Date: 2019-12-26 00:27:17 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 15916 163
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:hclust | 6 | 1.000 | 0.979 | 0.987 | ** | 2,3 |
| SD:pam | 6 | 1.000 | 0.978 | 0.989 | ** | 2,3,4,5 |
| CV:hclust | 6 | 1.000 | 1.000 | 1.000 | ** | 2,3 |
| CV:mclust | 6 | 1.000 | 0.966 | 0.983 | ** | 3,4,5 |
| MAD:hclust | 6 | 1.000 | 0.984 | 0.992 | ** | 2 |
| MAD:kmeans | 2 | 1.000 | 0.960 | 0.981 | ** | |
| ATC:kmeans | 3 | 1.000 | 1.000 | 1.000 | ** | 2 |
| ATC:mclust | 3 | 0.999 | 0.963 | 0.976 | ** | |
| SD:kmeans | 3 | 0.993 | 0.972 | 0.975 | ** | |
| ATC:NMF | 3 | 0.993 | 0.964 | 0.985 | ** | 2 |
| CV:skmeans | 5 | 0.970 | 0.927 | 0.953 | ** | 2,3,4 |
| MAD:skmeans | 5 | 0.949 | 0.856 | 0.921 | * | 2,4 |
| MAD:NMF | 4 | 0.948 | 0.902 | 0.956 | * | 2,3 |
| ATC:skmeans | 4 | 0.946 | 0.934 | 0.960 | * | 2,3 |
| SD:mclust | 6 | 0.946 | 0.899 | 0.956 | * | 3,4 |
| CV:pam | 6 | 0.940 | 0.972 | 0.973 | * | 2,3,4,5 |
| ATC:pam | 6 | 0.936 | 0.958 | 0.931 | * | 2,3,4,5 |
| MAD:pam | 6 | 0.933 | 0.945 | 0.947 | * | 2,3,4,5 |
| MAD:mclust | 6 | 0.928 | 0.888 | 0.947 | * | 2,3,4,5 |
| SD:skmeans | 6 | 0.924 | 0.944 | 0.949 | * | 2,3,4,5 |
| ATC:hclust | 6 | 0.923 | 0.905 | 0.944 | * | 2,3,4,5 |
| CV:NMF | 6 | 0.905 | 0.813 | 0.810 | * | 2,3,4 |
| SD:NMF | 6 | 0.901 | 0.676 | 0.855 | * | 2,3,4 |
| CV:kmeans | 2 | 0.763 | 0.895 | 0.953 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 1.000 0.974 0.989 0.499 0.499 0.499
#> CV:NMF 2 1.000 0.986 0.993 0.502 0.499 0.499
#> MAD:NMF 2 1.000 1.000 1.000 0.501 0.499 0.499
#> ATC:NMF 2 1.000 0.981 0.993 0.475 0.529 0.529
#> SD:skmeans 2 1.000 0.981 0.992 0.502 0.499 0.499
#> CV:skmeans 2 1.000 0.966 0.986 0.503 0.497 0.497
#> MAD:skmeans 2 1.000 0.946 0.979 0.503 0.499 0.499
#> ATC:skmeans 2 1.000 1.000 1.000 0.501 0.499 0.499
#> SD:mclust 2 0.654 0.878 0.933 0.412 0.627 0.627
#> CV:mclust 2 0.768 0.859 0.929 0.480 0.499 0.499
#> MAD:mclust 2 1.000 0.997 0.998 0.374 0.627 0.627
#> ATC:mclust 2 0.637 0.749 0.905 0.436 0.550 0.550
#> SD:kmeans 2 0.628 0.898 0.939 0.478 0.499 0.499
#> CV:kmeans 2 0.763 0.895 0.953 0.493 0.499 0.499
#> MAD:kmeans 2 1.000 0.960 0.981 0.502 0.499 0.499
#> ATC:kmeans 2 1.000 0.962 0.986 0.498 0.505 0.505
#> SD:pam 2 1.000 0.991 0.996 0.459 0.539 0.539
#> CV:pam 2 0.961 0.968 0.982 0.497 0.499 0.499
#> MAD:pam 2 1.000 0.993 0.996 0.500 0.499 0.499
#> ATC:pam 2 1.000 0.986 0.994 0.438 0.567 0.567
#> SD:hclust 2 1.000 0.969 0.988 0.502 0.499 0.499
#> CV:hclust 2 1.000 0.999 0.999 0.503 0.497 0.497
#> MAD:hclust 2 0.926 0.972 0.987 0.503 0.497 0.497
#> ATC:hclust 2 1.000 0.996 0.997 0.375 0.627 0.627
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 1.000 0.967 0.987 0.277 0.821 0.657
#> CV:NMF 3 1.000 0.974 0.989 0.295 0.796 0.614
#> MAD:NMF 3 1.000 0.962 0.983 0.276 0.826 0.665
#> ATC:NMF 3 0.993 0.964 0.985 0.332 0.774 0.598
#> SD:skmeans 3 1.000 0.997 0.998 0.278 0.811 0.639
#> CV:skmeans 3 1.000 0.983 0.992 0.281 0.834 0.674
#> MAD:skmeans 3 0.891 0.933 0.966 0.266 0.811 0.639
#> ATC:skmeans 3 0.977 0.924 0.970 0.225 0.861 0.726
#> SD:mclust 3 1.000 0.985 0.994 0.526 0.745 0.594
#> CV:mclust 3 1.000 0.984 0.994 0.313 0.623 0.396
#> MAD:mclust 3 1.000 0.989 0.996 0.673 0.745 0.594
#> ATC:mclust 3 0.999 0.963 0.976 0.447 0.670 0.474
#> SD:kmeans 3 0.993 0.972 0.975 0.328 0.798 0.618
#> CV:kmeans 3 0.628 0.708 0.862 0.313 0.721 0.499
#> MAD:kmeans 3 0.646 0.602 0.833 0.284 0.760 0.553
#> ATC:kmeans 3 1.000 1.000 1.000 0.292 0.776 0.588
#> SD:pam 3 0.987 0.978 0.989 0.374 0.771 0.599
#> CV:pam 3 0.917 0.939 0.974 0.315 0.782 0.591
#> MAD:pam 3 1.000 0.958 0.983 0.265 0.846 0.700
#> ATC:pam 3 1.000 1.000 1.000 0.471 0.729 0.547
#> SD:hclust 3 1.000 0.969 0.988 0.229 0.891 0.782
#> CV:hclust 3 0.925 0.951 0.971 0.237 0.876 0.750
#> MAD:hclust 3 0.812 0.924 0.931 0.242 0.876 0.750
#> ATC:hclust 3 1.000 0.998 0.999 0.717 0.729 0.569
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.943 0.909 0.951 0.1606 0.846 0.604
#> CV:NMF 4 1.000 0.991 0.988 0.1568 0.820 0.538
#> MAD:NMF 4 0.948 0.902 0.956 0.1479 0.823 0.561
#> ATC:NMF 4 0.875 0.836 0.901 0.0883 0.895 0.727
#> SD:skmeans 4 1.000 0.957 0.974 0.1684 0.885 0.682
#> CV:skmeans 4 1.000 0.992 0.995 0.1666 0.885 0.682
#> MAD:skmeans 4 0.934 0.912 0.953 0.1696 0.885 0.682
#> ATC:skmeans 4 0.946 0.934 0.960 0.0893 0.932 0.824
#> SD:mclust 4 0.915 0.941 0.956 0.1750 0.875 0.663
#> CV:mclust 4 0.941 0.961 0.978 0.1719 0.892 0.710
#> MAD:mclust 4 1.000 0.985 0.988 0.1971 0.875 0.663
#> ATC:mclust 4 0.854 0.930 0.951 0.1252 0.924 0.796
#> SD:kmeans 4 0.795 0.907 0.873 0.1399 0.883 0.676
#> CV:kmeans 4 0.675 0.802 0.829 0.1201 0.860 0.612
#> MAD:kmeans 4 0.746 0.855 0.836 0.1158 0.805 0.499
#> ATC:kmeans 4 0.802 0.731 0.870 0.1139 0.926 0.793
#> SD:pam 4 1.000 0.977 0.988 0.1932 0.874 0.662
#> CV:pam 4 1.000 0.982 0.992 0.1527 0.860 0.620
#> MAD:pam 4 1.000 0.953 0.983 0.1892 0.862 0.637
#> ATC:pam 4 1.000 0.983 0.994 0.1677 0.882 0.673
#> SD:hclust 4 0.873 0.922 0.947 0.1117 0.937 0.838
#> CV:hclust 4 0.805 0.928 0.916 0.1600 0.874 0.662
#> MAD:hclust 4 0.789 0.906 0.935 0.0891 0.965 0.907
#> ATC:hclust 4 1.000 0.964 0.986 0.0375 0.981 0.948
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.842 0.882 0.903 0.0607 0.890 0.621
#> CV:NMF 5 0.895 0.911 0.924 0.0501 0.939 0.765
#> MAD:NMF 5 0.843 0.883 0.909 0.0653 0.883 0.606
#> ATC:NMF 5 0.818 0.810 0.878 0.0744 0.945 0.824
#> SD:skmeans 5 0.953 0.929 0.950 0.0466 0.950 0.806
#> CV:skmeans 5 0.970 0.927 0.953 0.0445 0.963 0.855
#> MAD:skmeans 5 0.949 0.856 0.921 0.0518 0.950 0.803
#> ATC:skmeans 5 0.812 0.680 0.818 0.0813 0.998 0.993
#> SD:mclust 5 0.892 0.824 0.867 0.0619 0.953 0.815
#> CV:mclust 5 0.950 0.918 0.948 0.0643 0.952 0.820
#> MAD:mclust 5 0.911 0.897 0.896 0.0544 0.960 0.839
#> ATC:mclust 5 0.845 0.843 0.877 0.0890 0.919 0.728
#> SD:kmeans 5 0.717 0.778 0.801 0.0575 0.975 0.901
#> CV:kmeans 5 0.725 0.798 0.819 0.0591 0.972 0.895
#> MAD:kmeans 5 0.695 0.768 0.809 0.0632 0.982 0.929
#> ATC:kmeans 5 0.741 0.844 0.831 0.0600 0.926 0.757
#> SD:pam 5 1.000 0.982 0.992 0.0405 0.970 0.880
#> CV:pam 5 1.000 0.998 0.999 0.0466 0.965 0.857
#> MAD:pam 5 1.000 0.968 0.989 0.0424 0.963 0.854
#> ATC:pam 5 0.932 0.933 0.929 0.0455 0.967 0.867
#> SD:hclust 5 0.834 0.895 0.834 0.0797 0.894 0.677
#> CV:hclust 5 0.886 0.971 0.940 0.0459 0.970 0.880
#> MAD:hclust 5 0.814 0.886 0.889 0.0594 0.970 0.913
#> ATC:hclust 5 1.000 0.961 0.979 0.0433 0.970 0.913
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.901 0.676 0.855 0.0341 0.964 0.835
#> CV:NMF 6 0.905 0.813 0.810 0.0290 0.957 0.800
#> MAD:NMF 6 0.906 0.865 0.887 0.0353 1.000 1.000
#> ATC:NMF 6 0.856 0.905 0.918 0.0544 0.897 0.649
#> SD:skmeans 6 0.924 0.944 0.949 0.0233 0.988 0.945
#> CV:skmeans 6 0.862 0.836 0.878 0.0277 0.955 0.805
#> MAD:skmeans 6 0.922 0.943 0.948 0.0226 0.992 0.960
#> ATC:skmeans 6 0.835 0.884 0.878 0.0565 0.869 0.611
#> SD:mclust 6 0.946 0.899 0.956 0.0351 0.956 0.800
#> CV:mclust 6 1.000 0.966 0.983 0.0355 0.967 0.846
#> MAD:mclust 6 0.928 0.888 0.947 0.0288 0.960 0.815
#> ATC:mclust 6 0.868 0.798 0.834 0.0440 0.948 0.773
#> SD:kmeans 6 0.782 0.652 0.764 0.0389 0.986 0.940
#> CV:kmeans 6 0.840 0.710 0.791 0.0436 0.983 0.933
#> MAD:kmeans 6 0.769 0.621 0.783 0.0403 0.977 0.906
#> ATC:kmeans 6 0.767 0.765 0.807 0.0481 0.970 0.879
#> SD:pam 6 1.000 0.978 0.989 0.0382 0.970 0.862
#> CV:pam 6 0.940 0.972 0.973 0.0336 0.968 0.853
#> MAD:pam 6 0.933 0.945 0.947 0.0423 0.966 0.842
#> ATC:pam 6 0.936 0.958 0.931 0.0426 0.966 0.842
#> SD:hclust 6 1.000 0.979 0.987 0.0807 0.976 0.892
#> CV:hclust 6 1.000 1.000 1.000 0.0552 0.986 0.935
#> MAD:hclust 6 1.000 0.984 0.992 0.1083 0.894 0.658
#> ATC:hclust 6 0.923 0.905 0.944 0.1265 0.894 0.658
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.969 0.988 0.5022 0.499 0.499
#> 3 3 1.000 0.969 0.988 0.2289 0.891 0.782
#> 4 4 0.873 0.922 0.947 0.1117 0.937 0.838
#> 5 5 0.834 0.895 0.834 0.0797 0.894 0.677
#> 6 6 1.000 0.979 0.987 0.0807 0.976 0.892
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 2 0.000 1.000 0.000 1.000
#> SRR1633241 2 0.000 1.000 0.000 1.000
#> SRR1633242 2 0.000 1.000 0.000 1.000
#> SRR1633243 2 0.000 1.000 0.000 1.000
#> SRR1633244 2 0.000 1.000 0.000 1.000
#> SRR1633245 2 0.000 1.000 0.000 1.000
#> SRR1633246 2 0.000 1.000 0.000 1.000
#> SRR1633247 2 0.000 1.000 0.000 1.000
#> SRR1633248 2 0.000 1.000 0.000 1.000
#> SRR1633249 2 0.000 1.000 0.000 1.000
#> SRR1633250 2 0.000 1.000 0.000 1.000
#> SRR1633251 2 0.000 1.000 0.000 1.000
#> SRR1633252 2 0.000 1.000 0.000 1.000
#> SRR1633253 2 0.000 1.000 0.000 1.000
#> SRR1633254 2 0.000 1.000 0.000 1.000
#> SRR1633255 2 0.000 1.000 0.000 1.000
#> SRR1633256 2 0.000 1.000 0.000 1.000
#> SRR1633257 2 0.000 1.000 0.000 1.000
#> SRR1633258 2 0.000 1.000 0.000 1.000
#> SRR1633259 2 0.000 1.000 0.000 1.000
#> SRR1633260 2 0.000 1.000 0.000 1.000
#> SRR1633261 2 0.000 1.000 0.000 1.000
#> SRR1633262 1 0.969 0.375 0.604 0.396
#> SRR1633263 1 0.969 0.375 0.604 0.396
#> SRR1633264 1 0.969 0.375 0.604 0.396
#> SRR1633265 1 0.969 0.375 0.604 0.396
#> SRR1633266 1 0.969 0.375 0.604 0.396
#> SRR1633267 2 0.000 1.000 0.000 1.000
#> SRR1633268 2 0.000 1.000 0.000 1.000
#> SRR1633269 2 0.000 1.000 0.000 1.000
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.977 1.000 0.000
#> SRR1633274 1 0.000 0.977 1.000 0.000
#> SRR1633275 1 0.000 0.977 1.000 0.000
#> SRR1633276 1 0.000 0.977 1.000 0.000
#> SRR1633277 1 0.000 0.977 1.000 0.000
#> SRR1633278 1 0.000 0.977 1.000 0.000
#> SRR1633279 1 0.000 0.977 1.000 0.000
#> SRR1633280 1 0.000 0.977 1.000 0.000
#> SRR1633281 1 0.000 0.977 1.000 0.000
#> SRR1633282 1 0.000 0.977 1.000 0.000
#> SRR1633284 1 0.000 0.977 1.000 0.000
#> SRR1633285 1 0.000 0.977 1.000 0.000
#> SRR1633286 1 0.000 0.977 1.000 0.000
#> SRR1633287 1 0.000 0.977 1.000 0.000
#> SRR1633288 1 0.000 0.977 1.000 0.000
#> SRR1633289 1 0.000 0.977 1.000 0.000
#> SRR1633290 1 0.000 0.977 1.000 0.000
#> SRR1633291 1 0.000 0.977 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.977 1.000 0.000
#> SRR1633297 1 0.000 0.977 1.000 0.000
#> SRR1633298 1 0.000 0.977 1.000 0.000
#> SRR1633299 1 0.000 0.977 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.977 1.000 0.000
#> SRR1633336 1 0.000 0.977 1.000 0.000
#> SRR1633337 1 0.000 0.977 1.000 0.000
#> SRR1633338 1 0.000 0.977 1.000 0.000
#> SRR1633339 1 0.000 0.977 1.000 0.000
#> SRR1633340 1 0.000 0.977 1.000 0.000
#> SRR1633341 1 0.000 0.977 1.000 0.000
#> SRR1633342 1 0.000 0.977 1.000 0.000
#> SRR1633345 1 0.000 0.977 1.000 0.000
#> SRR1633346 1 0.000 0.977 1.000 0.000
#> SRR1633343 1 0.000 0.977 1.000 0.000
#> SRR1633344 1 0.000 0.977 1.000 0.000
#> SRR1633347 1 0.000 0.977 1.000 0.000
#> SRR1633348 1 0.000 0.977 1.000 0.000
#> SRR1633350 1 0.000 0.977 1.000 0.000
#> SRR1633351 1 0.000 0.977 1.000 0.000
#> SRR1633352 1 0.000 0.977 1.000 0.000
#> SRR1633353 1 0.000 0.977 1.000 0.000
#> SRR1633354 1 0.000 0.977 1.000 0.000
#> SRR1633355 1 0.000 0.977 1.000 0.000
#> SRR1633356 1 0.000 0.977 1.000 0.000
#> SRR1633357 1 0.000 0.977 1.000 0.000
#> SRR1633358 1 0.000 0.977 1.000 0.000
#> SRR1633362 1 0.000 0.977 1.000 0.000
#> SRR1633363 1 0.000 0.977 1.000 0.000
#> SRR1633364 1 0.000 0.977 1.000 0.000
#> SRR1633359 1 0.000 0.977 1.000 0.000
#> SRR1633360 1 0.000 0.977 1.000 0.000
#> SRR1633361 1 0.000 0.977 1.000 0.000
#> SRR2038492 1 0.000 0.977 1.000 0.000
#> SRR2038491 1 0.000 0.977 1.000 0.000
#> SRR2038490 1 0.000 0.977 1.000 0.000
#> SRR2038489 1 0.000 0.977 1.000 0.000
#> SRR2038488 1 0.000 0.977 1.000 0.000
#> SRR2038487 1 0.000 0.977 1.000 0.000
#> SRR2038486 1 0.000 0.977 1.000 0.000
#> SRR2038485 1 0.000 0.977 1.000 0.000
#> SRR2038484 1 0.000 0.977 1.000 0.000
#> SRR2038483 1 0.000 0.977 1.000 0.000
#> SRR2038482 1 0.000 0.977 1.000 0.000
#> SRR2038481 1 0.000 0.977 1.000 0.000
#> SRR2038480 1 0.000 0.977 1.000 0.000
#> SRR2038479 1 0.000 0.977 1.000 0.000
#> SRR2038477 1 0.000 0.977 1.000 0.000
#> SRR2038478 1 0.000 0.977 1.000 0.000
#> SRR2038476 1 0.000 0.977 1.000 0.000
#> SRR2038475 1 0.000 0.977 1.000 0.000
#> SRR2038474 1 0.000 0.977 1.000 0.000
#> SRR2038473 1 0.000 0.977 1.000 0.000
#> SRR2038472 1 0.000 0.977 1.000 0.000
#> SRR2038471 1 0.000 0.977 1.000 0.000
#> SRR2038470 1 0.000 0.977 1.000 0.000
#> SRR2038469 1 0.000 0.977 1.000 0.000
#> SRR2038468 1 0.000 0.977 1.000 0.000
#> SRR2038467 1 0.000 0.977 1.000 0.000
#> SRR2038466 1 0.000 0.977 1.000 0.000
#> SRR2038465 1 0.000 0.977 1.000 0.000
#> SRR2038464 1 0.000 0.977 1.000 0.000
#> SRR2038463 1 0.000 0.977 1.000 0.000
#> SRR2038462 1 0.000 0.977 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.000 1.000 0.000 1 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000
#> SRR1633236 3 0.000 1.000 0.000 0 1.000
#> SRR1633237 3 0.000 1.000 0.000 0 1.000
#> SRR1633238 3 0.000 1.000 0.000 0 1.000
#> SRR1633239 3 0.000 1.000 0.000 0 1.000
#> SRR1633240 3 0.000 1.000 0.000 0 1.000
#> SRR1633241 3 0.000 1.000 0.000 0 1.000
#> SRR1633242 3 0.000 1.000 0.000 0 1.000
#> SRR1633243 3 0.000 1.000 0.000 0 1.000
#> SRR1633244 3 0.000 1.000 0.000 0 1.000
#> SRR1633245 3 0.000 1.000 0.000 0 1.000
#> SRR1633246 3 0.000 1.000 0.000 0 1.000
#> SRR1633247 3 0.000 1.000 0.000 0 1.000
#> SRR1633248 3 0.000 1.000 0.000 0 1.000
#> SRR1633249 3 0.000 1.000 0.000 0 1.000
#> SRR1633250 3 0.000 1.000 0.000 0 1.000
#> SRR1633251 3 0.000 1.000 0.000 0 1.000
#> SRR1633252 3 0.000 1.000 0.000 0 1.000
#> SRR1633253 3 0.000 1.000 0.000 0 1.000
#> SRR1633254 3 0.000 1.000 0.000 0 1.000
#> SRR1633255 3 0.000 1.000 0.000 0 1.000
#> SRR1633256 3 0.000 1.000 0.000 0 1.000
#> SRR1633257 3 0.000 1.000 0.000 0 1.000
#> SRR1633258 3 0.000 1.000 0.000 0 1.000
#> SRR1633259 3 0.000 1.000 0.000 0 1.000
#> SRR1633260 3 0.000 1.000 0.000 0 1.000
#> SRR1633261 3 0.000 1.000 0.000 0 1.000
#> SRR1633262 1 0.611 0.375 0.604 0 0.396
#> SRR1633263 1 0.611 0.375 0.604 0 0.396
#> SRR1633264 1 0.611 0.375 0.604 0 0.396
#> SRR1633265 1 0.611 0.375 0.604 0 0.396
#> SRR1633266 1 0.611 0.375 0.604 0 0.396
#> SRR1633267 3 0.000 1.000 0.000 0 1.000
#> SRR1633268 3 0.000 1.000 0.000 0 1.000
#> SRR1633269 3 0.000 1.000 0.000 0 1.000
#> SRR1633270 3 0.000 1.000 0.000 0 1.000
#> SRR1633271 3 0.000 1.000 0.000 0 1.000
#> SRR1633272 3 0.000 1.000 0.000 0 1.000
#> SRR1633273 1 0.000 0.977 1.000 0 0.000
#> SRR1633274 1 0.000 0.977 1.000 0 0.000
#> SRR1633275 1 0.000 0.977 1.000 0 0.000
#> SRR1633276 1 0.000 0.977 1.000 0 0.000
#> SRR1633277 1 0.000 0.977 1.000 0 0.000
#> SRR1633278 1 0.000 0.977 1.000 0 0.000
#> SRR1633279 1 0.000 0.977 1.000 0 0.000
#> SRR1633280 1 0.000 0.977 1.000 0 0.000
#> SRR1633281 1 0.000 0.977 1.000 0 0.000
#> SRR1633282 1 0.000 0.977 1.000 0 0.000
#> SRR1633284 1 0.000 0.977 1.000 0 0.000
#> SRR1633285 1 0.000 0.977 1.000 0 0.000
#> SRR1633286 1 0.000 0.977 1.000 0 0.000
#> SRR1633287 1 0.000 0.977 1.000 0 0.000
#> SRR1633288 1 0.000 0.977 1.000 0 0.000
#> SRR1633289 1 0.000 0.977 1.000 0 0.000
#> SRR1633290 1 0.000 0.977 1.000 0 0.000
#> SRR1633291 1 0.000 0.977 1.000 0 0.000
#> SRR1633292 3 0.000 1.000 0.000 0 1.000
#> SRR1633293 3 0.000 1.000 0.000 0 1.000
#> SRR1633294 3 0.000 1.000 0.000 0 1.000
#> SRR1633295 3 0.000 1.000 0.000 0 1.000
#> SRR1633296 1 0.000 0.977 1.000 0 0.000
#> SRR1633297 1 0.000 0.977 1.000 0 0.000
#> SRR1633298 1 0.000 0.977 1.000 0 0.000
#> SRR1633299 1 0.000 0.977 1.000 0 0.000
#> SRR1633300 2 0.000 1.000 0.000 1 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000
#> SRR1633335 1 0.000 0.977 1.000 0 0.000
#> SRR1633336 1 0.000 0.977 1.000 0 0.000
#> SRR1633337 1 0.000 0.977 1.000 0 0.000
#> SRR1633338 1 0.000 0.977 1.000 0 0.000
#> SRR1633339 1 0.000 0.977 1.000 0 0.000
#> SRR1633340 1 0.000 0.977 1.000 0 0.000
#> SRR1633341 1 0.000 0.977 1.000 0 0.000
#> SRR1633342 1 0.000 0.977 1.000 0 0.000
#> SRR1633345 1 0.000 0.977 1.000 0 0.000
#> SRR1633346 1 0.000 0.977 1.000 0 0.000
#> SRR1633343 1 0.000 0.977 1.000 0 0.000
#> SRR1633344 1 0.000 0.977 1.000 0 0.000
#> SRR1633347 1 0.000 0.977 1.000 0 0.000
#> SRR1633348 1 0.000 0.977 1.000 0 0.000
#> SRR1633350 1 0.000 0.977 1.000 0 0.000
#> SRR1633351 1 0.000 0.977 1.000 0 0.000
#> SRR1633352 1 0.000 0.977 1.000 0 0.000
#> SRR1633353 1 0.000 0.977 1.000 0 0.000
#> SRR1633354 1 0.000 0.977 1.000 0 0.000
#> SRR1633355 1 0.000 0.977 1.000 0 0.000
#> SRR1633356 1 0.000 0.977 1.000 0 0.000
#> SRR1633357 1 0.000 0.977 1.000 0 0.000
#> SRR1633358 1 0.000 0.977 1.000 0 0.000
#> SRR1633362 1 0.000 0.977 1.000 0 0.000
#> SRR1633363 1 0.000 0.977 1.000 0 0.000
#> SRR1633364 1 0.000 0.977 1.000 0 0.000
#> SRR1633359 1 0.000 0.977 1.000 0 0.000
#> SRR1633360 1 0.000 0.977 1.000 0 0.000
#> SRR1633361 1 0.000 0.977 1.000 0 0.000
#> SRR2038492 1 0.000 0.977 1.000 0 0.000
#> SRR2038491 1 0.000 0.977 1.000 0 0.000
#> SRR2038490 1 0.000 0.977 1.000 0 0.000
#> SRR2038489 1 0.000 0.977 1.000 0 0.000
#> SRR2038488 1 0.000 0.977 1.000 0 0.000
#> SRR2038487 1 0.000 0.977 1.000 0 0.000
#> SRR2038486 1 0.000 0.977 1.000 0 0.000
#> SRR2038485 1 0.000 0.977 1.000 0 0.000
#> SRR2038484 1 0.000 0.977 1.000 0 0.000
#> SRR2038483 1 0.000 0.977 1.000 0 0.000
#> SRR2038482 1 0.000 0.977 1.000 0 0.000
#> SRR2038481 1 0.000 0.977 1.000 0 0.000
#> SRR2038480 1 0.000 0.977 1.000 0 0.000
#> SRR2038479 1 0.000 0.977 1.000 0 0.000
#> SRR2038477 1 0.000 0.977 1.000 0 0.000
#> SRR2038478 1 0.000 0.977 1.000 0 0.000
#> SRR2038476 1 0.000 0.977 1.000 0 0.000
#> SRR2038475 1 0.000 0.977 1.000 0 0.000
#> SRR2038474 1 0.000 0.977 1.000 0 0.000
#> SRR2038473 1 0.000 0.977 1.000 0 0.000
#> SRR2038472 1 0.000 0.977 1.000 0 0.000
#> SRR2038471 1 0.000 0.977 1.000 0 0.000
#> SRR2038470 1 0.000 0.977 1.000 0 0.000
#> SRR2038469 1 0.000 0.977 1.000 0 0.000
#> SRR2038468 1 0.000 0.977 1.000 0 0.000
#> SRR2038467 1 0.000 0.977 1.000 0 0.000
#> SRR2038466 1 0.000 0.977 1.000 0 0.000
#> SRR2038465 1 0.000 0.977 1.000 0 0.000
#> SRR2038464 1 0.000 0.977 1.000 0 0.000
#> SRR2038463 1 0.000 0.977 1.000 0 0.000
#> SRR2038462 1 0.000 0.977 1.000 0 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633236 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633237 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633238 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633239 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633240 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633241 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633242 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633243 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633244 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633245 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633246 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633247 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633248 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633249 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633250 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633251 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633252 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633253 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633254 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633255 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633256 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633257 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633258 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633259 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633260 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633261 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633262 4 0.484 0.622 0.000 0 0.396 0.604
#> SRR1633263 4 0.484 0.622 0.000 0 0.396 0.604
#> SRR1633264 4 0.484 0.622 0.000 0 0.396 0.604
#> SRR1633265 4 0.484 0.622 0.000 0 0.396 0.604
#> SRR1633266 4 0.484 0.622 0.000 0 0.396 0.604
#> SRR1633267 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633268 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633269 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633270 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633271 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633272 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633273 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633274 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633275 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633276 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633277 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633278 4 0.000 0.795 0.000 0 0.000 1.000
#> SRR1633279 4 0.000 0.795 0.000 0 0.000 1.000
#> SRR1633280 4 0.000 0.795 0.000 0 0.000 1.000
#> SRR1633281 4 0.000 0.795 0.000 0 0.000 1.000
#> SRR1633282 4 0.000 0.795 0.000 0 0.000 1.000
#> SRR1633284 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633285 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633286 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633287 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633288 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633289 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633290 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633291 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633292 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633293 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633294 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633295 3 0.000 1.000 0.000 0 1.000 0.000
#> SRR1633296 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633297 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633298 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633299 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633300 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633335 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633336 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633337 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633338 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633339 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633340 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633341 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633342 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633345 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633346 1 0.373 0.837 0.788 0 0.000 0.212
#> SRR1633343 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633344 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633347 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633348 1 0.376 0.835 0.784 0 0.000 0.216
#> SRR1633350 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633351 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633352 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633353 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633354 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633355 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633356 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633357 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633358 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633362 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633363 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633364 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633359 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633360 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR1633361 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038492 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038491 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038490 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038489 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038488 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038487 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038486 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038485 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038484 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038483 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038482 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038481 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038480 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038479 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038477 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038478 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038476 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038475 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038474 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038473 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038472 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038471 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038470 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038469 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038468 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038467 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038466 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038465 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038464 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038463 1 0.000 0.898 1.000 0 0.000 0.000
#> SRR2038462 4 0.000 0.795 0.000 0 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633236 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633237 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633238 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633239 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633240 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633241 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633242 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633243 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633244 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633245 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633246 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633247 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633248 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633249 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633250 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633251 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633252 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633253 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633254 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633255 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633256 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633257 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633258 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633259 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633260 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633261 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633262 3 0.6478 0.684 0.396 0 0.420 0.184 0.000
#> SRR1633263 3 0.6478 0.684 0.396 0 0.420 0.184 0.000
#> SRR1633264 3 0.6478 0.684 0.396 0 0.420 0.184 0.000
#> SRR1633265 3 0.6478 0.684 0.396 0 0.420 0.184 0.000
#> SRR1633266 3 0.6478 0.684 0.396 0 0.420 0.184 0.000
#> SRR1633267 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633268 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633269 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633270 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633271 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633272 5 0.4262 0.654 0.440 0 0.000 0.000 0.560
#> SRR1633273 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633274 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633275 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633276 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633277 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633278 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
#> SRR1633279 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
#> SRR1633280 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
#> SRR1633281 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
#> SRR1633282 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
#> SRR1633284 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633285 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633286 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633287 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633288 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633289 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633290 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633291 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633292 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633293 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633294 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633295 5 0.2966 0.540 0.000 0 0.184 0.000 0.816
#> SRR1633296 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633297 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633298 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633299 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633335 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633336 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633337 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633338 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633341 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633342 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633345 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633346 4 0.0000 0.996 0.000 0 0.000 1.000 0.000
#> SRR1633343 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633344 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633347 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633348 4 0.0162 0.996 0.000 0 0.004 0.996 0.000
#> SRR1633350 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633351 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633352 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633353 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633354 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633355 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633356 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633357 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633358 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633362 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633363 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633364 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633359 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633360 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR1633361 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038492 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038491 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038490 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038489 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038488 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038487 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038486 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038485 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038484 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038483 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038482 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038481 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038480 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038479 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038477 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038478 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038476 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038475 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038474 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038473 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038472 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038471 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038470 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038469 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038468 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038467 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038466 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038465 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038464 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038463 1 0.4262 1.000 0.560 0 0.000 0.440 0.000
#> SRR2038462 3 0.2966 0.792 0.000 0 0.816 0.184 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633236 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633237 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633238 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633239 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633240 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633241 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633242 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633243 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633244 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633245 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633246 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633247 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633248 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633249 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633250 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633251 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633252 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633253 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633254 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633255 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633256 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633257 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633258 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633259 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633260 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633261 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633262 6 0.3881 0.605 0.000 0 0.396 0.004 0 0.600
#> SRR1633263 6 0.3881 0.605 0.000 0 0.396 0.004 0 0.600
#> SRR1633264 6 0.3881 0.605 0.000 0 0.396 0.004 0 0.600
#> SRR1633265 6 0.3881 0.605 0.000 0 0.396 0.004 0 0.600
#> SRR1633266 6 0.3881 0.605 0.000 0 0.396 0.004 0 0.600
#> SRR1633267 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633268 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633269 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633270 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633271 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633272 3 0.0000 1.000 0.000 0 1.000 0.000 0 0.000
#> SRR1633273 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633274 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633275 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633276 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633277 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633278 6 0.0000 0.764 0.000 0 0.000 0.000 0 1.000
#> SRR1633279 6 0.0000 0.764 0.000 0 0.000 0.000 0 1.000
#> SRR1633280 6 0.0000 0.764 0.000 0 0.000 0.000 0 1.000
#> SRR1633281 6 0.0000 0.764 0.000 0 0.000 0.000 0 1.000
#> SRR1633282 6 0.0146 0.763 0.000 0 0.000 0.004 0 0.996
#> SRR1633284 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633285 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633286 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633287 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633288 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633289 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633290 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633291 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633292 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633293 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633294 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633295 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633296 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633297 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633298 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633299 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633335 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633336 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633337 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633338 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633339 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633340 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633341 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633342 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633345 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633346 4 0.0146 0.997 0.004 0 0.000 0.996 0 0.000
#> SRR1633343 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633344 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633347 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633348 4 0.0000 0.997 0.000 0 0.000 1.000 0 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0 0.000 0.000 0 0.000
#> SRR2038462 6 0.0000 0.764 0.000 0 0.000 0.000 0 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.628 0.898 0.939 0.4778 0.499 0.499
#> 3 3 0.993 0.972 0.975 0.3276 0.798 0.618
#> 4 4 0.795 0.907 0.873 0.1399 0.883 0.676
#> 5 5 0.717 0.778 0.801 0.0575 0.975 0.901
#> 6 6 0.782 0.652 0.764 0.0389 0.986 0.940
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.1184 0.881 0.016 0.984
#> SRR1633231 2 0.1184 0.881 0.016 0.984
#> SRR1633232 2 0.1184 0.881 0.016 0.984
#> SRR1633233 2 0.1184 0.881 0.016 0.984
#> SRR1633234 2 0.1184 0.881 0.016 0.984
#> SRR1633236 2 0.0000 0.874 0.000 1.000
#> SRR1633237 2 0.0000 0.874 0.000 1.000
#> SRR1633238 2 0.0000 0.874 0.000 1.000
#> SRR1633239 2 0.0000 0.874 0.000 1.000
#> SRR1633240 2 0.5519 0.837 0.128 0.872
#> SRR1633241 2 0.5519 0.837 0.128 0.872
#> SRR1633242 2 0.5519 0.837 0.128 0.872
#> SRR1633243 2 0.5519 0.837 0.128 0.872
#> SRR1633244 2 0.5519 0.837 0.128 0.872
#> SRR1633245 2 0.5519 0.837 0.128 0.872
#> SRR1633246 2 0.5519 0.837 0.128 0.872
#> SRR1633247 2 0.9129 0.661 0.328 0.672
#> SRR1633248 2 0.9129 0.661 0.328 0.672
#> SRR1633249 2 0.9129 0.661 0.328 0.672
#> SRR1633250 2 0.9129 0.661 0.328 0.672
#> SRR1633251 2 0.9129 0.661 0.328 0.672
#> SRR1633252 2 0.9129 0.661 0.328 0.672
#> SRR1633253 2 0.9129 0.661 0.328 0.672
#> SRR1633254 2 0.9129 0.661 0.328 0.672
#> SRR1633255 2 0.9129 0.661 0.328 0.672
#> SRR1633256 2 0.9129 0.661 0.328 0.672
#> SRR1633257 2 0.9129 0.661 0.328 0.672
#> SRR1633258 2 0.9129 0.661 0.328 0.672
#> SRR1633259 2 0.9129 0.661 0.328 0.672
#> SRR1633260 2 0.9129 0.661 0.328 0.672
#> SRR1633261 2 0.9129 0.661 0.328 0.672
#> SRR1633262 1 0.4431 0.896 0.908 0.092
#> SRR1633263 1 0.4431 0.896 0.908 0.092
#> SRR1633264 1 0.4431 0.896 0.908 0.092
#> SRR1633265 1 0.4431 0.896 0.908 0.092
#> SRR1633266 1 0.4431 0.896 0.908 0.092
#> SRR1633267 2 0.9129 0.661 0.328 0.672
#> SRR1633268 2 0.9129 0.661 0.328 0.672
#> SRR1633269 2 0.9129 0.661 0.328 0.672
#> SRR1633270 2 0.8661 0.706 0.288 0.712
#> SRR1633271 2 0.8661 0.706 0.288 0.712
#> SRR1633272 2 0.8661 0.706 0.288 0.712
#> SRR1633273 1 0.0672 0.979 0.992 0.008
#> SRR1633274 1 0.0672 0.979 0.992 0.008
#> SRR1633275 1 0.0672 0.979 0.992 0.008
#> SRR1633276 1 0.0672 0.979 0.992 0.008
#> SRR1633277 1 0.0672 0.979 0.992 0.008
#> SRR1633278 1 0.6148 0.795 0.848 0.152
#> SRR1633279 1 0.6148 0.795 0.848 0.152
#> SRR1633280 1 0.6148 0.795 0.848 0.152
#> SRR1633281 1 0.6148 0.795 0.848 0.152
#> SRR1633282 1 0.0672 0.979 0.992 0.008
#> SRR1633284 1 0.0000 0.984 1.000 0.000
#> SRR1633285 1 0.0000 0.984 1.000 0.000
#> SRR1633286 1 0.0000 0.984 1.000 0.000
#> SRR1633287 1 0.0000 0.984 1.000 0.000
#> SRR1633288 1 0.0000 0.984 1.000 0.000
#> SRR1633289 1 0.0000 0.984 1.000 0.000
#> SRR1633290 1 0.0672 0.979 0.992 0.008
#> SRR1633291 1 0.0672 0.979 0.992 0.008
#> SRR1633292 2 0.4939 0.846 0.108 0.892
#> SRR1633293 2 0.4939 0.846 0.108 0.892
#> SRR1633294 2 0.4939 0.846 0.108 0.892
#> SRR1633295 2 0.4939 0.846 0.108 0.892
#> SRR1633296 1 0.0672 0.979 0.992 0.008
#> SRR1633297 1 0.0672 0.979 0.992 0.008
#> SRR1633298 1 0.0672 0.979 0.992 0.008
#> SRR1633299 1 0.0672 0.979 0.992 0.008
#> SRR1633300 2 0.1184 0.881 0.016 0.984
#> SRR1633301 2 0.1184 0.881 0.016 0.984
#> SRR1633302 2 0.1184 0.881 0.016 0.984
#> SRR1633303 2 0.1184 0.881 0.016 0.984
#> SRR1633304 2 0.1184 0.881 0.016 0.984
#> SRR1633305 2 0.1184 0.881 0.016 0.984
#> SRR1633306 2 0.1184 0.881 0.016 0.984
#> SRR1633307 2 0.1184 0.881 0.016 0.984
#> SRR1633308 2 0.1184 0.881 0.016 0.984
#> SRR1633309 2 0.1184 0.881 0.016 0.984
#> SRR1633310 2 0.1184 0.881 0.016 0.984
#> SRR1633311 2 0.1184 0.881 0.016 0.984
#> SRR1633312 2 0.1184 0.881 0.016 0.984
#> SRR1633313 2 0.1184 0.881 0.016 0.984
#> SRR1633314 2 0.1184 0.881 0.016 0.984
#> SRR1633315 2 0.1184 0.881 0.016 0.984
#> SRR1633316 2 0.1184 0.881 0.016 0.984
#> SRR1633317 2 0.1184 0.881 0.016 0.984
#> SRR1633318 2 0.1184 0.881 0.016 0.984
#> SRR1633319 2 0.1184 0.881 0.016 0.984
#> SRR1633320 2 0.1184 0.881 0.016 0.984
#> SRR1633321 2 0.1184 0.881 0.016 0.984
#> SRR1633322 2 0.1184 0.881 0.016 0.984
#> SRR1633323 2 0.1184 0.881 0.016 0.984
#> SRR1633324 2 0.1184 0.881 0.016 0.984
#> SRR1633325 2 0.1184 0.881 0.016 0.984
#> SRR1633326 2 0.1184 0.881 0.016 0.984
#> SRR1633327 2 0.1184 0.881 0.016 0.984
#> SRR1633328 2 0.1184 0.881 0.016 0.984
#> SRR1633329 2 0.1184 0.881 0.016 0.984
#> SRR1633330 2 0.1184 0.881 0.016 0.984
#> SRR1633331 2 0.1184 0.881 0.016 0.984
#> SRR1633332 2 0.1184 0.881 0.016 0.984
#> SRR1633333 2 0.1184 0.881 0.016 0.984
#> SRR1633334 2 0.1184 0.881 0.016 0.984
#> SRR1633335 1 0.0000 0.984 1.000 0.000
#> SRR1633336 1 0.0000 0.984 1.000 0.000
#> SRR1633337 1 0.0000 0.984 1.000 0.000
#> SRR1633338 1 0.0376 0.982 0.996 0.004
#> SRR1633339 1 0.0376 0.982 0.996 0.004
#> SRR1633340 1 0.0376 0.982 0.996 0.004
#> SRR1633341 1 0.0000 0.984 1.000 0.000
#> SRR1633342 1 0.0000 0.984 1.000 0.000
#> SRR1633345 1 0.0000 0.984 1.000 0.000
#> SRR1633346 1 0.0000 0.984 1.000 0.000
#> SRR1633343 1 0.0672 0.979 0.992 0.008
#> SRR1633344 1 0.0672 0.979 0.992 0.008
#> SRR1633347 1 0.0672 0.979 0.992 0.008
#> SRR1633348 1 0.0672 0.979 0.992 0.008
#> SRR1633350 1 0.0000 0.984 1.000 0.000
#> SRR1633351 1 0.0000 0.984 1.000 0.000
#> SRR1633352 1 0.0000 0.984 1.000 0.000
#> SRR1633353 1 0.0000 0.984 1.000 0.000
#> SRR1633354 1 0.0000 0.984 1.000 0.000
#> SRR1633355 1 0.0000 0.984 1.000 0.000
#> SRR1633356 1 0.0000 0.984 1.000 0.000
#> SRR1633357 1 0.0000 0.984 1.000 0.000
#> SRR1633358 1 0.0000 0.984 1.000 0.000
#> SRR1633362 1 0.0000 0.984 1.000 0.000
#> SRR1633363 1 0.0000 0.984 1.000 0.000
#> SRR1633364 1 0.0000 0.984 1.000 0.000
#> SRR1633359 1 0.0000 0.984 1.000 0.000
#> SRR1633360 1 0.0000 0.984 1.000 0.000
#> SRR1633361 1 0.0000 0.984 1.000 0.000
#> SRR2038492 1 0.0000 0.984 1.000 0.000
#> SRR2038491 1 0.0000 0.984 1.000 0.000
#> SRR2038490 1 0.0000 0.984 1.000 0.000
#> SRR2038489 1 0.0000 0.984 1.000 0.000
#> SRR2038488 1 0.0000 0.984 1.000 0.000
#> SRR2038487 1 0.0000 0.984 1.000 0.000
#> SRR2038486 1 0.0000 0.984 1.000 0.000
#> SRR2038485 1 0.0000 0.984 1.000 0.000
#> SRR2038484 1 0.0000 0.984 1.000 0.000
#> SRR2038483 1 0.0000 0.984 1.000 0.000
#> SRR2038482 1 0.0000 0.984 1.000 0.000
#> SRR2038481 1 0.0000 0.984 1.000 0.000
#> SRR2038480 1 0.0000 0.984 1.000 0.000
#> SRR2038479 1 0.0000 0.984 1.000 0.000
#> SRR2038477 1 0.0000 0.984 1.000 0.000
#> SRR2038478 1 0.0000 0.984 1.000 0.000
#> SRR2038476 1 0.0000 0.984 1.000 0.000
#> SRR2038475 1 0.0000 0.984 1.000 0.000
#> SRR2038474 1 0.0000 0.984 1.000 0.000
#> SRR2038473 1 0.0000 0.984 1.000 0.000
#> SRR2038472 1 0.0000 0.984 1.000 0.000
#> SRR2038471 1 0.0000 0.984 1.000 0.000
#> SRR2038470 1 0.0000 0.984 1.000 0.000
#> SRR2038469 1 0.0000 0.984 1.000 0.000
#> SRR2038468 1 0.0000 0.984 1.000 0.000
#> SRR2038467 1 0.0000 0.984 1.000 0.000
#> SRR2038466 1 0.0000 0.984 1.000 0.000
#> SRR2038465 1 0.0000 0.984 1.000 0.000
#> SRR2038464 1 0.0000 0.984 1.000 0.000
#> SRR2038463 1 0.0000 0.984 1.000 0.000
#> SRR2038462 1 0.0376 0.982 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633231 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633232 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633233 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633234 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633236 3 0.3551 0.847 0.000 0.132 0.868
#> SRR1633237 3 0.3686 0.839 0.000 0.140 0.860
#> SRR1633238 3 0.3686 0.839 0.000 0.140 0.860
#> SRR1633239 3 0.3686 0.839 0.000 0.140 0.860
#> SRR1633240 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633241 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633242 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633243 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633244 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633245 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633246 3 0.1919 0.944 0.020 0.024 0.956
#> SRR1633247 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633248 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633249 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633250 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633251 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633252 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633253 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633254 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633255 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633256 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633257 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633258 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633259 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633260 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633261 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633262 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633263 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633264 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633265 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633266 3 0.1411 0.948 0.036 0.000 0.964
#> SRR1633267 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633268 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633269 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633270 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633271 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633272 3 0.1999 0.952 0.036 0.012 0.952
#> SRR1633273 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633274 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633275 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633276 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633277 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633278 3 0.4399 0.811 0.188 0.000 0.812
#> SRR1633279 3 0.4399 0.811 0.188 0.000 0.812
#> SRR1633280 3 0.4399 0.811 0.188 0.000 0.812
#> SRR1633281 3 0.4399 0.811 0.188 0.000 0.812
#> SRR1633282 3 0.4399 0.811 0.188 0.000 0.812
#> SRR1633284 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633285 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633286 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633287 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633288 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633289 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633290 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633291 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633292 3 0.1905 0.941 0.016 0.028 0.956
#> SRR1633293 3 0.1905 0.941 0.016 0.028 0.956
#> SRR1633294 3 0.1905 0.941 0.016 0.028 0.956
#> SRR1633295 3 0.1905 0.941 0.016 0.028 0.956
#> SRR1633296 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633297 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633298 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633299 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633300 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633301 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633302 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633303 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633304 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633305 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633306 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633307 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633308 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633309 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633310 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633311 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633312 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633313 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633314 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633315 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633316 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633317 2 0.0237 0.994 0.000 0.996 0.004
#> SRR1633318 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633319 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633320 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633321 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633322 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633323 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633324 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633325 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633326 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633327 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633328 2 0.0424 0.993 0.000 0.992 0.008
#> SRR1633329 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633330 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633331 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633332 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633333 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633334 2 0.1289 0.984 0.000 0.968 0.032
#> SRR1633335 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633336 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633337 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633338 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633339 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633340 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633341 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633342 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633345 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633346 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633343 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633344 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633347 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633348 1 0.0592 0.993 0.988 0.000 0.012
#> SRR1633350 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038462 3 0.5254 0.701 0.264 0.000 0.736
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0707 0.946 0.000 0.980 0.000 0.020
#> SRR1633231 2 0.0707 0.946 0.000 0.980 0.000 0.020
#> SRR1633232 2 0.0592 0.947 0.000 0.984 0.000 0.016
#> SRR1633233 2 0.0592 0.947 0.000 0.984 0.000 0.016
#> SRR1633234 2 0.0592 0.947 0.000 0.984 0.000 0.016
#> SRR1633236 3 0.4332 0.829 0.000 0.072 0.816 0.112
#> SRR1633237 3 0.4332 0.829 0.000 0.072 0.816 0.112
#> SRR1633238 3 0.4332 0.829 0.000 0.072 0.816 0.112
#> SRR1633239 3 0.4332 0.829 0.000 0.072 0.816 0.112
#> SRR1633240 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633241 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633242 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633243 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633244 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633245 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633246 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633247 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633248 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633249 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633250 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633251 3 0.1637 0.879 0.000 0.000 0.940 0.060
#> SRR1633252 3 0.1637 0.879 0.000 0.000 0.940 0.060
#> SRR1633253 3 0.1637 0.879 0.000 0.000 0.940 0.060
#> SRR1633254 3 0.1637 0.879 0.000 0.000 0.940 0.060
#> SRR1633255 3 0.1637 0.879 0.000 0.000 0.940 0.060
#> SRR1633256 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633257 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633258 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633259 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633260 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633261 3 0.0336 0.888 0.000 0.008 0.992 0.000
#> SRR1633262 3 0.4222 0.715 0.000 0.000 0.728 0.272
#> SRR1633263 3 0.4222 0.715 0.000 0.000 0.728 0.272
#> SRR1633264 3 0.4222 0.715 0.000 0.000 0.728 0.272
#> SRR1633265 3 0.4222 0.715 0.000 0.000 0.728 0.272
#> SRR1633266 3 0.4222 0.715 0.000 0.000 0.728 0.272
#> SRR1633267 3 0.1890 0.881 0.000 0.008 0.936 0.056
#> SRR1633268 3 0.1890 0.881 0.000 0.008 0.936 0.056
#> SRR1633269 3 0.1890 0.881 0.000 0.008 0.936 0.056
#> SRR1633270 3 0.1722 0.882 0.000 0.008 0.944 0.048
#> SRR1633271 3 0.1722 0.882 0.000 0.008 0.944 0.048
#> SRR1633272 3 0.1722 0.882 0.000 0.008 0.944 0.048
#> SRR1633273 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633274 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633275 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633276 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633277 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633278 3 0.6411 0.501 0.092 0.000 0.600 0.308
#> SRR1633279 3 0.6411 0.501 0.092 0.000 0.600 0.308
#> SRR1633280 3 0.6411 0.501 0.092 0.000 0.600 0.308
#> SRR1633281 3 0.6411 0.501 0.092 0.000 0.600 0.308
#> SRR1633282 4 0.5705 0.559 0.092 0.000 0.204 0.704
#> SRR1633284 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633285 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633286 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633287 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633288 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633289 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633290 4 0.5252 0.898 0.336 0.000 0.020 0.644
#> SRR1633291 4 0.5252 0.898 0.336 0.000 0.020 0.644
#> SRR1633292 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633293 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633294 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633295 3 0.2737 0.870 0.000 0.008 0.888 0.104
#> SRR1633296 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633297 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633298 4 0.5910 0.760 0.208 0.000 0.104 0.688
#> SRR1633299 4 0.5910 0.760 0.208 0.000 0.104 0.688
#> SRR1633300 2 0.2149 0.946 0.000 0.912 0.000 0.088
#> SRR1633301 2 0.2149 0.946 0.000 0.912 0.000 0.088
#> SRR1633302 2 0.2149 0.946 0.000 0.912 0.000 0.088
#> SRR1633303 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633304 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633305 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633306 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633307 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633308 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633309 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633310 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633311 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633312 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633313 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633314 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633315 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633316 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633317 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633318 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633319 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633320 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633321 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633322 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633323 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633324 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633325 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633326 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633327 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633328 2 0.0188 0.949 0.000 0.996 0.000 0.004
#> SRR1633329 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633330 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633331 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633332 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633333 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633334 2 0.1792 0.927 0.000 0.932 0.000 0.068
#> SRR1633335 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633336 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633337 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633338 4 0.4746 0.893 0.368 0.000 0.000 0.632
#> SRR1633339 4 0.4746 0.893 0.368 0.000 0.000 0.632
#> SRR1633340 4 0.4746 0.893 0.368 0.000 0.000 0.632
#> SRR1633341 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633342 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633345 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633346 4 0.4898 0.873 0.416 0.000 0.000 0.584
#> SRR1633343 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633344 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633347 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633348 4 0.5331 0.898 0.332 0.000 0.024 0.644
#> SRR1633350 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633351 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633352 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633353 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633354 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633355 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633356 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633357 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633358 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633362 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633363 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633364 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633359 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633360 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR1633361 1 0.0336 0.993 0.992 0.000 0.008 0.000
#> SRR2038492 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.5747 0.578 0.100 0.000 0.196 0.704
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.2701 0.886 0.000 0.896 0.048 0.044 0.012
#> SRR1633231 2 0.2701 0.886 0.000 0.896 0.048 0.044 0.012
#> SRR1633232 2 0.2701 0.886 0.000 0.896 0.048 0.044 0.012
#> SRR1633233 2 0.2701 0.886 0.000 0.896 0.048 0.044 0.012
#> SRR1633234 2 0.2701 0.886 0.000 0.896 0.048 0.044 0.012
#> SRR1633236 5 0.2267 0.555 0.000 0.048 0.008 0.028 0.916
#> SRR1633237 5 0.2267 0.555 0.000 0.048 0.008 0.028 0.916
#> SRR1633238 5 0.2267 0.555 0.000 0.048 0.008 0.028 0.916
#> SRR1633239 5 0.2267 0.555 0.000 0.048 0.008 0.028 0.916
#> SRR1633240 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.4498 0.596 0.000 0.000 0.280 0.032 0.688
#> SRR1633248 5 0.4498 0.596 0.000 0.000 0.280 0.032 0.688
#> SRR1633249 5 0.4498 0.596 0.000 0.000 0.280 0.032 0.688
#> SRR1633250 5 0.4498 0.596 0.000 0.000 0.280 0.032 0.688
#> SRR1633251 5 0.5193 0.457 0.000 0.000 0.364 0.052 0.584
#> SRR1633252 5 0.5193 0.457 0.000 0.000 0.364 0.052 0.584
#> SRR1633253 5 0.5193 0.457 0.000 0.000 0.364 0.052 0.584
#> SRR1633254 5 0.5193 0.457 0.000 0.000 0.364 0.052 0.584
#> SRR1633255 5 0.5193 0.457 0.000 0.000 0.364 0.052 0.584
#> SRR1633256 5 0.4562 0.588 0.000 0.000 0.292 0.032 0.676
#> SRR1633257 5 0.4562 0.588 0.000 0.000 0.292 0.032 0.676
#> SRR1633258 5 0.4562 0.588 0.000 0.000 0.292 0.032 0.676
#> SRR1633259 5 0.4520 0.594 0.000 0.000 0.284 0.032 0.684
#> SRR1633260 5 0.4520 0.594 0.000 0.000 0.284 0.032 0.684
#> SRR1633261 5 0.4520 0.594 0.000 0.000 0.284 0.032 0.684
#> SRR1633262 3 0.6653 0.823 0.000 0.000 0.432 0.240 0.328
#> SRR1633263 3 0.6653 0.823 0.000 0.000 0.432 0.240 0.328
#> SRR1633264 3 0.6653 0.823 0.000 0.000 0.432 0.240 0.328
#> SRR1633265 3 0.6653 0.823 0.000 0.000 0.432 0.240 0.328
#> SRR1633266 3 0.6653 0.823 0.000 0.000 0.432 0.240 0.328
#> SRR1633267 5 0.5395 0.427 0.000 0.000 0.356 0.068 0.576
#> SRR1633268 5 0.5395 0.427 0.000 0.000 0.356 0.068 0.576
#> SRR1633269 5 0.5395 0.427 0.000 0.000 0.356 0.068 0.576
#> SRR1633270 5 0.5341 0.439 0.000 0.000 0.356 0.064 0.580
#> SRR1633271 5 0.5341 0.439 0.000 0.000 0.356 0.064 0.580
#> SRR1633272 5 0.5341 0.439 0.000 0.000 0.356 0.064 0.580
#> SRR1633273 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633274 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633275 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633276 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633277 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633278 3 0.7044 0.814 0.012 0.000 0.404 0.328 0.256
#> SRR1633279 3 0.7044 0.814 0.012 0.000 0.404 0.328 0.256
#> SRR1633280 3 0.7044 0.814 0.012 0.000 0.404 0.328 0.256
#> SRR1633281 3 0.7044 0.814 0.012 0.000 0.404 0.328 0.256
#> SRR1633282 4 0.5504 -0.315 0.012 0.000 0.432 0.516 0.040
#> SRR1633284 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633285 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633286 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633287 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633288 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633289 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633290 4 0.4637 0.825 0.196 0.000 0.076 0.728 0.000
#> SRR1633291 4 0.4637 0.825 0.196 0.000 0.076 0.728 0.000
#> SRR1633292 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.614 0.000 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633297 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633298 4 0.4581 0.594 0.072 0.000 0.196 0.732 0.000
#> SRR1633299 4 0.4581 0.594 0.072 0.000 0.196 0.732 0.000
#> SRR1633300 2 0.3365 0.885 0.000 0.836 0.120 0.044 0.000
#> SRR1633301 2 0.3365 0.885 0.000 0.836 0.120 0.044 0.000
#> SRR1633302 2 0.3365 0.885 0.000 0.836 0.120 0.044 0.000
#> SRR1633303 2 0.3764 0.880 0.000 0.800 0.156 0.044 0.000
#> SRR1633304 2 0.3764 0.880 0.000 0.800 0.156 0.044 0.000
#> SRR1633305 2 0.3764 0.880 0.000 0.800 0.156 0.044 0.000
#> SRR1633306 2 0.3723 0.881 0.000 0.804 0.152 0.044 0.000
#> SRR1633307 2 0.3723 0.881 0.000 0.804 0.152 0.044 0.000
#> SRR1633308 2 0.3723 0.881 0.000 0.804 0.152 0.044 0.000
#> SRR1633309 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633310 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633311 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633312 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633313 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633314 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633315 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633316 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633317 2 0.3691 0.881 0.000 0.804 0.156 0.040 0.000
#> SRR1633318 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633319 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633320 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633321 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633322 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633323 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633324 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633325 2 0.1701 0.888 0.000 0.944 0.028 0.016 0.012
#> SRR1633326 2 0.1799 0.888 0.000 0.940 0.028 0.020 0.012
#> SRR1633327 2 0.1799 0.888 0.000 0.940 0.028 0.020 0.012
#> SRR1633328 2 0.1799 0.888 0.000 0.940 0.028 0.020 0.012
#> SRR1633329 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633330 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633331 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633332 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633333 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633334 2 0.2949 0.859 0.000 0.880 0.072 0.036 0.012
#> SRR1633335 4 0.3861 0.830 0.284 0.000 0.004 0.712 0.000
#> SRR1633336 4 0.3861 0.830 0.284 0.000 0.004 0.712 0.000
#> SRR1633337 4 0.3861 0.830 0.284 0.000 0.004 0.712 0.000
#> SRR1633338 4 0.3424 0.835 0.240 0.000 0.000 0.760 0.000
#> SRR1633339 4 0.3424 0.835 0.240 0.000 0.000 0.760 0.000
#> SRR1633340 4 0.3424 0.835 0.240 0.000 0.000 0.760 0.000
#> SRR1633341 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633342 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633345 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633346 4 0.3980 0.830 0.284 0.000 0.008 0.708 0.000
#> SRR1633343 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633344 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633347 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633348 4 0.4786 0.820 0.188 0.000 0.092 0.720 0.000
#> SRR1633350 1 0.3242 0.828 0.784 0.000 0.216 0.000 0.000
#> SRR1633351 1 0.3242 0.828 0.784 0.000 0.216 0.000 0.000
#> SRR1633352 1 0.3242 0.828 0.784 0.000 0.216 0.000 0.000
#> SRR1633353 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633354 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633355 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633356 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633357 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633358 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633362 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633363 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633364 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633359 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633360 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR1633361 1 0.3519 0.824 0.776 0.000 0.216 0.008 0.000
#> SRR2038492 1 0.0451 0.909 0.988 0.000 0.004 0.008 0.000
#> SRR2038491 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038489 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038488 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038485 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0290 0.914 0.992 0.000 0.008 0.000 0.000
#> SRR2038483 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038479 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038475 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038468 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0162 0.915 0.996 0.000 0.004 0.000 0.000
#> SRR2038463 1 0.0000 0.917 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.5525 -0.253 0.024 0.000 0.412 0.536 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.1262 0.849 0.000 0.956 0.000 0.020 0.016 0.008
#> SRR1633231 2 0.1262 0.849 0.000 0.956 0.000 0.020 0.016 0.008
#> SRR1633232 2 0.1167 0.850 0.000 0.960 0.000 0.020 0.012 0.008
#> SRR1633233 2 0.1167 0.850 0.000 0.960 0.000 0.020 0.012 0.008
#> SRR1633234 2 0.1167 0.850 0.000 0.960 0.000 0.020 0.012 0.008
#> SRR1633236 3 0.2547 0.523 0.000 0.044 0.896 0.008 0.040 0.012
#> SRR1633237 3 0.2547 0.523 0.000 0.044 0.896 0.008 0.040 0.012
#> SRR1633238 3 0.2547 0.523 0.000 0.044 0.896 0.008 0.040 0.012
#> SRR1633239 3 0.2547 0.523 0.000 0.044 0.896 0.008 0.040 0.012
#> SRR1633240 3 0.0291 0.580 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1633241 3 0.0291 0.580 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1633242 3 0.0291 0.580 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1633243 3 0.0291 0.580 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1633244 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633245 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633246 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633247 3 0.5251 0.608 0.000 0.000 0.572 0.020 0.064 0.344
#> SRR1633248 3 0.5251 0.608 0.000 0.000 0.572 0.020 0.064 0.344
#> SRR1633249 3 0.5251 0.608 0.000 0.000 0.572 0.020 0.064 0.344
#> SRR1633250 3 0.5251 0.608 0.000 0.000 0.572 0.020 0.064 0.344
#> SRR1633251 3 0.5791 0.538 0.000 0.000 0.484 0.028 0.092 0.396
#> SRR1633252 3 0.5791 0.538 0.000 0.000 0.484 0.028 0.092 0.396
#> SRR1633253 3 0.5791 0.538 0.000 0.000 0.484 0.028 0.092 0.396
#> SRR1633254 3 0.5791 0.538 0.000 0.000 0.484 0.028 0.092 0.396
#> SRR1633255 3 0.5791 0.538 0.000 0.000 0.484 0.028 0.092 0.396
#> SRR1633256 3 0.5330 0.601 0.000 0.000 0.556 0.020 0.068 0.356
#> SRR1633257 3 0.5330 0.601 0.000 0.000 0.556 0.020 0.068 0.356
#> SRR1633258 3 0.5330 0.601 0.000 0.000 0.556 0.020 0.068 0.356
#> SRR1633259 3 0.5262 0.607 0.000 0.000 0.568 0.020 0.064 0.348
#> SRR1633260 3 0.5262 0.607 0.000 0.000 0.568 0.020 0.064 0.348
#> SRR1633261 3 0.5262 0.607 0.000 0.000 0.568 0.020 0.064 0.348
#> SRR1633262 4 0.7419 -0.653 0.000 0.000 0.228 0.336 0.128 0.308
#> SRR1633263 4 0.7419 -0.653 0.000 0.000 0.228 0.336 0.128 0.308
#> SRR1633264 4 0.7419 -0.653 0.000 0.000 0.228 0.336 0.128 0.308
#> SRR1633265 4 0.7419 -0.653 0.000 0.000 0.228 0.336 0.128 0.308
#> SRR1633266 4 0.7419 -0.653 0.000 0.000 0.228 0.336 0.128 0.308
#> SRR1633267 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633268 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633269 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633270 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633271 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633272 3 0.6347 0.484 0.000 0.000 0.476 0.048 0.136 0.340
#> SRR1633273 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633274 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633275 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633276 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633277 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633278 5 0.7751 0.000 0.008 0.000 0.144 0.272 0.288 0.288
#> SRR1633279 5 0.7751 0.000 0.008 0.000 0.144 0.272 0.288 0.288
#> SRR1633280 6 0.7751 0.000 0.008 0.000 0.144 0.272 0.288 0.288
#> SRR1633281 6 0.7751 0.000 0.008 0.000 0.144 0.272 0.288 0.288
#> SRR1633282 4 0.6514 -0.673 0.008 0.000 0.012 0.424 0.284 0.272
#> SRR1633284 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633285 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633286 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633287 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633288 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633289 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633290 4 0.2426 0.674 0.092 0.000 0.000 0.884 0.012 0.012
#> SRR1633291 4 0.2426 0.674 0.092 0.000 0.000 0.884 0.012 0.012
#> SRR1633292 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633293 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633294 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633295 3 0.0146 0.581 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1633296 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633297 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633298 4 0.1765 0.563 0.024 0.000 0.000 0.924 0.000 0.052
#> SRR1633299 4 0.1765 0.563 0.024 0.000 0.000 0.924 0.000 0.052
#> SRR1633300 2 0.3991 0.850 0.000 0.744 0.000 0.016 0.212 0.028
#> SRR1633301 2 0.3991 0.850 0.000 0.744 0.000 0.016 0.212 0.028
#> SRR1633302 2 0.3991 0.850 0.000 0.744 0.000 0.016 0.212 0.028
#> SRR1633303 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633304 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633305 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633306 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633307 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633308 2 0.3809 0.845 0.000 0.716 0.000 0.008 0.264 0.012
#> SRR1633309 2 0.3330 0.845 0.000 0.716 0.000 0.000 0.284 0.000
#> SRR1633310 2 0.3330 0.845 0.000 0.716 0.000 0.000 0.284 0.000
#> SRR1633311 2 0.3330 0.845 0.000 0.716 0.000 0.000 0.284 0.000
#> SRR1633312 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633313 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633314 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633315 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633316 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633317 2 0.3534 0.846 0.000 0.716 0.000 0.008 0.276 0.000
#> SRR1633318 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.856 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0146 0.856 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1633327 2 0.0146 0.856 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1633328 2 0.0146 0.856 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1633329 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633330 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633331 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633332 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633333 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633334 2 0.3971 0.783 0.000 0.772 0.000 0.012 0.156 0.060
#> SRR1633335 4 0.5225 0.677 0.144 0.000 0.000 0.680 0.140 0.036
#> SRR1633336 4 0.5225 0.677 0.144 0.000 0.000 0.680 0.140 0.036
#> SRR1633337 4 0.5225 0.677 0.144 0.000 0.000 0.680 0.140 0.036
#> SRR1633338 4 0.4776 0.679 0.108 0.000 0.000 0.724 0.136 0.032
#> SRR1633339 4 0.4776 0.679 0.108 0.000 0.000 0.724 0.136 0.032
#> SRR1633340 4 0.4776 0.679 0.108 0.000 0.000 0.724 0.136 0.032
#> SRR1633341 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633342 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633345 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633346 4 0.5328 0.675 0.144 0.000 0.000 0.668 0.152 0.036
#> SRR1633343 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633344 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633347 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633348 4 0.1858 0.668 0.092 0.000 0.000 0.904 0.000 0.004
#> SRR1633350 1 0.0935 0.720 0.964 0.000 0.000 0.000 0.032 0.004
#> SRR1633351 1 0.0935 0.720 0.964 0.000 0.000 0.000 0.032 0.004
#> SRR1633352 1 0.0935 0.720 0.964 0.000 0.000 0.000 0.032 0.004
#> SRR1633353 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.719 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038492 1 0.4758 0.841 0.580 0.000 0.000 0.000 0.060 0.360
#> SRR2038491 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038490 1 0.4707 0.844 0.584 0.000 0.000 0.000 0.056 0.360
#> SRR2038489 1 0.4463 0.850 0.588 0.000 0.000 0.000 0.036 0.376
#> SRR2038488 1 0.4088 0.861 0.616 0.000 0.000 0.000 0.016 0.368
#> SRR2038487 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038486 1 0.4463 0.850 0.588 0.000 0.000 0.000 0.036 0.376
#> SRR2038485 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038484 1 0.3819 0.859 0.624 0.000 0.000 0.000 0.004 0.372
#> SRR2038483 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038482 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038481 1 0.4446 0.857 0.612 0.000 0.000 0.000 0.040 0.348
#> SRR2038480 1 0.4707 0.844 0.584 0.000 0.000 0.000 0.056 0.360
#> SRR2038479 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038477 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038478 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038476 1 0.4696 0.846 0.588 0.000 0.000 0.000 0.056 0.356
#> SRR2038475 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038474 1 0.3717 0.861 0.616 0.000 0.000 0.000 0.000 0.384
#> SRR2038473 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038472 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038471 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038470 1 0.4446 0.857 0.612 0.000 0.000 0.000 0.040 0.348
#> SRR2038469 1 0.4409 0.851 0.588 0.000 0.000 0.000 0.032 0.380
#> SRR2038468 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038467 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038466 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038465 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038464 1 0.4696 0.846 0.588 0.000 0.000 0.000 0.056 0.356
#> SRR2038463 1 0.3727 0.862 0.612 0.000 0.000 0.000 0.000 0.388
#> SRR2038462 4 0.6624 -0.694 0.008 0.000 0.016 0.404 0.288 0.284
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.981 0.992 0.5020 0.499 0.499
#> 3 3 1.000 0.997 0.998 0.2784 0.811 0.639
#> 4 4 1.000 0.957 0.974 0.1684 0.885 0.682
#> 5 5 0.953 0.929 0.950 0.0466 0.950 0.806
#> 6 6 0.924 0.944 0.949 0.0233 0.988 0.945
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.00 1.00
#> SRR1633231 2 0.000 1.000 0.00 1.00
#> SRR1633232 2 0.000 1.000 0.00 1.00
#> SRR1633233 2 0.000 1.000 0.00 1.00
#> SRR1633234 2 0.000 1.000 0.00 1.00
#> SRR1633236 2 0.000 1.000 0.00 1.00
#> SRR1633237 2 0.000 1.000 0.00 1.00
#> SRR1633238 2 0.000 1.000 0.00 1.00
#> SRR1633239 2 0.000 1.000 0.00 1.00
#> SRR1633240 2 0.000 1.000 0.00 1.00
#> SRR1633241 2 0.000 1.000 0.00 1.00
#> SRR1633242 2 0.000 1.000 0.00 1.00
#> SRR1633243 2 0.000 1.000 0.00 1.00
#> SRR1633244 2 0.000 1.000 0.00 1.00
#> SRR1633245 2 0.000 1.000 0.00 1.00
#> SRR1633246 2 0.000 1.000 0.00 1.00
#> SRR1633247 2 0.000 1.000 0.00 1.00
#> SRR1633248 2 0.000 1.000 0.00 1.00
#> SRR1633249 2 0.000 1.000 0.00 1.00
#> SRR1633250 2 0.000 1.000 0.00 1.00
#> SRR1633251 2 0.000 1.000 0.00 1.00
#> SRR1633252 2 0.000 1.000 0.00 1.00
#> SRR1633253 2 0.000 1.000 0.00 1.00
#> SRR1633254 2 0.000 1.000 0.00 1.00
#> SRR1633255 2 0.000 1.000 0.00 1.00
#> SRR1633256 2 0.000 1.000 0.00 1.00
#> SRR1633257 2 0.000 1.000 0.00 1.00
#> SRR1633258 2 0.000 1.000 0.00 1.00
#> SRR1633259 2 0.000 1.000 0.00 1.00
#> SRR1633260 2 0.000 1.000 0.00 1.00
#> SRR1633261 2 0.000 1.000 0.00 1.00
#> SRR1633262 1 0.000 0.985 1.00 0.00
#> SRR1633263 1 0.000 0.985 1.00 0.00
#> SRR1633264 1 0.000 0.985 1.00 0.00
#> SRR1633265 1 0.000 0.985 1.00 0.00
#> SRR1633266 1 0.000 0.985 1.00 0.00
#> SRR1633267 2 0.000 1.000 0.00 1.00
#> SRR1633268 2 0.000 1.000 0.00 1.00
#> SRR1633269 2 0.000 1.000 0.00 1.00
#> SRR1633270 2 0.000 1.000 0.00 1.00
#> SRR1633271 2 0.000 1.000 0.00 1.00
#> SRR1633272 2 0.000 1.000 0.00 1.00
#> SRR1633273 1 0.000 0.985 1.00 0.00
#> SRR1633274 1 0.000 0.985 1.00 0.00
#> SRR1633275 1 0.000 0.985 1.00 0.00
#> SRR1633276 1 0.000 0.985 1.00 0.00
#> SRR1633277 1 0.000 0.985 1.00 0.00
#> SRR1633278 1 0.904 0.546 0.68 0.32
#> SRR1633279 1 0.904 0.546 0.68 0.32
#> SRR1633280 1 0.904 0.546 0.68 0.32
#> SRR1633281 1 0.904 0.546 0.68 0.32
#> SRR1633282 1 0.000 0.985 1.00 0.00
#> SRR1633284 1 0.000 0.985 1.00 0.00
#> SRR1633285 1 0.000 0.985 1.00 0.00
#> SRR1633286 1 0.000 0.985 1.00 0.00
#> SRR1633287 1 0.000 0.985 1.00 0.00
#> SRR1633288 1 0.000 0.985 1.00 0.00
#> SRR1633289 1 0.000 0.985 1.00 0.00
#> SRR1633290 1 0.000 0.985 1.00 0.00
#> SRR1633291 1 0.000 0.985 1.00 0.00
#> SRR1633292 2 0.000 1.000 0.00 1.00
#> SRR1633293 2 0.000 1.000 0.00 1.00
#> SRR1633294 2 0.000 1.000 0.00 1.00
#> SRR1633295 2 0.000 1.000 0.00 1.00
#> SRR1633296 1 0.000 0.985 1.00 0.00
#> SRR1633297 1 0.000 0.985 1.00 0.00
#> SRR1633298 1 0.000 0.985 1.00 0.00
#> SRR1633299 1 0.000 0.985 1.00 0.00
#> SRR1633300 2 0.000 1.000 0.00 1.00
#> SRR1633301 2 0.000 1.000 0.00 1.00
#> SRR1633302 2 0.000 1.000 0.00 1.00
#> SRR1633303 2 0.000 1.000 0.00 1.00
#> SRR1633304 2 0.000 1.000 0.00 1.00
#> SRR1633305 2 0.000 1.000 0.00 1.00
#> SRR1633306 2 0.000 1.000 0.00 1.00
#> SRR1633307 2 0.000 1.000 0.00 1.00
#> SRR1633308 2 0.000 1.000 0.00 1.00
#> SRR1633309 2 0.000 1.000 0.00 1.00
#> SRR1633310 2 0.000 1.000 0.00 1.00
#> SRR1633311 2 0.000 1.000 0.00 1.00
#> SRR1633312 2 0.000 1.000 0.00 1.00
#> SRR1633313 2 0.000 1.000 0.00 1.00
#> SRR1633314 2 0.000 1.000 0.00 1.00
#> SRR1633315 2 0.000 1.000 0.00 1.00
#> SRR1633316 2 0.000 1.000 0.00 1.00
#> SRR1633317 2 0.000 1.000 0.00 1.00
#> SRR1633318 2 0.000 1.000 0.00 1.00
#> SRR1633319 2 0.000 1.000 0.00 1.00
#> SRR1633320 2 0.000 1.000 0.00 1.00
#> SRR1633321 2 0.000 1.000 0.00 1.00
#> SRR1633322 2 0.000 1.000 0.00 1.00
#> SRR1633323 2 0.000 1.000 0.00 1.00
#> SRR1633324 2 0.000 1.000 0.00 1.00
#> SRR1633325 2 0.000 1.000 0.00 1.00
#> SRR1633326 2 0.000 1.000 0.00 1.00
#> SRR1633327 2 0.000 1.000 0.00 1.00
#> SRR1633328 2 0.000 1.000 0.00 1.00
#> SRR1633329 2 0.000 1.000 0.00 1.00
#> SRR1633330 2 0.000 1.000 0.00 1.00
#> SRR1633331 2 0.000 1.000 0.00 1.00
#> SRR1633332 2 0.000 1.000 0.00 1.00
#> SRR1633333 2 0.000 1.000 0.00 1.00
#> SRR1633334 2 0.000 1.000 0.00 1.00
#> SRR1633335 1 0.000 0.985 1.00 0.00
#> SRR1633336 1 0.000 0.985 1.00 0.00
#> SRR1633337 1 0.000 0.985 1.00 0.00
#> SRR1633338 1 0.000 0.985 1.00 0.00
#> SRR1633339 1 0.000 0.985 1.00 0.00
#> SRR1633340 1 0.000 0.985 1.00 0.00
#> SRR1633341 1 0.000 0.985 1.00 0.00
#> SRR1633342 1 0.000 0.985 1.00 0.00
#> SRR1633345 1 0.000 0.985 1.00 0.00
#> SRR1633346 1 0.000 0.985 1.00 0.00
#> SRR1633343 1 0.000 0.985 1.00 0.00
#> SRR1633344 1 0.000 0.985 1.00 0.00
#> SRR1633347 1 0.000 0.985 1.00 0.00
#> SRR1633348 1 0.000 0.985 1.00 0.00
#> SRR1633350 1 0.000 0.985 1.00 0.00
#> SRR1633351 1 0.000 0.985 1.00 0.00
#> SRR1633352 1 0.000 0.985 1.00 0.00
#> SRR1633353 1 0.000 0.985 1.00 0.00
#> SRR1633354 1 0.000 0.985 1.00 0.00
#> SRR1633355 1 0.000 0.985 1.00 0.00
#> SRR1633356 1 0.000 0.985 1.00 0.00
#> SRR1633357 1 0.000 0.985 1.00 0.00
#> SRR1633358 1 0.000 0.985 1.00 0.00
#> SRR1633362 1 0.000 0.985 1.00 0.00
#> SRR1633363 1 0.000 0.985 1.00 0.00
#> SRR1633364 1 0.000 0.985 1.00 0.00
#> SRR1633359 1 0.000 0.985 1.00 0.00
#> SRR1633360 1 0.000 0.985 1.00 0.00
#> SRR1633361 1 0.000 0.985 1.00 0.00
#> SRR2038492 1 0.000 0.985 1.00 0.00
#> SRR2038491 1 0.000 0.985 1.00 0.00
#> SRR2038490 1 0.000 0.985 1.00 0.00
#> SRR2038489 1 0.000 0.985 1.00 0.00
#> SRR2038488 1 0.000 0.985 1.00 0.00
#> SRR2038487 1 0.000 0.985 1.00 0.00
#> SRR2038486 1 0.000 0.985 1.00 0.00
#> SRR2038485 1 0.000 0.985 1.00 0.00
#> SRR2038484 1 0.000 0.985 1.00 0.00
#> SRR2038483 1 0.000 0.985 1.00 0.00
#> SRR2038482 1 0.000 0.985 1.00 0.00
#> SRR2038481 1 0.000 0.985 1.00 0.00
#> SRR2038480 1 0.000 0.985 1.00 0.00
#> SRR2038479 1 0.000 0.985 1.00 0.00
#> SRR2038477 1 0.000 0.985 1.00 0.00
#> SRR2038478 1 0.000 0.985 1.00 0.00
#> SRR2038476 1 0.000 0.985 1.00 0.00
#> SRR2038475 1 0.000 0.985 1.00 0.00
#> SRR2038474 1 0.000 0.985 1.00 0.00
#> SRR2038473 1 0.000 0.985 1.00 0.00
#> SRR2038472 1 0.000 0.985 1.00 0.00
#> SRR2038471 1 0.000 0.985 1.00 0.00
#> SRR2038470 1 0.000 0.985 1.00 0.00
#> SRR2038469 1 0.000 0.985 1.00 0.00
#> SRR2038468 1 0.000 0.985 1.00 0.00
#> SRR2038467 1 0.000 0.985 1.00 0.00
#> SRR2038466 1 0.000 0.985 1.00 0.00
#> SRR2038465 1 0.000 0.985 1.00 0.00
#> SRR2038464 1 0.000 0.985 1.00 0.00
#> SRR2038463 1 0.000 0.985 1.00 0.00
#> SRR2038462 1 0.000 0.985 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633237 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633238 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633240 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633241 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633242 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633243 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633244 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633245 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633246 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633247 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.995 0.000 0.000 1.000
#> SRR1633273 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633274 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633275 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633276 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633277 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633278 3 0.1129 0.977 0.020 0.004 0.976
#> SRR1633279 3 0.1129 0.977 0.020 0.004 0.976
#> SRR1633280 3 0.1129 0.977 0.020 0.004 0.976
#> SRR1633281 3 0.1129 0.977 0.020 0.004 0.976
#> SRR1633282 3 0.1031 0.974 0.024 0.000 0.976
#> SRR1633284 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633290 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633291 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633292 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633293 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633294 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633295 3 0.0424 0.992 0.000 0.008 0.992
#> SRR1633296 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633297 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633298 1 0.0237 0.995 0.996 0.000 0.004
#> SRR1633299 1 0.0237 0.995 0.996 0.000 0.004
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633343 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633344 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633347 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633348 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633350 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038462 1 0.2537 0.914 0.920 0.000 0.080
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633236 2 0.1888 0.945 0.000 0.940 0.044 0.016
#> SRR1633237 2 0.1888 0.945 0.000 0.940 0.044 0.016
#> SRR1633238 2 0.1888 0.945 0.000 0.940 0.044 0.016
#> SRR1633239 2 0.1888 0.945 0.000 0.940 0.044 0.016
#> SRR1633240 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633241 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633242 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633243 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633244 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633245 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633246 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633247 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633252 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633253 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633254 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633255 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633256 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.3074 0.826 0.000 0.000 0.848 0.152
#> SRR1633263 3 0.3074 0.826 0.000 0.000 0.848 0.152
#> SRR1633264 3 0.3074 0.826 0.000 0.000 0.848 0.152
#> SRR1633265 3 0.3074 0.826 0.000 0.000 0.848 0.152
#> SRR1633266 3 0.3074 0.826 0.000 0.000 0.848 0.152
#> SRR1633267 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633268 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633269 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633270 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633271 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633272 3 0.0000 0.929 0.000 0.000 1.000 0.000
#> SRR1633273 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633274 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633275 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633276 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633277 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633278 3 0.5875 0.400 0.024 0.008 0.572 0.396
#> SRR1633279 3 0.5875 0.400 0.024 0.008 0.572 0.396
#> SRR1633280 3 0.5875 0.400 0.024 0.008 0.572 0.396
#> SRR1633281 3 0.5875 0.400 0.024 0.008 0.572 0.396
#> SRR1633282 4 0.0921 0.952 0.000 0.000 0.028 0.972
#> SRR1633284 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633285 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633286 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633287 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633288 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633289 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633290 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633291 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633292 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633293 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633294 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633295 3 0.1297 0.920 0.000 0.020 0.964 0.016
#> SRR1633296 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633297 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633298 4 0.0657 0.972 0.012 0.000 0.004 0.984
#> SRR1633299 4 0.0657 0.972 0.012 0.000 0.004 0.984
#> SRR1633300 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633336 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633337 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633338 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633339 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633340 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633341 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633342 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633345 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633346 4 0.1637 0.965 0.060 0.000 0.000 0.940
#> SRR1633343 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633344 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633347 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633348 4 0.0592 0.976 0.016 0.000 0.000 0.984
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.0817 0.955 0.000 0.000 0.024 0.976
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 5 0.2929 0.768 0.000 0.180 0.000 0.000 0.820
#> SRR1633237 5 0.2929 0.768 0.000 0.180 0.000 0.000 0.820
#> SRR1633238 5 0.2929 0.768 0.000 0.180 0.000 0.000 0.820
#> SRR1633239 5 0.2929 0.768 0.000 0.180 0.000 0.000 0.820
#> SRR1633240 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633241 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633242 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633243 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633244 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633245 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633246 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633247 3 0.4242 0.642 0.000 0.000 0.572 0.000 0.428
#> SRR1633248 3 0.4242 0.642 0.000 0.000 0.572 0.000 0.428
#> SRR1633249 3 0.4242 0.642 0.000 0.000 0.572 0.000 0.428
#> SRR1633250 3 0.4242 0.642 0.000 0.000 0.572 0.000 0.428
#> SRR1633251 3 0.3480 0.778 0.000 0.000 0.752 0.000 0.248
#> SRR1633252 3 0.3480 0.778 0.000 0.000 0.752 0.000 0.248
#> SRR1633253 3 0.3480 0.778 0.000 0.000 0.752 0.000 0.248
#> SRR1633254 3 0.3480 0.778 0.000 0.000 0.752 0.000 0.248
#> SRR1633255 3 0.3480 0.778 0.000 0.000 0.752 0.000 0.248
#> SRR1633256 3 0.4210 0.662 0.000 0.000 0.588 0.000 0.412
#> SRR1633257 3 0.4210 0.662 0.000 0.000 0.588 0.000 0.412
#> SRR1633258 3 0.4210 0.662 0.000 0.000 0.588 0.000 0.412
#> SRR1633259 3 0.4227 0.653 0.000 0.000 0.580 0.000 0.420
#> SRR1633260 3 0.4227 0.653 0.000 0.000 0.580 0.000 0.420
#> SRR1633261 3 0.4227 0.653 0.000 0.000 0.580 0.000 0.420
#> SRR1633262 3 0.1764 0.786 0.000 0.000 0.928 0.008 0.064
#> SRR1633263 3 0.1764 0.786 0.000 0.000 0.928 0.008 0.064
#> SRR1633264 3 0.1764 0.786 0.000 0.000 0.928 0.008 0.064
#> SRR1633265 3 0.1764 0.786 0.000 0.000 0.928 0.008 0.064
#> SRR1633266 3 0.1764 0.786 0.000 0.000 0.928 0.008 0.064
#> SRR1633267 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633268 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633269 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633270 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633271 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633272 3 0.2280 0.799 0.000 0.000 0.880 0.000 0.120
#> SRR1633273 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633274 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633275 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633276 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633277 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633278 3 0.1205 0.744 0.000 0.000 0.956 0.040 0.004
#> SRR1633279 3 0.1205 0.744 0.000 0.000 0.956 0.040 0.004
#> SRR1633280 3 0.1205 0.744 0.000 0.000 0.956 0.040 0.004
#> SRR1633281 3 0.1205 0.744 0.000 0.000 0.956 0.040 0.004
#> SRR1633282 3 0.3048 0.581 0.000 0.000 0.820 0.176 0.004
#> SRR1633284 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633285 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633286 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633287 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633288 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633289 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633290 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633291 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633292 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633293 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633294 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633295 5 0.0324 0.908 0.000 0.004 0.004 0.000 0.992
#> SRR1633296 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633297 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633298 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633299 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633335 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633336 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633337 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633338 4 0.0000 0.980 0.000 0.000 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.980 0.000 0.000 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.980 0.000 0.000 0.000 1.000 0.000
#> SRR1633341 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633342 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633345 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633346 4 0.0290 0.980 0.008 0.000 0.000 0.992 0.000
#> SRR1633343 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633344 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633347 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633348 4 0.0880 0.981 0.000 0.000 0.032 0.968 0.000
#> SRR1633350 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633351 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633352 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633353 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633354 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633355 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633356 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633357 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633358 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633362 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633363 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633364 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633359 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633360 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR1633361 1 0.0451 0.993 0.988 0.000 0.008 0.000 0.004
#> SRR2038492 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.3579 0.536 0.000 0.000 0.756 0.240 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633231 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633232 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633233 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633234 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633236 5 0.0547 0.973 0.000 0.020 0.000 0.000 0.980 0.000
#> SRR1633237 5 0.0547 0.973 0.000 0.020 0.000 0.000 0.980 0.000
#> SRR1633238 5 0.0547 0.973 0.000 0.020 0.000 0.000 0.980 0.000
#> SRR1633239 5 0.0547 0.973 0.000 0.020 0.000 0.000 0.980 0.000
#> SRR1633240 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633241 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633242 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633243 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633244 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633245 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633246 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633247 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633248 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633249 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633250 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633251 3 0.0790 0.910 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1633252 3 0.0790 0.910 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1633253 3 0.0790 0.910 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1633254 3 0.0790 0.910 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1633255 3 0.0790 0.910 0.000 0.000 0.968 0.000 0.032 0.000
#> SRR1633256 3 0.1714 0.895 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1633257 3 0.1714 0.895 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1633258 3 0.1714 0.895 0.000 0.000 0.908 0.000 0.092 0.000
#> SRR1633259 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633260 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633261 3 0.1765 0.893 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633262 3 0.1411 0.874 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1633263 3 0.1411 0.874 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1633264 3 0.1411 0.874 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1633265 3 0.1411 0.874 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1633266 3 0.1411 0.874 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1633267 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633268 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633269 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633270 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633271 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633272 3 0.1196 0.897 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR1633273 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633274 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633275 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633276 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633277 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633278 6 0.3330 0.943 0.000 0.000 0.284 0.000 0.000 0.716
#> SRR1633279 6 0.3330 0.943 0.000 0.000 0.284 0.000 0.000 0.716
#> SRR1633280 6 0.3330 0.943 0.000 0.000 0.284 0.000 0.000 0.716
#> SRR1633281 6 0.3330 0.943 0.000 0.000 0.284 0.000 0.000 0.716
#> SRR1633282 6 0.4229 0.895 0.000 0.000 0.220 0.068 0.000 0.712
#> SRR1633284 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633285 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633286 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633287 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633288 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633289 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633290 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633291 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633292 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633293 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633294 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633295 5 0.0146 0.990 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633296 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633297 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633298 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633299 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633300 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633301 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633302 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633303 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633304 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633305 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633306 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633307 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633308 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633309 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633310 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633311 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633312 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633313 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633314 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633315 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633316 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633317 2 0.0937 0.977 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1633318 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633319 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633320 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633321 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633322 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633323 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633324 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633325 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633326 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633327 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633328 2 0.0146 0.980 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633329 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633330 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633331 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633332 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633333 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633334 2 0.0363 0.976 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633335 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633336 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633337 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633338 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633339 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633340 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633341 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633342 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633345 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633346 4 0.1908 0.947 0.000 0.000 0.000 0.900 0.004 0.096
#> SRR1633343 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633344 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633347 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633348 4 0.0260 0.944 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633350 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633351 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633352 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633353 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633354 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633355 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633356 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633357 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633358 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633362 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633363 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633364 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633359 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633360 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR1633361 1 0.2416 0.892 0.844 0.000 0.000 0.000 0.000 0.156
#> SRR2038492 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.948 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 6 0.3714 0.863 0.000 0.000 0.196 0.044 0.000 0.760
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.996 0.4592 0.539 0.539
#> 3 3 0.987 0.978 0.989 0.3745 0.771 0.599
#> 4 4 1.000 0.977 0.988 0.1932 0.874 0.662
#> 5 5 1.000 0.982 0.992 0.0405 0.970 0.880
#> 6 6 1.000 0.978 0.989 0.0382 0.970 0.862
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.988 0.000 1.000
#> SRR1633231 2 0.0000 0.988 0.000 1.000
#> SRR1633232 2 0.0000 0.988 0.000 1.000
#> SRR1633233 2 0.0000 0.988 0.000 1.000
#> SRR1633234 2 0.0000 0.988 0.000 1.000
#> SRR1633236 2 0.0000 0.988 0.000 1.000
#> SRR1633237 2 0.0000 0.988 0.000 1.000
#> SRR1633238 2 0.0000 0.988 0.000 1.000
#> SRR1633239 2 0.0000 0.988 0.000 1.000
#> SRR1633240 2 0.0376 0.985 0.004 0.996
#> SRR1633241 2 0.0376 0.985 0.004 0.996
#> SRR1633242 2 0.0000 0.988 0.000 1.000
#> SRR1633243 2 0.0000 0.988 0.000 1.000
#> SRR1633244 2 0.1633 0.968 0.024 0.976
#> SRR1633245 2 0.1633 0.968 0.024 0.976
#> SRR1633246 2 0.1633 0.968 0.024 0.976
#> SRR1633247 1 0.0000 1.000 1.000 0.000
#> SRR1633248 1 0.0000 1.000 1.000 0.000
#> SRR1633249 1 0.0000 1.000 1.000 0.000
#> SRR1633250 1 0.0000 1.000 1.000 0.000
#> SRR1633251 1 0.0000 1.000 1.000 0.000
#> SRR1633252 1 0.0000 1.000 1.000 0.000
#> SRR1633253 1 0.0000 1.000 1.000 0.000
#> SRR1633254 1 0.0000 1.000 1.000 0.000
#> SRR1633255 1 0.0000 1.000 1.000 0.000
#> SRR1633256 1 0.0000 1.000 1.000 0.000
#> SRR1633257 1 0.0000 1.000 1.000 0.000
#> SRR1633258 1 0.0000 1.000 1.000 0.000
#> SRR1633259 1 0.0000 1.000 1.000 0.000
#> SRR1633260 1 0.0000 1.000 1.000 0.000
#> SRR1633261 1 0.0000 1.000 1.000 0.000
#> SRR1633262 1 0.0000 1.000 1.000 0.000
#> SRR1633263 1 0.0000 1.000 1.000 0.000
#> SRR1633264 1 0.0000 1.000 1.000 0.000
#> SRR1633265 1 0.0000 1.000 1.000 0.000
#> SRR1633266 1 0.0000 1.000 1.000 0.000
#> SRR1633267 1 0.0000 1.000 1.000 0.000
#> SRR1633268 1 0.0000 1.000 1.000 0.000
#> SRR1633269 1 0.0000 1.000 1.000 0.000
#> SRR1633270 2 0.7376 0.748 0.208 0.792
#> SRR1633271 2 0.7219 0.760 0.200 0.800
#> SRR1633272 2 0.7219 0.760 0.200 0.800
#> SRR1633273 1 0.0000 1.000 1.000 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0.000
#> SRR1633278 1 0.0000 1.000 1.000 0.000
#> SRR1633279 1 0.0000 1.000 1.000 0.000
#> SRR1633280 1 0.0000 1.000 1.000 0.000
#> SRR1633281 1 0.0000 1.000 1.000 0.000
#> SRR1633282 1 0.0000 1.000 1.000 0.000
#> SRR1633284 1 0.0000 1.000 1.000 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0.000
#> SRR1633292 2 0.0000 0.988 0.000 1.000
#> SRR1633293 2 0.0000 0.988 0.000 1.000
#> SRR1633294 2 0.0000 0.988 0.000 1.000
#> SRR1633295 2 0.0000 0.988 0.000 1.000
#> SRR1633296 1 0.0000 1.000 1.000 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0.000
#> SRR1633300 2 0.0000 0.988 0.000 1.000
#> SRR1633301 2 0.0000 0.988 0.000 1.000
#> SRR1633302 2 0.0000 0.988 0.000 1.000
#> SRR1633303 2 0.0000 0.988 0.000 1.000
#> SRR1633304 2 0.0000 0.988 0.000 1.000
#> SRR1633305 2 0.0000 0.988 0.000 1.000
#> SRR1633306 2 0.0000 0.988 0.000 1.000
#> SRR1633307 2 0.0000 0.988 0.000 1.000
#> SRR1633308 2 0.0000 0.988 0.000 1.000
#> SRR1633309 2 0.0000 0.988 0.000 1.000
#> SRR1633310 2 0.0000 0.988 0.000 1.000
#> SRR1633311 2 0.0000 0.988 0.000 1.000
#> SRR1633312 2 0.0000 0.988 0.000 1.000
#> SRR1633313 2 0.0000 0.988 0.000 1.000
#> SRR1633314 2 0.0000 0.988 0.000 1.000
#> SRR1633315 2 0.0000 0.988 0.000 1.000
#> SRR1633316 2 0.0000 0.988 0.000 1.000
#> SRR1633317 2 0.0000 0.988 0.000 1.000
#> SRR1633318 2 0.0000 0.988 0.000 1.000
#> SRR1633319 2 0.0000 0.988 0.000 1.000
#> SRR1633320 2 0.0000 0.988 0.000 1.000
#> SRR1633321 2 0.0000 0.988 0.000 1.000
#> SRR1633322 2 0.0000 0.988 0.000 1.000
#> SRR1633323 2 0.0000 0.988 0.000 1.000
#> SRR1633324 2 0.0000 0.988 0.000 1.000
#> SRR1633325 2 0.0000 0.988 0.000 1.000
#> SRR1633326 2 0.0000 0.988 0.000 1.000
#> SRR1633327 2 0.0000 0.988 0.000 1.000
#> SRR1633328 2 0.0000 0.988 0.000 1.000
#> SRR1633329 2 0.0000 0.988 0.000 1.000
#> SRR1633330 2 0.0000 0.988 0.000 1.000
#> SRR1633331 2 0.0000 0.988 0.000 1.000
#> SRR1633332 2 0.0000 0.988 0.000 1.000
#> SRR1633333 2 0.0000 0.988 0.000 1.000
#> SRR1633334 2 0.0000 0.988 0.000 1.000
#> SRR1633335 1 0.0000 1.000 1.000 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000
#> SRR2038462 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.141 0.957 0.000 0.036 0.964
#> SRR1633237 3 0.341 0.863 0.000 0.124 0.876
#> SRR1633238 3 0.312 0.882 0.000 0.108 0.892
#> SRR1633239 3 0.334 0.868 0.000 0.120 0.880
#> SRR1633240 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633241 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633242 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633243 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633244 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633245 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633246 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633247 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633248 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633249 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633250 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633251 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633252 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633253 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633254 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633255 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633256 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633257 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633258 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633259 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633260 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633261 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633262 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633263 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633264 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633265 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633266 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633267 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633268 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633269 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633270 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633271 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633272 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633273 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633274 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633275 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633276 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633277 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633278 1 0.429 0.801 0.820 0.000 0.180
#> SRR1633279 1 0.424 0.807 0.824 0.000 0.176
#> SRR1633280 1 0.424 0.807 0.824 0.000 0.176
#> SRR1633281 1 0.424 0.807 0.824 0.000 0.176
#> SRR1633282 1 0.406 0.821 0.836 0.000 0.164
#> SRR1633284 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633285 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633286 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633287 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633288 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633289 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633290 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633291 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633292 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633293 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633294 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633295 3 0.000 0.990 0.000 0.000 1.000
#> SRR1633296 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633297 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633298 1 0.406 0.821 0.836 0.000 0.164
#> SRR1633299 1 0.406 0.821 0.836 0.000 0.164
#> SRR1633300 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633336 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633337 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633338 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633339 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633340 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633341 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633342 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633345 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633346 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633343 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633344 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633347 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633348 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633350 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633351 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633352 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633353 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633354 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633355 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633356 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633357 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633358 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633362 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633363 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633364 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633359 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633360 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633361 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038492 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038491 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038490 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038489 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038488 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038487 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038486 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038485 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038484 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038483 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038482 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038481 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038480 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038479 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038477 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038478 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038476 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038475 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038474 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038473 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038472 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038471 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038470 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038469 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038468 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038467 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038466 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038465 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038464 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038463 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038462 1 0.394 0.831 0.844 0.000 0.156
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.0188 0.967 0.000 0.004 0.996 0.000
#> SRR1633237 3 0.1302 0.936 0.000 0.044 0.956 0.000
#> SRR1633238 3 0.0921 0.949 0.000 0.028 0.972 0.000
#> SRR1633239 3 0.1302 0.936 0.000 0.044 0.956 0.000
#> SRR1633240 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633252 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633253 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633254 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633255 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633256 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633257 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633258 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633259 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633260 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633261 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633262 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1633263 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1633264 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1633265 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1633266 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1633267 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633268 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633269 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633270 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633271 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633272 3 0.0188 0.969 0.000 0.000 0.996 0.004
#> SRR1633273 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633278 4 0.3311 0.799 0.000 0.000 0.172 0.828
#> SRR1633279 4 0.3266 0.804 0.000 0.000 0.168 0.832
#> SRR1633280 4 0.2921 0.841 0.000 0.000 0.140 0.860
#> SRR1633281 4 0.2973 0.836 0.000 0.000 0.144 0.856
#> SRR1633282 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633284 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633285 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633286 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633287 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633288 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633289 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633290 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633336 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633337 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633338 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633339 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633340 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633341 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633342 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633345 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633346 4 0.0188 0.979 0.004 0.000 0.000 0.996
#> SRR1633343 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.0000 0.980 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.0000 0.980 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633231 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633232 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633233 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633234 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633236 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633237 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633238 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633239 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633240 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633241 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633242 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633243 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633244 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633245 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633246 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633247 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633248 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633249 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633250 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633251 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633252 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633253 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633254 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633255 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633256 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633257 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633258 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633259 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633260 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633261 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633262 3 0.0703 0.975 0 0 0.976 0.024 0
#> SRR1633263 3 0.0703 0.975 0 0 0.976 0.024 0
#> SRR1633264 3 0.0703 0.975 0 0 0.976 0.024 0
#> SRR1633265 3 0.0703 0.975 0 0 0.976 0.024 0
#> SRR1633266 3 0.0703 0.975 0 0 0.976 0.024 0
#> SRR1633267 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633268 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633269 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633270 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633271 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633272 3 0.0000 0.994 0 0 1.000 0.000 0
#> SRR1633273 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633274 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633275 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633276 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633277 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633278 4 0.3837 0.587 0 0 0.308 0.692 0
#> SRR1633279 4 0.3796 0.602 0 0 0.300 0.700 0
#> SRR1633280 4 0.3561 0.670 0 0 0.260 0.740 0
#> SRR1633281 4 0.3586 0.664 0 0 0.264 0.736 0
#> SRR1633282 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633284 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633285 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633286 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633287 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633288 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633289 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633290 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633291 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633292 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633293 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633294 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633295 5 0.0000 1.000 0 0 0.000 0.000 1
#> SRR1633296 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633297 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633298 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633299 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633300 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633301 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633302 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633303 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633304 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633305 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633306 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633307 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633308 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633309 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633310 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633311 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633312 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633313 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633314 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633315 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633316 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633317 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633318 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633319 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633320 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633321 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633322 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633323 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633324 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633325 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633326 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633327 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633328 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633329 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633330 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633331 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633332 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633333 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633334 2 0.0000 1.000 0 1 0.000 0.000 0
#> SRR1633335 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633336 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633337 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633338 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633339 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633340 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633341 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633342 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633345 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633346 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633343 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633344 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633347 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633348 4 0.0000 0.965 0 0 0.000 1.000 0
#> SRR1633350 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633351 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633352 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633353 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633354 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633355 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633356 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633357 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633358 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633362 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633363 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633364 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633359 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633360 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR1633361 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038492 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038491 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038490 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038489 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038488 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038487 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038486 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038485 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038484 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038483 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038482 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038481 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038480 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038479 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038477 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038478 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038476 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038475 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038474 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038473 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038472 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038471 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038470 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038469 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038468 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038467 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038466 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038465 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038464 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038463 1 0.0000 1.000 1 0 0.000 0.000 0
#> SRR2038462 4 0.0000 0.965 0 0 0.000 1.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.1204 0.944 0 0.944 0.000 0.000 0 0.056
#> SRR1633231 2 0.1007 0.953 0 0.956 0.000 0.000 0 0.044
#> SRR1633232 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633233 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633234 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633236 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633237 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633238 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633239 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633240 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633241 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633242 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633243 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633244 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633245 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633246 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633247 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633248 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633249 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633250 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633251 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633252 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633253 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633254 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633255 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633256 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633257 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633258 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633259 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633260 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633261 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633262 3 0.0632 0.975 0 0.000 0.976 0.024 0 0.000
#> SRR1633263 3 0.0632 0.975 0 0.000 0.976 0.024 0 0.000
#> SRR1633264 3 0.0632 0.975 0 0.000 0.976 0.024 0 0.000
#> SRR1633265 3 0.0632 0.975 0 0.000 0.976 0.024 0 0.000
#> SRR1633266 3 0.0632 0.975 0 0.000 0.976 0.024 0 0.000
#> SRR1633267 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633268 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633269 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633270 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633271 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633272 3 0.0000 0.994 0 0.000 1.000 0.000 0 0.000
#> SRR1633273 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633274 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633275 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633276 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633277 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633278 4 0.3446 0.587 0 0.000 0.308 0.692 0 0.000
#> SRR1633279 4 0.3409 0.602 0 0.000 0.300 0.700 0 0.000
#> SRR1633280 4 0.3198 0.670 0 0.000 0.260 0.740 0 0.000
#> SRR1633281 4 0.3221 0.664 0 0.000 0.264 0.736 0 0.000
#> SRR1633282 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633284 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633285 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633286 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633287 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633288 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633289 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633290 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633291 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633292 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633293 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633294 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633295 5 0.0000 1.000 0 0.000 0.000 0.000 1 0.000
#> SRR1633296 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633297 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633298 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633299 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633300 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633301 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633302 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633303 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633304 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633305 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633306 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633307 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633308 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633309 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633310 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633311 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633312 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633313 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633314 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633315 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633316 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633317 6 0.0000 1.000 0 0.000 0.000 0.000 0 1.000
#> SRR1633318 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633319 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633320 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633321 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633322 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633323 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633324 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633325 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633326 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633327 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633328 2 0.0000 0.977 0 1.000 0.000 0.000 0 0.000
#> SRR1633329 2 0.1075 0.958 0 0.952 0.000 0.000 0 0.048
#> SRR1633330 2 0.1387 0.945 0 0.932 0.000 0.000 0 0.068
#> SRR1633331 2 0.1204 0.953 0 0.944 0.000 0.000 0 0.056
#> SRR1633332 2 0.1204 0.953 0 0.944 0.000 0.000 0 0.056
#> SRR1633333 2 0.1387 0.945 0 0.932 0.000 0.000 0 0.068
#> SRR1633334 2 0.1387 0.945 0 0.932 0.000 0.000 0 0.068
#> SRR1633335 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633336 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633337 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633338 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633339 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633340 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633341 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633342 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633345 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633346 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633343 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633344 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633347 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633348 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
#> SRR1633350 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633351 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633352 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633353 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633354 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633355 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633356 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633357 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633358 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633362 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633363 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633364 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633359 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633360 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR1633361 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038492 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038491 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038490 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038489 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038488 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038487 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038486 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038485 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038484 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038483 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038482 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038481 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038480 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038479 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038477 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038478 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038476 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038475 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038474 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038473 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038472 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038471 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038470 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038469 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038468 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038467 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038466 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038465 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038464 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038463 1 0.0000 1.000 1 0.000 0.000 0.000 0 0.000
#> SRR2038462 4 0.0000 0.965 0 0.000 0.000 1.000 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.654 0.878 0.933 0.4121 0.627 0.627
#> 3 3 1.000 0.985 0.994 0.5259 0.745 0.594
#> 4 4 0.915 0.941 0.956 0.1750 0.875 0.663
#> 5 5 0.892 0.824 0.867 0.0619 0.953 0.815
#> 6 6 0.946 0.899 0.956 0.0351 0.956 0.800
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3 4
There is also optional best \(k\) = 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 1 0.886 0.689 0.696 0.304
#> SRR1633237 1 0.886 0.689 0.696 0.304
#> SRR1633238 1 0.886 0.689 0.696 0.304
#> SRR1633239 1 0.886 0.689 0.696 0.304
#> SRR1633240 1 0.886 0.689 0.696 0.304
#> SRR1633241 1 0.886 0.689 0.696 0.304
#> SRR1633242 1 0.886 0.689 0.696 0.304
#> SRR1633243 1 0.886 0.689 0.696 0.304
#> SRR1633244 1 0.886 0.689 0.696 0.304
#> SRR1633245 1 0.886 0.689 0.696 0.304
#> SRR1633246 1 0.886 0.689 0.696 0.304
#> SRR1633247 1 0.886 0.689 0.696 0.304
#> SRR1633248 1 0.886 0.689 0.696 0.304
#> SRR1633249 1 0.886 0.689 0.696 0.304
#> SRR1633250 1 0.886 0.689 0.696 0.304
#> SRR1633251 1 0.886 0.689 0.696 0.304
#> SRR1633252 1 0.886 0.689 0.696 0.304
#> SRR1633253 1 0.886 0.689 0.696 0.304
#> SRR1633254 1 0.886 0.689 0.696 0.304
#> SRR1633255 1 0.886 0.689 0.696 0.304
#> SRR1633256 1 0.886 0.689 0.696 0.304
#> SRR1633257 1 0.886 0.689 0.696 0.304
#> SRR1633258 1 0.886 0.689 0.696 0.304
#> SRR1633259 1 0.886 0.689 0.696 0.304
#> SRR1633260 1 0.886 0.689 0.696 0.304
#> SRR1633261 1 0.886 0.689 0.696 0.304
#> SRR1633262 1 0.000 0.901 1.000 0.000
#> SRR1633263 1 0.000 0.901 1.000 0.000
#> SRR1633264 1 0.000 0.901 1.000 0.000
#> SRR1633265 1 0.000 0.901 1.000 0.000
#> SRR1633266 1 0.000 0.901 1.000 0.000
#> SRR1633267 1 0.886 0.689 0.696 0.304
#> SRR1633268 1 0.886 0.689 0.696 0.304
#> SRR1633269 1 0.886 0.689 0.696 0.304
#> SRR1633270 1 0.886 0.689 0.696 0.304
#> SRR1633271 1 0.886 0.689 0.696 0.304
#> SRR1633272 1 0.886 0.689 0.696 0.304
#> SRR1633273 1 0.000 0.901 1.000 0.000
#> SRR1633274 1 0.000 0.901 1.000 0.000
#> SRR1633275 1 0.000 0.901 1.000 0.000
#> SRR1633276 1 0.000 0.901 1.000 0.000
#> SRR1633277 1 0.000 0.901 1.000 0.000
#> SRR1633278 1 0.000 0.901 1.000 0.000
#> SRR1633279 1 0.000 0.901 1.000 0.000
#> SRR1633280 1 0.000 0.901 1.000 0.000
#> SRR1633281 1 0.000 0.901 1.000 0.000
#> SRR1633282 1 0.000 0.901 1.000 0.000
#> SRR1633284 1 0.000 0.901 1.000 0.000
#> SRR1633285 1 0.000 0.901 1.000 0.000
#> SRR1633286 1 0.000 0.901 1.000 0.000
#> SRR1633287 1 0.000 0.901 1.000 0.000
#> SRR1633288 1 0.000 0.901 1.000 0.000
#> SRR1633289 1 0.000 0.901 1.000 0.000
#> SRR1633290 1 0.000 0.901 1.000 0.000
#> SRR1633291 1 0.000 0.901 1.000 0.000
#> SRR1633292 1 0.886 0.689 0.696 0.304
#> SRR1633293 1 0.886 0.689 0.696 0.304
#> SRR1633294 1 0.886 0.689 0.696 0.304
#> SRR1633295 1 0.886 0.689 0.696 0.304
#> SRR1633296 1 0.000 0.901 1.000 0.000
#> SRR1633297 1 0.000 0.901 1.000 0.000
#> SRR1633298 1 0.000 0.901 1.000 0.000
#> SRR1633299 1 0.000 0.901 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.901 1.000 0.000
#> SRR1633336 1 0.000 0.901 1.000 0.000
#> SRR1633337 1 0.000 0.901 1.000 0.000
#> SRR1633338 1 0.000 0.901 1.000 0.000
#> SRR1633339 1 0.000 0.901 1.000 0.000
#> SRR1633340 1 0.000 0.901 1.000 0.000
#> SRR1633341 1 0.000 0.901 1.000 0.000
#> SRR1633342 1 0.000 0.901 1.000 0.000
#> SRR1633345 1 0.000 0.901 1.000 0.000
#> SRR1633346 1 0.000 0.901 1.000 0.000
#> SRR1633343 1 0.000 0.901 1.000 0.000
#> SRR1633344 1 0.000 0.901 1.000 0.000
#> SRR1633347 1 0.000 0.901 1.000 0.000
#> SRR1633348 1 0.000 0.901 1.000 0.000
#> SRR1633350 1 0.000 0.901 1.000 0.000
#> SRR1633351 1 0.000 0.901 1.000 0.000
#> SRR1633352 1 0.000 0.901 1.000 0.000
#> SRR1633353 1 0.000 0.901 1.000 0.000
#> SRR1633354 1 0.000 0.901 1.000 0.000
#> SRR1633355 1 0.000 0.901 1.000 0.000
#> SRR1633356 1 0.000 0.901 1.000 0.000
#> SRR1633357 1 0.000 0.901 1.000 0.000
#> SRR1633358 1 0.000 0.901 1.000 0.000
#> SRR1633362 1 0.000 0.901 1.000 0.000
#> SRR1633363 1 0.000 0.901 1.000 0.000
#> SRR1633364 1 0.000 0.901 1.000 0.000
#> SRR1633359 1 0.000 0.901 1.000 0.000
#> SRR1633360 1 0.000 0.901 1.000 0.000
#> SRR1633361 1 0.000 0.901 1.000 0.000
#> SRR2038492 1 0.000 0.901 1.000 0.000
#> SRR2038491 1 0.000 0.901 1.000 0.000
#> SRR2038490 1 0.000 0.901 1.000 0.000
#> SRR2038489 1 0.000 0.901 1.000 0.000
#> SRR2038488 1 0.000 0.901 1.000 0.000
#> SRR2038487 1 0.000 0.901 1.000 0.000
#> SRR2038486 1 0.000 0.901 1.000 0.000
#> SRR2038485 1 0.000 0.901 1.000 0.000
#> SRR2038484 1 0.000 0.901 1.000 0.000
#> SRR2038483 1 0.000 0.901 1.000 0.000
#> SRR2038482 1 0.000 0.901 1.000 0.000
#> SRR2038481 1 0.000 0.901 1.000 0.000
#> SRR2038480 1 0.000 0.901 1.000 0.000
#> SRR2038479 1 0.000 0.901 1.000 0.000
#> SRR2038477 1 0.000 0.901 1.000 0.000
#> SRR2038478 1 0.000 0.901 1.000 0.000
#> SRR2038476 1 0.000 0.901 1.000 0.000
#> SRR2038475 1 0.000 0.901 1.000 0.000
#> SRR2038474 1 0.000 0.901 1.000 0.000
#> SRR2038473 1 0.000 0.901 1.000 0.000
#> SRR2038472 1 0.000 0.901 1.000 0.000
#> SRR2038471 1 0.000 0.901 1.000 0.000
#> SRR2038470 1 0.000 0.901 1.000 0.000
#> SRR2038469 1 0.000 0.901 1.000 0.000
#> SRR2038468 1 0.000 0.901 1.000 0.000
#> SRR2038467 1 0.000 0.901 1.000 0.000
#> SRR2038466 1 0.000 0.901 1.000 0.000
#> SRR2038465 1 0.000 0.901 1.000 0.000
#> SRR2038464 1 0.000 0.901 1.000 0.000
#> SRR2038463 1 0.000 0.901 1.000 0.000
#> SRR2038462 1 0.000 0.901 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.000 1.000 0.000 1 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000
#> SRR1633236 3 0.000 0.990 0.000 0 1.000
#> SRR1633237 3 0.000 0.990 0.000 0 1.000
#> SRR1633238 3 0.000 0.990 0.000 0 1.000
#> SRR1633239 3 0.000 0.990 0.000 0 1.000
#> SRR1633240 3 0.000 0.990 0.000 0 1.000
#> SRR1633241 3 0.000 0.990 0.000 0 1.000
#> SRR1633242 3 0.000 0.990 0.000 0 1.000
#> SRR1633243 3 0.000 0.990 0.000 0 1.000
#> SRR1633244 3 0.000 0.990 0.000 0 1.000
#> SRR1633245 3 0.000 0.990 0.000 0 1.000
#> SRR1633246 3 0.000 0.990 0.000 0 1.000
#> SRR1633247 3 0.000 0.990 0.000 0 1.000
#> SRR1633248 3 0.000 0.990 0.000 0 1.000
#> SRR1633249 3 0.000 0.990 0.000 0 1.000
#> SRR1633250 3 0.000 0.990 0.000 0 1.000
#> SRR1633251 3 0.000 0.990 0.000 0 1.000
#> SRR1633252 3 0.000 0.990 0.000 0 1.000
#> SRR1633253 3 0.000 0.990 0.000 0 1.000
#> SRR1633254 3 0.000 0.990 0.000 0 1.000
#> SRR1633255 3 0.000 0.990 0.000 0 1.000
#> SRR1633256 3 0.000 0.990 0.000 0 1.000
#> SRR1633257 3 0.000 0.990 0.000 0 1.000
#> SRR1633258 3 0.000 0.990 0.000 0 1.000
#> SRR1633259 3 0.000 0.990 0.000 0 1.000
#> SRR1633260 3 0.000 0.990 0.000 0 1.000
#> SRR1633261 3 0.000 0.990 0.000 0 1.000
#> SRR1633262 3 0.000 0.990 0.000 0 1.000
#> SRR1633263 3 0.000 0.990 0.000 0 1.000
#> SRR1633264 3 0.000 0.990 0.000 0 1.000
#> SRR1633265 3 0.000 0.990 0.000 0 1.000
#> SRR1633266 3 0.000 0.990 0.000 0 1.000
#> SRR1633267 3 0.000 0.990 0.000 0 1.000
#> SRR1633268 3 0.000 0.990 0.000 0 1.000
#> SRR1633269 3 0.000 0.990 0.000 0 1.000
#> SRR1633270 3 0.000 0.990 0.000 0 1.000
#> SRR1633271 3 0.000 0.990 0.000 0 1.000
#> SRR1633272 3 0.000 0.990 0.000 0 1.000
#> SRR1633273 3 0.000 0.990 0.000 0 1.000
#> SRR1633274 3 0.000 0.990 0.000 0 1.000
#> SRR1633275 3 0.000 0.990 0.000 0 1.000
#> SRR1633276 3 0.000 0.990 0.000 0 1.000
#> SRR1633277 3 0.000 0.990 0.000 0 1.000
#> SRR1633278 3 0.000 0.990 0.000 0 1.000
#> SRR1633279 3 0.000 0.990 0.000 0 1.000
#> SRR1633280 3 0.000 0.990 0.000 0 1.000
#> SRR1633281 3 0.000 0.990 0.000 0 1.000
#> SRR1633282 3 0.000 0.990 0.000 0 1.000
#> SRR1633284 3 0.000 0.990 0.000 0 1.000
#> SRR1633285 3 0.000 0.990 0.000 0 1.000
#> SRR1633286 3 0.000 0.990 0.000 0 1.000
#> SRR1633287 3 0.000 0.990 0.000 0 1.000
#> SRR1633288 3 0.000 0.990 0.000 0 1.000
#> SRR1633289 3 0.000 0.990 0.000 0 1.000
#> SRR1633290 3 0.000 0.990 0.000 0 1.000
#> SRR1633291 3 0.000 0.990 0.000 0 1.000
#> SRR1633292 3 0.000 0.990 0.000 0 1.000
#> SRR1633293 3 0.000 0.990 0.000 0 1.000
#> SRR1633294 3 0.000 0.990 0.000 0 1.000
#> SRR1633295 3 0.000 0.990 0.000 0 1.000
#> SRR1633296 3 0.000 0.990 0.000 0 1.000
#> SRR1633297 3 0.000 0.990 0.000 0 1.000
#> SRR1633298 3 0.000 0.990 0.000 0 1.000
#> SRR1633299 3 0.000 0.990 0.000 0 1.000
#> SRR1633300 2 0.000 1.000 0.000 1 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000
#> SRR1633335 3 0.000 0.990 0.000 0 1.000
#> SRR1633336 3 0.000 0.990 0.000 0 1.000
#> SRR1633337 3 0.000 0.990 0.000 0 1.000
#> SRR1633338 3 0.000 0.990 0.000 0 1.000
#> SRR1633339 3 0.000 0.990 0.000 0 1.000
#> SRR1633340 3 0.000 0.990 0.000 0 1.000
#> SRR1633341 3 0.000 0.990 0.000 0 1.000
#> SRR1633342 3 0.000 0.990 0.000 0 1.000
#> SRR1633345 3 0.000 0.990 0.000 0 1.000
#> SRR1633346 3 0.000 0.990 0.000 0 1.000
#> SRR1633343 3 0.000 0.990 0.000 0 1.000
#> SRR1633344 3 0.000 0.990 0.000 0 1.000
#> SRR1633347 3 0.000 0.990 0.000 0 1.000
#> SRR1633348 3 0.000 0.990 0.000 0 1.000
#> SRR1633350 1 0.000 0.991 1.000 0 0.000
#> SRR1633351 1 0.000 0.991 1.000 0 0.000
#> SRR1633352 1 0.000 0.991 1.000 0 0.000
#> SRR1633353 1 0.000 0.991 1.000 0 0.000
#> SRR1633354 1 0.000 0.991 1.000 0 0.000
#> SRR1633355 1 0.000 0.991 1.000 0 0.000
#> SRR1633356 1 0.000 0.991 1.000 0 0.000
#> SRR1633357 1 0.000 0.991 1.000 0 0.000
#> SRR1633358 1 0.000 0.991 1.000 0 0.000
#> SRR1633362 1 0.000 0.991 1.000 0 0.000
#> SRR1633363 1 0.000 0.991 1.000 0 0.000
#> SRR1633364 1 0.000 0.991 1.000 0 0.000
#> SRR1633359 1 0.000 0.991 1.000 0 0.000
#> SRR1633360 1 0.000 0.991 1.000 0 0.000
#> SRR1633361 1 0.000 0.991 1.000 0 0.000
#> SRR2038492 3 0.440 0.774 0.188 0 0.812
#> SRR2038491 1 0.000 0.991 1.000 0 0.000
#> SRR2038490 3 0.445 0.768 0.192 0 0.808
#> SRR2038489 1 0.000 0.991 1.000 0 0.000
#> SRR2038488 1 0.000 0.991 1.000 0 0.000
#> SRR2038487 1 0.000 0.991 1.000 0 0.000
#> SRR2038486 1 0.000 0.991 1.000 0 0.000
#> SRR2038485 1 0.000 0.991 1.000 0 0.000
#> SRR2038484 1 0.000 0.991 1.000 0 0.000
#> SRR2038483 1 0.000 0.991 1.000 0 0.000
#> SRR2038482 1 0.000 0.991 1.000 0 0.000
#> SRR2038481 1 0.116 0.960 0.972 0 0.028
#> SRR2038480 3 0.445 0.768 0.192 0 0.808
#> SRR2038479 1 0.000 0.991 1.000 0 0.000
#> SRR2038477 1 0.000 0.991 1.000 0 0.000
#> SRR2038478 1 0.000 0.991 1.000 0 0.000
#> SRR2038476 3 0.445 0.768 0.192 0 0.808
#> SRR2038475 1 0.000 0.991 1.000 0 0.000
#> SRR2038474 1 0.000 0.991 1.000 0 0.000
#> SRR2038473 1 0.000 0.991 1.000 0 0.000
#> SRR2038472 1 0.000 0.991 1.000 0 0.000
#> SRR2038471 1 0.000 0.991 1.000 0 0.000
#> SRR2038470 1 0.116 0.960 0.972 0 0.028
#> SRR2038469 1 0.000 0.991 1.000 0 0.000
#> SRR2038468 1 0.000 0.991 1.000 0 0.000
#> SRR2038467 1 0.000 0.991 1.000 0 0.000
#> SRR2038466 1 0.000 0.991 1.000 0 0.000
#> SRR2038465 1 0.000 0.991 1.000 0 0.000
#> SRR2038464 1 0.493 0.692 0.768 0 0.232
#> SRR2038463 1 0.000 0.991 1.000 0 0.000
#> SRR2038462 3 0.000 0.990 0.000 0 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633236 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633237 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633238 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633239 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633240 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633241 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633242 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633243 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633244 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633245 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633246 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633247 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633248 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633249 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633250 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633251 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633252 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633253 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633254 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633255 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633256 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633257 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633258 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633259 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633260 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633261 3 0.428 0.816 0.000 0 0.720 0.280
#> SRR1633262 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633263 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633264 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633265 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633266 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633267 3 0.480 0.672 0.000 0 0.616 0.384
#> SRR1633268 3 0.480 0.672 0.000 0 0.616 0.384
#> SRR1633269 3 0.480 0.672 0.000 0 0.616 0.384
#> SRR1633270 3 0.430 0.812 0.000 0 0.716 0.284
#> SRR1633271 3 0.430 0.812 0.000 0 0.716 0.284
#> SRR1633272 3 0.430 0.812 0.000 0 0.716 0.284
#> SRR1633273 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633274 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633275 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633276 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633277 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633278 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633279 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633280 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633281 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633282 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633284 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633285 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633286 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633287 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633288 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633289 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633290 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633291 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633292 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633293 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633294 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633295 3 0.000 0.801 0.000 0 1.000 0.000
#> SRR1633296 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633297 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633298 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633299 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633300 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000 0.000
#> SRR1633335 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633336 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633337 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633338 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633339 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633340 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633341 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633342 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633345 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633346 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633343 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633344 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633347 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633348 4 0.000 0.979 0.000 0 0.000 1.000
#> SRR1633350 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633351 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633352 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633353 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633354 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633355 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633356 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633357 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633358 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633362 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633363 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633364 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633359 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633360 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR1633361 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038492 4 0.322 0.774 0.164 0 0.000 0.836
#> SRR2038491 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038490 4 0.340 0.752 0.180 0 0.000 0.820
#> SRR2038489 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038488 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038487 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038486 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038485 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038484 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038483 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038482 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038481 1 0.194 0.914 0.924 0 0.000 0.076
#> SRR2038480 4 0.340 0.752 0.180 0 0.000 0.820
#> SRR2038479 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038477 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038478 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038476 4 0.340 0.752 0.180 0 0.000 0.820
#> SRR2038475 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038474 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038473 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038472 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038471 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038470 1 0.194 0.914 0.924 0 0.000 0.076
#> SRR2038469 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038468 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038467 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038466 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038465 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038464 1 0.130 0.949 0.956 0 0.000 0.044
#> SRR2038463 1 0.000 0.994 1.000 0 0.000 0.000
#> SRR2038462 4 0.000 0.979 0.000 0 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633236 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633237 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633238 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633239 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633240 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633241 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633242 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633243 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633244 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633245 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633246 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633247 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633248 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633249 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633250 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633251 3 0.426 0.531 0.000 0 0.564 0.000 0.436
#> SRR1633252 3 0.426 0.531 0.000 0 0.564 0.000 0.436
#> SRR1633253 3 0.426 0.531 0.000 0 0.564 0.000 0.436
#> SRR1633254 3 0.426 0.531 0.000 0 0.564 0.000 0.436
#> SRR1633255 3 0.426 0.531 0.000 0 0.564 0.000 0.436
#> SRR1633256 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633257 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633258 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633259 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633260 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633261 3 0.427 0.526 0.000 0 0.556 0.000 0.444
#> SRR1633262 3 0.405 -0.321 0.000 0 0.644 0.356 0.000
#> SRR1633263 3 0.405 -0.321 0.000 0 0.644 0.356 0.000
#> SRR1633264 3 0.405 -0.321 0.000 0 0.644 0.356 0.000
#> SRR1633265 3 0.405 -0.321 0.000 0 0.644 0.356 0.000
#> SRR1633266 3 0.405 -0.321 0.000 0 0.644 0.356 0.000
#> SRR1633267 3 0.559 0.502 0.000 0 0.608 0.108 0.284
#> SRR1633268 3 0.559 0.502 0.000 0 0.608 0.108 0.284
#> SRR1633269 3 0.559 0.502 0.000 0 0.608 0.108 0.284
#> SRR1633270 3 0.561 0.502 0.000 0 0.604 0.108 0.288
#> SRR1633271 3 0.561 0.502 0.000 0 0.604 0.108 0.288
#> SRR1633272 3 0.561 0.502 0.000 0 0.604 0.108 0.288
#> SRR1633273 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633274 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633275 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633276 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633277 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633278 4 0.120 0.792 0.000 0 0.048 0.952 0.000
#> SRR1633279 4 0.120 0.792 0.000 0 0.048 0.952 0.000
#> SRR1633280 4 0.120 0.792 0.000 0 0.048 0.952 0.000
#> SRR1633281 4 0.120 0.792 0.000 0 0.048 0.952 0.000
#> SRR1633282 4 0.304 0.761 0.000 0 0.192 0.808 0.000
#> SRR1633284 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633285 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633286 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633287 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633288 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633289 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633290 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633291 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633292 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633293 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633294 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633295 5 0.000 1.000 0.000 0 0.000 0.000 1.000
#> SRR1633296 4 0.425 0.677 0.000 0 0.432 0.568 0.000
#> SRR1633297 4 0.425 0.677 0.000 0 0.432 0.568 0.000
#> SRR1633298 4 0.425 0.677 0.000 0 0.432 0.568 0.000
#> SRR1633299 4 0.425 0.677 0.000 0 0.432 0.568 0.000
#> SRR1633300 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633335 4 0.120 0.794 0.000 0 0.048 0.952 0.000
#> SRR1633336 4 0.120 0.794 0.000 0 0.048 0.952 0.000
#> SRR1633337 4 0.112 0.793 0.000 0 0.044 0.956 0.000
#> SRR1633338 4 0.141 0.794 0.000 0 0.060 0.940 0.000
#> SRR1633339 4 0.141 0.794 0.000 0 0.060 0.940 0.000
#> SRR1633340 4 0.141 0.794 0.000 0 0.060 0.940 0.000
#> SRR1633341 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633342 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633345 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633346 4 0.000 0.786 0.000 0 0.000 1.000 0.000
#> SRR1633343 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633344 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633347 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633348 4 0.427 0.670 0.000 0 0.444 0.556 0.000
#> SRR1633350 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633351 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633352 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633353 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633354 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633355 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633356 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633357 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633358 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633362 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633363 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633364 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633359 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633360 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR1633361 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038492 4 0.228 0.697 0.120 0 0.000 0.880 0.000
#> SRR2038491 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038490 4 0.238 0.688 0.128 0 0.000 0.872 0.000
#> SRR2038489 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038488 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038487 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038484 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038483 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038481 1 0.300 0.793 0.812 0 0.000 0.188 0.000
#> SRR2038480 4 0.238 0.688 0.128 0 0.000 0.872 0.000
#> SRR2038479 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038476 4 0.238 0.688 0.128 0 0.000 0.872 0.000
#> SRR2038475 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038470 1 0.300 0.793 0.812 0 0.000 0.188 0.000
#> SRR2038469 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038464 1 0.324 0.745 0.784 0 0.000 0.216 0.000
#> SRR2038463 1 0.000 0.985 1.000 0 0.000 0.000 0.000
#> SRR2038462 4 0.202 0.789 0.000 0 0.100 0.900 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633236 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633237 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633238 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633239 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633240 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633241 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633242 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633243 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633244 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633245 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633246 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633247 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633248 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633249 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633250 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633251 3 0.0632 0.874 0.000 0 0.976 0.024 0 0.000
#> SRR1633252 3 0.0632 0.874 0.000 0 0.976 0.024 0 0.000
#> SRR1633253 3 0.0632 0.874 0.000 0 0.976 0.024 0 0.000
#> SRR1633254 3 0.0632 0.874 0.000 0 0.976 0.024 0 0.000
#> SRR1633255 3 0.0632 0.874 0.000 0 0.976 0.024 0 0.000
#> SRR1633256 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633257 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633258 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633259 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633260 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633261 3 0.0000 0.877 0.000 0 1.000 0.000 0 0.000
#> SRR1633262 4 0.3695 0.366 0.000 0 0.376 0.624 0 0.000
#> SRR1633263 4 0.3695 0.366 0.000 0 0.376 0.624 0 0.000
#> SRR1633264 4 0.3695 0.366 0.000 0 0.376 0.624 0 0.000
#> SRR1633265 4 0.3695 0.366 0.000 0 0.376 0.624 0 0.000
#> SRR1633266 4 0.3695 0.366 0.000 0 0.376 0.624 0 0.000
#> SRR1633267 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633268 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633269 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633270 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633271 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633272 3 0.3482 0.606 0.000 0 0.684 0.316 0 0.000
#> SRR1633273 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633274 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633275 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633276 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633277 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633278 6 0.1957 0.798 0.000 0 0.000 0.112 0 0.888
#> SRR1633279 6 0.1957 0.798 0.000 0 0.000 0.112 0 0.888
#> SRR1633280 6 0.1957 0.798 0.000 0 0.000 0.112 0 0.888
#> SRR1633281 6 0.1957 0.798 0.000 0 0.000 0.112 0 0.888
#> SRR1633282 4 0.1714 0.809 0.000 0 0.000 0.908 0 0.092
#> SRR1633284 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633285 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633286 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633287 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633288 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633289 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633290 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633291 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633292 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633293 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633294 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633295 5 0.0000 1.000 0.000 0 0.000 0.000 1 0.000
#> SRR1633296 4 0.0713 0.865 0.000 0 0.000 0.972 0 0.028
#> SRR1633297 4 0.0713 0.865 0.000 0 0.000 0.972 0 0.028
#> SRR1633298 4 0.0632 0.866 0.000 0 0.000 0.976 0 0.024
#> SRR1633299 4 0.0632 0.866 0.000 0 0.000 0.976 0 0.024
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000 0.000 0 0.000
#> SRR1633335 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633336 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633337 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633338 6 0.1863 0.811 0.000 0 0.000 0.104 0 0.896
#> SRR1633339 6 0.1863 0.811 0.000 0 0.000 0.104 0 0.896
#> SRR1633340 6 0.1863 0.811 0.000 0 0.000 0.104 0 0.896
#> SRR1633341 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633342 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633345 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633346 6 0.0000 0.865 0.000 0 0.000 0.000 0 1.000
#> SRR1633343 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633344 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633347 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633348 4 0.0458 0.872 0.000 0 0.000 0.984 0 0.016
#> SRR1633350 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633351 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633352 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633353 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633354 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633355 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633356 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633357 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633358 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633362 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633363 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633364 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633359 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633360 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR1633361 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038492 6 0.3789 0.355 0.416 0 0.000 0.000 0 0.584
#> SRR2038491 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038490 6 0.3789 0.355 0.416 0 0.000 0.000 0 0.584
#> SRR2038489 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038488 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038487 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038486 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038485 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038484 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038483 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038482 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038481 1 0.2300 0.819 0.856 0 0.000 0.000 0 0.144
#> SRR2038480 6 0.3789 0.355 0.416 0 0.000 0.000 0 0.584
#> SRR2038479 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038477 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038478 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038476 6 0.3789 0.355 0.416 0 0.000 0.000 0 0.584
#> SRR2038475 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038474 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038473 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038472 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038471 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038470 1 0.2300 0.819 0.856 0 0.000 0.000 0 0.144
#> SRR2038469 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038468 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038467 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038466 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038465 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038464 1 0.2178 0.834 0.868 0 0.000 0.000 0 0.132
#> SRR2038463 1 0.0000 0.988 1.000 0 0.000 0.000 0 0.000
#> SRR2038462 6 0.2003 0.795 0.000 0 0.000 0.116 0 0.884
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.989 0.4992 0.499 0.499
#> 3 3 1.000 0.967 0.987 0.2767 0.821 0.657
#> 4 4 0.943 0.909 0.951 0.1606 0.846 0.604
#> 5 5 0.842 0.882 0.903 0.0607 0.890 0.621
#> 6 6 0.901 0.676 0.855 0.0341 0.964 0.835
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4
There is also optional best \(k\) = 2 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.977 0.000 1.000
#> SRR1633231 2 0.0000 0.977 0.000 1.000
#> SRR1633232 2 0.0000 0.977 0.000 1.000
#> SRR1633233 2 0.0000 0.977 0.000 1.000
#> SRR1633234 2 0.0000 0.977 0.000 1.000
#> SRR1633236 2 0.0000 0.977 0.000 1.000
#> SRR1633237 2 0.0000 0.977 0.000 1.000
#> SRR1633238 2 0.0000 0.977 0.000 1.000
#> SRR1633239 2 0.0000 0.977 0.000 1.000
#> SRR1633240 2 0.0000 0.977 0.000 1.000
#> SRR1633241 2 0.0000 0.977 0.000 1.000
#> SRR1633242 2 0.0000 0.977 0.000 1.000
#> SRR1633243 2 0.0000 0.977 0.000 1.000
#> SRR1633244 2 0.0000 0.977 0.000 1.000
#> SRR1633245 2 0.0000 0.977 0.000 1.000
#> SRR1633246 2 0.0000 0.977 0.000 1.000
#> SRR1633247 2 0.0376 0.973 0.004 0.996
#> SRR1633248 2 0.0000 0.977 0.000 1.000
#> SRR1633249 2 0.0000 0.977 0.000 1.000
#> SRR1633250 2 0.0000 0.977 0.000 1.000
#> SRR1633251 2 0.9044 0.551 0.320 0.680
#> SRR1633252 2 0.9323 0.494 0.348 0.652
#> SRR1633253 2 0.9522 0.439 0.372 0.628
#> SRR1633254 2 0.9248 0.511 0.340 0.660
#> SRR1633255 2 0.9323 0.494 0.348 0.652
#> SRR1633256 2 0.0000 0.977 0.000 1.000
#> SRR1633257 2 0.0000 0.977 0.000 1.000
#> SRR1633258 2 0.0000 0.977 0.000 1.000
#> SRR1633259 2 0.0000 0.977 0.000 1.000
#> SRR1633260 2 0.0000 0.977 0.000 1.000
#> SRR1633261 2 0.0000 0.977 0.000 1.000
#> SRR1633262 1 0.0000 1.000 1.000 0.000
#> SRR1633263 1 0.0000 1.000 1.000 0.000
#> SRR1633264 1 0.0000 1.000 1.000 0.000
#> SRR1633265 1 0.0000 1.000 1.000 0.000
#> SRR1633266 1 0.0000 1.000 1.000 0.000
#> SRR1633267 2 0.0000 0.977 0.000 1.000
#> SRR1633268 2 0.0000 0.977 0.000 1.000
#> SRR1633269 2 0.0000 0.977 0.000 1.000
#> SRR1633270 2 0.0000 0.977 0.000 1.000
#> SRR1633271 2 0.0000 0.977 0.000 1.000
#> SRR1633272 2 0.0000 0.977 0.000 1.000
#> SRR1633273 1 0.0000 1.000 1.000 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0.000
#> SRR1633278 1 0.0000 1.000 1.000 0.000
#> SRR1633279 1 0.0000 1.000 1.000 0.000
#> SRR1633280 1 0.0000 1.000 1.000 0.000
#> SRR1633281 1 0.0000 1.000 1.000 0.000
#> SRR1633282 1 0.0000 1.000 1.000 0.000
#> SRR1633284 1 0.0000 1.000 1.000 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0.000
#> SRR1633292 2 0.0000 0.977 0.000 1.000
#> SRR1633293 2 0.0000 0.977 0.000 1.000
#> SRR1633294 2 0.0000 0.977 0.000 1.000
#> SRR1633295 2 0.0000 0.977 0.000 1.000
#> SRR1633296 1 0.0000 1.000 1.000 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0.000
#> SRR1633300 2 0.0000 0.977 0.000 1.000
#> SRR1633301 2 0.0000 0.977 0.000 1.000
#> SRR1633302 2 0.0000 0.977 0.000 1.000
#> SRR1633303 2 0.0000 0.977 0.000 1.000
#> SRR1633304 2 0.0000 0.977 0.000 1.000
#> SRR1633305 2 0.0000 0.977 0.000 1.000
#> SRR1633306 2 0.0000 0.977 0.000 1.000
#> SRR1633307 2 0.0000 0.977 0.000 1.000
#> SRR1633308 2 0.0000 0.977 0.000 1.000
#> SRR1633309 2 0.0000 0.977 0.000 1.000
#> SRR1633310 2 0.0000 0.977 0.000 1.000
#> SRR1633311 2 0.0000 0.977 0.000 1.000
#> SRR1633312 2 0.0000 0.977 0.000 1.000
#> SRR1633313 2 0.0000 0.977 0.000 1.000
#> SRR1633314 2 0.0000 0.977 0.000 1.000
#> SRR1633315 2 0.0000 0.977 0.000 1.000
#> SRR1633316 2 0.0000 0.977 0.000 1.000
#> SRR1633317 2 0.0000 0.977 0.000 1.000
#> SRR1633318 2 0.0000 0.977 0.000 1.000
#> SRR1633319 2 0.0000 0.977 0.000 1.000
#> SRR1633320 2 0.0000 0.977 0.000 1.000
#> SRR1633321 2 0.0000 0.977 0.000 1.000
#> SRR1633322 2 0.0000 0.977 0.000 1.000
#> SRR1633323 2 0.0000 0.977 0.000 1.000
#> SRR1633324 2 0.0000 0.977 0.000 1.000
#> SRR1633325 2 0.0000 0.977 0.000 1.000
#> SRR1633326 2 0.0000 0.977 0.000 1.000
#> SRR1633327 2 0.0000 0.977 0.000 1.000
#> SRR1633328 2 0.0000 0.977 0.000 1.000
#> SRR1633329 2 0.0000 0.977 0.000 1.000
#> SRR1633330 2 0.0000 0.977 0.000 1.000
#> SRR1633331 2 0.0000 0.977 0.000 1.000
#> SRR1633332 2 0.0000 0.977 0.000 1.000
#> SRR1633333 2 0.0000 0.977 0.000 1.000
#> SRR1633334 2 0.0000 0.977 0.000 1.000
#> SRR1633335 1 0.0000 1.000 1.000 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000
#> SRR2038462 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.6140 0.361 0.000 0.404 0.596
#> SRR1633237 3 0.6252 0.256 0.000 0.444 0.556
#> SRR1633238 3 0.6252 0.256 0.000 0.444 0.556
#> SRR1633239 3 0.6280 0.207 0.000 0.460 0.540
#> SRR1633240 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633273 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633274 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633275 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633276 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633277 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633278 1 0.1163 0.971 0.972 0.000 0.028
#> SRR1633279 1 0.1163 0.971 0.972 0.000 0.028
#> SRR1633280 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1633281 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1633282 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633284 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633290 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633291 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633292 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633296 1 0.3192 0.879 0.888 0.000 0.112
#> SRR1633297 1 0.3752 0.837 0.856 0.000 0.144
#> SRR1633298 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633299 3 0.0000 0.958 0.000 0.000 1.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633343 1 0.0237 0.992 0.996 0.000 0.004
#> SRR1633344 1 0.0237 0.992 0.996 0.000 0.004
#> SRR1633347 1 0.1163 0.971 0.972 0.000 0.028
#> SRR1633348 1 0.1163 0.971 0.972 0.000 0.028
#> SRR1633350 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.995 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.995 1.000 0.000 0.000
#> SRR2038462 1 0.1411 0.964 0.964 0.000 0.036
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.1557 0.934 0.000 0.056 0.944 0.000
#> SRR1633237 3 0.1557 0.934 0.000 0.056 0.944 0.000
#> SRR1633238 3 0.1557 0.934 0.000 0.056 0.944 0.000
#> SRR1633239 3 0.1557 0.934 0.000 0.056 0.944 0.000
#> SRR1633240 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.2011 0.913 0.000 0.000 0.920 0.080
#> SRR1633252 3 0.2081 0.908 0.000 0.000 0.916 0.084
#> SRR1633253 3 0.2408 0.885 0.000 0.000 0.896 0.104
#> SRR1633254 3 0.1792 0.924 0.000 0.000 0.932 0.068
#> SRR1633255 3 0.2011 0.913 0.000 0.000 0.920 0.080
#> SRR1633256 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633262 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633263 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633264 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633265 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633266 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633267 4 0.2081 0.872 0.000 0.000 0.084 0.916
#> SRR1633268 4 0.2081 0.872 0.000 0.000 0.084 0.916
#> SRR1633269 4 0.2081 0.872 0.000 0.000 0.084 0.916
#> SRR1633270 4 0.4992 0.151 0.000 0.000 0.476 0.524
#> SRR1633271 4 0.4996 0.124 0.000 0.000 0.484 0.516
#> SRR1633272 4 0.4925 0.296 0.000 0.000 0.428 0.572
#> SRR1633273 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633274 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633275 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633276 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633277 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633278 4 0.0188 0.886 0.004 0.000 0.000 0.996
#> SRR1633279 4 0.0188 0.886 0.004 0.000 0.000 0.996
#> SRR1633280 4 0.0188 0.886 0.004 0.000 0.000 0.996
#> SRR1633281 4 0.0188 0.886 0.004 0.000 0.000 0.996
#> SRR1633282 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633284 1 0.3356 0.793 0.824 0.000 0.000 0.176
#> SRR1633285 1 0.3356 0.792 0.824 0.000 0.000 0.176
#> SRR1633286 1 0.3172 0.810 0.840 0.000 0.000 0.160
#> SRR1633287 1 0.3024 0.822 0.852 0.000 0.000 0.148
#> SRR1633288 1 0.3486 0.778 0.812 0.000 0.000 0.188
#> SRR1633289 1 0.3172 0.810 0.840 0.000 0.000 0.160
#> SRR1633290 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633291 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633292 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.1820 0.902 0.036 0.000 0.020 0.944
#> SRR1633297 4 0.1820 0.902 0.036 0.000 0.020 0.944
#> SRR1633298 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633299 4 0.1557 0.891 0.000 0.000 0.056 0.944
#> SRR1633300 2 0.0921 0.979 0.000 0.972 0.000 0.028
#> SRR1633301 2 0.0921 0.979 0.000 0.972 0.000 0.028
#> SRR1633302 2 0.0921 0.979 0.000 0.972 0.000 0.028
#> SRR1633303 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633304 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633305 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633306 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633307 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633308 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633309 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633310 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633311 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633312 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633313 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633314 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633315 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633316 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633317 2 0.1474 0.974 0.000 0.948 0.000 0.052
#> SRR1633318 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.982 0.000 1.000 0.000 0.000
#> SRR1633335 1 0.4222 0.652 0.728 0.000 0.000 0.272
#> SRR1633336 1 0.4222 0.653 0.728 0.000 0.000 0.272
#> SRR1633337 1 0.4193 0.659 0.732 0.000 0.000 0.268
#> SRR1633338 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633339 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633340 4 0.1557 0.901 0.056 0.000 0.000 0.944
#> SRR1633341 1 0.4855 0.370 0.600 0.000 0.000 0.400
#> SRR1633342 1 0.4746 0.454 0.632 0.000 0.000 0.368
#> SRR1633345 4 0.4855 0.307 0.400 0.000 0.000 0.600
#> SRR1633346 4 0.4730 0.409 0.364 0.000 0.000 0.636
#> SRR1633343 4 0.1661 0.902 0.052 0.000 0.004 0.944
#> SRR1633344 4 0.1661 0.902 0.052 0.000 0.004 0.944
#> SRR1633347 4 0.1767 0.903 0.044 0.000 0.012 0.944
#> SRR1633348 4 0.1767 0.903 0.044 0.000 0.012 0.944
#> SRR1633350 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.2124 0.891 0.068 0.000 0.008 0.924
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> SRR1633231 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> SRR1633232 2 0.0162 0.903 0.000 0.996 0.004 0.000 0.000
#> SRR1633233 2 0.0162 0.903 0.000 0.996 0.004 0.000 0.000
#> SRR1633234 2 0.0162 0.903 0.000 0.996 0.004 0.000 0.000
#> SRR1633236 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633237 5 0.0162 0.932 0.000 0.004 0.000 0.000 0.996
#> SRR1633238 5 0.0162 0.932 0.000 0.004 0.000 0.000 0.996
#> SRR1633239 5 0.0290 0.927 0.000 0.008 0.000 0.000 0.992
#> SRR1633240 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.3177 0.693 0.000 0.000 0.208 0.000 0.792
#> SRR1633248 5 0.3242 0.679 0.000 0.000 0.216 0.000 0.784
#> SRR1633249 5 0.3242 0.679 0.000 0.000 0.216 0.000 0.784
#> SRR1633250 5 0.3177 0.693 0.000 0.000 0.208 0.000 0.792
#> SRR1633251 3 0.4067 0.701 0.000 0.000 0.692 0.008 0.300
#> SRR1633252 3 0.4067 0.701 0.000 0.000 0.692 0.008 0.300
#> SRR1633253 3 0.4067 0.701 0.000 0.000 0.692 0.008 0.300
#> SRR1633254 3 0.4067 0.701 0.000 0.000 0.692 0.008 0.300
#> SRR1633255 3 0.4067 0.701 0.000 0.000 0.692 0.008 0.300
#> SRR1633256 3 0.3876 0.682 0.000 0.000 0.684 0.000 0.316
#> SRR1633257 3 0.3876 0.682 0.000 0.000 0.684 0.000 0.316
#> SRR1633258 3 0.3876 0.682 0.000 0.000 0.684 0.000 0.316
#> SRR1633259 3 0.4161 0.568 0.000 0.000 0.608 0.000 0.392
#> SRR1633260 3 0.4138 0.583 0.000 0.000 0.616 0.000 0.384
#> SRR1633261 3 0.4138 0.583 0.000 0.000 0.616 0.000 0.384
#> SRR1633262 3 0.3837 0.718 0.000 0.000 0.692 0.308 0.000
#> SRR1633263 3 0.3837 0.718 0.000 0.000 0.692 0.308 0.000
#> SRR1633264 3 0.3837 0.718 0.000 0.000 0.692 0.308 0.000
#> SRR1633265 3 0.3837 0.718 0.000 0.000 0.692 0.308 0.000
#> SRR1633266 3 0.3837 0.718 0.000 0.000 0.692 0.308 0.000
#> SRR1633267 3 0.4622 0.742 0.000 0.000 0.692 0.264 0.044
#> SRR1633268 3 0.4527 0.740 0.000 0.000 0.692 0.272 0.036
#> SRR1633269 3 0.4622 0.742 0.000 0.000 0.692 0.264 0.044
#> SRR1633270 3 0.4576 0.720 0.000 0.000 0.692 0.040 0.268
#> SRR1633271 3 0.4576 0.720 0.000 0.000 0.692 0.040 0.268
#> SRR1633272 3 0.4622 0.721 0.000 0.000 0.692 0.044 0.264
#> SRR1633273 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633274 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633275 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633276 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633277 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633278 3 0.2605 0.714 0.000 0.000 0.852 0.148 0.000
#> SRR1633279 3 0.2763 0.713 0.004 0.000 0.848 0.148 0.000
#> SRR1633280 3 0.2536 0.704 0.004 0.000 0.868 0.128 0.000
#> SRR1633281 3 0.2488 0.702 0.004 0.000 0.872 0.124 0.000
#> SRR1633282 3 0.4074 0.638 0.000 0.000 0.636 0.364 0.000
#> SRR1633284 4 0.2732 0.833 0.160 0.000 0.000 0.840 0.000
#> SRR1633285 4 0.2813 0.825 0.168 0.000 0.000 0.832 0.000
#> SRR1633286 4 0.2929 0.811 0.180 0.000 0.000 0.820 0.000
#> SRR1633287 4 0.3039 0.795 0.192 0.000 0.000 0.808 0.000
#> SRR1633288 4 0.2773 0.829 0.164 0.000 0.000 0.836 0.000
#> SRR1633289 4 0.2852 0.821 0.172 0.000 0.000 0.828 0.000
#> SRR1633290 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633291 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633292 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633297 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633298 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633299 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633300 2 0.2127 0.892 0.000 0.892 0.108 0.000 0.000
#> SRR1633301 2 0.2179 0.891 0.000 0.888 0.112 0.000 0.000
#> SRR1633302 2 0.2179 0.891 0.000 0.888 0.112 0.000 0.000
#> SRR1633303 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633304 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633305 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633306 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633307 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633308 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633309 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633310 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633311 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633312 2 0.3561 0.860 0.000 0.740 0.260 0.000 0.000
#> SRR1633313 2 0.3586 0.859 0.000 0.736 0.264 0.000 0.000
#> SRR1633314 2 0.3586 0.859 0.000 0.736 0.264 0.000 0.000
#> SRR1633315 2 0.3586 0.859 0.000 0.736 0.264 0.000 0.000
#> SRR1633316 2 0.3586 0.859 0.000 0.736 0.264 0.000 0.000
#> SRR1633317 2 0.3612 0.857 0.000 0.732 0.268 0.000 0.000
#> SRR1633318 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633330 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633331 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633332 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633333 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633334 2 0.0404 0.901 0.000 0.988 0.012 0.000 0.000
#> SRR1633335 4 0.2516 0.850 0.140 0.000 0.000 0.860 0.000
#> SRR1633336 4 0.2561 0.847 0.144 0.000 0.000 0.856 0.000
#> SRR1633337 4 0.2561 0.847 0.144 0.000 0.000 0.856 0.000
#> SRR1633338 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.910 0.000 0.000 0.000 1.000 0.000
#> SRR1633341 4 0.1965 0.875 0.096 0.000 0.000 0.904 0.000
#> SRR1633342 4 0.2179 0.867 0.112 0.000 0.000 0.888 0.000
#> SRR1633345 4 0.1043 0.900 0.040 0.000 0.000 0.960 0.000
#> SRR1633346 4 0.0880 0.902 0.032 0.000 0.000 0.968 0.000
#> SRR1633343 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633344 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633347 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633348 4 0.0162 0.911 0.000 0.000 0.004 0.996 0.000
#> SRR1633350 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633351 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633352 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633353 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633354 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633355 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633356 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633357 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633358 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633362 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633363 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633364 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633359 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633360 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1633361 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR2038492 1 0.0404 0.987 0.988 0.000 0.000 0.012 0.000
#> SRR2038491 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0162 0.995 0.996 0.000 0.000 0.004 0.000
#> SRR2038489 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.4356 0.645 0.012 0.000 0.648 0.340 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0363 0.651 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633231 2 0.0363 0.651 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1633232 2 0.0260 0.654 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633233 2 0.0260 0.654 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633234 2 0.0260 0.654 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633236 5 0.3982 0.677 0.000 0.004 0.460 0.000 0.536 0.000
#> SRR1633237 5 0.3982 0.677 0.000 0.004 0.460 0.000 0.536 0.000
#> SRR1633238 5 0.3982 0.677 0.000 0.004 0.460 0.000 0.536 0.000
#> SRR1633239 5 0.3982 0.677 0.000 0.004 0.460 0.000 0.536 0.000
#> SRR1633240 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633241 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633242 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633243 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633244 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633245 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633246 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633247 5 0.0000 0.245 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633248 5 0.0458 0.213 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633249 5 0.0260 0.230 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633250 5 0.0146 0.238 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1633251 3 0.3868 0.852 0.000 0.000 0.504 0.000 0.496 0.000
#> SRR1633252 3 0.3868 0.857 0.000 0.000 0.508 0.000 0.492 0.000
#> SRR1633253 3 0.3867 0.860 0.000 0.000 0.512 0.000 0.488 0.000
#> SRR1633254 3 0.3868 0.856 0.000 0.000 0.508 0.000 0.492 0.000
#> SRR1633255 3 0.3868 0.856 0.000 0.000 0.508 0.000 0.492 0.000
#> SRR1633256 5 0.3868 -0.849 0.000 0.000 0.492 0.000 0.508 0.000
#> SRR1633257 5 0.3868 -0.849 0.000 0.000 0.492 0.000 0.508 0.000
#> SRR1633258 5 0.3868 -0.849 0.000 0.000 0.492 0.000 0.508 0.000
#> SRR1633259 5 0.3592 -0.620 0.000 0.000 0.344 0.000 0.656 0.000
#> SRR1633260 5 0.3620 -0.634 0.000 0.000 0.352 0.000 0.648 0.000
#> SRR1633261 5 0.3592 -0.620 0.000 0.000 0.344 0.000 0.656 0.000
#> SRR1633262 3 0.4465 0.866 0.000 0.000 0.512 0.028 0.460 0.000
#> SRR1633263 3 0.4465 0.866 0.000 0.000 0.512 0.028 0.460 0.000
#> SRR1633264 3 0.4465 0.866 0.000 0.000 0.512 0.028 0.460 0.000
#> SRR1633265 3 0.4465 0.866 0.000 0.000 0.512 0.028 0.460 0.000
#> SRR1633266 3 0.4465 0.866 0.000 0.000 0.512 0.028 0.460 0.000
#> SRR1633267 3 0.4172 0.870 0.000 0.000 0.528 0.012 0.460 0.000
#> SRR1633268 3 0.4172 0.870 0.000 0.000 0.528 0.012 0.460 0.000
#> SRR1633269 3 0.4172 0.870 0.000 0.000 0.528 0.012 0.460 0.000
#> SRR1633270 3 0.3860 0.867 0.000 0.000 0.528 0.000 0.472 0.000
#> SRR1633271 3 0.3860 0.867 0.000 0.000 0.528 0.000 0.472 0.000
#> SRR1633272 3 0.3860 0.867 0.000 0.000 0.528 0.000 0.472 0.000
#> SRR1633273 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633274 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633275 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633276 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633277 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633278 3 0.6068 0.695 0.012 0.000 0.516 0.004 0.188 0.280
#> SRR1633279 3 0.6068 0.695 0.012 0.000 0.516 0.004 0.188 0.280
#> SRR1633280 3 0.6081 0.693 0.012 0.000 0.512 0.004 0.188 0.284
#> SRR1633281 3 0.6081 0.691 0.012 0.004 0.512 0.000 0.188 0.284
#> SRR1633282 3 0.6584 0.671 0.000 0.000 0.504 0.068 0.172 0.256
#> SRR1633284 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633285 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633286 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633287 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633288 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633289 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633290 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633291 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633292 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633293 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633294 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633295 5 0.3851 0.679 0.000 0.000 0.460 0.000 0.540 0.000
#> SRR1633296 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633297 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633298 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633299 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633300 2 0.3266 -0.041 0.000 0.728 0.000 0.000 0.000 0.272
#> SRR1633301 2 0.3330 -0.105 0.000 0.716 0.000 0.000 0.000 0.284
#> SRR1633302 2 0.3288 -0.062 0.000 0.724 0.000 0.000 0.000 0.276
#> SRR1633303 6 0.3868 0.992 0.000 0.496 0.000 0.000 0.000 0.504
#> SRR1633304 6 0.3868 0.992 0.000 0.496 0.000 0.000 0.000 0.504
#> SRR1633305 6 0.3868 0.992 0.000 0.496 0.000 0.000 0.000 0.504
#> SRR1633306 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633307 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633308 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633309 6 0.3869 0.994 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633310 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633311 6 0.3869 0.994 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633312 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633313 6 0.3869 0.994 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633314 6 0.3869 0.994 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633315 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633316 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633317 2 0.3869 -0.995 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR1633318 2 0.0146 0.658 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633319 2 0.0146 0.658 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633320 2 0.0000 0.658 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.658 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.658 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0260 0.657 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633324 2 0.0260 0.657 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633325 2 0.0260 0.657 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1633326 2 0.0000 0.658 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.658 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0146 0.658 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633329 2 0.2020 0.606 0.000 0.896 0.008 0.000 0.000 0.096
#> SRR1633330 2 0.2020 0.606 0.000 0.896 0.008 0.000 0.000 0.096
#> SRR1633331 2 0.2020 0.606 0.000 0.896 0.008 0.000 0.000 0.096
#> SRR1633332 2 0.2020 0.606 0.000 0.896 0.008 0.000 0.000 0.096
#> SRR1633333 2 0.2070 0.605 0.000 0.892 0.008 0.000 0.000 0.100
#> SRR1633334 2 0.2020 0.606 0.000 0.896 0.008 0.000 0.000 0.096
#> SRR1633335 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633336 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633337 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633338 4 0.0363 0.988 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633339 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633340 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633341 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633342 4 0.0260 0.983 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1633345 4 0.0000 0.985 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 0.985 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633344 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633347 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633348 4 0.0458 0.989 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1633350 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633351 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633352 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633353 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633354 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633355 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633356 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633357 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633358 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633362 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633363 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633364 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633359 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633360 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR1633361 1 0.2581 0.910 0.856 0.000 0.000 0.016 0.000 0.128
#> SRR2038492 1 0.1245 0.929 0.952 0.000 0.000 0.016 0.000 0.032
#> SRR2038491 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038490 1 0.0458 0.947 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR2038489 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038488 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038487 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038485 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038484 1 0.0458 0.949 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR2038483 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038482 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038481 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038480 1 0.1141 0.926 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR2038479 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038477 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038478 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0937 0.934 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR2038475 1 0.0146 0.951 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038474 1 0.0146 0.951 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038473 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038470 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038469 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038468 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038467 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038466 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0146 0.952 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038463 1 0.0000 0.952 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.7146 0.589 0.052 0.000 0.504 0.072 0.108 0.264
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.999 0.5034 0.497 0.497
#> 3 3 0.925 0.951 0.971 0.2370 0.876 0.750
#> 4 4 0.805 0.928 0.916 0.1600 0.874 0.662
#> 5 5 0.886 0.971 0.940 0.0459 0.970 0.880
#> 6 6 1.000 1.000 1.000 0.0552 0.986 0.935
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.999 0.000 1.000
#> SRR1633231 2 0.0000 0.999 0.000 1.000
#> SRR1633232 2 0.0000 0.999 0.000 1.000
#> SRR1633233 2 0.0000 0.999 0.000 1.000
#> SRR1633234 2 0.0000 0.999 0.000 1.000
#> SRR1633236 2 0.0000 0.999 0.000 1.000
#> SRR1633237 2 0.0000 0.999 0.000 1.000
#> SRR1633238 2 0.0000 0.999 0.000 1.000
#> SRR1633239 2 0.0000 0.999 0.000 1.000
#> SRR1633240 2 0.0000 0.999 0.000 1.000
#> SRR1633241 2 0.0000 0.999 0.000 1.000
#> SRR1633242 2 0.0000 0.999 0.000 1.000
#> SRR1633243 2 0.0000 0.999 0.000 1.000
#> SRR1633244 2 0.0000 0.999 0.000 1.000
#> SRR1633245 2 0.0000 0.999 0.000 1.000
#> SRR1633246 2 0.0000 0.999 0.000 1.000
#> SRR1633247 2 0.0376 0.997 0.004 0.996
#> SRR1633248 2 0.0376 0.997 0.004 0.996
#> SRR1633249 2 0.0376 0.997 0.004 0.996
#> SRR1633250 2 0.0376 0.997 0.004 0.996
#> SRR1633251 2 0.0376 0.997 0.004 0.996
#> SRR1633252 2 0.0376 0.997 0.004 0.996
#> SRR1633253 2 0.0376 0.997 0.004 0.996
#> SRR1633254 2 0.0376 0.997 0.004 0.996
#> SRR1633255 2 0.0376 0.997 0.004 0.996
#> SRR1633256 2 0.0376 0.997 0.004 0.996
#> SRR1633257 2 0.0376 0.997 0.004 0.996
#> SRR1633258 2 0.0376 0.997 0.004 0.996
#> SRR1633259 2 0.0376 0.997 0.004 0.996
#> SRR1633260 2 0.0376 0.997 0.004 0.996
#> SRR1633261 2 0.0376 0.997 0.004 0.996
#> SRR1633262 2 0.0376 0.997 0.004 0.996
#> SRR1633263 2 0.0376 0.997 0.004 0.996
#> SRR1633264 2 0.0376 0.997 0.004 0.996
#> SRR1633265 2 0.0376 0.997 0.004 0.996
#> SRR1633266 2 0.0376 0.997 0.004 0.996
#> SRR1633267 2 0.0376 0.997 0.004 0.996
#> SRR1633268 2 0.0376 0.997 0.004 0.996
#> SRR1633269 2 0.0376 0.997 0.004 0.996
#> SRR1633270 2 0.0376 0.997 0.004 0.996
#> SRR1633271 2 0.0376 0.997 0.004 0.996
#> SRR1633272 2 0.0376 0.997 0.004 0.996
#> SRR1633273 1 0.0000 1.000 1.000 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0.000
#> SRR1633278 1 0.0000 1.000 1.000 0.000
#> SRR1633279 1 0.0000 1.000 1.000 0.000
#> SRR1633280 1 0.0000 1.000 1.000 0.000
#> SRR1633281 1 0.0000 1.000 1.000 0.000
#> SRR1633282 1 0.0000 1.000 1.000 0.000
#> SRR1633284 1 0.0000 1.000 1.000 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0.000
#> SRR1633292 2 0.0000 0.999 0.000 1.000
#> SRR1633293 2 0.0000 0.999 0.000 1.000
#> SRR1633294 2 0.0000 0.999 0.000 1.000
#> SRR1633295 2 0.0000 0.999 0.000 1.000
#> SRR1633296 1 0.0000 1.000 1.000 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0.000
#> SRR1633300 2 0.0000 0.999 0.000 1.000
#> SRR1633301 2 0.0000 0.999 0.000 1.000
#> SRR1633302 2 0.0000 0.999 0.000 1.000
#> SRR1633303 2 0.0000 0.999 0.000 1.000
#> SRR1633304 2 0.0000 0.999 0.000 1.000
#> SRR1633305 2 0.0000 0.999 0.000 1.000
#> SRR1633306 2 0.0000 0.999 0.000 1.000
#> SRR1633307 2 0.0000 0.999 0.000 1.000
#> SRR1633308 2 0.0000 0.999 0.000 1.000
#> SRR1633309 2 0.0000 0.999 0.000 1.000
#> SRR1633310 2 0.0000 0.999 0.000 1.000
#> SRR1633311 2 0.0000 0.999 0.000 1.000
#> SRR1633312 2 0.0000 0.999 0.000 1.000
#> SRR1633313 2 0.0000 0.999 0.000 1.000
#> SRR1633314 2 0.0000 0.999 0.000 1.000
#> SRR1633315 2 0.0000 0.999 0.000 1.000
#> SRR1633316 2 0.0000 0.999 0.000 1.000
#> SRR1633317 2 0.0000 0.999 0.000 1.000
#> SRR1633318 2 0.0000 0.999 0.000 1.000
#> SRR1633319 2 0.0000 0.999 0.000 1.000
#> SRR1633320 2 0.0000 0.999 0.000 1.000
#> SRR1633321 2 0.0000 0.999 0.000 1.000
#> SRR1633322 2 0.0000 0.999 0.000 1.000
#> SRR1633323 2 0.0000 0.999 0.000 1.000
#> SRR1633324 2 0.0000 0.999 0.000 1.000
#> SRR1633325 2 0.0000 0.999 0.000 1.000
#> SRR1633326 2 0.0000 0.999 0.000 1.000
#> SRR1633327 2 0.0000 0.999 0.000 1.000
#> SRR1633328 2 0.0000 0.999 0.000 1.000
#> SRR1633329 2 0.0000 0.999 0.000 1.000
#> SRR1633330 2 0.0000 0.999 0.000 1.000
#> SRR1633331 2 0.0000 0.999 0.000 1.000
#> SRR1633332 2 0.0000 0.999 0.000 1.000
#> SRR1633333 2 0.0000 0.999 0.000 1.000
#> SRR1633334 2 0.0000 0.999 0.000 1.000
#> SRR1633335 1 0.0000 1.000 1.000 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000
#> SRR2038462 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633237 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633238 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633239 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633240 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633241 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633242 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633243 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633244 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633245 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633246 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633247 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.871 0.000 0.000 1.000
#> SRR1633273 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633278 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633279 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633280 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633281 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633282 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633284 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633292 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633293 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633294 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633295 3 0.5678 0.700 0.000 0.316 0.684
#> SRR1633296 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038462 1 0.0237 0.996 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633237 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633238 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633239 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633240 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633241 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633242 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633243 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633244 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633245 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633246 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633247 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633252 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633253 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633254 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633255 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633256 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633263 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633264 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633265 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633266 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633267 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633268 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633269 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633270 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633271 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633272 3 0.000 0.871 0.000 0.000 1.000 0.000
#> SRR1633273 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633274 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633275 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633276 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633277 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633278 4 0.000 0.695 0.000 0.000 0.000 1.000
#> SRR1633279 4 0.000 0.695 0.000 0.000 0.000 1.000
#> SRR1633280 4 0.000 0.695 0.000 0.000 0.000 1.000
#> SRR1633281 4 0.000 0.695 0.000 0.000 0.000 1.000
#> SRR1633282 4 0.000 0.695 0.000 0.000 0.000 1.000
#> SRR1633284 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633285 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633286 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633287 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633288 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633289 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633290 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633291 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633292 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633293 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633294 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633295 3 0.450 0.700 0.000 0.316 0.684 0.000
#> SRR1633296 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633297 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633298 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633299 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633300 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633336 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633337 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633338 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633339 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633340 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633341 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633342 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633345 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633346 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633343 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633344 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633347 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633348 4 0.433 0.933 0.288 0.000 0.000 0.712
#> SRR1633350 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.000 0.695 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633236 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633237 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633238 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633239 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633240 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633241 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633242 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633243 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633244 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633245 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633246 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633247 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633248 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633249 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633250 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633251 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633252 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633253 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633254 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633255 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633256 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633257 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633258 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633259 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633260 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633261 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633262 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633263 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633264 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633265 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633266 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633267 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633268 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633269 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633270 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633271 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633272 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1633273 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633274 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633275 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633276 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633277 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633278 4 0.476 0.593 0.288 0.000 0 0.668 0.044
#> SRR1633279 4 0.476 0.593 0.288 0.000 0 0.668 0.044
#> SRR1633280 4 0.476 0.593 0.288 0.000 0 0.668 0.044
#> SRR1633281 4 0.476 0.593 0.288 0.000 0 0.668 0.044
#> SRR1633282 4 0.476 0.593 0.288 0.000 0 0.668 0.044
#> SRR1633284 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633285 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633286 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633287 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633288 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633289 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633290 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633291 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633292 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633293 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633294 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633295 5 0.112 1.000 0.000 0.044 0 0.000 0.956
#> SRR1633296 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633297 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633298 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633299 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0 0.000 0.000
#> SRR1633335 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633336 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633337 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633338 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633339 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633340 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633341 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633342 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633345 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633346 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633343 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633344 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633347 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633348 4 0.000 0.927 0.000 0.000 0 1.000 0.000
#> SRR1633350 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633351 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633352 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633353 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633354 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633355 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633356 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633357 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633358 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633362 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633363 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633364 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633359 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633360 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR1633361 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038492 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038491 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038490 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038489 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038488 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038487 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038486 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038485 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038484 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038483 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038482 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038481 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038480 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038479 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038477 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038478 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038476 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038475 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038474 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038473 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038472 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038471 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038470 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038469 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038468 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038467 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038466 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038465 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038464 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038463 1 0.373 1.000 0.712 0.000 0 0.288 0.000
#> SRR2038462 4 0.476 0.593 0.288 0.000 0 0.668 0.044
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0 1 0 1 0 0 0 0
#> SRR1633231 2 0 1 0 1 0 0 0 0
#> SRR1633232 2 0 1 0 1 0 0 0 0
#> SRR1633233 2 0 1 0 1 0 0 0 0
#> SRR1633234 2 0 1 0 1 0 0 0 0
#> SRR1633236 5 0 1 0 0 0 0 1 0
#> SRR1633237 5 0 1 0 0 0 0 1 0
#> SRR1633238 5 0 1 0 0 0 0 1 0
#> SRR1633239 5 0 1 0 0 0 0 1 0
#> SRR1633240 5 0 1 0 0 0 0 1 0
#> SRR1633241 5 0 1 0 0 0 0 1 0
#> SRR1633242 5 0 1 0 0 0 0 1 0
#> SRR1633243 5 0 1 0 0 0 0 1 0
#> SRR1633244 5 0 1 0 0 0 0 1 0
#> SRR1633245 5 0 1 0 0 0 0 1 0
#> SRR1633246 5 0 1 0 0 0 0 1 0
#> SRR1633247 3 0 1 0 0 1 0 0 0
#> SRR1633248 3 0 1 0 0 1 0 0 0
#> SRR1633249 3 0 1 0 0 1 0 0 0
#> SRR1633250 3 0 1 0 0 1 0 0 0
#> SRR1633251 3 0 1 0 0 1 0 0 0
#> SRR1633252 3 0 1 0 0 1 0 0 0
#> SRR1633253 3 0 1 0 0 1 0 0 0
#> SRR1633254 3 0 1 0 0 1 0 0 0
#> SRR1633255 3 0 1 0 0 1 0 0 0
#> SRR1633256 3 0 1 0 0 1 0 0 0
#> SRR1633257 3 0 1 0 0 1 0 0 0
#> SRR1633258 3 0 1 0 0 1 0 0 0
#> SRR1633259 3 0 1 0 0 1 0 0 0
#> SRR1633260 3 0 1 0 0 1 0 0 0
#> SRR1633261 3 0 1 0 0 1 0 0 0
#> SRR1633262 3 0 1 0 0 1 0 0 0
#> SRR1633263 3 0 1 0 0 1 0 0 0
#> SRR1633264 3 0 1 0 0 1 0 0 0
#> SRR1633265 3 0 1 0 0 1 0 0 0
#> SRR1633266 3 0 1 0 0 1 0 0 0
#> SRR1633267 3 0 1 0 0 1 0 0 0
#> SRR1633268 3 0 1 0 0 1 0 0 0
#> SRR1633269 3 0 1 0 0 1 0 0 0
#> SRR1633270 3 0 1 0 0 1 0 0 0
#> SRR1633271 3 0 1 0 0 1 0 0 0
#> SRR1633272 3 0 1 0 0 1 0 0 0
#> SRR1633273 4 0 1 0 0 0 1 0 0
#> SRR1633274 4 0 1 0 0 0 1 0 0
#> SRR1633275 4 0 1 0 0 0 1 0 0
#> SRR1633276 4 0 1 0 0 0 1 0 0
#> SRR1633277 4 0 1 0 0 0 1 0 0
#> SRR1633278 6 0 1 0 0 0 0 0 1
#> SRR1633279 6 0 1 0 0 0 0 0 1
#> SRR1633280 6 0 1 0 0 0 0 0 1
#> SRR1633281 6 0 1 0 0 0 0 0 1
#> SRR1633282 6 0 1 0 0 0 0 0 1
#> SRR1633284 4 0 1 0 0 0 1 0 0
#> SRR1633285 4 0 1 0 0 0 1 0 0
#> SRR1633286 4 0 1 0 0 0 1 0 0
#> SRR1633287 4 0 1 0 0 0 1 0 0
#> SRR1633288 4 0 1 0 0 0 1 0 0
#> SRR1633289 4 0 1 0 0 0 1 0 0
#> SRR1633290 4 0 1 0 0 0 1 0 0
#> SRR1633291 4 0 1 0 0 0 1 0 0
#> SRR1633292 5 0 1 0 0 0 0 1 0
#> SRR1633293 5 0 1 0 0 0 0 1 0
#> SRR1633294 5 0 1 0 0 0 0 1 0
#> SRR1633295 5 0 1 0 0 0 0 1 0
#> SRR1633296 4 0 1 0 0 0 1 0 0
#> SRR1633297 4 0 1 0 0 0 1 0 0
#> SRR1633298 4 0 1 0 0 0 1 0 0
#> SRR1633299 4 0 1 0 0 0 1 0 0
#> SRR1633300 2 0 1 0 1 0 0 0 0
#> SRR1633301 2 0 1 0 1 0 0 0 0
#> SRR1633302 2 0 1 0 1 0 0 0 0
#> SRR1633303 2 0 1 0 1 0 0 0 0
#> SRR1633304 2 0 1 0 1 0 0 0 0
#> SRR1633305 2 0 1 0 1 0 0 0 0
#> SRR1633306 2 0 1 0 1 0 0 0 0
#> SRR1633307 2 0 1 0 1 0 0 0 0
#> SRR1633308 2 0 1 0 1 0 0 0 0
#> SRR1633309 2 0 1 0 1 0 0 0 0
#> SRR1633310 2 0 1 0 1 0 0 0 0
#> SRR1633311 2 0 1 0 1 0 0 0 0
#> SRR1633312 2 0 1 0 1 0 0 0 0
#> SRR1633313 2 0 1 0 1 0 0 0 0
#> SRR1633314 2 0 1 0 1 0 0 0 0
#> SRR1633315 2 0 1 0 1 0 0 0 0
#> SRR1633316 2 0 1 0 1 0 0 0 0
#> SRR1633317 2 0 1 0 1 0 0 0 0
#> SRR1633318 2 0 1 0 1 0 0 0 0
#> SRR1633319 2 0 1 0 1 0 0 0 0
#> SRR1633320 2 0 1 0 1 0 0 0 0
#> SRR1633321 2 0 1 0 1 0 0 0 0
#> SRR1633322 2 0 1 0 1 0 0 0 0
#> SRR1633323 2 0 1 0 1 0 0 0 0
#> SRR1633324 2 0 1 0 1 0 0 0 0
#> SRR1633325 2 0 1 0 1 0 0 0 0
#> SRR1633326 2 0 1 0 1 0 0 0 0
#> SRR1633327 2 0 1 0 1 0 0 0 0
#> SRR1633328 2 0 1 0 1 0 0 0 0
#> SRR1633329 2 0 1 0 1 0 0 0 0
#> SRR1633330 2 0 1 0 1 0 0 0 0
#> SRR1633331 2 0 1 0 1 0 0 0 0
#> SRR1633332 2 0 1 0 1 0 0 0 0
#> SRR1633333 2 0 1 0 1 0 0 0 0
#> SRR1633334 2 0 1 0 1 0 0 0 0
#> SRR1633335 4 0 1 0 0 0 1 0 0
#> SRR1633336 4 0 1 0 0 0 1 0 0
#> SRR1633337 4 0 1 0 0 0 1 0 0
#> SRR1633338 4 0 1 0 0 0 1 0 0
#> SRR1633339 4 0 1 0 0 0 1 0 0
#> SRR1633340 4 0 1 0 0 0 1 0 0
#> SRR1633341 4 0 1 0 0 0 1 0 0
#> SRR1633342 4 0 1 0 0 0 1 0 0
#> SRR1633345 4 0 1 0 0 0 1 0 0
#> SRR1633346 4 0 1 0 0 0 1 0 0
#> SRR1633343 4 0 1 0 0 0 1 0 0
#> SRR1633344 4 0 1 0 0 0 1 0 0
#> SRR1633347 4 0 1 0 0 0 1 0 0
#> SRR1633348 4 0 1 0 0 0 1 0 0
#> SRR1633350 1 0 1 1 0 0 0 0 0
#> SRR1633351 1 0 1 1 0 0 0 0 0
#> SRR1633352 1 0 1 1 0 0 0 0 0
#> SRR1633353 1 0 1 1 0 0 0 0 0
#> SRR1633354 1 0 1 1 0 0 0 0 0
#> SRR1633355 1 0 1 1 0 0 0 0 0
#> SRR1633356 1 0 1 1 0 0 0 0 0
#> SRR1633357 1 0 1 1 0 0 0 0 0
#> SRR1633358 1 0 1 1 0 0 0 0 0
#> SRR1633362 1 0 1 1 0 0 0 0 0
#> SRR1633363 1 0 1 1 0 0 0 0 0
#> SRR1633364 1 0 1 1 0 0 0 0 0
#> SRR1633359 1 0 1 1 0 0 0 0 0
#> SRR1633360 1 0 1 1 0 0 0 0 0
#> SRR1633361 1 0 1 1 0 0 0 0 0
#> SRR2038492 1 0 1 1 0 0 0 0 0
#> SRR2038491 1 0 1 1 0 0 0 0 0
#> SRR2038490 1 0 1 1 0 0 0 0 0
#> SRR2038489 1 0 1 1 0 0 0 0 0
#> SRR2038488 1 0 1 1 0 0 0 0 0
#> SRR2038487 1 0 1 1 0 0 0 0 0
#> SRR2038486 1 0 1 1 0 0 0 0 0
#> SRR2038485 1 0 1 1 0 0 0 0 0
#> SRR2038484 1 0 1 1 0 0 0 0 0
#> SRR2038483 1 0 1 1 0 0 0 0 0
#> SRR2038482 1 0 1 1 0 0 0 0 0
#> SRR2038481 1 0 1 1 0 0 0 0 0
#> SRR2038480 1 0 1 1 0 0 0 0 0
#> SRR2038479 1 0 1 1 0 0 0 0 0
#> SRR2038477 1 0 1 1 0 0 0 0 0
#> SRR2038478 1 0 1 1 0 0 0 0 0
#> SRR2038476 1 0 1 1 0 0 0 0 0
#> SRR2038475 1 0 1 1 0 0 0 0 0
#> SRR2038474 1 0 1 1 0 0 0 0 0
#> SRR2038473 1 0 1 1 0 0 0 0 0
#> SRR2038472 1 0 1 1 0 0 0 0 0
#> SRR2038471 1 0 1 1 0 0 0 0 0
#> SRR2038470 1 0 1 1 0 0 0 0 0
#> SRR2038469 1 0 1 1 0 0 0 0 0
#> SRR2038468 1 0 1 1 0 0 0 0 0
#> SRR2038467 1 0 1 1 0 0 0 0 0
#> SRR2038466 1 0 1 1 0 0 0 0 0
#> SRR2038465 1 0 1 1 0 0 0 0 0
#> SRR2038464 1 0 1 1 0 0 0 0 0
#> SRR2038463 1 0 1 1 0 0 0 0 0
#> SRR2038462 6 0 1 0 0 0 0 0 1
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.763 0.895 0.953 0.4928 0.499 0.499
#> 3 3 0.628 0.708 0.862 0.3129 0.721 0.499
#> 4 4 0.675 0.802 0.829 0.1201 0.860 0.612
#> 5 5 0.725 0.798 0.819 0.0591 0.972 0.895
#> 6 6 0.840 0.710 0.791 0.0436 0.983 0.933
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.942 0.000 1.000
#> SRR1633231 2 0.0000 0.942 0.000 1.000
#> SRR1633232 2 0.0000 0.942 0.000 1.000
#> SRR1633233 2 0.0000 0.942 0.000 1.000
#> SRR1633234 2 0.0000 0.942 0.000 1.000
#> SRR1633236 2 0.0376 0.941 0.004 0.996
#> SRR1633237 2 0.0376 0.941 0.004 0.996
#> SRR1633238 2 0.0376 0.941 0.004 0.996
#> SRR1633239 2 0.0376 0.941 0.004 0.996
#> SRR1633240 2 0.0376 0.941 0.004 0.996
#> SRR1633241 2 0.0376 0.941 0.004 0.996
#> SRR1633242 2 0.0376 0.941 0.004 0.996
#> SRR1633243 2 0.0376 0.941 0.004 0.996
#> SRR1633244 2 0.0376 0.941 0.004 0.996
#> SRR1633245 2 0.0376 0.941 0.004 0.996
#> SRR1633246 2 0.0376 0.941 0.004 0.996
#> SRR1633247 2 0.6887 0.824 0.184 0.816
#> SRR1633248 2 0.6887 0.824 0.184 0.816
#> SRR1633249 2 0.6887 0.824 0.184 0.816
#> SRR1633250 2 0.6887 0.824 0.184 0.816
#> SRR1633251 2 0.6887 0.824 0.184 0.816
#> SRR1633252 2 0.6887 0.824 0.184 0.816
#> SRR1633253 2 0.6887 0.824 0.184 0.816
#> SRR1633254 2 0.6887 0.824 0.184 0.816
#> SRR1633255 2 0.6887 0.824 0.184 0.816
#> SRR1633256 2 0.6887 0.824 0.184 0.816
#> SRR1633257 2 0.6887 0.824 0.184 0.816
#> SRR1633258 2 0.6887 0.824 0.184 0.816
#> SRR1633259 2 0.6887 0.824 0.184 0.816
#> SRR1633260 2 0.6887 0.824 0.184 0.816
#> SRR1633261 2 0.6887 0.824 0.184 0.816
#> SRR1633262 1 0.9850 0.214 0.572 0.428
#> SRR1633263 1 0.9850 0.214 0.572 0.428
#> SRR1633264 1 0.9850 0.214 0.572 0.428
#> SRR1633265 1 0.9850 0.214 0.572 0.428
#> SRR1633266 1 0.9850 0.214 0.572 0.428
#> SRR1633267 2 0.6887 0.824 0.184 0.816
#> SRR1633268 2 0.6887 0.824 0.184 0.816
#> SRR1633269 2 0.6887 0.824 0.184 0.816
#> SRR1633270 2 0.6887 0.824 0.184 0.816
#> SRR1633271 2 0.6887 0.824 0.184 0.816
#> SRR1633272 2 0.6887 0.824 0.184 0.816
#> SRR1633273 1 0.0000 0.951 1.000 0.000
#> SRR1633274 1 0.0000 0.951 1.000 0.000
#> SRR1633275 1 0.0000 0.951 1.000 0.000
#> SRR1633276 1 0.0000 0.951 1.000 0.000
#> SRR1633277 1 0.0000 0.951 1.000 0.000
#> SRR1633278 1 0.9522 0.376 0.628 0.372
#> SRR1633279 1 0.9522 0.376 0.628 0.372
#> SRR1633280 1 0.9522 0.376 0.628 0.372
#> SRR1633281 1 0.9522 0.376 0.628 0.372
#> SRR1633282 1 0.0000 0.951 1.000 0.000
#> SRR1633284 1 0.0000 0.951 1.000 0.000
#> SRR1633285 1 0.0000 0.951 1.000 0.000
#> SRR1633286 1 0.0000 0.951 1.000 0.000
#> SRR1633287 1 0.0000 0.951 1.000 0.000
#> SRR1633288 1 0.0000 0.951 1.000 0.000
#> SRR1633289 1 0.0000 0.951 1.000 0.000
#> SRR1633290 1 0.0000 0.951 1.000 0.000
#> SRR1633291 1 0.0000 0.951 1.000 0.000
#> SRR1633292 2 0.0376 0.941 0.004 0.996
#> SRR1633293 2 0.0376 0.941 0.004 0.996
#> SRR1633294 2 0.0376 0.941 0.004 0.996
#> SRR1633295 2 0.0376 0.941 0.004 0.996
#> SRR1633296 1 0.0000 0.951 1.000 0.000
#> SRR1633297 1 0.0000 0.951 1.000 0.000
#> SRR1633298 1 0.0000 0.951 1.000 0.000
#> SRR1633299 1 0.0000 0.951 1.000 0.000
#> SRR1633300 2 0.0000 0.942 0.000 1.000
#> SRR1633301 2 0.0000 0.942 0.000 1.000
#> SRR1633302 2 0.0000 0.942 0.000 1.000
#> SRR1633303 2 0.0000 0.942 0.000 1.000
#> SRR1633304 2 0.0000 0.942 0.000 1.000
#> SRR1633305 2 0.0000 0.942 0.000 1.000
#> SRR1633306 2 0.0000 0.942 0.000 1.000
#> SRR1633307 2 0.0000 0.942 0.000 1.000
#> SRR1633308 2 0.0000 0.942 0.000 1.000
#> SRR1633309 2 0.0000 0.942 0.000 1.000
#> SRR1633310 2 0.0000 0.942 0.000 1.000
#> SRR1633311 2 0.0000 0.942 0.000 1.000
#> SRR1633312 2 0.0000 0.942 0.000 1.000
#> SRR1633313 2 0.0000 0.942 0.000 1.000
#> SRR1633314 2 0.0000 0.942 0.000 1.000
#> SRR1633315 2 0.0000 0.942 0.000 1.000
#> SRR1633316 2 0.0000 0.942 0.000 1.000
#> SRR1633317 2 0.0000 0.942 0.000 1.000
#> SRR1633318 2 0.0000 0.942 0.000 1.000
#> SRR1633319 2 0.0000 0.942 0.000 1.000
#> SRR1633320 2 0.0000 0.942 0.000 1.000
#> SRR1633321 2 0.0000 0.942 0.000 1.000
#> SRR1633322 2 0.0000 0.942 0.000 1.000
#> SRR1633323 2 0.0000 0.942 0.000 1.000
#> SRR1633324 2 0.0000 0.942 0.000 1.000
#> SRR1633325 2 0.0000 0.942 0.000 1.000
#> SRR1633326 2 0.0000 0.942 0.000 1.000
#> SRR1633327 2 0.0000 0.942 0.000 1.000
#> SRR1633328 2 0.0000 0.942 0.000 1.000
#> SRR1633329 2 0.0000 0.942 0.000 1.000
#> SRR1633330 2 0.0000 0.942 0.000 1.000
#> SRR1633331 2 0.0000 0.942 0.000 1.000
#> SRR1633332 2 0.0000 0.942 0.000 1.000
#> SRR1633333 2 0.0000 0.942 0.000 1.000
#> SRR1633334 2 0.0000 0.942 0.000 1.000
#> SRR1633335 1 0.0000 0.951 1.000 0.000
#> SRR1633336 1 0.0000 0.951 1.000 0.000
#> SRR1633337 1 0.0000 0.951 1.000 0.000
#> SRR1633338 1 0.0000 0.951 1.000 0.000
#> SRR1633339 1 0.0000 0.951 1.000 0.000
#> SRR1633340 1 0.0000 0.951 1.000 0.000
#> SRR1633341 1 0.0000 0.951 1.000 0.000
#> SRR1633342 1 0.0000 0.951 1.000 0.000
#> SRR1633345 1 0.0000 0.951 1.000 0.000
#> SRR1633346 1 0.0000 0.951 1.000 0.000
#> SRR1633343 1 0.0000 0.951 1.000 0.000
#> SRR1633344 1 0.0000 0.951 1.000 0.000
#> SRR1633347 1 0.0000 0.951 1.000 0.000
#> SRR1633348 1 0.0000 0.951 1.000 0.000
#> SRR1633350 1 0.0376 0.951 0.996 0.004
#> SRR1633351 1 0.0376 0.951 0.996 0.004
#> SRR1633352 1 0.0376 0.951 0.996 0.004
#> SRR1633353 1 0.0376 0.951 0.996 0.004
#> SRR1633354 1 0.0376 0.951 0.996 0.004
#> SRR1633355 1 0.0376 0.951 0.996 0.004
#> SRR1633356 1 0.0376 0.951 0.996 0.004
#> SRR1633357 1 0.0376 0.951 0.996 0.004
#> SRR1633358 1 0.0376 0.951 0.996 0.004
#> SRR1633362 1 0.0376 0.951 0.996 0.004
#> SRR1633363 1 0.0376 0.951 0.996 0.004
#> SRR1633364 1 0.0376 0.951 0.996 0.004
#> SRR1633359 1 0.0376 0.951 0.996 0.004
#> SRR1633360 1 0.0376 0.951 0.996 0.004
#> SRR1633361 1 0.0376 0.951 0.996 0.004
#> SRR2038492 1 0.0376 0.951 0.996 0.004
#> SRR2038491 1 0.0376 0.951 0.996 0.004
#> SRR2038490 1 0.0376 0.951 0.996 0.004
#> SRR2038489 1 0.0376 0.951 0.996 0.004
#> SRR2038488 1 0.0376 0.951 0.996 0.004
#> SRR2038487 1 0.0376 0.951 0.996 0.004
#> SRR2038486 1 0.0376 0.951 0.996 0.004
#> SRR2038485 1 0.0376 0.951 0.996 0.004
#> SRR2038484 1 0.0376 0.951 0.996 0.004
#> SRR2038483 1 0.0376 0.951 0.996 0.004
#> SRR2038482 1 0.0376 0.951 0.996 0.004
#> SRR2038481 1 0.0376 0.951 0.996 0.004
#> SRR2038480 1 0.0376 0.951 0.996 0.004
#> SRR2038479 1 0.0376 0.951 0.996 0.004
#> SRR2038477 1 0.0376 0.951 0.996 0.004
#> SRR2038478 1 0.0376 0.951 0.996 0.004
#> SRR2038476 1 0.0376 0.951 0.996 0.004
#> SRR2038475 1 0.0376 0.951 0.996 0.004
#> SRR2038474 1 0.0376 0.951 0.996 0.004
#> SRR2038473 1 0.0376 0.951 0.996 0.004
#> SRR2038472 1 0.0376 0.951 0.996 0.004
#> SRR2038471 1 0.0376 0.951 0.996 0.004
#> SRR2038470 1 0.0376 0.951 0.996 0.004
#> SRR2038469 1 0.0376 0.951 0.996 0.004
#> SRR2038468 1 0.0376 0.951 0.996 0.004
#> SRR2038467 1 0.0376 0.951 0.996 0.004
#> SRR2038466 1 0.0376 0.951 0.996 0.004
#> SRR2038465 1 0.0376 0.951 0.996 0.004
#> SRR2038464 1 0.0376 0.951 0.996 0.004
#> SRR2038463 1 0.0376 0.951 0.996 0.004
#> SRR2038462 1 0.0000 0.951 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633231 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633232 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633233 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633234 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633236 2 0.4842 0.6613 0.000 0.776 0.224
#> SRR1633237 2 0.4555 0.7067 0.000 0.800 0.200
#> SRR1633238 2 0.4555 0.7067 0.000 0.800 0.200
#> SRR1633239 2 0.4555 0.7067 0.000 0.800 0.200
#> SRR1633240 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633241 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633242 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633243 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633244 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633245 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633246 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633247 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633248 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633249 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633250 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633251 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633252 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633253 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633254 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633255 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633256 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633257 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633258 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633259 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633260 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633261 3 0.5536 0.5882 0.012 0.236 0.752
#> SRR1633262 3 0.0892 0.6122 0.020 0.000 0.980
#> SRR1633263 3 0.0892 0.6122 0.020 0.000 0.980
#> SRR1633264 3 0.0892 0.6122 0.020 0.000 0.980
#> SRR1633265 3 0.0892 0.6122 0.020 0.000 0.980
#> SRR1633266 3 0.0892 0.6122 0.020 0.000 0.980
#> SRR1633267 3 0.3695 0.6135 0.012 0.108 0.880
#> SRR1633268 3 0.3695 0.6135 0.012 0.108 0.880
#> SRR1633269 3 0.3695 0.6135 0.012 0.108 0.880
#> SRR1633270 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633271 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633272 3 0.5493 0.5908 0.012 0.232 0.756
#> SRR1633273 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633274 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633275 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633276 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633277 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633278 3 0.3619 0.5475 0.136 0.000 0.864
#> SRR1633279 3 0.3619 0.5475 0.136 0.000 0.864
#> SRR1633280 3 0.3619 0.5475 0.136 0.000 0.864
#> SRR1633281 3 0.3619 0.5475 0.136 0.000 0.864
#> SRR1633282 3 0.3619 0.5475 0.136 0.000 0.864
#> SRR1633284 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633285 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633286 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633287 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633288 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633289 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633290 1 0.6299 0.2642 0.524 0.000 0.476
#> SRR1633291 1 0.6299 0.2642 0.524 0.000 0.476
#> SRR1633292 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633293 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633294 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633295 3 0.6286 0.2272 0.000 0.464 0.536
#> SRR1633296 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633297 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633298 3 0.6126 0.0848 0.400 0.000 0.600
#> SRR1633299 3 0.6126 0.0848 0.400 0.000 0.600
#> SRR1633300 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633301 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633302 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633303 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633304 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633305 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633306 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633307 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633308 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633309 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633310 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633311 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633312 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633313 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633314 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633315 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633316 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633317 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633318 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633319 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633320 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633321 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633322 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633323 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633324 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633325 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633326 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633327 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633328 2 0.0000 0.9726 0.000 1.000 0.000
#> SRR1633329 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633330 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633331 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633332 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633333 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633334 2 0.0892 0.9584 0.000 0.980 0.020
#> SRR1633335 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633336 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633337 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633338 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633339 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633340 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633341 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633342 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633345 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633346 1 0.4887 0.7668 0.772 0.000 0.228
#> SRR1633343 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633344 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633347 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633348 3 0.6299 -0.1317 0.476 0.000 0.524
#> SRR1633350 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.9139 1.000 0.000 0.000
#> SRR2038462 3 0.5216 0.3815 0.260 0.000 0.740
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.203 0.897 0.000 0.936 0.036 0.028
#> SRR1633231 2 0.203 0.897 0.000 0.936 0.036 0.028
#> SRR1633232 2 0.193 0.898 0.000 0.940 0.036 0.024
#> SRR1633233 2 0.193 0.898 0.000 0.940 0.036 0.024
#> SRR1633234 2 0.193 0.898 0.000 0.940 0.036 0.024
#> SRR1633236 2 0.569 0.191 0.000 0.500 0.476 0.024
#> SRR1633237 2 0.558 0.411 0.000 0.576 0.400 0.024
#> SRR1633238 2 0.558 0.411 0.000 0.576 0.400 0.024
#> SRR1633239 2 0.558 0.411 0.000 0.576 0.400 0.024
#> SRR1633240 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633241 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633242 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633243 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633244 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633245 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633246 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633247 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633248 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633249 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633250 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633251 3 0.529 0.860 0.000 0.056 0.720 0.224
#> SRR1633252 3 0.529 0.860 0.000 0.056 0.720 0.224
#> SRR1633253 3 0.529 0.860 0.000 0.056 0.720 0.224
#> SRR1633254 3 0.529 0.860 0.000 0.056 0.720 0.224
#> SRR1633255 3 0.529 0.860 0.000 0.056 0.720 0.224
#> SRR1633256 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633257 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633258 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633259 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633260 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633261 3 0.518 0.863 0.000 0.060 0.736 0.204
#> SRR1633262 3 0.498 0.561 0.000 0.000 0.536 0.464
#> SRR1633263 3 0.498 0.561 0.000 0.000 0.536 0.464
#> SRR1633264 3 0.498 0.561 0.000 0.000 0.536 0.464
#> SRR1633265 3 0.498 0.561 0.000 0.000 0.536 0.464
#> SRR1633266 3 0.498 0.561 0.000 0.000 0.536 0.464
#> SRR1633267 3 0.511 0.832 0.000 0.032 0.704 0.264
#> SRR1633268 3 0.511 0.832 0.000 0.032 0.704 0.264
#> SRR1633269 3 0.511 0.832 0.000 0.032 0.704 0.264
#> SRR1633270 3 0.542 0.853 0.000 0.056 0.704 0.240
#> SRR1633271 3 0.542 0.853 0.000 0.056 0.704 0.240
#> SRR1633272 3 0.542 0.853 0.000 0.056 0.704 0.240
#> SRR1633273 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633274 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633275 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633276 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633277 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633278 4 0.600 -0.250 0.044 0.000 0.404 0.552
#> SRR1633279 4 0.600 -0.250 0.044 0.000 0.404 0.552
#> SRR1633280 4 0.600 -0.250 0.044 0.000 0.404 0.552
#> SRR1633281 4 0.600 -0.250 0.044 0.000 0.404 0.552
#> SRR1633282 4 0.385 0.521 0.044 0.000 0.116 0.840
#> SRR1633284 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633285 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633286 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633287 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633288 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633289 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633290 4 0.448 0.770 0.248 0.000 0.012 0.740
#> SRR1633291 4 0.448 0.770 0.248 0.000 0.012 0.740
#> SRR1633292 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633293 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633294 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633295 3 0.302 0.766 0.000 0.148 0.852 0.000
#> SRR1633296 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633297 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633298 4 0.405 0.668 0.124 0.000 0.048 0.828
#> SRR1633299 4 0.405 0.668 0.124 0.000 0.048 0.828
#> SRR1633300 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633301 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633302 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633303 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633304 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633305 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633306 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633307 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633308 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633309 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633310 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633311 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633312 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633313 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633314 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633315 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633316 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633317 2 0.228 0.893 0.000 0.904 0.000 0.096
#> SRR1633318 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633319 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633320 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633321 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633322 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633323 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633324 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633325 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633326 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633327 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633328 2 0.158 0.901 0.000 0.952 0.036 0.012
#> SRR1633329 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633330 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633331 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633332 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633333 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633334 2 0.222 0.892 0.000 0.928 0.040 0.032
#> SRR1633335 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633336 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633337 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633338 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633339 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633340 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633341 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633342 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633345 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633346 4 0.488 0.706 0.408 0.000 0.000 0.592
#> SRR1633343 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633344 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633347 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633348 4 0.485 0.776 0.220 0.000 0.036 0.744
#> SRR1633350 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633351 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633352 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633353 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633354 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633355 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633356 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633357 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633358 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633362 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633363 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633364 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633359 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633360 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR1633361 1 0.334 0.887 0.856 0.000 0.128 0.016
#> SRR2038492 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.131 0.925 0.960 0.000 0.036 0.004
#> SRR2038487 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.222 0.910 0.924 0.000 0.060 0.016
#> SRR2038483 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.000 0.939 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.397 0.597 0.080 0.000 0.080 0.840
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.2359 0.8451 0.000 0.904 0.000 0.036 NA
#> SRR1633231 2 0.2359 0.8451 0.000 0.904 0.000 0.036 NA
#> SRR1633232 2 0.1965 0.8508 0.000 0.924 0.000 0.024 NA
#> SRR1633233 2 0.1965 0.8508 0.000 0.924 0.000 0.024 NA
#> SRR1633234 2 0.1965 0.8508 0.000 0.924 0.000 0.024 NA
#> SRR1633236 3 0.7085 0.2568 0.000 0.316 0.420 0.016 NA
#> SRR1633237 2 0.7114 -0.0481 0.000 0.400 0.336 0.016 NA
#> SRR1633238 2 0.7114 -0.0481 0.000 0.400 0.336 0.016 NA
#> SRR1633239 2 0.7114 -0.0481 0.000 0.400 0.336 0.016 NA
#> SRR1633240 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633241 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633242 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633243 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633244 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633245 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633246 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633247 3 0.1668 0.7929 0.000 0.032 0.940 0.000 NA
#> SRR1633248 3 0.1668 0.7929 0.000 0.032 0.940 0.000 NA
#> SRR1633249 3 0.1668 0.7929 0.000 0.032 0.940 0.000 NA
#> SRR1633250 3 0.1668 0.7929 0.000 0.032 0.940 0.000 NA
#> SRR1633251 3 0.2269 0.7916 0.000 0.028 0.920 0.020 NA
#> SRR1633252 3 0.2269 0.7916 0.000 0.028 0.920 0.020 NA
#> SRR1633253 3 0.2269 0.7916 0.000 0.028 0.920 0.020 NA
#> SRR1633254 3 0.2269 0.7916 0.000 0.028 0.920 0.020 NA
#> SRR1633255 3 0.2269 0.7916 0.000 0.028 0.920 0.020 NA
#> SRR1633256 3 0.0880 0.7960 0.000 0.032 0.968 0.000 NA
#> SRR1633257 3 0.0880 0.7960 0.000 0.032 0.968 0.000 NA
#> SRR1633258 3 0.0880 0.7960 0.000 0.032 0.968 0.000 NA
#> SRR1633259 3 0.1041 0.7958 0.000 0.032 0.964 0.000 NA
#> SRR1633260 3 0.1041 0.7958 0.000 0.032 0.964 0.000 NA
#> SRR1633261 3 0.1041 0.7958 0.000 0.032 0.964 0.000 NA
#> SRR1633262 3 0.5798 0.5694 0.000 0.000 0.608 0.236 NA
#> SRR1633263 3 0.5798 0.5694 0.000 0.000 0.608 0.236 NA
#> SRR1633264 3 0.5798 0.5694 0.000 0.000 0.608 0.236 NA
#> SRR1633265 3 0.5798 0.5694 0.000 0.000 0.608 0.236 NA
#> SRR1633266 3 0.5798 0.5694 0.000 0.000 0.608 0.236 NA
#> SRR1633267 3 0.4184 0.7464 0.000 0.008 0.792 0.068 NA
#> SRR1633268 3 0.4184 0.7464 0.000 0.008 0.792 0.068 NA
#> SRR1633269 3 0.4184 0.7464 0.000 0.008 0.792 0.068 NA
#> SRR1633270 3 0.4336 0.7577 0.000 0.028 0.792 0.048 NA
#> SRR1633271 3 0.4336 0.7577 0.000 0.028 0.792 0.048 NA
#> SRR1633272 3 0.4336 0.7577 0.000 0.028 0.792 0.048 NA
#> SRR1633273 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633274 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633275 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633276 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633277 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633278 3 0.6759 0.3426 0.008 0.000 0.436 0.360 NA
#> SRR1633279 3 0.6759 0.3426 0.008 0.000 0.436 0.360 NA
#> SRR1633280 3 0.6759 0.3426 0.008 0.000 0.436 0.360 NA
#> SRR1633281 3 0.6759 0.3426 0.008 0.000 0.436 0.360 NA
#> SRR1633282 4 0.6135 0.3937 0.008 0.000 0.184 0.596 NA
#> SRR1633284 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633285 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633286 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633287 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633288 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633289 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633290 4 0.4001 0.9031 0.152 0.000 0.024 0.800 NA
#> SRR1633291 4 0.4001 0.9031 0.152 0.000 0.024 0.800 NA
#> SRR1633292 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633293 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633294 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633295 3 0.4701 0.7181 0.000 0.060 0.704 0.000 NA
#> SRR1633296 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633297 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633298 4 0.4028 0.7570 0.032 0.000 0.124 0.812 NA
#> SRR1633299 4 0.4028 0.7570 0.032 0.000 0.124 0.812 NA
#> SRR1633300 2 0.3555 0.8568 0.000 0.824 0.000 0.052 NA
#> SRR1633301 2 0.3555 0.8568 0.000 0.824 0.000 0.052 NA
#> SRR1633302 2 0.3555 0.8568 0.000 0.824 0.000 0.052 NA
#> SRR1633303 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633304 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633305 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633306 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633307 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633308 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633309 2 0.3409 0.8566 0.000 0.824 0.000 0.032 NA
#> SRR1633310 2 0.3409 0.8566 0.000 0.824 0.000 0.032 NA
#> SRR1633311 2 0.3409 0.8566 0.000 0.824 0.000 0.032 NA
#> SRR1633312 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633313 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633314 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633315 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633316 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633317 2 0.3477 0.8566 0.000 0.824 0.000 0.040 NA
#> SRR1633318 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633319 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633320 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633321 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633322 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633323 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633324 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633325 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633326 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633327 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633328 2 0.0992 0.8618 0.000 0.968 0.000 0.008 NA
#> SRR1633329 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633330 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633331 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633332 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633333 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633334 2 0.2949 0.8333 0.000 0.876 0.004 0.052 NA
#> SRR1633335 4 0.3944 0.8945 0.200 0.000 0.000 0.768 NA
#> SRR1633336 4 0.3944 0.8945 0.200 0.000 0.000 0.768 NA
#> SRR1633337 4 0.3944 0.8945 0.200 0.000 0.000 0.768 NA
#> SRR1633338 4 0.3492 0.9008 0.188 0.000 0.000 0.796 NA
#> SRR1633339 4 0.3492 0.9008 0.188 0.000 0.000 0.796 NA
#> SRR1633340 4 0.3492 0.9008 0.188 0.000 0.000 0.796 NA
#> SRR1633341 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633342 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633345 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633346 4 0.3863 0.8960 0.200 0.000 0.000 0.772 NA
#> SRR1633343 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633344 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633347 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633348 4 0.4286 0.8998 0.140 0.000 0.036 0.792 NA
#> SRR1633350 1 0.3838 0.7973 0.716 0.000 0.000 0.004 NA
#> SRR1633351 1 0.3838 0.7973 0.716 0.000 0.000 0.004 NA
#> SRR1633352 1 0.3838 0.7973 0.716 0.000 0.000 0.004 NA
#> SRR1633353 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633354 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633355 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633356 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633357 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633358 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633362 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633363 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633364 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633359 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633360 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR1633361 1 0.3884 0.7935 0.708 0.000 0.000 0.004 NA
#> SRR2038492 1 0.1082 0.8907 0.964 0.000 0.028 0.008 NA
#> SRR2038491 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038490 1 0.0794 0.8948 0.972 0.000 0.028 0.000 NA
#> SRR2038489 1 0.0703 0.8956 0.976 0.000 0.024 0.000 NA
#> SRR2038488 1 0.0671 0.8970 0.980 0.000 0.016 0.000 NA
#> SRR2038487 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038486 1 0.0703 0.8956 0.976 0.000 0.024 0.000 NA
#> SRR2038485 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038484 1 0.0671 0.8951 0.980 0.000 0.004 0.000 NA
#> SRR2038483 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038482 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038481 1 0.0609 0.8961 0.980 0.000 0.020 0.000 NA
#> SRR2038480 1 0.0794 0.8948 0.972 0.000 0.028 0.000 NA
#> SRR2038479 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038477 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038478 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038476 1 0.0794 0.8948 0.972 0.000 0.028 0.000 NA
#> SRR2038475 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038474 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038473 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038472 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038471 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038470 1 0.0609 0.8961 0.980 0.000 0.020 0.000 NA
#> SRR2038469 1 0.0510 0.8964 0.984 0.000 0.016 0.000 NA
#> SRR2038468 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038467 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038466 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038465 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038464 1 0.0794 0.8948 0.972 0.000 0.028 0.000 NA
#> SRR2038463 1 0.0000 0.8981 1.000 0.000 0.000 0.000 NA
#> SRR2038462 4 0.6247 0.4280 0.020 0.000 0.172 0.608 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.2434 0.7884 0.000 0.892 0.000 0.008 NA 0.064
#> SRR1633231 2 0.2434 0.7884 0.000 0.892 0.000 0.008 NA 0.064
#> SRR1633232 2 0.2146 0.7950 0.000 0.908 0.000 0.008 NA 0.060
#> SRR1633233 2 0.2146 0.7950 0.000 0.908 0.000 0.008 NA 0.060
#> SRR1633234 2 0.2146 0.7950 0.000 0.908 0.000 0.008 NA 0.060
#> SRR1633236 3 0.7389 0.3313 0.000 0.212 0.444 0.008 NA 0.144
#> SRR1633237 2 0.7568 -0.1299 0.000 0.328 0.324 0.004 NA 0.152
#> SRR1633238 2 0.7568 -0.1299 0.000 0.328 0.324 0.004 NA 0.152
#> SRR1633239 2 0.7568 -0.1299 0.000 0.328 0.324 0.004 NA 0.152
#> SRR1633240 3 0.5308 0.6095 0.000 0.032 0.676 0.004 NA 0.120
#> SRR1633241 3 0.5308 0.6095 0.000 0.032 0.676 0.004 NA 0.120
#> SRR1633242 3 0.5308 0.6095 0.000 0.032 0.676 0.004 NA 0.120
#> SRR1633243 3 0.5308 0.6095 0.000 0.032 0.676 0.004 NA 0.120
#> SRR1633244 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633245 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633246 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633247 3 0.0146 0.7032 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1633248 3 0.0146 0.7032 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1633249 3 0.0146 0.7032 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1633250 3 0.0146 0.7032 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1633251 3 0.1471 0.6801 0.000 0.000 0.932 0.004 NA 0.064
#> SRR1633252 3 0.1471 0.6801 0.000 0.000 0.932 0.004 NA 0.064
#> SRR1633253 3 0.1471 0.6801 0.000 0.000 0.932 0.004 NA 0.064
#> SRR1633254 3 0.1471 0.6801 0.000 0.000 0.932 0.004 NA 0.064
#> SRR1633255 3 0.1471 0.6801 0.000 0.000 0.932 0.004 NA 0.064
#> SRR1633256 3 0.0146 0.7027 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1633257 3 0.0146 0.7027 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1633258 3 0.0146 0.7027 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1633259 3 0.0000 0.7032 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633260 3 0.0000 0.7032 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633261 3 0.0000 0.7032 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633262 3 0.5268 -0.0772 0.000 0.000 0.532 0.108 NA 0.360
#> SRR1633263 3 0.5268 -0.0772 0.000 0.000 0.532 0.108 NA 0.360
#> SRR1633264 3 0.5268 -0.0772 0.000 0.000 0.532 0.108 NA 0.360
#> SRR1633265 3 0.5268 -0.0772 0.000 0.000 0.532 0.108 NA 0.360
#> SRR1633266 3 0.5268 -0.0772 0.000 0.000 0.532 0.108 NA 0.360
#> SRR1633267 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633268 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633269 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633270 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633271 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633272 3 0.3243 0.5670 0.000 0.000 0.780 0.008 NA 0.208
#> SRR1633273 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633274 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633275 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633276 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633277 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633278 6 0.6945 0.8936 0.004 0.000 0.236 0.248 NA 0.448
#> SRR1633279 6 0.6945 0.8936 0.004 0.000 0.236 0.248 NA 0.448
#> SRR1633280 6 0.6945 0.8936 0.004 0.000 0.236 0.248 NA 0.448
#> SRR1633281 6 0.6945 0.8936 0.004 0.000 0.236 0.248 NA 0.448
#> SRR1633282 6 0.6416 0.7359 0.004 0.000 0.108 0.344 NA 0.484
#> SRR1633284 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633285 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633286 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633287 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633288 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633289 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633290 4 0.4944 0.7601 0.060 0.000 0.008 0.708 NA 0.188
#> SRR1633291 4 0.4944 0.7601 0.060 0.000 0.008 0.708 NA 0.188
#> SRR1633292 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633293 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633294 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633295 3 0.5300 0.6097 0.000 0.032 0.676 0.004 NA 0.116
#> SRR1633296 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633297 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633298 4 0.4945 0.6619 0.008 0.000 0.052 0.700 NA 0.204
#> SRR1633299 4 0.4945 0.6619 0.008 0.000 0.052 0.700 NA 0.204
#> SRR1633300 2 0.4179 0.7987 0.000 0.744 0.000 0.016 NA 0.048
#> SRR1633301 2 0.4179 0.7987 0.000 0.744 0.000 0.016 NA 0.048
#> SRR1633302 2 0.4179 0.7987 0.000 0.744 0.000 0.016 NA 0.048
#> SRR1633303 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633304 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633305 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633306 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633307 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633308 2 0.3825 0.7993 0.000 0.744 0.000 0.004 NA 0.032
#> SRR1633309 2 0.3941 0.7993 0.000 0.744 0.000 0.004 NA 0.044
#> SRR1633310 2 0.3941 0.7993 0.000 0.744 0.000 0.004 NA 0.044
#> SRR1633311 2 0.3941 0.7993 0.000 0.744 0.000 0.004 NA 0.044
#> SRR1633312 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633313 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633314 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633315 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633316 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633317 2 0.4020 0.7994 0.000 0.744 0.000 0.008 NA 0.044
#> SRR1633318 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633319 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633320 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633321 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633322 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633323 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633324 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633325 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633326 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633327 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633328 2 0.1082 0.8082 0.000 0.956 0.000 0.004 NA 0.040
#> SRR1633329 2 0.3126 0.7833 0.000 0.856 0.000 0.028 NA 0.072
#> SRR1633330 2 0.3113 0.7833 0.000 0.856 0.000 0.028 NA 0.076
#> SRR1633331 2 0.3113 0.7833 0.000 0.856 0.000 0.028 NA 0.076
#> SRR1633332 2 0.3126 0.7833 0.000 0.856 0.000 0.028 NA 0.072
#> SRR1633333 2 0.3133 0.7834 0.000 0.856 0.000 0.032 NA 0.072
#> SRR1633334 2 0.3145 0.7834 0.000 0.856 0.000 0.032 NA 0.068
#> SRR1633335 4 0.2147 0.7748 0.084 0.000 0.000 0.896 NA 0.000
#> SRR1633336 4 0.2147 0.7748 0.084 0.000 0.000 0.896 NA 0.000
#> SRR1633337 4 0.2147 0.7748 0.084 0.000 0.000 0.896 NA 0.000
#> SRR1633338 4 0.2039 0.7801 0.076 0.000 0.000 0.904 NA 0.020
#> SRR1633339 4 0.2039 0.7801 0.076 0.000 0.000 0.904 NA 0.020
#> SRR1633340 4 0.2039 0.7801 0.076 0.000 0.000 0.904 NA 0.020
#> SRR1633341 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633342 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633345 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633346 4 0.2831 0.7669 0.084 0.000 0.000 0.868 NA 0.016
#> SRR1633343 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633344 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633347 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633348 4 0.5071 0.7557 0.052 0.000 0.016 0.700 NA 0.196
#> SRR1633350 1 0.4109 0.6784 0.576 0.000 0.000 0.000 NA 0.012
#> SRR1633351 1 0.4109 0.6784 0.576 0.000 0.000 0.000 NA 0.012
#> SRR1633352 1 0.4109 0.6784 0.576 0.000 0.000 0.000 NA 0.012
#> SRR1633353 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633354 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633355 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633356 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633357 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633358 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633362 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633363 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633364 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633359 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633360 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR1633361 1 0.4141 0.6681 0.556 0.000 0.000 0.012 NA 0.000
#> SRR2038492 1 0.1719 0.8253 0.924 0.000 0.000 0.016 NA 0.060
#> SRR2038491 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038490 1 0.1267 0.8342 0.940 0.000 0.000 0.000 NA 0.060
#> SRR2038489 1 0.1204 0.8349 0.944 0.000 0.000 0.000 NA 0.056
#> SRR2038488 1 0.1333 0.8370 0.944 0.000 0.000 0.000 NA 0.048
#> SRR2038487 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038486 1 0.1204 0.8349 0.944 0.000 0.000 0.000 NA 0.056
#> SRR2038485 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038484 1 0.0820 0.8403 0.972 0.000 0.000 0.000 NA 0.016
#> SRR2038483 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038482 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038481 1 0.0937 0.8376 0.960 0.000 0.000 0.000 NA 0.040
#> SRR2038480 1 0.1267 0.8342 0.940 0.000 0.000 0.000 NA 0.060
#> SRR2038479 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038477 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038478 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038476 1 0.1267 0.8342 0.940 0.000 0.000 0.000 NA 0.060
#> SRR2038475 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038474 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038473 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038472 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038471 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038470 1 0.0937 0.8376 0.960 0.000 0.000 0.000 NA 0.040
#> SRR2038469 1 0.1204 0.8349 0.944 0.000 0.000 0.000 NA 0.056
#> SRR2038468 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038467 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038466 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038465 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038464 1 0.1267 0.8342 0.940 0.000 0.000 0.000 NA 0.060
#> SRR2038463 1 0.0000 0.8421 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2038462 6 0.6498 0.7375 0.004 0.000 0.108 0.368 NA 0.456
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.966 0.986 0.5029 0.497 0.497
#> 3 3 1.000 0.983 0.992 0.2806 0.834 0.674
#> 4 4 1.000 0.992 0.995 0.1666 0.885 0.682
#> 5 5 0.970 0.927 0.953 0.0445 0.963 0.855
#> 6 6 0.862 0.836 0.878 0.0277 0.955 0.805
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 3 4
There is also optional best \(k\) = 2 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 0.993 0.000 1.000
#> SRR1633231 2 0.000 0.993 0.000 1.000
#> SRR1633232 2 0.000 0.993 0.000 1.000
#> SRR1633233 2 0.000 0.993 0.000 1.000
#> SRR1633234 2 0.000 0.993 0.000 1.000
#> SRR1633236 2 0.000 0.993 0.000 1.000
#> SRR1633237 2 0.000 0.993 0.000 1.000
#> SRR1633238 2 0.000 0.993 0.000 1.000
#> SRR1633239 2 0.000 0.993 0.000 1.000
#> SRR1633240 2 0.000 0.993 0.000 1.000
#> SRR1633241 2 0.000 0.993 0.000 1.000
#> SRR1633242 2 0.000 0.993 0.000 1.000
#> SRR1633243 2 0.000 0.993 0.000 1.000
#> SRR1633244 2 0.000 0.993 0.000 1.000
#> SRR1633245 2 0.000 0.993 0.000 1.000
#> SRR1633246 2 0.000 0.993 0.000 1.000
#> SRR1633247 2 0.000 0.993 0.000 1.000
#> SRR1633248 2 0.000 0.993 0.000 1.000
#> SRR1633249 2 0.000 0.993 0.000 1.000
#> SRR1633250 2 0.000 0.993 0.000 1.000
#> SRR1633251 2 0.000 0.993 0.000 1.000
#> SRR1633252 2 0.000 0.993 0.000 1.000
#> SRR1633253 2 0.000 0.993 0.000 1.000
#> SRR1633254 2 0.000 0.993 0.000 1.000
#> SRR1633255 2 0.000 0.993 0.000 1.000
#> SRR1633256 2 0.000 0.993 0.000 1.000
#> SRR1633257 2 0.000 0.993 0.000 1.000
#> SRR1633258 2 0.000 0.993 0.000 1.000
#> SRR1633259 2 0.000 0.993 0.000 1.000
#> SRR1633260 2 0.000 0.993 0.000 1.000
#> SRR1633261 2 0.000 0.993 0.000 1.000
#> SRR1633262 2 0.506 0.876 0.112 0.888
#> SRR1633263 2 0.506 0.876 0.112 0.888
#> SRR1633264 2 0.506 0.876 0.112 0.888
#> SRR1633265 2 0.506 0.876 0.112 0.888
#> SRR1633266 2 0.506 0.876 0.112 0.888
#> SRR1633267 2 0.000 0.993 0.000 1.000
#> SRR1633268 2 0.000 0.993 0.000 1.000
#> SRR1633269 2 0.000 0.993 0.000 1.000
#> SRR1633270 2 0.000 0.993 0.000 1.000
#> SRR1633271 2 0.000 0.993 0.000 1.000
#> SRR1633272 2 0.000 0.993 0.000 1.000
#> SRR1633273 1 0.000 0.979 1.000 0.000
#> SRR1633274 1 0.000 0.979 1.000 0.000
#> SRR1633275 1 0.000 0.979 1.000 0.000
#> SRR1633276 1 0.000 0.979 1.000 0.000
#> SRR1633277 1 0.000 0.979 1.000 0.000
#> SRR1633278 1 0.978 0.311 0.588 0.412
#> SRR1633279 1 0.978 0.311 0.588 0.412
#> SRR1633280 1 0.978 0.311 0.588 0.412
#> SRR1633281 1 0.978 0.311 0.588 0.412
#> SRR1633282 1 0.000 0.979 1.000 0.000
#> SRR1633284 1 0.000 0.979 1.000 0.000
#> SRR1633285 1 0.000 0.979 1.000 0.000
#> SRR1633286 1 0.000 0.979 1.000 0.000
#> SRR1633287 1 0.000 0.979 1.000 0.000
#> SRR1633288 1 0.000 0.979 1.000 0.000
#> SRR1633289 1 0.000 0.979 1.000 0.000
#> SRR1633290 1 0.000 0.979 1.000 0.000
#> SRR1633291 1 0.000 0.979 1.000 0.000
#> SRR1633292 2 0.000 0.993 0.000 1.000
#> SRR1633293 2 0.000 0.993 0.000 1.000
#> SRR1633294 2 0.000 0.993 0.000 1.000
#> SRR1633295 2 0.000 0.993 0.000 1.000
#> SRR1633296 1 0.000 0.979 1.000 0.000
#> SRR1633297 1 0.000 0.979 1.000 0.000
#> SRR1633298 1 0.000 0.979 1.000 0.000
#> SRR1633299 1 0.000 0.979 1.000 0.000
#> SRR1633300 2 0.000 0.993 0.000 1.000
#> SRR1633301 2 0.000 0.993 0.000 1.000
#> SRR1633302 2 0.000 0.993 0.000 1.000
#> SRR1633303 2 0.000 0.993 0.000 1.000
#> SRR1633304 2 0.000 0.993 0.000 1.000
#> SRR1633305 2 0.000 0.993 0.000 1.000
#> SRR1633306 2 0.000 0.993 0.000 1.000
#> SRR1633307 2 0.000 0.993 0.000 1.000
#> SRR1633308 2 0.000 0.993 0.000 1.000
#> SRR1633309 2 0.000 0.993 0.000 1.000
#> SRR1633310 2 0.000 0.993 0.000 1.000
#> SRR1633311 2 0.000 0.993 0.000 1.000
#> SRR1633312 2 0.000 0.993 0.000 1.000
#> SRR1633313 2 0.000 0.993 0.000 1.000
#> SRR1633314 2 0.000 0.993 0.000 1.000
#> SRR1633315 2 0.000 0.993 0.000 1.000
#> SRR1633316 2 0.000 0.993 0.000 1.000
#> SRR1633317 2 0.000 0.993 0.000 1.000
#> SRR1633318 2 0.000 0.993 0.000 1.000
#> SRR1633319 2 0.000 0.993 0.000 1.000
#> SRR1633320 2 0.000 0.993 0.000 1.000
#> SRR1633321 2 0.000 0.993 0.000 1.000
#> SRR1633322 2 0.000 0.993 0.000 1.000
#> SRR1633323 2 0.000 0.993 0.000 1.000
#> SRR1633324 2 0.000 0.993 0.000 1.000
#> SRR1633325 2 0.000 0.993 0.000 1.000
#> SRR1633326 2 0.000 0.993 0.000 1.000
#> SRR1633327 2 0.000 0.993 0.000 1.000
#> SRR1633328 2 0.000 0.993 0.000 1.000
#> SRR1633329 2 0.000 0.993 0.000 1.000
#> SRR1633330 2 0.000 0.993 0.000 1.000
#> SRR1633331 2 0.000 0.993 0.000 1.000
#> SRR1633332 2 0.000 0.993 0.000 1.000
#> SRR1633333 2 0.000 0.993 0.000 1.000
#> SRR1633334 2 0.000 0.993 0.000 1.000
#> SRR1633335 1 0.000 0.979 1.000 0.000
#> SRR1633336 1 0.000 0.979 1.000 0.000
#> SRR1633337 1 0.000 0.979 1.000 0.000
#> SRR1633338 1 0.000 0.979 1.000 0.000
#> SRR1633339 1 0.000 0.979 1.000 0.000
#> SRR1633340 1 0.000 0.979 1.000 0.000
#> SRR1633341 1 0.000 0.979 1.000 0.000
#> SRR1633342 1 0.000 0.979 1.000 0.000
#> SRR1633345 1 0.000 0.979 1.000 0.000
#> SRR1633346 1 0.000 0.979 1.000 0.000
#> SRR1633343 1 0.000 0.979 1.000 0.000
#> SRR1633344 1 0.000 0.979 1.000 0.000
#> SRR1633347 1 0.000 0.979 1.000 0.000
#> SRR1633348 1 0.000 0.979 1.000 0.000
#> SRR1633350 1 0.000 0.979 1.000 0.000
#> SRR1633351 1 0.000 0.979 1.000 0.000
#> SRR1633352 1 0.000 0.979 1.000 0.000
#> SRR1633353 1 0.000 0.979 1.000 0.000
#> SRR1633354 1 0.000 0.979 1.000 0.000
#> SRR1633355 1 0.000 0.979 1.000 0.000
#> SRR1633356 1 0.000 0.979 1.000 0.000
#> SRR1633357 1 0.000 0.979 1.000 0.000
#> SRR1633358 1 0.000 0.979 1.000 0.000
#> SRR1633362 1 0.000 0.979 1.000 0.000
#> SRR1633363 1 0.000 0.979 1.000 0.000
#> SRR1633364 1 0.000 0.979 1.000 0.000
#> SRR1633359 1 0.000 0.979 1.000 0.000
#> SRR1633360 1 0.000 0.979 1.000 0.000
#> SRR1633361 1 0.000 0.979 1.000 0.000
#> SRR2038492 1 0.000 0.979 1.000 0.000
#> SRR2038491 1 0.000 0.979 1.000 0.000
#> SRR2038490 1 0.000 0.979 1.000 0.000
#> SRR2038489 1 0.000 0.979 1.000 0.000
#> SRR2038488 1 0.000 0.979 1.000 0.000
#> SRR2038487 1 0.000 0.979 1.000 0.000
#> SRR2038486 1 0.000 0.979 1.000 0.000
#> SRR2038485 1 0.000 0.979 1.000 0.000
#> SRR2038484 1 0.000 0.979 1.000 0.000
#> SRR2038483 1 0.000 0.979 1.000 0.000
#> SRR2038482 1 0.000 0.979 1.000 0.000
#> SRR2038481 1 0.000 0.979 1.000 0.000
#> SRR2038480 1 0.000 0.979 1.000 0.000
#> SRR2038479 1 0.000 0.979 1.000 0.000
#> SRR2038477 1 0.000 0.979 1.000 0.000
#> SRR2038478 1 0.000 0.979 1.000 0.000
#> SRR2038476 1 0.000 0.979 1.000 0.000
#> SRR2038475 1 0.000 0.979 1.000 0.000
#> SRR2038474 1 0.000 0.979 1.000 0.000
#> SRR2038473 1 0.000 0.979 1.000 0.000
#> SRR2038472 1 0.000 0.979 1.000 0.000
#> SRR2038471 1 0.000 0.979 1.000 0.000
#> SRR2038470 1 0.000 0.979 1.000 0.000
#> SRR2038469 1 0.000 0.979 1.000 0.000
#> SRR2038468 1 0.000 0.979 1.000 0.000
#> SRR2038467 1 0.000 0.979 1.000 0.000
#> SRR2038466 1 0.000 0.979 1.000 0.000
#> SRR2038465 1 0.000 0.979 1.000 0.000
#> SRR2038464 1 0.000 0.979 1.000 0.000
#> SRR2038463 1 0.000 0.979 1.000 0.000
#> SRR2038462 1 0.000 0.979 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 2 0.0424 0.992 0.000 0.992 0.008
#> SRR1633237 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633238 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633240 3 0.1031 0.979 0.000 0.024 0.976
#> SRR1633241 3 0.1031 0.979 0.000 0.024 0.976
#> SRR1633242 3 0.1031 0.979 0.000 0.024 0.976
#> SRR1633243 3 0.1031 0.979 0.000 0.024 0.976
#> SRR1633244 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633245 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633246 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633247 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633273 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633274 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633275 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633276 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633277 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633278 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633279 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633280 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633281 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633282 3 0.0000 0.992 0.000 0.000 1.000
#> SRR1633284 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633290 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633291 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633292 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633293 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633294 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633295 3 0.1289 0.974 0.000 0.032 0.968
#> SRR1633296 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633297 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633298 1 0.4291 0.791 0.820 0.000 0.180
#> SRR1633299 1 0.4291 0.791 0.820 0.000 0.180
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633343 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633344 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633347 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633348 1 0.0747 0.977 0.984 0.000 0.016
#> SRR1633350 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.987 1.000 0.000 0.000
#> SRR2038462 1 0.6260 0.214 0.552 0.000 0.448
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633236 2 0.1716 0.931 0.000 0.936 0.064 0.000
#> SRR1633237 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1633238 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1633239 2 0.0188 0.995 0.000 0.996 0.004 0.000
#> SRR1633240 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633252 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633253 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633254 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633255 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633256 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.0592 0.980 0.000 0.000 0.984 0.016
#> SRR1633263 3 0.0592 0.980 0.000 0.000 0.984 0.016
#> SRR1633264 3 0.0592 0.980 0.000 0.000 0.984 0.016
#> SRR1633265 3 0.0592 0.980 0.000 0.000 0.984 0.016
#> SRR1633266 3 0.0592 0.980 0.000 0.000 0.984 0.016
#> SRR1633267 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633268 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633269 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633270 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633271 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633272 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633273 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633278 3 0.2281 0.908 0.000 0.000 0.904 0.096
#> SRR1633279 3 0.2281 0.908 0.000 0.000 0.904 0.096
#> SRR1633280 3 0.2281 0.908 0.000 0.000 0.904 0.096
#> SRR1633281 3 0.2281 0.908 0.000 0.000 0.904 0.096
#> SRR1633282 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633284 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633285 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633286 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633287 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633288 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633289 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633290 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633336 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633337 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633338 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633339 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633340 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633341 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633342 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633345 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633346 4 0.0592 0.989 0.016 0.000 0.000 0.984
#> SRR1633343 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.0000 0.993 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.0000 0.993 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633236 2 0.4300 0.268 0.00 0.524 0.000 0.000 0.476
#> SRR1633237 2 0.4300 0.268 0.00 0.524 0.000 0.000 0.476
#> SRR1633238 2 0.4300 0.268 0.00 0.524 0.000 0.000 0.476
#> SRR1633239 2 0.4300 0.268 0.00 0.524 0.000 0.000 0.476
#> SRR1633240 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633248 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633249 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633250 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633251 5 0.3913 0.704 0.00 0.000 0.324 0.000 0.676
#> SRR1633252 5 0.3913 0.704 0.00 0.000 0.324 0.000 0.676
#> SRR1633253 5 0.3913 0.704 0.00 0.000 0.324 0.000 0.676
#> SRR1633254 5 0.3913 0.704 0.00 0.000 0.324 0.000 0.676
#> SRR1633255 5 0.3913 0.704 0.00 0.000 0.324 0.000 0.676
#> SRR1633256 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633257 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633258 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633259 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633260 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633261 5 0.3177 0.831 0.00 0.000 0.208 0.000 0.792
#> SRR1633262 3 0.1410 0.953 0.00 0.000 0.940 0.000 0.060
#> SRR1633263 3 0.1410 0.953 0.00 0.000 0.940 0.000 0.060
#> SRR1633264 3 0.1410 0.953 0.00 0.000 0.940 0.000 0.060
#> SRR1633265 3 0.1410 0.953 0.00 0.000 0.940 0.000 0.060
#> SRR1633266 3 0.1410 0.953 0.00 0.000 0.940 0.000 0.060
#> SRR1633267 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633268 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633269 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633270 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633271 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633272 3 0.1851 0.943 0.00 0.000 0.912 0.000 0.088
#> SRR1633273 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633274 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633275 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633276 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633277 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633278 3 0.1124 0.944 0.00 0.000 0.960 0.004 0.036
#> SRR1633279 3 0.1124 0.944 0.00 0.000 0.960 0.004 0.036
#> SRR1633280 3 0.1124 0.944 0.00 0.000 0.960 0.004 0.036
#> SRR1633281 3 0.1124 0.944 0.00 0.000 0.960 0.004 0.036
#> SRR1633282 3 0.1792 0.844 0.00 0.000 0.916 0.084 0.000
#> SRR1633284 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633285 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633286 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633287 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633288 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633289 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633290 4 0.0703 0.987 0.00 0.000 0.024 0.976 0.000
#> SRR1633291 4 0.0703 0.987 0.00 0.000 0.024 0.976 0.000
#> SRR1633292 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.818 0.00 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633297 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633298 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633299 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633300 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633301 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633302 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633303 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633304 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633305 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633306 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633307 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633308 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633309 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633310 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633311 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633312 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633313 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633314 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633315 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633316 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633317 2 0.0404 0.950 0.00 0.988 0.012 0.000 0.000
#> SRR1633318 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 0.951 0.00 1.000 0.000 0.000 0.000
#> SRR1633335 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633336 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633337 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633338 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633341 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633342 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633345 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633346 4 0.0000 0.988 0.00 0.000 0.000 1.000 0.000
#> SRR1633343 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633344 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633347 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633348 4 0.0794 0.986 0.00 0.000 0.028 0.972 0.000
#> SRR1633350 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633351 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633352 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633353 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633354 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633355 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633356 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633357 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633358 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633362 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633363 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633364 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633359 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633360 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR1633361 1 0.0609 0.988 0.98 0.000 0.020 0.000 0.000
#> SRR2038492 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.994 1.00 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.2020 0.836 0.00 0.000 0.900 0.100 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0790 0.940 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633231 2 0.0790 0.940 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633232 2 0.0790 0.940 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633233 2 0.0790 0.940 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633234 2 0.0790 0.940 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633236 5 0.3500 0.705 0.000 0.204 0.000 0.000 0.768 0.028
#> SRR1633237 5 0.3500 0.705 0.000 0.204 0.000 0.000 0.768 0.028
#> SRR1633238 5 0.3500 0.705 0.000 0.204 0.000 0.000 0.768 0.028
#> SRR1633239 5 0.3500 0.705 0.000 0.204 0.000 0.000 0.768 0.028
#> SRR1633240 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633241 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633242 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633243 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633244 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633245 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633246 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633247 3 0.3672 0.613 0.000 0.000 0.632 0.000 0.368 0.000
#> SRR1633248 3 0.3672 0.613 0.000 0.000 0.632 0.000 0.368 0.000
#> SRR1633249 3 0.3672 0.613 0.000 0.000 0.632 0.000 0.368 0.000
#> SRR1633250 3 0.3672 0.613 0.000 0.000 0.632 0.000 0.368 0.000
#> SRR1633251 3 0.3244 0.626 0.000 0.000 0.732 0.000 0.268 0.000
#> SRR1633252 3 0.3244 0.626 0.000 0.000 0.732 0.000 0.268 0.000
#> SRR1633253 3 0.3244 0.626 0.000 0.000 0.732 0.000 0.268 0.000
#> SRR1633254 3 0.3244 0.626 0.000 0.000 0.732 0.000 0.268 0.000
#> SRR1633255 3 0.3244 0.626 0.000 0.000 0.732 0.000 0.268 0.000
#> SRR1633256 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633257 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633258 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633259 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633260 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633261 3 0.3659 0.618 0.000 0.000 0.636 0.000 0.364 0.000
#> SRR1633262 3 0.2664 0.112 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1633263 3 0.2664 0.112 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1633264 3 0.2664 0.112 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1633265 3 0.2664 0.112 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1633266 3 0.2664 0.112 0.000 0.000 0.816 0.000 0.000 0.184
#> SRR1633267 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633268 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633269 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633270 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633271 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633272 3 0.3104 0.150 0.000 0.000 0.800 0.000 0.016 0.184
#> SRR1633273 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633274 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633275 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633276 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633277 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633278 6 0.3975 0.939 0.000 0.000 0.452 0.004 0.000 0.544
#> SRR1633279 6 0.3975 0.939 0.000 0.000 0.452 0.004 0.000 0.544
#> SRR1633280 6 0.3975 0.939 0.000 0.000 0.452 0.004 0.000 0.544
#> SRR1633281 6 0.3975 0.939 0.000 0.000 0.452 0.004 0.000 0.544
#> SRR1633282 6 0.4552 0.879 0.000 0.000 0.388 0.040 0.000 0.572
#> SRR1633284 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633285 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633286 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633287 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633288 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633289 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633290 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633291 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633292 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633293 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633294 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633295 5 0.0363 0.885 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633296 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633297 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633298 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633299 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633300 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633301 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633302 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633303 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633304 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633305 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633306 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633307 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633308 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633309 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633310 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633311 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633312 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633313 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633314 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633315 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633316 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633317 2 0.1918 0.940 0.000 0.904 0.000 0.000 0.008 0.088
#> SRR1633318 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633319 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633320 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633321 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633322 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633323 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633324 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633325 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633326 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633327 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633328 2 0.0458 0.945 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1633329 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633330 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633331 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633332 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633333 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633334 2 0.0790 0.939 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1633335 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633336 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633337 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633338 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633339 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633340 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633341 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633342 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633345 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 0.942 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633344 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633347 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633348 4 0.2358 0.938 0.000 0.000 0.016 0.876 0.000 0.108
#> SRR1633350 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633351 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633352 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633353 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633354 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633355 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633356 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633357 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633358 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633362 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633363 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633364 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633359 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633360 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR1633361 1 0.3023 0.850 0.784 0.000 0.000 0.000 0.004 0.212
#> SRR2038492 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.930 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 6 0.4957 0.872 0.000 0.000 0.384 0.072 0.000 0.544
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.961 0.968 0.982 0.4966 0.499 0.499
#> 3 3 0.917 0.939 0.974 0.3154 0.782 0.591
#> 4 4 1.000 0.982 0.992 0.1527 0.860 0.620
#> 5 5 1.000 0.998 0.999 0.0466 0.965 0.857
#> 6 6 0.940 0.972 0.973 0.0336 0.968 0.853
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.966 0.000 1.000
#> SRR1633231 2 0.0000 0.966 0.000 1.000
#> SRR1633232 2 0.0000 0.966 0.000 1.000
#> SRR1633233 2 0.0000 0.966 0.000 1.000
#> SRR1633234 2 0.0000 0.966 0.000 1.000
#> SRR1633236 2 0.0000 0.966 0.000 1.000
#> SRR1633237 2 0.0000 0.966 0.000 1.000
#> SRR1633238 2 0.0000 0.966 0.000 1.000
#> SRR1633239 2 0.0000 0.966 0.000 1.000
#> SRR1633240 2 0.0000 0.966 0.000 1.000
#> SRR1633241 2 0.0000 0.966 0.000 1.000
#> SRR1633242 2 0.0000 0.966 0.000 1.000
#> SRR1633243 2 0.0000 0.966 0.000 1.000
#> SRR1633244 2 0.0000 0.966 0.000 1.000
#> SRR1633245 2 0.0000 0.966 0.000 1.000
#> SRR1633246 2 0.0000 0.966 0.000 1.000
#> SRR1633247 2 0.4562 0.903 0.096 0.904
#> SRR1633248 2 0.4161 0.912 0.084 0.916
#> SRR1633249 2 0.4562 0.903 0.096 0.904
#> SRR1633250 2 0.4815 0.896 0.104 0.896
#> SRR1633251 2 0.7139 0.798 0.196 0.804
#> SRR1633252 2 0.7139 0.798 0.196 0.804
#> SRR1633253 2 0.7139 0.798 0.196 0.804
#> SRR1633254 2 0.7139 0.798 0.196 0.804
#> SRR1633255 2 0.7139 0.798 0.196 0.804
#> SRR1633256 2 0.5294 0.881 0.120 0.880
#> SRR1633257 2 0.5408 0.878 0.124 0.876
#> SRR1633258 2 0.5059 0.889 0.112 0.888
#> SRR1633259 2 0.2043 0.950 0.032 0.968
#> SRR1633260 2 0.2043 0.950 0.032 0.968
#> SRR1633261 2 0.2043 0.950 0.032 0.968
#> SRR1633262 1 0.2603 0.956 0.956 0.044
#> SRR1633263 1 0.2603 0.956 0.956 0.044
#> SRR1633264 1 0.2603 0.956 0.956 0.044
#> SRR1633265 1 0.2603 0.956 0.956 0.044
#> SRR1633266 1 0.2603 0.956 0.956 0.044
#> SRR1633267 2 0.7139 0.798 0.196 0.804
#> SRR1633268 2 0.7139 0.798 0.196 0.804
#> SRR1633269 2 0.7139 0.798 0.196 0.804
#> SRR1633270 2 0.2043 0.950 0.032 0.968
#> SRR1633271 2 0.2043 0.950 0.032 0.968
#> SRR1633272 2 0.2043 0.950 0.032 0.968
#> SRR1633273 1 0.0000 0.995 1.000 0.000
#> SRR1633274 1 0.0000 0.995 1.000 0.000
#> SRR1633275 1 0.0000 0.995 1.000 0.000
#> SRR1633276 1 0.0000 0.995 1.000 0.000
#> SRR1633277 1 0.0000 0.995 1.000 0.000
#> SRR1633278 1 0.2603 0.956 0.956 0.044
#> SRR1633279 1 0.2603 0.956 0.956 0.044
#> SRR1633280 1 0.2423 0.960 0.960 0.040
#> SRR1633281 1 0.2423 0.960 0.960 0.040
#> SRR1633282 1 0.0376 0.992 0.996 0.004
#> SRR1633284 1 0.0000 0.995 1.000 0.000
#> SRR1633285 1 0.0000 0.995 1.000 0.000
#> SRR1633286 1 0.0000 0.995 1.000 0.000
#> SRR1633287 1 0.0000 0.995 1.000 0.000
#> SRR1633288 1 0.0000 0.995 1.000 0.000
#> SRR1633289 1 0.0000 0.995 1.000 0.000
#> SRR1633290 1 0.0000 0.995 1.000 0.000
#> SRR1633291 1 0.0000 0.995 1.000 0.000
#> SRR1633292 2 0.0000 0.966 0.000 1.000
#> SRR1633293 2 0.0000 0.966 0.000 1.000
#> SRR1633294 2 0.0000 0.966 0.000 1.000
#> SRR1633295 2 0.0000 0.966 0.000 1.000
#> SRR1633296 1 0.0000 0.995 1.000 0.000
#> SRR1633297 1 0.0000 0.995 1.000 0.000
#> SRR1633298 1 0.0000 0.995 1.000 0.000
#> SRR1633299 1 0.0000 0.995 1.000 0.000
#> SRR1633300 2 0.0000 0.966 0.000 1.000
#> SRR1633301 2 0.0000 0.966 0.000 1.000
#> SRR1633302 2 0.0000 0.966 0.000 1.000
#> SRR1633303 2 0.0000 0.966 0.000 1.000
#> SRR1633304 2 0.0000 0.966 0.000 1.000
#> SRR1633305 2 0.0000 0.966 0.000 1.000
#> SRR1633306 2 0.0000 0.966 0.000 1.000
#> SRR1633307 2 0.0000 0.966 0.000 1.000
#> SRR1633308 2 0.0000 0.966 0.000 1.000
#> SRR1633309 2 0.0000 0.966 0.000 1.000
#> SRR1633310 2 0.0000 0.966 0.000 1.000
#> SRR1633311 2 0.0000 0.966 0.000 1.000
#> SRR1633312 2 0.0000 0.966 0.000 1.000
#> SRR1633313 2 0.0000 0.966 0.000 1.000
#> SRR1633314 2 0.0000 0.966 0.000 1.000
#> SRR1633315 2 0.0000 0.966 0.000 1.000
#> SRR1633316 2 0.0000 0.966 0.000 1.000
#> SRR1633317 2 0.0000 0.966 0.000 1.000
#> SRR1633318 2 0.0000 0.966 0.000 1.000
#> SRR1633319 2 0.0000 0.966 0.000 1.000
#> SRR1633320 2 0.0000 0.966 0.000 1.000
#> SRR1633321 2 0.0000 0.966 0.000 1.000
#> SRR1633322 2 0.0000 0.966 0.000 1.000
#> SRR1633323 2 0.0000 0.966 0.000 1.000
#> SRR1633324 2 0.0000 0.966 0.000 1.000
#> SRR1633325 2 0.0000 0.966 0.000 1.000
#> SRR1633326 2 0.0000 0.966 0.000 1.000
#> SRR1633327 2 0.0000 0.966 0.000 1.000
#> SRR1633328 2 0.0000 0.966 0.000 1.000
#> SRR1633329 2 0.0000 0.966 0.000 1.000
#> SRR1633330 2 0.0000 0.966 0.000 1.000
#> SRR1633331 2 0.0000 0.966 0.000 1.000
#> SRR1633332 2 0.0000 0.966 0.000 1.000
#> SRR1633333 2 0.0000 0.966 0.000 1.000
#> SRR1633334 2 0.0000 0.966 0.000 1.000
#> SRR1633335 1 0.0000 0.995 1.000 0.000
#> SRR1633336 1 0.0000 0.995 1.000 0.000
#> SRR1633337 1 0.0000 0.995 1.000 0.000
#> SRR1633338 1 0.0000 0.995 1.000 0.000
#> SRR1633339 1 0.0000 0.995 1.000 0.000
#> SRR1633340 1 0.0000 0.995 1.000 0.000
#> SRR1633341 1 0.0000 0.995 1.000 0.000
#> SRR1633342 1 0.0000 0.995 1.000 0.000
#> SRR1633345 1 0.0000 0.995 1.000 0.000
#> SRR1633346 1 0.0000 0.995 1.000 0.000
#> SRR1633343 1 0.0000 0.995 1.000 0.000
#> SRR1633344 1 0.0000 0.995 1.000 0.000
#> SRR1633347 1 0.0000 0.995 1.000 0.000
#> SRR1633348 1 0.0000 0.995 1.000 0.000
#> SRR1633350 1 0.0000 0.995 1.000 0.000
#> SRR1633351 1 0.0000 0.995 1.000 0.000
#> SRR1633352 1 0.0000 0.995 1.000 0.000
#> SRR1633353 1 0.0000 0.995 1.000 0.000
#> SRR1633354 1 0.0000 0.995 1.000 0.000
#> SRR1633355 1 0.0000 0.995 1.000 0.000
#> SRR1633356 1 0.0000 0.995 1.000 0.000
#> SRR1633357 1 0.0000 0.995 1.000 0.000
#> SRR1633358 1 0.0000 0.995 1.000 0.000
#> SRR1633362 1 0.0000 0.995 1.000 0.000
#> SRR1633363 1 0.0000 0.995 1.000 0.000
#> SRR1633364 1 0.0000 0.995 1.000 0.000
#> SRR1633359 1 0.0000 0.995 1.000 0.000
#> SRR1633360 1 0.0000 0.995 1.000 0.000
#> SRR1633361 1 0.0000 0.995 1.000 0.000
#> SRR2038492 1 0.0000 0.995 1.000 0.000
#> SRR2038491 1 0.0000 0.995 1.000 0.000
#> SRR2038490 1 0.0000 0.995 1.000 0.000
#> SRR2038489 1 0.0000 0.995 1.000 0.000
#> SRR2038488 1 0.0000 0.995 1.000 0.000
#> SRR2038487 1 0.0000 0.995 1.000 0.000
#> SRR2038486 1 0.0000 0.995 1.000 0.000
#> SRR2038485 1 0.0000 0.995 1.000 0.000
#> SRR2038484 1 0.0000 0.995 1.000 0.000
#> SRR2038483 1 0.0000 0.995 1.000 0.000
#> SRR2038482 1 0.0000 0.995 1.000 0.000
#> SRR2038481 1 0.0000 0.995 1.000 0.000
#> SRR2038480 1 0.0000 0.995 1.000 0.000
#> SRR2038479 1 0.0000 0.995 1.000 0.000
#> SRR2038477 1 0.0000 0.995 1.000 0.000
#> SRR2038478 1 0.0000 0.995 1.000 0.000
#> SRR2038476 1 0.0000 0.995 1.000 0.000
#> SRR2038475 1 0.0000 0.995 1.000 0.000
#> SRR2038474 1 0.0000 0.995 1.000 0.000
#> SRR2038473 1 0.0000 0.995 1.000 0.000
#> SRR2038472 1 0.0000 0.995 1.000 0.000
#> SRR2038471 1 0.0000 0.995 1.000 0.000
#> SRR2038470 1 0.0000 0.995 1.000 0.000
#> SRR2038469 1 0.0000 0.995 1.000 0.000
#> SRR2038468 1 0.0000 0.995 1.000 0.000
#> SRR2038467 1 0.0000 0.995 1.000 0.000
#> SRR2038466 1 0.0000 0.995 1.000 0.000
#> SRR2038465 1 0.0000 0.995 1.000 0.000
#> SRR2038464 1 0.0000 0.995 1.000 0.000
#> SRR2038463 1 0.0000 0.995 1.000 0.000
#> SRR2038462 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633231 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633232 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633233 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633234 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633236 2 0.5926 0.4627 0.000 0.644 0.356
#> SRR1633237 2 0.3686 0.8331 0.000 0.860 0.140
#> SRR1633238 2 0.3816 0.8229 0.000 0.852 0.148
#> SRR1633239 2 0.3941 0.8122 0.000 0.844 0.156
#> SRR1633240 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633273 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633274 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633275 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633276 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633277 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633278 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633279 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633280 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633281 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633282 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633284 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633290 1 0.1643 0.9212 0.956 0.000 0.044
#> SRR1633291 1 0.0892 0.9413 0.980 0.000 0.020
#> SRR1633292 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.9863 0.000 0.000 1.000
#> SRR1633296 1 0.6215 0.3198 0.572 0.000 0.428
#> SRR1633297 3 0.6274 0.0623 0.456 0.000 0.544
#> SRR1633298 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633299 3 0.0424 0.9803 0.008 0.000 0.992
#> SRR1633300 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633301 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633302 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633303 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633304 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633305 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633306 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633307 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633308 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633309 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633310 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633311 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633312 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633313 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633314 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633315 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633316 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633317 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633318 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633319 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633320 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633321 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633322 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633323 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633324 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633325 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633326 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633327 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633328 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633329 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633330 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633331 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633332 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633333 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633334 2 0.0000 0.9806 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633343 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633344 1 0.5327 0.6624 0.728 0.000 0.272
#> SRR1633347 1 0.5397 0.6493 0.720 0.000 0.280
#> SRR1633348 1 0.5363 0.6560 0.724 0.000 0.276
#> SRR1633350 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.9569 1.000 0.000 0.000
#> SRR2038462 3 0.0424 0.9803 0.008 0.000 0.992
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633231 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633232 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633233 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633234 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633236 2 0.478 0.434 0 0.624 0.376 0.000
#> SRR1633237 2 0.349 0.782 0 0.812 0.188 0.000
#> SRR1633238 2 0.340 0.793 0 0.820 0.180 0.000
#> SRR1633239 2 0.349 0.782 0 0.812 0.188 0.000
#> SRR1633240 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633241 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633242 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633243 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633244 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633245 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633246 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633247 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633248 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633249 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633250 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633251 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633252 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633253 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633254 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633255 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633256 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633257 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633258 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633259 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633260 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633261 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633262 3 0.228 0.903 0 0.000 0.904 0.096
#> SRR1633263 3 0.215 0.911 0 0.000 0.912 0.088
#> SRR1633264 3 0.241 0.894 0 0.000 0.896 0.104
#> SRR1633265 3 0.208 0.915 0 0.000 0.916 0.084
#> SRR1633266 3 0.201 0.919 0 0.000 0.920 0.080
#> SRR1633267 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633268 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633269 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633270 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633271 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633272 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633273 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633274 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633275 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633276 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633277 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633278 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633279 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633280 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633281 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633282 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633284 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633285 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633286 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633287 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633288 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633289 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633290 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633291 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633292 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633293 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633294 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633295 3 0.000 0.987 0 0.000 1.000 0.000
#> SRR1633296 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633297 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633298 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633299 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633300 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633301 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633302 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633303 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633304 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633305 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633306 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633307 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633308 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633309 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633310 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633311 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633312 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633313 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633314 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633315 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633316 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633317 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633318 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633319 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633320 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633321 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633322 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633323 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633324 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633325 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633326 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633327 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633328 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633329 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633330 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633331 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633332 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633333 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633334 2 0.000 0.978 0 1.000 0.000 0.000
#> SRR1633335 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633336 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633337 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633338 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633339 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633340 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633341 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633342 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633345 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633346 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633343 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633344 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633347 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633348 4 0.000 1.000 0 0.000 0.000 1.000
#> SRR1633350 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633351 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633352 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633353 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633354 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633355 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633356 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633357 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633358 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633362 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633363 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633364 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633359 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633360 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR1633361 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038492 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038491 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038490 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038489 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038488 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038487 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038486 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038485 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038484 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038483 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038482 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038481 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038480 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038479 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038477 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038478 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038476 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038475 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038474 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038473 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038472 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038471 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038470 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038469 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038468 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038467 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038466 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038465 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038464 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038463 1 0.000 1.000 1 0.000 0.000 0.000
#> SRR2038462 4 0.000 1.000 0 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633231 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633232 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633233 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633234 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633236 2 0.1121 0.947 0 0.956 0.044 0.000 0
#> SRR1633237 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633238 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633239 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633240 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633241 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633242 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633243 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633244 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633245 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633246 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633247 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633248 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633249 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633250 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633251 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633252 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633253 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633254 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633255 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633256 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633257 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633258 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633259 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633260 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633261 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633262 3 0.0794 0.971 0 0.000 0.972 0.028 0
#> SRR1633263 3 0.0703 0.975 0 0.000 0.976 0.024 0
#> SRR1633264 3 0.0794 0.971 0 0.000 0.972 0.028 0
#> SRR1633265 3 0.0703 0.975 0 0.000 0.976 0.024 0
#> SRR1633266 3 0.0703 0.975 0 0.000 0.976 0.024 0
#> SRR1633267 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633268 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633269 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633270 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633271 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633272 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633273 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633274 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633275 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633276 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633277 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633278 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633279 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633280 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633281 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633282 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633284 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633285 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633286 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633287 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633288 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633289 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633290 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633291 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633292 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633293 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633294 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633295 3 0.0000 0.996 0 0.000 1.000 0.000 0
#> SRR1633296 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633297 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633298 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633299 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633300 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633301 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633302 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633303 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633304 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633305 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633306 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633307 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633308 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633309 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633310 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633311 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633312 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633313 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633314 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633315 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633316 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633317 5 0.0000 1.000 0 0.000 0.000 0.000 1
#> SRR1633318 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633319 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633320 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633321 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633322 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633323 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633324 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633325 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633326 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633327 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633328 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633329 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633330 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633331 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633332 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633333 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633334 2 0.0000 0.998 0 1.000 0.000 0.000 0
#> SRR1633335 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633336 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633337 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633338 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633339 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633340 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633341 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633342 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633345 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633346 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633343 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633344 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633347 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633348 4 0.0000 1.000 0 0.000 0.000 1.000 0
#> SRR1633350 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633351 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633352 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633353 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633354 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633355 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633356 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633357 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633358 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633362 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633363 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633364 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633359 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633360 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR1633361 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038492 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038491 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038490 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038489 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038488 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038487 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038486 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038485 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038484 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038483 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038482 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038481 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038480 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038479 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038477 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038478 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038476 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038475 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038474 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038473 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038472 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038471 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038470 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038469 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038468 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038467 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038466 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038465 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038464 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038463 1 0.0000 1.000 1 0.000 0.000 0.000 0
#> SRR2038462 4 0.0000 1.000 0 0.000 0.000 1.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.2278 0.848 0.000 0.128 0.004 0.000 0.868 0.000
#> SRR1633237 5 0.2178 0.844 0.000 0.132 0.000 0.000 0.868 0.000
#> SRR1633238 5 0.2178 0.844 0.000 0.132 0.000 0.000 0.868 0.000
#> SRR1633239 5 0.2178 0.844 0.000 0.132 0.000 0.000 0.868 0.000
#> SRR1633240 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633241 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633242 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633243 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633244 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633245 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633246 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633247 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633248 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633249 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633250 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633251 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633252 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633253 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633254 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633255 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633256 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633257 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633258 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633259 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633260 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633261 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633262 3 0.0146 0.994 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1633263 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633264 3 0.0146 0.994 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1633265 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633266 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633267 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633268 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633269 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633270 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633271 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633272 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633273 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633274 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633275 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633276 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633277 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633278 4 0.2135 0.856 0.000 0.000 0.128 0.872 0.000 0.000
#> SRR1633279 4 0.1957 0.875 0.000 0.000 0.112 0.888 0.000 0.000
#> SRR1633280 4 0.1444 0.920 0.000 0.000 0.072 0.928 0.000 0.000
#> SRR1633281 4 0.1327 0.928 0.000 0.000 0.064 0.936 0.000 0.000
#> SRR1633282 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633284 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633285 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633286 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633287 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633288 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633289 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633290 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633291 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633292 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633293 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633294 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633295 5 0.2178 0.947 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1633296 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633297 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633298 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633299 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633300 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633301 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633302 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633303 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633304 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633305 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633306 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633307 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633308 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633309 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633310 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633311 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633312 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633313 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633314 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633315 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633316 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633317 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633330 2 0.0146 0.996 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633335 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633336 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633337 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633338 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633339 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633340 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633341 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633342 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633345 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633344 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633347 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633348 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633350 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633351 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633352 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633353 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633354 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633355 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633356 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633357 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633358 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633362 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633363 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633364 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633359 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633360 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR1633361 1 0.2178 0.916 0.868 0.000 0.000 0.000 0.132 0.000
#> SRR2038492 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.768 0.859 0.929 0.4802 0.499 0.499
#> 3 3 1.000 0.984 0.994 0.3125 0.623 0.396
#> 4 4 0.941 0.961 0.978 0.1719 0.892 0.710
#> 5 5 0.950 0.918 0.948 0.0643 0.952 0.820
#> 6 6 1.000 0.966 0.983 0.0355 0.967 0.846
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3 4 5
There is also optional best \(k\) = 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.858 0.000 1.000
#> SRR1633231 2 0.0000 0.858 0.000 1.000
#> SRR1633232 2 0.0000 0.858 0.000 1.000
#> SRR1633233 2 0.0000 0.858 0.000 1.000
#> SRR1633234 2 0.0000 0.858 0.000 1.000
#> SRR1633236 2 0.4022 0.837 0.080 0.920
#> SRR1633237 2 0.4022 0.837 0.080 0.920
#> SRR1633238 2 0.4022 0.837 0.080 0.920
#> SRR1633239 2 0.4022 0.837 0.080 0.920
#> SRR1633240 2 0.4022 0.837 0.080 0.920
#> SRR1633241 2 0.4022 0.837 0.080 0.920
#> SRR1633242 2 0.4022 0.837 0.080 0.920
#> SRR1633243 2 0.4022 0.837 0.080 0.920
#> SRR1633244 2 0.4022 0.837 0.080 0.920
#> SRR1633245 2 0.4022 0.837 0.080 0.920
#> SRR1633246 2 0.4022 0.837 0.080 0.920
#> SRR1633247 2 0.9775 0.521 0.412 0.588
#> SRR1633248 2 0.9775 0.521 0.412 0.588
#> SRR1633249 2 0.9775 0.521 0.412 0.588
#> SRR1633250 2 0.9775 0.521 0.412 0.588
#> SRR1633251 2 0.9775 0.521 0.412 0.588
#> SRR1633252 2 0.9775 0.521 0.412 0.588
#> SRR1633253 2 0.9775 0.521 0.412 0.588
#> SRR1633254 2 0.9775 0.521 0.412 0.588
#> SRR1633255 2 0.9775 0.521 0.412 0.588
#> SRR1633256 2 0.9775 0.521 0.412 0.588
#> SRR1633257 2 0.9775 0.521 0.412 0.588
#> SRR1633258 2 0.9775 0.521 0.412 0.588
#> SRR1633259 2 0.9775 0.521 0.412 0.588
#> SRR1633260 2 0.9775 0.521 0.412 0.588
#> SRR1633261 2 0.9775 0.521 0.412 0.588
#> SRR1633262 1 0.0376 0.972 0.996 0.004
#> SRR1633263 1 0.0376 0.972 0.996 0.004
#> SRR1633264 1 0.0376 0.972 0.996 0.004
#> SRR1633265 1 0.0376 0.972 0.996 0.004
#> SRR1633266 1 0.0376 0.972 0.996 0.004
#> SRR1633267 2 0.9775 0.521 0.412 0.588
#> SRR1633268 2 0.9775 0.521 0.412 0.588
#> SRR1633269 2 0.9775 0.521 0.412 0.588
#> SRR1633270 2 0.9775 0.521 0.412 0.588
#> SRR1633271 2 0.9775 0.521 0.412 0.588
#> SRR1633272 2 0.9775 0.521 0.412 0.588
#> SRR1633273 1 0.0000 0.976 1.000 0.000
#> SRR1633274 1 0.0000 0.976 1.000 0.000
#> SRR1633275 1 0.0000 0.976 1.000 0.000
#> SRR1633276 1 0.0000 0.976 1.000 0.000
#> SRR1633277 1 0.0000 0.976 1.000 0.000
#> SRR1633278 1 0.9170 0.446 0.668 0.332
#> SRR1633279 1 0.9170 0.446 0.668 0.332
#> SRR1633280 1 0.9170 0.446 0.668 0.332
#> SRR1633281 1 0.9170 0.446 0.668 0.332
#> SRR1633282 1 0.0000 0.976 1.000 0.000
#> SRR1633284 1 0.0000 0.976 1.000 0.000
#> SRR1633285 1 0.0000 0.976 1.000 0.000
#> SRR1633286 1 0.0000 0.976 1.000 0.000
#> SRR1633287 1 0.0000 0.976 1.000 0.000
#> SRR1633288 1 0.0000 0.976 1.000 0.000
#> SRR1633289 1 0.0000 0.976 1.000 0.000
#> SRR1633290 1 0.0000 0.976 1.000 0.000
#> SRR1633291 1 0.0000 0.976 1.000 0.000
#> SRR1633292 2 0.4022 0.837 0.080 0.920
#> SRR1633293 2 0.4022 0.837 0.080 0.920
#> SRR1633294 2 0.4022 0.837 0.080 0.920
#> SRR1633295 2 0.4022 0.837 0.080 0.920
#> SRR1633296 1 0.0000 0.976 1.000 0.000
#> SRR1633297 1 0.0000 0.976 1.000 0.000
#> SRR1633298 1 0.0000 0.976 1.000 0.000
#> SRR1633299 1 0.0000 0.976 1.000 0.000
#> SRR1633300 2 0.0000 0.858 0.000 1.000
#> SRR1633301 2 0.0000 0.858 0.000 1.000
#> SRR1633302 2 0.0000 0.858 0.000 1.000
#> SRR1633303 2 0.0000 0.858 0.000 1.000
#> SRR1633304 2 0.0000 0.858 0.000 1.000
#> SRR1633305 2 0.0000 0.858 0.000 1.000
#> SRR1633306 2 0.0000 0.858 0.000 1.000
#> SRR1633307 2 0.0000 0.858 0.000 1.000
#> SRR1633308 2 0.0000 0.858 0.000 1.000
#> SRR1633309 2 0.0000 0.858 0.000 1.000
#> SRR1633310 2 0.0000 0.858 0.000 1.000
#> SRR1633311 2 0.0000 0.858 0.000 1.000
#> SRR1633312 2 0.0000 0.858 0.000 1.000
#> SRR1633313 2 0.0000 0.858 0.000 1.000
#> SRR1633314 2 0.0000 0.858 0.000 1.000
#> SRR1633315 2 0.0000 0.858 0.000 1.000
#> SRR1633316 2 0.0000 0.858 0.000 1.000
#> SRR1633317 2 0.0000 0.858 0.000 1.000
#> SRR1633318 2 0.0000 0.858 0.000 1.000
#> SRR1633319 2 0.0000 0.858 0.000 1.000
#> SRR1633320 2 0.0000 0.858 0.000 1.000
#> SRR1633321 2 0.0000 0.858 0.000 1.000
#> SRR1633322 2 0.0000 0.858 0.000 1.000
#> SRR1633323 2 0.0000 0.858 0.000 1.000
#> SRR1633324 2 0.0000 0.858 0.000 1.000
#> SRR1633325 2 0.0000 0.858 0.000 1.000
#> SRR1633326 2 0.0000 0.858 0.000 1.000
#> SRR1633327 2 0.0000 0.858 0.000 1.000
#> SRR1633328 2 0.0000 0.858 0.000 1.000
#> SRR1633329 2 0.0000 0.858 0.000 1.000
#> SRR1633330 2 0.0000 0.858 0.000 1.000
#> SRR1633331 2 0.0000 0.858 0.000 1.000
#> SRR1633332 2 0.0000 0.858 0.000 1.000
#> SRR1633333 2 0.0000 0.858 0.000 1.000
#> SRR1633334 2 0.0000 0.858 0.000 1.000
#> SRR1633335 1 0.0000 0.976 1.000 0.000
#> SRR1633336 1 0.0000 0.976 1.000 0.000
#> SRR1633337 1 0.0000 0.976 1.000 0.000
#> SRR1633338 1 0.0000 0.976 1.000 0.000
#> SRR1633339 1 0.0000 0.976 1.000 0.000
#> SRR1633340 1 0.0000 0.976 1.000 0.000
#> SRR1633341 1 0.0000 0.976 1.000 0.000
#> SRR1633342 1 0.0000 0.976 1.000 0.000
#> SRR1633345 1 0.0000 0.976 1.000 0.000
#> SRR1633346 1 0.0000 0.976 1.000 0.000
#> SRR1633343 1 0.0000 0.976 1.000 0.000
#> SRR1633344 1 0.0000 0.976 1.000 0.000
#> SRR1633347 1 0.0000 0.976 1.000 0.000
#> SRR1633348 1 0.0000 0.976 1.000 0.000
#> SRR1633350 1 0.0000 0.976 1.000 0.000
#> SRR1633351 1 0.0000 0.976 1.000 0.000
#> SRR1633352 1 0.0000 0.976 1.000 0.000
#> SRR1633353 1 0.0000 0.976 1.000 0.000
#> SRR1633354 1 0.0000 0.976 1.000 0.000
#> SRR1633355 1 0.0000 0.976 1.000 0.000
#> SRR1633356 1 0.0000 0.976 1.000 0.000
#> SRR1633357 1 0.0000 0.976 1.000 0.000
#> SRR1633358 1 0.0000 0.976 1.000 0.000
#> SRR1633362 1 0.0000 0.976 1.000 0.000
#> SRR1633363 1 0.0000 0.976 1.000 0.000
#> SRR1633364 1 0.0000 0.976 1.000 0.000
#> SRR1633359 1 0.0000 0.976 1.000 0.000
#> SRR1633360 1 0.0000 0.976 1.000 0.000
#> SRR1633361 1 0.0000 0.976 1.000 0.000
#> SRR2038492 1 0.0000 0.976 1.000 0.000
#> SRR2038491 1 0.0000 0.976 1.000 0.000
#> SRR2038490 1 0.0000 0.976 1.000 0.000
#> SRR2038489 1 0.0000 0.976 1.000 0.000
#> SRR2038488 1 0.0000 0.976 1.000 0.000
#> SRR2038487 1 0.0000 0.976 1.000 0.000
#> SRR2038486 1 0.0000 0.976 1.000 0.000
#> SRR2038485 1 0.0000 0.976 1.000 0.000
#> SRR2038484 1 0.0000 0.976 1.000 0.000
#> SRR2038483 1 0.0000 0.976 1.000 0.000
#> SRR2038482 1 0.0000 0.976 1.000 0.000
#> SRR2038481 1 0.0000 0.976 1.000 0.000
#> SRR2038480 1 0.0000 0.976 1.000 0.000
#> SRR2038479 1 0.0000 0.976 1.000 0.000
#> SRR2038477 1 0.0000 0.976 1.000 0.000
#> SRR2038478 1 0.0000 0.976 1.000 0.000
#> SRR2038476 1 0.0000 0.976 1.000 0.000
#> SRR2038475 1 0.0000 0.976 1.000 0.000
#> SRR2038474 1 0.0000 0.976 1.000 0.000
#> SRR2038473 1 0.0000 0.976 1.000 0.000
#> SRR2038472 1 0.0000 0.976 1.000 0.000
#> SRR2038471 1 0.0000 0.976 1.000 0.000
#> SRR2038470 1 0.0000 0.976 1.000 0.000
#> SRR2038469 1 0.0000 0.976 1.000 0.000
#> SRR2038468 1 0.0000 0.976 1.000 0.000
#> SRR2038467 1 0.0000 0.976 1.000 0.000
#> SRR2038466 1 0.0000 0.976 1.000 0.000
#> SRR2038465 1 0.0000 0.976 1.000 0.000
#> SRR2038464 1 0.0000 0.976 1.000 0.000
#> SRR2038463 1 0.0000 0.976 1.000 0.000
#> SRR2038462 1 0.9087 0.463 0.676 0.324
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0424 0.990 0.000 0.992 0.008
#> SRR1633231 2 0.0424 0.990 0.000 0.992 0.008
#> SRR1633232 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633233 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633234 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633236 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633237 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633238 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633239 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633240 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633273 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633274 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633275 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633276 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633277 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633278 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633279 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633280 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633281 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633282 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633284 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633285 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633286 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633287 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633288 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633289 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633290 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633291 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633296 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633297 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633298 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633299 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633300 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633301 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633302 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633303 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633304 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633305 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633306 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633307 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633308 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633309 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633310 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633311 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633312 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633313 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633314 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633315 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633316 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633317 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633318 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633319 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633320 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633321 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633322 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633323 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633324 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633325 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633326 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633327 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633328 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633329 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633330 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633331 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633332 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633333 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633334 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1633335 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633336 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633337 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633338 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633339 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633340 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633341 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633342 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633345 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633346 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633343 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633344 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633347 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633348 3 0.0000 0.988 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038492 3 0.1289 0.957 0.032 0.000 0.968
#> SRR2038491 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038490 3 0.5706 0.541 0.320 0.000 0.680
#> SRR2038489 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038481 1 0.1031 0.971 0.976 0.000 0.024
#> SRR2038480 3 0.5706 0.541 0.320 0.000 0.680
#> SRR2038479 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038476 3 0.5678 0.549 0.316 0.000 0.684
#> SRR2038475 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038470 1 0.1031 0.971 0.976 0.000 0.024
#> SRR2038469 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.999 1.000 0.000 0.000
#> SRR2038462 3 0.0000 0.988 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0376 0.991 0.000 0.992 0.004 0.004
#> SRR1633231 2 0.0376 0.991 0.000 0.992 0.004 0.004
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.1209 0.967 0.000 0.004 0.964 0.032
#> SRR1633237 3 0.1209 0.967 0.000 0.004 0.964 0.032
#> SRR1633238 3 0.1209 0.967 0.000 0.004 0.964 0.032
#> SRR1633239 3 0.1209 0.967 0.000 0.004 0.964 0.032
#> SRR1633240 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633251 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633252 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633253 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633254 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633255 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633256 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633262 4 0.1389 0.923 0.000 0.000 0.048 0.952
#> SRR1633263 4 0.1389 0.923 0.000 0.000 0.048 0.952
#> SRR1633264 4 0.1389 0.923 0.000 0.000 0.048 0.952
#> SRR1633265 4 0.1389 0.923 0.000 0.000 0.048 0.952
#> SRR1633266 4 0.1389 0.923 0.000 0.000 0.048 0.952
#> SRR1633267 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633268 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633269 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633270 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633271 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633272 4 0.3266 0.840 0.000 0.000 0.168 0.832
#> SRR1633273 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633278 4 0.1305 0.928 0.004 0.000 0.036 0.960
#> SRR1633279 4 0.1305 0.928 0.004 0.000 0.036 0.960
#> SRR1633280 4 0.1305 0.928 0.004 0.000 0.036 0.960
#> SRR1633281 4 0.1305 0.928 0.004 0.000 0.036 0.960
#> SRR1633282 4 0.0336 0.937 0.000 0.000 0.008 0.992
#> SRR1633284 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633285 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633286 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633287 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633288 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633289 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633290 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.994 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633336 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633337 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633338 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633339 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633340 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633341 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633342 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633345 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633346 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633343 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038492 4 0.1118 0.919 0.036 0.000 0.000 0.964
#> SRR2038491 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038490 4 0.4564 0.551 0.328 0.000 0.000 0.672
#> SRR2038489 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0921 0.968 0.972 0.000 0.000 0.028
#> SRR2038480 4 0.4564 0.551 0.328 0.000 0.000 0.672
#> SRR2038479 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038476 4 0.4564 0.551 0.328 0.000 0.000 0.672
#> SRR2038475 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0921 0.968 0.972 0.000 0.000 0.028
#> SRR2038469 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.1305 0.928 0.004 0.000 0.036 0.960
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0290 0.992 0.000 0.992 0.008 0.000 0.000
#> SRR1633231 2 0.0290 0.992 0.000 0.992 0.008 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 5 0.1197 0.953 0.000 0.000 0.048 0.000 0.952
#> SRR1633237 5 0.1197 0.953 0.000 0.000 0.048 0.000 0.952
#> SRR1633238 5 0.1197 0.953 0.000 0.000 0.048 0.000 0.952
#> SRR1633239 5 0.1197 0.953 0.000 0.000 0.048 0.000 0.952
#> SRR1633240 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633248 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633249 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633250 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633251 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633252 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633253 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633254 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633255 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633256 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633257 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633258 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633259 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633260 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633261 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633262 3 0.1012 0.767 0.000 0.000 0.968 0.020 0.012
#> SRR1633263 3 0.1012 0.767 0.000 0.000 0.968 0.020 0.012
#> SRR1633264 3 0.1012 0.767 0.000 0.000 0.968 0.020 0.012
#> SRR1633265 3 0.1012 0.767 0.000 0.000 0.968 0.020 0.012
#> SRR1633266 3 0.1012 0.767 0.000 0.000 0.968 0.020 0.012
#> SRR1633267 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633268 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633269 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633270 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633271 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633272 3 0.1310 0.764 0.000 0.000 0.956 0.020 0.024
#> SRR1633273 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633274 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633275 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633276 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633277 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633278 3 0.0609 0.766 0.000 0.000 0.980 0.020 0.000
#> SRR1633279 3 0.0609 0.766 0.000 0.000 0.980 0.020 0.000
#> SRR1633280 3 0.0609 0.766 0.000 0.000 0.980 0.020 0.000
#> SRR1633281 3 0.0609 0.766 0.000 0.000 0.980 0.020 0.000
#> SRR1633282 3 0.3684 0.691 0.000 0.000 0.720 0.280 0.000
#> SRR1633284 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633285 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633286 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633287 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633288 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633289 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633290 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633291 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633292 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.991 0.000 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633297 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633298 4 0.0404 0.985 0.000 0.000 0.012 0.988 0.000
#> SRR1633299 4 0.0404 0.985 0.000 0.000 0.012 0.988 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633335 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633336 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633337 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633338 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633339 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633340 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633341 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633342 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633345 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633346 3 0.4045 0.652 0.000 0.000 0.644 0.356 0.000
#> SRR1633343 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633344 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633347 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633348 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000
#> SRR1633350 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038492 3 0.5179 0.640 0.072 0.000 0.640 0.288 0.000
#> SRR2038491 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 3 0.4787 0.514 0.324 0.000 0.640 0.036 0.000
#> SRR2038489 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.1341 0.935 0.944 0.000 0.056 0.000 0.000
#> SRR2038480 3 0.4787 0.514 0.324 0.000 0.640 0.036 0.000
#> SRR2038479 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 3 0.4787 0.514 0.324 0.000 0.640 0.036 0.000
#> SRR2038475 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.1341 0.935 0.944 0.000 0.056 0.000 0.000
#> SRR2038469 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.997 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.0609 0.766 0.000 0.000 0.980 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0405 0.988 0.000 0.988 0.008 0.000 0.004 0.000
#> SRR1633231 2 0.0405 0.988 0.000 0.988 0.008 0.000 0.004 0.000
#> SRR1633232 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.1075 0.953 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1633237 5 0.1075 0.953 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1633238 5 0.1075 0.953 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1633239 5 0.1075 0.953 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1633240 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633241 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633242 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633243 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633244 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633245 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633246 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633247 5 0.0363 0.985 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633248 5 0.0363 0.985 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633249 5 0.0363 0.985 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633250 5 0.0363 0.985 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1633251 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633252 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633253 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633254 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633255 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633256 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633257 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633258 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633259 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633260 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633261 5 0.0458 0.984 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1633262 3 0.0405 0.980 0.000 0.000 0.988 0.008 0.000 0.004
#> SRR1633263 3 0.0405 0.980 0.000 0.000 0.988 0.008 0.000 0.004
#> SRR1633264 3 0.0405 0.980 0.000 0.000 0.988 0.008 0.000 0.004
#> SRR1633265 3 0.0405 0.980 0.000 0.000 0.988 0.008 0.000 0.004
#> SRR1633266 3 0.0405 0.980 0.000 0.000 0.988 0.008 0.000 0.004
#> SRR1633267 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633268 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633269 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633270 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633271 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633272 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633273 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633274 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633275 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633276 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633277 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633278 3 0.1267 0.954 0.000 0.000 0.940 0.060 0.000 0.000
#> SRR1633279 3 0.1267 0.954 0.000 0.000 0.940 0.060 0.000 0.000
#> SRR1633280 3 0.1267 0.954 0.000 0.000 0.940 0.060 0.000 0.000
#> SRR1633281 3 0.1267 0.954 0.000 0.000 0.940 0.060 0.000 0.000
#> SRR1633282 3 0.1564 0.951 0.000 0.000 0.936 0.024 0.000 0.040
#> SRR1633284 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633285 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633286 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633287 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633288 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633289 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633290 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633291 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633292 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633293 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633294 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633295 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633296 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633297 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633298 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633299 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633335 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633336 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633337 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633338 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633339 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633340 4 0.0363 0.888 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1633341 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633342 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633345 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 0.891 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633344 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633347 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633348 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038492 4 0.4506 0.513 0.348 0.000 0.044 0.608 0.000 0.000
#> SRR2038491 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 4 0.4506 0.513 0.348 0.000 0.044 0.608 0.000 0.000
#> SRR2038489 1 0.0363 0.982 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR2038488 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0363 0.982 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR2038483 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.2100 0.861 0.884 0.000 0.004 0.112 0.000 0.000
#> SRR2038480 4 0.4506 0.513 0.348 0.000 0.044 0.608 0.000 0.000
#> SRR2038479 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 4 0.4506 0.513 0.348 0.000 0.044 0.608 0.000 0.000
#> SRR2038475 1 0.0547 0.975 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR2038474 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.2100 0.861 0.884 0.000 0.004 0.112 0.000 0.000
#> SRR2038469 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0260 0.986 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2038467 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0260 0.986 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2038463 1 0.0000 0.992 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.1267 0.954 0.000 0.000 0.940 0.060 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.993 0.5017 0.499 0.499
#> 3 3 1.000 0.974 0.989 0.2946 0.796 0.614
#> 4 4 1.000 0.991 0.988 0.1568 0.820 0.538
#> 5 5 0.895 0.911 0.924 0.0501 0.939 0.765
#> 6 6 0.905 0.813 0.810 0.0290 0.957 0.800
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4
There is also optional best \(k\) = 2 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 2 0.000 1.000 0.000 1.000
#> SRR1633241 2 0.000 1.000 0.000 1.000
#> SRR1633242 2 0.000 1.000 0.000 1.000
#> SRR1633243 2 0.000 1.000 0.000 1.000
#> SRR1633244 2 0.000 1.000 0.000 1.000
#> SRR1633245 2 0.000 1.000 0.000 1.000
#> SRR1633246 2 0.000 1.000 0.000 1.000
#> SRR1633247 2 0.000 1.000 0.000 1.000
#> SRR1633248 2 0.000 1.000 0.000 1.000
#> SRR1633249 2 0.000 1.000 0.000 1.000
#> SRR1633250 2 0.000 1.000 0.000 1.000
#> SRR1633251 2 0.000 1.000 0.000 1.000
#> SRR1633252 2 0.000 1.000 0.000 1.000
#> SRR1633253 2 0.000 1.000 0.000 1.000
#> SRR1633254 2 0.000 1.000 0.000 1.000
#> SRR1633255 2 0.000 1.000 0.000 1.000
#> SRR1633256 2 0.000 1.000 0.000 1.000
#> SRR1633257 2 0.000 1.000 0.000 1.000
#> SRR1633258 2 0.000 1.000 0.000 1.000
#> SRR1633259 2 0.000 1.000 0.000 1.000
#> SRR1633260 2 0.000 1.000 0.000 1.000
#> SRR1633261 2 0.000 1.000 0.000 1.000
#> SRR1633262 1 0.714 0.771 0.804 0.196
#> SRR1633263 1 0.706 0.777 0.808 0.192
#> SRR1633264 1 0.697 0.782 0.812 0.188
#> SRR1633265 1 0.714 0.771 0.804 0.196
#> SRR1633266 1 0.714 0.771 0.804 0.196
#> SRR1633267 2 0.000 1.000 0.000 1.000
#> SRR1633268 2 0.000 1.000 0.000 1.000
#> SRR1633269 2 0.000 1.000 0.000 1.000
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.987 1.000 0.000
#> SRR1633274 1 0.000 0.987 1.000 0.000
#> SRR1633275 1 0.000 0.987 1.000 0.000
#> SRR1633276 1 0.000 0.987 1.000 0.000
#> SRR1633277 1 0.000 0.987 1.000 0.000
#> SRR1633278 1 0.311 0.937 0.944 0.056
#> SRR1633279 1 0.260 0.948 0.956 0.044
#> SRR1633280 1 0.141 0.970 0.980 0.020
#> SRR1633281 1 0.141 0.970 0.980 0.020
#> SRR1633282 1 0.000 0.987 1.000 0.000
#> SRR1633284 1 0.000 0.987 1.000 0.000
#> SRR1633285 1 0.000 0.987 1.000 0.000
#> SRR1633286 1 0.000 0.987 1.000 0.000
#> SRR1633287 1 0.000 0.987 1.000 0.000
#> SRR1633288 1 0.000 0.987 1.000 0.000
#> SRR1633289 1 0.000 0.987 1.000 0.000
#> SRR1633290 1 0.000 0.987 1.000 0.000
#> SRR1633291 1 0.000 0.987 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.987 1.000 0.000
#> SRR1633297 1 0.000 0.987 1.000 0.000
#> SRR1633298 1 0.000 0.987 1.000 0.000
#> SRR1633299 1 0.000 0.987 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.987 1.000 0.000
#> SRR1633336 1 0.000 0.987 1.000 0.000
#> SRR1633337 1 0.000 0.987 1.000 0.000
#> SRR1633338 1 0.000 0.987 1.000 0.000
#> SRR1633339 1 0.000 0.987 1.000 0.000
#> SRR1633340 1 0.000 0.987 1.000 0.000
#> SRR1633341 1 0.000 0.987 1.000 0.000
#> SRR1633342 1 0.000 0.987 1.000 0.000
#> SRR1633345 1 0.000 0.987 1.000 0.000
#> SRR1633346 1 0.000 0.987 1.000 0.000
#> SRR1633343 1 0.000 0.987 1.000 0.000
#> SRR1633344 1 0.000 0.987 1.000 0.000
#> SRR1633347 1 0.000 0.987 1.000 0.000
#> SRR1633348 1 0.000 0.987 1.000 0.000
#> SRR1633350 1 0.000 0.987 1.000 0.000
#> SRR1633351 1 0.000 0.987 1.000 0.000
#> SRR1633352 1 0.000 0.987 1.000 0.000
#> SRR1633353 1 0.000 0.987 1.000 0.000
#> SRR1633354 1 0.000 0.987 1.000 0.000
#> SRR1633355 1 0.000 0.987 1.000 0.000
#> SRR1633356 1 0.000 0.987 1.000 0.000
#> SRR1633357 1 0.000 0.987 1.000 0.000
#> SRR1633358 1 0.000 0.987 1.000 0.000
#> SRR1633362 1 0.000 0.987 1.000 0.000
#> SRR1633363 1 0.000 0.987 1.000 0.000
#> SRR1633364 1 0.000 0.987 1.000 0.000
#> SRR1633359 1 0.000 0.987 1.000 0.000
#> SRR1633360 1 0.000 0.987 1.000 0.000
#> SRR1633361 1 0.000 0.987 1.000 0.000
#> SRR2038492 1 0.000 0.987 1.000 0.000
#> SRR2038491 1 0.000 0.987 1.000 0.000
#> SRR2038490 1 0.000 0.987 1.000 0.000
#> SRR2038489 1 0.000 0.987 1.000 0.000
#> SRR2038488 1 0.000 0.987 1.000 0.000
#> SRR2038487 1 0.000 0.987 1.000 0.000
#> SRR2038486 1 0.000 0.987 1.000 0.000
#> SRR2038485 1 0.000 0.987 1.000 0.000
#> SRR2038484 1 0.000 0.987 1.000 0.000
#> SRR2038483 1 0.000 0.987 1.000 0.000
#> SRR2038482 1 0.000 0.987 1.000 0.000
#> SRR2038481 1 0.000 0.987 1.000 0.000
#> SRR2038480 1 0.000 0.987 1.000 0.000
#> SRR2038479 1 0.000 0.987 1.000 0.000
#> SRR2038477 1 0.000 0.987 1.000 0.000
#> SRR2038478 1 0.000 0.987 1.000 0.000
#> SRR2038476 1 0.000 0.987 1.000 0.000
#> SRR2038475 1 0.000 0.987 1.000 0.000
#> SRR2038474 1 0.000 0.987 1.000 0.000
#> SRR2038473 1 0.000 0.987 1.000 0.000
#> SRR2038472 1 0.000 0.987 1.000 0.000
#> SRR2038471 1 0.000 0.987 1.000 0.000
#> SRR2038470 1 0.000 0.987 1.000 0.000
#> SRR2038469 1 0.000 0.987 1.000 0.000
#> SRR2038468 1 0.000 0.987 1.000 0.000
#> SRR2038467 1 0.000 0.987 1.000 0.000
#> SRR2038466 1 0.000 0.987 1.000 0.000
#> SRR2038465 1 0.000 0.987 1.000 0.000
#> SRR2038464 1 0.000 0.987 1.000 0.000
#> SRR2038463 1 0.000 0.987 1.000 0.000
#> SRR2038462 1 0.000 0.987 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633231 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633232 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633233 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633234 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633236 2 0.5948 0.436 0.000 0.64 0.360
#> SRR1633237 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633238 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633239 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633240 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633241 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633242 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633243 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633244 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633245 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633246 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633247 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633248 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633249 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633250 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633251 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633252 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633253 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633254 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633255 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633256 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633257 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633258 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633259 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633260 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633261 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633262 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633263 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633264 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633265 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633266 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633267 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633268 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633269 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633270 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633271 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633272 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633273 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633274 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633275 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633276 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633277 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633278 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633279 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633280 3 0.0892 0.977 0.020 0.00 0.980
#> SRR1633281 3 0.1163 0.967 0.028 0.00 0.972
#> SRR1633282 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633284 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633285 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633286 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633287 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633288 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633289 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633290 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633291 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633292 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633293 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633294 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633295 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633296 1 0.5678 0.560 0.684 0.00 0.316
#> SRR1633297 1 0.6126 0.365 0.600 0.00 0.400
#> SRR1633298 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633299 3 0.0000 0.999 0.000 0.00 1.000
#> SRR1633300 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633301 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633302 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633303 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633304 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633305 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633306 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633307 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633308 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633309 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633310 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633311 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633312 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633313 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633314 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633315 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633316 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633317 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633318 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633319 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633320 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633321 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633322 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633323 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633324 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633325 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633326 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633327 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633328 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633329 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633330 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633331 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633332 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633333 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633334 2 0.0000 0.992 0.000 1.00 0.000
#> SRR1633335 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633336 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633337 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633338 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633339 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633340 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633341 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633342 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633345 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633346 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633343 1 0.1643 0.942 0.956 0.00 0.044
#> SRR1633344 1 0.1643 0.942 0.956 0.00 0.044
#> SRR1633347 1 0.4702 0.742 0.788 0.00 0.212
#> SRR1633348 1 0.4750 0.736 0.784 0.00 0.216
#> SRR1633350 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633351 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633352 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633353 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633354 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633355 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633356 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633357 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633358 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633362 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633363 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633364 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633359 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633360 1 0.0000 0.981 1.000 0.00 0.000
#> SRR1633361 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038492 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038491 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038490 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038489 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038488 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038487 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038486 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038485 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038484 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038483 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038482 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038481 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038480 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038479 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038477 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038478 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038476 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038475 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038474 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038473 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038472 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038471 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038470 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038469 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038468 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038467 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038466 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038465 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038464 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038463 1 0.0000 0.981 1.000 0.00 0.000
#> SRR2038462 1 0.4178 0.798 0.828 0.00 0.172
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633231 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633232 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633233 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633234 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633236 3 0.1109 0.971 0.000 0.004 0.968 0.028
#> SRR1633237 3 0.1388 0.965 0.000 0.012 0.960 0.028
#> SRR1633238 3 0.1256 0.968 0.000 0.008 0.964 0.028
#> SRR1633239 3 0.1256 0.968 0.000 0.008 0.964 0.028
#> SRR1633240 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633252 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633253 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633254 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633255 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633256 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633262 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633263 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633264 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633265 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633266 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633267 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633268 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633269 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633270 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633271 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633272 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633273 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633274 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633275 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633276 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633277 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633278 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633279 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633280 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633281 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633282 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633284 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633285 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633286 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633287 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633288 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633289 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633290 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633291 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633292 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633297 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633298 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633299 4 0.1022 0.976 0.000 0.000 0.032 0.968
#> SRR1633300 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633301 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633302 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633303 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633304 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633305 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633306 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633307 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633308 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633309 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633310 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633311 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633312 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633313 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633314 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633315 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633316 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633317 2 0.0188 0.990 0.000 0.996 0.000 0.004
#> SRR1633318 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633319 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633320 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633321 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633322 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633323 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633324 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633325 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633326 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633327 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633328 2 0.0921 0.986 0.000 0.972 0.000 0.028
#> SRR1633329 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633336 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633337 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633338 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633339 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633340 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633341 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633342 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633345 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633346 4 0.1022 0.984 0.032 0.000 0.000 0.968
#> SRR1633343 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633344 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633347 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633348 4 0.1151 0.980 0.008 0.000 0.024 0.968
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.1174 0.981 0.012 0.000 0.020 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.1671 0.937 0.000 0.924 0.076 0.000 0.000
#> SRR1633231 2 0.1671 0.937 0.000 0.924 0.076 0.000 0.000
#> SRR1633232 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633233 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633234 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633236 5 0.2260 0.842 0.000 0.028 0.064 0.000 0.908
#> SRR1633237 5 0.3055 0.801 0.000 0.064 0.072 0.000 0.864
#> SRR1633238 5 0.3055 0.801 0.000 0.064 0.072 0.000 0.864
#> SRR1633239 5 0.3055 0.801 0.000 0.064 0.072 0.000 0.864
#> SRR1633240 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.0404 0.907 0.000 0.000 0.012 0.000 0.988
#> SRR1633248 5 0.0510 0.904 0.000 0.000 0.016 0.000 0.984
#> SRR1633249 5 0.0404 0.907 0.000 0.000 0.012 0.000 0.988
#> SRR1633250 5 0.0404 0.907 0.000 0.000 0.012 0.000 0.988
#> SRR1633251 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633252 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633253 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633254 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633255 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633256 3 0.3730 0.750 0.000 0.000 0.712 0.000 0.288
#> SRR1633257 3 0.3730 0.750 0.000 0.000 0.712 0.000 0.288
#> SRR1633258 3 0.3730 0.750 0.000 0.000 0.712 0.000 0.288
#> SRR1633259 5 0.3707 0.495 0.000 0.000 0.284 0.000 0.716
#> SRR1633260 5 0.3752 0.474 0.000 0.000 0.292 0.000 0.708
#> SRR1633261 5 0.3534 0.557 0.000 0.000 0.256 0.000 0.744
#> SRR1633262 3 0.3636 0.722 0.000 0.000 0.728 0.272 0.000
#> SRR1633263 3 0.3636 0.722 0.000 0.000 0.728 0.272 0.000
#> SRR1633264 3 0.3636 0.722 0.000 0.000 0.728 0.272 0.000
#> SRR1633265 3 0.3636 0.722 0.000 0.000 0.728 0.272 0.000
#> SRR1633266 3 0.3636 0.722 0.000 0.000 0.728 0.272 0.000
#> SRR1633267 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633268 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633269 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633270 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633271 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633272 3 0.3636 0.767 0.000 0.000 0.728 0.000 0.272
#> SRR1633273 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633274 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633275 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633276 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633277 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633278 3 0.4119 0.743 0.036 0.000 0.752 0.212 0.000
#> SRR1633279 3 0.4119 0.743 0.036 0.000 0.752 0.212 0.000
#> SRR1633280 3 0.4119 0.743 0.036 0.000 0.752 0.212 0.000
#> SRR1633281 3 0.4237 0.741 0.048 0.000 0.752 0.200 0.000
#> SRR1633282 3 0.4227 0.438 0.000 0.000 0.580 0.420 0.000
#> SRR1633284 4 0.0290 0.993 0.000 0.000 0.008 0.992 0.000
#> SRR1633285 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633286 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633287 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633288 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633289 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633290 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633291 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633292 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.912 0.000 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633297 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633298 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633299 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633300 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633301 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633302 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633303 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633304 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633305 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633306 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633307 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633308 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633309 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633310 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633311 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633312 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633313 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633314 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633315 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633316 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633317 2 0.1410 0.946 0.000 0.940 0.060 0.000 0.000
#> SRR1633318 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633319 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633320 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633321 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633322 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633323 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633324 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633325 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633326 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633327 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633328 2 0.1608 0.938 0.000 0.928 0.072 0.000 0.000
#> SRR1633329 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633330 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633331 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633332 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633333 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633334 2 0.0162 0.948 0.000 0.996 0.004 0.000 0.000
#> SRR1633335 4 0.0404 0.989 0.000 0.000 0.012 0.988 0.000
#> SRR1633336 4 0.0404 0.989 0.000 0.000 0.012 0.988 0.000
#> SRR1633337 4 0.0404 0.989 0.000 0.000 0.012 0.988 0.000
#> SRR1633338 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633341 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633342 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633345 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633346 4 0.0162 0.996 0.000 0.000 0.004 0.996 0.000
#> SRR1633343 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633344 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633347 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633348 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000
#> SRR1633350 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633351 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633352 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633353 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633354 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633355 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633356 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633357 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633358 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633362 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633363 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633364 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633359 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633360 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR1633361 1 0.2179 0.938 0.888 0.000 0.112 0.000 0.000
#> SRR2038492 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0510 0.963 0.984 0.000 0.016 0.000 0.000
#> SRR2038486 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0880 0.961 0.968 0.000 0.032 0.000 0.000
#> SRR2038483 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0510 0.963 0.984 0.000 0.016 0.000 0.000
#> SRR2038476 1 0.0162 0.964 0.996 0.000 0.004 0.000 0.000
#> SRR2038475 1 0.0794 0.962 0.972 0.000 0.028 0.000 0.000
#> SRR2038474 1 0.0794 0.962 0.972 0.000 0.028 0.000 0.000
#> SRR2038473 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0162 0.965 0.996 0.000 0.004 0.000 0.000
#> SRR2038464 1 0.0000 0.965 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0794 0.962 0.972 0.000 0.028 0.000 0.000
#> SRR2038462 3 0.4612 0.724 0.084 0.000 0.736 0.180 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.3843 0.648 0.000 0.548 0.000 0.000 0.000 0.452
#> SRR1633231 2 0.3843 0.648 0.000 0.548 0.000 0.000 0.000 0.452
#> SRR1633232 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633233 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633234 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633236 5 0.1549 0.845 0.000 0.044 0.020 0.000 0.936 0.000
#> SRR1633237 5 0.1327 0.830 0.000 0.064 0.000 0.000 0.936 0.000
#> SRR1633238 5 0.1327 0.830 0.000 0.064 0.000 0.000 0.936 0.000
#> SRR1633239 5 0.1327 0.830 0.000 0.064 0.000 0.000 0.936 0.000
#> SRR1633240 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633241 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633242 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633243 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633244 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633245 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633246 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633247 5 0.3860 0.318 0.000 0.000 0.472 0.000 0.528 0.000
#> SRR1633248 5 0.3866 0.287 0.000 0.000 0.484 0.000 0.516 0.000
#> SRR1633249 5 0.3864 0.298 0.000 0.000 0.480 0.000 0.520 0.000
#> SRR1633250 5 0.3857 0.328 0.000 0.000 0.468 0.000 0.532 0.000
#> SRR1633251 3 0.0146 0.839 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633252 3 0.0146 0.839 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633253 3 0.0146 0.839 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633254 3 0.0146 0.839 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633255 3 0.0146 0.839 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633256 3 0.0260 0.837 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1633257 3 0.0260 0.837 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1633258 3 0.0260 0.837 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1633259 3 0.3288 0.457 0.000 0.000 0.724 0.000 0.276 0.000
#> SRR1633260 3 0.3288 0.457 0.000 0.000 0.724 0.000 0.276 0.000
#> SRR1633261 3 0.3330 0.438 0.000 0.000 0.716 0.000 0.284 0.000
#> SRR1633262 3 0.1204 0.816 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1633263 3 0.1204 0.816 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1633264 3 0.1204 0.816 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1633265 3 0.1204 0.816 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1633266 3 0.1204 0.816 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1633267 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633268 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633269 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633270 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633271 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633272 3 0.0000 0.840 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633273 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633274 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633275 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633276 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633277 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633278 3 0.5177 0.522 0.028 0.424 0.516 0.028 0.004 0.000
#> SRR1633279 3 0.5089 0.526 0.016 0.424 0.520 0.036 0.004 0.000
#> SRR1633280 3 0.5178 0.518 0.032 0.428 0.512 0.024 0.004 0.000
#> SRR1633281 3 0.5151 0.515 0.040 0.428 0.512 0.016 0.004 0.000
#> SRR1633282 3 0.5511 0.479 0.000 0.416 0.468 0.112 0.004 0.000
#> SRR1633284 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633285 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633286 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633287 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633288 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633289 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633290 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633291 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633292 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633293 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633294 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633295 5 0.1204 0.871 0.000 0.000 0.056 0.000 0.944 0.000
#> SRR1633296 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633297 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633298 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633299 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633300 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633301 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633302 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633303 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633304 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633305 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633306 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633307 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633308 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633309 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633310 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633311 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633312 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633313 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633314 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633315 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633316 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633317 6 0.3547 1.000 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR1633318 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633319 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633320 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633321 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633322 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633323 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633324 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633325 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633326 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633327 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633328 2 0.3851 0.662 0.000 0.540 0.000 0.000 0.000 0.460
#> SRR1633329 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633330 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633331 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633332 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633333 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633334 2 0.3868 0.367 0.000 0.508 0.000 0.000 0.000 0.492
#> SRR1633335 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633336 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633337 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633338 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633339 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633340 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633341 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633342 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633345 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633344 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633347 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633348 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633350 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633351 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633352 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633353 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633354 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633355 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633356 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633357 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633358 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633362 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633363 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633364 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633359 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633360 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR1633361 1 0.4567 0.760 0.616 0.000 0.000 0.000 0.052 0.332
#> SRR2038492 1 0.0458 0.860 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0713 0.865 0.972 0.000 0.000 0.000 0.000 0.028
#> SRR2038490 1 0.0458 0.860 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0363 0.861 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0291 0.864 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR2038487 1 0.1349 0.862 0.940 0.000 0.000 0.000 0.004 0.056
#> SRR2038486 1 0.0363 0.861 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0260 0.862 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.2948 0.827 0.804 0.000 0.000 0.000 0.008 0.188
#> SRR2038483 1 0.0260 0.862 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0520 0.864 0.984 0.008 0.000 0.000 0.000 0.008
#> SRR2038480 1 0.0458 0.860 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0260 0.862 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0146 0.863 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.1267 0.862 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR2038476 1 0.0458 0.860 0.984 0.016 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.2118 0.852 0.888 0.000 0.000 0.000 0.008 0.104
#> SRR2038474 1 0.1918 0.855 0.904 0.000 0.000 0.000 0.008 0.088
#> SRR2038473 1 0.0632 0.865 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR2038472 1 0.0632 0.865 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR2038471 1 0.0146 0.863 0.996 0.004 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0405 0.863 0.988 0.008 0.000 0.000 0.000 0.004
#> SRR2038469 1 0.0363 0.861 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.864 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0260 0.862 0.992 0.008 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0632 0.865 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR2038465 1 0.0935 0.864 0.964 0.000 0.000 0.000 0.004 0.032
#> SRR2038464 1 0.0363 0.861 0.988 0.012 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.1958 0.854 0.896 0.000 0.000 0.000 0.004 0.100
#> SRR2038462 2 0.6511 -0.417 0.316 0.428 0.232 0.020 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.926 0.972 0.987 0.5027 0.497 0.497
#> 3 3 0.812 0.924 0.931 0.2425 0.876 0.750
#> 4 4 0.789 0.906 0.935 0.0891 0.965 0.907
#> 5 5 0.814 0.886 0.889 0.0594 0.970 0.913
#> 6 6 1.000 0.984 0.992 0.1083 0.894 0.658
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 0.983 0.000 1.000
#> SRR1633231 2 0.000 0.983 0.000 1.000
#> SRR1633232 2 0.000 0.983 0.000 1.000
#> SRR1633233 2 0.000 0.983 0.000 1.000
#> SRR1633234 2 0.000 0.983 0.000 1.000
#> SRR1633236 2 0.000 0.983 0.000 1.000
#> SRR1633237 2 0.000 0.983 0.000 1.000
#> SRR1633238 2 0.000 0.983 0.000 1.000
#> SRR1633239 2 0.000 0.983 0.000 1.000
#> SRR1633240 2 0.000 0.983 0.000 1.000
#> SRR1633241 2 0.000 0.983 0.000 1.000
#> SRR1633242 2 0.000 0.983 0.000 1.000
#> SRR1633243 2 0.000 0.983 0.000 1.000
#> SRR1633244 2 0.000 0.983 0.000 1.000
#> SRR1633245 2 0.000 0.983 0.000 1.000
#> SRR1633246 2 0.000 0.983 0.000 1.000
#> SRR1633247 2 0.000 0.983 0.000 1.000
#> SRR1633248 2 0.000 0.983 0.000 1.000
#> SRR1633249 2 0.000 0.983 0.000 1.000
#> SRR1633250 2 0.000 0.983 0.000 1.000
#> SRR1633251 2 0.000 0.983 0.000 1.000
#> SRR1633252 2 0.000 0.983 0.000 1.000
#> SRR1633253 2 0.000 0.983 0.000 1.000
#> SRR1633254 2 0.000 0.983 0.000 1.000
#> SRR1633255 2 0.000 0.983 0.000 1.000
#> SRR1633256 2 0.000 0.983 0.000 1.000
#> SRR1633257 2 0.000 0.983 0.000 1.000
#> SRR1633258 2 0.000 0.983 0.000 1.000
#> SRR1633259 2 0.000 0.983 0.000 1.000
#> SRR1633260 2 0.000 0.983 0.000 1.000
#> SRR1633261 2 0.000 0.983 0.000 1.000
#> SRR1633262 2 0.827 0.659 0.260 0.740
#> SRR1633263 2 0.827 0.659 0.260 0.740
#> SRR1633264 2 0.827 0.659 0.260 0.740
#> SRR1633265 2 0.827 0.659 0.260 0.740
#> SRR1633266 2 0.827 0.659 0.260 0.740
#> SRR1633267 2 0.000 0.983 0.000 1.000
#> SRR1633268 2 0.000 0.983 0.000 1.000
#> SRR1633269 2 0.000 0.983 0.000 1.000
#> SRR1633270 2 0.000 0.983 0.000 1.000
#> SRR1633271 2 0.000 0.983 0.000 1.000
#> SRR1633272 2 0.000 0.983 0.000 1.000
#> SRR1633273 1 0.000 0.990 1.000 0.000
#> SRR1633274 1 0.000 0.990 1.000 0.000
#> SRR1633275 1 0.000 0.990 1.000 0.000
#> SRR1633276 1 0.000 0.990 1.000 0.000
#> SRR1633277 1 0.000 0.990 1.000 0.000
#> SRR1633278 1 0.552 0.858 0.872 0.128
#> SRR1633279 1 0.552 0.858 0.872 0.128
#> SRR1633280 1 0.552 0.858 0.872 0.128
#> SRR1633281 1 0.552 0.858 0.872 0.128
#> SRR1633282 1 0.552 0.858 0.872 0.128
#> SRR1633284 1 0.000 0.990 1.000 0.000
#> SRR1633285 1 0.000 0.990 1.000 0.000
#> SRR1633286 1 0.000 0.990 1.000 0.000
#> SRR1633287 1 0.000 0.990 1.000 0.000
#> SRR1633288 1 0.000 0.990 1.000 0.000
#> SRR1633289 1 0.000 0.990 1.000 0.000
#> SRR1633290 1 0.000 0.990 1.000 0.000
#> SRR1633291 1 0.000 0.990 1.000 0.000
#> SRR1633292 2 0.000 0.983 0.000 1.000
#> SRR1633293 2 0.000 0.983 0.000 1.000
#> SRR1633294 2 0.000 0.983 0.000 1.000
#> SRR1633295 2 0.000 0.983 0.000 1.000
#> SRR1633296 1 0.000 0.990 1.000 0.000
#> SRR1633297 1 0.000 0.990 1.000 0.000
#> SRR1633298 1 0.000 0.990 1.000 0.000
#> SRR1633299 1 0.000 0.990 1.000 0.000
#> SRR1633300 2 0.000 0.983 0.000 1.000
#> SRR1633301 2 0.000 0.983 0.000 1.000
#> SRR1633302 2 0.000 0.983 0.000 1.000
#> SRR1633303 2 0.000 0.983 0.000 1.000
#> SRR1633304 2 0.000 0.983 0.000 1.000
#> SRR1633305 2 0.000 0.983 0.000 1.000
#> SRR1633306 2 0.000 0.983 0.000 1.000
#> SRR1633307 2 0.000 0.983 0.000 1.000
#> SRR1633308 2 0.000 0.983 0.000 1.000
#> SRR1633309 2 0.000 0.983 0.000 1.000
#> SRR1633310 2 0.000 0.983 0.000 1.000
#> SRR1633311 2 0.000 0.983 0.000 1.000
#> SRR1633312 2 0.000 0.983 0.000 1.000
#> SRR1633313 2 0.000 0.983 0.000 1.000
#> SRR1633314 2 0.000 0.983 0.000 1.000
#> SRR1633315 2 0.000 0.983 0.000 1.000
#> SRR1633316 2 0.000 0.983 0.000 1.000
#> SRR1633317 2 0.000 0.983 0.000 1.000
#> SRR1633318 2 0.000 0.983 0.000 1.000
#> SRR1633319 2 0.000 0.983 0.000 1.000
#> SRR1633320 2 0.000 0.983 0.000 1.000
#> SRR1633321 2 0.000 0.983 0.000 1.000
#> SRR1633322 2 0.000 0.983 0.000 1.000
#> SRR1633323 2 0.000 0.983 0.000 1.000
#> SRR1633324 2 0.000 0.983 0.000 1.000
#> SRR1633325 2 0.000 0.983 0.000 1.000
#> SRR1633326 2 0.000 0.983 0.000 1.000
#> SRR1633327 2 0.000 0.983 0.000 1.000
#> SRR1633328 2 0.000 0.983 0.000 1.000
#> SRR1633329 2 0.000 0.983 0.000 1.000
#> SRR1633330 2 0.000 0.983 0.000 1.000
#> SRR1633331 2 0.000 0.983 0.000 1.000
#> SRR1633332 2 0.000 0.983 0.000 1.000
#> SRR1633333 2 0.000 0.983 0.000 1.000
#> SRR1633334 2 0.000 0.983 0.000 1.000
#> SRR1633335 1 0.000 0.990 1.000 0.000
#> SRR1633336 1 0.000 0.990 1.000 0.000
#> SRR1633337 1 0.000 0.990 1.000 0.000
#> SRR1633338 1 0.000 0.990 1.000 0.000
#> SRR1633339 1 0.000 0.990 1.000 0.000
#> SRR1633340 1 0.000 0.990 1.000 0.000
#> SRR1633341 1 0.000 0.990 1.000 0.000
#> SRR1633342 1 0.000 0.990 1.000 0.000
#> SRR1633345 1 0.000 0.990 1.000 0.000
#> SRR1633346 1 0.000 0.990 1.000 0.000
#> SRR1633343 1 0.000 0.990 1.000 0.000
#> SRR1633344 1 0.000 0.990 1.000 0.000
#> SRR1633347 1 0.000 0.990 1.000 0.000
#> SRR1633348 1 0.000 0.990 1.000 0.000
#> SRR1633350 1 0.000 0.990 1.000 0.000
#> SRR1633351 1 0.000 0.990 1.000 0.000
#> SRR1633352 1 0.000 0.990 1.000 0.000
#> SRR1633353 1 0.000 0.990 1.000 0.000
#> SRR1633354 1 0.000 0.990 1.000 0.000
#> SRR1633355 1 0.000 0.990 1.000 0.000
#> SRR1633356 1 0.000 0.990 1.000 0.000
#> SRR1633357 1 0.000 0.990 1.000 0.000
#> SRR1633358 1 0.000 0.990 1.000 0.000
#> SRR1633362 1 0.000 0.990 1.000 0.000
#> SRR1633363 1 0.000 0.990 1.000 0.000
#> SRR1633364 1 0.000 0.990 1.000 0.000
#> SRR1633359 1 0.000 0.990 1.000 0.000
#> SRR1633360 1 0.000 0.990 1.000 0.000
#> SRR1633361 1 0.000 0.990 1.000 0.000
#> SRR2038492 1 0.000 0.990 1.000 0.000
#> SRR2038491 1 0.000 0.990 1.000 0.000
#> SRR2038490 1 0.000 0.990 1.000 0.000
#> SRR2038489 1 0.000 0.990 1.000 0.000
#> SRR2038488 1 0.000 0.990 1.000 0.000
#> SRR2038487 1 0.000 0.990 1.000 0.000
#> SRR2038486 1 0.000 0.990 1.000 0.000
#> SRR2038485 1 0.000 0.990 1.000 0.000
#> SRR2038484 1 0.000 0.990 1.000 0.000
#> SRR2038483 1 0.000 0.990 1.000 0.000
#> SRR2038482 1 0.000 0.990 1.000 0.000
#> SRR2038481 1 0.000 0.990 1.000 0.000
#> SRR2038480 1 0.000 0.990 1.000 0.000
#> SRR2038479 1 0.000 0.990 1.000 0.000
#> SRR2038477 1 0.000 0.990 1.000 0.000
#> SRR2038478 1 0.000 0.990 1.000 0.000
#> SRR2038476 1 0.000 0.990 1.000 0.000
#> SRR2038475 1 0.000 0.990 1.000 0.000
#> SRR2038474 1 0.000 0.990 1.000 0.000
#> SRR2038473 1 0.000 0.990 1.000 0.000
#> SRR2038472 1 0.000 0.990 1.000 0.000
#> SRR2038471 1 0.000 0.990 1.000 0.000
#> SRR2038470 1 0.000 0.990 1.000 0.000
#> SRR2038469 1 0.000 0.990 1.000 0.000
#> SRR2038468 1 0.000 0.990 1.000 0.000
#> SRR2038467 1 0.000 0.990 1.000 0.000
#> SRR2038466 1 0.000 0.990 1.000 0.000
#> SRR2038465 1 0.000 0.990 1.000 0.000
#> SRR2038464 1 0.000 0.990 1.000 0.000
#> SRR2038463 1 0.000 0.990 1.000 0.000
#> SRR2038462 1 0.552 0.858 0.872 0.128
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633237 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633238 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633239 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633240 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633241 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633242 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633243 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633244 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633245 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633246 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633247 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633248 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633249 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633250 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633251 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633252 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633253 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633254 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633255 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633256 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633257 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633258 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633259 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633260 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633261 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633262 3 0.394 0.659 0.156 0.000 0.844
#> SRR1633263 3 0.394 0.659 0.156 0.000 0.844
#> SRR1633264 3 0.394 0.659 0.156 0.000 0.844
#> SRR1633265 3 0.394 0.659 0.156 0.000 0.844
#> SRR1633266 3 0.394 0.659 0.156 0.000 0.844
#> SRR1633267 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633268 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633269 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633270 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633271 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633272 3 0.304 0.869 0.000 0.104 0.896
#> SRR1633273 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633274 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633275 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633276 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633277 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633278 1 0.493 0.814 0.768 0.000 0.232
#> SRR1633279 1 0.493 0.814 0.768 0.000 0.232
#> SRR1633280 1 0.493 0.814 0.768 0.000 0.232
#> SRR1633281 1 0.493 0.814 0.768 0.000 0.232
#> SRR1633282 1 0.493 0.814 0.768 0.000 0.232
#> SRR1633284 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633285 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633286 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633287 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633288 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633289 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633290 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633291 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633292 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633293 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633294 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633295 3 0.553 0.785 0.000 0.296 0.704
#> SRR1633296 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633297 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633298 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633299 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633300 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633336 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633337 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633338 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633339 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633340 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633341 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633342 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633345 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633346 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633343 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633344 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633347 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633348 1 0.254 0.947 0.920 0.000 0.080
#> SRR1633350 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633351 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633352 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633353 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633354 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633355 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633356 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633357 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633358 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633362 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633363 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633364 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633359 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633360 1 0.000 0.958 1.000 0.000 0.000
#> SRR1633361 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038492 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038491 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038490 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038489 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038488 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038487 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038486 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038485 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038484 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038483 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038482 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038481 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038480 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038479 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038477 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038478 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038476 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038475 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038474 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038473 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038472 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038471 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038470 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038469 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038468 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038467 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038466 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038465 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038464 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038463 1 0.000 0.958 1.000 0.000 0.000
#> SRR2038462 1 0.493 0.814 0.768 0.000 0.232
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633237 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633238 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633239 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633240 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633241 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633242 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633243 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633244 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633245 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633246 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633247 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633248 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633249 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633250 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633251 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633252 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633253 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633254 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633255 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633256 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633257 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633258 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633259 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633260 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633261 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633262 3 0.466 0.548 0.000 0.000 0.652 0.348
#> SRR1633263 3 0.466 0.548 0.000 0.000 0.652 0.348
#> SRR1633264 3 0.466 0.548 0.000 0.000 0.652 0.348
#> SRR1633265 3 0.466 0.548 0.000 0.000 0.652 0.348
#> SRR1633266 3 0.466 0.548 0.000 0.000 0.652 0.348
#> SRR1633267 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633268 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633269 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633270 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633271 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633272 3 0.215 0.865 0.000 0.000 0.912 0.088
#> SRR1633273 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633274 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633275 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633276 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633277 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633278 4 0.102 1.000 0.000 0.000 0.032 0.968
#> SRR1633279 4 0.102 1.000 0.000 0.000 0.032 0.968
#> SRR1633280 4 0.102 1.000 0.000 0.000 0.032 0.968
#> SRR1633281 4 0.102 1.000 0.000 0.000 0.032 0.968
#> SRR1633282 4 0.102 1.000 0.000 0.000 0.032 0.968
#> SRR1633284 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633285 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633286 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633287 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633288 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633289 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633290 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633291 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633292 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633293 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633294 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633295 3 0.241 0.816 0.000 0.104 0.896 0.000
#> SRR1633296 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633297 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633298 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633299 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633300 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0.000 0.000
#> SRR1633335 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633336 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633337 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633338 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633339 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633340 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633341 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633342 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633345 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633346 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633343 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633344 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633347 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633348 1 0.336 0.876 0.824 0.000 0.000 0.176
#> SRR1633350 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.000 0.920 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.102 1.000 0.000 0.000 0.032 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633231 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633232 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633233 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633234 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633236 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633237 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633238 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633239 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633240 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633241 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633242 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633243 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633244 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633245 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633246 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633247 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633248 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633249 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633250 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633251 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633252 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633253 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633254 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633255 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633256 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633257 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633258 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633259 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633260 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633261 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633262 3 0.356 0.642 0.000 0.000 0.74 0.26 0.000
#> SRR1633263 3 0.356 0.642 0.000 0.000 0.74 0.26 0.000
#> SRR1633264 3 0.356 0.642 0.000 0.000 0.74 0.26 0.000
#> SRR1633265 3 0.356 0.642 0.000 0.000 0.74 0.26 0.000
#> SRR1633266 3 0.356 0.642 0.000 0.000 0.74 0.26 0.000
#> SRR1633267 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633268 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633269 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633270 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633271 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633272 3 0.000 0.925 0.000 0.000 1.00 0.00 0.000
#> SRR1633273 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633274 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633275 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633276 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633277 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633278 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
#> SRR1633279 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
#> SRR1633280 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
#> SRR1633281 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
#> SRR1633282 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
#> SRR1633284 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633285 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633286 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633287 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633288 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633289 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633290 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633291 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633292 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633293 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633294 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633295 5 0.543 1.000 0.000 0.104 0.26 0.00 0.636
#> SRR1633296 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633297 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633298 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633299 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633301 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633302 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633303 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633304 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633305 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633306 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633307 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633308 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633309 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633310 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633311 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633312 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633313 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633314 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633315 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633316 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633317 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633318 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633319 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633320 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633321 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633322 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633323 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633324 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633325 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633326 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633327 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633328 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633329 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633330 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633331 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633332 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633333 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633334 2 0.000 1.000 0.000 1.000 0.00 0.00 0.000
#> SRR1633335 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633336 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633337 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633338 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633339 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633340 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633341 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633342 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633345 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633346 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633343 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633344 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633347 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633348 1 0.000 0.745 1.000 0.000 0.00 0.00 0.000
#> SRR1633350 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633351 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633352 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633353 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633354 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633355 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633356 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633357 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633358 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633362 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633363 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633364 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633359 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633360 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR1633361 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038492 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038491 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038490 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038489 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038488 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038487 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038486 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038485 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038484 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038483 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038482 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038481 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038480 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038479 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038477 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038478 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038476 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038475 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038474 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038473 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038472 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038471 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038470 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038469 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038468 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038467 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038466 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038465 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038464 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038463 1 0.407 0.836 0.636 0.000 0.00 0.00 0.364
#> SRR2038462 4 0.000 1.000 0.000 0.000 0.00 1.00 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633231 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633232 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633233 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633234 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633236 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633237 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633238 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633239 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633240 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633241 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633242 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633243 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633244 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633245 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633246 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633247 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633248 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633249 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633250 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633251 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633252 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633253 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633254 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633255 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633256 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633257 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633258 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633259 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633260 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633261 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633262 3 0.32 0.705 0 0 0.74 0 0 0.26
#> SRR1633263 3 0.32 0.705 0 0 0.74 0 0 0.26
#> SRR1633264 3 0.32 0.705 0 0 0.74 0 0 0.26
#> SRR1633265 3 0.32 0.705 0 0 0.74 0 0 0.26
#> SRR1633266 3 0.32 0.705 0 0 0.74 0 0 0.26
#> SRR1633267 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633268 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633269 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633270 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633271 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633272 3 0.00 0.945 0 0 1.00 0 0 0.00
#> SRR1633273 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633274 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633275 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633276 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633277 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633278 6 0.00 1.000 0 0 0.00 0 0 1.00
#> SRR1633279 6 0.00 1.000 0 0 0.00 0 0 1.00
#> SRR1633280 6 0.00 1.000 0 0 0.00 0 0 1.00
#> SRR1633281 6 0.00 1.000 0 0 0.00 0 0 1.00
#> SRR1633282 6 0.00 1.000 0 0 0.00 0 0 1.00
#> SRR1633284 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633285 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633286 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633287 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633288 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633289 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633290 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633291 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633292 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633293 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633294 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633295 5 0.00 1.000 0 0 0.00 0 1 0.00
#> SRR1633296 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633297 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633298 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633299 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633300 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633301 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633302 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633303 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633304 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633305 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633306 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633307 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633308 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633309 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633310 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633311 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633312 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633313 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633314 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633315 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633316 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633317 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633318 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633319 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633320 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633321 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633322 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633323 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633324 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633325 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633326 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633327 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633328 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633329 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633330 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633331 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633332 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633333 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633334 2 0.00 1.000 0 1 0.00 0 0 0.00
#> SRR1633335 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633336 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633337 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633338 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633339 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633340 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633341 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633342 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633345 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633346 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633343 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633344 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633347 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633348 4 0.00 1.000 0 0 0.00 1 0 0.00
#> SRR1633350 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633351 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633352 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633353 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633354 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633355 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633356 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633357 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633358 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633362 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633363 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633364 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633359 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633360 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR1633361 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038492 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038491 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038490 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038489 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038488 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038487 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038486 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038485 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038484 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038483 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038482 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038481 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038480 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038479 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038477 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038478 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038476 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038475 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038474 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038473 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038472 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038471 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038470 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038469 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038468 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038467 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038466 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038465 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038464 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038463 1 0.00 1.000 1 0 0.00 0 0 0.00
#> SRR2038462 6 0.00 1.000 0 0 0.00 0 0 1.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.960 0.981 0.5018 0.499 0.499
#> 3 3 0.646 0.602 0.833 0.2838 0.760 0.553
#> 4 4 0.746 0.855 0.836 0.1158 0.805 0.499
#> 5 5 0.695 0.768 0.809 0.0632 0.982 0.929
#> 6 6 0.769 0.621 0.783 0.0403 0.977 0.906
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 0.998 0.000 1.000
#> SRR1633231 2 0.0000 0.998 0.000 1.000
#> SRR1633232 2 0.0000 0.998 0.000 1.000
#> SRR1633233 2 0.0000 0.998 0.000 1.000
#> SRR1633234 2 0.0000 0.998 0.000 1.000
#> SRR1633236 2 0.0000 0.998 0.000 1.000
#> SRR1633237 2 0.0000 0.998 0.000 1.000
#> SRR1633238 2 0.0000 0.998 0.000 1.000
#> SRR1633239 2 0.0000 0.998 0.000 1.000
#> SRR1633240 2 0.0376 0.998 0.004 0.996
#> SRR1633241 2 0.0376 0.998 0.004 0.996
#> SRR1633242 2 0.0376 0.998 0.004 0.996
#> SRR1633243 2 0.0376 0.998 0.004 0.996
#> SRR1633244 2 0.0376 0.998 0.004 0.996
#> SRR1633245 2 0.0376 0.998 0.004 0.996
#> SRR1633246 2 0.0376 0.998 0.004 0.996
#> SRR1633247 2 0.0376 0.998 0.004 0.996
#> SRR1633248 2 0.0376 0.998 0.004 0.996
#> SRR1633249 2 0.0376 0.998 0.004 0.996
#> SRR1633250 2 0.0376 0.998 0.004 0.996
#> SRR1633251 2 0.0376 0.998 0.004 0.996
#> SRR1633252 2 0.0376 0.998 0.004 0.996
#> SRR1633253 2 0.0376 0.998 0.004 0.996
#> SRR1633254 2 0.0376 0.998 0.004 0.996
#> SRR1633255 2 0.0376 0.998 0.004 0.996
#> SRR1633256 2 0.0376 0.998 0.004 0.996
#> SRR1633257 2 0.0376 0.998 0.004 0.996
#> SRR1633258 2 0.0376 0.998 0.004 0.996
#> SRR1633259 2 0.0376 0.998 0.004 0.996
#> SRR1633260 2 0.0376 0.998 0.004 0.996
#> SRR1633261 2 0.0376 0.998 0.004 0.996
#> SRR1633262 1 0.8763 0.614 0.704 0.296
#> SRR1633263 1 0.8763 0.614 0.704 0.296
#> SRR1633264 1 0.8763 0.614 0.704 0.296
#> SRR1633265 1 0.8763 0.614 0.704 0.296
#> SRR1633266 1 0.8763 0.614 0.704 0.296
#> SRR1633267 2 0.0376 0.998 0.004 0.996
#> SRR1633268 2 0.0376 0.998 0.004 0.996
#> SRR1633269 2 0.0376 0.998 0.004 0.996
#> SRR1633270 2 0.0376 0.998 0.004 0.996
#> SRR1633271 2 0.0376 0.998 0.004 0.996
#> SRR1633272 2 0.0376 0.998 0.004 0.996
#> SRR1633273 1 0.0000 0.965 1.000 0.000
#> SRR1633274 1 0.0000 0.965 1.000 0.000
#> SRR1633275 1 0.0000 0.965 1.000 0.000
#> SRR1633276 1 0.0000 0.965 1.000 0.000
#> SRR1633277 1 0.0000 0.965 1.000 0.000
#> SRR1633278 1 0.9087 0.562 0.676 0.324
#> SRR1633279 1 0.9087 0.562 0.676 0.324
#> SRR1633280 1 0.9087 0.562 0.676 0.324
#> SRR1633281 1 0.9087 0.562 0.676 0.324
#> SRR1633282 1 0.0000 0.965 1.000 0.000
#> SRR1633284 1 0.0000 0.965 1.000 0.000
#> SRR1633285 1 0.0000 0.965 1.000 0.000
#> SRR1633286 1 0.0000 0.965 1.000 0.000
#> SRR1633287 1 0.0000 0.965 1.000 0.000
#> SRR1633288 1 0.0000 0.965 1.000 0.000
#> SRR1633289 1 0.0000 0.965 1.000 0.000
#> SRR1633290 1 0.0000 0.965 1.000 0.000
#> SRR1633291 1 0.0000 0.965 1.000 0.000
#> SRR1633292 2 0.0376 0.998 0.004 0.996
#> SRR1633293 2 0.0376 0.998 0.004 0.996
#> SRR1633294 2 0.0376 0.998 0.004 0.996
#> SRR1633295 2 0.0376 0.998 0.004 0.996
#> SRR1633296 1 0.0000 0.965 1.000 0.000
#> SRR1633297 1 0.0000 0.965 1.000 0.000
#> SRR1633298 1 0.0000 0.965 1.000 0.000
#> SRR1633299 1 0.0000 0.965 1.000 0.000
#> SRR1633300 2 0.0000 0.998 0.000 1.000
#> SRR1633301 2 0.0000 0.998 0.000 1.000
#> SRR1633302 2 0.0000 0.998 0.000 1.000
#> SRR1633303 2 0.0000 0.998 0.000 1.000
#> SRR1633304 2 0.0000 0.998 0.000 1.000
#> SRR1633305 2 0.0000 0.998 0.000 1.000
#> SRR1633306 2 0.0000 0.998 0.000 1.000
#> SRR1633307 2 0.0000 0.998 0.000 1.000
#> SRR1633308 2 0.0000 0.998 0.000 1.000
#> SRR1633309 2 0.0000 0.998 0.000 1.000
#> SRR1633310 2 0.0000 0.998 0.000 1.000
#> SRR1633311 2 0.0000 0.998 0.000 1.000
#> SRR1633312 2 0.0000 0.998 0.000 1.000
#> SRR1633313 2 0.0000 0.998 0.000 1.000
#> SRR1633314 2 0.0000 0.998 0.000 1.000
#> SRR1633315 2 0.0000 0.998 0.000 1.000
#> SRR1633316 2 0.0000 0.998 0.000 1.000
#> SRR1633317 2 0.0000 0.998 0.000 1.000
#> SRR1633318 2 0.0000 0.998 0.000 1.000
#> SRR1633319 2 0.0000 0.998 0.000 1.000
#> SRR1633320 2 0.0000 0.998 0.000 1.000
#> SRR1633321 2 0.0000 0.998 0.000 1.000
#> SRR1633322 2 0.0000 0.998 0.000 1.000
#> SRR1633323 2 0.0000 0.998 0.000 1.000
#> SRR1633324 2 0.0000 0.998 0.000 1.000
#> SRR1633325 2 0.0000 0.998 0.000 1.000
#> SRR1633326 2 0.0000 0.998 0.000 1.000
#> SRR1633327 2 0.0000 0.998 0.000 1.000
#> SRR1633328 2 0.0000 0.998 0.000 1.000
#> SRR1633329 2 0.0000 0.998 0.000 1.000
#> SRR1633330 2 0.0000 0.998 0.000 1.000
#> SRR1633331 2 0.0000 0.998 0.000 1.000
#> SRR1633332 2 0.0000 0.998 0.000 1.000
#> SRR1633333 2 0.0000 0.998 0.000 1.000
#> SRR1633334 2 0.0000 0.998 0.000 1.000
#> SRR1633335 1 0.0000 0.965 1.000 0.000
#> SRR1633336 1 0.0000 0.965 1.000 0.000
#> SRR1633337 1 0.0000 0.965 1.000 0.000
#> SRR1633338 1 0.0000 0.965 1.000 0.000
#> SRR1633339 1 0.0000 0.965 1.000 0.000
#> SRR1633340 1 0.0000 0.965 1.000 0.000
#> SRR1633341 1 0.0000 0.965 1.000 0.000
#> SRR1633342 1 0.0000 0.965 1.000 0.000
#> SRR1633345 1 0.0000 0.965 1.000 0.000
#> SRR1633346 1 0.0000 0.965 1.000 0.000
#> SRR1633343 1 0.0000 0.965 1.000 0.000
#> SRR1633344 1 0.0000 0.965 1.000 0.000
#> SRR1633347 1 0.0000 0.965 1.000 0.000
#> SRR1633348 1 0.0000 0.965 1.000 0.000
#> SRR1633350 1 0.0376 0.965 0.996 0.004
#> SRR1633351 1 0.0376 0.965 0.996 0.004
#> SRR1633352 1 0.0376 0.965 0.996 0.004
#> SRR1633353 1 0.0376 0.965 0.996 0.004
#> SRR1633354 1 0.0376 0.965 0.996 0.004
#> SRR1633355 1 0.0376 0.965 0.996 0.004
#> SRR1633356 1 0.0376 0.965 0.996 0.004
#> SRR1633357 1 0.0376 0.965 0.996 0.004
#> SRR1633358 1 0.0376 0.965 0.996 0.004
#> SRR1633362 1 0.0376 0.965 0.996 0.004
#> SRR1633363 1 0.0376 0.965 0.996 0.004
#> SRR1633364 1 0.0376 0.965 0.996 0.004
#> SRR1633359 1 0.0376 0.965 0.996 0.004
#> SRR1633360 1 0.0376 0.965 0.996 0.004
#> SRR1633361 1 0.0376 0.965 0.996 0.004
#> SRR2038492 1 0.0376 0.965 0.996 0.004
#> SRR2038491 1 0.0376 0.965 0.996 0.004
#> SRR2038490 1 0.0376 0.965 0.996 0.004
#> SRR2038489 1 0.0376 0.965 0.996 0.004
#> SRR2038488 1 0.0376 0.965 0.996 0.004
#> SRR2038487 1 0.0376 0.965 0.996 0.004
#> SRR2038486 1 0.0376 0.965 0.996 0.004
#> SRR2038485 1 0.0376 0.965 0.996 0.004
#> SRR2038484 1 0.0376 0.965 0.996 0.004
#> SRR2038483 1 0.0376 0.965 0.996 0.004
#> SRR2038482 1 0.0376 0.965 0.996 0.004
#> SRR2038481 1 0.0376 0.965 0.996 0.004
#> SRR2038480 1 0.0376 0.965 0.996 0.004
#> SRR2038479 1 0.0376 0.965 0.996 0.004
#> SRR2038477 1 0.0376 0.965 0.996 0.004
#> SRR2038478 1 0.0376 0.965 0.996 0.004
#> SRR2038476 1 0.0376 0.965 0.996 0.004
#> SRR2038475 1 0.0376 0.965 0.996 0.004
#> SRR2038474 1 0.0376 0.965 0.996 0.004
#> SRR2038473 1 0.0376 0.965 0.996 0.004
#> SRR2038472 1 0.0376 0.965 0.996 0.004
#> SRR2038471 1 0.0376 0.965 0.996 0.004
#> SRR2038470 1 0.0376 0.965 0.996 0.004
#> SRR2038469 1 0.0376 0.965 0.996 0.004
#> SRR2038468 1 0.0376 0.965 0.996 0.004
#> SRR2038467 1 0.0376 0.965 0.996 0.004
#> SRR2038466 1 0.0376 0.965 0.996 0.004
#> SRR2038465 1 0.0376 0.965 0.996 0.004
#> SRR2038464 1 0.0376 0.965 0.996 0.004
#> SRR2038463 1 0.0376 0.965 0.996 0.004
#> SRR2038462 1 0.0000 0.965 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633231 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633232 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633233 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633234 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633236 2 0.5785 0.545 0.000 0.668 0.332
#> SRR1633237 2 0.5785 0.545 0.000 0.668 0.332
#> SRR1633238 2 0.5785 0.545 0.000 0.668 0.332
#> SRR1633239 2 0.5785 0.545 0.000 0.668 0.332
#> SRR1633240 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633241 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633242 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633243 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633244 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633245 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633246 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633247 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633248 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633249 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633250 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633251 3 0.5835 0.278 0.000 0.340 0.660
#> SRR1633252 3 0.5835 0.278 0.000 0.340 0.660
#> SRR1633253 3 0.5835 0.278 0.000 0.340 0.660
#> SRR1633254 3 0.5835 0.278 0.000 0.340 0.660
#> SRR1633255 3 0.5835 0.278 0.000 0.340 0.660
#> SRR1633256 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633257 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633258 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633259 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633260 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633261 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633262 3 0.0592 0.500 0.012 0.000 0.988
#> SRR1633263 3 0.0592 0.500 0.012 0.000 0.988
#> SRR1633264 3 0.0592 0.500 0.012 0.000 0.988
#> SRR1633265 3 0.0592 0.500 0.012 0.000 0.988
#> SRR1633266 3 0.0592 0.500 0.012 0.000 0.988
#> SRR1633267 3 0.5948 0.252 0.000 0.360 0.640
#> SRR1633268 3 0.5948 0.252 0.000 0.360 0.640
#> SRR1633269 3 0.5948 0.252 0.000 0.360 0.640
#> SRR1633270 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633271 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633272 3 0.5968 0.245 0.000 0.364 0.636
#> SRR1633273 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633274 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633275 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633276 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633277 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633278 3 0.3412 0.460 0.124 0.000 0.876
#> SRR1633279 3 0.3412 0.460 0.124 0.000 0.876
#> SRR1633280 3 0.3412 0.460 0.124 0.000 0.876
#> SRR1633281 3 0.3412 0.460 0.124 0.000 0.876
#> SRR1633282 3 0.3412 0.460 0.124 0.000 0.876
#> SRR1633284 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633285 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633286 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633287 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633288 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633289 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633290 1 0.6204 0.456 0.576 0.000 0.424
#> SRR1633291 1 0.6204 0.456 0.576 0.000 0.424
#> SRR1633292 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633293 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633294 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633295 2 0.6180 0.420 0.000 0.584 0.416
#> SRR1633296 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633297 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633298 3 0.6215 -0.127 0.428 0.000 0.572
#> SRR1633299 3 0.6215 -0.127 0.428 0.000 0.572
#> SRR1633300 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633301 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633302 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633303 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633304 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633305 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633306 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633307 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633308 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633309 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633310 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633311 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633312 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633313 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633314 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633315 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633316 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633317 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633318 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633319 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633320 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633321 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633322 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633323 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633324 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633325 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633326 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633327 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633328 2 0.0000 0.859 0.000 1.000 0.000
#> SRR1633329 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633330 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633331 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633332 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633333 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633334 2 0.0592 0.851 0.000 0.988 0.012
#> SRR1633335 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633336 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633337 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633338 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633339 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633340 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633341 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633342 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633345 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633346 1 0.5905 0.595 0.648 0.000 0.352
#> SRR1633343 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633344 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633347 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633348 3 0.6308 -0.262 0.492 0.000 0.508
#> SRR1633350 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.872 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.872 1.000 0.000 0.000
#> SRR2038462 3 0.4842 0.324 0.224 0.000 0.776
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.1118 0.952 0.000 0.964 0.000 0.036
#> SRR1633231 2 0.1118 0.952 0.000 0.964 0.000 0.036
#> SRR1633232 2 0.1118 0.952 0.000 0.964 0.000 0.036
#> SRR1633233 2 0.1118 0.952 0.000 0.964 0.000 0.036
#> SRR1633234 2 0.1118 0.952 0.000 0.964 0.000 0.036
#> SRR1633236 3 0.7073 0.496 0.000 0.364 0.504 0.132
#> SRR1633237 3 0.7073 0.496 0.000 0.364 0.504 0.132
#> SRR1633238 3 0.7073 0.496 0.000 0.364 0.504 0.132
#> SRR1633239 3 0.7073 0.496 0.000 0.364 0.504 0.132
#> SRR1633240 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633241 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633242 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633243 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633244 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633245 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633246 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633247 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633248 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633249 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633250 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633251 3 0.3004 0.806 0.000 0.060 0.892 0.048
#> SRR1633252 3 0.3004 0.806 0.000 0.060 0.892 0.048
#> SRR1633253 3 0.3004 0.806 0.000 0.060 0.892 0.048
#> SRR1633254 3 0.3004 0.806 0.000 0.060 0.892 0.048
#> SRR1633255 3 0.3004 0.806 0.000 0.060 0.892 0.048
#> SRR1633256 3 0.1940 0.810 0.000 0.076 0.924 0.000
#> SRR1633257 3 0.1940 0.810 0.000 0.076 0.924 0.000
#> SRR1633258 3 0.1940 0.810 0.000 0.076 0.924 0.000
#> SRR1633259 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633260 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633261 3 0.2125 0.810 0.000 0.076 0.920 0.004
#> SRR1633262 3 0.4164 0.635 0.000 0.000 0.736 0.264
#> SRR1633263 3 0.4164 0.635 0.000 0.000 0.736 0.264
#> SRR1633264 3 0.4164 0.635 0.000 0.000 0.736 0.264
#> SRR1633265 3 0.4164 0.635 0.000 0.000 0.736 0.264
#> SRR1633266 3 0.4164 0.635 0.000 0.000 0.736 0.264
#> SRR1633267 3 0.3245 0.805 0.000 0.064 0.880 0.056
#> SRR1633268 3 0.3245 0.805 0.000 0.064 0.880 0.056
#> SRR1633269 3 0.3245 0.805 0.000 0.064 0.880 0.056
#> SRR1633270 3 0.3216 0.807 0.000 0.076 0.880 0.044
#> SRR1633271 3 0.3216 0.807 0.000 0.076 0.880 0.044
#> SRR1633272 3 0.3216 0.807 0.000 0.076 0.880 0.044
#> SRR1633273 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633274 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633275 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633276 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633277 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633278 3 0.5460 0.511 0.028 0.000 0.632 0.340
#> SRR1633279 3 0.5460 0.511 0.028 0.000 0.632 0.340
#> SRR1633280 3 0.5460 0.511 0.028 0.000 0.632 0.340
#> SRR1633281 3 0.5460 0.511 0.028 0.000 0.632 0.340
#> SRR1633282 4 0.5626 0.124 0.028 0.000 0.384 0.588
#> SRR1633284 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633285 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633286 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633287 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633288 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633289 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633290 4 0.5404 0.875 0.328 0.000 0.028 0.644
#> SRR1633291 4 0.5404 0.875 0.328 0.000 0.028 0.644
#> SRR1633292 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633293 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633294 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633295 3 0.6346 0.679 0.000 0.244 0.640 0.116
#> SRR1633296 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633297 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633298 4 0.5963 0.751 0.196 0.000 0.116 0.688
#> SRR1633299 4 0.5963 0.751 0.196 0.000 0.116 0.688
#> SRR1633300 2 0.1867 0.950 0.000 0.928 0.000 0.072
#> SRR1633301 2 0.1867 0.950 0.000 0.928 0.000 0.072
#> SRR1633302 2 0.1867 0.950 0.000 0.928 0.000 0.072
#> SRR1633303 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633304 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633305 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633306 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633307 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633308 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633309 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633310 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633311 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633312 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633313 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633314 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633315 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633316 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633317 2 0.2281 0.947 0.000 0.904 0.000 0.096
#> SRR1633318 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633319 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633320 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633321 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633322 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633323 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633324 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633325 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633326 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633327 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633328 2 0.0336 0.953 0.000 0.992 0.000 0.008
#> SRR1633329 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633330 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633331 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633332 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633333 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633334 2 0.1211 0.940 0.000 0.960 0.000 0.040
#> SRR1633335 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633336 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633337 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633338 4 0.4697 0.869 0.356 0.000 0.000 0.644
#> SRR1633339 4 0.4697 0.869 0.356 0.000 0.000 0.644
#> SRR1633340 4 0.4697 0.869 0.356 0.000 0.000 0.644
#> SRR1633341 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633342 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633345 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633346 4 0.4866 0.849 0.404 0.000 0.000 0.596
#> SRR1633343 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633344 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633347 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633348 4 0.5712 0.874 0.308 0.000 0.048 0.644
#> SRR1633350 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633351 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633352 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633353 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633354 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633355 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633356 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633357 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633358 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633362 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633363 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633364 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633359 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633360 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR1633361 1 0.2125 0.933 0.920 0.000 0.076 0.004
#> SRR2038492 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0188 0.965 0.996 0.000 0.000 0.004
#> SRR2038487 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0188 0.965 0.996 0.000 0.000 0.004
#> SRR2038483 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.5897 0.184 0.044 0.000 0.368 0.588
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.3303 0.923 0.000 0.848 0.000 0.076 0.076
#> SRR1633231 2 0.3303 0.923 0.000 0.848 0.000 0.076 0.076
#> SRR1633232 2 0.3303 0.923 0.000 0.848 0.000 0.076 0.076
#> SRR1633233 2 0.3303 0.923 0.000 0.848 0.000 0.076 0.076
#> SRR1633234 2 0.3303 0.923 0.000 0.848 0.000 0.076 0.076
#> SRR1633236 3 0.5932 0.473 0.000 0.180 0.664 0.036 0.120
#> SRR1633237 3 0.5932 0.473 0.000 0.180 0.664 0.036 0.120
#> SRR1633238 3 0.5932 0.473 0.000 0.180 0.664 0.036 0.120
#> SRR1633239 3 0.5932 0.473 0.000 0.180 0.664 0.036 0.120
#> SRR1633240 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633241 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633242 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633243 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633244 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633245 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633246 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633247 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633248 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633249 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633250 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633251 3 0.5162 0.551 0.000 0.036 0.680 0.028 0.256
#> SRR1633252 3 0.5162 0.551 0.000 0.036 0.680 0.028 0.256
#> SRR1633253 3 0.5162 0.551 0.000 0.036 0.680 0.028 0.256
#> SRR1633254 3 0.5162 0.551 0.000 0.036 0.680 0.028 0.256
#> SRR1633255 3 0.5162 0.551 0.000 0.036 0.680 0.028 0.256
#> SRR1633256 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633257 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633258 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633259 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633260 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633261 3 0.4514 0.609 0.000 0.044 0.756 0.016 0.184
#> SRR1633262 3 0.6763 -0.499 0.000 0.000 0.392 0.276 0.332
#> SRR1633263 3 0.6763 -0.499 0.000 0.000 0.392 0.276 0.332
#> SRR1633264 3 0.6763 -0.499 0.000 0.000 0.392 0.276 0.332
#> SRR1633265 3 0.6763 -0.499 0.000 0.000 0.392 0.276 0.332
#> SRR1633266 3 0.6763 -0.499 0.000 0.000 0.392 0.276 0.332
#> SRR1633267 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633268 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633269 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633270 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633271 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633272 3 0.5587 0.512 0.000 0.044 0.640 0.036 0.280
#> SRR1633273 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633274 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633275 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633276 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633277 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633278 5 0.6651 0.864 0.004 0.000 0.244 0.268 0.484
#> SRR1633279 5 0.6651 0.864 0.004 0.000 0.244 0.268 0.484
#> SRR1633280 5 0.6651 0.864 0.004 0.000 0.244 0.268 0.484
#> SRR1633281 5 0.6651 0.864 0.004 0.000 0.244 0.268 0.484
#> SRR1633282 5 0.6132 0.760 0.004 0.000 0.112 0.412 0.472
#> SRR1633284 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633285 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633286 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633287 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633288 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633289 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633290 4 0.3123 0.867 0.160 0.000 0.000 0.828 0.012
#> SRR1633291 4 0.3123 0.867 0.160 0.000 0.000 0.828 0.012
#> SRR1633292 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633293 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633294 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633295 3 0.4057 0.572 0.000 0.120 0.792 0.000 0.088
#> SRR1633296 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633297 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633298 4 0.4086 0.643 0.052 0.000 0.024 0.812 0.112
#> SRR1633299 4 0.4086 0.643 0.052 0.000 0.024 0.812 0.112
#> SRR1633300 2 0.1251 0.917 0.000 0.956 0.000 0.008 0.036
#> SRR1633301 2 0.1251 0.917 0.000 0.956 0.000 0.008 0.036
#> SRR1633302 2 0.1251 0.917 0.000 0.956 0.000 0.008 0.036
#> SRR1633303 2 0.0451 0.919 0.000 0.988 0.000 0.008 0.004
#> SRR1633304 2 0.0451 0.919 0.000 0.988 0.000 0.008 0.004
#> SRR1633305 2 0.0451 0.919 0.000 0.988 0.000 0.008 0.004
#> SRR1633306 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633307 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633308 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633309 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633310 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633311 2 0.0162 0.920 0.000 0.996 0.000 0.004 0.000
#> SRR1633312 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633313 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633314 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633315 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633316 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633317 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> SRR1633318 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633319 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633320 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633321 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633322 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633323 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633324 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633325 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633326 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633327 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633328 2 0.3239 0.925 0.000 0.852 0.000 0.068 0.080
#> SRR1633329 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633330 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633331 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633332 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633333 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633334 2 0.4098 0.889 0.000 0.780 0.000 0.064 0.156
#> SRR1633335 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633336 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633337 4 0.4083 0.863 0.228 0.000 0.000 0.744 0.028
#> SRR1633338 4 0.3574 0.869 0.168 0.000 0.000 0.804 0.028
#> SRR1633339 4 0.3574 0.869 0.168 0.000 0.000 0.804 0.028
#> SRR1633340 4 0.3574 0.869 0.168 0.000 0.000 0.804 0.028
#> SRR1633341 4 0.4000 0.864 0.228 0.000 0.000 0.748 0.024
#> SRR1633342 4 0.4000 0.864 0.228 0.000 0.000 0.748 0.024
#> SRR1633345 4 0.4000 0.864 0.228 0.000 0.000 0.748 0.024
#> SRR1633346 4 0.4000 0.864 0.228 0.000 0.000 0.748 0.024
#> SRR1633343 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633344 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633347 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633348 4 0.4082 0.848 0.140 0.000 0.008 0.796 0.056
#> SRR1633350 1 0.3550 0.829 0.760 0.000 0.004 0.000 0.236
#> SRR1633351 1 0.3550 0.829 0.760 0.000 0.004 0.000 0.236
#> SRR1633352 1 0.3550 0.829 0.760 0.000 0.004 0.000 0.236
#> SRR1633353 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633354 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633355 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633356 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633357 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633358 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633362 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633363 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633364 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633359 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633360 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR1633361 1 0.3452 0.827 0.756 0.000 0.000 0.000 0.244
#> SRR2038492 1 0.1646 0.892 0.944 0.000 0.032 0.004 0.020
#> SRR2038491 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.1168 0.903 0.960 0.000 0.032 0.000 0.008
#> SRR2038489 1 0.0609 0.909 0.980 0.000 0.020 0.000 0.000
#> SRR2038488 1 0.0771 0.909 0.976 0.000 0.020 0.000 0.004
#> SRR2038487 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0609 0.909 0.980 0.000 0.020 0.000 0.000
#> SRR2038485 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0451 0.911 0.988 0.000 0.004 0.000 0.008
#> SRR2038483 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0609 0.909 0.980 0.000 0.020 0.000 0.000
#> SRR2038480 1 0.1281 0.901 0.956 0.000 0.032 0.000 0.012
#> SRR2038479 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.1041 0.905 0.964 0.000 0.032 0.000 0.004
#> SRR2038475 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0609 0.909 0.980 0.000 0.020 0.000 0.000
#> SRR2038469 1 0.0609 0.909 0.980 0.000 0.020 0.000 0.000
#> SRR2038468 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0880 0.907 0.968 0.000 0.032 0.000 0.000
#> SRR2038463 1 0.0000 0.911 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 5 0.6193 0.751 0.008 0.000 0.108 0.400 0.484
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.1938 0.8581 0.000 0.920 0.000 0.004 0.036 0.040
#> SRR1633231 2 0.1938 0.8581 0.000 0.920 0.000 0.004 0.036 0.040
#> SRR1633232 2 0.1938 0.8581 0.000 0.920 0.000 0.004 0.036 0.040
#> SRR1633233 2 0.1938 0.8581 0.000 0.920 0.000 0.004 0.036 0.040
#> SRR1633234 2 0.1938 0.8581 0.000 0.920 0.000 0.004 0.036 0.040
#> SRR1633236 5 0.6775 1.0000 0.000 0.172 0.364 0.020 0.416 0.028
#> SRR1633237 5 0.6775 1.0000 0.000 0.172 0.364 0.020 0.416 0.028
#> SRR1633238 5 0.6775 1.0000 0.000 0.172 0.364 0.020 0.416 0.028
#> SRR1633239 5 0.6775 1.0000 0.000 0.172 0.364 0.020 0.416 0.028
#> SRR1633240 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633241 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633242 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633243 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633244 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633245 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633246 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633247 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633248 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633249 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633250 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633251 3 0.1196 0.5492 0.000 0.008 0.952 0.000 0.000 0.040
#> SRR1633252 3 0.1196 0.5492 0.000 0.008 0.952 0.000 0.000 0.040
#> SRR1633253 3 0.1196 0.5492 0.000 0.008 0.952 0.000 0.000 0.040
#> SRR1633254 3 0.1196 0.5492 0.000 0.008 0.952 0.000 0.000 0.040
#> SRR1633255 3 0.1196 0.5492 0.000 0.008 0.952 0.000 0.000 0.040
#> SRR1633256 3 0.1124 0.5279 0.000 0.008 0.956 0.000 0.036 0.000
#> SRR1633257 3 0.1124 0.5279 0.000 0.008 0.956 0.000 0.036 0.000
#> SRR1633258 3 0.1124 0.5279 0.000 0.008 0.956 0.000 0.036 0.000
#> SRR1633259 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633260 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633261 3 0.1196 0.5254 0.000 0.008 0.952 0.000 0.040 0.000
#> SRR1633262 3 0.4756 -0.0688 0.000 0.000 0.664 0.112 0.000 0.224
#> SRR1633263 3 0.4756 -0.0688 0.000 0.000 0.664 0.112 0.000 0.224
#> SRR1633264 3 0.4756 -0.0688 0.000 0.000 0.664 0.112 0.000 0.224
#> SRR1633265 3 0.4756 -0.0688 0.000 0.000 0.664 0.112 0.000 0.224
#> SRR1633266 3 0.4756 -0.0688 0.000 0.000 0.664 0.112 0.000 0.224
#> SRR1633267 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633268 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633269 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633270 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633271 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633272 3 0.2420 0.5198 0.000 0.008 0.876 0.008 0.000 0.108
#> SRR1633273 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633274 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633275 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633276 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633277 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633278 3 0.6575 -0.7499 0.008 0.000 0.420 0.144 0.040 0.388
#> SRR1633279 3 0.6575 -0.7499 0.008 0.000 0.420 0.144 0.040 0.388
#> SRR1633280 3 0.6575 -0.7499 0.008 0.000 0.420 0.144 0.040 0.388
#> SRR1633281 3 0.6575 -0.7499 0.008 0.000 0.420 0.144 0.040 0.388
#> SRR1633282 6 0.6759 0.9678 0.004 0.000 0.332 0.212 0.040 0.412
#> SRR1633284 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633285 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633286 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633287 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633288 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633289 4 0.2758 0.8204 0.112 0.000 0.000 0.860 0.012 0.016
#> SRR1633290 4 0.4667 0.8172 0.096 0.000 0.004 0.712 0.008 0.180
#> SRR1633291 4 0.4667 0.8172 0.096 0.000 0.004 0.712 0.008 0.180
#> SRR1633292 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633293 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633294 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633295 3 0.5181 -0.6255 0.000 0.088 0.484 0.000 0.428 0.000
#> SRR1633296 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633297 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633298 4 0.4947 0.6957 0.024 0.000 0.056 0.684 0.008 0.228
#> SRR1633299 4 0.4947 0.6957 0.024 0.000 0.056 0.684 0.008 0.228
#> SRR1633300 2 0.3235 0.8559 0.000 0.820 0.000 0.000 0.052 0.128
#> SRR1633301 2 0.3235 0.8559 0.000 0.820 0.000 0.000 0.052 0.128
#> SRR1633302 2 0.3235 0.8559 0.000 0.820 0.000 0.000 0.052 0.128
#> SRR1633303 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633304 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633305 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633306 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633307 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633308 2 0.3712 0.8487 0.000 0.768 0.000 0.000 0.052 0.180
#> SRR1633309 2 0.3920 0.8487 0.000 0.768 0.000 0.008 0.056 0.168
#> SRR1633310 2 0.3920 0.8487 0.000 0.768 0.000 0.008 0.056 0.168
#> SRR1633311 2 0.3920 0.8487 0.000 0.768 0.000 0.008 0.056 0.168
#> SRR1633312 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633313 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633314 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633315 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633316 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633317 2 0.3911 0.8488 0.000 0.768 0.000 0.004 0.068 0.160
#> SRR1633318 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633319 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633320 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633321 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633322 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633323 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633324 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633325 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633326 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633327 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633328 2 0.0951 0.8558 0.000 0.968 0.000 0.004 0.020 0.008
#> SRR1633329 2 0.3351 0.8037 0.000 0.844 0.000 0.048 0.040 0.068
#> SRR1633330 2 0.3338 0.8036 0.000 0.844 0.000 0.048 0.036 0.072
#> SRR1633331 2 0.3353 0.8037 0.000 0.844 0.000 0.044 0.044 0.068
#> SRR1633332 2 0.3338 0.8036 0.000 0.844 0.000 0.048 0.036 0.072
#> SRR1633333 2 0.3342 0.8036 0.000 0.844 0.000 0.044 0.040 0.072
#> SRR1633334 2 0.3342 0.8036 0.000 0.844 0.000 0.044 0.040 0.072
#> SRR1633335 4 0.2455 0.8245 0.112 0.000 0.000 0.872 0.012 0.004
#> SRR1633336 4 0.2455 0.8245 0.112 0.000 0.000 0.872 0.012 0.004
#> SRR1633337 4 0.2455 0.8245 0.112 0.000 0.000 0.872 0.012 0.004
#> SRR1633338 4 0.2800 0.8294 0.100 0.000 0.000 0.860 0.004 0.036
#> SRR1633339 4 0.2800 0.8294 0.100 0.000 0.000 0.860 0.004 0.036
#> SRR1633340 4 0.2800 0.8294 0.100 0.000 0.000 0.860 0.004 0.036
#> SRR1633341 4 0.2666 0.8219 0.112 0.000 0.000 0.864 0.012 0.012
#> SRR1633342 4 0.2666 0.8219 0.112 0.000 0.000 0.864 0.012 0.012
#> SRR1633345 4 0.2666 0.8219 0.112 0.000 0.000 0.864 0.012 0.012
#> SRR1633346 4 0.2666 0.8219 0.112 0.000 0.000 0.864 0.012 0.012
#> SRR1633343 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633344 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633347 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633348 4 0.5096 0.8039 0.084 0.000 0.016 0.672 0.008 0.220
#> SRR1633350 1 0.1644 0.6940 0.920 0.000 0.004 0.000 0.000 0.076
#> SRR1633351 1 0.1644 0.6940 0.920 0.000 0.004 0.000 0.000 0.076
#> SRR1633352 1 0.1644 0.6940 0.920 0.000 0.004 0.000 0.000 0.076
#> SRR1633353 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633354 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633355 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633356 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633357 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633358 1 0.1895 0.6873 0.912 0.000 0.000 0.016 0.000 0.072
#> SRR1633362 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR1633363 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR1633364 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR1633359 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR1633360 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR1633361 1 0.1982 0.6873 0.912 0.000 0.000 0.016 0.004 0.068
#> SRR2038492 1 0.5312 0.8019 0.604 0.000 0.000 0.032 0.300 0.064
#> SRR2038491 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038490 1 0.4587 0.8264 0.640 0.000 0.000 0.000 0.296 0.064
#> SRR2038489 1 0.4300 0.8331 0.640 0.000 0.000 0.000 0.324 0.036
#> SRR2038488 1 0.4152 0.8415 0.664 0.000 0.000 0.000 0.304 0.032
#> SRR2038487 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038486 1 0.4300 0.8331 0.640 0.000 0.000 0.000 0.324 0.036
#> SRR2038485 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038484 1 0.4028 0.8433 0.668 0.000 0.000 0.000 0.308 0.024
#> SRR2038483 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038482 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038481 1 0.4291 0.8401 0.664 0.000 0.000 0.000 0.292 0.044
#> SRR2038480 1 0.4587 0.8264 0.640 0.000 0.000 0.000 0.296 0.064
#> SRR2038479 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038477 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038478 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038476 1 0.4587 0.8264 0.640 0.000 0.000 0.000 0.296 0.064
#> SRR2038475 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038474 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038473 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038472 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038471 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038470 1 0.4291 0.8401 0.664 0.000 0.000 0.000 0.292 0.044
#> SRR2038469 1 0.4300 0.8331 0.640 0.000 0.000 0.000 0.324 0.036
#> SRR2038468 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038467 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038466 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038465 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038464 1 0.4587 0.8264 0.640 0.000 0.000 0.000 0.296 0.064
#> SRR2038463 1 0.3547 0.8460 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR2038462 6 0.6860 0.9679 0.004 0.000 0.328 0.228 0.044 0.396
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.946 0.979 0.5028 0.499 0.499
#> 3 3 0.891 0.933 0.966 0.2662 0.811 0.639
#> 4 4 0.934 0.912 0.953 0.1696 0.885 0.682
#> 5 5 0.949 0.856 0.921 0.0518 0.950 0.803
#> 6 6 0.922 0.943 0.948 0.0226 0.992 0.960
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 4
There is also optional best \(k\) = 2 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 2 0.000 1.000 0.000 1.000
#> SRR1633241 2 0.000 1.000 0.000 1.000
#> SRR1633242 2 0.000 1.000 0.000 1.000
#> SRR1633243 2 0.000 1.000 0.000 1.000
#> SRR1633244 2 0.000 1.000 0.000 1.000
#> SRR1633245 2 0.000 1.000 0.000 1.000
#> SRR1633246 2 0.000 1.000 0.000 1.000
#> SRR1633247 2 0.000 1.000 0.000 1.000
#> SRR1633248 2 0.000 1.000 0.000 1.000
#> SRR1633249 2 0.000 1.000 0.000 1.000
#> SRR1633250 2 0.000 1.000 0.000 1.000
#> SRR1633251 2 0.000 1.000 0.000 1.000
#> SRR1633252 2 0.000 1.000 0.000 1.000
#> SRR1633253 2 0.000 1.000 0.000 1.000
#> SRR1633254 2 0.000 1.000 0.000 1.000
#> SRR1633255 2 0.000 1.000 0.000 1.000
#> SRR1633256 2 0.000 1.000 0.000 1.000
#> SRR1633257 2 0.000 1.000 0.000 1.000
#> SRR1633258 2 0.000 1.000 0.000 1.000
#> SRR1633259 2 0.000 1.000 0.000 1.000
#> SRR1633260 2 0.000 1.000 0.000 1.000
#> SRR1633261 2 0.000 1.000 0.000 1.000
#> SRR1633262 1 0.997 0.176 0.532 0.468
#> SRR1633263 1 0.997 0.176 0.532 0.468
#> SRR1633264 1 0.997 0.176 0.532 0.468
#> SRR1633265 1 0.997 0.176 0.532 0.468
#> SRR1633266 1 0.997 0.176 0.532 0.468
#> SRR1633267 2 0.000 1.000 0.000 1.000
#> SRR1633268 2 0.000 1.000 0.000 1.000
#> SRR1633269 2 0.000 1.000 0.000 1.000
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.959 1.000 0.000
#> SRR1633274 1 0.000 0.959 1.000 0.000
#> SRR1633275 1 0.000 0.959 1.000 0.000
#> SRR1633276 1 0.000 0.959 1.000 0.000
#> SRR1633277 1 0.000 0.959 1.000 0.000
#> SRR1633278 1 0.844 0.640 0.728 0.272
#> SRR1633279 1 0.844 0.640 0.728 0.272
#> SRR1633280 1 0.844 0.640 0.728 0.272
#> SRR1633281 1 0.844 0.640 0.728 0.272
#> SRR1633282 1 0.000 0.959 1.000 0.000
#> SRR1633284 1 0.000 0.959 1.000 0.000
#> SRR1633285 1 0.000 0.959 1.000 0.000
#> SRR1633286 1 0.000 0.959 1.000 0.000
#> SRR1633287 1 0.000 0.959 1.000 0.000
#> SRR1633288 1 0.000 0.959 1.000 0.000
#> SRR1633289 1 0.000 0.959 1.000 0.000
#> SRR1633290 1 0.000 0.959 1.000 0.000
#> SRR1633291 1 0.000 0.959 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.959 1.000 0.000
#> SRR1633297 1 0.000 0.959 1.000 0.000
#> SRR1633298 1 0.000 0.959 1.000 0.000
#> SRR1633299 1 0.000 0.959 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.959 1.000 0.000
#> SRR1633336 1 0.000 0.959 1.000 0.000
#> SRR1633337 1 0.000 0.959 1.000 0.000
#> SRR1633338 1 0.000 0.959 1.000 0.000
#> SRR1633339 1 0.000 0.959 1.000 0.000
#> SRR1633340 1 0.000 0.959 1.000 0.000
#> SRR1633341 1 0.000 0.959 1.000 0.000
#> SRR1633342 1 0.000 0.959 1.000 0.000
#> SRR1633345 1 0.000 0.959 1.000 0.000
#> SRR1633346 1 0.000 0.959 1.000 0.000
#> SRR1633343 1 0.000 0.959 1.000 0.000
#> SRR1633344 1 0.000 0.959 1.000 0.000
#> SRR1633347 1 0.000 0.959 1.000 0.000
#> SRR1633348 1 0.000 0.959 1.000 0.000
#> SRR1633350 1 0.000 0.959 1.000 0.000
#> SRR1633351 1 0.000 0.959 1.000 0.000
#> SRR1633352 1 0.000 0.959 1.000 0.000
#> SRR1633353 1 0.000 0.959 1.000 0.000
#> SRR1633354 1 0.000 0.959 1.000 0.000
#> SRR1633355 1 0.000 0.959 1.000 0.000
#> SRR1633356 1 0.000 0.959 1.000 0.000
#> SRR1633357 1 0.000 0.959 1.000 0.000
#> SRR1633358 1 0.000 0.959 1.000 0.000
#> SRR1633362 1 0.000 0.959 1.000 0.000
#> SRR1633363 1 0.000 0.959 1.000 0.000
#> SRR1633364 1 0.000 0.959 1.000 0.000
#> SRR1633359 1 0.000 0.959 1.000 0.000
#> SRR1633360 1 0.000 0.959 1.000 0.000
#> SRR1633361 1 0.000 0.959 1.000 0.000
#> SRR2038492 1 0.000 0.959 1.000 0.000
#> SRR2038491 1 0.000 0.959 1.000 0.000
#> SRR2038490 1 0.000 0.959 1.000 0.000
#> SRR2038489 1 0.000 0.959 1.000 0.000
#> SRR2038488 1 0.000 0.959 1.000 0.000
#> SRR2038487 1 0.000 0.959 1.000 0.000
#> SRR2038486 1 0.000 0.959 1.000 0.000
#> SRR2038485 1 0.000 0.959 1.000 0.000
#> SRR2038484 1 0.000 0.959 1.000 0.000
#> SRR2038483 1 0.000 0.959 1.000 0.000
#> SRR2038482 1 0.000 0.959 1.000 0.000
#> SRR2038481 1 0.000 0.959 1.000 0.000
#> SRR2038480 1 0.000 0.959 1.000 0.000
#> SRR2038479 1 0.000 0.959 1.000 0.000
#> SRR2038477 1 0.000 0.959 1.000 0.000
#> SRR2038478 1 0.000 0.959 1.000 0.000
#> SRR2038476 1 0.000 0.959 1.000 0.000
#> SRR2038475 1 0.000 0.959 1.000 0.000
#> SRR2038474 1 0.000 0.959 1.000 0.000
#> SRR2038473 1 0.000 0.959 1.000 0.000
#> SRR2038472 1 0.000 0.959 1.000 0.000
#> SRR2038471 1 0.000 0.959 1.000 0.000
#> SRR2038470 1 0.000 0.959 1.000 0.000
#> SRR2038469 1 0.000 0.959 1.000 0.000
#> SRR2038468 1 0.000 0.959 1.000 0.000
#> SRR2038467 1 0.000 0.959 1.000 0.000
#> SRR2038466 1 0.000 0.959 1.000 0.000
#> SRR2038465 1 0.000 0.959 1.000 0.000
#> SRR2038464 1 0.000 0.959 1.000 0.000
#> SRR2038463 1 0.000 0.959 1.000 0.000
#> SRR2038462 1 0.000 0.959 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633237 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633238 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633240 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633241 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633242 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633243 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633244 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633245 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633246 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633247 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633248 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633249 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633250 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633251 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633252 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633253 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633254 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633255 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633256 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633257 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633258 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633259 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633260 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633261 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633262 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1633267 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633268 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633269 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633270 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633271 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633272 3 0.0424 0.866 0.000 0.008 0.992
#> SRR1633273 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633274 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633275 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633276 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633277 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633278 3 0.3500 0.778 0.116 0.004 0.880
#> SRR1633279 3 0.3500 0.778 0.116 0.004 0.880
#> SRR1633280 3 0.3500 0.778 0.116 0.004 0.880
#> SRR1633281 3 0.3500 0.778 0.116 0.004 0.880
#> SRR1633282 3 0.3686 0.756 0.140 0.000 0.860
#> SRR1633284 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633285 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633286 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633287 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633288 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633289 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633290 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633291 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633292 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633293 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633294 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633295 3 0.6111 0.504 0.000 0.396 0.604
#> SRR1633296 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633297 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633298 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633299 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633336 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633337 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633338 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633339 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633340 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633341 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633342 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633345 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633346 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633343 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633344 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633347 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633348 1 0.0424 0.993 0.992 0.000 0.008
#> SRR1633350 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.994 1.000 0.000 0.000
#> SRR2038462 1 0.4842 0.722 0.776 0.000 0.224
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633236 2 0.1174 0.968 0.000 0.968 0.020 0.012
#> SRR1633237 2 0.1174 0.968 0.000 0.968 0.020 0.012
#> SRR1633238 2 0.1174 0.968 0.000 0.968 0.020 0.012
#> SRR1633239 2 0.1174 0.968 0.000 0.968 0.020 0.012
#> SRR1633240 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633241 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633242 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633243 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633244 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633245 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633246 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633247 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633252 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633253 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633254 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633255 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633256 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.822 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.1389 0.801 0.000 0.000 0.952 0.048
#> SRR1633263 3 0.1389 0.801 0.000 0.000 0.952 0.048
#> SRR1633264 3 0.1389 0.801 0.000 0.000 0.952 0.048
#> SRR1633265 3 0.1389 0.801 0.000 0.000 0.952 0.048
#> SRR1633266 3 0.1389 0.801 0.000 0.000 0.952 0.048
#> SRR1633267 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633268 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633269 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633270 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633271 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633272 3 0.0188 0.822 0.000 0.000 0.996 0.004
#> SRR1633273 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633274 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633275 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633276 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633277 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633278 3 0.7544 0.261 0.368 0.048 0.512 0.072
#> SRR1633279 3 0.7544 0.261 0.368 0.048 0.512 0.072
#> SRR1633280 3 0.7544 0.261 0.368 0.048 0.512 0.072
#> SRR1633281 3 0.7544 0.261 0.368 0.048 0.512 0.072
#> SRR1633282 4 0.2814 0.835 0.000 0.000 0.132 0.868
#> SRR1633284 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633285 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633286 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633287 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633288 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633289 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633290 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633291 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633292 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633293 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633294 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633295 3 0.5159 0.519 0.000 0.364 0.624 0.012
#> SRR1633296 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633297 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633298 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633299 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633300 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633336 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633337 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633338 4 0.0817 0.974 0.024 0.000 0.000 0.976
#> SRR1633339 4 0.0817 0.974 0.024 0.000 0.000 0.976
#> SRR1633340 4 0.0817 0.974 0.024 0.000 0.000 0.976
#> SRR1633341 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633342 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633345 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633346 4 0.1557 0.967 0.056 0.000 0.000 0.944
#> SRR1633343 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633344 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633347 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633348 4 0.0592 0.974 0.016 0.000 0.000 0.984
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.1109 0.948 0.004 0.000 0.028 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 5 0.6491 0.533 0.000 0.264 0.244 0.000 0.492
#> SRR1633237 5 0.6491 0.533 0.000 0.264 0.244 0.000 0.492
#> SRR1633238 5 0.6491 0.533 0.000 0.264 0.244 0.000 0.492
#> SRR1633239 5 0.6491 0.533 0.000 0.264 0.244 0.000 0.492
#> SRR1633240 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633241 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633242 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633243 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633244 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633245 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633246 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633247 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633248 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633249 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633250 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633251 3 0.3109 0.742 0.000 0.000 0.800 0.000 0.200
#> SRR1633252 3 0.3109 0.742 0.000 0.000 0.800 0.000 0.200
#> SRR1633253 3 0.3109 0.742 0.000 0.000 0.800 0.000 0.200
#> SRR1633254 3 0.3109 0.742 0.000 0.000 0.800 0.000 0.200
#> SRR1633255 3 0.3109 0.742 0.000 0.000 0.800 0.000 0.200
#> SRR1633256 3 0.0000 0.643 0.000 0.000 1.000 0.000 0.000
#> SRR1633257 3 0.0000 0.643 0.000 0.000 1.000 0.000 0.000
#> SRR1633258 3 0.0000 0.643 0.000 0.000 1.000 0.000 0.000
#> SRR1633259 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633260 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633261 3 0.0162 0.639 0.000 0.000 0.996 0.000 0.004
#> SRR1633262 3 0.4276 0.712 0.000 0.000 0.616 0.004 0.380
#> SRR1633263 3 0.4276 0.712 0.000 0.000 0.616 0.004 0.380
#> SRR1633264 3 0.4276 0.712 0.000 0.000 0.616 0.004 0.380
#> SRR1633265 3 0.4276 0.712 0.000 0.000 0.616 0.004 0.380
#> SRR1633266 3 0.4276 0.712 0.000 0.000 0.616 0.004 0.380
#> SRR1633267 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633268 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633269 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633270 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633271 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633272 3 0.4060 0.727 0.000 0.000 0.640 0.000 0.360
#> SRR1633273 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633274 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633275 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633276 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633277 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633278 5 0.5058 -0.589 0.004 0.008 0.484 0.012 0.492
#> SRR1633279 5 0.5058 -0.589 0.004 0.008 0.484 0.012 0.492
#> SRR1633280 5 0.5058 -0.589 0.004 0.008 0.484 0.012 0.492
#> SRR1633281 5 0.5058 -0.589 0.004 0.008 0.484 0.012 0.492
#> SRR1633282 5 0.6386 -0.400 0.000 0.000 0.320 0.188 0.492
#> SRR1633284 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633285 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633286 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633287 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633288 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633289 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633290 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633291 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633292 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633293 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633294 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633295 5 0.5501 0.625 0.000 0.064 0.444 0.000 0.492
#> SRR1633296 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633297 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633298 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633299 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633335 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633336 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633337 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633338 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633339 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633340 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633341 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633342 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633345 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633346 4 0.0162 0.978 0.004 0.000 0.000 0.996 0.000
#> SRR1633343 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633344 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633347 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633348 4 0.0290 0.978 0.000 0.000 0.000 0.992 0.008
#> SRR1633350 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633351 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633352 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633353 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633354 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633355 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633356 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633357 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633358 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633362 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633363 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633364 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633359 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633360 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR1633361 1 0.0510 0.991 0.984 0.000 0.000 0.000 0.016
#> SRR2038492 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.4747 0.255 0.000 0.000 0.016 0.496 0.488
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.1701 0.925 0.000 0.072 0.008 0.000 0.920 0.000
#> SRR1633237 5 0.1701 0.925 0.000 0.072 0.008 0.000 0.920 0.000
#> SRR1633238 5 0.1701 0.925 0.000 0.072 0.008 0.000 0.920 0.000
#> SRR1633239 5 0.1701 0.925 0.000 0.072 0.008 0.000 0.920 0.000
#> SRR1633240 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633241 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633242 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633243 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633244 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633245 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633246 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633247 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633248 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633249 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633250 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633251 3 0.0547 0.921 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1633252 3 0.0547 0.921 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1633253 3 0.0547 0.921 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1633254 3 0.0547 0.921 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1633255 3 0.0547 0.921 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR1633256 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633257 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633258 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633259 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633260 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633261 3 0.1610 0.906 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1633262 3 0.1980 0.874 0.000 0.000 0.920 0.008 0.036 0.036
#> SRR1633263 3 0.1980 0.874 0.000 0.000 0.920 0.008 0.036 0.036
#> SRR1633264 3 0.1980 0.874 0.000 0.000 0.920 0.008 0.036 0.036
#> SRR1633265 3 0.1980 0.874 0.000 0.000 0.920 0.008 0.036 0.036
#> SRR1633266 3 0.1980 0.874 0.000 0.000 0.920 0.008 0.036 0.036
#> SRR1633267 3 0.0790 0.908 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1633268 3 0.0790 0.908 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1633269 3 0.0790 0.908 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1633270 3 0.0713 0.910 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1633271 3 0.0713 0.910 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1633272 3 0.0713 0.910 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1633273 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633274 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633275 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633276 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633277 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633278 6 0.2941 0.927 0.000 0.000 0.220 0.000 0.000 0.780
#> SRR1633279 6 0.2941 0.927 0.000 0.000 0.220 0.000 0.000 0.780
#> SRR1633280 6 0.2941 0.927 0.000 0.000 0.220 0.000 0.000 0.780
#> SRR1633281 6 0.2941 0.927 0.000 0.000 0.220 0.000 0.000 0.780
#> SRR1633282 6 0.3961 0.878 0.000 0.000 0.148 0.080 0.004 0.768
#> SRR1633284 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633285 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633286 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633287 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633288 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633289 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633290 4 0.0790 0.928 0.000 0.000 0.000 0.968 0.032 0.000
#> SRR1633291 4 0.0790 0.928 0.000 0.000 0.000 0.968 0.032 0.000
#> SRR1633292 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633293 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633294 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633295 5 0.1549 0.973 0.000 0.020 0.044 0.000 0.936 0.000
#> SRR1633296 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633297 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633298 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633299 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633300 2 0.1075 0.970 0.000 0.952 0.000 0.000 0.000 0.048
#> SRR1633301 2 0.1075 0.970 0.000 0.952 0.000 0.000 0.000 0.048
#> SRR1633302 2 0.1075 0.970 0.000 0.952 0.000 0.000 0.000 0.048
#> SRR1633303 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633304 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633305 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633306 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633307 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633308 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633309 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633310 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633311 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633312 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633313 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633314 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633315 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633316 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633317 2 0.1204 0.968 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1633318 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.973 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633330 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633331 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633332 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633333 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633334 2 0.0632 0.962 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1633335 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633336 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633337 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633338 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633339 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633340 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633341 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633342 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633345 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633346 4 0.1757 0.933 0.008 0.000 0.000 0.916 0.000 0.076
#> SRR1633343 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633344 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633347 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633348 4 0.1141 0.926 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1633350 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633351 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633352 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633353 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633354 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633355 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633356 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633357 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633358 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633362 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633363 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633364 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633359 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633360 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR1633361 1 0.2070 0.929 0.896 0.000 0.000 0.000 0.012 0.092
#> SRR2038492 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 6 0.3375 0.797 0.000 0.000 0.088 0.096 0.000 0.816
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.996 0.5001 0.499 0.499
#> 3 3 1.000 0.958 0.983 0.2652 0.846 0.700
#> 4 4 1.000 0.953 0.983 0.1892 0.862 0.637
#> 5 5 1.000 0.968 0.989 0.0424 0.963 0.854
#> 6 6 0.933 0.945 0.947 0.0423 0.966 0.842
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 0.991 0.000 1.000
#> SRR1633231 2 0.000 0.991 0.000 1.000
#> SRR1633232 2 0.000 0.991 0.000 1.000
#> SRR1633233 2 0.000 0.991 0.000 1.000
#> SRR1633234 2 0.000 0.991 0.000 1.000
#> SRR1633236 2 0.000 0.991 0.000 1.000
#> SRR1633237 2 0.000 0.991 0.000 1.000
#> SRR1633238 2 0.000 0.991 0.000 1.000
#> SRR1633239 2 0.000 0.991 0.000 1.000
#> SRR1633240 2 0.000 0.991 0.000 1.000
#> SRR1633241 2 0.000 0.991 0.000 1.000
#> SRR1633242 2 0.000 0.991 0.000 1.000
#> SRR1633243 2 0.000 0.991 0.000 1.000
#> SRR1633244 2 0.000 0.991 0.000 1.000
#> SRR1633245 2 0.000 0.991 0.000 1.000
#> SRR1633246 2 0.000 0.991 0.000 1.000
#> SRR1633247 2 0.311 0.949 0.056 0.944
#> SRR1633248 2 0.311 0.949 0.056 0.944
#> SRR1633249 2 0.311 0.949 0.056 0.944
#> SRR1633250 2 0.311 0.949 0.056 0.944
#> SRR1633251 2 0.327 0.945 0.060 0.940
#> SRR1633252 2 0.327 0.945 0.060 0.940
#> SRR1633253 2 0.327 0.945 0.060 0.940
#> SRR1633254 2 0.327 0.945 0.060 0.940
#> SRR1633255 2 0.327 0.945 0.060 0.940
#> SRR1633256 2 0.295 0.952 0.052 0.948
#> SRR1633257 2 0.295 0.952 0.052 0.948
#> SRR1633258 2 0.295 0.952 0.052 0.948
#> SRR1633259 2 0.000 0.991 0.000 1.000
#> SRR1633260 2 0.000 0.991 0.000 1.000
#> SRR1633261 2 0.000 0.991 0.000 1.000
#> SRR1633262 1 0.000 1.000 1.000 0.000
#> SRR1633263 1 0.000 1.000 1.000 0.000
#> SRR1633264 1 0.000 1.000 1.000 0.000
#> SRR1633265 1 0.000 1.000 1.000 0.000
#> SRR1633266 1 0.000 1.000 1.000 0.000
#> SRR1633267 2 0.000 0.991 0.000 1.000
#> SRR1633268 2 0.000 0.991 0.000 1.000
#> SRR1633269 2 0.000 0.991 0.000 1.000
#> SRR1633270 2 0.000 0.991 0.000 1.000
#> SRR1633271 2 0.000 0.991 0.000 1.000
#> SRR1633272 2 0.000 0.991 0.000 1.000
#> SRR1633273 1 0.000 1.000 1.000 0.000
#> SRR1633274 1 0.000 1.000 1.000 0.000
#> SRR1633275 1 0.000 1.000 1.000 0.000
#> SRR1633276 1 0.000 1.000 1.000 0.000
#> SRR1633277 1 0.000 1.000 1.000 0.000
#> SRR1633278 1 0.000 1.000 1.000 0.000
#> SRR1633279 1 0.000 1.000 1.000 0.000
#> SRR1633280 1 0.000 1.000 1.000 0.000
#> SRR1633281 1 0.000 1.000 1.000 0.000
#> SRR1633282 1 0.000 1.000 1.000 0.000
#> SRR1633284 1 0.000 1.000 1.000 0.000
#> SRR1633285 1 0.000 1.000 1.000 0.000
#> SRR1633286 1 0.000 1.000 1.000 0.000
#> SRR1633287 1 0.000 1.000 1.000 0.000
#> SRR1633288 1 0.000 1.000 1.000 0.000
#> SRR1633289 1 0.000 1.000 1.000 0.000
#> SRR1633290 1 0.000 1.000 1.000 0.000
#> SRR1633291 1 0.000 1.000 1.000 0.000
#> SRR1633292 2 0.000 0.991 0.000 1.000
#> SRR1633293 2 0.000 0.991 0.000 1.000
#> SRR1633294 2 0.000 0.991 0.000 1.000
#> SRR1633295 2 0.000 0.991 0.000 1.000
#> SRR1633296 1 0.000 1.000 1.000 0.000
#> SRR1633297 1 0.000 1.000 1.000 0.000
#> SRR1633298 1 0.000 1.000 1.000 0.000
#> SRR1633299 1 0.000 1.000 1.000 0.000
#> SRR1633300 2 0.000 0.991 0.000 1.000
#> SRR1633301 2 0.000 0.991 0.000 1.000
#> SRR1633302 2 0.000 0.991 0.000 1.000
#> SRR1633303 2 0.000 0.991 0.000 1.000
#> SRR1633304 2 0.000 0.991 0.000 1.000
#> SRR1633305 2 0.000 0.991 0.000 1.000
#> SRR1633306 2 0.000 0.991 0.000 1.000
#> SRR1633307 2 0.000 0.991 0.000 1.000
#> SRR1633308 2 0.000 0.991 0.000 1.000
#> SRR1633309 2 0.000 0.991 0.000 1.000
#> SRR1633310 2 0.000 0.991 0.000 1.000
#> SRR1633311 2 0.000 0.991 0.000 1.000
#> SRR1633312 2 0.000 0.991 0.000 1.000
#> SRR1633313 2 0.000 0.991 0.000 1.000
#> SRR1633314 2 0.000 0.991 0.000 1.000
#> SRR1633315 2 0.000 0.991 0.000 1.000
#> SRR1633316 2 0.000 0.991 0.000 1.000
#> SRR1633317 2 0.000 0.991 0.000 1.000
#> SRR1633318 2 0.000 0.991 0.000 1.000
#> SRR1633319 2 0.000 0.991 0.000 1.000
#> SRR1633320 2 0.000 0.991 0.000 1.000
#> SRR1633321 2 0.000 0.991 0.000 1.000
#> SRR1633322 2 0.000 0.991 0.000 1.000
#> SRR1633323 2 0.000 0.991 0.000 1.000
#> SRR1633324 2 0.000 0.991 0.000 1.000
#> SRR1633325 2 0.000 0.991 0.000 1.000
#> SRR1633326 2 0.000 0.991 0.000 1.000
#> SRR1633327 2 0.000 0.991 0.000 1.000
#> SRR1633328 2 0.000 0.991 0.000 1.000
#> SRR1633329 2 0.000 0.991 0.000 1.000
#> SRR1633330 2 0.000 0.991 0.000 1.000
#> SRR1633331 2 0.000 0.991 0.000 1.000
#> SRR1633332 2 0.000 0.991 0.000 1.000
#> SRR1633333 2 0.000 0.991 0.000 1.000
#> SRR1633334 2 0.000 0.991 0.000 1.000
#> SRR1633335 1 0.000 1.000 1.000 0.000
#> SRR1633336 1 0.000 1.000 1.000 0.000
#> SRR1633337 1 0.000 1.000 1.000 0.000
#> SRR1633338 1 0.000 1.000 1.000 0.000
#> SRR1633339 1 0.000 1.000 1.000 0.000
#> SRR1633340 1 0.000 1.000 1.000 0.000
#> SRR1633341 1 0.000 1.000 1.000 0.000
#> SRR1633342 1 0.000 1.000 1.000 0.000
#> SRR1633345 1 0.000 1.000 1.000 0.000
#> SRR1633346 1 0.000 1.000 1.000 0.000
#> SRR1633343 1 0.000 1.000 1.000 0.000
#> SRR1633344 1 0.000 1.000 1.000 0.000
#> SRR1633347 1 0.000 1.000 1.000 0.000
#> SRR1633348 1 0.000 1.000 1.000 0.000
#> SRR1633350 1 0.000 1.000 1.000 0.000
#> SRR1633351 1 0.000 1.000 1.000 0.000
#> SRR1633352 1 0.000 1.000 1.000 0.000
#> SRR1633353 1 0.000 1.000 1.000 0.000
#> SRR1633354 1 0.000 1.000 1.000 0.000
#> SRR1633355 1 0.000 1.000 1.000 0.000
#> SRR1633356 1 0.000 1.000 1.000 0.000
#> SRR1633357 1 0.000 1.000 1.000 0.000
#> SRR1633358 1 0.000 1.000 1.000 0.000
#> SRR1633362 1 0.000 1.000 1.000 0.000
#> SRR1633363 1 0.000 1.000 1.000 0.000
#> SRR1633364 1 0.000 1.000 1.000 0.000
#> SRR1633359 1 0.000 1.000 1.000 0.000
#> SRR1633360 1 0.000 1.000 1.000 0.000
#> SRR1633361 1 0.000 1.000 1.000 0.000
#> SRR2038492 1 0.000 1.000 1.000 0.000
#> SRR2038491 1 0.000 1.000 1.000 0.000
#> SRR2038490 1 0.000 1.000 1.000 0.000
#> SRR2038489 1 0.000 1.000 1.000 0.000
#> SRR2038488 1 0.000 1.000 1.000 0.000
#> SRR2038487 1 0.000 1.000 1.000 0.000
#> SRR2038486 1 0.000 1.000 1.000 0.000
#> SRR2038485 1 0.000 1.000 1.000 0.000
#> SRR2038484 1 0.000 1.000 1.000 0.000
#> SRR2038483 1 0.000 1.000 1.000 0.000
#> SRR2038482 1 0.000 1.000 1.000 0.000
#> SRR2038481 1 0.000 1.000 1.000 0.000
#> SRR2038480 1 0.000 1.000 1.000 0.000
#> SRR2038479 1 0.000 1.000 1.000 0.000
#> SRR2038477 1 0.000 1.000 1.000 0.000
#> SRR2038478 1 0.000 1.000 1.000 0.000
#> SRR2038476 1 0.000 1.000 1.000 0.000
#> SRR2038475 1 0.000 1.000 1.000 0.000
#> SRR2038474 1 0.000 1.000 1.000 0.000
#> SRR2038473 1 0.000 1.000 1.000 0.000
#> SRR2038472 1 0.000 1.000 1.000 0.000
#> SRR2038471 1 0.000 1.000 1.000 0.000
#> SRR2038470 1 0.000 1.000 1.000 0.000
#> SRR2038469 1 0.000 1.000 1.000 0.000
#> SRR2038468 1 0.000 1.000 1.000 0.000
#> SRR2038467 1 0.000 1.000 1.000 0.000
#> SRR2038466 1 0.000 1.000 1.000 0.000
#> SRR2038465 1 0.000 1.000 1.000 0.000
#> SRR2038464 1 0.000 1.000 1.000 0.000
#> SRR2038463 1 0.000 1.000 1.000 0.000
#> SRR2038462 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.5098 0.691 0.000 0.248 0.752
#> SRR1633237 3 0.4887 0.723 0.000 0.228 0.772
#> SRR1633238 3 0.4702 0.746 0.000 0.212 0.788
#> SRR1633239 3 0.4974 0.711 0.000 0.236 0.764
#> SRR1633240 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633264 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633265 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633270 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633271 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633272 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633273 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633274 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633275 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633276 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633277 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633278 1 0.6274 0.197 0.544 0.000 0.456
#> SRR1633279 1 0.6286 0.172 0.536 0.000 0.464
#> SRR1633280 1 0.6252 0.234 0.556 0.000 0.444
#> SRR1633281 1 0.6180 0.312 0.584 0.000 0.416
#> SRR1633282 1 0.0592 0.967 0.988 0.000 0.012
#> SRR1633284 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633290 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633291 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633292 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.976 0.000 0.000 1.000
#> SRR1633296 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633297 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633298 1 0.0592 0.967 0.988 0.000 0.012
#> SRR1633299 1 0.0592 0.967 0.988 0.000 0.012
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633343 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633344 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633347 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633348 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633350 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.977 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2038462 1 0.0592 0.967 0.988 0.000 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.4040 0.67796 0.000 0.248 0.752 0.000
#> SRR1633237 3 0.3873 0.70897 0.000 0.228 0.772 0.000
#> SRR1633238 3 0.3726 0.73156 0.000 0.212 0.788 0.000
#> SRR1633239 3 0.3942 0.69702 0.000 0.236 0.764 0.000
#> SRR1633240 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633251 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633252 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633253 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633254 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633255 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633256 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.0336 0.94618 0.000 0.000 0.992 0.008
#> SRR1633263 3 0.0336 0.94618 0.000 0.000 0.992 0.008
#> SRR1633264 3 0.0336 0.94618 0.000 0.000 0.992 0.008
#> SRR1633265 3 0.0336 0.94618 0.000 0.000 0.992 0.008
#> SRR1633266 3 0.0336 0.94618 0.000 0.000 0.992 0.008
#> SRR1633267 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633268 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633269 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633270 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633271 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633272 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633273 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633278 3 0.4977 0.11393 0.000 0.000 0.540 0.460
#> SRR1633279 3 0.4981 0.09983 0.000 0.000 0.536 0.464
#> SRR1633280 4 0.5000 0.00253 0.000 0.000 0.496 0.504
#> SRR1633281 4 0.4961 0.17369 0.000 0.000 0.448 0.552
#> SRR1633282 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633284 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633285 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633286 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633287 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633288 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633289 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633290 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.95133 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 1.00000 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633336 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633337 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633338 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633339 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633340 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633341 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633342 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633345 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633346 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633343 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.0000 0.97083 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0188 0.99599 0.996 0.000 0.000 0.004
#> SRR2038491 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.99991 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.0000 0.97083 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633231 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633232 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633233 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633234 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633236 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633237 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633238 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633239 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633240 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633241 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633242 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633243 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633244 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633245 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633246 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633247 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633248 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633249 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633250 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633251 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633252 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633253 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633254 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633255 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633256 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633257 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633258 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633259 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633260 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633261 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633262 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633263 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633264 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633265 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633266 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633267 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633268 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633269 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633270 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633271 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633272 3 0.0000 0.9454 0.000 0 1.000 0.000 0
#> SRR1633273 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633274 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633275 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633276 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633277 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633278 3 0.4182 0.3575 0.000 0 0.600 0.400 0
#> SRR1633279 3 0.4192 0.3475 0.000 0 0.596 0.404 0
#> SRR1633280 3 0.4278 0.2092 0.000 0 0.548 0.452 0
#> SRR1633281 4 0.4306 -0.0683 0.000 0 0.492 0.508 0
#> SRR1633282 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633284 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633285 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633286 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633287 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633288 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633289 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633290 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633291 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633292 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633293 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633294 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633295 5 0.0000 1.0000 0.000 0 0.000 0.000 1
#> SRR1633296 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633297 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633298 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633299 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633300 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633301 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633302 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633303 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633304 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633305 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633306 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633307 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633308 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633309 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633310 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633311 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633312 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633313 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633314 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633315 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633316 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633317 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633318 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633319 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633320 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633321 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633322 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633323 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633324 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633325 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633326 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633327 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633328 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633329 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633330 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633331 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633332 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633333 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633334 2 0.0000 1.0000 0.000 1 0.000 0.000 0
#> SRR1633335 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633336 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633337 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633338 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633339 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633340 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633341 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633342 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633345 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633346 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633343 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633344 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633347 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633348 4 0.0000 0.9831 0.000 0 0.000 1.000 0
#> SRR1633350 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633351 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633352 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633353 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633354 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633355 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633356 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633357 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633358 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633362 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633363 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633364 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633359 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633360 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR1633361 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038492 1 0.0162 0.9954 0.996 0 0.000 0.004 0
#> SRR2038491 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038490 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038489 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038488 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038487 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038486 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038485 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038484 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038483 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038482 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038481 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038480 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038479 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038477 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038478 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038476 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038475 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038474 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038473 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038472 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038471 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038470 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038469 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038468 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038467 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038466 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038465 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038464 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038463 1 0.0000 0.9999 1.000 0 0.000 0.000 0
#> SRR2038462 4 0.0000 0.9831 0.000 0 0.000 1.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633231 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633232 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633233 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633234 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633236 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633237 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633238 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633239 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633240 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633241 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633242 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633243 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633244 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633245 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633246 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633247 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633248 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633249 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633250 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633251 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633252 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633253 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633254 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633255 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633256 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633257 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633258 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633259 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633260 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633261 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633262 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633263 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633264 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633265 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633266 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633267 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633268 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633269 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633270 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633271 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633272 3 0.0000 0.9454 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633273 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633274 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633275 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633276 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633277 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633278 3 0.3756 0.3575 0.000 0.000 0.600 0.400 0 0.000
#> SRR1633279 3 0.3765 0.3475 0.000 0.000 0.596 0.404 0 0.000
#> SRR1633280 3 0.3843 0.2092 0.000 0.000 0.548 0.452 0 0.000
#> SRR1633281 4 0.3868 -0.0683 0.000 0.000 0.492 0.508 0 0.000
#> SRR1633282 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633284 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633285 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633286 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633287 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633288 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633289 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633290 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633291 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633292 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633293 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633294 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633295 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633296 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633297 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633298 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633299 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633300 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633301 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633302 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633303 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633304 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633305 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633306 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633307 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633308 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633309 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633310 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633311 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633312 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633313 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633314 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633315 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633316 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633317 2 0.0000 0.8915 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633318 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633319 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633320 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633321 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633322 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633323 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633324 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633325 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633326 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633327 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633328 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633329 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633330 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633331 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633332 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633333 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633334 2 0.2854 0.9126 0.000 0.792 0.000 0.000 0 0.208
#> SRR1633335 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633336 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633337 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633338 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633339 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633340 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633341 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633342 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633345 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633346 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633343 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633344 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633347 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633348 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
#> SRR1633350 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633351 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633352 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633353 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633354 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633355 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633356 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633357 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633358 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633362 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633363 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633364 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633359 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633360 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR1633361 6 0.2854 1.0000 0.208 0.000 0.000 0.000 0 0.792
#> SRR2038492 1 0.0146 0.9933 0.996 0.000 0.000 0.004 0 0.000
#> SRR2038491 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038490 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038489 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038488 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038487 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038486 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038485 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038484 1 0.0146 0.9948 0.996 0.000 0.000 0.000 0 0.004
#> SRR2038483 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038482 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038481 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038480 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038479 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038477 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038478 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038476 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038475 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038474 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038473 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038472 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038471 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038470 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038469 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038468 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038467 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038466 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038465 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038464 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038463 1 0.0000 0.9996 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038462 4 0.0000 0.9831 0.000 0.000 0.000 1.000 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.997 0.998 0.3742 0.627 0.627
#> 3 3 1.000 0.989 0.996 0.6734 0.745 0.594
#> 4 4 1.000 0.985 0.988 0.1971 0.875 0.663
#> 5 5 0.911 0.897 0.896 0.0544 0.960 0.839
#> 6 6 0.928 0.888 0.947 0.0288 0.960 0.815
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 1.000 0.000 1.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000
#> SRR1633236 1 0.0672 0.994 0.992 0.008
#> SRR1633237 1 0.0672 0.994 0.992 0.008
#> SRR1633238 1 0.0672 0.994 0.992 0.008
#> SRR1633239 1 0.0672 0.994 0.992 0.008
#> SRR1633240 1 0.0672 0.994 0.992 0.008
#> SRR1633241 1 0.0672 0.994 0.992 0.008
#> SRR1633242 1 0.0672 0.994 0.992 0.008
#> SRR1633243 1 0.0672 0.994 0.992 0.008
#> SRR1633244 1 0.0672 0.994 0.992 0.008
#> SRR1633245 1 0.0672 0.994 0.992 0.008
#> SRR1633246 1 0.0672 0.994 0.992 0.008
#> SRR1633247 1 0.0672 0.994 0.992 0.008
#> SRR1633248 1 0.0672 0.994 0.992 0.008
#> SRR1633249 1 0.0672 0.994 0.992 0.008
#> SRR1633250 1 0.0672 0.994 0.992 0.008
#> SRR1633251 1 0.0672 0.994 0.992 0.008
#> SRR1633252 1 0.0672 0.994 0.992 0.008
#> SRR1633253 1 0.0672 0.994 0.992 0.008
#> SRR1633254 1 0.0672 0.994 0.992 0.008
#> SRR1633255 1 0.0672 0.994 0.992 0.008
#> SRR1633256 1 0.0672 0.994 0.992 0.008
#> SRR1633257 1 0.0672 0.994 0.992 0.008
#> SRR1633258 1 0.0672 0.994 0.992 0.008
#> SRR1633259 1 0.0672 0.994 0.992 0.008
#> SRR1633260 1 0.0672 0.994 0.992 0.008
#> SRR1633261 1 0.0672 0.994 0.992 0.008
#> SRR1633262 1 0.0000 0.998 1.000 0.000
#> SRR1633263 1 0.0000 0.998 1.000 0.000
#> SRR1633264 1 0.0000 0.998 1.000 0.000
#> SRR1633265 1 0.0000 0.998 1.000 0.000
#> SRR1633266 1 0.0000 0.998 1.000 0.000
#> SRR1633267 1 0.0672 0.994 0.992 0.008
#> SRR1633268 1 0.0672 0.994 0.992 0.008
#> SRR1633269 1 0.0672 0.994 0.992 0.008
#> SRR1633270 1 0.0672 0.994 0.992 0.008
#> SRR1633271 1 0.0672 0.994 0.992 0.008
#> SRR1633272 1 0.0672 0.994 0.992 0.008
#> SRR1633273 1 0.0000 0.998 1.000 0.000
#> SRR1633274 1 0.0000 0.998 1.000 0.000
#> SRR1633275 1 0.0000 0.998 1.000 0.000
#> SRR1633276 1 0.0000 0.998 1.000 0.000
#> SRR1633277 1 0.0000 0.998 1.000 0.000
#> SRR1633278 1 0.0000 0.998 1.000 0.000
#> SRR1633279 1 0.0000 0.998 1.000 0.000
#> SRR1633280 1 0.0000 0.998 1.000 0.000
#> SRR1633281 1 0.0000 0.998 1.000 0.000
#> SRR1633282 1 0.0000 0.998 1.000 0.000
#> SRR1633284 1 0.0000 0.998 1.000 0.000
#> SRR1633285 1 0.0000 0.998 1.000 0.000
#> SRR1633286 1 0.0000 0.998 1.000 0.000
#> SRR1633287 1 0.0000 0.998 1.000 0.000
#> SRR1633288 1 0.0000 0.998 1.000 0.000
#> SRR1633289 1 0.0000 0.998 1.000 0.000
#> SRR1633290 1 0.0000 0.998 1.000 0.000
#> SRR1633291 1 0.0000 0.998 1.000 0.000
#> SRR1633292 1 0.0672 0.994 0.992 0.008
#> SRR1633293 1 0.0672 0.994 0.992 0.008
#> SRR1633294 1 0.0672 0.994 0.992 0.008
#> SRR1633295 1 0.0672 0.994 0.992 0.008
#> SRR1633296 1 0.0000 0.998 1.000 0.000
#> SRR1633297 1 0.0000 0.998 1.000 0.000
#> SRR1633298 1 0.0000 0.998 1.000 0.000
#> SRR1633299 1 0.0000 0.998 1.000 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000
#> SRR1633335 1 0.0000 0.998 1.000 0.000
#> SRR1633336 1 0.0000 0.998 1.000 0.000
#> SRR1633337 1 0.0000 0.998 1.000 0.000
#> SRR1633338 1 0.0000 0.998 1.000 0.000
#> SRR1633339 1 0.0000 0.998 1.000 0.000
#> SRR1633340 1 0.0000 0.998 1.000 0.000
#> SRR1633341 1 0.0000 0.998 1.000 0.000
#> SRR1633342 1 0.0000 0.998 1.000 0.000
#> SRR1633345 1 0.0000 0.998 1.000 0.000
#> SRR1633346 1 0.0000 0.998 1.000 0.000
#> SRR1633343 1 0.0000 0.998 1.000 0.000
#> SRR1633344 1 0.0000 0.998 1.000 0.000
#> SRR1633347 1 0.0000 0.998 1.000 0.000
#> SRR1633348 1 0.0000 0.998 1.000 0.000
#> SRR1633350 1 0.0000 0.998 1.000 0.000
#> SRR1633351 1 0.0000 0.998 1.000 0.000
#> SRR1633352 1 0.0000 0.998 1.000 0.000
#> SRR1633353 1 0.0000 0.998 1.000 0.000
#> SRR1633354 1 0.0000 0.998 1.000 0.000
#> SRR1633355 1 0.0000 0.998 1.000 0.000
#> SRR1633356 1 0.0000 0.998 1.000 0.000
#> SRR1633357 1 0.0000 0.998 1.000 0.000
#> SRR1633358 1 0.0000 0.998 1.000 0.000
#> SRR1633362 1 0.0000 0.998 1.000 0.000
#> SRR1633363 1 0.0000 0.998 1.000 0.000
#> SRR1633364 1 0.0000 0.998 1.000 0.000
#> SRR1633359 1 0.0000 0.998 1.000 0.000
#> SRR1633360 1 0.0000 0.998 1.000 0.000
#> SRR1633361 1 0.0000 0.998 1.000 0.000
#> SRR2038492 1 0.0000 0.998 1.000 0.000
#> SRR2038491 1 0.0000 0.998 1.000 0.000
#> SRR2038490 1 0.0000 0.998 1.000 0.000
#> SRR2038489 1 0.0000 0.998 1.000 0.000
#> SRR2038488 1 0.0000 0.998 1.000 0.000
#> SRR2038487 1 0.0000 0.998 1.000 0.000
#> SRR2038486 1 0.0000 0.998 1.000 0.000
#> SRR2038485 1 0.0000 0.998 1.000 0.000
#> SRR2038484 1 0.0000 0.998 1.000 0.000
#> SRR2038483 1 0.0000 0.998 1.000 0.000
#> SRR2038482 1 0.0000 0.998 1.000 0.000
#> SRR2038481 1 0.0000 0.998 1.000 0.000
#> SRR2038480 1 0.0000 0.998 1.000 0.000
#> SRR2038479 1 0.0000 0.998 1.000 0.000
#> SRR2038477 1 0.0000 0.998 1.000 0.000
#> SRR2038478 1 0.0000 0.998 1.000 0.000
#> SRR2038476 1 0.0000 0.998 1.000 0.000
#> SRR2038475 1 0.0000 0.998 1.000 0.000
#> SRR2038474 1 0.0000 0.998 1.000 0.000
#> SRR2038473 1 0.0000 0.998 1.000 0.000
#> SRR2038472 1 0.0000 0.998 1.000 0.000
#> SRR2038471 1 0.0000 0.998 1.000 0.000
#> SRR2038470 1 0.0000 0.998 1.000 0.000
#> SRR2038469 1 0.0000 0.998 1.000 0.000
#> SRR2038468 1 0.0000 0.998 1.000 0.000
#> SRR2038467 1 0.0000 0.998 1.000 0.000
#> SRR2038466 1 0.0000 0.998 1.000 0.000
#> SRR2038465 1 0.0000 0.998 1.000 0.000
#> SRR2038464 1 0.0000 0.998 1.000 0.000
#> SRR2038463 1 0.0000 0.998 1.000 0.000
#> SRR2038462 1 0.0000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633231 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633232 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633233 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633234 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633236 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633237 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633238 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633239 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633240 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633241 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633242 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633243 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633244 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633245 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633246 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633247 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633248 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633249 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633250 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633251 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633252 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633253 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633254 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633255 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633256 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633257 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633258 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633259 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633260 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633261 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633262 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633263 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633264 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633265 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633266 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633267 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633268 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633269 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633270 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633271 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633272 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633273 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633274 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633275 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633276 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633277 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633278 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633279 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633280 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633281 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633282 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633284 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633285 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633286 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633287 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633288 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633289 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633290 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633291 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633292 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633293 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633294 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633295 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633296 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633297 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633298 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633299 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633300 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633301 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633302 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633303 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633304 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633305 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633306 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633307 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633308 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633309 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633310 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633311 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633312 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633313 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633314 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633315 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633316 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633317 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633318 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633319 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633320 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633321 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633322 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633323 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633324 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633325 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633326 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633327 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633328 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633329 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633330 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633331 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633332 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633333 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633334 2 0.0000 1.000000 0.000 1 0.000
#> SRR1633335 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633336 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633337 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633338 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633339 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633340 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633341 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633342 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633345 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633346 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633343 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633344 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633347 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633348 3 0.0000 0.999147 0.000 0 1.000
#> SRR1633350 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633351 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633352 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633353 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633354 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633355 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633356 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633357 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633358 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633362 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633363 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633364 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633359 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633360 1 0.0000 0.983424 1.000 0 0.000
#> SRR1633361 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038492 3 0.0237 0.995403 0.004 0 0.996
#> SRR2038491 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038490 3 0.1964 0.939916 0.056 0 0.944
#> SRR2038489 1 0.0237 0.979294 0.996 0 0.004
#> SRR2038488 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038487 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038486 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038485 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038484 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038483 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038482 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038481 1 0.0747 0.966137 0.984 0 0.016
#> SRR2038480 3 0.0237 0.995403 0.004 0 0.996
#> SRR2038479 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038477 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038478 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038476 3 0.0237 0.995403 0.004 0 0.996
#> SRR2038475 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038474 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038473 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038472 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038471 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038470 1 0.0747 0.966137 0.984 0 0.016
#> SRR2038469 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038468 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038467 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038466 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038465 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038464 1 0.6309 -0.000754 0.500 0 0.500
#> SRR2038463 1 0.0000 0.983424 1.000 0 0.000
#> SRR2038462 3 0.0000 0.999147 0.000 0 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.3486 0.770 0.000 0.812 0.188 0.000
#> SRR1633231 2 0.3486 0.770 0.000 0.812 0.188 0.000
#> SRR1633232 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633237 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633238 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633239 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633240 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633248 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633249 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633250 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633251 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633252 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633253 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633254 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633255 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633256 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633257 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633258 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633259 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633260 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633261 3 0.1792 0.967 0.000 0.000 0.932 0.068
#> SRR1633262 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633263 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633264 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633265 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633266 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633267 3 0.2011 0.959 0.000 0.000 0.920 0.080
#> SRR1633268 3 0.2011 0.959 0.000 0.000 0.920 0.080
#> SRR1633269 3 0.2011 0.959 0.000 0.000 0.920 0.080
#> SRR1633270 3 0.1940 0.962 0.000 0.000 0.924 0.076
#> SRR1633271 3 0.1940 0.962 0.000 0.000 0.924 0.076
#> SRR1633272 3 0.1940 0.962 0.000 0.000 0.924 0.076
#> SRR1633273 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633278 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633279 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633280 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633281 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633282 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633284 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633285 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633286 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633287 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633288 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633289 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633290 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.955 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.990 0.000 1.000 0.000 0.000
#> SRR1633335 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633336 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633337 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633338 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633339 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633340 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633341 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633342 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633345 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633346 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633343 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038492 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR2038491 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038490 4 0.0188 0.996 0.004 0.000 0.000 0.996
#> SRR2038489 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0707 0.978 0.980 0.000 0.000 0.020
#> SRR2038480 4 0.0336 0.992 0.008 0.000 0.000 0.992
#> SRR2038479 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038476 4 0.0336 0.992 0.008 0.000 0.000 0.992
#> SRR2038475 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0707 0.978 0.980 0.000 0.000 0.020
#> SRR2038469 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.1557 0.936 0.944 0.000 0.000 0.056
#> SRR2038463 1 0.0000 0.997 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.0000 1.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.1671 0.9127 0.000 0.924 0.076 0.000 0.000
#> SRR1633231 2 0.1671 0.9127 0.000 0.924 0.076 0.000 0.000
#> SRR1633232 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633237 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633238 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633239 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633240 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633241 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633242 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633243 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633244 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633245 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633246 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633247 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633248 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633249 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633250 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633251 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633252 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633253 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633254 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633255 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633256 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633257 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633258 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633259 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633260 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633261 3 0.3550 0.8586 0.000 0.000 0.760 0.236 0.004
#> SRR1633262 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633263 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633264 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633265 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633266 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633267 3 0.6410 0.5918 0.000 0.000 0.496 0.200 0.304
#> SRR1633268 3 0.6410 0.5918 0.000 0.000 0.496 0.200 0.304
#> SRR1633269 3 0.6410 0.5918 0.000 0.000 0.496 0.200 0.304
#> SRR1633270 3 0.6398 0.5981 0.000 0.000 0.500 0.200 0.300
#> SRR1633271 3 0.6398 0.5981 0.000 0.000 0.500 0.200 0.300
#> SRR1633272 3 0.6398 0.5981 0.000 0.000 0.500 0.200 0.300
#> SRR1633273 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633274 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633275 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633276 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633277 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633278 4 0.4287 0.7004 0.000 0.000 0.000 0.540 0.460
#> SRR1633279 4 0.4287 0.7004 0.000 0.000 0.000 0.540 0.460
#> SRR1633280 4 0.4287 0.7004 0.000 0.000 0.000 0.540 0.460
#> SRR1633281 4 0.4287 0.7004 0.000 0.000 0.000 0.540 0.460
#> SRR1633282 5 0.3913 0.0236 0.000 0.000 0.000 0.324 0.676
#> SRR1633284 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633285 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633286 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633287 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633288 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633289 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633290 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633291 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633293 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633294 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633295 3 0.0000 0.8449 0.000 0.000 1.000 0.000 0.000
#> SRR1633296 5 0.0510 0.9504 0.000 0.000 0.000 0.016 0.984
#> SRR1633297 5 0.0510 0.9504 0.000 0.000 0.000 0.016 0.984
#> SRR1633298 5 0.0794 0.9342 0.000 0.000 0.000 0.028 0.972
#> SRR1633299 5 0.0794 0.9342 0.000 0.000 0.000 0.028 0.972
#> SRR1633300 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 0.9957 0.000 1.000 0.000 0.000 0.000
#> SRR1633335 4 0.3857 0.8469 0.000 0.000 0.000 0.688 0.312
#> SRR1633336 4 0.3857 0.8469 0.000 0.000 0.000 0.688 0.312
#> SRR1633337 4 0.3857 0.8469 0.000 0.000 0.000 0.688 0.312
#> SRR1633338 4 0.4306 0.6416 0.000 0.000 0.000 0.508 0.492
#> SRR1633339 4 0.4306 0.6416 0.000 0.000 0.000 0.508 0.492
#> SRR1633340 4 0.4306 0.6416 0.000 0.000 0.000 0.508 0.492
#> SRR1633341 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633342 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633345 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633346 4 0.3395 0.8714 0.000 0.000 0.000 0.764 0.236
#> SRR1633343 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633344 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633347 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633348 5 0.0000 0.9669 0.000 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038492 4 0.3809 0.8655 0.008 0.000 0.000 0.736 0.256
#> SRR2038491 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 4 0.3728 0.8674 0.008 0.000 0.000 0.748 0.244
#> SRR2038489 1 0.2280 0.8598 0.880 0.000 0.000 0.120 0.000
#> SRR2038488 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0290 0.9629 0.992 0.000 0.000 0.008 0.000
#> SRR2038485 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.4339 0.5370 0.652 0.000 0.000 0.336 0.012
#> SRR2038480 4 0.3612 0.8631 0.008 0.000 0.000 0.764 0.228
#> SRR2038479 1 0.0290 0.9629 0.992 0.000 0.000 0.008 0.000
#> SRR2038477 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0290 0.9629 0.992 0.000 0.000 0.008 0.000
#> SRR2038476 4 0.3612 0.8631 0.008 0.000 0.000 0.764 0.228
#> SRR2038475 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.4306 0.5526 0.660 0.000 0.000 0.328 0.012
#> SRR2038469 1 0.0290 0.9629 0.992 0.000 0.000 0.008 0.000
#> SRR2038468 1 0.0703 0.9507 0.976 0.000 0.000 0.024 0.000
#> SRR2038467 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.4632 0.2557 0.540 0.000 0.000 0.448 0.012
#> SRR2038463 1 0.0000 0.9677 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.4294 0.6883 0.000 0.000 0.000 0.532 0.468
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0717 0.977 0.000 0.976 0.000 0.000 0.016 0.008
#> SRR1633231 2 0.0717 0.977 0.000 0.976 0.000 0.000 0.016 0.008
#> SRR1633232 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633237 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633238 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633239 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633240 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633241 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633242 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633243 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633244 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633245 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633246 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633248 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633249 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633250 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633251 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633252 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633253 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633254 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633255 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633256 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633257 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633258 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633259 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633260 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633261 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633262 4 0.1556 0.814 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1633263 4 0.1556 0.814 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1633264 4 0.1556 0.814 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1633265 4 0.1556 0.814 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1633266 4 0.1556 0.814 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1633267 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633268 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633269 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633270 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633271 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633272 4 0.3867 0.290 0.000 0.000 0.488 0.512 0.000 0.000
#> SRR1633273 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633274 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633275 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633276 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633277 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633278 6 0.3101 0.654 0.000 0.000 0.000 0.244 0.000 0.756
#> SRR1633279 6 0.3101 0.654 0.000 0.000 0.000 0.244 0.000 0.756
#> SRR1633280 6 0.3101 0.654 0.000 0.000 0.000 0.244 0.000 0.756
#> SRR1633281 6 0.3101 0.654 0.000 0.000 0.000 0.244 0.000 0.756
#> SRR1633282 4 0.2941 0.595 0.000 0.000 0.000 0.780 0.000 0.220
#> SRR1633284 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633285 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633286 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633287 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633288 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633289 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633290 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633291 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633292 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633293 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633294 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633295 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.1007 0.834 0.000 0.000 0.000 0.956 0.000 0.044
#> SRR1633297 4 0.1007 0.834 0.000 0.000 0.000 0.956 0.000 0.044
#> SRR1633298 4 0.1141 0.826 0.000 0.000 0.000 0.948 0.000 0.052
#> SRR1633299 4 0.1141 0.826 0.000 0.000 0.000 0.948 0.000 0.052
#> SRR1633300 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633301 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633302 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633318 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633330 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633331 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633332 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633333 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633334 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633335 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633336 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633337 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633338 6 0.2219 0.764 0.000 0.000 0.000 0.136 0.000 0.864
#> SRR1633339 6 0.2219 0.764 0.000 0.000 0.000 0.136 0.000 0.864
#> SRR1633340 6 0.2219 0.764 0.000 0.000 0.000 0.136 0.000 0.864
#> SRR1633341 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633342 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633345 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633346 6 0.0146 0.836 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1633343 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633344 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633347 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633348 4 0.0713 0.842 0.000 0.000 0.000 0.972 0.000 0.028
#> SRR1633350 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038492 6 0.3737 0.365 0.392 0.000 0.000 0.000 0.000 0.608
#> SRR2038491 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 6 0.3737 0.365 0.392 0.000 0.000 0.000 0.000 0.608
#> SRR2038489 1 0.2135 0.836 0.872 0.000 0.000 0.000 0.000 0.128
#> SRR2038488 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0547 0.953 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR2038485 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.3515 0.512 0.676 0.000 0.000 0.000 0.000 0.324
#> SRR2038480 6 0.3737 0.365 0.392 0.000 0.000 0.000 0.000 0.608
#> SRR2038479 1 0.0547 0.953 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR2038477 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0547 0.953 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR2038476 6 0.3737 0.365 0.392 0.000 0.000 0.000 0.000 0.608
#> SRR2038475 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.3499 0.521 0.680 0.000 0.000 0.000 0.000 0.320
#> SRR2038469 1 0.0547 0.953 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR2038468 1 0.0547 0.953 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR2038467 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.3684 0.392 0.628 0.000 0.000 0.000 0.000 0.372
#> SRR2038463 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 6 0.3101 0.654 0.000 0.000 0.000 0.244 0.000 0.756
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5013 0.499 0.499
#> 3 3 1.000 0.962 0.983 0.2758 0.826 0.665
#> 4 4 0.948 0.902 0.956 0.1479 0.823 0.561
#> 5 5 0.843 0.883 0.909 0.0653 0.883 0.606
#> 6 6 0.906 0.865 0.887 0.0353 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0 1 0 1
#> SRR1633231 2 0 1 0 1
#> SRR1633232 2 0 1 0 1
#> SRR1633233 2 0 1 0 1
#> SRR1633234 2 0 1 0 1
#> SRR1633236 2 0 1 0 1
#> SRR1633237 2 0 1 0 1
#> SRR1633238 2 0 1 0 1
#> SRR1633239 2 0 1 0 1
#> SRR1633240 2 0 1 0 1
#> SRR1633241 2 0 1 0 1
#> SRR1633242 2 0 1 0 1
#> SRR1633243 2 0 1 0 1
#> SRR1633244 2 0 1 0 1
#> SRR1633245 2 0 1 0 1
#> SRR1633246 2 0 1 0 1
#> SRR1633247 2 0 1 0 1
#> SRR1633248 2 0 1 0 1
#> SRR1633249 2 0 1 0 1
#> SRR1633250 2 0 1 0 1
#> SRR1633251 2 0 1 0 1
#> SRR1633252 2 0 1 0 1
#> SRR1633253 2 0 1 0 1
#> SRR1633254 2 0 1 0 1
#> SRR1633255 2 0 1 0 1
#> SRR1633256 2 0 1 0 1
#> SRR1633257 2 0 1 0 1
#> SRR1633258 2 0 1 0 1
#> SRR1633259 2 0 1 0 1
#> SRR1633260 2 0 1 0 1
#> SRR1633261 2 0 1 0 1
#> SRR1633262 1 0 1 1 0
#> SRR1633263 1 0 1 1 0
#> SRR1633264 1 0 1 1 0
#> SRR1633265 1 0 1 1 0
#> SRR1633266 1 0 1 1 0
#> SRR1633267 2 0 1 0 1
#> SRR1633268 2 0 1 0 1
#> SRR1633269 2 0 1 0 1
#> SRR1633270 2 0 1 0 1
#> SRR1633271 2 0 1 0 1
#> SRR1633272 2 0 1 0 1
#> SRR1633273 1 0 1 1 0
#> SRR1633274 1 0 1 1 0
#> SRR1633275 1 0 1 1 0
#> SRR1633276 1 0 1 1 0
#> SRR1633277 1 0 1 1 0
#> SRR1633278 1 0 1 1 0
#> SRR1633279 1 0 1 1 0
#> SRR1633280 1 0 1 1 0
#> SRR1633281 1 0 1 1 0
#> SRR1633282 1 0 1 1 0
#> SRR1633284 1 0 1 1 0
#> SRR1633285 1 0 1 1 0
#> SRR1633286 1 0 1 1 0
#> SRR1633287 1 0 1 1 0
#> SRR1633288 1 0 1 1 0
#> SRR1633289 1 0 1 1 0
#> SRR1633290 1 0 1 1 0
#> SRR1633291 1 0 1 1 0
#> SRR1633292 2 0 1 0 1
#> SRR1633293 2 0 1 0 1
#> SRR1633294 2 0 1 0 1
#> SRR1633295 2 0 1 0 1
#> SRR1633296 1 0 1 1 0
#> SRR1633297 1 0 1 1 0
#> SRR1633298 1 0 1 1 0
#> SRR1633299 1 0 1 1 0
#> SRR1633300 2 0 1 0 1
#> SRR1633301 2 0 1 0 1
#> SRR1633302 2 0 1 0 1
#> SRR1633303 2 0 1 0 1
#> SRR1633304 2 0 1 0 1
#> SRR1633305 2 0 1 0 1
#> SRR1633306 2 0 1 0 1
#> SRR1633307 2 0 1 0 1
#> SRR1633308 2 0 1 0 1
#> SRR1633309 2 0 1 0 1
#> SRR1633310 2 0 1 0 1
#> SRR1633311 2 0 1 0 1
#> SRR1633312 2 0 1 0 1
#> SRR1633313 2 0 1 0 1
#> SRR1633314 2 0 1 0 1
#> SRR1633315 2 0 1 0 1
#> SRR1633316 2 0 1 0 1
#> SRR1633317 2 0 1 0 1
#> SRR1633318 2 0 1 0 1
#> SRR1633319 2 0 1 0 1
#> SRR1633320 2 0 1 0 1
#> SRR1633321 2 0 1 0 1
#> SRR1633322 2 0 1 0 1
#> SRR1633323 2 0 1 0 1
#> SRR1633324 2 0 1 0 1
#> SRR1633325 2 0 1 0 1
#> SRR1633326 2 0 1 0 1
#> SRR1633327 2 0 1 0 1
#> SRR1633328 2 0 1 0 1
#> SRR1633329 2 0 1 0 1
#> SRR1633330 2 0 1 0 1
#> SRR1633331 2 0 1 0 1
#> SRR1633332 2 0 1 0 1
#> SRR1633333 2 0 1 0 1
#> SRR1633334 2 0 1 0 1
#> SRR1633335 1 0 1 1 0
#> SRR1633336 1 0 1 1 0
#> SRR1633337 1 0 1 1 0
#> SRR1633338 1 0 1 1 0
#> SRR1633339 1 0 1 1 0
#> SRR1633340 1 0 1 1 0
#> SRR1633341 1 0 1 1 0
#> SRR1633342 1 0 1 1 0
#> SRR1633345 1 0 1 1 0
#> SRR1633346 1 0 1 1 0
#> SRR1633343 1 0 1 1 0
#> SRR1633344 1 0 1 1 0
#> SRR1633347 1 0 1 1 0
#> SRR1633348 1 0 1 1 0
#> SRR1633350 1 0 1 1 0
#> SRR1633351 1 0 1 1 0
#> SRR1633352 1 0 1 1 0
#> SRR1633353 1 0 1 1 0
#> SRR1633354 1 0 1 1 0
#> SRR1633355 1 0 1 1 0
#> SRR1633356 1 0 1 1 0
#> SRR1633357 1 0 1 1 0
#> SRR1633358 1 0 1 1 0
#> SRR1633362 1 0 1 1 0
#> SRR1633363 1 0 1 1 0
#> SRR1633364 1 0 1 1 0
#> SRR1633359 1 0 1 1 0
#> SRR1633360 1 0 1 1 0
#> SRR1633361 1 0 1 1 0
#> SRR2038492 1 0 1 1 0
#> SRR2038491 1 0 1 1 0
#> SRR2038490 1 0 1 1 0
#> SRR2038489 1 0 1 1 0
#> SRR2038488 1 0 1 1 0
#> SRR2038487 1 0 1 1 0
#> SRR2038486 1 0 1 1 0
#> SRR2038485 1 0 1 1 0
#> SRR2038484 1 0 1 1 0
#> SRR2038483 1 0 1 1 0
#> SRR2038482 1 0 1 1 0
#> SRR2038481 1 0 1 1 0
#> SRR2038480 1 0 1 1 0
#> SRR2038479 1 0 1 1 0
#> SRR2038477 1 0 1 1 0
#> SRR2038478 1 0 1 1 0
#> SRR2038476 1 0 1 1 0
#> SRR2038475 1 0 1 1 0
#> SRR2038474 1 0 1 1 0
#> SRR2038473 1 0 1 1 0
#> SRR2038472 1 0 1 1 0
#> SRR2038471 1 0 1 1 0
#> SRR2038470 1 0 1 1 0
#> SRR2038469 1 0 1 1 0
#> SRR2038468 1 0 1 1 0
#> SRR2038467 1 0 1 1 0
#> SRR2038466 1 0 1 1 0
#> SRR2038465 1 0 1 1 0
#> SRR2038464 1 0 1 1 0
#> SRR2038463 1 0 1 1 0
#> SRR2038462 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633231 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633232 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633233 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633234 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633236 2 0.576 0.544 0.000 0.672 0.328
#> SRR1633237 2 0.601 0.450 0.000 0.628 0.372
#> SRR1633238 2 0.590 0.495 0.000 0.648 0.352
#> SRR1633239 2 0.576 0.544 0.000 0.672 0.328
#> SRR1633240 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633241 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633242 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633243 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633244 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633245 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633246 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633247 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633248 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633249 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633250 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633251 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633252 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633253 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633254 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633255 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633256 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633257 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633258 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633259 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633260 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633261 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633262 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633263 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633264 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633265 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633266 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633267 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633268 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633269 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633270 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633271 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633272 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633273 1 0.103 0.965 0.976 0.000 0.024
#> SRR1633274 1 0.103 0.965 0.976 0.000 0.024
#> SRR1633275 1 0.103 0.965 0.976 0.000 0.024
#> SRR1633276 1 0.103 0.965 0.976 0.000 0.024
#> SRR1633277 1 0.175 0.943 0.952 0.000 0.048
#> SRR1633278 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633279 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633280 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633281 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633282 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633284 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633285 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633286 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633287 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633288 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633289 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633290 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633291 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633292 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633293 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633294 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633295 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633296 1 0.595 0.468 0.640 0.000 0.360
#> SRR1633297 1 0.601 0.439 0.628 0.000 0.372
#> SRR1633298 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633299 3 0.000 1.000 0.000 0.000 1.000
#> SRR1633300 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633301 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633302 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633303 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633304 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633305 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633306 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633307 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633308 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633309 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633310 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633311 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633312 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633313 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633314 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633315 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633316 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633317 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633318 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633319 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633320 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633321 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633322 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633323 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633324 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633325 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633326 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633327 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633328 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633329 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633330 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633331 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633332 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633333 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633334 2 0.000 0.967 0.000 1.000 0.000
#> SRR1633335 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633336 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633337 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633338 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633339 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633340 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633341 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633342 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633345 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633346 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633343 1 0.226 0.924 0.932 0.000 0.068
#> SRR1633344 1 0.216 0.928 0.936 0.000 0.064
#> SRR1633347 1 0.375 0.838 0.856 0.000 0.144
#> SRR1633348 1 0.400 0.818 0.840 0.000 0.160
#> SRR1633350 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633351 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633352 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633353 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633354 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633355 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633356 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633357 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633358 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633362 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633363 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633364 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633359 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633360 1 0.000 0.983 1.000 0.000 0.000
#> SRR1633361 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038492 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038491 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038490 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038489 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038488 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038487 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038486 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038485 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038484 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038483 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038482 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038481 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038480 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038479 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038477 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038478 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038476 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038475 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038474 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038473 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038472 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038471 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038470 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038469 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038468 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038467 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038466 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038465 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038464 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038463 1 0.000 0.983 1.000 0.000 0.000
#> SRR2038462 1 0.000 0.983 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633237 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633238 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633239 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633240 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633248 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633249 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633250 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633251 4 0.494 0.3188 0.000 0.000 0.436 0.564
#> SRR1633252 4 0.489 0.3761 0.000 0.000 0.412 0.588
#> SRR1633253 4 0.485 0.4048 0.000 0.000 0.400 0.600
#> SRR1633254 4 0.497 0.2663 0.000 0.000 0.456 0.544
#> SRR1633255 4 0.484 0.4123 0.000 0.000 0.396 0.604
#> SRR1633256 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633257 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633258 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633259 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633262 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633263 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633264 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633265 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633266 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633267 4 0.156 0.8445 0.000 0.000 0.056 0.944
#> SRR1633268 4 0.156 0.8445 0.000 0.000 0.056 0.944
#> SRR1633269 4 0.156 0.8445 0.000 0.000 0.056 0.944
#> SRR1633270 4 0.222 0.8194 0.000 0.000 0.092 0.908
#> SRR1633271 4 0.208 0.8255 0.000 0.000 0.084 0.916
#> SRR1633272 4 0.201 0.8281 0.000 0.000 0.080 0.920
#> SRR1633273 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633274 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633275 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633276 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633277 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633278 4 0.767 0.0741 0.212 0.392 0.000 0.396
#> SRR1633279 4 0.766 0.1102 0.212 0.380 0.000 0.408
#> SRR1633280 2 0.781 0.0285 0.272 0.416 0.000 0.312
#> SRR1633281 2 0.781 0.0299 0.272 0.416 0.000 0.312
#> SRR1633282 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633284 1 0.194 0.9210 0.924 0.000 0.000 0.076
#> SRR1633285 1 0.187 0.9242 0.928 0.000 0.000 0.072
#> SRR1633286 1 0.179 0.9271 0.932 0.000 0.000 0.068
#> SRR1633287 1 0.179 0.9271 0.932 0.000 0.000 0.068
#> SRR1633288 1 0.194 0.9210 0.924 0.000 0.000 0.076
#> SRR1633289 1 0.187 0.9242 0.928 0.000 0.000 0.072
#> SRR1633290 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633291 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633292 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633297 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633298 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633299 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633300 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.000 0.9713 0.000 1.000 0.000 0.000
#> SRR1633335 1 0.322 0.8315 0.836 0.000 0.000 0.164
#> SRR1633336 1 0.307 0.8457 0.848 0.000 0.000 0.152
#> SRR1633337 1 0.336 0.8165 0.824 0.000 0.000 0.176
#> SRR1633338 4 0.215 0.8088 0.088 0.000 0.000 0.912
#> SRR1633339 4 0.215 0.8088 0.088 0.000 0.000 0.912
#> SRR1633340 4 0.194 0.8191 0.076 0.000 0.000 0.924
#> SRR1633341 1 0.270 0.8771 0.876 0.000 0.000 0.124
#> SRR1633342 1 0.253 0.8889 0.888 0.000 0.000 0.112
#> SRR1633345 1 0.361 0.7849 0.800 0.000 0.000 0.200
#> SRR1633346 1 0.387 0.7432 0.772 0.000 0.000 0.228
#> SRR1633343 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633344 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633347 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633348 4 0.000 0.8733 0.000 0.000 0.000 1.000
#> SRR1633350 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.000 0.9716 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.475 0.3677 0.368 0.000 0.000 0.632
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.1732 0.919 0.000 0.920 0.080 0.000 0.000
#> SRR1633231 2 0.1732 0.919 0.000 0.920 0.080 0.000 0.000
#> SRR1633232 2 0.0162 0.926 0.000 0.996 0.004 0.000 0.000
#> SRR1633233 2 0.0290 0.926 0.000 0.992 0.008 0.000 0.000
#> SRR1633234 2 0.0162 0.926 0.000 0.996 0.004 0.000 0.000
#> SRR1633236 5 0.0162 0.933 0.000 0.004 0.000 0.000 0.996
#> SRR1633237 5 0.0162 0.933 0.000 0.004 0.000 0.000 0.996
#> SRR1633238 5 0.0162 0.933 0.000 0.004 0.000 0.000 0.996
#> SRR1633239 5 0.0162 0.933 0.000 0.004 0.000 0.000 0.996
#> SRR1633240 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633241 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633242 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633243 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633244 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633245 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633246 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633247 5 0.3177 0.705 0.000 0.000 0.208 0.000 0.792
#> SRR1633248 5 0.3336 0.670 0.000 0.000 0.228 0.000 0.772
#> SRR1633249 5 0.3210 0.699 0.000 0.000 0.212 0.000 0.788
#> SRR1633250 5 0.3242 0.692 0.000 0.000 0.216 0.000 0.784
#> SRR1633251 3 0.4818 0.764 0.000 0.000 0.720 0.100 0.180
#> SRR1633252 3 0.4855 0.771 0.000 0.000 0.720 0.112 0.168
#> SRR1633253 3 0.4855 0.771 0.000 0.000 0.720 0.112 0.168
#> SRR1633254 3 0.4832 0.767 0.000 0.000 0.720 0.104 0.176
#> SRR1633255 3 0.4832 0.767 0.000 0.000 0.720 0.104 0.176
#> SRR1633256 3 0.3895 0.613 0.000 0.000 0.680 0.000 0.320
#> SRR1633257 3 0.3837 0.628 0.000 0.000 0.692 0.000 0.308
#> SRR1633258 3 0.3895 0.613 0.000 0.000 0.680 0.000 0.320
#> SRR1633259 3 0.4268 0.373 0.000 0.000 0.556 0.000 0.444
#> SRR1633260 3 0.4256 0.394 0.000 0.000 0.564 0.000 0.436
#> SRR1633261 3 0.4278 0.351 0.000 0.000 0.548 0.000 0.452
#> SRR1633262 3 0.3707 0.791 0.000 0.000 0.716 0.284 0.000
#> SRR1633263 3 0.3707 0.791 0.000 0.000 0.716 0.284 0.000
#> SRR1633264 3 0.3730 0.789 0.000 0.000 0.712 0.288 0.000
#> SRR1633265 3 0.3730 0.789 0.000 0.000 0.712 0.288 0.000
#> SRR1633266 3 0.3730 0.789 0.000 0.000 0.712 0.288 0.000
#> SRR1633267 3 0.3942 0.799 0.000 0.000 0.728 0.260 0.012
#> SRR1633268 3 0.3942 0.799 0.000 0.000 0.728 0.260 0.012
#> SRR1633269 3 0.3942 0.799 0.000 0.000 0.728 0.260 0.012
#> SRR1633270 3 0.4337 0.805 0.000 0.000 0.748 0.196 0.056
#> SRR1633271 3 0.4272 0.805 0.000 0.000 0.752 0.196 0.052
#> SRR1633272 3 0.4272 0.805 0.000 0.000 0.752 0.196 0.052
#> SRR1633273 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633274 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633275 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633276 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633277 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633278 3 0.2523 0.722 0.028 0.024 0.908 0.040 0.000
#> SRR1633279 3 0.2607 0.723 0.032 0.024 0.904 0.040 0.000
#> SRR1633280 3 0.2597 0.720 0.040 0.020 0.904 0.036 0.000
#> SRR1633281 3 0.2597 0.723 0.036 0.020 0.904 0.040 0.000
#> SRR1633282 3 0.3636 0.793 0.000 0.000 0.728 0.272 0.000
#> SRR1633284 4 0.3395 0.788 0.236 0.000 0.000 0.764 0.000
#> SRR1633285 4 0.3336 0.795 0.228 0.000 0.000 0.772 0.000
#> SRR1633286 4 0.3395 0.788 0.236 0.000 0.000 0.764 0.000
#> SRR1633287 4 0.3395 0.788 0.236 0.000 0.000 0.764 0.000
#> SRR1633288 4 0.3366 0.792 0.232 0.000 0.000 0.768 0.000
#> SRR1633289 4 0.3395 0.788 0.236 0.000 0.000 0.764 0.000
#> SRR1633290 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633291 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633292 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633293 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633294 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633295 5 0.0000 0.936 0.000 0.000 0.000 0.000 1.000
#> SRR1633296 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633297 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633298 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633299 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633300 2 0.1544 0.921 0.000 0.932 0.068 0.000 0.000
#> SRR1633301 2 0.1544 0.921 0.000 0.932 0.068 0.000 0.000
#> SRR1633302 2 0.1544 0.921 0.000 0.932 0.068 0.000 0.000
#> SRR1633303 2 0.3143 0.894 0.000 0.796 0.204 0.000 0.000
#> SRR1633304 2 0.3143 0.894 0.000 0.796 0.204 0.000 0.000
#> SRR1633305 2 0.3143 0.894 0.000 0.796 0.204 0.000 0.000
#> SRR1633306 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633307 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633308 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633309 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633310 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633311 2 0.3109 0.896 0.000 0.800 0.200 0.000 0.000
#> SRR1633312 2 0.3039 0.899 0.000 0.808 0.192 0.000 0.000
#> SRR1633313 2 0.3039 0.899 0.000 0.808 0.192 0.000 0.000
#> SRR1633314 2 0.3074 0.898 0.000 0.804 0.196 0.000 0.000
#> SRR1633315 2 0.3039 0.899 0.000 0.808 0.192 0.000 0.000
#> SRR1633316 2 0.3074 0.898 0.000 0.804 0.196 0.000 0.000
#> SRR1633317 2 0.3074 0.898 0.000 0.804 0.196 0.000 0.000
#> SRR1633318 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633330 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633331 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633332 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633333 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633334 2 0.0510 0.920 0.000 0.984 0.016 0.000 0.000
#> SRR1633335 4 0.3074 0.820 0.196 0.000 0.000 0.804 0.000
#> SRR1633336 4 0.3109 0.817 0.200 0.000 0.000 0.800 0.000
#> SRR1633337 4 0.3039 0.822 0.192 0.000 0.000 0.808 0.000
#> SRR1633338 4 0.1410 0.854 0.060 0.000 0.000 0.940 0.000
#> SRR1633339 4 0.1270 0.854 0.052 0.000 0.000 0.948 0.000
#> SRR1633340 4 0.1341 0.854 0.056 0.000 0.000 0.944 0.000
#> SRR1633341 4 0.3039 0.822 0.192 0.000 0.000 0.808 0.000
#> SRR1633342 4 0.3074 0.820 0.196 0.000 0.000 0.804 0.000
#> SRR1633345 4 0.2852 0.829 0.172 0.000 0.000 0.828 0.000
#> SRR1633346 4 0.2690 0.834 0.156 0.000 0.000 0.844 0.000
#> SRR1633343 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633344 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633347 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633348 4 0.0000 0.851 0.000 0.000 0.000 1.000 0.000
#> SRR1633350 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038492 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038491 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038489 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038488 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038487 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038485 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038484 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038482 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038479 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038478 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038475 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038471 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038470 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038468 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038467 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038466 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0162 0.997 0.996 0.000 0.004 0.000 0.000
#> SRR2038463 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.5059 0.679 0.112 0.000 0.696 0.192 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.2048 0.800 0.000 0.880 0.000 0.000 0.000 0.120
#> SRR1633231 2 0.2048 0.800 0.000 0.880 0.000 0.000 0.000 0.120
#> SRR1633232 2 0.1387 0.802 0.000 0.932 0.000 0.000 0.000 0.068
#> SRR1633233 2 0.1444 0.802 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR1633234 2 0.1327 0.802 0.000 0.936 0.000 0.000 0.000 0.064
#> SRR1633236 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633237 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633238 5 0.0146 0.899 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1633239 5 0.0146 0.899 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1633240 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633241 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633242 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633243 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633244 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633245 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633246 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633247 5 0.3727 0.423 0.000 0.000 0.388 0.000 0.612 0.000
#> SRR1633248 5 0.3727 0.423 0.000 0.000 0.388 0.000 0.612 0.000
#> SRR1633249 5 0.3706 0.440 0.000 0.000 0.380 0.000 0.620 0.000
#> SRR1633250 5 0.3717 0.432 0.000 0.000 0.384 0.000 0.616 0.000
#> SRR1633251 3 0.1074 0.878 0.000 0.000 0.960 0.028 0.012 0.000
#> SRR1633252 3 0.1074 0.878 0.000 0.000 0.960 0.028 0.012 0.000
#> SRR1633253 3 0.0972 0.879 0.000 0.000 0.964 0.028 0.008 0.000
#> SRR1633254 3 0.1074 0.878 0.000 0.000 0.960 0.028 0.012 0.000
#> SRR1633255 3 0.1074 0.878 0.000 0.000 0.960 0.028 0.012 0.000
#> SRR1633256 3 0.1765 0.828 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633257 3 0.1765 0.828 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633258 3 0.1765 0.828 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633259 3 0.3221 0.614 0.000 0.000 0.736 0.000 0.264 0.000
#> SRR1633260 3 0.2996 0.674 0.000 0.000 0.772 0.000 0.228 0.000
#> SRR1633261 3 0.3175 0.628 0.000 0.000 0.744 0.000 0.256 0.000
#> SRR1633262 3 0.0937 0.878 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1633263 3 0.0937 0.878 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1633264 3 0.0937 0.878 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1633265 3 0.0937 0.878 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1633266 3 0.0937 0.878 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1633267 3 0.0935 0.879 0.000 0.000 0.964 0.032 0.000 0.004
#> SRR1633268 3 0.0935 0.879 0.000 0.000 0.964 0.032 0.000 0.004
#> SRR1633269 3 0.0935 0.879 0.000 0.000 0.964 0.032 0.000 0.004
#> SRR1633270 3 0.0972 0.878 0.000 0.000 0.964 0.028 0.000 0.008
#> SRR1633271 3 0.0972 0.878 0.000 0.000 0.964 0.028 0.000 0.008
#> SRR1633272 3 0.0972 0.878 0.000 0.000 0.964 0.028 0.000 0.008
#> SRR1633273 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633274 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633275 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633276 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633277 4 0.0146 0.972 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633278 3 0.4241 0.669 0.012 0.016 0.656 0.000 0.000 0.316
#> SRR1633279 3 0.4241 0.669 0.012 0.016 0.656 0.000 0.000 0.316
#> SRR1633280 3 0.4224 0.672 0.012 0.016 0.660 0.000 0.000 0.312
#> SRR1633281 3 0.4224 0.672 0.012 0.016 0.660 0.000 0.000 0.312
#> SRR1633282 3 0.3671 0.755 0.000 0.000 0.756 0.036 0.000 0.208
#> SRR1633284 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633285 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633286 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633287 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633288 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633289 4 0.0937 0.969 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1633290 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633291 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633292 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633293 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633294 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633295 5 0.0000 0.902 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0146 0.972 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633297 4 0.0146 0.972 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633298 4 0.0260 0.970 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633299 4 0.0260 0.970 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1633300 2 0.2664 0.792 0.000 0.816 0.000 0.000 0.000 0.184
#> SRR1633301 2 0.2697 0.791 0.000 0.812 0.000 0.000 0.000 0.188
#> SRR1633302 2 0.2762 0.790 0.000 0.804 0.000 0.000 0.000 0.196
#> SRR1633303 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633304 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633305 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633306 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633307 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633308 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633309 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633310 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633311 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633312 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633313 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633314 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633315 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633316 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633317 2 0.3862 0.733 0.000 0.524 0.000 0.000 0.000 0.476
#> SRR1633318 2 0.0146 0.796 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633319 2 0.0146 0.796 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633320 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0146 0.797 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633322 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0146 0.796 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633324 2 0.0146 0.796 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633325 2 0.0146 0.796 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1633326 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.1958 0.755 0.000 0.896 0.004 0.000 0.000 0.100
#> SRR1633330 2 0.1908 0.757 0.000 0.900 0.004 0.000 0.000 0.096
#> SRR1633331 2 0.1908 0.757 0.000 0.900 0.004 0.000 0.000 0.096
#> SRR1633332 2 0.1958 0.755 0.000 0.896 0.004 0.000 0.000 0.100
#> SRR1633333 2 0.1958 0.755 0.000 0.896 0.004 0.000 0.000 0.100
#> SRR1633334 2 0.1908 0.757 0.000 0.900 0.004 0.000 0.000 0.096
#> SRR1633335 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633336 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633337 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633338 4 0.0713 0.974 0.028 0.000 0.000 0.972 0.000 0.000
#> SRR1633339 4 0.0632 0.974 0.024 0.000 0.000 0.976 0.000 0.000
#> SRR1633340 4 0.0547 0.975 0.020 0.000 0.000 0.980 0.000 0.000
#> SRR1633341 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633342 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633345 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633346 4 0.0865 0.972 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1633343 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633344 4 0.0000 0.974 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633347 4 0.0146 0.972 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633348 4 0.0146 0.972 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633350 1 0.2048 0.923 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1633351 1 0.2048 0.923 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1633352 1 0.2048 0.923 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1633353 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633354 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633355 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633356 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633357 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633358 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633362 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633363 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633364 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633359 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633360 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR1633361 1 0.2191 0.922 0.876 0.000 0.000 0.004 0.000 0.120
#> SRR2038492 1 0.1584 0.915 0.928 0.000 0.008 0.000 0.000 0.064
#> SRR2038491 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.0291 0.954 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR2038489 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0146 0.957 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038483 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.1643 0.912 0.924 0.000 0.008 0.000 0.000 0.068
#> SRR2038479 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.1686 0.912 0.924 0.000 0.012 0.000 0.000 0.064
#> SRR2038475 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0146 0.955 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.957 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.4792 0.657 0.024 0.000 0.624 0.032 0.000 0.320
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.997 0.3747 0.627 0.627
#> 3 3 1.000 0.998 0.999 0.7166 0.729 0.569
#> 4 4 1.000 0.964 0.986 0.0375 0.981 0.948
#> 5 5 1.000 0.961 0.979 0.0433 0.970 0.913
#> 6 6 0.923 0.905 0.944 0.1265 0.894 0.658
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.0000 1.000 0.000 1.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000
#> SRR1633236 1 0.0938 0.991 0.988 0.012
#> SRR1633237 1 0.0938 0.991 0.988 0.012
#> SRR1633238 1 0.0938 0.991 0.988 0.012
#> SRR1633239 1 0.0938 0.991 0.988 0.012
#> SRR1633240 1 0.0938 0.991 0.988 0.012
#> SRR1633241 1 0.0938 0.991 0.988 0.012
#> SRR1633242 1 0.0938 0.991 0.988 0.012
#> SRR1633243 1 0.0938 0.991 0.988 0.012
#> SRR1633244 1 0.0938 0.991 0.988 0.012
#> SRR1633245 1 0.0938 0.991 0.988 0.012
#> SRR1633246 1 0.0938 0.991 0.988 0.012
#> SRR1633247 1 0.0938 0.991 0.988 0.012
#> SRR1633248 1 0.0938 0.991 0.988 0.012
#> SRR1633249 1 0.0938 0.991 0.988 0.012
#> SRR1633250 1 0.0938 0.991 0.988 0.012
#> SRR1633251 1 0.0938 0.991 0.988 0.012
#> SRR1633252 1 0.0938 0.991 0.988 0.012
#> SRR1633253 1 0.0938 0.991 0.988 0.012
#> SRR1633254 1 0.0938 0.991 0.988 0.012
#> SRR1633255 1 0.0938 0.991 0.988 0.012
#> SRR1633256 1 0.0938 0.991 0.988 0.012
#> SRR1633257 1 0.0938 0.991 0.988 0.012
#> SRR1633258 1 0.0938 0.991 0.988 0.012
#> SRR1633259 1 0.0938 0.991 0.988 0.012
#> SRR1633260 1 0.0938 0.991 0.988 0.012
#> SRR1633261 1 0.0938 0.991 0.988 0.012
#> SRR1633262 1 0.0000 0.996 1.000 0.000
#> SRR1633263 1 0.0000 0.996 1.000 0.000
#> SRR1633264 1 0.0000 0.996 1.000 0.000
#> SRR1633265 1 0.0000 0.996 1.000 0.000
#> SRR1633266 1 0.0000 0.996 1.000 0.000
#> SRR1633267 1 0.0938 0.991 0.988 0.012
#> SRR1633268 1 0.0938 0.991 0.988 0.012
#> SRR1633269 1 0.0938 0.991 0.988 0.012
#> SRR1633270 1 0.0938 0.991 0.988 0.012
#> SRR1633271 1 0.0938 0.991 0.988 0.012
#> SRR1633272 1 0.0938 0.991 0.988 0.012
#> SRR1633273 1 0.0000 0.996 1.000 0.000
#> SRR1633274 1 0.0000 0.996 1.000 0.000
#> SRR1633275 1 0.0000 0.996 1.000 0.000
#> SRR1633276 1 0.0000 0.996 1.000 0.000
#> SRR1633277 1 0.0000 0.996 1.000 0.000
#> SRR1633278 1 0.0000 0.996 1.000 0.000
#> SRR1633279 1 0.0000 0.996 1.000 0.000
#> SRR1633280 1 0.0000 0.996 1.000 0.000
#> SRR1633281 1 0.0000 0.996 1.000 0.000
#> SRR1633282 1 0.0000 0.996 1.000 0.000
#> SRR1633284 1 0.0000 0.996 1.000 0.000
#> SRR1633285 1 0.0000 0.996 1.000 0.000
#> SRR1633286 1 0.0000 0.996 1.000 0.000
#> SRR1633287 1 0.0000 0.996 1.000 0.000
#> SRR1633288 1 0.0000 0.996 1.000 0.000
#> SRR1633289 1 0.0000 0.996 1.000 0.000
#> SRR1633290 1 0.0000 0.996 1.000 0.000
#> SRR1633291 1 0.0000 0.996 1.000 0.000
#> SRR1633292 1 0.0938 0.991 0.988 0.012
#> SRR1633293 1 0.0938 0.991 0.988 0.012
#> SRR1633294 1 0.0938 0.991 0.988 0.012
#> SRR1633295 1 0.0938 0.991 0.988 0.012
#> SRR1633296 1 0.0000 0.996 1.000 0.000
#> SRR1633297 1 0.0000 0.996 1.000 0.000
#> SRR1633298 1 0.0000 0.996 1.000 0.000
#> SRR1633299 1 0.0000 0.996 1.000 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000
#> SRR1633335 1 0.0000 0.996 1.000 0.000
#> SRR1633336 1 0.0000 0.996 1.000 0.000
#> SRR1633337 1 0.0000 0.996 1.000 0.000
#> SRR1633338 1 0.0000 0.996 1.000 0.000
#> SRR1633339 1 0.0000 0.996 1.000 0.000
#> SRR1633340 1 0.0000 0.996 1.000 0.000
#> SRR1633341 1 0.0000 0.996 1.000 0.000
#> SRR1633342 1 0.0000 0.996 1.000 0.000
#> SRR1633345 1 0.0000 0.996 1.000 0.000
#> SRR1633346 1 0.0000 0.996 1.000 0.000
#> SRR1633343 1 0.0000 0.996 1.000 0.000
#> SRR1633344 1 0.0000 0.996 1.000 0.000
#> SRR1633347 1 0.0000 0.996 1.000 0.000
#> SRR1633348 1 0.0000 0.996 1.000 0.000
#> SRR1633350 1 0.0000 0.996 1.000 0.000
#> SRR1633351 1 0.0000 0.996 1.000 0.000
#> SRR1633352 1 0.0000 0.996 1.000 0.000
#> SRR1633353 1 0.0000 0.996 1.000 0.000
#> SRR1633354 1 0.0000 0.996 1.000 0.000
#> SRR1633355 1 0.0000 0.996 1.000 0.000
#> SRR1633356 1 0.0000 0.996 1.000 0.000
#> SRR1633357 1 0.0000 0.996 1.000 0.000
#> SRR1633358 1 0.0000 0.996 1.000 0.000
#> SRR1633362 1 0.0000 0.996 1.000 0.000
#> SRR1633363 1 0.0000 0.996 1.000 0.000
#> SRR1633364 1 0.0000 0.996 1.000 0.000
#> SRR1633359 1 0.0000 0.996 1.000 0.000
#> SRR1633360 1 0.0000 0.996 1.000 0.000
#> SRR1633361 1 0.0000 0.996 1.000 0.000
#> SRR2038492 1 0.0000 0.996 1.000 0.000
#> SRR2038491 1 0.0000 0.996 1.000 0.000
#> SRR2038490 1 0.0000 0.996 1.000 0.000
#> SRR2038489 1 0.0000 0.996 1.000 0.000
#> SRR2038488 1 0.0000 0.996 1.000 0.000
#> SRR2038487 1 0.0000 0.996 1.000 0.000
#> SRR2038486 1 0.0000 0.996 1.000 0.000
#> SRR2038485 1 0.0000 0.996 1.000 0.000
#> SRR2038484 1 0.0000 0.996 1.000 0.000
#> SRR2038483 1 0.0000 0.996 1.000 0.000
#> SRR2038482 1 0.0000 0.996 1.000 0.000
#> SRR2038481 1 0.0000 0.996 1.000 0.000
#> SRR2038480 1 0.0000 0.996 1.000 0.000
#> SRR2038479 1 0.0000 0.996 1.000 0.000
#> SRR2038477 1 0.0000 0.996 1.000 0.000
#> SRR2038478 1 0.0000 0.996 1.000 0.000
#> SRR2038476 1 0.0000 0.996 1.000 0.000
#> SRR2038475 1 0.0000 0.996 1.000 0.000
#> SRR2038474 1 0.0000 0.996 1.000 0.000
#> SRR2038473 1 0.0000 0.996 1.000 0.000
#> SRR2038472 1 0.0000 0.996 1.000 0.000
#> SRR2038471 1 0.0000 0.996 1.000 0.000
#> SRR2038470 1 0.0000 0.996 1.000 0.000
#> SRR2038469 1 0.0000 0.996 1.000 0.000
#> SRR2038468 1 0.0000 0.996 1.000 0.000
#> SRR2038467 1 0.0000 0.996 1.000 0.000
#> SRR2038466 1 0.0000 0.996 1.000 0.000
#> SRR2038465 1 0.0000 0.996 1.000 0.000
#> SRR2038464 1 0.0000 0.996 1.000 0.000
#> SRR2038463 1 0.0000 0.996 1.000 0.000
#> SRR2038462 1 0.0000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000
#> SRR1633236 3 0.0000 0.997 0.000 0 1.000
#> SRR1633237 3 0.0000 0.997 0.000 0 1.000
#> SRR1633238 3 0.0000 0.997 0.000 0 1.000
#> SRR1633239 3 0.0000 0.997 0.000 0 1.000
#> SRR1633240 3 0.0000 0.997 0.000 0 1.000
#> SRR1633241 3 0.0000 0.997 0.000 0 1.000
#> SRR1633242 3 0.0000 0.997 0.000 0 1.000
#> SRR1633243 3 0.0000 0.997 0.000 0 1.000
#> SRR1633244 3 0.0000 0.997 0.000 0 1.000
#> SRR1633245 3 0.0000 0.997 0.000 0 1.000
#> SRR1633246 3 0.0000 0.997 0.000 0 1.000
#> SRR1633247 3 0.0000 0.997 0.000 0 1.000
#> SRR1633248 3 0.0000 0.997 0.000 0 1.000
#> SRR1633249 3 0.0000 0.997 0.000 0 1.000
#> SRR1633250 3 0.0000 0.997 0.000 0 1.000
#> SRR1633251 3 0.0000 0.997 0.000 0 1.000
#> SRR1633252 3 0.0000 0.997 0.000 0 1.000
#> SRR1633253 3 0.0000 0.997 0.000 0 1.000
#> SRR1633254 3 0.0000 0.997 0.000 0 1.000
#> SRR1633255 3 0.0000 0.997 0.000 0 1.000
#> SRR1633256 3 0.0000 0.997 0.000 0 1.000
#> SRR1633257 3 0.0000 0.997 0.000 0 1.000
#> SRR1633258 3 0.0000 0.997 0.000 0 1.000
#> SRR1633259 3 0.0000 0.997 0.000 0 1.000
#> SRR1633260 3 0.0000 0.997 0.000 0 1.000
#> SRR1633261 3 0.0000 0.997 0.000 0 1.000
#> SRR1633262 3 0.0592 0.989 0.012 0 0.988
#> SRR1633263 3 0.0592 0.989 0.012 0 0.988
#> SRR1633264 3 0.0592 0.989 0.012 0 0.988
#> SRR1633265 3 0.0592 0.989 0.012 0 0.988
#> SRR1633266 3 0.0592 0.989 0.012 0 0.988
#> SRR1633267 3 0.0000 0.997 0.000 0 1.000
#> SRR1633268 3 0.0000 0.997 0.000 0 1.000
#> SRR1633269 3 0.0000 0.997 0.000 0 1.000
#> SRR1633270 3 0.0000 0.997 0.000 0 1.000
#> SRR1633271 3 0.0000 0.997 0.000 0 1.000
#> SRR1633272 3 0.0000 0.997 0.000 0 1.000
#> SRR1633273 1 0.0000 1.000 1.000 0 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0 0.000
#> SRR1633278 3 0.0592 0.989 0.012 0 0.988
#> SRR1633279 3 0.0592 0.989 0.012 0 0.988
#> SRR1633280 3 0.0592 0.989 0.012 0 0.988
#> SRR1633281 3 0.0592 0.989 0.012 0 0.988
#> SRR1633282 3 0.0592 0.989 0.012 0 0.988
#> SRR1633284 1 0.0000 1.000 1.000 0 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0 0.000
#> SRR1633292 3 0.0000 0.997 0.000 0 1.000
#> SRR1633293 3 0.0000 0.997 0.000 0 1.000
#> SRR1633294 3 0.0000 0.997 0.000 0 1.000
#> SRR1633295 3 0.0000 0.997 0.000 0 1.000
#> SRR1633296 1 0.0000 1.000 1.000 0 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000
#> SRR1633335 1 0.0000 1.000 1.000 0 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0 0.000
#> SRR2038462 3 0.0592 0.989 0.012 0 0.988
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633231 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633232 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633233 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633234 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633236 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633237 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633238 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633239 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633240 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633241 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633242 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633243 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633244 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633245 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633246 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633247 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633248 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633249 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633250 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633251 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633252 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633253 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633254 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633255 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633256 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633257 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633258 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633259 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633260 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633261 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633262 3 0.497 0.258 0 0 0.548 0.452
#> SRR1633263 3 0.497 0.258 0 0 0.548 0.452
#> SRR1633264 3 0.497 0.258 0 0 0.548 0.452
#> SRR1633265 3 0.497 0.258 0 0 0.548 0.452
#> SRR1633266 3 0.497 0.258 0 0 0.548 0.452
#> SRR1633267 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633268 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633269 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633270 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633271 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633272 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633273 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633274 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633275 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633276 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633277 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633278 4 0.000 1.000 0 0 0.000 1.000
#> SRR1633279 4 0.000 1.000 0 0 0.000 1.000
#> SRR1633280 4 0.000 1.000 0 0 0.000 1.000
#> SRR1633281 4 0.000 1.000 0 0 0.000 1.000
#> SRR1633282 4 0.000 1.000 0 0 0.000 1.000
#> SRR1633284 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633285 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633286 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633287 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633288 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633289 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633290 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633291 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633292 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633293 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633294 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633295 3 0.000 0.940 0 0 1.000 0.000
#> SRR1633296 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633297 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633298 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633299 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633300 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633301 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633302 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633303 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633304 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633305 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633306 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633307 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633308 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633309 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633310 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633311 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633312 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633313 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633314 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633315 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633316 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633317 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633318 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633319 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633320 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633321 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633322 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633323 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633324 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633325 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633326 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633327 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633328 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633329 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633330 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633331 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633332 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633333 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633334 2 0.000 1.000 0 1 0.000 0.000
#> SRR1633335 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633336 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633337 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633338 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633339 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633340 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633341 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633342 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633345 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633346 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633343 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633344 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633347 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633348 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633350 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633351 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633352 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633353 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633354 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633355 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633356 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633357 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633358 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633362 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633363 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633364 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633359 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633360 1 0.000 1.000 1 0 0.000 0.000
#> SRR1633361 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038492 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038491 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038490 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038489 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038488 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038487 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038486 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038485 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038484 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038483 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038482 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038481 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038480 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038479 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038477 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038478 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038476 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038475 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038474 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038473 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038472 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038471 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038470 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038469 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038468 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038467 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038466 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038465 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038464 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038463 1 0.000 1.000 1 0 0.000 0.000
#> SRR2038462 4 0.000 1.000 0 0 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633231 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633232 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633233 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633234 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633236 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633237 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633238 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633239 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633240 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633241 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633242 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633243 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633244 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633245 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633246 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633247 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633248 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633249 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633250 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633251 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633252 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633253 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633254 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633255 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633256 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633257 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633258 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633259 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633260 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633261 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633262 3 0.428 0.306 0.000 0 0.548 0.452 0.000
#> SRR1633263 3 0.428 0.306 0.000 0 0.548 0.452 0.000
#> SRR1633264 3 0.428 0.306 0.000 0 0.548 0.452 0.000
#> SRR1633265 3 0.428 0.306 0.000 0 0.548 0.452 0.000
#> SRR1633266 3 0.428 0.306 0.000 0 0.548 0.452 0.000
#> SRR1633267 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633268 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633269 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633270 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633271 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633272 3 0.000 0.902 0.000 0 1.000 0.000 0.000
#> SRR1633273 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633274 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633275 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633276 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633277 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633278 4 0.000 1.000 0.000 0 0.000 1.000 0.000
#> SRR1633279 4 0.000 1.000 0.000 0 0.000 1.000 0.000
#> SRR1633280 4 0.000 1.000 0.000 0 0.000 1.000 0.000
#> SRR1633281 4 0.000 1.000 0.000 0 0.000 1.000 0.000
#> SRR1633282 4 0.000 1.000 0.000 0 0.000 1.000 0.000
#> SRR1633284 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633285 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633286 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633287 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633288 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633289 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633290 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633291 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633292 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633293 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633294 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633295 5 0.088 1.000 0.000 0 0.032 0.000 0.968
#> SRR1633296 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633297 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633298 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633299 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633300 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633301 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633302 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633303 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633304 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633305 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633306 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633307 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633308 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633309 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633310 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633311 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633312 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633313 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633314 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633315 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633316 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633317 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633318 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633319 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633320 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633321 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633322 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633323 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633324 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633325 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633326 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633327 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633328 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633329 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633330 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633331 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633332 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633333 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633334 2 0.000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633335 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633336 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633337 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633338 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633339 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633340 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633341 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633342 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633345 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633346 1 0.051 0.988 0.984 0 0.000 0.000 0.016
#> SRR1633343 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633344 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633347 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633348 1 0.088 0.980 0.968 0 0.000 0.000 0.032
#> SRR1633350 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633351 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633352 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633353 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633354 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633355 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633356 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633357 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633358 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633362 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633363 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633364 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633359 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633360 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR1633361 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038492 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038491 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038490 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038489 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038488 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038487 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038484 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038483 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038481 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038480 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038479 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038476 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038475 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038470 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038469 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038464 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038463 1 0.000 0.991 1.000 0 0.000 0.000 0.000
#> SRR2038462 4 0.000 1.000 0.000 0 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633231 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633232 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633233 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633234 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633236 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633237 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633238 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633239 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633240 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633241 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633242 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633243 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633244 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633245 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633246 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633247 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633248 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633249 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633250 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633251 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633252 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633253 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633254 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633255 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633256 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633257 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633258 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633259 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633260 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633261 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633262 3 0.3843 0.307 0.000 0.000 0.548 0.000 0 0.452
#> SRR1633263 3 0.3843 0.307 0.000 0.000 0.548 0.000 0 0.452
#> SRR1633264 3 0.3843 0.307 0.000 0.000 0.548 0.000 0 0.452
#> SRR1633265 3 0.3843 0.307 0.000 0.000 0.548 0.000 0 0.452
#> SRR1633266 3 0.3843 0.307 0.000 0.000 0.548 0.000 0 0.452
#> SRR1633267 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633268 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633269 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633270 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633271 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633272 3 0.0000 0.902 0.000 0.000 1.000 0.000 0 0.000
#> SRR1633273 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633274 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633275 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633276 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633277 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633278 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1633279 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1633280 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1633281 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1633282 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
#> SRR1633284 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633285 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633286 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633287 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633288 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633289 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633290 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633291 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633292 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633293 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633294 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633295 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1633296 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633297 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633298 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633299 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633300 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633301 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633302 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633303 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633304 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633305 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633306 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633307 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633308 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633309 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633310 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633311 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633312 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633313 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633314 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633315 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633316 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633317 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633318 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633319 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633320 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633321 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633322 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633323 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633324 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633325 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633326 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633327 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633328 2 0.0000 0.994 0.000 1.000 0.000 0.000 0 0.000
#> SRR1633329 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633330 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633331 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633332 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633333 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633334 2 0.0713 0.979 0.000 0.972 0.000 0.028 0 0.000
#> SRR1633335 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633336 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633337 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633338 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633339 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633340 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633341 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633342 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633345 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633346 4 0.3737 0.701 0.392 0.000 0.000 0.608 0 0.000
#> SRR1633343 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633344 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633347 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633348 4 0.0713 0.683 0.028 0.000 0.000 0.972 0 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038492 1 0.0146 0.995 0.996 0.000 0.000 0.004 0 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000 0.000 0 0.000
#> SRR2038462 6 0.0000 1.000 0.000 0.000 0.000 0.000 0 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.962 0.986 0.4980 0.505 0.505
#> 3 3 1.000 1.000 1.000 0.2923 0.776 0.588
#> 4 4 0.802 0.731 0.870 0.1139 0.926 0.793
#> 5 5 0.741 0.844 0.831 0.0600 0.926 0.757
#> 6 6 0.767 0.765 0.807 0.0481 0.970 0.879
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 2 0.000 1.000 0.000 1.000
#> SRR1633241 2 0.000 1.000 0.000 1.000
#> SRR1633242 2 0.000 1.000 0.000 1.000
#> SRR1633243 2 0.000 1.000 0.000 1.000
#> SRR1633244 2 0.000 1.000 0.000 1.000
#> SRR1633245 2 0.000 1.000 0.000 1.000
#> SRR1633246 2 0.000 1.000 0.000 1.000
#> SRR1633247 2 0.000 1.000 0.000 1.000
#> SRR1633248 2 0.000 1.000 0.000 1.000
#> SRR1633249 2 0.000 1.000 0.000 1.000
#> SRR1633250 2 0.000 1.000 0.000 1.000
#> SRR1633251 1 0.994 0.199 0.544 0.456
#> SRR1633252 1 0.994 0.199 0.544 0.456
#> SRR1633253 1 0.994 0.199 0.544 0.456
#> SRR1633254 1 0.994 0.199 0.544 0.456
#> SRR1633255 1 0.994 0.199 0.544 0.456
#> SRR1633256 2 0.000 1.000 0.000 1.000
#> SRR1633257 2 0.000 1.000 0.000 1.000
#> SRR1633258 2 0.000 1.000 0.000 1.000
#> SRR1633259 2 0.000 1.000 0.000 1.000
#> SRR1633260 2 0.000 1.000 0.000 1.000
#> SRR1633261 2 0.000 1.000 0.000 1.000
#> SRR1633262 1 0.000 0.975 1.000 0.000
#> SRR1633263 1 0.000 0.975 1.000 0.000
#> SRR1633264 1 0.000 0.975 1.000 0.000
#> SRR1633265 1 0.000 0.975 1.000 0.000
#> SRR1633266 1 0.000 0.975 1.000 0.000
#> SRR1633267 2 0.000 1.000 0.000 1.000
#> SRR1633268 2 0.000 1.000 0.000 1.000
#> SRR1633269 2 0.000 1.000 0.000 1.000
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.975 1.000 0.000
#> SRR1633274 1 0.000 0.975 1.000 0.000
#> SRR1633275 1 0.000 0.975 1.000 0.000
#> SRR1633276 1 0.000 0.975 1.000 0.000
#> SRR1633277 1 0.000 0.975 1.000 0.000
#> SRR1633278 1 0.000 0.975 1.000 0.000
#> SRR1633279 1 0.000 0.975 1.000 0.000
#> SRR1633280 1 0.000 0.975 1.000 0.000
#> SRR1633281 1 0.000 0.975 1.000 0.000
#> SRR1633282 1 0.000 0.975 1.000 0.000
#> SRR1633284 1 0.000 0.975 1.000 0.000
#> SRR1633285 1 0.000 0.975 1.000 0.000
#> SRR1633286 1 0.000 0.975 1.000 0.000
#> SRR1633287 1 0.000 0.975 1.000 0.000
#> SRR1633288 1 0.000 0.975 1.000 0.000
#> SRR1633289 1 0.000 0.975 1.000 0.000
#> SRR1633290 1 0.000 0.975 1.000 0.000
#> SRR1633291 1 0.000 0.975 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.975 1.000 0.000
#> SRR1633297 1 0.000 0.975 1.000 0.000
#> SRR1633298 1 0.000 0.975 1.000 0.000
#> SRR1633299 1 0.000 0.975 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.975 1.000 0.000
#> SRR1633336 1 0.000 0.975 1.000 0.000
#> SRR1633337 1 0.000 0.975 1.000 0.000
#> SRR1633338 1 0.000 0.975 1.000 0.000
#> SRR1633339 1 0.000 0.975 1.000 0.000
#> SRR1633340 1 0.000 0.975 1.000 0.000
#> SRR1633341 1 0.000 0.975 1.000 0.000
#> SRR1633342 1 0.000 0.975 1.000 0.000
#> SRR1633345 1 0.000 0.975 1.000 0.000
#> SRR1633346 1 0.000 0.975 1.000 0.000
#> SRR1633343 1 0.000 0.975 1.000 0.000
#> SRR1633344 1 0.000 0.975 1.000 0.000
#> SRR1633347 1 0.000 0.975 1.000 0.000
#> SRR1633348 1 0.000 0.975 1.000 0.000
#> SRR1633350 1 0.000 0.975 1.000 0.000
#> SRR1633351 1 0.000 0.975 1.000 0.000
#> SRR1633352 1 0.000 0.975 1.000 0.000
#> SRR1633353 1 0.000 0.975 1.000 0.000
#> SRR1633354 1 0.000 0.975 1.000 0.000
#> SRR1633355 1 0.000 0.975 1.000 0.000
#> SRR1633356 1 0.000 0.975 1.000 0.000
#> SRR1633357 1 0.000 0.975 1.000 0.000
#> SRR1633358 1 0.000 0.975 1.000 0.000
#> SRR1633362 1 0.000 0.975 1.000 0.000
#> SRR1633363 1 0.000 0.975 1.000 0.000
#> SRR1633364 1 0.000 0.975 1.000 0.000
#> SRR1633359 1 0.000 0.975 1.000 0.000
#> SRR1633360 1 0.000 0.975 1.000 0.000
#> SRR1633361 1 0.000 0.975 1.000 0.000
#> SRR2038492 1 0.000 0.975 1.000 0.000
#> SRR2038491 1 0.000 0.975 1.000 0.000
#> SRR2038490 1 0.000 0.975 1.000 0.000
#> SRR2038489 1 0.000 0.975 1.000 0.000
#> SRR2038488 1 0.000 0.975 1.000 0.000
#> SRR2038487 1 0.000 0.975 1.000 0.000
#> SRR2038486 1 0.000 0.975 1.000 0.000
#> SRR2038485 1 0.000 0.975 1.000 0.000
#> SRR2038484 1 0.000 0.975 1.000 0.000
#> SRR2038483 1 0.000 0.975 1.000 0.000
#> SRR2038482 1 0.000 0.975 1.000 0.000
#> SRR2038481 1 0.000 0.975 1.000 0.000
#> SRR2038480 1 0.000 0.975 1.000 0.000
#> SRR2038479 1 0.000 0.975 1.000 0.000
#> SRR2038477 1 0.000 0.975 1.000 0.000
#> SRR2038478 1 0.000 0.975 1.000 0.000
#> SRR2038476 1 0.000 0.975 1.000 0.000
#> SRR2038475 1 0.000 0.975 1.000 0.000
#> SRR2038474 1 0.000 0.975 1.000 0.000
#> SRR2038473 1 0.000 0.975 1.000 0.000
#> SRR2038472 1 0.000 0.975 1.000 0.000
#> SRR2038471 1 0.000 0.975 1.000 0.000
#> SRR2038470 1 0.000 0.975 1.000 0.000
#> SRR2038469 1 0.000 0.975 1.000 0.000
#> SRR2038468 1 0.000 0.975 1.000 0.000
#> SRR2038467 1 0.000 0.975 1.000 0.000
#> SRR2038466 1 0.000 0.975 1.000 0.000
#> SRR2038465 1 0.000 0.975 1.000 0.000
#> SRR2038464 1 0.000 0.975 1.000 0.000
#> SRR2038463 1 0.000 0.975 1.000 0.000
#> SRR2038462 1 0.000 0.975 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0 1 0 1 0
#> SRR1633231 2 0 1 0 1 0
#> SRR1633232 2 0 1 0 1 0
#> SRR1633233 2 0 1 0 1 0
#> SRR1633234 2 0 1 0 1 0
#> SRR1633236 3 0 1 0 0 1
#> SRR1633237 3 0 1 0 0 1
#> SRR1633238 3 0 1 0 0 1
#> SRR1633239 3 0 1 0 0 1
#> SRR1633240 3 0 1 0 0 1
#> SRR1633241 3 0 1 0 0 1
#> SRR1633242 3 0 1 0 0 1
#> SRR1633243 3 0 1 0 0 1
#> SRR1633244 3 0 1 0 0 1
#> SRR1633245 3 0 1 0 0 1
#> SRR1633246 3 0 1 0 0 1
#> SRR1633247 3 0 1 0 0 1
#> SRR1633248 3 0 1 0 0 1
#> SRR1633249 3 0 1 0 0 1
#> SRR1633250 3 0 1 0 0 1
#> SRR1633251 3 0 1 0 0 1
#> SRR1633252 3 0 1 0 0 1
#> SRR1633253 3 0 1 0 0 1
#> SRR1633254 3 0 1 0 0 1
#> SRR1633255 3 0 1 0 0 1
#> SRR1633256 3 0 1 0 0 1
#> SRR1633257 3 0 1 0 0 1
#> SRR1633258 3 0 1 0 0 1
#> SRR1633259 3 0 1 0 0 1
#> SRR1633260 3 0 1 0 0 1
#> SRR1633261 3 0 1 0 0 1
#> SRR1633262 3 0 1 0 0 1
#> SRR1633263 3 0 1 0 0 1
#> SRR1633264 3 0 1 0 0 1
#> SRR1633265 3 0 1 0 0 1
#> SRR1633266 3 0 1 0 0 1
#> SRR1633267 3 0 1 0 0 1
#> SRR1633268 3 0 1 0 0 1
#> SRR1633269 3 0 1 0 0 1
#> SRR1633270 3 0 1 0 0 1
#> SRR1633271 3 0 1 0 0 1
#> SRR1633272 3 0 1 0 0 1
#> SRR1633273 1 0 1 1 0 0
#> SRR1633274 1 0 1 1 0 0
#> SRR1633275 1 0 1 1 0 0
#> SRR1633276 1 0 1 1 0 0
#> SRR1633277 1 0 1 1 0 0
#> SRR1633278 3 0 1 0 0 1
#> SRR1633279 3 0 1 0 0 1
#> SRR1633280 3 0 1 0 0 1
#> SRR1633281 3 0 1 0 0 1
#> SRR1633282 3 0 1 0 0 1
#> SRR1633284 1 0 1 1 0 0
#> SRR1633285 1 0 1 1 0 0
#> SRR1633286 1 0 1 1 0 0
#> SRR1633287 1 0 1 1 0 0
#> SRR1633288 1 0 1 1 0 0
#> SRR1633289 1 0 1 1 0 0
#> SRR1633290 1 0 1 1 0 0
#> SRR1633291 1 0 1 1 0 0
#> SRR1633292 3 0 1 0 0 1
#> SRR1633293 3 0 1 0 0 1
#> SRR1633294 3 0 1 0 0 1
#> SRR1633295 3 0 1 0 0 1
#> SRR1633296 1 0 1 1 0 0
#> SRR1633297 1 0 1 1 0 0
#> SRR1633298 1 0 1 1 0 0
#> SRR1633299 1 0 1 1 0 0
#> SRR1633300 2 0 1 0 1 0
#> SRR1633301 2 0 1 0 1 0
#> SRR1633302 2 0 1 0 1 0
#> SRR1633303 2 0 1 0 1 0
#> SRR1633304 2 0 1 0 1 0
#> SRR1633305 2 0 1 0 1 0
#> SRR1633306 2 0 1 0 1 0
#> SRR1633307 2 0 1 0 1 0
#> SRR1633308 2 0 1 0 1 0
#> SRR1633309 2 0 1 0 1 0
#> SRR1633310 2 0 1 0 1 0
#> SRR1633311 2 0 1 0 1 0
#> SRR1633312 2 0 1 0 1 0
#> SRR1633313 2 0 1 0 1 0
#> SRR1633314 2 0 1 0 1 0
#> SRR1633315 2 0 1 0 1 0
#> SRR1633316 2 0 1 0 1 0
#> SRR1633317 2 0 1 0 1 0
#> SRR1633318 2 0 1 0 1 0
#> SRR1633319 2 0 1 0 1 0
#> SRR1633320 2 0 1 0 1 0
#> SRR1633321 2 0 1 0 1 0
#> SRR1633322 2 0 1 0 1 0
#> SRR1633323 2 0 1 0 1 0
#> SRR1633324 2 0 1 0 1 0
#> SRR1633325 2 0 1 0 1 0
#> SRR1633326 2 0 1 0 1 0
#> SRR1633327 2 0 1 0 1 0
#> SRR1633328 2 0 1 0 1 0
#> SRR1633329 2 0 1 0 1 0
#> SRR1633330 2 0 1 0 1 0
#> SRR1633331 2 0 1 0 1 0
#> SRR1633332 2 0 1 0 1 0
#> SRR1633333 2 0 1 0 1 0
#> SRR1633334 2 0 1 0 1 0
#> SRR1633335 1 0 1 1 0 0
#> SRR1633336 1 0 1 1 0 0
#> SRR1633337 1 0 1 1 0 0
#> SRR1633338 1 0 1 1 0 0
#> SRR1633339 1 0 1 1 0 0
#> SRR1633340 1 0 1 1 0 0
#> SRR1633341 1 0 1 1 0 0
#> SRR1633342 1 0 1 1 0 0
#> SRR1633345 1 0 1 1 0 0
#> SRR1633346 1 0 1 1 0 0
#> SRR1633343 1 0 1 1 0 0
#> SRR1633344 1 0 1 1 0 0
#> SRR1633347 1 0 1 1 0 0
#> SRR1633348 1 0 1 1 0 0
#> SRR1633350 1 0 1 1 0 0
#> SRR1633351 1 0 1 1 0 0
#> SRR1633352 1 0 1 1 0 0
#> SRR1633353 1 0 1 1 0 0
#> SRR1633354 1 0 1 1 0 0
#> SRR1633355 1 0 1 1 0 0
#> SRR1633356 1 0 1 1 0 0
#> SRR1633357 1 0 1 1 0 0
#> SRR1633358 1 0 1 1 0 0
#> SRR1633362 1 0 1 1 0 0
#> SRR1633363 1 0 1 1 0 0
#> SRR1633364 1 0 1 1 0 0
#> SRR1633359 1 0 1 1 0 0
#> SRR1633360 1 0 1 1 0 0
#> SRR1633361 1 0 1 1 0 0
#> SRR2038492 1 0 1 1 0 0
#> SRR2038491 1 0 1 1 0 0
#> SRR2038490 1 0 1 1 0 0
#> SRR2038489 1 0 1 1 0 0
#> SRR2038488 1 0 1 1 0 0
#> SRR2038487 1 0 1 1 0 0
#> SRR2038486 1 0 1 1 0 0
#> SRR2038485 1 0 1 1 0 0
#> SRR2038484 1 0 1 1 0 0
#> SRR2038483 1 0 1 1 0 0
#> SRR2038482 1 0 1 1 0 0
#> SRR2038481 1 0 1 1 0 0
#> SRR2038480 1 0 1 1 0 0
#> SRR2038479 1 0 1 1 0 0
#> SRR2038477 1 0 1 1 0 0
#> SRR2038478 1 0 1 1 0 0
#> SRR2038476 1 0 1 1 0 0
#> SRR2038475 1 0 1 1 0 0
#> SRR2038474 1 0 1 1 0 0
#> SRR2038473 1 0 1 1 0 0
#> SRR2038472 1 0 1 1 0 0
#> SRR2038471 1 0 1 1 0 0
#> SRR2038470 1 0 1 1 0 0
#> SRR2038469 1 0 1 1 0 0
#> SRR2038468 1 0 1 1 0 0
#> SRR2038467 1 0 1 1 0 0
#> SRR2038466 1 0 1 1 0 0
#> SRR2038465 1 0 1 1 0 0
#> SRR2038464 1 0 1 1 0 0
#> SRR2038463 1 0 1 1 0 0
#> SRR2038462 3 0 1 0 0 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.2216 0.944 0.000 0.908 0.000 0.092
#> SRR1633231 2 0.2216 0.944 0.000 0.908 0.000 0.092
#> SRR1633232 2 0.2216 0.944 0.000 0.908 0.000 0.092
#> SRR1633233 2 0.2216 0.944 0.000 0.908 0.000 0.092
#> SRR1633234 2 0.2216 0.944 0.000 0.908 0.000 0.092
#> SRR1633236 3 0.1867 0.914 0.000 0.000 0.928 0.072
#> SRR1633237 3 0.1867 0.914 0.000 0.000 0.928 0.072
#> SRR1633238 3 0.1867 0.914 0.000 0.000 0.928 0.072
#> SRR1633239 3 0.1867 0.914 0.000 0.000 0.928 0.072
#> SRR1633240 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633241 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633242 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633243 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633244 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633245 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633246 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633247 3 0.0336 0.926 0.000 0.000 0.992 0.008
#> SRR1633248 3 0.0336 0.926 0.000 0.000 0.992 0.008
#> SRR1633249 3 0.0336 0.926 0.000 0.000 0.992 0.008
#> SRR1633250 3 0.0336 0.926 0.000 0.000 0.992 0.008
#> SRR1633251 3 0.1389 0.918 0.000 0.000 0.952 0.048
#> SRR1633252 3 0.1389 0.918 0.000 0.000 0.952 0.048
#> SRR1633253 3 0.1389 0.918 0.000 0.000 0.952 0.048
#> SRR1633254 3 0.1389 0.918 0.000 0.000 0.952 0.048
#> SRR1633255 3 0.1389 0.918 0.000 0.000 0.952 0.048
#> SRR1633256 3 0.0469 0.925 0.000 0.000 0.988 0.012
#> SRR1633257 3 0.0469 0.925 0.000 0.000 0.988 0.012
#> SRR1633258 3 0.0469 0.925 0.000 0.000 0.988 0.012
#> SRR1633259 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1633260 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1633261 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1633262 3 0.4040 0.805 0.000 0.000 0.752 0.248
#> SRR1633263 3 0.4040 0.805 0.000 0.000 0.752 0.248
#> SRR1633264 3 0.4040 0.805 0.000 0.000 0.752 0.248
#> SRR1633265 3 0.4040 0.805 0.000 0.000 0.752 0.248
#> SRR1633266 3 0.4040 0.805 0.000 0.000 0.752 0.248
#> SRR1633267 3 0.0592 0.925 0.000 0.000 0.984 0.016
#> SRR1633268 3 0.0592 0.925 0.000 0.000 0.984 0.016
#> SRR1633269 3 0.0592 0.925 0.000 0.000 0.984 0.016
#> SRR1633270 3 0.0707 0.926 0.000 0.000 0.980 0.020
#> SRR1633271 3 0.0707 0.926 0.000 0.000 0.980 0.020
#> SRR1633272 3 0.0707 0.926 0.000 0.000 0.980 0.020
#> SRR1633273 4 0.4916 0.878 0.424 0.000 0.000 0.576
#> SRR1633274 4 0.4916 0.878 0.424 0.000 0.000 0.576
#> SRR1633275 4 0.4916 0.878 0.424 0.000 0.000 0.576
#> SRR1633276 4 0.4916 0.878 0.424 0.000 0.000 0.576
#> SRR1633277 4 0.4916 0.878 0.424 0.000 0.000 0.576
#> SRR1633278 3 0.4193 0.791 0.000 0.000 0.732 0.268
#> SRR1633279 3 0.4193 0.791 0.000 0.000 0.732 0.268
#> SRR1633280 3 0.4193 0.791 0.000 0.000 0.732 0.268
#> SRR1633281 3 0.4193 0.791 0.000 0.000 0.732 0.268
#> SRR1633282 4 0.4560 0.149 0.004 0.000 0.296 0.700
#> SRR1633284 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633285 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633286 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633287 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633288 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633289 1 0.4907 -0.390 0.580 0.000 0.000 0.420
#> SRR1633290 4 0.4961 0.826 0.448 0.000 0.000 0.552
#> SRR1633291 4 0.4961 0.826 0.448 0.000 0.000 0.552
#> SRR1633292 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633293 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633294 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633295 3 0.1792 0.916 0.000 0.000 0.932 0.068
#> SRR1633296 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633297 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633298 4 0.4624 0.788 0.340 0.000 0.000 0.660
#> SRR1633299 4 0.4624 0.788 0.340 0.000 0.000 0.660
#> SRR1633300 2 0.2081 0.917 0.000 0.916 0.000 0.084
#> SRR1633301 2 0.2081 0.917 0.000 0.916 0.000 0.084
#> SRR1633302 2 0.2081 0.917 0.000 0.916 0.000 0.084
#> SRR1633303 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633304 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633305 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633306 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633307 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633308 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633309 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633310 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633311 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633312 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633313 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633314 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633315 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633316 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633317 2 0.2647 0.941 0.000 0.880 0.000 0.120
#> SRR1633318 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0817 0.937 0.000 0.976 0.000 0.024
#> SRR1633324 2 0.0817 0.937 0.000 0.976 0.000 0.024
#> SRR1633325 2 0.0817 0.937 0.000 0.976 0.000 0.024
#> SRR1633326 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633330 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633331 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633332 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633333 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633334 2 0.2081 0.918 0.000 0.916 0.000 0.084
#> SRR1633335 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633336 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633337 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633338 1 0.4981 -0.549 0.536 0.000 0.000 0.464
#> SRR1633339 1 0.4981 -0.549 0.536 0.000 0.000 0.464
#> SRR1633340 1 0.4981 -0.549 0.536 0.000 0.000 0.464
#> SRR1633341 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633342 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633345 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633346 1 0.4948 -0.455 0.560 0.000 0.000 0.440
#> SRR1633343 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633344 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633347 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633348 4 0.4888 0.884 0.412 0.000 0.000 0.588
#> SRR1633350 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633351 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633352 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633353 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633354 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633355 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633356 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633357 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633358 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633362 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633363 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633364 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633359 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633360 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR1633361 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR2038492 1 0.2589 0.618 0.884 0.000 0.000 0.116
#> SRR2038491 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR2038487 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0707 0.770 0.980 0.000 0.000 0.020
#> SRR2038483 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0469 0.767 0.988 0.000 0.000 0.012
#> SRR2038479 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.778 1.000 0.000 0.000 0.000
#> SRR2038462 3 0.4431 0.752 0.000 0.000 0.696 0.304
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.2773 0.917 0.000 0.868 0.000 0.020 NA
#> SRR1633231 2 0.2773 0.917 0.000 0.868 0.000 0.020 NA
#> SRR1633232 2 0.2773 0.917 0.000 0.868 0.000 0.020 NA
#> SRR1633233 2 0.2773 0.917 0.000 0.868 0.000 0.020 NA
#> SRR1633234 2 0.2773 0.917 0.000 0.868 0.000 0.020 NA
#> SRR1633236 3 0.3602 0.825 0.000 0.000 0.796 0.024 NA
#> SRR1633237 3 0.3602 0.825 0.000 0.000 0.796 0.024 NA
#> SRR1633238 3 0.3602 0.825 0.000 0.000 0.796 0.024 NA
#> SRR1633239 3 0.3602 0.825 0.000 0.000 0.796 0.024 NA
#> SRR1633240 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633241 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633242 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633243 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633244 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633245 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633246 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633247 3 0.0162 0.859 0.000 0.000 0.996 0.000 NA
#> SRR1633248 3 0.0162 0.859 0.000 0.000 0.996 0.000 NA
#> SRR1633249 3 0.0162 0.859 0.000 0.000 0.996 0.000 NA
#> SRR1633250 3 0.0162 0.859 0.000 0.000 0.996 0.000 NA
#> SRR1633251 3 0.2012 0.847 0.000 0.000 0.920 0.020 NA
#> SRR1633252 3 0.2012 0.847 0.000 0.000 0.920 0.020 NA
#> SRR1633253 3 0.2012 0.847 0.000 0.000 0.920 0.020 NA
#> SRR1633254 3 0.2012 0.847 0.000 0.000 0.920 0.020 NA
#> SRR1633255 3 0.2012 0.847 0.000 0.000 0.920 0.020 NA
#> SRR1633256 3 0.0566 0.858 0.000 0.000 0.984 0.004 NA
#> SRR1633257 3 0.0566 0.858 0.000 0.000 0.984 0.004 NA
#> SRR1633258 3 0.0566 0.858 0.000 0.000 0.984 0.004 NA
#> SRR1633259 3 0.0000 0.859 0.000 0.000 1.000 0.000 NA
#> SRR1633260 3 0.0000 0.859 0.000 0.000 1.000 0.000 NA
#> SRR1633261 3 0.0000 0.859 0.000 0.000 1.000 0.000 NA
#> SRR1633262 3 0.5925 0.676 0.000 0.000 0.596 0.216 NA
#> SRR1633263 3 0.5925 0.676 0.000 0.000 0.596 0.216 NA
#> SRR1633264 3 0.5925 0.676 0.000 0.000 0.596 0.216 NA
#> SRR1633265 3 0.5925 0.676 0.000 0.000 0.596 0.216 NA
#> SRR1633266 3 0.5925 0.676 0.000 0.000 0.596 0.216 NA
#> SRR1633267 3 0.1701 0.854 0.000 0.000 0.936 0.016 NA
#> SRR1633268 3 0.1701 0.854 0.000 0.000 0.936 0.016 NA
#> SRR1633269 3 0.1701 0.854 0.000 0.000 0.936 0.016 NA
#> SRR1633270 3 0.1774 0.855 0.000 0.000 0.932 0.016 NA
#> SRR1633271 3 0.1774 0.855 0.000 0.000 0.932 0.016 NA
#> SRR1633272 3 0.1774 0.855 0.000 0.000 0.932 0.016 NA
#> SRR1633273 4 0.3452 0.866 0.244 0.000 0.000 0.756 NA
#> SRR1633274 4 0.3452 0.866 0.244 0.000 0.000 0.756 NA
#> SRR1633275 4 0.3452 0.866 0.244 0.000 0.000 0.756 NA
#> SRR1633276 4 0.3452 0.866 0.244 0.000 0.000 0.756 NA
#> SRR1633277 4 0.3452 0.866 0.244 0.000 0.000 0.756 NA
#> SRR1633278 3 0.6080 0.660 0.000 0.000 0.572 0.228 NA
#> SRR1633279 3 0.6080 0.660 0.000 0.000 0.572 0.228 NA
#> SRR1633280 3 0.6080 0.660 0.000 0.000 0.572 0.228 NA
#> SRR1633281 3 0.6080 0.660 0.000 0.000 0.572 0.228 NA
#> SRR1633282 4 0.5967 0.169 0.004 0.000 0.184 0.608 NA
#> SRR1633284 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633285 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633286 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633287 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633288 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633289 4 0.5686 0.788 0.356 0.000 0.000 0.552 NA
#> SRR1633290 4 0.3863 0.868 0.248 0.000 0.000 0.740 NA
#> SRR1633291 4 0.3863 0.868 0.248 0.000 0.000 0.740 NA
#> SRR1633292 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633293 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633294 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633295 3 0.3203 0.831 0.000 0.000 0.820 0.012 NA
#> SRR1633296 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633297 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633298 4 0.2329 0.747 0.124 0.000 0.000 0.876 NA
#> SRR1633299 4 0.2329 0.747 0.124 0.000 0.000 0.876 NA
#> SRR1633300 2 0.3289 0.865 0.000 0.844 0.000 0.048 NA
#> SRR1633301 2 0.3289 0.865 0.000 0.844 0.000 0.048 NA
#> SRR1633302 2 0.3289 0.865 0.000 0.844 0.000 0.048 NA
#> SRR1633303 2 0.2773 0.913 0.000 0.836 0.000 0.000 NA
#> SRR1633304 2 0.2773 0.913 0.000 0.836 0.000 0.000 NA
#> SRR1633305 2 0.2773 0.913 0.000 0.836 0.000 0.000 NA
#> SRR1633306 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633307 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633308 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633309 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633310 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633311 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633312 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633313 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633314 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633315 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633316 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633317 2 0.2732 0.914 0.000 0.840 0.000 0.000 NA
#> SRR1633318 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633319 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633320 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633321 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633322 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633323 2 0.1117 0.909 0.000 0.964 0.000 0.020 NA
#> SRR1633324 2 0.1117 0.909 0.000 0.964 0.000 0.020 NA
#> SRR1633325 2 0.1117 0.909 0.000 0.964 0.000 0.020 NA
#> SRR1633326 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633327 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633328 2 0.0290 0.914 0.000 0.992 0.000 0.008 NA
#> SRR1633329 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633330 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633331 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633332 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633333 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633334 2 0.2879 0.878 0.000 0.868 0.000 0.032 NA
#> SRR1633335 4 0.5516 0.849 0.296 0.000 0.000 0.608 NA
#> SRR1633336 4 0.5516 0.849 0.296 0.000 0.000 0.608 NA
#> SRR1633337 4 0.5516 0.849 0.296 0.000 0.000 0.608 NA
#> SRR1633338 4 0.5047 0.862 0.284 0.000 0.000 0.652 NA
#> SRR1633339 4 0.5047 0.862 0.284 0.000 0.000 0.652 NA
#> SRR1633340 4 0.5047 0.862 0.284 0.000 0.000 0.652 NA
#> SRR1633341 4 0.5440 0.850 0.300 0.000 0.000 0.612 NA
#> SRR1633342 4 0.5440 0.850 0.300 0.000 0.000 0.612 NA
#> SRR1633345 4 0.5440 0.850 0.300 0.000 0.000 0.612 NA
#> SRR1633346 4 0.5440 0.850 0.300 0.000 0.000 0.612 NA
#> SRR1633343 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633344 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633347 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633348 4 0.3395 0.863 0.236 0.000 0.000 0.764 NA
#> SRR1633350 1 0.2813 0.823 0.832 0.000 0.000 0.000 NA
#> SRR1633351 1 0.2813 0.823 0.832 0.000 0.000 0.000 NA
#> SRR1633352 1 0.2813 0.823 0.832 0.000 0.000 0.000 NA
#> SRR1633353 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633354 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633355 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633356 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633357 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633358 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633362 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633363 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633364 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633359 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633360 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR1633361 1 0.3508 0.772 0.748 0.000 0.000 0.000 NA
#> SRR2038492 1 0.3760 0.565 0.784 0.000 0.000 0.188 NA
#> SRR2038491 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038490 1 0.0609 0.887 0.980 0.000 0.000 0.000 NA
#> SRR2038489 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038488 1 0.0162 0.896 0.996 0.000 0.000 0.000 NA
#> SRR2038487 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038486 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038485 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038484 1 0.0162 0.896 0.996 0.000 0.000 0.000 NA
#> SRR2038483 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038482 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038481 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038480 1 0.1195 0.870 0.960 0.000 0.000 0.012 NA
#> SRR2038479 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038477 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038478 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038476 1 0.0609 0.885 0.980 0.000 0.000 0.000 NA
#> SRR2038475 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038474 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038473 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038472 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038471 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038470 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038469 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038468 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038467 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038466 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038465 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038464 1 0.0404 0.890 0.988 0.000 0.000 0.000 NA
#> SRR2038463 1 0.0000 0.897 1.000 0.000 0.000 0.000 NA
#> SRR2038462 3 0.6476 0.541 0.000 0.000 0.476 0.320 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.2769 0.858 0.000 0.880 0.000 0.032 NA 0.052
#> SRR1633231 2 0.2769 0.858 0.000 0.880 0.000 0.032 NA 0.052
#> SRR1633232 2 0.2769 0.858 0.000 0.880 0.000 0.032 NA 0.052
#> SRR1633233 2 0.2769 0.858 0.000 0.880 0.000 0.032 NA 0.052
#> SRR1633234 2 0.2769 0.858 0.000 0.880 0.000 0.032 NA 0.052
#> SRR1633236 3 0.0891 0.610 0.000 0.000 0.968 0.008 NA 0.000
#> SRR1633237 3 0.0891 0.610 0.000 0.000 0.968 0.008 NA 0.000
#> SRR1633238 3 0.0891 0.610 0.000 0.000 0.968 0.008 NA 0.000
#> SRR1633239 3 0.0891 0.610 0.000 0.000 0.968 0.008 NA 0.000
#> SRR1633240 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633241 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633242 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633243 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633244 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633245 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633246 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633247 3 0.4524 0.600 0.000 0.000 0.648 0.008 NA 0.304
#> SRR1633248 3 0.4524 0.600 0.000 0.000 0.648 0.008 NA 0.304
#> SRR1633249 3 0.4524 0.600 0.000 0.000 0.648 0.008 NA 0.304
#> SRR1633250 3 0.4524 0.600 0.000 0.000 0.648 0.008 NA 0.304
#> SRR1633251 3 0.4841 0.488 0.000 0.000 0.576 0.012 NA 0.372
#> SRR1633252 3 0.4841 0.488 0.000 0.000 0.576 0.012 NA 0.372
#> SRR1633253 3 0.4841 0.488 0.000 0.000 0.576 0.012 NA 0.372
#> SRR1633254 3 0.4841 0.488 0.000 0.000 0.576 0.012 NA 0.372
#> SRR1633255 3 0.4841 0.488 0.000 0.000 0.576 0.012 NA 0.372
#> SRR1633256 3 0.4572 0.589 0.000 0.000 0.636 0.008 NA 0.316
#> SRR1633257 3 0.4572 0.589 0.000 0.000 0.636 0.008 NA 0.316
#> SRR1633258 3 0.4572 0.589 0.000 0.000 0.636 0.008 NA 0.316
#> SRR1633259 3 0.4506 0.604 0.000 0.000 0.652 0.008 NA 0.300
#> SRR1633260 3 0.4506 0.604 0.000 0.000 0.652 0.008 NA 0.300
#> SRR1633261 3 0.4506 0.604 0.000 0.000 0.652 0.008 NA 0.300
#> SRR1633262 6 0.4601 0.786 0.000 0.000 0.312 0.060 NA 0.628
#> SRR1633263 6 0.4601 0.786 0.000 0.000 0.312 0.060 NA 0.628
#> SRR1633264 6 0.4601 0.786 0.000 0.000 0.312 0.060 NA 0.628
#> SRR1633265 6 0.4601 0.786 0.000 0.000 0.312 0.060 NA 0.628
#> SRR1633266 6 0.4601 0.786 0.000 0.000 0.312 0.060 NA 0.628
#> SRR1633267 3 0.4881 0.536 0.000 0.000 0.588 0.000 NA 0.336
#> SRR1633268 3 0.4881 0.536 0.000 0.000 0.588 0.000 NA 0.336
#> SRR1633269 3 0.4881 0.536 0.000 0.000 0.588 0.000 NA 0.336
#> SRR1633270 3 0.4813 0.557 0.000 0.000 0.608 0.000 NA 0.316
#> SRR1633271 3 0.4813 0.557 0.000 0.000 0.608 0.000 NA 0.316
#> SRR1633272 3 0.4813 0.557 0.000 0.000 0.608 0.000 NA 0.316
#> SRR1633273 4 0.1967 0.874 0.084 0.000 0.000 0.904 NA 0.012
#> SRR1633274 4 0.1967 0.874 0.084 0.000 0.000 0.904 NA 0.012
#> SRR1633275 4 0.1967 0.874 0.084 0.000 0.000 0.904 NA 0.012
#> SRR1633276 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633277 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633278 6 0.4788 0.819 0.000 0.000 0.248 0.072 NA 0.668
#> SRR1633279 6 0.4788 0.819 0.000 0.000 0.248 0.072 NA 0.668
#> SRR1633280 6 0.4788 0.819 0.000 0.000 0.248 0.072 NA 0.668
#> SRR1633281 6 0.4788 0.819 0.000 0.000 0.248 0.072 NA 0.668
#> SRR1633282 6 0.4062 0.439 0.000 0.000 0.012 0.344 NA 0.640
#> SRR1633284 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633285 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633286 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633287 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633288 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633289 4 0.5385 0.808 0.272 0.000 0.000 0.616 NA 0.032
#> SRR1633290 4 0.2110 0.878 0.084 0.000 0.000 0.900 NA 0.004
#> SRR1633291 4 0.2110 0.878 0.084 0.000 0.000 0.900 NA 0.004
#> SRR1633292 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633293 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633294 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633295 3 0.0000 0.624 0.000 0.000 1.000 0.000 NA 0.000
#> SRR1633296 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633297 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633298 4 0.2106 0.848 0.064 0.000 0.000 0.904 NA 0.032
#> SRR1633299 4 0.2106 0.848 0.064 0.000 0.000 0.904 NA 0.032
#> SRR1633300 2 0.4659 0.791 0.000 0.612 0.000 0.004 NA 0.048
#> SRR1633301 2 0.4659 0.791 0.000 0.612 0.000 0.004 NA 0.048
#> SRR1633302 2 0.4659 0.791 0.000 0.612 0.000 0.004 NA 0.048
#> SRR1633303 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633304 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633305 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633306 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633307 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633308 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633309 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633310 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633311 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633312 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633313 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633314 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633315 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633316 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633317 2 0.0458 0.856 0.000 0.984 0.000 0.000 NA 0.016
#> SRR1633318 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633319 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633320 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633321 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633322 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633323 2 0.4322 0.850 0.000 0.736 0.000 0.020 NA 0.052
#> SRR1633324 2 0.4322 0.850 0.000 0.736 0.000 0.020 NA 0.052
#> SRR1633325 2 0.4322 0.850 0.000 0.736 0.000 0.020 NA 0.052
#> SRR1633326 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633327 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633328 2 0.4010 0.857 0.000 0.764 0.000 0.020 NA 0.040
#> SRR1633329 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633330 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633331 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633332 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633333 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633334 2 0.4763 0.790 0.000 0.608 0.000 0.008 NA 0.048
#> SRR1633335 4 0.5452 0.853 0.196 0.000 0.000 0.656 NA 0.056
#> SRR1633336 4 0.5452 0.853 0.196 0.000 0.000 0.656 NA 0.056
#> SRR1633337 4 0.5452 0.853 0.196 0.000 0.000 0.656 NA 0.056
#> SRR1633338 4 0.4602 0.874 0.136 0.000 0.000 0.744 NA 0.044
#> SRR1633339 4 0.4602 0.874 0.136 0.000 0.000 0.744 NA 0.044
#> SRR1633340 4 0.4602 0.874 0.136 0.000 0.000 0.744 NA 0.044
#> SRR1633341 4 0.4678 0.870 0.176 0.000 0.000 0.720 NA 0.028
#> SRR1633342 4 0.4678 0.870 0.176 0.000 0.000 0.720 NA 0.028
#> SRR1633345 4 0.4647 0.871 0.172 0.000 0.000 0.724 NA 0.028
#> SRR1633346 4 0.4647 0.871 0.172 0.000 0.000 0.724 NA 0.028
#> SRR1633343 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633344 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633347 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633348 4 0.1866 0.875 0.084 0.000 0.000 0.908 NA 0.008
#> SRR1633350 1 0.2350 0.729 0.880 0.000 0.000 0.000 NA 0.020
#> SRR1633351 1 0.2350 0.729 0.880 0.000 0.000 0.000 NA 0.020
#> SRR1633352 1 0.2350 0.729 0.880 0.000 0.000 0.000 NA 0.020
#> SRR1633353 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633354 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633355 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633356 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633357 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633358 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633362 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633363 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633364 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633359 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633360 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR1633361 1 0.0363 0.661 0.988 0.000 0.000 0.012 NA 0.000
#> SRR2038492 1 0.6729 0.616 0.420 0.000 0.000 0.136 NA 0.080
#> SRR2038491 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038490 1 0.5031 0.802 0.528 0.000 0.000 0.004 NA 0.064
#> SRR2038489 1 0.3923 0.846 0.580 0.000 0.000 0.004 NA 0.000
#> SRR2038488 1 0.4084 0.845 0.588 0.000 0.000 0.000 NA 0.012
#> SRR2038487 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038486 1 0.3923 0.846 0.580 0.000 0.000 0.004 NA 0.000
#> SRR2038485 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038484 1 0.4084 0.845 0.588 0.000 0.000 0.000 NA 0.012
#> SRR2038483 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038482 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038481 1 0.4218 0.845 0.584 0.000 0.000 0.004 NA 0.012
#> SRR2038480 1 0.5171 0.787 0.512 0.000 0.000 0.004 NA 0.076
#> SRR2038479 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038477 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038478 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038476 1 0.5160 0.793 0.520 0.000 0.000 0.004 NA 0.076
#> SRR2038475 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038474 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038473 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038472 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038471 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038470 1 0.4218 0.845 0.584 0.000 0.000 0.004 NA 0.012
#> SRR2038469 1 0.4041 0.847 0.584 0.000 0.000 0.004 NA 0.004
#> SRR2038468 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038467 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038466 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038465 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038464 1 0.4963 0.813 0.544 0.000 0.000 0.004 NA 0.060
#> SRR2038463 1 0.3915 0.847 0.584 0.000 0.000 0.004 NA 0.000
#> SRR2038462 6 0.4720 0.734 0.000 0.000 0.176 0.128 NA 0.692
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5013 0.499 0.499
#> 3 3 0.977 0.924 0.970 0.2247 0.861 0.726
#> 4 4 0.946 0.934 0.960 0.0893 0.932 0.824
#> 5 5 0.812 0.680 0.818 0.0813 0.998 0.993
#> 6 6 0.835 0.884 0.878 0.0565 0.869 0.611
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0 1 0 1
#> SRR1633231 2 0 1 0 1
#> SRR1633232 2 0 1 0 1
#> SRR1633233 2 0 1 0 1
#> SRR1633234 2 0 1 0 1
#> SRR1633236 2 0 1 0 1
#> SRR1633237 2 0 1 0 1
#> SRR1633238 2 0 1 0 1
#> SRR1633239 2 0 1 0 1
#> SRR1633240 2 0 1 0 1
#> SRR1633241 2 0 1 0 1
#> SRR1633242 2 0 1 0 1
#> SRR1633243 2 0 1 0 1
#> SRR1633244 2 0 1 0 1
#> SRR1633245 2 0 1 0 1
#> SRR1633246 2 0 1 0 1
#> SRR1633247 2 0 1 0 1
#> SRR1633248 2 0 1 0 1
#> SRR1633249 2 0 1 0 1
#> SRR1633250 2 0 1 0 1
#> SRR1633251 2 0 1 0 1
#> SRR1633252 2 0 1 0 1
#> SRR1633253 2 0 1 0 1
#> SRR1633254 2 0 1 0 1
#> SRR1633255 2 0 1 0 1
#> SRR1633256 2 0 1 0 1
#> SRR1633257 2 0 1 0 1
#> SRR1633258 2 0 1 0 1
#> SRR1633259 2 0 1 0 1
#> SRR1633260 2 0 1 0 1
#> SRR1633261 2 0 1 0 1
#> SRR1633262 1 0 1 1 0
#> SRR1633263 1 0 1 1 0
#> SRR1633264 1 0 1 1 0
#> SRR1633265 1 0 1 1 0
#> SRR1633266 1 0 1 1 0
#> SRR1633267 2 0 1 0 1
#> SRR1633268 2 0 1 0 1
#> SRR1633269 2 0 1 0 1
#> SRR1633270 2 0 1 0 1
#> SRR1633271 2 0 1 0 1
#> SRR1633272 2 0 1 0 1
#> SRR1633273 1 0 1 1 0
#> SRR1633274 1 0 1 1 0
#> SRR1633275 1 0 1 1 0
#> SRR1633276 1 0 1 1 0
#> SRR1633277 1 0 1 1 0
#> SRR1633278 1 0 1 1 0
#> SRR1633279 1 0 1 1 0
#> SRR1633280 1 0 1 1 0
#> SRR1633281 1 0 1 1 0
#> SRR1633282 1 0 1 1 0
#> SRR1633284 1 0 1 1 0
#> SRR1633285 1 0 1 1 0
#> SRR1633286 1 0 1 1 0
#> SRR1633287 1 0 1 1 0
#> SRR1633288 1 0 1 1 0
#> SRR1633289 1 0 1 1 0
#> SRR1633290 1 0 1 1 0
#> SRR1633291 1 0 1 1 0
#> SRR1633292 2 0 1 0 1
#> SRR1633293 2 0 1 0 1
#> SRR1633294 2 0 1 0 1
#> SRR1633295 2 0 1 0 1
#> SRR1633296 1 0 1 1 0
#> SRR1633297 1 0 1 1 0
#> SRR1633298 1 0 1 1 0
#> SRR1633299 1 0 1 1 0
#> SRR1633300 2 0 1 0 1
#> SRR1633301 2 0 1 0 1
#> SRR1633302 2 0 1 0 1
#> SRR1633303 2 0 1 0 1
#> SRR1633304 2 0 1 0 1
#> SRR1633305 2 0 1 0 1
#> SRR1633306 2 0 1 0 1
#> SRR1633307 2 0 1 0 1
#> SRR1633308 2 0 1 0 1
#> SRR1633309 2 0 1 0 1
#> SRR1633310 2 0 1 0 1
#> SRR1633311 2 0 1 0 1
#> SRR1633312 2 0 1 0 1
#> SRR1633313 2 0 1 0 1
#> SRR1633314 2 0 1 0 1
#> SRR1633315 2 0 1 0 1
#> SRR1633316 2 0 1 0 1
#> SRR1633317 2 0 1 0 1
#> SRR1633318 2 0 1 0 1
#> SRR1633319 2 0 1 0 1
#> SRR1633320 2 0 1 0 1
#> SRR1633321 2 0 1 0 1
#> SRR1633322 2 0 1 0 1
#> SRR1633323 2 0 1 0 1
#> SRR1633324 2 0 1 0 1
#> SRR1633325 2 0 1 0 1
#> SRR1633326 2 0 1 0 1
#> SRR1633327 2 0 1 0 1
#> SRR1633328 2 0 1 0 1
#> SRR1633329 2 0 1 0 1
#> SRR1633330 2 0 1 0 1
#> SRR1633331 2 0 1 0 1
#> SRR1633332 2 0 1 0 1
#> SRR1633333 2 0 1 0 1
#> SRR1633334 2 0 1 0 1
#> SRR1633335 1 0 1 1 0
#> SRR1633336 1 0 1 1 0
#> SRR1633337 1 0 1 1 0
#> SRR1633338 1 0 1 1 0
#> SRR1633339 1 0 1 1 0
#> SRR1633340 1 0 1 1 0
#> SRR1633341 1 0 1 1 0
#> SRR1633342 1 0 1 1 0
#> SRR1633345 1 0 1 1 0
#> SRR1633346 1 0 1 1 0
#> SRR1633343 1 0 1 1 0
#> SRR1633344 1 0 1 1 0
#> SRR1633347 1 0 1 1 0
#> SRR1633348 1 0 1 1 0
#> SRR1633350 1 0 1 1 0
#> SRR1633351 1 0 1 1 0
#> SRR1633352 1 0 1 1 0
#> SRR1633353 1 0 1 1 0
#> SRR1633354 1 0 1 1 0
#> SRR1633355 1 0 1 1 0
#> SRR1633356 1 0 1 1 0
#> SRR1633357 1 0 1 1 0
#> SRR1633358 1 0 1 1 0
#> SRR1633362 1 0 1 1 0
#> SRR1633363 1 0 1 1 0
#> SRR1633364 1 0 1 1 0
#> SRR1633359 1 0 1 1 0
#> SRR1633360 1 0 1 1 0
#> SRR1633361 1 0 1 1 0
#> SRR2038492 1 0 1 1 0
#> SRR2038491 1 0 1 1 0
#> SRR2038490 1 0 1 1 0
#> SRR2038489 1 0 1 1 0
#> SRR2038488 1 0 1 1 0
#> SRR2038487 1 0 1 1 0
#> SRR2038486 1 0 1 1 0
#> SRR2038485 1 0 1 1 0
#> SRR2038484 1 0 1 1 0
#> SRR2038483 1 0 1 1 0
#> SRR2038482 1 0 1 1 0
#> SRR2038481 1 0 1 1 0
#> SRR2038480 1 0 1 1 0
#> SRR2038479 1 0 1 1 0
#> SRR2038477 1 0 1 1 0
#> SRR2038478 1 0 1 1 0
#> SRR2038476 1 0 1 1 0
#> SRR2038475 1 0 1 1 0
#> SRR2038474 1 0 1 1 0
#> SRR2038473 1 0 1 1 0
#> SRR2038472 1 0 1 1 0
#> SRR2038471 1 0 1 1 0
#> SRR2038470 1 0 1 1 0
#> SRR2038469 1 0 1 1 0
#> SRR2038468 1 0 1 1 0
#> SRR2038467 1 0 1 1 0
#> SRR2038466 1 0 1 1 0
#> SRR2038465 1 0 1 1 0
#> SRR2038464 1 0 1 1 0
#> SRR2038463 1 0 1 1 0
#> SRR2038462 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633231 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633232 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633233 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633234 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633236 2 0.4974 0.708 0.000 0.764 0.236
#> SRR1633237 2 0.5678 0.591 0.000 0.684 0.316
#> SRR1633238 2 0.5678 0.591 0.000 0.684 0.316
#> SRR1633239 2 0.5678 0.591 0.000 0.684 0.316
#> SRR1633240 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633241 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633242 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633243 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633244 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633245 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633246 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633247 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1633262 3 0.6280 0.266 0.460 0.000 0.540
#> SRR1633263 3 0.6280 0.266 0.460 0.000 0.540
#> SRR1633264 3 0.6280 0.266 0.460 0.000 0.540
#> SRR1633265 3 0.6280 0.266 0.460 0.000 0.540
#> SRR1633266 3 0.6280 0.266 0.460 0.000 0.540
#> SRR1633267 2 0.6192 0.352 0.000 0.580 0.420
#> SRR1633268 2 0.6192 0.352 0.000 0.580 0.420
#> SRR1633269 2 0.6192 0.352 0.000 0.580 0.420
#> SRR1633270 2 0.1753 0.910 0.000 0.952 0.048
#> SRR1633271 2 0.1753 0.910 0.000 0.952 0.048
#> SRR1633272 2 0.1753 0.910 0.000 0.952 0.048
#> SRR1633273 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633278 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633279 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633280 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633281 1 0.0237 0.996 0.996 0.000 0.004
#> SRR1633282 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633284 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633292 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633293 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633294 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633295 3 0.0237 0.895 0.000 0.004 0.996
#> SRR1633296 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633300 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633301 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633302 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633303 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633304 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633305 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633306 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633307 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633308 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633309 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633310 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633311 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633312 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633313 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633314 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633315 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633316 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633317 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633318 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633319 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633320 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633321 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633322 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633323 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633324 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633325 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633326 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633327 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633328 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633329 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633330 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633331 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633332 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633333 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633334 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1633335 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0.000 0.000
#> SRR2038462 1 0.0000 1.000 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.4697 0.437 0.000 0.356 0.644 0.000
#> SRR1633237 3 0.4331 0.539 0.000 0.288 0.712 0.000
#> SRR1633238 3 0.4331 0.539 0.000 0.288 0.712 0.000
#> SRR1633239 3 0.4331 0.539 0.000 0.288 0.712 0.000
#> SRR1633240 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633241 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633242 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633243 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633244 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633245 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633246 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633248 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633249 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633250 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633251 3 0.3311 0.848 0.000 0.000 0.828 0.172
#> SRR1633252 3 0.3311 0.848 0.000 0.000 0.828 0.172
#> SRR1633253 3 0.3311 0.848 0.000 0.000 0.828 0.172
#> SRR1633254 3 0.3311 0.848 0.000 0.000 0.828 0.172
#> SRR1633255 3 0.3311 0.848 0.000 0.000 0.828 0.172
#> SRR1633256 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633257 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633258 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633259 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633260 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633261 3 0.2973 0.863 0.000 0.000 0.856 0.144
#> SRR1633262 4 0.0779 0.825 0.004 0.000 0.016 0.980
#> SRR1633263 4 0.0779 0.825 0.004 0.000 0.016 0.980
#> SRR1633264 4 0.0779 0.825 0.004 0.000 0.016 0.980
#> SRR1633265 4 0.0779 0.825 0.004 0.000 0.016 0.980
#> SRR1633266 4 0.0779 0.825 0.004 0.000 0.016 0.980
#> SRR1633267 2 0.4410 0.777 0.000 0.808 0.064 0.128
#> SRR1633268 2 0.4410 0.777 0.000 0.808 0.064 0.128
#> SRR1633269 2 0.4410 0.777 0.000 0.808 0.064 0.128
#> SRR1633270 2 0.1398 0.945 0.000 0.956 0.004 0.040
#> SRR1633271 2 0.1398 0.945 0.000 0.956 0.004 0.040
#> SRR1633272 2 0.1398 0.945 0.000 0.956 0.004 0.040
#> SRR1633273 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633274 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633275 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633276 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633277 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633278 4 0.3123 0.860 0.156 0.000 0.000 0.844
#> SRR1633279 4 0.3123 0.860 0.156 0.000 0.000 0.844
#> SRR1633280 4 0.3123 0.860 0.156 0.000 0.000 0.844
#> SRR1633281 4 0.3123 0.860 0.156 0.000 0.000 0.844
#> SRR1633282 4 0.3172 0.828 0.160 0.000 0.000 0.840
#> SRR1633284 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633285 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633286 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633287 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633288 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633289 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633290 1 0.1474 0.961 0.948 0.000 0.000 0.052
#> SRR1633291 1 0.1474 0.961 0.948 0.000 0.000 0.052
#> SRR1633292 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633293 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633294 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633295 3 0.0000 0.852 0.000 0.000 1.000 0.000
#> SRR1633296 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633297 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633298 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633299 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633300 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633301 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633302 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633303 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> SRR1633335 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633336 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633337 1 0.0707 0.979 0.980 0.000 0.000 0.020
#> SRR1633338 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633339 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633340 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633341 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633342 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633345 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633346 1 0.0817 0.977 0.976 0.000 0.000 0.024
#> SRR1633343 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633344 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633347 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633348 1 0.1557 0.958 0.944 0.000 0.000 0.056
#> SRR1633350 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.984 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.3400 0.810 0.180 0.000 0.000 0.820
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633231 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633232 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 4 0.798 0.000 0.000 0.180 0.108 0.360 0.352
#> SRR1633237 5 0.793 -0.955 0.000 0.168 0.108 0.360 0.364
#> SRR1633238 5 0.793 -0.955 0.000 0.168 0.108 0.360 0.364
#> SRR1633239 5 0.793 -0.955 0.000 0.168 0.108 0.360 0.364
#> SRR1633240 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633241 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633242 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633243 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633244 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633245 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633246 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633247 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633248 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633249 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633250 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633251 5 0.252 0.474 0.000 0.000 0.012 0.108 0.880
#> SRR1633252 5 0.252 0.474 0.000 0.000 0.012 0.108 0.880
#> SRR1633253 5 0.252 0.474 0.000 0.000 0.012 0.108 0.880
#> SRR1633254 5 0.252 0.474 0.000 0.000 0.012 0.108 0.880
#> SRR1633255 5 0.252 0.474 0.000 0.000 0.012 0.108 0.880
#> SRR1633256 5 0.029 0.524 0.000 0.000 0.000 0.008 0.992
#> SRR1633257 5 0.029 0.524 0.000 0.000 0.000 0.008 0.992
#> SRR1633258 5 0.029 0.524 0.000 0.000 0.000 0.008 0.992
#> SRR1633259 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633260 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633261 5 0.000 0.526 0.000 0.000 0.000 0.000 1.000
#> SRR1633262 3 0.618 0.723 0.000 0.000 0.556 0.212 0.232
#> SRR1633263 3 0.618 0.723 0.000 0.000 0.556 0.212 0.232
#> SRR1633264 3 0.618 0.723 0.000 0.000 0.556 0.212 0.232
#> SRR1633265 3 0.618 0.723 0.000 0.000 0.556 0.212 0.232
#> SRR1633266 3 0.618 0.723 0.000 0.000 0.556 0.212 0.232
#> SRR1633267 2 0.516 0.446 0.000 0.640 0.012 0.040 0.308
#> SRR1633268 2 0.516 0.446 0.000 0.640 0.012 0.040 0.308
#> SRR1633269 2 0.516 0.446 0.000 0.640 0.012 0.040 0.308
#> SRR1633270 2 0.298 0.815 0.000 0.860 0.000 0.032 0.108
#> SRR1633271 2 0.298 0.815 0.000 0.860 0.000 0.032 0.108
#> SRR1633272 2 0.298 0.815 0.000 0.860 0.000 0.032 0.108
#> SRR1633273 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633274 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633275 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633276 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633277 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633278 3 0.213 0.746 0.108 0.000 0.892 0.000 0.000
#> SRR1633279 3 0.213 0.746 0.108 0.000 0.892 0.000 0.000
#> SRR1633280 3 0.213 0.746 0.108 0.000 0.892 0.000 0.000
#> SRR1633281 3 0.213 0.746 0.108 0.000 0.892 0.000 0.000
#> SRR1633282 3 0.228 0.708 0.000 0.000 0.880 0.120 0.000
#> SRR1633284 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633285 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633286 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633287 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633288 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633289 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633290 1 0.452 0.648 0.552 0.000 0.008 0.440 0.000
#> SRR1633291 1 0.452 0.648 0.552 0.000 0.008 0.440 0.000
#> SRR1633292 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633293 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633294 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633295 5 0.586 -0.072 0.000 0.000 0.108 0.360 0.532
#> SRR1633296 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633297 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633298 1 0.455 0.630 0.532 0.000 0.008 0.460 0.000
#> SRR1633299 1 0.455 0.630 0.532 0.000 0.008 0.460 0.000
#> SRR1633300 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633301 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633302 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633303 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633319 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633320 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633321 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633322 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633323 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633324 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633325 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633326 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633327 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633328 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633329 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633330 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633331 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633332 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633333 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633334 2 0.000 0.960 0.000 1.000 0.000 0.000 0.000
#> SRR1633335 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633336 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633337 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633338 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633339 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633340 1 0.416 0.687 0.608 0.000 0.000 0.392 0.000
#> SRR1633341 1 0.417 0.685 0.604 0.000 0.000 0.396 0.000
#> SRR1633342 1 0.417 0.685 0.604 0.000 0.000 0.396 0.000
#> SRR1633345 1 0.417 0.685 0.604 0.000 0.000 0.396 0.000
#> SRR1633346 1 0.417 0.685 0.604 0.000 0.000 0.396 0.000
#> SRR1633343 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633344 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633347 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633348 1 0.454 0.635 0.536 0.000 0.008 0.456 0.000
#> SRR1633350 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038492 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038491 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038489 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038479 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.000 0.803 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 3 0.228 0.708 0.000 0.000 0.880 0.120 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.1753 0.8715 0.004 0.084 0.000 0.000 0.912 0.000
#> SRR1633237 5 0.1644 0.8855 0.004 0.076 0.000 0.000 0.920 0.000
#> SRR1633238 5 0.1644 0.8855 0.004 0.076 0.000 0.000 0.920 0.000
#> SRR1633239 5 0.1644 0.8855 0.004 0.076 0.000 0.000 0.920 0.000
#> SRR1633240 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633241 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633242 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633243 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633244 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633245 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633246 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633247 3 0.3101 0.7487 0.000 0.000 0.756 0.000 0.244 0.000
#> SRR1633248 3 0.3101 0.7487 0.000 0.000 0.756 0.000 0.244 0.000
#> SRR1633249 3 0.3101 0.7487 0.000 0.000 0.756 0.000 0.244 0.000
#> SRR1633250 3 0.3101 0.7487 0.000 0.000 0.756 0.000 0.244 0.000
#> SRR1633251 3 0.1765 0.7408 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633252 3 0.1765 0.7408 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633253 3 0.1765 0.7408 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633254 3 0.1765 0.7408 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633255 3 0.1765 0.7408 0.000 0.000 0.904 0.000 0.096 0.000
#> SRR1633256 3 0.2941 0.7587 0.000 0.000 0.780 0.000 0.220 0.000
#> SRR1633257 3 0.2941 0.7587 0.000 0.000 0.780 0.000 0.220 0.000
#> SRR1633258 3 0.2941 0.7587 0.000 0.000 0.780 0.000 0.220 0.000
#> SRR1633259 3 0.3076 0.7516 0.000 0.000 0.760 0.000 0.240 0.000
#> SRR1633260 3 0.3076 0.7516 0.000 0.000 0.760 0.000 0.240 0.000
#> SRR1633261 3 0.3076 0.7516 0.000 0.000 0.760 0.000 0.240 0.000
#> SRR1633262 3 0.6579 0.1676 0.164 0.000 0.500 0.068 0.000 0.268
#> SRR1633263 3 0.6579 0.1676 0.164 0.000 0.500 0.068 0.000 0.268
#> SRR1633264 3 0.6579 0.1676 0.164 0.000 0.500 0.068 0.000 0.268
#> SRR1633265 3 0.6579 0.1676 0.164 0.000 0.500 0.068 0.000 0.268
#> SRR1633266 3 0.6579 0.1676 0.164 0.000 0.500 0.068 0.000 0.268
#> SRR1633267 2 0.5579 0.0327 0.120 0.444 0.432 0.000 0.000 0.004
#> SRR1633268 2 0.5579 0.0327 0.120 0.444 0.432 0.000 0.000 0.004
#> SRR1633269 2 0.5579 0.0327 0.120 0.444 0.432 0.000 0.000 0.004
#> SRR1633270 2 0.5066 0.5092 0.116 0.632 0.248 0.000 0.000 0.004
#> SRR1633271 2 0.5066 0.5092 0.116 0.632 0.248 0.000 0.000 0.004
#> SRR1633272 2 0.5066 0.5092 0.116 0.632 0.248 0.000 0.000 0.004
#> SRR1633273 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633274 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633275 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633276 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633277 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633278 6 0.0260 0.9945 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1633279 6 0.0260 0.9945 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1633280 6 0.0260 0.9945 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1633281 6 0.0260 0.9945 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1633282 6 0.0405 0.9891 0.000 0.000 0.004 0.008 0.000 0.988
#> SRR1633284 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633285 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633286 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633287 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633288 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633289 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633290 4 0.1080 0.9265 0.032 0.000 0.004 0.960 0.000 0.004
#> SRR1633291 4 0.1080 0.9265 0.032 0.000 0.004 0.960 0.000 0.004
#> SRR1633292 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633293 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633294 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633295 5 0.0000 0.9574 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1633296 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633297 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633298 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633299 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633300 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633301 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633302 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633303 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.9337 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633318 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633319 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633320 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633321 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633322 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633323 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633324 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633325 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633326 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633327 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633328 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633329 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633330 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633331 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633332 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633333 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633334 2 0.0146 0.9336 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1633335 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633336 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633337 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633338 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633339 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633340 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633341 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633342 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633345 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633346 4 0.2110 0.9272 0.084 0.000 0.004 0.900 0.000 0.012
#> SRR1633343 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633344 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633347 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633348 4 0.0363 0.9199 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1633350 1 0.3476 0.9880 0.732 0.000 0.004 0.260 0.000 0.004
#> SRR1633351 1 0.3476 0.9880 0.732 0.000 0.004 0.260 0.000 0.004
#> SRR1633352 1 0.3476 0.9880 0.732 0.000 0.004 0.260 0.000 0.004
#> SRR1633353 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633354 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633355 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633356 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633357 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633358 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633362 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633363 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633364 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633359 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633360 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR1633361 1 0.3650 0.9791 0.716 0.000 0.004 0.272 0.000 0.008
#> SRR2038492 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038491 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038490 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038489 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038488 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038487 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038486 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038485 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038484 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038483 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038482 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038481 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038480 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038479 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038477 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038478 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038476 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038475 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038474 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038473 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038472 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038471 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038470 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038469 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038468 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038467 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038466 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038465 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038464 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038463 1 0.3198 0.9916 0.740 0.000 0.000 0.260 0.000 0.000
#> SRR2038462 6 0.0146 0.9850 0.000 0.000 0.000 0.004 0.000 0.996
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.994 0.4376 0.567 0.567
#> 3 3 1.000 1.000 1.000 0.4706 0.729 0.547
#> 4 4 1.000 0.983 0.994 0.1677 0.882 0.673
#> 5 5 0.932 0.933 0.929 0.0455 0.967 0.867
#> 6 6 0.936 0.958 0.931 0.0426 0.966 0.842
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 1 0.680 0.787 0.820 0.180
#> SRR1633241 1 0.714 0.765 0.804 0.196
#> SRR1633242 1 0.706 0.770 0.808 0.192
#> SRR1633243 1 0.917 0.519 0.668 0.332
#> SRR1633244 1 0.163 0.970 0.976 0.024
#> SRR1633245 1 0.163 0.970 0.976 0.024
#> SRR1633246 1 0.163 0.970 0.976 0.024
#> SRR1633247 1 0.000 0.991 1.000 0.000
#> SRR1633248 1 0.000 0.991 1.000 0.000
#> SRR1633249 1 0.000 0.991 1.000 0.000
#> SRR1633250 1 0.000 0.991 1.000 0.000
#> SRR1633251 1 0.000 0.991 1.000 0.000
#> SRR1633252 1 0.000 0.991 1.000 0.000
#> SRR1633253 1 0.000 0.991 1.000 0.000
#> SRR1633254 1 0.000 0.991 1.000 0.000
#> SRR1633255 1 0.000 0.991 1.000 0.000
#> SRR1633256 1 0.000 0.991 1.000 0.000
#> SRR1633257 1 0.000 0.991 1.000 0.000
#> SRR1633258 1 0.000 0.991 1.000 0.000
#> SRR1633259 1 0.000 0.991 1.000 0.000
#> SRR1633260 1 0.000 0.991 1.000 0.000
#> SRR1633261 1 0.000 0.991 1.000 0.000
#> SRR1633262 1 0.000 0.991 1.000 0.000
#> SRR1633263 1 0.000 0.991 1.000 0.000
#> SRR1633264 1 0.000 0.991 1.000 0.000
#> SRR1633265 1 0.000 0.991 1.000 0.000
#> SRR1633266 1 0.000 0.991 1.000 0.000
#> SRR1633267 1 0.163 0.970 0.976 0.024
#> SRR1633268 1 0.163 0.970 0.976 0.024
#> SRR1633269 1 0.163 0.970 0.976 0.024
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.991 1.000 0.000
#> SRR1633274 1 0.000 0.991 1.000 0.000
#> SRR1633275 1 0.000 0.991 1.000 0.000
#> SRR1633276 1 0.000 0.991 1.000 0.000
#> SRR1633277 1 0.000 0.991 1.000 0.000
#> SRR1633278 1 0.000 0.991 1.000 0.000
#> SRR1633279 1 0.000 0.991 1.000 0.000
#> SRR1633280 1 0.000 0.991 1.000 0.000
#> SRR1633281 1 0.000 0.991 1.000 0.000
#> SRR1633282 1 0.000 0.991 1.000 0.000
#> SRR1633284 1 0.000 0.991 1.000 0.000
#> SRR1633285 1 0.000 0.991 1.000 0.000
#> SRR1633286 1 0.000 0.991 1.000 0.000
#> SRR1633287 1 0.000 0.991 1.000 0.000
#> SRR1633288 1 0.000 0.991 1.000 0.000
#> SRR1633289 1 0.000 0.991 1.000 0.000
#> SRR1633290 1 0.000 0.991 1.000 0.000
#> SRR1633291 1 0.000 0.991 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.991 1.000 0.000
#> SRR1633297 1 0.000 0.991 1.000 0.000
#> SRR1633298 1 0.000 0.991 1.000 0.000
#> SRR1633299 1 0.000 0.991 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.991 1.000 0.000
#> SRR1633336 1 0.000 0.991 1.000 0.000
#> SRR1633337 1 0.000 0.991 1.000 0.000
#> SRR1633338 1 0.000 0.991 1.000 0.000
#> SRR1633339 1 0.000 0.991 1.000 0.000
#> SRR1633340 1 0.000 0.991 1.000 0.000
#> SRR1633341 1 0.000 0.991 1.000 0.000
#> SRR1633342 1 0.000 0.991 1.000 0.000
#> SRR1633345 1 0.000 0.991 1.000 0.000
#> SRR1633346 1 0.000 0.991 1.000 0.000
#> SRR1633343 1 0.000 0.991 1.000 0.000
#> SRR1633344 1 0.000 0.991 1.000 0.000
#> SRR1633347 1 0.000 0.991 1.000 0.000
#> SRR1633348 1 0.000 0.991 1.000 0.000
#> SRR1633350 1 0.000 0.991 1.000 0.000
#> SRR1633351 1 0.000 0.991 1.000 0.000
#> SRR1633352 1 0.000 0.991 1.000 0.000
#> SRR1633353 1 0.000 0.991 1.000 0.000
#> SRR1633354 1 0.000 0.991 1.000 0.000
#> SRR1633355 1 0.000 0.991 1.000 0.000
#> SRR1633356 1 0.000 0.991 1.000 0.000
#> SRR1633357 1 0.000 0.991 1.000 0.000
#> SRR1633358 1 0.000 0.991 1.000 0.000
#> SRR1633362 1 0.000 0.991 1.000 0.000
#> SRR1633363 1 0.000 0.991 1.000 0.000
#> SRR1633364 1 0.000 0.991 1.000 0.000
#> SRR1633359 1 0.000 0.991 1.000 0.000
#> SRR1633360 1 0.000 0.991 1.000 0.000
#> SRR1633361 1 0.000 0.991 1.000 0.000
#> SRR2038492 1 0.000 0.991 1.000 0.000
#> SRR2038491 1 0.000 0.991 1.000 0.000
#> SRR2038490 1 0.000 0.991 1.000 0.000
#> SRR2038489 1 0.000 0.991 1.000 0.000
#> SRR2038488 1 0.000 0.991 1.000 0.000
#> SRR2038487 1 0.000 0.991 1.000 0.000
#> SRR2038486 1 0.000 0.991 1.000 0.000
#> SRR2038485 1 0.000 0.991 1.000 0.000
#> SRR2038484 1 0.000 0.991 1.000 0.000
#> SRR2038483 1 0.000 0.991 1.000 0.000
#> SRR2038482 1 0.000 0.991 1.000 0.000
#> SRR2038481 1 0.000 0.991 1.000 0.000
#> SRR2038480 1 0.000 0.991 1.000 0.000
#> SRR2038479 1 0.000 0.991 1.000 0.000
#> SRR2038477 1 0.000 0.991 1.000 0.000
#> SRR2038478 1 0.000 0.991 1.000 0.000
#> SRR2038476 1 0.000 0.991 1.000 0.000
#> SRR2038475 1 0.000 0.991 1.000 0.000
#> SRR2038474 1 0.000 0.991 1.000 0.000
#> SRR2038473 1 0.000 0.991 1.000 0.000
#> SRR2038472 1 0.000 0.991 1.000 0.000
#> SRR2038471 1 0.000 0.991 1.000 0.000
#> SRR2038470 1 0.000 0.991 1.000 0.000
#> SRR2038469 1 0.000 0.991 1.000 0.000
#> SRR2038468 1 0.000 0.991 1.000 0.000
#> SRR2038467 1 0.000 0.991 1.000 0.000
#> SRR2038466 1 0.000 0.991 1.000 0.000
#> SRR2038465 1 0.000 0.991 1.000 0.000
#> SRR2038464 1 0.000 0.991 1.000 0.000
#> SRR2038463 1 0.000 0.991 1.000 0.000
#> SRR2038462 1 0.000 0.991 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000
#> SRR1633236 3 0.0000 1.000 0.000 0 1.000
#> SRR1633237 3 0.0000 1.000 0.000 0 1.000
#> SRR1633238 3 0.0000 1.000 0.000 0 1.000
#> SRR1633239 3 0.0000 1.000 0.000 0 1.000
#> SRR1633240 3 0.0000 1.000 0.000 0 1.000
#> SRR1633241 3 0.0000 1.000 0.000 0 1.000
#> SRR1633242 3 0.0000 1.000 0.000 0 1.000
#> SRR1633243 3 0.0000 1.000 0.000 0 1.000
#> SRR1633244 3 0.0000 1.000 0.000 0 1.000
#> SRR1633245 3 0.0000 1.000 0.000 0 1.000
#> SRR1633246 3 0.0000 1.000 0.000 0 1.000
#> SRR1633247 3 0.0000 1.000 0.000 0 1.000
#> SRR1633248 3 0.0000 1.000 0.000 0 1.000
#> SRR1633249 3 0.0000 1.000 0.000 0 1.000
#> SRR1633250 3 0.0000 1.000 0.000 0 1.000
#> SRR1633251 3 0.0000 1.000 0.000 0 1.000
#> SRR1633252 3 0.0000 1.000 0.000 0 1.000
#> SRR1633253 3 0.0000 1.000 0.000 0 1.000
#> SRR1633254 3 0.0000 1.000 0.000 0 1.000
#> SRR1633255 3 0.0000 1.000 0.000 0 1.000
#> SRR1633256 3 0.0000 1.000 0.000 0 1.000
#> SRR1633257 3 0.0000 1.000 0.000 0 1.000
#> SRR1633258 3 0.0000 1.000 0.000 0 1.000
#> SRR1633259 3 0.0000 1.000 0.000 0 1.000
#> SRR1633260 3 0.0000 1.000 0.000 0 1.000
#> SRR1633261 3 0.0000 1.000 0.000 0 1.000
#> SRR1633262 3 0.0000 1.000 0.000 0 1.000
#> SRR1633263 3 0.0000 1.000 0.000 0 1.000
#> SRR1633264 3 0.0000 1.000 0.000 0 1.000
#> SRR1633265 3 0.0000 1.000 0.000 0 1.000
#> SRR1633266 3 0.0000 1.000 0.000 0 1.000
#> SRR1633267 3 0.0000 1.000 0.000 0 1.000
#> SRR1633268 3 0.0000 1.000 0.000 0 1.000
#> SRR1633269 3 0.0000 1.000 0.000 0 1.000
#> SRR1633270 3 0.0000 1.000 0.000 0 1.000
#> SRR1633271 3 0.0000 1.000 0.000 0 1.000
#> SRR1633272 3 0.0000 1.000 0.000 0 1.000
#> SRR1633273 1 0.0000 1.000 1.000 0 0.000
#> SRR1633274 1 0.0000 1.000 1.000 0 0.000
#> SRR1633275 1 0.0000 1.000 1.000 0 0.000
#> SRR1633276 1 0.0000 1.000 1.000 0 0.000
#> SRR1633277 1 0.0000 1.000 1.000 0 0.000
#> SRR1633278 3 0.0000 1.000 0.000 0 1.000
#> SRR1633279 3 0.0000 1.000 0.000 0 1.000
#> SRR1633280 3 0.0000 1.000 0.000 0 1.000
#> SRR1633281 3 0.0000 1.000 0.000 0 1.000
#> SRR1633282 3 0.0237 0.995 0.004 0 0.996
#> SRR1633284 1 0.0000 1.000 1.000 0 0.000
#> SRR1633285 1 0.0000 1.000 1.000 0 0.000
#> SRR1633286 1 0.0000 1.000 1.000 0 0.000
#> SRR1633287 1 0.0000 1.000 1.000 0 0.000
#> SRR1633288 1 0.0000 1.000 1.000 0 0.000
#> SRR1633289 1 0.0000 1.000 1.000 0 0.000
#> SRR1633290 1 0.0000 1.000 1.000 0 0.000
#> SRR1633291 1 0.0000 1.000 1.000 0 0.000
#> SRR1633292 3 0.0000 1.000 0.000 0 1.000
#> SRR1633293 3 0.0000 1.000 0.000 0 1.000
#> SRR1633294 3 0.0000 1.000 0.000 0 1.000
#> SRR1633295 3 0.0000 1.000 0.000 0 1.000
#> SRR1633296 1 0.0000 1.000 1.000 0 0.000
#> SRR1633297 1 0.0000 1.000 1.000 0 0.000
#> SRR1633298 1 0.0000 1.000 1.000 0 0.000
#> SRR1633299 1 0.0000 1.000 1.000 0 0.000
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000
#> SRR1633335 1 0.0000 1.000 1.000 0 0.000
#> SRR1633336 1 0.0000 1.000 1.000 0 0.000
#> SRR1633337 1 0.0000 1.000 1.000 0 0.000
#> SRR1633338 1 0.0000 1.000 1.000 0 0.000
#> SRR1633339 1 0.0000 1.000 1.000 0 0.000
#> SRR1633340 1 0.0000 1.000 1.000 0 0.000
#> SRR1633341 1 0.0000 1.000 1.000 0 0.000
#> SRR1633342 1 0.0000 1.000 1.000 0 0.000
#> SRR1633345 1 0.0000 1.000 1.000 0 0.000
#> SRR1633346 1 0.0000 1.000 1.000 0 0.000
#> SRR1633343 1 0.0000 1.000 1.000 0 0.000
#> SRR1633344 1 0.0000 1.000 1.000 0 0.000
#> SRR1633347 1 0.0000 1.000 1.000 0 0.000
#> SRR1633348 1 0.0000 1.000 1.000 0 0.000
#> SRR1633350 1 0.0000 1.000 1.000 0 0.000
#> SRR1633351 1 0.0000 1.000 1.000 0 0.000
#> SRR1633352 1 0.0000 1.000 1.000 0 0.000
#> SRR1633353 1 0.0000 1.000 1.000 0 0.000
#> SRR1633354 1 0.0000 1.000 1.000 0 0.000
#> SRR1633355 1 0.0000 1.000 1.000 0 0.000
#> SRR1633356 1 0.0000 1.000 1.000 0 0.000
#> SRR1633357 1 0.0000 1.000 1.000 0 0.000
#> SRR1633358 1 0.0000 1.000 1.000 0 0.000
#> SRR1633362 1 0.0000 1.000 1.000 0 0.000
#> SRR1633363 1 0.0000 1.000 1.000 0 0.000
#> SRR1633364 1 0.0000 1.000 1.000 0 0.000
#> SRR1633359 1 0.0000 1.000 1.000 0 0.000
#> SRR1633360 1 0.0000 1.000 1.000 0 0.000
#> SRR1633361 1 0.0000 1.000 1.000 0 0.000
#> SRR2038492 1 0.0000 1.000 1.000 0 0.000
#> SRR2038491 1 0.0000 1.000 1.000 0 0.000
#> SRR2038490 1 0.0000 1.000 1.000 0 0.000
#> SRR2038489 1 0.0000 1.000 1.000 0 0.000
#> SRR2038488 1 0.0000 1.000 1.000 0 0.000
#> SRR2038487 1 0.0000 1.000 1.000 0 0.000
#> SRR2038486 1 0.0000 1.000 1.000 0 0.000
#> SRR2038485 1 0.0000 1.000 1.000 0 0.000
#> SRR2038484 1 0.0000 1.000 1.000 0 0.000
#> SRR2038483 1 0.0000 1.000 1.000 0 0.000
#> SRR2038482 1 0.0000 1.000 1.000 0 0.000
#> SRR2038481 1 0.0000 1.000 1.000 0 0.000
#> SRR2038480 1 0.0000 1.000 1.000 0 0.000
#> SRR2038479 1 0.0000 1.000 1.000 0 0.000
#> SRR2038477 1 0.0000 1.000 1.000 0 0.000
#> SRR2038478 1 0.0000 1.000 1.000 0 0.000
#> SRR2038476 1 0.0000 1.000 1.000 0 0.000
#> SRR2038475 1 0.0000 1.000 1.000 0 0.000
#> SRR2038474 1 0.0000 1.000 1.000 0 0.000
#> SRR2038473 1 0.0000 1.000 1.000 0 0.000
#> SRR2038472 1 0.0000 1.000 1.000 0 0.000
#> SRR2038471 1 0.0000 1.000 1.000 0 0.000
#> SRR2038470 1 0.0000 1.000 1.000 0 0.000
#> SRR2038469 1 0.0000 1.000 1.000 0 0.000
#> SRR2038468 1 0.0000 1.000 1.000 0 0.000
#> SRR2038467 1 0.0000 1.000 1.000 0 0.000
#> SRR2038466 1 0.0000 1.000 1.000 0 0.000
#> SRR2038465 1 0.0000 1.000 1.000 0 0.000
#> SRR2038464 1 0.0000 1.000 1.000 0 0.000
#> SRR2038463 1 0.0000 1.000 1.000 0 0.000
#> SRR2038462 3 0.0000 1.000 0.000 0 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633236 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633237 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633238 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633239 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633240 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633241 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633242 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633243 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633244 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633245 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633246 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633247 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633248 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633249 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633250 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633251 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633252 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633253 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633254 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633255 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633256 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633257 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633258 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633259 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633260 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633261 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633262 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633263 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633264 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633265 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633266 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633267 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633268 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633269 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633270 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633271 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633272 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633273 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633274 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633275 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633276 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633277 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633278 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633279 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633280 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633281 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633282 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633284 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633285 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633286 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633287 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633288 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633289 4 0.0188 0.972 0.004 0 0.000 0.996
#> SRR1633290 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633291 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633292 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633293 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633294 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633295 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR1633296 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633297 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633298 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633299 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR1633335 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633336 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633337 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633338 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633339 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633340 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633341 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633342 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633345 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633346 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633343 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633344 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633347 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633348 4 0.0000 0.974 0.000 0 0.000 1.000
#> SRR1633350 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633351 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633352 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633353 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633354 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633355 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633356 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633357 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633358 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633362 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633363 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633364 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633359 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633360 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR1633361 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038492 4 0.4500 0.524 0.316 0 0.000 0.684
#> SRR2038491 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038490 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038489 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038488 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038487 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038486 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038485 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038484 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038483 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038482 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038481 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038480 1 0.3649 0.739 0.796 0 0.000 0.204
#> SRR2038479 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038477 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038478 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038476 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038475 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038474 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038473 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038472 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038471 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038470 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038469 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038468 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038467 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038466 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038465 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038464 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038463 1 0.0000 0.995 1.000 0 0.000 0.000
#> SRR2038462 4 0.4998 0.044 0.000 0 0.488 0.512
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633236 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633237 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633238 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633239 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633240 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633241 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633242 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633243 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633244 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633245 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633246 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633247 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633248 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633249 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633250 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633251 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633252 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633253 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633254 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633255 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633256 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633257 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633258 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633259 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633260 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633261 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633262 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633263 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633264 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633265 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633266 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633267 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633268 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633269 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633270 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633271 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633272 3 0.0000 0.898 0.000 0 1.000 0.000 0.000
#> SRR1633273 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633274 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633275 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633276 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633277 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633278 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633279 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633280 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633281 3 0.0290 0.895 0.000 0 0.992 0.000 0.008
#> SRR1633282 4 0.0290 0.961 0.000 0 0.000 0.992 0.008
#> SRR1633284 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633285 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633286 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633287 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633288 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633289 4 0.0162 0.964 0.004 0 0.000 0.996 0.000
#> SRR1633290 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633291 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633292 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633293 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633294 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633295 3 0.3913 0.781 0.000 0 0.676 0.000 0.324
#> SRR1633296 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633297 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633298 4 0.0290 0.961 0.000 0 0.000 0.992 0.008
#> SRR1633299 4 0.0290 0.961 0.000 0 0.000 0.992 0.008
#> SRR1633300 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR1633335 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633336 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633337 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633338 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633339 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633340 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633341 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633342 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633345 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633346 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633343 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633344 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633347 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633348 4 0.0000 0.966 0.000 0 0.000 1.000 0.000
#> SRR1633350 5 0.3949 0.986 0.332 0 0.000 0.000 0.668
#> SRR1633351 5 0.3949 0.986 0.332 0 0.000 0.000 0.668
#> SRR1633352 5 0.3949 0.986 0.332 0 0.000 0.000 0.668
#> SRR1633353 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633354 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633355 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633356 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633357 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633358 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633362 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633363 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633364 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633359 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633360 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR1633361 5 0.4183 0.997 0.324 0 0.000 0.008 0.668
#> SRR2038492 4 0.4268 0.203 0.444 0 0.000 0.556 0.000
#> SRR2038491 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038490 1 0.3171 0.619 0.816 0 0.000 0.008 0.176
#> SRR2038489 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038480 1 0.4229 0.451 0.704 0 0.000 0.276 0.020
#> SRR2038479 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.971 1.000 0 0.000 0.000 0.000
#> SRR2038462 4 0.4559 0.136 0.000 0 0.480 0.512 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633237 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633238 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633239 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633240 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633241 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633242 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633243 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633244 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633245 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633246 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633247 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633248 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633249 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633250 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633251 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633252 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633253 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633254 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633255 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633256 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633257 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633258 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633259 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633260 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633261 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633262 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633263 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633264 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633265 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633266 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633267 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633268 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633269 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633270 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633271 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633272 3 0.0146 0.9987 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1633273 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633274 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633275 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633276 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633277 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633278 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633279 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633280 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633281 3 0.0000 0.9969 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1633282 4 0.0146 0.9636 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633284 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633285 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633286 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633287 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633288 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633289 4 0.0146 0.9637 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1633290 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633291 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633292 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633293 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633294 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633295 5 0.0260 1.0000 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1633296 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633297 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633298 4 0.0146 0.9636 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633299 4 0.0146 0.9636 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1633300 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633301 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633302 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633303 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.9376 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633318 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633319 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633320 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633321 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633322 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633323 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633324 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633325 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633326 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633327 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633328 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633329 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633330 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633331 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633332 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633333 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633334 2 0.2389 0.9376 0.000 0.864 0.000 0.000 0.008 0.128
#> SRR1633335 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633336 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633337 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633338 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633339 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633340 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633341 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633342 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633345 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633346 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633343 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633344 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633347 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633348 4 0.0000 0.9665 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1633350 6 0.2135 0.9947 0.128 0.000 0.000 0.000 0.000 0.872
#> SRR1633351 6 0.2135 0.9947 0.128 0.000 0.000 0.000 0.000 0.872
#> SRR1633352 6 0.2135 0.9947 0.128 0.000 0.000 0.000 0.000 0.872
#> SRR1633353 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633354 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633355 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633356 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633357 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633358 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633362 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633363 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633364 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633359 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633360 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR1633361 6 0.2234 0.9987 0.124 0.000 0.000 0.004 0.000 0.872
#> SRR2038492 4 0.3823 0.2004 0.436 0.000 0.000 0.564 0.000 0.000
#> SRR2038491 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038490 1 0.2838 0.7114 0.808 0.000 0.000 0.004 0.000 0.188
#> SRR2038489 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038480 1 0.4018 0.4854 0.656 0.000 0.000 0.324 0.000 0.020
#> SRR2038479 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.9757 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.3867 0.0457 0.000 0.000 0.488 0.512 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.637 0.749 0.905 0.436 0.550 0.550
#> 3 3 0.999 0.963 0.976 0.447 0.670 0.474
#> 4 4 0.854 0.930 0.951 0.125 0.924 0.796
#> 5 5 0.845 0.843 0.877 0.089 0.919 0.728
#> 6 6 0.868 0.798 0.834 0.044 0.948 0.773
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 0.847 0.000 1.000
#> SRR1633231 2 0.000 0.847 0.000 1.000
#> SRR1633232 2 0.000 0.847 0.000 1.000
#> SRR1633233 2 0.000 0.847 0.000 1.000
#> SRR1633234 2 0.000 0.847 0.000 1.000
#> SRR1633236 2 0.993 0.238 0.452 0.548
#> SRR1633237 2 0.993 0.238 0.452 0.548
#> SRR1633238 2 0.993 0.238 0.452 0.548
#> SRR1633239 2 0.993 0.238 0.452 0.548
#> SRR1633240 2 0.993 0.238 0.452 0.548
#> SRR1633241 2 0.993 0.238 0.452 0.548
#> SRR1633242 2 0.993 0.238 0.452 0.548
#> SRR1633243 2 0.993 0.238 0.452 0.548
#> SRR1633244 2 0.993 0.238 0.452 0.548
#> SRR1633245 2 0.993 0.238 0.452 0.548
#> SRR1633246 2 0.993 0.238 0.452 0.548
#> SRR1633247 1 0.993 0.129 0.548 0.452
#> SRR1633248 1 0.993 0.129 0.548 0.452
#> SRR1633249 1 0.993 0.129 0.548 0.452
#> SRR1633250 1 0.993 0.129 0.548 0.452
#> SRR1633251 1 0.975 0.272 0.592 0.408
#> SRR1633252 1 0.975 0.272 0.592 0.408
#> SRR1633253 1 0.975 0.272 0.592 0.408
#> SRR1633254 1 0.975 0.272 0.592 0.408
#> SRR1633255 1 0.975 0.272 0.592 0.408
#> SRR1633256 1 0.993 0.129 0.548 0.452
#> SRR1633257 1 0.993 0.129 0.548 0.452
#> SRR1633258 1 0.993 0.129 0.548 0.452
#> SRR1633259 1 0.993 0.129 0.548 0.452
#> SRR1633260 1 0.993 0.129 0.548 0.452
#> SRR1633261 1 0.993 0.129 0.548 0.452
#> SRR1633262 1 0.416 0.841 0.916 0.084
#> SRR1633263 1 0.416 0.841 0.916 0.084
#> SRR1633264 1 0.416 0.841 0.916 0.084
#> SRR1633265 1 0.416 0.841 0.916 0.084
#> SRR1633266 1 0.416 0.841 0.916 0.084
#> SRR1633267 1 0.595 0.778 0.856 0.144
#> SRR1633268 1 0.595 0.778 0.856 0.144
#> SRR1633269 1 0.595 0.778 0.856 0.144
#> SRR1633270 1 0.795 0.643 0.760 0.240
#> SRR1633271 1 0.795 0.643 0.760 0.240
#> SRR1633272 1 0.795 0.643 0.760 0.240
#> SRR1633273 1 0.000 0.900 1.000 0.000
#> SRR1633274 1 0.000 0.900 1.000 0.000
#> SRR1633275 1 0.000 0.900 1.000 0.000
#> SRR1633276 1 0.000 0.900 1.000 0.000
#> SRR1633277 1 0.000 0.900 1.000 0.000
#> SRR1633278 1 0.416 0.841 0.916 0.084
#> SRR1633279 1 0.416 0.841 0.916 0.084
#> SRR1633280 1 0.416 0.841 0.916 0.084
#> SRR1633281 1 0.416 0.841 0.916 0.084
#> SRR1633282 1 0.416 0.841 0.916 0.084
#> SRR1633284 1 0.000 0.900 1.000 0.000
#> SRR1633285 1 0.000 0.900 1.000 0.000
#> SRR1633286 1 0.000 0.900 1.000 0.000
#> SRR1633287 1 0.000 0.900 1.000 0.000
#> SRR1633288 1 0.000 0.900 1.000 0.000
#> SRR1633289 1 0.000 0.900 1.000 0.000
#> SRR1633290 1 0.000 0.900 1.000 0.000
#> SRR1633291 1 0.000 0.900 1.000 0.000
#> SRR1633292 2 0.993 0.238 0.452 0.548
#> SRR1633293 2 0.993 0.238 0.452 0.548
#> SRR1633294 2 0.993 0.238 0.452 0.548
#> SRR1633295 2 0.993 0.238 0.452 0.548
#> SRR1633296 1 0.000 0.900 1.000 0.000
#> SRR1633297 1 0.000 0.900 1.000 0.000
#> SRR1633298 1 0.000 0.900 1.000 0.000
#> SRR1633299 1 0.000 0.900 1.000 0.000
#> SRR1633300 2 0.000 0.847 0.000 1.000
#> SRR1633301 2 0.000 0.847 0.000 1.000
#> SRR1633302 2 0.000 0.847 0.000 1.000
#> SRR1633303 2 0.000 0.847 0.000 1.000
#> SRR1633304 2 0.000 0.847 0.000 1.000
#> SRR1633305 2 0.000 0.847 0.000 1.000
#> SRR1633306 2 0.000 0.847 0.000 1.000
#> SRR1633307 2 0.000 0.847 0.000 1.000
#> SRR1633308 2 0.000 0.847 0.000 1.000
#> SRR1633309 2 0.000 0.847 0.000 1.000
#> SRR1633310 2 0.000 0.847 0.000 1.000
#> SRR1633311 2 0.000 0.847 0.000 1.000
#> SRR1633312 2 0.000 0.847 0.000 1.000
#> SRR1633313 2 0.000 0.847 0.000 1.000
#> SRR1633314 2 0.000 0.847 0.000 1.000
#> SRR1633315 2 0.000 0.847 0.000 1.000
#> SRR1633316 2 0.000 0.847 0.000 1.000
#> SRR1633317 2 0.000 0.847 0.000 1.000
#> SRR1633318 2 0.000 0.847 0.000 1.000
#> SRR1633319 2 0.000 0.847 0.000 1.000
#> SRR1633320 2 0.000 0.847 0.000 1.000
#> SRR1633321 2 0.000 0.847 0.000 1.000
#> SRR1633322 2 0.000 0.847 0.000 1.000
#> SRR1633323 2 0.000 0.847 0.000 1.000
#> SRR1633324 2 0.000 0.847 0.000 1.000
#> SRR1633325 2 0.000 0.847 0.000 1.000
#> SRR1633326 2 0.000 0.847 0.000 1.000
#> SRR1633327 2 0.000 0.847 0.000 1.000
#> SRR1633328 2 0.000 0.847 0.000 1.000
#> SRR1633329 2 0.000 0.847 0.000 1.000
#> SRR1633330 2 0.000 0.847 0.000 1.000
#> SRR1633331 2 0.000 0.847 0.000 1.000
#> SRR1633332 2 0.000 0.847 0.000 1.000
#> SRR1633333 2 0.000 0.847 0.000 1.000
#> SRR1633334 2 0.000 0.847 0.000 1.000
#> SRR1633335 1 0.000 0.900 1.000 0.000
#> SRR1633336 1 0.000 0.900 1.000 0.000
#> SRR1633337 1 0.000 0.900 1.000 0.000
#> SRR1633338 1 0.000 0.900 1.000 0.000
#> SRR1633339 1 0.000 0.900 1.000 0.000
#> SRR1633340 1 0.000 0.900 1.000 0.000
#> SRR1633341 1 0.000 0.900 1.000 0.000
#> SRR1633342 1 0.000 0.900 1.000 0.000
#> SRR1633345 1 0.000 0.900 1.000 0.000
#> SRR1633346 1 0.000 0.900 1.000 0.000
#> SRR1633343 1 0.000 0.900 1.000 0.000
#> SRR1633344 1 0.000 0.900 1.000 0.000
#> SRR1633347 1 0.000 0.900 1.000 0.000
#> SRR1633348 1 0.000 0.900 1.000 0.000
#> SRR1633350 1 0.000 0.900 1.000 0.000
#> SRR1633351 1 0.000 0.900 1.000 0.000
#> SRR1633352 1 0.000 0.900 1.000 0.000
#> SRR1633353 1 0.000 0.900 1.000 0.000
#> SRR1633354 1 0.000 0.900 1.000 0.000
#> SRR1633355 1 0.000 0.900 1.000 0.000
#> SRR1633356 1 0.000 0.900 1.000 0.000
#> SRR1633357 1 0.000 0.900 1.000 0.000
#> SRR1633358 1 0.000 0.900 1.000 0.000
#> SRR1633362 1 0.000 0.900 1.000 0.000
#> SRR1633363 1 0.000 0.900 1.000 0.000
#> SRR1633364 1 0.000 0.900 1.000 0.000
#> SRR1633359 1 0.000 0.900 1.000 0.000
#> SRR1633360 1 0.000 0.900 1.000 0.000
#> SRR1633361 1 0.000 0.900 1.000 0.000
#> SRR2038492 1 0.000 0.900 1.000 0.000
#> SRR2038491 1 0.000 0.900 1.000 0.000
#> SRR2038490 1 0.000 0.900 1.000 0.000
#> SRR2038489 1 0.000 0.900 1.000 0.000
#> SRR2038488 1 0.000 0.900 1.000 0.000
#> SRR2038487 1 0.000 0.900 1.000 0.000
#> SRR2038486 1 0.000 0.900 1.000 0.000
#> SRR2038485 1 0.000 0.900 1.000 0.000
#> SRR2038484 1 0.000 0.900 1.000 0.000
#> SRR2038483 1 0.000 0.900 1.000 0.000
#> SRR2038482 1 0.000 0.900 1.000 0.000
#> SRR2038481 1 0.000 0.900 1.000 0.000
#> SRR2038480 1 0.000 0.900 1.000 0.000
#> SRR2038479 1 0.000 0.900 1.000 0.000
#> SRR2038477 1 0.000 0.900 1.000 0.000
#> SRR2038478 1 0.000 0.900 1.000 0.000
#> SRR2038476 1 0.000 0.900 1.000 0.000
#> SRR2038475 1 0.000 0.900 1.000 0.000
#> SRR2038474 1 0.000 0.900 1.000 0.000
#> SRR2038473 1 0.000 0.900 1.000 0.000
#> SRR2038472 1 0.000 0.900 1.000 0.000
#> SRR2038471 1 0.000 0.900 1.000 0.000
#> SRR2038470 1 0.000 0.900 1.000 0.000
#> SRR2038469 1 0.000 0.900 1.000 0.000
#> SRR2038468 1 0.000 0.900 1.000 0.000
#> SRR2038467 1 0.000 0.900 1.000 0.000
#> SRR2038466 1 0.000 0.900 1.000 0.000
#> SRR2038465 1 0.000 0.900 1.000 0.000
#> SRR2038464 1 0.000 0.900 1.000 0.000
#> SRR2038463 1 0.000 0.900 1.000 0.000
#> SRR2038462 1 0.416 0.841 0.916 0.084
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633236 3 0.0424 0.952 0.000 0.008 0.992
#> SRR1633237 3 0.0424 0.952 0.000 0.008 0.992
#> SRR1633238 3 0.0424 0.952 0.000 0.008 0.992
#> SRR1633239 3 0.0424 0.952 0.000 0.008 0.992
#> SRR1633240 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633262 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633263 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633264 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633265 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633266 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633267 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633268 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633269 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633270 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633271 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633272 3 0.0237 0.955 0.004 0.000 0.996
#> SRR1633273 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633274 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633275 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633276 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633277 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633278 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633279 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633280 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633281 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633282 3 0.0424 0.955 0.008 0.000 0.992
#> SRR1633284 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633285 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633286 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633287 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633288 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633289 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633290 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633291 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633292 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.954 0.000 0.000 1.000
#> SRR1633296 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633297 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633298 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633299 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1633335 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633336 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633337 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633338 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633339 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633340 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633341 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633342 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633345 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633346 3 0.2796 0.929 0.092 0.000 0.908
#> SRR1633343 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633344 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633347 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633348 3 0.2261 0.942 0.068 0.000 0.932
#> SRR1633350 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.996 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038492 3 0.5016 0.749 0.240 0.000 0.760
#> SRR2038491 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038490 3 0.5859 0.570 0.344 0.000 0.656
#> SRR2038489 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038480 3 0.5733 0.605 0.324 0.000 0.676
#> SRR2038479 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038476 3 0.5733 0.605 0.324 0.000 0.676
#> SRR2038475 1 0.0592 0.982 0.988 0.000 0.012
#> SRR2038474 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038464 1 0.3482 0.836 0.872 0.000 0.128
#> SRR2038463 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2038462 3 0.0424 0.955 0.008 0.000 0.992
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633237 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633238 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633239 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633240 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633241 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633242 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633243 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633244 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633245 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633246 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633247 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633248 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633249 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633250 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633251 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633252 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633253 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633254 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633255 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633256 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633257 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633258 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633259 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633260 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633261 4 0.2704 0.868 0.000 0.000 0.124 0.876
#> SRR1633262 4 0.1867 0.881 0.000 0.000 0.072 0.928
#> SRR1633263 4 0.1867 0.881 0.000 0.000 0.072 0.928
#> SRR1633264 4 0.1867 0.881 0.000 0.000 0.072 0.928
#> SRR1633265 4 0.1867 0.881 0.000 0.000 0.072 0.928
#> SRR1633266 4 0.1867 0.881 0.000 0.000 0.072 0.928
#> SRR1633267 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633268 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633269 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633270 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633271 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633272 4 0.2589 0.871 0.000 0.000 0.116 0.884
#> SRR1633273 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633274 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633275 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633276 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633277 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633278 4 0.2561 0.881 0.004 0.016 0.068 0.912
#> SRR1633279 4 0.2561 0.881 0.004 0.016 0.068 0.912
#> SRR1633280 4 0.2561 0.881 0.004 0.016 0.068 0.912
#> SRR1633281 4 0.2561 0.881 0.004 0.016 0.068 0.912
#> SRR1633282 4 0.1792 0.881 0.000 0.000 0.068 0.932
#> SRR1633284 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633285 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633286 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633287 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633288 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633289 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633290 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633291 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633292 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633293 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633294 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633295 3 0.0592 1.000 0.000 0.000 0.984 0.016
#> SRR1633296 4 0.1557 0.877 0.056 0.000 0.000 0.944
#> SRR1633297 4 0.1474 0.878 0.052 0.000 0.000 0.948
#> SRR1633298 4 0.0188 0.879 0.004 0.000 0.000 0.996
#> SRR1633299 4 0.0188 0.879 0.004 0.000 0.000 0.996
#> SRR1633300 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633301 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633302 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633303 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633319 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633320 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633321 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633322 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633323 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633324 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633325 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633326 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633327 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633328 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633329 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633330 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633331 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633332 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633333 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633334 2 0.0592 0.993 0.000 0.984 0.016 0.000
#> SRR1633335 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633336 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633337 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633338 4 0.1637 0.876 0.060 0.000 0.000 0.940
#> SRR1633339 4 0.1637 0.876 0.060 0.000 0.000 0.940
#> SRR1633340 4 0.1637 0.876 0.060 0.000 0.000 0.940
#> SRR1633341 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633342 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633345 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633346 4 0.2408 0.858 0.104 0.000 0.000 0.896
#> SRR1633343 4 0.0592 0.880 0.016 0.000 0.000 0.984
#> SRR1633344 4 0.0592 0.880 0.016 0.000 0.000 0.984
#> SRR1633347 4 0.0592 0.880 0.016 0.000 0.000 0.984
#> SRR1633348 4 0.0592 0.880 0.016 0.000 0.000 0.984
#> SRR1633350 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038492 4 0.4730 0.491 0.364 0.000 0.000 0.636
#> SRR2038491 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038490 4 0.4994 0.238 0.480 0.000 0.000 0.520
#> SRR2038489 1 0.0188 0.990 0.996 0.000 0.000 0.004
#> SRR2038488 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0469 0.982 0.988 0.000 0.000 0.012
#> SRR2038480 4 0.4955 0.313 0.444 0.000 0.000 0.556
#> SRR2038479 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038476 4 0.4907 0.381 0.420 0.000 0.000 0.580
#> SRR2038475 1 0.0817 0.966 0.976 0.000 0.000 0.024
#> SRR2038474 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0469 0.982 0.988 0.000 0.000 0.012
#> SRR2038469 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.2760 0.820 0.872 0.000 0.000 0.128
#> SRR2038463 1 0.0000 0.994 1.000 0.000 0.000 0.000
#> SRR2038462 4 0.2561 0.881 0.004 0.016 0.068 0.912
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633236 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633237 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633238 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633239 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633240 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633241 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633242 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633243 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633244 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633245 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633246 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633247 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633248 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633249 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633250 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633251 3 0.2179 0.8753 0.000 0.000 0.888 0.112 0.000
#> SRR1633252 3 0.2179 0.8753 0.000 0.000 0.888 0.112 0.000
#> SRR1633253 3 0.2179 0.8753 0.000 0.000 0.888 0.112 0.000
#> SRR1633254 3 0.2179 0.8753 0.000 0.000 0.888 0.112 0.000
#> SRR1633255 3 0.2179 0.8753 0.000 0.000 0.888 0.112 0.000
#> SRR1633256 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633257 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633258 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633259 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633260 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633261 3 0.1341 0.8664 0.000 0.000 0.944 0.056 0.000
#> SRR1633262 3 0.3983 0.6211 0.000 0.000 0.660 0.340 0.000
#> SRR1633263 3 0.3983 0.6211 0.000 0.000 0.660 0.340 0.000
#> SRR1633264 3 0.3983 0.6211 0.000 0.000 0.660 0.340 0.000
#> SRR1633265 3 0.3983 0.6211 0.000 0.000 0.660 0.340 0.000
#> SRR1633266 3 0.3983 0.6211 0.000 0.000 0.660 0.340 0.000
#> SRR1633267 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633268 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633269 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633270 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633271 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633272 3 0.2929 0.8492 0.000 0.000 0.820 0.180 0.000
#> SRR1633273 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633274 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633275 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633276 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633277 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633278 4 0.4451 -0.0837 0.004 0.000 0.492 0.504 0.000
#> SRR1633279 4 0.4451 -0.0837 0.004 0.000 0.492 0.504 0.000
#> SRR1633280 4 0.4451 -0.0837 0.004 0.000 0.492 0.504 0.000
#> SRR1633281 4 0.4451 -0.0837 0.004 0.000 0.492 0.504 0.000
#> SRR1633282 4 0.4451 -0.0788 0.004 0.000 0.492 0.504 0.000
#> SRR1633284 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633285 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633286 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633287 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633288 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633289 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633290 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633291 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633292 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633293 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633294 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633295 5 0.0510 1.0000 0.000 0.000 0.016 0.000 0.984
#> SRR1633296 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633297 4 0.2824 0.7810 0.032 0.000 0.096 0.872 0.000
#> SRR1633298 4 0.3109 0.6654 0.000 0.000 0.200 0.800 0.000
#> SRR1633299 4 0.3109 0.6654 0.000 0.000 0.200 0.800 0.000
#> SRR1633300 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633301 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633302 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633303 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.9395 0.000 1.000 0.000 0.000 0.000
#> SRR1633318 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633319 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633320 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633321 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633322 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633323 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633324 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633325 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633326 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633327 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633328 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633329 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633330 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633331 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633332 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633333 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633334 2 0.3226 0.9395 0.000 0.868 0.056 0.060 0.016
#> SRR1633335 4 0.1638 0.7830 0.064 0.000 0.004 0.932 0.000
#> SRR1633336 4 0.1638 0.7830 0.064 0.000 0.004 0.932 0.000
#> SRR1633337 4 0.1638 0.7830 0.064 0.000 0.004 0.932 0.000
#> SRR1633338 4 0.2370 0.7821 0.040 0.000 0.056 0.904 0.000
#> SRR1633339 4 0.2370 0.7821 0.040 0.000 0.056 0.904 0.000
#> SRR1633340 4 0.2370 0.7821 0.040 0.000 0.056 0.904 0.000
#> SRR1633341 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633342 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633345 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633346 4 0.1478 0.7822 0.064 0.000 0.000 0.936 0.000
#> SRR1633343 4 0.2624 0.7618 0.012 0.000 0.116 0.872 0.000
#> SRR1633344 4 0.2677 0.7664 0.016 0.000 0.112 0.872 0.000
#> SRR1633347 4 0.2761 0.7746 0.024 0.000 0.104 0.872 0.000
#> SRR1633348 4 0.2761 0.7746 0.024 0.000 0.104 0.872 0.000
#> SRR1633350 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038492 4 0.5576 0.3711 0.388 0.000 0.076 0.536 0.000
#> SRR2038491 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038490 4 0.4744 0.1842 0.476 0.000 0.016 0.508 0.000
#> SRR2038489 1 0.2074 0.8717 0.896 0.000 0.000 0.104 0.000
#> SRR2038488 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038486 1 0.0404 0.9682 0.988 0.000 0.000 0.012 0.000
#> SRR2038485 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038481 1 0.2329 0.8472 0.876 0.000 0.000 0.124 0.000
#> SRR2038480 4 0.5173 0.2103 0.460 0.000 0.040 0.500 0.000
#> SRR2038479 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038476 4 0.5296 0.1653 0.472 0.000 0.048 0.480 0.000
#> SRR2038475 1 0.1410 0.9176 0.940 0.000 0.000 0.060 0.000
#> SRR2038474 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038470 1 0.2280 0.8524 0.880 0.000 0.000 0.120 0.000
#> SRR2038469 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038464 1 0.3857 0.4888 0.688 0.000 0.000 0.312 0.000
#> SRR2038463 1 0.0000 0.9786 1.000 0.000 0.000 0.000 0.000
#> SRR2038462 4 0.4451 -0.0837 0.004 0.000 0.492 0.504 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633231 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633232 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633233 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633234 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633236 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633237 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633238 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633239 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633240 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633241 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633242 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633243 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633244 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633245 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633246 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633247 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633248 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633249 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633250 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633251 3 0.0547 0.8396 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1633252 3 0.0547 0.8396 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1633253 3 0.0547 0.8396 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1633254 3 0.0547 0.8396 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1633255 3 0.0547 0.8396 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1633256 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633257 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633258 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633259 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633260 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633261 3 0.0632 0.8355 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1633262 3 0.4684 0.6293 0.000 0.000 0.656 0.256 0.000 0.088
#> SRR1633263 3 0.4684 0.6293 0.000 0.000 0.656 0.256 0.000 0.088
#> SRR1633264 3 0.4684 0.6293 0.000 0.000 0.656 0.256 0.000 0.088
#> SRR1633265 3 0.4684 0.6293 0.000 0.000 0.656 0.256 0.000 0.088
#> SRR1633266 3 0.4771 0.6171 0.000 0.000 0.648 0.256 0.000 0.096
#> SRR1633267 3 0.3158 0.7909 0.000 0.000 0.812 0.164 0.004 0.020
#> SRR1633268 3 0.3158 0.7909 0.000 0.000 0.812 0.164 0.004 0.020
#> SRR1633269 3 0.3158 0.7909 0.000 0.000 0.812 0.164 0.004 0.020
#> SRR1633270 3 0.3361 0.7784 0.000 0.000 0.788 0.188 0.004 0.020
#> SRR1633271 3 0.3361 0.7784 0.000 0.000 0.788 0.188 0.004 0.020
#> SRR1633272 3 0.3361 0.7784 0.000 0.000 0.788 0.188 0.004 0.020
#> SRR1633273 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633274 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633275 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633276 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633277 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633278 6 0.5425 0.1235 0.000 0.004 0.396 0.104 0.000 0.496
#> SRR1633279 6 0.5425 0.1235 0.000 0.004 0.396 0.104 0.000 0.496
#> SRR1633280 6 0.5425 0.1235 0.000 0.004 0.396 0.104 0.000 0.496
#> SRR1633281 6 0.5425 0.1235 0.000 0.004 0.396 0.104 0.000 0.496
#> SRR1633282 3 0.6039 -0.2054 0.000 0.000 0.392 0.252 0.000 0.356
#> SRR1633284 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633285 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633286 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633287 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633288 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633289 6 0.0632 0.7227 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR1633290 4 0.4671 0.9673 0.000 0.000 0.044 0.532 0.000 0.424
#> SRR1633291 4 0.4671 0.9673 0.000 0.000 0.044 0.532 0.000 0.424
#> SRR1633292 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633293 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633294 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633295 5 0.3930 1.0000 0.000 0.000 0.000 0.420 0.576 0.004
#> SRR1633296 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633297 4 0.4721 0.9731 0.000 0.000 0.048 0.532 0.000 0.420
#> SRR1633298 4 0.5137 0.8521 0.000 0.000 0.096 0.552 0.000 0.352
#> SRR1633299 4 0.5137 0.8521 0.000 0.000 0.096 0.552 0.000 0.352
#> SRR1633300 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633301 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633302 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633303 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633304 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633305 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633306 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633307 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633308 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633309 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633310 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633311 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633312 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633313 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633314 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633315 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633316 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633317 2 0.0000 0.7875 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1633318 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633319 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633320 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633321 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633322 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633323 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633324 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633325 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633326 2 0.3797 0.7871 0.000 0.580 0.000 0.000 0.420 0.000
#> SRR1633327 2 0.3797 0.7871 0.000 0.580 0.000 0.000 0.420 0.000
#> SRR1633328 2 0.3797 0.7871 0.000 0.580 0.000 0.000 0.420 0.000
#> SRR1633329 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633330 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633331 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633332 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633333 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633334 2 0.3804 0.7868 0.000 0.576 0.000 0.000 0.424 0.000
#> SRR1633335 6 0.0777 0.7215 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633336 6 0.0777 0.7215 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633337 6 0.0777 0.7215 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633338 6 0.2393 0.5938 0.004 0.000 0.040 0.064 0.000 0.892
#> SRR1633339 6 0.2393 0.5938 0.004 0.000 0.040 0.064 0.000 0.892
#> SRR1633340 6 0.2393 0.5938 0.004 0.000 0.040 0.064 0.000 0.892
#> SRR1633341 6 0.0777 0.7220 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633342 6 0.0777 0.7220 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633345 6 0.0777 0.7220 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633346 6 0.0777 0.7220 0.024 0.000 0.000 0.004 0.000 0.972
#> SRR1633343 4 0.4726 0.9703 0.000 0.000 0.048 0.528 0.000 0.424
#> SRR1633344 4 0.4726 0.9703 0.000 0.000 0.048 0.528 0.000 0.424
#> SRR1633347 4 0.4726 0.9703 0.000 0.000 0.048 0.528 0.000 0.424
#> SRR1633348 4 0.4726 0.9703 0.000 0.000 0.048 0.528 0.000 0.424
#> SRR1633350 1 0.0000 0.9246 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9246 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9246 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1633353 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633354 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633355 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633356 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633357 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633358 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633362 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633363 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633364 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633359 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633360 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1633361 1 0.0146 0.9244 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2038492 1 0.4760 0.0706 0.496 0.000 0.032 0.008 0.000 0.464
#> SRR2038491 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038490 1 0.4631 0.0922 0.504 0.000 0.024 0.008 0.000 0.464
#> SRR2038489 1 0.2092 0.8166 0.876 0.000 0.000 0.000 0.000 0.124
#> SRR2038488 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038487 1 0.0260 0.9239 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR2038486 1 0.0458 0.9187 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR2038485 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038484 1 0.0291 0.9238 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR2038483 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038482 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038481 1 0.2632 0.7699 0.832 0.000 0.000 0.004 0.000 0.164
#> SRR2038480 1 0.4629 0.1041 0.508 0.000 0.024 0.008 0.000 0.460
#> SRR2038479 1 0.0405 0.9236 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR2038477 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038478 1 0.0260 0.9239 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR2038476 1 0.4844 0.0739 0.500 0.000 0.032 0.012 0.000 0.456
#> SRR2038475 1 0.1141 0.8873 0.948 0.000 0.000 0.000 0.000 0.052
#> SRR2038474 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038473 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038472 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038471 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038470 1 0.2632 0.7699 0.832 0.000 0.000 0.004 0.000 0.164
#> SRR2038469 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038468 1 0.0260 0.9239 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR2038467 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038466 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038465 1 0.0146 0.9251 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2038464 1 0.3742 0.4357 0.648 0.000 0.004 0.000 0.000 0.348
#> SRR2038463 1 0.0291 0.9228 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR2038462 6 0.5425 0.1235 0.000 0.004 0.396 0.104 0.000 0.496
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15916 rows and 163 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.981 0.993 0.4752 0.529 0.529
#> 3 3 0.993 0.964 0.985 0.3322 0.774 0.598
#> 4 4 0.875 0.836 0.901 0.0883 0.895 0.727
#> 5 5 0.818 0.810 0.878 0.0744 0.945 0.824
#> 6 6 0.856 0.905 0.918 0.0544 0.897 0.649
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1633230 2 0.000 1.000 0.000 1.000
#> SRR1633231 2 0.000 1.000 0.000 1.000
#> SRR1633232 2 0.000 1.000 0.000 1.000
#> SRR1633233 2 0.000 1.000 0.000 1.000
#> SRR1633234 2 0.000 1.000 0.000 1.000
#> SRR1633236 2 0.000 1.000 0.000 1.000
#> SRR1633237 2 0.000 1.000 0.000 1.000
#> SRR1633238 2 0.000 1.000 0.000 1.000
#> SRR1633239 2 0.000 1.000 0.000 1.000
#> SRR1633240 2 0.000 1.000 0.000 1.000
#> SRR1633241 2 0.000 1.000 0.000 1.000
#> SRR1633242 2 0.000 1.000 0.000 1.000
#> SRR1633243 2 0.000 1.000 0.000 1.000
#> SRR1633244 2 0.000 1.000 0.000 1.000
#> SRR1633245 2 0.000 1.000 0.000 1.000
#> SRR1633246 2 0.000 1.000 0.000 1.000
#> SRR1633247 1 0.000 0.988 1.000 0.000
#> SRR1633248 1 0.000 0.988 1.000 0.000
#> SRR1633249 1 0.000 0.988 1.000 0.000
#> SRR1633250 1 0.000 0.988 1.000 0.000
#> SRR1633251 1 0.000 0.988 1.000 0.000
#> SRR1633252 1 0.000 0.988 1.000 0.000
#> SRR1633253 1 0.000 0.988 1.000 0.000
#> SRR1633254 1 0.000 0.988 1.000 0.000
#> SRR1633255 1 0.000 0.988 1.000 0.000
#> SRR1633256 1 0.141 0.969 0.980 0.020
#> SRR1633257 1 0.141 0.969 0.980 0.020
#> SRR1633258 1 0.163 0.965 0.976 0.024
#> SRR1633259 1 0.961 0.391 0.616 0.384
#> SRR1633260 1 0.946 0.440 0.636 0.364
#> SRR1633261 1 0.971 0.349 0.600 0.400
#> SRR1633262 1 0.000 0.988 1.000 0.000
#> SRR1633263 1 0.000 0.988 1.000 0.000
#> SRR1633264 1 0.000 0.988 1.000 0.000
#> SRR1633265 1 0.000 0.988 1.000 0.000
#> SRR1633266 1 0.000 0.988 1.000 0.000
#> SRR1633267 2 0.000 1.000 0.000 1.000
#> SRR1633268 2 0.000 1.000 0.000 1.000
#> SRR1633269 2 0.000 1.000 0.000 1.000
#> SRR1633270 2 0.000 1.000 0.000 1.000
#> SRR1633271 2 0.000 1.000 0.000 1.000
#> SRR1633272 2 0.000 1.000 0.000 1.000
#> SRR1633273 1 0.000 0.988 1.000 0.000
#> SRR1633274 1 0.000 0.988 1.000 0.000
#> SRR1633275 1 0.000 0.988 1.000 0.000
#> SRR1633276 1 0.000 0.988 1.000 0.000
#> SRR1633277 1 0.000 0.988 1.000 0.000
#> SRR1633278 1 0.000 0.988 1.000 0.000
#> SRR1633279 1 0.000 0.988 1.000 0.000
#> SRR1633280 1 0.000 0.988 1.000 0.000
#> SRR1633281 1 0.000 0.988 1.000 0.000
#> SRR1633282 1 0.000 0.988 1.000 0.000
#> SRR1633284 1 0.000 0.988 1.000 0.000
#> SRR1633285 1 0.000 0.988 1.000 0.000
#> SRR1633286 1 0.000 0.988 1.000 0.000
#> SRR1633287 1 0.000 0.988 1.000 0.000
#> SRR1633288 1 0.000 0.988 1.000 0.000
#> SRR1633289 1 0.000 0.988 1.000 0.000
#> SRR1633290 1 0.000 0.988 1.000 0.000
#> SRR1633291 1 0.000 0.988 1.000 0.000
#> SRR1633292 2 0.000 1.000 0.000 1.000
#> SRR1633293 2 0.000 1.000 0.000 1.000
#> SRR1633294 2 0.000 1.000 0.000 1.000
#> SRR1633295 2 0.000 1.000 0.000 1.000
#> SRR1633296 1 0.000 0.988 1.000 0.000
#> SRR1633297 1 0.000 0.988 1.000 0.000
#> SRR1633298 1 0.000 0.988 1.000 0.000
#> SRR1633299 1 0.000 0.988 1.000 0.000
#> SRR1633300 2 0.000 1.000 0.000 1.000
#> SRR1633301 2 0.000 1.000 0.000 1.000
#> SRR1633302 2 0.000 1.000 0.000 1.000
#> SRR1633303 2 0.000 1.000 0.000 1.000
#> SRR1633304 2 0.000 1.000 0.000 1.000
#> SRR1633305 2 0.000 1.000 0.000 1.000
#> SRR1633306 2 0.000 1.000 0.000 1.000
#> SRR1633307 2 0.000 1.000 0.000 1.000
#> SRR1633308 2 0.000 1.000 0.000 1.000
#> SRR1633309 2 0.000 1.000 0.000 1.000
#> SRR1633310 2 0.000 1.000 0.000 1.000
#> SRR1633311 2 0.000 1.000 0.000 1.000
#> SRR1633312 2 0.000 1.000 0.000 1.000
#> SRR1633313 2 0.000 1.000 0.000 1.000
#> SRR1633314 2 0.000 1.000 0.000 1.000
#> SRR1633315 2 0.000 1.000 0.000 1.000
#> SRR1633316 2 0.000 1.000 0.000 1.000
#> SRR1633317 2 0.000 1.000 0.000 1.000
#> SRR1633318 2 0.000 1.000 0.000 1.000
#> SRR1633319 2 0.000 1.000 0.000 1.000
#> SRR1633320 2 0.000 1.000 0.000 1.000
#> SRR1633321 2 0.000 1.000 0.000 1.000
#> SRR1633322 2 0.000 1.000 0.000 1.000
#> SRR1633323 2 0.000 1.000 0.000 1.000
#> SRR1633324 2 0.000 1.000 0.000 1.000
#> SRR1633325 2 0.000 1.000 0.000 1.000
#> SRR1633326 2 0.000 1.000 0.000 1.000
#> SRR1633327 2 0.000 1.000 0.000 1.000
#> SRR1633328 2 0.000 1.000 0.000 1.000
#> SRR1633329 2 0.000 1.000 0.000 1.000
#> SRR1633330 2 0.000 1.000 0.000 1.000
#> SRR1633331 2 0.000 1.000 0.000 1.000
#> SRR1633332 2 0.000 1.000 0.000 1.000
#> SRR1633333 2 0.000 1.000 0.000 1.000
#> SRR1633334 2 0.000 1.000 0.000 1.000
#> SRR1633335 1 0.000 0.988 1.000 0.000
#> SRR1633336 1 0.000 0.988 1.000 0.000
#> SRR1633337 1 0.000 0.988 1.000 0.000
#> SRR1633338 1 0.000 0.988 1.000 0.000
#> SRR1633339 1 0.000 0.988 1.000 0.000
#> SRR1633340 1 0.000 0.988 1.000 0.000
#> SRR1633341 1 0.000 0.988 1.000 0.000
#> SRR1633342 1 0.000 0.988 1.000 0.000
#> SRR1633345 1 0.000 0.988 1.000 0.000
#> SRR1633346 1 0.000 0.988 1.000 0.000
#> SRR1633343 1 0.000 0.988 1.000 0.000
#> SRR1633344 1 0.000 0.988 1.000 0.000
#> SRR1633347 1 0.000 0.988 1.000 0.000
#> SRR1633348 1 0.000 0.988 1.000 0.000
#> SRR1633350 1 0.000 0.988 1.000 0.000
#> SRR1633351 1 0.000 0.988 1.000 0.000
#> SRR1633352 1 0.000 0.988 1.000 0.000
#> SRR1633353 1 0.000 0.988 1.000 0.000
#> SRR1633354 1 0.000 0.988 1.000 0.000
#> SRR1633355 1 0.000 0.988 1.000 0.000
#> SRR1633356 1 0.000 0.988 1.000 0.000
#> SRR1633357 1 0.000 0.988 1.000 0.000
#> SRR1633358 1 0.000 0.988 1.000 0.000
#> SRR1633362 1 0.000 0.988 1.000 0.000
#> SRR1633363 1 0.000 0.988 1.000 0.000
#> SRR1633364 1 0.000 0.988 1.000 0.000
#> SRR1633359 1 0.000 0.988 1.000 0.000
#> SRR1633360 1 0.000 0.988 1.000 0.000
#> SRR1633361 1 0.000 0.988 1.000 0.000
#> SRR2038492 1 0.000 0.988 1.000 0.000
#> SRR2038491 1 0.000 0.988 1.000 0.000
#> SRR2038490 1 0.000 0.988 1.000 0.000
#> SRR2038489 1 0.000 0.988 1.000 0.000
#> SRR2038488 1 0.000 0.988 1.000 0.000
#> SRR2038487 1 0.000 0.988 1.000 0.000
#> SRR2038486 1 0.000 0.988 1.000 0.000
#> SRR2038485 1 0.000 0.988 1.000 0.000
#> SRR2038484 1 0.000 0.988 1.000 0.000
#> SRR2038483 1 0.000 0.988 1.000 0.000
#> SRR2038482 1 0.000 0.988 1.000 0.000
#> SRR2038481 1 0.000 0.988 1.000 0.000
#> SRR2038480 1 0.000 0.988 1.000 0.000
#> SRR2038479 1 0.000 0.988 1.000 0.000
#> SRR2038477 1 0.000 0.988 1.000 0.000
#> SRR2038478 1 0.000 0.988 1.000 0.000
#> SRR2038476 1 0.000 0.988 1.000 0.000
#> SRR2038475 1 0.000 0.988 1.000 0.000
#> SRR2038474 1 0.000 0.988 1.000 0.000
#> SRR2038473 1 0.000 0.988 1.000 0.000
#> SRR2038472 1 0.000 0.988 1.000 0.000
#> SRR2038471 1 0.000 0.988 1.000 0.000
#> SRR2038470 1 0.000 0.988 1.000 0.000
#> SRR2038469 1 0.000 0.988 1.000 0.000
#> SRR2038468 1 0.000 0.988 1.000 0.000
#> SRR2038467 1 0.000 0.988 1.000 0.000
#> SRR2038466 1 0.000 0.988 1.000 0.000
#> SRR2038465 1 0.000 0.988 1.000 0.000
#> SRR2038464 1 0.000 0.988 1.000 0.000
#> SRR2038463 1 0.000 0.988 1.000 0.000
#> SRR2038462 1 0.000 0.988 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1633230 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633231 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633232 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633233 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633234 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633236 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633237 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633238 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633239 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633240 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633241 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633242 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633243 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633244 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633245 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633246 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633247 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633248 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633249 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633250 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633251 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633252 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633253 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633254 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633255 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633256 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633257 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633258 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633259 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633260 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633261 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633262 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633263 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633264 3 0.0424 0.9656 0.008 0.000 0.992
#> SRR1633265 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633266 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633267 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633268 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633269 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633270 3 0.3412 0.8480 0.000 0.124 0.876
#> SRR1633271 3 0.3941 0.8090 0.000 0.156 0.844
#> SRR1633272 3 0.4291 0.7764 0.000 0.180 0.820
#> SRR1633273 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633274 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633275 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633276 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633277 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633278 1 0.6026 0.4129 0.624 0.000 0.376
#> SRR1633279 1 0.5859 0.4892 0.656 0.000 0.344
#> SRR1633280 1 0.4750 0.7289 0.784 0.000 0.216
#> SRR1633281 1 0.4702 0.7350 0.788 0.000 0.212
#> SRR1633282 3 0.6307 0.0119 0.488 0.000 0.512
#> SRR1633284 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633285 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633286 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633287 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633288 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633289 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633290 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633291 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633292 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633293 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633294 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633295 3 0.0000 0.9738 0.000 0.000 1.000
#> SRR1633296 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633297 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633298 1 0.3482 0.8469 0.872 0.000 0.128
#> SRR1633299 1 0.3551 0.8421 0.868 0.000 0.132
#> SRR1633300 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633301 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633302 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633303 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633304 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633305 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633306 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633307 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633308 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633309 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633310 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633311 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633312 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633313 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633314 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633315 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633316 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633317 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633318 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633319 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633320 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633321 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633322 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633323 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633324 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633325 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633326 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633327 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633328 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633329 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633330 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633331 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633332 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633333 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633334 2 0.0000 1.0000 0.000 1.000 0.000
#> SRR1633335 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633336 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633337 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633338 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633339 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633340 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633341 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633342 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633345 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633346 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633343 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633344 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633347 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633348 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633350 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633351 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633352 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633353 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633354 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633355 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633356 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633357 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633358 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633362 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633363 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633364 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633359 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633360 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR1633361 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038492 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038491 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038490 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038489 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038488 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038487 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038486 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038485 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038484 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038483 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038482 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038481 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038480 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038479 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038477 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038478 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038476 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038475 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038474 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038473 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038472 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038471 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038470 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038469 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038468 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038467 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038466 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038465 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038464 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038463 1 0.0000 0.9820 1.000 0.000 0.000
#> SRR2038462 1 0.0000 0.9820 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1633230 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633231 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633232 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633233 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633234 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633236 3 0.2216 0.867 0.000 0.000 0.908 0.092
#> SRR1633237 3 0.2647 0.846 0.000 0.000 0.880 0.120
#> SRR1633238 3 0.2760 0.840 0.000 0.000 0.872 0.128
#> SRR1633239 3 0.2704 0.843 0.000 0.000 0.876 0.124
#> SRR1633240 3 0.0188 0.923 0.000 0.000 0.996 0.004
#> SRR1633241 3 0.0188 0.921 0.000 0.000 0.996 0.004
#> SRR1633242 3 0.0188 0.923 0.000 0.000 0.996 0.004
#> SRR1633243 3 0.0188 0.923 0.000 0.000 0.996 0.004
#> SRR1633244 3 0.0469 0.918 0.000 0.000 0.988 0.012
#> SRR1633245 3 0.0469 0.918 0.000 0.000 0.988 0.012
#> SRR1633246 3 0.0469 0.918 0.000 0.000 0.988 0.012
#> SRR1633247 3 0.2081 0.915 0.000 0.000 0.916 0.084
#> SRR1633248 3 0.2081 0.915 0.000 0.000 0.916 0.084
#> SRR1633249 3 0.2081 0.915 0.000 0.000 0.916 0.084
#> SRR1633250 3 0.2081 0.915 0.000 0.000 0.916 0.084
#> SRR1633251 4 0.4250 0.549 0.000 0.000 0.276 0.724
#> SRR1633252 4 0.4250 0.550 0.000 0.000 0.276 0.724
#> SRR1633253 4 0.4277 0.545 0.000 0.000 0.280 0.720
#> SRR1633254 4 0.4331 0.535 0.000 0.000 0.288 0.712
#> SRR1633255 4 0.4304 0.540 0.000 0.000 0.284 0.716
#> SRR1633256 3 0.3172 0.857 0.000 0.000 0.840 0.160
#> SRR1633257 3 0.3266 0.848 0.000 0.000 0.832 0.168
#> SRR1633258 3 0.3172 0.857 0.000 0.000 0.840 0.160
#> SRR1633259 3 0.2530 0.900 0.000 0.000 0.888 0.112
#> SRR1633260 3 0.2530 0.900 0.000 0.000 0.888 0.112
#> SRR1633261 3 0.2530 0.900 0.000 0.000 0.888 0.112
#> SRR1633262 4 0.4228 0.587 0.008 0.000 0.232 0.760
#> SRR1633263 4 0.4472 0.596 0.020 0.000 0.220 0.760
#> SRR1633264 4 0.4472 0.596 0.020 0.000 0.220 0.760
#> SRR1633265 4 0.4538 0.597 0.024 0.000 0.216 0.760
#> SRR1633266 4 0.4472 0.596 0.020 0.000 0.220 0.760
#> SRR1633267 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633268 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633269 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633270 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633271 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633272 4 0.3975 0.580 0.000 0.000 0.240 0.760
#> SRR1633273 4 0.4999 0.307 0.492 0.000 0.000 0.508
#> SRR1633274 4 0.4999 0.307 0.492 0.000 0.000 0.508
#> SRR1633275 4 0.4996 0.330 0.484 0.000 0.000 0.516
#> SRR1633276 1 0.5000 -0.316 0.500 0.000 0.000 0.500
#> SRR1633277 1 0.5000 -0.316 0.500 0.000 0.000 0.500
#> SRR1633278 1 0.7079 0.130 0.556 0.000 0.168 0.276
#> SRR1633279 1 0.6581 0.339 0.624 0.000 0.144 0.232
#> SRR1633280 1 0.5248 0.603 0.748 0.000 0.088 0.164
#> SRR1633281 1 0.4710 0.677 0.792 0.000 0.088 0.120
#> SRR1633282 4 0.4980 0.596 0.304 0.000 0.016 0.680
#> SRR1633284 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633285 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633286 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633287 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633288 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633289 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633290 1 0.4804 0.144 0.616 0.000 0.000 0.384
#> SRR1633291 1 0.4804 0.144 0.616 0.000 0.000 0.384
#> SRR1633292 3 0.1118 0.926 0.000 0.000 0.964 0.036
#> SRR1633293 3 0.1118 0.926 0.000 0.000 0.964 0.036
#> SRR1633294 3 0.1022 0.925 0.000 0.000 0.968 0.032
#> SRR1633295 3 0.1118 0.926 0.000 0.000 0.964 0.036
#> SRR1633296 4 0.4888 0.472 0.412 0.000 0.000 0.588
#> SRR1633297 4 0.4877 0.479 0.408 0.000 0.000 0.592
#> SRR1633298 4 0.4643 0.558 0.344 0.000 0.000 0.656
#> SRR1633299 4 0.4624 0.563 0.340 0.000 0.000 0.660
#> SRR1633300 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1633301 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1633302 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1633303 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633304 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633305 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633306 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633307 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633308 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633309 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633310 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633311 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633312 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633313 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633314 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633315 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633316 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633317 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633318 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633319 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633320 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633321 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633322 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633323 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633324 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633325 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633326 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633327 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633328 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633329 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633330 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633331 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633332 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633333 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633334 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1633335 1 0.0469 0.932 0.988 0.000 0.000 0.012
#> SRR1633336 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633337 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633338 1 0.0817 0.920 0.976 0.000 0.000 0.024
#> SRR1633339 1 0.0469 0.932 0.988 0.000 0.000 0.012
#> SRR1633340 1 0.0469 0.932 0.988 0.000 0.000 0.012
#> SRR1633341 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633342 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633345 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633346 1 0.0188 0.939 0.996 0.000 0.000 0.004
#> SRR1633343 4 0.4998 0.319 0.488 0.000 0.000 0.512
#> SRR1633344 4 0.4981 0.377 0.464 0.000 0.000 0.536
#> SRR1633347 4 0.4996 0.329 0.484 0.000 0.000 0.516
#> SRR1633348 4 0.4967 0.402 0.452 0.000 0.000 0.548
#> SRR1633350 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633351 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633352 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633353 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633354 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633355 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633356 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633357 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633358 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633362 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633363 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633364 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633359 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633360 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR1633361 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038492 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038491 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038490 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038489 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038488 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038487 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038486 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038485 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038484 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038483 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038482 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038481 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038480 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038479 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038477 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038478 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038476 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038475 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038474 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038473 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038472 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038471 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038470 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038469 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038468 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038467 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038466 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038465 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038464 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038463 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> SRR2038462 1 0.2011 0.852 0.920 0.000 0.000 0.080
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1633230 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633231 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633232 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633233 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633234 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633236 5 0.2536 0.856 0.000 0.004 0.000 NA 0.868
#> SRR1633237 5 0.2732 0.841 0.000 0.000 0.000 NA 0.840
#> SRR1633238 5 0.2690 0.843 0.000 0.000 0.000 NA 0.844
#> SRR1633239 5 0.2690 0.843 0.000 0.000 0.000 NA 0.844
#> SRR1633240 5 0.0609 0.899 0.000 0.000 0.000 NA 0.980
#> SRR1633241 5 0.0703 0.898 0.000 0.000 0.000 NA 0.976
#> SRR1633242 5 0.0404 0.900 0.000 0.000 0.000 NA 0.988
#> SRR1633243 5 0.0404 0.900 0.000 0.000 0.000 NA 0.988
#> SRR1633244 5 0.0963 0.895 0.000 0.000 0.000 NA 0.964
#> SRR1633245 5 0.0880 0.896 0.000 0.000 0.000 NA 0.968
#> SRR1633246 5 0.0609 0.899 0.000 0.000 0.000 NA 0.980
#> SRR1633247 5 0.1386 0.894 0.000 0.000 0.016 NA 0.952
#> SRR1633248 5 0.1522 0.891 0.000 0.000 0.012 NA 0.944
#> SRR1633249 5 0.1444 0.892 0.000 0.000 0.012 NA 0.948
#> SRR1633250 5 0.1444 0.892 0.000 0.000 0.012 NA 0.948
#> SRR1633251 3 0.6148 0.536 0.000 0.000 0.552 NA 0.180
#> SRR1633252 3 0.6254 0.512 0.000 0.000 0.536 NA 0.196
#> SRR1633253 3 0.6148 0.532 0.000 0.000 0.552 NA 0.180
#> SRR1633254 3 0.6158 0.533 0.000 0.000 0.552 NA 0.184
#> SRR1633255 3 0.6031 0.554 0.000 0.000 0.568 NA 0.164
#> SRR1633256 5 0.4629 0.690 0.000 0.000 0.052 NA 0.704
#> SRR1633257 5 0.4655 0.687 0.000 0.000 0.052 NA 0.700
#> SRR1633258 5 0.4629 0.690 0.000 0.000 0.052 NA 0.704
#> SRR1633259 5 0.3565 0.795 0.000 0.000 0.024 NA 0.800
#> SRR1633260 5 0.3687 0.788 0.000 0.000 0.028 NA 0.792
#> SRR1633261 5 0.3687 0.788 0.000 0.000 0.028 NA 0.792
#> SRR1633262 3 0.4110 0.660 0.008 0.000 0.736 NA 0.012
#> SRR1633263 3 0.4110 0.661 0.012 0.000 0.736 NA 0.008
#> SRR1633264 3 0.4217 0.662 0.012 0.000 0.732 NA 0.012
#> SRR1633265 3 0.4061 0.660 0.004 0.000 0.740 NA 0.016
#> SRR1633266 3 0.4061 0.660 0.004 0.000 0.740 NA 0.016
#> SRR1633267 3 0.4597 0.641 0.000 0.000 0.696 NA 0.044
#> SRR1633268 3 0.4597 0.641 0.000 0.000 0.696 NA 0.044
#> SRR1633269 3 0.4597 0.641 0.000 0.000 0.696 NA 0.044
#> SRR1633270 3 0.5720 0.577 0.000 0.000 0.604 NA 0.128
#> SRR1633271 3 0.5790 0.582 0.000 0.004 0.608 NA 0.120
#> SRR1633272 3 0.5869 0.575 0.000 0.004 0.600 NA 0.128
#> SRR1633273 3 0.3177 0.581 0.208 0.000 0.792 NA 0.000
#> SRR1633274 3 0.3177 0.581 0.208 0.000 0.792 NA 0.000
#> SRR1633275 3 0.3210 0.575 0.212 0.000 0.788 NA 0.000
#> SRR1633276 3 0.3336 0.550 0.228 0.000 0.772 NA 0.000
#> SRR1633277 3 0.3242 0.569 0.216 0.000 0.784 NA 0.000
#> SRR1633278 3 0.6355 0.590 0.132 0.000 0.492 NA 0.008
#> SRR1633279 3 0.6442 0.584 0.144 0.000 0.484 NA 0.008
#> SRR1633280 3 0.6677 0.547 0.212 0.000 0.456 NA 0.004
#> SRR1633281 3 0.6724 0.539 0.228 0.000 0.452 NA 0.004
#> SRR1633282 3 0.3631 0.668 0.008 0.000 0.788 NA 0.008
#> SRR1633284 1 0.3796 0.683 0.700 0.000 0.300 NA 0.000
#> SRR1633285 1 0.3796 0.683 0.700 0.000 0.300 NA 0.000
#> SRR1633286 1 0.3752 0.691 0.708 0.000 0.292 NA 0.000
#> SRR1633287 1 0.3752 0.691 0.708 0.000 0.292 NA 0.000
#> SRR1633288 1 0.3774 0.687 0.704 0.000 0.296 NA 0.000
#> SRR1633289 1 0.3752 0.691 0.708 0.000 0.292 NA 0.000
#> SRR1633290 3 0.4045 0.249 0.356 0.000 0.644 NA 0.000
#> SRR1633291 3 0.4060 0.239 0.360 0.000 0.640 NA 0.000
#> SRR1633292 5 0.0404 0.901 0.000 0.000 0.000 NA 0.988
#> SRR1633293 5 0.0404 0.901 0.000 0.000 0.000 NA 0.988
#> SRR1633294 5 0.0290 0.900 0.000 0.000 0.000 NA 0.992
#> SRR1633295 5 0.0290 0.900 0.000 0.000 0.000 NA 0.992
#> SRR1633296 3 0.2488 0.645 0.124 0.000 0.872 NA 0.000
#> SRR1633297 3 0.2329 0.646 0.124 0.000 0.876 NA 0.000
#> SRR1633298 3 0.1430 0.646 0.052 0.000 0.944 NA 0.004
#> SRR1633299 3 0.1408 0.645 0.044 0.000 0.948 NA 0.008
#> SRR1633300 2 0.0510 0.976 0.000 0.984 0.000 NA 0.000
#> SRR1633301 2 0.0510 0.976 0.000 0.984 0.000 NA 0.000
#> SRR1633302 2 0.0510 0.976 0.000 0.984 0.000 NA 0.000
#> SRR1633303 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633304 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633305 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633306 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633307 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633308 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633309 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633310 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633311 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633312 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633313 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633314 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633315 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633316 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633317 2 0.1043 0.980 0.000 0.960 0.000 NA 0.000
#> SRR1633318 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633319 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633320 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633321 2 0.0162 0.979 0.000 0.996 0.000 NA 0.000
#> SRR1633322 2 0.0162 0.979 0.000 0.996 0.000 NA 0.000
#> SRR1633323 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633324 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633325 2 0.0290 0.978 0.000 0.992 0.000 NA 0.000
#> SRR1633326 2 0.0000 0.979 0.000 1.000 0.000 NA 0.000
#> SRR1633327 2 0.0000 0.979 0.000 1.000 0.000 NA 0.000
#> SRR1633328 2 0.0000 0.979 0.000 1.000 0.000 NA 0.000
#> SRR1633329 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633330 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633331 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633332 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633333 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633334 2 0.0404 0.977 0.000 0.988 0.000 NA 0.000
#> SRR1633335 1 0.4108 0.674 0.684 0.000 0.308 NA 0.000
#> SRR1633336 1 0.4127 0.670 0.680 0.000 0.312 NA 0.000
#> SRR1633337 1 0.4009 0.670 0.684 0.000 0.312 NA 0.000
#> SRR1633338 1 0.4074 0.588 0.636 0.000 0.364 NA 0.000
#> SRR1633339 1 0.3999 0.622 0.656 0.000 0.344 NA 0.000
#> SRR1633340 1 0.3983 0.629 0.660 0.000 0.340 NA 0.000
#> SRR1633341 1 0.3895 0.659 0.680 0.000 0.320 NA 0.000
#> SRR1633342 1 0.3895 0.659 0.680 0.000 0.320 NA 0.000
#> SRR1633345 1 0.3932 0.648 0.672 0.000 0.328 NA 0.000
#> SRR1633346 1 0.3913 0.653 0.676 0.000 0.324 NA 0.000
#> SRR1633343 3 0.3210 0.575 0.212 0.000 0.788 NA 0.000
#> SRR1633344 3 0.3003 0.603 0.188 0.000 0.812 NA 0.000
#> SRR1633347 3 0.3039 0.599 0.192 0.000 0.808 NA 0.000
#> SRR1633348 3 0.2813 0.622 0.168 0.000 0.832 NA 0.000
#> SRR1633350 1 0.0451 0.886 0.988 0.000 0.004 NA 0.000
#> SRR1633351 1 0.0451 0.886 0.988 0.000 0.004 NA 0.000
#> SRR1633352 1 0.0451 0.886 0.988 0.000 0.004 NA 0.000
#> SRR1633353 1 0.0992 0.883 0.968 0.000 0.024 NA 0.000
#> SRR1633354 1 0.0898 0.884 0.972 0.000 0.020 NA 0.000
#> SRR1633355 1 0.0898 0.884 0.972 0.000 0.020 NA 0.000
#> SRR1633356 1 0.1168 0.881 0.960 0.000 0.032 NA 0.000
#> SRR1633357 1 0.0898 0.884 0.972 0.000 0.020 NA 0.000
#> SRR1633358 1 0.0898 0.884 0.972 0.000 0.020 NA 0.000
#> SRR1633362 1 0.1251 0.879 0.956 0.000 0.036 NA 0.000
#> SRR1633363 1 0.0992 0.883 0.968 0.000 0.024 NA 0.000
#> SRR1633364 1 0.0992 0.883 0.968 0.000 0.024 NA 0.000
#> SRR1633359 1 0.1408 0.875 0.948 0.000 0.044 NA 0.000
#> SRR1633360 1 0.1251 0.879 0.956 0.000 0.036 NA 0.000
#> SRR1633361 1 0.1251 0.879 0.956 0.000 0.036 NA 0.000
#> SRR2038492 1 0.0290 0.888 0.992 0.000 0.008 NA 0.000
#> SRR2038491 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038490 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038489 1 0.0000 0.888 1.000 0.000 0.000 NA 0.000
#> SRR2038488 1 0.0162 0.886 0.996 0.000 0.000 NA 0.000
#> SRR2038487 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038486 1 0.0000 0.888 1.000 0.000 0.000 NA 0.000
#> SRR2038485 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038484 1 0.0162 0.886 0.996 0.000 0.000 NA 0.000
#> SRR2038483 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038482 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038481 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038480 1 0.0290 0.885 0.992 0.000 0.000 NA 0.000
#> SRR2038479 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038477 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038478 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038476 1 0.0290 0.885 0.992 0.000 0.000 NA 0.000
#> SRR2038475 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038474 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038473 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038472 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038471 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038470 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038469 1 0.0000 0.888 1.000 0.000 0.000 NA 0.000
#> SRR2038468 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038467 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038466 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038465 1 0.0290 0.888 0.992 0.000 0.008 NA 0.000
#> SRR2038464 1 0.0290 0.884 0.992 0.000 0.000 NA 0.000
#> SRR2038463 1 0.0162 0.889 0.996 0.000 0.004 NA 0.000
#> SRR2038462 1 0.6131 0.217 0.564 0.000 0.208 NA 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1633230 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1633231 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1633232 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1633233 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1633234 2 0.0000 0.983 0.000 1.000 0.000 0.000 0.000 NA
#> SRR1633236 5 0.1714 0.910 0.000 0.000 0.000 0.000 0.908 NA
#> SRR1633237 5 0.1765 0.906 0.000 0.000 0.000 0.000 0.904 NA
#> SRR1633238 5 0.1863 0.904 0.000 0.000 0.000 0.000 0.896 NA
#> SRR1633239 5 0.1814 0.906 0.000 0.000 0.000 0.000 0.900 NA
#> SRR1633240 5 0.0458 0.937 0.000 0.000 0.000 0.000 0.984 NA
#> SRR1633241 5 0.0458 0.937 0.000 0.000 0.000 0.000 0.984 NA
#> SRR1633242 5 0.0458 0.937 0.000 0.000 0.000 0.000 0.984 NA
#> SRR1633243 5 0.0458 0.937 0.000 0.000 0.000 0.000 0.984 NA
#> SRR1633244 5 0.0547 0.936 0.000 0.000 0.000 0.000 0.980 NA
#> SRR1633245 5 0.0547 0.936 0.000 0.000 0.000 0.000 0.980 NA
#> SRR1633246 5 0.0547 0.936 0.000 0.000 0.000 0.000 0.980 NA
#> SRR1633247 5 0.0717 0.935 0.000 0.000 0.008 0.000 0.976 NA
#> SRR1633248 5 0.0820 0.934 0.000 0.000 0.012 0.000 0.972 NA
#> SRR1633249 5 0.0914 0.933 0.000 0.000 0.016 0.000 0.968 NA
#> SRR1633250 5 0.0914 0.933 0.000 0.000 0.016 0.000 0.968 NA
#> SRR1633251 3 0.2506 0.905 0.000 0.000 0.880 0.068 0.052 NA
#> SRR1633252 3 0.2376 0.908 0.000 0.000 0.888 0.068 0.044 NA
#> SRR1633253 3 0.2442 0.907 0.000 0.000 0.884 0.068 0.048 NA
#> SRR1633254 3 0.3039 0.899 0.000 0.000 0.848 0.088 0.060 NA
#> SRR1633255 3 0.2897 0.899 0.000 0.000 0.852 0.088 0.060 NA
#> SRR1633256 5 0.3404 0.726 0.000 0.000 0.224 0.000 0.760 NA
#> SRR1633257 5 0.3871 0.574 0.000 0.000 0.308 0.000 0.676 NA
#> SRR1633258 5 0.3582 0.681 0.000 0.000 0.252 0.000 0.732 NA
#> SRR1633259 5 0.1682 0.915 0.000 0.000 0.052 0.000 0.928 NA
#> SRR1633260 5 0.1657 0.913 0.000 0.000 0.056 0.000 0.928 NA
#> SRR1633261 5 0.1682 0.915 0.000 0.000 0.052 0.000 0.928 NA
#> SRR1633262 3 0.2300 0.894 0.000 0.000 0.856 0.144 0.000 NA
#> SRR1633263 3 0.2260 0.895 0.000 0.000 0.860 0.140 0.000 NA
#> SRR1633264 3 0.2340 0.892 0.000 0.000 0.852 0.148 0.000 NA
#> SRR1633265 3 0.2664 0.872 0.000 0.000 0.816 0.184 0.000 NA
#> SRR1633266 3 0.2664 0.872 0.000 0.000 0.816 0.184 0.000 NA
#> SRR1633267 3 0.1863 0.911 0.000 0.000 0.920 0.060 0.016 NA
#> SRR1633268 3 0.1523 0.909 0.000 0.000 0.940 0.044 0.008 NA
#> SRR1633269 3 0.1668 0.911 0.000 0.000 0.928 0.060 0.008 NA
#> SRR1633270 3 0.1675 0.902 0.000 0.000 0.936 0.024 0.032 NA
#> SRR1633271 3 0.1592 0.902 0.000 0.000 0.940 0.020 0.032 NA
#> SRR1633272 3 0.1605 0.900 0.000 0.000 0.940 0.016 0.032 NA
#> SRR1633273 4 0.2009 0.838 0.068 0.000 0.024 0.908 0.000 NA
#> SRR1633274 4 0.1970 0.833 0.060 0.000 0.028 0.912 0.000 NA
#> SRR1633275 4 0.2030 0.836 0.064 0.000 0.028 0.908 0.000 NA
#> SRR1633276 4 0.2066 0.840 0.072 0.000 0.024 0.904 0.000 NA
#> SRR1633277 4 0.2046 0.832 0.060 0.000 0.032 0.908 0.000 NA
#> SRR1633278 3 0.3540 0.822 0.020 0.000 0.812 0.024 0.004 NA
#> SRR1633279 3 0.3620 0.819 0.024 0.000 0.808 0.024 0.004 NA
#> SRR1633280 3 0.3425 0.829 0.032 0.000 0.824 0.024 0.000 NA
#> SRR1633281 3 0.3598 0.829 0.040 0.000 0.816 0.028 0.000 NA
#> SRR1633282 3 0.2199 0.908 0.000 0.000 0.892 0.088 0.000 NA
#> SRR1633284 4 0.3175 0.836 0.256 0.000 0.000 0.744 0.000 NA
#> SRR1633285 4 0.3175 0.836 0.256 0.000 0.000 0.744 0.000 NA
#> SRR1633286 4 0.3244 0.820 0.268 0.000 0.000 0.732 0.000 NA
#> SRR1633287 4 0.3221 0.826 0.264 0.000 0.000 0.736 0.000 NA
#> SRR1633288 4 0.3175 0.836 0.256 0.000 0.000 0.744 0.000 NA
#> SRR1633289 4 0.3221 0.826 0.264 0.000 0.000 0.736 0.000 NA
#> SRR1633290 4 0.2402 0.856 0.120 0.000 0.012 0.868 0.000 NA
#> SRR1633291 4 0.2446 0.857 0.124 0.000 0.012 0.864 0.000 NA
#> SRR1633292 5 0.0458 0.936 0.000 0.000 0.000 0.000 0.984 NA
#> SRR1633293 5 0.0363 0.937 0.000 0.000 0.000 0.000 0.988 NA
#> SRR1633294 5 0.0363 0.937 0.000 0.000 0.000 0.000 0.988 NA
#> SRR1633295 5 0.0363 0.937 0.000 0.000 0.000 0.000 0.988 NA
#> SRR1633296 4 0.2197 0.801 0.044 0.000 0.056 0.900 0.000 NA
#> SRR1633297 4 0.2129 0.796 0.040 0.000 0.056 0.904 0.000 NA
#> SRR1633298 4 0.2100 0.711 0.004 0.000 0.112 0.884 0.000 NA
#> SRR1633299 4 0.2100 0.711 0.004 0.000 0.112 0.884 0.000 NA
#> SRR1633300 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.000 NA
#> SRR1633301 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.000 NA
#> SRR1633302 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633303 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633304 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633305 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633306 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633307 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633308 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633309 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633310 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633311 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633312 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633313 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633314 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633315 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633316 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633317 2 0.0146 0.983 0.000 0.996 0.000 0.004 0.000 NA
#> SRR1633318 2 0.0865 0.981 0.000 0.964 0.000 0.000 0.000 NA
#> SRR1633319 2 0.0865 0.981 0.000 0.964 0.000 0.000 0.000 NA
#> SRR1633320 2 0.0790 0.982 0.000 0.968 0.000 0.000 0.000 NA
#> SRR1633321 2 0.0790 0.982 0.000 0.968 0.000 0.000 0.000 NA
#> SRR1633322 2 0.0790 0.982 0.000 0.968 0.000 0.000 0.000 NA
#> SRR1633323 2 0.0937 0.980 0.000 0.960 0.000 0.000 0.000 NA
#> SRR1633324 2 0.0937 0.980 0.000 0.960 0.000 0.000 0.000 NA
#> SRR1633325 2 0.0937 0.980 0.000 0.960 0.000 0.000 0.000 NA
#> SRR1633326 2 0.0632 0.983 0.000 0.976 0.000 0.000 0.000 NA
#> SRR1633327 2 0.0632 0.983 0.000 0.976 0.000 0.000 0.000 NA
#> SRR1633328 2 0.0632 0.983 0.000 0.976 0.000 0.000 0.000 NA
#> SRR1633329 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633330 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633331 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633332 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633333 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633334 2 0.1007 0.979 0.000 0.956 0.000 0.000 0.000 NA
#> SRR1633335 4 0.3050 0.849 0.236 0.000 0.000 0.764 0.000 NA
#> SRR1633336 4 0.3101 0.843 0.244 0.000 0.000 0.756 0.000 NA
#> SRR1633337 4 0.3126 0.840 0.248 0.000 0.000 0.752 0.000 NA
#> SRR1633338 4 0.2969 0.855 0.224 0.000 0.000 0.776 0.000 NA
#> SRR1633339 4 0.3215 0.850 0.240 0.000 0.004 0.756 0.000 NA
#> SRR1633340 4 0.3240 0.848 0.244 0.000 0.004 0.752 0.000 NA
#> SRR1633341 4 0.2969 0.856 0.224 0.000 0.000 0.776 0.000 NA
#> SRR1633342 4 0.2996 0.855 0.228 0.000 0.000 0.772 0.000 NA
#> SRR1633345 4 0.2941 0.857 0.220 0.000 0.000 0.780 0.000 NA
#> SRR1633346 4 0.3109 0.857 0.224 0.000 0.004 0.772 0.000 NA
#> SRR1633343 4 0.2030 0.836 0.064 0.000 0.028 0.908 0.000 NA
#> SRR1633344 4 0.2046 0.832 0.060 0.000 0.032 0.908 0.000 NA
#> SRR1633347 4 0.2088 0.836 0.068 0.000 0.028 0.904 0.000 NA
#> SRR1633348 4 0.2058 0.825 0.056 0.000 0.036 0.908 0.000 NA
#> SRR1633350 1 0.0891 0.934 0.968 0.000 0.000 0.024 0.000 NA
#> SRR1633351 1 0.0806 0.935 0.972 0.000 0.000 0.020 0.000 NA
#> SRR1633352 1 0.0806 0.935 0.972 0.000 0.000 0.020 0.000 NA
#> SRR1633353 1 0.2631 0.843 0.840 0.000 0.000 0.152 0.000 NA
#> SRR1633354 1 0.2389 0.864 0.864 0.000 0.000 0.128 0.000 NA
#> SRR1633355 1 0.2346 0.869 0.868 0.000 0.000 0.124 0.000 NA
#> SRR1633356 1 0.2814 0.819 0.820 0.000 0.000 0.172 0.000 NA
#> SRR1633357 1 0.2553 0.851 0.848 0.000 0.000 0.144 0.000 NA
#> SRR1633358 1 0.2553 0.851 0.848 0.000 0.000 0.144 0.000 NA
#> SRR1633362 1 0.3073 0.770 0.788 0.000 0.000 0.204 0.000 NA
#> SRR1633363 1 0.2706 0.834 0.832 0.000 0.000 0.160 0.000 NA
#> SRR1633364 1 0.2706 0.834 0.832 0.000 0.000 0.160 0.000 NA
#> SRR1633359 1 0.3190 0.749 0.772 0.000 0.000 0.220 0.000 NA
#> SRR1633360 1 0.2814 0.819 0.820 0.000 0.000 0.172 0.000 NA
#> SRR1633361 1 0.2882 0.808 0.812 0.000 0.000 0.180 0.000 NA
#> SRR2038492 1 0.0436 0.931 0.988 0.000 0.004 0.004 0.000 NA
#> SRR2038491 1 0.0363 0.938 0.988 0.000 0.000 0.012 0.000 NA
#> SRR2038490 1 0.0363 0.938 0.988 0.000 0.000 0.012 0.000 NA
#> SRR2038489 1 0.0146 0.933 0.996 0.000 0.004 0.000 0.000 NA
#> SRR2038488 1 0.0146 0.936 0.996 0.000 0.000 0.004 0.000 NA
#> SRR2038487 1 0.0363 0.938 0.988 0.000 0.000 0.012 0.000 NA
#> SRR2038486 1 0.0146 0.933 0.996 0.000 0.004 0.000 0.000 NA
#> SRR2038485 1 0.0363 0.938 0.988 0.000 0.000 0.012 0.000 NA
#> SRR2038484 1 0.0146 0.936 0.996 0.000 0.000 0.004 0.000 NA
#> SRR2038483 1 0.0146 0.933 0.996 0.000 0.004 0.000 0.000 NA
#> SRR2038482 1 0.0547 0.937 0.980 0.000 0.000 0.020 0.000 NA
#> SRR2038481 1 0.0790 0.933 0.968 0.000 0.000 0.032 0.000 NA
#> SRR2038480 1 0.0520 0.925 0.984 0.000 0.008 0.000 0.000 NA
#> SRR2038479 1 0.0790 0.933 0.968 0.000 0.000 0.032 0.000 NA
#> SRR2038477 1 0.0291 0.935 0.992 0.000 0.004 0.004 0.000 NA
#> SRR2038478 1 0.0713 0.935 0.972 0.000 0.000 0.028 0.000 NA
#> SRR2038476 1 0.0603 0.921 0.980 0.000 0.004 0.000 0.000 NA
#> SRR2038475 1 0.0713 0.935 0.972 0.000 0.000 0.028 0.000 NA
#> SRR2038474 1 0.0363 0.938 0.988 0.000 0.000 0.012 0.000 NA
#> SRR2038473 1 0.0291 0.935 0.992 0.000 0.004 0.004 0.000 NA
#> SRR2038472 1 0.0260 0.937 0.992 0.000 0.000 0.008 0.000 NA
#> SRR2038471 1 0.0146 0.936 0.996 0.000 0.000 0.004 0.000 NA
#> SRR2038470 1 0.0790 0.933 0.968 0.000 0.000 0.032 0.000 NA
#> SRR2038469 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 NA
#> SRR2038468 1 0.0146 0.933 0.996 0.000 0.004 0.000 0.000 NA
#> SRR2038467 1 0.0291 0.935 0.992 0.000 0.004 0.004 0.000 NA
#> SRR2038466 1 0.0260 0.937 0.992 0.000 0.000 0.008 0.000 NA
#> SRR2038465 1 0.0458 0.937 0.984 0.000 0.000 0.016 0.000 NA
#> SRR2038464 1 0.0436 0.929 0.988 0.000 0.004 0.004 0.000 NA
#> SRR2038463 1 0.0632 0.936 0.976 0.000 0.000 0.024 0.000 NA
#> SRR2038462 3 0.3395 0.778 0.132 0.000 0.820 0.028 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0