Date: 2019-12-25 23:17:49 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 13175 rows and 123 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] 13175 123
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:pam | 6 | 1.000 | 0.953 | 0.982 | ** | 2,4,5 |
| SD:NMF | 4 | 1.000 | 0.990 | 0.996 | ** | 2,3 |
| CV:hclust | 5 | 1.000 | 0.957 | 0.983 | ** | 2,3,4 |
| CV:pam | 6 | 1.000 | 0.982 | 0.993 | ** | 2,3,4,5 |
| CV:mclust | 6 | 1.000 | 0.970 | 0.990 | ** | 2,4,5 |
| MAD:hclust | 2 | 1.000 | 0.990 | 0.981 | ** | |
| MAD:pam | 5 | 1.000 | 0.973 | 0.988 | ** | 2,4 |
| MAD:NMF | 4 | 1.000 | 0.974 | 0.990 | ** | 2,3 |
| ATC:kmeans | 2 | 1.000 | 0.999 | 0.998 | ** | |
| ATC:pam | 6 | 0.995 | 0.959 | 0.979 | ** | 2,4,5 |
| ATC:skmeans | 6 | 0.990 | 0.968 | 0.955 | ** | 2,3 |
| MAD:mclust | 5 | 0.986 | 0.940 | 0.972 | ** | 3,4 |
| SD:mclust | 6 | 0.980 | 0.920 | 0.958 | ** | 2,3,4,5 |
| SD:hclust | 6 | 0.977 | 0.946 | 0.961 | ** | 3 |
| MAD:skmeans | 6 | 0.963 | 0.960 | 0.947 | ** | 2,3,4,5 |
| CV:NMF | 6 | 0.945 | 0.943 | 0.939 | * | 2,4,5 |
| SD:skmeans | 6 | 0.944 | 0.953 | 0.950 | * | 2,3,4,5 |
| ATC:mclust | 5 | 0.943 | 0.949 | 0.969 | * | 3 |
| CV:skmeans | 6 | 0.928 | 0.864 | 0.926 | * | 2,4,5 |
| ATC:hclust | 5 | 0.921 | 0.895 | 0.949 | * | 2,3 |
| SD:kmeans | 6 | 0.909 | 0.863 | 0.867 | * | |
| ATC:NMF | 6 | 0.908 | 0.818 | 0.894 | * | 2,3,4 |
| MAD:kmeans | 5 | 0.809 | 0.934 | 0.891 | ||
| CV:kmeans | 4 | 0.718 | 0.951 | 0.851 |
**: 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 0.985 0.993 0.469 0.528 0.528
#> CV:NMF 2 1.000 0.981 0.990 0.502 0.497 0.497
#> MAD:NMF 2 0.933 0.957 0.980 0.469 0.528 0.528
#> ATC:NMF 2 1.000 0.982 0.992 0.453 0.552 0.552
#> SD:skmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> CV:skmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> MAD:skmeans 2 1.000 0.998 0.998 0.504 0.497 0.497
#> ATC:skmeans 2 1.000 0.981 0.991 0.470 0.528 0.528
#> SD:mclust 2 1.000 1.000 1.000 0.504 0.497 0.497
#> CV:mclust 2 1.000 1.000 1.000 0.504 0.497 0.497
#> MAD:mclust 2 0.807 0.941 0.970 0.496 0.497 0.497
#> ATC:mclust 2 0.584 0.892 0.946 0.463 0.528 0.528
#> SD:kmeans 2 0.589 0.687 0.878 0.443 0.528 0.528
#> CV:kmeans 2 0.376 0.572 0.747 0.405 0.497 0.497
#> MAD:kmeans 2 0.589 0.876 0.908 0.464 0.497 0.497
#> ATC:kmeans 2 1.000 0.999 0.998 0.355 0.645 0.645
#> SD:pam 2 1.000 0.999 1.000 0.356 0.645 0.645
#> CV:pam 2 1.000 0.987 0.994 0.361 0.645 0.645
#> MAD:pam 2 1.000 0.956 0.982 0.373 0.645 0.645
#> ATC:pam 2 1.000 1.000 1.000 0.355 0.645 0.645
#> SD:hclust 2 0.620 0.858 0.897 0.443 0.497 0.497
#> CV:hclust 2 1.000 0.996 0.994 0.356 0.645 0.645
#> MAD:hclust 2 1.000 0.990 0.981 0.491 0.497 0.497
#> ATC:hclust 2 1.000 1.000 1.000 0.355 0.645 0.645
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 1.000 0.987 0.995 0.321 0.794 0.630
#> CV:NMF 3 0.759 0.838 0.902 0.266 0.637 0.403
#> MAD:NMF 3 1.000 0.983 0.994 0.320 0.797 0.635
#> ATC:NMF 3 1.000 0.999 1.000 0.368 0.776 0.613
#> SD:skmeans 3 1.000 0.999 0.998 0.230 0.884 0.767
#> CV:skmeans 3 0.710 0.787 0.851 0.263 0.632 0.396
#> MAD:skmeans 3 1.000 0.998 0.996 0.230 0.884 0.767
#> ATC:skmeans 3 1.000 0.971 0.988 0.318 0.797 0.635
#> SD:mclust 3 0.969 0.971 0.980 0.224 0.884 0.767
#> CV:mclust 3 0.594 0.829 0.844 0.296 0.708 0.479
#> MAD:mclust 3 0.910 0.974 0.980 0.243 0.884 0.767
#> ATC:mclust 3 1.000 0.963 0.982 0.335 0.797 0.635
#> SD:kmeans 3 0.502 0.859 0.792 0.390 0.791 0.626
#> CV:kmeans 3 0.616 0.793 0.827 0.530 0.624 0.395
#> MAD:kmeans 3 0.639 0.875 0.811 0.323 0.884 0.767
#> ATC:kmeans 3 0.630 0.956 0.945 0.691 0.736 0.590
#> SD:pam 3 0.790 0.930 0.958 0.766 0.736 0.590
#> CV:pam 3 1.000 0.977 0.990 0.708 0.740 0.597
#> MAD:pam 3 0.822 0.890 0.947 0.699 0.736 0.590
#> ATC:pam 3 0.758 0.868 0.930 0.784 0.736 0.590
#> SD:hclust 3 1.000 0.998 0.999 0.397 0.884 0.767
#> CV:hclust 3 1.000 0.993 0.997 0.770 0.724 0.572
#> MAD:hclust 3 0.831 0.916 0.862 0.222 0.884 0.767
#> ATC:hclust 3 1.000 0.988 0.992 0.728 0.736 0.590
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 1.000 0.990 0.996 0.2190 0.856 0.627
#> CV:NMF 4 1.000 0.994 0.997 0.1873 0.872 0.649
#> MAD:NMF 4 1.000 0.974 0.990 0.2186 0.849 0.611
#> ATC:NMF 4 1.000 0.994 0.976 0.2010 0.864 0.643
#> SD:skmeans 4 1.000 0.999 0.996 0.2171 0.864 0.643
#> CV:skmeans 4 1.000 0.995 0.998 0.1867 0.862 0.625
#> MAD:skmeans 4 1.000 0.999 0.998 0.2184 0.864 0.643
#> ATC:skmeans 4 0.831 0.945 0.860 0.1561 0.864 0.643
#> SD:mclust 4 0.916 0.894 0.950 0.2129 0.816 0.550
#> CV:mclust 4 0.931 0.899 0.955 0.1478 0.824 0.534
#> MAD:mclust 4 0.953 0.985 0.989 0.2119 0.818 0.553
#> ATC:mclust 4 0.819 0.892 0.892 0.1659 0.819 0.554
#> SD:kmeans 4 0.702 0.949 0.853 0.1478 0.864 0.643
#> CV:kmeans 4 0.718 0.951 0.851 0.1490 0.876 0.664
#> MAD:kmeans 4 0.741 0.928 0.835 0.1455 0.864 0.643
#> ATC:kmeans 4 0.841 0.936 0.856 0.1830 0.864 0.643
#> SD:pam 4 1.000 0.969 0.987 0.2014 0.847 0.608
#> CV:pam 4 1.000 0.986 0.994 0.2213 0.861 0.639
#> MAD:pam 4 1.000 0.983 0.993 0.1898 0.847 0.608
#> ATC:pam 4 1.000 0.994 0.995 0.1906 0.864 0.643
#> SD:hclust 4 0.824 0.698 0.888 0.1271 0.947 0.860
#> CV:hclust 4 1.000 0.993 0.996 0.1991 0.876 0.664
#> MAD:hclust 4 0.842 0.699 0.878 0.2055 0.907 0.755
#> ATC:hclust 4 1.000 0.984 0.992 0.0122 0.992 0.980
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.932 0.799 0.926 0.0312 0.992 0.968
#> CV:NMF 5 0.943 0.907 0.915 0.0398 0.943 0.778
#> MAD:NMF 5 0.916 0.899 0.921 0.0317 1.000 1.000
#> ATC:NMF 5 0.915 0.832 0.920 0.0430 0.996 0.984
#> SD:skmeans 5 0.969 0.929 0.944 0.0360 0.971 0.882
#> CV:skmeans 5 0.944 0.951 0.934 0.0397 0.969 0.873
#> MAD:skmeans 5 0.969 0.962 0.969 0.0353 0.971 0.882
#> ATC:skmeans 5 0.838 0.936 0.882 0.0751 0.961 0.842
#> SD:mclust 5 0.976 0.931 0.969 0.0609 0.943 0.778
#> CV:mclust 5 1.000 0.988 0.995 0.0632 0.939 0.762
#> MAD:mclust 5 0.986 0.940 0.972 0.0576 0.953 0.815
#> ATC:mclust 5 0.943 0.949 0.969 0.0711 0.974 0.898
#> SD:kmeans 5 0.795 0.887 0.879 0.0670 0.988 0.952
#> CV:kmeans 5 0.897 0.855 0.859 0.0784 0.984 0.935
#> MAD:kmeans 5 0.809 0.934 0.891 0.0753 0.971 0.881
#> ATC:kmeans 5 0.795 0.894 0.877 0.0649 1.000 1.000
#> SD:pam 5 0.958 0.944 0.962 0.0373 0.972 0.887
#> CV:pam 5 1.000 0.971 0.986 0.0389 0.972 0.888
#> MAD:pam 5 1.000 0.973 0.988 0.0385 0.972 0.888
#> ATC:pam 5 1.000 0.974 0.989 0.0432 0.968 0.869
#> SD:hclust 5 0.883 0.936 0.937 0.0699 0.867 0.618
#> CV:hclust 5 1.000 0.957 0.983 0.0316 0.981 0.921
#> MAD:hclust 5 0.880 0.722 0.861 0.0111 0.939 0.818
#> ATC:hclust 5 0.921 0.895 0.949 0.0982 0.961 0.895
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.875 0.858 0.887 0.03624 0.960 0.836
#> CV:NMF 6 0.945 0.943 0.939 0.03349 0.977 0.889
#> MAD:NMF 6 0.879 0.824 0.879 0.03990 0.921 0.683
#> ATC:NMF 6 0.908 0.818 0.894 0.03516 0.933 0.729
#> SD:skmeans 6 0.944 0.953 0.950 0.03023 0.972 0.874
#> CV:skmeans 6 0.928 0.864 0.926 0.02431 0.988 0.943
#> MAD:skmeans 6 0.963 0.960 0.947 0.03199 0.972 0.874
#> ATC:skmeans 6 0.990 0.968 0.955 0.03210 0.974 0.880
#> SD:mclust 6 0.980 0.920 0.958 0.03132 0.947 0.756
#> CV:mclust 6 1.000 0.970 0.990 0.01107 0.983 0.919
#> MAD:mclust 6 0.909 0.881 0.910 0.02225 0.990 0.952
#> ATC:mclust 6 0.876 0.869 0.907 0.03207 1.000 1.000
#> SD:kmeans 6 0.909 0.863 0.867 0.04181 0.982 0.925
#> CV:kmeans 6 0.869 0.790 0.862 0.04433 1.000 1.000
#> MAD:kmeans 6 0.906 0.881 0.881 0.04239 1.000 1.000
#> ATC:kmeans 6 0.757 0.874 0.846 0.02532 0.980 0.917
#> SD:pam 6 1.000 0.953 0.982 0.01810 0.987 0.941
#> CV:pam 6 1.000 0.982 0.993 0.01545 0.987 0.941
#> MAD:pam 6 0.987 0.944 0.971 0.01343 0.991 0.959
#> ATC:pam 6 0.995 0.959 0.979 0.01696 0.987 0.940
#> SD:hclust 6 0.977 0.946 0.961 0.04920 0.959 0.829
#> CV:hclust 6 1.000 0.956 0.984 0.00994 0.992 0.966
#> MAD:hclust 6 0.871 0.897 0.912 0.05092 0.892 0.663
#> ATC:hclust 6 0.799 0.824 0.893 0.03998 0.956 0.869
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 13175 rows and 123 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 0.620 0.858 0.897 0.4434 0.497 0.497
#> 3 3 1.000 0.998 0.999 0.3968 0.884 0.767
#> 4 4 0.824 0.698 0.888 0.1271 0.947 0.860
#> 5 5 0.883 0.936 0.937 0.0699 0.867 0.618
#> 6 6 0.977 0.946 0.961 0.0492 0.959 0.829
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
There is also optional best \(k\) = 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
#> SRR445718 2 0.000 0.750 0.000 1.000
#> SRR445719 2 0.000 0.750 0.000 1.000
#> SRR445720 2 0.000 0.750 0.000 1.000
#> SRR445721 2 0.000 0.750 0.000 1.000
#> SRR445722 2 0.000 0.750 0.000 1.000
#> SRR445723 2 0.000 0.750 0.000 1.000
#> SRR445724 2 0.000 0.750 0.000 1.000
#> SRR445725 2 0.000 0.750 0.000 1.000
#> SRR445726 2 0.000 0.750 0.000 1.000
#> SRR445727 2 0.000 0.750 0.000 1.000
#> SRR445728 2 0.000 0.750 0.000 1.000
#> SRR445729 2 0.000 0.750 0.000 1.000
#> SRR445730 1 0.000 1.000 1.000 0.000
#> SRR445731 1 0.000 1.000 1.000 0.000
#> SRR490961 2 0.000 0.750 0.000 1.000
#> SRR490962 2 0.000 0.750 0.000 1.000
#> SRR490963 2 0.000 0.750 0.000 1.000
#> SRR490964 2 0.000 0.750 0.000 1.000
#> SRR490965 2 0.000 0.750 0.000 1.000
#> SRR490966 2 0.000 0.750 0.000 1.000
#> SRR490967 2 0.000 0.750 0.000 1.000
#> SRR490968 2 0.000 0.750 0.000 1.000
#> SRR490969 2 0.000 0.750 0.000 1.000
#> SRR490970 2 0.000 0.750 0.000 1.000
#> SRR490971 2 0.000 0.750 0.000 1.000
#> SRR490972 2 0.000 0.750 0.000 1.000
#> SRR490973 2 0.978 0.660 0.412 0.588
#> SRR490974 2 0.978 0.660 0.412 0.588
#> SRR490975 2 0.978 0.660 0.412 0.588
#> SRR490976 2 0.978 0.660 0.412 0.588
#> SRR490977 2 0.978 0.660 0.412 0.588
#> SRR490978 2 0.978 0.660 0.412 0.588
#> SRR490979 2 0.978 0.660 0.412 0.588
#> SRR490980 2 0.978 0.660 0.412 0.588
#> SRR490981 2 0.000 0.750 0.000 1.000
#> SRR490982 2 0.000 0.750 0.000 1.000
#> SRR490983 2 0.000 0.750 0.000 1.000
#> SRR490984 2 0.000 0.750 0.000 1.000
#> SRR490985 2 0.978 0.660 0.412 0.588
#> SRR490986 2 0.978 0.660 0.412 0.588
#> SRR490987 2 0.978 0.660 0.412 0.588
#> SRR490988 2 0.978 0.660 0.412 0.588
#> SRR490989 2 0.978 0.660 0.412 0.588
#> SRR490990 2 0.978 0.660 0.412 0.588
#> SRR490991 2 0.978 0.660 0.412 0.588
#> SRR490992 2 0.978 0.660 0.412 0.588
#> SRR490993 2 0.978 0.660 0.412 0.588
#> SRR490994 2 0.978 0.660 0.412 0.588
#> SRR490995 2 0.946 0.675 0.364 0.636
#> SRR490996 2 0.978 0.660 0.412 0.588
#> SRR490997 2 0.978 0.660 0.412 0.588
#> SRR490998 2 0.978 0.660 0.412 0.588
#> SRR491000 2 0.946 0.675 0.364 0.636
#> SRR491001 2 0.978 0.660 0.412 0.588
#> SRR491002 2 0.978 0.660 0.412 0.588
#> SRR491003 2 0.978 0.660 0.412 0.588
#> SRR491004 2 0.978 0.660 0.412 0.588
#> SRR491005 2 0.978 0.660 0.412 0.588
#> SRR491006 2 0.978 0.660 0.412 0.588
#> SRR491007 2 0.978 0.660 0.412 0.588
#> SRR491008 2 0.978 0.660 0.412 0.588
#> SRR491009 1 0.000 1.000 1.000 0.000
#> SRR491010 1 0.000 1.000 1.000 0.000
#> SRR491011 1 0.000 1.000 1.000 0.000
#> SRR491012 1 0.000 1.000 1.000 0.000
#> SRR491013 1 0.000 1.000 1.000 0.000
#> SRR491014 1 0.000 1.000 1.000 0.000
#> SRR491015 1 0.000 1.000 1.000 0.000
#> SRR491016 1 0.000 1.000 1.000 0.000
#> SRR491017 1 0.000 1.000 1.000 0.000
#> SRR491018 1 0.000 1.000 1.000 0.000
#> SRR491019 1 0.000 1.000 1.000 0.000
#> SRR491020 1 0.000 1.000 1.000 0.000
#> SRR491021 1 0.000 1.000 1.000 0.000
#> SRR491022 1 0.000 1.000 1.000 0.000
#> SRR491023 1 0.000 1.000 1.000 0.000
#> SRR491024 1 0.000 1.000 1.000 0.000
#> SRR491025 1 0.000 1.000 1.000 0.000
#> SRR491026 1 0.000 1.000 1.000 0.000
#> SRR491027 1 0.000 1.000 1.000 0.000
#> SRR491028 1 0.000 1.000 1.000 0.000
#> SRR491029 1 0.000 1.000 1.000 0.000
#> SRR491030 1 0.000 1.000 1.000 0.000
#> SRR491031 1 0.000 1.000 1.000 0.000
#> SRR491032 1 0.000 1.000 1.000 0.000
#> SRR491033 1 0.000 1.000 1.000 0.000
#> SRR491034 1 0.000 1.000 1.000 0.000
#> SRR491035 1 0.000 1.000 1.000 0.000
#> SRR491036 1 0.000 1.000 1.000 0.000
#> SRR491037 1 0.000 1.000 1.000 0.000
#> SRR491038 1 0.000 1.000 1.000 0.000
#> SRR491039 1 0.000 1.000 1.000 0.000
#> SRR491040 1 0.000 1.000 1.000 0.000
#> SRR491041 1 0.000 1.000 1.000 0.000
#> SRR491042 1 0.000 1.000 1.000 0.000
#> SRR491043 1 0.000 1.000 1.000 0.000
#> SRR491045 1 0.000 1.000 1.000 0.000
#> SRR491065 1 0.000 1.000 1.000 0.000
#> SRR491066 1 0.000 1.000 1.000 0.000
#> SRR491067 1 0.000 1.000 1.000 0.000
#> SRR491068 1 0.000 1.000 1.000 0.000
#> SRR491069 1 0.000 1.000 1.000 0.000
#> SRR491070 1 0.000 1.000 1.000 0.000
#> SRR491071 1 0.000 1.000 1.000 0.000
#> SRR491072 1 0.000 1.000 1.000 0.000
#> SRR491073 1 0.000 1.000 1.000 0.000
#> SRR491074 1 0.000 1.000 1.000 0.000
#> SRR491075 1 0.000 1.000 1.000 0.000
#> SRR491076 1 0.000 1.000 1.000 0.000
#> SRR491077 1 0.000 1.000 1.000 0.000
#> SRR491078 1 0.000 1.000 1.000 0.000
#> SRR491079 1 0.000 1.000 1.000 0.000
#> SRR491080 1 0.000 1.000 1.000 0.000
#> SRR491081 1 0.000 1.000 1.000 0.000
#> SRR491082 1 0.000 1.000 1.000 0.000
#> SRR491083 1 0.000 1.000 1.000 0.000
#> SRR491084 1 0.000 1.000 1.000 0.000
#> SRR491085 1 0.000 1.000 1.000 0.000
#> SRR491086 1 0.000 1.000 1.000 0.000
#> SRR491087 1 0.000 1.000 1.000 0.000
#> SRR491088 1 0.000 1.000 1.000 0.000
#> SRR491089 1 0.000 1.000 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.000 1.000 0 1.000 0.000
#> SRR445719 2 0.000 1.000 0 1.000 0.000
#> SRR445720 2 0.000 1.000 0 1.000 0.000
#> SRR445721 2 0.000 1.000 0 1.000 0.000
#> SRR445722 2 0.000 1.000 0 1.000 0.000
#> SRR445723 2 0.000 1.000 0 1.000 0.000
#> SRR445724 2 0.000 1.000 0 1.000 0.000
#> SRR445725 2 0.000 1.000 0 1.000 0.000
#> SRR445726 2 0.000 1.000 0 1.000 0.000
#> SRR445727 2 0.000 1.000 0 1.000 0.000
#> SRR445728 2 0.000 1.000 0 1.000 0.000
#> SRR445729 2 0.000 1.000 0 1.000 0.000
#> SRR445730 1 0.000 1.000 1 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0.000 0.000
#> SRR490961 2 0.000 1.000 0 1.000 0.000
#> SRR490962 2 0.000 1.000 0 1.000 0.000
#> SRR490963 2 0.000 1.000 0 1.000 0.000
#> SRR490964 2 0.000 1.000 0 1.000 0.000
#> SRR490965 2 0.000 1.000 0 1.000 0.000
#> SRR490966 2 0.000 1.000 0 1.000 0.000
#> SRR490967 2 0.000 1.000 0 1.000 0.000
#> SRR490968 2 0.000 1.000 0 1.000 0.000
#> SRR490969 2 0.000 1.000 0 1.000 0.000
#> SRR490970 2 0.000 1.000 0 1.000 0.000
#> SRR490971 2 0.000 1.000 0 1.000 0.000
#> SRR490972 2 0.000 1.000 0 1.000 0.000
#> SRR490973 3 0.000 0.997 0 0.000 1.000
#> SRR490974 3 0.000 0.997 0 0.000 1.000
#> SRR490975 3 0.000 0.997 0 0.000 1.000
#> SRR490976 3 0.000 0.997 0 0.000 1.000
#> SRR490977 3 0.000 0.997 0 0.000 1.000
#> SRR490978 3 0.000 0.997 0 0.000 1.000
#> SRR490979 3 0.000 0.997 0 0.000 1.000
#> SRR490980 3 0.000 0.997 0 0.000 1.000
#> SRR490981 2 0.000 1.000 0 1.000 0.000
#> SRR490982 2 0.000 1.000 0 1.000 0.000
#> SRR490983 2 0.000 1.000 0 1.000 0.000
#> SRR490984 2 0.000 1.000 0 1.000 0.000
#> SRR490985 3 0.000 0.997 0 0.000 1.000
#> SRR490986 3 0.000 0.997 0 0.000 1.000
#> SRR490987 3 0.000 0.997 0 0.000 1.000
#> SRR490988 3 0.000 0.997 0 0.000 1.000
#> SRR490989 3 0.000 0.997 0 0.000 1.000
#> SRR490990 3 0.000 0.997 0 0.000 1.000
#> SRR490991 3 0.000 0.997 0 0.000 1.000
#> SRR490992 3 0.000 0.997 0 0.000 1.000
#> SRR490993 3 0.000 0.997 0 0.000 1.000
#> SRR490994 3 0.000 0.997 0 0.000 1.000
#> SRR490995 3 0.175 0.951 0 0.048 0.952
#> SRR490996 3 0.000 0.997 0 0.000 1.000
#> SRR490997 3 0.000 0.997 0 0.000 1.000
#> SRR490998 3 0.000 0.997 0 0.000 1.000
#> SRR491000 3 0.175 0.951 0 0.048 0.952
#> SRR491001 3 0.000 0.997 0 0.000 1.000
#> SRR491002 3 0.000 0.997 0 0.000 1.000
#> SRR491003 3 0.000 0.997 0 0.000 1.000
#> SRR491004 3 0.000 0.997 0 0.000 1.000
#> SRR491005 3 0.000 0.997 0 0.000 1.000
#> SRR491006 3 0.000 0.997 0 0.000 1.000
#> SRR491007 3 0.000 0.997 0 0.000 1.000
#> SRR491008 3 0.000 0.997 0 0.000 1.000
#> SRR491009 1 0.000 1.000 1 0.000 0.000
#> SRR491010 1 0.000 1.000 1 0.000 0.000
#> SRR491011 1 0.000 1.000 1 0.000 0.000
#> SRR491012 1 0.000 1.000 1 0.000 0.000
#> SRR491013 1 0.000 1.000 1 0.000 0.000
#> SRR491014 1 0.000 1.000 1 0.000 0.000
#> SRR491015 1 0.000 1.000 1 0.000 0.000
#> SRR491016 1 0.000 1.000 1 0.000 0.000
#> SRR491017 1 0.000 1.000 1 0.000 0.000
#> SRR491018 1 0.000 1.000 1 0.000 0.000
#> SRR491019 1 0.000 1.000 1 0.000 0.000
#> SRR491020 1 0.000 1.000 1 0.000 0.000
#> SRR491021 1 0.000 1.000 1 0.000 0.000
#> SRR491022 1 0.000 1.000 1 0.000 0.000
#> SRR491023 1 0.000 1.000 1 0.000 0.000
#> SRR491024 1 0.000 1.000 1 0.000 0.000
#> SRR491025 1 0.000 1.000 1 0.000 0.000
#> SRR491026 1 0.000 1.000 1 0.000 0.000
#> SRR491027 1 0.000 1.000 1 0.000 0.000
#> SRR491028 1 0.000 1.000 1 0.000 0.000
#> SRR491029 1 0.000 1.000 1 0.000 0.000
#> SRR491030 1 0.000 1.000 1 0.000 0.000
#> SRR491031 1 0.000 1.000 1 0.000 0.000
#> SRR491032 1 0.000 1.000 1 0.000 0.000
#> SRR491033 1 0.000 1.000 1 0.000 0.000
#> SRR491034 1 0.000 1.000 1 0.000 0.000
#> SRR491035 1 0.000 1.000 1 0.000 0.000
#> SRR491036 1 0.000 1.000 1 0.000 0.000
#> SRR491037 1 0.000 1.000 1 0.000 0.000
#> SRR491038 1 0.000 1.000 1 0.000 0.000
#> SRR491039 1 0.000 1.000 1 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR445731 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR490961 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490974 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490975 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490976 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490977 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490978 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490979 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490980 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490981 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490982 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490983 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490984 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490985 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490986 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490987 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490988 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490989 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490990 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490991 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490992 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490993 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490994 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490995 3 0.470 0.7200 0.000 0.004 0.676 0.320
#> SRR490996 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490997 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR490998 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491000 3 0.470 0.7200 0.000 0.004 0.676 0.320
#> SRR491001 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491002 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491003 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491004 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491005 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491006 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491007 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491008 3 0.000 0.9835 0.000 0.000 1.000 0.000
#> SRR491009 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491010 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491011 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491012 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491013 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491014 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491015 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491016 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491017 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491018 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491019 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491020 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491021 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491022 4 0.476 0.9948 0.372 0.000 0.000 0.628
#> SRR491023 4 0.476 0.9948 0.372 0.000 0.000 0.628
#> SRR491024 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491025 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491026 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491027 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491028 4 0.476 0.9948 0.372 0.000 0.000 0.628
#> SRR491029 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491030 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491031 4 0.475 0.9932 0.368 0.000 0.000 0.632
#> SRR491032 4 0.476 0.9948 0.372 0.000 0.000 0.628
#> SRR491033 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491034 4 0.475 0.9932 0.368 0.000 0.000 0.632
#> SRR491035 4 0.475 0.9932 0.368 0.000 0.000 0.632
#> SRR491036 1 0.491 0.0536 0.580 0.000 0.000 0.420
#> SRR491037 1 0.489 0.0883 0.588 0.000 0.000 0.412
#> SRR491038 1 0.491 0.0536 0.580 0.000 0.000 0.420
#> SRR491039 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491040 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491041 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491042 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491043 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491045 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491065 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491066 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491067 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491068 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491069 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491070 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491071 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491072 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491073 1 0.419 0.0967 0.732 0.000 0.000 0.268
#> SRR491074 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491075 1 0.419 0.0967 0.732 0.000 0.000 0.268
#> SRR491076 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491077 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491078 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491079 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491080 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491081 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491082 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491083 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491084 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491085 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491086 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491087 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491088 1 0.419 0.0967 0.732 0.000 0.000 0.268
#> SRR491089 1 0.000 0.6179 1.000 0.000 0.000 0.000
#> SRR491090 1 0.419 0.0967 0.732 0.000 0.000 0.268
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445719 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445720 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445721 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445722 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445723 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445724 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445725 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445726 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445727 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445728 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445729 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445730 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR445731 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR490961 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490962 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490963 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490964 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490965 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490966 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490967 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490968 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490969 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490970 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490971 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490972 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490973 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490974 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490975 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490976 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490977 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490978 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490979 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490980 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490981 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490982 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490983 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490984 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490985 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490986 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490987 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490988 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490989 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490990 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490991 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490992 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490993 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490994 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490995 5 0.000 1.000 0.000 0 0 0.000 1
#> SRR490996 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490997 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490998 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491000 5 0.000 1.000 0.000 0 0 0.000 1
#> SRR491001 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491002 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491003 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491004 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491005 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491006 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491007 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491008 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491009 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491010 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491011 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491012 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491013 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491014 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491015 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491016 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491017 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491018 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491019 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491020 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491021 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491022 4 0.127 0.619 0.052 0 0 0.948 0
#> SRR491023 4 0.127 0.619 0.052 0 0 0.948 0
#> SRR491024 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491025 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491026 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491027 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491028 4 0.127 0.619 0.052 0 0 0.948 0
#> SRR491029 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491030 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491031 4 0.120 0.613 0.048 0 0 0.952 0
#> SRR491032 4 0.127 0.619 0.052 0 0 0.948 0
#> SRR491033 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491034 4 0.120 0.613 0.048 0 0 0.952 0
#> SRR491035 4 0.120 0.613 0.048 0 0 0.952 0
#> SRR491036 4 0.356 0.898 0.260 0 0 0.740 0
#> SRR491037 4 0.361 0.904 0.268 0 0 0.732 0
#> SRR491038 4 0.356 0.898 0.260 0 0 0.740 0
#> SRR491039 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491040 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491041 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491042 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491043 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491045 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491065 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491066 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491067 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491068 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491069 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491070 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491071 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491072 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491073 1 0.361 0.593 0.732 0 0 0.268 0
#> SRR491074 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491075 1 0.361 0.593 0.732 0 0 0.268 0
#> SRR491076 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491077 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491078 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491079 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491080 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491081 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491082 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491083 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491084 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491085 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491086 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491087 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491088 1 0.361 0.593 0.732 0 0 0.268 0
#> SRR491089 1 0.000 0.956 1.000 0 0 0.000 0
#> SRR491090 1 0.361 0.593 0.732 0 0 0.268 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445719 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445720 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445721 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445722 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445723 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445724 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445725 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445726 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445727 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445728 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445729 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445730 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR445731 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR490961 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490962 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490963 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490964 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490965 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490966 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490967 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490968 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490969 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490970 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490971 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490972 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490973 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490974 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490975 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490976 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490977 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490978 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490979 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490980 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490981 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490982 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490983 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490984 2 0.0000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490985 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490986 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490987 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490988 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490989 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490990 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490991 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490992 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490993 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490994 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490995 6 0.0000 1.000 0.000 0 0 0.000 0.000 1
#> SRR490996 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490997 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490998 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491000 6 0.0000 1.000 0.000 0 0 0.000 0.000 1
#> SRR491001 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491002 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491003 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491004 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491005 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491006 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491007 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491008 3 0.0000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491009 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491010 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491011 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491012 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491013 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491014 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491015 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491016 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491017 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491018 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491019 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491020 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491021 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491022 5 0.3866 0.537 0.000 0 0 0.484 0.516 0
#> SRR491023 5 0.3866 0.537 0.000 0 0 0.484 0.516 0
#> SRR491024 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491025 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491026 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491027 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491028 5 0.3866 0.537 0.000 0 0 0.484 0.516 0
#> SRR491029 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491030 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491031 5 0.3864 0.541 0.000 0 0 0.480 0.520 0
#> SRR491032 5 0.3866 0.537 0.000 0 0 0.484 0.516 0
#> SRR491033 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491034 5 0.3864 0.541 0.000 0 0 0.480 0.520 0
#> SRR491035 5 0.3864 0.541 0.000 0 0 0.480 0.520 0
#> SRR491036 4 0.0891 0.989 0.024 0 0 0.968 0.008 0
#> SRR491037 4 0.0632 0.999 0.024 0 0 0.976 0.000 0
#> SRR491038 4 0.0891 0.989 0.024 0 0 0.968 0.008 0
#> SRR491039 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491040 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491041 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491042 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491043 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491045 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491065 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491066 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491067 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491068 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491069 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491070 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491071 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491072 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491073 5 0.3076 0.150 0.240 0 0 0.000 0.760 0
#> SRR491074 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491075 5 0.3076 0.150 0.240 0 0 0.000 0.760 0
#> SRR491076 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491077 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491078 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491079 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491080 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491081 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491082 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491083 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491084 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491085 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491086 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491087 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491088 5 0.2996 0.147 0.228 0 0 0.000 0.772 0
#> SRR491089 1 0.0000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491090 5 0.2996 0.147 0.228 0 0 0.000 0.772 0
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 13175 rows and 123 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 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 0.589 0.687 0.878 0.4430 0.528 0.528
#> 3 3 0.502 0.859 0.792 0.3901 0.791 0.626
#> 4 4 0.702 0.949 0.853 0.1478 0.864 0.643
#> 5 5 0.795 0.887 0.879 0.0670 0.988 0.952
#> 6 6 0.909 0.863 0.867 0.0418 0.982 0.925
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> SRR445718 2 0.118 0.798 0.016 0.984
#> SRR445719 2 0.118 0.798 0.016 0.984
#> SRR445720 2 0.118 0.798 0.016 0.984
#> SRR445721 2 0.118 0.798 0.016 0.984
#> SRR445722 2 0.118 0.798 0.016 0.984
#> SRR445723 2 0.118 0.798 0.016 0.984
#> SRR445724 2 0.118 0.798 0.016 0.984
#> SRR445725 2 0.118 0.798 0.016 0.984
#> SRR445726 2 0.118 0.798 0.016 0.984
#> SRR445727 2 0.118 0.798 0.016 0.984
#> SRR445728 2 0.118 0.798 0.016 0.984
#> SRR445729 2 0.118 0.798 0.016 0.984
#> SRR445730 1 0.184 0.871 0.972 0.028
#> SRR445731 1 0.184 0.871 0.972 0.028
#> SRR490961 2 0.118 0.798 0.016 0.984
#> SRR490962 2 0.118 0.798 0.016 0.984
#> SRR490963 2 0.118 0.798 0.016 0.984
#> SRR490964 2 0.118 0.798 0.016 0.984
#> SRR490965 2 0.118 0.798 0.016 0.984
#> SRR490966 2 0.118 0.798 0.016 0.984
#> SRR490967 2 0.118 0.798 0.016 0.984
#> SRR490968 2 0.118 0.798 0.016 0.984
#> SRR490969 2 0.118 0.798 0.016 0.984
#> SRR490970 2 0.118 0.798 0.016 0.984
#> SRR490971 2 0.118 0.798 0.016 0.984
#> SRR490972 2 0.118 0.798 0.016 0.984
#> SRR490973 2 0.978 0.488 0.412 0.588
#> SRR490974 2 0.973 0.505 0.404 0.596
#> SRR490975 2 0.973 0.505 0.404 0.596
#> SRR490976 2 0.978 0.488 0.412 0.588
#> SRR490977 2 0.978 0.488 0.412 0.588
#> SRR490978 2 0.978 0.488 0.412 0.588
#> SRR490979 2 0.978 0.488 0.412 0.588
#> SRR490980 2 0.973 0.505 0.404 0.596
#> SRR490981 2 0.000 0.789 0.000 1.000
#> SRR490982 2 0.000 0.789 0.000 1.000
#> SRR490983 2 0.000 0.789 0.000 1.000
#> SRR490984 2 0.000 0.789 0.000 1.000
#> SRR490985 2 0.971 0.512 0.400 0.600
#> SRR490986 2 0.971 0.512 0.400 0.600
#> SRR490987 2 0.971 0.512 0.400 0.600
#> SRR490988 2 0.971 0.512 0.400 0.600
#> SRR490989 2 0.971 0.512 0.400 0.600
#> SRR490990 2 0.971 0.512 0.400 0.600
#> SRR490991 2 0.971 0.512 0.400 0.600
#> SRR490992 2 0.973 0.505 0.404 0.596
#> SRR490993 1 1.000 -0.195 0.512 0.488
#> SRR490994 1 1.000 -0.195 0.512 0.488
#> SRR490995 2 0.973 0.497 0.404 0.596
#> SRR490996 1 1.000 -0.195 0.512 0.488
#> SRR490997 1 1.000 -0.195 0.512 0.488
#> SRR490998 1 1.000 -0.195 0.512 0.488
#> SRR491000 2 0.973 0.497 0.404 0.596
#> SRR491001 1 1.000 -0.195 0.512 0.488
#> SRR491002 1 1.000 -0.195 0.512 0.488
#> SRR491003 1 1.000 -0.195 0.512 0.488
#> SRR491004 1 1.000 -0.195 0.512 0.488
#> SRR491005 1 1.000 -0.195 0.512 0.488
#> SRR491006 1 1.000 -0.195 0.512 0.488
#> SRR491007 1 1.000 -0.195 0.512 0.488
#> SRR491008 1 1.000 -0.195 0.512 0.488
#> SRR491009 1 0.000 0.871 1.000 0.000
#> SRR491010 1 0.000 0.871 1.000 0.000
#> SRR491011 1 0.000 0.871 1.000 0.000
#> SRR491012 1 0.000 0.871 1.000 0.000
#> SRR491013 1 0.000 0.871 1.000 0.000
#> SRR491014 1 0.000 0.871 1.000 0.000
#> SRR491015 1 0.000 0.871 1.000 0.000
#> SRR491016 1 0.000 0.871 1.000 0.000
#> SRR491017 1 0.000 0.871 1.000 0.000
#> SRR491018 1 0.000 0.871 1.000 0.000
#> SRR491019 1 0.000 0.871 1.000 0.000
#> SRR491020 1 0.000 0.871 1.000 0.000
#> SRR491021 1 0.000 0.871 1.000 0.000
#> SRR491022 1 0.000 0.871 1.000 0.000
#> SRR491023 1 0.000 0.871 1.000 0.000
#> SRR491024 1 0.000 0.871 1.000 0.000
#> SRR491025 1 0.000 0.871 1.000 0.000
#> SRR491026 1 0.000 0.871 1.000 0.000
#> SRR491027 1 0.000 0.871 1.000 0.000
#> SRR491028 1 0.000 0.871 1.000 0.000
#> SRR491029 1 0.000 0.871 1.000 0.000
#> SRR491030 1 0.000 0.871 1.000 0.000
#> SRR491031 1 0.000 0.871 1.000 0.000
#> SRR491032 1 0.000 0.871 1.000 0.000
#> SRR491033 1 0.000 0.871 1.000 0.000
#> SRR491034 1 0.000 0.871 1.000 0.000
#> SRR491035 1 0.000 0.871 1.000 0.000
#> SRR491036 1 0.000 0.871 1.000 0.000
#> SRR491037 1 0.000 0.871 1.000 0.000
#> SRR491038 1 0.000 0.871 1.000 0.000
#> SRR491039 1 0.184 0.871 0.972 0.028
#> SRR491040 1 0.184 0.871 0.972 0.028
#> SRR491041 1 0.184 0.871 0.972 0.028
#> SRR491042 1 0.184 0.871 0.972 0.028
#> SRR491043 1 0.184 0.871 0.972 0.028
#> SRR491045 1 0.184 0.871 0.972 0.028
#> SRR491065 1 0.184 0.871 0.972 0.028
#> SRR491066 1 0.184 0.871 0.972 0.028
#> SRR491067 1 0.184 0.871 0.972 0.028
#> SRR491068 1 0.184 0.871 0.972 0.028
#> SRR491069 1 0.184 0.871 0.972 0.028
#> SRR491070 1 0.184 0.871 0.972 0.028
#> SRR491071 1 0.184 0.871 0.972 0.028
#> SRR491072 1 0.184 0.871 0.972 0.028
#> SRR491073 1 0.184 0.871 0.972 0.028
#> SRR491074 1 0.184 0.871 0.972 0.028
#> SRR491075 1 0.184 0.871 0.972 0.028
#> SRR491076 1 0.184 0.871 0.972 0.028
#> SRR491077 1 0.184 0.871 0.972 0.028
#> SRR491078 1 0.184 0.871 0.972 0.028
#> SRR491079 1 0.184 0.871 0.972 0.028
#> SRR491080 1 0.184 0.871 0.972 0.028
#> SRR491081 1 0.184 0.871 0.972 0.028
#> SRR491082 1 0.184 0.871 0.972 0.028
#> SRR491083 1 0.184 0.871 0.972 0.028
#> SRR491084 1 0.184 0.871 0.972 0.028
#> SRR491085 1 0.184 0.871 0.972 0.028
#> SRR491086 1 0.184 0.871 0.972 0.028
#> SRR491087 1 0.184 0.871 0.972 0.028
#> SRR491088 1 0.184 0.871 0.972 0.028
#> SRR491089 1 0.184 0.871 0.972 0.028
#> SRR491090 1 0.184 0.871 0.972 0.028
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445719 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445720 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445721 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445722 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445723 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445724 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445725 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445726 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445727 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445728 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445729 2 0.5397 0.992 0.000 0.720 0.280
#> SRR445730 1 0.6715 0.779 0.716 0.228 0.056
#> SRR445731 1 0.6715 0.779 0.716 0.228 0.056
#> SRR490961 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490962 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490963 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490964 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490965 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490966 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490967 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490968 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490969 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490970 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490971 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490972 2 0.5327 0.993 0.000 0.728 0.272
#> SRR490973 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490974 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490975 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490976 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490977 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490978 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490979 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490980 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490981 2 0.5291 0.993 0.000 0.732 0.268
#> SRR490982 2 0.5291 0.993 0.000 0.732 0.268
#> SRR490983 2 0.5291 0.993 0.000 0.732 0.268
#> SRR490984 2 0.5291 0.993 0.000 0.732 0.268
#> SRR490985 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490986 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490987 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490988 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490989 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490990 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490991 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490992 3 0.1989 0.986 0.048 0.004 0.948
#> SRR490993 3 0.1860 0.984 0.052 0.000 0.948
#> SRR490994 3 0.1860 0.984 0.052 0.000 0.948
#> SRR490995 3 0.3530 0.871 0.032 0.068 0.900
#> SRR490996 3 0.1860 0.984 0.052 0.000 0.948
#> SRR490997 3 0.1860 0.984 0.052 0.000 0.948
#> SRR490998 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491000 3 0.3530 0.871 0.032 0.068 0.900
#> SRR491001 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491002 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491003 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491004 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491005 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491006 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491007 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491008 3 0.1860 0.984 0.052 0.000 0.948
#> SRR491009 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491010 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491011 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491012 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491013 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491014 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491015 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491016 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491017 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491018 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491019 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491020 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491021 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491022 1 0.3340 0.721 0.880 0.000 0.120
#> SRR491023 1 0.4002 0.708 0.840 0.000 0.160
#> SRR491024 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491025 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491026 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491027 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491028 1 0.4002 0.708 0.840 0.000 0.160
#> SRR491029 1 0.4002 0.708 0.840 0.000 0.160
#> SRR491030 1 0.4062 0.706 0.836 0.000 0.164
#> SRR491031 1 0.5901 0.662 0.768 0.040 0.192
#> SRR491032 1 0.3816 0.713 0.852 0.000 0.148
#> SRR491033 1 0.3879 0.712 0.848 0.000 0.152
#> SRR491034 1 0.0424 0.741 0.992 0.000 0.008
#> SRR491035 1 0.0000 0.742 1.000 0.000 0.000
#> SRR491036 1 0.4575 0.695 0.812 0.004 0.184
#> SRR491037 1 0.3879 0.712 0.848 0.000 0.152
#> SRR491038 1 0.3879 0.712 0.848 0.000 0.152
#> SRR491039 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491040 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491041 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491042 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491043 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491045 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491065 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491066 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491067 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491068 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491069 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491070 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491071 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491072 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491073 1 0.7433 0.739 0.660 0.268 0.072
#> SRR491074 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491075 1 0.7400 0.741 0.664 0.264 0.072
#> SRR491076 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491077 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491078 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491079 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491080 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491081 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491082 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491083 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491084 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491085 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491086 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491087 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491088 1 0.7433 0.739 0.660 0.268 0.072
#> SRR491089 1 0.6715 0.779 0.716 0.228 0.056
#> SRR491090 1 0.7433 0.739 0.660 0.268 0.072
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.1576 0.960 0.000 0.948 0.004 0.048
#> SRR445719 2 0.1576 0.960 0.000 0.948 0.004 0.048
#> SRR445720 2 0.1576 0.960 0.000 0.948 0.004 0.048
#> SRR445721 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445722 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445723 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445724 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445725 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445726 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445727 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445728 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445729 2 0.1004 0.968 0.000 0.972 0.004 0.024
#> SRR445730 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0469 0.969 0.000 0.988 0.000 0.012
#> SRR490962 2 0.0469 0.969 0.000 0.988 0.000 0.012
#> SRR490963 2 0.0469 0.969 0.000 0.988 0.000 0.012
#> SRR490964 2 0.0469 0.969 0.000 0.988 0.000 0.012
#> SRR490965 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490966 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490967 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490968 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490969 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490970 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490971 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490972 2 0.0336 0.970 0.000 0.992 0.000 0.008
#> SRR490973 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490974 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490975 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490976 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490977 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490978 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490979 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490980 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490981 2 0.2831 0.896 0.000 0.876 0.004 0.120
#> SRR490982 2 0.2831 0.896 0.000 0.876 0.004 0.120
#> SRR490983 2 0.2831 0.896 0.000 0.876 0.004 0.120
#> SRR490984 2 0.2831 0.896 0.000 0.876 0.004 0.120
#> SRR490985 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490986 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490987 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490988 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490989 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490990 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490991 3 0.5395 0.931 0.012 0.100 0.764 0.124
#> SRR490992 3 0.5287 0.932 0.012 0.100 0.772 0.116
#> SRR490993 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR490994 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR490995 3 0.5878 0.623 0.000 0.056 0.632 0.312
#> SRR490996 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR490997 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR490998 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491000 3 0.5878 0.623 0.000 0.056 0.632 0.312
#> SRR491001 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491002 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491003 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491004 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491005 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491006 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491007 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491008 3 0.2805 0.925 0.012 0.100 0.888 0.000
#> SRR491009 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491010 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491011 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491012 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491013 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491014 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491015 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491016 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491017 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491018 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491019 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491020 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491021 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491022 4 0.5778 0.962 0.356 0.000 0.040 0.604
#> SRR491023 4 0.5778 0.962 0.356 0.000 0.040 0.604
#> SRR491024 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491025 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491026 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491027 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491028 4 0.5698 0.965 0.356 0.000 0.036 0.608
#> SRR491029 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491030 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491031 4 0.6383 0.813 0.292 0.000 0.096 0.612
#> SRR491032 4 0.5698 0.965 0.356 0.000 0.036 0.608
#> SRR491033 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491034 4 0.5713 0.958 0.360 0.000 0.036 0.604
#> SRR491035 4 0.5713 0.958 0.360 0.000 0.036 0.604
#> SRR491036 4 0.4837 0.977 0.348 0.000 0.004 0.648
#> SRR491037 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491038 4 0.4872 0.985 0.356 0.000 0.004 0.640
#> SRR491039 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491073 1 0.3959 0.808 0.840 0.000 0.092 0.068
#> SRR491074 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491075 1 0.3828 0.816 0.848 0.000 0.084 0.068
#> SRR491076 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491088 1 0.3959 0.808 0.840 0.000 0.092 0.068
#> SRR491089 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> SRR491090 1 0.3959 0.808 0.840 0.000 0.092 0.068
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.3947 0.9201 0.000 0.832 0.068 0.048 0.052
#> SRR445719 2 0.3947 0.9201 0.000 0.832 0.068 0.048 0.052
#> SRR445720 2 0.3947 0.9201 0.000 0.832 0.068 0.048 0.052
#> SRR445721 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445722 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445723 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445724 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445725 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445726 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445727 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445728 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445729 2 0.3418 0.9316 0.000 0.860 0.068 0.028 0.044
#> SRR445730 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR445731 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR490961 2 0.2396 0.9324 0.000 0.904 0.068 0.004 0.024
#> SRR490962 2 0.2396 0.9324 0.000 0.904 0.068 0.004 0.024
#> SRR490963 2 0.2396 0.9324 0.000 0.904 0.068 0.004 0.024
#> SRR490964 2 0.2396 0.9324 0.000 0.904 0.068 0.004 0.024
#> SRR490965 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490966 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490967 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490968 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490969 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490970 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490971 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490972 2 0.2206 0.9344 0.000 0.912 0.068 0.004 0.016
#> SRR490973 3 0.3231 0.8923 0.000 0.000 0.800 0.004 0.196
#> SRR490974 3 0.3266 0.8916 0.000 0.000 0.796 0.004 0.200
#> SRR490975 3 0.3266 0.8916 0.000 0.000 0.796 0.004 0.200
#> SRR490976 3 0.3231 0.8923 0.000 0.000 0.800 0.004 0.196
#> SRR490977 3 0.3231 0.8923 0.000 0.000 0.800 0.004 0.196
#> SRR490978 3 0.3231 0.8923 0.000 0.000 0.800 0.004 0.196
#> SRR490979 3 0.3231 0.8923 0.000 0.000 0.800 0.004 0.196
#> SRR490980 3 0.3266 0.8916 0.000 0.000 0.796 0.004 0.200
#> SRR490981 2 0.5089 0.8245 0.000 0.756 0.068 0.072 0.104
#> SRR490982 2 0.5138 0.8203 0.000 0.752 0.068 0.072 0.108
#> SRR490983 2 0.5089 0.8245 0.000 0.756 0.068 0.072 0.104
#> SRR490984 2 0.5089 0.8245 0.000 0.756 0.068 0.072 0.104
#> SRR490985 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490986 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490987 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490988 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490989 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490990 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490991 3 0.3210 0.8892 0.000 0.000 0.788 0.000 0.212
#> SRR490992 3 0.3177 0.8905 0.000 0.000 0.792 0.000 0.208
#> SRR490993 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR490994 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR490995 5 0.5593 0.6667 0.000 0.020 0.200 0.104 0.676
#> SRR490996 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR490997 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR490998 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491000 5 0.5593 0.6667 0.000 0.020 0.200 0.104 0.676
#> SRR491001 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491002 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491003 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491004 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491005 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491006 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491007 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491008 3 0.0000 0.8674 0.000 0.000 1.000 0.000 0.000
#> SRR491009 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491010 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491011 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491012 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491013 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491014 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491015 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491016 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491017 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491018 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491019 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491020 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491021 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491022 4 0.5180 0.8786 0.164 0.024 0.000 0.724 0.088
#> SRR491023 4 0.5127 0.8824 0.164 0.024 0.000 0.728 0.084
#> SRR491024 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491025 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491026 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491027 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491028 4 0.4582 0.9175 0.164 0.024 0.000 0.764 0.048
#> SRR491029 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491030 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491031 5 0.6593 -0.0121 0.116 0.024 0.000 0.388 0.472
#> SRR491032 4 0.4582 0.9175 0.164 0.024 0.000 0.764 0.048
#> SRR491033 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491034 4 0.5232 0.8742 0.164 0.024 0.000 0.720 0.092
#> SRR491035 4 0.5232 0.8742 0.164 0.024 0.000 0.720 0.092
#> SRR491036 4 0.2732 0.9698 0.160 0.000 0.000 0.840 0.000
#> SRR491037 4 0.2930 0.9728 0.164 0.004 0.000 0.832 0.000
#> SRR491038 4 0.2773 0.9736 0.164 0.000 0.000 0.836 0.000
#> SRR491039 1 0.0324 0.9305 0.992 0.004 0.000 0.000 0.004
#> SRR491040 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR491041 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR491042 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR491043 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR491045 1 0.0451 0.9291 0.988 0.004 0.000 0.000 0.008
#> SRR491065 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491066 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491067 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491068 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491070 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491071 1 0.0404 0.9303 0.988 0.012 0.000 0.000 0.000
#> SRR491072 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491073 1 0.5218 0.2024 0.516 0.008 0.000 0.028 0.448
#> SRR491074 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491075 1 0.5076 0.3818 0.592 0.008 0.000 0.028 0.372
#> SRR491076 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491077 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.9323 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0671 0.9273 0.980 0.016 0.000 0.000 0.004
#> SRR491087 1 0.0510 0.9289 0.984 0.016 0.000 0.000 0.000
#> SRR491088 1 0.4971 0.1857 0.512 0.000 0.000 0.028 0.460
#> SRR491089 1 0.0162 0.9322 0.996 0.004 0.000 0.000 0.000
#> SRR491090 1 0.4971 0.1857 0.512 0.000 0.000 0.028 0.460
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.2992 0.886 0.000 0.864 0.000 0.044 0.068 NA
#> SRR445719 2 0.2980 0.886 0.000 0.864 0.000 0.040 0.072 NA
#> SRR445720 2 0.2980 0.886 0.000 0.864 0.000 0.040 0.072 NA
#> SRR445721 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445722 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445723 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445724 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445725 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445726 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445727 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445728 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445729 2 0.2252 0.902 0.000 0.908 0.000 0.028 0.044 NA
#> SRR445730 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR445731 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR490961 2 0.1251 0.901 0.000 0.956 0.000 0.008 0.012 NA
#> SRR490962 2 0.1251 0.901 0.000 0.956 0.000 0.008 0.012 NA
#> SRR490963 2 0.1251 0.901 0.000 0.956 0.000 0.008 0.012 NA
#> SRR490964 2 0.1251 0.901 0.000 0.956 0.000 0.008 0.012 NA
#> SRR490965 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490966 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490967 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490968 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490969 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490970 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490971 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490972 2 0.1078 0.904 0.000 0.964 0.000 0.016 0.008 NA
#> SRR490973 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490974 3 0.0806 0.825 0.000 0.020 0.972 0.000 0.008 NA
#> SRR490975 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490976 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490977 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490978 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490979 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490980 3 0.0909 0.825 0.000 0.020 0.968 0.000 0.012 NA
#> SRR490981 2 0.4866 0.713 0.000 0.712 0.040 0.008 0.048 NA
#> SRR490982 2 0.4990 0.703 0.000 0.704 0.048 0.008 0.048 NA
#> SRR490983 2 0.4866 0.713 0.000 0.712 0.040 0.008 0.048 NA
#> SRR490984 2 0.4866 0.713 0.000 0.712 0.040 0.008 0.048 NA
#> SRR490985 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490986 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490987 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490988 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490989 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490990 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490991 3 0.1680 0.820 0.000 0.020 0.940 0.024 0.004 NA
#> SRR490992 3 0.1579 0.821 0.000 0.020 0.944 0.024 0.004 NA
#> SRR490993 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR490994 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR490995 5 0.4226 0.583 0.000 0.004 0.008 0.000 0.504 NA
#> SRR490996 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR490997 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR490998 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR491000 5 0.4226 0.583 0.000 0.004 0.008 0.000 0.504 NA
#> SRR491001 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR491002 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR491003 3 0.4213 0.791 0.000 0.020 0.636 0.004 0.000 NA
#> SRR491004 3 0.4213 0.791 0.000 0.020 0.636 0.004 0.000 NA
#> SRR491005 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR491006 3 0.4213 0.791 0.000 0.020 0.636 0.004 0.000 NA
#> SRR491007 3 0.4213 0.791 0.000 0.020 0.636 0.004 0.000 NA
#> SRR491008 3 0.4092 0.791 0.000 0.020 0.636 0.000 0.000 NA
#> SRR491009 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491010 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491011 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491012 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491013 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491014 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491015 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491016 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491017 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491018 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491019 4 0.2264 0.921 0.096 0.000 0.004 0.888 0.000 NA
#> SRR491020 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491021 4 0.1765 0.924 0.096 0.000 0.000 0.904 0.000 NA
#> SRR491022 4 0.5001 0.669 0.096 0.000 0.000 0.596 0.308 NA
#> SRR491023 4 0.4971 0.678 0.096 0.000 0.000 0.604 0.300 NA
#> SRR491024 4 0.2443 0.920 0.096 0.000 0.004 0.880 0.000 NA
#> SRR491025 4 0.2443 0.920 0.096 0.000 0.004 0.880 0.000 NA
#> SRR491026 4 0.2443 0.920 0.096 0.000 0.004 0.880 0.000 NA
#> SRR491027 4 0.2443 0.920 0.096 0.000 0.004 0.880 0.000 NA
#> SRR491028 4 0.4905 0.697 0.096 0.000 0.000 0.620 0.284 NA
#> SRR491029 4 0.2121 0.923 0.096 0.000 0.000 0.892 0.000 NA
#> SRR491030 4 0.2443 0.920 0.096 0.000 0.004 0.880 0.000 NA
#> SRR491031 5 0.3933 0.277 0.036 0.000 0.000 0.248 0.716 NA
#> SRR491032 4 0.4771 0.725 0.096 0.000 0.000 0.648 0.256 NA
#> SRR491033 4 0.2526 0.919 0.096 0.000 0.004 0.876 0.000 NA
#> SRR491034 4 0.5070 0.642 0.096 0.000 0.000 0.576 0.328 NA
#> SRR491035 4 0.5070 0.642 0.096 0.000 0.000 0.576 0.328 NA
#> SRR491036 4 0.2983 0.901 0.092 0.000 0.000 0.856 0.040 NA
#> SRR491037 4 0.2526 0.919 0.096 0.000 0.004 0.876 0.000 NA
#> SRR491038 4 0.2121 0.923 0.096 0.000 0.000 0.892 0.000 NA
#> SRR491039 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491040 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491041 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491042 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491043 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491045 1 0.1167 0.958 0.960 0.000 0.008 0.000 0.020 NA
#> SRR491065 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491066 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491067 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491068 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491069 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491070 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491071 1 0.0632 0.965 0.976 0.000 0.000 0.000 0.000 NA
#> SRR491072 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491073 5 0.5894 0.654 0.328 0.000 0.008 0.000 0.492 NA
#> SRR491074 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491075 5 0.5929 0.603 0.360 0.000 0.008 0.000 0.464 NA
#> SRR491076 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491077 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491078 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491079 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491080 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491081 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491082 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491083 1 0.0291 0.972 0.992 0.000 0.000 0.000 0.004 NA
#> SRR491084 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491085 1 0.0291 0.972 0.992 0.000 0.000 0.000 0.004 NA
#> SRR491086 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491087 1 0.0865 0.958 0.964 0.000 0.000 0.000 0.000 NA
#> SRR491088 5 0.5667 0.683 0.312 0.000 0.008 0.000 0.536 NA
#> SRR491089 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491090 5 0.5667 0.683 0.312 0.000 0.008 0.000 0.536 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.
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 13175 rows and 123 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 1.000 1.000 0.5038 0.497 0.497
#> 3 3 1.000 0.999 0.998 0.2301 0.884 0.767
#> 4 4 1.000 0.999 0.996 0.2171 0.864 0.643
#> 5 5 0.969 0.929 0.944 0.0360 0.971 0.882
#> 6 6 0.944 0.953 0.950 0.0302 0.972 0.874
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
#> SRR445718 2 0 1 0 1
#> SRR445719 2 0 1 0 1
#> SRR445720 2 0 1 0 1
#> SRR445721 2 0 1 0 1
#> SRR445722 2 0 1 0 1
#> SRR445723 2 0 1 0 1
#> SRR445724 2 0 1 0 1
#> SRR445725 2 0 1 0 1
#> SRR445726 2 0 1 0 1
#> SRR445727 2 0 1 0 1
#> SRR445728 2 0 1 0 1
#> SRR445729 2 0 1 0 1
#> SRR445730 1 0 1 1 0
#> SRR445731 1 0 1 1 0
#> SRR490961 2 0 1 0 1
#> SRR490962 2 0 1 0 1
#> SRR490963 2 0 1 0 1
#> SRR490964 2 0 1 0 1
#> SRR490965 2 0 1 0 1
#> SRR490966 2 0 1 0 1
#> SRR490967 2 0 1 0 1
#> SRR490968 2 0 1 0 1
#> SRR490969 2 0 1 0 1
#> SRR490970 2 0 1 0 1
#> SRR490971 2 0 1 0 1
#> SRR490972 2 0 1 0 1
#> SRR490973 2 0 1 0 1
#> SRR490974 2 0 1 0 1
#> SRR490975 2 0 1 0 1
#> SRR490976 2 0 1 0 1
#> SRR490977 2 0 1 0 1
#> SRR490978 2 0 1 0 1
#> SRR490979 2 0 1 0 1
#> SRR490980 2 0 1 0 1
#> SRR490981 2 0 1 0 1
#> SRR490982 2 0 1 0 1
#> SRR490983 2 0 1 0 1
#> SRR490984 2 0 1 0 1
#> SRR490985 2 0 1 0 1
#> SRR490986 2 0 1 0 1
#> SRR490987 2 0 1 0 1
#> SRR490988 2 0 1 0 1
#> SRR490989 2 0 1 0 1
#> SRR490990 2 0 1 0 1
#> SRR490991 2 0 1 0 1
#> SRR490992 2 0 1 0 1
#> SRR490993 2 0 1 0 1
#> SRR490994 2 0 1 0 1
#> SRR490995 2 0 1 0 1
#> SRR490996 2 0 1 0 1
#> SRR490997 2 0 1 0 1
#> SRR490998 2 0 1 0 1
#> SRR491000 2 0 1 0 1
#> SRR491001 2 0 1 0 1
#> SRR491002 2 0 1 0 1
#> SRR491003 2 0 1 0 1
#> SRR491004 2 0 1 0 1
#> SRR491005 2 0 1 0 1
#> SRR491006 2 0 1 0 1
#> SRR491007 2 0 1 0 1
#> SRR491008 2 0 1 0 1
#> SRR491009 1 0 1 1 0
#> SRR491010 1 0 1 1 0
#> SRR491011 1 0 1 1 0
#> SRR491012 1 0 1 1 0
#> SRR491013 1 0 1 1 0
#> SRR491014 1 0 1 1 0
#> SRR491015 1 0 1 1 0
#> SRR491016 1 0 1 1 0
#> SRR491017 1 0 1 1 0
#> SRR491018 1 0 1 1 0
#> SRR491019 1 0 1 1 0
#> SRR491020 1 0 1 1 0
#> SRR491021 1 0 1 1 0
#> SRR491022 1 0 1 1 0
#> SRR491023 1 0 1 1 0
#> SRR491024 1 0 1 1 0
#> SRR491025 1 0 1 1 0
#> SRR491026 1 0 1 1 0
#> SRR491027 1 0 1 1 0
#> SRR491028 1 0 1 1 0
#> SRR491029 1 0 1 1 0
#> SRR491030 1 0 1 1 0
#> SRR491031 1 0 1 1 0
#> SRR491032 1 0 1 1 0
#> SRR491033 1 0 1 1 0
#> SRR491034 1 0 1 1 0
#> SRR491035 1 0 1 1 0
#> SRR491036 1 0 1 1 0
#> SRR491037 1 0 1 1 0
#> SRR491038 1 0 1 1 0
#> SRR491039 1 0 1 1 0
#> SRR491040 1 0 1 1 0
#> SRR491041 1 0 1 1 0
#> SRR491042 1 0 1 1 0
#> SRR491043 1 0 1 1 0
#> SRR491045 1 0 1 1 0
#> SRR491065 1 0 1 1 0
#> SRR491066 1 0 1 1 0
#> SRR491067 1 0 1 1 0
#> SRR491068 1 0 1 1 0
#> SRR491069 1 0 1 1 0
#> SRR491070 1 0 1 1 0
#> SRR491071 1 0 1 1 0
#> SRR491072 1 0 1 1 0
#> SRR491073 1 0 1 1 0
#> SRR491074 1 0 1 1 0
#> SRR491075 1 0 1 1 0
#> SRR491076 1 0 1 1 0
#> SRR491077 1 0 1 1 0
#> SRR491078 1 0 1 1 0
#> SRR491079 1 0 1 1 0
#> SRR491080 1 0 1 1 0
#> SRR491081 1 0 1 1 0
#> SRR491082 1 0 1 1 0
#> SRR491083 1 0 1 1 0
#> SRR491084 1 0 1 1 0
#> SRR491085 1 0 1 1 0
#> SRR491086 1 0 1 1 0
#> SRR491087 1 0 1 1 0
#> SRR491088 1 0 1 1 0
#> SRR491089 1 0 1 1 0
#> SRR491090 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445730 1 0.0000 0.998 1.000 0.000 0.000
#> SRR445731 1 0.0000 0.998 1.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490973 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490974 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490975 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490976 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490977 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490978 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490979 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490980 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490985 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490986 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490987 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490988 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490989 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490990 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490991 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490992 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490993 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490994 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490995 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490996 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490997 3 0.0237 1.000 0.000 0.004 0.996
#> SRR490998 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491000 2 0.0000 1.000 0.000 1.000 0.000
#> SRR491001 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491002 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491003 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491004 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491005 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491006 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491007 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491008 3 0.0237 1.000 0.000 0.004 0.996
#> SRR491009 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491010 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491011 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491012 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491013 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491014 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491015 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491016 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491017 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491018 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491019 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491020 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491021 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491022 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491023 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491024 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491025 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491026 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491027 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491028 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491029 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491030 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491031 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491032 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491033 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491034 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491035 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491036 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491037 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491038 1 0.0237 0.998 0.996 0.000 0.004
#> SRR491039 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491040 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491041 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491042 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491043 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491045 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491065 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491066 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491067 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491068 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491069 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491070 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491071 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491072 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491073 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491074 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491075 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491076 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491077 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491078 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491079 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491080 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491081 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491082 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491083 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491084 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491085 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491086 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491087 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491088 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491089 1 0.0000 0.998 1.000 0.000 0.000
#> SRR491090 1 0.0000 0.998 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490977 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490978 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490985 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 0.996 0.000 0 1.000 0.000
#> SRR490993 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR490994 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR490995 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490996 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR490997 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR490998 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491000 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR491001 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491002 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491003 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491004 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491005 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491006 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491007 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491008 3 0.0469 0.995 0.000 0 0.988 0.012
#> SRR491009 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491010 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491011 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491012 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491013 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491014 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491015 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491016 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491017 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491018 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491019 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491020 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491021 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491022 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491023 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491024 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491025 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491026 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491027 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491028 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491029 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491030 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491031 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491032 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491033 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491034 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491035 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491036 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491037 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491038 4 0.0469 1.000 0.012 0 0.000 0.988
#> SRR491039 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491090 1 0.0000 1.000 1.000 0 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445719 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445720 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445721 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445722 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445723 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445724 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445725 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445726 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445727 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445728 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445729 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR445730 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR445731 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR490961 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490962 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490963 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490964 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490965 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490966 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490967 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490968 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490969 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490970 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490971 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490972 2 0.000 0.998 0.000 1.000 0.0 0.000 0.000
#> SRR490973 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490974 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490975 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490976 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490977 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490978 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490979 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490980 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490981 2 0.051 0.985 0.000 0.984 0.0 0.000 0.016
#> SRR490982 2 0.051 0.985 0.000 0.984 0.0 0.000 0.016
#> SRR490983 2 0.051 0.985 0.000 0.984 0.0 0.000 0.016
#> SRR490984 2 0.051 0.985 0.000 0.984 0.0 0.000 0.016
#> SRR490985 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490986 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490987 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490988 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490989 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490990 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490991 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490992 3 0.000 0.865 0.000 0.000 1.0 0.000 0.000
#> SRR490993 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR490994 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR490995 5 0.398 0.387 0.000 0.340 0.0 0.000 0.660
#> SRR490996 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR490997 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR490998 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491000 5 0.398 0.387 0.000 0.340 0.0 0.000 0.660
#> SRR491001 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491002 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491003 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491004 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491005 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491006 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491007 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491008 3 0.380 0.829 0.000 0.000 0.7 0.000 0.300
#> SRR491009 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491010 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491011 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491012 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491013 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491014 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491015 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491016 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491017 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491018 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491019 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491020 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491021 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491022 4 0.167 0.935 0.000 0.000 0.0 0.924 0.076
#> SRR491023 4 0.173 0.931 0.000 0.000 0.0 0.920 0.080
#> SRR491024 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491025 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491026 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491027 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491028 4 0.120 0.956 0.000 0.000 0.0 0.952 0.048
#> SRR491029 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491030 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491031 5 0.393 0.270 0.000 0.000 0.0 0.328 0.672
#> SRR491032 4 0.104 0.962 0.000 0.000 0.0 0.960 0.040
#> SRR491033 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491034 4 0.179 0.928 0.000 0.000 0.0 0.916 0.084
#> SRR491035 4 0.167 0.935 0.000 0.000 0.0 0.924 0.076
#> SRR491036 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491037 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491038 4 0.000 0.986 0.000 0.000 0.0 1.000 0.000
#> SRR491039 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491040 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491041 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491042 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491043 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491045 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491065 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491066 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491067 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491068 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491069 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491070 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491071 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491072 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491073 5 0.409 0.618 0.368 0.000 0.0 0.000 0.632
#> SRR491074 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491075 5 0.418 0.563 0.400 0.000 0.0 0.000 0.600
#> SRR491076 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491077 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491078 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491079 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491080 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491081 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491082 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491083 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491084 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491085 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491086 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491087 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491088 5 0.407 0.622 0.364 0.000 0.0 0.000 0.636
#> SRR491089 1 0.000 1.000 1.000 0.000 0.0 0.000 0.000
#> SRR491090 5 0.407 0.622 0.364 0.000 0.0 0.000 0.636
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490973 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490974 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490975 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490976 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490977 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490978 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490979 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490980 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490981 2 0.1649 0.939 0.000 0.932 0.032 0.000 0.036 0.000
#> SRR490982 2 0.1649 0.939 0.000 0.932 0.032 0.000 0.036 0.000
#> SRR490983 2 0.1649 0.939 0.000 0.932 0.032 0.000 0.036 0.000
#> SRR490984 2 0.1649 0.939 0.000 0.932 0.032 0.000 0.036 0.000
#> SRR490985 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490986 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490987 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490988 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490989 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490990 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490991 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490992 3 0.2730 1.000 0.000 0.000 0.808 0.000 0.000 0.192
#> SRR490993 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 5 0.3149 0.691 0.000 0.132 0.044 0.000 0.824 0.000
#> SRR490996 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 5 0.3149 0.691 0.000 0.132 0.044 0.000 0.824 0.000
#> SRR491001 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491022 4 0.4599 0.699 0.000 0.000 0.140 0.696 0.164 0.000
#> SRR491023 4 0.4693 0.685 0.000 0.000 0.140 0.684 0.176 0.000
#> SRR491024 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 4 0.4318 0.732 0.000 0.000 0.140 0.728 0.132 0.000
#> SRR491029 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 5 0.3790 0.617 0.000 0.000 0.116 0.104 0.780 0.000
#> SRR491032 4 0.4204 0.743 0.000 0.000 0.132 0.740 0.128 0.000
#> SRR491033 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491034 4 0.4723 0.679 0.000 0.000 0.140 0.680 0.180 0.000
#> SRR491035 4 0.4631 0.695 0.000 0.000 0.140 0.692 0.168 0.000
#> SRR491036 4 0.0260 0.933 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR491037 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491038 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491039 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0363 0.988 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491066 1 0.0458 0.985 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR491067 1 0.0363 0.988 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491068 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0458 0.985 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR491070 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491073 5 0.2941 0.797 0.220 0.000 0.000 0.000 0.780 0.000
#> SRR491074 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491075 5 0.3409 0.699 0.300 0.000 0.000 0.000 0.700 0.000
#> SRR491076 1 0.0547 0.981 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR491077 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0547 0.981 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR491087 1 0.0363 0.988 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491088 5 0.2854 0.802 0.208 0.000 0.000 0.000 0.792 0.000
#> SRR491089 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491090 5 0.2854 0.802 0.208 0.000 0.000 0.000 0.792 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", "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 13175 rows and 123 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 0.999 1.000 0.3556 0.645 0.645
#> 3 3 0.790 0.930 0.958 0.7660 0.736 0.590
#> 4 4 1.000 0.969 0.987 0.2014 0.847 0.608
#> 5 5 0.958 0.944 0.962 0.0373 0.972 0.887
#> 6 6 1.000 0.953 0.982 0.0181 0.987 0.941
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 4 5
There is also optional best \(k\) = 2 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
#> SRR445718 2 0.0000 1.000 0.000 1.000
#> SRR445719 2 0.0000 1.000 0.000 1.000
#> SRR445720 2 0.0000 1.000 0.000 1.000
#> SRR445721 2 0.0000 1.000 0.000 1.000
#> SRR445722 2 0.0000 1.000 0.000 1.000
#> SRR445723 2 0.0000 1.000 0.000 1.000
#> SRR445724 2 0.0000 1.000 0.000 1.000
#> SRR445725 2 0.0000 1.000 0.000 1.000
#> SRR445726 2 0.0000 1.000 0.000 1.000
#> SRR445727 2 0.0000 1.000 0.000 1.000
#> SRR445728 2 0.0000 1.000 0.000 1.000
#> SRR445729 2 0.0000 1.000 0.000 1.000
#> SRR445730 1 0.0000 0.999 1.000 0.000
#> SRR445731 1 0.0000 0.999 1.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000
#> SRR490962 2 0.0000 1.000 0.000 1.000
#> SRR490963 2 0.0000 1.000 0.000 1.000
#> SRR490964 2 0.0000 1.000 0.000 1.000
#> SRR490965 2 0.0000 1.000 0.000 1.000
#> SRR490966 2 0.0000 1.000 0.000 1.000
#> SRR490967 2 0.0000 1.000 0.000 1.000
#> SRR490968 2 0.0000 1.000 0.000 1.000
#> SRR490969 2 0.0000 1.000 0.000 1.000
#> SRR490970 2 0.0000 1.000 0.000 1.000
#> SRR490971 2 0.0000 1.000 0.000 1.000
#> SRR490972 2 0.0000 1.000 0.000 1.000
#> SRR490973 1 0.0000 0.999 1.000 0.000
#> SRR490974 1 0.0000 0.999 1.000 0.000
#> SRR490975 1 0.0000 0.999 1.000 0.000
#> SRR490976 1 0.0000 0.999 1.000 0.000
#> SRR490977 1 0.0000 0.999 1.000 0.000
#> SRR490978 1 0.0000 0.999 1.000 0.000
#> SRR490979 1 0.0000 0.999 1.000 0.000
#> SRR490980 1 0.0000 0.999 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1.000
#> SRR490982 2 0.0000 1.000 0.000 1.000
#> SRR490983 2 0.0000 1.000 0.000 1.000
#> SRR490984 2 0.0000 1.000 0.000 1.000
#> SRR490985 1 0.0376 0.996 0.996 0.004
#> SRR490986 1 0.0672 0.992 0.992 0.008
#> SRR490987 1 0.0000 0.999 1.000 0.000
#> SRR490988 1 0.2043 0.968 0.968 0.032
#> SRR490989 1 0.0376 0.996 0.996 0.004
#> SRR490990 1 0.0000 0.999 1.000 0.000
#> SRR490991 1 0.0000 0.999 1.000 0.000
#> SRR490992 1 0.0000 0.999 1.000 0.000
#> SRR490993 1 0.0000 0.999 1.000 0.000
#> SRR490994 1 0.0000 0.999 1.000 0.000
#> SRR490995 1 0.0376 0.996 0.996 0.004
#> SRR490996 1 0.0000 0.999 1.000 0.000
#> SRR490997 1 0.0000 0.999 1.000 0.000
#> SRR490998 1 0.0000 0.999 1.000 0.000
#> SRR491000 1 0.0376 0.996 0.996 0.004
#> SRR491001 1 0.0000 0.999 1.000 0.000
#> SRR491002 1 0.0000 0.999 1.000 0.000
#> SRR491003 1 0.0000 0.999 1.000 0.000
#> SRR491004 1 0.0000 0.999 1.000 0.000
#> SRR491005 1 0.0000 0.999 1.000 0.000
#> SRR491006 1 0.0000 0.999 1.000 0.000
#> SRR491007 1 0.0000 0.999 1.000 0.000
#> SRR491008 1 0.0000 0.999 1.000 0.000
#> SRR491009 1 0.0000 0.999 1.000 0.000
#> SRR491010 1 0.0000 0.999 1.000 0.000
#> SRR491011 1 0.0000 0.999 1.000 0.000
#> SRR491012 1 0.0000 0.999 1.000 0.000
#> SRR491013 1 0.0000 0.999 1.000 0.000
#> SRR491014 1 0.0000 0.999 1.000 0.000
#> SRR491015 1 0.0000 0.999 1.000 0.000
#> SRR491016 1 0.0000 0.999 1.000 0.000
#> SRR491017 1 0.0000 0.999 1.000 0.000
#> SRR491018 1 0.0000 0.999 1.000 0.000
#> SRR491019 1 0.0000 0.999 1.000 0.000
#> SRR491020 1 0.0000 0.999 1.000 0.000
#> SRR491021 1 0.0000 0.999 1.000 0.000
#> SRR491022 1 0.0000 0.999 1.000 0.000
#> SRR491023 1 0.0000 0.999 1.000 0.000
#> SRR491024 1 0.0000 0.999 1.000 0.000
#> SRR491025 1 0.0000 0.999 1.000 0.000
#> SRR491026 1 0.0000 0.999 1.000 0.000
#> SRR491027 1 0.0000 0.999 1.000 0.000
#> SRR491028 1 0.0000 0.999 1.000 0.000
#> SRR491029 1 0.0000 0.999 1.000 0.000
#> SRR491030 1 0.0000 0.999 1.000 0.000
#> SRR491031 1 0.0000 0.999 1.000 0.000
#> SRR491032 1 0.0000 0.999 1.000 0.000
#> SRR491033 1 0.0000 0.999 1.000 0.000
#> SRR491034 1 0.0000 0.999 1.000 0.000
#> SRR491035 1 0.0000 0.999 1.000 0.000
#> SRR491036 1 0.0000 0.999 1.000 0.000
#> SRR491037 1 0.0000 0.999 1.000 0.000
#> SRR491038 1 0.0000 0.999 1.000 0.000
#> SRR491039 1 0.0000 0.999 1.000 0.000
#> SRR491040 1 0.0000 0.999 1.000 0.000
#> SRR491041 1 0.0000 0.999 1.000 0.000
#> SRR491042 1 0.0000 0.999 1.000 0.000
#> SRR491043 1 0.0000 0.999 1.000 0.000
#> SRR491045 1 0.0000 0.999 1.000 0.000
#> SRR491065 1 0.0000 0.999 1.000 0.000
#> SRR491066 1 0.0000 0.999 1.000 0.000
#> SRR491067 1 0.0000 0.999 1.000 0.000
#> SRR491068 1 0.0000 0.999 1.000 0.000
#> SRR491069 1 0.0000 0.999 1.000 0.000
#> SRR491070 1 0.0000 0.999 1.000 0.000
#> SRR491071 1 0.0000 0.999 1.000 0.000
#> SRR491072 1 0.0000 0.999 1.000 0.000
#> SRR491073 1 0.0000 0.999 1.000 0.000
#> SRR491074 1 0.0000 0.999 1.000 0.000
#> SRR491075 1 0.0000 0.999 1.000 0.000
#> SRR491076 1 0.0000 0.999 1.000 0.000
#> SRR491077 1 0.0000 0.999 1.000 0.000
#> SRR491078 1 0.0000 0.999 1.000 0.000
#> SRR491079 1 0.0000 0.999 1.000 0.000
#> SRR491080 1 0.0000 0.999 1.000 0.000
#> SRR491081 1 0.0000 0.999 1.000 0.000
#> SRR491082 1 0.0000 0.999 1.000 0.000
#> SRR491083 1 0.0000 0.999 1.000 0.000
#> SRR491084 1 0.0000 0.999 1.000 0.000
#> SRR491085 1 0.0000 0.999 1.000 0.000
#> SRR491086 1 0.0000 0.999 1.000 0.000
#> SRR491087 1 0.0000 0.999 1.000 0.000
#> SRR491088 1 0.0000 0.999 1.000 0.000
#> SRR491089 1 0.0000 0.999 1.000 0.000
#> SRR491090 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 0.996 0.000 1.000 0.000
#> SRR445719 2 0.000 0.996 0.000 1.000 0.000
#> SRR445720 2 0.000 0.996 0.000 1.000 0.000
#> SRR445721 2 0.000 0.996 0.000 1.000 0.000
#> SRR445722 2 0.000 0.996 0.000 1.000 0.000
#> SRR445723 2 0.000 0.996 0.000 1.000 0.000
#> SRR445724 2 0.000 0.996 0.000 1.000 0.000
#> SRR445725 2 0.000 0.996 0.000 1.000 0.000
#> SRR445726 2 0.000 0.996 0.000 1.000 0.000
#> SRR445727 2 0.000 0.996 0.000 1.000 0.000
#> SRR445728 2 0.000 0.996 0.000 1.000 0.000
#> SRR445729 2 0.000 0.996 0.000 1.000 0.000
#> SRR445730 1 0.000 0.915 1.000 0.000 0.000
#> SRR445731 1 0.000 0.915 1.000 0.000 0.000
#> SRR490961 2 0.000 0.996 0.000 1.000 0.000
#> SRR490962 2 0.000 0.996 0.000 1.000 0.000
#> SRR490963 2 0.000 0.996 0.000 1.000 0.000
#> SRR490964 2 0.000 0.996 0.000 1.000 0.000
#> SRR490965 2 0.000 0.996 0.000 1.000 0.000
#> SRR490966 2 0.000 0.996 0.000 1.000 0.000
#> SRR490967 2 0.000 0.996 0.000 1.000 0.000
#> SRR490968 2 0.000 0.996 0.000 1.000 0.000
#> SRR490969 2 0.000 0.996 0.000 1.000 0.000
#> SRR490970 2 0.000 0.996 0.000 1.000 0.000
#> SRR490971 2 0.000 0.996 0.000 1.000 0.000
#> SRR490972 2 0.000 0.996 0.000 1.000 0.000
#> SRR490973 3 0.000 0.999 0.000 0.000 1.000
#> SRR490974 3 0.000 0.999 0.000 0.000 1.000
#> SRR490975 3 0.000 0.999 0.000 0.000 1.000
#> SRR490976 3 0.000 0.999 0.000 0.000 1.000
#> SRR490977 3 0.000 0.999 0.000 0.000 1.000
#> SRR490978 3 0.000 0.999 0.000 0.000 1.000
#> SRR490979 3 0.000 0.999 0.000 0.000 1.000
#> SRR490980 3 0.000 0.999 0.000 0.000 1.000
#> SRR490981 2 0.000 0.996 0.000 1.000 0.000
#> SRR490982 2 0.288 0.895 0.000 0.904 0.096
#> SRR490983 2 0.000 0.996 0.000 1.000 0.000
#> SRR490984 2 0.000 0.996 0.000 1.000 0.000
#> SRR490985 3 0.000 0.999 0.000 0.000 1.000
#> SRR490986 3 0.000 0.999 0.000 0.000 1.000
#> SRR490987 3 0.000 0.999 0.000 0.000 1.000
#> SRR490988 3 0.000 0.999 0.000 0.000 1.000
#> SRR490989 3 0.000 0.999 0.000 0.000 1.000
#> SRR490990 3 0.000 0.999 0.000 0.000 1.000
#> SRR490991 3 0.000 0.999 0.000 0.000 1.000
#> SRR490992 3 0.000 0.999 0.000 0.000 1.000
#> SRR490993 3 0.000 0.999 0.000 0.000 1.000
#> SRR490994 3 0.000 0.999 0.000 0.000 1.000
#> SRR490995 3 0.000 0.999 0.000 0.000 1.000
#> SRR490996 3 0.000 0.999 0.000 0.000 1.000
#> SRR490997 3 0.000 0.999 0.000 0.000 1.000
#> SRR490998 3 0.000 0.999 0.000 0.000 1.000
#> SRR491000 3 0.116 0.971 0.000 0.028 0.972
#> SRR491001 3 0.000 0.999 0.000 0.000 1.000
#> SRR491002 3 0.000 0.999 0.000 0.000 1.000
#> SRR491003 3 0.000 0.999 0.000 0.000 1.000
#> SRR491004 3 0.000 0.999 0.000 0.000 1.000
#> SRR491005 3 0.000 0.999 0.000 0.000 1.000
#> SRR491006 3 0.000 0.999 0.000 0.000 1.000
#> SRR491007 3 0.000 0.999 0.000 0.000 1.000
#> SRR491008 3 0.000 0.999 0.000 0.000 1.000
#> SRR491009 1 0.514 0.767 0.748 0.000 0.252
#> SRR491010 1 0.514 0.767 0.748 0.000 0.252
#> SRR491011 1 0.514 0.767 0.748 0.000 0.252
#> SRR491012 1 0.514 0.767 0.748 0.000 0.252
#> SRR491013 1 0.510 0.771 0.752 0.000 0.248
#> SRR491014 1 0.514 0.767 0.748 0.000 0.252
#> SRR491015 1 0.514 0.767 0.748 0.000 0.252
#> SRR491016 1 0.514 0.767 0.748 0.000 0.252
#> SRR491017 1 0.514 0.767 0.748 0.000 0.252
#> SRR491018 1 0.514 0.767 0.748 0.000 0.252
#> SRR491019 1 0.000 0.915 1.000 0.000 0.000
#> SRR491020 1 0.514 0.767 0.748 0.000 0.252
#> SRR491021 1 0.514 0.767 0.748 0.000 0.252
#> SRR491022 1 0.000 0.915 1.000 0.000 0.000
#> SRR491023 1 0.514 0.767 0.748 0.000 0.252
#> SRR491024 1 0.418 0.825 0.828 0.000 0.172
#> SRR491025 1 0.514 0.767 0.748 0.000 0.252
#> SRR491026 1 0.000 0.915 1.000 0.000 0.000
#> SRR491027 1 0.334 0.855 0.880 0.000 0.120
#> SRR491028 1 0.514 0.767 0.748 0.000 0.252
#> SRR491029 1 0.514 0.767 0.748 0.000 0.252
#> SRR491030 1 0.502 0.777 0.760 0.000 0.240
#> SRR491031 1 0.514 0.767 0.748 0.000 0.252
#> SRR491032 1 0.000 0.915 1.000 0.000 0.000
#> SRR491033 1 0.000 0.915 1.000 0.000 0.000
#> SRR491034 1 0.000 0.915 1.000 0.000 0.000
#> SRR491035 1 0.000 0.915 1.000 0.000 0.000
#> SRR491036 1 0.440 0.814 0.812 0.000 0.188
#> SRR491037 1 0.000 0.915 1.000 0.000 0.000
#> SRR491038 1 0.000 0.915 1.000 0.000 0.000
#> SRR491039 1 0.000 0.915 1.000 0.000 0.000
#> SRR491040 1 0.000 0.915 1.000 0.000 0.000
#> SRR491041 1 0.000 0.915 1.000 0.000 0.000
#> SRR491042 1 0.000 0.915 1.000 0.000 0.000
#> SRR491043 1 0.000 0.915 1.000 0.000 0.000
#> SRR491045 1 0.000 0.915 1.000 0.000 0.000
#> SRR491065 1 0.000 0.915 1.000 0.000 0.000
#> SRR491066 1 0.000 0.915 1.000 0.000 0.000
#> SRR491067 1 0.000 0.915 1.000 0.000 0.000
#> SRR491068 1 0.000 0.915 1.000 0.000 0.000
#> SRR491069 1 0.000 0.915 1.000 0.000 0.000
#> SRR491070 1 0.000 0.915 1.000 0.000 0.000
#> SRR491071 1 0.000 0.915 1.000 0.000 0.000
#> SRR491072 1 0.000 0.915 1.000 0.000 0.000
#> SRR491073 1 0.000 0.915 1.000 0.000 0.000
#> SRR491074 1 0.000 0.915 1.000 0.000 0.000
#> SRR491075 1 0.000 0.915 1.000 0.000 0.000
#> SRR491076 1 0.000 0.915 1.000 0.000 0.000
#> SRR491077 1 0.000 0.915 1.000 0.000 0.000
#> SRR491078 1 0.000 0.915 1.000 0.000 0.000
#> SRR491079 1 0.000 0.915 1.000 0.000 0.000
#> SRR491080 1 0.000 0.915 1.000 0.000 0.000
#> SRR491081 1 0.000 0.915 1.000 0.000 0.000
#> SRR491082 1 0.000 0.915 1.000 0.000 0.000
#> SRR491083 1 0.000 0.915 1.000 0.000 0.000
#> SRR491084 1 0.000 0.915 1.000 0.000 0.000
#> SRR491085 1 0.000 0.915 1.000 0.000 0.000
#> SRR491086 1 0.000 0.915 1.000 0.000 0.000
#> SRR491087 1 0.000 0.915 1.000 0.000 0.000
#> SRR491088 1 0.000 0.915 1.000 0.000 0.000
#> SRR491089 1 0.000 0.915 1.000 0.000 0.000
#> SRR491090 1 0.000 0.915 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490981 2 0.0188 0.993 0.000 0.996 0.004 0.000
#> SRR490982 2 0.2281 0.894 0.000 0.904 0.096 0.000
#> SRR490983 2 0.0188 0.993 0.000 0.996 0.004 0.000
#> SRR490984 2 0.0188 0.993 0.000 0.996 0.004 0.000
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490995 4 0.1557 0.928 0.000 0.000 0.056 0.944
#> SRR490996 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491000 4 0.1557 0.928 0.000 0.000 0.056 0.944
#> SRR491001 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491022 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491023 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491024 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491029 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491031 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491032 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491033 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491034 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491035 4 0.1302 0.937 0.044 0.000 0.000 0.956
#> SRR491036 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491037 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491039 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491073 1 0.4866 0.305 0.596 0.000 0.000 0.404
#> SRR491074 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491075 1 0.4304 0.590 0.716 0.000 0.000 0.284
#> SRR491076 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491088 4 0.4431 0.564 0.304 0.000 0.000 0.696
#> SRR491089 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR491090 4 0.4454 0.556 0.308 0.000 0.000 0.692
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490974 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490975 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490976 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490977 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490978 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490979 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490980 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490981 2 0.256 0.868 0.000 0.856 0.144 0.000 0.000
#> SRR490982 2 0.411 0.551 0.000 0.624 0.376 0.000 0.000
#> SRR490983 2 0.304 0.826 0.000 0.808 0.192 0.000 0.000
#> SRR490984 2 0.281 0.848 0.000 0.832 0.168 0.000 0.000
#> SRR490985 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490986 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490987 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490988 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490989 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490990 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490991 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490992 3 0.238 1.000 0.000 0.000 0.872 0.000 0.128
#> SRR490993 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 4 0.534 0.593 0.000 0.000 0.124 0.664 0.212
#> SRR490996 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 4 0.541 0.581 0.000 0.000 0.128 0.656 0.216
#> SRR491001 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 5 0.000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491023 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491024 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491029 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491032 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491033 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491035 4 0.196 0.859 0.096 0.000 0.000 0.904 0.000
#> SRR491036 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.000 0.954 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491067 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491068 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491070 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491073 1 0.419 0.286 0.596 0.000 0.000 0.404 0.000
#> SRR491074 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491075 1 0.371 0.580 0.716 0.000 0.000 0.284 0.000
#> SRR491076 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491087 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491088 4 0.395 0.514 0.332 0.000 0.000 0.668 0.000
#> SRR491089 1 0.000 0.973 1.000 0.000 0.000 0.000 0.000
#> SRR491090 4 0.397 0.506 0.336 0.000 0.000 0.664 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445719 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445720 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445721 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445722 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445723 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445724 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445725 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445726 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445727 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445728 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445729 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR445730 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR445731 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR490961 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490962 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490963 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490964 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490965 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490966 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490967 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490968 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490969 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490970 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490971 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490972 2 0.000 1.000 0.000 1 0 0.000 0.000 0.000
#> SRR490973 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490974 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490975 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490976 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490977 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490978 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490979 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490980 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490981 6 0.000 1.000 0.000 0 0 0.000 0.000 1.000
#> SRR490982 6 0.000 1.000 0.000 0 0 0.000 0.000 1.000
#> SRR490983 6 0.000 1.000 0.000 0 0 0.000 0.000 1.000
#> SRR490984 6 0.000 1.000 0.000 0 0 0.000 0.000 1.000
#> SRR490985 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490986 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490987 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490988 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490989 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490990 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490991 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490992 3 0.000 1.000 0.000 0 1 0.000 0.000 0.000
#> SRR490993 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR490994 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR490995 4 0.468 0.524 0.000 0 0 0.652 0.264 0.084
#> SRR490996 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR490997 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR490998 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491000 4 0.479 0.504 0.000 0 0 0.640 0.268 0.092
#> SRR491001 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491002 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491003 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491004 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491005 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491006 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491007 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491008 5 0.000 1.000 0.000 0 0 0.000 1.000 0.000
#> SRR491009 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491010 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491011 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491012 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491013 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491014 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491015 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491016 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491017 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491018 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491019 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491020 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491021 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491022 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491023 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491024 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491025 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491026 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491027 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491028 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491029 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491030 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491031 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491032 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491033 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491034 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491035 4 0.176 0.838 0.096 0 0 0.904 0.000 0.000
#> SRR491036 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491037 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491038 4 0.000 0.946 0.000 0 0 1.000 0.000 0.000
#> SRR491039 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491040 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491041 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491042 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491043 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491045 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491065 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491066 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491067 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491068 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491069 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491070 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491071 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491072 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491073 1 0.377 0.286 0.596 0 0 0.404 0.000 0.000
#> SRR491074 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491075 1 0.333 0.542 0.716 0 0 0.284 0.000 0.000
#> SRR491076 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491077 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491078 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491079 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491080 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491081 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491082 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491083 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491084 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491085 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491086 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491087 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491088 4 0.356 0.483 0.336 0 0 0.664 0.000 0.000
#> SRR491089 1 0.000 0.970 1.000 0 0 0.000 0.000 0.000
#> SRR491090 4 0.358 0.477 0.340 0 0 0.660 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["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 13175 rows and 123 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 1.000 1.000 1.000 0.5038 0.497 0.497
#> 3 3 0.969 0.971 0.980 0.2242 0.884 0.767
#> 4 4 0.916 0.894 0.950 0.2129 0.816 0.550
#> 5 5 0.976 0.931 0.969 0.0609 0.943 0.778
#> 6 6 0.980 0.920 0.958 0.0313 0.947 0.756
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
#> SRR445718 2 0.0000 1.000 0.000 1.000
#> SRR445719 2 0.0000 1.000 0.000 1.000
#> SRR445720 2 0.0000 1.000 0.000 1.000
#> SRR445721 2 0.0000 1.000 0.000 1.000
#> SRR445722 2 0.0000 1.000 0.000 1.000
#> SRR445723 2 0.0000 1.000 0.000 1.000
#> SRR445724 2 0.0000 1.000 0.000 1.000
#> SRR445725 2 0.0000 1.000 0.000 1.000
#> SRR445726 2 0.0000 1.000 0.000 1.000
#> SRR445727 2 0.0000 1.000 0.000 1.000
#> SRR445728 2 0.0000 1.000 0.000 1.000
#> SRR445729 2 0.0000 1.000 0.000 1.000
#> SRR445730 1 0.0000 1.000 1.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000
#> SRR490962 2 0.0000 1.000 0.000 1.000
#> SRR490963 2 0.0000 1.000 0.000 1.000
#> SRR490964 2 0.0000 1.000 0.000 1.000
#> SRR490965 2 0.0000 1.000 0.000 1.000
#> SRR490966 2 0.0000 1.000 0.000 1.000
#> SRR490967 2 0.0000 1.000 0.000 1.000
#> SRR490968 2 0.0000 1.000 0.000 1.000
#> SRR490969 2 0.0000 1.000 0.000 1.000
#> SRR490970 2 0.0000 1.000 0.000 1.000
#> SRR490971 2 0.0000 1.000 0.000 1.000
#> SRR490972 2 0.0000 1.000 0.000 1.000
#> SRR490973 2 0.0000 1.000 0.000 1.000
#> SRR490974 2 0.0000 1.000 0.000 1.000
#> SRR490975 2 0.0000 1.000 0.000 1.000
#> SRR490976 2 0.0000 1.000 0.000 1.000
#> SRR490977 2 0.0000 1.000 0.000 1.000
#> SRR490978 2 0.0000 1.000 0.000 1.000
#> SRR490979 2 0.0000 1.000 0.000 1.000
#> SRR490980 2 0.0000 1.000 0.000 1.000
#> SRR490981 2 0.0000 1.000 0.000 1.000
#> SRR490982 2 0.0000 1.000 0.000 1.000
#> SRR490983 2 0.0000 1.000 0.000 1.000
#> SRR490984 2 0.0000 1.000 0.000 1.000
#> SRR490985 2 0.0000 1.000 0.000 1.000
#> SRR490986 2 0.0000 1.000 0.000 1.000
#> SRR490987 2 0.0000 1.000 0.000 1.000
#> SRR490988 2 0.0000 1.000 0.000 1.000
#> SRR490989 2 0.0000 1.000 0.000 1.000
#> SRR490990 2 0.0000 1.000 0.000 1.000
#> SRR490991 2 0.0000 1.000 0.000 1.000
#> SRR490992 2 0.0000 1.000 0.000 1.000
#> SRR490993 2 0.0000 1.000 0.000 1.000
#> SRR490994 2 0.0000 1.000 0.000 1.000
#> SRR490995 2 0.0376 0.996 0.004 0.996
#> SRR490996 2 0.0000 1.000 0.000 1.000
#> SRR490997 2 0.0000 1.000 0.000 1.000
#> SRR490998 2 0.0000 1.000 0.000 1.000
#> SRR491000 2 0.0376 0.996 0.004 0.996
#> SRR491001 2 0.0000 1.000 0.000 1.000
#> SRR491002 2 0.0000 1.000 0.000 1.000
#> SRR491003 2 0.0000 1.000 0.000 1.000
#> SRR491004 2 0.0000 1.000 0.000 1.000
#> SRR491005 2 0.0000 1.000 0.000 1.000
#> SRR491006 2 0.0000 1.000 0.000 1.000
#> SRR491007 2 0.0000 1.000 0.000 1.000
#> SRR491008 2 0.0000 1.000 0.000 1.000
#> SRR491009 1 0.0000 1.000 1.000 0.000
#> SRR491010 1 0.0000 1.000 1.000 0.000
#> SRR491011 1 0.0000 1.000 1.000 0.000
#> SRR491012 1 0.0000 1.000 1.000 0.000
#> SRR491013 1 0.0000 1.000 1.000 0.000
#> SRR491014 1 0.0000 1.000 1.000 0.000
#> SRR491015 1 0.0000 1.000 1.000 0.000
#> SRR491016 1 0.0000 1.000 1.000 0.000
#> SRR491017 1 0.0000 1.000 1.000 0.000
#> SRR491018 1 0.0000 1.000 1.000 0.000
#> SRR491019 1 0.0000 1.000 1.000 0.000
#> SRR491020 1 0.0000 1.000 1.000 0.000
#> SRR491021 1 0.0000 1.000 1.000 0.000
#> SRR491022 1 0.0000 1.000 1.000 0.000
#> SRR491023 1 0.0000 1.000 1.000 0.000
#> SRR491024 1 0.0000 1.000 1.000 0.000
#> SRR491025 1 0.0000 1.000 1.000 0.000
#> SRR491026 1 0.0000 1.000 1.000 0.000
#> SRR491027 1 0.0000 1.000 1.000 0.000
#> SRR491028 1 0.0000 1.000 1.000 0.000
#> SRR491029 1 0.0000 1.000 1.000 0.000
#> SRR491030 1 0.0000 1.000 1.000 0.000
#> SRR491031 1 0.0000 1.000 1.000 0.000
#> SRR491032 1 0.0000 1.000 1.000 0.000
#> SRR491033 1 0.0000 1.000 1.000 0.000
#> SRR491034 1 0.0000 1.000 1.000 0.000
#> SRR491035 1 0.0000 1.000 1.000 0.000
#> SRR491036 1 0.0000 1.000 1.000 0.000
#> SRR491037 1 0.0000 1.000 1.000 0.000
#> SRR491038 1 0.0000 1.000 1.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.000 1.000 0 1.000 0.000
#> SRR445719 2 0.000 1.000 0 1.000 0.000
#> SRR445720 2 0.000 1.000 0 1.000 0.000
#> SRR445721 2 0.000 1.000 0 1.000 0.000
#> SRR445722 2 0.000 1.000 0 1.000 0.000
#> SRR445723 2 0.000 1.000 0 1.000 0.000
#> SRR445724 2 0.000 1.000 0 1.000 0.000
#> SRR445725 2 0.000 1.000 0 1.000 0.000
#> SRR445726 2 0.000 1.000 0 1.000 0.000
#> SRR445727 2 0.000 1.000 0 1.000 0.000
#> SRR445728 2 0.000 1.000 0 1.000 0.000
#> SRR445729 2 0.000 1.000 0 1.000 0.000
#> SRR445730 1 0.000 1.000 1 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0.000 0.000
#> SRR490961 2 0.000 1.000 0 1.000 0.000
#> SRR490962 2 0.000 1.000 0 1.000 0.000
#> SRR490963 2 0.000 1.000 0 1.000 0.000
#> SRR490964 2 0.000 1.000 0 1.000 0.000
#> SRR490965 2 0.000 1.000 0 1.000 0.000
#> SRR490966 2 0.000 1.000 0 1.000 0.000
#> SRR490967 2 0.000 1.000 0 1.000 0.000
#> SRR490968 2 0.000 1.000 0 1.000 0.000
#> SRR490969 2 0.000 1.000 0 1.000 0.000
#> SRR490970 2 0.000 1.000 0 1.000 0.000
#> SRR490971 2 0.000 1.000 0 1.000 0.000
#> SRR490972 2 0.000 1.000 0 1.000 0.000
#> SRR490973 3 0.263 0.917 0 0.084 0.916
#> SRR490974 3 0.355 0.900 0 0.132 0.868
#> SRR490975 3 0.348 0.902 0 0.128 0.872
#> SRR490976 3 0.263 0.917 0 0.084 0.916
#> SRR490977 3 0.263 0.917 0 0.084 0.916
#> SRR490978 3 0.263 0.917 0 0.084 0.916
#> SRR490979 3 0.263 0.917 0 0.084 0.916
#> SRR490980 3 0.341 0.904 0 0.124 0.876
#> SRR490981 2 0.000 1.000 0 1.000 0.000
#> SRR490982 2 0.000 1.000 0 1.000 0.000
#> SRR490983 2 0.000 1.000 0 1.000 0.000
#> SRR490984 2 0.000 1.000 0 1.000 0.000
#> SRR490985 3 0.375 0.892 0 0.144 0.856
#> SRR490986 3 0.375 0.892 0 0.144 0.856
#> SRR490987 3 0.355 0.900 0 0.132 0.868
#> SRR490988 3 0.382 0.888 0 0.148 0.852
#> SRR490989 3 0.382 0.888 0 0.148 0.852
#> SRR490990 3 0.355 0.900 0 0.132 0.868
#> SRR490991 3 0.362 0.898 0 0.136 0.864
#> SRR490992 3 0.263 0.917 0 0.084 0.916
#> SRR490993 3 0.000 0.912 0 0.000 1.000
#> SRR490994 3 0.000 0.912 0 0.000 1.000
#> SRR490995 3 0.573 0.527 0 0.324 0.676
#> SRR490996 3 0.000 0.912 0 0.000 1.000
#> SRR490997 3 0.000 0.912 0 0.000 1.000
#> SRR490998 3 0.000 0.912 0 0.000 1.000
#> SRR491000 3 0.573 0.527 0 0.324 0.676
#> SRR491001 3 0.000 0.912 0 0.000 1.000
#> SRR491002 3 0.000 0.912 0 0.000 1.000
#> SRR491003 3 0.000 0.912 0 0.000 1.000
#> SRR491004 3 0.000 0.912 0 0.000 1.000
#> SRR491005 3 0.000 0.912 0 0.000 1.000
#> SRR491006 3 0.000 0.912 0 0.000 1.000
#> SRR491007 3 0.000 0.912 0 0.000 1.000
#> SRR491008 3 0.000 0.912 0 0.000 1.000
#> SRR491009 1 0.000 1.000 1 0.000 0.000
#> SRR491010 1 0.000 1.000 1 0.000 0.000
#> SRR491011 1 0.000 1.000 1 0.000 0.000
#> SRR491012 1 0.000 1.000 1 0.000 0.000
#> SRR491013 1 0.000 1.000 1 0.000 0.000
#> SRR491014 1 0.000 1.000 1 0.000 0.000
#> SRR491015 1 0.000 1.000 1 0.000 0.000
#> SRR491016 1 0.000 1.000 1 0.000 0.000
#> SRR491017 1 0.000 1.000 1 0.000 0.000
#> SRR491018 1 0.000 1.000 1 0.000 0.000
#> SRR491019 1 0.000 1.000 1 0.000 0.000
#> SRR491020 1 0.000 1.000 1 0.000 0.000
#> SRR491021 1 0.000 1.000 1 0.000 0.000
#> SRR491022 1 0.000 1.000 1 0.000 0.000
#> SRR491023 1 0.000 1.000 1 0.000 0.000
#> SRR491024 1 0.000 1.000 1 0.000 0.000
#> SRR491025 1 0.000 1.000 1 0.000 0.000
#> SRR491026 1 0.000 1.000 1 0.000 0.000
#> SRR491027 1 0.000 1.000 1 0.000 0.000
#> SRR491028 1 0.000 1.000 1 0.000 0.000
#> SRR491029 1 0.000 1.000 1 0.000 0.000
#> SRR491030 1 0.000 1.000 1 0.000 0.000
#> SRR491031 1 0.000 1.000 1 0.000 0.000
#> SRR491032 1 0.000 1.000 1 0.000 0.000
#> SRR491033 1 0.000 1.000 1 0.000 0.000
#> SRR491034 1 0.000 1.000 1 0.000 0.000
#> SRR491035 1 0.000 1.000 1 0.000 0.000
#> SRR491036 1 0.000 1.000 1 0.000 0.000
#> SRR491037 1 0.000 1.000 1 0.000 0.000
#> SRR491038 1 0.000 1.000 1 0.000 0.000
#> SRR491039 1 0.000 1.000 1 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR445731 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR490961 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490974 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490975 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490976 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490977 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490978 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490979 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490980 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490981 4 0.627 0.3110 0.000 0.056 0.452 0.492
#> SRR490982 4 0.627 0.3110 0.000 0.056 0.452 0.492
#> SRR490983 4 0.627 0.3110 0.000 0.056 0.452 0.492
#> SRR490984 4 0.627 0.3110 0.000 0.056 0.452 0.492
#> SRR490985 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490986 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490987 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490988 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490989 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490990 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490991 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490992 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490993 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490994 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490995 4 0.541 0.2700 0.012 0.000 0.488 0.500
#> SRR490996 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490997 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR490998 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491000 4 0.541 0.2700 0.012 0.000 0.488 0.500
#> SRR491001 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491002 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491003 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491004 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491005 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491006 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491007 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491008 3 0.000 1.0000 0.000 0.000 1.000 0.000
#> SRR491009 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491010 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491011 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491012 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491013 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491014 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491015 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491016 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491017 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491018 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491019 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491020 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491021 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491022 4 0.442 0.6782 0.008 0.000 0.256 0.736
#> SRR491023 4 0.233 0.8237 0.004 0.000 0.088 0.908
#> SRR491024 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491025 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491026 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491027 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491028 4 0.215 0.8245 0.000 0.000 0.088 0.912
#> SRR491029 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491030 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491031 4 0.215 0.8245 0.000 0.000 0.088 0.912
#> SRR491032 4 0.233 0.8237 0.004 0.000 0.088 0.908
#> SRR491033 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491034 4 0.320 0.7898 0.008 0.000 0.136 0.856
#> SRR491035 4 0.442 0.6782 0.008 0.000 0.256 0.736
#> SRR491036 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491037 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491038 4 0.000 0.8601 0.000 0.000 0.000 1.000
#> SRR491039 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491040 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491041 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491042 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491043 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491045 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491065 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491066 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491067 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491068 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491069 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491070 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491071 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491072 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491073 1 0.659 0.0123 0.496 0.000 0.080 0.424
#> SRR491074 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491075 1 0.650 0.1793 0.544 0.000 0.080 0.376
#> SRR491076 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491077 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491078 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491079 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491080 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491081 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491082 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491083 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491084 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491085 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491086 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491087 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491088 4 0.771 0.3406 0.244 0.000 0.316 0.440
#> SRR491089 1 0.000 0.9670 1.000 0.000 0.000 0.000
#> SRR491090 4 0.760 0.3649 0.216 0.000 0.324 0.460
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445730 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR445731 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490981 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR490982 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR490983 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR490984 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR490985 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490995 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR490996 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491000 5 0.0000 0.767 0.000 0 0 0.000 1.000
#> SRR491001 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491009 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491010 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491011 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491012 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491013 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491014 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491015 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491016 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491017 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491018 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491019 4 0.0162 0.962 0.000 0 0 0.996 0.004
#> SRR491020 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491021 4 0.0794 0.945 0.000 0 0 0.972 0.028
#> SRR491022 5 0.2891 0.693 0.000 0 0 0.176 0.824
#> SRR491023 5 0.4268 0.271 0.000 0 0 0.444 0.556
#> SRR491024 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491025 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491026 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491027 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491028 4 0.4182 0.219 0.000 0 0 0.600 0.400
#> SRR491029 4 0.0162 0.962 0.000 0 0 0.996 0.004
#> SRR491030 4 0.0000 0.964 0.000 0 0 1.000 0.000
#> SRR491031 5 0.4294 0.203 0.000 0 0 0.468 0.532
#> SRR491032 5 0.4291 0.216 0.000 0 0 0.464 0.536
#> SRR491033 4 0.0703 0.948 0.000 0 0 0.976 0.024
#> SRR491034 5 0.3336 0.643 0.000 0 0 0.228 0.772
#> SRR491035 5 0.2813 0.698 0.000 0 0 0.168 0.832
#> SRR491036 4 0.2516 0.822 0.000 0 0 0.860 0.140
#> SRR491037 4 0.1043 0.934 0.000 0 0 0.960 0.040
#> SRR491038 4 0.1732 0.893 0.000 0 0 0.920 0.080
#> SRR491039 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491040 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491041 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491042 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491043 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491045 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491065 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491066 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491067 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491068 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491069 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491070 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491071 1 0.0510 0.983 0.984 0 0 0.000 0.016
#> SRR491072 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491073 5 0.4088 0.399 0.368 0 0 0.000 0.632
#> SRR491074 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491075 5 0.4219 0.296 0.416 0 0 0.000 0.584
#> SRR491076 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491077 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491078 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491079 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491080 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491081 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491082 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491083 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491084 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491085 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491086 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491087 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491088 5 0.2773 0.700 0.164 0 0 0.000 0.836
#> SRR491089 1 0.0000 0.999 1.000 0 0 0.000 0.000
#> SRR491090 5 0.2561 0.717 0.144 0 0 0.000 0.856
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.0000 0.000 1 0.000 0.000 0.000 0.000
#> SRR490973 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490974 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490975 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490976 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490977 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490978 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490979 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490980 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490981 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR490982 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR490983 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR490984 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR490985 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490986 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490987 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490988 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490989 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490990 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490991 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490992 3 0.0000 1.0000 0.000 0 1.000 0.000 0.000 0.000
#> SRR490993 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR490994 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR490995 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR490996 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR490997 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR490998 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491000 5 0.0000 0.8656 0.000 0 0.000 0.000 1.000 0.000
#> SRR491001 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491002 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491003 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491004 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491005 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491006 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491007 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491008 6 0.1444 1.0000 0.000 0 0.072 0.000 0.000 0.928
#> SRR491009 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0363 0.9166 0.000 0 0.000 0.988 0.000 0.012
#> SRR491020 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0865 0.9043 0.000 0 0.000 0.964 0.000 0.036
#> SRR491022 5 0.3641 0.7551 0.000 0 0.000 0.140 0.788 0.072
#> SRR491023 4 0.4893 0.3368 0.000 0 0.000 0.572 0.356 0.072
#> SRR491024 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0260 0.9182 0.000 0 0.000 0.992 0.000 0.008
#> SRR491026 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491028 4 0.4871 0.3569 0.000 0 0.000 0.580 0.348 0.072
#> SRR491029 4 0.0260 0.9182 0.000 0 0.000 0.992 0.000 0.008
#> SRR491030 4 0.0000 0.9204 0.000 0 0.000 1.000 0.000 0.000
#> SRR491031 4 0.4893 0.3368 0.000 0 0.000 0.572 0.356 0.072
#> SRR491032 4 0.4859 0.3664 0.000 0 0.000 0.584 0.344 0.072
#> SRR491033 4 0.0820 0.9081 0.000 0 0.000 0.972 0.016 0.012
#> SRR491034 5 0.4991 0.1676 0.000 0 0.000 0.404 0.524 0.072
#> SRR491035 5 0.3396 0.7810 0.000 0 0.000 0.116 0.812 0.072
#> SRR491036 4 0.1265 0.8935 0.000 0 0.000 0.948 0.008 0.044
#> SRR491037 4 0.0405 0.9169 0.000 0 0.000 0.988 0.004 0.008
#> SRR491038 4 0.1584 0.8786 0.000 0 0.000 0.928 0.008 0.064
#> SRR491039 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491073 1 0.3867 0.0523 0.512 0 0.000 0.000 0.488 0.000
#> SRR491074 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491075 1 0.3847 0.1609 0.544 0 0.000 0.000 0.456 0.000
#> SRR491076 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491088 5 0.2442 0.7683 0.144 0 0.000 0.000 0.852 0.004
#> SRR491089 1 0.0000 0.9679 1.000 0 0.000 0.000 0.000 0.000
#> SRR491090 5 0.2442 0.7683 0.144 0 0.000 0.000 0.852 0.004
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 13175 rows and 123 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 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 0.985 0.993 0.4688 0.528 0.528
#> 3 3 1.000 0.987 0.995 0.3214 0.794 0.630
#> 4 4 1.000 0.990 0.996 0.2190 0.856 0.627
#> 5 5 0.932 0.799 0.926 0.0312 0.992 0.968
#> 6 6 0.875 0.858 0.887 0.0362 0.960 0.836
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
#> SRR445718 2 0.0000 0.981 0.000 1.000
#> SRR445719 2 0.0000 0.981 0.000 1.000
#> SRR445720 2 0.0000 0.981 0.000 1.000
#> SRR445721 2 0.0000 0.981 0.000 1.000
#> SRR445722 2 0.0000 0.981 0.000 1.000
#> SRR445723 2 0.0000 0.981 0.000 1.000
#> SRR445724 2 0.0000 0.981 0.000 1.000
#> SRR445725 2 0.0000 0.981 0.000 1.000
#> SRR445726 2 0.0000 0.981 0.000 1.000
#> SRR445727 2 0.0000 0.981 0.000 1.000
#> SRR445728 2 0.0000 0.981 0.000 1.000
#> SRR445729 2 0.0000 0.981 0.000 1.000
#> SRR445730 1 0.0000 1.000 1.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000
#> SRR490961 2 0.0000 0.981 0.000 1.000
#> SRR490962 2 0.0000 0.981 0.000 1.000
#> SRR490963 2 0.0000 0.981 0.000 1.000
#> SRR490964 2 0.0000 0.981 0.000 1.000
#> SRR490965 2 0.0000 0.981 0.000 1.000
#> SRR490966 2 0.0000 0.981 0.000 1.000
#> SRR490967 2 0.0000 0.981 0.000 1.000
#> SRR490968 2 0.0000 0.981 0.000 1.000
#> SRR490969 2 0.0000 0.981 0.000 1.000
#> SRR490970 2 0.0000 0.981 0.000 1.000
#> SRR490971 2 0.0000 0.981 0.000 1.000
#> SRR490972 2 0.0000 0.981 0.000 1.000
#> SRR490973 2 0.4022 0.910 0.080 0.920
#> SRR490974 2 0.0376 0.979 0.004 0.996
#> SRR490975 2 0.0000 0.981 0.000 1.000
#> SRR490976 2 0.7745 0.724 0.228 0.772
#> SRR490977 2 0.8081 0.691 0.248 0.752
#> SRR490978 2 0.5408 0.864 0.124 0.876
#> SRR490979 2 0.5946 0.840 0.144 0.856
#> SRR490980 2 0.0376 0.979 0.004 0.996
#> SRR490981 2 0.0000 0.981 0.000 1.000
#> SRR490982 2 0.0000 0.981 0.000 1.000
#> SRR490983 2 0.0000 0.981 0.000 1.000
#> SRR490984 2 0.0000 0.981 0.000 1.000
#> SRR490985 2 0.0000 0.981 0.000 1.000
#> SRR490986 2 0.0000 0.981 0.000 1.000
#> SRR490987 2 0.0000 0.981 0.000 1.000
#> SRR490988 2 0.0000 0.981 0.000 1.000
#> SRR490989 2 0.0000 0.981 0.000 1.000
#> SRR490990 2 0.0000 0.981 0.000 1.000
#> SRR490991 2 0.0000 0.981 0.000 1.000
#> SRR490992 2 0.0376 0.979 0.004 0.996
#> SRR490993 1 0.1414 0.979 0.980 0.020
#> SRR490994 1 0.0000 1.000 1.000 0.000
#> SRR490995 2 0.0000 0.981 0.000 1.000
#> SRR490996 1 0.0000 1.000 1.000 0.000
#> SRR490997 1 0.0000 1.000 1.000 0.000
#> SRR490998 1 0.0000 1.000 1.000 0.000
#> SRR491000 2 0.0000 0.981 0.000 1.000
#> SRR491001 1 0.0000 1.000 1.000 0.000
#> SRR491002 1 0.0000 1.000 1.000 0.000
#> SRR491003 1 0.0000 1.000 1.000 0.000
#> SRR491004 1 0.0000 1.000 1.000 0.000
#> SRR491005 1 0.0000 1.000 1.000 0.000
#> SRR491006 1 0.0672 0.992 0.992 0.008
#> SRR491007 1 0.0000 1.000 1.000 0.000
#> SRR491008 1 0.0000 1.000 1.000 0.000
#> SRR491009 1 0.0000 1.000 1.000 0.000
#> SRR491010 1 0.0000 1.000 1.000 0.000
#> SRR491011 1 0.0000 1.000 1.000 0.000
#> SRR491012 1 0.0000 1.000 1.000 0.000
#> SRR491013 1 0.0000 1.000 1.000 0.000
#> SRR491014 1 0.0000 1.000 1.000 0.000
#> SRR491015 1 0.0000 1.000 1.000 0.000
#> SRR491016 1 0.0000 1.000 1.000 0.000
#> SRR491017 1 0.0000 1.000 1.000 0.000
#> SRR491018 1 0.0000 1.000 1.000 0.000
#> SRR491019 1 0.0000 1.000 1.000 0.000
#> SRR491020 1 0.0000 1.000 1.000 0.000
#> SRR491021 1 0.0000 1.000 1.000 0.000
#> SRR491022 1 0.0000 1.000 1.000 0.000
#> SRR491023 1 0.0000 1.000 1.000 0.000
#> SRR491024 1 0.0000 1.000 1.000 0.000
#> SRR491025 1 0.0000 1.000 1.000 0.000
#> SRR491026 1 0.0000 1.000 1.000 0.000
#> SRR491027 1 0.0000 1.000 1.000 0.000
#> SRR491028 1 0.0000 1.000 1.000 0.000
#> SRR491029 1 0.0000 1.000 1.000 0.000
#> SRR491030 1 0.0000 1.000 1.000 0.000
#> SRR491031 1 0.0000 1.000 1.000 0.000
#> SRR491032 1 0.0000 1.000 1.000 0.000
#> SRR491033 1 0.0000 1.000 1.000 0.000
#> SRR491034 1 0.0000 1.000 1.000 0.000
#> SRR491035 1 0.0000 1.000 1.000 0.000
#> SRR491036 1 0.0000 1.000 1.000 0.000
#> SRR491037 1 0.0000 1.000 1.000 0.000
#> SRR491038 1 0.0000 1.000 1.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.000 0.992 0 1.000 0.000
#> SRR445719 2 0.000 0.992 0 1.000 0.000
#> SRR445720 2 0.000 0.992 0 1.000 0.000
#> SRR445721 2 0.000 0.992 0 1.000 0.000
#> SRR445722 2 0.000 0.992 0 1.000 0.000
#> SRR445723 2 0.000 0.992 0 1.000 0.000
#> SRR445724 2 0.000 0.992 0 1.000 0.000
#> SRR445725 2 0.000 0.992 0 1.000 0.000
#> SRR445726 2 0.000 0.992 0 1.000 0.000
#> SRR445727 2 0.000 0.992 0 1.000 0.000
#> SRR445728 2 0.000 0.992 0 1.000 0.000
#> SRR445729 2 0.000 0.992 0 1.000 0.000
#> SRR445730 1 0.000 1.000 1 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0.000 0.000
#> SRR490961 2 0.000 0.992 0 1.000 0.000
#> SRR490962 2 0.000 0.992 0 1.000 0.000
#> SRR490963 2 0.000 0.992 0 1.000 0.000
#> SRR490964 2 0.000 0.992 0 1.000 0.000
#> SRR490965 2 0.000 0.992 0 1.000 0.000
#> SRR490966 2 0.000 0.992 0 1.000 0.000
#> SRR490967 2 0.000 0.992 0 1.000 0.000
#> SRR490968 2 0.000 0.992 0 1.000 0.000
#> SRR490969 2 0.000 0.992 0 1.000 0.000
#> SRR490970 2 0.000 0.992 0 1.000 0.000
#> SRR490971 2 0.000 0.992 0 1.000 0.000
#> SRR490972 2 0.000 0.992 0 1.000 0.000
#> SRR490973 3 0.000 0.986 0 0.000 1.000
#> SRR490974 3 0.000 0.986 0 0.000 1.000
#> SRR490975 3 0.000 0.986 0 0.000 1.000
#> SRR490976 3 0.000 0.986 0 0.000 1.000
#> SRR490977 3 0.000 0.986 0 0.000 1.000
#> SRR490978 3 0.000 0.986 0 0.000 1.000
#> SRR490979 3 0.000 0.986 0 0.000 1.000
#> SRR490980 3 0.000 0.986 0 0.000 1.000
#> SRR490981 2 0.000 0.992 0 1.000 0.000
#> SRR490982 2 0.000 0.992 0 1.000 0.000
#> SRR490983 2 0.000 0.992 0 1.000 0.000
#> SRR490984 2 0.000 0.992 0 1.000 0.000
#> SRR490985 3 0.000 0.986 0 0.000 1.000
#> SRR490986 3 0.000 0.986 0 0.000 1.000
#> SRR490987 3 0.000 0.986 0 0.000 1.000
#> SRR490988 3 0.000 0.986 0 0.000 1.000
#> SRR490989 3 0.000 0.986 0 0.000 1.000
#> SRR490990 3 0.000 0.986 0 0.000 1.000
#> SRR490991 3 0.000 0.986 0 0.000 1.000
#> SRR490992 3 0.000 0.986 0 0.000 1.000
#> SRR490993 3 0.000 0.986 0 0.000 1.000
#> SRR490994 3 0.000 0.986 0 0.000 1.000
#> SRR490995 3 0.615 0.296 0 0.408 0.592
#> SRR490996 3 0.000 0.986 0 0.000 1.000
#> SRR490997 3 0.000 0.986 0 0.000 1.000
#> SRR490998 3 0.000 0.986 0 0.000 1.000
#> SRR491000 2 0.480 0.711 0 0.780 0.220
#> SRR491001 3 0.000 0.986 0 0.000 1.000
#> SRR491002 3 0.000 0.986 0 0.000 1.000
#> SRR491003 3 0.000 0.986 0 0.000 1.000
#> SRR491004 3 0.000 0.986 0 0.000 1.000
#> SRR491005 3 0.000 0.986 0 0.000 1.000
#> SRR491006 3 0.000 0.986 0 0.000 1.000
#> SRR491007 3 0.000 0.986 0 0.000 1.000
#> SRR491008 3 0.000 0.986 0 0.000 1.000
#> SRR491009 1 0.000 1.000 1 0.000 0.000
#> SRR491010 1 0.000 1.000 1 0.000 0.000
#> SRR491011 1 0.000 1.000 1 0.000 0.000
#> SRR491012 1 0.000 1.000 1 0.000 0.000
#> SRR491013 1 0.000 1.000 1 0.000 0.000
#> SRR491014 1 0.000 1.000 1 0.000 0.000
#> SRR491015 1 0.000 1.000 1 0.000 0.000
#> SRR491016 1 0.000 1.000 1 0.000 0.000
#> SRR491017 1 0.000 1.000 1 0.000 0.000
#> SRR491018 1 0.000 1.000 1 0.000 0.000
#> SRR491019 1 0.000 1.000 1 0.000 0.000
#> SRR491020 1 0.000 1.000 1 0.000 0.000
#> SRR491021 1 0.000 1.000 1 0.000 0.000
#> SRR491022 1 0.000 1.000 1 0.000 0.000
#> SRR491023 1 0.000 1.000 1 0.000 0.000
#> SRR491024 1 0.000 1.000 1 0.000 0.000
#> SRR491025 1 0.000 1.000 1 0.000 0.000
#> SRR491026 1 0.000 1.000 1 0.000 0.000
#> SRR491027 1 0.000 1.000 1 0.000 0.000
#> SRR491028 1 0.000 1.000 1 0.000 0.000
#> SRR491029 1 0.000 1.000 1 0.000 0.000
#> SRR491030 1 0.000 1.000 1 0.000 0.000
#> SRR491031 1 0.000 1.000 1 0.000 0.000
#> SRR491032 1 0.000 1.000 1 0.000 0.000
#> SRR491033 1 0.000 1.000 1 0.000 0.000
#> SRR491034 1 0.000 1.000 1 0.000 0.000
#> SRR491035 1 0.000 1.000 1 0.000 0.000
#> SRR491036 1 0.000 1.000 1 0.000 0.000
#> SRR491037 1 0.000 1.000 1 0.000 0.000
#> SRR491038 1 0.000 1.000 1 0.000 0.000
#> SRR491039 1 0.000 1.000 1 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490982 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490985 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490995 3 0.2081 0.900 0.000 0.084 0.916 0.000
#> SRR490996 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491000 3 0.4817 0.374 0.000 0.388 0.612 0.000
#> SRR491001 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 0.984 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491022 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491023 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491024 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491029 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491031 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491032 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491033 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491034 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491035 4 0.0469 0.987 0.012 0.000 0.000 0.988
#> SRR491036 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491037 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491090 1 0.0000 1.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0609 0.9705 0.980 0.000 0.000 0.000 0.020
#> SRR445731 1 0.0609 0.9705 0.980 0.000 0.000 0.000 0.020
#> SRR490961 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.1197 0.7398 0.000 0.000 0.952 0.000 0.048
#> SRR490974 3 0.1792 0.7099 0.000 0.000 0.916 0.000 0.084
#> SRR490975 3 0.2127 0.6815 0.000 0.000 0.892 0.000 0.108
#> SRR490976 3 0.1121 0.7422 0.000 0.000 0.956 0.000 0.044
#> SRR490977 3 0.1121 0.7422 0.000 0.000 0.956 0.000 0.044
#> SRR490978 3 0.1121 0.7422 0.000 0.000 0.956 0.000 0.044
#> SRR490979 3 0.1121 0.7422 0.000 0.000 0.956 0.000 0.044
#> SRR490980 3 0.1732 0.7139 0.000 0.000 0.920 0.000 0.080
#> SRR490981 2 0.4300 0.1534 0.000 0.524 0.000 0.000 0.476
#> SRR490982 2 0.4748 0.0341 0.000 0.492 0.016 0.000 0.492
#> SRR490983 2 0.4304 0.1278 0.000 0.516 0.000 0.000 0.484
#> SRR490984 2 0.4302 0.1409 0.000 0.520 0.000 0.000 0.480
#> SRR490985 3 0.4305 -0.5228 0.000 0.000 0.512 0.000 0.488
#> SRR490986 3 0.4305 -0.5228 0.000 0.000 0.512 0.000 0.488
#> SRR490987 3 0.4227 -0.3088 0.000 0.000 0.580 0.000 0.420
#> SRR490988 3 0.4305 -0.5228 0.000 0.000 0.512 0.000 0.488
#> SRR490989 3 0.4300 -0.4893 0.000 0.000 0.524 0.000 0.476
#> SRR490990 3 0.4291 -0.4549 0.000 0.000 0.536 0.000 0.464
#> SRR490991 3 0.4305 -0.5228 0.000 0.000 0.512 0.000 0.488
#> SRR490992 3 0.2929 0.5612 0.000 0.000 0.820 0.000 0.180
#> SRR490993 3 0.0290 0.7506 0.000 0.000 0.992 0.000 0.008
#> SRR490994 3 0.0404 0.7487 0.000 0.000 0.988 0.000 0.012
#> SRR490995 5 0.5381 0.6406 0.000 0.056 0.428 0.000 0.516
#> SRR490996 3 0.0162 0.7508 0.000 0.000 0.996 0.000 0.004
#> SRR490997 3 0.0404 0.7487 0.000 0.000 0.988 0.000 0.012
#> SRR490998 3 0.0404 0.7487 0.000 0.000 0.988 0.000 0.012
#> SRR491000 5 0.6229 0.7137 0.000 0.164 0.320 0.000 0.516
#> SRR491001 3 0.0609 0.7436 0.000 0.000 0.980 0.000 0.020
#> SRR491002 3 0.0510 0.7465 0.000 0.000 0.984 0.000 0.016
#> SRR491003 3 0.0290 0.7501 0.000 0.000 0.992 0.000 0.008
#> SRR491004 3 0.0290 0.7501 0.000 0.000 0.992 0.000 0.008
#> SRR491005 3 0.0703 0.7400 0.000 0.000 0.976 0.000 0.024
#> SRR491006 3 0.0000 0.7512 0.000 0.000 1.000 0.000 0.000
#> SRR491007 3 0.0000 0.7512 0.000 0.000 1.000 0.000 0.000
#> SRR491008 3 0.0609 0.7436 0.000 0.000 0.980 0.000 0.020
#> SRR491009 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.2921 0.8857 0.004 0.000 0.004 0.844 0.148
#> SRR491023 4 0.2329 0.9084 0.000 0.000 0.000 0.876 0.124
#> SRR491024 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.1608 0.9400 0.000 0.000 0.000 0.928 0.072
#> SRR491029 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.2377 0.9069 0.000 0.000 0.000 0.872 0.128
#> SRR491032 4 0.1544 0.9422 0.000 0.000 0.000 0.932 0.068
#> SRR491033 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.2471 0.9007 0.000 0.000 0.000 0.864 0.136
#> SRR491035 4 0.2605 0.8918 0.000 0.000 0.000 0.852 0.148
#> SRR491036 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.9767 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0404 0.9727 0.988 0.000 0.000 0.000 0.012
#> SRR491040 1 0.0794 0.9677 0.972 0.000 0.000 0.000 0.028
#> SRR491041 1 0.0794 0.9677 0.972 0.000 0.000 0.000 0.028
#> SRR491042 1 0.0609 0.9705 0.980 0.000 0.000 0.000 0.020
#> SRR491043 1 0.0794 0.9677 0.972 0.000 0.000 0.000 0.028
#> SRR491045 1 0.0609 0.9705 0.980 0.000 0.000 0.000 0.020
#> SRR491065 1 0.1121 0.9626 0.956 0.000 0.000 0.000 0.044
#> SRR491066 1 0.0963 0.9658 0.964 0.000 0.000 0.000 0.036
#> SRR491067 1 0.0963 0.9658 0.964 0.000 0.000 0.000 0.036
#> SRR491068 1 0.0000 0.9734 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0794 0.9685 0.972 0.000 0.000 0.000 0.028
#> SRR491070 1 0.0404 0.9725 0.988 0.000 0.000 0.000 0.012
#> SRR491071 1 0.0404 0.9725 0.988 0.000 0.000 0.000 0.012
#> SRR491072 1 0.0404 0.9725 0.988 0.000 0.000 0.000 0.012
#> SRR491073 1 0.2179 0.9242 0.888 0.000 0.000 0.000 0.112
#> SRR491074 1 0.0290 0.9729 0.992 0.000 0.000 0.000 0.008
#> SRR491075 1 0.2179 0.9242 0.888 0.000 0.000 0.000 0.112
#> SRR491076 1 0.1965 0.9345 0.904 0.000 0.000 0.000 0.096
#> SRR491077 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491078 1 0.0404 0.9725 0.988 0.000 0.000 0.000 0.012
#> SRR491079 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491080 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491081 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491082 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491083 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491084 1 0.0290 0.9732 0.992 0.000 0.000 0.000 0.008
#> SRR491085 1 0.0404 0.9727 0.988 0.000 0.000 0.000 0.012
#> SRR491086 1 0.1908 0.9370 0.908 0.000 0.000 0.000 0.092
#> SRR491087 1 0.0703 0.9697 0.976 0.000 0.000 0.000 0.024
#> SRR491088 1 0.2179 0.9242 0.888 0.000 0.000 0.000 0.112
#> SRR491089 1 0.0404 0.9725 0.988 0.000 0.000 0.000 0.012
#> SRR491090 1 0.2179 0.9242 0.888 0.000 0.000 0.000 0.112
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445719 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445720 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445721 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445722 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445723 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445724 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445725 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445726 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445727 2 0.0146 0.997 0.000 0.996 0.000 0.000 0.004 NA
#> SRR445728 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445729 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445730 1 0.1866 0.840 0.908 0.000 0.000 0.000 0.008 NA
#> SRR445731 1 0.1643 0.847 0.924 0.000 0.000 0.000 0.008 NA
#> SRR490961 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490962 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490963 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490964 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490965 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490966 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490967 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490968 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490969 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490970 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490971 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490972 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490973 3 0.2454 0.832 0.000 0.000 0.840 0.000 0.160 NA
#> SRR490974 3 0.3050 0.738 0.000 0.000 0.764 0.000 0.236 NA
#> SRR490975 3 0.3309 0.660 0.000 0.000 0.720 0.000 0.280 NA
#> SRR490976 3 0.2260 0.848 0.000 0.000 0.860 0.000 0.140 NA
#> SRR490977 3 0.2300 0.846 0.000 0.000 0.856 0.000 0.144 NA
#> SRR490978 3 0.2300 0.846 0.000 0.000 0.856 0.000 0.144 NA
#> SRR490979 3 0.2260 0.848 0.000 0.000 0.860 0.000 0.140 NA
#> SRR490980 3 0.2664 0.807 0.000 0.000 0.816 0.000 0.184 NA
#> SRR490981 5 0.3593 0.669 0.000 0.228 0.000 0.000 0.748 NA
#> SRR490982 5 0.3385 0.713 0.000 0.180 0.000 0.000 0.788 NA
#> SRR490983 5 0.3572 0.699 0.000 0.204 0.000 0.000 0.764 NA
#> SRR490984 5 0.3529 0.696 0.000 0.208 0.000 0.000 0.764 NA
#> SRR490985 5 0.3023 0.752 0.000 0.000 0.232 0.000 0.768 NA
#> SRR490986 5 0.2912 0.757 0.000 0.000 0.216 0.000 0.784 NA
#> SRR490987 5 0.3634 0.538 0.000 0.000 0.356 0.000 0.644 NA
#> SRR490988 5 0.3050 0.749 0.000 0.000 0.236 0.000 0.764 NA
#> SRR490989 5 0.3151 0.732 0.000 0.000 0.252 0.000 0.748 NA
#> SRR490990 5 0.3351 0.681 0.000 0.000 0.288 0.000 0.712 NA
#> SRR490991 5 0.3023 0.752 0.000 0.000 0.232 0.000 0.768 NA
#> SRR490992 3 0.3756 0.482 0.000 0.000 0.644 0.000 0.352 NA
#> SRR490993 3 0.0713 0.891 0.000 0.000 0.972 0.000 0.028 NA
#> SRR490994 3 0.0146 0.888 0.000 0.000 0.996 0.000 0.004 NA
#> SRR490995 5 0.3549 0.757 0.000 0.016 0.128 0.000 0.812 NA
#> SRR490996 3 0.0260 0.892 0.000 0.000 0.992 0.000 0.008 NA
#> SRR490997 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490998 3 0.0146 0.888 0.000 0.000 0.996 0.000 0.004 NA
#> SRR491000 5 0.3805 0.755 0.000 0.056 0.088 0.000 0.812 NA
#> SRR491001 3 0.0146 0.888 0.000 0.000 0.996 0.000 0.004 NA
#> SRR491002 3 0.0260 0.886 0.000 0.000 0.992 0.000 0.008 NA
#> SRR491003 3 0.0363 0.893 0.000 0.000 0.988 0.000 0.012 NA
#> SRR491004 3 0.0363 0.893 0.000 0.000 0.988 0.000 0.012 NA
#> SRR491005 3 0.0260 0.886 0.000 0.000 0.992 0.000 0.008 NA
#> SRR491006 3 0.0458 0.893 0.000 0.000 0.984 0.000 0.016 NA
#> SRR491007 3 0.0458 0.893 0.000 0.000 0.984 0.000 0.016 NA
#> SRR491008 3 0.0000 0.890 0.000 0.000 1.000 0.000 0.000 NA
#> SRR491009 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491010 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491011 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491012 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491013 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491014 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491015 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491016 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491017 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491018 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491019 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491020 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491021 4 0.0146 0.915 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491022 4 0.5208 0.583 0.036 0.000 0.004 0.532 0.024 NA
#> SRR491023 4 0.4487 0.664 0.004 0.000 0.004 0.608 0.024 NA
#> SRR491024 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491025 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491026 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491027 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491028 4 0.4002 0.704 0.000 0.000 0.000 0.660 0.020 NA
#> SRR491029 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491030 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491031 4 0.4364 0.664 0.000 0.000 0.004 0.608 0.024 NA
#> SRR491032 4 0.3952 0.713 0.000 0.000 0.000 0.672 0.020 NA
#> SRR491033 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491034 4 0.4519 0.642 0.008 0.000 0.000 0.584 0.024 NA
#> SRR491035 4 0.4301 0.642 0.000 0.000 0.000 0.584 0.024 NA
#> SRR491036 4 0.0146 0.915 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491037 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491038 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491039 1 0.1049 0.861 0.960 0.000 0.000 0.000 0.008 NA
#> SRR491040 1 0.2170 0.828 0.888 0.000 0.000 0.000 0.012 NA
#> SRR491041 1 0.1701 0.846 0.920 0.000 0.000 0.000 0.008 NA
#> SRR491042 1 0.1584 0.849 0.928 0.000 0.000 0.000 0.008 NA
#> SRR491043 1 0.2070 0.833 0.896 0.000 0.000 0.000 0.012 NA
#> SRR491045 1 0.1462 0.852 0.936 0.000 0.000 0.000 0.008 NA
#> SRR491065 1 0.3309 0.805 0.720 0.000 0.000 0.000 0.000 NA
#> SRR491066 1 0.3175 0.818 0.744 0.000 0.000 0.000 0.000 NA
#> SRR491067 1 0.3101 0.823 0.756 0.000 0.000 0.000 0.000 NA
#> SRR491068 1 0.0363 0.870 0.988 0.000 0.000 0.000 0.000 NA
#> SRR491069 1 0.3126 0.821 0.752 0.000 0.000 0.000 0.000 NA
#> SRR491070 1 0.1863 0.865 0.896 0.000 0.000 0.000 0.000 NA
#> SRR491071 1 0.2003 0.863 0.884 0.000 0.000 0.000 0.000 NA
#> SRR491072 1 0.2378 0.854 0.848 0.000 0.000 0.000 0.000 NA
#> SRR491073 1 0.3899 0.723 0.592 0.000 0.000 0.000 0.004 NA
#> SRR491074 1 0.1327 0.869 0.936 0.000 0.000 0.000 0.000 NA
#> SRR491075 1 0.3899 0.723 0.592 0.000 0.000 0.000 0.004 NA
#> SRR491076 1 0.3862 0.735 0.608 0.000 0.000 0.000 0.004 NA
#> SRR491077 1 0.0291 0.869 0.992 0.000 0.000 0.000 0.004 NA
#> SRR491078 1 0.1556 0.868 0.920 0.000 0.000 0.000 0.000 NA
#> SRR491079 1 0.0405 0.867 0.988 0.000 0.000 0.000 0.004 NA
#> SRR491080 1 0.0405 0.870 0.988 0.000 0.000 0.000 0.004 NA
#> SRR491081 1 0.0260 0.870 0.992 0.000 0.000 0.000 0.000 NA
#> SRR491082 1 0.0547 0.871 0.980 0.000 0.000 0.000 0.000 NA
#> SRR491083 1 0.0622 0.866 0.980 0.000 0.000 0.000 0.008 NA
#> SRR491084 1 0.0291 0.868 0.992 0.000 0.000 0.000 0.004 NA
#> SRR491085 1 0.1196 0.858 0.952 0.000 0.000 0.000 0.008 NA
#> SRR491086 1 0.3841 0.741 0.616 0.000 0.000 0.000 0.004 NA
#> SRR491087 1 0.2996 0.829 0.772 0.000 0.000 0.000 0.000 NA
#> SRR491088 1 0.3899 0.723 0.592 0.000 0.000 0.000 0.004 NA
#> SRR491089 1 0.2048 0.862 0.880 0.000 0.000 0.000 0.000 NA
#> SRR491090 1 0.3899 0.723 0.592 0.000 0.000 0.000 0.004 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.
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 13175 rows and 123 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 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 0.996 0.994 0.35571 0.645 0.645
#> 3 3 1 0.993 0.997 0.77040 0.724 0.572
#> 4 4 1 0.993 0.996 0.19910 0.876 0.664
#> 5 5 1 0.957 0.983 0.03161 0.981 0.921
#> 6 6 1 0.956 0.984 0.00994 0.992 0.966
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
#> SRR445718 2 0.0000 1.000 0.000 1.000
#> SRR445719 2 0.0000 1.000 0.000 1.000
#> SRR445720 2 0.0000 1.000 0.000 1.000
#> SRR445721 2 0.0000 1.000 0.000 1.000
#> SRR445722 2 0.0000 1.000 0.000 1.000
#> SRR445723 2 0.0000 1.000 0.000 1.000
#> SRR445724 2 0.0000 1.000 0.000 1.000
#> SRR445725 2 0.0000 1.000 0.000 1.000
#> SRR445726 2 0.0000 1.000 0.000 1.000
#> SRR445727 2 0.0000 1.000 0.000 1.000
#> SRR445728 2 0.0000 1.000 0.000 1.000
#> SRR445729 2 0.0000 1.000 0.000 1.000
#> SRR445730 1 0.0000 0.992 1.000 0.000
#> SRR445731 1 0.0000 0.992 1.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000
#> SRR490962 2 0.0000 1.000 0.000 1.000
#> SRR490963 2 0.0000 1.000 0.000 1.000
#> SRR490964 2 0.0000 1.000 0.000 1.000
#> SRR490965 2 0.0000 1.000 0.000 1.000
#> SRR490966 2 0.0000 1.000 0.000 1.000
#> SRR490967 2 0.0000 1.000 0.000 1.000
#> SRR490968 2 0.0000 1.000 0.000 1.000
#> SRR490969 2 0.0000 1.000 0.000 1.000
#> SRR490970 2 0.0000 1.000 0.000 1.000
#> SRR490971 2 0.0000 1.000 0.000 1.000
#> SRR490972 2 0.0000 1.000 0.000 1.000
#> SRR490973 1 0.0938 0.996 0.988 0.012
#> SRR490974 1 0.0938 0.996 0.988 0.012
#> SRR490975 1 0.0938 0.996 0.988 0.012
#> SRR490976 1 0.0938 0.996 0.988 0.012
#> SRR490977 1 0.0938 0.996 0.988 0.012
#> SRR490978 1 0.0938 0.996 0.988 0.012
#> SRR490979 1 0.0938 0.996 0.988 0.012
#> SRR490980 1 0.0938 0.996 0.988 0.012
#> SRR490981 2 0.0000 1.000 0.000 1.000
#> SRR490982 2 0.0000 1.000 0.000 1.000
#> SRR490983 2 0.0000 1.000 0.000 1.000
#> SRR490984 2 0.0000 1.000 0.000 1.000
#> SRR490985 1 0.0938 0.996 0.988 0.012
#> SRR490986 1 0.0938 0.996 0.988 0.012
#> SRR490987 1 0.0938 0.996 0.988 0.012
#> SRR490988 1 0.0938 0.996 0.988 0.012
#> SRR490989 1 0.0938 0.996 0.988 0.012
#> SRR490990 1 0.0938 0.996 0.988 0.012
#> SRR490991 1 0.0938 0.996 0.988 0.012
#> SRR490992 1 0.0938 0.996 0.988 0.012
#> SRR490993 1 0.0938 0.996 0.988 0.012
#> SRR490994 1 0.0938 0.996 0.988 0.012
#> SRR490995 1 0.0938 0.996 0.988 0.012
#> SRR490996 1 0.0938 0.996 0.988 0.012
#> SRR490997 1 0.0938 0.996 0.988 0.012
#> SRR490998 1 0.0938 0.996 0.988 0.012
#> SRR491000 1 0.0938 0.996 0.988 0.012
#> SRR491001 1 0.0938 0.996 0.988 0.012
#> SRR491002 1 0.0938 0.996 0.988 0.012
#> SRR491003 1 0.0938 0.996 0.988 0.012
#> SRR491004 1 0.0938 0.996 0.988 0.012
#> SRR491005 1 0.0938 0.996 0.988 0.012
#> SRR491006 1 0.0938 0.996 0.988 0.012
#> SRR491007 1 0.0938 0.996 0.988 0.012
#> SRR491008 1 0.0938 0.996 0.988 0.012
#> SRR491009 1 0.0938 0.996 0.988 0.012
#> SRR491010 1 0.0938 0.996 0.988 0.012
#> SRR491011 1 0.0938 0.996 0.988 0.012
#> SRR491012 1 0.0938 0.996 0.988 0.012
#> SRR491013 1 0.0938 0.996 0.988 0.012
#> SRR491014 1 0.0938 0.996 0.988 0.012
#> SRR491015 1 0.0938 0.996 0.988 0.012
#> SRR491016 1 0.0938 0.996 0.988 0.012
#> SRR491017 1 0.0938 0.996 0.988 0.012
#> SRR491018 1 0.0938 0.996 0.988 0.012
#> SRR491019 1 0.0938 0.996 0.988 0.012
#> SRR491020 1 0.0938 0.996 0.988 0.012
#> SRR491021 1 0.0938 0.996 0.988 0.012
#> SRR491022 1 0.0938 0.996 0.988 0.012
#> SRR491023 1 0.0938 0.996 0.988 0.012
#> SRR491024 1 0.0938 0.996 0.988 0.012
#> SRR491025 1 0.0938 0.996 0.988 0.012
#> SRR491026 1 0.0938 0.996 0.988 0.012
#> SRR491027 1 0.0938 0.996 0.988 0.012
#> SRR491028 1 0.0938 0.996 0.988 0.012
#> SRR491029 1 0.0938 0.996 0.988 0.012
#> SRR491030 1 0.0938 0.996 0.988 0.012
#> SRR491031 1 0.0938 0.996 0.988 0.012
#> SRR491032 1 0.0938 0.996 0.988 0.012
#> SRR491033 1 0.0938 0.996 0.988 0.012
#> SRR491034 1 0.0938 0.996 0.988 0.012
#> SRR491035 1 0.0938 0.996 0.988 0.012
#> SRR491036 1 0.0938 0.996 0.988 0.012
#> SRR491037 1 0.0938 0.996 0.988 0.012
#> SRR491038 1 0.0938 0.996 0.988 0.012
#> SRR491039 1 0.0000 0.992 1.000 0.000
#> SRR491040 1 0.0000 0.992 1.000 0.000
#> SRR491041 1 0.0000 0.992 1.000 0.000
#> SRR491042 1 0.0000 0.992 1.000 0.000
#> SRR491043 1 0.0000 0.992 1.000 0.000
#> SRR491045 1 0.0000 0.992 1.000 0.000
#> SRR491065 1 0.0000 0.992 1.000 0.000
#> SRR491066 1 0.0000 0.992 1.000 0.000
#> SRR491067 1 0.0000 0.992 1.000 0.000
#> SRR491068 1 0.0000 0.992 1.000 0.000
#> SRR491069 1 0.0000 0.992 1.000 0.000
#> SRR491070 1 0.0000 0.992 1.000 0.000
#> SRR491071 1 0.0000 0.992 1.000 0.000
#> SRR491072 1 0.0000 0.992 1.000 0.000
#> SRR491073 1 0.0000 0.992 1.000 0.000
#> SRR491074 1 0.0000 0.992 1.000 0.000
#> SRR491075 1 0.0000 0.992 1.000 0.000
#> SRR491076 1 0.0000 0.992 1.000 0.000
#> SRR491077 1 0.0000 0.992 1.000 0.000
#> SRR491078 1 0.0000 0.992 1.000 0.000
#> SRR491079 1 0.0000 0.992 1.000 0.000
#> SRR491080 1 0.0000 0.992 1.000 0.000
#> SRR491081 1 0.0000 0.992 1.000 0.000
#> SRR491082 1 0.0000 0.992 1.000 0.000
#> SRR491083 1 0.0000 0.992 1.000 0.000
#> SRR491084 1 0.0000 0.992 1.000 0.000
#> SRR491085 1 0.0000 0.992 1.000 0.000
#> SRR491086 1 0.0000 0.992 1.000 0.000
#> SRR491087 1 0.0000 0.992 1.000 0.000
#> SRR491088 1 0.0000 0.992 1.000 0.000
#> SRR491089 1 0.0000 0.992 1.000 0.000
#> SRR491090 1 0.0000 0.992 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 1.000 0.000 1 0.000
#> SRR445719 2 0.000 1.000 0.000 1 0.000
#> SRR445720 2 0.000 1.000 0.000 1 0.000
#> SRR445721 2 0.000 1.000 0.000 1 0.000
#> SRR445722 2 0.000 1.000 0.000 1 0.000
#> SRR445723 2 0.000 1.000 0.000 1 0.000
#> SRR445724 2 0.000 1.000 0.000 1 0.000
#> SRR445725 2 0.000 1.000 0.000 1 0.000
#> SRR445726 2 0.000 1.000 0.000 1 0.000
#> SRR445727 2 0.000 1.000 0.000 1 0.000
#> SRR445728 2 0.000 1.000 0.000 1 0.000
#> SRR445729 2 0.000 1.000 0.000 1 0.000
#> SRR445730 1 0.000 0.986 1.000 0 0.000
#> SRR445731 1 0.000 0.986 1.000 0 0.000
#> SRR490961 2 0.000 1.000 0.000 1 0.000
#> SRR490962 2 0.000 1.000 0.000 1 0.000
#> SRR490963 2 0.000 1.000 0.000 1 0.000
#> SRR490964 2 0.000 1.000 0.000 1 0.000
#> SRR490965 2 0.000 1.000 0.000 1 0.000
#> SRR490966 2 0.000 1.000 0.000 1 0.000
#> SRR490967 2 0.000 1.000 0.000 1 0.000
#> SRR490968 2 0.000 1.000 0.000 1 0.000
#> SRR490969 2 0.000 1.000 0.000 1 0.000
#> SRR490970 2 0.000 1.000 0.000 1 0.000
#> SRR490971 2 0.000 1.000 0.000 1 0.000
#> SRR490972 2 0.000 1.000 0.000 1 0.000
#> SRR490973 3 0.000 1.000 0.000 0 1.000
#> SRR490974 3 0.000 1.000 0.000 0 1.000
#> SRR490975 3 0.000 1.000 0.000 0 1.000
#> SRR490976 3 0.000 1.000 0.000 0 1.000
#> SRR490977 3 0.000 1.000 0.000 0 1.000
#> SRR490978 3 0.000 1.000 0.000 0 1.000
#> SRR490979 3 0.000 1.000 0.000 0 1.000
#> SRR490980 3 0.000 1.000 0.000 0 1.000
#> SRR490981 2 0.000 1.000 0.000 1 0.000
#> SRR490982 2 0.000 1.000 0.000 1 0.000
#> SRR490983 2 0.000 1.000 0.000 1 0.000
#> SRR490984 2 0.000 1.000 0.000 1 0.000
#> SRR490985 3 0.000 1.000 0.000 0 1.000
#> SRR490986 3 0.000 1.000 0.000 0 1.000
#> SRR490987 3 0.000 1.000 0.000 0 1.000
#> SRR490988 3 0.000 1.000 0.000 0 1.000
#> SRR490989 3 0.000 1.000 0.000 0 1.000
#> SRR490990 3 0.000 1.000 0.000 0 1.000
#> SRR490991 3 0.000 1.000 0.000 0 1.000
#> SRR490992 3 0.000 1.000 0.000 0 1.000
#> SRR490993 3 0.000 1.000 0.000 0 1.000
#> SRR490994 3 0.000 1.000 0.000 0 1.000
#> SRR490995 3 0.000 1.000 0.000 0 1.000
#> SRR490996 3 0.000 1.000 0.000 0 1.000
#> SRR490997 3 0.000 1.000 0.000 0 1.000
#> SRR490998 3 0.000 1.000 0.000 0 1.000
#> SRR491000 3 0.000 1.000 0.000 0 1.000
#> SRR491001 3 0.000 1.000 0.000 0 1.000
#> SRR491002 3 0.000 1.000 0.000 0 1.000
#> SRR491003 3 0.000 1.000 0.000 0 1.000
#> SRR491004 3 0.000 1.000 0.000 0 1.000
#> SRR491005 3 0.000 1.000 0.000 0 1.000
#> SRR491006 3 0.000 1.000 0.000 0 1.000
#> SRR491007 3 0.000 1.000 0.000 0 1.000
#> SRR491008 3 0.000 1.000 0.000 0 1.000
#> SRR491009 3 0.000 1.000 0.000 0 1.000
#> SRR491010 3 0.000 1.000 0.000 0 1.000
#> SRR491011 3 0.000 1.000 0.000 0 1.000
#> SRR491012 3 0.000 1.000 0.000 0 1.000
#> SRR491013 3 0.000 1.000 0.000 0 1.000
#> SRR491014 3 0.000 1.000 0.000 0 1.000
#> SRR491015 3 0.000 1.000 0.000 0 1.000
#> SRR491016 3 0.000 1.000 0.000 0 1.000
#> SRR491017 3 0.000 1.000 0.000 0 1.000
#> SRR491018 3 0.000 1.000 0.000 0 1.000
#> SRR491019 3 0.000 1.000 0.000 0 1.000
#> SRR491020 3 0.000 1.000 0.000 0 1.000
#> SRR491021 3 0.000 1.000 0.000 0 1.000
#> SRR491022 3 0.000 1.000 0.000 0 1.000
#> SRR491023 3 0.000 1.000 0.000 0 1.000
#> SRR491024 3 0.000 1.000 0.000 0 1.000
#> SRR491025 3 0.000 1.000 0.000 0 1.000
#> SRR491026 3 0.000 1.000 0.000 0 1.000
#> SRR491027 3 0.000 1.000 0.000 0 1.000
#> SRR491028 3 0.000 1.000 0.000 0 1.000
#> SRR491029 3 0.000 1.000 0.000 0 1.000
#> SRR491030 3 0.000 1.000 0.000 0 1.000
#> SRR491031 3 0.000 1.000 0.000 0 1.000
#> SRR491032 3 0.000 1.000 0.000 0 1.000
#> SRR491033 3 0.000 1.000 0.000 0 1.000
#> SRR491034 3 0.000 1.000 0.000 0 1.000
#> SRR491035 3 0.000 1.000 0.000 0 1.000
#> SRR491036 3 0.000 1.000 0.000 0 1.000
#> SRR491037 3 0.000 1.000 0.000 0 1.000
#> SRR491038 3 0.000 1.000 0.000 0 1.000
#> SRR491039 1 0.000 0.986 1.000 0 0.000
#> SRR491040 1 0.000 0.986 1.000 0 0.000
#> SRR491041 1 0.000 0.986 1.000 0 0.000
#> SRR491042 1 0.000 0.986 1.000 0 0.000
#> SRR491043 1 0.000 0.986 1.000 0 0.000
#> SRR491045 1 0.000 0.986 1.000 0 0.000
#> SRR491065 1 0.000 0.986 1.000 0 0.000
#> SRR491066 1 0.000 0.986 1.000 0 0.000
#> SRR491067 1 0.000 0.986 1.000 0 0.000
#> SRR491068 1 0.000 0.986 1.000 0 0.000
#> SRR491069 1 0.000 0.986 1.000 0 0.000
#> SRR491070 1 0.000 0.986 1.000 0 0.000
#> SRR491071 1 0.000 0.986 1.000 0 0.000
#> SRR491072 1 0.000 0.986 1.000 0 0.000
#> SRR491073 1 0.288 0.887 0.904 0 0.096
#> SRR491074 1 0.000 0.986 1.000 0 0.000
#> SRR491075 1 0.288 0.887 0.904 0 0.096
#> SRR491076 1 0.000 0.986 1.000 0 0.000
#> SRR491077 1 0.000 0.986 1.000 0 0.000
#> SRR491078 1 0.000 0.986 1.000 0 0.000
#> SRR491079 1 0.000 0.986 1.000 0 0.000
#> SRR491080 1 0.000 0.986 1.000 0 0.000
#> SRR491081 1 0.000 0.986 1.000 0 0.000
#> SRR491082 1 0.000 0.986 1.000 0 0.000
#> SRR491083 1 0.000 0.986 1.000 0 0.000
#> SRR491084 1 0.000 0.986 1.000 0 0.000
#> SRR491085 1 0.000 0.986 1.000 0 0.000
#> SRR491086 1 0.000 0.986 1.000 0 0.000
#> SRR491087 1 0.000 0.986 1.000 0 0.000
#> SRR491088 1 0.288 0.887 0.904 0 0.096
#> SRR491089 1 0.000 0.986 1.000 0 0.000
#> SRR491090 1 0.288 0.887 0.904 0 0.096
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR445731 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490977 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490978 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490985 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490993 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490994 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490995 3 0.0817 0.976 0.000 0 0.976 0.024
#> SRR490996 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490997 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR490998 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491000 3 0.0817 0.976 0.000 0 0.976 0.024
#> SRR491001 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491002 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491003 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491004 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491005 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491006 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491007 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491008 3 0.0000 0.998 0.000 0 1.000 0.000
#> SRR491009 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491010 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491011 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491012 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491013 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491014 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491015 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491016 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491017 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491018 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491019 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491020 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491021 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491022 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491023 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491024 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491025 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491026 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491027 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491028 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491029 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491030 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491031 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491032 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491033 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491034 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491035 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491036 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491037 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491038 4 0.0000 1.000 0.000 0 0.000 1.000
#> SRR491039 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491040 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491041 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491042 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491043 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491045 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491065 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491066 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491067 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491068 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491069 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491070 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491071 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491072 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491073 1 0.2281 0.903 0.904 0 0.000 0.096
#> SRR491074 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491075 1 0.2281 0.903 0.904 0 0.000 0.096
#> SRR491076 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491077 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491078 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491079 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491080 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491081 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491082 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491083 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491084 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491085 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491086 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491087 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491088 1 0.2281 0.903 0.904 0 0.000 0.096
#> SRR491089 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491090 1 0.2281 0.903 0.904 0 0.000 0.096
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490974 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490975 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490976 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490977 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490978 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490979 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490980 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490985 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490986 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490987 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490988 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490989 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490990 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490991 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490992 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490993 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490994 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490995 3 0.0703 0.969 0.000 0 0.976 0.024 0.000
#> SRR490996 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490997 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR490998 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491000 3 0.0703 0.969 0.000 0 0.976 0.024 0.000
#> SRR491001 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491002 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491003 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491004 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491005 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491006 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491007 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491008 3 0.0000 0.998 0.000 0 1.000 0.000 0.000
#> SRR491009 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491010 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491011 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491012 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491013 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491014 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491015 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491016 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491017 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491018 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491019 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491020 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491021 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491022 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491023 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491024 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491025 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491026 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491027 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491028 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491029 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491030 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491031 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491032 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491033 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491034 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491035 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491036 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491037 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491038 4 0.0000 1.000 0.000 0 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491066 1 0.4287 0.210 0.540 0 0.000 0.000 0.460
#> SRR491067 1 0.4287 0.210 0.540 0 0.000 0.000 0.460
#> SRR491068 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491069 1 0.4192 0.356 0.596 0 0.000 0.000 0.404
#> SRR491070 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491071 1 0.1197 0.890 0.952 0 0.000 0.000 0.048
#> SRR491072 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491073 5 0.0000 0.964 0.000 0 0.000 0.000 1.000
#> SRR491074 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491075 5 0.0000 0.964 0.000 0 0.000 0.000 1.000
#> SRR491076 1 0.1197 0.890 0.952 0 0.000 0.000 0.048
#> SRR491077 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491086 5 0.2329 0.837 0.124 0 0.000 0.000 0.876
#> SRR491087 1 0.4287 0.209 0.540 0 0.000 0.000 0.460
#> SRR491088 5 0.0000 0.964 0.000 0 0.000 0.000 1.000
#> SRR491089 1 0.0000 0.927 1.000 0 0.000 0.000 0.000
#> SRR491090 5 0.0000 0.964 0.000 0 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445719 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445720 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445721 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445722 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445723 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445724 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445725 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445726 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445727 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445728 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445729 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR445730 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR445731 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR490961 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490962 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490963 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490964 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490965 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490966 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490967 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490968 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490969 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490970 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490971 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490972 2 0.0000 0.999 0.000 1.000 0.000 0 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490981 2 0.0146 0.997 0.000 0.996 0.000 0 0.000 0.004
#> SRR490982 2 0.0146 0.997 0.000 0.996 0.000 0 0.000 0.004
#> SRR490983 2 0.0146 0.997 0.000 0.996 0.000 0 0.000 0.004
#> SRR490984 2 0.0146 0.997 0.000 0.996 0.000 0 0.000 0.004
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490995 6 0.0146 1.000 0.000 0.000 0.004 0 0.000 0.996
#> SRR490996 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491000 6 0.0146 1.000 0.000 0.000 0.004 0 0.000 0.996
#> SRR491001 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0.000 1.000 0 0.000 0.000
#> SRR491009 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491010 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491011 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491012 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491013 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491014 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491015 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491016 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491017 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491018 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491019 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491020 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491021 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491022 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491023 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491024 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491025 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491026 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491027 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491028 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491029 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491030 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491031 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491032 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491033 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491034 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491035 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491036 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491037 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491038 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR491039 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491040 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491041 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491042 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491043 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491045 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491065 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491066 1 0.3851 0.210 0.540 0.000 0.000 0 0.460 0.000
#> SRR491067 1 0.3851 0.210 0.540 0.000 0.000 0 0.460 0.000
#> SRR491068 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491069 1 0.3765 0.356 0.596 0.000 0.000 0 0.404 0.000
#> SRR491070 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491071 1 0.1075 0.890 0.952 0.000 0.000 0 0.048 0.000
#> SRR491072 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491073 5 0.0000 0.934 0.000 0.000 0.000 0 1.000 0.000
#> SRR491074 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491075 5 0.0000 0.934 0.000 0.000 0.000 0 1.000 0.000
#> SRR491076 1 0.1075 0.890 0.952 0.000 0.000 0 0.048 0.000
#> SRR491077 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491078 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491079 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491080 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491081 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491082 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491083 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491084 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491085 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491086 5 0.2092 0.741 0.124 0.000 0.000 0 0.876 0.000
#> SRR491087 1 0.3851 0.209 0.540 0.000 0.000 0 0.460 0.000
#> SRR491088 5 0.0000 0.934 0.000 0.000 0.000 0 1.000 0.000
#> SRR491089 1 0.0000 0.927 1.000 0.000 0.000 0 0.000 0.000
#> SRR491090 5 0.0000 0.934 0.000 0.000 0.000 0 1.000 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", "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 13175 rows and 123 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 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 0.376 0.572 0.747 0.4050 0.497 0.497
#> 3 3 0.616 0.793 0.827 0.5298 0.624 0.395
#> 4 4 0.718 0.951 0.851 0.1490 0.876 0.664
#> 5 5 0.897 0.855 0.859 0.0784 0.984 0.935
#> 6 6 0.869 0.790 0.862 0.0443 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
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
#> SRR445718 2 0.0376 0.631 0.004 0.996
#> SRR445719 2 0.0376 0.631 0.004 0.996
#> SRR445720 2 0.0376 0.631 0.004 0.996
#> SRR445721 2 0.0376 0.631 0.004 0.996
#> SRR445722 2 0.0376 0.631 0.004 0.996
#> SRR445723 2 0.0376 0.631 0.004 0.996
#> SRR445724 2 0.0376 0.631 0.004 0.996
#> SRR445725 2 0.0376 0.631 0.004 0.996
#> SRR445726 2 0.0376 0.631 0.004 0.996
#> SRR445727 2 0.0376 0.631 0.004 0.996
#> SRR445728 2 0.0376 0.631 0.004 0.996
#> SRR445729 2 0.0376 0.631 0.004 0.996
#> SRR445730 1 0.1843 0.725 0.972 0.028
#> SRR445731 1 0.1843 0.725 0.972 0.028
#> SRR490961 2 0.0376 0.631 0.004 0.996
#> SRR490962 2 0.0376 0.631 0.004 0.996
#> SRR490963 2 0.0376 0.631 0.004 0.996
#> SRR490964 2 0.0376 0.631 0.004 0.996
#> SRR490965 2 0.0376 0.631 0.004 0.996
#> SRR490966 2 0.0376 0.631 0.004 0.996
#> SRR490967 2 0.0376 0.631 0.004 0.996
#> SRR490968 2 0.0376 0.631 0.004 0.996
#> SRR490969 2 0.0376 0.631 0.004 0.996
#> SRR490970 2 0.0376 0.631 0.004 0.996
#> SRR490971 2 0.0376 0.631 0.004 0.996
#> SRR490972 2 0.0376 0.631 0.004 0.996
#> SRR490973 2 0.9944 0.387 0.456 0.544
#> SRR490974 2 0.9944 0.387 0.456 0.544
#> SRR490975 2 0.9944 0.387 0.456 0.544
#> SRR490976 2 0.9944 0.387 0.456 0.544
#> SRR490977 2 0.9944 0.387 0.456 0.544
#> SRR490978 2 0.9944 0.387 0.456 0.544
#> SRR490979 2 0.9944 0.387 0.456 0.544
#> SRR490980 2 0.9944 0.387 0.456 0.544
#> SRR490981 2 0.0376 0.631 0.004 0.996
#> SRR490982 2 0.0376 0.631 0.004 0.996
#> SRR490983 2 0.0376 0.631 0.004 0.996
#> SRR490984 2 0.0376 0.631 0.004 0.996
#> SRR490985 2 0.9944 0.387 0.456 0.544
#> SRR490986 2 0.9944 0.387 0.456 0.544
#> SRR490987 2 0.9944 0.387 0.456 0.544
#> SRR490988 2 0.9944 0.387 0.456 0.544
#> SRR490989 2 0.9944 0.387 0.456 0.544
#> SRR490990 2 0.9944 0.387 0.456 0.544
#> SRR490991 2 0.9944 0.387 0.456 0.544
#> SRR490992 2 0.9944 0.387 0.456 0.544
#> SRR490993 2 0.9944 0.387 0.456 0.544
#> SRR490994 2 0.9944 0.387 0.456 0.544
#> SRR490995 2 0.9954 0.378 0.460 0.540
#> SRR490996 2 0.9944 0.387 0.456 0.544
#> SRR490997 2 0.9944 0.387 0.456 0.544
#> SRR490998 2 0.9944 0.387 0.456 0.544
#> SRR491000 2 0.9954 0.378 0.460 0.540
#> SRR491001 2 0.9944 0.387 0.456 0.544
#> SRR491002 2 0.9944 0.387 0.456 0.544
#> SRR491003 2 0.9944 0.387 0.456 0.544
#> SRR491004 2 0.9944 0.387 0.456 0.544
#> SRR491005 2 0.9944 0.387 0.456 0.544
#> SRR491006 2 0.9944 0.387 0.456 0.544
#> SRR491007 2 0.9944 0.387 0.456 0.544
#> SRR491008 2 0.9944 0.387 0.456 0.544
#> SRR491009 1 0.9087 0.535 0.676 0.324
#> SRR491010 1 0.9087 0.535 0.676 0.324
#> SRR491011 1 0.9087 0.535 0.676 0.324
#> SRR491012 1 0.9087 0.535 0.676 0.324
#> SRR491013 1 0.9087 0.535 0.676 0.324
#> SRR491014 1 0.9087 0.535 0.676 0.324
#> SRR491015 1 0.9087 0.535 0.676 0.324
#> SRR491016 1 0.9087 0.535 0.676 0.324
#> SRR491017 1 0.9087 0.535 0.676 0.324
#> SRR491018 1 0.9087 0.535 0.676 0.324
#> SRR491019 1 0.9087 0.535 0.676 0.324
#> SRR491020 1 0.9087 0.535 0.676 0.324
#> SRR491021 1 0.9087 0.535 0.676 0.324
#> SRR491022 1 0.9087 0.535 0.676 0.324
#> SRR491023 1 0.9087 0.535 0.676 0.324
#> SRR491024 1 0.9087 0.535 0.676 0.324
#> SRR491025 1 0.9087 0.535 0.676 0.324
#> SRR491026 1 0.9087 0.535 0.676 0.324
#> SRR491027 1 0.9087 0.535 0.676 0.324
#> SRR491028 1 0.9087 0.535 0.676 0.324
#> SRR491029 1 0.9087 0.535 0.676 0.324
#> SRR491030 1 0.9087 0.535 0.676 0.324
#> SRR491031 1 0.9087 0.535 0.676 0.324
#> SRR491032 1 0.9087 0.535 0.676 0.324
#> SRR491033 1 0.9087 0.535 0.676 0.324
#> SRR491034 1 0.9087 0.535 0.676 0.324
#> SRR491035 1 0.9087 0.535 0.676 0.324
#> SRR491036 1 0.9087 0.535 0.676 0.324
#> SRR491037 1 0.9087 0.535 0.676 0.324
#> SRR491038 1 0.9087 0.535 0.676 0.324
#> SRR491039 1 0.1843 0.725 0.972 0.028
#> SRR491040 1 0.1843 0.725 0.972 0.028
#> SRR491041 1 0.1843 0.725 0.972 0.028
#> SRR491042 1 0.1843 0.725 0.972 0.028
#> SRR491043 1 0.1843 0.725 0.972 0.028
#> SRR491045 1 0.1843 0.725 0.972 0.028
#> SRR491065 1 0.1843 0.725 0.972 0.028
#> SRR491066 1 0.1633 0.725 0.976 0.024
#> SRR491067 1 0.1633 0.725 0.976 0.024
#> SRR491068 1 0.1843 0.725 0.972 0.028
#> SRR491069 1 0.1633 0.725 0.976 0.024
#> SRR491070 1 0.1843 0.725 0.972 0.028
#> SRR491071 1 0.1843 0.725 0.972 0.028
#> SRR491072 1 0.1843 0.725 0.972 0.028
#> SRR491073 1 0.0938 0.721 0.988 0.012
#> SRR491074 1 0.1843 0.725 0.972 0.028
#> SRR491075 1 0.0938 0.721 0.988 0.012
#> SRR491076 1 0.1843 0.725 0.972 0.028
#> SRR491077 1 0.1843 0.725 0.972 0.028
#> SRR491078 1 0.1843 0.725 0.972 0.028
#> SRR491079 1 0.1843 0.725 0.972 0.028
#> SRR491080 1 0.1843 0.725 0.972 0.028
#> SRR491081 1 0.1843 0.725 0.972 0.028
#> SRR491082 1 0.1843 0.725 0.972 0.028
#> SRR491083 1 0.1843 0.725 0.972 0.028
#> SRR491084 1 0.1843 0.725 0.972 0.028
#> SRR491085 1 0.1843 0.725 0.972 0.028
#> SRR491086 1 0.1633 0.725 0.976 0.024
#> SRR491087 1 0.1633 0.725 0.976 0.024
#> SRR491088 1 0.0938 0.721 0.988 0.012
#> SRR491089 1 0.1843 0.725 0.972 0.028
#> SRR491090 1 0.1184 0.720 0.984 0.016
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445719 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445720 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445721 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445722 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445723 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445724 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445725 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445726 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445727 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445728 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445729 2 0.4172 0.994 0.004 0.840 0.156
#> SRR445730 1 0.0237 0.977 0.996 0.004 0.000
#> SRR445731 1 0.0237 0.977 0.996 0.004 0.000
#> SRR490961 2 0.4233 0.988 0.004 0.836 0.160
#> SRR490962 2 0.4233 0.988 0.004 0.836 0.160
#> SRR490963 2 0.4233 0.988 0.004 0.836 0.160
#> SRR490964 2 0.4293 0.989 0.004 0.832 0.164
#> SRR490965 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490966 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490967 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490968 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490969 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490970 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490971 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490972 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490973 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490974 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490975 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490976 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490977 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490978 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490979 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490980 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490981 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490982 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490983 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490984 2 0.3983 0.995 0.004 0.852 0.144
#> SRR490985 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490986 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490987 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490988 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490989 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490990 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490991 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490992 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490993 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490994 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490995 3 0.2599 0.662 0.052 0.016 0.932
#> SRR490996 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490997 3 0.2066 0.671 0.060 0.000 0.940
#> SRR490998 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491000 3 0.2599 0.662 0.052 0.016 0.932
#> SRR491001 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491002 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491003 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491004 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491005 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491006 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491007 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491008 3 0.2066 0.671 0.060 0.000 0.940
#> SRR491009 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491010 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491011 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491012 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491013 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491014 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491015 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491016 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491017 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491018 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491019 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491020 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491021 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491022 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491023 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491024 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491025 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491026 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491027 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491028 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491029 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491030 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491031 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491032 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491033 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491034 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491035 3 0.9154 0.534 0.384 0.148 0.468
#> SRR491036 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491037 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491038 3 0.9282 0.558 0.368 0.164 0.468
#> SRR491039 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491040 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491041 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491042 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491043 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491045 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491065 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491066 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491067 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491068 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491069 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491070 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491071 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491072 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491073 1 0.0000 0.972 1.000 0.000 0.000
#> SRR491074 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491075 1 0.0000 0.972 1.000 0.000 0.000
#> SRR491076 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491077 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491078 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491079 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491080 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491081 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491082 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491083 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491084 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491085 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491086 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491087 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491088 1 0.4605 0.588 0.796 0.000 0.204
#> SRR491089 1 0.0237 0.977 0.996 0.004 0.000
#> SRR491090 1 0.5656 0.337 0.712 0.004 0.284
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.2521 0.975 0.000 0.912 0.064 0.024
#> SRR445719 2 0.2521 0.975 0.000 0.912 0.064 0.024
#> SRR445720 2 0.2521 0.975 0.000 0.912 0.064 0.024
#> SRR445721 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445722 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445723 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445724 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445725 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445726 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445727 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445728 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445729 2 0.2300 0.977 0.000 0.920 0.064 0.016
#> SRR445730 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR445731 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR490961 2 0.3081 0.970 0.000 0.888 0.064 0.048
#> SRR490962 2 0.3081 0.970 0.000 0.888 0.064 0.048
#> SRR490963 2 0.3081 0.970 0.000 0.888 0.064 0.048
#> SRR490964 2 0.3081 0.970 0.000 0.888 0.064 0.048
#> SRR490965 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490966 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490967 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490968 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490969 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490970 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490971 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490972 2 0.2623 0.975 0.000 0.908 0.064 0.028
#> SRR490973 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490974 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490975 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490976 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490977 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490978 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490979 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490980 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490981 2 0.3392 0.951 0.000 0.872 0.072 0.056
#> SRR490982 2 0.3392 0.951 0.000 0.872 0.072 0.056
#> SRR490983 2 0.3392 0.951 0.000 0.872 0.072 0.056
#> SRR490984 2 0.3392 0.951 0.000 0.872 0.072 0.056
#> SRR490985 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490986 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490987 3 0.0376 0.962 0.004 0.000 0.992 0.004
#> SRR490988 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490989 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490990 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490991 3 0.0524 0.961 0.004 0.000 0.988 0.008
#> SRR490992 3 0.0188 0.963 0.004 0.000 0.996 0.000
#> SRR490993 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR490994 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR490995 3 0.3088 0.826 0.000 0.008 0.864 0.128
#> SRR490996 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR490997 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR490998 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491000 3 0.3088 0.826 0.000 0.008 0.864 0.128
#> SRR491001 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491002 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491003 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491004 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491005 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491006 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491007 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491008 3 0.1902 0.958 0.004 0.000 0.932 0.064
#> SRR491009 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491010 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491011 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491012 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491013 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491014 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491015 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491016 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491017 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491018 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491019 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491020 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491021 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491022 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491023 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491024 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491025 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491026 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491027 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491028 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491029 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491030 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491031 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491032 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491033 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491034 4 0.6855 0.996 0.152 0.008 0.216 0.624
#> SRR491035 4 0.6528 0.956 0.156 0.004 0.188 0.652
#> SRR491036 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491037 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491038 4 0.6824 0.998 0.152 0.008 0.212 0.628
#> SRR491039 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491040 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491041 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491042 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491043 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491045 1 0.0657 0.930 0.984 0.004 0.000 0.012
#> SRR491065 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491066 1 0.4820 0.828 0.772 0.060 0.000 0.168
#> SRR491067 1 0.4820 0.828 0.772 0.060 0.000 0.168
#> SRR491068 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491069 1 0.4701 0.833 0.780 0.056 0.000 0.164
#> SRR491070 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0376 0.932 0.992 0.004 0.000 0.004
#> SRR491072 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491073 1 0.4949 0.819 0.760 0.060 0.000 0.180
#> SRR491074 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491075 1 0.4949 0.819 0.760 0.060 0.000 0.180
#> SRR491076 1 0.1724 0.915 0.948 0.020 0.000 0.032
#> SRR491077 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0188 0.933 0.996 0.004 0.000 0.000
#> SRR491084 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491086 1 0.4820 0.828 0.772 0.060 0.000 0.168
#> SRR491087 1 0.4820 0.828 0.772 0.060 0.000 0.168
#> SRR491088 1 0.6150 0.691 0.656 0.060 0.012 0.272
#> SRR491089 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> SRR491090 1 0.6355 0.629 0.620 0.060 0.012 0.308
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.1978 0.9288 0.024 0.928 0.004 0.000 0.044
#> SRR445719 2 0.1978 0.9288 0.024 0.928 0.004 0.000 0.044
#> SRR445720 2 0.1978 0.9288 0.024 0.928 0.004 0.000 0.044
#> SRR445721 2 0.0992 0.9425 0.008 0.968 0.000 0.000 0.024
#> SRR445722 2 0.0992 0.9425 0.008 0.968 0.000 0.000 0.024
#> SRR445723 2 0.0992 0.9425 0.008 0.968 0.000 0.000 0.024
#> SRR445724 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445725 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445726 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445727 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445728 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445729 2 0.0865 0.9432 0.004 0.972 0.000 0.000 0.024
#> SRR445730 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR445731 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR490961 2 0.1617 0.9386 0.020 0.948 0.012 0.000 0.020
#> SRR490962 2 0.1617 0.9386 0.020 0.948 0.012 0.000 0.020
#> SRR490963 2 0.1617 0.9386 0.020 0.948 0.012 0.000 0.020
#> SRR490964 2 0.1617 0.9386 0.020 0.948 0.012 0.000 0.020
#> SRR490965 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490966 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490967 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490968 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490969 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490970 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490971 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490972 2 0.1074 0.9437 0.012 0.968 0.004 0.000 0.016
#> SRR490973 3 0.2209 0.8998 0.000 0.032 0.912 0.056 0.000
#> SRR490974 3 0.2494 0.8988 0.000 0.032 0.904 0.056 0.008
#> SRR490975 3 0.2494 0.8988 0.000 0.032 0.904 0.056 0.008
#> SRR490976 3 0.2209 0.8998 0.000 0.032 0.912 0.056 0.000
#> SRR490977 3 0.2369 0.8999 0.000 0.032 0.908 0.056 0.004
#> SRR490978 3 0.2209 0.8998 0.000 0.032 0.912 0.056 0.000
#> SRR490979 3 0.2209 0.8998 0.000 0.032 0.912 0.056 0.000
#> SRR490980 3 0.2369 0.8994 0.000 0.032 0.908 0.056 0.004
#> SRR490981 2 0.4118 0.8069 0.012 0.780 0.032 0.000 0.176
#> SRR490982 2 0.4118 0.8069 0.012 0.780 0.032 0.000 0.176
#> SRR490983 2 0.4118 0.8069 0.012 0.780 0.032 0.000 0.176
#> SRR490984 2 0.4118 0.8069 0.012 0.780 0.032 0.000 0.176
#> SRR490985 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490986 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490987 3 0.2899 0.8949 0.000 0.032 0.888 0.056 0.024
#> SRR490988 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490989 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490990 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490991 3 0.2987 0.8937 0.000 0.032 0.884 0.056 0.028
#> SRR490992 3 0.2710 0.8971 0.000 0.032 0.896 0.056 0.016
#> SRR490993 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR490994 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR490995 3 0.6852 0.5257 0.000 0.032 0.536 0.188 0.244
#> SRR490996 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR490997 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR490998 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491000 3 0.6852 0.5257 0.000 0.032 0.536 0.188 0.244
#> SRR491001 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491002 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491003 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491004 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491005 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491006 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491007 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491008 3 0.4953 0.8862 0.000 0.032 0.740 0.056 0.172
#> SRR491009 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.2852 0.8232 0.000 0.000 0.000 0.828 0.172
#> SRR491023 4 0.2852 0.8232 0.000 0.000 0.000 0.828 0.172
#> SRR491024 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.2773 0.8306 0.000 0.000 0.000 0.836 0.164
#> SRR491029 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.2852 0.8232 0.000 0.000 0.000 0.828 0.172
#> SRR491032 4 0.2773 0.8306 0.000 0.000 0.000 0.836 0.164
#> SRR491033 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.2852 0.8232 0.000 0.000 0.000 0.828 0.172
#> SRR491035 4 0.2929 0.8141 0.000 0.000 0.000 0.820 0.180
#> SRR491036 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.9530 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491040 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491041 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491042 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491043 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491045 1 0.3078 0.8357 0.872 0.000 0.064 0.056 0.008
#> SRR491065 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491066 1 0.5351 0.0937 0.592 0.000 0.004 0.056 0.348
#> SRR491067 1 0.5364 0.0806 0.588 0.000 0.004 0.056 0.352
#> SRR491068 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491069 1 0.5279 0.1589 0.612 0.000 0.004 0.056 0.328
#> SRR491070 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491071 1 0.2005 0.8520 0.924 0.000 0.004 0.056 0.016
#> SRR491072 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491073 5 0.5486 0.7962 0.352 0.000 0.000 0.076 0.572
#> SRR491074 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491075 5 0.5320 0.7585 0.368 0.000 0.000 0.060 0.572
#> SRR491076 1 0.3142 0.7843 0.864 0.000 0.004 0.056 0.076
#> SRR491077 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491078 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491079 1 0.1341 0.8628 0.944 0.000 0.000 0.056 0.000
#> SRR491080 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491081 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491082 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491083 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491084 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491085 1 0.1502 0.8628 0.940 0.000 0.004 0.056 0.000
#> SRR491086 1 0.5304 -0.0676 0.560 0.000 0.000 0.056 0.384
#> SRR491087 1 0.5364 0.0806 0.588 0.000 0.004 0.056 0.352
#> SRR491088 5 0.6004 0.8294 0.256 0.000 0.000 0.168 0.576
#> SRR491089 1 0.1502 0.8625 0.940 0.000 0.004 0.056 0.000
#> SRR491090 5 0.6023 0.8195 0.248 0.000 0.000 0.176 0.576
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.2380 0.8757 0.004 0.892 0.000 0.000 0.036 NA
#> SRR445719 2 0.2380 0.8757 0.004 0.892 0.000 0.000 0.036 NA
#> SRR445720 2 0.2380 0.8757 0.004 0.892 0.000 0.000 0.036 NA
#> SRR445721 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445722 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445723 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445724 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445725 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445726 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445727 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445728 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445729 2 0.1152 0.8944 0.000 0.952 0.000 0.000 0.004 NA
#> SRR445730 1 0.3449 0.7675 0.796 0.000 0.004 0.024 0.004 NA
#> SRR445731 1 0.3343 0.7675 0.796 0.000 0.000 0.024 0.004 NA
#> SRR490961 2 0.3150 0.8677 0.008 0.844 0.000 0.000 0.088 NA
#> SRR490962 2 0.3150 0.8677 0.008 0.844 0.000 0.000 0.088 NA
#> SRR490963 2 0.3150 0.8677 0.008 0.844 0.000 0.000 0.088 NA
#> SRR490964 2 0.3099 0.8687 0.008 0.848 0.000 0.000 0.084 NA
#> SRR490965 2 0.1867 0.8934 0.000 0.916 0.000 0.000 0.064 NA
#> SRR490966 2 0.1867 0.8934 0.000 0.916 0.000 0.000 0.064 NA
#> SRR490967 2 0.1867 0.8934 0.000 0.916 0.000 0.000 0.064 NA
#> SRR490968 2 0.1867 0.8934 0.000 0.916 0.000 0.000 0.064 NA
#> SRR490969 2 0.1807 0.8938 0.000 0.920 0.000 0.000 0.060 NA
#> SRR490970 2 0.1807 0.8938 0.000 0.920 0.000 0.000 0.060 NA
#> SRR490971 2 0.1807 0.8938 0.000 0.920 0.000 0.000 0.060 NA
#> SRR490972 2 0.1807 0.8938 0.000 0.920 0.000 0.000 0.060 NA
#> SRR490973 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490974 3 0.3784 0.8258 0.004 0.004 0.696 0.000 0.004 NA
#> SRR490975 3 0.3784 0.8258 0.004 0.004 0.696 0.000 0.004 NA
#> SRR490976 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490977 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490978 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490979 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490980 3 0.3764 0.8265 0.004 0.004 0.700 0.000 0.004 NA
#> SRR490981 2 0.4612 0.7053 0.004 0.688 0.000 0.000 0.088 NA
#> SRR490982 2 0.4612 0.7053 0.004 0.688 0.000 0.000 0.088 NA
#> SRR490983 2 0.4612 0.7053 0.004 0.688 0.000 0.000 0.088 NA
#> SRR490984 2 0.4632 0.7054 0.004 0.688 0.000 0.000 0.092 NA
#> SRR490985 3 0.3652 0.8188 0.000 0.004 0.672 0.000 0.000 NA
#> SRR490986 3 0.3652 0.8188 0.000 0.004 0.672 0.000 0.000 NA
#> SRR490987 3 0.3636 0.8204 0.000 0.004 0.676 0.000 0.000 NA
#> SRR490988 3 0.3652 0.8188 0.000 0.004 0.672 0.000 0.000 NA
#> SRR490989 3 0.3652 0.8188 0.000 0.004 0.672 0.000 0.000 NA
#> SRR490990 3 0.3636 0.8204 0.000 0.004 0.676 0.000 0.000 NA
#> SRR490991 3 0.3636 0.8204 0.000 0.004 0.676 0.000 0.000 NA
#> SRR490992 3 0.3619 0.8216 0.000 0.004 0.680 0.000 0.000 NA
#> SRR490993 3 0.0291 0.7982 0.004 0.004 0.992 0.000 0.000 NA
#> SRR490994 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR490995 3 0.7125 0.0483 0.000 0.004 0.384 0.088 0.348 NA
#> SRR490996 3 0.0291 0.7982 0.004 0.004 0.992 0.000 0.000 NA
#> SRR490997 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR490998 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491000 3 0.7125 0.0483 0.000 0.004 0.384 0.088 0.348 NA
#> SRR491001 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491002 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491003 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491004 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491005 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491006 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491007 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491008 3 0.0146 0.7985 0.000 0.004 0.996 0.000 0.000 NA
#> SRR491009 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491010 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491011 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491012 4 0.0458 0.8959 0.000 0.000 0.000 0.984 0.000 NA
#> SRR491013 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491014 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491015 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491016 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491017 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491018 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491019 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491020 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491021 4 0.0000 0.8983 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491022 4 0.4934 0.5728 0.000 0.000 0.000 0.632 0.256 NA
#> SRR491023 4 0.4934 0.5728 0.000 0.000 0.000 0.632 0.256 NA
#> SRR491024 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491025 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491026 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491027 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491028 4 0.4871 0.5863 0.000 0.000 0.000 0.644 0.244 NA
#> SRR491029 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491030 4 0.0363 0.8972 0.000 0.000 0.000 0.988 0.000 NA
#> SRR491031 4 0.4934 0.5728 0.000 0.000 0.000 0.632 0.256 NA
#> SRR491032 4 0.4871 0.5863 0.000 0.000 0.000 0.644 0.244 NA
#> SRR491033 4 0.0547 0.8947 0.000 0.000 0.000 0.980 0.000 NA
#> SRR491034 4 0.4934 0.5728 0.000 0.000 0.000 0.632 0.256 NA
#> SRR491035 4 0.4934 0.5728 0.000 0.000 0.000 0.632 0.256 NA
#> SRR491036 4 0.0547 0.8947 0.000 0.000 0.000 0.980 0.000 NA
#> SRR491037 4 0.0547 0.8947 0.000 0.000 0.000 0.980 0.000 NA
#> SRR491038 4 0.0547 0.8947 0.000 0.000 0.000 0.980 0.000 NA
#> SRR491039 1 0.3343 0.7675 0.796 0.000 0.000 0.024 0.004 NA
#> SRR491040 1 0.3449 0.7675 0.796 0.000 0.004 0.024 0.004 NA
#> SRR491041 1 0.3343 0.7675 0.796 0.000 0.000 0.024 0.004 NA
#> SRR491042 1 0.3449 0.7675 0.796 0.000 0.004 0.024 0.004 NA
#> SRR491043 1 0.3449 0.7675 0.796 0.000 0.004 0.024 0.004 NA
#> SRR491045 1 0.3343 0.7675 0.796 0.000 0.000 0.024 0.004 NA
#> SRR491065 1 0.1564 0.8180 0.936 0.000 0.000 0.024 0.000 NA
#> SRR491066 1 0.5220 0.1945 0.540 0.000 0.000 0.024 0.388 NA
#> SRR491067 1 0.5220 0.1945 0.540 0.000 0.000 0.024 0.388 NA
#> SRR491068 1 0.0777 0.8341 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491069 1 0.5241 0.2333 0.552 0.000 0.000 0.024 0.372 NA
#> SRR491070 1 0.0777 0.8340 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491071 1 0.2084 0.8084 0.916 0.000 0.000 0.024 0.016 NA
#> SRR491072 1 0.0993 0.8340 0.964 0.000 0.000 0.024 0.000 NA
#> SRR491073 5 0.3543 0.9134 0.200 0.000 0.000 0.032 0.768 NA
#> SRR491074 1 0.0891 0.8330 0.968 0.000 0.000 0.024 0.000 NA
#> SRR491075 5 0.3543 0.9134 0.200 0.000 0.000 0.032 0.768 NA
#> SRR491076 1 0.2685 0.7788 0.884 0.000 0.000 0.024 0.052 NA
#> SRR491077 1 0.0777 0.8341 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491078 1 0.0891 0.8330 0.968 0.000 0.000 0.024 0.000 NA
#> SRR491079 1 0.0632 0.8343 0.976 0.000 0.000 0.024 0.000 NA
#> SRR491080 1 0.0777 0.8341 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491081 1 0.0777 0.8341 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491082 1 0.0777 0.8340 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491083 1 0.0777 0.8341 0.972 0.000 0.000 0.024 0.000 NA
#> SRR491084 1 0.0632 0.8343 0.976 0.000 0.000 0.024 0.000 NA
#> SRR491085 1 0.0632 0.8343 0.976 0.000 0.000 0.024 0.000 NA
#> SRR491086 1 0.4869 0.0168 0.500 0.000 0.000 0.024 0.456 NA
#> SRR491087 1 0.5273 0.1872 0.536 0.000 0.000 0.024 0.388 NA
#> SRR491088 5 0.3717 0.9191 0.148 0.000 0.000 0.072 0.780 NA
#> SRR491089 1 0.0891 0.8330 0.968 0.000 0.000 0.024 0.000 NA
#> SRR491090 5 0.3775 0.8949 0.128 0.000 0.000 0.092 0.780 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.
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 13175 rows and 123 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 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 1.000 1.000 0.5038 0.497 0.497
#> 3 3 0.710 0.787 0.851 0.2631 0.632 0.396
#> 4 4 1.000 0.995 0.998 0.1867 0.862 0.625
#> 5 5 0.944 0.951 0.934 0.0397 0.969 0.873
#> 6 6 0.928 0.864 0.926 0.0243 0.988 0.943
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 4 5
There is also optional best \(k\) = 2 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
#> SRR445718 2 0 1 0 1
#> SRR445719 2 0 1 0 1
#> SRR445720 2 0 1 0 1
#> SRR445721 2 0 1 0 1
#> SRR445722 2 0 1 0 1
#> SRR445723 2 0 1 0 1
#> SRR445724 2 0 1 0 1
#> SRR445725 2 0 1 0 1
#> SRR445726 2 0 1 0 1
#> SRR445727 2 0 1 0 1
#> SRR445728 2 0 1 0 1
#> SRR445729 2 0 1 0 1
#> SRR445730 1 0 1 1 0
#> SRR445731 1 0 1 1 0
#> SRR490961 2 0 1 0 1
#> SRR490962 2 0 1 0 1
#> SRR490963 2 0 1 0 1
#> SRR490964 2 0 1 0 1
#> SRR490965 2 0 1 0 1
#> SRR490966 2 0 1 0 1
#> SRR490967 2 0 1 0 1
#> SRR490968 2 0 1 0 1
#> SRR490969 2 0 1 0 1
#> SRR490970 2 0 1 0 1
#> SRR490971 2 0 1 0 1
#> SRR490972 2 0 1 0 1
#> SRR490973 2 0 1 0 1
#> SRR490974 2 0 1 0 1
#> SRR490975 2 0 1 0 1
#> SRR490976 2 0 1 0 1
#> SRR490977 2 0 1 0 1
#> SRR490978 2 0 1 0 1
#> SRR490979 2 0 1 0 1
#> SRR490980 2 0 1 0 1
#> SRR490981 2 0 1 0 1
#> SRR490982 2 0 1 0 1
#> SRR490983 2 0 1 0 1
#> SRR490984 2 0 1 0 1
#> SRR490985 2 0 1 0 1
#> SRR490986 2 0 1 0 1
#> SRR490987 2 0 1 0 1
#> SRR490988 2 0 1 0 1
#> SRR490989 2 0 1 0 1
#> SRR490990 2 0 1 0 1
#> SRR490991 2 0 1 0 1
#> SRR490992 2 0 1 0 1
#> SRR490993 2 0 1 0 1
#> SRR490994 2 0 1 0 1
#> SRR490995 2 0 1 0 1
#> SRR490996 2 0 1 0 1
#> SRR490997 2 0 1 0 1
#> SRR490998 2 0 1 0 1
#> SRR491000 2 0 1 0 1
#> SRR491001 2 0 1 0 1
#> SRR491002 2 0 1 0 1
#> SRR491003 2 0 1 0 1
#> SRR491004 2 0 1 0 1
#> SRR491005 2 0 1 0 1
#> SRR491006 2 0 1 0 1
#> SRR491007 2 0 1 0 1
#> SRR491008 2 0 1 0 1
#> SRR491009 1 0 1 1 0
#> SRR491010 1 0 1 1 0
#> SRR491011 1 0 1 1 0
#> SRR491012 1 0 1 1 0
#> SRR491013 1 0 1 1 0
#> SRR491014 1 0 1 1 0
#> SRR491015 1 0 1 1 0
#> SRR491016 1 0 1 1 0
#> SRR491017 1 0 1 1 0
#> SRR491018 1 0 1 1 0
#> SRR491019 1 0 1 1 0
#> SRR491020 1 0 1 1 0
#> SRR491021 1 0 1 1 0
#> SRR491022 1 0 1 1 0
#> SRR491023 1 0 1 1 0
#> SRR491024 1 0 1 1 0
#> SRR491025 1 0 1 1 0
#> SRR491026 1 0 1 1 0
#> SRR491027 1 0 1 1 0
#> SRR491028 1 0 1 1 0
#> SRR491029 1 0 1 1 0
#> SRR491030 1 0 1 1 0
#> SRR491031 1 0 1 1 0
#> SRR491032 1 0 1 1 0
#> SRR491033 1 0 1 1 0
#> SRR491034 1 0 1 1 0
#> SRR491035 1 0 1 1 0
#> SRR491036 1 0 1 1 0
#> SRR491037 1 0 1 1 0
#> SRR491038 1 0 1 1 0
#> SRR491039 1 0 1 1 0
#> SRR491040 1 0 1 1 0
#> SRR491041 1 0 1 1 0
#> SRR491042 1 0 1 1 0
#> SRR491043 1 0 1 1 0
#> SRR491045 1 0 1 1 0
#> SRR491065 1 0 1 1 0
#> SRR491066 1 0 1 1 0
#> SRR491067 1 0 1 1 0
#> SRR491068 1 0 1 1 0
#> SRR491069 1 0 1 1 0
#> SRR491070 1 0 1 1 0
#> SRR491071 1 0 1 1 0
#> SRR491072 1 0 1 1 0
#> SRR491073 1 0 1 1 0
#> SRR491074 1 0 1 1 0
#> SRR491075 1 0 1 1 0
#> SRR491076 1 0 1 1 0
#> SRR491077 1 0 1 1 0
#> SRR491078 1 0 1 1 0
#> SRR491079 1 0 1 1 0
#> SRR491080 1 0 1 1 0
#> SRR491081 1 0 1 1 0
#> SRR491082 1 0 1 1 0
#> SRR491083 1 0 1 1 0
#> SRR491084 1 0 1 1 0
#> SRR491085 1 0 1 1 0
#> SRR491086 1 0 1 1 0
#> SRR491087 1 0 1 1 0
#> SRR491088 1 0 1 1 0
#> SRR491089 1 0 1 1 0
#> SRR491090 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 0.936 0.000 1.000 0.000
#> SRR445719 2 0.000 0.936 0.000 1.000 0.000
#> SRR445720 2 0.000 0.936 0.000 1.000 0.000
#> SRR445721 2 0.000 0.936 0.000 1.000 0.000
#> SRR445722 2 0.000 0.936 0.000 1.000 0.000
#> SRR445723 2 0.000 0.936 0.000 1.000 0.000
#> SRR445724 2 0.000 0.936 0.000 1.000 0.000
#> SRR445725 2 0.000 0.936 0.000 1.000 0.000
#> SRR445726 2 0.000 0.936 0.000 1.000 0.000
#> SRR445727 2 0.000 0.936 0.000 1.000 0.000
#> SRR445728 2 0.000 0.936 0.000 1.000 0.000
#> SRR445729 2 0.000 0.936 0.000 1.000 0.000
#> SRR445730 1 0.593 0.968 0.644 0.000 0.356
#> SRR445731 1 0.593 0.968 0.644 0.000 0.356
#> SRR490961 2 0.000 0.936 0.000 1.000 0.000
#> SRR490962 2 0.000 0.936 0.000 1.000 0.000
#> SRR490963 2 0.000 0.936 0.000 1.000 0.000
#> SRR490964 2 0.000 0.936 0.000 1.000 0.000
#> SRR490965 2 0.000 0.936 0.000 1.000 0.000
#> SRR490966 2 0.000 0.936 0.000 1.000 0.000
#> SRR490967 2 0.000 0.936 0.000 1.000 0.000
#> SRR490968 2 0.000 0.936 0.000 1.000 0.000
#> SRR490969 2 0.000 0.936 0.000 1.000 0.000
#> SRR490970 2 0.000 0.936 0.000 1.000 0.000
#> SRR490971 2 0.000 0.936 0.000 1.000 0.000
#> SRR490972 2 0.000 0.936 0.000 1.000 0.000
#> SRR490973 3 0.782 0.671 0.356 0.064 0.580
#> SRR490974 3 0.950 0.524 0.356 0.192 0.452
#> SRR490975 3 0.950 0.524 0.356 0.192 0.452
#> SRR490976 3 0.774 0.675 0.356 0.060 0.584
#> SRR490977 3 0.757 0.681 0.356 0.052 0.592
#> SRR490978 3 0.774 0.675 0.356 0.060 0.584
#> SRR490979 3 0.782 0.671 0.356 0.064 0.580
#> SRR490980 3 0.950 0.524 0.356 0.192 0.452
#> SRR490981 2 0.000 0.936 0.000 1.000 0.000
#> SRR490982 2 0.000 0.936 0.000 1.000 0.000
#> SRR490983 2 0.000 0.936 0.000 1.000 0.000
#> SRR490984 2 0.000 0.936 0.000 1.000 0.000
#> SRR490985 2 0.993 -0.151 0.356 0.368 0.276
#> SRR490986 1 0.996 -0.470 0.356 0.356 0.288
#> SRR490987 3 0.956 0.512 0.356 0.200 0.444
#> SRR490988 2 0.993 -0.151 0.356 0.368 0.276
#> SRR490989 2 0.993 -0.151 0.356 0.368 0.276
#> SRR490990 3 0.961 0.498 0.356 0.208 0.436
#> SRR490991 3 0.961 0.498 0.356 0.208 0.436
#> SRR490992 3 0.940 0.541 0.356 0.180 0.464
#> SRR490993 3 0.593 0.712 0.356 0.000 0.644
#> SRR490994 3 0.593 0.712 0.356 0.000 0.644
#> SRR490995 3 0.674 0.702 0.356 0.020 0.624
#> SRR490996 3 0.593 0.712 0.356 0.000 0.644
#> SRR490997 3 0.593 0.712 0.356 0.000 0.644
#> SRR490998 3 0.593 0.712 0.356 0.000 0.644
#> SRR491000 3 0.661 0.704 0.356 0.016 0.628
#> SRR491001 3 0.593 0.712 0.356 0.000 0.644
#> SRR491002 3 0.593 0.712 0.356 0.000 0.644
#> SRR491003 3 0.593 0.712 0.356 0.000 0.644
#> SRR491004 3 0.593 0.712 0.356 0.000 0.644
#> SRR491005 3 0.593 0.712 0.356 0.000 0.644
#> SRR491006 3 0.593 0.712 0.356 0.000 0.644
#> SRR491007 3 0.593 0.712 0.356 0.000 0.644
#> SRR491008 3 0.593 0.712 0.356 0.000 0.644
#> SRR491009 3 0.000 0.699 0.000 0.000 1.000
#> SRR491010 3 0.000 0.699 0.000 0.000 1.000
#> SRR491011 3 0.000 0.699 0.000 0.000 1.000
#> SRR491012 3 0.000 0.699 0.000 0.000 1.000
#> SRR491013 3 0.000 0.699 0.000 0.000 1.000
#> SRR491014 3 0.000 0.699 0.000 0.000 1.000
#> SRR491015 3 0.000 0.699 0.000 0.000 1.000
#> SRR491016 3 0.000 0.699 0.000 0.000 1.000
#> SRR491017 3 0.000 0.699 0.000 0.000 1.000
#> SRR491018 3 0.000 0.699 0.000 0.000 1.000
#> SRR491019 3 0.000 0.699 0.000 0.000 1.000
#> SRR491020 3 0.000 0.699 0.000 0.000 1.000
#> SRR491021 3 0.000 0.699 0.000 0.000 1.000
#> SRR491022 3 0.000 0.699 0.000 0.000 1.000
#> SRR491023 3 0.000 0.699 0.000 0.000 1.000
#> SRR491024 3 0.000 0.699 0.000 0.000 1.000
#> SRR491025 3 0.000 0.699 0.000 0.000 1.000
#> SRR491026 3 0.000 0.699 0.000 0.000 1.000
#> SRR491027 3 0.000 0.699 0.000 0.000 1.000
#> SRR491028 3 0.000 0.699 0.000 0.000 1.000
#> SRR491029 3 0.000 0.699 0.000 0.000 1.000
#> SRR491030 3 0.000 0.699 0.000 0.000 1.000
#> SRR491031 3 0.000 0.699 0.000 0.000 1.000
#> SRR491032 3 0.000 0.699 0.000 0.000 1.000
#> SRR491033 3 0.000 0.699 0.000 0.000 1.000
#> SRR491034 3 0.000 0.699 0.000 0.000 1.000
#> SRR491035 3 0.000 0.699 0.000 0.000 1.000
#> SRR491036 3 0.000 0.699 0.000 0.000 1.000
#> SRR491037 3 0.000 0.699 0.000 0.000 1.000
#> SRR491038 3 0.000 0.699 0.000 0.000 1.000
#> SRR491039 1 0.593 0.968 0.644 0.000 0.356
#> SRR491040 1 0.593 0.968 0.644 0.000 0.356
#> SRR491041 1 0.593 0.968 0.644 0.000 0.356
#> SRR491042 1 0.593 0.968 0.644 0.000 0.356
#> SRR491043 1 0.593 0.968 0.644 0.000 0.356
#> SRR491045 1 0.593 0.968 0.644 0.000 0.356
#> SRR491065 1 0.593 0.968 0.644 0.000 0.356
#> SRR491066 1 0.593 0.968 0.644 0.000 0.356
#> SRR491067 1 0.593 0.968 0.644 0.000 0.356
#> SRR491068 1 0.593 0.968 0.644 0.000 0.356
#> SRR491069 1 0.593 0.968 0.644 0.000 0.356
#> SRR491070 1 0.593 0.968 0.644 0.000 0.356
#> SRR491071 1 0.593 0.968 0.644 0.000 0.356
#> SRR491072 1 0.593 0.968 0.644 0.000 0.356
#> SRR491073 1 0.593 0.968 0.644 0.000 0.356
#> SRR491074 1 0.593 0.968 0.644 0.000 0.356
#> SRR491075 1 0.593 0.968 0.644 0.000 0.356
#> SRR491076 1 0.593 0.968 0.644 0.000 0.356
#> SRR491077 1 0.593 0.968 0.644 0.000 0.356
#> SRR491078 1 0.593 0.968 0.644 0.000 0.356
#> SRR491079 1 0.593 0.968 0.644 0.000 0.356
#> SRR491080 1 0.593 0.968 0.644 0.000 0.356
#> SRR491081 1 0.593 0.968 0.644 0.000 0.356
#> SRR491082 1 0.593 0.968 0.644 0.000 0.356
#> SRR491083 1 0.593 0.968 0.644 0.000 0.356
#> SRR491084 1 0.593 0.968 0.644 0.000 0.356
#> SRR491085 1 0.593 0.968 0.644 0.000 0.356
#> SRR491086 1 0.593 0.968 0.644 0.000 0.356
#> SRR491087 1 0.593 0.968 0.644 0.000 0.356
#> SRR491088 1 0.593 0.968 0.644 0.000 0.356
#> SRR491089 1 0.593 0.968 0.644 0.000 0.356
#> SRR491090 1 0.593 0.968 0.644 0.000 0.356
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.000 1.000 0 1 0.000 0.000
#> SRR445719 2 0.000 1.000 0 1 0.000 0.000
#> SRR445720 2 0.000 1.000 0 1 0.000 0.000
#> SRR445721 2 0.000 1.000 0 1 0.000 0.000
#> SRR445722 2 0.000 1.000 0 1 0.000 0.000
#> SRR445723 2 0.000 1.000 0 1 0.000 0.000
#> SRR445724 2 0.000 1.000 0 1 0.000 0.000
#> SRR445725 2 0.000 1.000 0 1 0.000 0.000
#> SRR445726 2 0.000 1.000 0 1 0.000 0.000
#> SRR445727 2 0.000 1.000 0 1 0.000 0.000
#> SRR445728 2 0.000 1.000 0 1 0.000 0.000
#> SRR445729 2 0.000 1.000 0 1 0.000 0.000
#> SRR445730 1 0.000 1.000 1 0 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0 0.000 0.000
#> SRR490961 2 0.000 1.000 0 1 0.000 0.000
#> SRR490962 2 0.000 1.000 0 1 0.000 0.000
#> SRR490963 2 0.000 1.000 0 1 0.000 0.000
#> SRR490964 2 0.000 1.000 0 1 0.000 0.000
#> SRR490965 2 0.000 1.000 0 1 0.000 0.000
#> SRR490966 2 0.000 1.000 0 1 0.000 0.000
#> SRR490967 2 0.000 1.000 0 1 0.000 0.000
#> SRR490968 2 0.000 1.000 0 1 0.000 0.000
#> SRR490969 2 0.000 1.000 0 1 0.000 0.000
#> SRR490970 2 0.000 1.000 0 1 0.000 0.000
#> SRR490971 2 0.000 1.000 0 1 0.000 0.000
#> SRR490972 2 0.000 1.000 0 1 0.000 0.000
#> SRR490973 3 0.000 0.990 0 0 1.000 0.000
#> SRR490974 3 0.000 0.990 0 0 1.000 0.000
#> SRR490975 3 0.000 0.990 0 0 1.000 0.000
#> SRR490976 3 0.000 0.990 0 0 1.000 0.000
#> SRR490977 3 0.000 0.990 0 0 1.000 0.000
#> SRR490978 3 0.000 0.990 0 0 1.000 0.000
#> SRR490979 3 0.000 0.990 0 0 1.000 0.000
#> SRR490980 3 0.000 0.990 0 0 1.000 0.000
#> SRR490981 2 0.000 1.000 0 1 0.000 0.000
#> SRR490982 2 0.000 1.000 0 1 0.000 0.000
#> SRR490983 2 0.000 1.000 0 1 0.000 0.000
#> SRR490984 2 0.000 1.000 0 1 0.000 0.000
#> SRR490985 3 0.000 0.990 0 0 1.000 0.000
#> SRR490986 3 0.000 0.990 0 0 1.000 0.000
#> SRR490987 3 0.000 0.990 0 0 1.000 0.000
#> SRR490988 3 0.000 0.990 0 0 1.000 0.000
#> SRR490989 3 0.000 0.990 0 0 1.000 0.000
#> SRR490990 3 0.000 0.990 0 0 1.000 0.000
#> SRR490991 3 0.000 0.990 0 0 1.000 0.000
#> SRR490992 3 0.000 0.990 0 0 1.000 0.000
#> SRR490993 3 0.000 0.990 0 0 1.000 0.000
#> SRR490994 3 0.000 0.990 0 0 1.000 0.000
#> SRR490995 3 0.234 0.889 0 0 0.900 0.100
#> SRR490996 3 0.000 0.990 0 0 1.000 0.000
#> SRR490997 3 0.000 0.990 0 0 1.000 0.000
#> SRR490998 3 0.000 0.990 0 0 1.000 0.000
#> SRR491000 3 0.349 0.774 0 0 0.812 0.188
#> SRR491001 3 0.000 0.990 0 0 1.000 0.000
#> SRR491002 3 0.000 0.990 0 0 1.000 0.000
#> SRR491003 3 0.000 0.990 0 0 1.000 0.000
#> SRR491004 3 0.000 0.990 0 0 1.000 0.000
#> SRR491005 3 0.000 0.990 0 0 1.000 0.000
#> SRR491006 3 0.000 0.990 0 0 1.000 0.000
#> SRR491007 3 0.000 0.990 0 0 1.000 0.000
#> SRR491008 3 0.000 0.990 0 0 1.000 0.000
#> SRR491009 4 0.000 1.000 0 0 0.000 1.000
#> SRR491010 4 0.000 1.000 0 0 0.000 1.000
#> SRR491011 4 0.000 1.000 0 0 0.000 1.000
#> SRR491012 4 0.000 1.000 0 0 0.000 1.000
#> SRR491013 4 0.000 1.000 0 0 0.000 1.000
#> SRR491014 4 0.000 1.000 0 0 0.000 1.000
#> SRR491015 4 0.000 1.000 0 0 0.000 1.000
#> SRR491016 4 0.000 1.000 0 0 0.000 1.000
#> SRR491017 4 0.000 1.000 0 0 0.000 1.000
#> SRR491018 4 0.000 1.000 0 0 0.000 1.000
#> SRR491019 4 0.000 1.000 0 0 0.000 1.000
#> SRR491020 4 0.000 1.000 0 0 0.000 1.000
#> SRR491021 4 0.000 1.000 0 0 0.000 1.000
#> SRR491022 4 0.000 1.000 0 0 0.000 1.000
#> SRR491023 4 0.000 1.000 0 0 0.000 1.000
#> SRR491024 4 0.000 1.000 0 0 0.000 1.000
#> SRR491025 4 0.000 1.000 0 0 0.000 1.000
#> SRR491026 4 0.000 1.000 0 0 0.000 1.000
#> SRR491027 4 0.000 1.000 0 0 0.000 1.000
#> SRR491028 4 0.000 1.000 0 0 0.000 1.000
#> SRR491029 4 0.000 1.000 0 0 0.000 1.000
#> SRR491030 4 0.000 1.000 0 0 0.000 1.000
#> SRR491031 4 0.000 1.000 0 0 0.000 1.000
#> SRR491032 4 0.000 1.000 0 0 0.000 1.000
#> SRR491033 4 0.000 1.000 0 0 0.000 1.000
#> SRR491034 4 0.000 1.000 0 0 0.000 1.000
#> SRR491035 4 0.000 1.000 0 0 0.000 1.000
#> SRR491036 4 0.000 1.000 0 0 0.000 1.000
#> SRR491037 4 0.000 1.000 0 0 0.000 1.000
#> SRR491038 4 0.000 1.000 0 0 0.000 1.000
#> SRR491039 1 0.000 1.000 1 0 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.4101 0.925 0.000 0.000 0.628 0.000 0.372
#> SRR490974 3 0.4088 0.927 0.000 0.000 0.632 0.000 0.368
#> SRR490975 3 0.4088 0.927 0.000 0.000 0.632 0.000 0.368
#> SRR490976 3 0.4101 0.925 0.000 0.000 0.628 0.000 0.372
#> SRR490977 3 0.4101 0.925 0.000 0.000 0.628 0.000 0.372
#> SRR490978 3 0.4101 0.925 0.000 0.000 0.628 0.000 0.372
#> SRR490979 3 0.4101 0.925 0.000 0.000 0.628 0.000 0.372
#> SRR490980 3 0.4088 0.927 0.000 0.000 0.632 0.000 0.368
#> SRR490981 2 0.0794 0.978 0.000 0.972 0.028 0.000 0.000
#> SRR490982 2 0.0794 0.978 0.000 0.972 0.028 0.000 0.000
#> SRR490983 2 0.0794 0.978 0.000 0.972 0.028 0.000 0.000
#> SRR490984 2 0.0794 0.978 0.000 0.972 0.028 0.000 0.000
#> SRR490985 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490986 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490987 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490988 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490989 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490990 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490991 3 0.4074 0.928 0.000 0.000 0.636 0.000 0.364
#> SRR490992 3 0.4088 0.927 0.000 0.000 0.632 0.000 0.368
#> SRR490993 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 3 0.2046 0.510 0.000 0.000 0.916 0.016 0.068
#> SRR490996 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 3 0.1981 0.506 0.000 0.000 0.920 0.016 0.064
#> SRR491001 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.3636 0.789 0.000 0.000 0.272 0.728 0.000
#> SRR491023 4 0.3636 0.789 0.000 0.000 0.272 0.728 0.000
#> SRR491024 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.2852 0.857 0.000 0.000 0.172 0.828 0.000
#> SRR491029 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.3636 0.789 0.000 0.000 0.272 0.728 0.000
#> SRR491032 4 0.2891 0.854 0.000 0.000 0.176 0.824 0.000
#> SRR491033 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.3636 0.789 0.000 0.000 0.272 0.728 0.000
#> SRR491035 4 0.3636 0.789 0.000 0.000 0.272 0.728 0.000
#> SRR491036 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0794 0.967 0.972 0.000 0.028 0.000 0.000
#> SRR491067 1 0.0609 0.971 0.980 0.000 0.020 0.000 0.000
#> SRR491068 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0404 0.975 0.988 0.000 0.012 0.000 0.000
#> SRR491070 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491073 1 0.2605 0.877 0.852 0.000 0.148 0.000 0.000
#> SRR491074 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491075 1 0.2424 0.891 0.868 0.000 0.132 0.000 0.000
#> SRR491076 1 0.0162 0.979 0.996 0.000 0.004 0.000 0.000
#> SRR491077 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.1043 0.960 0.960 0.000 0.040 0.000 0.000
#> SRR491087 1 0.0510 0.973 0.984 0.000 0.016 0.000 0.000
#> SRR491088 1 0.2966 0.844 0.816 0.000 0.184 0.000 0.000
#> SRR491089 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR491090 1 0.3109 0.828 0.800 0.000 0.200 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR445731 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR490961 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490973 3 0.1003 0.9776 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR490974 3 0.0820 0.9794 0.000 0.000 0.972 0.000 0.012 0.016
#> SRR490975 3 0.0914 0.9789 0.000 0.000 0.968 0.000 0.016 0.016
#> SRR490976 3 0.1003 0.9776 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR490977 3 0.1003 0.9776 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR490978 3 0.1003 0.9776 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR490979 3 0.1003 0.9776 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR490980 3 0.0914 0.9789 0.000 0.000 0.968 0.000 0.016 0.016
#> SRR490981 2 0.3433 0.8306 0.000 0.816 0.012 0.000 0.132 0.040
#> SRR490982 2 0.3433 0.8306 0.000 0.816 0.012 0.000 0.132 0.040
#> SRR490983 2 0.3433 0.8306 0.000 0.816 0.012 0.000 0.132 0.040
#> SRR490984 2 0.3433 0.8306 0.000 0.816 0.012 0.000 0.132 0.040
#> SRR490985 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490986 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490987 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490988 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490989 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490990 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490991 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490992 3 0.0146 0.9800 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490993 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR490994 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR490995 5 0.4459 0.3807 0.000 0.000 0.204 0.000 0.700 0.096
#> SRR490996 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR490997 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR490998 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491000 5 0.4374 0.3901 0.000 0.000 0.192 0.000 0.712 0.096
#> SRR491001 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491002 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491003 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491004 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491005 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491006 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491007 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491008 6 0.2527 1.0000 0.000 0.000 0.168 0.000 0.000 0.832
#> SRR491009 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491022 4 0.3869 -0.3105 0.000 0.000 0.000 0.500 0.500 0.000
#> SRR491023 4 0.3868 -0.2979 0.000 0.000 0.000 0.504 0.496 0.000
#> SRR491024 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 4 0.3515 0.3346 0.000 0.000 0.000 0.676 0.324 0.000
#> SRR491029 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 4 0.3868 -0.2986 0.000 0.000 0.000 0.504 0.496 0.000
#> SRR491032 4 0.3482 0.3573 0.000 0.000 0.000 0.684 0.316 0.000
#> SRR491033 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491034 5 0.3868 0.0637 0.000 0.000 0.000 0.496 0.504 0.000
#> SRR491035 5 0.3868 0.0654 0.000 0.000 0.000 0.496 0.504 0.000
#> SRR491036 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491037 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491038 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491039 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491040 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491041 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491042 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491043 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491045 1 0.0363 0.9366 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR491065 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.1088 0.9191 0.960 0.000 0.000 0.000 0.024 0.016
#> SRR491067 1 0.1003 0.9215 0.964 0.000 0.000 0.000 0.020 0.016
#> SRR491068 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0725 0.9283 0.976 0.000 0.000 0.000 0.012 0.012
#> SRR491070 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491073 1 0.4928 0.4755 0.572 0.000 0.000 0.000 0.352 0.076
#> SRR491074 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491075 1 0.4813 0.5338 0.608 0.000 0.000 0.000 0.316 0.076
#> SRR491076 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0146 0.9385 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491080 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0146 0.9385 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491082 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0146 0.9385 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491084 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0146 0.9385 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491086 1 0.2129 0.8775 0.904 0.000 0.000 0.000 0.056 0.040
#> SRR491087 1 0.0820 0.9261 0.972 0.000 0.000 0.000 0.016 0.012
#> SRR491088 1 0.5040 0.3714 0.516 0.000 0.000 0.000 0.408 0.076
#> SRR491089 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491090 1 0.5069 0.3010 0.484 0.000 0.000 0.000 0.440 0.076
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 13175 rows and 123 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 1 0.987 0.994 0.3614 0.645 0.645
#> 3 3 1 0.977 0.990 0.7080 0.740 0.597
#> 4 4 1 0.986 0.994 0.2213 0.861 0.639
#> 5 5 1 0.971 0.986 0.0389 0.972 0.888
#> 6 6 1 0.982 0.993 0.0155 0.987 0.941
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
#> SRR445718 2 0.000 1.000 0.000 1.000
#> SRR445719 2 0.000 1.000 0.000 1.000
#> SRR445720 2 0.000 1.000 0.000 1.000
#> SRR445721 2 0.000 1.000 0.000 1.000
#> SRR445722 2 0.000 1.000 0.000 1.000
#> SRR445723 2 0.000 1.000 0.000 1.000
#> SRR445724 2 0.000 1.000 0.000 1.000
#> SRR445725 2 0.000 1.000 0.000 1.000
#> SRR445726 2 0.000 1.000 0.000 1.000
#> SRR445727 2 0.000 1.000 0.000 1.000
#> SRR445728 2 0.000 1.000 0.000 1.000
#> SRR445729 2 0.000 1.000 0.000 1.000
#> SRR445730 1 0.000 0.992 1.000 0.000
#> SRR445731 1 0.000 0.992 1.000 0.000
#> SRR490961 2 0.000 1.000 0.000 1.000
#> SRR490962 2 0.000 1.000 0.000 1.000
#> SRR490963 2 0.000 1.000 0.000 1.000
#> SRR490964 2 0.000 1.000 0.000 1.000
#> SRR490965 2 0.000 1.000 0.000 1.000
#> SRR490966 2 0.000 1.000 0.000 1.000
#> SRR490967 2 0.000 1.000 0.000 1.000
#> SRR490968 2 0.000 1.000 0.000 1.000
#> SRR490969 2 0.000 1.000 0.000 1.000
#> SRR490970 2 0.000 1.000 0.000 1.000
#> SRR490971 2 0.000 1.000 0.000 1.000
#> SRR490972 2 0.000 1.000 0.000 1.000
#> SRR490973 1 0.000 0.992 1.000 0.000
#> SRR490974 1 0.000 0.992 1.000 0.000
#> SRR490975 1 0.000 0.992 1.000 0.000
#> SRR490976 1 0.000 0.992 1.000 0.000
#> SRR490977 1 0.000 0.992 1.000 0.000
#> SRR490978 1 0.000 0.992 1.000 0.000
#> SRR490979 1 0.000 0.992 1.000 0.000
#> SRR490980 1 0.000 0.992 1.000 0.000
#> SRR490981 2 0.000 1.000 0.000 1.000
#> SRR490982 2 0.000 1.000 0.000 1.000
#> SRR490983 2 0.000 1.000 0.000 1.000
#> SRR490984 2 0.000 1.000 0.000 1.000
#> SRR490985 1 0.781 0.704 0.768 0.232
#> SRR490986 1 0.000 0.992 1.000 0.000
#> SRR490987 1 0.000 0.992 1.000 0.000
#> SRR490988 1 0.871 0.597 0.708 0.292
#> SRR490989 1 0.722 0.753 0.800 0.200
#> SRR490990 1 0.000 0.992 1.000 0.000
#> SRR490991 1 0.000 0.992 1.000 0.000
#> SRR490992 1 0.000 0.992 1.000 0.000
#> SRR490993 1 0.000 0.992 1.000 0.000
#> SRR490994 1 0.000 0.992 1.000 0.000
#> SRR490995 1 0.000 0.992 1.000 0.000
#> SRR490996 1 0.000 0.992 1.000 0.000
#> SRR490997 1 0.000 0.992 1.000 0.000
#> SRR490998 1 0.000 0.992 1.000 0.000
#> SRR491000 1 0.000 0.992 1.000 0.000
#> SRR491001 1 0.000 0.992 1.000 0.000
#> SRR491002 1 0.000 0.992 1.000 0.000
#> SRR491003 1 0.000 0.992 1.000 0.000
#> SRR491004 1 0.000 0.992 1.000 0.000
#> SRR491005 1 0.000 0.992 1.000 0.000
#> SRR491006 1 0.000 0.992 1.000 0.000
#> SRR491007 1 0.000 0.992 1.000 0.000
#> SRR491008 1 0.000 0.992 1.000 0.000
#> SRR491009 1 0.000 0.992 1.000 0.000
#> SRR491010 1 0.000 0.992 1.000 0.000
#> SRR491011 1 0.000 0.992 1.000 0.000
#> SRR491012 1 0.000 0.992 1.000 0.000
#> SRR491013 1 0.000 0.992 1.000 0.000
#> SRR491014 1 0.000 0.992 1.000 0.000
#> SRR491015 1 0.000 0.992 1.000 0.000
#> SRR491016 1 0.000 0.992 1.000 0.000
#> SRR491017 1 0.000 0.992 1.000 0.000
#> SRR491018 1 0.000 0.992 1.000 0.000
#> SRR491019 1 0.000 0.992 1.000 0.000
#> SRR491020 1 0.000 0.992 1.000 0.000
#> SRR491021 1 0.000 0.992 1.000 0.000
#> SRR491022 1 0.000 0.992 1.000 0.000
#> SRR491023 1 0.000 0.992 1.000 0.000
#> SRR491024 1 0.000 0.992 1.000 0.000
#> SRR491025 1 0.000 0.992 1.000 0.000
#> SRR491026 1 0.000 0.992 1.000 0.000
#> SRR491027 1 0.000 0.992 1.000 0.000
#> SRR491028 1 0.000 0.992 1.000 0.000
#> SRR491029 1 0.000 0.992 1.000 0.000
#> SRR491030 1 0.000 0.992 1.000 0.000
#> SRR491031 1 0.000 0.992 1.000 0.000
#> SRR491032 1 0.000 0.992 1.000 0.000
#> SRR491033 1 0.000 0.992 1.000 0.000
#> SRR491034 1 0.000 0.992 1.000 0.000
#> SRR491035 1 0.000 0.992 1.000 0.000
#> SRR491036 1 0.000 0.992 1.000 0.000
#> SRR491037 1 0.000 0.992 1.000 0.000
#> SRR491038 1 0.000 0.992 1.000 0.000
#> SRR491039 1 0.000 0.992 1.000 0.000
#> SRR491040 1 0.000 0.992 1.000 0.000
#> SRR491041 1 0.000 0.992 1.000 0.000
#> SRR491042 1 0.000 0.992 1.000 0.000
#> SRR491043 1 0.000 0.992 1.000 0.000
#> SRR491045 1 0.000 0.992 1.000 0.000
#> SRR491065 1 0.000 0.992 1.000 0.000
#> SRR491066 1 0.000 0.992 1.000 0.000
#> SRR491067 1 0.000 0.992 1.000 0.000
#> SRR491068 1 0.000 0.992 1.000 0.000
#> SRR491069 1 0.000 0.992 1.000 0.000
#> SRR491070 1 0.000 0.992 1.000 0.000
#> SRR491071 1 0.000 0.992 1.000 0.000
#> SRR491072 1 0.000 0.992 1.000 0.000
#> SRR491073 1 0.000 0.992 1.000 0.000
#> SRR491074 1 0.000 0.992 1.000 0.000
#> SRR491075 1 0.000 0.992 1.000 0.000
#> SRR491076 1 0.000 0.992 1.000 0.000
#> SRR491077 1 0.000 0.992 1.000 0.000
#> SRR491078 1 0.000 0.992 1.000 0.000
#> SRR491079 1 0.000 0.992 1.000 0.000
#> SRR491080 1 0.000 0.992 1.000 0.000
#> SRR491081 1 0.000 0.992 1.000 0.000
#> SRR491082 1 0.000 0.992 1.000 0.000
#> SRR491083 1 0.000 0.992 1.000 0.000
#> SRR491084 1 0.000 0.992 1.000 0.000
#> SRR491085 1 0.000 0.992 1.000 0.000
#> SRR491086 1 0.000 0.992 1.000 0.000
#> SRR491087 1 0.000 0.992 1.000 0.000
#> SRR491088 1 0.000 0.992 1.000 0.000
#> SRR491089 1 0.000 0.992 1.000 0.000
#> SRR491090 1 0.000 0.992 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445730 1 0.0000 0.985 1.000 0.000 0.000
#> SRR445731 1 0.0000 0.985 1.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490973 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490974 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490975 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490976 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490977 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490978 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490979 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490980 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490985 3 0.2448 0.913 0.000 0.076 0.924
#> SRR490986 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490987 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490988 3 0.3116 0.878 0.000 0.108 0.892
#> SRR490989 3 0.1289 0.957 0.000 0.032 0.968
#> SRR490990 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490991 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490992 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490993 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490994 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490995 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490996 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490997 3 0.0000 0.985 0.000 0.000 1.000
#> SRR490998 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491000 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491001 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491002 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491003 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491004 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491005 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491006 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491007 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491008 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491009 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491010 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491011 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491012 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491013 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491014 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491015 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491016 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491017 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491018 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491019 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491020 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491021 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491022 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491023 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491024 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491025 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491026 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491027 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491028 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491029 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491030 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491031 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491032 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491033 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491034 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491035 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491036 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491037 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491038 3 0.0000 0.985 0.000 0.000 1.000
#> SRR491039 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491040 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491041 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491042 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491043 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491045 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491065 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491066 1 0.4555 0.735 0.800 0.000 0.200
#> SRR491067 1 0.0592 0.973 0.988 0.000 0.012
#> SRR491068 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491069 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491070 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491071 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491072 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491073 3 0.4555 0.757 0.200 0.000 0.800
#> SRR491074 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491075 3 0.4555 0.757 0.200 0.000 0.800
#> SRR491076 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491077 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491078 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491079 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491080 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491081 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491082 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491083 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491084 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491085 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491086 1 0.3340 0.843 0.880 0.000 0.120
#> SRR491087 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491088 3 0.4452 0.769 0.192 0.000 0.808
#> SRR491089 1 0.0000 0.985 1.000 0.000 0.000
#> SRR491090 3 0.3412 0.858 0.124 0.000 0.876
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR445731 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490985 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490995 4 0.4072 0.673 0.000 0 0.252 0.748
#> SRR490996 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491000 4 0.2081 0.907 0.000 0 0.084 0.916
#> SRR491001 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491009 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491010 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491011 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491012 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491013 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491014 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491015 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491016 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491017 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491018 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491019 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491020 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491021 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491022 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491023 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491024 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491025 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491026 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491027 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491028 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491029 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491030 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491031 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491032 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491033 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491034 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491035 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491036 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491037 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491038 4 0.0000 0.987 0.000 0 0.000 1.000
#> SRR491039 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491040 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491041 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491042 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491043 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491045 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491065 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491066 1 0.3610 0.753 0.800 0 0.000 0.200
#> SRR491067 1 0.0469 0.977 0.988 0 0.000 0.012
#> SRR491068 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491069 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491070 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491071 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491072 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491073 4 0.1940 0.914 0.076 0 0.000 0.924
#> SRR491074 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491075 4 0.0592 0.974 0.016 0 0.000 0.984
#> SRR491076 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491077 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491078 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491079 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491080 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491081 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491082 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491083 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491084 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491085 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491086 1 0.2647 0.864 0.880 0 0.000 0.120
#> SRR491087 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491088 4 0.0817 0.967 0.024 0 0.000 0.976
#> SRR491089 1 0.0000 0.988 1.000 0 0.000 0.000
#> SRR491090 4 0.0000 0.987 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
#> SRR445718 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.980 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.1197 0.958 0.000 0.000 0.952 0.000 0.048
#> SRR490974 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490975 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490976 3 0.1270 0.956 0.000 0.000 0.948 0.000 0.052
#> SRR490977 3 0.1270 0.956 0.000 0.000 0.948 0.000 0.052
#> SRR490978 3 0.1270 0.956 0.000 0.000 0.948 0.000 0.052
#> SRR490979 3 0.1270 0.956 0.000 0.000 0.948 0.000 0.052
#> SRR490980 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490981 2 0.1908 0.905 0.000 0.908 0.092 0.000 0.000
#> SRR490982 2 0.3707 0.633 0.000 0.716 0.284 0.000 0.000
#> SRR490983 2 0.1410 0.934 0.000 0.940 0.060 0.000 0.000
#> SRR490984 2 0.2020 0.897 0.000 0.900 0.100 0.000 0.000
#> SRR490985 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490986 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490987 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490988 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490989 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490990 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490991 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490992 3 0.0000 0.981 0.000 0.000 1.000 0.000 0.000
#> SRR490993 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 4 0.3882 0.700 0.000 0.000 0.020 0.756 0.224
#> SRR490996 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 4 0.3039 0.771 0.000 0.000 0.000 0.808 0.192
#> SRR491001 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491023 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491024 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491029 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491032 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491033 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491035 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491036 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.3109 0.731 0.800 0.000 0.000 0.200 0.000
#> SRR491067 1 0.0404 0.973 0.988 0.000 0.000 0.012 0.000
#> SRR491068 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491073 4 0.1671 0.904 0.076 0.000 0.000 0.924 0.000
#> SRR491074 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491075 4 0.0609 0.965 0.020 0.000 0.000 0.980 0.000
#> SRR491076 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.2280 0.843 0.880 0.000 0.000 0.120 0.000
#> SRR491087 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491088 4 0.0794 0.958 0.028 0.000 0.000 0.972 0.000
#> SRR491089 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR491090 4 0.0000 0.984 0.000 0.000 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
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR445730 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR445731 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000 0.000 0
#> SRR490973 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490974 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490975 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490976 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490977 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490978 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490979 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490980 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490981 6 0.0000 1.000 0.000 0 0.000 0.000 0.000 1
#> SRR490982 6 0.0000 1.000 0.000 0 0.000 0.000 0.000 1
#> SRR490983 6 0.0000 1.000 0.000 0 0.000 0.000 0.000 1
#> SRR490984 6 0.0000 1.000 0.000 0 0.000 0.000 0.000 1
#> SRR490985 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490986 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490987 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490988 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490989 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490990 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490991 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490992 3 0.0000 1.000 0.000 0 1.000 0.000 0.000 0
#> SRR490993 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR490994 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR490995 4 0.3539 0.688 0.000 0 0.024 0.756 0.220 0
#> SRR490996 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR490997 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR490998 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491000 4 0.2697 0.765 0.000 0 0.000 0.812 0.188 0
#> SRR491001 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491002 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491003 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491004 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491005 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491006 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491007 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491008 5 0.0000 1.000 0.000 0 0.000 0.000 1.000 0
#> SRR491009 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491010 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491011 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491012 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491013 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491014 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491015 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491016 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491017 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491018 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491019 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491020 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491021 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491022 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491023 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491024 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491025 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491026 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491027 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491028 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491029 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491030 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491031 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491032 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491033 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491034 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491035 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491036 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491037 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491038 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
#> SRR491039 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491040 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491041 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491042 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491043 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491045 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491065 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491066 1 0.2793 0.686 0.800 0 0.000 0.200 0.000 0
#> SRR491067 1 0.0363 0.970 0.988 0 0.000 0.012 0.000 0
#> SRR491068 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491069 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491070 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491071 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491072 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491073 4 0.1501 0.894 0.076 0 0.000 0.924 0.000 0
#> SRR491074 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491075 4 0.0547 0.962 0.020 0 0.000 0.980 0.000 0
#> SRR491076 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491077 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491078 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491079 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491080 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491081 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491082 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491083 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491084 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491085 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491086 1 0.2048 0.819 0.880 0 0.000 0.120 0.000 0
#> SRR491087 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491088 4 0.0713 0.953 0.028 0 0.000 0.972 0.000 0
#> SRR491089 1 0.0000 0.984 1.000 0 0.000 0.000 0.000 0
#> SRR491090 4 0.0000 0.982 0.000 0 0.000 1.000 0.000 0
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 13175 rows and 123 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 1.000 1.000 1.000 0.5038 0.497 0.497
#> 3 3 0.594 0.829 0.844 0.2956 0.708 0.479
#> 4 4 0.931 0.899 0.955 0.1478 0.824 0.534
#> 5 5 1.000 0.988 0.995 0.0632 0.939 0.762
#> 6 6 1.000 0.970 0.990 0.0111 0.983 0.919
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 4 5
There is also optional best \(k\) = 2 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
#> SRR445718 2 0.0000 1.000 0.000 1.000
#> SRR445719 2 0.0000 1.000 0.000 1.000
#> SRR445720 2 0.0000 1.000 0.000 1.000
#> SRR445721 2 0.0000 1.000 0.000 1.000
#> SRR445722 2 0.0000 1.000 0.000 1.000
#> SRR445723 2 0.0000 1.000 0.000 1.000
#> SRR445724 2 0.0000 1.000 0.000 1.000
#> SRR445725 2 0.0000 1.000 0.000 1.000
#> SRR445726 2 0.0000 1.000 0.000 1.000
#> SRR445727 2 0.0000 1.000 0.000 1.000
#> SRR445728 2 0.0000 1.000 0.000 1.000
#> SRR445729 2 0.0000 1.000 0.000 1.000
#> SRR445730 1 0.0000 1.000 1.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000
#> SRR490962 2 0.0000 1.000 0.000 1.000
#> SRR490963 2 0.0000 1.000 0.000 1.000
#> SRR490964 2 0.0000 1.000 0.000 1.000
#> SRR490965 2 0.0000 1.000 0.000 1.000
#> SRR490966 2 0.0000 1.000 0.000 1.000
#> SRR490967 2 0.0000 1.000 0.000 1.000
#> SRR490968 2 0.0000 1.000 0.000 1.000
#> SRR490969 2 0.0000 1.000 0.000 1.000
#> SRR490970 2 0.0000 1.000 0.000 1.000
#> SRR490971 2 0.0000 1.000 0.000 1.000
#> SRR490972 2 0.0000 1.000 0.000 1.000
#> SRR490973 2 0.0000 1.000 0.000 1.000
#> SRR490974 2 0.0000 1.000 0.000 1.000
#> SRR490975 2 0.0000 1.000 0.000 1.000
#> SRR490976 2 0.0000 1.000 0.000 1.000
#> SRR490977 2 0.0000 1.000 0.000 1.000
#> SRR490978 2 0.0000 1.000 0.000 1.000
#> SRR490979 2 0.0000 1.000 0.000 1.000
#> SRR490980 2 0.0000 1.000 0.000 1.000
#> SRR490981 2 0.0000 1.000 0.000 1.000
#> SRR490982 2 0.0000 1.000 0.000 1.000
#> SRR490983 2 0.0000 1.000 0.000 1.000
#> SRR490984 2 0.0000 1.000 0.000 1.000
#> SRR490985 2 0.0000 1.000 0.000 1.000
#> SRR490986 2 0.0000 1.000 0.000 1.000
#> SRR490987 2 0.0000 1.000 0.000 1.000
#> SRR490988 2 0.0000 1.000 0.000 1.000
#> SRR490989 2 0.0000 1.000 0.000 1.000
#> SRR490990 2 0.0000 1.000 0.000 1.000
#> SRR490991 2 0.0000 1.000 0.000 1.000
#> SRR490992 2 0.0000 1.000 0.000 1.000
#> SRR490993 2 0.0000 1.000 0.000 1.000
#> SRR490994 2 0.0000 1.000 0.000 1.000
#> SRR490995 2 0.0376 0.996 0.004 0.996
#> SRR490996 2 0.0000 1.000 0.000 1.000
#> SRR490997 2 0.0000 1.000 0.000 1.000
#> SRR490998 2 0.0000 1.000 0.000 1.000
#> SRR491000 2 0.0376 0.996 0.004 0.996
#> SRR491001 2 0.0000 1.000 0.000 1.000
#> SRR491002 2 0.0000 1.000 0.000 1.000
#> SRR491003 2 0.0000 1.000 0.000 1.000
#> SRR491004 2 0.0000 1.000 0.000 1.000
#> SRR491005 2 0.0000 1.000 0.000 1.000
#> SRR491006 2 0.0000 1.000 0.000 1.000
#> SRR491007 2 0.0000 1.000 0.000 1.000
#> SRR491008 2 0.0000 1.000 0.000 1.000
#> SRR491009 1 0.0000 1.000 1.000 0.000
#> SRR491010 1 0.0000 1.000 1.000 0.000
#> SRR491011 1 0.0000 1.000 1.000 0.000
#> SRR491012 1 0.0000 1.000 1.000 0.000
#> SRR491013 1 0.0000 1.000 1.000 0.000
#> SRR491014 1 0.0000 1.000 1.000 0.000
#> SRR491015 1 0.0000 1.000 1.000 0.000
#> SRR491016 1 0.0000 1.000 1.000 0.000
#> SRR491017 1 0.0000 1.000 1.000 0.000
#> SRR491018 1 0.0000 1.000 1.000 0.000
#> SRR491019 1 0.0000 1.000 1.000 0.000
#> SRR491020 1 0.0000 1.000 1.000 0.000
#> SRR491021 1 0.0000 1.000 1.000 0.000
#> SRR491022 1 0.0000 1.000 1.000 0.000
#> SRR491023 1 0.0000 1.000 1.000 0.000
#> SRR491024 1 0.0000 1.000 1.000 0.000
#> SRR491025 1 0.0000 1.000 1.000 0.000
#> SRR491026 1 0.0000 1.000 1.000 0.000
#> SRR491027 1 0.0000 1.000 1.000 0.000
#> SRR491028 1 0.0000 1.000 1.000 0.000
#> SRR491029 1 0.0000 1.000 1.000 0.000
#> SRR491030 1 0.0000 1.000 1.000 0.000
#> SRR491031 1 0.0000 1.000 1.000 0.000
#> SRR491032 1 0.0000 1.000 1.000 0.000
#> SRR491033 1 0.0000 1.000 1.000 0.000
#> SRR491034 1 0.0000 1.000 1.000 0.000
#> SRR491035 1 0.0000 1.000 1.000 0.000
#> SRR491036 1 0.0000 1.000 1.000 0.000
#> SRR491037 1 0.0000 1.000 1.000 0.000
#> SRR491038 1 0.0000 1.000 1.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.000 0.885 0.000 1.000 0.000
#> SRR445719 2 0.000 0.885 0.000 1.000 0.000
#> SRR445720 2 0.000 0.885 0.000 1.000 0.000
#> SRR445721 2 0.000 0.885 0.000 1.000 0.000
#> SRR445722 2 0.000 0.885 0.000 1.000 0.000
#> SRR445723 2 0.000 0.885 0.000 1.000 0.000
#> SRR445724 2 0.000 0.885 0.000 1.000 0.000
#> SRR445725 2 0.000 0.885 0.000 1.000 0.000
#> SRR445726 2 0.000 0.885 0.000 1.000 0.000
#> SRR445727 2 0.000 0.885 0.000 1.000 0.000
#> SRR445728 2 0.000 0.885 0.000 1.000 0.000
#> SRR445729 2 0.000 0.885 0.000 1.000 0.000
#> SRR445730 3 0.000 0.848 0.000 0.000 1.000
#> SRR445731 3 0.000 0.848 0.000 0.000 1.000
#> SRR490961 2 0.000 0.885 0.000 1.000 0.000
#> SRR490962 2 0.000 0.885 0.000 1.000 0.000
#> SRR490963 2 0.000 0.885 0.000 1.000 0.000
#> SRR490964 2 0.000 0.885 0.000 1.000 0.000
#> SRR490965 2 0.000 0.885 0.000 1.000 0.000
#> SRR490966 2 0.000 0.885 0.000 1.000 0.000
#> SRR490967 2 0.000 0.885 0.000 1.000 0.000
#> SRR490968 2 0.000 0.885 0.000 1.000 0.000
#> SRR490969 2 0.000 0.885 0.000 1.000 0.000
#> SRR490970 2 0.000 0.885 0.000 1.000 0.000
#> SRR490971 2 0.000 0.885 0.000 1.000 0.000
#> SRR490972 2 0.000 0.885 0.000 1.000 0.000
#> SRR490973 2 0.687 0.784 0.276 0.680 0.044
#> SRR490974 2 0.662 0.808 0.248 0.708 0.044
#> SRR490975 2 0.659 0.811 0.244 0.712 0.044
#> SRR490976 2 0.687 0.784 0.276 0.680 0.044
#> SRR490977 2 0.687 0.784 0.276 0.680 0.044
#> SRR490978 2 0.687 0.784 0.276 0.680 0.044
#> SRR490979 2 0.687 0.784 0.276 0.680 0.044
#> SRR490980 2 0.659 0.811 0.244 0.712 0.044
#> SRR490981 2 0.388 0.859 0.068 0.888 0.044
#> SRR490982 2 0.388 0.859 0.068 0.888 0.044
#> SRR490983 2 0.388 0.859 0.068 0.888 0.044
#> SRR490984 2 0.388 0.859 0.068 0.888 0.044
#> SRR490985 2 0.659 0.811 0.244 0.712 0.044
#> SRR490986 2 0.659 0.811 0.244 0.712 0.044
#> SRR490987 2 0.659 0.811 0.244 0.712 0.044
#> SRR490988 2 0.659 0.811 0.244 0.712 0.044
#> SRR490989 2 0.659 0.811 0.244 0.712 0.044
#> SRR490990 2 0.659 0.811 0.244 0.712 0.044
#> SRR490991 2 0.659 0.811 0.244 0.712 0.044
#> SRR490992 2 0.659 0.811 0.244 0.712 0.044
#> SRR490993 3 0.603 0.642 0.376 0.000 0.624
#> SRR490994 3 0.603 0.642 0.376 0.000 0.624
#> SRR490995 1 0.566 0.692 0.796 0.052 0.152
#> SRR490996 3 0.603 0.642 0.376 0.000 0.624
#> SRR490997 3 0.603 0.642 0.376 0.000 0.624
#> SRR490998 3 0.603 0.642 0.376 0.000 0.624
#> SRR491000 1 0.566 0.692 0.796 0.052 0.152
#> SRR491001 3 0.603 0.642 0.376 0.000 0.624
#> SRR491002 3 0.603 0.642 0.376 0.000 0.624
#> SRR491003 3 0.603 0.642 0.376 0.000 0.624
#> SRR491004 3 0.603 0.642 0.376 0.000 0.624
#> SRR491005 3 0.603 0.642 0.376 0.000 0.624
#> SRR491006 3 0.603 0.642 0.376 0.000 0.624
#> SRR491007 3 0.603 0.642 0.376 0.000 0.624
#> SRR491008 3 0.603 0.642 0.376 0.000 0.624
#> SRR491009 1 0.418 0.938 0.828 0.000 0.172
#> SRR491010 1 0.418 0.938 0.828 0.000 0.172
#> SRR491011 1 0.418 0.938 0.828 0.000 0.172
#> SRR491012 1 0.418 0.938 0.828 0.000 0.172
#> SRR491013 1 0.418 0.938 0.828 0.000 0.172
#> SRR491014 1 0.418 0.938 0.828 0.000 0.172
#> SRR491015 1 0.418 0.938 0.828 0.000 0.172
#> SRR491016 1 0.418 0.938 0.828 0.000 0.172
#> SRR491017 1 0.418 0.938 0.828 0.000 0.172
#> SRR491018 1 0.418 0.938 0.828 0.000 0.172
#> SRR491019 1 0.418 0.938 0.828 0.000 0.172
#> SRR491020 1 0.418 0.938 0.828 0.000 0.172
#> SRR491021 1 0.418 0.938 0.828 0.000 0.172
#> SRR491022 1 0.565 0.810 0.688 0.000 0.312
#> SRR491023 1 0.546 0.838 0.712 0.000 0.288
#> SRR491024 1 0.418 0.938 0.828 0.000 0.172
#> SRR491025 1 0.418 0.938 0.828 0.000 0.172
#> SRR491026 1 0.418 0.938 0.828 0.000 0.172
#> SRR491027 1 0.418 0.938 0.828 0.000 0.172
#> SRR491028 1 0.470 0.911 0.788 0.000 0.212
#> SRR491029 1 0.418 0.938 0.828 0.000 0.172
#> SRR491030 1 0.418 0.938 0.828 0.000 0.172
#> SRR491031 1 0.470 0.911 0.788 0.000 0.212
#> SRR491032 1 0.475 0.907 0.784 0.000 0.216
#> SRR491033 1 0.418 0.938 0.828 0.000 0.172
#> SRR491034 1 0.565 0.810 0.688 0.000 0.312
#> SRR491035 1 0.576 0.793 0.672 0.000 0.328
#> SRR491036 1 0.418 0.938 0.828 0.000 0.172
#> SRR491037 1 0.418 0.938 0.828 0.000 0.172
#> SRR491038 1 0.418 0.938 0.828 0.000 0.172
#> SRR491039 3 0.000 0.848 0.000 0.000 1.000
#> SRR491040 3 0.000 0.848 0.000 0.000 1.000
#> SRR491041 3 0.000 0.848 0.000 0.000 1.000
#> SRR491042 3 0.000 0.848 0.000 0.000 1.000
#> SRR491043 3 0.000 0.848 0.000 0.000 1.000
#> SRR491045 3 0.000 0.848 0.000 0.000 1.000
#> SRR491065 3 0.000 0.848 0.000 0.000 1.000
#> SRR491066 3 0.000 0.848 0.000 0.000 1.000
#> SRR491067 3 0.000 0.848 0.000 0.000 1.000
#> SRR491068 3 0.000 0.848 0.000 0.000 1.000
#> SRR491069 3 0.000 0.848 0.000 0.000 1.000
#> SRR491070 3 0.000 0.848 0.000 0.000 1.000
#> SRR491071 3 0.000 0.848 0.000 0.000 1.000
#> SRR491072 3 0.000 0.848 0.000 0.000 1.000
#> SRR491073 3 0.571 0.182 0.320 0.000 0.680
#> SRR491074 3 0.000 0.848 0.000 0.000 1.000
#> SRR491075 3 0.571 0.182 0.320 0.000 0.680
#> SRR491076 3 0.000 0.848 0.000 0.000 1.000
#> SRR491077 3 0.000 0.848 0.000 0.000 1.000
#> SRR491078 3 0.000 0.848 0.000 0.000 1.000
#> SRR491079 3 0.000 0.848 0.000 0.000 1.000
#> SRR491080 3 0.000 0.848 0.000 0.000 1.000
#> SRR491081 3 0.000 0.848 0.000 0.000 1.000
#> SRR491082 3 0.000 0.848 0.000 0.000 1.000
#> SRR491083 3 0.000 0.848 0.000 0.000 1.000
#> SRR491084 3 0.000 0.848 0.000 0.000 1.000
#> SRR491085 3 0.000 0.848 0.000 0.000 1.000
#> SRR491086 3 0.000 0.848 0.000 0.000 1.000
#> SRR491087 3 0.000 0.848 0.000 0.000 1.000
#> SRR491088 1 0.614 0.679 0.596 0.000 0.404
#> SRR491089 3 0.000 0.848 0.000 0.000 1.000
#> SRR491090 1 0.610 0.701 0.608 0.000 0.392
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 0.96057 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490981 2 0.4436 0.71564 0.020 0.764 0.216 0.000
#> SRR490982 2 0.4436 0.71564 0.020 0.764 0.216 0.000
#> SRR490983 2 0.4436 0.71564 0.020 0.764 0.216 0.000
#> SRR490984 2 0.4436 0.71564 0.020 0.764 0.216 0.000
#> SRR490985 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490995 1 0.7660 0.00403 0.428 0.000 0.216 0.356
#> SRR490996 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491000 1 0.7660 0.00403 0.428 0.000 0.216 0.356
#> SRR491001 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 1.00000 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0921 0.87871 0.000 0.000 0.028 0.972
#> SRR491022 4 0.7626 0.30435 0.336 0.000 0.216 0.448
#> SRR491023 4 0.7498 0.39707 0.292 0.000 0.216 0.492
#> SRR491024 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491028 4 0.4436 0.68282 0.020 0.000 0.216 0.764
#> SRR491029 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491031 4 0.6664 0.58108 0.164 0.000 0.216 0.620
#> SRR491032 4 0.4538 0.68105 0.024 0.000 0.216 0.760
#> SRR491033 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491034 4 0.7626 0.30435 0.336 0.000 0.216 0.448
#> SRR491035 4 0.7276 0.20925 0.404 0.000 0.148 0.448
#> SRR491036 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491037 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 0.89721 0.000 0.000 0.000 1.000
#> SRR491039 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491073 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491074 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491075 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491076 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491088 1 0.3764 0.70199 0.784 0.000 0.216 0.000
#> SRR491089 1 0.0000 0.95247 1.000 0.000 0.000 0.000
#> SRR491090 1 0.3764 0.70199 0.784 0.000 0.216 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR445730 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR445731 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490981 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR490982 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR490983 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR490984 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR490985 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490995 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR490996 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491000 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491001 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0 1 0.000 0.000
#> SRR491009 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491010 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491011 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491012 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491013 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491014 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491015 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491016 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491017 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491018 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491019 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491020 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491021 4 0.3210 0.731 0.000 0 0 0.788 0.212
#> SRR491022 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491023 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491024 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491025 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491026 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491027 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491028 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491029 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491030 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491031 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491032 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491033 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491034 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491035 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491036 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491037 4 0.0000 0.990 0.000 0 0 1.000 0.000
#> SRR491038 4 0.0162 0.987 0.000 0 0 0.996 0.004
#> SRR491039 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491040 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491041 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491042 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491043 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491045 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491065 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491066 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491067 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491068 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491069 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491070 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491071 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491072 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491073 1 0.3480 0.680 0.752 0 0 0.000 0.248
#> SRR491074 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491075 1 0.2852 0.795 0.828 0 0 0.000 0.172
#> SRR491076 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491077 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491078 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491079 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491080 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491081 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491082 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491083 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491084 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491085 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491086 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491087 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491088 5 0.0000 1.000 0.000 0 0 0.000 1.000
#> SRR491089 1 0.0000 0.986 1.000 0 0 0.000 0.000
#> SRR491090 5 0.0000 1.000 0.000 0 0 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445719 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445720 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445721 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445722 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445723 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445724 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445725 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445726 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445727 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445728 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445729 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR445730 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR445731 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR490961 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490962 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490963 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490964 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490965 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490966 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490967 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490968 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490969 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490970 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490971 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490972 2 0.0000 1.0000 0.000 1 0 0.000 0.000 0
#> SRR490973 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490974 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490975 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490976 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490977 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490978 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490979 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490980 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490981 6 0.0000 1.0000 0.000 0 0 0.000 0.000 1
#> SRR490982 6 0.0000 1.0000 0.000 0 0 0.000 0.000 1
#> SRR490983 6 0.0000 1.0000 0.000 0 0 0.000 0.000 1
#> SRR490984 6 0.0000 1.0000 0.000 0 0 0.000 0.000 1
#> SRR490985 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490986 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490987 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490988 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490989 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490990 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490991 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490992 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490993 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490994 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490995 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR490996 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490997 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR490998 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491000 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR491001 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491002 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491003 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491004 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491005 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491006 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491007 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491008 3 0.0000 1.0000 0.000 0 1 0.000 0.000 0
#> SRR491009 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491010 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491011 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491012 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491013 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491014 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491015 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491016 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491017 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491018 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491019 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491020 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491021 4 0.3860 0.0984 0.000 0 0 0.528 0.472 0
#> SRR491022 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR491023 5 0.0632 0.8885 0.000 0 0 0.024 0.976 0
#> SRR491024 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491025 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491026 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491027 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491028 5 0.1204 0.8585 0.000 0 0 0.056 0.944 0
#> SRR491029 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491030 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491031 5 0.0458 0.8936 0.000 0 0 0.016 0.984 0
#> SRR491032 5 0.1141 0.8630 0.000 0 0 0.052 0.948 0
#> SRR491033 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491034 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR491035 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR491036 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491037 4 0.0000 0.9752 0.000 0 0 1.000 0.000 0
#> SRR491038 4 0.0146 0.9712 0.000 0 0 0.996 0.004 0
#> SRR491039 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491040 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491041 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491042 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491043 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491045 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491065 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491066 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491067 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491068 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491069 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491070 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491071 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491072 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491073 5 0.2854 0.6328 0.208 0 0 0.000 0.792 0
#> SRR491074 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491075 5 0.3727 0.3582 0.388 0 0 0.000 0.612 0
#> SRR491076 1 0.0363 0.9866 0.988 0 0 0.000 0.012 0
#> SRR491077 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491078 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491079 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491080 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491081 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491082 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491083 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491084 1 0.0260 0.9912 0.992 0 0 0.000 0.008 0
#> SRR491085 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491086 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491087 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491088 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
#> SRR491089 1 0.0000 0.9992 1.000 0 0 0.000 0.000 0
#> SRR491090 5 0.0000 0.9004 0.000 0 0 0.000 1.000 0
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 13175 rows and 123 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 0.981 0.990 0.5024 0.497 0.497
#> 3 3 0.759 0.838 0.902 0.2663 0.637 0.403
#> 4 4 1.000 0.994 0.997 0.1873 0.872 0.649
#> 5 5 0.943 0.907 0.915 0.0398 0.943 0.778
#> 6 6 0.945 0.943 0.939 0.0335 0.977 0.889
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 4 5
There is also optional best \(k\) = 2 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
#> SRR445718 2 0.0376 0.981 0.004 0.996
#> SRR445719 2 0.0376 0.981 0.004 0.996
#> SRR445720 2 0.0376 0.981 0.004 0.996
#> SRR445721 2 0.0376 0.981 0.004 0.996
#> SRR445722 2 0.0376 0.981 0.004 0.996
#> SRR445723 2 0.0376 0.981 0.004 0.996
#> SRR445724 2 0.0376 0.981 0.004 0.996
#> SRR445725 2 0.0376 0.981 0.004 0.996
#> SRR445726 2 0.0376 0.981 0.004 0.996
#> SRR445727 2 0.0376 0.981 0.004 0.996
#> SRR445728 2 0.0376 0.981 0.004 0.996
#> SRR445729 2 0.0376 0.981 0.004 0.996
#> SRR445730 1 0.0000 0.998 1.000 0.000
#> SRR445731 1 0.0000 0.998 1.000 0.000
#> SRR490961 2 0.0376 0.981 0.004 0.996
#> SRR490962 2 0.0376 0.981 0.004 0.996
#> SRR490963 2 0.0376 0.981 0.004 0.996
#> SRR490964 2 0.0376 0.981 0.004 0.996
#> SRR490965 2 0.0376 0.981 0.004 0.996
#> SRR490966 2 0.0376 0.981 0.004 0.996
#> SRR490967 2 0.0376 0.981 0.004 0.996
#> SRR490968 2 0.0376 0.981 0.004 0.996
#> SRR490969 2 0.0376 0.981 0.004 0.996
#> SRR490970 2 0.0376 0.981 0.004 0.996
#> SRR490971 2 0.0376 0.981 0.004 0.996
#> SRR490972 2 0.0376 0.981 0.004 0.996
#> SRR490973 2 0.0000 0.980 0.000 1.000
#> SRR490974 2 0.0000 0.980 0.000 1.000
#> SRR490975 2 0.0000 0.980 0.000 1.000
#> SRR490976 2 0.0000 0.980 0.000 1.000
#> SRR490977 2 0.0000 0.980 0.000 1.000
#> SRR490978 2 0.0000 0.980 0.000 1.000
#> SRR490979 2 0.0000 0.980 0.000 1.000
#> SRR490980 2 0.0000 0.980 0.000 1.000
#> SRR490981 2 0.0376 0.981 0.004 0.996
#> SRR490982 2 0.0376 0.981 0.004 0.996
#> SRR490983 2 0.0376 0.981 0.004 0.996
#> SRR490984 2 0.0376 0.981 0.004 0.996
#> SRR490985 2 0.0000 0.980 0.000 1.000
#> SRR490986 2 0.0000 0.980 0.000 1.000
#> SRR490987 2 0.0000 0.980 0.000 1.000
#> SRR490988 2 0.0000 0.980 0.000 1.000
#> SRR490989 2 0.0000 0.980 0.000 1.000
#> SRR490990 2 0.0000 0.980 0.000 1.000
#> SRR490991 2 0.0000 0.980 0.000 1.000
#> SRR490992 2 0.0000 0.980 0.000 1.000
#> SRR490993 2 0.0000 0.980 0.000 1.000
#> SRR490994 2 0.0000 0.980 0.000 1.000
#> SRR490995 2 0.0376 0.981 0.004 0.996
#> SRR490996 2 0.2948 0.940 0.052 0.948
#> SRR490997 2 0.4939 0.883 0.108 0.892
#> SRR490998 2 0.1184 0.971 0.016 0.984
#> SRR491000 2 0.0376 0.981 0.004 0.996
#> SRR491001 2 0.7674 0.731 0.224 0.776
#> SRR491002 2 0.7299 0.761 0.204 0.796
#> SRR491003 2 0.2236 0.955 0.036 0.964
#> SRR491004 2 0.0672 0.976 0.008 0.992
#> SRR491005 2 0.8081 0.692 0.248 0.752
#> SRR491006 2 0.0672 0.976 0.008 0.992
#> SRR491007 2 0.1843 0.961 0.028 0.972
#> SRR491008 2 0.4161 0.909 0.084 0.916
#> SRR491009 1 0.0376 0.998 0.996 0.004
#> SRR491010 1 0.0376 0.998 0.996 0.004
#> SRR491011 1 0.0376 0.998 0.996 0.004
#> SRR491012 1 0.0376 0.998 0.996 0.004
#> SRR491013 1 0.0376 0.998 0.996 0.004
#> SRR491014 1 0.0376 0.998 0.996 0.004
#> SRR491015 1 0.0376 0.998 0.996 0.004
#> SRR491016 1 0.0376 0.998 0.996 0.004
#> SRR491017 1 0.0376 0.998 0.996 0.004
#> SRR491018 1 0.0376 0.998 0.996 0.004
#> SRR491019 1 0.0376 0.998 0.996 0.004
#> SRR491020 1 0.0376 0.998 0.996 0.004
#> SRR491021 1 0.0376 0.998 0.996 0.004
#> SRR491022 1 0.0376 0.998 0.996 0.004
#> SRR491023 1 0.0376 0.998 0.996 0.004
#> SRR491024 1 0.0376 0.998 0.996 0.004
#> SRR491025 1 0.0376 0.998 0.996 0.004
#> SRR491026 1 0.0376 0.998 0.996 0.004
#> SRR491027 1 0.0376 0.998 0.996 0.004
#> SRR491028 1 0.0376 0.998 0.996 0.004
#> SRR491029 1 0.0376 0.998 0.996 0.004
#> SRR491030 1 0.0376 0.998 0.996 0.004
#> SRR491031 1 0.0376 0.998 0.996 0.004
#> SRR491032 1 0.0376 0.998 0.996 0.004
#> SRR491033 1 0.0376 0.998 0.996 0.004
#> SRR491034 1 0.0376 0.998 0.996 0.004
#> SRR491035 1 0.0376 0.998 0.996 0.004
#> SRR491036 1 0.0376 0.998 0.996 0.004
#> SRR491037 1 0.0376 0.998 0.996 0.004
#> SRR491038 1 0.0376 0.998 0.996 0.004
#> SRR491039 1 0.0000 0.998 1.000 0.000
#> SRR491040 1 0.0000 0.998 1.000 0.000
#> SRR491041 1 0.0000 0.998 1.000 0.000
#> SRR491042 1 0.0000 0.998 1.000 0.000
#> SRR491043 1 0.0000 0.998 1.000 0.000
#> SRR491045 1 0.0000 0.998 1.000 0.000
#> SRR491065 1 0.0000 0.998 1.000 0.000
#> SRR491066 1 0.0000 0.998 1.000 0.000
#> SRR491067 1 0.0000 0.998 1.000 0.000
#> SRR491068 1 0.0000 0.998 1.000 0.000
#> SRR491069 1 0.0000 0.998 1.000 0.000
#> SRR491070 1 0.0000 0.998 1.000 0.000
#> SRR491071 1 0.0000 0.998 1.000 0.000
#> SRR491072 1 0.0000 0.998 1.000 0.000
#> SRR491073 1 0.0000 0.998 1.000 0.000
#> SRR491074 1 0.0000 0.998 1.000 0.000
#> SRR491075 1 0.0000 0.998 1.000 0.000
#> SRR491076 1 0.0000 0.998 1.000 0.000
#> SRR491077 1 0.0000 0.998 1.000 0.000
#> SRR491078 1 0.0000 0.998 1.000 0.000
#> SRR491079 1 0.0000 0.998 1.000 0.000
#> SRR491080 1 0.0000 0.998 1.000 0.000
#> SRR491081 1 0.0000 0.998 1.000 0.000
#> SRR491082 1 0.0000 0.998 1.000 0.000
#> SRR491083 1 0.0000 0.998 1.000 0.000
#> SRR491084 1 0.0000 0.998 1.000 0.000
#> SRR491085 1 0.0000 0.998 1.000 0.000
#> SRR491086 1 0.0000 0.998 1.000 0.000
#> SRR491087 1 0.0000 0.998 1.000 0.000
#> SRR491088 1 0.0000 0.998 1.000 0.000
#> SRR491089 1 0.0000 0.998 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445719 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445720 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445721 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445722 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445723 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445724 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445725 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445726 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445727 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445728 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445729 2 0.6045 0.996 0.380 0.620 0.000
#> SRR445730 1 0.6045 0.997 0.620 0.000 0.380
#> SRR445731 1 0.6045 0.997 0.620 0.000 0.380
#> SRR490961 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490962 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490963 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490964 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490965 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490966 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490967 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490968 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490969 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490970 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490971 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490972 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490973 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490974 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490975 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490976 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490977 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490978 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490979 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490980 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490981 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490982 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490983 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490984 2 0.6045 0.996 0.380 0.620 0.000
#> SRR490985 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490986 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490987 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490988 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490989 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490990 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490991 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490992 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490993 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490994 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490995 2 0.5058 0.863 0.244 0.756 0.000
#> SRR490996 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490997 3 0.6045 0.758 0.000 0.380 0.620
#> SRR490998 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491000 2 0.6045 0.996 0.380 0.620 0.000
#> SRR491001 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491002 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491003 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491004 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491005 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491006 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491007 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491008 3 0.6045 0.758 0.000 0.380 0.620
#> SRR491009 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491010 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491011 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491012 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491013 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491014 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491015 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491016 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491017 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491018 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491019 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491020 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491021 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491022 3 0.6295 -0.753 0.472 0.000 0.528
#> SRR491023 3 0.0237 0.653 0.004 0.000 0.996
#> SRR491024 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491025 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491026 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491027 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491028 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491029 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491030 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491031 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491032 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491033 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491034 3 0.6008 -0.511 0.372 0.000 0.628
#> SRR491035 1 0.6267 0.890 0.548 0.000 0.452
#> SRR491036 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491037 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491038 3 0.0000 0.659 0.000 0.000 1.000
#> SRR491039 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491040 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491041 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491042 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491043 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491045 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491065 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491066 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491067 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491068 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491069 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491070 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491071 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491072 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491073 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491074 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491075 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491076 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491077 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491078 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491079 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491080 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491081 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491082 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491083 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491084 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491085 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491086 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491087 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491088 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491089 1 0.6045 0.997 0.620 0.000 0.380
#> SRR491090 1 0.6045 0.997 0.620 0.000 0.380
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445719 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445720 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445721 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445722 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445723 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445724 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445725 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445726 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445727 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445728 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445729 2 0.0000 0.990 0 1.000 0.000 0
#> SRR445730 1 0.0000 1.000 1 0.000 0.000 0
#> SRR445731 1 0.0000 1.000 1 0.000 0.000 0
#> SRR490961 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490962 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490963 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490964 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490965 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490966 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490967 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490968 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490969 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490970 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490971 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490972 2 0.0000 0.990 0 1.000 0.000 0
#> SRR490973 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490974 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490975 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490976 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490977 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490978 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490979 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490980 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490981 2 0.0469 0.982 0 0.988 0.012 0
#> SRR490982 2 0.0817 0.972 0 0.976 0.024 0
#> SRR490983 2 0.0469 0.982 0 0.988 0.012 0
#> SRR490984 2 0.0817 0.972 0 0.976 0.024 0
#> SRR490985 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490986 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490987 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490988 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490989 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490990 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490991 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490992 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490993 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490994 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490995 3 0.2216 0.897 0 0.092 0.908 0
#> SRR490996 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490997 3 0.0000 0.997 0 0.000 1.000 0
#> SRR490998 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491000 2 0.3649 0.747 0 0.796 0.204 0
#> SRR491001 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491002 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491003 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491004 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491005 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491006 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491007 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491008 3 0.0000 0.997 0 0.000 1.000 0
#> SRR491009 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491010 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491011 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491012 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491013 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491014 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491015 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491016 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491017 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491018 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491019 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491020 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491021 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491022 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491023 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491024 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491025 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491026 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491027 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491028 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491029 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491030 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491031 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491032 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491033 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491034 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491035 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491036 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491037 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491038 4 0.0000 1.000 0 0.000 0.000 1
#> SRR491039 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491040 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491041 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491042 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491043 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491045 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491065 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491066 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491067 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491068 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491069 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491070 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491071 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491072 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491073 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491074 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491075 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491076 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491077 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491078 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491079 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491080 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491081 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491082 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491083 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491084 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491085 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491086 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491087 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491088 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491089 1 0.0000 1.000 1 0.000 0.000 0
#> SRR491090 1 0.0000 1.000 1 0.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.0290 0.776 0.000 0.000 0.992 0.000 0.008
#> SRR490974 3 0.0290 0.776 0.000 0.000 0.992 0.000 0.008
#> SRR490975 3 0.0290 0.776 0.000 0.000 0.992 0.000 0.008
#> SRR490976 3 0.1908 0.649 0.000 0.000 0.908 0.000 0.092
#> SRR490977 3 0.3452 0.186 0.000 0.000 0.756 0.000 0.244
#> SRR490978 3 0.0609 0.763 0.000 0.000 0.980 0.000 0.020
#> SRR490979 3 0.1341 0.713 0.000 0.000 0.944 0.000 0.056
#> SRR490980 3 0.0290 0.776 0.000 0.000 0.992 0.000 0.008
#> SRR490981 3 0.4276 0.411 0.000 0.380 0.616 0.000 0.004
#> SRR490982 3 0.4276 0.411 0.000 0.380 0.616 0.000 0.004
#> SRR490983 3 0.4288 0.401 0.000 0.384 0.612 0.000 0.004
#> SRR490984 3 0.4276 0.411 0.000 0.380 0.616 0.000 0.004
#> SRR490985 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490986 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490987 3 0.0162 0.777 0.000 0.000 0.996 0.000 0.004
#> SRR490988 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490989 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490990 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490991 3 0.0000 0.778 0.000 0.000 1.000 0.000 0.000
#> SRR490992 3 0.0290 0.776 0.000 0.000 0.992 0.000 0.008
#> SRR490993 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR490994 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR490995 3 0.0566 0.769 0.000 0.012 0.984 0.000 0.004
#> SRR490996 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR490997 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR490998 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491000 3 0.3662 0.560 0.000 0.252 0.744 0.000 0.004
#> SRR491001 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491002 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491003 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491004 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491005 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491006 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491007 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491008 5 0.4150 1.000 0.000 0.000 0.388 0.000 0.612
#> SRR491009 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.3336 0.808 0.000 0.000 0.000 0.772 0.228
#> SRR491022 4 0.4276 0.718 0.004 0.000 0.000 0.616 0.380
#> SRR491023 4 0.4114 0.724 0.000 0.000 0.000 0.624 0.376
#> SRR491024 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.4074 0.732 0.000 0.000 0.000 0.636 0.364
#> SRR491029 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.4114 0.724 0.000 0.000 0.000 0.624 0.376
#> SRR491032 4 0.4074 0.732 0.000 0.000 0.000 0.636 0.364
#> SRR491033 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.4126 0.721 0.000 0.000 0.000 0.620 0.380
#> SRR491035 4 0.4126 0.721 0.000 0.000 0.000 0.620 0.380
#> SRR491036 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.918 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491073 1 0.0510 0.988 0.984 0.000 0.000 0.000 0.016
#> SRR491074 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491075 1 0.0404 0.991 0.988 0.000 0.000 0.000 0.012
#> SRR491076 1 0.0162 0.996 0.996 0.000 0.000 0.000 0.004
#> SRR491077 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0404 0.991 0.988 0.000 0.000 0.000 0.012
#> SRR491087 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491088 1 0.0510 0.988 0.984 0.000 0.000 0.000 0.016
#> SRR491089 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR491090 1 0.0510 0.988 0.984 0.000 0.000 0.000 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.977 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR445731 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR490961 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490962 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490963 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490964 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490965 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490966 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490967 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490968 2 0.1219 0.976 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR490969 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR490970 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR490971 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR490972 2 0.1075 0.977 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR490973 3 0.1267 0.858 0.000 0.000 0.940 0.000 0.000 0.060
#> SRR490974 3 0.1267 0.858 0.000 0.000 0.940 0.000 0.000 0.060
#> SRR490975 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490976 3 0.3023 0.670 0.000 0.000 0.768 0.000 0.000 0.232
#> SRR490977 3 0.3847 0.101 0.000 0.000 0.544 0.000 0.000 0.456
#> SRR490978 3 0.1444 0.850 0.000 0.000 0.928 0.000 0.000 0.072
#> SRR490979 3 0.2416 0.772 0.000 0.000 0.844 0.000 0.000 0.156
#> SRR490980 3 0.0790 0.871 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR490981 3 0.3953 0.767 0.000 0.060 0.744 0.000 0.196 0.000
#> SRR490982 3 0.3892 0.773 0.000 0.060 0.752 0.000 0.188 0.000
#> SRR490983 3 0.3892 0.774 0.000 0.060 0.752 0.000 0.188 0.000
#> SRR490984 3 0.3892 0.773 0.000 0.060 0.752 0.000 0.188 0.000
#> SRR490985 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490986 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490987 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490988 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490989 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490990 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490991 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490992 3 0.0146 0.881 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR490993 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR490994 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR490995 3 0.2980 0.809 0.000 0.012 0.808 0.000 0.180 0.000
#> SRR490996 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR490997 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR490998 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491000 3 0.3683 0.786 0.000 0.048 0.768 0.000 0.184 0.000
#> SRR491001 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491002 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491003 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491004 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491005 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491006 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491007 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491008 6 0.1267 1.000 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR491009 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0146 0.996 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491013 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 5 0.3782 0.776 0.000 0.000 0.000 0.412 0.588 0.000
#> SRR491022 5 0.3518 0.936 0.012 0.000 0.000 0.256 0.732 0.000
#> SRR491023 5 0.3309 0.947 0.000 0.000 0.000 0.280 0.720 0.000
#> SRR491024 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 5 0.3428 0.935 0.000 0.000 0.000 0.304 0.696 0.000
#> SRR491029 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 5 0.3288 0.947 0.000 0.000 0.000 0.276 0.724 0.000
#> SRR491032 5 0.3428 0.935 0.000 0.000 0.000 0.304 0.696 0.000
#> SRR491033 4 0.0146 0.996 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491034 5 0.3175 0.939 0.000 0.000 0.000 0.256 0.744 0.000
#> SRR491035 5 0.3175 0.939 0.000 0.000 0.000 0.256 0.744 0.000
#> SRR491036 4 0.0146 0.996 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491037 4 0.0146 0.996 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491038 4 0.0146 0.996 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491039 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491040 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR491041 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR491042 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR491043 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR491045 1 0.0291 0.969 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR491065 1 0.1682 0.950 0.928 0.000 0.000 0.000 0.020 0.052
#> SRR491066 1 0.1418 0.958 0.944 0.000 0.000 0.000 0.032 0.024
#> SRR491067 1 0.1480 0.955 0.940 0.000 0.000 0.000 0.020 0.040
#> SRR491068 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491069 1 0.1461 0.956 0.940 0.000 0.000 0.000 0.016 0.044
#> SRR491070 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.1334 0.958 0.948 0.000 0.000 0.000 0.020 0.032
#> SRR491072 1 0.0520 0.968 0.984 0.000 0.000 0.000 0.008 0.008
#> SRR491073 1 0.2389 0.927 0.888 0.000 0.000 0.000 0.060 0.052
#> SRR491074 1 0.0291 0.970 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR491075 1 0.2389 0.927 0.888 0.000 0.000 0.000 0.060 0.052
#> SRR491076 1 0.2134 0.937 0.904 0.000 0.000 0.000 0.044 0.052
#> SRR491077 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491078 1 0.0291 0.970 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR491079 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491081 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491082 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491083 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR491084 1 0.0000 0.970 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0405 0.968 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR491086 1 0.2389 0.927 0.888 0.000 0.000 0.000 0.060 0.052
#> SRR491087 1 0.1633 0.952 0.932 0.000 0.000 0.000 0.024 0.044
#> SRR491088 1 0.2389 0.927 0.888 0.000 0.000 0.000 0.060 0.052
#> SRR491089 1 0.0146 0.970 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR491090 1 0.2389 0.927 0.888 0.000 0.000 0.000 0.060 0.052
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 13175 rows and 123 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 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 0.990 0.981 0.4908 0.497 0.497
#> 3 3 0.831 0.916 0.862 0.2219 0.884 0.767
#> 4 4 0.842 0.699 0.878 0.2055 0.907 0.755
#> 5 5 0.880 0.722 0.861 0.0111 0.939 0.818
#> 6 6 0.871 0.897 0.912 0.0509 0.892 0.663
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
#> SRR445718 2 0.0000 0.985 0.000 1.000
#> SRR445719 2 0.0000 0.985 0.000 1.000
#> SRR445720 2 0.0000 0.985 0.000 1.000
#> SRR445721 2 0.0000 0.985 0.000 1.000
#> SRR445722 2 0.0000 0.985 0.000 1.000
#> SRR445723 2 0.0000 0.985 0.000 1.000
#> SRR445724 2 0.0000 0.985 0.000 1.000
#> SRR445725 2 0.0000 0.985 0.000 1.000
#> SRR445726 2 0.0000 0.985 0.000 1.000
#> SRR445727 2 0.0000 0.985 0.000 1.000
#> SRR445728 2 0.0000 0.985 0.000 1.000
#> SRR445729 2 0.0000 0.985 0.000 1.000
#> SRR445730 1 0.1633 0.998 0.976 0.024
#> SRR445731 1 0.1633 0.998 0.976 0.024
#> SRR490961 2 0.0000 0.985 0.000 1.000
#> SRR490962 2 0.0000 0.985 0.000 1.000
#> SRR490963 2 0.0000 0.985 0.000 1.000
#> SRR490964 2 0.0000 0.985 0.000 1.000
#> SRR490965 2 0.0000 0.985 0.000 1.000
#> SRR490966 2 0.0000 0.985 0.000 1.000
#> SRR490967 2 0.0000 0.985 0.000 1.000
#> SRR490968 2 0.0000 0.985 0.000 1.000
#> SRR490969 2 0.0000 0.985 0.000 1.000
#> SRR490970 2 0.0000 0.985 0.000 1.000
#> SRR490971 2 0.0000 0.985 0.000 1.000
#> SRR490972 2 0.0000 0.985 0.000 1.000
#> SRR490973 2 0.1843 0.984 0.028 0.972
#> SRR490974 2 0.1843 0.984 0.028 0.972
#> SRR490975 2 0.1843 0.984 0.028 0.972
#> SRR490976 2 0.1843 0.984 0.028 0.972
#> SRR490977 2 0.1843 0.984 0.028 0.972
#> SRR490978 2 0.1843 0.984 0.028 0.972
#> SRR490979 2 0.1843 0.984 0.028 0.972
#> SRR490980 2 0.1843 0.984 0.028 0.972
#> SRR490981 2 0.0000 0.985 0.000 1.000
#> SRR490982 2 0.0000 0.985 0.000 1.000
#> SRR490983 2 0.0000 0.985 0.000 1.000
#> SRR490984 2 0.0000 0.985 0.000 1.000
#> SRR490985 2 0.1843 0.984 0.028 0.972
#> SRR490986 2 0.1843 0.984 0.028 0.972
#> SRR490987 2 0.1843 0.984 0.028 0.972
#> SRR490988 2 0.1843 0.984 0.028 0.972
#> SRR490989 2 0.1843 0.984 0.028 0.972
#> SRR490990 2 0.1843 0.984 0.028 0.972
#> SRR490991 2 0.1843 0.984 0.028 0.972
#> SRR490992 2 0.1843 0.984 0.028 0.972
#> SRR490993 2 0.1843 0.984 0.028 0.972
#> SRR490994 2 0.1843 0.984 0.028 0.972
#> SRR490995 2 0.1633 0.965 0.024 0.976
#> SRR490996 2 0.1843 0.984 0.028 0.972
#> SRR490997 2 0.1843 0.984 0.028 0.972
#> SRR490998 2 0.1843 0.984 0.028 0.972
#> SRR491000 2 0.1633 0.965 0.024 0.976
#> SRR491001 2 0.1843 0.984 0.028 0.972
#> SRR491002 2 0.1843 0.984 0.028 0.972
#> SRR491003 2 0.1843 0.984 0.028 0.972
#> SRR491004 2 0.1843 0.984 0.028 0.972
#> SRR491005 2 0.1843 0.984 0.028 0.972
#> SRR491006 2 0.1843 0.984 0.028 0.972
#> SRR491007 2 0.1843 0.984 0.028 0.972
#> SRR491008 2 0.1843 0.984 0.028 0.972
#> SRR491009 1 0.1633 0.998 0.976 0.024
#> SRR491010 1 0.1633 0.998 0.976 0.024
#> SRR491011 1 0.1633 0.998 0.976 0.024
#> SRR491012 1 0.1633 0.998 0.976 0.024
#> SRR491013 1 0.1633 0.998 0.976 0.024
#> SRR491014 1 0.1633 0.998 0.976 0.024
#> SRR491015 1 0.1633 0.998 0.976 0.024
#> SRR491016 1 0.1633 0.998 0.976 0.024
#> SRR491017 1 0.1633 0.998 0.976 0.024
#> SRR491018 1 0.1633 0.998 0.976 0.024
#> SRR491019 1 0.1633 0.998 0.976 0.024
#> SRR491020 1 0.1633 0.998 0.976 0.024
#> SRR491021 1 0.1633 0.998 0.976 0.024
#> SRR491022 1 0.1633 0.998 0.976 0.024
#> SRR491023 1 0.1633 0.998 0.976 0.024
#> SRR491024 1 0.1633 0.998 0.976 0.024
#> SRR491025 1 0.1633 0.998 0.976 0.024
#> SRR491026 1 0.1633 0.998 0.976 0.024
#> SRR491027 1 0.1633 0.998 0.976 0.024
#> SRR491028 1 0.1633 0.998 0.976 0.024
#> SRR491029 1 0.1633 0.998 0.976 0.024
#> SRR491030 1 0.1633 0.998 0.976 0.024
#> SRR491031 1 0.0672 0.984 0.992 0.008
#> SRR491032 1 0.1633 0.998 0.976 0.024
#> SRR491033 1 0.1633 0.998 0.976 0.024
#> SRR491034 1 0.1633 0.998 0.976 0.024
#> SRR491035 1 0.1633 0.998 0.976 0.024
#> SRR491036 1 0.1633 0.998 0.976 0.024
#> SRR491037 1 0.1633 0.998 0.976 0.024
#> SRR491038 1 0.1633 0.998 0.976 0.024
#> SRR491039 1 0.1633 0.998 0.976 0.024
#> SRR491040 1 0.1633 0.998 0.976 0.024
#> SRR491041 1 0.1633 0.998 0.976 0.024
#> SRR491042 1 0.1633 0.998 0.976 0.024
#> SRR491043 1 0.1633 0.998 0.976 0.024
#> SRR491045 1 0.1633 0.998 0.976 0.024
#> SRR491065 1 0.1633 0.998 0.976 0.024
#> SRR491066 1 0.1633 0.998 0.976 0.024
#> SRR491067 1 0.1633 0.998 0.976 0.024
#> SRR491068 1 0.1633 0.998 0.976 0.024
#> SRR491069 1 0.1633 0.998 0.976 0.024
#> SRR491070 1 0.1633 0.998 0.976 0.024
#> SRR491071 1 0.1633 0.998 0.976 0.024
#> SRR491072 1 0.1633 0.998 0.976 0.024
#> SRR491073 1 0.0000 0.976 1.000 0.000
#> SRR491074 1 0.1633 0.998 0.976 0.024
#> SRR491075 1 0.0000 0.976 1.000 0.000
#> SRR491076 1 0.1633 0.998 0.976 0.024
#> SRR491077 1 0.1633 0.998 0.976 0.024
#> SRR491078 1 0.1633 0.998 0.976 0.024
#> SRR491079 1 0.1633 0.998 0.976 0.024
#> SRR491080 1 0.1633 0.998 0.976 0.024
#> SRR491081 1 0.1633 0.998 0.976 0.024
#> SRR491082 1 0.1633 0.998 0.976 0.024
#> SRR491083 1 0.1633 0.998 0.976 0.024
#> SRR491084 1 0.1633 0.998 0.976 0.024
#> SRR491085 1 0.1633 0.998 0.976 0.024
#> SRR491086 1 0.1633 0.998 0.976 0.024
#> SRR491087 1 0.1633 0.998 0.976 0.024
#> SRR491088 1 0.0000 0.976 1.000 0.000
#> SRR491089 1 0.1633 0.998 0.976 0.024
#> SRR491090 1 0.0000 0.976 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445719 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445720 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445721 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445722 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445723 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445724 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445725 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445726 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445727 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445728 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445729 2 0.5835 1.000 0.000 0.660 0.340
#> SRR445730 1 0.0000 0.944 1.000 0.000 0.000
#> SRR445731 1 0.0000 0.944 1.000 0.000 0.000
#> SRR490961 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490962 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490963 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490964 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490965 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490966 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490967 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490968 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490969 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490970 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490971 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490972 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490973 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490974 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490975 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490976 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490977 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490978 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490979 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490980 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490981 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490982 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490983 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490984 2 0.5835 1.000 0.000 0.660 0.340
#> SRR490985 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490986 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490987 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490988 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490989 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490990 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490991 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490992 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490993 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490994 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490995 3 0.6192 -0.411 0.000 0.420 0.580
#> SRR490996 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490997 3 0.1643 0.957 0.044 0.000 0.956
#> SRR490998 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491000 3 0.6192 -0.411 0.000 0.420 0.580
#> SRR491001 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491002 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491003 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491004 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491005 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491006 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491007 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491008 3 0.1643 0.957 0.044 0.000 0.956
#> SRR491009 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491010 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491011 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491012 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491013 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491014 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491015 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491016 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491017 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491018 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491019 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491020 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491021 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491022 1 0.6625 0.716 0.660 0.316 0.024
#> SRR491023 1 0.6625 0.716 0.660 0.316 0.024
#> SRR491024 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491025 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491026 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491027 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491028 1 0.5919 0.767 0.724 0.260 0.016
#> SRR491029 1 0.0983 0.940 0.980 0.016 0.004
#> SRR491030 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491031 1 0.7186 0.687 0.624 0.336 0.040
#> SRR491032 1 0.5919 0.767 0.724 0.260 0.016
#> SRR491033 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491034 1 0.6843 0.700 0.640 0.332 0.028
#> SRR491035 1 0.6843 0.700 0.640 0.332 0.028
#> SRR491036 1 0.1647 0.931 0.960 0.036 0.004
#> SRR491037 1 0.0592 0.943 0.988 0.012 0.000
#> SRR491038 1 0.1647 0.931 0.960 0.036 0.004
#> SRR491039 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491040 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491041 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491042 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491043 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491045 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491065 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491066 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491067 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491068 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491069 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491070 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491071 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491072 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491073 1 0.7238 0.681 0.628 0.328 0.044
#> SRR491074 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491075 1 0.7238 0.681 0.628 0.328 0.044
#> SRR491076 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491077 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491078 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491079 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491080 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491081 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491082 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491083 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491084 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491085 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491086 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491087 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491088 1 0.7238 0.681 0.628 0.328 0.044
#> SRR491089 1 0.0000 0.944 1.000 0.000 0.000
#> SRR491090 1 0.7238 0.681 0.628 0.328 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490974 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490975 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490976 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490977 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490978 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490979 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490980 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490981 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490982 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490983 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490984 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490985 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490986 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490987 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490988 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490989 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490990 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490991 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490992 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490993 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490994 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490995 3 0.5611 0.2814 0.000 0.412 0.564 0.024
#> SRR490996 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490997 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR490998 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491000 3 0.5611 0.2814 0.000 0.412 0.564 0.024
#> SRR491001 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491002 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491003 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491004 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491005 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491006 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491007 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491008 3 0.0188 0.9678 0.004 0.000 0.996 0.000
#> SRR491009 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491010 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491011 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491012 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491013 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491014 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491015 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491016 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491017 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491018 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491019 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491020 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491021 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491022 4 0.2589 0.5480 0.116 0.000 0.000 0.884
#> SRR491023 4 0.2589 0.5480 0.116 0.000 0.000 0.884
#> SRR491024 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491025 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491026 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491027 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491028 4 0.3444 0.4971 0.184 0.000 0.000 0.816
#> SRR491029 4 0.5000 -0.3082 0.496 0.000 0.000 0.504
#> SRR491030 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491031 4 0.1211 0.5513 0.040 0.000 0.000 0.960
#> SRR491032 4 0.3444 0.4971 0.184 0.000 0.000 0.816
#> SRR491033 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491034 4 0.1557 0.5572 0.056 0.000 0.000 0.944
#> SRR491035 4 0.1557 0.5572 0.056 0.000 0.000 0.944
#> SRR491036 4 0.4916 -0.0809 0.424 0.000 0.000 0.576
#> SRR491037 1 0.5000 0.2615 0.504 0.000 0.000 0.496
#> SRR491038 4 0.4916 -0.0809 0.424 0.000 0.000 0.576
#> SRR491039 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491073 4 0.5000 0.2827 0.496 0.000 0.000 0.504
#> SRR491074 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491075 4 0.5000 0.2827 0.496 0.000 0.000 0.504
#> SRR491076 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491088 4 0.5000 0.2827 0.496 0.000 0.000 0.504
#> SRR491089 1 0.0000 0.6562 1.000 0.000 0.000 0.000
#> SRR491090 4 0.5000 0.2827 0.496 0.000 0.000 0.504
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445719 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445720 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445721 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445722 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445723 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445724 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445725 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445726 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445727 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445728 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445729 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR445730 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR445731 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR490961 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490962 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490963 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490964 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490965 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490966 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490967 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490968 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490969 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490970 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490971 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490972 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490973 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490974 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490975 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490976 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490977 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490978 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490979 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490980 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490981 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490982 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490983 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490984 2 0.000 1.000 0.000 1 0 0.000 0
#> SRR490985 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490986 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490987 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490988 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490989 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490990 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490991 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490992 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490993 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490994 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490995 5 0.000 1.000 0.000 0 0 0.000 1
#> SRR490996 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490997 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR490998 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491000 5 0.000 1.000 0.000 0 0 0.000 1
#> SRR491001 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491002 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491003 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491004 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491005 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491006 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491007 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491008 3 0.000 1.000 0.000 0 1 0.000 0
#> SRR491009 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491010 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491011 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491012 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491013 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491014 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491015 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491016 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491017 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491018 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491019 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491020 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491021 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491022 1 0.426 -0.424 0.564 0 0 0.436 0
#> SRR491023 1 0.426 -0.424 0.564 0 0 0.436 0
#> SRR491024 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491025 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491026 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491027 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491028 1 0.409 -0.296 0.632 0 0 0.368 0
#> SRR491029 1 0.029 0.476 0.992 0 0 0.008 0
#> SRR491030 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491031 4 0.430 0.339 0.472 0 0 0.528 0
#> SRR491032 1 0.409 -0.296 0.632 0 0 0.368 0
#> SRR491033 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491034 4 0.430 0.354 0.488 0 0 0.512 0
#> SRR491035 4 0.430 0.354 0.488 0 0 0.512 0
#> SRR491036 1 0.218 0.350 0.888 0 0 0.112 0
#> SRR491037 1 0.000 0.485 1.000 0 0 0.000 0
#> SRR491038 1 0.218 0.350 0.888 0 0 0.112 0
#> SRR491039 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491040 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491041 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491042 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491043 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491045 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491065 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491066 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491067 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491068 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491069 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491070 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491071 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491072 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491073 4 0.000 0.517 0.000 0 0 1.000 0
#> SRR491074 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491075 4 0.000 0.517 0.000 0 0 1.000 0
#> SRR491076 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491077 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491078 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491079 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491080 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491081 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491082 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491083 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491084 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491085 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491086 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491087 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491088 4 0.000 0.517 0.000 0 0 1.000 0
#> SRR491089 1 0.431 0.573 0.504 0 0 0.496 0
#> SRR491090 4 0.000 0.517 0.000 0 0 1.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445719 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445720 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445721 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445722 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445723 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445724 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445725 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445726 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445727 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445728 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445729 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR445730 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR445731 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR490961 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490962 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490963 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490964 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490965 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490966 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490967 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490968 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490969 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490970 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490971 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490972 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490973 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490974 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490975 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490976 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490977 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490978 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490979 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490980 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490981 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490982 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490983 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490984 2 0.000 1.000 0.000 1 0 0.000 0.000 0
#> SRR490985 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490986 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490987 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490988 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490989 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490990 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490991 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490992 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490993 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490994 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490995 6 0.000 1.000 0.000 0 0 0.000 0.000 1
#> SRR490996 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490997 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR490998 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491000 6 0.000 1.000 0.000 0 0 0.000 0.000 1
#> SRR491001 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491002 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491003 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491004 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491005 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491006 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491007 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491008 3 0.000 1.000 0.000 0 1 0.000 0.000 0
#> SRR491009 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491010 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491011 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491012 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491013 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491014 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491015 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491016 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491017 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491018 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491019 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491020 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491021 4 0.302 0.865 0.232 0 0 0.768 0.000 0
#> SRR491022 4 0.398 -0.422 0.004 0 0 0.540 0.456 0
#> SRR491023 4 0.398 -0.422 0.004 0 0 0.540 0.456 0
#> SRR491024 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491025 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491026 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491027 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491028 4 0.467 -0.276 0.044 0 0 0.532 0.424 0
#> SRR491029 4 0.337 0.858 0.232 0 0 0.756 0.012 0
#> SRR491030 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491031 5 0.384 0.369 0.000 0 0 0.452 0.548 0
#> SRR491032 4 0.467 -0.276 0.044 0 0 0.532 0.424 0
#> SRR491033 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491034 5 0.399 0.371 0.004 0 0 0.464 0.532 0
#> SRR491035 5 0.399 0.371 0.004 0 0 0.464 0.532 0
#> SRR491036 4 0.500 0.738 0.228 0 0 0.636 0.136 0
#> SRR491037 4 0.305 0.863 0.236 0 0 0.764 0.000 0
#> SRR491038 4 0.500 0.738 0.228 0 0 0.636 0.136 0
#> SRR491039 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491040 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491041 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491042 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491043 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491045 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491065 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491066 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491067 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491068 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491069 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491070 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491071 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491072 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491073 5 0.315 0.494 0.252 0 0 0.000 0.748 0
#> SRR491074 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491075 5 0.315 0.494 0.252 0 0 0.000 0.748 0
#> SRR491076 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491077 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491078 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491079 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491080 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491081 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491082 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491083 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491084 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491085 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491086 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491087 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491088 5 0.315 0.494 0.252 0 0 0.000 0.748 0
#> SRR491089 1 0.000 1.000 1.000 0 0 0.000 0.000 0
#> SRR491090 5 0.315 0.494 0.252 0 0 0.000 0.748 0
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 13175 rows and 123 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 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 0.589 0.876 0.908 0.4639 0.497 0.497
#> 3 3 0.639 0.875 0.811 0.3227 0.884 0.767
#> 4 4 0.741 0.928 0.835 0.1455 0.864 0.643
#> 5 5 0.809 0.934 0.891 0.0753 0.971 0.881
#> 6 6 0.906 0.881 0.881 0.0424 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] 5
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
#> SRR445718 2 0.1633 0.818 0.024 0.976
#> SRR445719 2 0.1633 0.818 0.024 0.976
#> SRR445720 2 0.1633 0.818 0.024 0.976
#> SRR445721 2 0.1633 0.818 0.024 0.976
#> SRR445722 2 0.1633 0.818 0.024 0.976
#> SRR445723 2 0.1633 0.818 0.024 0.976
#> SRR445724 2 0.1633 0.818 0.024 0.976
#> SRR445725 2 0.1633 0.818 0.024 0.976
#> SRR445726 2 0.1633 0.818 0.024 0.976
#> SRR445727 2 0.1633 0.818 0.024 0.976
#> SRR445728 2 0.1633 0.818 0.024 0.976
#> SRR445729 2 0.1633 0.818 0.024 0.976
#> SRR445730 1 0.2043 0.979 0.968 0.032
#> SRR445731 1 0.2043 0.979 0.968 0.032
#> SRR490961 2 0.1633 0.818 0.024 0.976
#> SRR490962 2 0.1633 0.818 0.024 0.976
#> SRR490963 2 0.1633 0.818 0.024 0.976
#> SRR490964 2 0.1633 0.818 0.024 0.976
#> SRR490965 2 0.1633 0.818 0.024 0.976
#> SRR490966 2 0.1633 0.818 0.024 0.976
#> SRR490967 2 0.1633 0.818 0.024 0.976
#> SRR490968 2 0.1633 0.818 0.024 0.976
#> SRR490969 2 0.1633 0.818 0.024 0.976
#> SRR490970 2 0.1633 0.818 0.024 0.976
#> SRR490971 2 0.1633 0.818 0.024 0.976
#> SRR490972 2 0.1633 0.818 0.024 0.976
#> SRR490973 2 0.8081 0.791 0.248 0.752
#> SRR490974 2 0.8081 0.791 0.248 0.752
#> SRR490975 2 0.8081 0.791 0.248 0.752
#> SRR490976 2 0.8081 0.791 0.248 0.752
#> SRR490977 2 0.8081 0.791 0.248 0.752
#> SRR490978 2 0.8081 0.791 0.248 0.752
#> SRR490979 2 0.8081 0.791 0.248 0.752
#> SRR490980 2 0.8081 0.791 0.248 0.752
#> SRR490981 2 0.0938 0.814 0.012 0.988
#> SRR490982 2 0.0938 0.814 0.012 0.988
#> SRR490983 2 0.0938 0.814 0.012 0.988
#> SRR490984 2 0.0938 0.814 0.012 0.988
#> SRR490985 2 0.8081 0.791 0.248 0.752
#> SRR490986 2 0.8081 0.791 0.248 0.752
#> SRR490987 2 0.8081 0.791 0.248 0.752
#> SRR490988 2 0.8081 0.791 0.248 0.752
#> SRR490989 2 0.8081 0.791 0.248 0.752
#> SRR490990 2 0.8081 0.791 0.248 0.752
#> SRR490991 2 0.8081 0.791 0.248 0.752
#> SRR490992 2 0.8081 0.791 0.248 0.752
#> SRR490993 2 0.9754 0.618 0.408 0.592
#> SRR490994 2 0.9754 0.618 0.408 0.592
#> SRR490995 2 0.8327 0.785 0.264 0.736
#> SRR490996 2 0.9754 0.618 0.408 0.592
#> SRR490997 2 0.9754 0.618 0.408 0.592
#> SRR490998 2 0.9754 0.618 0.408 0.592
#> SRR491000 2 0.8327 0.785 0.264 0.736
#> SRR491001 2 0.9754 0.618 0.408 0.592
#> SRR491002 2 0.9754 0.618 0.408 0.592
#> SRR491003 2 0.9754 0.618 0.408 0.592
#> SRR491004 2 0.9754 0.618 0.408 0.592
#> SRR491005 2 0.9754 0.618 0.408 0.592
#> SRR491006 2 0.9754 0.618 0.408 0.592
#> SRR491007 2 0.9754 0.618 0.408 0.592
#> SRR491008 2 0.9754 0.618 0.408 0.592
#> SRR491009 1 0.0000 0.978 1.000 0.000
#> SRR491010 1 0.0000 0.978 1.000 0.000
#> SRR491011 1 0.0000 0.978 1.000 0.000
#> SRR491012 1 0.0000 0.978 1.000 0.000
#> SRR491013 1 0.0000 0.978 1.000 0.000
#> SRR491014 1 0.0000 0.978 1.000 0.000
#> SRR491015 1 0.0000 0.978 1.000 0.000
#> SRR491016 1 0.0000 0.978 1.000 0.000
#> SRR491017 1 0.0000 0.978 1.000 0.000
#> SRR491018 1 0.0000 0.978 1.000 0.000
#> SRR491019 1 0.0000 0.978 1.000 0.000
#> SRR491020 1 0.0000 0.978 1.000 0.000
#> SRR491021 1 0.0000 0.978 1.000 0.000
#> SRR491022 1 0.0000 0.978 1.000 0.000
#> SRR491023 1 0.0000 0.978 1.000 0.000
#> SRR491024 1 0.0000 0.978 1.000 0.000
#> SRR491025 1 0.0000 0.978 1.000 0.000
#> SRR491026 1 0.0000 0.978 1.000 0.000
#> SRR491027 1 0.0000 0.978 1.000 0.000
#> SRR491028 1 0.0000 0.978 1.000 0.000
#> SRR491029 1 0.0000 0.978 1.000 0.000
#> SRR491030 1 0.0000 0.978 1.000 0.000
#> SRR491031 1 0.0000 0.978 1.000 0.000
#> SRR491032 1 0.0000 0.978 1.000 0.000
#> SRR491033 1 0.0000 0.978 1.000 0.000
#> SRR491034 1 0.0000 0.978 1.000 0.000
#> SRR491035 1 0.0000 0.978 1.000 0.000
#> SRR491036 1 0.0000 0.978 1.000 0.000
#> SRR491037 1 0.0000 0.978 1.000 0.000
#> SRR491038 1 0.0000 0.978 1.000 0.000
#> SRR491039 1 0.2043 0.979 0.968 0.032
#> SRR491040 1 0.2043 0.979 0.968 0.032
#> SRR491041 1 0.2043 0.979 0.968 0.032
#> SRR491042 1 0.2043 0.979 0.968 0.032
#> SRR491043 1 0.2043 0.979 0.968 0.032
#> SRR491045 1 0.2043 0.979 0.968 0.032
#> SRR491065 1 0.2043 0.979 0.968 0.032
#> SRR491066 1 0.2043 0.979 0.968 0.032
#> SRR491067 1 0.2043 0.979 0.968 0.032
#> SRR491068 1 0.2043 0.979 0.968 0.032
#> SRR491069 1 0.2043 0.979 0.968 0.032
#> SRR491070 1 0.2043 0.979 0.968 0.032
#> SRR491071 1 0.2043 0.979 0.968 0.032
#> SRR491072 1 0.2043 0.979 0.968 0.032
#> SRR491073 1 0.1414 0.980 0.980 0.020
#> SRR491074 1 0.2043 0.979 0.968 0.032
#> SRR491075 1 0.1414 0.980 0.980 0.020
#> SRR491076 1 0.2043 0.979 0.968 0.032
#> SRR491077 1 0.2043 0.979 0.968 0.032
#> SRR491078 1 0.2043 0.979 0.968 0.032
#> SRR491079 1 0.2043 0.979 0.968 0.032
#> SRR491080 1 0.2043 0.979 0.968 0.032
#> SRR491081 1 0.2043 0.979 0.968 0.032
#> SRR491082 1 0.2043 0.979 0.968 0.032
#> SRR491083 1 0.2043 0.979 0.968 0.032
#> SRR491084 1 0.2043 0.979 0.968 0.032
#> SRR491085 1 0.2043 0.979 0.968 0.032
#> SRR491086 1 0.2043 0.979 0.968 0.032
#> SRR491087 1 0.2043 0.979 0.968 0.032
#> SRR491088 1 0.1414 0.980 0.980 0.020
#> SRR491089 1 0.2043 0.979 0.968 0.032
#> SRR491090 1 0.1414 0.980 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.6282 0.985 0.004 0.612 0.384
#> SRR445719 2 0.6282 0.985 0.004 0.612 0.384
#> SRR445720 2 0.6282 0.985 0.004 0.612 0.384
#> SRR445721 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445722 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445723 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445724 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445725 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445726 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445727 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445728 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445729 2 0.6264 0.987 0.004 0.616 0.380
#> SRR445730 1 0.6512 0.822 0.676 0.300 0.024
#> SRR445731 1 0.6512 0.822 0.676 0.300 0.024
#> SRR490961 2 0.6314 0.984 0.004 0.604 0.392
#> SRR490962 2 0.6314 0.984 0.004 0.604 0.392
#> SRR490963 2 0.6314 0.984 0.004 0.604 0.392
#> SRR490964 2 0.6314 0.984 0.004 0.604 0.392
#> SRR490965 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490966 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490967 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490968 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490969 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490970 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490971 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490972 2 0.6298 0.986 0.004 0.608 0.388
#> SRR490973 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490974 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490975 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490976 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490977 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490978 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490979 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490980 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490981 2 0.6026 0.985 0.000 0.624 0.376
#> SRR490982 2 0.6026 0.985 0.000 0.624 0.376
#> SRR490983 2 0.6026 0.985 0.000 0.624 0.376
#> SRR490984 2 0.6026 0.985 0.000 0.624 0.376
#> SRR490985 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490986 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490987 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490988 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490989 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490990 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490991 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490992 3 0.1643 0.930 0.044 0.000 0.956
#> SRR490993 3 0.4092 0.919 0.088 0.036 0.876
#> SRR490994 3 0.4092 0.919 0.088 0.036 0.876
#> SRR490995 3 0.3764 0.865 0.040 0.068 0.892
#> SRR490996 3 0.4092 0.919 0.088 0.036 0.876
#> SRR490997 3 0.4092 0.919 0.088 0.036 0.876
#> SRR490998 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491000 3 0.3764 0.865 0.040 0.068 0.892
#> SRR491001 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491002 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491003 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491004 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491005 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491006 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491007 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491008 3 0.4092 0.919 0.088 0.036 0.876
#> SRR491009 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491010 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491011 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491012 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491013 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491014 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491015 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491016 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491017 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491018 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491019 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491020 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491021 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491022 1 0.0237 0.800 0.996 0.000 0.004
#> SRR491023 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491024 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491025 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491026 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491027 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491028 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491029 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491030 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491031 1 0.4281 0.743 0.872 0.072 0.056
#> SRR491032 1 0.1411 0.795 0.964 0.000 0.036
#> SRR491033 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491034 1 0.0000 0.800 1.000 0.000 0.000
#> SRR491035 1 0.0592 0.802 0.988 0.012 0.000
#> SRR491036 1 0.3583 0.763 0.900 0.044 0.056
#> SRR491037 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491038 1 0.1643 0.794 0.956 0.000 0.044
#> SRR491039 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491040 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491041 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491042 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491043 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491045 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491065 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491066 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491067 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491068 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491069 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491070 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491071 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491072 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491073 1 0.6912 0.784 0.628 0.344 0.028
#> SRR491074 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491075 1 0.6912 0.784 0.628 0.344 0.028
#> SRR491076 1 0.6570 0.819 0.668 0.308 0.024
#> SRR491077 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491078 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491079 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491080 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491081 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491082 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491083 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491084 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491085 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491086 1 0.6570 0.819 0.668 0.308 0.024
#> SRR491087 1 0.6541 0.821 0.672 0.304 0.024
#> SRR491088 1 0.6912 0.784 0.628 0.344 0.028
#> SRR491089 1 0.6512 0.822 0.676 0.300 0.024
#> SRR491090 1 0.6912 0.784 0.628 0.344 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.1545 0.962 0.040 0.952 0.008 0.000
#> SRR445719 2 0.1545 0.962 0.040 0.952 0.008 0.000
#> SRR445720 2 0.1545 0.962 0.040 0.952 0.008 0.000
#> SRR445721 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445722 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445723 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445724 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445725 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445726 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445727 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445728 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445729 2 0.1151 0.968 0.024 0.968 0.008 0.000
#> SRR445730 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR445731 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR490961 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490962 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490963 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490964 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490965 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490966 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490967 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490968 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490969 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490970 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490971 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490972 2 0.0707 0.970 0.020 0.980 0.000 0.000
#> SRR490973 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490974 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490975 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490976 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490977 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490978 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490979 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490980 3 0.3933 0.935 0.000 0.200 0.792 0.008
#> SRR490981 2 0.1792 0.935 0.068 0.932 0.000 0.000
#> SRR490982 2 0.1792 0.935 0.068 0.932 0.000 0.000
#> SRR490983 2 0.1792 0.935 0.068 0.932 0.000 0.000
#> SRR490984 2 0.1792 0.935 0.068 0.932 0.000 0.000
#> SRR490985 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490986 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490987 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490988 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490989 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490990 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490991 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490992 3 0.4381 0.933 0.012 0.200 0.780 0.008
#> SRR490993 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR490994 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR490995 3 0.7588 0.526 0.268 0.068 0.584 0.080
#> SRR490996 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR490997 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR490998 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491000 3 0.7588 0.526 0.268 0.068 0.584 0.080
#> SRR491001 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491002 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491003 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491004 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491005 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491006 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491007 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491008 3 0.5728 0.931 0.064 0.184 0.732 0.020
#> SRR491009 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491022 4 0.1022 0.955 0.000 0.000 0.032 0.968
#> SRR491023 4 0.1022 0.955 0.000 0.000 0.032 0.968
#> SRR491024 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0592 0.966 0.000 0.000 0.016 0.984
#> SRR491029 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491031 4 0.5979 0.610 0.136 0.000 0.172 0.692
#> SRR491032 4 0.0921 0.958 0.000 0.000 0.028 0.972
#> SRR491033 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491034 4 0.1398 0.945 0.004 0.000 0.040 0.956
#> SRR491035 4 0.1398 0.945 0.004 0.000 0.040 0.956
#> SRR491036 4 0.2489 0.878 0.068 0.000 0.020 0.912
#> SRR491037 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491039 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491040 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491041 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491042 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491043 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491045 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491065 1 0.5110 0.942 0.656 0.000 0.016 0.328
#> SRR491066 1 0.5110 0.942 0.656 0.000 0.016 0.328
#> SRR491067 1 0.5110 0.942 0.656 0.000 0.016 0.328
#> SRR491068 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491069 1 0.5110 0.942 0.656 0.000 0.016 0.328
#> SRR491070 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491071 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491072 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491073 1 0.7234 0.516 0.544 0.000 0.204 0.252
#> SRR491074 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491075 1 0.7205 0.542 0.548 0.000 0.200 0.252
#> SRR491076 1 0.5213 0.939 0.652 0.000 0.020 0.328
#> SRR491077 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491078 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491079 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491080 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491081 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491082 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491083 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491084 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491085 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491086 1 0.5213 0.939 0.652 0.000 0.020 0.328
#> SRR491087 1 0.5110 0.942 0.656 0.000 0.016 0.328
#> SRR491088 1 0.7277 0.503 0.536 0.000 0.204 0.260
#> SRR491089 1 0.4999 0.944 0.660 0.000 0.012 0.328
#> SRR491090 1 0.7277 0.503 0.536 0.000 0.204 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.2450 0.926 0.000 0.900 0.000 0.052 0.048
#> SRR445719 2 0.2450 0.926 0.000 0.900 0.000 0.052 0.048
#> SRR445720 2 0.2450 0.926 0.000 0.900 0.000 0.052 0.048
#> SRR445721 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445722 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445723 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445724 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445725 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445726 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445727 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445728 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445729 2 0.1485 0.945 0.000 0.948 0.000 0.032 0.020
#> SRR445730 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR445731 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR490961 2 0.0968 0.945 0.000 0.972 0.004 0.012 0.012
#> SRR490962 2 0.0968 0.945 0.000 0.972 0.004 0.012 0.012
#> SRR490963 2 0.0968 0.945 0.000 0.972 0.004 0.012 0.012
#> SRR490964 2 0.0968 0.945 0.000 0.972 0.004 0.012 0.012
#> SRR490965 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490966 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490967 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490968 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490969 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490970 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490971 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490972 2 0.0727 0.947 0.000 0.980 0.004 0.004 0.012
#> SRR490973 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490974 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490975 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490976 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490977 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490978 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490979 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490980 3 0.2124 0.922 0.004 0.096 0.900 0.000 0.000
#> SRR490981 2 0.3022 0.861 0.000 0.848 0.012 0.004 0.136
#> SRR490982 2 0.3022 0.861 0.000 0.848 0.012 0.004 0.136
#> SRR490983 2 0.3022 0.861 0.000 0.848 0.012 0.004 0.136
#> SRR490984 2 0.3022 0.861 0.000 0.848 0.012 0.004 0.136
#> SRR490985 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490986 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490987 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490988 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490989 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490990 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490991 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490992 3 0.2804 0.917 0.004 0.096 0.880 0.012 0.008
#> SRR490993 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR490994 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR490995 5 0.3594 0.589 0.000 0.028 0.096 0.032 0.844
#> SRR490996 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR490997 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR490998 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491000 5 0.3594 0.589 0.000 0.028 0.096 0.032 0.844
#> SRR491001 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491002 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491003 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491004 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491005 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491006 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491007 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491008 3 0.5336 0.907 0.004 0.096 0.744 0.056 0.100
#> SRR491009 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491010 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491011 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491012 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491013 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491014 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491015 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491016 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491017 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491018 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491019 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491020 4 0.2424 0.981 0.132 0.000 0.000 0.868 0.000
#> SRR491021 4 0.2818 0.978 0.132 0.000 0.012 0.856 0.000
#> SRR491022 4 0.3786 0.954 0.132 0.000 0.044 0.816 0.008
#> SRR491023 4 0.3786 0.954 0.132 0.000 0.044 0.816 0.008
#> SRR491024 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491025 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491026 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491027 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491028 4 0.3663 0.957 0.132 0.000 0.044 0.820 0.004
#> SRR491029 4 0.2818 0.978 0.132 0.000 0.012 0.856 0.000
#> SRR491030 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491031 5 0.6400 0.403 0.096 0.000 0.040 0.288 0.576
#> SRR491032 4 0.3663 0.957 0.132 0.000 0.044 0.820 0.004
#> SRR491033 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491034 4 0.3996 0.948 0.132 0.000 0.044 0.808 0.016
#> SRR491035 4 0.4247 0.932 0.136 0.000 0.056 0.792 0.016
#> SRR491036 4 0.3272 0.965 0.120 0.000 0.016 0.848 0.016
#> SRR491037 4 0.2818 0.979 0.132 0.000 0.012 0.856 0.000
#> SRR491038 4 0.2920 0.976 0.132 0.000 0.016 0.852 0.000
#> SRR491039 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491040 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491041 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491042 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491043 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491045 1 0.0290 0.989 0.992 0.000 0.008 0.000 0.000
#> SRR491065 1 0.0404 0.984 0.988 0.000 0.012 0.000 0.000
#> SRR491066 1 0.0566 0.981 0.984 0.000 0.012 0.000 0.004
#> SRR491067 1 0.0566 0.981 0.984 0.000 0.012 0.000 0.004
#> SRR491068 1 0.0162 0.990 0.996 0.000 0.004 0.000 0.000
#> SRR491069 1 0.0566 0.981 0.984 0.000 0.012 0.000 0.004
#> SRR491070 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491073 5 0.5244 0.720 0.312 0.000 0.012 0.044 0.632
#> SRR491074 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491075 5 0.5355 0.680 0.340 0.000 0.012 0.044 0.604
#> SRR491076 1 0.1281 0.952 0.956 0.000 0.012 0.000 0.032
#> SRR491077 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0162 0.990 0.996 0.000 0.004 0.000 0.000
#> SRR491082 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0162 0.990 0.996 0.000 0.004 0.000 0.000
#> SRR491084 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0162 0.990 0.996 0.000 0.004 0.000 0.000
#> SRR491086 1 0.1281 0.952 0.956 0.000 0.012 0.000 0.032
#> SRR491087 1 0.0566 0.981 0.984 0.000 0.012 0.000 0.004
#> SRR491088 5 0.4836 0.731 0.304 0.000 0.000 0.044 0.652
#> SRR491089 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR491090 5 0.4836 0.731 0.304 0.000 0.000 0.044 0.652
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.3989 0.879 0.000 0.800 0.044 0.016 0.020 0.120
#> SRR445719 2 0.3989 0.879 0.000 0.800 0.044 0.016 0.020 0.120
#> SRR445720 2 0.3989 0.879 0.000 0.800 0.044 0.016 0.020 0.120
#> SRR445721 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445722 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445723 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445724 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445725 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445726 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445727 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445728 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445729 2 0.2914 0.907 0.000 0.860 0.040 0.004 0.004 0.092
#> SRR445730 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR445731 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR490961 2 0.2446 0.901 0.000 0.900 0.044 0.008 0.008 0.040
#> SRR490962 2 0.2446 0.901 0.000 0.900 0.044 0.008 0.008 0.040
#> SRR490963 2 0.2446 0.901 0.000 0.900 0.044 0.008 0.008 0.040
#> SRR490964 2 0.2446 0.901 0.000 0.900 0.044 0.008 0.008 0.040
#> SRR490965 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490966 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490967 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490968 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490969 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490970 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490971 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490972 2 0.1737 0.911 0.000 0.932 0.040 0.008 0.000 0.020
#> SRR490973 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490974 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490975 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490976 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490977 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490978 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490979 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490980 3 0.4593 0.824 0.004 0.000 0.672 0.020 0.028 0.276
#> SRR490981 2 0.4601 0.799 0.000 0.760 0.040 0.008 0.108 0.084
#> SRR490982 2 0.4601 0.799 0.000 0.760 0.040 0.008 0.108 0.084
#> SRR490983 2 0.4601 0.799 0.000 0.760 0.040 0.008 0.108 0.084
#> SRR490984 2 0.4601 0.799 0.000 0.760 0.040 0.008 0.108 0.084
#> SRR490985 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490986 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490987 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490988 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490989 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490990 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490991 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490992 3 0.3930 0.809 0.004 0.000 0.628 0.004 0.000 0.364
#> SRR490993 3 0.0405 0.799 0.004 0.000 0.988 0.008 0.000 0.000
#> SRR490994 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR490995 5 0.5188 0.659 0.000 0.020 0.064 0.012 0.656 0.248
#> SRR490996 3 0.0405 0.799 0.004 0.000 0.988 0.008 0.000 0.000
#> SRR490997 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR490998 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491000 5 0.5188 0.659 0.000 0.020 0.064 0.012 0.656 0.248
#> SRR491001 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491002 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491003 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491004 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491005 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491006 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491007 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491008 3 0.0291 0.799 0.004 0.000 0.992 0.004 0.000 0.000
#> SRR491009 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491010 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491011 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491012 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491013 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491014 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491015 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491016 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491017 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491018 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491019 4 0.2240 0.924 0.056 0.008 0.000 0.904 0.000 0.032
#> SRR491020 4 0.1204 0.934 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR491021 4 0.1349 0.933 0.056 0.000 0.000 0.940 0.000 0.004
#> SRR491022 4 0.4586 0.801 0.060 0.000 0.000 0.744 0.052 0.144
#> SRR491023 4 0.4529 0.805 0.056 0.000 0.000 0.748 0.052 0.144
#> SRR491024 4 0.2341 0.924 0.056 0.012 0.000 0.900 0.000 0.032
#> SRR491025 4 0.2128 0.925 0.056 0.004 0.000 0.908 0.000 0.032
#> SRR491026 4 0.2341 0.924 0.056 0.012 0.000 0.900 0.000 0.032
#> SRR491027 4 0.2341 0.924 0.056 0.012 0.000 0.900 0.000 0.032
#> SRR491028 4 0.4406 0.812 0.056 0.000 0.000 0.756 0.044 0.144
#> SRR491029 4 0.1606 0.932 0.056 0.008 0.000 0.932 0.000 0.004
#> SRR491030 4 0.2341 0.924 0.056 0.012 0.000 0.900 0.000 0.032
#> SRR491031 5 0.5753 0.474 0.032 0.000 0.000 0.224 0.600 0.144
#> SRR491032 4 0.4431 0.812 0.056 0.000 0.000 0.756 0.048 0.140
#> SRR491033 4 0.2341 0.924 0.056 0.012 0.000 0.900 0.000 0.032
#> SRR491034 4 0.5397 0.720 0.060 0.000 0.000 0.676 0.120 0.144
#> SRR491035 4 0.5644 0.680 0.064 0.000 0.000 0.648 0.120 0.168
#> SRR491036 4 0.3695 0.852 0.040 0.008 0.000 0.824 0.096 0.032
#> SRR491037 4 0.2240 0.925 0.056 0.008 0.000 0.904 0.000 0.032
#> SRR491038 4 0.1606 0.932 0.056 0.008 0.000 0.932 0.000 0.004
#> SRR491039 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491040 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491041 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491042 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491043 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491045 1 0.0622 0.974 0.980 0.008 0.000 0.000 0.000 0.012
#> SRR491065 1 0.1010 0.959 0.960 0.004 0.000 0.000 0.000 0.036
#> SRR491066 1 0.1268 0.954 0.952 0.004 0.000 0.000 0.008 0.036
#> SRR491067 1 0.1010 0.959 0.960 0.004 0.000 0.000 0.000 0.036
#> SRR491068 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.1155 0.957 0.956 0.004 0.000 0.000 0.004 0.036
#> SRR491070 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0291 0.978 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR491072 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491073 5 0.3917 0.778 0.204 0.000 0.000 0.012 0.752 0.032
#> SRR491074 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491075 5 0.4056 0.757 0.224 0.000 0.000 0.012 0.732 0.032
#> SRR491076 1 0.1867 0.927 0.924 0.004 0.000 0.000 0.036 0.036
#> SRR491077 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.1867 0.927 0.924 0.004 0.000 0.000 0.036 0.036
#> SRR491087 1 0.1010 0.959 0.960 0.004 0.000 0.000 0.000 0.036
#> SRR491088 5 0.3078 0.793 0.192 0.000 0.000 0.012 0.796 0.000
#> SRR491089 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491090 5 0.3078 0.793 0.192 0.000 0.000 0.012 0.796 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", "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 13175 rows and 123 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 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 0.998 0.998 0.5038 0.497 0.497
#> 3 3 1.000 0.998 0.996 0.2302 0.884 0.767
#> 4 4 1.000 0.999 0.998 0.2184 0.864 0.643
#> 5 5 0.969 0.962 0.969 0.0353 0.971 0.882
#> 6 6 0.963 0.960 0.947 0.0320 0.972 0.874
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
#> SRR445718 2 0.0000 0.998 0.000 1.000
#> SRR445719 2 0.0000 0.998 0.000 1.000
#> SRR445720 2 0.0000 0.998 0.000 1.000
#> SRR445721 2 0.0000 0.998 0.000 1.000
#> SRR445722 2 0.0000 0.998 0.000 1.000
#> SRR445723 2 0.0000 0.998 0.000 1.000
#> SRR445724 2 0.0000 0.998 0.000 1.000
#> SRR445725 2 0.0000 0.998 0.000 1.000
#> SRR445726 2 0.0000 0.998 0.000 1.000
#> SRR445727 2 0.0000 0.998 0.000 1.000
#> SRR445728 2 0.0000 0.998 0.000 1.000
#> SRR445729 2 0.0000 0.998 0.000 1.000
#> SRR445730 1 0.0376 0.998 0.996 0.004
#> SRR445731 1 0.0376 0.998 0.996 0.004
#> SRR490961 2 0.0000 0.998 0.000 1.000
#> SRR490962 2 0.0000 0.998 0.000 1.000
#> SRR490963 2 0.0000 0.998 0.000 1.000
#> SRR490964 2 0.0000 0.998 0.000 1.000
#> SRR490965 2 0.0000 0.998 0.000 1.000
#> SRR490966 2 0.0000 0.998 0.000 1.000
#> SRR490967 2 0.0000 0.998 0.000 1.000
#> SRR490968 2 0.0000 0.998 0.000 1.000
#> SRR490969 2 0.0000 0.998 0.000 1.000
#> SRR490970 2 0.0000 0.998 0.000 1.000
#> SRR490971 2 0.0000 0.998 0.000 1.000
#> SRR490972 2 0.0000 0.998 0.000 1.000
#> SRR490973 2 0.0376 0.998 0.004 0.996
#> SRR490974 2 0.0376 0.998 0.004 0.996
#> SRR490975 2 0.0376 0.998 0.004 0.996
#> SRR490976 2 0.0376 0.998 0.004 0.996
#> SRR490977 2 0.0376 0.998 0.004 0.996
#> SRR490978 2 0.0376 0.998 0.004 0.996
#> SRR490979 2 0.0376 0.998 0.004 0.996
#> SRR490980 2 0.0376 0.998 0.004 0.996
#> SRR490981 2 0.0000 0.998 0.000 1.000
#> SRR490982 2 0.0000 0.998 0.000 1.000
#> SRR490983 2 0.0000 0.998 0.000 1.000
#> SRR490984 2 0.0000 0.998 0.000 1.000
#> SRR490985 2 0.0376 0.998 0.004 0.996
#> SRR490986 2 0.0376 0.998 0.004 0.996
#> SRR490987 2 0.0376 0.998 0.004 0.996
#> SRR490988 2 0.0376 0.998 0.004 0.996
#> SRR490989 2 0.0376 0.998 0.004 0.996
#> SRR490990 2 0.0376 0.998 0.004 0.996
#> SRR490991 2 0.0376 0.998 0.004 0.996
#> SRR490992 2 0.0376 0.998 0.004 0.996
#> SRR490993 2 0.0376 0.998 0.004 0.996
#> SRR490994 2 0.0376 0.998 0.004 0.996
#> SRR490995 2 0.0376 0.998 0.004 0.996
#> SRR490996 2 0.0376 0.998 0.004 0.996
#> SRR490997 2 0.0376 0.998 0.004 0.996
#> SRR490998 2 0.0376 0.998 0.004 0.996
#> SRR491000 2 0.0376 0.998 0.004 0.996
#> SRR491001 2 0.0376 0.998 0.004 0.996
#> SRR491002 2 0.0376 0.998 0.004 0.996
#> SRR491003 2 0.0376 0.998 0.004 0.996
#> SRR491004 2 0.0376 0.998 0.004 0.996
#> SRR491005 2 0.0376 0.998 0.004 0.996
#> SRR491006 2 0.0376 0.998 0.004 0.996
#> SRR491007 2 0.0376 0.998 0.004 0.996
#> SRR491008 2 0.0376 0.998 0.004 0.996
#> SRR491009 1 0.0000 0.998 1.000 0.000
#> SRR491010 1 0.0000 0.998 1.000 0.000
#> SRR491011 1 0.0000 0.998 1.000 0.000
#> SRR491012 1 0.0000 0.998 1.000 0.000
#> SRR491013 1 0.0000 0.998 1.000 0.000
#> SRR491014 1 0.0000 0.998 1.000 0.000
#> SRR491015 1 0.0000 0.998 1.000 0.000
#> SRR491016 1 0.0000 0.998 1.000 0.000
#> SRR491017 1 0.0000 0.998 1.000 0.000
#> SRR491018 1 0.0000 0.998 1.000 0.000
#> SRR491019 1 0.0000 0.998 1.000 0.000
#> SRR491020 1 0.0000 0.998 1.000 0.000
#> SRR491021 1 0.0000 0.998 1.000 0.000
#> SRR491022 1 0.0000 0.998 1.000 0.000
#> SRR491023 1 0.0000 0.998 1.000 0.000
#> SRR491024 1 0.0000 0.998 1.000 0.000
#> SRR491025 1 0.0000 0.998 1.000 0.000
#> SRR491026 1 0.0000 0.998 1.000 0.000
#> SRR491027 1 0.0000 0.998 1.000 0.000
#> SRR491028 1 0.0000 0.998 1.000 0.000
#> SRR491029 1 0.0000 0.998 1.000 0.000
#> SRR491030 1 0.0000 0.998 1.000 0.000
#> SRR491031 1 0.0000 0.998 1.000 0.000
#> SRR491032 1 0.0000 0.998 1.000 0.000
#> SRR491033 1 0.0000 0.998 1.000 0.000
#> SRR491034 1 0.0000 0.998 1.000 0.000
#> SRR491035 1 0.0000 0.998 1.000 0.000
#> SRR491036 1 0.0000 0.998 1.000 0.000
#> SRR491037 1 0.0000 0.998 1.000 0.000
#> SRR491038 1 0.0000 0.998 1.000 0.000
#> SRR491039 1 0.0376 0.998 0.996 0.004
#> SRR491040 1 0.0376 0.998 0.996 0.004
#> SRR491041 1 0.0376 0.998 0.996 0.004
#> SRR491042 1 0.0376 0.998 0.996 0.004
#> SRR491043 1 0.0376 0.998 0.996 0.004
#> SRR491045 1 0.0376 0.998 0.996 0.004
#> SRR491065 1 0.0376 0.998 0.996 0.004
#> SRR491066 1 0.0376 0.998 0.996 0.004
#> SRR491067 1 0.0376 0.998 0.996 0.004
#> SRR491068 1 0.0376 0.998 0.996 0.004
#> SRR491069 1 0.0376 0.998 0.996 0.004
#> SRR491070 1 0.0376 0.998 0.996 0.004
#> SRR491071 1 0.0376 0.998 0.996 0.004
#> SRR491072 1 0.0376 0.998 0.996 0.004
#> SRR491073 1 0.0000 0.998 1.000 0.000
#> SRR491074 1 0.0376 0.998 0.996 0.004
#> SRR491075 1 0.0000 0.998 1.000 0.000
#> SRR491076 1 0.0376 0.998 0.996 0.004
#> SRR491077 1 0.0376 0.998 0.996 0.004
#> SRR491078 1 0.0376 0.998 0.996 0.004
#> SRR491079 1 0.0376 0.998 0.996 0.004
#> SRR491080 1 0.0376 0.998 0.996 0.004
#> SRR491081 1 0.0376 0.998 0.996 0.004
#> SRR491082 1 0.0376 0.998 0.996 0.004
#> SRR491083 1 0.0376 0.998 0.996 0.004
#> SRR491084 1 0.0376 0.998 0.996 0.004
#> SRR491085 1 0.0376 0.998 0.996 0.004
#> SRR491086 1 0.0376 0.998 0.996 0.004
#> SRR491087 1 0.0376 0.998 0.996 0.004
#> SRR491088 1 0.0000 0.998 1.000 0.000
#> SRR491089 1 0.0376 0.998 0.996 0.004
#> SRR491090 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
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445730 1 0.0000 0.997 1.000 0.000 0.000
#> SRR445731 1 0.0000 0.997 1.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490973 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490974 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490975 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490976 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490977 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490978 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490979 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490980 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490985 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490986 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490987 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490988 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490989 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490990 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490991 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490992 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490993 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490994 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490995 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490996 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490997 3 0.0424 1.000 0.000 0.008 0.992
#> SRR490998 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491000 2 0.0000 1.000 0.000 1.000 0.000
#> SRR491001 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491002 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491003 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491004 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491005 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491006 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491007 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491008 3 0.0424 1.000 0.000 0.008 0.992
#> SRR491009 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491010 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491011 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491012 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491013 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491014 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491015 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491016 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491017 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491018 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491019 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491020 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491021 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491022 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491023 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491024 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491025 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491026 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491027 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491028 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491029 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491030 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491031 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491032 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491033 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491034 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491035 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491036 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491037 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491038 1 0.0424 0.996 0.992 0.000 0.008
#> SRR491039 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491040 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491041 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491042 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491043 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491045 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491065 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491066 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491067 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491068 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491069 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491070 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491071 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491072 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491073 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491074 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491075 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491076 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491077 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491078 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491079 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491080 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491081 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491082 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491083 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491084 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491085 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491086 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491087 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491088 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491089 1 0.0000 0.997 1.000 0.000 0.000
#> SRR491090 1 0.0000 0.997 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490977 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490978 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490985 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 0.999 0.000 0 1.000 0.000
#> SRR490993 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR490994 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR490995 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490996 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR490997 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR490998 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491000 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR491001 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491002 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491003 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491004 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491005 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491006 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491007 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491008 3 0.0188 0.998 0.000 0 0.996 0.004
#> SRR491009 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491010 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491011 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491012 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491013 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491014 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491015 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491016 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491017 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491018 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491019 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491020 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491021 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491022 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491023 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491024 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491025 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491026 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491027 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491028 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491029 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491030 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491031 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491032 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491033 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491034 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491035 4 0.0817 0.979 0.024 0 0.000 0.976
#> SRR491036 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491037 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491038 4 0.0188 0.999 0.004 0 0.000 0.996
#> SRR491039 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491090 1 0.0000 1.000 1.000 0 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490974 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490975 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490976 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490977 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490978 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490979 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490980 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490981 2 0.0794 0.975 0.000 0.972 0.000 0.000 0.028
#> SRR490982 2 0.0794 0.975 0.000 0.972 0.000 0.000 0.028
#> SRR490983 2 0.0794 0.975 0.000 0.972 0.000 0.000 0.028
#> SRR490984 2 0.0794 0.975 0.000 0.972 0.000 0.000 0.028
#> SRR490985 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490986 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490987 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490988 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490989 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490990 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490991 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490992 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000
#> SRR490993 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR490994 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR490995 5 0.3246 0.732 0.000 0.184 0.008 0.000 0.808
#> SRR490996 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR490997 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR490998 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491000 5 0.3246 0.732 0.000 0.184 0.008 0.000 0.808
#> SRR491001 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491002 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491003 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491004 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491005 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491006 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491007 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491008 3 0.2561 0.921 0.000 0.000 0.856 0.000 0.144
#> SRR491009 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.0880 0.967 0.000 0.000 0.000 0.968 0.032
#> SRR491023 4 0.1671 0.929 0.000 0.000 0.000 0.924 0.076
#> SRR491024 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.0510 0.979 0.000 0.000 0.000 0.984 0.016
#> SRR491029 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491031 5 0.3039 0.674 0.000 0.000 0.000 0.192 0.808
#> SRR491032 4 0.0609 0.976 0.000 0.000 0.000 0.980 0.020
#> SRR491033 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.1851 0.917 0.000 0.000 0.000 0.912 0.088
#> SRR491035 4 0.1965 0.910 0.000 0.000 0.000 0.904 0.096
#> SRR491036 4 0.0880 0.966 0.000 0.000 0.000 0.968 0.032
#> SRR491037 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0510 0.985 0.984 0.000 0.000 0.000 0.016
#> SRR491066 1 0.0609 0.982 0.980 0.000 0.000 0.000 0.020
#> SRR491067 1 0.0510 0.985 0.984 0.000 0.000 0.000 0.016
#> SRR491068 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0609 0.982 0.980 0.000 0.000 0.000 0.020
#> SRR491070 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491073 5 0.2929 0.839 0.180 0.000 0.000 0.000 0.820
#> SRR491074 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491075 5 0.3143 0.815 0.204 0.000 0.000 0.000 0.796
#> SRR491076 1 0.0609 0.982 0.980 0.000 0.000 0.000 0.020
#> SRR491077 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0703 0.979 0.976 0.000 0.000 0.000 0.024
#> SRR491087 1 0.0510 0.985 0.984 0.000 0.000 0.000 0.016
#> SRR491088 5 0.2891 0.841 0.176 0.000 0.000 0.000 0.824
#> SRR491089 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000
#> SRR491090 5 0.2891 0.841 0.176 0.000 0.000 0.000 0.824
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445719 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445720 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445721 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445722 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445723 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445724 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445725 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445726 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445727 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445728 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445729 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR445730 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR445731 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR490961 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490962 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490963 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490964 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490965 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490966 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490967 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490968 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490969 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490970 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490971 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490972 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.00
#> SRR490973 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490974 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490975 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490976 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490977 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490978 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490979 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490980 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490981 2 0.1995 0.920 0.000 0.912 0.052 0.000 0.036 0.00
#> SRR490982 2 0.1995 0.920 0.000 0.912 0.052 0.000 0.036 0.00
#> SRR490983 2 0.1995 0.920 0.000 0.912 0.052 0.000 0.036 0.00
#> SRR490984 2 0.1995 0.920 0.000 0.912 0.052 0.000 0.036 0.00
#> SRR490985 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490986 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490987 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490988 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490989 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490990 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490991 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490992 3 0.3198 1.000 0.000 0.000 0.740 0.000 0.000 0.26
#> SRR490993 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR490994 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR490995 5 0.2672 0.857 0.000 0.052 0.080 0.000 0.868 0.00
#> SRR490996 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR490997 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR490998 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491000 5 0.2672 0.857 0.000 0.052 0.080 0.000 0.868 0.00
#> SRR491001 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491002 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491003 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491004 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491005 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491006 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491007 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491008 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.00
#> SRR491009 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491010 4 0.0146 0.945 0.000 0.000 0.004 0.996 0.000 0.00
#> SRR491011 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491012 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491013 4 0.0146 0.945 0.000 0.000 0.004 0.996 0.000 0.00
#> SRR491014 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491015 4 0.0146 0.945 0.000 0.000 0.004 0.996 0.000 0.00
#> SRR491016 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491017 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491018 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491019 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491020 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491021 4 0.0713 0.935 0.000 0.000 0.028 0.972 0.000 0.00
#> SRR491022 4 0.3707 0.801 0.000 0.000 0.136 0.784 0.080 0.00
#> SRR491023 4 0.4121 0.765 0.000 0.000 0.136 0.748 0.116 0.00
#> SRR491024 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491025 4 0.0146 0.945 0.000 0.000 0.004 0.996 0.000 0.00
#> SRR491026 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491027 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491028 4 0.3108 0.841 0.000 0.000 0.128 0.828 0.044 0.00
#> SRR491029 4 0.0632 0.937 0.000 0.000 0.024 0.976 0.000 0.00
#> SRR491030 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491031 5 0.3013 0.822 0.000 0.000 0.088 0.068 0.844 0.00
#> SRR491032 4 0.3336 0.827 0.000 0.000 0.132 0.812 0.056 0.00
#> SRR491033 4 0.0260 0.945 0.000 0.000 0.008 0.992 0.000 0.00
#> SRR491034 4 0.4533 0.710 0.000 0.000 0.140 0.704 0.156 0.00
#> SRR491035 4 0.4707 0.714 0.008 0.000 0.152 0.704 0.136 0.00
#> SRR491036 4 0.1984 0.901 0.000 0.000 0.032 0.912 0.056 0.00
#> SRR491037 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.00
#> SRR491038 4 0.0363 0.942 0.000 0.000 0.012 0.988 0.000 0.00
#> SRR491039 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491040 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491041 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491042 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491043 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491045 1 0.0146 0.985 0.996 0.000 0.004 0.000 0.000 0.00
#> SRR491065 1 0.0891 0.969 0.968 0.000 0.024 0.000 0.008 0.00
#> SRR491066 1 0.1765 0.936 0.924 0.000 0.024 0.000 0.052 0.00
#> SRR491067 1 0.1088 0.964 0.960 0.000 0.024 0.000 0.016 0.00
#> SRR491068 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491069 1 0.1418 0.953 0.944 0.000 0.024 0.000 0.032 0.00
#> SRR491070 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491071 1 0.0458 0.978 0.984 0.000 0.016 0.000 0.000 0.00
#> SRR491072 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491073 5 0.1219 0.902 0.048 0.000 0.004 0.000 0.948 0.00
#> SRR491074 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491075 5 0.2278 0.833 0.128 0.000 0.004 0.000 0.868 0.00
#> SRR491076 1 0.1765 0.936 0.924 0.000 0.024 0.000 0.052 0.00
#> SRR491077 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491078 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491079 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491080 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491081 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491082 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491083 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491084 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491085 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491086 1 0.1765 0.936 0.924 0.000 0.024 0.000 0.052 0.00
#> SRR491087 1 0.1088 0.964 0.960 0.000 0.024 0.000 0.016 0.00
#> SRR491088 5 0.0937 0.905 0.040 0.000 0.000 0.000 0.960 0.00
#> SRR491089 1 0.0000 0.985 1.000 0.000 0.000 0.000 0.000 0.00
#> SRR491090 5 0.0937 0.905 0.040 0.000 0.000 0.000 0.960 0.00
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", "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 13175 rows and 123 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 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 0.956 0.982 0.3731 0.645 0.645
#> 3 3 0.822 0.890 0.947 0.6987 0.736 0.590
#> 4 4 1.000 0.983 0.993 0.1898 0.847 0.608
#> 5 5 1.000 0.973 0.988 0.0385 0.972 0.888
#> 6 6 0.987 0.944 0.971 0.0134 0.991 0.959
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
#> SRR445718 2 0.000 1.000 0.000 1.000
#> SRR445719 2 0.000 1.000 0.000 1.000
#> SRR445720 2 0.000 1.000 0.000 1.000
#> SRR445721 2 0.000 1.000 0.000 1.000
#> SRR445722 2 0.000 1.000 0.000 1.000
#> SRR445723 2 0.000 1.000 0.000 1.000
#> SRR445724 2 0.000 1.000 0.000 1.000
#> SRR445725 2 0.000 1.000 0.000 1.000
#> SRR445726 2 0.000 1.000 0.000 1.000
#> SRR445727 2 0.000 1.000 0.000 1.000
#> SRR445728 2 0.000 1.000 0.000 1.000
#> SRR445729 2 0.000 1.000 0.000 1.000
#> SRR445730 1 0.000 0.976 1.000 0.000
#> SRR445731 1 0.000 0.976 1.000 0.000
#> SRR490961 2 0.000 1.000 0.000 1.000
#> SRR490962 2 0.000 1.000 0.000 1.000
#> SRR490963 2 0.000 1.000 0.000 1.000
#> SRR490964 2 0.000 1.000 0.000 1.000
#> SRR490965 2 0.000 1.000 0.000 1.000
#> SRR490966 2 0.000 1.000 0.000 1.000
#> SRR490967 2 0.000 1.000 0.000 1.000
#> SRR490968 2 0.000 1.000 0.000 1.000
#> SRR490969 2 0.000 1.000 0.000 1.000
#> SRR490970 2 0.000 1.000 0.000 1.000
#> SRR490971 2 0.000 1.000 0.000 1.000
#> SRR490972 2 0.000 1.000 0.000 1.000
#> SRR490973 1 0.118 0.965 0.984 0.016
#> SRR490974 1 0.224 0.948 0.964 0.036
#> SRR490975 1 0.141 0.962 0.980 0.020
#> SRR490976 1 0.118 0.965 0.984 0.016
#> SRR490977 1 0.118 0.965 0.984 0.016
#> SRR490978 1 0.141 0.962 0.980 0.020
#> SRR490979 1 0.118 0.965 0.984 0.016
#> SRR490980 1 0.163 0.959 0.976 0.024
#> SRR490981 2 0.000 1.000 0.000 1.000
#> SRR490982 2 0.000 1.000 0.000 1.000
#> SRR490983 2 0.000 1.000 0.000 1.000
#> SRR490984 2 0.000 1.000 0.000 1.000
#> SRR490985 1 0.876 0.603 0.704 0.296
#> SRR490986 1 0.999 0.120 0.516 0.484
#> SRR490987 1 0.388 0.909 0.924 0.076
#> SRR490988 1 0.997 0.175 0.532 0.468
#> SRR490989 1 0.909 0.549 0.676 0.324
#> SRR490990 1 0.671 0.792 0.824 0.176
#> SRR490991 1 0.738 0.747 0.792 0.208
#> SRR490992 1 0.141 0.962 0.980 0.020
#> SRR490993 1 0.000 0.976 1.000 0.000
#> SRR490994 1 0.000 0.976 1.000 0.000
#> SRR490995 1 0.141 0.962 0.980 0.020
#> SRR490996 1 0.000 0.976 1.000 0.000
#> SRR490997 1 0.000 0.976 1.000 0.000
#> SRR490998 1 0.000 0.976 1.000 0.000
#> SRR491000 1 0.141 0.962 0.980 0.020
#> SRR491001 1 0.000 0.976 1.000 0.000
#> SRR491002 1 0.000 0.976 1.000 0.000
#> SRR491003 1 0.000 0.976 1.000 0.000
#> SRR491004 1 0.000 0.976 1.000 0.000
#> SRR491005 1 0.000 0.976 1.000 0.000
#> SRR491006 1 0.000 0.976 1.000 0.000
#> SRR491007 1 0.000 0.976 1.000 0.000
#> SRR491008 1 0.000 0.976 1.000 0.000
#> SRR491009 1 0.000 0.976 1.000 0.000
#> SRR491010 1 0.000 0.976 1.000 0.000
#> SRR491011 1 0.000 0.976 1.000 0.000
#> SRR491012 1 0.000 0.976 1.000 0.000
#> SRR491013 1 0.000 0.976 1.000 0.000
#> SRR491014 1 0.000 0.976 1.000 0.000
#> SRR491015 1 0.000 0.976 1.000 0.000
#> SRR491016 1 0.000 0.976 1.000 0.000
#> SRR491017 1 0.000 0.976 1.000 0.000
#> SRR491018 1 0.000 0.976 1.000 0.000
#> SRR491019 1 0.000 0.976 1.000 0.000
#> SRR491020 1 0.000 0.976 1.000 0.000
#> SRR491021 1 0.000 0.976 1.000 0.000
#> SRR491022 1 0.000 0.976 1.000 0.000
#> SRR491023 1 0.000 0.976 1.000 0.000
#> SRR491024 1 0.000 0.976 1.000 0.000
#> SRR491025 1 0.000 0.976 1.000 0.000
#> SRR491026 1 0.000 0.976 1.000 0.000
#> SRR491027 1 0.000 0.976 1.000 0.000
#> SRR491028 1 0.000 0.976 1.000 0.000
#> SRR491029 1 0.000 0.976 1.000 0.000
#> SRR491030 1 0.000 0.976 1.000 0.000
#> SRR491031 1 0.000 0.976 1.000 0.000
#> SRR491032 1 0.000 0.976 1.000 0.000
#> SRR491033 1 0.000 0.976 1.000 0.000
#> SRR491034 1 0.000 0.976 1.000 0.000
#> SRR491035 1 0.000 0.976 1.000 0.000
#> SRR491036 1 0.000 0.976 1.000 0.000
#> SRR491037 1 0.000 0.976 1.000 0.000
#> SRR491038 1 0.000 0.976 1.000 0.000
#> SRR491039 1 0.000 0.976 1.000 0.000
#> SRR491040 1 0.000 0.976 1.000 0.000
#> SRR491041 1 0.000 0.976 1.000 0.000
#> SRR491042 1 0.000 0.976 1.000 0.000
#> SRR491043 1 0.000 0.976 1.000 0.000
#> SRR491045 1 0.000 0.976 1.000 0.000
#> SRR491065 1 0.000 0.976 1.000 0.000
#> SRR491066 1 0.000 0.976 1.000 0.000
#> SRR491067 1 0.000 0.976 1.000 0.000
#> SRR491068 1 0.000 0.976 1.000 0.000
#> SRR491069 1 0.000 0.976 1.000 0.000
#> SRR491070 1 0.000 0.976 1.000 0.000
#> SRR491071 1 0.000 0.976 1.000 0.000
#> SRR491072 1 0.000 0.976 1.000 0.000
#> SRR491073 1 0.000 0.976 1.000 0.000
#> SRR491074 1 0.000 0.976 1.000 0.000
#> SRR491075 1 0.000 0.976 1.000 0.000
#> SRR491076 1 0.000 0.976 1.000 0.000
#> SRR491077 1 0.000 0.976 1.000 0.000
#> SRR491078 1 0.000 0.976 1.000 0.000
#> SRR491079 1 0.000 0.976 1.000 0.000
#> SRR491080 1 0.000 0.976 1.000 0.000
#> SRR491081 1 0.000 0.976 1.000 0.000
#> SRR491082 1 0.000 0.976 1.000 0.000
#> SRR491083 1 0.000 0.976 1.000 0.000
#> SRR491084 1 0.000 0.976 1.000 0.000
#> SRR491085 1 0.000 0.976 1.000 0.000
#> SRR491086 1 0.000 0.976 1.000 0.000
#> SRR491087 1 0.000 0.976 1.000 0.000
#> SRR491088 1 0.000 0.976 1.000 0.000
#> SRR491089 1 0.000 0.976 1.000 0.000
#> SRR491090 1 0.000 0.976 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445719 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445720 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445721 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445722 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445723 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445724 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445725 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445726 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445727 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445728 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445729 2 0.0000 0.998 0.000 1.000 0.000
#> SRR445730 1 0.0000 0.893 1.000 0.000 0.000
#> SRR445731 1 0.0000 0.893 1.000 0.000 0.000
#> SRR490961 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490962 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490963 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490964 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490965 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490966 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490967 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490968 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490969 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490970 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490971 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490972 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490973 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490974 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490975 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490976 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490977 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490978 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490979 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490980 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490981 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490982 2 0.1753 0.951 0.000 0.952 0.048
#> SRR490983 2 0.0424 0.991 0.000 0.992 0.008
#> SRR490984 2 0.0000 0.998 0.000 1.000 0.000
#> SRR490985 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490986 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490987 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490988 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490989 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490990 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490991 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490992 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490993 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490994 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490995 3 0.3038 0.890 0.000 0.104 0.896
#> SRR490996 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490997 3 0.0000 0.994 0.000 0.000 1.000
#> SRR490998 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491000 3 0.2796 0.904 0.000 0.092 0.908
#> SRR491001 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491002 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491003 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491004 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491005 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491006 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491007 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491008 3 0.0000 0.994 0.000 0.000 1.000
#> SRR491009 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491010 1 0.1860 0.862 0.948 0.000 0.052
#> SRR491011 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491012 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491013 1 0.0237 0.890 0.996 0.000 0.004
#> SRR491014 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491015 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491016 1 0.6095 0.502 0.608 0.000 0.392
#> SRR491017 1 0.5968 0.542 0.636 0.000 0.364
#> SRR491018 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491019 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491020 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491021 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491022 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491023 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491024 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491025 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491026 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491027 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491028 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491029 1 0.6062 0.514 0.616 0.000 0.384
#> SRR491030 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491031 1 0.6140 0.483 0.596 0.000 0.404
#> SRR491032 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491033 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491034 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491035 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491036 1 0.3192 0.818 0.888 0.000 0.112
#> SRR491037 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491038 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491039 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491040 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491041 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491042 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491043 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491045 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491065 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491066 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491067 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491068 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491069 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491070 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491071 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491072 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491073 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491074 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491075 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491076 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491077 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491078 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491079 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491080 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491081 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491082 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491083 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491084 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491085 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491086 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491087 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491088 1 0.2261 0.851 0.932 0.000 0.068
#> SRR491089 1 0.0000 0.893 1.000 0.000 0.000
#> SRR491090 1 0.1529 0.869 0.960 0.000 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490981 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490982 2 0.2081 0.909 0.000 0.916 0.084 0.000
#> SRR490983 2 0.0469 0.986 0.000 0.988 0.012 0.000
#> SRR490984 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490995 4 0.1022 0.949 0.000 0.000 0.032 0.968
#> SRR490996 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491000 4 0.0592 0.964 0.000 0.000 0.016 0.984
#> SRR491001 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491022 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491023 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491024 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491029 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491031 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491032 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491033 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491034 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491035 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491036 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491037 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 0.976 0.000 0.000 0.000 1.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491073 4 0.3764 0.734 0.216 0.000 0.000 0.784
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491075 4 0.4804 0.406 0.384 0.000 0.000 0.616
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491088 4 0.3219 0.806 0.164 0.000 0.000 0.836
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491090 4 0.0336 0.970 0.008 0.000 0.000 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.990 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490981 2 0.0703 0.971 0.000 0.976 0.024 0.000 0.000
#> SRR490982 2 0.2891 0.795 0.000 0.824 0.176 0.000 0.000
#> SRR490983 2 0.1197 0.948 0.000 0.952 0.048 0.000 0.000
#> SRR490984 2 0.0703 0.971 0.000 0.976 0.024 0.000 0.000
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR490993 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 4 0.3143 0.752 0.000 0.000 0.000 0.796 0.204
#> SRR490996 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 4 0.3074 0.763 0.000 0.000 0.000 0.804 0.196
#> SRR491001 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491022 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491023 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491024 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491028 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491029 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491031 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491032 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491033 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491035 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491036 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.963 0.000 0.000 0.000 1.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491073 4 0.3242 0.727 0.216 0.000 0.000 0.784 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491075 4 0.4138 0.417 0.384 0.000 0.000 0.616 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491088 4 0.2773 0.792 0.164 0.000 0.000 0.836 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR491090 4 0.0290 0.956 0.008 0.000 0.000 0.992 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.933 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490974 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490975 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490976 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490977 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490978 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490979 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490980 3 0.0000 0.998 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490981 2 0.3854 0.441 0.000 0.536 0.000 0.000 0.464 0.000
#> SRR490982 2 0.3854 0.441 0.000 0.536 0.000 0.000 0.464 0.000
#> SRR490983 2 0.3854 0.441 0.000 0.536 0.000 0.000 0.464 0.000
#> SRR490984 2 0.3854 0.441 0.000 0.536 0.000 0.000 0.464 0.000
#> SRR490985 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490986 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490987 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490988 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490989 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490990 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490991 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490992 3 0.0146 0.998 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490993 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 5 0.5471 0.970 0.000 0.000 0.000 0.268 0.560 0.172
#> SRR490996 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 5 0.5522 0.970 0.000 0.000 0.000 0.256 0.556 0.188
#> SRR491001 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491022 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491023 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491024 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491029 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491032 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491033 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491034 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491035 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491036 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491037 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491038 4 0.0000 0.958 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491073 4 0.2912 0.536 0.216 0.000 0.000 0.784 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491075 4 0.3717 0.135 0.384 0.000 0.000 0.616 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491088 4 0.2491 0.653 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491090 4 0.0260 0.946 0.008 0.000 0.000 0.992 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["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 13175 rows and 123 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 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 0.807 0.941 0.970 0.4961 0.497 0.497
#> 3 3 0.910 0.974 0.980 0.2427 0.884 0.767
#> 4 4 0.953 0.985 0.989 0.2119 0.818 0.553
#> 5 5 0.986 0.940 0.972 0.0576 0.953 0.815
#> 6 6 0.909 0.881 0.910 0.0222 0.990 0.952
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] 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
#> SRR445718 2 0.000 0.933 0.000 1.000
#> SRR445719 2 0.000 0.933 0.000 1.000
#> SRR445720 2 0.000 0.933 0.000 1.000
#> SRR445721 2 0.000 0.933 0.000 1.000
#> SRR445722 2 0.000 0.933 0.000 1.000
#> SRR445723 2 0.000 0.933 0.000 1.000
#> SRR445724 2 0.000 0.933 0.000 1.000
#> SRR445725 2 0.000 0.933 0.000 1.000
#> SRR445726 2 0.000 0.933 0.000 1.000
#> SRR445727 2 0.000 0.933 0.000 1.000
#> SRR445728 2 0.000 0.933 0.000 1.000
#> SRR445729 2 0.000 0.933 0.000 1.000
#> SRR445730 1 0.000 1.000 1.000 0.000
#> SRR445731 1 0.000 1.000 1.000 0.000
#> SRR490961 2 0.000 0.933 0.000 1.000
#> SRR490962 2 0.000 0.933 0.000 1.000
#> SRR490963 2 0.000 0.933 0.000 1.000
#> SRR490964 2 0.000 0.933 0.000 1.000
#> SRR490965 2 0.000 0.933 0.000 1.000
#> SRR490966 2 0.000 0.933 0.000 1.000
#> SRR490967 2 0.000 0.933 0.000 1.000
#> SRR490968 2 0.000 0.933 0.000 1.000
#> SRR490969 2 0.000 0.933 0.000 1.000
#> SRR490970 2 0.000 0.933 0.000 1.000
#> SRR490971 2 0.000 0.933 0.000 1.000
#> SRR490972 2 0.000 0.933 0.000 1.000
#> SRR490973 2 0.000 0.933 0.000 1.000
#> SRR490974 2 0.000 0.933 0.000 1.000
#> SRR490975 2 0.000 0.933 0.000 1.000
#> SRR490976 2 0.000 0.933 0.000 1.000
#> SRR490977 2 0.000 0.933 0.000 1.000
#> SRR490978 2 0.000 0.933 0.000 1.000
#> SRR490979 2 0.000 0.933 0.000 1.000
#> SRR490980 2 0.000 0.933 0.000 1.000
#> SRR490981 2 0.000 0.933 0.000 1.000
#> SRR490982 2 0.000 0.933 0.000 1.000
#> SRR490983 2 0.000 0.933 0.000 1.000
#> SRR490984 2 0.000 0.933 0.000 1.000
#> SRR490985 2 0.000 0.933 0.000 1.000
#> SRR490986 2 0.000 0.933 0.000 1.000
#> SRR490987 2 0.000 0.933 0.000 1.000
#> SRR490988 2 0.000 0.933 0.000 1.000
#> SRR490989 2 0.000 0.933 0.000 1.000
#> SRR490990 2 0.000 0.933 0.000 1.000
#> SRR490991 2 0.000 0.933 0.000 1.000
#> SRR490992 2 0.000 0.933 0.000 1.000
#> SRR490993 2 0.767 0.767 0.224 0.776
#> SRR490994 2 0.767 0.767 0.224 0.776
#> SRR490995 2 0.971 0.360 0.400 0.600
#> SRR490996 2 0.767 0.767 0.224 0.776
#> SRR490997 2 0.767 0.767 0.224 0.776
#> SRR490998 2 0.767 0.767 0.224 0.776
#> SRR491000 2 0.971 0.360 0.400 0.600
#> SRR491001 2 0.767 0.767 0.224 0.776
#> SRR491002 2 0.767 0.767 0.224 0.776
#> SRR491003 2 0.767 0.767 0.224 0.776
#> SRR491004 2 0.767 0.767 0.224 0.776
#> SRR491005 2 0.767 0.767 0.224 0.776
#> SRR491006 2 0.767 0.767 0.224 0.776
#> SRR491007 2 0.767 0.767 0.224 0.776
#> SRR491008 2 0.767 0.767 0.224 0.776
#> SRR491009 1 0.000 1.000 1.000 0.000
#> SRR491010 1 0.000 1.000 1.000 0.000
#> SRR491011 1 0.000 1.000 1.000 0.000
#> SRR491012 1 0.000 1.000 1.000 0.000
#> SRR491013 1 0.000 1.000 1.000 0.000
#> SRR491014 1 0.000 1.000 1.000 0.000
#> SRR491015 1 0.000 1.000 1.000 0.000
#> SRR491016 1 0.000 1.000 1.000 0.000
#> SRR491017 1 0.000 1.000 1.000 0.000
#> SRR491018 1 0.000 1.000 1.000 0.000
#> SRR491019 1 0.000 1.000 1.000 0.000
#> SRR491020 1 0.000 1.000 1.000 0.000
#> SRR491021 1 0.000 1.000 1.000 0.000
#> SRR491022 1 0.000 1.000 1.000 0.000
#> SRR491023 1 0.000 1.000 1.000 0.000
#> SRR491024 1 0.000 1.000 1.000 0.000
#> SRR491025 1 0.000 1.000 1.000 0.000
#> SRR491026 1 0.000 1.000 1.000 0.000
#> SRR491027 1 0.000 1.000 1.000 0.000
#> SRR491028 1 0.000 1.000 1.000 0.000
#> SRR491029 1 0.000 1.000 1.000 0.000
#> SRR491030 1 0.000 1.000 1.000 0.000
#> SRR491031 1 0.000 1.000 1.000 0.000
#> SRR491032 1 0.000 1.000 1.000 0.000
#> SRR491033 1 0.000 1.000 1.000 0.000
#> SRR491034 1 0.000 1.000 1.000 0.000
#> SRR491035 1 0.000 1.000 1.000 0.000
#> SRR491036 1 0.000 1.000 1.000 0.000
#> SRR491037 1 0.000 1.000 1.000 0.000
#> SRR491038 1 0.000 1.000 1.000 0.000
#> SRR491039 1 0.000 1.000 1.000 0.000
#> SRR491040 1 0.000 1.000 1.000 0.000
#> SRR491041 1 0.000 1.000 1.000 0.000
#> SRR491042 1 0.000 1.000 1.000 0.000
#> SRR491043 1 0.000 1.000 1.000 0.000
#> SRR491045 1 0.000 1.000 1.000 0.000
#> SRR491065 1 0.000 1.000 1.000 0.000
#> SRR491066 1 0.000 1.000 1.000 0.000
#> SRR491067 1 0.000 1.000 1.000 0.000
#> SRR491068 1 0.000 1.000 1.000 0.000
#> SRR491069 1 0.000 1.000 1.000 0.000
#> SRR491070 1 0.000 1.000 1.000 0.000
#> SRR491071 1 0.000 1.000 1.000 0.000
#> SRR491072 1 0.000 1.000 1.000 0.000
#> SRR491073 1 0.000 1.000 1.000 0.000
#> SRR491074 1 0.000 1.000 1.000 0.000
#> SRR491075 1 0.000 1.000 1.000 0.000
#> SRR491076 1 0.000 1.000 1.000 0.000
#> SRR491077 1 0.000 1.000 1.000 0.000
#> SRR491078 1 0.000 1.000 1.000 0.000
#> SRR491079 1 0.000 1.000 1.000 0.000
#> SRR491080 1 0.000 1.000 1.000 0.000
#> SRR491081 1 0.000 1.000 1.000 0.000
#> SRR491082 1 0.000 1.000 1.000 0.000
#> SRR491083 1 0.000 1.000 1.000 0.000
#> SRR491084 1 0.000 1.000 1.000 0.000
#> SRR491085 1 0.000 1.000 1.000 0.000
#> SRR491086 1 0.000 1.000 1.000 0.000
#> SRR491087 1 0.000 1.000 1.000 0.000
#> SRR491088 1 0.000 1.000 1.000 0.000
#> SRR491089 1 0.000 1.000 1.000 0.000
#> SRR491090 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
#> SRR445718 2 0.000 0.980 0 1.000 0.000
#> SRR445719 2 0.000 0.980 0 1.000 0.000
#> SRR445720 2 0.000 0.980 0 1.000 0.000
#> SRR445721 2 0.000 0.980 0 1.000 0.000
#> SRR445722 2 0.000 0.980 0 1.000 0.000
#> SRR445723 2 0.000 0.980 0 1.000 0.000
#> SRR445724 2 0.000 0.980 0 1.000 0.000
#> SRR445725 2 0.000 0.980 0 1.000 0.000
#> SRR445726 2 0.000 0.980 0 1.000 0.000
#> SRR445727 2 0.000 0.980 0 1.000 0.000
#> SRR445728 2 0.000 0.980 0 1.000 0.000
#> SRR445729 2 0.000 0.980 0 1.000 0.000
#> SRR445730 1 0.000 1.000 1 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0.000 0.000
#> SRR490961 2 0.000 0.980 0 1.000 0.000
#> SRR490962 2 0.000 0.980 0 1.000 0.000
#> SRR490963 2 0.000 0.980 0 1.000 0.000
#> SRR490964 2 0.000 0.980 0 1.000 0.000
#> SRR490965 2 0.000 0.980 0 1.000 0.000
#> SRR490966 2 0.000 0.980 0 1.000 0.000
#> SRR490967 2 0.000 0.980 0 1.000 0.000
#> SRR490968 2 0.000 0.980 0 1.000 0.000
#> SRR490969 2 0.000 0.980 0 1.000 0.000
#> SRR490970 2 0.000 0.980 0 1.000 0.000
#> SRR490971 2 0.000 0.980 0 1.000 0.000
#> SRR490972 2 0.000 0.980 0 1.000 0.000
#> SRR490973 3 0.319 0.940 0 0.112 0.888
#> SRR490974 3 0.319 0.940 0 0.112 0.888
#> SRR490975 3 0.319 0.940 0 0.112 0.888
#> SRR490976 3 0.319 0.940 0 0.112 0.888
#> SRR490977 3 0.319 0.940 0 0.112 0.888
#> SRR490978 3 0.319 0.940 0 0.112 0.888
#> SRR490979 3 0.319 0.940 0 0.112 0.888
#> SRR490980 3 0.319 0.940 0 0.112 0.888
#> SRR490981 2 0.000 0.980 0 1.000 0.000
#> SRR490982 2 0.000 0.980 0 1.000 0.000
#> SRR490983 2 0.000 0.980 0 1.000 0.000
#> SRR490984 2 0.000 0.980 0 1.000 0.000
#> SRR490985 3 0.319 0.940 0 0.112 0.888
#> SRR490986 3 0.319 0.940 0 0.112 0.888
#> SRR490987 3 0.319 0.940 0 0.112 0.888
#> SRR490988 3 0.319 0.940 0 0.112 0.888
#> SRR490989 3 0.319 0.940 0 0.112 0.888
#> SRR490990 3 0.319 0.940 0 0.112 0.888
#> SRR490991 3 0.319 0.940 0 0.112 0.888
#> SRR490992 3 0.319 0.940 0 0.112 0.888
#> SRR490993 3 0.000 0.930 0 0.000 1.000
#> SRR490994 3 0.000 0.930 0 0.000 1.000
#> SRR490995 2 0.559 0.625 0 0.696 0.304
#> SRR490996 3 0.000 0.930 0 0.000 1.000
#> SRR490997 3 0.000 0.930 0 0.000 1.000
#> SRR490998 3 0.000 0.930 0 0.000 1.000
#> SRR491000 2 0.559 0.625 0 0.696 0.304
#> SRR491001 3 0.000 0.930 0 0.000 1.000
#> SRR491002 3 0.000 0.930 0 0.000 1.000
#> SRR491003 3 0.000 0.930 0 0.000 1.000
#> SRR491004 3 0.000 0.930 0 0.000 1.000
#> SRR491005 3 0.000 0.930 0 0.000 1.000
#> SRR491006 3 0.000 0.930 0 0.000 1.000
#> SRR491007 3 0.000 0.930 0 0.000 1.000
#> SRR491008 3 0.000 0.930 0 0.000 1.000
#> SRR491009 1 0.000 1.000 1 0.000 0.000
#> SRR491010 1 0.000 1.000 1 0.000 0.000
#> SRR491011 1 0.000 1.000 1 0.000 0.000
#> SRR491012 1 0.000 1.000 1 0.000 0.000
#> SRR491013 1 0.000 1.000 1 0.000 0.000
#> SRR491014 1 0.000 1.000 1 0.000 0.000
#> SRR491015 1 0.000 1.000 1 0.000 0.000
#> SRR491016 1 0.000 1.000 1 0.000 0.000
#> SRR491017 1 0.000 1.000 1 0.000 0.000
#> SRR491018 1 0.000 1.000 1 0.000 0.000
#> SRR491019 1 0.000 1.000 1 0.000 0.000
#> SRR491020 1 0.000 1.000 1 0.000 0.000
#> SRR491021 1 0.000 1.000 1 0.000 0.000
#> SRR491022 1 0.000 1.000 1 0.000 0.000
#> SRR491023 1 0.000 1.000 1 0.000 0.000
#> SRR491024 1 0.000 1.000 1 0.000 0.000
#> SRR491025 1 0.000 1.000 1 0.000 0.000
#> SRR491026 1 0.000 1.000 1 0.000 0.000
#> SRR491027 1 0.000 1.000 1 0.000 0.000
#> SRR491028 1 0.000 1.000 1 0.000 0.000
#> SRR491029 1 0.000 1.000 1 0.000 0.000
#> SRR491030 1 0.000 1.000 1 0.000 0.000
#> SRR491031 1 0.000 1.000 1 0.000 0.000
#> SRR491032 1 0.000 1.000 1 0.000 0.000
#> SRR491033 1 0.000 1.000 1 0.000 0.000
#> SRR491034 1 0.000 1.000 1 0.000 0.000
#> SRR491035 1 0.000 1.000 1 0.000 0.000
#> SRR491036 1 0.000 1.000 1 0.000 0.000
#> SRR491037 1 0.000 1.000 1 0.000 0.000
#> SRR491038 1 0.000 1.000 1 0.000 0.000
#> SRR491039 1 0.000 1.000 1 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490981 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR490982 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR490983 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR490984 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR490985 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490995 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR490996 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491000 4 0.2530 0.905 0.004 0 0.100 0.896
#> SRR491001 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0 1.000 0.000
#> SRR491009 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491010 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491011 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491012 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491013 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491014 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491015 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491016 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491017 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491018 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491019 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491020 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491021 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491022 4 0.1356 0.956 0.008 0 0.032 0.960
#> SRR491023 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491024 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491025 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491026 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491027 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491028 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491029 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491030 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491031 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491032 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491033 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491034 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491035 4 0.1452 0.954 0.008 0 0.036 0.956
#> SRR491036 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491037 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491038 4 0.0336 0.972 0.008 0 0.000 0.992
#> SRR491039 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491067 1 0.0188 0.995 0.996 0 0.000 0.004
#> SRR491068 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491073 4 0.3421 0.896 0.088 0 0.044 0.868
#> SRR491074 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491075 4 0.3850 0.865 0.116 0 0.044 0.840
#> SRR491076 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491088 4 0.2500 0.935 0.040 0 0.044 0.916
#> SRR491089 1 0.0000 1.000 1.000 0 0.000 0.000
#> SRR491090 4 0.2500 0.935 0.040 0 0.044 0.916
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445719 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445720 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445721 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445722 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445723 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445724 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445725 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445726 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445727 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445728 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445729 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR445730 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR445731 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR490961 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490962 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490963 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490964 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490965 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490966 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490967 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490968 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490969 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490970 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490971 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490972 2 0.0000 1.0000 0.000 1 0 0.000 0.000
#> SRR490973 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490974 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490975 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490976 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490977 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490978 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490979 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490980 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490981 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR490982 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR490983 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR490984 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR490985 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490986 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490987 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490988 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490989 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490990 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490991 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490992 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490993 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490994 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490995 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR490996 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490997 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR490998 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491000 5 0.0000 0.7968 0.000 0 0 0.000 1.000
#> SRR491001 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491002 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491003 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491004 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491005 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491006 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491007 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491008 3 0.0000 1.0000 0.000 0 1 0.000 0.000
#> SRR491009 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491010 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491011 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491012 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491013 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491014 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491015 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491016 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491017 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491018 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491019 4 0.0609 0.9372 0.000 0 0 0.980 0.020
#> SRR491020 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491021 4 0.0404 0.9440 0.000 0 0 0.988 0.012
#> SRR491022 5 0.4114 0.5678 0.000 0 0 0.376 0.624
#> SRR491023 4 0.4182 0.0972 0.000 0 0 0.600 0.400
#> SRR491024 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491025 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491026 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491027 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491028 4 0.0703 0.9350 0.000 0 0 0.976 0.024
#> SRR491029 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491030 4 0.0000 0.9511 0.000 0 0 1.000 0.000
#> SRR491031 4 0.4101 0.2028 0.000 0 0 0.628 0.372
#> SRR491032 4 0.1851 0.8605 0.000 0 0 0.912 0.088
#> SRR491033 4 0.1410 0.8954 0.000 0 0 0.940 0.060
#> SRR491034 5 0.4297 0.3325 0.000 0 0 0.472 0.528
#> SRR491035 5 0.4192 0.5127 0.000 0 0 0.404 0.596
#> SRR491036 4 0.0162 0.9488 0.000 0 0 0.996 0.004
#> SRR491037 4 0.0162 0.9488 0.000 0 0 0.996 0.004
#> SRR491038 4 0.0510 0.9414 0.000 0 0 0.984 0.016
#> SRR491039 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491040 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491041 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491042 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491043 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491045 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491065 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491066 1 0.0404 0.9813 0.988 0 0 0.000 0.012
#> SRR491067 1 0.3039 0.7511 0.808 0 0 0.000 0.192
#> SRR491068 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491069 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491070 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491071 1 0.0290 0.9854 0.992 0 0 0.000 0.008
#> SRR491072 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491073 5 0.4210 0.7560 0.036 0 0 0.224 0.740
#> SRR491074 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491075 5 0.4284 0.7542 0.040 0 0 0.224 0.736
#> SRR491076 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491077 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491078 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491079 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491080 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491081 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491082 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491083 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491084 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491085 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491086 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491087 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491088 5 0.3789 0.7599 0.016 0 0 0.224 0.760
#> SRR491089 1 0.0000 0.9923 1.000 0 0 0.000 0.000
#> SRR491090 5 0.3789 0.7599 0.016 0 0 0.224 0.760
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.00 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490974 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490975 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490976 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490977 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490978 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490979 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490980 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490981 6 0.0000 0.997 0.000 0 0.00 0.000 0.000 1.000
#> SRR490982 6 0.0000 0.997 0.000 0 0.00 0.000 0.000 1.000
#> SRR490983 6 0.0000 0.997 0.000 0 0.00 0.000 0.000 1.000
#> SRR490984 6 0.0000 0.997 0.000 0 0.00 0.000 0.000 1.000
#> SRR490985 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490986 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490987 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490988 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490989 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490990 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490991 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490992 3 0.0000 0.900 0.000 0 1.00 0.000 0.000 0.000
#> SRR490993 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR490994 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR490995 6 0.0260 0.993 0.000 0 0.00 0.000 0.008 0.992
#> SRR490996 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR490997 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR490998 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491000 6 0.0260 0.993 0.000 0 0.00 0.000 0.008 0.992
#> SRR491001 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491002 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491003 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491004 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491005 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491006 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491007 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491008 3 0.2941 0.874 0.000 0 0.78 0.000 0.220 0.000
#> SRR491009 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491011 4 0.0713 0.863 0.000 0 0.00 0.972 0.028 0.000
#> SRR491012 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491019 4 0.3405 0.587 0.000 0 0.00 0.724 0.272 0.004
#> SRR491020 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491021 4 0.2871 0.719 0.000 0 0.00 0.804 0.192 0.004
#> SRR491022 5 0.5362 0.896 0.000 0 0.00 0.228 0.588 0.184
#> SRR491023 4 0.4316 0.406 0.000 0 0.00 0.648 0.312 0.040
#> SRR491024 4 0.0146 0.875 0.000 0 0.00 0.996 0.004 0.000
#> SRR491025 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491026 4 0.0547 0.866 0.000 0 0.00 0.980 0.020 0.000
#> SRR491027 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491028 4 0.2964 0.703 0.000 0 0.00 0.792 0.204 0.004
#> SRR491029 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491031 4 0.4089 0.530 0.000 0 0.00 0.696 0.264 0.040
#> SRR491032 4 0.3448 0.586 0.000 0 0.00 0.716 0.280 0.004
#> SRR491033 4 0.3052 0.678 0.000 0 0.00 0.780 0.216 0.004
#> SRR491034 4 0.5412 -0.230 0.000 0 0.00 0.496 0.384 0.120
#> SRR491035 5 0.5429 0.889 0.000 0 0.00 0.236 0.576 0.188
#> SRR491036 4 0.0000 0.877 0.000 0 0.00 1.000 0.000 0.000
#> SRR491037 4 0.0363 0.871 0.000 0 0.00 0.988 0.012 0.000
#> SRR491038 4 0.2482 0.768 0.000 0 0.00 0.848 0.148 0.004
#> SRR491039 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491065 1 0.1714 0.870 0.908 0 0.00 0.000 0.092 0.000
#> SRR491066 1 0.3695 0.602 0.624 0 0.00 0.000 0.376 0.000
#> SRR491067 1 0.4004 0.596 0.620 0 0.00 0.000 0.368 0.012
#> SRR491068 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491069 1 0.3578 0.655 0.660 0 0.00 0.000 0.340 0.000
#> SRR491070 1 0.0260 0.917 0.992 0 0.00 0.000 0.008 0.000
#> SRR491071 1 0.2697 0.805 0.812 0 0.00 0.000 0.188 0.000
#> SRR491072 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491073 5 0.6303 0.921 0.048 0 0.00 0.208 0.540 0.204
#> SRR491074 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491075 5 0.6497 0.881 0.080 0 0.00 0.208 0.540 0.172
#> SRR491076 1 0.3515 0.676 0.676 0 0.00 0.000 0.324 0.000
#> SRR491077 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491086 1 0.3531 0.671 0.672 0 0.00 0.000 0.328 0.000
#> SRR491087 1 0.3531 0.671 0.672 0 0.00 0.000 0.328 0.000
#> SRR491088 5 0.6133 0.928 0.032 0 0.00 0.208 0.544 0.216
#> SRR491089 1 0.0000 0.921 1.000 0 0.00 0.000 0.000 0.000
#> SRR491090 5 0.6154 0.926 0.032 0 0.00 0.208 0.540 0.220
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 13175 rows and 123 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 0.933 0.957 0.980 0.4693 0.528 0.528
#> 3 3 1.000 0.983 0.994 0.3200 0.797 0.635
#> 4 4 1.000 0.974 0.990 0.2186 0.849 0.611
#> 5 5 0.916 0.899 0.921 0.0317 1.000 1.000
#> 6 6 0.879 0.824 0.879 0.0399 0.921 0.683
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
#> SRR445718 2 0.0000 0.968 0.000 1.000
#> SRR445719 2 0.0000 0.968 0.000 1.000
#> SRR445720 2 0.0000 0.968 0.000 1.000
#> SRR445721 2 0.0000 0.968 0.000 1.000
#> SRR445722 2 0.0000 0.968 0.000 1.000
#> SRR445723 2 0.0000 0.968 0.000 1.000
#> SRR445724 2 0.0000 0.968 0.000 1.000
#> SRR445725 2 0.0000 0.968 0.000 1.000
#> SRR445726 2 0.0000 0.968 0.000 1.000
#> SRR445727 2 0.0000 0.968 0.000 1.000
#> SRR445728 2 0.0000 0.968 0.000 1.000
#> SRR445729 2 0.0000 0.968 0.000 1.000
#> SRR445730 1 0.0000 0.985 1.000 0.000
#> SRR445731 1 0.0000 0.985 1.000 0.000
#> SRR490961 2 0.0000 0.968 0.000 1.000
#> SRR490962 2 0.0000 0.968 0.000 1.000
#> SRR490963 2 0.0000 0.968 0.000 1.000
#> SRR490964 2 0.0000 0.968 0.000 1.000
#> SRR490965 2 0.0000 0.968 0.000 1.000
#> SRR490966 2 0.0000 0.968 0.000 1.000
#> SRR490967 2 0.0000 0.968 0.000 1.000
#> SRR490968 2 0.0000 0.968 0.000 1.000
#> SRR490969 2 0.0000 0.968 0.000 1.000
#> SRR490970 2 0.0000 0.968 0.000 1.000
#> SRR490971 2 0.0000 0.968 0.000 1.000
#> SRR490972 2 0.0000 0.968 0.000 1.000
#> SRR490973 2 0.6531 0.798 0.168 0.832
#> SRR490974 2 0.0376 0.965 0.004 0.996
#> SRR490975 2 0.0000 0.968 0.000 1.000
#> SRR490976 2 0.8713 0.608 0.292 0.708
#> SRR490977 2 0.9129 0.535 0.328 0.672
#> SRR490978 2 0.7602 0.727 0.220 0.780
#> SRR490979 2 0.8861 0.585 0.304 0.696
#> SRR490980 2 0.0376 0.965 0.004 0.996
#> SRR490981 2 0.0000 0.968 0.000 1.000
#> SRR490982 2 0.0000 0.968 0.000 1.000
#> SRR490983 2 0.0000 0.968 0.000 1.000
#> SRR490984 2 0.0000 0.968 0.000 1.000
#> SRR490985 2 0.0000 0.968 0.000 1.000
#> SRR490986 2 0.0000 0.968 0.000 1.000
#> SRR490987 2 0.0376 0.965 0.004 0.996
#> SRR490988 2 0.0000 0.968 0.000 1.000
#> SRR490989 2 0.0000 0.968 0.000 1.000
#> SRR490990 2 0.0000 0.968 0.000 1.000
#> SRR490991 2 0.0000 0.968 0.000 1.000
#> SRR490992 2 0.3584 0.909 0.068 0.932
#> SRR490993 1 0.6531 0.806 0.832 0.168
#> SRR490994 1 0.3584 0.924 0.932 0.068
#> SRR490995 2 0.0000 0.968 0.000 1.000
#> SRR490996 1 0.5737 0.848 0.864 0.136
#> SRR490997 1 0.0672 0.979 0.992 0.008
#> SRR490998 1 0.2043 0.959 0.968 0.032
#> SRR491000 2 0.0000 0.968 0.000 1.000
#> SRR491001 1 0.0672 0.979 0.992 0.008
#> SRR491002 1 0.0672 0.979 0.992 0.008
#> SRR491003 1 0.6247 0.823 0.844 0.156
#> SRR491004 1 0.6148 0.828 0.848 0.152
#> SRR491005 1 0.0938 0.976 0.988 0.012
#> SRR491006 1 0.6148 0.828 0.848 0.152
#> SRR491007 1 0.5408 0.863 0.876 0.124
#> SRR491008 1 0.2603 0.948 0.956 0.044
#> SRR491009 1 0.0000 0.985 1.000 0.000
#> SRR491010 1 0.0000 0.985 1.000 0.000
#> SRR491011 1 0.0000 0.985 1.000 0.000
#> SRR491012 1 0.0000 0.985 1.000 0.000
#> SRR491013 1 0.0000 0.985 1.000 0.000
#> SRR491014 1 0.0000 0.985 1.000 0.000
#> SRR491015 1 0.0000 0.985 1.000 0.000
#> SRR491016 1 0.0000 0.985 1.000 0.000
#> SRR491017 1 0.0000 0.985 1.000 0.000
#> SRR491018 1 0.0000 0.985 1.000 0.000
#> SRR491019 1 0.0000 0.985 1.000 0.000
#> SRR491020 1 0.0000 0.985 1.000 0.000
#> SRR491021 1 0.0000 0.985 1.000 0.000
#> SRR491022 1 0.0000 0.985 1.000 0.000
#> SRR491023 1 0.0000 0.985 1.000 0.000
#> SRR491024 1 0.0000 0.985 1.000 0.000
#> SRR491025 1 0.0000 0.985 1.000 0.000
#> SRR491026 1 0.0000 0.985 1.000 0.000
#> SRR491027 1 0.0000 0.985 1.000 0.000
#> SRR491028 1 0.0000 0.985 1.000 0.000
#> SRR491029 1 0.0000 0.985 1.000 0.000
#> SRR491030 1 0.0000 0.985 1.000 0.000
#> SRR491031 1 0.0000 0.985 1.000 0.000
#> SRR491032 1 0.0000 0.985 1.000 0.000
#> SRR491033 1 0.0000 0.985 1.000 0.000
#> SRR491034 1 0.0000 0.985 1.000 0.000
#> SRR491035 1 0.0000 0.985 1.000 0.000
#> SRR491036 1 0.0000 0.985 1.000 0.000
#> SRR491037 1 0.0000 0.985 1.000 0.000
#> SRR491038 1 0.0000 0.985 1.000 0.000
#> SRR491039 1 0.0000 0.985 1.000 0.000
#> SRR491040 1 0.0000 0.985 1.000 0.000
#> SRR491041 1 0.0000 0.985 1.000 0.000
#> SRR491042 1 0.0000 0.985 1.000 0.000
#> SRR491043 1 0.0000 0.985 1.000 0.000
#> SRR491045 1 0.0000 0.985 1.000 0.000
#> SRR491065 1 0.0000 0.985 1.000 0.000
#> SRR491066 1 0.0000 0.985 1.000 0.000
#> SRR491067 1 0.0000 0.985 1.000 0.000
#> SRR491068 1 0.0000 0.985 1.000 0.000
#> SRR491069 1 0.0000 0.985 1.000 0.000
#> SRR491070 1 0.0000 0.985 1.000 0.000
#> SRR491071 1 0.0000 0.985 1.000 0.000
#> SRR491072 1 0.0000 0.985 1.000 0.000
#> SRR491073 1 0.0000 0.985 1.000 0.000
#> SRR491074 1 0.0000 0.985 1.000 0.000
#> SRR491075 1 0.0000 0.985 1.000 0.000
#> SRR491076 1 0.0000 0.985 1.000 0.000
#> SRR491077 1 0.0000 0.985 1.000 0.000
#> SRR491078 1 0.0000 0.985 1.000 0.000
#> SRR491079 1 0.0000 0.985 1.000 0.000
#> SRR491080 1 0.0000 0.985 1.000 0.000
#> SRR491081 1 0.0000 0.985 1.000 0.000
#> SRR491082 1 0.0000 0.985 1.000 0.000
#> SRR491083 1 0.0000 0.985 1.000 0.000
#> SRR491084 1 0.0000 0.985 1.000 0.000
#> SRR491085 1 0.0000 0.985 1.000 0.000
#> SRR491086 1 0.0000 0.985 1.000 0.000
#> SRR491087 1 0.0000 0.985 1.000 0.000
#> SRR491088 1 0.0000 0.985 1.000 0.000
#> SRR491089 1 0.0000 0.985 1.000 0.000
#> SRR491090 1 0.0000 0.985 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 0.973 0 1.000 0.000
#> SRR445719 2 0.000 0.973 0 1.000 0.000
#> SRR445720 2 0.000 0.973 0 1.000 0.000
#> SRR445721 2 0.000 0.973 0 1.000 0.000
#> SRR445722 2 0.000 0.973 0 1.000 0.000
#> SRR445723 2 0.000 0.973 0 1.000 0.000
#> SRR445724 2 0.000 0.973 0 1.000 0.000
#> SRR445725 2 0.000 0.973 0 1.000 0.000
#> SRR445726 2 0.000 0.973 0 1.000 0.000
#> SRR445727 2 0.000 0.973 0 1.000 0.000
#> SRR445728 2 0.000 0.973 0 1.000 0.000
#> SRR445729 2 0.000 0.973 0 1.000 0.000
#> SRR445730 1 0.000 1.000 1 0.000 0.000
#> SRR445731 1 0.000 1.000 1 0.000 0.000
#> SRR490961 2 0.000 0.973 0 1.000 0.000
#> SRR490962 2 0.000 0.973 0 1.000 0.000
#> SRR490963 2 0.000 0.973 0 1.000 0.000
#> SRR490964 2 0.000 0.973 0 1.000 0.000
#> SRR490965 2 0.000 0.973 0 1.000 0.000
#> SRR490966 2 0.000 0.973 0 1.000 0.000
#> SRR490967 2 0.000 0.973 0 1.000 0.000
#> SRR490968 2 0.000 0.973 0 1.000 0.000
#> SRR490969 2 0.000 0.973 0 1.000 0.000
#> SRR490970 2 0.000 0.973 0 1.000 0.000
#> SRR490971 2 0.000 0.973 0 1.000 0.000
#> SRR490972 2 0.000 0.973 0 1.000 0.000
#> SRR490973 3 0.000 1.000 0 0.000 1.000
#> SRR490974 3 0.000 1.000 0 0.000 1.000
#> SRR490975 3 0.000 1.000 0 0.000 1.000
#> SRR490976 3 0.000 1.000 0 0.000 1.000
#> SRR490977 3 0.000 1.000 0 0.000 1.000
#> SRR490978 3 0.000 1.000 0 0.000 1.000
#> SRR490979 3 0.000 1.000 0 0.000 1.000
#> SRR490980 3 0.000 1.000 0 0.000 1.000
#> SRR490981 2 0.000 0.973 0 1.000 0.000
#> SRR490982 2 0.000 0.973 0 1.000 0.000
#> SRR490983 2 0.000 0.973 0 1.000 0.000
#> SRR490984 2 0.000 0.973 0 1.000 0.000
#> SRR490985 3 0.000 1.000 0 0.000 1.000
#> SRR490986 3 0.000 1.000 0 0.000 1.000
#> SRR490987 3 0.000 1.000 0 0.000 1.000
#> SRR490988 3 0.000 1.000 0 0.000 1.000
#> SRR490989 3 0.000 1.000 0 0.000 1.000
#> SRR490990 3 0.000 1.000 0 0.000 1.000
#> SRR490991 3 0.000 1.000 0 0.000 1.000
#> SRR490992 3 0.000 1.000 0 0.000 1.000
#> SRR490993 3 0.000 1.000 0 0.000 1.000
#> SRR490994 3 0.000 1.000 0 0.000 1.000
#> SRR490995 2 0.624 0.240 0 0.560 0.440
#> SRR490996 3 0.000 1.000 0 0.000 1.000
#> SRR490997 3 0.000 1.000 0 0.000 1.000
#> SRR490998 3 0.000 1.000 0 0.000 1.000
#> SRR491000 2 0.586 0.487 0 0.656 0.344
#> SRR491001 3 0.000 1.000 0 0.000 1.000
#> SRR491002 3 0.000 1.000 0 0.000 1.000
#> SRR491003 3 0.000 1.000 0 0.000 1.000
#> SRR491004 3 0.000 1.000 0 0.000 1.000
#> SRR491005 3 0.000 1.000 0 0.000 1.000
#> SRR491006 3 0.000 1.000 0 0.000 1.000
#> SRR491007 3 0.000 1.000 0 0.000 1.000
#> SRR491008 3 0.000 1.000 0 0.000 1.000
#> SRR491009 1 0.000 1.000 1 0.000 0.000
#> SRR491010 1 0.000 1.000 1 0.000 0.000
#> SRR491011 1 0.000 1.000 1 0.000 0.000
#> SRR491012 1 0.000 1.000 1 0.000 0.000
#> SRR491013 1 0.000 1.000 1 0.000 0.000
#> SRR491014 1 0.000 1.000 1 0.000 0.000
#> SRR491015 1 0.000 1.000 1 0.000 0.000
#> SRR491016 1 0.000 1.000 1 0.000 0.000
#> SRR491017 1 0.000 1.000 1 0.000 0.000
#> SRR491018 1 0.000 1.000 1 0.000 0.000
#> SRR491019 1 0.000 1.000 1 0.000 0.000
#> SRR491020 1 0.000 1.000 1 0.000 0.000
#> SRR491021 1 0.000 1.000 1 0.000 0.000
#> SRR491022 1 0.000 1.000 1 0.000 0.000
#> SRR491023 1 0.000 1.000 1 0.000 0.000
#> SRR491024 1 0.000 1.000 1 0.000 0.000
#> SRR491025 1 0.000 1.000 1 0.000 0.000
#> SRR491026 1 0.000 1.000 1 0.000 0.000
#> SRR491027 1 0.000 1.000 1 0.000 0.000
#> SRR491028 1 0.000 1.000 1 0.000 0.000
#> SRR491029 1 0.000 1.000 1 0.000 0.000
#> SRR491030 1 0.000 1.000 1 0.000 0.000
#> SRR491031 1 0.000 1.000 1 0.000 0.000
#> SRR491032 1 0.000 1.000 1 0.000 0.000
#> SRR491033 1 0.000 1.000 1 0.000 0.000
#> SRR491034 1 0.000 1.000 1 0.000 0.000
#> SRR491035 1 0.000 1.000 1 0.000 0.000
#> SRR491036 1 0.000 1.000 1 0.000 0.000
#> SRR491037 1 0.000 1.000 1 0.000 0.000
#> SRR491038 1 0.000 1.000 1 0.000 0.000
#> SRR491039 1 0.000 1.000 1 0.000 0.000
#> SRR491040 1 0.000 1.000 1 0.000 0.000
#> SRR491041 1 0.000 1.000 1 0.000 0.000
#> SRR491042 1 0.000 1.000 1 0.000 0.000
#> SRR491043 1 0.000 1.000 1 0.000 0.000
#> SRR491045 1 0.000 1.000 1 0.000 0.000
#> SRR491065 1 0.000 1.000 1 0.000 0.000
#> SRR491066 1 0.000 1.000 1 0.000 0.000
#> SRR491067 1 0.000 1.000 1 0.000 0.000
#> SRR491068 1 0.000 1.000 1 0.000 0.000
#> SRR491069 1 0.000 1.000 1 0.000 0.000
#> SRR491070 1 0.000 1.000 1 0.000 0.000
#> SRR491071 1 0.000 1.000 1 0.000 0.000
#> SRR491072 1 0.000 1.000 1 0.000 0.000
#> SRR491073 1 0.000 1.000 1 0.000 0.000
#> SRR491074 1 0.000 1.000 1 0.000 0.000
#> SRR491075 1 0.000 1.000 1 0.000 0.000
#> SRR491076 1 0.000 1.000 1 0.000 0.000
#> SRR491077 1 0.000 1.000 1 0.000 0.000
#> SRR491078 1 0.000 1.000 1 0.000 0.000
#> SRR491079 1 0.000 1.000 1 0.000 0.000
#> SRR491080 1 0.000 1.000 1 0.000 0.000
#> SRR491081 1 0.000 1.000 1 0.000 0.000
#> SRR491082 1 0.000 1.000 1 0.000 0.000
#> SRR491083 1 0.000 1.000 1 0.000 0.000
#> SRR491084 1 0.000 1.000 1 0.000 0.000
#> SRR491085 1 0.000 1.000 1 0.000 0.000
#> SRR491086 1 0.000 1.000 1 0.000 0.000
#> SRR491087 1 0.000 1.000 1 0.000 0.000
#> SRR491088 1 0.000 1.000 1 0.000 0.000
#> SRR491089 1 0.000 1.000 1 0.000 0.000
#> SRR491090 1 0.000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490982 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> SRR490983 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR490985 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490995 3 0.4431 0.572 0.000 0.304 0.696 0.000
#> SRR490996 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491000 3 0.4877 0.331 0.000 0.408 0.592 0.000
#> SRR491001 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491020 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491021 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491022 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491023 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491024 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491025 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491027 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491029 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491030 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491031 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491032 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491033 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491034 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491035 4 0.4989 0.106 0.472 0.000 0.000 0.528
#> SRR491036 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491037 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491038 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR491090 1 0.0000 1.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0162 0.9618 0.000 0.996 0.000 0.000 NA
#> SRR445719 2 0.0290 0.9597 0.000 0.992 0.000 0.000 NA
#> SRR445720 2 0.0290 0.9597 0.000 0.992 0.000 0.000 NA
#> SRR445721 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445722 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445723 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445724 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445725 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445726 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445727 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445728 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445729 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR445730 1 0.0510 0.9121 0.984 0.000 0.000 0.000 NA
#> SRR445731 1 0.0609 0.9112 0.980 0.000 0.000 0.000 NA
#> SRR490961 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490962 2 0.0162 0.9622 0.000 0.996 0.000 0.000 NA
#> SRR490963 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490964 2 0.0162 0.9622 0.000 0.996 0.000 0.000 NA
#> SRR490965 2 0.0162 0.9622 0.000 0.996 0.000 0.000 NA
#> SRR490966 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490967 2 0.0162 0.9622 0.000 0.996 0.000 0.000 NA
#> SRR490968 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490969 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490970 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490971 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490972 2 0.0000 0.9635 0.000 1.000 0.000 0.000 NA
#> SRR490973 3 0.0404 0.8897 0.000 0.000 0.988 0.000 NA
#> SRR490974 3 0.0963 0.8862 0.000 0.000 0.964 0.000 NA
#> SRR490975 3 0.1043 0.8854 0.000 0.000 0.960 0.000 NA
#> SRR490976 3 0.0162 0.8903 0.000 0.000 0.996 0.000 NA
#> SRR490977 3 0.0162 0.8905 0.000 0.000 0.996 0.000 NA
#> SRR490978 3 0.0000 0.8904 0.000 0.000 1.000 0.000 NA
#> SRR490979 3 0.0000 0.8904 0.000 0.000 1.000 0.000 NA
#> SRR490980 3 0.0794 0.8875 0.000 0.000 0.972 0.000 NA
#> SRR490981 2 0.4210 0.7627 0.000 0.740 0.036 0.000 NA
#> SRR490982 2 0.4693 0.7229 0.000 0.700 0.056 0.000 NA
#> SRR490983 2 0.4384 0.7510 0.000 0.728 0.044 0.000 NA
#> SRR490984 2 0.4313 0.7554 0.000 0.732 0.040 0.000 NA
#> SRR490985 3 0.2891 0.8400 0.000 0.000 0.824 0.000 NA
#> SRR490986 3 0.3003 0.8333 0.000 0.000 0.812 0.000 NA
#> SRR490987 3 0.2561 0.8542 0.000 0.000 0.856 0.000 NA
#> SRR490988 3 0.2852 0.8422 0.000 0.000 0.828 0.000 NA
#> SRR490989 3 0.2732 0.8476 0.000 0.000 0.840 0.000 NA
#> SRR490990 3 0.2773 0.8459 0.000 0.000 0.836 0.000 NA
#> SRR490991 3 0.2852 0.8422 0.000 0.000 0.828 0.000 NA
#> SRR490992 3 0.2516 0.8557 0.000 0.000 0.860 0.000 NA
#> SRR490993 3 0.1965 0.8868 0.000 0.000 0.904 0.000 NA
#> SRR490994 3 0.2329 0.8807 0.000 0.000 0.876 0.000 NA
#> SRR490995 3 0.6593 0.3139 0.000 0.284 0.464 0.000 NA
#> SRR490996 3 0.2179 0.8835 0.000 0.000 0.888 0.000 NA
#> SRR490997 3 0.2280 0.8816 0.000 0.000 0.880 0.000 NA
#> SRR490998 3 0.2329 0.8807 0.000 0.000 0.876 0.000 NA
#> SRR491000 3 0.6728 0.0475 0.000 0.368 0.380 0.000 NA
#> SRR491001 3 0.2280 0.8816 0.000 0.000 0.880 0.000 NA
#> SRR491002 3 0.2329 0.8807 0.000 0.000 0.876 0.000 NA
#> SRR491003 3 0.1908 0.8870 0.000 0.000 0.908 0.000 NA
#> SRR491004 3 0.1965 0.8865 0.000 0.000 0.904 0.000 NA
#> SRR491005 3 0.2329 0.8807 0.000 0.000 0.876 0.000 NA
#> SRR491006 3 0.1965 0.8865 0.000 0.000 0.904 0.000 NA
#> SRR491007 3 0.1965 0.8865 0.000 0.000 0.904 0.000 NA
#> SRR491008 3 0.2329 0.8807 0.000 0.000 0.876 0.000 NA
#> SRR491009 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491010 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491011 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491012 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491013 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491014 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491015 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491016 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491017 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491018 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491019 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491020 4 0.0162 0.9768 0.000 0.000 0.000 0.996 NA
#> SRR491021 4 0.0162 0.9772 0.000 0.000 0.000 0.996 NA
#> SRR491022 4 0.2193 0.9221 0.008 0.000 0.000 0.900 NA
#> SRR491023 4 0.0880 0.9673 0.000 0.000 0.000 0.968 NA
#> SRR491024 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491025 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491026 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491027 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491028 4 0.0510 0.9742 0.000 0.000 0.000 0.984 NA
#> SRR491029 4 0.0162 0.9768 0.000 0.000 0.000 0.996 NA
#> SRR491030 4 0.0000 0.9773 0.000 0.000 0.000 1.000 NA
#> SRR491031 4 0.1792 0.9325 0.000 0.000 0.000 0.916 NA
#> SRR491032 4 0.0703 0.9694 0.000 0.000 0.000 0.976 NA
#> SRR491033 4 0.0162 0.9768 0.000 0.000 0.000 0.996 NA
#> SRR491034 4 0.1121 0.9592 0.000 0.000 0.000 0.956 NA
#> SRR491035 4 0.6004 0.3984 0.256 0.000 0.000 0.576 NA
#> SRR491036 4 0.0609 0.9713 0.000 0.000 0.000 0.980 NA
#> SRR491037 4 0.0162 0.9768 0.000 0.000 0.000 0.996 NA
#> SRR491038 4 0.0290 0.9758 0.000 0.000 0.000 0.992 NA
#> SRR491039 1 0.0510 0.9121 0.984 0.000 0.000 0.000 NA
#> SRR491040 1 0.0963 0.9060 0.964 0.000 0.000 0.000 NA
#> SRR491041 1 0.0963 0.9060 0.964 0.000 0.000 0.000 NA
#> SRR491042 1 0.0609 0.9112 0.980 0.000 0.000 0.000 NA
#> SRR491043 1 0.0703 0.9101 0.976 0.000 0.000 0.000 NA
#> SRR491045 1 0.0703 0.9101 0.976 0.000 0.000 0.000 NA
#> SRR491065 1 0.3586 0.8515 0.736 0.000 0.000 0.000 NA
#> SRR491066 1 0.3336 0.8666 0.772 0.000 0.000 0.000 NA
#> SRR491067 1 0.3452 0.8602 0.756 0.000 0.000 0.000 NA
#> SRR491068 1 0.0162 0.9148 0.996 0.000 0.000 0.000 NA
#> SRR491069 1 0.3336 0.8666 0.772 0.000 0.000 0.000 NA
#> SRR491070 1 0.1965 0.9049 0.904 0.000 0.000 0.000 NA
#> SRR491071 1 0.1671 0.9089 0.924 0.000 0.000 0.000 NA
#> SRR491072 1 0.2424 0.8963 0.868 0.000 0.000 0.000 NA
#> SRR491073 1 0.3932 0.8185 0.672 0.000 0.000 0.000 NA
#> SRR491074 1 0.0794 0.9152 0.972 0.000 0.000 0.000 NA
#> SRR491075 1 0.3913 0.8209 0.676 0.000 0.000 0.000 NA
#> SRR491076 1 0.3857 0.8277 0.688 0.000 0.000 0.000 NA
#> SRR491077 1 0.0000 0.9144 1.000 0.000 0.000 0.000 NA
#> SRR491078 1 0.0963 0.9149 0.964 0.000 0.000 0.000 NA
#> SRR491079 1 0.0290 0.9135 0.992 0.000 0.000 0.000 NA
#> SRR491080 1 0.0404 0.9151 0.988 0.000 0.000 0.000 NA
#> SRR491081 1 0.0000 0.9144 1.000 0.000 0.000 0.000 NA
#> SRR491082 1 0.0609 0.9156 0.980 0.000 0.000 0.000 NA
#> SRR491083 1 0.0290 0.9135 0.992 0.000 0.000 0.000 NA
#> SRR491084 1 0.0290 0.9150 0.992 0.000 0.000 0.000 NA
#> SRR491085 1 0.0609 0.9112 0.980 0.000 0.000 0.000 NA
#> SRR491086 1 0.3837 0.8298 0.692 0.000 0.000 0.000 NA
#> SRR491087 1 0.3274 0.8699 0.780 0.000 0.000 0.000 NA
#> SRR491088 1 0.3949 0.8159 0.668 0.000 0.000 0.000 NA
#> SRR491089 1 0.1270 0.9127 0.948 0.000 0.000 0.000 NA
#> SRR491090 1 0.3932 0.8185 0.672 0.000 0.000 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0363 0.992 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445719 2 0.0363 0.992 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445720 2 0.0363 0.992 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445721 2 0.0260 0.995 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445722 2 0.0260 0.995 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445723 2 0.0260 0.995 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445724 2 0.0260 0.995 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445725 2 0.0260 0.995 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445726 2 0.0146 0.996 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445727 2 0.0146 0.996 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445728 2 0.0146 0.996 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445729 2 0.0146 0.996 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445730 1 0.3371 0.889 0.708 0.000 0.000 0.000 0.292 0.000
#> SRR445731 1 0.3266 0.882 0.728 0.000 0.000 0.000 0.272 0.000
#> SRR490961 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0146 0.994 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR490966 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0146 0.994 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR490968 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.996 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490973 3 0.2793 0.766 0.000 0.000 0.800 0.000 0.000 0.200
#> SRR490974 3 0.2793 0.766 0.000 0.000 0.800 0.000 0.000 0.200
#> SRR490975 3 0.3076 0.705 0.000 0.000 0.760 0.000 0.000 0.240
#> SRR490976 3 0.2697 0.778 0.000 0.000 0.812 0.000 0.000 0.188
#> SRR490977 3 0.2597 0.788 0.000 0.000 0.824 0.000 0.000 0.176
#> SRR490978 3 0.2597 0.789 0.000 0.000 0.824 0.000 0.000 0.176
#> SRR490979 3 0.2562 0.791 0.000 0.000 0.828 0.000 0.000 0.172
#> SRR490980 3 0.2823 0.761 0.000 0.000 0.796 0.000 0.000 0.204
#> SRR490981 6 0.3620 0.475 0.000 0.352 0.000 0.000 0.000 0.648
#> SRR490982 6 0.3565 0.550 0.000 0.304 0.004 0.000 0.000 0.692
#> SRR490983 6 0.3547 0.514 0.000 0.332 0.000 0.000 0.000 0.668
#> SRR490984 6 0.3578 0.500 0.000 0.340 0.000 0.000 0.000 0.660
#> SRR490985 6 0.3428 0.607 0.000 0.000 0.304 0.000 0.000 0.696
#> SRR490986 6 0.3309 0.618 0.000 0.000 0.280 0.000 0.000 0.720
#> SRR490987 6 0.3774 0.414 0.000 0.000 0.408 0.000 0.000 0.592
#> SRR490988 6 0.3464 0.601 0.000 0.000 0.312 0.000 0.000 0.688
#> SRR490989 6 0.3647 0.534 0.000 0.000 0.360 0.000 0.000 0.640
#> SRR490990 6 0.3647 0.534 0.000 0.000 0.360 0.000 0.000 0.640
#> SRR490991 6 0.3515 0.588 0.000 0.000 0.324 0.000 0.000 0.676
#> SRR490992 3 0.3869 -0.165 0.000 0.000 0.500 0.000 0.000 0.500
#> SRR490993 3 0.0363 0.857 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR490994 3 0.0508 0.851 0.004 0.000 0.984 0.000 0.000 0.012
#> SRR490995 6 0.3566 0.652 0.008 0.076 0.104 0.000 0.000 0.812
#> SRR490996 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490997 3 0.0405 0.853 0.004 0.000 0.988 0.000 0.000 0.008
#> SRR490998 3 0.0622 0.849 0.008 0.000 0.980 0.000 0.000 0.012
#> SRR491000 6 0.3577 0.650 0.012 0.084 0.088 0.000 0.000 0.816
#> SRR491001 3 0.0806 0.843 0.008 0.000 0.972 0.000 0.000 0.020
#> SRR491002 3 0.0717 0.847 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR491003 3 0.0458 0.857 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR491004 3 0.0458 0.857 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR491005 3 0.0806 0.843 0.008 0.000 0.972 0.000 0.000 0.020
#> SRR491006 3 0.0363 0.857 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR491007 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR491008 3 0.0622 0.849 0.008 0.000 0.980 0.000 0.000 0.012
#> SRR491009 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0146 0.946 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR491011 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491012 4 0.0146 0.946 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491013 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491014 4 0.0146 0.946 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR491015 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491016 4 0.0000 0.946 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491018 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491019 4 0.0146 0.946 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR491020 4 0.0260 0.946 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR491021 4 0.0508 0.942 0.004 0.000 0.000 0.984 0.000 0.012
#> SRR491022 4 0.4183 0.786 0.180 0.000 0.004 0.740 0.000 0.076
#> SRR491023 4 0.3666 0.848 0.096 0.000 0.004 0.812 0.008 0.080
#> SRR491024 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491025 4 0.0405 0.945 0.008 0.000 0.000 0.988 0.000 0.004
#> SRR491026 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491027 4 0.0291 0.946 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR491028 4 0.2744 0.881 0.072 0.000 0.000 0.864 0.000 0.064
#> SRR491029 4 0.0260 0.944 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR491030 4 0.0405 0.945 0.008 0.000 0.000 0.988 0.000 0.004
#> SRR491031 4 0.4777 0.803 0.096 0.000 0.008 0.752 0.064 0.080
#> SRR491032 4 0.3165 0.869 0.076 0.000 0.000 0.844 0.008 0.072
#> SRR491033 4 0.0146 0.946 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491034 4 0.4188 0.829 0.104 0.000 0.004 0.784 0.028 0.080
#> SRR491035 4 0.6406 0.450 0.092 0.000 0.004 0.516 0.308 0.080
#> SRR491036 4 0.1518 0.926 0.024 0.000 0.000 0.944 0.024 0.008
#> SRR491037 4 0.0405 0.943 0.004 0.000 0.000 0.988 0.000 0.008
#> SRR491038 4 0.0717 0.939 0.016 0.000 0.000 0.976 0.000 0.008
#> SRR491039 1 0.3409 0.889 0.700 0.000 0.000 0.000 0.300 0.000
#> SRR491040 1 0.3076 0.848 0.760 0.000 0.000 0.000 0.240 0.000
#> SRR491041 1 0.2969 0.825 0.776 0.000 0.000 0.000 0.224 0.000
#> SRR491042 1 0.3244 0.879 0.732 0.000 0.000 0.000 0.268 0.000
#> SRR491043 1 0.3198 0.872 0.740 0.000 0.000 0.000 0.260 0.000
#> SRR491045 1 0.3244 0.879 0.732 0.000 0.000 0.000 0.268 0.000
#> SRR491065 5 0.2003 0.755 0.116 0.000 0.000 0.000 0.884 0.000
#> SRR491066 5 0.2697 0.692 0.188 0.000 0.000 0.000 0.812 0.000
#> SRR491067 5 0.2730 0.686 0.192 0.000 0.000 0.000 0.808 0.000
#> SRR491068 1 0.3578 0.874 0.660 0.000 0.000 0.000 0.340 0.000
#> SRR491069 5 0.2969 0.626 0.224 0.000 0.000 0.000 0.776 0.000
#> SRR491070 1 0.3864 0.622 0.520 0.000 0.000 0.000 0.480 0.000
#> SRR491071 1 0.3862 0.635 0.524 0.000 0.000 0.000 0.476 0.000
#> SRR491072 5 0.3828 -0.375 0.440 0.000 0.000 0.000 0.560 0.000
#> SRR491073 5 0.0000 0.774 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491074 1 0.3717 0.827 0.616 0.000 0.000 0.000 0.384 0.000
#> SRR491075 5 0.0146 0.776 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR491076 5 0.0790 0.783 0.032 0.000 0.000 0.000 0.968 0.000
#> SRR491077 1 0.3515 0.884 0.676 0.000 0.000 0.000 0.324 0.000
#> SRR491078 1 0.3804 0.760 0.576 0.000 0.000 0.000 0.424 0.000
#> SRR491079 1 0.3351 0.889 0.712 0.000 0.000 0.000 0.288 0.000
#> SRR491080 1 0.3547 0.881 0.668 0.000 0.000 0.000 0.332 0.000
#> SRR491081 1 0.3482 0.887 0.684 0.000 0.000 0.000 0.316 0.000
#> SRR491082 1 0.3737 0.815 0.608 0.000 0.000 0.000 0.392 0.000
#> SRR491083 1 0.3309 0.886 0.720 0.000 0.000 0.000 0.280 0.000
#> SRR491084 1 0.3531 0.882 0.672 0.000 0.000 0.000 0.328 0.000
#> SRR491085 1 0.3221 0.876 0.736 0.000 0.000 0.000 0.264 0.000
#> SRR491086 5 0.0937 0.783 0.040 0.000 0.000 0.000 0.960 0.000
#> SRR491087 5 0.3175 0.541 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR491088 5 0.0000 0.774 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491089 1 0.3817 0.745 0.568 0.000 0.000 0.000 0.432 0.000
#> SRR491090 5 0.0000 0.774 0.000 0.000 0.000 0.000 1.000 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", "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 13175 rows and 123 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 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 1.000 1.000 0.3552 0.645 0.645
#> 3 3 1.000 0.988 0.992 0.7278 0.736 0.590
#> 4 4 1.000 0.984 0.992 0.0122 0.992 0.980
#> 5 5 0.921 0.895 0.949 0.0982 0.961 0.895
#> 6 6 0.799 0.824 0.893 0.0400 0.956 0.869
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
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
#> SRR445718 2 0 1 0 1
#> SRR445719 2 0 1 0 1
#> SRR445720 2 0 1 0 1
#> SRR445721 2 0 1 0 1
#> SRR445722 2 0 1 0 1
#> SRR445723 2 0 1 0 1
#> SRR445724 2 0 1 0 1
#> SRR445725 2 0 1 0 1
#> SRR445726 2 0 1 0 1
#> SRR445727 2 0 1 0 1
#> SRR445728 2 0 1 0 1
#> SRR445729 2 0 1 0 1
#> SRR445730 1 0 1 1 0
#> SRR445731 1 0 1 1 0
#> SRR490961 2 0 1 0 1
#> SRR490962 2 0 1 0 1
#> SRR490963 2 0 1 0 1
#> SRR490964 2 0 1 0 1
#> SRR490965 2 0 1 0 1
#> SRR490966 2 0 1 0 1
#> SRR490967 2 0 1 0 1
#> SRR490968 2 0 1 0 1
#> SRR490969 2 0 1 0 1
#> SRR490970 2 0 1 0 1
#> SRR490971 2 0 1 0 1
#> SRR490972 2 0 1 0 1
#> SRR490973 1 0 1 1 0
#> SRR490974 1 0 1 1 0
#> SRR490975 1 0 1 1 0
#> SRR490976 1 0 1 1 0
#> SRR490977 1 0 1 1 0
#> SRR490978 1 0 1 1 0
#> SRR490979 1 0 1 1 0
#> SRR490980 1 0 1 1 0
#> SRR490981 2 0 1 0 1
#> SRR490982 2 0 1 0 1
#> SRR490983 2 0 1 0 1
#> SRR490984 2 0 1 0 1
#> SRR490985 1 0 1 1 0
#> SRR490986 1 0 1 1 0
#> SRR490987 1 0 1 1 0
#> SRR490988 1 0 1 1 0
#> SRR490989 1 0 1 1 0
#> SRR490990 1 0 1 1 0
#> SRR490991 1 0 1 1 0
#> SRR490992 1 0 1 1 0
#> SRR490993 1 0 1 1 0
#> SRR490994 1 0 1 1 0
#> SRR490995 1 0 1 1 0
#> SRR490996 1 0 1 1 0
#> SRR490997 1 0 1 1 0
#> SRR490998 1 0 1 1 0
#> SRR491000 1 0 1 1 0
#> SRR491001 1 0 1 1 0
#> SRR491002 1 0 1 1 0
#> SRR491003 1 0 1 1 0
#> SRR491004 1 0 1 1 0
#> SRR491005 1 0 1 1 0
#> SRR491006 1 0 1 1 0
#> SRR491007 1 0 1 1 0
#> SRR491008 1 0 1 1 0
#> SRR491009 1 0 1 1 0
#> SRR491010 1 0 1 1 0
#> SRR491011 1 0 1 1 0
#> SRR491012 1 0 1 1 0
#> SRR491013 1 0 1 1 0
#> SRR491014 1 0 1 1 0
#> SRR491015 1 0 1 1 0
#> SRR491016 1 0 1 1 0
#> SRR491017 1 0 1 1 0
#> SRR491018 1 0 1 1 0
#> SRR491019 1 0 1 1 0
#> SRR491020 1 0 1 1 0
#> SRR491021 1 0 1 1 0
#> SRR491022 1 0 1 1 0
#> SRR491023 1 0 1 1 0
#> SRR491024 1 0 1 1 0
#> SRR491025 1 0 1 1 0
#> SRR491026 1 0 1 1 0
#> SRR491027 1 0 1 1 0
#> SRR491028 1 0 1 1 0
#> SRR491029 1 0 1 1 0
#> SRR491030 1 0 1 1 0
#> SRR491031 1 0 1 1 0
#> SRR491032 1 0 1 1 0
#> SRR491033 1 0 1 1 0
#> SRR491034 1 0 1 1 0
#> SRR491035 1 0 1 1 0
#> SRR491036 1 0 1 1 0
#> SRR491037 1 0 1 1 0
#> SRR491038 1 0 1 1 0
#> SRR491039 1 0 1 1 0
#> SRR491040 1 0 1 1 0
#> SRR491041 1 0 1 1 0
#> SRR491042 1 0 1 1 0
#> SRR491043 1 0 1 1 0
#> SRR491045 1 0 1 1 0
#> SRR491065 1 0 1 1 0
#> SRR491066 1 0 1 1 0
#> SRR491067 1 0 1 1 0
#> SRR491068 1 0 1 1 0
#> SRR491069 1 0 1 1 0
#> SRR491070 1 0 1 1 0
#> SRR491071 1 0 1 1 0
#> SRR491072 1 0 1 1 0
#> SRR491073 1 0 1 1 0
#> SRR491074 1 0 1 1 0
#> SRR491075 1 0 1 1 0
#> SRR491076 1 0 1 1 0
#> SRR491077 1 0 1 1 0
#> SRR491078 1 0 1 1 0
#> SRR491079 1 0 1 1 0
#> SRR491080 1 0 1 1 0
#> SRR491081 1 0 1 1 0
#> SRR491082 1 0 1 1 0
#> SRR491083 1 0 1 1 0
#> SRR491084 1 0 1 1 0
#> SRR491085 1 0 1 1 0
#> SRR491086 1 0 1 1 0
#> SRR491087 1 0 1 1 0
#> SRR491088 1 0 1 1 0
#> SRR491089 1 0 1 1 0
#> SRR491090 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 1.000 0.000 1 0.000
#> SRR445719 2 0.000 1.000 0.000 1 0.000
#> SRR445720 2 0.000 1.000 0.000 1 0.000
#> SRR445721 2 0.000 1.000 0.000 1 0.000
#> SRR445722 2 0.000 1.000 0.000 1 0.000
#> SRR445723 2 0.000 1.000 0.000 1 0.000
#> SRR445724 2 0.000 1.000 0.000 1 0.000
#> SRR445725 2 0.000 1.000 0.000 1 0.000
#> SRR445726 2 0.000 1.000 0.000 1 0.000
#> SRR445727 2 0.000 1.000 0.000 1 0.000
#> SRR445728 2 0.000 1.000 0.000 1 0.000
#> SRR445729 2 0.000 1.000 0.000 1 0.000
#> SRR445730 1 0.000 1.000 1.000 0 0.000
#> SRR445731 1 0.000 1.000 1.000 0 0.000
#> SRR490961 2 0.000 1.000 0.000 1 0.000
#> SRR490962 2 0.000 1.000 0.000 1 0.000
#> SRR490963 2 0.000 1.000 0.000 1 0.000
#> SRR490964 2 0.000 1.000 0.000 1 0.000
#> SRR490965 2 0.000 1.000 0.000 1 0.000
#> SRR490966 2 0.000 1.000 0.000 1 0.000
#> SRR490967 2 0.000 1.000 0.000 1 0.000
#> SRR490968 2 0.000 1.000 0.000 1 0.000
#> SRR490969 2 0.000 1.000 0.000 1 0.000
#> SRR490970 2 0.000 1.000 0.000 1 0.000
#> SRR490971 2 0.000 1.000 0.000 1 0.000
#> SRR490972 2 0.000 1.000 0.000 1 0.000
#> SRR490973 3 0.000 0.960 0.000 0 1.000
#> SRR490974 3 0.000 0.960 0.000 0 1.000
#> SRR490975 3 0.000 0.960 0.000 0 1.000
#> SRR490976 3 0.000 0.960 0.000 0 1.000
#> SRR490977 3 0.000 0.960 0.000 0 1.000
#> SRR490978 3 0.000 0.960 0.000 0 1.000
#> SRR490979 3 0.000 0.960 0.000 0 1.000
#> SRR490980 3 0.000 0.960 0.000 0 1.000
#> SRR490981 2 0.000 1.000 0.000 1 0.000
#> SRR490982 2 0.000 1.000 0.000 1 0.000
#> SRR490983 2 0.000 1.000 0.000 1 0.000
#> SRR490984 2 0.000 1.000 0.000 1 0.000
#> SRR490985 3 0.000 0.960 0.000 0 1.000
#> SRR490986 3 0.000 0.960 0.000 0 1.000
#> SRR490987 3 0.000 0.960 0.000 0 1.000
#> SRR490988 3 0.000 0.960 0.000 0 1.000
#> SRR490989 3 0.000 0.960 0.000 0 1.000
#> SRR490990 3 0.000 0.960 0.000 0 1.000
#> SRR490991 3 0.000 0.960 0.000 0 1.000
#> SRR490992 3 0.000 0.960 0.000 0 1.000
#> SRR490993 3 0.236 0.944 0.072 0 0.928
#> SRR490994 3 0.236 0.944 0.072 0 0.928
#> SRR490995 3 0.000 0.960 0.000 0 1.000
#> SRR490996 3 0.236 0.944 0.072 0 0.928
#> SRR490997 3 0.236 0.944 0.072 0 0.928
#> SRR490998 3 0.236 0.944 0.072 0 0.928
#> SRR491000 3 0.000 0.960 0.000 0 1.000
#> SRR491001 3 0.236 0.944 0.072 0 0.928
#> SRR491002 3 0.236 0.944 0.072 0 0.928
#> SRR491003 3 0.236 0.944 0.072 0 0.928
#> SRR491004 3 0.236 0.944 0.072 0 0.928
#> SRR491005 3 0.236 0.944 0.072 0 0.928
#> SRR491006 3 0.236 0.944 0.072 0 0.928
#> SRR491007 3 0.236 0.944 0.072 0 0.928
#> SRR491008 3 0.236 0.944 0.072 0 0.928
#> SRR491009 1 0.000 1.000 1.000 0 0.000
#> SRR491010 1 0.000 1.000 1.000 0 0.000
#> SRR491011 1 0.000 1.000 1.000 0 0.000
#> SRR491012 1 0.000 1.000 1.000 0 0.000
#> SRR491013 1 0.000 1.000 1.000 0 0.000
#> SRR491014 1 0.000 1.000 1.000 0 0.000
#> SRR491015 1 0.000 1.000 1.000 0 0.000
#> SRR491016 1 0.000 1.000 1.000 0 0.000
#> SRR491017 1 0.000 1.000 1.000 0 0.000
#> SRR491018 1 0.000 1.000 1.000 0 0.000
#> SRR491019 1 0.000 1.000 1.000 0 0.000
#> SRR491020 1 0.000 1.000 1.000 0 0.000
#> SRR491021 1 0.000 1.000 1.000 0 0.000
#> SRR491022 1 0.000 1.000 1.000 0 0.000
#> SRR491023 1 0.000 1.000 1.000 0 0.000
#> SRR491024 1 0.000 1.000 1.000 0 0.000
#> SRR491025 1 0.000 1.000 1.000 0 0.000
#> SRR491026 1 0.000 1.000 1.000 0 0.000
#> SRR491027 1 0.000 1.000 1.000 0 0.000
#> SRR491028 1 0.000 1.000 1.000 0 0.000
#> SRR491029 1 0.000 1.000 1.000 0 0.000
#> SRR491030 1 0.000 1.000 1.000 0 0.000
#> SRR491031 1 0.000 1.000 1.000 0 0.000
#> SRR491032 1 0.000 1.000 1.000 0 0.000
#> SRR491033 1 0.000 1.000 1.000 0 0.000
#> SRR491034 1 0.000 1.000 1.000 0 0.000
#> SRR491035 1 0.000 1.000 1.000 0 0.000
#> SRR491036 1 0.000 1.000 1.000 0 0.000
#> SRR491037 1 0.000 1.000 1.000 0 0.000
#> SRR491038 1 0.000 1.000 1.000 0 0.000
#> SRR491039 1 0.000 1.000 1.000 0 0.000
#> SRR491040 1 0.000 1.000 1.000 0 0.000
#> SRR491041 1 0.000 1.000 1.000 0 0.000
#> SRR491042 1 0.000 1.000 1.000 0 0.000
#> SRR491043 1 0.000 1.000 1.000 0 0.000
#> SRR491045 1 0.000 1.000 1.000 0 0.000
#> SRR491065 1 0.000 1.000 1.000 0 0.000
#> SRR491066 1 0.000 1.000 1.000 0 0.000
#> SRR491067 1 0.000 1.000 1.000 0 0.000
#> SRR491068 1 0.000 1.000 1.000 0 0.000
#> SRR491069 1 0.000 1.000 1.000 0 0.000
#> SRR491070 1 0.000 1.000 1.000 0 0.000
#> SRR491071 1 0.000 1.000 1.000 0 0.000
#> SRR491072 1 0.000 1.000 1.000 0 0.000
#> SRR491073 1 0.000 1.000 1.000 0 0.000
#> SRR491074 1 0.000 1.000 1.000 0 0.000
#> SRR491075 1 0.000 1.000 1.000 0 0.000
#> SRR491076 1 0.000 1.000 1.000 0 0.000
#> SRR491077 1 0.000 1.000 1.000 0 0.000
#> SRR491078 1 0.000 1.000 1.000 0 0.000
#> SRR491079 1 0.000 1.000 1.000 0 0.000
#> SRR491080 1 0.000 1.000 1.000 0 0.000
#> SRR491081 1 0.000 1.000 1.000 0 0.000
#> SRR491082 1 0.000 1.000 1.000 0 0.000
#> SRR491083 1 0.000 1.000 1.000 0 0.000
#> SRR491084 1 0.000 1.000 1.000 0 0.000
#> SRR491085 1 0.000 1.000 1.000 0 0.000
#> SRR491086 1 0.000 1.000 1.000 0 0.000
#> SRR491087 1 0.000 1.000 1.000 0 0.000
#> SRR491088 1 0.000 1.000 1.000 0 0.000
#> SRR491089 1 0.000 1.000 1.000 0 0.000
#> SRR491090 1 0.000 1.000 1.000 0 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.000 1.000 0.000 1 0.000 0
#> SRR445719 2 0.000 1.000 0.000 1 0.000 0
#> SRR445720 2 0.000 1.000 0.000 1 0.000 0
#> SRR445721 2 0.000 1.000 0.000 1 0.000 0
#> SRR445722 2 0.000 1.000 0.000 1 0.000 0
#> SRR445723 2 0.000 1.000 0.000 1 0.000 0
#> SRR445724 2 0.000 1.000 0.000 1 0.000 0
#> SRR445725 2 0.000 1.000 0.000 1 0.000 0
#> SRR445726 2 0.000 1.000 0.000 1 0.000 0
#> SRR445727 2 0.000 1.000 0.000 1 0.000 0
#> SRR445728 2 0.000 1.000 0.000 1 0.000 0
#> SRR445729 2 0.000 1.000 0.000 1 0.000 0
#> SRR445730 1 0.000 1.000 1.000 0 0.000 0
#> SRR445731 1 0.000 1.000 1.000 0 0.000 0
#> SRR490961 2 0.000 1.000 0.000 1 0.000 0
#> SRR490962 2 0.000 1.000 0.000 1 0.000 0
#> SRR490963 2 0.000 1.000 0.000 1 0.000 0
#> SRR490964 2 0.000 1.000 0.000 1 0.000 0
#> SRR490965 2 0.000 1.000 0.000 1 0.000 0
#> SRR490966 2 0.000 1.000 0.000 1 0.000 0
#> SRR490967 2 0.000 1.000 0.000 1 0.000 0
#> SRR490968 2 0.000 1.000 0.000 1 0.000 0
#> SRR490969 2 0.000 1.000 0.000 1 0.000 0
#> SRR490970 2 0.000 1.000 0.000 1 0.000 0
#> SRR490971 2 0.000 1.000 0.000 1 0.000 0
#> SRR490972 2 0.000 1.000 0.000 1 0.000 0
#> SRR490973 3 0.000 0.940 0.000 0 1.000 0
#> SRR490974 3 0.000 0.940 0.000 0 1.000 0
#> SRR490975 3 0.000 0.940 0.000 0 1.000 0
#> SRR490976 3 0.000 0.940 0.000 0 1.000 0
#> SRR490977 3 0.000 0.940 0.000 0 1.000 0
#> SRR490978 3 0.000 0.940 0.000 0 1.000 0
#> SRR490979 3 0.000 0.940 0.000 0 1.000 0
#> SRR490980 3 0.000 0.940 0.000 0 1.000 0
#> SRR490981 2 0.000 1.000 0.000 1 0.000 0
#> SRR490982 2 0.000 1.000 0.000 1 0.000 0
#> SRR490983 2 0.000 1.000 0.000 1 0.000 0
#> SRR490984 2 0.000 1.000 0.000 1 0.000 0
#> SRR490985 3 0.000 0.940 0.000 0 1.000 0
#> SRR490986 3 0.000 0.940 0.000 0 1.000 0
#> SRR490987 3 0.000 0.940 0.000 0 1.000 0
#> SRR490988 3 0.000 0.940 0.000 0 1.000 0
#> SRR490989 3 0.000 0.940 0.000 0 1.000 0
#> SRR490990 3 0.000 0.940 0.000 0 1.000 0
#> SRR490991 3 0.000 0.940 0.000 0 1.000 0
#> SRR490992 3 0.000 0.940 0.000 0 1.000 0
#> SRR490993 3 0.187 0.926 0.072 0 0.928 0
#> SRR490994 3 0.187 0.926 0.072 0 0.928 0
#> SRR490995 4 0.000 1.000 0.000 0 0.000 1
#> SRR490996 3 0.187 0.926 0.072 0 0.928 0
#> SRR490997 3 0.187 0.926 0.072 0 0.928 0
#> SRR490998 3 0.187 0.926 0.072 0 0.928 0
#> SRR491000 4 0.000 1.000 0.000 0 0.000 1
#> SRR491001 3 0.187 0.926 0.072 0 0.928 0
#> SRR491002 3 0.187 0.926 0.072 0 0.928 0
#> SRR491003 3 0.187 0.926 0.072 0 0.928 0
#> SRR491004 3 0.187 0.926 0.072 0 0.928 0
#> SRR491005 3 0.187 0.926 0.072 0 0.928 0
#> SRR491006 3 0.187 0.926 0.072 0 0.928 0
#> SRR491007 3 0.187 0.926 0.072 0 0.928 0
#> SRR491008 3 0.187 0.926 0.072 0 0.928 0
#> SRR491009 1 0.000 1.000 1.000 0 0.000 0
#> SRR491010 1 0.000 1.000 1.000 0 0.000 0
#> SRR491011 1 0.000 1.000 1.000 0 0.000 0
#> SRR491012 1 0.000 1.000 1.000 0 0.000 0
#> SRR491013 1 0.000 1.000 1.000 0 0.000 0
#> SRR491014 1 0.000 1.000 1.000 0 0.000 0
#> SRR491015 1 0.000 1.000 1.000 0 0.000 0
#> SRR491016 1 0.000 1.000 1.000 0 0.000 0
#> SRR491017 1 0.000 1.000 1.000 0 0.000 0
#> SRR491018 1 0.000 1.000 1.000 0 0.000 0
#> SRR491019 1 0.000 1.000 1.000 0 0.000 0
#> SRR491020 1 0.000 1.000 1.000 0 0.000 0
#> SRR491021 1 0.000 1.000 1.000 0 0.000 0
#> SRR491022 1 0.000 1.000 1.000 0 0.000 0
#> SRR491023 1 0.000 1.000 1.000 0 0.000 0
#> SRR491024 1 0.000 1.000 1.000 0 0.000 0
#> SRR491025 1 0.000 1.000 1.000 0 0.000 0
#> SRR491026 1 0.000 1.000 1.000 0 0.000 0
#> SRR491027 1 0.000 1.000 1.000 0 0.000 0
#> SRR491028 1 0.000 1.000 1.000 0 0.000 0
#> SRR491029 1 0.000 1.000 1.000 0 0.000 0
#> SRR491030 1 0.000 1.000 1.000 0 0.000 0
#> SRR491031 1 0.000 1.000 1.000 0 0.000 0
#> SRR491032 1 0.000 1.000 1.000 0 0.000 0
#> SRR491033 1 0.000 1.000 1.000 0 0.000 0
#> SRR491034 1 0.000 1.000 1.000 0 0.000 0
#> SRR491035 1 0.000 1.000 1.000 0 0.000 0
#> SRR491036 1 0.000 1.000 1.000 0 0.000 0
#> SRR491037 1 0.000 1.000 1.000 0 0.000 0
#> SRR491038 1 0.000 1.000 1.000 0 0.000 0
#> SRR491039 1 0.000 1.000 1.000 0 0.000 0
#> SRR491040 1 0.000 1.000 1.000 0 0.000 0
#> SRR491041 1 0.000 1.000 1.000 0 0.000 0
#> SRR491042 1 0.000 1.000 1.000 0 0.000 0
#> SRR491043 1 0.000 1.000 1.000 0 0.000 0
#> SRR491045 1 0.000 1.000 1.000 0 0.000 0
#> SRR491065 1 0.000 1.000 1.000 0 0.000 0
#> SRR491066 1 0.000 1.000 1.000 0 0.000 0
#> SRR491067 1 0.000 1.000 1.000 0 0.000 0
#> SRR491068 1 0.000 1.000 1.000 0 0.000 0
#> SRR491069 1 0.000 1.000 1.000 0 0.000 0
#> SRR491070 1 0.000 1.000 1.000 0 0.000 0
#> SRR491071 1 0.000 1.000 1.000 0 0.000 0
#> SRR491072 1 0.000 1.000 1.000 0 0.000 0
#> SRR491073 1 0.000 1.000 1.000 0 0.000 0
#> SRR491074 1 0.000 1.000 1.000 0 0.000 0
#> SRR491075 1 0.000 1.000 1.000 0 0.000 0
#> SRR491076 1 0.000 1.000 1.000 0 0.000 0
#> SRR491077 1 0.000 1.000 1.000 0 0.000 0
#> SRR491078 1 0.000 1.000 1.000 0 0.000 0
#> SRR491079 1 0.000 1.000 1.000 0 0.000 0
#> SRR491080 1 0.000 1.000 1.000 0 0.000 0
#> SRR491081 1 0.000 1.000 1.000 0 0.000 0
#> SRR491082 1 0.000 1.000 1.000 0 0.000 0
#> SRR491083 1 0.000 1.000 1.000 0 0.000 0
#> SRR491084 1 0.000 1.000 1.000 0 0.000 0
#> SRR491085 1 0.000 1.000 1.000 0 0.000 0
#> SRR491086 1 0.000 1.000 1.000 0 0.000 0
#> SRR491087 1 0.000 1.000 1.000 0 0.000 0
#> SRR491088 1 0.000 1.000 1.000 0 0.000 0
#> SRR491089 1 0.000 1.000 1.000 0 0.000 0
#> SRR491090 1 0.000 1.000 1.000 0 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445719 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445720 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445721 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445722 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445723 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445724 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445725 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445726 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445727 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445728 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445729 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR445730 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR445731 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR490961 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490962 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490963 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490964 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490965 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490966 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490967 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490968 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490969 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490970 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490971 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490972 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490973 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490974 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490975 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490976 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490977 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490978 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490979 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490980 3 0.0000 0.926 0.000 0 1.00 0.000 0
#> SRR490981 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490982 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490983 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490984 2 0.0000 1.000 0.000 1 0.00 0.000 0
#> SRR490985 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490986 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490987 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490988 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490989 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490990 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490991 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490992 3 0.2280 0.876 0.120 0 0.88 0.000 0
#> SRR490993 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR490994 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR490995 5 0.0000 1.000 0.000 0 0.00 0.000 1
#> SRR490996 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR490997 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR490998 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491000 5 0.0000 1.000 0.000 0 0.00 0.000 1
#> SRR491001 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491002 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491003 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491004 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491005 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491006 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491007 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491008 3 0.1732 0.925 0.080 0 0.92 0.000 0
#> SRR491009 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491010 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491011 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491012 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491013 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491014 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491015 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491016 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491017 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491018 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491019 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491020 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491021 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491022 4 0.4088 0.202 0.368 0 0.00 0.632 0
#> SRR491023 4 0.4088 0.202 0.368 0 0.00 0.632 0
#> SRR491024 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491025 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491026 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491027 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491028 4 0.4045 0.246 0.356 0 0.00 0.644 0
#> SRR491029 4 0.0880 0.899 0.032 0 0.00 0.968 0
#> SRR491030 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491031 1 0.3452 0.902 0.756 0 0.00 0.244 0
#> SRR491032 4 0.4101 0.189 0.372 0 0.00 0.628 0
#> SRR491033 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491034 4 0.4306 -0.311 0.492 0 0.00 0.508 0
#> SRR491035 4 0.4302 -0.268 0.480 0 0.00 0.520 0
#> SRR491036 4 0.3534 0.538 0.256 0 0.00 0.744 0
#> SRR491037 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491038 4 0.3534 0.538 0.256 0 0.00 0.744 0
#> SRR491039 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491040 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491041 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491042 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491043 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491045 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491065 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491066 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491067 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491068 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491069 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491070 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491071 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491072 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491073 1 0.3109 0.974 0.800 0 0.00 0.200 0
#> SRR491074 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491075 1 0.3109 0.974 0.800 0 0.00 0.200 0
#> SRR491076 4 0.0703 0.907 0.024 0 0.00 0.976 0
#> SRR491077 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491078 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491079 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491080 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491081 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491082 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491083 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491084 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491085 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491086 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491087 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491088 1 0.3109 0.974 0.800 0 0.00 0.200 0
#> SRR491089 4 0.0000 0.932 0.000 0 0.00 1.000 0
#> SRR491090 1 0.3109 0.974 0.800 0 0.00 0.200 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.971 0.000 1.0 0.000 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490974 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490975 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490976 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490977 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490978 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490979 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490980 3 0.0000 0.853 0.000 0.0 1.000 0.000 0.000 0.000
#> SRR490981 2 0.2793 0.796 0.000 0.8 0.000 0.000 0.200 0.000
#> SRR490982 2 0.2793 0.796 0.000 0.8 0.000 0.000 0.200 0.000
#> SRR490983 2 0.2793 0.796 0.000 0.8 0.000 0.000 0.200 0.000
#> SRR490984 2 0.2793 0.796 0.000 0.8 0.000 0.000 0.200 0.000
#> SRR490985 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490986 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490987 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490988 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490989 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490990 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490991 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490992 3 0.3482 0.670 0.000 0.0 0.684 0.000 0.000 0.316
#> SRR490993 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR490994 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR490995 6 0.3482 1.000 0.000 0.0 0.000 0.316 0.000 0.684
#> SRR490996 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR490997 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR490998 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491000 6 0.3482 1.000 0.000 0.0 0.000 0.316 0.000 0.684
#> SRR491001 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491002 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491003 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491004 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491005 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491006 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491007 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491008 3 0.1765 0.855 0.000 0.0 0.904 0.096 0.000 0.000
#> SRR491009 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491010 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491011 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491012 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491013 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491014 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491015 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491016 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491017 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491018 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491019 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491020 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491021 1 0.2491 0.760 0.836 0.0 0.000 0.164 0.000 0.000
#> SRR491022 4 0.4703 0.762 0.464 0.0 0.000 0.492 0.044 0.000
#> SRR491023 4 0.4703 0.762 0.464 0.0 0.000 0.492 0.044 0.000
#> SRR491024 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491025 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491026 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491027 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491028 4 0.4705 0.737 0.476 0.0 0.000 0.480 0.044 0.000
#> SRR491029 1 0.2762 0.700 0.804 0.0 0.000 0.196 0.000 0.000
#> SRR491030 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491031 4 0.5884 -0.345 0.212 0.0 0.000 0.452 0.336 0.000
#> SRR491032 4 0.4172 0.754 0.460 0.0 0.000 0.528 0.012 0.000
#> SRR491033 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491034 4 0.4964 0.694 0.388 0.0 0.000 0.540 0.072 0.000
#> SRR491035 4 0.4845 0.710 0.400 0.0 0.000 0.540 0.060 0.000
#> SRR491036 1 0.4601 -0.126 0.628 0.0 0.000 0.312 0.060 0.000
#> SRR491037 1 0.2219 0.789 0.864 0.0 0.000 0.136 0.000 0.000
#> SRR491038 1 0.4601 -0.126 0.628 0.0 0.000 0.312 0.060 0.000
#> SRR491039 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491070 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491073 5 0.2793 1.000 0.200 0.0 0.000 0.000 0.800 0.000
#> SRR491074 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491075 5 0.2793 1.000 0.200 0.0 0.000 0.000 0.800 0.000
#> SRR491076 1 0.0632 0.823 0.976 0.0 0.000 0.000 0.024 0.000
#> SRR491077 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491088 5 0.2793 1.000 0.200 0.0 0.000 0.000 0.800 0.000
#> SRR491089 1 0.0000 0.855 1.000 0.0 0.000 0.000 0.000 0.000
#> SRR491090 5 0.2793 1.000 0.200 0.0 0.000 0.000 0.800 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", "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 13175 rows and 123 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 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 0.999 0.998 0.3552 0.645 0.645
#> 3 3 0.630 0.956 0.945 0.6906 0.736 0.590
#> 4 4 0.841 0.936 0.856 0.1830 0.864 0.643
#> 5 5 0.795 0.894 0.877 0.0649 1.000 1.000
#> 6 6 0.757 0.874 0.846 0.0253 0.980 0.917
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
#> SRR445718 2 0.0376 1.000 0.004 0.996
#> SRR445719 2 0.0376 1.000 0.004 0.996
#> SRR445720 2 0.0376 1.000 0.004 0.996
#> SRR445721 2 0.0376 1.000 0.004 0.996
#> SRR445722 2 0.0376 1.000 0.004 0.996
#> SRR445723 2 0.0376 1.000 0.004 0.996
#> SRR445724 2 0.0376 1.000 0.004 0.996
#> SRR445725 2 0.0376 1.000 0.004 0.996
#> SRR445726 2 0.0376 1.000 0.004 0.996
#> SRR445727 2 0.0376 1.000 0.004 0.996
#> SRR445728 2 0.0376 1.000 0.004 0.996
#> SRR445729 2 0.0376 1.000 0.004 0.996
#> SRR445730 1 0.0000 0.999 1.000 0.000
#> SRR445731 1 0.0000 0.999 1.000 0.000
#> SRR490961 2 0.0376 1.000 0.004 0.996
#> SRR490962 2 0.0376 1.000 0.004 0.996
#> SRR490963 2 0.0376 1.000 0.004 0.996
#> SRR490964 2 0.0376 1.000 0.004 0.996
#> SRR490965 2 0.0376 1.000 0.004 0.996
#> SRR490966 2 0.0376 1.000 0.004 0.996
#> SRR490967 2 0.0376 1.000 0.004 0.996
#> SRR490968 2 0.0376 1.000 0.004 0.996
#> SRR490969 2 0.0376 1.000 0.004 0.996
#> SRR490970 2 0.0376 1.000 0.004 0.996
#> SRR490971 2 0.0376 1.000 0.004 0.996
#> SRR490972 2 0.0376 1.000 0.004 0.996
#> SRR490973 1 0.0376 0.997 0.996 0.004
#> SRR490974 1 0.0376 0.997 0.996 0.004
#> SRR490975 1 0.0376 0.997 0.996 0.004
#> SRR490976 1 0.0376 0.997 0.996 0.004
#> SRR490977 1 0.0376 0.997 0.996 0.004
#> SRR490978 1 0.0376 0.997 0.996 0.004
#> SRR490979 1 0.0376 0.997 0.996 0.004
#> SRR490980 1 0.0376 0.997 0.996 0.004
#> SRR490981 2 0.0376 1.000 0.004 0.996
#> SRR490982 2 0.0376 1.000 0.004 0.996
#> SRR490983 2 0.0376 1.000 0.004 0.996
#> SRR490984 2 0.0376 1.000 0.004 0.996
#> SRR490985 1 0.0376 0.997 0.996 0.004
#> SRR490986 1 0.0376 0.997 0.996 0.004
#> SRR490987 1 0.0376 0.997 0.996 0.004
#> SRR490988 1 0.0376 0.997 0.996 0.004
#> SRR490989 1 0.0376 0.997 0.996 0.004
#> SRR490990 1 0.0376 0.997 0.996 0.004
#> SRR490991 1 0.0376 0.997 0.996 0.004
#> SRR490992 1 0.0376 0.997 0.996 0.004
#> SRR490993 1 0.0376 0.997 0.996 0.004
#> SRR490994 1 0.0376 0.997 0.996 0.004
#> SRR490995 1 0.0376 0.997 0.996 0.004
#> SRR490996 1 0.0376 0.997 0.996 0.004
#> SRR490997 1 0.0376 0.997 0.996 0.004
#> SRR490998 1 0.0376 0.997 0.996 0.004
#> SRR491000 1 0.0376 0.997 0.996 0.004
#> SRR491001 1 0.0376 0.997 0.996 0.004
#> SRR491002 1 0.0376 0.997 0.996 0.004
#> SRR491003 1 0.0376 0.997 0.996 0.004
#> SRR491004 1 0.0376 0.997 0.996 0.004
#> SRR491005 1 0.0376 0.997 0.996 0.004
#> SRR491006 1 0.0376 0.997 0.996 0.004
#> SRR491007 1 0.0376 0.997 0.996 0.004
#> SRR491008 1 0.0376 0.997 0.996 0.004
#> SRR491009 1 0.0000 0.999 1.000 0.000
#> SRR491010 1 0.0000 0.999 1.000 0.000
#> SRR491011 1 0.0000 0.999 1.000 0.000
#> SRR491012 1 0.0000 0.999 1.000 0.000
#> SRR491013 1 0.0000 0.999 1.000 0.000
#> SRR491014 1 0.0000 0.999 1.000 0.000
#> SRR491015 1 0.0000 0.999 1.000 0.000
#> SRR491016 1 0.0000 0.999 1.000 0.000
#> SRR491017 1 0.0000 0.999 1.000 0.000
#> SRR491018 1 0.0000 0.999 1.000 0.000
#> SRR491019 1 0.0000 0.999 1.000 0.000
#> SRR491020 1 0.0000 0.999 1.000 0.000
#> SRR491021 1 0.0000 0.999 1.000 0.000
#> SRR491022 1 0.0000 0.999 1.000 0.000
#> SRR491023 1 0.0000 0.999 1.000 0.000
#> SRR491024 1 0.0000 0.999 1.000 0.000
#> SRR491025 1 0.0000 0.999 1.000 0.000
#> SRR491026 1 0.0000 0.999 1.000 0.000
#> SRR491027 1 0.0000 0.999 1.000 0.000
#> SRR491028 1 0.0000 0.999 1.000 0.000
#> SRR491029 1 0.0000 0.999 1.000 0.000
#> SRR491030 1 0.0000 0.999 1.000 0.000
#> SRR491031 1 0.0000 0.999 1.000 0.000
#> SRR491032 1 0.0000 0.999 1.000 0.000
#> SRR491033 1 0.0000 0.999 1.000 0.000
#> SRR491034 1 0.0000 0.999 1.000 0.000
#> SRR491035 1 0.0000 0.999 1.000 0.000
#> SRR491036 1 0.0000 0.999 1.000 0.000
#> SRR491037 1 0.0000 0.999 1.000 0.000
#> SRR491038 1 0.0000 0.999 1.000 0.000
#> SRR491039 1 0.0000 0.999 1.000 0.000
#> SRR491040 1 0.0000 0.999 1.000 0.000
#> SRR491041 1 0.0000 0.999 1.000 0.000
#> SRR491042 1 0.0000 0.999 1.000 0.000
#> SRR491043 1 0.0000 0.999 1.000 0.000
#> SRR491045 1 0.0000 0.999 1.000 0.000
#> SRR491065 1 0.0000 0.999 1.000 0.000
#> SRR491066 1 0.0000 0.999 1.000 0.000
#> SRR491067 1 0.0000 0.999 1.000 0.000
#> SRR491068 1 0.0000 0.999 1.000 0.000
#> SRR491069 1 0.0000 0.999 1.000 0.000
#> SRR491070 1 0.0000 0.999 1.000 0.000
#> SRR491071 1 0.0000 0.999 1.000 0.000
#> SRR491072 1 0.0000 0.999 1.000 0.000
#> SRR491073 1 0.0000 0.999 1.000 0.000
#> SRR491074 1 0.0000 0.999 1.000 0.000
#> SRR491075 1 0.0000 0.999 1.000 0.000
#> SRR491076 1 0.0000 0.999 1.000 0.000
#> SRR491077 1 0.0000 0.999 1.000 0.000
#> SRR491078 1 0.0000 0.999 1.000 0.000
#> SRR491079 1 0.0000 0.999 1.000 0.000
#> SRR491080 1 0.0000 0.999 1.000 0.000
#> SRR491081 1 0.0000 0.999 1.000 0.000
#> SRR491082 1 0.0000 0.999 1.000 0.000
#> SRR491083 1 0.0000 0.999 1.000 0.000
#> SRR491084 1 0.0000 0.999 1.000 0.000
#> SRR491085 1 0.0000 0.999 1.000 0.000
#> SRR491086 1 0.0000 0.999 1.000 0.000
#> SRR491087 1 0.0000 0.999 1.000 0.000
#> SRR491088 1 0.0000 0.999 1.000 0.000
#> SRR491089 1 0.0000 0.999 1.000 0.000
#> SRR491090 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.1529 0.981 0.000 0.960 0.040
#> SRR445719 2 0.1529 0.981 0.000 0.960 0.040
#> SRR445720 2 0.1529 0.981 0.000 0.960 0.040
#> SRR445721 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445722 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445723 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445724 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445725 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445726 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445727 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445728 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445729 2 0.0747 0.992 0.000 0.984 0.016
#> SRR445730 1 0.0237 0.934 0.996 0.000 0.004
#> SRR445731 1 0.0237 0.934 0.996 0.000 0.004
#> SRR490961 2 0.0237 0.992 0.000 0.996 0.004
#> SRR490962 2 0.0237 0.992 0.000 0.996 0.004
#> SRR490963 2 0.0237 0.992 0.000 0.996 0.004
#> SRR490964 2 0.0237 0.992 0.000 0.996 0.004
#> SRR490965 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490966 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490967 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490968 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490969 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490970 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490971 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490972 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490973 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490974 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490975 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490976 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490977 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490978 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490979 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490980 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490981 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490982 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490983 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490984 2 0.0000 0.993 0.000 1.000 0.000
#> SRR490985 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490986 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490987 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490988 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490989 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490990 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490991 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490992 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490993 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490994 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490995 3 0.1753 0.933 0.048 0.000 0.952
#> SRR490996 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490997 3 0.2878 0.996 0.096 0.000 0.904
#> SRR490998 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491000 3 0.1753 0.933 0.048 0.000 0.952
#> SRR491001 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491002 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491003 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491004 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491005 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491006 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491007 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491008 3 0.2878 0.996 0.096 0.000 0.904
#> SRR491009 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491010 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491011 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491012 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491013 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491014 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491015 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491016 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491017 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491018 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491019 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491020 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491021 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491022 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491023 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491024 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491025 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491026 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491027 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491028 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491029 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491030 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491031 1 0.4002 0.893 0.840 0.000 0.160
#> SRR491032 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491033 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491034 1 0.1964 0.926 0.944 0.000 0.056
#> SRR491035 1 0.0000 0.932 1.000 0.000 0.000
#> SRR491036 1 0.3340 0.913 0.880 0.000 0.120
#> SRR491037 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491038 1 0.3267 0.914 0.884 0.000 0.116
#> SRR491039 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491040 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491041 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491042 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491043 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491045 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491065 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491066 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491067 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491068 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491069 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491070 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491071 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491072 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491073 1 0.1643 0.900 0.956 0.000 0.044
#> SRR491074 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491075 1 0.1643 0.900 0.956 0.000 0.044
#> SRR491076 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491077 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491078 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491079 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491080 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491081 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491082 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491083 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491084 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491085 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491086 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491087 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491088 1 0.1643 0.900 0.956 0.000 0.044
#> SRR491089 1 0.0237 0.934 0.996 0.000 0.004
#> SRR491090 1 0.1643 0.900 0.956 0.000 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.1637 0.968 0.000 0.940 0.000 0.060
#> SRR445719 2 0.1637 0.968 0.000 0.940 0.000 0.060
#> SRR445720 2 0.1637 0.968 0.000 0.940 0.000 0.060
#> SRR445721 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445722 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445723 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445724 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445725 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445726 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445727 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445728 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445729 2 0.1118 0.977 0.000 0.964 0.000 0.036
#> SRR445730 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR490961 2 0.0336 0.978 0.000 0.992 0.000 0.008
#> SRR490962 2 0.0336 0.978 0.000 0.992 0.000 0.008
#> SRR490963 2 0.0336 0.978 0.000 0.992 0.000 0.008
#> SRR490964 2 0.0336 0.978 0.000 0.992 0.000 0.008
#> SRR490965 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490966 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490967 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490968 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490969 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490970 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490971 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490972 2 0.0188 0.978 0.000 0.996 0.000 0.004
#> SRR490973 3 0.0188 0.955 0.004 0.000 0.996 0.000
#> SRR490974 3 0.1824 0.949 0.004 0.000 0.936 0.060
#> SRR490975 3 0.1824 0.949 0.004 0.000 0.936 0.060
#> SRR490976 3 0.0188 0.955 0.004 0.000 0.996 0.000
#> SRR490977 3 0.0376 0.955 0.004 0.000 0.992 0.004
#> SRR490978 3 0.0188 0.955 0.004 0.000 0.996 0.000
#> SRR490979 3 0.0188 0.955 0.004 0.000 0.996 0.000
#> SRR490980 3 0.1824 0.949 0.004 0.000 0.936 0.060
#> SRR490981 2 0.1792 0.951 0.000 0.932 0.000 0.068
#> SRR490982 2 0.1792 0.951 0.000 0.932 0.000 0.068
#> SRR490983 2 0.1792 0.951 0.000 0.932 0.000 0.068
#> SRR490984 2 0.1792 0.951 0.000 0.932 0.000 0.068
#> SRR490985 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490986 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490987 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490988 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490989 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490990 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490991 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490992 3 0.1902 0.948 0.004 0.000 0.932 0.064
#> SRR490993 3 0.1824 0.952 0.004 0.000 0.936 0.060
#> SRR490994 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR490995 3 0.3172 0.901 0.000 0.000 0.840 0.160
#> SRR490996 3 0.1824 0.952 0.004 0.000 0.936 0.060
#> SRR490997 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR490998 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491000 3 0.3172 0.901 0.000 0.000 0.840 0.160
#> SRR491001 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491002 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491003 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491004 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491005 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491006 3 0.1824 0.952 0.004 0.000 0.936 0.060
#> SRR491007 3 0.1824 0.952 0.004 0.000 0.936 0.060
#> SRR491008 3 0.2124 0.951 0.008 0.000 0.924 0.068
#> SRR491009 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491010 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491011 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491012 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491013 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491014 4 0.5028 0.986 0.400 0.000 0.004 0.596
#> SRR491015 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491016 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491017 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491018 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491019 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491020 4 0.5028 0.986 0.400 0.000 0.004 0.596
#> SRR491021 4 0.5028 0.986 0.400 0.000 0.004 0.596
#> SRR491022 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491023 4 0.5028 0.986 0.400 0.000 0.004 0.596
#> SRR491024 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491025 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491026 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491027 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491028 4 0.5028 0.986 0.400 0.000 0.004 0.596
#> SRR491029 4 0.5016 0.982 0.396 0.000 0.004 0.600
#> SRR491030 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491031 4 0.4792 0.842 0.312 0.000 0.008 0.680
#> SRR491032 4 0.5016 0.982 0.396 0.000 0.004 0.600
#> SRR491033 4 0.5039 0.988 0.404 0.000 0.004 0.592
#> SRR491034 4 0.4855 0.971 0.400 0.000 0.000 0.600
#> SRR491035 4 0.4855 0.971 0.400 0.000 0.000 0.600
#> SRR491036 4 0.4964 0.953 0.380 0.000 0.004 0.616
#> SRR491037 4 0.5028 0.985 0.400 0.000 0.004 0.596
#> SRR491038 4 0.5016 0.982 0.396 0.000 0.004 0.600
#> SRR491039 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491065 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491066 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491067 1 0.0336 0.932 0.992 0.000 0.000 0.008
#> SRR491068 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491069 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491070 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491073 1 0.4920 0.164 0.628 0.000 0.004 0.368
#> SRR491074 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491075 1 0.4655 0.343 0.684 0.000 0.004 0.312
#> SRR491076 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491077 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491086 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491087 1 0.0188 0.936 0.996 0.000 0.000 0.004
#> SRR491088 1 0.4920 0.164 0.628 0.000 0.004 0.368
#> SRR491089 1 0.0000 0.939 1.000 0.000 0.000 0.000
#> SRR491090 1 0.4819 0.270 0.652 0.000 0.004 0.344
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.3106 0.919 0.000 0.840 0.000 0.020 NA
#> SRR445719 2 0.3106 0.919 0.000 0.840 0.000 0.020 NA
#> SRR445720 2 0.3106 0.919 0.000 0.840 0.000 0.020 NA
#> SRR445721 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445722 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445723 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445724 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445725 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445726 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445727 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445728 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445729 2 0.2304 0.937 0.000 0.892 0.000 0.008 NA
#> SRR445730 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR445731 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR490961 2 0.0324 0.943 0.000 0.992 0.000 0.004 NA
#> SRR490962 2 0.0324 0.943 0.000 0.992 0.000 0.004 NA
#> SRR490963 2 0.0324 0.943 0.000 0.992 0.000 0.004 NA
#> SRR490964 2 0.0324 0.943 0.000 0.992 0.000 0.004 NA
#> SRR490965 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490966 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490967 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490968 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490969 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490970 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490971 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490972 2 0.0162 0.943 0.000 0.996 0.000 0.000 NA
#> SRR490973 3 0.3353 0.893 0.000 0.000 0.796 0.008 NA
#> SRR490974 3 0.3783 0.887 0.000 0.000 0.740 0.008 NA
#> SRR490975 3 0.3783 0.887 0.000 0.000 0.740 0.008 NA
#> SRR490976 3 0.3353 0.893 0.000 0.000 0.796 0.008 NA
#> SRR490977 3 0.3318 0.893 0.000 0.000 0.800 0.008 NA
#> SRR490978 3 0.3353 0.893 0.000 0.000 0.796 0.008 NA
#> SRR490979 3 0.3353 0.893 0.000 0.000 0.796 0.008 NA
#> SRR490980 3 0.3783 0.887 0.000 0.000 0.740 0.008 NA
#> SRR490981 2 0.3479 0.880 0.000 0.836 0.000 0.084 NA
#> SRR490982 2 0.3479 0.880 0.000 0.836 0.000 0.084 NA
#> SRR490983 2 0.3479 0.880 0.000 0.836 0.000 0.084 NA
#> SRR490984 2 0.3479 0.880 0.000 0.836 0.000 0.084 NA
#> SRR490985 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490986 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490987 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490988 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490989 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490990 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490991 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490992 3 0.3715 0.886 0.000 0.000 0.736 0.004 NA
#> SRR490993 3 0.0000 0.880 0.000 0.000 1.000 0.000 NA
#> SRR490994 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR490995 3 0.4996 0.689 0.000 0.000 0.548 0.032 NA
#> SRR490996 3 0.0000 0.880 0.000 0.000 1.000 0.000 NA
#> SRR490997 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR490998 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491000 3 0.4996 0.689 0.000 0.000 0.548 0.032 NA
#> SRR491001 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491002 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491003 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491004 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491005 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491006 3 0.0000 0.880 0.000 0.000 1.000 0.000 NA
#> SRR491007 3 0.0000 0.880 0.000 0.000 1.000 0.000 NA
#> SRR491008 3 0.0290 0.879 0.000 0.000 0.992 0.008 NA
#> SRR491009 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491010 4 0.3246 0.970 0.184 0.000 0.000 0.808 NA
#> SRR491011 4 0.3123 0.970 0.184 0.000 0.000 0.812 NA
#> SRR491012 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491013 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491014 4 0.3318 0.968 0.180 0.000 0.000 0.808 NA
#> SRR491015 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491016 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491017 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491018 4 0.3355 0.969 0.184 0.000 0.000 0.804 NA
#> SRR491019 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491020 4 0.3318 0.968 0.180 0.000 0.000 0.808 NA
#> SRR491021 4 0.3318 0.968 0.180 0.000 0.000 0.808 NA
#> SRR491022 4 0.3456 0.969 0.184 0.000 0.000 0.800 NA
#> SRR491023 4 0.3419 0.967 0.180 0.000 0.000 0.804 NA
#> SRR491024 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491025 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491026 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491027 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491028 4 0.3419 0.967 0.180 0.000 0.000 0.804 NA
#> SRR491029 4 0.3123 0.968 0.184 0.000 0.000 0.812 NA
#> SRR491030 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491031 4 0.6153 0.544 0.136 0.000 0.000 0.484 NA
#> SRR491032 4 0.3355 0.965 0.184 0.000 0.000 0.804 NA
#> SRR491033 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491034 4 0.4666 0.907 0.180 0.000 0.000 0.732 NA
#> SRR491035 4 0.4666 0.907 0.180 0.000 0.000 0.732 NA
#> SRR491036 4 0.4444 0.886 0.156 0.000 0.000 0.756 NA
#> SRR491037 4 0.2966 0.969 0.184 0.000 0.000 0.816 NA
#> SRR491038 4 0.4031 0.944 0.184 0.000 0.000 0.772 NA
#> SRR491039 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491040 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491041 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491042 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491043 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491045 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491065 1 0.0162 0.926 0.996 0.000 0.000 0.004 NA
#> SRR491066 1 0.0451 0.922 0.988 0.000 0.000 0.004 NA
#> SRR491067 1 0.0451 0.922 0.988 0.000 0.000 0.004 NA
#> SRR491068 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491069 1 0.0451 0.922 0.988 0.000 0.000 0.004 NA
#> SRR491070 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491071 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491072 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491073 1 0.6659 0.130 0.396 0.000 0.000 0.228 NA
#> SRR491074 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491075 1 0.6564 0.189 0.420 0.000 0.000 0.204 NA
#> SRR491076 1 0.0451 0.922 0.988 0.000 0.000 0.004 NA
#> SRR491077 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491078 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491079 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491080 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491081 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491082 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491083 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491084 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491085 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491086 1 0.0324 0.924 0.992 0.000 0.000 0.004 NA
#> SRR491087 1 0.0324 0.924 0.992 0.000 0.000 0.004 NA
#> SRR491088 1 0.6659 0.130 0.396 0.000 0.000 0.228 NA
#> SRR491089 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA
#> SRR491090 1 0.6527 0.219 0.428 0.000 0.000 0.196 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.4073 0.861 0.064 0.796 0.000 0.000 0.068 NA
#> SRR445719 2 0.4073 0.861 0.064 0.796 0.000 0.000 0.068 NA
#> SRR445720 2 0.4073 0.861 0.064 0.796 0.000 0.000 0.068 NA
#> SRR445721 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445722 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445723 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445724 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445725 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445726 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445727 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445728 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445729 2 0.3291 0.881 0.060 0.848 0.000 0.000 0.036 NA
#> SRR445730 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR445731 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR490961 2 0.1483 0.873 0.008 0.944 0.000 0.000 0.036 NA
#> SRR490962 2 0.1483 0.873 0.008 0.944 0.000 0.000 0.036 NA
#> SRR490963 2 0.1483 0.873 0.008 0.944 0.000 0.000 0.036 NA
#> SRR490964 2 0.1483 0.873 0.008 0.944 0.000 0.000 0.036 NA
#> SRR490965 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490966 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490967 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490968 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490969 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490970 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490971 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490972 2 0.0547 0.884 0.020 0.980 0.000 0.000 0.000 NA
#> SRR490973 3 0.4217 0.797 0.016 0.000 0.700 0.000 0.260 NA
#> SRR490974 3 0.4632 0.780 0.016 0.000 0.600 0.000 0.360 NA
#> SRR490975 3 0.4632 0.780 0.016 0.000 0.600 0.000 0.360 NA
#> SRR490976 3 0.4217 0.797 0.016 0.000 0.700 0.000 0.260 NA
#> SRR490977 3 0.3998 0.798 0.016 0.000 0.736 0.000 0.224 NA
#> SRR490978 3 0.4217 0.797 0.016 0.000 0.700 0.000 0.260 NA
#> SRR490979 3 0.4217 0.797 0.016 0.000 0.700 0.000 0.260 NA
#> SRR490980 3 0.4632 0.780 0.016 0.000 0.600 0.000 0.360 NA
#> SRR490981 2 0.3684 0.711 0.000 0.664 0.000 0.000 0.004 NA
#> SRR490982 2 0.3684 0.711 0.000 0.664 0.000 0.000 0.004 NA
#> SRR490983 2 0.3684 0.711 0.000 0.664 0.000 0.000 0.004 NA
#> SRR490984 2 0.3684 0.711 0.000 0.664 0.000 0.000 0.004 NA
#> SRR490985 3 0.3774 0.778 0.000 0.000 0.592 0.000 0.408 NA
#> SRR490986 3 0.3774 0.778 0.000 0.000 0.592 0.000 0.408 NA
#> SRR490987 3 0.3765 0.779 0.000 0.000 0.596 0.000 0.404 NA
#> SRR490988 3 0.3774 0.778 0.000 0.000 0.592 0.000 0.408 NA
#> SRR490989 3 0.3774 0.778 0.000 0.000 0.592 0.000 0.408 NA
#> SRR490990 3 0.3765 0.779 0.000 0.000 0.596 0.000 0.404 NA
#> SRR490991 3 0.3765 0.779 0.000 0.000 0.596 0.000 0.404 NA
#> SRR490992 3 0.3765 0.779 0.000 0.000 0.596 0.000 0.404 NA
#> SRR490993 3 0.0000 0.780 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490994 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR490995 3 0.6648 0.344 0.028 0.000 0.364 0.000 0.288 NA
#> SRR490996 3 0.0000 0.780 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490997 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR490998 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR491000 3 0.6648 0.344 0.028 0.000 0.364 0.000 0.288 NA
#> SRR491001 3 0.0291 0.779 0.004 0.000 0.992 0.004 0.000 NA
#> SRR491002 3 0.0291 0.779 0.004 0.000 0.992 0.004 0.000 NA
#> SRR491003 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR491004 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR491005 3 0.0291 0.779 0.004 0.000 0.992 0.004 0.000 NA
#> SRR491006 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR491007 3 0.0146 0.779 0.000 0.000 0.996 0.004 0.000 NA
#> SRR491008 3 0.0291 0.779 0.004 0.000 0.992 0.004 0.000 NA
#> SRR491009 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491010 4 0.0260 0.944 0.000 0.000 0.000 0.992 0.000 NA
#> SRR491011 4 0.0458 0.943 0.000 0.000 0.000 0.984 0.000 NA
#> SRR491012 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491013 4 0.0458 0.943 0.000 0.000 0.000 0.984 0.000 NA
#> SRR491014 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491015 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491016 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491017 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491018 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491019 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491020 4 0.0146 0.944 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491021 4 0.0865 0.931 0.000 0.000 0.000 0.964 0.000 NA
#> SRR491022 4 0.1564 0.912 0.000 0.000 0.000 0.936 0.024 NA
#> SRR491023 4 0.1564 0.912 0.000 0.000 0.000 0.936 0.024 NA
#> SRR491024 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491025 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491026 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491027 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491028 4 0.1564 0.912 0.000 0.000 0.000 0.936 0.024 NA
#> SRR491029 4 0.0622 0.942 0.000 0.000 0.000 0.980 0.008 NA
#> SRR491030 4 0.1007 0.935 0.000 0.000 0.000 0.956 0.000 NA
#> SRR491031 5 0.6224 0.640 0.012 0.000 0.000 0.364 0.412 NA
#> SRR491032 4 0.1930 0.902 0.000 0.000 0.000 0.916 0.036 NA
#> SRR491033 4 0.1219 0.933 0.000 0.000 0.000 0.948 0.004 NA
#> SRR491034 4 0.3072 0.804 0.000 0.000 0.000 0.840 0.084 NA
#> SRR491035 4 0.3020 0.811 0.000 0.000 0.000 0.844 0.076 NA
#> SRR491036 4 0.3462 0.758 0.004 0.000 0.000 0.816 0.100 NA
#> SRR491037 4 0.1219 0.933 0.000 0.000 0.000 0.948 0.004 NA
#> SRR491038 4 0.1257 0.926 0.000 0.000 0.000 0.952 0.020 NA
#> SRR491039 1 0.3175 0.971 0.808 0.000 0.000 0.164 0.000 NA
#> SRR491040 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR491041 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR491042 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR491043 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR491045 1 0.3248 0.969 0.804 0.000 0.000 0.164 0.000 NA
#> SRR491065 1 0.2841 0.980 0.824 0.000 0.000 0.164 0.000 NA
#> SRR491066 1 0.3158 0.975 0.812 0.000 0.000 0.164 0.004 NA
#> SRR491067 1 0.3158 0.975 0.812 0.000 0.000 0.164 0.004 NA
#> SRR491068 1 0.2841 0.981 0.824 0.000 0.000 0.164 0.000 NA
#> SRR491069 1 0.3158 0.975 0.812 0.000 0.000 0.164 0.004 NA
#> SRR491070 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491071 1 0.2743 0.981 0.828 0.000 0.000 0.164 0.000 NA
#> SRR491072 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491073 5 0.7158 0.907 0.140 0.000 0.000 0.232 0.448 NA
#> SRR491074 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491075 5 0.7195 0.900 0.156 0.000 0.000 0.216 0.448 NA
#> SRR491076 1 0.3073 0.975 0.816 0.000 0.000 0.164 0.004 NA
#> SRR491077 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491078 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491079 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491080 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491081 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491082 1 0.2743 0.982 0.828 0.000 0.000 0.164 0.000 NA
#> SRR491083 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491084 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491085 1 0.2632 0.982 0.832 0.000 0.000 0.164 0.000 NA
#> SRR491086 1 0.2982 0.978 0.820 0.000 0.000 0.164 0.004 NA
#> SRR491087 1 0.2982 0.978 0.820 0.000 0.000 0.164 0.004 NA
#> SRR491088 5 0.7147 0.904 0.136 0.000 0.000 0.236 0.448 NA
#> SRR491089 1 0.2743 0.982 0.828 0.000 0.000 0.164 0.000 NA
#> SRR491090 5 0.7217 0.878 0.172 0.000 0.000 0.200 0.448 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.
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 13175 rows and 123 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 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 0.991 0.4697 0.528 0.528
#> 3 3 1.000 0.971 0.988 0.3177 0.797 0.635
#> 4 4 0.831 0.945 0.860 0.1561 0.864 0.643
#> 5 5 0.838 0.936 0.882 0.0751 0.961 0.842
#> 6 6 0.990 0.968 0.955 0.0321 0.974 0.880
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
#> SRR445718 2 0.000 0.982 0.000 1.000
#> SRR445719 2 0.000 0.982 0.000 1.000
#> SRR445720 2 0.000 0.982 0.000 1.000
#> SRR445721 2 0.000 0.982 0.000 1.000
#> SRR445722 2 0.000 0.982 0.000 1.000
#> SRR445723 2 0.000 0.982 0.000 1.000
#> SRR445724 2 0.000 0.982 0.000 1.000
#> SRR445725 2 0.000 0.982 0.000 1.000
#> SRR445726 2 0.000 0.982 0.000 1.000
#> SRR445727 2 0.000 0.982 0.000 1.000
#> SRR445728 2 0.000 0.982 0.000 1.000
#> SRR445729 2 0.000 0.982 0.000 1.000
#> SRR445730 1 0.000 0.996 1.000 0.000
#> SRR445731 1 0.000 0.996 1.000 0.000
#> SRR490961 2 0.000 0.982 0.000 1.000
#> SRR490962 2 0.000 0.982 0.000 1.000
#> SRR490963 2 0.000 0.982 0.000 1.000
#> SRR490964 2 0.000 0.982 0.000 1.000
#> SRR490965 2 0.000 0.982 0.000 1.000
#> SRR490966 2 0.000 0.982 0.000 1.000
#> SRR490967 2 0.000 0.982 0.000 1.000
#> SRR490968 2 0.000 0.982 0.000 1.000
#> SRR490969 2 0.000 0.982 0.000 1.000
#> SRR490970 2 0.000 0.982 0.000 1.000
#> SRR490971 2 0.000 0.982 0.000 1.000
#> SRR490972 2 0.000 0.982 0.000 1.000
#> SRR490973 2 0.388 0.914 0.076 0.924
#> SRR490974 2 0.000 0.982 0.000 1.000
#> SRR490975 2 0.000 0.982 0.000 1.000
#> SRR490976 2 0.625 0.824 0.156 0.844
#> SRR490977 2 0.871 0.605 0.292 0.708
#> SRR490978 2 0.615 0.829 0.152 0.848
#> SRR490979 2 0.541 0.863 0.124 0.876
#> SRR490980 2 0.000 0.982 0.000 1.000
#> SRR490981 2 0.000 0.982 0.000 1.000
#> SRR490982 2 0.000 0.982 0.000 1.000
#> SRR490983 2 0.000 0.982 0.000 1.000
#> SRR490984 2 0.000 0.982 0.000 1.000
#> SRR490985 2 0.000 0.982 0.000 1.000
#> SRR490986 2 0.000 0.982 0.000 1.000
#> SRR490987 2 0.000 0.982 0.000 1.000
#> SRR490988 2 0.000 0.982 0.000 1.000
#> SRR490989 2 0.000 0.982 0.000 1.000
#> SRR490990 2 0.000 0.982 0.000 1.000
#> SRR490991 2 0.000 0.982 0.000 1.000
#> SRR490992 2 0.000 0.982 0.000 1.000
#> SRR490993 1 0.343 0.936 0.936 0.064
#> SRR490994 1 0.141 0.981 0.980 0.020
#> SRR490995 2 0.000 0.982 0.000 1.000
#> SRR490996 1 0.242 0.962 0.960 0.040
#> SRR490997 1 0.141 0.981 0.980 0.020
#> SRR490998 1 0.141 0.981 0.980 0.020
#> SRR491000 2 0.000 0.982 0.000 1.000
#> SRR491001 1 0.141 0.981 0.980 0.020
#> SRR491002 1 0.141 0.981 0.980 0.020
#> SRR491003 1 0.141 0.981 0.980 0.020
#> SRR491004 1 0.141 0.981 0.980 0.020
#> SRR491005 1 0.141 0.981 0.980 0.020
#> SRR491006 1 0.141 0.981 0.980 0.020
#> SRR491007 1 0.141 0.981 0.980 0.020
#> SRR491008 1 0.141 0.981 0.980 0.020
#> SRR491009 1 0.000 0.996 1.000 0.000
#> SRR491010 1 0.000 0.996 1.000 0.000
#> SRR491011 1 0.000 0.996 1.000 0.000
#> SRR491012 1 0.000 0.996 1.000 0.000
#> SRR491013 1 0.000 0.996 1.000 0.000
#> SRR491014 1 0.000 0.996 1.000 0.000
#> SRR491015 1 0.000 0.996 1.000 0.000
#> SRR491016 1 0.000 0.996 1.000 0.000
#> SRR491017 1 0.000 0.996 1.000 0.000
#> SRR491018 1 0.000 0.996 1.000 0.000
#> SRR491019 1 0.000 0.996 1.000 0.000
#> SRR491020 1 0.000 0.996 1.000 0.000
#> SRR491021 1 0.000 0.996 1.000 0.000
#> SRR491022 1 0.000 0.996 1.000 0.000
#> SRR491023 1 0.000 0.996 1.000 0.000
#> SRR491024 1 0.000 0.996 1.000 0.000
#> SRR491025 1 0.000 0.996 1.000 0.000
#> SRR491026 1 0.000 0.996 1.000 0.000
#> SRR491027 1 0.000 0.996 1.000 0.000
#> SRR491028 1 0.000 0.996 1.000 0.000
#> SRR491029 1 0.000 0.996 1.000 0.000
#> SRR491030 1 0.000 0.996 1.000 0.000
#> SRR491031 1 0.000 0.996 1.000 0.000
#> SRR491032 1 0.000 0.996 1.000 0.000
#> SRR491033 1 0.000 0.996 1.000 0.000
#> SRR491034 1 0.000 0.996 1.000 0.000
#> SRR491035 1 0.000 0.996 1.000 0.000
#> SRR491036 1 0.000 0.996 1.000 0.000
#> SRR491037 1 0.000 0.996 1.000 0.000
#> SRR491038 1 0.000 0.996 1.000 0.000
#> SRR491039 1 0.000 0.996 1.000 0.000
#> SRR491040 1 0.000 0.996 1.000 0.000
#> SRR491041 1 0.000 0.996 1.000 0.000
#> SRR491042 1 0.000 0.996 1.000 0.000
#> SRR491043 1 0.000 0.996 1.000 0.000
#> SRR491045 1 0.000 0.996 1.000 0.000
#> SRR491065 1 0.000 0.996 1.000 0.000
#> SRR491066 1 0.000 0.996 1.000 0.000
#> SRR491067 1 0.000 0.996 1.000 0.000
#> SRR491068 1 0.000 0.996 1.000 0.000
#> SRR491069 1 0.000 0.996 1.000 0.000
#> SRR491070 1 0.000 0.996 1.000 0.000
#> SRR491071 1 0.000 0.996 1.000 0.000
#> SRR491072 1 0.000 0.996 1.000 0.000
#> SRR491073 1 0.000 0.996 1.000 0.000
#> SRR491074 1 0.000 0.996 1.000 0.000
#> SRR491075 1 0.000 0.996 1.000 0.000
#> SRR491076 1 0.000 0.996 1.000 0.000
#> SRR491077 1 0.000 0.996 1.000 0.000
#> SRR491078 1 0.000 0.996 1.000 0.000
#> SRR491079 1 0.000 0.996 1.000 0.000
#> SRR491080 1 0.000 0.996 1.000 0.000
#> SRR491081 1 0.000 0.996 1.000 0.000
#> SRR491082 1 0.000 0.996 1.000 0.000
#> SRR491083 1 0.000 0.996 1.000 0.000
#> SRR491084 1 0.000 0.996 1.000 0.000
#> SRR491085 1 0.000 0.996 1.000 0.000
#> SRR491086 1 0.000 0.996 1.000 0.000
#> SRR491087 1 0.000 0.996 1.000 0.000
#> SRR491088 1 0.000 0.996 1.000 0.000
#> SRR491089 1 0.000 0.996 1.000 0.000
#> SRR491090 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.000 0.974 0 1.00 0.00
#> SRR445719 2 0.000 0.974 0 1.00 0.00
#> SRR445720 2 0.000 0.974 0 1.00 0.00
#> SRR445721 2 0.000 0.974 0 1.00 0.00
#> SRR445722 2 0.000 0.974 0 1.00 0.00
#> SRR445723 2 0.000 0.974 0 1.00 0.00
#> SRR445724 2 0.000 0.974 0 1.00 0.00
#> SRR445725 2 0.000 0.974 0 1.00 0.00
#> SRR445726 2 0.000 0.974 0 1.00 0.00
#> SRR445727 2 0.000 0.974 0 1.00 0.00
#> SRR445728 2 0.000 0.974 0 1.00 0.00
#> SRR445729 2 0.000 0.974 0 1.00 0.00
#> SRR445730 1 0.000 1.000 1 0.00 0.00
#> SRR445731 1 0.000 1.000 1 0.00 0.00
#> SRR490961 2 0.000 0.974 0 1.00 0.00
#> SRR490962 2 0.000 0.974 0 1.00 0.00
#> SRR490963 2 0.000 0.974 0 1.00 0.00
#> SRR490964 2 0.000 0.974 0 1.00 0.00
#> SRR490965 2 0.000 0.974 0 1.00 0.00
#> SRR490966 2 0.000 0.974 0 1.00 0.00
#> SRR490967 2 0.000 0.974 0 1.00 0.00
#> SRR490968 2 0.000 0.974 0 1.00 0.00
#> SRR490969 2 0.000 0.974 0 1.00 0.00
#> SRR490970 2 0.000 0.974 0 1.00 0.00
#> SRR490971 2 0.000 0.974 0 1.00 0.00
#> SRR490972 2 0.000 0.974 0 1.00 0.00
#> SRR490973 3 0.000 0.970 0 0.00 1.00
#> SRR490974 3 0.000 0.970 0 0.00 1.00
#> SRR490975 3 0.000 0.970 0 0.00 1.00
#> SRR490976 3 0.000 0.970 0 0.00 1.00
#> SRR490977 3 0.000 0.970 0 0.00 1.00
#> SRR490978 3 0.000 0.970 0 0.00 1.00
#> SRR490979 3 0.000 0.970 0 0.00 1.00
#> SRR490980 3 0.000 0.970 0 0.00 1.00
#> SRR490981 2 0.000 0.974 0 1.00 0.00
#> SRR490982 2 0.000 0.974 0 1.00 0.00
#> SRR490983 2 0.000 0.974 0 1.00 0.00
#> SRR490984 2 0.000 0.974 0 1.00 0.00
#> SRR490985 3 0.455 0.766 0 0.20 0.80
#> SRR490986 3 0.455 0.766 0 0.20 0.80
#> SRR490987 3 0.000 0.970 0 0.00 1.00
#> SRR490988 3 0.455 0.766 0 0.20 0.80
#> SRR490989 3 0.455 0.766 0 0.20 0.80
#> SRR490990 3 0.000 0.970 0 0.00 1.00
#> SRR490991 3 0.000 0.970 0 0.00 1.00
#> SRR490992 3 0.000 0.970 0 0.00 1.00
#> SRR490993 3 0.000 0.970 0 0.00 1.00
#> SRR490994 3 0.000 0.970 0 0.00 1.00
#> SRR490995 2 0.595 0.417 0 0.64 0.36
#> SRR490996 3 0.000 0.970 0 0.00 1.00
#> SRR490997 3 0.000 0.970 0 0.00 1.00
#> SRR490998 3 0.000 0.970 0 0.00 1.00
#> SRR491000 2 0.595 0.417 0 0.64 0.36
#> SRR491001 3 0.000 0.970 0 0.00 1.00
#> SRR491002 3 0.000 0.970 0 0.00 1.00
#> SRR491003 3 0.000 0.970 0 0.00 1.00
#> SRR491004 3 0.000 0.970 0 0.00 1.00
#> SRR491005 3 0.000 0.970 0 0.00 1.00
#> SRR491006 3 0.000 0.970 0 0.00 1.00
#> SRR491007 3 0.000 0.970 0 0.00 1.00
#> SRR491008 3 0.000 0.970 0 0.00 1.00
#> SRR491009 1 0.000 1.000 1 0.00 0.00
#> SRR491010 1 0.000 1.000 1 0.00 0.00
#> SRR491011 1 0.000 1.000 1 0.00 0.00
#> SRR491012 1 0.000 1.000 1 0.00 0.00
#> SRR491013 1 0.000 1.000 1 0.00 0.00
#> SRR491014 1 0.000 1.000 1 0.00 0.00
#> SRR491015 1 0.000 1.000 1 0.00 0.00
#> SRR491016 1 0.000 1.000 1 0.00 0.00
#> SRR491017 1 0.000 1.000 1 0.00 0.00
#> SRR491018 1 0.000 1.000 1 0.00 0.00
#> SRR491019 1 0.000 1.000 1 0.00 0.00
#> SRR491020 1 0.000 1.000 1 0.00 0.00
#> SRR491021 1 0.000 1.000 1 0.00 0.00
#> SRR491022 1 0.000 1.000 1 0.00 0.00
#> SRR491023 1 0.000 1.000 1 0.00 0.00
#> SRR491024 1 0.000 1.000 1 0.00 0.00
#> SRR491025 1 0.000 1.000 1 0.00 0.00
#> SRR491026 1 0.000 1.000 1 0.00 0.00
#> SRR491027 1 0.000 1.000 1 0.00 0.00
#> SRR491028 1 0.000 1.000 1 0.00 0.00
#> SRR491029 1 0.000 1.000 1 0.00 0.00
#> SRR491030 1 0.000 1.000 1 0.00 0.00
#> SRR491031 1 0.000 1.000 1 0.00 0.00
#> SRR491032 1 0.000 1.000 1 0.00 0.00
#> SRR491033 1 0.000 1.000 1 0.00 0.00
#> SRR491034 1 0.000 1.000 1 0.00 0.00
#> SRR491035 1 0.000 1.000 1 0.00 0.00
#> SRR491036 1 0.000 1.000 1 0.00 0.00
#> SRR491037 1 0.000 1.000 1 0.00 0.00
#> SRR491038 1 0.000 1.000 1 0.00 0.00
#> SRR491039 1 0.000 1.000 1 0.00 0.00
#> SRR491040 1 0.000 1.000 1 0.00 0.00
#> SRR491041 1 0.000 1.000 1 0.00 0.00
#> SRR491042 1 0.000 1.000 1 0.00 0.00
#> SRR491043 1 0.000 1.000 1 0.00 0.00
#> SRR491045 1 0.000 1.000 1 0.00 0.00
#> SRR491065 1 0.000 1.000 1 0.00 0.00
#> SRR491066 1 0.000 1.000 1 0.00 0.00
#> SRR491067 1 0.000 1.000 1 0.00 0.00
#> SRR491068 1 0.000 1.000 1 0.00 0.00
#> SRR491069 1 0.000 1.000 1 0.00 0.00
#> SRR491070 1 0.000 1.000 1 0.00 0.00
#> SRR491071 1 0.000 1.000 1 0.00 0.00
#> SRR491072 1 0.000 1.000 1 0.00 0.00
#> SRR491073 1 0.000 1.000 1 0.00 0.00
#> SRR491074 1 0.000 1.000 1 0.00 0.00
#> SRR491075 1 0.000 1.000 1 0.00 0.00
#> SRR491076 1 0.000 1.000 1 0.00 0.00
#> SRR491077 1 0.000 1.000 1 0.00 0.00
#> SRR491078 1 0.000 1.000 1 0.00 0.00
#> SRR491079 1 0.000 1.000 1 0.00 0.00
#> SRR491080 1 0.000 1.000 1 0.00 0.00
#> SRR491081 1 0.000 1.000 1 0.00 0.00
#> SRR491082 1 0.000 1.000 1 0.00 0.00
#> SRR491083 1 0.000 1.000 1 0.00 0.00
#> SRR491084 1 0.000 1.000 1 0.00 0.00
#> SRR491085 1 0.000 1.000 1 0.00 0.00
#> SRR491086 1 0.000 1.000 1 0.00 0.00
#> SRR491087 1 0.000 1.000 1 0.00 0.00
#> SRR491088 1 0.000 1.000 1 0.00 0.00
#> SRR491089 1 0.000 1.000 1 0.00 0.00
#> SRR491090 1 0.000 1.000 1 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445719 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445720 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445721 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445722 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445723 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445724 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445725 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445726 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445727 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445728 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445729 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR445730 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR445731 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR490961 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490962 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490963 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490964 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490965 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490966 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490967 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490968 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490969 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490970 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490971 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490972 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490973 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490974 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490975 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490976 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490977 3 0.460 0.863 0.000 0.00 0.664 0.336
#> SRR490978 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490979 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490980 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490981 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490982 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490983 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490984 2 0.000 0.973 0.000 1.00 0.000 0.000
#> SRR490985 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490986 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490987 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490988 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490989 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490990 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490991 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490992 3 0.475 0.866 0.000 0.00 0.632 0.368
#> SRR490993 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR490994 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR490995 2 0.499 0.397 0.000 0.64 0.352 0.008
#> SRR490996 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR490997 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR490998 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491000 2 0.499 0.397 0.000 0.64 0.352 0.008
#> SRR491001 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491002 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491003 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491004 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491005 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491006 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491007 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491008 3 0.000 0.834 0.000 0.00 1.000 0.000
#> SRR491009 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491010 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491011 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491012 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491013 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491014 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491015 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491016 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491017 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491018 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491019 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491020 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491021 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491022 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491023 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491024 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491025 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491026 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491027 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491028 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491029 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491030 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491031 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491032 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491033 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491034 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491035 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491036 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491037 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491038 4 0.475 1.000 0.368 0.00 0.000 0.632
#> SRR491039 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491040 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491041 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491042 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491043 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491045 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491065 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491066 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491067 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491068 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491069 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491070 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491071 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491072 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491073 1 0.112 0.944 0.964 0.00 0.000 0.036
#> SRR491074 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491075 1 0.112 0.944 0.964 0.00 0.000 0.036
#> SRR491076 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491077 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491078 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491079 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491080 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491081 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491082 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491083 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491084 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491085 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491086 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491087 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491088 1 0.121 0.938 0.960 0.00 0.000 0.040
#> SRR491089 1 0.000 0.993 1.000 0.00 0.000 0.000
#> SRR491090 1 0.130 0.932 0.956 0.00 0.000 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.3636 0.882 0.000 0.000 0.728 0.000 0.272
#> SRR490974 3 0.3612 0.884 0.000 0.000 0.732 0.000 0.268
#> SRR490975 3 0.3612 0.884 0.000 0.000 0.732 0.000 0.268
#> SRR490976 3 0.3612 0.884 0.000 0.000 0.732 0.000 0.268
#> SRR490977 3 0.3796 0.852 0.000 0.000 0.700 0.000 0.300
#> SRR490978 3 0.3636 0.882 0.000 0.000 0.728 0.000 0.272
#> SRR490979 3 0.3636 0.882 0.000 0.000 0.728 0.000 0.272
#> SRR490980 3 0.3612 0.884 0.000 0.000 0.732 0.000 0.268
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000
#> SRR490985 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490986 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490987 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490988 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490989 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490990 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490991 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490992 3 0.4181 0.885 0.000 0.000 0.712 0.020 0.268
#> SRR490993 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490994 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490995 3 0.5769 0.206 0.000 0.304 0.596 0.092 0.008
#> SRR490996 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490997 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR490998 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491000 3 0.5752 0.214 0.000 0.300 0.600 0.092 0.008
#> SRR491001 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491002 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491003 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491004 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491005 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491006 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491007 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491008 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR491009 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491010 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491011 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491012 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491013 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491014 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491015 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491016 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491017 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491018 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491019 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491020 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491021 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491022 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491023 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491024 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491025 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491026 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491027 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491028 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491029 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491030 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491031 4 0.5537 0.721 0.112 0.000 0.264 0.624 0.000
#> SRR491032 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491033 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491034 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491035 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491036 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491037 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491038 4 0.2179 0.992 0.112 0.000 0.000 0.888 0.000
#> SRR491039 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491067 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491068 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.0162 0.943 0.996 0.000 0.000 0.004 0.000
#> SRR491070 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491073 1 0.6261 0.408 0.536 0.000 0.264 0.200 0.000
#> SRR491074 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491075 1 0.6211 0.426 0.544 0.000 0.264 0.192 0.000
#> SRR491076 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491088 1 0.6237 0.417 0.540 0.000 0.264 0.196 0.000
#> SRR491089 1 0.0000 0.947 1.000 0.000 0.000 0.000 0.000
#> SRR491090 1 0.6286 0.398 0.532 0.000 0.264 0.204 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR445731 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490973 3 0.2532 0.942 0.052 0.000 0.884 0.000 0.060 0.004
#> SRR490974 3 0.2389 0.942 0.052 0.000 0.888 0.000 0.060 0.000
#> SRR490975 3 0.2389 0.942 0.052 0.000 0.888 0.000 0.060 0.000
#> SRR490976 3 0.2532 0.942 0.052 0.000 0.884 0.000 0.060 0.004
#> SRR490977 3 0.2923 0.932 0.052 0.000 0.868 0.000 0.060 0.020
#> SRR490978 3 0.2532 0.942 0.052 0.000 0.884 0.000 0.060 0.004
#> SRR490979 3 0.2532 0.942 0.052 0.000 0.884 0.000 0.060 0.004
#> SRR490980 3 0.2389 0.942 0.052 0.000 0.888 0.000 0.060 0.000
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490985 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490986 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490987 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490988 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490989 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490990 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490991 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490992 3 0.0146 0.944 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490993 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR490994 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR490995 5 0.2693 0.492 0.000 0.052 0.028 0.000 0.884 0.036
#> SRR490996 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR490997 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR490998 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491000 5 0.2693 0.492 0.000 0.052 0.028 0.000 0.884 0.036
#> SRR491001 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491002 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491003 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491004 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491005 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491006 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491007 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491008 6 0.0790 1.000 0.000 0.000 0.032 0.000 0.000 0.968
#> SRR491009 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491022 4 0.0405 0.985 0.000 0.000 0.000 0.988 0.008 0.004
#> SRR491023 4 0.0508 0.981 0.000 0.000 0.000 0.984 0.012 0.004
#> SRR491024 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 4 0.0405 0.985 0.000 0.000 0.000 0.988 0.008 0.004
#> SRR491029 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 5 0.3823 0.455 0.000 0.000 0.000 0.436 0.564 0.000
#> SRR491032 4 0.0405 0.985 0.000 0.000 0.000 0.988 0.008 0.004
#> SRR491033 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491034 4 0.1010 0.956 0.000 0.000 0.000 0.960 0.036 0.004
#> SRR491035 4 0.1010 0.956 0.000 0.000 0.000 0.960 0.036 0.004
#> SRR491036 4 0.0790 0.963 0.000 0.000 0.000 0.968 0.032 0.000
#> SRR491037 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491038 4 0.0000 0.993 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491039 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491040 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491041 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491042 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491043 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491045 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491065 1 0.1285 0.995 0.944 0.000 0.000 0.052 0.004 0.000
#> SRR491066 1 0.1398 0.993 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR491067 1 0.1398 0.993 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR491068 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491069 1 0.1462 0.988 0.936 0.000 0.000 0.056 0.008 0.000
#> SRR491070 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491071 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491072 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491073 5 0.5406 0.755 0.160 0.000 0.000 0.272 0.568 0.000
#> SRR491074 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491075 5 0.5539 0.740 0.188 0.000 0.000 0.260 0.552 0.000
#> SRR491076 1 0.1398 0.993 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR491077 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491078 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491079 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491080 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491081 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491082 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491083 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491084 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491085 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491086 1 0.1398 0.993 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR491087 1 0.1398 0.993 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR491088 5 0.5348 0.756 0.152 0.000 0.000 0.272 0.576 0.000
#> SRR491089 1 0.1141 0.998 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR491090 5 0.5372 0.758 0.160 0.000 0.000 0.264 0.576 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", "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 13175 rows and 123 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 1.000 1.000 0.3552 0.645 0.645
#> 3 3 0.758 0.868 0.930 0.7837 0.736 0.590
#> 4 4 1.000 0.994 0.995 0.1906 0.864 0.643
#> 5 5 1.000 0.974 0.989 0.0432 0.968 0.869
#> 6 6 0.995 0.959 0.979 0.0170 0.987 0.940
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 4 5
There is also optional best \(k\) = 2 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
#> SRR445718 2 0 1 0 1
#> SRR445719 2 0 1 0 1
#> SRR445720 2 0 1 0 1
#> SRR445721 2 0 1 0 1
#> SRR445722 2 0 1 0 1
#> SRR445723 2 0 1 0 1
#> SRR445724 2 0 1 0 1
#> SRR445725 2 0 1 0 1
#> SRR445726 2 0 1 0 1
#> SRR445727 2 0 1 0 1
#> SRR445728 2 0 1 0 1
#> SRR445729 2 0 1 0 1
#> SRR445730 1 0 1 1 0
#> SRR445731 1 0 1 1 0
#> SRR490961 2 0 1 0 1
#> SRR490962 2 0 1 0 1
#> SRR490963 2 0 1 0 1
#> SRR490964 2 0 1 0 1
#> SRR490965 2 0 1 0 1
#> SRR490966 2 0 1 0 1
#> SRR490967 2 0 1 0 1
#> SRR490968 2 0 1 0 1
#> SRR490969 2 0 1 0 1
#> SRR490970 2 0 1 0 1
#> SRR490971 2 0 1 0 1
#> SRR490972 2 0 1 0 1
#> SRR490973 1 0 1 1 0
#> SRR490974 1 0 1 1 0
#> SRR490975 1 0 1 1 0
#> SRR490976 1 0 1 1 0
#> SRR490977 1 0 1 1 0
#> SRR490978 1 0 1 1 0
#> SRR490979 1 0 1 1 0
#> SRR490980 1 0 1 1 0
#> SRR490981 2 0 1 0 1
#> SRR490982 2 0 1 0 1
#> SRR490983 2 0 1 0 1
#> SRR490984 2 0 1 0 1
#> SRR490985 1 0 1 1 0
#> SRR490986 1 0 1 1 0
#> SRR490987 1 0 1 1 0
#> SRR490988 1 0 1 1 0
#> SRR490989 1 0 1 1 0
#> SRR490990 1 0 1 1 0
#> SRR490991 1 0 1 1 0
#> SRR490992 1 0 1 1 0
#> SRR490993 1 0 1 1 0
#> SRR490994 1 0 1 1 0
#> SRR490995 1 0 1 1 0
#> SRR490996 1 0 1 1 0
#> SRR490997 1 0 1 1 0
#> SRR490998 1 0 1 1 0
#> SRR491000 1 0 1 1 0
#> SRR491001 1 0 1 1 0
#> SRR491002 1 0 1 1 0
#> SRR491003 1 0 1 1 0
#> SRR491004 1 0 1 1 0
#> SRR491005 1 0 1 1 0
#> SRR491006 1 0 1 1 0
#> SRR491007 1 0 1 1 0
#> SRR491008 1 0 1 1 0
#> SRR491009 1 0 1 1 0
#> SRR491010 1 0 1 1 0
#> SRR491011 1 0 1 1 0
#> SRR491012 1 0 1 1 0
#> SRR491013 1 0 1 1 0
#> SRR491014 1 0 1 1 0
#> SRR491015 1 0 1 1 0
#> SRR491016 1 0 1 1 0
#> SRR491017 1 0 1 1 0
#> SRR491018 1 0 1 1 0
#> SRR491019 1 0 1 1 0
#> SRR491020 1 0 1 1 0
#> SRR491021 1 0 1 1 0
#> SRR491022 1 0 1 1 0
#> SRR491023 1 0 1 1 0
#> SRR491024 1 0 1 1 0
#> SRR491025 1 0 1 1 0
#> SRR491026 1 0 1 1 0
#> SRR491027 1 0 1 1 0
#> SRR491028 1 0 1 1 0
#> SRR491029 1 0 1 1 0
#> SRR491030 1 0 1 1 0
#> SRR491031 1 0 1 1 0
#> SRR491032 1 0 1 1 0
#> SRR491033 1 0 1 1 0
#> SRR491034 1 0 1 1 0
#> SRR491035 1 0 1 1 0
#> SRR491036 1 0 1 1 0
#> SRR491037 1 0 1 1 0
#> SRR491038 1 0 1 1 0
#> SRR491039 1 0 1 1 0
#> SRR491040 1 0 1 1 0
#> SRR491041 1 0 1 1 0
#> SRR491042 1 0 1 1 0
#> SRR491043 1 0 1 1 0
#> SRR491045 1 0 1 1 0
#> SRR491065 1 0 1 1 0
#> SRR491066 1 0 1 1 0
#> SRR491067 1 0 1 1 0
#> SRR491068 1 0 1 1 0
#> SRR491069 1 0 1 1 0
#> SRR491070 1 0 1 1 0
#> SRR491071 1 0 1 1 0
#> SRR491072 1 0 1 1 0
#> SRR491073 1 0 1 1 0
#> SRR491074 1 0 1 1 0
#> SRR491075 1 0 1 1 0
#> SRR491076 1 0 1 1 0
#> SRR491077 1 0 1 1 0
#> SRR491078 1 0 1 1 0
#> SRR491079 1 0 1 1 0
#> SRR491080 1 0 1 1 0
#> SRR491081 1 0 1 1 0
#> SRR491082 1 0 1 1 0
#> SRR491083 1 0 1 1 0
#> SRR491084 1 0 1 1 0
#> SRR491085 1 0 1 1 0
#> SRR491086 1 0 1 1 0
#> SRR491087 1 0 1 1 0
#> SRR491088 1 0 1 1 0
#> SRR491089 1 0 1 1 0
#> SRR491090 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 1.000 0.000 1 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000
#> SRR445730 1 0.0000 0.850 1.000 0 0.000
#> SRR445731 1 0.0000 0.850 1.000 0 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000
#> SRR490973 3 0.0000 1.000 0.000 0 1.000
#> SRR490974 3 0.0000 1.000 0.000 0 1.000
#> SRR490975 3 0.0000 1.000 0.000 0 1.000
#> SRR490976 3 0.0000 1.000 0.000 0 1.000
#> SRR490977 3 0.0000 1.000 0.000 0 1.000
#> SRR490978 3 0.0000 1.000 0.000 0 1.000
#> SRR490979 3 0.0000 1.000 0.000 0 1.000
#> SRR490980 3 0.0000 1.000 0.000 0 1.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000
#> SRR490985 3 0.0000 1.000 0.000 0 1.000
#> SRR490986 3 0.0000 1.000 0.000 0 1.000
#> SRR490987 3 0.0000 1.000 0.000 0 1.000
#> SRR490988 3 0.0000 1.000 0.000 0 1.000
#> SRR490989 3 0.0000 1.000 0.000 0 1.000
#> SRR490990 3 0.0000 1.000 0.000 0 1.000
#> SRR490991 3 0.0000 1.000 0.000 0 1.000
#> SRR490992 3 0.0000 1.000 0.000 0 1.000
#> SRR490993 3 0.0000 1.000 0.000 0 1.000
#> SRR490994 3 0.0000 1.000 0.000 0 1.000
#> SRR490995 3 0.0000 1.000 0.000 0 1.000
#> SRR490996 3 0.0000 1.000 0.000 0 1.000
#> SRR490997 3 0.0000 1.000 0.000 0 1.000
#> SRR490998 3 0.0000 1.000 0.000 0 1.000
#> SRR491000 3 0.0000 1.000 0.000 0 1.000
#> SRR491001 3 0.0000 1.000 0.000 0 1.000
#> SRR491002 3 0.0000 1.000 0.000 0 1.000
#> SRR491003 3 0.0000 1.000 0.000 0 1.000
#> SRR491004 3 0.0000 1.000 0.000 0 1.000
#> SRR491005 3 0.0000 1.000 0.000 0 1.000
#> SRR491006 3 0.0000 1.000 0.000 0 1.000
#> SRR491007 3 0.0000 1.000 0.000 0 1.000
#> SRR491008 3 0.0000 1.000 0.000 0 1.000
#> SRR491009 1 0.6215 0.498 0.572 0 0.428
#> SRR491010 1 0.6126 0.539 0.600 0 0.400
#> SRR491011 1 0.6204 0.504 0.576 0 0.424
#> SRR491012 1 0.6215 0.498 0.572 0 0.428
#> SRR491013 1 0.6126 0.539 0.600 0 0.400
#> SRR491014 1 0.6225 0.490 0.568 0 0.432
#> SRR491015 1 0.6225 0.490 0.568 0 0.432
#> SRR491016 1 0.6225 0.490 0.568 0 0.432
#> SRR491017 1 0.6204 0.504 0.576 0 0.424
#> SRR491018 1 0.6204 0.504 0.576 0 0.424
#> SRR491019 1 0.3752 0.783 0.856 0 0.144
#> SRR491020 1 0.6225 0.490 0.568 0 0.432
#> SRR491021 1 0.6225 0.490 0.568 0 0.432
#> SRR491022 1 0.3412 0.795 0.876 0 0.124
#> SRR491023 1 0.6225 0.490 0.568 0 0.432
#> SRR491024 1 0.3752 0.783 0.856 0 0.144
#> SRR491025 1 0.6126 0.539 0.600 0 0.400
#> SRR491026 1 0.0000 0.850 1.000 0 0.000
#> SRR491027 1 0.1163 0.840 0.972 0 0.028
#> SRR491028 1 0.6225 0.490 0.568 0 0.432
#> SRR491029 1 0.6192 0.510 0.580 0 0.420
#> SRR491030 1 0.4605 0.741 0.796 0 0.204
#> SRR491031 1 0.6225 0.490 0.568 0 0.432
#> SRR491032 1 0.4605 0.741 0.796 0 0.204
#> SRR491033 1 0.0000 0.850 1.000 0 0.000
#> SRR491034 1 0.1860 0.830 0.948 0 0.052
#> SRR491035 1 0.0000 0.850 1.000 0 0.000
#> SRR491036 1 0.5988 0.579 0.632 0 0.368
#> SRR491037 1 0.0000 0.850 1.000 0 0.000
#> SRR491038 1 0.4002 0.773 0.840 0 0.160
#> SRR491039 1 0.0000 0.850 1.000 0 0.000
#> SRR491040 1 0.0000 0.850 1.000 0 0.000
#> SRR491041 1 0.0000 0.850 1.000 0 0.000
#> SRR491042 1 0.0000 0.850 1.000 0 0.000
#> SRR491043 1 0.0000 0.850 1.000 0 0.000
#> SRR491045 1 0.0000 0.850 1.000 0 0.000
#> SRR491065 1 0.0000 0.850 1.000 0 0.000
#> SRR491066 1 0.0000 0.850 1.000 0 0.000
#> SRR491067 1 0.0000 0.850 1.000 0 0.000
#> SRR491068 1 0.0000 0.850 1.000 0 0.000
#> SRR491069 1 0.0000 0.850 1.000 0 0.000
#> SRR491070 1 0.0000 0.850 1.000 0 0.000
#> SRR491071 1 0.0000 0.850 1.000 0 0.000
#> SRR491072 1 0.0000 0.850 1.000 0 0.000
#> SRR491073 1 0.0000 0.850 1.000 0 0.000
#> SRR491074 1 0.0000 0.850 1.000 0 0.000
#> SRR491075 1 0.0000 0.850 1.000 0 0.000
#> SRR491076 1 0.0000 0.850 1.000 0 0.000
#> SRR491077 1 0.0000 0.850 1.000 0 0.000
#> SRR491078 1 0.0000 0.850 1.000 0 0.000
#> SRR491079 1 0.0000 0.850 1.000 0 0.000
#> SRR491080 1 0.0000 0.850 1.000 0 0.000
#> SRR491081 1 0.0000 0.850 1.000 0 0.000
#> SRR491082 1 0.0000 0.850 1.000 0 0.000
#> SRR491083 1 0.0000 0.850 1.000 0 0.000
#> SRR491084 1 0.0000 0.850 1.000 0 0.000
#> SRR491085 1 0.0000 0.850 1.000 0 0.000
#> SRR491086 1 0.0000 0.850 1.000 0 0.000
#> SRR491087 1 0.0000 0.850 1.000 0 0.000
#> SRR491088 1 0.0000 0.850 1.000 0 0.000
#> SRR491089 1 0.0000 0.850 1.000 0 0.000
#> SRR491090 1 0.0237 0.849 0.996 0 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR445730 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR445731 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490973 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490974 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490975 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490976 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490977 3 0.0188 0.996 0.004 0 0.996 0.000
#> SRR490978 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490979 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490980 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000
#> SRR490985 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490986 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490987 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490988 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490989 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490990 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490991 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490992 3 0.0000 0.997 0.000 0 1.000 0.000
#> SRR490993 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR490994 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR490995 3 0.0657 0.987 0.004 0 0.984 0.012
#> SRR490996 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR490997 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR490998 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491000 3 0.0469 0.987 0.000 0 0.988 0.012
#> SRR491001 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491002 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491003 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491004 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491005 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491006 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491007 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491008 3 0.0336 0.996 0.008 0 0.992 0.000
#> SRR491009 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491010 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491011 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491012 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491013 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491014 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491015 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491016 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491017 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491018 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491019 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491020 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491021 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491022 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491023 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491024 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491025 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491026 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491027 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491028 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491029 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491030 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491031 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491032 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491033 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491034 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491035 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491036 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491037 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491038 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491039 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491040 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491041 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491042 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491043 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491045 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491065 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491066 1 0.0592 0.985 0.984 0 0.000 0.016
#> SRR491067 1 0.1118 0.966 0.964 0 0.000 0.036
#> SRR491068 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491069 1 0.3400 0.791 0.820 0 0.000 0.180
#> SRR491070 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491071 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491072 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491073 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491074 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491075 4 0.1940 0.916 0.076 0 0.000 0.924
#> SRR491076 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491077 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491078 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491079 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491080 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491081 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491082 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491083 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491084 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491085 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491086 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491087 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491088 4 0.0000 0.998 0.000 0 0.000 1.000
#> SRR491089 1 0.0336 0.992 0.992 0 0.000 0.008
#> SRR491090 4 0.0000 0.998 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
#> SRR445718 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490973 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490974 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490975 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490976 3 0.0703 0.959 0.000 0 0.976 0.000 0.024
#> SRR490977 3 0.3366 0.690 0.000 0 0.768 0.000 0.232
#> SRR490978 3 0.1341 0.928 0.000 0 0.944 0.000 0.056
#> SRR490979 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490980 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490981 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> SRR490985 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490986 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490987 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490988 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490989 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490990 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490991 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490992 3 0.0000 0.978 0.000 0 1.000 0.000 0.000
#> SRR490993 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR490994 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR490995 5 0.3895 0.555 0.000 0 0.320 0.000 0.680
#> SRR490996 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR490997 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR490998 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491000 5 0.4171 0.391 0.000 0 0.396 0.000 0.604
#> SRR491001 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491002 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491003 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491004 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491005 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491006 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491007 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491008 5 0.0000 0.947 0.000 0 0.000 0.000 1.000
#> SRR491009 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491010 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491011 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491012 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491013 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491014 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491015 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491016 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491017 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491018 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491019 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491020 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491021 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491022 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491023 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491024 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491025 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491026 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491027 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491028 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491029 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491030 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491031 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491032 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491033 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491034 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491035 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491036 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491037 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491038 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491039 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491066 1 0.0290 0.983 0.992 0 0.000 0.008 0.000
#> SRR491067 1 0.0880 0.956 0.968 0 0.000 0.032 0.000
#> SRR491068 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491069 1 0.2929 0.759 0.820 0 0.000 0.180 0.000
#> SRR491070 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491073 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491074 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491075 4 0.1671 0.907 0.076 0 0.000 0.924 0.000
#> SRR491076 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491088 4 0.0000 0.997 0.000 0 0.000 1.000 0.000
#> SRR491089 1 0.0000 0.991 1.000 0 0.000 0.000 0.000
#> SRR491090 4 0.0000 0.997 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
#> SRR445718 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445719 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445720 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445721 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445722 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445723 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445724 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445725 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445726 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445728 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR445731 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR490961 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490962 2 0.0146 0.959 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR490963 2 0.1501 0.935 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR490964 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490965 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490966 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490967 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490968 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490969 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490970 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490971 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490972 2 0.1714 0.929 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR490973 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490974 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490975 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490976 3 0.0632 0.953 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR490977 3 0.3023 0.681 0.000 0.000 0.768 0.000 0.232 0.000
#> SRR490978 3 0.1204 0.919 0.000 0.000 0.944 0.000 0.056 0.000
#> SRR490979 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490980 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490981 6 0.1387 1.000 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR490982 6 0.1387 1.000 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR490983 6 0.1387 1.000 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR490984 6 0.1387 1.000 0.000 0.068 0.000 0.000 0.000 0.932
#> SRR490985 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490986 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490987 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490988 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490989 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490990 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490991 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490992 3 0.0000 0.974 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR490993 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR490994 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR490995 5 0.4732 0.483 0.000 0.000 0.320 0.000 0.612 0.068
#> SRR490996 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR490997 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR490998 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491000 5 0.4933 0.305 0.000 0.000 0.396 0.000 0.536 0.068
#> SRR491001 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491002 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491003 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491004 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491005 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491006 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491007 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491008 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491010 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491011 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491012 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491013 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491014 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491015 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491016 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491017 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491018 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491020 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491021 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491022 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491023 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491024 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491025 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491026 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491028 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491029 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491030 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491031 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491032 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491033 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491034 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491035 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491036 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491037 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491038 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491039 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491040 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491041 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491042 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491043 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491045 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491065 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491066 1 0.0260 0.980 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR491067 1 0.0790 0.950 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR491068 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491069 1 0.2631 0.721 0.820 0.000 0.000 0.180 0.000 0.000
#> SRR491070 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491071 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491072 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491073 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491074 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491075 4 0.1501 0.895 0.076 0.000 0.000 0.924 0.000 0.000
#> SRR491076 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491077 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491078 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491079 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491080 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491081 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491082 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491083 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491084 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491085 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491086 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491087 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491088 4 0.0000 0.997 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491089 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR491090 4 0.0000 0.997 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["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 13175 rows and 123 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 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 0.584 0.892 0.946 0.4629 0.528 0.528
#> 3 3 1.000 0.963 0.982 0.3350 0.797 0.635
#> 4 4 0.819 0.892 0.892 0.1659 0.819 0.554
#> 5 5 0.943 0.949 0.969 0.0711 0.974 0.898
#> 6 6 0.876 0.869 0.907 0.0321 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] 5
#> attr(,"optional")
#> [1] 3
There is also optional best \(k\) = 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
#> SRR445718 2 0.000 0.922 0.000 1.000
#> SRR445719 2 0.000 0.922 0.000 1.000
#> SRR445720 2 0.000 0.922 0.000 1.000
#> SRR445721 2 0.000 0.922 0.000 1.000
#> SRR445722 2 0.000 0.922 0.000 1.000
#> SRR445723 2 0.000 0.922 0.000 1.000
#> SRR445724 2 0.000 0.922 0.000 1.000
#> SRR445725 2 0.000 0.922 0.000 1.000
#> SRR445726 2 0.000 0.922 0.000 1.000
#> SRR445727 2 0.000 0.922 0.000 1.000
#> SRR445728 2 0.000 0.922 0.000 1.000
#> SRR445729 2 0.000 0.922 0.000 1.000
#> SRR445730 1 0.000 0.946 1.000 0.000
#> SRR445731 1 0.000 0.946 1.000 0.000
#> SRR490961 2 0.000 0.922 0.000 1.000
#> SRR490962 2 0.000 0.922 0.000 1.000
#> SRR490963 2 0.000 0.922 0.000 1.000
#> SRR490964 2 0.000 0.922 0.000 1.000
#> SRR490965 2 0.000 0.922 0.000 1.000
#> SRR490966 2 0.000 0.922 0.000 1.000
#> SRR490967 2 0.000 0.922 0.000 1.000
#> SRR490968 2 0.000 0.922 0.000 1.000
#> SRR490969 2 0.000 0.922 0.000 1.000
#> SRR490970 2 0.000 0.922 0.000 1.000
#> SRR490971 2 0.000 0.922 0.000 1.000
#> SRR490972 2 0.000 0.922 0.000 1.000
#> SRR490973 2 0.671 0.844 0.176 0.824
#> SRR490974 2 0.671 0.844 0.176 0.824
#> SRR490975 2 0.671 0.844 0.176 0.824
#> SRR490976 2 0.671 0.844 0.176 0.824
#> SRR490977 2 0.671 0.844 0.176 0.824
#> SRR490978 2 0.671 0.844 0.176 0.824
#> SRR490979 2 0.671 0.844 0.176 0.824
#> SRR490980 2 0.671 0.844 0.176 0.824
#> SRR490981 2 0.000 0.922 0.000 1.000
#> SRR490982 2 0.000 0.922 0.000 1.000
#> SRR490983 2 0.000 0.922 0.000 1.000
#> SRR490984 2 0.000 0.922 0.000 1.000
#> SRR490985 2 0.671 0.844 0.176 0.824
#> SRR490986 2 0.671 0.844 0.176 0.824
#> SRR490987 2 0.671 0.844 0.176 0.824
#> SRR490988 2 0.671 0.844 0.176 0.824
#> SRR490989 2 0.671 0.844 0.176 0.824
#> SRR490990 2 0.671 0.844 0.176 0.824
#> SRR490991 2 0.671 0.844 0.176 0.824
#> SRR490992 2 0.671 0.844 0.176 0.824
#> SRR490993 1 0.850 0.635 0.724 0.276
#> SRR490994 1 0.850 0.635 0.724 0.276
#> SRR490995 2 0.574 0.829 0.136 0.864
#> SRR490996 1 0.850 0.635 0.724 0.276
#> SRR490997 1 0.850 0.635 0.724 0.276
#> SRR490998 1 0.850 0.635 0.724 0.276
#> SRR491000 2 0.574 0.829 0.136 0.864
#> SRR491001 1 0.850 0.635 0.724 0.276
#> SRR491002 1 0.850 0.635 0.724 0.276
#> SRR491003 1 0.850 0.635 0.724 0.276
#> SRR491004 1 0.850 0.635 0.724 0.276
#> SRR491005 1 0.850 0.635 0.724 0.276
#> SRR491006 1 0.850 0.635 0.724 0.276
#> SRR491007 1 0.850 0.635 0.724 0.276
#> SRR491008 1 0.850 0.635 0.724 0.276
#> SRR491009 1 0.000 0.946 1.000 0.000
#> SRR491010 1 0.000 0.946 1.000 0.000
#> SRR491011 1 0.000 0.946 1.000 0.000
#> SRR491012 1 0.000 0.946 1.000 0.000
#> SRR491013 1 0.000 0.946 1.000 0.000
#> SRR491014 1 0.000 0.946 1.000 0.000
#> SRR491015 1 0.000 0.946 1.000 0.000
#> SRR491016 1 0.000 0.946 1.000 0.000
#> SRR491017 1 0.000 0.946 1.000 0.000
#> SRR491018 1 0.000 0.946 1.000 0.000
#> SRR491019 1 0.000 0.946 1.000 0.000
#> SRR491020 1 0.000 0.946 1.000 0.000
#> SRR491021 1 0.000 0.946 1.000 0.000
#> SRR491022 1 0.000 0.946 1.000 0.000
#> SRR491023 1 0.000 0.946 1.000 0.000
#> SRR491024 1 0.000 0.946 1.000 0.000
#> SRR491025 1 0.000 0.946 1.000 0.000
#> SRR491026 1 0.000 0.946 1.000 0.000
#> SRR491027 1 0.000 0.946 1.000 0.000
#> SRR491028 1 0.000 0.946 1.000 0.000
#> SRR491029 1 0.000 0.946 1.000 0.000
#> SRR491030 1 0.000 0.946 1.000 0.000
#> SRR491031 1 0.000 0.946 1.000 0.000
#> SRR491032 1 0.000 0.946 1.000 0.000
#> SRR491033 1 0.000 0.946 1.000 0.000
#> SRR491034 1 0.000 0.946 1.000 0.000
#> SRR491035 1 0.000 0.946 1.000 0.000
#> SRR491036 1 0.000 0.946 1.000 0.000
#> SRR491037 1 0.000 0.946 1.000 0.000
#> SRR491038 1 0.000 0.946 1.000 0.000
#> SRR491039 1 0.000 0.946 1.000 0.000
#> SRR491040 1 0.000 0.946 1.000 0.000
#> SRR491041 1 0.000 0.946 1.000 0.000
#> SRR491042 1 0.000 0.946 1.000 0.000
#> SRR491043 1 0.000 0.946 1.000 0.000
#> SRR491045 1 0.000 0.946 1.000 0.000
#> SRR491065 1 0.000 0.946 1.000 0.000
#> SRR491066 1 0.000 0.946 1.000 0.000
#> SRR491067 1 0.000 0.946 1.000 0.000
#> SRR491068 1 0.000 0.946 1.000 0.000
#> SRR491069 1 0.000 0.946 1.000 0.000
#> SRR491070 1 0.000 0.946 1.000 0.000
#> SRR491071 1 0.000 0.946 1.000 0.000
#> SRR491072 1 0.000 0.946 1.000 0.000
#> SRR491073 1 0.000 0.946 1.000 0.000
#> SRR491074 1 0.000 0.946 1.000 0.000
#> SRR491075 1 0.000 0.946 1.000 0.000
#> SRR491076 1 0.000 0.946 1.000 0.000
#> SRR491077 1 0.000 0.946 1.000 0.000
#> SRR491078 1 0.000 0.946 1.000 0.000
#> SRR491079 1 0.000 0.946 1.000 0.000
#> SRR491080 1 0.000 0.946 1.000 0.000
#> SRR491081 1 0.000 0.946 1.000 0.000
#> SRR491082 1 0.000 0.946 1.000 0.000
#> SRR491083 1 0.000 0.946 1.000 0.000
#> SRR491084 1 0.000 0.946 1.000 0.000
#> SRR491085 1 0.000 0.946 1.000 0.000
#> SRR491086 1 0.000 0.946 1.000 0.000
#> SRR491087 1 0.000 0.946 1.000 0.000
#> SRR491088 1 0.000 0.946 1.000 0.000
#> SRR491089 1 0.000 0.946 1.000 0.000
#> SRR491090 1 0.000 0.946 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 0.926 0 1.000 0.000
#> SRR445719 2 0.0000 0.926 0 1.000 0.000
#> SRR445720 2 0.0000 0.926 0 1.000 0.000
#> SRR445721 2 0.0000 0.926 0 1.000 0.000
#> SRR445722 2 0.0000 0.926 0 1.000 0.000
#> SRR445723 2 0.0000 0.926 0 1.000 0.000
#> SRR445724 2 0.0000 0.926 0 1.000 0.000
#> SRR445725 2 0.0000 0.926 0 1.000 0.000
#> SRR445726 2 0.0000 0.926 0 1.000 0.000
#> SRR445727 2 0.0000 0.926 0 1.000 0.000
#> SRR445728 2 0.0000 0.926 0 1.000 0.000
#> SRR445729 2 0.0000 0.926 0 1.000 0.000
#> SRR445730 1 0.0000 1.000 1 0.000 0.000
#> SRR445731 1 0.0000 1.000 1 0.000 0.000
#> SRR490961 2 0.0000 0.926 0 1.000 0.000
#> SRR490962 2 0.0000 0.926 0 1.000 0.000
#> SRR490963 2 0.0000 0.926 0 1.000 0.000
#> SRR490964 2 0.0000 0.926 0 1.000 0.000
#> SRR490965 2 0.0000 0.926 0 1.000 0.000
#> SRR490966 2 0.0000 0.926 0 1.000 0.000
#> SRR490967 2 0.0000 0.926 0 1.000 0.000
#> SRR490968 2 0.0000 0.926 0 1.000 0.000
#> SRR490969 2 0.0000 0.926 0 1.000 0.000
#> SRR490970 2 0.0000 0.926 0 1.000 0.000
#> SRR490971 2 0.0000 0.926 0 1.000 0.000
#> SRR490972 2 0.0000 0.926 0 1.000 0.000
#> SRR490973 3 0.0592 0.994 0 0.012 0.988
#> SRR490974 3 0.0592 0.994 0 0.012 0.988
#> SRR490975 3 0.0592 0.994 0 0.012 0.988
#> SRR490976 3 0.0592 0.994 0 0.012 0.988
#> SRR490977 3 0.0592 0.994 0 0.012 0.988
#> SRR490978 3 0.0592 0.994 0 0.012 0.988
#> SRR490979 3 0.0592 0.994 0 0.012 0.988
#> SRR490980 3 0.0592 0.994 0 0.012 0.988
#> SRR490981 2 0.5810 0.574 0 0.664 0.336
#> SRR490982 2 0.5810 0.574 0 0.664 0.336
#> SRR490983 2 0.5810 0.574 0 0.664 0.336
#> SRR490984 2 0.5810 0.574 0 0.664 0.336
#> SRR490985 3 0.0592 0.994 0 0.012 0.988
#> SRR490986 3 0.0592 0.994 0 0.012 0.988
#> SRR490987 3 0.0592 0.994 0 0.012 0.988
#> SRR490988 3 0.0592 0.994 0 0.012 0.988
#> SRR490989 3 0.0592 0.994 0 0.012 0.988
#> SRR490990 3 0.0592 0.994 0 0.012 0.988
#> SRR490991 3 0.0592 0.994 0 0.012 0.988
#> SRR490992 3 0.0592 0.994 0 0.012 0.988
#> SRR490993 3 0.0000 0.992 0 0.000 1.000
#> SRR490994 3 0.0000 0.992 0 0.000 1.000
#> SRR490995 2 0.5882 0.564 0 0.652 0.348
#> SRR490996 3 0.0000 0.992 0 0.000 1.000
#> SRR490997 3 0.0000 0.992 0 0.000 1.000
#> SRR490998 3 0.0000 0.992 0 0.000 1.000
#> SRR491000 2 0.5882 0.564 0 0.652 0.348
#> SRR491001 3 0.0000 0.992 0 0.000 1.000
#> SRR491002 3 0.0000 0.992 0 0.000 1.000
#> SRR491003 3 0.0000 0.992 0 0.000 1.000
#> SRR491004 3 0.0000 0.992 0 0.000 1.000
#> SRR491005 3 0.0000 0.992 0 0.000 1.000
#> SRR491006 3 0.0000 0.992 0 0.000 1.000
#> SRR491007 3 0.0000 0.992 0 0.000 1.000
#> SRR491008 3 0.0000 0.992 0 0.000 1.000
#> SRR491009 1 0.0000 1.000 1 0.000 0.000
#> SRR491010 1 0.0000 1.000 1 0.000 0.000
#> SRR491011 1 0.0000 1.000 1 0.000 0.000
#> SRR491012 1 0.0000 1.000 1 0.000 0.000
#> SRR491013 1 0.0000 1.000 1 0.000 0.000
#> SRR491014 1 0.0000 1.000 1 0.000 0.000
#> SRR491015 1 0.0000 1.000 1 0.000 0.000
#> SRR491016 1 0.0000 1.000 1 0.000 0.000
#> SRR491017 1 0.0000 1.000 1 0.000 0.000
#> SRR491018 1 0.0000 1.000 1 0.000 0.000
#> SRR491019 1 0.0000 1.000 1 0.000 0.000
#> SRR491020 1 0.0000 1.000 1 0.000 0.000
#> SRR491021 1 0.0000 1.000 1 0.000 0.000
#> SRR491022 1 0.0000 1.000 1 0.000 0.000
#> SRR491023 1 0.0000 1.000 1 0.000 0.000
#> SRR491024 1 0.0000 1.000 1 0.000 0.000
#> SRR491025 1 0.0000 1.000 1 0.000 0.000
#> SRR491026 1 0.0000 1.000 1 0.000 0.000
#> SRR491027 1 0.0000 1.000 1 0.000 0.000
#> SRR491028 1 0.0000 1.000 1 0.000 0.000
#> SRR491029 1 0.0000 1.000 1 0.000 0.000
#> SRR491030 1 0.0000 1.000 1 0.000 0.000
#> SRR491031 1 0.0000 1.000 1 0.000 0.000
#> SRR491032 1 0.0000 1.000 1 0.000 0.000
#> SRR491033 1 0.0000 1.000 1 0.000 0.000
#> SRR491034 1 0.0000 1.000 1 0.000 0.000
#> SRR491035 1 0.0000 1.000 1 0.000 0.000
#> SRR491036 1 0.0000 1.000 1 0.000 0.000
#> SRR491037 1 0.0000 1.000 1 0.000 0.000
#> SRR491038 1 0.0000 1.000 1 0.000 0.000
#> SRR491039 1 0.0000 1.000 1 0.000 0.000
#> SRR491040 1 0.0000 1.000 1 0.000 0.000
#> SRR491041 1 0.0000 1.000 1 0.000 0.000
#> SRR491042 1 0.0000 1.000 1 0.000 0.000
#> SRR491043 1 0.0000 1.000 1 0.000 0.000
#> SRR491045 1 0.0000 1.000 1 0.000 0.000
#> SRR491065 1 0.0000 1.000 1 0.000 0.000
#> SRR491066 1 0.0000 1.000 1 0.000 0.000
#> SRR491067 1 0.0000 1.000 1 0.000 0.000
#> SRR491068 1 0.0000 1.000 1 0.000 0.000
#> SRR491069 1 0.0000 1.000 1 0.000 0.000
#> SRR491070 1 0.0000 1.000 1 0.000 0.000
#> SRR491071 1 0.0000 1.000 1 0.000 0.000
#> SRR491072 1 0.0000 1.000 1 0.000 0.000
#> SRR491073 1 0.0000 1.000 1 0.000 0.000
#> SRR491074 1 0.0000 1.000 1 0.000 0.000
#> SRR491075 1 0.0000 1.000 1 0.000 0.000
#> SRR491076 1 0.0000 1.000 1 0.000 0.000
#> SRR491077 1 0.0000 1.000 1 0.000 0.000
#> SRR491078 1 0.0000 1.000 1 0.000 0.000
#> SRR491079 1 0.0000 1.000 1 0.000 0.000
#> SRR491080 1 0.0000 1.000 1 0.000 0.000
#> SRR491081 1 0.0000 1.000 1 0.000 0.000
#> SRR491082 1 0.0000 1.000 1 0.000 0.000
#> SRR491083 1 0.0000 1.000 1 0.000 0.000
#> SRR491084 1 0.0000 1.000 1 0.000 0.000
#> SRR491085 1 0.0000 1.000 1 0.000 0.000
#> SRR491086 1 0.0000 1.000 1 0.000 0.000
#> SRR491087 1 0.0000 1.000 1 0.000 0.000
#> SRR491088 1 0.0000 1.000 1 0.000 0.000
#> SRR491089 1 0.0000 1.000 1 0.000 0.000
#> SRR491090 1 0.0000 1.000 1 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR445718 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445719 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445720 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445721 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445722 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445723 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445724 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445725 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445726 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445727 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445728 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445729 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR445730 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR445731 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR490961 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490962 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490963 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490964 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490965 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490966 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490967 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490968 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490969 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490970 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490971 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490972 2 0.0000 1.0000 0.000 1.000 0.000 0.000
#> SRR490973 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490974 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490975 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490976 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490977 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490978 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490979 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490980 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490981 3 0.9292 0.0996 0.080 0.304 0.328 0.288
#> SRR490982 3 0.9292 0.0996 0.080 0.304 0.328 0.288
#> SRR490983 3 0.9292 0.0996 0.080 0.304 0.328 0.288
#> SRR490984 3 0.9292 0.0996 0.080 0.304 0.328 0.288
#> SRR490985 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490986 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490987 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490988 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490989 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490990 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490991 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490992 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490993 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490994 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490995 4 0.7786 0.0223 0.256 0.000 0.328 0.416
#> SRR490996 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490997 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR490998 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491000 4 0.7786 0.0223 0.256 0.000 0.328 0.416
#> SRR491001 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491002 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491003 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491004 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491005 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491006 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491007 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491008 3 0.0000 0.9230 0.000 0.000 1.000 0.000
#> SRR491009 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491010 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491011 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491012 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491013 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491014 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491015 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491016 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491017 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491018 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491019 4 0.0592 0.9055 0.016 0.000 0.000 0.984
#> SRR491020 4 0.0188 0.9091 0.004 0.000 0.000 0.996
#> SRR491021 4 0.0188 0.9091 0.004 0.000 0.000 0.996
#> SRR491022 4 0.0188 0.9103 0.004 0.000 0.000 0.996
#> SRR491023 4 0.0188 0.9091 0.004 0.000 0.000 0.996
#> SRR491024 4 0.0336 0.9088 0.008 0.000 0.000 0.992
#> SRR491025 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491026 4 0.0707 0.9035 0.020 0.000 0.000 0.980
#> SRR491027 4 0.0000 0.9109 0.000 0.000 0.000 1.000
#> SRR491028 4 0.0336 0.9066 0.008 0.000 0.000 0.992
#> SRR491029 4 0.0336 0.9089 0.008 0.000 0.000 0.992
#> SRR491030 4 0.0188 0.9102 0.004 0.000 0.000 0.996
#> SRR491031 4 0.1389 0.8805 0.048 0.000 0.000 0.952
#> SRR491032 4 0.1118 0.8930 0.036 0.000 0.000 0.964
#> SRR491033 4 0.1022 0.8963 0.032 0.000 0.000 0.968
#> SRR491034 4 0.2921 0.7810 0.140 0.000 0.000 0.860
#> SRR491035 4 0.1716 0.8675 0.064 0.000 0.000 0.936
#> SRR491036 4 0.2469 0.8407 0.108 0.000 0.000 0.892
#> SRR491037 4 0.1211 0.8896 0.040 0.000 0.000 0.960
#> SRR491038 4 0.2760 0.7960 0.128 0.000 0.000 0.872
#> SRR491039 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491040 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491041 1 0.4564 0.9047 0.672 0.000 0.000 0.328
#> SRR491042 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491043 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491045 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491065 1 0.4331 0.9473 0.712 0.000 0.000 0.288
#> SRR491066 1 0.4746 0.8518 0.632 0.000 0.000 0.368
#> SRR491067 1 0.4564 0.9073 0.672 0.000 0.000 0.328
#> SRR491068 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491069 1 0.4746 0.8518 0.632 0.000 0.000 0.368
#> SRR491070 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491071 1 0.4761 0.8469 0.628 0.000 0.000 0.372
#> SRR491072 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491073 4 0.3219 0.8008 0.164 0.000 0.000 0.836
#> SRR491074 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491075 4 0.3311 0.7799 0.172 0.000 0.000 0.828
#> SRR491076 1 0.4356 0.9435 0.708 0.000 0.000 0.292
#> SRR491077 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491078 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491079 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491080 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491081 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491082 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491083 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491084 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491085 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491086 1 0.4454 0.9294 0.692 0.000 0.000 0.308
#> SRR491087 1 0.4454 0.9292 0.692 0.000 0.000 0.308
#> SRR491088 4 0.3266 0.7990 0.168 0.000 0.000 0.832
#> SRR491089 1 0.4103 0.9685 0.744 0.000 0.000 0.256
#> SRR491090 4 0.3311 0.7937 0.172 0.000 0.000 0.828
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR445730 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR445731 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.00 0.000 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490981 5 0.3551 0.895 0.000 0.008 0.22 0.000 0.772
#> SRR490982 5 0.3551 0.895 0.000 0.008 0.22 0.000 0.772
#> SRR490983 5 0.3551 0.895 0.000 0.008 0.22 0.000 0.772
#> SRR490984 5 0.3551 0.895 0.000 0.008 0.22 0.000 0.772
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490993 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490994 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490995 5 0.1012 0.819 0.000 0.000 0.02 0.012 0.968
#> SRR490996 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490997 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR490998 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491000 5 0.1012 0.819 0.000 0.000 0.02 0.012 0.968
#> SRR491001 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491002 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491003 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491004 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491005 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491006 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491007 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491008 3 0.0000 1.000 0.000 0.000 1.00 0.000 0.000
#> SRR491009 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491010 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491011 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491012 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491013 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491014 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491015 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491016 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491017 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491018 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491019 4 0.1331 0.941 0.040 0.000 0.00 0.952 0.008
#> SRR491020 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491021 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491022 4 0.1121 0.946 0.000 0.000 0.00 0.956 0.044
#> SRR491023 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491024 4 0.0992 0.955 0.024 0.000 0.00 0.968 0.008
#> SRR491025 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491026 4 0.0798 0.960 0.016 0.000 0.00 0.976 0.008
#> SRR491027 4 0.0451 0.964 0.004 0.000 0.00 0.988 0.008
#> SRR491028 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491029 4 0.1485 0.947 0.020 0.000 0.00 0.948 0.032
#> SRR491030 4 0.0290 0.964 0.008 0.000 0.00 0.992 0.000
#> SRR491031 4 0.0000 0.966 0.000 0.000 0.00 1.000 0.000
#> SRR491032 4 0.0880 0.953 0.032 0.000 0.00 0.968 0.000
#> SRR491033 4 0.0579 0.963 0.008 0.000 0.00 0.984 0.008
#> SRR491034 4 0.1121 0.943 0.044 0.000 0.00 0.956 0.000
#> SRR491035 4 0.1478 0.924 0.064 0.000 0.00 0.936 0.000
#> SRR491036 4 0.2104 0.918 0.060 0.000 0.00 0.916 0.024
#> SRR491037 4 0.0703 0.958 0.024 0.000 0.00 0.976 0.000
#> SRR491038 4 0.0404 0.963 0.012 0.000 0.00 0.988 0.000
#> SRR491039 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491040 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491041 1 0.2230 0.843 0.884 0.000 0.00 0.116 0.000
#> SRR491042 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491043 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491045 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491065 1 0.1648 0.894 0.940 0.000 0.00 0.040 0.020
#> SRR491066 1 0.4229 0.652 0.704 0.000 0.00 0.276 0.020
#> SRR491067 1 0.3438 0.781 0.808 0.000 0.00 0.172 0.020
#> SRR491068 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491069 1 0.4229 0.652 0.704 0.000 0.00 0.276 0.020
#> SRR491070 1 0.0290 0.921 0.992 0.000 0.00 0.008 0.000
#> SRR491071 1 0.4367 0.721 0.748 0.000 0.00 0.192 0.060
#> SRR491072 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491073 4 0.2411 0.888 0.008 0.000 0.00 0.884 0.108
#> SRR491074 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491075 4 0.3590 0.838 0.080 0.000 0.00 0.828 0.092
#> SRR491076 1 0.3016 0.819 0.848 0.000 0.00 0.132 0.020
#> SRR491077 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491078 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491079 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491080 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491081 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491082 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491083 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491084 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491085 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491086 1 0.3231 0.763 0.800 0.000 0.00 0.196 0.004
#> SRR491087 1 0.3183 0.799 0.828 0.000 0.00 0.156 0.016
#> SRR491088 4 0.2561 0.855 0.000 0.000 0.00 0.856 0.144
#> SRR491089 1 0.0000 0.925 1.000 0.000 0.00 0.000 0.000
#> SRR491090 4 0.2953 0.847 0.012 0.000 0.00 0.844 0.144
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR445730 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR445731 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 NA
#> SRR490973 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490974 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490975 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490976 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490977 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490978 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490979 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490980 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490981 5 0.2771 0.924 0.000 0.032 0.116 0.000 0.852 NA
#> SRR490982 5 0.2771 0.924 0.000 0.032 0.116 0.000 0.852 NA
#> SRR490983 5 0.2771 0.924 0.000 0.032 0.116 0.000 0.852 NA
#> SRR490984 5 0.2771 0.924 0.000 0.032 0.116 0.000 0.852 NA
#> SRR490985 3 0.0865 0.813 0.000 0.000 0.964 0.000 0.036 NA
#> SRR490986 3 0.0865 0.813 0.000 0.000 0.964 0.000 0.036 NA
#> SRR490987 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490988 3 0.0865 0.813 0.000 0.000 0.964 0.000 0.036 NA
#> SRR490989 3 0.0865 0.813 0.000 0.000 0.964 0.000 0.036 NA
#> SRR490990 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490991 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490992 3 0.0000 0.835 0.000 0.000 1.000 0.000 0.000 NA
#> SRR490993 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR490994 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR490995 5 0.1524 0.863 0.000 0.000 0.000 0.008 0.932 NA
#> SRR490996 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR490997 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR490998 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491000 5 0.1524 0.863 0.000 0.000 0.000 0.008 0.932 NA
#> SRR491001 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491002 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491003 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491004 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491005 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491006 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491007 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491008 3 0.3409 0.798 0.000 0.000 0.700 0.000 0.000 NA
#> SRR491009 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491010 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491011 4 0.0146 0.900 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491012 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491013 4 0.0146 0.900 0.000 0.000 0.000 0.996 0.000 NA
#> SRR491014 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491015 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491016 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491017 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491018 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491019 4 0.1856 0.890 0.048 0.000 0.000 0.920 0.000 NA
#> SRR491020 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491021 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491022 4 0.3330 0.732 0.000 0.000 0.000 0.716 0.000 NA
#> SRR491023 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491024 4 0.2325 0.878 0.060 0.000 0.000 0.892 0.000 NA
#> SRR491025 4 0.0935 0.896 0.032 0.000 0.000 0.964 0.000 NA
#> SRR491026 4 0.2775 0.851 0.104 0.000 0.000 0.856 0.000 NA
#> SRR491027 4 0.1720 0.892 0.032 0.000 0.000 0.928 0.000 NA
#> SRR491028 4 0.0000 0.901 0.000 0.000 0.000 1.000 0.000 NA
#> SRR491029 4 0.2680 0.873 0.056 0.000 0.000 0.868 0.000 NA
#> SRR491030 4 0.1863 0.888 0.044 0.000 0.000 0.920 0.000 NA
#> SRR491031 4 0.0790 0.894 0.000 0.000 0.000 0.968 0.000 NA
#> SRR491032 4 0.2685 0.872 0.060 0.000 0.000 0.868 0.000 NA
#> SRR491033 4 0.2728 0.853 0.100 0.000 0.000 0.860 0.000 NA
#> SRR491034 4 0.3295 0.846 0.056 0.000 0.000 0.816 0.000 NA
#> SRR491035 4 0.3834 0.793 0.144 0.000 0.000 0.772 0.000 NA
#> SRR491036 4 0.3475 0.836 0.060 0.000 0.000 0.800 0.000 NA
#> SRR491037 4 0.2020 0.869 0.096 0.000 0.000 0.896 0.000 NA
#> SRR491038 4 0.1700 0.892 0.024 0.000 0.000 0.928 0.000 NA
#> SRR491039 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491040 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491041 1 0.2842 0.809 0.852 0.000 0.000 0.044 0.000 NA
#> SRR491042 1 0.0146 0.902 0.996 0.000 0.000 0.000 0.000 NA
#> SRR491043 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491045 1 0.0260 0.898 0.992 0.000 0.000 0.008 0.000 NA
#> SRR491065 1 0.3668 0.750 0.744 0.000 0.000 0.028 0.000 NA
#> SRR491066 1 0.4844 0.497 0.504 0.000 0.000 0.056 0.000 NA
#> SRR491067 1 0.4614 0.679 0.684 0.000 0.000 0.108 0.000 NA
#> SRR491068 1 0.0146 0.901 0.996 0.000 0.000 0.004 0.000 NA
#> SRR491069 1 0.4936 0.491 0.500 0.000 0.000 0.064 0.000 NA
#> SRR491070 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491071 1 0.5059 0.587 0.588 0.000 0.000 0.072 0.008 NA
#> SRR491072 1 0.0146 0.902 0.996 0.000 0.000 0.000 0.000 NA
#> SRR491073 4 0.3965 0.713 0.008 0.000 0.000 0.720 0.024 NA
#> SRR491074 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491075 4 0.5587 0.554 0.084 0.000 0.000 0.532 0.024 NA
#> SRR491076 1 0.3938 0.677 0.660 0.000 0.000 0.016 0.000 NA
#> SRR491077 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491078 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491079 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491080 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491081 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491082 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491083 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491084 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491085 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491086 1 0.3445 0.784 0.796 0.000 0.000 0.048 0.000 NA
#> SRR491087 1 0.4331 0.708 0.704 0.000 0.000 0.076 0.000 NA
#> SRR491088 4 0.4218 0.627 0.000 0.000 0.000 0.616 0.024 NA
#> SRR491089 1 0.0000 0.903 1.000 0.000 0.000 0.000 0.000 NA
#> SRR491090 4 0.4490 0.608 0.008 0.000 0.000 0.596 0.024 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.
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 13175 rows and 123 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 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 0.982 0.992 0.4527 0.552 0.552
#> 3 3 1.000 0.999 1.000 0.3684 0.776 0.613
#> 4 4 1.000 0.994 0.976 0.2010 0.864 0.643
#> 5 5 0.915 0.832 0.920 0.0430 0.996 0.984
#> 6 6 0.908 0.818 0.894 0.0352 0.933 0.729
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
#> SRR445718 2 0.0000 0.998 0.000 1.000
#> SRR445719 2 0.0000 0.998 0.000 1.000
#> SRR445720 2 0.0000 0.998 0.000 1.000
#> SRR445721 2 0.0000 0.998 0.000 1.000
#> SRR445722 2 0.0000 0.998 0.000 1.000
#> SRR445723 2 0.0000 0.998 0.000 1.000
#> SRR445724 2 0.0000 0.998 0.000 1.000
#> SRR445725 2 0.0000 0.998 0.000 1.000
#> SRR445726 2 0.0000 0.998 0.000 1.000
#> SRR445727 2 0.0000 0.998 0.000 1.000
#> SRR445728 2 0.0000 0.998 0.000 1.000
#> SRR445729 2 0.0000 0.998 0.000 1.000
#> SRR445730 1 0.0000 0.989 1.000 0.000
#> SRR445731 1 0.0000 0.989 1.000 0.000
#> SRR490961 2 0.0000 0.998 0.000 1.000
#> SRR490962 2 0.0000 0.998 0.000 1.000
#> SRR490963 2 0.0000 0.998 0.000 1.000
#> SRR490964 2 0.0000 0.998 0.000 1.000
#> SRR490965 2 0.0000 0.998 0.000 1.000
#> SRR490966 2 0.0000 0.998 0.000 1.000
#> SRR490967 2 0.0000 0.998 0.000 1.000
#> SRR490968 2 0.0000 0.998 0.000 1.000
#> SRR490969 2 0.0000 0.998 0.000 1.000
#> SRR490970 2 0.0000 0.998 0.000 1.000
#> SRR490971 2 0.0000 0.998 0.000 1.000
#> SRR490972 2 0.0000 0.998 0.000 1.000
#> SRR490973 1 0.9580 0.401 0.620 0.380
#> SRR490974 2 0.0000 0.998 0.000 1.000
#> SRR490975 2 0.0000 0.998 0.000 1.000
#> SRR490976 1 0.5842 0.837 0.860 0.140
#> SRR490977 1 0.0000 0.989 1.000 0.000
#> SRR490978 1 0.6531 0.800 0.832 0.168
#> SRR490979 1 0.7376 0.742 0.792 0.208
#> SRR490980 2 0.0376 0.994 0.004 0.996
#> SRR490981 2 0.0000 0.998 0.000 1.000
#> SRR490982 2 0.0000 0.998 0.000 1.000
#> SRR490983 2 0.0000 0.998 0.000 1.000
#> SRR490984 2 0.0000 0.998 0.000 1.000
#> SRR490985 2 0.0000 0.998 0.000 1.000
#> SRR490986 2 0.0000 0.998 0.000 1.000
#> SRR490987 2 0.0000 0.998 0.000 1.000
#> SRR490988 2 0.0000 0.998 0.000 1.000
#> SRR490989 2 0.0000 0.998 0.000 1.000
#> SRR490990 2 0.0000 0.998 0.000 1.000
#> SRR490991 2 0.0000 0.998 0.000 1.000
#> SRR490992 2 0.4161 0.906 0.084 0.916
#> SRR490993 1 0.0000 0.989 1.000 0.000
#> SRR490994 1 0.0000 0.989 1.000 0.000
#> SRR490995 2 0.0000 0.998 0.000 1.000
#> SRR490996 1 0.0000 0.989 1.000 0.000
#> SRR490997 1 0.0000 0.989 1.000 0.000
#> SRR490998 1 0.0000 0.989 1.000 0.000
#> SRR491000 2 0.0000 0.998 0.000 1.000
#> SRR491001 1 0.0000 0.989 1.000 0.000
#> SRR491002 1 0.0000 0.989 1.000 0.000
#> SRR491003 1 0.0000 0.989 1.000 0.000
#> SRR491004 1 0.0000 0.989 1.000 0.000
#> SRR491005 1 0.0000 0.989 1.000 0.000
#> SRR491006 1 0.0000 0.989 1.000 0.000
#> SRR491007 1 0.0000 0.989 1.000 0.000
#> SRR491008 1 0.0000 0.989 1.000 0.000
#> SRR491009 1 0.0000 0.989 1.000 0.000
#> SRR491010 1 0.0000 0.989 1.000 0.000
#> SRR491011 1 0.0000 0.989 1.000 0.000
#> SRR491012 1 0.0000 0.989 1.000 0.000
#> SRR491013 1 0.0000 0.989 1.000 0.000
#> SRR491014 1 0.0000 0.989 1.000 0.000
#> SRR491015 1 0.0000 0.989 1.000 0.000
#> SRR491016 1 0.0000 0.989 1.000 0.000
#> SRR491017 1 0.0000 0.989 1.000 0.000
#> SRR491018 1 0.0000 0.989 1.000 0.000
#> SRR491019 1 0.0000 0.989 1.000 0.000
#> SRR491020 1 0.0000 0.989 1.000 0.000
#> SRR491021 1 0.0000 0.989 1.000 0.000
#> SRR491022 1 0.0000 0.989 1.000 0.000
#> SRR491023 1 0.0000 0.989 1.000 0.000
#> SRR491024 1 0.0000 0.989 1.000 0.000
#> SRR491025 1 0.0000 0.989 1.000 0.000
#> SRR491026 1 0.0000 0.989 1.000 0.000
#> SRR491027 1 0.0000 0.989 1.000 0.000
#> SRR491028 1 0.0000 0.989 1.000 0.000
#> SRR491029 1 0.0000 0.989 1.000 0.000
#> SRR491030 1 0.0000 0.989 1.000 0.000
#> SRR491031 1 0.0000 0.989 1.000 0.000
#> SRR491032 1 0.0000 0.989 1.000 0.000
#> SRR491033 1 0.0000 0.989 1.000 0.000
#> SRR491034 1 0.0000 0.989 1.000 0.000
#> SRR491035 1 0.0000 0.989 1.000 0.000
#> SRR491036 1 0.0000 0.989 1.000 0.000
#> SRR491037 1 0.0000 0.989 1.000 0.000
#> SRR491038 1 0.0000 0.989 1.000 0.000
#> SRR491039 1 0.0000 0.989 1.000 0.000
#> SRR491040 1 0.0000 0.989 1.000 0.000
#> SRR491041 1 0.0000 0.989 1.000 0.000
#> SRR491042 1 0.0000 0.989 1.000 0.000
#> SRR491043 1 0.0000 0.989 1.000 0.000
#> SRR491045 1 0.0000 0.989 1.000 0.000
#> SRR491065 1 0.0000 0.989 1.000 0.000
#> SRR491066 1 0.0000 0.989 1.000 0.000
#> SRR491067 1 0.0000 0.989 1.000 0.000
#> SRR491068 1 0.0000 0.989 1.000 0.000
#> SRR491069 1 0.0000 0.989 1.000 0.000
#> SRR491070 1 0.0000 0.989 1.000 0.000
#> SRR491071 1 0.0000 0.989 1.000 0.000
#> SRR491072 1 0.0000 0.989 1.000 0.000
#> SRR491073 1 0.0000 0.989 1.000 0.000
#> SRR491074 1 0.0000 0.989 1.000 0.000
#> SRR491075 1 0.0000 0.989 1.000 0.000
#> SRR491076 1 0.0000 0.989 1.000 0.000
#> SRR491077 1 0.0000 0.989 1.000 0.000
#> SRR491078 1 0.0000 0.989 1.000 0.000
#> SRR491079 1 0.0000 0.989 1.000 0.000
#> SRR491080 1 0.0000 0.989 1.000 0.000
#> SRR491081 1 0.0000 0.989 1.000 0.000
#> SRR491082 1 0.0000 0.989 1.000 0.000
#> SRR491083 1 0.0000 0.989 1.000 0.000
#> SRR491084 1 0.0000 0.989 1.000 0.000
#> SRR491085 1 0.0000 0.989 1.000 0.000
#> SRR491086 1 0.0000 0.989 1.000 0.000
#> SRR491087 1 0.0000 0.989 1.000 0.000
#> SRR491088 1 0.0000 0.989 1.000 0.000
#> SRR491089 1 0.0000 0.989 1.000 0.000
#> SRR491090 1 0.0000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR445718 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445719 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445720 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445721 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445722 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445723 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445724 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445725 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445726 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445727 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445728 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445729 2 0.0000 1.000 0.000 1.000 0.000
#> SRR445730 1 0.0000 1.000 1.000 0.000 0.000
#> SRR445731 1 0.0000 1.000 1.000 0.000 0.000
#> SRR490961 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490962 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490963 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490964 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490965 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490966 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490967 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490968 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490969 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490970 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490971 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490972 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490973 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490974 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490975 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490976 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490977 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490978 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490979 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490980 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490981 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490982 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490983 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490984 2 0.0000 1.000 0.000 1.000 0.000
#> SRR490985 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490986 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490987 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490988 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490989 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490990 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490991 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490992 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490993 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490994 3 0.0237 0.997 0.004 0.000 0.996
#> SRR490995 2 0.0237 0.996 0.000 0.996 0.004
#> SRR490996 3 0.0000 0.998 0.000 0.000 1.000
#> SRR490997 3 0.0237 0.997 0.004 0.000 0.996
#> SRR490998 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491000 2 0.0424 0.993 0.000 0.992 0.008
#> SRR491001 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491002 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491003 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491004 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491005 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491006 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491007 3 0.0000 0.998 0.000 0.000 1.000
#> SRR491008 3 0.0237 0.997 0.004 0.000 0.996
#> SRR491009 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491010 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491011 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491012 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491013 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491014 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491015 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491016 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491017 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491018 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491019 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491020 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491021 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491022 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491023 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491024 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491025 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491026 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491027 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491028 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491029 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491030 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491031 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491032 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491033 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491034 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491035 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491036 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491037 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491038 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491039 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491040 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491041 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491042 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491043 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491045 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491065 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491066 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491067 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491068 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491069 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491070 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491071 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491072 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491073 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491074 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491075 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491076 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491077 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491078 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491079 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491080 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491081 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491082 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491083 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491084 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491085 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491086 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491087 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491088 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491089 1 0.0000 1.000 1.000 0.000 0.000
#> SRR491090 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
#> SRR445718 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445719 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445720 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445721 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445722 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445723 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445724 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445725 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445726 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445727 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445728 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445729 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR445730 1 0.2081 0.987 0.916 0.000 0.00 0.084
#> SRR445731 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR490961 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490962 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490963 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490964 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490965 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490966 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490967 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490968 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490969 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490970 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490971 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490972 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490973 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490974 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490975 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490976 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490977 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490978 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490979 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490980 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490981 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490982 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490983 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490984 2 0.0000 0.999 0.000 1.000 0.00 0.000
#> SRR490985 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490986 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490987 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490988 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490989 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490990 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490991 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490992 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490993 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490994 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490995 2 0.0336 0.994 0.008 0.992 0.00 0.000
#> SRR490996 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490997 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR490998 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491000 2 0.1042 0.975 0.008 0.972 0.02 0.000
#> SRR491001 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491002 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491003 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491004 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491005 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491006 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491007 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491008 3 0.0000 1.000 0.000 0.000 1.00 0.000
#> SRR491009 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491010 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491011 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491012 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491013 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491014 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491015 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491016 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491017 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491018 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491019 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491020 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491021 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491022 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491023 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491024 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491025 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491026 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491027 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491028 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491029 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491030 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491031 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491032 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491033 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491034 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491035 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491036 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491037 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491038 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR491039 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491040 1 0.2081 0.987 0.916 0.000 0.00 0.084
#> SRR491041 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491042 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491043 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491045 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491065 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491066 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491067 1 0.2281 0.986 0.904 0.000 0.00 0.096
#> SRR491068 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491069 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491070 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491071 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491072 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491073 1 0.2973 0.938 0.856 0.000 0.00 0.144
#> SRR491074 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491075 1 0.3801 0.839 0.780 0.000 0.00 0.220
#> SRR491076 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491077 1 0.2149 0.989 0.912 0.000 0.00 0.088
#> SRR491078 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491079 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491080 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491081 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491082 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491083 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491084 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491085 1 0.2011 0.985 0.920 0.000 0.00 0.080
#> SRR491086 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491087 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491088 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491089 1 0.2216 0.989 0.908 0.000 0.00 0.092
#> SRR491090 1 0.2647 0.964 0.880 0.000 0.00 0.120
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR445718 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445719 2 0.0404 0.8685 0.000 0.988 0.000 0.000 0.012
#> SRR445720 2 0.0404 0.8685 0.000 0.988 0.000 0.000 0.012
#> SRR445721 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445722 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445723 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445724 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445725 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445726 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445727 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445728 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445729 2 0.0290 0.8726 0.000 0.992 0.000 0.000 0.008
#> SRR445730 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR445731 1 0.0955 0.9055 0.968 0.000 0.000 0.028 0.004
#> SRR490961 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490962 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490963 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490964 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490965 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490966 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490967 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490968 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490969 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490971 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490972 2 0.0000 0.8731 0.000 1.000 0.000 0.000 0.000
#> SRR490973 3 0.0609 0.9236 0.000 0.000 0.980 0.000 0.020
#> SRR490974 3 0.0794 0.9213 0.000 0.000 0.972 0.000 0.028
#> SRR490975 3 0.0794 0.9213 0.000 0.000 0.972 0.000 0.028
#> SRR490976 3 0.0510 0.9242 0.000 0.000 0.984 0.000 0.016
#> SRR490977 3 0.0510 0.9242 0.000 0.000 0.984 0.000 0.016
#> SRR490978 3 0.0609 0.9235 0.000 0.000 0.980 0.000 0.020
#> SRR490979 3 0.0510 0.9242 0.000 0.000 0.984 0.000 0.016
#> SRR490980 3 0.0703 0.9225 0.000 0.000 0.976 0.000 0.024
#> SRR490981 2 0.3534 0.0644 0.000 0.744 0.000 0.000 0.256
#> SRR490982 2 0.3876 -0.2964 0.000 0.684 0.000 0.000 0.316
#> SRR490983 2 0.3452 0.1211 0.000 0.756 0.000 0.000 0.244
#> SRR490984 2 0.3752 -0.1616 0.000 0.708 0.000 0.000 0.292
#> SRR490985 3 0.4030 0.6479 0.000 0.000 0.648 0.000 0.352
#> SRR490986 3 0.4030 0.6479 0.000 0.000 0.648 0.000 0.352
#> SRR490987 3 0.1851 0.8902 0.000 0.000 0.912 0.000 0.088
#> SRR490988 3 0.3684 0.7302 0.000 0.000 0.720 0.000 0.280
#> SRR490989 3 0.3480 0.7617 0.000 0.000 0.752 0.000 0.248
#> SRR490990 3 0.3983 0.6634 0.000 0.000 0.660 0.000 0.340
#> SRR490991 3 0.3913 0.6829 0.000 0.000 0.676 0.000 0.324
#> SRR490992 3 0.1478 0.9049 0.000 0.000 0.936 0.000 0.064
#> SRR490993 3 0.0162 0.9250 0.000 0.000 0.996 0.000 0.004
#> SRR490994 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR490995 2 0.5049 -0.9920 0.000 0.484 0.032 0.000 0.484
#> SRR490996 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR490997 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR490998 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491000 5 0.4980 0.0000 0.000 0.484 0.028 0.000 0.488
#> SRR491001 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491002 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491003 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491004 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491005 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491006 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491007 3 0.0000 0.9250 0.000 0.000 1.000 0.000 0.000
#> SRR491008 3 0.0162 0.9231 0.000 0.000 0.996 0.000 0.004
#> SRR491009 4 0.0162 0.9656 0.000 0.000 0.000 0.996 0.004
#> SRR491010 4 0.0162 0.9668 0.000 0.000 0.000 0.996 0.004
#> SRR491011 4 0.0290 0.9659 0.000 0.000 0.000 0.992 0.008
#> SRR491012 4 0.0000 0.9667 0.000 0.000 0.000 1.000 0.000
#> SRR491013 4 0.0290 0.9659 0.000 0.000 0.000 0.992 0.008
#> SRR491014 4 0.0290 0.9659 0.000 0.000 0.000 0.992 0.008
#> SRR491015 4 0.0404 0.9647 0.000 0.000 0.000 0.988 0.012
#> SRR491016 4 0.0000 0.9667 0.000 0.000 0.000 1.000 0.000
#> SRR491017 4 0.0404 0.9647 0.000 0.000 0.000 0.988 0.012
#> SRR491018 4 0.0162 0.9664 0.000 0.000 0.000 0.996 0.004
#> SRR491019 4 0.0404 0.9647 0.000 0.000 0.000 0.988 0.012
#> SRR491020 4 0.0000 0.9667 0.000 0.000 0.000 1.000 0.000
#> SRR491021 4 0.0162 0.9668 0.000 0.000 0.000 0.996 0.004
#> SRR491022 4 0.0404 0.9648 0.000 0.000 0.000 0.988 0.012
#> SRR491023 4 0.0404 0.9648 0.000 0.000 0.000 0.988 0.012
#> SRR491024 4 0.0162 0.9668 0.000 0.000 0.000 0.996 0.004
#> SRR491025 4 0.0162 0.9656 0.000 0.000 0.000 0.996 0.004
#> SRR491026 4 0.0162 0.9668 0.000 0.000 0.000 0.996 0.004
#> SRR491027 4 0.0162 0.9668 0.000 0.000 0.000 0.996 0.004
#> SRR491028 4 0.0000 0.9667 0.000 0.000 0.000 1.000 0.000
#> SRR491029 4 0.0955 0.9525 0.004 0.000 0.000 0.968 0.028
#> SRR491030 4 0.0290 0.9659 0.000 0.000 0.000 0.992 0.008
#> SRR491031 4 0.4090 0.7162 0.016 0.000 0.000 0.716 0.268
#> SRR491032 4 0.1124 0.9484 0.004 0.000 0.000 0.960 0.036
#> SRR491033 4 0.0000 0.9667 0.000 0.000 0.000 1.000 0.000
#> SRR491034 4 0.2864 0.8637 0.012 0.000 0.000 0.852 0.136
#> SRR491035 4 0.3852 0.7682 0.020 0.000 0.000 0.760 0.220
#> SRR491036 4 0.2674 0.8767 0.012 0.000 0.000 0.868 0.120
#> SRR491037 4 0.0609 0.9590 0.000 0.000 0.000 0.980 0.020
#> SRR491038 4 0.1041 0.9502 0.004 0.000 0.000 0.964 0.032
#> SRR491039 1 0.0794 0.9053 0.972 0.000 0.000 0.028 0.000
#> SRR491040 1 0.0992 0.9023 0.968 0.000 0.000 0.024 0.008
#> SRR491041 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491042 1 0.1251 0.9046 0.956 0.000 0.000 0.036 0.008
#> SRR491043 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491045 1 0.0794 0.9053 0.972 0.000 0.000 0.028 0.000
#> SRR491065 1 0.3283 0.8608 0.832 0.000 0.000 0.028 0.140
#> SRR491066 1 0.3995 0.8355 0.776 0.000 0.000 0.044 0.180
#> SRR491067 1 0.4728 0.7899 0.700 0.000 0.000 0.060 0.240
#> SRR491068 1 0.1981 0.8975 0.924 0.000 0.000 0.028 0.048
#> SRR491069 1 0.3848 0.8410 0.788 0.000 0.000 0.040 0.172
#> SRR491070 1 0.1830 0.9000 0.932 0.000 0.000 0.028 0.040
#> SRR491071 1 0.0703 0.9047 0.976 0.000 0.000 0.024 0.000
#> SRR491072 1 0.1399 0.9048 0.952 0.000 0.000 0.028 0.020
#> SRR491073 1 0.5928 0.6475 0.548 0.000 0.000 0.124 0.328
#> SRR491074 1 0.0955 0.9055 0.968 0.000 0.000 0.028 0.004
#> SRR491075 1 0.6406 0.5585 0.484 0.000 0.000 0.188 0.328
#> SRR491076 1 0.4713 0.7730 0.676 0.000 0.000 0.044 0.280
#> SRR491077 1 0.0703 0.9047 0.976 0.000 0.000 0.024 0.000
#> SRR491078 1 0.1399 0.9046 0.952 0.000 0.000 0.028 0.020
#> SRR491079 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491080 1 0.0703 0.9047 0.976 0.000 0.000 0.024 0.000
#> SRR491081 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491082 1 0.1082 0.9054 0.964 0.000 0.000 0.028 0.008
#> SRR491083 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491084 1 0.1082 0.9054 0.964 0.000 0.000 0.028 0.008
#> SRR491085 1 0.0865 0.9040 0.972 0.000 0.000 0.024 0.004
#> SRR491086 1 0.4644 0.7757 0.680 0.000 0.000 0.040 0.280
#> SRR491087 1 0.4313 0.8102 0.732 0.000 0.000 0.040 0.228
#> SRR491088 1 0.5405 0.7025 0.596 0.000 0.000 0.076 0.328
#> SRR491089 1 0.1485 0.9038 0.948 0.000 0.000 0.032 0.020
#> SRR491090 1 0.5599 0.6856 0.580 0.000 0.000 0.092 0.328
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR445718 2 0.0363 0.9876 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445719 2 0.0363 0.9876 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445720 2 0.0363 0.9876 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR445721 2 0.0260 0.9904 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR445722 2 0.0146 0.9924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445723 2 0.0146 0.9924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445724 2 0.0146 0.9924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445725 2 0.0146 0.9924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445726 2 0.0000 0.9930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445727 2 0.0146 0.9924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR445728 2 0.0000 0.9930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445729 2 0.0000 0.9930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR445730 1 0.0291 0.8669 0.992 0.000 0.000 0.004 0.004 0.000
#> SRR445731 1 0.0692 0.8675 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR490961 2 0.0260 0.9913 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR490962 2 0.0146 0.9925 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR490963 2 0.0260 0.9913 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR490964 2 0.0260 0.9913 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR490965 2 0.0363 0.9894 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR490966 2 0.0363 0.9894 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR490967 2 0.0363 0.9894 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR490968 2 0.0363 0.9894 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR490969 2 0.0000 0.9930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490970 2 0.0000 0.9930 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR490971 2 0.0146 0.9922 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR490972 2 0.0260 0.9911 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR490973 3 0.1333 0.9345 0.000 0.000 0.944 0.000 0.008 0.048
#> SRR490974 3 0.1701 0.9161 0.000 0.000 0.920 0.000 0.008 0.072
#> SRR490975 3 0.1701 0.9161 0.000 0.000 0.920 0.000 0.008 0.072
#> SRR490976 3 0.1124 0.9413 0.000 0.000 0.956 0.000 0.008 0.036
#> SRR490977 3 0.0777 0.9468 0.000 0.000 0.972 0.000 0.004 0.024
#> SRR490978 3 0.1196 0.9391 0.000 0.000 0.952 0.000 0.008 0.040
#> SRR490979 3 0.1010 0.9426 0.000 0.000 0.960 0.000 0.004 0.036
#> SRR490980 3 0.1462 0.9291 0.000 0.000 0.936 0.000 0.008 0.056
#> SRR490981 6 0.3823 0.2859 0.000 0.436 0.000 0.000 0.000 0.564
#> SRR490982 6 0.3620 0.4377 0.000 0.352 0.000 0.000 0.000 0.648
#> SRR490983 6 0.3869 0.1076 0.000 0.500 0.000 0.000 0.000 0.500
#> SRR490984 6 0.3672 0.4151 0.000 0.368 0.000 0.000 0.000 0.632
#> SRR490985 6 0.3531 0.5084 0.000 0.000 0.328 0.000 0.000 0.672
#> SRR490986 6 0.3531 0.5082 0.000 0.000 0.328 0.000 0.000 0.672
#> SRR490987 3 0.3189 0.6723 0.000 0.000 0.760 0.000 0.004 0.236
#> SRR490988 6 0.3782 0.3866 0.000 0.000 0.412 0.000 0.000 0.588
#> SRR490989 6 0.3862 0.2093 0.000 0.000 0.476 0.000 0.000 0.524
#> SRR490990 6 0.3659 0.4722 0.000 0.000 0.364 0.000 0.000 0.636
#> SRR490991 6 0.3684 0.4617 0.000 0.000 0.372 0.000 0.000 0.628
#> SRR490992 3 0.2743 0.7989 0.000 0.000 0.828 0.000 0.008 0.164
#> SRR490993 3 0.0363 0.9504 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR490994 3 0.0260 0.9495 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR490995 6 0.4059 0.4745 0.000 0.088 0.004 0.000 0.148 0.760
#> SRR490996 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490997 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR490998 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491000 6 0.4059 0.4745 0.000 0.088 0.004 0.000 0.148 0.760
#> SRR491001 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491002 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491003 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491004 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491005 3 0.0146 0.9520 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR491006 3 0.0000 0.9521 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR491007 3 0.0000 0.9521 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR491008 3 0.0260 0.9495 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR491009 4 0.0458 0.9386 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR491010 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491011 4 0.0260 0.9391 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR491012 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491013 4 0.0260 0.9391 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR491014 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491015 4 0.0260 0.9391 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR491016 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491017 4 0.0363 0.9379 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR491018 4 0.0000 0.9414 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491019 4 0.0260 0.9391 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR491020 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491021 4 0.0146 0.9416 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR491022 4 0.1088 0.9267 0.000 0.000 0.000 0.960 0.024 0.016
#> SRR491023 4 0.1168 0.9251 0.000 0.000 0.000 0.956 0.028 0.016
#> SRR491024 4 0.0146 0.9414 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491025 4 0.0458 0.9386 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR491026 4 0.0000 0.9414 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR491027 4 0.0146 0.9419 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491028 4 0.1074 0.9304 0.000 0.000 0.000 0.960 0.028 0.012
#> SRR491029 4 0.1616 0.9023 0.020 0.000 0.000 0.932 0.048 0.000
#> SRR491030 4 0.0146 0.9414 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR491031 5 0.5166 0.1479 0.060 0.000 0.000 0.400 0.528 0.012
#> SRR491032 4 0.2043 0.8983 0.012 0.000 0.000 0.912 0.064 0.012
#> SRR491033 4 0.0458 0.9386 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR491034 4 0.4648 0.5309 0.048 0.000 0.000 0.656 0.284 0.012
#> SRR491035 4 0.5110 0.2092 0.056 0.000 0.000 0.536 0.396 0.012
#> SRR491036 4 0.3776 0.6913 0.052 0.000 0.000 0.760 0.188 0.000
#> SRR491037 4 0.0713 0.9336 0.000 0.000 0.000 0.972 0.028 0.000
#> SRR491038 4 0.2009 0.8809 0.024 0.000 0.000 0.908 0.068 0.000
#> SRR491039 1 0.0291 0.8670 0.992 0.000 0.000 0.004 0.004 0.000
#> SRR491040 1 0.1219 0.8317 0.948 0.000 0.000 0.004 0.048 0.000
#> SRR491041 1 0.1010 0.8446 0.960 0.000 0.000 0.004 0.036 0.000
#> SRR491042 1 0.1257 0.8578 0.952 0.000 0.000 0.020 0.028 0.000
#> SRR491043 1 0.0692 0.8572 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR491045 1 0.0508 0.8677 0.984 0.000 0.000 0.004 0.012 0.000
#> SRR491065 1 0.3373 0.5721 0.744 0.000 0.000 0.008 0.248 0.000
#> SRR491066 1 0.3952 0.3936 0.672 0.000 0.000 0.020 0.308 0.000
#> SRR491067 1 0.4439 -0.1654 0.540 0.000 0.000 0.028 0.432 0.000
#> SRR491068 1 0.2350 0.7972 0.880 0.000 0.000 0.020 0.100 0.000
#> SRR491069 1 0.3969 0.3837 0.668 0.000 0.000 0.020 0.312 0.000
#> SRR491070 1 0.2199 0.8115 0.892 0.000 0.000 0.020 0.088 0.000
#> SRR491071 1 0.0405 0.8674 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR491072 1 0.1588 0.8415 0.924 0.000 0.000 0.004 0.072 0.000
#> SRR491073 5 0.4305 0.7648 0.260 0.000 0.000 0.056 0.684 0.000
#> SRR491074 1 0.0603 0.8678 0.980 0.000 0.000 0.004 0.016 0.000
#> SRR491075 5 0.4582 0.7366 0.216 0.000 0.000 0.100 0.684 0.000
#> SRR491076 5 0.4261 0.5596 0.408 0.000 0.000 0.020 0.572 0.000
#> SRR491077 1 0.0692 0.8678 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR491078 1 0.1524 0.8486 0.932 0.000 0.000 0.008 0.060 0.000
#> SRR491079 1 0.0692 0.8562 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR491080 1 0.0603 0.8657 0.980 0.000 0.000 0.004 0.016 0.000
#> SRR491081 1 0.0405 0.8625 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR491082 1 0.1049 0.8637 0.960 0.000 0.000 0.008 0.032 0.000
#> SRR491083 1 0.0692 0.8564 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR491084 1 0.0993 0.8647 0.964 0.000 0.000 0.012 0.024 0.000
#> SRR491085 1 0.0935 0.8477 0.964 0.000 0.000 0.004 0.032 0.000
#> SRR491086 5 0.4269 0.5500 0.412 0.000 0.000 0.020 0.568 0.000
#> SRR491087 1 0.4246 0.0331 0.580 0.000 0.000 0.020 0.400 0.000
#> SRR491088 5 0.4268 0.7561 0.272 0.000 0.004 0.040 0.684 0.000
#> SRR491089 1 0.1594 0.8476 0.932 0.000 0.000 0.016 0.052 0.000
#> SRR491090 5 0.4386 0.7645 0.260 0.000 0.004 0.052 0.684 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.
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