Date: 2019-12-26 00:39:16 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 15185 rows and 159 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] 15185 159
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:kmeans | 2 | 1.000 | 0.982 | 0.986 | ** | |
| SD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:hclust | 6 | 1.000 | 0.984 | 0.986 | ** | 2,3,4,5 |
| MAD:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:skmeans | 4 | 1.000 | 0.991 | 0.992 | ** | 2,3 |
| MAD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:pam | 4 | 1.000 | 0.983 | 0.993 | ** | 2 |
| ATC:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:skmeans | 6 | 0.995 | 0.989 | 0.976 | ** | 2,4,5 |
| ATC:hclust | 6 | 0.968 | 0.976 | 0.983 | ** | 2,3,4,5 |
| ATC:skmeans | 5 | 0.949 | 0.974 | 0.959 | * | 2,3,4 |
| MAD:pam | 5 | 0.930 | 0.895 | 0.924 | * | 2,4 |
| SD:hclust | 6 | 0.926 | 0.951 | 0.954 | * | 2,3,4,5 |
| SD:pam | 6 | 0.924 | 0.881 | 0.914 | * | 2,4,5 |
| CV:pam | 6 | 0.920 | 0.968 | 0.966 | * | 2 |
| SD:skmeans | 6 | 0.911 | 0.913 | 0.890 | * | 2,3,4 |
| ATC:mclust | 5 | 0.902 | 0.910 | 0.941 | * | 2 |
| CV:hclust | 5 | 0.859 | 0.934 | 0.935 | ||
| CV:NMF | 2 | 0.725 | 0.893 | 0.946 | ||
| CV:kmeans | 2 | 0.426 | 0.719 | 0.852 |
**: 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 1.000 1.000 0.504 0.497 0.497
#> CV:NMF 2 0.725 0.893 0.946 0.494 0.503 0.503
#> MAD:NMF 2 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:NMF 2 1.000 1.000 1.000 0.504 0.497 0.497
#> SD:skmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> CV:skmeans 2 1.000 0.982 0.986 0.504 0.497 0.497
#> MAD:skmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:skmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> 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 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:mclust 2 1.000 1.000 1.000 0.504 0.497 0.497
#> SD:kmeans 2 1.000 0.982 0.986 0.504 0.497 0.497
#> CV:kmeans 2 0.426 0.719 0.852 0.497 0.497 0.497
#> MAD:kmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:kmeans 2 1.000 1.000 1.000 0.504 0.497 0.497
#> SD:pam 2 1.000 1.000 1.000 0.504 0.497 0.497
#> CV:pam 2 1.000 1.000 1.000 0.504 0.497 0.497
#> MAD:pam 2 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:pam 2 1.000 1.000 1.000 0.504 0.497 0.497
#> SD:hclust 2 1.000 0.958 0.960 0.502 0.497 0.497
#> CV:hclust 2 0.515 0.767 0.909 0.471 0.519 0.519
#> MAD:hclust 2 1.000 1.000 1.000 0.504 0.497 0.497
#> ATC:hclust 2 1.000 1.000 1.000 0.504 0.497 0.497
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.680 0.771 0.883 0.288 0.783 0.589
#> CV:NMF 3 0.568 0.711 0.808 0.327 0.671 0.438
#> MAD:NMF 3 0.754 0.810 0.893 0.280 0.783 0.589
#> ATC:NMF 3 0.770 0.894 0.947 0.297 0.783 0.589
#> SD:skmeans 3 1.000 0.992 0.988 0.236 0.882 0.762
#> CV:skmeans 3 0.879 0.897 0.947 0.317 0.763 0.557
#> MAD:skmeans 3 1.000 0.992 0.984 0.235 0.882 0.762
#> ATC:skmeans 3 0.920 0.914 0.955 0.185 0.924 0.846
#> SD:mclust 3 0.784 0.825 0.892 0.221 0.906 0.811
#> CV:mclust 3 0.666 0.732 0.816 0.218 0.878 0.754
#> MAD:mclust 3 0.734 0.866 0.911 0.215 0.906 0.811
#> ATC:mclust 3 0.811 0.914 0.888 0.189 0.904 0.808
#> SD:kmeans 3 0.633 0.756 0.750 0.257 1.000 1.000
#> CV:kmeans 3 0.516 0.593 0.790 0.316 0.702 0.470
#> MAD:kmeans 3 0.670 0.353 0.761 0.243 0.976 0.952
#> ATC:kmeans 3 0.753 0.492 0.801 0.237 0.954 0.908
#> SD:pam 3 0.751 0.910 0.829 0.246 0.877 0.753
#> CV:pam 3 0.873 0.976 0.986 0.251 0.877 0.753
#> MAD:pam 3 0.751 0.901 0.812 0.246 0.877 0.753
#> ATC:pam 3 0.749 0.910 0.835 0.240 0.878 0.754
#> SD:hclust 3 0.920 0.949 0.965 0.247 0.882 0.762
#> CV:hclust 3 0.577 0.700 0.789 0.368 0.780 0.605
#> MAD:hclust 3 1.000 0.996 0.997 0.235 0.882 0.762
#> ATC:hclust 3 1.000 0.998 0.999 0.152 0.924 0.846
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.869 0.961 0.896 0.0843 0.765 0.435
#> CV:NMF 4 0.690 0.838 0.853 0.1227 0.877 0.657
#> MAD:NMF 4 0.829 0.930 0.854 0.0865 0.765 0.435
#> ATC:NMF 4 0.750 0.804 0.894 0.0169 0.872 0.691
#> SD:skmeans 4 1.000 0.995 0.996 0.1953 0.878 0.677
#> CV:skmeans 4 1.000 0.994 0.996 0.1221 0.800 0.495
#> MAD:skmeans 4 1.000 0.991 0.992 0.1951 0.878 0.677
#> ATC:skmeans 4 0.920 0.907 0.953 0.2035 0.878 0.709
#> SD:mclust 4 0.697 0.718 0.809 0.1263 0.934 0.836
#> CV:mclust 4 0.553 0.542 0.729 0.1356 0.858 0.672
#> MAD:mclust 4 0.668 0.769 0.830 0.1275 0.934 0.836
#> ATC:mclust 4 0.839 0.932 0.919 0.1537 0.906 0.766
#> SD:kmeans 4 0.629 0.716 0.754 0.1201 0.758 0.513
#> CV:kmeans 4 0.662 0.765 0.829 0.1187 0.890 0.684
#> MAD:kmeans 4 0.642 0.754 0.771 0.1316 0.755 0.497
#> ATC:kmeans 4 0.660 0.876 0.809 0.1207 0.775 0.516
#> SD:pam 4 1.000 0.996 0.998 0.1984 0.874 0.663
#> CV:pam 4 0.764 0.917 0.925 0.1789 0.878 0.673
#> MAD:pam 4 1.000 0.997 0.997 0.1978 0.874 0.663
#> ATC:pam 4 1.000 0.983 0.993 0.1959 0.877 0.673
#> SD:hclust 4 0.920 0.970 0.981 0.1897 0.878 0.677
#> CV:hclust 4 0.781 0.895 0.937 0.1558 0.878 0.676
#> MAD:hclust 4 1.000 0.990 0.993 0.1964 0.878 0.677
#> ATC:hclust 4 1.000 0.998 0.999 0.2105 0.878 0.709
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.786 0.903 0.883 0.0331 1.000 1.000
#> CV:NMF 5 0.891 0.905 0.922 0.0230 1.000 1.000
#> MAD:NMF 5 0.779 0.887 0.848 0.0300 1.000 1.000
#> ATC:NMF 5 0.595 0.575 0.824 0.0577 0.940 0.838
#> SD:skmeans 5 0.879 0.898 0.883 0.0542 0.959 0.841
#> CV:skmeans 5 1.000 0.990 0.984 0.0544 0.959 0.841
#> MAD:skmeans 5 0.838 0.373 0.623 0.0548 0.837 0.486
#> ATC:skmeans 5 0.949 0.974 0.959 0.0531 0.939 0.795
#> SD:mclust 5 0.657 0.502 0.758 0.1166 0.878 0.637
#> CV:mclust 5 0.566 0.413 0.649 0.0953 0.739 0.380
#> MAD:mclust 5 0.715 0.698 0.794 0.1211 0.884 0.656
#> ATC:mclust 5 0.902 0.910 0.941 0.0976 0.933 0.782
#> SD:kmeans 5 0.659 0.649 0.722 0.0735 0.918 0.724
#> CV:kmeans 5 0.720 0.422 0.604 0.0600 0.874 0.590
#> MAD:kmeans 5 0.664 0.672 0.712 0.0793 0.918 0.725
#> ATC:kmeans 5 0.592 0.679 0.700 0.0665 0.959 0.841
#> SD:pam 5 0.928 0.907 0.927 0.0253 0.896 0.644
#> CV:pam 5 0.866 0.921 0.939 0.0376 0.976 0.905
#> MAD:pam 5 0.930 0.895 0.924 0.0269 0.896 0.644
#> ATC:pam 5 0.852 0.775 0.871 0.0505 0.983 0.932
#> SD:hclust 5 0.920 0.951 0.958 0.0537 0.959 0.841
#> CV:hclust 5 0.859 0.934 0.935 0.0520 0.962 0.850
#> MAD:hclust 5 1.000 0.984 0.984 0.0546 0.959 0.841
#> ATC:hclust 5 1.000 0.999 0.999 0.1154 0.918 0.727
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.760 0.879 0.892 0.0340 1.000 1.000
#> CV:NMF 6 0.854 0.893 0.914 0.0195 1.000 1.000
#> MAD:NMF 6 0.684 0.818 0.851 0.0315 0.988 0.953
#> ATC:NMF 6 0.612 0.633 0.755 0.0814 0.838 0.545
#> SD:skmeans 6 0.911 0.913 0.890 0.0499 0.920 0.660
#> CV:skmeans 6 0.995 0.989 0.976 0.0484 0.959 0.811
#> MAD:skmeans 6 0.859 0.888 0.907 0.0501 0.838 0.415
#> ATC:skmeans 6 0.870 0.885 0.905 0.0299 1.000 1.000
#> SD:mclust 6 0.731 0.529 0.706 0.0430 0.871 0.509
#> CV:mclust 6 0.677 0.579 0.720 0.0384 0.792 0.380
#> MAD:mclust 6 0.755 0.686 0.789 0.0510 0.944 0.751
#> ATC:mclust 6 0.779 0.841 0.845 0.0408 0.979 0.913
#> SD:kmeans 6 0.750 0.607 0.662 0.0469 0.861 0.546
#> CV:kmeans 6 0.758 0.649 0.692 0.0409 0.869 0.541
#> MAD:kmeans 6 0.717 0.520 0.612 0.0362 0.875 0.558
#> ATC:kmeans 6 0.573 0.726 0.720 0.0535 0.925 0.707
#> SD:pam 6 0.924 0.881 0.914 0.0268 0.894 0.619
#> CV:pam 6 0.920 0.968 0.966 0.0523 0.959 0.821
#> MAD:pam 6 0.860 0.866 0.908 0.0277 0.894 0.619
#> ATC:pam 6 0.857 0.746 0.870 0.0465 0.917 0.677
#> SD:hclust 6 0.926 0.951 0.954 0.0508 0.959 0.811
#> CV:hclust 6 0.838 0.923 0.937 0.0518 0.959 0.812
#> MAD:hclust 6 1.000 0.984 0.986 0.0519 0.959 0.811
#> ATC:hclust 6 0.968 0.976 0.983 0.0527 0.959 0.812
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 15185 rows and 159 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.958 0.960 0.5019 0.497 0.497
#> 3 3 0.920 0.949 0.965 0.2468 0.882 0.762
#> 4 4 0.920 0.970 0.981 0.1897 0.878 0.677
#> 5 5 0.920 0.951 0.958 0.0537 0.959 0.841
#> 6 6 0.926 0.951 0.954 0.0508 0.959 0.811
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
#> SRR1706767 1 0.000 0.947 1.00 0.00
#> SRR1706768 1 0.000 0.947 1.00 0.00
#> SRR1706769 1 0.000 0.947 1.00 0.00
#> SRR1706770 1 0.000 0.947 1.00 0.00
#> SRR1706771 1 0.000 0.947 1.00 0.00
#> SRR1706772 1 0.000 0.947 1.00 0.00
#> SRR1706773 1 0.000 0.947 1.00 0.00
#> SRR1706774 1 0.000 0.947 1.00 0.00
#> SRR1706775 1 0.402 0.965 0.92 0.08
#> SRR1706776 1 0.402 0.965 0.92 0.08
#> SRR1706777 1 0.402 0.965 0.92 0.08
#> SRR1706778 1 0.402 0.965 0.92 0.08
#> SRR1706779 1 0.402 0.965 0.92 0.08
#> SRR1706780 1 0.402 0.965 0.92 0.08
#> SRR1706781 1 0.402 0.965 0.92 0.08
#> SRR1706782 1 0.402 0.965 0.92 0.08
#> SRR1706783 1 0.402 0.965 0.92 0.08
#> SRR1706784 1 0.402 0.965 0.92 0.08
#> SRR1706785 1 0.402 0.965 0.92 0.08
#> SRR1706786 1 0.402 0.965 0.92 0.08
#> SRR1706787 1 0.000 0.947 1.00 0.00
#> SRR1706788 1 0.000 0.947 1.00 0.00
#> SRR1706789 1 0.000 0.947 1.00 0.00
#> SRR1706790 1 0.000 0.947 1.00 0.00
#> SRR1706791 1 0.000 0.947 1.00 0.00
#> SRR1706792 1 0.000 0.947 1.00 0.00
#> SRR1706793 1 0.000 0.947 1.00 0.00
#> SRR1706794 1 0.000 0.947 1.00 0.00
#> SRR1706795 1 0.402 0.965 0.92 0.08
#> SRR1706796 1 0.402 0.965 0.92 0.08
#> SRR1706797 1 0.402 0.965 0.92 0.08
#> SRR1706798 1 0.402 0.965 0.92 0.08
#> SRR1706799 1 0.402 0.965 0.92 0.08
#> SRR1706800 1 0.402 0.965 0.92 0.08
#> SRR1706801 1 0.402 0.965 0.92 0.08
#> SRR1706802 1 0.402 0.965 0.92 0.08
#> SRR1706803 1 0.402 0.965 0.92 0.08
#> SRR1706804 1 0.402 0.965 0.92 0.08
#> SRR1706805 1 0.402 0.965 0.92 0.08
#> SRR1706806 1 0.402 0.965 0.92 0.08
#> SRR1706811 1 0.000 0.947 1.00 0.00
#> SRR1706812 1 0.000 0.947 1.00 0.00
#> SRR1706813 1 0.000 0.947 1.00 0.00
#> SRR1706814 1 0.000 0.947 1.00 0.00
#> SRR1706807 1 0.000 0.947 1.00 0.00
#> SRR1706808 1 0.000 0.947 1.00 0.00
#> SRR1706809 1 0.000 0.947 1.00 0.00
#> SRR1706810 1 0.000 0.947 1.00 0.00
#> SRR1706815 1 0.402 0.965 0.92 0.08
#> SRR1706816 1 0.402 0.965 0.92 0.08
#> SRR1706817 1 0.402 0.965 0.92 0.08
#> SRR1706818 1 0.402 0.965 0.92 0.08
#> SRR1706819 1 0.402 0.965 0.92 0.08
#> SRR1706820 1 0.402 0.965 0.92 0.08
#> SRR1706821 1 0.402 0.965 0.92 0.08
#> SRR1706822 1 0.402 0.965 0.92 0.08
#> SRR1706823 1 0.402 0.965 0.92 0.08
#> SRR1706824 1 0.402 0.965 0.92 0.08
#> SRR1706825 1 0.402 0.965 0.92 0.08
#> SRR1706826 1 0.402 0.965 0.92 0.08
#> SRR1706827 1 0.000 0.947 1.00 0.00
#> SRR1706828 1 0.000 0.947 1.00 0.00
#> SRR1706829 1 0.000 0.947 1.00 0.00
#> SRR1706830 1 0.000 0.947 1.00 0.00
#> SRR1706835 1 0.402 0.965 0.92 0.08
#> SRR1706836 1 0.402 0.965 0.92 0.08
#> SRR1706837 1 0.402 0.965 0.92 0.08
#> SRR1706838 1 0.402 0.965 0.92 0.08
#> SRR1706831 1 0.000 0.947 1.00 0.00
#> SRR1706832 1 0.000 0.947 1.00 0.00
#> SRR1706833 1 0.000 0.947 1.00 0.00
#> SRR1706834 1 0.000 0.947 1.00 0.00
#> SRR1706839 1 0.402 0.965 0.92 0.08
#> SRR1706840 1 0.402 0.965 0.92 0.08
#> SRR1706841 1 0.402 0.965 0.92 0.08
#> SRR1706842 1 0.402 0.965 0.92 0.08
#> SRR1706847 2 0.402 0.947 0.08 0.92
#> SRR1706848 2 0.402 0.947 0.08 0.92
#> SRR1706849 2 0.402 0.947 0.08 0.92
#> SRR1706850 2 0.402 0.947 0.08 0.92
#> SRR1706843 1 0.402 0.965 0.92 0.08
#> SRR1706844 1 0.402 0.965 0.92 0.08
#> SRR1706845 1 0.402 0.965 0.92 0.08
#> SRR1706846 1 0.402 0.965 0.92 0.08
#> SRR1706851 2 0.402 0.947 0.08 0.92
#> SRR1706852 2 0.402 0.947 0.08 0.92
#> SRR1706853 2 0.402 0.947 0.08 0.92
#> SRR1706854 2 0.402 0.947 0.08 0.92
#> SRR1706855 2 0.000 0.965 0.00 1.00
#> SRR1706856 2 0.000 0.965 0.00 1.00
#> SRR1706857 2 0.000 0.965 0.00 1.00
#> SRR1706858 2 0.000 0.965 0.00 1.00
#> SRR1706859 2 0.000 0.965 0.00 1.00
#> SRR1706860 2 0.000 0.965 0.00 1.00
#> SRR1706861 2 0.000 0.965 0.00 1.00
#> SRR1706862 2 0.000 0.965 0.00 1.00
#> SRR1706867 2 0.402 0.947 0.08 0.92
#> SRR1706869 2 0.402 0.947 0.08 0.92
#> SRR1706870 2 0.402 0.947 0.08 0.92
#> SRR1706863 2 0.000 0.965 0.00 1.00
#> SRR1706864 2 0.000 0.965 0.00 1.00
#> SRR1706865 2 0.000 0.965 0.00 1.00
#> SRR1706866 2 0.000 0.965 0.00 1.00
#> SRR1706871 2 0.402 0.947 0.08 0.92
#> SRR1706872 2 0.402 0.947 0.08 0.92
#> SRR1706873 2 0.402 0.947 0.08 0.92
#> SRR1706874 2 0.402 0.947 0.08 0.92
#> SRR1706879 2 0.000 0.965 0.00 1.00
#> SRR1706880 2 0.000 0.965 0.00 1.00
#> SRR1706881 2 0.000 0.965 0.00 1.00
#> SRR1706882 2 0.000 0.965 0.00 1.00
#> SRR1706883 2 0.000 0.965 0.00 1.00
#> SRR1706884 2 0.000 0.965 0.00 1.00
#> SRR1706885 2 0.000 0.965 0.00 1.00
#> SRR1706886 2 0.000 0.965 0.00 1.00
#> SRR1706875 2 0.000 0.965 0.00 1.00
#> SRR1706876 2 0.000 0.965 0.00 1.00
#> SRR1706877 2 0.000 0.965 0.00 1.00
#> SRR1706878 2 0.000 0.965 0.00 1.00
#> SRR1706887 2 0.402 0.947 0.08 0.92
#> SRR1706888 2 0.402 0.947 0.08 0.92
#> SRR1706889 2 0.402 0.947 0.08 0.92
#> SRR1706890 2 0.402 0.947 0.08 0.92
#> SRR1706891 2 0.402 0.947 0.08 0.92
#> SRR1706892 2 0.402 0.947 0.08 0.92
#> SRR1706893 2 0.402 0.947 0.08 0.92
#> SRR1706894 2 0.402 0.947 0.08 0.92
#> SRR1706895 2 0.000 0.965 0.00 1.00
#> SRR1706896 2 0.000 0.965 0.00 1.00
#> SRR1706897 2 0.000 0.965 0.00 1.00
#> SRR1706898 2 0.000 0.965 0.00 1.00
#> SRR1706899 2 0.000 0.965 0.00 1.00
#> SRR1706900 2 0.000 0.965 0.00 1.00
#> SRR1706901 2 0.000 0.965 0.00 1.00
#> SRR1706902 2 0.000 0.965 0.00 1.00
#> SRR1706907 2 0.402 0.947 0.08 0.92
#> SRR1706908 2 0.402 0.947 0.08 0.92
#> SRR1706909 2 0.402 0.947 0.08 0.92
#> SRR1706910 2 0.402 0.947 0.08 0.92
#> SRR1706903 2 0.000 0.965 0.00 1.00
#> SRR1706904 2 0.000 0.965 0.00 1.00
#> SRR1706905 2 0.000 0.965 0.00 1.00
#> SRR1706906 2 0.000 0.965 0.00 1.00
#> SRR1706911 2 0.402 0.947 0.08 0.92
#> SRR1706912 2 0.402 0.947 0.08 0.92
#> SRR1706913 2 0.402 0.947 0.08 0.92
#> SRR1706914 2 0.402 0.947 0.08 0.92
#> SRR1706919 2 0.000 0.965 0.00 1.00
#> SRR1706920 2 0.000 0.965 0.00 1.00
#> SRR1706921 2 0.000 0.965 0.00 1.00
#> SRR1706922 2 0.000 0.965 0.00 1.00
#> SRR1706915 2 0.000 0.965 0.00 1.00
#> SRR1706916 2 0.000 0.965 0.00 1.00
#> SRR1706917 2 0.000 0.965 0.00 1.00
#> SRR1706918 2 0.000 0.965 0.00 1.00
#> SRR1706923 2 0.000 0.965 0.00 1.00
#> SRR1706924 2 0.000 0.965 0.00 1.00
#> SRR1706925 2 0.000 0.965 0.00 1.00
#> SRR1706926 2 0.000 0.965 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706768 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706769 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706770 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706771 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706772 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706773 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706774 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706775 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706776 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706777 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706778 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706779 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706780 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706781 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706782 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706783 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706784 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706785 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706786 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706787 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706788 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706789 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706790 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706791 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706792 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706793 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706794 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706795 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706796 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706797 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706798 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706799 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706800 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706801 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706802 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706803 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706804 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706805 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706806 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706811 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706812 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706813 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706814 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706807 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706808 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706809 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706810 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706815 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706816 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706817 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706818 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706819 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706820 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706821 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706822 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706823 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706824 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706825 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706826 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706827 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706828 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706829 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706830 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706835 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706836 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706837 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706838 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706831 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706832 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706833 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706834 1 0.254 0.947 0.92 0.000 0.080
#> SRR1706839 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706840 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706841 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706842 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706847 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706848 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706849 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706850 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706843 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706844 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706845 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706846 1 0.000 0.966 1.00 0.000 0.000
#> SRR1706851 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706852 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706853 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706854 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706855 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706856 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706857 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706858 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706859 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706860 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706861 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706862 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706867 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706869 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706870 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706863 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706864 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706865 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706866 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706871 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706872 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706873 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706874 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706879 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706880 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706881 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706882 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706883 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706884 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706885 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706886 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706875 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706876 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706877 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706878 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706887 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706888 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706889 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706890 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706891 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706892 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706893 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706894 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706895 2 0.412 0.853 0.00 0.832 0.168
#> SRR1706896 2 0.412 0.853 0.00 0.832 0.168
#> SRR1706897 2 0.412 0.853 0.00 0.832 0.168
#> SRR1706898 2 0.412 0.853 0.00 0.832 0.168
#> SRR1706899 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706900 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706901 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706902 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706907 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706908 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706909 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706910 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706903 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706904 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706905 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706906 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706911 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706912 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706913 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706914 3 0.000 1.000 0.00 0.000 1.000
#> SRR1706919 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706920 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706921 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706922 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706915 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706916 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706917 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706918 2 0.450 0.833 0.00 0.804 0.196
#> SRR1706923 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706924 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706925 2 0.000 0.932 0.00 1.000 0.000
#> SRR1706926 2 0.000 0.932 0.00 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706772 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706773 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706774 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706775 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706776 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706777 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706778 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706779 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706792 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706793 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706794 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706795 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706796 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706797 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706798 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706799 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706812 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706813 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706814 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706815 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706816 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706817 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706818 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706819 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706835 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706836 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706837 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706838 1 0.0188 0.997 0.996 0.000 0.000 0.004
#> SRR1706831 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706832 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706833 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706834 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706839 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706843 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.999 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706856 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706857 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706858 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706859 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706863 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706876 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706877 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706878 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706891 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.3266 0.853 0.000 0.832 0.168 0.000
#> SRR1706896 2 0.3266 0.853 0.000 0.832 0.168 0.000
#> SRR1706897 2 0.3266 0.853 0.000 0.832 0.168 0.000
#> SRR1706898 2 0.3266 0.853 0.000 0.832 0.168 0.000
#> SRR1706899 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706903 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706915 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706916 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706917 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706918 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> SRR1706923 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.932 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706772 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706773 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706774 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706775 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706776 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706777 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706778 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706779 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706794 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706796 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706797 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706798 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706799 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706812 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706813 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706814 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706815 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706816 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706817 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706818 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706819 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706836 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706837 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706838 1 0.0162 0.997 0.996 0.000 0.000 0.004 0.000
#> SRR1706831 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.998 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706852 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706853 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706854 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706855 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706856 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706857 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706858 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706859 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706860 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706861 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706862 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706867 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706863 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706864 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706865 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706866 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706871 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706872 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706873 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706874 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706879 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706880 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706881 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706882 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706883 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706884 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706885 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706886 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706875 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706876 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706877 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706878 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706887 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706888 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706889 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706890 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706891 3 0.2891 0.898 0.000 0.176 0.824 0.000 0.000
#> SRR1706892 3 0.2891 0.898 0.000 0.176 0.824 0.000 0.000
#> SRR1706893 3 0.2891 0.898 0.000 0.176 0.824 0.000 0.000
#> SRR1706894 3 0.2891 0.898 0.000 0.176 0.824 0.000 0.000
#> SRR1706895 2 0.0963 0.870 0.000 0.964 0.000 0.000 0.036
#> SRR1706896 2 0.0963 0.870 0.000 0.964 0.000 0.000 0.036
#> SRR1706897 2 0.0963 0.870 0.000 0.964 0.000 0.000 0.036
#> SRR1706898 2 0.0963 0.870 0.000 0.964 0.000 0.000 0.036
#> SRR1706899 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706900 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706901 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706902 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706907 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.0000 0.893 0.000 0.000 1.000 0.000 0.000
#> SRR1706903 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706904 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706905 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706906 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706911 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706912 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706913 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706914 3 0.3143 0.894 0.000 0.204 0.796 0.000 0.000
#> SRR1706919 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706920 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706921 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706922 2 0.3143 0.862 0.000 0.796 0.000 0.000 0.204
#> SRR1706915 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706916 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706917 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706918 2 0.0290 0.866 0.000 0.992 0.000 0.000 0.008
#> SRR1706923 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706924 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706925 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706926 5 0.0000 1.000 0.000 0.000 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
#> SRR1706767 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706768 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706769 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706770 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706771 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706772 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706773 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706774 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706775 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706776 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706777 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706778 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706779 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706780 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706781 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706782 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706783 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706784 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706785 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706786 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706787 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706788 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706789 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706790 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706791 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706792 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706793 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706794 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706795 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706796 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706797 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706798 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706799 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706800 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706801 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706802 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706803 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706804 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706805 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706806 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706811 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706812 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706813 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706814 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706807 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706808 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706809 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706810 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706815 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706816 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706817 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706818 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706819 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706820 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706821 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706822 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706823 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706824 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706825 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706826 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706827 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706828 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706829 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706830 4 0.0000 0.998 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706835 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706836 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706837 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706838 5 0.0000 0.997 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706831 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706832 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706833 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706834 4 0.0146 0.998 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706839 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706840 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706841 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706842 5 0.0146 0.997 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1706847 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706848 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706849 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706850 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706843 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706844 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706845 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706846 1 0.1007 1.000 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1706851 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706852 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706853 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706854 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706855 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706856 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706857 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706858 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706859 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706860 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706861 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706862 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706867 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706869 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706870 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706863 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706871 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706872 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706873 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706874 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706879 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706880 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706881 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706882 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706883 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706875 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706876 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706877 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706878 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706887 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706888 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706889 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706890 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706891 3 0.2778 0.897 0.008 0.000 0.824 0.000 0.000 0.168
#> SRR1706892 3 0.2778 0.897 0.008 0.000 0.824 0.000 0.000 0.168
#> SRR1706893 3 0.2778 0.897 0.008 0.000 0.824 0.000 0.000 0.168
#> SRR1706894 3 0.2778 0.897 0.008 0.000 0.824 0.000 0.000 0.168
#> SRR1706895 6 0.1007 0.868 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR1706896 6 0.1007 0.868 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR1706897 6 0.1007 0.868 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR1706898 6 0.1007 0.868 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR1706899 6 0.3354 0.855 0.036 0.168 0.000 0.000 0.000 0.796
#> SRR1706900 6 0.3354 0.855 0.036 0.168 0.000 0.000 0.000 0.796
#> SRR1706901 6 0.3354 0.855 0.036 0.168 0.000 0.000 0.000 0.796
#> SRR1706902 6 0.3354 0.855 0.036 0.168 0.000 0.000 0.000 0.796
#> SRR1706907 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706908 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706909 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706910 3 0.0000 0.892 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706903 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706911 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706912 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706913 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706914 3 0.3012 0.893 0.008 0.000 0.796 0.000 0.000 0.196
#> SRR1706919 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706920 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706921 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706922 6 0.2762 0.855 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1706915 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706916 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706917 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706918 6 0.0000 0.871 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706923 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1.000 0.000 0.000 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["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 15185 rows and 159 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 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 0.982 0.986 0.5036 0.497 0.497
#> 3 3 0.633 0.756 0.750 0.2566 1.000 1.000
#> 4 4 0.629 0.716 0.754 0.1201 0.758 0.513
#> 5 5 0.659 0.649 0.722 0.0735 0.918 0.724
#> 6 6 0.750 0.607 0.662 0.0469 0.861 0.546
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
#> SRR1706767 1 0.000 0.985 1.000 0.000
#> SRR1706768 1 0.000 0.985 1.000 0.000
#> SRR1706769 1 0.000 0.985 1.000 0.000
#> SRR1706770 1 0.000 0.985 1.000 0.000
#> SRR1706771 1 0.000 0.985 1.000 0.000
#> SRR1706772 1 0.000 0.985 1.000 0.000
#> SRR1706773 1 0.000 0.985 1.000 0.000
#> SRR1706774 1 0.000 0.985 1.000 0.000
#> SRR1706775 1 0.000 0.985 1.000 0.000
#> SRR1706776 1 0.000 0.985 1.000 0.000
#> SRR1706777 1 0.000 0.985 1.000 0.000
#> SRR1706778 1 0.000 0.985 1.000 0.000
#> SRR1706779 1 0.224 0.977 0.964 0.036
#> SRR1706780 1 0.224 0.977 0.964 0.036
#> SRR1706781 1 0.224 0.977 0.964 0.036
#> SRR1706782 1 0.224 0.977 0.964 0.036
#> SRR1706783 1 0.224 0.977 0.964 0.036
#> SRR1706784 1 0.224 0.977 0.964 0.036
#> SRR1706785 1 0.224 0.977 0.964 0.036
#> SRR1706786 1 0.224 0.977 0.964 0.036
#> SRR1706787 1 0.000 0.985 1.000 0.000
#> SRR1706788 1 0.000 0.985 1.000 0.000
#> SRR1706789 1 0.000 0.985 1.000 0.000
#> SRR1706790 1 0.000 0.985 1.000 0.000
#> SRR1706791 1 0.000 0.985 1.000 0.000
#> SRR1706792 1 0.000 0.985 1.000 0.000
#> SRR1706793 1 0.000 0.985 1.000 0.000
#> SRR1706794 1 0.000 0.985 1.000 0.000
#> SRR1706795 1 0.000 0.985 1.000 0.000
#> SRR1706796 1 0.000 0.985 1.000 0.000
#> SRR1706797 1 0.000 0.985 1.000 0.000
#> SRR1706798 1 0.000 0.985 1.000 0.000
#> SRR1706799 1 0.224 0.977 0.964 0.036
#> SRR1706800 1 0.224 0.977 0.964 0.036
#> SRR1706801 1 0.224 0.977 0.964 0.036
#> SRR1706802 1 0.224 0.977 0.964 0.036
#> SRR1706803 1 0.224 0.977 0.964 0.036
#> SRR1706804 1 0.224 0.977 0.964 0.036
#> SRR1706805 1 0.224 0.977 0.964 0.036
#> SRR1706806 1 0.224 0.977 0.964 0.036
#> SRR1706811 1 0.000 0.985 1.000 0.000
#> SRR1706812 1 0.000 0.985 1.000 0.000
#> SRR1706813 1 0.000 0.985 1.000 0.000
#> SRR1706814 1 0.000 0.985 1.000 0.000
#> SRR1706807 1 0.000 0.985 1.000 0.000
#> SRR1706808 1 0.000 0.985 1.000 0.000
#> SRR1706809 1 0.000 0.985 1.000 0.000
#> SRR1706810 1 0.000 0.985 1.000 0.000
#> SRR1706815 1 0.000 0.985 1.000 0.000
#> SRR1706816 1 0.000 0.985 1.000 0.000
#> SRR1706817 1 0.000 0.985 1.000 0.000
#> SRR1706818 1 0.000 0.985 1.000 0.000
#> SRR1706819 1 0.224 0.977 0.964 0.036
#> SRR1706820 1 0.224 0.977 0.964 0.036
#> SRR1706821 1 0.224 0.977 0.964 0.036
#> SRR1706822 1 0.224 0.977 0.964 0.036
#> SRR1706823 1 0.224 0.977 0.964 0.036
#> SRR1706824 1 0.224 0.977 0.964 0.036
#> SRR1706825 1 0.224 0.977 0.964 0.036
#> SRR1706826 1 0.224 0.977 0.964 0.036
#> SRR1706827 1 0.000 0.985 1.000 0.000
#> SRR1706828 1 0.000 0.985 1.000 0.000
#> SRR1706829 1 0.000 0.985 1.000 0.000
#> SRR1706830 1 0.000 0.985 1.000 0.000
#> SRR1706835 1 0.000 0.985 1.000 0.000
#> SRR1706836 1 0.000 0.985 1.000 0.000
#> SRR1706837 1 0.000 0.985 1.000 0.000
#> SRR1706838 1 0.000 0.985 1.000 0.000
#> SRR1706831 1 0.000 0.985 1.000 0.000
#> SRR1706832 1 0.000 0.985 1.000 0.000
#> SRR1706833 1 0.000 0.985 1.000 0.000
#> SRR1706834 1 0.000 0.985 1.000 0.000
#> SRR1706839 1 0.224 0.977 0.964 0.036
#> SRR1706840 1 0.224 0.977 0.964 0.036
#> SRR1706841 1 0.224 0.977 0.964 0.036
#> SRR1706842 1 0.224 0.977 0.964 0.036
#> SRR1706847 2 0.224 0.977 0.036 0.964
#> SRR1706848 2 0.224 0.977 0.036 0.964
#> SRR1706849 2 0.224 0.977 0.036 0.964
#> SRR1706850 2 0.224 0.977 0.036 0.964
#> SRR1706843 1 0.224 0.977 0.964 0.036
#> SRR1706844 1 0.224 0.977 0.964 0.036
#> SRR1706845 1 0.224 0.977 0.964 0.036
#> SRR1706846 1 0.224 0.977 0.964 0.036
#> SRR1706851 2 0.224 0.977 0.036 0.964
#> SRR1706852 2 0.224 0.977 0.036 0.964
#> SRR1706853 2 0.224 0.977 0.036 0.964
#> SRR1706854 2 0.224 0.977 0.036 0.964
#> SRR1706855 2 0.000 0.985 0.000 1.000
#> SRR1706856 2 0.000 0.985 0.000 1.000
#> SRR1706857 2 0.000 0.985 0.000 1.000
#> SRR1706858 2 0.000 0.985 0.000 1.000
#> SRR1706859 2 0.000 0.985 0.000 1.000
#> SRR1706860 2 0.000 0.985 0.000 1.000
#> SRR1706861 2 0.000 0.985 0.000 1.000
#> SRR1706862 2 0.000 0.985 0.000 1.000
#> SRR1706867 2 0.224 0.977 0.036 0.964
#> SRR1706869 2 0.224 0.977 0.036 0.964
#> SRR1706870 2 0.224 0.977 0.036 0.964
#> SRR1706863 2 0.000 0.985 0.000 1.000
#> SRR1706864 2 0.000 0.985 0.000 1.000
#> SRR1706865 2 0.000 0.985 0.000 1.000
#> SRR1706866 2 0.000 0.985 0.000 1.000
#> SRR1706871 2 0.224 0.977 0.036 0.964
#> SRR1706872 2 0.224 0.977 0.036 0.964
#> SRR1706873 2 0.224 0.977 0.036 0.964
#> SRR1706874 2 0.224 0.977 0.036 0.964
#> SRR1706879 2 0.000 0.985 0.000 1.000
#> SRR1706880 2 0.000 0.985 0.000 1.000
#> SRR1706881 2 0.000 0.985 0.000 1.000
#> SRR1706882 2 0.000 0.985 0.000 1.000
#> SRR1706883 2 0.000 0.985 0.000 1.000
#> SRR1706884 2 0.000 0.985 0.000 1.000
#> SRR1706885 2 0.000 0.985 0.000 1.000
#> SRR1706886 2 0.000 0.985 0.000 1.000
#> SRR1706875 2 0.000 0.985 0.000 1.000
#> SRR1706876 2 0.000 0.985 0.000 1.000
#> SRR1706877 2 0.000 0.985 0.000 1.000
#> SRR1706878 2 0.000 0.985 0.000 1.000
#> SRR1706887 2 0.224 0.977 0.036 0.964
#> SRR1706888 2 0.224 0.977 0.036 0.964
#> SRR1706889 2 0.224 0.977 0.036 0.964
#> SRR1706890 2 0.224 0.977 0.036 0.964
#> SRR1706891 2 0.224 0.977 0.036 0.964
#> SRR1706892 2 0.224 0.977 0.036 0.964
#> SRR1706893 2 0.224 0.977 0.036 0.964
#> SRR1706894 2 0.224 0.977 0.036 0.964
#> SRR1706895 2 0.000 0.985 0.000 1.000
#> SRR1706896 2 0.000 0.985 0.000 1.000
#> SRR1706897 2 0.000 0.985 0.000 1.000
#> SRR1706898 2 0.000 0.985 0.000 1.000
#> SRR1706899 2 0.000 0.985 0.000 1.000
#> SRR1706900 2 0.000 0.985 0.000 1.000
#> SRR1706901 2 0.000 0.985 0.000 1.000
#> SRR1706902 2 0.000 0.985 0.000 1.000
#> SRR1706907 2 0.224 0.977 0.036 0.964
#> SRR1706908 2 0.224 0.977 0.036 0.964
#> SRR1706909 2 0.224 0.977 0.036 0.964
#> SRR1706910 2 0.224 0.977 0.036 0.964
#> SRR1706903 2 0.000 0.985 0.000 1.000
#> SRR1706904 2 0.000 0.985 0.000 1.000
#> SRR1706905 2 0.000 0.985 0.000 1.000
#> SRR1706906 2 0.000 0.985 0.000 1.000
#> SRR1706911 2 0.224 0.977 0.036 0.964
#> SRR1706912 2 0.224 0.977 0.036 0.964
#> SRR1706913 2 0.224 0.977 0.036 0.964
#> SRR1706914 2 0.224 0.977 0.036 0.964
#> SRR1706919 2 0.000 0.985 0.000 1.000
#> SRR1706920 2 0.000 0.985 0.000 1.000
#> SRR1706921 2 0.000 0.985 0.000 1.000
#> SRR1706922 2 0.000 0.985 0.000 1.000
#> SRR1706915 2 0.000 0.985 0.000 1.000
#> SRR1706916 2 0.000 0.985 0.000 1.000
#> SRR1706917 2 0.000 0.985 0.000 1.000
#> SRR1706918 2 0.000 0.985 0.000 1.000
#> SRR1706923 2 0.000 0.985 0.000 1.000
#> SRR1706924 2 0.000 0.985 0.000 1.000
#> SRR1706925 2 0.000 0.985 0.000 1.000
#> SRR1706926 2 0.000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706768 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706769 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706770 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706771 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706772 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706773 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706774 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706775 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706776 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706777 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706778 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706779 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706780 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706781 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706782 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706783 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706784 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706785 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706786 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706787 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706788 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706789 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706790 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706791 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706792 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706793 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706794 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706795 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706796 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706797 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706798 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706799 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706800 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706801 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706802 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706803 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706804 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706805 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706806 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706811 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706812 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706813 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706814 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706807 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706808 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706809 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706810 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706815 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706816 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706817 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706818 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706819 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706820 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706821 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706822 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706823 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706824 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706825 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706826 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706827 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706828 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706829 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706830 1 0.2939 0.718 0.916 0.012 0.072
#> SRR1706835 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706836 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706837 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706838 1 0.5553 0.810 0.724 0.004 0.272
#> SRR1706831 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706832 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706833 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706834 1 0.0000 0.761 1.000 0.000 0.000
#> SRR1706839 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706840 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706841 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706842 1 0.6483 0.792 0.544 0.004 0.452
#> SRR1706847 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706848 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706849 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706850 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706843 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706844 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706845 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706846 1 0.6305 0.781 0.516 0.000 0.484
#> SRR1706851 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706855 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706856 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706857 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706858 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706859 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706860 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706861 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706862 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706867 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706869 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706870 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706863 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706864 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706865 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706866 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706871 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706879 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706880 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706881 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706882 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706883 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706884 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706885 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706886 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706875 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706876 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706877 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706878 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706887 2 0.6488 0.584 0.160 0.756 0.084
#> SRR1706888 2 0.6488 0.584 0.160 0.756 0.084
#> SRR1706889 2 0.6488 0.584 0.160 0.756 0.084
#> SRR1706890 2 0.6488 0.584 0.160 0.756 0.084
#> SRR1706891 2 0.0424 0.740 0.000 0.992 0.008
#> SRR1706892 2 0.0424 0.740 0.000 0.992 0.008
#> SRR1706893 2 0.0424 0.740 0.000 0.992 0.008
#> SRR1706894 2 0.0424 0.740 0.000 0.992 0.008
#> SRR1706895 2 0.5397 0.798 0.000 0.720 0.280
#> SRR1706896 2 0.5397 0.798 0.000 0.720 0.280
#> SRR1706897 2 0.5397 0.798 0.000 0.720 0.280
#> SRR1706898 2 0.5397 0.798 0.000 0.720 0.280
#> SRR1706899 2 0.6260 0.787 0.000 0.552 0.448
#> SRR1706900 2 0.6260 0.787 0.000 0.552 0.448
#> SRR1706901 2 0.6260 0.787 0.000 0.552 0.448
#> SRR1706902 2 0.6260 0.787 0.000 0.552 0.448
#> SRR1706907 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706908 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706909 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706910 2 0.6324 0.587 0.160 0.764 0.076
#> SRR1706903 2 0.6305 0.774 0.000 0.516 0.484
#> SRR1706904 2 0.6305 0.774 0.000 0.516 0.484
#> SRR1706905 2 0.6305 0.774 0.000 0.516 0.484
#> SRR1706906 2 0.6305 0.774 0.000 0.516 0.484
#> SRR1706911 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.741 0.000 1.000 0.000
#> SRR1706919 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706920 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706921 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706922 2 0.6244 0.788 0.000 0.560 0.440
#> SRR1706915 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706916 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706917 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706918 2 0.5327 0.799 0.000 0.728 0.272
#> SRR1706923 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706924 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706925 2 0.6299 0.776 0.000 0.524 0.476
#> SRR1706926 2 0.6299 0.776 0.000 0.524 0.476
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706768 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706769 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706770 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706771 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706772 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706773 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706774 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706775 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706776 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706777 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706778 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706779 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706784 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706785 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706786 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706787 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706788 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706789 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706790 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706791 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706792 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706793 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706794 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706795 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706796 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706797 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706798 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706799 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706804 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706805 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706806 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706811 4 0.4804 0.91445 0.276 0.016 0.000 0.708
#> SRR1706812 4 0.4804 0.91445 0.276 0.016 0.000 0.708
#> SRR1706813 4 0.4804 0.91445 0.276 0.016 0.000 0.708
#> SRR1706814 4 0.4804 0.91445 0.276 0.016 0.000 0.708
#> SRR1706807 4 0.6144 0.91765 0.252 0.084 0.004 0.660
#> SRR1706808 4 0.6144 0.91765 0.252 0.084 0.004 0.660
#> SRR1706809 4 0.6144 0.91765 0.252 0.084 0.004 0.660
#> SRR1706810 4 0.6144 0.91765 0.252 0.084 0.004 0.660
#> SRR1706815 1 0.5511 0.51370 0.676 0.036 0.004 0.284
#> SRR1706816 1 0.5511 0.51370 0.676 0.036 0.004 0.284
#> SRR1706817 1 0.5511 0.51370 0.676 0.036 0.004 0.284
#> SRR1706818 1 0.5511 0.51370 0.676 0.036 0.004 0.284
#> SRR1706819 1 0.0712 0.80646 0.984 0.008 0.004 0.004
#> SRR1706820 1 0.0712 0.80646 0.984 0.008 0.004 0.004
#> SRR1706821 1 0.0712 0.80646 0.984 0.008 0.004 0.004
#> SRR1706822 1 0.0712 0.80646 0.984 0.008 0.004 0.004
#> SRR1706823 1 0.2629 0.79228 0.912 0.060 0.024 0.004
#> SRR1706824 1 0.2629 0.79228 0.912 0.060 0.024 0.004
#> SRR1706825 1 0.2629 0.79228 0.912 0.060 0.024 0.004
#> SRR1706826 1 0.2629 0.79228 0.912 0.060 0.024 0.004
#> SRR1706827 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706828 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706829 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706830 4 0.5986 0.92509 0.256 0.072 0.004 0.668
#> SRR1706835 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706836 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706837 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706838 1 0.5088 0.51871 0.688 0.024 0.000 0.288
#> SRR1706831 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706832 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706833 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706834 4 0.4277 0.92171 0.280 0.000 0.000 0.720
#> SRR1706839 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.80928 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.3356 0.66182 0.000 0.000 0.824 0.176
#> SRR1706848 3 0.3356 0.66182 0.000 0.000 0.824 0.176
#> SRR1706849 3 0.3356 0.66182 0.000 0.000 0.824 0.176
#> SRR1706850 3 0.3356 0.66182 0.000 0.000 0.824 0.176
#> SRR1706843 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706844 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706845 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706846 1 0.2174 0.79714 0.928 0.052 0.020 0.000
#> SRR1706851 3 0.1706 0.68572 0.000 0.036 0.948 0.016
#> SRR1706852 3 0.1706 0.68572 0.000 0.036 0.948 0.016
#> SRR1706853 3 0.1706 0.68572 0.000 0.036 0.948 0.016
#> SRR1706854 3 0.1706 0.68572 0.000 0.036 0.948 0.016
#> SRR1706855 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706856 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706857 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706858 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706859 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706860 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706861 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706862 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706867 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706869 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706870 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706863 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706864 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706865 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706866 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706871 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706872 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706873 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706874 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706879 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706880 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706881 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706882 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706883 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706884 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706885 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706886 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706875 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706876 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706877 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706878 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706887 3 0.3356 0.66181 0.000 0.000 0.824 0.176
#> SRR1706888 3 0.3356 0.66181 0.000 0.000 0.824 0.176
#> SRR1706889 3 0.3356 0.66181 0.000 0.000 0.824 0.176
#> SRR1706890 3 0.3356 0.66181 0.000 0.000 0.824 0.176
#> SRR1706891 3 0.1305 0.68887 0.000 0.036 0.960 0.004
#> SRR1706892 3 0.1305 0.68887 0.000 0.036 0.960 0.004
#> SRR1706893 3 0.1305 0.68887 0.000 0.036 0.960 0.004
#> SRR1706894 3 0.1305 0.68887 0.000 0.036 0.960 0.004
#> SRR1706895 3 0.6209 0.00593 0.000 0.456 0.492 0.052
#> SRR1706896 3 0.6209 0.00593 0.000 0.456 0.492 0.052
#> SRR1706897 3 0.6209 0.00593 0.000 0.456 0.492 0.052
#> SRR1706898 3 0.6209 0.00593 0.000 0.456 0.492 0.052
#> SRR1706899 2 0.4459 0.90743 0.000 0.780 0.188 0.032
#> SRR1706900 2 0.4459 0.90743 0.000 0.780 0.188 0.032
#> SRR1706901 2 0.4459 0.90743 0.000 0.780 0.188 0.032
#> SRR1706902 2 0.4459 0.90743 0.000 0.780 0.188 0.032
#> SRR1706907 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706908 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706909 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706910 3 0.3311 0.66227 0.000 0.000 0.828 0.172
#> SRR1706903 2 0.5204 0.91440 0.000 0.752 0.160 0.088
#> SRR1706904 2 0.5204 0.91440 0.000 0.752 0.160 0.088
#> SRR1706905 2 0.5204 0.91440 0.000 0.752 0.160 0.088
#> SRR1706906 2 0.5204 0.91440 0.000 0.752 0.160 0.088
#> SRR1706911 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706912 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706913 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706914 3 0.1118 0.68914 0.000 0.036 0.964 0.000
#> SRR1706919 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706920 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706921 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706922 2 0.3486 0.92225 0.000 0.812 0.188 0.000
#> SRR1706915 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706916 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706917 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706918 3 0.5861 -0.00437 0.000 0.476 0.492 0.032
#> SRR1706923 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706924 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706925 2 0.4875 0.92506 0.000 0.772 0.160 0.068
#> SRR1706926 2 0.4875 0.92506 0.000 0.772 0.160 0.068
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.3661 0.657 0.000 0.000 0.000 0.724 0.276
#> SRR1706768 4 0.3661 0.657 0.000 0.000 0.000 0.724 0.276
#> SRR1706769 4 0.3661 0.657 0.000 0.000 0.000 0.724 0.276
#> SRR1706770 4 0.3661 0.657 0.000 0.000 0.000 0.724 0.276
#> SRR1706771 4 0.0955 0.695 0.000 0.004 0.000 0.968 0.028
#> SRR1706772 4 0.0955 0.695 0.000 0.004 0.000 0.968 0.028
#> SRR1706773 4 0.0955 0.695 0.000 0.004 0.000 0.968 0.028
#> SRR1706774 4 0.0955 0.695 0.000 0.004 0.000 0.968 0.028
#> SRR1706775 4 0.6716 0.265 0.292 0.024 0.000 0.524 0.160
#> SRR1706776 4 0.6716 0.265 0.292 0.024 0.000 0.524 0.160
#> SRR1706777 4 0.6716 0.265 0.292 0.024 0.000 0.524 0.160
#> SRR1706778 4 0.6716 0.265 0.292 0.024 0.000 0.524 0.160
#> SRR1706779 1 0.5052 0.886 0.728 0.012 0.000 0.144 0.116
#> SRR1706780 1 0.5052 0.886 0.728 0.012 0.000 0.144 0.116
#> SRR1706781 1 0.5052 0.886 0.728 0.012 0.000 0.144 0.116
#> SRR1706782 1 0.5052 0.886 0.728 0.012 0.000 0.144 0.116
#> SRR1706783 1 0.3351 0.889 0.836 0.028 0.000 0.132 0.004
#> SRR1706784 1 0.3351 0.889 0.836 0.028 0.000 0.132 0.004
#> SRR1706785 1 0.3351 0.889 0.836 0.028 0.000 0.132 0.004
#> SRR1706786 1 0.3351 0.889 0.836 0.028 0.000 0.132 0.004
#> SRR1706787 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706788 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706789 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706790 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706791 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706794 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706796 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706797 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706798 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706799 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706800 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706801 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706802 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706803 1 0.3264 0.890 0.840 0.024 0.000 0.132 0.004
#> SRR1706804 1 0.3264 0.890 0.840 0.024 0.000 0.132 0.004
#> SRR1706805 1 0.3264 0.890 0.840 0.024 0.000 0.132 0.004
#> SRR1706806 1 0.3264 0.890 0.840 0.024 0.000 0.132 0.004
#> SRR1706811 4 0.0671 0.696 0.004 0.000 0.000 0.980 0.016
#> SRR1706812 4 0.0671 0.696 0.004 0.000 0.000 0.980 0.016
#> SRR1706813 4 0.0671 0.696 0.004 0.000 0.000 0.980 0.016
#> SRR1706814 4 0.0671 0.696 0.004 0.000 0.000 0.980 0.016
#> SRR1706807 4 0.3662 0.660 0.004 0.000 0.000 0.744 0.252
#> SRR1706808 4 0.3662 0.660 0.004 0.000 0.000 0.744 0.252
#> SRR1706809 4 0.3662 0.660 0.004 0.000 0.000 0.744 0.252
#> SRR1706810 4 0.3662 0.660 0.004 0.000 0.000 0.744 0.252
#> SRR1706815 4 0.6771 0.264 0.284 0.024 0.000 0.520 0.172
#> SRR1706816 4 0.6771 0.264 0.284 0.024 0.000 0.520 0.172
#> SRR1706817 4 0.6771 0.264 0.284 0.024 0.000 0.520 0.172
#> SRR1706818 4 0.6771 0.264 0.284 0.024 0.000 0.520 0.172
#> SRR1706819 1 0.5199 0.882 0.720 0.016 0.000 0.140 0.124
#> SRR1706820 1 0.5199 0.882 0.720 0.016 0.000 0.140 0.124
#> SRR1706821 1 0.5199 0.882 0.720 0.016 0.000 0.140 0.124
#> SRR1706822 1 0.5199 0.882 0.720 0.016 0.000 0.140 0.124
#> SRR1706823 1 0.3648 0.881 0.828 0.020 0.000 0.128 0.024
#> SRR1706824 1 0.3648 0.881 0.828 0.020 0.000 0.128 0.024
#> SRR1706825 1 0.3648 0.881 0.828 0.020 0.000 0.128 0.024
#> SRR1706826 1 0.3648 0.881 0.828 0.020 0.000 0.128 0.024
#> SRR1706827 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706828 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706829 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706830 4 0.3480 0.662 0.000 0.000 0.000 0.752 0.248
#> SRR1706835 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706836 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706837 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706838 4 0.6637 0.267 0.292 0.020 0.000 0.528 0.160
#> SRR1706831 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 0.698 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706840 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706841 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706842 1 0.5006 0.887 0.732 0.012 0.000 0.144 0.112
#> SRR1706847 3 0.4108 0.602 0.008 0.000 0.684 0.000 0.308
#> SRR1706848 3 0.4108 0.602 0.008 0.000 0.684 0.000 0.308
#> SRR1706849 3 0.4108 0.602 0.008 0.000 0.684 0.000 0.308
#> SRR1706850 3 0.4108 0.602 0.008 0.000 0.684 0.000 0.308
#> SRR1706843 1 0.3193 0.890 0.840 0.028 0.000 0.132 0.000
#> SRR1706844 1 0.3193 0.890 0.840 0.028 0.000 0.132 0.000
#> SRR1706845 1 0.3193 0.890 0.840 0.028 0.000 0.132 0.000
#> SRR1706846 1 0.3193 0.890 0.840 0.028 0.000 0.132 0.000
#> SRR1706851 3 0.0671 0.657 0.000 0.016 0.980 0.000 0.004
#> SRR1706852 3 0.0671 0.657 0.000 0.016 0.980 0.000 0.004
#> SRR1706853 3 0.0671 0.657 0.000 0.016 0.980 0.000 0.004
#> SRR1706854 3 0.0671 0.657 0.000 0.016 0.980 0.000 0.004
#> SRR1706855 3 0.5562 0.182 0.020 0.452 0.496 0.000 0.032
#> SRR1706856 3 0.5562 0.182 0.020 0.452 0.496 0.000 0.032
#> SRR1706857 3 0.5562 0.182 0.020 0.452 0.496 0.000 0.032
#> SRR1706858 3 0.5562 0.182 0.020 0.452 0.496 0.000 0.032
#> SRR1706859 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706860 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706861 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706862 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706867 3 0.4009 0.601 0.004 0.000 0.684 0.000 0.312
#> SRR1706869 3 0.4009 0.601 0.004 0.000 0.684 0.000 0.312
#> SRR1706870 3 0.4009 0.601 0.004 0.000 0.684 0.000 0.312
#> SRR1706863 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706864 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706865 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706866 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706871 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706872 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706873 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706874 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706879 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706880 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706881 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706882 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706883 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706884 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706885 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706886 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706875 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706876 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706877 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706878 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706887 3 0.4792 0.595 0.020 0.012 0.656 0.000 0.312
#> SRR1706888 3 0.4792 0.595 0.020 0.012 0.656 0.000 0.312
#> SRR1706889 3 0.4792 0.595 0.020 0.012 0.656 0.000 0.312
#> SRR1706890 3 0.4792 0.595 0.020 0.012 0.656 0.000 0.312
#> SRR1706891 3 0.1997 0.652 0.016 0.028 0.932 0.000 0.024
#> SRR1706892 3 0.1997 0.652 0.016 0.028 0.932 0.000 0.024
#> SRR1706893 3 0.1997 0.652 0.016 0.028 0.932 0.000 0.024
#> SRR1706894 3 0.1997 0.652 0.016 0.028 0.932 0.000 0.024
#> SRR1706895 3 0.6415 0.181 0.044 0.424 0.468 0.000 0.064
#> SRR1706896 3 0.6415 0.181 0.044 0.424 0.468 0.000 0.064
#> SRR1706897 3 0.6415 0.181 0.044 0.424 0.468 0.000 0.064
#> SRR1706898 3 0.6415 0.181 0.044 0.424 0.468 0.000 0.064
#> SRR1706899 2 0.4349 0.784 0.036 0.800 0.108 0.000 0.056
#> SRR1706900 2 0.4349 0.784 0.036 0.800 0.108 0.000 0.056
#> SRR1706901 2 0.4349 0.784 0.036 0.800 0.108 0.000 0.056
#> SRR1706902 2 0.4349 0.784 0.036 0.800 0.108 0.000 0.056
#> SRR1706907 3 0.3876 0.601 0.000 0.000 0.684 0.000 0.316
#> SRR1706908 3 0.3876 0.601 0.000 0.000 0.684 0.000 0.316
#> SRR1706909 3 0.3876 0.601 0.000 0.000 0.684 0.000 0.316
#> SRR1706910 3 0.3876 0.601 0.000 0.000 0.684 0.000 0.316
#> SRR1706903 2 0.5642 0.818 0.064 0.700 0.068 0.000 0.168
#> SRR1706904 2 0.5642 0.818 0.064 0.700 0.068 0.000 0.168
#> SRR1706905 2 0.5642 0.818 0.064 0.700 0.068 0.000 0.168
#> SRR1706906 2 0.5642 0.818 0.064 0.700 0.068 0.000 0.168
#> SRR1706911 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706912 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706913 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706914 3 0.0510 0.657 0.000 0.016 0.984 0.000 0.000
#> SRR1706919 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706920 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706921 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706922 2 0.2280 0.828 0.000 0.880 0.120 0.000 0.000
#> SRR1706915 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706916 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706917 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706918 3 0.5559 0.187 0.020 0.448 0.500 0.000 0.032
#> SRR1706923 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706924 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706925 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
#> SRR1706926 2 0.5341 0.841 0.044 0.724 0.080 0.000 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.6584 0.7987 0.068 0.052 0.076 0.584 NA 0.000
#> SRR1706768 4 0.6584 0.7987 0.068 0.052 0.076 0.584 NA 0.000
#> SRR1706769 4 0.6584 0.7987 0.068 0.052 0.076 0.584 NA 0.000
#> SRR1706770 4 0.6584 0.7987 0.068 0.052 0.076 0.584 NA 0.000
#> SRR1706771 4 0.2604 0.7894 0.080 0.008 0.008 0.884 NA 0.000
#> SRR1706772 4 0.2604 0.7894 0.080 0.008 0.008 0.884 NA 0.000
#> SRR1706773 4 0.2604 0.7894 0.080 0.008 0.008 0.884 NA 0.000
#> SRR1706774 4 0.2604 0.7894 0.080 0.008 0.008 0.884 NA 0.000
#> SRR1706775 1 0.6715 0.4010 0.500 0.076 0.036 0.324 NA 0.000
#> SRR1706776 1 0.6715 0.4010 0.500 0.076 0.036 0.324 NA 0.000
#> SRR1706777 1 0.6715 0.4010 0.500 0.076 0.036 0.324 NA 0.000
#> SRR1706778 1 0.6715 0.4010 0.500 0.076 0.036 0.324 NA 0.000
#> SRR1706779 1 0.0146 0.6733 0.996 0.000 0.004 0.000 NA 0.000
#> SRR1706780 1 0.0146 0.6733 0.996 0.000 0.004 0.000 NA 0.000
#> SRR1706781 1 0.0146 0.6733 0.996 0.000 0.004 0.000 NA 0.000
#> SRR1706782 1 0.0146 0.6733 0.996 0.000 0.004 0.000 NA 0.000
#> SRR1706783 1 0.4101 0.6165 0.632 0.008 0.008 0.000 NA 0.000
#> SRR1706784 1 0.4101 0.6165 0.632 0.008 0.008 0.000 NA 0.000
#> SRR1706785 1 0.4101 0.6165 0.632 0.008 0.008 0.000 NA 0.000
#> SRR1706786 1 0.4101 0.6165 0.632 0.008 0.008 0.000 NA 0.000
#> SRR1706787 4 0.6296 0.8062 0.068 0.040 0.072 0.612 NA 0.000
#> SRR1706788 4 0.6296 0.8062 0.068 0.040 0.072 0.612 NA 0.000
#> SRR1706789 4 0.6296 0.8062 0.068 0.040 0.072 0.612 NA 0.000
#> SRR1706790 4 0.6296 0.8062 0.068 0.040 0.072 0.612 NA 0.000
#> SRR1706791 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706792 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706793 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706794 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706795 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706796 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706797 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706798 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706799 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706800 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706801 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706802 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706803 1 0.3659 0.6165 0.636 0.000 0.000 0.000 NA 0.000
#> SRR1706804 1 0.3659 0.6165 0.636 0.000 0.000 0.000 NA 0.000
#> SRR1706805 1 0.3659 0.6165 0.636 0.000 0.000 0.000 NA 0.000
#> SRR1706806 1 0.3659 0.6165 0.636 0.000 0.000 0.000 NA 0.000
#> SRR1706811 4 0.2505 0.7853 0.080 0.012 0.004 0.888 NA 0.000
#> SRR1706812 4 0.2505 0.7853 0.080 0.012 0.004 0.888 NA 0.000
#> SRR1706813 4 0.2505 0.7853 0.080 0.012 0.004 0.888 NA 0.000
#> SRR1706814 4 0.2505 0.7853 0.080 0.012 0.004 0.888 NA 0.000
#> SRR1706807 4 0.6537 0.8013 0.068 0.052 0.080 0.596 NA 0.000
#> SRR1706808 4 0.6537 0.8013 0.068 0.052 0.080 0.596 NA 0.000
#> SRR1706809 4 0.6537 0.8013 0.068 0.052 0.080 0.596 NA 0.000
#> SRR1706810 4 0.6537 0.8013 0.068 0.052 0.080 0.596 NA 0.000
#> SRR1706815 1 0.7110 0.3861 0.464 0.092 0.036 0.320 NA 0.000
#> SRR1706816 1 0.7110 0.3861 0.464 0.092 0.036 0.320 NA 0.000
#> SRR1706817 1 0.7110 0.3861 0.464 0.092 0.036 0.320 NA 0.000
#> SRR1706818 1 0.7110 0.3861 0.464 0.092 0.036 0.320 NA 0.000
#> SRR1706819 1 0.1977 0.6615 0.920 0.040 0.008 0.000 NA 0.000
#> SRR1706820 1 0.1977 0.6615 0.920 0.040 0.008 0.000 NA 0.000
#> SRR1706821 1 0.1977 0.6615 0.920 0.040 0.008 0.000 NA 0.000
#> SRR1706822 1 0.1977 0.6615 0.920 0.040 0.008 0.000 NA 0.000
#> SRR1706823 1 0.4528 0.6052 0.588 0.020 0.012 0.000 NA 0.000
#> SRR1706824 1 0.4528 0.6052 0.588 0.020 0.012 0.000 NA 0.000
#> SRR1706825 1 0.4528 0.6052 0.588 0.020 0.012 0.000 NA 0.000
#> SRR1706826 1 0.4528 0.6052 0.588 0.020 0.012 0.000 NA 0.000
#> SRR1706827 4 0.6272 0.8062 0.068 0.040 0.068 0.612 NA 0.000
#> SRR1706828 4 0.6272 0.8062 0.068 0.040 0.068 0.612 NA 0.000
#> SRR1706829 4 0.6272 0.8062 0.068 0.040 0.068 0.612 NA 0.000
#> SRR1706830 4 0.6272 0.8062 0.068 0.040 0.068 0.612 NA 0.000
#> SRR1706835 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706836 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706837 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706838 1 0.6588 0.3991 0.500 0.076 0.032 0.336 NA 0.000
#> SRR1706831 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706832 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706833 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706834 4 0.1556 0.7954 0.080 0.000 0.000 0.920 NA 0.000
#> SRR1706839 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706840 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706841 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706842 1 0.0000 0.6735 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706847 3 0.3191 0.9643 0.000 0.000 0.812 0.012 NA 0.164
#> SRR1706848 3 0.3191 0.9643 0.000 0.000 0.812 0.012 NA 0.164
#> SRR1706849 3 0.3191 0.9643 0.000 0.000 0.812 0.012 NA 0.164
#> SRR1706850 3 0.3191 0.9643 0.000 0.000 0.812 0.012 NA 0.164
#> SRR1706843 1 0.3782 0.6165 0.636 0.004 0.000 0.000 NA 0.000
#> SRR1706844 1 0.3782 0.6165 0.636 0.004 0.000 0.000 NA 0.000
#> SRR1706845 1 0.3782 0.6165 0.636 0.004 0.000 0.000 NA 0.000
#> SRR1706846 1 0.3782 0.6165 0.636 0.004 0.000 0.000 NA 0.000
#> SRR1706851 6 0.5337 0.1455 0.000 0.000 0.396 0.008 NA 0.512
#> SRR1706852 6 0.5337 0.1455 0.000 0.000 0.396 0.008 NA 0.512
#> SRR1706853 6 0.5337 0.1455 0.000 0.000 0.396 0.008 NA 0.512
#> SRR1706854 6 0.5337 0.1455 0.000 0.000 0.396 0.008 NA 0.512
#> SRR1706855 6 0.0665 0.5141 0.000 0.004 0.000 0.008 NA 0.980
#> SRR1706856 6 0.0665 0.5141 0.000 0.004 0.000 0.008 NA 0.980
#> SRR1706857 6 0.0665 0.5141 0.000 0.004 0.000 0.008 NA 0.980
#> SRR1706858 6 0.0665 0.5141 0.000 0.004 0.000 0.008 NA 0.980
#> SRR1706859 2 0.5448 0.7518 0.000 0.468 0.000 0.008 NA 0.432
#> SRR1706860 2 0.5448 0.7518 0.000 0.468 0.000 0.008 NA 0.432
#> SRR1706861 2 0.5448 0.7518 0.000 0.468 0.000 0.008 NA 0.432
#> SRR1706862 2 0.5448 0.7518 0.000 0.468 0.000 0.008 NA 0.432
#> SRR1706867 3 0.2982 0.9693 0.000 0.000 0.820 0.004 NA 0.164
#> SRR1706869 3 0.2982 0.9693 0.000 0.000 0.820 0.004 NA 0.164
#> SRR1706870 3 0.2982 0.9693 0.000 0.000 0.820 0.004 NA 0.164
#> SRR1706863 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706864 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706865 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706866 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706871 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706872 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706873 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706874 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706879 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706880 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706881 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706882 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706883 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706884 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706885 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706886 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706875 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706876 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706877 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706878 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706887 3 0.3649 0.9479 0.000 0.012 0.804 0.012 NA 0.148
#> SRR1706888 3 0.3649 0.9479 0.000 0.012 0.804 0.012 NA 0.148
#> SRR1706889 3 0.3649 0.9479 0.000 0.012 0.804 0.012 NA 0.148
#> SRR1706890 3 0.3649 0.9479 0.000 0.012 0.804 0.012 NA 0.148
#> SRR1706891 6 0.5980 0.0943 0.000 0.016 0.408 0.012 NA 0.464
#> SRR1706892 6 0.5980 0.0943 0.000 0.016 0.408 0.012 NA 0.464
#> SRR1706893 6 0.5980 0.0943 0.000 0.016 0.408 0.012 NA 0.464
#> SRR1706894 6 0.5980 0.0943 0.000 0.016 0.408 0.012 NA 0.464
#> SRR1706895 6 0.2645 0.4873 0.000 0.020 0.012 0.004 NA 0.880
#> SRR1706896 6 0.2645 0.4873 0.000 0.020 0.012 0.004 NA 0.880
#> SRR1706897 6 0.2645 0.4873 0.000 0.020 0.012 0.004 NA 0.880
#> SRR1706898 6 0.2645 0.4873 0.000 0.020 0.012 0.004 NA 0.880
#> SRR1706899 6 0.6269 -0.6499 0.000 0.404 0.012 0.008 NA 0.408
#> SRR1706900 6 0.6269 -0.6499 0.000 0.404 0.012 0.008 NA 0.408
#> SRR1706901 6 0.6269 -0.6499 0.000 0.404 0.012 0.008 NA 0.408
#> SRR1706902 6 0.6269 -0.6499 0.000 0.404 0.012 0.008 NA 0.408
#> SRR1706907 3 0.2491 0.9708 0.000 0.000 0.836 0.000 NA 0.164
#> SRR1706908 3 0.2491 0.9708 0.000 0.000 0.836 0.000 NA 0.164
#> SRR1706909 3 0.2491 0.9708 0.000 0.000 0.836 0.000 NA 0.164
#> SRR1706910 3 0.2491 0.9708 0.000 0.000 0.836 0.000 NA 0.164
#> SRR1706903 2 0.4389 0.7916 0.000 0.736 0.012 0.016 NA 0.200
#> SRR1706904 2 0.4389 0.7916 0.000 0.736 0.012 0.016 NA 0.200
#> SRR1706905 2 0.4389 0.7916 0.000 0.736 0.012 0.016 NA 0.200
#> SRR1706906 2 0.4389 0.7916 0.000 0.736 0.012 0.016 NA 0.200
#> SRR1706911 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706912 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706913 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706914 6 0.5312 0.1377 0.000 0.000 0.408 0.008 NA 0.504
#> SRR1706919 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706920 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706921 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706922 2 0.5316 0.7526 0.000 0.468 0.000 0.004 NA 0.440
#> SRR1706915 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706916 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706917 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706918 6 0.0000 0.5215 0.000 0.000 0.000 0.000 NA 1.000
#> SRR1706923 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706924 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706925 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
#> SRR1706926 2 0.2996 0.8241 0.000 0.772 0.000 0.000 NA 0.228
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 15185 rows and 159 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 0.5036 0.497 0.497
#> 3 3 1.000 0.992 0.988 0.2361 0.882 0.762
#> 4 4 1.000 0.995 0.996 0.1953 0.878 0.677
#> 5 5 0.879 0.898 0.883 0.0542 0.959 0.841
#> 6 6 0.911 0.913 0.890 0.0499 0.920 0.660
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706768 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706769 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706770 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706771 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706772 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706773 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706774 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706775 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706776 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706777 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706778 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706779 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706780 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706781 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706782 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706783 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706784 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706785 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706786 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706787 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706788 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706789 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706790 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706791 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706792 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706793 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706794 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706795 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706796 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706797 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706798 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706799 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706800 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706801 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706802 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706803 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706804 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706805 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706806 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706811 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706812 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706813 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706814 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706807 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706808 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706809 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706810 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706815 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706816 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706817 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706818 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706819 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706820 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706821 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706822 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706823 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706824 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706825 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706826 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706827 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706828 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706829 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706830 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706835 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706836 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706837 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706838 1 0.0000 0.988 1.000 0.000 0.000
#> SRR1706831 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706832 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706833 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706834 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1706839 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706840 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706841 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706842 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706847 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706848 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706849 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706850 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706843 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706844 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706845 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706846 1 0.0892 0.986 0.980 0.000 0.020
#> SRR1706851 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706852 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706853 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706854 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706855 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706856 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706857 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706858 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706859 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706860 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706861 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706862 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706867 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706869 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706870 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706863 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706871 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706872 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706873 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706874 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706879 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706880 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706881 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706882 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706883 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706875 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706876 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706877 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706878 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706887 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706888 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706889 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706890 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706891 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706892 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706893 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706894 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706895 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706896 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706897 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706898 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706899 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706900 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706901 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706902 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706907 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706908 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706909 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706910 3 0.0892 0.992 0.000 0.020 0.980
#> SRR1706903 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706911 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706912 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706913 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706914 3 0.1289 0.993 0.000 0.032 0.968
#> SRR1706919 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706920 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706921 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706922 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706915 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706916 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706917 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706918 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706923 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706768 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706769 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706770 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706771 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706772 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706773 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706774 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706775 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706776 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706777 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706778 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706779 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706780 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706781 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706782 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706783 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706784 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706785 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706786 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706787 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706788 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706789 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706790 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706791 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706792 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706793 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706794 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706795 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706796 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706797 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706798 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706799 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706800 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706801 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706802 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706803 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706804 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706805 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706806 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706811 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706812 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706813 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706814 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706807 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706808 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706809 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706810 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706815 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706816 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706817 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706818 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706819 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706820 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706821 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706822 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706823 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706824 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706825 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706826 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706827 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706828 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706829 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706830 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706835 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706836 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706837 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706838 4 0.112 0.975 0.036 0 0 0.964
#> SRR1706831 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706832 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706833 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706834 4 0.000 0.988 0.000 0 0 1.000
#> SRR1706839 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706840 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706841 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706842 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706847 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706848 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706849 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706850 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706843 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706844 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706845 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706846 1 0.000 1.000 1.000 0 0 0.000
#> SRR1706851 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706852 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706853 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706854 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706855 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706856 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706857 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706858 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706859 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706860 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706861 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706862 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706867 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706869 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706870 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706863 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706864 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706865 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706866 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706871 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706872 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706873 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706874 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706879 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706880 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706881 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706882 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706883 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706884 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706885 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706886 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706875 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706876 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706877 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706878 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706887 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706888 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706889 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706890 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706891 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706892 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706893 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706894 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706895 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706896 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706897 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706898 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706899 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706900 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706901 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706902 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706907 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706908 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706909 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706910 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706903 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706904 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706905 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706906 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706911 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706912 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706913 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706914 3 0.000 1.000 0.000 0 1 0.000
#> SRR1706919 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706920 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706921 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706922 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706915 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706916 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706917 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706918 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706923 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706924 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706925 2 0.000 1.000 0.000 1 0 0.000
#> SRR1706926 2 0.000 1.000 0.000 1 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706772 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706773 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706774 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706775 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706776 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706777 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706778 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706779 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706780 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706781 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706782 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706783 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706794 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706796 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706797 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706798 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706799 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706800 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706801 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706802 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706803 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706812 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706813 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706814 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706807 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706815 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706816 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706817 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706818 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706819 1 0.029 0.987 0.992 0.000 0.000 0.000 0.008
#> SRR1706820 1 0.029 0.987 0.992 0.000 0.000 0.000 0.008
#> SRR1706821 1 0.029 0.987 0.992 0.000 0.000 0.000 0.008
#> SRR1706822 1 0.029 0.987 0.992 0.000 0.000 0.000 0.008
#> SRR1706823 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706836 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706837 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706838 5 0.480 1.000 0.028 0.000 0.000 0.368 0.604
#> SRR1706831 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706840 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706841 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706842 1 0.088 0.981 0.968 0.000 0.000 0.000 0.032
#> SRR1706847 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.000 0.987 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706852 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706853 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706854 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706855 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706856 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706857 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706858 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706859 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706860 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706861 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706862 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706867 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706863 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706864 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706865 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706866 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706871 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706872 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706873 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706874 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706879 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706880 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706881 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706882 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706883 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706884 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706885 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706886 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706875 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706876 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706877 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706878 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706887 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706888 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706889 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706890 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706891 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706892 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706893 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706894 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706895 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706896 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706897 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706898 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706899 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706900 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706901 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706902 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706907 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.000 0.802 0.000 0.000 1.000 0.000 0.000
#> SRR1706903 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706904 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706905 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706906 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706911 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706912 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706913 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706914 3 0.403 0.819 0.000 0.352 0.648 0.000 0.000
#> SRR1706919 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706920 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706921 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706922 2 0.415 0.861 0.000 0.612 0.000 0.000 0.388
#> SRR1706915 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706916 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706917 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706918 2 0.000 0.661 0.000 1.000 0.000 0.000 0.000
#> SRR1706923 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706924 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706925 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
#> SRR1706926 2 0.417 0.860 0.000 0.604 0.000 0.000 0.396
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706768 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706769 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706770 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706771 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706772 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706773 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706774 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706775 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706776 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706777 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706778 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706779 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706780 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706781 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706782 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706783 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706788 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706789 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706790 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706791 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706792 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706793 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706794 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706795 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706796 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706797 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706798 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706799 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706800 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706801 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706802 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706803 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706812 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706813 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706814 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706807 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706808 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706809 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706810 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706815 5 0.1194 0.998 0.008 0.000 0.000 0.032 0.956 0.004
#> SRR1706816 5 0.1194 0.998 0.008 0.000 0.000 0.032 0.956 0.004
#> SRR1706817 5 0.1194 0.998 0.008 0.000 0.000 0.032 0.956 0.004
#> SRR1706818 5 0.1194 0.998 0.008 0.000 0.000 0.032 0.956 0.004
#> SRR1706819 1 0.1572 0.915 0.936 0.000 0.000 0.000 0.036 0.028
#> SRR1706820 1 0.1572 0.915 0.936 0.000 0.000 0.000 0.036 0.028
#> SRR1706821 1 0.1572 0.915 0.936 0.000 0.000 0.000 0.036 0.028
#> SRR1706822 1 0.1572 0.915 0.936 0.000 0.000 0.000 0.036 0.028
#> SRR1706823 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706828 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706829 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706830 4 0.0000 0.988 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706835 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706836 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706837 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706838 5 0.1049 0.999 0.008 0.000 0.000 0.032 0.960 0.000
#> SRR1706831 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706832 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706833 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706834 4 0.0692 0.988 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1706839 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706840 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706841 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706842 1 0.3062 0.875 0.816 0.000 0.000 0.000 0.160 0.024
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706843 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706851 6 0.4264 0.687 0.000 0.000 0.352 0.000 0.028 0.620
#> SRR1706852 6 0.4264 0.687 0.000 0.000 0.352 0.000 0.028 0.620
#> SRR1706853 6 0.4264 0.687 0.000 0.000 0.352 0.000 0.028 0.620
#> SRR1706854 6 0.4264 0.687 0.000 0.000 0.352 0.000 0.028 0.620
#> SRR1706855 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706856 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706857 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706858 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706859 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706860 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706861 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706862 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706863 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706871 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706872 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706873 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706874 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706879 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706880 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706881 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706882 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706883 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706875 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706876 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706877 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706878 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706891 6 0.4458 0.682 0.000 0.000 0.352 0.000 0.040 0.608
#> SRR1706892 6 0.4458 0.682 0.000 0.000 0.352 0.000 0.040 0.608
#> SRR1706893 6 0.4458 0.682 0.000 0.000 0.352 0.000 0.040 0.608
#> SRR1706894 6 0.4458 0.682 0.000 0.000 0.352 0.000 0.040 0.608
#> SRR1706895 6 0.1151 0.756 0.000 0.032 0.000 0.000 0.012 0.956
#> SRR1706896 6 0.1151 0.756 0.000 0.032 0.000 0.000 0.012 0.956
#> SRR1706897 6 0.1151 0.756 0.000 0.032 0.000 0.000 0.012 0.956
#> SRR1706898 6 0.1151 0.756 0.000 0.032 0.000 0.000 0.012 0.956
#> SRR1706899 2 0.1765 0.948 0.000 0.904 0.000 0.000 0.000 0.096
#> SRR1706900 2 0.1765 0.948 0.000 0.904 0.000 0.000 0.000 0.096
#> SRR1706901 2 0.1765 0.948 0.000 0.904 0.000 0.000 0.000 0.096
#> SRR1706902 2 0.1765 0.948 0.000 0.904 0.000 0.000 0.000 0.096
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706903 2 0.0146 0.957 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706904 2 0.0146 0.957 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706905 2 0.0146 0.957 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706906 2 0.0146 0.957 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706911 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706912 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706913 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706914 6 0.4289 0.682 0.000 0.000 0.360 0.000 0.028 0.612
#> SRR1706919 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706920 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706921 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706922 2 0.1501 0.958 0.000 0.924 0.000 0.000 0.000 0.076
#> SRR1706915 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706916 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706917 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706918 6 0.0937 0.760 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1706923 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.958 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.958 0.000 1.000 0.000 0.000 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["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 15185 rows and 159 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.751 0.910 0.829 0.2455 0.877 0.753
#> 4 4 1.000 0.996 0.998 0.1984 0.874 0.663
#> 5 5 0.928 0.907 0.927 0.0253 0.896 0.644
#> 6 6 0.924 0.881 0.914 0.0268 0.894 0.619
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706768 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706769 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706770 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706771 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706772 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706773 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706774 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706775 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706776 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706777 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706778 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706779 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706780 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706781 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706782 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706783 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706784 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706785 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706786 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706787 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706788 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706789 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706790 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706791 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706792 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706793 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706794 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706795 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706796 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706797 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706798 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706799 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706800 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706801 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706802 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706803 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706804 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706805 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706806 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706811 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706812 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706813 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706814 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706807 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706808 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706809 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706810 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706815 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706816 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706817 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706818 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706819 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706820 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706821 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706822 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706823 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706824 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706825 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706826 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706827 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706828 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706829 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706830 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706835 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706836 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706837 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706838 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706831 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706832 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706833 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706834 1 0.000 0.805 1.00 0.000 0.000
#> SRR1706839 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706840 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706841 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706842 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706847 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706848 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706849 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706850 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706843 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706844 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706845 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706846 1 0.604 0.844 0.62 0.000 0.380
#> SRR1706851 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706852 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706853 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706854 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706855 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706856 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706857 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706858 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706859 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706860 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706861 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706862 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706867 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706869 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706870 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706863 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706864 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706865 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706866 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706871 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706872 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706873 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706874 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706879 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706880 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706881 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706882 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706883 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706884 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706885 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706886 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706875 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706876 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706877 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706878 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706887 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706888 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706889 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706890 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706891 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706892 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706893 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706894 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706895 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706896 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706897 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706898 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706899 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706900 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706901 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706902 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706907 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706908 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706909 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706910 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706903 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706904 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706905 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706906 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706911 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706912 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706913 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706914 3 0.604 0.994 0.00 0.380 0.620
#> SRR1706919 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706920 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706921 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706922 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706915 3 0.618 0.944 0.00 0.416 0.584
#> SRR1706916 3 0.618 0.944 0.00 0.416 0.584
#> SRR1706917 3 0.618 0.944 0.00 0.416 0.584
#> SRR1706918 3 0.614 0.962 0.00 0.404 0.596
#> SRR1706923 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706924 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706925 2 0.000 1.000 0.00 1.000 0.000
#> SRR1706926 2 0.000 1.000 0.00 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706772 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706773 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706774 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706775 1 0.0188 0.996 0.996 0.000 0.000 0.004
#> SRR1706776 1 0.0188 0.996 0.996 0.000 0.000 0.004
#> SRR1706777 1 0.0188 0.996 0.996 0.000 0.000 0.004
#> SRR1706778 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706792 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706793 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706794 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706795 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706796 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706797 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706798 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706812 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706813 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706814 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706815 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706816 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706817 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706818 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706835 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706836 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706837 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706838 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706831 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706832 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706833 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706834 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706848 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706849 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706850 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706856 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706857 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706858 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706859 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706869 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706870 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706863 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706876 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706877 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706878 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706887 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706888 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706889 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706890 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706891 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706896 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706897 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706898 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706899 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706908 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706909 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706910 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706903 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706915 3 0.2281 0.903 0.000 0.096 0.904 0.000
#> SRR1706916 3 0.1940 0.924 0.000 0.076 0.924 0.000
#> SRR1706917 3 0.2216 0.907 0.000 0.092 0.908 0.000
#> SRR1706918 3 0.2011 0.920 0.000 0.080 0.920 0.000
#> SRR1706923 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706771 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706772 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706773 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706774 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706775 1 0.0162 0.996 0.996 0.000 0 0.004 0.000
#> SRR1706776 1 0.0162 0.996 0.996 0.000 0 0.004 0.000
#> SRR1706777 1 0.0162 0.995 0.996 0.000 0 0.004 0.000
#> SRR1706778 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706791 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706792 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706793 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706794 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706795 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706796 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706797 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706798 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706811 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706812 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706813 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706814 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706815 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706816 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706817 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706818 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706835 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706836 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706837 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706838 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706831 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706832 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706833 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706834 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0 0.000 0.000
#> SRR1706851 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706852 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706853 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706854 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706855 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706856 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706857 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706858 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706859 2 0.1121 0.645 0.000 0.956 0 0.000 0.044
#> SRR1706860 2 0.1478 0.609 0.000 0.936 0 0.000 0.064
#> SRR1706861 2 0.0794 0.670 0.000 0.972 0 0.000 0.028
#> SRR1706862 2 0.1410 0.617 0.000 0.940 0 0.000 0.060
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706863 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706864 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706865 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706866 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706871 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706872 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706873 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706874 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706879 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706880 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706881 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706882 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706883 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706884 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706885 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706886 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706875 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706876 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706877 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706878 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706891 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706892 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706893 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706894 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706895 2 0.0162 0.710 0.000 0.996 0 0.000 0.004
#> SRR1706896 2 0.0162 0.710 0.000 0.996 0 0.000 0.004
#> SRR1706897 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706898 2 0.0162 0.710 0.000 0.996 0 0.000 0.004
#> SRR1706899 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706900 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706901 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706902 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR1706903 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706904 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706905 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706906 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706911 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706912 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706913 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706914 2 0.4192 0.679 0.000 0.596 0 0.000 0.404
#> SRR1706919 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706920 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706921 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706922 2 0.0000 0.710 0.000 1.000 0 0.000 0.000
#> SRR1706915 2 0.4060 0.685 0.000 0.640 0 0.000 0.360
#> SRR1706916 2 0.4114 0.683 0.000 0.624 0 0.000 0.376
#> SRR1706917 2 0.4060 0.685 0.000 0.640 0 0.000 0.360
#> SRR1706918 2 0.4074 0.685 0.000 0.636 0 0.000 0.364
#> SRR1706923 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706924 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706925 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
#> SRR1706926 5 0.4192 1.000 0.000 0.404 0 0.000 0.596
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706771 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706772 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706773 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706774 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706775 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706776 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706777 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706778 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706779 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706780 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706781 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706782 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706791 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706792 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706793 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706794 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706795 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706796 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706797 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706798 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706799 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706800 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706801 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706802 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706811 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706812 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706813 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706814 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706815 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706816 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706817 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706818 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706819 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706820 5 0.1387 0.920 0.068 0.000 0 0.000 0.932 0.000
#> SRR1706821 5 0.1714 0.900 0.092 0.000 0 0.000 0.908 0.000
#> SRR1706822 5 0.1267 0.926 0.060 0.000 0 0.000 0.940 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706835 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706836 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706837 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706838 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706831 5 0.3515 0.549 0.000 0.000 0 0.324 0.676 0.000
#> SRR1706832 5 0.3515 0.549 0.000 0.000 0 0.324 0.676 0.000
#> SRR1706833 5 0.3515 0.549 0.000 0.000 0 0.324 0.676 0.000
#> SRR1706834 5 0.3515 0.549 0.000 0.000 0 0.324 0.676 0.000
#> SRR1706839 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706840 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706841 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706842 5 0.0000 0.955 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706851 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706852 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706853 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706854 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706855 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706856 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706857 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706858 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706859 6 0.3838 0.645 0.000 0.448 0 0.000 0.000 0.552
#> SRR1706860 6 0.3857 0.609 0.000 0.468 0 0.000 0.000 0.532
#> SRR1706861 6 0.3817 0.670 0.000 0.432 0 0.000 0.000 0.568
#> SRR1706862 6 0.3854 0.617 0.000 0.464 0 0.000 0.000 0.536
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706863 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706871 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706872 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706873 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706874 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706879 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706880 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706881 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706882 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706883 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706875 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706876 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706877 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706878 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706891 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706892 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706893 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706894 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706895 6 0.3756 0.710 0.000 0.400 0 0.000 0.000 0.600
#> SRR1706896 6 0.3756 0.710 0.000 0.400 0 0.000 0.000 0.600
#> SRR1706897 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706898 6 0.3756 0.710 0.000 0.400 0 0.000 0.000 0.600
#> SRR1706899 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706900 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706901 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706902 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706903 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706911 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706912 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706913 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706914 6 0.0000 0.679 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706919 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706920 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706921 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706922 6 0.3765 0.710 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706915 6 0.1007 0.685 0.000 0.044 0 0.000 0.000 0.956
#> SRR1706916 6 0.0713 0.683 0.000 0.028 0 0.000 0.000 0.972
#> SRR1706917 6 0.1007 0.685 0.000 0.044 0 0.000 0.000 0.956
#> SRR1706918 6 0.0937 0.685 0.000 0.040 0 0.000 0.000 0.960
#> SRR1706923 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1.000 0 0.000 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["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 15185 rows and 159 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 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 1.000 1.000 0.504 0.497 0.497
#> 3 3 0.784 0.825 0.892 0.221 0.906 0.811
#> 4 4 0.697 0.718 0.809 0.126 0.934 0.836
#> 5 5 0.657 0.502 0.758 0.117 0.878 0.637
#> 6 6 0.731 0.529 0.706 0.043 0.871 0.509
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706768 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706769 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706770 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706771 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706772 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706773 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706774 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706775 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706776 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706777 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706778 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706779 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706783 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706784 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706785 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706786 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706787 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706788 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706789 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706790 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706791 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706792 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706793 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706794 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706795 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706796 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706797 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706798 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706799 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706802 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706803 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706804 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706805 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706806 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706811 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706812 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706813 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706814 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706807 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706808 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706809 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706810 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706815 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706816 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706817 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706818 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706819 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706820 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706821 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706822 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706823 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706824 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706825 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706826 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706827 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706828 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706829 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706830 1 0.0892 0.917 0.980 0.020 0.000
#> SRR1706835 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706836 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706837 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706838 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706831 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706832 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706833 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706834 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706839 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1706847 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706848 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706849 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706850 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706843 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706844 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706845 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706846 1 0.5882 0.655 0.652 0.348 0.000
#> SRR1706851 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706852 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706853 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706854 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706855 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706856 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706857 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706858 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706859 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706860 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706861 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706862 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706867 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706869 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706870 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706863 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706864 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706865 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706866 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706871 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706872 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706873 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706874 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706879 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706880 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706881 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706882 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706883 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706884 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706885 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706886 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706875 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706876 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706877 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706878 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706887 3 0.1753 0.832 0.000 0.048 0.952
#> SRR1706888 3 0.1753 0.832 0.000 0.048 0.952
#> SRR1706889 3 0.1753 0.832 0.000 0.048 0.952
#> SRR1706890 3 0.1753 0.832 0.000 0.048 0.952
#> SRR1706891 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706892 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706893 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706894 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706895 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706896 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706897 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706898 3 0.0000 0.853 0.000 0.000 1.000
#> SRR1706899 3 0.0892 0.847 0.000 0.020 0.980
#> SRR1706900 3 0.0892 0.847 0.000 0.020 0.980
#> SRR1706901 3 0.0892 0.847 0.000 0.020 0.980
#> SRR1706902 3 0.0892 0.847 0.000 0.020 0.980
#> SRR1706907 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706908 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706909 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706910 2 0.5905 0.851 0.000 0.648 0.352
#> SRR1706903 3 0.6126 0.582 0.000 0.400 0.600
#> SRR1706904 3 0.6126 0.582 0.000 0.400 0.600
#> SRR1706905 3 0.6126 0.582 0.000 0.400 0.600
#> SRR1706906 3 0.6126 0.582 0.000 0.400 0.600
#> SRR1706911 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706912 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706913 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706914 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706919 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706920 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706921 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706922 2 0.5905 0.853 0.000 0.648 0.352
#> SRR1706915 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706916 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706917 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706918 2 0.6008 0.857 0.000 0.628 0.372
#> SRR1706923 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706924 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706925 2 0.1163 0.474 0.000 0.972 0.028
#> SRR1706926 2 0.1163 0.474 0.000 0.972 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706768 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706769 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706770 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706771 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706772 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706773 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706774 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706775 1 0.0336 0.845 0.992 0.008 0.000 0.000
#> SRR1706776 1 0.0000 0.845 1.000 0.000 0.000 0.000
#> SRR1706777 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706778 1 0.1022 0.841 0.968 0.032 0.000 0.000
#> SRR1706779 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706780 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706781 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706782 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706783 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706784 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706785 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706786 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706787 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706788 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706789 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706790 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706791 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706792 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706793 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706794 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706795 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706796 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706797 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706798 1 0.1211 0.839 0.960 0.040 0.000 0.000
#> SRR1706799 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706800 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706801 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706802 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706803 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706804 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706805 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706806 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706811 1 0.0376 0.845 0.992 0.004 0.000 0.004
#> SRR1706812 1 0.0376 0.845 0.992 0.004 0.000 0.004
#> SRR1706813 1 0.0376 0.845 0.992 0.004 0.000 0.004
#> SRR1706814 1 0.0376 0.845 0.992 0.004 0.000 0.004
#> SRR1706807 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706808 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706809 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706810 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706815 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706816 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706817 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706818 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706819 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706820 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706821 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706822 1 0.0188 0.845 0.996 0.004 0.000 0.000
#> SRR1706823 1 0.6197 0.620 0.604 0.324 0.000 0.072
#> SRR1706824 1 0.6197 0.620 0.604 0.324 0.000 0.072
#> SRR1706825 1 0.6197 0.620 0.604 0.324 0.000 0.072
#> SRR1706826 1 0.6197 0.620 0.604 0.324 0.000 0.072
#> SRR1706827 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706828 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706829 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706830 1 0.4454 0.696 0.692 0.000 0.000 0.308
#> SRR1706835 1 0.0000 0.845 1.000 0.000 0.000 0.000
#> SRR1706836 1 0.0000 0.845 1.000 0.000 0.000 0.000
#> SRR1706837 1 0.0000 0.845 1.000 0.000 0.000 0.000
#> SRR1706838 1 0.0000 0.845 1.000 0.000 0.000 0.000
#> SRR1706831 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706832 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706833 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706834 1 0.0188 0.845 0.996 0.000 0.000 0.004
#> SRR1706839 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706840 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706841 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706842 1 0.1118 0.840 0.964 0.036 0.000 0.000
#> SRR1706847 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706848 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706849 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706850 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706843 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706844 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706845 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706846 1 0.6337 0.609 0.568 0.360 0.000 0.072
#> SRR1706851 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706856 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706857 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706858 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706859 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706860 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706861 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706862 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706867 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706869 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706870 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706863 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706864 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706865 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706866 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706871 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706880 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706881 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706882 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706883 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706884 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706885 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706886 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706875 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706876 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706877 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706878 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706887 4 0.2670 0.465 0.000 0.072 0.024 0.904
#> SRR1706888 4 0.2670 0.465 0.000 0.072 0.024 0.904
#> SRR1706889 4 0.2670 0.465 0.000 0.072 0.024 0.904
#> SRR1706890 4 0.2670 0.465 0.000 0.072 0.024 0.904
#> SRR1706891 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706892 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706893 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706894 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706895 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706896 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706897 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706898 4 0.5269 0.720 0.000 0.016 0.364 0.620
#> SRR1706899 4 0.5355 0.719 0.000 0.020 0.360 0.620
#> SRR1706900 4 0.5355 0.719 0.000 0.020 0.360 0.620
#> SRR1706901 4 0.5355 0.719 0.000 0.020 0.360 0.620
#> SRR1706902 4 0.5355 0.719 0.000 0.020 0.360 0.620
#> SRR1706907 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706908 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706909 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706910 3 0.6058 0.656 0.000 0.072 0.632 0.296
#> SRR1706903 4 0.5231 0.525 0.000 0.384 0.012 0.604
#> SRR1706904 4 0.5231 0.525 0.000 0.384 0.012 0.604
#> SRR1706905 4 0.5231 0.525 0.000 0.384 0.012 0.604
#> SRR1706906 4 0.5231 0.525 0.000 0.384 0.012 0.604
#> SRR1706911 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.657 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706920 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706921 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706922 2 0.4955 0.770 0.000 0.556 0.444 0.000
#> SRR1706915 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706916 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706917 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706918 2 0.4961 0.768 0.000 0.552 0.448 0.000
#> SRR1706923 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706924 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706925 2 0.1211 0.554 0.000 0.960 0.040 0.000
#> SRR1706926 2 0.1211 0.554 0.000 0.960 0.040 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706768 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706769 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706770 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706771 4 0.0290 0.512 0.008 0.000 0.000 0.992 0.000
#> SRR1706772 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706773 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706774 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706775 4 0.3796 0.213 0.300 0.000 0.000 0.700 0.000
#> SRR1706776 4 0.3796 0.213 0.300 0.000 0.000 0.700 0.000
#> SRR1706777 4 0.3816 0.206 0.304 0.000 0.000 0.696 0.000
#> SRR1706778 4 0.3895 0.175 0.320 0.000 0.000 0.680 0.000
#> SRR1706779 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706780 1 0.4201 0.421 0.592 0.000 0.000 0.408 0.000
#> SRR1706781 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706782 1 0.4201 0.421 0.592 0.000 0.000 0.408 0.000
#> SRR1706783 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706784 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706785 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706786 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706787 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706788 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706789 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706790 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706791 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.0162 0.513 0.004 0.000 0.000 0.996 0.000
#> SRR1706794 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 4 0.4294 -0.147 0.468 0.000 0.000 0.532 0.000
#> SRR1706796 4 0.4294 -0.147 0.468 0.000 0.000 0.532 0.000
#> SRR1706797 4 0.4294 -0.147 0.468 0.000 0.000 0.532 0.000
#> SRR1706798 4 0.4294 -0.147 0.468 0.000 0.000 0.532 0.000
#> SRR1706799 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706800 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706801 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706802 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706803 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706804 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706805 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706806 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706811 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706812 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706813 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706814 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706807 1 0.5776 0.272 0.588 0.000 0.288 0.124 0.000
#> SRR1706808 1 0.5776 0.272 0.588 0.000 0.288 0.124 0.000
#> SRR1706809 1 0.5776 0.272 0.588 0.000 0.288 0.124 0.000
#> SRR1706810 1 0.5776 0.272 0.588 0.000 0.288 0.124 0.000
#> SRR1706815 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706816 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706817 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706818 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706819 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706820 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706821 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706822 4 0.4302 -0.167 0.480 0.000 0.000 0.520 0.000
#> SRR1706823 1 0.6146 0.586 0.468 0.000 0.000 0.132 0.400
#> SRR1706824 1 0.6146 0.586 0.468 0.000 0.000 0.132 0.400
#> SRR1706825 1 0.6146 0.586 0.468 0.000 0.000 0.132 0.400
#> SRR1706826 1 0.6146 0.586 0.468 0.000 0.000 0.132 0.400
#> SRR1706827 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706828 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706829 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706830 4 0.5655 0.341 0.112 0.000 0.288 0.600 0.000
#> SRR1706835 4 0.0794 0.503 0.028 0.000 0.000 0.972 0.000
#> SRR1706836 4 0.1197 0.493 0.048 0.000 0.000 0.952 0.000
#> SRR1706837 4 0.1121 0.496 0.044 0.000 0.000 0.956 0.000
#> SRR1706838 4 0.0880 0.501 0.032 0.000 0.000 0.968 0.000
#> SRR1706831 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 0.514 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706840 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706841 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706842 1 0.4210 0.419 0.588 0.000 0.000 0.412 0.000
#> SRR1706847 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706844 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706845 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706846 1 0.4446 0.652 0.592 0.000 0.000 0.008 0.400
#> SRR1706851 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706852 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706853 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706854 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706855 2 0.4249 0.767 0.296 0.688 0.016 0.000 0.000
#> SRR1706856 2 0.4249 0.767 0.296 0.688 0.016 0.000 0.000
#> SRR1706857 2 0.4249 0.767 0.296 0.688 0.016 0.000 0.000
#> SRR1706858 2 0.4249 0.767 0.296 0.688 0.016 0.000 0.000
#> SRR1706859 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706860 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706861 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706862 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706867 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706863 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706864 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706865 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706866 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706871 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706872 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706873 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706874 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706879 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706880 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706881 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706882 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706883 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706884 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706885 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706886 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706875 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706876 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706877 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706878 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706887 5 0.4256 0.544 0.000 0.000 0.436 0.000 0.564
#> SRR1706888 5 0.4256 0.544 0.000 0.000 0.436 0.000 0.564
#> SRR1706889 5 0.4256 0.544 0.000 0.000 0.436 0.000 0.564
#> SRR1706890 5 0.4256 0.544 0.000 0.000 0.436 0.000 0.564
#> SRR1706891 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706892 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706893 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706894 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706895 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706896 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706897 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706898 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706899 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706900 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706901 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706902 5 0.5991 0.774 0.288 0.000 0.148 0.000 0.564
#> SRR1706907 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.0000 0.583 0.000 0.000 1.000 0.000 0.000
#> SRR1706903 5 0.5991 0.625 0.000 0.288 0.148 0.000 0.564
#> SRR1706904 5 0.5991 0.625 0.000 0.288 0.148 0.000 0.564
#> SRR1706905 5 0.5991 0.625 0.000 0.288 0.148 0.000 0.564
#> SRR1706906 5 0.5991 0.625 0.000 0.288 0.148 0.000 0.564
#> SRR1706911 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706912 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706913 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706914 3 0.6024 0.624 0.296 0.148 0.556 0.000 0.000
#> SRR1706919 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706920 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706921 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706922 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706915 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706916 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706917 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706918 2 0.3774 0.783 0.296 0.704 0.000 0.000 0.000
#> SRR1706923 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706924 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706925 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
#> SRR1706926 2 0.3115 0.535 0.000 0.852 0.112 0.000 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706768 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706769 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706770 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706771 4 0.3993 0.61463 0.008 0.000 0.000 0.592 0.400 0.000
#> SRR1706772 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706773 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706774 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706775 5 0.6041 -0.38869 0.344 0.000 0.000 0.256 0.400 0.000
#> SRR1706776 5 0.6047 -0.38450 0.340 0.000 0.000 0.260 0.400 0.000
#> SRR1706777 5 0.6041 -0.38869 0.344 0.000 0.000 0.256 0.400 0.000
#> SRR1706778 5 0.6041 -0.38869 0.344 0.000 0.000 0.256 0.400 0.000
#> SRR1706779 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706780 1 0.3756 0.60878 0.600 0.000 0.000 0.000 0.400 0.000
#> SRR1706781 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706782 1 0.3756 0.60878 0.600 0.000 0.000 0.000 0.400 0.000
#> SRR1706783 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706788 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706789 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706790 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706791 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706792 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706793 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706794 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706795 1 0.5368 0.46481 0.488 0.000 0.000 0.112 0.400 0.000
#> SRR1706796 1 0.5368 0.46481 0.488 0.000 0.000 0.112 0.400 0.000
#> SRR1706797 1 0.5368 0.46481 0.488 0.000 0.000 0.112 0.400 0.000
#> SRR1706798 1 0.5368 0.46481 0.488 0.000 0.000 0.112 0.400 0.000
#> SRR1706799 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706800 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706801 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706802 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706803 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706811 5 0.6759 -0.11248 0.088 0.384 0.000 0.128 0.400 0.000
#> SRR1706812 5 0.6759 -0.11248 0.088 0.384 0.000 0.128 0.400 0.000
#> SRR1706813 5 0.6759 -0.11248 0.088 0.384 0.000 0.128 0.400 0.000
#> SRR1706814 5 0.6759 -0.11248 0.088 0.384 0.000 0.128 0.400 0.000
#> SRR1706807 4 0.5086 0.24528 0.084 0.384 0.000 0.532 0.000 0.000
#> SRR1706808 4 0.5086 0.24528 0.084 0.384 0.000 0.532 0.000 0.000
#> SRR1706809 4 0.5086 0.24528 0.084 0.384 0.000 0.532 0.000 0.000
#> SRR1706810 4 0.5086 0.24528 0.084 0.384 0.000 0.532 0.000 0.000
#> SRR1706815 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706816 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706817 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706818 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706819 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706820 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706821 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706822 5 0.6779 -0.09478 0.104 0.384 0.000 0.112 0.400 0.000
#> SRR1706823 1 0.5443 0.19680 0.492 0.384 0.000 0.124 0.000 0.000
#> SRR1706824 1 0.5443 0.19680 0.492 0.384 0.000 0.124 0.000 0.000
#> SRR1706825 1 0.5443 0.19680 0.492 0.384 0.000 0.124 0.000 0.000
#> SRR1706826 1 0.5443 0.19680 0.492 0.384 0.000 0.124 0.000 0.000
#> SRR1706827 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706828 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706829 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706830 4 0.0000 0.62486 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706835 4 0.4634 0.56838 0.044 0.000 0.000 0.556 0.400 0.000
#> SRR1706836 4 0.4845 0.55141 0.060 0.000 0.000 0.540 0.400 0.000
#> SRR1706837 4 0.4845 0.55141 0.060 0.000 0.000 0.540 0.400 0.000
#> SRR1706838 4 0.4634 0.56838 0.044 0.000 0.000 0.556 0.400 0.000
#> SRR1706831 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706832 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706833 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706834 4 0.3756 0.62262 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1706839 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706840 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706841 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706842 1 0.3890 0.60827 0.596 0.000 0.000 0.004 0.400 0.000
#> SRR1706847 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706848 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706849 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706850 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706843 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.61106 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706851 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706852 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706853 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706854 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706855 6 0.0865 0.94351 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR1706856 6 0.0865 0.94351 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR1706857 6 0.0865 0.94351 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR1706858 6 0.0865 0.94351 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR1706859 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706860 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706861 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706862 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706867 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706869 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706870 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706863 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706864 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706865 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706866 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706871 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706872 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706873 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706874 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706879 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706880 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706881 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706882 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706883 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706884 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706885 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706886 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706875 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706876 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706877 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706878 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706887 3 0.0000 0.62863 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706888 3 0.0000 0.62863 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706889 3 0.0000 0.62863 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706890 3 0.0000 0.62863 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706891 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706892 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706893 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706894 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706895 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706896 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706897 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706898 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706899 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706900 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706901 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706902 3 0.3351 0.78197 0.000 0.000 0.712 0.000 0.000 0.288
#> SRR1706907 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706908 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706909 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706910 5 0.3756 0.19024 0.000 0.000 0.400 0.000 0.600 0.000
#> SRR1706903 3 0.3747 0.43234 0.000 0.396 0.604 0.000 0.000 0.000
#> SRR1706904 3 0.3747 0.43234 0.000 0.396 0.604 0.000 0.000 0.000
#> SRR1706905 3 0.3747 0.43234 0.000 0.396 0.604 0.000 0.000 0.000
#> SRR1706906 3 0.3747 0.43234 0.000 0.396 0.604 0.000 0.000 0.000
#> SRR1706911 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706912 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706913 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706914 5 0.3851 0.00874 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1706919 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706920 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706921 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706922 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706915 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706916 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706917 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706918 6 0.0000 0.98861 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706923 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706924 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706925 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
#> SRR1706926 2 0.3717 1.00000 0.000 0.616 0.000 0.000 0.000 0.384
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 15185 rows and 159 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 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.680 0.771 0.883 0.2882 0.783 0.589
#> 4 4 0.869 0.961 0.896 0.0843 0.765 0.435
#> 5 5 0.786 0.903 0.883 0.0331 1.000 1.000
#> 6 6 0.760 0.879 0.892 0.0340 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] 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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706768 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706769 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706770 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706771 1 0.418 0.795 0.828 0.000 0.172
#> SRR1706772 1 0.412 0.796 0.832 0.000 0.168
#> SRR1706773 1 0.412 0.797 0.832 0.000 0.168
#> SRR1706774 1 0.400 0.799 0.840 0.000 0.160
#> SRR1706775 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706776 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706777 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706778 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706779 3 0.116 0.765 0.028 0.000 0.972
#> SRR1706780 3 0.129 0.761 0.032 0.000 0.968
#> SRR1706781 3 0.141 0.757 0.036 0.000 0.964
#> SRR1706782 3 0.129 0.761 0.032 0.000 0.968
#> SRR1706783 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706784 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706785 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706786 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706787 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706788 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706789 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706790 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706791 1 0.382 0.803 0.852 0.000 0.148
#> SRR1706792 1 0.369 0.804 0.860 0.000 0.140
#> SRR1706793 1 0.362 0.805 0.864 0.000 0.136
#> SRR1706794 1 0.382 0.803 0.852 0.000 0.148
#> SRR1706795 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706796 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706797 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706798 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706799 3 0.103 0.768 0.024 0.000 0.976
#> SRR1706800 3 0.103 0.768 0.024 0.000 0.976
#> SRR1706801 3 0.129 0.761 0.032 0.000 0.968
#> SRR1706802 3 0.103 0.768 0.024 0.000 0.976
#> SRR1706803 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706804 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706805 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706806 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706811 1 0.263 0.805 0.916 0.000 0.084
#> SRR1706812 1 0.296 0.807 0.900 0.000 0.100
#> SRR1706813 1 0.271 0.806 0.912 0.000 0.088
#> SRR1706814 1 0.296 0.807 0.900 0.000 0.100
#> SRR1706807 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706808 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706809 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706810 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706815 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706816 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706817 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706818 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706819 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706820 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706821 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706822 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706823 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706824 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706825 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706826 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706827 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706828 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706829 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706830 1 0.000 0.788 1.000 0.000 0.000
#> SRR1706835 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706836 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706837 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706838 1 0.625 0.622 0.556 0.000 0.444
#> SRR1706831 1 0.319 0.808 0.888 0.000 0.112
#> SRR1706832 1 0.327 0.808 0.884 0.000 0.116
#> SRR1706833 1 0.334 0.807 0.880 0.000 0.120
#> SRR1706834 1 0.327 0.808 0.884 0.000 0.116
#> SRR1706839 3 0.207 0.726 0.060 0.000 0.940
#> SRR1706840 3 0.164 0.748 0.044 0.000 0.956
#> SRR1706841 3 0.175 0.743 0.048 0.000 0.952
#> SRR1706842 3 0.207 0.726 0.060 0.000 0.940
#> SRR1706847 2 0.312 0.871 0.108 0.892 0.000
#> SRR1706848 2 0.312 0.871 0.108 0.892 0.000
#> SRR1706849 2 0.312 0.871 0.108 0.892 0.000
#> SRR1706850 2 0.312 0.871 0.108 0.892 0.000
#> SRR1706843 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706844 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706845 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706846 3 0.000 0.783 0.000 0.000 1.000
#> SRR1706851 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706852 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706853 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706854 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706855 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706856 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706857 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706858 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706859 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706860 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706861 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706862 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706867 2 0.429 0.804 0.180 0.820 0.000
#> SRR1706869 2 0.440 0.795 0.188 0.812 0.000
#> SRR1706870 2 0.424 0.808 0.176 0.824 0.000
#> SRR1706863 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706864 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706865 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706866 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706871 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706872 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706873 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706874 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706879 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706880 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706881 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706882 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706883 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706884 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706885 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706886 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706875 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706876 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706877 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706878 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706887 2 0.296 0.876 0.100 0.900 0.000
#> SRR1706888 2 0.304 0.874 0.104 0.896 0.000
#> SRR1706889 2 0.304 0.874 0.104 0.896 0.000
#> SRR1706890 2 0.296 0.876 0.100 0.900 0.000
#> SRR1706891 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706892 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706893 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706894 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706895 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706896 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706897 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706898 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706899 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706900 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706901 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706902 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706907 2 0.388 0.833 0.152 0.848 0.000
#> SRR1706908 2 0.400 0.825 0.160 0.840 0.000
#> SRR1706909 2 0.369 0.844 0.140 0.860 0.000
#> SRR1706910 2 0.362 0.847 0.136 0.864 0.000
#> SRR1706903 2 0.614 0.183 0.000 0.596 0.404
#> SRR1706904 2 0.615 0.169 0.000 0.592 0.408
#> SRR1706905 2 0.615 0.169 0.000 0.592 0.408
#> SRR1706906 2 0.613 0.196 0.000 0.600 0.400
#> SRR1706911 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706912 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706913 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706914 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706919 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706920 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706921 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706922 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706915 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706916 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706917 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706918 2 0.000 0.937 0.000 1.000 0.000
#> SRR1706923 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706924 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706925 3 0.625 0.329 0.000 0.444 0.556
#> SRR1706926 3 0.625 0.329 0.000 0.444 0.556
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.0707 0.971 0.000 0.020 0.000 0.980
#> SRR1706772 4 0.0336 0.974 0.000 0.008 0.000 0.992
#> SRR1706773 4 0.0592 0.972 0.000 0.016 0.000 0.984
#> SRR1706774 4 0.0469 0.973 0.000 0.012 0.000 0.988
#> SRR1706775 1 0.1209 0.954 0.964 0.032 0.000 0.004
#> SRR1706776 1 0.1109 0.956 0.968 0.028 0.000 0.004
#> SRR1706777 1 0.1305 0.952 0.960 0.036 0.000 0.004
#> SRR1706778 1 0.0895 0.958 0.976 0.020 0.000 0.004
#> SRR1706779 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706784 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706785 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706786 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706787 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.1004 0.969 0.004 0.024 0.000 0.972
#> SRR1706792 4 0.0779 0.972 0.004 0.016 0.000 0.980
#> SRR1706793 4 0.0779 0.972 0.004 0.016 0.000 0.980
#> SRR1706794 4 0.0895 0.970 0.004 0.020 0.000 0.976
#> SRR1706795 1 0.1118 0.954 0.964 0.036 0.000 0.000
#> SRR1706796 1 0.0817 0.958 0.976 0.024 0.000 0.000
#> SRR1706797 1 0.1211 0.952 0.960 0.040 0.000 0.000
#> SRR1706798 1 0.1302 0.950 0.956 0.044 0.000 0.000
#> SRR1706799 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706804 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706805 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706806 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706811 4 0.3908 0.858 0.004 0.212 0.000 0.784
#> SRR1706812 4 0.3908 0.858 0.004 0.212 0.000 0.784
#> SRR1706813 4 0.3982 0.853 0.004 0.220 0.000 0.776
#> SRR1706814 4 0.3870 0.861 0.004 0.208 0.000 0.788
#> SRR1706807 4 0.0336 0.974 0.000 0.008 0.000 0.992
#> SRR1706808 4 0.0336 0.974 0.000 0.008 0.000 0.992
#> SRR1706809 4 0.0336 0.974 0.000 0.008 0.000 0.992
#> SRR1706810 4 0.0336 0.974 0.000 0.008 0.000 0.992
#> SRR1706815 1 0.6221 0.649 0.644 0.256 0.000 0.100
#> SRR1706816 1 0.6350 0.635 0.636 0.252 0.000 0.112
#> SRR1706817 1 0.6352 0.633 0.632 0.260 0.000 0.108
#> SRR1706818 1 0.6084 0.666 0.656 0.252 0.000 0.092
#> SRR1706819 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR1706820 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR1706822 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR1706823 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706824 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706825 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706826 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706827 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> SRR1706835 1 0.1902 0.938 0.932 0.064 0.000 0.004
#> SRR1706836 1 0.1824 0.940 0.936 0.060 0.000 0.004
#> SRR1706837 1 0.1824 0.940 0.936 0.060 0.000 0.004
#> SRR1706838 1 0.1978 0.935 0.928 0.068 0.000 0.004
#> SRR1706831 4 0.0524 0.973 0.004 0.008 0.000 0.988
#> SRR1706832 4 0.0524 0.973 0.004 0.008 0.000 0.988
#> SRR1706833 4 0.0779 0.972 0.004 0.016 0.000 0.980
#> SRR1706834 4 0.0779 0.972 0.004 0.016 0.000 0.980
#> SRR1706839 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.965 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706843 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706844 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706845 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706846 1 0.0188 0.965 0.996 0.004 0.000 0.000
#> SRR1706851 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.4916 0.968 0.000 0.576 0.424 0.000
#> SRR1706856 2 0.4907 0.971 0.000 0.580 0.420 0.000
#> SRR1706857 2 0.4907 0.971 0.000 0.580 0.420 0.000
#> SRR1706858 2 0.4916 0.968 0.000 0.576 0.424 0.000
#> SRR1706859 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706860 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706861 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706862 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706863 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706864 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706865 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706866 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706871 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706880 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706881 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706882 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706883 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706884 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706885 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706886 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706875 2 0.4972 0.932 0.000 0.544 0.456 0.000
#> SRR1706876 2 0.4961 0.943 0.000 0.552 0.448 0.000
#> SRR1706877 2 0.4948 0.952 0.000 0.560 0.440 0.000
#> SRR1706878 2 0.4981 0.920 0.000 0.536 0.464 0.000
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706891 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.4967 0.936 0.000 0.548 0.452 0.000
#> SRR1706896 2 0.4961 0.940 0.000 0.552 0.448 0.000
#> SRR1706897 2 0.4967 0.936 0.000 0.548 0.452 0.000
#> SRR1706898 2 0.4967 0.936 0.000 0.548 0.452 0.000
#> SRR1706899 2 0.4888 0.975 0.000 0.588 0.412 0.000
#> SRR1706900 2 0.4888 0.975 0.000 0.588 0.412 0.000
#> SRR1706901 2 0.4888 0.975 0.000 0.588 0.412 0.000
#> SRR1706902 2 0.4888 0.975 0.000 0.588 0.412 0.000
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706903 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706904 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706905 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706906 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706911 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706920 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706921 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706922 2 0.4877 0.975 0.000 0.592 0.408 0.000
#> SRR1706915 2 0.4967 0.938 0.000 0.548 0.452 0.000
#> SRR1706916 2 0.4989 0.906 0.000 0.528 0.472 0.000
#> SRR1706917 2 0.4977 0.926 0.000 0.540 0.460 0.000
#> SRR1706918 2 0.4992 0.899 0.000 0.524 0.476 0.000
#> SRR1706923 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706924 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706925 2 0.4888 0.976 0.000 0.588 0.412 0.000
#> SRR1706926 2 0.4888 0.976 0.000 0.588 0.412 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0290 0.973 0.000 0.000 0.000 0.992 NA
#> SRR1706768 4 0.0404 0.973 0.000 0.000 0.000 0.988 NA
#> SRR1706769 4 0.0404 0.973 0.000 0.000 0.000 0.988 NA
#> SRR1706770 4 0.0404 0.973 0.000 0.000 0.000 0.988 NA
#> SRR1706771 4 0.1116 0.967 0.028 0.000 0.004 0.964 NA
#> SRR1706772 4 0.0955 0.967 0.028 0.000 0.000 0.968 NA
#> SRR1706773 4 0.0955 0.967 0.028 0.000 0.000 0.968 NA
#> SRR1706774 4 0.0955 0.967 0.028 0.000 0.000 0.968 NA
#> SRR1706775 1 0.1502 0.927 0.940 0.000 0.004 0.056 NA
#> SRR1706776 1 0.1041 0.938 0.964 0.000 0.004 0.032 NA
#> SRR1706777 1 0.1768 0.916 0.924 0.000 0.004 0.072 NA
#> SRR1706778 1 0.1121 0.934 0.956 0.000 0.000 0.044 NA
#> SRR1706779 1 0.0162 0.947 0.996 0.000 0.000 0.000 NA
#> SRR1706780 1 0.0324 0.947 0.992 0.000 0.004 0.000 NA
#> SRR1706781 1 0.0162 0.947 0.996 0.000 0.004 0.000 NA
#> SRR1706782 1 0.0324 0.947 0.992 0.000 0.004 0.000 NA
#> SRR1706783 1 0.0566 0.947 0.984 0.000 0.012 0.000 NA
#> SRR1706784 1 0.0693 0.946 0.980 0.000 0.012 0.000 NA
#> SRR1706785 1 0.0566 0.947 0.984 0.000 0.012 0.000 NA
#> SRR1706786 1 0.0566 0.947 0.984 0.000 0.012 0.000 NA
#> SRR1706787 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706788 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706789 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706790 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706791 4 0.1493 0.964 0.024 0.000 0.028 0.948 NA
#> SRR1706792 4 0.0992 0.970 0.024 0.000 0.008 0.968 NA
#> SRR1706793 4 0.1211 0.968 0.024 0.000 0.016 0.960 NA
#> SRR1706794 4 0.1211 0.968 0.024 0.000 0.016 0.960 NA
#> SRR1706795 1 0.1626 0.929 0.940 0.000 0.016 0.044 NA
#> SRR1706796 1 0.1522 0.931 0.944 0.000 0.012 0.044 NA
#> SRR1706797 1 0.1626 0.929 0.940 0.000 0.016 0.044 NA
#> SRR1706798 1 0.1549 0.932 0.944 0.000 0.016 0.040 NA
#> SRR1706799 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706800 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706801 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706802 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706803 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706804 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706805 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706806 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706811 4 0.3397 0.891 0.012 0.008 0.124 0.844 NA
#> SRR1706812 4 0.3444 0.889 0.012 0.008 0.128 0.840 NA
#> SRR1706813 4 0.3397 0.891 0.012 0.008 0.124 0.844 NA
#> SRR1706814 4 0.3139 0.899 0.012 0.008 0.112 0.860 NA
#> SRR1706807 4 0.0162 0.973 0.000 0.000 0.004 0.996 NA
#> SRR1706808 4 0.0162 0.973 0.000 0.000 0.004 0.996 NA
#> SRR1706809 4 0.0162 0.973 0.000 0.000 0.004 0.996 NA
#> SRR1706810 4 0.0162 0.973 0.000 0.000 0.004 0.996 NA
#> SRR1706815 1 0.5447 0.782 0.732 0.012 0.116 0.112 NA
#> SRR1706816 1 0.5442 0.785 0.732 0.012 0.124 0.104 NA
#> SRR1706817 1 0.5582 0.767 0.720 0.012 0.116 0.124 NA
#> SRR1706818 1 0.5492 0.778 0.728 0.012 0.120 0.112 NA
#> SRR1706819 1 0.2408 0.911 0.892 0.016 0.092 0.000 NA
#> SRR1706820 1 0.2408 0.911 0.892 0.016 0.092 0.000 NA
#> SRR1706821 1 0.2408 0.911 0.892 0.016 0.092 0.000 NA
#> SRR1706822 1 0.2408 0.911 0.892 0.016 0.092 0.000 NA
#> SRR1706823 1 0.2464 0.911 0.888 0.016 0.096 0.000 NA
#> SRR1706824 1 0.2464 0.911 0.888 0.016 0.096 0.000 NA
#> SRR1706825 1 0.2464 0.911 0.888 0.016 0.096 0.000 NA
#> SRR1706826 1 0.2464 0.911 0.888 0.016 0.096 0.000 NA
#> SRR1706827 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706828 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706829 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706830 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA
#> SRR1706835 1 0.2370 0.912 0.904 0.000 0.040 0.056 NA
#> SRR1706836 1 0.2209 0.916 0.912 0.000 0.032 0.056 NA
#> SRR1706837 1 0.2278 0.913 0.908 0.000 0.032 0.060 NA
#> SRR1706838 1 0.2438 0.909 0.900 0.000 0.040 0.060 NA
#> SRR1706831 4 0.0703 0.971 0.024 0.000 0.000 0.976 NA
#> SRR1706832 4 0.0703 0.971 0.024 0.000 0.000 0.976 NA
#> SRR1706833 4 0.0703 0.971 0.024 0.000 0.000 0.976 NA
#> SRR1706834 4 0.0703 0.971 0.024 0.000 0.000 0.976 NA
#> SRR1706839 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706840 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706841 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706842 1 0.0000 0.947 1.000 0.000 0.000 0.000 NA
#> SRR1706847 3 0.4009 0.949 0.000 0.312 0.684 0.000 NA
#> SRR1706848 3 0.4009 0.949 0.000 0.312 0.684 0.000 NA
#> SRR1706849 3 0.4009 0.949 0.000 0.312 0.684 0.000 NA
#> SRR1706850 3 0.4009 0.949 0.000 0.312 0.684 0.000 NA
#> SRR1706843 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706844 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706845 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706846 1 0.0404 0.947 0.988 0.000 0.012 0.000 NA
#> SRR1706851 3 0.3895 0.945 0.000 0.320 0.680 0.000 NA
#> SRR1706852 3 0.3895 0.945 0.000 0.320 0.680 0.000 NA
#> SRR1706853 3 0.3895 0.945 0.000 0.320 0.680 0.000 NA
#> SRR1706854 3 0.3895 0.945 0.000 0.320 0.680 0.000 NA
#> SRR1706855 2 0.2278 0.862 0.000 0.908 0.060 0.000 NA
#> SRR1706856 2 0.2344 0.857 0.000 0.904 0.064 0.000 NA
#> SRR1706857 2 0.2124 0.864 0.000 0.916 0.056 0.000 NA
#> SRR1706858 2 0.2370 0.859 0.000 0.904 0.056 0.000 NA
#> SRR1706859 2 0.0162 0.886 0.000 0.996 0.004 0.000 NA
#> SRR1706860 2 0.0290 0.885 0.000 0.992 0.008 0.000 NA
#> SRR1706861 2 0.0162 0.886 0.000 0.996 0.004 0.000 NA
#> SRR1706862 2 0.0290 0.885 0.000 0.992 0.008 0.000 NA
#> SRR1706867 3 0.3774 0.943 0.000 0.296 0.704 0.000 NA
#> SRR1706869 3 0.3774 0.943 0.000 0.296 0.704 0.000 NA
#> SRR1706870 3 0.3774 0.943 0.000 0.296 0.704 0.000 NA
#> SRR1706863 2 0.0451 0.885 0.000 0.988 0.004 0.000 NA
#> SRR1706864 2 0.0451 0.885 0.000 0.988 0.004 0.000 NA
#> SRR1706865 2 0.0693 0.883 0.000 0.980 0.012 0.000 NA
#> SRR1706866 2 0.0579 0.886 0.000 0.984 0.008 0.000 NA
#> SRR1706871 3 0.3857 0.950 0.000 0.312 0.688 0.000 NA
#> SRR1706872 3 0.3857 0.950 0.000 0.312 0.688 0.000 NA
#> SRR1706873 3 0.3857 0.950 0.000 0.312 0.688 0.000 NA
#> SRR1706874 3 0.3857 0.950 0.000 0.312 0.688 0.000 NA
#> SRR1706879 2 0.0000 0.886 0.000 1.000 0.000 0.000 NA
#> SRR1706880 2 0.0290 0.885 0.000 0.992 0.008 0.000 NA
#> SRR1706881 2 0.0162 0.886 0.000 0.996 0.004 0.000 NA
#> SRR1706882 2 0.0000 0.886 0.000 1.000 0.000 0.000 NA
#> SRR1706883 2 0.2352 0.830 0.032 0.912 0.048 0.000 NA
#> SRR1706884 2 0.2278 0.835 0.032 0.916 0.044 0.000 NA
#> SRR1706885 2 0.2438 0.825 0.040 0.908 0.044 0.000 NA
#> SRR1706886 2 0.2513 0.820 0.040 0.904 0.048 0.000 NA
#> SRR1706875 2 0.1732 0.851 0.000 0.920 0.080 0.000 NA
#> SRR1706876 2 0.1608 0.857 0.000 0.928 0.072 0.000 NA
#> SRR1706877 2 0.1671 0.855 0.000 0.924 0.076 0.000 NA
#> SRR1706878 2 0.1671 0.855 0.000 0.924 0.076 0.000 NA
#> SRR1706887 3 0.4562 0.924 0.000 0.292 0.676 0.000 NA
#> SRR1706888 3 0.4562 0.924 0.000 0.292 0.676 0.000 NA
#> SRR1706889 3 0.4562 0.924 0.000 0.292 0.676 0.000 NA
#> SRR1706890 3 0.4562 0.924 0.000 0.292 0.676 0.000 NA
#> SRR1706891 3 0.6511 0.735 0.004 0.284 0.508 0.000 NA
#> SRR1706892 3 0.6440 0.747 0.004 0.284 0.520 0.000 NA
#> SRR1706893 3 0.6511 0.735 0.004 0.284 0.508 0.000 NA
#> SRR1706894 3 0.6380 0.739 0.000 0.288 0.508 0.000 NA
#> SRR1706895 2 0.5204 0.643 0.008 0.684 0.080 0.000 NA
#> SRR1706896 2 0.5094 0.662 0.008 0.696 0.076 0.000 NA
#> SRR1706897 2 0.5231 0.638 0.008 0.680 0.080 0.000 NA
#> SRR1706898 2 0.5148 0.655 0.008 0.692 0.080 0.000 NA
#> SRR1706899 2 0.3835 0.774 0.000 0.796 0.048 0.000 NA
#> SRR1706900 2 0.3835 0.774 0.000 0.796 0.048 0.000 NA
#> SRR1706901 2 0.3794 0.778 0.000 0.800 0.048 0.000 NA
#> SRR1706902 2 0.3835 0.774 0.000 0.796 0.048 0.000 NA
#> SRR1706907 3 0.3816 0.948 0.000 0.304 0.696 0.000 NA
#> SRR1706908 3 0.3837 0.949 0.000 0.308 0.692 0.000 NA
#> SRR1706909 3 0.3837 0.949 0.000 0.308 0.692 0.000 NA
#> SRR1706910 3 0.3816 0.948 0.000 0.304 0.696 0.000 NA
#> SRR1706903 2 0.2505 0.853 0.000 0.888 0.020 0.000 NA
#> SRR1706904 2 0.2561 0.853 0.000 0.884 0.020 0.000 NA
#> SRR1706905 2 0.2505 0.856 0.000 0.888 0.020 0.000 NA
#> SRR1706906 2 0.2561 0.853 0.000 0.884 0.020 0.000 NA
#> SRR1706911 3 0.3876 0.948 0.000 0.316 0.684 0.000 NA
#> SRR1706912 3 0.3876 0.948 0.000 0.316 0.684 0.000 NA
#> SRR1706913 3 0.3876 0.948 0.000 0.316 0.684 0.000 NA
#> SRR1706914 3 0.3876 0.948 0.000 0.316 0.684 0.000 NA
#> SRR1706919 2 0.0510 0.884 0.000 0.984 0.016 0.000 NA
#> SRR1706920 2 0.0404 0.885 0.000 0.988 0.012 0.000 NA
#> SRR1706921 2 0.0609 0.883 0.000 0.980 0.020 0.000 NA
#> SRR1706922 2 0.0510 0.884 0.000 0.984 0.016 0.000 NA
#> SRR1706915 2 0.2077 0.847 0.000 0.908 0.084 0.000 NA
#> SRR1706916 2 0.2448 0.839 0.000 0.892 0.088 0.000 NA
#> SRR1706917 2 0.2130 0.849 0.000 0.908 0.080 0.000 NA
#> SRR1706918 2 0.2189 0.846 0.000 0.904 0.084 0.000 NA
#> SRR1706923 2 0.2131 0.847 0.016 0.920 0.056 0.000 NA
#> SRR1706924 2 0.2131 0.847 0.016 0.920 0.056 0.000 NA
#> SRR1706925 2 0.2060 0.851 0.016 0.924 0.052 0.000 NA
#> SRR1706926 2 0.2131 0.847 0.016 0.920 0.056 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.1168 0.963 0.000 0.000 0.016 0.956 NA 0.000
#> SRR1706768 4 0.1168 0.963 0.000 0.000 0.016 0.956 NA 0.000
#> SRR1706769 4 0.1245 0.962 0.000 0.000 0.016 0.952 NA 0.000
#> SRR1706770 4 0.1168 0.963 0.000 0.000 0.016 0.956 NA 0.000
#> SRR1706771 4 0.1956 0.955 0.016 0.000 0.016 0.928 NA 0.008
#> SRR1706772 4 0.1956 0.955 0.016 0.000 0.016 0.928 NA 0.008
#> SRR1706773 4 0.1956 0.955 0.016 0.000 0.016 0.928 NA 0.008
#> SRR1706774 4 0.1843 0.956 0.016 0.000 0.016 0.932 NA 0.004
#> SRR1706775 1 0.3228 0.817 0.804 0.000 0.012 0.176 NA 0.004
#> SRR1706776 1 0.3135 0.832 0.816 0.000 0.008 0.164 NA 0.008
#> SRR1706777 1 0.3130 0.820 0.808 0.000 0.008 0.176 NA 0.004
#> SRR1706778 1 0.3061 0.830 0.816 0.000 0.008 0.168 NA 0.004
#> SRR1706779 1 0.0146 0.944 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706780 1 0.0146 0.944 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706781 1 0.0146 0.944 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706782 1 0.0260 0.943 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706783 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706784 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706785 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706786 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706787 4 0.0260 0.972 0.000 0.000 0.000 0.992 NA 0.008
#> SRR1706788 4 0.0260 0.972 0.000 0.000 0.000 0.992 NA 0.008
#> SRR1706789 4 0.0260 0.972 0.000 0.000 0.000 0.992 NA 0.008
#> SRR1706790 4 0.0260 0.972 0.000 0.000 0.000 0.992 NA 0.008
#> SRR1706791 4 0.1015 0.969 0.012 0.000 0.004 0.968 NA 0.012
#> SRR1706792 4 0.0767 0.970 0.012 0.000 0.004 0.976 NA 0.008
#> SRR1706793 4 0.0912 0.970 0.012 0.000 0.004 0.972 NA 0.008
#> SRR1706794 4 0.0912 0.970 0.012 0.000 0.004 0.972 NA 0.008
#> SRR1706795 1 0.1949 0.908 0.904 0.000 0.004 0.088 NA 0.004
#> SRR1706796 1 0.2149 0.898 0.888 0.000 0.004 0.104 NA 0.004
#> SRR1706797 1 0.2051 0.904 0.896 0.000 0.004 0.096 NA 0.004
#> SRR1706798 1 0.2001 0.906 0.900 0.000 0.004 0.092 NA 0.004
#> SRR1706799 1 0.0146 0.943 0.996 0.000 0.000 0.000 NA 0.004
#> SRR1706800 1 0.0146 0.943 0.996 0.000 0.000 0.000 NA 0.004
#> SRR1706801 1 0.0146 0.943 0.996 0.000 0.000 0.000 NA 0.004
#> SRR1706802 1 0.0146 0.943 0.996 0.000 0.000 0.000 NA 0.004
#> SRR1706803 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706804 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706805 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706806 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706811 4 0.2465 0.938 0.024 0.000 0.004 0.900 NA 0.048
#> SRR1706812 4 0.2687 0.931 0.028 0.000 0.004 0.888 NA 0.052
#> SRR1706813 4 0.2544 0.936 0.024 0.000 0.004 0.896 NA 0.048
#> SRR1706814 4 0.2459 0.939 0.024 0.004 0.004 0.904 NA 0.044
#> SRR1706807 4 0.1086 0.965 0.000 0.000 0.012 0.964 NA 0.012
#> SRR1706808 4 0.1180 0.964 0.000 0.000 0.016 0.960 NA 0.012
#> SRR1706809 4 0.1180 0.964 0.000 0.000 0.016 0.960 NA 0.012
#> SRR1706810 4 0.1180 0.964 0.000 0.000 0.016 0.960 NA 0.012
#> SRR1706815 1 0.3446 0.870 0.832 0.004 0.000 0.104 NA 0.040
#> SRR1706816 1 0.3284 0.879 0.844 0.004 0.000 0.092 NA 0.044
#> SRR1706817 1 0.3473 0.868 0.828 0.004 0.000 0.108 NA 0.044
#> SRR1706818 1 0.3314 0.876 0.840 0.004 0.000 0.100 NA 0.040
#> SRR1706819 1 0.1367 0.932 0.944 0.012 0.000 0.000 NA 0.044
#> SRR1706820 1 0.1367 0.932 0.944 0.012 0.000 0.000 NA 0.044
#> SRR1706821 1 0.1196 0.933 0.952 0.008 0.000 0.000 NA 0.040
#> SRR1706822 1 0.1367 0.932 0.944 0.012 0.000 0.000 NA 0.044
#> SRR1706823 1 0.1575 0.927 0.936 0.032 0.000 0.000 NA 0.032
#> SRR1706824 1 0.1418 0.931 0.944 0.024 0.000 0.000 NA 0.032
#> SRR1706825 1 0.1649 0.926 0.932 0.032 0.000 0.000 NA 0.036
#> SRR1706826 1 0.1644 0.926 0.932 0.028 0.000 0.000 NA 0.040
#> SRR1706827 4 0.0146 0.972 0.000 0.000 0.000 0.996 NA 0.004
#> SRR1706828 4 0.0146 0.972 0.000 0.000 0.000 0.996 NA 0.004
#> SRR1706829 4 0.0146 0.972 0.000 0.000 0.000 0.996 NA 0.004
#> SRR1706830 4 0.0146 0.972 0.000 0.000 0.000 0.996 NA 0.004
#> SRR1706835 1 0.2400 0.889 0.872 0.000 0.008 0.116 NA 0.004
#> SRR1706836 1 0.2573 0.875 0.856 0.000 0.008 0.132 NA 0.004
#> SRR1706837 1 0.2615 0.871 0.852 0.000 0.008 0.136 NA 0.004
#> SRR1706838 1 0.2531 0.878 0.860 0.000 0.008 0.128 NA 0.004
#> SRR1706831 4 0.0405 0.972 0.008 0.000 0.004 0.988 NA 0.000
#> SRR1706832 4 0.0436 0.972 0.004 0.000 0.004 0.988 NA 0.000
#> SRR1706833 4 0.0405 0.972 0.008 0.000 0.004 0.988 NA 0.000
#> SRR1706834 4 0.0405 0.972 0.008 0.000 0.004 0.988 NA 0.000
#> SRR1706839 1 0.0000 0.944 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706840 1 0.0000 0.944 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706841 1 0.0000 0.944 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706842 1 0.0000 0.944 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706847 3 0.3923 0.878 0.000 0.080 0.772 0.000 NA 0.004
#> SRR1706848 3 0.3923 0.878 0.000 0.080 0.772 0.000 NA 0.004
#> SRR1706849 3 0.3923 0.878 0.000 0.080 0.772 0.000 NA 0.004
#> SRR1706850 3 0.3923 0.878 0.000 0.080 0.772 0.000 NA 0.004
#> SRR1706843 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706844 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706845 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706846 1 0.0146 0.944 0.996 0.004 0.000 0.000 NA 0.000
#> SRR1706851 3 0.4900 0.824 0.000 0.108 0.656 0.000 NA 0.004
#> SRR1706852 3 0.4900 0.824 0.000 0.108 0.656 0.000 NA 0.004
#> SRR1706853 3 0.4923 0.821 0.000 0.108 0.652 0.000 NA 0.004
#> SRR1706854 3 0.4923 0.821 0.000 0.108 0.652 0.000 NA 0.004
#> SRR1706855 2 0.3836 0.770 0.000 0.764 0.188 0.000 NA 0.040
#> SRR1706856 2 0.3897 0.763 0.000 0.756 0.196 0.000 NA 0.040
#> SRR1706857 2 0.3772 0.777 0.000 0.772 0.180 0.000 NA 0.040
#> SRR1706858 2 0.3867 0.766 0.000 0.760 0.192 0.000 NA 0.040
#> SRR1706859 2 0.0993 0.855 0.000 0.964 0.024 0.000 NA 0.012
#> SRR1706860 2 0.1074 0.855 0.000 0.960 0.028 0.000 NA 0.012
#> SRR1706861 2 0.1074 0.855 0.000 0.960 0.028 0.000 NA 0.012
#> SRR1706862 2 0.1003 0.855 0.000 0.964 0.020 0.000 NA 0.016
#> SRR1706867 3 0.2737 0.908 0.000 0.084 0.868 0.000 NA 0.004
#> SRR1706869 3 0.2737 0.908 0.000 0.084 0.868 0.000 NA 0.004
#> SRR1706870 3 0.2737 0.908 0.000 0.084 0.868 0.000 NA 0.004
#> SRR1706863 2 0.1204 0.849 0.004 0.960 0.016 0.000 NA 0.016
#> SRR1706864 2 0.1204 0.849 0.004 0.960 0.016 0.000 NA 0.016
#> SRR1706865 2 0.1204 0.849 0.004 0.960 0.016 0.000 NA 0.016
#> SRR1706866 2 0.1377 0.852 0.004 0.952 0.024 0.000 NA 0.016
#> SRR1706871 3 0.2070 0.914 0.000 0.092 0.896 0.000 NA 0.000
#> SRR1706872 3 0.2163 0.914 0.000 0.092 0.892 0.000 NA 0.000
#> SRR1706873 3 0.2163 0.914 0.000 0.092 0.892 0.000 NA 0.000
#> SRR1706874 3 0.2070 0.914 0.000 0.092 0.896 0.000 NA 0.000
#> SRR1706879 2 0.1176 0.855 0.000 0.956 0.024 0.000 NA 0.020
#> SRR1706880 2 0.1003 0.853 0.000 0.964 0.016 0.000 NA 0.020
#> SRR1706881 2 0.1092 0.854 0.000 0.960 0.020 0.000 NA 0.020
#> SRR1706882 2 0.1176 0.855 0.000 0.956 0.024 0.000 NA 0.020
#> SRR1706883 2 0.1623 0.830 0.020 0.940 0.004 0.000 NA 0.032
#> SRR1706884 2 0.1546 0.829 0.020 0.944 0.004 0.000 NA 0.028
#> SRR1706885 2 0.1623 0.830 0.020 0.940 0.004 0.000 NA 0.032
#> SRR1706886 2 0.1546 0.829 0.020 0.944 0.004 0.000 NA 0.028
#> SRR1706875 2 0.3500 0.761 0.000 0.768 0.204 0.000 NA 0.028
#> SRR1706876 2 0.3572 0.763 0.000 0.764 0.204 0.000 NA 0.032
#> SRR1706877 2 0.3481 0.773 0.000 0.776 0.192 0.000 NA 0.032
#> SRR1706878 2 0.3630 0.752 0.000 0.756 0.212 0.000 NA 0.032
#> SRR1706887 3 0.2711 0.905 0.000 0.080 0.876 0.000 NA 0.024
#> SRR1706888 3 0.2711 0.905 0.000 0.080 0.876 0.000 NA 0.024
#> SRR1706889 3 0.2711 0.905 0.000 0.080 0.876 0.000 NA 0.024
#> SRR1706890 3 0.2711 0.905 0.000 0.080 0.876 0.000 NA 0.024
#> SRR1706891 3 0.4422 0.855 0.000 0.096 0.768 0.000 NA 0.076
#> SRR1706892 3 0.4375 0.853 0.000 0.092 0.772 0.000 NA 0.076
#> SRR1706893 3 0.4375 0.853 0.000 0.092 0.772 0.000 NA 0.076
#> SRR1706894 3 0.4375 0.853 0.000 0.092 0.772 0.000 NA 0.076
#> SRR1706895 2 0.6205 0.366 0.000 0.500 0.324 0.000 NA 0.136
#> SRR1706896 2 0.6225 0.400 0.000 0.516 0.300 0.000 NA 0.140
#> SRR1706897 2 0.6260 0.362 0.000 0.496 0.324 0.000 NA 0.136
#> SRR1706898 2 0.6258 0.350 0.000 0.488 0.336 0.000 NA 0.132
#> SRR1706899 2 0.3607 0.810 0.000 0.796 0.092 0.000 NA 0.112
#> SRR1706900 2 0.3413 0.818 0.000 0.812 0.080 0.000 NA 0.108
#> SRR1706901 2 0.3361 0.820 0.000 0.816 0.076 0.000 NA 0.108
#> SRR1706902 2 0.3563 0.811 0.000 0.800 0.092 0.000 NA 0.108
#> SRR1706907 3 0.2733 0.908 0.000 0.080 0.864 0.000 NA 0.000
#> SRR1706908 3 0.2936 0.905 0.000 0.080 0.856 0.000 NA 0.004
#> SRR1706909 3 0.2876 0.906 0.000 0.080 0.860 0.000 NA 0.004
#> SRR1706910 3 0.2733 0.907 0.000 0.080 0.864 0.000 NA 0.000
#> SRR1706903 2 0.2860 0.830 0.000 0.852 0.048 0.000 NA 0.100
#> SRR1706904 2 0.2860 0.830 0.000 0.852 0.048 0.000 NA 0.100
#> SRR1706905 2 0.2712 0.833 0.000 0.864 0.048 0.000 NA 0.088
#> SRR1706906 2 0.2860 0.830 0.000 0.852 0.048 0.000 NA 0.100
#> SRR1706911 3 0.2199 0.914 0.000 0.088 0.892 0.000 NA 0.000
#> SRR1706912 3 0.2425 0.915 0.000 0.088 0.884 0.000 NA 0.004
#> SRR1706913 3 0.2342 0.914 0.000 0.088 0.888 0.000 NA 0.004
#> SRR1706914 3 0.2342 0.915 0.000 0.088 0.888 0.000 NA 0.004
#> SRR1706919 2 0.1245 0.854 0.000 0.952 0.032 0.000 NA 0.016
#> SRR1706920 2 0.1245 0.854 0.000 0.952 0.032 0.000 NA 0.016
#> SRR1706921 2 0.1088 0.854 0.000 0.960 0.024 0.000 NA 0.016
#> SRR1706922 2 0.1320 0.855 0.000 0.948 0.036 0.000 NA 0.016
#> SRR1706915 2 0.4088 0.715 0.000 0.716 0.240 0.000 NA 0.040
#> SRR1706916 2 0.4222 0.701 0.000 0.700 0.252 0.000 NA 0.044
#> SRR1706917 2 0.4113 0.709 0.000 0.712 0.244 0.000 NA 0.040
#> SRR1706918 2 0.4136 0.709 0.000 0.708 0.248 0.000 NA 0.040
#> SRR1706923 2 0.1621 0.847 0.008 0.944 0.020 0.000 NA 0.016
#> SRR1706924 2 0.1533 0.845 0.008 0.948 0.016 0.000 NA 0.016
#> SRR1706925 2 0.1621 0.847 0.008 0.944 0.020 0.000 NA 0.016
#> SRR1706926 2 0.1723 0.845 0.012 0.940 0.020 0.000 NA 0.016
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 15185 rows and 159 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 0.515 0.767 0.909 0.4712 0.519 0.519
#> 3 3 0.577 0.700 0.789 0.3683 0.780 0.605
#> 4 4 0.781 0.895 0.937 0.1558 0.878 0.676
#> 5 5 0.859 0.934 0.935 0.0520 0.962 0.850
#> 6 6 0.838 0.923 0.937 0.0518 0.959 0.812
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
#> SRR1706767 1 0.000 0.891 1.000 0.000
#> SRR1706768 1 0.000 0.891 1.000 0.000
#> SRR1706769 1 0.000 0.891 1.000 0.000
#> SRR1706770 1 0.000 0.891 1.000 0.000
#> SRR1706771 1 0.000 0.891 1.000 0.000
#> SRR1706772 1 0.000 0.891 1.000 0.000
#> SRR1706773 1 0.000 0.891 1.000 0.000
#> SRR1706774 1 0.000 0.891 1.000 0.000
#> SRR1706775 2 0.482 0.822 0.104 0.896
#> SRR1706776 2 0.482 0.822 0.104 0.896
#> SRR1706777 2 0.482 0.822 0.104 0.896
#> SRR1706778 2 0.482 0.822 0.104 0.896
#> SRR1706779 2 0.000 0.886 0.000 1.000
#> SRR1706780 2 0.000 0.886 0.000 1.000
#> SRR1706781 2 0.000 0.886 0.000 1.000
#> SRR1706782 2 0.000 0.886 0.000 1.000
#> SRR1706783 2 0.000 0.886 0.000 1.000
#> SRR1706784 2 0.000 0.886 0.000 1.000
#> SRR1706785 2 0.000 0.886 0.000 1.000
#> SRR1706786 2 0.000 0.886 0.000 1.000
#> SRR1706787 1 0.000 0.891 1.000 0.000
#> SRR1706788 1 0.000 0.891 1.000 0.000
#> SRR1706789 1 0.000 0.891 1.000 0.000
#> SRR1706790 1 0.000 0.891 1.000 0.000
#> SRR1706791 1 0.000 0.891 1.000 0.000
#> SRR1706792 1 0.000 0.891 1.000 0.000
#> SRR1706793 1 0.000 0.891 1.000 0.000
#> SRR1706794 1 0.000 0.891 1.000 0.000
#> SRR1706795 2 0.482 0.822 0.104 0.896
#> SRR1706796 2 0.482 0.822 0.104 0.896
#> SRR1706797 2 0.482 0.822 0.104 0.896
#> SRR1706798 2 0.482 0.822 0.104 0.896
#> SRR1706799 2 0.000 0.886 0.000 1.000
#> SRR1706800 2 0.000 0.886 0.000 1.000
#> SRR1706801 2 0.000 0.886 0.000 1.000
#> SRR1706802 2 0.000 0.886 0.000 1.000
#> SRR1706803 2 0.000 0.886 0.000 1.000
#> SRR1706804 2 0.000 0.886 0.000 1.000
#> SRR1706805 2 0.000 0.886 0.000 1.000
#> SRR1706806 2 0.000 0.886 0.000 1.000
#> SRR1706811 1 0.000 0.891 1.000 0.000
#> SRR1706812 1 0.000 0.891 1.000 0.000
#> SRR1706813 1 0.000 0.891 1.000 0.000
#> SRR1706814 1 0.000 0.891 1.000 0.000
#> SRR1706807 1 0.000 0.891 1.000 0.000
#> SRR1706808 1 0.000 0.891 1.000 0.000
#> SRR1706809 1 0.000 0.891 1.000 0.000
#> SRR1706810 1 0.000 0.891 1.000 0.000
#> SRR1706815 2 0.482 0.822 0.104 0.896
#> SRR1706816 2 0.482 0.822 0.104 0.896
#> SRR1706817 2 0.482 0.822 0.104 0.896
#> SRR1706818 2 0.482 0.822 0.104 0.896
#> SRR1706819 2 0.000 0.886 0.000 1.000
#> SRR1706820 2 0.000 0.886 0.000 1.000
#> SRR1706821 2 0.000 0.886 0.000 1.000
#> SRR1706822 2 0.000 0.886 0.000 1.000
#> SRR1706823 2 0.000 0.886 0.000 1.000
#> SRR1706824 2 0.000 0.886 0.000 1.000
#> SRR1706825 2 0.000 0.886 0.000 1.000
#> SRR1706826 2 0.000 0.886 0.000 1.000
#> SRR1706827 1 0.000 0.891 1.000 0.000
#> SRR1706828 1 0.000 0.891 1.000 0.000
#> SRR1706829 1 0.000 0.891 1.000 0.000
#> SRR1706830 1 0.000 0.891 1.000 0.000
#> SRR1706835 2 0.482 0.822 0.104 0.896
#> SRR1706836 2 0.482 0.822 0.104 0.896
#> SRR1706837 2 0.482 0.822 0.104 0.896
#> SRR1706838 2 0.482 0.822 0.104 0.896
#> SRR1706831 1 0.000 0.891 1.000 0.000
#> SRR1706832 1 0.000 0.891 1.000 0.000
#> SRR1706833 1 0.000 0.891 1.000 0.000
#> SRR1706834 1 0.000 0.891 1.000 0.000
#> SRR1706839 2 0.000 0.886 0.000 1.000
#> SRR1706840 2 0.000 0.886 0.000 1.000
#> SRR1706841 2 0.000 0.886 0.000 1.000
#> SRR1706842 2 0.000 0.886 0.000 1.000
#> SRR1706847 1 0.000 0.891 1.000 0.000
#> SRR1706848 1 0.000 0.891 1.000 0.000
#> SRR1706849 1 0.000 0.891 1.000 0.000
#> SRR1706850 1 0.000 0.891 1.000 0.000
#> SRR1706843 2 0.000 0.886 0.000 1.000
#> SRR1706844 2 0.000 0.886 0.000 1.000
#> SRR1706845 2 0.000 0.886 0.000 1.000
#> SRR1706846 2 0.000 0.886 0.000 1.000
#> SRR1706851 1 0.925 0.513 0.660 0.340
#> SRR1706852 1 0.925 0.513 0.660 0.340
#> SRR1706853 1 0.925 0.513 0.660 0.340
#> SRR1706854 1 0.925 0.513 0.660 0.340
#> SRR1706855 2 0.995 0.129 0.460 0.540
#> SRR1706856 2 0.995 0.129 0.460 0.540
#> SRR1706857 2 0.995 0.129 0.460 0.540
#> SRR1706858 2 0.995 0.129 0.460 0.540
#> SRR1706859 2 0.000 0.886 0.000 1.000
#> SRR1706860 2 0.000 0.886 0.000 1.000
#> SRR1706861 2 0.000 0.886 0.000 1.000
#> SRR1706862 2 0.000 0.886 0.000 1.000
#> SRR1706867 1 0.000 0.891 1.000 0.000
#> SRR1706869 1 0.000 0.891 1.000 0.000
#> SRR1706870 1 0.000 0.891 1.000 0.000
#> SRR1706863 2 0.000 0.886 0.000 1.000
#> SRR1706864 2 0.000 0.886 0.000 1.000
#> SRR1706865 2 0.000 0.886 0.000 1.000
#> SRR1706866 2 0.000 0.886 0.000 1.000
#> SRR1706871 1 0.925 0.513 0.660 0.340
#> SRR1706872 1 0.925 0.513 0.660 0.340
#> SRR1706873 1 0.925 0.513 0.660 0.340
#> SRR1706874 1 0.925 0.513 0.660 0.340
#> SRR1706879 2 0.000 0.886 0.000 1.000
#> SRR1706880 2 0.000 0.886 0.000 1.000
#> SRR1706881 2 0.000 0.886 0.000 1.000
#> SRR1706882 2 0.000 0.886 0.000 1.000
#> SRR1706883 2 0.000 0.886 0.000 1.000
#> SRR1706884 2 0.000 0.886 0.000 1.000
#> SRR1706885 2 0.000 0.886 0.000 1.000
#> SRR1706886 2 0.000 0.886 0.000 1.000
#> SRR1706875 2 0.995 0.129 0.460 0.540
#> SRR1706876 2 0.995 0.129 0.460 0.540
#> SRR1706877 2 0.995 0.129 0.460 0.540
#> SRR1706878 2 0.995 0.129 0.460 0.540
#> SRR1706887 1 0.000 0.891 1.000 0.000
#> SRR1706888 1 0.000 0.891 1.000 0.000
#> SRR1706889 1 0.000 0.891 1.000 0.000
#> SRR1706890 1 0.000 0.891 1.000 0.000
#> SRR1706891 1 0.925 0.513 0.660 0.340
#> SRR1706892 1 0.925 0.513 0.660 0.340
#> SRR1706893 1 0.925 0.513 0.660 0.340
#> SRR1706894 1 0.925 0.513 0.660 0.340
#> SRR1706895 2 0.995 0.129 0.460 0.540
#> SRR1706896 2 0.995 0.129 0.460 0.540
#> SRR1706897 2 0.995 0.129 0.460 0.540
#> SRR1706898 2 0.995 0.129 0.460 0.540
#> SRR1706899 2 0.000 0.886 0.000 1.000
#> SRR1706900 2 0.000 0.886 0.000 1.000
#> SRR1706901 2 0.000 0.886 0.000 1.000
#> SRR1706902 2 0.000 0.886 0.000 1.000
#> SRR1706907 1 0.000 0.891 1.000 0.000
#> SRR1706908 1 0.000 0.891 1.000 0.000
#> SRR1706909 1 0.000 0.891 1.000 0.000
#> SRR1706910 1 0.000 0.891 1.000 0.000
#> SRR1706903 2 0.000 0.886 0.000 1.000
#> SRR1706904 2 0.000 0.886 0.000 1.000
#> SRR1706905 2 0.000 0.886 0.000 1.000
#> SRR1706906 2 0.000 0.886 0.000 1.000
#> SRR1706911 1 0.925 0.513 0.660 0.340
#> SRR1706912 1 0.925 0.513 0.660 0.340
#> SRR1706913 1 0.925 0.513 0.660 0.340
#> SRR1706914 1 0.925 0.513 0.660 0.340
#> SRR1706919 2 0.000 0.886 0.000 1.000
#> SRR1706920 2 0.000 0.886 0.000 1.000
#> SRR1706921 2 0.000 0.886 0.000 1.000
#> SRR1706922 2 0.000 0.886 0.000 1.000
#> SRR1706915 2 0.995 0.129 0.460 0.540
#> SRR1706916 2 0.995 0.129 0.460 0.540
#> SRR1706917 2 0.995 0.129 0.460 0.540
#> SRR1706918 2 0.995 0.129 0.460 0.540
#> SRR1706923 2 0.000 0.886 0.000 1.000
#> SRR1706924 2 0.000 0.886 0.000 1.000
#> SRR1706925 2 0.000 0.886 0.000 1.000
#> SRR1706926 2 0.000 0.886 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706768 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706769 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706770 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706771 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706772 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706773 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706774 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706775 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706776 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706777 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706778 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706779 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706780 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706781 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706782 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706783 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706784 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706785 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706786 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706787 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706788 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706789 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706790 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706791 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706792 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706793 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706794 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706795 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706796 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706797 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706798 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706799 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706800 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706801 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706802 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706803 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706804 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706805 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706806 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706811 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706812 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706813 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706814 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706807 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706808 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706809 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706810 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706815 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706816 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706817 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706818 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706819 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706820 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706821 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706822 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706823 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706824 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706825 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706826 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706827 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706828 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706829 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706830 1 0.0000 0.997 1.000 0.000 0.000
#> SRR1706835 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706836 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706837 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706838 2 0.3193 0.698 0.100 0.896 0.004
#> SRR1706831 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706832 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706833 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706834 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706839 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706840 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706841 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706842 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706847 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706848 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706849 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706850 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706843 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706844 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706845 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706846 2 0.0000 0.748 0.000 1.000 0.000
#> SRR1706851 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706852 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706853 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706854 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706855 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706856 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706857 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706858 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706859 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706860 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706861 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706862 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706867 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706869 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706870 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706863 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706864 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706865 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706866 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706871 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706872 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706873 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706874 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706879 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706880 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706881 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706882 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706883 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706884 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706885 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706886 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706875 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706876 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706877 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706878 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706887 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706888 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706889 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706890 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706891 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706892 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706893 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706894 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706895 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706896 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706897 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706898 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706899 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706900 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706901 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706902 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706907 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706908 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706909 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706910 3 0.6305 0.325 0.484 0.000 0.516
#> SRR1706903 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706904 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706905 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706906 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706911 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706912 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706913 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706914 3 0.3686 0.736 0.140 0.000 0.860
#> SRR1706919 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706920 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706921 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706922 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706915 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706916 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706917 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706918 3 0.2066 0.663 0.000 0.060 0.940
#> SRR1706923 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706924 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706925 2 0.6302 0.533 0.000 0.520 0.480
#> SRR1706926 2 0.6302 0.533 0.000 0.520 0.480
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706768 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706769 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706770 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706771 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706772 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706773 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706774 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706775 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706776 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706777 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706778 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706779 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706780 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706781 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706782 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706783 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706784 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706785 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706786 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706787 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706788 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706789 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706790 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706791 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706792 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706793 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706794 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706795 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706796 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706797 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706798 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706799 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706800 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706801 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706802 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706803 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706804 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706805 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706806 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706811 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706812 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706813 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706814 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706807 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706808 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706809 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706810 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706815 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706816 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706817 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706818 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706819 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706820 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706821 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706822 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706823 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706824 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706825 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706826 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706827 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706828 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706829 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706830 4 0.0000 0.997 0.000 0.0 0.000 1.000
#> SRR1706835 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706836 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706837 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706838 1 0.2530 0.923 0.896 0.0 0.004 0.100
#> SRR1706831 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706832 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706833 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706834 4 0.0188 0.997 0.000 0.0 0.004 0.996
#> SRR1706839 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706840 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706841 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706842 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706847 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706848 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706849 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706850 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706843 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706844 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706845 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706846 1 0.0000 0.964 1.000 0.0 0.000 0.000
#> SRR1706851 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706852 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706853 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706854 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706855 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706856 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706857 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706858 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706859 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706860 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706861 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706862 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706867 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706869 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706870 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706863 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706871 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706872 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706873 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706874 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706879 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706880 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706881 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706882 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706883 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706875 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706876 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706877 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706878 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706887 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706888 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706889 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706890 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706891 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706892 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706893 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706894 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706895 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706896 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706897 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706898 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706899 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706900 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706901 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706902 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706907 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706908 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706909 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706910 3 0.4643 0.569 0.000 0.0 0.656 0.344
#> SRR1706903 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706911 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706912 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706913 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706914 3 0.0000 0.791 0.000 0.0 1.000 0.000
#> SRR1706919 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706920 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706921 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706922 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706915 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706916 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706917 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706918 3 0.3610 0.720 0.000 0.2 0.800 0.000
#> SRR1706923 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.0 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 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
#> SRR1706767 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706768 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706769 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706770 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706771 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706772 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706773 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706774 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706775 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706776 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706777 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706778 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706779 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706784 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706785 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706786 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706787 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706788 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706789 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706790 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706791 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706792 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706793 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706794 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706795 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706796 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706797 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706798 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706799 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706804 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706805 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706806 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706811 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706812 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706813 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706814 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706807 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706808 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706809 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706810 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706815 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706816 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706817 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706818 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706819 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706823 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706824 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706825 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706826 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706827 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706828 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706829 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706830 4 0.0162 0.966 0.000 0.000 0.000 0.996 0.004
#> SRR1706835 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706836 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706837 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706838 1 0.2645 0.917 0.888 0.000 0.068 0.044 0.000
#> SRR1706831 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706832 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706833 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706834 4 0.1410 0.966 0.000 0.000 0.060 0.940 0.000
#> SRR1706839 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.956 1.000 0.000 0.000 0.000 0.000
#> SRR1706847 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706848 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706849 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706850 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706843 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706844 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706845 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706846 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> SRR1706851 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706852 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706853 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706854 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706855 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706856 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706857 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706858 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706859 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706860 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706861 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706862 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706867 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706869 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706870 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706863 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706871 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706872 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706873 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706874 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706879 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706880 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706881 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706882 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706883 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706875 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706876 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706877 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706878 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706887 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706888 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706889 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706890 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706891 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706892 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706893 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706894 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706895 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706896 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706897 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706898 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706899 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706900 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706901 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706902 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706907 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706908 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706909 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706910 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706903 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706904 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706905 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706906 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706911 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706912 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706913 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706914 3 0.3109 0.847 0.000 0.000 0.800 0.000 0.200
#> SRR1706919 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706920 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706921 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706922 2 0.2230 0.938 0.000 0.884 0.116 0.000 0.000
#> SRR1706915 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706916 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706917 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706918 3 0.1792 0.863 0.000 0.084 0.916 0.000 0.000
#> SRR1706923 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.938 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706768 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706769 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706770 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706771 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706772 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706773 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706774 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706775 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706776 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706777 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706778 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706779 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706780 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706781 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706782 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706783 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706784 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706785 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706786 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706787 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706788 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706789 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706790 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706791 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706792 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706793 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706794 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706795 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706796 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706797 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706798 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706799 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706800 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706801 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706802 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706803 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706804 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706805 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706806 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706811 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706812 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706813 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706814 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706807 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706808 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706809 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706810 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706815 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706816 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706817 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706818 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706819 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706820 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706821 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706822 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706823 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706824 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706825 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706826 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706827 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706828 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706829 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706830 4 0.000 0.944 0.00 0.0 0.0 1.000 0.000 0.0
#> SRR1706835 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706836 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706837 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706838 5 0.000 0.925 0.00 0.0 0.0 0.000 1.000 0.0
#> SRR1706831 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706832 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706833 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706834 4 0.186 0.944 0.00 0.0 0.0 0.896 0.104 0.0
#> SRR1706839 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706840 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706841 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706842 5 0.205 0.921 0.12 0.0 0.0 0.000 0.880 0.0
#> SRR1706847 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706848 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706849 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706850 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706843 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706844 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706845 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706846 1 0.000 1.000 1.00 0.0 0.0 0.000 0.000 0.0
#> SRR1706851 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706852 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706853 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706854 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706855 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706856 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706857 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706858 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706859 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706860 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706861 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706862 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706867 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706869 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706870 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706863 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706864 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706865 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706866 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706871 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706872 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706873 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706874 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706879 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706880 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706881 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706882 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706883 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706884 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706885 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706886 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706875 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706876 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706877 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706878 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706887 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706888 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706889 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706890 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706891 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706892 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706893 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706894 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706895 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706896 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706897 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706898 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706899 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706900 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706901 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706902 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706907 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706908 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706909 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706910 3 0.000 1.000 0.00 0.0 1.0 0.000 0.000 0.0
#> SRR1706903 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706904 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706905 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706906 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706911 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706912 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706913 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706914 6 0.279 0.851 0.00 0.0 0.2 0.000 0.000 0.8
#> SRR1706919 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706920 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706921 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706922 2 0.279 0.890 0.00 0.8 0.0 0.000 0.000 0.2
#> SRR1706915 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706916 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706917 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706918 6 0.000 0.866 0.00 0.0 0.0 0.000 0.000 1.0
#> SRR1706923 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706924 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706925 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
#> SRR1706926 2 0.000 0.889 0.00 1.0 0.0 0.000 0.000 0.0
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", "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 15185 rows and 159 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.426 0.719 0.852 0.4969 0.497 0.497
#> 3 3 0.516 0.593 0.790 0.3161 0.702 0.470
#> 4 4 0.662 0.765 0.829 0.1187 0.890 0.684
#> 5 5 0.720 0.422 0.604 0.0600 0.874 0.590
#> 6 6 0.758 0.649 0.692 0.0409 0.869 0.541
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
#> SRR1706767 1 0.000 0.828 1.000 0.000
#> SRR1706768 1 0.000 0.828 1.000 0.000
#> SRR1706769 1 0.000 0.828 1.000 0.000
#> SRR1706770 1 0.000 0.828 1.000 0.000
#> SRR1706771 1 0.000 0.828 1.000 0.000
#> SRR1706772 1 0.000 0.828 1.000 0.000
#> SRR1706773 1 0.000 0.828 1.000 0.000
#> SRR1706774 1 0.000 0.828 1.000 0.000
#> SRR1706775 1 0.000 0.828 1.000 0.000
#> SRR1706776 1 0.000 0.828 1.000 0.000
#> SRR1706777 1 0.000 0.828 1.000 0.000
#> SRR1706778 1 0.000 0.828 1.000 0.000
#> SRR1706779 2 0.998 0.467 0.476 0.524
#> SRR1706780 2 0.998 0.467 0.476 0.524
#> SRR1706781 2 0.998 0.467 0.476 0.524
#> SRR1706782 2 0.998 0.467 0.476 0.524
#> SRR1706783 2 0.952 0.605 0.372 0.628
#> SRR1706784 2 0.952 0.605 0.372 0.628
#> SRR1706785 2 0.952 0.605 0.372 0.628
#> SRR1706786 2 0.952 0.605 0.372 0.628
#> SRR1706787 1 0.000 0.828 1.000 0.000
#> SRR1706788 1 0.000 0.828 1.000 0.000
#> SRR1706789 1 0.000 0.828 1.000 0.000
#> SRR1706790 1 0.000 0.828 1.000 0.000
#> SRR1706791 1 0.000 0.828 1.000 0.000
#> SRR1706792 1 0.000 0.828 1.000 0.000
#> SRR1706793 1 0.000 0.828 1.000 0.000
#> SRR1706794 1 0.000 0.828 1.000 0.000
#> SRR1706795 1 0.000 0.828 1.000 0.000
#> SRR1706796 1 0.000 0.828 1.000 0.000
#> SRR1706797 1 0.000 0.828 1.000 0.000
#> SRR1706798 1 0.000 0.828 1.000 0.000
#> SRR1706799 2 0.998 0.467 0.476 0.524
#> SRR1706800 2 0.998 0.467 0.476 0.524
#> SRR1706801 2 0.998 0.467 0.476 0.524
#> SRR1706802 2 0.998 0.467 0.476 0.524
#> SRR1706803 2 0.952 0.605 0.372 0.628
#> SRR1706804 2 0.952 0.605 0.372 0.628
#> SRR1706805 2 0.952 0.605 0.372 0.628
#> SRR1706806 2 0.952 0.605 0.372 0.628
#> SRR1706811 1 0.000 0.828 1.000 0.000
#> SRR1706812 1 0.000 0.828 1.000 0.000
#> SRR1706813 1 0.000 0.828 1.000 0.000
#> SRR1706814 1 0.000 0.828 1.000 0.000
#> SRR1706807 1 0.000 0.828 1.000 0.000
#> SRR1706808 1 0.000 0.828 1.000 0.000
#> SRR1706809 1 0.000 0.828 1.000 0.000
#> SRR1706810 1 0.000 0.828 1.000 0.000
#> SRR1706815 1 0.000 0.828 1.000 0.000
#> SRR1706816 1 0.000 0.828 1.000 0.000
#> SRR1706817 1 0.000 0.828 1.000 0.000
#> SRR1706818 1 0.000 0.828 1.000 0.000
#> SRR1706819 2 0.992 0.512 0.448 0.552
#> SRR1706820 2 0.992 0.512 0.448 0.552
#> SRR1706821 2 0.992 0.512 0.448 0.552
#> SRR1706822 2 0.992 0.512 0.448 0.552
#> SRR1706823 2 0.952 0.605 0.372 0.628
#> SRR1706824 2 0.952 0.605 0.372 0.628
#> SRR1706825 2 0.952 0.605 0.372 0.628
#> SRR1706826 2 0.952 0.605 0.372 0.628
#> SRR1706827 1 0.000 0.828 1.000 0.000
#> SRR1706828 1 0.000 0.828 1.000 0.000
#> SRR1706829 1 0.000 0.828 1.000 0.000
#> SRR1706830 1 0.000 0.828 1.000 0.000
#> SRR1706835 1 0.000 0.828 1.000 0.000
#> SRR1706836 1 0.000 0.828 1.000 0.000
#> SRR1706837 1 0.000 0.828 1.000 0.000
#> SRR1706838 1 0.000 0.828 1.000 0.000
#> SRR1706831 1 0.000 0.828 1.000 0.000
#> SRR1706832 1 0.000 0.828 1.000 0.000
#> SRR1706833 1 0.000 0.828 1.000 0.000
#> SRR1706834 1 0.000 0.828 1.000 0.000
#> SRR1706839 2 0.998 0.467 0.476 0.524
#> SRR1706840 2 0.998 0.467 0.476 0.524
#> SRR1706841 2 0.998 0.467 0.476 0.524
#> SRR1706842 2 0.998 0.467 0.476 0.524
#> SRR1706847 1 0.802 0.711 0.756 0.244
#> SRR1706848 1 0.802 0.711 0.756 0.244
#> SRR1706849 1 0.802 0.711 0.756 0.244
#> SRR1706850 1 0.802 0.711 0.756 0.244
#> SRR1706843 2 0.952 0.605 0.372 0.628
#> SRR1706844 2 0.952 0.605 0.372 0.628
#> SRR1706845 2 0.952 0.605 0.372 0.628
#> SRR1706846 2 0.952 0.605 0.372 0.628
#> SRR1706851 1 0.973 0.542 0.596 0.404
#> SRR1706852 1 0.973 0.542 0.596 0.404
#> SRR1706853 1 0.973 0.542 0.596 0.404
#> SRR1706854 1 0.973 0.542 0.596 0.404
#> SRR1706855 2 0.000 0.789 0.000 1.000
#> SRR1706856 2 0.000 0.789 0.000 1.000
#> SRR1706857 2 0.000 0.789 0.000 1.000
#> SRR1706858 2 0.000 0.789 0.000 1.000
#> SRR1706859 2 0.000 0.789 0.000 1.000
#> SRR1706860 2 0.000 0.789 0.000 1.000
#> SRR1706861 2 0.000 0.789 0.000 1.000
#> SRR1706862 2 0.000 0.789 0.000 1.000
#> SRR1706867 1 0.795 0.715 0.760 0.240
#> SRR1706869 1 0.795 0.715 0.760 0.240
#> SRR1706870 1 0.795 0.715 0.760 0.240
#> SRR1706863 2 0.000 0.789 0.000 1.000
#> SRR1706864 2 0.000 0.789 0.000 1.000
#> SRR1706865 2 0.000 0.789 0.000 1.000
#> SRR1706866 2 0.000 0.789 0.000 1.000
#> SRR1706871 1 0.973 0.542 0.596 0.404
#> SRR1706872 1 0.973 0.542 0.596 0.404
#> SRR1706873 1 0.973 0.542 0.596 0.404
#> SRR1706874 1 0.973 0.542 0.596 0.404
#> SRR1706879 2 0.000 0.789 0.000 1.000
#> SRR1706880 2 0.000 0.789 0.000 1.000
#> SRR1706881 2 0.000 0.789 0.000 1.000
#> SRR1706882 2 0.000 0.789 0.000 1.000
#> SRR1706883 2 0.000 0.789 0.000 1.000
#> SRR1706884 2 0.000 0.789 0.000 1.000
#> SRR1706885 2 0.000 0.789 0.000 1.000
#> SRR1706886 2 0.000 0.789 0.000 1.000
#> SRR1706875 2 0.000 0.789 0.000 1.000
#> SRR1706876 2 0.000 0.789 0.000 1.000
#> SRR1706877 2 0.000 0.789 0.000 1.000
#> SRR1706878 2 0.000 0.789 0.000 1.000
#> SRR1706887 1 0.795 0.715 0.760 0.240
#> SRR1706888 1 0.795 0.715 0.760 0.240
#> SRR1706889 1 0.795 0.715 0.760 0.240
#> SRR1706890 1 0.795 0.715 0.760 0.240
#> SRR1706891 1 0.973 0.542 0.596 0.404
#> SRR1706892 1 0.973 0.542 0.596 0.404
#> SRR1706893 1 0.973 0.542 0.596 0.404
#> SRR1706894 1 0.973 0.542 0.596 0.404
#> SRR1706895 2 0.000 0.789 0.000 1.000
#> SRR1706896 2 0.000 0.789 0.000 1.000
#> SRR1706897 2 0.000 0.789 0.000 1.000
#> SRR1706898 2 0.000 0.789 0.000 1.000
#> SRR1706899 2 0.000 0.789 0.000 1.000
#> SRR1706900 2 0.000 0.789 0.000 1.000
#> SRR1706901 2 0.000 0.789 0.000 1.000
#> SRR1706902 2 0.000 0.789 0.000 1.000
#> SRR1706907 1 0.795 0.715 0.760 0.240
#> SRR1706908 1 0.795 0.715 0.760 0.240
#> SRR1706909 1 0.795 0.715 0.760 0.240
#> SRR1706910 1 0.795 0.715 0.760 0.240
#> SRR1706903 2 0.000 0.789 0.000 1.000
#> SRR1706904 2 0.000 0.789 0.000 1.000
#> SRR1706905 2 0.000 0.789 0.000 1.000
#> SRR1706906 2 0.000 0.789 0.000 1.000
#> SRR1706911 1 0.973 0.542 0.596 0.404
#> SRR1706912 1 0.973 0.542 0.596 0.404
#> SRR1706913 1 0.973 0.542 0.596 0.404
#> SRR1706914 1 0.973 0.542 0.596 0.404
#> SRR1706919 2 0.000 0.789 0.000 1.000
#> SRR1706920 2 0.000 0.789 0.000 1.000
#> SRR1706921 2 0.000 0.789 0.000 1.000
#> SRR1706922 2 0.000 0.789 0.000 1.000
#> SRR1706915 2 0.000 0.789 0.000 1.000
#> SRR1706916 2 0.000 0.789 0.000 1.000
#> SRR1706917 2 0.000 0.789 0.000 1.000
#> SRR1706918 2 0.000 0.789 0.000 1.000
#> SRR1706923 2 0.000 0.789 0.000 1.000
#> SRR1706924 2 0.000 0.789 0.000 1.000
#> SRR1706925 2 0.000 0.789 0.000 1.000
#> SRR1706926 2 0.000 0.789 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706768 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706769 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706770 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706771 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706772 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706773 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706774 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706775 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706776 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706777 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706778 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706779 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706780 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706781 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706782 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706783 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706784 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706785 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706786 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706787 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706788 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706789 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706790 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706791 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706792 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706793 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706794 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706795 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706796 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706797 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706798 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706799 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706800 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706801 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706802 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706803 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706804 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706805 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706806 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706811 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706812 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706813 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706814 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706807 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706808 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706809 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706810 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706815 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706816 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706817 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706818 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706819 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706820 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706821 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706822 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706823 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706824 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706825 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706826 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706827 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706828 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706829 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706830 3 0.6180 0.403 0.416 0.000 0.584
#> SRR1706835 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706836 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706837 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706838 1 0.0892 0.682 0.980 0.000 0.020
#> SRR1706831 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706832 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706833 1 0.6309 -0.273 0.500 0.000 0.500
#> SRR1706834 3 0.6309 0.246 0.500 0.000 0.500
#> SRR1706839 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706840 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706841 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706842 1 0.3816 0.737 0.852 0.148 0.000
#> SRR1706847 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706848 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706849 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706850 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706843 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706844 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706845 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706846 1 0.5497 0.672 0.708 0.292 0.000
#> SRR1706851 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706852 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706853 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706854 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706855 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706856 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706857 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706858 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706859 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706860 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706861 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706862 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706867 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706869 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706870 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706863 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706864 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706865 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706866 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706871 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706872 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706873 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706874 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706879 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706880 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706881 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706882 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706883 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706884 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706885 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706886 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706875 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706876 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706877 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706878 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706887 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706888 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706889 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706890 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706891 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706892 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706893 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706894 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706895 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706896 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706897 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706898 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706899 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706900 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706901 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706902 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706907 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706908 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706909 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706910 3 0.0424 0.632 0.000 0.008 0.992
#> SRR1706903 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706904 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706905 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706906 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706911 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706912 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706913 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706914 3 0.6487 0.383 0.032 0.268 0.700
#> SRR1706919 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706920 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706921 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706922 2 0.0983 0.882 0.004 0.980 0.016
#> SRR1706915 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706916 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706917 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706918 2 0.6187 0.747 0.028 0.724 0.248
#> SRR1706923 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706924 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706925 2 0.0592 0.874 0.012 0.988 0.000
#> SRR1706926 2 0.0592 0.874 0.012 0.988 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706768 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706769 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706770 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706771 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706772 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706773 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706774 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706775 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706776 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706777 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706778 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706779 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706780 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706781 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706782 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706783 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706784 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706785 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706786 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706787 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706788 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706789 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706790 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706791 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706792 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706793 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706794 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706795 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706796 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706797 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706798 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706799 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706800 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706801 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706802 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706803 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706804 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706805 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706806 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706811 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706812 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706813 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706814 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706807 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706808 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706809 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706810 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706815 1 0.4999 0.580 0.660 0.000 0.012 0.328
#> SRR1706816 1 0.4999 0.580 0.660 0.000 0.012 0.328
#> SRR1706817 1 0.4978 0.584 0.664 0.000 0.012 0.324
#> SRR1706818 1 0.4978 0.584 0.664 0.000 0.012 0.324
#> SRR1706819 1 0.0524 0.833 0.988 0.000 0.004 0.008
#> SRR1706820 1 0.0524 0.833 0.988 0.000 0.004 0.008
#> SRR1706821 1 0.0524 0.833 0.988 0.000 0.004 0.008
#> SRR1706822 1 0.0524 0.833 0.988 0.000 0.004 0.008
#> SRR1706823 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706824 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706825 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706826 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706827 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706828 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706829 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706830 4 0.4401 0.925 0.112 0.000 0.076 0.812
#> SRR1706835 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706836 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706837 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706838 1 0.5018 0.574 0.656 0.000 0.012 0.332
#> SRR1706831 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706832 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706833 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706834 4 0.3606 0.922 0.140 0.000 0.020 0.840
#> SRR1706839 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706840 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706841 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706842 1 0.0336 0.834 0.992 0.000 0.000 0.008
#> SRR1706847 3 0.4535 0.784 0.000 0.016 0.744 0.240
#> SRR1706848 3 0.4535 0.784 0.000 0.016 0.744 0.240
#> SRR1706849 3 0.4535 0.784 0.000 0.016 0.744 0.240
#> SRR1706850 3 0.4535 0.784 0.000 0.016 0.744 0.240
#> SRR1706843 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706844 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706845 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706846 1 0.1624 0.825 0.952 0.028 0.020 0.000
#> SRR1706851 3 0.2741 0.780 0.000 0.096 0.892 0.012
#> SRR1706852 3 0.2741 0.780 0.000 0.096 0.892 0.012
#> SRR1706853 3 0.2741 0.780 0.000 0.096 0.892 0.012
#> SRR1706854 3 0.2741 0.780 0.000 0.096 0.892 0.012
#> SRR1706855 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706856 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706857 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706858 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706859 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706860 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706861 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706862 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706867 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706869 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706870 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706863 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706864 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706865 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706866 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706871 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706872 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706873 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706874 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706879 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706880 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706881 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706882 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706883 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706884 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706885 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706886 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706875 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706876 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706877 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706878 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706887 3 0.4642 0.781 0.000 0.020 0.740 0.240
#> SRR1706888 3 0.4642 0.781 0.000 0.020 0.740 0.240
#> SRR1706889 3 0.4642 0.781 0.000 0.020 0.740 0.240
#> SRR1706890 3 0.4642 0.781 0.000 0.020 0.740 0.240
#> SRR1706891 3 0.2611 0.780 0.000 0.096 0.896 0.008
#> SRR1706892 3 0.2611 0.780 0.000 0.096 0.896 0.008
#> SRR1706893 3 0.2611 0.780 0.000 0.096 0.896 0.008
#> SRR1706894 3 0.2611 0.780 0.000 0.096 0.896 0.008
#> SRR1706895 2 0.5842 0.422 0.000 0.520 0.448 0.032
#> SRR1706896 2 0.5842 0.422 0.000 0.520 0.448 0.032
#> SRR1706897 2 0.5842 0.422 0.000 0.520 0.448 0.032
#> SRR1706898 2 0.5842 0.422 0.000 0.520 0.448 0.032
#> SRR1706899 2 0.2221 0.788 0.020 0.936 0.020 0.024
#> SRR1706900 2 0.2221 0.788 0.020 0.936 0.020 0.024
#> SRR1706901 2 0.2221 0.788 0.020 0.936 0.020 0.024
#> SRR1706902 2 0.2221 0.788 0.020 0.936 0.020 0.024
#> SRR1706907 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706908 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706909 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706910 3 0.4535 0.782 0.000 0.016 0.744 0.240
#> SRR1706903 2 0.3120 0.777 0.036 0.896 0.012 0.056
#> SRR1706904 2 0.3120 0.777 0.036 0.896 0.012 0.056
#> SRR1706905 2 0.3120 0.777 0.036 0.896 0.012 0.056
#> SRR1706906 2 0.3120 0.777 0.036 0.896 0.012 0.056
#> SRR1706911 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706912 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706913 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706914 3 0.2611 0.781 0.000 0.096 0.896 0.008
#> SRR1706919 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706920 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706921 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706922 2 0.1174 0.793 0.020 0.968 0.012 0.000
#> SRR1706915 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706916 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706917 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706918 2 0.5478 0.438 0.000 0.540 0.444 0.016
#> SRR1706923 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706924 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706925 2 0.2317 0.785 0.036 0.928 0.004 0.032
#> SRR1706926 2 0.2317 0.785 0.036 0.928 0.004 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.4040 0.983 0.276 0.000 0.000 0.712 0.012
#> SRR1706768 4 0.4040 0.983 0.276 0.000 0.000 0.712 0.012
#> SRR1706769 4 0.4040 0.983 0.276 0.000 0.000 0.712 0.012
#> SRR1706770 4 0.4040 0.983 0.276 0.000 0.000 0.712 0.012
#> SRR1706771 1 0.4876 -0.427 0.544 0.000 0.008 0.436 0.012
#> SRR1706772 1 0.4876 -0.427 0.544 0.000 0.008 0.436 0.012
#> SRR1706773 1 0.4876 -0.427 0.544 0.000 0.008 0.436 0.012
#> SRR1706774 1 0.4876 -0.427 0.544 0.000 0.008 0.436 0.012
#> SRR1706775 1 0.0324 0.416 0.992 0.000 0.004 0.004 0.000
#> SRR1706776 1 0.0324 0.416 0.992 0.000 0.004 0.004 0.000
#> SRR1706777 1 0.0324 0.416 0.992 0.000 0.004 0.004 0.000
#> SRR1706778 1 0.0324 0.416 0.992 0.000 0.004 0.004 0.000
#> SRR1706779 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706780 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706781 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706782 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706783 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706784 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706785 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706786 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706787 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706788 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706789 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706790 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706791 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706792 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706793 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706794 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706795 1 0.0162 0.417 0.996 0.000 0.004 0.000 0.000
#> SRR1706796 1 0.0162 0.417 0.996 0.000 0.004 0.000 0.000
#> SRR1706797 1 0.0162 0.417 0.996 0.000 0.004 0.000 0.000
#> SRR1706798 1 0.0162 0.417 0.996 0.000 0.004 0.000 0.000
#> SRR1706799 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706800 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706801 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706802 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706803 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706804 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706805 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706806 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706811 1 0.4397 -0.409 0.564 0.000 0.004 0.432 0.000
#> SRR1706812 1 0.4397 -0.409 0.564 0.000 0.004 0.432 0.000
#> SRR1706813 1 0.4397 -0.409 0.564 0.000 0.004 0.432 0.000
#> SRR1706814 1 0.4397 -0.409 0.564 0.000 0.004 0.432 0.000
#> SRR1706807 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706808 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706809 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706810 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706815 1 0.0324 0.412 0.992 0.000 0.004 0.000 0.004
#> SRR1706816 1 0.0324 0.412 0.992 0.000 0.004 0.000 0.004
#> SRR1706817 1 0.0324 0.412 0.992 0.000 0.004 0.000 0.004
#> SRR1706818 1 0.0451 0.407 0.988 0.000 0.004 0.000 0.008
#> SRR1706819 1 0.5153 -0.648 0.508 0.008 0.024 0.000 0.460
#> SRR1706820 1 0.5153 -0.648 0.508 0.008 0.024 0.000 0.460
#> SRR1706821 1 0.5153 -0.648 0.508 0.008 0.024 0.000 0.460
#> SRR1706822 1 0.5153 -0.648 0.508 0.008 0.024 0.000 0.460
#> SRR1706823 5 0.4333 0.996 0.352 0.004 0.004 0.000 0.640
#> SRR1706824 5 0.4333 0.996 0.352 0.004 0.004 0.000 0.640
#> SRR1706825 5 0.4333 0.996 0.352 0.004 0.004 0.000 0.640
#> SRR1706826 5 0.4333 0.996 0.352 0.004 0.004 0.000 0.640
#> SRR1706827 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706828 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706829 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706830 4 0.3684 0.994 0.280 0.000 0.000 0.720 0.000
#> SRR1706835 1 0.0000 0.418 1.000 0.000 0.000 0.000 0.000
#> SRR1706836 1 0.0000 0.418 1.000 0.000 0.000 0.000 0.000
#> SRR1706837 1 0.0000 0.418 1.000 0.000 0.000 0.000 0.000
#> SRR1706838 1 0.0000 0.418 1.000 0.000 0.000 0.000 0.000
#> SRR1706831 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706832 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706833 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706834 1 0.4415 -0.425 0.552 0.000 0.004 0.444 0.000
#> SRR1706839 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706840 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706841 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706842 1 0.5069 -0.626 0.520 0.008 0.020 0.000 0.452
#> SRR1706847 3 0.5708 0.669 0.000 0.000 0.588 0.300 0.112
#> SRR1706848 3 0.5708 0.669 0.000 0.000 0.588 0.300 0.112
#> SRR1706849 3 0.5708 0.669 0.000 0.000 0.588 0.300 0.112
#> SRR1706850 3 0.5708 0.669 0.000 0.000 0.588 0.300 0.112
#> SRR1706843 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706844 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706845 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706846 5 0.4182 0.999 0.352 0.004 0.000 0.000 0.644
#> SRR1706851 3 0.2321 0.691 0.000 0.044 0.916 0.024 0.016
#> SRR1706852 3 0.2321 0.691 0.000 0.044 0.916 0.024 0.016
#> SRR1706853 3 0.2321 0.691 0.000 0.044 0.916 0.024 0.016
#> SRR1706854 3 0.2321 0.691 0.000 0.044 0.916 0.024 0.016
#> SRR1706855 2 0.6464 0.213 0.000 0.464 0.424 0.044 0.068
#> SRR1706856 2 0.6464 0.213 0.000 0.464 0.424 0.044 0.068
#> SRR1706857 2 0.6464 0.213 0.000 0.464 0.424 0.044 0.068
#> SRR1706858 2 0.6464 0.213 0.000 0.464 0.424 0.044 0.068
#> SRR1706859 2 0.0162 0.721 0.000 0.996 0.000 0.004 0.000
#> SRR1706860 2 0.0162 0.721 0.000 0.996 0.000 0.004 0.000
#> SRR1706861 2 0.0162 0.721 0.000 0.996 0.000 0.004 0.000
#> SRR1706862 2 0.0162 0.721 0.000 0.996 0.000 0.004 0.000
#> SRR1706867 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706869 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706870 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706863 2 0.4479 0.696 0.000 0.744 0.000 0.072 0.184
#> SRR1706864 2 0.4479 0.696 0.000 0.744 0.000 0.072 0.184
#> SRR1706865 2 0.4479 0.696 0.000 0.744 0.000 0.072 0.184
#> SRR1706866 2 0.4479 0.696 0.000 0.744 0.000 0.072 0.184
#> SRR1706871 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706872 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706873 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706874 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706879 2 0.0000 0.721 0.000 1.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 0.721 0.000 1.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 0.721 0.000 1.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 0.721 0.000 1.000 0.000 0.000 0.000
#> SRR1706883 2 0.4421 0.696 0.000 0.748 0.000 0.068 0.184
#> SRR1706884 2 0.4421 0.696 0.000 0.748 0.000 0.068 0.184
#> SRR1706885 2 0.4421 0.696 0.000 0.748 0.000 0.068 0.184
#> SRR1706886 2 0.4421 0.696 0.000 0.748 0.000 0.068 0.184
#> SRR1706875 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706876 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706877 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706878 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706887 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706888 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706889 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706890 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706891 3 0.0963 0.704 0.000 0.036 0.964 0.000 0.000
#> SRR1706892 3 0.0963 0.704 0.000 0.036 0.964 0.000 0.000
#> SRR1706893 3 0.0963 0.704 0.000 0.036 0.964 0.000 0.000
#> SRR1706894 3 0.0963 0.704 0.000 0.036 0.964 0.000 0.000
#> SRR1706895 3 0.6658 -0.213 0.000 0.428 0.444 0.048 0.080
#> SRR1706896 3 0.6658 -0.213 0.000 0.428 0.444 0.048 0.080
#> SRR1706897 3 0.6658 -0.213 0.000 0.428 0.444 0.048 0.080
#> SRR1706898 3 0.6658 -0.213 0.000 0.428 0.444 0.048 0.080
#> SRR1706899 2 0.1854 0.710 0.000 0.936 0.008 0.036 0.020
#> SRR1706900 2 0.1854 0.710 0.000 0.936 0.008 0.036 0.020
#> SRR1706901 2 0.1854 0.710 0.000 0.936 0.008 0.036 0.020
#> SRR1706902 2 0.1854 0.710 0.000 0.936 0.008 0.036 0.020
#> SRR1706907 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706908 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706909 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706910 3 0.5596 0.669 0.000 0.000 0.596 0.304 0.100
#> SRR1706903 2 0.5092 0.684 0.000 0.708 0.008 0.092 0.192
#> SRR1706904 2 0.5092 0.684 0.000 0.708 0.008 0.092 0.192
#> SRR1706905 2 0.5092 0.684 0.000 0.708 0.008 0.092 0.192
#> SRR1706906 2 0.5092 0.684 0.000 0.708 0.008 0.092 0.192
#> SRR1706911 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706912 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706913 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706914 3 0.1121 0.704 0.000 0.044 0.956 0.000 0.000
#> SRR1706919 2 0.0162 0.721 0.000 0.996 0.000 0.000 0.004
#> SRR1706920 2 0.0162 0.721 0.000 0.996 0.000 0.000 0.004
#> SRR1706921 2 0.0162 0.721 0.000 0.996 0.000 0.000 0.004
#> SRR1706922 2 0.0162 0.721 0.000 0.996 0.000 0.000 0.004
#> SRR1706915 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706916 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706917 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706918 2 0.6188 0.217 0.000 0.472 0.432 0.024 0.072
#> SRR1706923 2 0.4455 0.696 0.000 0.744 0.000 0.068 0.188
#> SRR1706924 2 0.4455 0.696 0.000 0.744 0.000 0.068 0.188
#> SRR1706925 2 0.4455 0.696 0.000 0.744 0.000 0.068 0.188
#> SRR1706926 2 0.4455 0.696 0.000 0.744 0.000 0.068 0.188
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.1452 0.75681 0.020 0.000 NA 0.948 0.012 0.000
#> SRR1706768 4 0.1452 0.75681 0.020 0.000 NA 0.948 0.012 0.000
#> SRR1706769 4 0.1452 0.75681 0.020 0.000 NA 0.948 0.012 0.000
#> SRR1706770 4 0.1452 0.75681 0.020 0.000 NA 0.948 0.012 0.000
#> SRR1706771 4 0.5333 0.69700 0.024 0.000 NA 0.604 0.292 0.000
#> SRR1706772 4 0.5333 0.69700 0.024 0.000 NA 0.604 0.292 0.000
#> SRR1706773 4 0.5333 0.69700 0.024 0.000 NA 0.604 0.292 0.000
#> SRR1706774 4 0.5333 0.69700 0.024 0.000 NA 0.604 0.292 0.000
#> SRR1706775 5 0.5172 0.97651 0.228 0.000 NA 0.128 0.636 0.000
#> SRR1706776 5 0.5172 0.97651 0.228 0.000 NA 0.128 0.636 0.000
#> SRR1706777 5 0.5172 0.97651 0.228 0.000 NA 0.128 0.636 0.000
#> SRR1706778 5 0.5172 0.97651 0.228 0.000 NA 0.128 0.636 0.000
#> SRR1706779 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706780 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706781 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706782 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706783 1 0.0291 0.75958 0.992 0.000 NA 0.000 0.000 0.004
#> SRR1706784 1 0.0291 0.75958 0.992 0.000 NA 0.000 0.000 0.004
#> SRR1706785 1 0.0291 0.75958 0.992 0.000 NA 0.000 0.000 0.004
#> SRR1706786 1 0.0291 0.75958 0.992 0.000 NA 0.000 0.000 0.004
#> SRR1706787 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706788 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706789 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706790 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706791 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706792 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706793 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706794 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706795 5 0.5350 0.97816 0.228 0.000 NA 0.128 0.628 0.000
#> SRR1706796 5 0.5350 0.97816 0.228 0.000 NA 0.128 0.628 0.000
#> SRR1706797 5 0.5350 0.97816 0.228 0.000 NA 0.128 0.628 0.000
#> SRR1706798 5 0.5350 0.97816 0.228 0.000 NA 0.128 0.628 0.000
#> SRR1706799 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706800 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706801 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706802 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706803 1 0.0260 0.75957 0.992 0.000 NA 0.000 0.000 0.000
#> SRR1706804 1 0.0260 0.75957 0.992 0.000 NA 0.000 0.000 0.000
#> SRR1706805 1 0.0260 0.75957 0.992 0.000 NA 0.000 0.000 0.000
#> SRR1706806 1 0.0260 0.75957 0.992 0.000 NA 0.000 0.000 0.000
#> SRR1706811 4 0.5155 0.68942 0.024 0.000 NA 0.608 0.308 0.000
#> SRR1706812 4 0.5155 0.68942 0.024 0.000 NA 0.608 0.308 0.000
#> SRR1706813 4 0.5155 0.68942 0.024 0.000 NA 0.608 0.308 0.000
#> SRR1706814 4 0.5155 0.68942 0.024 0.000 NA 0.608 0.308 0.000
#> SRR1706807 4 0.0951 0.76277 0.020 0.000 NA 0.968 0.004 0.000
#> SRR1706808 4 0.0951 0.76277 0.020 0.000 NA 0.968 0.004 0.000
#> SRR1706809 4 0.0951 0.76277 0.020 0.000 NA 0.968 0.004 0.000
#> SRR1706810 4 0.0951 0.76277 0.020 0.000 NA 0.968 0.004 0.000
#> SRR1706815 5 0.5628 0.96645 0.232 0.000 NA 0.124 0.612 0.000
#> SRR1706816 5 0.5628 0.96645 0.232 0.000 NA 0.124 0.612 0.000
#> SRR1706817 5 0.5628 0.96645 0.232 0.000 NA 0.124 0.612 0.000
#> SRR1706818 5 0.5614 0.95997 0.236 0.000 NA 0.120 0.612 0.000
#> SRR1706819 1 0.5027 0.70085 0.640 0.000 NA 0.000 0.200 0.000
#> SRR1706820 1 0.5027 0.70085 0.640 0.000 NA 0.000 0.200 0.000
#> SRR1706821 1 0.5027 0.70085 0.640 0.000 NA 0.000 0.200 0.000
#> SRR1706822 1 0.5027 0.70085 0.640 0.000 NA 0.000 0.200 0.000
#> SRR1706823 1 0.1477 0.74531 0.940 0.000 NA 0.000 0.008 0.004
#> SRR1706824 1 0.1477 0.74531 0.940 0.000 NA 0.000 0.008 0.004
#> SRR1706825 1 0.1477 0.74531 0.940 0.000 NA 0.000 0.008 0.004
#> SRR1706826 1 0.1477 0.74531 0.940 0.000 NA 0.000 0.008 0.004
#> SRR1706827 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706828 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706829 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706830 4 0.0547 0.76405 0.020 0.000 NA 0.980 0.000 0.000
#> SRR1706835 5 0.4932 0.97720 0.228 0.000 NA 0.128 0.644 0.000
#> SRR1706836 5 0.4932 0.97720 0.228 0.000 NA 0.128 0.644 0.000
#> SRR1706837 5 0.4932 0.97720 0.228 0.000 NA 0.128 0.644 0.000
#> SRR1706838 5 0.4932 0.97720 0.228 0.000 NA 0.128 0.644 0.000
#> SRR1706831 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706832 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706833 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706834 4 0.5090 0.70328 0.024 0.000 NA 0.624 0.292 0.000
#> SRR1706839 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706840 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706841 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706842 1 0.4871 0.70236 0.652 0.000 NA 0.000 0.224 0.000
#> SRR1706847 6 0.6742 0.57121 0.000 0.000 NA 0.176 0.140 0.528
#> SRR1706848 6 0.6742 0.57121 0.000 0.000 NA 0.176 0.140 0.528
#> SRR1706849 6 0.6742 0.57121 0.000 0.000 NA 0.176 0.140 0.528
#> SRR1706850 6 0.6742 0.57121 0.000 0.000 NA 0.176 0.140 0.528
#> SRR1706843 1 0.0000 0.75977 1.000 0.000 NA 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.75977 1.000 0.000 NA 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.75977 1.000 0.000 NA 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.75977 1.000 0.000 NA 0.000 0.000 0.000
#> SRR1706851 6 0.2307 0.62417 0.000 0.012 NA 0.000 0.024 0.900
#> SRR1706852 6 0.2307 0.62417 0.000 0.012 NA 0.000 0.024 0.900
#> SRR1706853 6 0.2307 0.62417 0.000 0.012 NA 0.000 0.024 0.900
#> SRR1706854 6 0.2307 0.62417 0.000 0.012 NA 0.000 0.024 0.900
#> SRR1706855 2 0.6885 -0.01724 0.000 0.380 NA 0.000 0.072 0.368
#> SRR1706856 2 0.6885 -0.01724 0.000 0.380 NA 0.000 0.072 0.368
#> SRR1706857 2 0.6885 -0.01724 0.000 0.380 NA 0.000 0.072 0.368
#> SRR1706858 2 0.6885 -0.01724 0.000 0.380 NA 0.000 0.072 0.368
#> SRR1706859 2 0.0632 0.71063 0.000 0.976 NA 0.000 0.000 0.000
#> SRR1706860 2 0.0632 0.71063 0.000 0.976 NA 0.000 0.000 0.000
#> SRR1706861 2 0.0632 0.71063 0.000 0.976 NA 0.000 0.000 0.000
#> SRR1706862 2 0.0632 0.71063 0.000 0.976 NA 0.000 0.000 0.000
#> SRR1706867 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706869 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706870 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706863 2 0.3955 0.70110 0.008 0.648 NA 0.000 0.004 0.000
#> SRR1706864 2 0.3955 0.70110 0.008 0.648 NA 0.000 0.004 0.000
#> SRR1706865 2 0.3955 0.70110 0.008 0.648 NA 0.000 0.004 0.000
#> SRR1706866 2 0.3955 0.70110 0.008 0.648 NA 0.000 0.004 0.000
#> SRR1706871 6 0.0363 0.64076 0.000 0.012 NA 0.000 0.000 0.988
#> SRR1706872 6 0.0363 0.64076 0.000 0.012 NA 0.000 0.000 0.988
#> SRR1706873 6 0.0363 0.64076 0.000 0.012 NA 0.000 0.000 0.988
#> SRR1706874 6 0.0363 0.64076 0.000 0.012 NA 0.000 0.000 0.988
#> SRR1706879 2 0.0146 0.71426 0.000 0.996 NA 0.004 0.000 0.000
#> SRR1706880 2 0.0146 0.71426 0.000 0.996 NA 0.004 0.000 0.000
#> SRR1706881 2 0.0146 0.71426 0.000 0.996 NA 0.004 0.000 0.000
#> SRR1706882 2 0.0146 0.71426 0.000 0.996 NA 0.004 0.000 0.000
#> SRR1706883 2 0.3910 0.70311 0.008 0.660 NA 0.000 0.004 0.000
#> SRR1706884 2 0.3910 0.70311 0.008 0.660 NA 0.000 0.004 0.000
#> SRR1706885 2 0.3910 0.70311 0.008 0.660 NA 0.000 0.004 0.000
#> SRR1706886 2 0.3910 0.70311 0.008 0.660 NA 0.000 0.004 0.000
#> SRR1706875 2 0.6697 -0.02786 0.000 0.388 NA 0.000 0.060 0.388
#> SRR1706876 6 0.6697 -0.02277 0.000 0.388 NA 0.000 0.060 0.388
#> SRR1706877 2 0.6697 -0.02786 0.000 0.388 NA 0.000 0.060 0.388
#> SRR1706878 2 0.6697 -0.02786 0.000 0.388 NA 0.000 0.060 0.388
#> SRR1706887 6 0.6749 0.56682 0.000 0.000 NA 0.188 0.160 0.524
#> SRR1706888 6 0.6749 0.56682 0.000 0.000 NA 0.188 0.160 0.524
#> SRR1706889 6 0.6749 0.56682 0.000 0.000 NA 0.188 0.160 0.524
#> SRR1706890 6 0.6749 0.56682 0.000 0.000 NA 0.188 0.160 0.524
#> SRR1706891 6 0.0912 0.63905 0.000 0.008 NA 0.004 0.012 0.972
#> SRR1706892 6 0.0912 0.63905 0.000 0.008 NA 0.004 0.012 0.972
#> SRR1706893 6 0.0912 0.63905 0.000 0.008 NA 0.004 0.012 0.972
#> SRR1706894 6 0.0912 0.63905 0.000 0.008 NA 0.004 0.012 0.972
#> SRR1706895 6 0.7040 -0.00108 0.000 0.356 NA 0.004 0.084 0.392
#> SRR1706896 6 0.7040 -0.00108 0.000 0.356 NA 0.004 0.084 0.392
#> SRR1706897 6 0.7040 -0.00108 0.000 0.356 NA 0.004 0.084 0.392
#> SRR1706898 6 0.7040 -0.00108 0.000 0.356 NA 0.004 0.084 0.392
#> SRR1706899 2 0.2450 0.69468 0.000 0.892 NA 0.004 0.068 0.004
#> SRR1706900 2 0.2450 0.69468 0.000 0.892 NA 0.004 0.068 0.004
#> SRR1706901 2 0.2450 0.69468 0.000 0.892 NA 0.004 0.068 0.004
#> SRR1706902 2 0.2450 0.69468 0.000 0.892 NA 0.004 0.068 0.004
#> SRR1706907 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706908 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706909 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706910 6 0.6717 0.56737 0.000 0.000 NA 0.188 0.160 0.528
#> SRR1706903 2 0.4780 0.68977 0.008 0.612 NA 0.000 0.040 0.004
#> SRR1706904 2 0.4780 0.68977 0.008 0.612 NA 0.000 0.040 0.004
#> SRR1706905 2 0.4780 0.68977 0.008 0.612 NA 0.000 0.040 0.004
#> SRR1706906 2 0.4780 0.68977 0.008 0.612 NA 0.000 0.040 0.004
#> SRR1706911 6 0.0508 0.64074 0.000 0.012 NA 0.000 0.000 0.984
#> SRR1706912 6 0.0508 0.64074 0.000 0.012 NA 0.000 0.000 0.984
#> SRR1706913 6 0.0508 0.64074 0.000 0.012 NA 0.000 0.000 0.984
#> SRR1706914 6 0.0508 0.64074 0.000 0.012 NA 0.000 0.000 0.984
#> SRR1706919 2 0.0000 0.71424 0.000 1.000 NA 0.000 0.000 0.000
#> SRR1706920 2 0.0000 0.71424 0.000 1.000 NA 0.000 0.000 0.000
#> SRR1706921 2 0.0000 0.71424 0.000 1.000 NA 0.000 0.000 0.000
#> SRR1706922 2 0.0000 0.71424 0.000 1.000 NA 0.000 0.000 0.000
#> SRR1706915 6 0.6716 -0.01724 0.000 0.384 NA 0.000 0.060 0.388
#> SRR1706916 6 0.6716 -0.01724 0.000 0.384 NA 0.000 0.060 0.388
#> SRR1706917 6 0.6716 -0.01724 0.000 0.384 NA 0.000 0.060 0.388
#> SRR1706918 6 0.6716 -0.01724 0.000 0.384 NA 0.000 0.060 0.388
#> SRR1706923 2 0.3789 0.70311 0.008 0.660 NA 0.000 0.000 0.000
#> SRR1706924 2 0.3789 0.70311 0.008 0.660 NA 0.000 0.000 0.000
#> SRR1706925 2 0.3789 0.70311 0.008 0.660 NA 0.000 0.000 0.000
#> SRR1706926 2 0.3789 0.70311 0.008 0.660 NA 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 15185 rows and 159 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 0.982 0.986 0.5036 0.497 0.497
#> 3 3 0.879 0.897 0.947 0.3172 0.763 0.557
#> 4 4 1.000 0.994 0.996 0.1221 0.800 0.495
#> 5 5 1.000 0.990 0.984 0.0544 0.959 0.841
#> 6 6 0.995 0.989 0.976 0.0484 0.959 0.811
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
#> SRR1706767 1 0.000 0.985 1.000 0.000
#> SRR1706768 1 0.000 0.985 1.000 0.000
#> SRR1706769 1 0.000 0.985 1.000 0.000
#> SRR1706770 1 0.000 0.985 1.000 0.000
#> SRR1706771 1 0.000 0.985 1.000 0.000
#> SRR1706772 1 0.000 0.985 1.000 0.000
#> SRR1706773 1 0.000 0.985 1.000 0.000
#> SRR1706774 1 0.000 0.985 1.000 0.000
#> SRR1706775 1 0.000 0.985 1.000 0.000
#> SRR1706776 1 0.000 0.985 1.000 0.000
#> SRR1706777 1 0.000 0.985 1.000 0.000
#> SRR1706778 1 0.000 0.985 1.000 0.000
#> SRR1706779 1 0.224 0.977 0.964 0.036
#> SRR1706780 1 0.224 0.977 0.964 0.036
#> SRR1706781 1 0.224 0.977 0.964 0.036
#> SRR1706782 1 0.224 0.977 0.964 0.036
#> SRR1706783 1 0.224 0.977 0.964 0.036
#> SRR1706784 1 0.224 0.977 0.964 0.036
#> SRR1706785 1 0.224 0.977 0.964 0.036
#> SRR1706786 1 0.224 0.977 0.964 0.036
#> SRR1706787 1 0.000 0.985 1.000 0.000
#> SRR1706788 1 0.000 0.985 1.000 0.000
#> SRR1706789 1 0.000 0.985 1.000 0.000
#> SRR1706790 1 0.000 0.985 1.000 0.000
#> SRR1706791 1 0.000 0.985 1.000 0.000
#> SRR1706792 1 0.000 0.985 1.000 0.000
#> SRR1706793 1 0.000 0.985 1.000 0.000
#> SRR1706794 1 0.000 0.985 1.000 0.000
#> SRR1706795 1 0.000 0.985 1.000 0.000
#> SRR1706796 1 0.000 0.985 1.000 0.000
#> SRR1706797 1 0.000 0.985 1.000 0.000
#> SRR1706798 1 0.000 0.985 1.000 0.000
#> SRR1706799 1 0.224 0.977 0.964 0.036
#> SRR1706800 1 0.224 0.977 0.964 0.036
#> SRR1706801 1 0.224 0.977 0.964 0.036
#> SRR1706802 1 0.224 0.977 0.964 0.036
#> SRR1706803 1 0.224 0.977 0.964 0.036
#> SRR1706804 1 0.224 0.977 0.964 0.036
#> SRR1706805 1 0.224 0.977 0.964 0.036
#> SRR1706806 1 0.224 0.977 0.964 0.036
#> SRR1706811 1 0.000 0.985 1.000 0.000
#> SRR1706812 1 0.000 0.985 1.000 0.000
#> SRR1706813 1 0.000 0.985 1.000 0.000
#> SRR1706814 1 0.000 0.985 1.000 0.000
#> SRR1706807 1 0.000 0.985 1.000 0.000
#> SRR1706808 1 0.000 0.985 1.000 0.000
#> SRR1706809 1 0.000 0.985 1.000 0.000
#> SRR1706810 1 0.000 0.985 1.000 0.000
#> SRR1706815 1 0.000 0.985 1.000 0.000
#> SRR1706816 1 0.000 0.985 1.000 0.000
#> SRR1706817 1 0.000 0.985 1.000 0.000
#> SRR1706818 1 0.000 0.985 1.000 0.000
#> SRR1706819 1 0.224 0.977 0.964 0.036
#> SRR1706820 1 0.224 0.977 0.964 0.036
#> SRR1706821 1 0.224 0.977 0.964 0.036
#> SRR1706822 1 0.224 0.977 0.964 0.036
#> SRR1706823 1 0.224 0.977 0.964 0.036
#> SRR1706824 1 0.224 0.977 0.964 0.036
#> SRR1706825 1 0.224 0.977 0.964 0.036
#> SRR1706826 1 0.224 0.977 0.964 0.036
#> SRR1706827 1 0.000 0.985 1.000 0.000
#> SRR1706828 1 0.000 0.985 1.000 0.000
#> SRR1706829 1 0.000 0.985 1.000 0.000
#> SRR1706830 1 0.000 0.985 1.000 0.000
#> SRR1706835 1 0.000 0.985 1.000 0.000
#> SRR1706836 1 0.000 0.985 1.000 0.000
#> SRR1706837 1 0.000 0.985 1.000 0.000
#> SRR1706838 1 0.000 0.985 1.000 0.000
#> SRR1706831 1 0.000 0.985 1.000 0.000
#> SRR1706832 1 0.000 0.985 1.000 0.000
#> SRR1706833 1 0.000 0.985 1.000 0.000
#> SRR1706834 1 0.000 0.985 1.000 0.000
#> SRR1706839 1 0.224 0.977 0.964 0.036
#> SRR1706840 1 0.224 0.977 0.964 0.036
#> SRR1706841 1 0.224 0.977 0.964 0.036
#> SRR1706842 1 0.224 0.977 0.964 0.036
#> SRR1706847 2 0.224 0.977 0.036 0.964
#> SRR1706848 2 0.224 0.977 0.036 0.964
#> SRR1706849 2 0.224 0.977 0.036 0.964
#> SRR1706850 2 0.224 0.977 0.036 0.964
#> SRR1706843 1 0.224 0.977 0.964 0.036
#> SRR1706844 1 0.224 0.977 0.964 0.036
#> SRR1706845 1 0.224 0.977 0.964 0.036
#> SRR1706846 1 0.224 0.977 0.964 0.036
#> SRR1706851 2 0.224 0.977 0.036 0.964
#> SRR1706852 2 0.224 0.977 0.036 0.964
#> SRR1706853 2 0.224 0.977 0.036 0.964
#> SRR1706854 2 0.224 0.977 0.036 0.964
#> SRR1706855 2 0.000 0.985 0.000 1.000
#> SRR1706856 2 0.000 0.985 0.000 1.000
#> SRR1706857 2 0.000 0.985 0.000 1.000
#> SRR1706858 2 0.000 0.985 0.000 1.000
#> SRR1706859 2 0.000 0.985 0.000 1.000
#> SRR1706860 2 0.000 0.985 0.000 1.000
#> SRR1706861 2 0.000 0.985 0.000 1.000
#> SRR1706862 2 0.000 0.985 0.000 1.000
#> SRR1706867 2 0.224 0.977 0.036 0.964
#> SRR1706869 2 0.224 0.977 0.036 0.964
#> SRR1706870 2 0.224 0.977 0.036 0.964
#> SRR1706863 2 0.000 0.985 0.000 1.000
#> SRR1706864 2 0.000 0.985 0.000 1.000
#> SRR1706865 2 0.000 0.985 0.000 1.000
#> SRR1706866 2 0.000 0.985 0.000 1.000
#> SRR1706871 2 0.224 0.977 0.036 0.964
#> SRR1706872 2 0.224 0.977 0.036 0.964
#> SRR1706873 2 0.224 0.977 0.036 0.964
#> SRR1706874 2 0.224 0.977 0.036 0.964
#> SRR1706879 2 0.000 0.985 0.000 1.000
#> SRR1706880 2 0.000 0.985 0.000 1.000
#> SRR1706881 2 0.000 0.985 0.000 1.000
#> SRR1706882 2 0.000 0.985 0.000 1.000
#> SRR1706883 2 0.000 0.985 0.000 1.000
#> SRR1706884 2 0.000 0.985 0.000 1.000
#> SRR1706885 2 0.000 0.985 0.000 1.000
#> SRR1706886 2 0.000 0.985 0.000 1.000
#> SRR1706875 2 0.000 0.985 0.000 1.000
#> SRR1706876 2 0.000 0.985 0.000 1.000
#> SRR1706877 2 0.000 0.985 0.000 1.000
#> SRR1706878 2 0.000 0.985 0.000 1.000
#> SRR1706887 2 0.224 0.977 0.036 0.964
#> SRR1706888 2 0.224 0.977 0.036 0.964
#> SRR1706889 2 0.224 0.977 0.036 0.964
#> SRR1706890 2 0.224 0.977 0.036 0.964
#> SRR1706891 2 0.224 0.977 0.036 0.964
#> SRR1706892 2 0.224 0.977 0.036 0.964
#> SRR1706893 2 0.224 0.977 0.036 0.964
#> SRR1706894 2 0.224 0.977 0.036 0.964
#> SRR1706895 2 0.000 0.985 0.000 1.000
#> SRR1706896 2 0.000 0.985 0.000 1.000
#> SRR1706897 2 0.000 0.985 0.000 1.000
#> SRR1706898 2 0.000 0.985 0.000 1.000
#> SRR1706899 2 0.000 0.985 0.000 1.000
#> SRR1706900 2 0.000 0.985 0.000 1.000
#> SRR1706901 2 0.000 0.985 0.000 1.000
#> SRR1706902 2 0.000 0.985 0.000 1.000
#> SRR1706907 2 0.224 0.977 0.036 0.964
#> SRR1706908 2 0.224 0.977 0.036 0.964
#> SRR1706909 2 0.224 0.977 0.036 0.964
#> SRR1706910 2 0.224 0.977 0.036 0.964
#> SRR1706903 2 0.000 0.985 0.000 1.000
#> SRR1706904 2 0.000 0.985 0.000 1.000
#> SRR1706905 2 0.000 0.985 0.000 1.000
#> SRR1706906 2 0.000 0.985 0.000 1.000
#> SRR1706911 2 0.224 0.977 0.036 0.964
#> SRR1706912 2 0.224 0.977 0.036 0.964
#> SRR1706913 2 0.224 0.977 0.036 0.964
#> SRR1706914 2 0.224 0.977 0.036 0.964
#> SRR1706919 2 0.000 0.985 0.000 1.000
#> SRR1706920 2 0.000 0.985 0.000 1.000
#> SRR1706921 2 0.000 0.985 0.000 1.000
#> SRR1706922 2 0.000 0.985 0.000 1.000
#> SRR1706915 2 0.000 0.985 0.000 1.000
#> SRR1706916 2 0.000 0.985 0.000 1.000
#> SRR1706917 2 0.000 0.985 0.000 1.000
#> SRR1706918 2 0.000 0.985 0.000 1.000
#> SRR1706923 2 0.000 0.985 0.000 1.000
#> SRR1706924 2 0.000 0.985 0.000 1.000
#> SRR1706925 2 0.000 0.985 0.000 1.000
#> SRR1706926 2 0.000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706768 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706769 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706770 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706771 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706772 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706773 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706774 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706775 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706776 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706777 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706778 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706779 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706783 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706784 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706785 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706786 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706787 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706788 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706789 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706790 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706791 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706792 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706793 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706794 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706795 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706796 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706797 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706798 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706799 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706802 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706803 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706804 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706805 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706806 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706811 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706812 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706813 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706814 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706807 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706808 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706809 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706810 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706815 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706816 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706817 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706818 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706819 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706820 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706821 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706822 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706823 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706824 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706825 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706826 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706827 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706828 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706829 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706830 3 0.1163 0.985 0.028 0.000 0.972
#> SRR1706835 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706836 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706837 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706838 1 0.0237 0.997 0.996 0.000 0.004
#> SRR1706831 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706832 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706833 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706834 3 0.1411 0.982 0.036 0.000 0.964
#> SRR1706839 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706847 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706848 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706849 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706850 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706843 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706844 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706845 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706846 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1706851 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706852 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706853 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706854 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706855 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706859 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706860 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706861 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706862 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706867 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706869 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706870 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706863 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706864 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706865 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706866 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706871 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706872 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706873 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706874 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706879 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706880 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706881 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706882 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706883 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706884 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706885 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706886 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706875 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706887 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706888 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706889 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706890 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706891 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706892 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706893 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706894 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706895 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706896 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706897 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706898 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706899 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706900 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706901 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706902 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706907 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706908 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706909 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706910 3 0.0237 0.971 0.000 0.004 0.996
#> SRR1706903 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706904 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706905 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706906 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706911 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706912 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706913 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706914 2 0.6252 0.406 0.000 0.556 0.444
#> SRR1706919 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706920 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706921 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706922 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706915 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.877 0.000 1.000 0.000
#> SRR1706923 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706924 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706925 2 0.0237 0.878 0.004 0.996 0.000
#> SRR1706926 2 0.0237 0.878 0.004 0.996 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706772 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706773 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706774 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706775 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706776 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706777 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706778 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706792 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706793 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706794 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706795 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706796 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706797 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706798 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706812 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706813 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706814 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706807 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706808 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706809 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706810 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706815 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706816 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706817 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706818 4 0.1474 0.955 0.052 0.000 0.000 0.948
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706835 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706836 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706837 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706838 4 0.0188 0.989 0.004 0.000 0.000 0.996
#> SRR1706831 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706832 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706833 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706834 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706848 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706849 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706850 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706856 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706857 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706858 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706859 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706869 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706870 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706863 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706876 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706877 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706878 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706887 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706888 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706889 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706890 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706891 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706896 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706897 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706898 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706899 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706908 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706909 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706910 3 0.0188 0.998 0.000 0.000 0.996 0.004
#> SRR1706903 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.998 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706915 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706916 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706917 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706918 2 0.0188 0.998 0.000 0.996 0.004 0.000
#> SRR1706923 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.999 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.999 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706768 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706769 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706770 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706771 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706772 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706773 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706774 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706775 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706776 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706777 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706778 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706779 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706780 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706781 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706782 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706783 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706784 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706785 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706786 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706787 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706788 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706789 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706790 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706791 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706792 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706793 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706794 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706795 5 0.183 0.996 0.004 0.00 0 0.076 0.92
#> SRR1706796 5 0.183 0.996 0.004 0.00 0 0.076 0.92
#> SRR1706797 5 0.183 0.996 0.004 0.00 0 0.076 0.92
#> SRR1706798 5 0.183 0.996 0.004 0.00 0 0.076 0.92
#> SRR1706799 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706800 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706801 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706802 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706803 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706804 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706805 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706806 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706811 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706812 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706813 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706814 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706807 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706808 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706809 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706810 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706815 5 0.189 0.993 0.008 0.00 0 0.072 0.92
#> SRR1706816 5 0.189 0.993 0.008 0.00 0 0.072 0.92
#> SRR1706817 5 0.189 0.993 0.008 0.00 0 0.072 0.92
#> SRR1706818 5 0.189 0.993 0.008 0.00 0 0.072 0.92
#> SRR1706819 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706820 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706821 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706822 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706823 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706824 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706825 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706826 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706827 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706828 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706829 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706830 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706835 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706836 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706837 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706838 5 0.173 0.996 0.000 0.00 0 0.080 0.92
#> SRR1706831 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706832 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706833 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706834 4 0.000 1.000 0.000 0.00 0 1.000 0.00
#> SRR1706839 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706840 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706841 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706842 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706847 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706848 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706849 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706850 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706843 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706844 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706845 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706846 1 0.000 1.000 1.000 0.00 0 0.000 0.00
#> SRR1706851 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706852 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706853 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706854 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706855 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706856 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706857 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706858 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706859 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706860 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706861 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706862 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706867 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706869 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706870 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706863 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706864 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706865 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706866 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706871 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706872 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706873 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706874 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706879 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706880 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706881 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706882 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706883 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706884 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706885 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706886 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706875 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706876 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706877 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706878 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706887 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706888 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706889 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706890 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706891 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706892 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706893 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706894 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706895 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706896 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706897 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706898 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706899 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706900 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706901 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706902 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706907 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706908 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706909 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706910 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706903 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706904 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706905 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706906 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706911 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706912 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706913 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706914 3 0.000 1.000 0.000 0.00 1 0.000 0.00
#> SRR1706919 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706920 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706921 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706922 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706915 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706916 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706917 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706918 2 0.173 0.951 0.000 0.92 0 0.000 0.08
#> SRR1706923 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706924 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706925 2 0.000 0.976 0.000 1.00 0 0.000 0.00
#> SRR1706926 2 0.000 0.976 0.000 1.00 0 0.000 0.00
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706768 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706769 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706770 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706771 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706772 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706773 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706774 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706775 5 0.1010 0.997 0.000 0.000 0.000 0.036 0.960 0.004
#> SRR1706776 5 0.1010 0.997 0.000 0.000 0.000 0.036 0.960 0.004
#> SRR1706777 5 0.1010 0.997 0.000 0.000 0.000 0.036 0.960 0.004
#> SRR1706778 5 0.1010 0.997 0.000 0.000 0.000 0.036 0.960 0.004
#> SRR1706779 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706780 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706781 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706782 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706783 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706788 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706789 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706790 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706791 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706792 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706793 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706794 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706795 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706796 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706797 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706798 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706799 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706800 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706801 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706802 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706803 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706812 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706813 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706814 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706807 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706808 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706809 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706810 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706815 5 0.1080 0.993 0.004 0.000 0.000 0.032 0.960 0.004
#> SRR1706816 5 0.1010 0.996 0.000 0.000 0.000 0.036 0.960 0.004
#> SRR1706817 5 0.1080 0.993 0.004 0.000 0.000 0.032 0.960 0.004
#> SRR1706818 5 0.1080 0.993 0.004 0.000 0.000 0.032 0.960 0.004
#> SRR1706819 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706820 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706821 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706822 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706823 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706828 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706829 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706830 4 0.0260 0.996 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1706835 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706836 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706837 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706838 5 0.0865 0.997 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1706831 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706832 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706833 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706834 4 0.0000 0.996 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706839 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706840 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706841 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706842 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1706847 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706848 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706849 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706850 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706843 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.2179 0.957 0.000 0.000 0.900 0.000 0.036 0.064
#> SRR1706852 3 0.2179 0.957 0.000 0.000 0.900 0.000 0.036 0.064
#> SRR1706853 3 0.2179 0.957 0.000 0.000 0.900 0.000 0.036 0.064
#> SRR1706854 3 0.2179 0.957 0.000 0.000 0.900 0.000 0.036 0.064
#> SRR1706855 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706856 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706857 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706858 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706859 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706860 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706861 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706862 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706867 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706869 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706870 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706863 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706871 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706872 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706873 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706874 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706879 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706883 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706875 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706876 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706877 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706878 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706887 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706888 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706889 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706890 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706891 3 0.2237 0.956 0.000 0.000 0.896 0.000 0.036 0.068
#> SRR1706892 3 0.2237 0.956 0.000 0.000 0.896 0.000 0.036 0.068
#> SRR1706893 3 0.2237 0.956 0.000 0.000 0.896 0.000 0.036 0.068
#> SRR1706894 3 0.2237 0.956 0.000 0.000 0.896 0.000 0.036 0.068
#> SRR1706895 6 0.1556 0.992 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR1706896 6 0.1556 0.992 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR1706897 6 0.1556 0.992 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR1706898 6 0.1556 0.992 0.000 0.080 0.000 0.000 0.000 0.920
#> SRR1706899 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706900 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706901 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706902 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706907 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706908 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706909 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706910 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706903 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706911 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706912 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706913 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706914 3 0.2119 0.958 0.000 0.000 0.904 0.000 0.036 0.060
#> SRR1706919 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706920 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706921 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706922 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706915 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706916 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706917 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706918 6 0.1663 0.997 0.000 0.088 0.000 0.000 0.000 0.912
#> SRR1706923 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 15185 rows and 159 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 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.873 0.976 0.986 0.2512 0.877 0.753
#> 4 4 0.764 0.917 0.925 0.1789 0.878 0.673
#> 5 5 0.866 0.921 0.939 0.0376 0.976 0.905
#> 6 6 0.920 0.968 0.966 0.0523 0.959 0.821
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706768 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706769 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706770 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706771 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706772 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706773 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706774 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706775 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706776 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706777 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706778 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706779 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706780 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706781 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706782 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706783 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706784 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706785 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706786 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706787 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706788 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706789 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706790 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706791 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706792 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706793 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706794 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706795 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706796 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706797 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706798 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706799 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706800 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706801 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706802 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706803 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706804 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706805 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706806 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706811 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706812 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706813 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706814 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706807 1 0.304 0.907 0.896 0.000 0.104
#> SRR1706808 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706809 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706810 1 0.304 0.907 0.896 0.000 0.104
#> SRR1706815 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706816 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706817 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706818 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706819 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706820 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706821 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706822 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706823 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706824 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706825 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706826 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706827 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706828 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706829 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706830 1 0.312 0.904 0.892 0.000 0.108
#> SRR1706835 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706836 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706837 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706838 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706831 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706832 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706833 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706834 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706839 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706840 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706841 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706842 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706847 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706848 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706849 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706850 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706843 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706844 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706845 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706846 1 0.000 0.978 1.000 0.000 0.000
#> SRR1706851 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706852 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706853 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706854 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706855 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706856 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706857 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706858 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706859 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706860 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706861 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706862 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706867 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706869 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706870 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706863 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706864 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706865 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706866 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706871 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706872 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706873 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706874 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706879 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706880 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706881 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706882 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706883 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706884 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706885 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706886 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706875 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706876 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706877 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706878 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706887 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706888 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706889 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706890 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706891 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706892 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706893 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706894 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706895 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706896 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706897 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706898 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706899 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706900 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706901 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706902 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706907 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706908 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706909 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706910 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706903 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706904 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706905 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706906 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706911 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706912 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706913 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706914 3 0.000 0.986 0.000 0.000 1.000
#> SRR1706919 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706920 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706921 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706922 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706915 3 0.319 0.875 0.000 0.112 0.888
#> SRR1706916 3 0.312 0.879 0.000 0.108 0.892
#> SRR1706917 3 0.312 0.879 0.000 0.108 0.892
#> SRR1706918 3 0.312 0.879 0.000 0.108 0.892
#> SRR1706923 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706924 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706925 2 0.000 1.000 0.000 1.000 0.000
#> SRR1706926 2 0.000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706772 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706773 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706774 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706775 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706776 4 0.4477 0.759 0.312 0.000 0.000 0.688
#> SRR1706777 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706778 4 0.4500 0.755 0.316 0.000 0.000 0.684
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0921 0.848 0.028 0.000 0.000 0.972
#> SRR1706789 4 0.0336 0.839 0.008 0.000 0.000 0.992
#> SRR1706790 4 0.0188 0.837 0.004 0.000 0.000 0.996
#> SRR1706791 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706792 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706793 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706794 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706795 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706796 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706797 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706798 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706812 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706813 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706814 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706807 4 0.1022 0.850 0.032 0.000 0.000 0.968
#> SRR1706808 4 0.1118 0.851 0.036 0.000 0.000 0.964
#> SRR1706809 4 0.0817 0.846 0.024 0.000 0.000 0.976
#> SRR1706810 4 0.1211 0.852 0.040 0.000 0.000 0.960
#> SRR1706815 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706816 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706817 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706818 4 0.4585 0.737 0.332 0.000 0.000 0.668
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> SRR1706835 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706836 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706837 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706838 4 0.4454 0.763 0.308 0.000 0.000 0.692
#> SRR1706831 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706832 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706833 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706834 4 0.2530 0.870 0.112 0.000 0.000 0.888
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706848 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706849 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706850 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706856 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706857 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706858 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706859 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706860 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706861 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706862 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706867 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706869 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706870 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706863 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706880 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706881 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706882 2 0.2345 0.956 0.000 0.900 0.100 0.000
#> SRR1706883 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706876 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706877 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706878 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706887 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706888 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706889 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706890 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706891 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706896 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706897 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706898 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706899 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706900 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706901 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706902 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706907 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706908 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706909 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706910 3 0.2530 0.928 0.000 0.000 0.888 0.112
#> SRR1706903 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706920 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706921 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706922 2 0.2530 0.958 0.000 0.888 0.112 0.000
#> SRR1706915 3 0.0188 0.942 0.000 0.004 0.996 0.000
#> SRR1706916 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706917 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706918 3 0.0000 0.946 0.000 0.000 1.000 0.000
#> SRR1706923 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.929 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.929 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706768 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706769 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706770 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706771 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706772 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706773 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706774 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706775 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706776 4 0.3480 0.760 0.248 0.000 0.000 0.752 0
#> SRR1706777 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706778 4 0.3561 0.748 0.260 0.000 0.000 0.740 0
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706787 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706788 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706789 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706790 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706791 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706792 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706793 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706794 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706795 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706796 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706797 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706798 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706811 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706812 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706813 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706814 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706807 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706808 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706809 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706810 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706815 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706816 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706817 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706818 4 0.3796 0.704 0.300 0.000 0.000 0.700 0
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706827 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706828 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706829 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706830 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706835 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706836 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706837 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706838 4 0.3452 0.764 0.244 0.000 0.000 0.756 0
#> SRR1706831 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706832 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706833 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706834 4 0.0000 0.890 0.000 0.000 0.000 1.000 0
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706847 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706848 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706849 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706850 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000 0
#> SRR1706851 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706852 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706853 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706854 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706855 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706856 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706857 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706858 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706859 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706860 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706861 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706862 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706867 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706869 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706870 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706863 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706864 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706865 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706866 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706871 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706872 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706873 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706874 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706879 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706880 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706881 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706882 2 0.2891 0.909 0.000 0.824 0.176 0.000 0
#> SRR1706883 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706884 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706885 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706886 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706875 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706876 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706877 2 0.3039 0.909 0.000 0.808 0.192 0.000 0
#> SRR1706878 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706887 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706888 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706889 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706890 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706891 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706892 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706893 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706894 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706895 2 0.3039 0.909 0.000 0.808 0.192 0.000 0
#> SRR1706896 2 0.3242 0.886 0.000 0.784 0.216 0.000 0
#> SRR1706897 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706898 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706899 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706900 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706901 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706902 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706907 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706908 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706909 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706910 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706903 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706904 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706905 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706906 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706911 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706912 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706913 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706914 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706919 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706920 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706921 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706922 2 0.3003 0.912 0.000 0.812 0.188 0.000 0
#> SRR1706915 3 0.0162 0.995 0.000 0.004 0.996 0.000 0
#> SRR1706916 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706917 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706918 3 0.0000 1.000 0.000 0.000 1.000 0.000 0
#> SRR1706923 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706924 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706925 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
#> SRR1706926 2 0.0000 0.857 0.000 1.000 0.000 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706771 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706772 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706773 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706774 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706775 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706776 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706777 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706778 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706779 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706780 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706781 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706782 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706783 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706791 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706792 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706793 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706794 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706795 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706796 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706797 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706798 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706799 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706800 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706801 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706802 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706803 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706811 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706812 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706813 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706814 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706815 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706816 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706817 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706818 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706819 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706820 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706821 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706822 1 0.0260 0.994 0.992 0.000 0 0.000 0.008 0.000
#> SRR1706823 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706835 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706836 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706837 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706838 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706831 5 0.0146 0.996 0.000 0.000 0 0.004 0.996 0.000
#> SRR1706832 5 0.0146 0.996 0.000 0.000 0 0.004 0.996 0.000
#> SRR1706833 5 0.0146 0.996 0.000 0.000 0 0.004 0.996 0.000
#> SRR1706834 5 0.0000 1.000 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706839 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706840 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706841 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706842 1 0.0363 0.992 0.988 0.000 0 0.000 0.012 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706843 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.994 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706851 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706852 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706853 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706854 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706855 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706856 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706857 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706858 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706859 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706860 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706861 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706862 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706863 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706871 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706872 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706873 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706874 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706879 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706880 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706881 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706882 2 0.2597 0.909 0.000 0.824 0 0.000 0.000 0.176
#> SRR1706883 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706875 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706876 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706877 2 0.2730 0.909 0.000 0.808 0 0.000 0.000 0.192
#> SRR1706878 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706891 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706892 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706893 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706894 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706895 2 0.2730 0.909 0.000 0.808 0 0.000 0.000 0.192
#> SRR1706896 2 0.2912 0.886 0.000 0.784 0 0.000 0.000 0.216
#> SRR1706897 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706898 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706899 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706900 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706901 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706902 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706903 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706904 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706905 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706906 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706911 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706912 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706913 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706914 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706919 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706920 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706921 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706922 2 0.2697 0.912 0.000 0.812 0 0.000 0.000 0.188
#> SRR1706915 6 0.0146 0.995 0.000 0.004 0 0.000 0.000 0.996
#> SRR1706916 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706917 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706918 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706923 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.857 0.000 1.000 0 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15185 rows and 159 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 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.666 0.732 0.816 0.2178 0.878 0.754
#> 4 4 0.553 0.542 0.729 0.1356 0.858 0.672
#> 5 5 0.566 0.413 0.649 0.0953 0.739 0.380
#> 6 6 0.677 0.579 0.720 0.0384 0.792 0.380
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706768 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706769 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706770 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706771 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706772 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706773 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706774 1 0.6062 0.129 0.616 0.000 0.384
#> SRR1706775 1 0.6062 0.128 0.616 0.000 0.384
#> SRR1706776 1 0.6045 0.141 0.620 0.000 0.380
#> SRR1706777 1 0.6062 0.129 0.616 0.000 0.384
#> SRR1706778 1 0.6079 0.114 0.612 0.000 0.388
#> SRR1706779 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706780 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706781 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706782 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706783 3 0.5058 0.727 0.244 0.000 0.756
#> SRR1706784 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706785 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706786 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706787 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706788 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706789 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706790 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706791 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706792 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706793 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706794 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706795 1 0.2796 0.694 0.908 0.000 0.092
#> SRR1706796 1 0.4002 0.644 0.840 0.000 0.160
#> SRR1706797 1 0.3038 0.688 0.896 0.000 0.104
#> SRR1706798 1 0.2878 0.692 0.904 0.000 0.096
#> SRR1706799 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706800 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706801 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706802 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706803 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706804 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706805 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706806 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706811 1 0.4178 0.633 0.828 0.000 0.172
#> SRR1706812 1 0.4062 0.643 0.836 0.000 0.164
#> SRR1706813 1 0.4062 0.643 0.836 0.000 0.164
#> SRR1706814 1 0.4062 0.643 0.836 0.000 0.164
#> SRR1706807 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706808 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706809 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706810 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706815 1 0.6079 0.114 0.612 0.000 0.388
#> SRR1706816 1 0.6079 0.114 0.612 0.000 0.388
#> SRR1706817 1 0.6079 0.114 0.612 0.000 0.388
#> SRR1706818 1 0.6079 0.114 0.612 0.000 0.388
#> SRR1706819 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706820 3 0.6305 0.579 0.484 0.000 0.516
#> SRR1706821 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706822 3 0.6305 0.579 0.484 0.000 0.516
#> SRR1706823 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706824 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706825 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706826 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706827 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706828 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706829 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706830 1 0.0237 0.669 0.996 0.000 0.004
#> SRR1706835 1 0.2711 0.695 0.912 0.000 0.088
#> SRR1706836 1 0.3941 0.652 0.844 0.000 0.156
#> SRR1706837 1 0.3619 0.669 0.864 0.000 0.136
#> SRR1706838 1 0.3038 0.689 0.896 0.000 0.104
#> SRR1706831 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706832 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706833 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706834 1 0.2066 0.699 0.940 0.000 0.060
#> SRR1706839 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706840 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706841 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706842 3 0.6302 0.593 0.480 0.000 0.520
#> SRR1706847 2 0.0592 0.926 0.000 0.988 0.012
#> SRR1706848 2 0.0592 0.926 0.000 0.988 0.012
#> SRR1706849 2 0.0592 0.926 0.000 0.988 0.012
#> SRR1706850 2 0.0592 0.926 0.000 0.988 0.012
#> SRR1706843 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706844 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706845 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706846 3 0.5016 0.728 0.240 0.000 0.760
#> SRR1706851 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1706855 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1706856 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1706857 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1706858 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1706859 2 0.1753 0.923 0.000 0.952 0.048
#> SRR1706860 2 0.1753 0.923 0.000 0.952 0.048
#> SRR1706861 2 0.1753 0.923 0.000 0.952 0.048
#> SRR1706862 2 0.1753 0.923 0.000 0.952 0.048
#> SRR1706867 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706869 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706870 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706863 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706864 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706865 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706866 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706871 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706872 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706873 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706874 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706879 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706880 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706881 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706882 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706883 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706884 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706885 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706886 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706875 2 0.0592 0.928 0.000 0.988 0.012
#> SRR1706876 2 0.0592 0.928 0.000 0.988 0.012
#> SRR1706877 2 0.0424 0.928 0.000 0.992 0.008
#> SRR1706878 2 0.0592 0.928 0.000 0.988 0.012
#> SRR1706887 2 0.3791 0.896 0.060 0.892 0.048
#> SRR1706888 2 0.3791 0.896 0.060 0.892 0.048
#> SRR1706889 2 0.3791 0.896 0.060 0.892 0.048
#> SRR1706890 2 0.3791 0.896 0.060 0.892 0.048
#> SRR1706891 2 0.1643 0.922 0.000 0.956 0.044
#> SRR1706892 2 0.1643 0.922 0.000 0.956 0.044
#> SRR1706893 2 0.1643 0.922 0.000 0.956 0.044
#> SRR1706894 2 0.1643 0.922 0.000 0.956 0.044
#> SRR1706895 2 0.3340 0.918 0.000 0.880 0.120
#> SRR1706896 2 0.3340 0.918 0.000 0.880 0.120
#> SRR1706897 2 0.3340 0.918 0.000 0.880 0.120
#> SRR1706898 2 0.3340 0.918 0.000 0.880 0.120
#> SRR1706899 2 0.4121 0.905 0.000 0.832 0.168
#> SRR1706900 2 0.4121 0.905 0.000 0.832 0.168
#> SRR1706901 2 0.4121 0.905 0.000 0.832 0.168
#> SRR1706902 2 0.4121 0.905 0.000 0.832 0.168
#> SRR1706907 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706908 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706909 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706910 2 0.3083 0.902 0.060 0.916 0.024
#> SRR1706903 2 0.4702 0.880 0.000 0.788 0.212
#> SRR1706904 2 0.4702 0.880 0.000 0.788 0.212
#> SRR1706905 2 0.4702 0.880 0.000 0.788 0.212
#> SRR1706906 2 0.4702 0.880 0.000 0.788 0.212
#> SRR1706911 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706912 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706913 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706914 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1706919 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706920 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706921 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706922 2 0.4002 0.900 0.000 0.840 0.160
#> SRR1706915 2 0.0747 0.928 0.000 0.984 0.016
#> SRR1706916 2 0.0592 0.928 0.000 0.988 0.012
#> SRR1706917 2 0.0592 0.928 0.000 0.988 0.012
#> SRR1706918 2 0.0747 0.928 0.000 0.984 0.016
#> SRR1706923 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706924 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706925 2 0.4974 0.867 0.000 0.764 0.236
#> SRR1706926 2 0.4974 0.867 0.000 0.764 0.236
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.6875 0.4311 0.368 0.112 0.000 0.520
#> SRR1706768 4 0.6875 0.4311 0.368 0.112 0.000 0.520
#> SRR1706769 4 0.6875 0.4311 0.368 0.112 0.000 0.520
#> SRR1706770 4 0.6875 0.4311 0.368 0.112 0.000 0.520
#> SRR1706771 4 0.5284 0.4650 0.368 0.016 0.000 0.616
#> SRR1706772 4 0.5284 0.4650 0.368 0.016 0.000 0.616
#> SRR1706773 4 0.5284 0.4650 0.368 0.016 0.000 0.616
#> SRR1706774 4 0.5284 0.4650 0.368 0.016 0.000 0.616
#> SRR1706775 4 0.5099 0.4588 0.380 0.008 0.000 0.612
#> SRR1706776 4 0.5099 0.4588 0.380 0.008 0.000 0.612
#> SRR1706777 4 0.5099 0.4588 0.380 0.008 0.000 0.612
#> SRR1706778 4 0.5099 0.4588 0.380 0.008 0.000 0.612
#> SRR1706779 4 0.4989 0.2425 0.472 0.000 0.000 0.528
#> SRR1706780 4 0.4992 0.2351 0.476 0.000 0.000 0.524
#> SRR1706781 4 0.4994 0.2240 0.480 0.000 0.000 0.520
#> SRR1706782 4 0.4989 0.2425 0.472 0.000 0.000 0.528
#> SRR1706783 1 0.4669 0.9361 0.780 0.052 0.000 0.168
#> SRR1706784 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706785 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706786 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706787 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706788 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706789 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706790 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706791 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706792 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706793 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706794 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706795 4 0.1474 0.6232 0.052 0.000 0.000 0.948
#> SRR1706796 4 0.2408 0.6222 0.104 0.000 0.000 0.896
#> SRR1706797 4 0.1716 0.6238 0.064 0.000 0.000 0.936
#> SRR1706798 4 0.1474 0.6232 0.052 0.000 0.000 0.948
#> SRR1706799 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706800 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706801 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706802 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706803 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706804 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706805 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706806 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706811 4 0.2921 0.6166 0.140 0.000 0.000 0.860
#> SRR1706812 4 0.2868 0.6178 0.136 0.000 0.000 0.864
#> SRR1706813 4 0.2868 0.6178 0.136 0.000 0.000 0.864
#> SRR1706814 4 0.2868 0.6178 0.136 0.000 0.000 0.864
#> SRR1706807 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706808 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706809 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706810 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706815 4 0.4624 0.4983 0.340 0.000 0.000 0.660
#> SRR1706816 4 0.4761 0.4628 0.372 0.000 0.000 0.628
#> SRR1706817 4 0.4697 0.4826 0.356 0.000 0.000 0.644
#> SRR1706818 4 0.4679 0.4868 0.352 0.000 0.000 0.648
#> SRR1706819 4 0.5000 0.1732 0.496 0.000 0.000 0.504
#> SRR1706820 4 0.4998 0.2001 0.488 0.000 0.000 0.512
#> SRR1706821 4 0.4994 0.2223 0.480 0.000 0.000 0.520
#> SRR1706822 4 0.4998 0.2000 0.488 0.000 0.000 0.512
#> SRR1706823 1 0.4578 0.9502 0.788 0.052 0.000 0.160
#> SRR1706824 1 0.4578 0.9502 0.788 0.052 0.000 0.160
#> SRR1706825 1 0.4624 0.9451 0.784 0.052 0.000 0.164
#> SRR1706826 1 0.4578 0.9502 0.788 0.052 0.000 0.160
#> SRR1706827 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706828 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706829 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706830 4 0.3601 0.5582 0.084 0.056 0.000 0.860
#> SRR1706835 4 0.1557 0.6241 0.056 0.000 0.000 0.944
#> SRR1706836 4 0.3024 0.6142 0.148 0.000 0.000 0.852
#> SRR1706837 4 0.2760 0.6196 0.128 0.000 0.000 0.872
#> SRR1706838 4 0.2216 0.6239 0.092 0.000 0.000 0.908
#> SRR1706831 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706832 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706833 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706834 4 0.0000 0.6169 0.000 0.000 0.000 1.000
#> SRR1706839 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706840 1 0.5000 -0.2229 0.500 0.000 0.000 0.500
#> SRR1706841 4 0.5000 0.1732 0.496 0.000 0.000 0.504
#> SRR1706842 4 0.5000 0.1610 0.500 0.000 0.000 0.500
#> SRR1706847 3 0.4440 0.6387 0.136 0.060 0.804 0.000
#> SRR1706848 3 0.4440 0.6387 0.136 0.060 0.804 0.000
#> SRR1706849 3 0.4440 0.6387 0.136 0.060 0.804 0.000
#> SRR1706850 3 0.4440 0.6387 0.136 0.060 0.804 0.000
#> SRR1706843 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706844 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706845 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706846 1 0.4532 0.9537 0.792 0.052 0.000 0.156
#> SRR1706851 3 0.4415 0.6228 0.056 0.140 0.804 0.000
#> SRR1706852 3 0.4415 0.6228 0.056 0.140 0.804 0.000
#> SRR1706853 3 0.4415 0.6228 0.056 0.140 0.804 0.000
#> SRR1706854 3 0.4415 0.6228 0.056 0.140 0.804 0.000
#> SRR1706855 3 0.4934 0.4920 0.028 0.252 0.720 0.000
#> SRR1706856 3 0.4934 0.4920 0.028 0.252 0.720 0.000
#> SRR1706857 3 0.4934 0.4920 0.028 0.252 0.720 0.000
#> SRR1706858 3 0.4934 0.4920 0.028 0.252 0.720 0.000
#> SRR1706859 3 0.5343 0.1971 0.028 0.316 0.656 0.000
#> SRR1706860 3 0.5343 0.1971 0.028 0.316 0.656 0.000
#> SRR1706861 3 0.5343 0.1971 0.028 0.316 0.656 0.000
#> SRR1706862 3 0.5343 0.1971 0.028 0.316 0.656 0.000
#> SRR1706867 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706869 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706870 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706863 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706864 2 0.4761 0.9931 0.004 0.664 0.332 0.000
#> SRR1706865 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706866 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706871 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706879 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706880 3 0.4933 -0.3127 0.000 0.432 0.568 0.000
#> SRR1706881 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706882 3 0.4933 -0.3127 0.000 0.432 0.568 0.000
#> SRR1706883 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706884 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706885 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706886 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706875 3 0.1584 0.6584 0.012 0.036 0.952 0.000
#> SRR1706876 3 0.1584 0.6584 0.012 0.036 0.952 0.000
#> SRR1706877 3 0.1584 0.6584 0.012 0.036 0.952 0.000
#> SRR1706878 3 0.1584 0.6584 0.012 0.036 0.952 0.000
#> SRR1706887 3 0.5905 0.5773 0.144 0.156 0.700 0.000
#> SRR1706888 3 0.5905 0.5773 0.144 0.156 0.700 0.000
#> SRR1706889 3 0.5905 0.5773 0.144 0.156 0.700 0.000
#> SRR1706890 3 0.5905 0.5773 0.144 0.156 0.700 0.000
#> SRR1706891 3 0.3399 0.6517 0.040 0.092 0.868 0.000
#> SRR1706892 3 0.3399 0.6517 0.040 0.092 0.868 0.000
#> SRR1706893 3 0.3399 0.6517 0.040 0.092 0.868 0.000
#> SRR1706894 3 0.3399 0.6517 0.040 0.092 0.868 0.000
#> SRR1706895 3 0.3224 0.6480 0.016 0.120 0.864 0.000
#> SRR1706896 3 0.3224 0.6480 0.016 0.120 0.864 0.000
#> SRR1706897 3 0.3224 0.6480 0.016 0.120 0.864 0.000
#> SRR1706898 3 0.3224 0.6480 0.016 0.120 0.864 0.000
#> SRR1706899 3 0.4630 0.5144 0.016 0.252 0.732 0.000
#> SRR1706900 3 0.4630 0.5144 0.016 0.252 0.732 0.000
#> SRR1706901 3 0.4630 0.5144 0.016 0.252 0.732 0.000
#> SRR1706902 3 0.4630 0.5144 0.016 0.252 0.732 0.000
#> SRR1706907 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706908 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706909 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706910 3 0.4706 0.6141 0.140 0.072 0.788 0.000
#> SRR1706903 3 0.5500 -0.0118 0.016 0.464 0.520 0.000
#> SRR1706904 3 0.5500 -0.0118 0.016 0.464 0.520 0.000
#> SRR1706905 3 0.5500 -0.0118 0.016 0.464 0.520 0.000
#> SRR1706906 3 0.5500 -0.0118 0.016 0.464 0.520 0.000
#> SRR1706911 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0188 0.6716 0.004 0.000 0.996 0.000
#> SRR1706913 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.6713 0.000 0.000 1.000 0.000
#> SRR1706919 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706920 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706921 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706922 3 0.4925 -0.3044 0.000 0.428 0.572 0.000
#> SRR1706915 3 0.1302 0.6584 0.000 0.044 0.956 0.000
#> SRR1706916 3 0.1118 0.6600 0.000 0.036 0.964 0.000
#> SRR1706917 3 0.1211 0.6599 0.000 0.040 0.960 0.000
#> SRR1706918 3 0.1211 0.6599 0.000 0.040 0.960 0.000
#> SRR1706923 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706924 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706925 2 0.4585 0.9994 0.000 0.668 0.332 0.000
#> SRR1706926 2 0.4585 0.9994 0.000 0.668 0.332 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 1 0.5632 0.1266 0.588 0.008 0.000 0.332 0.072
#> SRR1706768 1 0.5632 0.1266 0.588 0.008 0.000 0.332 0.072
#> SRR1706769 1 0.5632 0.1266 0.588 0.008 0.000 0.332 0.072
#> SRR1706770 1 0.5617 0.1357 0.592 0.008 0.000 0.328 0.072
#> SRR1706771 1 0.4252 0.4028 0.784 0.008 0.000 0.144 0.064
#> SRR1706772 1 0.4167 0.4128 0.792 0.008 0.000 0.136 0.064
#> SRR1706773 1 0.4210 0.4079 0.788 0.008 0.000 0.140 0.064
#> SRR1706774 1 0.4210 0.4079 0.788 0.008 0.000 0.140 0.064
#> SRR1706775 1 0.3814 0.4331 0.816 0.004 0.000 0.116 0.064
#> SRR1706776 1 0.3862 0.4292 0.812 0.004 0.000 0.120 0.064
#> SRR1706777 1 0.3765 0.4367 0.820 0.004 0.000 0.112 0.064
#> SRR1706778 1 0.3715 0.4401 0.824 0.004 0.000 0.108 0.064
#> SRR1706779 1 0.1124 0.4935 0.960 0.000 0.000 0.036 0.004
#> SRR1706780 1 0.0992 0.4978 0.968 0.000 0.000 0.024 0.008
#> SRR1706781 1 0.1168 0.4956 0.960 0.000 0.000 0.032 0.008
#> SRR1706782 1 0.1082 0.4967 0.964 0.000 0.000 0.028 0.008
#> SRR1706783 1 0.7735 0.3832 0.408 0.064 0.000 0.260 0.268
#> SRR1706784 1 0.7755 0.3799 0.400 0.064 0.000 0.264 0.272
#> SRR1706785 1 0.7745 0.3816 0.404 0.064 0.000 0.260 0.272
#> SRR1706786 1 0.7745 0.3828 0.404 0.064 0.000 0.260 0.272
#> SRR1706787 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706788 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706789 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706790 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706791 4 0.4219 0.5297 0.416 0.000 0.000 0.584 0.000
#> SRR1706792 4 0.4219 0.5297 0.416 0.000 0.000 0.584 0.000
#> SRR1706793 4 0.4201 0.5408 0.408 0.000 0.000 0.592 0.000
#> SRR1706794 4 0.4219 0.5297 0.416 0.000 0.000 0.584 0.000
#> SRR1706795 1 0.4262 -0.1376 0.560 0.000 0.000 0.440 0.000
#> SRR1706796 1 0.4201 -0.0384 0.592 0.000 0.000 0.408 0.000
#> SRR1706797 1 0.4256 -0.1225 0.564 0.000 0.000 0.436 0.000
#> SRR1706798 1 0.4262 -0.1343 0.560 0.000 0.000 0.440 0.000
#> SRR1706799 1 0.2890 0.4416 0.836 0.000 0.000 0.160 0.004
#> SRR1706800 1 0.3086 0.4264 0.816 0.000 0.000 0.180 0.004
#> SRR1706801 1 0.2890 0.4416 0.836 0.000 0.000 0.160 0.004
#> SRR1706802 1 0.3048 0.4298 0.820 0.000 0.000 0.176 0.004
#> SRR1706803 1 0.7733 0.3877 0.408 0.064 0.000 0.256 0.272
#> SRR1706804 1 0.7722 0.3902 0.412 0.064 0.000 0.252 0.272
#> SRR1706805 1 0.7668 0.3948 0.428 0.064 0.000 0.236 0.272
#> SRR1706806 1 0.7696 0.3914 0.420 0.064 0.000 0.244 0.272
#> SRR1706811 4 0.4452 0.3570 0.496 0.000 0.000 0.500 0.004
#> SRR1706812 1 0.4452 -0.3843 0.500 0.000 0.000 0.496 0.004
#> SRR1706813 4 0.4452 0.3570 0.496 0.000 0.000 0.500 0.004
#> SRR1706814 4 0.4452 0.3570 0.496 0.000 0.000 0.500 0.004
#> SRR1706807 4 0.1851 0.6884 0.088 0.000 0.000 0.912 0.000
#> SRR1706808 4 0.1851 0.6884 0.088 0.000 0.000 0.912 0.000
#> SRR1706809 4 0.1851 0.6884 0.088 0.000 0.000 0.912 0.000
#> SRR1706810 4 0.1908 0.6879 0.092 0.000 0.000 0.908 0.000
#> SRR1706815 1 0.2179 0.4382 0.888 0.000 0.000 0.112 0.000
#> SRR1706816 1 0.2179 0.4382 0.888 0.000 0.000 0.112 0.000
#> SRR1706817 1 0.2179 0.4382 0.888 0.000 0.000 0.112 0.000
#> SRR1706818 1 0.2179 0.4382 0.888 0.000 0.000 0.112 0.000
#> SRR1706819 1 0.0609 0.5007 0.980 0.000 0.000 0.020 0.000
#> SRR1706820 1 0.1270 0.5000 0.948 0.000 0.000 0.052 0.000
#> SRR1706821 1 0.0404 0.5003 0.988 0.000 0.000 0.012 0.000
#> SRR1706822 1 0.1357 0.5008 0.948 0.000 0.000 0.048 0.004
#> SRR1706823 1 0.7784 0.3761 0.388 0.064 0.000 0.276 0.272
#> SRR1706824 1 0.7784 0.3761 0.388 0.064 0.000 0.276 0.272
#> SRR1706825 1 0.7784 0.3761 0.388 0.064 0.000 0.276 0.272
#> SRR1706826 1 0.7784 0.3761 0.388 0.064 0.000 0.276 0.272
#> SRR1706827 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706828 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706829 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706830 4 0.1792 0.6890 0.084 0.000 0.000 0.916 0.000
#> SRR1706835 1 0.4287 -0.2182 0.540 0.000 0.000 0.460 0.000
#> SRR1706836 1 0.4359 -0.0583 0.584 0.000 0.000 0.412 0.004
#> SRR1706837 1 0.4420 -0.1840 0.548 0.000 0.000 0.448 0.004
#> SRR1706838 1 0.4410 -0.1541 0.556 0.000 0.000 0.440 0.004
#> SRR1706831 4 0.4201 0.5408 0.408 0.000 0.000 0.592 0.000
#> SRR1706832 4 0.4201 0.5408 0.408 0.000 0.000 0.592 0.000
#> SRR1706833 4 0.4201 0.5408 0.408 0.000 0.000 0.592 0.000
#> SRR1706834 4 0.4201 0.5408 0.408 0.000 0.000 0.592 0.000
#> SRR1706839 1 0.3010 0.4333 0.824 0.000 0.000 0.172 0.004
#> SRR1706840 1 0.2930 0.4390 0.832 0.000 0.000 0.164 0.004
#> SRR1706841 1 0.3231 0.4117 0.800 0.000 0.000 0.196 0.004
#> SRR1706842 1 0.3010 0.4329 0.824 0.000 0.000 0.172 0.004
#> SRR1706847 5 0.5851 0.6025 0.000 0.000 0.272 0.140 0.588
#> SRR1706848 5 0.5851 0.6025 0.000 0.000 0.272 0.140 0.588
#> SRR1706849 5 0.5851 0.6025 0.000 0.000 0.272 0.140 0.588
#> SRR1706850 5 0.5851 0.6025 0.000 0.000 0.272 0.140 0.588
#> SRR1706843 1 0.7652 0.3949 0.432 0.064 0.000 0.232 0.272
#> SRR1706844 1 0.7765 0.3808 0.396 0.064 0.000 0.268 0.272
#> SRR1706845 1 0.7745 0.3855 0.404 0.064 0.000 0.260 0.272
#> SRR1706846 1 0.7696 0.3909 0.420 0.064 0.000 0.244 0.272
#> SRR1706851 5 0.3910 0.8016 0.000 0.008 0.272 0.000 0.720
#> SRR1706852 5 0.3910 0.8016 0.000 0.008 0.272 0.000 0.720
#> SRR1706853 5 0.3910 0.8016 0.000 0.008 0.272 0.000 0.720
#> SRR1706854 5 0.3910 0.8016 0.000 0.008 0.272 0.000 0.720
#> SRR1706855 5 0.5028 0.8102 0.000 0.072 0.260 0.000 0.668
#> SRR1706856 5 0.5028 0.8102 0.000 0.072 0.260 0.000 0.668
#> SRR1706857 5 0.5028 0.8102 0.000 0.072 0.260 0.000 0.668
#> SRR1706858 5 0.5028 0.8102 0.000 0.072 0.260 0.000 0.668
#> SRR1706859 5 0.6139 0.7323 0.004 0.148 0.288 0.000 0.560
#> SRR1706860 5 0.6139 0.7323 0.004 0.148 0.288 0.000 0.560
#> SRR1706861 5 0.6139 0.7323 0.004 0.148 0.288 0.000 0.560
#> SRR1706862 5 0.6139 0.7323 0.004 0.148 0.288 0.000 0.560
#> SRR1706867 3 0.8272 0.3312 0.000 0.208 0.372 0.272 0.148
#> SRR1706869 3 0.8272 0.3312 0.000 0.208 0.372 0.272 0.148
#> SRR1706870 3 0.8272 0.3312 0.000 0.208 0.372 0.272 0.148
#> SRR1706863 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706864 2 0.3242 0.5538 0.000 0.852 0.076 0.000 0.072
#> SRR1706865 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706866 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706871 3 0.5693 0.2828 0.000 0.236 0.620 0.000 0.144
#> SRR1706872 3 0.5693 0.2828 0.000 0.236 0.620 0.000 0.144
#> SRR1706873 3 0.5693 0.2828 0.000 0.236 0.620 0.000 0.144
#> SRR1706874 3 0.5693 0.2828 0.000 0.236 0.620 0.000 0.144
#> SRR1706879 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706880 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706881 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706882 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706883 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706884 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706885 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706886 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706875 2 0.6557 0.2818 0.000 0.428 0.368 0.000 0.204
#> SRR1706876 2 0.6557 0.2818 0.000 0.428 0.368 0.000 0.204
#> SRR1706877 2 0.6557 0.2818 0.000 0.428 0.368 0.000 0.204
#> SRR1706878 2 0.6557 0.2818 0.000 0.428 0.368 0.000 0.204
#> SRR1706887 3 0.3790 0.3528 0.000 0.000 0.724 0.272 0.004
#> SRR1706888 3 0.3790 0.3528 0.000 0.000 0.724 0.272 0.004
#> SRR1706889 3 0.3790 0.3528 0.000 0.000 0.724 0.272 0.004
#> SRR1706890 3 0.3790 0.3528 0.000 0.000 0.724 0.272 0.004
#> SRR1706891 3 0.0290 0.4129 0.000 0.008 0.992 0.000 0.000
#> SRR1706892 3 0.0290 0.4129 0.000 0.008 0.992 0.000 0.000
#> SRR1706893 3 0.0290 0.4129 0.000 0.008 0.992 0.000 0.000
#> SRR1706894 3 0.0290 0.4129 0.000 0.008 0.992 0.000 0.000
#> SRR1706895 3 0.1851 0.3880 0.000 0.088 0.912 0.000 0.000
#> SRR1706896 3 0.2471 0.3283 0.000 0.136 0.864 0.000 0.000
#> SRR1706897 3 0.2179 0.3616 0.000 0.112 0.888 0.000 0.000
#> SRR1706898 3 0.2329 0.3462 0.000 0.124 0.876 0.000 0.000
#> SRR1706899 3 0.3707 0.0741 0.000 0.284 0.716 0.000 0.000
#> SRR1706900 3 0.3707 0.0741 0.000 0.284 0.716 0.000 0.000
#> SRR1706901 3 0.3707 0.0741 0.000 0.284 0.716 0.000 0.000
#> SRR1706902 3 0.3707 0.0741 0.000 0.284 0.716 0.000 0.000
#> SRR1706907 3 0.8316 0.3281 0.000 0.208 0.364 0.272 0.156
#> SRR1706908 3 0.8316 0.3281 0.000 0.208 0.364 0.272 0.156
#> SRR1706909 3 0.8316 0.3281 0.000 0.208 0.364 0.272 0.156
#> SRR1706910 3 0.8316 0.3281 0.000 0.208 0.364 0.272 0.156
#> SRR1706903 2 0.4302 0.1694 0.000 0.520 0.480 0.000 0.000
#> SRR1706904 2 0.4302 0.1694 0.000 0.520 0.480 0.000 0.000
#> SRR1706905 2 0.4302 0.1694 0.000 0.520 0.480 0.000 0.000
#> SRR1706906 2 0.4302 0.1694 0.000 0.520 0.480 0.000 0.000
#> SRR1706911 3 0.5831 0.2625 0.000 0.236 0.604 0.000 0.160
#> SRR1706912 3 0.5831 0.2625 0.000 0.236 0.604 0.000 0.160
#> SRR1706913 3 0.5831 0.2625 0.000 0.236 0.604 0.000 0.160
#> SRR1706914 3 0.5831 0.2625 0.000 0.236 0.604 0.000 0.160
#> SRR1706919 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706920 2 0.5491 0.4693 0.000 0.600 0.312 0.000 0.088
#> SRR1706921 2 0.5491 0.4688 0.000 0.600 0.312 0.000 0.088
#> SRR1706922 2 0.5443 0.4721 0.000 0.604 0.312 0.000 0.084
#> SRR1706915 2 0.6325 0.2294 0.000 0.424 0.420 0.000 0.156
#> SRR1706916 2 0.6300 0.2347 0.000 0.428 0.420 0.000 0.152
#> SRR1706917 2 0.6325 0.2294 0.000 0.424 0.420 0.000 0.156
#> SRR1706918 2 0.6325 0.2294 0.000 0.424 0.420 0.000 0.156
#> SRR1706923 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706924 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706925 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
#> SRR1706926 2 0.3116 0.5555 0.000 0.860 0.076 0.000 0.064
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 5 0.4280 0.6124 0.012 0.088 0.032 0.080 0.788 0.000
#> SRR1706768 5 0.4280 0.6124 0.012 0.088 0.032 0.080 0.788 0.000
#> SRR1706769 5 0.4280 0.6124 0.012 0.088 0.032 0.080 0.788 0.000
#> SRR1706770 5 0.4280 0.6124 0.012 0.088 0.032 0.080 0.788 0.000
#> SRR1706771 5 0.2715 0.6597 0.012 0.088 0.028 0.000 0.872 0.000
#> SRR1706772 5 0.2715 0.6597 0.012 0.088 0.028 0.000 0.872 0.000
#> SRR1706773 5 0.2715 0.6597 0.012 0.088 0.028 0.000 0.872 0.000
#> SRR1706774 5 0.2715 0.6597 0.012 0.088 0.028 0.000 0.872 0.000
#> SRR1706775 5 0.2231 0.6809 0.016 0.048 0.028 0.000 0.908 0.000
#> SRR1706776 5 0.2231 0.6809 0.016 0.048 0.028 0.000 0.908 0.000
#> SRR1706777 5 0.2231 0.6809 0.016 0.048 0.028 0.000 0.908 0.000
#> SRR1706778 5 0.2318 0.6813 0.020 0.048 0.028 0.000 0.904 0.000
#> SRR1706779 5 0.3956 0.6372 0.204 0.008 0.000 0.040 0.748 0.000
#> SRR1706780 5 0.3956 0.6372 0.204 0.008 0.000 0.040 0.748 0.000
#> SRR1706781 5 0.4020 0.6384 0.204 0.008 0.000 0.044 0.744 0.000
#> SRR1706782 5 0.3956 0.6372 0.204 0.008 0.000 0.040 0.748 0.000
#> SRR1706783 1 0.2191 0.9825 0.876 0.000 0.000 0.004 0.120 0.000
#> SRR1706784 1 0.2263 0.9801 0.884 0.000 0.000 0.016 0.100 0.000
#> SRR1706785 1 0.2100 0.9844 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1706786 1 0.2212 0.9843 0.880 0.000 0.000 0.008 0.112 0.000
#> SRR1706787 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706788 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706789 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706790 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706791 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706792 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706793 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706794 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706795 5 0.3699 0.5865 0.004 0.000 0.000 0.336 0.660 0.000
#> SRR1706796 5 0.3409 0.6106 0.000 0.000 0.000 0.300 0.700 0.000
#> SRR1706797 5 0.3699 0.5865 0.004 0.000 0.000 0.336 0.660 0.000
#> SRR1706798 5 0.3563 0.5872 0.000 0.000 0.000 0.336 0.664 0.000
#> SRR1706799 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706800 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706801 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706802 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706803 1 0.2446 0.9723 0.864 0.000 0.000 0.012 0.124 0.000
#> SRR1706804 1 0.2100 0.9848 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1706805 1 0.2100 0.9848 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1706806 1 0.2003 0.9823 0.884 0.000 0.000 0.000 0.116 0.000
#> SRR1706811 5 0.4236 0.5880 0.036 0.000 0.000 0.308 0.656 0.000
#> SRR1706812 5 0.4236 0.5880 0.036 0.000 0.000 0.308 0.656 0.000
#> SRR1706813 5 0.4236 0.5880 0.036 0.000 0.000 0.308 0.656 0.000
#> SRR1706814 5 0.4236 0.5880 0.036 0.000 0.000 0.308 0.656 0.000
#> SRR1706807 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706808 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706809 4 0.0260 0.9955 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1706810 4 0.0363 0.9921 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1706815 5 0.1088 0.7083 0.016 0.000 0.000 0.024 0.960 0.000
#> SRR1706816 5 0.0603 0.7032 0.016 0.000 0.000 0.004 0.980 0.000
#> SRR1706817 5 0.0717 0.7045 0.016 0.000 0.000 0.008 0.976 0.000
#> SRR1706818 5 0.0717 0.7045 0.016 0.000 0.000 0.008 0.976 0.000
#> SRR1706819 5 0.3168 0.6343 0.192 0.000 0.000 0.016 0.792 0.000
#> SRR1706820 5 0.3523 0.6519 0.180 0.000 0.000 0.040 0.780 0.000
#> SRR1706821 5 0.2572 0.6589 0.136 0.000 0.000 0.012 0.852 0.000
#> SRR1706822 5 0.3551 0.6450 0.192 0.000 0.000 0.036 0.772 0.000
#> SRR1706823 1 0.2383 0.9740 0.880 0.000 0.000 0.024 0.096 0.000
#> SRR1706824 1 0.2432 0.9750 0.876 0.000 0.000 0.024 0.100 0.000
#> SRR1706825 1 0.2311 0.9802 0.880 0.000 0.000 0.016 0.104 0.000
#> SRR1706826 1 0.2462 0.9706 0.876 0.000 0.000 0.028 0.096 0.000
#> SRR1706827 4 0.0363 0.9916 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1706828 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706829 4 0.0146 0.9975 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1706830 4 0.0260 0.9955 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1706835 5 0.3668 0.5964 0.004 0.000 0.000 0.328 0.668 0.000
#> SRR1706836 5 0.3886 0.6477 0.028 0.000 0.000 0.264 0.708 0.000
#> SRR1706837 5 0.4066 0.6402 0.036 0.000 0.000 0.272 0.692 0.000
#> SRR1706838 5 0.3952 0.6118 0.020 0.000 0.000 0.308 0.672 0.000
#> SRR1706831 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706832 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706833 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706834 5 0.3854 0.4106 0.000 0.000 0.000 0.464 0.536 0.000
#> SRR1706839 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706840 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706841 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706842 5 0.4305 0.6372 0.216 0.000 0.000 0.076 0.708 0.000
#> SRR1706847 6 0.7273 0.2601 0.104 0.304 0.192 0.004 0.000 0.396
#> SRR1706848 6 0.7273 0.2601 0.104 0.304 0.192 0.004 0.000 0.396
#> SRR1706849 6 0.7273 0.2601 0.104 0.304 0.192 0.004 0.000 0.396
#> SRR1706850 6 0.7273 0.2601 0.104 0.304 0.192 0.004 0.000 0.396
#> SRR1706843 1 0.2146 0.9842 0.880 0.000 0.000 0.004 0.116 0.000
#> SRR1706844 1 0.2212 0.9847 0.880 0.000 0.000 0.008 0.112 0.000
#> SRR1706845 1 0.2146 0.9838 0.880 0.000 0.000 0.004 0.116 0.000
#> SRR1706846 1 0.2212 0.9847 0.880 0.000 0.000 0.008 0.112 0.000
#> SRR1706851 6 0.6841 0.3806 0.112 0.340 0.076 0.004 0.008 0.460
#> SRR1706852 6 0.6841 0.3806 0.112 0.340 0.076 0.004 0.008 0.460
#> SRR1706853 6 0.6841 0.3806 0.112 0.340 0.076 0.004 0.008 0.460
#> SRR1706854 6 0.6841 0.3806 0.112 0.340 0.076 0.004 0.008 0.460
#> SRR1706855 6 0.6145 0.3949 0.092 0.372 0.056 0.000 0.000 0.480
#> SRR1706856 6 0.6145 0.3949 0.092 0.372 0.056 0.000 0.000 0.480
#> SRR1706857 6 0.6145 0.3949 0.092 0.372 0.056 0.000 0.000 0.480
#> SRR1706858 6 0.6145 0.3949 0.092 0.372 0.056 0.000 0.000 0.480
#> SRR1706859 6 0.4127 0.4516 0.004 0.400 0.008 0.000 0.000 0.588
#> SRR1706860 6 0.4127 0.4516 0.004 0.400 0.008 0.000 0.000 0.588
#> SRR1706861 6 0.4127 0.4516 0.004 0.400 0.008 0.000 0.000 0.588
#> SRR1706862 6 0.4127 0.4516 0.004 0.400 0.008 0.000 0.000 0.588
#> SRR1706867 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706869 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706870 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706863 2 0.3823 0.9943 0.000 0.564 0.000 0.000 0.000 0.436
#> SRR1706864 2 0.3828 0.9881 0.000 0.560 0.000 0.000 0.000 0.440
#> SRR1706865 2 0.3823 0.9943 0.000 0.564 0.000 0.000 0.000 0.436
#> SRR1706866 2 0.3823 0.9943 0.000 0.564 0.000 0.000 0.000 0.436
#> SRR1706871 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706872 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706873 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706874 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706879 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706880 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706881 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706882 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706883 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706884 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706885 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706886 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706875 6 0.0520 0.4964 0.000 0.008 0.008 0.000 0.000 0.984
#> SRR1706876 6 0.0520 0.4964 0.000 0.008 0.008 0.000 0.000 0.984
#> SRR1706877 6 0.0520 0.4964 0.000 0.008 0.008 0.000 0.000 0.984
#> SRR1706878 6 0.0520 0.4964 0.000 0.008 0.008 0.000 0.000 0.984
#> SRR1706887 3 0.1196 0.4997 0.000 0.000 0.952 0.008 0.000 0.040
#> SRR1706888 3 0.1196 0.4997 0.000 0.000 0.952 0.008 0.000 0.040
#> SRR1706889 3 0.1196 0.4997 0.000 0.000 0.952 0.008 0.000 0.040
#> SRR1706890 3 0.1196 0.4997 0.000 0.000 0.952 0.008 0.000 0.040
#> SRR1706891 3 0.3563 0.2583 0.000 0.000 0.664 0.000 0.000 0.336
#> SRR1706892 3 0.3563 0.2583 0.000 0.000 0.664 0.000 0.000 0.336
#> SRR1706893 3 0.3563 0.2583 0.000 0.000 0.664 0.000 0.000 0.336
#> SRR1706894 3 0.3563 0.2583 0.000 0.000 0.664 0.000 0.000 0.336
#> SRR1706895 6 0.4407 -0.0374 0.000 0.024 0.484 0.000 0.000 0.492
#> SRR1706896 6 0.4406 -0.0260 0.000 0.024 0.476 0.000 0.000 0.500
#> SRR1706897 6 0.4407 -0.0312 0.000 0.024 0.480 0.000 0.000 0.496
#> SRR1706898 6 0.4407 -0.0312 0.000 0.024 0.480 0.000 0.000 0.496
#> SRR1706899 6 0.4561 0.0568 0.000 0.040 0.392 0.000 0.000 0.568
#> SRR1706900 6 0.4561 0.0568 0.000 0.040 0.392 0.000 0.000 0.568
#> SRR1706901 6 0.4561 0.0568 0.000 0.040 0.392 0.000 0.000 0.568
#> SRR1706902 6 0.4561 0.0568 0.000 0.040 0.392 0.000 0.000 0.568
#> SRR1706907 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706908 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706909 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706910 3 0.4761 0.4040 0.012 0.024 0.628 0.012 0.000 0.324
#> SRR1706903 3 0.6085 -0.2531 0.000 0.288 0.392 0.000 0.000 0.320
#> SRR1706904 3 0.6085 -0.2531 0.000 0.288 0.392 0.000 0.000 0.320
#> SRR1706905 3 0.6085 -0.2531 0.000 0.288 0.392 0.000 0.000 0.320
#> SRR1706906 3 0.6085 -0.2531 0.000 0.288 0.392 0.000 0.000 0.320
#> SRR1706911 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706912 6 0.3950 0.3468 0.004 0.016 0.268 0.004 0.000 0.708
#> SRR1706913 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706914 6 0.3971 0.3420 0.004 0.016 0.272 0.004 0.000 0.704
#> SRR1706919 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706920 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706921 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706922 6 0.1007 0.4426 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1706915 6 0.0458 0.4959 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1706916 6 0.0458 0.4959 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1706917 6 0.0458 0.4959 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1706918 6 0.0458 0.4959 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1706923 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706924 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706925 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
#> SRR1706926 2 0.3817 0.9970 0.000 0.568 0.000 0.000 0.000 0.432
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 15185 rows and 159 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.725 0.893 0.946 0.4936 0.503 0.503
#> 3 3 0.568 0.711 0.808 0.3266 0.671 0.438
#> 4 4 0.690 0.838 0.853 0.1227 0.877 0.657
#> 5 5 0.891 0.905 0.922 0.0230 1.000 1.000
#> 6 6 0.854 0.893 0.914 0.0195 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] 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
#> SRR1706767 1 0.000 0.922 1.000 0.000
#> SRR1706768 1 0.000 0.922 1.000 0.000
#> SRR1706769 1 0.000 0.922 1.000 0.000
#> SRR1706770 1 0.000 0.922 1.000 0.000
#> SRR1706771 1 0.000 0.922 1.000 0.000
#> SRR1706772 1 0.000 0.922 1.000 0.000
#> SRR1706773 1 0.000 0.922 1.000 0.000
#> SRR1706774 1 0.000 0.922 1.000 0.000
#> SRR1706775 1 0.855 0.610 0.720 0.280
#> SRR1706776 1 0.844 0.625 0.728 0.272
#> SRR1706777 1 0.563 0.828 0.868 0.132
#> SRR1706778 1 0.932 0.457 0.652 0.348
#> SRR1706779 2 0.141 0.952 0.020 0.980
#> SRR1706780 2 0.141 0.952 0.020 0.980
#> SRR1706781 2 0.141 0.952 0.020 0.980
#> SRR1706782 2 0.141 0.952 0.020 0.980
#> SRR1706783 2 0.141 0.952 0.020 0.980
#> SRR1706784 2 0.141 0.952 0.020 0.980
#> SRR1706785 2 0.141 0.952 0.020 0.980
#> SRR1706786 2 0.141 0.952 0.020 0.980
#> SRR1706787 1 0.000 0.922 1.000 0.000
#> SRR1706788 1 0.000 0.922 1.000 0.000
#> SRR1706789 1 0.000 0.922 1.000 0.000
#> SRR1706790 1 0.000 0.922 1.000 0.000
#> SRR1706791 1 0.000 0.922 1.000 0.000
#> SRR1706792 1 0.000 0.922 1.000 0.000
#> SRR1706793 1 0.000 0.922 1.000 0.000
#> SRR1706794 1 0.000 0.922 1.000 0.000
#> SRR1706795 2 0.939 0.491 0.356 0.644
#> SRR1706796 2 0.925 0.525 0.340 0.660
#> SRR1706797 2 0.961 0.424 0.384 0.616
#> SRR1706798 2 0.936 0.500 0.352 0.648
#> SRR1706799 2 0.141 0.952 0.020 0.980
#> SRR1706800 2 0.141 0.952 0.020 0.980
#> SRR1706801 2 0.141 0.952 0.020 0.980
#> SRR1706802 2 0.141 0.952 0.020 0.980
#> SRR1706803 2 0.141 0.952 0.020 0.980
#> SRR1706804 2 0.141 0.952 0.020 0.980
#> SRR1706805 2 0.141 0.952 0.020 0.980
#> SRR1706806 2 0.141 0.952 0.020 0.980
#> SRR1706811 1 0.000 0.922 1.000 0.000
#> SRR1706812 1 0.000 0.922 1.000 0.000
#> SRR1706813 1 0.000 0.922 1.000 0.000
#> SRR1706814 1 0.000 0.922 1.000 0.000
#> SRR1706807 1 0.000 0.922 1.000 0.000
#> SRR1706808 1 0.000 0.922 1.000 0.000
#> SRR1706809 1 0.000 0.922 1.000 0.000
#> SRR1706810 1 0.000 0.922 1.000 0.000
#> SRR1706815 2 0.936 0.500 0.352 0.648
#> SRR1706816 2 0.973 0.371 0.404 0.596
#> SRR1706817 2 0.925 0.525 0.340 0.660
#> SRR1706818 2 0.917 0.540 0.332 0.668
#> SRR1706819 2 0.141 0.952 0.020 0.980
#> SRR1706820 2 0.141 0.952 0.020 0.980
#> SRR1706821 2 0.141 0.952 0.020 0.980
#> SRR1706822 2 0.141 0.952 0.020 0.980
#> SRR1706823 2 0.141 0.952 0.020 0.980
#> SRR1706824 2 0.141 0.952 0.020 0.980
#> SRR1706825 2 0.141 0.952 0.020 0.980
#> SRR1706826 2 0.141 0.952 0.020 0.980
#> SRR1706827 1 0.000 0.922 1.000 0.000
#> SRR1706828 1 0.000 0.922 1.000 0.000
#> SRR1706829 1 0.000 0.922 1.000 0.000
#> SRR1706830 1 0.000 0.922 1.000 0.000
#> SRR1706835 1 0.563 0.828 0.868 0.132
#> SRR1706836 1 0.781 0.695 0.768 0.232
#> SRR1706837 1 0.482 0.855 0.896 0.104
#> SRR1706838 1 0.430 0.868 0.912 0.088
#> SRR1706831 1 0.000 0.922 1.000 0.000
#> SRR1706832 1 0.000 0.922 1.000 0.000
#> SRR1706833 1 0.000 0.922 1.000 0.000
#> SRR1706834 1 0.000 0.922 1.000 0.000
#> SRR1706839 2 0.224 0.942 0.036 0.964
#> SRR1706840 2 0.184 0.948 0.028 0.972
#> SRR1706841 2 0.163 0.950 0.024 0.976
#> SRR1706842 2 0.204 0.945 0.032 0.968
#> SRR1706847 1 0.141 0.919 0.980 0.020
#> SRR1706848 1 0.141 0.919 0.980 0.020
#> SRR1706849 1 0.141 0.919 0.980 0.020
#> SRR1706850 1 0.141 0.919 0.980 0.020
#> SRR1706843 2 0.141 0.952 0.020 0.980
#> SRR1706844 2 0.141 0.952 0.020 0.980
#> SRR1706845 2 0.141 0.952 0.020 0.980
#> SRR1706846 2 0.141 0.952 0.020 0.980
#> SRR1706851 1 0.671 0.824 0.824 0.176
#> SRR1706852 1 0.680 0.820 0.820 0.180
#> SRR1706853 1 0.680 0.820 0.820 0.180
#> SRR1706854 1 0.680 0.820 0.820 0.180
#> SRR1706855 2 0.000 0.956 0.000 1.000
#> SRR1706856 2 0.000 0.956 0.000 1.000
#> SRR1706857 2 0.000 0.956 0.000 1.000
#> SRR1706858 2 0.000 0.956 0.000 1.000
#> SRR1706859 2 0.000 0.956 0.000 1.000
#> SRR1706860 2 0.000 0.956 0.000 1.000
#> SRR1706861 2 0.000 0.956 0.000 1.000
#> SRR1706862 2 0.000 0.956 0.000 1.000
#> SRR1706867 1 0.141 0.919 0.980 0.020
#> SRR1706869 1 0.141 0.919 0.980 0.020
#> SRR1706870 1 0.141 0.919 0.980 0.020
#> SRR1706863 2 0.000 0.956 0.000 1.000
#> SRR1706864 2 0.000 0.956 0.000 1.000
#> SRR1706865 2 0.000 0.956 0.000 1.000
#> SRR1706866 2 0.000 0.956 0.000 1.000
#> SRR1706871 1 0.689 0.816 0.816 0.184
#> SRR1706872 1 0.697 0.812 0.812 0.188
#> SRR1706873 1 0.722 0.799 0.800 0.200
#> SRR1706874 1 0.697 0.812 0.812 0.188
#> SRR1706879 2 0.000 0.956 0.000 1.000
#> SRR1706880 2 0.000 0.956 0.000 1.000
#> SRR1706881 2 0.000 0.956 0.000 1.000
#> SRR1706882 2 0.000 0.956 0.000 1.000
#> SRR1706883 2 0.000 0.956 0.000 1.000
#> SRR1706884 2 0.000 0.956 0.000 1.000
#> SRR1706885 2 0.000 0.956 0.000 1.000
#> SRR1706886 2 0.000 0.956 0.000 1.000
#> SRR1706875 2 0.000 0.956 0.000 1.000
#> SRR1706876 2 0.000 0.956 0.000 1.000
#> SRR1706877 2 0.000 0.956 0.000 1.000
#> SRR1706878 2 0.000 0.956 0.000 1.000
#> SRR1706887 1 0.141 0.919 0.980 0.020
#> SRR1706888 1 0.141 0.919 0.980 0.020
#> SRR1706889 1 0.141 0.919 0.980 0.020
#> SRR1706890 1 0.141 0.919 0.980 0.020
#> SRR1706891 1 0.855 0.686 0.720 0.280
#> SRR1706892 1 0.821 0.724 0.744 0.256
#> SRR1706893 1 0.788 0.753 0.764 0.236
#> SRR1706894 1 0.781 0.759 0.768 0.232
#> SRR1706895 2 0.000 0.956 0.000 1.000
#> SRR1706896 2 0.000 0.956 0.000 1.000
#> SRR1706897 2 0.000 0.956 0.000 1.000
#> SRR1706898 2 0.000 0.956 0.000 1.000
#> SRR1706899 2 0.000 0.956 0.000 1.000
#> SRR1706900 2 0.000 0.956 0.000 1.000
#> SRR1706901 2 0.000 0.956 0.000 1.000
#> SRR1706902 2 0.000 0.956 0.000 1.000
#> SRR1706907 1 0.141 0.919 0.980 0.020
#> SRR1706908 1 0.141 0.919 0.980 0.020
#> SRR1706909 1 0.141 0.919 0.980 0.020
#> SRR1706910 1 0.141 0.919 0.980 0.020
#> SRR1706903 2 0.000 0.956 0.000 1.000
#> SRR1706904 2 0.000 0.956 0.000 1.000
#> SRR1706905 2 0.000 0.956 0.000 1.000
#> SRR1706906 2 0.000 0.956 0.000 1.000
#> SRR1706911 1 0.662 0.828 0.828 0.172
#> SRR1706912 1 0.671 0.824 0.824 0.176
#> SRR1706913 1 0.653 0.831 0.832 0.168
#> SRR1706914 1 0.653 0.831 0.832 0.168
#> SRR1706919 2 0.000 0.956 0.000 1.000
#> SRR1706920 2 0.000 0.956 0.000 1.000
#> SRR1706921 2 0.000 0.956 0.000 1.000
#> SRR1706922 2 0.000 0.956 0.000 1.000
#> SRR1706915 2 0.000 0.956 0.000 1.000
#> SRR1706916 2 0.000 0.956 0.000 1.000
#> SRR1706917 2 0.000 0.956 0.000 1.000
#> SRR1706918 2 0.000 0.956 0.000 1.000
#> SRR1706923 2 0.000 0.956 0.000 1.000
#> SRR1706924 2 0.000 0.956 0.000 1.000
#> SRR1706925 2 0.000 0.956 0.000 1.000
#> SRR1706926 2 0.000 0.956 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 3 0.597 0.500 0.364 0.000 0.636
#> SRR1706768 3 0.601 0.491 0.372 0.000 0.628
#> SRR1706769 3 0.595 0.505 0.360 0.000 0.640
#> SRR1706770 3 0.601 0.491 0.372 0.000 0.628
#> SRR1706771 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706772 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706773 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706774 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706775 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706776 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706777 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706778 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706779 1 0.348 0.808 0.872 0.128 0.000
#> SRR1706780 1 0.341 0.809 0.876 0.124 0.000
#> SRR1706781 1 0.341 0.809 0.876 0.124 0.000
#> SRR1706782 1 0.327 0.811 0.884 0.116 0.000
#> SRR1706783 1 0.601 0.627 0.628 0.372 0.000
#> SRR1706784 1 0.610 0.595 0.608 0.392 0.000
#> SRR1706785 1 0.599 0.633 0.632 0.368 0.000
#> SRR1706786 1 0.606 0.609 0.616 0.384 0.000
#> SRR1706787 3 0.627 0.374 0.452 0.000 0.548
#> SRR1706788 3 0.631 0.285 0.492 0.000 0.508
#> SRR1706789 3 0.630 0.324 0.476 0.000 0.524
#> SRR1706790 3 0.627 0.374 0.452 0.000 0.548
#> SRR1706791 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706792 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706793 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706794 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706795 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706796 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706797 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706798 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706799 1 0.334 0.810 0.880 0.120 0.000
#> SRR1706800 1 0.424 0.788 0.824 0.176 0.000
#> SRR1706801 1 0.355 0.806 0.868 0.132 0.000
#> SRR1706802 1 0.382 0.800 0.852 0.148 0.000
#> SRR1706803 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706804 1 0.597 0.639 0.636 0.364 0.000
#> SRR1706805 1 0.606 0.608 0.616 0.384 0.000
#> SRR1706806 1 0.595 0.644 0.640 0.360 0.000
#> SRR1706811 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706812 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706813 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706814 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706807 3 0.631 0.296 0.488 0.000 0.512
#> SRR1706808 3 0.631 0.275 0.496 0.000 0.504
#> SRR1706809 1 0.631 -0.280 0.504 0.000 0.496
#> SRR1706810 1 0.631 -0.291 0.500 0.000 0.500
#> SRR1706815 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706816 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706817 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706818 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706819 1 0.470 0.769 0.788 0.212 0.000
#> SRR1706820 1 0.455 0.776 0.800 0.200 0.000
#> SRR1706821 1 0.450 0.778 0.804 0.196 0.000
#> SRR1706822 1 0.455 0.776 0.800 0.200 0.000
#> SRR1706823 1 0.595 0.644 0.640 0.360 0.000
#> SRR1706824 1 0.595 0.644 0.640 0.360 0.000
#> SRR1706825 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706826 1 0.597 0.639 0.636 0.364 0.000
#> SRR1706827 3 0.629 0.342 0.468 0.000 0.532
#> SRR1706828 3 0.628 0.359 0.460 0.000 0.540
#> SRR1706829 3 0.624 0.395 0.440 0.000 0.560
#> SRR1706830 3 0.625 0.387 0.444 0.000 0.556
#> SRR1706835 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706836 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706837 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706838 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706831 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706832 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706833 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706834 1 0.000 0.820 1.000 0.000 0.000
#> SRR1706839 1 0.327 0.811 0.884 0.116 0.000
#> SRR1706840 1 0.312 0.812 0.892 0.108 0.000
#> SRR1706841 1 0.296 0.813 0.900 0.100 0.000
#> SRR1706842 1 0.304 0.813 0.896 0.104 0.000
#> SRR1706847 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706848 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706849 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706850 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706843 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706844 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706845 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706846 1 0.593 0.649 0.644 0.356 0.000
#> SRR1706851 3 0.460 0.600 0.000 0.204 0.796
#> SRR1706852 3 0.475 0.586 0.000 0.216 0.784
#> SRR1706853 3 0.465 0.596 0.000 0.208 0.792
#> SRR1706854 3 0.470 0.591 0.000 0.212 0.788
#> SRR1706855 2 0.533 0.692 0.000 0.728 0.272
#> SRR1706856 2 0.529 0.697 0.000 0.732 0.268
#> SRR1706857 2 0.529 0.697 0.000 0.732 0.268
#> SRR1706858 2 0.529 0.697 0.000 0.732 0.268
#> SRR1706859 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706860 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706861 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706862 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706867 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706869 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706870 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706863 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706864 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706865 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706866 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706871 3 0.475 0.587 0.000 0.216 0.784
#> SRR1706872 3 0.493 0.564 0.000 0.232 0.768
#> SRR1706873 3 0.489 0.570 0.000 0.228 0.772
#> SRR1706874 3 0.475 0.587 0.000 0.216 0.784
#> SRR1706879 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706880 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706881 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706882 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706883 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706884 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706885 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706886 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706875 2 0.510 0.719 0.000 0.752 0.248
#> SRR1706876 2 0.493 0.732 0.000 0.768 0.232
#> SRR1706877 2 0.510 0.719 0.000 0.752 0.248
#> SRR1706878 2 0.497 0.729 0.000 0.764 0.236
#> SRR1706887 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706888 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706889 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706890 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706891 3 0.573 0.392 0.000 0.324 0.676
#> SRR1706892 3 0.573 0.392 0.000 0.324 0.676
#> SRR1706893 3 0.565 0.419 0.000 0.312 0.688
#> SRR1706894 3 0.573 0.392 0.000 0.324 0.676
#> SRR1706895 2 0.506 0.722 0.000 0.756 0.244
#> SRR1706896 2 0.522 0.707 0.000 0.740 0.260
#> SRR1706897 2 0.522 0.707 0.000 0.740 0.260
#> SRR1706898 2 0.518 0.711 0.000 0.744 0.256
#> SRR1706899 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706900 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706901 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706902 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706907 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706908 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706909 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706910 3 0.000 0.713 0.000 0.000 1.000
#> SRR1706903 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706904 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706905 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706906 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706911 3 0.435 0.618 0.000 0.184 0.816
#> SRR1706912 3 0.440 0.615 0.000 0.188 0.812
#> SRR1706913 3 0.424 0.624 0.000 0.176 0.824
#> SRR1706914 3 0.424 0.624 0.000 0.176 0.824
#> SRR1706919 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706920 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706921 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706922 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706915 2 0.533 0.692 0.000 0.728 0.272
#> SRR1706916 2 0.533 0.692 0.000 0.728 0.272
#> SRR1706917 2 0.533 0.692 0.000 0.728 0.272
#> SRR1706918 2 0.533 0.692 0.000 0.728 0.272
#> SRR1706923 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706924 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706925 2 0.000 0.883 0.000 1.000 0.000
#> SRR1706926 2 0.000 0.883 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706768 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706769 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706770 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706771 4 0.1042 0.859 0.008 0.000 0.020 0.972
#> SRR1706772 4 0.1042 0.859 0.008 0.000 0.020 0.972
#> SRR1706773 4 0.1004 0.862 0.024 0.000 0.004 0.972
#> SRR1706774 4 0.0921 0.863 0.028 0.000 0.000 0.972
#> SRR1706775 1 0.0779 0.760 0.980 0.016 0.000 0.004
#> SRR1706776 1 0.0817 0.767 0.976 0.024 0.000 0.000
#> SRR1706777 1 0.0336 0.756 0.992 0.008 0.000 0.000
#> SRR1706778 1 0.0469 0.760 0.988 0.012 0.000 0.000
#> SRR1706779 1 0.4331 0.849 0.712 0.288 0.000 0.000
#> SRR1706780 1 0.4331 0.849 0.712 0.288 0.000 0.000
#> SRR1706781 1 0.4331 0.849 0.712 0.288 0.000 0.000
#> SRR1706782 1 0.4331 0.849 0.712 0.288 0.000 0.000
#> SRR1706783 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706784 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706785 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706786 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706787 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706788 4 0.1059 0.861 0.016 0.000 0.012 0.972
#> SRR1706789 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706790 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706791 4 0.4304 0.802 0.284 0.000 0.000 0.716
#> SRR1706792 4 0.4356 0.798 0.292 0.000 0.000 0.708
#> SRR1706793 4 0.4277 0.805 0.280 0.000 0.000 0.720
#> SRR1706794 4 0.4406 0.793 0.300 0.000 0.000 0.700
#> SRR1706795 1 0.0336 0.756 0.992 0.008 0.000 0.000
#> SRR1706796 1 0.0336 0.756 0.992 0.008 0.000 0.000
#> SRR1706797 1 0.0469 0.760 0.988 0.012 0.000 0.000
#> SRR1706798 1 0.0336 0.756 0.992 0.008 0.000 0.000
#> SRR1706799 1 0.4222 0.851 0.728 0.272 0.000 0.000
#> SRR1706800 1 0.4304 0.850 0.716 0.284 0.000 0.000
#> SRR1706801 1 0.4250 0.851 0.724 0.276 0.000 0.000
#> SRR1706802 1 0.4277 0.850 0.720 0.280 0.000 0.000
#> SRR1706803 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706804 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706805 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706806 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706811 4 0.4406 0.793 0.300 0.000 0.000 0.700
#> SRR1706812 4 0.4406 0.793 0.300 0.000 0.000 0.700
#> SRR1706813 4 0.4406 0.793 0.300 0.000 0.000 0.700
#> SRR1706814 4 0.4406 0.793 0.300 0.000 0.000 0.700
#> SRR1706807 4 0.1854 0.863 0.048 0.000 0.012 0.940
#> SRR1706808 4 0.2546 0.861 0.092 0.000 0.008 0.900
#> SRR1706809 4 0.2675 0.860 0.100 0.000 0.008 0.892
#> SRR1706810 4 0.3249 0.852 0.140 0.000 0.008 0.852
#> SRR1706815 1 0.0336 0.744 0.992 0.000 0.000 0.008
#> SRR1706816 1 0.0336 0.744 0.992 0.000 0.000 0.008
#> SRR1706817 1 0.1302 0.710 0.956 0.000 0.000 0.044
#> SRR1706818 1 0.0336 0.744 0.992 0.000 0.000 0.008
#> SRR1706819 1 0.2973 0.825 0.856 0.144 0.000 0.000
#> SRR1706820 1 0.3024 0.827 0.852 0.148 0.000 0.000
#> SRR1706821 1 0.1637 0.787 0.940 0.060 0.000 0.000
#> SRR1706822 1 0.3024 0.827 0.852 0.148 0.000 0.000
#> SRR1706823 1 0.4382 0.845 0.704 0.296 0.000 0.000
#> SRR1706824 1 0.4382 0.845 0.704 0.296 0.000 0.000
#> SRR1706825 1 0.4356 0.847 0.708 0.292 0.000 0.000
#> SRR1706826 1 0.4382 0.845 0.704 0.296 0.000 0.000
#> SRR1706827 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706828 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706829 4 0.0921 0.855 0.000 0.000 0.028 0.972
#> SRR1706830 4 0.1004 0.857 0.004 0.000 0.024 0.972
#> SRR1706835 1 0.0336 0.744 0.992 0.000 0.000 0.008
#> SRR1706836 1 0.1211 0.714 0.960 0.000 0.000 0.040
#> SRR1706837 1 0.1389 0.705 0.952 0.000 0.000 0.048
#> SRR1706838 1 0.2081 0.658 0.916 0.000 0.000 0.084
#> SRR1706831 4 0.3764 0.833 0.216 0.000 0.000 0.784
#> SRR1706832 4 0.4040 0.820 0.248 0.000 0.000 0.752
#> SRR1706833 4 0.3569 0.839 0.196 0.000 0.000 0.804
#> SRR1706834 4 0.3907 0.827 0.232 0.000 0.000 0.768
#> SRR1706839 1 0.4250 0.851 0.724 0.276 0.000 0.000
#> SRR1706840 1 0.4222 0.851 0.728 0.272 0.000 0.000
#> SRR1706841 1 0.4277 0.850 0.720 0.280 0.000 0.000
#> SRR1706842 1 0.4250 0.851 0.724 0.276 0.000 0.000
#> SRR1706847 3 0.4193 0.817 0.000 0.000 0.732 0.268
#> SRR1706848 3 0.4164 0.821 0.000 0.000 0.736 0.264
#> SRR1706849 3 0.4193 0.817 0.000 0.000 0.732 0.268
#> SRR1706850 3 0.4193 0.817 0.000 0.000 0.732 0.268
#> SRR1706843 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706844 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706845 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706846 1 0.4406 0.843 0.700 0.300 0.000 0.000
#> SRR1706851 3 0.2216 0.892 0.000 0.000 0.908 0.092
#> SRR1706852 3 0.2011 0.891 0.000 0.000 0.920 0.080
#> SRR1706853 3 0.2216 0.892 0.000 0.000 0.908 0.092
#> SRR1706854 3 0.2281 0.892 0.000 0.000 0.904 0.096
#> SRR1706855 2 0.3486 0.825 0.000 0.812 0.188 0.000
#> SRR1706856 2 0.3982 0.797 0.000 0.776 0.220 0.004
#> SRR1706857 2 0.3668 0.823 0.000 0.808 0.188 0.004
#> SRR1706858 2 0.3528 0.822 0.000 0.808 0.192 0.000
#> SRR1706859 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.4072 0.832 0.000 0.000 0.748 0.252
#> SRR1706869 3 0.4164 0.821 0.000 0.000 0.736 0.264
#> SRR1706870 3 0.4134 0.825 0.000 0.000 0.740 0.260
#> SRR1706863 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0921 0.879 0.000 0.000 0.972 0.028
#> SRR1706872 3 0.1022 0.881 0.000 0.000 0.968 0.032
#> SRR1706873 3 0.1022 0.881 0.000 0.000 0.968 0.032
#> SRR1706874 3 0.1118 0.883 0.000 0.000 0.964 0.036
#> SRR1706879 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.3444 0.827 0.000 0.816 0.184 0.000
#> SRR1706876 2 0.3444 0.827 0.000 0.816 0.184 0.000
#> SRR1706877 2 0.3710 0.820 0.000 0.804 0.192 0.004
#> SRR1706878 2 0.3528 0.822 0.000 0.808 0.192 0.000
#> SRR1706887 3 0.2530 0.890 0.000 0.000 0.888 0.112
#> SRR1706888 3 0.2921 0.884 0.000 0.000 0.860 0.140
#> SRR1706889 3 0.2704 0.888 0.000 0.000 0.876 0.124
#> SRR1706890 3 0.2973 0.883 0.000 0.000 0.856 0.144
#> SRR1706891 3 0.0000 0.862 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 0.862 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 0.862 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 0.862 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.4882 0.732 0.000 0.708 0.272 0.020
#> SRR1706896 2 0.4776 0.737 0.000 0.712 0.272 0.016
#> SRR1706897 2 0.4882 0.732 0.000 0.708 0.272 0.020
#> SRR1706898 2 0.4882 0.732 0.000 0.708 0.272 0.020
#> SRR1706899 2 0.3142 0.852 0.000 0.860 0.132 0.008
#> SRR1706900 2 0.2593 0.864 0.000 0.892 0.104 0.004
#> SRR1706901 2 0.2654 0.863 0.000 0.888 0.108 0.004
#> SRR1706902 2 0.2530 0.865 0.000 0.896 0.100 0.004
#> SRR1706907 3 0.4040 0.835 0.000 0.000 0.752 0.248
#> SRR1706908 3 0.3942 0.843 0.000 0.000 0.764 0.236
#> SRR1706909 3 0.3975 0.841 0.000 0.000 0.760 0.240
#> SRR1706910 3 0.3907 0.845 0.000 0.000 0.768 0.232
#> SRR1706903 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0188 0.886 0.000 0.996 0.000 0.004
#> SRR1706906 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.1211 0.884 0.000 0.000 0.960 0.040
#> SRR1706912 3 0.1022 0.881 0.000 0.000 0.968 0.032
#> SRR1706913 3 0.1118 0.883 0.000 0.000 0.964 0.036
#> SRR1706914 3 0.1118 0.883 0.000 0.000 0.964 0.036
#> SRR1706919 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706915 2 0.4452 0.757 0.000 0.732 0.260 0.008
#> SRR1706916 2 0.4283 0.764 0.000 0.740 0.256 0.004
#> SRR1706917 2 0.4372 0.752 0.000 0.728 0.268 0.004
#> SRR1706918 2 0.4343 0.756 0.000 0.732 0.264 0.004
#> SRR1706923 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.888 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.888 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.1484 0.947 0.000 0.000 0.008 0.944 NA
#> SRR1706768 4 0.1557 0.946 0.000 0.000 0.008 0.940 NA
#> SRR1706769 4 0.1697 0.944 0.000 0.000 0.008 0.932 NA
#> SRR1706770 4 0.1408 0.949 0.000 0.000 0.008 0.948 NA
#> SRR1706771 4 0.3038 0.916 0.040 0.000 0.008 0.872 NA
#> SRR1706772 4 0.2992 0.917 0.044 0.000 0.008 0.876 NA
#> SRR1706773 4 0.3325 0.897 0.056 0.000 0.008 0.856 NA
#> SRR1706774 4 0.2838 0.921 0.036 0.000 0.008 0.884 NA
#> SRR1706775 1 0.5022 0.645 0.672 0.016 0.000 0.276 NA
#> SRR1706776 1 0.4829 0.651 0.684 0.012 0.000 0.272 NA
#> SRR1706777 1 0.4824 0.663 0.688 0.016 0.000 0.268 NA
#> SRR1706778 1 0.5184 0.643 0.668 0.016 0.000 0.268 NA
#> SRR1706779 1 0.1670 0.923 0.936 0.052 0.000 0.000 NA
#> SRR1706780 1 0.1670 0.923 0.936 0.052 0.000 0.000 NA
#> SRR1706781 1 0.1670 0.923 0.936 0.052 0.000 0.000 NA
#> SRR1706782 1 0.1670 0.923 0.936 0.052 0.000 0.000 NA
#> SRR1706783 1 0.1341 0.922 0.944 0.056 0.000 0.000 NA
#> SRR1706784 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706785 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706786 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706787 4 0.0566 0.955 0.000 0.000 0.012 0.984 NA
#> SRR1706788 4 0.0566 0.955 0.000 0.000 0.012 0.984 NA
#> SRR1706789 4 0.0807 0.955 0.000 0.000 0.012 0.976 NA
#> SRR1706790 4 0.0693 0.955 0.000 0.000 0.012 0.980 NA
#> SRR1706791 4 0.1741 0.946 0.040 0.000 0.000 0.936 NA
#> SRR1706792 4 0.1386 0.949 0.032 0.000 0.000 0.952 NA
#> SRR1706793 4 0.1485 0.949 0.032 0.000 0.000 0.948 NA
#> SRR1706794 4 0.1399 0.949 0.028 0.000 0.000 0.952 NA
#> SRR1706795 1 0.1877 0.892 0.924 0.000 0.000 0.064 NA
#> SRR1706796 1 0.2037 0.894 0.920 0.004 0.000 0.064 NA
#> SRR1706797 1 0.1764 0.891 0.928 0.000 0.000 0.064 NA
#> SRR1706798 1 0.2228 0.887 0.908 0.004 0.000 0.076 NA
#> SRR1706799 1 0.1243 0.922 0.960 0.028 0.000 0.004 NA
#> SRR1706800 1 0.1285 0.923 0.956 0.036 0.000 0.004 NA
#> SRR1706801 1 0.1251 0.923 0.956 0.036 0.000 0.000 NA
#> SRR1706802 1 0.1243 0.921 0.960 0.028 0.000 0.004 NA
#> SRR1706803 1 0.1628 0.922 0.936 0.056 0.000 0.000 NA
#> SRR1706804 1 0.1740 0.922 0.932 0.056 0.000 0.000 NA
#> SRR1706805 1 0.1628 0.922 0.936 0.056 0.000 0.000 NA
#> SRR1706806 1 0.1740 0.922 0.932 0.056 0.000 0.000 NA
#> SRR1706811 4 0.2450 0.937 0.052 0.000 0.000 0.900 NA
#> SRR1706812 4 0.2450 0.937 0.048 0.000 0.000 0.900 NA
#> SRR1706813 4 0.2378 0.937 0.048 0.000 0.000 0.904 NA
#> SRR1706814 4 0.2376 0.937 0.052 0.000 0.000 0.904 NA
#> SRR1706807 4 0.1364 0.952 0.000 0.000 0.012 0.952 NA
#> SRR1706808 4 0.1444 0.951 0.000 0.000 0.012 0.948 NA
#> SRR1706809 4 0.1364 0.952 0.000 0.000 0.012 0.952 NA
#> SRR1706810 4 0.1364 0.952 0.000 0.000 0.012 0.952 NA
#> SRR1706815 1 0.1845 0.893 0.928 0.000 0.000 0.056 NA
#> SRR1706816 1 0.1893 0.896 0.928 0.000 0.000 0.048 NA
#> SRR1706817 1 0.2136 0.875 0.904 0.000 0.000 0.088 NA
#> SRR1706818 1 0.1741 0.899 0.936 0.000 0.000 0.040 NA
#> SRR1706819 1 0.1568 0.918 0.944 0.020 0.000 0.000 NA
#> SRR1706820 1 0.1469 0.916 0.948 0.016 0.000 0.000 NA
#> SRR1706821 1 0.1251 0.913 0.956 0.008 0.000 0.000 NA
#> SRR1706822 1 0.1568 0.918 0.944 0.020 0.000 0.000 NA
#> SRR1706823 1 0.2370 0.919 0.904 0.056 0.000 0.000 NA
#> SRR1706824 1 0.2370 0.919 0.904 0.056 0.000 0.000 NA
#> SRR1706825 1 0.2370 0.919 0.904 0.056 0.000 0.000 NA
#> SRR1706826 1 0.2370 0.919 0.904 0.056 0.000 0.000 NA
#> SRR1706827 4 0.0912 0.954 0.000 0.000 0.012 0.972 NA
#> SRR1706828 4 0.1012 0.954 0.000 0.000 0.012 0.968 NA
#> SRR1706829 4 0.1012 0.954 0.000 0.000 0.012 0.968 NA
#> SRR1706830 4 0.0807 0.955 0.000 0.000 0.012 0.976 NA
#> SRR1706835 1 0.3123 0.785 0.812 0.000 0.000 0.184 NA
#> SRR1706836 1 0.3231 0.770 0.800 0.000 0.000 0.196 NA
#> SRR1706837 1 0.3461 0.735 0.772 0.000 0.000 0.224 NA
#> SRR1706838 1 0.4046 0.619 0.696 0.000 0.000 0.296 NA
#> SRR1706831 4 0.1412 0.952 0.036 0.000 0.004 0.952 NA
#> SRR1706832 4 0.1364 0.949 0.036 0.000 0.000 0.952 NA
#> SRR1706833 4 0.1455 0.954 0.032 0.000 0.008 0.952 NA
#> SRR1706834 4 0.1331 0.950 0.040 0.000 0.000 0.952 NA
#> SRR1706839 1 0.1251 0.923 0.956 0.036 0.000 0.000 NA
#> SRR1706840 1 0.1251 0.923 0.956 0.036 0.000 0.000 NA
#> SRR1706841 1 0.1124 0.923 0.960 0.036 0.000 0.000 NA
#> SRR1706842 1 0.1285 0.923 0.956 0.036 0.000 0.004 NA
#> SRR1706847 3 0.2708 0.932 0.000 0.000 0.884 0.044 NA
#> SRR1706848 3 0.2580 0.935 0.000 0.000 0.892 0.044 NA
#> SRR1706849 3 0.2708 0.932 0.000 0.000 0.884 0.044 NA
#> SRR1706850 3 0.2645 0.934 0.000 0.000 0.888 0.044 NA
#> SRR1706843 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706844 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706845 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706846 1 0.1502 0.922 0.940 0.056 0.000 0.000 NA
#> SRR1706851 3 0.4234 0.885 0.000 0.012 0.776 0.040 NA
#> SRR1706852 3 0.4138 0.894 0.000 0.016 0.792 0.040 NA
#> SRR1706853 3 0.4334 0.883 0.000 0.016 0.772 0.040 NA
#> SRR1706854 3 0.4515 0.877 0.000 0.024 0.764 0.040 NA
#> SRR1706855 2 0.4930 0.612 0.000 0.676 0.268 0.004 NA
#> SRR1706856 2 0.4533 0.652 0.000 0.704 0.260 0.004 NA
#> SRR1706857 2 0.4726 0.641 0.000 0.696 0.256 0.004 NA
#> SRR1706858 2 0.4649 0.662 0.000 0.708 0.244 0.004 NA
#> SRR1706859 2 0.0671 0.933 0.004 0.980 0.000 0.000 NA
#> SRR1706860 2 0.0671 0.933 0.004 0.980 0.000 0.000 NA
#> SRR1706861 2 0.0671 0.933 0.004 0.980 0.000 0.000 NA
#> SRR1706862 2 0.0671 0.933 0.004 0.980 0.000 0.000 NA
#> SRR1706867 3 0.1701 0.947 0.000 0.000 0.936 0.048 NA
#> SRR1706869 3 0.1597 0.946 0.000 0.000 0.940 0.048 NA
#> SRR1706870 3 0.1484 0.946 0.000 0.000 0.944 0.048 NA
#> SRR1706863 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706864 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706865 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706866 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706871 3 0.1012 0.941 0.000 0.020 0.968 0.000 NA
#> SRR1706872 3 0.1012 0.941 0.000 0.020 0.968 0.000 NA
#> SRR1706873 3 0.1117 0.941 0.000 0.020 0.964 0.000 NA
#> SRR1706874 3 0.1012 0.941 0.000 0.020 0.968 0.000 NA
#> SRR1706879 2 0.0000 0.935 0.000 1.000 0.000 0.000 NA
#> SRR1706880 2 0.0000 0.935 0.000 1.000 0.000 0.000 NA
#> SRR1706881 2 0.0162 0.934 0.000 0.996 0.000 0.000 NA
#> SRR1706882 2 0.0000 0.935 0.000 1.000 0.000 0.000 NA
#> SRR1706883 2 0.0162 0.934 0.004 0.996 0.000 0.000 NA
#> SRR1706884 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706885 2 0.0162 0.934 0.004 0.996 0.000 0.000 NA
#> SRR1706886 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706875 2 0.1357 0.920 0.000 0.948 0.048 0.000 NA
#> SRR1706876 2 0.1484 0.919 0.000 0.944 0.048 0.000 NA
#> SRR1706877 2 0.1430 0.918 0.000 0.944 0.052 0.000 NA
#> SRR1706878 2 0.1341 0.918 0.000 0.944 0.056 0.000 NA
#> SRR1706887 3 0.1493 0.949 0.000 0.000 0.948 0.028 NA
#> SRR1706888 3 0.1661 0.948 0.000 0.000 0.940 0.036 NA
#> SRR1706889 3 0.1399 0.949 0.000 0.000 0.952 0.028 NA
#> SRR1706890 3 0.1582 0.950 0.000 0.000 0.944 0.028 NA
#> SRR1706891 3 0.2318 0.930 0.008 0.020 0.916 0.004 NA
#> SRR1706892 3 0.2246 0.932 0.008 0.020 0.920 0.004 NA
#> SRR1706893 3 0.2246 0.932 0.008 0.020 0.920 0.004 NA
#> SRR1706894 3 0.2318 0.930 0.008 0.020 0.916 0.004 NA
#> SRR1706895 2 0.1940 0.913 0.008 0.936 0.028 0.004 NA
#> SRR1706896 2 0.1940 0.913 0.008 0.936 0.028 0.004 NA
#> SRR1706897 2 0.1940 0.913 0.008 0.936 0.028 0.004 NA
#> SRR1706898 2 0.1940 0.913 0.008 0.936 0.028 0.004 NA
#> SRR1706899 2 0.1631 0.920 0.004 0.948 0.024 0.004 NA
#> SRR1706900 2 0.1631 0.920 0.004 0.948 0.024 0.004 NA
#> SRR1706901 2 0.1631 0.920 0.004 0.948 0.024 0.004 NA
#> SRR1706902 2 0.1631 0.920 0.004 0.948 0.024 0.004 NA
#> SRR1706907 3 0.1444 0.948 0.000 0.000 0.948 0.040 NA
#> SRR1706908 3 0.1205 0.949 0.000 0.000 0.956 0.040 NA
#> SRR1706909 3 0.1205 0.949 0.000 0.000 0.956 0.040 NA
#> SRR1706910 3 0.1331 0.949 0.000 0.000 0.952 0.040 NA
#> SRR1706903 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706904 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706905 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706906 2 0.0613 0.934 0.004 0.984 0.004 0.000 NA
#> SRR1706911 3 0.1299 0.950 0.000 0.008 0.960 0.012 NA
#> SRR1706912 3 0.1095 0.948 0.000 0.012 0.968 0.008 NA
#> SRR1706913 3 0.1173 0.950 0.000 0.004 0.964 0.012 NA
#> SRR1706914 3 0.0613 0.948 0.000 0.004 0.984 0.004 NA
#> SRR1706919 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706920 2 0.0162 0.935 0.000 0.996 0.000 0.000 NA
#> SRR1706921 2 0.0162 0.935 0.000 0.996 0.000 0.000 NA
#> SRR1706922 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706915 2 0.3750 0.745 0.000 0.756 0.232 0.000 NA
#> SRR1706916 2 0.3890 0.715 0.000 0.736 0.252 0.000 NA
#> SRR1706917 2 0.3582 0.757 0.000 0.768 0.224 0.000 NA
#> SRR1706918 2 0.4270 0.606 0.000 0.668 0.320 0.000 NA
#> SRR1706923 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706924 2 0.0324 0.934 0.004 0.992 0.000 0.000 NA
#> SRR1706925 2 0.0451 0.934 0.004 0.988 0.000 0.000 NA
#> SRR1706926 2 0.0451 0.935 0.004 0.988 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
#> SRR1706767 4 0.1779 0.947 0.000 0.000 0.000 0.920 NA 0.064
#> SRR1706768 4 0.1779 0.948 0.000 0.000 0.000 0.920 NA 0.064
#> SRR1706769 4 0.1779 0.948 0.000 0.000 0.000 0.920 NA 0.064
#> SRR1706770 4 0.1657 0.950 0.000 0.000 0.000 0.928 NA 0.056
#> SRR1706771 4 0.2834 0.915 0.020 0.000 0.000 0.864 NA 0.096
#> SRR1706772 4 0.3058 0.904 0.024 0.000 0.000 0.848 NA 0.108
#> SRR1706773 4 0.2816 0.918 0.024 0.000 0.000 0.868 NA 0.088
#> SRR1706774 4 0.3067 0.902 0.020 0.000 0.000 0.844 NA 0.116
#> SRR1706775 1 0.3520 0.798 0.776 0.000 0.000 0.188 NA 0.036
#> SRR1706776 1 0.3543 0.786 0.768 0.000 0.000 0.200 NA 0.032
#> SRR1706777 1 0.3649 0.785 0.764 0.000 0.000 0.196 NA 0.040
#> SRR1706778 1 0.3827 0.808 0.776 0.000 0.000 0.164 NA 0.052
#> SRR1706779 1 0.1480 0.926 0.940 0.000 0.000 0.000 NA 0.020
#> SRR1706780 1 0.1789 0.921 0.924 0.000 0.000 0.000 NA 0.032
#> SRR1706781 1 0.1564 0.925 0.936 0.000 0.000 0.000 NA 0.024
#> SRR1706782 1 0.1480 0.926 0.940 0.000 0.000 0.000 NA 0.020
#> SRR1706783 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706784 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706785 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706786 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706787 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706788 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706789 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706790 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706791 4 0.0653 0.973 0.004 0.000 0.000 0.980 NA 0.004
#> SRR1706792 4 0.0603 0.973 0.000 0.000 0.000 0.980 NA 0.004
#> SRR1706793 4 0.0603 0.973 0.000 0.000 0.000 0.980 NA 0.004
#> SRR1706794 4 0.0603 0.973 0.000 0.000 0.000 0.980 NA 0.004
#> SRR1706795 1 0.1444 0.914 0.928 0.000 0.000 0.072 NA 0.000
#> SRR1706796 1 0.1788 0.911 0.916 0.000 0.000 0.076 NA 0.004
#> SRR1706797 1 0.1765 0.900 0.904 0.000 0.000 0.096 NA 0.000
#> SRR1706798 1 0.1863 0.895 0.896 0.000 0.000 0.104 NA 0.000
#> SRR1706799 1 0.0146 0.936 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706800 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706801 1 0.0146 0.936 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706802 1 0.0146 0.936 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706803 1 0.0146 0.935 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706804 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706805 1 0.0146 0.935 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706806 1 0.0260 0.936 0.992 0.000 0.000 0.000 NA 0.000
#> SRR1706811 4 0.0632 0.970 0.000 0.000 0.000 0.976 NA 0.000
#> SRR1706812 4 0.0632 0.970 0.000 0.000 0.000 0.976 NA 0.000
#> SRR1706813 4 0.0632 0.970 0.000 0.000 0.000 0.976 NA 0.000
#> SRR1706814 4 0.0777 0.971 0.000 0.000 0.000 0.972 NA 0.004
#> SRR1706807 4 0.0260 0.973 0.000 0.000 0.000 0.992 NA 0.000
#> SRR1706808 4 0.0363 0.973 0.000 0.000 0.000 0.988 NA 0.000
#> SRR1706809 4 0.0363 0.973 0.000 0.000 0.000 0.988 NA 0.000
#> SRR1706810 4 0.0458 0.972 0.000 0.000 0.000 0.984 NA 0.000
#> SRR1706815 1 0.2361 0.901 0.884 0.000 0.000 0.088 NA 0.000
#> SRR1706816 1 0.2201 0.907 0.896 0.000 0.000 0.076 NA 0.000
#> SRR1706817 1 0.3065 0.849 0.820 0.000 0.000 0.152 NA 0.000
#> SRR1706818 1 0.1908 0.917 0.916 0.000 0.000 0.056 NA 0.000
#> SRR1706819 1 0.0713 0.932 0.972 0.000 0.000 0.000 NA 0.000
#> SRR1706820 1 0.0713 0.932 0.972 0.000 0.000 0.000 NA 0.000
#> SRR1706821 1 0.0713 0.932 0.972 0.000 0.000 0.000 NA 0.000
#> SRR1706822 1 0.0790 0.932 0.968 0.000 0.000 0.000 NA 0.000
#> SRR1706823 1 0.0790 0.932 0.968 0.000 0.000 0.000 NA 0.000
#> SRR1706824 1 0.0790 0.932 0.968 0.000 0.000 0.000 NA 0.000
#> SRR1706825 1 0.0790 0.932 0.968 0.000 0.000 0.000 NA 0.000
#> SRR1706826 1 0.0790 0.932 0.968 0.000 0.000 0.000 NA 0.000
#> SRR1706827 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706828 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706829 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706830 4 0.0000 0.974 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706835 1 0.3081 0.783 0.776 0.000 0.000 0.220 NA 0.000
#> SRR1706836 1 0.2964 0.803 0.792 0.000 0.000 0.204 NA 0.000
#> SRR1706837 1 0.3360 0.721 0.732 0.000 0.000 0.264 NA 0.000
#> SRR1706838 1 0.3725 0.625 0.676 0.000 0.000 0.316 NA 0.000
#> SRR1706831 4 0.0508 0.973 0.000 0.000 0.000 0.984 NA 0.004
#> SRR1706832 4 0.0508 0.973 0.000 0.000 0.000 0.984 NA 0.004
#> SRR1706833 4 0.0508 0.973 0.000 0.000 0.000 0.984 NA 0.004
#> SRR1706834 4 0.0603 0.973 0.000 0.000 0.000 0.980 NA 0.004
#> SRR1706839 1 0.0547 0.935 0.980 0.000 0.000 0.000 NA 0.000
#> SRR1706840 1 0.0713 0.934 0.972 0.000 0.000 0.000 NA 0.000
#> SRR1706841 1 0.0865 0.932 0.964 0.000 0.000 0.000 NA 0.000
#> SRR1706842 1 0.0458 0.935 0.984 0.000 0.000 0.000 NA 0.000
#> SRR1706847 3 0.2416 0.882 0.000 0.000 0.844 0.000 NA 0.156
#> SRR1706848 3 0.2178 0.892 0.000 0.000 0.868 0.000 NA 0.132
#> SRR1706849 3 0.3309 0.823 0.000 0.000 0.720 0.000 NA 0.280
#> SRR1706850 3 0.2969 0.851 0.000 0.000 0.776 0.000 NA 0.224
#> SRR1706843 1 0.0000 0.935 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706844 1 0.0146 0.935 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706845 1 0.0146 0.935 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1706846 1 0.0000 0.935 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706851 3 0.3986 0.745 0.000 0.004 0.608 0.000 NA 0.384
#> SRR1706852 3 0.3634 0.803 0.000 0.008 0.696 0.000 NA 0.296
#> SRR1706853 3 0.3805 0.784 0.000 0.004 0.664 0.000 NA 0.328
#> SRR1706854 3 0.4109 0.736 0.000 0.004 0.596 0.000 NA 0.392
#> SRR1706855 2 0.4276 0.770 0.000 0.756 0.160 0.000 NA 0.056
#> SRR1706856 2 0.3930 0.787 0.000 0.780 0.152 0.000 NA 0.048
#> SRR1706857 2 0.4021 0.790 0.000 0.780 0.144 0.000 NA 0.044
#> SRR1706858 2 0.3966 0.783 0.000 0.776 0.156 0.000 NA 0.048
#> SRR1706859 2 0.1297 0.888 0.000 0.948 0.000 0.000 NA 0.012
#> SRR1706860 2 0.1643 0.883 0.000 0.924 0.000 0.000 NA 0.008
#> SRR1706861 2 0.1219 0.888 0.000 0.948 0.000 0.000 NA 0.004
#> SRR1706862 2 0.1010 0.889 0.000 0.960 0.000 0.000 NA 0.004
#> SRR1706867 3 0.0405 0.933 0.000 0.000 0.988 0.000 NA 0.008
#> SRR1706869 3 0.0405 0.933 0.000 0.000 0.988 0.000 NA 0.008
#> SRR1706870 3 0.0291 0.933 0.000 0.000 0.992 0.000 NA 0.004
#> SRR1706863 2 0.0363 0.890 0.000 0.988 0.000 0.000 NA 0.000
#> SRR1706864 2 0.0458 0.891 0.000 0.984 0.000 0.000 NA 0.000
#> SRR1706865 2 0.0146 0.890 0.000 0.996 0.000 0.000 NA 0.000
#> SRR1706866 2 0.0260 0.890 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706871 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706872 3 0.0551 0.932 0.000 0.004 0.984 0.000 NA 0.008
#> SRR1706873 3 0.0405 0.934 0.000 0.000 0.988 0.000 NA 0.004
#> SRR1706874 3 0.0622 0.933 0.000 0.000 0.980 0.000 NA 0.008
#> SRR1706879 2 0.0260 0.890 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706880 2 0.0291 0.890 0.000 0.992 0.000 0.000 NA 0.004
#> SRR1706881 2 0.0603 0.889 0.000 0.980 0.000 0.000 NA 0.016
#> SRR1706882 2 0.0508 0.890 0.000 0.984 0.000 0.000 NA 0.012
#> SRR1706883 2 0.0405 0.890 0.000 0.988 0.000 0.000 NA 0.004
#> SRR1706884 2 0.0806 0.889 0.000 0.972 0.000 0.000 NA 0.020
#> SRR1706885 2 0.0520 0.890 0.000 0.984 0.000 0.000 NA 0.008
#> SRR1706886 2 0.0547 0.891 0.000 0.980 0.000 0.000 NA 0.000
#> SRR1706875 2 0.2062 0.867 0.000 0.900 0.088 0.000 NA 0.004
#> SRR1706876 2 0.2002 0.872 0.000 0.908 0.076 0.000 NA 0.012
#> SRR1706877 2 0.1956 0.870 0.000 0.908 0.080 0.000 NA 0.004
#> SRR1706878 2 0.1918 0.868 0.000 0.904 0.088 0.000 NA 0.008
#> SRR1706887 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706888 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706889 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706890 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706891 3 0.2290 0.896 0.000 0.004 0.892 0.000 NA 0.084
#> SRR1706892 3 0.2149 0.900 0.000 0.004 0.900 0.000 NA 0.080
#> SRR1706893 3 0.2149 0.900 0.000 0.004 0.900 0.000 NA 0.080
#> SRR1706894 3 0.2320 0.898 0.000 0.004 0.892 0.000 NA 0.080
#> SRR1706895 2 0.4172 0.798 0.000 0.724 0.000 0.000 NA 0.204
#> SRR1706896 2 0.4172 0.798 0.000 0.724 0.000 0.000 NA 0.204
#> SRR1706897 2 0.4310 0.797 0.000 0.720 0.004 0.000 NA 0.204
#> SRR1706898 2 0.4310 0.797 0.000 0.720 0.004 0.000 NA 0.204
#> SRR1706899 2 0.3907 0.815 0.000 0.756 0.000 0.000 NA 0.176
#> SRR1706900 2 0.3907 0.815 0.000 0.756 0.000 0.000 NA 0.176
#> SRR1706901 2 0.3907 0.815 0.000 0.756 0.000 0.000 NA 0.176
#> SRR1706902 2 0.3907 0.815 0.000 0.756 0.000 0.000 NA 0.176
#> SRR1706907 3 0.0260 0.933 0.000 0.000 0.992 0.000 NA 0.000
#> SRR1706908 3 0.0260 0.933 0.000 0.000 0.992 0.000 NA 0.000
#> SRR1706909 3 0.0260 0.933 0.000 0.000 0.992 0.000 NA 0.000
#> SRR1706910 3 0.0405 0.934 0.000 0.000 0.988 0.000 NA 0.004
#> SRR1706903 2 0.3395 0.840 0.000 0.808 0.000 0.000 NA 0.132
#> SRR1706904 2 0.3453 0.840 0.000 0.804 0.000 0.000 NA 0.132
#> SRR1706905 2 0.3395 0.840 0.000 0.808 0.000 0.000 NA 0.132
#> SRR1706906 2 0.3354 0.842 0.000 0.812 0.000 0.000 NA 0.128
#> SRR1706911 3 0.0520 0.933 0.000 0.000 0.984 0.000 NA 0.008
#> SRR1706912 3 0.0405 0.933 0.000 0.000 0.988 0.000 NA 0.004
#> SRR1706913 3 0.0622 0.933 0.000 0.000 0.980 0.000 NA 0.008
#> SRR1706914 3 0.0508 0.933 0.000 0.000 0.984 0.000 NA 0.004
#> SRR1706919 2 0.1267 0.883 0.000 0.940 0.000 0.000 NA 0.000
#> SRR1706920 2 0.1204 0.885 0.000 0.944 0.000 0.000 NA 0.000
#> SRR1706921 2 0.1007 0.886 0.000 0.956 0.000 0.000 NA 0.000
#> SRR1706922 2 0.1267 0.883 0.000 0.940 0.000 0.000 NA 0.000
#> SRR1706915 2 0.3917 0.630 0.000 0.692 0.284 0.000 NA 0.000
#> SRR1706916 2 0.4028 0.583 0.000 0.668 0.308 0.000 NA 0.000
#> SRR1706917 2 0.3608 0.661 0.000 0.716 0.272 0.000 NA 0.000
#> SRR1706918 2 0.4438 0.521 0.000 0.628 0.328 0.000 NA 0.000
#> SRR1706923 2 0.0547 0.889 0.000 0.980 0.000 0.000 NA 0.000
#> SRR1706924 2 0.0363 0.890 0.000 0.988 0.000 0.000 NA 0.000
#> SRR1706925 2 0.0790 0.888 0.000 0.968 0.000 0.000 NA 0.000
#> SRR1706926 2 0.0865 0.887 0.000 0.964 0.000 0.000 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15185 rows and 159 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 1.000 1.000 0.5036 0.497 0.497
#> 3 3 1 0.996 0.997 0.2351 0.882 0.762
#> 4 4 1 0.990 0.993 0.1964 0.878 0.677
#> 5 5 1 0.984 0.984 0.0546 0.959 0.841
#> 6 6 1 0.984 0.986 0.0519 0.959 0.811
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.000 1.000 1 0.000 0.000
#> SRR1706768 1 0.000 1.000 1 0.000 0.000
#> SRR1706769 1 0.000 1.000 1 0.000 0.000
#> SRR1706770 1 0.000 1.000 1 0.000 0.000
#> SRR1706771 1 0.000 1.000 1 0.000 0.000
#> SRR1706772 1 0.000 1.000 1 0.000 0.000
#> SRR1706773 1 0.000 1.000 1 0.000 0.000
#> SRR1706774 1 0.000 1.000 1 0.000 0.000
#> SRR1706775 1 0.000 1.000 1 0.000 0.000
#> SRR1706776 1 0.000 1.000 1 0.000 0.000
#> SRR1706777 1 0.000 1.000 1 0.000 0.000
#> SRR1706778 1 0.000 1.000 1 0.000 0.000
#> SRR1706779 1 0.000 1.000 1 0.000 0.000
#> SRR1706780 1 0.000 1.000 1 0.000 0.000
#> SRR1706781 1 0.000 1.000 1 0.000 0.000
#> SRR1706782 1 0.000 1.000 1 0.000 0.000
#> SRR1706783 1 0.000 1.000 1 0.000 0.000
#> SRR1706784 1 0.000 1.000 1 0.000 0.000
#> SRR1706785 1 0.000 1.000 1 0.000 0.000
#> SRR1706786 1 0.000 1.000 1 0.000 0.000
#> SRR1706787 1 0.000 1.000 1 0.000 0.000
#> SRR1706788 1 0.000 1.000 1 0.000 0.000
#> SRR1706789 1 0.000 1.000 1 0.000 0.000
#> SRR1706790 1 0.000 1.000 1 0.000 0.000
#> SRR1706791 1 0.000 1.000 1 0.000 0.000
#> SRR1706792 1 0.000 1.000 1 0.000 0.000
#> SRR1706793 1 0.000 1.000 1 0.000 0.000
#> SRR1706794 1 0.000 1.000 1 0.000 0.000
#> SRR1706795 1 0.000 1.000 1 0.000 0.000
#> SRR1706796 1 0.000 1.000 1 0.000 0.000
#> SRR1706797 1 0.000 1.000 1 0.000 0.000
#> SRR1706798 1 0.000 1.000 1 0.000 0.000
#> SRR1706799 1 0.000 1.000 1 0.000 0.000
#> SRR1706800 1 0.000 1.000 1 0.000 0.000
#> SRR1706801 1 0.000 1.000 1 0.000 0.000
#> SRR1706802 1 0.000 1.000 1 0.000 0.000
#> SRR1706803 1 0.000 1.000 1 0.000 0.000
#> SRR1706804 1 0.000 1.000 1 0.000 0.000
#> SRR1706805 1 0.000 1.000 1 0.000 0.000
#> SRR1706806 1 0.000 1.000 1 0.000 0.000
#> SRR1706811 1 0.000 1.000 1 0.000 0.000
#> SRR1706812 1 0.000 1.000 1 0.000 0.000
#> SRR1706813 1 0.000 1.000 1 0.000 0.000
#> SRR1706814 1 0.000 1.000 1 0.000 0.000
#> SRR1706807 1 0.000 1.000 1 0.000 0.000
#> SRR1706808 1 0.000 1.000 1 0.000 0.000
#> SRR1706809 1 0.000 1.000 1 0.000 0.000
#> SRR1706810 1 0.000 1.000 1 0.000 0.000
#> SRR1706815 1 0.000 1.000 1 0.000 0.000
#> SRR1706816 1 0.000 1.000 1 0.000 0.000
#> SRR1706817 1 0.000 1.000 1 0.000 0.000
#> SRR1706818 1 0.000 1.000 1 0.000 0.000
#> SRR1706819 1 0.000 1.000 1 0.000 0.000
#> SRR1706820 1 0.000 1.000 1 0.000 0.000
#> SRR1706821 1 0.000 1.000 1 0.000 0.000
#> SRR1706822 1 0.000 1.000 1 0.000 0.000
#> SRR1706823 1 0.000 1.000 1 0.000 0.000
#> SRR1706824 1 0.000 1.000 1 0.000 0.000
#> SRR1706825 1 0.000 1.000 1 0.000 0.000
#> SRR1706826 1 0.000 1.000 1 0.000 0.000
#> SRR1706827 1 0.000 1.000 1 0.000 0.000
#> SRR1706828 1 0.000 1.000 1 0.000 0.000
#> SRR1706829 1 0.000 1.000 1 0.000 0.000
#> SRR1706830 1 0.000 1.000 1 0.000 0.000
#> SRR1706835 1 0.000 1.000 1 0.000 0.000
#> SRR1706836 1 0.000 1.000 1 0.000 0.000
#> SRR1706837 1 0.000 1.000 1 0.000 0.000
#> SRR1706838 1 0.000 1.000 1 0.000 0.000
#> SRR1706831 1 0.000 1.000 1 0.000 0.000
#> SRR1706832 1 0.000 1.000 1 0.000 0.000
#> SRR1706833 1 0.000 1.000 1 0.000 0.000
#> SRR1706834 1 0.000 1.000 1 0.000 0.000
#> SRR1706839 1 0.000 1.000 1 0.000 0.000
#> SRR1706840 1 0.000 1.000 1 0.000 0.000
#> SRR1706841 1 0.000 1.000 1 0.000 0.000
#> SRR1706842 1 0.000 1.000 1 0.000 0.000
#> SRR1706847 3 0.000 1.000 0 0.000 1.000
#> SRR1706848 3 0.000 1.000 0 0.000 1.000
#> SRR1706849 3 0.000 1.000 0 0.000 1.000
#> SRR1706850 3 0.000 1.000 0 0.000 1.000
#> SRR1706843 1 0.000 1.000 1 0.000 0.000
#> SRR1706844 1 0.000 1.000 1 0.000 0.000
#> SRR1706845 1 0.000 1.000 1 0.000 0.000
#> SRR1706846 1 0.000 1.000 1 0.000 0.000
#> SRR1706851 3 0.000 1.000 0 0.000 1.000
#> SRR1706852 3 0.000 1.000 0 0.000 1.000
#> SRR1706853 3 0.000 1.000 0 0.000 1.000
#> SRR1706854 3 0.000 1.000 0 0.000 1.000
#> SRR1706855 2 0.129 0.977 0 0.968 0.032
#> SRR1706856 2 0.129 0.977 0 0.968 0.032
#> SRR1706857 2 0.129 0.977 0 0.968 0.032
#> SRR1706858 2 0.129 0.977 0 0.968 0.032
#> SRR1706859 2 0.000 0.989 0 1.000 0.000
#> SRR1706860 2 0.000 0.989 0 1.000 0.000
#> SRR1706861 2 0.000 0.989 0 1.000 0.000
#> SRR1706862 2 0.000 0.989 0 1.000 0.000
#> SRR1706867 3 0.000 1.000 0 0.000 1.000
#> SRR1706869 3 0.000 1.000 0 0.000 1.000
#> SRR1706870 3 0.000 1.000 0 0.000 1.000
#> SRR1706863 2 0.000 0.989 0 1.000 0.000
#> SRR1706864 2 0.000 0.989 0 1.000 0.000
#> SRR1706865 2 0.000 0.989 0 1.000 0.000
#> SRR1706866 2 0.000 0.989 0 1.000 0.000
#> SRR1706871 3 0.000 1.000 0 0.000 1.000
#> SRR1706872 3 0.000 1.000 0 0.000 1.000
#> SRR1706873 3 0.000 1.000 0 0.000 1.000
#> SRR1706874 3 0.000 1.000 0 0.000 1.000
#> SRR1706879 2 0.000 0.989 0 1.000 0.000
#> SRR1706880 2 0.000 0.989 0 1.000 0.000
#> SRR1706881 2 0.000 0.989 0 1.000 0.000
#> SRR1706882 2 0.000 0.989 0 1.000 0.000
#> SRR1706883 2 0.000 0.989 0 1.000 0.000
#> SRR1706884 2 0.000 0.989 0 1.000 0.000
#> SRR1706885 2 0.000 0.989 0 1.000 0.000
#> SRR1706886 2 0.000 0.989 0 1.000 0.000
#> SRR1706875 2 0.129 0.977 0 0.968 0.032
#> SRR1706876 2 0.129 0.977 0 0.968 0.032
#> SRR1706877 2 0.129 0.977 0 0.968 0.032
#> SRR1706878 2 0.129 0.977 0 0.968 0.032
#> SRR1706887 3 0.000 1.000 0 0.000 1.000
#> SRR1706888 3 0.000 1.000 0 0.000 1.000
#> SRR1706889 3 0.000 1.000 0 0.000 1.000
#> SRR1706890 3 0.000 1.000 0 0.000 1.000
#> SRR1706891 3 0.000 1.000 0 0.000 1.000
#> SRR1706892 3 0.000 1.000 0 0.000 1.000
#> SRR1706893 3 0.000 1.000 0 0.000 1.000
#> SRR1706894 3 0.000 1.000 0 0.000 1.000
#> SRR1706895 2 0.129 0.977 0 0.968 0.032
#> SRR1706896 2 0.129 0.977 0 0.968 0.032
#> SRR1706897 2 0.129 0.977 0 0.968 0.032
#> SRR1706898 2 0.129 0.977 0 0.968 0.032
#> SRR1706899 2 0.000 0.989 0 1.000 0.000
#> SRR1706900 2 0.000 0.989 0 1.000 0.000
#> SRR1706901 2 0.000 0.989 0 1.000 0.000
#> SRR1706902 2 0.000 0.989 0 1.000 0.000
#> SRR1706907 3 0.000 1.000 0 0.000 1.000
#> SRR1706908 3 0.000 1.000 0 0.000 1.000
#> SRR1706909 3 0.000 1.000 0 0.000 1.000
#> SRR1706910 3 0.000 1.000 0 0.000 1.000
#> SRR1706903 2 0.000 0.989 0 1.000 0.000
#> SRR1706904 2 0.000 0.989 0 1.000 0.000
#> SRR1706905 2 0.000 0.989 0 1.000 0.000
#> SRR1706906 2 0.000 0.989 0 1.000 0.000
#> SRR1706911 3 0.000 1.000 0 0.000 1.000
#> SRR1706912 3 0.000 1.000 0 0.000 1.000
#> SRR1706913 3 0.000 1.000 0 0.000 1.000
#> SRR1706914 3 0.000 1.000 0 0.000 1.000
#> SRR1706919 2 0.000 0.989 0 1.000 0.000
#> SRR1706920 2 0.000 0.989 0 1.000 0.000
#> SRR1706921 2 0.000 0.989 0 1.000 0.000
#> SRR1706922 2 0.000 0.989 0 1.000 0.000
#> SRR1706915 2 0.129 0.977 0 0.968 0.032
#> SRR1706916 2 0.129 0.977 0 0.968 0.032
#> SRR1706917 2 0.129 0.977 0 0.968 0.032
#> SRR1706918 2 0.129 0.977 0 0.968 0.032
#> SRR1706923 2 0.000 0.989 0 1.000 0.000
#> SRR1706924 2 0.000 0.989 0 1.000 0.000
#> SRR1706925 2 0.000 0.989 0 1.000 0.000
#> SRR1706926 2 0.000 0.989 0 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706768 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706769 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706770 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706771 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706772 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706773 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706774 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706775 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706776 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706777 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706778 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706779 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706780 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706781 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706782 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706783 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706784 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706785 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706786 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706787 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706788 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706789 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706790 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706791 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706792 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706793 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706794 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706795 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706796 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706797 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706798 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706799 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706800 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706801 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706802 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706803 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706804 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706805 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706806 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706811 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706812 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706813 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706814 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706807 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706808 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706809 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706810 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706815 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706816 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706817 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706818 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706819 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706820 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706821 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706822 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706823 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706824 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706825 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706826 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706827 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706828 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706829 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706830 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706835 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706836 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706837 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706838 1 0.121 0.972 0.96 0.000 0.000 0.04
#> SRR1706831 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706832 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706833 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706834 4 0.000 1.000 0.00 0.000 0.000 1.00
#> SRR1706839 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706840 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706841 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706842 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706847 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706848 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706849 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706850 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706843 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706844 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706845 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706846 1 0.000 0.986 1.00 0.000 0.000 0.00
#> SRR1706851 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706852 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706853 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706854 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706855 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706856 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706857 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706858 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706859 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706860 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706861 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706862 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706867 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706869 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706870 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706863 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706864 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706865 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706866 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706871 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706872 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706873 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706874 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706879 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706880 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706881 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706882 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706883 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706884 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706885 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706886 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706875 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706876 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706877 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706878 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706887 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706888 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706889 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706890 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706891 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706892 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706893 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706894 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706895 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706896 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706897 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706898 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706899 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706900 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706901 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706902 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706907 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706908 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706909 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706910 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706903 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706904 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706905 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706906 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706911 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706912 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706913 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706914 3 0.000 1.000 0.00 0.000 1.000 0.00
#> SRR1706919 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706920 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706921 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706922 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706915 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706916 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706917 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706918 2 0.102 0.977 0.00 0.968 0.032 0.00
#> SRR1706923 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706924 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706925 2 0.000 0.989 0.00 1.000 0.000 0.00
#> SRR1706926 2 0.000 0.989 0.00 1.000 0.000 0.00
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706772 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706773 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706774 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706775 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706776 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706777 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706778 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706779 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706792 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706793 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706794 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706795 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706796 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706797 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706798 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706799 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706812 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706813 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706814 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706807 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706815 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706816 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706817 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706818 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706819 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 0.985 0.00 0.000 0.000 1.000 0.000
#> SRR1706835 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706836 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706837 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706838 1 0.1195 0.972 0.96 0.000 0.000 0.012 0.028
#> SRR1706831 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706832 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706833 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706834 4 0.0794 0.985 0.00 0.000 0.000 0.972 0.028
#> SRR1706839 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.986 1.00 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706852 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706853 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706854 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706855 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706856 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706857 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706858 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706859 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706860 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706861 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706862 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706867 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706863 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706864 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706865 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706866 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706871 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706872 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706873 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706874 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706879 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706880 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706881 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706882 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706883 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706884 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706885 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706886 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706875 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706876 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706877 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706878 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706887 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706888 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706889 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706890 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706891 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706892 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706893 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706894 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706895 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706896 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706897 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706898 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706899 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706900 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706901 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706902 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706907 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.0000 0.983 0.00 0.000 1.000 0.000 0.000
#> SRR1706903 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706904 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706905 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706906 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706911 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706912 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706913 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706914 3 0.0880 0.984 0.00 0.032 0.968 0.000 0.000
#> SRR1706919 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706920 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706921 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706922 2 0.0880 0.980 0.00 0.968 0.000 0.000 0.032
#> SRR1706915 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706916 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706917 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706918 2 0.0000 0.980 0.00 1.000 0.000 0.000 0.000
#> SRR1706923 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706924 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706925 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
#> SRR1706926 5 0.0794 1.000 0.00 0.028 0.000 0.000 0.972
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706768 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706769 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706770 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706771 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706772 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706773 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706774 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706775 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706776 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706777 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706778 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706779 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706780 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706781 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706782 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706783 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706784 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706785 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706786 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706787 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706788 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706789 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706790 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706791 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706792 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706793 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706794 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706795 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706796 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706797 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706798 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706799 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706800 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706801 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706802 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706803 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706804 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706805 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706806 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706811 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706812 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706813 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706814 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706807 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706808 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706809 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706810 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706815 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706816 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706817 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706818 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706819 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706820 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706821 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706822 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706823 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706824 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706825 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706826 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706827 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706828 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706829 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706830 4 0.0000 0.979 0.00 0.000 0.000 1.00 0.00 0.000
#> SRR1706835 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706836 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706837 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706838 5 0.0000 0.976 0.00 0.000 0.000 0.00 1.00 0.000
#> SRR1706831 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706832 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706833 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706834 4 0.0937 0.979 0.00 0.000 0.000 0.96 0.04 0.000
#> SRR1706839 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706840 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706841 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706842 5 0.0937 0.975 0.04 0.000 0.000 0.00 0.96 0.000
#> SRR1706847 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706848 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706849 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706850 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706843 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706844 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706845 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706846 1 0.0000 1.000 1.00 0.000 0.000 0.00 0.00 0.000
#> SRR1706851 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706852 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706853 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706854 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706855 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706856 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706857 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706858 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706859 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706860 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706861 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706862 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706867 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706869 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706870 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706863 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706864 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706865 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706866 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706871 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706872 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706873 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706874 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706879 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706880 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706881 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706882 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706883 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706884 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706885 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706886 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706875 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706876 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706877 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706878 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706887 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706888 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706889 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706890 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706891 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706892 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706893 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706894 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706895 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706896 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706897 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706898 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706899 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706900 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706901 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706902 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706907 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706908 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706909 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706910 3 0.0000 0.983 0.00 0.000 1.000 0.00 0.00 0.000
#> SRR1706903 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706904 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706905 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706906 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706911 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706912 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706913 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706914 3 0.0790 0.984 0.00 0.000 0.968 0.00 0.00 0.032
#> SRR1706919 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706920 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706921 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706922 6 0.0790 0.980 0.00 0.032 0.000 0.00 0.00 0.968
#> SRR1706915 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706916 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706917 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706918 6 0.0000 0.981 0.00 0.000 0.000 0.00 0.00 1.000
#> SRR1706923 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706924 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706925 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 0.000
#> SRR1706926 2 0.0000 1.000 0.00 1.000 0.000 0.00 0.00 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", "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 15185 rows and 159 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.670 0.353 0.761 0.2434 0.976 0.952
#> 4 4 0.642 0.754 0.771 0.1316 0.755 0.497
#> 5 5 0.664 0.672 0.712 0.0793 0.918 0.725
#> 6 6 0.717 0.520 0.612 0.0362 0.875 0.558
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706768 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706769 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706770 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706771 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706772 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706773 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706774 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706775 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706776 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706777 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706778 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706779 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706783 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706784 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706785 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706786 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706787 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706788 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706789 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706790 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706791 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706792 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706793 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706794 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706795 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706796 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706797 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706798 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706799 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706802 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706803 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706804 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706805 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706806 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706811 1 0.6291 0.7984 0.532 0.000 0.468
#> SRR1706812 1 0.6291 0.7984 0.532 0.000 0.468
#> SRR1706813 1 0.6291 0.7984 0.532 0.000 0.468
#> SRR1706814 1 0.6291 0.7984 0.532 0.000 0.468
#> SRR1706807 1 0.6307 0.7900 0.512 0.000 0.488
#> SRR1706808 1 0.6307 0.7900 0.512 0.000 0.488
#> SRR1706809 1 0.6307 0.7900 0.512 0.000 0.488
#> SRR1706810 1 0.6307 0.7900 0.512 0.000 0.488
#> SRR1706815 1 0.5138 0.8254 0.748 0.000 0.252
#> SRR1706816 1 0.5138 0.8254 0.748 0.000 0.252
#> SRR1706817 1 0.5138 0.8254 0.748 0.000 0.252
#> SRR1706818 1 0.5138 0.8254 0.748 0.000 0.252
#> SRR1706819 1 0.0424 0.7931 0.992 0.000 0.008
#> SRR1706820 1 0.0424 0.7931 0.992 0.000 0.008
#> SRR1706821 1 0.0424 0.7931 0.992 0.000 0.008
#> SRR1706822 1 0.0424 0.7931 0.992 0.000 0.008
#> SRR1706823 1 0.1163 0.7848 0.972 0.000 0.028
#> SRR1706824 1 0.1163 0.7848 0.972 0.000 0.028
#> SRR1706825 1 0.1163 0.7848 0.972 0.000 0.028
#> SRR1706826 1 0.1163 0.7848 0.972 0.000 0.028
#> SRR1706827 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706828 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706829 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706830 1 0.6302 0.7920 0.520 0.000 0.480
#> SRR1706835 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706836 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706837 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706838 1 0.5098 0.8260 0.752 0.000 0.248
#> SRR1706831 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706832 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706833 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706834 1 0.6280 0.7995 0.540 0.000 0.460
#> SRR1706839 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.7943 1.000 0.000 0.000
#> SRR1706847 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706848 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706849 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706850 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706843 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706844 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706845 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706846 1 0.0892 0.7869 0.980 0.000 0.020
#> SRR1706851 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706855 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706856 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706857 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706858 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706859 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706860 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706861 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706862 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706867 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706869 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706870 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706863 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706864 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706865 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706866 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706871 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706879 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706880 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706881 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706882 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706883 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706884 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706885 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706886 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706875 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706876 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706877 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706878 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706887 2 0.2959 0.4210 0.000 0.900 0.100
#> SRR1706888 2 0.2959 0.4210 0.000 0.900 0.100
#> SRR1706889 2 0.2959 0.4210 0.000 0.900 0.100
#> SRR1706890 2 0.2959 0.4210 0.000 0.900 0.100
#> SRR1706891 2 0.0592 0.4366 0.000 0.988 0.012
#> SRR1706892 2 0.0592 0.4366 0.000 0.988 0.012
#> SRR1706893 2 0.0592 0.4366 0.000 0.988 0.012
#> SRR1706894 2 0.0592 0.4366 0.000 0.988 0.012
#> SRR1706895 2 0.5465 0.0139 0.000 0.712 0.288
#> SRR1706896 2 0.5465 0.0139 0.000 0.712 0.288
#> SRR1706897 2 0.5465 0.0139 0.000 0.712 0.288
#> SRR1706898 2 0.5465 0.0139 0.000 0.712 0.288
#> SRR1706899 2 0.6305 -0.9068 0.000 0.516 0.484
#> SRR1706900 2 0.6305 -0.9068 0.000 0.516 0.484
#> SRR1706901 2 0.6305 -0.9068 0.000 0.516 0.484
#> SRR1706902 2 0.6305 -0.9068 0.000 0.516 0.484
#> SRR1706907 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706908 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706909 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706910 2 0.2711 0.4260 0.000 0.912 0.088
#> SRR1706903 3 0.6309 1.0000 0.000 0.496 0.504
#> SRR1706904 3 0.6309 1.0000 0.000 0.496 0.504
#> SRR1706905 3 0.6309 1.0000 0.000 0.496 0.504
#> SRR1706906 3 0.6309 1.0000 0.000 0.496 0.504
#> SRR1706911 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.4392 0.000 1.000 0.000
#> SRR1706919 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706920 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706921 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706922 2 0.6295 -0.8819 0.000 0.528 0.472
#> SRR1706915 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706916 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706917 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706918 2 0.5363 0.0231 0.000 0.724 0.276
#> SRR1706923 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706924 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706925 2 0.6308 -0.9501 0.000 0.508 0.492
#> SRR1706926 2 0.6308 -0.9501 0.000 0.508 0.492
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.5864 0.941 0.264 0.000 0.072 0.664
#> SRR1706768 4 0.5864 0.941 0.264 0.000 0.072 0.664
#> SRR1706769 4 0.5864 0.941 0.264 0.000 0.072 0.664
#> SRR1706770 4 0.5864 0.941 0.264 0.000 0.072 0.664
#> SRR1706771 4 0.4748 0.939 0.268 0.000 0.016 0.716
#> SRR1706772 4 0.4748 0.939 0.268 0.000 0.016 0.716
#> SRR1706773 4 0.4748 0.939 0.268 0.000 0.016 0.716
#> SRR1706774 4 0.4748 0.939 0.268 0.000 0.016 0.716
#> SRR1706775 1 0.5442 0.374 0.636 0.000 0.028 0.336
#> SRR1706776 1 0.5442 0.374 0.636 0.000 0.028 0.336
#> SRR1706777 1 0.5442 0.374 0.636 0.000 0.028 0.336
#> SRR1706778 1 0.5442 0.374 0.636 0.000 0.028 0.336
#> SRR1706779 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706780 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706781 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706782 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706783 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706784 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706785 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706786 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706787 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706788 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706789 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706790 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706791 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706792 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706793 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706794 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706795 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706796 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706797 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706798 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706799 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706800 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706801 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706802 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706803 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706804 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706805 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706806 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706811 4 0.4599 0.929 0.248 0.000 0.016 0.736
#> SRR1706812 4 0.4599 0.929 0.248 0.000 0.016 0.736
#> SRR1706813 4 0.4599 0.929 0.248 0.000 0.016 0.736
#> SRR1706814 4 0.4599 0.929 0.248 0.000 0.016 0.736
#> SRR1706807 4 0.5657 0.927 0.244 0.000 0.068 0.688
#> SRR1706808 4 0.5657 0.927 0.244 0.000 0.068 0.688
#> SRR1706809 4 0.5657 0.927 0.244 0.000 0.068 0.688
#> SRR1706810 4 0.5657 0.927 0.244 0.000 0.068 0.688
#> SRR1706815 1 0.5839 0.356 0.604 0.000 0.044 0.352
#> SRR1706816 1 0.5839 0.356 0.604 0.000 0.044 0.352
#> SRR1706817 1 0.5839 0.356 0.604 0.000 0.044 0.352
#> SRR1706818 1 0.5839 0.356 0.604 0.000 0.044 0.352
#> SRR1706819 1 0.1488 0.774 0.956 0.000 0.012 0.032
#> SRR1706820 1 0.1488 0.774 0.956 0.000 0.012 0.032
#> SRR1706821 1 0.1488 0.774 0.956 0.000 0.012 0.032
#> SRR1706822 1 0.1488 0.774 0.956 0.000 0.012 0.032
#> SRR1706823 1 0.2489 0.760 0.912 0.000 0.068 0.020
#> SRR1706824 1 0.2489 0.760 0.912 0.000 0.068 0.020
#> SRR1706825 1 0.2489 0.760 0.912 0.000 0.068 0.020
#> SRR1706826 1 0.2489 0.760 0.912 0.000 0.068 0.020
#> SRR1706827 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706828 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706829 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706830 4 0.5592 0.945 0.264 0.000 0.056 0.680
#> SRR1706835 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706836 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706837 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706838 1 0.5368 0.372 0.636 0.000 0.024 0.340
#> SRR1706831 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706832 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706833 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706834 4 0.4193 0.942 0.268 0.000 0.000 0.732
#> SRR1706839 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706840 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706841 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706842 1 0.0469 0.783 0.988 0.000 0.000 0.012
#> SRR1706847 3 0.5376 0.910 0.000 0.176 0.736 0.088
#> SRR1706848 3 0.5376 0.910 0.000 0.176 0.736 0.088
#> SRR1706849 3 0.5376 0.910 0.000 0.176 0.736 0.088
#> SRR1706850 3 0.5376 0.910 0.000 0.176 0.736 0.088
#> SRR1706843 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706844 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706845 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706846 1 0.1557 0.774 0.944 0.000 0.056 0.000
#> SRR1706851 3 0.4175 0.905 0.000 0.212 0.776 0.012
#> SRR1706852 3 0.4175 0.905 0.000 0.212 0.776 0.012
#> SRR1706853 3 0.4175 0.905 0.000 0.212 0.776 0.012
#> SRR1706854 3 0.4175 0.905 0.000 0.212 0.776 0.012
#> SRR1706855 2 0.6058 0.419 0.000 0.604 0.336 0.060
#> SRR1706856 2 0.6058 0.419 0.000 0.604 0.336 0.060
#> SRR1706857 2 0.6058 0.419 0.000 0.604 0.336 0.060
#> SRR1706858 2 0.6058 0.419 0.000 0.604 0.336 0.060
#> SRR1706859 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706860 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706861 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706862 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706867 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706869 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706870 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706863 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706864 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706865 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706866 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706871 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706872 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706873 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706874 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706879 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706880 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706881 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706882 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706883 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706884 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706885 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706886 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706875 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706876 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706877 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706878 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706887 3 0.5309 0.902 0.000 0.164 0.744 0.092
#> SRR1706888 3 0.5309 0.902 0.000 0.164 0.744 0.092
#> SRR1706889 3 0.5309 0.902 0.000 0.164 0.744 0.092
#> SRR1706890 3 0.5309 0.902 0.000 0.164 0.744 0.092
#> SRR1706891 3 0.3933 0.906 0.000 0.200 0.792 0.008
#> SRR1706892 3 0.3933 0.906 0.000 0.200 0.792 0.008
#> SRR1706893 3 0.3933 0.906 0.000 0.200 0.792 0.008
#> SRR1706894 3 0.3933 0.906 0.000 0.200 0.792 0.008
#> SRR1706895 2 0.6263 0.392 0.000 0.576 0.356 0.068
#> SRR1706896 2 0.6263 0.392 0.000 0.576 0.356 0.068
#> SRR1706897 2 0.6263 0.392 0.000 0.576 0.356 0.068
#> SRR1706898 2 0.6263 0.392 0.000 0.576 0.356 0.068
#> SRR1706899 2 0.1975 0.763 0.000 0.936 0.016 0.048
#> SRR1706900 2 0.1975 0.763 0.000 0.936 0.016 0.048
#> SRR1706901 2 0.1975 0.763 0.000 0.936 0.016 0.048
#> SRR1706902 2 0.1975 0.763 0.000 0.936 0.016 0.048
#> SRR1706907 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706908 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706909 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706910 3 0.5314 0.911 0.000 0.176 0.740 0.084
#> SRR1706903 2 0.2676 0.757 0.000 0.896 0.012 0.092
#> SRR1706904 2 0.2676 0.757 0.000 0.896 0.012 0.092
#> SRR1706905 2 0.2676 0.757 0.000 0.896 0.012 0.092
#> SRR1706906 2 0.2676 0.757 0.000 0.896 0.012 0.092
#> SRR1706911 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706912 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706913 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706914 3 0.3726 0.911 0.000 0.212 0.788 0.000
#> SRR1706919 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706920 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706921 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706922 2 0.0188 0.777 0.000 0.996 0.004 0.000
#> SRR1706915 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706916 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706917 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706918 2 0.6091 0.403 0.000 0.596 0.344 0.060
#> SRR1706923 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706924 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706925 2 0.2081 0.764 0.000 0.916 0.000 0.084
#> SRR1706926 2 0.2081 0.764 0.000 0.916 0.000 0.084
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.5935 0.678 0.108 0.000 0.040 0.664 0.188
#> SRR1706768 4 0.5935 0.678 0.108 0.000 0.040 0.664 0.188
#> SRR1706769 4 0.5935 0.678 0.108 0.000 0.040 0.664 0.188
#> SRR1706770 4 0.5935 0.678 0.108 0.000 0.040 0.664 0.188
#> SRR1706771 4 0.2445 0.709 0.108 0.000 0.004 0.884 0.004
#> SRR1706772 4 0.2445 0.709 0.108 0.000 0.004 0.884 0.004
#> SRR1706773 4 0.2445 0.709 0.108 0.000 0.004 0.884 0.004
#> SRR1706774 4 0.2445 0.709 0.108 0.000 0.004 0.884 0.004
#> SRR1706775 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706776 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706777 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706778 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706779 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706780 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706781 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706782 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706783 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706788 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706789 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706790 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706791 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706792 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706793 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706794 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706795 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706796 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706797 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706798 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706799 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706800 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706801 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706802 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706803 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.3019 0.707 0.108 0.000 0.016 0.864 0.012
#> SRR1706812 4 0.3019 0.707 0.108 0.000 0.016 0.864 0.012
#> SRR1706813 4 0.3019 0.707 0.108 0.000 0.016 0.864 0.012
#> SRR1706814 4 0.3019 0.707 0.108 0.000 0.016 0.864 0.012
#> SRR1706807 4 0.5834 0.676 0.108 0.000 0.028 0.660 0.204
#> SRR1706808 4 0.5834 0.676 0.108 0.000 0.028 0.660 0.204
#> SRR1706809 4 0.5834 0.676 0.108 0.000 0.028 0.660 0.204
#> SRR1706810 4 0.5834 0.676 0.108 0.000 0.028 0.660 0.204
#> SRR1706815 4 0.7251 0.266 0.320 0.000 0.020 0.380 0.280
#> SRR1706816 4 0.7251 0.266 0.320 0.000 0.020 0.380 0.280
#> SRR1706817 4 0.7251 0.266 0.320 0.000 0.020 0.380 0.280
#> SRR1706818 4 0.7251 0.266 0.320 0.000 0.020 0.380 0.280
#> SRR1706819 1 0.3671 0.844 0.756 0.000 0.008 0.000 0.236
#> SRR1706820 1 0.3671 0.844 0.756 0.000 0.008 0.000 0.236
#> SRR1706821 1 0.3671 0.844 0.756 0.000 0.008 0.000 0.236
#> SRR1706822 1 0.3671 0.844 0.756 0.000 0.008 0.000 0.236
#> SRR1706823 1 0.0771 0.849 0.976 0.000 0.004 0.000 0.020
#> SRR1706824 1 0.0771 0.849 0.976 0.000 0.004 0.000 0.020
#> SRR1706825 1 0.0771 0.849 0.976 0.000 0.004 0.000 0.020
#> SRR1706826 1 0.0771 0.849 0.976 0.000 0.004 0.000 0.020
#> SRR1706827 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706828 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706829 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706830 4 0.5775 0.678 0.108 0.000 0.028 0.668 0.196
#> SRR1706835 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706836 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706837 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706838 4 0.6740 0.278 0.328 0.000 0.000 0.404 0.268
#> SRR1706831 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706832 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706833 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706834 4 0.2127 0.709 0.108 0.000 0.000 0.892 0.000
#> SRR1706839 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706840 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706841 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706842 1 0.3554 0.850 0.776 0.000 0.004 0.004 0.216
#> SRR1706847 3 0.5482 0.825 0.000 0.092 0.636 0.004 0.268
#> SRR1706848 3 0.5482 0.825 0.000 0.092 0.636 0.004 0.268
#> SRR1706849 3 0.5482 0.825 0.000 0.092 0.636 0.004 0.268
#> SRR1706850 3 0.5482 0.825 0.000 0.092 0.636 0.004 0.268
#> SRR1706843 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.857 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.2249 0.822 0.000 0.096 0.896 0.000 0.008
#> SRR1706852 3 0.2249 0.822 0.000 0.096 0.896 0.000 0.008
#> SRR1706853 3 0.2249 0.822 0.000 0.096 0.896 0.000 0.008
#> SRR1706854 3 0.2249 0.822 0.000 0.096 0.896 0.000 0.008
#> SRR1706855 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706856 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706857 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706858 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706859 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706860 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706861 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706862 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706867 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706869 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706870 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706863 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706864 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706865 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706866 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706871 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706872 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706873 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706874 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706879 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706880 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706881 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706882 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706883 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706884 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706885 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706886 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706875 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706876 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706877 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706878 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706887 3 0.5062 0.812 0.000 0.068 0.656 0.000 0.276
#> SRR1706888 3 0.5062 0.812 0.000 0.068 0.656 0.000 0.276
#> SRR1706889 3 0.5062 0.812 0.000 0.068 0.656 0.000 0.276
#> SRR1706890 3 0.5062 0.812 0.000 0.068 0.656 0.000 0.276
#> SRR1706891 3 0.2403 0.810 0.000 0.072 0.904 0.012 0.012
#> SRR1706892 3 0.2403 0.810 0.000 0.072 0.904 0.012 0.012
#> SRR1706893 3 0.2403 0.810 0.000 0.072 0.904 0.012 0.012
#> SRR1706894 3 0.2403 0.810 0.000 0.072 0.904 0.012 0.012
#> SRR1706895 2 0.6544 0.294 0.000 0.444 0.436 0.040 0.080
#> SRR1706896 2 0.6544 0.294 0.000 0.444 0.436 0.040 0.080
#> SRR1706897 2 0.6544 0.294 0.000 0.444 0.436 0.040 0.080
#> SRR1706898 2 0.6544 0.294 0.000 0.444 0.436 0.040 0.080
#> SRR1706899 2 0.2902 0.703 0.000 0.888 0.028 0.028 0.056
#> SRR1706900 2 0.2902 0.703 0.000 0.888 0.028 0.028 0.056
#> SRR1706901 2 0.2902 0.703 0.000 0.888 0.028 0.028 0.056
#> SRR1706902 2 0.2902 0.703 0.000 0.888 0.028 0.028 0.056
#> SRR1706907 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706908 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706909 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706910 3 0.5331 0.825 0.000 0.092 0.640 0.000 0.268
#> SRR1706903 2 0.4622 0.682 0.000 0.768 0.024 0.060 0.148
#> SRR1706904 2 0.4622 0.682 0.000 0.768 0.024 0.060 0.148
#> SRR1706905 2 0.4622 0.682 0.000 0.768 0.024 0.060 0.148
#> SRR1706906 2 0.4622 0.682 0.000 0.768 0.024 0.060 0.148
#> SRR1706911 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706912 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706913 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706914 3 0.1965 0.825 0.000 0.096 0.904 0.000 0.000
#> SRR1706919 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706920 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706921 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706922 2 0.0162 0.721 0.000 0.996 0.004 0.000 0.000
#> SRR1706915 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706916 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706917 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706918 2 0.6030 0.324 0.000 0.496 0.420 0.024 0.060
#> SRR1706923 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706924 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706925 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
#> SRR1706926 2 0.3409 0.698 0.000 0.824 0.000 0.032 0.144
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.1003 0.7444 0.000 0.004 0.028 0.964 0.004 0.000
#> SRR1706768 4 0.1003 0.7444 0.000 0.004 0.028 0.964 0.004 0.000
#> SRR1706769 4 0.1003 0.7444 0.000 0.004 0.028 0.964 0.004 0.000
#> SRR1706770 4 0.1003 0.7444 0.000 0.004 0.028 0.964 0.004 0.000
#> SRR1706771 4 0.4976 0.6802 0.000 0.012 0.088 0.652 0.248 0.000
#> SRR1706772 4 0.4976 0.6802 0.000 0.012 0.088 0.652 0.248 0.000
#> SRR1706773 4 0.4976 0.6802 0.000 0.012 0.088 0.652 0.248 0.000
#> SRR1706774 4 0.4976 0.6802 0.000 0.012 0.088 0.652 0.248 0.000
#> SRR1706775 5 0.5462 0.9638 0.128 0.004 0.016 0.224 0.628 0.000
#> SRR1706776 5 0.5462 0.9638 0.128 0.004 0.016 0.224 0.628 0.000
#> SRR1706777 5 0.5462 0.9638 0.128 0.004 0.016 0.224 0.628 0.000
#> SRR1706778 5 0.5462 0.9638 0.128 0.004 0.016 0.224 0.628 0.000
#> SRR1706779 1 0.6053 0.7270 0.600 0.004 0.076 0.096 0.224 0.000
#> SRR1706780 1 0.6053 0.7270 0.600 0.004 0.076 0.096 0.224 0.000
#> SRR1706781 1 0.6053 0.7270 0.600 0.004 0.076 0.096 0.224 0.000
#> SRR1706782 1 0.6053 0.7270 0.600 0.004 0.076 0.096 0.224 0.000
#> SRR1706783 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706784 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706785 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706786 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706787 4 0.0146 0.7470 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1706788 4 0.0146 0.7470 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1706789 4 0.0146 0.7470 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1706790 4 0.0146 0.7470 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1706791 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706792 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706793 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706794 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706795 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706796 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706797 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706798 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706799 1 0.5973 0.7252 0.600 0.004 0.064 0.096 0.236 0.000
#> SRR1706800 1 0.5973 0.7252 0.600 0.004 0.064 0.096 0.236 0.000
#> SRR1706801 1 0.5973 0.7252 0.600 0.004 0.064 0.096 0.236 0.000
#> SRR1706802 1 0.5973 0.7252 0.600 0.004 0.064 0.096 0.236 0.000
#> SRR1706803 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706804 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706805 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706806 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706811 4 0.5067 0.6530 0.000 0.000 0.120 0.612 0.268 0.000
#> SRR1706812 4 0.5067 0.6530 0.000 0.000 0.120 0.612 0.268 0.000
#> SRR1706813 4 0.5067 0.6530 0.000 0.000 0.120 0.612 0.268 0.000
#> SRR1706814 4 0.5067 0.6530 0.000 0.000 0.120 0.612 0.268 0.000
#> SRR1706807 4 0.1138 0.7359 0.000 0.004 0.012 0.960 0.024 0.000
#> SRR1706808 4 0.1138 0.7359 0.000 0.004 0.012 0.960 0.024 0.000
#> SRR1706809 4 0.1138 0.7359 0.000 0.004 0.012 0.960 0.024 0.000
#> SRR1706810 4 0.1138 0.7359 0.000 0.004 0.012 0.960 0.024 0.000
#> SRR1706815 5 0.5819 0.9310 0.120 0.008 0.040 0.208 0.624 0.000
#> SRR1706816 5 0.5819 0.9310 0.120 0.008 0.040 0.208 0.624 0.000
#> SRR1706817 5 0.5819 0.9310 0.120 0.008 0.040 0.208 0.624 0.000
#> SRR1706818 5 0.5819 0.9310 0.120 0.008 0.040 0.208 0.624 0.000
#> SRR1706819 1 0.6718 0.7033 0.552 0.020 0.104 0.096 0.228 0.000
#> SRR1706820 1 0.6718 0.7033 0.552 0.020 0.104 0.096 0.228 0.000
#> SRR1706821 1 0.6718 0.7033 0.552 0.020 0.104 0.096 0.228 0.000
#> SRR1706822 1 0.6718 0.7033 0.552 0.020 0.104 0.096 0.228 0.000
#> SRR1706823 1 0.3542 0.7567 0.832 0.012 0.044 0.096 0.016 0.000
#> SRR1706824 1 0.3542 0.7567 0.832 0.012 0.044 0.096 0.016 0.000
#> SRR1706825 1 0.3542 0.7567 0.832 0.012 0.044 0.096 0.016 0.000
#> SRR1706826 1 0.3542 0.7567 0.832 0.012 0.044 0.096 0.016 0.000
#> SRR1706827 4 0.0260 0.7471 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1706828 4 0.0260 0.7471 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1706829 4 0.0260 0.7471 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1706830 4 0.0260 0.7471 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1706835 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706836 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706837 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706838 5 0.4908 0.9713 0.128 0.000 0.000 0.224 0.648 0.000
#> SRR1706831 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706832 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706833 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706834 4 0.4707 0.6812 0.000 0.000 0.092 0.656 0.252 0.000
#> SRR1706839 1 0.5994 0.7216 0.596 0.004 0.064 0.096 0.240 0.000
#> SRR1706840 1 0.5994 0.7216 0.596 0.004 0.064 0.096 0.240 0.000
#> SRR1706841 1 0.5994 0.7216 0.596 0.004 0.064 0.096 0.240 0.000
#> SRR1706842 1 0.5994 0.7216 0.596 0.004 0.064 0.096 0.240 0.000
#> SRR1706847 2 0.6304 -0.9485 0.016 0.412 0.404 0.000 0.008 0.160
#> SRR1706848 2 0.6304 -0.9485 0.016 0.412 0.404 0.000 0.008 0.160
#> SRR1706849 2 0.6304 -0.9485 0.016 0.412 0.404 0.000 0.008 0.160
#> SRR1706850 2 0.6304 -0.9485 0.016 0.412 0.404 0.000 0.008 0.160
#> SRR1706843 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706844 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706845 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706846 1 0.1765 0.7800 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR1706851 2 0.5689 -0.2570 0.004 0.480 0.056 0.000 0.036 0.424
#> SRR1706852 2 0.5689 -0.2570 0.004 0.480 0.056 0.000 0.036 0.424
#> SRR1706853 2 0.5689 -0.2570 0.004 0.480 0.056 0.000 0.036 0.424
#> SRR1706854 2 0.5689 -0.2570 0.004 0.480 0.056 0.000 0.036 0.424
#> SRR1706855 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706856 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706857 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706858 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706859 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706860 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706861 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706862 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706867 3 0.5955 0.9624 0.004 0.408 0.424 0.000 0.004 0.160
#> SRR1706869 3 0.5955 0.9624 0.004 0.408 0.424 0.000 0.004 0.160
#> SRR1706870 3 0.5955 0.9624 0.004 0.408 0.424 0.000 0.004 0.160
#> SRR1706863 2 0.7434 -0.0250 0.000 0.388 0.160 0.000 0.212 0.240
#> SRR1706864 2 0.7434 -0.0250 0.000 0.388 0.160 0.000 0.212 0.240
#> SRR1706865 2 0.7434 -0.0250 0.000 0.388 0.160 0.000 0.212 0.240
#> SRR1706866 2 0.7434 -0.0250 0.000 0.388 0.160 0.000 0.212 0.240
#> SRR1706871 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706872 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706873 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706874 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706879 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706880 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706881 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706882 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706883 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706884 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706885 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706886 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706875 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706876 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706877 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706878 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706887 3 0.6194 0.9403 0.012 0.400 0.436 0.000 0.012 0.140
#> SRR1706888 3 0.6194 0.9403 0.012 0.400 0.436 0.000 0.012 0.140
#> SRR1706889 3 0.6194 0.9403 0.012 0.400 0.436 0.000 0.012 0.140
#> SRR1706890 3 0.6194 0.9403 0.012 0.400 0.436 0.000 0.012 0.140
#> SRR1706891 2 0.6484 -0.2983 0.008 0.464 0.100 0.000 0.060 0.368
#> SRR1706892 2 0.6484 -0.2983 0.008 0.464 0.100 0.000 0.060 0.368
#> SRR1706893 2 0.6484 -0.2983 0.008 0.464 0.100 0.000 0.060 0.368
#> SRR1706894 2 0.6484 -0.2983 0.008 0.464 0.100 0.000 0.060 0.368
#> SRR1706895 6 0.2874 0.5701 0.024 0.024 0.044 0.000 0.024 0.884
#> SRR1706896 6 0.2874 0.5701 0.024 0.024 0.044 0.000 0.024 0.884
#> SRR1706897 6 0.2874 0.5701 0.024 0.024 0.044 0.000 0.024 0.884
#> SRR1706898 6 0.2874 0.5701 0.024 0.024 0.044 0.000 0.024 0.884
#> SRR1706899 6 0.6049 0.5676 0.048 0.384 0.048 0.000 0.020 0.500
#> SRR1706900 6 0.6049 0.5676 0.048 0.384 0.048 0.000 0.020 0.500
#> SRR1706901 6 0.6049 0.5676 0.048 0.384 0.048 0.000 0.020 0.500
#> SRR1706902 6 0.6049 0.5676 0.048 0.384 0.048 0.000 0.020 0.500
#> SRR1706907 3 0.5703 0.9602 0.000 0.412 0.428 0.000 0.000 0.160
#> SRR1706908 3 0.5703 0.9602 0.000 0.412 0.428 0.000 0.000 0.160
#> SRR1706909 3 0.5703 0.9602 0.000 0.412 0.428 0.000 0.000 0.160
#> SRR1706910 3 0.5703 0.9602 0.000 0.412 0.428 0.000 0.000 0.160
#> SRR1706903 2 0.7806 -0.0517 0.016 0.380 0.220 0.000 0.172 0.212
#> SRR1706904 2 0.7806 -0.0517 0.016 0.380 0.220 0.000 0.172 0.212
#> SRR1706905 2 0.7806 -0.0517 0.016 0.380 0.220 0.000 0.172 0.212
#> SRR1706906 2 0.7806 -0.0517 0.016 0.380 0.220 0.000 0.172 0.212
#> SRR1706911 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706912 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706913 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706914 2 0.5635 -0.2609 0.000 0.480 0.068 0.000 0.032 0.420
#> SRR1706919 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706920 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706921 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706922 6 0.4517 0.5942 0.024 0.444 0.004 0.000 0.000 0.528
#> SRR1706915 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706916 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706917 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706918 6 0.0000 0.6020 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706923 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706924 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706925 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
#> SRR1706926 2 0.7439 -0.0238 0.000 0.388 0.164 0.000 0.208 0.240
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 15185 rows and 159 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 1.000 0.992 0.984 0.2353 0.882 0.762
#> 4 4 1.000 0.991 0.992 0.1951 0.878 0.677
#> 5 5 0.838 0.373 0.623 0.0548 0.837 0.486
#> 6 6 0.859 0.888 0.907 0.0501 0.838 0.415
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706768 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706769 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706770 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706771 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706772 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706773 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706774 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706775 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706776 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706777 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706778 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706779 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706780 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706781 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706782 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706783 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706784 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706785 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706786 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706787 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706788 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706789 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706790 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706791 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706792 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706793 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706794 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706795 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706796 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706797 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706798 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706799 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706800 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706801 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706802 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706803 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706804 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706805 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706806 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706811 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706812 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706813 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706814 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706807 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706808 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706809 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706810 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706815 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706816 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706817 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706818 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706819 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706820 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706821 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706822 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706823 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706824 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706825 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706826 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706827 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706828 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706829 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706830 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706835 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706836 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706837 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706838 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706831 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706832 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706833 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706834 1 0.000 0.986 1.00 0.00 0.00
#> SRR1706839 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706840 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706841 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706842 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706847 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706848 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706849 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706850 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706843 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706844 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706845 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706846 1 0.153 0.979 0.96 0.00 0.04
#> SRR1706851 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706852 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706853 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706854 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706855 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706856 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706857 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706858 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706859 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706860 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706861 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706862 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706867 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706869 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706870 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706863 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706864 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706865 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706866 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706871 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706872 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706873 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706874 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706879 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706880 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706881 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706882 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706883 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706884 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706885 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706886 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706875 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706876 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706877 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706878 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706887 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706888 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706889 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706890 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706891 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706892 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706893 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706894 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706895 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706896 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706897 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706898 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706899 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706900 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706901 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706902 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706907 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706908 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706909 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706910 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706903 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706904 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706905 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706906 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706911 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706912 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706913 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706914 3 0.153 1.000 0.00 0.04 0.96
#> SRR1706919 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706920 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706921 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706922 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706915 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706916 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706917 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706918 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706923 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706924 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706925 2 0.000 1.000 0.00 1.00 0.00
#> SRR1706926 2 0.000 1.000 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706768 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706769 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706770 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706771 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706772 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706773 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706774 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706775 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706776 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706777 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706778 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706779 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706780 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706781 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706782 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706783 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706784 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706785 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706786 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706787 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706792 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706793 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706794 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706795 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706796 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706797 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706798 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706799 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706800 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706801 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706802 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706803 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706804 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706805 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706806 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706811 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706812 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706813 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706814 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706807 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706808 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706809 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706810 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706815 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706816 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706817 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706818 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706819 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706820 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706821 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706822 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706823 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706824 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706825 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706826 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706827 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706828 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706829 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706830 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706835 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706836 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706837 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706838 4 0.1389 0.965 0.048 0.000 0.000 0.952
#> SRR1706831 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706832 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706833 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706834 4 0.0000 0.983 0.000 0.000 0.000 1.000
#> SRR1706839 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706840 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706841 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706842 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706847 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706848 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706849 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706850 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706843 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706844 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706845 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706846 1 0.0336 1.000 0.992 0.000 0.000 0.008
#> SRR1706851 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706852 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706853 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706854 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706855 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706856 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706857 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706858 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706859 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706869 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706870 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706863 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706876 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706877 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706878 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706887 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706888 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706889 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706890 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706891 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706895 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706896 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706897 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706898 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706899 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706908 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706909 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706910 3 0.0336 0.996 0.008 0.000 0.992 0.000
#> SRR1706903 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706915 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706916 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706917 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706918 2 0.0336 0.995 0.000 0.992 0.008 0.000
#> SRR1706923 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.998 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.998 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706768 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706769 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706770 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706771 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706772 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706773 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706774 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706775 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706776 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706777 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706778 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706779 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706780 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706781 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706782 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706783 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706784 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706785 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706786 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706787 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706788 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706789 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706790 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706791 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706792 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706793 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706794 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706795 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706796 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706797 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706798 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706799 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706800 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706801 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706802 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706803 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706804 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706805 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706806 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706811 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706812 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706813 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706814 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706807 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706808 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706809 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706810 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706815 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706816 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706817 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706818 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706819 4 0.641 -0.163 0.180 0.000 0.000 0.468 0.352
#> SRR1706820 4 0.641 -0.163 0.180 0.000 0.000 0.468 0.352
#> SRR1706821 4 0.641 -0.163 0.180 0.000 0.000 0.468 0.352
#> SRR1706822 4 0.641 -0.163 0.180 0.000 0.000 0.468 0.352
#> SRR1706823 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706824 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706825 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706826 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706827 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706828 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706829 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706830 4 0.429 0.287 0.468 0.000 0.000 0.532 0.000
#> SRR1706835 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706836 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706837 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706838 1 0.000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706831 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706832 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706833 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706834 4 0.430 0.285 0.472 0.000 0.000 0.528 0.000
#> SRR1706839 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706840 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706841 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706842 4 0.643 -0.160 0.184 0.000 0.000 0.468 0.348
#> SRR1706847 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706843 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706844 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706845 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706846 5 0.524 0.268 0.044 0.000 0.000 0.468 0.488
#> SRR1706851 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706852 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706853 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706854 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706855 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706856 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706857 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706858 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706859 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706860 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706861 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706862 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706867 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706863 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706864 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706865 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706866 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706871 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706872 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706873 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706874 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706879 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706880 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706881 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706882 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706883 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706884 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706885 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706886 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706875 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706876 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706877 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706878 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706887 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706888 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706889 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706890 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706891 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706892 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706893 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706894 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706895 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706896 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706897 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706898 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706899 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706900 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706901 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706902 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706907 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.000 0.730 0.000 0.000 1.000 0.000 0.000
#> SRR1706903 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706904 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706905 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706906 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706911 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706912 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706913 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706914 3 0.430 0.716 0.000 0.484 0.516 0.000 0.000
#> SRR1706919 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706920 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706921 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706922 2 0.431 0.500 0.000 0.508 0.000 0.000 0.492
#> SRR1706915 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706916 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706917 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706918 2 0.000 0.622 0.000 1.000 0.000 0.000 0.000
#> SRR1706923 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706924 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706925 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
#> SRR1706926 5 0.430 -0.500 0.000 0.488 0.000 0.000 0.512
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706768 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706769 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706770 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706771 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706772 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706773 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706774 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706775 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706776 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706777 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706778 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706779 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706780 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706781 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706782 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706783 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706784 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706785 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706786 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706787 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706788 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706789 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706790 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706791 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706792 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706793 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706794 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706795 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706796 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706797 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706798 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706799 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706800 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706801 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706802 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706803 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706804 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706805 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706806 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706811 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706812 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706813 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706814 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706807 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706808 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706809 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706810 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706815 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706816 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706817 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706818 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706819 1 0.0000 0.741 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 0.741 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 0.741 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 0.741 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706823 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706824 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706825 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706826 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706827 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706828 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706829 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706830 4 0.0000 0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706835 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706836 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706837 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706838 5 0.4332 1.000 0.316 0.000 0.000 0.040 0.644 0.000
#> SRR1706831 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706832 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706833 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706834 4 0.1444 0.959 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1706839 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706840 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706841 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706842 1 0.0363 0.736 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706843 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706844 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706845 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706846 1 0.3986 0.793 0.664 0.000 0.000 0.000 0.316 0.020
#> SRR1706851 6 0.4127 0.757 0.000 0.000 0.284 0.000 0.036 0.680
#> SRR1706852 6 0.4127 0.757 0.000 0.000 0.284 0.000 0.036 0.680
#> SRR1706853 6 0.4127 0.757 0.000 0.000 0.284 0.000 0.036 0.680
#> SRR1706854 6 0.4127 0.757 0.000 0.000 0.284 0.000 0.036 0.680
#> SRR1706855 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706856 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706857 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706858 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706859 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706860 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706861 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706862 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706863 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706871 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706872 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706873 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706874 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706879 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706880 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706881 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706882 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706883 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706875 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706876 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706877 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706878 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706891 6 0.4229 0.751 0.000 0.000 0.292 0.000 0.040 0.668
#> SRR1706892 6 0.4229 0.751 0.000 0.000 0.292 0.000 0.040 0.668
#> SRR1706893 6 0.4229 0.751 0.000 0.000 0.292 0.000 0.040 0.668
#> SRR1706894 6 0.4229 0.751 0.000 0.000 0.292 0.000 0.040 0.668
#> SRR1706895 6 0.0692 0.798 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR1706896 6 0.0692 0.798 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR1706897 6 0.0692 0.798 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR1706898 6 0.0692 0.798 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR1706899 2 0.1910 0.942 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR1706900 2 0.1910 0.942 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR1706901 2 0.1910 0.942 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR1706902 2 0.1910 0.942 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1706903 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706904 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706905 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706906 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706911 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706912 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706913 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706914 6 0.4201 0.748 0.000 0.000 0.300 0.000 0.036 0.664
#> SRR1706919 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706920 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706921 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706922 2 0.1863 0.944 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1706915 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706916 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706917 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706918 6 0.0632 0.800 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR1706923 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.944 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.944 0.000 1.000 0.000 0.000 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["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 15185 rows and 159 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.751 0.901 0.812 0.2460 0.877 0.753
#> 4 4 1.000 0.997 0.997 0.1978 0.874 0.663
#> 5 5 0.930 0.895 0.924 0.0269 0.896 0.644
#> 6 6 0.860 0.866 0.908 0.0277 0.894 0.619
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706768 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706769 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706770 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706771 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706772 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706773 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706774 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706775 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706776 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706777 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706778 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706779 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706780 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706781 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706782 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706783 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706784 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706785 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706786 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706787 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706788 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706789 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706790 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706791 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706792 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706793 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706794 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706795 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706796 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706797 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706798 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706799 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706800 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706801 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706802 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706803 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706804 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706805 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706806 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706811 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706812 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706813 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706814 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706807 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706808 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706809 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706810 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706815 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706816 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706817 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706818 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706819 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706820 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706821 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706822 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706823 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706824 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706825 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706826 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706827 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706828 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706829 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706830 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706835 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706836 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706837 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706838 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706831 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706832 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706833 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706834 1 0.0000 0.784 1.00 0.000 0.000
#> SRR1706839 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706840 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706841 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706842 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706847 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706848 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706849 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706850 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706843 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706844 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706845 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706846 1 0.6192 0.828 0.58 0.420 0.000
#> SRR1706851 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706852 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706853 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706854 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706855 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706856 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706857 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706858 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706859 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706860 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706861 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706862 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706867 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706869 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706870 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706863 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706864 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706865 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706866 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706871 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706872 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706873 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706874 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706879 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706880 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706881 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706882 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706883 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706884 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706885 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706886 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706875 2 0.6235 0.973 0.00 0.564 0.436
#> SRR1706876 2 0.6244 0.967 0.00 0.560 0.440
#> SRR1706877 2 0.6215 0.986 0.00 0.572 0.428
#> SRR1706878 2 0.6215 0.986 0.00 0.572 0.428
#> SRR1706887 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706888 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706889 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706890 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706891 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706892 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706893 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706894 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706895 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706896 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706897 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706898 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706899 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706900 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706901 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706902 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706907 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706908 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706909 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706910 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706903 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706904 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706905 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706906 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706911 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706912 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706913 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706914 3 0.0000 0.997 0.00 0.000 1.000
#> SRR1706919 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706920 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706921 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706922 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706915 3 0.0592 0.980 0.00 0.012 0.988
#> SRR1706916 3 0.0592 0.980 0.00 0.012 0.988
#> SRR1706917 3 0.0892 0.967 0.00 0.020 0.980
#> SRR1706918 3 0.0592 0.980 0.00 0.012 0.988
#> SRR1706923 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706924 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706925 2 0.6192 0.998 0.00 0.580 0.420
#> SRR1706926 2 0.6192 0.998 0.00 0.580 0.420
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706768 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706769 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706770 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706771 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706772 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706773 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706774 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706775 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706776 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706777 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706778 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706788 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706789 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706790 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706791 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706792 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706793 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706794 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706795 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706796 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706797 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706798 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706812 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706813 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706814 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706807 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706808 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706809 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706810 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706815 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706816 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706817 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706818 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706828 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706829 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706830 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706835 4 0.0469 0.992 0.012 0.000 0.000 0.988
#> SRR1706836 4 0.0469 0.992 0.012 0.000 0.000 0.988
#> SRR1706837 4 0.0469 0.992 0.012 0.000 0.000 0.988
#> SRR1706838 4 0.0469 0.992 0.012 0.000 0.000 0.988
#> SRR1706831 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706832 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706833 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706834 4 0.0188 0.999 0.004 0.000 0.000 0.996
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706847 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706848 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706849 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706850 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706852 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706853 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706854 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706855 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706856 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706857 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706858 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706859 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706869 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706870 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706863 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706864 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706865 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706866 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706871 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706872 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706873 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706874 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706879 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706884 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706885 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706886 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706875 2 0.0592 0.984 0.000 0.984 0.016 0.000
#> SRR1706876 2 0.0707 0.981 0.000 0.980 0.020 0.000
#> SRR1706877 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1706878 2 0.0336 0.991 0.000 0.992 0.008 0.000
#> SRR1706887 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706888 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706889 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706890 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706891 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706892 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706893 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706894 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706895 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706896 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706897 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706898 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706899 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706908 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706909 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706910 3 0.0000 0.996 0.000 0.000 1.000 0.000
#> SRR1706903 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706904 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706905 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706906 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706911 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706912 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706913 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706914 3 0.0188 0.996 0.000 0.004 0.996 0.000
#> SRR1706919 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR1706915 3 0.0707 0.983 0.000 0.020 0.980 0.000
#> SRR1706916 3 0.0707 0.983 0.000 0.020 0.980 0.000
#> SRR1706917 3 0.0921 0.976 0.000 0.028 0.972 0.000
#> SRR1706918 3 0.0707 0.983 0.000 0.020 0.980 0.000
#> SRR1706923 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706924 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706925 2 0.0188 0.997 0.000 0.996 0.000 0.004
#> SRR1706926 2 0.0188 0.997 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706772 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706773 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706774 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706775 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706776 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706777 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706778 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706779 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706794 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706796 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706797 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706798 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706799 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706812 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706813 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706814 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706807 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706815 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706816 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706817 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706818 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706819 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 4 0.0290 0.991 0.008 0.000 0.000 0.992 0.000
#> SRR1706836 4 0.0290 0.991 0.008 0.000 0.000 0.992 0.000
#> SRR1706837 4 0.0290 0.991 0.008 0.000 0.000 0.992 0.000
#> SRR1706838 4 0.0290 0.991 0.008 0.000 0.000 0.992 0.000
#> SRR1706831 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 0.999 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706847 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706848 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706849 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706850 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706852 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706853 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706854 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706855 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706856 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706857 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706858 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706859 2 0.2690 0.371 0.000 0.844 0.000 0.000 0.156
#> SRR1706860 2 0.2690 0.371 0.000 0.844 0.000 0.000 0.156
#> SRR1706861 2 0.2471 0.425 0.000 0.864 0.000 0.000 0.136
#> SRR1706862 2 0.2813 0.334 0.000 0.832 0.000 0.000 0.168
#> SRR1706867 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706869 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706870 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706863 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706864 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706865 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706866 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706871 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706872 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706873 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706874 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706879 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706883 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706884 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706885 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706886 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706875 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706876 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706877 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706878 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706887 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706888 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706889 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706890 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706891 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706892 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706893 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706894 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706895 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706896 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706897 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706898 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706899 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706900 2 0.0290 0.674 0.000 0.992 0.000 0.000 0.008
#> SRR1706901 2 0.0162 0.680 0.000 0.996 0.000 0.000 0.004
#> SRR1706902 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706907 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706908 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706909 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706910 3 0.4192 1.000 0.000 0.000 0.596 0.000 0.404
#> SRR1706903 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706904 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706905 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706906 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706911 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706912 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706913 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706914 2 0.4192 0.675 0.000 0.596 0.404 0.000 0.000
#> SRR1706919 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706920 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706921 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706922 2 0.0000 0.685 0.000 1.000 0.000 0.000 0.000
#> SRR1706915 2 0.4138 0.677 0.000 0.616 0.384 0.000 0.000
#> SRR1706916 2 0.4161 0.676 0.000 0.608 0.392 0.000 0.000
#> SRR1706917 2 0.4150 0.677 0.000 0.612 0.388 0.000 0.000
#> SRR1706918 2 0.4150 0.677 0.000 0.612 0.388 0.000 0.000
#> SRR1706923 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706924 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706925 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
#> SRR1706926 5 0.4192 1.000 0.000 0.404 0.000 0.000 0.596
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706771 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706772 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706773 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706774 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706775 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706776 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706777 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706778 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706779 5 0.2048 0.877 0.120 0.000 0 0.000 0.880 0.000
#> SRR1706780 5 0.2092 0.874 0.124 0.000 0 0.000 0.876 0.000
#> SRR1706781 5 0.2135 0.871 0.128 0.000 0 0.000 0.872 0.000
#> SRR1706782 5 0.2092 0.874 0.124 0.000 0 0.000 0.876 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706791 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706792 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706793 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706794 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706795 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706796 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706797 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706798 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706799 5 0.2340 0.855 0.148 0.000 0 0.000 0.852 0.000
#> SRR1706800 5 0.2260 0.862 0.140 0.000 0 0.000 0.860 0.000
#> SRR1706801 5 0.2416 0.848 0.156 0.000 0 0.000 0.844 0.000
#> SRR1706802 5 0.2135 0.871 0.128 0.000 0 0.000 0.872 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706811 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706812 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706813 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706814 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706815 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706816 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706817 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706818 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706819 5 0.2454 0.844 0.160 0.000 0 0.000 0.840 0.000
#> SRR1706820 5 0.2340 0.855 0.148 0.000 0 0.000 0.852 0.000
#> SRR1706821 5 0.2454 0.844 0.160 0.000 0 0.000 0.840 0.000
#> SRR1706822 5 0.2491 0.840 0.164 0.000 0 0.000 0.836 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> SRR1706835 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706836 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706837 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706838 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706831 5 0.3023 0.707 0.000 0.000 0 0.232 0.768 0.000
#> SRR1706832 5 0.3023 0.707 0.000 0.000 0 0.232 0.768 0.000
#> SRR1706833 5 0.3050 0.701 0.000 0.000 0 0.236 0.764 0.000
#> SRR1706834 5 0.2823 0.747 0.000 0.000 0 0.204 0.796 0.000
#> SRR1706839 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706840 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706841 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706842 5 0.0000 0.942 0.000 0.000 0 0.000 1.000 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0.000 0 0.000 0.000 0.000
#> SRR1706851 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706852 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706853 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706854 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706855 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706856 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706857 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706858 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706859 6 0.2416 0.371 0.000 0.156 0 0.000 0.000 0.844
#> SRR1706860 6 0.2416 0.371 0.000 0.156 0 0.000 0.000 0.844
#> SRR1706861 6 0.2219 0.425 0.000 0.136 0 0.000 0.000 0.864
#> SRR1706862 6 0.2527 0.334 0.000 0.168 0 0.000 0.000 0.832
#> SRR1706867 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706863 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706864 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706865 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706866 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706871 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706872 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706873 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706874 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706879 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706880 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706881 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706882 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706883 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706884 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706885 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706886 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706875 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706876 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706877 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706878 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706891 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706892 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706893 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706894 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706895 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706896 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706897 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706898 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706899 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706900 6 0.0260 0.674 0.000 0.008 0 0.000 0.000 0.992
#> SRR1706901 6 0.0146 0.680 0.000 0.004 0 0.000 0.000 0.996
#> SRR1706902 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706907 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> SRR1706903 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706904 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706905 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706906 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706911 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706912 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706913 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706914 6 0.3765 0.675 0.000 0.404 0 0.000 0.000 0.596
#> SRR1706919 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706920 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706921 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706922 6 0.0000 0.685 0.000 0.000 0 0.000 0.000 1.000
#> SRR1706915 6 0.3717 0.677 0.000 0.384 0 0.000 0.000 0.616
#> SRR1706916 6 0.3737 0.676 0.000 0.392 0 0.000 0.000 0.608
#> SRR1706917 6 0.3727 0.677 0.000 0.388 0 0.000 0.000 0.612
#> SRR1706918 6 0.3727 0.677 0.000 0.388 0 0.000 0.000 0.612
#> SRR1706923 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706924 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706925 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
#> SRR1706926 2 0.3765 1.000 0.000 0.596 0 0.000 0.000 0.404
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 15185 rows and 159 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 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 1.000 1.000 0.504 0.497 0.497
#> 3 3 0.734 0.866 0.911 0.215 0.906 0.811
#> 4 4 0.668 0.769 0.830 0.128 0.934 0.836
#> 5 5 0.715 0.698 0.794 0.121 0.884 0.656
#> 6 6 0.755 0.686 0.789 0.051 0.944 0.751
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706768 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706769 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706770 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706771 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706772 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706773 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706774 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706775 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706776 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706777 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706778 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706779 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706783 1 0.4796 0.801 0.780 0.000 0.220
#> SRR1706784 1 0.4796 0.801 0.780 0.000 0.220
#> SRR1706785 1 0.4796 0.801 0.780 0.000 0.220
#> SRR1706786 1 0.4796 0.801 0.780 0.000 0.220
#> SRR1706787 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706788 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706789 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706790 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706791 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706792 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706793 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706794 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706795 1 0.0424 0.951 0.992 0.000 0.008
#> SRR1706796 1 0.0424 0.951 0.992 0.000 0.008
#> SRR1706797 1 0.0424 0.951 0.992 0.000 0.008
#> SRR1706798 1 0.0424 0.951 0.992 0.000 0.008
#> SRR1706799 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706802 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706803 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706804 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706805 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706806 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706811 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706812 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706813 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706814 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706807 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706808 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706809 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706810 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706815 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706816 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706817 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706818 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706819 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706820 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706821 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706822 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706823 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706824 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706825 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706826 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706827 1 0.0592 0.951 0.988 0.000 0.012
#> SRR1706828 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706829 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706830 1 0.0424 0.952 0.992 0.000 0.008
#> SRR1706835 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706836 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706837 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706838 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706831 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706832 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706833 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706834 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706839 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1706847 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706848 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706849 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706850 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706843 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706844 1 0.4750 0.804 0.784 0.000 0.216
#> SRR1706845 1 0.4750 0.804 0.784 0.000 0.216
#> SRR1706846 1 0.4702 0.806 0.788 0.000 0.212
#> SRR1706851 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706855 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706859 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706860 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706861 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706862 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706867 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706869 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706870 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706863 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706864 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706865 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706866 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706871 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706879 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706880 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706881 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706882 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706883 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706884 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706885 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706886 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706875 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706887 3 0.5178 0.899 0.000 0.256 0.744
#> SRR1706888 3 0.5178 0.899 0.000 0.256 0.744
#> SRR1706889 3 0.5178 0.899 0.000 0.256 0.744
#> SRR1706890 3 0.5178 0.899 0.000 0.256 0.744
#> SRR1706891 3 0.5138 0.906 0.000 0.252 0.748
#> SRR1706892 3 0.5138 0.906 0.000 0.252 0.748
#> SRR1706893 3 0.5138 0.906 0.000 0.252 0.748
#> SRR1706894 3 0.5138 0.906 0.000 0.252 0.748
#> SRR1706895 3 0.5098 0.907 0.000 0.248 0.752
#> SRR1706896 3 0.5098 0.907 0.000 0.248 0.752
#> SRR1706897 3 0.5098 0.907 0.000 0.248 0.752
#> SRR1706898 3 0.5098 0.907 0.000 0.248 0.752
#> SRR1706899 3 0.4796 0.898 0.000 0.220 0.780
#> SRR1706900 3 0.4796 0.898 0.000 0.220 0.780
#> SRR1706901 3 0.4796 0.898 0.000 0.220 0.780
#> SRR1706902 3 0.4796 0.898 0.000 0.220 0.780
#> SRR1706907 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706908 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706909 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706910 2 0.3686 0.782 0.000 0.860 0.140
#> SRR1706903 3 0.1031 0.728 0.000 0.024 0.976
#> SRR1706904 3 0.1031 0.728 0.000 0.024 0.976
#> SRR1706905 3 0.1031 0.728 0.000 0.024 0.976
#> SRR1706906 3 0.1031 0.728 0.000 0.024 0.976
#> SRR1706911 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706919 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706920 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706921 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706922 2 0.1163 0.854 0.000 0.972 0.028
#> SRR1706915 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.858 0.000 1.000 0.000
#> SRR1706923 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706924 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706925 2 0.6079 0.586 0.000 0.612 0.388
#> SRR1706926 2 0.6079 0.586 0.000 0.612 0.388
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706768 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706769 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706770 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706771 1 0.1474 0.861 0.948 0.000 0.052 0.000
#> SRR1706772 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706773 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706774 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706775 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706776 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706777 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706778 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706779 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706780 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706781 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706782 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706783 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706784 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706785 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706786 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706787 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706788 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706789 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706790 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706791 1 0.1474 0.861 0.948 0.000 0.052 0.000
#> SRR1706792 1 0.1474 0.861 0.948 0.000 0.052 0.000
#> SRR1706793 1 0.1474 0.861 0.948 0.000 0.052 0.000
#> SRR1706794 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706795 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706796 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706797 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706798 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706799 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706800 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706801 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706802 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706803 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706804 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706805 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706806 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706811 1 0.1637 0.861 0.940 0.000 0.060 0.000
#> SRR1706812 1 0.1637 0.861 0.940 0.000 0.060 0.000
#> SRR1706813 1 0.1637 0.861 0.940 0.000 0.060 0.000
#> SRR1706814 1 0.1637 0.861 0.940 0.000 0.060 0.000
#> SRR1706807 1 0.4804 0.683 0.616 0.000 0.384 0.000
#> SRR1706808 1 0.4804 0.683 0.616 0.000 0.384 0.000
#> SRR1706809 1 0.4804 0.683 0.616 0.000 0.384 0.000
#> SRR1706810 1 0.4804 0.683 0.616 0.000 0.384 0.000
#> SRR1706815 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706816 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706817 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706818 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706819 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706820 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706821 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706822 1 0.0336 0.867 0.992 0.000 0.008 0.000
#> SRR1706823 1 0.4747 0.768 0.764 0.204 0.008 0.024
#> SRR1706824 1 0.4747 0.768 0.764 0.204 0.008 0.024
#> SRR1706825 1 0.4747 0.768 0.764 0.204 0.008 0.024
#> SRR1706826 1 0.4747 0.768 0.764 0.204 0.008 0.024
#> SRR1706827 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706828 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706829 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706830 1 0.4817 0.680 0.612 0.000 0.388 0.000
#> SRR1706835 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> SRR1706836 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706837 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706838 1 0.0000 0.867 1.000 0.000 0.000 0.000
#> SRR1706831 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706832 1 0.1474 0.861 0.948 0.000 0.052 0.000
#> SRR1706833 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706834 1 0.1557 0.860 0.944 0.000 0.056 0.000
#> SRR1706839 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706840 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706841 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706842 1 0.0188 0.867 0.996 0.000 0.004 0.000
#> SRR1706847 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706848 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706849 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706850 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706843 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706844 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706845 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706846 1 0.5095 0.760 0.752 0.204 0.020 0.024
#> SRR1706851 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706852 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706853 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706854 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706855 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706856 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706857 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706858 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706859 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706860 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706861 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706862 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706867 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706869 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706870 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706863 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706864 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706865 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706866 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706871 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706872 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706873 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706874 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706879 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706880 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706881 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706882 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706883 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706884 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706885 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706886 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706875 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706876 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706877 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706878 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706887 4 0.4919 0.676 0.000 0.048 0.200 0.752
#> SRR1706888 4 0.4919 0.676 0.000 0.048 0.200 0.752
#> SRR1706889 4 0.4919 0.676 0.000 0.048 0.200 0.752
#> SRR1706890 4 0.4919 0.676 0.000 0.048 0.200 0.752
#> SRR1706891 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706892 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706893 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706894 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706895 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706896 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706897 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706898 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706899 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706900 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706901 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706902 4 0.0000 0.847 0.000 0.000 0.000 1.000
#> SRR1706907 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706908 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706909 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706910 3 0.4614 0.685 0.000 0.064 0.792 0.144
#> SRR1706903 4 0.4072 0.716 0.000 0.252 0.000 0.748
#> SRR1706904 4 0.4072 0.716 0.000 0.252 0.000 0.748
#> SRR1706905 4 0.4072 0.716 0.000 0.252 0.000 0.748
#> SRR1706906 4 0.4072 0.716 0.000 0.252 0.000 0.748
#> SRR1706911 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706912 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706913 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706914 3 0.6917 0.722 0.000 0.204 0.592 0.204
#> SRR1706919 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706920 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706921 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706922 2 0.3649 0.794 0.000 0.796 0.000 0.204
#> SRR1706915 2 0.5184 0.726 0.000 0.736 0.060 0.204
#> SRR1706916 2 0.5035 0.737 0.000 0.744 0.052 0.204
#> SRR1706917 2 0.5256 0.721 0.000 0.732 0.064 0.204
#> SRR1706918 2 0.5035 0.737 0.000 0.744 0.052 0.204
#> SRR1706923 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706924 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706925 2 0.3486 0.574 0.000 0.812 0.000 0.188
#> SRR1706926 2 0.3486 0.574 0.000 0.812 0.000 0.188
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706768 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706769 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706770 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706771 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706772 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706773 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706774 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706775 1 0.3177 0.439 0.792 0.000 0.000 0.208 0.0
#> SRR1706776 1 0.3210 0.429 0.788 0.000 0.000 0.212 0.0
#> SRR1706777 1 0.3210 0.429 0.788 0.000 0.000 0.212 0.0
#> SRR1706778 1 0.2966 0.495 0.816 0.000 0.000 0.184 0.0
#> SRR1706779 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706780 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706781 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706782 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706783 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706784 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706785 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706786 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706787 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706788 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706789 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706790 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706791 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706792 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706793 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706794 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706795 1 0.0162 0.774 0.996 0.000 0.000 0.004 0.0
#> SRR1706796 1 0.0162 0.774 0.996 0.000 0.000 0.004 0.0
#> SRR1706797 1 0.0162 0.774 0.996 0.000 0.000 0.004 0.0
#> SRR1706798 1 0.0162 0.774 0.996 0.000 0.000 0.004 0.0
#> SRR1706799 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706800 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706801 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706802 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706803 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706804 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706805 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706806 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706811 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706812 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706813 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706814 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706807 1 0.5844 0.469 0.608 0.000 0.208 0.184 0.0
#> SRR1706808 1 0.5844 0.469 0.608 0.000 0.208 0.184 0.0
#> SRR1706809 1 0.5844 0.469 0.608 0.000 0.208 0.184 0.0
#> SRR1706810 1 0.5844 0.469 0.608 0.000 0.208 0.184 0.0
#> SRR1706815 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706816 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706817 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706818 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706819 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706820 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706821 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706822 1 0.0290 0.775 0.992 0.000 0.008 0.000 0.0
#> SRR1706823 1 0.4998 0.690 0.712 0.080 0.008 0.200 0.0
#> SRR1706824 1 0.4998 0.690 0.712 0.080 0.008 0.200 0.0
#> SRR1706825 1 0.4998 0.690 0.712 0.080 0.008 0.200 0.0
#> SRR1706826 1 0.4998 0.690 0.712 0.080 0.008 0.200 0.0
#> SRR1706827 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706828 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706829 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706830 4 0.3109 0.646 0.000 0.000 0.200 0.800 0.0
#> SRR1706835 4 0.4283 0.635 0.456 0.000 0.000 0.544 0.0
#> SRR1706836 4 0.4268 0.655 0.444 0.000 0.000 0.556 0.0
#> SRR1706837 4 0.4278 0.642 0.452 0.000 0.000 0.548 0.0
#> SRR1706838 4 0.4287 0.627 0.460 0.000 0.000 0.540 0.0
#> SRR1706831 4 0.4235 0.680 0.424 0.000 0.000 0.576 0.0
#> SRR1706832 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706833 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706834 4 0.4182 0.709 0.400 0.000 0.000 0.600 0.0
#> SRR1706839 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706840 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706841 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706842 1 0.0000 0.776 1.000 0.000 0.000 0.000 0.0
#> SRR1706847 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706848 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706849 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706850 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706843 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706844 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706845 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706846 1 0.5904 0.636 0.600 0.200 0.000 0.200 0.0
#> SRR1706851 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706852 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706853 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706854 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706855 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706856 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706857 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706858 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706859 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706860 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706861 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706862 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706867 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706869 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706870 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706863 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706864 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706865 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706866 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706871 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706872 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706873 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706874 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706879 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706880 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706881 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706882 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706883 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706884 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706885 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706886 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706875 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706876 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706877 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706878 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706887 5 0.4182 0.698 0.000 0.000 0.400 0.000 0.6
#> SRR1706888 5 0.4182 0.698 0.000 0.000 0.400 0.000 0.6
#> SRR1706889 5 0.4182 0.698 0.000 0.000 0.400 0.000 0.6
#> SRR1706890 5 0.4182 0.698 0.000 0.000 0.400 0.000 0.6
#> SRR1706891 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706892 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706893 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706894 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706895 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706896 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706897 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706898 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706899 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706900 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706901 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706902 5 0.3109 0.849 0.000 0.000 0.200 0.000 0.8
#> SRR1706907 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706908 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706909 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706910 3 0.0290 0.617 0.000 0.008 0.992 0.000 0.0
#> SRR1706903 5 0.5904 0.742 0.000 0.200 0.200 0.000 0.6
#> SRR1706904 5 0.5904 0.742 0.000 0.200 0.200 0.000 0.6
#> SRR1706905 5 0.5904 0.742 0.000 0.200 0.200 0.000 0.6
#> SRR1706906 5 0.5904 0.742 0.000 0.200 0.200 0.000 0.6
#> SRR1706911 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706912 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706913 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706914 3 0.4446 0.671 0.000 0.008 0.592 0.000 0.4
#> SRR1706919 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706920 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706921 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706922 2 0.4182 0.795 0.000 0.600 0.000 0.000 0.4
#> SRR1706915 2 0.5394 0.733 0.000 0.540 0.060 0.000 0.4
#> SRR1706916 2 0.5394 0.733 0.000 0.540 0.060 0.000 0.4
#> SRR1706917 2 0.5447 0.728 0.000 0.536 0.064 0.000 0.4
#> SRR1706918 2 0.5338 0.738 0.000 0.544 0.056 0.000 0.4
#> SRR1706923 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706924 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706925 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
#> SRR1706926 2 0.3109 0.560 0.000 0.800 0.200 0.000 0.0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706768 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706769 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706770 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706771 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706772 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706773 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706774 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706775 1 0.1663 0.618 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1706776 1 0.1663 0.618 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR1706777 1 0.1814 0.602 0.900 0.000 0.000 0.100 0.000 0.000
#> SRR1706778 1 0.1610 0.623 0.916 0.000 0.000 0.084 0.000 0.000
#> SRR1706779 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706784 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706785 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706786 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706787 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706788 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706789 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706790 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706791 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706792 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706793 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706794 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706795 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706796 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706797 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706798 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706799 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706804 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706805 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706806 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706811 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706812 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706813 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706814 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706807 5 0.4131 0.478 0.016 0.000 0.000 0.384 0.600 0.000
#> SRR1706808 5 0.4131 0.478 0.016 0.000 0.000 0.384 0.600 0.000
#> SRR1706809 5 0.4131 0.478 0.016 0.000 0.000 0.384 0.600 0.000
#> SRR1706810 5 0.4131 0.478 0.016 0.000 0.000 0.384 0.600 0.000
#> SRR1706815 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706816 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706817 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706818 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706819 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706820 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706821 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706822 5 0.3756 0.759 0.400 0.000 0.000 0.000 0.600 0.000
#> SRR1706823 5 0.5067 0.605 0.120 0.000 0.268 0.000 0.612 0.000
#> SRR1706824 5 0.5067 0.605 0.120 0.000 0.268 0.000 0.612 0.000
#> SRR1706825 5 0.5067 0.605 0.120 0.000 0.268 0.000 0.612 0.000
#> SRR1706826 5 0.5067 0.605 0.120 0.000 0.268 0.000 0.612 0.000
#> SRR1706827 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706828 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706829 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706830 4 0.0000 0.635 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1706835 4 0.3868 0.524 0.496 0.000 0.000 0.504 0.000 0.000
#> SRR1706836 4 0.3854 0.577 0.464 0.000 0.000 0.536 0.000 0.000
#> SRR1706837 4 0.3868 0.524 0.496 0.000 0.000 0.504 0.000 0.000
#> SRR1706838 1 0.3868 -0.531 0.508 0.000 0.000 0.492 0.000 0.000
#> SRR1706831 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706832 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706833 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706834 4 0.3756 0.651 0.400 0.000 0.000 0.600 0.000 0.000
#> SRR1706839 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.707 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1706847 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706848 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706849 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706850 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706843 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706844 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706845 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706846 1 0.4057 0.591 0.600 0.000 0.388 0.000 0.012 0.000
#> SRR1706851 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706852 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706853 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706854 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706855 2 0.0146 0.802 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706856 2 0.0146 0.802 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706857 2 0.0146 0.802 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706858 2 0.0146 0.802 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1706859 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706860 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706861 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706862 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706867 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706869 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706870 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706863 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706864 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706865 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706866 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706871 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706872 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706873 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706874 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706879 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706883 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706884 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706885 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706886 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706875 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706876 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706877 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706878 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706887 3 0.3756 0.719 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR1706888 3 0.3756 0.719 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR1706889 3 0.3756 0.719 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR1706890 3 0.3756 0.719 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR1706891 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706892 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706893 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706894 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706895 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706896 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706897 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706898 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706899 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706900 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706901 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706902 3 0.5227 0.852 0.000 0.200 0.612 0.000 0.000 0.188
#> SRR1706907 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706908 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706909 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706910 6 0.0000 0.641 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1706903 3 0.5219 0.738 0.000 0.000 0.612 0.000 0.212 0.176
#> SRR1706904 3 0.5219 0.738 0.000 0.000 0.612 0.000 0.212 0.176
#> SRR1706905 3 0.5219 0.738 0.000 0.000 0.612 0.000 0.212 0.176
#> SRR1706906 3 0.5219 0.738 0.000 0.000 0.612 0.000 0.212 0.176
#> SRR1706911 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706912 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706913 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706914 6 0.3756 0.699 0.000 0.400 0.000 0.000 0.000 0.600
#> SRR1706919 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706920 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706921 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706922 2 0.0000 0.805 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1706915 2 0.1387 0.732 0.000 0.932 0.000 0.000 0.000 0.068
#> SRR1706916 2 0.1204 0.747 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1706917 2 0.1444 0.726 0.000 0.928 0.000 0.000 0.000 0.072
#> SRR1706918 2 0.1327 0.737 0.000 0.936 0.000 0.000 0.000 0.064
#> SRR1706923 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706924 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706925 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 0.000
#> SRR1706926 2 0.4057 0.645 0.000 0.600 0.012 0.000 0.388 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", "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 15185 rows and 159 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 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.754 0.810 0.893 0.2796 0.783 0.589
#> 4 4 0.829 0.930 0.854 0.0865 0.765 0.435
#> 5 5 0.779 0.887 0.848 0.0300 1.000 1.000
#> 6 6 0.684 0.818 0.851 0.0315 0.988 0.953
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706768 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706769 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706770 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706771 1 0.4346 0.8252 0.816 0.000 0.184
#> SRR1706772 1 0.4399 0.8238 0.812 0.000 0.188
#> SRR1706773 1 0.4346 0.8252 0.816 0.000 0.184
#> SRR1706774 1 0.4399 0.8238 0.812 0.000 0.188
#> SRR1706775 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706776 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706777 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706778 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706779 3 0.2356 0.7483 0.072 0.000 0.928
#> SRR1706780 3 0.2261 0.7520 0.068 0.000 0.932
#> SRR1706781 3 0.2878 0.7198 0.096 0.000 0.904
#> SRR1706782 3 0.2356 0.7483 0.072 0.000 0.928
#> SRR1706783 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706784 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706785 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706786 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706787 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706788 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706789 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706790 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706791 1 0.4002 0.8319 0.840 0.000 0.160
#> SRR1706792 1 0.3752 0.8343 0.856 0.000 0.144
#> SRR1706793 1 0.4002 0.8319 0.840 0.000 0.160
#> SRR1706794 1 0.3941 0.8326 0.844 0.000 0.156
#> SRR1706795 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706796 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706797 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706798 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706799 3 0.2261 0.7519 0.068 0.000 0.932
#> SRR1706800 3 0.2261 0.7519 0.068 0.000 0.932
#> SRR1706801 3 0.2448 0.7440 0.076 0.000 0.924
#> SRR1706802 3 0.2356 0.7483 0.072 0.000 0.928
#> SRR1706803 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706804 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706805 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706806 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706811 1 0.2711 0.8334 0.912 0.000 0.088
#> SRR1706812 1 0.2711 0.8334 0.912 0.000 0.088
#> SRR1706813 1 0.2625 0.8327 0.916 0.000 0.084
#> SRR1706814 1 0.2796 0.8339 0.908 0.000 0.092
#> SRR1706807 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706808 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706809 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706810 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706815 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706816 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706817 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706818 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706819 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706820 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706821 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706822 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706823 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706824 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706825 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706826 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706827 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706828 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706829 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706830 1 0.0000 0.8134 1.000 0.000 0.000
#> SRR1706835 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706836 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706837 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706838 1 0.6008 0.7214 0.628 0.000 0.372
#> SRR1706831 1 0.3412 0.8357 0.876 0.000 0.124
#> SRR1706832 1 0.3340 0.8356 0.880 0.000 0.120
#> SRR1706833 1 0.3551 0.8356 0.868 0.000 0.132
#> SRR1706834 1 0.3551 0.8356 0.868 0.000 0.132
#> SRR1706839 3 0.3267 0.6916 0.116 0.000 0.884
#> SRR1706840 3 0.3038 0.7091 0.104 0.000 0.896
#> SRR1706841 3 0.3267 0.6916 0.116 0.000 0.884
#> SRR1706842 3 0.3267 0.6916 0.116 0.000 0.884
#> SRR1706847 2 0.0592 0.9549 0.012 0.988 0.000
#> SRR1706848 2 0.0592 0.9549 0.012 0.988 0.000
#> SRR1706849 2 0.0424 0.9577 0.008 0.992 0.000
#> SRR1706850 2 0.0592 0.9549 0.012 0.988 0.000
#> SRR1706843 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706844 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706845 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706846 3 0.0000 0.7966 0.000 0.000 1.000
#> SRR1706851 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706855 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706859 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706860 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706861 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706862 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706867 2 0.1529 0.9298 0.040 0.960 0.000
#> SRR1706869 2 0.1529 0.9298 0.040 0.960 0.000
#> SRR1706870 2 0.1529 0.9298 0.040 0.960 0.000
#> SRR1706863 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706864 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706865 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706866 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706871 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706879 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706880 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706881 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706882 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706883 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706884 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706885 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706886 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706875 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706887 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706888 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706889 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706890 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706891 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706892 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706893 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706894 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706895 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706896 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706897 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706898 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706899 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706900 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706901 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706902 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706907 2 0.0892 0.9484 0.020 0.980 0.000
#> SRR1706908 2 0.0892 0.9484 0.020 0.980 0.000
#> SRR1706909 2 0.0747 0.9518 0.016 0.984 0.000
#> SRR1706910 2 0.0747 0.9518 0.016 0.984 0.000
#> SRR1706903 2 0.6280 0.0178 0.000 0.540 0.460
#> SRR1706904 2 0.6291 -0.0141 0.000 0.532 0.468
#> SRR1706905 2 0.6295 -0.0302 0.000 0.528 0.472
#> SRR1706906 2 0.6286 0.0020 0.000 0.536 0.464
#> SRR1706911 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706919 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706920 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706921 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706922 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706915 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.9633 0.000 1.000 0.000
#> SRR1706923 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706924 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706925 3 0.6008 0.4765 0.000 0.372 0.628
#> SRR1706926 3 0.6008 0.4765 0.000 0.372 0.628
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0921 0.940 0.000 0.000 0.028 0.972
#> SRR1706768 4 0.1118 0.939 0.000 0.000 0.036 0.964
#> SRR1706769 4 0.0921 0.940 0.000 0.000 0.028 0.972
#> SRR1706770 4 0.0921 0.940 0.000 0.000 0.028 0.972
#> SRR1706771 4 0.2011 0.930 0.000 0.000 0.080 0.920
#> SRR1706772 4 0.2081 0.930 0.000 0.000 0.084 0.916
#> SRR1706773 4 0.2011 0.930 0.000 0.000 0.080 0.920
#> SRR1706774 4 0.2011 0.930 0.000 0.000 0.080 0.920
#> SRR1706775 1 0.3636 0.856 0.820 0.000 0.172 0.008
#> SRR1706776 1 0.3355 0.864 0.836 0.000 0.160 0.004
#> SRR1706777 1 0.3681 0.854 0.816 0.000 0.176 0.008
#> SRR1706778 1 0.3494 0.859 0.824 0.000 0.172 0.004
#> SRR1706779 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> SRR1706788 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> SRR1706789 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> SRR1706790 4 0.0000 0.941 0.000 0.000 0.000 1.000
#> SRR1706791 4 0.2345 0.924 0.000 0.000 0.100 0.900
#> SRR1706792 4 0.2081 0.929 0.000 0.000 0.084 0.916
#> SRR1706793 4 0.2011 0.931 0.000 0.000 0.080 0.920
#> SRR1706794 4 0.2408 0.922 0.000 0.000 0.104 0.896
#> SRR1706795 1 0.3494 0.858 0.824 0.000 0.172 0.004
#> SRR1706796 1 0.3306 0.865 0.840 0.000 0.156 0.004
#> SRR1706797 1 0.3402 0.862 0.832 0.000 0.164 0.004
#> SRR1706798 1 0.3355 0.864 0.836 0.000 0.160 0.004
#> SRR1706799 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706800 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706801 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706802 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706803 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706811 4 0.4643 0.748 0.000 0.000 0.344 0.656
#> SRR1706812 4 0.4643 0.748 0.000 0.000 0.344 0.656
#> SRR1706813 4 0.4643 0.748 0.000 0.000 0.344 0.656
#> SRR1706814 4 0.4643 0.748 0.000 0.000 0.344 0.656
#> SRR1706807 4 0.0336 0.942 0.000 0.000 0.008 0.992
#> SRR1706808 4 0.0469 0.941 0.000 0.000 0.012 0.988
#> SRR1706809 4 0.0336 0.942 0.000 0.000 0.008 0.992
#> SRR1706810 4 0.0336 0.942 0.000 0.000 0.008 0.992
#> SRR1706815 1 0.7220 0.421 0.472 0.000 0.384 0.144
#> SRR1706816 1 0.7242 0.419 0.476 0.000 0.376 0.148
#> SRR1706817 1 0.7338 0.393 0.464 0.000 0.376 0.160
#> SRR1706818 1 0.7098 0.453 0.492 0.000 0.376 0.132
#> SRR1706819 1 0.0895 0.917 0.976 0.000 0.020 0.004
#> SRR1706820 1 0.0895 0.917 0.976 0.000 0.020 0.004
#> SRR1706821 1 0.0895 0.917 0.976 0.000 0.020 0.004
#> SRR1706822 1 0.0779 0.918 0.980 0.000 0.016 0.004
#> SRR1706823 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706827 4 0.0188 0.940 0.000 0.000 0.004 0.996
#> SRR1706828 4 0.0188 0.940 0.000 0.000 0.004 0.996
#> SRR1706829 4 0.0188 0.940 0.000 0.000 0.004 0.996
#> SRR1706830 4 0.0188 0.940 0.000 0.000 0.004 0.996
#> SRR1706835 1 0.4086 0.830 0.776 0.000 0.216 0.008
#> SRR1706836 1 0.3972 0.837 0.788 0.000 0.204 0.008
#> SRR1706837 1 0.4011 0.835 0.784 0.000 0.208 0.008
#> SRR1706838 1 0.4049 0.832 0.780 0.000 0.212 0.008
#> SRR1706831 4 0.1716 0.936 0.000 0.000 0.064 0.936
#> SRR1706832 4 0.1867 0.934 0.000 0.000 0.072 0.928
#> SRR1706833 4 0.2149 0.929 0.000 0.000 0.088 0.912
#> SRR1706834 4 0.2011 0.932 0.000 0.000 0.080 0.920
#> SRR1706839 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706840 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706841 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706842 1 0.0188 0.922 0.996 0.000 0.004 0.000
#> SRR1706847 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706848 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706849 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706850 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706843 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.922 1.000 0.000 0.000 0.000
#> SRR1706851 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706852 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706853 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706854 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706855 2 0.0592 0.965 0.000 0.984 0.016 0.000
#> SRR1706856 2 0.0592 0.965 0.000 0.984 0.016 0.000
#> SRR1706857 2 0.0592 0.965 0.000 0.984 0.016 0.000
#> SRR1706858 2 0.0592 0.965 0.000 0.984 0.016 0.000
#> SRR1706859 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706860 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706861 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706862 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706867 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706869 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706870 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706863 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706864 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706865 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706866 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706871 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706872 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706873 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706874 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706879 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706880 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706881 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706882 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706883 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706884 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706885 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706886 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706875 2 0.0921 0.949 0.000 0.972 0.028 0.000
#> SRR1706876 2 0.0817 0.955 0.000 0.976 0.024 0.000
#> SRR1706877 2 0.0707 0.960 0.000 0.980 0.020 0.000
#> SRR1706878 2 0.1022 0.944 0.000 0.968 0.032 0.000
#> SRR1706887 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706888 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706889 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706890 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706891 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706892 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706893 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706894 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706895 2 0.0817 0.953 0.000 0.976 0.024 0.000
#> SRR1706896 2 0.1022 0.939 0.000 0.968 0.032 0.000
#> SRR1706897 2 0.1118 0.932 0.000 0.964 0.036 0.000
#> SRR1706898 2 0.0469 0.966 0.000 0.988 0.012 0.000
#> SRR1706899 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706900 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706901 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706902 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706907 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706908 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706909 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706910 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706903 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706904 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706905 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706906 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706911 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706912 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706913 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706914 3 0.5000 1.000 0.000 0.496 0.504 0.000
#> SRR1706919 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706920 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706921 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706922 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706915 2 0.1557 0.897 0.000 0.944 0.056 0.000
#> SRR1706916 2 0.2081 0.830 0.000 0.916 0.084 0.000
#> SRR1706917 2 0.1637 0.889 0.000 0.940 0.060 0.000
#> SRR1706918 2 0.1389 0.914 0.000 0.952 0.048 0.000
#> SRR1706923 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706924 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706925 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR1706926 2 0.0000 0.979 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706768 4 0.0162 0.969 0.000 0.000 0.000 0.996 NA
#> SRR1706769 4 0.0162 0.969 0.000 0.000 0.000 0.996 NA
#> SRR1706770 4 0.0162 0.969 0.000 0.000 0.000 0.996 NA
#> SRR1706771 4 0.0771 0.964 0.020 0.000 0.004 0.976 NA
#> SRR1706772 4 0.0771 0.964 0.020 0.000 0.004 0.976 NA
#> SRR1706773 4 0.0771 0.964 0.020 0.000 0.004 0.976 NA
#> SRR1706774 4 0.0609 0.964 0.020 0.000 0.000 0.980 NA
#> SRR1706775 1 0.2390 0.908 0.908 0.000 0.044 0.044 NA
#> SRR1706776 1 0.1996 0.915 0.928 0.000 0.036 0.032 NA
#> SRR1706777 1 0.2609 0.903 0.896 0.000 0.052 0.048 NA
#> SRR1706778 1 0.2078 0.914 0.924 0.000 0.036 0.036 NA
#> SRR1706779 1 0.0510 0.931 0.984 0.000 0.000 0.000 NA
#> SRR1706780 1 0.0510 0.931 0.984 0.000 0.000 0.000 NA
#> SRR1706781 1 0.0404 0.931 0.988 0.000 0.000 0.000 NA
#> SRR1706782 1 0.0510 0.931 0.984 0.000 0.000 0.000 NA
#> SRR1706783 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706784 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706785 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706786 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706787 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706788 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706789 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706790 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706791 4 0.2199 0.948 0.008 0.000 0.060 0.916 NA
#> SRR1706792 4 0.1988 0.953 0.008 0.000 0.048 0.928 NA
#> SRR1706793 4 0.2131 0.950 0.008 0.000 0.056 0.920 NA
#> SRR1706794 4 0.2131 0.950 0.008 0.000 0.056 0.920 NA
#> SRR1706795 1 0.2854 0.897 0.880 0.000 0.084 0.028 NA
#> SRR1706796 1 0.2609 0.904 0.896 0.000 0.068 0.028 NA
#> SRR1706797 1 0.2673 0.903 0.892 0.000 0.072 0.028 NA
#> SRR1706798 1 0.2795 0.899 0.884 0.000 0.080 0.028 NA
#> SRR1706799 1 0.0162 0.932 0.996 0.000 0.000 0.000 NA
#> SRR1706800 1 0.0162 0.932 0.996 0.000 0.000 0.000 NA
#> SRR1706801 1 0.0162 0.932 0.996 0.000 0.000 0.000 NA
#> SRR1706802 1 0.0162 0.932 0.996 0.000 0.000 0.000 NA
#> SRR1706803 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706804 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706805 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706806 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706811 4 0.3129 0.897 0.008 0.000 0.156 0.832 NA
#> SRR1706812 4 0.3129 0.897 0.008 0.000 0.156 0.832 NA
#> SRR1706813 4 0.3087 0.899 0.008 0.000 0.152 0.836 NA
#> SRR1706814 4 0.3129 0.897 0.008 0.000 0.156 0.832 NA
#> SRR1706807 4 0.0671 0.968 0.000 0.000 0.016 0.980 NA
#> SRR1706808 4 0.0671 0.968 0.000 0.000 0.016 0.980 NA
#> SRR1706809 4 0.0671 0.968 0.000 0.000 0.016 0.980 NA
#> SRR1706810 4 0.0510 0.967 0.000 0.000 0.016 0.984 NA
#> SRR1706815 1 0.6336 0.596 0.588 0.000 0.196 0.200 NA
#> SRR1706816 1 0.6281 0.610 0.596 0.000 0.196 0.192 NA
#> SRR1706817 1 0.6271 0.590 0.596 0.000 0.176 0.212 NA
#> SRR1706818 1 0.6155 0.613 0.612 0.000 0.168 0.204 NA
#> SRR1706819 1 0.2136 0.903 0.904 0.000 0.088 0.008 NA
#> SRR1706820 1 0.2136 0.903 0.904 0.000 0.088 0.008 NA
#> SRR1706821 1 0.2136 0.903 0.904 0.000 0.088 0.008 NA
#> SRR1706822 1 0.2136 0.903 0.904 0.000 0.088 0.008 NA
#> SRR1706823 1 0.1952 0.906 0.912 0.004 0.084 0.000 NA
#> SRR1706824 1 0.1952 0.906 0.912 0.004 0.084 0.000 NA
#> SRR1706825 1 0.1952 0.906 0.912 0.004 0.084 0.000 NA
#> SRR1706826 1 0.1952 0.906 0.912 0.004 0.084 0.000 NA
#> SRR1706827 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706828 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706829 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706830 4 0.0000 0.970 0.000 0.000 0.000 1.000 NA
#> SRR1706835 1 0.3963 0.863 0.820 0.000 0.104 0.056 NA
#> SRR1706836 1 0.3504 0.878 0.844 0.000 0.100 0.044 NA
#> SRR1706837 1 0.3504 0.878 0.844 0.000 0.100 0.044 NA
#> SRR1706838 1 0.3678 0.873 0.836 0.000 0.100 0.048 NA
#> SRR1706831 4 0.1186 0.965 0.008 0.000 0.020 0.964 NA
#> SRR1706832 4 0.1393 0.963 0.008 0.000 0.024 0.956 NA
#> SRR1706833 4 0.1393 0.963 0.008 0.000 0.024 0.956 NA
#> SRR1706834 4 0.1280 0.964 0.008 0.000 0.024 0.960 NA
#> SRR1706839 1 0.0451 0.931 0.988 0.000 0.000 0.004 NA
#> SRR1706840 1 0.0290 0.931 0.992 0.000 0.000 0.000 NA
#> SRR1706841 1 0.0566 0.930 0.984 0.000 0.000 0.004 NA
#> SRR1706842 1 0.0451 0.931 0.988 0.000 0.000 0.004 NA
#> SRR1706847 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706848 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706849 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706850 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706843 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706844 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706845 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706846 1 0.0162 0.932 0.996 0.000 0.004 0.000 NA
#> SRR1706851 3 0.4651 0.933 0.000 0.372 0.608 0.000 NA
#> SRR1706852 3 0.4651 0.933 0.000 0.372 0.608 0.000 NA
#> SRR1706853 3 0.4651 0.933 0.000 0.372 0.608 0.000 NA
#> SRR1706854 3 0.4651 0.933 0.000 0.372 0.608 0.000 NA
#> SRR1706855 2 0.3695 0.809 0.000 0.800 0.036 0.000 NA
#> SRR1706856 2 0.3695 0.809 0.000 0.800 0.036 0.000 NA
#> SRR1706857 2 0.3695 0.809 0.000 0.800 0.036 0.000 NA
#> SRR1706858 2 0.3848 0.802 0.000 0.788 0.040 0.000 NA
#> SRR1706859 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706860 2 0.0162 0.863 0.000 0.996 0.004 0.000 NA
#> SRR1706861 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706862 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706867 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706869 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706870 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706863 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706864 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706865 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706866 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706871 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706872 3 0.4354 0.939 0.000 0.368 0.624 0.000 NA
#> SRR1706873 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706874 3 0.4354 0.939 0.000 0.368 0.624 0.000 NA
#> SRR1706879 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706880 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706881 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706882 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706883 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706884 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706885 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706886 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706875 2 0.3019 0.835 0.000 0.864 0.048 0.000 NA
#> SRR1706876 2 0.2889 0.839 0.000 0.872 0.044 0.000 NA
#> SRR1706877 2 0.2889 0.839 0.000 0.872 0.044 0.000 NA
#> SRR1706878 2 0.2962 0.836 0.000 0.868 0.048 0.000 NA
#> SRR1706887 3 0.4908 0.917 0.000 0.356 0.608 0.000 NA
#> SRR1706888 3 0.4908 0.917 0.000 0.356 0.608 0.000 NA
#> SRR1706889 3 0.4836 0.919 0.000 0.356 0.612 0.000 NA
#> SRR1706890 3 0.4908 0.917 0.000 0.356 0.608 0.000 NA
#> SRR1706891 3 0.6678 0.644 0.000 0.360 0.404 0.000 NA
#> SRR1706892 3 0.6678 0.644 0.000 0.360 0.404 0.000 NA
#> SRR1706893 3 0.6678 0.644 0.000 0.360 0.404 0.000 NA
#> SRR1706894 3 0.6678 0.644 0.000 0.360 0.404 0.000 NA
#> SRR1706895 2 0.4881 0.682 0.004 0.696 0.060 0.000 NA
#> SRR1706896 2 0.4820 0.690 0.004 0.700 0.056 0.000 NA
#> SRR1706897 2 0.4820 0.690 0.004 0.700 0.056 0.000 NA
#> SRR1706898 2 0.4820 0.690 0.004 0.700 0.056 0.000 NA
#> SRR1706899 2 0.3562 0.799 0.000 0.788 0.016 0.000 NA
#> SRR1706900 2 0.3562 0.799 0.000 0.788 0.016 0.000 NA
#> SRR1706901 2 0.3562 0.799 0.000 0.788 0.016 0.000 NA
#> SRR1706902 2 0.3527 0.802 0.000 0.792 0.016 0.000 NA
#> SRR1706907 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706908 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706909 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706910 3 0.4341 0.937 0.000 0.364 0.628 0.000 NA
#> SRR1706903 2 0.2890 0.830 0.000 0.836 0.004 0.000 NA
#> SRR1706904 2 0.2890 0.830 0.000 0.836 0.004 0.000 NA
#> SRR1706905 2 0.2763 0.836 0.000 0.848 0.004 0.000 NA
#> SRR1706906 2 0.2971 0.831 0.000 0.836 0.008 0.000 NA
#> SRR1706911 3 0.4088 0.939 0.000 0.368 0.632 0.000 NA
#> SRR1706912 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706913 3 0.4238 0.939 0.000 0.368 0.628 0.000 NA
#> SRR1706914 3 0.4251 0.937 0.000 0.372 0.624 0.000 NA
#> SRR1706919 2 0.0290 0.862 0.000 0.992 0.008 0.000 NA
#> SRR1706920 2 0.0290 0.862 0.000 0.992 0.008 0.000 NA
#> SRR1706921 2 0.0290 0.862 0.000 0.992 0.008 0.000 NA
#> SRR1706922 2 0.0162 0.863 0.000 0.996 0.004 0.000 NA
#> SRR1706915 2 0.3710 0.813 0.000 0.808 0.048 0.000 NA
#> SRR1706916 2 0.3930 0.800 0.000 0.792 0.056 0.000 NA
#> SRR1706917 2 0.3863 0.804 0.000 0.796 0.052 0.000 NA
#> SRR1706918 2 0.3736 0.812 0.000 0.808 0.052 0.000 NA
#> SRR1706923 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706924 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706925 2 0.0000 0.865 0.000 1.000 0.000 0.000 NA
#> SRR1706926 2 0.0000 0.865 0.000 1.000 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
#> SRR1706767 4 0.1745 0.932 0.000 0.000 0.068 0.920 NA NA
#> SRR1706768 4 0.1838 0.930 0.000 0.000 0.068 0.916 NA NA
#> SRR1706769 4 0.1643 0.933 0.000 0.000 0.068 0.924 NA NA
#> SRR1706770 4 0.1838 0.930 0.000 0.000 0.068 0.916 NA NA
#> SRR1706771 4 0.3020 0.904 0.040 0.000 0.064 0.864 NA NA
#> SRR1706772 4 0.3020 0.904 0.040 0.000 0.064 0.864 NA NA
#> SRR1706773 4 0.2950 0.907 0.036 0.000 0.064 0.868 NA NA
#> SRR1706774 4 0.3020 0.904 0.040 0.000 0.064 0.864 NA NA
#> SRR1706775 1 0.4113 0.751 0.748 0.000 0.056 0.188 NA NA
#> SRR1706776 1 0.3812 0.780 0.772 0.000 0.056 0.168 NA NA
#> SRR1706777 1 0.4174 0.741 0.740 0.000 0.056 0.196 NA NA
#> SRR1706778 1 0.3812 0.779 0.772 0.000 0.056 0.168 NA NA
#> SRR1706779 1 0.0458 0.912 0.984 0.000 0.000 0.000 NA NA
#> SRR1706780 1 0.0547 0.912 0.980 0.000 0.000 0.000 NA NA
#> SRR1706781 1 0.0547 0.912 0.980 0.000 0.000 0.000 NA NA
#> SRR1706782 1 0.0547 0.912 0.980 0.000 0.000 0.000 NA NA
#> SRR1706783 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706784 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706785 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706786 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706787 4 0.0291 0.953 0.000 0.000 0.004 0.992 NA NA
#> SRR1706788 4 0.0291 0.953 0.000 0.000 0.004 0.992 NA NA
#> SRR1706789 4 0.0146 0.953 0.000 0.000 0.004 0.996 NA NA
#> SRR1706790 4 0.0291 0.953 0.000 0.000 0.004 0.992 NA NA
#> SRR1706791 4 0.0964 0.951 0.004 0.000 0.012 0.968 NA NA
#> SRR1706792 4 0.0779 0.952 0.008 0.000 0.008 0.976 NA NA
#> SRR1706793 4 0.0870 0.952 0.004 0.000 0.012 0.972 NA NA
#> SRR1706794 4 0.1026 0.951 0.008 0.000 0.008 0.968 NA NA
#> SRR1706795 1 0.2615 0.851 0.852 0.000 0.000 0.136 NA NA
#> SRR1706796 1 0.2362 0.855 0.860 0.000 0.000 0.136 NA NA
#> SRR1706797 1 0.2462 0.855 0.860 0.000 0.000 0.132 NA NA
#> SRR1706798 1 0.2584 0.848 0.848 0.000 0.000 0.144 NA NA
#> SRR1706799 1 0.0000 0.913 1.000 0.000 0.000 0.000 NA NA
#> SRR1706800 1 0.0000 0.913 1.000 0.000 0.000 0.000 NA NA
#> SRR1706801 1 0.0000 0.913 1.000 0.000 0.000 0.000 NA NA
#> SRR1706802 1 0.0000 0.913 1.000 0.000 0.000 0.000 NA NA
#> SRR1706803 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706804 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706805 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706806 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706811 4 0.3298 0.884 0.024 0.004 0.012 0.836 NA NA
#> SRR1706812 4 0.3492 0.875 0.032 0.004 0.012 0.824 NA NA
#> SRR1706813 4 0.3284 0.881 0.024 0.004 0.008 0.832 NA NA
#> SRR1706814 4 0.3284 0.881 0.024 0.004 0.008 0.832 NA NA
#> SRR1706807 4 0.1364 0.945 0.000 0.000 0.016 0.952 NA NA
#> SRR1706808 4 0.1364 0.945 0.000 0.000 0.016 0.952 NA NA
#> SRR1706809 4 0.1364 0.945 0.000 0.000 0.016 0.952 NA NA
#> SRR1706810 4 0.1364 0.945 0.000 0.000 0.016 0.952 NA NA
#> SRR1706815 1 0.4841 0.764 0.712 0.004 0.008 0.136 NA NA
#> SRR1706816 1 0.4589 0.777 0.728 0.004 0.008 0.132 NA NA
#> SRR1706817 1 0.4879 0.742 0.696 0.004 0.008 0.156 NA NA
#> SRR1706818 1 0.4739 0.762 0.712 0.004 0.008 0.140 NA NA
#> SRR1706819 1 0.2110 0.887 0.900 0.012 0.004 0.000 NA NA
#> SRR1706820 1 0.2203 0.886 0.896 0.016 0.004 0.000 NA NA
#> SRR1706821 1 0.2110 0.887 0.900 0.012 0.004 0.000 NA NA
#> SRR1706822 1 0.2110 0.887 0.900 0.012 0.004 0.000 NA NA
#> SRR1706823 1 0.2203 0.888 0.896 0.016 0.004 0.000 NA NA
#> SRR1706824 1 0.2203 0.888 0.896 0.016 0.004 0.000 NA NA
#> SRR1706825 1 0.2418 0.883 0.884 0.016 0.008 0.000 NA NA
#> SRR1706826 1 0.2255 0.887 0.892 0.016 0.004 0.000 NA NA
#> SRR1706827 4 0.0146 0.953 0.000 0.000 0.004 0.996 NA NA
#> SRR1706828 4 0.0146 0.953 0.000 0.000 0.004 0.996 NA NA
#> SRR1706829 4 0.0146 0.953 0.000 0.000 0.004 0.996 NA NA
#> SRR1706830 4 0.0146 0.953 0.000 0.000 0.004 0.996 NA NA
#> SRR1706835 1 0.3196 0.830 0.816 0.000 0.000 0.156 NA NA
#> SRR1706836 1 0.3546 0.803 0.788 0.000 0.004 0.180 NA NA
#> SRR1706837 1 0.3611 0.795 0.780 0.000 0.004 0.188 NA NA
#> SRR1706838 1 0.3438 0.801 0.788 0.000 0.000 0.184 NA NA
#> SRR1706831 4 0.0436 0.953 0.004 0.000 0.004 0.988 NA NA
#> SRR1706832 4 0.0436 0.953 0.004 0.000 0.004 0.988 NA NA
#> SRR1706833 4 0.0436 0.953 0.004 0.000 0.004 0.988 NA NA
#> SRR1706834 4 0.0551 0.953 0.004 0.000 0.004 0.984 NA NA
#> SRR1706839 1 0.0146 0.913 0.996 0.000 0.000 0.000 NA NA
#> SRR1706840 1 0.0291 0.913 0.992 0.000 0.004 0.000 NA NA
#> SRR1706841 1 0.0291 0.913 0.992 0.000 0.004 0.000 NA NA
#> SRR1706842 1 0.0291 0.913 0.992 0.000 0.004 0.000 NA NA
#> SRR1706847 3 0.4846 0.772 0.000 0.188 0.676 0.000 NA NA
#> SRR1706848 3 0.4882 0.770 0.000 0.188 0.672 0.000 NA NA
#> SRR1706849 3 0.4882 0.769 0.000 0.188 0.672 0.000 NA NA
#> SRR1706850 3 0.4809 0.772 0.000 0.188 0.680 0.000 NA NA
#> SRR1706843 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706844 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706845 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706846 1 0.0405 0.913 0.988 0.000 0.004 0.000 NA NA
#> SRR1706851 3 0.5372 0.745 0.000 0.236 0.600 0.000 NA NA
#> SRR1706852 3 0.5280 0.750 0.000 0.236 0.612 0.000 NA NA
#> SRR1706853 3 0.5383 0.735 0.000 0.244 0.596 0.000 NA NA
#> SRR1706854 3 0.5372 0.745 0.000 0.236 0.600 0.000 NA NA
#> SRR1706855 2 0.3755 0.701 0.000 0.768 0.192 0.000 NA NA
#> SRR1706856 2 0.3858 0.692 0.000 0.760 0.196 0.000 NA NA
#> SRR1706857 2 0.3692 0.709 0.000 0.776 0.184 0.000 NA NA
#> SRR1706858 2 0.3858 0.692 0.000 0.760 0.196 0.000 NA NA
#> SRR1706859 2 0.0909 0.817 0.000 0.968 0.012 0.000 NA NA
#> SRR1706860 2 0.0806 0.817 0.000 0.972 0.008 0.000 NA NA
#> SRR1706861 2 0.0806 0.817 0.000 0.972 0.008 0.000 NA NA
#> SRR1706862 2 0.0806 0.817 0.000 0.972 0.008 0.000 NA NA
#> SRR1706867 3 0.4613 0.829 0.000 0.204 0.704 0.000 NA NA
#> SRR1706869 3 0.4661 0.829 0.000 0.204 0.700 0.000 NA NA
#> SRR1706870 3 0.4613 0.829 0.000 0.204 0.704 0.000 NA NA
#> SRR1706863 2 0.1053 0.809 0.012 0.964 0.004 0.000 NA NA
#> SRR1706864 2 0.1053 0.809 0.012 0.964 0.004 0.000 NA NA
#> SRR1706865 2 0.1138 0.808 0.012 0.960 0.004 0.000 NA NA
#> SRR1706866 2 0.0964 0.810 0.012 0.968 0.004 0.000 NA NA
#> SRR1706871 3 0.3566 0.845 0.000 0.224 0.752 0.000 NA NA
#> SRR1706872 3 0.3566 0.845 0.000 0.224 0.752 0.000 NA NA
#> SRR1706873 3 0.3566 0.845 0.000 0.224 0.752 0.000 NA NA
#> SRR1706874 3 0.3566 0.845 0.000 0.224 0.752 0.000 NA NA
#> SRR1706879 2 0.0806 0.817 0.000 0.972 0.008 0.000 NA NA
#> SRR1706880 2 0.0891 0.816 0.000 0.968 0.008 0.000 NA NA
#> SRR1706881 2 0.0692 0.816 0.000 0.976 0.004 0.000 NA NA
#> SRR1706882 2 0.0909 0.817 0.000 0.968 0.012 0.000 NA NA
#> SRR1706883 2 0.1787 0.786 0.020 0.932 0.016 0.000 NA NA
#> SRR1706884 2 0.1777 0.783 0.024 0.932 0.012 0.000 NA NA
#> SRR1706885 2 0.1851 0.779 0.024 0.928 0.012 0.000 NA NA
#> SRR1706886 2 0.1787 0.786 0.020 0.932 0.016 0.000 NA NA
#> SRR1706875 2 0.3564 0.699 0.000 0.772 0.200 0.000 NA NA
#> SRR1706876 2 0.3502 0.705 0.000 0.780 0.192 0.000 NA NA
#> SRR1706877 2 0.3406 0.720 0.000 0.792 0.180 0.000 NA NA
#> SRR1706878 2 0.3564 0.698 0.000 0.772 0.200 0.000 NA NA
#> SRR1706887 3 0.3770 0.834 0.000 0.212 0.752 0.000 NA NA
#> SRR1706888 3 0.3741 0.832 0.000 0.208 0.756 0.000 NA NA
#> SRR1706889 3 0.3669 0.833 0.000 0.208 0.760 0.000 NA NA
#> SRR1706890 3 0.3669 0.833 0.000 0.208 0.760 0.000 NA NA
#> SRR1706891 3 0.4732 0.764 0.000 0.220 0.668 0.000 NA NA
#> SRR1706892 3 0.4691 0.768 0.000 0.220 0.672 0.000 NA NA
#> SRR1706893 3 0.4732 0.764 0.000 0.220 0.668 0.000 NA NA
#> SRR1706894 3 0.4732 0.764 0.000 0.220 0.668 0.000 NA NA
#> SRR1706895 3 0.5452 0.226 0.000 0.436 0.444 0.000 NA NA
#> SRR1706896 2 0.5578 -0.223 0.000 0.448 0.428 0.000 NA NA
#> SRR1706897 3 0.5452 0.226 0.000 0.436 0.444 0.000 NA NA
#> SRR1706898 2 0.5452 -0.232 0.000 0.444 0.436 0.000 NA NA
#> SRR1706899 2 0.4175 0.699 0.000 0.748 0.172 0.000 NA NA
#> SRR1706900 2 0.4122 0.704 0.000 0.752 0.172 0.000 NA NA
#> SRR1706901 2 0.4122 0.704 0.000 0.752 0.172 0.000 NA NA
#> SRR1706902 2 0.4122 0.704 0.000 0.752 0.172 0.000 NA NA
#> SRR1706907 3 0.4744 0.835 0.000 0.224 0.684 0.000 NA NA
#> SRR1706908 3 0.4744 0.835 0.000 0.224 0.684 0.000 NA NA
#> SRR1706909 3 0.4744 0.835 0.000 0.224 0.684 0.000 NA NA
#> SRR1706910 3 0.4696 0.836 0.000 0.224 0.688 0.000 NA NA
#> SRR1706903 2 0.3266 0.766 0.004 0.824 0.136 0.000 NA NA
#> SRR1706904 2 0.3306 0.763 0.004 0.820 0.140 0.000 NA NA
#> SRR1706905 2 0.3266 0.766 0.004 0.824 0.136 0.000 NA NA
#> SRR1706906 2 0.3266 0.766 0.004 0.824 0.136 0.000 NA NA
#> SRR1706911 3 0.3420 0.841 0.000 0.240 0.748 0.000 NA NA
#> SRR1706912 3 0.3509 0.841 0.000 0.240 0.744 0.000 NA NA
#> SRR1706913 3 0.3420 0.841 0.000 0.240 0.748 0.000 NA NA
#> SRR1706914 3 0.3509 0.842 0.000 0.240 0.744 0.000 NA NA
#> SRR1706919 2 0.1074 0.817 0.000 0.960 0.012 0.000 NA NA
#> SRR1706920 2 0.0909 0.817 0.000 0.968 0.012 0.000 NA NA
#> SRR1706921 2 0.1074 0.817 0.000 0.960 0.012 0.000 NA NA
#> SRR1706922 2 0.0993 0.817 0.000 0.964 0.012 0.000 NA NA
#> SRR1706915 2 0.4091 0.651 0.000 0.736 0.216 0.000 NA NA
#> SRR1706916 2 0.4213 0.635 0.000 0.724 0.224 0.000 NA NA
#> SRR1706917 2 0.4238 0.627 0.000 0.720 0.228 0.000 NA NA
#> SRR1706918 2 0.4132 0.652 0.000 0.736 0.212 0.000 NA NA
#> SRR1706923 2 0.1334 0.794 0.020 0.948 0.000 0.000 NA NA
#> SRR1706924 2 0.1334 0.794 0.020 0.948 0.000 0.000 NA NA
#> SRR1706925 2 0.1257 0.796 0.020 0.952 0.000 0.000 NA NA
#> SRR1706926 2 0.1257 0.797 0.020 0.952 0.000 0.000 NA 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", "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 15185 rows and 159 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 1.000 0.998 0.999 0.1524 0.924 0.846
#> 4 4 1.000 0.998 0.999 0.2105 0.878 0.709
#> 5 5 1.000 0.999 0.999 0.1154 0.918 0.727
#> 6 6 0.968 0.976 0.983 0.0527 0.959 0.812
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0000 1.000 1 0.000 0.000
#> SRR1706768 1 0.0000 1.000 1 0.000 0.000
#> SRR1706769 1 0.0000 1.000 1 0.000 0.000
#> SRR1706770 1 0.0000 1.000 1 0.000 0.000
#> SRR1706771 1 0.0000 1.000 1 0.000 0.000
#> SRR1706772 1 0.0000 1.000 1 0.000 0.000
#> SRR1706773 1 0.0000 1.000 1 0.000 0.000
#> SRR1706774 1 0.0000 1.000 1 0.000 0.000
#> SRR1706775 1 0.0000 1.000 1 0.000 0.000
#> SRR1706776 1 0.0000 1.000 1 0.000 0.000
#> SRR1706777 1 0.0000 1.000 1 0.000 0.000
#> SRR1706778 1 0.0000 1.000 1 0.000 0.000
#> SRR1706779 1 0.0000 1.000 1 0.000 0.000
#> SRR1706780 1 0.0000 1.000 1 0.000 0.000
#> SRR1706781 1 0.0000 1.000 1 0.000 0.000
#> SRR1706782 1 0.0000 1.000 1 0.000 0.000
#> SRR1706783 1 0.0000 1.000 1 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1 0.000 0.000
#> SRR1706787 1 0.0000 1.000 1 0.000 0.000
#> SRR1706788 1 0.0000 1.000 1 0.000 0.000
#> SRR1706789 1 0.0000 1.000 1 0.000 0.000
#> SRR1706790 1 0.0000 1.000 1 0.000 0.000
#> SRR1706791 1 0.0000 1.000 1 0.000 0.000
#> SRR1706792 1 0.0000 1.000 1 0.000 0.000
#> SRR1706793 1 0.0000 1.000 1 0.000 0.000
#> SRR1706794 1 0.0000 1.000 1 0.000 0.000
#> SRR1706795 1 0.0000 1.000 1 0.000 0.000
#> SRR1706796 1 0.0000 1.000 1 0.000 0.000
#> SRR1706797 1 0.0000 1.000 1 0.000 0.000
#> SRR1706798 1 0.0000 1.000 1 0.000 0.000
#> SRR1706799 1 0.0000 1.000 1 0.000 0.000
#> SRR1706800 1 0.0000 1.000 1 0.000 0.000
#> SRR1706801 1 0.0000 1.000 1 0.000 0.000
#> SRR1706802 1 0.0000 1.000 1 0.000 0.000
#> SRR1706803 1 0.0000 1.000 1 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1 0.000 0.000
#> SRR1706811 1 0.0000 1.000 1 0.000 0.000
#> SRR1706812 1 0.0000 1.000 1 0.000 0.000
#> SRR1706813 1 0.0000 1.000 1 0.000 0.000
#> SRR1706814 1 0.0000 1.000 1 0.000 0.000
#> SRR1706807 1 0.0000 1.000 1 0.000 0.000
#> SRR1706808 1 0.0000 1.000 1 0.000 0.000
#> SRR1706809 1 0.0000 1.000 1 0.000 0.000
#> SRR1706810 1 0.0000 1.000 1 0.000 0.000
#> SRR1706815 1 0.0000 1.000 1 0.000 0.000
#> SRR1706816 1 0.0000 1.000 1 0.000 0.000
#> SRR1706817 1 0.0000 1.000 1 0.000 0.000
#> SRR1706818 1 0.0000 1.000 1 0.000 0.000
#> SRR1706819 1 0.0000 1.000 1 0.000 0.000
#> SRR1706820 1 0.0000 1.000 1 0.000 0.000
#> SRR1706821 1 0.0000 1.000 1 0.000 0.000
#> SRR1706822 1 0.0000 1.000 1 0.000 0.000
#> SRR1706823 1 0.0000 1.000 1 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1 0.000 0.000
#> SRR1706827 1 0.0000 1.000 1 0.000 0.000
#> SRR1706828 1 0.0000 1.000 1 0.000 0.000
#> SRR1706829 1 0.0000 1.000 1 0.000 0.000
#> SRR1706830 1 0.0000 1.000 1 0.000 0.000
#> SRR1706835 1 0.0000 1.000 1 0.000 0.000
#> SRR1706836 1 0.0000 1.000 1 0.000 0.000
#> SRR1706837 1 0.0000 1.000 1 0.000 0.000
#> SRR1706838 1 0.0000 1.000 1 0.000 0.000
#> SRR1706831 1 0.0000 1.000 1 0.000 0.000
#> SRR1706832 1 0.0000 1.000 1 0.000 0.000
#> SRR1706833 1 0.0000 1.000 1 0.000 0.000
#> SRR1706834 1 0.0000 1.000 1 0.000 0.000
#> SRR1706839 1 0.0000 1.000 1 0.000 0.000
#> SRR1706840 1 0.0000 1.000 1 0.000 0.000
#> SRR1706841 1 0.0000 1.000 1 0.000 0.000
#> SRR1706842 1 0.0000 1.000 1 0.000 0.000
#> SRR1706847 3 0.0000 1.000 0 0.000 1.000
#> SRR1706848 3 0.0000 1.000 0 0.000 1.000
#> SRR1706849 3 0.0000 1.000 0 0.000 1.000
#> SRR1706850 3 0.0000 1.000 0 0.000 1.000
#> SRR1706843 1 0.0000 1.000 1 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1 0.000 0.000
#> SRR1706851 2 0.0592 0.990 0 0.988 0.012
#> SRR1706852 2 0.0592 0.990 0 0.988 0.012
#> SRR1706853 2 0.0592 0.990 0 0.988 0.012
#> SRR1706854 2 0.0592 0.990 0 0.988 0.012
#> SRR1706855 2 0.0000 0.998 0 1.000 0.000
#> SRR1706856 2 0.0000 0.998 0 1.000 0.000
#> SRR1706857 2 0.0000 0.998 0 1.000 0.000
#> SRR1706858 2 0.0000 0.998 0 1.000 0.000
#> SRR1706859 2 0.0000 0.998 0 1.000 0.000
#> SRR1706860 2 0.0000 0.998 0 1.000 0.000
#> SRR1706861 2 0.0000 0.998 0 1.000 0.000
#> SRR1706862 2 0.0000 0.998 0 1.000 0.000
#> SRR1706867 3 0.0000 1.000 0 0.000 1.000
#> SRR1706869 3 0.0000 1.000 0 0.000 1.000
#> SRR1706870 3 0.0000 1.000 0 0.000 1.000
#> SRR1706863 2 0.0000 0.998 0 1.000 0.000
#> SRR1706864 2 0.0000 0.998 0 1.000 0.000
#> SRR1706865 2 0.0000 0.998 0 1.000 0.000
#> SRR1706866 2 0.0000 0.998 0 1.000 0.000
#> SRR1706871 2 0.0592 0.990 0 0.988 0.012
#> SRR1706872 2 0.0592 0.990 0 0.988 0.012
#> SRR1706873 2 0.0592 0.990 0 0.988 0.012
#> SRR1706874 2 0.0592 0.990 0 0.988 0.012
#> SRR1706879 2 0.0000 0.998 0 1.000 0.000
#> SRR1706880 2 0.0000 0.998 0 1.000 0.000
#> SRR1706881 2 0.0000 0.998 0 1.000 0.000
#> SRR1706882 2 0.0000 0.998 0 1.000 0.000
#> SRR1706883 2 0.0000 0.998 0 1.000 0.000
#> SRR1706884 2 0.0000 0.998 0 1.000 0.000
#> SRR1706885 2 0.0000 0.998 0 1.000 0.000
#> SRR1706886 2 0.0000 0.998 0 1.000 0.000
#> SRR1706875 2 0.0000 0.998 0 1.000 0.000
#> SRR1706876 2 0.0000 0.998 0 1.000 0.000
#> SRR1706877 2 0.0000 0.998 0 1.000 0.000
#> SRR1706878 2 0.0000 0.998 0 1.000 0.000
#> SRR1706887 3 0.0000 1.000 0 0.000 1.000
#> SRR1706888 3 0.0000 1.000 0 0.000 1.000
#> SRR1706889 3 0.0000 1.000 0 0.000 1.000
#> SRR1706890 3 0.0000 1.000 0 0.000 1.000
#> SRR1706891 2 0.0000 0.998 0 1.000 0.000
#> SRR1706892 2 0.0000 0.998 0 1.000 0.000
#> SRR1706893 2 0.0000 0.998 0 1.000 0.000
#> SRR1706894 2 0.0000 0.998 0 1.000 0.000
#> SRR1706895 2 0.0000 0.998 0 1.000 0.000
#> SRR1706896 2 0.0000 0.998 0 1.000 0.000
#> SRR1706897 2 0.0000 0.998 0 1.000 0.000
#> SRR1706898 2 0.0000 0.998 0 1.000 0.000
#> SRR1706899 2 0.0000 0.998 0 1.000 0.000
#> SRR1706900 2 0.0000 0.998 0 1.000 0.000
#> SRR1706901 2 0.0000 0.998 0 1.000 0.000
#> SRR1706902 2 0.0000 0.998 0 1.000 0.000
#> SRR1706907 3 0.0000 1.000 0 0.000 1.000
#> SRR1706908 3 0.0000 1.000 0 0.000 1.000
#> SRR1706909 3 0.0000 1.000 0 0.000 1.000
#> SRR1706910 3 0.0000 1.000 0 0.000 1.000
#> SRR1706903 2 0.0000 0.998 0 1.000 0.000
#> SRR1706904 2 0.0000 0.998 0 1.000 0.000
#> SRR1706905 2 0.0000 0.998 0 1.000 0.000
#> SRR1706906 2 0.0000 0.998 0 1.000 0.000
#> SRR1706911 2 0.0592 0.990 0 0.988 0.012
#> SRR1706912 2 0.0592 0.990 0 0.988 0.012
#> SRR1706913 2 0.0592 0.990 0 0.988 0.012
#> SRR1706914 2 0.0592 0.990 0 0.988 0.012
#> SRR1706919 2 0.0000 0.998 0 1.000 0.000
#> SRR1706920 2 0.0000 0.998 0 1.000 0.000
#> SRR1706921 2 0.0000 0.998 0 1.000 0.000
#> SRR1706922 2 0.0000 0.998 0 1.000 0.000
#> SRR1706915 2 0.0000 0.998 0 1.000 0.000
#> SRR1706916 2 0.0000 0.998 0 1.000 0.000
#> SRR1706917 2 0.0000 0.998 0 1.000 0.000
#> SRR1706918 2 0.0000 0.998 0 1.000 0.000
#> SRR1706923 2 0.0000 0.998 0 1.000 0.000
#> SRR1706924 2 0.0000 0.998 0 1.000 0.000
#> SRR1706925 2 0.0000 0.998 0 1.000 0.000
#> SRR1706926 2 0.0000 0.998 0 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706768 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706769 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706770 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706771 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706772 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706773 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706774 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706775 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706776 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706777 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706778 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706779 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706780 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706781 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706782 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706783 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706784 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706785 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706786 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706787 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706788 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706789 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706790 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706791 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706792 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706793 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706794 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706795 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706796 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706797 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706798 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706799 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706800 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706801 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706802 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706803 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706804 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706805 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706806 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706811 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706812 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706813 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706814 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706807 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706808 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706809 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706810 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706815 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706816 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706817 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706818 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706819 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706820 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706821 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706822 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706823 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706824 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706825 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706826 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706827 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706828 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706829 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706830 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706835 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706836 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706837 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706838 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706831 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706832 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706833 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706834 4 0.0000 1.000 0 0.000 0.000 1
#> SRR1706839 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706840 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706841 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706842 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706847 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706848 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706849 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706850 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706843 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706844 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706845 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706846 1 0.0000 1.000 1 0.000 0.000 0
#> SRR1706851 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706852 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706853 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706854 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706855 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706856 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706857 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706858 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706859 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706860 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706861 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706862 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706867 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706869 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706870 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706863 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706864 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706865 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706866 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706871 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706872 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706873 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706874 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706879 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706880 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706881 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706882 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706883 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706884 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706885 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706886 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706875 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706876 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706877 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706878 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706887 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706888 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706889 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706890 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706891 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706892 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706893 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706894 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706895 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706896 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706897 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706898 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706899 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706900 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706901 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706902 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706907 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706908 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706909 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706910 3 0.0000 1.000 0 0.000 1.000 0
#> SRR1706903 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706904 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706905 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706906 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706911 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706912 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706913 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706914 2 0.0469 0.990 0 0.988 0.012 0
#> SRR1706919 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706920 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706921 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706922 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706915 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706916 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706917 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706918 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706923 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706924 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706925 2 0.0000 0.998 0 1.000 0.000 0
#> SRR1706926 2 0.0000 0.998 0 1.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706768 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706769 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706770 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706771 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706772 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706773 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706774 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706775 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706776 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706777 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706778 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706779 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706780 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706781 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706782 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706783 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706784 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706785 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706786 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706787 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706788 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706789 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706790 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706791 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706792 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706793 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706794 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706795 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706796 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706797 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706798 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706799 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706800 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706801 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706802 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706803 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706804 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706805 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706806 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706811 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706812 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706813 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706814 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706807 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706808 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706809 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706810 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706815 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706816 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706817 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706818 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706819 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706820 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706821 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706822 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706823 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706824 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706825 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706826 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706827 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706828 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706829 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706830 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706835 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706836 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706837 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706838 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706831 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706832 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706833 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706834 4 0.0000 1.000 0 0 0.000 1 0.000
#> SRR1706839 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706840 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706841 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706842 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706847 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706848 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706849 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706850 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706843 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706844 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706845 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706846 1 0.0000 1.000 1 0 0.000 0 0.000
#> SRR1706851 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706852 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706853 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706854 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706855 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706856 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706857 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706858 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706859 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706860 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706861 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706862 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706867 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706869 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706870 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706863 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706864 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706865 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706866 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706871 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706872 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706873 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706874 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706879 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706880 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706881 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706882 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706883 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706884 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706885 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706886 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706875 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706876 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706877 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706878 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706887 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706888 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706889 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706890 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706891 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706892 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706893 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706894 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706895 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706896 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706897 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706898 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706899 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706900 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706901 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706902 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706907 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706908 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706909 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706910 5 0.0000 1.000 0 0 0.000 0 1.000
#> SRR1706903 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706904 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706905 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706906 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706911 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706912 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706913 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706914 3 0.0404 0.992 0 0 0.988 0 0.012
#> SRR1706919 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706920 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706921 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706922 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706915 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706916 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706917 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706918 3 0.0000 0.995 0 0 1.000 0 0.000
#> SRR1706923 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706924 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706925 2 0.0000 1.000 0 1 0.000 0 0.000
#> SRR1706926 2 0.0000 1.000 0 1 0.000 0 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706768 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706769 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706770 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706771 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706772 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706773 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706774 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706775 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706776 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706777 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706778 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706779 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706780 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706781 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706782 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706783 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706784 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706785 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706786 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706787 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706788 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706789 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706790 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706791 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706792 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706793 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706794 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706795 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706796 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706797 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706798 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706799 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706800 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706801 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706802 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706803 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706804 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706805 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706806 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706811 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706812 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706813 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706814 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706807 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706808 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706809 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706810 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706815 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706816 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706817 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706818 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706819 5 0.3309 0.742 0.280 0 0.000 0 0.720 0.000
#> SRR1706820 5 0.3309 0.742 0.280 0 0.000 0 0.720 0.000
#> SRR1706821 5 0.3309 0.742 0.280 0 0.000 0 0.720 0.000
#> SRR1706822 5 0.3309 0.742 0.280 0 0.000 0 0.720 0.000
#> SRR1706823 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706824 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706825 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706826 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706827 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706828 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706829 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706830 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706835 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706836 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706837 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706838 5 0.0000 0.913 0.000 0 0.000 0 1.000 0.000
#> SRR1706831 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706832 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706833 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706834 4 0.0000 1.000 0.000 0 0.000 1 0.000 0.000
#> SRR1706839 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706840 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706841 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706842 5 0.2092 0.902 0.124 0 0.000 0 0.876 0.000
#> SRR1706847 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706848 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706849 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706850 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706843 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706844 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706845 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706846 1 0.0000 1.000 1.000 0 0.000 0 0.000 0.000
#> SRR1706851 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706852 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706853 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706854 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706855 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706856 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706857 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706858 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706859 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706860 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706861 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706862 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706867 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706869 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706870 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706863 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706864 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706865 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706866 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706871 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706872 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706873 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706874 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706879 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706880 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706881 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706882 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706883 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706884 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706885 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706886 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706875 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706876 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706877 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706878 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706887 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706891 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706892 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706893 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706894 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706895 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706896 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706897 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706898 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706899 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706900 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706901 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706902 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706907 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706908 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706909 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706910 3 0.0000 1.000 0.000 0 1.000 0 0.000 0.000
#> SRR1706903 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706904 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706905 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706906 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706911 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706912 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706913 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706914 6 0.0363 0.992 0.000 0 0.012 0 0.000 0.988
#> SRR1706919 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706920 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706921 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706922 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706915 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706916 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706917 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706918 6 0.0000 0.995 0.000 0 0.000 0 0.000 1.000
#> SRR1706923 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706924 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706925 2 0.0000 1.000 0.000 1 0.000 0 0.000 0.000
#> SRR1706926 2 0.0000 1.000 0.000 1 0.000 0 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", "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 15185 rows and 159 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.753 0.492 0.801 0.2374 0.954 0.908
#> 4 4 0.660 0.876 0.809 0.1207 0.775 0.516
#> 5 5 0.592 0.679 0.700 0.0665 0.959 0.841
#> 6 6 0.573 0.726 0.720 0.0535 0.925 0.707
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706768 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706769 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706770 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706771 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706772 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706773 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706774 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706775 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706776 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706777 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706778 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706779 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706783 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706784 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706785 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706786 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706787 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706788 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706789 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706790 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706791 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706792 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706793 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706794 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706795 1 0.0747 0.623 0.984 0.000 0.016
#> SRR1706796 1 0.0747 0.623 0.984 0.000 0.016
#> SRR1706797 1 0.0747 0.623 0.984 0.000 0.016
#> SRR1706798 1 0.0747 0.623 0.984 0.000 0.016
#> SRR1706799 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706802 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706803 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706804 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706805 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706806 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706811 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706812 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706813 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706814 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706807 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706808 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706809 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706810 3 0.6302 1.000 0.480 0.000 0.520
#> SRR1706815 1 0.2711 0.580 0.912 0.000 0.088
#> SRR1706816 1 0.2711 0.580 0.912 0.000 0.088
#> SRR1706817 1 0.2711 0.580 0.912 0.000 0.088
#> SRR1706818 1 0.2711 0.580 0.912 0.000 0.088
#> SRR1706819 1 0.1163 0.615 0.972 0.000 0.028
#> SRR1706820 1 0.1163 0.615 0.972 0.000 0.028
#> SRR1706821 1 0.1163 0.615 0.972 0.000 0.028
#> SRR1706822 1 0.1163 0.615 0.972 0.000 0.028
#> SRR1706823 1 0.1529 0.609 0.960 0.000 0.040
#> SRR1706824 1 0.1529 0.609 0.960 0.000 0.040
#> SRR1706825 1 0.1529 0.609 0.960 0.000 0.040
#> SRR1706826 1 0.1529 0.609 0.960 0.000 0.040
#> SRR1706827 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706828 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706829 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706830 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706835 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706836 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706837 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706838 1 0.2066 0.589 0.940 0.000 0.060
#> SRR1706831 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706832 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706833 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706834 1 0.6308 -0.911 0.508 0.000 0.492
#> SRR1706839 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.629 1.000 0.000 0.000
#> SRR1706847 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706848 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706849 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706850 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706843 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706844 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706845 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706846 1 0.0592 0.626 0.988 0.000 0.012
#> SRR1706851 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706852 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706853 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706854 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706855 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706859 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706860 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706861 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706862 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706867 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706869 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706870 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706863 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706864 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706865 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706866 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706871 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706872 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706873 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706874 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706879 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706880 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706881 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706882 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706883 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706884 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706885 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706886 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706875 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706887 2 0.6302 0.720 0.000 0.520 0.480
#> SRR1706888 2 0.6302 0.720 0.000 0.520 0.480
#> SRR1706889 2 0.6302 0.720 0.000 0.520 0.480
#> SRR1706890 2 0.6302 0.720 0.000 0.520 0.480
#> SRR1706891 2 0.6291 0.727 0.000 0.532 0.468
#> SRR1706892 2 0.6291 0.727 0.000 0.532 0.468
#> SRR1706893 2 0.6291 0.727 0.000 0.532 0.468
#> SRR1706894 2 0.6291 0.727 0.000 0.532 0.468
#> SRR1706895 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706896 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706897 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706898 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706899 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706900 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706901 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706902 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706907 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706908 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706909 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706910 2 0.6291 0.724 0.000 0.532 0.468
#> SRR1706903 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706904 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706905 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706906 2 0.0592 0.832 0.000 0.988 0.012
#> SRR1706911 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706912 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706913 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706914 2 0.6274 0.730 0.000 0.544 0.456
#> SRR1706919 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706920 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706921 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706922 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706915 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706923 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706924 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706925 2 0.0000 0.835 0.000 1.000 0.000
#> SRR1706926 2 0.0000 0.835 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.6107 0.938 0.264 0.088 0.000 0.648
#> SRR1706768 4 0.6107 0.938 0.264 0.088 0.000 0.648
#> SRR1706769 4 0.6107 0.938 0.264 0.088 0.000 0.648
#> SRR1706770 4 0.6107 0.938 0.264 0.088 0.000 0.648
#> SRR1706771 4 0.4826 0.934 0.264 0.020 0.000 0.716
#> SRR1706772 4 0.4826 0.934 0.264 0.020 0.000 0.716
#> SRR1706773 4 0.4826 0.934 0.264 0.020 0.000 0.716
#> SRR1706774 4 0.4826 0.934 0.264 0.020 0.000 0.716
#> SRR1706775 1 0.5517 0.659 0.724 0.092 0.000 0.184
#> SRR1706776 1 0.5517 0.659 0.724 0.092 0.000 0.184
#> SRR1706777 1 0.5517 0.659 0.724 0.092 0.000 0.184
#> SRR1706778 1 0.5517 0.659 0.724 0.092 0.000 0.184
#> SRR1706779 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706783 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706784 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706785 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706786 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706787 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706788 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706789 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706790 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706791 4 0.4606 0.934 0.264 0.012 0.000 0.724
#> SRR1706792 4 0.4606 0.934 0.264 0.012 0.000 0.724
#> SRR1706793 4 0.4606 0.934 0.264 0.012 0.000 0.724
#> SRR1706794 4 0.4606 0.934 0.264 0.012 0.000 0.724
#> SRR1706795 1 0.4710 0.738 0.792 0.088 0.000 0.120
#> SRR1706796 1 0.4710 0.738 0.792 0.088 0.000 0.120
#> SRR1706797 1 0.4710 0.738 0.792 0.088 0.000 0.120
#> SRR1706798 1 0.4710 0.738 0.792 0.088 0.000 0.120
#> SRR1706799 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.843 1.000 0.000 0.000 0.000
#> SRR1706803 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706804 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706805 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706806 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706811 4 0.4661 0.934 0.256 0.016 0.000 0.728
#> SRR1706812 4 0.4661 0.934 0.256 0.016 0.000 0.728
#> SRR1706813 4 0.4661 0.934 0.256 0.016 0.000 0.728
#> SRR1706814 4 0.4661 0.934 0.256 0.016 0.000 0.728
#> SRR1706807 4 0.6054 0.935 0.256 0.088 0.000 0.656
#> SRR1706808 4 0.6054 0.935 0.256 0.088 0.000 0.656
#> SRR1706809 4 0.6054 0.935 0.256 0.088 0.000 0.656
#> SRR1706810 4 0.6054 0.935 0.256 0.088 0.000 0.656
#> SRR1706815 1 0.6013 0.637 0.684 0.120 0.000 0.196
#> SRR1706816 1 0.6013 0.637 0.684 0.120 0.000 0.196
#> SRR1706817 1 0.6013 0.637 0.684 0.120 0.000 0.196
#> SRR1706818 1 0.6013 0.637 0.684 0.120 0.000 0.196
#> SRR1706819 1 0.1807 0.833 0.940 0.052 0.000 0.008
#> SRR1706820 1 0.1807 0.833 0.940 0.052 0.000 0.008
#> SRR1706821 1 0.1807 0.833 0.940 0.052 0.000 0.008
#> SRR1706822 1 0.1807 0.833 0.940 0.052 0.000 0.008
#> SRR1706823 1 0.2918 0.813 0.876 0.116 0.000 0.008
#> SRR1706824 1 0.2918 0.813 0.876 0.116 0.000 0.008
#> SRR1706825 1 0.2918 0.813 0.876 0.116 0.000 0.008
#> SRR1706826 1 0.2918 0.813 0.876 0.116 0.000 0.008
#> SRR1706827 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706828 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706829 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706830 4 0.5927 0.941 0.264 0.076 0.000 0.660
#> SRR1706835 1 0.5496 0.657 0.724 0.088 0.000 0.188
#> SRR1706836 1 0.5496 0.657 0.724 0.088 0.000 0.188
#> SRR1706837 1 0.5496 0.657 0.724 0.088 0.000 0.188
#> SRR1706838 1 0.5496 0.657 0.724 0.088 0.000 0.188
#> SRR1706831 4 0.4164 0.939 0.264 0.000 0.000 0.736
#> SRR1706832 4 0.4164 0.939 0.264 0.000 0.000 0.736
#> SRR1706833 4 0.4164 0.939 0.264 0.000 0.000 0.736
#> SRR1706834 4 0.4164 0.939 0.264 0.000 0.000 0.736
#> SRR1706839 1 0.0188 0.842 0.996 0.004 0.000 0.000
#> SRR1706840 1 0.0188 0.842 0.996 0.004 0.000 0.000
#> SRR1706841 1 0.0188 0.842 0.996 0.004 0.000 0.000
#> SRR1706842 1 0.0188 0.842 0.996 0.004 0.000 0.000
#> SRR1706847 3 0.1792 0.950 0.000 0.000 0.932 0.068
#> SRR1706848 3 0.1792 0.950 0.000 0.000 0.932 0.068
#> SRR1706849 3 0.1792 0.950 0.000 0.000 0.932 0.068
#> SRR1706850 3 0.1792 0.950 0.000 0.000 0.932 0.068
#> SRR1706843 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706844 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706845 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706846 1 0.2081 0.827 0.916 0.084 0.000 0.000
#> SRR1706851 3 0.0469 0.946 0.000 0.000 0.988 0.012
#> SRR1706852 3 0.0469 0.946 0.000 0.000 0.988 0.012
#> SRR1706853 3 0.0469 0.946 0.000 0.000 0.988 0.012
#> SRR1706854 3 0.0469 0.946 0.000 0.000 0.988 0.012
#> SRR1706855 2 0.6820 0.851 0.000 0.528 0.364 0.108
#> SRR1706856 2 0.6820 0.851 0.000 0.528 0.364 0.108
#> SRR1706857 2 0.6820 0.851 0.000 0.528 0.364 0.108
#> SRR1706858 2 0.6820 0.851 0.000 0.528 0.364 0.108
#> SRR1706859 2 0.4431 0.912 0.000 0.696 0.304 0.000
#> SRR1706860 2 0.4431 0.912 0.000 0.696 0.304 0.000
#> SRR1706861 2 0.4431 0.912 0.000 0.696 0.304 0.000
#> SRR1706862 2 0.4431 0.912 0.000 0.696 0.304 0.000
#> SRR1706867 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706869 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706870 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706863 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706864 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706865 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706866 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706871 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706872 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706873 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706874 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706879 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706880 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706881 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706882 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706883 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706884 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706885 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706886 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706875 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706876 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706877 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706878 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706887 3 0.2081 0.946 0.000 0.000 0.916 0.084
#> SRR1706888 3 0.2081 0.946 0.000 0.000 0.916 0.084
#> SRR1706889 3 0.2081 0.946 0.000 0.000 0.916 0.084
#> SRR1706890 3 0.2081 0.946 0.000 0.000 0.916 0.084
#> SRR1706891 3 0.1557 0.917 0.000 0.000 0.944 0.056
#> SRR1706892 3 0.1557 0.917 0.000 0.000 0.944 0.056
#> SRR1706893 3 0.1557 0.917 0.000 0.000 0.944 0.056
#> SRR1706894 3 0.1557 0.917 0.000 0.000 0.944 0.056
#> SRR1706895 2 0.7302 0.850 0.000 0.500 0.332 0.168
#> SRR1706896 2 0.7302 0.850 0.000 0.500 0.332 0.168
#> SRR1706897 2 0.7302 0.850 0.000 0.500 0.332 0.168
#> SRR1706898 2 0.7302 0.850 0.000 0.500 0.332 0.168
#> SRR1706899 2 0.5972 0.890 0.000 0.632 0.304 0.064
#> SRR1706900 2 0.5972 0.890 0.000 0.632 0.304 0.064
#> SRR1706901 2 0.5972 0.890 0.000 0.632 0.304 0.064
#> SRR1706902 2 0.5972 0.890 0.000 0.632 0.304 0.064
#> SRR1706907 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706908 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706909 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706910 3 0.1792 0.949 0.000 0.000 0.932 0.068
#> SRR1706903 2 0.5905 0.889 0.000 0.636 0.304 0.060
#> SRR1706904 2 0.5905 0.889 0.000 0.636 0.304 0.060
#> SRR1706905 2 0.5905 0.889 0.000 0.636 0.304 0.060
#> SRR1706906 2 0.5905 0.889 0.000 0.636 0.304 0.060
#> SRR1706911 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706912 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706913 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706914 3 0.0000 0.950 0.000 0.000 1.000 0.000
#> SRR1706919 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706920 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706921 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706922 2 0.5535 0.910 0.000 0.656 0.304 0.040
#> SRR1706915 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706916 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706917 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706918 2 0.6830 0.848 0.000 0.524 0.368 0.108
#> SRR1706923 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706924 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706925 2 0.4608 0.911 0.000 0.692 0.304 0.004
#> SRR1706926 2 0.4608 0.911 0.000 0.692 0.304 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.1661 0.876 0.000 0.000 0.036 0.940 0.024
#> SRR1706768 4 0.1661 0.876 0.000 0.000 0.036 0.940 0.024
#> SRR1706769 4 0.1661 0.876 0.000 0.000 0.036 0.940 0.024
#> SRR1706770 4 0.1661 0.876 0.000 0.000 0.036 0.940 0.024
#> SRR1706771 4 0.4314 0.871 0.120 0.000 0.060 0.796 0.024
#> SRR1706772 4 0.4314 0.871 0.120 0.000 0.060 0.796 0.024
#> SRR1706773 4 0.4314 0.871 0.120 0.000 0.060 0.796 0.024
#> SRR1706774 4 0.4314 0.871 0.120 0.000 0.060 0.796 0.024
#> SRR1706775 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706776 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706777 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706778 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706779 1 0.6793 -0.312 0.504 0.000 0.016 0.212 0.268
#> SRR1706780 1 0.6793 -0.312 0.504 0.000 0.016 0.212 0.268
#> SRR1706781 1 0.6793 -0.312 0.504 0.000 0.016 0.212 0.268
#> SRR1706782 1 0.6793 -0.312 0.504 0.000 0.016 0.212 0.268
#> SRR1706783 5 0.6845 0.927 0.380 0.000 0.008 0.212 0.400
#> SRR1706784 5 0.6845 0.927 0.380 0.000 0.008 0.212 0.400
#> SRR1706785 5 0.6845 0.927 0.380 0.000 0.008 0.212 0.400
#> SRR1706786 5 0.6845 0.927 0.380 0.000 0.008 0.212 0.400
#> SRR1706787 4 0.0162 0.886 0.000 0.000 0.004 0.996 0.000
#> SRR1706788 4 0.0162 0.886 0.000 0.000 0.004 0.996 0.000
#> SRR1706789 4 0.0162 0.886 0.000 0.000 0.004 0.996 0.000
#> SRR1706790 4 0.0162 0.886 0.000 0.000 0.004 0.996 0.000
#> SRR1706791 4 0.3433 0.877 0.132 0.000 0.032 0.832 0.004
#> SRR1706792 4 0.3433 0.877 0.132 0.000 0.032 0.832 0.004
#> SRR1706793 4 0.3433 0.877 0.132 0.000 0.032 0.832 0.004
#> SRR1706794 4 0.3433 0.877 0.132 0.000 0.032 0.832 0.004
#> SRR1706795 1 0.3480 0.494 0.752 0.000 0.000 0.248 0.000
#> SRR1706796 1 0.3480 0.494 0.752 0.000 0.000 0.248 0.000
#> SRR1706797 1 0.3480 0.494 0.752 0.000 0.000 0.248 0.000
#> SRR1706798 1 0.3480 0.494 0.752 0.000 0.000 0.248 0.000
#> SRR1706799 1 0.6760 -0.269 0.512 0.000 0.016 0.212 0.260
#> SRR1706800 1 0.6760 -0.269 0.512 0.000 0.016 0.212 0.260
#> SRR1706801 1 0.6760 -0.269 0.512 0.000 0.016 0.212 0.260
#> SRR1706802 1 0.6760 -0.269 0.512 0.000 0.016 0.212 0.260
#> SRR1706803 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706804 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706805 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706806 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706811 4 0.3940 0.872 0.136 0.000 0.044 0.808 0.012
#> SRR1706812 4 0.3940 0.872 0.136 0.000 0.044 0.808 0.012
#> SRR1706813 4 0.3940 0.872 0.136 0.000 0.044 0.808 0.012
#> SRR1706814 4 0.3940 0.872 0.136 0.000 0.044 0.808 0.012
#> SRR1706807 4 0.0968 0.876 0.012 0.000 0.004 0.972 0.012
#> SRR1706808 4 0.0968 0.876 0.012 0.000 0.004 0.972 0.012
#> SRR1706809 4 0.0968 0.876 0.012 0.000 0.004 0.972 0.012
#> SRR1706810 4 0.0968 0.876 0.012 0.000 0.004 0.972 0.012
#> SRR1706815 1 0.5469 0.482 0.644 0.000 0.036 0.284 0.036
#> SRR1706816 1 0.5469 0.482 0.644 0.000 0.036 0.284 0.036
#> SRR1706817 1 0.5469 0.482 0.644 0.000 0.036 0.284 0.036
#> SRR1706818 1 0.5469 0.482 0.644 0.000 0.036 0.284 0.036
#> SRR1706819 1 0.7398 -0.266 0.484 0.000 0.060 0.200 0.256
#> SRR1706820 1 0.7398 -0.266 0.484 0.000 0.060 0.200 0.256
#> SRR1706821 1 0.7398 -0.266 0.484 0.000 0.060 0.200 0.256
#> SRR1706822 1 0.7398 -0.266 0.484 0.000 0.060 0.200 0.256
#> SRR1706823 5 0.7301 0.792 0.356 0.000 0.036 0.200 0.408
#> SRR1706824 5 0.7301 0.792 0.356 0.000 0.036 0.200 0.408
#> SRR1706825 5 0.7301 0.792 0.356 0.000 0.036 0.200 0.408
#> SRR1706826 5 0.7301 0.792 0.356 0.000 0.036 0.200 0.408
#> SRR1706827 4 0.0162 0.885 0.000 0.000 0.000 0.996 0.004
#> SRR1706828 4 0.0162 0.885 0.000 0.000 0.000 0.996 0.004
#> SRR1706829 4 0.0162 0.885 0.000 0.000 0.000 0.996 0.004
#> SRR1706830 4 0.0162 0.885 0.000 0.000 0.000 0.996 0.004
#> SRR1706835 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706836 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706837 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706838 1 0.3816 0.518 0.696 0.000 0.000 0.304 0.000
#> SRR1706831 4 0.3510 0.879 0.128 0.000 0.032 0.832 0.008
#> SRR1706832 4 0.3510 0.879 0.128 0.000 0.032 0.832 0.008
#> SRR1706833 4 0.3510 0.879 0.128 0.000 0.032 0.832 0.008
#> SRR1706834 4 0.3510 0.879 0.128 0.000 0.032 0.832 0.008
#> SRR1706839 1 0.6725 -0.241 0.520 0.000 0.016 0.212 0.252
#> SRR1706840 1 0.6725 -0.241 0.520 0.000 0.016 0.212 0.252
#> SRR1706841 1 0.6725 -0.241 0.520 0.000 0.016 0.212 0.252
#> SRR1706842 1 0.6725 -0.241 0.520 0.000 0.016 0.212 0.252
#> SRR1706847 3 0.6996 0.846 0.088 0.168 0.580 0.000 0.164
#> SRR1706848 3 0.6996 0.846 0.088 0.168 0.580 0.000 0.164
#> SRR1706849 3 0.6996 0.846 0.088 0.168 0.580 0.000 0.164
#> SRR1706850 3 0.6996 0.846 0.088 0.168 0.580 0.000 0.164
#> SRR1706843 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706844 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706845 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706846 5 0.6740 0.929 0.380 0.000 0.004 0.212 0.404
#> SRR1706851 3 0.4642 0.821 0.032 0.168 0.760 0.000 0.040
#> SRR1706852 3 0.4642 0.821 0.032 0.168 0.760 0.000 0.040
#> SRR1706853 3 0.4642 0.821 0.032 0.168 0.760 0.000 0.040
#> SRR1706854 3 0.4642 0.821 0.032 0.168 0.760 0.000 0.040
#> SRR1706855 2 0.6034 0.659 0.036 0.656 0.140 0.000 0.168
#> SRR1706856 2 0.6034 0.659 0.036 0.656 0.140 0.000 0.168
#> SRR1706857 2 0.6034 0.659 0.036 0.656 0.140 0.000 0.168
#> SRR1706858 2 0.6034 0.659 0.036 0.656 0.140 0.000 0.168
#> SRR1706859 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000
#> SRR1706860 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000
#> SRR1706861 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000
#> SRR1706862 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000
#> SRR1706867 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706869 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706870 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706863 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706864 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706865 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706866 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706871 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706872 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706873 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706874 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706879 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706880 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706881 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706882 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706883 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706884 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706885 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706886 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706875 2 0.6575 0.593 0.036 0.588 0.208 0.000 0.168
#> SRR1706876 2 0.6575 0.593 0.036 0.588 0.208 0.000 0.168
#> SRR1706877 2 0.6575 0.593 0.036 0.588 0.208 0.000 0.168
#> SRR1706878 2 0.6575 0.593 0.036 0.588 0.208 0.000 0.168
#> SRR1706887 3 0.6784 0.843 0.076 0.156 0.600 0.000 0.168
#> SRR1706888 3 0.6784 0.843 0.076 0.156 0.600 0.000 0.168
#> SRR1706889 3 0.6784 0.843 0.076 0.156 0.600 0.000 0.168
#> SRR1706890 3 0.6784 0.843 0.076 0.156 0.600 0.000 0.168
#> SRR1706891 3 0.5605 0.780 0.032 0.156 0.696 0.000 0.116
#> SRR1706892 3 0.5605 0.780 0.032 0.156 0.696 0.000 0.116
#> SRR1706893 3 0.5605 0.780 0.032 0.156 0.696 0.000 0.116
#> SRR1706894 3 0.5605 0.780 0.032 0.156 0.696 0.000 0.116
#> SRR1706895 2 0.5608 0.677 0.016 0.616 0.064 0.000 0.304
#> SRR1706896 2 0.5608 0.677 0.016 0.616 0.064 0.000 0.304
#> SRR1706897 2 0.5608 0.677 0.016 0.616 0.064 0.000 0.304
#> SRR1706898 2 0.5608 0.677 0.016 0.616 0.064 0.000 0.304
#> SRR1706899 2 0.3543 0.772 0.024 0.828 0.012 0.000 0.136
#> SRR1706900 2 0.3543 0.772 0.024 0.828 0.012 0.000 0.136
#> SRR1706901 2 0.3543 0.772 0.024 0.828 0.012 0.000 0.136
#> SRR1706902 2 0.3543 0.772 0.024 0.828 0.012 0.000 0.136
#> SRR1706907 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706908 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706909 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706910 3 0.6729 0.848 0.056 0.168 0.592 0.000 0.184
#> SRR1706903 2 0.3666 0.773 0.032 0.824 0.012 0.000 0.132
#> SRR1706904 2 0.3666 0.773 0.032 0.824 0.012 0.000 0.132
#> SRR1706905 2 0.3666 0.773 0.032 0.824 0.012 0.000 0.132
#> SRR1706906 2 0.3666 0.773 0.032 0.824 0.012 0.000 0.132
#> SRR1706911 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706912 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706913 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706914 3 0.2813 0.847 0.000 0.168 0.832 0.000 0.000
#> SRR1706919 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706920 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706921 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706922 2 0.0880 0.823 0.000 0.968 0.000 0.000 0.032
#> SRR1706915 2 0.6522 0.599 0.036 0.596 0.200 0.000 0.168
#> SRR1706916 2 0.6522 0.599 0.036 0.596 0.200 0.000 0.168
#> SRR1706917 2 0.6522 0.599 0.036 0.596 0.200 0.000 0.168
#> SRR1706918 2 0.6522 0.599 0.036 0.596 0.200 0.000 0.168
#> SRR1706923 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706924 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706925 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
#> SRR1706926 2 0.0671 0.823 0.016 0.980 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.4190 0.875 0.000 0.000 0.020 0.756 NA 0.168
#> SRR1706768 4 0.4190 0.875 0.000 0.000 0.020 0.756 NA 0.168
#> SRR1706769 4 0.4190 0.875 0.000 0.000 0.020 0.756 NA 0.168
#> SRR1706770 4 0.4190 0.875 0.000 0.000 0.020 0.756 NA 0.168
#> SRR1706771 4 0.1737 0.870 0.000 0.000 0.008 0.932 NA 0.040
#> SRR1706772 4 0.1737 0.870 0.000 0.000 0.008 0.932 NA 0.040
#> SRR1706773 4 0.1737 0.870 0.000 0.000 0.008 0.932 NA 0.040
#> SRR1706774 4 0.1737 0.870 0.000 0.000 0.008 0.932 NA 0.040
#> SRR1706775 1 0.6856 0.600 0.500 0.000 0.008 0.260 NA 0.088
#> SRR1706776 1 0.6856 0.600 0.500 0.000 0.008 0.260 NA 0.088
#> SRR1706777 1 0.6856 0.600 0.500 0.000 0.008 0.260 NA 0.088
#> SRR1706778 1 0.6856 0.600 0.500 0.000 0.008 0.260 NA 0.088
#> SRR1706779 1 0.2118 0.759 0.888 0.000 0.000 0.104 NA 0.000
#> SRR1706780 1 0.2118 0.759 0.888 0.000 0.000 0.104 NA 0.000
#> SRR1706781 1 0.2118 0.759 0.888 0.000 0.000 0.104 NA 0.000
#> SRR1706782 1 0.2118 0.759 0.888 0.000 0.000 0.104 NA 0.000
#> SRR1706783 1 0.5030 0.713 0.628 0.000 0.004 0.104 NA 0.000
#> SRR1706784 1 0.5030 0.713 0.628 0.000 0.004 0.104 NA 0.000
#> SRR1706785 1 0.5030 0.713 0.628 0.000 0.004 0.104 NA 0.000
#> SRR1706786 1 0.5030 0.713 0.628 0.000 0.004 0.104 NA 0.000
#> SRR1706787 4 0.3565 0.881 0.000 0.000 0.008 0.796 NA 0.156
#> SRR1706788 4 0.3565 0.881 0.000 0.000 0.008 0.796 NA 0.156
#> SRR1706789 4 0.3565 0.881 0.000 0.000 0.008 0.796 NA 0.156
#> SRR1706790 4 0.3565 0.881 0.000 0.000 0.008 0.796 NA 0.156
#> SRR1706791 4 0.0806 0.869 0.000 0.000 0.000 0.972 NA 0.020
#> SRR1706792 4 0.0806 0.869 0.000 0.000 0.000 0.972 NA 0.020
#> SRR1706793 4 0.0806 0.869 0.000 0.000 0.000 0.972 NA 0.020
#> SRR1706794 4 0.0806 0.869 0.000 0.000 0.000 0.972 NA 0.020
#> SRR1706795 1 0.6540 0.649 0.552 0.000 0.004 0.212 NA 0.088
#> SRR1706796 1 0.6540 0.649 0.552 0.000 0.004 0.212 NA 0.088
#> SRR1706797 1 0.6540 0.649 0.552 0.000 0.004 0.212 NA 0.088
#> SRR1706798 1 0.6540 0.649 0.552 0.000 0.004 0.212 NA 0.088
#> SRR1706799 1 0.1863 0.759 0.896 0.000 0.000 0.104 NA 0.000
#> SRR1706800 1 0.1863 0.759 0.896 0.000 0.000 0.104 NA 0.000
#> SRR1706801 1 0.1863 0.759 0.896 0.000 0.000 0.104 NA 0.000
#> SRR1706802 1 0.1863 0.759 0.896 0.000 0.000 0.104 NA 0.000
#> SRR1706803 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706804 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706805 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706806 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706811 4 0.1414 0.871 0.004 0.000 0.012 0.952 NA 0.012
#> SRR1706812 4 0.1414 0.871 0.004 0.000 0.012 0.952 NA 0.012
#> SRR1706813 4 0.1414 0.871 0.004 0.000 0.012 0.952 NA 0.012
#> SRR1706814 4 0.1414 0.871 0.004 0.000 0.012 0.952 NA 0.012
#> SRR1706807 4 0.4064 0.875 0.004 0.000 0.012 0.768 NA 0.164
#> SRR1706808 4 0.4064 0.875 0.004 0.000 0.012 0.768 NA 0.164
#> SRR1706809 4 0.4064 0.875 0.004 0.000 0.012 0.768 NA 0.164
#> SRR1706810 4 0.4064 0.875 0.004 0.000 0.012 0.768 NA 0.164
#> SRR1706815 1 0.7587 0.582 0.452 0.000 0.048 0.256 NA 0.092
#> SRR1706816 1 0.7587 0.582 0.452 0.000 0.048 0.256 NA 0.092
#> SRR1706817 1 0.7587 0.582 0.452 0.000 0.048 0.256 NA 0.092
#> SRR1706818 1 0.7587 0.582 0.452 0.000 0.048 0.256 NA 0.092
#> SRR1706819 1 0.5730 0.733 0.692 0.000 0.044 0.100 NA 0.060
#> SRR1706820 1 0.5730 0.733 0.692 0.000 0.044 0.100 NA 0.060
#> SRR1706821 1 0.5730 0.733 0.692 0.000 0.044 0.100 NA 0.060
#> SRR1706822 1 0.5730 0.733 0.692 0.000 0.044 0.100 NA 0.060
#> SRR1706823 1 0.6134 0.694 0.564 0.000 0.020 0.100 NA 0.032
#> SRR1706824 1 0.6134 0.694 0.564 0.000 0.020 0.100 NA 0.032
#> SRR1706825 1 0.6134 0.694 0.564 0.000 0.020 0.100 NA 0.032
#> SRR1706826 1 0.6134 0.694 0.564 0.000 0.020 0.100 NA 0.032
#> SRR1706827 4 0.3555 0.881 0.000 0.000 0.000 0.780 NA 0.176
#> SRR1706828 4 0.3555 0.881 0.000 0.000 0.000 0.780 NA 0.176
#> SRR1706829 4 0.3555 0.881 0.000 0.000 0.000 0.780 NA 0.176
#> SRR1706830 4 0.3555 0.881 0.000 0.000 0.000 0.780 NA 0.176
#> SRR1706835 1 0.6771 0.599 0.500 0.000 0.004 0.264 NA 0.088
#> SRR1706836 1 0.6771 0.599 0.500 0.000 0.004 0.264 NA 0.088
#> SRR1706837 1 0.6771 0.599 0.500 0.000 0.004 0.264 NA 0.088
#> SRR1706838 1 0.6771 0.599 0.500 0.000 0.004 0.264 NA 0.088
#> SRR1706831 4 0.0603 0.878 0.000 0.000 0.000 0.980 NA 0.016
#> SRR1706832 4 0.0603 0.878 0.000 0.000 0.000 0.980 NA 0.016
#> SRR1706833 4 0.0603 0.878 0.000 0.000 0.000 0.980 NA 0.016
#> SRR1706834 4 0.0603 0.878 0.000 0.000 0.000 0.980 NA 0.016
#> SRR1706839 1 0.2454 0.758 0.876 0.000 0.000 0.104 NA 0.004
#> SRR1706840 1 0.2454 0.758 0.876 0.000 0.000 0.104 NA 0.004
#> SRR1706841 1 0.2454 0.758 0.876 0.000 0.000 0.104 NA 0.004
#> SRR1706842 1 0.2454 0.758 0.876 0.000 0.000 0.104 NA 0.004
#> SRR1706847 3 0.3559 0.784 0.028 0.116 0.824 0.000 NA 0.008
#> SRR1706848 3 0.3559 0.784 0.028 0.116 0.824 0.000 NA 0.008
#> SRR1706849 3 0.3559 0.784 0.028 0.116 0.824 0.000 NA 0.008
#> SRR1706850 3 0.3559 0.784 0.028 0.116 0.824 0.000 NA 0.008
#> SRR1706843 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706844 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706845 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706846 1 0.4914 0.713 0.628 0.000 0.000 0.104 NA 0.000
#> SRR1706851 3 0.6598 0.707 0.028 0.116 0.528 0.000 NA 0.284
#> SRR1706852 3 0.6598 0.707 0.028 0.116 0.528 0.000 NA 0.284
#> SRR1706853 3 0.6598 0.707 0.028 0.116 0.528 0.000 NA 0.284
#> SRR1706854 3 0.6598 0.707 0.028 0.116 0.528 0.000 NA 0.284
#> SRR1706855 6 0.4175 0.918 0.000 0.464 0.000 0.000 NA 0.524
#> SRR1706856 6 0.4175 0.918 0.000 0.464 0.000 0.000 NA 0.524
#> SRR1706857 6 0.4175 0.918 0.000 0.464 0.000 0.000 NA 0.524
#> SRR1706858 6 0.4175 0.918 0.000 0.464 0.000 0.000 NA 0.524
#> SRR1706859 2 0.1167 0.712 0.008 0.960 0.000 0.000 NA 0.012
#> SRR1706860 2 0.1167 0.712 0.008 0.960 0.000 0.000 NA 0.012
#> SRR1706861 2 0.1167 0.712 0.008 0.960 0.000 0.000 NA 0.012
#> SRR1706862 2 0.1167 0.712 0.008 0.960 0.000 0.000 NA 0.012
#> SRR1706867 3 0.2536 0.787 0.000 0.116 0.864 0.000 NA 0.000
#> SRR1706869 3 0.2536 0.787 0.000 0.116 0.864 0.000 NA 0.000
#> SRR1706870 3 0.2536 0.787 0.000 0.116 0.864 0.000 NA 0.000
#> SRR1706863 2 0.0622 0.720 0.008 0.980 0.000 0.000 NA 0.000
#> SRR1706864 2 0.0622 0.720 0.008 0.980 0.000 0.000 NA 0.000
#> SRR1706865 2 0.0622 0.720 0.008 0.980 0.000 0.000 NA 0.000
#> SRR1706866 2 0.0622 0.720 0.008 0.980 0.000 0.000 NA 0.000
#> SRR1706871 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706872 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706873 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706874 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706879 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706880 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706881 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706882 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706883 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706884 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706885 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706886 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706875 6 0.3804 0.957 0.000 0.424 0.000 0.000 NA 0.576
#> SRR1706876 6 0.3804 0.957 0.000 0.424 0.000 0.000 NA 0.576
#> SRR1706877 6 0.3804 0.957 0.000 0.424 0.000 0.000 NA 0.576
#> SRR1706878 6 0.3804 0.957 0.000 0.424 0.000 0.000 NA 0.576
#> SRR1706887 3 0.4354 0.761 0.012 0.108 0.780 0.000 NA 0.032
#> SRR1706888 3 0.4354 0.761 0.012 0.108 0.780 0.000 NA 0.032
#> SRR1706889 3 0.4354 0.761 0.012 0.108 0.780 0.000 NA 0.032
#> SRR1706890 3 0.4354 0.761 0.012 0.108 0.780 0.000 NA 0.032
#> SRR1706891 3 0.7600 0.586 0.048 0.108 0.424 0.000 NA 0.304
#> SRR1706892 3 0.7600 0.586 0.048 0.108 0.424 0.000 NA 0.304
#> SRR1706893 3 0.7600 0.586 0.048 0.108 0.424 0.000 NA 0.304
#> SRR1706894 3 0.7600 0.586 0.048 0.108 0.424 0.000 NA 0.304
#> SRR1706895 2 0.6672 -0.214 0.020 0.408 0.008 0.000 NA 0.300
#> SRR1706896 2 0.6672 -0.214 0.020 0.408 0.008 0.000 NA 0.300
#> SRR1706897 2 0.6672 -0.214 0.020 0.408 0.008 0.000 NA 0.300
#> SRR1706898 2 0.6672 -0.214 0.020 0.408 0.008 0.000 NA 0.300
#> SRR1706899 2 0.4173 0.528 0.000 0.692 0.008 0.000 NA 0.028
#> SRR1706900 2 0.4173 0.528 0.000 0.692 0.008 0.000 NA 0.028
#> SRR1706901 2 0.4173 0.528 0.000 0.692 0.008 0.000 NA 0.028
#> SRR1706902 2 0.4173 0.528 0.000 0.692 0.008 0.000 NA 0.028
#> SRR1706907 3 0.2400 0.788 0.004 0.116 0.872 0.000 NA 0.000
#> SRR1706908 3 0.2400 0.788 0.004 0.116 0.872 0.000 NA 0.000
#> SRR1706909 3 0.2400 0.788 0.004 0.116 0.872 0.000 NA 0.000
#> SRR1706910 3 0.2400 0.788 0.004 0.116 0.872 0.000 NA 0.000
#> SRR1706903 2 0.4172 0.542 0.004 0.704 0.008 0.000 NA 0.024
#> SRR1706904 2 0.4172 0.542 0.004 0.704 0.008 0.000 NA 0.024
#> SRR1706905 2 0.4172 0.542 0.004 0.704 0.008 0.000 NA 0.024
#> SRR1706906 2 0.4172 0.542 0.004 0.704 0.008 0.000 NA 0.024
#> SRR1706911 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706912 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706913 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706914 3 0.5960 0.758 0.020 0.116 0.608 0.000 NA 0.228
#> SRR1706919 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706920 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706921 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706922 2 0.2342 0.645 0.004 0.888 0.000 0.000 NA 0.088
#> SRR1706915 6 0.3937 0.960 0.000 0.424 0.000 0.000 NA 0.572
#> SRR1706916 6 0.3937 0.960 0.000 0.424 0.000 0.000 NA 0.572
#> SRR1706917 6 0.3937 0.960 0.000 0.424 0.000 0.000 NA 0.572
#> SRR1706918 6 0.3937 0.960 0.000 0.424 0.000 0.000 NA 0.572
#> SRR1706923 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706924 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706925 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 0.000
#> SRR1706926 2 0.0260 0.723 0.000 0.992 0.000 0.000 NA 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", "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 15185 rows and 159 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 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 0.5036 0.497 0.497
#> 3 3 0.920 0.914 0.955 0.1850 0.924 0.846
#> 4 4 0.920 0.907 0.953 0.2035 0.878 0.709
#> 5 5 0.949 0.974 0.959 0.0531 0.939 0.795
#> 6 6 0.870 0.885 0.905 0.0299 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] 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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706768 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706769 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706770 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706771 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706772 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706773 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706774 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706775 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706776 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706777 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706778 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706779 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706780 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706781 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706782 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706783 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706784 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706785 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706786 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706787 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706788 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706789 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706790 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706791 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706792 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706793 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706794 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706795 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706796 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706797 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706798 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706799 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706800 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706801 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706802 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706803 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706804 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706805 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706806 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706811 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706812 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706813 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706814 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706807 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706808 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706809 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706810 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706815 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706816 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706817 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706818 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706819 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706820 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706821 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706822 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706823 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706824 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706825 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706826 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706827 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706828 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706829 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706830 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706835 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706836 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706837 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706838 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706831 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706832 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706833 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706834 1 0.000 0.991 1.000 0.000 0.000
#> SRR1706839 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706840 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706841 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706842 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706847 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706848 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706849 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706850 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706843 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706844 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706845 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706846 1 0.103 0.987 0.976 0.000 0.024
#> SRR1706851 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706852 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706853 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706854 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706855 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706856 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706857 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706858 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706859 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706860 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706861 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706862 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706867 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706869 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706870 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706863 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706864 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706865 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706866 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706871 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706872 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706873 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706874 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706879 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706880 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706881 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706882 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706883 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706884 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706885 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706886 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706875 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706876 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706877 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706878 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706887 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706888 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706889 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706890 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706891 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706892 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706893 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706894 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706895 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706896 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706897 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706898 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706899 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706900 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706901 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706902 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706907 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706908 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706909 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706910 3 0.103 1.000 0.000 0.024 0.976
#> SRR1706903 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706904 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706905 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706906 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706911 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706912 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706913 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706914 2 0.603 0.523 0.000 0.624 0.376
#> SRR1706919 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706920 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706921 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706922 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706915 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706916 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706917 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706918 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706923 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706924 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706925 2 0.000 0.891 0.000 1.000 0.000
#> SRR1706926 2 0.000 0.891 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706768 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706769 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706770 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706771 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706772 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706773 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706774 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706775 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706776 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706777 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706778 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706779 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706780 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706781 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706782 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706783 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706784 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706785 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706786 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706787 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706788 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706789 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706790 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706791 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706792 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706793 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706794 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706795 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706796 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706797 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706798 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706799 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706800 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706801 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706802 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706803 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706804 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706805 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706806 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706811 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706812 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706813 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706814 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706807 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706808 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706809 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706810 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706815 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706816 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706817 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706818 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706819 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706820 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706821 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706822 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706823 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706824 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706825 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706826 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706827 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706828 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706829 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706830 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706835 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706836 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706837 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706838 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706831 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706832 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706833 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706834 4 0.0000 1.000 0.00 0.000 0.000 1.00
#> SRR1706839 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706840 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706841 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706842 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706847 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706848 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706849 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706850 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706843 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706844 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706845 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706846 1 0.0707 1.000 0.98 0.000 0.000 0.02
#> SRR1706851 2 0.5476 0.460 0.02 0.584 0.396 0.00
#> SRR1706852 2 0.5476 0.460 0.02 0.584 0.396 0.00
#> SRR1706853 2 0.5476 0.460 0.02 0.584 0.396 0.00
#> SRR1706854 2 0.5476 0.460 0.02 0.584 0.396 0.00
#> SRR1706855 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706856 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706857 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706858 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706859 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706860 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706861 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706862 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706867 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706869 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706870 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706863 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706864 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706865 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706866 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706871 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706872 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706873 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706874 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706879 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706880 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706881 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706882 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706883 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706884 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706885 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706886 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706875 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706876 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706877 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706878 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706887 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706888 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706889 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706890 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706891 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706892 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706893 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706894 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706895 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706896 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706897 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706898 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706899 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706900 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706901 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706902 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706907 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706908 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706909 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706910 3 0.0000 1.000 0.00 0.000 1.000 0.00
#> SRR1706903 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706904 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706905 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706906 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706911 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706912 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706913 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706914 2 0.5517 0.434 0.02 0.568 0.412 0.00
#> SRR1706919 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706920 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706921 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706922 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706915 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706916 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706917 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706918 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706923 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706924 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706925 2 0.0000 0.880 0.00 1.000 0.000 0.00
#> SRR1706926 2 0.0000 0.880 0.00 1.000 0.000 0.00
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706772 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706773 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706774 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706775 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706776 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706777 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706778 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706779 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706784 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706785 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706786 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706787 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706792 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706793 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706794 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706795 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706796 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706797 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706798 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706799 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706804 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706805 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706806 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706811 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706812 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706813 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706814 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706807 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706815 4 0.0671 0.977 0.016 0.000 0.004 0.980 0.000
#> SRR1706816 4 0.0671 0.977 0.016 0.000 0.004 0.980 0.000
#> SRR1706817 4 0.0671 0.977 0.016 0.000 0.004 0.980 0.000
#> SRR1706818 4 0.0671 0.977 0.016 0.000 0.004 0.980 0.000
#> SRR1706819 1 0.0963 0.955 0.964 0.000 0.036 0.000 0.000
#> SRR1706820 1 0.0963 0.955 0.964 0.000 0.036 0.000 0.000
#> SRR1706821 1 0.0963 0.955 0.964 0.000 0.036 0.000 0.000
#> SRR1706822 1 0.0963 0.955 0.964 0.000 0.036 0.000 0.000
#> SRR1706823 1 0.1792 0.958 0.916 0.000 0.084 0.000 0.000
#> SRR1706824 1 0.1792 0.958 0.916 0.000 0.084 0.000 0.000
#> SRR1706825 1 0.1792 0.958 0.916 0.000 0.084 0.000 0.000
#> SRR1706826 1 0.1792 0.958 0.916 0.000 0.084 0.000 0.000
#> SRR1706827 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706836 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706837 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706838 4 0.1430 0.958 0.052 0.000 0.004 0.944 0.000
#> SRR1706831 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 0.984 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> SRR1706847 5 0.1792 0.944 0.000 0.000 0.084 0.000 0.916
#> SRR1706848 5 0.1792 0.944 0.000 0.000 0.084 0.000 0.916
#> SRR1706849 5 0.1792 0.944 0.000 0.000 0.084 0.000 0.916
#> SRR1706850 5 0.1792 0.944 0.000 0.000 0.084 0.000 0.916
#> SRR1706843 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706844 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706845 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706846 1 0.1410 0.965 0.940 0.000 0.060 0.000 0.000
#> SRR1706851 3 0.4367 0.969 0.000 0.192 0.748 0.000 0.060
#> SRR1706852 3 0.4367 0.969 0.000 0.192 0.748 0.000 0.060
#> SRR1706853 3 0.4367 0.969 0.000 0.192 0.748 0.000 0.060
#> SRR1706854 3 0.4367 0.969 0.000 0.192 0.748 0.000 0.060
#> SRR1706855 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706856 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706857 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706858 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706859 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706860 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706861 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706862 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706867 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706869 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706870 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706863 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706871 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706872 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706873 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706874 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706879 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706883 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706875 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706876 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706877 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706878 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706887 5 0.1270 0.953 0.000 0.000 0.052 0.000 0.948
#> SRR1706888 5 0.1270 0.953 0.000 0.000 0.052 0.000 0.948
#> SRR1706889 5 0.1270 0.953 0.000 0.000 0.052 0.000 0.948
#> SRR1706890 5 0.1270 0.953 0.000 0.000 0.052 0.000 0.948
#> SRR1706891 3 0.3236 0.940 0.000 0.152 0.828 0.000 0.020
#> SRR1706892 3 0.3236 0.940 0.000 0.152 0.828 0.000 0.020
#> SRR1706893 3 0.3236 0.940 0.000 0.152 0.828 0.000 0.020
#> SRR1706894 3 0.3236 0.940 0.000 0.152 0.828 0.000 0.020
#> SRR1706895 2 0.1121 0.961 0.000 0.956 0.044 0.000 0.000
#> SRR1706896 2 0.1121 0.961 0.000 0.956 0.044 0.000 0.000
#> SRR1706897 2 0.1121 0.961 0.000 0.956 0.044 0.000 0.000
#> SRR1706898 2 0.1121 0.961 0.000 0.956 0.044 0.000 0.000
#> SRR1706899 2 0.0794 0.973 0.000 0.972 0.028 0.000 0.000
#> SRR1706900 2 0.0794 0.973 0.000 0.972 0.028 0.000 0.000
#> SRR1706901 2 0.0794 0.973 0.000 0.972 0.028 0.000 0.000
#> SRR1706902 2 0.0794 0.973 0.000 0.972 0.028 0.000 0.000
#> SRR1706907 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706908 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706909 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706910 5 0.0000 0.969 0.000 0.000 0.000 0.000 1.000
#> SRR1706903 2 0.0703 0.975 0.000 0.976 0.024 0.000 0.000
#> SRR1706904 2 0.0703 0.975 0.000 0.976 0.024 0.000 0.000
#> SRR1706905 2 0.0703 0.975 0.000 0.976 0.024 0.000 0.000
#> SRR1706906 2 0.0703 0.975 0.000 0.976 0.024 0.000 0.000
#> SRR1706911 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706912 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706913 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706914 3 0.4325 0.976 0.000 0.180 0.756 0.000 0.064
#> SRR1706919 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706920 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706921 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706922 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706915 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706916 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706917 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706918 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706923 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.991 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706768 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706769 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706770 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706771 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706772 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706773 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706774 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706775 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706776 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706777 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706778 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706779 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706780 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706781 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706782 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706783 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706784 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706785 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706786 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706787 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706788 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706789 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706790 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706791 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706792 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706793 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706794 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706795 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706796 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706797 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706798 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706799 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706800 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706801 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706802 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706803 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706804 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706805 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706806 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706811 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706812 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706813 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706814 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706807 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706808 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706809 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706810 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706815 4 0.2250 0.882 0.092 0.000 0.000 0.888 NA 0.000
#> SRR1706816 4 0.2250 0.882 0.092 0.000 0.000 0.888 NA 0.000
#> SRR1706817 4 0.2250 0.882 0.092 0.000 0.000 0.888 NA 0.000
#> SRR1706818 4 0.2250 0.882 0.092 0.000 0.000 0.888 NA 0.000
#> SRR1706819 1 0.2178 0.793 0.868 0.000 0.000 0.000 NA 0.000
#> SRR1706820 1 0.2178 0.793 0.868 0.000 0.000 0.000 NA 0.000
#> SRR1706821 1 0.2178 0.793 0.868 0.000 0.000 0.000 NA 0.000
#> SRR1706822 1 0.2178 0.793 0.868 0.000 0.000 0.000 NA 0.000
#> SRR1706823 1 0.3499 0.810 0.680 0.000 0.000 0.000 NA 0.000
#> SRR1706824 1 0.3499 0.810 0.680 0.000 0.000 0.000 NA 0.000
#> SRR1706825 1 0.3499 0.810 0.680 0.000 0.000 0.000 NA 0.000
#> SRR1706826 1 0.3499 0.810 0.680 0.000 0.000 0.000 NA 0.000
#> SRR1706827 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706828 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706829 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706830 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706835 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706836 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706837 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706838 4 0.3374 0.812 0.208 0.000 0.000 0.772 NA 0.000
#> SRR1706831 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706832 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706833 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706834 4 0.0000 0.929 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1706839 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706840 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706841 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706842 1 0.0000 0.849 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1706847 3 0.4122 0.779 0.000 0.000 0.704 0.000 NA 0.048
#> SRR1706848 3 0.4122 0.779 0.000 0.000 0.704 0.000 NA 0.048
#> SRR1706849 3 0.4122 0.779 0.000 0.000 0.704 0.000 NA 0.048
#> SRR1706850 3 0.4122 0.779 0.000 0.000 0.704 0.000 NA 0.048
#> SRR1706843 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706844 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706845 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706846 1 0.3101 0.842 0.756 0.000 0.000 0.000 NA 0.000
#> SRR1706851 6 0.5283 0.903 0.000 0.064 0.020 0.000 NA 0.560
#> SRR1706852 6 0.5283 0.903 0.000 0.064 0.020 0.000 NA 0.560
#> SRR1706853 6 0.5283 0.903 0.000 0.064 0.020 0.000 NA 0.560
#> SRR1706854 6 0.5283 0.903 0.000 0.064 0.020 0.000 NA 0.560
#> SRR1706855 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706856 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706857 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706858 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706859 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706860 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706861 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706862 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706867 3 0.0146 0.878 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1706869 3 0.0146 0.878 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1706870 3 0.0146 0.878 0.000 0.000 0.996 0.000 NA 0.004
#> SRR1706863 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706864 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706865 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706866 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706871 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706872 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706873 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706874 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706879 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706880 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706881 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706882 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706883 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706884 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706885 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706886 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706875 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706876 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706877 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706878 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706887 3 0.3221 0.809 0.000 0.000 0.792 0.000 NA 0.188
#> SRR1706888 3 0.3221 0.809 0.000 0.000 0.792 0.000 NA 0.188
#> SRR1706889 3 0.3221 0.809 0.000 0.000 0.792 0.000 NA 0.188
#> SRR1706890 3 0.3221 0.809 0.000 0.000 0.792 0.000 NA 0.188
#> SRR1706891 6 0.1141 0.680 0.000 0.052 0.000 0.000 NA 0.948
#> SRR1706892 6 0.1141 0.680 0.000 0.052 0.000 0.000 NA 0.948
#> SRR1706893 6 0.1141 0.680 0.000 0.052 0.000 0.000 NA 0.948
#> SRR1706894 6 0.1141 0.680 0.000 0.052 0.000 0.000 NA 0.948
#> SRR1706895 2 0.3052 0.791 0.000 0.780 0.000 0.000 NA 0.216
#> SRR1706896 2 0.3052 0.791 0.000 0.780 0.000 0.000 NA 0.216
#> SRR1706897 2 0.3052 0.791 0.000 0.780 0.000 0.000 NA 0.216
#> SRR1706898 2 0.3052 0.791 0.000 0.780 0.000 0.000 NA 0.216
#> SRR1706899 2 0.2595 0.848 0.000 0.836 0.000 0.000 NA 0.160
#> SRR1706900 2 0.2595 0.848 0.000 0.836 0.000 0.000 NA 0.160
#> SRR1706901 2 0.2595 0.848 0.000 0.836 0.000 0.000 NA 0.160
#> SRR1706902 2 0.2595 0.848 0.000 0.836 0.000 0.000 NA 0.160
#> SRR1706907 3 0.0146 0.879 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1706908 3 0.0146 0.879 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1706909 3 0.0146 0.879 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1706910 3 0.0146 0.879 0.000 0.000 0.996 0.000 NA 0.000
#> SRR1706903 2 0.1411 0.926 0.000 0.936 0.000 0.000 NA 0.060
#> SRR1706904 2 0.1411 0.926 0.000 0.936 0.000 0.000 NA 0.060
#> SRR1706905 2 0.1411 0.926 0.000 0.936 0.000 0.000 NA 0.060
#> SRR1706906 2 0.1411 0.926 0.000 0.936 0.000 0.000 NA 0.060
#> SRR1706911 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706912 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706913 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706914 6 0.5306 0.904 0.000 0.060 0.024 0.000 NA 0.560
#> SRR1706919 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706920 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706921 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706922 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706915 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706916 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706917 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706918 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706923 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706924 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706925 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 0.000
#> SRR1706926 2 0.0000 0.963 0.000 1.000 0.000 0.000 NA 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 15185 rows and 159 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.749 0.910 0.835 0.2405 0.878 0.754
#> 4 4 1.000 0.983 0.993 0.1959 0.877 0.673
#> 5 5 0.852 0.775 0.871 0.0505 0.983 0.932
#> 6 6 0.857 0.746 0.870 0.0465 0.917 0.677
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
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706768 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706769 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706770 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706771 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706772 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706773 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706774 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706775 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706776 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706777 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706778 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706779 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706780 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706781 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706782 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706783 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706784 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706785 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706786 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706787 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706788 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706789 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706790 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706791 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706792 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706793 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706794 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706795 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706796 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706797 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706798 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706799 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706800 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706801 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706802 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706803 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706804 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706805 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706806 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706811 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706812 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706813 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706814 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706807 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706808 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706809 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706810 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706815 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706816 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706817 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706818 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706819 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706820 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706821 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706822 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706823 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706824 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706825 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706826 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706827 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706828 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706829 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706830 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706835 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706836 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706837 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706838 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706831 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706832 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706833 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706834 3 0.0000 1.000 0.000 0.000 1.000
#> SRR1706839 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706840 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706841 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706842 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706847 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706848 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706849 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706850 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706843 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706844 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706845 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706846 1 0.6154 1.000 0.592 0.000 0.408
#> SRR1706851 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706852 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706853 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706854 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706855 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706859 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706860 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706861 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706862 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706867 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706869 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706870 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706863 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706864 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706865 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706866 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706871 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706872 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706873 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706874 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706879 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706880 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706881 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706882 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706883 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706884 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706885 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706886 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706875 2 0.3879 0.826 0.152 0.848 0.000
#> SRR1706876 2 0.3686 0.828 0.140 0.860 0.000
#> SRR1706877 2 0.0424 0.851 0.008 0.992 0.000
#> SRR1706878 2 0.4002 0.824 0.160 0.840 0.000
#> SRR1706887 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706888 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706889 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706890 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706891 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706892 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706893 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706894 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706895 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706896 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706897 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706898 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706899 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706900 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706901 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706902 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706907 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706908 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706909 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706910 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706903 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706904 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706905 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706906 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706911 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706912 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706913 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706914 2 0.6154 0.772 0.408 0.592 0.000
#> SRR1706919 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706920 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706921 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706922 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706915 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706923 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706924 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706925 2 0.0000 0.852 0.000 1.000 0.000
#> SRR1706926 2 0.0000 0.852 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706768 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706769 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706770 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706771 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706772 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706773 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706774 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706775 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706776 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706777 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706778 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706779 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706780 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706781 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706782 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706783 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706784 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706785 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706786 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706787 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706788 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706789 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706790 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706791 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706792 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706793 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706794 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706795 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706796 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706797 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706798 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706799 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706800 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706801 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706802 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706803 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706804 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706805 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706806 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706811 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706812 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706813 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706814 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706807 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706808 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706809 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706810 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706815 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706816 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706817 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706818 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706819 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706820 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706821 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706822 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706823 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706824 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706825 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706826 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706827 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706828 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706829 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706830 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706835 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706836 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706837 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706838 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706831 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706832 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706833 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706834 4 0.000 1.000 0 0.000 0.000 1
#> SRR1706839 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706840 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706841 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706842 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706847 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706848 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706849 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706850 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706843 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706844 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706845 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706846 1 0.000 1.000 1 0.000 0.000 0
#> SRR1706851 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706852 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706853 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706854 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706855 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706856 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706857 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706858 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706859 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706860 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706861 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706862 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706867 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706869 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706870 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706863 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706864 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706865 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706866 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706871 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706872 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706873 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706874 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706879 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706880 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706881 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706882 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706883 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706884 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706885 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706886 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706875 3 0.410 0.679 0 0.256 0.744 0
#> SRR1706876 3 0.419 0.660 0 0.268 0.732 0
#> SRR1706877 3 0.485 0.382 0 0.400 0.600 0
#> SRR1706878 3 0.404 0.691 0 0.248 0.752 0
#> SRR1706887 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706888 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706889 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706890 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706891 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706892 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706893 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706894 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706895 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706896 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706897 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706898 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706899 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706900 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706901 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706902 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706907 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706908 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706909 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706910 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706903 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706904 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706905 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706906 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706911 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706912 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706913 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706914 3 0.000 0.962 0 0.000 1.000 0
#> SRR1706919 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706920 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706921 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706922 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706915 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706916 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706917 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706918 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706923 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706924 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706925 2 0.000 1.000 0 1.000 0.000 0
#> SRR1706926 2 0.000 1.000 0 1.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706768 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706769 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706770 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706771 4 0.3876 0.711 0.000 0.000 0.000 0.684 0.316
#> SRR1706772 4 0.3395 0.778 0.000 0.000 0.000 0.764 0.236
#> SRR1706773 4 0.3424 0.775 0.000 0.000 0.000 0.760 0.240
#> SRR1706774 4 0.1792 0.878 0.000 0.000 0.000 0.916 0.084
#> SRR1706775 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706776 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706777 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706778 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706779 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706780 1 0.1121 0.859 0.956 0.000 0.000 0.000 0.044
#> SRR1706781 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706782 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706783 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706788 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706789 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706790 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706791 4 0.4088 0.662 0.000 0.000 0.000 0.632 0.368
#> SRR1706792 4 0.4088 0.662 0.000 0.000 0.000 0.632 0.368
#> SRR1706793 4 0.4088 0.662 0.000 0.000 0.000 0.632 0.368
#> SRR1706794 4 0.4088 0.662 0.000 0.000 0.000 0.632 0.368
#> SRR1706795 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706796 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706797 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706798 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706799 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706800 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706801 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706802 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706803 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706804 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706806 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706811 4 0.0404 0.911 0.000 0.000 0.000 0.988 0.012
#> SRR1706812 4 0.1410 0.891 0.000 0.000 0.000 0.940 0.060
#> SRR1706813 4 0.2377 0.852 0.000 0.000 0.000 0.872 0.128
#> SRR1706814 4 0.0963 0.902 0.000 0.000 0.000 0.964 0.036
#> SRR1706807 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706808 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706809 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706810 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706815 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706816 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706817 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706818 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706819 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706820 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706821 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706822 1 0.1197 0.859 0.952 0.000 0.000 0.000 0.048
#> SRR1706823 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706824 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706825 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706826 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706827 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706829 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706836 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706837 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706838 1 0.4088 0.783 0.632 0.000 0.000 0.000 0.368
#> SRR1706831 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706834 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.3274 0.827 0.780 0.000 0.000 0.000 0.220
#> SRR1706840 1 0.3305 0.826 0.776 0.000 0.000 0.000 0.224
#> SRR1706841 1 0.3913 0.797 0.676 0.000 0.000 0.000 0.324
#> SRR1706842 1 0.3274 0.827 0.780 0.000 0.000 0.000 0.220
#> SRR1706847 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706848 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706849 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706850 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.851 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706852 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706853 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706854 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706855 2 0.4182 -0.153 0.000 0.600 0.000 0.000 0.400
#> SRR1706856 2 0.4182 -0.153 0.000 0.600 0.000 0.000 0.400
#> SRR1706857 2 0.4182 -0.153 0.000 0.600 0.000 0.000 0.400
#> SRR1706858 2 0.4182 -0.153 0.000 0.600 0.000 0.000 0.400
#> SRR1706859 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706860 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706861 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706862 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706867 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706869 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706870 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706863 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706864 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706865 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706866 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706871 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706872 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706873 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706874 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706879 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706880 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706881 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706882 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706883 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706884 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706885 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706886 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706875 5 0.6022 0.751 0.000 0.280 0.156 0.000 0.564
#> SRR1706876 5 0.5867 0.747 0.000 0.268 0.144 0.000 0.588
#> SRR1706877 5 0.4590 0.681 0.000 0.420 0.012 0.000 0.568
#> SRR1706878 5 0.6012 0.739 0.000 0.264 0.164 0.000 0.572
#> SRR1706887 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706888 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706889 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706890 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706891 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706892 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706893 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706894 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706895 2 0.4268 -0.197 0.000 0.556 0.000 0.000 0.444
#> SRR1706896 2 0.4268 -0.197 0.000 0.556 0.000 0.000 0.444
#> SRR1706897 2 0.4268 -0.197 0.000 0.556 0.000 0.000 0.444
#> SRR1706898 2 0.4268 -0.197 0.000 0.556 0.000 0.000 0.444
#> SRR1706899 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706900 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706901 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706902 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706907 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706908 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706909 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706910 3 0.0000 0.895 0.000 0.000 1.000 0.000 0.000
#> SRR1706903 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706904 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706905 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706906 2 0.1121 0.784 0.000 0.956 0.000 0.000 0.044
#> SRR1706911 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706912 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706913 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706914 3 0.3003 0.898 0.000 0.000 0.812 0.000 0.188
#> SRR1706919 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706920 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706921 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706922 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706915 2 0.4201 -0.192 0.000 0.592 0.000 0.000 0.408
#> SRR1706916 5 0.4306 0.534 0.000 0.492 0.000 0.000 0.508
#> SRR1706917 2 0.4182 -0.153 0.000 0.600 0.000 0.000 0.400
#> SRR1706918 5 0.4300 0.584 0.000 0.476 0.000 0.000 0.524
#> SRR1706923 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706924 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706925 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
#> SRR1706926 2 0.0000 0.814 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706768 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706769 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706770 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706771 4 0.3482 0.6387 0.000 0.000 0.000 0.684 0.316 0.0
#> SRR1706772 4 0.3050 0.7431 0.000 0.000 0.000 0.764 0.236 0.0
#> SRR1706773 4 0.3076 0.7375 0.000 0.000 0.000 0.760 0.240 0.0
#> SRR1706774 4 0.1610 0.8966 0.000 0.000 0.000 0.916 0.084 0.0
#> SRR1706775 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706776 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706777 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706778 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706779 5 0.3531 0.6516 0.328 0.000 0.000 0.000 0.672 0.0
#> SRR1706780 5 0.3563 0.6414 0.336 0.000 0.000 0.000 0.664 0.0
#> SRR1706781 5 0.3563 0.6418 0.336 0.000 0.000 0.000 0.664 0.0
#> SRR1706782 5 0.3499 0.6603 0.320 0.000 0.000 0.000 0.680 0.0
#> SRR1706783 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706784 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706785 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706786 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706787 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706788 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706789 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706790 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706791 5 0.1204 0.7931 0.000 0.000 0.000 0.056 0.944 0.0
#> SRR1706792 5 0.1501 0.7783 0.000 0.000 0.000 0.076 0.924 0.0
#> SRR1706793 5 0.1501 0.7783 0.000 0.000 0.000 0.076 0.924 0.0
#> SRR1706794 5 0.1663 0.7678 0.000 0.000 0.000 0.088 0.912 0.0
#> SRR1706795 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706796 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706797 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706798 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706799 5 0.3499 0.6603 0.320 0.000 0.000 0.000 0.680 0.0
#> SRR1706800 5 0.3499 0.6603 0.320 0.000 0.000 0.000 0.680 0.0
#> SRR1706801 5 0.3515 0.6561 0.324 0.000 0.000 0.000 0.676 0.0
#> SRR1706802 5 0.3499 0.6603 0.320 0.000 0.000 0.000 0.680 0.0
#> SRR1706803 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706804 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706805 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706806 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706811 4 0.0363 0.9481 0.000 0.000 0.000 0.988 0.012 0.0
#> SRR1706812 4 0.1267 0.9162 0.000 0.000 0.000 0.940 0.060 0.0
#> SRR1706813 4 0.2135 0.8574 0.000 0.000 0.000 0.872 0.128 0.0
#> SRR1706814 4 0.0865 0.9335 0.000 0.000 0.000 0.964 0.036 0.0
#> SRR1706807 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706808 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706809 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706810 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706815 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706816 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706817 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706818 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706819 5 0.3547 0.6465 0.332 0.000 0.000 0.000 0.668 0.0
#> SRR1706820 5 0.3647 0.6035 0.360 0.000 0.000 0.000 0.640 0.0
#> SRR1706821 5 0.3607 0.6229 0.348 0.000 0.000 0.000 0.652 0.0
#> SRR1706822 5 0.3684 0.5826 0.372 0.000 0.000 0.000 0.628 0.0
#> SRR1706823 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706824 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706825 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706826 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706827 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706828 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706829 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706830 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706835 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706836 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706837 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706838 5 0.0000 0.8362 0.000 0.000 0.000 0.000 1.000 0.0
#> SRR1706831 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706832 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706833 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706834 4 0.0000 0.9546 0.000 0.000 0.000 1.000 0.000 0.0
#> SRR1706839 5 0.2340 0.7842 0.148 0.000 0.000 0.000 0.852 0.0
#> SRR1706840 5 0.2300 0.7862 0.144 0.000 0.000 0.000 0.856 0.0
#> SRR1706841 5 0.1007 0.8253 0.044 0.000 0.000 0.000 0.956 0.0
#> SRR1706842 5 0.2340 0.7842 0.148 0.000 0.000 0.000 0.852 0.0
#> SRR1706847 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706848 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706849 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706850 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706843 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706844 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706845 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706846 1 0.1075 1.0000 0.952 0.000 0.000 0.000 0.048 0.0
#> SRR1706851 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706852 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706853 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706854 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706855 2 0.3756 0.0548 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706856 2 0.3756 0.0548 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706857 2 0.3756 0.0548 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706858 2 0.3756 0.0548 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706859 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706860 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706861 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706862 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706867 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706869 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706870 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706863 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706864 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706865 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706866 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706871 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706872 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706873 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706874 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706879 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706880 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706881 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706882 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706883 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706884 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706885 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706886 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706875 6 0.6070 0.5139 0.000 0.280 0.320 0.000 0.000 0.4
#> SRR1706876 6 0.6041 0.5126 0.000 0.256 0.344 0.000 0.000 0.4
#> SRR1706877 2 0.5782 -0.3558 0.000 0.424 0.176 0.000 0.000 0.4
#> SRR1706878 6 0.6067 0.5147 0.000 0.276 0.324 0.000 0.000 0.4
#> SRR1706887 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706888 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706889 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706890 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706891 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706892 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706893 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706894 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706895 6 0.2793 0.6202 0.000 0.200 0.000 0.000 0.000 0.8
#> SRR1706896 6 0.2793 0.6202 0.000 0.200 0.000 0.000 0.000 0.8
#> SRR1706897 6 0.2793 0.6202 0.000 0.200 0.000 0.000 0.000 0.8
#> SRR1706898 6 0.2793 0.6202 0.000 0.200 0.000 0.000 0.000 0.8
#> SRR1706899 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706900 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706901 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706902 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706907 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706908 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706909 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706910 3 0.3806 0.8580 0.048 0.000 0.752 0.000 0.000 0.2
#> SRR1706903 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706904 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706905 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706906 2 0.3756 0.2856 0.000 0.600 0.000 0.000 0.000 0.4
#> SRR1706911 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706912 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706913 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706914 3 0.0000 0.8636 0.000 0.000 1.000 0.000 0.000 0.0
#> SRR1706919 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706920 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706921 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706922 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706915 2 0.3993 0.0339 0.000 0.592 0.008 0.000 0.000 0.4
#> SRR1706916 2 0.5575 -0.2864 0.000 0.460 0.140 0.000 0.000 0.4
#> SRR1706917 2 0.4388 -0.0140 0.000 0.572 0.028 0.000 0.000 0.4
#> SRR1706918 2 0.5651 -0.3099 0.000 0.448 0.152 0.000 0.000 0.4
#> SRR1706923 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706924 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706925 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
#> SRR1706926 2 0.0000 0.7165 0.000 1.000 0.000 0.000 0.000 0.0
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["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 15185 rows and 159 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 1.000 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.811 0.914 0.888 0.1895 0.904 0.808
#> 4 4 0.839 0.932 0.919 0.1537 0.906 0.766
#> 5 5 0.902 0.910 0.941 0.0976 0.933 0.782
#> 6 6 0.779 0.841 0.845 0.0408 0.979 0.913
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
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0592 0.985 0.988 0.000 0.012
#> SRR1706768 1 0.0592 0.985 0.988 0.000 0.012
#> SRR1706769 1 0.0592 0.985 0.988 0.000 0.012
#> SRR1706770 1 0.0747 0.981 0.984 0.000 0.016
#> SRR1706771 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706772 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706773 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706774 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706775 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706776 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706777 1 0.0424 0.987 0.992 0.000 0.008
#> SRR1706778 1 0.0592 0.983 0.988 0.000 0.012
#> SRR1706779 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706780 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706781 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706782 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706783 1 0.0747 0.981 0.984 0.000 0.016
#> SRR1706784 1 0.0747 0.981 0.984 0.000 0.016
#> SRR1706785 1 0.0747 0.981 0.984 0.000 0.016
#> SRR1706786 1 0.0747 0.981 0.984 0.000 0.016
#> SRR1706787 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706788 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706789 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706790 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706791 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706792 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706793 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706794 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706795 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706796 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706797 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706798 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706799 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706800 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706801 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706802 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1706803 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706804 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706805 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706806 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706811 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706812 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706813 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706814 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706807 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706808 3 0.6215 0.980 0.428 0.000 0.572
#> SRR1706809 3 0.6215 0.980 0.428 0.000 0.572
#> SRR1706810 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706815 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706816 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706817 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706818 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706819 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706820 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706821 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706822 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706823 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706824 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706825 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706826 3 0.6244 0.998 0.440 0.000 0.560
#> SRR1706827 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706828 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706829 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706830 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706835 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706836 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706837 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706838 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706831 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706832 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706833 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706834 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706839 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706840 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706841 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706842 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706847 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706848 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706849 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706850 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706843 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706844 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706845 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706846 1 0.0000 0.994 1.000 0.000 0.000
#> SRR1706851 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706855 2 0.0237 0.897 0.000 0.996 0.004
#> SRR1706856 2 0.0237 0.897 0.000 0.996 0.004
#> SRR1706857 2 0.0237 0.897 0.000 0.996 0.004
#> SRR1706858 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706859 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706860 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706861 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706862 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706867 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706869 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706870 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706863 2 0.0892 0.890 0.000 0.980 0.020
#> SRR1706864 2 0.0747 0.892 0.000 0.984 0.016
#> SRR1706865 2 0.0747 0.892 0.000 0.984 0.016
#> SRR1706866 2 0.0892 0.890 0.000 0.980 0.020
#> SRR1706871 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706879 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706880 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706881 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706882 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706883 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706884 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706885 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706886 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706875 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706887 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706888 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706889 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706890 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706891 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706892 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706893 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706894 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706895 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706896 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706897 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706898 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706899 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706900 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706901 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706902 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706907 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706908 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706909 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706910 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706903 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706904 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706905 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706906 2 0.6235 0.652 0.000 0.564 0.436
#> SRR1706911 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706919 2 0.0592 0.894 0.000 0.988 0.012
#> SRR1706920 2 0.0237 0.897 0.000 0.996 0.004
#> SRR1706921 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706922 2 0.0237 0.897 0.000 0.996 0.004
#> SRR1706915 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706923 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706924 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706925 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1706926 2 0.0000 0.898 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 1 0.3123 0.805 0.844 0.000 0.000 0.156
#> SRR1706768 1 0.3123 0.805 0.844 0.000 0.000 0.156
#> SRR1706769 1 0.3123 0.805 0.844 0.000 0.000 0.156
#> SRR1706770 1 0.3219 0.791 0.836 0.000 0.000 0.164
#> SRR1706771 1 0.1716 0.930 0.936 0.000 0.000 0.064
#> SRR1706772 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706773 1 0.1557 0.937 0.944 0.000 0.000 0.056
#> SRR1706774 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706775 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706776 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706777 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706778 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706779 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706780 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706781 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706782 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706783 1 0.0592 0.959 0.984 0.000 0.000 0.016
#> SRR1706784 1 0.0592 0.959 0.984 0.000 0.000 0.016
#> SRR1706785 1 0.0592 0.959 0.984 0.000 0.000 0.016
#> SRR1706786 1 0.0592 0.959 0.984 0.000 0.000 0.016
#> SRR1706787 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706788 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706789 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706790 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706791 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706792 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706793 1 0.0336 0.960 0.992 0.000 0.000 0.008
#> SRR1706794 1 0.0469 0.959 0.988 0.000 0.000 0.012
#> SRR1706795 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706796 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706797 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706798 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706799 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706800 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706801 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706802 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706803 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706804 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706805 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706806 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706811 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706812 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706813 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706814 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706807 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706808 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706809 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706810 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706815 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706816 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706817 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706818 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706819 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706820 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706821 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706822 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706823 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706824 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706825 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706826 4 0.3907 1.000 0.232 0.000 0.000 0.768
#> SRR1706827 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706828 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706829 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706830 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706835 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706836 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706837 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706838 1 0.0188 0.961 0.996 0.000 0.000 0.004
#> SRR1706831 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706832 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706833 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706834 1 0.1637 0.934 0.940 0.000 0.000 0.060
#> SRR1706839 1 0.0188 0.959 0.996 0.000 0.000 0.004
#> SRR1706840 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> SRR1706847 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706848 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706849 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706850 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706843 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706844 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706845 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706846 1 0.0336 0.958 0.992 0.000 0.000 0.008
#> SRR1706851 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706852 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706853 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706854 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706855 2 0.1637 0.877 0.000 0.940 0.060 0.000
#> SRR1706856 2 0.1637 0.877 0.000 0.940 0.060 0.000
#> SRR1706857 2 0.1637 0.877 0.000 0.940 0.060 0.000
#> SRR1706858 2 0.1637 0.877 0.000 0.940 0.060 0.000
#> SRR1706859 2 0.5361 0.829 0.000 0.716 0.060 0.224
#> SRR1706860 2 0.5361 0.829 0.000 0.716 0.060 0.224
#> SRR1706861 2 0.5361 0.829 0.000 0.716 0.060 0.224
#> SRR1706862 2 0.5361 0.829 0.000 0.716 0.060 0.224
#> SRR1706867 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706869 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706870 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706863 2 0.5500 0.823 0.000 0.708 0.068 0.224
#> SRR1706864 2 0.5500 0.823 0.000 0.708 0.068 0.224
#> SRR1706865 2 0.5500 0.823 0.000 0.708 0.068 0.224
#> SRR1706866 2 0.5500 0.823 0.000 0.708 0.068 0.224
#> SRR1706871 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706872 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706873 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706874 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706879 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706880 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706881 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706882 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706883 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706884 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706885 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706886 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706875 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706876 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706877 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706878 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706887 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706888 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706889 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706890 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706891 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706892 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706893 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706894 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706895 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706896 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706897 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706898 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706899 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706900 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706901 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706902 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706907 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706908 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706909 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706910 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706903 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706904 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706905 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706906 3 0.0000 1.000 0.000 0.000 1.000 0.000
#> SRR1706911 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706912 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706913 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706914 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> SRR1706919 2 0.5361 0.829 0.000 0.716 0.060 0.224
#> SRR1706920 2 0.4609 0.848 0.000 0.752 0.024 0.224
#> SRR1706921 2 0.4799 0.845 0.000 0.744 0.032 0.224
#> SRR1706922 2 0.4609 0.848 0.000 0.752 0.024 0.224
#> SRR1706915 2 0.0336 0.900 0.000 0.992 0.008 0.000
#> SRR1706916 2 0.0469 0.899 0.000 0.988 0.012 0.000
#> SRR1706917 2 0.0336 0.900 0.000 0.992 0.008 0.000
#> SRR1706918 2 0.0469 0.899 0.000 0.988 0.012 0.000
#> SRR1706923 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706924 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706925 2 0.3837 0.857 0.000 0.776 0.000 0.224
#> SRR1706926 2 0.3837 0.857 0.000 0.776 0.000 0.224
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 1 0.3876 0.601 0.684 0.000 0.000 0.316 0
#> SRR1706768 1 0.3895 0.593 0.680 0.000 0.000 0.320 0
#> SRR1706769 1 0.3895 0.593 0.680 0.000 0.000 0.320 0
#> SRR1706770 1 0.3895 0.593 0.680 0.000 0.000 0.320 0
#> SRR1706771 1 0.1121 0.957 0.956 0.000 0.000 0.044 0
#> SRR1706772 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706773 1 0.0880 0.963 0.968 0.000 0.000 0.032 0
#> SRR1706774 1 0.1043 0.960 0.960 0.000 0.000 0.040 0
#> SRR1706775 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706776 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706777 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706778 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706779 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706780 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706781 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706782 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706783 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706784 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706785 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706786 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706787 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706788 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706789 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706790 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706791 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706792 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706793 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706794 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706795 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706796 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706797 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706798 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706799 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706800 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706801 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706802 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706803 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706804 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706805 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706806 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706811 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706812 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706813 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706814 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706807 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706808 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706809 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706810 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706815 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706816 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706817 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706818 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706819 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706820 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706821 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706822 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706823 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706824 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706825 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706826 4 0.0000 1.000 0.000 0.000 0.000 1.000 0
#> SRR1706827 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706828 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706829 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706830 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706835 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706836 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706837 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706838 1 0.0703 0.966 0.976 0.000 0.000 0.024 0
#> SRR1706831 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706832 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706833 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706834 1 0.0963 0.962 0.964 0.000 0.000 0.036 0
#> SRR1706839 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706840 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706841 1 0.0162 0.964 0.996 0.000 0.000 0.004 0
#> SRR1706842 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706847 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706848 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706849 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706850 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706843 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706844 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706845 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706846 1 0.0000 0.963 1.000 0.000 0.000 0.000 0
#> SRR1706851 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706852 3 0.2732 0.845 0.000 0.160 0.840 0.000 0
#> SRR1706853 3 0.2690 0.846 0.000 0.156 0.844 0.000 0
#> SRR1706854 3 0.2648 0.848 0.000 0.152 0.848 0.000 0
#> SRR1706855 3 0.3534 0.750 0.000 0.256 0.744 0.000 0
#> SRR1706856 3 0.3534 0.750 0.000 0.256 0.744 0.000 0
#> SRR1706857 3 0.3534 0.750 0.000 0.256 0.744 0.000 0
#> SRR1706858 3 0.3534 0.750 0.000 0.256 0.744 0.000 0
#> SRR1706859 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706860 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706861 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706862 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706867 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706869 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706870 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706863 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706864 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706865 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706866 2 0.0510 0.861 0.000 0.984 0.016 0.000 0
#> SRR1706871 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706872 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706873 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706874 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706879 2 0.3452 0.761 0.000 0.756 0.244 0.000 0
#> SRR1706880 2 0.3508 0.749 0.000 0.748 0.252 0.000 0
#> SRR1706881 2 0.3561 0.741 0.000 0.740 0.260 0.000 0
#> SRR1706882 2 0.3534 0.748 0.000 0.744 0.256 0.000 0
#> SRR1706883 2 0.2471 0.817 0.000 0.864 0.136 0.000 0
#> SRR1706884 2 0.2471 0.817 0.000 0.864 0.136 0.000 0
#> SRR1706885 2 0.2471 0.817 0.000 0.864 0.136 0.000 0
#> SRR1706886 2 0.2471 0.817 0.000 0.864 0.136 0.000 0
#> SRR1706875 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706876 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706877 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706878 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706887 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706888 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706889 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706890 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706891 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706892 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706893 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706894 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706895 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706896 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706897 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706898 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706899 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706900 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706901 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706902 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706907 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706908 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706909 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706910 3 0.0000 0.852 0.000 0.000 1.000 0.000 0
#> SRR1706903 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706904 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706905 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706906 5 0.0000 1.000 0.000 0.000 0.000 0.000 1
#> SRR1706911 3 0.2329 0.853 0.000 0.124 0.876 0.000 0
#> SRR1706912 3 0.2230 0.855 0.000 0.116 0.884 0.000 0
#> SRR1706913 3 0.2648 0.846 0.000 0.152 0.848 0.000 0
#> SRR1706914 3 0.2127 0.856 0.000 0.108 0.892 0.000 0
#> SRR1706919 2 0.2377 0.862 0.000 0.872 0.128 0.000 0
#> SRR1706920 2 0.2377 0.862 0.000 0.872 0.128 0.000 0
#> SRR1706921 2 0.2377 0.862 0.000 0.872 0.128 0.000 0
#> SRR1706922 2 0.2377 0.862 0.000 0.872 0.128 0.000 0
#> SRR1706915 3 0.4171 0.553 0.000 0.396 0.604 0.000 0
#> SRR1706916 3 0.4030 0.615 0.000 0.352 0.648 0.000 0
#> SRR1706917 3 0.4060 0.606 0.000 0.360 0.640 0.000 0
#> SRR1706918 3 0.4030 0.615 0.000 0.352 0.648 0.000 0
#> SRR1706923 2 0.2773 0.849 0.000 0.836 0.164 0.000 0
#> SRR1706924 2 0.2773 0.849 0.000 0.836 0.164 0.000 0
#> SRR1706925 2 0.2773 0.849 0.000 0.836 0.164 0.000 0
#> SRR1706926 2 0.2891 0.841 0.000 0.824 0.176 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706768 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706769 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706770 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706771 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706772 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706773 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706774 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706775 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706776 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706777 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706778 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706779 1 0.0458 0.743 0.984 0.000 0.016 0.000 0 0.000
#> SRR1706780 1 0.0547 0.742 0.980 0.000 0.020 0.000 0 0.000
#> SRR1706781 1 0.0547 0.742 0.980 0.000 0.020 0.000 0 0.000
#> SRR1706782 1 0.0547 0.742 0.980 0.000 0.020 0.000 0 0.000
#> SRR1706783 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706784 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706785 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706786 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706787 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706788 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706789 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706790 1 0.5444 0.667 0.576 0.000 0.216 0.208 0 0.000
#> SRR1706791 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706792 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706793 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706794 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706795 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706796 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706797 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706798 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706799 1 0.0632 0.740 0.976 0.000 0.024 0.000 0 0.000
#> SRR1706800 1 0.0146 0.746 0.996 0.000 0.004 0.000 0 0.000
#> SRR1706801 1 0.0000 0.746 1.000 0.000 0.000 0.000 0 0.000
#> SRR1706802 1 0.0632 0.740 0.976 0.000 0.024 0.000 0 0.000
#> SRR1706803 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706804 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706805 1 0.1082 0.735 0.956 0.000 0.040 0.004 0 0.000
#> SRR1706806 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706811 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706812 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706813 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706814 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706807 4 0.2912 0.794 0.000 0.000 0.216 0.784 0 0.000
#> SRR1706808 4 0.2912 0.794 0.000 0.000 0.216 0.784 0 0.000
#> SRR1706809 4 0.2912 0.794 0.000 0.000 0.216 0.784 0 0.000
#> SRR1706810 4 0.2912 0.794 0.000 0.000 0.216 0.784 0 0.000
#> SRR1706815 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706816 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706817 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706818 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706819 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706820 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706821 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706822 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706823 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706824 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706825 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706826 4 0.0000 0.952 0.000 0.000 0.000 1.000 0 0.000
#> SRR1706827 1 0.5421 0.671 0.580 0.000 0.212 0.208 0 0.000
#> SRR1706828 1 0.5421 0.671 0.580 0.000 0.212 0.208 0 0.000
#> SRR1706829 1 0.5421 0.671 0.580 0.000 0.212 0.208 0 0.000
#> SRR1706830 1 0.5421 0.671 0.580 0.000 0.212 0.208 0 0.000
#> SRR1706835 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706836 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706837 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706838 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706831 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706832 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706833 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706834 1 0.3446 0.761 0.692 0.000 0.000 0.308 0 0.000
#> SRR1706839 1 0.0000 0.746 1.000 0.000 0.000 0.000 0 0.000
#> SRR1706840 1 0.0260 0.748 0.992 0.000 0.000 0.008 0 0.000
#> SRR1706841 1 0.0713 0.750 0.972 0.000 0.000 0.028 0 0.000
#> SRR1706842 1 0.0260 0.748 0.992 0.000 0.000 0.008 0 0.000
#> SRR1706847 3 0.3758 0.941 0.000 0.016 0.700 0.000 0 0.284
#> SRR1706848 3 0.3738 0.945 0.000 0.016 0.704 0.000 0 0.280
#> SRR1706849 3 0.3758 0.941 0.000 0.016 0.700 0.000 0 0.284
#> SRR1706850 3 0.3758 0.941 0.000 0.016 0.700 0.000 0 0.284
#> SRR1706843 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706844 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706845 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706846 1 0.0937 0.733 0.960 0.000 0.040 0.000 0 0.000
#> SRR1706851 6 0.0458 0.856 0.000 0.016 0.000 0.000 0 0.984
#> SRR1706852 6 0.0458 0.856 0.000 0.016 0.000 0.000 0 0.984
#> SRR1706853 6 0.0458 0.856 0.000 0.016 0.000 0.000 0 0.984
#> SRR1706854 6 0.0547 0.858 0.000 0.020 0.000 0.000 0 0.980
#> SRR1706855 6 0.2854 0.828 0.000 0.208 0.000 0.000 0 0.792
#> SRR1706856 6 0.2854 0.828 0.000 0.208 0.000 0.000 0 0.792
#> SRR1706857 6 0.2854 0.828 0.000 0.208 0.000 0.000 0 0.792
#> SRR1706858 6 0.2854 0.828 0.000 0.208 0.000 0.000 0 0.792
#> SRR1706859 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706860 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706861 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706862 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706867 3 0.3175 0.959 0.000 0.000 0.744 0.000 0 0.256
#> SRR1706869 3 0.3175 0.959 0.000 0.000 0.744 0.000 0 0.256
#> SRR1706870 3 0.3175 0.959 0.000 0.000 0.744 0.000 0 0.256
#> SRR1706863 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706864 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706865 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706866 2 0.1141 0.872 0.000 0.948 0.000 0.000 0 0.052
#> SRR1706871 6 0.1765 0.853 0.000 0.096 0.000 0.000 0 0.904
#> SRR1706872 6 0.1765 0.853 0.000 0.096 0.000 0.000 0 0.904
#> SRR1706873 6 0.1765 0.853 0.000 0.096 0.000 0.000 0 0.904
#> SRR1706874 6 0.1765 0.853 0.000 0.096 0.000 0.000 0 0.904
#> SRR1706879 2 0.2378 0.842 0.000 0.848 0.000 0.000 0 0.152
#> SRR1706880 2 0.2416 0.844 0.000 0.844 0.000 0.000 0 0.156
#> SRR1706881 2 0.2378 0.842 0.000 0.848 0.000 0.000 0 0.152
#> SRR1706882 2 0.2416 0.844 0.000 0.844 0.000 0.000 0 0.156
#> SRR1706883 2 0.2092 0.856 0.000 0.876 0.000 0.000 0 0.124
#> SRR1706884 2 0.2092 0.856 0.000 0.876 0.000 0.000 0 0.124
#> SRR1706885 2 0.2092 0.856 0.000 0.876 0.000 0.000 0 0.124
#> SRR1706886 2 0.2092 0.856 0.000 0.876 0.000 0.000 0 0.124
#> SRR1706875 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> SRR1706876 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> SRR1706877 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> SRR1706878 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> SRR1706887 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706888 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706889 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706890 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706891 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706892 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706893 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706894 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706895 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706896 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706897 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706898 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706899 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706900 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706901 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706902 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706907 3 0.3244 0.963 0.000 0.000 0.732 0.000 0 0.268
#> SRR1706908 3 0.3288 0.962 0.000 0.000 0.724 0.000 0 0.276
#> SRR1706909 3 0.3198 0.962 0.000 0.000 0.740 0.000 0 0.260
#> SRR1706910 3 0.3244 0.963 0.000 0.000 0.732 0.000 0 0.268
#> SRR1706903 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706904 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706905 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706906 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> SRR1706911 6 0.1910 0.857 0.000 0.108 0.000 0.000 0 0.892
#> SRR1706912 6 0.2003 0.858 0.000 0.116 0.000 0.000 0 0.884
#> SRR1706913 6 0.2135 0.857 0.000 0.128 0.000 0.000 0 0.872
#> SRR1706914 6 0.1957 0.857 0.000 0.112 0.000 0.000 0 0.888
#> SRR1706919 2 0.2664 0.870 0.000 0.816 0.000 0.000 0 0.184
#> SRR1706920 2 0.2664 0.870 0.000 0.816 0.000 0.000 0 0.184
#> SRR1706921 2 0.2664 0.870 0.000 0.816 0.000 0.000 0 0.184
#> SRR1706922 2 0.2664 0.870 0.000 0.816 0.000 0.000 0 0.184
#> SRR1706915 6 0.2854 0.749 0.000 0.208 0.000 0.000 0 0.792
#> SRR1706916 6 0.2340 0.799 0.000 0.148 0.000 0.000 0 0.852
#> SRR1706917 6 0.2491 0.790 0.000 0.164 0.000 0.000 0 0.836
#> SRR1706918 6 0.2260 0.801 0.000 0.140 0.000 0.000 0 0.860
#> SRR1706923 2 0.2762 0.866 0.000 0.804 0.000 0.000 0 0.196
#> SRR1706924 2 0.2762 0.866 0.000 0.804 0.000 0.000 0 0.196
#> SRR1706925 2 0.2762 0.866 0.000 0.804 0.000 0.000 0 0.196
#> SRR1706926 2 0.2762 0.866 0.000 0.804 0.000 0.000 0 0.196
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 15185 rows and 159 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 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 1.000 1.000 0.5036 0.497 0.497
#> 3 3 0.770 0.894 0.947 0.2971 0.783 0.589
#> 4 4 0.750 0.804 0.894 0.0169 0.872 0.691
#> 5 5 0.595 0.575 0.824 0.0577 0.940 0.838
#> 6 6 0.612 0.633 0.755 0.0814 0.838 0.545
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
#> SRR1706767 1 0 1 1 0
#> SRR1706768 1 0 1 1 0
#> SRR1706769 1 0 1 1 0
#> SRR1706770 1 0 1 1 0
#> SRR1706771 1 0 1 1 0
#> SRR1706772 1 0 1 1 0
#> SRR1706773 1 0 1 1 0
#> SRR1706774 1 0 1 1 0
#> SRR1706775 1 0 1 1 0
#> SRR1706776 1 0 1 1 0
#> SRR1706777 1 0 1 1 0
#> SRR1706778 1 0 1 1 0
#> SRR1706779 1 0 1 1 0
#> SRR1706780 1 0 1 1 0
#> SRR1706781 1 0 1 1 0
#> SRR1706782 1 0 1 1 0
#> SRR1706783 1 0 1 1 0
#> SRR1706784 1 0 1 1 0
#> SRR1706785 1 0 1 1 0
#> SRR1706786 1 0 1 1 0
#> SRR1706787 1 0 1 1 0
#> SRR1706788 1 0 1 1 0
#> SRR1706789 1 0 1 1 0
#> SRR1706790 1 0 1 1 0
#> SRR1706791 1 0 1 1 0
#> SRR1706792 1 0 1 1 0
#> SRR1706793 1 0 1 1 0
#> SRR1706794 1 0 1 1 0
#> SRR1706795 1 0 1 1 0
#> SRR1706796 1 0 1 1 0
#> SRR1706797 1 0 1 1 0
#> SRR1706798 1 0 1 1 0
#> SRR1706799 1 0 1 1 0
#> SRR1706800 1 0 1 1 0
#> SRR1706801 1 0 1 1 0
#> SRR1706802 1 0 1 1 0
#> SRR1706803 1 0 1 1 0
#> SRR1706804 1 0 1 1 0
#> SRR1706805 1 0 1 1 0
#> SRR1706806 1 0 1 1 0
#> SRR1706811 1 0 1 1 0
#> SRR1706812 1 0 1 1 0
#> SRR1706813 1 0 1 1 0
#> SRR1706814 1 0 1 1 0
#> SRR1706807 1 0 1 1 0
#> SRR1706808 1 0 1 1 0
#> SRR1706809 1 0 1 1 0
#> SRR1706810 1 0 1 1 0
#> SRR1706815 1 0 1 1 0
#> SRR1706816 1 0 1 1 0
#> SRR1706817 1 0 1 1 0
#> SRR1706818 1 0 1 1 0
#> SRR1706819 1 0 1 1 0
#> SRR1706820 1 0 1 1 0
#> SRR1706821 1 0 1 1 0
#> SRR1706822 1 0 1 1 0
#> SRR1706823 1 0 1 1 0
#> SRR1706824 1 0 1 1 0
#> SRR1706825 1 0 1 1 0
#> SRR1706826 1 0 1 1 0
#> SRR1706827 1 0 1 1 0
#> SRR1706828 1 0 1 1 0
#> SRR1706829 1 0 1 1 0
#> SRR1706830 1 0 1 1 0
#> SRR1706835 1 0 1 1 0
#> SRR1706836 1 0 1 1 0
#> SRR1706837 1 0 1 1 0
#> SRR1706838 1 0 1 1 0
#> SRR1706831 1 0 1 1 0
#> SRR1706832 1 0 1 1 0
#> SRR1706833 1 0 1 1 0
#> SRR1706834 1 0 1 1 0
#> SRR1706839 1 0 1 1 0
#> SRR1706840 1 0 1 1 0
#> SRR1706841 1 0 1 1 0
#> SRR1706842 1 0 1 1 0
#> SRR1706847 2 0 1 0 1
#> SRR1706848 2 0 1 0 1
#> SRR1706849 2 0 1 0 1
#> SRR1706850 2 0 1 0 1
#> SRR1706843 1 0 1 1 0
#> SRR1706844 1 0 1 1 0
#> SRR1706845 1 0 1 1 0
#> SRR1706846 1 0 1 1 0
#> SRR1706851 2 0 1 0 1
#> SRR1706852 2 0 1 0 1
#> SRR1706853 2 0 1 0 1
#> SRR1706854 2 0 1 0 1
#> SRR1706855 2 0 1 0 1
#> SRR1706856 2 0 1 0 1
#> SRR1706857 2 0 1 0 1
#> SRR1706858 2 0 1 0 1
#> SRR1706859 2 0 1 0 1
#> SRR1706860 2 0 1 0 1
#> SRR1706861 2 0 1 0 1
#> SRR1706862 2 0 1 0 1
#> SRR1706867 2 0 1 0 1
#> SRR1706869 2 0 1 0 1
#> SRR1706870 2 0 1 0 1
#> SRR1706863 2 0 1 0 1
#> SRR1706864 2 0 1 0 1
#> SRR1706865 2 0 1 0 1
#> SRR1706866 2 0 1 0 1
#> SRR1706871 2 0 1 0 1
#> SRR1706872 2 0 1 0 1
#> SRR1706873 2 0 1 0 1
#> SRR1706874 2 0 1 0 1
#> SRR1706879 2 0 1 0 1
#> SRR1706880 2 0 1 0 1
#> SRR1706881 2 0 1 0 1
#> SRR1706882 2 0 1 0 1
#> SRR1706883 2 0 1 0 1
#> SRR1706884 2 0 1 0 1
#> SRR1706885 2 0 1 0 1
#> SRR1706886 2 0 1 0 1
#> SRR1706875 2 0 1 0 1
#> SRR1706876 2 0 1 0 1
#> SRR1706877 2 0 1 0 1
#> SRR1706878 2 0 1 0 1
#> SRR1706887 2 0 1 0 1
#> SRR1706888 2 0 1 0 1
#> SRR1706889 2 0 1 0 1
#> SRR1706890 2 0 1 0 1
#> SRR1706891 2 0 1 0 1
#> SRR1706892 2 0 1 0 1
#> SRR1706893 2 0 1 0 1
#> SRR1706894 2 0 1 0 1
#> SRR1706895 2 0 1 0 1
#> SRR1706896 2 0 1 0 1
#> SRR1706897 2 0 1 0 1
#> SRR1706898 2 0 1 0 1
#> SRR1706899 2 0 1 0 1
#> SRR1706900 2 0 1 0 1
#> SRR1706901 2 0 1 0 1
#> SRR1706902 2 0 1 0 1
#> SRR1706907 2 0 1 0 1
#> SRR1706908 2 0 1 0 1
#> SRR1706909 2 0 1 0 1
#> SRR1706910 2 0 1 0 1
#> SRR1706903 2 0 1 0 1
#> SRR1706904 2 0 1 0 1
#> SRR1706905 2 0 1 0 1
#> SRR1706906 2 0 1 0 1
#> SRR1706911 2 0 1 0 1
#> SRR1706912 2 0 1 0 1
#> SRR1706913 2 0 1 0 1
#> SRR1706914 2 0 1 0 1
#> SRR1706919 2 0 1 0 1
#> SRR1706920 2 0 1 0 1
#> SRR1706921 2 0 1 0 1
#> SRR1706922 2 0 1 0 1
#> SRR1706915 2 0 1 0 1
#> SRR1706916 2 0 1 0 1
#> SRR1706917 2 0 1 0 1
#> SRR1706918 2 0 1 0 1
#> SRR1706923 2 0 1 0 1
#> SRR1706924 2 0 1 0 1
#> SRR1706925 2 0 1 0 1
#> SRR1706926 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1706767 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706768 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706769 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706770 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706771 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706772 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706773 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706774 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706775 1 0.4750 0.803 0.784 0.000 0.216
#> SRR1706776 1 0.4796 0.798 0.780 0.000 0.220
#> SRR1706777 1 0.4399 0.833 0.812 0.000 0.188
#> SRR1706778 1 0.4605 0.816 0.796 0.000 0.204
#> SRR1706779 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706780 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706781 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706782 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706783 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706784 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706785 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706786 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706787 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706788 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706789 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706790 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706791 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706792 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706793 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706794 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706795 1 0.4555 0.821 0.800 0.000 0.200
#> SRR1706796 1 0.4931 0.781 0.768 0.000 0.232
#> SRR1706797 1 0.4796 0.797 0.780 0.000 0.220
#> SRR1706798 1 0.4842 0.792 0.776 0.000 0.224
#> SRR1706799 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706800 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706801 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706802 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706803 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706804 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706805 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706806 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706811 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706812 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706813 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706814 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706807 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706808 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706809 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706810 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706815 1 0.3879 0.862 0.848 0.000 0.152
#> SRR1706816 1 0.3619 0.873 0.864 0.000 0.136
#> SRR1706817 1 0.4399 0.830 0.812 0.000 0.188
#> SRR1706818 1 0.3879 0.862 0.848 0.000 0.152
#> SRR1706819 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706820 3 0.0237 0.935 0.004 0.000 0.996
#> SRR1706821 3 0.0237 0.935 0.004 0.000 0.996
#> SRR1706822 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706823 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706824 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706825 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706826 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706827 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706828 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706829 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706830 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706835 1 0.3340 0.883 0.880 0.000 0.120
#> SRR1706836 1 0.3816 0.866 0.852 0.000 0.148
#> SRR1706837 1 0.3686 0.871 0.860 0.000 0.140
#> SRR1706838 1 0.2959 0.895 0.900 0.000 0.100
#> SRR1706831 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706832 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706833 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706834 1 0.0000 0.938 1.000 0.000 0.000
#> SRR1706839 3 0.0237 0.935 0.004 0.000 0.996
#> SRR1706840 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706841 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706842 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706847 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706848 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706849 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706850 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706843 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706844 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706845 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706846 3 0.0000 0.938 0.000 0.000 1.000
#> SRR1706851 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706852 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706853 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706854 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706855 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706856 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706857 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706858 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706859 2 0.3412 0.860 0.000 0.876 0.124
#> SRR1706860 2 0.3340 0.864 0.000 0.880 0.120
#> SRR1706861 2 0.3340 0.864 0.000 0.880 0.120
#> SRR1706862 2 0.3412 0.860 0.000 0.876 0.124
#> SRR1706867 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706869 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706870 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706863 3 0.5327 0.656 0.000 0.272 0.728
#> SRR1706864 3 0.5291 0.664 0.000 0.268 0.732
#> SRR1706865 3 0.5397 0.642 0.000 0.280 0.720
#> SRR1706866 3 0.5431 0.634 0.000 0.284 0.716
#> SRR1706871 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706872 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706873 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706874 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706879 2 0.3482 0.856 0.000 0.872 0.128
#> SRR1706880 2 0.3412 0.860 0.000 0.876 0.124
#> SRR1706881 2 0.3482 0.856 0.000 0.872 0.128
#> SRR1706882 2 0.3412 0.860 0.000 0.876 0.124
#> SRR1706883 3 0.3192 0.869 0.000 0.112 0.888
#> SRR1706884 3 0.3116 0.872 0.000 0.108 0.892
#> SRR1706885 3 0.3116 0.872 0.000 0.108 0.892
#> SRR1706886 3 0.3192 0.869 0.000 0.112 0.888
#> SRR1706875 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706876 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706877 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706878 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706887 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706888 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706889 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706890 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706891 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706892 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706893 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706894 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706895 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706896 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706897 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706898 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706899 2 0.2711 0.891 0.000 0.912 0.088
#> SRR1706900 2 0.2711 0.891 0.000 0.912 0.088
#> SRR1706901 2 0.2796 0.888 0.000 0.908 0.092
#> SRR1706902 2 0.2711 0.891 0.000 0.912 0.088
#> SRR1706907 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706908 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706909 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706910 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706903 2 0.6215 0.271 0.000 0.572 0.428
#> SRR1706904 2 0.6225 0.258 0.000 0.568 0.432
#> SRR1706905 2 0.6225 0.258 0.000 0.568 0.432
#> SRR1706906 2 0.6215 0.271 0.000 0.572 0.428
#> SRR1706911 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706912 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706913 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706914 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706919 2 0.2796 0.888 0.000 0.908 0.092
#> SRR1706920 2 0.2537 0.896 0.000 0.920 0.080
#> SRR1706921 2 0.2711 0.891 0.000 0.912 0.088
#> SRR1706922 2 0.2959 0.881 0.000 0.900 0.100
#> SRR1706915 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706916 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706917 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706918 2 0.0000 0.943 0.000 1.000 0.000
#> SRR1706923 3 0.4002 0.821 0.000 0.160 0.840
#> SRR1706924 3 0.4121 0.811 0.000 0.168 0.832
#> SRR1706925 3 0.4002 0.821 0.000 0.160 0.840
#> SRR1706926 3 0.3816 0.834 0.000 0.148 0.852
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1706767 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706768 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706769 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706770 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706771 4 0.0817 0.9268 0.024 0.000 NA 0.976
#> SRR1706772 4 0.0707 0.9293 0.020 0.000 NA 0.980
#> SRR1706773 4 0.0817 0.9268 0.024 0.000 NA 0.976
#> SRR1706774 4 0.0817 0.9268 0.024 0.000 NA 0.976
#> SRR1706775 1 0.4277 0.5994 0.720 0.000 NA 0.280
#> SRR1706776 1 0.4103 0.6310 0.744 0.000 NA 0.256
#> SRR1706777 1 0.4304 0.5949 0.716 0.000 NA 0.284
#> SRR1706778 1 0.4522 0.5450 0.680 0.000 NA 0.320
#> SRR1706779 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706780 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706781 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706782 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706783 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706784 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706785 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706786 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706787 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706788 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706789 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706790 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706791 4 0.0469 0.9348 0.012 0.000 NA 0.988
#> SRR1706792 4 0.0336 0.9371 0.008 0.000 NA 0.992
#> SRR1706793 4 0.0336 0.9371 0.008 0.000 NA 0.992
#> SRR1706794 4 0.0336 0.9371 0.008 0.000 NA 0.992
#> SRR1706795 1 0.4431 0.5602 0.696 0.000 NA 0.304
#> SRR1706796 1 0.3975 0.6434 0.760 0.000 NA 0.240
#> SRR1706797 1 0.4250 0.6011 0.724 0.000 NA 0.276
#> SRR1706798 1 0.4331 0.5850 0.712 0.000 NA 0.288
#> SRR1706799 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706800 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706801 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706802 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706803 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706804 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706805 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706806 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706811 4 0.0817 0.9303 0.000 0.000 NA 0.976
#> SRR1706812 4 0.0921 0.9281 0.000 0.000 NA 0.972
#> SRR1706813 4 0.1022 0.9258 0.000 0.000 NA 0.968
#> SRR1706814 4 0.1022 0.9258 0.000 0.000 NA 0.968
#> SRR1706807 4 0.0188 0.9388 0.000 0.000 NA 0.996
#> SRR1706808 4 0.0188 0.9388 0.000 0.000 NA 0.996
#> SRR1706809 4 0.0188 0.9388 0.000 0.000 NA 0.996
#> SRR1706810 4 0.0188 0.9388 0.000 0.000 NA 0.996
#> SRR1706815 4 0.7438 0.3342 0.244 0.000 NA 0.512
#> SRR1706816 4 0.7438 0.3332 0.244 0.000 NA 0.512
#> SRR1706817 4 0.7437 0.3275 0.248 0.000 NA 0.512
#> SRR1706818 4 0.7362 0.3293 0.256 0.000 NA 0.524
#> SRR1706819 1 0.3764 0.7475 0.784 0.000 NA 0.000
#> SRR1706820 1 0.3726 0.7496 0.788 0.000 NA 0.000
#> SRR1706821 1 0.3688 0.7517 0.792 0.000 NA 0.000
#> SRR1706822 1 0.3726 0.7496 0.788 0.000 NA 0.000
#> SRR1706823 1 0.2921 0.7800 0.860 0.000 NA 0.000
#> SRR1706824 1 0.2921 0.7798 0.860 0.000 NA 0.000
#> SRR1706825 1 0.3400 0.7633 0.820 0.000 NA 0.000
#> SRR1706826 1 0.3123 0.7733 0.844 0.000 NA 0.000
#> SRR1706827 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706828 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706829 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706830 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706835 1 0.4994 0.1710 0.520 0.000 NA 0.480
#> SRR1706836 1 0.4925 0.3166 0.572 0.000 NA 0.428
#> SRR1706837 1 0.4955 0.2737 0.556 0.000 NA 0.444
#> SRR1706838 1 0.4981 0.2177 0.536 0.000 NA 0.464
#> SRR1706831 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706832 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706833 4 0.0000 0.9399 0.000 0.000 NA 1.000
#> SRR1706834 4 0.0188 0.9387 0.004 0.000 NA 0.996
#> SRR1706839 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706840 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706841 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706842 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706847 2 0.1637 0.9145 0.000 0.940 NA 0.000
#> SRR1706848 2 0.1557 0.9156 0.000 0.944 NA 0.000
#> SRR1706849 2 0.1557 0.9156 0.000 0.944 NA 0.000
#> SRR1706850 2 0.1557 0.9156 0.000 0.944 NA 0.000
#> SRR1706843 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706844 1 0.0188 0.8240 0.996 0.000 NA 0.000
#> SRR1706845 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706846 1 0.0000 0.8242 1.000 0.000 NA 0.000
#> SRR1706851 2 0.1389 0.9153 0.000 0.952 NA 0.000
#> SRR1706852 2 0.1302 0.9161 0.000 0.956 NA 0.000
#> SRR1706853 2 0.1389 0.9153 0.000 0.952 NA 0.000
#> SRR1706854 2 0.1302 0.9161 0.000 0.956 NA 0.000
#> SRR1706855 2 0.0592 0.9188 0.000 0.984 NA 0.000
#> SRR1706856 2 0.0469 0.9189 0.000 0.988 NA 0.000
#> SRR1706857 2 0.0469 0.9189 0.000 0.988 NA 0.000
#> SRR1706858 2 0.0469 0.9189 0.000 0.988 NA 0.000
#> SRR1706859 2 0.0672 0.9178 0.008 0.984 NA 0.000
#> SRR1706860 2 0.0672 0.9178 0.008 0.984 NA 0.000
#> SRR1706861 2 0.0672 0.9178 0.008 0.984 NA 0.000
#> SRR1706862 2 0.0779 0.9178 0.004 0.980 NA 0.000
#> SRR1706867 2 0.1302 0.9166 0.000 0.956 NA 0.000
#> SRR1706869 2 0.1389 0.9167 0.000 0.952 NA 0.000
#> SRR1706870 2 0.1389 0.9167 0.000 0.952 NA 0.000
#> SRR1706863 2 0.5062 0.5914 0.284 0.692 NA 0.000
#> SRR1706864 2 0.5157 0.5934 0.284 0.688 NA 0.000
#> SRR1706865 2 0.4983 0.6142 0.272 0.704 NA 0.000
#> SRR1706866 2 0.4898 0.6331 0.260 0.716 NA 0.000
#> SRR1706871 2 0.0921 0.9178 0.000 0.972 NA 0.000
#> SRR1706872 2 0.0921 0.9178 0.000 0.972 NA 0.000
#> SRR1706873 2 0.0921 0.9178 0.000 0.972 NA 0.000
#> SRR1706874 2 0.0817 0.9181 0.000 0.976 NA 0.000
#> SRR1706879 2 0.1109 0.9160 0.004 0.968 NA 0.000
#> SRR1706880 2 0.1356 0.9147 0.008 0.960 NA 0.000
#> SRR1706881 2 0.1356 0.9147 0.008 0.960 NA 0.000
#> SRR1706882 2 0.1109 0.9162 0.004 0.968 NA 0.000
#> SRR1706883 1 0.5848 0.3024 0.584 0.376 NA 0.000
#> SRR1706884 1 0.5596 0.4158 0.632 0.332 NA 0.000
#> SRR1706885 1 0.5677 0.4134 0.628 0.332 NA 0.000
#> SRR1706886 1 0.5645 0.3413 0.604 0.364 NA 0.000
#> SRR1706875 2 0.0921 0.9167 0.000 0.972 NA 0.000
#> SRR1706876 2 0.0469 0.9174 0.000 0.988 NA 0.000
#> SRR1706877 2 0.1022 0.9164 0.000 0.968 NA 0.000
#> SRR1706878 2 0.0817 0.9173 0.000 0.976 NA 0.000
#> SRR1706887 2 0.2281 0.9013 0.000 0.904 NA 0.000
#> SRR1706888 2 0.2281 0.9013 0.000 0.904 NA 0.000
#> SRR1706889 2 0.2281 0.9013 0.000 0.904 NA 0.000
#> SRR1706890 2 0.2281 0.9013 0.000 0.904 NA 0.000
#> SRR1706891 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706892 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706893 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706894 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706895 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706896 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706897 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706898 2 0.2408 0.8985 0.000 0.896 NA 0.000
#> SRR1706899 2 0.2530 0.8961 0.000 0.888 NA 0.000
#> SRR1706900 2 0.2530 0.8961 0.000 0.888 NA 0.000
#> SRR1706901 2 0.2530 0.8961 0.000 0.888 NA 0.000
#> SRR1706902 2 0.2530 0.8961 0.000 0.888 NA 0.000
#> SRR1706907 2 0.1118 0.9180 0.000 0.964 NA 0.000
#> SRR1706908 2 0.1118 0.9170 0.000 0.964 NA 0.000
#> SRR1706909 2 0.1211 0.9172 0.000 0.960 NA 0.000
#> SRR1706910 2 0.1118 0.9170 0.000 0.964 NA 0.000
#> SRR1706903 2 0.4727 0.8264 0.100 0.792 NA 0.000
#> SRR1706904 2 0.4786 0.8222 0.104 0.788 NA 0.000
#> SRR1706905 2 0.4786 0.8197 0.108 0.788 NA 0.000
#> SRR1706906 2 0.4605 0.8340 0.092 0.800 NA 0.000
#> SRR1706911 2 0.0592 0.9181 0.000 0.984 NA 0.000
#> SRR1706912 2 0.0469 0.9180 0.000 0.988 NA 0.000
#> SRR1706913 2 0.0592 0.9179 0.000 0.984 NA 0.000
#> SRR1706914 2 0.0592 0.9181 0.000 0.984 NA 0.000
#> SRR1706919 2 0.1902 0.9061 0.004 0.932 NA 0.000
#> SRR1706920 2 0.1867 0.9047 0.000 0.928 NA 0.000
#> SRR1706921 2 0.2266 0.8970 0.004 0.912 NA 0.000
#> SRR1706922 2 0.2081 0.8993 0.000 0.916 NA 0.000
#> SRR1706915 2 0.1637 0.9086 0.000 0.940 NA 0.000
#> SRR1706916 2 0.1474 0.9114 0.000 0.948 NA 0.000
#> SRR1706917 2 0.1474 0.9115 0.000 0.948 NA 0.000
#> SRR1706918 2 0.1557 0.9101 0.000 0.944 NA 0.000
#> SRR1706923 2 0.6077 0.0839 0.460 0.496 NA 0.000
#> SRR1706924 2 0.6148 0.0492 0.468 0.484 NA 0.000
#> SRR1706925 2 0.6074 0.0955 0.456 0.500 NA 0.000
#> SRR1706926 1 0.6265 0.0793 0.500 0.444 NA 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1706767 4 0.1732 0.8901 0.000 0.000 0.080 0.920 0.000
#> SRR1706768 4 0.1792 0.8887 0.000 0.000 0.084 0.916 0.000
#> SRR1706769 4 0.1792 0.8887 0.000 0.000 0.084 0.916 0.000
#> SRR1706770 4 0.1792 0.8887 0.000 0.000 0.084 0.916 0.000
#> SRR1706771 4 0.2828 0.8658 0.020 0.000 0.104 0.872 0.004
#> SRR1706772 4 0.2464 0.8780 0.012 0.000 0.092 0.892 0.004
#> SRR1706773 4 0.2520 0.8761 0.012 0.000 0.096 0.888 0.004
#> SRR1706774 4 0.2349 0.8815 0.012 0.000 0.084 0.900 0.004
#> SRR1706775 1 0.3550 0.3469 0.760 0.000 0.000 0.236 0.004
#> SRR1706776 1 0.3398 0.3933 0.780 0.000 0.000 0.216 0.004
#> SRR1706777 1 0.3461 0.3739 0.772 0.000 0.000 0.224 0.004
#> SRR1706778 1 0.3689 0.2959 0.740 0.000 0.000 0.256 0.004
#> SRR1706779 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706780 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706781 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706782 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706783 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706784 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706785 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706786 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706787 4 0.2813 0.8417 0.000 0.000 0.168 0.832 0.000
#> SRR1706788 4 0.2813 0.8417 0.000 0.000 0.168 0.832 0.000
#> SRR1706789 4 0.2773 0.8445 0.000 0.000 0.164 0.836 0.000
#> SRR1706790 4 0.2773 0.8445 0.000 0.000 0.164 0.836 0.000
#> SRR1706791 4 0.1043 0.8872 0.040 0.000 0.000 0.960 0.000
#> SRR1706792 4 0.0794 0.8964 0.028 0.000 0.000 0.972 0.000
#> SRR1706793 4 0.0290 0.9043 0.008 0.000 0.000 0.992 0.000
#> SRR1706794 4 0.0510 0.9019 0.016 0.000 0.000 0.984 0.000
#> SRR1706795 1 0.1282 0.6632 0.952 0.000 0.004 0.044 0.000
#> SRR1706796 1 0.0880 0.6761 0.968 0.000 0.000 0.032 0.000
#> SRR1706797 1 0.1121 0.6654 0.956 0.000 0.000 0.044 0.000
#> SRR1706798 1 0.1197 0.6610 0.952 0.000 0.000 0.048 0.000
#> SRR1706799 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706800 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706801 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706802 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706803 1 0.0290 0.6972 0.992 0.000 0.000 0.000 0.008
#> SRR1706804 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706805 1 0.0162 0.6996 0.996 0.000 0.000 0.000 0.004
#> SRR1706806 1 0.0290 0.6972 0.992 0.000 0.000 0.000 0.008
#> SRR1706811 4 0.3143 0.7847 0.000 0.000 0.000 0.796 0.204
#> SRR1706812 4 0.3003 0.7985 0.000 0.000 0.000 0.812 0.188
#> SRR1706813 4 0.3395 0.7471 0.000 0.000 0.000 0.764 0.236
#> SRR1706814 4 0.3039 0.7944 0.000 0.000 0.000 0.808 0.192
#> SRR1706807 4 0.3037 0.8674 0.000 0.000 0.100 0.860 0.040
#> SRR1706808 4 0.2628 0.8781 0.000 0.000 0.088 0.884 0.028
#> SRR1706809 4 0.2983 0.8697 0.000 0.000 0.096 0.864 0.040
#> SRR1706810 4 0.2927 0.8718 0.000 0.000 0.092 0.868 0.040
#> SRR1706815 5 0.6194 0.9534 0.352 0.000 0.000 0.148 0.500
#> SRR1706816 5 0.6206 0.9473 0.344 0.000 0.000 0.152 0.504
#> SRR1706817 5 0.6194 0.9544 0.352 0.000 0.000 0.148 0.500
#> SRR1706818 5 0.6264 0.8832 0.400 0.000 0.000 0.148 0.452
#> SRR1706819 1 0.4030 0.0193 0.648 0.000 0.000 0.000 0.352
#> SRR1706820 1 0.3999 0.0562 0.656 0.000 0.000 0.000 0.344
#> SRR1706821 1 0.3966 0.0890 0.664 0.000 0.000 0.000 0.336
#> SRR1706822 1 0.3999 0.0539 0.656 0.000 0.000 0.000 0.344
#> SRR1706823 1 0.3480 0.3553 0.752 0.000 0.000 0.000 0.248
#> SRR1706824 1 0.3424 0.3752 0.760 0.000 0.000 0.000 0.240
#> SRR1706825 1 0.3774 0.2197 0.704 0.000 0.000 0.000 0.296
#> SRR1706826 1 0.3586 0.3142 0.736 0.000 0.000 0.000 0.264
#> SRR1706827 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706828 4 0.0162 0.9056 0.000 0.000 0.004 0.996 0.000
#> SRR1706829 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706830 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706835 1 0.2852 0.4798 0.828 0.000 0.000 0.172 0.000
#> SRR1706836 1 0.2280 0.5667 0.880 0.000 0.000 0.120 0.000
#> SRR1706837 1 0.2648 0.5151 0.848 0.000 0.000 0.152 0.000
#> SRR1706838 1 0.2732 0.5005 0.840 0.000 0.000 0.160 0.000
#> SRR1706831 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706832 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706833 4 0.0162 0.9051 0.004 0.000 0.000 0.996 0.000
#> SRR1706834 4 0.0000 0.9055 0.000 0.000 0.000 1.000 0.000
#> SRR1706839 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706840 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706841 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706842 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706847 3 0.4278 0.9650 0.000 0.452 0.548 0.000 0.000
#> SRR1706848 3 0.4256 0.9773 0.000 0.436 0.564 0.000 0.000
#> SRR1706849 3 0.4262 0.9834 0.000 0.440 0.560 0.000 0.000
#> SRR1706850 3 0.4262 0.9831 0.000 0.440 0.560 0.000 0.000
#> SRR1706843 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706844 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706845 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706846 1 0.0000 0.7019 1.000 0.000 0.000 0.000 0.000
#> SRR1706851 2 0.4540 -0.1659 0.000 0.640 0.340 0.000 0.020
#> SRR1706852 2 0.4608 -0.1562 0.000 0.640 0.336 0.000 0.024
#> SRR1706853 2 0.4655 -0.1207 0.000 0.644 0.328 0.000 0.028
#> SRR1706854 2 0.4508 -0.1181 0.000 0.648 0.332 0.000 0.020
#> SRR1706855 2 0.2921 0.5220 0.000 0.856 0.124 0.000 0.020
#> SRR1706856 2 0.2674 0.5338 0.000 0.868 0.120 0.000 0.012
#> SRR1706857 2 0.2777 0.5307 0.000 0.864 0.120 0.000 0.016
#> SRR1706858 2 0.2723 0.5283 0.000 0.864 0.124 0.000 0.012
#> SRR1706859 2 0.4801 0.4648 0.120 0.768 0.036 0.000 0.076
#> SRR1706860 2 0.4813 0.4761 0.104 0.772 0.044 0.000 0.080
#> SRR1706861 2 0.4663 0.4816 0.112 0.780 0.040 0.000 0.068
#> SRR1706862 2 0.4851 0.4727 0.104 0.768 0.040 0.000 0.088
#> SRR1706867 2 0.2660 0.5600 0.000 0.864 0.128 0.000 0.008
#> SRR1706869 2 0.2660 0.5600 0.000 0.864 0.128 0.000 0.008
#> SRR1706870 2 0.2439 0.5680 0.000 0.876 0.120 0.000 0.004
#> SRR1706863 2 0.5597 0.0463 0.448 0.488 0.004 0.000 0.060
#> SRR1706864 2 0.5494 0.0448 0.460 0.484 0.004 0.000 0.052
#> SRR1706865 2 0.5645 0.0558 0.436 0.500 0.008 0.000 0.056
#> SRR1706866 2 0.5596 0.0516 0.444 0.496 0.008 0.000 0.052
#> SRR1706871 2 0.1608 0.5926 0.000 0.928 0.072 0.000 0.000
#> SRR1706872 2 0.1608 0.5926 0.000 0.928 0.072 0.000 0.000
#> SRR1706873 2 0.1544 0.5933 0.000 0.932 0.068 0.000 0.000
#> SRR1706874 2 0.1544 0.5941 0.000 0.932 0.068 0.000 0.000
#> SRR1706879 2 0.5029 0.4354 0.112 0.740 0.020 0.000 0.128
#> SRR1706880 2 0.4889 0.4450 0.108 0.748 0.016 0.000 0.128
#> SRR1706881 2 0.5026 0.4280 0.116 0.736 0.016 0.000 0.132
#> SRR1706882 2 0.4940 0.4432 0.112 0.748 0.020 0.000 0.120
#> SRR1706883 1 0.5880 0.1401 0.568 0.304 0.000 0.000 0.128
#> SRR1706884 1 0.5666 0.1708 0.592 0.300 0.000 0.000 0.108
#> SRR1706885 1 0.5629 0.1664 0.588 0.312 0.000 0.000 0.100
#> SRR1706886 1 0.5724 0.1620 0.584 0.304 0.000 0.000 0.112
#> SRR1706875 2 0.2685 0.5657 0.000 0.880 0.028 0.000 0.092
#> SRR1706876 2 0.2344 0.5812 0.000 0.904 0.032 0.000 0.064
#> SRR1706877 2 0.2735 0.5659 0.000 0.880 0.036 0.000 0.084
#> SRR1706878 2 0.2793 0.5649 0.000 0.876 0.036 0.000 0.088
#> SRR1706887 2 0.3527 0.5283 0.000 0.828 0.116 0.000 0.056
#> SRR1706888 2 0.3527 0.5283 0.000 0.828 0.116 0.000 0.056
#> SRR1706889 2 0.3527 0.5283 0.000 0.828 0.116 0.000 0.056
#> SRR1706890 2 0.3527 0.5283 0.000 0.828 0.116 0.000 0.056
#> SRR1706891 2 0.3281 0.5511 0.000 0.848 0.092 0.000 0.060
#> SRR1706892 2 0.3336 0.5478 0.000 0.844 0.096 0.000 0.060
#> SRR1706893 2 0.3169 0.5565 0.000 0.856 0.084 0.000 0.060
#> SRR1706894 2 0.3336 0.5478 0.000 0.844 0.096 0.000 0.060
#> SRR1706895 2 0.2629 0.5873 0.008 0.896 0.032 0.000 0.064
#> SRR1706896 2 0.2629 0.5873 0.008 0.896 0.032 0.000 0.064
#> SRR1706897 2 0.2629 0.5873 0.008 0.896 0.032 0.000 0.064
#> SRR1706898 2 0.2629 0.5873 0.008 0.896 0.032 0.000 0.064
#> SRR1706899 2 0.3546 0.5698 0.060 0.852 0.024 0.000 0.064
#> SRR1706900 2 0.3674 0.5645 0.064 0.844 0.024 0.000 0.068
#> SRR1706901 2 0.3546 0.5698 0.060 0.852 0.024 0.000 0.064
#> SRR1706902 2 0.3410 0.5780 0.052 0.860 0.024 0.000 0.064
#> SRR1706907 2 0.2020 0.5814 0.000 0.900 0.100 0.000 0.000
#> SRR1706908 2 0.2233 0.5775 0.000 0.892 0.104 0.000 0.004
#> SRR1706909 2 0.2233 0.5782 0.000 0.892 0.104 0.000 0.004
#> SRR1706910 2 0.2358 0.5786 0.000 0.888 0.104 0.000 0.008
#> SRR1706903 2 0.3934 0.4724 0.168 0.792 0.008 0.000 0.032
#> SRR1706904 2 0.4082 0.4486 0.184 0.776 0.008 0.000 0.032
#> SRR1706905 2 0.4160 0.4435 0.184 0.772 0.008 0.000 0.036
#> SRR1706906 2 0.4072 0.4370 0.192 0.772 0.008 0.000 0.028
#> SRR1706911 2 0.2172 0.5839 0.000 0.908 0.076 0.000 0.016
#> SRR1706912 2 0.1956 0.5856 0.000 0.916 0.076 0.000 0.008
#> SRR1706913 2 0.2130 0.5832 0.000 0.908 0.080 0.000 0.012
#> SRR1706914 2 0.1956 0.5856 0.000 0.916 0.076 0.000 0.008
#> SRR1706919 2 0.5129 0.3988 0.068 0.724 0.028 0.000 0.180
#> SRR1706920 2 0.5163 0.3919 0.068 0.720 0.028 0.000 0.184
#> SRR1706921 2 0.5384 0.3330 0.068 0.692 0.028 0.000 0.212
#> SRR1706922 2 0.5389 0.3227 0.072 0.688 0.024 0.000 0.216
#> SRR1706915 2 0.3844 0.4884 0.000 0.804 0.064 0.000 0.132
#> SRR1706916 2 0.3705 0.5035 0.000 0.816 0.064 0.000 0.120
#> SRR1706917 2 0.3657 0.5085 0.000 0.820 0.064 0.000 0.116
#> SRR1706918 2 0.3798 0.4930 0.000 0.808 0.064 0.000 0.128
#> SRR1706923 1 0.6166 0.0723 0.512 0.340 0.000 0.000 0.148
#> SRR1706924 1 0.6310 0.0767 0.516 0.328 0.004 0.000 0.152
#> SRR1706925 1 0.6166 0.0719 0.512 0.340 0.000 0.000 0.148
#> SRR1706926 1 0.6321 0.0808 0.524 0.312 0.004 0.000 0.160
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1706767 4 0.1610 0.8752 0.000 0.000 0.000 0.916 NA 0.084
#> SRR1706768 4 0.1610 0.8752 0.000 0.000 0.000 0.916 NA 0.084
#> SRR1706769 4 0.1610 0.8752 0.000 0.000 0.000 0.916 NA 0.084
#> SRR1706770 4 0.1610 0.8752 0.000 0.000 0.000 0.916 NA 0.084
#> SRR1706771 4 0.3275 0.8425 0.036 0.000 0.000 0.816 NA 0.144
#> SRR1706772 4 0.3065 0.8579 0.052 0.000 0.000 0.844 NA 0.100
#> SRR1706773 4 0.3096 0.8558 0.048 0.000 0.000 0.840 NA 0.108
#> SRR1706774 4 0.3127 0.8552 0.056 0.000 0.000 0.840 NA 0.100
#> SRR1706775 1 0.1364 0.8559 0.944 0.000 0.000 0.048 NA 0.004
#> SRR1706776 1 0.1440 0.8584 0.944 0.004 0.000 0.044 NA 0.004
#> SRR1706777 1 0.1429 0.8532 0.940 0.000 0.000 0.052 NA 0.004
#> SRR1706778 1 0.1493 0.8513 0.936 0.000 0.000 0.056 NA 0.004
#> SRR1706779 1 0.0291 0.8744 0.992 0.000 0.004 0.000 NA 0.004
#> SRR1706780 1 0.0436 0.8748 0.988 0.004 0.004 0.000 NA 0.004
#> SRR1706781 1 0.0436 0.8748 0.988 0.004 0.004 0.000 NA 0.004
#> SRR1706782 1 0.0436 0.8748 0.988 0.004 0.004 0.000 NA 0.004
#> SRR1706783 1 0.0767 0.8745 0.976 0.008 0.004 0.000 NA 0.012
#> SRR1706784 1 0.0767 0.8745 0.976 0.008 0.004 0.000 NA 0.012
#> SRR1706785 1 0.0665 0.8745 0.980 0.008 0.004 0.000 NA 0.008
#> SRR1706786 1 0.0767 0.8745 0.976 0.008 0.004 0.000 NA 0.012
#> SRR1706787 4 0.2679 0.8660 0.000 0.000 0.096 0.868 NA 0.004
#> SRR1706788 4 0.2679 0.8660 0.000 0.000 0.096 0.868 NA 0.004
#> SRR1706789 4 0.2679 0.8660 0.000 0.000 0.096 0.868 NA 0.004
#> SRR1706790 4 0.2679 0.8660 0.000 0.000 0.096 0.868 NA 0.004
#> SRR1706791 4 0.3819 0.7494 0.176 0.000 0.052 0.768 NA 0.000
#> SRR1706792 4 0.3654 0.7880 0.144 0.000 0.060 0.792 NA 0.000
#> SRR1706793 4 0.3381 0.7923 0.148 0.000 0.040 0.808 NA 0.000
#> SRR1706794 4 0.3265 0.8238 0.112 0.000 0.056 0.828 NA 0.000
#> SRR1706795 1 0.1194 0.8695 0.956 0.008 0.000 0.032 NA 0.004
#> SRR1706796 1 0.1261 0.8713 0.956 0.008 0.000 0.028 NA 0.004
#> SRR1706797 1 0.1225 0.8704 0.956 0.004 0.000 0.032 NA 0.004
#> SRR1706798 1 0.1194 0.8695 0.956 0.008 0.000 0.032 NA 0.004
#> SRR1706799 1 0.0692 0.8724 0.976 0.000 0.000 0.000 NA 0.004
#> SRR1706800 1 0.0603 0.8730 0.980 0.000 0.000 0.000 NA 0.004
#> SRR1706801 1 0.0458 0.8729 0.984 0.000 0.000 0.000 NA 0.000
#> SRR1706802 1 0.0458 0.8729 0.984 0.000 0.000 0.000 NA 0.000
#> SRR1706803 1 0.0547 0.8723 0.980 0.000 0.000 0.000 NA 0.000
#> SRR1706804 1 0.0547 0.8723 0.980 0.000 0.000 0.000 NA 0.000
#> SRR1706805 1 0.0547 0.8723 0.980 0.000 0.000 0.000 NA 0.000
#> SRR1706806 1 0.0547 0.8723 0.980 0.000 0.000 0.000 NA 0.000
#> SRR1706811 4 0.3499 0.8116 0.008 0.032 0.000 0.796 NA 0.000
#> SRR1706812 4 0.3534 0.8063 0.008 0.032 0.000 0.792 NA 0.000
#> SRR1706813 4 0.3800 0.7930 0.008 0.048 0.000 0.776 NA 0.000
#> SRR1706814 4 0.3667 0.8018 0.012 0.036 0.000 0.788 NA 0.000
#> SRR1706807 4 0.2445 0.8642 0.000 0.000 0.020 0.872 NA 0.000
#> SRR1706808 4 0.2491 0.8624 0.000 0.000 0.020 0.868 NA 0.000
#> SRR1706809 4 0.2618 0.8612 0.000 0.000 0.024 0.860 NA 0.000
#> SRR1706810 4 0.2445 0.8644 0.000 0.000 0.020 0.872 NA 0.000
#> SRR1706815 2 0.7368 -0.0781 0.316 0.324 0.000 0.112 NA 0.000
#> SRR1706816 1 0.7325 0.0623 0.328 0.316 0.000 0.104 NA 0.000
#> SRR1706817 2 0.7267 -0.0936 0.328 0.328 0.000 0.096 NA 0.000
#> SRR1706818 1 0.7305 0.0992 0.348 0.316 0.000 0.108 NA 0.000
#> SRR1706819 1 0.5468 0.3674 0.540 0.332 0.000 0.000 NA 0.004
#> SRR1706820 1 0.5468 0.3674 0.540 0.332 0.000 0.000 NA 0.004
#> SRR1706821 1 0.5339 0.4198 0.568 0.312 0.000 0.000 NA 0.004
#> SRR1706822 1 0.5457 0.3758 0.544 0.328 0.000 0.000 NA 0.004
#> SRR1706823 1 0.4475 0.6320 0.700 0.200 0.000 0.000 NA 0.000
#> SRR1706824 1 0.4297 0.6630 0.724 0.176 0.000 0.000 NA 0.000
#> SRR1706825 1 0.4769 0.5703 0.656 0.240 0.000 0.000 NA 0.000
#> SRR1706826 1 0.4625 0.6066 0.680 0.216 0.000 0.000 NA 0.000
#> SRR1706827 4 0.1719 0.8781 0.000 0.000 0.060 0.924 NA 0.000
#> SRR1706828 4 0.1863 0.8776 0.000 0.000 0.060 0.920 NA 0.004
#> SRR1706829 4 0.1528 0.8797 0.000 0.000 0.048 0.936 NA 0.000
#> SRR1706830 4 0.1863 0.8776 0.000 0.000 0.060 0.920 NA 0.004
#> SRR1706835 1 0.2259 0.8512 0.908 0.024 0.004 0.056 NA 0.004
#> SRR1706836 1 0.1981 0.8612 0.924 0.020 0.004 0.044 NA 0.004
#> SRR1706837 1 0.2339 0.8495 0.904 0.028 0.004 0.056 NA 0.004
#> SRR1706838 1 0.2259 0.8512 0.908 0.024 0.004 0.056 NA 0.004
#> SRR1706831 4 0.1180 0.8842 0.016 0.012 0.012 0.960 NA 0.000
#> SRR1706832 4 0.1364 0.8838 0.012 0.016 0.020 0.952 NA 0.000
#> SRR1706833 4 0.1414 0.8844 0.012 0.012 0.020 0.952 NA 0.000
#> SRR1706834 4 0.1180 0.8840 0.012 0.012 0.016 0.960 NA 0.000
#> SRR1706839 1 0.0725 0.8740 0.976 0.012 0.000 0.000 NA 0.012
#> SRR1706840 1 0.0725 0.8740 0.976 0.012 0.000 0.000 NA 0.012
#> SRR1706841 1 0.0622 0.8742 0.980 0.012 0.000 0.000 NA 0.008
#> SRR1706842 1 0.0725 0.8740 0.976 0.012 0.000 0.000 NA 0.012
#> SRR1706847 6 0.4419 0.7781 0.000 0.116 0.100 0.000 NA 0.756
#> SRR1706848 6 0.4403 0.7735 0.000 0.112 0.096 0.000 NA 0.760
#> SRR1706849 6 0.4721 0.7685 0.000 0.116 0.092 0.000 NA 0.740
#> SRR1706850 6 0.4373 0.7770 0.000 0.116 0.096 0.000 NA 0.760
#> SRR1706843 1 0.0653 0.8754 0.980 0.012 0.000 0.000 NA 0.004
#> SRR1706844 1 0.0622 0.8747 0.980 0.012 0.000 0.000 NA 0.008
#> SRR1706845 1 0.0653 0.8754 0.980 0.012 0.000 0.000 NA 0.004
#> SRR1706846 1 0.0653 0.8754 0.980 0.012 0.000 0.000 NA 0.004
#> SRR1706851 6 0.5560 0.7289 0.000 0.240 0.112 0.000 NA 0.616
#> SRR1706852 6 0.5657 0.7280 0.000 0.232 0.120 0.000 NA 0.612
#> SRR1706853 6 0.5724 0.7212 0.000 0.240 0.116 0.000 NA 0.604
#> SRR1706854 6 0.5714 0.7137 0.000 0.236 0.124 0.000 NA 0.604
#> SRR1706855 2 0.5958 -0.0251 0.000 0.568 0.260 0.000 NA 0.132
#> SRR1706856 2 0.5672 -0.0677 0.000 0.568 0.296 0.000 NA 0.112
#> SRR1706857 2 0.5766 -0.0644 0.000 0.564 0.284 0.000 NA 0.128
#> SRR1706858 2 0.5852 -0.0693 0.000 0.560 0.280 0.000 NA 0.132
#> SRR1706859 2 0.3344 0.6105 0.104 0.828 0.060 0.000 NA 0.000
#> SRR1706860 2 0.3200 0.6055 0.092 0.840 0.060 0.000 NA 0.000
#> SRR1706861 2 0.3249 0.6077 0.096 0.836 0.060 0.000 NA 0.000
#> SRR1706862 2 0.3200 0.6064 0.092 0.840 0.060 0.000 NA 0.000
#> SRR1706867 3 0.6944 0.3978 0.000 0.208 0.428 0.000 NA 0.076
#> SRR1706869 3 0.6952 0.3909 0.000 0.208 0.424 0.000 NA 0.076
#> SRR1706870 3 0.6941 0.4141 0.000 0.212 0.432 0.000 NA 0.076
#> SRR1706863 2 0.4008 0.6069 0.184 0.760 0.044 0.000 NA 0.004
#> SRR1706864 2 0.4070 0.6050 0.192 0.752 0.044 0.000 NA 0.004
#> SRR1706865 2 0.4008 0.6069 0.184 0.760 0.044 0.000 NA 0.004
#> SRR1706866 2 0.4040 0.6068 0.188 0.756 0.044 0.000 NA 0.004
#> SRR1706871 3 0.6478 0.5702 0.000 0.308 0.488 0.000 NA 0.060
#> SRR1706872 3 0.6524 0.5665 0.000 0.316 0.476 0.000 NA 0.060
#> SRR1706873 3 0.6478 0.5702 0.000 0.308 0.488 0.000 NA 0.060
#> SRR1706874 3 0.6576 0.5641 0.000 0.308 0.476 0.000 NA 0.064
#> SRR1706879 2 0.2933 0.6160 0.096 0.856 0.040 0.000 NA 0.000
#> SRR1706880 2 0.2863 0.6161 0.096 0.860 0.036 0.000 NA 0.000
#> SRR1706881 2 0.3028 0.6178 0.104 0.848 0.040 0.000 NA 0.000
#> SRR1706882 2 0.2933 0.6151 0.096 0.856 0.040 0.000 NA 0.000
#> SRR1706883 2 0.4078 0.5471 0.272 0.700 0.008 0.000 NA 0.004
#> SRR1706884 2 0.3902 0.5670 0.256 0.720 0.008 0.000 NA 0.004
#> SRR1706885 2 0.4095 0.5574 0.260 0.708 0.008 0.000 NA 0.004
#> SRR1706886 2 0.4035 0.5560 0.264 0.708 0.008 0.000 NA 0.004
#> SRR1706875 2 0.5202 -0.0359 0.000 0.588 0.320 0.000 NA 0.012
#> SRR1706876 2 0.5186 -0.1063 0.000 0.572 0.344 0.000 NA 0.012
#> SRR1706877 2 0.5082 0.0358 0.000 0.612 0.300 0.000 NA 0.012
#> SRR1706878 2 0.5174 0.0023 0.000 0.596 0.312 0.000 NA 0.012
#> SRR1706887 3 0.3737 0.6232 0.000 0.188 0.772 0.000 NA 0.024
#> SRR1706888 3 0.3737 0.6232 0.000 0.188 0.772 0.000 NA 0.024
#> SRR1706889 3 0.3769 0.6260 0.000 0.192 0.768 0.000 NA 0.024
#> SRR1706890 3 0.3737 0.6232 0.000 0.188 0.772 0.000 NA 0.024
#> SRR1706891 3 0.2933 0.6296 0.004 0.200 0.796 0.000 NA 0.000
#> SRR1706892 3 0.2902 0.6280 0.004 0.196 0.800 0.000 NA 0.000
#> SRR1706893 3 0.2871 0.6273 0.004 0.192 0.804 0.000 NA 0.000
#> SRR1706894 3 0.2902 0.6280 0.004 0.196 0.800 0.000 NA 0.000
#> SRR1706895 3 0.3935 0.5895 0.012 0.288 0.692 0.000 NA 0.000
#> SRR1706896 3 0.3895 0.5871 0.016 0.284 0.696 0.000 NA 0.000
#> SRR1706897 3 0.4002 0.5875 0.016 0.284 0.692 0.000 NA 0.000
#> SRR1706898 3 0.3982 0.5899 0.016 0.280 0.696 0.000 NA 0.000
#> SRR1706899 3 0.4900 0.4470 0.044 0.372 0.572 0.000 NA 0.000
#> SRR1706900 3 0.4869 0.4670 0.044 0.360 0.584 0.000 NA 0.000
#> SRR1706901 3 0.4880 0.4617 0.044 0.364 0.580 0.000 NA 0.000
#> SRR1706902 3 0.4880 0.4617 0.044 0.364 0.580 0.000 NA 0.000
#> SRR1706907 3 0.7091 0.3681 0.000 0.236 0.388 0.000 NA 0.080
#> SRR1706908 3 0.7032 0.3850 0.000 0.224 0.412 0.000 NA 0.080
#> SRR1706909 3 0.7027 0.3863 0.000 0.220 0.412 0.000 NA 0.080
#> SRR1706910 3 0.7032 0.3858 0.000 0.224 0.412 0.000 NA 0.080
#> SRR1706903 2 0.5473 0.2177 0.108 0.548 0.336 0.000 NA 0.004
#> SRR1706904 2 0.5573 0.1916 0.116 0.528 0.348 0.000 NA 0.004
#> SRR1706905 2 0.5558 0.1808 0.104 0.540 0.344 0.000 NA 0.004
#> SRR1706906 2 0.5604 0.1765 0.108 0.532 0.348 0.000 NA 0.004
#> SRR1706911 3 0.6842 0.4900 0.000 0.356 0.396 0.000 NA 0.068
#> SRR1706912 3 0.6781 0.4972 0.000 0.352 0.408 0.000 NA 0.064
#> SRR1706913 3 0.6772 0.4828 0.000 0.368 0.396 0.000 NA 0.064
#> SRR1706914 3 0.6781 0.4971 0.000 0.352 0.408 0.000 NA 0.064
#> SRR1706919 2 0.2265 0.5976 0.056 0.904 0.012 0.000 NA 0.000
#> SRR1706920 2 0.2445 0.5953 0.056 0.896 0.020 0.000 NA 0.000
#> SRR1706921 2 0.2434 0.5963 0.056 0.896 0.016 0.000 NA 0.000
#> SRR1706922 2 0.2341 0.5958 0.056 0.900 0.012 0.000 NA 0.000
#> SRR1706915 2 0.4667 0.3199 0.000 0.724 0.172 0.000 NA 0.032
#> SRR1706916 2 0.4732 0.2805 0.000 0.704 0.200 0.000 NA 0.024
#> SRR1706917 2 0.4708 0.3429 0.008 0.728 0.168 0.000 NA 0.020
#> SRR1706918 2 0.4615 0.3113 0.000 0.720 0.184 0.000 NA 0.024
#> SRR1706923 2 0.3976 0.5785 0.224 0.740 0.016 0.000 NA 0.004
#> SRR1706924 2 0.3605 0.5799 0.224 0.756 0.008 0.000 NA 0.004
#> SRR1706925 2 0.3970 0.5777 0.224 0.740 0.012 0.000 NA 0.004
#> SRR1706926 2 0.3887 0.5788 0.224 0.744 0.012 0.000 NA 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.
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