Date: 2019-12-26 00:30:30 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 13572 rows and 129 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] 13572 129
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 | ||
|---|---|---|---|---|---|---|
| ATC:pam | 4 | 1.000 | 0.983 | 0.994 | ** | 2,3 |
| ATC:skmeans | 3 | 0.992 | 0.934 | 0.972 | ** | 2 |
| SD:pam | 2 | 0.984 | 0.964 | 0.985 | ** | |
| MAD:kmeans | 2 | 0.983 | 0.956 | 0.982 | ** | |
| CV:kmeans | 2 | 0.980 | 0.942 | 0.974 | ** | |
| SD:kmeans | 2 | 0.968 | 0.957 | 0.983 | ** | |
| ATC:mclust | 2 | 0.967 | 0.955 | 0.976 | ** | |
| ATC:kmeans | 3 | 0.965 | 0.932 | 0.973 | ** | 2 |
| MAD:skmeans | 3 | 0.961 | 0.926 | 0.971 | ** | 2 |
| CV:NMF | 2 | 0.947 | 0.941 | 0.974 | * | |
| SD:skmeans | 3 | 0.937 | 0.933 | 0.972 | * | 2 |
| MAD:pam | 2 | 0.937 | 0.953 | 0.981 | * | |
| CV:skmeans | 3 | 0.928 | 0.900 | 0.958 | * | 2 |
| CV:mclust | 3 | 0.912 | 0.912 | 0.965 | * | 2 |
| ATC:hclust | 2 | 0.864 | 0.910 | 0.954 | ||
| ATC:NMF | 2 | 0.827 | 0.890 | 0.954 | ||
| MAD:mclust | 5 | 0.776 | 0.841 | 0.896 | ||
| MAD:NMF | 2 | 0.719 | 0.866 | 0.942 | ||
| SD:NMF | 2 | 0.715 | 0.884 | 0.945 | ||
| SD:mclust | 5 | 0.712 | 0.802 | 0.872 | ||
| CV:hclust | 2 | 0.707 | 0.865 | 0.933 | ||
| SD:hclust | 3 | 0.610 | 0.787 | 0.887 | ||
| MAD:hclust | 3 | 0.579 | 0.768 | 0.879 | ||
| CV:pam | 2 | 0.569 | 0.875 | 0.925 |
**: 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 0.715 0.884 0.945 0.489 0.512 0.512
#> CV:NMF 2 0.947 0.941 0.974 0.493 0.512 0.512
#> MAD:NMF 2 0.719 0.866 0.942 0.493 0.507 0.507
#> ATC:NMF 2 0.827 0.890 0.954 0.481 0.522 0.522
#> SD:skmeans 2 1.000 0.947 0.980 0.494 0.507 0.507
#> CV:skmeans 2 0.984 0.944 0.979 0.498 0.503 0.503
#> MAD:skmeans 2 1.000 0.956 0.983 0.494 0.505 0.505
#> ATC:skmeans 2 1.000 0.998 0.999 0.491 0.509 0.509
#> SD:mclust 2 0.597 0.796 0.906 0.363 0.705 0.705
#> CV:mclust 2 0.923 0.928 0.964 0.320 0.649 0.649
#> MAD:mclust 2 0.683 0.897 0.946 0.331 0.705 0.705
#> ATC:mclust 2 0.967 0.955 0.976 0.260 0.715 0.715
#> SD:kmeans 2 0.968 0.957 0.983 0.465 0.538 0.538
#> CV:kmeans 2 0.980 0.942 0.974 0.468 0.525 0.525
#> MAD:kmeans 2 0.983 0.956 0.982 0.466 0.538 0.538
#> ATC:kmeans 2 1.000 0.988 0.995 0.473 0.525 0.525
#> SD:pam 2 0.984 0.964 0.985 0.433 0.563 0.563
#> CV:pam 2 0.569 0.875 0.925 0.454 0.552 0.552
#> MAD:pam 2 0.937 0.953 0.981 0.438 0.563 0.563
#> ATC:pam 2 1.000 0.986 0.994 0.480 0.518 0.518
#> SD:hclust 2 0.741 0.898 0.950 0.385 0.640 0.640
#> CV:hclust 2 0.707 0.865 0.933 0.379 0.594 0.594
#> MAD:hclust 2 0.733 0.871 0.938 0.399 0.624 0.624
#> ATC:hclust 2 0.864 0.910 0.954 0.472 0.522 0.522
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.766 0.847 0.927 0.276 0.789 0.617
#> CV:NMF 3 0.692 0.774 0.902 0.335 0.751 0.550
#> MAD:NMF 3 0.715 0.827 0.914 0.274 0.811 0.646
#> ATC:NMF 3 0.433 0.629 0.801 0.352 0.718 0.513
#> SD:skmeans 3 0.937 0.933 0.972 0.344 0.802 0.619
#> CV:skmeans 3 0.928 0.900 0.958 0.323 0.776 0.579
#> MAD:skmeans 3 0.961 0.926 0.971 0.344 0.796 0.609
#> ATC:skmeans 3 0.992 0.934 0.972 0.309 0.827 0.664
#> SD:mclust 3 0.489 0.623 0.757 0.624 0.664 0.527
#> CV:mclust 3 0.912 0.912 0.965 0.919 0.619 0.464
#> MAD:mclust 3 0.457 0.506 0.737 0.757 0.729 0.621
#> ATC:mclust 3 0.773 0.850 0.924 1.377 0.618 0.474
#> SD:kmeans 3 0.735 0.855 0.914 0.362 0.747 0.564
#> CV:kmeans 3 0.755 0.864 0.920 0.385 0.673 0.452
#> MAD:kmeans 3 0.726 0.831 0.904 0.368 0.739 0.553
#> ATC:kmeans 3 0.965 0.932 0.973 0.384 0.682 0.466
#> SD:pam 3 0.653 0.722 0.880 0.513 0.693 0.492
#> CV:pam 3 0.628 0.802 0.908 0.374 0.815 0.665
#> MAD:pam 3 0.653 0.745 0.888 0.506 0.699 0.499
#> ATC:pam 3 0.999 0.957 0.981 0.353 0.681 0.464
#> SD:hclust 3 0.610 0.787 0.887 0.594 0.729 0.579
#> CV:hclust 3 0.508 0.776 0.862 0.253 0.962 0.936
#> MAD:hclust 3 0.579 0.768 0.879 0.562 0.714 0.551
#> ATC:hclust 3 0.581 0.674 0.799 0.297 0.903 0.813
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.634 0.685 0.848 0.1578 0.790 0.511
#> CV:NMF 4 0.664 0.697 0.836 0.1327 0.773 0.455
#> MAD:NMF 4 0.663 0.686 0.856 0.1562 0.802 0.527
#> ATC:NMF 4 0.527 0.537 0.746 0.1511 0.674 0.297
#> SD:skmeans 4 0.806 0.861 0.922 0.1018 0.896 0.705
#> CV:skmeans 4 0.897 0.905 0.950 0.1211 0.858 0.617
#> MAD:skmeans 4 0.838 0.880 0.933 0.1029 0.881 0.669
#> ATC:skmeans 4 0.766 0.763 0.899 0.0751 0.894 0.721
#> SD:mclust 4 0.639 0.698 0.823 0.1562 0.855 0.662
#> CV:mclust 4 0.521 0.477 0.752 0.1214 0.866 0.691
#> MAD:mclust 4 0.638 0.723 0.821 0.1682 0.603 0.327
#> ATC:mclust 4 0.680 0.816 0.884 0.1218 0.855 0.627
#> SD:kmeans 4 0.810 0.873 0.923 0.1780 0.795 0.506
#> CV:kmeans 4 0.725 0.757 0.877 0.1126 0.874 0.666
#> MAD:kmeans 4 0.764 0.851 0.908 0.1696 0.797 0.508
#> ATC:kmeans 4 0.886 0.881 0.940 0.1143 0.798 0.512
#> SD:pam 4 0.737 0.737 0.881 0.1333 0.797 0.489
#> CV:pam 4 0.854 0.862 0.936 0.1858 0.854 0.619
#> MAD:pam 4 0.737 0.709 0.874 0.1238 0.775 0.449
#> ATC:pam 4 1.000 0.983 0.994 0.1363 0.864 0.638
#> SD:hclust 4 0.640 0.754 0.881 0.0987 0.949 0.866
#> CV:hclust 4 0.463 0.670 0.793 0.3152 0.742 0.544
#> MAD:hclust 4 0.673 0.767 0.874 0.1158 0.962 0.896
#> ATC:hclust 4 0.627 0.625 0.724 0.1392 0.821 0.601
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.616 0.678 0.813 0.0817 0.849 0.526
#> CV:NMF 5 0.559 0.412 0.671 0.0713 0.863 0.546
#> MAD:NMF 5 0.673 0.690 0.827 0.0791 0.840 0.496
#> ATC:NMF 5 0.522 0.410 0.639 0.0634 0.872 0.558
#> SD:skmeans 5 0.758 0.666 0.845 0.0466 0.959 0.848
#> CV:skmeans 5 0.838 0.785 0.893 0.0483 0.933 0.759
#> MAD:skmeans 5 0.773 0.644 0.838 0.0457 0.975 0.906
#> ATC:skmeans 5 0.752 0.676 0.822 0.0494 0.918 0.749
#> SD:mclust 5 0.712 0.802 0.872 0.1023 0.822 0.525
#> CV:mclust 5 0.587 0.571 0.752 0.1054 0.771 0.412
#> MAD:mclust 5 0.776 0.841 0.896 0.1110 0.822 0.525
#> ATC:mclust 5 0.836 0.879 0.915 0.0991 0.894 0.654
#> SD:kmeans 5 0.735 0.727 0.824 0.0676 0.918 0.692
#> CV:kmeans 5 0.713 0.548 0.768 0.0804 0.864 0.576
#> MAD:kmeans 5 0.763 0.766 0.857 0.0713 0.914 0.681
#> ATC:kmeans 5 0.747 0.682 0.842 0.0785 0.893 0.641
#> SD:pam 5 0.674 0.631 0.805 0.0624 0.912 0.678
#> CV:pam 5 0.861 0.861 0.925 0.0406 0.970 0.885
#> MAD:pam 5 0.671 0.598 0.802 0.0629 0.897 0.633
#> ATC:pam 5 0.704 0.383 0.705 0.0644 0.881 0.636
#> SD:hclust 5 0.613 0.613 0.740 0.1157 0.991 0.973
#> CV:hclust 5 0.518 0.636 0.750 0.0950 0.947 0.840
#> MAD:hclust 5 0.653 0.595 0.768 0.0895 0.948 0.839
#> ATC:hclust 5 0.659 0.674 0.783 0.1147 0.847 0.541
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.640 0.642 0.784 0.0445 0.929 0.695
#> CV:NMF 6 0.701 0.646 0.796 0.0448 0.866 0.471
#> MAD:NMF 6 0.617 0.588 0.752 0.0436 0.952 0.780
#> ATC:NMF 6 0.595 0.519 0.686 0.0356 0.893 0.560
#> SD:skmeans 6 0.779 0.726 0.855 0.0437 0.894 0.604
#> CV:skmeans 6 0.839 0.672 0.842 0.0284 0.969 0.868
#> MAD:skmeans 6 0.807 0.770 0.875 0.0437 0.900 0.629
#> ATC:skmeans 6 0.865 0.824 0.916 0.0447 0.957 0.849
#> SD:mclust 6 0.798 0.782 0.875 0.0724 0.891 0.591
#> CV:mclust 6 0.720 0.679 0.825 0.0589 0.919 0.660
#> MAD:mclust 6 0.837 0.819 0.899 0.0723 0.883 0.569
#> ATC:mclust 6 0.688 0.725 0.778 0.0353 0.961 0.836
#> SD:kmeans 6 0.763 0.661 0.821 0.0391 0.948 0.763
#> CV:kmeans 6 0.747 0.623 0.799 0.0475 0.866 0.501
#> MAD:kmeans 6 0.791 0.667 0.824 0.0368 0.957 0.798
#> ATC:kmeans 6 0.760 0.722 0.827 0.0489 0.918 0.646
#> SD:pam 6 0.806 0.729 0.865 0.0511 0.890 0.545
#> CV:pam 6 0.795 0.745 0.877 0.0325 0.973 0.887
#> MAD:pam 6 0.798 0.682 0.855 0.0510 0.885 0.531
#> ATC:pam 6 0.751 0.678 0.795 0.0490 0.836 0.463
#> SD:hclust 6 0.664 0.529 0.715 0.0570 0.859 0.590
#> CV:hclust 6 0.618 0.645 0.812 0.0638 0.942 0.814
#> MAD:hclust 6 0.684 0.634 0.728 0.0546 0.911 0.692
#> ATC:hclust 6 0.781 0.772 0.865 0.0545 0.960 0.818
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.741 0.898 0.950 0.3853 0.640 0.640
#> 3 3 0.610 0.787 0.887 0.5937 0.729 0.579
#> 4 4 0.640 0.754 0.881 0.0987 0.949 0.866
#> 5 5 0.613 0.613 0.740 0.1157 0.991 0.973
#> 6 6 0.664 0.529 0.715 0.0570 0.859 0.590
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.0000 0.964 0.000 1.000
#> SRR1656464 1 0.0000 0.941 1.000 0.000
#> SRR1656462 1 0.0000 0.941 1.000 0.000
#> SRR1656465 1 0.0000 0.941 1.000 0.000
#> SRR1656467 2 0.8207 0.631 0.256 0.744
#> SRR1656466 1 0.0000 0.941 1.000 0.000
#> SRR1656468 1 0.6048 0.837 0.852 0.148
#> SRR1656472 1 0.0672 0.938 0.992 0.008
#> SRR1656471 1 0.0000 0.941 1.000 0.000
#> SRR1656470 2 0.0000 0.964 0.000 1.000
#> SRR1656469 1 0.0376 0.940 0.996 0.004
#> SRR1656473 2 0.0000 0.964 0.000 1.000
#> SRR1656474 2 0.0000 0.964 0.000 1.000
#> SRR1656475 2 0.0000 0.964 0.000 1.000
#> SRR1656478 1 0.0000 0.941 1.000 0.000
#> SRR1656477 1 0.7602 0.760 0.780 0.220
#> SRR1656479 1 0.0376 0.940 0.996 0.004
#> SRR1656480 1 0.7674 0.755 0.776 0.224
#> SRR1656476 2 0.0000 0.964 0.000 1.000
#> SRR1656481 1 0.5946 0.840 0.856 0.144
#> SRR1656482 2 0.0376 0.962 0.004 0.996
#> SRR1656483 2 0.0000 0.964 0.000 1.000
#> SRR1656485 1 0.0000 0.941 1.000 0.000
#> SRR1656487 1 0.0000 0.941 1.000 0.000
#> SRR1656486 1 0.0000 0.941 1.000 0.000
#> SRR1656488 1 0.0000 0.941 1.000 0.000
#> SRR1656484 1 0.0000 0.941 1.000 0.000
#> SRR1656489 1 0.0000 0.941 1.000 0.000
#> SRR1656491 1 0.0672 0.938 0.992 0.008
#> SRR1656490 1 0.0376 0.940 0.996 0.004
#> SRR1656492 1 0.0000 0.941 1.000 0.000
#> SRR1656493 1 0.0376 0.940 0.996 0.004
#> SRR1656495 1 0.0672 0.938 0.992 0.008
#> SRR1656496 1 0.0376 0.940 0.996 0.004
#> SRR1656494 2 0.3733 0.910 0.072 0.928
#> SRR1656497 2 0.0000 0.964 0.000 1.000
#> SRR1656499 1 0.0000 0.941 1.000 0.000
#> SRR1656500 1 0.0000 0.941 1.000 0.000
#> SRR1656501 1 0.0000 0.941 1.000 0.000
#> SRR1656498 1 0.0000 0.941 1.000 0.000
#> SRR1656504 2 0.0000 0.964 0.000 1.000
#> SRR1656502 1 0.0672 0.938 0.992 0.008
#> SRR1656503 1 0.0000 0.941 1.000 0.000
#> SRR1656507 1 0.0000 0.941 1.000 0.000
#> SRR1656508 1 0.0000 0.941 1.000 0.000
#> SRR1656505 1 0.7815 0.745 0.768 0.232
#> SRR1656506 1 0.0000 0.941 1.000 0.000
#> SRR1656509 1 0.6531 0.817 0.832 0.168
#> SRR1656510 1 0.3733 0.897 0.928 0.072
#> SRR1656511 1 0.8555 0.676 0.720 0.280
#> SRR1656513 2 0.2423 0.940 0.040 0.960
#> SRR1656512 2 0.0000 0.964 0.000 1.000
#> SRR1656514 1 0.0000 0.941 1.000 0.000
#> SRR1656515 2 0.9286 0.425 0.344 0.656
#> SRR1656516 1 0.0000 0.941 1.000 0.000
#> SRR1656518 1 0.0000 0.941 1.000 0.000
#> SRR1656517 1 0.0000 0.941 1.000 0.000
#> SRR1656519 1 0.0000 0.941 1.000 0.000
#> SRR1656522 1 0.0000 0.941 1.000 0.000
#> SRR1656523 1 0.4690 0.877 0.900 0.100
#> SRR1656521 2 0.0000 0.964 0.000 1.000
#> SRR1656520 1 0.0000 0.941 1.000 0.000
#> SRR1656524 1 0.0672 0.938 0.992 0.008
#> SRR1656525 1 0.0000 0.941 1.000 0.000
#> SRR1656526 2 0.0000 0.964 0.000 1.000
#> SRR1656527 2 0.2043 0.946 0.032 0.968
#> SRR1656530 1 0.0000 0.941 1.000 0.000
#> SRR1656529 1 0.0000 0.941 1.000 0.000
#> SRR1656531 1 0.0000 0.941 1.000 0.000
#> SRR1656528 1 0.0000 0.941 1.000 0.000
#> SRR1656534 1 0.0000 0.941 1.000 0.000
#> SRR1656533 1 0.0000 0.941 1.000 0.000
#> SRR1656536 1 0.0672 0.938 0.992 0.008
#> SRR1656532 2 0.2778 0.933 0.048 0.952
#> SRR1656537 1 0.0000 0.941 1.000 0.000
#> SRR1656538 1 0.0000 0.941 1.000 0.000
#> SRR1656535 2 0.0000 0.964 0.000 1.000
#> SRR1656539 1 0.0672 0.938 0.992 0.008
#> SRR1656544 1 0.0000 0.941 1.000 0.000
#> SRR1656542 1 0.0000 0.941 1.000 0.000
#> SRR1656543 1 0.0000 0.941 1.000 0.000
#> SRR1656545 2 0.0000 0.964 0.000 1.000
#> SRR1656540 1 0.0000 0.941 1.000 0.000
#> SRR1656546 1 0.2603 0.916 0.956 0.044
#> SRR1656541 2 0.0000 0.964 0.000 1.000
#> SRR1656547 1 0.9977 0.206 0.528 0.472
#> SRR1656548 1 0.0000 0.941 1.000 0.000
#> SRR1656549 1 0.1414 0.931 0.980 0.020
#> SRR1656551 1 0.0672 0.938 0.992 0.008
#> SRR1656553 1 0.0000 0.941 1.000 0.000
#> SRR1656550 1 0.7950 0.735 0.760 0.240
#> SRR1656552 1 0.7950 0.736 0.760 0.240
#> SRR1656554 1 0.0000 0.941 1.000 0.000
#> SRR1656555 1 0.5946 0.842 0.856 0.144
#> SRR1656556 1 0.6148 0.832 0.848 0.152
#> SRR1656557 1 0.0000 0.941 1.000 0.000
#> SRR1656558 1 0.0000 0.941 1.000 0.000
#> SRR1656559 1 0.0000 0.941 1.000 0.000
#> SRR1656560 1 0.0000 0.941 1.000 0.000
#> SRR1656561 1 0.0000 0.941 1.000 0.000
#> SRR1656562 1 0.8144 0.719 0.748 0.252
#> SRR1656563 1 0.0000 0.941 1.000 0.000
#> SRR1656564 2 0.0000 0.964 0.000 1.000
#> SRR1656565 2 0.4690 0.878 0.100 0.900
#> SRR1656566 1 0.0000 0.941 1.000 0.000
#> SRR1656568 2 0.0000 0.964 0.000 1.000
#> SRR1656567 1 0.7950 0.735 0.760 0.240
#> SRR1656569 1 0.0000 0.941 1.000 0.000
#> SRR1656570 1 0.0000 0.941 1.000 0.000
#> SRR1656571 2 0.0000 0.964 0.000 1.000
#> SRR1656573 1 0.1184 0.934 0.984 0.016
#> SRR1656572 1 0.8555 0.676 0.720 0.280
#> SRR1656574 1 0.0000 0.941 1.000 0.000
#> SRR1656575 1 0.0000 0.941 1.000 0.000
#> SRR1656576 1 0.8555 0.676 0.720 0.280
#> SRR1656578 2 0.2423 0.940 0.040 0.960
#> SRR1656577 1 0.0000 0.941 1.000 0.000
#> SRR1656579 1 0.9963 0.232 0.536 0.464
#> SRR1656580 1 0.0000 0.941 1.000 0.000
#> SRR1656581 1 0.4562 0.880 0.904 0.096
#> SRR1656582 2 0.0000 0.964 0.000 1.000
#> SRR1656585 1 0.7299 0.779 0.796 0.204
#> SRR1656584 1 0.0000 0.941 1.000 0.000
#> SRR1656583 1 0.7219 0.783 0.800 0.200
#> SRR1656586 2 0.0000 0.964 0.000 1.000
#> SRR1656587 1 0.7299 0.779 0.796 0.204
#> SRR1656588 1 0.8207 0.714 0.744 0.256
#> SRR1656589 2 0.0000 0.964 0.000 1.000
#> SRR1656590 1 0.0000 0.941 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656464 1 0.3879 0.7947 0.848 0.000 0.152
#> SRR1656462 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656465 1 0.2625 0.8647 0.916 0.000 0.084
#> SRR1656467 2 0.6095 0.4472 0.000 0.608 0.392
#> SRR1656466 1 0.0892 0.8927 0.980 0.000 0.020
#> SRR1656468 3 0.4937 0.7477 0.148 0.028 0.824
#> SRR1656472 3 0.5327 0.6144 0.272 0.000 0.728
#> SRR1656471 1 0.2959 0.8653 0.900 0.000 0.100
#> SRR1656470 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656469 1 0.3941 0.8166 0.844 0.000 0.156
#> SRR1656473 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656478 1 0.2711 0.8692 0.912 0.000 0.088
#> SRR1656477 3 0.2280 0.7362 0.008 0.052 0.940
#> SRR1656479 1 0.4555 0.7690 0.800 0.000 0.200
#> SRR1656480 3 0.2384 0.7348 0.008 0.056 0.936
#> SRR1656476 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656481 3 0.4874 0.7478 0.144 0.028 0.828
#> SRR1656482 2 0.1289 0.9414 0.000 0.968 0.032
#> SRR1656483 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656485 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656487 1 0.0892 0.8927 0.980 0.000 0.020
#> SRR1656486 1 0.4178 0.8029 0.828 0.000 0.172
#> SRR1656488 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656484 1 0.4452 0.7846 0.808 0.000 0.192
#> SRR1656489 1 0.0747 0.8949 0.984 0.000 0.016
#> SRR1656491 1 0.5431 0.6305 0.716 0.000 0.284
#> SRR1656490 1 0.4750 0.7550 0.784 0.000 0.216
#> SRR1656492 1 0.1031 0.8935 0.976 0.000 0.024
#> SRR1656493 3 0.5968 0.4517 0.364 0.000 0.636
#> SRR1656495 3 0.5529 0.5684 0.296 0.000 0.704
#> SRR1656496 1 0.4504 0.7743 0.804 0.000 0.196
#> SRR1656494 2 0.3752 0.8586 0.000 0.856 0.144
#> SRR1656497 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656501 1 0.4178 0.8029 0.828 0.000 0.172
#> SRR1656498 1 0.5650 0.5800 0.688 0.000 0.312
#> SRR1656504 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656502 3 0.5327 0.6144 0.272 0.000 0.728
#> SRR1656503 1 0.0747 0.8949 0.984 0.000 0.016
#> SRR1656507 1 0.2711 0.8692 0.912 0.000 0.088
#> SRR1656508 1 0.2878 0.8661 0.904 0.000 0.096
#> SRR1656505 3 0.2584 0.7323 0.008 0.064 0.928
#> SRR1656506 1 0.2625 0.8677 0.916 0.000 0.084
#> SRR1656509 3 0.1989 0.7484 0.048 0.004 0.948
#> SRR1656510 3 0.6252 0.5466 0.344 0.008 0.648
#> SRR1656511 3 0.3573 0.7038 0.004 0.120 0.876
#> SRR1656513 2 0.3116 0.8946 0.000 0.892 0.108
#> SRR1656512 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656514 1 0.1289 0.8908 0.968 0.000 0.032
#> SRR1656515 3 0.6308 -0.1635 0.000 0.492 0.508
#> SRR1656516 1 0.0424 0.8950 0.992 0.000 0.008
#> SRR1656518 1 0.4178 0.8029 0.828 0.000 0.172
#> SRR1656517 1 0.2796 0.8674 0.908 0.000 0.092
#> SRR1656519 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656523 3 0.5072 0.7107 0.196 0.012 0.792
#> SRR1656521 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656520 1 0.0747 0.8940 0.984 0.000 0.016
#> SRR1656524 3 0.5529 0.5684 0.296 0.000 0.704
#> SRR1656525 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656526 2 0.1860 0.9322 0.000 0.948 0.052
#> SRR1656527 2 0.2537 0.9144 0.000 0.920 0.080
#> SRR1656530 1 0.0892 0.8927 0.980 0.000 0.020
#> SRR1656529 1 0.1031 0.8926 0.976 0.000 0.024
#> SRR1656531 3 0.6305 0.0960 0.484 0.000 0.516
#> SRR1656528 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656534 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656533 1 0.2959 0.8643 0.900 0.000 0.100
#> SRR1656536 1 0.4504 0.7741 0.804 0.000 0.196
#> SRR1656532 2 0.3340 0.8839 0.000 0.880 0.120
#> SRR1656537 1 0.5926 0.5002 0.644 0.000 0.356
#> SRR1656538 1 0.0424 0.8950 0.992 0.000 0.008
#> SRR1656535 2 0.0237 0.9512 0.000 0.996 0.004
#> SRR1656539 1 0.4452 0.7792 0.808 0.000 0.192
#> SRR1656544 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656542 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656543 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656540 1 0.1031 0.8925 0.976 0.000 0.024
#> SRR1656546 3 0.5926 0.4810 0.356 0.000 0.644
#> SRR1656541 2 0.1860 0.9322 0.000 0.948 0.052
#> SRR1656547 3 0.6228 0.4219 0.012 0.316 0.672
#> SRR1656548 1 0.0237 0.8946 0.996 0.000 0.004
#> SRR1656549 3 0.6252 0.2459 0.444 0.000 0.556
#> SRR1656551 1 0.4504 0.7741 0.804 0.000 0.196
#> SRR1656553 1 0.0747 0.8949 0.984 0.000 0.016
#> SRR1656550 3 0.2356 0.7265 0.000 0.072 0.928
#> SRR1656552 3 0.4092 0.7369 0.036 0.088 0.876
#> SRR1656554 1 0.2537 0.8694 0.920 0.000 0.080
#> SRR1656555 3 0.5792 0.7183 0.192 0.036 0.772
#> SRR1656556 3 0.6224 0.5475 0.296 0.016 0.688
#> SRR1656557 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656558 1 0.2711 0.8692 0.912 0.000 0.088
#> SRR1656559 1 0.0000 0.8939 1.000 0.000 0.000
#> SRR1656560 1 0.0892 0.8927 0.980 0.000 0.020
#> SRR1656561 1 0.0237 0.8946 0.996 0.000 0.004
#> SRR1656562 3 0.3207 0.7270 0.012 0.084 0.904
#> SRR1656563 1 0.2959 0.8643 0.900 0.000 0.100
#> SRR1656564 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656565 2 0.4235 0.8187 0.000 0.824 0.176
#> SRR1656566 3 0.6302 0.0927 0.480 0.000 0.520
#> SRR1656568 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656567 3 0.2356 0.7265 0.000 0.072 0.928
#> SRR1656569 1 0.2625 0.8677 0.916 0.000 0.084
#> SRR1656570 1 0.2959 0.8643 0.900 0.000 0.100
#> SRR1656571 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656573 1 0.5201 0.7146 0.760 0.004 0.236
#> SRR1656572 3 0.3573 0.7038 0.004 0.120 0.876
#> SRR1656574 1 0.2878 0.8661 0.904 0.000 0.096
#> SRR1656575 1 0.4178 0.8029 0.828 0.000 0.172
#> SRR1656576 3 0.3573 0.7038 0.004 0.120 0.876
#> SRR1656578 2 0.3116 0.8946 0.000 0.892 0.108
#> SRR1656577 1 0.0747 0.8915 0.984 0.000 0.016
#> SRR1656579 3 0.5958 0.4412 0.008 0.300 0.692
#> SRR1656580 1 0.0237 0.8946 0.996 0.000 0.004
#> SRR1656581 3 0.4912 0.7098 0.196 0.008 0.796
#> SRR1656582 2 0.1529 0.9372 0.000 0.960 0.040
#> SRR1656585 3 0.2550 0.7441 0.024 0.040 0.936
#> SRR1656584 1 0.6299 0.0297 0.524 0.000 0.476
#> SRR1656583 3 0.2443 0.7445 0.028 0.032 0.940
#> SRR1656586 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656587 3 0.2550 0.7441 0.024 0.040 0.936
#> SRR1656588 3 0.2711 0.7196 0.000 0.088 0.912
#> SRR1656589 2 0.0000 0.9524 0.000 1.000 0.000
#> SRR1656590 1 0.5988 0.4730 0.632 0.000 0.368
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656464 3 0.4795 0.4971 0.292 0.000 0.696 0.012
#> SRR1656462 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656465 3 0.2281 0.8058 0.000 0.000 0.904 0.096
#> SRR1656467 2 0.4916 0.3072 0.000 0.576 0.000 0.424
#> SRR1656466 3 0.0921 0.8401 0.000 0.000 0.972 0.028
#> SRR1656468 4 0.3684 0.7331 0.020 0.004 0.132 0.844
#> SRR1656472 1 0.2530 0.5681 0.896 0.000 0.004 0.100
#> SRR1656471 3 0.2805 0.8041 0.012 0.000 0.888 0.100
#> SRR1656470 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.3577 0.7437 0.012 0.000 0.832 0.156
#> SRR1656473 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656478 3 0.3569 0.7166 0.196 0.000 0.804 0.000
#> SRR1656477 4 0.0469 0.8062 0.000 0.012 0.000 0.988
#> SRR1656479 3 0.4801 0.6799 0.048 0.000 0.764 0.188
#> SRR1656480 4 0.0592 0.8073 0.000 0.016 0.000 0.984
#> SRR1656476 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.2944 0.7321 0.004 0.000 0.128 0.868
#> SRR1656482 2 0.1118 0.9307 0.000 0.964 0.000 0.036
#> SRR1656483 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.0921 0.8401 0.000 0.000 0.972 0.028
#> SRR1656486 3 0.4431 0.5373 0.304 0.000 0.696 0.000
#> SRR1656488 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656484 3 0.5003 0.5116 0.308 0.000 0.676 0.016
#> SRR1656489 3 0.0921 0.8435 0.028 0.000 0.972 0.000
#> SRR1656491 3 0.5256 0.5472 0.036 0.000 0.692 0.272
#> SRR1656490 3 0.5279 0.6561 0.072 0.000 0.736 0.192
#> SRR1656492 3 0.1042 0.8433 0.008 0.000 0.972 0.020
#> SRR1656493 1 0.3495 0.6934 0.844 0.000 0.140 0.016
#> SRR1656495 1 0.0188 0.6192 0.996 0.000 0.000 0.004
#> SRR1656496 3 0.4916 0.6809 0.056 0.000 0.760 0.184
#> SRR1656494 2 0.3306 0.8326 0.004 0.840 0.000 0.156
#> SRR1656497 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656501 3 0.4431 0.5373 0.304 0.000 0.696 0.000
#> SRR1656498 3 0.4999 -0.1464 0.492 0.000 0.508 0.000
#> SRR1656504 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.2530 0.5681 0.896 0.000 0.004 0.100
#> SRR1656503 3 0.0592 0.8449 0.016 0.000 0.984 0.000
#> SRR1656507 3 0.3569 0.7166 0.196 0.000 0.804 0.000
#> SRR1656508 3 0.3688 0.7014 0.208 0.000 0.792 0.000
#> SRR1656505 4 0.0817 0.8082 0.000 0.024 0.000 0.976
#> SRR1656506 3 0.2466 0.8053 0.004 0.000 0.900 0.096
#> SRR1656509 4 0.2466 0.7878 0.056 0.000 0.028 0.916
#> SRR1656510 4 0.5793 0.3972 0.048 0.000 0.324 0.628
#> SRR1656511 4 0.2775 0.7963 0.020 0.084 0.000 0.896
#> SRR1656513 2 0.2831 0.8712 0.004 0.876 0.000 0.120
#> SRR1656512 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.2412 0.8143 0.084 0.000 0.908 0.008
#> SRR1656515 4 0.4967 0.0893 0.000 0.452 0.000 0.548
#> SRR1656516 3 0.0592 0.8454 0.016 0.000 0.984 0.000
#> SRR1656518 3 0.4431 0.5373 0.304 0.000 0.696 0.000
#> SRR1656517 3 0.3610 0.7096 0.200 0.000 0.800 0.000
#> SRR1656519 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.0707 0.8435 0.020 0.000 0.980 0.000
#> SRR1656523 4 0.5843 0.6160 0.156 0.004 0.124 0.716
#> SRR1656521 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.1256 0.8402 0.028 0.000 0.964 0.008
#> SRR1656524 1 0.0188 0.6192 0.996 0.000 0.000 0.004
#> SRR1656525 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.1792 0.9149 0.000 0.932 0.000 0.068
#> SRR1656527 2 0.2266 0.8987 0.004 0.912 0.000 0.084
#> SRR1656530 3 0.0921 0.8401 0.000 0.000 0.972 0.028
#> SRR1656529 3 0.0817 0.8421 0.000 0.000 0.976 0.024
#> SRR1656531 1 0.3726 0.7133 0.788 0.000 0.212 0.000
#> SRR1656528 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656534 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656533 3 0.3726 0.6968 0.212 0.000 0.788 0.000
#> SRR1656536 3 0.4095 0.6989 0.016 0.000 0.792 0.192
#> SRR1656532 2 0.2999 0.8591 0.004 0.864 0.000 0.132
#> SRR1656537 1 0.4967 0.2753 0.548 0.000 0.452 0.000
#> SRR1656538 3 0.0592 0.8454 0.016 0.000 0.984 0.000
#> SRR1656535 2 0.0188 0.9428 0.000 0.996 0.000 0.004
#> SRR1656539 3 0.4054 0.7042 0.016 0.000 0.796 0.188
#> SRR1656544 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.1452 0.8369 0.036 0.000 0.956 0.008
#> SRR1656546 4 0.7553 0.0657 0.324 0.000 0.208 0.468
#> SRR1656541 2 0.1792 0.9149 0.000 0.932 0.000 0.068
#> SRR1656547 4 0.4746 0.5782 0.008 0.276 0.004 0.712
#> SRR1656548 3 0.0469 0.8456 0.012 0.000 0.988 0.000
#> SRR1656549 1 0.5334 0.6600 0.680 0.000 0.284 0.036
#> SRR1656551 3 0.4095 0.6989 0.016 0.000 0.792 0.192
#> SRR1656553 3 0.0592 0.8449 0.016 0.000 0.984 0.000
#> SRR1656550 4 0.1022 0.8085 0.000 0.032 0.000 0.968
#> SRR1656552 4 0.3255 0.8028 0.044 0.048 0.016 0.892
#> SRR1656554 3 0.2401 0.8080 0.004 0.000 0.904 0.092
#> SRR1656555 4 0.4449 0.6682 0.016 0.016 0.172 0.796
#> SRR1656556 4 0.4910 0.4469 0.020 0.000 0.276 0.704
#> SRR1656557 3 0.0000 0.8445 0.000 0.000 1.000 0.000
#> SRR1656558 3 0.3569 0.7166 0.196 0.000 0.804 0.000
#> SRR1656559 3 0.0707 0.8435 0.020 0.000 0.980 0.000
#> SRR1656560 3 0.0921 0.8401 0.000 0.000 0.972 0.028
#> SRR1656561 3 0.0469 0.8456 0.012 0.000 0.988 0.000
#> SRR1656562 4 0.1953 0.8094 0.012 0.044 0.004 0.940
#> SRR1656563 3 0.3726 0.6968 0.212 0.000 0.788 0.000
#> SRR1656564 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.3626 0.7951 0.004 0.812 0.000 0.184
#> SRR1656566 1 0.4277 0.6705 0.720 0.000 0.280 0.000
#> SRR1656568 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.1022 0.8085 0.000 0.032 0.000 0.968
#> SRR1656569 3 0.2466 0.8053 0.004 0.000 0.900 0.096
#> SRR1656570 3 0.3726 0.6968 0.212 0.000 0.788 0.000
#> SRR1656571 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656573 3 0.4988 0.6239 0.036 0.000 0.728 0.236
#> SRR1656572 4 0.2775 0.7963 0.020 0.084 0.000 0.896
#> SRR1656574 3 0.3688 0.7014 0.208 0.000 0.792 0.000
#> SRR1656575 3 0.4431 0.5373 0.304 0.000 0.696 0.000
#> SRR1656576 4 0.2775 0.7963 0.020 0.084 0.000 0.896
#> SRR1656578 2 0.2831 0.8712 0.004 0.876 0.000 0.120
#> SRR1656577 3 0.1474 0.8305 0.052 0.000 0.948 0.000
#> SRR1656579 4 0.4134 0.5937 0.000 0.260 0.000 0.740
#> SRR1656580 3 0.0469 0.8456 0.012 0.000 0.988 0.000
#> SRR1656581 4 0.5664 0.6153 0.156 0.000 0.124 0.720
#> SRR1656582 2 0.1302 0.9265 0.000 0.956 0.000 0.044
#> SRR1656585 4 0.2188 0.8078 0.032 0.020 0.012 0.936
#> SRR1656584 1 0.4624 0.5949 0.660 0.000 0.340 0.000
#> SRR1656583 4 0.1821 0.8052 0.032 0.012 0.008 0.948
#> SRR1656586 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.2188 0.8078 0.032 0.020 0.012 0.936
#> SRR1656588 4 0.1389 0.8072 0.000 0.048 0.000 0.952
#> SRR1656589 2 0.0000 0.9440 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.4948 0.3137 0.560 0.000 0.440 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656464 3 0.6326 0.1557 0.336 0.000 0.492 0.000 NA
#> SRR1656462 3 0.2740 0.5910 0.096 0.000 0.876 0.000 NA
#> SRR1656465 3 0.5299 0.5074 0.016 0.000 0.612 0.036 NA
#> SRR1656467 2 0.5394 0.2929 0.000 0.540 0.000 0.400 NA
#> SRR1656466 3 0.4582 0.5437 0.016 0.000 0.684 0.012 NA
#> SRR1656468 4 0.3964 0.7439 0.016 0.000 0.020 0.788 NA
#> SRR1656472 1 0.5341 0.5619 0.504 0.000 0.000 0.052 NA
#> SRR1656471 3 0.5355 0.4998 0.016 0.000 0.596 0.036 NA
#> SRR1656470 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656469 3 0.6070 0.4740 0.016 0.000 0.576 0.100 NA
#> SRR1656473 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656474 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656475 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656478 3 0.4440 0.2615 0.468 0.000 0.528 0.000 NA
#> SRR1656477 4 0.1408 0.7942 0.000 0.008 0.000 0.948 NA
#> SRR1656479 3 0.7283 0.3908 0.076 0.000 0.468 0.120 NA
#> SRR1656480 4 0.1331 0.7944 0.000 0.008 0.000 0.952 NA
#> SRR1656476 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656481 4 0.3907 0.7414 0.016 0.000 0.016 0.788 NA
#> SRR1656482 2 0.1469 0.9199 0.000 0.948 0.000 0.016 NA
#> SRR1656483 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656485 3 0.2561 0.5943 0.096 0.000 0.884 0.000 NA
#> SRR1656487 3 0.4582 0.5437 0.016 0.000 0.684 0.012 NA
#> SRR1656486 3 0.4517 0.1877 0.436 0.000 0.556 0.000 NA
#> SRR1656488 3 0.2230 0.6000 0.000 0.000 0.884 0.000 NA
#> SRR1656484 3 0.5225 0.2010 0.392 0.000 0.564 0.004 NA
#> SRR1656489 3 0.1270 0.6097 0.052 0.000 0.948 0.000 NA
#> SRR1656491 3 0.7269 0.3173 0.036 0.000 0.424 0.196 NA
#> SRR1656490 3 0.7602 0.3716 0.104 0.000 0.456 0.132 NA
#> SRR1656492 3 0.4455 0.5571 0.036 0.000 0.704 0.000 NA
#> SRR1656493 1 0.5355 0.6949 0.636 0.000 0.064 0.008 NA
#> SRR1656495 1 0.4015 0.6434 0.652 0.000 0.000 0.000 NA
#> SRR1656496 3 0.7301 0.3957 0.080 0.000 0.476 0.120 NA
#> SRR1656494 2 0.3779 0.8144 0.000 0.804 0.000 0.144 NA
#> SRR1656497 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656499 3 0.2230 0.6000 0.000 0.000 0.884 0.000 NA
#> SRR1656500 3 0.2653 0.5902 0.096 0.000 0.880 0.000 NA
#> SRR1656501 3 0.4517 0.1877 0.436 0.000 0.556 0.000 NA
#> SRR1656498 1 0.4269 0.4601 0.732 0.000 0.232 0.000 NA
#> SRR1656504 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656502 1 0.5341 0.5619 0.504 0.000 0.000 0.052 NA
#> SRR1656503 3 0.1043 0.6108 0.040 0.000 0.960 0.000 NA
#> SRR1656507 3 0.4440 0.2615 0.468 0.000 0.528 0.000 NA
#> SRR1656508 3 0.4448 0.2381 0.480 0.000 0.516 0.000 NA
#> SRR1656505 4 0.0451 0.7938 0.000 0.008 0.000 0.988 NA
#> SRR1656506 3 0.5483 0.5020 0.024 0.000 0.600 0.036 NA
#> SRR1656509 4 0.3455 0.7687 0.024 0.000 0.020 0.844 NA
#> SRR1656510 4 0.6743 0.4844 0.036 0.000 0.184 0.564 NA
#> SRR1656511 4 0.3237 0.7758 0.000 0.048 0.000 0.848 NA
#> SRR1656513 2 0.3359 0.8513 0.000 0.840 0.000 0.108 NA
#> SRR1656512 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656514 3 0.4898 0.4481 0.248 0.000 0.684 0.000 NA
#> SRR1656515 4 0.5473 0.0887 0.000 0.416 0.000 0.520 NA
#> SRR1656516 3 0.1831 0.6077 0.076 0.000 0.920 0.000 NA
#> SRR1656518 3 0.4622 0.1758 0.440 0.000 0.548 0.000 NA
#> SRR1656517 3 0.4443 0.2506 0.472 0.000 0.524 0.000 NA
#> SRR1656519 3 0.2653 0.5902 0.096 0.000 0.880 0.000 NA
#> SRR1656522 3 0.3779 0.5237 0.200 0.000 0.776 0.000 NA
#> SRR1656523 4 0.5711 0.6340 0.116 0.000 0.012 0.648 NA
#> SRR1656521 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656520 3 0.3390 0.5765 0.100 0.000 0.840 0.000 NA
#> SRR1656524 1 0.4015 0.6434 0.652 0.000 0.000 0.000 NA
#> SRR1656525 3 0.2338 0.6011 0.004 0.000 0.884 0.000 NA
#> SRR1656526 2 0.2376 0.9002 0.000 0.904 0.000 0.044 NA
#> SRR1656527 2 0.2628 0.8806 0.000 0.884 0.000 0.088 NA
#> SRR1656530 3 0.4582 0.5437 0.016 0.000 0.684 0.012 NA
#> SRR1656529 3 0.4420 0.5549 0.016 0.000 0.712 0.012 NA
#> SRR1656531 1 0.6089 0.6955 0.532 0.000 0.144 0.000 NA
#> SRR1656528 3 0.3456 0.5845 0.016 0.000 0.800 0.000 NA
#> SRR1656534 3 0.2653 0.5902 0.096 0.000 0.880 0.000 NA
#> SRR1656533 3 0.4658 0.2237 0.484 0.000 0.504 0.000 NA
#> SRR1656536 3 0.6377 0.4371 0.016 0.000 0.532 0.124 NA
#> SRR1656532 2 0.3507 0.8404 0.000 0.828 0.000 0.120 NA
#> SRR1656537 1 0.4100 0.5426 0.764 0.000 0.192 0.000 NA
#> SRR1656538 3 0.1831 0.6077 0.076 0.000 0.920 0.000 NA
#> SRR1656535 2 0.0162 0.9358 0.000 0.996 0.000 0.004 NA
#> SRR1656539 3 0.6353 0.4384 0.016 0.000 0.532 0.120 NA
#> SRR1656544 3 0.2707 0.5948 0.100 0.000 0.876 0.000 NA
#> SRR1656542 3 0.2707 0.5948 0.100 0.000 0.876 0.000 NA
#> SRR1656543 3 0.2740 0.5910 0.096 0.000 0.876 0.000 NA
#> SRR1656545 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656540 3 0.3517 0.5723 0.100 0.000 0.832 0.000 NA
#> SRR1656546 4 0.7729 0.0778 0.324 0.000 0.108 0.428 NA
#> SRR1656541 2 0.2376 0.9002 0.000 0.904 0.000 0.044 NA
#> SRR1656547 4 0.5116 0.5737 0.000 0.248 0.000 0.668 NA
#> SRR1656548 3 0.1704 0.6098 0.068 0.000 0.928 0.000 NA
#> SRR1656549 1 0.6390 0.6697 0.584 0.000 0.144 0.024 NA
#> SRR1656551 3 0.6377 0.4371 0.016 0.000 0.532 0.124 NA
#> SRR1656553 3 0.1043 0.6108 0.040 0.000 0.960 0.000 NA
#> SRR1656550 4 0.0807 0.7923 0.000 0.012 0.000 0.976 NA
#> SRR1656552 4 0.3251 0.7878 0.008 0.028 0.008 0.864 NA
#> SRR1656554 3 0.5439 0.4996 0.024 0.000 0.596 0.032 NA
#> SRR1656555 4 0.5365 0.6878 0.028 0.000 0.080 0.704 NA
#> SRR1656556 4 0.5779 0.5084 0.004 0.000 0.220 0.628 NA
#> SRR1656557 3 0.2740 0.5910 0.096 0.000 0.876 0.000 NA
#> SRR1656558 3 0.4440 0.2615 0.468 0.000 0.528 0.000 NA
#> SRR1656559 3 0.3779 0.5237 0.200 0.000 0.776 0.000 NA
#> SRR1656560 3 0.4582 0.5437 0.016 0.000 0.684 0.012 NA
#> SRR1656561 3 0.1704 0.6098 0.068 0.000 0.928 0.000 NA
#> SRR1656562 4 0.2470 0.7894 0.000 0.012 0.000 0.884 NA
#> SRR1656563 3 0.4560 0.2242 0.484 0.000 0.508 0.000 NA
#> SRR1656564 2 0.0162 0.9359 0.000 0.996 0.000 0.004 NA
#> SRR1656565 2 0.4010 0.7870 0.000 0.784 0.000 0.160 NA
#> SRR1656566 1 0.5305 0.6795 0.676 0.000 0.152 0.000 NA
#> SRR1656568 2 0.0162 0.9359 0.000 0.996 0.000 0.004 NA
#> SRR1656567 4 0.0807 0.7923 0.000 0.012 0.000 0.976 NA
#> SRR1656569 3 0.5483 0.5020 0.024 0.000 0.600 0.036 NA
#> SRR1656570 3 0.4560 0.2242 0.484 0.000 0.508 0.000 NA
#> SRR1656571 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656573 3 0.7412 0.3587 0.060 0.000 0.440 0.164 NA
#> SRR1656572 4 0.3237 0.7758 0.000 0.048 0.000 0.848 NA
#> SRR1656574 3 0.4448 0.2381 0.480 0.000 0.516 0.000 NA
#> SRR1656575 3 0.4517 0.1877 0.436 0.000 0.556 0.000 NA
#> SRR1656576 4 0.3237 0.7758 0.000 0.048 0.000 0.848 NA
#> SRR1656578 2 0.3359 0.8513 0.000 0.840 0.000 0.108 NA
#> SRR1656577 3 0.4169 0.4936 0.240 0.000 0.732 0.000 NA
#> SRR1656579 4 0.4481 0.5916 0.000 0.232 0.000 0.720 NA
#> SRR1656580 3 0.1638 0.6107 0.064 0.000 0.932 0.000 NA
#> SRR1656581 4 0.5657 0.6340 0.116 0.000 0.012 0.656 NA
#> SRR1656582 2 0.1670 0.9148 0.000 0.936 0.000 0.012 NA
#> SRR1656585 4 0.2612 0.7868 0.016 0.004 0.004 0.892 NA
#> SRR1656584 1 0.5572 0.6370 0.644 0.000 0.192 0.000 NA
#> SRR1656583 4 0.2511 0.7842 0.016 0.000 0.004 0.892 NA
#> SRR1656586 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656587 4 0.2612 0.7868 0.016 0.004 0.004 0.892 NA
#> SRR1656588 4 0.1493 0.7911 0.000 0.028 0.000 0.948 NA
#> SRR1656589 2 0.0000 0.9367 0.000 1.000 0.000 0.000 NA
#> SRR1656590 1 0.3995 0.5591 0.776 0.000 0.180 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.6737 0.348 0.212 0.000 0.516 0.000 0.108 0.164
#> SRR1656462 3 0.6053 0.366 0.000 0.000 0.412 0.000 0.308 0.280
#> SRR1656465 5 0.1078 0.701 0.000 0.000 0.008 0.016 0.964 0.012
#> SRR1656467 2 0.5052 0.267 0.000 0.532 0.000 0.388 0.000 0.080
#> SRR1656466 5 0.1649 0.707 0.000 0.000 0.032 0.000 0.932 0.036
#> SRR1656468 4 0.3730 0.550 0.000 0.000 0.000 0.772 0.168 0.060
#> SRR1656472 1 0.3669 0.531 0.784 0.000 0.016 0.012 0.008 0.180
#> SRR1656471 5 0.1520 0.699 0.008 0.000 0.008 0.016 0.948 0.020
#> SRR1656470 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656469 5 0.1829 0.675 0.000 0.000 0.000 0.056 0.920 0.024
#> SRR1656473 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656474 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656475 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656478 3 0.1138 0.456 0.004 0.000 0.960 0.000 0.024 0.012
#> SRR1656477 4 0.1605 0.676 0.004 0.000 0.000 0.936 0.016 0.044
#> SRR1656479 5 0.5158 0.537 0.016 0.000 0.104 0.072 0.728 0.080
#> SRR1656480 4 0.1461 0.677 0.000 0.000 0.000 0.940 0.016 0.044
#> SRR1656476 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.3786 0.552 0.004 0.000 0.000 0.772 0.172 0.052
#> SRR1656482 2 0.1367 0.913 0.000 0.944 0.000 0.012 0.000 0.044
#> SRR1656483 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656485 3 0.6027 0.374 0.000 0.000 0.424 0.000 0.304 0.272
#> SRR1656487 5 0.1649 0.707 0.000 0.000 0.032 0.000 0.932 0.036
#> SRR1656486 3 0.4723 0.271 0.108 0.000 0.704 0.000 0.176 0.012
#> SRR1656488 5 0.5346 0.278 0.000 0.000 0.164 0.000 0.584 0.252
#> SRR1656484 3 0.5972 0.236 0.152 0.000 0.560 0.000 0.256 0.032
#> SRR1656489 3 0.5759 0.213 0.004 0.000 0.440 0.000 0.408 0.148
#> SRR1656491 5 0.4785 0.491 0.012 0.000 0.024 0.140 0.736 0.088
#> SRR1656490 5 0.5713 0.461 0.020 0.000 0.148 0.076 0.676 0.080
#> SRR1656492 5 0.3221 0.671 0.000 0.000 0.096 0.000 0.828 0.076
#> SRR1656493 1 0.4005 0.559 0.748 0.000 0.192 0.000 0.004 0.056
#> SRR1656495 1 0.1524 0.617 0.932 0.000 0.060 0.000 0.000 0.008
#> SRR1656496 5 0.5332 0.525 0.016 0.000 0.116 0.072 0.712 0.084
#> SRR1656494 2 0.3602 0.804 0.000 0.792 0.000 0.136 0.000 0.072
#> SRR1656497 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656499 5 0.5374 0.269 0.000 0.000 0.168 0.000 0.580 0.252
#> SRR1656500 3 0.6014 0.390 0.000 0.000 0.432 0.000 0.288 0.280
#> SRR1656501 3 0.4723 0.271 0.108 0.000 0.704 0.000 0.176 0.012
#> SRR1656498 3 0.4308 -0.187 0.280 0.000 0.680 0.000 0.012 0.028
#> SRR1656504 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 1 0.3669 0.531 0.784 0.000 0.016 0.012 0.008 0.180
#> SRR1656503 5 0.5744 -0.226 0.000 0.000 0.408 0.000 0.424 0.168
#> SRR1656507 3 0.1138 0.456 0.004 0.000 0.960 0.000 0.024 0.012
#> SRR1656508 3 0.0837 0.445 0.004 0.000 0.972 0.000 0.020 0.004
#> SRR1656505 4 0.0146 0.679 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1656506 5 0.1802 0.703 0.000 0.000 0.020 0.024 0.932 0.024
#> SRR1656509 4 0.4154 0.606 0.060 0.000 0.004 0.796 0.064 0.076
#> SRR1656510 6 0.6825 0.292 0.004 0.000 0.036 0.268 0.300 0.392
#> SRR1656511 4 0.3534 0.627 0.000 0.036 0.000 0.796 0.008 0.160
#> SRR1656513 2 0.3206 0.840 0.000 0.828 0.000 0.104 0.000 0.068
#> SRR1656512 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656514 3 0.5673 0.505 0.040 0.000 0.628 0.000 0.156 0.176
#> SRR1656515 4 0.5123 0.126 0.000 0.408 0.000 0.508 0.000 0.084
#> SRR1656516 3 0.5363 0.242 0.000 0.000 0.492 0.000 0.396 0.112
#> SRR1656518 3 0.4723 0.260 0.108 0.000 0.704 0.000 0.176 0.012
#> SRR1656517 3 0.0632 0.448 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1656519 3 0.6004 0.395 0.000 0.000 0.436 0.000 0.284 0.280
#> SRR1656522 3 0.5348 0.489 0.000 0.000 0.592 0.000 0.192 0.216
#> SRR1656523 4 0.6853 0.186 0.048 0.000 0.068 0.564 0.120 0.200
#> SRR1656521 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.6356 0.401 0.016 0.000 0.432 0.000 0.264 0.288
#> SRR1656524 1 0.1524 0.617 0.932 0.000 0.060 0.000 0.000 0.008
#> SRR1656525 5 0.5510 0.223 0.000 0.000 0.192 0.000 0.560 0.248
#> SRR1656526 2 0.2328 0.887 0.000 0.892 0.000 0.052 0.000 0.056
#> SRR1656527 2 0.2579 0.869 0.000 0.872 0.000 0.088 0.000 0.040
#> SRR1656530 5 0.1649 0.707 0.000 0.000 0.032 0.000 0.932 0.036
#> SRR1656529 5 0.2164 0.694 0.000 0.000 0.032 0.000 0.900 0.068
#> SRR1656531 1 0.5246 0.560 0.604 0.000 0.280 0.000 0.008 0.108
#> SRR1656528 5 0.4253 0.528 0.000 0.000 0.108 0.000 0.732 0.160
#> SRR1656534 3 0.6004 0.395 0.000 0.000 0.436 0.000 0.284 0.280
#> SRR1656533 3 0.1065 0.432 0.008 0.000 0.964 0.000 0.020 0.008
#> SRR1656536 5 0.2563 0.643 0.000 0.000 0.000 0.072 0.876 0.052
#> SRR1656532 2 0.3341 0.829 0.000 0.816 0.000 0.116 0.000 0.068
#> SRR1656537 3 0.4406 -0.286 0.344 0.000 0.624 0.000 0.008 0.024
#> SRR1656538 3 0.5363 0.242 0.000 0.000 0.492 0.000 0.396 0.112
#> SRR1656535 2 0.0146 0.928 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656539 5 0.2506 0.646 0.000 0.000 0.000 0.068 0.880 0.052
#> SRR1656544 3 0.6006 0.379 0.000 0.000 0.432 0.000 0.304 0.264
#> SRR1656542 3 0.6006 0.379 0.000 0.000 0.432 0.000 0.304 0.264
#> SRR1656543 3 0.6053 0.366 0.000 0.000 0.412 0.000 0.308 0.280
#> SRR1656545 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656540 3 0.6427 0.398 0.020 0.000 0.428 0.000 0.264 0.288
#> SRR1656546 6 0.7735 0.153 0.192 0.000 0.240 0.136 0.024 0.408
#> SRR1656541 2 0.2328 0.887 0.000 0.892 0.000 0.052 0.000 0.056
#> SRR1656547 4 0.5062 0.429 0.000 0.240 0.000 0.648 0.012 0.100
#> SRR1656548 3 0.5372 0.235 0.000 0.000 0.484 0.000 0.404 0.112
#> SRR1656549 1 0.5987 0.374 0.452 0.000 0.420 0.016 0.012 0.100
#> SRR1656551 5 0.2563 0.643 0.000 0.000 0.000 0.072 0.876 0.052
#> SRR1656553 5 0.5744 -0.226 0.000 0.000 0.408 0.000 0.424 0.168
#> SRR1656550 4 0.0858 0.677 0.000 0.004 0.000 0.968 0.000 0.028
#> SRR1656552 4 0.5289 0.241 0.004 0.024 0.008 0.572 0.032 0.360
#> SRR1656554 5 0.1448 0.700 0.000 0.000 0.012 0.016 0.948 0.024
#> SRR1656555 4 0.5373 0.408 0.000 0.000 0.024 0.648 0.176 0.152
#> SRR1656556 4 0.5460 0.171 0.028 0.000 0.000 0.576 0.320 0.076
#> SRR1656557 3 0.6053 0.366 0.000 0.000 0.412 0.000 0.308 0.280
#> SRR1656558 3 0.1138 0.456 0.004 0.000 0.960 0.000 0.024 0.012
#> SRR1656559 3 0.5348 0.489 0.000 0.000 0.592 0.000 0.192 0.216
#> SRR1656560 5 0.1720 0.706 0.000 0.000 0.032 0.000 0.928 0.040
#> SRR1656561 3 0.5372 0.235 0.000 0.000 0.484 0.000 0.404 0.112
#> SRR1656562 4 0.3114 0.650 0.004 0.004 0.000 0.832 0.024 0.136
#> SRR1656563 3 0.0951 0.436 0.008 0.000 0.968 0.000 0.020 0.004
#> SRR1656564 2 0.0291 0.927 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1656565 2 0.3821 0.778 0.000 0.772 0.000 0.148 0.000 0.080
#> SRR1656566 1 0.4297 0.424 0.532 0.000 0.452 0.000 0.004 0.012
#> SRR1656568 2 0.0291 0.927 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1656567 4 0.0858 0.677 0.000 0.004 0.000 0.968 0.000 0.028
#> SRR1656569 5 0.1802 0.703 0.000 0.000 0.020 0.024 0.932 0.024
#> SRR1656570 3 0.0951 0.436 0.008 0.000 0.968 0.000 0.020 0.004
#> SRR1656571 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656573 5 0.5215 0.497 0.008 0.000 0.084 0.108 0.716 0.084
#> SRR1656572 4 0.3534 0.627 0.000 0.036 0.000 0.796 0.008 0.160
#> SRR1656574 3 0.0837 0.445 0.004 0.000 0.972 0.000 0.020 0.004
#> SRR1656575 3 0.4723 0.271 0.108 0.000 0.704 0.000 0.176 0.012
#> SRR1656576 4 0.3497 0.630 0.000 0.036 0.000 0.800 0.008 0.156
#> SRR1656578 2 0.3206 0.840 0.000 0.828 0.000 0.104 0.000 0.068
#> SRR1656577 3 0.4974 0.512 0.000 0.000 0.648 0.000 0.160 0.192
#> SRR1656579 4 0.4091 0.460 0.000 0.224 0.000 0.720 0.000 0.056
#> SRR1656580 3 0.5376 0.227 0.000 0.000 0.480 0.000 0.408 0.112
#> SRR1656581 4 0.6805 0.187 0.048 0.000 0.068 0.572 0.120 0.192
#> SRR1656582 2 0.1563 0.906 0.000 0.932 0.000 0.012 0.000 0.056
#> SRR1656585 4 0.3286 0.647 0.040 0.000 0.000 0.848 0.044 0.068
#> SRR1656584 3 0.4484 -0.510 0.460 0.000 0.516 0.000 0.008 0.016
#> SRR1656583 4 0.3528 0.640 0.044 0.000 0.000 0.832 0.048 0.076
#> SRR1656586 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656587 4 0.3286 0.647 0.040 0.000 0.000 0.848 0.044 0.068
#> SRR1656588 4 0.1480 0.676 0.000 0.020 0.000 0.940 0.000 0.040
#> SRR1656589 2 0.0146 0.928 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656590 3 0.4444 -0.303 0.356 0.000 0.612 0.000 0.008 0.024
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 13572 rows and 129 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 0.968 0.957 0.983 0.4646 0.538 0.538
#> 3 3 0.735 0.855 0.914 0.3618 0.747 0.564
#> 4 4 0.810 0.873 0.923 0.1780 0.795 0.506
#> 5 5 0.735 0.727 0.824 0.0676 0.918 0.692
#> 6 6 0.763 0.661 0.821 0.0391 0.948 0.763
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
#> SRR1656463 2 0.000 0.980 0.000 1.000
#> SRR1656464 1 0.000 0.983 1.000 0.000
#> SRR1656462 1 0.000 0.983 1.000 0.000
#> SRR1656465 1 0.000 0.983 1.000 0.000
#> SRR1656467 2 0.000 0.980 0.000 1.000
#> SRR1656466 1 0.000 0.983 1.000 0.000
#> SRR1656468 1 0.881 0.581 0.700 0.300
#> SRR1656472 1 0.000 0.983 1.000 0.000
#> SRR1656471 1 0.000 0.983 1.000 0.000
#> SRR1656470 2 0.000 0.980 0.000 1.000
#> SRR1656469 1 0.000 0.983 1.000 0.000
#> SRR1656473 2 0.000 0.980 0.000 1.000
#> SRR1656474 2 0.000 0.980 0.000 1.000
#> SRR1656475 2 0.000 0.980 0.000 1.000
#> SRR1656478 1 0.000 0.983 1.000 0.000
#> SRR1656477 1 0.971 0.331 0.600 0.400
#> SRR1656479 1 0.000 0.983 1.000 0.000
#> SRR1656480 2 0.000 0.980 0.000 1.000
#> SRR1656476 2 0.000 0.980 0.000 1.000
#> SRR1656481 1 0.529 0.859 0.880 0.120
#> SRR1656482 2 0.000 0.980 0.000 1.000
#> SRR1656483 2 0.000 0.980 0.000 1.000
#> SRR1656485 1 0.000 0.983 1.000 0.000
#> SRR1656487 1 0.000 0.983 1.000 0.000
#> SRR1656486 1 0.000 0.983 1.000 0.000
#> SRR1656488 1 0.000 0.983 1.000 0.000
#> SRR1656484 1 0.000 0.983 1.000 0.000
#> SRR1656489 1 0.000 0.983 1.000 0.000
#> SRR1656491 1 0.000 0.983 1.000 0.000
#> SRR1656490 1 0.000 0.983 1.000 0.000
#> SRR1656492 1 0.000 0.983 1.000 0.000
#> SRR1656493 1 0.000 0.983 1.000 0.000
#> SRR1656495 2 0.000 0.980 0.000 1.000
#> SRR1656496 1 0.000 0.983 1.000 0.000
#> SRR1656494 2 0.000 0.980 0.000 1.000
#> SRR1656497 2 0.000 0.980 0.000 1.000
#> SRR1656499 1 0.000 0.983 1.000 0.000
#> SRR1656500 1 0.000 0.983 1.000 0.000
#> SRR1656501 1 0.000 0.983 1.000 0.000
#> SRR1656498 1 0.000 0.983 1.000 0.000
#> SRR1656504 2 0.000 0.980 0.000 1.000
#> SRR1656502 1 0.000 0.983 1.000 0.000
#> SRR1656503 1 0.000 0.983 1.000 0.000
#> SRR1656507 1 0.000 0.983 1.000 0.000
#> SRR1656508 1 0.000 0.983 1.000 0.000
#> SRR1656505 2 0.000 0.980 0.000 1.000
#> SRR1656506 1 0.000 0.983 1.000 0.000
#> SRR1656509 1 0.000 0.983 1.000 0.000
#> SRR1656510 1 0.388 0.909 0.924 0.076
#> SRR1656511 2 0.000 0.980 0.000 1.000
#> SRR1656513 2 0.000 0.980 0.000 1.000
#> SRR1656512 2 0.000 0.980 0.000 1.000
#> SRR1656514 1 0.000 0.983 1.000 0.000
#> SRR1656515 2 0.000 0.980 0.000 1.000
#> SRR1656516 1 0.000 0.983 1.000 0.000
#> SRR1656518 1 0.000 0.983 1.000 0.000
#> SRR1656517 1 0.000 0.983 1.000 0.000
#> SRR1656519 1 0.000 0.983 1.000 0.000
#> SRR1656522 1 0.000 0.983 1.000 0.000
#> SRR1656523 2 0.983 0.259 0.424 0.576
#> SRR1656521 2 0.000 0.980 0.000 1.000
#> SRR1656520 1 0.000 0.983 1.000 0.000
#> SRR1656524 1 0.000 0.983 1.000 0.000
#> SRR1656525 1 0.000 0.983 1.000 0.000
#> SRR1656526 2 0.000 0.980 0.000 1.000
#> SRR1656527 2 0.000 0.980 0.000 1.000
#> SRR1656530 1 0.000 0.983 1.000 0.000
#> SRR1656529 1 0.000 0.983 1.000 0.000
#> SRR1656531 1 0.000 0.983 1.000 0.000
#> SRR1656528 1 0.000 0.983 1.000 0.000
#> SRR1656534 1 0.000 0.983 1.000 0.000
#> SRR1656533 1 0.000 0.983 1.000 0.000
#> SRR1656536 1 0.000 0.983 1.000 0.000
#> SRR1656532 2 0.000 0.980 0.000 1.000
#> SRR1656537 1 0.000 0.983 1.000 0.000
#> SRR1656538 1 0.000 0.983 1.000 0.000
#> SRR1656535 2 0.000 0.980 0.000 1.000
#> SRR1656539 1 0.000 0.983 1.000 0.000
#> SRR1656544 1 0.000 0.983 1.000 0.000
#> SRR1656542 1 0.000 0.983 1.000 0.000
#> SRR1656543 1 0.000 0.983 1.000 0.000
#> SRR1656545 2 0.000 0.980 0.000 1.000
#> SRR1656540 1 0.000 0.983 1.000 0.000
#> SRR1656546 1 0.000 0.983 1.000 0.000
#> SRR1656541 2 0.000 0.980 0.000 1.000
#> SRR1656547 2 0.000 0.980 0.000 1.000
#> SRR1656548 1 0.000 0.983 1.000 0.000
#> SRR1656549 1 0.000 0.983 1.000 0.000
#> SRR1656551 1 0.000 0.983 1.000 0.000
#> SRR1656553 1 0.000 0.983 1.000 0.000
#> SRR1656550 2 0.000 0.980 0.000 1.000
#> SRR1656552 2 0.000 0.980 0.000 1.000
#> SRR1656554 1 0.000 0.983 1.000 0.000
#> SRR1656555 1 0.689 0.771 0.816 0.184
#> SRR1656556 1 0.529 0.859 0.880 0.120
#> SRR1656557 1 0.000 0.983 1.000 0.000
#> SRR1656558 1 0.000 0.983 1.000 0.000
#> SRR1656559 1 0.000 0.983 1.000 0.000
#> SRR1656560 1 0.000 0.983 1.000 0.000
#> SRR1656561 1 0.000 0.983 1.000 0.000
#> SRR1656562 2 0.000 0.980 0.000 1.000
#> SRR1656563 1 0.000 0.983 1.000 0.000
#> SRR1656564 2 0.000 0.980 0.000 1.000
#> SRR1656565 2 0.000 0.980 0.000 1.000
#> SRR1656566 1 0.000 0.983 1.000 0.000
#> SRR1656568 2 0.000 0.980 0.000 1.000
#> SRR1656567 2 0.000 0.980 0.000 1.000
#> SRR1656569 1 0.000 0.983 1.000 0.000
#> SRR1656570 1 0.000 0.983 1.000 0.000
#> SRR1656571 2 0.000 0.980 0.000 1.000
#> SRR1656573 1 0.000 0.983 1.000 0.000
#> SRR1656572 2 0.000 0.980 0.000 1.000
#> SRR1656574 1 0.000 0.983 1.000 0.000
#> SRR1656575 1 0.000 0.983 1.000 0.000
#> SRR1656576 2 0.000 0.980 0.000 1.000
#> SRR1656578 2 0.000 0.980 0.000 1.000
#> SRR1656577 1 0.000 0.983 1.000 0.000
#> SRR1656579 2 0.000 0.980 0.000 1.000
#> SRR1656580 1 0.000 0.983 1.000 0.000
#> SRR1656581 1 0.000 0.983 1.000 0.000
#> SRR1656582 2 0.000 0.980 0.000 1.000
#> SRR1656585 1 0.563 0.845 0.868 0.132
#> SRR1656584 1 0.000 0.983 1.000 0.000
#> SRR1656583 2 0.985 0.240 0.428 0.572
#> SRR1656586 2 0.000 0.980 0.000 1.000
#> SRR1656587 2 0.242 0.940 0.040 0.960
#> SRR1656588 2 0.000 0.980 0.000 1.000
#> SRR1656589 2 0.000 0.980 0.000 1.000
#> SRR1656590 1 0.000 0.983 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656464 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656462 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656465 1 0.6126 0.485 0.600 0.000 0.400
#> SRR1656467 2 0.0892 0.952 0.000 0.980 0.020
#> SRR1656466 1 0.5058 0.762 0.756 0.000 0.244
#> SRR1656468 3 0.0892 0.849 0.020 0.000 0.980
#> SRR1656472 3 0.4346 0.775 0.184 0.000 0.816
#> SRR1656471 1 0.5254 0.723 0.736 0.000 0.264
#> SRR1656470 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656469 1 0.5254 0.741 0.736 0.000 0.264
#> SRR1656473 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656478 1 0.1289 0.904 0.968 0.000 0.032
#> SRR1656477 3 0.1289 0.857 0.000 0.032 0.968
#> SRR1656479 1 0.2537 0.913 0.920 0.000 0.080
#> SRR1656480 3 0.2959 0.850 0.000 0.100 0.900
#> SRR1656476 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656481 3 0.0892 0.849 0.020 0.000 0.980
#> SRR1656482 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656485 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656487 1 0.3619 0.875 0.864 0.000 0.136
#> SRR1656486 1 0.1964 0.912 0.944 0.000 0.056
#> SRR1656488 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656484 1 0.1411 0.902 0.964 0.000 0.036
#> SRR1656489 1 0.0592 0.908 0.988 0.000 0.012
#> SRR1656491 1 0.3038 0.906 0.896 0.000 0.104
#> SRR1656490 3 0.5431 0.621 0.284 0.000 0.716
#> SRR1656492 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1656493 3 0.5291 0.684 0.268 0.000 0.732
#> SRR1656495 3 0.2682 0.836 0.076 0.004 0.920
#> SRR1656496 1 0.2711 0.913 0.912 0.000 0.088
#> SRR1656494 3 0.3192 0.845 0.000 0.112 0.888
#> SRR1656497 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656499 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656500 1 0.2261 0.914 0.932 0.000 0.068
#> SRR1656501 1 0.1411 0.909 0.964 0.000 0.036
#> SRR1656498 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656504 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656502 3 0.3116 0.826 0.108 0.000 0.892
#> SRR1656503 1 0.1289 0.904 0.968 0.000 0.032
#> SRR1656507 1 0.1289 0.904 0.968 0.000 0.032
#> SRR1656508 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656505 3 0.2959 0.850 0.000 0.100 0.900
#> SRR1656506 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656509 3 0.5098 0.571 0.248 0.000 0.752
#> SRR1656510 3 0.0747 0.851 0.016 0.000 0.984
#> SRR1656511 3 0.3192 0.844 0.000 0.112 0.888
#> SRR1656513 2 0.4399 0.750 0.000 0.812 0.188
#> SRR1656512 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656514 1 0.0237 0.909 0.996 0.000 0.004
#> SRR1656515 2 0.5098 0.653 0.000 0.752 0.248
#> SRR1656516 1 0.1163 0.909 0.972 0.000 0.028
#> SRR1656518 1 0.1411 0.902 0.964 0.000 0.036
#> SRR1656517 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656519 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1656522 1 0.0000 0.910 1.000 0.000 0.000
#> SRR1656523 3 0.1411 0.859 0.000 0.036 0.964
#> SRR1656521 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656520 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656524 3 0.5291 0.684 0.268 0.000 0.732
#> SRR1656525 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656526 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656527 2 0.0237 0.966 0.000 0.996 0.004
#> SRR1656530 1 0.2796 0.910 0.908 0.000 0.092
#> SRR1656529 1 0.3816 0.872 0.852 0.000 0.148
#> SRR1656531 1 0.0892 0.905 0.980 0.000 0.020
#> SRR1656528 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656534 1 0.2066 0.915 0.940 0.000 0.060
#> SRR1656533 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656536 3 0.0892 0.849 0.020 0.000 0.980
#> SRR1656532 3 0.3412 0.836 0.000 0.124 0.876
#> SRR1656537 1 0.1411 0.902 0.964 0.000 0.036
#> SRR1656538 1 0.2165 0.915 0.936 0.000 0.064
#> SRR1656535 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656539 1 0.6260 0.363 0.552 0.000 0.448
#> SRR1656544 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656542 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1656543 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656545 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656540 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656546 3 0.6079 0.460 0.388 0.000 0.612
#> SRR1656541 2 0.5254 0.613 0.000 0.736 0.264
#> SRR1656547 3 0.3192 0.845 0.000 0.112 0.888
#> SRR1656548 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656549 1 0.6079 0.276 0.612 0.000 0.388
#> SRR1656551 3 0.0892 0.849 0.020 0.000 0.980
#> SRR1656553 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1656550 3 0.2537 0.856 0.000 0.080 0.920
#> SRR1656552 3 0.3116 0.846 0.000 0.108 0.892
#> SRR1656554 1 0.5327 0.723 0.728 0.000 0.272
#> SRR1656555 3 0.0424 0.852 0.008 0.000 0.992
#> SRR1656556 3 0.4452 0.686 0.192 0.000 0.808
#> SRR1656557 1 0.2448 0.913 0.924 0.000 0.076
#> SRR1656558 1 0.1163 0.905 0.972 0.000 0.028
#> SRR1656559 1 0.0237 0.909 0.996 0.000 0.004
#> SRR1656560 1 0.2537 0.912 0.920 0.000 0.080
#> SRR1656561 1 0.2165 0.915 0.936 0.000 0.064
#> SRR1656562 3 0.2878 0.850 0.000 0.096 0.904
#> SRR1656563 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656564 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656565 3 0.3551 0.830 0.000 0.132 0.868
#> SRR1656566 3 0.6302 0.200 0.480 0.000 0.520
#> SRR1656568 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656567 3 0.3267 0.842 0.000 0.116 0.884
#> SRR1656569 1 0.3879 0.871 0.848 0.000 0.152
#> SRR1656570 1 0.0747 0.907 0.984 0.000 0.016
#> SRR1656571 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656573 3 0.0237 0.853 0.004 0.000 0.996
#> SRR1656572 3 0.3192 0.844 0.000 0.112 0.888
#> SRR1656574 1 0.0592 0.908 0.988 0.000 0.012
#> SRR1656575 1 0.1289 0.904 0.968 0.000 0.032
#> SRR1656576 3 0.3267 0.842 0.000 0.116 0.884
#> SRR1656578 2 0.0237 0.966 0.000 0.996 0.004
#> SRR1656577 1 0.0237 0.909 0.996 0.000 0.004
#> SRR1656579 3 0.3267 0.842 0.000 0.116 0.884
#> SRR1656580 1 0.1753 0.915 0.952 0.000 0.048
#> SRR1656581 3 0.0000 0.854 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656585 3 0.0237 0.854 0.000 0.004 0.996
#> SRR1656584 1 0.1411 0.902 0.964 0.000 0.036
#> SRR1656583 3 0.2066 0.858 0.000 0.060 0.940
#> SRR1656586 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656587 3 0.3083 0.853 0.024 0.060 0.916
#> SRR1656588 3 0.5465 0.633 0.000 0.288 0.712
#> SRR1656589 2 0.0000 0.969 0.000 1.000 0.000
#> SRR1656590 1 0.6008 0.323 0.628 0.000 0.372
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.2216 0.8834 0.908 0.000 0.092 0.000
#> SRR1656462 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656465 3 0.4123 0.7031 0.008 0.000 0.772 0.220
#> SRR1656467 4 0.4605 0.5124 0.000 0.336 0.000 0.664
#> SRR1656466 3 0.1722 0.8795 0.008 0.000 0.944 0.048
#> SRR1656468 4 0.1305 0.9164 0.004 0.000 0.036 0.960
#> SRR1656472 1 0.4399 0.6516 0.760 0.000 0.016 0.224
#> SRR1656471 3 0.3852 0.7396 0.008 0.000 0.800 0.192
#> SRR1656470 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.2198 0.8672 0.008 0.000 0.920 0.072
#> SRR1656473 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.1792 0.8944 0.932 0.000 0.068 0.000
#> SRR1656477 4 0.1209 0.9181 0.004 0.000 0.032 0.964
#> SRR1656479 1 0.2216 0.8881 0.908 0.000 0.092 0.000
#> SRR1656480 4 0.0336 0.9239 0.000 0.000 0.008 0.992
#> SRR1656476 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.1545 0.9135 0.008 0.000 0.040 0.952
#> SRR1656482 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.1022 0.9218 0.032 0.000 0.968 0.000
#> SRR1656487 3 0.1576 0.8819 0.004 0.000 0.948 0.048
#> SRR1656486 1 0.3528 0.8125 0.808 0.000 0.192 0.000
#> SRR1656488 3 0.1022 0.9218 0.032 0.000 0.968 0.000
#> SRR1656484 1 0.1557 0.8944 0.944 0.000 0.056 0.000
#> SRR1656489 1 0.3801 0.7690 0.780 0.000 0.220 0.000
#> SRR1656491 3 0.4798 0.7007 0.180 0.000 0.768 0.052
#> SRR1656490 1 0.2759 0.8501 0.904 0.000 0.052 0.044
#> SRR1656492 3 0.1118 0.9221 0.036 0.000 0.964 0.000
#> SRR1656493 1 0.1022 0.8618 0.968 0.000 0.000 0.032
#> SRR1656495 1 0.4008 0.6393 0.756 0.000 0.000 0.244
#> SRR1656496 1 0.2408 0.8840 0.896 0.000 0.104 0.000
#> SRR1656494 4 0.0921 0.9174 0.028 0.000 0.000 0.972
#> SRR1656497 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.1118 0.9219 0.036 0.000 0.964 0.000
#> SRR1656500 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656501 1 0.3486 0.8117 0.812 0.000 0.188 0.000
#> SRR1656498 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656504 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.4399 0.6516 0.760 0.000 0.016 0.224
#> SRR1656503 1 0.3975 0.7507 0.760 0.000 0.240 0.000
#> SRR1656507 1 0.1792 0.8944 0.932 0.000 0.068 0.000
#> SRR1656508 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656505 4 0.0336 0.9239 0.000 0.000 0.008 0.992
#> SRR1656506 3 0.0336 0.9145 0.008 0.000 0.992 0.000
#> SRR1656509 1 0.6234 0.4209 0.584 0.000 0.068 0.348
#> SRR1656510 4 0.2255 0.8923 0.012 0.000 0.068 0.920
#> SRR1656511 4 0.1557 0.9069 0.056 0.000 0.000 0.944
#> SRR1656513 4 0.4872 0.4655 0.004 0.356 0.000 0.640
#> SRR1656512 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.4624 0.5218 0.340 0.000 0.660 0.000
#> SRR1656515 4 0.2921 0.8179 0.000 0.140 0.000 0.860
#> SRR1656516 1 0.3486 0.8117 0.812 0.000 0.188 0.000
#> SRR1656518 1 0.1637 0.8946 0.940 0.000 0.060 0.000
#> SRR1656517 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656519 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656522 3 0.2011 0.9017 0.080 0.000 0.920 0.000
#> SRR1656523 4 0.0895 0.9236 0.020 0.000 0.004 0.976
#> SRR1656521 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656524 1 0.1118 0.8587 0.964 0.000 0.000 0.036
#> SRR1656525 3 0.1211 0.9217 0.040 0.000 0.960 0.000
#> SRR1656526 2 0.1211 0.9590 0.000 0.960 0.000 0.040
#> SRR1656527 2 0.2089 0.9372 0.020 0.932 0.000 0.048
#> SRR1656530 3 0.0657 0.9112 0.012 0.000 0.984 0.004
#> SRR1656529 3 0.1970 0.8742 0.008 0.000 0.932 0.060
#> SRR1656531 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656528 3 0.0592 0.9174 0.016 0.000 0.984 0.000
#> SRR1656534 3 0.1940 0.9046 0.076 0.000 0.924 0.000
#> SRR1656533 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656536 4 0.1545 0.9135 0.008 0.000 0.040 0.952
#> SRR1656532 4 0.2282 0.8960 0.052 0.024 0.000 0.924
#> SRR1656537 1 0.0921 0.8886 0.972 0.000 0.028 0.000
#> SRR1656538 3 0.1940 0.9046 0.076 0.000 0.924 0.000
#> SRR1656535 2 0.0336 0.9853 0.000 0.992 0.000 0.008
#> SRR1656539 3 0.4086 0.7092 0.008 0.000 0.776 0.216
#> SRR1656544 3 0.1022 0.9218 0.032 0.000 0.968 0.000
#> SRR1656542 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656543 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656545 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.1118 0.9219 0.036 0.000 0.964 0.000
#> SRR1656546 1 0.1724 0.8763 0.948 0.000 0.020 0.032
#> SRR1656541 4 0.4103 0.6679 0.000 0.256 0.000 0.744
#> SRR1656547 4 0.0000 0.9236 0.000 0.000 0.000 1.000
#> SRR1656548 3 0.1211 0.9217 0.040 0.000 0.960 0.000
#> SRR1656549 1 0.0524 0.8789 0.988 0.000 0.004 0.008
#> SRR1656551 4 0.1545 0.9135 0.008 0.000 0.040 0.952
#> SRR1656553 3 0.1211 0.9217 0.040 0.000 0.960 0.000
#> SRR1656550 4 0.0469 0.9236 0.000 0.000 0.012 0.988
#> SRR1656552 4 0.0188 0.9238 0.004 0.000 0.000 0.996
#> SRR1656554 3 0.2198 0.8661 0.008 0.000 0.920 0.072
#> SRR1656555 4 0.0524 0.9238 0.004 0.000 0.008 0.988
#> SRR1656556 4 0.5203 0.2596 0.008 0.000 0.416 0.576
#> SRR1656557 3 0.1302 0.9216 0.044 0.000 0.956 0.000
#> SRR1656558 1 0.1716 0.8944 0.936 0.000 0.064 0.000
#> SRR1656559 3 0.2647 0.8654 0.120 0.000 0.880 0.000
#> SRR1656560 3 0.0779 0.9161 0.016 0.000 0.980 0.004
#> SRR1656561 3 0.1867 0.9053 0.072 0.000 0.928 0.000
#> SRR1656562 4 0.0707 0.9200 0.020 0.000 0.000 0.980
#> SRR1656563 1 0.2216 0.8833 0.908 0.000 0.092 0.000
#> SRR1656564 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656565 4 0.1610 0.9098 0.032 0.016 0.000 0.952
#> SRR1656566 1 0.0524 0.8789 0.988 0.000 0.004 0.008
#> SRR1656568 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0000 0.9236 0.000 0.000 0.000 1.000
#> SRR1656569 3 0.2124 0.8693 0.008 0.000 0.924 0.068
#> SRR1656570 1 0.2281 0.8832 0.904 0.000 0.096 0.000
#> SRR1656571 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1798 0.9131 0.016 0.000 0.040 0.944
#> SRR1656572 4 0.1557 0.9069 0.056 0.000 0.000 0.944
#> SRR1656574 1 0.4996 0.0479 0.516 0.000 0.484 0.000
#> SRR1656575 1 0.1716 0.8947 0.936 0.000 0.064 0.000
#> SRR1656576 4 0.0000 0.9236 0.000 0.000 0.000 1.000
#> SRR1656578 2 0.2706 0.9032 0.020 0.900 0.000 0.080
#> SRR1656577 3 0.3688 0.7600 0.208 0.000 0.792 0.000
#> SRR1656579 4 0.0000 0.9236 0.000 0.000 0.000 1.000
#> SRR1656580 3 0.1940 0.9046 0.076 0.000 0.924 0.000
#> SRR1656581 4 0.1833 0.9174 0.024 0.000 0.032 0.944
#> SRR1656582 2 0.0469 0.9829 0.000 0.988 0.000 0.012
#> SRR1656585 4 0.1406 0.9222 0.024 0.000 0.016 0.960
#> SRR1656584 1 0.1022 0.8889 0.968 0.000 0.032 0.000
#> SRR1656583 4 0.0927 0.9233 0.008 0.000 0.016 0.976
#> SRR1656586 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.1389 0.9124 0.048 0.000 0.000 0.952
#> SRR1656588 4 0.2081 0.8735 0.000 0.084 0.000 0.916
#> SRR1656589 2 0.0000 0.9902 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0188 0.8786 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 1 0.6178 0.2624 0.484 0.000 0.376 0.000 0.140
#> SRR1656462 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656465 5 0.5060 0.7360 0.000 0.000 0.224 0.092 0.684
#> SRR1656467 4 0.3196 0.7034 0.000 0.192 0.000 0.804 0.004
#> SRR1656466 5 0.3949 0.6833 0.000 0.000 0.332 0.000 0.668
#> SRR1656468 4 0.4045 0.4587 0.000 0.000 0.000 0.644 0.356
#> SRR1656472 1 0.6190 0.5366 0.536 0.000 0.004 0.140 0.320
#> SRR1656471 5 0.4924 0.7264 0.000 0.000 0.272 0.060 0.668
#> SRR1656470 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.5441 0.7203 0.048 0.000 0.232 0.040 0.680
#> SRR1656473 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.1251 0.8331 0.956 0.000 0.036 0.000 0.008
#> SRR1656477 4 0.3816 0.5617 0.000 0.000 0.000 0.696 0.304
#> SRR1656479 1 0.4473 0.5149 0.656 0.000 0.020 0.000 0.324
#> SRR1656480 4 0.1671 0.8245 0.000 0.000 0.000 0.924 0.076
#> SRR1656476 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.4201 0.2908 0.000 0.000 0.000 0.408 0.592
#> SRR1656482 2 0.0566 0.9490 0.000 0.984 0.000 0.012 0.004
#> SRR1656483 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.2020 0.7494 0.000 0.000 0.900 0.000 0.100
#> SRR1656487 5 0.4101 0.6256 0.000 0.000 0.372 0.000 0.628
#> SRR1656486 1 0.2863 0.8054 0.876 0.000 0.060 0.000 0.064
#> SRR1656488 3 0.1908 0.7559 0.000 0.000 0.908 0.000 0.092
#> SRR1656484 1 0.1310 0.8356 0.956 0.000 0.020 0.000 0.024
#> SRR1656489 3 0.4622 0.2120 0.440 0.000 0.548 0.000 0.012
#> SRR1656491 5 0.5480 0.6869 0.076 0.000 0.248 0.016 0.660
#> SRR1656490 1 0.4639 0.4521 0.612 0.000 0.000 0.020 0.368
#> SRR1656492 3 0.6285 -0.0359 0.152 0.000 0.456 0.000 0.392
#> SRR1656493 1 0.3196 0.7726 0.804 0.000 0.004 0.000 0.192
#> SRR1656495 1 0.6904 0.2375 0.396 0.000 0.004 0.308 0.292
#> SRR1656496 1 0.4734 0.4111 0.604 0.000 0.024 0.000 0.372
#> SRR1656494 4 0.1608 0.8155 0.000 0.000 0.000 0.928 0.072
#> SRR1656497 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0324 0.7977 0.004 0.000 0.992 0.000 0.004
#> SRR1656500 3 0.0290 0.7984 0.008 0.000 0.992 0.000 0.000
#> SRR1656501 1 0.2193 0.8218 0.912 0.000 0.060 0.000 0.028
#> SRR1656498 1 0.1106 0.8349 0.964 0.000 0.024 0.000 0.012
#> SRR1656504 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.6190 0.5366 0.536 0.000 0.004 0.140 0.320
#> SRR1656503 1 0.3586 0.7108 0.792 0.000 0.188 0.000 0.020
#> SRR1656507 1 0.1331 0.8322 0.952 0.000 0.040 0.000 0.008
#> SRR1656508 1 0.0955 0.8344 0.968 0.000 0.028 0.000 0.004
#> SRR1656505 4 0.1671 0.8245 0.000 0.000 0.000 0.924 0.076
#> SRR1656506 5 0.4306 0.3287 0.000 0.000 0.492 0.000 0.508
#> SRR1656509 5 0.5868 0.6022 0.120 0.000 0.056 0.136 0.688
#> SRR1656510 4 0.4835 0.3296 0.000 0.000 0.028 0.592 0.380
#> SRR1656511 4 0.1928 0.8140 0.004 0.000 0.004 0.920 0.072
#> SRR1656513 4 0.3669 0.7264 0.000 0.128 0.000 0.816 0.056
#> SRR1656512 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.3835 0.6461 0.244 0.000 0.744 0.000 0.012
#> SRR1656515 4 0.2006 0.8100 0.000 0.072 0.000 0.916 0.012
#> SRR1656516 1 0.2409 0.8162 0.900 0.000 0.068 0.000 0.032
#> SRR1656518 1 0.1310 0.8353 0.956 0.000 0.024 0.000 0.020
#> SRR1656517 1 0.1331 0.8321 0.952 0.000 0.040 0.000 0.008
#> SRR1656519 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656522 3 0.2513 0.7518 0.116 0.000 0.876 0.000 0.008
#> SRR1656523 4 0.2424 0.7915 0.000 0.000 0.000 0.868 0.132
#> SRR1656521 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656524 1 0.4170 0.7114 0.712 0.000 0.004 0.012 0.272
#> SRR1656525 3 0.1892 0.7650 0.004 0.000 0.916 0.000 0.080
#> SRR1656526 2 0.3366 0.7479 0.000 0.784 0.000 0.212 0.004
#> SRR1656527 2 0.4399 0.7571 0.004 0.768 0.004 0.168 0.056
#> SRR1656530 5 0.4196 0.6498 0.004 0.000 0.356 0.000 0.640
#> SRR1656529 5 0.4323 0.6877 0.000 0.000 0.332 0.012 0.656
#> SRR1656531 1 0.2017 0.8191 0.912 0.000 0.008 0.000 0.080
#> SRR1656528 3 0.3274 0.5942 0.000 0.000 0.780 0.000 0.220
#> SRR1656534 3 0.1502 0.7871 0.056 0.000 0.940 0.000 0.004
#> SRR1656533 1 0.0955 0.8344 0.968 0.000 0.028 0.000 0.004
#> SRR1656536 5 0.4138 0.3535 0.000 0.000 0.000 0.384 0.616
#> SRR1656532 4 0.3595 0.7260 0.004 0.008 0.004 0.796 0.188
#> SRR1656537 1 0.2719 0.7977 0.852 0.000 0.004 0.000 0.144
#> SRR1656538 3 0.1768 0.7796 0.072 0.000 0.924 0.000 0.004
#> SRR1656535 2 0.0671 0.9468 0.000 0.980 0.000 0.016 0.004
#> SRR1656539 5 0.5060 0.7360 0.000 0.000 0.224 0.092 0.684
#> SRR1656544 3 0.3003 0.6475 0.000 0.000 0.812 0.000 0.188
#> SRR1656542 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656543 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656545 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.1270 0.7684 0.000 0.000 0.948 0.000 0.052
#> SRR1656546 1 0.1717 0.8314 0.936 0.000 0.008 0.004 0.052
#> SRR1656541 4 0.3053 0.7296 0.000 0.164 0.000 0.828 0.008
#> SRR1656547 4 0.0963 0.8334 0.000 0.000 0.000 0.964 0.036
#> SRR1656548 3 0.3123 0.6531 0.004 0.000 0.812 0.000 0.184
#> SRR1656549 1 0.1043 0.8301 0.960 0.000 0.000 0.000 0.040
#> SRR1656551 5 0.4126 0.3628 0.000 0.000 0.000 0.380 0.620
#> SRR1656553 3 0.1697 0.7827 0.008 0.000 0.932 0.000 0.060
#> SRR1656550 4 0.1671 0.8245 0.000 0.000 0.000 0.924 0.076
#> SRR1656552 4 0.1121 0.8330 0.000 0.000 0.000 0.956 0.044
#> SRR1656554 5 0.4637 0.7205 0.000 0.000 0.292 0.036 0.672
#> SRR1656555 4 0.3707 0.5896 0.000 0.000 0.000 0.716 0.284
#> SRR1656556 5 0.5426 0.6162 0.000 0.000 0.108 0.252 0.640
#> SRR1656557 3 0.0162 0.7987 0.004 0.000 0.996 0.000 0.000
#> SRR1656558 1 0.1251 0.8331 0.956 0.000 0.036 0.000 0.008
#> SRR1656559 3 0.2753 0.7364 0.136 0.000 0.856 0.000 0.008
#> SRR1656560 3 0.3774 0.4326 0.000 0.000 0.704 0.000 0.296
#> SRR1656561 3 0.5472 0.6032 0.156 0.000 0.656 0.000 0.188
#> SRR1656562 4 0.1121 0.8304 0.000 0.000 0.000 0.956 0.044
#> SRR1656563 1 0.2929 0.7447 0.840 0.000 0.152 0.000 0.008
#> SRR1656564 2 0.0566 0.9490 0.000 0.984 0.000 0.012 0.004
#> SRR1656565 4 0.1638 0.8183 0.000 0.000 0.004 0.932 0.064
#> SRR1656566 1 0.2516 0.7978 0.860 0.000 0.000 0.000 0.140
#> SRR1656568 2 0.0324 0.9525 0.000 0.992 0.000 0.004 0.004
#> SRR1656567 4 0.1043 0.8337 0.000 0.000 0.000 0.960 0.040
#> SRR1656569 5 0.4419 0.7067 0.000 0.000 0.312 0.020 0.668
#> SRR1656570 1 0.1670 0.8270 0.936 0.000 0.052 0.000 0.012
#> SRR1656571 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.4138 0.3392 0.000 0.000 0.000 0.384 0.616
#> SRR1656572 4 0.1662 0.8232 0.004 0.000 0.004 0.936 0.056
#> SRR1656574 3 0.4517 0.2227 0.436 0.000 0.556 0.000 0.008
#> SRR1656575 1 0.1310 0.8353 0.956 0.000 0.024 0.000 0.020
#> SRR1656576 4 0.0771 0.8343 0.000 0.004 0.000 0.976 0.020
#> SRR1656578 2 0.5781 0.4257 0.004 0.576 0.004 0.336 0.080
#> SRR1656577 3 0.3487 0.6684 0.212 0.000 0.780 0.000 0.008
#> SRR1656579 4 0.1197 0.8325 0.000 0.000 0.000 0.952 0.048
#> SRR1656580 3 0.2011 0.7719 0.088 0.000 0.908 0.000 0.004
#> SRR1656581 4 0.4294 0.1450 0.000 0.000 0.000 0.532 0.468
#> SRR1656582 2 0.1892 0.8964 0.000 0.916 0.000 0.080 0.004
#> SRR1656585 4 0.3676 0.6712 0.004 0.000 0.004 0.760 0.232
#> SRR1656584 1 0.0566 0.8338 0.984 0.000 0.004 0.000 0.012
#> SRR1656583 4 0.1908 0.8279 0.000 0.000 0.000 0.908 0.092
#> SRR1656586 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.2228 0.8143 0.004 0.000 0.004 0.900 0.092
#> SRR1656588 4 0.1648 0.8267 0.000 0.040 0.000 0.940 0.020
#> SRR1656589 2 0.0000 0.9555 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.3550 0.7473 0.760 0.000 0.004 0.000 0.236
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0547 0.88755 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1656464 3 0.5363 0.33899 0.156 0.000 0.600 0.000 0.004 0.240
#> SRR1656462 3 0.0858 0.83121 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1656465 5 0.1245 0.80557 0.000 0.000 0.032 0.016 0.952 0.000
#> SRR1656467 4 0.3422 0.73643 0.000 0.036 0.000 0.788 0.000 0.176
#> SRR1656466 5 0.1625 0.80121 0.000 0.000 0.060 0.000 0.928 0.012
#> SRR1656468 4 0.4051 0.10327 0.000 0.000 0.000 0.560 0.432 0.008
#> SRR1656472 6 0.4983 0.80480 0.220 0.000 0.008 0.052 0.032 0.688
#> SRR1656471 5 0.1429 0.80467 0.000 0.000 0.052 0.004 0.940 0.004
#> SRR1656470 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.1268 0.80103 0.008 0.000 0.036 0.000 0.952 0.004
#> SRR1656473 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.1346 0.70374 0.952 0.000 0.024 0.000 0.008 0.016
#> SRR1656477 4 0.3566 0.55610 0.000 0.000 0.000 0.752 0.224 0.024
#> SRR1656479 1 0.4621 0.38470 0.604 0.000 0.016 0.000 0.356 0.024
#> SRR1656480 4 0.0914 0.76359 0.000 0.000 0.000 0.968 0.016 0.016
#> SRR1656476 2 0.0547 0.88755 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1656481 5 0.4018 0.54188 0.000 0.000 0.000 0.324 0.656 0.020
#> SRR1656482 2 0.2489 0.82590 0.000 0.860 0.000 0.012 0.000 0.128
#> SRR1656483 2 0.0547 0.88755 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1656485 3 0.3756 0.66059 0.000 0.000 0.712 0.000 0.268 0.020
#> SRR1656487 5 0.1838 0.79547 0.000 0.000 0.068 0.000 0.916 0.016
#> SRR1656486 1 0.2419 0.69511 0.896 0.000 0.028 0.000 0.060 0.016
#> SRR1656488 3 0.3566 0.69676 0.000 0.000 0.744 0.000 0.236 0.020
#> SRR1656484 1 0.1590 0.70044 0.936 0.000 0.008 0.000 0.048 0.008
#> SRR1656489 1 0.4117 0.43007 0.672 0.000 0.296 0.000 0.000 0.032
#> SRR1656491 5 0.1693 0.79694 0.012 0.000 0.032 0.000 0.936 0.020
#> SRR1656490 1 0.5024 0.18880 0.500 0.000 0.000 0.008 0.440 0.052
#> SRR1656492 1 0.5945 0.15085 0.436 0.000 0.116 0.000 0.424 0.024
#> SRR1656493 1 0.4109 -0.23083 0.576 0.000 0.000 0.000 0.012 0.412
#> SRR1656495 6 0.3419 0.69866 0.116 0.000 0.000 0.056 0.008 0.820
#> SRR1656496 1 0.4738 0.33362 0.556 0.000 0.020 0.000 0.404 0.020
#> SRR1656494 4 0.3175 0.73550 0.000 0.000 0.000 0.744 0.000 0.256
#> SRR1656497 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.1296 0.82921 0.004 0.000 0.952 0.000 0.032 0.012
#> SRR1656500 3 0.0717 0.82983 0.008 0.000 0.976 0.000 0.016 0.000
#> SRR1656501 1 0.2358 0.69677 0.900 0.000 0.028 0.000 0.056 0.016
#> SRR1656498 1 0.1575 0.69470 0.936 0.000 0.032 0.000 0.000 0.032
#> SRR1656504 2 0.0547 0.88755 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1656502 6 0.4983 0.80480 0.220 0.000 0.008 0.052 0.032 0.688
#> SRR1656503 1 0.4303 0.53062 0.732 0.000 0.204 0.000 0.040 0.024
#> SRR1656507 1 0.1346 0.70374 0.952 0.000 0.024 0.000 0.008 0.016
#> SRR1656508 1 0.1572 0.69577 0.936 0.000 0.036 0.000 0.000 0.028
#> SRR1656505 4 0.0914 0.76359 0.000 0.000 0.000 0.968 0.016 0.016
#> SRR1656506 5 0.2715 0.74943 0.004 0.000 0.112 0.000 0.860 0.024
#> SRR1656509 5 0.4228 0.70409 0.032 0.000 0.012 0.088 0.792 0.076
#> SRR1656510 4 0.4597 -0.06453 0.004 0.000 0.004 0.488 0.484 0.020
#> SRR1656511 4 0.3753 0.71460 0.000 0.000 0.004 0.696 0.008 0.292
#> SRR1656513 4 0.4216 0.69228 0.000 0.032 0.004 0.676 0.000 0.288
#> SRR1656512 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.3449 0.69298 0.116 0.000 0.808 0.000 0.000 0.076
#> SRR1656515 4 0.2340 0.76096 0.000 0.000 0.000 0.852 0.000 0.148
#> SRR1656516 1 0.2467 0.69942 0.896 0.000 0.036 0.000 0.048 0.020
#> SRR1656518 1 0.1452 0.69603 0.948 0.000 0.012 0.000 0.020 0.020
#> SRR1656517 1 0.1575 0.69672 0.936 0.000 0.032 0.000 0.000 0.032
#> SRR1656519 3 0.0777 0.83111 0.004 0.000 0.972 0.000 0.024 0.000
#> SRR1656522 3 0.1950 0.78895 0.064 0.000 0.912 0.000 0.000 0.024
#> SRR1656523 4 0.3669 0.67567 0.004 0.000 0.004 0.784 0.172 0.036
#> SRR1656521 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0858 0.83121 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1656524 6 0.3992 0.70254 0.364 0.000 0.000 0.000 0.012 0.624
#> SRR1656525 3 0.3705 0.70615 0.004 0.000 0.748 0.000 0.224 0.024
#> SRR1656526 2 0.5771 0.32458 0.000 0.500 0.004 0.328 0.000 0.168
#> SRR1656527 2 0.5280 0.52326 0.000 0.564 0.004 0.104 0.000 0.328
#> SRR1656530 5 0.1682 0.79936 0.000 0.000 0.052 0.000 0.928 0.020
#> SRR1656529 5 0.1625 0.80108 0.000 0.000 0.060 0.000 0.928 0.012
#> SRR1656531 1 0.4290 -0.00967 0.612 0.000 0.020 0.000 0.004 0.364
#> SRR1656528 3 0.4273 0.46800 0.000 0.000 0.596 0.000 0.380 0.024
#> SRR1656534 3 0.1194 0.82065 0.032 0.000 0.956 0.000 0.008 0.004
#> SRR1656533 1 0.1498 0.69693 0.940 0.000 0.032 0.000 0.000 0.028
#> SRR1656536 5 0.3859 0.58468 0.000 0.000 0.000 0.288 0.692 0.020
#> SRR1656532 4 0.3950 0.59427 0.000 0.000 0.004 0.564 0.000 0.432
#> SRR1656537 1 0.4421 -0.30772 0.552 0.000 0.020 0.000 0.004 0.424
#> SRR1656538 3 0.1767 0.81971 0.036 0.000 0.932 0.000 0.012 0.020
#> SRR1656535 2 0.3274 0.78465 0.000 0.804 0.004 0.024 0.000 0.168
#> SRR1656539 5 0.1269 0.80010 0.000 0.000 0.020 0.012 0.956 0.012
#> SRR1656544 3 0.4165 0.60044 0.004 0.000 0.664 0.000 0.308 0.024
#> SRR1656542 3 0.1218 0.83118 0.004 0.000 0.956 0.000 0.028 0.012
#> SRR1656543 3 0.0858 0.83121 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1656545 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.1349 0.81929 0.000 0.000 0.940 0.000 0.056 0.004
#> SRR1656546 1 0.1909 0.66859 0.920 0.000 0.004 0.000 0.024 0.052
#> SRR1656541 4 0.4344 0.67002 0.000 0.096 0.000 0.716 0.000 0.188
#> SRR1656547 4 0.1524 0.77736 0.000 0.000 0.000 0.932 0.008 0.060
#> SRR1656548 3 0.4287 0.59141 0.008 0.000 0.656 0.000 0.312 0.024
#> SRR1656549 1 0.1498 0.67474 0.940 0.000 0.000 0.000 0.028 0.032
#> SRR1656551 5 0.3738 0.59497 0.000 0.000 0.000 0.280 0.704 0.016
#> SRR1656553 3 0.2810 0.76876 0.004 0.000 0.832 0.000 0.156 0.008
#> SRR1656550 4 0.1003 0.76269 0.000 0.000 0.000 0.964 0.020 0.016
#> SRR1656552 4 0.3670 0.76872 0.000 0.000 0.004 0.788 0.056 0.152
#> SRR1656554 5 0.1398 0.80428 0.000 0.000 0.052 0.000 0.940 0.008
#> SRR1656555 4 0.4206 0.32601 0.000 0.000 0.000 0.620 0.356 0.024
#> SRR1656556 5 0.3987 0.65845 0.000 0.000 0.012 0.236 0.728 0.024
#> SRR1656557 3 0.0858 0.83121 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1656558 1 0.1257 0.70036 0.952 0.000 0.028 0.000 0.000 0.020
#> SRR1656559 3 0.2066 0.78216 0.072 0.000 0.904 0.000 0.000 0.024
#> SRR1656560 5 0.4305 -0.05784 0.000 0.000 0.436 0.000 0.544 0.020
#> SRR1656561 1 0.6643 0.08542 0.404 0.000 0.280 0.000 0.284 0.032
#> SRR1656562 4 0.2402 0.77163 0.000 0.000 0.000 0.868 0.012 0.120
#> SRR1656563 1 0.2402 0.68725 0.896 0.000 0.060 0.000 0.012 0.032
#> SRR1656564 2 0.2615 0.82178 0.000 0.852 0.004 0.008 0.000 0.136
#> SRR1656565 4 0.3426 0.72484 0.000 0.000 0.004 0.720 0.000 0.276
#> SRR1656566 1 0.3940 0.00612 0.640 0.000 0.000 0.000 0.012 0.348
#> SRR1656568 2 0.1155 0.87964 0.000 0.956 0.004 0.004 0.000 0.036
#> SRR1656567 4 0.0622 0.77244 0.000 0.000 0.000 0.980 0.008 0.012
#> SRR1656569 5 0.1265 0.80396 0.000 0.000 0.044 0.000 0.948 0.008
#> SRR1656570 1 0.2415 0.70064 0.900 0.000 0.024 0.000 0.040 0.036
#> SRR1656571 2 0.0547 0.88755 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1656573 5 0.4319 0.57942 0.012 0.000 0.000 0.256 0.696 0.036
#> SRR1656572 4 0.3746 0.72940 0.000 0.000 0.004 0.712 0.012 0.272
#> SRR1656574 3 0.4395 0.20398 0.404 0.000 0.568 0.000 0.000 0.028
#> SRR1656575 1 0.1173 0.70193 0.960 0.000 0.016 0.000 0.016 0.008
#> SRR1656576 4 0.2632 0.76100 0.000 0.000 0.000 0.832 0.004 0.164
#> SRR1656578 2 0.6074 0.24566 0.000 0.424 0.004 0.224 0.000 0.348
#> SRR1656577 3 0.2066 0.78216 0.072 0.000 0.904 0.000 0.000 0.024
#> SRR1656579 4 0.0622 0.77106 0.000 0.000 0.000 0.980 0.012 0.008
#> SRR1656580 3 0.1718 0.81413 0.044 0.000 0.932 0.000 0.008 0.016
#> SRR1656581 5 0.4924 0.20558 0.004 0.000 0.004 0.416 0.532 0.044
#> SRR1656582 2 0.4415 0.71108 0.000 0.724 0.004 0.104 0.000 0.168
#> SRR1656585 4 0.4556 0.61039 0.000 0.000 0.000 0.696 0.188 0.116
#> SRR1656584 1 0.1088 0.68717 0.960 0.000 0.000 0.000 0.016 0.024
#> SRR1656583 4 0.2999 0.73814 0.000 0.000 0.000 0.840 0.048 0.112
#> SRR1656586 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 4 0.3345 0.72679 0.000 0.000 0.000 0.776 0.020 0.204
#> SRR1656588 4 0.0865 0.77648 0.000 0.000 0.000 0.964 0.000 0.036
#> SRR1656589 2 0.0000 0.88888 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 6 0.4303 0.66210 0.392 0.000 0.008 0.000 0.012 0.588
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.947 0.980 0.4943 0.507 0.507
#> 3 3 0.937 0.933 0.972 0.3443 0.802 0.619
#> 4 4 0.806 0.861 0.922 0.1018 0.896 0.705
#> 5 5 0.758 0.666 0.845 0.0466 0.959 0.848
#> 6 6 0.779 0.726 0.855 0.0437 0.894 0.604
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.000 0.979 0.000 1.000
#> SRR1656464 1 0.000 0.979 1.000 0.000
#> SRR1656462 1 0.000 0.979 1.000 0.000
#> SRR1656465 1 0.000 0.979 1.000 0.000
#> SRR1656467 2 0.000 0.979 0.000 1.000
#> SRR1656466 1 0.000 0.979 1.000 0.000
#> SRR1656468 2 0.000 0.979 0.000 1.000
#> SRR1656472 1 0.000 0.979 1.000 0.000
#> SRR1656471 1 0.000 0.979 1.000 0.000
#> SRR1656470 2 0.000 0.979 0.000 1.000
#> SRR1656469 1 0.000 0.979 1.000 0.000
#> SRR1656473 2 0.000 0.979 0.000 1.000
#> SRR1656474 2 0.000 0.979 0.000 1.000
#> SRR1656475 2 0.000 0.979 0.000 1.000
#> SRR1656478 1 0.000 0.979 1.000 0.000
#> SRR1656477 2 0.000 0.979 0.000 1.000
#> SRR1656479 1 0.000 0.979 1.000 0.000
#> SRR1656480 2 0.000 0.979 0.000 1.000
#> SRR1656476 2 0.000 0.979 0.000 1.000
#> SRR1656481 2 0.000 0.979 0.000 1.000
#> SRR1656482 2 0.000 0.979 0.000 1.000
#> SRR1656483 2 0.000 0.979 0.000 1.000
#> SRR1656485 1 0.000 0.979 1.000 0.000
#> SRR1656487 1 0.000 0.979 1.000 0.000
#> SRR1656486 1 0.000 0.979 1.000 0.000
#> SRR1656488 1 0.000 0.979 1.000 0.000
#> SRR1656484 1 0.000 0.979 1.000 0.000
#> SRR1656489 1 0.000 0.979 1.000 0.000
#> SRR1656491 1 0.000 0.979 1.000 0.000
#> SRR1656490 1 0.000 0.979 1.000 0.000
#> SRR1656492 1 0.000 0.979 1.000 0.000
#> SRR1656493 1 0.000 0.979 1.000 0.000
#> SRR1656495 2 0.000 0.979 0.000 1.000
#> SRR1656496 1 0.000 0.979 1.000 0.000
#> SRR1656494 2 0.000 0.979 0.000 1.000
#> SRR1656497 2 0.000 0.979 0.000 1.000
#> SRR1656499 1 0.000 0.979 1.000 0.000
#> SRR1656500 1 0.000 0.979 1.000 0.000
#> SRR1656501 1 0.000 0.979 1.000 0.000
#> SRR1656498 1 0.000 0.979 1.000 0.000
#> SRR1656504 2 0.000 0.979 0.000 1.000
#> SRR1656502 1 0.855 0.605 0.720 0.280
#> SRR1656503 1 0.000 0.979 1.000 0.000
#> SRR1656507 1 0.000 0.979 1.000 0.000
#> SRR1656508 1 0.000 0.979 1.000 0.000
#> SRR1656505 2 0.000 0.979 0.000 1.000
#> SRR1656506 1 0.000 0.979 1.000 0.000
#> SRR1656509 1 0.000 0.979 1.000 0.000
#> SRR1656510 2 0.971 0.331 0.400 0.600
#> SRR1656511 2 0.000 0.979 0.000 1.000
#> SRR1656513 2 0.000 0.979 0.000 1.000
#> SRR1656512 2 0.000 0.979 0.000 1.000
#> SRR1656514 1 0.000 0.979 1.000 0.000
#> SRR1656515 2 0.000 0.979 0.000 1.000
#> SRR1656516 1 0.000 0.979 1.000 0.000
#> SRR1656518 1 0.000 0.979 1.000 0.000
#> SRR1656517 1 0.000 0.979 1.000 0.000
#> SRR1656519 1 0.000 0.979 1.000 0.000
#> SRR1656522 1 0.000 0.979 1.000 0.000
#> SRR1656523 2 0.000 0.979 0.000 1.000
#> SRR1656521 2 0.000 0.979 0.000 1.000
#> SRR1656520 1 0.000 0.979 1.000 0.000
#> SRR1656524 2 0.895 0.540 0.312 0.688
#> SRR1656525 1 0.000 0.979 1.000 0.000
#> SRR1656526 2 0.000 0.979 0.000 1.000
#> SRR1656527 2 0.000 0.979 0.000 1.000
#> SRR1656530 1 0.000 0.979 1.000 0.000
#> SRR1656529 1 0.000 0.979 1.000 0.000
#> SRR1656531 1 0.000 0.979 1.000 0.000
#> SRR1656528 1 0.000 0.979 1.000 0.000
#> SRR1656534 1 0.000 0.979 1.000 0.000
#> SRR1656533 1 0.000 0.979 1.000 0.000
#> SRR1656536 1 0.998 0.108 0.528 0.472
#> SRR1656532 2 0.000 0.979 0.000 1.000
#> SRR1656537 1 0.000 0.979 1.000 0.000
#> SRR1656538 1 0.000 0.979 1.000 0.000
#> SRR1656535 2 0.000 0.979 0.000 1.000
#> SRR1656539 1 0.000 0.979 1.000 0.000
#> SRR1656544 1 0.000 0.979 1.000 0.000
#> SRR1656542 1 0.000 0.979 1.000 0.000
#> SRR1656543 1 0.000 0.979 1.000 0.000
#> SRR1656545 2 0.000 0.979 0.000 1.000
#> SRR1656540 1 0.000 0.979 1.000 0.000
#> SRR1656546 1 0.971 0.316 0.600 0.400
#> SRR1656541 2 0.000 0.979 0.000 1.000
#> SRR1656547 2 0.000 0.979 0.000 1.000
#> SRR1656548 1 0.000 0.979 1.000 0.000
#> SRR1656549 1 0.000 0.979 1.000 0.000
#> SRR1656551 2 0.958 0.373 0.380 0.620
#> SRR1656553 1 0.000 0.979 1.000 0.000
#> SRR1656550 2 0.000 0.979 0.000 1.000
#> SRR1656552 2 0.000 0.979 0.000 1.000
#> SRR1656554 1 0.000 0.979 1.000 0.000
#> SRR1656555 2 0.000 0.979 0.000 1.000
#> SRR1656556 1 0.917 0.498 0.668 0.332
#> SRR1656557 1 0.000 0.979 1.000 0.000
#> SRR1656558 1 0.000 0.979 1.000 0.000
#> SRR1656559 1 0.000 0.979 1.000 0.000
#> SRR1656560 1 0.000 0.979 1.000 0.000
#> SRR1656561 1 0.000 0.979 1.000 0.000
#> SRR1656562 2 0.000 0.979 0.000 1.000
#> SRR1656563 1 0.000 0.979 1.000 0.000
#> SRR1656564 2 0.000 0.979 0.000 1.000
#> SRR1656565 2 0.000 0.979 0.000 1.000
#> SRR1656566 1 0.000 0.979 1.000 0.000
#> SRR1656568 2 0.000 0.979 0.000 1.000
#> SRR1656567 2 0.000 0.979 0.000 1.000
#> SRR1656569 1 0.000 0.979 1.000 0.000
#> SRR1656570 1 0.000 0.979 1.000 0.000
#> SRR1656571 2 0.000 0.979 0.000 1.000
#> SRR1656573 1 0.000 0.979 1.000 0.000
#> SRR1656572 2 0.000 0.979 0.000 1.000
#> SRR1656574 1 0.000 0.979 1.000 0.000
#> SRR1656575 1 0.000 0.979 1.000 0.000
#> SRR1656576 2 0.000 0.979 0.000 1.000
#> SRR1656578 2 0.000 0.979 0.000 1.000
#> SRR1656577 1 0.000 0.979 1.000 0.000
#> SRR1656579 2 0.000 0.979 0.000 1.000
#> SRR1656580 1 0.000 0.979 1.000 0.000
#> SRR1656581 2 0.000 0.979 0.000 1.000
#> SRR1656582 2 0.000 0.979 0.000 1.000
#> SRR1656585 2 0.000 0.979 0.000 1.000
#> SRR1656584 1 0.000 0.979 1.000 0.000
#> SRR1656583 2 0.000 0.979 0.000 1.000
#> SRR1656586 2 0.000 0.979 0.000 1.000
#> SRR1656587 2 0.000 0.979 0.000 1.000
#> SRR1656588 2 0.000 0.979 0.000 1.000
#> SRR1656589 2 0.000 0.979 0.000 1.000
#> SRR1656590 1 0.000 0.979 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656462 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656465 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656468 2 0.4504 0.7700 0.000 0.804 0.196
#> SRR1656472 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656471 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656470 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656478 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656477 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656479 1 0.6154 0.2337 0.592 0.000 0.408
#> SRR1656480 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656476 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656481 3 0.0237 0.9423 0.000 0.004 0.996
#> SRR1656482 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656487 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656486 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656488 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656484 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656489 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656491 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656490 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656492 3 0.4504 0.7844 0.196 0.000 0.804
#> SRR1656493 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656495 2 0.0237 0.9808 0.004 0.996 0.000
#> SRR1656496 1 0.6274 0.0649 0.544 0.000 0.456
#> SRR1656494 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656497 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656499 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656500 3 0.4452 0.7891 0.192 0.000 0.808
#> SRR1656501 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656498 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656502 1 0.0424 0.9644 0.992 0.008 0.000
#> SRR1656503 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656507 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656508 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656505 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656506 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656509 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656510 2 0.6126 0.3785 0.000 0.600 0.400
#> SRR1656511 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656516 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656518 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656517 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656519 3 0.4399 0.7933 0.188 0.000 0.812
#> SRR1656522 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656523 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656520 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656524 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656525 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656526 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656530 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656528 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656534 3 0.4555 0.7793 0.200 0.000 0.800
#> SRR1656533 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656537 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656538 3 0.4504 0.7844 0.196 0.000 0.804
#> SRR1656535 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656544 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656542 3 0.4452 0.7891 0.192 0.000 0.808
#> SRR1656543 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656545 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656540 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656546 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656541 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656548 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656549 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656551 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656553 3 0.4121 0.8130 0.168 0.000 0.832
#> SRR1656550 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656552 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656555 2 0.4555 0.7648 0.000 0.800 0.200
#> SRR1656556 3 0.0237 0.9423 0.000 0.004 0.996
#> SRR1656557 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656558 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656559 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656561 3 0.4504 0.7844 0.196 0.000 0.804
#> SRR1656562 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656563 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656566 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656568 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656567 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656569 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656570 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656571 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.9453 0.000 0.000 1.000
#> SRR1656572 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656574 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656575 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656576 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656578 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656580 3 0.6235 0.2881 0.436 0.000 0.564
#> SRR1656581 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656582 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656585 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656584 1 0.0000 0.9733 1.000 0.000 0.000
#> SRR1656583 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656586 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656587 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656588 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656589 2 0.0000 0.9845 0.000 1.000 0.000
#> SRR1656590 1 0.0000 0.9733 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.2921 0.856 0.860 0.000 0.140 0.000
#> SRR1656462 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656465 3 0.4382 0.691 0.000 0.000 0.704 0.296
#> SRR1656467 2 0.0188 0.971 0.000 0.996 0.000 0.004
#> SRR1656466 3 0.3569 0.794 0.000 0.000 0.804 0.196
#> SRR1656468 4 0.0000 0.773 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.2319 0.868 0.924 0.000 0.036 0.040
#> SRR1656471 3 0.4304 0.707 0.000 0.000 0.716 0.284
#> SRR1656470 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.3801 0.772 0.000 0.000 0.780 0.220
#> SRR1656473 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0921 0.894 0.972 0.000 0.028 0.000
#> SRR1656477 4 0.1302 0.796 0.000 0.044 0.000 0.956
#> SRR1656479 3 0.4624 0.437 0.340 0.000 0.660 0.000
#> SRR1656480 4 0.3486 0.812 0.000 0.188 0.000 0.812
#> SRR1656476 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.773 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.3837 0.768 0.000 0.000 0.776 0.224
#> SRR1656486 1 0.3649 0.815 0.796 0.000 0.204 0.000
#> SRR1656488 3 0.0817 0.903 0.000 0.000 0.976 0.024
#> SRR1656484 1 0.1389 0.889 0.952 0.000 0.048 0.000
#> SRR1656489 1 0.3610 0.819 0.800 0.000 0.200 0.000
#> SRR1656491 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656490 1 0.0592 0.895 0.984 0.000 0.016 0.000
#> SRR1656492 3 0.1398 0.889 0.040 0.000 0.956 0.004
#> SRR1656493 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656495 2 0.3569 0.704 0.196 0.804 0.000 0.000
#> SRR1656496 3 0.3024 0.759 0.148 0.000 0.852 0.000
#> SRR1656494 2 0.0188 0.971 0.000 0.996 0.000 0.004
#> SRR1656497 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656501 1 0.3610 0.818 0.800 0.000 0.200 0.000
#> SRR1656498 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656504 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.2319 0.868 0.924 0.000 0.036 0.040
#> SRR1656503 1 0.4454 0.712 0.692 0.000 0.308 0.000
#> SRR1656507 1 0.0921 0.894 0.972 0.000 0.028 0.000
#> SRR1656508 1 0.0336 0.894 0.992 0.000 0.008 0.000
#> SRR1656505 4 0.3873 0.786 0.000 0.228 0.000 0.772
#> SRR1656506 3 0.0188 0.908 0.000 0.000 0.996 0.004
#> SRR1656509 3 0.1584 0.886 0.012 0.000 0.952 0.036
#> SRR1656510 2 0.7149 0.263 0.004 0.576 0.240 0.180
#> SRR1656511 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656513 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656514 1 0.4713 0.555 0.640 0.000 0.360 0.000
#> SRR1656515 2 0.2281 0.862 0.000 0.904 0.000 0.096
#> SRR1656516 1 0.3837 0.802 0.776 0.000 0.224 0.000
#> SRR1656518 1 0.0469 0.895 0.988 0.000 0.012 0.000
#> SRR1656517 1 0.0469 0.895 0.988 0.000 0.012 0.000
#> SRR1656519 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656522 1 0.4624 0.660 0.660 0.000 0.340 0.000
#> SRR1656523 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656521 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656525 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656530 3 0.1389 0.894 0.000 0.000 0.952 0.048
#> SRR1656529 3 0.3837 0.768 0.000 0.000 0.776 0.224
#> SRR1656531 1 0.0188 0.893 0.996 0.000 0.004 0.000
#> SRR1656528 3 0.1389 0.894 0.000 0.000 0.952 0.048
#> SRR1656534 3 0.0188 0.907 0.004 0.000 0.996 0.000
#> SRR1656533 1 0.0336 0.894 0.992 0.000 0.008 0.000
#> SRR1656536 4 0.0000 0.773 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656537 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656538 3 0.0188 0.907 0.004 0.000 0.996 0.000
#> SRR1656535 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656539 3 0.3942 0.756 0.000 0.000 0.764 0.236
#> SRR1656544 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0188 0.908 0.000 0.000 0.996 0.004
#> SRR1656546 1 0.3577 0.732 0.832 0.156 0.012 0.000
#> SRR1656541 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656547 2 0.0592 0.959 0.000 0.984 0.000 0.016
#> SRR1656548 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656549 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656551 4 0.0188 0.771 0.000 0.000 0.004 0.996
#> SRR1656553 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.3444 0.813 0.000 0.184 0.000 0.816
#> SRR1656552 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656554 3 0.3907 0.760 0.000 0.000 0.768 0.232
#> SRR1656555 4 0.4285 0.779 0.000 0.156 0.040 0.804
#> SRR1656556 4 0.4643 0.226 0.000 0.000 0.344 0.656
#> SRR1656557 3 0.0000 0.909 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.0469 0.895 0.988 0.000 0.012 0.000
#> SRR1656559 1 0.4250 0.752 0.724 0.000 0.276 0.000
#> SRR1656560 3 0.1389 0.894 0.000 0.000 0.952 0.048
#> SRR1656561 3 0.0817 0.899 0.024 0.000 0.976 0.000
#> SRR1656562 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656563 1 0.2469 0.871 0.892 0.000 0.108 0.000
#> SRR1656564 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656566 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656568 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.3942 0.780 0.000 0.236 0.000 0.764
#> SRR1656569 3 0.3837 0.768 0.000 0.000 0.776 0.224
#> SRR1656570 1 0.2408 0.872 0.896 0.000 0.104 0.000
#> SRR1656571 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.4761 0.187 0.000 0.000 0.372 0.628
#> SRR1656572 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656574 1 0.2760 0.862 0.872 0.000 0.128 0.000
#> SRR1656575 1 0.0469 0.895 0.988 0.000 0.012 0.000
#> SRR1656576 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656578 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656577 1 0.3907 0.798 0.768 0.000 0.232 0.000
#> SRR1656579 4 0.4193 0.747 0.000 0.268 0.000 0.732
#> SRR1656580 3 0.0188 0.907 0.004 0.000 0.996 0.000
#> SRR1656581 4 0.4955 0.716 0.024 0.268 0.000 0.708
#> SRR1656582 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.3610 0.807 0.000 0.200 0.000 0.800
#> SRR1656584 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> SRR1656583 4 0.3486 0.812 0.000 0.188 0.000 0.812
#> SRR1656586 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656587 2 0.1118 0.937 0.000 0.964 0.000 0.036
#> SRR1656588 4 0.4103 0.761 0.000 0.256 0.000 0.744
#> SRR1656589 2 0.0000 0.975 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0000 0.892 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 1 0.6590 0.4036 0.452 0.000 0.320 0.228 0.000
#> SRR1656462 3 0.1831 0.7626 0.004 0.000 0.920 0.076 0.000
#> SRR1656465 3 0.4425 0.4651 0.000 0.000 0.600 0.008 0.392
#> SRR1656467 2 0.0290 0.9604 0.000 0.992 0.000 0.008 0.000
#> SRR1656466 3 0.4088 0.5775 0.000 0.000 0.688 0.008 0.304
#> SRR1656468 5 0.0000 0.5697 0.000 0.000 0.000 0.000 1.000
#> SRR1656472 4 0.0404 0.5450 0.012 0.000 0.000 0.988 0.000
#> SRR1656471 3 0.4403 0.4761 0.000 0.000 0.608 0.008 0.384
#> SRR1656470 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 3 0.4088 0.5771 0.000 0.000 0.688 0.008 0.304
#> SRR1656473 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.0162 0.7072 0.996 0.000 0.000 0.004 0.000
#> SRR1656477 5 0.1792 0.5454 0.000 0.000 0.000 0.084 0.916
#> SRR1656479 1 0.5790 0.2911 0.500 0.000 0.408 0.092 0.000
#> SRR1656480 5 0.4109 0.6025 0.000 0.288 0.000 0.012 0.700
#> SRR1656476 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.0404 0.5710 0.000 0.000 0.000 0.012 0.988
#> SRR1656482 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.0290 0.7738 0.000 0.000 0.992 0.008 0.000
#> SRR1656487 3 0.4088 0.5771 0.000 0.000 0.688 0.008 0.304
#> SRR1656486 1 0.0865 0.7124 0.972 0.000 0.024 0.004 0.000
#> SRR1656488 3 0.0579 0.7707 0.000 0.000 0.984 0.008 0.008
#> SRR1656484 1 0.1809 0.7076 0.928 0.000 0.012 0.060 0.000
#> SRR1656489 1 0.5260 0.5665 0.648 0.000 0.264 0.088 0.000
#> SRR1656491 3 0.0324 0.7744 0.004 0.000 0.992 0.004 0.000
#> SRR1656490 1 0.0290 0.7067 0.992 0.000 0.000 0.008 0.000
#> SRR1656492 3 0.3171 0.6636 0.176 0.000 0.816 0.008 0.000
#> SRR1656493 1 0.3913 0.3104 0.676 0.000 0.000 0.324 0.000
#> SRR1656495 4 0.2286 0.4981 0.004 0.108 0.000 0.888 0.000
#> SRR1656496 3 0.5752 -0.0795 0.412 0.000 0.500 0.088 0.000
#> SRR1656494 2 0.2471 0.8176 0.000 0.864 0.000 0.136 0.000
#> SRR1656497 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0162 0.7731 0.000 0.000 0.996 0.004 0.000
#> SRR1656500 3 0.2450 0.7511 0.028 0.000 0.896 0.076 0.000
#> SRR1656501 1 0.0703 0.7130 0.976 0.000 0.024 0.000 0.000
#> SRR1656498 1 0.0510 0.7113 0.984 0.000 0.000 0.016 0.000
#> SRR1656504 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 4 0.0404 0.5450 0.012 0.000 0.000 0.988 0.000
#> SRR1656503 1 0.5723 0.3753 0.520 0.000 0.392 0.088 0.000
#> SRR1656507 1 0.0000 0.7090 1.000 0.000 0.000 0.000 0.000
#> SRR1656508 1 0.2077 0.7002 0.908 0.000 0.008 0.084 0.000
#> SRR1656505 5 0.4130 0.6006 0.000 0.292 0.000 0.012 0.696
#> SRR1656506 3 0.0000 0.7739 0.000 0.000 1.000 0.000 0.000
#> SRR1656509 4 0.4288 0.2135 0.012 0.000 0.324 0.664 0.000
#> SRR1656510 2 0.7379 -0.1361 0.048 0.448 0.160 0.004 0.340
#> SRR1656511 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 1 0.5872 0.3181 0.492 0.000 0.408 0.100 0.000
#> SRR1656515 2 0.2077 0.8607 0.000 0.908 0.000 0.008 0.084
#> SRR1656516 1 0.5144 0.5510 0.632 0.000 0.304 0.064 0.000
#> SRR1656518 1 0.0162 0.7072 0.996 0.000 0.000 0.004 0.000
#> SRR1656517 1 0.0000 0.7090 1.000 0.000 0.000 0.000 0.000
#> SRR1656519 3 0.1831 0.7626 0.004 0.000 0.920 0.076 0.000
#> SRR1656522 1 0.5742 0.3471 0.508 0.000 0.404 0.088 0.000
#> SRR1656523 2 0.0162 0.9641 0.000 0.996 0.000 0.004 0.000
#> SRR1656521 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.1831 0.7626 0.004 0.000 0.920 0.076 0.000
#> SRR1656524 4 0.4305 0.0972 0.488 0.000 0.000 0.512 0.000
#> SRR1656525 3 0.0162 0.7742 0.004 0.000 0.996 0.000 0.000
#> SRR1656526 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656530 3 0.1830 0.7526 0.000 0.000 0.924 0.008 0.068
#> SRR1656529 3 0.4088 0.5771 0.000 0.000 0.688 0.008 0.304
#> SRR1656531 1 0.2970 0.6670 0.828 0.000 0.004 0.168 0.000
#> SRR1656528 3 0.1956 0.7493 0.000 0.000 0.916 0.008 0.076
#> SRR1656534 3 0.3362 0.7122 0.080 0.000 0.844 0.076 0.000
#> SRR1656533 1 0.0404 0.7106 0.988 0.000 0.000 0.012 0.000
#> SRR1656536 5 0.0162 0.5707 0.000 0.000 0.000 0.004 0.996
#> SRR1656532 2 0.2516 0.8123 0.000 0.860 0.000 0.140 0.000
#> SRR1656537 1 0.3983 0.3089 0.660 0.000 0.000 0.340 0.000
#> SRR1656538 3 0.5122 0.3089 0.312 0.000 0.628 0.060 0.000
#> SRR1656535 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 3 0.4283 0.5258 0.000 0.000 0.644 0.008 0.348
#> SRR1656544 3 0.0771 0.7736 0.004 0.000 0.976 0.020 0.000
#> SRR1656542 3 0.2300 0.7558 0.024 0.000 0.904 0.072 0.000
#> SRR1656543 3 0.1704 0.7649 0.004 0.000 0.928 0.068 0.000
#> SRR1656545 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.1831 0.7626 0.004 0.000 0.920 0.076 0.000
#> SRR1656546 1 0.3882 0.5037 0.788 0.044 0.000 0.168 0.000
#> SRR1656541 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0609 0.9484 0.000 0.980 0.000 0.000 0.020
#> SRR1656548 3 0.0000 0.7739 0.000 0.000 1.000 0.000 0.000
#> SRR1656549 1 0.2471 0.6012 0.864 0.000 0.000 0.136 0.000
#> SRR1656551 5 0.0000 0.5697 0.000 0.000 0.000 0.000 1.000
#> SRR1656553 3 0.2595 0.7460 0.032 0.000 0.888 0.080 0.000
#> SRR1656550 5 0.3659 0.6141 0.000 0.220 0.000 0.012 0.768
#> SRR1656552 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 3 0.4235 0.5406 0.000 0.000 0.656 0.008 0.336
#> SRR1656555 5 0.4524 0.5084 0.000 0.336 0.020 0.000 0.644
#> SRR1656556 5 0.5580 0.2141 0.000 0.000 0.336 0.088 0.576
#> SRR1656557 3 0.1831 0.7626 0.004 0.000 0.920 0.076 0.000
#> SRR1656558 1 0.0290 0.7054 0.992 0.000 0.000 0.008 0.000
#> SRR1656559 1 0.5663 0.4332 0.548 0.000 0.364 0.088 0.000
#> SRR1656560 3 0.1956 0.7493 0.000 0.000 0.916 0.008 0.076
#> SRR1656561 3 0.3876 0.3646 0.316 0.000 0.684 0.000 0.000
#> SRR1656562 2 0.0510 0.9534 0.000 0.984 0.000 0.016 0.000
#> SRR1656563 1 0.2616 0.6969 0.888 0.000 0.036 0.076 0.000
#> SRR1656564 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656566 1 0.3895 0.3181 0.680 0.000 0.000 0.320 0.000
#> SRR1656568 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 5 0.4339 0.5763 0.000 0.336 0.000 0.012 0.652
#> SRR1656569 3 0.4088 0.5771 0.000 0.000 0.688 0.008 0.304
#> SRR1656570 1 0.1701 0.7106 0.936 0.000 0.016 0.048 0.000
#> SRR1656571 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.4551 0.1386 0.008 0.000 0.348 0.008 0.636
#> SRR1656572 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656574 1 0.5010 0.5902 0.688 0.000 0.224 0.088 0.000
#> SRR1656575 1 0.0404 0.7106 0.988 0.000 0.000 0.012 0.000
#> SRR1656576 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.1121 0.9258 0.000 0.956 0.000 0.044 0.000
#> SRR1656577 1 0.5491 0.5190 0.600 0.000 0.312 0.088 0.000
#> SRR1656579 5 0.4252 0.5732 0.000 0.340 0.000 0.008 0.652
#> SRR1656580 3 0.5618 0.1505 0.348 0.000 0.564 0.088 0.000
#> SRR1656581 5 0.4809 0.5255 0.036 0.296 0.000 0.004 0.664
#> SRR1656582 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 4 0.4497 -0.0532 0.000 0.008 0.000 0.568 0.424
#> SRR1656584 1 0.2127 0.6268 0.892 0.000 0.000 0.108 0.000
#> SRR1656583 5 0.4443 0.1001 0.000 0.004 0.000 0.472 0.524
#> SRR1656586 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.4201 0.1822 0.000 0.408 0.000 0.592 0.000
#> SRR1656588 5 0.4339 0.5763 0.000 0.336 0.000 0.012 0.652
#> SRR1656589 2 0.0000 0.9673 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 4 0.4210 0.2298 0.412 0.000 0.000 0.588 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.2489 0.7113 0.012 0.000 0.860 0.000 0.000 0.128
#> SRR1656462 3 0.2454 0.7619 0.000 0.000 0.840 0.000 0.160 0.000
#> SRR1656465 5 0.0937 0.7802 0.000 0.000 0.000 0.040 0.960 0.000
#> SRR1656467 2 0.1327 0.9135 0.000 0.936 0.000 0.064 0.000 0.000
#> SRR1656466 5 0.1779 0.7926 0.000 0.000 0.064 0.016 0.920 0.000
#> SRR1656468 4 0.2191 0.7083 0.000 0.000 0.004 0.876 0.120 0.000
#> SRR1656472 6 0.0725 0.6652 0.012 0.000 0.012 0.000 0.000 0.976
#> SRR1656471 5 0.1010 0.7835 0.000 0.000 0.004 0.036 0.960 0.000
#> SRR1656470 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.0692 0.7988 0.000 0.000 0.020 0.004 0.976 0.000
#> SRR1656473 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2006 0.7843 0.892 0.000 0.104 0.000 0.000 0.004
#> SRR1656477 4 0.1478 0.7167 0.000 0.004 0.000 0.944 0.020 0.032
#> SRR1656479 3 0.3201 0.6649 0.140 0.000 0.824 0.008 0.028 0.000
#> SRR1656480 4 0.1204 0.7504 0.000 0.056 0.000 0.944 0.000 0.000
#> SRR1656476 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.2178 0.7043 0.000 0.000 0.000 0.868 0.132 0.000
#> SRR1656482 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.3789 0.3210 0.000 0.000 0.584 0.000 0.416 0.000
#> SRR1656487 5 0.1082 0.7992 0.000 0.000 0.040 0.004 0.956 0.000
#> SRR1656486 1 0.2504 0.7795 0.856 0.000 0.136 0.004 0.004 0.000
#> SRR1656488 5 0.3446 0.5398 0.000 0.000 0.308 0.000 0.692 0.000
#> SRR1656484 1 0.3969 0.6521 0.652 0.000 0.332 0.016 0.000 0.000
#> SRR1656489 3 0.3221 0.4498 0.264 0.000 0.736 0.000 0.000 0.000
#> SRR1656491 3 0.3601 0.6081 0.004 0.000 0.684 0.000 0.312 0.000
#> SRR1656490 1 0.1738 0.7655 0.928 0.000 0.052 0.016 0.004 0.000
#> SRR1656492 5 0.6044 0.2245 0.264 0.000 0.268 0.004 0.464 0.000
#> SRR1656493 1 0.3695 0.5205 0.732 0.000 0.000 0.024 0.000 0.244
#> SRR1656495 6 0.2146 0.6472 0.060 0.008 0.000 0.024 0.000 0.908
#> SRR1656496 3 0.1268 0.7717 0.036 0.000 0.952 0.004 0.008 0.000
#> SRR1656494 2 0.2685 0.8526 0.000 0.868 0.000 0.072 0.000 0.060
#> SRR1656497 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.3843 0.2088 0.000 0.000 0.548 0.000 0.452 0.000
#> SRR1656500 3 0.2178 0.7735 0.000 0.000 0.868 0.000 0.132 0.000
#> SRR1656501 1 0.2378 0.7815 0.848 0.000 0.152 0.000 0.000 0.000
#> SRR1656498 1 0.2955 0.7782 0.816 0.000 0.172 0.008 0.000 0.004
#> SRR1656504 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656502 6 0.0725 0.6652 0.012 0.000 0.012 0.000 0.000 0.976
#> SRR1656503 3 0.0632 0.7750 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1656507 1 0.2362 0.7860 0.860 0.000 0.136 0.000 0.000 0.004
#> SRR1656508 1 0.4062 0.4769 0.552 0.000 0.440 0.008 0.000 0.000
#> SRR1656505 4 0.1204 0.7504 0.000 0.056 0.000 0.944 0.000 0.000
#> SRR1656506 5 0.3288 0.5676 0.000 0.000 0.276 0.000 0.724 0.000
#> SRR1656509 6 0.4214 0.4581 0.000 0.000 0.276 0.000 0.044 0.680
#> SRR1656510 4 0.7701 0.2694 0.116 0.196 0.020 0.344 0.324 0.000
#> SRR1656511 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656513 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.1341 0.7664 0.028 0.000 0.948 0.000 0.000 0.024
#> SRR1656515 2 0.3684 0.3831 0.000 0.628 0.000 0.372 0.000 0.000
#> SRR1656516 3 0.3563 0.2828 0.336 0.000 0.664 0.000 0.000 0.000
#> SRR1656518 1 0.1765 0.7822 0.904 0.000 0.096 0.000 0.000 0.000
#> SRR1656517 1 0.2320 0.7856 0.864 0.000 0.132 0.000 0.000 0.004
#> SRR1656519 3 0.2340 0.7677 0.000 0.000 0.852 0.000 0.148 0.000
#> SRR1656522 3 0.0632 0.7750 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1656523 2 0.2976 0.8668 0.012 0.884 0.016 0.040 0.032 0.016
#> SRR1656521 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.2416 0.7636 0.000 0.000 0.844 0.000 0.156 0.000
#> SRR1656524 1 0.4206 0.3268 0.620 0.000 0.000 0.024 0.000 0.356
#> SRR1656525 3 0.2969 0.7046 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR1656526 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656530 5 0.2883 0.6989 0.000 0.000 0.212 0.000 0.788 0.000
#> SRR1656529 5 0.0547 0.7987 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR1656531 1 0.5983 0.4408 0.424 0.000 0.396 0.008 0.000 0.172
#> SRR1656528 5 0.2178 0.7657 0.000 0.000 0.132 0.000 0.868 0.000
#> SRR1656534 3 0.2070 0.7800 0.008 0.000 0.892 0.000 0.100 0.000
#> SRR1656533 1 0.2706 0.7813 0.832 0.000 0.160 0.008 0.000 0.000
#> SRR1656536 4 0.3265 0.6218 0.000 0.000 0.000 0.748 0.248 0.004
#> SRR1656532 2 0.1814 0.8804 0.000 0.900 0.000 0.000 0.000 0.100
#> SRR1656537 1 0.4427 0.5161 0.692 0.000 0.028 0.024 0.000 0.256
#> SRR1656538 3 0.1700 0.7742 0.048 0.000 0.928 0.000 0.024 0.000
#> SRR1656535 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.0891 0.7911 0.000 0.000 0.008 0.024 0.968 0.000
#> SRR1656544 3 0.2697 0.7404 0.000 0.000 0.812 0.000 0.188 0.000
#> SRR1656542 3 0.2378 0.7666 0.000 0.000 0.848 0.000 0.152 0.000
#> SRR1656543 3 0.2631 0.7463 0.000 0.000 0.820 0.000 0.180 0.000
#> SRR1656545 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.2838 0.7392 0.000 0.000 0.808 0.000 0.188 0.004
#> SRR1656546 1 0.0984 0.7387 0.968 0.000 0.012 0.008 0.000 0.012
#> SRR1656541 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.2793 0.7409 0.000 0.800 0.000 0.200 0.000 0.000
#> SRR1656548 3 0.3833 0.2701 0.000 0.000 0.556 0.000 0.444 0.000
#> SRR1656549 1 0.1218 0.7250 0.956 0.000 0.004 0.028 0.000 0.012
#> SRR1656551 4 0.3937 0.4138 0.000 0.000 0.000 0.572 0.424 0.004
#> SRR1656553 3 0.2048 0.7766 0.000 0.000 0.880 0.000 0.120 0.000
#> SRR1656550 4 0.1285 0.7499 0.000 0.052 0.000 0.944 0.004 0.000
#> SRR1656552 2 0.0291 0.9654 0.004 0.992 0.000 0.004 0.000 0.000
#> SRR1656554 5 0.0000 0.7914 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656555 4 0.6284 0.4306 0.000 0.240 0.016 0.536 0.192 0.016
#> SRR1656556 5 0.5364 0.0433 0.000 0.000 0.024 0.416 0.504 0.056
#> SRR1656557 3 0.2454 0.7619 0.000 0.000 0.840 0.000 0.160 0.000
#> SRR1656558 1 0.1588 0.7754 0.924 0.000 0.072 0.000 0.000 0.004
#> SRR1656559 3 0.0632 0.7750 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1656560 5 0.2912 0.6954 0.000 0.000 0.216 0.000 0.784 0.000
#> SRR1656561 3 0.4111 0.6756 0.144 0.000 0.748 0.000 0.108 0.000
#> SRR1656562 2 0.0508 0.9589 0.000 0.984 0.000 0.012 0.000 0.004
#> SRR1656563 1 0.4056 0.5064 0.576 0.000 0.416 0.004 0.004 0.000
#> SRR1656564 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656565 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656566 1 0.3539 0.5526 0.756 0.000 0.000 0.024 0.000 0.220
#> SRR1656568 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656567 4 0.1444 0.7454 0.000 0.072 0.000 0.928 0.000 0.000
#> SRR1656569 5 0.0000 0.7914 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656570 1 0.3954 0.5816 0.620 0.000 0.372 0.004 0.004 0.000
#> SRR1656571 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.4138 0.5521 0.016 0.000 0.024 0.164 0.772 0.024
#> SRR1656572 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656574 3 0.3081 0.5307 0.220 0.000 0.776 0.004 0.000 0.000
#> SRR1656575 1 0.2454 0.7819 0.840 0.000 0.160 0.000 0.000 0.000
#> SRR1656576 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 2 0.0363 0.9610 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1656577 3 0.1501 0.7380 0.076 0.000 0.924 0.000 0.000 0.000
#> SRR1656579 4 0.1610 0.7385 0.000 0.084 0.000 0.916 0.000 0.000
#> SRR1656580 3 0.0777 0.7759 0.024 0.000 0.972 0.000 0.004 0.000
#> SRR1656581 4 0.6257 0.4627 0.056 0.212 0.016 0.620 0.080 0.016
#> SRR1656582 2 0.0146 0.9674 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656585 6 0.3902 0.4703 0.000 0.012 0.008 0.256 0.004 0.720
#> SRR1656584 1 0.1332 0.7512 0.952 0.000 0.028 0.012 0.000 0.008
#> SRR1656583 6 0.4250 0.1186 0.000 0.016 0.000 0.456 0.000 0.528
#> SRR1656586 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 6 0.3711 0.4316 0.000 0.260 0.000 0.020 0.000 0.720
#> SRR1656588 4 0.1556 0.7411 0.000 0.080 0.000 0.920 0.000 0.000
#> SRR1656589 2 0.0000 0.9690 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 6 0.4632 0.2272 0.360 0.000 0.016 0.024 0.000 0.600
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 13572 rows and 129 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 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 0.984 0.964 0.985 0.4331 0.563 0.563
#> 3 3 0.653 0.722 0.880 0.5127 0.693 0.492
#> 4 4 0.737 0.737 0.881 0.1333 0.797 0.489
#> 5 5 0.674 0.631 0.805 0.0624 0.912 0.678
#> 6 6 0.806 0.729 0.865 0.0511 0.890 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
#> SRR1656463 2 0.0000 0.967 0.000 1.000
#> SRR1656464 1 0.0000 0.992 1.000 0.000
#> SRR1656462 1 0.0000 0.992 1.000 0.000
#> SRR1656465 1 0.0000 0.992 1.000 0.000
#> SRR1656467 2 0.0000 0.967 0.000 1.000
#> SRR1656466 1 0.0000 0.992 1.000 0.000
#> SRR1656468 1 0.0672 0.986 0.992 0.008
#> SRR1656472 1 0.0000 0.992 1.000 0.000
#> SRR1656471 1 0.0000 0.992 1.000 0.000
#> SRR1656470 2 0.0000 0.967 0.000 1.000
#> SRR1656469 1 0.0000 0.992 1.000 0.000
#> SRR1656473 2 0.0000 0.967 0.000 1.000
#> SRR1656474 2 0.0000 0.967 0.000 1.000
#> SRR1656475 2 0.0000 0.967 0.000 1.000
#> SRR1656478 1 0.0000 0.992 1.000 0.000
#> SRR1656477 1 0.2043 0.964 0.968 0.032
#> SRR1656479 1 0.0000 0.992 1.000 0.000
#> SRR1656480 2 0.0000 0.967 0.000 1.000
#> SRR1656476 2 0.0000 0.967 0.000 1.000
#> SRR1656481 1 0.1633 0.972 0.976 0.024
#> SRR1656482 2 0.0000 0.967 0.000 1.000
#> SRR1656483 2 0.0000 0.967 0.000 1.000
#> SRR1656485 1 0.0000 0.992 1.000 0.000
#> SRR1656487 1 0.0000 0.992 1.000 0.000
#> SRR1656486 1 0.0000 0.992 1.000 0.000
#> SRR1656488 1 0.0000 0.992 1.000 0.000
#> SRR1656484 1 0.0000 0.992 1.000 0.000
#> SRR1656489 1 0.0000 0.992 1.000 0.000
#> SRR1656491 1 0.0000 0.992 1.000 0.000
#> SRR1656490 1 0.0000 0.992 1.000 0.000
#> SRR1656492 1 0.0000 0.992 1.000 0.000
#> SRR1656493 1 0.0000 0.992 1.000 0.000
#> SRR1656495 1 0.5059 0.875 0.888 0.112
#> SRR1656496 1 0.0000 0.992 1.000 0.000
#> SRR1656494 2 0.0000 0.967 0.000 1.000
#> SRR1656497 2 0.0000 0.967 0.000 1.000
#> SRR1656499 1 0.0000 0.992 1.000 0.000
#> SRR1656500 1 0.0000 0.992 1.000 0.000
#> SRR1656501 1 0.0000 0.992 1.000 0.000
#> SRR1656498 1 0.0000 0.992 1.000 0.000
#> SRR1656504 2 0.0000 0.967 0.000 1.000
#> SRR1656502 1 0.0000 0.992 1.000 0.000
#> SRR1656503 1 0.0000 0.992 1.000 0.000
#> SRR1656507 1 0.0000 0.992 1.000 0.000
#> SRR1656508 1 0.0000 0.992 1.000 0.000
#> SRR1656505 2 0.9850 0.255 0.428 0.572
#> SRR1656506 1 0.0000 0.992 1.000 0.000
#> SRR1656509 1 0.0000 0.992 1.000 0.000
#> SRR1656510 1 0.0000 0.992 1.000 0.000
#> SRR1656511 2 0.6247 0.803 0.156 0.844
#> SRR1656513 2 0.0000 0.967 0.000 1.000
#> SRR1656512 2 0.0000 0.967 0.000 1.000
#> SRR1656514 1 0.0000 0.992 1.000 0.000
#> SRR1656515 2 0.0000 0.967 0.000 1.000
#> SRR1656516 1 0.0000 0.992 1.000 0.000
#> SRR1656518 1 0.0000 0.992 1.000 0.000
#> SRR1656517 1 0.0000 0.992 1.000 0.000
#> SRR1656519 1 0.0000 0.992 1.000 0.000
#> SRR1656522 1 0.0000 0.992 1.000 0.000
#> SRR1656523 1 0.4939 0.880 0.892 0.108
#> SRR1656521 2 0.0000 0.967 0.000 1.000
#> SRR1656520 1 0.0000 0.992 1.000 0.000
#> SRR1656524 1 0.0000 0.992 1.000 0.000
#> SRR1656525 1 0.0000 0.992 1.000 0.000
#> SRR1656526 2 0.0000 0.967 0.000 1.000
#> SRR1656527 2 0.0000 0.967 0.000 1.000
#> SRR1656530 1 0.0000 0.992 1.000 0.000
#> SRR1656529 1 0.0000 0.992 1.000 0.000
#> SRR1656531 1 0.0000 0.992 1.000 0.000
#> SRR1656528 1 0.0000 0.992 1.000 0.000
#> SRR1656534 1 0.0000 0.992 1.000 0.000
#> SRR1656533 1 0.0000 0.992 1.000 0.000
#> SRR1656536 1 0.0000 0.992 1.000 0.000
#> SRR1656532 2 0.0000 0.967 0.000 1.000
#> SRR1656537 1 0.0000 0.992 1.000 0.000
#> SRR1656538 1 0.0000 0.992 1.000 0.000
#> SRR1656535 2 0.0000 0.967 0.000 1.000
#> SRR1656539 1 0.0000 0.992 1.000 0.000
#> SRR1656544 1 0.0000 0.992 1.000 0.000
#> SRR1656542 1 0.0000 0.992 1.000 0.000
#> SRR1656543 1 0.0000 0.992 1.000 0.000
#> SRR1656545 2 0.0000 0.967 0.000 1.000
#> SRR1656540 1 0.0000 0.992 1.000 0.000
#> SRR1656546 1 0.0000 0.992 1.000 0.000
#> SRR1656541 2 0.0000 0.967 0.000 1.000
#> SRR1656547 2 0.0000 0.967 0.000 1.000
#> SRR1656548 1 0.0000 0.992 1.000 0.000
#> SRR1656549 1 0.0000 0.992 1.000 0.000
#> SRR1656551 1 0.0000 0.992 1.000 0.000
#> SRR1656553 1 0.0000 0.992 1.000 0.000
#> SRR1656550 2 0.0000 0.967 0.000 1.000
#> SRR1656552 2 0.9608 0.397 0.384 0.616
#> SRR1656554 1 0.0000 0.992 1.000 0.000
#> SRR1656555 1 0.0000 0.992 1.000 0.000
#> SRR1656556 1 0.1633 0.972 0.976 0.024
#> SRR1656557 1 0.0000 0.992 1.000 0.000
#> SRR1656558 1 0.0000 0.992 1.000 0.000
#> SRR1656559 1 0.0000 0.992 1.000 0.000
#> SRR1656560 1 0.0000 0.992 1.000 0.000
#> SRR1656561 1 0.0000 0.992 1.000 0.000
#> SRR1656562 1 0.5178 0.870 0.884 0.116
#> SRR1656563 1 0.0000 0.992 1.000 0.000
#> SRR1656564 2 0.0000 0.967 0.000 1.000
#> SRR1656565 2 0.0000 0.967 0.000 1.000
#> SRR1656566 1 0.0000 0.992 1.000 0.000
#> SRR1656568 2 0.0000 0.967 0.000 1.000
#> SRR1656567 2 0.0000 0.967 0.000 1.000
#> SRR1656569 1 0.0000 0.992 1.000 0.000
#> SRR1656570 1 0.0000 0.992 1.000 0.000
#> SRR1656571 2 0.0000 0.967 0.000 1.000
#> SRR1656573 1 0.0000 0.992 1.000 0.000
#> SRR1656572 2 0.9209 0.509 0.336 0.664
#> SRR1656574 1 0.0000 0.992 1.000 0.000
#> SRR1656575 1 0.0000 0.992 1.000 0.000
#> SRR1656576 2 0.0000 0.967 0.000 1.000
#> SRR1656578 2 0.0000 0.967 0.000 1.000
#> SRR1656577 1 0.0000 0.992 1.000 0.000
#> SRR1656579 2 0.0000 0.967 0.000 1.000
#> SRR1656580 1 0.0000 0.992 1.000 0.000
#> SRR1656581 1 0.0938 0.982 0.988 0.012
#> SRR1656582 2 0.0000 0.967 0.000 1.000
#> SRR1656585 1 0.0938 0.982 0.988 0.012
#> SRR1656584 1 0.0000 0.992 1.000 0.000
#> SRR1656583 1 0.3733 0.924 0.928 0.072
#> SRR1656586 2 0.0000 0.967 0.000 1.000
#> SRR1656587 1 0.5519 0.856 0.872 0.128
#> SRR1656588 2 0.0000 0.967 0.000 1.000
#> SRR1656589 2 0.0000 0.967 0.000 1.000
#> SRR1656590 1 0.0000 0.992 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656462 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656465 3 0.5948 0.4283 0.360 0.000 0.640
#> SRR1656467 2 0.1529 0.9185 0.000 0.960 0.040
#> SRR1656466 1 0.6111 0.2450 0.604 0.000 0.396
#> SRR1656468 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656472 3 0.2165 0.7422 0.064 0.000 0.936
#> SRR1656471 1 0.6260 0.0738 0.552 0.000 0.448
#> SRR1656470 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656469 3 0.5560 0.5570 0.300 0.000 0.700
#> SRR1656473 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656478 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656477 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656479 3 0.6225 0.3609 0.432 0.000 0.568
#> SRR1656480 3 0.3619 0.6406 0.000 0.136 0.864
#> SRR1656476 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656485 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656487 1 0.1411 0.8687 0.964 0.000 0.036
#> SRR1656486 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656488 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656484 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656489 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656491 3 0.1529 0.7478 0.040 0.000 0.960
#> SRR1656490 3 0.2448 0.7415 0.076 0.000 0.924
#> SRR1656492 1 0.5733 0.3782 0.676 0.000 0.324
#> SRR1656493 3 0.1860 0.7463 0.052 0.000 0.948
#> SRR1656495 3 0.4784 0.5975 0.004 0.200 0.796
#> SRR1656496 3 0.6140 0.4140 0.404 0.000 0.596
#> SRR1656494 2 0.5529 0.6547 0.000 0.704 0.296
#> SRR1656497 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656501 3 0.6308 0.2261 0.492 0.000 0.508
#> SRR1656498 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656502 3 0.1529 0.7478 0.040 0.000 0.960
#> SRR1656503 1 0.6295 -0.1491 0.528 0.000 0.472
#> SRR1656507 1 0.5397 0.4856 0.720 0.000 0.280
#> SRR1656508 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656505 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656506 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656509 3 0.5397 0.5506 0.280 0.000 0.720
#> SRR1656510 3 0.4178 0.6872 0.172 0.000 0.828
#> SRR1656511 3 0.6095 0.1806 0.000 0.392 0.608
#> SRR1656513 2 0.2959 0.8811 0.000 0.900 0.100
#> SRR1656512 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656515 2 0.1964 0.9108 0.000 0.944 0.056
#> SRR1656516 1 0.2625 0.8162 0.916 0.000 0.084
#> SRR1656518 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656517 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656523 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656521 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656520 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656524 3 0.3340 0.7193 0.120 0.000 0.880
#> SRR1656525 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656526 2 0.0892 0.9285 0.000 0.980 0.020
#> SRR1656527 2 0.0747 0.9295 0.000 0.984 0.016
#> SRR1656530 1 0.4555 0.6750 0.800 0.000 0.200
#> SRR1656529 1 0.6307 -0.0645 0.512 0.000 0.488
#> SRR1656531 1 0.0592 0.8919 0.988 0.000 0.012
#> SRR1656528 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656534 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656533 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656532 2 0.4235 0.8111 0.000 0.824 0.176
#> SRR1656537 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656538 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656535 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656539 3 0.5397 0.5506 0.280 0.000 0.720
#> SRR1656544 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656542 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656543 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656540 1 0.1529 0.8649 0.960 0.000 0.040
#> SRR1656546 3 0.4555 0.6695 0.200 0.000 0.800
#> SRR1656541 2 0.3267 0.8691 0.000 0.884 0.116
#> SRR1656547 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656548 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656549 3 0.5905 0.4933 0.352 0.000 0.648
#> SRR1656551 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656553 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656550 3 0.4555 0.5853 0.000 0.200 0.800
#> SRR1656552 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656554 3 0.5465 0.5418 0.288 0.000 0.712
#> SRR1656555 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656556 3 0.5431 0.5408 0.284 0.000 0.716
#> SRR1656557 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656558 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656559 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656560 1 0.2625 0.8144 0.916 0.000 0.084
#> SRR1656561 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656562 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656563 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656565 2 0.4235 0.8111 0.000 0.824 0.176
#> SRR1656566 3 0.6168 0.3988 0.412 0.000 0.588
#> SRR1656568 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656567 2 0.6267 0.3433 0.000 0.548 0.452
#> SRR1656569 3 0.5216 0.6167 0.260 0.000 0.740
#> SRR1656570 1 0.2537 0.8211 0.920 0.000 0.080
#> SRR1656571 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656573 3 0.0237 0.7504 0.004 0.000 0.996
#> SRR1656572 3 0.5098 0.4778 0.000 0.248 0.752
#> SRR1656574 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656575 1 0.6045 0.1877 0.620 0.000 0.380
#> SRR1656576 2 0.6235 0.3912 0.000 0.564 0.436
#> SRR1656578 2 0.1643 0.9147 0.000 0.956 0.044
#> SRR1656577 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656579 3 0.6140 0.0516 0.000 0.404 0.596
#> SRR1656580 1 0.0000 0.9022 1.000 0.000 0.000
#> SRR1656581 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656584 3 0.6307 0.2366 0.488 0.000 0.512
#> SRR1656583 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656586 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656587 3 0.0000 0.7499 0.000 0.000 1.000
#> SRR1656588 2 0.2959 0.8810 0.000 0.900 0.100
#> SRR1656589 2 0.0000 0.9354 0.000 1.000 0.000
#> SRR1656590 3 0.4452 0.6801 0.192 0.000 0.808
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.3024 0.7898 0.852 0.000 0.148 0.000
#> SRR1656462 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656465 4 0.4500 0.5915 0.000 0.000 0.316 0.684
#> SRR1656467 2 0.4522 0.6271 0.000 0.680 0.000 0.320
#> SRR1656466 4 0.4500 0.5915 0.000 0.000 0.316 0.684
#> SRR1656468 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.3157 0.7630 0.852 0.000 0.004 0.144
#> SRR1656471 4 0.4500 0.5915 0.000 0.000 0.316 0.684
#> SRR1656470 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656469 4 0.4677 0.5901 0.004 0.000 0.316 0.680
#> SRR1656473 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656477 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656479 3 0.3754 0.7741 0.064 0.000 0.852 0.084
#> SRR1656480 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.2081 0.8195 0.000 0.000 0.916 0.084
#> SRR1656486 3 0.3024 0.7754 0.148 0.000 0.852 0.000
#> SRR1656488 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656484 1 0.4500 0.4604 0.684 0.000 0.316 0.000
#> SRR1656489 3 0.2081 0.8078 0.084 0.000 0.916 0.000
#> SRR1656491 4 0.6033 0.5542 0.064 0.000 0.316 0.620
#> SRR1656490 4 0.5532 0.6585 0.068 0.000 0.228 0.704
#> SRR1656492 3 0.0469 0.8654 0.012 0.000 0.988 0.000
#> SRR1656493 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656495 1 0.4356 0.5137 0.708 0.000 0.000 0.292
#> SRR1656496 3 0.6013 0.3530 0.064 0.000 0.624 0.312
#> SRR1656494 4 0.4843 0.1056 0.000 0.396 0.000 0.604
#> SRR1656497 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656501 3 0.2973 0.7793 0.144 0.000 0.856 0.000
#> SRR1656498 1 0.1716 0.8302 0.936 0.000 0.064 0.000
#> SRR1656504 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.2281 0.8054 0.904 0.000 0.000 0.096
#> SRR1656503 3 0.2081 0.8195 0.000 0.000 0.916 0.084
#> SRR1656507 1 0.4072 0.6441 0.748 0.000 0.252 0.000
#> SRR1656508 1 0.3024 0.7898 0.852 0.000 0.148 0.000
#> SRR1656505 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656506 3 0.2081 0.8195 0.000 0.000 0.916 0.084
#> SRR1656509 4 0.4677 0.5901 0.004 0.000 0.316 0.680
#> SRR1656510 4 0.1716 0.7664 0.064 0.000 0.000 0.936
#> SRR1656511 4 0.6376 -0.0845 0.064 0.432 0.000 0.504
#> SRR1656513 2 0.4522 0.6271 0.000 0.680 0.000 0.320
#> SRR1656512 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656514 1 0.4643 0.5179 0.656 0.000 0.344 0.000
#> SRR1656515 2 0.4643 0.5935 0.000 0.656 0.000 0.344
#> SRR1656516 3 0.2081 0.8232 0.084 0.000 0.916 0.000
#> SRR1656518 3 0.4994 0.1736 0.480 0.000 0.520 0.000
#> SRR1656517 1 0.3024 0.7803 0.852 0.000 0.148 0.000
#> SRR1656519 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.4406 0.4917 0.300 0.000 0.700 0.000
#> SRR1656523 4 0.1716 0.7664 0.064 0.000 0.000 0.936
#> SRR1656521 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656525 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.1302 0.8803 0.000 0.956 0.000 0.044
#> SRR1656527 2 0.0188 0.9038 0.000 0.996 0.000 0.004
#> SRR1656530 3 0.5558 0.3723 0.036 0.000 0.640 0.324
#> SRR1656529 4 0.4522 0.5865 0.000 0.000 0.320 0.680
#> SRR1656531 1 0.3024 0.7898 0.852 0.000 0.148 0.000
#> SRR1656528 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656534 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656533 1 0.1716 0.8302 0.936 0.000 0.064 0.000
#> SRR1656536 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.5538 0.5897 0.036 0.644 0.000 0.320
#> SRR1656537 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656535 2 0.0188 0.9038 0.000 0.996 0.000 0.004
#> SRR1656539 4 0.4677 0.5901 0.004 0.000 0.316 0.680
#> SRR1656544 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0469 0.8655 0.000 0.000 0.988 0.012
#> SRR1656546 1 0.4647 0.5143 0.704 0.000 0.008 0.288
#> SRR1656541 2 0.4624 0.6001 0.000 0.660 0.000 0.340
#> SRR1656547 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656548 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656549 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656551 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656553 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656552 4 0.3400 0.7284 0.064 0.064 0.000 0.872
#> SRR1656554 4 0.4677 0.5901 0.004 0.000 0.316 0.680
#> SRR1656555 4 0.0188 0.7917 0.004 0.000 0.000 0.996
#> SRR1656556 4 0.4222 0.6374 0.000 0.000 0.272 0.728
#> SRR1656557 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.1716 0.8302 0.936 0.000 0.064 0.000
#> SRR1656559 3 0.4522 0.4533 0.320 0.000 0.680 0.000
#> SRR1656560 3 0.2530 0.7856 0.000 0.000 0.888 0.112
#> SRR1656561 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656562 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656563 3 0.4406 0.4917 0.300 0.000 0.700 0.000
#> SRR1656564 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.4585 0.6122 0.000 0.668 0.000 0.332
#> SRR1656566 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656568 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656569 4 0.6276 0.4296 0.064 0.000 0.380 0.556
#> SRR1656570 3 0.3486 0.7408 0.188 0.000 0.812 0.000
#> SRR1656571 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1716 0.7664 0.064 0.000 0.000 0.936
#> SRR1656572 4 0.6013 0.3115 0.064 0.312 0.000 0.624
#> SRR1656574 3 0.4406 0.4917 0.300 0.000 0.700 0.000
#> SRR1656575 1 0.5000 -0.1365 0.500 0.000 0.500 0.000
#> SRR1656576 4 0.4500 0.3395 0.000 0.316 0.000 0.684
#> SRR1656578 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656577 3 0.4406 0.4917 0.300 0.000 0.700 0.000
#> SRR1656579 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656580 3 0.0000 0.8707 0.000 0.000 1.000 0.000
#> SRR1656581 4 0.1716 0.7664 0.064 0.000 0.000 0.936
#> SRR1656582 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656584 1 0.0000 0.8403 1.000 0.000 0.000 0.000
#> SRR1656583 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656586 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.0000 0.7931 0.000 0.000 0.000 1.000
#> SRR1656588 2 0.4790 0.5271 0.000 0.620 0.000 0.380
#> SRR1656589 2 0.0000 0.9056 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0000 0.8403 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.4235 0.1941 0.000 0.576 0.000 0.424 0.000
#> SRR1656464 1 0.1792 0.7205 0.916 0.000 0.084 0.000 0.000
#> SRR1656462 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 5 0.4065 0.6737 0.000 0.000 0.264 0.016 0.720
#> SRR1656467 4 0.5180 0.6054 0.000 0.196 0.000 0.684 0.120
#> SRR1656466 5 0.4065 0.6737 0.000 0.000 0.264 0.016 0.720
#> SRR1656468 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656472 1 0.4449 0.5052 0.688 0.000 0.004 0.020 0.288
#> SRR1656471 5 0.3684 0.6651 0.000 0.000 0.280 0.000 0.720
#> SRR1656470 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.2813 0.6832 0.000 0.000 0.168 0.000 0.832
#> SRR1656473 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.2179 0.7104 0.888 0.000 0.000 0.112 0.000
#> SRR1656477 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656479 3 0.5962 0.4931 0.000 0.000 0.584 0.168 0.248
#> SRR1656480 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656476 2 0.1608 0.8422 0.000 0.928 0.000 0.072 0.000
#> SRR1656481 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656482 2 0.4307 -0.0725 0.000 0.500 0.000 0.500 0.000
#> SRR1656483 2 0.0404 0.8691 0.000 0.988 0.000 0.012 0.000
#> SRR1656485 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656487 3 0.1908 0.7867 0.000 0.000 0.908 0.000 0.092
#> SRR1656486 3 0.6884 0.4865 0.084 0.000 0.592 0.168 0.156
#> SRR1656488 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656484 1 0.7576 0.4855 0.520 0.000 0.156 0.168 0.156
#> SRR1656489 3 0.1544 0.8019 0.068 0.000 0.932 0.000 0.000
#> SRR1656491 5 0.5195 0.5711 0.000 0.000 0.144 0.168 0.688
#> SRR1656490 5 0.5222 0.5578 0.000 0.000 0.124 0.196 0.680
#> SRR1656492 3 0.4444 0.6676 0.000 0.000 0.756 0.088 0.156
#> SRR1656493 1 0.6226 0.4355 0.504 0.000 0.000 0.340 0.156
#> SRR1656495 1 0.6268 0.4091 0.484 0.000 0.000 0.360 0.156
#> SRR1656496 5 0.6342 0.1441 0.000 0.000 0.356 0.168 0.476
#> SRR1656494 4 0.4138 0.4448 0.000 0.000 0.000 0.616 0.384
#> SRR1656497 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656500 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656501 3 0.6786 0.5031 0.084 0.000 0.604 0.160 0.152
#> SRR1656498 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656504 2 0.1671 0.8395 0.000 0.924 0.000 0.076 0.000
#> SRR1656502 1 0.3878 0.5814 0.748 0.000 0.000 0.016 0.236
#> SRR1656503 3 0.3242 0.7166 0.000 0.000 0.784 0.000 0.216
#> SRR1656507 1 0.1410 0.7321 0.940 0.000 0.060 0.000 0.000
#> SRR1656508 1 0.1792 0.7205 0.916 0.000 0.084 0.000 0.000
#> SRR1656505 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656506 3 0.1792 0.7913 0.000 0.000 0.916 0.000 0.084
#> SRR1656509 5 0.3586 0.6733 0.000 0.000 0.264 0.000 0.736
#> SRR1656510 5 0.3895 0.5981 0.000 0.000 0.000 0.320 0.680
#> SRR1656511 4 0.2648 0.5021 0.000 0.000 0.000 0.848 0.152
#> SRR1656513 4 0.2970 0.6348 0.000 0.168 0.000 0.828 0.004
#> SRR1656512 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 1 0.3586 0.5295 0.736 0.000 0.264 0.000 0.000
#> SRR1656515 4 0.5378 0.6208 0.000 0.160 0.000 0.668 0.172
#> SRR1656516 3 0.2966 0.7308 0.184 0.000 0.816 0.000 0.000
#> SRR1656518 3 0.8068 0.1608 0.248 0.000 0.428 0.168 0.156
#> SRR1656517 1 0.1965 0.7149 0.904 0.000 0.096 0.000 0.000
#> SRR1656519 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.3730 0.5047 0.288 0.000 0.712 0.000 0.000
#> SRR1656523 5 0.4088 0.3889 0.000 0.000 0.000 0.368 0.632
#> SRR1656521 2 0.0609 0.8664 0.000 0.980 0.000 0.020 0.000
#> SRR1656520 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 1 0.3954 0.6224 0.772 0.000 0.000 0.192 0.036
#> SRR1656525 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656526 4 0.3913 0.4478 0.000 0.324 0.000 0.676 0.000
#> SRR1656527 4 0.4219 0.2387 0.000 0.416 0.000 0.584 0.000
#> SRR1656530 3 0.4430 0.5004 0.000 0.000 0.708 0.036 0.256
#> SRR1656529 5 0.4060 0.5773 0.000 0.000 0.360 0.000 0.640
#> SRR1656531 1 0.1792 0.7205 0.916 0.000 0.084 0.000 0.000
#> SRR1656528 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656534 3 0.0609 0.8405 0.020 0.000 0.980 0.000 0.000
#> SRR1656533 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656536 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656532 4 0.1478 0.6391 0.000 0.064 0.000 0.936 0.000
#> SRR1656537 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656535 4 0.3932 0.4402 0.000 0.328 0.000 0.672 0.000
#> SRR1656539 5 0.3586 0.6733 0.000 0.000 0.264 0.000 0.736
#> SRR1656544 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656542 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656543 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.1544 0.8114 0.000 0.000 0.932 0.000 0.068
#> SRR1656546 1 0.6487 0.4171 0.488 0.000 0.004 0.332 0.176
#> SRR1656541 4 0.3495 0.6435 0.000 0.160 0.000 0.812 0.028
#> SRR1656547 4 0.4307 0.1786 0.000 0.000 0.000 0.504 0.496
#> SRR1656548 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656549 1 0.6145 0.4654 0.532 0.000 0.000 0.312 0.156
#> SRR1656551 5 0.2329 0.6773 0.000 0.000 0.000 0.124 0.876
#> SRR1656553 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656550 5 0.2690 0.6694 0.000 0.000 0.000 0.156 0.844
#> SRR1656552 4 0.2561 0.5067 0.000 0.000 0.000 0.856 0.144
#> SRR1656554 5 0.3707 0.6628 0.000 0.000 0.284 0.000 0.716
#> SRR1656555 5 0.3561 0.6264 0.000 0.000 0.000 0.260 0.740
#> SRR1656556 5 0.4223 0.6781 0.000 0.000 0.248 0.028 0.724
#> SRR1656557 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656559 3 0.4291 0.1188 0.464 0.000 0.536 0.000 0.000
#> SRR1656560 3 0.1792 0.7977 0.000 0.000 0.916 0.000 0.084
#> SRR1656561 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656562 5 0.3816 0.5884 0.000 0.000 0.000 0.304 0.696
#> SRR1656563 3 0.4291 0.1400 0.464 0.000 0.536 0.000 0.000
#> SRR1656564 2 0.4278 0.1198 0.000 0.548 0.000 0.452 0.000
#> SRR1656565 4 0.3203 0.6386 0.000 0.168 0.000 0.820 0.012
#> SRR1656566 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656568 2 0.1851 0.8302 0.000 0.912 0.000 0.088 0.000
#> SRR1656567 4 0.4219 0.3917 0.000 0.000 0.000 0.584 0.416
#> SRR1656569 5 0.5344 0.5729 0.000 0.000 0.160 0.168 0.672
#> SRR1656570 3 0.4016 0.6151 0.272 0.000 0.716 0.012 0.000
#> SRR1656571 2 0.1608 0.8422 0.000 0.928 0.000 0.072 0.000
#> SRR1656573 5 0.2813 0.5728 0.000 0.000 0.000 0.168 0.832
#> SRR1656572 4 0.2516 0.5128 0.000 0.000 0.000 0.860 0.140
#> SRR1656574 1 0.4304 -0.0385 0.516 0.000 0.484 0.000 0.000
#> SRR1656575 1 0.7564 0.3195 0.468 0.000 0.284 0.164 0.084
#> SRR1656576 4 0.2966 0.6183 0.000 0.000 0.000 0.816 0.184
#> SRR1656578 4 0.3913 0.4443 0.000 0.324 0.000 0.676 0.000
#> SRR1656577 1 0.4304 -0.0385 0.516 0.000 0.484 0.000 0.000
#> SRR1656579 4 0.4182 0.4207 0.000 0.000 0.000 0.600 0.400
#> SRR1656580 3 0.0000 0.8488 0.000 0.000 1.000 0.000 0.000
#> SRR1656581 5 0.3949 0.4410 0.000 0.000 0.000 0.332 0.668
#> SRR1656582 4 0.3949 0.4340 0.000 0.332 0.000 0.668 0.000
#> SRR1656585 5 0.0162 0.6704 0.000 0.000 0.000 0.004 0.996
#> SRR1656584 1 0.0000 0.7399 1.000 0.000 0.000 0.000 0.000
#> SRR1656583 5 0.2813 0.6546 0.000 0.000 0.000 0.168 0.832
#> SRR1656586 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 5 0.2732 0.6669 0.000 0.000 0.000 0.160 0.840
#> SRR1656588 4 0.5899 0.6014 0.000 0.160 0.000 0.592 0.248
#> SRR1656589 2 0.0000 0.8715 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.0000 0.7399 1.000 0.000 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
#> SRR1656463 4 0.3309 0.4539 0.000 0.280 0.000 0.720 0.000 0.000
#> SRR1656464 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656462 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.3175 0.7065 0.000 0.000 0.256 0.000 0.744 0.000
#> SRR1656467 4 0.1663 0.7355 0.000 0.000 0.000 0.912 0.088 0.000
#> SRR1656466 5 0.3175 0.7065 0.000 0.000 0.256 0.000 0.744 0.000
#> SRR1656468 5 0.0000 0.7656 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656472 6 0.6110 0.2875 0.340 0.000 0.000 0.192 0.012 0.456
#> SRR1656471 5 0.3244 0.6916 0.000 0.000 0.268 0.000 0.732 0.000
#> SRR1656470 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.4295 0.7141 0.000 0.000 0.160 0.000 0.728 0.112
#> SRR1656473 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2883 0.7269 0.788 0.000 0.000 0.000 0.000 0.212
#> SRR1656477 5 0.0000 0.7656 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656479 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656480 5 0.0260 0.7613 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1656476 2 0.3151 0.7527 0.000 0.748 0.000 0.252 0.000 0.000
#> SRR1656481 5 0.0000 0.7656 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656482 4 0.2730 0.5926 0.000 0.192 0.000 0.808 0.000 0.000
#> SRR1656483 2 0.3244 0.7400 0.000 0.732 0.000 0.268 0.000 0.000
#> SRR1656485 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656487 3 0.1610 0.8407 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1656486 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656488 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656484 6 0.0146 0.8103 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1656489 3 0.1663 0.8458 0.088 0.000 0.912 0.000 0.000 0.000
#> SRR1656491 6 0.1556 0.7824 0.000 0.000 0.000 0.000 0.080 0.920
#> SRR1656490 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656492 6 0.3175 0.5553 0.000 0.000 0.256 0.000 0.000 0.744
#> SRR1656493 6 0.1556 0.7852 0.080 0.000 0.000 0.000 0.000 0.920
#> SRR1656495 6 0.4156 0.6561 0.080 0.000 0.000 0.188 0.000 0.732
#> SRR1656496 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656494 4 0.3244 0.6738 0.000 0.000 0.000 0.732 0.268 0.000
#> SRR1656497 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656500 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656501 6 0.0458 0.8051 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR1656498 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656504 2 0.3288 0.7295 0.000 0.724 0.000 0.276 0.000 0.000
#> SRR1656502 6 0.6074 0.1587 0.392 0.000 0.000 0.192 0.008 0.408
#> SRR1656503 3 0.3371 0.6003 0.000 0.000 0.708 0.000 0.000 0.292
#> SRR1656507 1 0.1556 0.8481 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR1656508 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656505 5 0.0000 0.7656 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656506 3 0.1700 0.8418 0.000 0.000 0.916 0.000 0.080 0.004
#> SRR1656509 5 0.3518 0.7020 0.000 0.000 0.256 0.000 0.732 0.012
#> SRR1656510 6 0.3647 0.5109 0.000 0.000 0.000 0.000 0.360 0.640
#> SRR1656511 6 0.3843 0.2970 0.000 0.000 0.000 0.452 0.000 0.548
#> SRR1656513 4 0.0000 0.7294 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 1 0.1556 0.8456 0.920 0.000 0.080 0.000 0.000 0.000
#> SRR1656515 4 0.3244 0.6738 0.000 0.000 0.000 0.732 0.268 0.000
#> SRR1656516 3 0.5037 0.2545 0.380 0.000 0.540 0.000 0.000 0.080
#> SRR1656518 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656517 1 0.1204 0.8610 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1656519 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656522 3 0.3309 0.5366 0.280 0.000 0.720 0.000 0.000 0.000
#> SRR1656523 6 0.1856 0.7943 0.000 0.000 0.000 0.032 0.048 0.920
#> SRR1656521 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 1 0.3862 -0.0511 0.524 0.000 0.000 0.000 0.000 0.476
#> SRR1656525 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656526 4 0.0458 0.7265 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656527 4 0.1765 0.6702 0.000 0.096 0.000 0.904 0.000 0.000
#> SRR1656530 3 0.3356 0.7742 0.000 0.000 0.808 0.000 0.052 0.140
#> SRR1656529 3 0.3695 0.2801 0.000 0.000 0.624 0.000 0.376 0.000
#> SRR1656531 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656528 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656534 3 0.0632 0.8933 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1656533 1 0.1204 0.8610 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1656536 5 0.0000 0.7656 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656532 4 0.0000 0.7294 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656537 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656535 4 0.1814 0.6829 0.000 0.100 0.000 0.900 0.000 0.000
#> SRR1656539 5 0.3518 0.7020 0.000 0.000 0.256 0.000 0.732 0.012
#> SRR1656544 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656542 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656543 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.1814 0.8276 0.000 0.000 0.900 0.000 0.100 0.000
#> SRR1656546 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656541 4 0.0713 0.7318 0.000 0.000 0.000 0.972 0.028 0.000
#> SRR1656547 4 0.3828 0.4309 0.000 0.000 0.000 0.560 0.440 0.000
#> SRR1656548 3 0.0146 0.9028 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656549 6 0.0000 0.8107 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656551 5 0.1141 0.7592 0.000 0.000 0.000 0.000 0.948 0.052
#> SRR1656553 3 0.0146 0.9028 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656550 5 0.0260 0.7613 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1656552 6 0.5572 0.3544 0.000 0.000 0.000 0.188 0.268 0.544
#> SRR1656554 5 0.3244 0.6916 0.000 0.000 0.268 0.000 0.732 0.000
#> SRR1656555 5 0.0713 0.7643 0.000 0.000 0.000 0.000 0.972 0.028
#> SRR1656556 5 0.3101 0.7166 0.000 0.000 0.244 0.000 0.756 0.000
#> SRR1656557 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.1204 0.8610 0.944 0.000 0.000 0.000 0.000 0.056
#> SRR1656559 1 0.2416 0.7857 0.844 0.000 0.156 0.000 0.000 0.000
#> SRR1656560 3 0.1444 0.8532 0.000 0.000 0.928 0.000 0.072 0.000
#> SRR1656561 3 0.1556 0.8548 0.000 0.000 0.920 0.000 0.000 0.080
#> SRR1656562 5 0.0291 0.7634 0.000 0.000 0.000 0.004 0.992 0.004
#> SRR1656563 1 0.3789 0.2842 0.584 0.000 0.416 0.000 0.000 0.000
#> SRR1656564 4 0.3351 0.4369 0.000 0.288 0.000 0.712 0.000 0.000
#> SRR1656565 4 0.3221 0.6764 0.000 0.000 0.000 0.736 0.264 0.000
#> SRR1656566 1 0.0000 0.8676 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656568 2 0.3244 0.7400 0.000 0.732 0.000 0.268 0.000 0.000
#> SRR1656567 4 0.3851 0.4941 0.000 0.000 0.000 0.540 0.460 0.000
#> SRR1656569 5 0.4819 0.2839 0.000 0.000 0.056 0.000 0.528 0.416
#> SRR1656570 3 0.4545 0.6144 0.224 0.000 0.684 0.000 0.000 0.092
#> SRR1656571 2 0.3244 0.7400 0.000 0.732 0.000 0.268 0.000 0.000
#> SRR1656573 6 0.1556 0.7824 0.000 0.000 0.000 0.000 0.080 0.920
#> SRR1656572 6 0.5361 0.4235 0.000 0.000 0.000 0.268 0.156 0.576
#> SRR1656574 1 0.1556 0.8456 0.920 0.000 0.080 0.000 0.000 0.000
#> SRR1656575 1 0.4666 0.2919 0.536 0.000 0.044 0.000 0.000 0.420
#> SRR1656576 4 0.3833 0.5126 0.000 0.000 0.000 0.556 0.444 0.000
#> SRR1656578 4 0.0000 0.7294 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656577 1 0.1556 0.8456 0.920 0.000 0.080 0.000 0.000 0.000
#> SRR1656579 4 0.3851 0.4941 0.000 0.000 0.000 0.540 0.460 0.000
#> SRR1656580 3 0.0000 0.9042 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656581 6 0.1556 0.7824 0.000 0.000 0.000 0.000 0.080 0.920
#> SRR1656582 4 0.2664 0.6026 0.000 0.184 0.000 0.816 0.000 0.000
#> SRR1656585 5 0.3244 0.5950 0.000 0.000 0.000 0.000 0.732 0.268
#> SRR1656584 1 0.1007 0.8636 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1656583 5 0.3509 0.4865 0.000 0.000 0.000 0.240 0.744 0.016
#> SRR1656586 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 5 0.2980 0.5582 0.000 0.000 0.000 0.192 0.800 0.008
#> SRR1656588 4 0.3244 0.6738 0.000 0.000 0.000 0.732 0.268 0.000
#> SRR1656589 2 0.0000 0.8923 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.0000 0.8676 1.000 0.000 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", "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 13572 rows and 129 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 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 0.597 0.796 0.906 0.3634 0.705 0.705
#> 3 3 0.489 0.623 0.757 0.6243 0.664 0.527
#> 4 4 0.639 0.698 0.823 0.1562 0.855 0.662
#> 5 5 0.712 0.802 0.872 0.1023 0.822 0.525
#> 6 6 0.798 0.782 0.875 0.0724 0.891 0.591
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
#> SRR1656463 2 0.0000 0.956 0.000 1.000
#> SRR1656464 1 0.0000 0.875 1.000 0.000
#> SRR1656462 1 0.0000 0.875 1.000 0.000
#> SRR1656465 1 0.0000 0.875 1.000 0.000
#> SRR1656467 1 0.9850 0.422 0.572 0.428
#> SRR1656466 1 0.0000 0.875 1.000 0.000
#> SRR1656468 1 0.9732 0.475 0.596 0.404
#> SRR1656472 1 0.0000 0.875 1.000 0.000
#> SRR1656471 1 0.0000 0.875 1.000 0.000
#> SRR1656470 2 0.0000 0.956 0.000 1.000
#> SRR1656469 1 0.0000 0.875 1.000 0.000
#> SRR1656473 2 0.0000 0.956 0.000 1.000
#> SRR1656474 2 0.0000 0.956 0.000 1.000
#> SRR1656475 2 0.0000 0.956 0.000 1.000
#> SRR1656478 1 0.0000 0.875 1.000 0.000
#> SRR1656477 1 0.9522 0.523 0.628 0.372
#> SRR1656479 1 0.0000 0.875 1.000 0.000
#> SRR1656480 1 0.9732 0.475 0.596 0.404
#> SRR1656476 2 0.0938 0.952 0.012 0.988
#> SRR1656481 1 0.5629 0.788 0.868 0.132
#> SRR1656482 2 0.0938 0.952 0.012 0.988
#> SRR1656483 2 0.0000 0.956 0.000 1.000
#> SRR1656485 1 0.0000 0.875 1.000 0.000
#> SRR1656487 1 0.0000 0.875 1.000 0.000
#> SRR1656486 1 0.0000 0.875 1.000 0.000
#> SRR1656488 1 0.0000 0.875 1.000 0.000
#> SRR1656484 1 0.0000 0.875 1.000 0.000
#> SRR1656489 1 0.0000 0.875 1.000 0.000
#> SRR1656491 1 0.0000 0.875 1.000 0.000
#> SRR1656490 1 0.2778 0.846 0.952 0.048
#> SRR1656492 1 0.0000 0.875 1.000 0.000
#> SRR1656493 1 0.0000 0.875 1.000 0.000
#> SRR1656495 1 0.0000 0.875 1.000 0.000
#> SRR1656496 1 0.0000 0.875 1.000 0.000
#> SRR1656494 1 0.9732 0.475 0.596 0.404
#> SRR1656497 2 0.0000 0.956 0.000 1.000
#> SRR1656499 1 0.0000 0.875 1.000 0.000
#> SRR1656500 1 0.0000 0.875 1.000 0.000
#> SRR1656501 1 0.0000 0.875 1.000 0.000
#> SRR1656498 1 0.0000 0.875 1.000 0.000
#> SRR1656504 2 0.1184 0.949 0.016 0.984
#> SRR1656502 1 0.0000 0.875 1.000 0.000
#> SRR1656503 1 0.0000 0.875 1.000 0.000
#> SRR1656507 1 0.0000 0.875 1.000 0.000
#> SRR1656508 1 0.0000 0.875 1.000 0.000
#> SRR1656505 1 0.9732 0.475 0.596 0.404
#> SRR1656506 1 0.0000 0.875 1.000 0.000
#> SRR1656509 1 0.0000 0.875 1.000 0.000
#> SRR1656510 1 0.6801 0.745 0.820 0.180
#> SRR1656511 1 0.9732 0.475 0.596 0.404
#> SRR1656513 1 0.9732 0.475 0.596 0.404
#> SRR1656512 2 0.0000 0.956 0.000 1.000
#> SRR1656514 1 0.0000 0.875 1.000 0.000
#> SRR1656515 1 0.9732 0.475 0.596 0.404
#> SRR1656516 1 0.0000 0.875 1.000 0.000
#> SRR1656518 1 0.0000 0.875 1.000 0.000
#> SRR1656517 1 0.0000 0.875 1.000 0.000
#> SRR1656519 1 0.0000 0.875 1.000 0.000
#> SRR1656522 1 0.0000 0.875 1.000 0.000
#> SRR1656523 1 0.9732 0.475 0.596 0.404
#> SRR1656521 2 0.0000 0.956 0.000 1.000
#> SRR1656520 1 0.0000 0.875 1.000 0.000
#> SRR1656524 1 0.0000 0.875 1.000 0.000
#> SRR1656525 1 0.0000 0.875 1.000 0.000
#> SRR1656526 2 0.3733 0.887 0.072 0.928
#> SRR1656527 2 0.8016 0.601 0.244 0.756
#> SRR1656530 1 0.0000 0.875 1.000 0.000
#> SRR1656529 1 0.0000 0.875 1.000 0.000
#> SRR1656531 1 0.0000 0.875 1.000 0.000
#> SRR1656528 1 0.0000 0.875 1.000 0.000
#> SRR1656534 1 0.0000 0.875 1.000 0.000
#> SRR1656533 1 0.0000 0.875 1.000 0.000
#> SRR1656536 1 0.5294 0.797 0.880 0.120
#> SRR1656532 1 0.9732 0.475 0.596 0.404
#> SRR1656537 1 0.0000 0.875 1.000 0.000
#> SRR1656538 1 0.0000 0.875 1.000 0.000
#> SRR1656535 2 0.0938 0.952 0.012 0.988
#> SRR1656539 1 0.0000 0.875 1.000 0.000
#> SRR1656544 1 0.0000 0.875 1.000 0.000
#> SRR1656542 1 0.0000 0.875 1.000 0.000
#> SRR1656543 1 0.0000 0.875 1.000 0.000
#> SRR1656545 2 0.0000 0.956 0.000 1.000
#> SRR1656540 1 0.0000 0.875 1.000 0.000
#> SRR1656546 1 0.0000 0.875 1.000 0.000
#> SRR1656541 2 0.9580 0.217 0.380 0.620
#> SRR1656547 1 0.9732 0.475 0.596 0.404
#> SRR1656548 1 0.0000 0.875 1.000 0.000
#> SRR1656549 1 0.0000 0.875 1.000 0.000
#> SRR1656551 1 0.5737 0.785 0.864 0.136
#> SRR1656553 1 0.0000 0.875 1.000 0.000
#> SRR1656550 1 0.9732 0.475 0.596 0.404
#> SRR1656552 1 0.9732 0.475 0.596 0.404
#> SRR1656554 1 0.0000 0.875 1.000 0.000
#> SRR1656555 1 0.9460 0.533 0.636 0.364
#> SRR1656556 1 0.0000 0.875 1.000 0.000
#> SRR1656557 1 0.0000 0.875 1.000 0.000
#> SRR1656558 1 0.0000 0.875 1.000 0.000
#> SRR1656559 1 0.0000 0.875 1.000 0.000
#> SRR1656560 1 0.0000 0.875 1.000 0.000
#> SRR1656561 1 0.0000 0.875 1.000 0.000
#> SRR1656562 1 0.9732 0.475 0.596 0.404
#> SRR1656563 1 0.0000 0.875 1.000 0.000
#> SRR1656564 2 0.0000 0.956 0.000 1.000
#> SRR1656565 1 0.9732 0.475 0.596 0.404
#> SRR1656566 1 0.0000 0.875 1.000 0.000
#> SRR1656568 2 0.0938 0.952 0.012 0.988
#> SRR1656567 1 0.9732 0.475 0.596 0.404
#> SRR1656569 1 0.0000 0.875 1.000 0.000
#> SRR1656570 1 0.0000 0.875 1.000 0.000
#> SRR1656571 2 0.0000 0.956 0.000 1.000
#> SRR1656573 1 0.6801 0.745 0.820 0.180
#> SRR1656572 1 0.9732 0.475 0.596 0.404
#> SRR1656574 1 0.0000 0.875 1.000 0.000
#> SRR1656575 1 0.0000 0.875 1.000 0.000
#> SRR1656576 1 0.9732 0.475 0.596 0.404
#> SRR1656578 1 0.9732 0.475 0.596 0.404
#> SRR1656577 1 0.0000 0.875 1.000 0.000
#> SRR1656579 1 0.9732 0.475 0.596 0.404
#> SRR1656580 1 0.0000 0.875 1.000 0.000
#> SRR1656581 1 0.9460 0.534 0.636 0.364
#> SRR1656582 2 0.0938 0.952 0.012 0.988
#> SRR1656585 1 0.9732 0.475 0.596 0.404
#> SRR1656584 1 0.0000 0.875 1.000 0.000
#> SRR1656583 1 0.6438 0.757 0.836 0.164
#> SRR1656586 2 0.0000 0.956 0.000 1.000
#> SRR1656587 1 0.9732 0.475 0.596 0.404
#> SRR1656588 1 0.9732 0.475 0.596 0.404
#> SRR1656589 2 0.0000 0.956 0.000 1.000
#> SRR1656590 1 0.0000 0.875 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.2200 0.8842 0.056 0.940 0.004
#> SRR1656464 1 0.6299 0.6921 0.524 0.000 0.476
#> SRR1656462 3 0.0424 0.6783 0.008 0.000 0.992
#> SRR1656465 3 0.3686 0.6444 0.140 0.000 0.860
#> SRR1656467 3 0.7878 0.4980 0.392 0.060 0.548
#> SRR1656466 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656468 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656472 1 0.3038 0.4743 0.896 0.000 0.104
#> SRR1656471 3 0.1529 0.6776 0.040 0.000 0.960
#> SRR1656470 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656469 3 0.0237 0.6814 0.004 0.000 0.996
#> SRR1656473 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656478 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656477 3 0.6111 0.5523 0.396 0.000 0.604
#> SRR1656479 3 0.3816 0.4151 0.148 0.000 0.852
#> SRR1656480 3 0.6095 0.5522 0.392 0.000 0.608
#> SRR1656476 2 0.2945 0.8750 0.088 0.908 0.004
#> SRR1656481 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656482 2 0.3644 0.8607 0.124 0.872 0.004
#> SRR1656483 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.6813 0.000 0.000 1.000
#> SRR1656487 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656486 3 0.6225 -0.5689 0.432 0.000 0.568
#> SRR1656488 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656484 1 0.6308 0.6853 0.508 0.000 0.492
#> SRR1656489 1 0.6302 0.6922 0.520 0.000 0.480
#> SRR1656491 3 0.0237 0.6806 0.004 0.000 0.996
#> SRR1656490 3 0.4796 0.1711 0.220 0.000 0.780
#> SRR1656492 3 0.0424 0.6783 0.008 0.000 0.992
#> SRR1656493 1 0.5835 0.6145 0.660 0.000 0.340
#> SRR1656495 1 0.2878 0.4680 0.904 0.000 0.096
#> SRR1656496 3 0.0892 0.6661 0.020 0.000 0.980
#> SRR1656494 1 0.6306 0.2797 0.748 0.052 0.200
#> SRR1656497 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656499 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656500 3 0.0424 0.6783 0.008 0.000 0.992
#> SRR1656501 1 0.6307 0.6870 0.512 0.000 0.488
#> SRR1656498 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656504 2 0.3826 0.8580 0.124 0.868 0.008
#> SRR1656502 1 0.2959 0.4713 0.900 0.000 0.100
#> SRR1656503 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656507 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656508 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656505 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656506 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656509 3 0.1643 0.6755 0.044 0.000 0.956
#> SRR1656510 3 0.4178 0.6281 0.172 0.000 0.828
#> SRR1656511 1 0.4920 0.4433 0.840 0.052 0.108
#> SRR1656513 1 0.8439 -0.2156 0.536 0.368 0.096
#> SRR1656512 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656514 1 0.6299 0.6921 0.524 0.000 0.476
#> SRR1656515 3 0.7724 0.5009 0.396 0.052 0.552
#> SRR1656516 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656518 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656517 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656519 3 0.0424 0.6783 0.008 0.000 0.992
#> SRR1656522 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656523 3 0.6079 0.5571 0.388 0.000 0.612
#> SRR1656521 2 0.0983 0.8913 0.016 0.980 0.004
#> SRR1656520 3 0.0892 0.6775 0.020 0.000 0.980
#> SRR1656524 1 0.3879 0.5042 0.848 0.000 0.152
#> SRR1656525 3 0.0237 0.6815 0.004 0.000 0.996
#> SRR1656526 2 0.6252 0.6709 0.344 0.648 0.008
#> SRR1656527 1 0.4749 0.2861 0.816 0.172 0.012
#> SRR1656530 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656529 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656531 1 0.6299 0.6921 0.524 0.000 0.476
#> SRR1656528 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656534 3 0.1163 0.6559 0.028 0.000 0.972
#> SRR1656533 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656536 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656532 1 0.4689 0.4398 0.852 0.052 0.096
#> SRR1656537 1 0.6299 0.6921 0.524 0.000 0.476
#> SRR1656538 3 0.2625 0.5586 0.084 0.000 0.916
#> SRR1656535 2 0.5982 0.6940 0.328 0.668 0.004
#> SRR1656539 3 0.0000 0.6813 0.000 0.000 1.000
#> SRR1656544 3 0.0237 0.6806 0.004 0.000 0.996
#> SRR1656542 3 0.0237 0.6806 0.004 0.000 0.996
#> SRR1656543 3 0.0237 0.6806 0.004 0.000 0.996
#> SRR1656545 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656540 3 0.0892 0.6775 0.020 0.000 0.980
#> SRR1656546 1 0.6299 0.6901 0.524 0.000 0.476
#> SRR1656541 2 0.8618 0.4796 0.388 0.508 0.104
#> SRR1656547 3 0.6062 0.5589 0.384 0.000 0.616
#> SRR1656548 3 0.0237 0.6806 0.004 0.000 0.996
#> SRR1656549 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656551 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656553 3 0.0592 0.6747 0.012 0.000 0.988
#> SRR1656550 3 0.6095 0.5522 0.392 0.000 0.608
#> SRR1656552 3 0.7129 0.5303 0.392 0.028 0.580
#> SRR1656554 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656555 3 0.6045 0.5609 0.380 0.000 0.620
#> SRR1656556 3 0.6111 0.5523 0.396 0.000 0.604
#> SRR1656557 3 0.0424 0.6783 0.008 0.000 0.992
#> SRR1656558 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656559 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656560 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656561 3 0.1411 0.6443 0.036 0.000 0.964
#> SRR1656562 1 0.6143 0.0809 0.684 0.012 0.304
#> SRR1656563 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656564 2 0.3573 0.8628 0.120 0.876 0.004
#> SRR1656565 1 0.6128 0.3792 0.780 0.084 0.136
#> SRR1656566 1 0.6305 0.6904 0.516 0.000 0.484
#> SRR1656568 2 0.4682 0.8138 0.192 0.804 0.004
#> SRR1656567 3 0.6095 0.5549 0.392 0.000 0.608
#> SRR1656569 3 0.0592 0.6800 0.012 0.000 0.988
#> SRR1656570 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656571 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656573 3 0.3192 0.6532 0.112 0.000 0.888
#> SRR1656572 1 0.4920 0.4433 0.840 0.052 0.108
#> SRR1656574 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656575 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656576 3 0.7699 0.5077 0.388 0.052 0.560
#> SRR1656578 1 0.5253 0.4465 0.828 0.076 0.096
#> SRR1656577 1 0.6307 0.6896 0.512 0.000 0.488
#> SRR1656579 3 0.6045 0.5594 0.380 0.000 0.620
#> SRR1656580 3 0.6062 -0.4574 0.384 0.000 0.616
#> SRR1656581 3 0.6045 0.5609 0.380 0.000 0.620
#> SRR1656582 2 0.5845 0.7136 0.308 0.688 0.004
#> SRR1656585 3 0.6095 0.5557 0.392 0.000 0.608
#> SRR1656584 1 0.6140 0.6877 0.596 0.000 0.404
#> SRR1656583 3 0.6140 0.5486 0.404 0.000 0.596
#> SRR1656586 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656587 1 0.3752 0.4231 0.856 0.000 0.144
#> SRR1656588 3 0.7030 0.5316 0.396 0.024 0.580
#> SRR1656589 2 0.0000 0.8933 0.000 1.000 0.000
#> SRR1656590 1 0.6295 0.6921 0.528 0.000 0.472
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.1389 0.754 0.000 0.952 0.000 0.048
#> SRR1656464 4 0.6299 0.424 0.080 0.000 0.320 0.600
#> SRR1656462 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656465 3 0.1474 0.729 0.000 0.000 0.948 0.052
#> SRR1656467 2 0.4855 0.643 0.000 0.600 0.000 0.400
#> SRR1656466 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656468 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656472 4 0.2814 0.646 0.132 0.000 0.000 0.868
#> SRR1656471 3 0.1938 0.745 0.012 0.000 0.936 0.052
#> SRR1656470 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656469 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656473 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656474 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656475 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656478 1 0.2081 0.844 0.916 0.000 0.084 0.000
#> SRR1656477 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656479 3 0.2281 0.722 0.096 0.000 0.904 0.000
#> SRR1656480 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656476 2 0.2011 0.761 0.000 0.920 0.000 0.080
#> SRR1656481 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656482 2 0.2589 0.766 0.000 0.884 0.000 0.116
#> SRR1656483 2 0.0000 0.738 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656487 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656486 1 0.4454 0.684 0.692 0.000 0.308 0.000
#> SRR1656488 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656484 1 0.2216 0.843 0.908 0.000 0.092 0.000
#> SRR1656489 1 0.3837 0.774 0.776 0.000 0.224 0.000
#> SRR1656491 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656490 3 0.4836 0.475 0.320 0.000 0.672 0.008
#> SRR1656492 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656493 1 0.1488 0.800 0.956 0.000 0.032 0.012
#> SRR1656495 4 0.2149 0.627 0.088 0.000 0.000 0.912
#> SRR1656496 3 0.1557 0.748 0.056 0.000 0.944 0.000
#> SRR1656494 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656497 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656499 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656500 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656501 1 0.4406 0.694 0.700 0.000 0.300 0.000
#> SRR1656498 1 0.1792 0.844 0.932 0.000 0.068 0.000
#> SRR1656504 2 0.2589 0.766 0.000 0.884 0.000 0.116
#> SRR1656502 4 0.2408 0.642 0.104 0.000 0.000 0.896
#> SRR1656503 1 0.4761 0.578 0.628 0.000 0.372 0.000
#> SRR1656507 1 0.3123 0.821 0.844 0.000 0.156 0.000
#> SRR1656508 1 0.1792 0.844 0.932 0.000 0.068 0.000
#> SRR1656505 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656506 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656509 3 0.3217 0.704 0.012 0.000 0.860 0.128
#> SRR1656510 3 0.4903 0.638 0.028 0.000 0.724 0.248
#> SRR1656511 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656513 2 0.4888 0.637 0.000 0.588 0.000 0.412
#> SRR1656512 2 0.0000 0.738 0.000 1.000 0.000 0.000
#> SRR1656514 4 0.6794 0.376 0.136 0.000 0.280 0.584
#> SRR1656515 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656516 1 0.4431 0.689 0.696 0.000 0.304 0.000
#> SRR1656518 1 0.1792 0.844 0.932 0.000 0.068 0.000
#> SRR1656517 1 0.1792 0.844 0.932 0.000 0.068 0.000
#> SRR1656519 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656522 1 0.4543 0.661 0.676 0.000 0.324 0.000
#> SRR1656523 3 0.5203 0.532 0.000 0.008 0.576 0.416
#> SRR1656521 2 0.0469 0.743 0.000 0.988 0.000 0.012
#> SRR1656520 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656524 1 0.3545 0.559 0.828 0.000 0.008 0.164
#> SRR1656525 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656526 2 0.3801 0.739 0.000 0.780 0.000 0.220
#> SRR1656527 2 0.4155 0.729 0.004 0.756 0.000 0.240
#> SRR1656530 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656529 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656531 1 0.3164 0.817 0.884 0.000 0.064 0.052
#> SRR1656528 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656534 3 0.1716 0.743 0.064 0.000 0.936 0.000
#> SRR1656533 1 0.1792 0.844 0.932 0.000 0.068 0.000
#> SRR1656536 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656532 2 0.5060 0.634 0.004 0.584 0.000 0.412
#> SRR1656537 1 0.2399 0.784 0.920 0.000 0.032 0.048
#> SRR1656538 3 0.2081 0.722 0.084 0.000 0.916 0.000
#> SRR1656535 2 0.2760 0.764 0.000 0.872 0.000 0.128
#> SRR1656539 3 0.0336 0.750 0.008 0.000 0.992 0.000
#> SRR1656544 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656542 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656543 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656545 2 0.0000 0.738 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656546 1 0.2300 0.831 0.920 0.000 0.064 0.016
#> SRR1656541 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656547 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656548 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656549 1 0.1474 0.833 0.948 0.000 0.052 0.000
#> SRR1656551 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656553 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656550 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656552 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656554 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656555 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656556 3 0.4888 0.546 0.000 0.000 0.588 0.412
#> SRR1656557 3 0.1211 0.756 0.040 0.000 0.960 0.000
#> SRR1656558 1 0.1716 0.841 0.936 0.000 0.064 0.000
#> SRR1656559 1 0.4356 0.704 0.708 0.000 0.292 0.000
#> SRR1656560 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656561 3 0.2345 0.711 0.100 0.000 0.900 0.000
#> SRR1656562 2 0.7343 0.330 0.000 0.428 0.156 0.416
#> SRR1656563 1 0.3219 0.815 0.836 0.000 0.164 0.000
#> SRR1656564 2 0.2589 0.766 0.000 0.884 0.000 0.116
#> SRR1656565 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656566 1 0.1022 0.812 0.968 0.000 0.032 0.000
#> SRR1656568 2 0.2589 0.766 0.000 0.884 0.000 0.116
#> SRR1656567 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656569 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656570 1 0.2921 0.828 0.860 0.000 0.140 0.000
#> SRR1656571 2 0.0000 0.738 0.000 1.000 0.000 0.000
#> SRR1656573 3 0.4730 0.572 0.000 0.000 0.636 0.364
#> SRR1656572 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656574 1 0.3764 0.780 0.784 0.000 0.216 0.000
#> SRR1656575 1 0.2081 0.844 0.916 0.000 0.084 0.000
#> SRR1656576 2 0.4898 0.634 0.000 0.584 0.000 0.416
#> SRR1656578 2 0.4053 0.734 0.004 0.768 0.000 0.228
#> SRR1656577 1 0.3837 0.774 0.776 0.000 0.224 0.000
#> SRR1656579 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656580 3 0.2530 0.693 0.112 0.000 0.888 0.000
#> SRR1656581 3 0.4866 0.549 0.000 0.000 0.596 0.404
#> SRR1656582 2 0.2760 0.764 0.000 0.872 0.000 0.128
#> SRR1656585 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656584 1 0.1637 0.839 0.940 0.000 0.060 0.000
#> SRR1656583 3 0.4898 0.543 0.000 0.000 0.584 0.416
#> SRR1656586 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656587 3 0.5526 0.513 0.000 0.020 0.564 0.416
#> SRR1656588 3 0.5708 0.500 0.000 0.028 0.556 0.416
#> SRR1656589 2 0.0188 0.738 0.004 0.996 0.000 0.000
#> SRR1656590 1 0.3377 0.660 0.848 0.000 0.012 0.140
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0609 0.862 0.000 0.980 0.000 0.020 0.000
#> SRR1656464 5 0.7607 0.170 0.276 0.000 0.280 0.048 0.396
#> SRR1656462 3 0.2331 0.876 0.020 0.000 0.900 0.080 0.000
#> SRR1656465 3 0.2153 0.878 0.000 0.000 0.916 0.040 0.044
#> SRR1656467 4 0.2708 0.807 0.000 0.072 0.044 0.884 0.000
#> SRR1656466 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656468 4 0.2424 0.870 0.000 0.000 0.132 0.868 0.000
#> SRR1656472 5 0.3771 0.794 0.040 0.000 0.000 0.164 0.796
#> SRR1656471 3 0.2103 0.895 0.020 0.000 0.920 0.056 0.004
#> SRR1656470 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656469 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656473 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656474 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656475 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656478 1 0.0510 0.840 0.984 0.000 0.016 0.000 0.000
#> SRR1656477 4 0.2280 0.872 0.000 0.000 0.120 0.880 0.000
#> SRR1656479 3 0.3123 0.772 0.184 0.000 0.812 0.004 0.000
#> SRR1656480 4 0.2329 0.872 0.000 0.000 0.124 0.876 0.000
#> SRR1656476 2 0.2230 0.850 0.000 0.912 0.000 0.044 0.044
#> SRR1656481 4 0.2424 0.870 0.000 0.000 0.132 0.868 0.000
#> SRR1656482 2 0.1410 0.857 0.000 0.940 0.000 0.060 0.000
#> SRR1656483 2 0.0510 0.862 0.000 0.984 0.000 0.016 0.000
#> SRR1656485 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656487 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656486 1 0.2280 0.780 0.880 0.000 0.120 0.000 0.000
#> SRR1656488 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656484 1 0.2127 0.795 0.892 0.000 0.108 0.000 0.000
#> SRR1656489 1 0.1121 0.837 0.956 0.000 0.044 0.000 0.000
#> SRR1656491 3 0.1377 0.921 0.020 0.000 0.956 0.004 0.020
#> SRR1656490 1 0.4724 0.573 0.732 0.000 0.164 0.104 0.000
#> SRR1656492 3 0.1041 0.921 0.032 0.000 0.964 0.004 0.000
#> SRR1656493 1 0.3849 0.710 0.808 0.000 0.000 0.112 0.080
#> SRR1656495 5 0.3771 0.792 0.040 0.000 0.000 0.164 0.796
#> SRR1656496 3 0.1952 0.894 0.084 0.000 0.912 0.004 0.000
#> SRR1656494 4 0.2069 0.765 0.000 0.012 0.000 0.912 0.076
#> SRR1656497 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656499 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656500 3 0.0609 0.922 0.020 0.000 0.980 0.000 0.000
#> SRR1656501 1 0.1732 0.813 0.920 0.000 0.080 0.000 0.000
#> SRR1656498 1 0.3113 0.789 0.868 0.000 0.008 0.044 0.080
#> SRR1656504 2 0.2580 0.843 0.000 0.892 0.000 0.064 0.044
#> SRR1656502 5 0.3771 0.794 0.040 0.000 0.000 0.164 0.796
#> SRR1656503 1 0.4138 0.362 0.616 0.000 0.384 0.000 0.000
#> SRR1656507 1 0.0703 0.841 0.976 0.000 0.024 0.000 0.000
#> SRR1656508 1 0.2124 0.831 0.924 0.000 0.020 0.044 0.012
#> SRR1656505 4 0.2424 0.870 0.000 0.000 0.132 0.868 0.000
#> SRR1656506 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656509 3 0.3013 0.771 0.000 0.000 0.832 0.160 0.008
#> SRR1656510 4 0.3861 0.654 0.000 0.000 0.284 0.712 0.004
#> SRR1656511 4 0.2746 0.742 0.000 0.112 0.008 0.872 0.008
#> SRR1656513 2 0.4341 0.485 0.000 0.628 0.000 0.364 0.008
#> SRR1656512 2 0.2074 0.856 0.000 0.896 0.000 0.000 0.104
#> SRR1656514 3 0.5356 0.644 0.136 0.000 0.728 0.048 0.088
#> SRR1656515 4 0.2305 0.863 0.000 0.012 0.092 0.896 0.000
#> SRR1656516 1 0.2471 0.761 0.864 0.000 0.136 0.000 0.000
#> SRR1656518 1 0.0404 0.839 0.988 0.000 0.012 0.000 0.000
#> SRR1656517 1 0.0609 0.841 0.980 0.000 0.020 0.000 0.000
#> SRR1656519 3 0.2012 0.893 0.020 0.000 0.920 0.060 0.000
#> SRR1656522 3 0.6221 0.335 0.276 0.000 0.600 0.044 0.080
#> SRR1656523 4 0.2445 0.868 0.000 0.004 0.108 0.884 0.004
#> SRR1656521 2 0.1485 0.864 0.000 0.948 0.000 0.020 0.032
#> SRR1656520 3 0.1808 0.899 0.020 0.000 0.936 0.040 0.004
#> SRR1656524 1 0.6036 0.253 0.564 0.000 0.000 0.160 0.276
#> SRR1656525 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656526 2 0.4264 0.687 0.000 0.744 0.000 0.212 0.044
#> SRR1656527 2 0.3789 0.706 0.000 0.768 0.000 0.212 0.020
#> SRR1656530 3 0.1443 0.911 0.004 0.000 0.948 0.004 0.044
#> SRR1656529 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656531 1 0.3301 0.783 0.856 0.000 0.008 0.048 0.088
#> SRR1656528 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656534 3 0.2927 0.862 0.068 0.000 0.872 0.060 0.000
#> SRR1656533 1 0.0609 0.841 0.980 0.000 0.020 0.000 0.000
#> SRR1656536 4 0.2424 0.870 0.000 0.000 0.132 0.868 0.000
#> SRR1656532 4 0.6981 -0.183 0.008 0.312 0.000 0.404 0.276
#> SRR1656537 1 0.3075 0.774 0.860 0.000 0.000 0.048 0.092
#> SRR1656538 3 0.1544 0.901 0.068 0.000 0.932 0.000 0.000
#> SRR1656535 2 0.2645 0.840 0.000 0.888 0.000 0.068 0.044
#> SRR1656539 3 0.1569 0.911 0.004 0.000 0.944 0.008 0.044
#> SRR1656544 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656542 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656543 3 0.2012 0.893 0.020 0.000 0.920 0.060 0.000
#> SRR1656545 2 0.2074 0.856 0.000 0.896 0.000 0.000 0.104
#> SRR1656540 3 0.2302 0.882 0.020 0.000 0.916 0.048 0.016
#> SRR1656546 1 0.1341 0.797 0.944 0.000 0.000 0.056 0.000
#> SRR1656541 4 0.4987 0.353 0.000 0.340 0.000 0.616 0.044
#> SRR1656547 4 0.2179 0.869 0.000 0.000 0.112 0.888 0.000
#> SRR1656548 3 0.0771 0.923 0.020 0.000 0.976 0.004 0.000
#> SRR1656549 1 0.0000 0.831 1.000 0.000 0.000 0.000 0.000
#> SRR1656551 4 0.2920 0.860 0.000 0.000 0.132 0.852 0.016
#> SRR1656553 3 0.0898 0.920 0.020 0.000 0.972 0.008 0.000
#> SRR1656550 4 0.2329 0.872 0.000 0.000 0.124 0.876 0.000
#> SRR1656552 4 0.2804 0.861 0.000 0.012 0.092 0.880 0.016
#> SRR1656554 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656555 4 0.2707 0.866 0.000 0.000 0.132 0.860 0.008
#> SRR1656556 4 0.4060 0.451 0.000 0.000 0.360 0.640 0.000
#> SRR1656557 3 0.2079 0.891 0.020 0.000 0.916 0.064 0.000
#> SRR1656558 1 0.0290 0.837 0.992 0.000 0.008 0.000 0.000
#> SRR1656559 1 0.6374 0.352 0.564 0.000 0.312 0.044 0.080
#> SRR1656560 3 0.1443 0.911 0.004 0.000 0.948 0.004 0.044
#> SRR1656561 3 0.1704 0.901 0.068 0.000 0.928 0.004 0.000
#> SRR1656562 4 0.1877 0.844 0.000 0.012 0.064 0.924 0.000
#> SRR1656563 1 0.1043 0.838 0.960 0.000 0.040 0.000 0.000
#> SRR1656564 2 0.1341 0.858 0.000 0.944 0.000 0.056 0.000
#> SRR1656565 4 0.1764 0.788 0.000 0.064 0.008 0.928 0.000
#> SRR1656566 1 0.2595 0.787 0.888 0.000 0.000 0.032 0.080
#> SRR1656568 2 0.2304 0.828 0.000 0.892 0.000 0.100 0.008
#> SRR1656567 4 0.2329 0.872 0.000 0.000 0.124 0.876 0.000
#> SRR1656569 3 0.1282 0.908 0.000 0.000 0.952 0.004 0.044
#> SRR1656570 1 0.0963 0.839 0.964 0.000 0.036 0.000 0.000
#> SRR1656571 2 0.0000 0.862 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 4 0.2732 0.845 0.000 0.000 0.160 0.840 0.000
#> SRR1656572 4 0.2522 0.736 0.000 0.108 0.000 0.880 0.012
#> SRR1656574 1 0.2230 0.831 0.912 0.000 0.044 0.044 0.000
#> SRR1656575 1 0.0609 0.841 0.980 0.000 0.020 0.000 0.000
#> SRR1656576 4 0.2772 0.832 0.000 0.012 0.052 0.892 0.044
#> SRR1656578 2 0.5618 0.518 0.000 0.632 0.000 0.224 0.144
#> SRR1656577 1 0.4041 0.742 0.804 0.000 0.136 0.044 0.016
#> SRR1656579 4 0.2424 0.870 0.000 0.000 0.132 0.868 0.000
#> SRR1656580 3 0.1410 0.906 0.060 0.000 0.940 0.000 0.000
#> SRR1656581 4 0.2583 0.869 0.000 0.000 0.132 0.864 0.004
#> SRR1656582 2 0.2770 0.836 0.000 0.880 0.000 0.076 0.044
#> SRR1656585 4 0.2230 0.871 0.000 0.000 0.116 0.884 0.000
#> SRR1656584 1 0.0000 0.831 1.000 0.000 0.000 0.000 0.000
#> SRR1656583 4 0.2124 0.831 0.000 0.000 0.056 0.916 0.028
#> SRR1656586 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656587 4 0.2017 0.766 0.000 0.008 0.000 0.912 0.080
#> SRR1656588 4 0.2230 0.871 0.000 0.000 0.116 0.884 0.000
#> SRR1656589 2 0.2127 0.856 0.000 0.892 0.000 0.000 0.108
#> SRR1656590 1 0.4964 0.571 0.700 0.000 0.000 0.096 0.204
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0146 0.8621 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656464 3 0.3087 0.6716 0.160 0.000 0.820 0.004 0.012 0.004
#> SRR1656462 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.2221 0.7729 0.000 0.000 0.072 0.032 0.896 0.000
#> SRR1656467 4 0.1865 0.8577 0.000 0.040 0.000 0.920 0.040 0.000
#> SRR1656466 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656468 4 0.3215 0.6573 0.000 0.000 0.004 0.756 0.240 0.000
#> SRR1656472 6 0.3035 0.9327 0.008 0.000 0.000 0.148 0.016 0.828
#> SRR1656471 5 0.4222 0.7324 0.000 0.000 0.184 0.088 0.728 0.000
#> SRR1656470 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656469 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656473 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656474 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656475 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656478 1 0.0547 0.8955 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1656477 4 0.0891 0.8984 0.000 0.008 0.000 0.968 0.024 0.000
#> SRR1656479 1 0.4729 0.5509 0.676 0.000 0.128 0.000 0.196 0.000
#> SRR1656480 4 0.0891 0.8984 0.000 0.008 0.000 0.968 0.024 0.000
#> SRR1656476 2 0.0146 0.8621 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656481 5 0.3966 0.2509 0.000 0.000 0.004 0.444 0.552 0.000
#> SRR1656482 2 0.1257 0.8459 0.000 0.952 0.000 0.020 0.028 0.000
#> SRR1656483 2 0.0000 0.8622 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.2793 0.7290 0.000 0.000 0.800 0.000 0.200 0.000
#> SRR1656487 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656486 1 0.1644 0.8601 0.920 0.000 0.076 0.000 0.004 0.000
#> SRR1656488 3 0.2883 0.7209 0.000 0.000 0.788 0.000 0.212 0.000
#> SRR1656484 1 0.0458 0.8962 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1656489 1 0.2219 0.7994 0.864 0.000 0.136 0.000 0.000 0.000
#> SRR1656491 5 0.4557 0.7311 0.020 0.000 0.180 0.076 0.724 0.000
#> SRR1656490 1 0.2088 0.8559 0.920 0.000 0.024 0.036 0.016 0.004
#> SRR1656492 3 0.4407 0.0806 0.024 0.000 0.492 0.000 0.484 0.000
#> SRR1656493 1 0.2972 0.6949 0.836 0.000 0.000 0.128 0.000 0.036
#> SRR1656495 6 0.2944 0.9293 0.008 0.000 0.000 0.148 0.012 0.832
#> SRR1656496 1 0.6065 -0.0101 0.404 0.000 0.280 0.000 0.316 0.000
#> SRR1656494 4 0.0935 0.8865 0.000 0.000 0.000 0.964 0.032 0.004
#> SRR1656497 2 0.2988 0.8433 0.000 0.824 0.000 0.000 0.024 0.152
#> SRR1656499 3 0.0146 0.8317 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656500 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656501 1 0.1219 0.8808 0.948 0.000 0.048 0.000 0.004 0.000
#> SRR1656498 1 0.0260 0.8960 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656504 2 0.0146 0.8621 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656502 6 0.3035 0.9327 0.008 0.000 0.000 0.148 0.016 0.828
#> SRR1656503 1 0.3023 0.6870 0.768 0.000 0.232 0.000 0.000 0.000
#> SRR1656507 1 0.1007 0.8842 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1656508 1 0.0260 0.8960 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656505 4 0.0790 0.8958 0.000 0.000 0.000 0.968 0.032 0.000
#> SRR1656506 3 0.3857 0.1936 0.000 0.000 0.532 0.000 0.468 0.000
#> SRR1656509 5 0.4333 0.6863 0.008 0.000 0.064 0.188 0.736 0.004
#> SRR1656510 4 0.3990 0.5518 0.000 0.000 0.028 0.688 0.284 0.000
#> SRR1656511 4 0.1367 0.8744 0.000 0.012 0.000 0.944 0.044 0.000
#> SRR1656513 4 0.2003 0.8427 0.000 0.044 0.000 0.912 0.044 0.000
#> SRR1656512 2 0.2950 0.8440 0.000 0.828 0.000 0.000 0.024 0.148
#> SRR1656514 3 0.1194 0.8086 0.032 0.000 0.956 0.000 0.008 0.004
#> SRR1656515 4 0.1124 0.8872 0.000 0.008 0.000 0.956 0.036 0.000
#> SRR1656516 1 0.2632 0.7671 0.832 0.000 0.164 0.000 0.004 0.000
#> SRR1656518 1 0.0260 0.8945 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656517 1 0.0260 0.8960 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656519 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656522 3 0.0858 0.8168 0.028 0.000 0.968 0.000 0.000 0.004
#> SRR1656523 4 0.2300 0.7863 0.000 0.000 0.000 0.856 0.144 0.000
#> SRR1656521 2 0.0146 0.8621 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656520 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 6 0.4847 0.8160 0.156 0.000 0.000 0.148 0.008 0.688
#> SRR1656525 3 0.2823 0.7267 0.000 0.000 0.796 0.000 0.204 0.000
#> SRR1656526 2 0.3874 0.5918 0.000 0.732 0.000 0.228 0.040 0.000
#> SRR1656527 2 0.4025 0.5897 0.000 0.720 0.000 0.232 0.048 0.000
#> SRR1656530 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656529 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656531 1 0.0520 0.8952 0.984 0.000 0.008 0.000 0.000 0.008
#> SRR1656528 3 0.3717 0.4316 0.000 0.000 0.616 0.000 0.384 0.000
#> SRR1656534 3 0.0260 0.8292 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656533 1 0.0260 0.8960 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656536 5 0.3290 0.6268 0.000 0.000 0.004 0.252 0.744 0.000
#> SRR1656532 4 0.2358 0.8430 0.000 0.012 0.000 0.900 0.048 0.040
#> SRR1656537 1 0.0865 0.8800 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1656538 3 0.0291 0.8316 0.004 0.000 0.992 0.000 0.004 0.000
#> SRR1656535 2 0.1564 0.8371 0.000 0.936 0.000 0.024 0.040 0.000
#> SRR1656539 5 0.2672 0.7738 0.000 0.000 0.080 0.052 0.868 0.000
#> SRR1656544 3 0.2823 0.7267 0.000 0.000 0.796 0.000 0.204 0.000
#> SRR1656542 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656543 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 2 0.2950 0.8440 0.000 0.828 0.000 0.000 0.024 0.148
#> SRR1656540 3 0.0146 0.8308 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656546 1 0.1434 0.8738 0.948 0.000 0.008 0.020 0.000 0.024
#> SRR1656541 4 0.2164 0.8337 0.000 0.068 0.000 0.900 0.032 0.000
#> SRR1656547 4 0.0806 0.8985 0.000 0.008 0.000 0.972 0.020 0.000
#> SRR1656548 3 0.2883 0.7190 0.000 0.000 0.788 0.000 0.212 0.000
#> SRR1656549 1 0.0260 0.8945 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656551 5 0.3360 0.6113 0.000 0.000 0.004 0.264 0.732 0.000
#> SRR1656553 3 0.3528 0.5969 0.004 0.000 0.700 0.000 0.296 0.000
#> SRR1656550 4 0.0891 0.8984 0.000 0.008 0.000 0.968 0.024 0.000
#> SRR1656552 4 0.0547 0.8982 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1656554 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656555 4 0.3136 0.6741 0.000 0.000 0.004 0.768 0.228 0.000
#> SRR1656556 5 0.4119 0.5222 0.000 0.000 0.016 0.336 0.644 0.004
#> SRR1656557 3 0.0000 0.8323 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.0000 0.8922 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656559 3 0.1082 0.8073 0.040 0.000 0.956 0.000 0.000 0.004
#> SRR1656560 5 0.2300 0.7651 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656561 3 0.3320 0.7137 0.016 0.000 0.772 0.000 0.212 0.000
#> SRR1656562 4 0.0547 0.8979 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1656563 1 0.0363 0.8965 0.988 0.000 0.012 0.000 0.000 0.000
#> SRR1656564 2 0.0820 0.8560 0.000 0.972 0.000 0.012 0.016 0.000
#> SRR1656565 4 0.1007 0.8806 0.000 0.000 0.000 0.956 0.044 0.000
#> SRR1656566 1 0.0865 0.8788 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1656568 2 0.0363 0.8599 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1656567 4 0.0891 0.8984 0.000 0.008 0.000 0.968 0.024 0.000
#> SRR1656569 5 0.2003 0.7874 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1656570 1 0.0458 0.8962 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1656571 2 0.0000 0.8622 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.4062 0.2587 0.000 0.000 0.008 0.440 0.552 0.000
#> SRR1656572 4 0.0405 0.8951 0.000 0.008 0.000 0.988 0.004 0.000
#> SRR1656574 3 0.3563 0.4181 0.336 0.000 0.664 0.000 0.000 0.000
#> SRR1656575 1 0.0458 0.8962 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1656576 4 0.1049 0.8866 0.000 0.008 0.000 0.960 0.032 0.000
#> SRR1656578 2 0.4987 0.3579 0.000 0.584 0.000 0.352 0.048 0.016
#> SRR1656577 3 0.1387 0.7840 0.068 0.000 0.932 0.000 0.000 0.000
#> SRR1656579 4 0.0891 0.8984 0.000 0.008 0.000 0.968 0.024 0.000
#> SRR1656580 3 0.0146 0.8311 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1656581 4 0.3360 0.6223 0.000 0.000 0.004 0.732 0.264 0.000
#> SRR1656582 2 0.1418 0.8414 0.000 0.944 0.000 0.024 0.032 0.000
#> SRR1656585 4 0.0692 0.8972 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1656584 1 0.0000 0.8922 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656583 4 0.0777 0.8969 0.000 0.000 0.000 0.972 0.024 0.004
#> SRR1656586 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656587 4 0.0692 0.8972 0.000 0.000 0.000 0.976 0.020 0.004
#> SRR1656588 4 0.1124 0.8872 0.000 0.008 0.000 0.956 0.036 0.000
#> SRR1656589 2 0.3025 0.8424 0.000 0.820 0.000 0.000 0.024 0.156
#> SRR1656590 1 0.2948 0.7229 0.804 0.000 0.000 0.008 0.000 0.188
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 13572 rows and 129 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 0.715 0.884 0.945 0.4886 0.512 0.512
#> 3 3 0.766 0.847 0.927 0.2763 0.789 0.617
#> 4 4 0.634 0.685 0.848 0.1578 0.790 0.511
#> 5 5 0.616 0.678 0.813 0.0817 0.849 0.526
#> 6 6 0.640 0.642 0.784 0.0445 0.929 0.695
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
#> SRR1656463 1 0.0000 0.9410 1.000 0.000
#> SRR1656464 2 0.0376 0.9340 0.004 0.996
#> SRR1656462 1 0.0000 0.9410 1.000 0.000
#> SRR1656465 1 0.0000 0.9410 1.000 0.000
#> SRR1656467 1 0.0000 0.9410 1.000 0.000
#> SRR1656466 1 0.0000 0.9410 1.000 0.000
#> SRR1656468 1 0.0000 0.9410 1.000 0.000
#> SRR1656472 2 0.4022 0.8839 0.080 0.920
#> SRR1656471 1 0.0000 0.9410 1.000 0.000
#> SRR1656470 1 0.0000 0.9410 1.000 0.000
#> SRR1656469 1 0.0000 0.9410 1.000 0.000
#> SRR1656473 2 0.7219 0.7674 0.200 0.800
#> SRR1656474 2 0.6247 0.8171 0.156 0.844
#> SRR1656475 2 0.9710 0.4188 0.400 0.600
#> SRR1656478 2 0.0000 0.9357 0.000 1.000
#> SRR1656477 1 0.0000 0.9410 1.000 0.000
#> SRR1656479 1 0.8813 0.6280 0.700 0.300
#> SRR1656480 1 0.0000 0.9410 1.000 0.000
#> SRR1656476 1 0.0000 0.9410 1.000 0.000
#> SRR1656481 1 0.0000 0.9410 1.000 0.000
#> SRR1656482 1 0.0000 0.9410 1.000 0.000
#> SRR1656483 1 0.0000 0.9410 1.000 0.000
#> SRR1656485 1 0.0000 0.9410 1.000 0.000
#> SRR1656487 1 0.0000 0.9410 1.000 0.000
#> SRR1656486 1 0.8608 0.6564 0.716 0.284
#> SRR1656488 1 0.0000 0.9410 1.000 0.000
#> SRR1656484 2 0.8267 0.6324 0.260 0.740
#> SRR1656489 2 0.0376 0.9341 0.004 0.996
#> SRR1656491 1 0.0376 0.9393 0.996 0.004
#> SRR1656490 1 0.9732 0.3963 0.596 0.404
#> SRR1656492 1 0.7139 0.7830 0.804 0.196
#> SRR1656493 2 0.0000 0.9357 0.000 1.000
#> SRR1656495 2 0.0000 0.9357 0.000 1.000
#> SRR1656496 1 0.7139 0.7834 0.804 0.196
#> SRR1656494 1 0.4161 0.8722 0.916 0.084
#> SRR1656497 1 0.0000 0.9410 1.000 0.000
#> SRR1656499 1 0.0000 0.9410 1.000 0.000
#> SRR1656500 1 0.2043 0.9237 0.968 0.032
#> SRR1656501 1 1.0000 0.0936 0.504 0.496
#> SRR1656498 2 0.0000 0.9357 0.000 1.000
#> SRR1656504 1 0.7376 0.7686 0.792 0.208
#> SRR1656502 2 0.0000 0.9357 0.000 1.000
#> SRR1656503 2 0.7299 0.7273 0.204 0.796
#> SRR1656507 2 0.0000 0.9357 0.000 1.000
#> SRR1656508 2 0.0000 0.9357 0.000 1.000
#> SRR1656505 1 0.0000 0.9410 1.000 0.000
#> SRR1656506 1 0.0376 0.9393 0.996 0.004
#> SRR1656509 1 0.0000 0.9410 1.000 0.000
#> SRR1656510 1 0.2603 0.9163 0.956 0.044
#> SRR1656511 2 0.0000 0.9357 0.000 1.000
#> SRR1656513 2 0.6438 0.8086 0.164 0.836
#> SRR1656512 2 0.0000 0.9357 0.000 1.000
#> SRR1656514 2 0.2778 0.9046 0.048 0.952
#> SRR1656515 1 0.0000 0.9410 1.000 0.000
#> SRR1656516 2 0.9209 0.4660 0.336 0.664
#> SRR1656518 2 0.0000 0.9357 0.000 1.000
#> SRR1656517 2 0.0000 0.9357 0.000 1.000
#> SRR1656519 1 0.0938 0.9352 0.988 0.012
#> SRR1656522 2 0.2423 0.9109 0.040 0.960
#> SRR1656523 1 0.7299 0.7736 0.796 0.204
#> SRR1656521 2 0.0000 0.9357 0.000 1.000
#> SRR1656520 1 0.0000 0.9410 1.000 0.000
#> SRR1656524 2 0.0000 0.9357 0.000 1.000
#> SRR1656525 1 0.0376 0.9393 0.996 0.004
#> SRR1656526 1 0.0000 0.9410 1.000 0.000
#> SRR1656527 2 0.0000 0.9357 0.000 1.000
#> SRR1656530 1 0.0000 0.9410 1.000 0.000
#> SRR1656529 1 0.0000 0.9410 1.000 0.000
#> SRR1656531 2 0.0000 0.9357 0.000 1.000
#> SRR1656528 1 0.0000 0.9410 1.000 0.000
#> SRR1656534 1 0.7139 0.7830 0.804 0.196
#> SRR1656533 2 0.0000 0.9357 0.000 1.000
#> SRR1656536 1 0.0000 0.9410 1.000 0.000
#> SRR1656532 2 0.0000 0.9357 0.000 1.000
#> SRR1656537 2 0.0000 0.9357 0.000 1.000
#> SRR1656538 1 0.7219 0.7784 0.800 0.200
#> SRR1656535 2 0.0000 0.9357 0.000 1.000
#> SRR1656539 1 0.0000 0.9410 1.000 0.000
#> SRR1656544 1 0.0376 0.9393 0.996 0.004
#> SRR1656542 1 0.4298 0.8837 0.912 0.088
#> SRR1656543 1 0.0000 0.9410 1.000 0.000
#> SRR1656545 2 0.6247 0.8173 0.156 0.844
#> SRR1656540 1 0.0000 0.9410 1.000 0.000
#> SRR1656546 2 0.0000 0.9357 0.000 1.000
#> SRR1656541 1 0.0000 0.9410 1.000 0.000
#> SRR1656547 1 0.0000 0.9410 1.000 0.000
#> SRR1656548 1 0.2603 0.9162 0.956 0.044
#> SRR1656549 2 0.0000 0.9357 0.000 1.000
#> SRR1656551 1 0.0000 0.9410 1.000 0.000
#> SRR1656553 1 0.0376 0.9393 0.996 0.004
#> SRR1656550 1 0.0000 0.9410 1.000 0.000
#> SRR1656552 1 0.6712 0.8042 0.824 0.176
#> SRR1656554 1 0.0000 0.9410 1.000 0.000
#> SRR1656555 1 0.0000 0.9410 1.000 0.000
#> SRR1656556 1 0.0000 0.9410 1.000 0.000
#> SRR1656557 1 0.0000 0.9410 1.000 0.000
#> SRR1656558 2 0.0000 0.9357 0.000 1.000
#> SRR1656559 2 0.0672 0.9318 0.008 0.992
#> SRR1656560 1 0.0000 0.9410 1.000 0.000
#> SRR1656561 1 0.7219 0.7784 0.800 0.200
#> SRR1656562 1 0.4431 0.8644 0.908 0.092
#> SRR1656563 2 0.0376 0.9341 0.004 0.996
#> SRR1656564 2 0.0000 0.9357 0.000 1.000
#> SRR1656565 2 0.8327 0.6246 0.264 0.736
#> SRR1656566 2 0.0000 0.9357 0.000 1.000
#> SRR1656568 2 0.0000 0.9357 0.000 1.000
#> SRR1656567 1 0.0000 0.9410 1.000 0.000
#> SRR1656569 1 0.0000 0.9410 1.000 0.000
#> SRR1656570 2 0.0376 0.9341 0.004 0.996
#> SRR1656571 2 0.3114 0.9019 0.056 0.944
#> SRR1656573 1 0.2948 0.9108 0.948 0.052
#> SRR1656572 2 0.0000 0.9357 0.000 1.000
#> SRR1656574 2 0.0376 0.9341 0.004 0.996
#> SRR1656575 2 0.0000 0.9357 0.000 1.000
#> SRR1656576 1 0.0000 0.9410 1.000 0.000
#> SRR1656578 2 0.0000 0.9357 0.000 1.000
#> SRR1656577 2 0.0000 0.9357 0.000 1.000
#> SRR1656579 1 0.0000 0.9410 1.000 0.000
#> SRR1656580 1 0.7139 0.7834 0.804 0.196
#> SRR1656581 1 0.7219 0.7784 0.800 0.200
#> SRR1656582 1 0.6048 0.8316 0.852 0.148
#> SRR1656585 1 0.0000 0.9410 1.000 0.000
#> SRR1656584 2 0.0000 0.9357 0.000 1.000
#> SRR1656583 1 0.0000 0.9410 1.000 0.000
#> SRR1656586 2 0.7950 0.7191 0.240 0.760
#> SRR1656587 2 0.7453 0.7632 0.212 0.788
#> SRR1656588 1 0.0000 0.9410 1.000 0.000
#> SRR1656589 2 0.7528 0.7496 0.216 0.784
#> SRR1656590 2 0.0000 0.9357 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 3 0.4555 0.72828 0.000 0.200 0.800
#> SRR1656464 1 0.3816 0.79139 0.852 0.148 0.000
#> SRR1656462 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656465 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656467 2 0.5016 0.66733 0.000 0.760 0.240
#> SRR1656466 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656468 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656472 2 0.0892 0.90512 0.020 0.980 0.000
#> SRR1656471 3 0.0892 0.91163 0.000 0.020 0.980
#> SRR1656470 2 0.0892 0.90399 0.000 0.980 0.020
#> SRR1656469 3 0.0892 0.91814 0.020 0.000 0.980
#> SRR1656473 2 0.0237 0.90719 0.004 0.996 0.000
#> SRR1656474 2 0.0424 0.90715 0.008 0.992 0.000
#> SRR1656475 2 0.0237 0.90709 0.000 0.996 0.004
#> SRR1656478 1 0.0892 0.91502 0.980 0.000 0.020
#> SRR1656477 3 0.2537 0.86947 0.000 0.080 0.920
#> SRR1656479 1 0.6280 0.11604 0.540 0.000 0.460
#> SRR1656480 3 0.1289 0.90461 0.000 0.032 0.968
#> SRR1656476 3 0.0592 0.92019 0.012 0.000 0.988
#> SRR1656481 3 0.0592 0.91562 0.000 0.012 0.988
#> SRR1656482 2 0.1031 0.90289 0.000 0.976 0.024
#> SRR1656483 2 0.1529 0.89393 0.000 0.960 0.040
#> SRR1656485 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656487 3 0.0237 0.91894 0.000 0.004 0.996
#> SRR1656486 1 0.4346 0.76509 0.816 0.000 0.184
#> SRR1656488 3 0.0747 0.91938 0.016 0.000 0.984
#> SRR1656484 1 0.2066 0.89366 0.940 0.000 0.060
#> SRR1656489 1 0.1529 0.90842 0.960 0.000 0.040
#> SRR1656491 3 0.0892 0.91814 0.020 0.000 0.980
#> SRR1656490 1 0.4887 0.70230 0.772 0.000 0.228
#> SRR1656492 3 0.3686 0.82951 0.140 0.000 0.860
#> SRR1656493 1 0.0592 0.90736 0.988 0.012 0.000
#> SRR1656495 1 0.3879 0.78691 0.848 0.152 0.000
#> SRR1656496 3 0.5621 0.58832 0.308 0.000 0.692
#> SRR1656494 2 0.0892 0.90399 0.000 0.980 0.020
#> SRR1656497 2 0.1031 0.90274 0.000 0.976 0.024
#> SRR1656499 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656500 3 0.1753 0.90491 0.048 0.000 0.952
#> SRR1656501 1 0.3619 0.82224 0.864 0.000 0.136
#> SRR1656498 1 0.0592 0.90736 0.988 0.012 0.000
#> SRR1656504 1 0.6286 0.10906 0.536 0.000 0.464
#> SRR1656502 2 0.1031 0.90378 0.024 0.976 0.000
#> SRR1656503 1 0.2796 0.86873 0.908 0.000 0.092
#> SRR1656507 1 0.1163 0.91323 0.972 0.000 0.028
#> SRR1656508 1 0.0592 0.90736 0.988 0.012 0.000
#> SRR1656505 3 0.0424 0.91743 0.000 0.008 0.992
#> SRR1656506 3 0.0892 0.91814 0.020 0.000 0.980
#> SRR1656509 3 0.6215 0.25948 0.000 0.428 0.572
#> SRR1656510 3 0.1964 0.89946 0.056 0.000 0.944
#> SRR1656511 1 0.0424 0.91418 0.992 0.000 0.008
#> SRR1656513 2 0.0747 0.90605 0.016 0.984 0.000
#> SRR1656512 2 0.2959 0.85233 0.100 0.900 0.000
#> SRR1656514 1 0.3434 0.86626 0.904 0.064 0.032
#> SRR1656515 3 0.4121 0.78118 0.000 0.168 0.832
#> SRR1656516 1 0.2959 0.86046 0.900 0.000 0.100
#> SRR1656518 1 0.1031 0.91415 0.976 0.000 0.024
#> SRR1656517 1 0.0747 0.91488 0.984 0.000 0.016
#> SRR1656519 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656522 1 0.2229 0.90916 0.944 0.012 0.044
#> SRR1656523 3 0.5859 0.51021 0.344 0.000 0.656
#> SRR1656521 1 0.0424 0.91422 0.992 0.000 0.008
#> SRR1656520 3 0.0892 0.91163 0.000 0.020 0.980
#> SRR1656524 1 0.1031 0.90108 0.976 0.024 0.000
#> SRR1656525 3 0.1163 0.91498 0.028 0.000 0.972
#> SRR1656526 3 0.0424 0.92065 0.008 0.000 0.992
#> SRR1656527 1 0.2356 0.86876 0.928 0.072 0.000
#> SRR1656530 3 0.1031 0.91663 0.024 0.000 0.976
#> SRR1656529 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656531 1 0.0892 0.90327 0.980 0.020 0.000
#> SRR1656528 3 0.0424 0.92065 0.008 0.000 0.992
#> SRR1656534 3 0.4702 0.74540 0.212 0.000 0.788
#> SRR1656533 1 0.0424 0.91418 0.992 0.000 0.008
#> SRR1656536 3 0.0592 0.91562 0.000 0.012 0.988
#> SRR1656532 1 0.3116 0.83518 0.892 0.108 0.000
#> SRR1656537 1 0.0892 0.90327 0.980 0.020 0.000
#> SRR1656538 3 0.4974 0.71092 0.236 0.000 0.764
#> SRR1656535 1 0.0592 0.91478 0.988 0.000 0.012
#> SRR1656539 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656544 3 0.0747 0.91944 0.016 0.000 0.984
#> SRR1656542 3 0.1860 0.90228 0.052 0.000 0.948
#> SRR1656543 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656545 2 0.0892 0.90512 0.020 0.980 0.000
#> SRR1656540 3 0.1163 0.90726 0.000 0.028 0.972
#> SRR1656546 1 0.1411 0.91044 0.964 0.000 0.036
#> SRR1656541 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656547 3 0.0237 0.91894 0.000 0.004 0.996
#> SRR1656548 3 0.1753 0.90454 0.048 0.000 0.952
#> SRR1656549 1 0.0237 0.91285 0.996 0.000 0.004
#> SRR1656551 3 0.0000 0.92024 0.000 0.000 1.000
#> SRR1656553 3 0.0592 0.92006 0.012 0.000 0.988
#> SRR1656550 3 0.1289 0.90461 0.000 0.032 0.968
#> SRR1656552 3 0.2959 0.86594 0.100 0.000 0.900
#> SRR1656554 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656555 3 0.0892 0.91814 0.020 0.000 0.980
#> SRR1656556 3 0.3412 0.83009 0.000 0.124 0.876
#> SRR1656557 3 0.0424 0.92084 0.008 0.000 0.992
#> SRR1656558 1 0.0424 0.91422 0.992 0.000 0.008
#> SRR1656559 1 0.0892 0.91502 0.980 0.000 0.020
#> SRR1656560 3 0.0237 0.92075 0.004 0.000 0.996
#> SRR1656561 3 0.5497 0.61611 0.292 0.000 0.708
#> SRR1656562 2 0.6521 -0.00329 0.004 0.500 0.496
#> SRR1656563 1 0.1529 0.90838 0.960 0.000 0.040
#> SRR1656564 2 0.5859 0.48125 0.344 0.656 0.000
#> SRR1656565 2 0.7860 0.61629 0.228 0.656 0.116
#> SRR1656566 1 0.0592 0.90736 0.988 0.012 0.000
#> SRR1656568 1 0.2165 0.87504 0.936 0.064 0.000
#> SRR1656567 3 0.1411 0.90228 0.000 0.036 0.964
#> SRR1656569 3 0.0592 0.92029 0.012 0.000 0.988
#> SRR1656570 1 0.1964 0.89780 0.944 0.000 0.056
#> SRR1656571 2 0.1031 0.90406 0.024 0.976 0.000
#> SRR1656573 3 0.1289 0.91377 0.032 0.000 0.968
#> SRR1656572 1 0.1411 0.91044 0.964 0.000 0.036
#> SRR1656574 1 0.0892 0.91502 0.980 0.000 0.020
#> SRR1656575 1 0.1163 0.91323 0.972 0.000 0.028
#> SRR1656576 3 0.0747 0.91938 0.016 0.000 0.984
#> SRR1656578 2 0.2878 0.85626 0.096 0.904 0.000
#> SRR1656577 1 0.0892 0.91502 0.980 0.000 0.020
#> SRR1656579 3 0.0747 0.91387 0.000 0.016 0.984
#> SRR1656580 3 0.6244 0.22759 0.440 0.000 0.560
#> SRR1656581 3 0.5058 0.70121 0.244 0.000 0.756
#> SRR1656582 3 0.3038 0.86261 0.104 0.000 0.896
#> SRR1656585 3 0.4842 0.71569 0.000 0.224 0.776
#> SRR1656584 1 0.0000 0.91137 1.000 0.000 0.000
#> SRR1656583 2 0.1529 0.89378 0.000 0.960 0.040
#> SRR1656586 2 0.0237 0.90709 0.000 0.996 0.004
#> SRR1656587 2 0.1529 0.89561 0.040 0.960 0.000
#> SRR1656588 3 0.2356 0.87682 0.000 0.072 0.928
#> SRR1656589 2 0.0000 0.90718 0.000 1.000 0.000
#> SRR1656590 1 0.1753 0.88652 0.952 0.048 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 3 0.4225 0.71475 0.000 0.184 0.792 0.024
#> SRR1656464 1 0.1943 0.79170 0.944 0.016 0.032 0.008
#> SRR1656462 3 0.0564 0.85818 0.004 0.004 0.988 0.004
#> SRR1656465 3 0.0592 0.85852 0.000 0.000 0.984 0.016
#> SRR1656467 3 0.5126 0.10403 0.000 0.444 0.552 0.004
#> SRR1656466 3 0.1211 0.85192 0.000 0.000 0.960 0.040
#> SRR1656468 3 0.2345 0.81881 0.000 0.000 0.900 0.100
#> SRR1656472 1 0.6246 -0.10892 0.488 0.464 0.044 0.004
#> SRR1656471 3 0.0779 0.85560 0.000 0.004 0.980 0.016
#> SRR1656470 2 0.0188 0.88705 0.000 0.996 0.000 0.004
#> SRR1656469 3 0.2868 0.76407 0.000 0.000 0.864 0.136
#> SRR1656473 2 0.0188 0.88771 0.004 0.996 0.000 0.000
#> SRR1656474 2 0.0188 0.88771 0.004 0.996 0.000 0.000
#> SRR1656475 2 0.0376 0.88764 0.004 0.992 0.000 0.004
#> SRR1656478 1 0.1743 0.80847 0.940 0.000 0.004 0.056
#> SRR1656477 3 0.0524 0.85753 0.000 0.004 0.988 0.008
#> SRR1656479 4 0.2773 0.75642 0.028 0.000 0.072 0.900
#> SRR1656480 3 0.1004 0.85419 0.000 0.004 0.972 0.024
#> SRR1656476 4 0.3837 0.69647 0.000 0.000 0.224 0.776
#> SRR1656481 3 0.0921 0.85458 0.000 0.000 0.972 0.028
#> SRR1656482 2 0.4193 0.62814 0.000 0.732 0.268 0.000
#> SRR1656483 3 0.5573 0.03807 0.004 0.476 0.508 0.012
#> SRR1656485 3 0.0524 0.85816 0.000 0.004 0.988 0.008
#> SRR1656487 3 0.0921 0.85681 0.000 0.000 0.972 0.028
#> SRR1656486 4 0.2197 0.72964 0.080 0.000 0.004 0.916
#> SRR1656488 3 0.1474 0.85105 0.000 0.000 0.948 0.052
#> SRR1656484 1 0.4872 0.50226 0.640 0.000 0.004 0.356
#> SRR1656489 1 0.2334 0.79325 0.908 0.000 0.004 0.088
#> SRR1656491 4 0.5143 0.13137 0.004 0.000 0.456 0.540
#> SRR1656490 4 0.4248 0.60088 0.220 0.000 0.012 0.768
#> SRR1656492 4 0.5530 0.51497 0.032 0.000 0.336 0.632
#> SRR1656493 1 0.0336 0.81078 0.992 0.000 0.000 0.008
#> SRR1656495 1 0.1488 0.79927 0.956 0.032 0.000 0.012
#> SRR1656496 4 0.3856 0.76009 0.032 0.000 0.136 0.832
#> SRR1656494 3 0.5433 0.08133 0.008 0.448 0.540 0.004
#> SRR1656497 2 0.2480 0.84312 0.000 0.904 0.008 0.088
#> SRR1656499 3 0.1022 0.85569 0.000 0.000 0.968 0.032
#> SRR1656500 3 0.0672 0.85852 0.008 0.000 0.984 0.008
#> SRR1656501 4 0.4697 0.49850 0.296 0.000 0.008 0.696
#> SRR1656498 1 0.0188 0.81011 0.996 0.000 0.000 0.004
#> SRR1656504 4 0.1854 0.74428 0.048 0.000 0.012 0.940
#> SRR1656502 1 0.5427 0.03851 0.544 0.444 0.008 0.004
#> SRR1656503 1 0.4656 0.73051 0.792 0.000 0.072 0.136
#> SRR1656507 1 0.3105 0.77137 0.856 0.000 0.004 0.140
#> SRR1656508 1 0.1867 0.80603 0.928 0.000 0.000 0.072
#> SRR1656505 3 0.1022 0.85472 0.000 0.000 0.968 0.032
#> SRR1656506 4 0.3569 0.71446 0.000 0.000 0.196 0.804
#> SRR1656509 3 0.1822 0.83990 0.004 0.044 0.944 0.008
#> SRR1656510 4 0.5269 0.46209 0.016 0.000 0.364 0.620
#> SRR1656511 4 0.1557 0.73713 0.056 0.000 0.000 0.944
#> SRR1656513 2 0.0376 0.88764 0.004 0.992 0.000 0.004
#> SRR1656512 2 0.1489 0.87690 0.004 0.952 0.000 0.044
#> SRR1656514 1 0.4741 0.54659 0.728 0.008 0.256 0.008
#> SRR1656515 3 0.1510 0.85141 0.000 0.028 0.956 0.016
#> SRR1656516 4 0.5604 -0.00445 0.476 0.000 0.020 0.504
#> SRR1656518 1 0.5097 0.27986 0.568 0.000 0.004 0.428
#> SRR1656517 1 0.3208 0.75691 0.848 0.000 0.004 0.148
#> SRR1656519 3 0.0188 0.85892 0.000 0.000 0.996 0.004
#> SRR1656522 1 0.2654 0.74817 0.888 0.000 0.108 0.004
#> SRR1656523 4 0.1388 0.75361 0.012 0.000 0.028 0.960
#> SRR1656521 1 0.5165 0.12257 0.512 0.000 0.004 0.484
#> SRR1656520 3 0.0844 0.85683 0.004 0.004 0.980 0.012
#> SRR1656524 1 0.0592 0.81038 0.984 0.000 0.000 0.016
#> SRR1656525 3 0.4331 0.55529 0.000 0.000 0.712 0.288
#> SRR1656526 4 0.1716 0.76101 0.000 0.000 0.064 0.936
#> SRR1656527 1 0.0524 0.81117 0.988 0.008 0.000 0.004
#> SRR1656530 3 0.2973 0.77702 0.000 0.000 0.856 0.144
#> SRR1656529 3 0.4456 0.57261 0.000 0.004 0.716 0.280
#> SRR1656531 1 0.0921 0.80801 0.972 0.000 0.000 0.028
#> SRR1656528 3 0.4978 0.34120 0.000 0.004 0.612 0.384
#> SRR1656534 3 0.2773 0.79789 0.072 0.000 0.900 0.028
#> SRR1656533 1 0.4193 0.65089 0.732 0.000 0.000 0.268
#> SRR1656536 3 0.0524 0.85816 0.000 0.004 0.988 0.008
#> SRR1656532 1 0.0779 0.80515 0.980 0.016 0.000 0.004
#> SRR1656537 1 0.0336 0.81051 0.992 0.000 0.000 0.008
#> SRR1656538 4 0.5850 0.65139 0.080 0.000 0.244 0.676
#> SRR1656535 4 0.4539 0.52754 0.272 0.000 0.008 0.720
#> SRR1656539 3 0.0524 0.85816 0.000 0.004 0.988 0.008
#> SRR1656544 3 0.0469 0.85901 0.000 0.000 0.988 0.012
#> SRR1656542 3 0.1978 0.84298 0.004 0.000 0.928 0.068
#> SRR1656543 3 0.0817 0.85665 0.000 0.000 0.976 0.024
#> SRR1656545 2 0.2334 0.85357 0.000 0.908 0.004 0.088
#> SRR1656540 3 0.0844 0.85683 0.004 0.004 0.980 0.012
#> SRR1656546 1 0.5050 0.34139 0.588 0.000 0.004 0.408
#> SRR1656541 3 0.4661 0.43068 0.000 0.000 0.652 0.348
#> SRR1656547 3 0.1305 0.85533 0.000 0.004 0.960 0.036
#> SRR1656548 4 0.3486 0.73240 0.000 0.000 0.188 0.812
#> SRR1656549 4 0.4193 0.48596 0.268 0.000 0.000 0.732
#> SRR1656551 3 0.1743 0.84249 0.000 0.004 0.940 0.056
#> SRR1656553 3 0.1610 0.84363 0.032 0.000 0.952 0.016
#> SRR1656550 3 0.0524 0.85816 0.000 0.004 0.988 0.008
#> SRR1656552 4 0.3161 0.74967 0.012 0.000 0.124 0.864
#> SRR1656554 3 0.5155 0.07309 0.000 0.004 0.528 0.468
#> SRR1656555 4 0.3486 0.72746 0.000 0.000 0.188 0.812
#> SRR1656556 3 0.0657 0.85826 0.000 0.004 0.984 0.012
#> SRR1656557 3 0.0469 0.85901 0.000 0.000 0.988 0.012
#> SRR1656558 1 0.1389 0.80908 0.952 0.000 0.000 0.048
#> SRR1656559 1 0.0927 0.80920 0.976 0.000 0.016 0.008
#> SRR1656560 3 0.1389 0.85102 0.000 0.000 0.952 0.048
#> SRR1656561 4 0.1929 0.75426 0.036 0.000 0.024 0.940
#> SRR1656562 2 0.4267 0.72179 0.000 0.788 0.188 0.024
#> SRR1656563 4 0.2216 0.71688 0.092 0.000 0.000 0.908
#> SRR1656564 2 0.2670 0.84908 0.040 0.908 0.000 0.052
#> SRR1656565 2 0.3201 0.84232 0.032 0.888 0.072 0.008
#> SRR1656566 1 0.1302 0.81120 0.956 0.000 0.000 0.044
#> SRR1656568 1 0.1305 0.81215 0.960 0.004 0.000 0.036
#> SRR1656567 3 0.0376 0.85876 0.000 0.004 0.992 0.004
#> SRR1656569 4 0.4855 0.47068 0.000 0.004 0.352 0.644
#> SRR1656570 4 0.1557 0.73713 0.056 0.000 0.000 0.944
#> SRR1656571 2 0.0336 0.88668 0.008 0.992 0.000 0.000
#> SRR1656573 4 0.4304 0.60524 0.000 0.000 0.284 0.716
#> SRR1656572 4 0.5119 0.10795 0.440 0.000 0.004 0.556
#> SRR1656574 1 0.3400 0.74477 0.820 0.000 0.000 0.180
#> SRR1656575 1 0.2814 0.77503 0.868 0.000 0.000 0.132
#> SRR1656576 4 0.3311 0.73832 0.000 0.000 0.172 0.828
#> SRR1656578 2 0.3801 0.67905 0.220 0.780 0.000 0.000
#> SRR1656577 1 0.0921 0.81246 0.972 0.000 0.000 0.028
#> SRR1656579 3 0.4964 0.36851 0.000 0.004 0.616 0.380
#> SRR1656580 3 0.7883 -0.28192 0.292 0.000 0.380 0.328
#> SRR1656581 4 0.1677 0.75756 0.012 0.000 0.040 0.948
#> SRR1656582 4 0.1743 0.75925 0.000 0.004 0.056 0.940
#> SRR1656585 3 0.5857 0.60720 0.004 0.172 0.712 0.112
#> SRR1656584 1 0.4072 0.66288 0.748 0.000 0.000 0.252
#> SRR1656583 2 0.5276 0.26326 0.004 0.560 0.432 0.004
#> SRR1656586 2 0.0188 0.88771 0.004 0.996 0.000 0.000
#> SRR1656587 1 0.7378 0.09600 0.448 0.140 0.408 0.004
#> SRR1656588 3 0.0672 0.85817 0.000 0.008 0.984 0.008
#> SRR1656589 2 0.0376 0.88722 0.004 0.992 0.004 0.000
#> SRR1656590 1 0.0779 0.80770 0.980 0.004 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 4 0.6638 0.3222 0.000 0.272 0.276 0.452 0.000
#> SRR1656464 1 0.1518 0.8037 0.944 0.004 0.048 0.004 0.000
#> SRR1656462 3 0.2124 0.7827 0.004 0.000 0.900 0.096 0.000
#> SRR1656465 3 0.2329 0.7645 0.000 0.000 0.876 0.124 0.000
#> SRR1656467 3 0.2408 0.7317 0.000 0.096 0.892 0.008 0.004
#> SRR1656466 4 0.4101 0.3974 0.000 0.000 0.372 0.628 0.000
#> SRR1656468 4 0.4084 0.4769 0.000 0.000 0.328 0.668 0.004
#> SRR1656472 1 0.5098 0.6014 0.716 0.092 0.180 0.012 0.000
#> SRR1656471 3 0.0324 0.7871 0.000 0.000 0.992 0.004 0.004
#> SRR1656470 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> SRR1656469 3 0.4984 0.4618 0.000 0.000 0.640 0.308 0.052
#> SRR1656473 2 0.0000 0.8869 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8869 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> SRR1656478 1 0.4192 0.5072 0.596 0.000 0.000 0.404 0.000
#> SRR1656477 3 0.0854 0.7854 0.000 0.008 0.976 0.012 0.004
#> SRR1656479 5 0.0865 0.8184 0.000 0.000 0.024 0.004 0.972
#> SRR1656480 3 0.0579 0.7880 0.000 0.000 0.984 0.008 0.008
#> SRR1656476 4 0.5064 0.5802 0.000 0.000 0.088 0.680 0.232
#> SRR1656481 3 0.3039 0.7006 0.000 0.000 0.808 0.192 0.000
#> SRR1656482 3 0.4718 0.3359 0.008 0.404 0.580 0.008 0.000
#> SRR1656483 2 0.3255 0.7730 0.000 0.848 0.100 0.052 0.000
#> SRR1656485 3 0.1410 0.7898 0.000 0.000 0.940 0.060 0.000
#> SRR1656487 3 0.3857 0.5490 0.000 0.000 0.688 0.312 0.000
#> SRR1656486 5 0.4275 0.6473 0.024 0.000 0.008 0.228 0.740
#> SRR1656488 4 0.4225 0.4062 0.000 0.000 0.364 0.632 0.004
#> SRR1656484 1 0.5415 0.6468 0.648 0.000 0.004 0.092 0.256
#> SRR1656489 1 0.3790 0.7162 0.724 0.000 0.000 0.272 0.004
#> SRR1656491 5 0.4724 0.7364 0.000 0.000 0.104 0.164 0.732
#> SRR1656490 5 0.2597 0.7796 0.060 0.000 0.004 0.040 0.896
#> SRR1656492 4 0.3304 0.7009 0.004 0.000 0.092 0.852 0.052
#> SRR1656493 1 0.2629 0.8046 0.860 0.000 0.000 0.136 0.004
#> SRR1656495 1 0.0854 0.8092 0.976 0.012 0.004 0.008 0.000
#> SRR1656496 5 0.1329 0.8240 0.008 0.000 0.032 0.004 0.956
#> SRR1656494 3 0.3375 0.7005 0.040 0.096 0.852 0.012 0.000
#> SRR1656497 2 0.2674 0.8518 0.000 0.868 0.000 0.120 0.012
#> SRR1656499 3 0.4297 0.1159 0.000 0.000 0.528 0.472 0.000
#> SRR1656500 3 0.1792 0.7838 0.000 0.000 0.916 0.084 0.000
#> SRR1656501 4 0.5268 0.6312 0.104 0.000 0.024 0.720 0.152
#> SRR1656498 1 0.0794 0.8184 0.972 0.000 0.000 0.028 0.000
#> SRR1656504 4 0.4270 0.4659 0.004 0.000 0.004 0.656 0.336
#> SRR1656502 1 0.3673 0.7204 0.836 0.092 0.060 0.012 0.000
#> SRR1656503 1 0.4256 0.7653 0.760 0.000 0.004 0.192 0.044
#> SRR1656507 4 0.3706 0.5433 0.236 0.000 0.004 0.756 0.004
#> SRR1656508 1 0.2179 0.7962 0.896 0.000 0.000 0.004 0.100
#> SRR1656505 3 0.3913 0.5062 0.000 0.000 0.676 0.324 0.000
#> SRR1656506 5 0.1908 0.8122 0.000 0.000 0.092 0.000 0.908
#> SRR1656509 3 0.1667 0.7713 0.024 0.012 0.948 0.012 0.004
#> SRR1656510 4 0.3112 0.6976 0.000 0.000 0.100 0.856 0.044
#> SRR1656511 5 0.0579 0.8168 0.008 0.000 0.000 0.008 0.984
#> SRR1656513 2 0.3796 0.8241 0.100 0.820 0.004 0.076 0.000
#> SRR1656512 2 0.2773 0.8531 0.000 0.868 0.000 0.112 0.020
#> SRR1656514 1 0.3328 0.7037 0.812 0.004 0.176 0.008 0.000
#> SRR1656515 3 0.4968 0.6700 0.000 0.136 0.712 0.152 0.000
#> SRR1656516 4 0.5371 0.6240 0.140 0.000 0.028 0.716 0.116
#> SRR1656518 4 0.5316 0.3819 0.284 0.000 0.000 0.632 0.084
#> SRR1656517 1 0.4252 0.6881 0.700 0.000 0.000 0.280 0.020
#> SRR1656519 3 0.0566 0.7905 0.004 0.000 0.984 0.012 0.000
#> SRR1656522 1 0.3413 0.7784 0.832 0.000 0.044 0.124 0.000
#> SRR1656523 5 0.0162 0.8189 0.000 0.000 0.004 0.000 0.996
#> SRR1656521 4 0.4049 0.6105 0.164 0.000 0.000 0.780 0.056
#> SRR1656520 3 0.0854 0.7824 0.008 0.000 0.976 0.012 0.004
#> SRR1656524 1 0.0771 0.8185 0.976 0.000 0.000 0.020 0.004
#> SRR1656525 5 0.6572 0.1390 0.000 0.000 0.364 0.208 0.428
#> SRR1656526 5 0.3828 0.7464 0.000 0.020 0.008 0.184 0.788
#> SRR1656527 1 0.2690 0.7955 0.844 0.000 0.000 0.156 0.000
#> SRR1656530 4 0.4009 0.5008 0.000 0.000 0.312 0.684 0.004
#> SRR1656529 3 0.6247 -0.0779 0.000 0.000 0.428 0.144 0.428
#> SRR1656531 1 0.1116 0.8119 0.964 0.000 0.004 0.004 0.028
#> SRR1656528 5 0.5557 0.5528 0.000 0.000 0.260 0.116 0.624
#> SRR1656534 3 0.3146 0.7247 0.052 0.000 0.856 0.000 0.092
#> SRR1656533 1 0.5004 0.6722 0.672 0.000 0.000 0.072 0.256
#> SRR1656536 3 0.1197 0.7912 0.000 0.000 0.952 0.048 0.000
#> SRR1656532 1 0.0451 0.8145 0.988 0.008 0.000 0.004 0.000
#> SRR1656537 1 0.0510 0.8171 0.984 0.000 0.000 0.016 0.000
#> SRR1656538 4 0.4147 0.6862 0.016 0.000 0.064 0.804 0.116
#> SRR1656535 4 0.4075 0.6490 0.060 0.000 0.000 0.780 0.160
#> SRR1656539 3 0.1851 0.7834 0.000 0.000 0.912 0.088 0.000
#> SRR1656544 3 0.2020 0.7801 0.000 0.000 0.900 0.100 0.000
#> SRR1656542 4 0.4744 0.3178 0.000 0.000 0.408 0.572 0.020
#> SRR1656543 3 0.3210 0.6955 0.000 0.000 0.788 0.212 0.000
#> SRR1656545 2 0.3242 0.8431 0.000 0.844 0.000 0.116 0.040
#> SRR1656540 3 0.0451 0.7863 0.004 0.000 0.988 0.008 0.000
#> SRR1656546 4 0.3639 0.5952 0.184 0.000 0.000 0.792 0.024
#> SRR1656541 4 0.3536 0.6051 0.000 0.000 0.156 0.812 0.032
#> SRR1656547 3 0.4420 0.4073 0.000 0.000 0.548 0.448 0.004
#> SRR1656548 5 0.3670 0.7659 0.000 0.000 0.068 0.112 0.820
#> SRR1656549 5 0.1774 0.7960 0.052 0.000 0.000 0.016 0.932
#> SRR1656551 3 0.1872 0.7815 0.000 0.000 0.928 0.020 0.052
#> SRR1656553 3 0.4886 0.4807 0.032 0.000 0.596 0.372 0.000
#> SRR1656550 3 0.1341 0.7918 0.000 0.000 0.944 0.056 0.000
#> SRR1656552 4 0.3193 0.6884 0.004 0.000 0.032 0.852 0.112
#> SRR1656554 5 0.3980 0.6405 0.000 0.000 0.284 0.008 0.708
#> SRR1656555 5 0.3929 0.7401 0.000 0.000 0.028 0.208 0.764
#> SRR1656556 3 0.0671 0.7897 0.000 0.004 0.980 0.016 0.000
#> SRR1656557 3 0.1908 0.7802 0.000 0.000 0.908 0.092 0.000
#> SRR1656558 1 0.3661 0.7091 0.724 0.000 0.000 0.276 0.000
#> SRR1656559 1 0.3048 0.7845 0.820 0.000 0.004 0.176 0.000
#> SRR1656560 4 0.4341 0.3037 0.000 0.000 0.404 0.592 0.004
#> SRR1656561 5 0.1461 0.8205 0.004 0.000 0.016 0.028 0.952
#> SRR1656562 2 0.6882 0.2510 0.000 0.476 0.020 0.184 0.320
#> SRR1656563 5 0.0451 0.8165 0.008 0.000 0.000 0.004 0.988
#> SRR1656564 5 0.5288 0.1693 0.052 0.404 0.000 0.000 0.544
#> SRR1656565 2 0.5245 0.6803 0.096 0.720 0.024 0.000 0.160
#> SRR1656566 1 0.2329 0.8078 0.876 0.000 0.000 0.124 0.000
#> SRR1656568 1 0.2451 0.8211 0.904 0.004 0.000 0.056 0.036
#> SRR1656567 3 0.2127 0.7754 0.000 0.000 0.892 0.108 0.000
#> SRR1656569 5 0.3616 0.7547 0.000 0.000 0.164 0.032 0.804
#> SRR1656570 5 0.0324 0.8173 0.004 0.000 0.000 0.004 0.992
#> SRR1656571 2 0.0960 0.8800 0.016 0.972 0.008 0.004 0.000
#> SRR1656573 5 0.2462 0.7997 0.000 0.000 0.112 0.008 0.880
#> SRR1656572 4 0.4767 0.5595 0.200 0.000 0.004 0.724 0.072
#> SRR1656574 1 0.3093 0.7654 0.824 0.000 0.000 0.008 0.168
#> SRR1656575 1 0.3366 0.7990 0.828 0.000 0.000 0.140 0.032
#> SRR1656576 5 0.2928 0.7981 0.000 0.000 0.064 0.064 0.872
#> SRR1656578 1 0.4249 0.2728 0.568 0.432 0.000 0.000 0.000
#> SRR1656577 1 0.1732 0.8169 0.920 0.000 0.000 0.080 0.000
#> SRR1656579 3 0.4641 -0.0371 0.000 0.000 0.532 0.012 0.456
#> SRR1656580 5 0.6090 0.6586 0.100 0.000 0.136 0.088 0.676
#> SRR1656581 5 0.0324 0.8191 0.000 0.000 0.004 0.004 0.992
#> SRR1656582 5 0.0162 0.8189 0.000 0.000 0.004 0.000 0.996
#> SRR1656585 3 0.5714 0.5121 0.024 0.076 0.676 0.008 0.216
#> SRR1656584 1 0.5198 0.7164 0.688 0.000 0.000 0.164 0.148
#> SRR1656583 3 0.3878 0.6829 0.020 0.140 0.816 0.016 0.008
#> SRR1656586 2 0.0000 0.8869 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 1 0.5278 0.4822 0.624 0.044 0.320 0.012 0.000
#> SRR1656588 3 0.1041 0.7917 0.000 0.004 0.964 0.032 0.000
#> SRR1656589 2 0.0613 0.8831 0.004 0.984 0.008 0.004 0.000
#> SRR1656590 1 0.0324 0.8134 0.992 0.000 0.004 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 3 0.6438 0.3547 0.000 0.312 0.464 0.000 0.188 0.036
#> SRR1656464 1 0.2871 0.7509 0.868 0.000 0.008 0.004 0.080 0.040
#> SRR1656462 5 0.3777 0.7675 0.008 0.000 0.040 0.004 0.784 0.164
#> SRR1656465 5 0.3166 0.7732 0.000 0.000 0.156 0.004 0.816 0.024
#> SRR1656467 5 0.1406 0.8080 0.000 0.008 0.004 0.016 0.952 0.020
#> SRR1656466 3 0.4533 0.5041 0.000 0.000 0.652 0.000 0.284 0.064
#> SRR1656468 3 0.4338 0.6016 0.000 0.000 0.716 0.004 0.208 0.072
#> SRR1656472 1 0.5095 0.5242 0.660 0.000 0.004 0.024 0.244 0.068
#> SRR1656471 5 0.0951 0.8122 0.000 0.000 0.004 0.008 0.968 0.020
#> SRR1656470 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.5645 0.0895 0.000 0.000 0.444 0.068 0.456 0.032
#> SRR1656473 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9192 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.4983 0.4456 0.564 0.000 0.356 0.000 0.000 0.080
#> SRR1656477 5 0.0881 0.8136 0.000 0.000 0.008 0.008 0.972 0.012
#> SRR1656479 4 0.2024 0.7245 0.028 0.000 0.000 0.920 0.036 0.016
#> SRR1656480 5 0.1251 0.8165 0.000 0.000 0.008 0.012 0.956 0.024
#> SRR1656476 3 0.3367 0.5926 0.000 0.000 0.804 0.164 0.020 0.012
#> SRR1656481 5 0.3292 0.7321 0.000 0.000 0.200 0.008 0.784 0.008
#> SRR1656482 5 0.4399 0.3914 0.000 0.384 0.004 0.004 0.592 0.016
#> SRR1656483 2 0.1390 0.8730 0.000 0.948 0.032 0.000 0.016 0.004
#> SRR1656485 5 0.2009 0.8128 0.000 0.000 0.068 0.000 0.908 0.024
#> SRR1656487 5 0.4593 0.3556 0.000 0.000 0.380 0.000 0.576 0.044
#> SRR1656486 4 0.5028 0.3355 0.020 0.000 0.384 0.556 0.000 0.040
#> SRR1656488 3 0.4570 0.5505 0.000 0.000 0.668 0.000 0.252 0.080
#> SRR1656484 4 0.6594 0.1983 0.332 0.000 0.148 0.472 0.020 0.028
#> SRR1656489 1 0.5280 0.6042 0.616 0.000 0.248 0.008 0.000 0.128
#> SRR1656491 6 0.4254 0.5039 0.000 0.000 0.004 0.352 0.020 0.624
#> SRR1656490 4 0.3360 0.6914 0.084 0.000 0.044 0.840 0.000 0.032
#> SRR1656492 3 0.1434 0.6992 0.000 0.000 0.948 0.008 0.024 0.020
#> SRR1656493 1 0.3468 0.7486 0.804 0.000 0.128 0.000 0.000 0.068
#> SRR1656495 1 0.1334 0.7692 0.948 0.000 0.000 0.020 0.000 0.032
#> SRR1656496 4 0.2641 0.7335 0.020 0.000 0.040 0.896 0.024 0.020
#> SRR1656494 5 0.2856 0.7677 0.064 0.000 0.004 0.004 0.868 0.060
#> SRR1656497 6 0.3911 0.3535 0.000 0.368 0.000 0.008 0.000 0.624
#> SRR1656499 3 0.5953 0.2824 0.000 0.000 0.448 0.000 0.308 0.244
#> SRR1656500 5 0.2770 0.8149 0.012 0.000 0.036 0.012 0.884 0.056
#> SRR1656501 3 0.4683 0.6501 0.096 0.000 0.744 0.052 0.000 0.108
#> SRR1656498 1 0.0820 0.7807 0.972 0.000 0.012 0.000 0.000 0.016
#> SRR1656504 3 0.2212 0.6608 0.000 0.000 0.880 0.112 0.000 0.008
#> SRR1656502 1 0.3984 0.6899 0.800 0.000 0.004 0.028 0.100 0.068
#> SRR1656503 6 0.3773 0.6031 0.164 0.000 0.012 0.028 0.008 0.788
#> SRR1656507 3 0.3319 0.6359 0.164 0.000 0.800 0.000 0.000 0.036
#> SRR1656508 1 0.3110 0.6782 0.792 0.000 0.000 0.196 0.000 0.012
#> SRR1656505 5 0.3967 0.4687 0.000 0.000 0.356 0.000 0.632 0.012
#> SRR1656506 4 0.3035 0.7175 0.000 0.000 0.040 0.860 0.076 0.024
#> SRR1656509 5 0.2935 0.7783 0.028 0.000 0.004 0.004 0.852 0.112
#> SRR1656510 3 0.0291 0.6979 0.000 0.000 0.992 0.004 0.004 0.000
#> SRR1656511 4 0.1167 0.7221 0.012 0.000 0.008 0.960 0.000 0.020
#> SRR1656513 6 0.4682 0.4996 0.064 0.240 0.000 0.004 0.008 0.684
#> SRR1656512 2 0.4083 -0.0353 0.000 0.532 0.000 0.008 0.000 0.460
#> SRR1656514 1 0.4083 0.5491 0.688 0.000 0.008 0.000 0.284 0.020
#> SRR1656515 5 0.5031 0.6998 0.000 0.060 0.092 0.000 0.712 0.136
#> SRR1656516 3 0.4272 0.6516 0.104 0.000 0.776 0.044 0.000 0.076
#> SRR1656518 3 0.4150 0.6061 0.168 0.000 0.760 0.048 0.000 0.024
#> SRR1656517 1 0.4729 0.6242 0.660 0.000 0.264 0.008 0.000 0.068
#> SRR1656519 5 0.1515 0.8172 0.000 0.000 0.020 0.008 0.944 0.028
#> SRR1656522 1 0.4178 0.7004 0.764 0.000 0.016 0.004 0.056 0.160
#> SRR1656523 4 0.1049 0.7195 0.000 0.000 0.008 0.960 0.000 0.032
#> SRR1656521 3 0.3978 0.6591 0.112 0.000 0.792 0.028 0.000 0.068
#> SRR1656520 5 0.0582 0.8159 0.004 0.000 0.004 0.004 0.984 0.004
#> SRR1656524 1 0.0870 0.7784 0.972 0.000 0.004 0.012 0.000 0.012
#> SRR1656525 6 0.5356 0.6723 0.000 0.000 0.084 0.124 0.104 0.688
#> SRR1656526 6 0.3468 0.6264 0.000 0.000 0.008 0.264 0.000 0.728
#> SRR1656527 1 0.3468 0.7504 0.804 0.000 0.128 0.000 0.000 0.068
#> SRR1656530 3 0.4316 0.6226 0.000 0.000 0.728 0.000 0.144 0.128
#> SRR1656529 6 0.7267 0.3090 0.000 0.000 0.124 0.232 0.232 0.412
#> SRR1656531 1 0.2594 0.7527 0.884 0.000 0.004 0.068 0.004 0.040
#> SRR1656528 4 0.6990 -0.1740 0.000 0.000 0.116 0.388 0.132 0.364
#> SRR1656534 5 0.3712 0.7061 0.024 0.000 0.004 0.168 0.788 0.016
#> SRR1656533 1 0.5023 0.3715 0.564 0.000 0.036 0.376 0.000 0.024
#> SRR1656536 5 0.2070 0.8188 0.000 0.000 0.044 0.000 0.908 0.048
#> SRR1656532 1 0.0790 0.7724 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR1656537 1 0.0260 0.7785 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656538 3 0.3361 0.6799 0.040 0.000 0.848 0.040 0.004 0.068
#> SRR1656535 3 0.2239 0.6919 0.040 0.004 0.912 0.028 0.000 0.016
#> SRR1656539 5 0.2404 0.8095 0.000 0.000 0.080 0.000 0.884 0.036
#> SRR1656544 5 0.3481 0.7772 0.000 0.000 0.124 0.000 0.804 0.072
#> SRR1656542 3 0.4250 0.4564 0.004 0.000 0.664 0.016 0.308 0.008
#> SRR1656543 5 0.5051 0.5941 0.000 0.000 0.208 0.004 0.648 0.140
#> SRR1656545 6 0.4789 0.4979 0.000 0.268 0.000 0.092 0.000 0.640
#> SRR1656540 5 0.0951 0.8148 0.000 0.000 0.008 0.004 0.968 0.020
#> SRR1656546 3 0.5000 0.5650 0.144 0.000 0.668 0.008 0.000 0.180
#> SRR1656541 6 0.4208 0.6631 0.000 0.000 0.140 0.028 0.064 0.768
#> SRR1656547 6 0.4013 0.6555 0.000 0.000 0.104 0.004 0.124 0.768
#> SRR1656548 4 0.4130 0.6035 0.000 0.000 0.264 0.700 0.008 0.028
#> SRR1656549 4 0.2063 0.7115 0.060 0.000 0.008 0.912 0.000 0.020
#> SRR1656551 5 0.2521 0.8130 0.000 0.000 0.020 0.032 0.892 0.056
#> SRR1656553 6 0.4281 0.6328 0.032 0.000 0.064 0.000 0.140 0.764
#> SRR1656550 5 0.2404 0.8093 0.000 0.000 0.036 0.000 0.884 0.080
#> SRR1656552 3 0.1841 0.6984 0.000 0.000 0.920 0.008 0.008 0.064
#> SRR1656554 4 0.3909 0.5958 0.000 0.000 0.020 0.732 0.236 0.012
#> SRR1656555 6 0.3692 0.6479 0.000 0.000 0.008 0.244 0.012 0.736
#> SRR1656556 5 0.1313 0.8191 0.000 0.000 0.016 0.004 0.952 0.028
#> SRR1656557 5 0.3334 0.7918 0.000 0.000 0.052 0.004 0.820 0.124
#> SRR1656558 1 0.4255 0.6745 0.708 0.000 0.224 0.000 0.000 0.068
#> SRR1656559 1 0.4622 0.7087 0.720 0.000 0.132 0.000 0.012 0.136
#> SRR1656560 3 0.5606 0.3403 0.000 0.000 0.512 0.000 0.324 0.164
#> SRR1656561 4 0.2679 0.7149 0.000 0.000 0.096 0.868 0.004 0.032
#> SRR1656562 6 0.3441 0.6849 0.000 0.060 0.004 0.092 0.012 0.832
#> SRR1656563 4 0.1059 0.7236 0.016 0.000 0.004 0.964 0.000 0.016
#> SRR1656564 4 0.5385 0.4672 0.132 0.228 0.000 0.624 0.000 0.016
#> SRR1656565 4 0.8219 0.0955 0.076 0.352 0.044 0.368 0.076 0.084
#> SRR1656566 1 0.3421 0.7600 0.824 0.000 0.116 0.016 0.000 0.044
#> SRR1656568 1 0.3123 0.7807 0.864 0.004 0.040 0.040 0.000 0.052
#> SRR1656567 5 0.2507 0.8080 0.000 0.004 0.072 0.000 0.884 0.040
#> SRR1656569 4 0.4621 0.6580 0.000 0.000 0.112 0.724 0.148 0.016
#> SRR1656570 4 0.0862 0.7239 0.004 0.000 0.008 0.972 0.000 0.016
#> SRR1656571 2 0.0146 0.9170 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656573 4 0.3988 0.6999 0.000 0.000 0.060 0.796 0.104 0.040
#> SRR1656572 3 0.4231 0.6404 0.140 0.000 0.760 0.016 0.000 0.084
#> SRR1656574 1 0.4713 0.4505 0.620 0.000 0.004 0.320 0.000 0.056
#> SRR1656575 1 0.4780 0.6900 0.708 0.000 0.180 0.024 0.000 0.088
#> SRR1656576 4 0.4800 0.6229 0.000 0.000 0.192 0.692 0.012 0.104
#> SRR1656578 1 0.3898 0.5345 0.684 0.296 0.000 0.000 0.000 0.020
#> SRR1656577 1 0.1644 0.7823 0.932 0.000 0.040 0.000 0.000 0.028
#> SRR1656579 4 0.5114 0.2156 0.000 0.000 0.052 0.492 0.444 0.012
#> SRR1656580 4 0.6861 0.4321 0.184 0.000 0.052 0.548 0.040 0.176
#> SRR1656581 4 0.1649 0.7297 0.000 0.000 0.040 0.936 0.008 0.016
#> SRR1656582 4 0.1088 0.7240 0.000 0.000 0.016 0.960 0.000 0.024
#> SRR1656585 5 0.5534 0.3601 0.016 0.000 0.004 0.284 0.592 0.104
#> SRR1656584 1 0.5517 0.6640 0.644 0.000 0.172 0.148 0.000 0.036
#> SRR1656583 5 0.2350 0.7937 0.000 0.016 0.004 0.008 0.896 0.076
#> SRR1656586 2 0.0146 0.9177 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1656587 5 0.5262 0.1824 0.404 0.000 0.004 0.008 0.520 0.064
#> SRR1656588 5 0.1265 0.8180 0.000 0.000 0.044 0.000 0.948 0.008
#> SRR1656589 2 0.0146 0.9177 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1656590 1 0.1232 0.7715 0.956 0.000 0.000 0.016 0.004 0.024
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 13572 rows and 129 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 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 0.707 0.865 0.933 0.3788 0.594 0.594
#> 3 3 0.508 0.776 0.862 0.2525 0.962 0.936
#> 4 4 0.463 0.670 0.793 0.3152 0.742 0.544
#> 5 5 0.518 0.636 0.750 0.0950 0.947 0.840
#> 6 6 0.618 0.645 0.812 0.0638 0.942 0.814
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
#> SRR1656463 2 0.0000 0.960 0.000 1.000
#> SRR1656464 1 0.0000 0.828 1.000 0.000
#> SRR1656462 1 0.1414 0.836 0.980 0.020
#> SRR1656465 2 0.1414 0.949 0.020 0.980
#> SRR1656467 2 0.0000 0.960 0.000 1.000
#> SRR1656466 2 0.3274 0.916 0.060 0.940
#> SRR1656468 2 0.0000 0.960 0.000 1.000
#> SRR1656472 1 0.0000 0.828 1.000 0.000
#> SRR1656471 2 0.5294 0.851 0.120 0.880
#> SRR1656470 2 0.0000 0.960 0.000 1.000
#> SRR1656469 2 0.0000 0.960 0.000 1.000
#> SRR1656473 2 0.0000 0.960 0.000 1.000
#> SRR1656474 2 0.0000 0.960 0.000 1.000
#> SRR1656475 2 0.0000 0.960 0.000 1.000
#> SRR1656478 1 0.9661 0.532 0.608 0.392
#> SRR1656477 2 0.0000 0.960 0.000 1.000
#> SRR1656479 2 0.0000 0.960 0.000 1.000
#> SRR1656480 2 0.0000 0.960 0.000 1.000
#> SRR1656476 2 0.0000 0.960 0.000 1.000
#> SRR1656481 2 0.0000 0.960 0.000 1.000
#> SRR1656482 2 0.0000 0.960 0.000 1.000
#> SRR1656483 2 0.0000 0.960 0.000 1.000
#> SRR1656485 2 0.2778 0.926 0.048 0.952
#> SRR1656487 2 0.3431 0.912 0.064 0.936
#> SRR1656486 2 0.0000 0.960 0.000 1.000
#> SRR1656488 2 0.3431 0.912 0.064 0.936
#> SRR1656484 2 0.6438 0.778 0.164 0.836
#> SRR1656489 1 0.9686 0.525 0.604 0.396
#> SRR1656491 2 0.0000 0.960 0.000 1.000
#> SRR1656490 2 0.0000 0.960 0.000 1.000
#> SRR1656492 2 0.2236 0.937 0.036 0.964
#> SRR1656493 1 0.4431 0.822 0.908 0.092
#> SRR1656495 1 0.3431 0.833 0.936 0.064
#> SRR1656496 2 0.1184 0.952 0.016 0.984
#> SRR1656494 2 0.0672 0.956 0.008 0.992
#> SRR1656497 2 0.0000 0.960 0.000 1.000
#> SRR1656499 2 0.8608 0.558 0.284 0.716
#> SRR1656500 1 0.9977 0.268 0.528 0.472
#> SRR1656501 2 0.0000 0.960 0.000 1.000
#> SRR1656498 1 0.2043 0.837 0.968 0.032
#> SRR1656504 2 0.0000 0.960 0.000 1.000
#> SRR1656502 1 0.0000 0.828 1.000 0.000
#> SRR1656503 2 0.6148 0.798 0.152 0.848
#> SRR1656507 1 0.9661 0.532 0.608 0.392
#> SRR1656508 1 0.2778 0.837 0.952 0.048
#> SRR1656505 2 0.0000 0.960 0.000 1.000
#> SRR1656506 2 0.1414 0.948 0.020 0.980
#> SRR1656509 2 0.5946 0.821 0.144 0.856
#> SRR1656510 2 0.0000 0.960 0.000 1.000
#> SRR1656511 2 0.0000 0.960 0.000 1.000
#> SRR1656513 2 0.0000 0.960 0.000 1.000
#> SRR1656512 2 0.0000 0.960 0.000 1.000
#> SRR1656514 1 0.0000 0.828 1.000 0.000
#> SRR1656515 2 0.0000 0.960 0.000 1.000
#> SRR1656516 2 0.6438 0.778 0.164 0.836
#> SRR1656518 1 0.9795 0.484 0.584 0.416
#> SRR1656517 1 0.2603 0.837 0.956 0.044
#> SRR1656519 1 0.2603 0.836 0.956 0.044
#> SRR1656522 1 0.0000 0.828 1.000 0.000
#> SRR1656523 2 0.0000 0.960 0.000 1.000
#> SRR1656521 2 0.0000 0.960 0.000 1.000
#> SRR1656520 1 0.1414 0.836 0.980 0.020
#> SRR1656524 1 0.3431 0.833 0.936 0.064
#> SRR1656525 2 0.4022 0.891 0.080 0.920
#> SRR1656526 2 0.0000 0.960 0.000 1.000
#> SRR1656527 2 0.0000 0.960 0.000 1.000
#> SRR1656530 2 0.0000 0.960 0.000 1.000
#> SRR1656529 2 0.1414 0.948 0.020 0.980
#> SRR1656531 1 0.0938 0.833 0.988 0.012
#> SRR1656528 2 0.1414 0.948 0.020 0.980
#> SRR1656534 1 0.1184 0.835 0.984 0.016
#> SRR1656533 1 0.6343 0.782 0.840 0.160
#> SRR1656536 2 0.0000 0.960 0.000 1.000
#> SRR1656532 2 0.0672 0.956 0.008 0.992
#> SRR1656537 1 0.1633 0.836 0.976 0.024
#> SRR1656538 2 0.7139 0.729 0.196 0.804
#> SRR1656535 2 0.0000 0.960 0.000 1.000
#> SRR1656539 2 0.1843 0.942 0.028 0.972
#> SRR1656544 2 0.7139 0.735 0.196 0.804
#> SRR1656542 1 0.9996 0.218 0.512 0.488
#> SRR1656543 1 0.1414 0.836 0.980 0.020
#> SRR1656545 2 0.0000 0.960 0.000 1.000
#> SRR1656540 1 0.1633 0.836 0.976 0.024
#> SRR1656546 2 0.0376 0.958 0.004 0.996
#> SRR1656541 2 0.0000 0.960 0.000 1.000
#> SRR1656547 2 0.0000 0.960 0.000 1.000
#> SRR1656548 2 0.0672 0.956 0.008 0.992
#> SRR1656549 2 0.0000 0.960 0.000 1.000
#> SRR1656551 2 0.0000 0.960 0.000 1.000
#> SRR1656553 2 0.7950 0.654 0.240 0.760
#> SRR1656550 2 0.0000 0.960 0.000 1.000
#> SRR1656552 2 0.0000 0.960 0.000 1.000
#> SRR1656554 2 0.1414 0.948 0.020 0.980
#> SRR1656555 2 0.0000 0.960 0.000 1.000
#> SRR1656556 2 0.7950 0.656 0.240 0.760
#> SRR1656557 1 0.1414 0.836 0.980 0.020
#> SRR1656558 1 0.9661 0.532 0.608 0.392
#> SRR1656559 1 0.0000 0.828 1.000 0.000
#> SRR1656560 2 0.1184 0.951 0.016 0.984
#> SRR1656561 2 0.0672 0.956 0.008 0.992
#> SRR1656562 2 0.0000 0.960 0.000 1.000
#> SRR1656563 1 0.9850 0.457 0.572 0.428
#> SRR1656564 2 0.0000 0.960 0.000 1.000
#> SRR1656565 2 0.0000 0.960 0.000 1.000
#> SRR1656566 1 0.9732 0.508 0.596 0.404
#> SRR1656568 2 0.0000 0.960 0.000 1.000
#> SRR1656567 2 0.0000 0.960 0.000 1.000
#> SRR1656569 2 0.1414 0.948 0.020 0.980
#> SRR1656570 1 0.9850 0.457 0.572 0.428
#> SRR1656571 2 0.0000 0.960 0.000 1.000
#> SRR1656573 2 0.0000 0.960 0.000 1.000
#> SRR1656572 2 0.0000 0.960 0.000 1.000
#> SRR1656574 1 0.6343 0.782 0.840 0.160
#> SRR1656575 2 0.4161 0.885 0.084 0.916
#> SRR1656576 2 0.0000 0.960 0.000 1.000
#> SRR1656578 2 0.0672 0.956 0.008 0.992
#> SRR1656577 1 0.2236 0.838 0.964 0.036
#> SRR1656579 2 0.0000 0.960 0.000 1.000
#> SRR1656580 2 0.6973 0.743 0.188 0.812
#> SRR1656581 2 0.0938 0.954 0.012 0.988
#> SRR1656582 2 0.0000 0.960 0.000 1.000
#> SRR1656585 2 0.0376 0.958 0.004 0.996
#> SRR1656584 1 0.9732 0.508 0.596 0.404
#> SRR1656583 2 0.9209 0.426 0.336 0.664
#> SRR1656586 2 0.0000 0.960 0.000 1.000
#> SRR1656587 2 0.0938 0.954 0.012 0.988
#> SRR1656588 2 0.0000 0.960 0.000 1.000
#> SRR1656589 2 0.0000 0.960 0.000 1.000
#> SRR1656590 1 0.3733 0.831 0.928 0.072
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656464 2 0.3879 0.808 0.152 0.848 0.000
#> SRR1656462 2 0.2537 0.817 0.080 0.920 0.000
#> SRR1656465 3 0.5722 0.828 0.112 0.084 0.804
#> SRR1656467 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656466 3 0.6526 0.795 0.112 0.128 0.760
#> SRR1656468 3 0.3802 0.870 0.080 0.032 0.888
#> SRR1656472 2 0.3879 0.808 0.152 0.848 0.000
#> SRR1656471 3 0.7524 0.716 0.116 0.196 0.688
#> SRR1656470 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656469 3 0.3802 0.870 0.080 0.032 0.888
#> SRR1656473 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656474 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656475 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656478 1 0.5058 0.622 0.756 0.000 0.244
#> SRR1656477 3 0.4316 0.863 0.088 0.044 0.868
#> SRR1656479 3 0.1989 0.884 0.048 0.004 0.948
#> SRR1656480 3 0.4206 0.864 0.088 0.040 0.872
#> SRR1656476 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656481 3 0.3802 0.870 0.080 0.032 0.888
#> SRR1656482 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656483 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656485 3 0.6394 0.802 0.116 0.116 0.768
#> SRR1656487 3 0.6783 0.780 0.116 0.140 0.744
#> SRR1656486 3 0.0592 0.886 0.012 0.000 0.988
#> SRR1656488 3 0.6783 0.780 0.116 0.140 0.744
#> SRR1656484 3 0.5431 0.696 0.284 0.000 0.716
#> SRR1656489 1 0.5098 0.620 0.752 0.000 0.248
#> SRR1656491 3 0.2584 0.880 0.064 0.008 0.928
#> SRR1656490 3 0.1989 0.884 0.048 0.004 0.948
#> SRR1656492 3 0.5371 0.837 0.140 0.048 0.812
#> SRR1656493 1 0.4295 0.613 0.864 0.104 0.032
#> SRR1656495 1 0.2959 0.592 0.900 0.100 0.000
#> SRR1656496 3 0.2945 0.877 0.088 0.004 0.908
#> SRR1656494 3 0.0747 0.886 0.016 0.000 0.984
#> SRR1656497 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656499 3 0.8649 0.423 0.112 0.360 0.528
#> SRR1656500 2 0.8567 0.115 0.128 0.576 0.296
#> SRR1656501 3 0.0592 0.886 0.012 0.000 0.988
#> SRR1656498 1 0.4121 0.541 0.832 0.168 0.000
#> SRR1656504 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656502 2 0.3879 0.808 0.152 0.848 0.000
#> SRR1656503 3 0.5480 0.721 0.264 0.004 0.732
#> SRR1656507 1 0.5058 0.622 0.756 0.000 0.244
#> SRR1656508 1 0.3918 0.570 0.856 0.140 0.004
#> SRR1656505 3 0.4035 0.867 0.080 0.040 0.880
#> SRR1656506 3 0.5731 0.828 0.108 0.088 0.804
#> SRR1656509 3 0.7807 0.693 0.144 0.184 0.672
#> SRR1656510 3 0.3802 0.870 0.080 0.032 0.888
#> SRR1656511 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656513 3 0.0237 0.884 0.004 0.000 0.996
#> SRR1656512 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656514 2 0.3816 0.809 0.148 0.852 0.000
#> SRR1656515 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656516 3 0.5986 0.682 0.284 0.012 0.704
#> SRR1656518 1 0.5291 0.601 0.732 0.000 0.268
#> SRR1656517 1 0.3551 0.577 0.868 0.132 0.000
#> SRR1656519 2 0.3752 0.791 0.096 0.884 0.020
#> SRR1656522 2 0.3879 0.807 0.152 0.848 0.000
#> SRR1656523 3 0.0237 0.885 0.004 0.000 0.996
#> SRR1656521 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656520 2 0.2448 0.813 0.076 0.924 0.000
#> SRR1656524 1 0.2959 0.592 0.900 0.100 0.000
#> SRR1656525 3 0.5008 0.812 0.180 0.016 0.804
#> SRR1656526 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656527 3 0.1411 0.874 0.036 0.000 0.964
#> SRR1656530 3 0.3445 0.873 0.088 0.016 0.896
#> SRR1656529 3 0.5804 0.825 0.112 0.088 0.800
#> SRR1656531 1 0.5810 0.237 0.664 0.336 0.000
#> SRR1656528 3 0.5804 0.825 0.112 0.088 0.800
#> SRR1656534 2 0.3192 0.812 0.112 0.888 0.000
#> SRR1656533 1 0.3899 0.630 0.888 0.056 0.056
#> SRR1656536 3 0.4316 0.863 0.088 0.044 0.868
#> SRR1656532 3 0.0747 0.886 0.016 0.000 0.984
#> SRR1656537 1 0.4452 0.505 0.808 0.192 0.000
#> SRR1656538 3 0.6473 0.625 0.312 0.020 0.668
#> SRR1656535 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656539 3 0.5883 0.822 0.112 0.092 0.796
#> SRR1656544 3 0.7267 0.649 0.268 0.064 0.668
#> SRR1656542 2 0.9962 -0.129 0.292 0.364 0.344
#> SRR1656543 2 0.2625 0.818 0.084 0.916 0.000
#> SRR1656545 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656540 2 0.2448 0.811 0.076 0.924 0.000
#> SRR1656546 3 0.1643 0.870 0.044 0.000 0.956
#> SRR1656541 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656547 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656548 3 0.3607 0.868 0.112 0.008 0.880
#> SRR1656549 3 0.0592 0.885 0.012 0.000 0.988
#> SRR1656551 3 0.4035 0.867 0.080 0.040 0.880
#> SRR1656553 3 0.8009 0.567 0.276 0.100 0.624
#> SRR1656550 3 0.4316 0.863 0.088 0.044 0.868
#> SRR1656552 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656554 3 0.5804 0.825 0.112 0.088 0.800
#> SRR1656555 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656556 3 0.8513 0.513 0.116 0.316 0.568
#> SRR1656557 2 0.2625 0.818 0.084 0.916 0.000
#> SRR1656558 1 0.5058 0.622 0.756 0.000 0.244
#> SRR1656559 2 0.3879 0.807 0.152 0.848 0.000
#> SRR1656560 3 0.5722 0.828 0.112 0.084 0.804
#> SRR1656561 3 0.3607 0.868 0.112 0.008 0.880
#> SRR1656562 3 0.0237 0.884 0.004 0.000 0.996
#> SRR1656563 1 0.5397 0.585 0.720 0.000 0.280
#> SRR1656564 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656565 3 0.1643 0.884 0.044 0.000 0.956
#> SRR1656566 1 0.5443 0.610 0.736 0.004 0.260
#> SRR1656568 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656567 3 0.4146 0.866 0.080 0.044 0.876
#> SRR1656569 3 0.5650 0.830 0.108 0.084 0.808
#> SRR1656570 1 0.5397 0.585 0.720 0.000 0.280
#> SRR1656571 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656573 3 0.2772 0.878 0.080 0.004 0.916
#> SRR1656572 3 0.0424 0.885 0.008 0.000 0.992
#> SRR1656574 1 0.3899 0.630 0.888 0.056 0.056
#> SRR1656575 3 0.4702 0.790 0.212 0.000 0.788
#> SRR1656576 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656578 3 0.0747 0.886 0.016 0.000 0.984
#> SRR1656577 1 0.4062 0.543 0.836 0.164 0.000
#> SRR1656579 3 0.0829 0.883 0.012 0.004 0.984
#> SRR1656580 3 0.6416 0.640 0.304 0.020 0.676
#> SRR1656581 3 0.2261 0.882 0.068 0.000 0.932
#> SRR1656582 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656585 3 0.2774 0.879 0.072 0.008 0.920
#> SRR1656584 1 0.5443 0.610 0.736 0.004 0.260
#> SRR1656583 3 0.9004 0.280 0.132 0.400 0.468
#> SRR1656586 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656587 3 0.1267 0.886 0.024 0.004 0.972
#> SRR1656588 3 0.4035 0.867 0.080 0.040 0.880
#> SRR1656589 3 0.0592 0.883 0.012 0.000 0.988
#> SRR1656590 1 0.4139 0.592 0.860 0.124 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656464 2 0.1211 0.87680 0.040 0.960 0.000 0.000
#> SRR1656462 2 0.3569 0.89544 0.000 0.804 0.196 0.000
#> SRR1656465 3 0.4222 0.75116 0.000 0.000 0.728 0.272
#> SRR1656467 4 0.0817 0.83011 0.000 0.000 0.024 0.976
#> SRR1656466 3 0.4088 0.74573 0.000 0.004 0.764 0.232
#> SRR1656468 3 0.4933 0.61298 0.000 0.000 0.568 0.432
#> SRR1656472 2 0.1302 0.87473 0.044 0.956 0.000 0.000
#> SRR1656471 3 0.5072 0.71526 0.000 0.052 0.740 0.208
#> SRR1656470 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656469 4 0.4985 -0.36068 0.000 0.000 0.468 0.532
#> SRR1656473 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656474 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656475 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656478 1 0.6215 0.69238 0.664 0.000 0.208 0.128
#> SRR1656477 3 0.4790 0.68875 0.000 0.000 0.620 0.380
#> SRR1656479 4 0.2944 0.73440 0.004 0.000 0.128 0.868
#> SRR1656480 3 0.4817 0.68125 0.000 0.000 0.612 0.388
#> SRR1656476 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656481 3 0.4994 0.49571 0.000 0.000 0.520 0.480
#> SRR1656482 4 0.0336 0.83878 0.000 0.000 0.008 0.992
#> SRR1656483 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656485 3 0.4122 0.74674 0.000 0.004 0.760 0.236
#> SRR1656487 3 0.3726 0.73760 0.000 0.000 0.788 0.212
#> SRR1656486 4 0.2053 0.80260 0.004 0.000 0.072 0.924
#> SRR1656488 3 0.3726 0.73760 0.000 0.000 0.788 0.212
#> SRR1656484 3 0.7494 0.52694 0.188 0.000 0.460 0.352
#> SRR1656489 1 0.6262 0.68776 0.660 0.000 0.208 0.132
#> SRR1656491 4 0.4103 0.48187 0.000 0.000 0.256 0.744
#> SRR1656490 4 0.2944 0.73440 0.004 0.000 0.128 0.868
#> SRR1656492 3 0.5383 0.71510 0.024 0.004 0.664 0.308
#> SRR1656493 1 0.1762 0.70590 0.952 0.016 0.020 0.012
#> SRR1656495 1 0.0895 0.69214 0.976 0.020 0.004 0.000
#> SRR1656496 4 0.4999 0.28480 0.012 0.000 0.328 0.660
#> SRR1656494 4 0.1902 0.80966 0.004 0.000 0.064 0.932
#> SRR1656497 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656499 3 0.4840 0.51585 0.000 0.116 0.784 0.100
#> SRR1656500 3 0.5170 -0.12159 0.008 0.324 0.660 0.008
#> SRR1656501 4 0.2125 0.79938 0.004 0.000 0.076 0.920
#> SRR1656498 1 0.3306 0.60663 0.840 0.156 0.004 0.000
#> SRR1656504 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656502 2 0.1302 0.87473 0.044 0.956 0.000 0.000
#> SRR1656503 4 0.7268 -0.28743 0.152 0.000 0.372 0.476
#> SRR1656507 1 0.6215 0.69238 0.664 0.000 0.208 0.128
#> SRR1656508 1 0.3335 0.63357 0.856 0.128 0.016 0.000
#> SRR1656505 3 0.4916 0.62896 0.000 0.000 0.576 0.424
#> SRR1656506 3 0.4624 0.71874 0.000 0.000 0.660 0.340
#> SRR1656509 3 0.5883 0.70966 0.024 0.044 0.700 0.232
#> SRR1656510 4 0.5000 -0.45272 0.000 0.000 0.496 0.504
#> SRR1656511 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656513 4 0.1022 0.82907 0.000 0.000 0.032 0.968
#> SRR1656512 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656514 2 0.0469 0.88580 0.012 0.988 0.000 0.000
#> SRR1656515 4 0.0188 0.83957 0.000 0.000 0.004 0.996
#> SRR1656516 3 0.7424 0.42997 0.168 0.000 0.424 0.408
#> SRR1656518 1 0.6472 0.66451 0.640 0.000 0.212 0.148
#> SRR1656517 1 0.1661 0.68263 0.944 0.052 0.004 0.000
#> SRR1656519 2 0.4682 0.87710 0.020 0.764 0.208 0.008
#> SRR1656522 2 0.0707 0.88476 0.020 0.980 0.000 0.000
#> SRR1656523 4 0.1209 0.82915 0.004 0.000 0.032 0.964
#> SRR1656521 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656520 2 0.3801 0.88792 0.000 0.780 0.220 0.000
#> SRR1656524 1 0.0895 0.69214 0.976 0.020 0.004 0.000
#> SRR1656525 4 0.6285 -0.19189 0.060 0.000 0.412 0.528
#> SRR1656526 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656527 4 0.1406 0.80379 0.024 0.000 0.016 0.960
#> SRR1656530 3 0.4855 0.66048 0.000 0.000 0.600 0.400
#> SRR1656529 3 0.4193 0.75120 0.000 0.000 0.732 0.268
#> SRR1656531 1 0.4781 0.34417 0.660 0.336 0.004 0.000
#> SRR1656528 3 0.4193 0.75120 0.000 0.000 0.732 0.268
#> SRR1656534 2 0.4095 0.89365 0.024 0.804 0.172 0.000
#> SRR1656533 1 0.3190 0.71742 0.880 0.008 0.096 0.016
#> SRR1656536 3 0.4790 0.68875 0.000 0.000 0.620 0.380
#> SRR1656532 4 0.1902 0.81154 0.004 0.000 0.064 0.932
#> SRR1656537 1 0.2714 0.64855 0.884 0.112 0.004 0.000
#> SRR1656538 3 0.7501 0.52763 0.196 0.000 0.472 0.332
#> SRR1656535 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656539 3 0.4164 0.75156 0.000 0.000 0.736 0.264
#> SRR1656544 3 0.7454 0.60827 0.176 0.004 0.512 0.308
#> SRR1656542 3 0.7946 0.03824 0.192 0.184 0.572 0.052
#> SRR1656543 2 0.3528 0.89649 0.000 0.808 0.192 0.000
#> SRR1656545 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656540 2 0.3873 0.88587 0.000 0.772 0.228 0.000
#> SRR1656546 4 0.1936 0.77911 0.032 0.000 0.028 0.940
#> SRR1656541 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656547 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656548 4 0.5039 0.00192 0.004 0.000 0.404 0.592
#> SRR1656549 4 0.1576 0.82274 0.004 0.000 0.048 0.948
#> SRR1656551 3 0.4916 0.62896 0.000 0.000 0.576 0.424
#> SRR1656553 3 0.7984 0.56914 0.164 0.040 0.540 0.256
#> SRR1656550 3 0.4790 0.68875 0.000 0.000 0.620 0.380
#> SRR1656552 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656554 3 0.4193 0.75120 0.000 0.000 0.732 0.268
#> SRR1656555 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656556 3 0.5102 0.59089 0.000 0.100 0.764 0.136
#> SRR1656557 2 0.3528 0.89649 0.000 0.808 0.192 0.000
#> SRR1656558 1 0.6215 0.69238 0.664 0.000 0.208 0.128
#> SRR1656559 2 0.0707 0.88476 0.020 0.980 0.000 0.000
#> SRR1656560 3 0.4250 0.75056 0.000 0.000 0.724 0.276
#> SRR1656561 4 0.5039 0.00192 0.004 0.000 0.404 0.592
#> SRR1656562 4 0.1022 0.82907 0.000 0.000 0.032 0.968
#> SRR1656563 1 0.6602 0.64465 0.628 0.000 0.208 0.164
#> SRR1656564 4 0.0376 0.83642 0.004 0.000 0.004 0.992
#> SRR1656565 4 0.3831 0.59939 0.004 0.000 0.204 0.792
#> SRR1656566 1 0.6320 0.68061 0.656 0.000 0.204 0.140
#> SRR1656568 4 0.0376 0.83647 0.004 0.000 0.004 0.992
#> SRR1656567 3 0.4855 0.66649 0.000 0.000 0.600 0.400
#> SRR1656569 3 0.4643 0.71718 0.000 0.000 0.656 0.344
#> SRR1656570 1 0.6602 0.64465 0.628 0.000 0.208 0.164
#> SRR1656571 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656573 4 0.4872 0.18401 0.004 0.000 0.356 0.640
#> SRR1656572 4 0.1978 0.80600 0.004 0.000 0.068 0.928
#> SRR1656574 1 0.3190 0.71742 0.880 0.008 0.096 0.016
#> SRR1656575 4 0.6346 0.29975 0.116 0.000 0.244 0.640
#> SRR1656576 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656578 4 0.1902 0.81154 0.004 0.000 0.064 0.932
#> SRR1656577 1 0.3450 0.60965 0.836 0.156 0.008 0.000
#> SRR1656579 4 0.1211 0.81838 0.000 0.000 0.040 0.960
#> SRR1656580 3 0.7469 0.52547 0.188 0.000 0.472 0.340
#> SRR1656581 4 0.4576 0.49977 0.012 0.000 0.260 0.728
#> SRR1656582 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656585 4 0.4837 0.18432 0.004 0.000 0.348 0.648
#> SRR1656584 1 0.6353 0.67888 0.652 0.000 0.208 0.140
#> SRR1656583 3 0.5327 0.40182 0.008 0.172 0.752 0.068
#> SRR1656586 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656587 4 0.2480 0.78369 0.008 0.000 0.088 0.904
#> SRR1656588 3 0.4916 0.62896 0.000 0.000 0.576 0.424
#> SRR1656589 4 0.0000 0.84141 0.000 0.000 0.000 1.000
#> SRR1656590 1 0.3160 0.65374 0.872 0.108 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 4 0.4654 0.8684 0.024 0.000 0.348 0.628 0.000
#> SRR1656462 3 0.1956 0.5734 0.000 0.000 0.916 0.076 0.008
#> SRR1656465 5 0.3615 0.7291 0.000 0.156 0.000 0.036 0.808
#> SRR1656467 2 0.1121 0.8444 0.000 0.956 0.000 0.000 0.044
#> SRR1656466 5 0.4307 0.7058 0.000 0.128 0.036 0.040 0.796
#> SRR1656468 5 0.4047 0.6723 0.000 0.320 0.000 0.004 0.676
#> SRR1656472 4 0.4540 0.8750 0.024 0.000 0.320 0.656 0.000
#> SRR1656471 5 0.4803 0.6403 0.000 0.096 0.092 0.040 0.772
#> SRR1656470 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.4622 0.4487 0.000 0.440 0.000 0.012 0.548
#> SRR1656473 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.6899 0.6444 0.520 0.036 0.000 0.284 0.160
#> SRR1656477 5 0.3741 0.7184 0.000 0.264 0.000 0.004 0.732
#> SRR1656479 2 0.3888 0.7315 0.000 0.800 0.000 0.064 0.136
#> SRR1656480 5 0.3838 0.7107 0.000 0.280 0.000 0.004 0.716
#> SRR1656476 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.4225 0.6087 0.000 0.364 0.000 0.004 0.632
#> SRR1656482 2 0.0404 0.8625 0.000 0.988 0.000 0.000 0.012
#> SRR1656483 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 5 0.3999 0.7049 0.000 0.124 0.020 0.044 0.812
#> SRR1656487 5 0.4042 0.6826 0.000 0.100 0.040 0.040 0.820
#> SRR1656486 2 0.3180 0.7947 0.000 0.856 0.000 0.068 0.076
#> SRR1656488 5 0.4042 0.6826 0.000 0.100 0.040 0.040 0.820
#> SRR1656484 5 0.7871 0.4465 0.152 0.244 0.000 0.144 0.460
#> SRR1656489 1 0.6928 0.6427 0.516 0.036 0.000 0.284 0.164
#> SRR1656491 2 0.4863 0.4236 0.000 0.656 0.000 0.048 0.296
#> SRR1656490 2 0.3888 0.7315 0.000 0.800 0.000 0.064 0.136
#> SRR1656492 5 0.4592 0.7151 0.004 0.228 0.008 0.032 0.728
#> SRR1656493 1 0.2086 0.6507 0.924 0.008 0.000 0.020 0.048
#> SRR1656495 1 0.0807 0.6367 0.976 0.000 0.000 0.012 0.012
#> SRR1656496 2 0.5829 0.1448 0.008 0.548 0.000 0.080 0.364
#> SRR1656494 2 0.2234 0.8341 0.004 0.916 0.000 0.044 0.036
#> SRR1656497 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 5 0.5615 0.4057 0.000 0.044 0.272 0.040 0.644
#> SRR1656500 3 0.5334 0.1396 0.000 0.000 0.512 0.052 0.436
#> SRR1656501 2 0.3239 0.7916 0.000 0.852 0.000 0.068 0.080
#> SRR1656498 1 0.2561 0.5591 0.856 0.000 0.000 0.144 0.000
#> SRR1656504 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 4 0.4540 0.8750 0.024 0.000 0.320 0.656 0.000
#> SRR1656503 5 0.7999 0.3488 0.124 0.352 0.000 0.160 0.364
#> SRR1656507 1 0.6899 0.6444 0.520 0.036 0.000 0.284 0.160
#> SRR1656508 1 0.2624 0.5819 0.872 0.000 0.000 0.116 0.012
#> SRR1656505 5 0.4066 0.6683 0.000 0.324 0.000 0.004 0.672
#> SRR1656506 5 0.4240 0.7329 0.000 0.228 0.000 0.036 0.736
#> SRR1656509 5 0.6135 0.6331 0.024 0.104 0.092 0.076 0.704
#> SRR1656510 5 0.4403 0.4747 0.000 0.436 0.000 0.004 0.560
#> SRR1656511 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.1661 0.8453 0.000 0.940 0.000 0.024 0.036
#> SRR1656512 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 4 0.4450 0.6208 0.004 0.000 0.488 0.508 0.000
#> SRR1656515 2 0.0703 0.8564 0.000 0.976 0.000 0.000 0.024
#> SRR1656516 5 0.8045 0.4317 0.120 0.256 0.000 0.208 0.416
#> SRR1656518 1 0.7226 0.6294 0.488 0.052 0.000 0.292 0.168
#> SRR1656517 1 0.1522 0.6232 0.944 0.000 0.000 0.044 0.012
#> SRR1656519 3 0.1851 0.5752 0.008 0.004 0.940 0.024 0.024
#> SRR1656522 3 0.4450 -0.6880 0.004 0.000 0.508 0.488 0.000
#> SRR1656523 2 0.2139 0.8357 0.000 0.916 0.000 0.052 0.032
#> SRR1656521 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0671 0.5936 0.000 0.000 0.980 0.004 0.016
#> SRR1656524 1 0.0807 0.6367 0.976 0.000 0.000 0.012 0.012
#> SRR1656525 5 0.7185 0.3113 0.036 0.400 0.004 0.148 0.412
#> SRR1656526 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.1651 0.8315 0.008 0.944 0.000 0.012 0.036
#> SRR1656530 5 0.4350 0.7077 0.000 0.268 0.000 0.028 0.704
#> SRR1656529 5 0.3489 0.7245 0.000 0.144 0.000 0.036 0.820
#> SRR1656531 1 0.4218 0.2699 0.660 0.000 0.008 0.332 0.000
#> SRR1656528 5 0.3531 0.7267 0.000 0.148 0.000 0.036 0.816
#> SRR1656534 3 0.1787 0.5720 0.016 0.000 0.940 0.032 0.012
#> SRR1656533 1 0.3759 0.6581 0.816 0.000 0.000 0.092 0.092
#> SRR1656536 5 0.3741 0.7184 0.000 0.264 0.000 0.004 0.732
#> SRR1656532 2 0.2313 0.8347 0.004 0.912 0.000 0.044 0.040
#> SRR1656537 1 0.2522 0.5816 0.880 0.000 0.000 0.108 0.012
#> SRR1656538 5 0.7908 0.4156 0.152 0.188 0.000 0.192 0.468
#> SRR1656535 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 5 0.3764 0.7232 0.000 0.148 0.004 0.040 0.808
#> SRR1656544 5 0.7891 0.5337 0.144 0.144 0.028 0.144 0.540
#> SRR1656542 5 0.8062 -0.0312 0.148 0.004 0.336 0.124 0.388
#> SRR1656543 3 0.1831 0.5701 0.000 0.000 0.920 0.076 0.004
#> SRR1656545 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0794 0.5901 0.000 0.000 0.972 0.000 0.028
#> SRR1656546 2 0.2198 0.8082 0.012 0.920 0.000 0.020 0.048
#> SRR1656541 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0290 0.8641 0.000 0.992 0.000 0.000 0.008
#> SRR1656548 2 0.6088 -0.0844 0.004 0.484 0.000 0.108 0.404
#> SRR1656549 2 0.2514 0.8243 0.000 0.896 0.000 0.060 0.044
#> SRR1656551 5 0.4047 0.6729 0.000 0.320 0.000 0.004 0.676
#> SRR1656553 5 0.8460 0.4788 0.136 0.144 0.076 0.136 0.508
#> SRR1656550 5 0.3715 0.7205 0.000 0.260 0.000 0.004 0.736
#> SRR1656552 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 5 0.3489 0.7245 0.000 0.144 0.000 0.036 0.820
#> SRR1656555 2 0.0162 0.8650 0.000 0.996 0.000 0.000 0.004
#> SRR1656556 5 0.5665 0.4873 0.000 0.068 0.220 0.040 0.672
#> SRR1656557 3 0.1831 0.5701 0.000 0.000 0.920 0.076 0.004
#> SRR1656558 1 0.6899 0.6444 0.520 0.036 0.000 0.284 0.160
#> SRR1656559 3 0.4450 -0.6880 0.004 0.000 0.508 0.488 0.000
#> SRR1656560 5 0.3573 0.7283 0.000 0.152 0.000 0.036 0.812
#> SRR1656561 2 0.6088 -0.0844 0.004 0.484 0.000 0.108 0.404
#> SRR1656562 2 0.1661 0.8453 0.000 0.940 0.000 0.024 0.036
#> SRR1656563 1 0.7546 0.6075 0.448 0.064 0.000 0.292 0.196
#> SRR1656564 2 0.0798 0.8560 0.000 0.976 0.000 0.008 0.016
#> SRR1656565 2 0.4555 0.5811 0.000 0.720 0.000 0.056 0.224
#> SRR1656566 1 0.7072 0.6388 0.508 0.048 0.000 0.288 0.156
#> SRR1656568 2 0.0693 0.8574 0.000 0.980 0.000 0.008 0.012
#> SRR1656567 5 0.3906 0.7006 0.000 0.292 0.000 0.004 0.704
#> SRR1656569 5 0.4297 0.7305 0.000 0.236 0.000 0.036 0.728
#> SRR1656570 1 0.7546 0.6075 0.448 0.064 0.000 0.292 0.196
#> SRR1656571 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 2 0.5513 0.0288 0.000 0.524 0.000 0.068 0.408
#> SRR1656572 2 0.3055 0.8008 0.000 0.864 0.000 0.064 0.072
#> SRR1656574 1 0.3759 0.6581 0.816 0.000 0.000 0.092 0.092
#> SRR1656575 2 0.7004 0.2998 0.064 0.556 0.000 0.156 0.224
#> SRR1656576 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.2387 0.8332 0.004 0.908 0.000 0.048 0.040
#> SRR1656577 1 0.2719 0.5614 0.852 0.000 0.000 0.144 0.004
#> SRR1656579 2 0.1671 0.8149 0.000 0.924 0.000 0.000 0.076
#> SRR1656580 5 0.7861 0.4323 0.152 0.180 0.000 0.192 0.476
#> SRR1656581 2 0.5275 0.4445 0.004 0.640 0.000 0.068 0.288
#> SRR1656582 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 2 0.5483 -0.0856 0.000 0.512 0.000 0.064 0.424
#> SRR1656584 1 0.7101 0.6376 0.504 0.048 0.000 0.288 0.160
#> SRR1656583 5 0.5805 0.2979 0.008 0.024 0.304 0.048 0.616
#> SRR1656586 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 2 0.3021 0.8038 0.004 0.872 0.000 0.064 0.060
#> SRR1656588 5 0.4047 0.6728 0.000 0.320 0.000 0.004 0.676
#> SRR1656589 2 0.0000 0.8660 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.2616 0.6044 0.880 0.000 0.000 0.100 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 6 0.1152 0.7354 0.000 0.000 0.044 0.004 0.000 0.952
#> SRR1656462 3 0.2001 0.7420 0.004 0.000 0.900 0.000 0.004 0.092
#> SRR1656465 5 0.1219 0.7123 0.004 0.048 0.000 0.000 0.948 0.000
#> SRR1656467 2 0.1610 0.8235 0.000 0.916 0.000 0.000 0.084 0.000
#> SRR1656466 5 0.2168 0.6988 0.016 0.036 0.028 0.000 0.916 0.004
#> SRR1656468 5 0.4092 0.6822 0.060 0.196 0.000 0.000 0.740 0.004
#> SRR1656472 6 0.0603 0.7305 0.000 0.000 0.016 0.004 0.000 0.980
#> SRR1656471 5 0.2905 0.6370 0.016 0.008 0.084 0.004 0.872 0.016
#> SRR1656470 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.4867 0.5301 0.068 0.320 0.000 0.000 0.608 0.004
#> SRR1656473 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2768 0.5323 0.832 0.012 0.000 0.156 0.000 0.000
#> SRR1656477 5 0.3576 0.7084 0.060 0.136 0.000 0.000 0.800 0.004
#> SRR1656479 2 0.4051 0.7205 0.164 0.756 0.000 0.000 0.076 0.004
#> SRR1656480 5 0.3835 0.7019 0.060 0.164 0.000 0.000 0.772 0.004
#> SRR1656476 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 5 0.4390 0.6400 0.064 0.232 0.000 0.000 0.700 0.004
#> SRR1656482 2 0.0458 0.8645 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1656483 2 0.0146 0.8669 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1656485 5 0.1774 0.6932 0.020 0.024 0.016 0.000 0.936 0.004
#> SRR1656487 5 0.1749 0.6781 0.016 0.012 0.032 0.000 0.936 0.004
#> SRR1656486 2 0.3094 0.7855 0.140 0.824 0.000 0.000 0.036 0.000
#> SRR1656488 5 0.1749 0.6781 0.016 0.012 0.032 0.000 0.936 0.004
#> SRR1656484 1 0.6215 0.0376 0.436 0.180 0.000 0.012 0.368 0.004
#> SRR1656489 1 0.2730 0.5358 0.836 0.012 0.000 0.152 0.000 0.000
#> SRR1656491 2 0.5480 0.3893 0.164 0.580 0.000 0.000 0.252 0.004
#> SRR1656490 2 0.4051 0.7205 0.164 0.756 0.000 0.000 0.076 0.004
#> SRR1656492 5 0.4912 0.6246 0.156 0.152 0.004 0.000 0.684 0.004
#> SRR1656493 4 0.2165 0.7710 0.108 0.008 0.000 0.884 0.000 0.000
#> SRR1656495 4 0.1075 0.7857 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR1656496 2 0.6029 0.0921 0.232 0.460 0.000 0.000 0.304 0.004
#> SRR1656494 2 0.2230 0.8327 0.084 0.892 0.000 0.000 0.024 0.000
#> SRR1656497 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 5 0.4310 0.4239 0.012 0.004 0.272 0.004 0.692 0.016
#> SRR1656500 3 0.4917 0.1645 0.024 0.000 0.512 0.004 0.444 0.016
#> SRR1656501 2 0.3163 0.7830 0.140 0.820 0.000 0.000 0.040 0.000
#> SRR1656498 4 0.4240 0.7804 0.124 0.000 0.000 0.736 0.000 0.140
#> SRR1656504 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.0603 0.7305 0.000 0.000 0.016 0.004 0.000 0.980
#> SRR1656503 1 0.5983 0.0719 0.432 0.244 0.000 0.000 0.324 0.000
#> SRR1656507 1 0.2768 0.5323 0.832 0.012 0.000 0.156 0.000 0.000
#> SRR1656508 4 0.4043 0.7885 0.128 0.000 0.000 0.756 0.000 0.116
#> SRR1656505 5 0.4121 0.6802 0.060 0.200 0.000 0.000 0.736 0.004
#> SRR1656506 5 0.2234 0.7030 0.004 0.124 0.000 0.000 0.872 0.000
#> SRR1656509 5 0.4340 0.5744 0.108 0.012 0.080 0.004 0.780 0.016
#> SRR1656510 5 0.4996 0.4528 0.064 0.384 0.000 0.000 0.548 0.004
#> SRR1656511 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656513 2 0.1745 0.8477 0.056 0.924 0.000 0.000 0.020 0.000
#> SRR1656512 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 6 0.3586 0.6582 0.004 0.000 0.280 0.000 0.004 0.712
#> SRR1656515 2 0.1075 0.8498 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR1656516 1 0.5637 0.1615 0.480 0.156 0.000 0.000 0.364 0.000
#> SRR1656518 1 0.2790 0.5450 0.844 0.024 0.000 0.132 0.000 0.000
#> SRR1656517 4 0.0777 0.7781 0.024 0.000 0.000 0.972 0.000 0.004
#> SRR1656519 3 0.1742 0.7507 0.040 0.004 0.936 0.008 0.008 0.004
#> SRR1656522 6 0.4310 0.4962 0.000 0.000 0.440 0.020 0.000 0.540
#> SRR1656523 2 0.2053 0.8270 0.108 0.888 0.000 0.000 0.004 0.000
#> SRR1656521 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.1086 0.7624 0.012 0.000 0.964 0.012 0.012 0.000
#> SRR1656524 4 0.1075 0.7857 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR1656525 5 0.6343 0.0565 0.332 0.288 0.004 0.000 0.372 0.004
#> SRR1656526 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.1349 0.8448 0.056 0.940 0.000 0.004 0.000 0.000
#> SRR1656530 5 0.4250 0.6700 0.108 0.144 0.000 0.000 0.744 0.004
#> SRR1656529 5 0.0790 0.7059 0.000 0.032 0.000 0.000 0.968 0.000
#> SRR1656531 4 0.3531 0.5618 0.000 0.000 0.000 0.672 0.000 0.328
#> SRR1656528 5 0.0865 0.7080 0.000 0.036 0.000 0.000 0.964 0.000
#> SRR1656534 3 0.2058 0.7439 0.024 0.000 0.924 0.012 0.012 0.028
#> SRR1656533 4 0.3862 0.5020 0.476 0.000 0.000 0.524 0.000 0.000
#> SRR1656536 5 0.3576 0.7084 0.060 0.136 0.000 0.000 0.800 0.004
#> SRR1656532 2 0.2121 0.8328 0.096 0.892 0.000 0.000 0.012 0.000
#> SRR1656537 4 0.1531 0.7367 0.004 0.000 0.000 0.928 0.000 0.068
#> SRR1656538 1 0.5201 0.1690 0.500 0.092 0.000 0.000 0.408 0.000
#> SRR1656535 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.1536 0.7065 0.016 0.040 0.000 0.000 0.940 0.004
#> SRR1656544 5 0.4994 0.1566 0.392 0.032 0.024 0.000 0.552 0.000
#> SRR1656542 1 0.6122 0.0741 0.356 0.000 0.336 0.000 0.308 0.000
#> SRR1656543 3 0.1714 0.7395 0.000 0.000 0.908 0.000 0.000 0.092
#> SRR1656545 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.1116 0.7620 0.008 0.000 0.960 0.000 0.028 0.004
#> SRR1656546 2 0.1700 0.8267 0.080 0.916 0.000 0.004 0.000 0.000
#> SRR1656541 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.0547 0.8640 0.000 0.980 0.000 0.000 0.020 0.000
#> SRR1656548 2 0.6170 -0.0785 0.264 0.404 0.000 0.000 0.328 0.004
#> SRR1656549 2 0.2278 0.8156 0.128 0.868 0.000 0.000 0.004 0.000
#> SRR1656551 5 0.4092 0.6834 0.060 0.196 0.000 0.000 0.740 0.004
#> SRR1656553 5 0.6199 0.0440 0.364 0.076 0.076 0.000 0.484 0.000
#> SRR1656550 5 0.3536 0.7090 0.060 0.132 0.000 0.000 0.804 0.004
#> SRR1656552 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656554 5 0.0790 0.7059 0.000 0.032 0.000 0.000 0.968 0.000
#> SRR1656555 2 0.0363 0.8654 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1656556 5 0.4033 0.5045 0.016 0.004 0.212 0.004 0.748 0.016
#> SRR1656557 3 0.1714 0.7395 0.000 0.000 0.908 0.000 0.000 0.092
#> SRR1656558 1 0.2768 0.5323 0.832 0.012 0.000 0.156 0.000 0.000
#> SRR1656559 6 0.4310 0.4962 0.000 0.000 0.440 0.020 0.000 0.540
#> SRR1656560 5 0.0937 0.7102 0.000 0.040 0.000 0.000 0.960 0.000
#> SRR1656561 2 0.6170 -0.0785 0.264 0.404 0.000 0.000 0.328 0.004
#> SRR1656562 2 0.1829 0.8472 0.056 0.920 0.000 0.000 0.024 0.000
#> SRR1656563 1 0.1668 0.5386 0.928 0.008 0.000 0.060 0.004 0.000
#> SRR1656564 2 0.0713 0.8603 0.028 0.972 0.000 0.000 0.000 0.000
#> SRR1656565 2 0.4799 0.5899 0.140 0.684 0.000 0.000 0.172 0.004
#> SRR1656566 1 0.2907 0.5335 0.828 0.020 0.000 0.152 0.000 0.000
#> SRR1656568 2 0.0632 0.8613 0.024 0.976 0.000 0.000 0.000 0.000
#> SRR1656567 5 0.4031 0.6871 0.060 0.188 0.000 0.000 0.748 0.004
#> SRR1656569 5 0.2362 0.7026 0.004 0.136 0.000 0.000 0.860 0.000
#> SRR1656570 1 0.1668 0.5386 0.928 0.008 0.000 0.060 0.004 0.000
#> SRR1656571 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 2 0.6033 -0.0431 0.212 0.432 0.000 0.000 0.352 0.004
#> SRR1656572 2 0.3014 0.7924 0.132 0.832 0.000 0.000 0.036 0.000
#> SRR1656574 4 0.3862 0.5020 0.476 0.000 0.000 0.524 0.000 0.000
#> SRR1656575 2 0.5536 0.2505 0.380 0.504 0.000 0.008 0.108 0.000
#> SRR1656576 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 2 0.2170 0.8310 0.100 0.888 0.000 0.000 0.012 0.000
#> SRR1656577 4 0.4316 0.7783 0.128 0.000 0.000 0.728 0.000 0.144
#> SRR1656579 2 0.2092 0.7822 0.000 0.876 0.000 0.000 0.124 0.000
#> SRR1656580 1 0.5061 0.1295 0.496 0.076 0.000 0.000 0.428 0.000
#> SRR1656581 2 0.5576 0.4073 0.204 0.572 0.000 0.000 0.220 0.004
#> SRR1656582 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 5 0.5963 0.1927 0.188 0.396 0.000 0.000 0.412 0.004
#> SRR1656584 1 0.2869 0.5377 0.832 0.020 0.000 0.148 0.000 0.000
#> SRR1656583 5 0.5252 0.3264 0.048 0.004 0.276 0.016 0.640 0.016
#> SRR1656586 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 2 0.3066 0.7933 0.124 0.832 0.000 0.000 0.044 0.000
#> SRR1656588 5 0.4092 0.6832 0.060 0.196 0.000 0.000 0.740 0.004
#> SRR1656589 2 0.0000 0.8678 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 4 0.4516 0.7753 0.188 0.000 0.000 0.700 0.000 0.112
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 13572 rows and 129 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 0.980 0.942 0.974 0.4677 0.525 0.525
#> 3 3 0.755 0.864 0.920 0.3853 0.673 0.452
#> 4 4 0.725 0.757 0.877 0.1126 0.874 0.666
#> 5 5 0.713 0.548 0.768 0.0804 0.864 0.576
#> 6 6 0.747 0.623 0.799 0.0475 0.866 0.501
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
#> SRR1656463 2 0.0000 0.988 0.000 1.000
#> SRR1656464 1 0.0000 0.948 1.000 0.000
#> SRR1656462 1 0.0000 0.948 1.000 0.000
#> SRR1656465 2 0.0376 0.985 0.004 0.996
#> SRR1656467 2 0.0000 0.988 0.000 1.000
#> SRR1656466 1 0.9775 0.358 0.588 0.412
#> SRR1656468 2 0.0000 0.988 0.000 1.000
#> SRR1656472 1 0.0000 0.948 1.000 0.000
#> SRR1656471 1 0.9323 0.514 0.652 0.348
#> SRR1656470 2 0.0000 0.988 0.000 1.000
#> SRR1656469 2 0.0000 0.988 0.000 1.000
#> SRR1656473 2 0.0000 0.988 0.000 1.000
#> SRR1656474 2 0.0000 0.988 0.000 1.000
#> SRR1656475 2 0.0000 0.988 0.000 1.000
#> SRR1656478 1 0.0000 0.948 1.000 0.000
#> SRR1656477 2 0.0000 0.988 0.000 1.000
#> SRR1656479 2 0.0000 0.988 0.000 1.000
#> SRR1656480 2 0.0000 0.988 0.000 1.000
#> SRR1656476 2 0.0000 0.988 0.000 1.000
#> SRR1656481 2 0.0000 0.988 0.000 1.000
#> SRR1656482 2 0.0000 0.988 0.000 1.000
#> SRR1656483 2 0.0000 0.988 0.000 1.000
#> SRR1656485 1 0.9170 0.546 0.668 0.332
#> SRR1656487 2 0.3733 0.919 0.072 0.928
#> SRR1656486 2 0.0000 0.988 0.000 1.000
#> SRR1656488 1 0.9286 0.522 0.656 0.344
#> SRR1656484 1 0.1414 0.935 0.980 0.020
#> SRR1656489 1 0.0000 0.948 1.000 0.000
#> SRR1656491 2 0.0000 0.988 0.000 1.000
#> SRR1656490 2 0.0000 0.988 0.000 1.000
#> SRR1656492 2 0.3733 0.919 0.072 0.928
#> SRR1656493 1 0.0000 0.948 1.000 0.000
#> SRR1656495 1 0.0000 0.948 1.000 0.000
#> SRR1656496 2 0.0376 0.985 0.004 0.996
#> SRR1656494 2 0.0000 0.988 0.000 1.000
#> SRR1656497 2 0.0000 0.988 0.000 1.000
#> SRR1656499 1 0.0000 0.948 1.000 0.000
#> SRR1656500 1 0.0000 0.948 1.000 0.000
#> SRR1656501 2 0.0000 0.988 0.000 1.000
#> SRR1656498 1 0.0000 0.948 1.000 0.000
#> SRR1656504 2 0.0000 0.988 0.000 1.000
#> SRR1656502 1 0.0000 0.948 1.000 0.000
#> SRR1656503 2 0.9393 0.431 0.356 0.644
#> SRR1656507 1 0.0000 0.948 1.000 0.000
#> SRR1656508 1 0.0000 0.948 1.000 0.000
#> SRR1656505 2 0.0000 0.988 0.000 1.000
#> SRR1656506 2 0.0000 0.988 0.000 1.000
#> SRR1656509 1 0.4562 0.870 0.904 0.096
#> SRR1656510 2 0.0000 0.988 0.000 1.000
#> SRR1656511 2 0.0000 0.988 0.000 1.000
#> SRR1656513 2 0.0000 0.988 0.000 1.000
#> SRR1656512 2 0.0000 0.988 0.000 1.000
#> SRR1656514 1 0.0000 0.948 1.000 0.000
#> SRR1656515 2 0.0000 0.988 0.000 1.000
#> SRR1656516 2 0.4298 0.901 0.088 0.912
#> SRR1656518 1 0.9087 0.535 0.676 0.324
#> SRR1656517 1 0.0000 0.948 1.000 0.000
#> SRR1656519 1 0.0000 0.948 1.000 0.000
#> SRR1656522 1 0.0000 0.948 1.000 0.000
#> SRR1656523 2 0.0000 0.988 0.000 1.000
#> SRR1656521 2 0.0000 0.988 0.000 1.000
#> SRR1656520 1 0.0000 0.948 1.000 0.000
#> SRR1656524 1 0.0000 0.948 1.000 0.000
#> SRR1656525 2 0.3733 0.919 0.072 0.928
#> SRR1656526 2 0.0000 0.988 0.000 1.000
#> SRR1656527 2 0.0000 0.988 0.000 1.000
#> SRR1656530 2 0.0000 0.988 0.000 1.000
#> SRR1656529 2 0.0000 0.988 0.000 1.000
#> SRR1656531 1 0.0000 0.948 1.000 0.000
#> SRR1656528 2 0.3584 0.924 0.068 0.932
#> SRR1656534 1 0.0000 0.948 1.000 0.000
#> SRR1656533 1 0.0000 0.948 1.000 0.000
#> SRR1656536 2 0.0000 0.988 0.000 1.000
#> SRR1656532 2 0.0000 0.988 0.000 1.000
#> SRR1656537 1 0.0000 0.948 1.000 0.000
#> SRR1656538 1 0.5519 0.838 0.872 0.128
#> SRR1656535 2 0.0000 0.988 0.000 1.000
#> SRR1656539 2 0.3584 0.924 0.068 0.932
#> SRR1656544 1 0.9358 0.506 0.648 0.352
#> SRR1656542 1 0.0000 0.948 1.000 0.000
#> SRR1656543 1 0.0000 0.948 1.000 0.000
#> SRR1656545 2 0.0000 0.988 0.000 1.000
#> SRR1656540 1 0.0000 0.948 1.000 0.000
#> SRR1656546 2 0.0000 0.988 0.000 1.000
#> SRR1656541 2 0.0000 0.988 0.000 1.000
#> SRR1656547 2 0.0000 0.988 0.000 1.000
#> SRR1656548 2 0.0000 0.988 0.000 1.000
#> SRR1656549 2 0.0000 0.988 0.000 1.000
#> SRR1656551 2 0.0000 0.988 0.000 1.000
#> SRR1656553 1 0.0000 0.948 1.000 0.000
#> SRR1656550 2 0.0000 0.988 0.000 1.000
#> SRR1656552 2 0.0000 0.988 0.000 1.000
#> SRR1656554 2 0.0000 0.988 0.000 1.000
#> SRR1656555 2 0.0000 0.988 0.000 1.000
#> SRR1656556 1 0.0938 0.940 0.988 0.012
#> SRR1656557 1 0.0000 0.948 1.000 0.000
#> SRR1656558 1 0.0000 0.948 1.000 0.000
#> SRR1656559 1 0.0000 0.948 1.000 0.000
#> SRR1656560 2 0.3584 0.924 0.068 0.932
#> SRR1656561 2 0.0000 0.988 0.000 1.000
#> SRR1656562 2 0.0000 0.988 0.000 1.000
#> SRR1656563 1 0.0000 0.948 1.000 0.000
#> SRR1656564 2 0.0000 0.988 0.000 1.000
#> SRR1656565 2 0.0000 0.988 0.000 1.000
#> SRR1656566 1 0.0000 0.948 1.000 0.000
#> SRR1656568 2 0.0000 0.988 0.000 1.000
#> SRR1656567 2 0.0000 0.988 0.000 1.000
#> SRR1656569 2 0.0000 0.988 0.000 1.000
#> SRR1656570 1 0.1184 0.938 0.984 0.016
#> SRR1656571 2 0.0000 0.988 0.000 1.000
#> SRR1656573 2 0.0000 0.988 0.000 1.000
#> SRR1656572 2 0.0000 0.988 0.000 1.000
#> SRR1656574 1 0.0000 0.948 1.000 0.000
#> SRR1656575 2 0.0376 0.985 0.004 0.996
#> SRR1656576 2 0.0000 0.988 0.000 1.000
#> SRR1656578 2 0.0000 0.988 0.000 1.000
#> SRR1656577 1 0.0000 0.948 1.000 0.000
#> SRR1656579 2 0.0000 0.988 0.000 1.000
#> SRR1656580 1 0.0000 0.948 1.000 0.000
#> SRR1656581 2 0.0000 0.988 0.000 1.000
#> SRR1656582 2 0.0000 0.988 0.000 1.000
#> SRR1656585 2 0.0000 0.988 0.000 1.000
#> SRR1656584 1 0.2236 0.923 0.964 0.036
#> SRR1656583 1 0.0000 0.948 1.000 0.000
#> SRR1656586 2 0.0000 0.988 0.000 1.000
#> SRR1656587 2 0.0376 0.985 0.004 0.996
#> SRR1656588 2 0.0000 0.988 0.000 1.000
#> SRR1656589 2 0.0000 0.988 0.000 1.000
#> SRR1656590 1 0.0000 0.948 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656464 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656462 1 0.4750 0.818 0.784 0.000 0.216
#> SRR1656465 3 0.2066 0.888 0.000 0.060 0.940
#> SRR1656467 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656466 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1656468 2 0.1643 0.911 0.000 0.956 0.044
#> SRR1656472 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656471 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1656470 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656469 3 0.5098 0.771 0.000 0.248 0.752
#> SRR1656473 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656478 1 0.2066 0.902 0.940 0.000 0.060
#> SRR1656477 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656479 3 0.4887 0.794 0.000 0.228 0.772
#> SRR1656480 3 0.5138 0.767 0.000 0.252 0.748
#> SRR1656476 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656481 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656482 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656485 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1656487 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1656486 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656488 3 0.0592 0.869 0.000 0.012 0.988
#> SRR1656484 3 0.4504 0.766 0.196 0.000 0.804
#> SRR1656489 1 0.2066 0.902 0.940 0.000 0.060
#> SRR1656491 3 0.4887 0.794 0.000 0.228 0.772
#> SRR1656490 3 0.5465 0.722 0.000 0.288 0.712
#> SRR1656492 3 0.1860 0.886 0.000 0.052 0.948
#> SRR1656493 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656495 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656496 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656494 3 0.4887 0.794 0.000 0.228 0.772
#> SRR1656497 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656499 3 0.0592 0.860 0.012 0.000 0.988
#> SRR1656500 1 0.5327 0.759 0.728 0.000 0.272
#> SRR1656501 3 0.5138 0.770 0.000 0.252 0.748
#> SRR1656498 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656504 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656502 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656503 3 0.1753 0.859 0.048 0.000 0.952
#> SRR1656507 3 0.5216 0.697 0.260 0.000 0.740
#> SRR1656508 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656505 2 0.6026 0.286 0.000 0.624 0.376
#> SRR1656506 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656509 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1656510 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656511 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656514 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656515 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656516 3 0.2527 0.882 0.020 0.044 0.936
#> SRR1656518 3 0.5327 0.684 0.272 0.000 0.728
#> SRR1656517 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656519 1 0.1860 0.920 0.948 0.000 0.052
#> SRR1656522 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656523 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656520 1 0.4750 0.818 0.784 0.000 0.216
#> SRR1656524 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656525 3 0.1964 0.887 0.000 0.056 0.944
#> SRR1656526 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656530 3 0.2959 0.877 0.000 0.100 0.900
#> SRR1656529 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656531 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1656528 3 0.2066 0.888 0.000 0.060 0.940
#> SRR1656534 1 0.1860 0.920 0.948 0.000 0.052
#> SRR1656533 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656536 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656532 2 0.0592 0.943 0.000 0.988 0.012
#> SRR1656537 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1656538 3 0.1999 0.869 0.036 0.012 0.952
#> SRR1656535 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656539 3 0.2066 0.888 0.000 0.060 0.940
#> SRR1656544 3 0.1163 0.877 0.000 0.028 0.972
#> SRR1656542 1 0.5926 0.652 0.644 0.000 0.356
#> SRR1656543 1 0.4750 0.818 0.784 0.000 0.216
#> SRR1656545 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656540 1 0.5098 0.785 0.752 0.000 0.248
#> SRR1656546 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656541 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656548 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656549 2 0.0424 0.948 0.000 0.992 0.008
#> SRR1656551 2 0.6154 0.176 0.000 0.592 0.408
#> SRR1656553 3 0.0000 0.862 0.000 0.000 1.000
#> SRR1656550 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656552 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656554 3 0.2165 0.888 0.000 0.064 0.936
#> SRR1656555 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656556 3 0.0592 0.860 0.012 0.000 0.988
#> SRR1656557 1 0.5098 0.785 0.752 0.000 0.248
#> SRR1656558 1 0.1753 0.909 0.952 0.000 0.048
#> SRR1656559 1 0.1753 0.921 0.952 0.000 0.048
#> SRR1656560 3 0.2066 0.888 0.000 0.060 0.940
#> SRR1656561 3 0.4291 0.834 0.000 0.180 0.820
#> SRR1656562 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656563 3 0.5327 0.684 0.272 0.000 0.728
#> SRR1656564 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656566 1 0.2066 0.902 0.940 0.000 0.060
#> SRR1656568 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656567 2 0.6026 0.286 0.000 0.624 0.376
#> SRR1656569 3 0.3752 0.858 0.000 0.144 0.856
#> SRR1656570 3 0.5327 0.684 0.272 0.000 0.728
#> SRR1656571 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656573 3 0.4887 0.794 0.000 0.228 0.772
#> SRR1656572 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656574 1 0.0592 0.924 0.988 0.000 0.012
#> SRR1656575 3 0.5803 0.755 0.016 0.248 0.736
#> SRR1656576 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656578 2 0.0592 0.943 0.000 0.988 0.012
#> SRR1656577 1 0.0000 0.924 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656580 3 0.1289 0.862 0.032 0.000 0.968
#> SRR1656581 2 0.6309 -0.213 0.000 0.500 0.500
#> SRR1656582 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656585 3 0.4750 0.805 0.000 0.216 0.784
#> SRR1656584 3 0.5327 0.684 0.272 0.000 0.728
#> SRR1656583 3 0.0592 0.860 0.012 0.000 0.988
#> SRR1656586 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656587 3 0.4750 0.805 0.000 0.216 0.784
#> SRR1656588 3 0.5327 0.739 0.000 0.272 0.728
#> SRR1656589 2 0.0000 0.956 0.000 1.000 0.000
#> SRR1656590 1 0.0592 0.924 0.988 0.000 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656464 2 0.2814 0.7989 0.132 0.868 0.000 0.000
#> SRR1656462 2 0.2048 0.7916 0.008 0.928 0.064 0.000
#> SRR1656465 3 0.0376 0.8576 0.004 0.004 0.992 0.000
#> SRR1656467 4 0.0657 0.9267 0.012 0.000 0.004 0.984
#> SRR1656466 3 0.1978 0.8300 0.004 0.068 0.928 0.000
#> SRR1656468 4 0.5912 0.1068 0.036 0.000 0.440 0.524
#> SRR1656472 2 0.2814 0.7989 0.132 0.868 0.000 0.000
#> SRR1656471 3 0.2654 0.8052 0.004 0.108 0.888 0.000
#> SRR1656470 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656469 3 0.2892 0.8404 0.036 0.000 0.896 0.068
#> SRR1656473 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656474 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656475 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656478 1 0.1510 0.7158 0.956 0.028 0.016 0.000
#> SRR1656477 3 0.0992 0.8606 0.012 0.004 0.976 0.008
#> SRR1656479 3 0.5036 0.6450 0.280 0.000 0.696 0.024
#> SRR1656480 3 0.2965 0.8388 0.036 0.000 0.892 0.072
#> SRR1656476 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656481 3 0.0992 0.8606 0.012 0.004 0.976 0.008
#> SRR1656482 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656483 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656485 3 0.2125 0.8256 0.004 0.076 0.920 0.000
#> SRR1656487 3 0.2466 0.8140 0.004 0.096 0.900 0.000
#> SRR1656486 4 0.3216 0.8509 0.076 0.000 0.044 0.880
#> SRR1656488 3 0.2999 0.7865 0.004 0.132 0.864 0.000
#> SRR1656484 1 0.4933 0.1428 0.568 0.000 0.432 0.000
#> SRR1656489 1 0.2222 0.7150 0.924 0.060 0.016 0.000
#> SRR1656491 3 0.2871 0.8469 0.072 0.000 0.896 0.032
#> SRR1656490 3 0.4688 0.7745 0.080 0.000 0.792 0.128
#> SRR1656492 3 0.1792 0.8525 0.068 0.000 0.932 0.000
#> SRR1656493 1 0.2149 0.7064 0.912 0.088 0.000 0.000
#> SRR1656495 1 0.2469 0.6939 0.892 0.108 0.000 0.000
#> SRR1656496 3 0.2831 0.8347 0.120 0.000 0.876 0.004
#> SRR1656494 3 0.5407 0.7385 0.152 0.000 0.740 0.108
#> SRR1656497 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656499 3 0.3810 0.7334 0.008 0.188 0.804 0.000
#> SRR1656500 2 0.3324 0.7124 0.012 0.852 0.136 0.000
#> SRR1656501 3 0.5614 0.5788 0.304 0.000 0.652 0.044
#> SRR1656498 1 0.4972 -0.1242 0.544 0.456 0.000 0.000
#> SRR1656504 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656502 2 0.2814 0.7989 0.132 0.868 0.000 0.000
#> SRR1656503 1 0.4985 -0.0184 0.532 0.000 0.468 0.000
#> SRR1656507 1 0.2469 0.6940 0.892 0.000 0.108 0.000
#> SRR1656508 1 0.3569 0.5909 0.804 0.196 0.000 0.000
#> SRR1656505 3 0.3787 0.8001 0.036 0.000 0.840 0.124
#> SRR1656506 3 0.1398 0.8588 0.040 0.000 0.956 0.004
#> SRR1656509 3 0.3266 0.8156 0.084 0.040 0.876 0.000
#> SRR1656510 4 0.3557 0.8148 0.036 0.000 0.108 0.856
#> SRR1656511 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656513 4 0.1305 0.9150 0.036 0.000 0.004 0.960
#> SRR1656512 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656514 2 0.2469 0.8031 0.108 0.892 0.000 0.000
#> SRR1656515 4 0.0657 0.9267 0.012 0.000 0.004 0.984
#> SRR1656516 3 0.4624 0.5497 0.340 0.000 0.660 0.000
#> SRR1656518 1 0.2469 0.6940 0.892 0.000 0.108 0.000
#> SRR1656517 1 0.3764 0.5564 0.784 0.216 0.000 0.000
#> SRR1656519 2 0.0937 0.8028 0.012 0.976 0.012 0.000
#> SRR1656522 2 0.2814 0.7989 0.132 0.868 0.000 0.000
#> SRR1656523 4 0.2124 0.8919 0.068 0.000 0.008 0.924
#> SRR1656521 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656520 2 0.2048 0.7916 0.008 0.928 0.064 0.000
#> SRR1656524 1 0.2345 0.6999 0.900 0.100 0.000 0.000
#> SRR1656525 3 0.0817 0.8597 0.024 0.000 0.976 0.000
#> SRR1656526 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656527 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656530 3 0.1798 0.8592 0.040 0.000 0.944 0.016
#> SRR1656529 3 0.0524 0.8613 0.004 0.000 0.988 0.008
#> SRR1656531 2 0.4961 0.3230 0.448 0.552 0.000 0.000
#> SRR1656528 3 0.0376 0.8576 0.004 0.004 0.992 0.000
#> SRR1656534 2 0.1545 0.8067 0.040 0.952 0.008 0.000
#> SRR1656533 1 0.2149 0.7064 0.912 0.088 0.000 0.000
#> SRR1656536 3 0.0804 0.8611 0.012 0.000 0.980 0.008
#> SRR1656532 4 0.6143 0.1688 0.456 0.000 0.048 0.496
#> SRR1656537 2 0.4994 0.2401 0.480 0.520 0.000 0.000
#> SRR1656538 3 0.2921 0.8133 0.140 0.000 0.860 0.000
#> SRR1656535 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656539 3 0.0376 0.8576 0.004 0.004 0.992 0.000
#> SRR1656544 3 0.0524 0.8566 0.004 0.008 0.988 0.000
#> SRR1656542 1 0.7568 0.1900 0.456 0.340 0.204 0.000
#> SRR1656543 2 0.2048 0.7916 0.008 0.928 0.064 0.000
#> SRR1656545 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656540 2 0.2053 0.7866 0.004 0.924 0.072 0.000
#> SRR1656546 4 0.3821 0.8136 0.120 0.000 0.040 0.840
#> SRR1656541 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656547 4 0.1305 0.9150 0.036 0.000 0.004 0.960
#> SRR1656548 3 0.1824 0.8553 0.060 0.000 0.936 0.004
#> SRR1656549 4 0.6200 0.2009 0.444 0.000 0.052 0.504
#> SRR1656551 3 0.3731 0.8042 0.036 0.000 0.844 0.120
#> SRR1656553 3 0.5470 0.6922 0.100 0.168 0.732 0.000
#> SRR1656550 3 0.0992 0.8606 0.012 0.004 0.976 0.008
#> SRR1656552 4 0.0657 0.9267 0.012 0.000 0.004 0.984
#> SRR1656554 3 0.0524 0.8615 0.004 0.000 0.988 0.008
#> SRR1656555 4 0.0657 0.9267 0.012 0.000 0.004 0.984
#> SRR1656556 3 0.5229 0.2573 0.008 0.428 0.564 0.000
#> SRR1656557 2 0.2048 0.7916 0.008 0.928 0.064 0.000
#> SRR1656558 1 0.2402 0.7109 0.912 0.076 0.012 0.000
#> SRR1656559 2 0.2814 0.7989 0.132 0.868 0.000 0.000
#> SRR1656560 3 0.0376 0.8576 0.004 0.004 0.992 0.000
#> SRR1656561 3 0.2546 0.8458 0.092 0.000 0.900 0.008
#> SRR1656562 4 0.1706 0.9071 0.036 0.000 0.016 0.948
#> SRR1656563 1 0.2589 0.6889 0.884 0.000 0.116 0.000
#> SRR1656564 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656565 4 0.1677 0.9077 0.040 0.000 0.012 0.948
#> SRR1656566 1 0.1610 0.7164 0.952 0.032 0.016 0.000
#> SRR1656568 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656567 3 0.3616 0.8108 0.036 0.000 0.852 0.112
#> SRR1656569 3 0.2197 0.8573 0.048 0.000 0.928 0.024
#> SRR1656570 1 0.2589 0.6889 0.884 0.000 0.116 0.000
#> SRR1656571 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656573 3 0.2943 0.8458 0.076 0.000 0.892 0.032
#> SRR1656572 4 0.1305 0.9150 0.036 0.000 0.004 0.960
#> SRR1656574 1 0.3486 0.5915 0.812 0.188 0.000 0.000
#> SRR1656575 1 0.6895 0.0756 0.492 0.000 0.400 0.108
#> SRR1656576 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656578 4 0.5650 0.2882 0.432 0.000 0.024 0.544
#> SRR1656577 2 0.4543 0.5732 0.324 0.676 0.000 0.000
#> SRR1656579 4 0.0657 0.9267 0.012 0.000 0.004 0.984
#> SRR1656580 3 0.4699 0.5557 0.320 0.004 0.676 0.000
#> SRR1656581 3 0.5998 0.6039 0.092 0.000 0.668 0.240
#> SRR1656582 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656585 3 0.2797 0.8482 0.068 0.000 0.900 0.032
#> SRR1656584 1 0.2345 0.6967 0.900 0.000 0.100 0.000
#> SRR1656583 3 0.5257 0.6777 0.060 0.212 0.728 0.000
#> SRR1656586 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656587 3 0.4713 0.7716 0.172 0.000 0.776 0.052
#> SRR1656588 3 0.2635 0.8415 0.020 0.000 0.904 0.076
#> SRR1656589 4 0.0000 0.9320 0.000 0.000 0.000 1.000
#> SRR1656590 1 0.2149 0.7064 0.912 0.088 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.3612 0.5754 0.268 0.000 0.732 0.000 0.000
#> SRR1656462 3 0.2439 0.7277 0.000 0.000 0.876 0.120 0.004
#> SRR1656465 5 0.3715 0.6321 0.000 0.000 0.004 0.260 0.736
#> SRR1656467 2 0.0510 0.9131 0.000 0.984 0.000 0.000 0.016
#> SRR1656466 5 0.3949 0.6081 0.000 0.000 0.004 0.300 0.696
#> SRR1656468 5 0.1671 0.5603 0.000 0.076 0.000 0.000 0.924
#> SRR1656472 3 0.3983 0.4870 0.340 0.000 0.660 0.000 0.000
#> SRR1656471 5 0.5684 0.4851 0.000 0.000 0.096 0.340 0.564
#> SRR1656470 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.1430 0.5689 0.000 0.004 0.000 0.052 0.944
#> SRR1656473 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.4171 0.3224 0.604 0.000 0.000 0.396 0.000
#> SRR1656477 5 0.3123 0.6622 0.000 0.000 0.004 0.184 0.812
#> SRR1656479 4 0.4557 0.3105 0.004 0.004 0.000 0.552 0.440
#> SRR1656480 5 0.0290 0.6087 0.000 0.008 0.000 0.000 0.992
#> SRR1656476 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.3160 0.6609 0.000 0.000 0.004 0.188 0.808
#> SRR1656482 2 0.0162 0.9183 0.000 0.996 0.000 0.000 0.004
#> SRR1656483 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 5 0.4716 0.5801 0.000 0.000 0.036 0.308 0.656
#> SRR1656487 5 0.4135 0.5794 0.000 0.000 0.004 0.340 0.656
#> SRR1656486 2 0.6556 0.1525 0.000 0.476 0.000 0.260 0.264
#> SRR1656488 5 0.5948 0.3842 0.000 0.000 0.108 0.408 0.484
#> SRR1656484 4 0.4981 0.4253 0.188 0.000 0.000 0.704 0.108
#> SRR1656489 1 0.3752 0.5467 0.708 0.000 0.000 0.292 0.000
#> SRR1656491 5 0.4074 0.0688 0.000 0.000 0.000 0.364 0.636
#> SRR1656490 5 0.5046 -0.2481 0.000 0.032 0.000 0.468 0.500
#> SRR1656492 4 0.4249 -0.3096 0.000 0.000 0.000 0.568 0.432
#> SRR1656493 1 0.1732 0.7764 0.920 0.000 0.000 0.080 0.000
#> SRR1656495 1 0.1478 0.7799 0.936 0.000 0.000 0.064 0.000
#> SRR1656496 4 0.4304 0.2250 0.000 0.000 0.000 0.516 0.484
#> SRR1656494 5 0.5350 -0.2755 0.000 0.052 0.000 0.460 0.488
#> SRR1656497 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 4 0.6687 -0.2053 0.000 0.000 0.332 0.420 0.248
#> SRR1656500 3 0.4989 0.4143 0.000 0.000 0.552 0.416 0.032
#> SRR1656501 4 0.4830 0.3435 0.016 0.004 0.000 0.560 0.420
#> SRR1656498 1 0.2648 0.6235 0.848 0.000 0.152 0.000 0.000
#> SRR1656504 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 3 0.3983 0.4870 0.340 0.000 0.660 0.000 0.000
#> SRR1656503 4 0.5941 0.4512 0.168 0.000 0.000 0.588 0.244
#> SRR1656507 4 0.4425 0.0351 0.452 0.000 0.000 0.544 0.004
#> SRR1656508 1 0.0693 0.7578 0.980 0.000 0.008 0.012 0.000
#> SRR1656505 5 0.1205 0.5894 0.000 0.040 0.000 0.004 0.956
#> SRR1656506 5 0.3003 0.6616 0.000 0.000 0.000 0.188 0.812
#> SRR1656509 4 0.5415 -0.2526 0.000 0.000 0.064 0.552 0.384
#> SRR1656510 2 0.5125 0.3433 0.000 0.544 0.000 0.040 0.416
#> SRR1656511 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.3795 0.7292 0.000 0.780 0.000 0.028 0.192
#> SRR1656512 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.3612 0.5754 0.268 0.000 0.732 0.000 0.000
#> SRR1656515 2 0.0404 0.9152 0.000 0.988 0.000 0.000 0.012
#> SRR1656516 4 0.5422 0.4193 0.072 0.000 0.000 0.580 0.348
#> SRR1656518 4 0.4297 -0.0146 0.472 0.000 0.000 0.528 0.000
#> SRR1656517 1 0.0693 0.7560 0.980 0.000 0.012 0.008 0.000
#> SRR1656519 3 0.2597 0.7267 0.004 0.000 0.872 0.120 0.004
#> SRR1656522 3 0.3684 0.5638 0.280 0.000 0.720 0.000 0.000
#> SRR1656523 2 0.4934 0.6414 0.000 0.708 0.000 0.104 0.188
#> SRR1656521 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.2439 0.7277 0.000 0.000 0.876 0.120 0.004
#> SRR1656524 1 0.1478 0.7799 0.936 0.000 0.000 0.064 0.000
#> SRR1656525 4 0.4288 -0.2239 0.000 0.000 0.004 0.612 0.384
#> SRR1656526 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0693 0.9108 0.000 0.980 0.000 0.008 0.012
#> SRR1656530 5 0.2074 0.5689 0.000 0.000 0.000 0.104 0.896
#> SRR1656529 5 0.3123 0.6622 0.000 0.000 0.004 0.184 0.812
#> SRR1656531 1 0.3612 0.4712 0.732 0.000 0.268 0.000 0.000
#> SRR1656528 5 0.3884 0.6169 0.000 0.000 0.004 0.288 0.708
#> SRR1656534 3 0.2563 0.7246 0.008 0.000 0.872 0.120 0.000
#> SRR1656533 1 0.1608 0.7795 0.928 0.000 0.000 0.072 0.000
#> SRR1656536 5 0.3123 0.6622 0.000 0.000 0.004 0.184 0.812
#> SRR1656532 4 0.7428 0.4179 0.124 0.144 0.000 0.532 0.200
#> SRR1656537 1 0.3534 0.4920 0.744 0.000 0.256 0.000 0.000
#> SRR1656538 4 0.3160 0.2349 0.000 0.000 0.004 0.808 0.188
#> SRR1656535 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 5 0.3790 0.6267 0.000 0.000 0.004 0.272 0.724
#> SRR1656544 5 0.4165 0.5988 0.000 0.000 0.008 0.320 0.672
#> SRR1656542 4 0.5343 -0.1271 0.032 0.000 0.344 0.604 0.020
#> SRR1656543 3 0.2439 0.7277 0.000 0.000 0.876 0.120 0.004
#> SRR1656545 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.2439 0.7277 0.000 0.000 0.876 0.120 0.004
#> SRR1656546 4 0.6670 0.1468 0.004 0.380 0.000 0.420 0.196
#> SRR1656541 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.3663 0.7247 0.000 0.776 0.000 0.016 0.208
#> SRR1656548 5 0.3913 0.4777 0.000 0.000 0.000 0.324 0.676
#> SRR1656549 4 0.6982 0.4363 0.152 0.072 0.000 0.568 0.208
#> SRR1656551 5 0.1043 0.5925 0.000 0.040 0.000 0.000 0.960
#> SRR1656553 4 0.5128 0.0121 0.000 0.000 0.268 0.656 0.076
#> SRR1656550 5 0.3123 0.6622 0.000 0.000 0.004 0.184 0.812
#> SRR1656552 2 0.0404 0.9152 0.000 0.988 0.000 0.000 0.012
#> SRR1656554 5 0.3039 0.6618 0.000 0.000 0.000 0.192 0.808
#> SRR1656555 2 0.0794 0.9051 0.000 0.972 0.000 0.000 0.028
#> SRR1656556 3 0.6299 0.2586 0.000 0.000 0.432 0.416 0.152
#> SRR1656557 3 0.2439 0.7277 0.000 0.000 0.876 0.120 0.004
#> SRR1656558 1 0.3424 0.6216 0.760 0.000 0.000 0.240 0.000
#> SRR1656559 3 0.3612 0.5754 0.268 0.000 0.732 0.000 0.000
#> SRR1656560 5 0.3814 0.6243 0.000 0.000 0.004 0.276 0.720
#> SRR1656561 5 0.4304 -0.2191 0.000 0.000 0.000 0.484 0.516
#> SRR1656562 2 0.4817 0.6024 0.000 0.680 0.000 0.056 0.264
#> SRR1656563 4 0.4297 -0.0212 0.472 0.000 0.000 0.528 0.000
#> SRR1656564 2 0.0162 0.9182 0.000 0.996 0.000 0.004 0.000
#> SRR1656565 2 0.5187 0.5636 0.000 0.656 0.000 0.084 0.260
#> SRR1656566 1 0.3730 0.5489 0.712 0.000 0.000 0.288 0.000
#> SRR1656568 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 5 0.1124 0.5979 0.000 0.036 0.000 0.004 0.960
#> SRR1656569 5 0.1851 0.6411 0.000 0.000 0.000 0.088 0.912
#> SRR1656570 4 0.4297 -0.0212 0.472 0.000 0.000 0.528 0.000
#> SRR1656571 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.3684 0.2628 0.000 0.000 0.000 0.280 0.720
#> SRR1656572 2 0.3795 0.7294 0.000 0.780 0.000 0.028 0.192
#> SRR1656574 1 0.2300 0.7604 0.908 0.000 0.040 0.052 0.000
#> SRR1656575 4 0.6417 0.4689 0.140 0.024 0.000 0.576 0.260
#> SRR1656576 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 4 0.7541 0.3890 0.096 0.208 0.000 0.504 0.192
#> SRR1656577 1 0.4238 0.2048 0.628 0.000 0.368 0.004 0.000
#> SRR1656579 2 0.0290 0.9169 0.000 0.992 0.000 0.000 0.008
#> SRR1656580 4 0.3342 0.3076 0.020 0.000 0.008 0.836 0.136
#> SRR1656581 5 0.4331 -0.0405 0.000 0.004 0.000 0.400 0.596
#> SRR1656582 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 5 0.3395 0.3541 0.000 0.000 0.000 0.236 0.764
#> SRR1656584 4 0.4307 -0.0829 0.496 0.000 0.000 0.504 0.000
#> SRR1656583 4 0.5878 -0.1294 0.000 0.000 0.336 0.548 0.116
#> SRR1656586 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.4881 0.2791 0.004 0.016 0.000 0.520 0.460
#> SRR1656588 5 0.0579 0.6127 0.000 0.008 0.000 0.008 0.984
#> SRR1656589 2 0.0000 0.9197 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.1732 0.7763 0.920 0.000 0.000 0.080 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 6 0.0748 0.76494 0.016 0.000 0.004 0.004 0.000 0.976
#> SRR1656462 3 0.4220 0.31128 0.000 0.000 0.520 0.008 0.004 0.468
#> SRR1656465 5 0.0865 0.73668 0.000 0.000 0.036 0.000 0.964 0.000
#> SRR1656467 2 0.2584 0.83309 0.000 0.848 0.004 0.144 0.004 0.000
#> SRR1656466 5 0.3531 0.49772 0.000 0.000 0.328 0.000 0.672 0.000
#> SRR1656468 5 0.3089 0.69050 0.000 0.008 0.004 0.188 0.800 0.000
#> SRR1656472 6 0.1411 0.76771 0.060 0.000 0.000 0.004 0.000 0.936
#> SRR1656471 5 0.3819 0.43242 0.000 0.000 0.372 0.000 0.624 0.004
#> SRR1656470 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.3043 0.68506 0.000 0.004 0.004 0.196 0.796 0.000
#> SRR1656473 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.3766 0.65192 0.784 0.000 0.144 0.068 0.000 0.004
#> SRR1656477 5 0.0790 0.75781 0.000 0.000 0.000 0.032 0.968 0.000
#> SRR1656479 4 0.3624 0.67490 0.028 0.000 0.100 0.824 0.044 0.004
#> SRR1656480 5 0.2946 0.69567 0.000 0.004 0.004 0.184 0.808 0.000
#> SRR1656476 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 5 0.0790 0.75781 0.000 0.000 0.000 0.032 0.968 0.000
#> SRR1656482 2 0.1152 0.91368 0.000 0.952 0.004 0.044 0.000 0.000
#> SRR1656483 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 5 0.3912 0.46918 0.000 0.000 0.340 0.012 0.648 0.000
#> SRR1656487 5 0.3531 0.49801 0.000 0.000 0.328 0.000 0.672 0.000
#> SRR1656486 4 0.1949 0.69235 0.000 0.088 0.004 0.904 0.004 0.000
#> SRR1656488 5 0.3672 0.44279 0.000 0.000 0.368 0.000 0.632 0.000
#> SRR1656484 4 0.6221 0.34269 0.180 0.000 0.240 0.544 0.032 0.004
#> SRR1656489 1 0.2294 0.70751 0.896 0.000 0.076 0.020 0.000 0.008
#> SRR1656491 4 0.2237 0.69309 0.000 0.000 0.020 0.896 0.080 0.004
#> SRR1656490 4 0.1411 0.69818 0.000 0.000 0.004 0.936 0.060 0.000
#> SRR1656492 5 0.6814 -0.01271 0.024 0.000 0.336 0.292 0.340 0.008
#> SRR1656493 1 0.0790 0.72004 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR1656495 1 0.0865 0.71885 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1656496 4 0.4162 0.65993 0.028 0.000 0.116 0.784 0.068 0.004
#> SRR1656494 4 0.2341 0.69905 0.000 0.032 0.012 0.900 0.056 0.000
#> SRR1656497 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.3088 0.48260 0.000 0.000 0.808 0.000 0.172 0.020
#> SRR1656500 3 0.2941 0.49001 0.000 0.000 0.856 0.004 0.064 0.076
#> SRR1656501 4 0.3621 0.67860 0.028 0.000 0.096 0.828 0.040 0.008
#> SRR1656498 1 0.3244 0.40425 0.732 0.000 0.000 0.000 0.000 0.268
#> SRR1656504 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.1411 0.76771 0.060 0.000 0.000 0.004 0.000 0.936
#> SRR1656503 4 0.4578 0.63270 0.076 0.000 0.136 0.752 0.028 0.008
#> SRR1656507 1 0.6167 0.24610 0.416 0.000 0.288 0.292 0.000 0.004
#> SRR1656508 1 0.1615 0.69742 0.928 0.000 0.004 0.004 0.000 0.064
#> SRR1656505 5 0.3089 0.69050 0.000 0.008 0.004 0.188 0.800 0.000
#> SRR1656506 5 0.1225 0.75686 0.000 0.000 0.012 0.036 0.952 0.000
#> SRR1656509 3 0.6156 0.11238 0.012 0.000 0.472 0.228 0.288 0.000
#> SRR1656510 4 0.5125 0.53293 0.000 0.204 0.004 0.640 0.152 0.000
#> SRR1656511 2 0.0790 0.92232 0.000 0.968 0.000 0.032 0.000 0.000
#> SRR1656513 4 0.3862 0.31340 0.000 0.388 0.004 0.608 0.000 0.000
#> SRR1656512 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 6 0.0717 0.74982 0.008 0.000 0.016 0.000 0.000 0.976
#> SRR1656515 2 0.2584 0.83475 0.000 0.848 0.004 0.144 0.004 0.000
#> SRR1656516 4 0.4628 0.63714 0.068 0.000 0.124 0.756 0.044 0.008
#> SRR1656518 1 0.5638 0.30871 0.504 0.000 0.140 0.352 0.000 0.004
#> SRR1656517 1 0.1327 0.69770 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR1656519 3 0.4175 0.31235 0.000 0.000 0.524 0.012 0.000 0.464
#> SRR1656522 6 0.0692 0.76729 0.020 0.000 0.004 0.000 0.000 0.976
#> SRR1656523 4 0.3728 0.40946 0.000 0.344 0.004 0.652 0.000 0.000
#> SRR1656521 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.4308 0.31152 0.000 0.000 0.516 0.012 0.004 0.468
#> SRR1656524 1 0.0865 0.71885 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR1656525 4 0.6591 -0.00319 0.020 0.000 0.372 0.388 0.212 0.008
#> SRR1656526 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.2597 0.80518 0.000 0.824 0.000 0.176 0.000 0.000
#> SRR1656530 5 0.3758 0.59867 0.000 0.000 0.024 0.232 0.740 0.004
#> SRR1656529 5 0.0935 0.75727 0.000 0.000 0.004 0.032 0.964 0.000
#> SRR1656531 6 0.3857 0.20190 0.468 0.000 0.000 0.000 0.000 0.532
#> SRR1656528 5 0.1141 0.73060 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1656534 3 0.4313 0.28856 0.004 0.000 0.504 0.012 0.000 0.480
#> SRR1656533 1 0.1194 0.72215 0.956 0.000 0.004 0.008 0.000 0.032
#> SRR1656536 5 0.0790 0.75781 0.000 0.000 0.000 0.032 0.968 0.000
#> SRR1656532 4 0.3150 0.69189 0.040 0.068 0.036 0.856 0.000 0.000
#> SRR1656537 6 0.3860 0.19981 0.472 0.000 0.000 0.000 0.000 0.528
#> SRR1656538 3 0.6645 -0.10917 0.040 0.000 0.400 0.396 0.156 0.008
#> SRR1656535 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.1267 0.72646 0.000 0.000 0.060 0.000 0.940 0.000
#> SRR1656544 5 0.4563 0.42108 0.000 0.000 0.348 0.048 0.604 0.000
#> SRR1656542 3 0.3106 0.47621 0.020 0.000 0.864 0.044 0.064 0.008
#> SRR1656543 3 0.4220 0.31128 0.000 0.000 0.520 0.008 0.004 0.468
#> SRR1656545 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.4220 0.31128 0.000 0.000 0.520 0.008 0.004 0.468
#> SRR1656546 4 0.2540 0.68560 0.004 0.104 0.020 0.872 0.000 0.000
#> SRR1656541 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.4224 0.08800 0.000 0.512 0.004 0.476 0.008 0.000
#> SRR1656548 5 0.5591 0.09652 0.016 0.000 0.072 0.384 0.520 0.008
#> SRR1656549 4 0.3043 0.69417 0.056 0.032 0.040 0.868 0.000 0.004
#> SRR1656551 5 0.3056 0.69314 0.000 0.008 0.004 0.184 0.804 0.000
#> SRR1656553 3 0.3620 0.47948 0.012 0.000 0.808 0.060 0.120 0.000
#> SRR1656550 5 0.0790 0.75781 0.000 0.000 0.000 0.032 0.968 0.000
#> SRR1656552 2 0.2933 0.77442 0.000 0.796 0.004 0.200 0.000 0.000
#> SRR1656554 5 0.1010 0.75766 0.000 0.000 0.004 0.036 0.960 0.000
#> SRR1656555 2 0.2595 0.81955 0.000 0.836 0.004 0.160 0.000 0.000
#> SRR1656556 3 0.3014 0.48832 0.000 0.000 0.832 0.000 0.132 0.036
#> SRR1656557 3 0.4217 0.31494 0.000 0.000 0.524 0.008 0.004 0.464
#> SRR1656558 1 0.1622 0.72243 0.940 0.000 0.028 0.016 0.000 0.016
#> SRR1656559 6 0.0725 0.75987 0.012 0.000 0.012 0.000 0.000 0.976
#> SRR1656560 5 0.1765 0.70672 0.000 0.000 0.096 0.000 0.904 0.000
#> SRR1656561 4 0.4238 0.66642 0.028 0.000 0.108 0.784 0.072 0.008
#> SRR1656562 4 0.3606 0.55867 0.000 0.264 0.004 0.724 0.008 0.000
#> SRR1656563 1 0.5458 0.47157 0.576 0.000 0.128 0.288 0.000 0.008
#> SRR1656564 2 0.2219 0.84412 0.000 0.864 0.000 0.136 0.000 0.000
#> SRR1656565 4 0.4058 0.45337 0.000 0.320 0.004 0.660 0.016 0.000
#> SRR1656566 1 0.1341 0.71971 0.948 0.000 0.028 0.024 0.000 0.000
#> SRR1656568 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656567 5 0.3089 0.69050 0.000 0.008 0.004 0.188 0.800 0.000
#> SRR1656569 5 0.1387 0.75204 0.000 0.000 0.000 0.068 0.932 0.000
#> SRR1656570 1 0.5474 0.46466 0.572 0.000 0.128 0.292 0.000 0.008
#> SRR1656571 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 4 0.4119 0.37713 0.000 0.000 0.016 0.644 0.336 0.004
#> SRR1656572 4 0.3915 0.24196 0.000 0.412 0.004 0.584 0.000 0.000
#> SRR1656574 1 0.1590 0.71496 0.936 0.000 0.008 0.008 0.000 0.048
#> SRR1656575 4 0.4173 0.65529 0.068 0.000 0.104 0.788 0.036 0.004
#> SRR1656576 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 4 0.3051 0.68735 0.032 0.088 0.024 0.856 0.000 0.000
#> SRR1656577 1 0.4114 -0.08904 0.532 0.000 0.004 0.004 0.000 0.460
#> SRR1656579 2 0.2333 0.85366 0.000 0.872 0.004 0.120 0.004 0.000
#> SRR1656580 3 0.6439 -0.11466 0.048 0.000 0.428 0.408 0.108 0.008
#> SRR1656581 4 0.2466 0.69014 0.012 0.000 0.012 0.888 0.084 0.004
#> SRR1656582 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 4 0.4129 0.16657 0.000 0.000 0.012 0.564 0.424 0.000
#> SRR1656584 1 0.4855 0.58071 0.672 0.000 0.124 0.200 0.000 0.004
#> SRR1656583 3 0.4088 0.48441 0.004 0.000 0.768 0.056 0.160 0.012
#> SRR1656586 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 4 0.3377 0.69550 0.012 0.012 0.080 0.844 0.052 0.000
#> SRR1656588 5 0.2773 0.71113 0.000 0.004 0.004 0.164 0.828 0.000
#> SRR1656589 2 0.0000 0.93870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.0972 0.72210 0.964 0.000 0.000 0.008 0.000 0.028
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.944 0.979 0.4984 0.503 0.503
#> 3 3 0.928 0.900 0.958 0.3235 0.776 0.579
#> 4 4 0.897 0.905 0.950 0.1211 0.858 0.617
#> 5 5 0.838 0.785 0.893 0.0483 0.933 0.759
#> 6 6 0.839 0.672 0.842 0.0284 0.969 0.868
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.000 0.971 0.000 1.000
#> SRR1656464 1 0.000 0.985 1.000 0.000
#> SRR1656462 1 0.000 0.985 1.000 0.000
#> SRR1656465 2 0.753 0.724 0.216 0.784
#> SRR1656467 2 0.000 0.971 0.000 1.000
#> SRR1656466 1 0.000 0.985 1.000 0.000
#> SRR1656468 2 0.000 0.971 0.000 1.000
#> SRR1656472 1 0.000 0.985 1.000 0.000
#> SRR1656471 1 0.000 0.985 1.000 0.000
#> SRR1656470 2 0.000 0.971 0.000 1.000
#> SRR1656469 2 0.000 0.971 0.000 1.000
#> SRR1656473 2 0.000 0.971 0.000 1.000
#> SRR1656474 2 0.000 0.971 0.000 1.000
#> SRR1656475 2 0.000 0.971 0.000 1.000
#> SRR1656478 1 0.000 0.985 1.000 0.000
#> SRR1656477 2 0.000 0.971 0.000 1.000
#> SRR1656479 2 0.141 0.954 0.020 0.980
#> SRR1656480 2 0.000 0.971 0.000 1.000
#> SRR1656476 2 0.000 0.971 0.000 1.000
#> SRR1656481 2 0.000 0.971 0.000 1.000
#> SRR1656482 2 0.000 0.971 0.000 1.000
#> SRR1656483 2 0.000 0.971 0.000 1.000
#> SRR1656485 1 0.000 0.985 1.000 0.000
#> SRR1656487 1 0.000 0.985 1.000 0.000
#> SRR1656486 2 0.000 0.971 0.000 1.000
#> SRR1656488 1 0.000 0.985 1.000 0.000
#> SRR1656484 1 0.000 0.985 1.000 0.000
#> SRR1656489 1 0.000 0.985 1.000 0.000
#> SRR1656491 2 0.000 0.971 0.000 1.000
#> SRR1656490 2 0.000 0.971 0.000 1.000
#> SRR1656492 1 0.000 0.985 1.000 0.000
#> SRR1656493 1 0.000 0.985 1.000 0.000
#> SRR1656495 1 0.000 0.985 1.000 0.000
#> SRR1656496 1 0.000 0.985 1.000 0.000
#> SRR1656494 2 0.000 0.971 0.000 1.000
#> SRR1656497 2 0.000 0.971 0.000 1.000
#> SRR1656499 1 0.000 0.985 1.000 0.000
#> SRR1656500 1 0.000 0.985 1.000 0.000
#> SRR1656501 2 0.000 0.971 0.000 1.000
#> SRR1656498 1 0.000 0.985 1.000 0.000
#> SRR1656504 2 0.000 0.971 0.000 1.000
#> SRR1656502 1 0.000 0.985 1.000 0.000
#> SRR1656503 1 0.000 0.985 1.000 0.000
#> SRR1656507 1 0.000 0.985 1.000 0.000
#> SRR1656508 1 0.000 0.985 1.000 0.000
#> SRR1656505 2 0.000 0.971 0.000 1.000
#> SRR1656506 2 0.730 0.742 0.204 0.796
#> SRR1656509 1 0.000 0.985 1.000 0.000
#> SRR1656510 2 0.000 0.971 0.000 1.000
#> SRR1656511 2 0.000 0.971 0.000 1.000
#> SRR1656513 2 0.000 0.971 0.000 1.000
#> SRR1656512 2 0.000 0.971 0.000 1.000
#> SRR1656514 1 0.000 0.985 1.000 0.000
#> SRR1656515 2 0.000 0.971 0.000 1.000
#> SRR1656516 1 0.000 0.985 1.000 0.000
#> SRR1656518 1 0.000 0.985 1.000 0.000
#> SRR1656517 1 0.000 0.985 1.000 0.000
#> SRR1656519 1 0.000 0.985 1.000 0.000
#> SRR1656522 1 0.000 0.985 1.000 0.000
#> SRR1656523 2 0.000 0.971 0.000 1.000
#> SRR1656521 2 0.000 0.971 0.000 1.000
#> SRR1656520 1 0.000 0.985 1.000 0.000
#> SRR1656524 1 0.000 0.985 1.000 0.000
#> SRR1656525 1 0.000 0.985 1.000 0.000
#> SRR1656526 2 0.000 0.971 0.000 1.000
#> SRR1656527 2 0.000 0.971 0.000 1.000
#> SRR1656530 2 0.000 0.971 0.000 1.000
#> SRR1656529 2 0.000 0.971 0.000 1.000
#> SRR1656531 1 0.000 0.985 1.000 0.000
#> SRR1656528 2 0.990 0.239 0.440 0.560
#> SRR1656534 1 0.000 0.985 1.000 0.000
#> SRR1656533 1 0.000 0.985 1.000 0.000
#> SRR1656536 2 0.000 0.971 0.000 1.000
#> SRR1656532 2 0.000 0.971 0.000 1.000
#> SRR1656537 1 0.000 0.985 1.000 0.000
#> SRR1656538 1 0.000 0.985 1.000 0.000
#> SRR1656535 2 0.000 0.971 0.000 1.000
#> SRR1656539 2 0.995 0.172 0.460 0.540
#> SRR1656544 1 0.000 0.985 1.000 0.000
#> SRR1656542 1 0.000 0.985 1.000 0.000
#> SRR1656543 1 0.000 0.985 1.000 0.000
#> SRR1656545 2 0.000 0.971 0.000 1.000
#> SRR1656540 1 0.000 0.985 1.000 0.000
#> SRR1656546 2 0.000 0.971 0.000 1.000
#> SRR1656541 2 0.000 0.971 0.000 1.000
#> SRR1656547 2 0.000 0.971 0.000 1.000
#> SRR1656548 2 0.722 0.747 0.200 0.800
#> SRR1656549 2 0.000 0.971 0.000 1.000
#> SRR1656551 2 0.000 0.971 0.000 1.000
#> SRR1656553 1 0.000 0.985 1.000 0.000
#> SRR1656550 2 0.000 0.971 0.000 1.000
#> SRR1656552 2 0.000 0.971 0.000 1.000
#> SRR1656554 2 0.000 0.971 0.000 1.000
#> SRR1656555 2 0.000 0.971 0.000 1.000
#> SRR1656556 1 0.000 0.985 1.000 0.000
#> SRR1656557 1 0.000 0.985 1.000 0.000
#> SRR1656558 1 0.000 0.985 1.000 0.000
#> SRR1656559 1 0.000 0.985 1.000 0.000
#> SRR1656560 2 0.969 0.362 0.396 0.604
#> SRR1656561 2 0.000 0.971 0.000 1.000
#> SRR1656562 2 0.000 0.971 0.000 1.000
#> SRR1656563 1 0.000 0.985 1.000 0.000
#> SRR1656564 2 0.000 0.971 0.000 1.000
#> SRR1656565 2 0.000 0.971 0.000 1.000
#> SRR1656566 1 0.000 0.985 1.000 0.000
#> SRR1656568 2 0.000 0.971 0.000 1.000
#> SRR1656567 2 0.000 0.971 0.000 1.000
#> SRR1656569 2 0.000 0.971 0.000 1.000
#> SRR1656570 1 0.000 0.985 1.000 0.000
#> SRR1656571 2 0.000 0.971 0.000 1.000
#> SRR1656573 2 0.000 0.971 0.000 1.000
#> SRR1656572 2 0.000 0.971 0.000 1.000
#> SRR1656574 1 0.000 0.985 1.000 0.000
#> SRR1656575 1 0.981 0.240 0.580 0.420
#> SRR1656576 2 0.000 0.971 0.000 1.000
#> SRR1656578 2 0.204 0.942 0.032 0.968
#> SRR1656577 1 0.000 0.985 1.000 0.000
#> SRR1656579 2 0.000 0.971 0.000 1.000
#> SRR1656580 1 0.000 0.985 1.000 0.000
#> SRR1656581 2 0.000 0.971 0.000 1.000
#> SRR1656582 2 0.000 0.971 0.000 1.000
#> SRR1656585 2 0.000 0.971 0.000 1.000
#> SRR1656584 1 0.000 0.985 1.000 0.000
#> SRR1656583 1 0.000 0.985 1.000 0.000
#> SRR1656586 2 0.000 0.971 0.000 1.000
#> SRR1656587 1 0.955 0.368 0.624 0.376
#> SRR1656588 2 0.000 0.971 0.000 1.000
#> SRR1656589 2 0.000 0.971 0.000 1.000
#> SRR1656590 1 0.000 0.985 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656462 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656465 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656468 2 0.6126 0.253 0.000 0.600 0.400
#> SRR1656472 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656471 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656470 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656469 3 0.6267 0.246 0.000 0.452 0.548
#> SRR1656473 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656478 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656477 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656479 2 0.5098 0.655 0.248 0.752 0.000
#> SRR1656480 3 0.5591 0.593 0.000 0.304 0.696
#> SRR1656476 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656487 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656486 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656488 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656484 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656489 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656491 2 0.5706 0.487 0.000 0.680 0.320
#> SRR1656490 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656492 3 0.2625 0.848 0.084 0.000 0.916
#> SRR1656493 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656495 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656496 3 0.4452 0.737 0.192 0.000 0.808
#> SRR1656494 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656497 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656499 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656500 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656501 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656498 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656502 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656503 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656507 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656508 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656505 2 0.6180 0.200 0.000 0.584 0.416
#> SRR1656506 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656509 1 0.2711 0.904 0.912 0.000 0.088
#> SRR1656510 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656511 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656516 1 0.1860 0.932 0.948 0.000 0.052
#> SRR1656518 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656517 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656523 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656520 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656524 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656525 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656526 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656530 3 0.0237 0.919 0.000 0.004 0.996
#> SRR1656529 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656528 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656534 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656533 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656532 2 0.1529 0.926 0.040 0.960 0.000
#> SRR1656537 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656538 1 0.3551 0.856 0.868 0.000 0.132
#> SRR1656535 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656544 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656542 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656543 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656545 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656540 1 0.1964 0.934 0.944 0.000 0.056
#> SRR1656546 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656541 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656548 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656549 2 0.1163 0.939 0.028 0.972 0.000
#> SRR1656551 3 0.5859 0.518 0.000 0.344 0.656
#> SRR1656553 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656550 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656552 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656555 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656556 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656557 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656558 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656559 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.921 0.000 0.000 1.000
#> SRR1656561 3 0.1163 0.906 0.000 0.028 0.972
#> SRR1656562 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656563 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656566 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656568 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656567 3 0.6305 0.141 0.000 0.484 0.516
#> SRR1656569 3 0.0892 0.910 0.000 0.020 0.980
#> SRR1656570 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656571 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656573 3 0.5291 0.659 0.000 0.268 0.732
#> SRR1656572 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656574 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656575 1 0.6180 0.310 0.584 0.416 0.000
#> SRR1656576 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656578 2 0.1529 0.926 0.040 0.960 0.000
#> SRR1656577 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656580 1 0.1529 0.945 0.960 0.000 0.040
#> SRR1656581 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656582 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656585 3 0.1753 0.891 0.000 0.048 0.952
#> SRR1656584 1 0.0000 0.965 1.000 0.000 0.000
#> SRR1656583 1 0.1643 0.943 0.956 0.000 0.044
#> SRR1656586 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656587 1 0.6111 0.374 0.604 0.396 0.000
#> SRR1656588 3 0.4452 0.755 0.000 0.192 0.808
#> SRR1656589 2 0.0000 0.965 0.000 1.000 0.000
#> SRR1656590 1 0.0000 0.965 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656464 3 0.2814 0.797 0.132 0.000 0.868 0.000
#> SRR1656462 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656465 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656466 3 0.4103 0.704 0.000 0.000 0.744 0.256
#> SRR1656468 4 0.0469 0.966 0.000 0.012 0.000 0.988
#> SRR1656472 1 0.4643 0.552 0.656 0.000 0.344 0.000
#> SRR1656471 3 0.4830 0.464 0.000 0.000 0.608 0.392
#> SRR1656470 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656469 4 0.0336 0.969 0.000 0.008 0.000 0.992
#> SRR1656473 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.1716 0.910 0.936 0.000 0.064 0.000
#> SRR1656477 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656479 1 0.3975 0.650 0.760 0.240 0.000 0.000
#> SRR1656480 4 0.0336 0.969 0.000 0.008 0.000 0.992
#> SRR1656476 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.2814 0.835 0.000 0.000 0.868 0.132
#> SRR1656487 4 0.1637 0.918 0.000 0.000 0.060 0.940
#> SRR1656486 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656488 3 0.4072 0.709 0.000 0.000 0.748 0.252
#> SRR1656484 1 0.3266 0.794 0.832 0.000 0.168 0.000
#> SRR1656489 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1656491 4 0.3266 0.770 0.000 0.168 0.000 0.832
#> SRR1656490 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656492 3 0.6822 0.316 0.104 0.000 0.512 0.384
#> SRR1656493 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1656495 1 0.0921 0.923 0.972 0.000 0.028 0.000
#> SRR1656496 4 0.3764 0.733 0.216 0.000 0.000 0.784
#> SRR1656494 2 0.0188 0.977 0.000 0.996 0.004 0.000
#> SRR1656497 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.1118 0.885 0.000 0.000 0.964 0.036
#> SRR1656500 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656501 2 0.3024 0.833 0.148 0.852 0.000 0.000
#> SRR1656498 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1656504 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.4522 0.599 0.680 0.000 0.320 0.000
#> SRR1656503 1 0.1637 0.899 0.940 0.000 0.060 0.000
#> SRR1656507 1 0.1211 0.922 0.960 0.000 0.040 0.000
#> SRR1656508 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1656505 4 0.0469 0.966 0.000 0.012 0.000 0.988
#> SRR1656506 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656509 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656510 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656511 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656513 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.1557 0.866 0.056 0.000 0.944 0.000
#> SRR1656515 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656516 1 0.0804 0.907 0.980 0.000 0.012 0.008
#> SRR1656518 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1656517 1 0.1022 0.924 0.968 0.000 0.032 0.000
#> SRR1656519 3 0.0336 0.892 0.008 0.000 0.992 0.000
#> SRR1656522 3 0.2973 0.782 0.144 0.000 0.856 0.000
#> SRR1656523 2 0.0188 0.977 0.004 0.996 0.000 0.000
#> SRR1656521 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0817 0.923 0.976 0.000 0.024 0.000
#> SRR1656525 3 0.3335 0.829 0.016 0.000 0.856 0.128
#> SRR1656526 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656530 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656529 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656531 1 0.1118 0.923 0.964 0.000 0.036 0.000
#> SRR1656528 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656534 3 0.0921 0.883 0.028 0.000 0.972 0.000
#> SRR1656533 1 0.0817 0.923 0.976 0.000 0.024 0.000
#> SRR1656536 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.4406 0.576 0.300 0.700 0.000 0.000
#> SRR1656537 1 0.1118 0.923 0.964 0.000 0.036 0.000
#> SRR1656538 3 0.3485 0.833 0.116 0.000 0.856 0.028
#> SRR1656535 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656539 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656544 3 0.3311 0.800 0.000 0.000 0.828 0.172
#> SRR1656542 3 0.0188 0.894 0.004 0.000 0.996 0.000
#> SRR1656543 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656546 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656541 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656547 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656548 4 0.0817 0.958 0.024 0.000 0.000 0.976
#> SRR1656549 2 0.3528 0.774 0.192 0.808 0.000 0.000
#> SRR1656551 4 0.0336 0.969 0.000 0.008 0.000 0.992
#> SRR1656553 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656552 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656554 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656555 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656556 3 0.0921 0.887 0.000 0.000 0.972 0.028
#> SRR1656557 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.0921 0.923 0.972 0.000 0.028 0.000
#> SRR1656559 3 0.2814 0.797 0.132 0.000 0.868 0.000
#> SRR1656560 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656561 4 0.0921 0.955 0.028 0.000 0.000 0.972
#> SRR1656562 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656563 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1656564 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656566 1 0.0817 0.923 0.976 0.000 0.024 0.000
#> SRR1656568 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0336 0.969 0.000 0.008 0.000 0.992
#> SRR1656569 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656570 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1656571 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1474 0.924 0.000 0.052 0.000 0.948
#> SRR1656572 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656574 1 0.1867 0.903 0.928 0.000 0.072 0.000
#> SRR1656575 1 0.1302 0.883 0.956 0.044 0.000 0.000
#> SRR1656576 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656578 2 0.3400 0.780 0.180 0.820 0.000 0.000
#> SRR1656577 1 0.2011 0.898 0.920 0.000 0.080 0.000
#> SRR1656579 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656580 3 0.1022 0.887 0.032 0.000 0.968 0.000
#> SRR1656581 2 0.1118 0.951 0.036 0.964 0.000 0.000
#> SRR1656582 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.0000 0.972 0.000 0.000 0.000 1.000
#> SRR1656584 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1656583 3 0.0000 0.895 0.000 0.000 1.000 0.000
#> SRR1656586 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656587 1 0.7429 0.359 0.492 0.316 0.192 0.000
#> SRR1656588 4 0.0188 0.971 0.000 0.004 0.000 0.996
#> SRR1656589 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.1022 0.924 0.968 0.000 0.032 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 1 0.4150 0.4761 0.612 0.000 0.388 0.000 0.000
#> SRR1656462 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656465 5 0.3165 0.8309 0.000 0.000 0.036 0.116 0.848
#> SRR1656467 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656466 3 0.4637 0.6644 0.000 0.000 0.744 0.128 0.128
#> SRR1656468 5 0.0290 0.8933 0.000 0.008 0.000 0.000 0.992
#> SRR1656472 1 0.3796 0.6125 0.700 0.000 0.300 0.000 0.000
#> SRR1656471 3 0.5772 0.3865 0.000 0.000 0.564 0.108 0.328
#> SRR1656470 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656473 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.0290 0.7954 0.992 0.000 0.008 0.000 0.000
#> SRR1656477 5 0.0162 0.8948 0.000 0.000 0.004 0.000 0.996
#> SRR1656479 4 0.3111 0.7320 0.144 0.012 0.000 0.840 0.004
#> SRR1656480 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656476 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.1117 0.8873 0.000 0.000 0.016 0.020 0.964
#> SRR1656482 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.3427 0.7423 0.000 0.000 0.836 0.108 0.056
#> SRR1656487 5 0.5905 0.4735 0.000 0.000 0.276 0.144 0.580
#> SRR1656486 2 0.1197 0.9329 0.000 0.952 0.000 0.048 0.000
#> SRR1656488 3 0.4717 0.6562 0.000 0.000 0.736 0.144 0.120
#> SRR1656484 1 0.2915 0.7455 0.860 0.000 0.116 0.024 0.000
#> SRR1656489 1 0.0000 0.7951 1.000 0.000 0.000 0.000 0.000
#> SRR1656491 5 0.6465 0.1749 0.000 0.220 0.000 0.288 0.492
#> SRR1656490 2 0.0290 0.9688 0.000 0.992 0.000 0.008 0.000
#> SRR1656492 3 0.6146 0.4970 0.020 0.000 0.592 0.276 0.112
#> SRR1656493 1 0.0000 0.7951 1.000 0.000 0.000 0.000 0.000
#> SRR1656495 1 0.0404 0.7877 0.988 0.000 0.000 0.012 0.000
#> SRR1656496 4 0.3948 0.5643 0.012 0.000 0.012 0.768 0.208
#> SRR1656494 2 0.0960 0.9526 0.004 0.972 0.008 0.016 0.000
#> SRR1656497 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.2179 0.7663 0.000 0.000 0.888 0.112 0.000
#> SRR1656500 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656501 4 0.3694 0.7143 0.084 0.084 0.000 0.828 0.004
#> SRR1656498 1 0.0162 0.7961 0.996 0.000 0.004 0.000 0.000
#> SRR1656504 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.3661 0.6409 0.724 0.000 0.276 0.000 0.000
#> SRR1656503 4 0.4723 0.2934 0.448 0.000 0.016 0.536 0.000
#> SRR1656507 1 0.0162 0.7961 0.996 0.000 0.004 0.000 0.000
#> SRR1656508 1 0.0000 0.7951 1.000 0.000 0.000 0.000 0.000
#> SRR1656505 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656506 5 0.2017 0.8736 0.000 0.000 0.008 0.080 0.912
#> SRR1656509 3 0.1205 0.8192 0.040 0.000 0.956 0.004 0.000
#> SRR1656510 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656511 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 1 0.4307 0.2073 0.504 0.000 0.496 0.000 0.000
#> SRR1656515 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656516 4 0.3086 0.7237 0.180 0.000 0.000 0.816 0.004
#> SRR1656518 1 0.4126 0.1469 0.620 0.000 0.000 0.380 0.000
#> SRR1656517 1 0.0000 0.7951 1.000 0.000 0.000 0.000 0.000
#> SRR1656519 3 0.2127 0.7694 0.108 0.000 0.892 0.000 0.000
#> SRR1656522 1 0.3999 0.5505 0.656 0.000 0.344 0.000 0.000
#> SRR1656523 2 0.2732 0.8018 0.000 0.840 0.000 0.160 0.000
#> SRR1656521 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656524 1 0.0880 0.7711 0.968 0.000 0.000 0.032 0.000
#> SRR1656525 3 0.4735 0.2851 0.000 0.000 0.524 0.460 0.016
#> SRR1656526 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656530 5 0.3909 0.7696 0.000 0.000 0.024 0.216 0.760
#> SRR1656529 5 0.1768 0.8780 0.000 0.000 0.004 0.072 0.924
#> SRR1656531 1 0.0162 0.7961 0.996 0.000 0.004 0.000 0.000
#> SRR1656528 5 0.3732 0.7998 0.000 0.000 0.032 0.176 0.792
#> SRR1656534 3 0.2813 0.6914 0.168 0.000 0.832 0.000 0.000
#> SRR1656533 1 0.0404 0.7877 0.988 0.000 0.000 0.012 0.000
#> SRR1656536 5 0.0162 0.8948 0.000 0.000 0.004 0.000 0.996
#> SRR1656532 2 0.4354 0.5959 0.256 0.712 0.000 0.032 0.000
#> SRR1656537 1 0.0162 0.7961 0.996 0.000 0.004 0.000 0.000
#> SRR1656538 4 0.4015 0.3115 0.004 0.000 0.284 0.708 0.004
#> SRR1656535 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 5 0.2889 0.8480 0.000 0.000 0.044 0.084 0.872
#> SRR1656544 3 0.3184 0.7528 0.000 0.000 0.852 0.100 0.048
#> SRR1656542 3 0.1478 0.8059 0.064 0.000 0.936 0.000 0.000
#> SRR1656543 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656545 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656546 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656541 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656548 4 0.4485 0.3098 0.000 0.000 0.028 0.680 0.292
#> SRR1656549 4 0.4086 0.5271 0.012 0.284 0.000 0.704 0.000
#> SRR1656551 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656553 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656550 5 0.0162 0.8948 0.000 0.000 0.004 0.000 0.996
#> SRR1656552 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 5 0.1956 0.8756 0.000 0.000 0.008 0.076 0.916
#> SRR1656555 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656556 3 0.0451 0.8152 0.008 0.000 0.988 0.004 0.000
#> SRR1656557 3 0.0880 0.8231 0.032 0.000 0.968 0.000 0.000
#> SRR1656558 1 0.0703 0.7777 0.976 0.000 0.000 0.024 0.000
#> SRR1656559 1 0.4114 0.4985 0.624 0.000 0.376 0.000 0.000
#> SRR1656560 5 0.4170 0.7687 0.000 0.000 0.048 0.192 0.760
#> SRR1656561 4 0.1331 0.6726 0.000 0.000 0.008 0.952 0.040
#> SRR1656562 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656563 4 0.3534 0.6814 0.256 0.000 0.000 0.744 0.000
#> SRR1656564 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 2 0.0290 0.9687 0.000 0.992 0.000 0.008 0.000
#> SRR1656566 1 0.1121 0.7608 0.956 0.000 0.000 0.044 0.000
#> SRR1656568 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656569 5 0.0510 0.8932 0.000 0.000 0.000 0.016 0.984
#> SRR1656570 4 0.3508 0.6852 0.252 0.000 0.000 0.748 0.000
#> SRR1656571 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.2448 0.8224 0.000 0.020 0.000 0.088 0.892
#> SRR1656572 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656574 1 0.1965 0.7620 0.904 0.000 0.096 0.000 0.000
#> SRR1656575 4 0.3521 0.6973 0.232 0.004 0.000 0.764 0.000
#> SRR1656576 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.3656 0.7148 0.196 0.784 0.000 0.020 0.000
#> SRR1656577 1 0.2329 0.7480 0.876 0.000 0.124 0.000 0.000
#> SRR1656579 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656580 3 0.5111 0.1562 0.028 0.000 0.516 0.452 0.004
#> SRR1656581 2 0.3521 0.6886 0.000 0.764 0.000 0.232 0.004
#> SRR1656582 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 5 0.0404 0.8926 0.000 0.000 0.000 0.012 0.988
#> SRR1656584 1 0.4192 0.0651 0.596 0.000 0.000 0.404 0.000
#> SRR1656583 3 0.1282 0.8174 0.044 0.000 0.952 0.004 0.000
#> SRR1656586 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 1 0.7196 0.2550 0.436 0.264 0.280 0.016 0.004
#> SRR1656588 5 0.0162 0.8953 0.000 0.004 0.000 0.000 0.996
#> SRR1656589 2 0.0000 0.9749 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.0162 0.7961 0.996 0.000 0.004 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 1 0.4335 0.2674 0.508 0.000 0.472 0.020 0.000 0.000
#> SRR1656462 3 0.0458 0.7280 0.016 0.000 0.984 0.000 0.000 0.000
#> SRR1656465 5 0.3852 0.3787 0.000 0.000 0.012 0.324 0.664 0.000
#> SRR1656467 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656466 3 0.5166 0.1736 0.000 0.000 0.552 0.348 0.100 0.000
#> SRR1656468 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656472 1 0.4362 0.4353 0.584 0.000 0.392 0.020 0.000 0.004
#> SRR1656471 3 0.5919 -0.0834 0.000 0.000 0.464 0.288 0.248 0.000
#> SRR1656470 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.0260 0.8573 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1656473 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.0520 0.7917 0.984 0.000 0.008 0.008 0.000 0.000
#> SRR1656477 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656479 6 0.0363 0.6628 0.000 0.000 0.000 0.012 0.000 0.988
#> SRR1656480 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656476 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 5 0.2165 0.7690 0.000 0.000 0.008 0.108 0.884 0.000
#> SRR1656482 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.4203 0.3802 0.000 0.000 0.652 0.316 0.032 0.000
#> SRR1656487 4 0.6023 0.3695 0.000 0.000 0.284 0.428 0.288 0.000
#> SRR1656486 2 0.1866 0.8746 0.000 0.908 0.000 0.008 0.000 0.084
#> SRR1656488 3 0.5011 0.1661 0.000 0.000 0.552 0.368 0.080 0.000
#> SRR1656484 1 0.4350 0.6841 0.760 0.000 0.136 0.032 0.000 0.072
#> SRR1656489 1 0.0405 0.7909 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1656491 6 0.7674 0.0120 0.000 0.220 0.000 0.208 0.272 0.300
#> SRR1656490 2 0.1500 0.9034 0.000 0.936 0.000 0.012 0.000 0.052
#> SRR1656492 4 0.6077 0.1238 0.012 0.000 0.372 0.504 0.052 0.060
#> SRR1656493 1 0.0508 0.7903 0.984 0.000 0.004 0.012 0.000 0.000
#> SRR1656495 1 0.0713 0.7847 0.972 0.000 0.000 0.028 0.000 0.000
#> SRR1656496 6 0.5446 0.3581 0.008 0.000 0.000 0.236 0.156 0.600
#> SRR1656494 2 0.4755 0.4934 0.004 0.616 0.012 0.340 0.004 0.024
#> SRR1656497 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.3515 0.4157 0.000 0.000 0.676 0.324 0.000 0.000
#> SRR1656500 3 0.0363 0.7281 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1656501 6 0.2903 0.6718 0.036 0.016 0.000 0.084 0.000 0.864
#> SRR1656498 1 0.0520 0.7919 0.984 0.000 0.008 0.008 0.000 0.000
#> SRR1656504 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 1 0.4313 0.4680 0.604 0.000 0.372 0.020 0.000 0.004
#> SRR1656503 6 0.5442 0.3910 0.336 0.000 0.008 0.108 0.000 0.548
#> SRR1656507 1 0.0405 0.7909 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1656508 1 0.0622 0.7920 0.980 0.000 0.008 0.012 0.000 0.000
#> SRR1656505 5 0.0146 0.8611 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1656506 5 0.3619 0.6280 0.000 0.000 0.000 0.232 0.744 0.024
#> SRR1656509 3 0.2723 0.6656 0.020 0.000 0.856 0.120 0.000 0.004
#> SRR1656510 2 0.0603 0.9334 0.000 0.980 0.000 0.004 0.016 0.000
#> SRR1656511 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656513 2 0.0260 0.9417 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656512 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.4067 -0.1219 0.444 0.000 0.548 0.008 0.000 0.000
#> SRR1656515 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656516 6 0.4104 0.6590 0.104 0.000 0.000 0.148 0.000 0.748
#> SRR1656518 1 0.4388 0.1444 0.572 0.000 0.000 0.028 0.000 0.400
#> SRR1656517 1 0.0405 0.7903 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1656519 3 0.1863 0.6789 0.104 0.000 0.896 0.000 0.000 0.000
#> SRR1656522 1 0.4072 0.3462 0.544 0.000 0.448 0.008 0.000 0.000
#> SRR1656523 2 0.3168 0.7443 0.000 0.792 0.000 0.016 0.000 0.192
#> SRR1656521 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0458 0.7280 0.016 0.000 0.984 0.000 0.000 0.000
#> SRR1656524 1 0.1564 0.7554 0.936 0.000 0.000 0.024 0.000 0.040
#> SRR1656525 4 0.5729 0.2340 0.000 0.000 0.288 0.528 0.004 0.180
#> SRR1656526 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.1007 0.9193 0.000 0.956 0.000 0.044 0.000 0.000
#> SRR1656530 4 0.4936 0.0621 0.000 0.000 0.004 0.480 0.464 0.052
#> SRR1656529 5 0.3514 0.6371 0.000 0.000 0.000 0.228 0.752 0.020
#> SRR1656531 1 0.0520 0.7919 0.984 0.000 0.008 0.008 0.000 0.000
#> SRR1656528 4 0.4563 0.0530 0.000 0.000 0.008 0.504 0.468 0.020
#> SRR1656534 3 0.2135 0.6580 0.128 0.000 0.872 0.000 0.000 0.000
#> SRR1656533 1 0.0520 0.7868 0.984 0.000 0.000 0.008 0.000 0.008
#> SRR1656536 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656532 2 0.6916 -0.0189 0.172 0.396 0.000 0.352 0.000 0.080
#> SRR1656537 1 0.0520 0.7919 0.984 0.000 0.008 0.008 0.000 0.000
#> SRR1656538 6 0.5983 0.0218 0.000 0.000 0.228 0.384 0.000 0.388
#> SRR1656535 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.4014 0.4976 0.000 0.000 0.044 0.240 0.716 0.000
#> SRR1656544 3 0.4130 0.4533 0.000 0.000 0.696 0.260 0.044 0.000
#> SRR1656542 3 0.1267 0.7095 0.060 0.000 0.940 0.000 0.000 0.000
#> SRR1656543 3 0.0363 0.7281 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0363 0.7281 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1656546 2 0.1075 0.9167 0.000 0.952 0.000 0.048 0.000 0.000
#> SRR1656541 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656548 4 0.5661 -0.0486 0.000 0.000 0.004 0.476 0.136 0.384
#> SRR1656549 6 0.2356 0.6165 0.004 0.096 0.000 0.016 0.000 0.884
#> SRR1656551 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656553 3 0.0458 0.7280 0.016 0.000 0.984 0.000 0.000 0.000
#> SRR1656550 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656552 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656554 5 0.3345 0.6739 0.000 0.000 0.000 0.204 0.776 0.020
#> SRR1656555 2 0.0146 0.9433 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656556 3 0.1444 0.6865 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR1656557 3 0.0363 0.7281 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR1656558 1 0.0622 0.7863 0.980 0.000 0.000 0.012 0.000 0.008
#> SRR1656559 1 0.4072 0.3455 0.544 0.000 0.448 0.008 0.000 0.000
#> SRR1656560 4 0.4644 0.1481 0.000 0.000 0.032 0.524 0.440 0.004
#> SRR1656561 6 0.3076 0.5733 0.000 0.000 0.000 0.240 0.000 0.760
#> SRR1656562 2 0.0260 0.9418 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656563 6 0.2491 0.6585 0.164 0.000 0.000 0.000 0.000 0.836
#> SRR1656564 2 0.1003 0.9249 0.000 0.964 0.000 0.016 0.000 0.020
#> SRR1656565 2 0.1418 0.9102 0.000 0.944 0.000 0.024 0.000 0.032
#> SRR1656566 1 0.2250 0.7115 0.888 0.000 0.000 0.020 0.000 0.092
#> SRR1656568 2 0.0260 0.9413 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656567 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656569 5 0.1333 0.8394 0.000 0.000 0.000 0.048 0.944 0.008
#> SRR1656570 6 0.2491 0.6585 0.164 0.000 0.000 0.000 0.000 0.836
#> SRR1656571 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.3620 0.6926 0.000 0.036 0.000 0.056 0.824 0.084
#> SRR1656572 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656574 1 0.3046 0.7040 0.800 0.000 0.188 0.012 0.000 0.000
#> SRR1656575 6 0.2741 0.6760 0.092 0.008 0.000 0.032 0.000 0.868
#> SRR1656576 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 2 0.6318 0.2161 0.124 0.480 0.000 0.344 0.000 0.052
#> SRR1656577 1 0.3245 0.6683 0.764 0.000 0.228 0.008 0.000 0.000
#> SRR1656579 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656580 3 0.5318 0.2226 0.004 0.000 0.560 0.108 0.000 0.328
#> SRR1656581 2 0.3956 0.6343 0.000 0.716 0.000 0.028 0.004 0.252
#> SRR1656582 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 5 0.1633 0.8356 0.000 0.000 0.000 0.044 0.932 0.024
#> SRR1656584 1 0.4276 0.1110 0.564 0.000 0.000 0.020 0.000 0.416
#> SRR1656583 3 0.2622 0.6738 0.024 0.000 0.868 0.104 0.000 0.004
#> SRR1656586 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 4 0.8177 -0.1077 0.216 0.140 0.256 0.352 0.012 0.024
#> SRR1656588 5 0.0000 0.8642 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656589 2 0.0000 0.9451 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.0622 0.7920 0.980 0.000 0.008 0.012 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 13572 rows and 129 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 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 0.569 0.875 0.925 0.4544 0.552 0.552
#> 3 3 0.628 0.802 0.908 0.3742 0.815 0.665
#> 4 4 0.854 0.862 0.936 0.1858 0.854 0.619
#> 5 5 0.861 0.861 0.925 0.0406 0.970 0.885
#> 6 6 0.795 0.745 0.877 0.0325 0.973 0.887
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
#> SRR1656463 2 0.0000 0.973 0.000 1.000
#> SRR1656464 1 0.0000 0.891 1.000 0.000
#> SRR1656462 1 0.0672 0.893 0.992 0.008
#> SRR1656465 1 0.6801 0.847 0.820 0.180
#> SRR1656467 2 0.0000 0.973 0.000 1.000
#> SRR1656466 1 0.2236 0.891 0.964 0.036
#> SRR1656468 2 0.0000 0.973 0.000 1.000
#> SRR1656472 1 0.0000 0.891 1.000 0.000
#> SRR1656471 1 0.2043 0.892 0.968 0.032
#> SRR1656470 2 0.0000 0.973 0.000 1.000
#> SRR1656469 1 0.9635 0.549 0.612 0.388
#> SRR1656473 2 0.0000 0.973 0.000 1.000
#> SRR1656474 2 0.0000 0.973 0.000 1.000
#> SRR1656475 2 0.0000 0.973 0.000 1.000
#> SRR1656478 1 0.0376 0.892 0.996 0.004
#> SRR1656477 1 0.7745 0.803 0.772 0.228
#> SRR1656479 1 0.6801 0.847 0.820 0.180
#> SRR1656480 1 1.0000 0.248 0.504 0.496
#> SRR1656476 2 0.0000 0.973 0.000 1.000
#> SRR1656481 1 0.8499 0.744 0.724 0.276
#> SRR1656482 2 0.0000 0.973 0.000 1.000
#> SRR1656483 2 0.0000 0.973 0.000 1.000
#> SRR1656485 1 0.1414 0.893 0.980 0.020
#> SRR1656487 1 0.1184 0.894 0.984 0.016
#> SRR1656486 1 0.8081 0.784 0.752 0.248
#> SRR1656488 1 0.1184 0.894 0.984 0.016
#> SRR1656484 1 0.6712 0.848 0.824 0.176
#> SRR1656489 1 0.0000 0.891 1.000 0.000
#> SRR1656491 1 0.6801 0.847 0.820 0.180
#> SRR1656490 1 0.6801 0.847 0.820 0.180
#> SRR1656492 1 0.1184 0.894 0.984 0.016
#> SRR1656493 1 0.0000 0.891 1.000 0.000
#> SRR1656495 1 0.0000 0.891 1.000 0.000
#> SRR1656496 1 0.6801 0.847 0.820 0.180
#> SRR1656494 1 0.9248 0.644 0.660 0.340
#> SRR1656497 2 0.0000 0.973 0.000 1.000
#> SRR1656499 1 0.1184 0.894 0.984 0.016
#> SRR1656500 1 0.0938 0.893 0.988 0.012
#> SRR1656501 1 0.6801 0.847 0.820 0.180
#> SRR1656498 1 0.0000 0.891 1.000 0.000
#> SRR1656504 2 0.0000 0.973 0.000 1.000
#> SRR1656502 1 0.0000 0.891 1.000 0.000
#> SRR1656503 1 0.6801 0.847 0.820 0.180
#> SRR1656507 1 0.0672 0.893 0.992 0.008
#> SRR1656508 1 0.0000 0.891 1.000 0.000
#> SRR1656505 2 0.0000 0.973 0.000 1.000
#> SRR1656506 1 0.6801 0.847 0.820 0.180
#> SRR1656509 1 0.6531 0.852 0.832 0.168
#> SRR1656510 2 0.0938 0.962 0.012 0.988
#> SRR1656511 2 0.0000 0.973 0.000 1.000
#> SRR1656513 2 0.0000 0.973 0.000 1.000
#> SRR1656512 2 0.0000 0.973 0.000 1.000
#> SRR1656514 1 0.0000 0.891 1.000 0.000
#> SRR1656515 2 0.0000 0.973 0.000 1.000
#> SRR1656516 1 0.6531 0.852 0.832 0.168
#> SRR1656518 1 0.0376 0.892 0.996 0.004
#> SRR1656517 1 0.0000 0.891 1.000 0.000
#> SRR1656519 1 0.0000 0.891 1.000 0.000
#> SRR1656522 1 0.0000 0.891 1.000 0.000
#> SRR1656523 1 0.9815 0.471 0.580 0.420
#> SRR1656521 2 0.0000 0.973 0.000 1.000
#> SRR1656520 1 0.0376 0.892 0.996 0.004
#> SRR1656524 1 0.0000 0.891 1.000 0.000
#> SRR1656525 1 0.1414 0.894 0.980 0.020
#> SRR1656526 2 0.0000 0.973 0.000 1.000
#> SRR1656527 2 0.0000 0.973 0.000 1.000
#> SRR1656530 1 0.6801 0.847 0.820 0.180
#> SRR1656529 1 0.6801 0.847 0.820 0.180
#> SRR1656531 1 0.0000 0.891 1.000 0.000
#> SRR1656528 1 0.6801 0.847 0.820 0.180
#> SRR1656534 1 0.0000 0.891 1.000 0.000
#> SRR1656533 1 0.0000 0.891 1.000 0.000
#> SRR1656536 1 0.9248 0.644 0.660 0.340
#> SRR1656532 1 0.9044 0.678 0.680 0.320
#> SRR1656537 1 0.0000 0.891 1.000 0.000
#> SRR1656538 1 0.0938 0.893 0.988 0.012
#> SRR1656535 2 0.0000 0.973 0.000 1.000
#> SRR1656539 1 0.6801 0.847 0.820 0.180
#> SRR1656544 1 0.1843 0.893 0.972 0.028
#> SRR1656542 1 0.0000 0.891 1.000 0.000
#> SRR1656543 1 0.0000 0.891 1.000 0.000
#> SRR1656545 2 0.0000 0.973 0.000 1.000
#> SRR1656540 1 0.0938 0.893 0.988 0.012
#> SRR1656546 2 0.3879 0.891 0.076 0.924
#> SRR1656541 2 0.0000 0.973 0.000 1.000
#> SRR1656547 2 0.0000 0.973 0.000 1.000
#> SRR1656548 1 0.6801 0.847 0.820 0.180
#> SRR1656549 1 0.8081 0.784 0.752 0.248
#> SRR1656551 2 0.8144 0.582 0.252 0.748
#> SRR1656553 1 0.1184 0.894 0.984 0.016
#> SRR1656550 1 0.9248 0.644 0.660 0.340
#> SRR1656552 2 0.0000 0.973 0.000 1.000
#> SRR1656554 1 0.6801 0.847 0.820 0.180
#> SRR1656555 2 0.0000 0.973 0.000 1.000
#> SRR1656556 1 0.1184 0.894 0.984 0.016
#> SRR1656557 1 0.0938 0.893 0.988 0.012
#> SRR1656558 1 0.0000 0.891 1.000 0.000
#> SRR1656559 1 0.0000 0.891 1.000 0.000
#> SRR1656560 1 0.6801 0.847 0.820 0.180
#> SRR1656561 1 0.6801 0.847 0.820 0.180
#> SRR1656562 2 0.9754 0.104 0.408 0.592
#> SRR1656563 1 0.0672 0.893 0.992 0.008
#> SRR1656564 2 0.0000 0.973 0.000 1.000
#> SRR1656565 2 0.0000 0.973 0.000 1.000
#> SRR1656566 1 0.1184 0.893 0.984 0.016
#> SRR1656568 2 0.0000 0.973 0.000 1.000
#> SRR1656567 2 0.0376 0.969 0.004 0.996
#> SRR1656569 1 0.6801 0.847 0.820 0.180
#> SRR1656570 1 0.0938 0.894 0.988 0.012
#> SRR1656571 2 0.0000 0.973 0.000 1.000
#> SRR1656573 1 0.6801 0.847 0.820 0.180
#> SRR1656572 2 0.0000 0.973 0.000 1.000
#> SRR1656574 1 0.0000 0.891 1.000 0.000
#> SRR1656575 1 0.6801 0.847 0.820 0.180
#> SRR1656576 2 0.0000 0.973 0.000 1.000
#> SRR1656578 2 0.6048 0.790 0.148 0.852
#> SRR1656577 1 0.0000 0.891 1.000 0.000
#> SRR1656579 2 0.0000 0.973 0.000 1.000
#> SRR1656580 1 0.1184 0.894 0.984 0.016
#> SRR1656581 1 0.8016 0.789 0.756 0.244
#> SRR1656582 2 0.0000 0.973 0.000 1.000
#> SRR1656585 1 0.6801 0.847 0.820 0.180
#> SRR1656584 1 0.5946 0.859 0.856 0.144
#> SRR1656583 1 0.1843 0.893 0.972 0.028
#> SRR1656586 2 0.0000 0.973 0.000 1.000
#> SRR1656587 1 0.6887 0.844 0.816 0.184
#> SRR1656588 2 0.2778 0.923 0.048 0.952
#> SRR1656589 2 0.0000 0.973 0.000 1.000
#> SRR1656590 1 0.0000 0.891 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656462 1 0.5733 0.616 0.676 0.000 0.324
#> SRR1656465 3 0.0747 0.859 0.000 0.016 0.984
#> SRR1656467 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656468 2 0.4235 0.770 0.000 0.824 0.176
#> SRR1656472 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656471 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656470 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656469 3 0.5178 0.698 0.000 0.256 0.744
#> SRR1656473 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656478 3 0.3267 0.762 0.116 0.000 0.884
#> SRR1656477 3 0.3482 0.828 0.000 0.128 0.872
#> SRR1656479 3 0.3193 0.846 0.004 0.100 0.896
#> SRR1656480 3 0.6045 0.465 0.000 0.380 0.620
#> SRR1656476 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656481 3 0.2796 0.830 0.000 0.092 0.908
#> SRR1656482 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656485 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656487 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656486 3 0.5623 0.681 0.004 0.280 0.716
#> SRR1656488 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656484 3 0.3193 0.846 0.004 0.100 0.896
#> SRR1656489 3 0.5254 0.600 0.264 0.000 0.736
#> SRR1656491 3 0.2945 0.850 0.004 0.088 0.908
#> SRR1656490 3 0.3272 0.844 0.004 0.104 0.892
#> SRR1656492 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656493 1 0.2711 0.808 0.912 0.000 0.088
#> SRR1656495 1 0.6079 0.361 0.612 0.000 0.388
#> SRR1656496 3 0.2945 0.850 0.004 0.088 0.908
#> SRR1656494 3 0.6095 0.498 0.000 0.392 0.608
#> SRR1656497 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656499 3 0.0892 0.846 0.020 0.000 0.980
#> SRR1656500 3 0.6062 0.147 0.384 0.000 0.616
#> SRR1656501 3 0.3193 0.846 0.004 0.100 0.896
#> SRR1656498 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656502 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656503 3 0.1878 0.859 0.004 0.044 0.952
#> SRR1656507 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656508 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656505 2 0.4235 0.770 0.000 0.824 0.176
#> SRR1656506 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656509 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656510 2 0.3116 0.852 0.000 0.892 0.108
#> SRR1656511 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656516 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656518 3 0.4914 0.819 0.068 0.088 0.844
#> SRR1656517 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656519 1 0.5760 0.608 0.672 0.000 0.328
#> SRR1656522 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656523 3 0.6386 0.429 0.004 0.412 0.584
#> SRR1656521 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656520 3 0.6302 -0.175 0.480 0.000 0.520
#> SRR1656524 1 0.6045 0.382 0.620 0.000 0.380
#> SRR1656525 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656526 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656530 3 0.2860 0.851 0.004 0.084 0.912
#> SRR1656529 3 0.2625 0.851 0.000 0.084 0.916
#> SRR1656531 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656528 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656534 1 0.0237 0.845 0.996 0.000 0.004
#> SRR1656533 1 0.0424 0.844 0.992 0.000 0.008
#> SRR1656536 3 0.4702 0.749 0.000 0.212 0.788
#> SRR1656532 3 0.6247 0.531 0.004 0.376 0.620
#> SRR1656537 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656538 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656535 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656539 3 0.0747 0.859 0.000 0.016 0.984
#> SRR1656544 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656542 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656543 1 0.3816 0.784 0.852 0.000 0.148
#> SRR1656545 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656540 1 0.6111 0.498 0.604 0.000 0.396
#> SRR1656546 2 0.2165 0.889 0.000 0.936 0.064
#> SRR1656541 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656548 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656549 3 0.5588 0.686 0.004 0.276 0.720
#> SRR1656551 2 0.6215 0.181 0.000 0.572 0.428
#> SRR1656553 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656550 3 0.4702 0.749 0.000 0.212 0.788
#> SRR1656552 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656554 3 0.2261 0.855 0.000 0.068 0.932
#> SRR1656555 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656556 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656557 1 0.5650 0.637 0.688 0.000 0.312
#> SRR1656558 1 0.6045 0.382 0.620 0.000 0.380
#> SRR1656559 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.857 0.000 0.000 1.000
#> SRR1656561 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656562 2 0.6095 0.203 0.000 0.608 0.392
#> SRR1656563 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656564 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656566 3 0.5497 0.559 0.292 0.000 0.708
#> SRR1656568 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656567 2 0.3412 0.833 0.000 0.876 0.124
#> SRR1656569 3 0.2959 0.846 0.000 0.100 0.900
#> SRR1656570 3 0.1878 0.859 0.004 0.044 0.952
#> SRR1656571 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656573 3 0.3272 0.844 0.004 0.104 0.892
#> SRR1656572 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656574 1 0.4121 0.751 0.832 0.000 0.168
#> SRR1656575 3 0.3851 0.826 0.004 0.136 0.860
#> SRR1656576 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656578 2 0.2537 0.873 0.000 0.920 0.080
#> SRR1656577 1 0.0000 0.846 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656580 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656581 3 0.3272 0.844 0.004 0.104 0.892
#> SRR1656582 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656585 3 0.3272 0.844 0.004 0.104 0.892
#> SRR1656584 3 0.5431 0.572 0.284 0.000 0.716
#> SRR1656583 3 0.0237 0.857 0.004 0.000 0.996
#> SRR1656586 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656587 3 0.4465 0.792 0.004 0.176 0.820
#> SRR1656588 2 0.4121 0.781 0.000 0.832 0.168
#> SRR1656589 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656590 1 0.6026 0.391 0.624 0.000 0.376
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656462 1 0.5321 0.681 0.716 0.000 0.056 0.228
#> SRR1656465 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656467 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656466 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656468 3 0.2647 0.818 0.000 0.120 0.880 0.000
#> SRR1656472 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656471 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656470 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.0188 0.940 0.000 0.004 0.996 0.000
#> SRR1656473 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656478 4 0.0000 0.914 0.000 0.000 0.000 1.000
#> SRR1656477 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656479 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656480 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656476 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656481 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656482 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656485 4 0.3172 0.813 0.000 0.000 0.160 0.840
#> SRR1656487 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656486 4 0.1489 0.905 0.000 0.044 0.004 0.952
#> SRR1656488 3 0.1302 0.913 0.000 0.000 0.956 0.044
#> SRR1656484 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656489 4 0.0000 0.914 0.000 0.000 0.000 1.000
#> SRR1656491 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656490 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656492 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656493 1 0.2704 0.786 0.876 0.000 0.000 0.124
#> SRR1656495 1 0.4855 0.379 0.600 0.000 0.000 0.400
#> SRR1656496 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656494 4 0.3401 0.782 0.000 0.152 0.008 0.840
#> SRR1656497 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.1489 0.910 0.004 0.000 0.952 0.044
#> SRR1656500 4 0.4817 0.197 0.388 0.000 0.000 0.612
#> SRR1656501 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656498 1 0.0188 0.840 0.996 0.000 0.000 0.004
#> SRR1656504 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656503 4 0.0188 0.913 0.004 0.000 0.000 0.996
#> SRR1656507 4 0.0188 0.916 0.000 0.000 0.004 0.996
#> SRR1656508 1 0.0188 0.840 0.996 0.000 0.000 0.004
#> SRR1656505 3 0.0188 0.940 0.000 0.004 0.996 0.000
#> SRR1656506 3 0.2760 0.806 0.000 0.000 0.872 0.128
#> SRR1656509 3 0.4898 0.329 0.000 0.000 0.584 0.416
#> SRR1656510 2 0.4372 0.622 0.000 0.728 0.268 0.004
#> SRR1656511 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656513 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656512 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656514 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656515 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656516 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656518 4 0.1388 0.917 0.028 0.000 0.012 0.960
#> SRR1656517 1 0.0188 0.840 0.996 0.000 0.000 0.004
#> SRR1656519 1 0.4877 0.417 0.592 0.000 0.000 0.408
#> SRR1656522 1 0.0336 0.838 0.992 0.000 0.000 0.008
#> SRR1656523 4 0.4464 0.692 0.000 0.208 0.024 0.768
#> SRR1656521 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656520 4 0.4817 0.197 0.388 0.000 0.000 0.612
#> SRR1656524 1 0.4817 0.407 0.612 0.000 0.000 0.388
#> SRR1656525 4 0.0188 0.915 0.000 0.000 0.004 0.996
#> SRR1656526 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656530 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656529 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656531 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656528 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656534 1 0.0336 0.840 0.992 0.000 0.000 0.008
#> SRR1656533 1 0.0469 0.838 0.988 0.000 0.000 0.012
#> SRR1656536 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656532 4 0.2888 0.820 0.000 0.124 0.004 0.872
#> SRR1656537 1 0.0188 0.840 0.996 0.000 0.000 0.004
#> SRR1656538 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656535 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656539 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656544 3 0.0336 0.940 0.000 0.000 0.992 0.008
#> SRR1656542 4 0.0188 0.913 0.004 0.000 0.000 0.996
#> SRR1656543 1 0.4174 0.714 0.816 0.000 0.140 0.044
#> SRR1656545 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.6007 0.224 0.408 0.000 0.548 0.044
#> SRR1656546 2 0.2593 0.857 0.000 0.892 0.004 0.104
#> SRR1656541 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656547 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656548 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656549 4 0.1452 0.911 0.000 0.036 0.008 0.956
#> SRR1656551 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656553 4 0.2466 0.836 0.004 0.000 0.096 0.900
#> SRR1656550 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656552 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656554 3 0.0000 0.942 0.000 0.000 1.000 0.000
#> SRR1656555 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656556 3 0.1489 0.910 0.004 0.000 0.952 0.044
#> SRR1656557 1 0.5171 0.705 0.760 0.000 0.128 0.112
#> SRR1656558 1 0.4817 0.407 0.612 0.000 0.000 0.388
#> SRR1656559 1 0.1118 0.827 0.964 0.000 0.000 0.036
#> SRR1656560 3 0.0188 0.941 0.000 0.000 0.996 0.004
#> SRR1656561 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656562 2 0.5016 0.294 0.000 0.600 0.004 0.396
#> SRR1656563 4 0.1211 0.928 0.000 0.000 0.040 0.960
#> SRR1656564 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656566 4 0.1637 0.895 0.060 0.000 0.000 0.940
#> SRR1656568 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656567 3 0.1022 0.915 0.000 0.032 0.968 0.000
#> SRR1656569 3 0.0817 0.924 0.000 0.000 0.976 0.024
#> SRR1656570 4 0.1211 0.928 0.000 0.000 0.040 0.960
#> SRR1656571 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656572 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656574 1 0.4679 0.488 0.648 0.000 0.000 0.352
#> SRR1656575 4 0.1624 0.922 0.000 0.020 0.028 0.952
#> SRR1656576 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656578 2 0.2654 0.853 0.000 0.888 0.004 0.108
#> SRR1656577 1 0.0000 0.840 1.000 0.000 0.000 0.000
#> SRR1656579 2 0.0188 0.969 0.000 0.996 0.004 0.000
#> SRR1656580 4 0.1302 0.928 0.000 0.000 0.044 0.956
#> SRR1656581 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656582 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.1389 0.928 0.000 0.000 0.048 0.952
#> SRR1656584 4 0.1302 0.906 0.044 0.000 0.000 0.956
#> SRR1656583 4 0.0188 0.913 0.004 0.000 0.000 0.996
#> SRR1656586 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.1635 0.906 0.000 0.044 0.008 0.948
#> SRR1656588 3 0.0469 0.935 0.000 0.012 0.988 0.000
#> SRR1656589 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.4804 0.415 0.616 0.000 0.000 0.384
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 1 0.0000 0.771 1.000 0.000 0.000 0.000 0.000
#> SRR1656462 3 0.3561 0.840 0.260 0.000 0.740 0.000 0.000
#> SRR1656465 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656466 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656468 5 0.2471 0.771 0.000 0.136 0.000 0.000 0.864
#> SRR1656472 1 0.0000 0.771 1.000 0.000 0.000 0.000 0.000
#> SRR1656471 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656470 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 4 0.3612 0.704 0.000 0.000 0.268 0.732 0.000
#> SRR1656477 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656479 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656480 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 4 0.2329 0.826 0.000 0.000 0.000 0.876 0.124
#> SRR1656487 5 0.2230 0.855 0.000 0.000 0.116 0.000 0.884
#> SRR1656486 4 0.0162 0.937 0.000 0.004 0.000 0.996 0.000
#> SRR1656488 5 0.2516 0.832 0.000 0.000 0.140 0.000 0.860
#> SRR1656484 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000
#> SRR1656489 4 0.1043 0.919 0.000 0.000 0.040 0.960 0.000
#> SRR1656491 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656490 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656492 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656493 1 0.6071 0.528 0.548 0.000 0.300 0.152 0.000
#> SRR1656495 1 0.5754 0.562 0.604 0.000 0.260 0.136 0.000
#> SRR1656496 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656494 4 0.2674 0.789 0.000 0.140 0.000 0.856 0.004
#> SRR1656497 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.3796 0.430 0.000 0.000 0.700 0.000 0.300
#> SRR1656500 3 0.3561 0.840 0.260 0.000 0.740 0.000 0.000
#> SRR1656501 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656498 1 0.1205 0.765 0.956 0.000 0.040 0.004 0.000
#> SRR1656504 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.0000 0.771 1.000 0.000 0.000 0.000 0.000
#> SRR1656503 4 0.0290 0.936 0.000 0.000 0.008 0.992 0.000
#> SRR1656507 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000
#> SRR1656508 1 0.1205 0.765 0.956 0.000 0.040 0.004 0.000
#> SRR1656505 5 0.0162 0.932 0.000 0.004 0.000 0.000 0.996
#> SRR1656506 5 0.2732 0.751 0.000 0.000 0.000 0.160 0.840
#> SRR1656509 5 0.4251 0.384 0.000 0.000 0.004 0.372 0.624
#> SRR1656510 2 0.3741 0.620 0.000 0.732 0.000 0.004 0.264
#> SRR1656511 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 1 0.0404 0.764 0.988 0.000 0.012 0.000 0.000
#> SRR1656515 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656516 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656518 4 0.3534 0.711 0.000 0.000 0.256 0.744 0.000
#> SRR1656517 1 0.2233 0.759 0.892 0.000 0.104 0.004 0.000
#> SRR1656519 3 0.6268 0.556 0.260 0.000 0.536 0.204 0.000
#> SRR1656522 1 0.1478 0.748 0.936 0.000 0.064 0.000 0.000
#> SRR1656523 4 0.3003 0.726 0.000 0.188 0.000 0.812 0.000
#> SRR1656521 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.3561 0.840 0.260 0.000 0.740 0.000 0.000
#> SRR1656524 1 0.4597 0.662 0.696 0.000 0.260 0.044 0.000
#> SRR1656525 4 0.0794 0.924 0.000 0.000 0.028 0.972 0.000
#> SRR1656526 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656530 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656529 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.771 1.000 0.000 0.000 0.000 0.000
#> SRR1656528 5 0.2230 0.855 0.000 0.000 0.116 0.000 0.884
#> SRR1656534 3 0.4287 0.538 0.460 0.000 0.540 0.000 0.000
#> SRR1656533 1 0.3949 0.667 0.696 0.000 0.300 0.004 0.000
#> SRR1656536 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656532 4 0.2179 0.828 0.000 0.112 0.000 0.888 0.000
#> SRR1656537 1 0.0000 0.771 1.000 0.000 0.000 0.000 0.000
#> SRR1656538 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656535 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656544 5 0.0162 0.933 0.000 0.000 0.000 0.004 0.996
#> SRR1656542 4 0.0162 0.938 0.000 0.000 0.004 0.996 0.000
#> SRR1656543 3 0.3561 0.840 0.260 0.000 0.740 0.000 0.000
#> SRR1656545 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.3796 0.813 0.300 0.000 0.700 0.000 0.000
#> SRR1656546 2 0.2127 0.843 0.000 0.892 0.000 0.108 0.000
#> SRR1656541 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656548 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656549 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000
#> SRR1656551 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656553 4 0.3530 0.725 0.000 0.000 0.204 0.784 0.012
#> SRR1656550 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656552 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656555 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656556 5 0.3074 0.767 0.000 0.000 0.196 0.000 0.804
#> SRR1656557 3 0.3561 0.840 0.260 0.000 0.740 0.000 0.000
#> SRR1656558 1 0.4597 0.662 0.696 0.000 0.260 0.044 0.000
#> SRR1656559 1 0.1197 0.760 0.952 0.000 0.048 0.000 0.000
#> SRR1656560 5 0.0000 0.935 0.000 0.000 0.000 0.000 1.000
#> SRR1656561 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656562 2 0.4171 0.330 0.000 0.604 0.000 0.396 0.000
#> SRR1656563 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000
#> SRR1656564 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656566 4 0.4132 0.683 0.020 0.000 0.260 0.720 0.000
#> SRR1656568 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 5 0.0162 0.932 0.000 0.004 0.000 0.000 0.996
#> SRR1656569 5 0.1043 0.901 0.000 0.000 0.000 0.040 0.960
#> SRR1656570 4 0.0000 0.938 0.000 0.000 0.000 1.000 0.000
#> SRR1656571 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656572 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656574 1 0.5086 0.152 0.564 0.000 0.040 0.396 0.000
#> SRR1656575 4 0.0162 0.937 0.000 0.004 0.000 0.996 0.000
#> SRR1656576 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.2127 0.843 0.000 0.892 0.000 0.108 0.000
#> SRR1656577 1 0.1205 0.765 0.956 0.000 0.040 0.004 0.000
#> SRR1656579 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656580 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656581 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656582 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 4 0.0162 0.939 0.000 0.000 0.000 0.996 0.004
#> SRR1656584 4 0.3561 0.706 0.000 0.000 0.260 0.740 0.000
#> SRR1656583 4 0.0290 0.936 0.000 0.000 0.008 0.992 0.000
#> SRR1656586 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.0290 0.935 0.000 0.008 0.000 0.992 0.000
#> SRR1656588 5 0.0162 0.932 0.000 0.004 0.000 0.000 0.996
#> SRR1656589 2 0.0000 0.971 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.4597 0.662 0.696 0.000 0.260 0.044 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656464 6 0.3076 0.851 0.240 0.000 0.000 0.000 0.000 0.760
#> SRR1656462 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656467 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656466 5 0.0363 0.905 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR1656468 5 0.3578 0.538 0.000 0.340 0.000 0.000 0.660 0.000
#> SRR1656472 6 0.3076 0.851 0.240 0.000 0.000 0.000 0.000 0.760
#> SRR1656471 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656470 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656469 5 0.0713 0.898 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1656473 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656474 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656475 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656478 1 0.4097 0.112 0.500 0.000 0.008 0.492 0.000 0.000
#> SRR1656477 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656479 4 0.0260 0.884 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1656480 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656476 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656481 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656482 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.2219 0.853 0.000 0.864 0.000 0.000 0.000 0.136
#> SRR1656485 4 0.2260 0.763 0.000 0.000 0.000 0.860 0.140 0.000
#> SRR1656487 5 0.1663 0.865 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1656486 4 0.3101 0.649 0.000 0.244 0.000 0.756 0.000 0.000
#> SRR1656488 5 0.1910 0.850 0.000 0.000 0.108 0.000 0.892 0.000
#> SRR1656484 4 0.0458 0.880 0.016 0.000 0.000 0.984 0.000 0.000
#> SRR1656489 4 0.3852 0.213 0.384 0.000 0.004 0.612 0.000 0.000
#> SRR1656491 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656490 4 0.0363 0.884 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1656492 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656493 1 0.1957 0.454 0.888 0.000 0.000 0.112 0.000 0.000
#> SRR1656495 1 0.2793 0.429 0.800 0.000 0.000 0.200 0.000 0.000
#> SRR1656496 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656494 4 0.3819 0.468 0.000 0.372 0.000 0.624 0.004 0.000
#> SRR1656497 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656499 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656500 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656501 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656498 1 0.1444 0.437 0.928 0.000 0.000 0.000 0.000 0.072
#> SRR1656504 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656502 6 0.3076 0.851 0.240 0.000 0.000 0.000 0.000 0.760
#> SRR1656503 4 0.0405 0.881 0.004 0.000 0.008 0.988 0.000 0.000
#> SRR1656507 4 0.0713 0.867 0.028 0.000 0.000 0.972 0.000 0.000
#> SRR1656508 1 0.1444 0.437 0.928 0.000 0.000 0.000 0.000 0.072
#> SRR1656505 5 0.3076 0.679 0.000 0.240 0.000 0.000 0.760 0.000
#> SRR1656506 5 0.2378 0.760 0.000 0.000 0.000 0.152 0.848 0.000
#> SRR1656509 5 0.3717 0.379 0.000 0.000 0.000 0.384 0.616 0.000
#> SRR1656510 2 0.3383 0.482 0.000 0.728 0.000 0.004 0.268 0.000
#> SRR1656511 2 0.1714 0.845 0.000 0.908 0.000 0.000 0.000 0.092
#> SRR1656513 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656512 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656514 6 0.5587 0.565 0.240 0.000 0.212 0.000 0.000 0.548
#> SRR1656515 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656516 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656518 4 0.3868 -0.135 0.496 0.000 0.000 0.504 0.000 0.000
#> SRR1656517 1 0.1444 0.437 0.928 0.000 0.000 0.000 0.000 0.072
#> SRR1656519 3 0.2793 0.643 0.000 0.000 0.800 0.200 0.000 0.000
#> SRR1656522 6 0.3076 0.851 0.240 0.000 0.000 0.000 0.000 0.760
#> SRR1656523 4 0.3756 0.506 0.000 0.352 0.000 0.644 0.004 0.000
#> SRR1656521 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656520 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 1 0.0000 0.469 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656525 4 0.1151 0.869 0.000 0.000 0.032 0.956 0.012 0.000
#> SRR1656526 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656527 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656530 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656529 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656531 6 0.3864 0.580 0.480 0.000 0.000 0.000 0.000 0.520
#> SRR1656528 5 0.1663 0.866 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1656534 3 0.2553 0.762 0.144 0.000 0.848 0.008 0.000 0.000
#> SRR1656533 1 0.0000 0.469 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656536 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656532 4 0.3531 0.534 0.000 0.328 0.000 0.672 0.000 0.000
#> SRR1656537 1 0.3867 -0.616 0.512 0.000 0.000 0.000 0.000 0.488
#> SRR1656538 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656535 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656539 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656544 5 0.0458 0.904 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR1656542 4 0.0260 0.882 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1656543 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656540 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656546 2 0.1910 0.728 0.000 0.892 0.000 0.108 0.000 0.000
#> SRR1656541 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656547 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656548 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656549 4 0.0458 0.880 0.016 0.000 0.000 0.984 0.000 0.000
#> SRR1656551 5 0.0937 0.891 0.000 0.040 0.000 0.000 0.960 0.000
#> SRR1656553 4 0.2980 0.695 0.000 0.000 0.192 0.800 0.008 0.000
#> SRR1656550 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656552 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656554 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656555 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656556 5 0.2597 0.784 0.000 0.000 0.176 0.000 0.824 0.000
#> SRR1656557 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.0146 0.467 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1656559 1 0.5160 -0.162 0.476 0.000 0.448 0.004 0.000 0.072
#> SRR1656560 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656561 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656562 2 0.2854 0.580 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656563 4 0.0458 0.880 0.016 0.000 0.000 0.984 0.000 0.000
#> SRR1656564 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656566 1 0.3857 0.162 0.532 0.000 0.000 0.468 0.000 0.000
#> SRR1656568 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656567 5 0.2362 0.799 0.000 0.136 0.000 0.004 0.860 0.000
#> SRR1656569 5 0.0865 0.888 0.000 0.000 0.000 0.036 0.964 0.000
#> SRR1656570 4 0.0458 0.880 0.016 0.000 0.000 0.984 0.000 0.000
#> SRR1656571 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656573 4 0.1320 0.863 0.000 0.036 0.000 0.948 0.016 0.000
#> SRR1656572 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656574 1 0.4569 0.344 0.636 0.000 0.000 0.304 0.000 0.060
#> SRR1656575 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656576 2 0.3050 0.866 0.000 0.764 0.000 0.000 0.000 0.236
#> SRR1656578 2 0.2048 0.712 0.000 0.880 0.000 0.120 0.000 0.000
#> SRR1656577 1 0.5002 -0.133 0.516 0.000 0.412 0.000 0.000 0.072
#> SRR1656579 2 0.0000 0.824 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656580 4 0.0458 0.883 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656581 4 0.2932 0.732 0.000 0.164 0.000 0.820 0.016 0.000
#> SRR1656582 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656585 4 0.0260 0.884 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1656584 1 0.3864 0.128 0.520 0.000 0.000 0.480 0.000 0.000
#> SRR1656583 4 0.0405 0.882 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1656586 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656587 4 0.0260 0.881 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1656588 5 0.0000 0.910 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656589 2 0.3076 0.866 0.000 0.760 0.000 0.000 0.000 0.240
#> SRR1656590 1 0.1444 0.437 0.928 0.000 0.000 0.000 0.000 0.072
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.923 0.928 0.964 0.3198 0.649 0.649
#> 3 3 0.912 0.912 0.965 0.9185 0.619 0.464
#> 4 4 0.521 0.477 0.752 0.1214 0.866 0.691
#> 5 5 0.587 0.571 0.752 0.1054 0.771 0.412
#> 6 6 0.720 0.679 0.825 0.0589 0.919 0.660
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.0000 0.856 0.000 1.000
#> SRR1656464 1 0.0000 0.990 1.000 0.000
#> SRR1656462 1 0.0000 0.990 1.000 0.000
#> SRR1656465 1 0.1184 0.982 0.984 0.016
#> SRR1656467 2 0.9732 0.503 0.404 0.596
#> SRR1656466 1 0.0000 0.990 1.000 0.000
#> SRR1656468 1 0.1184 0.982 0.984 0.016
#> SRR1656472 1 0.0000 0.990 1.000 0.000
#> SRR1656471 1 0.1184 0.982 0.984 0.016
#> SRR1656470 2 0.0000 0.856 0.000 1.000
#> SRR1656469 1 0.1184 0.982 0.984 0.016
#> SRR1656473 2 0.0000 0.856 0.000 1.000
#> SRR1656474 2 0.0000 0.856 0.000 1.000
#> SRR1656475 2 0.0000 0.856 0.000 1.000
#> SRR1656478 1 0.0000 0.990 1.000 0.000
#> SRR1656477 1 0.1184 0.982 0.984 0.016
#> SRR1656479 1 0.0000 0.990 1.000 0.000
#> SRR1656480 1 0.1184 0.982 0.984 0.016
#> SRR1656476 2 0.0000 0.856 0.000 1.000
#> SRR1656481 1 0.1184 0.982 0.984 0.016
#> SRR1656482 2 0.8909 0.631 0.308 0.692
#> SRR1656483 2 0.0376 0.855 0.004 0.996
#> SRR1656485 1 0.0000 0.990 1.000 0.000
#> SRR1656487 1 0.1184 0.982 0.984 0.016
#> SRR1656486 1 0.0938 0.984 0.988 0.012
#> SRR1656488 1 0.1184 0.982 0.984 0.016
#> SRR1656484 1 0.0000 0.990 1.000 0.000
#> SRR1656489 1 0.0000 0.990 1.000 0.000
#> SRR1656491 1 0.1184 0.982 0.984 0.016
#> SRR1656490 1 0.1184 0.982 0.984 0.016
#> SRR1656492 1 0.0000 0.990 1.000 0.000
#> SRR1656493 1 0.0000 0.990 1.000 0.000
#> SRR1656495 1 0.0000 0.990 1.000 0.000
#> SRR1656496 1 0.0000 0.990 1.000 0.000
#> SRR1656494 1 0.0000 0.990 1.000 0.000
#> SRR1656497 2 0.0000 0.856 0.000 1.000
#> SRR1656499 1 0.0000 0.990 1.000 0.000
#> SRR1656500 1 0.0000 0.990 1.000 0.000
#> SRR1656501 1 0.0376 0.988 0.996 0.004
#> SRR1656498 1 0.0000 0.990 1.000 0.000
#> SRR1656504 2 0.1843 0.847 0.028 0.972
#> SRR1656502 1 0.0000 0.990 1.000 0.000
#> SRR1656503 1 0.0000 0.990 1.000 0.000
#> SRR1656507 1 0.0000 0.990 1.000 0.000
#> SRR1656508 1 0.0000 0.990 1.000 0.000
#> SRR1656505 1 0.1184 0.982 0.984 0.016
#> SRR1656506 1 0.0000 0.990 1.000 0.000
#> SRR1656509 1 0.0000 0.990 1.000 0.000
#> SRR1656510 1 0.1184 0.982 0.984 0.016
#> SRR1656511 2 0.9732 0.503 0.404 0.596
#> SRR1656513 2 0.9775 0.486 0.412 0.588
#> SRR1656512 2 0.0000 0.856 0.000 1.000
#> SRR1656514 1 0.0000 0.990 1.000 0.000
#> SRR1656515 1 0.1414 0.979 0.980 0.020
#> SRR1656516 1 0.0000 0.990 1.000 0.000
#> SRR1656518 1 0.0000 0.990 1.000 0.000
#> SRR1656517 1 0.0000 0.990 1.000 0.000
#> SRR1656519 1 0.0000 0.990 1.000 0.000
#> SRR1656522 1 0.0000 0.990 1.000 0.000
#> SRR1656523 1 0.0000 0.990 1.000 0.000
#> SRR1656521 2 0.0000 0.856 0.000 1.000
#> SRR1656520 1 0.0000 0.990 1.000 0.000
#> SRR1656524 1 0.0000 0.990 1.000 0.000
#> SRR1656525 1 0.0000 0.990 1.000 0.000
#> SRR1656526 2 0.0000 0.856 0.000 1.000
#> SRR1656527 2 0.9815 0.483 0.420 0.580
#> SRR1656530 1 0.1184 0.982 0.984 0.016
#> SRR1656529 1 0.0000 0.990 1.000 0.000
#> SRR1656531 1 0.0000 0.990 1.000 0.000
#> SRR1656528 1 0.0000 0.990 1.000 0.000
#> SRR1656534 1 0.0000 0.990 1.000 0.000
#> SRR1656533 1 0.0000 0.990 1.000 0.000
#> SRR1656536 1 0.1184 0.982 0.984 0.016
#> SRR1656532 1 0.0000 0.990 1.000 0.000
#> SRR1656537 1 0.0000 0.990 1.000 0.000
#> SRR1656538 1 0.0000 0.990 1.000 0.000
#> SRR1656535 2 0.0000 0.856 0.000 1.000
#> SRR1656539 1 0.1184 0.982 0.984 0.016
#> SRR1656544 1 0.0000 0.990 1.000 0.000
#> SRR1656542 1 0.0000 0.990 1.000 0.000
#> SRR1656543 1 0.0000 0.990 1.000 0.000
#> SRR1656545 2 0.0000 0.856 0.000 1.000
#> SRR1656540 1 0.0000 0.990 1.000 0.000
#> SRR1656546 1 0.0000 0.990 1.000 0.000
#> SRR1656541 2 0.0000 0.856 0.000 1.000
#> SRR1656547 1 0.1843 0.971 0.972 0.028
#> SRR1656548 1 0.0000 0.990 1.000 0.000
#> SRR1656549 1 0.0000 0.990 1.000 0.000
#> SRR1656551 1 0.1184 0.982 0.984 0.016
#> SRR1656553 1 0.0000 0.990 1.000 0.000
#> SRR1656550 1 0.1184 0.982 0.984 0.016
#> SRR1656552 2 0.9732 0.503 0.404 0.596
#> SRR1656554 1 0.0000 0.990 1.000 0.000
#> SRR1656555 1 0.1184 0.982 0.984 0.016
#> SRR1656556 1 0.0000 0.990 1.000 0.000
#> SRR1656557 1 0.0000 0.990 1.000 0.000
#> SRR1656558 1 0.0000 0.990 1.000 0.000
#> SRR1656559 1 0.0000 0.990 1.000 0.000
#> SRR1656560 1 0.1184 0.982 0.984 0.016
#> SRR1656561 1 0.0000 0.990 1.000 0.000
#> SRR1656562 1 0.4815 0.874 0.896 0.104
#> SRR1656563 1 0.0000 0.990 1.000 0.000
#> SRR1656564 2 0.9732 0.503 0.404 0.596
#> SRR1656565 1 0.4815 0.874 0.896 0.104
#> SRR1656566 1 0.0000 0.990 1.000 0.000
#> SRR1656568 2 0.9710 0.510 0.400 0.600
#> SRR1656567 1 0.1184 0.982 0.984 0.016
#> SRR1656569 1 0.0672 0.986 0.992 0.008
#> SRR1656570 1 0.0000 0.990 1.000 0.000
#> SRR1656571 2 0.0000 0.856 0.000 1.000
#> SRR1656573 1 0.1184 0.982 0.984 0.016
#> SRR1656572 1 0.6712 0.755 0.824 0.176
#> SRR1656574 1 0.0000 0.990 1.000 0.000
#> SRR1656575 1 0.0000 0.990 1.000 0.000
#> SRR1656576 2 0.4022 0.823 0.080 0.920
#> SRR1656578 1 0.0000 0.990 1.000 0.000
#> SRR1656577 1 0.0000 0.990 1.000 0.000
#> SRR1656579 2 1.0000 0.245 0.496 0.504
#> SRR1656580 1 0.0000 0.990 1.000 0.000
#> SRR1656581 1 0.0000 0.990 1.000 0.000
#> SRR1656582 2 0.4431 0.816 0.092 0.908
#> SRR1656585 1 0.1184 0.982 0.984 0.016
#> SRR1656584 1 0.0000 0.990 1.000 0.000
#> SRR1656583 1 0.0000 0.990 1.000 0.000
#> SRR1656586 2 0.0000 0.856 0.000 1.000
#> SRR1656587 1 0.0000 0.990 1.000 0.000
#> SRR1656588 1 0.1184 0.982 0.984 0.016
#> SRR1656589 2 0.0000 0.856 0.000 1.000
#> SRR1656590 1 0.0000 0.990 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656462 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656465 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656467 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656466 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656468 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656472 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656471 1 0.3482 0.8187 0.872 0.000 0.128
#> SRR1656470 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656478 1 0.1163 0.9362 0.972 0.000 0.028
#> SRR1656477 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656479 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656480 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656482 3 0.6235 0.2389 0.000 0.436 0.564
#> SRR1656483 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656487 3 0.1411 0.9234 0.036 0.000 0.964
#> SRR1656486 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656488 1 0.6079 0.3825 0.612 0.000 0.388
#> SRR1656484 3 0.2356 0.8899 0.072 0.000 0.928
#> SRR1656489 1 0.0747 0.9463 0.984 0.000 0.016
#> SRR1656491 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656490 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656492 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656493 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656495 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656496 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656494 3 0.3482 0.8330 0.128 0.000 0.872
#> SRR1656497 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656499 1 0.0424 0.9519 0.992 0.000 0.008
#> SRR1656500 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656501 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656498 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656502 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656503 3 0.0424 0.9461 0.008 0.000 0.992
#> SRR1656507 3 0.6307 0.0203 0.488 0.000 0.512
#> SRR1656508 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656505 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656506 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656509 1 0.1289 0.9313 0.968 0.000 0.032
#> SRR1656510 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656511 3 0.5291 0.6265 0.000 0.268 0.732
#> SRR1656513 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656512 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656515 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656516 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656518 3 0.6026 0.3893 0.376 0.000 0.624
#> SRR1656517 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656523 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656521 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656520 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656524 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656525 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656526 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656527 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656530 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656528 3 0.1529 0.9197 0.040 0.000 0.960
#> SRR1656534 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656533 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656532 3 0.3340 0.8409 0.120 0.000 0.880
#> SRR1656537 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656538 1 0.6168 0.3152 0.588 0.000 0.412
#> SRR1656535 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656544 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656542 1 0.0592 0.9493 0.988 0.000 0.012
#> SRR1656543 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656540 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656546 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656541 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656547 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656548 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656549 3 0.1289 0.9269 0.032 0.000 0.968
#> SRR1656551 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656553 1 0.4887 0.6912 0.772 0.000 0.228
#> SRR1656550 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656552 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656554 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656555 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656556 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656557 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656558 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656559 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656561 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656562 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656563 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656564 3 0.4654 0.7225 0.000 0.208 0.792
#> SRR1656565 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656566 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656568 3 0.6244 0.2257 0.000 0.440 0.560
#> SRR1656567 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656569 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656570 1 0.1643 0.9214 0.956 0.000 0.044
#> SRR1656571 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656572 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656574 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656575 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656576 2 0.1529 0.9472 0.000 0.960 0.040
#> SRR1656578 3 0.4452 0.7571 0.192 0.000 0.808
#> SRR1656577 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656579 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656580 1 0.1964 0.9101 0.944 0.000 0.056
#> SRR1656581 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656582 2 0.4121 0.7902 0.000 0.832 0.168
#> SRR1656585 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656584 1 0.1753 0.9174 0.952 0.000 0.048
#> SRR1656583 1 0.0000 0.9574 1.000 0.000 0.000
#> SRR1656586 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656587 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656588 3 0.0000 0.9523 0.000 0.000 1.000
#> SRR1656589 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656590 1 0.0000 0.9574 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656462 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656465 3 0.2760 0.3081 0.000 0.000 0.872 0.128
#> SRR1656467 4 0.7844 0.5368 0.000 0.264 0.368 0.368
#> SRR1656466 3 0.3099 0.3978 0.104 0.000 0.876 0.020
#> SRR1656468 4 0.4996 0.5846 0.000 0.000 0.484 0.516
#> SRR1656472 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656471 3 0.5050 0.1747 0.408 0.000 0.588 0.004
#> SRR1656470 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.4730 -0.0737 0.000 0.000 0.636 0.364
#> SRR1656473 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.7544 0.5719 0.484 0.000 0.224 0.292
#> SRR1656477 3 0.4643 -0.0370 0.000 0.000 0.656 0.344
#> SRR1656479 3 0.4008 0.3297 0.000 0.000 0.756 0.244
#> SRR1656480 4 0.4967 0.5848 0.000 0.000 0.452 0.548
#> SRR1656476 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656481 3 0.4643 -0.0396 0.000 0.000 0.656 0.344
#> SRR1656482 2 0.7628 -0.3619 0.000 0.440 0.212 0.348
#> SRR1656483 2 0.0336 0.9421 0.000 0.992 0.000 0.008
#> SRR1656485 3 0.2466 0.4096 0.096 0.000 0.900 0.004
#> SRR1656487 3 0.2593 0.4066 0.104 0.000 0.892 0.004
#> SRR1656486 3 0.4543 0.1929 0.000 0.000 0.676 0.324
#> SRR1656488 3 0.2654 0.4044 0.108 0.000 0.888 0.004
#> SRR1656484 3 0.5339 0.3512 0.272 0.000 0.688 0.040
#> SRR1656489 1 0.7222 0.6286 0.528 0.000 0.172 0.300
#> SRR1656491 3 0.3907 0.2745 0.000 0.000 0.768 0.232
#> SRR1656490 3 0.4072 0.2359 0.000 0.000 0.748 0.252
#> SRR1656492 3 0.3156 0.4231 0.068 0.000 0.884 0.048
#> SRR1656493 1 0.4599 0.7743 0.736 0.000 0.016 0.248
#> SRR1656495 1 0.0779 0.8242 0.980 0.000 0.004 0.016
#> SRR1656496 3 0.3591 0.3528 0.008 0.000 0.824 0.168
#> SRR1656494 3 0.6794 0.2832 0.328 0.000 0.556 0.116
#> SRR1656497 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656499 1 0.4431 0.5283 0.696 0.000 0.304 0.000
#> SRR1656500 1 0.0469 0.8233 0.988 0.000 0.012 0.000
#> SRR1656501 3 0.4040 0.3263 0.000 0.000 0.752 0.248
#> SRR1656498 1 0.2466 0.8137 0.900 0.000 0.004 0.096
#> SRR1656504 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656503 3 0.5655 0.3719 0.212 0.000 0.704 0.084
#> SRR1656507 3 0.6080 0.0253 0.468 0.000 0.488 0.044
#> SRR1656508 1 0.4155 0.7801 0.756 0.000 0.004 0.240
#> SRR1656505 4 0.4898 0.6255 0.000 0.000 0.416 0.584
#> SRR1656506 3 0.1004 0.4029 0.024 0.000 0.972 0.004
#> SRR1656509 1 0.4888 0.2207 0.588 0.000 0.412 0.000
#> SRR1656510 3 0.4697 -0.0556 0.000 0.000 0.644 0.356
#> SRR1656511 3 0.7681 -0.4115 0.000 0.224 0.432 0.344
#> SRR1656513 3 0.6979 -0.2960 0.000 0.128 0.528 0.344
#> SRR1656512 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656514 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656515 4 0.7603 0.5769 0.000 0.204 0.360 0.436
#> SRR1656516 3 0.4175 0.3613 0.012 0.000 0.776 0.212
#> SRR1656518 1 0.7614 0.5523 0.468 0.000 0.232 0.300
#> SRR1656517 1 0.4283 0.7744 0.740 0.000 0.004 0.256
#> SRR1656519 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656522 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656523 3 0.4661 0.1185 0.000 0.000 0.652 0.348
#> SRR1656521 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656520 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656524 1 0.4155 0.7801 0.756 0.000 0.004 0.240
#> SRR1656525 3 0.2363 0.4180 0.056 0.000 0.920 0.024
#> SRR1656526 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656527 3 0.7109 -0.3071 0.000 0.144 0.520 0.336
#> SRR1656530 3 0.2281 0.3639 0.000 0.000 0.904 0.096
#> SRR1656529 3 0.1182 0.3943 0.016 0.000 0.968 0.016
#> SRR1656531 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656528 3 0.2530 0.4084 0.100 0.000 0.896 0.004
#> SRR1656534 1 0.0188 0.8239 0.996 0.000 0.004 0.000
#> SRR1656533 1 0.5512 0.7396 0.660 0.000 0.040 0.300
#> SRR1656536 3 0.4776 -0.1090 0.000 0.000 0.624 0.376
#> SRR1656532 3 0.6835 0.2880 0.316 0.000 0.560 0.124
#> SRR1656537 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656538 3 0.6007 0.2072 0.408 0.000 0.548 0.044
#> SRR1656535 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656539 3 0.2466 0.3287 0.004 0.000 0.900 0.096
#> SRR1656544 3 0.1902 0.4136 0.064 0.000 0.932 0.004
#> SRR1656542 1 0.5646 0.5633 0.672 0.000 0.272 0.056
#> SRR1656543 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656545 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656540 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656546 3 0.4250 0.2927 0.000 0.000 0.724 0.276
#> SRR1656541 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656547 3 0.5099 -0.1424 0.000 0.008 0.612 0.380
#> SRR1656548 3 0.1510 0.4105 0.028 0.000 0.956 0.016
#> SRR1656549 3 0.6159 0.3718 0.132 0.000 0.672 0.196
#> SRR1656551 4 0.4998 0.5737 0.000 0.000 0.488 0.512
#> SRR1656553 3 0.4624 0.3175 0.340 0.000 0.660 0.000
#> SRR1656550 3 0.4661 -0.0448 0.000 0.000 0.652 0.348
#> SRR1656552 3 0.6991 -0.3065 0.000 0.128 0.524 0.348
#> SRR1656554 3 0.1305 0.4086 0.036 0.000 0.960 0.004
#> SRR1656555 3 0.5323 -0.0930 0.000 0.020 0.628 0.352
#> SRR1656556 1 0.3172 0.6850 0.840 0.000 0.160 0.000
#> SRR1656557 1 0.0336 0.8236 0.992 0.000 0.008 0.000
#> SRR1656558 1 0.6720 0.6837 0.580 0.000 0.120 0.300
#> SRR1656559 1 0.0000 0.8234 1.000 0.000 0.000 0.000
#> SRR1656560 3 0.0921 0.3768 0.000 0.000 0.972 0.028
#> SRR1656561 3 0.4248 0.3598 0.012 0.000 0.768 0.220
#> SRR1656562 3 0.5682 -0.0937 0.000 0.036 0.612 0.352
#> SRR1656563 1 0.7453 0.5943 0.496 0.000 0.204 0.300
#> SRR1656564 3 0.7451 -0.3789 0.000 0.184 0.472 0.344
#> SRR1656565 3 0.6106 -0.1320 0.000 0.060 0.592 0.348
#> SRR1656566 1 0.6578 0.6959 0.592 0.000 0.108 0.300
#> SRR1656568 3 0.7885 -0.4157 0.000 0.288 0.372 0.340
#> SRR1656567 4 0.4996 0.5287 0.000 0.000 0.484 0.516
#> SRR1656569 3 0.1211 0.3666 0.000 0.000 0.960 0.040
#> SRR1656570 1 0.7572 0.5659 0.476 0.000 0.224 0.300
#> SRR1656571 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656573 3 0.3907 0.2775 0.000 0.000 0.768 0.232
#> SRR1656572 3 0.6156 -0.1335 0.000 0.064 0.592 0.344
#> SRR1656574 1 0.4188 0.7787 0.752 0.000 0.004 0.244
#> SRR1656575 3 0.4360 0.3269 0.008 0.000 0.744 0.248
#> SRR1656576 2 0.4274 0.7297 0.000 0.808 0.044 0.148
#> SRR1656578 3 0.6602 0.2742 0.356 0.000 0.552 0.092
#> SRR1656577 1 0.0779 0.8243 0.980 0.000 0.004 0.016
#> SRR1656579 4 0.7844 0.5368 0.000 0.264 0.368 0.368
#> SRR1656580 3 0.6395 -0.0245 0.464 0.000 0.472 0.064
#> SRR1656581 3 0.4155 0.3213 0.004 0.000 0.756 0.240
#> SRR1656582 2 0.2983 0.8258 0.000 0.892 0.068 0.040
#> SRR1656585 3 0.4008 0.2421 0.000 0.000 0.756 0.244
#> SRR1656584 1 0.7572 0.5659 0.476 0.000 0.224 0.300
#> SRR1656583 1 0.3400 0.6682 0.820 0.000 0.180 0.000
#> SRR1656586 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656587 3 0.6316 0.2741 0.156 0.000 0.660 0.184
#> SRR1656588 3 0.4925 -0.4048 0.000 0.000 0.572 0.428
#> SRR1656589 2 0.0000 0.9485 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.4011 0.7892 0.784 0.000 0.008 0.208
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656462 3 0.2302 0.7887 0.048 0.000 0.916 0.016 0.020
#> SRR1656465 5 0.3146 0.6740 0.028 0.000 0.000 0.128 0.844
#> SRR1656467 4 0.3155 0.5973 0.016 0.128 0.000 0.848 0.008
#> SRR1656466 5 0.3794 0.6926 0.000 0.000 0.048 0.152 0.800
#> SRR1656468 4 0.5985 0.1856 0.112 0.000 0.000 0.480 0.408
#> SRR1656472 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656471 5 0.5632 0.5522 0.000 0.000 0.232 0.140 0.628
#> SRR1656470 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 4 0.4987 0.2397 0.044 0.000 0.000 0.616 0.340
#> SRR1656473 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.5490 0.6702 0.708 0.000 0.164 0.040 0.088
#> SRR1656477 5 0.3339 0.5258 0.124 0.000 0.000 0.040 0.836
#> SRR1656479 4 0.6659 0.0139 0.248 0.000 0.000 0.436 0.316
#> SRR1656480 5 0.6289 0.0794 0.160 0.000 0.000 0.356 0.484
#> SRR1656476 2 0.0162 0.9816 0.000 0.996 0.000 0.004 0.000
#> SRR1656481 5 0.3099 0.5337 0.124 0.000 0.000 0.028 0.848
#> SRR1656482 4 0.2648 0.5914 0.000 0.152 0.000 0.848 0.000
#> SRR1656483 2 0.2773 0.8106 0.000 0.836 0.000 0.164 0.000
#> SRR1656485 5 0.3535 0.6955 0.000 0.000 0.028 0.164 0.808
#> SRR1656487 5 0.3608 0.6928 0.000 0.000 0.040 0.148 0.812
#> SRR1656486 4 0.1831 0.6379 0.004 0.000 0.000 0.920 0.076
#> SRR1656488 5 0.3752 0.6908 0.000 0.000 0.048 0.148 0.804
#> SRR1656484 1 0.7690 -0.0380 0.384 0.000 0.092 0.148 0.376
#> SRR1656489 1 0.4138 0.6746 0.780 0.000 0.148 0.000 0.072
#> SRR1656491 4 0.4430 -0.0253 0.004 0.000 0.000 0.540 0.456
#> SRR1656490 4 0.3906 0.3946 0.004 0.000 0.000 0.704 0.292
#> SRR1656492 5 0.4412 0.6797 0.040 0.000 0.012 0.192 0.756
#> SRR1656493 1 0.3913 0.5104 0.676 0.000 0.324 0.000 0.000
#> SRR1656495 1 0.4242 0.3517 0.572 0.000 0.428 0.000 0.000
#> SRR1656496 5 0.5285 0.4387 0.060 0.000 0.000 0.356 0.584
#> SRR1656494 4 0.8184 -0.0256 0.204 0.000 0.136 0.388 0.272
#> SRR1656497 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 5 0.5893 0.1947 0.000 0.000 0.436 0.100 0.464
#> SRR1656500 3 0.3931 0.7104 0.056 0.000 0.832 0.040 0.072
#> SRR1656501 4 0.6622 0.0926 0.260 0.000 0.000 0.456 0.284
#> SRR1656498 3 0.3480 0.6180 0.248 0.000 0.752 0.000 0.000
#> SRR1656504 2 0.0290 0.9785 0.000 0.992 0.000 0.008 0.000
#> SRR1656502 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656503 1 0.7774 0.1105 0.412 0.000 0.076 0.212 0.300
#> SRR1656507 1 0.7364 0.4838 0.536 0.000 0.144 0.112 0.208
#> SRR1656508 3 0.4302 0.1042 0.480 0.000 0.520 0.000 0.000
#> SRR1656505 4 0.5981 0.2001 0.112 0.000 0.000 0.484 0.404
#> SRR1656506 5 0.3353 0.6871 0.008 0.000 0.000 0.196 0.796
#> SRR1656509 5 0.7283 0.2980 0.056 0.000 0.352 0.148 0.444
#> SRR1656510 4 0.1205 0.6533 0.004 0.000 0.000 0.956 0.040
#> SRR1656511 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656513 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656512 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656515 4 0.4195 0.5844 0.048 0.080 0.000 0.816 0.056
#> SRR1656516 5 0.6824 0.0838 0.328 0.000 0.000 0.328 0.344
#> SRR1656518 1 0.5048 0.6809 0.744 0.000 0.132 0.028 0.096
#> SRR1656517 3 0.4268 0.2401 0.444 0.000 0.556 0.000 0.000
#> SRR1656519 3 0.2395 0.7948 0.072 0.000 0.904 0.016 0.008
#> SRR1656522 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656523 4 0.1877 0.6432 0.012 0.000 0.000 0.924 0.064
#> SRR1656521 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.2302 0.7887 0.048 0.000 0.916 0.016 0.020
#> SRR1656524 1 0.4060 0.4483 0.640 0.000 0.360 0.000 0.000
#> SRR1656525 5 0.3648 0.6902 0.016 0.000 0.004 0.188 0.792
#> SRR1656526 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656530 5 0.3890 0.6359 0.012 0.000 0.000 0.252 0.736
#> SRR1656529 5 0.3561 0.6909 0.008 0.000 0.008 0.188 0.796
#> SRR1656531 3 0.1851 0.7754 0.088 0.000 0.912 0.000 0.000
#> SRR1656528 5 0.3894 0.6951 0.008 0.000 0.036 0.156 0.800
#> SRR1656534 3 0.2020 0.7935 0.100 0.000 0.900 0.000 0.000
#> SRR1656533 1 0.3707 0.5523 0.716 0.000 0.284 0.000 0.000
#> SRR1656536 5 0.3339 0.5258 0.124 0.000 0.000 0.040 0.836
#> SRR1656532 4 0.7779 0.1246 0.272 0.000 0.116 0.456 0.156
#> SRR1656537 3 0.1043 0.8005 0.040 0.000 0.960 0.000 0.000
#> SRR1656538 5 0.6978 0.4296 0.244 0.000 0.072 0.128 0.556
#> SRR1656535 2 0.0162 0.9818 0.000 0.996 0.000 0.004 0.000
#> SRR1656539 5 0.3241 0.6832 0.024 0.000 0.000 0.144 0.832
#> SRR1656544 5 0.3381 0.6948 0.000 0.000 0.016 0.176 0.808
#> SRR1656542 1 0.7673 0.3574 0.356 0.000 0.300 0.048 0.296
#> SRR1656543 3 0.2302 0.7887 0.048 0.000 0.916 0.016 0.020
#> SRR1656545 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.2400 0.7870 0.048 0.000 0.912 0.020 0.020
#> SRR1656546 4 0.2798 0.5835 0.008 0.000 0.000 0.852 0.140
#> SRR1656541 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 4 0.1612 0.6590 0.012 0.024 0.000 0.948 0.016
#> SRR1656548 5 0.3897 0.6784 0.028 0.000 0.000 0.204 0.768
#> SRR1656549 1 0.7229 -0.0233 0.412 0.000 0.044 0.384 0.160
#> SRR1656551 4 0.5968 0.0819 0.108 0.000 0.000 0.448 0.444
#> SRR1656553 5 0.7464 0.2539 0.228 0.000 0.164 0.096 0.512
#> SRR1656550 5 0.3339 0.5258 0.124 0.000 0.000 0.040 0.836
#> SRR1656552 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656554 5 0.3474 0.6893 0.008 0.000 0.004 0.192 0.796
#> SRR1656555 4 0.1205 0.6545 0.000 0.004 0.000 0.956 0.040
#> SRR1656556 3 0.4216 0.5922 0.000 0.000 0.780 0.100 0.120
#> SRR1656557 3 0.2302 0.7887 0.048 0.000 0.916 0.016 0.020
#> SRR1656558 1 0.4264 0.6393 0.744 0.000 0.212 0.000 0.044
#> SRR1656559 3 0.0794 0.8063 0.028 0.000 0.972 0.000 0.000
#> SRR1656560 5 0.2929 0.6928 0.000 0.000 0.000 0.180 0.820
#> SRR1656561 5 0.5447 0.3272 0.064 0.000 0.000 0.400 0.536
#> SRR1656562 4 0.1211 0.6629 0.000 0.024 0.000 0.960 0.016
#> SRR1656563 1 0.4870 0.6838 0.748 0.000 0.152 0.020 0.080
#> SRR1656564 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656565 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656566 1 0.4409 0.6676 0.752 0.000 0.176 0.000 0.072
#> SRR1656568 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656567 5 0.6315 0.0360 0.160 0.000 0.000 0.372 0.468
#> SRR1656569 5 0.3612 0.6649 0.008 0.000 0.000 0.228 0.764
#> SRR1656570 1 0.5026 0.6834 0.740 0.000 0.148 0.024 0.088
#> SRR1656571 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 4 0.4434 -0.0393 0.004 0.000 0.000 0.536 0.460
#> SRR1656572 4 0.1168 0.6643 0.000 0.032 0.000 0.960 0.008
#> SRR1656574 3 0.4283 0.2399 0.456 0.000 0.544 0.000 0.000
#> SRR1656575 4 0.6603 0.1117 0.388 0.000 0.000 0.400 0.212
#> SRR1656576 4 0.4297 -0.1029 0.000 0.472 0.000 0.528 0.000
#> SRR1656578 4 0.7857 0.0873 0.276 0.000 0.124 0.444 0.156
#> SRR1656577 3 0.2329 0.7592 0.124 0.000 0.876 0.000 0.000
#> SRR1656579 4 0.2857 0.6096 0.012 0.112 0.000 0.868 0.008
#> SRR1656580 5 0.7701 0.0353 0.328 0.000 0.140 0.104 0.428
#> SRR1656581 4 0.4010 0.5007 0.032 0.000 0.000 0.760 0.208
#> SRR1656582 2 0.1792 0.9000 0.000 0.916 0.000 0.084 0.000
#> SRR1656585 5 0.4294 0.2499 0.000 0.000 0.000 0.468 0.532
#> SRR1656584 1 0.4311 0.6793 0.776 0.000 0.144 0.004 0.076
#> SRR1656583 3 0.5880 0.2393 0.004 0.000 0.584 0.116 0.296
#> SRR1656586 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.7504 -0.0574 0.192 0.000 0.056 0.424 0.328
#> SRR1656588 5 0.5653 0.3937 0.160 0.000 0.000 0.208 0.632
#> SRR1656589 2 0.0000 0.9842 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.3752 0.5283 0.708 0.000 0.292 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.2053 0.7571 0.108 0.000 0.888 0.000 0.000 0.004
#> SRR1656462 3 0.3706 0.7346 0.004 0.000 0.796 0.000 0.104 0.096
#> SRR1656465 5 0.2805 0.5907 0.000 0.000 0.004 0.000 0.812 0.184
#> SRR1656467 4 0.0820 0.7666 0.000 0.012 0.000 0.972 0.000 0.016
#> SRR1656466 5 0.1838 0.6942 0.000 0.000 0.068 0.000 0.916 0.016
#> SRR1656468 6 0.4348 0.6770 0.000 0.000 0.000 0.248 0.064 0.688
#> SRR1656472 3 0.2053 0.7571 0.108 0.000 0.888 0.000 0.000 0.004
#> SRR1656471 5 0.4022 0.5406 0.000 0.000 0.252 0.000 0.708 0.040
#> SRR1656470 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 4 0.4866 0.3821 0.000 0.000 0.000 0.648 0.116 0.236
#> SRR1656473 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2053 0.7660 0.888 0.000 0.004 0.000 0.108 0.000
#> SRR1656477 5 0.3950 0.0524 0.000 0.000 0.004 0.000 0.564 0.432
#> SRR1656479 4 0.5673 0.0434 0.116 0.000 0.004 0.456 0.420 0.004
#> SRR1656480 6 0.3278 0.7646 0.000 0.000 0.000 0.088 0.088 0.824
#> SRR1656476 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 6 0.3830 0.5225 0.000 0.000 0.004 0.000 0.376 0.620
#> SRR1656482 4 0.0458 0.7714 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656483 2 0.1444 0.9094 0.000 0.928 0.000 0.072 0.000 0.000
#> SRR1656485 5 0.0914 0.7099 0.000 0.000 0.016 0.000 0.968 0.016
#> SRR1656487 5 0.2006 0.6868 0.000 0.000 0.080 0.000 0.904 0.016
#> SRR1656486 4 0.0146 0.7783 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656488 5 0.2006 0.6868 0.000 0.000 0.080 0.000 0.904 0.016
#> SRR1656484 5 0.4570 0.5673 0.252 0.000 0.000 0.080 0.668 0.000
#> SRR1656489 1 0.0146 0.8125 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1656491 4 0.4415 0.2229 0.020 0.000 0.000 0.556 0.420 0.004
#> SRR1656490 4 0.1320 0.7574 0.016 0.000 0.000 0.948 0.036 0.000
#> SRR1656492 5 0.1578 0.7136 0.000 0.000 0.004 0.048 0.936 0.012
#> SRR1656493 1 0.2538 0.7721 0.860 0.000 0.124 0.000 0.000 0.016
#> SRR1656495 1 0.2896 0.7458 0.824 0.000 0.160 0.000 0.000 0.016
#> SRR1656496 5 0.4086 0.5797 0.036 0.000 0.004 0.212 0.740 0.008
#> SRR1656494 4 0.7138 0.1920 0.176 0.000 0.128 0.440 0.256 0.000
#> SRR1656497 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 5 0.4740 0.4282 0.004 0.000 0.300 0.000 0.632 0.064
#> SRR1656500 3 0.5567 0.1565 0.020 0.000 0.476 0.000 0.424 0.080
#> SRR1656501 4 0.5893 0.3875 0.232 0.000 0.004 0.532 0.228 0.004
#> SRR1656498 3 0.4184 0.3313 0.408 0.000 0.576 0.000 0.000 0.016
#> SRR1656504 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 3 0.2053 0.7571 0.108 0.000 0.888 0.000 0.000 0.004
#> SRR1656503 5 0.5872 0.2164 0.400 0.000 0.000 0.196 0.404 0.000
#> SRR1656507 1 0.4034 0.3227 0.652 0.000 0.000 0.020 0.328 0.000
#> SRR1656508 1 0.3431 0.6497 0.756 0.000 0.228 0.000 0.000 0.016
#> SRR1656505 6 0.4294 0.6746 0.000 0.000 0.000 0.248 0.060 0.692
#> SRR1656506 5 0.1485 0.7137 0.000 0.000 0.004 0.024 0.944 0.028
#> SRR1656509 5 0.5167 0.5523 0.108 0.000 0.204 0.008 0.668 0.012
#> SRR1656510 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656511 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656513 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.2100 0.7566 0.112 0.000 0.884 0.000 0.000 0.004
#> SRR1656515 4 0.1610 0.7188 0.000 0.000 0.000 0.916 0.000 0.084
#> SRR1656516 5 0.5812 0.4599 0.212 0.000 0.004 0.212 0.564 0.008
#> SRR1656518 1 0.1204 0.8016 0.944 0.000 0.000 0.000 0.056 0.000
#> SRR1656517 1 0.3558 0.6138 0.736 0.000 0.248 0.000 0.000 0.016
#> SRR1656519 3 0.5248 0.7467 0.100 0.000 0.700 0.000 0.104 0.096
#> SRR1656522 3 0.2100 0.7566 0.112 0.000 0.884 0.000 0.000 0.004
#> SRR1656523 4 0.0632 0.7730 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656521 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.3706 0.7346 0.004 0.000 0.796 0.000 0.104 0.096
#> SRR1656524 1 0.2821 0.7537 0.832 0.000 0.152 0.000 0.000 0.016
#> SRR1656525 5 0.1138 0.7175 0.000 0.000 0.004 0.024 0.960 0.012
#> SRR1656526 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 4 0.0146 0.7783 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1656530 5 0.2678 0.6681 0.000 0.000 0.004 0.116 0.860 0.020
#> SRR1656529 5 0.1218 0.7105 0.000 0.000 0.004 0.012 0.956 0.028
#> SRR1656531 3 0.2968 0.7156 0.168 0.000 0.816 0.000 0.000 0.016
#> SRR1656528 5 0.0603 0.7102 0.000 0.000 0.004 0.000 0.980 0.016
#> SRR1656534 3 0.5224 0.7433 0.124 0.000 0.700 0.000 0.092 0.084
#> SRR1656533 1 0.0603 0.8082 0.980 0.000 0.004 0.000 0.000 0.016
#> SRR1656536 6 0.3769 0.5577 0.000 0.000 0.004 0.000 0.356 0.640
#> SRR1656532 4 0.4793 0.5539 0.240 0.000 0.008 0.668 0.084 0.000
#> SRR1656537 3 0.2581 0.7422 0.120 0.000 0.860 0.000 0.000 0.020
#> SRR1656538 5 0.1931 0.7070 0.068 0.000 0.008 0.004 0.916 0.004
#> SRR1656535 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.2738 0.6008 0.000 0.000 0.004 0.000 0.820 0.176
#> SRR1656544 5 0.0862 0.7136 0.000 0.000 0.004 0.008 0.972 0.016
#> SRR1656542 5 0.5610 0.4389 0.236 0.000 0.148 0.004 0.600 0.012
#> SRR1656543 3 0.3706 0.7346 0.004 0.000 0.796 0.000 0.104 0.096
#> SRR1656545 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.3706 0.7346 0.004 0.000 0.796 0.000 0.104 0.096
#> SRR1656546 4 0.0865 0.7673 0.000 0.000 0.000 0.964 0.036 0.000
#> SRR1656541 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 4 0.0458 0.7723 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR1656548 5 0.1982 0.7031 0.000 0.000 0.004 0.068 0.912 0.016
#> SRR1656549 4 0.5105 0.4483 0.340 0.000 0.000 0.564 0.096 0.000
#> SRR1656551 6 0.4431 0.6985 0.000 0.000 0.000 0.228 0.080 0.692
#> SRR1656553 5 0.3616 0.6820 0.088 0.000 0.068 0.008 0.824 0.012
#> SRR1656550 6 0.3795 0.5455 0.000 0.000 0.004 0.000 0.364 0.632
#> SRR1656552 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656554 5 0.0858 0.7104 0.000 0.000 0.000 0.004 0.968 0.028
#> SRR1656555 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656556 3 0.5108 0.1515 0.004 0.000 0.496 0.000 0.432 0.068
#> SRR1656557 3 0.3706 0.7346 0.004 0.000 0.796 0.000 0.104 0.096
#> SRR1656558 1 0.0146 0.8120 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1656559 3 0.2100 0.7566 0.112 0.000 0.884 0.000 0.000 0.004
#> SRR1656560 5 0.1528 0.7023 0.000 0.000 0.000 0.016 0.936 0.048
#> SRR1656561 5 0.4440 0.4843 0.032 0.000 0.004 0.284 0.672 0.008
#> SRR1656562 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656563 1 0.1204 0.8021 0.944 0.000 0.000 0.000 0.056 0.000
#> SRR1656564 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656565 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656566 1 0.0000 0.8124 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656568 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656567 6 0.3172 0.7644 0.000 0.000 0.000 0.076 0.092 0.832
#> SRR1656569 5 0.2604 0.6741 0.000 0.000 0.004 0.096 0.872 0.028
#> SRR1656570 1 0.1267 0.7991 0.940 0.000 0.000 0.000 0.060 0.000
#> SRR1656571 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 4 0.4124 0.0683 0.004 0.000 0.000 0.516 0.476 0.004
#> SRR1656572 4 0.0000 0.7787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656574 1 0.3290 0.6187 0.744 0.000 0.252 0.000 0.000 0.004
#> SRR1656575 4 0.5305 0.3265 0.404 0.000 0.000 0.492 0.104 0.000
#> SRR1656576 4 0.3862 -0.0131 0.000 0.476 0.000 0.524 0.000 0.000
#> SRR1656578 4 0.5291 0.5272 0.244 0.000 0.032 0.640 0.084 0.000
#> SRR1656577 3 0.3266 0.6322 0.272 0.000 0.728 0.000 0.000 0.000
#> SRR1656579 4 0.0790 0.7629 0.000 0.000 0.000 0.968 0.000 0.032
#> SRR1656580 5 0.2837 0.6674 0.144 0.000 0.004 0.004 0.840 0.008
#> SRR1656581 4 0.2420 0.7082 0.000 0.000 0.004 0.864 0.128 0.004
#> SRR1656582 2 0.1863 0.8599 0.000 0.896 0.000 0.104 0.000 0.000
#> SRR1656585 5 0.3937 0.2042 0.004 0.000 0.000 0.424 0.572 0.000
#> SRR1656584 1 0.1007 0.8066 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1656583 5 0.5151 0.3379 0.048 0.000 0.356 0.008 0.576 0.012
#> SRR1656586 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 5 0.6926 0.2692 0.120 0.000 0.124 0.324 0.432 0.000
#> SRR1656588 6 0.3023 0.7512 0.000 0.000 0.000 0.044 0.120 0.836
#> SRR1656589 2 0.0000 0.9878 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.2821 0.7538 0.832 0.000 0.152 0.000 0.000 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", "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 13572 rows and 129 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 0.947 0.941 0.974 0.4928 0.512 0.512
#> 3 3 0.692 0.774 0.902 0.3353 0.751 0.550
#> 4 4 0.664 0.697 0.836 0.1327 0.773 0.455
#> 5 5 0.559 0.412 0.671 0.0713 0.863 0.546
#> 6 6 0.701 0.646 0.796 0.0448 0.866 0.471
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
#> SRR1656463 2 0.0000 0.962 0.000 1.000
#> SRR1656464 1 0.0000 0.988 1.000 0.000
#> SRR1656462 2 0.0938 0.955 0.012 0.988
#> SRR1656465 2 0.0000 0.962 0.000 1.000
#> SRR1656467 2 0.0000 0.962 0.000 1.000
#> SRR1656466 2 0.0000 0.962 0.000 1.000
#> SRR1656468 2 0.0000 0.962 0.000 1.000
#> SRR1656472 1 0.0000 0.988 1.000 0.000
#> SRR1656471 2 0.0000 0.962 0.000 1.000
#> SRR1656470 2 0.0000 0.962 0.000 1.000
#> SRR1656469 2 0.0000 0.962 0.000 1.000
#> SRR1656473 1 0.3114 0.942 0.944 0.056
#> SRR1656474 1 0.0000 0.988 1.000 0.000
#> SRR1656475 2 0.0000 0.962 0.000 1.000
#> SRR1656478 1 0.0000 0.988 1.000 0.000
#> SRR1656477 2 0.0000 0.962 0.000 1.000
#> SRR1656479 1 0.0000 0.988 1.000 0.000
#> SRR1656480 2 0.0000 0.962 0.000 1.000
#> SRR1656476 2 0.0000 0.962 0.000 1.000
#> SRR1656481 2 0.0000 0.962 0.000 1.000
#> SRR1656482 2 0.0000 0.962 0.000 1.000
#> SRR1656483 2 0.0000 0.962 0.000 1.000
#> SRR1656485 2 0.0000 0.962 0.000 1.000
#> SRR1656487 2 0.0000 0.962 0.000 1.000
#> SRR1656486 1 0.0000 0.988 1.000 0.000
#> SRR1656488 2 0.0000 0.962 0.000 1.000
#> SRR1656484 1 0.2236 0.959 0.964 0.036
#> SRR1656489 1 0.0000 0.988 1.000 0.000
#> SRR1656491 2 0.0000 0.962 0.000 1.000
#> SRR1656490 2 0.9996 0.103 0.488 0.512
#> SRR1656492 2 0.0000 0.962 0.000 1.000
#> SRR1656493 1 0.0000 0.988 1.000 0.000
#> SRR1656495 1 0.0000 0.988 1.000 0.000
#> SRR1656496 2 0.5842 0.835 0.140 0.860
#> SRR1656494 1 0.1184 0.976 0.984 0.016
#> SRR1656497 2 0.0000 0.962 0.000 1.000
#> SRR1656499 2 0.0000 0.962 0.000 1.000
#> SRR1656500 2 0.0000 0.962 0.000 1.000
#> SRR1656501 1 0.0376 0.985 0.996 0.004
#> SRR1656498 1 0.0000 0.988 1.000 0.000
#> SRR1656504 2 0.4298 0.891 0.088 0.912
#> SRR1656502 1 0.0000 0.988 1.000 0.000
#> SRR1656503 1 0.0000 0.988 1.000 0.000
#> SRR1656507 1 0.0000 0.988 1.000 0.000
#> SRR1656508 1 0.0000 0.988 1.000 0.000
#> SRR1656505 2 0.0000 0.962 0.000 1.000
#> SRR1656506 2 0.0000 0.962 0.000 1.000
#> SRR1656509 2 0.1843 0.944 0.028 0.972
#> SRR1656510 2 0.0000 0.962 0.000 1.000
#> SRR1656511 1 0.0000 0.988 1.000 0.000
#> SRR1656513 1 0.0000 0.988 1.000 0.000
#> SRR1656512 1 0.3431 0.934 0.936 0.064
#> SRR1656514 1 0.0000 0.988 1.000 0.000
#> SRR1656515 2 0.0000 0.962 0.000 1.000
#> SRR1656516 1 0.3431 0.935 0.936 0.064
#> SRR1656518 1 0.0000 0.988 1.000 0.000
#> SRR1656517 1 0.0000 0.988 1.000 0.000
#> SRR1656519 1 0.6148 0.817 0.848 0.152
#> SRR1656522 1 0.0000 0.988 1.000 0.000
#> SRR1656523 1 0.2948 0.946 0.948 0.052
#> SRR1656521 1 0.0000 0.988 1.000 0.000
#> SRR1656520 2 0.0000 0.962 0.000 1.000
#> SRR1656524 1 0.0000 0.988 1.000 0.000
#> SRR1656525 2 0.0000 0.962 0.000 1.000
#> SRR1656526 2 0.0000 0.962 0.000 1.000
#> SRR1656527 1 0.0000 0.988 1.000 0.000
#> SRR1656530 2 0.0000 0.962 0.000 1.000
#> SRR1656529 2 0.0000 0.962 0.000 1.000
#> SRR1656531 1 0.0000 0.988 1.000 0.000
#> SRR1656528 2 0.0000 0.962 0.000 1.000
#> SRR1656534 2 0.9909 0.250 0.444 0.556
#> SRR1656533 1 0.0000 0.988 1.000 0.000
#> SRR1656536 2 0.0000 0.962 0.000 1.000
#> SRR1656532 1 0.0000 0.988 1.000 0.000
#> SRR1656537 1 0.0000 0.988 1.000 0.000
#> SRR1656538 2 0.0000 0.962 0.000 1.000
#> SRR1656535 2 0.8327 0.662 0.264 0.736
#> SRR1656539 2 0.0000 0.962 0.000 1.000
#> SRR1656544 2 0.0000 0.962 0.000 1.000
#> SRR1656542 1 0.3733 0.923 0.928 0.072
#> SRR1656543 2 0.0000 0.962 0.000 1.000
#> SRR1656545 2 0.1633 0.947 0.024 0.976
#> SRR1656540 2 0.0000 0.962 0.000 1.000
#> SRR1656546 1 0.0000 0.988 1.000 0.000
#> SRR1656541 2 0.0000 0.962 0.000 1.000
#> SRR1656547 2 0.0000 0.962 0.000 1.000
#> SRR1656548 2 0.0000 0.962 0.000 1.000
#> SRR1656549 1 0.0000 0.988 1.000 0.000
#> SRR1656551 2 0.0000 0.962 0.000 1.000
#> SRR1656553 2 0.5408 0.854 0.124 0.876
#> SRR1656550 2 0.0000 0.962 0.000 1.000
#> SRR1656552 2 0.2778 0.927 0.048 0.952
#> SRR1656554 2 0.0000 0.962 0.000 1.000
#> SRR1656555 2 0.0000 0.962 0.000 1.000
#> SRR1656556 2 0.0000 0.962 0.000 1.000
#> SRR1656557 2 0.0000 0.962 0.000 1.000
#> SRR1656558 1 0.0000 0.988 1.000 0.000
#> SRR1656559 1 0.0000 0.988 1.000 0.000
#> SRR1656560 2 0.0000 0.962 0.000 1.000
#> SRR1656561 2 0.0376 0.960 0.004 0.996
#> SRR1656562 2 0.4939 0.870 0.108 0.892
#> SRR1656563 1 0.0000 0.988 1.000 0.000
#> SRR1656564 1 0.0000 0.988 1.000 0.000
#> SRR1656565 2 0.9833 0.319 0.424 0.576
#> SRR1656566 1 0.0000 0.988 1.000 0.000
#> SRR1656568 1 0.0000 0.988 1.000 0.000
#> SRR1656567 2 0.0000 0.962 0.000 1.000
#> SRR1656569 2 0.0000 0.962 0.000 1.000
#> SRR1656570 1 0.0000 0.988 1.000 0.000
#> SRR1656571 2 0.3274 0.919 0.060 0.940
#> SRR1656573 2 0.0000 0.962 0.000 1.000
#> SRR1656572 1 0.0376 0.985 0.996 0.004
#> SRR1656574 1 0.0000 0.988 1.000 0.000
#> SRR1656575 1 0.0000 0.988 1.000 0.000
#> SRR1656576 2 0.0000 0.962 0.000 1.000
#> SRR1656578 1 0.0000 0.988 1.000 0.000
#> SRR1656577 1 0.0000 0.988 1.000 0.000
#> SRR1656579 2 0.0000 0.962 0.000 1.000
#> SRR1656580 2 0.2603 0.931 0.044 0.956
#> SRR1656581 2 0.7056 0.770 0.192 0.808
#> SRR1656582 2 0.0000 0.962 0.000 1.000
#> SRR1656585 2 0.0000 0.962 0.000 1.000
#> SRR1656584 1 0.0000 0.988 1.000 0.000
#> SRR1656583 2 0.0938 0.955 0.012 0.988
#> SRR1656586 2 0.1414 0.950 0.020 0.980
#> SRR1656587 1 0.3584 0.930 0.932 0.068
#> SRR1656588 2 0.0000 0.962 0.000 1.000
#> SRR1656589 2 0.8267 0.664 0.260 0.740
#> SRR1656590 1 0.0000 0.988 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656464 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656462 3 0.4796 0.7164 0.220 0.000 0.780
#> SRR1656465 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656467 3 0.0237 0.8897 0.004 0.000 0.996
#> SRR1656466 3 0.0424 0.8882 0.008 0.000 0.992
#> SRR1656468 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656472 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656471 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656470 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656469 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656473 2 0.0237 0.8605 0.000 0.996 0.004
#> SRR1656474 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656475 3 0.6299 0.0667 0.000 0.476 0.524
#> SRR1656478 1 0.1411 0.8952 0.964 0.036 0.000
#> SRR1656477 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656479 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656480 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656476 2 0.5216 0.6238 0.000 0.740 0.260
#> SRR1656481 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656482 3 0.0424 0.8882 0.008 0.000 0.992
#> SRR1656483 3 0.0237 0.8897 0.004 0.000 0.996
#> SRR1656485 3 0.0237 0.8897 0.004 0.000 0.996
#> SRR1656487 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656486 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656488 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656484 2 0.5443 0.5391 0.260 0.736 0.004
#> SRR1656489 1 0.0892 0.9000 0.980 0.020 0.000
#> SRR1656491 3 0.6225 0.2177 0.000 0.432 0.568
#> SRR1656490 2 0.7741 0.3367 0.056 0.568 0.376
#> SRR1656492 2 0.6280 0.1352 0.000 0.540 0.460
#> SRR1656493 1 0.0424 0.9025 0.992 0.008 0.000
#> SRR1656495 1 0.0237 0.9027 0.996 0.004 0.000
#> SRR1656496 2 0.0892 0.8565 0.000 0.980 0.020
#> SRR1656494 1 0.0592 0.8952 0.988 0.000 0.012
#> SRR1656497 2 0.6111 0.3336 0.000 0.604 0.396
#> SRR1656499 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656500 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656501 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656498 1 0.1289 0.8963 0.968 0.032 0.000
#> SRR1656504 2 0.0424 0.8599 0.000 0.992 0.008
#> SRR1656502 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656503 1 0.5058 0.7170 0.756 0.244 0.000
#> SRR1656507 1 0.1529 0.8932 0.960 0.040 0.000
#> SRR1656508 1 0.3879 0.8188 0.848 0.152 0.000
#> SRR1656505 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656506 3 0.5882 0.4407 0.000 0.348 0.652
#> SRR1656509 3 0.5178 0.6708 0.256 0.000 0.744
#> SRR1656510 3 0.4178 0.7354 0.000 0.172 0.828
#> SRR1656511 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656513 1 0.0237 0.9027 0.996 0.004 0.000
#> SRR1656512 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656515 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656516 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656518 2 0.5098 0.5622 0.248 0.752 0.000
#> SRR1656517 1 0.3619 0.8293 0.864 0.136 0.000
#> SRR1656519 1 0.3816 0.7535 0.852 0.000 0.148
#> SRR1656522 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656523 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656520 3 0.3482 0.8083 0.128 0.000 0.872
#> SRR1656524 1 0.1643 0.8911 0.956 0.044 0.000
#> SRR1656525 3 0.6026 0.3723 0.000 0.376 0.624
#> SRR1656526 3 0.6286 0.1118 0.000 0.464 0.536
#> SRR1656527 1 0.0424 0.9025 0.992 0.008 0.000
#> SRR1656530 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656531 1 0.0424 0.9025 0.992 0.008 0.000
#> SRR1656528 3 0.1860 0.8555 0.000 0.052 0.948
#> SRR1656534 3 0.7828 0.5551 0.160 0.168 0.672
#> SRR1656533 1 0.6274 0.3058 0.544 0.456 0.000
#> SRR1656536 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656532 1 0.0237 0.9027 0.996 0.004 0.000
#> SRR1656537 1 0.0237 0.9027 0.996 0.004 0.000
#> SRR1656538 2 0.0424 0.8599 0.000 0.992 0.008
#> SRR1656535 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656539 3 0.0237 0.8897 0.004 0.000 0.996
#> SRR1656544 3 0.0424 0.8882 0.008 0.000 0.992
#> SRR1656542 1 0.1525 0.8856 0.964 0.004 0.032
#> SRR1656543 3 0.2448 0.8467 0.076 0.000 0.924
#> SRR1656545 2 0.3551 0.7770 0.000 0.868 0.132
#> SRR1656540 3 0.2711 0.8381 0.088 0.000 0.912
#> SRR1656546 1 0.5216 0.6953 0.740 0.260 0.000
#> SRR1656541 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656547 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656548 2 0.2066 0.8339 0.000 0.940 0.060
#> SRR1656549 2 0.0237 0.8587 0.004 0.996 0.000
#> SRR1656551 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656553 3 0.5926 0.4974 0.356 0.000 0.644
#> SRR1656550 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656552 2 0.2959 0.8109 0.000 0.900 0.100
#> SRR1656554 3 0.1031 0.8774 0.000 0.024 0.976
#> SRR1656555 3 0.6291 0.0982 0.000 0.468 0.532
#> SRR1656556 3 0.0592 0.8864 0.012 0.000 0.988
#> SRR1656557 3 0.0747 0.8844 0.016 0.000 0.984
#> SRR1656558 1 0.2796 0.8617 0.908 0.092 0.000
#> SRR1656559 1 0.0000 0.9017 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656561 2 0.0424 0.8599 0.000 0.992 0.008
#> SRR1656562 2 0.4796 0.6744 0.000 0.780 0.220
#> SRR1656563 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656564 2 0.1163 0.8417 0.028 0.972 0.000
#> SRR1656565 2 0.0237 0.8606 0.000 0.996 0.004
#> SRR1656566 1 0.5465 0.6561 0.712 0.288 0.000
#> SRR1656568 1 0.6126 0.4433 0.600 0.400 0.000
#> SRR1656567 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656569 3 0.0424 0.8869 0.000 0.008 0.992
#> SRR1656570 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656571 2 0.8503 0.3442 0.104 0.544 0.352
#> SRR1656573 3 0.4887 0.6677 0.000 0.228 0.772
#> SRR1656572 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656574 1 0.6267 0.3146 0.548 0.452 0.000
#> SRR1656575 2 0.6095 0.1971 0.392 0.608 0.000
#> SRR1656576 2 0.5327 0.6029 0.000 0.728 0.272
#> SRR1656578 1 0.0237 0.9027 0.996 0.004 0.000
#> SRR1656577 1 0.4399 0.7845 0.812 0.188 0.000
#> SRR1656579 3 0.0424 0.8868 0.000 0.008 0.992
#> SRR1656580 2 0.0000 0.8609 0.000 1.000 0.000
#> SRR1656581 2 0.0424 0.8599 0.000 0.992 0.008
#> SRR1656582 2 0.1860 0.8386 0.000 0.948 0.052
#> SRR1656585 3 0.0237 0.8897 0.004 0.000 0.996
#> SRR1656584 2 0.4750 0.6180 0.216 0.784 0.000
#> SRR1656583 3 0.4796 0.7176 0.220 0.000 0.780
#> SRR1656586 3 0.4796 0.7215 0.220 0.000 0.780
#> SRR1656587 1 0.0592 0.8957 0.988 0.000 0.012
#> SRR1656588 3 0.0000 0.8907 0.000 0.000 1.000
#> SRR1656589 3 0.6286 0.2392 0.464 0.000 0.536
#> SRR1656590 1 0.0237 0.9027 0.996 0.004 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 3 0.3547 0.7616 0.000 0.016 0.840 0.144
#> SRR1656464 4 0.2973 0.7432 0.144 0.000 0.000 0.856
#> SRR1656462 4 0.5231 0.5215 0.028 0.000 0.296 0.676
#> SRR1656465 3 0.0592 0.8472 0.000 0.000 0.984 0.016
#> SRR1656467 3 0.4898 0.6140 0.000 0.024 0.716 0.260
#> SRR1656466 3 0.0188 0.8485 0.000 0.000 0.996 0.004
#> SRR1656468 3 0.0188 0.8482 0.004 0.000 0.996 0.000
#> SRR1656472 4 0.2469 0.7553 0.108 0.000 0.000 0.892
#> SRR1656471 3 0.1576 0.8376 0.000 0.004 0.948 0.048
#> SRR1656470 3 0.6411 0.4185 0.000 0.308 0.600 0.092
#> SRR1656469 3 0.0188 0.8485 0.000 0.000 0.996 0.004
#> SRR1656473 2 0.3486 0.7831 0.000 0.812 0.000 0.188
#> SRR1656474 4 0.2300 0.7358 0.000 0.064 0.016 0.920
#> SRR1656475 2 0.4692 0.7327 0.000 0.756 0.032 0.212
#> SRR1656478 1 0.1661 0.7721 0.944 0.000 0.004 0.052
#> SRR1656477 3 0.1661 0.8357 0.000 0.004 0.944 0.052
#> SRR1656479 2 0.1022 0.8495 0.032 0.968 0.000 0.000
#> SRR1656480 3 0.5522 0.6646 0.000 0.120 0.732 0.148
#> SRR1656476 2 0.0859 0.8624 0.008 0.980 0.008 0.004
#> SRR1656481 3 0.0336 0.8481 0.000 0.000 0.992 0.008
#> SRR1656482 4 0.6204 0.5067 0.000 0.164 0.164 0.672
#> SRR1656483 3 0.7527 0.1495 0.000 0.192 0.452 0.356
#> SRR1656485 3 0.0188 0.8482 0.004 0.000 0.996 0.000
#> SRR1656487 3 0.0188 0.8482 0.004 0.000 0.996 0.000
#> SRR1656486 1 0.4328 0.6878 0.748 0.244 0.008 0.000
#> SRR1656488 3 0.1022 0.8429 0.032 0.000 0.968 0.000
#> SRR1656484 2 0.5913 0.1674 0.352 0.600 0.000 0.048
#> SRR1656489 1 0.1474 0.7726 0.948 0.000 0.000 0.052
#> SRR1656491 2 0.5612 0.6685 0.044 0.716 0.224 0.016
#> SRR1656490 1 0.5220 0.7326 0.772 0.116 0.104 0.008
#> SRR1656492 3 0.5992 0.1474 0.444 0.040 0.516 0.000
#> SRR1656493 1 0.3219 0.7023 0.836 0.000 0.000 0.164
#> SRR1656495 4 0.3649 0.7008 0.204 0.000 0.000 0.796
#> SRR1656496 2 0.1474 0.8585 0.000 0.948 0.000 0.052
#> SRR1656494 4 0.0927 0.7609 0.016 0.000 0.008 0.976
#> SRR1656497 2 0.3591 0.8391 0.032 0.872 0.016 0.080
#> SRR1656499 3 0.1302 0.8387 0.044 0.000 0.956 0.000
#> SRR1656500 3 0.0376 0.8487 0.004 0.000 0.992 0.004
#> SRR1656501 1 0.3324 0.7632 0.852 0.136 0.012 0.000
#> SRR1656498 1 0.2868 0.7269 0.864 0.000 0.000 0.136
#> SRR1656504 2 0.0707 0.8555 0.020 0.980 0.000 0.000
#> SRR1656502 4 0.2704 0.7510 0.124 0.000 0.000 0.876
#> SRR1656503 1 0.1377 0.7784 0.964 0.008 0.008 0.020
#> SRR1656507 1 0.1637 0.7714 0.940 0.000 0.000 0.060
#> SRR1656508 1 0.5700 0.2192 0.560 0.028 0.000 0.412
#> SRR1656505 3 0.0592 0.8472 0.000 0.000 0.984 0.016
#> SRR1656506 2 0.3080 0.8246 0.000 0.880 0.096 0.024
#> SRR1656509 4 0.2101 0.7421 0.000 0.012 0.060 0.928
#> SRR1656510 3 0.2944 0.7848 0.128 0.004 0.868 0.000
#> SRR1656511 2 0.0592 0.8555 0.016 0.984 0.000 0.000
#> SRR1656513 4 0.2271 0.7553 0.076 0.008 0.000 0.916
#> SRR1656512 2 0.2408 0.8407 0.000 0.896 0.000 0.104
#> SRR1656514 4 0.3266 0.7301 0.168 0.000 0.000 0.832
#> SRR1656515 3 0.1398 0.8456 0.004 0.000 0.956 0.040
#> SRR1656516 1 0.3495 0.7652 0.844 0.140 0.016 0.000
#> SRR1656518 1 0.3105 0.7835 0.868 0.120 0.000 0.012
#> SRR1656517 1 0.1557 0.7748 0.944 0.000 0.000 0.056
#> SRR1656519 3 0.7877 -0.1441 0.304 0.000 0.388 0.308
#> SRR1656522 4 0.4776 0.4574 0.376 0.000 0.000 0.624
#> SRR1656523 2 0.0376 0.8592 0.004 0.992 0.000 0.004
#> SRR1656521 1 0.3836 0.7576 0.816 0.168 0.016 0.000
#> SRR1656520 3 0.5165 0.0212 0.004 0.000 0.512 0.484
#> SRR1656524 1 0.3688 0.6544 0.792 0.000 0.000 0.208
#> SRR1656525 3 0.3695 0.7904 0.108 0.028 0.856 0.008
#> SRR1656526 2 0.5322 0.7187 0.048 0.752 0.184 0.016
#> SRR1656527 1 0.4382 0.5120 0.704 0.000 0.000 0.296
#> SRR1656530 3 0.2081 0.8202 0.084 0.000 0.916 0.000
#> SRR1656529 3 0.3004 0.7934 0.008 0.100 0.884 0.008
#> SRR1656531 4 0.4277 0.6136 0.280 0.000 0.000 0.720
#> SRR1656528 3 0.1489 0.8395 0.044 0.004 0.952 0.000
#> SRR1656534 3 0.6961 -0.0637 0.452 0.076 0.460 0.012
#> SRR1656533 1 0.3647 0.7877 0.852 0.108 0.000 0.040
#> SRR1656536 3 0.1022 0.8440 0.000 0.000 0.968 0.032
#> SRR1656532 4 0.4164 0.6350 0.264 0.000 0.000 0.736
#> SRR1656537 4 0.4790 0.4340 0.380 0.000 0.000 0.620
#> SRR1656538 1 0.5571 0.4213 0.580 0.396 0.024 0.000
#> SRR1656535 2 0.2909 0.8235 0.092 0.888 0.000 0.020
#> SRR1656539 3 0.0817 0.8459 0.000 0.000 0.976 0.024
#> SRR1656544 3 0.0188 0.8485 0.000 0.000 0.996 0.004
#> SRR1656542 1 0.3471 0.7444 0.868 0.000 0.072 0.060
#> SRR1656543 3 0.1398 0.8399 0.040 0.000 0.956 0.004
#> SRR1656545 2 0.3725 0.8233 0.028 0.848 0.004 0.120
#> SRR1656540 4 0.4679 0.4225 0.000 0.000 0.352 0.648
#> SRR1656546 1 0.1059 0.7793 0.972 0.012 0.016 0.000
#> SRR1656541 3 0.2179 0.8283 0.064 0.000 0.924 0.012
#> SRR1656547 3 0.1854 0.8354 0.048 0.000 0.940 0.012
#> SRR1656548 2 0.2816 0.8279 0.064 0.900 0.036 0.000
#> SRR1656549 1 0.4994 0.3106 0.520 0.480 0.000 0.000
#> SRR1656551 3 0.0336 0.8481 0.000 0.000 0.992 0.008
#> SRR1656553 3 0.3552 0.7788 0.128 0.000 0.848 0.024
#> SRR1656550 3 0.0921 0.8453 0.000 0.000 0.972 0.028
#> SRR1656552 1 0.7785 0.1579 0.404 0.248 0.348 0.000
#> SRR1656554 3 0.5643 0.1864 0.000 0.428 0.548 0.024
#> SRR1656555 2 0.5509 0.6820 0.048 0.724 0.216 0.012
#> SRR1656556 3 0.0921 0.8454 0.000 0.000 0.972 0.028
#> SRR1656557 3 0.1022 0.8426 0.032 0.000 0.968 0.000
#> SRR1656558 1 0.1474 0.7745 0.948 0.000 0.000 0.052
#> SRR1656559 1 0.4040 0.5951 0.752 0.000 0.000 0.248
#> SRR1656560 3 0.1637 0.8327 0.060 0.000 0.940 0.000
#> SRR1656561 2 0.2281 0.8161 0.096 0.904 0.000 0.000
#> SRR1656562 2 0.4334 0.7926 0.032 0.804 0.004 0.160
#> SRR1656563 2 0.1792 0.8260 0.068 0.932 0.000 0.000
#> SRR1656564 2 0.2329 0.8552 0.012 0.916 0.000 0.072
#> SRR1656565 2 0.2814 0.8263 0.000 0.868 0.000 0.132
#> SRR1656566 1 0.3833 0.7812 0.848 0.072 0.000 0.080
#> SRR1656568 1 0.4150 0.7821 0.824 0.120 0.000 0.056
#> SRR1656567 3 0.1211 0.8416 0.000 0.000 0.960 0.040
#> SRR1656569 3 0.2002 0.8326 0.000 0.044 0.936 0.020
#> SRR1656570 2 0.1474 0.8381 0.052 0.948 0.000 0.000
#> SRR1656571 2 0.5298 0.4952 0.000 0.612 0.016 0.372
#> SRR1656573 2 0.2246 0.8519 0.004 0.928 0.052 0.016
#> SRR1656572 1 0.4941 0.3769 0.564 0.436 0.000 0.000
#> SRR1656574 1 0.5292 0.7562 0.744 0.168 0.000 0.088
#> SRR1656575 1 0.3447 0.7839 0.852 0.128 0.000 0.020
#> SRR1656576 2 0.2742 0.8444 0.000 0.900 0.024 0.076
#> SRR1656578 4 0.2216 0.7580 0.092 0.000 0.000 0.908
#> SRR1656577 1 0.3082 0.7788 0.884 0.032 0.000 0.084
#> SRR1656579 3 0.6330 0.0418 0.000 0.448 0.492 0.060
#> SRR1656580 2 0.1389 0.8468 0.048 0.952 0.000 0.000
#> SRR1656581 2 0.0817 0.8531 0.024 0.976 0.000 0.000
#> SRR1656582 2 0.0188 0.8599 0.000 0.996 0.000 0.004
#> SRR1656585 4 0.6887 -0.1509 0.000 0.440 0.104 0.456
#> SRR1656584 1 0.4059 0.7577 0.788 0.200 0.000 0.012
#> SRR1656583 4 0.2623 0.7356 0.000 0.028 0.064 0.908
#> SRR1656586 4 0.4839 0.5595 0.000 0.200 0.044 0.756
#> SRR1656587 4 0.1820 0.7629 0.036 0.000 0.020 0.944
#> SRR1656588 3 0.0921 0.8451 0.000 0.000 0.972 0.028
#> SRR1656589 4 0.2021 0.7439 0.000 0.012 0.056 0.932
#> SRR1656590 4 0.3311 0.7273 0.172 0.000 0.000 0.828
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.8119 0.1521 0.000 0.416 0.164 0.164 0.256
#> SRR1656464 3 0.2583 0.6977 0.132 0.004 0.864 0.000 0.000
#> SRR1656462 3 0.5257 0.5534 0.020 0.160 0.716 0.000 0.104
#> SRR1656465 5 0.0000 0.7747 0.000 0.000 0.000 0.000 1.000
#> SRR1656467 5 0.4456 0.6510 0.000 0.080 0.100 0.028 0.792
#> SRR1656466 5 0.2249 0.7299 0.008 0.096 0.000 0.000 0.896
#> SRR1656468 5 0.1043 0.7662 0.000 0.040 0.000 0.000 0.960
#> SRR1656472 3 0.1892 0.7084 0.080 0.004 0.916 0.000 0.000
#> SRR1656471 5 0.0854 0.7719 0.000 0.008 0.012 0.004 0.976
#> SRR1656470 2 0.8007 0.0184 0.000 0.364 0.088 0.300 0.248
#> SRR1656469 5 0.1671 0.7507 0.000 0.076 0.000 0.000 0.924
#> SRR1656473 4 0.6553 0.1661 0.000 0.292 0.236 0.472 0.000
#> SRR1656474 3 0.5358 0.3989 0.000 0.248 0.648 0.104 0.000
#> SRR1656475 4 0.6499 0.1203 0.000 0.368 0.192 0.440 0.000
#> SRR1656478 1 0.3321 0.6019 0.832 0.136 0.032 0.000 0.000
#> SRR1656477 5 0.0854 0.7718 0.000 0.012 0.008 0.004 0.976
#> SRR1656479 4 0.3810 0.4640 0.176 0.036 0.000 0.788 0.000
#> SRR1656480 5 0.3736 0.6953 0.000 0.072 0.024 0.064 0.840
#> SRR1656476 4 0.4113 0.4029 0.000 0.232 0.000 0.740 0.028
#> SRR1656481 5 0.0510 0.7726 0.000 0.016 0.000 0.000 0.984
#> SRR1656482 5 0.8375 -0.0780 0.000 0.232 0.308 0.148 0.312
#> SRR1656483 5 0.7966 0.0532 0.000 0.284 0.176 0.120 0.420
#> SRR1656485 5 0.1341 0.7590 0.000 0.056 0.000 0.000 0.944
#> SRR1656487 5 0.0703 0.7710 0.000 0.024 0.000 0.000 0.976
#> SRR1656486 1 0.6006 0.4541 0.584 0.220 0.000 0.196 0.000
#> SRR1656488 5 0.3596 0.6261 0.016 0.200 0.000 0.000 0.784
#> SRR1656484 1 0.5118 0.1892 0.548 0.040 0.000 0.412 0.000
#> SRR1656489 1 0.5036 0.4257 0.628 0.320 0.052 0.000 0.000
#> SRR1656491 4 0.4630 0.1845 0.000 0.396 0.000 0.588 0.016
#> SRR1656490 1 0.4503 0.5377 0.756 0.000 0.000 0.120 0.124
#> SRR1656492 1 0.7121 0.0537 0.400 0.332 0.000 0.016 0.252
#> SRR1656493 1 0.2230 0.5695 0.884 0.000 0.116 0.000 0.000
#> SRR1656495 3 0.3300 0.6759 0.204 0.004 0.792 0.000 0.000
#> SRR1656496 4 0.5297 0.3277 0.272 0.060 0.000 0.656 0.012
#> SRR1656494 3 0.1235 0.6968 0.012 0.016 0.964 0.004 0.004
#> SRR1656497 2 0.5106 -0.0892 0.000 0.508 0.036 0.456 0.000
#> SRR1656499 5 0.4528 0.2425 0.008 0.444 0.000 0.000 0.548
#> SRR1656500 5 0.0162 0.7750 0.000 0.000 0.004 0.000 0.996
#> SRR1656501 1 0.6530 0.2000 0.424 0.380 0.000 0.196 0.000
#> SRR1656498 1 0.2583 0.5614 0.864 0.004 0.132 0.000 0.000
#> SRR1656504 4 0.3921 0.4721 0.044 0.172 0.000 0.784 0.000
#> SRR1656502 3 0.2233 0.7058 0.104 0.004 0.892 0.000 0.000
#> SRR1656503 2 0.5164 0.3822 0.120 0.732 0.024 0.124 0.000
#> SRR1656507 1 0.4054 0.5132 0.732 0.248 0.020 0.000 0.000
#> SRR1656508 1 0.4697 0.3263 0.660 0.000 0.304 0.036 0.000
#> SRR1656505 5 0.0000 0.7747 0.000 0.000 0.000 0.000 1.000
#> SRR1656506 4 0.3942 0.3550 0.000 0.012 0.000 0.728 0.260
#> SRR1656509 3 0.2338 0.6616 0.000 0.036 0.916 0.032 0.016
#> SRR1656510 5 0.5703 0.1400 0.084 0.408 0.000 0.000 0.508
#> SRR1656511 4 0.2270 0.4975 0.076 0.020 0.000 0.904 0.000
#> SRR1656513 2 0.5509 -0.1454 0.000 0.468 0.468 0.064 0.000
#> SRR1656512 4 0.5776 0.2538 0.000 0.288 0.124 0.588 0.000
#> SRR1656514 3 0.3944 0.6508 0.224 0.004 0.756 0.000 0.016
#> SRR1656515 5 0.6111 0.2069 0.000 0.364 0.068 0.028 0.540
#> SRR1656516 2 0.6495 -0.1702 0.388 0.424 0.000 0.188 0.000
#> SRR1656518 1 0.2270 0.6228 0.904 0.020 0.000 0.076 0.000
#> SRR1656517 1 0.1605 0.6133 0.944 0.012 0.040 0.004 0.000
#> SRR1656519 3 0.7906 0.2079 0.280 0.192 0.424 0.000 0.104
#> SRR1656522 3 0.5144 0.5200 0.292 0.068 0.640 0.000 0.000
#> SRR1656523 4 0.1502 0.4991 0.056 0.004 0.000 0.940 0.000
#> SRR1656521 1 0.5921 0.2380 0.460 0.448 0.000 0.088 0.004
#> SRR1656520 5 0.3906 0.5527 0.000 0.004 0.292 0.000 0.704
#> SRR1656524 1 0.2563 0.5686 0.872 0.000 0.120 0.008 0.000
#> SRR1656525 2 0.5199 0.3880 0.036 0.728 0.000 0.164 0.072
#> SRR1656526 2 0.4860 -0.0455 0.000 0.540 0.016 0.440 0.004
#> SRR1656527 1 0.5559 -0.0248 0.544 0.076 0.380 0.000 0.000
#> SRR1656530 2 0.5800 0.3722 0.032 0.628 0.000 0.064 0.276
#> SRR1656529 4 0.6605 0.0282 0.000 0.288 0.000 0.460 0.252
#> SRR1656531 3 0.4138 0.4586 0.384 0.000 0.616 0.000 0.000
#> SRR1656528 4 0.6692 -0.0222 0.000 0.336 0.000 0.416 0.248
#> SRR1656534 5 0.6174 0.1056 0.396 0.008 0.004 0.092 0.500
#> SRR1656533 1 0.2997 0.5897 0.840 0.000 0.012 0.148 0.000
#> SRR1656536 5 0.0162 0.7750 0.000 0.000 0.004 0.000 0.996
#> SRR1656532 3 0.3550 0.6416 0.236 0.004 0.760 0.000 0.000
#> SRR1656537 3 0.4074 0.4829 0.364 0.000 0.636 0.000 0.000
#> SRR1656538 4 0.6134 0.0908 0.116 0.384 0.000 0.496 0.004
#> SRR1656535 2 0.6919 -0.1382 0.148 0.416 0.028 0.408 0.000
#> SRR1656539 5 0.0162 0.7750 0.000 0.000 0.004 0.000 0.996
#> SRR1656544 5 0.4119 0.6054 0.000 0.212 0.036 0.000 0.752
#> SRR1656542 1 0.7174 0.2679 0.476 0.336 0.124 0.000 0.064
#> SRR1656543 5 0.6587 0.2288 0.040 0.360 0.092 0.000 0.508
#> SRR1656545 2 0.5778 -0.1089 0.000 0.464 0.088 0.448 0.000
#> SRR1656540 5 0.4238 0.4206 0.000 0.004 0.368 0.000 0.628
#> SRR1656546 1 0.4562 0.2018 0.500 0.492 0.008 0.000 0.000
#> SRR1656541 2 0.3988 0.4520 0.024 0.776 0.000 0.008 0.192
#> SRR1656547 2 0.4487 0.4427 0.008 0.776 0.004 0.072 0.140
#> SRR1656548 4 0.4173 0.4319 0.028 0.204 0.000 0.760 0.008
#> SRR1656549 1 0.4256 0.1893 0.564 0.000 0.000 0.436 0.000
#> SRR1656551 5 0.0162 0.7743 0.000 0.004 0.000 0.000 0.996
#> SRR1656553 2 0.6827 0.2998 0.144 0.608 0.116 0.000 0.132
#> SRR1656550 5 0.0324 0.7748 0.000 0.004 0.004 0.000 0.992
#> SRR1656552 2 0.4918 0.2659 0.236 0.704 0.000 0.044 0.016
#> SRR1656554 5 0.2813 0.6574 0.000 0.000 0.000 0.168 0.832
#> SRR1656555 4 0.4420 0.1277 0.000 0.448 0.000 0.548 0.004
#> SRR1656556 5 0.0510 0.7733 0.000 0.000 0.016 0.000 0.984
#> SRR1656557 5 0.6415 0.3428 0.008 0.276 0.176 0.000 0.540
#> SRR1656558 1 0.2848 0.6120 0.868 0.104 0.028 0.000 0.000
#> SRR1656559 1 0.5650 -0.1555 0.468 0.076 0.456 0.000 0.000
#> SRR1656560 2 0.4837 0.2370 0.020 0.624 0.000 0.008 0.348
#> SRR1656561 4 0.3710 0.4679 0.048 0.144 0.000 0.808 0.000
#> SRR1656562 4 0.5765 0.0562 0.000 0.424 0.088 0.488 0.000
#> SRR1656563 4 0.4380 0.2054 0.376 0.008 0.000 0.616 0.000
#> SRR1656564 4 0.7478 0.1911 0.320 0.200 0.052 0.428 0.000
#> SRR1656565 4 0.7430 0.2685 0.268 0.176 0.044 0.500 0.012
#> SRR1656566 1 0.2446 0.6179 0.900 0.000 0.044 0.056 0.000
#> SRR1656568 1 0.3992 0.5911 0.812 0.028 0.032 0.128 0.000
#> SRR1656567 5 0.0162 0.7751 0.000 0.004 0.000 0.000 0.996
#> SRR1656569 5 0.0703 0.7699 0.000 0.000 0.000 0.024 0.976
#> SRR1656570 4 0.4060 0.2422 0.360 0.000 0.000 0.640 0.000
#> SRR1656571 4 0.6724 0.1212 0.000 0.284 0.296 0.420 0.000
#> SRR1656573 4 0.4763 0.3822 0.020 0.032 0.000 0.716 0.232
#> SRR1656572 4 0.6433 0.0857 0.188 0.340 0.000 0.472 0.000
#> SRR1656574 1 0.4750 0.4740 0.692 0.004 0.044 0.260 0.000
#> SRR1656575 1 0.7507 0.2832 0.436 0.292 0.052 0.220 0.000
#> SRR1656576 4 0.3639 0.4368 0.000 0.144 0.044 0.812 0.000
#> SRR1656578 3 0.2124 0.6966 0.028 0.056 0.916 0.000 0.000
#> SRR1656577 1 0.4169 0.5522 0.792 0.044 0.148 0.016 0.000
#> SRR1656579 5 0.3810 0.6119 0.000 0.036 0.000 0.176 0.788
#> SRR1656580 4 0.3794 0.4642 0.048 0.152 0.000 0.800 0.000
#> SRR1656581 4 0.5730 0.1001 0.400 0.052 0.000 0.532 0.016
#> SRR1656582 4 0.1918 0.4987 0.036 0.036 0.000 0.928 0.000
#> SRR1656585 4 0.7474 0.1738 0.000 0.076 0.292 0.472 0.160
#> SRR1656584 1 0.3336 0.5183 0.772 0.000 0.000 0.228 0.000
#> SRR1656583 3 0.4339 0.5835 0.000 0.100 0.800 0.072 0.028
#> SRR1656586 3 0.6902 -0.1096 0.000 0.280 0.392 0.324 0.004
#> SRR1656587 3 0.1243 0.7049 0.028 0.008 0.960 0.000 0.004
#> SRR1656588 5 0.0162 0.7750 0.000 0.000 0.004 0.000 0.996
#> SRR1656589 3 0.5695 0.3760 0.000 0.276 0.624 0.088 0.012
#> SRR1656590 3 0.3561 0.6277 0.260 0.000 0.740 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.3060 0.79415 0.004 0.868 0.040 0.008 0.068 0.012
#> SRR1656464 6 0.0508 0.78450 0.000 0.012 0.004 0.000 0.000 0.984
#> SRR1656462 6 0.3429 0.70191 0.000 0.016 0.144 0.000 0.028 0.812
#> SRR1656465 5 0.0951 0.84973 0.004 0.008 0.020 0.000 0.968 0.000
#> SRR1656467 5 0.1757 0.81443 0.000 0.076 0.000 0.000 0.916 0.008
#> SRR1656466 5 0.3301 0.68409 0.004 0.008 0.216 0.000 0.772 0.000
#> SRR1656468 5 0.1872 0.83168 0.004 0.008 0.064 0.004 0.920 0.000
#> SRR1656472 6 0.0858 0.78396 0.000 0.028 0.004 0.000 0.000 0.968
#> SRR1656471 5 0.0291 0.85134 0.000 0.004 0.000 0.000 0.992 0.004
#> SRR1656470 2 0.3092 0.79298 0.004 0.860 0.028 0.028 0.080 0.000
#> SRR1656469 5 0.3722 0.74425 0.012 0.032 0.140 0.012 0.804 0.000
#> SRR1656473 2 0.1858 0.81476 0.000 0.912 0.000 0.076 0.000 0.012
#> SRR1656474 2 0.2300 0.76972 0.000 0.856 0.000 0.000 0.000 0.144
#> SRR1656475 2 0.1951 0.82041 0.000 0.916 0.020 0.060 0.000 0.004
#> SRR1656478 1 0.3297 0.69134 0.832 0.000 0.100 0.008 0.000 0.060
#> SRR1656477 5 0.0000 0.85130 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656479 4 0.0363 0.75781 0.012 0.000 0.000 0.988 0.000 0.000
#> SRR1656480 5 0.1129 0.84406 0.000 0.012 0.004 0.012 0.964 0.008
#> SRR1656476 4 0.4103 0.68931 0.004 0.192 0.040 0.752 0.012 0.000
#> SRR1656481 5 0.1628 0.84317 0.008 0.012 0.036 0.004 0.940 0.000
#> SRR1656482 2 0.3533 0.67222 0.000 0.780 0.004 0.004 0.192 0.020
#> SRR1656483 2 0.2809 0.77670 0.000 0.848 0.000 0.020 0.128 0.004
#> SRR1656485 5 0.3061 0.74509 0.004 0.008 0.168 0.000 0.816 0.004
#> SRR1656487 5 0.1644 0.83752 0.004 0.012 0.052 0.000 0.932 0.000
#> SRR1656486 1 0.5355 -0.13305 0.468 0.000 0.424 0.108 0.000 0.000
#> SRR1656488 5 0.4208 0.16160 0.004 0.008 0.452 0.000 0.536 0.000
#> SRR1656484 1 0.3643 0.70463 0.808 0.076 0.000 0.108 0.004 0.004
#> SRR1656489 3 0.5322 0.35258 0.352 0.004 0.552 0.004 0.000 0.088
#> SRR1656491 4 0.3812 0.65509 0.000 0.024 0.264 0.712 0.000 0.000
#> SRR1656490 1 0.2504 0.73014 0.892 0.004 0.008 0.032 0.064 0.000
#> SRR1656492 3 0.5919 0.39416 0.356 0.016 0.528 0.028 0.072 0.000
#> SRR1656493 1 0.1531 0.74527 0.928 0.000 0.004 0.000 0.000 0.068
#> SRR1656495 6 0.2383 0.78464 0.096 0.024 0.000 0.000 0.000 0.880
#> SRR1656496 4 0.6242 -0.14154 0.420 0.084 0.000 0.428 0.068 0.000
#> SRR1656494 6 0.2048 0.75645 0.000 0.120 0.000 0.000 0.000 0.880
#> SRR1656497 2 0.4002 0.76272 0.000 0.744 0.188 0.068 0.000 0.000
#> SRR1656499 3 0.3109 0.62794 0.000 0.016 0.812 0.000 0.168 0.004
#> SRR1656500 5 0.0748 0.85000 0.000 0.000 0.004 0.004 0.976 0.016
#> SRR1656501 3 0.5465 0.14592 0.112 0.000 0.508 0.376 0.000 0.004
#> SRR1656498 1 0.3171 0.62303 0.784 0.000 0.012 0.000 0.000 0.204
#> SRR1656504 4 0.4097 0.69458 0.012 0.164 0.064 0.760 0.000 0.000
#> SRR1656502 6 0.0777 0.78454 0.000 0.024 0.004 0.000 0.000 0.972
#> SRR1656503 3 0.2826 0.63018 0.000 0.024 0.856 0.008 0.000 0.112
#> SRR1656507 1 0.4520 0.42614 0.688 0.000 0.248 0.012 0.000 0.052
#> SRR1656508 6 0.3946 0.69872 0.192 0.000 0.004 0.052 0.000 0.752
#> SRR1656505 5 0.0964 0.85019 0.004 0.012 0.016 0.000 0.968 0.000
#> SRR1656506 4 0.1806 0.74466 0.000 0.004 0.000 0.908 0.088 0.000
#> SRR1656509 6 0.2624 0.73846 0.000 0.148 0.004 0.004 0.000 0.844
#> SRR1656510 3 0.5501 0.50612 0.048 0.040 0.640 0.020 0.252 0.000
#> SRR1656511 4 0.0891 0.75802 0.024 0.008 0.000 0.968 0.000 0.000
#> SRR1656513 2 0.3970 0.68560 0.000 0.692 0.280 0.000 0.000 0.028
#> SRR1656512 2 0.2402 0.79633 0.000 0.856 0.004 0.140 0.000 0.000
#> SRR1656514 6 0.4249 0.69091 0.184 0.004 0.004 0.000 0.068 0.740
#> SRR1656515 2 0.4198 0.71070 0.000 0.708 0.232 0.000 0.060 0.000
#> SRR1656516 4 0.5701 0.16932 0.100 0.012 0.392 0.492 0.000 0.004
#> SRR1656518 1 0.1630 0.74987 0.940 0.000 0.016 0.024 0.000 0.020
#> SRR1656517 1 0.1410 0.75004 0.944 0.000 0.004 0.008 0.000 0.044
#> SRR1656519 6 0.3909 0.66318 0.060 0.000 0.160 0.000 0.008 0.772
#> SRR1656522 6 0.1564 0.77793 0.024 0.000 0.040 0.000 0.000 0.936
#> SRR1656523 4 0.2003 0.74180 0.044 0.044 0.000 0.912 0.000 0.000
#> SRR1656521 3 0.6176 0.37560 0.352 0.108 0.496 0.040 0.000 0.004
#> SRR1656520 5 0.4424 0.43079 0.012 0.020 0.000 0.000 0.624 0.344
#> SRR1656524 1 0.1610 0.74141 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1656525 3 0.1480 0.63895 0.000 0.020 0.940 0.040 0.000 0.000
#> SRR1656526 2 0.4168 0.52803 0.000 0.584 0.400 0.016 0.000 0.000
#> SRR1656527 1 0.5163 0.51061 0.640 0.252 0.020 0.000 0.000 0.088
#> SRR1656530 3 0.3831 0.55517 0.004 0.024 0.780 0.172 0.020 0.000
#> SRR1656529 4 0.5007 0.65793 0.000 0.016 0.168 0.692 0.120 0.004
#> SRR1656531 6 0.2446 0.77481 0.124 0.012 0.000 0.000 0.000 0.864
#> SRR1656528 4 0.4436 0.64038 0.000 0.020 0.272 0.680 0.028 0.000
#> SRR1656534 5 0.5420 0.04781 0.440 0.016 0.004 0.036 0.492 0.012
#> SRR1656533 1 0.2340 0.74235 0.896 0.044 0.000 0.056 0.000 0.004
#> SRR1656536 5 0.0146 0.85202 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1656532 6 0.1410 0.78985 0.044 0.008 0.004 0.000 0.000 0.944
#> SRR1656537 6 0.3405 0.64197 0.272 0.000 0.004 0.000 0.000 0.724
#> SRR1656538 4 0.3708 0.72263 0.052 0.012 0.116 0.812 0.008 0.000
#> SRR1656535 2 0.3859 0.76253 0.024 0.796 0.056 0.124 0.000 0.000
#> SRR1656539 5 0.0146 0.85202 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1656544 5 0.5920 0.33145 0.004 0.008 0.264 0.000 0.536 0.188
#> SRR1656542 3 0.6319 0.41311 0.084 0.008 0.544 0.000 0.076 0.288
#> SRR1656543 3 0.5703 0.49774 0.008 0.008 0.596 0.000 0.188 0.200
#> SRR1656545 2 0.3352 0.77687 0.000 0.792 0.176 0.032 0.000 0.000
#> SRR1656540 5 0.3843 0.20825 0.000 0.000 0.000 0.000 0.548 0.452
#> SRR1656546 3 0.4075 0.48580 0.312 0.012 0.668 0.004 0.000 0.004
#> SRR1656541 3 0.3480 0.49490 0.016 0.200 0.776 0.000 0.008 0.000
#> SRR1656547 3 0.1155 0.65314 0.000 0.036 0.956 0.000 0.004 0.004
#> SRR1656548 4 0.2746 0.75106 0.008 0.020 0.100 0.868 0.004 0.000
#> SRR1656549 1 0.3201 0.68656 0.780 0.012 0.000 0.208 0.000 0.000
#> SRR1656551 5 0.0146 0.85202 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1656553 3 0.2182 0.66308 0.004 0.020 0.900 0.000 0.000 0.076
#> SRR1656550 5 0.0146 0.85100 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1656552 3 0.6367 -0.00332 0.100 0.388 0.444 0.068 0.000 0.000
#> SRR1656554 5 0.2544 0.74384 0.004 0.000 0.000 0.140 0.852 0.004
#> SRR1656555 4 0.4428 0.49930 0.000 0.032 0.388 0.580 0.000 0.000
#> SRR1656556 5 0.1396 0.84866 0.004 0.008 0.012 0.000 0.952 0.024
#> SRR1656557 6 0.5416 0.40361 0.000 0.004 0.228 0.000 0.172 0.596
#> SRR1656558 1 0.2519 0.72618 0.888 0.000 0.048 0.008 0.000 0.056
#> SRR1656559 6 0.4392 0.60955 0.256 0.000 0.064 0.000 0.000 0.680
#> SRR1656560 3 0.1584 0.66560 0.000 0.008 0.928 0.000 0.064 0.000
#> SRR1656561 4 0.2126 0.75876 0.004 0.020 0.072 0.904 0.000 0.000
#> SRR1656562 2 0.5125 0.66427 0.000 0.632 0.232 0.132 0.000 0.004
#> SRR1656563 4 0.1867 0.73943 0.064 0.020 0.000 0.916 0.000 0.000
#> SRR1656564 1 0.5607 0.22286 0.448 0.408 0.000 0.144 0.000 0.000
#> SRR1656565 1 0.6646 0.38506 0.488 0.312 0.004 0.144 0.044 0.008
#> SRR1656566 1 0.1390 0.75322 0.948 0.000 0.004 0.016 0.000 0.032
#> SRR1656568 1 0.2177 0.74850 0.908 0.052 0.000 0.032 0.000 0.008
#> SRR1656567 5 0.0146 0.85116 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1656569 5 0.1007 0.83802 0.000 0.000 0.000 0.044 0.956 0.000
#> SRR1656570 4 0.1643 0.74269 0.068 0.008 0.000 0.924 0.000 0.000
#> SRR1656571 2 0.1950 0.81589 0.000 0.912 0.000 0.064 0.000 0.024
#> SRR1656573 4 0.2361 0.74527 0.000 0.028 0.000 0.884 0.088 0.000
#> SRR1656572 4 0.5912 0.08093 0.056 0.064 0.424 0.456 0.000 0.000
#> SRR1656574 1 0.3655 0.70082 0.796 0.044 0.000 0.148 0.000 0.012
#> SRR1656575 4 0.6840 0.36318 0.088 0.012 0.136 0.516 0.000 0.248
#> SRR1656576 4 0.2809 0.74072 0.000 0.128 0.020 0.848 0.004 0.000
#> SRR1656578 6 0.3892 0.43367 0.004 0.352 0.004 0.000 0.000 0.640
#> SRR1656577 6 0.5258 0.24758 0.408 0.000 0.060 0.016 0.000 0.516
#> SRR1656579 5 0.1549 0.83970 0.004 0.024 0.004 0.024 0.944 0.000
#> SRR1656580 4 0.2306 0.75639 0.004 0.016 0.092 0.888 0.000 0.000
#> SRR1656581 1 0.5851 0.43926 0.560 0.096 0.000 0.300 0.044 0.000
#> SRR1656582 4 0.1788 0.74798 0.040 0.028 0.000 0.928 0.004 0.000
#> SRR1656585 4 0.5156 0.65950 0.000 0.100 0.004 0.716 0.100 0.080
#> SRR1656584 1 0.1349 0.75310 0.940 0.000 0.004 0.056 0.000 0.000
#> SRR1656583 6 0.4865 0.49170 0.000 0.296 0.004 0.016 0.044 0.640
#> SRR1656586 2 0.1858 0.80586 0.000 0.912 0.000 0.012 0.000 0.076
#> SRR1656587 6 0.1444 0.77273 0.000 0.072 0.000 0.000 0.000 0.928
#> SRR1656588 5 0.0665 0.85127 0.004 0.008 0.008 0.000 0.980 0.000
#> SRR1656589 2 0.1949 0.79933 0.004 0.904 0.004 0.000 0.000 0.088
#> SRR1656590 6 0.2149 0.78336 0.104 0.004 0.004 0.000 0.000 0.888
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.733 0.871 0.938 0.3986 0.624 0.624
#> 3 3 0.579 0.768 0.879 0.5615 0.714 0.551
#> 4 4 0.673 0.767 0.874 0.1158 0.962 0.896
#> 5 5 0.653 0.595 0.768 0.0895 0.948 0.839
#> 6 6 0.684 0.634 0.728 0.0546 0.911 0.692
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.0000 0.9468 0.000 1.000
#> SRR1656464 1 0.0000 0.9266 1.000 0.000
#> SRR1656462 1 0.0000 0.9266 1.000 0.000
#> SRR1656465 1 0.3733 0.8902 0.928 0.072
#> SRR1656467 2 0.4161 0.8690 0.084 0.916
#> SRR1656466 1 0.0000 0.9266 1.000 0.000
#> SRR1656468 1 0.7674 0.7586 0.776 0.224
#> SRR1656472 1 0.4161 0.8835 0.916 0.084
#> SRR1656471 1 0.5059 0.8608 0.888 0.112
#> SRR1656470 2 0.0000 0.9468 0.000 1.000
#> SRR1656469 1 0.2236 0.9121 0.964 0.036
#> SRR1656473 2 0.0000 0.9468 0.000 1.000
#> SRR1656474 2 0.0000 0.9468 0.000 1.000
#> SRR1656475 2 0.0000 0.9468 0.000 1.000
#> SRR1656478 1 0.0000 0.9266 1.000 0.000
#> SRR1656477 1 0.8763 0.6618 0.704 0.296
#> SRR1656479 1 0.0938 0.9228 0.988 0.012
#> SRR1656480 1 0.8955 0.6368 0.688 0.312
#> SRR1656476 2 0.0000 0.9468 0.000 1.000
#> SRR1656481 1 0.7602 0.7626 0.780 0.220
#> SRR1656482 2 0.0000 0.9468 0.000 1.000
#> SRR1656483 2 0.0000 0.9468 0.000 1.000
#> SRR1656485 1 0.0000 0.9266 1.000 0.000
#> SRR1656487 1 0.0000 0.9266 1.000 0.000
#> SRR1656486 1 0.0000 0.9266 1.000 0.000
#> SRR1656488 1 0.0000 0.9266 1.000 0.000
#> SRR1656484 1 0.0000 0.9266 1.000 0.000
#> SRR1656489 1 0.0000 0.9266 1.000 0.000
#> SRR1656491 1 0.2603 0.9090 0.956 0.044
#> SRR1656490 1 0.1184 0.9213 0.984 0.016
#> SRR1656492 1 0.0000 0.9266 1.000 0.000
#> SRR1656493 1 0.1184 0.9214 0.984 0.016
#> SRR1656495 1 0.1184 0.9214 0.984 0.016
#> SRR1656496 1 0.0938 0.9228 0.988 0.012
#> SRR1656494 2 0.1414 0.9356 0.020 0.980
#> SRR1656497 2 0.0000 0.9468 0.000 1.000
#> SRR1656499 1 0.0000 0.9266 1.000 0.000
#> SRR1656500 1 0.0000 0.9266 1.000 0.000
#> SRR1656501 1 0.0000 0.9266 1.000 0.000
#> SRR1656498 1 0.0000 0.9266 1.000 0.000
#> SRR1656504 2 0.0000 0.9468 0.000 1.000
#> SRR1656502 1 0.4161 0.8835 0.916 0.084
#> SRR1656503 1 0.0000 0.9266 1.000 0.000
#> SRR1656507 1 0.0000 0.9266 1.000 0.000
#> SRR1656508 1 0.0000 0.9266 1.000 0.000
#> SRR1656505 1 0.8909 0.6423 0.692 0.308
#> SRR1656506 1 0.0000 0.9266 1.000 0.000
#> SRR1656509 1 0.7950 0.7395 0.760 0.240
#> SRR1656510 1 0.4562 0.8735 0.904 0.096
#> SRR1656511 1 0.8909 0.6428 0.692 0.308
#> SRR1656513 2 0.1184 0.9388 0.016 0.984
#> SRR1656512 2 0.0000 0.9468 0.000 1.000
#> SRR1656514 1 0.0000 0.9266 1.000 0.000
#> SRR1656515 2 0.8713 0.5264 0.292 0.708
#> SRR1656516 1 0.0000 0.9266 1.000 0.000
#> SRR1656518 1 0.0000 0.9266 1.000 0.000
#> SRR1656517 1 0.0000 0.9266 1.000 0.000
#> SRR1656519 1 0.0000 0.9266 1.000 0.000
#> SRR1656522 1 0.0000 0.9266 1.000 0.000
#> SRR1656523 1 0.7883 0.7435 0.764 0.236
#> SRR1656521 2 0.0000 0.9468 0.000 1.000
#> SRR1656520 1 0.0000 0.9266 1.000 0.000
#> SRR1656524 1 0.1184 0.9214 0.984 0.016
#> SRR1656525 1 0.0000 0.9266 1.000 0.000
#> SRR1656526 2 0.0000 0.9468 0.000 1.000
#> SRR1656527 2 0.1184 0.9388 0.016 0.984
#> SRR1656530 1 0.0000 0.9266 1.000 0.000
#> SRR1656529 1 0.0000 0.9266 1.000 0.000
#> SRR1656531 1 0.0000 0.9266 1.000 0.000
#> SRR1656528 1 0.0000 0.9266 1.000 0.000
#> SRR1656534 1 0.0000 0.9266 1.000 0.000
#> SRR1656533 1 0.0000 0.9266 1.000 0.000
#> SRR1656536 1 0.2948 0.9042 0.948 0.052
#> SRR1656532 2 0.1184 0.9388 0.016 0.984
#> SRR1656537 1 0.0000 0.9266 1.000 0.000
#> SRR1656538 1 0.0000 0.9266 1.000 0.000
#> SRR1656535 2 0.0376 0.9450 0.004 0.996
#> SRR1656539 1 0.2948 0.9042 0.948 0.052
#> SRR1656544 1 0.0000 0.9266 1.000 0.000
#> SRR1656542 1 0.0000 0.9266 1.000 0.000
#> SRR1656543 1 0.0000 0.9266 1.000 0.000
#> SRR1656545 2 0.0000 0.9468 0.000 1.000
#> SRR1656540 1 0.0000 0.9266 1.000 0.000
#> SRR1656546 1 0.3584 0.8931 0.932 0.068
#> SRR1656541 2 0.0000 0.9468 0.000 1.000
#> SRR1656547 2 0.9970 -0.0244 0.468 0.532
#> SRR1656548 1 0.0000 0.9266 1.000 0.000
#> SRR1656549 1 0.1414 0.9194 0.980 0.020
#> SRR1656551 1 0.2948 0.9042 0.948 0.052
#> SRR1656553 1 0.0000 0.9266 1.000 0.000
#> SRR1656550 1 0.9000 0.6298 0.684 0.316
#> SRR1656552 1 0.8267 0.7132 0.740 0.260
#> SRR1656554 1 0.0000 0.9266 1.000 0.000
#> SRR1656555 1 0.6343 0.8228 0.840 0.160
#> SRR1656556 1 0.6531 0.8143 0.832 0.168
#> SRR1656557 1 0.0000 0.9266 1.000 0.000
#> SRR1656558 1 0.0000 0.9266 1.000 0.000
#> SRR1656559 1 0.0000 0.9266 1.000 0.000
#> SRR1656560 1 0.0000 0.9266 1.000 0.000
#> SRR1656561 1 0.0000 0.9266 1.000 0.000
#> SRR1656562 1 0.8713 0.6679 0.708 0.292
#> SRR1656563 1 0.0000 0.9266 1.000 0.000
#> SRR1656564 2 0.0000 0.9468 0.000 1.000
#> SRR1656565 2 0.2603 0.9136 0.044 0.956
#> SRR1656566 1 0.0000 0.9266 1.000 0.000
#> SRR1656568 2 0.0000 0.9468 0.000 1.000
#> SRR1656567 1 0.9000 0.6298 0.684 0.316
#> SRR1656569 1 0.0000 0.9266 1.000 0.000
#> SRR1656570 1 0.0000 0.9266 1.000 0.000
#> SRR1656571 2 0.0000 0.9468 0.000 1.000
#> SRR1656573 1 0.2423 0.9110 0.960 0.040
#> SRR1656572 1 0.8909 0.6428 0.692 0.308
#> SRR1656574 1 0.0000 0.9266 1.000 0.000
#> SRR1656575 1 0.0000 0.9266 1.000 0.000
#> SRR1656576 1 0.9044 0.6218 0.680 0.320
#> SRR1656578 2 0.1184 0.9388 0.016 0.984
#> SRR1656577 1 0.0000 0.9266 1.000 0.000
#> SRR1656579 2 0.9944 0.0226 0.456 0.544
#> SRR1656580 1 0.0000 0.9266 1.000 0.000
#> SRR1656581 1 0.7883 0.7435 0.764 0.236
#> SRR1656582 2 0.0000 0.9468 0.000 1.000
#> SRR1656585 1 0.8081 0.7295 0.752 0.248
#> SRR1656584 1 0.0000 0.9266 1.000 0.000
#> SRR1656583 1 0.8386 0.7029 0.732 0.268
#> SRR1656586 2 0.0000 0.9468 0.000 1.000
#> SRR1656587 1 0.8081 0.7295 0.752 0.248
#> SRR1656588 1 0.9358 0.5596 0.648 0.352
#> SRR1656589 2 0.0000 0.9468 0.000 1.000
#> SRR1656590 1 0.0000 0.9266 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656464 1 0.4121 0.757 0.832 0.000 0.168
#> SRR1656462 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656465 1 0.3551 0.802 0.868 0.000 0.132
#> SRR1656467 2 0.5431 0.695 0.000 0.716 0.284
#> SRR1656466 1 0.1163 0.881 0.972 0.000 0.028
#> SRR1656468 3 0.4056 0.746 0.092 0.032 0.876
#> SRR1656472 3 0.5016 0.665 0.240 0.000 0.760
#> SRR1656471 1 0.4654 0.723 0.792 0.000 0.208
#> SRR1656470 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656469 1 0.4750 0.711 0.784 0.000 0.216
#> SRR1656473 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656478 1 0.3551 0.828 0.868 0.000 0.132
#> SRR1656477 3 0.2550 0.718 0.012 0.056 0.932
#> SRR1656479 1 0.5497 0.590 0.708 0.000 0.292
#> SRR1656480 3 0.2356 0.708 0.000 0.072 0.928
#> SRR1656476 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656481 3 0.4068 0.741 0.120 0.016 0.864
#> SRR1656482 2 0.2165 0.926 0.000 0.936 0.064
#> SRR1656483 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656485 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656487 1 0.1289 0.880 0.968 0.000 0.032
#> SRR1656486 1 0.3619 0.829 0.864 0.000 0.136
#> SRR1656488 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656484 1 0.3752 0.819 0.856 0.000 0.144
#> SRR1656489 1 0.1411 0.882 0.964 0.000 0.036
#> SRR1656491 1 0.5678 0.525 0.684 0.000 0.316
#> SRR1656490 1 0.5678 0.538 0.684 0.000 0.316
#> SRR1656492 1 0.0424 0.888 0.992 0.000 0.008
#> SRR1656493 3 0.5810 0.527 0.336 0.000 0.664
#> SRR1656495 3 0.5733 0.543 0.324 0.000 0.676
#> SRR1656496 1 0.5327 0.628 0.728 0.000 0.272
#> SRR1656494 2 0.3941 0.865 0.000 0.844 0.156
#> SRR1656497 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656501 1 0.3619 0.829 0.864 0.000 0.136
#> SRR1656498 3 0.6299 0.206 0.476 0.000 0.524
#> SRR1656504 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656502 3 0.5016 0.665 0.240 0.000 0.760
#> SRR1656503 1 0.1163 0.883 0.972 0.000 0.028
#> SRR1656507 1 0.3551 0.828 0.868 0.000 0.132
#> SRR1656508 1 0.3619 0.825 0.864 0.000 0.136
#> SRR1656505 3 0.2356 0.709 0.000 0.072 0.928
#> SRR1656506 1 0.2165 0.863 0.936 0.000 0.064
#> SRR1656509 3 0.1964 0.740 0.056 0.000 0.944
#> SRR1656510 3 0.5536 0.681 0.236 0.012 0.752
#> SRR1656511 3 0.3695 0.702 0.012 0.108 0.880
#> SRR1656513 2 0.3816 0.871 0.000 0.852 0.148
#> SRR1656512 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656514 1 0.0424 0.887 0.992 0.000 0.008
#> SRR1656515 3 0.6302 -0.150 0.000 0.480 0.520
#> SRR1656516 1 0.0747 0.887 0.984 0.000 0.016
#> SRR1656518 1 0.3619 0.829 0.864 0.000 0.136
#> SRR1656517 1 0.3267 0.842 0.884 0.000 0.116
#> SRR1656519 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656523 3 0.4179 0.743 0.072 0.052 0.876
#> SRR1656521 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656520 1 0.0424 0.887 0.992 0.000 0.008
#> SRR1656524 3 0.5733 0.543 0.324 0.000 0.676
#> SRR1656525 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656526 2 0.1964 0.930 0.000 0.944 0.056
#> SRR1656527 2 0.3619 0.880 0.000 0.864 0.136
#> SRR1656530 1 0.1163 0.881 0.972 0.000 0.028
#> SRR1656529 1 0.1753 0.873 0.952 0.000 0.048
#> SRR1656531 3 0.6180 0.383 0.416 0.000 0.584
#> SRR1656528 1 0.0237 0.886 0.996 0.000 0.004
#> SRR1656534 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656533 1 0.3267 0.842 0.884 0.000 0.116
#> SRR1656536 1 0.5678 0.531 0.684 0.000 0.316
#> SRR1656532 2 0.3816 0.871 0.000 0.852 0.148
#> SRR1656537 3 0.6295 0.211 0.472 0.000 0.528
#> SRR1656538 1 0.0747 0.887 0.984 0.000 0.016
#> SRR1656535 2 0.0237 0.947 0.000 0.996 0.004
#> SRR1656539 1 0.5650 0.540 0.688 0.000 0.312
#> SRR1656544 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656542 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656543 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656540 1 0.0424 0.887 0.992 0.000 0.008
#> SRR1656546 3 0.5465 0.602 0.288 0.000 0.712
#> SRR1656541 2 0.1964 0.930 0.000 0.944 0.056
#> SRR1656547 3 0.6307 0.368 0.012 0.328 0.660
#> SRR1656548 1 0.0424 0.887 0.992 0.000 0.008
#> SRR1656549 3 0.6079 0.441 0.388 0.000 0.612
#> SRR1656551 1 0.5678 0.531 0.684 0.000 0.316
#> SRR1656553 1 0.1163 0.883 0.972 0.000 0.028
#> SRR1656550 3 0.2448 0.706 0.000 0.076 0.924
#> SRR1656552 3 0.4914 0.732 0.068 0.088 0.844
#> SRR1656554 1 0.2165 0.863 0.936 0.000 0.064
#> SRR1656555 3 0.4741 0.732 0.152 0.020 0.828
#> SRR1656556 3 0.6495 0.158 0.460 0.004 0.536
#> SRR1656557 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656558 1 0.3551 0.828 0.868 0.000 0.132
#> SRR1656559 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656560 1 0.1163 0.881 0.972 0.000 0.028
#> SRR1656561 1 0.0424 0.887 0.992 0.000 0.008
#> SRR1656562 3 0.2845 0.719 0.012 0.068 0.920
#> SRR1656563 1 0.3686 0.822 0.860 0.000 0.140
#> SRR1656564 2 0.0424 0.947 0.000 0.992 0.008
#> SRR1656565 2 0.4121 0.850 0.000 0.832 0.168
#> SRR1656566 3 0.6180 0.377 0.416 0.000 0.584
#> SRR1656568 2 0.0424 0.947 0.000 0.992 0.008
#> SRR1656567 3 0.2448 0.706 0.000 0.076 0.924
#> SRR1656569 1 0.2165 0.863 0.936 0.000 0.064
#> SRR1656570 1 0.3686 0.822 0.860 0.000 0.140
#> SRR1656571 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656573 1 0.6057 0.472 0.656 0.004 0.340
#> SRR1656572 3 0.3695 0.702 0.012 0.108 0.880
#> SRR1656574 1 0.2796 0.856 0.908 0.000 0.092
#> SRR1656575 1 0.3619 0.829 0.864 0.000 0.136
#> SRR1656576 3 0.3038 0.696 0.000 0.104 0.896
#> SRR1656578 2 0.3816 0.871 0.000 0.852 0.148
#> SRR1656577 1 0.1031 0.884 0.976 0.000 0.024
#> SRR1656579 3 0.5733 0.364 0.000 0.324 0.676
#> SRR1656580 1 0.0747 0.887 0.984 0.000 0.016
#> SRR1656581 3 0.4179 0.743 0.072 0.052 0.876
#> SRR1656582 2 0.1860 0.932 0.000 0.948 0.052
#> SRR1656585 3 0.2280 0.738 0.052 0.008 0.940
#> SRR1656584 3 0.6180 0.377 0.416 0.000 0.584
#> SRR1656583 3 0.2443 0.730 0.032 0.028 0.940
#> SRR1656586 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656587 3 0.2280 0.738 0.052 0.008 0.940
#> SRR1656588 3 0.3192 0.680 0.000 0.112 0.888
#> SRR1656589 2 0.0000 0.948 0.000 1.000 0.000
#> SRR1656590 3 0.6291 0.218 0.468 0.000 0.532
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656464 3 0.5085 0.5314 0.304 0.000 0.676 0.020
#> SRR1656462 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656465 3 0.3266 0.7455 0.000 0.000 0.832 0.168
#> SRR1656467 2 0.4454 0.6080 0.000 0.692 0.000 0.308
#> SRR1656466 3 0.1022 0.8166 0.000 0.000 0.968 0.032
#> SRR1656468 4 0.2179 0.8089 0.000 0.012 0.064 0.924
#> SRR1656472 1 0.4284 0.6440 0.780 0.000 0.020 0.200
#> SRR1656471 3 0.4262 0.6831 0.008 0.000 0.756 0.236
#> SRR1656470 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.4262 0.6654 0.008 0.000 0.756 0.236
#> SRR1656473 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656478 3 0.4500 0.5912 0.316 0.000 0.684 0.000
#> SRR1656477 4 0.1109 0.8341 0.000 0.028 0.004 0.968
#> SRR1656479 3 0.6501 0.5357 0.116 0.000 0.616 0.268
#> SRR1656480 4 0.1302 0.8358 0.000 0.044 0.000 0.956
#> SRR1656476 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.2081 0.7811 0.000 0.000 0.084 0.916
#> SRR1656482 2 0.1867 0.9129 0.000 0.928 0.000 0.072
#> SRR1656483 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.1118 0.8165 0.000 0.000 0.964 0.036
#> SRR1656486 3 0.4564 0.5803 0.328 0.000 0.672 0.000
#> SRR1656488 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656484 3 0.4722 0.6123 0.300 0.000 0.692 0.008
#> SRR1656489 3 0.2469 0.7861 0.108 0.000 0.892 0.000
#> SRR1656491 3 0.5496 0.5415 0.036 0.000 0.652 0.312
#> SRR1656490 3 0.6634 0.4961 0.116 0.000 0.592 0.292
#> SRR1656492 3 0.0927 0.8230 0.016 0.000 0.976 0.008
#> SRR1656493 1 0.1743 0.8413 0.940 0.000 0.056 0.004
#> SRR1656495 1 0.0000 0.8055 1.000 0.000 0.000 0.000
#> SRR1656496 3 0.6448 0.5572 0.120 0.000 0.628 0.252
#> SRR1656494 2 0.3577 0.8439 0.012 0.832 0.000 0.156
#> SRR1656497 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656501 3 0.4564 0.5803 0.328 0.000 0.672 0.000
#> SRR1656498 1 0.3688 0.7955 0.792 0.000 0.208 0.000
#> SRR1656504 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.4284 0.6440 0.780 0.000 0.020 0.200
#> SRR1656503 3 0.1302 0.8144 0.044 0.000 0.956 0.000
#> SRR1656507 3 0.4500 0.5912 0.316 0.000 0.684 0.000
#> SRR1656508 3 0.4431 0.6065 0.304 0.000 0.696 0.000
#> SRR1656505 4 0.1302 0.8354 0.000 0.044 0.000 0.956
#> SRR1656506 3 0.1902 0.7991 0.004 0.000 0.932 0.064
#> SRR1656509 4 0.1854 0.8121 0.048 0.000 0.012 0.940
#> SRR1656510 4 0.5172 0.6308 0.068 0.000 0.188 0.744
#> SRR1656511 4 0.2412 0.8261 0.008 0.084 0.000 0.908
#> SRR1656513 2 0.3479 0.8510 0.012 0.840 0.000 0.148
#> SRR1656512 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.2256 0.8073 0.056 0.000 0.924 0.020
#> SRR1656515 4 0.4967 0.1360 0.000 0.452 0.000 0.548
#> SRR1656516 3 0.1022 0.8187 0.032 0.000 0.968 0.000
#> SRR1656518 3 0.4564 0.5803 0.328 0.000 0.672 0.000
#> SRR1656517 3 0.4331 0.6292 0.288 0.000 0.712 0.000
#> SRR1656519 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.0188 0.8213 0.004 0.000 0.996 0.000
#> SRR1656523 4 0.3316 0.8157 0.064 0.028 0.020 0.888
#> SRR1656521 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.1488 0.8169 0.032 0.000 0.956 0.012
#> SRR1656524 1 0.0000 0.8055 1.000 0.000 0.000 0.000
#> SRR1656525 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.1792 0.9152 0.000 0.932 0.000 0.068
#> SRR1656527 2 0.3324 0.8613 0.012 0.852 0.000 0.136
#> SRR1656530 3 0.1022 0.8166 0.000 0.000 0.968 0.032
#> SRR1656529 3 0.1474 0.8073 0.000 0.000 0.948 0.052
#> SRR1656531 1 0.2469 0.8625 0.892 0.000 0.108 0.000
#> SRR1656528 3 0.0336 0.8205 0.000 0.000 0.992 0.008
#> SRR1656534 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656533 3 0.4382 0.6206 0.296 0.000 0.704 0.000
#> SRR1656536 3 0.4917 0.5363 0.008 0.000 0.656 0.336
#> SRR1656532 2 0.3479 0.8510 0.012 0.840 0.000 0.148
#> SRR1656537 1 0.3356 0.8300 0.824 0.000 0.176 0.000
#> SRR1656538 3 0.1022 0.8187 0.032 0.000 0.968 0.000
#> SRR1656535 2 0.0188 0.9378 0.000 0.996 0.000 0.004
#> SRR1656539 3 0.4897 0.5432 0.008 0.000 0.660 0.332
#> SRR1656544 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.1488 0.8169 0.032 0.000 0.956 0.012
#> SRR1656546 4 0.6718 0.1328 0.380 0.000 0.096 0.524
#> SRR1656541 2 0.1792 0.9152 0.000 0.932 0.000 0.068
#> SRR1656547 4 0.4722 0.5603 0.008 0.300 0.000 0.692
#> SRR1656548 3 0.0707 0.8212 0.020 0.000 0.980 0.000
#> SRR1656549 1 0.3659 0.8580 0.840 0.000 0.136 0.024
#> SRR1656551 3 0.4917 0.5363 0.008 0.000 0.656 0.336
#> SRR1656553 3 0.1302 0.8144 0.044 0.000 0.956 0.000
#> SRR1656550 4 0.1389 0.8356 0.000 0.048 0.000 0.952
#> SRR1656552 4 0.4003 0.8096 0.072 0.064 0.012 0.852
#> SRR1656554 3 0.1902 0.7991 0.004 0.000 0.932 0.064
#> SRR1656555 4 0.3272 0.7481 0.004 0.008 0.128 0.860
#> SRR1656556 4 0.5088 0.0852 0.004 0.000 0.424 0.572
#> SRR1656557 3 0.0000 0.8213 0.000 0.000 1.000 0.000
#> SRR1656558 3 0.4500 0.5912 0.316 0.000 0.684 0.000
#> SRR1656559 3 0.0188 0.8213 0.004 0.000 0.996 0.000
#> SRR1656560 3 0.1022 0.8166 0.000 0.000 0.968 0.032
#> SRR1656561 3 0.0707 0.8212 0.020 0.000 0.980 0.000
#> SRR1656562 4 0.1635 0.8365 0.008 0.044 0.000 0.948
#> SRR1656563 3 0.4522 0.5871 0.320 0.000 0.680 0.000
#> SRR1656564 2 0.0592 0.9355 0.000 0.984 0.000 0.016
#> SRR1656565 2 0.3636 0.8274 0.008 0.820 0.000 0.172
#> SRR1656566 1 0.2814 0.8636 0.868 0.000 0.132 0.000
#> SRR1656568 2 0.0592 0.9355 0.000 0.984 0.000 0.016
#> SRR1656567 4 0.1389 0.8356 0.000 0.048 0.000 0.952
#> SRR1656569 3 0.1902 0.7991 0.004 0.000 0.932 0.064
#> SRR1656570 3 0.4522 0.5871 0.320 0.000 0.680 0.000
#> SRR1656571 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656573 3 0.6602 0.4703 0.088 0.004 0.584 0.324
#> SRR1656572 4 0.2412 0.8261 0.008 0.084 0.000 0.908
#> SRR1656574 3 0.4040 0.6712 0.248 0.000 0.752 0.000
#> SRR1656575 3 0.4564 0.5803 0.328 0.000 0.672 0.000
#> SRR1656576 4 0.2011 0.8289 0.000 0.080 0.000 0.920
#> SRR1656578 2 0.3479 0.8510 0.012 0.840 0.000 0.148
#> SRR1656577 3 0.1389 0.8139 0.048 0.000 0.952 0.000
#> SRR1656579 4 0.4382 0.5697 0.000 0.296 0.000 0.704
#> SRR1656580 3 0.1022 0.8187 0.032 0.000 0.968 0.000
#> SRR1656581 4 0.3316 0.8157 0.064 0.028 0.020 0.888
#> SRR1656582 2 0.1716 0.9169 0.000 0.936 0.000 0.064
#> SRR1656585 4 0.1953 0.8168 0.044 0.004 0.012 0.940
#> SRR1656584 1 0.2973 0.8596 0.856 0.000 0.144 0.000
#> SRR1656583 4 0.2284 0.8259 0.036 0.020 0.012 0.932
#> SRR1656586 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.1953 0.8168 0.044 0.004 0.012 0.940
#> SRR1656588 4 0.2081 0.8230 0.000 0.084 0.000 0.916
#> SRR1656589 2 0.0000 0.9388 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.3494 0.8342 0.824 0.000 0.172 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.5577 0.2882 0.256 0.000 0.624 0.000 0.120
#> SRR1656462 3 0.0404 0.5755 0.000 0.000 0.988 0.000 0.012
#> SRR1656465 3 0.5939 -0.4118 0.000 0.000 0.492 0.108 0.400
#> SRR1656467 2 0.4907 0.6090 0.000 0.664 0.000 0.280 0.056
#> SRR1656466 3 0.4497 -0.0183 0.000 0.000 0.632 0.016 0.352
#> SRR1656468 4 0.2574 0.7368 0.000 0.000 0.012 0.876 0.112
#> SRR1656472 1 0.5731 0.6217 0.560 0.000 0.004 0.084 0.352
#> SRR1656471 3 0.6299 -0.5625 0.000 0.000 0.432 0.152 0.416
#> SRR1656470 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 3 0.6439 -0.6850 0.004 0.000 0.432 0.152 0.412
#> SRR1656473 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 3 0.4682 0.3954 0.420 0.000 0.564 0.000 0.016
#> SRR1656477 4 0.1522 0.7679 0.000 0.012 0.000 0.944 0.044
#> SRR1656479 5 0.7811 0.8273 0.104 0.000 0.304 0.164 0.428
#> SRR1656480 4 0.1661 0.7707 0.000 0.024 0.000 0.940 0.036
#> SRR1656476 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 4 0.2818 0.7071 0.000 0.000 0.012 0.856 0.132
#> SRR1656482 2 0.2304 0.9050 0.000 0.908 0.000 0.044 0.048
#> SRR1656483 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.1121 0.5524 0.000 0.000 0.956 0.000 0.044
#> SRR1656487 3 0.4511 -0.0275 0.000 0.000 0.628 0.016 0.356
#> SRR1656486 3 0.5195 0.4099 0.420 0.000 0.536 0.000 0.044
#> SRR1656488 3 0.3074 0.3813 0.000 0.000 0.804 0.000 0.196
#> SRR1656484 3 0.5510 0.4133 0.380 0.000 0.548 0.000 0.072
#> SRR1656489 3 0.3759 0.5621 0.136 0.000 0.808 0.000 0.056
#> SRR1656491 5 0.7041 0.8461 0.020 0.000 0.336 0.208 0.436
#> SRR1656490 5 0.7896 0.8356 0.104 0.000 0.284 0.188 0.424
#> SRR1656492 3 0.4809 0.1717 0.036 0.000 0.664 0.004 0.296
#> SRR1656493 1 0.3565 0.8124 0.800 0.000 0.024 0.000 0.176
#> SRR1656495 1 0.3109 0.7949 0.800 0.000 0.000 0.000 0.200
#> SRR1656496 5 0.7902 0.7976 0.120 0.000 0.308 0.156 0.416
#> SRR1656494 2 0.4016 0.8336 0.000 0.796 0.000 0.112 0.092
#> SRR1656497 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.2966 0.3982 0.000 0.000 0.816 0.000 0.184
#> SRR1656500 3 0.0510 0.5693 0.000 0.000 0.984 0.000 0.016
#> SRR1656501 3 0.5195 0.4099 0.420 0.000 0.536 0.000 0.044
#> SRR1656498 1 0.3719 0.7806 0.816 0.000 0.116 0.000 0.068
#> SRR1656504 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.5731 0.6217 0.560 0.000 0.004 0.084 0.352
#> SRR1656503 3 0.2863 0.5783 0.064 0.000 0.876 0.000 0.060
#> SRR1656507 3 0.4682 0.3954 0.420 0.000 0.564 0.000 0.016
#> SRR1656508 3 0.4649 0.4208 0.404 0.000 0.580 0.000 0.016
#> SRR1656505 4 0.0912 0.7728 0.000 0.016 0.000 0.972 0.012
#> SRR1656506 3 0.4757 -0.1685 0.000 0.000 0.596 0.024 0.380
#> SRR1656509 4 0.3912 0.7033 0.020 0.000 0.004 0.768 0.208
#> SRR1656510 4 0.6309 0.5010 0.052 0.000 0.148 0.640 0.160
#> SRR1656511 4 0.3460 0.7556 0.000 0.044 0.000 0.828 0.128
#> SRR1656513 2 0.3970 0.8381 0.000 0.800 0.000 0.104 0.096
#> SRR1656512 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.2659 0.5622 0.060 0.000 0.888 0.000 0.052
#> SRR1656515 4 0.5320 0.0997 0.000 0.424 0.000 0.524 0.052
#> SRR1656516 3 0.3043 0.5745 0.080 0.000 0.864 0.000 0.056
#> SRR1656518 3 0.5195 0.4099 0.420 0.000 0.536 0.000 0.044
#> SRR1656517 3 0.4620 0.4366 0.392 0.000 0.592 0.000 0.016
#> SRR1656519 3 0.0404 0.5755 0.000 0.000 0.988 0.000 0.012
#> SRR1656522 3 0.1168 0.5819 0.032 0.000 0.960 0.000 0.008
#> SRR1656523 4 0.3386 0.7486 0.040 0.000 0.000 0.832 0.128
#> SRR1656521 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.1648 0.5728 0.020 0.000 0.940 0.000 0.040
#> SRR1656524 1 0.3109 0.7949 0.800 0.000 0.000 0.000 0.200
#> SRR1656525 3 0.2930 0.4293 0.004 0.000 0.832 0.000 0.164
#> SRR1656526 2 0.2228 0.9069 0.000 0.912 0.000 0.040 0.048
#> SRR1656527 2 0.3806 0.8455 0.000 0.812 0.000 0.104 0.084
#> SRR1656530 3 0.4497 -0.0183 0.000 0.000 0.632 0.016 0.352
#> SRR1656529 3 0.4613 -0.0802 0.000 0.000 0.620 0.020 0.360
#> SRR1656531 1 0.3991 0.8257 0.780 0.000 0.048 0.000 0.172
#> SRR1656528 3 0.3689 0.2772 0.000 0.000 0.740 0.004 0.256
#> SRR1656534 3 0.0404 0.5755 0.000 0.000 0.988 0.000 0.012
#> SRR1656533 3 0.4640 0.4278 0.400 0.000 0.584 0.000 0.016
#> SRR1656536 5 0.6761 0.8486 0.004 0.000 0.336 0.228 0.432
#> SRR1656532 2 0.3970 0.8381 0.000 0.800 0.000 0.104 0.096
#> SRR1656537 1 0.3301 0.8081 0.848 0.000 0.080 0.000 0.072
#> SRR1656538 3 0.3043 0.5745 0.080 0.000 0.864 0.000 0.056
#> SRR1656535 2 0.0162 0.9312 0.000 0.996 0.000 0.000 0.004
#> SRR1656539 5 0.6751 0.8449 0.004 0.000 0.340 0.224 0.432
#> SRR1656544 3 0.1205 0.5584 0.004 0.000 0.956 0.000 0.040
#> SRR1656542 3 0.1205 0.5584 0.004 0.000 0.956 0.000 0.040
#> SRR1656543 3 0.0404 0.5755 0.000 0.000 0.988 0.000 0.012
#> SRR1656545 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.1725 0.5713 0.020 0.000 0.936 0.000 0.044
#> SRR1656546 4 0.7057 0.0713 0.376 0.000 0.036 0.436 0.152
#> SRR1656541 2 0.2228 0.9069 0.000 0.912 0.000 0.040 0.048
#> SRR1656547 4 0.5136 0.5345 0.000 0.260 0.000 0.660 0.080
#> SRR1656548 3 0.3051 0.5667 0.060 0.000 0.864 0.000 0.076
#> SRR1656549 1 0.2575 0.8156 0.904 0.000 0.044 0.016 0.036
#> SRR1656551 5 0.6761 0.8486 0.004 0.000 0.336 0.228 0.432
#> SRR1656553 3 0.2863 0.5783 0.064 0.000 0.876 0.000 0.060
#> SRR1656550 4 0.0992 0.7722 0.000 0.024 0.000 0.968 0.008
#> SRR1656552 4 0.4733 0.7299 0.052 0.028 0.000 0.756 0.164
#> SRR1656554 3 0.4757 -0.1685 0.000 0.000 0.596 0.024 0.380
#> SRR1656555 4 0.4499 0.6688 0.004 0.000 0.096 0.764 0.136
#> SRR1656556 4 0.6253 -0.4238 0.000 0.000 0.148 0.464 0.388
#> SRR1656557 3 0.0404 0.5755 0.000 0.000 0.988 0.000 0.012
#> SRR1656558 3 0.4682 0.3954 0.420 0.000 0.564 0.000 0.016
#> SRR1656559 3 0.1168 0.5819 0.032 0.000 0.960 0.000 0.008
#> SRR1656560 3 0.4497 -0.0183 0.000 0.000 0.632 0.016 0.352
#> SRR1656561 3 0.3051 0.5667 0.060 0.000 0.864 0.000 0.076
#> SRR1656562 4 0.2886 0.7680 0.004 0.016 0.000 0.864 0.116
#> SRR1656563 3 0.4689 0.3935 0.424 0.000 0.560 0.000 0.016
#> SRR1656564 2 0.1106 0.9246 0.000 0.964 0.000 0.024 0.012
#> SRR1656565 2 0.4158 0.8192 0.000 0.784 0.000 0.124 0.092
#> SRR1656566 1 0.1830 0.8209 0.932 0.000 0.040 0.000 0.028
#> SRR1656568 2 0.1106 0.9246 0.000 0.964 0.000 0.024 0.012
#> SRR1656567 4 0.0992 0.7722 0.000 0.024 0.000 0.968 0.008
#> SRR1656569 3 0.4757 -0.1685 0.000 0.000 0.596 0.024 0.380
#> SRR1656570 3 0.4689 0.3935 0.424 0.000 0.560 0.000 0.016
#> SRR1656571 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.7679 0.8434 0.072 0.000 0.272 0.216 0.440
#> SRR1656572 4 0.3460 0.7556 0.000 0.044 0.000 0.828 0.128
#> SRR1656574 3 0.4467 0.4673 0.344 0.000 0.640 0.000 0.016
#> SRR1656575 3 0.5195 0.4099 0.420 0.000 0.536 0.000 0.044
#> SRR1656576 4 0.3184 0.7630 0.000 0.048 0.000 0.852 0.100
#> SRR1656578 2 0.3970 0.8381 0.000 0.800 0.000 0.104 0.096
#> SRR1656577 3 0.2193 0.5791 0.092 0.000 0.900 0.000 0.008
#> SRR1656579 4 0.4754 0.5433 0.000 0.264 0.000 0.684 0.052
#> SRR1656580 3 0.2616 0.5807 0.076 0.000 0.888 0.000 0.036
#> SRR1656581 4 0.3386 0.7486 0.040 0.000 0.000 0.832 0.128
#> SRR1656582 2 0.2209 0.9079 0.000 0.912 0.000 0.032 0.056
#> SRR1656585 4 0.3846 0.7088 0.020 0.000 0.004 0.776 0.200
#> SRR1656584 1 0.1893 0.8141 0.928 0.000 0.048 0.000 0.024
#> SRR1656583 4 0.3631 0.7256 0.012 0.004 0.004 0.800 0.180
#> SRR1656586 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.3846 0.7088 0.020 0.000 0.004 0.776 0.200
#> SRR1656588 4 0.1697 0.7681 0.000 0.060 0.000 0.932 0.008
#> SRR1656589 2 0.0000 0.9321 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.3421 0.8092 0.840 0.000 0.080 0.000 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.5536 0.3770 0.180 0.000 0.632 0.000 0.028 0.160
#> SRR1656462 3 0.2263 0.6000 0.000 0.000 0.896 0.000 0.048 0.056
#> SRR1656465 5 0.4515 0.7409 0.000 0.000 0.192 0.072 0.720 0.016
#> SRR1656467 2 0.4724 0.5964 0.000 0.656 0.000 0.276 0.012 0.056
#> SRR1656466 5 0.3784 0.7177 0.000 0.000 0.308 0.000 0.680 0.012
#> SRR1656468 4 0.2491 0.7153 0.000 0.000 0.000 0.836 0.164 0.000
#> SRR1656472 6 0.4212 1.0000 0.424 0.000 0.000 0.000 0.016 0.560
#> SRR1656471 5 0.4610 0.7262 0.000 0.000 0.152 0.100 0.728 0.020
#> SRR1656470 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.4105 0.7524 0.000 0.000 0.152 0.080 0.760 0.008
#> SRR1656473 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 3 0.4285 0.4063 0.432 0.000 0.552 0.000 0.008 0.008
#> SRR1656477 4 0.1787 0.7291 0.000 0.004 0.000 0.920 0.068 0.008
#> SRR1656479 5 0.5696 0.6480 0.088 0.000 0.092 0.060 0.700 0.060
#> SRR1656480 4 0.1976 0.7312 0.000 0.016 0.000 0.916 0.060 0.008
#> SRR1656476 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.2838 0.6754 0.000 0.000 0.000 0.808 0.188 0.004
#> SRR1656482 2 0.2201 0.8969 0.000 0.900 0.000 0.048 0.000 0.052
#> SRR1656483 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.3493 0.4969 0.000 0.000 0.796 0.000 0.148 0.056
#> SRR1656487 5 0.3766 0.7203 0.000 0.000 0.304 0.000 0.684 0.012
#> SRR1656486 3 0.4962 0.3929 0.428 0.000 0.516 0.000 0.048 0.008
#> SRR1656488 3 0.4788 -0.1765 0.000 0.000 0.548 0.000 0.396 0.056
#> SRR1656484 3 0.5851 0.3982 0.356 0.000 0.516 0.000 0.092 0.036
#> SRR1656489 3 0.3886 0.6253 0.124 0.000 0.784 0.000 0.084 0.008
#> SRR1656491 5 0.5164 0.7029 0.024 0.000 0.124 0.100 0.720 0.032
#> SRR1656490 5 0.5954 0.6298 0.088 0.000 0.088 0.084 0.680 0.060
#> SRR1656492 5 0.4963 0.5733 0.044 0.000 0.364 0.000 0.576 0.016
#> SRR1656493 1 0.3742 0.3366 0.788 0.000 0.008 0.000 0.056 0.148
#> SRR1656495 1 0.3802 0.1588 0.748 0.000 0.000 0.000 0.044 0.208
#> SRR1656496 5 0.5807 0.6378 0.104 0.000 0.096 0.056 0.688 0.056
#> SRR1656494 2 0.4037 0.8227 0.000 0.784 0.000 0.108 0.020 0.088
#> SRR1656497 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.4707 -0.0683 0.000 0.000 0.584 0.000 0.360 0.056
#> SRR1656500 3 0.3078 0.5526 0.000 0.000 0.836 0.000 0.108 0.056
#> SRR1656501 3 0.4962 0.3929 0.428 0.000 0.516 0.000 0.048 0.008
#> SRR1656498 1 0.3838 0.5467 0.784 0.000 0.116 0.000 0.004 0.096
#> SRR1656504 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.4212 1.0000 0.424 0.000 0.000 0.000 0.016 0.560
#> SRR1656503 3 0.2763 0.6191 0.036 0.000 0.868 0.000 0.088 0.008
#> SRR1656507 3 0.4285 0.4063 0.432 0.000 0.552 0.000 0.008 0.008
#> SRR1656508 3 0.4261 0.4267 0.416 0.000 0.568 0.000 0.008 0.008
#> SRR1656505 4 0.0665 0.7348 0.000 0.008 0.000 0.980 0.008 0.004
#> SRR1656506 5 0.3309 0.7425 0.000 0.000 0.280 0.000 0.720 0.000
#> SRR1656509 4 0.5104 0.5724 0.004 0.000 0.000 0.628 0.120 0.248
#> SRR1656510 4 0.7885 0.3094 0.040 0.000 0.108 0.376 0.196 0.280
#> SRR1656511 4 0.4279 0.7106 0.000 0.036 0.000 0.772 0.080 0.112
#> SRR1656513 2 0.3995 0.8272 0.000 0.788 0.000 0.100 0.020 0.092
#> SRR1656512 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.3066 0.6235 0.024 0.000 0.860 0.000 0.056 0.060
#> SRR1656515 4 0.5169 0.1047 0.000 0.416 0.000 0.516 0.016 0.052
#> SRR1656516 3 0.3099 0.6304 0.060 0.000 0.848 0.000 0.084 0.008
#> SRR1656518 3 0.4962 0.3929 0.428 0.000 0.516 0.000 0.048 0.008
#> SRR1656517 3 0.4238 0.4372 0.404 0.000 0.580 0.000 0.008 0.008
#> SRR1656519 3 0.2263 0.6000 0.000 0.000 0.896 0.000 0.048 0.056
#> SRR1656522 3 0.1564 0.6346 0.024 0.000 0.936 0.000 0.040 0.000
#> SRR1656523 4 0.4487 0.6987 0.032 0.000 0.004 0.748 0.160 0.056
#> SRR1656521 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.2937 0.5989 0.000 0.000 0.848 0.000 0.056 0.096
#> SRR1656524 1 0.3802 0.1588 0.748 0.000 0.000 0.000 0.044 0.208
#> SRR1656525 3 0.4634 0.1310 0.004 0.000 0.640 0.000 0.300 0.056
#> SRR1656526 2 0.2278 0.8972 0.000 0.900 0.000 0.044 0.004 0.052
#> SRR1656527 2 0.3846 0.8352 0.000 0.800 0.000 0.100 0.020 0.080
#> SRR1656530 5 0.3784 0.7177 0.000 0.000 0.308 0.000 0.680 0.012
#> SRR1656529 5 0.3428 0.7261 0.000 0.000 0.304 0.000 0.696 0.000
#> SRR1656531 1 0.4133 0.3486 0.708 0.000 0.032 0.000 0.008 0.252
#> SRR1656528 5 0.4814 0.4932 0.000 0.000 0.412 0.000 0.532 0.056
#> SRR1656534 3 0.2263 0.6000 0.000 0.000 0.896 0.000 0.048 0.056
#> SRR1656533 3 0.4336 0.4299 0.408 0.000 0.572 0.000 0.012 0.008
#> SRR1656536 5 0.4587 0.6955 0.000 0.000 0.104 0.136 0.736 0.024
#> SRR1656532 2 0.3995 0.8272 0.000 0.788 0.000 0.100 0.020 0.092
#> SRR1656537 1 0.3605 0.5759 0.804 0.000 0.084 0.000 0.004 0.108
#> SRR1656538 3 0.3099 0.6304 0.060 0.000 0.848 0.000 0.084 0.008
#> SRR1656535 2 0.0146 0.9249 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1656539 5 0.4549 0.6980 0.000 0.000 0.104 0.132 0.740 0.024
#> SRR1656544 3 0.3416 0.5094 0.000 0.000 0.804 0.000 0.140 0.056
#> SRR1656542 3 0.3416 0.5094 0.000 0.000 0.804 0.000 0.140 0.056
#> SRR1656543 3 0.2390 0.5954 0.000 0.000 0.888 0.000 0.056 0.056
#> SRR1656545 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.2997 0.5983 0.000 0.000 0.844 0.000 0.060 0.096
#> SRR1656546 1 0.7423 0.0569 0.396 0.000 0.012 0.208 0.096 0.288
#> SRR1656541 2 0.2278 0.8972 0.000 0.900 0.000 0.044 0.004 0.052
#> SRR1656547 4 0.5052 0.5156 0.000 0.252 0.000 0.652 0.024 0.072
#> SRR1656548 3 0.3382 0.6023 0.048 0.000 0.820 0.000 0.124 0.008
#> SRR1656549 1 0.2074 0.5941 0.924 0.000 0.028 0.016 0.016 0.016
#> SRR1656551 5 0.4587 0.6955 0.000 0.000 0.104 0.136 0.736 0.024
#> SRR1656553 3 0.2763 0.6191 0.036 0.000 0.868 0.000 0.088 0.008
#> SRR1656550 4 0.1148 0.7323 0.000 0.016 0.000 0.960 0.020 0.004
#> SRR1656552 4 0.6754 0.4766 0.040 0.020 0.004 0.496 0.136 0.304
#> SRR1656554 5 0.3309 0.7425 0.000 0.000 0.280 0.000 0.720 0.000
#> SRR1656555 4 0.5129 0.6678 0.000 0.000 0.068 0.704 0.140 0.088
#> SRR1656556 5 0.4959 0.2463 0.000 0.000 0.020 0.380 0.564 0.036
#> SRR1656557 3 0.2263 0.6000 0.000 0.000 0.896 0.000 0.048 0.056
#> SRR1656558 3 0.4285 0.4063 0.432 0.000 0.552 0.000 0.008 0.008
#> SRR1656559 3 0.1564 0.6346 0.024 0.000 0.936 0.000 0.040 0.000
#> SRR1656560 5 0.3784 0.7177 0.000 0.000 0.308 0.000 0.680 0.012
#> SRR1656561 3 0.3382 0.6023 0.048 0.000 0.820 0.000 0.124 0.008
#> SRR1656562 4 0.3710 0.7271 0.000 0.012 0.000 0.804 0.076 0.108
#> SRR1656563 3 0.4375 0.4030 0.432 0.000 0.548 0.000 0.012 0.008
#> SRR1656564 2 0.1080 0.9181 0.000 0.960 0.000 0.032 0.004 0.004
#> SRR1656565 2 0.4092 0.8138 0.000 0.776 0.000 0.112 0.016 0.096
#> SRR1656566 1 0.1408 0.6074 0.944 0.000 0.036 0.000 0.000 0.020
#> SRR1656568 2 0.1080 0.9181 0.000 0.960 0.000 0.032 0.004 0.004
#> SRR1656567 4 0.1148 0.7323 0.000 0.016 0.000 0.960 0.020 0.004
#> SRR1656569 5 0.3309 0.7425 0.000 0.000 0.280 0.000 0.720 0.000
#> SRR1656570 3 0.4375 0.4030 0.432 0.000 0.548 0.000 0.012 0.008
#> SRR1656571 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.5634 0.6374 0.052 0.000 0.068 0.120 0.700 0.060
#> SRR1656572 4 0.4279 0.7106 0.000 0.036 0.000 0.772 0.080 0.112
#> SRR1656574 3 0.4278 0.4801 0.352 0.000 0.624 0.000 0.016 0.008
#> SRR1656575 3 0.4962 0.3929 0.428 0.000 0.516 0.000 0.048 0.008
#> SRR1656576 4 0.3779 0.7245 0.000 0.036 0.000 0.812 0.060 0.092
#> SRR1656578 2 0.3995 0.8272 0.000 0.788 0.000 0.100 0.020 0.092
#> SRR1656577 3 0.2679 0.6478 0.096 0.000 0.864 0.000 0.040 0.000
#> SRR1656579 4 0.4342 0.5283 0.000 0.252 0.000 0.692 0.004 0.052
#> SRR1656580 3 0.2765 0.6385 0.056 0.000 0.872 0.000 0.064 0.008
#> SRR1656581 4 0.4487 0.6987 0.032 0.000 0.004 0.748 0.160 0.056
#> SRR1656582 2 0.2138 0.9015 0.000 0.908 0.000 0.036 0.004 0.052
#> SRR1656585 4 0.5045 0.5785 0.004 0.000 0.000 0.636 0.116 0.244
#> SRR1656584 1 0.1124 0.6054 0.956 0.000 0.036 0.000 0.000 0.008
#> SRR1656583 4 0.4954 0.6031 0.004 0.004 0.000 0.660 0.100 0.232
#> SRR1656586 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 4 0.5045 0.5785 0.004 0.000 0.000 0.636 0.116 0.244
#> SRR1656588 4 0.1826 0.7274 0.000 0.052 0.000 0.924 0.020 0.004
#> SRR1656589 2 0.0000 0.9258 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.3693 0.5721 0.796 0.000 0.084 0.000 0.004 0.116
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 13572 rows and 129 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 0.983 0.956 0.982 0.4655 0.538 0.538
#> 3 3 0.726 0.831 0.904 0.3684 0.739 0.553
#> 4 4 0.764 0.851 0.908 0.1696 0.797 0.508
#> 5 5 0.763 0.766 0.857 0.0713 0.914 0.681
#> 6 6 0.791 0.667 0.824 0.0368 0.957 0.798
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
#> SRR1656463 2 0.0000 0.981 0.000 1.000
#> SRR1656464 1 0.0000 0.981 1.000 0.000
#> SRR1656462 1 0.0000 0.981 1.000 0.000
#> SRR1656465 1 0.0000 0.981 1.000 0.000
#> SRR1656467 2 0.0000 0.981 0.000 1.000
#> SRR1656466 1 0.0000 0.981 1.000 0.000
#> SRR1656468 1 0.9833 0.279 0.576 0.424
#> SRR1656472 1 0.0000 0.981 1.000 0.000
#> SRR1656471 1 0.0000 0.981 1.000 0.000
#> SRR1656470 2 0.0000 0.981 0.000 1.000
#> SRR1656469 1 0.0000 0.981 1.000 0.000
#> SRR1656473 2 0.0000 0.981 0.000 1.000
#> SRR1656474 2 0.0000 0.981 0.000 1.000
#> SRR1656475 2 0.0000 0.981 0.000 1.000
#> SRR1656478 1 0.0000 0.981 1.000 0.000
#> SRR1656477 1 0.9710 0.338 0.600 0.400
#> SRR1656479 1 0.0000 0.981 1.000 0.000
#> SRR1656480 2 0.0000 0.981 0.000 1.000
#> SRR1656476 2 0.0000 0.981 0.000 1.000
#> SRR1656481 1 0.7219 0.750 0.800 0.200
#> SRR1656482 2 0.0000 0.981 0.000 1.000
#> SRR1656483 2 0.0000 0.981 0.000 1.000
#> SRR1656485 1 0.0000 0.981 1.000 0.000
#> SRR1656487 1 0.0000 0.981 1.000 0.000
#> SRR1656486 1 0.0000 0.981 1.000 0.000
#> SRR1656488 1 0.0000 0.981 1.000 0.000
#> SRR1656484 1 0.0000 0.981 1.000 0.000
#> SRR1656489 1 0.0000 0.981 1.000 0.000
#> SRR1656491 1 0.0000 0.981 1.000 0.000
#> SRR1656490 1 0.0000 0.981 1.000 0.000
#> SRR1656492 1 0.0000 0.981 1.000 0.000
#> SRR1656493 1 0.0000 0.981 1.000 0.000
#> SRR1656495 2 0.6247 0.812 0.156 0.844
#> SRR1656496 1 0.0000 0.981 1.000 0.000
#> SRR1656494 2 0.0000 0.981 0.000 1.000
#> SRR1656497 2 0.0000 0.981 0.000 1.000
#> SRR1656499 1 0.0000 0.981 1.000 0.000
#> SRR1656500 1 0.0000 0.981 1.000 0.000
#> SRR1656501 1 0.0000 0.981 1.000 0.000
#> SRR1656498 1 0.0000 0.981 1.000 0.000
#> SRR1656504 2 0.0000 0.981 0.000 1.000
#> SRR1656502 1 0.0000 0.981 1.000 0.000
#> SRR1656503 1 0.0000 0.981 1.000 0.000
#> SRR1656507 1 0.0000 0.981 1.000 0.000
#> SRR1656508 1 0.0000 0.981 1.000 0.000
#> SRR1656505 2 0.0000 0.981 0.000 1.000
#> SRR1656506 1 0.0000 0.981 1.000 0.000
#> SRR1656509 1 0.0000 0.981 1.000 0.000
#> SRR1656510 1 0.4298 0.894 0.912 0.088
#> SRR1656511 2 0.0000 0.981 0.000 1.000
#> SRR1656513 2 0.0000 0.981 0.000 1.000
#> SRR1656512 2 0.0000 0.981 0.000 1.000
#> SRR1656514 1 0.0000 0.981 1.000 0.000
#> SRR1656515 2 0.0000 0.981 0.000 1.000
#> SRR1656516 1 0.0000 0.981 1.000 0.000
#> SRR1656518 1 0.0000 0.981 1.000 0.000
#> SRR1656517 1 0.0000 0.981 1.000 0.000
#> SRR1656519 1 0.0000 0.981 1.000 0.000
#> SRR1656522 1 0.0000 0.981 1.000 0.000
#> SRR1656523 2 0.5519 0.847 0.128 0.872
#> SRR1656521 2 0.0000 0.981 0.000 1.000
#> SRR1656520 1 0.0000 0.981 1.000 0.000
#> SRR1656524 1 0.0000 0.981 1.000 0.000
#> SRR1656525 1 0.0000 0.981 1.000 0.000
#> SRR1656526 2 0.0000 0.981 0.000 1.000
#> SRR1656527 2 0.0000 0.981 0.000 1.000
#> SRR1656530 1 0.0000 0.981 1.000 0.000
#> SRR1656529 1 0.0000 0.981 1.000 0.000
#> SRR1656531 1 0.0000 0.981 1.000 0.000
#> SRR1656528 1 0.0000 0.981 1.000 0.000
#> SRR1656534 1 0.0000 0.981 1.000 0.000
#> SRR1656533 1 0.0000 0.981 1.000 0.000
#> SRR1656536 1 0.2603 0.941 0.956 0.044
#> SRR1656532 2 0.0000 0.981 0.000 1.000
#> SRR1656537 1 0.0000 0.981 1.000 0.000
#> SRR1656538 1 0.0000 0.981 1.000 0.000
#> SRR1656535 2 0.0000 0.981 0.000 1.000
#> SRR1656539 1 0.0000 0.981 1.000 0.000
#> SRR1656544 1 0.0000 0.981 1.000 0.000
#> SRR1656542 1 0.0000 0.981 1.000 0.000
#> SRR1656543 1 0.0000 0.981 1.000 0.000
#> SRR1656545 2 0.0000 0.981 0.000 1.000
#> SRR1656540 1 0.0000 0.981 1.000 0.000
#> SRR1656546 1 0.0000 0.981 1.000 0.000
#> SRR1656541 2 0.0000 0.981 0.000 1.000
#> SRR1656547 2 0.0000 0.981 0.000 1.000
#> SRR1656548 1 0.0000 0.981 1.000 0.000
#> SRR1656549 1 0.0000 0.981 1.000 0.000
#> SRR1656551 1 0.0000 0.981 1.000 0.000
#> SRR1656553 1 0.0000 0.981 1.000 0.000
#> SRR1656550 2 0.0000 0.981 0.000 1.000
#> SRR1656552 2 0.0000 0.981 0.000 1.000
#> SRR1656554 1 0.0000 0.981 1.000 0.000
#> SRR1656555 1 0.7219 0.746 0.800 0.200
#> SRR1656556 1 0.3584 0.917 0.932 0.068
#> SRR1656557 1 0.0000 0.981 1.000 0.000
#> SRR1656558 1 0.0000 0.981 1.000 0.000
#> SRR1656559 1 0.0000 0.981 1.000 0.000
#> SRR1656560 1 0.0000 0.981 1.000 0.000
#> SRR1656561 1 0.0000 0.981 1.000 0.000
#> SRR1656562 2 0.0000 0.981 0.000 1.000
#> SRR1656563 1 0.0000 0.981 1.000 0.000
#> SRR1656564 2 0.0000 0.981 0.000 1.000
#> SRR1656565 2 0.0000 0.981 0.000 1.000
#> SRR1656566 1 0.0000 0.981 1.000 0.000
#> SRR1656568 2 0.0000 0.981 0.000 1.000
#> SRR1656567 2 0.0000 0.981 0.000 1.000
#> SRR1656569 1 0.0000 0.981 1.000 0.000
#> SRR1656570 1 0.0000 0.981 1.000 0.000
#> SRR1656571 2 0.0000 0.981 0.000 1.000
#> SRR1656573 1 0.0000 0.981 1.000 0.000
#> SRR1656572 2 0.0000 0.981 0.000 1.000
#> SRR1656574 1 0.0000 0.981 1.000 0.000
#> SRR1656575 1 0.0000 0.981 1.000 0.000
#> SRR1656576 2 0.0000 0.981 0.000 1.000
#> SRR1656578 2 0.0000 0.981 0.000 1.000
#> SRR1656577 1 0.0000 0.981 1.000 0.000
#> SRR1656579 2 0.0000 0.981 0.000 1.000
#> SRR1656580 1 0.0000 0.981 1.000 0.000
#> SRR1656581 1 0.0376 0.978 0.996 0.004
#> SRR1656582 2 0.0000 0.981 0.000 1.000
#> SRR1656585 1 0.3733 0.913 0.928 0.072
#> SRR1656584 1 0.0000 0.981 1.000 0.000
#> SRR1656583 2 0.9710 0.324 0.400 0.600
#> SRR1656586 2 0.0000 0.981 0.000 1.000
#> SRR1656587 2 0.6148 0.814 0.152 0.848
#> SRR1656588 2 0.0000 0.981 0.000 1.000
#> SRR1656589 2 0.0000 0.981 0.000 1.000
#> SRR1656590 1 0.0000 0.981 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656464 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656462 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656465 1 0.6235 0.420 0.564 0.000 0.436
#> SRR1656467 2 0.0747 0.976 0.000 0.984 0.016
#> SRR1656466 1 0.5835 0.618 0.660 0.000 0.340
#> SRR1656468 3 0.0747 0.828 0.016 0.000 0.984
#> SRR1656472 3 0.3686 0.793 0.140 0.000 0.860
#> SRR1656471 1 0.5882 0.568 0.652 0.000 0.348
#> SRR1656470 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656469 1 0.6111 0.526 0.604 0.000 0.396
#> SRR1656473 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656478 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656477 3 0.0829 0.832 0.004 0.012 0.984
#> SRR1656479 1 0.2959 0.882 0.900 0.000 0.100
#> SRR1656480 3 0.2625 0.828 0.000 0.084 0.916
#> SRR1656476 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656481 3 0.0747 0.828 0.016 0.000 0.984
#> SRR1656482 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656485 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656487 1 0.5591 0.642 0.696 0.000 0.304
#> SRR1656486 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656488 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656484 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656489 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656491 1 0.4121 0.845 0.832 0.000 0.168
#> SRR1656490 3 0.5621 0.575 0.308 0.000 0.692
#> SRR1656492 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656493 3 0.6008 0.477 0.372 0.000 0.628
#> SRR1656495 3 0.2796 0.811 0.092 0.000 0.908
#> SRR1656496 1 0.3482 0.880 0.872 0.000 0.128
#> SRR1656494 3 0.3686 0.801 0.000 0.140 0.860
#> SRR1656497 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656499 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656500 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656501 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656498 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656504 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656502 3 0.3038 0.808 0.104 0.000 0.896
#> SRR1656503 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656507 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656508 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656505 3 0.2625 0.828 0.000 0.084 0.916
#> SRR1656506 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656509 3 0.4555 0.623 0.200 0.000 0.800
#> SRR1656510 3 0.0000 0.832 0.000 0.000 1.000
#> SRR1656511 3 0.3816 0.795 0.000 0.148 0.852
#> SRR1656513 2 0.2625 0.902 0.000 0.916 0.084
#> SRR1656512 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656515 2 0.3116 0.875 0.000 0.892 0.108
#> SRR1656516 1 0.1964 0.878 0.944 0.000 0.056
#> SRR1656518 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656517 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656519 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656522 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656523 3 0.2261 0.831 0.000 0.068 0.932
#> SRR1656521 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656520 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656524 3 0.5926 0.508 0.356 0.000 0.644
#> SRR1656525 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656526 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656530 1 0.3482 0.871 0.872 0.000 0.128
#> SRR1656529 1 0.5835 0.618 0.660 0.000 0.340
#> SRR1656531 1 0.0892 0.884 0.980 0.000 0.020
#> SRR1656528 1 0.2625 0.884 0.916 0.000 0.084
#> SRR1656534 1 0.2165 0.892 0.936 0.000 0.064
#> SRR1656533 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656536 3 0.0747 0.828 0.016 0.000 0.984
#> SRR1656532 3 0.3879 0.791 0.000 0.152 0.848
#> SRR1656537 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656538 1 0.2165 0.892 0.936 0.000 0.064
#> SRR1656535 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656539 1 0.6286 0.361 0.536 0.000 0.464
#> SRR1656544 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656542 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656543 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656545 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656540 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656546 3 0.6192 0.368 0.420 0.000 0.580
#> SRR1656541 2 0.1411 0.957 0.000 0.964 0.036
#> SRR1656547 3 0.3412 0.810 0.000 0.124 0.876
#> SRR1656548 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656549 3 0.6299 0.209 0.476 0.000 0.524
#> SRR1656551 3 0.0747 0.828 0.016 0.000 0.984
#> SRR1656553 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656550 3 0.2448 0.830 0.000 0.076 0.924
#> SRR1656552 3 0.3551 0.805 0.000 0.132 0.868
#> SRR1656554 1 0.6062 0.537 0.616 0.000 0.384
#> SRR1656555 3 0.0000 0.832 0.000 0.000 1.000
#> SRR1656556 3 0.4605 0.633 0.204 0.000 0.796
#> SRR1656557 1 0.2261 0.891 0.932 0.000 0.068
#> SRR1656558 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656559 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656560 1 0.2796 0.879 0.908 0.000 0.092
#> SRR1656561 1 0.2165 0.892 0.936 0.000 0.064
#> SRR1656562 3 0.2625 0.828 0.000 0.084 0.916
#> SRR1656563 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656564 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656565 3 0.4974 0.701 0.000 0.236 0.764
#> SRR1656566 3 0.6299 0.209 0.476 0.000 0.524
#> SRR1656568 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656567 3 0.3551 0.805 0.000 0.132 0.868
#> SRR1656569 1 0.5859 0.615 0.656 0.000 0.344
#> SRR1656570 1 0.0892 0.884 0.980 0.000 0.020
#> SRR1656571 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.832 0.000 0.000 1.000
#> SRR1656572 3 0.3551 0.805 0.000 0.132 0.868
#> SRR1656574 1 0.0747 0.885 0.984 0.000 0.016
#> SRR1656575 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656576 3 0.3816 0.795 0.000 0.148 0.852
#> SRR1656578 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.887 1.000 0.000 0.000
#> SRR1656579 3 0.3551 0.805 0.000 0.132 0.868
#> SRR1656580 1 0.0747 0.890 0.984 0.000 0.016
#> SRR1656581 3 0.0000 0.832 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.832 0.000 0.000 1.000
#> SRR1656584 1 0.2165 0.875 0.936 0.000 0.064
#> SRR1656583 3 0.1860 0.834 0.000 0.052 0.948
#> SRR1656586 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656587 3 0.2550 0.831 0.012 0.056 0.932
#> SRR1656588 3 0.5835 0.532 0.000 0.340 0.660
#> SRR1656589 2 0.0000 0.990 0.000 1.000 0.000
#> SRR1656590 1 0.5988 0.335 0.632 0.000 0.368
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.3356 0.8615 0.824 0.000 0.176 0.000
#> SRR1656462 3 0.1118 0.8973 0.036 0.000 0.964 0.000
#> SRR1656465 3 0.4262 0.6916 0.008 0.000 0.756 0.236
#> SRR1656467 4 0.4972 0.2233 0.000 0.456 0.000 0.544
#> SRR1656466 3 0.2345 0.8345 0.000 0.000 0.900 0.100
#> SRR1656468 4 0.1677 0.8831 0.012 0.000 0.040 0.948
#> SRR1656472 1 0.3610 0.6774 0.800 0.000 0.000 0.200
#> SRR1656471 3 0.3486 0.7609 0.000 0.000 0.812 0.188
#> SRR1656470 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.4175 0.7178 0.012 0.000 0.776 0.212
#> SRR1656473 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.2589 0.9025 0.884 0.000 0.116 0.000
#> SRR1656477 4 0.1174 0.8904 0.012 0.000 0.020 0.968
#> SRR1656479 1 0.2868 0.8986 0.864 0.000 0.136 0.000
#> SRR1656480 4 0.0804 0.8921 0.012 0.000 0.008 0.980
#> SRR1656476 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.1854 0.8792 0.012 0.000 0.048 0.940
#> SRR1656482 2 0.0469 0.9829 0.000 0.988 0.000 0.012
#> SRR1656483 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0592 0.8956 0.016 0.000 0.984 0.000
#> SRR1656487 3 0.2345 0.8345 0.000 0.000 0.900 0.100
#> SRR1656486 1 0.3355 0.8837 0.836 0.000 0.160 0.004
#> SRR1656488 3 0.0707 0.8963 0.020 0.000 0.980 0.000
#> SRR1656484 1 0.2469 0.9032 0.892 0.000 0.108 0.000
#> SRR1656489 1 0.2973 0.8868 0.856 0.000 0.144 0.000
#> SRR1656491 3 0.6084 0.5587 0.244 0.000 0.660 0.096
#> SRR1656490 1 0.2670 0.8442 0.904 0.000 0.024 0.072
#> SRR1656492 3 0.1118 0.8964 0.036 0.000 0.964 0.000
#> SRR1656493 1 0.1022 0.8502 0.968 0.000 0.000 0.032
#> SRR1656495 1 0.3400 0.7018 0.820 0.000 0.000 0.180
#> SRR1656496 1 0.3444 0.8674 0.816 0.000 0.184 0.000
#> SRR1656494 4 0.1792 0.8768 0.068 0.000 0.000 0.932
#> SRR1656497 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0817 0.8966 0.024 0.000 0.976 0.000
#> SRR1656500 3 0.1474 0.8939 0.052 0.000 0.948 0.000
#> SRR1656501 1 0.3123 0.8838 0.844 0.000 0.156 0.000
#> SRR1656498 1 0.2530 0.9025 0.888 0.000 0.112 0.000
#> SRR1656504 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.3610 0.6774 0.800 0.000 0.000 0.200
#> SRR1656503 1 0.3569 0.8453 0.804 0.000 0.196 0.000
#> SRR1656507 1 0.2704 0.9002 0.876 0.000 0.124 0.000
#> SRR1656508 1 0.2530 0.9025 0.888 0.000 0.112 0.000
#> SRR1656505 4 0.0804 0.8921 0.012 0.000 0.008 0.980
#> SRR1656506 3 0.0336 0.8936 0.008 0.000 0.992 0.000
#> SRR1656509 1 0.6521 0.2268 0.512 0.000 0.076 0.412
#> SRR1656510 4 0.1488 0.8865 0.012 0.000 0.032 0.956
#> SRR1656511 4 0.2466 0.8613 0.096 0.004 0.000 0.900
#> SRR1656513 4 0.5257 0.2457 0.008 0.444 0.000 0.548
#> SRR1656512 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.3219 0.7954 0.164 0.000 0.836 0.000
#> SRR1656515 4 0.4008 0.6732 0.000 0.244 0.000 0.756
#> SRR1656516 1 0.3486 0.8538 0.812 0.000 0.188 0.000
#> SRR1656518 1 0.2281 0.9021 0.904 0.000 0.096 0.000
#> SRR1656517 1 0.2530 0.9025 0.888 0.000 0.112 0.000
#> SRR1656519 3 0.1474 0.8939 0.052 0.000 0.948 0.000
#> SRR1656522 3 0.2081 0.8718 0.084 0.000 0.916 0.000
#> SRR1656523 4 0.1302 0.8897 0.044 0.000 0.000 0.956
#> SRR1656521 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.1118 0.8973 0.036 0.000 0.964 0.000
#> SRR1656524 1 0.1022 0.8502 0.968 0.000 0.000 0.032
#> SRR1656525 3 0.1211 0.8960 0.040 0.000 0.960 0.000
#> SRR1656526 2 0.0469 0.9829 0.000 0.988 0.000 0.012
#> SRR1656527 2 0.2983 0.8998 0.040 0.892 0.000 0.068
#> SRR1656530 3 0.1022 0.8767 0.000 0.000 0.968 0.032
#> SRR1656529 3 0.2647 0.8203 0.000 0.000 0.880 0.120
#> SRR1656531 1 0.2216 0.9018 0.908 0.000 0.092 0.000
#> SRR1656528 3 0.0524 0.8923 0.008 0.000 0.988 0.004
#> SRR1656534 3 0.1474 0.8939 0.052 0.000 0.948 0.000
#> SRR1656533 1 0.2530 0.9025 0.888 0.000 0.112 0.000
#> SRR1656536 4 0.1854 0.8792 0.012 0.000 0.048 0.940
#> SRR1656532 4 0.3037 0.8507 0.100 0.020 0.000 0.880
#> SRR1656537 1 0.1716 0.8949 0.936 0.000 0.064 0.000
#> SRR1656538 3 0.1557 0.8920 0.056 0.000 0.944 0.000
#> SRR1656535 2 0.0469 0.9829 0.000 0.988 0.000 0.012
#> SRR1656539 3 0.4284 0.7019 0.012 0.000 0.764 0.224
#> SRR1656544 3 0.0707 0.8963 0.020 0.000 0.980 0.000
#> SRR1656542 3 0.1474 0.8939 0.052 0.000 0.948 0.000
#> SRR1656543 3 0.1118 0.8973 0.036 0.000 0.964 0.000
#> SRR1656545 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.1022 0.8971 0.032 0.000 0.968 0.000
#> SRR1656546 1 0.1724 0.8654 0.948 0.000 0.020 0.032
#> SRR1656541 4 0.4907 0.3396 0.000 0.420 0.000 0.580
#> SRR1656547 4 0.0000 0.8928 0.000 0.000 0.000 1.000
#> SRR1656548 3 0.1211 0.8960 0.040 0.000 0.960 0.000
#> SRR1656549 1 0.1042 0.8651 0.972 0.000 0.008 0.020
#> SRR1656551 4 0.1854 0.8792 0.012 0.000 0.048 0.940
#> SRR1656553 3 0.1389 0.8943 0.048 0.000 0.952 0.000
#> SRR1656550 4 0.1174 0.8904 0.012 0.000 0.020 0.968
#> SRR1656552 4 0.0707 0.8915 0.020 0.000 0.000 0.980
#> SRR1656554 3 0.3610 0.7458 0.000 0.000 0.800 0.200
#> SRR1656555 4 0.1388 0.8876 0.012 0.000 0.028 0.960
#> SRR1656556 4 0.5378 0.0944 0.012 0.000 0.448 0.540
#> SRR1656557 3 0.1474 0.8939 0.052 0.000 0.948 0.000
#> SRR1656558 1 0.2530 0.9025 0.888 0.000 0.112 0.000
#> SRR1656559 3 0.2589 0.8431 0.116 0.000 0.884 0.000
#> SRR1656560 3 0.1151 0.8844 0.008 0.000 0.968 0.024
#> SRR1656561 3 0.1302 0.8951 0.044 0.000 0.956 0.000
#> SRR1656562 4 0.1940 0.8796 0.076 0.000 0.000 0.924
#> SRR1656563 1 0.2589 0.9014 0.884 0.000 0.116 0.000
#> SRR1656564 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656565 4 0.2816 0.8617 0.064 0.036 0.000 0.900
#> SRR1656566 1 0.0927 0.8665 0.976 0.000 0.008 0.016
#> SRR1656568 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0000 0.8928 0.000 0.000 0.000 1.000
#> SRR1656569 3 0.2704 0.8174 0.000 0.000 0.876 0.124
#> SRR1656570 1 0.2647 0.9015 0.880 0.000 0.120 0.000
#> SRR1656571 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1488 0.8863 0.012 0.000 0.032 0.956
#> SRR1656572 4 0.2466 0.8613 0.096 0.004 0.000 0.900
#> SRR1656574 3 0.4994 0.0156 0.480 0.000 0.520 0.000
#> SRR1656575 1 0.2530 0.9031 0.888 0.000 0.112 0.000
#> SRR1656576 4 0.1557 0.8726 0.000 0.056 0.000 0.944
#> SRR1656578 2 0.2983 0.8998 0.040 0.892 0.000 0.068
#> SRR1656577 3 0.3528 0.7526 0.192 0.000 0.808 0.000
#> SRR1656579 4 0.0000 0.8928 0.000 0.000 0.000 1.000
#> SRR1656580 3 0.1557 0.8920 0.056 0.000 0.944 0.000
#> SRR1656581 4 0.1488 0.8932 0.032 0.000 0.012 0.956
#> SRR1656582 2 0.0469 0.9829 0.000 0.988 0.000 0.012
#> SRR1656585 4 0.1452 0.8928 0.036 0.000 0.008 0.956
#> SRR1656584 1 0.1792 0.8950 0.932 0.000 0.068 0.000
#> SRR1656583 4 0.0707 0.8936 0.020 0.000 0.000 0.980
#> SRR1656586 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.2081 0.8763 0.084 0.000 0.000 0.916
#> SRR1656588 4 0.2921 0.7995 0.000 0.140 0.000 0.860
#> SRR1656589 2 0.0000 0.9890 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0469 0.8641 0.988 0.000 0.000 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.5470 0.410 0.332 0.000 0.588 0.000 0.080
#> SRR1656462 3 0.0290 0.810 0.000 0.000 0.992 0.000 0.008
#> SRR1656465 5 0.3795 0.801 0.000 0.000 0.192 0.028 0.780
#> SRR1656467 4 0.3430 0.692 0.000 0.220 0.000 0.776 0.004
#> SRR1656466 5 0.3715 0.763 0.000 0.000 0.260 0.004 0.736
#> SRR1656468 4 0.4182 0.428 0.000 0.000 0.000 0.600 0.400
#> SRR1656472 1 0.5726 0.664 0.636 0.000 0.004 0.148 0.212
#> SRR1656471 5 0.4141 0.784 0.000 0.000 0.236 0.028 0.736
#> SRR1656470 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.4100 0.797 0.016 0.000 0.172 0.028 0.784
#> SRR1656473 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.0992 0.864 0.968 0.000 0.024 0.000 0.008
#> SRR1656477 4 0.4015 0.550 0.000 0.000 0.000 0.652 0.348
#> SRR1656479 1 0.4313 0.446 0.636 0.000 0.008 0.000 0.356
#> SRR1656480 4 0.2230 0.831 0.000 0.000 0.000 0.884 0.116
#> SRR1656476 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.3837 0.482 0.000 0.000 0.000 0.308 0.692
#> SRR1656482 2 0.0162 0.964 0.000 0.996 0.000 0.004 0.000
#> SRR1656483 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.2377 0.740 0.000 0.000 0.872 0.000 0.128
#> SRR1656487 5 0.3636 0.750 0.000 0.000 0.272 0.000 0.728
#> SRR1656486 1 0.1195 0.863 0.960 0.000 0.028 0.000 0.012
#> SRR1656488 3 0.2074 0.763 0.000 0.000 0.896 0.000 0.104
#> SRR1656484 1 0.0693 0.865 0.980 0.000 0.012 0.000 0.008
#> SRR1656489 3 0.4597 0.336 0.424 0.000 0.564 0.000 0.012
#> SRR1656491 5 0.4104 0.773 0.032 0.000 0.220 0.000 0.748
#> SRR1656490 1 0.4286 0.510 0.652 0.000 0.004 0.004 0.340
#> SRR1656492 5 0.6253 0.213 0.148 0.000 0.388 0.000 0.464
#> SRR1656493 1 0.3081 0.812 0.832 0.000 0.000 0.012 0.156
#> SRR1656495 1 0.6469 0.345 0.468 0.000 0.000 0.336 0.196
#> SRR1656496 1 0.5376 0.172 0.520 0.000 0.056 0.000 0.424
#> SRR1656494 4 0.0703 0.834 0.000 0.000 0.000 0.976 0.024
#> SRR1656497 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0510 0.807 0.000 0.000 0.984 0.000 0.016
#> SRR1656500 3 0.0404 0.812 0.012 0.000 0.988 0.000 0.000
#> SRR1656501 1 0.1300 0.862 0.956 0.000 0.028 0.000 0.016
#> SRR1656498 1 0.1106 0.863 0.964 0.000 0.024 0.000 0.012
#> SRR1656504 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.5726 0.664 0.636 0.000 0.004 0.148 0.212
#> SRR1656503 1 0.3081 0.757 0.832 0.000 0.156 0.000 0.012
#> SRR1656507 1 0.1830 0.840 0.924 0.000 0.068 0.000 0.008
#> SRR1656508 1 0.1106 0.863 0.964 0.000 0.024 0.000 0.012
#> SRR1656505 4 0.2230 0.831 0.000 0.000 0.000 0.884 0.116
#> SRR1656506 5 0.4045 0.618 0.000 0.000 0.356 0.000 0.644
#> SRR1656509 5 0.5387 0.723 0.084 0.000 0.088 0.092 0.736
#> SRR1656510 4 0.4597 0.331 0.000 0.000 0.012 0.564 0.424
#> SRR1656511 4 0.0992 0.832 0.008 0.000 0.000 0.968 0.024
#> SRR1656513 4 0.3106 0.738 0.000 0.140 0.000 0.840 0.020
#> SRR1656512 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.2771 0.769 0.128 0.000 0.860 0.000 0.012
#> SRR1656515 4 0.2408 0.814 0.000 0.092 0.000 0.892 0.016
#> SRR1656516 1 0.2423 0.825 0.896 0.000 0.080 0.000 0.024
#> SRR1656518 1 0.0566 0.865 0.984 0.000 0.012 0.000 0.004
#> SRR1656517 1 0.0865 0.864 0.972 0.000 0.024 0.000 0.004
#> SRR1656519 3 0.0162 0.811 0.000 0.000 0.996 0.000 0.004
#> SRR1656522 3 0.2723 0.771 0.124 0.000 0.864 0.000 0.012
#> SRR1656523 4 0.1892 0.846 0.004 0.000 0.000 0.916 0.080
#> SRR1656521 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0290 0.810 0.000 0.000 0.992 0.000 0.008
#> SRR1656524 1 0.3994 0.772 0.772 0.000 0.000 0.040 0.188
#> SRR1656525 3 0.1965 0.768 0.000 0.000 0.904 0.000 0.096
#> SRR1656526 2 0.1792 0.898 0.000 0.916 0.000 0.084 0.000
#> SRR1656527 2 0.4064 0.718 0.004 0.756 0.000 0.216 0.024
#> SRR1656530 5 0.3774 0.719 0.000 0.000 0.296 0.000 0.704
#> SRR1656529 5 0.3461 0.792 0.000 0.000 0.224 0.004 0.772
#> SRR1656531 1 0.1892 0.852 0.916 0.000 0.004 0.000 0.080
#> SRR1656528 3 0.4060 0.282 0.000 0.000 0.640 0.000 0.360
#> SRR1656534 3 0.0963 0.809 0.036 0.000 0.964 0.000 0.000
#> SRR1656533 1 0.0992 0.864 0.968 0.000 0.024 0.000 0.008
#> SRR1656536 5 0.3816 0.491 0.000 0.000 0.000 0.304 0.696
#> SRR1656532 4 0.2660 0.764 0.008 0.000 0.000 0.864 0.128
#> SRR1656537 1 0.2338 0.836 0.884 0.000 0.004 0.000 0.112
#> SRR1656538 3 0.2358 0.785 0.104 0.000 0.888 0.000 0.008
#> SRR1656535 2 0.0703 0.951 0.000 0.976 0.000 0.024 0.000
#> SRR1656539 5 0.3760 0.801 0.000 0.000 0.188 0.028 0.784
#> SRR1656544 3 0.2424 0.737 0.000 0.000 0.868 0.000 0.132
#> SRR1656542 3 0.0566 0.813 0.012 0.000 0.984 0.000 0.004
#> SRR1656543 3 0.0162 0.811 0.000 0.000 0.996 0.000 0.004
#> SRR1656545 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0703 0.801 0.000 0.000 0.976 0.000 0.024
#> SRR1656546 1 0.1568 0.857 0.944 0.000 0.000 0.020 0.036
#> SRR1656541 4 0.3160 0.735 0.000 0.188 0.000 0.808 0.004
#> SRR1656547 4 0.1478 0.846 0.000 0.000 0.000 0.936 0.064
#> SRR1656548 3 0.2813 0.690 0.000 0.000 0.832 0.000 0.168
#> SRR1656549 1 0.1478 0.853 0.936 0.000 0.000 0.000 0.064
#> SRR1656551 5 0.3730 0.518 0.000 0.000 0.000 0.288 0.712
#> SRR1656553 3 0.2130 0.792 0.012 0.000 0.908 0.000 0.080
#> SRR1656550 4 0.2230 0.831 0.000 0.000 0.000 0.884 0.116
#> SRR1656552 4 0.1341 0.847 0.000 0.000 0.000 0.944 0.056
#> SRR1656554 5 0.3863 0.800 0.000 0.000 0.200 0.028 0.772
#> SRR1656555 4 0.3752 0.641 0.000 0.000 0.000 0.708 0.292
#> SRR1656556 5 0.4333 0.676 0.000 0.000 0.060 0.188 0.752
#> SRR1656557 3 0.0162 0.811 0.000 0.000 0.996 0.000 0.004
#> SRR1656558 1 0.0865 0.864 0.972 0.000 0.024 0.000 0.004
#> SRR1656559 3 0.2771 0.769 0.128 0.000 0.860 0.000 0.012
#> SRR1656560 3 0.4126 0.216 0.000 0.000 0.620 0.000 0.380
#> SRR1656561 3 0.5309 0.667 0.160 0.000 0.676 0.000 0.164
#> SRR1656562 4 0.0404 0.837 0.000 0.000 0.000 0.988 0.012
#> SRR1656563 1 0.1942 0.839 0.920 0.000 0.068 0.000 0.012
#> SRR1656564 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 4 0.0798 0.834 0.000 0.008 0.000 0.976 0.016
#> SRR1656566 1 0.2563 0.828 0.872 0.000 0.000 0.008 0.120
#> SRR1656568 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 4 0.1732 0.844 0.000 0.000 0.000 0.920 0.080
#> SRR1656569 5 0.3628 0.796 0.000 0.000 0.216 0.012 0.772
#> SRR1656570 1 0.1195 0.863 0.960 0.000 0.028 0.000 0.012
#> SRR1656571 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.3837 0.483 0.000 0.000 0.000 0.308 0.692
#> SRR1656572 4 0.0992 0.832 0.008 0.000 0.000 0.968 0.024
#> SRR1656574 3 0.4306 0.534 0.328 0.000 0.660 0.000 0.012
#> SRR1656575 1 0.0566 0.865 0.984 0.000 0.012 0.000 0.004
#> SRR1656576 4 0.1697 0.847 0.000 0.008 0.000 0.932 0.060
#> SRR1656578 2 0.4822 0.516 0.004 0.636 0.000 0.332 0.028
#> SRR1656577 3 0.3318 0.725 0.180 0.000 0.808 0.000 0.012
#> SRR1656579 4 0.1732 0.844 0.000 0.000 0.000 0.920 0.080
#> SRR1656580 3 0.2522 0.780 0.108 0.000 0.880 0.000 0.012
#> SRR1656581 4 0.4350 0.391 0.004 0.000 0.000 0.588 0.408
#> SRR1656582 2 0.0880 0.945 0.000 0.968 0.000 0.032 0.000
#> SRR1656585 4 0.3366 0.731 0.004 0.000 0.000 0.784 0.212
#> SRR1656584 1 0.0324 0.864 0.992 0.000 0.004 0.000 0.004
#> SRR1656583 4 0.2074 0.837 0.000 0.000 0.000 0.896 0.104
#> SRR1656586 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.1124 0.835 0.004 0.000 0.000 0.960 0.036
#> SRR1656588 4 0.2450 0.838 0.000 0.048 0.000 0.900 0.052
#> SRR1656589 2 0.0000 0.966 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.3132 0.802 0.820 0.000 0.000 0.008 0.172
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.4020 0.6689 0.068 0.000 0.764 0.000 0.008 0.160
#> SRR1656462 3 0.0260 0.8609 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1656465 5 0.0964 0.7993 0.000 0.000 0.016 0.012 0.968 0.004
#> SRR1656467 4 0.3892 0.6555 0.000 0.048 0.000 0.740 0.000 0.212
#> SRR1656466 5 0.1124 0.7955 0.000 0.000 0.036 0.000 0.956 0.008
#> SRR1656468 4 0.3756 0.3249 0.000 0.000 0.000 0.644 0.352 0.004
#> SRR1656472 6 0.4414 0.5953 0.256 0.000 0.004 0.024 0.020 0.696
#> SRR1656471 5 0.1453 0.7980 0.000 0.000 0.040 0.008 0.944 0.008
#> SRR1656470 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.0862 0.7991 0.000 0.000 0.016 0.008 0.972 0.004
#> SRR1656473 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.0820 0.7717 0.972 0.000 0.016 0.000 0.000 0.012
#> SRR1656477 4 0.2980 0.6236 0.000 0.000 0.000 0.808 0.180 0.012
#> SRR1656479 1 0.4706 0.4287 0.624 0.000 0.008 0.000 0.320 0.048
#> SRR1656480 4 0.1007 0.7116 0.000 0.000 0.000 0.956 0.044 0.000
#> SRR1656476 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 5 0.4101 0.3545 0.000 0.000 0.000 0.408 0.580 0.012
#> SRR1656482 2 0.2260 0.8142 0.000 0.860 0.000 0.000 0.000 0.140
#> SRR1656483 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.3725 0.5921 0.000 0.000 0.676 0.000 0.316 0.008
#> SRR1656487 5 0.1196 0.7943 0.000 0.000 0.040 0.000 0.952 0.008
#> SRR1656486 1 0.1511 0.7656 0.940 0.000 0.012 0.000 0.044 0.004
#> SRR1656488 3 0.3595 0.6351 0.000 0.000 0.704 0.000 0.288 0.008
#> SRR1656484 1 0.1644 0.7689 0.932 0.000 0.000 0.000 0.040 0.028
#> SRR1656489 1 0.3780 0.5445 0.744 0.000 0.224 0.000 0.004 0.028
#> SRR1656491 5 0.1549 0.7966 0.004 0.000 0.024 0.004 0.944 0.024
#> SRR1656490 1 0.4734 0.4388 0.660 0.000 0.000 0.016 0.272 0.052
#> SRR1656492 5 0.5637 -0.0590 0.432 0.000 0.104 0.000 0.452 0.012
#> SRR1656493 1 0.3690 0.3051 0.684 0.000 0.000 0.000 0.008 0.308
#> SRR1656495 6 0.3377 0.5672 0.148 0.000 0.000 0.028 0.012 0.812
#> SRR1656496 1 0.4989 0.3088 0.540 0.000 0.016 0.000 0.404 0.040
#> SRR1656494 4 0.3782 0.6209 0.000 0.000 0.000 0.636 0.004 0.360
#> SRR1656497 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.0622 0.8596 0.000 0.000 0.980 0.000 0.012 0.008
#> SRR1656500 3 0.0291 0.8608 0.004 0.000 0.992 0.000 0.004 0.000
#> SRR1656501 1 0.1536 0.7663 0.940 0.000 0.016 0.000 0.040 0.004
#> SRR1656498 1 0.1370 0.7694 0.948 0.000 0.012 0.000 0.004 0.036
#> SRR1656504 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.4414 0.5953 0.256 0.000 0.004 0.024 0.020 0.696
#> SRR1656503 1 0.3592 0.6620 0.812 0.000 0.124 0.000 0.044 0.020
#> SRR1656507 1 0.0972 0.7714 0.964 0.000 0.028 0.000 0.000 0.008
#> SRR1656508 1 0.1194 0.7705 0.956 0.000 0.008 0.000 0.004 0.032
#> SRR1656505 4 0.1007 0.7116 0.000 0.000 0.000 0.956 0.044 0.000
#> SRR1656506 5 0.2110 0.7598 0.004 0.000 0.084 0.000 0.900 0.012
#> SRR1656509 5 0.4181 0.6889 0.036 0.000 0.004 0.072 0.788 0.100
#> SRR1656510 4 0.4136 0.1763 0.000 0.000 0.000 0.560 0.428 0.012
#> SRR1656511 4 0.3890 0.5969 0.000 0.000 0.000 0.596 0.004 0.400
#> SRR1656513 4 0.4333 0.5881 0.000 0.028 0.000 0.596 0.000 0.376
#> SRR1656512 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.1245 0.8435 0.032 0.000 0.952 0.000 0.000 0.016
#> SRR1656515 4 0.3200 0.6831 0.000 0.016 0.000 0.788 0.000 0.196
#> SRR1656516 1 0.2195 0.7603 0.912 0.000 0.028 0.000 0.036 0.024
#> SRR1656518 1 0.0508 0.7693 0.984 0.000 0.004 0.000 0.000 0.012
#> SRR1656517 1 0.0717 0.7725 0.976 0.000 0.008 0.000 0.000 0.016
#> SRR1656519 3 0.0260 0.8609 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1656522 3 0.1225 0.8443 0.036 0.000 0.952 0.000 0.000 0.012
#> SRR1656523 4 0.3066 0.7154 0.000 0.000 0.000 0.832 0.044 0.124
#> SRR1656521 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0260 0.8609 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1656524 6 0.4083 0.3246 0.460 0.000 0.000 0.000 0.008 0.532
#> SRR1656525 3 0.3853 0.6477 0.008 0.000 0.708 0.000 0.272 0.012
#> SRR1656526 2 0.5062 0.5874 0.000 0.636 0.000 0.168 0.000 0.196
#> SRR1656527 2 0.5683 0.3482 0.000 0.484 0.000 0.168 0.000 0.348
#> SRR1656530 5 0.1297 0.7928 0.000 0.000 0.040 0.000 0.948 0.012
#> SRR1656529 5 0.0951 0.7998 0.000 0.000 0.020 0.004 0.968 0.008
#> SRR1656531 1 0.4317 0.0155 0.572 0.000 0.004 0.000 0.016 0.408
#> SRR1656528 5 0.4062 0.3114 0.004 0.000 0.344 0.000 0.640 0.012
#> SRR1656534 3 0.0291 0.8608 0.004 0.000 0.992 0.000 0.004 0.000
#> SRR1656533 1 0.1218 0.7707 0.956 0.000 0.012 0.000 0.004 0.028
#> SRR1656536 5 0.3927 0.4595 0.000 0.000 0.000 0.344 0.644 0.012
#> SRR1656532 6 0.3989 -0.5361 0.000 0.000 0.000 0.468 0.004 0.528
#> SRR1656537 1 0.3878 0.2931 0.668 0.000 0.004 0.000 0.008 0.320
#> SRR1656538 3 0.1194 0.8531 0.032 0.000 0.956 0.000 0.004 0.008
#> SRR1656535 2 0.3551 0.7474 0.000 0.772 0.000 0.036 0.000 0.192
#> SRR1656539 5 0.1167 0.7945 0.000 0.000 0.012 0.020 0.960 0.008
#> SRR1656544 3 0.3903 0.6024 0.004 0.000 0.680 0.000 0.304 0.012
#> SRR1656542 3 0.1307 0.8535 0.008 0.000 0.952 0.000 0.032 0.008
#> SRR1656543 3 0.0260 0.8609 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1656545 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0717 0.8567 0.000 0.000 0.976 0.000 0.016 0.008
#> SRR1656546 1 0.1285 0.7527 0.944 0.000 0.004 0.000 0.000 0.052
#> SRR1656541 4 0.4357 0.6212 0.000 0.072 0.000 0.696 0.000 0.232
#> SRR1656547 4 0.1588 0.7264 0.000 0.000 0.000 0.924 0.004 0.072
#> SRR1656548 3 0.4139 0.5478 0.008 0.000 0.644 0.000 0.336 0.012
#> SRR1656549 1 0.1196 0.7481 0.952 0.000 0.000 0.000 0.008 0.040
#> SRR1656551 5 0.3802 0.4992 0.000 0.000 0.000 0.312 0.676 0.012
#> SRR1656553 3 0.3329 0.7068 0.004 0.000 0.768 0.000 0.220 0.008
#> SRR1656550 4 0.1204 0.7076 0.000 0.000 0.000 0.944 0.056 0.000
#> SRR1656552 4 0.3373 0.6898 0.000 0.000 0.000 0.744 0.008 0.248
#> SRR1656554 5 0.0806 0.8005 0.000 0.000 0.020 0.008 0.972 0.000
#> SRR1656555 4 0.3867 0.4333 0.000 0.000 0.000 0.660 0.328 0.012
#> SRR1656556 5 0.3376 0.6594 0.000 0.000 0.000 0.220 0.764 0.016
#> SRR1656557 3 0.0260 0.8609 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1656558 1 0.0622 0.7691 0.980 0.000 0.008 0.000 0.000 0.012
#> SRR1656559 3 0.1225 0.8443 0.036 0.000 0.952 0.000 0.000 0.012
#> SRR1656560 5 0.3848 0.4388 0.004 0.000 0.292 0.000 0.692 0.012
#> SRR1656561 1 0.6355 0.0474 0.396 0.000 0.272 0.000 0.320 0.012
#> SRR1656562 4 0.2964 0.6957 0.000 0.000 0.000 0.792 0.004 0.204
#> SRR1656563 1 0.2195 0.7630 0.912 0.000 0.028 0.000 0.024 0.036
#> SRR1656564 2 0.2454 0.8023 0.000 0.840 0.000 0.000 0.000 0.160
#> SRR1656565 4 0.3819 0.6133 0.000 0.000 0.000 0.624 0.004 0.372
#> SRR1656566 1 0.3445 0.4270 0.732 0.000 0.000 0.000 0.008 0.260
#> SRR1656568 2 0.0458 0.8856 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1656567 4 0.0717 0.7233 0.000 0.000 0.000 0.976 0.008 0.016
#> SRR1656569 5 0.0692 0.8005 0.000 0.000 0.020 0.004 0.976 0.000
#> SRR1656570 1 0.1933 0.7671 0.924 0.000 0.012 0.000 0.032 0.032
#> SRR1656571 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.4406 0.4184 0.000 0.000 0.000 0.336 0.624 0.040
#> SRR1656572 4 0.3862 0.6077 0.000 0.000 0.000 0.608 0.004 0.388
#> SRR1656574 3 0.4385 0.3953 0.328 0.000 0.636 0.000 0.004 0.032
#> SRR1656575 1 0.0837 0.7721 0.972 0.000 0.004 0.000 0.004 0.020
#> SRR1656576 4 0.3052 0.6871 0.000 0.000 0.000 0.780 0.004 0.216
#> SRR1656578 2 0.5901 0.1671 0.000 0.408 0.000 0.204 0.000 0.388
#> SRR1656577 3 0.1442 0.8389 0.040 0.000 0.944 0.000 0.004 0.012
#> SRR1656579 4 0.0717 0.7233 0.000 0.000 0.000 0.976 0.008 0.016
#> SRR1656580 3 0.1196 0.8470 0.040 0.000 0.952 0.000 0.000 0.008
#> SRR1656581 4 0.5044 0.4050 0.000 0.000 0.000 0.584 0.320 0.096
#> SRR1656582 2 0.3679 0.7369 0.000 0.760 0.000 0.040 0.000 0.200
#> SRR1656585 4 0.4643 0.6176 0.000 0.000 0.000 0.688 0.128 0.184
#> SRR1656584 1 0.0547 0.7656 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1656583 4 0.3396 0.6781 0.000 0.000 0.000 0.812 0.072 0.116
#> SRR1656586 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 4 0.3483 0.6583 0.000 0.000 0.000 0.748 0.016 0.236
#> SRR1656588 4 0.1340 0.7252 0.000 0.008 0.000 0.948 0.004 0.040
#> SRR1656589 2 0.0000 0.8925 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 6 0.4238 0.3202 0.444 0.000 0.000 0.000 0.016 0.540
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.956 0.983 0.4941 0.505 0.505
#> 3 3 0.961 0.926 0.971 0.3440 0.796 0.609
#> 4 4 0.838 0.880 0.933 0.1029 0.881 0.669
#> 5 5 0.773 0.644 0.838 0.0457 0.975 0.906
#> 6 6 0.807 0.770 0.875 0.0437 0.900 0.629
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.0000 0.973 0.000 1.000
#> SRR1656464 1 0.0000 0.990 1.000 0.000
#> SRR1656462 1 0.0000 0.990 1.000 0.000
#> SRR1656465 1 0.0000 0.990 1.000 0.000
#> SRR1656467 2 0.0000 0.973 0.000 1.000
#> SRR1656466 1 0.0000 0.990 1.000 0.000
#> SRR1656468 2 0.0000 0.973 0.000 1.000
#> SRR1656472 1 0.0000 0.990 1.000 0.000
#> SRR1656471 1 0.0000 0.990 1.000 0.000
#> SRR1656470 2 0.0000 0.973 0.000 1.000
#> SRR1656469 1 0.0000 0.990 1.000 0.000
#> SRR1656473 2 0.0000 0.973 0.000 1.000
#> SRR1656474 2 0.0000 0.973 0.000 1.000
#> SRR1656475 2 0.0000 0.973 0.000 1.000
#> SRR1656478 1 0.0000 0.990 1.000 0.000
#> SRR1656477 2 0.0000 0.973 0.000 1.000
#> SRR1656479 1 0.0000 0.990 1.000 0.000
#> SRR1656480 2 0.0000 0.973 0.000 1.000
#> SRR1656476 2 0.0000 0.973 0.000 1.000
#> SRR1656481 2 0.0000 0.973 0.000 1.000
#> SRR1656482 2 0.0000 0.973 0.000 1.000
#> SRR1656483 2 0.0000 0.973 0.000 1.000
#> SRR1656485 1 0.0000 0.990 1.000 0.000
#> SRR1656487 1 0.0000 0.990 1.000 0.000
#> SRR1656486 1 0.0000 0.990 1.000 0.000
#> SRR1656488 1 0.0000 0.990 1.000 0.000
#> SRR1656484 1 0.0000 0.990 1.000 0.000
#> SRR1656489 1 0.0000 0.990 1.000 0.000
#> SRR1656491 1 0.0000 0.990 1.000 0.000
#> SRR1656490 1 0.0000 0.990 1.000 0.000
#> SRR1656492 1 0.0000 0.990 1.000 0.000
#> SRR1656493 1 0.0000 0.990 1.000 0.000
#> SRR1656495 2 0.0000 0.973 0.000 1.000
#> SRR1656496 1 0.0000 0.990 1.000 0.000
#> SRR1656494 2 0.0000 0.973 0.000 1.000
#> SRR1656497 2 0.0000 0.973 0.000 1.000
#> SRR1656499 1 0.0000 0.990 1.000 0.000
#> SRR1656500 1 0.0000 0.990 1.000 0.000
#> SRR1656501 1 0.0000 0.990 1.000 0.000
#> SRR1656498 1 0.0000 0.990 1.000 0.000
#> SRR1656504 2 0.0000 0.973 0.000 1.000
#> SRR1656502 1 0.0376 0.986 0.996 0.004
#> SRR1656503 1 0.0000 0.990 1.000 0.000
#> SRR1656507 1 0.0000 0.990 1.000 0.000
#> SRR1656508 1 0.0000 0.990 1.000 0.000
#> SRR1656505 2 0.0000 0.973 0.000 1.000
#> SRR1656506 1 0.0000 0.990 1.000 0.000
#> SRR1656509 1 0.0000 0.990 1.000 0.000
#> SRR1656510 2 0.9710 0.341 0.400 0.600
#> SRR1656511 2 0.0000 0.973 0.000 1.000
#> SRR1656513 2 0.0000 0.973 0.000 1.000
#> SRR1656512 2 0.0000 0.973 0.000 1.000
#> SRR1656514 1 0.0000 0.990 1.000 0.000
#> SRR1656515 2 0.0000 0.973 0.000 1.000
#> SRR1656516 1 0.0000 0.990 1.000 0.000
#> SRR1656518 1 0.0000 0.990 1.000 0.000
#> SRR1656517 1 0.0000 0.990 1.000 0.000
#> SRR1656519 1 0.0000 0.990 1.000 0.000
#> SRR1656522 1 0.0000 0.990 1.000 0.000
#> SRR1656523 2 0.0000 0.973 0.000 1.000
#> SRR1656521 2 0.0000 0.973 0.000 1.000
#> SRR1656520 1 0.0000 0.990 1.000 0.000
#> SRR1656524 2 0.9866 0.250 0.432 0.568
#> SRR1656525 1 0.0000 0.990 1.000 0.000
#> SRR1656526 2 0.0000 0.973 0.000 1.000
#> SRR1656527 2 0.0000 0.973 0.000 1.000
#> SRR1656530 1 0.0000 0.990 1.000 0.000
#> SRR1656529 1 0.0000 0.990 1.000 0.000
#> SRR1656531 1 0.0000 0.990 1.000 0.000
#> SRR1656528 1 0.0000 0.990 1.000 0.000
#> SRR1656534 1 0.0000 0.990 1.000 0.000
#> SRR1656533 1 0.0000 0.990 1.000 0.000
#> SRR1656536 2 0.9815 0.282 0.420 0.580
#> SRR1656532 2 0.0000 0.973 0.000 1.000
#> SRR1656537 1 0.0000 0.990 1.000 0.000
#> SRR1656538 1 0.0000 0.990 1.000 0.000
#> SRR1656535 2 0.0000 0.973 0.000 1.000
#> SRR1656539 1 0.0000 0.990 1.000 0.000
#> SRR1656544 1 0.0000 0.990 1.000 0.000
#> SRR1656542 1 0.0000 0.990 1.000 0.000
#> SRR1656543 1 0.0000 0.990 1.000 0.000
#> SRR1656545 2 0.0000 0.973 0.000 1.000
#> SRR1656540 1 0.0000 0.990 1.000 0.000
#> SRR1656546 1 0.9710 0.306 0.600 0.400
#> SRR1656541 2 0.0000 0.973 0.000 1.000
#> SRR1656547 2 0.0000 0.973 0.000 1.000
#> SRR1656548 1 0.0000 0.990 1.000 0.000
#> SRR1656549 1 0.0000 0.990 1.000 0.000
#> SRR1656551 2 0.6623 0.780 0.172 0.828
#> SRR1656553 1 0.0000 0.990 1.000 0.000
#> SRR1656550 2 0.0000 0.973 0.000 1.000
#> SRR1656552 2 0.0000 0.973 0.000 1.000
#> SRR1656554 1 0.0000 0.990 1.000 0.000
#> SRR1656555 2 0.0000 0.973 0.000 1.000
#> SRR1656556 1 0.8955 0.527 0.688 0.312
#> SRR1656557 1 0.0000 0.990 1.000 0.000
#> SRR1656558 1 0.0000 0.990 1.000 0.000
#> SRR1656559 1 0.0000 0.990 1.000 0.000
#> SRR1656560 1 0.0000 0.990 1.000 0.000
#> SRR1656561 1 0.0000 0.990 1.000 0.000
#> SRR1656562 2 0.0000 0.973 0.000 1.000
#> SRR1656563 1 0.0000 0.990 1.000 0.000
#> SRR1656564 2 0.0000 0.973 0.000 1.000
#> SRR1656565 2 0.0000 0.973 0.000 1.000
#> SRR1656566 1 0.0000 0.990 1.000 0.000
#> SRR1656568 2 0.0000 0.973 0.000 1.000
#> SRR1656567 2 0.0000 0.973 0.000 1.000
#> SRR1656569 1 0.0000 0.990 1.000 0.000
#> SRR1656570 1 0.0000 0.990 1.000 0.000
#> SRR1656571 2 0.0000 0.973 0.000 1.000
#> SRR1656573 1 0.0000 0.990 1.000 0.000
#> SRR1656572 2 0.0000 0.973 0.000 1.000
#> SRR1656574 1 0.0000 0.990 1.000 0.000
#> SRR1656575 1 0.0000 0.990 1.000 0.000
#> SRR1656576 2 0.0000 0.973 0.000 1.000
#> SRR1656578 2 0.0000 0.973 0.000 1.000
#> SRR1656577 1 0.0000 0.990 1.000 0.000
#> SRR1656579 2 0.0000 0.973 0.000 1.000
#> SRR1656580 1 0.0000 0.990 1.000 0.000
#> SRR1656581 2 0.0000 0.973 0.000 1.000
#> SRR1656582 2 0.0000 0.973 0.000 1.000
#> SRR1656585 2 0.0000 0.973 0.000 1.000
#> SRR1656584 1 0.0000 0.990 1.000 0.000
#> SRR1656583 2 0.0000 0.973 0.000 1.000
#> SRR1656586 2 0.0000 0.973 0.000 1.000
#> SRR1656587 2 0.0000 0.973 0.000 1.000
#> SRR1656588 2 0.0000 0.973 0.000 1.000
#> SRR1656589 2 0.0000 0.973 0.000 1.000
#> SRR1656590 1 0.0000 0.990 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656462 3 0.0424 0.9553 0.008 0.000 0.992
#> SRR1656465 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656468 2 0.1289 0.9593 0.000 0.968 0.032
#> SRR1656472 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656471 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656470 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656478 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656477 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656479 1 0.6225 0.2079 0.568 0.000 0.432
#> SRR1656480 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656476 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656487 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656486 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656488 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656484 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656489 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656491 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656490 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656492 3 0.4452 0.7724 0.192 0.000 0.808
#> SRR1656493 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656495 2 0.4002 0.7981 0.160 0.840 0.000
#> SRR1656496 1 0.6309 -0.0225 0.504 0.000 0.496
#> SRR1656494 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656497 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656499 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656500 3 0.2537 0.9017 0.080 0.000 0.920
#> SRR1656501 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656498 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656502 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656503 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656507 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656508 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656505 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656506 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656509 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656510 2 0.6126 0.3669 0.000 0.600 0.400
#> SRR1656511 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656514 1 0.5016 0.6517 0.760 0.000 0.240
#> SRR1656515 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656516 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656518 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656517 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656519 3 0.2537 0.9017 0.080 0.000 0.920
#> SRR1656522 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656523 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656520 3 0.0592 0.9534 0.012 0.000 0.988
#> SRR1656524 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656525 3 0.0592 0.9534 0.012 0.000 0.988
#> SRR1656526 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656530 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656528 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656534 3 0.4555 0.7610 0.200 0.000 0.800
#> SRR1656533 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656537 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656538 3 0.5560 0.5888 0.300 0.000 0.700
#> SRR1656535 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656544 3 0.0424 0.9553 0.008 0.000 0.992
#> SRR1656542 3 0.2537 0.9017 0.080 0.000 0.920
#> SRR1656543 3 0.0424 0.9553 0.008 0.000 0.992
#> SRR1656545 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656540 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656546 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656541 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656548 3 0.0892 0.9482 0.020 0.000 0.980
#> SRR1656549 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656551 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656553 3 0.3482 0.8526 0.128 0.000 0.872
#> SRR1656550 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656552 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656555 2 0.2165 0.9282 0.000 0.936 0.064
#> SRR1656556 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656557 3 0.0592 0.9534 0.012 0.000 0.988
#> SRR1656558 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656559 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656560 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656561 3 0.5810 0.5107 0.336 0.000 0.664
#> SRR1656562 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656563 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656566 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656568 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656567 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656569 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656570 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656571 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.9581 0.000 0.000 1.000
#> SRR1656572 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656574 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656575 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656576 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656578 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656580 1 0.6309 -0.0225 0.504 0.000 0.496
#> SRR1656581 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656582 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656585 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656584 1 0.0000 0.9511 1.000 0.000 0.000
#> SRR1656583 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656586 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656587 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656588 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656589 2 0.0000 0.9871 0.000 1.000 0.000
#> SRR1656590 1 0.0000 0.9511 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.3528 0.827 0.808 0.000 0.192 0.000
#> SRR1656462 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656465 3 0.4193 0.741 0.000 0.000 0.732 0.268
#> SRR1656467 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656466 3 0.3311 0.829 0.000 0.000 0.828 0.172
#> SRR1656468 4 0.0000 0.784 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.1940 0.902 0.924 0.000 0.076 0.000
#> SRR1656471 3 0.4134 0.752 0.000 0.000 0.740 0.260
#> SRR1656470 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.3486 0.817 0.000 0.000 0.812 0.188
#> SRR1656473 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0592 0.929 0.984 0.000 0.016 0.000
#> SRR1656477 4 0.2011 0.824 0.000 0.080 0.000 0.920
#> SRR1656479 3 0.4250 0.585 0.276 0.000 0.724 0.000
#> SRR1656480 4 0.3311 0.833 0.000 0.172 0.000 0.828
#> SRR1656476 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.784 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.3486 0.817 0.000 0.000 0.812 0.188
#> SRR1656486 1 0.1637 0.916 0.940 0.000 0.060 0.000
#> SRR1656488 3 0.1792 0.892 0.000 0.000 0.932 0.068
#> SRR1656484 1 0.0817 0.928 0.976 0.000 0.024 0.000
#> SRR1656489 1 0.2149 0.900 0.912 0.000 0.088 0.000
#> SRR1656491 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656490 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1656492 3 0.1867 0.875 0.072 0.000 0.928 0.000
#> SRR1656493 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656495 2 0.3942 0.637 0.236 0.764 0.000 0.000
#> SRR1656496 3 0.1716 0.864 0.064 0.000 0.936 0.000
#> SRR1656494 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656497 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656501 1 0.1716 0.915 0.936 0.000 0.064 0.000
#> SRR1656498 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656504 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.1867 0.904 0.928 0.000 0.072 0.000
#> SRR1656503 1 0.3649 0.816 0.796 0.000 0.204 0.000
#> SRR1656507 1 0.1022 0.926 0.968 0.000 0.032 0.000
#> SRR1656508 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656505 4 0.3569 0.820 0.000 0.196 0.000 0.804
#> SRR1656506 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656509 3 0.0592 0.908 0.000 0.000 0.984 0.016
#> SRR1656510 2 0.6854 0.318 0.000 0.596 0.232 0.172
#> SRR1656511 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656513 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.3942 0.636 0.236 0.000 0.764 0.000
#> SRR1656515 2 0.3172 0.759 0.000 0.840 0.000 0.160
#> SRR1656516 1 0.3528 0.827 0.808 0.000 0.192 0.000
#> SRR1656518 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1656517 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1656519 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656522 1 0.4040 0.765 0.752 0.000 0.248 0.000
#> SRR1656523 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656521 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656525 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656530 3 0.2011 0.886 0.000 0.000 0.920 0.080
#> SRR1656529 3 0.3486 0.817 0.000 0.000 0.812 0.188
#> SRR1656531 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656528 3 0.2081 0.885 0.000 0.000 0.916 0.084
#> SRR1656534 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656533 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1656536 4 0.0000 0.784 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656537 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656535 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656539 3 0.3801 0.790 0.000 0.000 0.780 0.220
#> SRR1656544 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656546 1 0.3486 0.704 0.812 0.188 0.000 0.000
#> SRR1656541 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656547 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656548 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656549 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656551 4 0.0000 0.784 0.000 0.000 0.000 1.000
#> SRR1656553 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.3311 0.833 0.000 0.172 0.000 0.828
#> SRR1656552 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656554 3 0.3649 0.803 0.000 0.000 0.796 0.204
#> SRR1656555 4 0.4072 0.691 0.000 0.252 0.000 0.748
#> SRR1656556 4 0.4761 0.141 0.000 0.000 0.372 0.628
#> SRR1656557 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656559 1 0.3837 0.794 0.776 0.000 0.224 0.000
#> SRR1656560 3 0.2081 0.885 0.000 0.000 0.916 0.084
#> SRR1656561 3 0.0000 0.915 0.000 0.000 1.000 0.000
#> SRR1656562 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656563 1 0.1211 0.924 0.960 0.000 0.040 0.000
#> SRR1656564 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656566 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656568 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.3569 0.820 0.000 0.196 0.000 0.804
#> SRR1656569 3 0.3486 0.817 0.000 0.000 0.812 0.188
#> SRR1656570 1 0.1118 0.925 0.964 0.000 0.036 0.000
#> SRR1656571 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.4193 0.460 0.000 0.000 0.268 0.732
#> SRR1656572 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656574 1 0.2814 0.874 0.868 0.000 0.132 0.000
#> SRR1656575 1 0.0188 0.930 0.996 0.000 0.004 0.000
#> SRR1656576 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656578 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656577 1 0.3569 0.824 0.804 0.000 0.196 0.000
#> SRR1656579 4 0.3873 0.792 0.000 0.228 0.000 0.772
#> SRR1656580 3 0.0188 0.912 0.004 0.000 0.996 0.000
#> SRR1656581 4 0.4800 0.627 0.004 0.340 0.000 0.656
#> SRR1656582 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.3311 0.833 0.000 0.172 0.000 0.828
#> SRR1656584 1 0.0000 0.929 1.000 0.000 0.000 0.000
#> SRR1656583 4 0.3311 0.833 0.000 0.172 0.000 0.828
#> SRR1656586 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656587 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656588 4 0.3726 0.807 0.000 0.212 0.000 0.788
#> SRR1656589 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0000 0.929 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 1 0.6127 0.2799 0.484 0.000 0.384 0.132 0.000
#> SRR1656462 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656465 3 0.4949 0.4269 0.000 0.000 0.572 0.032 0.396
#> SRR1656467 2 0.0451 0.9555 0.000 0.988 0.000 0.004 0.008
#> SRR1656466 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656468 5 0.0290 0.5112 0.000 0.000 0.000 0.008 0.992
#> SRR1656472 4 0.1579 0.5899 0.032 0.000 0.024 0.944 0.000
#> SRR1656471 3 0.4930 0.4381 0.000 0.000 0.580 0.032 0.388
#> SRR1656470 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656473 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.0771 0.7283 0.976 0.000 0.004 0.020 0.000
#> SRR1656477 5 0.1270 0.4928 0.000 0.000 0.000 0.052 0.948
#> SRR1656479 3 0.5114 -0.0597 0.476 0.000 0.488 0.036 0.000
#> SRR1656480 5 0.4491 0.5492 0.000 0.328 0.000 0.020 0.652
#> SRR1656476 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.0000 0.5113 0.000 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.0162 0.7370 0.000 0.000 0.996 0.004 0.000
#> SRR1656487 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656486 1 0.0771 0.7283 0.976 0.000 0.004 0.020 0.000
#> SRR1656488 3 0.0880 0.7311 0.000 0.000 0.968 0.032 0.000
#> SRR1656484 1 0.1582 0.7138 0.944 0.000 0.028 0.028 0.000
#> SRR1656489 1 0.4352 0.5581 0.720 0.000 0.244 0.036 0.000
#> SRR1656491 3 0.0162 0.7373 0.000 0.000 0.996 0.004 0.000
#> SRR1656490 1 0.0609 0.7273 0.980 0.000 0.000 0.020 0.000
#> SRR1656492 3 0.3550 0.6254 0.184 0.000 0.796 0.020 0.000
#> SRR1656493 1 0.4088 0.4470 0.632 0.000 0.000 0.368 0.000
#> SRR1656495 4 0.2632 0.5547 0.040 0.072 0.000 0.888 0.000
#> SRR1656496 3 0.5028 0.1710 0.400 0.000 0.564 0.036 0.000
#> SRR1656494 2 0.2612 0.8143 0.000 0.868 0.000 0.124 0.008
#> SRR1656497 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0404 0.7360 0.000 0.000 0.988 0.012 0.000
#> SRR1656500 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656501 1 0.0771 0.7283 0.976 0.000 0.004 0.020 0.000
#> SRR1656498 1 0.0000 0.7276 1.000 0.000 0.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 4 0.1579 0.5899 0.032 0.000 0.024 0.944 0.000
#> SRR1656503 1 0.5000 0.3375 0.576 0.000 0.388 0.036 0.000
#> SRR1656507 1 0.1012 0.7282 0.968 0.000 0.012 0.020 0.000
#> SRR1656508 1 0.0880 0.7212 0.968 0.000 0.000 0.032 0.000
#> SRR1656505 5 0.4252 0.5501 0.000 0.340 0.000 0.008 0.652
#> SRR1656506 3 0.0703 0.7333 0.000 0.000 0.976 0.024 0.000
#> SRR1656509 4 0.4874 0.2205 0.032 0.000 0.368 0.600 0.000
#> SRR1656510 2 0.7612 -0.2453 0.084 0.416 0.064 0.032 0.404
#> SRR1656511 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.5137 0.0893 0.424 0.000 0.536 0.040 0.000
#> SRR1656515 2 0.2338 0.8178 0.000 0.884 0.000 0.004 0.112
#> SRR1656516 1 0.4671 0.4466 0.640 0.000 0.332 0.028 0.000
#> SRR1656518 1 0.0609 0.7273 0.980 0.000 0.000 0.020 0.000
#> SRR1656517 1 0.0609 0.7273 0.980 0.000 0.000 0.020 0.000
#> SRR1656519 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656522 1 0.5077 0.2329 0.536 0.000 0.428 0.036 0.000
#> SRR1656523 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656521 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.1124 0.7351 0.004 0.000 0.960 0.036 0.000
#> SRR1656524 1 0.4307 0.2170 0.504 0.000 0.000 0.496 0.000
#> SRR1656525 3 0.0000 0.7373 0.000 0.000 1.000 0.000 0.000
#> SRR1656526 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656530 3 0.1992 0.7152 0.000 0.000 0.924 0.032 0.044
#> SRR1656529 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656531 1 0.3635 0.5992 0.748 0.000 0.004 0.248 0.000
#> SRR1656528 3 0.2067 0.7132 0.000 0.000 0.920 0.032 0.048
#> SRR1656534 3 0.1836 0.7225 0.032 0.000 0.932 0.036 0.000
#> SRR1656533 1 0.0000 0.7276 1.000 0.000 0.000 0.000 0.000
#> SRR1656536 5 0.0000 0.5113 0.000 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.2516 0.8044 0.000 0.860 0.000 0.140 0.000
#> SRR1656537 1 0.4015 0.4526 0.652 0.000 0.000 0.348 0.000
#> SRR1656538 3 0.4934 0.2562 0.364 0.000 0.600 0.036 0.000
#> SRR1656535 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656544 3 0.0794 0.7367 0.000 0.000 0.972 0.028 0.000
#> SRR1656542 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656543 3 0.0963 0.7356 0.000 0.000 0.964 0.036 0.000
#> SRR1656545 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0963 0.7356 0.000 0.000 0.964 0.036 0.000
#> SRR1656546 1 0.4490 0.5490 0.724 0.052 0.000 0.224 0.000
#> SRR1656541 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0162 0.9630 0.000 0.996 0.000 0.004 0.000
#> SRR1656548 3 0.0290 0.7366 0.000 0.000 0.992 0.008 0.000
#> SRR1656549 1 0.3876 0.5084 0.684 0.000 0.000 0.316 0.000
#> SRR1656551 5 0.0404 0.5061 0.000 0.000 0.000 0.012 0.988
#> SRR1656553 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656550 5 0.3835 0.5573 0.000 0.244 0.000 0.012 0.744
#> SRR1656552 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656555 5 0.4735 0.3056 0.000 0.460 0.000 0.016 0.524
#> SRR1656556 5 0.5036 0.1817 0.000 0.000 0.320 0.052 0.628
#> SRR1656557 3 0.1364 0.7336 0.012 0.000 0.952 0.036 0.000
#> SRR1656558 1 0.1043 0.7201 0.960 0.000 0.000 0.040 0.000
#> SRR1656559 1 0.5019 0.3185 0.568 0.000 0.396 0.036 0.000
#> SRR1656560 3 0.2067 0.7132 0.000 0.000 0.920 0.032 0.048
#> SRR1656561 3 0.4114 0.2558 0.376 0.000 0.624 0.000 0.000
#> SRR1656562 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656563 1 0.1750 0.7103 0.936 0.000 0.036 0.028 0.000
#> SRR1656564 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656566 1 0.4074 0.4522 0.636 0.000 0.000 0.364 0.000
#> SRR1656568 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 5 0.4252 0.5501 0.000 0.340 0.000 0.008 0.652
#> SRR1656569 3 0.4808 0.4869 0.000 0.000 0.620 0.032 0.348
#> SRR1656570 1 0.0609 0.7245 0.980 0.000 0.000 0.020 0.000
#> SRR1656571 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.4197 0.2744 0.000 0.000 0.244 0.028 0.728
#> SRR1656572 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656574 1 0.4527 0.5250 0.692 0.000 0.272 0.036 0.000
#> SRR1656575 1 0.0000 0.7276 1.000 0.000 0.000 0.000 0.000
#> SRR1656576 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.0703 0.9424 0.000 0.976 0.000 0.024 0.000
#> SRR1656577 1 0.4980 0.3550 0.584 0.000 0.380 0.036 0.000
#> SRR1656579 5 0.4211 0.5334 0.000 0.360 0.000 0.004 0.636
#> SRR1656580 3 0.5071 0.1025 0.424 0.000 0.540 0.036 0.000
#> SRR1656581 5 0.4264 0.4737 0.000 0.376 0.000 0.004 0.620
#> SRR1656582 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 4 0.4305 -0.0642 0.000 0.000 0.000 0.512 0.488
#> SRR1656584 1 0.2852 0.6421 0.828 0.000 0.000 0.172 0.000
#> SRR1656583 5 0.4306 -0.1209 0.000 0.000 0.000 0.492 0.508
#> SRR1656586 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.4489 0.2014 0.000 0.420 0.000 0.572 0.008
#> SRR1656588 5 0.4252 0.5501 0.000 0.340 0.000 0.008 0.652
#> SRR1656589 2 0.0000 0.9669 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.4302 0.2151 0.520 0.000 0.000 0.480 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.2264 0.758 0.012 0.000 0.888 0.000 0.004 0.096
#> SRR1656462 3 0.2300 0.805 0.000 0.000 0.856 0.000 0.144 0.000
#> SRR1656465 5 0.1225 0.812 0.000 0.000 0.012 0.036 0.952 0.000
#> SRR1656467 2 0.0632 0.955 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656466 5 0.1075 0.823 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1656468 4 0.1204 0.746 0.000 0.000 0.000 0.944 0.056 0.000
#> SRR1656472 6 0.0653 0.773 0.004 0.000 0.012 0.004 0.000 0.980
#> SRR1656471 5 0.1074 0.818 0.000 0.000 0.012 0.028 0.960 0.000
#> SRR1656470 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.0692 0.826 0.000 0.000 0.020 0.004 0.976 0.000
#> SRR1656473 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2020 0.804 0.896 0.000 0.096 0.008 0.000 0.000
#> SRR1656477 4 0.0993 0.745 0.000 0.000 0.000 0.964 0.024 0.012
#> SRR1656479 3 0.1787 0.779 0.068 0.000 0.920 0.000 0.008 0.004
#> SRR1656480 4 0.0858 0.760 0.000 0.028 0.000 0.968 0.000 0.004
#> SRR1656476 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.1863 0.726 0.000 0.000 0.000 0.896 0.104 0.000
#> SRR1656482 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.3756 0.374 0.000 0.000 0.600 0.000 0.400 0.000
#> SRR1656487 5 0.0858 0.826 0.000 0.000 0.028 0.004 0.968 0.000
#> SRR1656486 1 0.2431 0.803 0.872 0.000 0.116 0.004 0.004 0.004
#> SRR1656488 5 0.2996 0.694 0.000 0.000 0.228 0.000 0.772 0.000
#> SRR1656484 1 0.3830 0.603 0.620 0.000 0.376 0.000 0.000 0.004
#> SRR1656489 3 0.3426 0.420 0.276 0.000 0.720 0.000 0.000 0.004
#> SRR1656491 3 0.3189 0.732 0.000 0.000 0.760 0.000 0.236 0.004
#> SRR1656490 1 0.1931 0.797 0.916 0.000 0.068 0.008 0.004 0.004
#> SRR1656492 5 0.5916 0.330 0.240 0.000 0.256 0.004 0.500 0.000
#> SRR1656493 1 0.3048 0.677 0.824 0.000 0.004 0.020 0.000 0.152
#> SRR1656495 6 0.2207 0.737 0.076 0.008 0.000 0.016 0.000 0.900
#> SRR1656496 3 0.0837 0.810 0.020 0.000 0.972 0.000 0.004 0.004
#> SRR1656494 2 0.2255 0.876 0.000 0.892 0.000 0.028 0.000 0.080
#> SRR1656497 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.3817 0.282 0.000 0.000 0.568 0.000 0.432 0.000
#> SRR1656500 3 0.2135 0.810 0.000 0.000 0.872 0.000 0.128 0.000
#> SRR1656501 1 0.2377 0.804 0.868 0.000 0.124 0.004 0.000 0.004
#> SRR1656498 1 0.2595 0.797 0.836 0.000 0.160 0.000 0.000 0.004
#> SRR1656504 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.0653 0.773 0.004 0.000 0.012 0.004 0.000 0.980
#> SRR1656503 3 0.0858 0.803 0.028 0.000 0.968 0.000 0.000 0.004
#> SRR1656507 1 0.2402 0.803 0.856 0.000 0.140 0.004 0.000 0.000
#> SRR1656508 1 0.3881 0.576 0.600 0.000 0.396 0.000 0.000 0.004
#> SRR1656505 4 0.1007 0.762 0.000 0.044 0.000 0.956 0.000 0.000
#> SRR1656506 5 0.3126 0.646 0.000 0.000 0.248 0.000 0.752 0.000
#> SRR1656509 6 0.3341 0.636 0.000 0.000 0.208 0.004 0.012 0.776
#> SRR1656510 4 0.6966 0.260 0.056 0.168 0.012 0.404 0.360 0.000
#> SRR1656511 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656513 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.0748 0.810 0.016 0.000 0.976 0.000 0.004 0.004
#> SRR1656515 2 0.3797 0.209 0.000 0.580 0.000 0.420 0.000 0.000
#> SRR1656516 3 0.3136 0.559 0.228 0.000 0.768 0.000 0.000 0.004
#> SRR1656518 1 0.1858 0.803 0.904 0.000 0.092 0.004 0.000 0.000
#> SRR1656517 1 0.2402 0.803 0.856 0.000 0.140 0.004 0.000 0.000
#> SRR1656519 3 0.2300 0.805 0.000 0.000 0.856 0.000 0.144 0.000
#> SRR1656522 3 0.0748 0.810 0.016 0.000 0.976 0.000 0.004 0.004
#> SRR1656523 2 0.1824 0.923 0.004 0.936 0.004 0.020 0.024 0.012
#> SRR1656521 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.2378 0.800 0.000 0.000 0.848 0.000 0.152 0.000
#> SRR1656524 1 0.3558 0.572 0.736 0.000 0.000 0.016 0.000 0.248
#> SRR1656525 3 0.2631 0.778 0.000 0.000 0.820 0.000 0.180 0.000
#> SRR1656526 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656530 5 0.2823 0.727 0.000 0.000 0.204 0.000 0.796 0.000
#> SRR1656529 5 0.0603 0.825 0.000 0.000 0.016 0.004 0.980 0.000
#> SRR1656531 1 0.5565 0.561 0.488 0.000 0.368 0.000 0.000 0.144
#> SRR1656528 5 0.1814 0.798 0.000 0.000 0.100 0.000 0.900 0.000
#> SRR1656534 3 0.2006 0.816 0.004 0.000 0.892 0.000 0.104 0.000
#> SRR1656533 1 0.2520 0.799 0.844 0.000 0.152 0.000 0.000 0.004
#> SRR1656536 4 0.2883 0.643 0.000 0.000 0.000 0.788 0.212 0.000
#> SRR1656532 2 0.2191 0.857 0.000 0.876 0.000 0.004 0.000 0.120
#> SRR1656537 1 0.3209 0.687 0.816 0.000 0.016 0.012 0.000 0.156
#> SRR1656538 3 0.1176 0.814 0.024 0.000 0.956 0.000 0.020 0.000
#> SRR1656535 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 5 0.0909 0.825 0.000 0.000 0.020 0.012 0.968 0.000
#> SRR1656544 3 0.2416 0.797 0.000 0.000 0.844 0.000 0.156 0.000
#> SRR1656542 3 0.2300 0.805 0.000 0.000 0.856 0.000 0.144 0.000
#> SRR1656543 3 0.2416 0.797 0.000 0.000 0.844 0.000 0.156 0.000
#> SRR1656545 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.2491 0.791 0.000 0.000 0.836 0.000 0.164 0.000
#> SRR1656546 1 0.0862 0.759 0.972 0.008 0.000 0.016 0.000 0.004
#> SRR1656541 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.1714 0.885 0.000 0.908 0.000 0.092 0.000 0.000
#> SRR1656548 3 0.3620 0.509 0.000 0.000 0.648 0.000 0.352 0.000
#> SRR1656549 1 0.1204 0.758 0.960 0.000 0.004 0.016 0.004 0.016
#> SRR1656551 4 0.3634 0.506 0.000 0.000 0.000 0.644 0.356 0.000
#> SRR1656553 3 0.1910 0.815 0.000 0.000 0.892 0.000 0.108 0.000
#> SRR1656550 4 0.0993 0.760 0.000 0.024 0.000 0.964 0.012 0.000
#> SRR1656552 2 0.0146 0.972 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656554 5 0.0508 0.823 0.000 0.000 0.012 0.004 0.984 0.000
#> SRR1656555 4 0.6163 0.307 0.000 0.332 0.004 0.472 0.180 0.012
#> SRR1656556 5 0.4720 0.184 0.000 0.000 0.012 0.404 0.556 0.028
#> SRR1656557 3 0.2300 0.805 0.000 0.000 0.856 0.000 0.144 0.000
#> SRR1656558 1 0.1524 0.794 0.932 0.000 0.060 0.008 0.000 0.000
#> SRR1656559 3 0.0922 0.807 0.024 0.000 0.968 0.000 0.004 0.004
#> SRR1656560 5 0.2762 0.736 0.000 0.000 0.196 0.000 0.804 0.000
#> SRR1656561 3 0.2685 0.788 0.080 0.000 0.872 0.000 0.044 0.004
#> SRR1656562 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656563 1 0.3993 0.553 0.592 0.000 0.400 0.000 0.000 0.008
#> SRR1656564 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656565 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656566 1 0.2714 0.695 0.848 0.000 0.004 0.012 0.000 0.136
#> SRR1656568 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656567 4 0.1075 0.761 0.000 0.048 0.000 0.952 0.000 0.000
#> SRR1656569 5 0.0603 0.825 0.000 0.000 0.016 0.004 0.980 0.000
#> SRR1656570 1 0.4009 0.627 0.632 0.000 0.356 0.000 0.004 0.008
#> SRR1656571 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.3558 0.550 0.004 0.000 0.008 0.192 0.780 0.016
#> SRR1656572 2 0.0146 0.973 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656574 3 0.2738 0.632 0.176 0.000 0.820 0.000 0.000 0.004
#> SRR1656575 1 0.2520 0.799 0.844 0.000 0.152 0.000 0.000 0.004
#> SRR1656576 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 2 0.0291 0.970 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1656577 3 0.1152 0.793 0.044 0.000 0.952 0.000 0.000 0.004
#> SRR1656579 4 0.1444 0.745 0.000 0.072 0.000 0.928 0.000 0.000
#> SRR1656580 3 0.0508 0.810 0.012 0.000 0.984 0.000 0.000 0.004
#> SRR1656581 4 0.5148 0.445 0.016 0.272 0.004 0.648 0.048 0.012
#> SRR1656582 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 6 0.3287 0.658 0.000 0.000 0.000 0.220 0.012 0.768
#> SRR1656584 1 0.0870 0.767 0.972 0.000 0.012 0.012 0.000 0.004
#> SRR1656583 6 0.3695 0.444 0.000 0.000 0.000 0.376 0.000 0.624
#> SRR1656586 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 6 0.2743 0.642 0.000 0.164 0.000 0.008 0.000 0.828
#> SRR1656588 4 0.1204 0.757 0.000 0.056 0.000 0.944 0.000 0.000
#> SRR1656589 2 0.0000 0.974 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.4555 0.214 0.532 0.000 0.016 0.012 0.000 0.440
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 13572 rows and 129 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 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 0.937 0.953 0.981 0.4383 0.563 0.563
#> 3 3 0.653 0.745 0.888 0.5059 0.699 0.499
#> 4 4 0.737 0.709 0.874 0.1238 0.775 0.449
#> 5 5 0.671 0.598 0.802 0.0629 0.897 0.633
#> 6 6 0.798 0.682 0.855 0.0510 0.885 0.531
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
#> SRR1656463 2 0.0000 0.971 0.000 1.000
#> SRR1656464 1 0.0000 0.984 1.000 0.000
#> SRR1656462 1 0.0000 0.984 1.000 0.000
#> SRR1656465 1 0.0000 0.984 1.000 0.000
#> SRR1656467 2 0.0000 0.971 0.000 1.000
#> SRR1656466 1 0.0000 0.984 1.000 0.000
#> SRR1656468 1 0.1184 0.970 0.984 0.016
#> SRR1656472 1 0.0000 0.984 1.000 0.000
#> SRR1656471 1 0.0000 0.984 1.000 0.000
#> SRR1656470 2 0.0000 0.971 0.000 1.000
#> SRR1656469 1 0.0000 0.984 1.000 0.000
#> SRR1656473 2 0.0000 0.971 0.000 1.000
#> SRR1656474 2 0.0000 0.971 0.000 1.000
#> SRR1656475 2 0.0000 0.971 0.000 1.000
#> SRR1656478 1 0.0000 0.984 1.000 0.000
#> SRR1656477 1 0.6973 0.763 0.812 0.188
#> SRR1656479 1 0.0000 0.984 1.000 0.000
#> SRR1656480 2 0.0000 0.971 0.000 1.000
#> SRR1656476 2 0.0000 0.971 0.000 1.000
#> SRR1656481 1 0.0672 0.977 0.992 0.008
#> SRR1656482 2 0.0000 0.971 0.000 1.000
#> SRR1656483 2 0.0000 0.971 0.000 1.000
#> SRR1656485 1 0.0000 0.984 1.000 0.000
#> SRR1656487 1 0.0000 0.984 1.000 0.000
#> SRR1656486 1 0.0000 0.984 1.000 0.000
#> SRR1656488 1 0.0000 0.984 1.000 0.000
#> SRR1656484 1 0.0000 0.984 1.000 0.000
#> SRR1656489 1 0.0000 0.984 1.000 0.000
#> SRR1656491 1 0.0000 0.984 1.000 0.000
#> SRR1656490 1 0.0000 0.984 1.000 0.000
#> SRR1656492 1 0.0000 0.984 1.000 0.000
#> SRR1656493 1 0.0000 0.984 1.000 0.000
#> SRR1656495 1 0.6973 0.769 0.812 0.188
#> SRR1656496 1 0.0000 0.984 1.000 0.000
#> SRR1656494 2 0.0000 0.971 0.000 1.000
#> SRR1656497 2 0.0000 0.971 0.000 1.000
#> SRR1656499 1 0.0000 0.984 1.000 0.000
#> SRR1656500 1 0.0000 0.984 1.000 0.000
#> SRR1656501 1 0.0000 0.984 1.000 0.000
#> SRR1656498 1 0.0000 0.984 1.000 0.000
#> SRR1656504 2 0.0000 0.971 0.000 1.000
#> SRR1656502 1 0.0000 0.984 1.000 0.000
#> SRR1656503 1 0.0000 0.984 1.000 0.000
#> SRR1656507 1 0.0000 0.984 1.000 0.000
#> SRR1656508 1 0.0000 0.984 1.000 0.000
#> SRR1656505 2 0.9710 0.318 0.400 0.600
#> SRR1656506 1 0.0000 0.984 1.000 0.000
#> SRR1656509 1 0.0000 0.984 1.000 0.000
#> SRR1656510 1 0.0000 0.984 1.000 0.000
#> SRR1656511 2 0.1414 0.953 0.020 0.980
#> SRR1656513 2 0.0000 0.971 0.000 1.000
#> SRR1656512 2 0.0000 0.971 0.000 1.000
#> SRR1656514 1 0.0000 0.984 1.000 0.000
#> SRR1656515 2 0.0000 0.971 0.000 1.000
#> SRR1656516 1 0.0000 0.984 1.000 0.000
#> SRR1656518 1 0.0000 0.984 1.000 0.000
#> SRR1656517 1 0.0000 0.984 1.000 0.000
#> SRR1656519 1 0.0000 0.984 1.000 0.000
#> SRR1656522 1 0.0000 0.984 1.000 0.000
#> SRR1656523 1 0.6973 0.769 0.812 0.188
#> SRR1656521 2 0.0000 0.971 0.000 1.000
#> SRR1656520 1 0.0000 0.984 1.000 0.000
#> SRR1656524 1 0.0000 0.984 1.000 0.000
#> SRR1656525 1 0.0000 0.984 1.000 0.000
#> SRR1656526 2 0.0000 0.971 0.000 1.000
#> SRR1656527 2 0.0000 0.971 0.000 1.000
#> SRR1656530 1 0.0000 0.984 1.000 0.000
#> SRR1656529 1 0.0000 0.984 1.000 0.000
#> SRR1656531 1 0.0000 0.984 1.000 0.000
#> SRR1656528 1 0.0000 0.984 1.000 0.000
#> SRR1656534 1 0.0000 0.984 1.000 0.000
#> SRR1656533 1 0.0000 0.984 1.000 0.000
#> SRR1656536 1 0.0000 0.984 1.000 0.000
#> SRR1656532 2 0.0000 0.971 0.000 1.000
#> SRR1656537 1 0.0000 0.984 1.000 0.000
#> SRR1656538 1 0.0000 0.984 1.000 0.000
#> SRR1656535 2 0.0000 0.971 0.000 1.000
#> SRR1656539 1 0.0000 0.984 1.000 0.000
#> SRR1656544 1 0.0000 0.984 1.000 0.000
#> SRR1656542 1 0.0000 0.984 1.000 0.000
#> SRR1656543 1 0.0000 0.984 1.000 0.000
#> SRR1656545 2 0.0000 0.971 0.000 1.000
#> SRR1656540 1 0.0000 0.984 1.000 0.000
#> SRR1656546 1 0.0000 0.984 1.000 0.000
#> SRR1656541 2 0.0000 0.971 0.000 1.000
#> SRR1656547 2 0.0000 0.971 0.000 1.000
#> SRR1656548 1 0.0000 0.984 1.000 0.000
#> SRR1656549 1 0.0000 0.984 1.000 0.000
#> SRR1656551 1 0.0000 0.984 1.000 0.000
#> SRR1656553 1 0.0000 0.984 1.000 0.000
#> SRR1656550 2 0.0000 0.971 0.000 1.000
#> SRR1656552 2 0.9661 0.358 0.392 0.608
#> SRR1656554 1 0.0000 0.984 1.000 0.000
#> SRR1656555 1 0.2778 0.938 0.952 0.048
#> SRR1656556 1 0.0672 0.977 0.992 0.008
#> SRR1656557 1 0.0000 0.984 1.000 0.000
#> SRR1656558 1 0.0000 0.984 1.000 0.000
#> SRR1656559 1 0.0000 0.984 1.000 0.000
#> SRR1656560 1 0.0000 0.984 1.000 0.000
#> SRR1656561 1 0.0000 0.984 1.000 0.000
#> SRR1656562 1 0.6973 0.769 0.812 0.188
#> SRR1656563 1 0.0000 0.984 1.000 0.000
#> SRR1656564 2 0.0000 0.971 0.000 1.000
#> SRR1656565 2 0.0000 0.971 0.000 1.000
#> SRR1656566 1 0.0000 0.984 1.000 0.000
#> SRR1656568 2 0.0000 0.971 0.000 1.000
#> SRR1656567 2 0.0000 0.971 0.000 1.000
#> SRR1656569 1 0.0000 0.984 1.000 0.000
#> SRR1656570 1 0.0000 0.984 1.000 0.000
#> SRR1656571 2 0.0000 0.971 0.000 1.000
#> SRR1656573 1 0.0000 0.984 1.000 0.000
#> SRR1656572 2 0.9000 0.538 0.316 0.684
#> SRR1656574 1 0.0000 0.984 1.000 0.000
#> SRR1656575 1 0.0000 0.984 1.000 0.000
#> SRR1656576 2 0.0000 0.971 0.000 1.000
#> SRR1656578 2 0.0000 0.971 0.000 1.000
#> SRR1656577 1 0.0000 0.984 1.000 0.000
#> SRR1656579 2 0.0000 0.971 0.000 1.000
#> SRR1656580 1 0.0000 0.984 1.000 0.000
#> SRR1656581 1 0.0000 0.984 1.000 0.000
#> SRR1656582 2 0.0000 0.971 0.000 1.000
#> SRR1656585 1 0.0000 0.984 1.000 0.000
#> SRR1656584 1 0.0000 0.984 1.000 0.000
#> SRR1656583 1 0.9248 0.480 0.660 0.340
#> SRR1656586 2 0.0000 0.971 0.000 1.000
#> SRR1656587 1 0.6973 0.769 0.812 0.188
#> SRR1656588 2 0.0000 0.971 0.000 1.000
#> SRR1656589 2 0.0000 0.971 0.000 1.000
#> SRR1656590 1 0.0000 0.984 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656462 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656465 3 0.6307 0.0270 0.488 0.000 0.512
#> SRR1656467 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656466 1 0.6192 0.2209 0.580 0.000 0.420
#> SRR1656468 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656472 3 0.0747 0.7453 0.016 0.000 0.984
#> SRR1656471 1 0.5835 0.3832 0.660 0.000 0.340
#> SRR1656470 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656469 3 0.5591 0.4659 0.304 0.000 0.696
#> SRR1656473 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656478 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656477 3 0.0237 0.7459 0.000 0.004 0.996
#> SRR1656479 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656480 3 0.5529 0.4146 0.000 0.296 0.704
#> SRR1656476 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656485 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656487 1 0.3482 0.7615 0.872 0.000 0.128
#> SRR1656486 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656488 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656484 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656489 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656491 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656490 3 0.2959 0.7222 0.100 0.000 0.900
#> SRR1656492 1 0.6309 -0.2143 0.504 0.000 0.496
#> SRR1656493 3 0.4750 0.6648 0.216 0.000 0.784
#> SRR1656495 3 0.5008 0.6462 0.016 0.180 0.804
#> SRR1656496 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656494 2 0.4062 0.8170 0.000 0.836 0.164
#> SRR1656497 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656501 3 0.5905 0.5458 0.352 0.000 0.648
#> SRR1656498 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656502 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656503 3 0.6111 0.4733 0.396 0.000 0.604
#> SRR1656507 1 0.5988 0.2167 0.632 0.000 0.368
#> SRR1656508 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656505 3 0.0237 0.7459 0.000 0.004 0.996
#> SRR1656506 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656509 3 0.6154 0.2610 0.408 0.000 0.592
#> SRR1656510 3 0.0592 0.7454 0.012 0.000 0.988
#> SRR1656511 3 0.6299 0.0646 0.000 0.476 0.524
#> SRR1656513 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656515 2 0.0747 0.9480 0.000 0.984 0.016
#> SRR1656516 1 0.2625 0.8230 0.916 0.000 0.084
#> SRR1656518 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656517 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656523 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656521 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656520 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656524 3 0.4974 0.6513 0.236 0.000 0.764
#> SRR1656525 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656526 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656530 1 0.3340 0.7872 0.880 0.000 0.120
#> SRR1656529 3 0.6305 0.0442 0.484 0.000 0.516
#> SRR1656531 1 0.4178 0.6853 0.828 0.000 0.172
#> SRR1656528 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656534 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656533 1 0.0237 0.9080 0.996 0.000 0.004
#> SRR1656536 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656532 2 0.0892 0.9453 0.000 0.980 0.020
#> SRR1656537 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656538 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656535 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656539 3 0.6140 0.2701 0.404 0.000 0.596
#> SRR1656544 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656542 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656543 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656540 1 0.1529 0.8735 0.960 0.000 0.040
#> SRR1656546 3 0.4750 0.6648 0.216 0.000 0.784
#> SRR1656541 2 0.3340 0.8627 0.000 0.880 0.120
#> SRR1656547 3 0.1643 0.7311 0.000 0.044 0.956
#> SRR1656548 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656549 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656551 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656553 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656550 3 0.5254 0.4757 0.000 0.264 0.736
#> SRR1656552 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656554 3 0.6154 0.2610 0.408 0.000 0.592
#> SRR1656555 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656556 3 0.6168 0.2511 0.412 0.000 0.588
#> SRR1656557 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656558 3 0.5988 0.5163 0.368 0.000 0.632
#> SRR1656559 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656560 1 0.2165 0.8468 0.936 0.000 0.064
#> SRR1656561 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656562 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656563 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656565 2 0.0892 0.9453 0.000 0.980 0.020
#> SRR1656566 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656568 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656567 2 0.5810 0.5699 0.000 0.664 0.336
#> SRR1656569 3 0.4555 0.6103 0.200 0.000 0.800
#> SRR1656570 1 0.3551 0.7688 0.868 0.000 0.132
#> SRR1656571 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656572 3 0.4555 0.5682 0.000 0.200 0.800
#> SRR1656574 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656575 1 0.6274 -0.1136 0.544 0.000 0.456
#> SRR1656576 2 0.4504 0.7798 0.000 0.804 0.196
#> SRR1656578 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656579 2 0.5810 0.5699 0.000 0.664 0.336
#> SRR1656580 1 0.0000 0.9114 1.000 0.000 0.000
#> SRR1656581 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656584 3 0.5810 0.5653 0.336 0.000 0.664
#> SRR1656583 3 0.1289 0.7367 0.000 0.032 0.968
#> SRR1656586 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656587 3 0.0000 0.7466 0.000 0.000 1.000
#> SRR1656588 2 0.3551 0.8509 0.000 0.868 0.132
#> SRR1656589 2 0.0000 0.9573 0.000 1.000 0.000
#> SRR1656590 3 0.5760 0.5735 0.328 0.000 0.672
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656464 1 0.3024 0.7921 0.852 0.000 0.148 0.000
#> SRR1656462 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656465 4 0.4790 0.4539 0.000 0.000 0.380 0.620
#> SRR1656467 2 0.4843 0.4605 0.000 0.604 0.000 0.396
#> SRR1656466 4 0.4790 0.4539 0.000 0.000 0.380 0.620
#> SRR1656468 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.2984 0.7862 0.888 0.000 0.028 0.084
#> SRR1656471 4 0.4790 0.4539 0.000 0.000 0.380 0.620
#> SRR1656470 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656469 4 0.5284 0.4658 0.016 0.000 0.368 0.616
#> SRR1656473 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656477 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656479 3 0.3754 0.7919 0.064 0.000 0.852 0.084
#> SRR1656480 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656483 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656487 3 0.3486 0.7088 0.000 0.000 0.812 0.188
#> SRR1656486 3 0.3024 0.7957 0.148 0.000 0.852 0.000
#> SRR1656488 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656484 1 0.4543 0.4291 0.676 0.000 0.324 0.000
#> SRR1656489 3 0.1302 0.8602 0.044 0.000 0.956 0.000
#> SRR1656491 4 0.6276 0.4077 0.064 0.000 0.380 0.556
#> SRR1656490 4 0.6140 0.4661 0.064 0.000 0.340 0.596
#> SRR1656492 3 0.1716 0.8518 0.064 0.000 0.936 0.000
#> SRR1656493 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656495 1 0.4454 0.4806 0.692 0.000 0.000 0.308
#> SRR1656496 3 0.5397 0.5747 0.064 0.000 0.716 0.220
#> SRR1656494 4 0.4933 -0.0624 0.000 0.432 0.000 0.568
#> SRR1656497 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656501 3 0.3024 0.7957 0.148 0.000 0.852 0.000
#> SRR1656498 1 0.1716 0.8334 0.936 0.000 0.064 0.000
#> SRR1656504 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.1940 0.8033 0.924 0.000 0.000 0.076
#> SRR1656503 3 0.2081 0.8377 0.000 0.000 0.916 0.084
#> SRR1656507 1 0.3726 0.7176 0.788 0.000 0.212 0.000
#> SRR1656508 1 0.3024 0.7921 0.852 0.000 0.148 0.000
#> SRR1656505 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656506 3 0.2081 0.8377 0.000 0.000 0.916 0.084
#> SRR1656509 4 0.5326 0.4464 0.016 0.000 0.380 0.604
#> SRR1656510 4 0.1716 0.7217 0.064 0.000 0.000 0.936
#> SRR1656511 2 0.6384 0.2523 0.064 0.496 0.000 0.440
#> SRR1656513 2 0.4830 0.4666 0.000 0.608 0.000 0.392
#> SRR1656512 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656514 1 0.4817 0.4303 0.612 0.000 0.388 0.000
#> SRR1656515 2 0.4941 0.3885 0.000 0.564 0.000 0.436
#> SRR1656516 3 0.2081 0.8426 0.084 0.000 0.916 0.000
#> SRR1656518 1 0.4955 0.1044 0.556 0.000 0.444 0.000
#> SRR1656517 1 0.1716 0.8334 0.936 0.000 0.064 0.000
#> SRR1656519 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.4454 0.4612 0.308 0.000 0.692 0.000
#> SRR1656523 4 0.1716 0.7217 0.064 0.000 0.000 0.936
#> SRR1656521 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656525 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656526 2 0.0707 0.8833 0.000 0.980 0.000 0.020
#> SRR1656527 2 0.1174 0.8765 0.020 0.968 0.000 0.012
#> SRR1656530 3 0.4094 0.7629 0.056 0.000 0.828 0.116
#> SRR1656529 4 0.5231 0.4422 0.012 0.000 0.384 0.604
#> SRR1656531 1 0.3024 0.7921 0.852 0.000 0.148 0.000
#> SRR1656528 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656534 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656533 1 0.1716 0.8334 0.936 0.000 0.064 0.000
#> SRR1656536 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.5766 0.4060 0.032 0.564 0.000 0.404
#> SRR1656537 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656535 2 0.0592 0.8855 0.000 0.984 0.000 0.016
#> SRR1656539 4 0.5326 0.4464 0.016 0.000 0.380 0.604
#> SRR1656544 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656546 1 0.4500 0.4679 0.684 0.000 0.000 0.316
#> SRR1656541 4 0.5000 -0.2565 0.000 0.496 0.000 0.504
#> SRR1656547 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656548 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656549 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656551 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656553 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656552 4 0.3754 0.6625 0.064 0.084 0.000 0.852
#> SRR1656554 4 0.5326 0.4464 0.016 0.000 0.380 0.604
#> SRR1656555 4 0.0592 0.7431 0.016 0.000 0.000 0.984
#> SRR1656556 4 0.4661 0.4992 0.000 0.000 0.348 0.652
#> SRR1656557 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.1637 0.8346 0.940 0.000 0.060 0.000
#> SRR1656559 3 0.4697 0.3561 0.356 0.000 0.644 0.000
#> SRR1656560 3 0.1211 0.8675 0.000 0.000 0.960 0.040
#> SRR1656561 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656562 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656563 3 0.4454 0.4612 0.308 0.000 0.692 0.000
#> SRR1656564 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656565 2 0.4941 0.3896 0.000 0.564 0.000 0.436
#> SRR1656566 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656568 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656569 4 0.6212 0.4114 0.060 0.000 0.380 0.560
#> SRR1656570 3 0.3074 0.7993 0.152 0.000 0.848 0.000
#> SRR1656571 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656573 4 0.1716 0.7217 0.064 0.000 0.000 0.936
#> SRR1656572 4 0.5431 0.4620 0.064 0.224 0.000 0.712
#> SRR1656574 3 0.4454 0.4612 0.308 0.000 0.692 0.000
#> SRR1656575 1 0.3764 0.6678 0.784 0.000 0.216 0.000
#> SRR1656576 4 0.4916 -0.0353 0.000 0.424 0.000 0.576
#> SRR1656578 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656577 3 0.4454 0.4612 0.308 0.000 0.692 0.000
#> SRR1656579 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656580 3 0.0000 0.8900 0.000 0.000 1.000 0.000
#> SRR1656581 4 0.1716 0.7217 0.064 0.000 0.000 0.936
#> SRR1656582 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656585 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656584 1 0.0000 0.8431 1.000 0.000 0.000 0.000
#> SRR1656583 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656586 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656587 4 0.0000 0.7486 0.000 0.000 0.000 1.000
#> SRR1656588 4 0.4989 -0.1873 0.000 0.472 0.000 0.528
#> SRR1656589 2 0.0000 0.8948 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0000 0.8431 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.3913 0.6151 0.000 0.676 0.000 0.324 0.000
#> SRR1656464 1 0.1851 0.7437 0.912 0.000 0.088 0.000 0.000
#> SRR1656462 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 5 0.4045 0.5213 0.000 0.000 0.356 0.000 0.644
#> SRR1656467 4 0.6551 0.3226 0.000 0.228 0.000 0.468 0.304
#> SRR1656466 5 0.4045 0.5213 0.000 0.000 0.356 0.000 0.644
#> SRR1656468 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.6774 0.4135 0.572 0.000 0.048 0.224 0.156
#> SRR1656471 5 0.4088 0.5036 0.000 0.000 0.368 0.000 0.632
#> SRR1656470 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.5345 0.5548 0.000 0.000 0.280 0.088 0.632
#> SRR1656473 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.0609 0.7504 0.980 0.000 0.000 0.020 0.000
#> SRR1656477 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656479 3 0.5505 0.5653 0.004 0.000 0.620 0.292 0.084
#> SRR1656480 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656476 2 0.3003 0.7778 0.000 0.812 0.000 0.188 0.000
#> SRR1656481 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.4150 0.4973 0.000 0.612 0.000 0.388 0.000
#> SRR1656483 2 0.1792 0.8282 0.000 0.916 0.000 0.084 0.000
#> SRR1656485 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656487 3 0.3274 0.6415 0.000 0.000 0.780 0.000 0.220
#> SRR1656486 3 0.5404 0.5399 0.088 0.000 0.620 0.292 0.000
#> SRR1656488 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656484 1 0.6371 0.3335 0.508 0.000 0.200 0.292 0.000
#> SRR1656489 3 0.2605 0.7335 0.148 0.000 0.852 0.000 0.000
#> SRR1656491 5 0.6582 0.4279 0.004 0.000 0.212 0.292 0.492
#> SRR1656490 5 0.6460 0.3201 0.004 0.000 0.156 0.400 0.440
#> SRR1656492 3 0.3884 0.6289 0.004 0.000 0.708 0.288 0.000
#> SRR1656493 4 0.4294 -0.0862 0.468 0.000 0.000 0.532 0.000
#> SRR1656495 4 0.4218 0.1998 0.332 0.000 0.000 0.660 0.008
#> SRR1656496 3 0.6582 0.3038 0.004 0.000 0.492 0.292 0.212
#> SRR1656494 5 0.4547 -0.0177 0.000 0.012 0.000 0.400 0.588
#> SRR1656497 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656500 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656501 3 0.5385 0.5448 0.088 0.000 0.624 0.288 0.000
#> SRR1656498 1 0.0162 0.7586 0.996 0.000 0.004 0.000 0.000
#> SRR1656504 2 0.3109 0.7691 0.000 0.800 0.000 0.200 0.000
#> SRR1656502 1 0.4974 0.5511 0.696 0.000 0.000 0.212 0.092
#> SRR1656503 3 0.4612 0.6908 0.000 0.000 0.736 0.180 0.084
#> SRR1656507 1 0.1478 0.7525 0.936 0.000 0.064 0.000 0.000
#> SRR1656508 1 0.1851 0.7437 0.912 0.000 0.088 0.000 0.000
#> SRR1656505 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656506 3 0.1792 0.8061 0.000 0.000 0.916 0.000 0.084
#> SRR1656509 5 0.4416 0.5147 0.000 0.000 0.356 0.012 0.632
#> SRR1656510 5 0.2179 0.5955 0.004 0.000 0.000 0.100 0.896
#> SRR1656511 4 0.1205 0.5279 0.004 0.000 0.000 0.956 0.040
#> SRR1656513 4 0.4951 0.5304 0.000 0.100 0.000 0.704 0.196
#> SRR1656512 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 1 0.3305 0.6753 0.776 0.000 0.224 0.000 0.000
#> SRR1656515 5 0.5822 -0.1081 0.000 0.108 0.000 0.344 0.548
#> SRR1656516 3 0.2230 0.7946 0.116 0.000 0.884 0.000 0.000
#> SRR1656518 1 0.6781 0.2195 0.388 0.000 0.320 0.292 0.000
#> SRR1656517 1 0.0162 0.7586 0.996 0.000 0.004 0.000 0.000
#> SRR1656519 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.4138 0.1663 0.384 0.000 0.616 0.000 0.000
#> SRR1656523 4 0.4151 0.1070 0.004 0.000 0.000 0.652 0.344
#> SRR1656521 2 0.0404 0.8502 0.000 0.988 0.000 0.012 0.000
#> SRR1656520 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 1 0.4088 0.3356 0.632 0.000 0.000 0.368 0.000
#> SRR1656525 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656526 4 0.3876 0.2933 0.000 0.316 0.000 0.684 0.000
#> SRR1656527 4 0.4088 0.1680 0.000 0.368 0.000 0.632 0.000
#> SRR1656530 3 0.3997 0.7247 0.004 0.000 0.804 0.076 0.116
#> SRR1656529 5 0.4138 0.4772 0.000 0.000 0.384 0.000 0.616
#> SRR1656531 1 0.1851 0.7437 0.912 0.000 0.088 0.000 0.000
#> SRR1656528 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656534 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656533 1 0.0162 0.7586 0.996 0.000 0.004 0.000 0.000
#> SRR1656536 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656532 4 0.4429 0.5548 0.000 0.064 0.000 0.744 0.192
#> SRR1656537 1 0.0000 0.7573 1.000 0.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656535 4 0.3876 0.2933 0.000 0.316 0.000 0.684 0.000
#> SRR1656539 5 0.4497 0.5176 0.000 0.000 0.352 0.016 0.632
#> SRR1656544 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656542 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656543 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0162 0.8683 0.000 0.000 0.996 0.000 0.004
#> SRR1656546 4 0.5877 0.0907 0.384 0.000 0.012 0.532 0.072
#> SRR1656541 4 0.4820 0.5419 0.000 0.068 0.000 0.696 0.236
#> SRR1656547 5 0.3684 0.3274 0.000 0.000 0.000 0.280 0.720
#> SRR1656548 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656549 4 0.4235 0.0108 0.424 0.000 0.000 0.576 0.000
#> SRR1656551 5 0.0290 0.6480 0.000 0.000 0.000 0.008 0.992
#> SRR1656553 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656550 5 0.0000 0.6481 0.000 0.000 0.000 0.000 1.000
#> SRR1656552 4 0.3861 0.5112 0.004 0.000 0.000 0.712 0.284
#> SRR1656554 5 0.4088 0.5036 0.000 0.000 0.368 0.000 0.632
#> SRR1656555 5 0.2773 0.5848 0.000 0.000 0.000 0.164 0.836
#> SRR1656556 5 0.3913 0.5566 0.000 0.000 0.324 0.000 0.676
#> SRR1656557 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.0162 0.7586 0.996 0.000 0.004 0.000 0.000
#> SRR1656559 1 0.4088 0.4773 0.632 0.000 0.368 0.000 0.000
#> SRR1656560 3 0.1121 0.8439 0.000 0.000 0.956 0.000 0.044
#> SRR1656561 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656562 5 0.3816 0.2873 0.000 0.000 0.000 0.304 0.696
#> SRR1656563 1 0.4227 0.3737 0.580 0.000 0.420 0.000 0.000
#> SRR1656564 2 0.4235 0.4277 0.000 0.576 0.000 0.424 0.000
#> SRR1656565 4 0.5404 0.5391 0.000 0.100 0.000 0.636 0.264
#> SRR1656566 1 0.0000 0.7573 1.000 0.000 0.000 0.000 0.000
#> SRR1656568 2 0.3730 0.6708 0.000 0.712 0.000 0.288 0.000
#> SRR1656567 5 0.3039 0.4595 0.000 0.000 0.000 0.192 0.808
#> SRR1656569 5 0.6383 0.4989 0.004 0.000 0.248 0.208 0.540
#> SRR1656570 3 0.4380 0.5275 0.304 0.000 0.676 0.020 0.000
#> SRR1656571 2 0.3074 0.7723 0.000 0.804 0.000 0.196 0.000
#> SRR1656573 5 0.3906 0.4874 0.004 0.000 0.000 0.292 0.704
#> SRR1656572 4 0.3607 0.5415 0.004 0.000 0.000 0.752 0.244
#> SRR1656574 1 0.4126 0.4567 0.620 0.000 0.380 0.000 0.000
#> SRR1656575 1 0.2661 0.7333 0.888 0.000 0.056 0.056 0.000
#> SRR1656576 4 0.4457 0.4387 0.000 0.012 0.000 0.620 0.368
#> SRR1656578 4 0.3752 0.3269 0.000 0.292 0.000 0.708 0.000
#> SRR1656577 1 0.4126 0.4567 0.620 0.000 0.380 0.000 0.000
#> SRR1656579 5 0.3074 0.4546 0.000 0.000 0.000 0.196 0.804
#> SRR1656580 3 0.0000 0.8706 0.000 0.000 1.000 0.000 0.000
#> SRR1656581 4 0.4425 -0.1210 0.004 0.000 0.000 0.544 0.452
#> SRR1656582 4 0.3876 0.2933 0.000 0.316 0.000 0.684 0.000
#> SRR1656585 5 0.3039 0.5775 0.000 0.000 0.000 0.192 0.808
#> SRR1656584 1 0.0000 0.7573 1.000 0.000 0.000 0.000 0.000
#> SRR1656583 5 0.0963 0.6427 0.000 0.000 0.000 0.036 0.964
#> SRR1656586 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 5 0.0609 0.6400 0.000 0.000 0.000 0.020 0.980
#> SRR1656588 5 0.4333 0.3717 0.000 0.048 0.000 0.212 0.740
#> SRR1656589 2 0.0000 0.8524 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.0000 0.7573 1.000 0.000 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
#> SRR1656463 4 0.3266 0.450 0.000 0.272 0.000 0.728 0.000 0.000
#> SRR1656464 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656462 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.3563 0.513 0.000 0.000 0.336 0.000 0.664 0.000
#> SRR1656467 4 0.1462 0.701 0.000 0.008 0.000 0.936 0.056 0.000
#> SRR1656466 5 0.3563 0.513 0.000 0.000 0.336 0.000 0.664 0.000
#> SRR1656468 5 0.0000 0.668 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656472 6 0.5225 0.575 0.204 0.000 0.000 0.184 0.000 0.612
#> SRR1656471 5 0.3607 0.493 0.000 0.000 0.348 0.000 0.652 0.000
#> SRR1656470 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.4843 0.564 0.000 0.000 0.232 0.000 0.652 0.116
#> SRR1656473 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2178 0.878 0.868 0.000 0.000 0.000 0.000 0.132
#> SRR1656477 5 0.0000 0.668 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656479 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656480 5 0.0260 0.664 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1656476 2 0.3390 0.640 0.000 0.704 0.000 0.296 0.000 0.000
#> SRR1656481 5 0.0000 0.668 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656482 4 0.2730 0.583 0.000 0.192 0.000 0.808 0.000 0.000
#> SRR1656483 2 0.3717 0.544 0.000 0.616 0.000 0.384 0.000 0.000
#> SRR1656485 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656487 3 0.2697 0.684 0.000 0.000 0.812 0.000 0.188 0.000
#> SRR1656486 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656488 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656484 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656489 3 0.3023 0.661 0.232 0.000 0.768 0.000 0.000 0.000
#> SRR1656491 6 0.1610 0.793 0.000 0.000 0.000 0.000 0.084 0.916
#> SRR1656490 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656492 6 0.2219 0.722 0.000 0.000 0.136 0.000 0.000 0.864
#> SRR1656493 6 0.1610 0.802 0.084 0.000 0.000 0.000 0.000 0.916
#> SRR1656495 6 0.2660 0.785 0.084 0.000 0.000 0.048 0.000 0.868
#> SRR1656496 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656494 4 0.3634 0.467 0.000 0.000 0.000 0.644 0.356 0.000
#> SRR1656497 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656500 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656501 6 0.0260 0.825 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1656498 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656504 2 0.3727 0.541 0.000 0.612 0.000 0.388 0.000 0.000
#> SRR1656502 6 0.5404 0.543 0.236 0.000 0.000 0.184 0.000 0.580
#> SRR1656503 3 0.3857 0.165 0.000 0.000 0.532 0.000 0.000 0.468
#> SRR1656507 1 0.2147 0.906 0.896 0.000 0.020 0.000 0.000 0.084
#> SRR1656508 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656505 5 0.0000 0.668 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656506 3 0.1610 0.806 0.000 0.000 0.916 0.000 0.084 0.000
#> SRR1656509 5 0.3898 0.507 0.000 0.000 0.336 0.000 0.652 0.012
#> SRR1656510 6 0.3817 0.368 0.000 0.000 0.000 0.000 0.432 0.568
#> SRR1656511 4 0.3867 -0.104 0.000 0.000 0.000 0.512 0.000 0.488
#> SRR1656513 4 0.0000 0.708 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656512 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 1 0.1610 0.902 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1656515 4 0.3634 0.467 0.000 0.000 0.000 0.644 0.356 0.000
#> SRR1656516 3 0.4617 0.559 0.252 0.000 0.664 0.000 0.000 0.084
#> SRR1656518 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656517 1 0.1610 0.911 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1656519 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656522 3 0.3620 0.360 0.352 0.000 0.648 0.000 0.000 0.000
#> SRR1656523 6 0.1838 0.795 0.000 0.000 0.000 0.068 0.016 0.916
#> SRR1656521 2 0.0260 0.840 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656520 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 6 0.3867 0.172 0.488 0.000 0.000 0.000 0.000 0.512
#> SRR1656525 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656526 4 0.1910 0.664 0.000 0.108 0.000 0.892 0.000 0.000
#> SRR1656527 4 0.1663 0.657 0.000 0.088 0.000 0.912 0.000 0.000
#> SRR1656530 3 0.3266 0.638 0.000 0.000 0.728 0.000 0.000 0.272
#> SRR1656529 3 0.3810 0.105 0.000 0.000 0.572 0.000 0.428 0.000
#> SRR1656531 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656528 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656534 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656533 1 0.1610 0.911 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1656536 5 0.0000 0.668 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656532 4 0.0000 0.708 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656537 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656538 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656535 4 0.2003 0.658 0.000 0.116 0.000 0.884 0.000 0.000
#> SRR1656539 5 0.3969 0.510 0.000 0.000 0.332 0.000 0.652 0.016
#> SRR1656544 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656542 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656543 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656546 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656541 4 0.0000 0.708 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656547 4 0.3774 0.372 0.000 0.000 0.000 0.592 0.408 0.000
#> SRR1656548 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656549 6 0.0000 0.828 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1656551 5 0.0547 0.667 0.000 0.000 0.000 0.000 0.980 0.020
#> SRR1656553 3 0.0260 0.878 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1656550 5 0.0146 0.666 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1656552 6 0.5470 0.308 0.000 0.000 0.000 0.136 0.348 0.516
#> SRR1656554 5 0.3607 0.493 0.000 0.000 0.348 0.000 0.652 0.000
#> SRR1656555 5 0.1765 0.645 0.000 0.000 0.000 0.000 0.904 0.096
#> SRR1656556 5 0.3446 0.545 0.000 0.000 0.308 0.000 0.692 0.000
#> SRR1656557 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.1610 0.911 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1656559 1 0.1610 0.902 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1656560 3 0.1075 0.847 0.000 0.000 0.952 0.000 0.048 0.000
#> SRR1656561 3 0.1610 0.821 0.000 0.000 0.916 0.000 0.000 0.084
#> SRR1656562 5 0.2562 0.541 0.000 0.000 0.000 0.172 0.828 0.000
#> SRR1656563 1 0.3244 0.667 0.732 0.000 0.268 0.000 0.000 0.000
#> SRR1656564 4 0.3288 0.437 0.000 0.276 0.000 0.724 0.000 0.000
#> SRR1656565 4 0.3221 0.565 0.000 0.000 0.000 0.736 0.264 0.000
#> SRR1656566 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1656568 2 0.3737 0.534 0.000 0.608 0.000 0.392 0.000 0.000
#> SRR1656567 5 0.3804 -0.104 0.000 0.000 0.000 0.424 0.576 0.000
#> SRR1656569 5 0.5678 0.245 0.000 0.000 0.160 0.000 0.464 0.376
#> SRR1656570 3 0.5135 0.518 0.240 0.000 0.616 0.000 0.000 0.144
#> SRR1656571 2 0.3717 0.544 0.000 0.616 0.000 0.384 0.000 0.000
#> SRR1656573 6 0.1610 0.793 0.000 0.000 0.000 0.000 0.084 0.916
#> SRR1656572 6 0.5670 0.268 0.000 0.000 0.000 0.296 0.188 0.516
#> SRR1656574 1 0.1610 0.902 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1656575 1 0.2562 0.833 0.828 0.000 0.000 0.000 0.000 0.172
#> SRR1656576 5 0.3867 -0.260 0.000 0.000 0.000 0.488 0.512 0.000
#> SRR1656578 4 0.0000 0.708 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656577 1 0.1610 0.902 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1656579 5 0.3804 -0.104 0.000 0.000 0.000 0.424 0.576 0.000
#> SRR1656580 3 0.0000 0.882 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656581 6 0.1610 0.793 0.000 0.000 0.000 0.000 0.084 0.916
#> SRR1656582 4 0.2664 0.587 0.000 0.184 0.000 0.816 0.000 0.000
#> SRR1656585 5 0.3607 0.364 0.000 0.000 0.000 0.000 0.652 0.348
#> SRR1656584 1 0.1610 0.911 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1656583 5 0.3231 0.511 0.000 0.000 0.000 0.200 0.784 0.016
#> SRR1656586 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 5 0.2664 0.528 0.000 0.000 0.000 0.184 0.816 0.000
#> SRR1656588 4 0.3634 0.467 0.000 0.000 0.000 0.644 0.356 0.000
#> SRR1656589 2 0.0000 0.844 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.0000 0.920 1.000 0.000 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", "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 13572 rows and 129 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.683 0.897 0.946 0.3306 0.705 0.705
#> 3 3 0.457 0.506 0.737 0.7573 0.729 0.621
#> 4 4 0.638 0.723 0.821 0.1682 0.603 0.327
#> 5 5 0.776 0.841 0.896 0.1110 0.822 0.525
#> 6 6 0.837 0.819 0.899 0.0723 0.883 0.569
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
#> SRR1656463 2 0.0000 0.963 0.000 1.000
#> SRR1656464 1 0.0000 0.935 1.000 0.000
#> SRR1656462 1 0.0000 0.935 1.000 0.000
#> SRR1656465 1 0.0000 0.935 1.000 0.000
#> SRR1656467 1 0.9170 0.609 0.668 0.332
#> SRR1656466 1 0.0000 0.935 1.000 0.000
#> SRR1656468 1 0.7602 0.782 0.780 0.220
#> SRR1656472 1 0.0000 0.935 1.000 0.000
#> SRR1656471 1 0.0000 0.935 1.000 0.000
#> SRR1656470 2 0.0000 0.963 0.000 1.000
#> SRR1656469 1 0.0000 0.935 1.000 0.000
#> SRR1656473 2 0.0000 0.963 0.000 1.000
#> SRR1656474 2 0.0000 0.963 0.000 1.000
#> SRR1656475 2 0.0000 0.963 0.000 1.000
#> SRR1656478 1 0.0000 0.935 1.000 0.000
#> SRR1656477 1 0.5842 0.848 0.860 0.140
#> SRR1656479 1 0.0000 0.935 1.000 0.000
#> SRR1656480 1 0.7602 0.782 0.780 0.220
#> SRR1656476 2 0.0376 0.962 0.004 0.996
#> SRR1656481 1 0.3114 0.905 0.944 0.056
#> SRR1656482 2 0.0376 0.962 0.004 0.996
#> SRR1656483 2 0.0000 0.963 0.000 1.000
#> SRR1656485 1 0.0000 0.935 1.000 0.000
#> SRR1656487 1 0.0000 0.935 1.000 0.000
#> SRR1656486 1 0.0000 0.935 1.000 0.000
#> SRR1656488 1 0.0000 0.935 1.000 0.000
#> SRR1656484 1 0.0000 0.935 1.000 0.000
#> SRR1656489 1 0.0000 0.935 1.000 0.000
#> SRR1656491 1 0.0000 0.935 1.000 0.000
#> SRR1656490 1 0.1184 0.927 0.984 0.016
#> SRR1656492 1 0.0000 0.935 1.000 0.000
#> SRR1656493 1 0.0000 0.935 1.000 0.000
#> SRR1656495 1 0.0000 0.935 1.000 0.000
#> SRR1656496 1 0.0000 0.935 1.000 0.000
#> SRR1656494 1 0.7602 0.782 0.780 0.220
#> SRR1656497 2 0.0000 0.963 0.000 1.000
#> SRR1656499 1 0.0000 0.935 1.000 0.000
#> SRR1656500 1 0.0000 0.935 1.000 0.000
#> SRR1656501 1 0.0000 0.935 1.000 0.000
#> SRR1656498 1 0.0000 0.935 1.000 0.000
#> SRR1656504 2 0.0376 0.962 0.004 0.996
#> SRR1656502 1 0.0000 0.935 1.000 0.000
#> SRR1656503 1 0.0000 0.935 1.000 0.000
#> SRR1656507 1 0.0000 0.935 1.000 0.000
#> SRR1656508 1 0.0000 0.935 1.000 0.000
#> SRR1656505 1 0.7602 0.782 0.780 0.220
#> SRR1656506 1 0.0000 0.935 1.000 0.000
#> SRR1656509 1 0.0000 0.935 1.000 0.000
#> SRR1656510 1 0.4815 0.874 0.896 0.104
#> SRR1656511 1 0.7602 0.782 0.780 0.220
#> SRR1656513 1 0.7815 0.768 0.768 0.232
#> SRR1656512 2 0.0000 0.963 0.000 1.000
#> SRR1656514 1 0.0000 0.935 1.000 0.000
#> SRR1656515 1 0.7883 0.763 0.764 0.236
#> SRR1656516 1 0.0000 0.935 1.000 0.000
#> SRR1656518 1 0.0000 0.935 1.000 0.000
#> SRR1656517 1 0.0000 0.935 1.000 0.000
#> SRR1656519 1 0.0000 0.935 1.000 0.000
#> SRR1656522 1 0.0000 0.935 1.000 0.000
#> SRR1656523 1 0.7602 0.782 0.780 0.220
#> SRR1656521 2 0.0000 0.963 0.000 1.000
#> SRR1656520 1 0.0000 0.935 1.000 0.000
#> SRR1656524 1 0.0000 0.935 1.000 0.000
#> SRR1656525 1 0.0000 0.935 1.000 0.000
#> SRR1656526 2 0.0376 0.962 0.004 0.996
#> SRR1656527 2 0.9460 0.345 0.364 0.636
#> SRR1656530 1 0.0000 0.935 1.000 0.000
#> SRR1656529 1 0.0000 0.935 1.000 0.000
#> SRR1656531 1 0.0000 0.935 1.000 0.000
#> SRR1656528 1 0.0000 0.935 1.000 0.000
#> SRR1656534 1 0.0000 0.935 1.000 0.000
#> SRR1656533 1 0.0000 0.935 1.000 0.000
#> SRR1656536 1 0.3114 0.905 0.944 0.056
#> SRR1656532 1 0.7602 0.782 0.780 0.220
#> SRR1656537 1 0.0000 0.935 1.000 0.000
#> SRR1656538 1 0.0000 0.935 1.000 0.000
#> SRR1656535 2 0.0376 0.962 0.004 0.996
#> SRR1656539 1 0.0000 0.935 1.000 0.000
#> SRR1656544 1 0.0000 0.935 1.000 0.000
#> SRR1656542 1 0.0000 0.935 1.000 0.000
#> SRR1656543 1 0.0000 0.935 1.000 0.000
#> SRR1656545 2 0.0000 0.963 0.000 1.000
#> SRR1656540 1 0.0000 0.935 1.000 0.000
#> SRR1656546 1 0.0000 0.935 1.000 0.000
#> SRR1656541 2 0.9129 0.443 0.328 0.672
#> SRR1656547 1 0.7602 0.782 0.780 0.220
#> SRR1656548 1 0.0000 0.935 1.000 0.000
#> SRR1656549 1 0.0000 0.935 1.000 0.000
#> SRR1656551 1 0.3431 0.901 0.936 0.064
#> SRR1656553 1 0.0000 0.935 1.000 0.000
#> SRR1656550 1 0.7602 0.782 0.780 0.220
#> SRR1656552 1 0.7602 0.782 0.780 0.220
#> SRR1656554 1 0.0000 0.935 1.000 0.000
#> SRR1656555 1 0.7602 0.782 0.780 0.220
#> SRR1656556 1 0.0000 0.935 1.000 0.000
#> SRR1656557 1 0.0000 0.935 1.000 0.000
#> SRR1656558 1 0.0000 0.935 1.000 0.000
#> SRR1656559 1 0.0000 0.935 1.000 0.000
#> SRR1656560 1 0.0000 0.935 1.000 0.000
#> SRR1656561 1 0.0000 0.935 1.000 0.000
#> SRR1656562 1 0.7602 0.782 0.780 0.220
#> SRR1656563 1 0.0000 0.935 1.000 0.000
#> SRR1656564 2 0.0000 0.963 0.000 1.000
#> SRR1656565 1 0.7602 0.782 0.780 0.220
#> SRR1656566 1 0.0000 0.935 1.000 0.000
#> SRR1656568 2 0.0376 0.962 0.004 0.996
#> SRR1656567 1 0.7602 0.782 0.780 0.220
#> SRR1656569 1 0.0000 0.935 1.000 0.000
#> SRR1656570 1 0.0000 0.935 1.000 0.000
#> SRR1656571 2 0.0000 0.963 0.000 1.000
#> SRR1656573 1 0.4431 0.882 0.908 0.092
#> SRR1656572 1 0.7602 0.782 0.780 0.220
#> SRR1656574 1 0.0000 0.935 1.000 0.000
#> SRR1656575 1 0.0000 0.935 1.000 0.000
#> SRR1656576 1 0.7602 0.782 0.780 0.220
#> SRR1656578 1 0.7602 0.782 0.780 0.220
#> SRR1656577 1 0.0000 0.935 1.000 0.000
#> SRR1656579 1 0.7602 0.782 0.780 0.220
#> SRR1656580 1 0.0000 0.935 1.000 0.000
#> SRR1656581 1 0.7602 0.782 0.780 0.220
#> SRR1656582 2 0.0376 0.962 0.004 0.996
#> SRR1656585 1 0.7602 0.782 0.780 0.220
#> SRR1656584 1 0.0000 0.935 1.000 0.000
#> SRR1656583 1 0.3274 0.903 0.940 0.060
#> SRR1656586 2 0.0000 0.963 0.000 1.000
#> SRR1656587 1 0.7602 0.782 0.780 0.220
#> SRR1656588 1 0.7602 0.782 0.780 0.220
#> SRR1656589 2 0.0000 0.963 0.000 1.000
#> SRR1656590 1 0.0000 0.935 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0424 0.9106 0.000 0.992 0.008
#> SRR1656464 1 0.0892 0.5572 0.980 0.000 0.020
#> SRR1656462 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656465 3 0.6274 0.0920 0.456 0.000 0.544
#> SRR1656467 3 0.5174 0.8124 0.128 0.048 0.824
#> SRR1656466 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656468 3 0.3752 0.8430 0.144 0.000 0.856
#> SRR1656472 1 0.4605 0.3758 0.796 0.000 0.204
#> SRR1656471 1 0.6305 0.1342 0.516 0.000 0.484
#> SRR1656470 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656469 1 0.6215 0.2748 0.572 0.000 0.428
#> SRR1656473 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656478 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656477 3 0.3482 0.8397 0.128 0.000 0.872
#> SRR1656479 1 0.5098 0.4224 0.752 0.000 0.248
#> SRR1656480 3 0.3784 0.8430 0.132 0.004 0.864
#> SRR1656476 2 0.1411 0.9025 0.000 0.964 0.036
#> SRR1656481 3 0.3752 0.8430 0.144 0.000 0.856
#> SRR1656482 2 0.2878 0.8788 0.000 0.904 0.096
#> SRR1656483 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656485 1 0.6215 0.2748 0.572 0.000 0.428
#> SRR1656487 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656486 1 0.0237 0.5613 0.996 0.000 0.004
#> SRR1656488 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656484 1 0.0237 0.5613 0.996 0.000 0.004
#> SRR1656489 1 0.0237 0.5615 0.996 0.000 0.004
#> SRR1656491 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656490 1 0.3551 0.5051 0.868 0.000 0.132
#> SRR1656492 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656493 1 0.2878 0.4914 0.904 0.000 0.096
#> SRR1656495 1 0.6008 0.0983 0.628 0.000 0.372
#> SRR1656496 1 0.6079 0.3073 0.612 0.000 0.388
#> SRR1656494 1 0.7178 -0.0854 0.512 0.024 0.464
#> SRR1656497 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656499 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656500 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656501 1 0.1031 0.5572 0.976 0.000 0.024
#> SRR1656498 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656504 2 0.2959 0.8766 0.000 0.900 0.100
#> SRR1656502 1 0.4750 0.3589 0.784 0.000 0.216
#> SRR1656503 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656507 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656508 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656505 3 0.3918 0.8438 0.140 0.004 0.856
#> SRR1656506 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656509 3 0.6302 -0.0482 0.480 0.000 0.520
#> SRR1656510 3 0.6252 0.1520 0.444 0.000 0.556
#> SRR1656511 1 0.7232 -0.0523 0.544 0.028 0.428
#> SRR1656513 3 0.9787 0.0482 0.248 0.328 0.424
#> SRR1656512 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656514 1 0.0892 0.5572 0.980 0.000 0.020
#> SRR1656515 3 0.4483 0.8249 0.128 0.024 0.848
#> SRR1656516 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656518 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656517 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656519 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656522 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656523 3 0.4733 0.7920 0.196 0.004 0.800
#> SRR1656521 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656520 1 0.6244 0.2593 0.560 0.000 0.440
#> SRR1656524 1 0.3879 0.4394 0.848 0.000 0.152
#> SRR1656525 1 0.6215 0.2748 0.572 0.000 0.428
#> SRR1656526 2 0.6180 0.5144 0.000 0.584 0.416
#> SRR1656527 1 0.9191 -0.0739 0.428 0.148 0.424
#> SRR1656530 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656529 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656531 1 0.0747 0.5584 0.984 0.000 0.016
#> SRR1656528 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656534 1 0.6192 0.2812 0.580 0.000 0.420
#> SRR1656533 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656536 3 0.3752 0.8430 0.144 0.000 0.856
#> SRR1656532 1 0.7159 -0.0579 0.528 0.024 0.448
#> SRR1656537 1 0.0747 0.5584 0.984 0.000 0.016
#> SRR1656538 1 0.5706 0.3634 0.680 0.000 0.320
#> SRR1656535 2 0.6314 0.5500 0.004 0.604 0.392
#> SRR1656539 1 0.6225 0.2664 0.568 0.000 0.432
#> SRR1656544 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656542 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656543 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656545 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656540 1 0.6252 0.2538 0.556 0.000 0.444
#> SRR1656546 1 0.0424 0.5593 0.992 0.000 0.008
#> SRR1656541 2 0.7974 0.3183 0.060 0.504 0.436
#> SRR1656547 3 0.3983 0.8439 0.144 0.004 0.852
#> SRR1656548 1 0.6215 0.2748 0.572 0.000 0.428
#> SRR1656549 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656551 3 0.4121 0.8238 0.168 0.000 0.832
#> SRR1656553 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656550 3 0.3715 0.8406 0.128 0.004 0.868
#> SRR1656552 3 0.4811 0.8323 0.148 0.024 0.828
#> SRR1656554 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656555 3 0.4291 0.8122 0.180 0.000 0.820
#> SRR1656556 3 0.4555 0.7624 0.200 0.000 0.800
#> SRR1656557 1 0.6204 0.2770 0.576 0.000 0.424
#> SRR1656558 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656559 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656560 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656561 1 0.5926 0.3330 0.644 0.000 0.356
#> SRR1656562 1 0.7138 -0.0625 0.540 0.024 0.436
#> SRR1656563 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656564 2 0.2878 0.8788 0.000 0.904 0.096
#> SRR1656565 1 0.7446 -0.0679 0.532 0.036 0.432
#> SRR1656566 1 0.0424 0.5593 0.992 0.000 0.008
#> SRR1656568 2 0.4335 0.8569 0.036 0.864 0.100
#> SRR1656567 3 0.3784 0.8430 0.132 0.004 0.864
#> SRR1656569 1 0.6225 0.2730 0.568 0.000 0.432
#> SRR1656570 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656571 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656573 3 0.6244 0.1669 0.440 0.000 0.560
#> SRR1656572 1 0.7121 -0.0486 0.548 0.024 0.428
#> SRR1656574 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656575 1 0.3116 0.5261 0.892 0.000 0.108
#> SRR1656576 3 0.4874 0.8300 0.144 0.028 0.828
#> SRR1656578 1 0.7360 -0.0574 0.528 0.032 0.440
#> SRR1656577 1 0.0000 0.5617 1.000 0.000 0.000
#> SRR1656579 3 0.3918 0.8438 0.140 0.004 0.856
#> SRR1656580 1 0.0892 0.5572 0.980 0.000 0.020
#> SRR1656581 3 0.4346 0.8071 0.184 0.000 0.816
#> SRR1656582 2 0.3340 0.8632 0.000 0.880 0.120
#> SRR1656585 3 0.3686 0.8442 0.140 0.000 0.860
#> SRR1656584 1 0.3340 0.5208 0.880 0.000 0.120
#> SRR1656583 3 0.3482 0.8397 0.128 0.000 0.872
#> SRR1656586 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656587 1 0.6505 -0.0653 0.528 0.004 0.468
#> SRR1656588 3 0.3715 0.8406 0.128 0.004 0.868
#> SRR1656589 2 0.0000 0.9122 0.000 1.000 0.000
#> SRR1656590 1 0.1163 0.5538 0.972 0.000 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.4679 0.777 0.000 0.648 0.000 0.352
#> SRR1656464 4 0.6753 0.482 0.164 0.000 0.228 0.608
#> SRR1656462 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656465 3 0.2530 0.708 0.000 0.112 0.888 0.000
#> SRR1656467 2 0.3123 0.743 0.000 0.844 0.000 0.156
#> SRR1656466 3 0.0469 0.751 0.012 0.000 0.988 0.000
#> SRR1656468 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656472 4 0.6800 0.701 0.204 0.156 0.008 0.632
#> SRR1656471 3 0.2844 0.750 0.048 0.052 0.900 0.000
#> SRR1656470 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656469 3 0.1022 0.755 0.032 0.000 0.968 0.000
#> SRR1656473 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656474 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656475 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656478 1 0.2345 0.880 0.900 0.000 0.100 0.000
#> SRR1656477 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656479 3 0.2814 0.730 0.132 0.000 0.868 0.000
#> SRR1656480 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656476 2 0.4679 0.777 0.000 0.648 0.000 0.352
#> SRR1656481 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656482 2 0.4356 0.786 0.000 0.708 0.000 0.292
#> SRR1656483 2 0.4697 0.775 0.000 0.644 0.000 0.356
#> SRR1656485 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656487 3 0.0817 0.752 0.024 0.000 0.976 0.000
#> SRR1656486 1 0.3801 0.807 0.780 0.000 0.220 0.000
#> SRR1656488 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656484 1 0.2868 0.873 0.864 0.000 0.136 0.000
#> SRR1656489 1 0.3486 0.839 0.812 0.000 0.188 0.000
#> SRR1656491 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656490 3 0.4372 0.577 0.268 0.004 0.728 0.000
#> SRR1656492 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656493 1 0.2032 0.786 0.936 0.028 0.036 0.000
#> SRR1656495 4 0.6782 0.689 0.212 0.148 0.008 0.632
#> SRR1656496 3 0.2216 0.757 0.092 0.000 0.908 0.000
#> SRR1656494 2 0.1284 0.643 0.012 0.964 0.024 0.000
#> SRR1656497 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656499 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656500 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656501 1 0.3726 0.817 0.788 0.000 0.212 0.000
#> SRR1656498 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656504 2 0.4356 0.786 0.000 0.708 0.000 0.292
#> SRR1656502 4 0.6823 0.696 0.184 0.176 0.008 0.632
#> SRR1656503 1 0.3837 0.802 0.776 0.000 0.224 0.000
#> SRR1656507 1 0.3024 0.866 0.852 0.000 0.148 0.000
#> SRR1656508 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656505 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656506 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656509 3 0.3370 0.741 0.048 0.080 0.872 0.000
#> SRR1656510 3 0.4957 0.632 0.016 0.300 0.684 0.000
#> SRR1656511 2 0.1118 0.642 0.000 0.964 0.036 0.000
#> SRR1656513 2 0.1022 0.687 0.000 0.968 0.000 0.032
#> SRR1656512 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656514 4 0.7213 0.335 0.140 0.000 0.408 0.452
#> SRR1656515 2 0.0937 0.655 0.000 0.976 0.012 0.012
#> SRR1656516 1 0.4134 0.751 0.740 0.000 0.260 0.000
#> SRR1656518 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656517 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656519 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656522 1 0.4193 0.738 0.732 0.000 0.268 0.000
#> SRR1656523 3 0.4898 0.518 0.000 0.416 0.584 0.000
#> SRR1656521 2 0.4855 0.776 0.004 0.644 0.000 0.352
#> SRR1656520 3 0.2216 0.759 0.092 0.000 0.908 0.000
#> SRR1656524 1 0.3586 0.634 0.872 0.040 0.012 0.076
#> SRR1656525 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656526 2 0.4040 0.779 0.000 0.752 0.000 0.248
#> SRR1656527 2 0.3266 0.755 0.000 0.832 0.000 0.168
#> SRR1656530 3 0.0817 0.752 0.024 0.000 0.976 0.000
#> SRR1656529 3 0.0817 0.752 0.024 0.000 0.976 0.000
#> SRR1656531 1 0.3601 0.841 0.860 0.000 0.084 0.056
#> SRR1656528 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656534 3 0.2216 0.757 0.092 0.000 0.908 0.000
#> SRR1656533 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656536 3 0.4679 0.606 0.000 0.352 0.648 0.000
#> SRR1656532 2 0.1362 0.647 0.012 0.964 0.004 0.020
#> SRR1656537 1 0.2565 0.779 0.912 0.000 0.032 0.056
#> SRR1656538 3 0.2589 0.736 0.116 0.000 0.884 0.000
#> SRR1656535 2 0.4040 0.779 0.000 0.752 0.000 0.248
#> SRR1656539 3 0.0336 0.745 0.000 0.008 0.992 0.000
#> SRR1656544 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656542 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656543 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656545 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656540 3 0.2216 0.759 0.092 0.000 0.908 0.000
#> SRR1656546 1 0.2973 0.864 0.884 0.020 0.096 0.000
#> SRR1656541 2 0.1004 0.654 0.000 0.972 0.024 0.004
#> SRR1656547 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656548 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656549 1 0.1211 0.828 0.960 0.000 0.040 0.000
#> SRR1656551 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656553 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656550 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656552 2 0.1118 0.642 0.000 0.964 0.036 0.000
#> SRR1656554 3 0.0000 0.748 0.000 0.000 1.000 0.000
#> SRR1656555 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656556 3 0.5093 0.600 0.012 0.348 0.640 0.000
#> SRR1656557 3 0.2011 0.764 0.080 0.000 0.920 0.000
#> SRR1656558 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656559 1 0.4040 0.769 0.752 0.000 0.248 0.000
#> SRR1656560 3 0.0817 0.752 0.024 0.000 0.976 0.000
#> SRR1656561 3 0.2704 0.727 0.124 0.000 0.876 0.000
#> SRR1656562 2 0.1557 0.615 0.000 0.944 0.056 0.000
#> SRR1656563 1 0.2760 0.875 0.872 0.000 0.128 0.000
#> SRR1656564 2 0.4331 0.786 0.000 0.712 0.000 0.288
#> SRR1656565 2 0.1118 0.642 0.000 0.964 0.036 0.000
#> SRR1656566 1 0.1118 0.824 0.964 0.000 0.036 0.000
#> SRR1656568 2 0.4331 0.786 0.000 0.712 0.000 0.288
#> SRR1656567 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656569 3 0.0817 0.752 0.024 0.000 0.976 0.000
#> SRR1656570 1 0.2704 0.876 0.876 0.000 0.124 0.000
#> SRR1656571 2 0.4697 0.775 0.000 0.644 0.000 0.356
#> SRR1656573 3 0.5152 0.623 0.020 0.316 0.664 0.000
#> SRR1656572 2 0.1118 0.642 0.000 0.964 0.036 0.000
#> SRR1656574 1 0.3486 0.839 0.812 0.000 0.188 0.000
#> SRR1656575 1 0.2281 0.880 0.904 0.000 0.096 0.000
#> SRR1656576 2 0.0921 0.649 0.000 0.972 0.028 0.000
#> SRR1656578 2 0.3718 0.752 0.012 0.820 0.000 0.168
#> SRR1656577 1 0.3610 0.829 0.800 0.000 0.200 0.000
#> SRR1656579 3 0.5127 0.593 0.000 0.356 0.632 0.012
#> SRR1656580 3 0.3837 0.575 0.224 0.000 0.776 0.000
#> SRR1656581 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656582 2 0.4356 0.786 0.000 0.708 0.000 0.292
#> SRR1656585 3 0.4697 0.603 0.000 0.356 0.644 0.000
#> SRR1656584 1 0.1792 0.859 0.932 0.000 0.068 0.000
#> SRR1656583 3 0.5127 0.593 0.012 0.356 0.632 0.000
#> SRR1656586 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656587 3 0.5159 0.584 0.012 0.364 0.624 0.000
#> SRR1656588 2 0.5404 -0.420 0.000 0.512 0.476 0.012
#> SRR1656589 2 0.4973 0.776 0.008 0.644 0.000 0.348
#> SRR1656590 1 0.3280 0.657 0.860 0.000 0.016 0.124
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0162 0.8920 0.000 0.996 0.000 0.004 0.000
#> SRR1656464 3 0.7208 -0.0699 0.308 0.000 0.404 0.020 0.268
#> SRR1656462 3 0.2189 0.8656 0.012 0.000 0.904 0.084 0.000
#> SRR1656465 3 0.3421 0.7486 0.004 0.000 0.816 0.164 0.016
#> SRR1656467 4 0.4629 0.6404 0.000 0.244 0.052 0.704 0.000
#> SRR1656466 3 0.1372 0.9153 0.004 0.000 0.956 0.024 0.016
#> SRR1656468 4 0.1908 0.8975 0.000 0.000 0.092 0.908 0.000
#> SRR1656472 5 0.2927 1.0000 0.060 0.000 0.000 0.068 0.872
#> SRR1656471 3 0.1628 0.9114 0.008 0.000 0.936 0.056 0.000
#> SRR1656470 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656469 3 0.1211 0.9171 0.000 0.000 0.960 0.024 0.016
#> SRR1656473 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656474 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656475 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656478 1 0.0703 0.8722 0.976 0.000 0.024 0.000 0.000
#> SRR1656477 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656479 3 0.2864 0.8469 0.112 0.000 0.864 0.024 0.000
#> SRR1656480 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656476 2 0.0771 0.8889 0.000 0.976 0.000 0.004 0.020
#> SRR1656481 4 0.1965 0.8963 0.000 0.000 0.096 0.904 0.000
#> SRR1656482 2 0.0609 0.8891 0.000 0.980 0.000 0.020 0.000
#> SRR1656483 2 0.0162 0.8920 0.000 0.996 0.000 0.004 0.000
#> SRR1656485 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656487 3 0.1372 0.9153 0.004 0.000 0.956 0.024 0.016
#> SRR1656486 1 0.1410 0.8770 0.940 0.000 0.060 0.000 0.000
#> SRR1656488 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656484 1 0.1908 0.8633 0.908 0.000 0.092 0.000 0.000
#> SRR1656489 1 0.1478 0.8751 0.936 0.000 0.064 0.000 0.000
#> SRR1656491 3 0.0992 0.9217 0.008 0.000 0.968 0.024 0.000
#> SRR1656490 1 0.4801 0.5822 0.728 0.000 0.148 0.124 0.000
#> SRR1656492 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656493 1 0.2770 0.8142 0.880 0.000 0.000 0.044 0.076
#> SRR1656495 5 0.2927 1.0000 0.060 0.000 0.000 0.068 0.872
#> SRR1656496 3 0.1211 0.9219 0.016 0.000 0.960 0.024 0.000
#> SRR1656494 4 0.2396 0.8131 0.000 0.024 0.004 0.904 0.068
#> SRR1656497 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656499 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656500 3 0.0912 0.9108 0.012 0.000 0.972 0.016 0.000
#> SRR1656501 1 0.1851 0.8610 0.912 0.000 0.088 0.000 0.000
#> SRR1656498 1 0.2367 0.8388 0.904 0.000 0.004 0.020 0.072
#> SRR1656504 2 0.0898 0.8886 0.000 0.972 0.000 0.008 0.020
#> SRR1656502 5 0.2927 1.0000 0.060 0.000 0.000 0.068 0.872
#> SRR1656503 1 0.3210 0.7331 0.788 0.000 0.212 0.000 0.000
#> SRR1656507 1 0.1410 0.8763 0.940 0.000 0.060 0.000 0.000
#> SRR1656508 1 0.2975 0.8646 0.884 0.000 0.048 0.020 0.048
#> SRR1656505 4 0.1908 0.8975 0.000 0.000 0.092 0.908 0.000
#> SRR1656506 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656509 3 0.2079 0.8747 0.000 0.000 0.916 0.064 0.020
#> SRR1656510 4 0.2230 0.8776 0.000 0.000 0.116 0.884 0.000
#> SRR1656511 4 0.3579 0.6291 0.000 0.240 0.000 0.756 0.004
#> SRR1656513 2 0.3816 0.5247 0.000 0.696 0.000 0.304 0.000
#> SRR1656512 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656514 3 0.3375 0.8166 0.048 0.000 0.860 0.020 0.072
#> SRR1656515 4 0.2388 0.8850 0.000 0.028 0.072 0.900 0.000
#> SRR1656516 1 0.3210 0.7116 0.788 0.000 0.212 0.000 0.000
#> SRR1656518 1 0.0162 0.8620 0.996 0.000 0.004 0.000 0.000
#> SRR1656517 1 0.1270 0.8772 0.948 0.000 0.052 0.000 0.000
#> SRR1656519 3 0.2189 0.8656 0.012 0.000 0.904 0.084 0.000
#> SRR1656522 3 0.5751 0.4058 0.292 0.000 0.616 0.020 0.072
#> SRR1656523 4 0.1851 0.8980 0.000 0.000 0.088 0.912 0.000
#> SRR1656521 2 0.1571 0.8890 0.000 0.936 0.000 0.004 0.060
#> SRR1656520 3 0.0912 0.9111 0.012 0.000 0.972 0.016 0.000
#> SRR1656524 1 0.4629 0.5902 0.704 0.000 0.000 0.052 0.244
#> SRR1656525 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656526 2 0.1216 0.8841 0.000 0.960 0.000 0.020 0.020
#> SRR1656527 2 0.2561 0.7729 0.000 0.856 0.000 0.144 0.000
#> SRR1656530 3 0.1211 0.9171 0.000 0.000 0.960 0.024 0.016
#> SRR1656529 3 0.1372 0.9153 0.004 0.000 0.956 0.024 0.016
#> SRR1656531 1 0.2367 0.8388 0.904 0.000 0.004 0.020 0.072
#> SRR1656528 3 0.0992 0.9217 0.008 0.000 0.968 0.024 0.000
#> SRR1656534 3 0.2390 0.8623 0.020 0.000 0.896 0.084 0.000
#> SRR1656533 1 0.1341 0.8770 0.944 0.000 0.056 0.000 0.000
#> SRR1656536 4 0.1965 0.8963 0.000 0.000 0.096 0.904 0.000
#> SRR1656532 2 0.6042 0.1506 0.000 0.484 0.000 0.396 0.120
#> SRR1656537 1 0.2490 0.8341 0.896 0.000 0.004 0.020 0.080
#> SRR1656538 3 0.1331 0.9134 0.040 0.000 0.952 0.008 0.000
#> SRR1656535 2 0.1216 0.8841 0.000 0.960 0.000 0.020 0.020
#> SRR1656539 3 0.1211 0.9171 0.000 0.000 0.960 0.024 0.016
#> SRR1656544 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656542 3 0.1012 0.9219 0.012 0.000 0.968 0.020 0.000
#> SRR1656543 3 0.2130 0.8679 0.012 0.000 0.908 0.080 0.000
#> SRR1656545 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656540 3 0.1885 0.8867 0.012 0.000 0.936 0.020 0.032
#> SRR1656546 1 0.0703 0.8510 0.976 0.000 0.000 0.024 0.000
#> SRR1656541 4 0.4774 0.2537 0.000 0.424 0.000 0.556 0.020
#> SRR1656547 4 0.1851 0.8980 0.000 0.000 0.088 0.912 0.000
#> SRR1656548 3 0.1106 0.9222 0.012 0.000 0.964 0.024 0.000
#> SRR1656549 1 0.0324 0.8575 0.992 0.000 0.000 0.004 0.004
#> SRR1656551 4 0.1965 0.8963 0.000 0.000 0.096 0.904 0.000
#> SRR1656553 3 0.0912 0.9111 0.012 0.000 0.972 0.016 0.000
#> SRR1656550 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656552 4 0.2800 0.8848 0.000 0.024 0.072 0.888 0.016
#> SRR1656554 3 0.1372 0.9153 0.004 0.000 0.956 0.024 0.016
#> SRR1656555 4 0.1908 0.8975 0.000 0.000 0.092 0.908 0.000
#> SRR1656556 4 0.4088 0.4566 0.000 0.000 0.368 0.632 0.000
#> SRR1656557 3 0.2189 0.8656 0.012 0.000 0.904 0.084 0.000
#> SRR1656558 1 0.0162 0.8620 0.996 0.000 0.004 0.000 0.000
#> SRR1656559 1 0.5821 0.4982 0.600 0.000 0.308 0.020 0.072
#> SRR1656560 3 0.1211 0.9171 0.000 0.000 0.960 0.024 0.016
#> SRR1656561 3 0.2171 0.8933 0.064 0.000 0.912 0.024 0.000
#> SRR1656562 4 0.2300 0.8861 0.000 0.024 0.072 0.904 0.000
#> SRR1656563 1 0.1478 0.8751 0.936 0.000 0.064 0.000 0.000
#> SRR1656564 2 0.0404 0.8920 0.000 0.988 0.000 0.012 0.000
#> SRR1656565 4 0.2011 0.8135 0.000 0.088 0.004 0.908 0.000
#> SRR1656566 1 0.2270 0.8344 0.904 0.000 0.000 0.020 0.076
#> SRR1656568 2 0.0703 0.8888 0.000 0.976 0.000 0.024 0.000
#> SRR1656567 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656569 3 0.1372 0.9153 0.004 0.000 0.956 0.024 0.016
#> SRR1656570 1 0.1478 0.8751 0.936 0.000 0.064 0.000 0.000
#> SRR1656571 2 0.0000 0.8922 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 4 0.1965 0.8963 0.000 0.000 0.096 0.904 0.000
#> SRR1656572 4 0.2970 0.7302 0.000 0.168 0.004 0.828 0.000
#> SRR1656574 1 0.2270 0.8682 0.904 0.000 0.076 0.020 0.000
#> SRR1656575 1 0.0963 0.8757 0.964 0.000 0.036 0.000 0.000
#> SRR1656576 4 0.3005 0.8790 0.000 0.032 0.068 0.880 0.020
#> SRR1656578 2 0.3780 0.7369 0.000 0.812 0.000 0.116 0.072
#> SRR1656577 1 0.3554 0.8246 0.836 0.000 0.120 0.020 0.024
#> SRR1656579 4 0.1908 0.8975 0.000 0.000 0.092 0.908 0.000
#> SRR1656580 3 0.1043 0.9077 0.040 0.000 0.960 0.000 0.000
#> SRR1656581 4 0.1908 0.8975 0.000 0.000 0.092 0.908 0.000
#> SRR1656582 2 0.1012 0.8876 0.000 0.968 0.000 0.012 0.020
#> SRR1656585 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656584 1 0.0324 0.8601 0.992 0.000 0.004 0.000 0.004
#> SRR1656583 4 0.2067 0.8573 0.000 0.000 0.032 0.920 0.048
#> SRR1656586 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656587 4 0.2270 0.8066 0.000 0.020 0.000 0.904 0.076
#> SRR1656588 4 0.1671 0.8979 0.000 0.000 0.076 0.924 0.000
#> SRR1656589 2 0.1908 0.8855 0.000 0.908 0.000 0.000 0.092
#> SRR1656590 1 0.2864 0.8077 0.864 0.000 0.000 0.024 0.112
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.1644 0.8291 0.076 0.000 0.920 0.000 0.004 0.000
#> SRR1656462 3 0.0146 0.8703 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656465 5 0.2357 0.8041 0.000 0.000 0.116 0.012 0.872 0.000
#> SRR1656467 4 0.2257 0.8291 0.000 0.116 0.000 0.876 0.008 0.000
#> SRR1656466 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656468 4 0.2969 0.7075 0.000 0.000 0.000 0.776 0.224 0.000
#> SRR1656472 6 0.1836 0.9305 0.008 0.000 0.004 0.048 0.012 0.928
#> SRR1656471 5 0.3210 0.8036 0.000 0.000 0.168 0.028 0.804 0.000
#> SRR1656470 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656469 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656473 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656474 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656475 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656478 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656477 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656479 1 0.4800 0.5013 0.672 0.000 0.160 0.000 0.168 0.000
#> SRR1656480 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656476 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 5 0.3578 0.4090 0.000 0.000 0.000 0.340 0.660 0.000
#> SRR1656482 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.2969 0.6819 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR1656487 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656486 1 0.0363 0.9129 0.988 0.000 0.012 0.000 0.000 0.000
#> SRR1656488 3 0.2969 0.6819 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR1656484 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656489 1 0.1141 0.8834 0.948 0.000 0.052 0.000 0.000 0.000
#> SRR1656491 5 0.3419 0.8023 0.004 0.000 0.152 0.040 0.804 0.000
#> SRR1656490 1 0.2176 0.8693 0.916 0.000 0.024 0.036 0.004 0.020
#> SRR1656492 5 0.4453 0.2634 0.028 0.000 0.444 0.000 0.528 0.000
#> SRR1656493 1 0.1812 0.8633 0.912 0.000 0.000 0.008 0.000 0.080
#> SRR1656495 6 0.1692 0.9275 0.008 0.000 0.000 0.048 0.012 0.932
#> SRR1656496 1 0.6040 -0.0669 0.420 0.000 0.284 0.000 0.296 0.000
#> SRR1656494 4 0.0547 0.9271 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1656497 2 0.2672 0.8920 0.000 0.868 0.000 0.000 0.080 0.052
#> SRR1656499 3 0.0146 0.8703 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656500 3 0.0260 0.8711 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656501 1 0.0458 0.9109 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1656498 1 0.0291 0.9143 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1656504 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.1836 0.9305 0.008 0.000 0.004 0.048 0.012 0.928
#> SRR1656503 1 0.2219 0.7909 0.864 0.000 0.136 0.000 0.000 0.000
#> SRR1656507 1 0.0865 0.8972 0.964 0.000 0.036 0.000 0.000 0.000
#> SRR1656508 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656505 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656506 5 0.3864 0.2005 0.000 0.000 0.480 0.000 0.520 0.000
#> SRR1656509 5 0.3477 0.7915 0.004 0.000 0.132 0.056 0.808 0.000
#> SRR1656510 4 0.4118 0.5057 0.000 0.000 0.028 0.660 0.312 0.000
#> SRR1656511 4 0.0777 0.9184 0.000 0.004 0.000 0.972 0.024 0.000
#> SRR1656513 4 0.2301 0.8338 0.000 0.096 0.000 0.884 0.020 0.000
#> SRR1656512 2 0.2672 0.8920 0.000 0.868 0.000 0.000 0.080 0.052
#> SRR1656514 3 0.0713 0.8660 0.028 0.000 0.972 0.000 0.000 0.000
#> SRR1656515 4 0.0632 0.9270 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656516 1 0.2340 0.7716 0.852 0.000 0.148 0.000 0.000 0.000
#> SRR1656518 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656517 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656519 3 0.0146 0.8712 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1656522 3 0.1007 0.8559 0.044 0.000 0.956 0.000 0.000 0.000
#> SRR1656523 4 0.0603 0.9261 0.004 0.000 0.000 0.980 0.016 0.000
#> SRR1656521 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0146 0.8703 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656524 6 0.3628 0.8006 0.168 0.000 0.000 0.044 0.004 0.784
#> SRR1656525 3 0.2854 0.7024 0.000 0.000 0.792 0.000 0.208 0.000
#> SRR1656526 2 0.1524 0.8603 0.000 0.932 0.000 0.060 0.008 0.000
#> SRR1656527 2 0.3374 0.6614 0.000 0.772 0.000 0.208 0.020 0.000
#> SRR1656530 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656529 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656531 1 0.0291 0.9143 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR1656528 5 0.3862 0.2195 0.000 0.000 0.476 0.000 0.524 0.000
#> SRR1656534 3 0.0547 0.8689 0.020 0.000 0.980 0.000 0.000 0.000
#> SRR1656533 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656536 5 0.2631 0.6486 0.000 0.000 0.000 0.180 0.820 0.000
#> SRR1656532 4 0.2545 0.8405 0.004 0.008 0.000 0.884 0.020 0.084
#> SRR1656537 1 0.0713 0.9036 0.972 0.000 0.000 0.000 0.000 0.028
#> SRR1656538 3 0.0632 0.8677 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1656535 2 0.0520 0.9037 0.000 0.984 0.000 0.008 0.008 0.000
#> SRR1656539 5 0.2219 0.8141 0.000 0.000 0.136 0.000 0.864 0.000
#> SRR1656544 3 0.2912 0.6927 0.000 0.000 0.784 0.000 0.216 0.000
#> SRR1656542 3 0.0291 0.8710 0.004 0.000 0.992 0.000 0.004 0.000
#> SRR1656543 3 0.0146 0.8703 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656545 2 0.2672 0.8920 0.000 0.868 0.000 0.000 0.080 0.052
#> SRR1656540 3 0.0146 0.8703 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656546 1 0.1549 0.8786 0.936 0.000 0.000 0.020 0.000 0.044
#> SRR1656541 4 0.2768 0.7634 0.000 0.156 0.000 0.832 0.012 0.000
#> SRR1656547 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656548 3 0.3151 0.6348 0.000 0.000 0.748 0.000 0.252 0.000
#> SRR1656549 1 0.0547 0.9046 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1656551 5 0.2772 0.6441 0.000 0.000 0.004 0.180 0.816 0.000
#> SRR1656553 3 0.3758 0.4612 0.008 0.000 0.668 0.000 0.324 0.000
#> SRR1656550 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656552 4 0.0603 0.9265 0.000 0.004 0.000 0.980 0.016 0.000
#> SRR1656554 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656555 4 0.2964 0.7268 0.000 0.000 0.004 0.792 0.204 0.000
#> SRR1656556 5 0.3314 0.7139 0.004 0.000 0.048 0.128 0.820 0.000
#> SRR1656557 3 0.0146 0.8712 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1656558 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656559 3 0.1141 0.8487 0.052 0.000 0.948 0.000 0.000 0.000
#> SRR1656560 5 0.2340 0.8152 0.000 0.000 0.148 0.000 0.852 0.000
#> SRR1656561 3 0.3989 0.6601 0.044 0.000 0.720 0.000 0.236 0.000
#> SRR1656562 4 0.0458 0.9271 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656563 1 0.0260 0.9141 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656565 4 0.0692 0.9189 0.000 0.004 0.000 0.976 0.020 0.000
#> SRR1656566 1 0.1267 0.8841 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1656568 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656567 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656569 5 0.2300 0.8165 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1656570 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656571 2 0.0000 0.9114 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 5 0.4394 0.2513 0.004 0.000 0.020 0.408 0.568 0.000
#> SRR1656572 4 0.0603 0.9224 0.004 0.000 0.000 0.980 0.016 0.000
#> SRR1656574 3 0.3050 0.5930 0.236 0.000 0.764 0.000 0.000 0.000
#> SRR1656575 1 0.0146 0.9153 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1656576 4 0.0777 0.9266 0.000 0.004 0.000 0.972 0.024 0.000
#> SRR1656578 2 0.3566 0.6207 0.000 0.744 0.000 0.236 0.020 0.000
#> SRR1656577 3 0.1327 0.8367 0.064 0.000 0.936 0.000 0.000 0.000
#> SRR1656579 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656580 3 0.0790 0.8639 0.032 0.000 0.968 0.000 0.000 0.000
#> SRR1656581 4 0.0777 0.9242 0.004 0.000 0.000 0.972 0.024 0.000
#> SRR1656582 2 0.0260 0.9079 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1656585 4 0.0458 0.9284 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656584 1 0.0146 0.9119 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1656583 4 0.0632 0.9284 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656586 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656587 4 0.0363 0.9279 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1656588 4 0.0632 0.9270 0.000 0.000 0.000 0.976 0.024 0.000
#> SRR1656589 2 0.2733 0.8910 0.000 0.864 0.000 0.000 0.080 0.056
#> SRR1656590 1 0.2416 0.7900 0.844 0.000 0.000 0.000 0.000 0.156
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 13572 rows and 129 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 0.719 0.866 0.942 0.4927 0.507 0.507
#> 3 3 0.715 0.827 0.914 0.2738 0.811 0.646
#> 4 4 0.663 0.686 0.856 0.1562 0.802 0.527
#> 5 5 0.673 0.690 0.827 0.0791 0.840 0.496
#> 6 6 0.617 0.588 0.752 0.0436 0.952 0.780
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
#> SRR1656463 1 0.0000 0.9326 1.000 0.000
#> SRR1656464 2 0.0000 0.9373 0.000 1.000
#> SRR1656462 1 0.0000 0.9326 1.000 0.000
#> SRR1656465 1 0.0000 0.9326 1.000 0.000
#> SRR1656467 1 0.0000 0.9326 1.000 0.000
#> SRR1656466 1 0.0000 0.9326 1.000 0.000
#> SRR1656468 1 0.0000 0.9326 1.000 0.000
#> SRR1656472 2 0.0000 0.9373 0.000 1.000
#> SRR1656471 1 0.0000 0.9326 1.000 0.000
#> SRR1656470 1 0.0000 0.9326 1.000 0.000
#> SRR1656469 1 0.0000 0.9326 1.000 0.000
#> SRR1656473 2 0.6438 0.7807 0.164 0.836
#> SRR1656474 2 0.5178 0.8358 0.116 0.884
#> SRR1656475 1 0.9795 0.2363 0.584 0.416
#> SRR1656478 2 0.0000 0.9373 0.000 1.000
#> SRR1656477 1 0.0000 0.9326 1.000 0.000
#> SRR1656479 1 0.9732 0.3771 0.596 0.404
#> SRR1656480 1 0.0000 0.9326 1.000 0.000
#> SRR1656476 1 0.0000 0.9326 1.000 0.000
#> SRR1656481 1 0.0000 0.9326 1.000 0.000
#> SRR1656482 1 0.0000 0.9326 1.000 0.000
#> SRR1656483 1 0.0000 0.9326 1.000 0.000
#> SRR1656485 1 0.0000 0.9326 1.000 0.000
#> SRR1656487 1 0.0000 0.9326 1.000 0.000
#> SRR1656486 2 0.9754 0.2632 0.408 0.592
#> SRR1656488 1 0.0000 0.9326 1.000 0.000
#> SRR1656484 2 0.7528 0.7001 0.216 0.784
#> SRR1656489 2 0.0000 0.9373 0.000 1.000
#> SRR1656491 1 0.0000 0.9326 1.000 0.000
#> SRR1656490 2 0.9977 0.0328 0.472 0.528
#> SRR1656492 1 0.7219 0.7643 0.800 0.200
#> SRR1656493 2 0.0000 0.9373 0.000 1.000
#> SRR1656495 2 0.0000 0.9373 0.000 1.000
#> SRR1656496 1 0.7815 0.7228 0.768 0.232
#> SRR1656494 1 0.3584 0.8799 0.932 0.068
#> SRR1656497 1 0.0000 0.9326 1.000 0.000
#> SRR1656499 1 0.0000 0.9326 1.000 0.000
#> SRR1656500 1 0.3431 0.8926 0.936 0.064
#> SRR1656501 2 0.6973 0.7427 0.188 0.812
#> SRR1656498 2 0.0000 0.9373 0.000 1.000
#> SRR1656504 1 0.9323 0.5164 0.652 0.348
#> SRR1656502 2 0.0000 0.9373 0.000 1.000
#> SRR1656503 2 0.2948 0.8975 0.052 0.948
#> SRR1656507 2 0.0000 0.9373 0.000 1.000
#> SRR1656508 2 0.0000 0.9373 0.000 1.000
#> SRR1656505 1 0.0000 0.9326 1.000 0.000
#> SRR1656506 1 0.0000 0.9326 1.000 0.000
#> SRR1656509 1 0.0000 0.9326 1.000 0.000
#> SRR1656510 1 0.4431 0.8718 0.908 0.092
#> SRR1656511 2 0.0000 0.9373 0.000 1.000
#> SRR1656513 2 0.0376 0.9347 0.004 0.996
#> SRR1656512 2 0.0000 0.9373 0.000 1.000
#> SRR1656514 2 0.4939 0.8466 0.108 0.892
#> SRR1656515 1 0.0000 0.9326 1.000 0.000
#> SRR1656516 2 0.3733 0.8798 0.072 0.928
#> SRR1656518 2 0.0000 0.9373 0.000 1.000
#> SRR1656517 2 0.0000 0.9373 0.000 1.000
#> SRR1656519 1 0.0672 0.9280 0.992 0.008
#> SRR1656522 2 0.0000 0.9373 0.000 1.000
#> SRR1656523 1 0.8813 0.6121 0.700 0.300
#> SRR1656521 2 0.0000 0.9373 0.000 1.000
#> SRR1656520 1 0.0000 0.9326 1.000 0.000
#> SRR1656524 2 0.0000 0.9373 0.000 1.000
#> SRR1656525 1 0.0000 0.9326 1.000 0.000
#> SRR1656526 1 0.0000 0.9326 1.000 0.000
#> SRR1656527 2 0.0000 0.9373 0.000 1.000
#> SRR1656530 1 0.0000 0.9326 1.000 0.000
#> SRR1656529 1 0.0000 0.9326 1.000 0.000
#> SRR1656531 2 0.0000 0.9373 0.000 1.000
#> SRR1656528 1 0.0000 0.9326 1.000 0.000
#> SRR1656534 1 0.7299 0.7596 0.796 0.204
#> SRR1656533 2 0.0000 0.9373 0.000 1.000
#> SRR1656536 1 0.0000 0.9326 1.000 0.000
#> SRR1656532 2 0.0000 0.9373 0.000 1.000
#> SRR1656537 2 0.0000 0.9373 0.000 1.000
#> SRR1656538 1 0.7674 0.7337 0.776 0.224
#> SRR1656535 2 0.0000 0.9373 0.000 1.000
#> SRR1656539 1 0.0000 0.9326 1.000 0.000
#> SRR1656544 1 0.0000 0.9326 1.000 0.000
#> SRR1656542 1 0.5629 0.8363 0.868 0.132
#> SRR1656543 1 0.0000 0.9326 1.000 0.000
#> SRR1656545 2 0.0000 0.9373 0.000 1.000
#> SRR1656540 1 0.0000 0.9326 1.000 0.000
#> SRR1656546 2 0.0000 0.9373 0.000 1.000
#> SRR1656541 1 0.0000 0.9326 1.000 0.000
#> SRR1656547 1 0.0000 0.9326 1.000 0.000
#> SRR1656548 1 0.4161 0.8780 0.916 0.084
#> SRR1656549 2 0.0000 0.9373 0.000 1.000
#> SRR1656551 1 0.0000 0.9326 1.000 0.000
#> SRR1656553 1 0.4562 0.8690 0.904 0.096
#> SRR1656550 1 0.0000 0.9326 1.000 0.000
#> SRR1656552 1 0.7219 0.7643 0.800 0.200
#> SRR1656554 1 0.0000 0.9326 1.000 0.000
#> SRR1656555 1 0.0000 0.9326 1.000 0.000
#> SRR1656556 1 0.0000 0.9326 1.000 0.000
#> SRR1656557 1 0.0000 0.9326 1.000 0.000
#> SRR1656558 2 0.0000 0.9373 0.000 1.000
#> SRR1656559 2 0.0000 0.9373 0.000 1.000
#> SRR1656560 1 0.0000 0.9326 1.000 0.000
#> SRR1656561 1 0.8267 0.6803 0.740 0.260
#> SRR1656562 1 0.8955 0.5261 0.688 0.312
#> SRR1656563 2 0.0000 0.9373 0.000 1.000
#> SRR1656564 2 0.0000 0.9373 0.000 1.000
#> SRR1656565 2 0.8909 0.5292 0.308 0.692
#> SRR1656566 2 0.0000 0.9373 0.000 1.000
#> SRR1656568 2 0.0000 0.9373 0.000 1.000
#> SRR1656567 1 0.0000 0.9326 1.000 0.000
#> SRR1656569 1 0.0000 0.9326 1.000 0.000
#> SRR1656570 2 0.0000 0.9373 0.000 1.000
#> SRR1656571 2 0.0376 0.9347 0.004 0.996
#> SRR1656573 1 0.4022 0.8811 0.920 0.080
#> SRR1656572 2 0.0000 0.9373 0.000 1.000
#> SRR1656574 2 0.0000 0.9373 0.000 1.000
#> SRR1656575 2 0.0000 0.9373 0.000 1.000
#> SRR1656576 1 0.0000 0.9326 1.000 0.000
#> SRR1656578 2 0.0000 0.9373 0.000 1.000
#> SRR1656577 2 0.0000 0.9373 0.000 1.000
#> SRR1656579 1 0.0000 0.9326 1.000 0.000
#> SRR1656580 1 0.9460 0.4792 0.636 0.364
#> SRR1656581 1 0.7219 0.7643 0.800 0.200
#> SRR1656582 1 0.6623 0.7958 0.828 0.172
#> SRR1656585 1 0.0000 0.9326 1.000 0.000
#> SRR1656584 2 0.0000 0.9373 0.000 1.000
#> SRR1656583 1 0.0000 0.9326 1.000 0.000
#> SRR1656586 2 0.9850 0.2876 0.428 0.572
#> SRR1656587 2 0.6343 0.7956 0.160 0.840
#> SRR1656588 1 0.0000 0.9326 1.000 0.000
#> SRR1656589 2 0.9323 0.4868 0.348 0.652
#> SRR1656590 2 0.0000 0.9373 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 3 0.4346 0.7506 0.000 0.184 0.816
#> SRR1656464 1 0.4235 0.7482 0.824 0.176 0.000
#> SRR1656462 3 0.0424 0.8989 0.000 0.008 0.992
#> SRR1656465 3 0.0424 0.8989 0.000 0.008 0.992
#> SRR1656467 2 0.5397 0.5977 0.000 0.720 0.280
#> SRR1656466 3 0.0237 0.9024 0.004 0.000 0.996
#> SRR1656468 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656472 2 0.1163 0.8792 0.028 0.972 0.000
#> SRR1656471 3 0.1964 0.8729 0.000 0.056 0.944
#> SRR1656470 2 0.1643 0.8716 0.000 0.956 0.044
#> SRR1656469 3 0.0747 0.9026 0.016 0.000 0.984
#> SRR1656473 2 0.0424 0.8811 0.008 0.992 0.000
#> SRR1656474 2 0.0592 0.8809 0.012 0.988 0.000
#> SRR1656475 2 0.0000 0.8815 0.000 1.000 0.000
#> SRR1656478 1 0.1031 0.9114 0.976 0.000 0.024
#> SRR1656477 3 0.4121 0.7785 0.000 0.168 0.832
#> SRR1656479 1 0.6295 0.0248 0.528 0.000 0.472
#> SRR1656480 3 0.2625 0.8531 0.000 0.084 0.916
#> SRR1656476 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656481 3 0.0592 0.8971 0.000 0.012 0.988
#> SRR1656482 2 0.1529 0.8735 0.000 0.960 0.040
#> SRR1656483 2 0.2165 0.8611 0.000 0.936 0.064
#> SRR1656485 3 0.0000 0.9015 0.000 0.000 1.000
#> SRR1656487 3 0.0424 0.8989 0.000 0.008 0.992
#> SRR1656486 1 0.3619 0.8231 0.864 0.000 0.136
#> SRR1656488 3 0.0892 0.9019 0.020 0.000 0.980
#> SRR1656484 1 0.1860 0.9013 0.948 0.000 0.052
#> SRR1656489 1 0.1860 0.9011 0.948 0.000 0.052
#> SRR1656491 3 0.0892 0.9019 0.020 0.000 0.980
#> SRR1656490 1 0.4654 0.7272 0.792 0.000 0.208
#> SRR1656492 3 0.4654 0.7523 0.208 0.000 0.792
#> SRR1656493 1 0.0424 0.9031 0.992 0.008 0.000
#> SRR1656495 1 0.4974 0.6551 0.764 0.236 0.000
#> SRR1656496 3 0.5760 0.5692 0.328 0.000 0.672
#> SRR1656494 2 0.0592 0.8797 0.000 0.988 0.012
#> SRR1656497 2 0.1411 0.8750 0.000 0.964 0.036
#> SRR1656499 3 0.0747 0.9026 0.016 0.000 0.984
#> SRR1656500 3 0.1860 0.8887 0.052 0.000 0.948
#> SRR1656501 1 0.3267 0.8448 0.884 0.000 0.116
#> SRR1656498 1 0.0424 0.9031 0.992 0.008 0.000
#> SRR1656504 3 0.6308 0.0713 0.492 0.000 0.508
#> SRR1656502 2 0.1753 0.8736 0.048 0.952 0.000
#> SRR1656503 1 0.1643 0.9052 0.956 0.000 0.044
#> SRR1656507 1 0.1860 0.9010 0.948 0.000 0.052
#> SRR1656508 1 0.0424 0.9037 0.992 0.008 0.000
#> SRR1656505 3 0.0424 0.8989 0.000 0.008 0.992
#> SRR1656506 3 0.0892 0.9019 0.020 0.000 0.980
#> SRR1656509 2 0.6225 0.2043 0.000 0.568 0.432
#> SRR1656510 3 0.2959 0.8573 0.100 0.000 0.900
#> SRR1656511 1 0.0592 0.9115 0.988 0.000 0.012
#> SRR1656513 2 0.1411 0.8776 0.036 0.964 0.000
#> SRR1656512 2 0.4235 0.7718 0.176 0.824 0.000
#> SRR1656514 1 0.5339 0.7893 0.824 0.080 0.096
#> SRR1656515 3 0.4178 0.7729 0.000 0.172 0.828
#> SRR1656516 1 0.2959 0.8608 0.900 0.000 0.100
#> SRR1656518 1 0.1411 0.9084 0.964 0.000 0.036
#> SRR1656517 1 0.0747 0.9119 0.984 0.000 0.016
#> SRR1656519 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656522 1 0.0892 0.9123 0.980 0.000 0.020
#> SRR1656523 3 0.6111 0.4013 0.396 0.000 0.604
#> SRR1656521 1 0.0592 0.9115 0.988 0.000 0.012
#> SRR1656520 3 0.1529 0.8824 0.000 0.040 0.960
#> SRR1656524 1 0.1643 0.8829 0.956 0.044 0.000
#> SRR1656525 3 0.1411 0.8958 0.036 0.000 0.964
#> SRR1656526 3 0.0592 0.9030 0.012 0.000 0.988
#> SRR1656527 1 0.2356 0.8636 0.928 0.072 0.000
#> SRR1656530 3 0.1163 0.8993 0.028 0.000 0.972
#> SRR1656529 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656531 1 0.1643 0.8829 0.956 0.044 0.000
#> SRR1656528 3 0.0592 0.9030 0.012 0.000 0.988
#> SRR1656534 3 0.4452 0.7726 0.192 0.000 0.808
#> SRR1656533 1 0.0592 0.9115 0.988 0.000 0.012
#> SRR1656536 3 0.0592 0.8971 0.000 0.012 0.988
#> SRR1656532 1 0.3879 0.7779 0.848 0.152 0.000
#> SRR1656537 1 0.1411 0.8876 0.964 0.036 0.000
#> SRR1656538 3 0.5810 0.5359 0.336 0.000 0.664
#> SRR1656535 1 0.0747 0.9119 0.984 0.000 0.016
#> SRR1656539 3 0.0237 0.9003 0.000 0.004 0.996
#> SRR1656544 3 0.1031 0.9007 0.024 0.000 0.976
#> SRR1656542 3 0.2537 0.8720 0.080 0.000 0.920
#> SRR1656543 3 0.0237 0.9024 0.004 0.000 0.996
#> SRR1656545 2 0.2448 0.8594 0.076 0.924 0.000
#> SRR1656540 3 0.2711 0.8509 0.000 0.088 0.912
#> SRR1656546 1 0.1753 0.9033 0.952 0.000 0.048
#> SRR1656541 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656547 3 0.0424 0.8989 0.000 0.008 0.992
#> SRR1656548 3 0.2356 0.8770 0.072 0.000 0.928
#> SRR1656549 1 0.0424 0.9102 0.992 0.000 0.008
#> SRR1656551 3 0.0237 0.9003 0.000 0.004 0.996
#> SRR1656553 3 0.2356 0.8774 0.072 0.000 0.928
#> SRR1656550 3 0.2448 0.8585 0.000 0.076 0.924
#> SRR1656552 3 0.4399 0.7776 0.188 0.000 0.812
#> SRR1656554 3 0.0237 0.9024 0.004 0.000 0.996
#> SRR1656555 3 0.1031 0.9007 0.024 0.000 0.976
#> SRR1656556 3 0.4002 0.7855 0.000 0.160 0.840
#> SRR1656557 3 0.0747 0.9026 0.016 0.000 0.984
#> SRR1656558 1 0.0424 0.9102 0.992 0.000 0.008
#> SRR1656559 1 0.0592 0.9116 0.988 0.000 0.012
#> SRR1656560 3 0.0592 0.9031 0.012 0.000 0.988
#> SRR1656561 3 0.6045 0.4331 0.380 0.000 0.620
#> SRR1656562 2 0.7988 0.6543 0.144 0.656 0.200
#> SRR1656563 1 0.1860 0.9010 0.948 0.000 0.052
#> SRR1656564 2 0.6140 0.3485 0.404 0.596 0.000
#> SRR1656565 2 0.7252 0.7029 0.196 0.704 0.100
#> SRR1656566 1 0.0424 0.9031 0.992 0.008 0.000
#> SRR1656568 1 0.2165 0.8695 0.936 0.064 0.000
#> SRR1656567 3 0.2537 0.8559 0.000 0.080 0.920
#> SRR1656569 3 0.0424 0.9030 0.008 0.000 0.992
#> SRR1656570 1 0.1964 0.8982 0.944 0.000 0.056
#> SRR1656571 2 0.1643 0.8753 0.044 0.956 0.000
#> SRR1656573 3 0.2066 0.8847 0.060 0.000 0.940
#> SRR1656572 1 0.1529 0.9067 0.960 0.000 0.040
#> SRR1656574 1 0.0892 0.9118 0.980 0.000 0.020
#> SRR1656575 1 0.1289 0.9094 0.968 0.000 0.032
#> SRR1656576 3 0.0892 0.9019 0.020 0.000 0.980
#> SRR1656578 2 0.3412 0.8243 0.124 0.876 0.000
#> SRR1656577 1 0.0747 0.9119 0.984 0.000 0.016
#> SRR1656579 3 0.0592 0.8971 0.000 0.012 0.988
#> SRR1656580 1 0.6008 0.4015 0.628 0.000 0.372
#> SRR1656581 3 0.4974 0.7200 0.236 0.000 0.764
#> SRR1656582 3 0.3551 0.8319 0.132 0.000 0.868
#> SRR1656585 3 0.5835 0.5041 0.000 0.340 0.660
#> SRR1656584 1 0.0424 0.9102 0.992 0.000 0.008
#> SRR1656583 2 0.1964 0.8656 0.000 0.944 0.056
#> SRR1656586 2 0.0000 0.8815 0.000 1.000 0.000
#> SRR1656587 2 0.2625 0.8559 0.084 0.916 0.000
#> SRR1656588 3 0.3116 0.8344 0.000 0.108 0.892
#> SRR1656589 2 0.0000 0.8815 0.000 1.000 0.000
#> SRR1656590 1 0.2261 0.8653 0.932 0.068 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 3 0.3710 0.70375 0.000 0.192 0.804 0.004
#> SRR1656464 1 0.1339 0.82404 0.964 0.008 0.024 0.004
#> SRR1656462 3 0.0188 0.85964 0.000 0.000 0.996 0.004
#> SRR1656465 3 0.0188 0.85976 0.000 0.000 0.996 0.004
#> SRR1656467 3 0.5292 -0.08887 0.000 0.480 0.512 0.008
#> SRR1656466 3 0.0707 0.85782 0.000 0.000 0.980 0.020
#> SRR1656468 3 0.3356 0.72595 0.000 0.000 0.824 0.176
#> SRR1656472 2 0.6174 0.17192 0.460 0.496 0.040 0.004
#> SRR1656471 3 0.0376 0.85872 0.000 0.004 0.992 0.004
#> SRR1656470 2 0.0000 0.84853 0.000 1.000 0.000 0.000
#> SRR1656469 3 0.3123 0.74156 0.000 0.000 0.844 0.156
#> SRR1656473 2 0.0000 0.84853 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.84853 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.84853 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0779 0.83630 0.980 0.000 0.004 0.016
#> SRR1656477 3 0.1109 0.84632 0.000 0.028 0.968 0.004
#> SRR1656479 4 0.2546 0.76315 0.028 0.000 0.060 0.912
#> SRR1656480 3 0.0937 0.85758 0.000 0.012 0.976 0.012
#> SRR1656476 4 0.3726 0.70109 0.000 0.000 0.212 0.788
#> SRR1656481 3 0.0336 0.85928 0.000 0.000 0.992 0.008
#> SRR1656482 2 0.4428 0.61828 0.000 0.720 0.276 0.004
#> SRR1656483 3 0.5303 0.16195 0.004 0.448 0.544 0.004
#> SRR1656485 3 0.0336 0.85995 0.000 0.000 0.992 0.008
#> SRR1656487 3 0.0817 0.85700 0.000 0.000 0.976 0.024
#> SRR1656486 4 0.2300 0.75177 0.064 0.000 0.016 0.920
#> SRR1656488 3 0.1211 0.85047 0.000 0.000 0.960 0.040
#> SRR1656484 1 0.4248 0.69822 0.768 0.000 0.012 0.220
#> SRR1656489 1 0.2021 0.82528 0.932 0.000 0.012 0.056
#> SRR1656491 4 0.4500 0.52424 0.000 0.000 0.316 0.684
#> SRR1656490 4 0.5088 0.49471 0.288 0.000 0.024 0.688
#> SRR1656492 4 0.5630 0.47363 0.032 0.000 0.360 0.608
#> SRR1656493 1 0.0188 0.83501 0.996 0.000 0.000 0.004
#> SRR1656495 1 0.2266 0.77681 0.912 0.084 0.000 0.004
#> SRR1656496 4 0.4322 0.74953 0.044 0.000 0.152 0.804
#> SRR1656494 2 0.5252 0.33232 0.004 0.572 0.420 0.004
#> SRR1656497 2 0.2704 0.78745 0.000 0.876 0.000 0.124
#> SRR1656499 3 0.0707 0.85777 0.000 0.000 0.980 0.020
#> SRR1656500 3 0.0937 0.85767 0.012 0.000 0.976 0.012
#> SRR1656501 4 0.5149 0.41261 0.336 0.000 0.016 0.648
#> SRR1656498 1 0.0188 0.83501 0.996 0.000 0.000 0.004
#> SRR1656504 4 0.0804 0.76213 0.012 0.000 0.008 0.980
#> SRR1656502 1 0.5771 -0.12467 0.504 0.472 0.020 0.004
#> SRR1656503 1 0.3032 0.79078 0.868 0.000 0.008 0.124
#> SRR1656507 1 0.2473 0.81554 0.908 0.000 0.012 0.080
#> SRR1656508 1 0.1792 0.82611 0.932 0.000 0.000 0.068
#> SRR1656505 3 0.0592 0.85922 0.000 0.000 0.984 0.016
#> SRR1656506 4 0.4250 0.60736 0.000 0.000 0.276 0.724
#> SRR1656509 3 0.2861 0.78813 0.004 0.092 0.892 0.012
#> SRR1656510 4 0.5517 0.35548 0.020 0.000 0.412 0.568
#> SRR1656511 4 0.0921 0.75597 0.028 0.000 0.000 0.972
#> SRR1656513 2 0.0188 0.84786 0.004 0.996 0.000 0.000
#> SRR1656512 2 0.2081 0.81850 0.000 0.916 0.000 0.084
#> SRR1656514 1 0.5165 0.35754 0.604 0.004 0.388 0.004
#> SRR1656515 3 0.1545 0.84507 0.000 0.040 0.952 0.008
#> SRR1656516 1 0.5658 0.46886 0.632 0.000 0.040 0.328
#> SRR1656518 1 0.5099 0.42198 0.612 0.000 0.008 0.380
#> SRR1656517 1 0.2530 0.79939 0.888 0.000 0.000 0.112
#> SRR1656519 3 0.0188 0.85934 0.004 0.000 0.996 0.000
#> SRR1656522 1 0.1209 0.82617 0.964 0.000 0.032 0.004
#> SRR1656523 4 0.0657 0.76324 0.004 0.000 0.012 0.984
#> SRR1656521 4 0.5147 -0.00283 0.460 0.000 0.004 0.536
#> SRR1656520 3 0.0188 0.85964 0.000 0.000 0.996 0.004
#> SRR1656524 1 0.0376 0.83443 0.992 0.004 0.000 0.004
#> SRR1656525 3 0.4804 0.33124 0.000 0.000 0.616 0.384
#> SRR1656526 4 0.0592 0.76358 0.000 0.000 0.016 0.984
#> SRR1656527 1 0.0524 0.83493 0.988 0.008 0.000 0.004
#> SRR1656530 3 0.3172 0.74271 0.000 0.000 0.840 0.160
#> SRR1656529 3 0.4605 0.45449 0.000 0.000 0.664 0.336
#> SRR1656531 1 0.0817 0.83302 0.976 0.000 0.000 0.024
#> SRR1656528 3 0.4989 0.06063 0.000 0.000 0.528 0.472
#> SRR1656534 3 0.1807 0.82974 0.052 0.000 0.940 0.008
#> SRR1656533 1 0.3400 0.75125 0.820 0.000 0.000 0.180
#> SRR1656536 3 0.0336 0.85995 0.000 0.000 0.992 0.008
#> SRR1656532 1 0.0524 0.83086 0.988 0.008 0.000 0.004
#> SRR1656537 1 0.0188 0.83501 0.996 0.000 0.000 0.004
#> SRR1656538 4 0.6691 0.61507 0.152 0.000 0.236 0.612
#> SRR1656535 4 0.4428 0.51140 0.276 0.000 0.004 0.720
#> SRR1656539 3 0.0336 0.85995 0.000 0.000 0.992 0.008
#> SRR1656544 3 0.0336 0.85995 0.000 0.000 0.992 0.008
#> SRR1656542 3 0.2413 0.82496 0.020 0.000 0.916 0.064
#> SRR1656543 3 0.0336 0.85928 0.000 0.000 0.992 0.008
#> SRR1656545 2 0.3024 0.77271 0.000 0.852 0.000 0.148
#> SRR1656540 3 0.0376 0.85923 0.004 0.000 0.992 0.004
#> SRR1656546 1 0.5193 0.33674 0.580 0.000 0.008 0.412
#> SRR1656541 3 0.4967 0.09724 0.000 0.000 0.548 0.452
#> SRR1656547 3 0.1792 0.82893 0.000 0.000 0.932 0.068
#> SRR1656548 4 0.3219 0.74126 0.000 0.000 0.164 0.836
#> SRR1656549 4 0.4661 0.28219 0.348 0.000 0.000 0.652
#> SRR1656551 3 0.1389 0.84625 0.000 0.000 0.952 0.048
#> SRR1656553 3 0.1545 0.84094 0.040 0.000 0.952 0.008
#> SRR1656550 3 0.0524 0.85970 0.000 0.004 0.988 0.008
#> SRR1656552 4 0.2198 0.77016 0.008 0.000 0.072 0.920
#> SRR1656554 3 0.4925 0.23712 0.000 0.000 0.572 0.428
#> SRR1656555 4 0.2345 0.76545 0.000 0.000 0.100 0.900
#> SRR1656556 3 0.0188 0.85892 0.000 0.004 0.996 0.000
#> SRR1656557 3 0.0188 0.85964 0.000 0.000 0.996 0.004
#> SRR1656558 1 0.0707 0.83604 0.980 0.000 0.000 0.020
#> SRR1656559 1 0.0524 0.83547 0.988 0.000 0.008 0.004
#> SRR1656560 3 0.1302 0.84830 0.000 0.000 0.956 0.044
#> SRR1656561 4 0.1510 0.77121 0.016 0.000 0.028 0.956
#> SRR1656562 2 0.1917 0.83697 0.012 0.944 0.008 0.036
#> SRR1656563 4 0.2216 0.71621 0.092 0.000 0.000 0.908
#> SRR1656564 2 0.3108 0.79644 0.016 0.872 0.000 0.112
#> SRR1656565 2 0.3134 0.80024 0.024 0.884 0.088 0.004
#> SRR1656566 1 0.0469 0.83615 0.988 0.000 0.000 0.012
#> SRR1656568 1 0.1305 0.83210 0.960 0.004 0.000 0.036
#> SRR1656567 3 0.0524 0.85994 0.000 0.004 0.988 0.008
#> SRR1656569 4 0.4790 0.40424 0.000 0.000 0.380 0.620
#> SRR1656570 4 0.1118 0.75266 0.036 0.000 0.000 0.964
#> SRR1656571 2 0.0469 0.84626 0.012 0.988 0.000 0.000
#> SRR1656573 4 0.4500 0.55054 0.000 0.000 0.316 0.684
#> SRR1656572 1 0.5097 0.30098 0.568 0.000 0.004 0.428
#> SRR1656574 1 0.2647 0.79526 0.880 0.000 0.000 0.120
#> SRR1656575 1 0.1305 0.83273 0.960 0.000 0.004 0.036
#> SRR1656576 4 0.3123 0.74194 0.000 0.000 0.156 0.844
#> SRR1656578 2 0.3908 0.67857 0.212 0.784 0.000 0.004
#> SRR1656577 1 0.0376 0.83550 0.992 0.000 0.004 0.004
#> SRR1656579 3 0.5132 0.17888 0.000 0.004 0.548 0.448
#> SRR1656580 1 0.7558 0.10994 0.488 0.000 0.256 0.256
#> SRR1656581 4 0.0707 0.76458 0.000 0.000 0.020 0.980
#> SRR1656582 4 0.1297 0.75948 0.000 0.016 0.020 0.964
#> SRR1656585 3 0.6261 -0.05523 0.000 0.440 0.504 0.056
#> SRR1656584 1 0.3123 0.77024 0.844 0.000 0.000 0.156
#> SRR1656583 2 0.4978 0.42374 0.000 0.612 0.384 0.004
#> SRR1656586 2 0.0000 0.84853 0.000 1.000 0.000 0.000
#> SRR1656587 1 0.7146 0.32013 0.560 0.152 0.284 0.004
#> SRR1656588 3 0.0188 0.85892 0.000 0.004 0.996 0.000
#> SRR1656589 2 0.0336 0.84744 0.000 0.992 0.008 0.000
#> SRR1656590 1 0.0376 0.83182 0.992 0.004 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 4 0.6497 0.3457 0.000 0.312 0.212 0.476 0.000
#> SRR1656464 1 0.0671 0.8381 0.980 0.000 0.016 0.004 0.000
#> SRR1656462 3 0.2068 0.8137 0.004 0.000 0.904 0.092 0.000
#> SRR1656465 3 0.3048 0.7553 0.000 0.000 0.820 0.176 0.004
#> SRR1656467 3 0.2084 0.7618 0.004 0.064 0.920 0.008 0.004
#> SRR1656466 4 0.3966 0.4323 0.000 0.000 0.336 0.664 0.000
#> SRR1656468 4 0.4306 0.4332 0.000 0.000 0.328 0.660 0.012
#> SRR1656472 1 0.3782 0.7380 0.836 0.048 0.096 0.016 0.004
#> SRR1656471 3 0.0324 0.8017 0.000 0.000 0.992 0.004 0.004
#> SRR1656470 2 0.0162 0.8654 0.000 0.996 0.000 0.004 0.000
#> SRR1656469 3 0.4642 0.5588 0.000 0.000 0.660 0.308 0.032
#> SRR1656473 2 0.0000 0.8658 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0162 0.8655 0.000 0.996 0.000 0.004 0.000
#> SRR1656475 2 0.0000 0.8658 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.4287 0.3781 0.540 0.000 0.000 0.460 0.000
#> SRR1656477 3 0.0290 0.8032 0.000 0.008 0.992 0.000 0.000
#> SRR1656479 5 0.1168 0.8327 0.008 0.000 0.032 0.000 0.960
#> SRR1656480 3 0.0290 0.8027 0.000 0.000 0.992 0.000 0.008
#> SRR1656476 4 0.4370 0.5603 0.000 0.000 0.040 0.724 0.236
#> SRR1656481 3 0.3689 0.6640 0.000 0.000 0.740 0.256 0.004
#> SRR1656482 2 0.4911 0.0238 0.008 0.504 0.476 0.012 0.000
#> SRR1656483 2 0.3924 0.7169 0.008 0.816 0.096 0.080 0.000
#> SRR1656485 3 0.1831 0.8136 0.000 0.000 0.920 0.076 0.004
#> SRR1656487 3 0.4268 0.5140 0.000 0.000 0.648 0.344 0.008
#> SRR1656486 5 0.4026 0.6259 0.020 0.000 0.000 0.244 0.736
#> SRR1656488 4 0.4218 0.4302 0.000 0.000 0.332 0.660 0.008
#> SRR1656484 1 0.4247 0.7829 0.776 0.000 0.000 0.132 0.092
#> SRR1656489 1 0.3969 0.6807 0.692 0.000 0.000 0.304 0.004
#> SRR1656491 5 0.3110 0.8116 0.000 0.000 0.080 0.060 0.860
#> SRR1656490 5 0.2514 0.7976 0.060 0.000 0.000 0.044 0.896
#> SRR1656492 4 0.2684 0.7146 0.024 0.000 0.032 0.900 0.044
#> SRR1656493 1 0.2230 0.8322 0.884 0.000 0.000 0.116 0.000
#> SRR1656495 1 0.0854 0.8339 0.976 0.012 0.000 0.008 0.004
#> SRR1656496 5 0.1788 0.8379 0.004 0.000 0.056 0.008 0.932
#> SRR1656494 3 0.3052 0.7282 0.036 0.072 0.876 0.016 0.000
#> SRR1656497 2 0.1768 0.8479 0.000 0.924 0.000 0.072 0.004
#> SRR1656499 4 0.4451 -0.0778 0.000 0.000 0.492 0.504 0.004
#> SRR1656500 3 0.2295 0.8103 0.004 0.000 0.900 0.088 0.008
#> SRR1656501 4 0.4316 0.6343 0.108 0.000 0.000 0.772 0.120
#> SRR1656498 1 0.1205 0.8437 0.956 0.000 0.000 0.040 0.004
#> SRR1656504 4 0.3684 0.5205 0.000 0.000 0.000 0.720 0.280
#> SRR1656502 1 0.2949 0.7738 0.884 0.048 0.052 0.016 0.000
#> SRR1656503 1 0.3780 0.7999 0.812 0.000 0.000 0.116 0.072
#> SRR1656507 4 0.2970 0.6264 0.168 0.000 0.004 0.828 0.000
#> SRR1656508 1 0.1638 0.8290 0.932 0.000 0.000 0.004 0.064
#> SRR1656505 3 0.4331 0.3800 0.000 0.000 0.596 0.400 0.004
#> SRR1656506 5 0.1851 0.8240 0.000 0.000 0.088 0.000 0.912
#> SRR1656509 3 0.1772 0.7779 0.024 0.012 0.944 0.016 0.004
#> SRR1656510 4 0.2434 0.7116 0.008 0.000 0.036 0.908 0.048
#> SRR1656511 5 0.0671 0.8350 0.004 0.000 0.000 0.016 0.980
#> SRR1656513 2 0.2529 0.8366 0.056 0.900 0.004 0.040 0.000
#> SRR1656512 2 0.2236 0.8436 0.000 0.908 0.000 0.068 0.024
#> SRR1656514 1 0.3635 0.6099 0.748 0.000 0.248 0.004 0.000
#> SRR1656515 3 0.4926 0.6800 0.000 0.112 0.712 0.176 0.000
#> SRR1656516 4 0.4294 0.6300 0.148 0.000 0.008 0.780 0.064
#> SRR1656518 4 0.4732 0.5250 0.208 0.000 0.000 0.716 0.076
#> SRR1656517 1 0.4339 0.6250 0.652 0.000 0.000 0.336 0.012
#> SRR1656519 3 0.1731 0.8167 0.004 0.000 0.932 0.060 0.004
#> SRR1656522 1 0.2171 0.8326 0.912 0.000 0.024 0.064 0.000
#> SRR1656523 5 0.0000 0.8369 0.000 0.000 0.000 0.000 1.000
#> SRR1656521 4 0.3670 0.6538 0.112 0.000 0.000 0.820 0.068
#> SRR1656520 3 0.0451 0.8008 0.008 0.000 0.988 0.004 0.000
#> SRR1656524 1 0.0671 0.8416 0.980 0.000 0.000 0.016 0.004
#> SRR1656525 5 0.6507 0.2177 0.000 0.000 0.316 0.212 0.472
#> SRR1656526 5 0.2511 0.8137 0.000 0.016 0.004 0.088 0.892
#> SRR1656527 1 0.2929 0.8040 0.820 0.000 0.000 0.180 0.000
#> SRR1656530 4 0.4003 0.4997 0.000 0.000 0.288 0.704 0.008
#> SRR1656529 3 0.6110 0.1011 0.000 0.000 0.476 0.128 0.396
#> SRR1656531 1 0.1281 0.8335 0.956 0.000 0.000 0.012 0.032
#> SRR1656528 5 0.5599 0.5261 0.000 0.000 0.260 0.120 0.620
#> SRR1656534 3 0.2151 0.8006 0.040 0.000 0.924 0.016 0.020
#> SRR1656533 1 0.4535 0.7690 0.752 0.000 0.000 0.108 0.140
#> SRR1656536 3 0.1544 0.8144 0.000 0.000 0.932 0.068 0.000
#> SRR1656532 1 0.0000 0.8385 1.000 0.000 0.000 0.000 0.000
#> SRR1656537 1 0.0865 0.8427 0.972 0.000 0.000 0.024 0.004
#> SRR1656538 4 0.3100 0.7103 0.040 0.000 0.020 0.876 0.064
#> SRR1656535 4 0.3543 0.6772 0.060 0.000 0.000 0.828 0.112
#> SRR1656539 3 0.1908 0.8105 0.000 0.000 0.908 0.092 0.000
#> SRR1656544 3 0.2497 0.8014 0.004 0.000 0.880 0.112 0.004
#> SRR1656542 4 0.4836 0.2332 0.008 0.000 0.412 0.568 0.012
#> SRR1656543 3 0.3452 0.6885 0.000 0.000 0.756 0.244 0.000
#> SRR1656545 2 0.2853 0.8268 0.000 0.876 0.000 0.072 0.052
#> SRR1656540 3 0.0693 0.8043 0.008 0.000 0.980 0.012 0.000
#> SRR1656546 4 0.3452 0.6335 0.148 0.000 0.000 0.820 0.032
#> SRR1656541 4 0.3323 0.6512 0.000 0.000 0.100 0.844 0.056
#> SRR1656547 3 0.4403 0.3946 0.000 0.000 0.560 0.436 0.004
#> SRR1656548 5 0.2951 0.7922 0.000 0.000 0.028 0.112 0.860
#> SRR1656549 5 0.1872 0.8184 0.052 0.000 0.000 0.020 0.928
#> SRR1656551 3 0.1800 0.8134 0.000 0.000 0.932 0.048 0.020
#> SRR1656553 3 0.5691 0.2252 0.084 0.000 0.516 0.400 0.000
#> SRR1656550 3 0.1410 0.8146 0.000 0.000 0.940 0.060 0.000
#> SRR1656552 4 0.2674 0.7031 0.020 0.000 0.008 0.888 0.084
#> SRR1656554 5 0.4436 0.4163 0.000 0.000 0.396 0.008 0.596
#> SRR1656555 5 0.2727 0.8178 0.000 0.000 0.016 0.116 0.868
#> SRR1656556 3 0.1331 0.8132 0.008 0.000 0.952 0.040 0.000
#> SRR1656557 3 0.2068 0.8104 0.004 0.000 0.904 0.092 0.000
#> SRR1656558 1 0.3774 0.6950 0.704 0.000 0.000 0.296 0.000
#> SRR1656559 1 0.2648 0.8177 0.848 0.000 0.000 0.152 0.000
#> SRR1656560 4 0.4527 0.2829 0.000 0.000 0.392 0.596 0.012
#> SRR1656561 5 0.1041 0.8370 0.000 0.000 0.004 0.032 0.964
#> SRR1656562 2 0.6123 0.1446 0.004 0.512 0.020 0.064 0.400
#> SRR1656563 5 0.0290 0.8365 0.000 0.000 0.000 0.008 0.992
#> SRR1656564 5 0.4965 0.4379 0.032 0.320 0.000 0.008 0.640
#> SRR1656565 2 0.5326 0.7070 0.096 0.740 0.044 0.004 0.116
#> SRR1656566 1 0.2280 0.8311 0.880 0.000 0.000 0.120 0.000
#> SRR1656568 1 0.1662 0.8443 0.936 0.004 0.000 0.056 0.004
#> SRR1656567 3 0.2516 0.7852 0.000 0.000 0.860 0.140 0.000
#> SRR1656569 5 0.4010 0.7129 0.000 0.000 0.208 0.032 0.760
#> SRR1656570 5 0.0290 0.8365 0.000 0.000 0.000 0.008 0.992
#> SRR1656571 2 0.0613 0.8636 0.008 0.984 0.004 0.004 0.000
#> SRR1656573 5 0.2305 0.8221 0.000 0.000 0.092 0.012 0.896
#> SRR1656572 4 0.4360 0.5743 0.184 0.000 0.000 0.752 0.064
#> SRR1656574 1 0.2068 0.8212 0.904 0.000 0.000 0.004 0.092
#> SRR1656575 1 0.2488 0.8302 0.872 0.000 0.000 0.124 0.004
#> SRR1656576 5 0.2378 0.8302 0.000 0.000 0.048 0.048 0.904
#> SRR1656578 1 0.4450 0.0809 0.508 0.488 0.000 0.004 0.000
#> SRR1656577 1 0.1410 0.8434 0.940 0.000 0.000 0.060 0.000
#> SRR1656579 3 0.4359 0.1193 0.000 0.000 0.584 0.004 0.412
#> SRR1656580 5 0.5272 0.6827 0.164 0.000 0.072 0.040 0.724
#> SRR1656581 5 0.0162 0.8367 0.000 0.000 0.000 0.004 0.996
#> SRR1656582 5 0.0000 0.8369 0.000 0.000 0.000 0.000 1.000
#> SRR1656585 3 0.5194 0.5998 0.040 0.044 0.748 0.016 0.152
#> SRR1656584 1 0.4528 0.7457 0.728 0.000 0.000 0.212 0.060
#> SRR1656583 3 0.2943 0.7368 0.016 0.068 0.888 0.016 0.012
#> SRR1656586 2 0.0162 0.8655 0.000 0.996 0.000 0.004 0.000
#> SRR1656587 1 0.4023 0.6960 0.792 0.028 0.164 0.016 0.000
#> SRR1656588 3 0.1544 0.8155 0.000 0.000 0.932 0.068 0.000
#> SRR1656589 2 0.0613 0.8635 0.004 0.984 0.008 0.004 0.000
#> SRR1656590 1 0.0451 0.8368 0.988 0.000 0.000 0.008 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 4 0.6928 0.26196 0.000 0.328 0.172 0.416 0.000 0.084
#> SRR1656464 1 0.3013 0.75569 0.864 0.000 0.044 0.028 0.000 0.064
#> SRR1656462 3 0.4198 0.62103 0.004 0.000 0.656 0.024 0.000 0.316
#> SRR1656465 3 0.3168 0.75995 0.000 0.000 0.828 0.116 0.000 0.056
#> SRR1656467 3 0.2030 0.74991 0.000 0.016 0.920 0.012 0.004 0.048
#> SRR1656466 4 0.5255 0.42652 0.000 0.000 0.272 0.588 0.000 0.140
#> SRR1656468 4 0.5070 0.52718 0.000 0.000 0.200 0.656 0.008 0.136
#> SRR1656472 1 0.5008 0.63239 0.728 0.008 0.136 0.020 0.012 0.096
#> SRR1656471 3 0.1003 0.76691 0.000 0.000 0.964 0.004 0.004 0.028
#> SRR1656470 2 0.0146 0.76381 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656469 3 0.5829 0.40606 0.000 0.000 0.548 0.324 0.052 0.076
#> SRR1656473 2 0.0000 0.76460 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.76460 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0146 0.76381 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1656478 1 0.4750 0.47716 0.596 0.000 0.000 0.340 0.000 0.064
#> SRR1656477 3 0.1218 0.76342 0.000 0.012 0.956 0.004 0.000 0.028
#> SRR1656479 5 0.2962 0.73157 0.024 0.000 0.060 0.020 0.876 0.020
#> SRR1656480 3 0.1010 0.77050 0.000 0.000 0.960 0.000 0.004 0.036
#> SRR1656476 4 0.4506 0.57806 0.000 0.000 0.048 0.752 0.136 0.064
#> SRR1656481 3 0.3806 0.68464 0.000 0.000 0.752 0.200 0.000 0.048
#> SRR1656482 2 0.4467 -0.04362 0.000 0.496 0.480 0.004 0.000 0.020
#> SRR1656483 2 0.3106 0.67686 0.000 0.860 0.048 0.036 0.000 0.056
#> SRR1656485 3 0.2499 0.77373 0.000 0.000 0.880 0.048 0.000 0.072
#> SRR1656487 3 0.5096 0.47657 0.000 0.000 0.596 0.292 0.000 0.112
#> SRR1656486 5 0.5124 0.16239 0.028 0.000 0.000 0.440 0.500 0.032
#> SRR1656488 4 0.5327 0.44812 0.000 0.000 0.248 0.588 0.000 0.164
#> SRR1656484 1 0.7466 0.10831 0.388 0.004 0.036 0.208 0.320 0.044
#> SRR1656489 1 0.4736 0.63957 0.680 0.000 0.000 0.212 0.004 0.104
#> SRR1656491 6 0.4654 0.37130 0.000 0.000 0.044 0.000 0.412 0.544
#> SRR1656490 5 0.4392 0.64019 0.120 0.000 0.000 0.088 0.760 0.032
#> SRR1656492 4 0.3029 0.65787 0.016 0.000 0.020 0.872 0.032 0.060
#> SRR1656493 1 0.3314 0.74256 0.820 0.000 0.000 0.128 0.004 0.048
#> SRR1656495 1 0.1937 0.75971 0.924 0.004 0.000 0.012 0.012 0.048
#> SRR1656496 5 0.3781 0.73725 0.028 0.000 0.064 0.040 0.832 0.036
#> SRR1656494 3 0.4017 0.66796 0.084 0.012 0.800 0.016 0.000 0.088
#> SRR1656497 2 0.3955 0.17772 0.000 0.560 0.000 0.000 0.004 0.436
#> SRR1656499 3 0.6120 -0.00335 0.000 0.000 0.364 0.320 0.000 0.316
#> SRR1656500 3 0.3005 0.76756 0.008 0.000 0.856 0.036 0.004 0.096
#> SRR1656501 4 0.5501 0.58656 0.136 0.000 0.000 0.660 0.052 0.152
#> SRR1656498 1 0.1480 0.77291 0.940 0.000 0.000 0.040 0.000 0.020
#> SRR1656504 4 0.3319 0.56776 0.004 0.000 0.004 0.800 0.176 0.016
#> SRR1656502 1 0.4345 0.68851 0.788 0.008 0.076 0.020 0.012 0.096
#> SRR1656503 6 0.4833 0.36140 0.288 0.000 0.000 0.012 0.060 0.640
#> SRR1656507 4 0.3236 0.61275 0.180 0.000 0.000 0.796 0.000 0.024
#> SRR1656508 1 0.3178 0.72282 0.832 0.000 0.000 0.012 0.128 0.028
#> SRR1656505 3 0.4859 0.48796 0.000 0.000 0.604 0.316 0.000 0.080
#> SRR1656506 5 0.3093 0.72008 0.000 0.000 0.104 0.024 0.848 0.024
#> SRR1656509 3 0.3620 0.71624 0.028 0.004 0.808 0.012 0.004 0.144
#> SRR1656510 4 0.2055 0.65907 0.008 0.000 0.020 0.924 0.020 0.028
#> SRR1656511 5 0.1620 0.73597 0.012 0.000 0.000 0.024 0.940 0.024
#> SRR1656513 6 0.4981 -0.07936 0.068 0.436 0.000 0.000 0.000 0.496
#> SRR1656512 2 0.4388 0.38850 0.000 0.648 0.000 0.004 0.036 0.312
#> SRR1656514 1 0.4812 0.39112 0.592 0.000 0.352 0.008 0.000 0.048
#> SRR1656515 3 0.5626 0.61150 0.000 0.068 0.644 0.096 0.000 0.192
#> SRR1656516 4 0.5503 0.57725 0.128 0.000 0.004 0.676 0.060 0.132
#> SRR1656518 4 0.4279 0.58925 0.184 0.000 0.000 0.744 0.048 0.024
#> SRR1656517 1 0.4066 0.62687 0.696 0.000 0.000 0.272 0.004 0.028
#> SRR1656519 3 0.1926 0.77096 0.000 0.000 0.912 0.020 0.000 0.068
#> SRR1656522 1 0.3399 0.72771 0.816 0.000 0.024 0.020 0.000 0.140
#> SRR1656523 5 0.0858 0.72811 0.000 0.000 0.000 0.004 0.968 0.028
#> SRR1656521 4 0.3758 0.63647 0.112 0.000 0.000 0.808 0.032 0.048
#> SRR1656520 3 0.1410 0.77230 0.000 0.000 0.944 0.008 0.004 0.044
#> SRR1656524 1 0.1059 0.77346 0.964 0.000 0.000 0.016 0.004 0.016
#> SRR1656525 6 0.5562 0.58193 0.004 0.000 0.152 0.040 0.148 0.656
#> SRR1656526 6 0.4355 0.38251 0.000 0.008 0.004 0.008 0.396 0.584
#> SRR1656527 1 0.3522 0.73613 0.804 0.004 0.000 0.148 0.004 0.040
#> SRR1656530 4 0.5731 0.43975 0.000 0.000 0.184 0.552 0.008 0.256
#> SRR1656529 6 0.6548 0.25519 0.000 0.000 0.304 0.056 0.164 0.476
#> SRR1656531 1 0.3745 0.72303 0.820 0.000 0.008 0.020 0.080 0.072
#> SRR1656528 6 0.6906 0.32264 0.000 0.000 0.180 0.076 0.340 0.404
#> SRR1656534 3 0.3532 0.73579 0.032 0.000 0.844 0.016 0.064 0.044
#> SRR1656533 1 0.4803 0.61789 0.672 0.000 0.000 0.108 0.216 0.004
#> SRR1656536 3 0.2558 0.77572 0.000 0.000 0.868 0.028 0.000 0.104
#> SRR1656532 1 0.1411 0.76011 0.936 0.000 0.000 0.004 0.000 0.060
#> SRR1656537 1 0.0603 0.77294 0.980 0.000 0.000 0.016 0.000 0.004
#> SRR1656538 4 0.4791 0.62139 0.048 0.000 0.024 0.752 0.048 0.128
#> SRR1656535 4 0.2119 0.65192 0.060 0.000 0.000 0.904 0.036 0.000
#> SRR1656539 3 0.2752 0.77351 0.000 0.000 0.856 0.036 0.000 0.108
#> SRR1656544 3 0.4471 0.71731 0.004 0.000 0.736 0.096 0.008 0.156
#> SRR1656542 4 0.4836 0.52562 0.004 0.000 0.240 0.680 0.020 0.056
#> SRR1656543 3 0.5367 0.53301 0.000 0.000 0.588 0.188 0.000 0.224
#> SRR1656545 6 0.5533 0.00690 0.000 0.420 0.000 0.000 0.132 0.448
#> SRR1656540 3 0.0806 0.77056 0.000 0.000 0.972 0.008 0.000 0.020
#> SRR1656546 4 0.5051 0.52191 0.208 0.000 0.000 0.656 0.008 0.128
#> SRR1656541 6 0.4949 0.48221 0.000 0.000 0.084 0.172 0.040 0.704
#> SRR1656547 6 0.5013 0.45503 0.000 0.000 0.220 0.116 0.008 0.656
#> SRR1656548 5 0.4280 0.60734 0.000 0.000 0.004 0.232 0.708 0.056
#> SRR1656549 5 0.2520 0.72055 0.068 0.000 0.000 0.032 0.888 0.012
#> SRR1656551 3 0.2851 0.76603 0.000 0.000 0.868 0.016 0.036 0.080
#> SRR1656553 6 0.5687 0.41531 0.068 0.000 0.208 0.092 0.000 0.632
#> SRR1656550 3 0.2831 0.75129 0.000 0.000 0.840 0.024 0.000 0.136
#> SRR1656552 4 0.3234 0.65748 0.024 0.000 0.008 0.856 0.040 0.072
#> SRR1656554 5 0.4450 0.37091 0.000 0.000 0.352 0.012 0.616 0.020
#> SRR1656555 6 0.4150 0.42947 0.000 0.000 0.012 0.004 0.372 0.612
#> SRR1656556 3 0.1643 0.77822 0.000 0.000 0.924 0.008 0.000 0.068
#> SRR1656557 3 0.4341 0.65524 0.004 0.000 0.684 0.036 0.004 0.272
#> SRR1656558 1 0.3969 0.68362 0.740 0.000 0.000 0.212 0.004 0.044
#> SRR1656559 1 0.3828 0.72600 0.776 0.000 0.000 0.100 0.000 0.124
#> SRR1656560 4 0.6085 0.11519 0.000 0.000 0.320 0.392 0.000 0.288
#> SRR1656561 5 0.2908 0.71256 0.000 0.000 0.000 0.104 0.848 0.048
#> SRR1656562 6 0.4819 0.55603 0.012 0.060 0.028 0.008 0.148 0.744
#> SRR1656563 5 0.1346 0.73823 0.024 0.000 0.000 0.016 0.952 0.008
#> SRR1656564 5 0.5075 0.54747 0.068 0.176 0.000 0.016 0.708 0.032
#> SRR1656565 2 0.8911 0.09298 0.080 0.376 0.108 0.092 0.252 0.092
#> SRR1656566 1 0.2877 0.75315 0.848 0.000 0.000 0.124 0.008 0.020
#> SRR1656568 1 0.2960 0.76544 0.868 0.004 0.000 0.076 0.024 0.028
#> SRR1656567 3 0.3045 0.76637 0.000 0.000 0.840 0.060 0.000 0.100
#> SRR1656569 5 0.5261 0.59102 0.000 0.000 0.196 0.096 0.668 0.040
#> SRR1656570 5 0.1078 0.73844 0.008 0.000 0.000 0.012 0.964 0.016
#> SRR1656571 2 0.0976 0.75346 0.008 0.968 0.000 0.008 0.000 0.016
#> SRR1656573 5 0.4421 0.66359 0.000 0.000 0.152 0.048 0.752 0.048
#> SRR1656572 4 0.5551 0.48678 0.244 0.000 0.000 0.612 0.028 0.116
#> SRR1656574 1 0.4593 0.65132 0.720 0.000 0.000 0.024 0.188 0.068
#> SRR1656575 1 0.4902 0.68982 0.720 0.000 0.000 0.120 0.044 0.116
#> SRR1656576 5 0.5283 0.56694 0.000 0.000 0.016 0.164 0.648 0.172
#> SRR1656578 1 0.4167 0.46382 0.632 0.344 0.000 0.000 0.000 0.024
#> SRR1656577 1 0.1844 0.77215 0.924 0.000 0.000 0.048 0.004 0.024
#> SRR1656579 3 0.5606 0.06824 0.000 0.000 0.516 0.036 0.384 0.064
#> SRR1656580 5 0.7141 0.06936 0.304 0.000 0.028 0.032 0.400 0.236
#> SRR1656581 5 0.2937 0.73340 0.000 0.000 0.020 0.080 0.864 0.036
#> SRR1656582 5 0.1549 0.73275 0.000 0.000 0.000 0.020 0.936 0.044
#> SRR1656585 3 0.6033 0.43784 0.024 0.004 0.624 0.024 0.188 0.136
#> SRR1656584 1 0.4331 0.68843 0.728 0.000 0.000 0.192 0.072 0.008
#> SRR1656583 3 0.2703 0.73829 0.000 0.028 0.876 0.016 0.000 0.080
#> SRR1656586 2 0.0000 0.76460 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 1 0.6104 0.24729 0.492 0.004 0.360 0.020 0.004 0.120
#> SRR1656588 3 0.2129 0.77589 0.000 0.000 0.904 0.040 0.000 0.056
#> SRR1656589 2 0.0291 0.76255 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1656590 1 0.1442 0.76275 0.944 0.000 0.000 0.012 0.004 0.040
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 13572 rows and 129 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 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 0.864 0.910 0.954 0.4720 0.522 0.522
#> 3 3 0.581 0.674 0.799 0.2969 0.903 0.813
#> 4 4 0.627 0.625 0.724 0.1392 0.821 0.601
#> 5 5 0.659 0.674 0.783 0.1147 0.847 0.541
#> 6 6 0.781 0.772 0.865 0.0545 0.960 0.818
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
#> SRR1656463 2 0.0000 0.950 0.000 1.000
#> SRR1656464 1 0.0000 0.951 1.000 0.000
#> SRR1656462 1 0.0000 0.951 1.000 0.000
#> SRR1656465 1 0.2236 0.950 0.964 0.036
#> SRR1656467 2 0.0000 0.950 0.000 1.000
#> SRR1656466 1 0.2423 0.949 0.960 0.040
#> SRR1656468 2 0.7674 0.704 0.224 0.776
#> SRR1656472 1 0.2423 0.949 0.960 0.040
#> SRR1656471 1 0.0000 0.951 1.000 0.000
#> SRR1656470 2 0.0000 0.950 0.000 1.000
#> SRR1656469 1 0.8499 0.661 0.724 0.276
#> SRR1656473 2 0.0000 0.950 0.000 1.000
#> SRR1656474 2 0.0000 0.950 0.000 1.000
#> SRR1656475 2 0.0000 0.950 0.000 1.000
#> SRR1656478 1 0.2603 0.947 0.956 0.044
#> SRR1656477 2 0.9552 0.394 0.376 0.624
#> SRR1656479 1 0.2236 0.950 0.964 0.036
#> SRR1656480 2 0.9129 0.512 0.328 0.672
#> SRR1656476 2 0.0000 0.950 0.000 1.000
#> SRR1656481 2 0.8443 0.624 0.272 0.728
#> SRR1656482 2 0.0000 0.950 0.000 1.000
#> SRR1656483 2 0.0000 0.950 0.000 1.000
#> SRR1656485 1 0.0000 0.951 1.000 0.000
#> SRR1656487 1 0.3274 0.938 0.940 0.060
#> SRR1656486 1 0.8499 0.661 0.724 0.276
#> SRR1656488 1 0.0000 0.951 1.000 0.000
#> SRR1656484 1 0.1184 0.953 0.984 0.016
#> SRR1656489 1 0.0000 0.951 1.000 0.000
#> SRR1656491 1 0.4161 0.919 0.916 0.084
#> SRR1656490 1 0.4939 0.898 0.892 0.108
#> SRR1656492 1 0.3431 0.935 0.936 0.064
#> SRR1656493 1 0.2423 0.949 0.960 0.040
#> SRR1656495 1 0.3733 0.929 0.928 0.072
#> SRR1656496 1 0.2603 0.947 0.956 0.044
#> SRR1656494 2 0.2603 0.922 0.044 0.956
#> SRR1656497 2 0.0000 0.950 0.000 1.000
#> SRR1656499 1 0.0000 0.951 1.000 0.000
#> SRR1656500 1 0.0000 0.951 1.000 0.000
#> SRR1656501 1 0.1184 0.953 0.984 0.016
#> SRR1656498 1 0.0000 0.951 1.000 0.000
#> SRR1656504 2 0.0000 0.950 0.000 1.000
#> SRR1656502 1 0.2423 0.949 0.960 0.040
#> SRR1656503 1 0.2423 0.949 0.960 0.040
#> SRR1656507 1 0.2603 0.947 0.956 0.044
#> SRR1656508 1 0.0000 0.951 1.000 0.000
#> SRR1656505 2 0.2778 0.919 0.048 0.952
#> SRR1656506 1 0.2043 0.951 0.968 0.032
#> SRR1656509 1 0.1184 0.953 0.984 0.016
#> SRR1656510 2 0.0672 0.948 0.008 0.992
#> SRR1656511 2 0.0376 0.949 0.004 0.996
#> SRR1656513 2 0.0376 0.949 0.004 0.996
#> SRR1656512 2 0.0000 0.950 0.000 1.000
#> SRR1656514 1 0.0000 0.951 1.000 0.000
#> SRR1656515 2 0.0000 0.950 0.000 1.000
#> SRR1656516 1 0.1184 0.953 0.984 0.016
#> SRR1656518 1 0.8386 0.674 0.732 0.268
#> SRR1656517 1 0.0000 0.951 1.000 0.000
#> SRR1656519 1 0.0000 0.951 1.000 0.000
#> SRR1656522 1 0.0000 0.951 1.000 0.000
#> SRR1656523 2 0.0672 0.947 0.008 0.992
#> SRR1656521 2 0.0000 0.950 0.000 1.000
#> SRR1656520 1 0.0000 0.951 1.000 0.000
#> SRR1656524 1 0.2423 0.949 0.960 0.040
#> SRR1656525 1 0.0672 0.952 0.992 0.008
#> SRR1656526 2 0.0000 0.950 0.000 1.000
#> SRR1656527 2 0.0000 0.950 0.000 1.000
#> SRR1656530 1 0.2423 0.949 0.960 0.040
#> SRR1656529 1 0.3114 0.940 0.944 0.056
#> SRR1656531 1 0.2423 0.949 0.960 0.040
#> SRR1656528 1 0.0938 0.953 0.988 0.012
#> SRR1656534 1 0.0000 0.951 1.000 0.000
#> SRR1656533 1 0.0000 0.951 1.000 0.000
#> SRR1656536 1 0.9248 0.533 0.660 0.340
#> SRR1656532 2 0.0376 0.949 0.004 0.996
#> SRR1656537 1 0.0000 0.951 1.000 0.000
#> SRR1656538 1 0.0000 0.951 1.000 0.000
#> SRR1656535 2 0.0000 0.950 0.000 1.000
#> SRR1656539 1 0.2043 0.951 0.968 0.032
#> SRR1656544 1 0.0376 0.952 0.996 0.004
#> SRR1656542 1 0.0376 0.952 0.996 0.004
#> SRR1656543 1 0.0000 0.951 1.000 0.000
#> SRR1656545 2 0.0000 0.950 0.000 1.000
#> SRR1656540 1 0.0000 0.951 1.000 0.000
#> SRR1656546 2 0.0672 0.948 0.008 0.992
#> SRR1656541 2 0.0000 0.950 0.000 1.000
#> SRR1656547 2 0.0376 0.949 0.004 0.996
#> SRR1656548 1 0.2043 0.951 0.968 0.032
#> SRR1656549 1 0.8499 0.661 0.724 0.276
#> SRR1656551 1 0.9427 0.485 0.640 0.360
#> SRR1656553 1 0.0672 0.952 0.992 0.008
#> SRR1656550 2 0.9661 0.348 0.392 0.608
#> SRR1656552 2 0.0376 0.949 0.004 0.996
#> SRR1656554 1 0.3114 0.940 0.944 0.056
#> SRR1656555 2 0.0672 0.947 0.008 0.992
#> SRR1656556 1 0.0000 0.951 1.000 0.000
#> SRR1656557 1 0.0000 0.951 1.000 0.000
#> SRR1656558 1 0.2603 0.947 0.956 0.044
#> SRR1656559 1 0.0000 0.951 1.000 0.000
#> SRR1656560 1 0.0672 0.952 0.992 0.008
#> SRR1656561 1 0.2043 0.951 0.968 0.032
#> SRR1656562 2 0.0672 0.947 0.008 0.992
#> SRR1656563 1 0.0000 0.951 1.000 0.000
#> SRR1656564 2 0.0000 0.950 0.000 1.000
#> SRR1656565 2 0.2778 0.919 0.048 0.952
#> SRR1656566 1 0.2603 0.947 0.956 0.044
#> SRR1656568 2 0.0000 0.950 0.000 1.000
#> SRR1656567 2 0.2778 0.919 0.048 0.952
#> SRR1656569 1 0.3114 0.940 0.944 0.056
#> SRR1656570 1 0.0000 0.951 1.000 0.000
#> SRR1656571 2 0.0000 0.950 0.000 1.000
#> SRR1656573 1 0.5059 0.894 0.888 0.112
#> SRR1656572 2 0.0376 0.949 0.004 0.996
#> SRR1656574 1 0.0000 0.951 1.000 0.000
#> SRR1656575 1 0.1184 0.953 0.984 0.016
#> SRR1656576 2 0.0376 0.949 0.004 0.996
#> SRR1656578 2 0.0376 0.949 0.004 0.996
#> SRR1656577 1 0.0000 0.951 1.000 0.000
#> SRR1656579 2 0.0376 0.949 0.004 0.996
#> SRR1656580 1 0.0000 0.951 1.000 0.000
#> SRR1656581 2 0.9286 0.476 0.344 0.656
#> SRR1656582 2 0.0000 0.950 0.000 1.000
#> SRR1656585 1 0.4815 0.902 0.896 0.104
#> SRR1656584 1 0.2603 0.947 0.956 0.044
#> SRR1656583 1 0.4815 0.902 0.896 0.104
#> SRR1656586 2 0.0000 0.950 0.000 1.000
#> SRR1656587 1 0.4815 0.902 0.896 0.104
#> SRR1656588 2 0.2778 0.919 0.048 0.952
#> SRR1656589 2 0.0000 0.950 0.000 1.000
#> SRR1656590 1 0.1184 0.953 0.984 0.016
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656464 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656462 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656465 3 0.1765 0.709 0.040 0.004 0.956
#> SRR1656467 2 0.4974 0.809 0.236 0.764 0.000
#> SRR1656466 3 0.2063 0.709 0.044 0.008 0.948
#> SRR1656468 2 0.5254 0.593 0.000 0.736 0.264
#> SRR1656472 3 0.0983 0.713 0.016 0.004 0.980
#> SRR1656471 3 0.6126 0.230 0.400 0.000 0.600
#> SRR1656470 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656469 3 0.4974 0.478 0.000 0.236 0.764
#> SRR1656473 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656474 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656475 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656478 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656477 2 0.6180 0.306 0.000 0.584 0.416
#> SRR1656479 3 0.0983 0.713 0.016 0.004 0.980
#> SRR1656480 2 0.5988 0.422 0.000 0.632 0.368
#> SRR1656476 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656481 2 0.5650 0.520 0.000 0.688 0.312
#> SRR1656482 2 0.4974 0.809 0.236 0.764 0.000
#> SRR1656483 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656485 3 0.6062 0.290 0.384 0.000 0.616
#> SRR1656487 3 0.0892 0.703 0.000 0.020 0.980
#> SRR1656486 3 0.4974 0.478 0.000 0.236 0.764
#> SRR1656488 3 0.6062 0.290 0.384 0.000 0.616
#> SRR1656484 3 0.5016 0.573 0.240 0.000 0.760
#> SRR1656489 3 0.5678 0.459 0.316 0.000 0.684
#> SRR1656491 3 0.1643 0.688 0.000 0.044 0.956
#> SRR1656490 3 0.2261 0.669 0.000 0.068 0.932
#> SRR1656492 3 0.1031 0.701 0.000 0.024 0.976
#> SRR1656493 3 0.0829 0.712 0.012 0.004 0.984
#> SRR1656495 3 0.1289 0.696 0.000 0.032 0.968
#> SRR1656496 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656494 2 0.2625 0.771 0.000 0.916 0.084
#> SRR1656497 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656499 3 0.6095 0.261 0.392 0.000 0.608
#> SRR1656500 3 0.6126 0.232 0.400 0.000 0.600
#> SRR1656501 3 0.4062 0.640 0.164 0.000 0.836
#> SRR1656498 3 0.5988 0.339 0.368 0.000 0.632
#> SRR1656504 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656502 3 0.0983 0.713 0.016 0.004 0.980
#> SRR1656503 3 0.1315 0.713 0.020 0.008 0.972
#> SRR1656507 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656508 3 0.5760 0.436 0.328 0.000 0.672
#> SRR1656505 2 0.2711 0.769 0.000 0.912 0.088
#> SRR1656506 3 0.2096 0.707 0.052 0.004 0.944
#> SRR1656509 3 0.4974 0.575 0.236 0.000 0.764
#> SRR1656510 2 0.1753 0.790 0.000 0.952 0.048
#> SRR1656511 2 0.1765 0.793 0.004 0.956 0.040
#> SRR1656513 2 0.1529 0.792 0.000 0.960 0.040
#> SRR1656512 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656514 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656515 2 0.4974 0.809 0.236 0.764 0.000
#> SRR1656516 3 0.4062 0.640 0.164 0.000 0.836
#> SRR1656518 3 0.4887 0.486 0.000 0.228 0.772
#> SRR1656517 3 0.5988 0.339 0.368 0.000 0.632
#> SRR1656519 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656522 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656523 2 0.1753 0.790 0.000 0.952 0.048
#> SRR1656521 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656520 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656524 3 0.0829 0.712 0.012 0.004 0.984
#> SRR1656525 3 0.5216 0.547 0.260 0.000 0.740
#> SRR1656526 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656527 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656530 3 0.1950 0.710 0.040 0.008 0.952
#> SRR1656529 3 0.0747 0.705 0.000 0.016 0.984
#> SRR1656531 3 0.0983 0.713 0.016 0.004 0.980
#> SRR1656528 3 0.5327 0.533 0.272 0.000 0.728
#> SRR1656534 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656533 3 0.5988 0.339 0.368 0.000 0.632
#> SRR1656536 3 0.5560 0.404 0.000 0.300 0.700
#> SRR1656532 2 0.1529 0.792 0.000 0.960 0.040
#> SRR1656537 3 0.5988 0.339 0.368 0.000 0.632
#> SRR1656538 3 0.6079 0.276 0.388 0.000 0.612
#> SRR1656535 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656539 3 0.2096 0.707 0.052 0.004 0.944
#> SRR1656544 3 0.5621 0.474 0.308 0.000 0.692
#> SRR1656542 3 0.5621 0.474 0.308 0.000 0.692
#> SRR1656543 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656545 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656540 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656546 2 0.1753 0.790 0.000 0.952 0.048
#> SRR1656541 2 0.4974 0.809 0.236 0.764 0.000
#> SRR1656547 2 0.1765 0.793 0.004 0.956 0.040
#> SRR1656548 3 0.2301 0.704 0.060 0.004 0.936
#> SRR1656549 3 0.4974 0.478 0.000 0.236 0.764
#> SRR1656551 3 0.5706 0.377 0.000 0.320 0.680
#> SRR1656553 3 0.5098 0.560 0.248 0.000 0.752
#> SRR1656550 2 0.6225 0.264 0.000 0.568 0.432
#> SRR1656552 2 0.1765 0.793 0.004 0.956 0.040
#> SRR1656554 3 0.0747 0.705 0.000 0.016 0.984
#> SRR1656555 2 0.1753 0.790 0.000 0.952 0.048
#> SRR1656556 3 0.6126 0.230 0.400 0.000 0.600
#> SRR1656557 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656558 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656559 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656560 3 0.5926 0.372 0.356 0.000 0.644
#> SRR1656561 3 0.2301 0.704 0.060 0.004 0.936
#> SRR1656562 2 0.1753 0.790 0.000 0.952 0.048
#> SRR1656563 3 0.5678 0.458 0.316 0.000 0.684
#> SRR1656564 2 0.5291 0.808 0.268 0.732 0.000
#> SRR1656565 2 0.2711 0.769 0.000 0.912 0.088
#> SRR1656566 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656568 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656567 2 0.2711 0.769 0.000 0.912 0.088
#> SRR1656569 3 0.0747 0.705 0.000 0.016 0.984
#> SRR1656570 3 0.5591 0.480 0.304 0.000 0.696
#> SRR1656571 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656573 3 0.2356 0.665 0.000 0.072 0.928
#> SRR1656572 2 0.1529 0.792 0.000 0.960 0.040
#> SRR1656574 3 0.6079 0.277 0.388 0.000 0.612
#> SRR1656575 3 0.4452 0.619 0.192 0.000 0.808
#> SRR1656576 2 0.1765 0.793 0.004 0.956 0.040
#> SRR1656578 2 0.1529 0.792 0.000 0.960 0.040
#> SRR1656577 1 0.5327 1.000 0.728 0.000 0.272
#> SRR1656579 2 0.1765 0.793 0.004 0.956 0.040
#> SRR1656580 3 0.6079 0.276 0.388 0.000 0.612
#> SRR1656581 2 0.6062 0.386 0.000 0.616 0.384
#> SRR1656582 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656585 3 0.2165 0.672 0.000 0.064 0.936
#> SRR1656584 3 0.1015 0.712 0.012 0.008 0.980
#> SRR1656583 3 0.2165 0.672 0.000 0.064 0.936
#> SRR1656586 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656587 3 0.2165 0.672 0.000 0.064 0.936
#> SRR1656588 2 0.2711 0.769 0.000 0.912 0.088
#> SRR1656589 2 0.5327 0.808 0.272 0.728 0.000
#> SRR1656590 3 0.5098 0.564 0.248 0.000 0.752
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656464 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656462 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656465 1 0.5463 0.6976 0.692 0.000 0.052 0.256
#> SRR1656467 2 0.4164 0.6156 0.000 0.736 0.000 0.264
#> SRR1656466 1 0.5434 0.6996 0.696 0.000 0.052 0.252
#> SRR1656468 4 0.1767 0.6080 0.012 0.044 0.000 0.944
#> SRR1656472 1 0.0188 0.6905 0.996 0.000 0.004 0.000
#> SRR1656471 3 0.4972 -0.0394 0.456 0.000 0.544 0.000
#> SRR1656470 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656469 4 0.4977 -0.2454 0.460 0.000 0.000 0.540
#> SRR1656473 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0469 0.6951 0.988 0.000 0.000 0.012
#> SRR1656477 4 0.2704 0.5431 0.124 0.000 0.000 0.876
#> SRR1656479 1 0.4284 0.7114 0.780 0.000 0.020 0.200
#> SRR1656480 4 0.2796 0.5829 0.092 0.016 0.000 0.892
#> SRR1656476 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.1388 0.5973 0.028 0.012 0.000 0.960
#> SRR1656482 2 0.4164 0.6156 0.000 0.736 0.000 0.264
#> SRR1656483 2 0.0188 0.9390 0.000 0.996 0.000 0.004
#> SRR1656485 3 0.5392 -0.0884 0.460 0.000 0.528 0.012
#> SRR1656487 1 0.4769 0.6744 0.684 0.000 0.008 0.308
#> SRR1656486 4 0.4977 -0.2454 0.460 0.000 0.000 0.540
#> SRR1656488 3 0.5408 -0.1977 0.488 0.000 0.500 0.012
#> SRR1656484 1 0.4155 0.6414 0.756 0.000 0.240 0.004
#> SRR1656489 1 0.4543 0.5610 0.676 0.000 0.324 0.000
#> SRR1656491 1 0.4522 0.6647 0.680 0.000 0.000 0.320
#> SRR1656490 1 0.4624 0.6468 0.660 0.000 0.000 0.340
#> SRR1656492 1 0.4655 0.6735 0.684 0.000 0.004 0.312
#> SRR1656493 1 0.0000 0.6909 1.000 0.000 0.000 0.000
#> SRR1656495 1 0.1716 0.6833 0.936 0.000 0.000 0.064
#> SRR1656496 1 0.4399 0.7073 0.768 0.000 0.020 0.212
#> SRR1656494 4 0.3837 0.6454 0.000 0.224 0.000 0.776
#> SRR1656497 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.5147 -0.0657 0.460 0.000 0.536 0.004
#> SRR1656500 1 0.4981 0.3001 0.536 0.000 0.464 0.000
#> SRR1656501 1 0.3355 0.6799 0.836 0.000 0.160 0.004
#> SRR1656498 1 0.4804 0.4786 0.616 0.000 0.384 0.000
#> SRR1656504 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.0188 0.6905 0.996 0.000 0.004 0.000
#> SRR1656503 1 0.4307 0.7120 0.784 0.000 0.024 0.192
#> SRR1656507 1 0.0469 0.6951 0.988 0.000 0.000 0.012
#> SRR1656508 1 0.4605 0.5486 0.664 0.000 0.336 0.000
#> SRR1656505 4 0.3801 0.6462 0.000 0.220 0.000 0.780
#> SRR1656506 1 0.5565 0.7010 0.684 0.000 0.056 0.260
#> SRR1656509 1 0.4328 0.6407 0.748 0.000 0.244 0.008
#> SRR1656510 4 0.4134 0.6318 0.000 0.260 0.000 0.740
#> SRR1656511 4 0.4477 0.5808 0.000 0.312 0.000 0.688
#> SRR1656513 4 0.4222 0.6231 0.000 0.272 0.000 0.728
#> SRR1656512 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656515 2 0.4164 0.6156 0.000 0.736 0.000 0.264
#> SRR1656516 1 0.3355 0.6799 0.836 0.000 0.160 0.004
#> SRR1656518 4 0.4985 -0.2685 0.468 0.000 0.000 0.532
#> SRR1656517 1 0.4804 0.4786 0.616 0.000 0.384 0.000
#> SRR1656519 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656523 4 0.4134 0.6321 0.000 0.260 0.000 0.740
#> SRR1656521 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.6909 1.000 0.000 0.000 0.000
#> SRR1656525 1 0.5953 0.6385 0.656 0.000 0.268 0.076
#> SRR1656526 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0188 0.9390 0.000 0.996 0.000 0.004
#> SRR1656530 1 0.5463 0.6979 0.692 0.000 0.052 0.256
#> SRR1656529 1 0.4746 0.6768 0.688 0.000 0.008 0.304
#> SRR1656531 1 0.0188 0.6905 0.996 0.000 0.004 0.000
#> SRR1656528 1 0.6028 0.6246 0.644 0.000 0.280 0.076
#> SRR1656534 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656533 1 0.4804 0.4786 0.616 0.000 0.384 0.000
#> SRR1656536 4 0.4877 -0.0918 0.408 0.000 0.000 0.592
#> SRR1656532 4 0.4222 0.6231 0.000 0.272 0.000 0.728
#> SRR1656537 1 0.4804 0.4786 0.616 0.000 0.384 0.000
#> SRR1656538 1 0.4866 0.4534 0.596 0.000 0.404 0.000
#> SRR1656535 2 0.0188 0.9390 0.000 0.996 0.000 0.004
#> SRR1656539 1 0.5417 0.7039 0.704 0.000 0.056 0.240
#> SRR1656544 1 0.4957 0.5690 0.668 0.000 0.320 0.012
#> SRR1656542 1 0.4957 0.5690 0.668 0.000 0.320 0.012
#> SRR1656543 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656546 4 0.4134 0.6321 0.000 0.260 0.000 0.740
#> SRR1656541 2 0.4008 0.6499 0.000 0.756 0.000 0.244
#> SRR1656547 4 0.4431 0.5901 0.000 0.304 0.000 0.696
#> SRR1656548 1 0.5579 0.7028 0.688 0.000 0.060 0.252
#> SRR1656549 4 0.4977 -0.2454 0.460 0.000 0.000 0.540
#> SRR1656551 4 0.4830 -0.0290 0.392 0.000 0.000 0.608
#> SRR1656553 1 0.4516 0.6421 0.736 0.000 0.252 0.012
#> SRR1656550 4 0.2921 0.5233 0.140 0.000 0.000 0.860
#> SRR1656552 4 0.4500 0.5756 0.000 0.316 0.000 0.684
#> SRR1656554 1 0.4746 0.6768 0.688 0.000 0.008 0.304
#> SRR1656555 4 0.4134 0.6321 0.000 0.260 0.000 0.740
#> SRR1656556 3 0.4972 -0.0394 0.456 0.000 0.544 0.000
#> SRR1656557 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.0469 0.6951 0.988 0.000 0.000 0.012
#> SRR1656559 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656560 1 0.5600 0.5006 0.596 0.000 0.376 0.028
#> SRR1656561 1 0.5579 0.7028 0.688 0.000 0.060 0.252
#> SRR1656562 4 0.4134 0.6321 0.000 0.260 0.000 0.740
#> SRR1656563 1 0.4522 0.5652 0.680 0.000 0.320 0.000
#> SRR1656564 2 0.1792 0.8803 0.000 0.932 0.000 0.068
#> SRR1656565 4 0.3801 0.6462 0.000 0.220 0.000 0.780
#> SRR1656566 1 0.0469 0.6951 0.988 0.000 0.000 0.012
#> SRR1656568 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.3801 0.6462 0.000 0.220 0.000 0.780
#> SRR1656569 1 0.4746 0.6768 0.688 0.000 0.008 0.304
#> SRR1656570 1 0.4454 0.5820 0.692 0.000 0.308 0.000
#> SRR1656571 2 0.0188 0.9390 0.000 0.996 0.000 0.004
#> SRR1656573 1 0.4643 0.6414 0.656 0.000 0.000 0.344
#> SRR1656572 4 0.4222 0.6231 0.000 0.272 0.000 0.728
#> SRR1656574 1 0.4888 0.4365 0.588 0.000 0.412 0.000
#> SRR1656575 1 0.3710 0.6649 0.804 0.000 0.192 0.004
#> SRR1656576 4 0.4500 0.5756 0.000 0.316 0.000 0.684
#> SRR1656578 4 0.4222 0.6231 0.000 0.272 0.000 0.728
#> SRR1656577 3 0.0000 0.7924 0.000 0.000 1.000 0.000
#> SRR1656579 4 0.4500 0.5756 0.000 0.316 0.000 0.684
#> SRR1656580 1 0.4866 0.4534 0.596 0.000 0.404 0.000
#> SRR1656581 4 0.2216 0.5773 0.092 0.000 0.000 0.908
#> SRR1656582 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656585 1 0.4605 0.6519 0.664 0.000 0.000 0.336
#> SRR1656584 1 0.0469 0.6951 0.988 0.000 0.000 0.012
#> SRR1656583 1 0.4605 0.6519 0.664 0.000 0.000 0.336
#> SRR1656586 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656587 1 0.4605 0.6519 0.664 0.000 0.000 0.336
#> SRR1656588 4 0.3801 0.6462 0.000 0.220 0.000 0.780
#> SRR1656589 2 0.0000 0.9409 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.4072 0.6284 0.748 0.000 0.252 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656462 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656465 5 0.2629 0.686 0.136 0.000 0.004 0.000 0.860
#> SRR1656467 2 0.3837 0.603 0.000 0.692 0.000 0.308 0.000
#> SRR1656466 5 0.2719 0.679 0.144 0.000 0.004 0.000 0.852
#> SRR1656468 4 0.3534 0.671 0.000 0.000 0.000 0.744 0.256
#> SRR1656472 1 0.6671 0.256 0.540 0.000 0.192 0.020 0.248
#> SRR1656471 1 0.5700 0.309 0.600 0.000 0.280 0.000 0.120
#> SRR1656470 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.4054 0.605 0.020 0.000 0.000 0.248 0.732
#> SRR1656473 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.6701 0.240 0.532 0.000 0.188 0.020 0.260
#> SRR1656477 4 0.4397 0.367 0.000 0.000 0.004 0.564 0.432
#> SRR1656479 5 0.3906 0.555 0.292 0.000 0.000 0.004 0.704
#> SRR1656480 4 0.4276 0.482 0.000 0.000 0.004 0.616 0.380
#> SRR1656476 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 4 0.3990 0.599 0.000 0.000 0.004 0.688 0.308
#> SRR1656482 2 0.3837 0.603 0.000 0.692 0.000 0.308 0.000
#> SRR1656483 2 0.0162 0.938 0.000 0.996 0.000 0.004 0.000
#> SRR1656485 1 0.5775 0.332 0.600 0.000 0.264 0.000 0.136
#> SRR1656487 5 0.1638 0.730 0.064 0.000 0.000 0.004 0.932
#> SRR1656486 5 0.4141 0.602 0.024 0.000 0.000 0.248 0.728
#> SRR1656488 1 0.5864 0.362 0.600 0.000 0.236 0.000 0.164
#> SRR1656484 1 0.3398 0.558 0.780 0.000 0.004 0.000 0.216
#> SRR1656489 1 0.3814 0.592 0.808 0.000 0.068 0.000 0.124
#> SRR1656491 5 0.3063 0.724 0.096 0.000 0.004 0.036 0.864
#> SRR1656490 5 0.2853 0.723 0.068 0.000 0.008 0.040 0.884
#> SRR1656492 5 0.1764 0.731 0.064 0.000 0.000 0.008 0.928
#> SRR1656493 1 0.6708 0.248 0.532 0.000 0.192 0.020 0.256
#> SRR1656495 5 0.7032 -0.023 0.388 0.000 0.196 0.020 0.396
#> SRR1656496 5 0.3814 0.581 0.276 0.000 0.000 0.004 0.720
#> SRR1656494 4 0.2032 0.859 0.000 0.020 0.004 0.924 0.052
#> SRR1656497 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 1 0.5740 0.321 0.600 0.000 0.272 0.000 0.128
#> SRR1656500 1 0.5444 0.472 0.656 0.000 0.204 0.000 0.140
#> SRR1656501 1 0.4902 0.417 0.648 0.000 0.048 0.000 0.304
#> SRR1656498 1 0.4262 0.557 0.776 0.000 0.124 0.000 0.100
#> SRR1656504 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.6671 0.256 0.540 0.000 0.192 0.020 0.248
#> SRR1656503 5 0.3969 0.531 0.304 0.000 0.000 0.004 0.692
#> SRR1656507 1 0.6742 0.219 0.520 0.000 0.184 0.020 0.276
#> SRR1656508 1 0.3888 0.588 0.804 0.000 0.076 0.000 0.120
#> SRR1656505 4 0.2005 0.857 0.000 0.016 0.004 0.924 0.056
#> SRR1656506 5 0.3635 0.548 0.248 0.000 0.000 0.004 0.748
#> SRR1656509 1 0.3783 0.521 0.740 0.000 0.008 0.000 0.252
#> SRR1656510 4 0.1012 0.867 0.000 0.020 0.000 0.968 0.012
#> SRR1656511 4 0.1478 0.851 0.000 0.064 0.000 0.936 0.000
#> SRR1656513 4 0.0703 0.866 0.000 0.024 0.000 0.976 0.000
#> SRR1656512 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656515 2 0.3837 0.603 0.000 0.692 0.000 0.308 0.000
#> SRR1656516 1 0.4902 0.417 0.648 0.000 0.048 0.000 0.304
#> SRR1656518 5 0.4352 0.607 0.036 0.000 0.000 0.244 0.720
#> SRR1656517 1 0.4262 0.557 0.776 0.000 0.124 0.000 0.100
#> SRR1656519 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656522 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656523 4 0.0898 0.867 0.000 0.020 0.000 0.972 0.008
#> SRR1656521 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656524 1 0.6726 0.242 0.528 0.000 0.192 0.020 0.260
#> SRR1656525 1 0.5131 0.366 0.540 0.000 0.040 0.000 0.420
#> SRR1656526 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0290 0.936 0.000 0.992 0.000 0.008 0.000
#> SRR1656530 5 0.2763 0.675 0.148 0.000 0.004 0.000 0.848
#> SRR1656529 5 0.1704 0.730 0.068 0.000 0.000 0.004 0.928
#> SRR1656531 1 0.6671 0.256 0.540 0.000 0.192 0.020 0.248
#> SRR1656528 1 0.5345 0.368 0.540 0.000 0.056 0.000 0.404
#> SRR1656534 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656533 1 0.4262 0.557 0.776 0.000 0.124 0.000 0.100
#> SRR1656536 5 0.3968 0.515 0.004 0.000 0.004 0.276 0.716
#> SRR1656532 4 0.0703 0.866 0.000 0.024 0.000 0.976 0.000
#> SRR1656537 1 0.4262 0.557 0.776 0.000 0.124 0.000 0.100
#> SRR1656538 1 0.4971 0.539 0.712 0.000 0.144 0.000 0.144
#> SRR1656535 2 0.0290 0.936 0.000 0.992 0.000 0.008 0.000
#> SRR1656539 5 0.3491 0.578 0.228 0.000 0.004 0.000 0.768
#> SRR1656544 1 0.4793 0.571 0.700 0.000 0.068 0.000 0.232
#> SRR1656542 1 0.4820 0.572 0.696 0.000 0.068 0.000 0.236
#> SRR1656543 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656545 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656546 4 0.0912 0.867 0.000 0.016 0.000 0.972 0.012
#> SRR1656541 2 0.3730 0.636 0.000 0.712 0.000 0.288 0.000
#> SRR1656547 4 0.1341 0.855 0.000 0.056 0.000 0.944 0.000
#> SRR1656548 5 0.4084 0.393 0.328 0.000 0.000 0.004 0.668
#> SRR1656549 5 0.4141 0.602 0.024 0.000 0.000 0.248 0.728
#> SRR1656551 5 0.3928 0.473 0.004 0.000 0.000 0.296 0.700
#> SRR1656553 1 0.4851 0.484 0.624 0.000 0.036 0.000 0.340
#> SRR1656550 4 0.4425 0.316 0.000 0.000 0.004 0.544 0.452
#> SRR1656552 4 0.1608 0.846 0.000 0.072 0.000 0.928 0.000
#> SRR1656554 5 0.1704 0.730 0.068 0.000 0.000 0.004 0.928
#> SRR1656555 4 0.0898 0.867 0.000 0.020 0.000 0.972 0.008
#> SRR1656556 1 0.5739 0.314 0.596 0.000 0.280 0.000 0.124
#> SRR1656557 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656558 1 0.6701 0.240 0.532 0.000 0.188 0.020 0.260
#> SRR1656559 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656560 1 0.5618 0.477 0.608 0.000 0.112 0.000 0.280
#> SRR1656561 5 0.4084 0.393 0.328 0.000 0.000 0.004 0.668
#> SRR1656562 4 0.0898 0.867 0.000 0.020 0.000 0.972 0.008
#> SRR1656563 1 0.3780 0.593 0.808 0.000 0.060 0.000 0.132
#> SRR1656564 2 0.1851 0.872 0.000 0.912 0.000 0.088 0.000
#> SRR1656565 4 0.2005 0.857 0.000 0.016 0.004 0.924 0.056
#> SRR1656566 1 0.6718 0.234 0.528 0.000 0.188 0.020 0.264
#> SRR1656568 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 4 0.2005 0.857 0.000 0.016 0.004 0.924 0.056
#> SRR1656569 5 0.1704 0.730 0.068 0.000 0.000 0.004 0.928
#> SRR1656570 1 0.3995 0.600 0.788 0.000 0.060 0.000 0.152
#> SRR1656571 2 0.0162 0.938 0.000 0.996 0.000 0.004 0.000
#> SRR1656573 5 0.2929 0.723 0.068 0.000 0.008 0.044 0.880
#> SRR1656572 4 0.0703 0.866 0.000 0.024 0.000 0.976 0.000
#> SRR1656574 1 0.4968 0.537 0.712 0.000 0.152 0.000 0.136
#> SRR1656575 1 0.4163 0.525 0.740 0.000 0.032 0.000 0.228
#> SRR1656576 4 0.1608 0.846 0.000 0.072 0.000 0.928 0.000
#> SRR1656578 4 0.0703 0.866 0.000 0.024 0.000 0.976 0.000
#> SRR1656577 3 0.3074 1.000 0.196 0.000 0.804 0.000 0.000
#> SRR1656579 4 0.1608 0.846 0.000 0.072 0.000 0.928 0.000
#> SRR1656580 1 0.4971 0.539 0.712 0.000 0.144 0.000 0.144
#> SRR1656581 4 0.4331 0.443 0.000 0.000 0.004 0.596 0.400
#> SRR1656582 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 5 0.2886 0.721 0.068 0.000 0.012 0.036 0.884
#> SRR1656584 1 0.6701 0.240 0.532 0.000 0.188 0.020 0.260
#> SRR1656583 5 0.2886 0.721 0.068 0.000 0.012 0.036 0.884
#> SRR1656586 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 5 0.2886 0.721 0.068 0.000 0.012 0.036 0.884
#> SRR1656588 4 0.2005 0.857 0.000 0.016 0.004 0.924 0.056
#> SRR1656589 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.3246 0.578 0.808 0.000 0.008 0.000 0.184
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656462 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656465 5 0.2488 0.719 0.124 0.000 0.000 0.004 0.864 0.008
#> SRR1656467 2 0.3584 0.607 0.000 0.688 0.000 0.308 0.000 0.004
#> SRR1656466 5 0.2723 0.713 0.128 0.000 0.000 0.004 0.852 0.016
#> SRR1656468 4 0.3445 0.637 0.000 0.000 0.000 0.732 0.260 0.008
#> SRR1656472 6 0.0862 0.936 0.016 0.000 0.004 0.000 0.008 0.972
#> SRR1656471 1 0.4366 0.671 0.712 0.000 0.212 0.004 0.072 0.000
#> SRR1656470 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.3571 0.620 0.008 0.000 0.000 0.240 0.744 0.008
#> SRR1656473 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 6 0.1245 0.947 0.016 0.000 0.000 0.000 0.032 0.952
#> SRR1656477 4 0.4189 0.284 0.000 0.000 0.004 0.552 0.436 0.008
#> SRR1656479 5 0.4949 0.560 0.256 0.000 0.000 0.004 0.640 0.100
#> SRR1656480 4 0.4090 0.420 0.000 0.000 0.004 0.604 0.384 0.008
#> SRR1656476 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.3844 0.556 0.000 0.000 0.004 0.676 0.312 0.008
#> SRR1656482 2 0.3584 0.607 0.000 0.688 0.000 0.308 0.000 0.004
#> SRR1656483 2 0.0291 0.932 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1656485 1 0.4253 0.678 0.728 0.000 0.196 0.004 0.072 0.000
#> SRR1656487 5 0.0713 0.761 0.028 0.000 0.000 0.000 0.972 0.000
#> SRR1656486 5 0.3667 0.618 0.008 0.000 0.000 0.240 0.740 0.012
#> SRR1656488 1 0.4371 0.688 0.728 0.000 0.168 0.004 0.100 0.000
#> SRR1656484 1 0.4166 0.664 0.728 0.000 0.000 0.000 0.076 0.196
#> SRR1656489 1 0.2114 0.765 0.904 0.000 0.012 0.000 0.008 0.076
#> SRR1656491 5 0.3717 0.742 0.036 0.000 0.000 0.036 0.808 0.120
#> SRR1656490 5 0.3434 0.730 0.008 0.000 0.004 0.040 0.820 0.128
#> SRR1656492 5 0.0858 0.761 0.028 0.000 0.000 0.000 0.968 0.004
#> SRR1656493 6 0.0909 0.946 0.012 0.000 0.000 0.000 0.020 0.968
#> SRR1656495 6 0.3565 0.586 0.004 0.000 0.004 0.000 0.276 0.716
#> SRR1656496 5 0.4798 0.587 0.236 0.000 0.000 0.004 0.664 0.096
#> SRR1656494 4 0.1573 0.848 0.000 0.004 0.004 0.936 0.052 0.004
#> SRR1656497 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 1 0.4310 0.675 0.720 0.000 0.204 0.004 0.072 0.000
#> SRR1656500 1 0.2357 0.759 0.872 0.000 0.116 0.000 0.012 0.000
#> SRR1656501 1 0.5347 0.294 0.504 0.000 0.000 0.000 0.112 0.384
#> SRR1656498 1 0.2745 0.756 0.864 0.000 0.068 0.000 0.000 0.068
#> SRR1656504 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.0862 0.936 0.016 0.000 0.004 0.000 0.008 0.972
#> SRR1656503 5 0.5303 0.513 0.260 0.000 0.000 0.004 0.600 0.136
#> SRR1656507 6 0.1745 0.927 0.020 0.000 0.000 0.000 0.056 0.924
#> SRR1656508 1 0.1745 0.768 0.924 0.000 0.020 0.000 0.000 0.056
#> SRR1656505 4 0.1637 0.847 0.000 0.004 0.004 0.932 0.056 0.004
#> SRR1656506 5 0.3259 0.602 0.216 0.000 0.000 0.000 0.772 0.012
#> SRR1656509 1 0.4634 0.625 0.688 0.000 0.000 0.000 0.124 0.188
#> SRR1656510 4 0.0748 0.857 0.000 0.004 0.000 0.976 0.016 0.004
#> SRR1656511 4 0.1152 0.841 0.000 0.044 0.000 0.952 0.000 0.004
#> SRR1656513 4 0.0291 0.856 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1656512 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656515 2 0.3584 0.607 0.000 0.688 0.000 0.308 0.000 0.004
#> SRR1656516 1 0.5347 0.294 0.504 0.000 0.000 0.000 0.112 0.384
#> SRR1656518 5 0.4412 0.609 0.008 0.000 0.000 0.236 0.700 0.056
#> SRR1656517 1 0.2745 0.756 0.864 0.000 0.068 0.000 0.000 0.068
#> SRR1656519 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656522 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656523 4 0.0405 0.858 0.000 0.004 0.000 0.988 0.008 0.000
#> SRR1656521 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656524 6 0.0692 0.944 0.004 0.000 0.000 0.000 0.020 0.976
#> SRR1656525 1 0.4343 0.451 0.584 0.000 0.004 0.004 0.396 0.012
#> SRR1656526 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.0405 0.930 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1656530 5 0.2631 0.714 0.128 0.000 0.000 0.004 0.856 0.012
#> SRR1656529 5 0.0547 0.763 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1656531 6 0.0862 0.936 0.016 0.000 0.004 0.000 0.008 0.972
#> SRR1656528 1 0.3965 0.463 0.616 0.000 0.004 0.004 0.376 0.000
#> SRR1656534 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656533 1 0.2745 0.756 0.864 0.000 0.068 0.000 0.000 0.068
#> SRR1656536 5 0.4161 0.557 0.000 0.000 0.004 0.264 0.696 0.036
#> SRR1656532 4 0.0291 0.856 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1656537 1 0.2745 0.756 0.864 0.000 0.068 0.000 0.000 0.068
#> SRR1656538 1 0.1657 0.773 0.928 0.000 0.056 0.000 0.016 0.000
#> SRR1656535 2 0.0405 0.930 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1656539 5 0.3426 0.615 0.220 0.000 0.000 0.004 0.764 0.012
#> SRR1656544 1 0.3737 0.736 0.780 0.000 0.008 0.000 0.168 0.044
#> SRR1656542 1 0.3771 0.734 0.776 0.000 0.008 0.000 0.172 0.044
#> SRR1656543 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656545 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656546 4 0.0458 0.857 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1656541 2 0.3489 0.640 0.000 0.708 0.000 0.288 0.000 0.004
#> SRR1656547 4 0.1010 0.846 0.000 0.036 0.000 0.960 0.000 0.004
#> SRR1656548 5 0.3748 0.444 0.300 0.000 0.000 0.000 0.688 0.012
#> SRR1656549 5 0.3667 0.618 0.008 0.000 0.000 0.240 0.740 0.012
#> SRR1656551 5 0.3670 0.526 0.000 0.000 0.000 0.284 0.704 0.012
#> SRR1656553 1 0.4895 0.616 0.632 0.000 0.000 0.000 0.264 0.104
#> SRR1656550 4 0.4211 0.223 0.000 0.000 0.004 0.532 0.456 0.008
#> SRR1656552 4 0.1285 0.837 0.000 0.052 0.000 0.944 0.000 0.004
#> SRR1656554 5 0.0547 0.763 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1656555 4 0.0405 0.858 0.000 0.004 0.000 0.988 0.008 0.000
#> SRR1656556 1 0.4416 0.673 0.708 0.000 0.212 0.004 0.076 0.000
#> SRR1656557 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656558 6 0.1245 0.947 0.016 0.000 0.000 0.000 0.032 0.952
#> SRR1656559 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656560 1 0.4051 0.663 0.728 0.000 0.044 0.004 0.224 0.000
#> SRR1656561 5 0.3748 0.444 0.300 0.000 0.000 0.000 0.688 0.012
#> SRR1656562 4 0.0405 0.858 0.000 0.004 0.000 0.988 0.008 0.000
#> SRR1656563 1 0.1349 0.767 0.940 0.000 0.000 0.000 0.004 0.056
#> SRR1656564 2 0.1806 0.867 0.000 0.908 0.000 0.088 0.000 0.004
#> SRR1656565 4 0.1637 0.847 0.000 0.004 0.004 0.932 0.056 0.004
#> SRR1656566 6 0.1151 0.947 0.012 0.000 0.000 0.000 0.032 0.956
#> SRR1656568 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656567 4 0.1637 0.847 0.000 0.004 0.004 0.932 0.056 0.004
#> SRR1656569 5 0.0547 0.763 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1656570 1 0.1807 0.768 0.920 0.000 0.000 0.000 0.020 0.060
#> SRR1656571 2 0.0291 0.932 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1656573 5 0.3475 0.730 0.008 0.000 0.004 0.040 0.816 0.132
#> SRR1656572 4 0.0291 0.856 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1656574 1 0.1584 0.773 0.928 0.000 0.064 0.000 0.008 0.000
#> SRR1656575 1 0.4750 0.582 0.652 0.000 0.000 0.000 0.096 0.252
#> SRR1656576 4 0.1285 0.837 0.000 0.052 0.000 0.944 0.000 0.004
#> SRR1656578 4 0.0291 0.856 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1656577 3 0.0260 1.000 0.008 0.000 0.992 0.000 0.000 0.000
#> SRR1656579 4 0.1285 0.837 0.000 0.052 0.000 0.944 0.000 0.004
#> SRR1656580 1 0.1657 0.773 0.928 0.000 0.056 0.000 0.016 0.000
#> SRR1656581 4 0.4135 0.375 0.000 0.000 0.004 0.584 0.404 0.008
#> SRR1656582 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 5 0.3447 0.727 0.008 0.000 0.004 0.036 0.816 0.136
#> SRR1656584 6 0.1320 0.945 0.016 0.000 0.000 0.000 0.036 0.948
#> SRR1656583 5 0.3447 0.727 0.008 0.000 0.004 0.036 0.816 0.136
#> SRR1656586 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 5 0.3447 0.727 0.008 0.000 0.004 0.036 0.816 0.136
#> SRR1656588 4 0.1637 0.847 0.000 0.004 0.004 0.932 0.056 0.004
#> SRR1656589 2 0.0000 0.935 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.3806 0.674 0.752 0.000 0.000 0.000 0.048 0.200
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 13572 rows and 129 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.4729 0.525 0.525
#> 3 3 0.965 0.932 0.973 0.3844 0.682 0.466
#> 4 4 0.886 0.881 0.940 0.1143 0.798 0.512
#> 5 5 0.747 0.682 0.842 0.0785 0.893 0.641
#> 6 6 0.760 0.722 0.827 0.0489 0.918 0.646
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.000 0.987 0.000 1.000
#> SRR1656464 1 0.000 1.000 1.000 0.000
#> SRR1656462 1 0.000 1.000 1.000 0.000
#> SRR1656465 1 0.000 1.000 1.000 0.000
#> SRR1656467 2 0.000 0.987 0.000 1.000
#> SRR1656466 1 0.000 1.000 1.000 0.000
#> SRR1656468 2 0.000 0.987 0.000 1.000
#> SRR1656472 1 0.000 1.000 1.000 0.000
#> SRR1656471 1 0.000 1.000 1.000 0.000
#> SRR1656470 2 0.000 0.987 0.000 1.000
#> SRR1656469 1 0.000 1.000 1.000 0.000
#> SRR1656473 2 0.000 0.987 0.000 1.000
#> SRR1656474 2 0.000 0.987 0.000 1.000
#> SRR1656475 2 0.000 0.987 0.000 1.000
#> SRR1656478 1 0.000 1.000 1.000 0.000
#> SRR1656477 1 0.000 1.000 1.000 0.000
#> SRR1656479 1 0.000 1.000 1.000 0.000
#> SRR1656480 2 0.000 0.987 0.000 1.000
#> SRR1656476 2 0.000 0.987 0.000 1.000
#> SRR1656481 2 0.980 0.292 0.416 0.584
#> SRR1656482 2 0.000 0.987 0.000 1.000
#> SRR1656483 2 0.000 0.987 0.000 1.000
#> SRR1656485 1 0.000 1.000 1.000 0.000
#> SRR1656487 1 0.000 1.000 1.000 0.000
#> SRR1656486 1 0.000 1.000 1.000 0.000
#> SRR1656488 1 0.000 1.000 1.000 0.000
#> SRR1656484 1 0.000 1.000 1.000 0.000
#> SRR1656489 1 0.000 1.000 1.000 0.000
#> SRR1656491 1 0.000 1.000 1.000 0.000
#> SRR1656490 1 0.000 1.000 1.000 0.000
#> SRR1656492 1 0.000 1.000 1.000 0.000
#> SRR1656493 1 0.000 1.000 1.000 0.000
#> SRR1656495 1 0.000 1.000 1.000 0.000
#> SRR1656496 1 0.000 1.000 1.000 0.000
#> SRR1656494 2 0.000 0.987 0.000 1.000
#> SRR1656497 2 0.000 0.987 0.000 1.000
#> SRR1656499 1 0.000 1.000 1.000 0.000
#> SRR1656500 1 0.000 1.000 1.000 0.000
#> SRR1656501 1 0.000 1.000 1.000 0.000
#> SRR1656498 1 0.000 1.000 1.000 0.000
#> SRR1656504 2 0.000 0.987 0.000 1.000
#> SRR1656502 1 0.000 1.000 1.000 0.000
#> SRR1656503 1 0.000 1.000 1.000 0.000
#> SRR1656507 1 0.000 1.000 1.000 0.000
#> SRR1656508 1 0.000 1.000 1.000 0.000
#> SRR1656505 2 0.000 0.987 0.000 1.000
#> SRR1656506 1 0.000 1.000 1.000 0.000
#> SRR1656509 1 0.000 1.000 1.000 0.000
#> SRR1656510 2 0.000 0.987 0.000 1.000
#> SRR1656511 2 0.000 0.987 0.000 1.000
#> SRR1656513 2 0.000 0.987 0.000 1.000
#> SRR1656512 2 0.000 0.987 0.000 1.000
#> SRR1656514 1 0.000 1.000 1.000 0.000
#> SRR1656515 2 0.000 0.987 0.000 1.000
#> SRR1656516 1 0.000 1.000 1.000 0.000
#> SRR1656518 1 0.000 1.000 1.000 0.000
#> SRR1656517 1 0.000 1.000 1.000 0.000
#> SRR1656519 1 0.000 1.000 1.000 0.000
#> SRR1656522 1 0.000 1.000 1.000 0.000
#> SRR1656523 2 0.000 0.987 0.000 1.000
#> SRR1656521 2 0.000 0.987 0.000 1.000
#> SRR1656520 1 0.000 1.000 1.000 0.000
#> SRR1656524 1 0.000 1.000 1.000 0.000
#> SRR1656525 1 0.000 1.000 1.000 0.000
#> SRR1656526 2 0.000 0.987 0.000 1.000
#> SRR1656527 2 0.000 0.987 0.000 1.000
#> SRR1656530 1 0.000 1.000 1.000 0.000
#> SRR1656529 1 0.000 1.000 1.000 0.000
#> SRR1656531 1 0.000 1.000 1.000 0.000
#> SRR1656528 1 0.000 1.000 1.000 0.000
#> SRR1656534 1 0.000 1.000 1.000 0.000
#> SRR1656533 1 0.000 1.000 1.000 0.000
#> SRR1656536 1 0.000 1.000 1.000 0.000
#> SRR1656532 2 0.000 0.987 0.000 1.000
#> SRR1656537 1 0.000 1.000 1.000 0.000
#> SRR1656538 1 0.000 1.000 1.000 0.000
#> SRR1656535 2 0.000 0.987 0.000 1.000
#> SRR1656539 1 0.000 1.000 1.000 0.000
#> SRR1656544 1 0.000 1.000 1.000 0.000
#> SRR1656542 1 0.000 1.000 1.000 0.000
#> SRR1656543 1 0.000 1.000 1.000 0.000
#> SRR1656545 2 0.000 0.987 0.000 1.000
#> SRR1656540 1 0.000 1.000 1.000 0.000
#> SRR1656546 2 0.000 0.987 0.000 1.000
#> SRR1656541 2 0.000 0.987 0.000 1.000
#> SRR1656547 2 0.000 0.987 0.000 1.000
#> SRR1656548 1 0.000 1.000 1.000 0.000
#> SRR1656549 1 0.000 1.000 1.000 0.000
#> SRR1656551 1 0.000 1.000 1.000 0.000
#> SRR1656553 1 0.000 1.000 1.000 0.000
#> SRR1656550 2 0.738 0.736 0.208 0.792
#> SRR1656552 2 0.000 0.987 0.000 1.000
#> SRR1656554 1 0.000 1.000 1.000 0.000
#> SRR1656555 2 0.000 0.987 0.000 1.000
#> SRR1656556 1 0.000 1.000 1.000 0.000
#> SRR1656557 1 0.000 1.000 1.000 0.000
#> SRR1656558 1 0.000 1.000 1.000 0.000
#> SRR1656559 1 0.000 1.000 1.000 0.000
#> SRR1656560 1 0.000 1.000 1.000 0.000
#> SRR1656561 1 0.000 1.000 1.000 0.000
#> SRR1656562 2 0.000 0.987 0.000 1.000
#> SRR1656563 1 0.000 1.000 1.000 0.000
#> SRR1656564 2 0.000 0.987 0.000 1.000
#> SRR1656565 2 0.000 0.987 0.000 1.000
#> SRR1656566 1 0.000 1.000 1.000 0.000
#> SRR1656568 2 0.000 0.987 0.000 1.000
#> SRR1656567 2 0.000 0.987 0.000 1.000
#> SRR1656569 1 0.000 1.000 1.000 0.000
#> SRR1656570 1 0.000 1.000 1.000 0.000
#> SRR1656571 2 0.000 0.987 0.000 1.000
#> SRR1656573 1 0.000 1.000 1.000 0.000
#> SRR1656572 2 0.000 0.987 0.000 1.000
#> SRR1656574 1 0.000 1.000 1.000 0.000
#> SRR1656575 1 0.000 1.000 1.000 0.000
#> SRR1656576 2 0.000 0.987 0.000 1.000
#> SRR1656578 2 0.000 0.987 0.000 1.000
#> SRR1656577 1 0.000 1.000 1.000 0.000
#> SRR1656579 2 0.000 0.987 0.000 1.000
#> SRR1656580 1 0.000 1.000 1.000 0.000
#> SRR1656581 2 0.000 0.987 0.000 1.000
#> SRR1656582 2 0.000 0.987 0.000 1.000
#> SRR1656585 1 0.000 1.000 1.000 0.000
#> SRR1656584 1 0.000 1.000 1.000 0.000
#> SRR1656583 1 0.000 1.000 1.000 0.000
#> SRR1656586 2 0.000 0.987 0.000 1.000
#> SRR1656587 1 0.000 1.000 1.000 0.000
#> SRR1656588 2 0.000 0.987 0.000 1.000
#> SRR1656589 2 0.000 0.987 0.000 1.000
#> SRR1656590 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656462 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656465 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656468 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656472 1 0.2356 0.9167 0.928 0.000 0.072
#> SRR1656471 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656470 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656478 3 0.0592 0.9486 0.012 0.000 0.988
#> SRR1656477 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656479 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656480 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656476 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656485 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656487 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656486 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656488 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656484 3 0.1031 0.9387 0.024 0.000 0.976
#> SRR1656489 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656491 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656490 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656492 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656493 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656495 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656496 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656494 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656497 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656501 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656498 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656502 1 0.3412 0.8583 0.876 0.000 0.124
#> SRR1656503 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656507 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656508 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656505 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656506 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656509 3 0.0592 0.9486 0.012 0.000 0.988
#> SRR1656510 3 0.6235 0.2309 0.000 0.436 0.564
#> SRR1656511 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656516 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656518 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656517 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656523 3 0.5431 0.5888 0.000 0.284 0.716
#> SRR1656521 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656520 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656524 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656525 3 0.5560 0.5620 0.300 0.000 0.700
#> SRR1656526 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656530 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656531 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656528 3 0.6302 0.0938 0.480 0.000 0.520
#> SRR1656534 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656533 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656532 2 0.0747 0.9640 0.000 0.984 0.016
#> SRR1656537 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656538 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656535 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656544 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656542 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656543 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656540 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656546 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656541 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656548 3 0.1031 0.9387 0.024 0.000 0.976
#> SRR1656549 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656551 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656553 3 0.5363 0.6065 0.276 0.000 0.724
#> SRR1656550 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656552 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656555 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656556 1 0.0237 0.9841 0.996 0.000 0.004
#> SRR1656557 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656558 3 0.0592 0.9486 0.012 0.000 0.988
#> SRR1656559 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656560 1 0.4291 0.7754 0.820 0.000 0.180
#> SRR1656561 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656562 3 0.0237 0.9539 0.000 0.004 0.996
#> SRR1656563 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656565 2 0.5835 0.4839 0.000 0.660 0.340
#> SRR1656566 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656568 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656567 2 0.5431 0.6002 0.000 0.716 0.284
#> SRR1656569 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656570 3 0.0592 0.9486 0.012 0.000 0.988
#> SRR1656571 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656572 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656574 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656575 3 0.0592 0.9486 0.012 0.000 0.988
#> SRR1656576 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656578 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656580 1 0.0000 0.9862 1.000 0.000 0.000
#> SRR1656581 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656584 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656583 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656586 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656587 3 0.0000 0.9563 0.000 0.000 1.000
#> SRR1656588 3 0.6307 0.0157 0.000 0.488 0.512
#> SRR1656589 2 0.0000 0.9793 0.000 1.000 0.000
#> SRR1656590 1 0.0747 0.9736 0.984 0.000 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656464 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656462 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656465 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656467 4 0.2281 0.9122 0.000 0.096 0.000 0.904
#> SRR1656466 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656468 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.4250 0.5908 0.724 0.000 0.276 0.000
#> SRR1656471 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656470 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656469 1 0.2281 0.8847 0.904 0.000 0.000 0.096
#> SRR1656473 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656478 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656477 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656479 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656480 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656481 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656482 2 0.4804 0.3287 0.000 0.616 0.000 0.384
#> SRR1656483 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656485 3 0.4961 0.1920 0.448 0.000 0.552 0.000
#> SRR1656487 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656486 1 0.2281 0.8847 0.904 0.000 0.000 0.096
#> SRR1656488 3 0.3873 0.7215 0.228 0.000 0.772 0.000
#> SRR1656484 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656489 1 0.4585 0.4304 0.668 0.000 0.332 0.000
#> SRR1656491 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656490 1 0.2011 0.8942 0.920 0.000 0.000 0.080
#> SRR1656492 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656493 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656495 1 0.4193 0.6275 0.732 0.000 0.000 0.268
#> SRR1656496 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656494 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656497 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656499 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656501 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656498 3 0.0921 0.9198 0.028 0.000 0.972 0.000
#> SRR1656504 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656502 1 0.4250 0.5908 0.724 0.000 0.276 0.000
#> SRR1656503 1 0.0921 0.8989 0.972 0.000 0.000 0.028
#> SRR1656507 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656508 3 0.2469 0.8780 0.108 0.000 0.892 0.000
#> SRR1656505 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656506 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656509 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656510 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656511 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656513 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656512 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656514 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656515 4 0.3219 0.8328 0.000 0.164 0.000 0.836
#> SRR1656516 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656518 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656517 3 0.0921 0.9198 0.028 0.000 0.972 0.000
#> SRR1656519 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656523 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656521 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656520 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656525 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656526 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656530 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656529 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656531 1 0.4304 0.5758 0.716 0.000 0.284 0.000
#> SRR1656528 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656534 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656533 3 0.2469 0.8780 0.108 0.000 0.892 0.000
#> SRR1656536 1 0.2281 0.8847 0.904 0.000 0.000 0.096
#> SRR1656532 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656537 3 0.1716 0.8993 0.064 0.000 0.936 0.000
#> SRR1656538 3 0.3219 0.8105 0.164 0.000 0.836 0.000
#> SRR1656535 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656539 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656544 1 0.4994 -0.0828 0.520 0.000 0.480 0.000
#> SRR1656542 1 0.2814 0.7806 0.868 0.000 0.132 0.000
#> SRR1656543 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656545 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656540 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656546 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656541 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656547 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656548 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656549 1 0.2011 0.8942 0.920 0.000 0.000 0.080
#> SRR1656551 1 0.4916 0.4051 0.576 0.000 0.000 0.424
#> SRR1656553 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656550 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656552 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656554 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656555 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656556 3 0.3024 0.8228 0.148 0.000 0.852 0.000
#> SRR1656557 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656559 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656560 1 0.3764 0.7103 0.784 0.000 0.216 0.000
#> SRR1656561 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656562 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656563 3 0.4008 0.7412 0.244 0.000 0.756 0.000
#> SRR1656564 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656565 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656566 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656568 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656567 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656569 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656570 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656571 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656573 1 0.4477 0.6344 0.688 0.000 0.000 0.312
#> SRR1656572 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656574 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656575 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656576 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656578 4 0.2281 0.9122 0.000 0.096 0.000 0.904
#> SRR1656577 3 0.0000 0.9303 0.000 0.000 1.000 0.000
#> SRR1656579 4 0.1716 0.9392 0.000 0.064 0.000 0.936
#> SRR1656580 3 0.0817 0.9203 0.024 0.000 0.976 0.000
#> SRR1656581 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656585 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656584 1 0.0000 0.8949 1.000 0.000 0.000 0.000
#> SRR1656583 1 0.1716 0.9017 0.936 0.000 0.000 0.064
#> SRR1656586 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656587 1 0.4585 0.6005 0.668 0.000 0.000 0.332
#> SRR1656588 4 0.0000 0.9594 0.000 0.000 0.000 1.000
#> SRR1656589 2 0.0000 0.9815 0.000 1.000 0.000 0.000
#> SRR1656590 1 0.0000 0.8949 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0880 0.9441 0.032 0.968 0.000 0.000 0.000
#> SRR1656464 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656462 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 5 0.0404 0.7017 0.012 0.000 0.000 0.000 0.988
#> SRR1656467 4 0.3875 0.8291 0.124 0.072 0.000 0.804 0.000
#> SRR1656466 5 0.0794 0.6991 0.028 0.000 0.000 0.000 0.972
#> SRR1656468 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656472 1 0.3593 0.6245 0.828 0.000 0.088 0.000 0.084
#> SRR1656471 3 0.5275 0.4356 0.276 0.000 0.640 0.000 0.084
#> SRR1656470 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.3409 0.6129 0.032 0.000 0.000 0.144 0.824
#> SRR1656473 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.3999 0.4584 0.656 0.000 0.000 0.000 0.344
#> SRR1656477 4 0.4063 0.5796 0.012 0.000 0.000 0.708 0.280
#> SRR1656479 5 0.1197 0.6936 0.048 0.000 0.000 0.000 0.952
#> SRR1656480 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656476 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 4 0.2470 0.8358 0.012 0.000 0.000 0.884 0.104
#> SRR1656482 2 0.6066 0.1645 0.124 0.488 0.000 0.388 0.000
#> SRR1656483 2 0.1043 0.9419 0.040 0.960 0.000 0.000 0.000
#> SRR1656485 5 0.5691 0.0636 0.376 0.000 0.088 0.000 0.536
#> SRR1656487 5 0.0609 0.7010 0.020 0.000 0.000 0.000 0.980
#> SRR1656486 5 0.3262 0.6341 0.036 0.000 0.000 0.124 0.840
#> SRR1656488 5 0.6564 -0.2002 0.376 0.000 0.204 0.000 0.420
#> SRR1656484 5 0.3876 0.3817 0.316 0.000 0.000 0.000 0.684
#> SRR1656489 1 0.4125 0.5903 0.772 0.000 0.056 0.000 0.172
#> SRR1656491 5 0.1410 0.6893 0.060 0.000 0.000 0.000 0.940
#> SRR1656490 5 0.3359 0.6453 0.072 0.000 0.000 0.084 0.844
#> SRR1656492 5 0.0794 0.6991 0.028 0.000 0.000 0.000 0.972
#> SRR1656493 1 0.3949 0.4679 0.668 0.000 0.000 0.000 0.332
#> SRR1656495 1 0.6325 0.0567 0.428 0.000 0.000 0.156 0.416
#> SRR1656496 5 0.1410 0.6893 0.060 0.000 0.000 0.000 0.940
#> SRR1656494 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656497 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.4233 0.6390 0.208 0.000 0.748 0.000 0.044
#> SRR1656500 3 0.0609 0.8936 0.020 0.000 0.980 0.000 0.000
#> SRR1656501 5 0.2929 0.6178 0.180 0.000 0.000 0.000 0.820
#> SRR1656498 3 0.3424 0.6473 0.240 0.000 0.760 0.000 0.000
#> SRR1656504 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 1 0.3593 0.6245 0.828 0.000 0.088 0.000 0.084
#> SRR1656503 5 0.1608 0.6851 0.072 0.000 0.000 0.000 0.928
#> SRR1656507 5 0.3336 0.5463 0.228 0.000 0.000 0.000 0.772
#> SRR1656508 1 0.3333 0.5032 0.788 0.000 0.208 0.000 0.004
#> SRR1656505 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656506 5 0.0510 0.7018 0.016 0.000 0.000 0.000 0.984
#> SRR1656509 5 0.4297 0.0753 0.472 0.000 0.000 0.000 0.528
#> SRR1656510 4 0.1410 0.9128 0.060 0.000 0.000 0.940 0.000
#> SRR1656511 4 0.1792 0.9085 0.084 0.000 0.000 0.916 0.000
#> SRR1656513 4 0.1851 0.9075 0.088 0.000 0.000 0.912 0.000
#> SRR1656512 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656515 4 0.3875 0.8291 0.124 0.072 0.000 0.804 0.000
#> SRR1656516 5 0.3684 0.4733 0.280 0.000 0.000 0.000 0.720
#> SRR1656518 5 0.1851 0.6820 0.088 0.000 0.000 0.000 0.912
#> SRR1656517 3 0.4302 0.1031 0.480 0.000 0.520 0.000 0.000
#> SRR1656519 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656523 4 0.0290 0.9138 0.008 0.000 0.000 0.992 0.000
#> SRR1656521 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 1 0.4192 0.3530 0.596 0.000 0.000 0.000 0.404
#> SRR1656525 5 0.4030 0.3188 0.352 0.000 0.000 0.000 0.648
#> SRR1656526 2 0.0794 0.9449 0.028 0.972 0.000 0.000 0.000
#> SRR1656527 2 0.2723 0.8865 0.124 0.864 0.000 0.012 0.000
#> SRR1656530 5 0.0510 0.7018 0.016 0.000 0.000 0.000 0.984
#> SRR1656529 5 0.0510 0.7016 0.016 0.000 0.000 0.000 0.984
#> SRR1656531 1 0.3359 0.6228 0.844 0.000 0.072 0.000 0.084
#> SRR1656528 5 0.3999 0.3338 0.344 0.000 0.000 0.000 0.656
#> SRR1656534 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656533 1 0.4132 0.4588 0.720 0.000 0.260 0.000 0.020
#> SRR1656536 5 0.3616 0.5944 0.032 0.000 0.000 0.164 0.804
#> SRR1656532 4 0.1792 0.9085 0.084 0.000 0.000 0.916 0.000
#> SRR1656537 1 0.3210 0.4966 0.788 0.000 0.212 0.000 0.000
#> SRR1656538 1 0.6620 0.3812 0.456 0.000 0.288 0.000 0.256
#> SRR1656535 2 0.2723 0.8865 0.124 0.864 0.000 0.012 0.000
#> SRR1656539 5 0.0510 0.7018 0.016 0.000 0.000 0.000 0.984
#> SRR1656544 1 0.5932 0.1463 0.456 0.000 0.104 0.000 0.440
#> SRR1656542 5 0.4740 -0.0231 0.468 0.000 0.016 0.000 0.516
#> SRR1656543 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656546 4 0.0703 0.9158 0.024 0.000 0.000 0.976 0.000
#> SRR1656541 2 0.2723 0.8865 0.124 0.864 0.000 0.012 0.000
#> SRR1656547 4 0.1792 0.9085 0.084 0.000 0.000 0.916 0.000
#> SRR1656548 5 0.3796 0.4098 0.300 0.000 0.000 0.000 0.700
#> SRR1656549 5 0.2782 0.6663 0.072 0.000 0.000 0.048 0.880
#> SRR1656551 5 0.3942 0.4958 0.012 0.000 0.000 0.260 0.728
#> SRR1656553 5 0.4302 0.0126 0.480 0.000 0.000 0.000 0.520
#> SRR1656550 4 0.3355 0.7387 0.012 0.000 0.000 0.804 0.184
#> SRR1656552 4 0.2280 0.8936 0.120 0.000 0.000 0.880 0.000
#> SRR1656554 5 0.0510 0.7016 0.016 0.000 0.000 0.000 0.984
#> SRR1656555 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656556 1 0.6665 0.3539 0.436 0.000 0.252 0.000 0.312
#> SRR1656557 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.3949 0.4693 0.668 0.000 0.000 0.000 0.332
#> SRR1656559 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656560 5 0.4341 0.2795 0.364 0.000 0.008 0.000 0.628
#> SRR1656561 5 0.2020 0.6528 0.100 0.000 0.000 0.000 0.900
#> SRR1656562 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656563 1 0.6141 0.4605 0.560 0.000 0.244 0.000 0.196
#> SRR1656564 2 0.2286 0.9028 0.108 0.888 0.000 0.004 0.000
#> SRR1656565 4 0.0609 0.9156 0.020 0.000 0.000 0.980 0.000
#> SRR1656566 1 0.4287 0.2362 0.540 0.000 0.000 0.000 0.460
#> SRR1656568 2 0.1043 0.9419 0.040 0.960 0.000 0.000 0.000
#> SRR1656567 4 0.0000 0.9147 0.000 0.000 0.000 1.000 0.000
#> SRR1656569 5 0.1117 0.6962 0.016 0.000 0.000 0.020 0.964
#> SRR1656570 5 0.4235 0.1246 0.424 0.000 0.000 0.000 0.576
#> SRR1656571 2 0.1043 0.9419 0.040 0.960 0.000 0.000 0.000
#> SRR1656573 5 0.4201 0.5475 0.044 0.000 0.000 0.204 0.752
#> SRR1656572 4 0.1792 0.9085 0.084 0.000 0.000 0.916 0.000
#> SRR1656574 3 0.0609 0.8927 0.020 0.000 0.980 0.000 0.000
#> SRR1656575 1 0.3876 0.4708 0.684 0.000 0.000 0.000 0.316
#> SRR1656576 4 0.2329 0.8915 0.124 0.000 0.000 0.876 0.000
#> SRR1656578 4 0.3992 0.8198 0.124 0.080 0.000 0.796 0.000
#> SRR1656577 3 0.0000 0.9045 0.000 0.000 1.000 0.000 0.000
#> SRR1656579 4 0.1792 0.9085 0.084 0.000 0.000 0.916 0.000
#> SRR1656580 1 0.5896 0.0500 0.452 0.000 0.448 0.000 0.100
#> SRR1656581 4 0.2069 0.8617 0.012 0.000 0.000 0.912 0.076
#> SRR1656582 2 0.0880 0.9441 0.032 0.968 0.000 0.000 0.000
#> SRR1656585 5 0.2632 0.6754 0.072 0.000 0.000 0.040 0.888
#> SRR1656584 1 0.4235 0.3167 0.576 0.000 0.000 0.000 0.424
#> SRR1656583 5 0.3184 0.6575 0.100 0.000 0.000 0.048 0.852
#> SRR1656586 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 5 0.5663 0.2546 0.084 0.000 0.000 0.384 0.532
#> SRR1656588 4 0.0404 0.9131 0.012 0.000 0.000 0.988 0.000
#> SRR1656589 2 0.0000 0.9486 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 1 0.2732 0.5912 0.840 0.000 0.000 0.000 0.160
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.1625 0.9143 0.060 0.928 0.000 0.000 0.000 0.012
#> SRR1656464 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656462 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.3360 0.7003 0.004 0.000 0.000 0.000 0.732 0.264
#> SRR1656467 4 0.3274 0.7654 0.168 0.004 0.000 0.804 0.000 0.024
#> SRR1656466 5 0.3969 0.6517 0.020 0.000 0.000 0.000 0.668 0.312
#> SRR1656468 4 0.2100 0.8092 0.004 0.000 0.000 0.884 0.112 0.000
#> SRR1656472 1 0.3863 0.6320 0.712 0.000 0.028 0.000 0.000 0.260
#> SRR1656471 6 0.3052 0.6416 0.004 0.000 0.216 0.000 0.000 0.780
#> SRR1656470 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.0622 0.7184 0.000 0.000 0.000 0.008 0.980 0.012
#> SRR1656473 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.4634 0.7076 0.688 0.000 0.000 0.000 0.188 0.124
#> SRR1656477 5 0.3707 0.3315 0.008 0.000 0.000 0.312 0.680 0.000
#> SRR1656479 5 0.2538 0.7493 0.016 0.000 0.000 0.000 0.860 0.124
#> SRR1656480 4 0.2257 0.8060 0.008 0.000 0.000 0.876 0.116 0.000
#> SRR1656476 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.4095 0.2672 0.008 0.000 0.000 0.512 0.480 0.000
#> SRR1656482 4 0.6209 0.1068 0.168 0.344 0.000 0.464 0.000 0.024
#> SRR1656483 2 0.2070 0.9029 0.092 0.896 0.000 0.000 0.000 0.012
#> SRR1656485 6 0.2019 0.7814 0.000 0.000 0.012 0.000 0.088 0.900
#> SRR1656487 5 0.3351 0.6831 0.000 0.000 0.000 0.000 0.712 0.288
#> SRR1656486 5 0.1082 0.7022 0.040 0.000 0.000 0.000 0.956 0.004
#> SRR1656488 6 0.2088 0.7860 0.000 0.000 0.028 0.000 0.068 0.904
#> SRR1656484 6 0.4062 0.6146 0.068 0.000 0.000 0.000 0.196 0.736
#> SRR1656489 6 0.2520 0.6355 0.152 0.000 0.000 0.000 0.004 0.844
#> SRR1656491 5 0.2538 0.7484 0.016 0.000 0.000 0.000 0.860 0.124
#> SRR1656490 5 0.0820 0.7187 0.016 0.000 0.000 0.000 0.972 0.012
#> SRR1656492 5 0.3934 0.6616 0.020 0.000 0.000 0.000 0.676 0.304
#> SRR1656493 1 0.3874 0.7179 0.760 0.000 0.000 0.000 0.172 0.068
#> SRR1656495 1 0.4593 0.4924 0.620 0.000 0.000 0.056 0.324 0.000
#> SRR1656496 5 0.2784 0.7476 0.028 0.000 0.000 0.000 0.848 0.124
#> SRR1656494 4 0.2212 0.8080 0.008 0.000 0.000 0.880 0.112 0.000
#> SRR1656497 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 6 0.3695 0.3582 0.000 0.000 0.376 0.000 0.000 0.624
#> SRR1656500 3 0.1141 0.9010 0.000 0.000 0.948 0.000 0.000 0.052
#> SRR1656501 5 0.5879 0.2597 0.284 0.000 0.000 0.000 0.476 0.240
#> SRR1656498 3 0.4336 0.6352 0.160 0.000 0.724 0.000 0.000 0.116
#> SRR1656504 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 1 0.3863 0.6320 0.712 0.000 0.028 0.000 0.000 0.260
#> SRR1656503 5 0.2923 0.7338 0.052 0.000 0.000 0.000 0.848 0.100
#> SRR1656507 5 0.5894 0.2456 0.284 0.000 0.000 0.000 0.472 0.244
#> SRR1656508 1 0.4219 0.5676 0.648 0.000 0.032 0.000 0.000 0.320
#> SRR1656505 4 0.2212 0.8080 0.008 0.000 0.000 0.880 0.112 0.000
#> SRR1656506 5 0.3634 0.6735 0.008 0.000 0.000 0.000 0.696 0.296
#> SRR1656509 6 0.6032 -0.1820 0.284 0.000 0.000 0.000 0.292 0.424
#> SRR1656510 4 0.1777 0.8247 0.024 0.000 0.000 0.928 0.044 0.004
#> SRR1656511 4 0.1858 0.8144 0.076 0.000 0.000 0.912 0.000 0.012
#> SRR1656513 4 0.2060 0.8106 0.084 0.000 0.000 0.900 0.000 0.016
#> SRR1656512 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656515 4 0.3274 0.7654 0.168 0.004 0.000 0.804 0.000 0.024
#> SRR1656516 5 0.6091 -0.0108 0.320 0.000 0.000 0.000 0.388 0.292
#> SRR1656518 5 0.4795 0.6243 0.152 0.000 0.000 0.000 0.672 0.176
#> SRR1656517 3 0.5682 0.1031 0.160 0.000 0.460 0.000 0.000 0.380
#> SRR1656519 3 0.0146 0.9360 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656522 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656523 4 0.1814 0.8136 0.000 0.000 0.000 0.900 0.100 0.000
#> SRR1656521 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0146 0.9360 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656524 1 0.3715 0.7114 0.764 0.000 0.000 0.000 0.188 0.048
#> SRR1656525 6 0.2135 0.7595 0.000 0.000 0.000 0.000 0.128 0.872
#> SRR1656526 2 0.1563 0.9152 0.056 0.932 0.000 0.000 0.000 0.012
#> SRR1656527 2 0.4948 0.7519 0.168 0.696 0.000 0.112 0.000 0.024
#> SRR1656530 5 0.3969 0.6517 0.020 0.000 0.000 0.000 0.668 0.312
#> SRR1656529 5 0.2664 0.7402 0.000 0.000 0.000 0.000 0.816 0.184
#> SRR1656531 1 0.3950 0.6174 0.696 0.000 0.028 0.000 0.000 0.276
#> SRR1656528 6 0.2340 0.7435 0.000 0.000 0.000 0.000 0.148 0.852
#> SRR1656534 3 0.0146 0.9360 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656533 6 0.4412 0.4125 0.236 0.000 0.056 0.000 0.008 0.700
#> SRR1656536 5 0.1555 0.6943 0.008 0.000 0.000 0.040 0.940 0.012
#> SRR1656532 4 0.1858 0.8144 0.076 0.000 0.000 0.912 0.000 0.012
#> SRR1656537 1 0.4020 0.6196 0.692 0.000 0.032 0.000 0.000 0.276
#> SRR1656538 6 0.1410 0.7762 0.008 0.000 0.044 0.000 0.004 0.944
#> SRR1656535 2 0.4948 0.7519 0.168 0.696 0.000 0.112 0.000 0.024
#> SRR1656539 5 0.3595 0.6810 0.008 0.000 0.000 0.000 0.704 0.288
#> SRR1656544 6 0.1346 0.7853 0.008 0.000 0.016 0.000 0.024 0.952
#> SRR1656542 6 0.1225 0.7834 0.012 0.000 0.000 0.000 0.036 0.952
#> SRR1656543 3 0.0146 0.9360 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1656545 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656546 4 0.1285 0.8225 0.004 0.000 0.000 0.944 0.052 0.000
#> SRR1656541 2 0.4989 0.7471 0.168 0.692 0.000 0.116 0.000 0.024
#> SRR1656547 4 0.1858 0.8144 0.076 0.000 0.000 0.912 0.000 0.012
#> SRR1656548 6 0.2915 0.6918 0.008 0.000 0.000 0.000 0.184 0.808
#> SRR1656549 5 0.2311 0.6776 0.104 0.000 0.000 0.000 0.880 0.016
#> SRR1656551 5 0.2257 0.6433 0.008 0.000 0.000 0.116 0.876 0.000
#> SRR1656553 6 0.1794 0.7738 0.036 0.000 0.000 0.000 0.040 0.924
#> SRR1656550 4 0.4097 0.2305 0.008 0.000 0.000 0.500 0.492 0.000
#> SRR1656552 4 0.3025 0.7749 0.156 0.000 0.000 0.820 0.000 0.024
#> SRR1656554 5 0.2793 0.7337 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1656555 4 0.1957 0.8103 0.000 0.000 0.000 0.888 0.112 0.000
#> SRR1656556 6 0.1577 0.7833 0.008 0.000 0.036 0.000 0.016 0.940
#> SRR1656557 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.4634 0.7076 0.688 0.000 0.000 0.000 0.188 0.124
#> SRR1656559 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656560 6 0.1910 0.7716 0.000 0.000 0.000 0.000 0.108 0.892
#> SRR1656561 5 0.4449 0.3925 0.028 0.000 0.000 0.000 0.532 0.440
#> SRR1656562 4 0.2212 0.8080 0.008 0.000 0.000 0.880 0.112 0.000
#> SRR1656563 6 0.2119 0.7575 0.036 0.000 0.044 0.000 0.008 0.912
#> SRR1656564 2 0.4210 0.8075 0.168 0.756 0.000 0.052 0.000 0.024
#> SRR1656565 4 0.0508 0.8229 0.004 0.000 0.000 0.984 0.012 0.000
#> SRR1656566 1 0.3934 0.6598 0.708 0.000 0.000 0.000 0.260 0.032
#> SRR1656568 2 0.2070 0.9029 0.092 0.896 0.000 0.000 0.000 0.012
#> SRR1656567 4 0.1141 0.8218 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1656569 5 0.2631 0.7414 0.000 0.000 0.000 0.000 0.820 0.180
#> SRR1656570 1 0.6119 0.1759 0.356 0.000 0.000 0.000 0.304 0.340
#> SRR1656571 2 0.2070 0.9029 0.092 0.896 0.000 0.000 0.000 0.012
#> SRR1656573 5 0.2404 0.6325 0.016 0.000 0.000 0.112 0.872 0.000
#> SRR1656572 4 0.1858 0.8144 0.076 0.000 0.000 0.912 0.000 0.012
#> SRR1656574 3 0.0713 0.9208 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1656575 1 0.5428 0.5805 0.568 0.000 0.000 0.000 0.168 0.264
#> SRR1656576 4 0.3062 0.7728 0.160 0.000 0.000 0.816 0.000 0.024
#> SRR1656578 4 0.3274 0.7654 0.168 0.004 0.000 0.804 0.000 0.024
#> SRR1656577 3 0.0000 0.9369 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656579 4 0.1858 0.8144 0.076 0.000 0.000 0.912 0.000 0.012
#> SRR1656580 6 0.1951 0.7509 0.016 0.000 0.076 0.000 0.000 0.908
#> SRR1656581 4 0.3945 0.4949 0.008 0.000 0.000 0.612 0.380 0.000
#> SRR1656582 2 0.1625 0.9143 0.060 0.928 0.000 0.000 0.000 0.012
#> SRR1656585 5 0.1401 0.7230 0.020 0.000 0.000 0.004 0.948 0.028
#> SRR1656584 1 0.4685 0.6759 0.664 0.000 0.000 0.000 0.240 0.096
#> SRR1656583 5 0.2216 0.7060 0.052 0.000 0.000 0.016 0.908 0.024
#> SRR1656586 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 5 0.3227 0.5985 0.088 0.000 0.000 0.084 0.828 0.000
#> SRR1656588 4 0.2212 0.8080 0.008 0.000 0.000 0.880 0.112 0.000
#> SRR1656589 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 1 0.3555 0.6325 0.712 0.000 0.000 0.000 0.008 0.280
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 13572 rows and 129 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.998 0.999 0.4913 0.509 0.509
#> 3 3 0.992 0.934 0.972 0.3086 0.827 0.664
#> 4 4 0.766 0.763 0.899 0.0751 0.894 0.721
#> 5 5 0.752 0.676 0.822 0.0494 0.918 0.749
#> 6 6 0.865 0.824 0.916 0.0447 0.957 0.849
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1656463 2 0.000 1.000 0.000 1.000
#> SRR1656464 1 0.000 0.998 1.000 0.000
#> SRR1656462 1 0.000 0.998 1.000 0.000
#> SRR1656465 1 0.000 0.998 1.000 0.000
#> SRR1656467 2 0.000 1.000 0.000 1.000
#> SRR1656466 1 0.000 0.998 1.000 0.000
#> SRR1656468 2 0.000 1.000 0.000 1.000
#> SRR1656472 1 0.000 0.998 1.000 0.000
#> SRR1656471 1 0.000 0.998 1.000 0.000
#> SRR1656470 2 0.000 1.000 0.000 1.000
#> SRR1656469 1 0.000 0.998 1.000 0.000
#> SRR1656473 2 0.000 1.000 0.000 1.000
#> SRR1656474 2 0.000 1.000 0.000 1.000
#> SRR1656475 2 0.000 1.000 0.000 1.000
#> SRR1656478 1 0.000 0.998 1.000 0.000
#> SRR1656477 2 0.000 1.000 0.000 1.000
#> SRR1656479 1 0.000 0.998 1.000 0.000
#> SRR1656480 2 0.000 1.000 0.000 1.000
#> SRR1656476 2 0.000 1.000 0.000 1.000
#> SRR1656481 2 0.000 1.000 0.000 1.000
#> SRR1656482 2 0.000 1.000 0.000 1.000
#> SRR1656483 2 0.000 1.000 0.000 1.000
#> SRR1656485 1 0.000 0.998 1.000 0.000
#> SRR1656487 1 0.000 0.998 1.000 0.000
#> SRR1656486 1 0.456 0.894 0.904 0.096
#> SRR1656488 1 0.000 0.998 1.000 0.000
#> SRR1656484 1 0.000 0.998 1.000 0.000
#> SRR1656489 1 0.000 0.998 1.000 0.000
#> SRR1656491 1 0.000 0.998 1.000 0.000
#> SRR1656490 1 0.000 0.998 1.000 0.000
#> SRR1656492 1 0.000 0.998 1.000 0.000
#> SRR1656493 1 0.000 0.998 1.000 0.000
#> SRR1656495 2 0.000 1.000 0.000 1.000
#> SRR1656496 1 0.000 0.998 1.000 0.000
#> SRR1656494 2 0.000 1.000 0.000 1.000
#> SRR1656497 2 0.000 1.000 0.000 1.000
#> SRR1656499 1 0.000 0.998 1.000 0.000
#> SRR1656500 1 0.000 0.998 1.000 0.000
#> SRR1656501 1 0.000 0.998 1.000 0.000
#> SRR1656498 1 0.000 0.998 1.000 0.000
#> SRR1656504 2 0.000 1.000 0.000 1.000
#> SRR1656502 1 0.000 0.998 1.000 0.000
#> SRR1656503 1 0.000 0.998 1.000 0.000
#> SRR1656507 1 0.000 0.998 1.000 0.000
#> SRR1656508 1 0.000 0.998 1.000 0.000
#> SRR1656505 2 0.000 1.000 0.000 1.000
#> SRR1656506 1 0.000 0.998 1.000 0.000
#> SRR1656509 1 0.000 0.998 1.000 0.000
#> SRR1656510 2 0.000 1.000 0.000 1.000
#> SRR1656511 2 0.000 1.000 0.000 1.000
#> SRR1656513 2 0.000 1.000 0.000 1.000
#> SRR1656512 2 0.000 1.000 0.000 1.000
#> SRR1656514 1 0.000 0.998 1.000 0.000
#> SRR1656515 2 0.000 1.000 0.000 1.000
#> SRR1656516 1 0.000 0.998 1.000 0.000
#> SRR1656518 1 0.000 0.998 1.000 0.000
#> SRR1656517 1 0.000 0.998 1.000 0.000
#> SRR1656519 1 0.000 0.998 1.000 0.000
#> SRR1656522 1 0.000 0.998 1.000 0.000
#> SRR1656523 2 0.000 1.000 0.000 1.000
#> SRR1656521 2 0.000 1.000 0.000 1.000
#> SRR1656520 1 0.000 0.998 1.000 0.000
#> SRR1656524 1 0.000 0.998 1.000 0.000
#> SRR1656525 1 0.000 0.998 1.000 0.000
#> SRR1656526 2 0.000 1.000 0.000 1.000
#> SRR1656527 2 0.000 1.000 0.000 1.000
#> SRR1656530 1 0.000 0.998 1.000 0.000
#> SRR1656529 1 0.000 0.998 1.000 0.000
#> SRR1656531 1 0.000 0.998 1.000 0.000
#> SRR1656528 1 0.000 0.998 1.000 0.000
#> SRR1656534 1 0.000 0.998 1.000 0.000
#> SRR1656533 1 0.000 0.998 1.000 0.000
#> SRR1656536 1 0.204 0.966 0.968 0.032
#> SRR1656532 2 0.000 1.000 0.000 1.000
#> SRR1656537 1 0.000 0.998 1.000 0.000
#> SRR1656538 1 0.000 0.998 1.000 0.000
#> SRR1656535 2 0.000 1.000 0.000 1.000
#> SRR1656539 1 0.000 0.998 1.000 0.000
#> SRR1656544 1 0.000 0.998 1.000 0.000
#> SRR1656542 1 0.000 0.998 1.000 0.000
#> SRR1656543 1 0.000 0.998 1.000 0.000
#> SRR1656545 2 0.000 1.000 0.000 1.000
#> SRR1656540 1 0.000 0.998 1.000 0.000
#> SRR1656546 2 0.000 1.000 0.000 1.000
#> SRR1656541 2 0.000 1.000 0.000 1.000
#> SRR1656547 2 0.000 1.000 0.000 1.000
#> SRR1656548 1 0.000 0.998 1.000 0.000
#> SRR1656549 1 0.000 0.998 1.000 0.000
#> SRR1656551 2 0.141 0.980 0.020 0.980
#> SRR1656553 1 0.000 0.998 1.000 0.000
#> SRR1656550 2 0.000 1.000 0.000 1.000
#> SRR1656552 2 0.000 1.000 0.000 1.000
#> SRR1656554 1 0.000 0.998 1.000 0.000
#> SRR1656555 2 0.000 1.000 0.000 1.000
#> SRR1656556 1 0.000 0.998 1.000 0.000
#> SRR1656557 1 0.000 0.998 1.000 0.000
#> SRR1656558 1 0.000 0.998 1.000 0.000
#> SRR1656559 1 0.000 0.998 1.000 0.000
#> SRR1656560 1 0.000 0.998 1.000 0.000
#> SRR1656561 1 0.000 0.998 1.000 0.000
#> SRR1656562 2 0.000 1.000 0.000 1.000
#> SRR1656563 1 0.000 0.998 1.000 0.000
#> SRR1656564 2 0.000 1.000 0.000 1.000
#> SRR1656565 2 0.000 1.000 0.000 1.000
#> SRR1656566 1 0.000 0.998 1.000 0.000
#> SRR1656568 2 0.000 1.000 0.000 1.000
#> SRR1656567 2 0.000 1.000 0.000 1.000
#> SRR1656569 1 0.000 0.998 1.000 0.000
#> SRR1656570 1 0.000 0.998 1.000 0.000
#> SRR1656571 2 0.000 1.000 0.000 1.000
#> SRR1656573 2 0.000 1.000 0.000 1.000
#> SRR1656572 2 0.000 1.000 0.000 1.000
#> SRR1656574 1 0.000 0.998 1.000 0.000
#> SRR1656575 1 0.000 0.998 1.000 0.000
#> SRR1656576 2 0.000 1.000 0.000 1.000
#> SRR1656578 2 0.000 1.000 0.000 1.000
#> SRR1656577 1 0.000 0.998 1.000 0.000
#> SRR1656579 2 0.000 1.000 0.000 1.000
#> SRR1656580 1 0.000 0.998 1.000 0.000
#> SRR1656581 2 0.000 1.000 0.000 1.000
#> SRR1656582 2 0.000 1.000 0.000 1.000
#> SRR1656585 1 0.000 0.998 1.000 0.000
#> SRR1656584 1 0.000 0.998 1.000 0.000
#> SRR1656583 1 0.000 0.998 1.000 0.000
#> SRR1656586 2 0.000 1.000 0.000 1.000
#> SRR1656587 2 0.000 1.000 0.000 1.000
#> SRR1656588 2 0.000 1.000 0.000 1.000
#> SRR1656589 2 0.000 1.000 0.000 1.000
#> SRR1656590 1 0.000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656464 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656462 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656465 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656466 3 0.6309 0.154 0.496 0.000 0.504
#> SRR1656468 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656472 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656471 1 0.3038 0.878 0.896 0.000 0.104
#> SRR1656470 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656478 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656477 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656479 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656480 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656476 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656481 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656482 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656485 3 0.6308 0.168 0.492 0.000 0.508
#> SRR1656487 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656486 3 0.0424 0.880 0.008 0.000 0.992
#> SRR1656488 3 0.6308 0.168 0.492 0.000 0.508
#> SRR1656484 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656489 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656491 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656490 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656492 3 0.0237 0.883 0.004 0.000 0.996
#> SRR1656493 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656495 2 0.1964 0.935 0.056 0.944 0.000
#> SRR1656496 3 0.0892 0.875 0.020 0.000 0.980
#> SRR1656494 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656497 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656499 1 0.1163 0.972 0.972 0.000 0.028
#> SRR1656500 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656501 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656498 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656502 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656503 1 0.0237 0.985 0.996 0.000 0.004
#> SRR1656507 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656508 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656505 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656506 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656509 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656510 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656511 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656513 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656512 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656514 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656515 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656516 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656518 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656517 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656519 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656522 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656523 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656521 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656520 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656524 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656525 3 0.2711 0.822 0.088 0.000 0.912
#> SRR1656526 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656530 3 0.0747 0.877 0.016 0.000 0.984
#> SRR1656529 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656531 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656528 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656534 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656533 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656537 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656538 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656535 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656539 3 0.6308 0.168 0.492 0.000 0.508
#> SRR1656544 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656542 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656543 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656545 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656540 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656546 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656541 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656547 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656548 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656549 3 0.0747 0.877 0.016 0.000 0.984
#> SRR1656551 3 0.0592 0.876 0.000 0.012 0.988
#> SRR1656553 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656550 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656552 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656555 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656556 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656557 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656558 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656559 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656560 3 0.6308 0.168 0.492 0.000 0.508
#> SRR1656561 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656562 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656563 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656564 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656565 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656566 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656568 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656567 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656569 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656570 1 0.4062 0.775 0.836 0.000 0.164
#> SRR1656571 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656573 3 0.5882 0.428 0.000 0.348 0.652
#> SRR1656572 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656574 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656575 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656576 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656578 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656577 1 0.0424 0.988 0.992 0.000 0.008
#> SRR1656579 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656580 1 0.0592 0.987 0.988 0.000 0.012
#> SRR1656581 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656582 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.885 0.000 0.000 1.000
#> SRR1656584 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1656583 1 0.0747 0.984 0.984 0.000 0.016
#> SRR1656586 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656587 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656588 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656589 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1656590 1 0.0000 0.987 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656464 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656462 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656465 2 0.2760 0.7801 0.000 0.872 0.128 0.000
#> SRR1656467 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656466 3 0.3444 0.6435 0.000 0.184 0.816 0.000
#> SRR1656468 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656472 1 0.4843 0.2713 0.604 0.000 0.396 0.000
#> SRR1656471 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656470 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656469 2 0.0000 0.7846 0.000 1.000 0.000 0.000
#> SRR1656473 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656474 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656475 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656478 1 0.4955 0.4341 0.556 0.000 0.444 0.000
#> SRR1656477 4 0.3937 0.7724 0.012 0.188 0.000 0.800
#> SRR1656479 2 0.3764 0.7083 0.000 0.784 0.216 0.000
#> SRR1656480 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656476 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656481 4 0.3626 0.7866 0.004 0.184 0.000 0.812
#> SRR1656482 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656483 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656485 3 0.3219 0.6684 0.000 0.164 0.836 0.000
#> SRR1656487 2 0.0469 0.7857 0.000 0.988 0.012 0.000
#> SRR1656486 2 0.4522 0.4700 0.320 0.680 0.000 0.000
#> SRR1656488 3 0.3486 0.6377 0.000 0.188 0.812 0.000
#> SRR1656484 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656489 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656491 2 0.3791 0.7235 0.004 0.796 0.200 0.000
#> SRR1656490 2 0.0376 0.7857 0.004 0.992 0.004 0.000
#> SRR1656492 2 0.5222 0.6663 0.132 0.756 0.112 0.000
#> SRR1656493 1 0.1557 0.5147 0.944 0.000 0.056 0.000
#> SRR1656495 1 0.3024 0.3866 0.852 0.000 0.000 0.148
#> SRR1656496 2 0.4961 0.2953 0.000 0.552 0.448 0.000
#> SRR1656494 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656497 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656499 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656500 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656501 1 0.4967 0.4147 0.548 0.000 0.452 0.000
#> SRR1656498 3 0.3356 0.6406 0.176 0.000 0.824 0.000
#> SRR1656504 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656502 1 0.4843 0.2713 0.604 0.000 0.396 0.000
#> SRR1656503 3 0.3037 0.7396 0.100 0.020 0.880 0.000
#> SRR1656507 1 0.4955 0.4341 0.556 0.000 0.444 0.000
#> SRR1656508 3 0.3311 0.6459 0.172 0.000 0.828 0.000
#> SRR1656505 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656506 2 0.2647 0.7830 0.000 0.880 0.120 0.000
#> SRR1656509 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656510 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656511 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656513 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656512 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656514 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656515 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656516 3 0.4830 0.0861 0.392 0.000 0.608 0.000
#> SRR1656518 1 0.5119 0.4367 0.556 0.004 0.440 0.000
#> SRR1656517 3 0.3400 0.6353 0.180 0.000 0.820 0.000
#> SRR1656519 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656522 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656523 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656521 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656520 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656524 1 0.0469 0.4816 0.988 0.000 0.012 0.000
#> SRR1656525 3 0.3942 0.5642 0.000 0.236 0.764 0.000
#> SRR1656526 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656527 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656530 2 0.4877 0.3569 0.000 0.592 0.408 0.000
#> SRR1656529 2 0.0000 0.7846 0.000 1.000 0.000 0.000
#> SRR1656531 3 0.4697 0.3127 0.356 0.000 0.644 0.000
#> SRR1656528 2 0.2704 0.7824 0.000 0.876 0.124 0.000
#> SRR1656534 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656533 3 0.3356 0.6406 0.176 0.000 0.824 0.000
#> SRR1656536 2 0.0469 0.7814 0.012 0.988 0.000 0.000
#> SRR1656532 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656537 3 0.4564 0.3837 0.328 0.000 0.672 0.000
#> SRR1656538 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656535 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656539 3 0.3400 0.6483 0.000 0.180 0.820 0.000
#> SRR1656544 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656542 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656543 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656545 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656540 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656546 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656541 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656547 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656548 2 0.2704 0.7824 0.000 0.876 0.124 0.000
#> SRR1656549 1 0.5147 -0.1276 0.536 0.460 0.004 0.000
#> SRR1656551 2 0.0469 0.7814 0.012 0.988 0.000 0.000
#> SRR1656553 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656550 4 0.3105 0.8563 0.012 0.120 0.000 0.868
#> SRR1656552 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656554 2 0.0000 0.7846 0.000 1.000 0.000 0.000
#> SRR1656555 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656556 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656557 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.4933 0.4511 0.568 0.000 0.432 0.000
#> SRR1656559 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656560 3 0.3610 0.6196 0.000 0.200 0.800 0.000
#> SRR1656561 2 0.2704 0.7824 0.000 0.876 0.124 0.000
#> SRR1656562 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656563 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656564 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656565 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656566 1 0.0707 0.4893 0.980 0.000 0.020 0.000
#> SRR1656568 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656567 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656569 2 0.0000 0.7846 0.000 1.000 0.000 0.000
#> SRR1656570 3 0.6391 0.1113 0.328 0.084 0.588 0.000
#> SRR1656571 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656573 2 0.4795 0.4433 0.012 0.696 0.000 0.292
#> SRR1656572 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656574 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656575 3 0.4304 0.4512 0.284 0.000 0.716 0.000
#> SRR1656576 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656578 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656577 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656579 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656580 3 0.0000 0.8385 0.000 0.000 1.000 0.000
#> SRR1656581 4 0.2831 0.8635 0.004 0.120 0.000 0.876
#> SRR1656582 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656585 2 0.7336 0.4276 0.216 0.528 0.256 0.000
#> SRR1656584 1 0.4933 0.4511 0.568 0.000 0.432 0.000
#> SRR1656583 3 0.5372 -0.0113 0.444 0.012 0.544 0.000
#> SRR1656586 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656587 4 0.4972 0.3108 0.456 0.000 0.000 0.544
#> SRR1656588 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656589 4 0.0000 0.9784 0.000 0.000 0.000 1.000
#> SRR1656590 3 0.4564 0.3837 0.328 0.000 0.672 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656464 3 0.0609 0.8193 0.000 0.000 0.980 0.020 0.000
#> SRR1656462 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 4 0.6655 0.3668 0.000 0.000 0.296 0.444 0.260
#> SRR1656467 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656466 3 0.4213 0.4244 0.000 0.000 0.680 0.308 0.012
#> SRR1656468 2 0.3242 0.7145 0.000 0.784 0.000 0.000 0.216
#> SRR1656472 4 0.5996 0.0799 0.136 0.000 0.316 0.548 0.000
#> SRR1656471 3 0.0290 0.8264 0.000 0.000 0.992 0.008 0.000
#> SRR1656470 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656469 5 0.4390 0.2388 0.004 0.000 0.000 0.428 0.568
#> SRR1656473 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656478 1 0.2516 0.7835 0.860 0.000 0.140 0.000 0.000
#> SRR1656477 5 0.3561 0.4326 0.000 0.260 0.000 0.000 0.740
#> SRR1656479 4 0.6549 0.3683 0.000 0.000 0.360 0.436 0.204
#> SRR1656480 2 0.3684 0.6010 0.000 0.720 0.000 0.000 0.280
#> SRR1656476 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656481 5 0.4150 0.3160 0.000 0.388 0.000 0.000 0.612
#> SRR1656482 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656485 3 0.3774 0.4677 0.000 0.000 0.704 0.296 0.000
#> SRR1656487 5 0.5852 0.0495 0.008 0.000 0.072 0.444 0.476
#> SRR1656486 1 0.5122 0.5041 0.688 0.000 0.000 0.200 0.112
#> SRR1656488 3 0.4108 0.4321 0.000 0.000 0.684 0.308 0.008
#> SRR1656484 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656489 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656491 4 0.6623 0.3680 0.000 0.000 0.300 0.452 0.248
#> SRR1656490 4 0.6200 -0.1578 0.036 0.000 0.056 0.464 0.444
#> SRR1656492 4 0.6479 -0.0535 0.428 0.000 0.024 0.448 0.100
#> SRR1656493 1 0.4355 0.6182 0.732 0.000 0.044 0.224 0.000
#> SRR1656495 4 0.6460 -0.1304 0.284 0.152 0.000 0.548 0.016
#> SRR1656496 3 0.6037 -0.2366 0.000 0.000 0.444 0.440 0.116
#> SRR1656494 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656497 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656499 3 0.0510 0.8217 0.000 0.000 0.984 0.016 0.000
#> SRR1656500 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656501 1 0.2424 0.7902 0.868 0.000 0.132 0.000 0.000
#> SRR1656498 3 0.1626 0.7949 0.044 0.000 0.940 0.016 0.000
#> SRR1656504 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656502 4 0.5996 0.0799 0.136 0.000 0.316 0.548 0.000
#> SRR1656503 3 0.3142 0.7094 0.032 0.000 0.856 0.108 0.004
#> SRR1656507 1 0.2329 0.7945 0.876 0.000 0.124 0.000 0.000
#> SRR1656508 3 0.2230 0.7722 0.044 0.000 0.912 0.044 0.000
#> SRR1656505 2 0.2929 0.7677 0.000 0.820 0.000 0.000 0.180
#> SRR1656506 4 0.6662 0.3531 0.000 0.000 0.280 0.444 0.276
#> SRR1656509 3 0.1197 0.7994 0.000 0.000 0.952 0.048 0.000
#> SRR1656510 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656511 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656514 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656516 1 0.4242 0.2994 0.572 0.000 0.428 0.000 0.000
#> SRR1656518 1 0.2411 0.7970 0.884 0.000 0.108 0.008 0.000
#> SRR1656517 3 0.1608 0.7843 0.072 0.000 0.928 0.000 0.000
#> SRR1656519 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656523 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656521 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656520 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 1 0.3534 0.5729 0.744 0.000 0.000 0.256 0.000
#> SRR1656525 3 0.4708 0.0841 0.000 0.000 0.548 0.436 0.016
#> SRR1656526 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656530 3 0.6068 -0.2110 0.000 0.000 0.452 0.428 0.120
#> SRR1656529 5 0.4268 0.2266 0.000 0.000 0.000 0.444 0.556
#> SRR1656531 3 0.4877 0.4853 0.072 0.000 0.692 0.236 0.000
#> SRR1656528 4 0.6633 0.3735 0.000 0.000 0.304 0.448 0.248
#> SRR1656534 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656533 3 0.1544 0.7873 0.068 0.000 0.932 0.000 0.000
#> SRR1656536 5 0.0000 0.3989 0.000 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656537 3 0.4871 0.5090 0.084 0.000 0.704 0.212 0.000
#> SRR1656538 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656535 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656539 3 0.3957 0.4834 0.000 0.000 0.712 0.280 0.008
#> SRR1656544 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656542 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656543 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656540 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656546 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656541 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656547 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656548 4 0.6621 0.3752 0.000 0.000 0.312 0.448 0.240
#> SRR1656549 1 0.3241 0.6681 0.832 0.000 0.000 0.144 0.024
#> SRR1656551 5 0.0290 0.3994 0.000 0.000 0.000 0.008 0.992
#> SRR1656553 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656550 5 0.3684 0.4263 0.000 0.280 0.000 0.000 0.720
#> SRR1656552 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656554 5 0.4262 0.2312 0.000 0.000 0.000 0.440 0.560
#> SRR1656555 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656556 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656557 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.2127 0.7991 0.892 0.000 0.108 0.000 0.000
#> SRR1656559 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656560 3 0.4108 0.4321 0.000 0.000 0.684 0.308 0.008
#> SRR1656561 4 0.7087 0.3675 0.020 0.000 0.288 0.448 0.244
#> SRR1656562 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656563 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656566 1 0.0579 0.7361 0.984 0.000 0.008 0.008 0.000
#> SRR1656568 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656567 2 0.2690 0.7996 0.000 0.844 0.000 0.000 0.156
#> SRR1656569 5 0.4262 0.2312 0.000 0.000 0.000 0.440 0.560
#> SRR1656570 3 0.6343 0.0877 0.376 0.000 0.500 0.108 0.016
#> SRR1656571 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656573 5 0.2230 0.4368 0.000 0.116 0.000 0.000 0.884
#> SRR1656572 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656574 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656575 3 0.3895 0.4698 0.320 0.000 0.680 0.000 0.000
#> SRR1656576 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656578 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656577 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656579 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656580 3 0.0000 0.8309 0.000 0.000 1.000 0.000 0.000
#> SRR1656581 5 0.4242 0.2116 0.000 0.428 0.000 0.000 0.572
#> SRR1656582 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656585 4 0.7405 0.1108 0.064 0.000 0.160 0.456 0.320
#> SRR1656584 1 0.2127 0.7991 0.892 0.000 0.108 0.000 0.000
#> SRR1656583 4 0.7250 0.0769 0.100 0.000 0.204 0.548 0.148
#> SRR1656586 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656587 4 0.6839 -0.0225 0.120 0.276 0.000 0.548 0.056
#> SRR1656588 2 0.3177 0.7269 0.000 0.792 0.000 0.000 0.208
#> SRR1656589 2 0.0000 0.9735 0.000 1.000 0.000 0.000 0.000
#> SRR1656590 3 0.4793 0.5143 0.076 0.000 0.708 0.216 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656464 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656462 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.1897 0.868 0.000 0.000 0.084 0.004 0.908 0.004
#> SRR1656467 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656466 3 0.4076 0.232 0.000 0.000 0.564 0.004 0.428 0.004
#> SRR1656468 2 0.3737 0.346 0.000 0.608 0.000 0.392 0.000 0.000
#> SRR1656472 6 0.1141 0.898 0.000 0.000 0.052 0.000 0.000 0.948
#> SRR1656471 3 0.0363 0.882 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR1656470 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656469 5 0.3743 0.744 0.028 0.000 0.000 0.160 0.788 0.024
#> SRR1656473 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656474 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656475 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656478 1 0.2191 0.702 0.876 0.000 0.120 0.000 0.000 0.004
#> SRR1656477 4 0.1333 0.891 0.000 0.048 0.000 0.944 0.000 0.008
#> SRR1656479 5 0.2708 0.854 0.016 0.000 0.104 0.004 0.868 0.008
#> SRR1656480 2 0.3765 0.312 0.000 0.596 0.000 0.404 0.000 0.000
#> SRR1656476 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656481 4 0.2003 0.832 0.000 0.116 0.000 0.884 0.000 0.000
#> SRR1656482 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656483 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656485 3 0.3756 0.426 0.000 0.000 0.644 0.000 0.352 0.004
#> SRR1656487 5 0.1933 0.876 0.000 0.000 0.044 0.032 0.920 0.004
#> SRR1656486 1 0.4960 0.402 0.600 0.000 0.000 0.044 0.336 0.020
#> SRR1656488 3 0.4088 0.207 0.000 0.000 0.556 0.004 0.436 0.004
#> SRR1656484 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656489 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656491 5 0.3447 0.852 0.020 0.000 0.072 0.012 0.844 0.052
#> SRR1656490 5 0.4087 0.764 0.052 0.000 0.004 0.080 0.800 0.064
#> SRR1656492 5 0.3708 0.661 0.220 0.000 0.000 0.020 0.752 0.008
#> SRR1656493 1 0.3830 0.597 0.744 0.000 0.044 0.000 0.000 0.212
#> SRR1656495 6 0.0914 0.894 0.016 0.016 0.000 0.000 0.000 0.968
#> SRR1656496 5 0.2500 0.849 0.012 0.000 0.116 0.000 0.868 0.004
#> SRR1656494 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656497 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656499 3 0.0508 0.880 0.000 0.000 0.984 0.000 0.012 0.004
#> SRR1656500 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656501 1 0.0937 0.750 0.960 0.000 0.040 0.000 0.000 0.000
#> SRR1656498 3 0.1556 0.837 0.080 0.000 0.920 0.000 0.000 0.000
#> SRR1656504 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656502 6 0.1141 0.898 0.000 0.000 0.052 0.000 0.000 0.948
#> SRR1656503 3 0.4685 0.646 0.100 0.000 0.728 0.008 0.152 0.012
#> SRR1656507 1 0.1285 0.746 0.944 0.000 0.052 0.000 0.000 0.004
#> SRR1656508 3 0.1398 0.853 0.052 0.000 0.940 0.000 0.000 0.008
#> SRR1656505 2 0.2854 0.722 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656506 5 0.1410 0.881 0.004 0.000 0.044 0.008 0.944 0.000
#> SRR1656509 3 0.0458 0.881 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR1656510 2 0.0146 0.960 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656511 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656513 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656512 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656514 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656515 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656516 1 0.3592 0.441 0.656 0.000 0.344 0.000 0.000 0.000
#> SRR1656518 1 0.1823 0.728 0.932 0.000 0.016 0.012 0.036 0.004
#> SRR1656517 3 0.2003 0.806 0.116 0.000 0.884 0.000 0.000 0.000
#> SRR1656519 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656522 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656523 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656521 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656520 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 1 0.3309 0.520 0.720 0.000 0.000 0.000 0.000 0.280
#> SRR1656525 5 0.2946 0.761 0.004 0.000 0.184 0.000 0.808 0.004
#> SRR1656526 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656527 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656530 5 0.2632 0.785 0.000 0.000 0.164 0.000 0.832 0.004
#> SRR1656529 5 0.1411 0.859 0.000 0.000 0.000 0.060 0.936 0.004
#> SRR1656531 3 0.3520 0.710 0.036 0.000 0.776 0.000 0.000 0.188
#> SRR1656528 5 0.1075 0.881 0.000 0.000 0.048 0.000 0.952 0.000
#> SRR1656534 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656533 3 0.1610 0.834 0.084 0.000 0.916 0.000 0.000 0.000
#> SRR1656536 4 0.1536 0.850 0.004 0.000 0.000 0.940 0.040 0.016
#> SRR1656532 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656537 3 0.4159 0.670 0.116 0.000 0.744 0.000 0.000 0.140
#> SRR1656538 3 0.0146 0.886 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656535 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656539 3 0.3769 0.425 0.000 0.000 0.640 0.000 0.356 0.004
#> SRR1656544 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656542 3 0.0146 0.886 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1656543 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656540 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656546 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656541 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656547 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656548 5 0.1349 0.881 0.004 0.000 0.056 0.000 0.940 0.000
#> SRR1656549 1 0.4129 0.595 0.744 0.000 0.000 0.036 0.200 0.020
#> SRR1656551 4 0.1036 0.855 0.004 0.000 0.000 0.964 0.024 0.008
#> SRR1656553 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656550 4 0.1462 0.889 0.000 0.056 0.000 0.936 0.000 0.008
#> SRR1656552 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656554 5 0.1471 0.858 0.000 0.000 0.000 0.064 0.932 0.004
#> SRR1656555 2 0.0146 0.960 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1656556 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656557 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.0972 0.748 0.964 0.000 0.028 0.000 0.000 0.008
#> SRR1656559 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656560 3 0.3979 0.153 0.000 0.000 0.540 0.000 0.456 0.004
#> SRR1656561 5 0.1628 0.879 0.012 0.000 0.036 0.008 0.940 0.004
#> SRR1656562 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656563 3 0.1124 0.867 0.036 0.000 0.956 0.000 0.008 0.000
#> SRR1656564 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656565 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656566 1 0.1075 0.724 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1656568 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656567 2 0.2454 0.790 0.000 0.840 0.000 0.160 0.000 0.000
#> SRR1656569 5 0.1531 0.857 0.000 0.000 0.000 0.068 0.928 0.004
#> SRR1656570 1 0.6219 0.209 0.384 0.000 0.304 0.000 0.308 0.004
#> SRR1656571 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656573 4 0.0951 0.880 0.000 0.020 0.000 0.968 0.004 0.008
#> SRR1656572 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656574 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656575 3 0.3620 0.447 0.352 0.000 0.648 0.000 0.000 0.000
#> SRR1656576 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656578 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656577 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656579 2 0.0260 0.957 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656580 3 0.0000 0.887 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656581 4 0.2260 0.793 0.000 0.140 0.000 0.860 0.000 0.000
#> SRR1656582 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656585 6 0.4329 0.729 0.008 0.000 0.016 0.064 0.152 0.760
#> SRR1656584 1 0.0972 0.748 0.964 0.000 0.028 0.000 0.000 0.008
#> SRR1656583 6 0.1232 0.904 0.004 0.000 0.024 0.016 0.000 0.956
#> SRR1656586 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656587 6 0.0858 0.888 0.000 0.028 0.000 0.004 0.000 0.968
#> SRR1656588 2 0.3578 0.477 0.000 0.660 0.000 0.340 0.000 0.000
#> SRR1656589 2 0.0000 0.963 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1656590 3 0.3637 0.718 0.056 0.000 0.780 0.000 0.000 0.164
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 13572 rows and 129 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 0.986 0.994 0.4798 0.518 0.518
#> 3 3 0.999 0.957 0.981 0.3527 0.681 0.464
#> 4 4 1.000 0.983 0.994 0.1363 0.864 0.638
#> 5 5 0.704 0.383 0.705 0.0644 0.881 0.636
#> 6 6 0.751 0.678 0.795 0.0490 0.836 0.463
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
#> SRR1656463 2 0.0000 0.985 0.000 1.000
#> SRR1656464 1 0.0000 1.000 1.000 0.000
#> SRR1656462 1 0.0000 1.000 1.000 0.000
#> SRR1656465 1 0.0000 1.000 1.000 0.000
#> SRR1656467 2 0.0000 0.985 0.000 1.000
#> SRR1656466 1 0.0000 1.000 1.000 0.000
#> SRR1656468 2 0.0000 0.985 0.000 1.000
#> SRR1656472 1 0.0000 1.000 1.000 0.000
#> SRR1656471 1 0.0000 1.000 1.000 0.000
#> SRR1656470 2 0.0000 0.985 0.000 1.000
#> SRR1656469 1 0.0000 1.000 1.000 0.000
#> SRR1656473 2 0.0000 0.985 0.000 1.000
#> SRR1656474 2 0.0000 0.985 0.000 1.000
#> SRR1656475 2 0.0000 0.985 0.000 1.000
#> SRR1656478 1 0.0000 1.000 1.000 0.000
#> SRR1656477 2 0.5629 0.848 0.132 0.868
#> SRR1656479 1 0.0000 1.000 1.000 0.000
#> SRR1656480 2 0.0000 0.985 0.000 1.000
#> SRR1656476 2 0.0000 0.985 0.000 1.000
#> SRR1656481 2 0.2948 0.938 0.052 0.948
#> SRR1656482 2 0.0000 0.985 0.000 1.000
#> SRR1656483 2 0.0000 0.985 0.000 1.000
#> SRR1656485 1 0.0000 1.000 1.000 0.000
#> SRR1656487 1 0.0000 1.000 1.000 0.000
#> SRR1656486 1 0.0000 1.000 1.000 0.000
#> SRR1656488 1 0.0000 1.000 1.000 0.000
#> SRR1656484 1 0.0000 1.000 1.000 0.000
#> SRR1656489 1 0.0000 1.000 1.000 0.000
#> SRR1656491 1 0.0000 1.000 1.000 0.000
#> SRR1656490 1 0.0000 1.000 1.000 0.000
#> SRR1656492 1 0.0000 1.000 1.000 0.000
#> SRR1656493 1 0.0000 1.000 1.000 0.000
#> SRR1656495 2 0.9922 0.204 0.448 0.552
#> SRR1656496 1 0.0000 1.000 1.000 0.000
#> SRR1656494 2 0.0000 0.985 0.000 1.000
#> SRR1656497 2 0.0000 0.985 0.000 1.000
#> SRR1656499 1 0.0000 1.000 1.000 0.000
#> SRR1656500 1 0.0000 1.000 1.000 0.000
#> SRR1656501 1 0.0000 1.000 1.000 0.000
#> SRR1656498 1 0.0000 1.000 1.000 0.000
#> SRR1656504 2 0.0000 0.985 0.000 1.000
#> SRR1656502 1 0.0000 1.000 1.000 0.000
#> SRR1656503 1 0.0000 1.000 1.000 0.000
#> SRR1656507 1 0.0000 1.000 1.000 0.000
#> SRR1656508 1 0.0000 1.000 1.000 0.000
#> SRR1656505 2 0.0000 0.985 0.000 1.000
#> SRR1656506 1 0.0000 1.000 1.000 0.000
#> SRR1656509 1 0.0000 1.000 1.000 0.000
#> SRR1656510 2 0.0000 0.985 0.000 1.000
#> SRR1656511 2 0.0000 0.985 0.000 1.000
#> SRR1656513 2 0.0000 0.985 0.000 1.000
#> SRR1656512 2 0.0000 0.985 0.000 1.000
#> SRR1656514 1 0.0000 1.000 1.000 0.000
#> SRR1656515 2 0.0000 0.985 0.000 1.000
#> SRR1656516 1 0.0000 1.000 1.000 0.000
#> SRR1656518 1 0.0000 1.000 1.000 0.000
#> SRR1656517 1 0.0000 1.000 1.000 0.000
#> SRR1656519 1 0.0000 1.000 1.000 0.000
#> SRR1656522 1 0.0000 1.000 1.000 0.000
#> SRR1656523 2 0.0000 0.985 0.000 1.000
#> SRR1656521 2 0.0000 0.985 0.000 1.000
#> SRR1656520 1 0.0000 1.000 1.000 0.000
#> SRR1656524 1 0.0000 1.000 1.000 0.000
#> SRR1656525 1 0.0000 1.000 1.000 0.000
#> SRR1656526 2 0.0000 0.985 0.000 1.000
#> SRR1656527 2 0.0000 0.985 0.000 1.000
#> SRR1656530 1 0.0000 1.000 1.000 0.000
#> SRR1656529 1 0.0000 1.000 1.000 0.000
#> SRR1656531 1 0.0000 1.000 1.000 0.000
#> SRR1656528 1 0.0000 1.000 1.000 0.000
#> SRR1656534 1 0.0000 1.000 1.000 0.000
#> SRR1656533 1 0.0000 1.000 1.000 0.000
#> SRR1656536 1 0.0000 1.000 1.000 0.000
#> SRR1656532 2 0.0000 0.985 0.000 1.000
#> SRR1656537 1 0.0000 1.000 1.000 0.000
#> SRR1656538 1 0.0000 1.000 1.000 0.000
#> SRR1656535 2 0.0000 0.985 0.000 1.000
#> SRR1656539 1 0.0000 1.000 1.000 0.000
#> SRR1656544 1 0.0000 1.000 1.000 0.000
#> SRR1656542 1 0.0000 1.000 1.000 0.000
#> SRR1656543 1 0.0000 1.000 1.000 0.000
#> SRR1656545 2 0.0000 0.985 0.000 1.000
#> SRR1656540 1 0.0000 1.000 1.000 0.000
#> SRR1656546 2 0.0000 0.985 0.000 1.000
#> SRR1656541 2 0.0000 0.985 0.000 1.000
#> SRR1656547 2 0.0000 0.985 0.000 1.000
#> SRR1656548 1 0.0000 1.000 1.000 0.000
#> SRR1656549 1 0.0000 1.000 1.000 0.000
#> SRR1656551 1 0.0000 1.000 1.000 0.000
#> SRR1656553 1 0.0000 1.000 1.000 0.000
#> SRR1656550 2 0.4298 0.899 0.088 0.912
#> SRR1656552 2 0.0000 0.985 0.000 1.000
#> SRR1656554 1 0.0000 1.000 1.000 0.000
#> SRR1656555 2 0.0000 0.985 0.000 1.000
#> SRR1656556 1 0.0000 1.000 1.000 0.000
#> SRR1656557 1 0.0000 1.000 1.000 0.000
#> SRR1656558 1 0.0000 1.000 1.000 0.000
#> SRR1656559 1 0.0000 1.000 1.000 0.000
#> SRR1656560 1 0.0000 1.000 1.000 0.000
#> SRR1656561 1 0.0000 1.000 1.000 0.000
#> SRR1656562 2 0.0000 0.985 0.000 1.000
#> SRR1656563 1 0.0000 1.000 1.000 0.000
#> SRR1656564 2 0.0000 0.985 0.000 1.000
#> SRR1656565 2 0.0000 0.985 0.000 1.000
#> SRR1656566 1 0.0000 1.000 1.000 0.000
#> SRR1656568 2 0.0000 0.985 0.000 1.000
#> SRR1656567 2 0.0000 0.985 0.000 1.000
#> SRR1656569 1 0.0000 1.000 1.000 0.000
#> SRR1656570 1 0.0000 1.000 1.000 0.000
#> SRR1656571 2 0.0000 0.985 0.000 1.000
#> SRR1656573 1 0.0000 1.000 1.000 0.000
#> SRR1656572 2 0.0000 0.985 0.000 1.000
#> SRR1656574 1 0.0000 1.000 1.000 0.000
#> SRR1656575 1 0.0000 1.000 1.000 0.000
#> SRR1656576 2 0.0000 0.985 0.000 1.000
#> SRR1656578 2 0.0000 0.985 0.000 1.000
#> SRR1656577 1 0.0000 1.000 1.000 0.000
#> SRR1656579 2 0.0000 0.985 0.000 1.000
#> SRR1656580 1 0.0000 1.000 1.000 0.000
#> SRR1656581 2 0.0672 0.979 0.008 0.992
#> SRR1656582 2 0.0000 0.985 0.000 1.000
#> SRR1656585 1 0.0000 1.000 1.000 0.000
#> SRR1656584 1 0.0000 1.000 1.000 0.000
#> SRR1656583 1 0.0000 1.000 1.000 0.000
#> SRR1656586 2 0.0000 0.985 0.000 1.000
#> SRR1656587 1 0.0000 1.000 1.000 0.000
#> SRR1656588 2 0.0000 0.985 0.000 1.000
#> SRR1656589 2 0.0000 0.985 0.000 1.000
#> SRR1656590 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656464 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656462 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656465 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656467 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656466 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656468 3 0.0892 0.959 0.000 0.020 0.980
#> SRR1656472 3 0.5650 0.562 0.312 0.000 0.688
#> SRR1656471 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656470 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656478 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656477 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656479 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656480 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656476 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656481 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656482 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656483 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656485 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656487 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656486 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656488 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656484 3 0.0747 0.962 0.016 0.000 0.984
#> SRR1656489 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656491 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656490 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656492 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656493 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656495 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656496 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656494 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656497 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656499 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656500 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656501 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656498 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656504 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656502 3 0.0237 0.973 0.004 0.000 0.996
#> SRR1656503 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656507 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656508 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656505 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656506 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656509 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656510 2 0.2537 0.912 0.000 0.920 0.080
#> SRR1656511 2 0.2261 0.923 0.000 0.932 0.068
#> SRR1656513 2 0.2261 0.923 0.000 0.932 0.068
#> SRR1656512 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656514 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656515 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656516 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656518 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656517 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656519 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656522 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656523 3 0.3192 0.862 0.000 0.112 0.888
#> SRR1656521 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656520 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656524 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656525 3 0.6126 0.356 0.400 0.000 0.600
#> SRR1656526 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656527 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656530 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656531 1 0.0747 0.982 0.984 0.000 0.016
#> SRR1656528 1 0.0237 0.995 0.996 0.000 0.004
#> SRR1656534 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656533 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656536 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656532 2 0.2625 0.908 0.000 0.916 0.084
#> SRR1656537 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656538 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656535 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656539 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656544 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656542 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656543 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656545 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656540 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656546 3 0.1163 0.952 0.000 0.028 0.972
#> SRR1656541 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656547 2 0.2261 0.923 0.000 0.932 0.068
#> SRR1656548 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656549 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656551 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656553 3 0.2261 0.911 0.068 0.000 0.932
#> SRR1656550 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656552 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656554 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656555 3 0.0892 0.959 0.000 0.020 0.980
#> SRR1656556 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656557 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656558 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656559 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656560 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656561 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656562 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656563 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656564 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656565 2 0.5465 0.632 0.000 0.712 0.288
#> SRR1656566 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656568 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656567 2 0.5397 0.647 0.000 0.720 0.280
#> SRR1656569 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656570 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656571 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656572 2 0.2261 0.923 0.000 0.932 0.068
#> SRR1656574 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656575 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656576 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656578 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656577 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656579 2 0.2261 0.923 0.000 0.932 0.068
#> SRR1656580 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1656581 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656582 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656585 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656584 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656583 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656586 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656587 3 0.0000 0.975 0.000 0.000 1.000
#> SRR1656588 3 0.3038 0.870 0.000 0.104 0.896
#> SRR1656589 2 0.0000 0.966 0.000 1.000 0.000
#> SRR1656590 3 0.5465 0.605 0.288 0.000 0.712
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656464 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656462 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656465 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656467 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656466 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656468 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656472 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656471 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656470 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656469 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656473 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656474 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656475 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656478 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656477 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656479 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656480 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656476 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656481 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656482 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656483 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656485 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656487 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656486 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656488 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656484 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656489 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656491 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656490 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656492 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656493 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656495 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656496 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656494 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656497 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656499 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656500 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656501 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656498 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656504 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656502 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656503 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656507 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656508 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656505 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656506 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656509 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656510 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656511 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656513 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656512 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656514 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656515 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656516 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656518 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656517 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656519 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656522 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656523 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656521 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656520 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656524 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656525 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656526 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656527 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656530 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656529 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656531 3 0.3610 0.742 0.200 0.000 0.80 0.000
#> SRR1656528 3 0.3400 0.770 0.180 0.000 0.82 0.000
#> SRR1656534 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656533 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656536 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656532 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656537 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656538 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656535 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656539 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656544 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656542 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656543 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656545 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656540 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656546 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656541 2 0.0188 0.996 0.000 0.996 0.00 0.004
#> SRR1656547 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656548 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656549 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656551 1 0.4925 0.252 0.572 0.000 0.00 0.428
#> SRR1656553 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656550 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656552 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656554 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656555 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656556 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656557 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656558 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656559 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656560 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656561 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656562 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656563 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656564 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656565 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656566 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656568 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656567 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656569 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656570 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656571 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656573 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656572 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656574 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656575 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656576 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656578 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656577 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656579 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656580 3 0.0000 0.986 0.000 0.000 1.00 0.000
#> SRR1656581 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656582 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656585 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656584 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656583 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656586 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656587 1 0.0000 0.990 1.000 0.000 0.00 0.000
#> SRR1656588 4 0.0000 1.000 0.000 0.000 0.00 1.000
#> SRR1656589 2 0.0000 1.000 0.000 1.000 0.00 0.000
#> SRR1656590 1 0.0000 0.990 1.000 0.000 0.00 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 2 0.5338 -0.0625 0.000 0.544 0.000 0.400 0.056
#> SRR1656464 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656462 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 1 0.0000 0.7000 1.000 0.000 0.000 0.000 0.000
#> SRR1656467 4 0.4114 0.5476 0.000 0.000 0.000 0.624 0.376
#> SRR1656466 1 0.2561 0.7347 0.856 0.000 0.000 0.144 0.000
#> SRR1656468 2 0.6510 -0.8515 0.004 0.456 0.000 0.168 0.372
#> SRR1656472 1 0.6782 0.7052 0.492 0.000 0.020 0.320 0.168
#> SRR1656471 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656470 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656469 1 0.4012 0.4984 0.788 0.036 0.000 0.168 0.008
#> SRR1656473 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656474 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656475 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656478 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656477 2 0.6991 -0.2666 0.348 0.456 0.000 0.168 0.028
#> SRR1656479 1 0.0000 0.7000 1.000 0.000 0.000 0.000 0.000
#> SRR1656480 2 0.7052 -0.8205 0.032 0.456 0.000 0.168 0.344
#> SRR1656476 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656481 2 0.7109 -0.2776 0.340 0.456 0.000 0.168 0.036
#> SRR1656482 4 0.6372 0.5304 0.000 0.168 0.000 0.456 0.376
#> SRR1656483 2 0.4867 -0.1013 0.000 0.544 0.000 0.432 0.024
#> SRR1656485 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656487 1 0.0162 0.6979 0.996 0.004 0.000 0.000 0.000
#> SRR1656486 1 0.4410 0.6498 0.556 0.004 0.000 0.440 0.000
#> SRR1656488 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656484 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656489 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656491 1 0.0162 0.6979 0.996 0.004 0.000 0.000 0.000
#> SRR1656490 1 0.2970 0.5548 0.828 0.004 0.000 0.168 0.000
#> SRR1656492 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656493 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656495 1 0.4244 0.6189 0.780 0.024 0.000 0.028 0.168
#> SRR1656496 1 0.0404 0.7043 0.988 0.000 0.000 0.012 0.000
#> SRR1656494 2 0.6372 -0.8554 0.000 0.456 0.000 0.168 0.376
#> SRR1656497 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656499 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656500 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656501 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656498 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656504 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656502 1 0.6131 0.7095 0.548 0.000 0.000 0.284 0.168
#> SRR1656503 1 0.0671 0.7040 0.980 0.004 0.000 0.016 0.000
#> SRR1656507 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656508 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656505 2 0.7816 -0.4063 0.260 0.456 0.000 0.168 0.116
#> SRR1656506 1 0.0000 0.7000 1.000 0.000 0.000 0.000 0.000
#> SRR1656509 1 0.6147 0.7118 0.544 0.000 0.000 0.288 0.168
#> SRR1656510 2 0.6396 -0.8611 0.000 0.452 0.000 0.172 0.376
#> SRR1656511 4 0.4264 0.5392 0.000 0.004 0.000 0.620 0.376
#> SRR1656513 4 0.4264 0.5392 0.000 0.004 0.000 0.620 0.376
#> SRR1656512 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656514 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656515 4 0.4114 0.5476 0.000 0.000 0.000 0.624 0.376
#> SRR1656516 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656518 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656517 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656519 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656523 2 0.6372 -0.8554 0.000 0.456 0.000 0.168 0.376
#> SRR1656521 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656520 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656525 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656526 2 0.5338 -0.0625 0.000 0.544 0.000 0.400 0.056
#> SRR1656527 2 0.5039 -0.1792 0.000 0.512 0.000 0.456 0.032
#> SRR1656530 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656529 1 0.0162 0.6979 0.996 0.004 0.000 0.000 0.000
#> SRR1656531 3 0.5022 0.7620 0.068 0.000 0.736 0.028 0.168
#> SRR1656528 3 0.5841 0.5710 0.256 0.000 0.596 0.148 0.000
#> SRR1656534 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656533 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656536 2 0.7031 -0.2487 0.384 0.420 0.000 0.168 0.028
#> SRR1656532 2 0.6396 -0.8611 0.000 0.452 0.000 0.172 0.376
#> SRR1656537 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656538 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656535 4 0.6608 0.5100 0.000 0.244 0.000 0.456 0.300
#> SRR1656539 1 0.0000 0.7000 1.000 0.000 0.000 0.000 0.000
#> SRR1656544 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656542 3 0.6563 0.2857 0.220 0.000 0.456 0.324 0.000
#> SRR1656543 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656540 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656546 2 0.6372 -0.8554 0.000 0.456 0.000 0.168 0.376
#> SRR1656541 4 0.6596 0.5140 0.000 0.236 0.000 0.456 0.308
#> SRR1656547 2 0.6645 -0.9535 0.000 0.400 0.000 0.224 0.376
#> SRR1656548 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656549 4 0.4304 -0.6809 0.484 0.000 0.000 0.516 0.000
#> SRR1656551 2 0.6991 -0.2666 0.348 0.456 0.000 0.168 0.028
#> SRR1656553 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656550 2 0.6991 -0.2666 0.348 0.456 0.000 0.168 0.028
#> SRR1656552 4 0.4114 0.5476 0.000 0.000 0.000 0.624 0.376
#> SRR1656554 1 0.0162 0.6979 0.996 0.004 0.000 0.000 0.000
#> SRR1656555 2 0.6372 -0.8554 0.000 0.456 0.000 0.168 0.376
#> SRR1656556 3 0.3574 0.8164 0.000 0.000 0.804 0.028 0.168
#> SRR1656557 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656559 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656560 3 0.4270 0.7834 0.204 0.000 0.748 0.048 0.000
#> SRR1656561 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656562 2 0.6372 -0.8554 0.000 0.456 0.000 0.168 0.376
#> SRR1656563 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656564 4 0.6183 0.2800 0.000 0.408 0.000 0.456 0.136
#> SRR1656565 2 0.6396 -0.8611 0.000 0.452 0.000 0.172 0.376
#> SRR1656566 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656568 2 0.5338 -0.0625 0.000 0.544 0.000 0.400 0.056
#> SRR1656567 2 0.6396 -0.8611 0.000 0.452 0.000 0.172 0.376
#> SRR1656569 1 0.0162 0.6979 0.996 0.004 0.000 0.000 0.000
#> SRR1656570 1 0.4015 0.7512 0.652 0.000 0.000 0.348 0.000
#> SRR1656571 2 0.5236 -0.0717 0.000 0.544 0.000 0.408 0.048
#> SRR1656573 2 0.6991 -0.2666 0.348 0.456 0.000 0.168 0.028
#> SRR1656572 4 0.4264 0.5388 0.000 0.004 0.000 0.620 0.376
#> SRR1656574 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656575 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656576 4 0.4114 0.5476 0.000 0.000 0.000 0.624 0.376
#> SRR1656578 4 0.4114 0.5476 0.000 0.000 0.000 0.624 0.376
#> SRR1656577 3 0.0000 0.8621 0.000 0.000 1.000 0.000 0.000
#> SRR1656579 5 0.6719 0.0000 0.000 0.376 0.000 0.248 0.376
#> SRR1656580 3 0.3074 0.8271 0.196 0.000 0.804 0.000 0.000
#> SRR1656581 2 0.7987 -0.5624 0.176 0.456 0.000 0.168 0.200
#> SRR1656582 2 0.5338 -0.0625 0.000 0.544 0.000 0.400 0.056
#> SRR1656585 1 0.3231 0.5449 0.800 0.004 0.000 0.196 0.000
#> SRR1656584 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
#> SRR1656583 1 0.3852 0.6314 0.796 0.008 0.000 0.028 0.168
#> SRR1656586 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656587 2 0.6772 -0.2563 0.348 0.456 0.000 0.184 0.012
#> SRR1656588 2 0.6534 -0.8573 0.004 0.452 0.000 0.172 0.372
#> SRR1656589 2 0.4283 0.2482 0.000 0.544 0.000 0.000 0.456
#> SRR1656590 1 0.6372 0.7029 0.456 0.000 0.000 0.376 0.168
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.0458 0.8313 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1656464 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656462 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656465 5 0.0260 0.6697 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR1656467 2 0.2854 0.8016 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656466 5 0.4672 -0.0376 0.348 0.000 0.000 0.000 0.596 0.056
#> SRR1656468 4 0.0713 0.8450 0.000 0.000 0.000 0.972 0.028 0.000
#> SRR1656472 1 0.6360 0.3956 0.572 0.000 0.096 0.000 0.180 0.152
#> SRR1656471 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656470 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656469 5 0.3578 0.5385 0.000 0.000 0.000 0.340 0.660 0.000
#> SRR1656473 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656474 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656475 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656478 1 0.0146 0.6580 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1656477 5 0.3823 0.4001 0.000 0.000 0.000 0.436 0.564 0.000
#> SRR1656479 5 0.0000 0.6719 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656480 4 0.1007 0.8314 0.000 0.000 0.000 0.956 0.044 0.000
#> SRR1656476 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656481 4 0.3756 0.0450 0.000 0.000 0.000 0.600 0.400 0.000
#> SRR1656482 2 0.2854 0.8016 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656483 2 0.0363 0.8333 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1656485 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656487 5 0.1349 0.6378 0.004 0.000 0.000 0.000 0.940 0.056
#> SRR1656486 1 0.5308 0.3567 0.592 0.000 0.000 0.244 0.164 0.000
#> SRR1656488 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656484 1 0.5290 0.5671 0.504 0.000 0.000 0.000 0.392 0.104
#> SRR1656489 3 0.5929 0.4551 0.008 0.000 0.464 0.000 0.360 0.168
#> SRR1656491 5 0.0000 0.6719 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656490 5 0.3101 0.6158 0.000 0.000 0.000 0.244 0.756 0.000
#> SRR1656492 1 0.3859 0.6643 0.692 0.000 0.000 0.000 0.288 0.020
#> SRR1656493 1 0.1866 0.6297 0.908 0.000 0.000 0.000 0.008 0.084
#> SRR1656495 5 0.4573 0.4324 0.244 0.000 0.000 0.000 0.672 0.084
#> SRR1656496 5 0.0363 0.6648 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR1656494 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656497 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656499 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656500 3 0.0405 0.7594 0.004 0.000 0.988 0.000 0.000 0.008
#> SRR1656501 1 0.3859 0.6645 0.692 0.000 0.000 0.000 0.288 0.020
#> SRR1656498 3 0.4945 0.5771 0.304 0.000 0.604 0.000 0.000 0.092
#> SRR1656504 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656502 1 0.5239 0.3810 0.600 0.000 0.000 0.000 0.248 0.152
#> SRR1656503 5 0.2597 0.4236 0.176 0.000 0.000 0.000 0.824 0.000
#> SRR1656507 1 0.3446 0.6587 0.692 0.000 0.000 0.000 0.308 0.000
#> SRR1656508 3 0.5156 0.5696 0.308 0.000 0.580 0.000 0.000 0.112
#> SRR1656505 4 0.1444 0.8001 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR1656506 5 0.1462 0.6352 0.008 0.000 0.000 0.000 0.936 0.056
#> SRR1656509 1 0.4932 0.3110 0.600 0.000 0.000 0.000 0.312 0.088
#> SRR1656510 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656511 4 0.3684 0.2192 0.000 0.372 0.000 0.628 0.000 0.000
#> SRR1656513 4 0.3684 0.2192 0.000 0.372 0.000 0.628 0.000 0.000
#> SRR1656512 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656514 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656515 2 0.2854 0.8016 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656516 1 0.4239 0.6646 0.696 0.000 0.000 0.000 0.248 0.056
#> SRR1656518 1 0.3482 0.6552 0.684 0.000 0.000 0.000 0.316 0.000
#> SRR1656517 3 0.4134 0.6313 0.316 0.000 0.656 0.000 0.000 0.028
#> SRR1656519 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656522 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656523 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656521 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656520 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656524 1 0.2060 0.6275 0.900 0.000 0.000 0.000 0.016 0.084
#> SRR1656525 1 0.4993 0.5872 0.560 0.000 0.000 0.000 0.360 0.080
#> SRR1656526 2 0.1267 0.7852 0.000 0.940 0.000 0.000 0.000 0.060
#> SRR1656527 2 0.0632 0.8464 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656530 1 0.4305 0.6633 0.684 0.000 0.000 0.000 0.260 0.056
#> SRR1656529 5 0.1141 0.6446 0.000 0.000 0.000 0.000 0.948 0.052
#> SRR1656531 3 0.7076 0.3721 0.308 0.000 0.420 0.000 0.120 0.152
#> SRR1656528 3 0.7006 0.1322 0.184 0.000 0.368 0.000 0.364 0.084
#> SRR1656534 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656533 3 0.4101 0.6364 0.308 0.000 0.664 0.000 0.000 0.028
#> SRR1656536 5 0.3659 0.5119 0.000 0.000 0.000 0.364 0.636 0.000
#> SRR1656532 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656537 3 0.5184 0.5616 0.316 0.000 0.572 0.000 0.000 0.112
#> SRR1656538 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656535 2 0.1663 0.8525 0.000 0.912 0.000 0.088 0.000 0.000
#> SRR1656539 5 0.0146 0.6707 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1656544 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656542 1 0.7160 0.1698 0.364 0.000 0.308 0.000 0.244 0.084
#> SRR1656543 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656545 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656540 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656546 4 0.1075 0.8248 0.000 0.000 0.000 0.952 0.048 0.000
#> SRR1656541 2 0.1765 0.8518 0.000 0.904 0.000 0.096 0.000 0.000
#> SRR1656547 4 0.0458 0.8553 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656548 1 0.5002 0.5833 0.556 0.000 0.000 0.000 0.364 0.080
#> SRR1656549 1 0.4573 0.4703 0.672 0.000 0.000 0.244 0.084 0.000
#> SRR1656551 5 0.3672 0.5070 0.000 0.000 0.000 0.368 0.632 0.000
#> SRR1656553 1 0.4526 0.6577 0.676 0.000 0.000 0.000 0.244 0.080
#> SRR1656550 5 0.3817 0.4086 0.000 0.000 0.000 0.432 0.568 0.000
#> SRR1656552 2 0.2854 0.8016 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656554 5 0.1075 0.6475 0.000 0.000 0.000 0.000 0.952 0.048
#> SRR1656555 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656556 3 0.4101 0.6364 0.308 0.000 0.664 0.000 0.000 0.028
#> SRR1656557 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656558 1 0.0146 0.6580 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1656559 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656560 3 0.5802 0.5894 0.068 0.000 0.604 0.000 0.244 0.084
#> SRR1656561 1 0.4284 0.6640 0.688 0.000 0.000 0.000 0.256 0.056
#> SRR1656562 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656563 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656564 2 0.0260 0.8418 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1656565 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656566 1 0.0632 0.6572 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1656568 2 0.0458 0.8313 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1656567 4 0.0000 0.8617 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1656569 5 0.0000 0.6719 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1656570 1 0.4284 0.6640 0.688 0.000 0.000 0.000 0.256 0.056
#> SRR1656571 2 0.0458 0.8313 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1656573 5 0.3672 0.5070 0.000 0.000 0.000 0.368 0.632 0.000
#> SRR1656572 4 0.3862 -0.1340 0.000 0.476 0.000 0.524 0.000 0.000
#> SRR1656574 3 0.0291 0.7596 0.004 0.000 0.992 0.000 0.000 0.004
#> SRR1656575 1 0.0363 0.6584 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1656576 2 0.2883 0.7971 0.000 0.788 0.000 0.212 0.000 0.000
#> SRR1656578 2 0.2854 0.8016 0.000 0.792 0.000 0.208 0.000 0.000
#> SRR1656577 3 0.0000 0.7599 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1656579 4 0.0547 0.8534 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1656580 3 0.4815 0.6593 0.008 0.000 0.664 0.000 0.244 0.084
#> SRR1656581 4 0.2135 0.7356 0.000 0.000 0.000 0.872 0.128 0.000
#> SRR1656582 2 0.0458 0.8313 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1656585 5 0.3101 0.6158 0.000 0.000 0.000 0.244 0.756 0.000
#> SRR1656584 1 0.0260 0.6573 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1656583 5 0.3101 0.5177 0.244 0.000 0.000 0.000 0.756 0.000
#> SRR1656586 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656587 5 0.3672 0.5070 0.000 0.000 0.000 0.368 0.632 0.000
#> SRR1656588 4 0.0260 0.8582 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1656589 6 0.2854 1.0000 0.000 0.208 0.000 0.000 0.000 0.792
#> SRR1656590 1 0.3657 0.5737 0.792 0.000 0.000 0.000 0.108 0.100
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 13572 rows and 129 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 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 0.967 0.955 0.976 0.2597 0.715 0.715
#> 3 3 0.773 0.850 0.924 1.3773 0.618 0.474
#> 4 4 0.680 0.816 0.884 0.1218 0.855 0.627
#> 5 5 0.836 0.879 0.915 0.0991 0.894 0.654
#> 6 6 0.688 0.725 0.778 0.0353 0.961 0.836
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
#> SRR1656463 2 0.000 0.867 0.000 1.000
#> SRR1656464 1 0.000 0.995 1.000 0.000
#> SRR1656462 1 0.000 0.995 1.000 0.000
#> SRR1656465 1 0.000 0.995 1.000 0.000
#> SRR1656467 1 0.000 0.995 1.000 0.000
#> SRR1656466 1 0.000 0.995 1.000 0.000
#> SRR1656468 1 0.000 0.995 1.000 0.000
#> SRR1656472 1 0.000 0.995 1.000 0.000
#> SRR1656471 1 0.000 0.995 1.000 0.000
#> SRR1656470 2 0.000 0.867 0.000 1.000
#> SRR1656469 1 0.000 0.995 1.000 0.000
#> SRR1656473 2 0.000 0.867 0.000 1.000
#> SRR1656474 2 0.000 0.867 0.000 1.000
#> SRR1656475 2 0.000 0.867 0.000 1.000
#> SRR1656478 1 0.000 0.995 1.000 0.000
#> SRR1656477 1 0.000 0.995 1.000 0.000
#> SRR1656479 1 0.000 0.995 1.000 0.000
#> SRR1656480 1 0.000 0.995 1.000 0.000
#> SRR1656476 2 0.900 0.677 0.316 0.684
#> SRR1656481 1 0.000 0.995 1.000 0.000
#> SRR1656482 2 0.946 0.602 0.364 0.636
#> SRR1656483 2 0.000 0.867 0.000 1.000
#> SRR1656485 1 0.000 0.995 1.000 0.000
#> SRR1656487 1 0.000 0.995 1.000 0.000
#> SRR1656486 1 0.000 0.995 1.000 0.000
#> SRR1656488 1 0.000 0.995 1.000 0.000
#> SRR1656484 1 0.000 0.995 1.000 0.000
#> SRR1656489 1 0.000 0.995 1.000 0.000
#> SRR1656491 1 0.000 0.995 1.000 0.000
#> SRR1656490 1 0.000 0.995 1.000 0.000
#> SRR1656492 1 0.000 0.995 1.000 0.000
#> SRR1656493 1 0.000 0.995 1.000 0.000
#> SRR1656495 1 0.000 0.995 1.000 0.000
#> SRR1656496 1 0.000 0.995 1.000 0.000
#> SRR1656494 1 0.000 0.995 1.000 0.000
#> SRR1656497 2 0.000 0.867 0.000 1.000
#> SRR1656499 1 0.000 0.995 1.000 0.000
#> SRR1656500 1 0.000 0.995 1.000 0.000
#> SRR1656501 1 0.000 0.995 1.000 0.000
#> SRR1656498 1 0.000 0.995 1.000 0.000
#> SRR1656504 2 0.900 0.677 0.316 0.684
#> SRR1656502 1 0.000 0.995 1.000 0.000
#> SRR1656503 1 0.000 0.995 1.000 0.000
#> SRR1656507 1 0.000 0.995 1.000 0.000
#> SRR1656508 1 0.000 0.995 1.000 0.000
#> SRR1656505 1 0.000 0.995 1.000 0.000
#> SRR1656506 1 0.000 0.995 1.000 0.000
#> SRR1656509 1 0.000 0.995 1.000 0.000
#> SRR1656510 1 0.000 0.995 1.000 0.000
#> SRR1656511 1 0.000 0.995 1.000 0.000
#> SRR1656513 1 0.000 0.995 1.000 0.000
#> SRR1656512 2 0.000 0.867 0.000 1.000
#> SRR1656514 1 0.000 0.995 1.000 0.000
#> SRR1656515 1 0.456 0.872 0.904 0.096
#> SRR1656516 1 0.000 0.995 1.000 0.000
#> SRR1656518 1 0.000 0.995 1.000 0.000
#> SRR1656517 1 0.000 0.995 1.000 0.000
#> SRR1656519 1 0.000 0.995 1.000 0.000
#> SRR1656522 1 0.000 0.995 1.000 0.000
#> SRR1656523 1 0.000 0.995 1.000 0.000
#> SRR1656521 2 0.000 0.867 0.000 1.000
#> SRR1656520 1 0.000 0.995 1.000 0.000
#> SRR1656524 1 0.000 0.995 1.000 0.000
#> SRR1656525 1 0.000 0.995 1.000 0.000
#> SRR1656526 2 0.921 0.655 0.336 0.664
#> SRR1656527 1 0.904 0.411 0.680 0.320
#> SRR1656530 1 0.000 0.995 1.000 0.000
#> SRR1656529 1 0.000 0.995 1.000 0.000
#> SRR1656531 1 0.000 0.995 1.000 0.000
#> SRR1656528 1 0.000 0.995 1.000 0.000
#> SRR1656534 1 0.000 0.995 1.000 0.000
#> SRR1656533 1 0.000 0.995 1.000 0.000
#> SRR1656536 1 0.000 0.995 1.000 0.000
#> SRR1656532 1 0.000 0.995 1.000 0.000
#> SRR1656537 1 0.000 0.995 1.000 0.000
#> SRR1656538 1 0.000 0.995 1.000 0.000
#> SRR1656535 2 0.925 0.649 0.340 0.660
#> SRR1656539 1 0.000 0.995 1.000 0.000
#> SRR1656544 1 0.000 0.995 1.000 0.000
#> SRR1656542 1 0.000 0.995 1.000 0.000
#> SRR1656543 1 0.000 0.995 1.000 0.000
#> SRR1656545 2 0.000 0.867 0.000 1.000
#> SRR1656540 1 0.000 0.995 1.000 0.000
#> SRR1656546 1 0.000 0.995 1.000 0.000
#> SRR1656541 2 0.925 0.649 0.340 0.660
#> SRR1656547 1 0.000 0.995 1.000 0.000
#> SRR1656548 1 0.000 0.995 1.000 0.000
#> SRR1656549 1 0.000 0.995 1.000 0.000
#> SRR1656551 1 0.000 0.995 1.000 0.000
#> SRR1656553 1 0.000 0.995 1.000 0.000
#> SRR1656550 1 0.000 0.995 1.000 0.000
#> SRR1656552 1 0.000 0.995 1.000 0.000
#> SRR1656554 1 0.000 0.995 1.000 0.000
#> SRR1656555 1 0.000 0.995 1.000 0.000
#> SRR1656556 1 0.000 0.995 1.000 0.000
#> SRR1656557 1 0.000 0.995 1.000 0.000
#> SRR1656558 1 0.000 0.995 1.000 0.000
#> SRR1656559 1 0.000 0.995 1.000 0.000
#> SRR1656560 1 0.000 0.995 1.000 0.000
#> SRR1656561 1 0.000 0.995 1.000 0.000
#> SRR1656562 1 0.000 0.995 1.000 0.000
#> SRR1656563 1 0.000 0.995 1.000 0.000
#> SRR1656564 2 0.141 0.859 0.020 0.980
#> SRR1656565 1 0.000 0.995 1.000 0.000
#> SRR1656566 1 0.000 0.995 1.000 0.000
#> SRR1656568 2 0.921 0.655 0.336 0.664
#> SRR1656567 1 0.000 0.995 1.000 0.000
#> SRR1656569 1 0.000 0.995 1.000 0.000
#> SRR1656570 1 0.000 0.995 1.000 0.000
#> SRR1656571 2 0.000 0.867 0.000 1.000
#> SRR1656573 1 0.000 0.995 1.000 0.000
#> SRR1656572 1 0.000 0.995 1.000 0.000
#> SRR1656574 1 0.000 0.995 1.000 0.000
#> SRR1656575 1 0.000 0.995 1.000 0.000
#> SRR1656576 1 0.000 0.995 1.000 0.000
#> SRR1656578 1 0.000 0.995 1.000 0.000
#> SRR1656577 1 0.000 0.995 1.000 0.000
#> SRR1656579 1 0.000 0.995 1.000 0.000
#> SRR1656580 1 0.000 0.995 1.000 0.000
#> SRR1656581 1 0.000 0.995 1.000 0.000
#> SRR1656582 2 0.925 0.649 0.340 0.660
#> SRR1656585 1 0.000 0.995 1.000 0.000
#> SRR1656584 1 0.000 0.995 1.000 0.000
#> SRR1656583 1 0.000 0.995 1.000 0.000
#> SRR1656586 2 0.000 0.867 0.000 1.000
#> SRR1656587 1 0.000 0.995 1.000 0.000
#> SRR1656588 1 0.000 0.995 1.000 0.000
#> SRR1656589 2 0.000 0.867 0.000 1.000
#> SRR1656590 1 0.000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656464 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656462 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656465 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656467 2 0.6229 0.603 0.008 0.652 0.340
#> SRR1656466 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656468 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656472 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656471 1 0.6140 0.415 0.596 0.000 0.404
#> SRR1656470 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656469 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656473 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656474 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656475 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656478 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656477 3 0.0237 0.961 0.004 0.000 0.996
#> SRR1656479 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656480 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656476 2 0.6018 0.649 0.008 0.684 0.308
#> SRR1656481 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656482 2 0.7366 0.670 0.072 0.668 0.260
#> SRR1656483 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656485 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656487 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656486 3 0.0424 0.959 0.008 0.000 0.992
#> SRR1656488 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656484 3 0.0424 0.959 0.008 0.000 0.992
#> SRR1656489 1 0.1289 0.907 0.968 0.000 0.032
#> SRR1656491 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656490 3 0.0424 0.959 0.008 0.000 0.992
#> SRR1656492 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656493 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656495 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656496 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656494 1 0.1163 0.909 0.972 0.000 0.028
#> SRR1656497 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656499 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656500 1 0.5882 0.536 0.652 0.000 0.348
#> SRR1656501 1 0.4235 0.766 0.824 0.000 0.176
#> SRR1656498 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656504 2 0.6018 0.649 0.008 0.684 0.308
#> SRR1656502 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656503 3 0.3686 0.794 0.140 0.000 0.860
#> SRR1656507 1 0.2165 0.869 0.936 0.000 0.064
#> SRR1656508 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656505 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656506 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656509 1 0.1411 0.905 0.964 0.000 0.036
#> SRR1656510 3 0.0747 0.953 0.016 0.000 0.984
#> SRR1656511 3 0.1711 0.931 0.008 0.032 0.960
#> SRR1656513 2 0.7394 0.563 0.284 0.652 0.064
#> SRR1656512 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656514 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656515 2 0.6565 0.445 0.008 0.576 0.416
#> SRR1656516 1 0.4235 0.778 0.824 0.000 0.176
#> SRR1656518 1 0.5098 0.700 0.752 0.000 0.248
#> SRR1656517 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656519 1 0.3038 0.852 0.896 0.000 0.104
#> SRR1656522 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656523 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656521 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656520 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656524 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656525 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656526 2 0.6129 0.630 0.008 0.668 0.324
#> SRR1656527 2 0.6282 0.537 0.324 0.664 0.012
#> SRR1656530 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656529 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656531 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656528 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656534 1 0.1860 0.894 0.948 0.000 0.052
#> SRR1656533 1 0.0747 0.905 0.984 0.000 0.016
#> SRR1656536 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656532 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656537 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656538 3 0.6008 0.341 0.372 0.000 0.628
#> SRR1656535 2 0.6255 0.635 0.012 0.668 0.320
#> SRR1656539 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656544 3 0.3941 0.773 0.156 0.000 0.844
#> SRR1656542 3 0.0747 0.952 0.016 0.000 0.984
#> SRR1656543 1 0.1753 0.897 0.952 0.000 0.048
#> SRR1656545 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656540 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656546 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656541 2 0.6129 0.630 0.008 0.668 0.324
#> SRR1656547 3 0.0237 0.961 0.004 0.000 0.996
#> SRR1656548 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656549 1 0.6026 0.501 0.624 0.000 0.376
#> SRR1656551 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656553 1 0.6252 0.307 0.556 0.000 0.444
#> SRR1656550 3 0.0424 0.959 0.008 0.000 0.992
#> SRR1656552 3 0.5202 0.635 0.008 0.220 0.772
#> SRR1656554 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656555 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656556 1 0.1163 0.909 0.972 0.000 0.028
#> SRR1656557 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656558 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656559 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656560 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656561 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656562 3 0.5733 0.460 0.324 0.000 0.676
#> SRR1656563 1 0.5591 0.635 0.696 0.000 0.304
#> SRR1656564 2 0.0424 0.821 0.000 0.992 0.008
#> SRR1656565 3 0.1525 0.933 0.032 0.004 0.964
#> SRR1656566 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656568 2 0.6129 0.542 0.324 0.668 0.008
#> SRR1656567 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656569 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656570 1 0.5926 0.545 0.644 0.000 0.356
#> SRR1656571 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656573 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656572 1 0.4737 0.826 0.852 0.064 0.084
#> SRR1656574 1 0.1163 0.909 0.972 0.000 0.028
#> SRR1656575 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656576 3 0.0661 0.957 0.008 0.004 0.988
#> SRR1656578 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656577 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656579 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656580 1 0.5760 0.580 0.672 0.000 0.328
#> SRR1656581 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1656582 2 0.6129 0.630 0.008 0.668 0.324
#> SRR1656585 3 0.0892 0.948 0.020 0.000 0.980
#> SRR1656584 1 0.0000 0.891 1.000 0.000 0.000
#> SRR1656583 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656586 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656587 1 0.1031 0.910 0.976 0.000 0.024
#> SRR1656588 3 0.0424 0.959 0.008 0.000 0.992
#> SRR1656589 2 0.0000 0.823 0.000 1.000 0.000
#> SRR1656590 1 0.1031 0.910 0.976 0.000 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 2 0.1629 0.731 0.024 0.952 0.000 0.024
#> SRR1656464 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656462 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656465 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656467 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656466 4 0.0469 0.931 0.012 0.000 0.000 0.988
#> SRR1656468 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656472 3 0.1042 0.910 0.008 0.000 0.972 0.020
#> SRR1656471 3 0.3074 0.755 0.000 0.000 0.848 0.152
#> SRR1656470 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656469 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656473 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656474 2 0.1867 0.713 0.072 0.928 0.000 0.000
#> SRR1656475 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656478 1 0.3392 0.890 0.856 0.000 0.124 0.020
#> SRR1656477 4 0.0804 0.931 0.000 0.012 0.008 0.980
#> SRR1656479 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656480 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656476 2 0.4188 0.740 0.004 0.752 0.000 0.244
#> SRR1656481 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656482 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656483 2 0.1520 0.732 0.020 0.956 0.000 0.024
#> SRR1656485 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656487 4 0.0592 0.929 0.016 0.000 0.000 0.984
#> SRR1656486 4 0.5408 -0.122 0.488 0.000 0.012 0.500
#> SRR1656488 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656484 4 0.1474 0.906 0.000 0.000 0.052 0.948
#> SRR1656489 1 0.7832 0.313 0.380 0.000 0.360 0.260
#> SRR1656491 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656490 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656492 4 0.0804 0.929 0.012 0.000 0.008 0.980
#> SRR1656493 3 0.4267 0.722 0.188 0.000 0.788 0.024
#> SRR1656495 3 0.0921 0.909 0.000 0.000 0.972 0.028
#> SRR1656496 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656494 3 0.2908 0.829 0.000 0.040 0.896 0.064
#> SRR1656497 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656499 4 0.1716 0.896 0.000 0.000 0.064 0.936
#> SRR1656500 3 0.4713 0.356 0.000 0.000 0.640 0.360
#> SRR1656501 1 0.3160 0.889 0.872 0.000 0.108 0.020
#> SRR1656498 3 0.4399 0.687 0.212 0.000 0.768 0.020
#> SRR1656504 2 0.4188 0.740 0.004 0.752 0.000 0.244
#> SRR1656502 3 0.1042 0.910 0.008 0.000 0.972 0.020
#> SRR1656503 4 0.1637 0.898 0.000 0.000 0.060 0.940
#> SRR1656507 1 0.3160 0.889 0.872 0.000 0.108 0.020
#> SRR1656508 3 0.1042 0.907 0.008 0.000 0.972 0.020
#> SRR1656505 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656506 4 0.0469 0.931 0.012 0.000 0.000 0.988
#> SRR1656509 3 0.3486 0.702 0.000 0.000 0.812 0.188
#> SRR1656510 4 0.1936 0.915 0.028 0.032 0.000 0.940
#> SRR1656511 2 0.4972 0.355 0.000 0.544 0.000 0.456
#> SRR1656513 2 0.5221 0.734 0.000 0.732 0.060 0.208
#> SRR1656512 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656514 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656515 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656516 1 0.3464 0.891 0.860 0.000 0.108 0.032
#> SRR1656518 1 0.4344 0.870 0.816 0.000 0.108 0.076
#> SRR1656517 1 0.3447 0.888 0.852 0.000 0.128 0.020
#> SRR1656519 3 0.1118 0.890 0.000 0.000 0.964 0.036
#> SRR1656522 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656523 4 0.2647 0.848 0.000 0.120 0.000 0.880
#> SRR1656521 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656520 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656524 3 0.4399 0.685 0.212 0.000 0.768 0.020
#> SRR1656525 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656526 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656527 2 0.5277 0.664 0.008 0.740 0.204 0.048
#> SRR1656530 4 0.0592 0.929 0.016 0.000 0.000 0.984
#> SRR1656529 4 0.0188 0.934 0.004 0.000 0.000 0.996
#> SRR1656531 3 0.1042 0.910 0.008 0.000 0.972 0.020
#> SRR1656528 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656534 3 0.0592 0.906 0.000 0.000 0.984 0.016
#> SRR1656533 1 0.3447 0.888 0.852 0.000 0.128 0.020
#> SRR1656536 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656532 2 0.5537 0.654 0.008 0.720 0.216 0.056
#> SRR1656537 3 0.1042 0.907 0.008 0.000 0.972 0.020
#> SRR1656538 4 0.4499 0.748 0.072 0.000 0.124 0.804
#> SRR1656535 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656539 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656544 4 0.2704 0.829 0.000 0.000 0.124 0.876
#> SRR1656542 4 0.2081 0.877 0.000 0.000 0.084 0.916
#> SRR1656543 3 0.0469 0.908 0.000 0.000 0.988 0.012
#> SRR1656545 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656540 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656546 1 0.3616 0.888 0.852 0.000 0.112 0.036
#> SRR1656541 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656547 4 0.3311 0.778 0.000 0.172 0.000 0.828
#> SRR1656548 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656549 1 0.5839 0.765 0.696 0.000 0.104 0.200
#> SRR1656551 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656553 4 0.2760 0.824 0.000 0.000 0.128 0.872
#> SRR1656550 4 0.0804 0.931 0.000 0.012 0.008 0.980
#> SRR1656552 2 0.4222 0.726 0.000 0.728 0.000 0.272
#> SRR1656554 4 0.0592 0.929 0.016 0.000 0.000 0.984
#> SRR1656555 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656556 3 0.0921 0.909 0.000 0.000 0.972 0.028
#> SRR1656557 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656558 1 0.3219 0.890 0.868 0.000 0.112 0.020
#> SRR1656559 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR1656560 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656561 4 0.1256 0.920 0.028 0.000 0.008 0.964
#> SRR1656562 4 0.2973 0.819 0.000 0.144 0.000 0.856
#> SRR1656563 1 0.6075 0.762 0.680 0.000 0.128 0.192
#> SRR1656564 2 0.1624 0.733 0.020 0.952 0.000 0.028
#> SRR1656565 2 0.4907 0.453 0.000 0.580 0.000 0.420
#> SRR1656566 1 0.3447 0.888 0.852 0.000 0.128 0.020
#> SRR1656568 2 0.5277 0.664 0.008 0.740 0.204 0.048
#> SRR1656567 4 0.1716 0.901 0.000 0.064 0.000 0.936
#> SRR1656569 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1656570 1 0.5766 0.770 0.704 0.000 0.104 0.192
#> SRR1656571 2 0.1297 0.729 0.020 0.964 0.000 0.016
#> SRR1656573 4 0.0707 0.926 0.000 0.020 0.000 0.980
#> SRR1656572 2 0.4631 0.733 0.008 0.728 0.004 0.260
#> SRR1656574 3 0.2011 0.861 0.000 0.000 0.920 0.080
#> SRR1656575 1 0.4609 0.865 0.788 0.000 0.156 0.056
#> SRR1656576 2 0.4992 0.295 0.000 0.524 0.000 0.476
#> SRR1656578 2 0.5466 0.661 0.008 0.728 0.208 0.056
#> SRR1656577 3 0.0336 0.907 0.008 0.000 0.992 0.000
#> SRR1656579 4 0.2647 0.848 0.000 0.120 0.000 0.880
#> SRR1656580 4 0.5173 0.492 0.020 0.000 0.320 0.660
#> SRR1656581 4 0.0817 0.925 0.000 0.024 0.000 0.976
#> SRR1656582 2 0.4193 0.730 0.000 0.732 0.000 0.268
#> SRR1656585 4 0.1557 0.904 0.000 0.000 0.056 0.944
#> SRR1656584 1 0.3219 0.890 0.868 0.000 0.112 0.020
#> SRR1656583 3 0.1109 0.909 0.000 0.004 0.968 0.028
#> SRR1656586 2 0.2647 0.699 0.120 0.880 0.000 0.000
#> SRR1656587 3 0.1557 0.888 0.000 0.000 0.944 0.056
#> SRR1656588 4 0.1970 0.903 0.000 0.060 0.008 0.932
#> SRR1656589 2 0.3166 0.708 0.116 0.868 0.000 0.016
#> SRR1656590 3 0.0895 0.908 0.004 0.000 0.976 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 4 0.1168 0.913 0.032 0.008 0.000 0.960 0.000
#> SRR1656464 3 0.1410 0.882 0.060 0.000 0.940 0.000 0.000
#> SRR1656462 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656465 5 0.0963 0.954 0.000 0.036 0.000 0.000 0.964
#> SRR1656467 4 0.0324 0.915 0.004 0.000 0.004 0.992 0.000
#> SRR1656466 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656468 5 0.1568 0.936 0.000 0.020 0.000 0.036 0.944
#> SRR1656472 3 0.3522 0.873 0.104 0.020 0.844 0.000 0.032
#> SRR1656471 3 0.2824 0.832 0.020 0.000 0.864 0.000 0.116
#> SRR1656470 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656469 5 0.0290 0.956 0.000 0.008 0.000 0.000 0.992
#> SRR1656473 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656474 2 0.4201 0.511 0.000 0.592 0.000 0.408 0.000
#> SRR1656475 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656478 1 0.1168 0.861 0.960 0.000 0.008 0.000 0.032
#> SRR1656477 5 0.1569 0.948 0.000 0.044 0.004 0.008 0.944
#> SRR1656479 5 0.1043 0.953 0.000 0.040 0.000 0.000 0.960
#> SRR1656480 5 0.2074 0.927 0.000 0.044 0.000 0.036 0.920
#> SRR1656476 4 0.1082 0.913 0.028 0.008 0.000 0.964 0.000
#> SRR1656481 5 0.1818 0.937 0.000 0.044 0.000 0.024 0.932
#> SRR1656482 4 0.1041 0.915 0.032 0.000 0.004 0.964 0.000
#> SRR1656483 4 0.1168 0.913 0.032 0.008 0.000 0.960 0.000
#> SRR1656485 5 0.0992 0.951 0.000 0.008 0.024 0.000 0.968
#> SRR1656487 5 0.1043 0.953 0.000 0.040 0.000 0.000 0.960
#> SRR1656486 1 0.4661 0.623 0.656 0.032 0.000 0.000 0.312
#> SRR1656488 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656484 5 0.0609 0.952 0.000 0.020 0.000 0.000 0.980
#> SRR1656489 1 0.6403 0.228 0.500 0.016 0.368 0.000 0.116
#> SRR1656491 5 0.1121 0.952 0.000 0.044 0.000 0.000 0.956
#> SRR1656490 5 0.0963 0.954 0.000 0.036 0.000 0.000 0.964
#> SRR1656492 5 0.0794 0.947 0.000 0.028 0.000 0.000 0.972
#> SRR1656493 3 0.4697 0.658 0.320 0.000 0.648 0.000 0.032
#> SRR1656495 3 0.3522 0.873 0.104 0.020 0.844 0.000 0.032
#> SRR1656496 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656494 4 0.2867 0.843 0.004 0.020 0.072 0.888 0.016
#> SRR1656497 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656499 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656500 3 0.0854 0.880 0.012 0.004 0.976 0.000 0.008
#> SRR1656501 1 0.2291 0.857 0.908 0.036 0.000 0.000 0.056
#> SRR1656498 3 0.4452 0.730 0.272 0.000 0.696 0.000 0.032
#> SRR1656504 4 0.1082 0.913 0.028 0.008 0.000 0.964 0.000
#> SRR1656502 3 0.3522 0.873 0.104 0.020 0.844 0.000 0.032
#> SRR1656503 5 0.0807 0.950 0.000 0.012 0.012 0.000 0.976
#> SRR1656507 1 0.1661 0.859 0.940 0.024 0.000 0.000 0.036
#> SRR1656508 3 0.2932 0.876 0.104 0.000 0.864 0.000 0.032
#> SRR1656505 5 0.2074 0.927 0.000 0.044 0.000 0.036 0.920
#> SRR1656506 5 0.0290 0.955 0.000 0.008 0.000 0.000 0.992
#> SRR1656509 3 0.3758 0.840 0.096 0.000 0.816 0.000 0.088
#> SRR1656510 4 0.4291 0.542 0.016 0.004 0.000 0.704 0.276
#> SRR1656511 4 0.1043 0.897 0.000 0.000 0.000 0.960 0.040
#> SRR1656513 4 0.0162 0.915 0.000 0.000 0.004 0.996 0.000
#> SRR1656512 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656514 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656515 4 0.0324 0.915 0.004 0.000 0.004 0.992 0.000
#> SRR1656516 1 0.2580 0.855 0.892 0.044 0.000 0.000 0.064
#> SRR1656518 1 0.2843 0.850 0.876 0.048 0.000 0.000 0.076
#> SRR1656517 1 0.1836 0.847 0.932 0.000 0.036 0.000 0.032
#> SRR1656519 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656522 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656523 4 0.3267 0.793 0.000 0.044 0.000 0.844 0.112
#> SRR1656521 2 0.2377 0.940 0.000 0.872 0.000 0.128 0.000
#> SRR1656520 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656524 3 0.4849 0.580 0.360 0.000 0.608 0.000 0.032
#> SRR1656525 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656526 4 0.1041 0.914 0.032 0.004 0.000 0.964 0.000
#> SRR1656527 4 0.1041 0.915 0.032 0.000 0.004 0.964 0.000
#> SRR1656530 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656529 5 0.0162 0.956 0.000 0.004 0.000 0.000 0.996
#> SRR1656531 3 0.3090 0.876 0.104 0.004 0.860 0.000 0.032
#> SRR1656528 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656534 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656533 1 0.1386 0.859 0.952 0.000 0.016 0.000 0.032
#> SRR1656536 5 0.1121 0.952 0.000 0.044 0.000 0.000 0.956
#> SRR1656532 4 0.0162 0.915 0.000 0.000 0.004 0.996 0.000
#> SRR1656537 3 0.3452 0.855 0.148 0.000 0.820 0.000 0.032
#> SRR1656538 5 0.4237 0.695 0.168 0.032 0.020 0.000 0.780
#> SRR1656535 4 0.1041 0.914 0.032 0.004 0.000 0.964 0.000
#> SRR1656539 5 0.0963 0.954 0.000 0.036 0.000 0.000 0.964
#> SRR1656544 5 0.1168 0.944 0.000 0.008 0.032 0.000 0.960
#> SRR1656542 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656543 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656545 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656540 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656546 1 0.1251 0.823 0.956 0.000 0.008 0.036 0.000
#> SRR1656541 4 0.0880 0.915 0.032 0.000 0.000 0.968 0.000
#> SRR1656547 4 0.2149 0.870 0.000 0.036 0.000 0.916 0.048
#> SRR1656548 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656549 1 0.4476 0.766 0.744 0.044 0.008 0.000 0.204
#> SRR1656551 5 0.1121 0.952 0.000 0.044 0.000 0.000 0.956
#> SRR1656553 5 0.1012 0.946 0.000 0.020 0.012 0.000 0.968
#> SRR1656550 5 0.2234 0.926 0.000 0.044 0.004 0.036 0.916
#> SRR1656552 4 0.0000 0.914 0.000 0.000 0.000 1.000 0.000
#> SRR1656554 5 0.0963 0.954 0.000 0.036 0.000 0.000 0.964
#> SRR1656555 5 0.2438 0.910 0.000 0.040 0.000 0.060 0.900
#> SRR1656556 3 0.2959 0.876 0.100 0.000 0.864 0.000 0.036
#> SRR1656557 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656558 1 0.1168 0.861 0.960 0.000 0.008 0.000 0.032
#> SRR1656559 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656560 5 0.0510 0.953 0.000 0.016 0.000 0.000 0.984
#> SRR1656561 5 0.0955 0.945 0.004 0.028 0.000 0.000 0.968
#> SRR1656562 4 0.2867 0.837 0.000 0.044 0.004 0.880 0.072
#> SRR1656563 1 0.4253 0.765 0.756 0.032 0.008 0.000 0.204
#> SRR1656564 4 0.1168 0.913 0.032 0.008 0.000 0.960 0.000
#> SRR1656565 4 0.1356 0.900 0.000 0.012 0.004 0.956 0.028
#> SRR1656566 1 0.1168 0.861 0.960 0.000 0.008 0.000 0.032
#> SRR1656568 4 0.1202 0.914 0.032 0.004 0.004 0.960 0.000
#> SRR1656567 4 0.3551 0.762 0.000 0.044 0.000 0.820 0.136
#> SRR1656569 5 0.0794 0.955 0.000 0.028 0.000 0.000 0.972
#> SRR1656570 1 0.4233 0.760 0.748 0.044 0.000 0.000 0.208
#> SRR1656571 4 0.1168 0.913 0.032 0.008 0.000 0.960 0.000
#> SRR1656573 5 0.1121 0.952 0.000 0.044 0.000 0.000 0.956
#> SRR1656572 4 0.0162 0.915 0.000 0.000 0.004 0.996 0.000
#> SRR1656574 3 0.2673 0.879 0.076 0.016 0.892 0.000 0.016
#> SRR1656575 1 0.3033 0.852 0.876 0.016 0.032 0.000 0.076
#> SRR1656576 4 0.1043 0.897 0.000 0.000 0.000 0.960 0.040
#> SRR1656578 4 0.0162 0.915 0.000 0.000 0.004 0.996 0.000
#> SRR1656577 3 0.0000 0.878 0.000 0.000 1.000 0.000 0.000
#> SRR1656579 4 0.2514 0.852 0.000 0.044 0.000 0.896 0.060
#> SRR1656580 3 0.5701 0.609 0.104 0.016 0.652 0.000 0.228
#> SRR1656581 5 0.2074 0.927 0.000 0.044 0.000 0.036 0.920
#> SRR1656582 4 0.1041 0.914 0.032 0.004 0.000 0.964 0.000
#> SRR1656585 5 0.2230 0.922 0.000 0.044 0.044 0.000 0.912
#> SRR1656584 1 0.1168 0.861 0.960 0.000 0.008 0.000 0.032
#> SRR1656583 3 0.3613 0.872 0.104 0.024 0.840 0.000 0.032
#> SRR1656586 2 0.2230 0.947 0.000 0.884 0.000 0.116 0.000
#> SRR1656587 3 0.4041 0.852 0.100 0.020 0.816 0.000 0.064
#> SRR1656588 4 0.3880 0.736 0.000 0.044 0.004 0.800 0.152
#> SRR1656589 2 0.3488 0.892 0.024 0.808 0.000 0.168 0.000
#> SRR1656590 3 0.2932 0.876 0.104 0.000 0.864 0.000 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 4 0.0458 0.8178 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656464 6 0.3351 -0.0732 0.000 0.000 0.288 0.000 0.000 0.712
#> SRR1656462 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656465 5 0.0632 0.8058 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1656467 4 0.2778 0.8411 0.168 0.000 0.000 0.824 0.008 0.000
#> SRR1656466 5 0.1285 0.7982 0.000 0.004 0.052 0.000 0.944 0.000
#> SRR1656468 5 0.4710 0.6993 0.176 0.000 0.116 0.008 0.700 0.000
#> SRR1656472 6 0.1779 0.6313 0.000 0.016 0.064 0.000 0.000 0.920
#> SRR1656471 6 0.4932 0.2395 0.000 0.000 0.072 0.000 0.372 0.556
#> SRR1656470 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656469 5 0.0458 0.8062 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1656473 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656474 2 0.3737 0.3555 0.000 0.608 0.000 0.392 0.000 0.000
#> SRR1656475 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656478 1 0.3163 0.7804 0.764 0.000 0.000 0.000 0.004 0.232
#> SRR1656477 5 0.3393 0.7659 0.020 0.000 0.140 0.008 0.820 0.012
#> SRR1656479 5 0.1957 0.7969 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1656480 5 0.5670 0.6440 0.176 0.000 0.128 0.056 0.640 0.000
#> SRR1656476 4 0.0935 0.8119 0.000 0.032 0.004 0.964 0.000 0.000
#> SRR1656481 5 0.3358 0.7678 0.052 0.000 0.116 0.008 0.824 0.000
#> SRR1656482 4 0.0810 0.8237 0.008 0.008 0.000 0.976 0.004 0.004
#> SRR1656483 4 0.0458 0.8194 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656485 5 0.3052 0.7277 0.000 0.000 0.004 0.000 0.780 0.216
#> SRR1656487 5 0.0937 0.8044 0.000 0.000 0.040 0.000 0.960 0.000
#> SRR1656486 5 0.5097 -0.0303 0.420 0.004 0.068 0.000 0.508 0.000
#> SRR1656488 5 0.3542 0.7373 0.000 0.000 0.052 0.000 0.788 0.160
#> SRR1656484 5 0.4672 0.7075 0.000 0.004 0.152 0.000 0.700 0.144
#> SRR1656489 1 0.6124 0.5424 0.488 0.004 0.056 0.000 0.076 0.376
#> SRR1656491 5 0.1714 0.8015 0.000 0.000 0.092 0.000 0.908 0.000
#> SRR1656490 5 0.2219 0.7916 0.000 0.000 0.136 0.000 0.864 0.000
#> SRR1656492 5 0.1728 0.7935 0.008 0.004 0.064 0.000 0.924 0.000
#> SRR1656493 6 0.3314 0.4513 0.224 0.000 0.012 0.000 0.000 0.764
#> SRR1656495 6 0.0665 0.6496 0.000 0.008 0.004 0.000 0.008 0.980
#> SRR1656496 5 0.2442 0.7761 0.000 0.004 0.144 0.000 0.852 0.000
#> SRR1656494 4 0.5795 0.7471 0.176 0.000 0.092 0.660 0.040 0.032
#> SRR1656497 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656499 5 0.3523 0.7299 0.000 0.000 0.040 0.000 0.780 0.180
#> SRR1656500 6 0.4537 -0.3396 0.000 0.000 0.412 0.000 0.036 0.552
#> SRR1656501 1 0.4716 0.7521 0.756 0.008 0.060 0.000 0.076 0.100
#> SRR1656498 6 0.2946 0.5485 0.176 0.000 0.012 0.000 0.000 0.812
#> SRR1656504 4 0.0935 0.8119 0.000 0.032 0.004 0.964 0.000 0.000
#> SRR1656502 6 0.1779 0.6313 0.000 0.016 0.064 0.000 0.000 0.920
#> SRR1656503 5 0.3477 0.7589 0.056 0.004 0.132 0.000 0.808 0.000
#> SRR1656507 1 0.4021 0.7779 0.780 0.004 0.040 0.000 0.024 0.152
#> SRR1656508 6 0.1679 0.6510 0.012 0.000 0.036 0.000 0.016 0.936
#> SRR1656505 5 0.5519 0.6558 0.176 0.000 0.112 0.056 0.656 0.000
#> SRR1656506 5 0.1007 0.7993 0.000 0.000 0.044 0.000 0.956 0.000
#> SRR1656509 6 0.4495 0.3713 0.000 0.000 0.064 0.000 0.276 0.660
#> SRR1656510 4 0.5998 0.5675 0.192 0.000 0.028 0.560 0.220 0.000
#> SRR1656511 4 0.2946 0.8382 0.176 0.000 0.000 0.812 0.012 0.000
#> SRR1656513 4 0.2879 0.8393 0.176 0.000 0.000 0.816 0.004 0.004
#> SRR1656512 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656514 3 0.3747 0.9771 0.000 0.000 0.604 0.000 0.000 0.396
#> SRR1656515 4 0.2669 0.8420 0.156 0.000 0.000 0.836 0.008 0.000
#> SRR1656516 1 0.4641 0.7744 0.736 0.008 0.076 0.000 0.020 0.160
#> SRR1656518 1 0.5113 0.6795 0.704 0.008 0.080 0.000 0.168 0.040
#> SRR1656517 1 0.3742 0.6526 0.648 0.000 0.004 0.000 0.000 0.348
#> SRR1656519 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656522 3 0.3833 0.8960 0.000 0.000 0.556 0.000 0.000 0.444
#> SRR1656523 4 0.5178 0.7615 0.176 0.000 0.060 0.688 0.076 0.000
#> SRR1656521 2 0.0937 0.9134 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1656520 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656524 6 0.3368 0.4319 0.232 0.000 0.012 0.000 0.000 0.756
#> SRR1656525 5 0.3044 0.7684 0.000 0.000 0.048 0.000 0.836 0.116
#> SRR1656526 4 0.0260 0.8202 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1656527 4 0.0520 0.8199 0.000 0.008 0.000 0.984 0.000 0.008
#> SRR1656530 5 0.1349 0.7972 0.000 0.004 0.056 0.000 0.940 0.000
#> SRR1656529 5 0.0363 0.8060 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1656531 6 0.0260 0.6490 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1656528 5 0.3417 0.7414 0.000 0.000 0.044 0.000 0.796 0.160
#> SRR1656534 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656533 1 0.3101 0.7765 0.756 0.000 0.000 0.000 0.000 0.244
#> SRR1656536 5 0.1957 0.7876 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1656532 4 0.2848 0.8388 0.176 0.000 0.000 0.816 0.000 0.008
#> SRR1656537 6 0.1398 0.6440 0.052 0.000 0.008 0.000 0.000 0.940
#> SRR1656538 5 0.5803 0.5811 0.088 0.004 0.068 0.000 0.628 0.212
#> SRR1656535 4 0.0260 0.8202 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1656539 5 0.0146 0.8070 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1656544 5 0.3564 0.6695 0.000 0.000 0.012 0.000 0.724 0.264
#> SRR1656542 5 0.3777 0.7314 0.000 0.004 0.056 0.000 0.776 0.164
#> SRR1656543 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656545 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656540 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656546 1 0.2199 0.6123 0.892 0.000 0.020 0.000 0.000 0.088
#> SRR1656541 4 0.0260 0.8202 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1656547 4 0.3452 0.8310 0.176 0.000 0.008 0.792 0.024 0.000
#> SRR1656548 5 0.2724 0.7820 0.000 0.000 0.052 0.000 0.864 0.084
#> SRR1656549 1 0.6071 0.5812 0.556 0.008 0.096 0.000 0.296 0.044
#> SRR1656551 5 0.1957 0.7885 0.000 0.000 0.112 0.000 0.888 0.000
#> SRR1656553 5 0.4873 0.6848 0.000 0.004 0.160 0.000 0.676 0.160
#> SRR1656550 5 0.5568 0.6767 0.156 0.000 0.140 0.008 0.660 0.036
#> SRR1656552 4 0.2597 0.8393 0.176 0.000 0.000 0.824 0.000 0.000
#> SRR1656554 5 0.1007 0.8036 0.000 0.000 0.044 0.000 0.956 0.000
#> SRR1656555 5 0.5996 0.6112 0.176 0.000 0.112 0.096 0.616 0.000
#> SRR1656556 6 0.3361 0.5591 0.000 0.000 0.076 0.000 0.108 0.816
#> SRR1656557 3 0.3862 0.9838 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1656558 1 0.3834 0.7602 0.708 0.000 0.024 0.000 0.000 0.268
#> SRR1656559 3 0.3747 0.9771 0.000 0.000 0.604 0.000 0.000 0.396
#> SRR1656560 5 0.3417 0.7414 0.000 0.000 0.044 0.000 0.796 0.160
#> SRR1656561 5 0.3403 0.7730 0.012 0.004 0.068 0.000 0.836 0.080
#> SRR1656562 4 0.4431 0.8013 0.176 0.000 0.036 0.740 0.048 0.000
#> SRR1656563 1 0.6336 0.6957 0.568 0.004 0.072 0.000 0.132 0.224
#> SRR1656564 4 0.0458 0.8194 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656565 4 0.3197 0.8361 0.176 0.000 0.008 0.804 0.012 0.000
#> SRR1656566 1 0.3971 0.7594 0.704 0.000 0.024 0.000 0.004 0.268
#> SRR1656568 4 0.0520 0.8199 0.000 0.008 0.000 0.984 0.000 0.008
#> SRR1656567 4 0.6514 0.6138 0.176 0.000 0.112 0.556 0.156 0.000
#> SRR1656569 5 0.0937 0.8041 0.000 0.000 0.040 0.000 0.960 0.000
#> SRR1656570 1 0.6144 0.6993 0.620 0.008 0.088 0.000 0.124 0.160
#> SRR1656571 4 0.0458 0.8194 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1656573 5 0.2165 0.7861 0.000 0.000 0.108 0.008 0.884 0.000
#> SRR1656572 4 0.2879 0.8393 0.176 0.000 0.000 0.816 0.004 0.004
#> SRR1656574 6 0.4172 -0.5354 0.000 0.000 0.460 0.000 0.012 0.528
#> SRR1656575 1 0.4773 0.7782 0.692 0.004 0.036 0.000 0.036 0.232
#> SRR1656576 4 0.2848 0.8391 0.176 0.000 0.000 0.816 0.008 0.000
#> SRR1656578 4 0.3099 0.8395 0.176 0.008 0.000 0.808 0.000 0.008
#> SRR1656577 3 0.3756 0.9727 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR1656579 4 0.5573 0.7317 0.176 0.000 0.112 0.652 0.060 0.000
#> SRR1656580 5 0.5453 0.3117 0.020 0.004 0.060 0.000 0.516 0.400
#> SRR1656581 5 0.4710 0.6956 0.176 0.000 0.116 0.008 0.700 0.000
#> SRR1656582 4 0.0260 0.8202 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR1656585 5 0.3558 0.7537 0.000 0.000 0.112 0.000 0.800 0.088
#> SRR1656584 1 0.3665 0.7715 0.728 0.000 0.020 0.000 0.000 0.252
#> SRR1656583 6 0.2224 0.6267 0.000 0.012 0.064 0.000 0.020 0.904
#> SRR1656586 2 0.0632 0.9243 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1656587 6 0.3055 0.6135 0.000 0.000 0.068 0.008 0.072 0.852
#> SRR1656588 4 0.7238 0.3792 0.176 0.000 0.140 0.420 0.264 0.000
#> SRR1656589 2 0.2664 0.8020 0.000 0.816 0.000 0.184 0.000 0.000
#> SRR1656590 6 0.1167 0.6559 0.008 0.000 0.012 0.000 0.020 0.960
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 13572 rows and 129 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 0.827 0.890 0.954 0.4810 0.522 0.522
#> 3 3 0.433 0.629 0.801 0.3523 0.718 0.513
#> 4 4 0.527 0.537 0.746 0.1511 0.674 0.297
#> 5 5 0.522 0.410 0.639 0.0634 0.872 0.558
#> 6 6 0.595 0.519 0.686 0.0356 0.893 0.560
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
#> SRR1656463 1 0.2236 0.92518 0.964 0.036
#> SRR1656464 2 0.0000 0.95886 0.000 1.000
#> SRR1656462 2 0.0376 0.95682 0.004 0.996
#> SRR1656465 1 0.0000 0.94459 1.000 0.000
#> SRR1656467 1 0.0000 0.94459 1.000 0.000
#> SRR1656466 1 0.0000 0.94459 1.000 0.000
#> SRR1656468 1 0.0000 0.94459 1.000 0.000
#> SRR1656472 2 0.0000 0.95886 0.000 1.000
#> SRR1656471 1 0.0000 0.94459 1.000 0.000
#> SRR1656470 1 0.9896 0.25954 0.560 0.440
#> SRR1656469 1 0.0000 0.94459 1.000 0.000
#> SRR1656473 2 0.0000 0.95886 0.000 1.000
#> SRR1656474 2 0.0000 0.95886 0.000 1.000
#> SRR1656475 2 0.0000 0.95886 0.000 1.000
#> SRR1656478 2 0.0000 0.95886 0.000 1.000
#> SRR1656477 1 0.0000 0.94459 1.000 0.000
#> SRR1656479 1 0.0000 0.94459 1.000 0.000
#> SRR1656480 1 0.0000 0.94459 1.000 0.000
#> SRR1656476 1 0.0000 0.94459 1.000 0.000
#> SRR1656481 1 0.0000 0.94459 1.000 0.000
#> SRR1656482 1 0.2603 0.91903 0.956 0.044
#> SRR1656483 2 0.9209 0.45837 0.336 0.664
#> SRR1656485 1 0.0000 0.94459 1.000 0.000
#> SRR1656487 1 0.0000 0.94459 1.000 0.000
#> SRR1656486 1 0.0000 0.94459 1.000 0.000
#> SRR1656488 1 0.0000 0.94459 1.000 0.000
#> SRR1656484 1 0.4161 0.88863 0.916 0.084
#> SRR1656489 2 0.2043 0.93488 0.032 0.968
#> SRR1656491 1 0.0000 0.94459 1.000 0.000
#> SRR1656490 1 0.1414 0.93410 0.980 0.020
#> SRR1656492 1 0.0000 0.94459 1.000 0.000
#> SRR1656493 2 0.0000 0.95886 0.000 1.000
#> SRR1656495 2 0.0000 0.95886 0.000 1.000
#> SRR1656496 1 0.0376 0.94268 0.996 0.004
#> SRR1656494 2 0.0000 0.95886 0.000 1.000
#> SRR1656497 1 0.0000 0.94459 1.000 0.000
#> SRR1656499 1 0.0000 0.94459 1.000 0.000
#> SRR1656500 1 0.5059 0.86339 0.888 0.112
#> SRR1656501 1 0.4562 0.87858 0.904 0.096
#> SRR1656498 2 0.0000 0.95886 0.000 1.000
#> SRR1656504 1 0.0000 0.94459 1.000 0.000
#> SRR1656502 2 0.0000 0.95886 0.000 1.000
#> SRR1656503 1 0.7602 0.73679 0.780 0.220
#> SRR1656507 2 0.9983 0.00863 0.476 0.524
#> SRR1656508 2 0.0000 0.95886 0.000 1.000
#> SRR1656505 1 0.0000 0.94459 1.000 0.000
#> SRR1656506 1 0.0000 0.94459 1.000 0.000
#> SRR1656509 2 0.2423 0.92748 0.040 0.960
#> SRR1656510 1 0.0000 0.94459 1.000 0.000
#> SRR1656511 1 0.0000 0.94459 1.000 0.000
#> SRR1656513 2 0.0376 0.95678 0.004 0.996
#> SRR1656512 2 0.0000 0.95886 0.000 1.000
#> SRR1656514 2 0.0000 0.95886 0.000 1.000
#> SRR1656515 1 0.0000 0.94459 1.000 0.000
#> SRR1656516 1 0.8267 0.67682 0.740 0.260
#> SRR1656518 1 0.6973 0.78114 0.812 0.188
#> SRR1656517 2 0.0000 0.95886 0.000 1.000
#> SRR1656519 1 0.6247 0.81803 0.844 0.156
#> SRR1656522 2 0.0000 0.95886 0.000 1.000
#> SRR1656523 1 0.0000 0.94459 1.000 0.000
#> SRR1656521 2 0.1414 0.94480 0.020 0.980
#> SRR1656520 2 0.8144 0.63454 0.252 0.748
#> SRR1656524 2 0.0000 0.95886 0.000 1.000
#> SRR1656525 1 0.0000 0.94459 1.000 0.000
#> SRR1656526 1 0.0000 0.94459 1.000 0.000
#> SRR1656527 2 0.0000 0.95886 0.000 1.000
#> SRR1656530 1 0.0000 0.94459 1.000 0.000
#> SRR1656529 1 0.0000 0.94459 1.000 0.000
#> SRR1656531 2 0.0000 0.95886 0.000 1.000
#> SRR1656528 1 0.0000 0.94459 1.000 0.000
#> SRR1656534 1 0.8207 0.68356 0.744 0.256
#> SRR1656533 2 0.0000 0.95886 0.000 1.000
#> SRR1656536 1 0.0000 0.94459 1.000 0.000
#> SRR1656532 2 0.0000 0.95886 0.000 1.000
#> SRR1656537 2 0.0000 0.95886 0.000 1.000
#> SRR1656538 1 0.0000 0.94459 1.000 0.000
#> SRR1656535 1 0.0938 0.93875 0.988 0.012
#> SRR1656539 1 0.0000 0.94459 1.000 0.000
#> SRR1656544 1 0.3584 0.90127 0.932 0.068
#> SRR1656542 1 0.2423 0.92162 0.960 0.040
#> SRR1656543 1 0.9850 0.31020 0.572 0.428
#> SRR1656545 1 0.0000 0.94459 1.000 0.000
#> SRR1656540 2 0.0672 0.95430 0.008 0.992
#> SRR1656546 1 0.9850 0.30928 0.572 0.428
#> SRR1656541 1 0.0000 0.94459 1.000 0.000
#> SRR1656547 1 0.0000 0.94459 1.000 0.000
#> SRR1656548 1 0.0000 0.94459 1.000 0.000
#> SRR1656549 1 0.0376 0.94265 0.996 0.004
#> SRR1656551 1 0.0000 0.94459 1.000 0.000
#> SRR1656553 1 0.9815 0.33232 0.580 0.420
#> SRR1656550 1 0.0000 0.94459 1.000 0.000
#> SRR1656552 1 0.0000 0.94459 1.000 0.000
#> SRR1656554 1 0.0000 0.94459 1.000 0.000
#> SRR1656555 1 0.0000 0.94459 1.000 0.000
#> SRR1656556 1 0.9686 0.39436 0.604 0.396
#> SRR1656557 2 0.1843 0.93837 0.028 0.972
#> SRR1656558 2 0.0000 0.95886 0.000 1.000
#> SRR1656559 2 0.0000 0.95886 0.000 1.000
#> SRR1656560 1 0.0000 0.94459 1.000 0.000
#> SRR1656561 1 0.0000 0.94459 1.000 0.000
#> SRR1656562 2 1.0000 -0.04371 0.496 0.504
#> SRR1656563 1 0.2603 0.91869 0.956 0.044
#> SRR1656564 2 0.0376 0.95666 0.004 0.996
#> SRR1656565 1 0.6148 0.82231 0.848 0.152
#> SRR1656566 2 0.0000 0.95886 0.000 1.000
#> SRR1656568 2 0.0000 0.95886 0.000 1.000
#> SRR1656567 1 0.0000 0.94459 1.000 0.000
#> SRR1656569 1 0.0000 0.94459 1.000 0.000
#> SRR1656570 1 0.4022 0.88920 0.920 0.080
#> SRR1656571 2 0.0000 0.95886 0.000 1.000
#> SRR1656573 1 0.0000 0.94459 1.000 0.000
#> SRR1656572 2 0.0672 0.95429 0.008 0.992
#> SRR1656574 2 0.5408 0.83359 0.124 0.876
#> SRR1656575 2 0.0376 0.95682 0.004 0.996
#> SRR1656576 1 0.0000 0.94459 1.000 0.000
#> SRR1656578 2 0.0000 0.95886 0.000 1.000
#> SRR1656577 2 0.0000 0.95886 0.000 1.000
#> SRR1656579 1 0.0000 0.94459 1.000 0.000
#> SRR1656580 1 0.5059 0.86402 0.888 0.112
#> SRR1656581 1 0.0000 0.94459 1.000 0.000
#> SRR1656582 1 0.0000 0.94459 1.000 0.000
#> SRR1656585 1 0.1843 0.92987 0.972 0.028
#> SRR1656584 2 0.0000 0.95886 0.000 1.000
#> SRR1656583 2 0.0938 0.95143 0.012 0.988
#> SRR1656586 2 0.0000 0.95886 0.000 1.000
#> SRR1656587 2 0.0000 0.95886 0.000 1.000
#> SRR1656588 1 0.0000 0.94459 1.000 0.000
#> SRR1656589 2 0.0000 0.95886 0.000 1.000
#> SRR1656590 2 0.0000 0.95886 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1656463 3 0.4569 0.7894 0.068 0.072 0.860
#> SRR1656464 2 0.4399 0.5533 0.188 0.812 0.000
#> SRR1656462 2 0.2982 0.6887 0.024 0.920 0.056
#> SRR1656465 3 0.2959 0.7653 0.000 0.100 0.900
#> SRR1656467 2 0.6309 0.0677 0.000 0.504 0.496
#> SRR1656466 3 0.4575 0.7695 0.184 0.004 0.812
#> SRR1656468 3 0.4291 0.7726 0.180 0.000 0.820
#> SRR1656472 2 0.3879 0.5933 0.152 0.848 0.000
#> SRR1656471 2 0.6225 0.2817 0.000 0.568 0.432
#> SRR1656470 2 0.4555 0.6720 0.000 0.800 0.200
#> SRR1656469 3 0.4504 0.7534 0.196 0.000 0.804
#> SRR1656473 2 0.3941 0.5900 0.156 0.844 0.000
#> SRR1656474 2 0.3267 0.6229 0.116 0.884 0.000
#> SRR1656475 2 0.2173 0.6625 0.048 0.944 0.008
#> SRR1656478 1 0.2400 0.7087 0.932 0.064 0.004
#> SRR1656477 3 0.5291 0.5708 0.000 0.268 0.732
#> SRR1656479 3 0.2066 0.7944 0.000 0.060 0.940
#> SRR1656480 3 0.4654 0.6604 0.000 0.208 0.792
#> SRR1656476 3 0.5098 0.7151 0.248 0.000 0.752
#> SRR1656481 3 0.2663 0.8063 0.024 0.044 0.932
#> SRR1656482 2 0.5497 0.5775 0.000 0.708 0.292
#> SRR1656483 2 0.7489 0.4979 0.256 0.664 0.080
#> SRR1656485 3 0.3551 0.7398 0.000 0.132 0.868
#> SRR1656487 3 0.1129 0.8081 0.004 0.020 0.976
#> SRR1656486 3 0.6295 0.3137 0.472 0.000 0.528
#> SRR1656488 3 0.3192 0.8045 0.112 0.000 0.888
#> SRR1656484 3 0.4569 0.7927 0.072 0.068 0.860
#> SRR1656489 1 0.3644 0.7016 0.872 0.124 0.004
#> SRR1656491 3 0.3551 0.7470 0.000 0.132 0.868
#> SRR1656490 3 0.2492 0.8135 0.048 0.016 0.936
#> SRR1656492 3 0.5621 0.6502 0.308 0.000 0.692
#> SRR1656493 1 0.5291 0.6205 0.732 0.268 0.000
#> SRR1656495 2 0.6267 -0.0933 0.452 0.548 0.000
#> SRR1656496 3 0.3686 0.7880 0.140 0.000 0.860
#> SRR1656494 2 0.2537 0.6928 0.000 0.920 0.080
#> SRR1656497 3 0.1031 0.8060 0.000 0.024 0.976
#> SRR1656499 3 0.3456 0.8013 0.036 0.060 0.904
#> SRR1656500 3 0.6195 0.5575 0.020 0.276 0.704
#> SRR1656501 1 0.4062 0.6358 0.836 0.000 0.164
#> SRR1656498 1 0.5058 0.6400 0.756 0.244 0.000
#> SRR1656504 3 0.5591 0.6532 0.304 0.000 0.696
#> SRR1656502 2 0.4121 0.5763 0.168 0.832 0.000
#> SRR1656503 1 0.6420 0.5219 0.688 0.024 0.288
#> SRR1656507 1 0.3192 0.6679 0.888 0.000 0.112
#> SRR1656508 1 0.6274 0.3235 0.544 0.456 0.000
#> SRR1656505 3 0.2703 0.8131 0.056 0.016 0.928
#> SRR1656506 3 0.0747 0.8134 0.016 0.000 0.984
#> SRR1656509 2 0.3784 0.6903 0.004 0.864 0.132
#> SRR1656510 3 0.6280 0.3710 0.460 0.000 0.540
#> SRR1656511 3 0.4605 0.7416 0.204 0.000 0.796
#> SRR1656513 2 0.3530 0.6691 0.068 0.900 0.032
#> SRR1656512 1 0.6421 0.3958 0.572 0.424 0.004
#> SRR1656514 2 0.2448 0.6491 0.076 0.924 0.000
#> SRR1656515 3 0.4045 0.7734 0.024 0.104 0.872
#> SRR1656516 1 0.3816 0.6479 0.852 0.000 0.148
#> SRR1656518 1 0.3752 0.6505 0.856 0.000 0.144
#> SRR1656517 1 0.2878 0.7059 0.904 0.096 0.000
#> SRR1656519 2 0.5845 0.5502 0.004 0.688 0.308
#> SRR1656522 2 0.6095 0.1284 0.392 0.608 0.000
#> SRR1656523 3 0.3500 0.7970 0.116 0.004 0.880
#> SRR1656521 1 0.2261 0.6859 0.932 0.000 0.068
#> SRR1656520 2 0.4555 0.6712 0.000 0.800 0.200
#> SRR1656524 1 0.5138 0.6352 0.748 0.252 0.000
#> SRR1656525 3 0.3340 0.7965 0.120 0.000 0.880
#> SRR1656526 3 0.3941 0.7792 0.156 0.000 0.844
#> SRR1656527 1 0.3816 0.6889 0.852 0.148 0.000
#> SRR1656530 3 0.4452 0.7651 0.192 0.000 0.808
#> SRR1656529 3 0.0000 0.8117 0.000 0.000 1.000
#> SRR1656531 2 0.6309 -0.2384 0.496 0.504 0.000
#> SRR1656528 3 0.0000 0.8117 0.000 0.000 1.000
#> SRR1656534 2 0.5953 0.5958 0.012 0.708 0.280
#> SRR1656533 1 0.2537 0.7088 0.920 0.080 0.000
#> SRR1656536 3 0.3941 0.7173 0.000 0.156 0.844
#> SRR1656532 1 0.5363 0.6127 0.724 0.276 0.000
#> SRR1656537 1 0.5621 0.5772 0.692 0.308 0.000
#> SRR1656538 3 0.5926 0.5858 0.356 0.000 0.644
#> SRR1656535 1 0.5098 0.5091 0.752 0.000 0.248
#> SRR1656539 3 0.4605 0.6656 0.000 0.204 0.796
#> SRR1656544 3 0.4702 0.6708 0.000 0.212 0.788
#> SRR1656542 3 0.4465 0.7750 0.176 0.004 0.820
#> SRR1656543 2 0.5331 0.6761 0.024 0.792 0.184
#> SRR1656545 3 0.4978 0.6527 0.004 0.216 0.780
#> SRR1656540 2 0.4099 0.6883 0.008 0.852 0.140
#> SRR1656546 1 0.3686 0.6530 0.860 0.000 0.140
#> SRR1656541 3 0.4842 0.7350 0.224 0.000 0.776
#> SRR1656547 3 0.1129 0.8091 0.004 0.020 0.976
#> SRR1656548 3 0.3816 0.7850 0.148 0.000 0.852
#> SRR1656549 1 0.5058 0.5421 0.756 0.000 0.244
#> SRR1656551 3 0.1289 0.8035 0.000 0.032 0.968
#> SRR1656553 1 0.7797 0.6005 0.672 0.140 0.188
#> SRR1656550 3 0.5706 0.4714 0.000 0.320 0.680
#> SRR1656552 3 0.5560 0.6630 0.300 0.000 0.700
#> SRR1656554 3 0.2261 0.7862 0.000 0.068 0.932
#> SRR1656555 3 0.3941 0.7792 0.156 0.000 0.844
#> SRR1656556 2 0.4796 0.6606 0.000 0.780 0.220
#> SRR1656557 2 0.3310 0.6896 0.028 0.908 0.064
#> SRR1656558 1 0.0237 0.7008 0.996 0.000 0.004
#> SRR1656559 2 0.6244 -0.0241 0.440 0.560 0.000
#> SRR1656560 3 0.2492 0.8136 0.048 0.016 0.936
#> SRR1656561 3 0.4887 0.7306 0.228 0.000 0.772
#> SRR1656562 1 0.9851 0.2784 0.420 0.296 0.284
#> SRR1656563 1 0.6416 0.3072 0.616 0.008 0.376
#> SRR1656564 2 0.5558 0.6110 0.152 0.800 0.048
#> SRR1656565 3 0.7083 0.3117 0.028 0.380 0.592
#> SRR1656566 1 0.2711 0.7077 0.912 0.088 0.000
#> SRR1656568 1 0.5016 0.6428 0.760 0.240 0.000
#> SRR1656567 3 0.3826 0.7509 0.008 0.124 0.868
#> SRR1656569 3 0.0237 0.8122 0.004 0.000 0.996
#> SRR1656570 1 0.5988 0.4944 0.688 0.008 0.304
#> SRR1656571 1 0.5178 0.6323 0.744 0.256 0.000
#> SRR1656573 3 0.0892 0.8072 0.000 0.020 0.980
#> SRR1656572 1 0.2096 0.6925 0.944 0.004 0.052
#> SRR1656574 2 0.6322 0.4017 0.276 0.700 0.024
#> SRR1656575 1 0.2860 0.7095 0.912 0.084 0.004
#> SRR1656576 3 0.4887 0.7311 0.228 0.000 0.772
#> SRR1656578 1 0.5882 0.5245 0.652 0.348 0.000
#> SRR1656577 1 0.6180 0.4001 0.584 0.416 0.000
#> SRR1656579 3 0.1163 0.8050 0.000 0.028 0.972
#> SRR1656580 3 0.7044 0.5329 0.348 0.032 0.620
#> SRR1656581 3 0.0983 0.8134 0.016 0.004 0.980
#> SRR1656582 3 0.1482 0.8134 0.020 0.012 0.968
#> SRR1656585 2 0.5650 0.5465 0.000 0.688 0.312
#> SRR1656584 1 0.1163 0.6971 0.972 0.000 0.028
#> SRR1656583 2 0.5016 0.6431 0.000 0.760 0.240
#> SRR1656586 2 0.2066 0.6906 0.000 0.940 0.060
#> SRR1656587 2 0.3619 0.6099 0.136 0.864 0.000
#> SRR1656588 3 0.6062 0.3134 0.000 0.384 0.616
#> SRR1656589 1 0.4931 0.6489 0.768 0.232 0.000
#> SRR1656590 1 0.6305 0.2396 0.516 0.484 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1656463 3 0.5167 0.5112 0.000 0.340 0.644 0.016
#> SRR1656464 1 0.5217 0.4241 0.608 0.012 0.380 0.000
#> SRR1656462 3 0.3528 0.5834 0.192 0.000 0.808 0.000
#> SRR1656465 3 0.6816 0.5215 0.000 0.184 0.604 0.212
#> SRR1656467 3 0.3764 0.6933 0.040 0.000 0.844 0.116
#> SRR1656466 2 0.5750 -0.1598 0.000 0.532 0.440 0.028
#> SRR1656468 2 0.7102 0.2344 0.000 0.540 0.156 0.304
#> SRR1656472 1 0.3873 0.5995 0.772 0.000 0.228 0.000
#> SRR1656471 3 0.4740 0.6667 0.080 0.000 0.788 0.132
#> SRR1656470 3 0.2216 0.6736 0.092 0.000 0.908 0.000
#> SRR1656469 4 0.3812 0.7719 0.000 0.140 0.028 0.832
#> SRR1656473 1 0.3831 0.6105 0.792 0.004 0.204 0.000
#> SRR1656474 1 0.4535 0.5248 0.704 0.004 0.292 0.000
#> SRR1656475 1 0.5449 0.4903 0.676 0.004 0.288 0.032
#> SRR1656478 2 0.4431 0.4028 0.304 0.696 0.000 0.000
#> SRR1656477 3 0.4292 0.6695 0.008 0.016 0.796 0.180
#> SRR1656479 4 0.1484 0.8328 0.020 0.004 0.016 0.960
#> SRR1656480 4 0.3683 0.7989 0.016 0.016 0.112 0.856
#> SRR1656476 2 0.6797 0.1582 0.000 0.536 0.108 0.356
#> SRR1656481 3 0.6170 0.4850 0.000 0.332 0.600 0.068
#> SRR1656482 3 0.2053 0.6883 0.072 0.000 0.924 0.004
#> SRR1656483 3 0.5607 0.2315 0.020 0.484 0.496 0.000
#> SRR1656485 3 0.5540 0.6336 0.000 0.108 0.728 0.164
#> SRR1656487 3 0.6519 0.4689 0.000 0.320 0.584 0.096
#> SRR1656486 4 0.5576 0.6184 0.068 0.212 0.004 0.716
#> SRR1656488 2 0.6885 -0.0562 0.000 0.516 0.372 0.112
#> SRR1656484 2 0.8862 -0.0174 0.056 0.396 0.324 0.224
#> SRR1656489 2 0.4955 0.1684 0.444 0.556 0.000 0.000
#> SRR1656491 4 0.2384 0.8094 0.072 0.004 0.008 0.916
#> SRR1656490 4 0.1362 0.8383 0.020 0.012 0.004 0.964
#> SRR1656492 2 0.4426 0.5046 0.000 0.812 0.092 0.096
#> SRR1656493 1 0.4697 0.3029 0.644 0.356 0.000 0.000
#> SRR1656495 1 0.1377 0.6432 0.964 0.008 0.020 0.008
#> SRR1656496 4 0.2023 0.8339 0.028 0.028 0.004 0.940
#> SRR1656494 3 0.4905 0.2600 0.364 0.000 0.632 0.004
#> SRR1656497 4 0.1598 0.8346 0.020 0.004 0.020 0.956
#> SRR1656499 3 0.5636 0.5366 0.000 0.308 0.648 0.044
#> SRR1656500 3 0.3380 0.7080 0.008 0.088 0.876 0.028
#> SRR1656501 2 0.5031 0.4945 0.212 0.740 0.000 0.048
#> SRR1656498 1 0.4830 0.2275 0.608 0.392 0.000 0.000
#> SRR1656504 2 0.6206 0.3349 0.000 0.632 0.088 0.280
#> SRR1656502 1 0.3726 0.6109 0.788 0.000 0.212 0.000
#> SRR1656503 4 0.6089 0.4609 0.328 0.064 0.000 0.608
#> SRR1656507 2 0.2149 0.5380 0.088 0.912 0.000 0.000
#> SRR1656508 1 0.2830 0.6182 0.904 0.032 0.004 0.060
#> SRR1656505 3 0.6644 0.3652 0.000 0.392 0.520 0.088
#> SRR1656506 4 0.0927 0.8377 0.000 0.008 0.016 0.976
#> SRR1656509 1 0.6058 0.2092 0.536 0.004 0.424 0.036
#> SRR1656510 2 0.3617 0.5300 0.000 0.860 0.076 0.064
#> SRR1656511 4 0.1356 0.8292 0.032 0.008 0.000 0.960
#> SRR1656513 1 0.5586 0.2048 0.528 0.000 0.452 0.020
#> SRR1656512 1 0.4809 0.4035 0.684 0.004 0.004 0.308
#> SRR1656514 3 0.3649 0.5726 0.204 0.000 0.796 0.000
#> SRR1656515 3 0.5720 0.5441 0.000 0.296 0.652 0.052
#> SRR1656516 2 0.5052 0.4741 0.244 0.720 0.000 0.036
#> SRR1656518 2 0.4214 0.4977 0.204 0.780 0.000 0.016
#> SRR1656517 2 0.4790 0.2957 0.380 0.620 0.000 0.000
#> SRR1656519 3 0.1256 0.7089 0.028 0.008 0.964 0.000
#> SRR1656522 1 0.3877 0.6471 0.840 0.048 0.112 0.000
#> SRR1656523 4 0.1305 0.8254 0.036 0.004 0.000 0.960
#> SRR1656521 2 0.3444 0.5064 0.184 0.816 0.000 0.000
#> SRR1656520 3 0.2530 0.6581 0.112 0.000 0.888 0.000
#> SRR1656524 1 0.3764 0.5317 0.816 0.172 0.000 0.012
#> SRR1656525 4 0.3198 0.8083 0.000 0.080 0.040 0.880
#> SRR1656526 4 0.1284 0.8373 0.000 0.024 0.012 0.964
#> SRR1656527 2 0.4961 0.1594 0.448 0.552 0.000 0.000
#> SRR1656530 2 0.6971 0.2887 0.000 0.568 0.156 0.276
#> SRR1656529 4 0.2224 0.8303 0.000 0.032 0.040 0.928
#> SRR1656531 1 0.2643 0.6328 0.916 0.016 0.016 0.052
#> SRR1656528 4 0.2670 0.8248 0.000 0.052 0.040 0.908
#> SRR1656534 3 0.1635 0.7033 0.044 0.008 0.948 0.000
#> SRR1656533 2 0.5388 0.1252 0.456 0.532 0.000 0.012
#> SRR1656536 3 0.5798 0.6148 0.000 0.112 0.704 0.184
#> SRR1656532 1 0.4585 0.3420 0.668 0.332 0.000 0.000
#> SRR1656537 1 0.4049 0.5190 0.780 0.212 0.008 0.000
#> SRR1656538 2 0.5139 0.5218 0.020 0.768 0.040 0.172
#> SRR1656535 2 0.1593 0.5524 0.024 0.956 0.004 0.016
#> SRR1656539 3 0.3877 0.6875 0.000 0.112 0.840 0.048
#> SRR1656544 3 0.6116 0.5804 0.052 0.020 0.672 0.256
#> SRR1656542 2 0.5395 0.4173 0.000 0.736 0.172 0.092
#> SRR1656543 3 0.2227 0.7109 0.036 0.036 0.928 0.000
#> SRR1656545 4 0.4063 0.7225 0.172 0.004 0.016 0.808
#> SRR1656540 3 0.2814 0.6425 0.132 0.000 0.868 0.000
#> SRR1656546 2 0.3808 0.5133 0.176 0.812 0.000 0.012
#> SRR1656541 4 0.6557 0.1096 0.000 0.448 0.076 0.476
#> SRR1656547 4 0.5812 0.6350 0.000 0.136 0.156 0.708
#> SRR1656548 4 0.2949 0.8136 0.000 0.088 0.024 0.888
#> SRR1656549 4 0.4332 0.7414 0.112 0.072 0.000 0.816
#> SRR1656551 4 0.5421 0.6462 0.000 0.076 0.200 0.724
#> SRR1656553 2 0.3994 0.5095 0.028 0.828 0.140 0.004
#> SRR1656550 3 0.1733 0.7216 0.000 0.024 0.948 0.028
#> SRR1656552 2 0.5092 0.4807 0.000 0.764 0.096 0.140
#> SRR1656554 4 0.2644 0.8244 0.000 0.032 0.060 0.908
#> SRR1656555 4 0.0895 0.8379 0.000 0.020 0.004 0.976
#> SRR1656556 3 0.1902 0.6925 0.064 0.004 0.932 0.000
#> SRR1656557 3 0.3266 0.6110 0.168 0.000 0.832 0.000
#> SRR1656558 2 0.5024 0.3338 0.360 0.632 0.000 0.008
#> SRR1656559 1 0.6320 0.5558 0.656 0.204 0.140 0.000
#> SRR1656560 3 0.6926 0.3304 0.000 0.392 0.496 0.112
#> SRR1656561 4 0.1732 0.8325 0.008 0.040 0.004 0.948
#> SRR1656562 4 0.4872 0.4585 0.356 0.004 0.000 0.640
#> SRR1656563 4 0.3217 0.7754 0.128 0.012 0.000 0.860
#> SRR1656564 1 0.6027 0.1490 0.552 0.004 0.036 0.408
#> SRR1656565 3 0.4065 0.7146 0.068 0.044 0.856 0.032
#> SRR1656566 1 0.5388 0.0287 0.532 0.456 0.000 0.012
#> SRR1656568 1 0.5119 0.1157 0.556 0.440 0.004 0.000
#> SRR1656567 3 0.5911 0.5992 0.000 0.196 0.692 0.112
#> SRR1656569 4 0.3810 0.7873 0.000 0.092 0.060 0.848
#> SRR1656570 4 0.3271 0.7710 0.132 0.012 0.000 0.856
#> SRR1656571 2 0.5402 0.0251 0.472 0.516 0.012 0.000
#> SRR1656573 4 0.1833 0.8343 0.000 0.024 0.032 0.944
#> SRR1656572 2 0.5536 0.2908 0.384 0.592 0.000 0.024
#> SRR1656574 1 0.2660 0.6506 0.908 0.012 0.072 0.008
#> SRR1656575 1 0.5308 0.3736 0.684 0.280 0.000 0.036
#> SRR1656576 4 0.1256 0.8329 0.008 0.028 0.000 0.964
#> SRR1656578 1 0.2198 0.6173 0.920 0.072 0.008 0.000
#> SRR1656577 1 0.4462 0.5817 0.792 0.164 0.044 0.000
#> SRR1656579 4 0.1936 0.8341 0.000 0.028 0.032 0.940
#> SRR1656580 4 0.6726 0.3404 0.364 0.100 0.000 0.536
#> SRR1656581 4 0.3004 0.8139 0.000 0.060 0.048 0.892
#> SRR1656582 4 0.0804 0.8366 0.008 0.000 0.012 0.980
#> SRR1656585 4 0.7333 0.2927 0.320 0.004 0.156 0.520
#> SRR1656584 2 0.5110 0.3454 0.352 0.636 0.000 0.012
#> SRR1656583 3 0.5596 0.3312 0.332 0.000 0.632 0.036
#> SRR1656586 3 0.4483 0.4402 0.284 0.000 0.712 0.004
#> SRR1656587 1 0.4250 0.5541 0.724 0.000 0.276 0.000
#> SRR1656588 3 0.2179 0.7152 0.000 0.064 0.924 0.012
#> SRR1656589 2 0.5867 0.4520 0.096 0.688 0.216 0.000
#> SRR1656590 1 0.1510 0.6395 0.956 0.028 0.016 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1656463 1 0.855 0.09557 0.392 0.152 0.268 0.016 0.172
#> SRR1656464 2 0.545 0.48766 0.008 0.668 0.220 0.104 0.000
#> SRR1656462 3 0.402 0.70522 0.000 0.096 0.796 0.108 0.000
#> SRR1656465 3 0.416 0.69439 0.128 0.000 0.800 0.016 0.056
#> SRR1656467 5 0.766 0.01649 0.024 0.348 0.216 0.020 0.392
#> SRR1656466 1 0.457 0.02634 0.576 0.000 0.412 0.000 0.012
#> SRR1656468 1 0.649 0.29286 0.552 0.000 0.208 0.012 0.228
#> SRR1656472 2 0.248 0.56178 0.000 0.900 0.068 0.028 0.004
#> SRR1656471 3 0.384 0.72980 0.000 0.048 0.832 0.092 0.028
#> SRR1656470 3 0.345 0.71024 0.000 0.156 0.816 0.028 0.000
#> SRR1656469 5 0.396 0.57960 0.204 0.012 0.004 0.008 0.772
#> SRR1656473 2 0.541 0.42939 0.000 0.612 0.084 0.304 0.000
#> SRR1656474 2 0.546 0.46749 0.000 0.652 0.136 0.212 0.000
#> SRR1656475 2 0.623 0.26014 0.000 0.484 0.112 0.396 0.008
#> SRR1656478 1 0.538 0.28930 0.632 0.076 0.004 0.288 0.000
#> SRR1656477 3 0.576 0.63401 0.004 0.100 0.696 0.040 0.160
#> SRR1656479 5 0.193 0.69338 0.000 0.016 0.004 0.052 0.928
#> SRR1656480 5 0.684 0.25495 0.008 0.000 0.232 0.312 0.448
#> SRR1656476 1 0.633 0.15273 0.536 0.000 0.108 0.020 0.336
#> SRR1656481 3 0.582 0.29428 0.388 0.004 0.540 0.012 0.056
#> SRR1656482 3 0.279 0.73909 0.000 0.100 0.872 0.028 0.000
#> SRR1656483 3 0.473 0.58893 0.240 0.000 0.700 0.060 0.000
#> SRR1656485 5 0.783 0.18108 0.108 0.104 0.308 0.016 0.464
#> SRR1656487 1 0.734 0.05764 0.372 0.008 0.332 0.012 0.276
#> SRR1656486 5 0.501 0.44555 0.320 0.020 0.000 0.020 0.640
#> SRR1656488 1 0.564 0.10119 0.552 0.000 0.372 0.004 0.072
#> SRR1656484 3 0.667 0.38606 0.204 0.008 0.544 0.236 0.008
#> SRR1656489 2 0.560 0.05991 0.424 0.520 0.004 0.008 0.044
#> SRR1656491 5 0.487 0.32871 0.000 0.004 0.016 0.444 0.536
#> SRR1656490 5 0.120 0.69508 0.000 0.004 0.000 0.040 0.956
#> SRR1656492 1 0.477 0.42393 0.724 0.000 0.096 0.000 0.180
#> SRR1656493 4 0.681 -0.06384 0.324 0.312 0.000 0.364 0.000
#> SRR1656495 2 0.335 0.53908 0.032 0.848 0.004 0.112 0.004
#> SRR1656496 5 0.501 0.50840 0.032 0.000 0.008 0.332 0.628
#> SRR1656494 2 0.451 0.47596 0.000 0.736 0.220 0.020 0.024
#> SRR1656497 4 0.489 -0.24388 0.000 0.000 0.024 0.524 0.452
#> SRR1656499 3 0.349 0.63533 0.228 0.000 0.768 0.000 0.004
#> SRR1656500 3 0.152 0.75658 0.048 0.004 0.944 0.004 0.000
#> SRR1656501 1 0.522 0.20141 0.576 0.032 0.004 0.384 0.004
#> SRR1656498 1 0.682 -0.00392 0.344 0.336 0.000 0.320 0.000
#> SRR1656504 1 0.579 0.28428 0.628 0.000 0.076 0.024 0.272
#> SRR1656502 2 0.268 0.56455 0.004 0.896 0.060 0.036 0.004
#> SRR1656503 5 0.650 0.15901 0.172 0.352 0.000 0.004 0.472
#> SRR1656507 1 0.306 0.43096 0.860 0.020 0.008 0.112 0.000
#> SRR1656508 4 0.507 -0.09498 0.020 0.392 0.000 0.576 0.012
#> SRR1656505 3 0.422 0.54648 0.300 0.000 0.688 0.004 0.008
#> SRR1656506 5 0.342 0.66960 0.008 0.000 0.016 0.152 0.824
#> SRR1656509 4 0.627 -0.04308 0.000 0.076 0.392 0.504 0.028
#> SRR1656510 1 0.403 0.44861 0.812 0.000 0.096 0.012 0.080
#> SRR1656511 5 0.325 0.65030 0.008 0.000 0.000 0.184 0.808
#> SRR1656513 2 0.373 0.53623 0.012 0.832 0.096 0.000 0.060
#> SRR1656512 4 0.435 0.37788 0.008 0.104 0.000 0.784 0.104
#> SRR1656514 3 0.381 0.71232 0.000 0.096 0.812 0.092 0.000
#> SRR1656515 3 0.503 0.51134 0.296 0.004 0.660 0.012 0.028
#> SRR1656516 4 0.575 -0.09509 0.456 0.024 0.024 0.488 0.008
#> SRR1656518 1 0.423 0.42417 0.768 0.184 0.000 0.040 0.008
#> SRR1656517 1 0.595 0.27328 0.584 0.164 0.000 0.252 0.000
#> SRR1656519 3 0.443 0.68420 0.032 0.200 0.752 0.016 0.000
#> SRR1656522 2 0.395 0.53732 0.052 0.816 0.016 0.116 0.000
#> SRR1656523 5 0.382 0.55178 0.000 0.000 0.000 0.304 0.696
#> SRR1656521 1 0.428 0.40390 0.744 0.224 0.004 0.024 0.004
#> SRR1656520 3 0.378 0.64308 0.000 0.236 0.752 0.012 0.000
#> SRR1656524 2 0.678 0.07807 0.232 0.424 0.000 0.340 0.004
#> SRR1656525 5 0.247 0.69051 0.084 0.000 0.008 0.012 0.896
#> SRR1656526 5 0.128 0.69226 0.044 0.000 0.000 0.004 0.952
#> SRR1656527 1 0.644 0.11143 0.448 0.372 0.000 0.180 0.000
#> SRR1656530 1 0.608 0.17501 0.564 0.000 0.324 0.016 0.096
#> SRR1656529 5 0.620 0.45914 0.024 0.000 0.088 0.336 0.552
#> SRR1656531 2 0.258 0.53349 0.044 0.892 0.000 0.000 0.064
#> SRR1656528 5 0.513 0.65340 0.036 0.000 0.084 0.140 0.740
#> SRR1656534 3 0.318 0.74685 0.028 0.108 0.856 0.008 0.000
#> SRR1656533 1 0.646 0.20862 0.500 0.244 0.000 0.256 0.000
#> SRR1656536 3 0.328 0.73657 0.056 0.000 0.868 0.052 0.024
#> SRR1656532 2 0.430 0.44513 0.192 0.752 0.000 0.056 0.000
#> SRR1656537 2 0.641 0.21349 0.244 0.512 0.000 0.244 0.000
#> SRR1656538 1 0.635 0.22365 0.532 0.000 0.132 0.324 0.012
#> SRR1656535 1 0.233 0.45402 0.916 0.004 0.016 0.052 0.012
#> SRR1656539 3 0.254 0.74522 0.044 0.000 0.900 0.052 0.004
#> SRR1656544 3 0.575 0.66581 0.012 0.096 0.716 0.048 0.128
#> SRR1656542 1 0.709 0.36938 0.572 0.100 0.104 0.004 0.220
#> SRR1656543 3 0.496 0.64644 0.056 0.228 0.704 0.012 0.000
#> SRR1656545 5 0.394 0.58602 0.000 0.200 0.000 0.032 0.768
#> SRR1656540 3 0.408 0.63890 0.000 0.228 0.744 0.028 0.000
#> SRR1656546 1 0.442 0.41912 0.760 0.148 0.000 0.092 0.000
#> SRR1656541 5 0.469 0.43022 0.320 0.012 0.008 0.004 0.656
#> SRR1656547 5 0.525 0.55505 0.172 0.060 0.020 0.016 0.732
#> SRR1656548 5 0.299 0.68599 0.100 0.000 0.008 0.024 0.868
#> SRR1656549 5 0.570 0.53014 0.092 0.028 0.000 0.212 0.668
#> SRR1656551 5 0.452 0.62339 0.036 0.004 0.164 0.024 0.772
#> SRR1656553 1 0.447 0.43593 0.772 0.156 0.060 0.004 0.008
#> SRR1656550 3 0.191 0.75657 0.000 0.032 0.932 0.032 0.004
#> SRR1656552 1 0.547 0.29368 0.632 0.036 0.032 0.000 0.300
#> SRR1656554 5 0.532 0.60722 0.016 0.000 0.152 0.124 0.708
#> SRR1656555 5 0.347 0.65807 0.012 0.000 0.004 0.180 0.804
#> SRR1656556 3 0.219 0.74425 0.000 0.092 0.900 0.008 0.000
#> SRR1656557 3 0.359 0.72162 0.000 0.092 0.828 0.080 0.000
#> SRR1656558 1 0.618 0.28141 0.556 0.224 0.000 0.220 0.000
#> SRR1656559 4 0.856 -0.13432 0.196 0.276 0.252 0.276 0.000
#> SRR1656560 3 0.419 0.60136 0.260 0.000 0.720 0.016 0.004
#> SRR1656561 5 0.279 0.69353 0.052 0.000 0.000 0.068 0.880
#> SRR1656562 4 0.386 0.29936 0.004 0.008 0.004 0.764 0.220
#> SRR1656563 4 0.498 0.08580 0.028 0.008 0.000 0.620 0.344
#> SRR1656564 2 0.520 0.29353 0.000 0.616 0.012 0.036 0.336
#> SRR1656565 3 0.540 0.46954 0.056 0.004 0.616 0.320 0.004
#> SRR1656566 1 0.688 0.07859 0.396 0.264 0.000 0.336 0.004
#> SRR1656568 2 0.660 -0.09514 0.392 0.396 0.000 0.212 0.000
#> SRR1656567 3 0.342 0.69698 0.152 0.000 0.824 0.016 0.008
#> SRR1656569 5 0.469 0.66242 0.056 0.000 0.088 0.072 0.784
#> SRR1656570 4 0.464 0.16125 0.024 0.004 0.000 0.664 0.308
#> SRR1656571 4 0.655 0.06129 0.364 0.040 0.088 0.508 0.000
#> SRR1656573 5 0.300 0.68355 0.008 0.000 0.020 0.108 0.864
#> SRR1656572 1 0.616 0.29766 0.576 0.268 0.000 0.148 0.008
#> SRR1656574 4 0.345 0.38242 0.020 0.028 0.084 0.860 0.008
#> SRR1656575 4 0.711 -0.09158 0.336 0.280 0.000 0.372 0.012
#> SRR1656576 5 0.550 0.33152 0.040 0.000 0.012 0.432 0.516
#> SRR1656578 2 0.432 0.48296 0.100 0.780 0.000 0.116 0.004
#> SRR1656577 4 0.750 0.10086 0.208 0.216 0.080 0.496 0.000
#> SRR1656579 5 0.186 0.68781 0.044 0.008 0.004 0.008 0.936
#> SRR1656580 4 0.645 0.37858 0.152 0.028 0.020 0.644 0.156
#> SRR1656581 5 0.234 0.67960 0.072 0.004 0.008 0.008 0.908
#> SRR1656582 5 0.120 0.69535 0.000 0.000 0.004 0.040 0.956
#> SRR1656585 4 0.621 0.27554 0.000 0.024 0.188 0.620 0.168
#> SRR1656584 1 0.595 0.21235 0.532 0.120 0.000 0.348 0.000
#> SRR1656583 2 0.662 0.13950 0.000 0.512 0.356 0.080 0.052
#> SRR1656586 2 0.471 0.39976 0.000 0.684 0.280 0.024 0.012
#> SRR1656587 4 0.681 -0.06639 0.004 0.232 0.372 0.392 0.000
#> SRR1656588 3 0.130 0.76095 0.016 0.028 0.956 0.000 0.000
#> SRR1656589 1 0.657 0.15283 0.500 0.052 0.376 0.072 0.000
#> SRR1656590 2 0.577 0.35503 0.072 0.588 0.008 0.328 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1656463 2 0.8398 0.3390 0.060 0.396 0.188 0.036 0.072 0.248
#> SRR1656464 6 0.4707 0.6393 0.048 0.000 0.096 0.116 0.000 0.740
#> SRR1656462 3 0.2321 0.7600 0.008 0.000 0.900 0.052 0.000 0.040
#> SRR1656465 3 0.5689 0.5516 0.000 0.232 0.628 0.032 0.096 0.012
#> SRR1656467 6 0.6417 0.2921 0.000 0.048 0.092 0.028 0.280 0.552
#> SRR1656466 2 0.3705 0.5538 0.024 0.748 0.224 0.004 0.000 0.000
#> SRR1656468 2 0.2247 0.6407 0.004 0.912 0.024 0.020 0.040 0.000
#> SRR1656472 6 0.1251 0.6619 0.008 0.000 0.012 0.024 0.000 0.956
#> SRR1656471 3 0.5357 0.6711 0.000 0.012 0.708 0.108 0.088 0.084
#> SRR1656470 3 0.3279 0.7227 0.000 0.000 0.796 0.028 0.000 0.176
#> SRR1656469 2 0.5031 -0.0332 0.016 0.504 0.000 0.000 0.440 0.040
#> SRR1656473 6 0.4134 0.5240 0.016 0.000 0.004 0.340 0.000 0.640
#> SRR1656474 6 0.4034 0.5913 0.008 0.000 0.024 0.260 0.000 0.708
#> SRR1656475 6 0.4440 0.4232 0.008 0.000 0.016 0.420 0.000 0.556
#> SRR1656478 1 0.3820 0.7090 0.796 0.056 0.020 0.128 0.000 0.000
#> SRR1656477 3 0.7370 0.2904 0.000 0.040 0.432 0.048 0.260 0.220
#> SRR1656479 5 0.1700 0.6670 0.000 0.012 0.000 0.028 0.936 0.024
#> SRR1656480 4 0.6993 0.0051 0.000 0.048 0.120 0.408 0.388 0.036
#> SRR1656476 2 0.2340 0.6256 0.004 0.896 0.000 0.044 0.056 0.000
#> SRR1656481 2 0.4341 0.3207 0.000 0.620 0.356 0.008 0.012 0.004
#> SRR1656482 3 0.3475 0.7408 0.000 0.008 0.816 0.040 0.004 0.132
#> SRR1656483 3 0.6659 0.1818 0.048 0.356 0.464 0.112 0.000 0.020
#> SRR1656485 5 0.6913 0.1836 0.000 0.080 0.308 0.016 0.476 0.120
#> SRR1656487 2 0.7006 0.3047 0.000 0.456 0.304 0.016 0.160 0.064
#> SRR1656486 5 0.5612 0.1213 0.096 0.432 0.000 0.008 0.460 0.004
#> SRR1656488 2 0.4574 0.3842 0.016 0.636 0.324 0.004 0.020 0.000
#> SRR1656484 3 0.5566 0.5684 0.120 0.036 0.672 0.156 0.016 0.000
#> SRR1656489 1 0.5700 0.5686 0.700 0.060 0.012 0.040 0.048 0.140
#> SRR1656491 5 0.4229 0.4132 0.000 0.012 0.008 0.324 0.652 0.004
#> SRR1656490 5 0.2651 0.6519 0.052 0.016 0.012 0.028 0.892 0.000
#> SRR1656492 2 0.2409 0.6483 0.040 0.904 0.024 0.004 0.028 0.000
#> SRR1656493 1 0.3316 0.7162 0.828 0.000 0.024 0.124 0.000 0.024
#> SRR1656495 6 0.4473 0.6104 0.212 0.008 0.000 0.072 0.000 0.708
#> SRR1656496 5 0.4836 0.3275 0.020 0.032 0.000 0.356 0.592 0.000
#> SRR1656494 6 0.1590 0.6512 0.000 0.008 0.048 0.008 0.000 0.936
#> SRR1656497 5 0.4452 0.2657 0.000 0.004 0.024 0.400 0.572 0.000
#> SRR1656499 3 0.2512 0.7558 0.020 0.048 0.900 0.012 0.020 0.000
#> SRR1656500 3 0.1918 0.7589 0.020 0.024 0.932 0.016 0.004 0.004
#> SRR1656501 1 0.5478 0.4374 0.544 0.128 0.000 0.324 0.000 0.004
#> SRR1656498 1 0.3992 0.7068 0.784 0.008 0.008 0.136 0.000 0.064
#> SRR1656504 2 0.2122 0.6292 0.008 0.912 0.000 0.040 0.040 0.000
#> SRR1656502 6 0.1555 0.6631 0.008 0.000 0.012 0.040 0.000 0.940
#> SRR1656503 5 0.7537 -0.0167 0.180 0.092 0.000 0.024 0.380 0.324
#> SRR1656507 1 0.4368 0.4879 0.640 0.328 0.012 0.020 0.000 0.000
#> SRR1656508 4 0.5586 -0.0156 0.152 0.000 0.000 0.544 0.004 0.300
#> SRR1656505 3 0.3486 0.6875 0.000 0.180 0.788 0.008 0.024 0.000
#> SRR1656506 5 0.4588 0.5982 0.000 0.120 0.000 0.156 0.716 0.008
#> SRR1656509 4 0.5935 0.1984 0.000 0.000 0.204 0.564 0.024 0.208
#> SRR1656510 2 0.1793 0.6318 0.040 0.932 0.008 0.016 0.004 0.000
#> SRR1656511 5 0.2588 0.6352 0.004 0.012 0.000 0.124 0.860 0.000
#> SRR1656513 6 0.8054 0.1911 0.304 0.024 0.076 0.040 0.168 0.388
#> SRR1656512 4 0.4228 0.4889 0.060 0.000 0.000 0.784 0.076 0.080
#> SRR1656514 3 0.2190 0.7623 0.008 0.012 0.916 0.032 0.000 0.032
#> SRR1656515 3 0.4825 0.3744 0.000 0.368 0.588 0.012 0.012 0.020
#> SRR1656516 4 0.6075 0.0324 0.304 0.192 0.000 0.492 0.008 0.004
#> SRR1656518 1 0.4098 0.6151 0.724 0.240 0.000 0.008 0.012 0.016
#> SRR1656517 1 0.3711 0.7245 0.828 0.032 0.044 0.084 0.000 0.012
#> SRR1656519 3 0.3585 0.7323 0.004 0.024 0.824 0.020 0.008 0.120
#> SRR1656522 6 0.4949 0.4918 0.308 0.000 0.016 0.056 0.000 0.620
#> SRR1656523 5 0.4574 0.4091 0.004 0.016 0.000 0.324 0.636 0.020
#> SRR1656521 1 0.5039 0.5820 0.700 0.184 0.004 0.024 0.004 0.084
#> SRR1656520 3 0.2295 0.7587 0.000 0.004 0.900 0.016 0.008 0.072
#> SRR1656524 1 0.4522 0.6582 0.720 0.008 0.000 0.168 0.000 0.104
#> SRR1656525 5 0.1988 0.6692 0.000 0.072 0.004 0.008 0.912 0.004
#> SRR1656526 5 0.1152 0.6688 0.000 0.044 0.000 0.000 0.952 0.004
#> SRR1656527 1 0.2957 0.6992 0.864 0.056 0.000 0.016 0.000 0.064
#> SRR1656530 3 0.5955 0.3029 0.036 0.336 0.536 0.008 0.084 0.000
#> SRR1656529 5 0.5103 0.3366 0.000 0.028 0.040 0.356 0.576 0.000
#> SRR1656531 6 0.3498 0.6293 0.096 0.016 0.000 0.016 0.036 0.836
#> SRR1656528 5 0.3747 0.6231 0.000 0.016 0.072 0.108 0.804 0.000
#> SRR1656534 3 0.2538 0.7547 0.012 0.020 0.892 0.008 0.000 0.068
#> SRR1656533 1 0.2738 0.7267 0.876 0.004 0.016 0.084 0.000 0.020
#> SRR1656536 3 0.3629 0.7378 0.000 0.028 0.828 0.064 0.076 0.004
#> SRR1656532 6 0.4468 0.4437 0.316 0.028 0.000 0.012 0.000 0.644
#> SRR1656537 1 0.4210 0.6732 0.756 0.000 0.008 0.116 0.000 0.120
#> SRR1656538 2 0.6609 0.0385 0.164 0.456 0.032 0.336 0.012 0.000
#> SRR1656535 2 0.4771 0.0625 0.412 0.544 0.008 0.036 0.000 0.000
#> SRR1656539 3 0.1592 0.7620 0.000 0.012 0.944 0.024 0.016 0.004
#> SRR1656544 3 0.5261 0.4410 0.008 0.012 0.600 0.028 0.332 0.020
#> SRR1656542 2 0.6861 0.5310 0.124 0.600 0.024 0.020 0.096 0.136
#> SRR1656543 3 0.3688 0.7181 0.008 0.024 0.804 0.020 0.000 0.144
#> SRR1656545 5 0.3363 0.6157 0.000 0.024 0.000 0.036 0.832 0.108
#> SRR1656540 3 0.3043 0.7087 0.000 0.000 0.792 0.008 0.000 0.200
#> SRR1656546 1 0.3308 0.6717 0.812 0.160 0.000 0.012 0.008 0.008
#> SRR1656541 5 0.5829 0.3738 0.052 0.272 0.004 0.016 0.608 0.048
#> SRR1656547 5 0.5999 0.4622 0.008 0.136 0.020 0.032 0.644 0.160
#> SRR1656548 5 0.3018 0.6511 0.000 0.168 0.000 0.012 0.816 0.004
#> SRR1656549 5 0.6333 0.3199 0.240 0.044 0.000 0.132 0.568 0.016
#> SRR1656551 5 0.3813 0.5639 0.000 0.024 0.180 0.016 0.776 0.004
#> SRR1656553 1 0.5934 0.6041 0.696 0.112 0.068 0.028 0.028 0.068
#> SRR1656550 3 0.1453 0.7619 0.000 0.008 0.944 0.008 0.040 0.000
#> SRR1656552 2 0.4152 0.6199 0.100 0.792 0.008 0.004 0.080 0.016
#> SRR1656554 5 0.3923 0.5735 0.000 0.004 0.144 0.080 0.772 0.000
#> SRR1656555 5 0.3263 0.6167 0.000 0.020 0.004 0.160 0.812 0.004
#> SRR1656556 3 0.0935 0.7641 0.000 0.000 0.964 0.000 0.004 0.032
#> SRR1656557 3 0.2008 0.7627 0.004 0.004 0.920 0.032 0.000 0.040
#> SRR1656558 1 0.1088 0.7271 0.960 0.024 0.000 0.000 0.000 0.016
#> SRR1656559 1 0.5812 0.2401 0.496 0.008 0.400 0.060 0.000 0.036
#> SRR1656560 3 0.3070 0.7467 0.028 0.060 0.868 0.008 0.036 0.000
#> SRR1656561 5 0.3658 0.6442 0.000 0.152 0.000 0.048 0.792 0.008
#> SRR1656562 4 0.4012 0.4752 0.024 0.000 0.000 0.712 0.256 0.008
#> SRR1656563 4 0.5147 0.3690 0.064 0.004 0.000 0.600 0.320 0.012
#> SRR1656564 5 0.7208 0.2874 0.144 0.012 0.040 0.064 0.540 0.200
#> SRR1656565 3 0.4978 0.3639 0.028 0.012 0.576 0.372 0.012 0.000
#> SRR1656566 1 0.3926 0.6951 0.768 0.016 0.000 0.176 0.000 0.040
#> SRR1656568 1 0.2244 0.7259 0.912 0.004 0.016 0.032 0.000 0.036
#> SRR1656567 3 0.4166 0.7018 0.000 0.156 0.772 0.040 0.024 0.008
#> SRR1656569 5 0.4607 0.6179 0.000 0.180 0.028 0.068 0.724 0.000
#> SRR1656570 4 0.4625 0.4792 0.044 0.004 0.000 0.688 0.248 0.016
#> SRR1656571 4 0.7744 0.1678 0.188 0.084 0.084 0.472 0.000 0.172
#> SRR1656573 5 0.2686 0.6602 0.000 0.032 0.012 0.080 0.876 0.000
#> SRR1656572 1 0.5033 0.2864 0.520 0.424 0.000 0.020 0.000 0.036
#> SRR1656574 4 0.5231 0.4516 0.136 0.008 0.164 0.676 0.016 0.000
#> SRR1656575 1 0.5337 0.6135 0.664 0.032 0.000 0.232 0.024 0.048
#> SRR1656576 4 0.6282 0.2556 0.012 0.328 0.000 0.420 0.240 0.000
#> SRR1656578 6 0.4524 0.6125 0.200 0.004 0.000 0.092 0.000 0.704
#> SRR1656577 1 0.6287 0.4564 0.536 0.004 0.156 0.264 0.000 0.040
#> SRR1656579 5 0.3164 0.6572 0.000 0.112 0.004 0.020 0.844 0.020
#> SRR1656580 4 0.5129 0.5491 0.100 0.024 0.000 0.700 0.164 0.012
#> SRR1656581 5 0.2742 0.6566 0.000 0.076 0.020 0.016 0.880 0.008
#> SRR1656582 5 0.2164 0.6686 0.000 0.044 0.000 0.028 0.912 0.016
#> SRR1656585 4 0.6208 0.2581 0.000 0.000 0.236 0.456 0.296 0.012
#> SRR1656584 1 0.4703 0.6436 0.692 0.096 0.000 0.204 0.000 0.008
#> SRR1656583 6 0.4107 0.5856 0.000 0.000 0.156 0.052 0.024 0.768
#> SRR1656586 6 0.2309 0.6353 0.000 0.000 0.084 0.028 0.000 0.888
#> SRR1656587 6 0.6897 0.1452 0.040 0.004 0.260 0.344 0.000 0.352
#> SRR1656588 3 0.2006 0.7639 0.000 0.024 0.924 0.008 0.008 0.036
#> SRR1656589 3 0.6172 0.2445 0.316 0.128 0.520 0.028 0.000 0.008
#> SRR1656590 6 0.5988 0.3499 0.288 0.000 0.004 0.232 0.000 0.476
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