Date: 2019-12-26 01:16:51 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 16873 rows and 72 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] 16873 72
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:kmeans | 3 | 1.000 | 0.972 | 0.977 | ** | |
| CV:NMF | 2 | 1.000 | 0.949 | 0.980 | ** | |
| MAD:kmeans | 3 | 1.000 | 0.980 | 0.991 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.995 | 0.998 | ** | |
| ATC:kmeans | 3 | 1.000 | 0.946 | 0.979 | ** | 2 |
| ATC:skmeans | 2 | 1.000 | 0.988 | 0.995 | ** | |
| CV:pam | 2 | 0.977 | 0.915 | 0.949 | ** | |
| MAD:NMF | 2 | 0.971 | 0.963 | 0.982 | ** | |
| ATC:pam | 4 | 0.952 | 0.914 | 0.963 | ** | |
| ATC:NMF | 2 | 0.945 | 0.951 | 0.980 | * | |
| SD:NMF | 2 | 0.913 | 0.948 | 0.976 | * | |
| SD:skmeans | 4 | 0.908 | 0.853 | 0.930 | * | 2,3 |
| CV:kmeans | 2 | 0.889 | 0.941 | 0.974 | ||
| CV:skmeans | 2 | 0.887 | 0.932 | 0.970 | ||
| MAD:pam | 3 | 0.854 | 0.942 | 0.973 | ||
| CV:mclust | 2 | 0.711 | 0.924 | 0.951 | ||
| ATC:hclust | 3 | 0.709 | 0.844 | 0.913 | ||
| MAD:hclust | 3 | 0.630 | 0.774 | 0.893 | ||
| SD:pam | 2 | 0.622 | 0.917 | 0.952 | ||
| ATC:mclust | 2 | 0.619 | 0.845 | 0.925 | ||
| CV:hclust | 3 | 0.520 | 0.763 | 0.864 | ||
| SD:hclust | 3 | 0.512 | 0.705 | 0.862 | ||
| MAD:mclust | 2 | 0.486 | 0.857 | 0.898 | ||
| SD:mclust | 2 | 0.428 | 0.793 | 0.894 |
**: 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 0.913 0.948 0.976 0.505 0.493 0.493
#> CV:NMF 2 1.000 0.949 0.980 0.498 0.503 0.503
#> MAD:NMF 2 0.971 0.963 0.982 0.506 0.493 0.493
#> ATC:NMF 2 0.945 0.951 0.980 0.502 0.496 0.496
#> SD:skmeans 2 1.000 0.995 0.998 0.507 0.493 0.493
#> CV:skmeans 2 0.887 0.932 0.970 0.505 0.495 0.495
#> MAD:skmeans 2 1.000 0.995 0.998 0.507 0.493 0.493
#> ATC:skmeans 2 1.000 0.988 0.995 0.506 0.495 0.495
#> SD:mclust 2 0.428 0.793 0.894 0.444 0.540 0.540
#> CV:mclust 2 0.711 0.924 0.951 0.484 0.518 0.518
#> MAD:mclust 2 0.486 0.857 0.898 0.468 0.525 0.525
#> ATC:mclust 2 0.619 0.845 0.925 0.466 0.503 0.503
#> SD:kmeans 2 0.803 0.848 0.944 0.494 0.503 0.503
#> CV:kmeans 2 0.889 0.941 0.974 0.480 0.512 0.512
#> MAD:kmeans 2 0.759 0.894 0.950 0.497 0.493 0.493
#> ATC:kmeans 2 1.000 0.992 0.997 0.474 0.525 0.525
#> SD:pam 2 0.622 0.917 0.952 0.490 0.503 0.503
#> CV:pam 2 0.977 0.915 0.949 0.441 0.549 0.549
#> MAD:pam 2 0.838 0.924 0.959 0.490 0.503 0.503
#> ATC:pam 2 0.767 0.908 0.958 0.490 0.512 0.512
#> SD:hclust 2 0.513 0.720 0.875 0.434 0.581 0.581
#> CV:hclust 2 0.419 0.673 0.847 0.423 0.581 0.581
#> MAD:hclust 2 0.453 0.723 0.865 0.455 0.493 0.493
#> ATC:hclust 2 0.815 0.917 0.955 0.418 0.593 0.593
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.463 0.478 0.744 0.261 0.856 0.718
#> CV:NMF 3 0.842 0.856 0.938 0.331 0.797 0.608
#> MAD:NMF 3 0.388 0.453 0.717 0.297 0.783 0.585
#> ATC:NMF 3 0.575 0.751 0.858 0.265 0.788 0.598
#> SD:skmeans 3 0.928 0.851 0.940 0.268 0.848 0.698
#> CV:skmeans 3 0.719 0.785 0.896 0.274 0.802 0.619
#> MAD:skmeans 3 0.826 0.917 0.956 0.267 0.823 0.654
#> ATC:skmeans 3 0.771 0.788 0.909 0.191 0.907 0.814
#> SD:mclust 3 0.423 0.564 0.805 0.221 0.898 0.820
#> CV:mclust 3 0.518 0.540 0.806 0.327 0.737 0.527
#> MAD:mclust 3 0.337 0.710 0.795 0.188 0.619 0.462
#> ATC:mclust 3 0.360 0.555 0.775 0.234 0.710 0.526
#> SD:kmeans 3 1.000 0.972 0.977 0.318 0.654 0.420
#> CV:kmeans 3 0.670 0.772 0.879 0.345 0.656 0.426
#> MAD:kmeans 3 1.000 0.980 0.991 0.318 0.672 0.436
#> ATC:kmeans 3 1.000 0.946 0.979 0.404 0.682 0.462
#> SD:pam 3 0.741 0.899 0.949 0.319 0.678 0.452
#> CV:pam 3 0.813 0.868 0.937 0.420 0.831 0.692
#> MAD:pam 3 0.854 0.942 0.973 0.317 0.678 0.452
#> ATC:pam 3 0.792 0.895 0.951 0.358 0.714 0.492
#> SD:hclust 3 0.512 0.705 0.862 0.455 0.665 0.469
#> CV:hclust 3 0.520 0.763 0.864 0.476 0.668 0.472
#> MAD:hclust 3 0.630 0.774 0.893 0.380 0.718 0.500
#> ATC:hclust 3 0.709 0.844 0.913 0.562 0.739 0.560
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.588 0.659 0.820 0.1621 0.683 0.336
#> CV:NMF 4 0.874 0.871 0.932 0.1409 0.805 0.492
#> MAD:NMF 4 0.585 0.669 0.795 0.1406 0.786 0.463
#> ATC:NMF 4 0.559 0.505 0.722 0.1220 0.842 0.607
#> SD:skmeans 4 0.908 0.853 0.930 0.0957 0.867 0.664
#> CV:skmeans 4 0.772 0.736 0.872 0.1260 0.832 0.570
#> MAD:skmeans 4 0.792 0.804 0.895 0.0931 0.913 0.766
#> ATC:skmeans 4 0.853 0.825 0.909 0.1141 0.868 0.690
#> SD:mclust 4 0.499 0.661 0.810 0.1517 0.807 0.653
#> CV:mclust 4 0.511 0.509 0.680 0.0568 0.815 0.523
#> MAD:mclust 4 0.419 0.496 0.727 0.2476 0.760 0.537
#> ATC:mclust 4 0.442 0.436 0.719 0.1528 0.796 0.571
#> SD:kmeans 4 0.659 0.498 0.754 0.1225 0.975 0.928
#> CV:kmeans 4 0.656 0.705 0.823 0.1379 0.828 0.548
#> MAD:kmeans 4 0.615 0.543 0.706 0.1193 0.939 0.826
#> ATC:kmeans 4 0.766 0.810 0.854 0.1065 0.894 0.700
#> SD:pam 4 0.681 0.653 0.806 0.1241 0.900 0.727
#> CV:pam 4 0.841 0.880 0.940 0.1600 0.856 0.640
#> MAD:pam 4 0.657 0.748 0.876 0.1448 0.837 0.580
#> ATC:pam 4 0.952 0.914 0.963 0.1109 0.891 0.689
#> SD:hclust 4 0.560 0.609 0.735 0.1010 1.000 1.000
#> CV:hclust 4 0.508 0.691 0.803 0.0819 0.977 0.932
#> MAD:hclust 4 0.636 0.682 0.795 0.1242 0.930 0.807
#> ATC:hclust 4 0.709 0.775 0.833 0.1072 0.944 0.838
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.691 0.681 0.825 0.0652 0.888 0.617
#> CV:NMF 5 0.793 0.786 0.874 0.0574 0.916 0.677
#> MAD:NMF 5 0.677 0.602 0.787 0.0673 0.870 0.544
#> ATC:NMF 5 0.583 0.536 0.728 0.0644 0.898 0.685
#> SD:skmeans 5 0.789 0.708 0.866 0.0618 0.928 0.770
#> CV:skmeans 5 0.798 0.743 0.881 0.0590 0.919 0.725
#> MAD:skmeans 5 0.721 0.682 0.823 0.0641 0.935 0.795
#> ATC:skmeans 5 0.840 0.758 0.879 0.0546 0.957 0.866
#> SD:mclust 5 0.522 0.574 0.760 0.1902 0.782 0.509
#> CV:mclust 5 0.784 0.795 0.921 0.1093 0.869 0.591
#> MAD:mclust 5 0.581 0.608 0.788 0.1065 0.797 0.431
#> ATC:mclust 5 0.618 0.609 0.795 0.1427 0.752 0.384
#> SD:kmeans 5 0.651 0.540 0.689 0.0614 0.749 0.362
#> CV:kmeans 5 0.652 0.551 0.756 0.0703 0.872 0.579
#> MAD:kmeans 5 0.633 0.431 0.662 0.0721 0.760 0.375
#> ATC:kmeans 5 0.746 0.677 0.790 0.0690 0.908 0.669
#> SD:pam 5 0.780 0.742 0.881 0.0739 0.840 0.519
#> CV:pam 5 0.698 0.662 0.802 0.0574 0.978 0.920
#> MAD:pam 5 0.627 0.553 0.716 0.0688 0.849 0.521
#> ATC:pam 5 0.858 0.796 0.901 0.0523 0.942 0.789
#> SD:hclust 5 0.550 0.362 0.670 0.0629 0.694 0.354
#> CV:hclust 5 0.609 0.499 0.768 0.0977 0.860 0.599
#> MAD:hclust 5 0.657 0.732 0.834 0.0723 0.919 0.738
#> ATC:hclust 5 0.738 0.713 0.841 0.0617 0.944 0.812
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.697 0.616 0.799 0.0347 0.923 0.675
#> CV:NMF 6 0.806 0.746 0.868 0.0336 0.924 0.660
#> MAD:NMF 6 0.701 0.562 0.770 0.0288 0.916 0.628
#> ATC:NMF 6 0.623 0.530 0.733 0.0554 0.898 0.635
#> SD:skmeans 6 0.763 0.688 0.829 0.0591 0.933 0.753
#> CV:skmeans 6 0.733 0.585 0.796 0.0385 0.973 0.890
#> MAD:skmeans 6 0.744 0.687 0.822 0.0543 0.929 0.746
#> ATC:skmeans 6 0.800 0.691 0.836 0.0415 0.930 0.771
#> SD:mclust 6 0.600 0.586 0.765 0.0704 0.896 0.608
#> CV:mclust 6 0.701 0.586 0.783 0.0613 0.912 0.660
#> MAD:mclust 6 0.647 0.632 0.794 0.0479 0.971 0.875
#> ATC:mclust 6 0.596 0.507 0.691 0.0517 0.867 0.556
#> SD:kmeans 6 0.698 0.678 0.796 0.0491 0.896 0.588
#> CV:kmeans 6 0.656 0.542 0.701 0.0427 0.883 0.565
#> MAD:kmeans 6 0.651 0.556 0.726 0.0397 0.848 0.462
#> ATC:kmeans 6 0.724 0.616 0.742 0.0423 0.918 0.657
#> SD:pam 6 0.725 0.651 0.830 0.0410 0.972 0.873
#> CV:pam 6 0.716 0.625 0.832 0.0507 0.871 0.541
#> MAD:pam 6 0.690 0.662 0.787 0.0432 0.911 0.624
#> ATC:pam 6 0.812 0.767 0.874 0.0535 0.923 0.681
#> SD:hclust 6 0.625 0.622 0.734 0.0427 0.799 0.431
#> CV:hclust 6 0.634 0.517 0.672 0.0545 0.873 0.573
#> MAD:hclust 6 0.718 0.758 0.828 0.0458 0.965 0.851
#> ATC:hclust 6 0.726 0.590 0.781 0.0358 0.969 0.876
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 16873 rows and 72 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 0.513 0.720 0.875 0.4344 0.581 0.581
#> 3 3 0.512 0.705 0.862 0.4551 0.665 0.469
#> 4 4 0.560 0.609 0.735 0.1010 1.000 1.000
#> 5 5 0.550 0.362 0.670 0.0629 0.694 0.354
#> 6 6 0.625 0.622 0.734 0.0427 0.799 0.431
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
#> SRR2313978 2 0.0000 0.819 0.000 1.000
#> SRR2313980 2 0.0000 0.819 0.000 1.000
#> SRR2313979 1 0.0000 0.893 1.000 0.000
#> SRR2313977 2 0.0000 0.819 0.000 1.000
#> SRR2313976 2 0.0000 0.819 0.000 1.000
#> SRR2313975 1 0.1184 0.895 0.984 0.016
#> SRR2313973 1 0.0000 0.893 1.000 0.000
#> SRR2313974 1 0.2778 0.878 0.952 0.048
#> SRR2313985 1 0.0000 0.893 1.000 0.000
#> SRR2313993 2 0.0000 0.819 0.000 1.000
#> SRR2313994 2 0.0000 0.819 0.000 1.000
#> SRR2313995 2 0.0000 0.819 0.000 1.000
#> SRR2314024 2 0.0000 0.819 0.000 1.000
#> SRR2314015 2 0.0000 0.819 0.000 1.000
#> SRR2314025 1 0.5408 0.804 0.876 0.124
#> SRR2314023 1 0.0000 0.893 1.000 0.000
#> SRR2314033 2 0.0000 0.819 0.000 1.000
#> SRR2314045 1 0.1184 0.895 0.984 0.016
#> SRR2314042 1 0.9686 0.213 0.604 0.396
#> SRR2314043 2 0.8813 0.593 0.300 0.700
#> SRR2314041 2 0.2236 0.809 0.036 0.964
#> SRR2314044 2 0.2603 0.805 0.044 0.956
#> SRR2314040 2 0.6048 0.741 0.148 0.852
#> SRR2314039 2 0.2236 0.809 0.036 0.964
#> SRR2314038 2 0.9833 0.414 0.424 0.576
#> SRR2314037 2 0.9866 0.398 0.432 0.568
#> SRR2314036 2 0.0000 0.819 0.000 1.000
#> SRR2314035 2 0.6048 0.741 0.148 0.852
#> SRR2314034 2 0.9933 0.345 0.452 0.548
#> SRR2314032 1 0.1184 0.895 0.984 0.016
#> SRR2314031 1 0.8763 0.523 0.704 0.296
#> SRR2314029 2 0.2236 0.809 0.036 0.964
#> SRR2314030 2 0.9833 0.414 0.424 0.576
#> SRR2314028 1 0.8861 0.500 0.696 0.304
#> SRR2314027 2 0.2236 0.809 0.036 0.964
#> SRR2314026 2 0.9866 0.398 0.432 0.568
#> SRR2314020 2 0.0000 0.819 0.000 1.000
#> SRR2314019 2 0.9866 0.398 0.432 0.568
#> SRR2314021 2 0.9866 0.398 0.432 0.568
#> SRR2314018 2 0.6048 0.741 0.148 0.852
#> SRR2314017 2 0.9833 0.414 0.424 0.576
#> SRR2314016 2 0.9833 0.414 0.424 0.576
#> SRR2314014 2 0.0000 0.819 0.000 1.000
#> SRR2314013 2 0.0000 0.819 0.000 1.000
#> SRR2314012 1 0.2948 0.875 0.948 0.052
#> SRR2314011 1 0.0000 0.893 1.000 0.000
#> SRR2314010 2 0.0000 0.819 0.000 1.000
#> SRR2314009 2 0.9866 0.398 0.432 0.568
#> SRR2314008 2 0.0000 0.819 0.000 1.000
#> SRR2314007 2 0.1184 0.815 0.016 0.984
#> SRR2314005 2 0.0000 0.819 0.000 1.000
#> SRR2314004 2 0.0000 0.819 0.000 1.000
#> SRR2314003 1 0.2423 0.883 0.960 0.040
#> SRR2314006 2 0.0000 0.819 0.000 1.000
#> SRR2314002 2 0.9983 0.283 0.476 0.524
#> SRR2314001 2 0.0000 0.819 0.000 1.000
#> SRR2314000 2 0.9044 0.573 0.320 0.680
#> SRR2313998 2 0.0376 0.818 0.004 0.996
#> SRR2313996 2 0.0000 0.819 0.000 1.000
#> SRR2313999 1 0.0938 0.895 0.988 0.012
#> SRR2313997 2 0.0000 0.819 0.000 1.000
#> SRR2313992 2 0.9866 0.398 0.432 0.568
#> SRR2313991 2 0.9833 0.414 0.424 0.576
#> SRR2313990 2 0.0000 0.819 0.000 1.000
#> SRR2313988 1 0.1184 0.895 0.984 0.016
#> SRR2313987 1 0.0000 0.893 1.000 0.000
#> SRR2313984 2 0.0000 0.819 0.000 1.000
#> SRR2313989 2 0.0000 0.819 0.000 1.000
#> SRR2313983 1 0.0000 0.893 1.000 0.000
#> SRR2313986 1 0.8763 0.523 0.704 0.296
#> SRR2313982 1 0.0000 0.893 1.000 0.000
#> SRR2313981 2 0.9866 0.398 0.432 0.568
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.4235 0.8676 0.000 0.824 0.176
#> SRR2313980 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.8697 1.000 0.000 0.000
#> SRR2313977 2 0.3686 0.8909 0.000 0.860 0.140
#> SRR2313976 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2313975 1 0.3340 0.8557 0.880 0.000 0.120
#> SRR2313973 1 0.0000 0.8697 1.000 0.000 0.000
#> SRR2313974 1 0.6260 0.4112 0.552 0.000 0.448
#> SRR2313985 1 0.0747 0.8755 0.984 0.000 0.016
#> SRR2313993 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2313994 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2313995 2 0.4178 0.8359 0.000 0.828 0.172
#> SRR2314024 2 0.0747 0.8809 0.000 0.984 0.016
#> SRR2314015 2 0.4235 0.8676 0.000 0.824 0.176
#> SRR2314025 3 0.6267 -0.2056 0.452 0.000 0.548
#> SRR2314023 1 0.0747 0.8755 0.984 0.000 0.016
#> SRR2314033 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2314045 1 0.2878 0.8686 0.904 0.000 0.096
#> SRR2314042 3 0.4575 0.5437 0.184 0.004 0.812
#> SRR2314043 3 0.3412 0.6990 0.000 0.124 0.876
#> SRR2314041 3 0.6286 0.0976 0.000 0.464 0.536
#> SRR2314044 3 0.6260 0.1542 0.000 0.448 0.552
#> SRR2314040 3 0.5591 0.4999 0.000 0.304 0.696
#> SRR2314039 3 0.6274 0.1249 0.000 0.456 0.544
#> SRR2314038 3 0.0000 0.7283 0.000 0.000 1.000
#> SRR2314037 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2314036 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2314035 3 0.5591 0.4999 0.000 0.304 0.696
#> SRR2314034 3 0.1525 0.7157 0.032 0.004 0.964
#> SRR2314032 1 0.2878 0.8686 0.904 0.000 0.096
#> SRR2314031 3 0.5397 0.3543 0.280 0.000 0.720
#> SRR2314029 3 0.6244 0.1790 0.000 0.440 0.560
#> SRR2314030 3 0.0000 0.7283 0.000 0.000 1.000
#> SRR2314028 3 0.5327 0.3821 0.272 0.000 0.728
#> SRR2314027 3 0.6274 0.1249 0.000 0.456 0.544
#> SRR2314026 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2314020 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2314019 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2314021 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2314018 3 0.5591 0.4999 0.000 0.304 0.696
#> SRR2314017 3 0.0000 0.7283 0.000 0.000 1.000
#> SRR2314016 3 0.0000 0.7283 0.000 0.000 1.000
#> SRR2314014 2 0.3686 0.8909 0.000 0.860 0.140
#> SRR2314013 2 0.3752 0.8881 0.000 0.856 0.144
#> SRR2314012 1 0.6154 0.4924 0.592 0.000 0.408
#> SRR2314011 1 0.1031 0.8766 0.976 0.000 0.024
#> SRR2314010 2 0.3267 0.8923 0.000 0.884 0.116
#> SRR2314009 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2314008 2 0.4504 0.8444 0.000 0.804 0.196
#> SRR2314007 3 0.6302 0.0350 0.000 0.480 0.520
#> SRR2314005 2 0.0424 0.8790 0.000 0.992 0.008
#> SRR2314004 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2314003 1 0.5650 0.6349 0.688 0.000 0.312
#> SRR2314006 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2314002 3 0.3482 0.6469 0.128 0.000 0.872
#> SRR2314001 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2314000 3 0.4280 0.6963 0.020 0.124 0.856
#> SRR2313998 2 0.4346 0.8598 0.000 0.816 0.184
#> SRR2313996 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2313999 1 0.3551 0.8393 0.868 0.000 0.132
#> SRR2313997 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2313992 3 0.0829 0.7281 0.012 0.004 0.984
#> SRR2313991 3 0.0000 0.7283 0.000 0.000 1.000
#> SRR2313990 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2313988 1 0.2625 0.8710 0.916 0.000 0.084
#> SRR2313987 1 0.2448 0.8669 0.924 0.000 0.076
#> SRR2313984 2 0.3879 0.8877 0.000 0.848 0.152
#> SRR2313989 2 0.0000 0.8768 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.8697 1.000 0.000 0.000
#> SRR2313986 3 0.5397 0.3543 0.280 0.000 0.720
#> SRR2313982 1 0.0000 0.8697 1.000 0.000 0.000
#> SRR2313981 3 0.0829 0.7281 0.012 0.004 0.984
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.7102 0.722 0.000 0.540 0.156 NA
#> SRR2313980 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2313979 1 0.4605 0.724 0.664 0.000 0.000 NA
#> SRR2313977 2 0.6856 0.744 0.000 0.576 0.140 NA
#> SRR2313976 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2313975 1 0.5572 0.695 0.716 0.000 0.088 NA
#> SRR2313973 1 0.4605 0.724 0.664 0.000 0.000 NA
#> SRR2313974 1 0.6042 0.319 0.560 0.000 0.392 NA
#> SRR2313985 1 0.4516 0.746 0.736 0.000 0.012 NA
#> SRR2313993 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2313994 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2313995 2 0.6994 0.692 0.000 0.560 0.152 NA
#> SRR2314024 2 0.1510 0.698 0.000 0.956 0.016 NA
#> SRR2314015 2 0.7102 0.722 0.000 0.540 0.156 NA
#> SRR2314025 3 0.6265 -0.138 0.444 0.000 0.500 NA
#> SRR2314023 1 0.4059 0.756 0.788 0.000 0.012 NA
#> SRR2314033 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2314045 1 0.5132 0.709 0.748 0.000 0.068 NA
#> SRR2314042 3 0.3853 0.543 0.160 0.000 0.820 NA
#> SRR2314043 3 0.3074 0.658 0.000 0.000 0.848 NA
#> SRR2314041 3 0.7382 0.185 0.000 0.208 0.516 NA
#> SRR2314044 3 0.7271 0.227 0.000 0.192 0.532 NA
#> SRR2314040 3 0.5130 0.517 0.000 0.020 0.668 NA
#> SRR2314039 3 0.7337 0.205 0.000 0.204 0.524 NA
#> SRR2314038 3 0.3836 0.626 0.016 0.000 0.816 NA
#> SRR2314037 3 0.0000 0.684 0.000 0.000 1.000 NA
#> SRR2314036 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2314035 3 0.5130 0.517 0.000 0.020 0.668 NA
#> SRR2314034 3 0.1059 0.677 0.016 0.000 0.972 NA
#> SRR2314032 1 0.5132 0.709 0.748 0.000 0.068 NA
#> SRR2314031 3 0.5646 0.332 0.272 0.000 0.672 NA
#> SRR2314029 3 0.7198 0.257 0.000 0.180 0.540 NA
#> SRR2314030 3 0.3836 0.626 0.016 0.000 0.816 NA
#> SRR2314028 3 0.5690 0.350 0.268 0.000 0.672 NA
#> SRR2314027 3 0.7337 0.205 0.000 0.204 0.524 NA
#> SRR2314026 3 0.0336 0.684 0.000 0.000 0.992 NA
#> SRR2314020 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2314019 3 0.0336 0.684 0.000 0.000 0.992 NA
#> SRR2314021 3 0.0336 0.684 0.000 0.000 0.992 NA
#> SRR2314018 3 0.5130 0.517 0.000 0.020 0.668 NA
#> SRR2314017 3 0.3836 0.626 0.016 0.000 0.816 NA
#> SRR2314016 3 0.3836 0.626 0.016 0.000 0.816 NA
#> SRR2314014 2 0.6856 0.744 0.000 0.576 0.140 NA
#> SRR2314013 2 0.6783 0.741 0.000 0.572 0.124 NA
#> SRR2314012 1 0.6121 0.386 0.588 0.000 0.352 NA
#> SRR2314011 1 0.2924 0.752 0.884 0.000 0.016 NA
#> SRR2314010 2 0.6566 0.746 0.000 0.600 0.112 NA
#> SRR2314009 3 0.0000 0.684 0.000 0.000 1.000 NA
#> SRR2314008 2 0.7270 0.697 0.000 0.520 0.176 NA
#> SRR2314007 3 0.7458 0.146 0.000 0.212 0.500 NA
#> SRR2314005 2 0.3249 0.717 0.000 0.852 0.008 NA
#> SRR2314004 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2314003 1 0.5619 0.539 0.688 0.000 0.248 NA
#> SRR2314006 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2314002 3 0.5788 0.537 0.084 0.000 0.688 NA
#> SRR2314001 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2314000 3 0.3757 0.654 0.020 0.000 0.828 NA
#> SRR2313998 2 0.7172 0.714 0.000 0.532 0.164 NA
#> SRR2313996 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2313999 1 0.3308 0.714 0.872 0.000 0.092 NA
#> SRR2313997 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2313992 3 0.0000 0.684 0.000 0.000 1.000 NA
#> SRR2313991 3 0.3836 0.626 0.016 0.000 0.816 NA
#> SRR2313990 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2313988 1 0.5132 0.732 0.748 0.000 0.068 NA
#> SRR2313987 1 0.5328 0.747 0.724 0.000 0.064 NA
#> SRR2313984 2 0.6956 0.741 0.000 0.564 0.148 NA
#> SRR2313989 2 0.0000 0.684 0.000 1.000 0.000 NA
#> SRR2313983 1 0.4564 0.731 0.672 0.000 0.000 NA
#> SRR2313986 3 0.5573 0.334 0.272 0.000 0.676 NA
#> SRR2313982 1 0.4605 0.724 0.664 0.000 0.000 NA
#> SRR2313981 3 0.0000 0.684 0.000 0.000 1.000 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.4350 0.2846 0.004 0.408 0.588 0.000 0.000
#> SRR2313980 2 0.0162 0.9519 0.000 0.996 0.004 0.000 0.000
#> SRR2313979 1 0.4201 0.9822 0.664 0.000 0.008 0.328 0.000
#> SRR2313977 3 0.4415 0.2362 0.004 0.444 0.552 0.000 0.000
#> SRR2313976 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2313975 4 0.5652 0.3119 0.400 0.000 0.060 0.532 0.008
#> SRR2313973 1 0.4201 0.9822 0.664 0.000 0.008 0.328 0.000
#> SRR2313974 4 0.5154 0.3458 0.016 0.000 0.052 0.680 0.252
#> SRR2313985 4 0.4297 -0.6901 0.472 0.000 0.000 0.528 0.000
#> SRR2313993 3 0.4397 0.2598 0.004 0.432 0.564 0.000 0.000
#> SRR2313994 3 0.4410 0.2526 0.004 0.440 0.556 0.000 0.000
#> SRR2313995 3 0.4434 0.1409 0.004 0.460 0.536 0.000 0.000
#> SRR2314024 2 0.1831 0.8717 0.004 0.920 0.076 0.000 0.000
#> SRR2314015 3 0.4350 0.2846 0.004 0.408 0.588 0.000 0.000
#> SRR2314025 4 0.5228 0.1819 0.000 0.000 0.056 0.588 0.356
#> SRR2314023 4 0.4150 -0.5355 0.388 0.000 0.000 0.612 0.000
#> SRR2314033 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 4 0.5624 0.2959 0.392 0.000 0.052 0.544 0.012
#> SRR2314042 5 0.6790 0.0914 0.000 0.000 0.328 0.292 0.380
#> SRR2314043 3 0.4731 0.1023 0.000 0.000 0.640 0.032 0.328
#> SRR2314041 3 0.3780 0.4873 0.000 0.116 0.812 0.000 0.072
#> SRR2314044 3 0.3962 0.4788 0.000 0.112 0.800 0.000 0.088
#> SRR2314040 3 0.3320 0.3625 0.004 0.000 0.820 0.012 0.164
#> SRR2314039 3 0.4002 0.4865 0.000 0.120 0.796 0.000 0.084
#> SRR2314038 5 0.0290 0.8403 0.000 0.000 0.008 0.000 0.992
#> SRR2314037 3 0.5944 -0.1402 0.000 0.000 0.488 0.108 0.404
#> SRR2314036 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.3320 0.3625 0.004 0.000 0.820 0.012 0.164
#> SRR2314034 3 0.6059 -0.1634 0.000 0.000 0.468 0.120 0.412
#> SRR2314032 4 0.5624 0.2959 0.392 0.000 0.052 0.544 0.012
#> SRR2314031 4 0.6582 -0.0720 0.000 0.000 0.208 0.416 0.376
#> SRR2314029 3 0.3749 0.4608 0.000 0.080 0.816 0.000 0.104
#> SRR2314030 5 0.0290 0.8403 0.000 0.000 0.008 0.000 0.992
#> SRR2314028 4 0.6650 -0.0842 0.000 0.000 0.228 0.412 0.360
#> SRR2314027 3 0.4002 0.4865 0.000 0.120 0.796 0.000 0.084
#> SRR2314026 3 0.5876 -0.1372 0.000 0.000 0.488 0.100 0.412
#> SRR2314020 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.5876 -0.1372 0.000 0.000 0.488 0.100 0.412
#> SRR2314021 3 0.5876 -0.1372 0.000 0.000 0.488 0.100 0.412
#> SRR2314018 3 0.3320 0.3625 0.004 0.000 0.820 0.012 0.164
#> SRR2314017 5 0.0290 0.8403 0.000 0.000 0.008 0.000 0.992
#> SRR2314016 5 0.0290 0.8403 0.000 0.000 0.008 0.000 0.992
#> SRR2314014 3 0.4415 0.2362 0.004 0.444 0.552 0.000 0.000
#> SRR2314013 3 0.4410 0.2286 0.004 0.440 0.556 0.000 0.000
#> SRR2314012 4 0.4415 0.3772 0.008 0.000 0.028 0.728 0.236
#> SRR2314011 4 0.3395 -0.1941 0.236 0.000 0.000 0.764 0.000
#> SRR2314010 3 0.4443 0.1626 0.004 0.472 0.524 0.000 0.000
#> SRR2314009 3 0.5944 -0.1402 0.000 0.000 0.488 0.108 0.404
#> SRR2314008 3 0.4585 0.2985 0.004 0.396 0.592 0.000 0.008
#> SRR2314007 3 0.3622 0.4899 0.000 0.124 0.820 0.000 0.056
#> SRR2314005 2 0.3480 0.5686 0.000 0.752 0.248 0.000 0.000
#> SRR2314004 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 4 0.5269 0.3898 0.060 0.000 0.048 0.724 0.168
#> SRR2314006 2 0.0162 0.9519 0.000 0.996 0.004 0.000 0.000
#> SRR2314002 5 0.3138 0.7007 0.032 0.000 0.032 0.060 0.876
#> SRR2314001 3 0.4397 0.2598 0.004 0.432 0.564 0.000 0.000
#> SRR2314000 3 0.4854 0.1053 0.000 0.000 0.648 0.044 0.308
#> SRR2313998 3 0.4507 0.2844 0.004 0.412 0.580 0.000 0.004
#> SRR2313996 3 0.4397 0.2598 0.004 0.432 0.564 0.000 0.000
#> SRR2313999 4 0.3301 0.1413 0.088 0.000 0.048 0.856 0.008
#> SRR2313997 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 3 0.5944 -0.1402 0.000 0.000 0.488 0.108 0.404
#> SRR2313991 5 0.0290 0.8403 0.000 0.000 0.008 0.000 0.992
#> SRR2313990 3 0.4397 0.2598 0.004 0.432 0.564 0.000 0.000
#> SRR2313988 4 0.4826 0.2042 0.472 0.000 0.020 0.508 0.000
#> SRR2313987 4 0.4937 -0.5222 0.364 0.000 0.028 0.604 0.004
#> SRR2313984 3 0.4397 0.2598 0.004 0.432 0.564 0.000 0.000
#> SRR2313989 2 0.0000 0.9538 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.4371 0.9453 0.644 0.000 0.012 0.344 0.000
#> SRR2313986 4 0.6579 -0.0716 0.000 0.000 0.208 0.420 0.372
#> SRR2313982 1 0.4201 0.9822 0.664 0.000 0.008 0.328 0.000
#> SRR2313981 3 0.5944 -0.1402 0.000 0.000 0.488 0.108 0.404
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.0260 0.7641 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR2313980 6 0.3371 0.9843 0.000 0.292 0.000 0.000 0.000 0.708
#> SRR2313979 1 0.4743 0.5589 0.652 0.000 0.000 0.012 0.056 0.280
#> SRR2313977 2 0.0713 0.7528 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR2313976 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2313975 4 0.1003 0.8658 0.000 0.000 0.020 0.964 0.016 0.000
#> SRR2313973 1 0.4743 0.5589 0.652 0.000 0.000 0.012 0.056 0.280
#> SRR2313974 3 0.6643 -0.0761 0.328 0.000 0.476 0.088 0.104 0.004
#> SRR2313985 1 0.4565 0.5511 0.744 0.000 0.020 0.100 0.004 0.132
#> SRR2313993 2 0.0458 0.7627 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2313994 2 0.0632 0.7600 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR2313995 2 0.4176 0.4771 0.000 0.720 0.068 0.000 0.000 0.212
#> SRR2314024 6 0.3672 0.8824 0.000 0.368 0.000 0.000 0.000 0.632
#> SRR2314015 2 0.0260 0.7641 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR2314025 3 0.5660 0.1196 0.324 0.000 0.560 0.016 0.092 0.008
#> SRR2314023 1 0.5696 0.5521 0.636 0.000 0.052 0.100 0.004 0.208
#> SRR2314033 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2314045 4 0.1563 0.9092 0.056 0.000 0.012 0.932 0.000 0.000
#> SRR2314042 3 0.4723 0.5488 0.112 0.084 0.748 0.000 0.052 0.004
#> SRR2314043 3 0.3840 0.5250 0.000 0.284 0.696 0.000 0.020 0.000
#> SRR2314041 2 0.3819 0.3299 0.000 0.624 0.372 0.000 0.004 0.000
#> SRR2314044 2 0.3955 0.2950 0.000 0.608 0.384 0.000 0.008 0.000
#> SRR2314040 3 0.4260 0.1245 0.000 0.472 0.512 0.000 0.016 0.000
#> SRR2314039 2 0.3706 0.3260 0.000 0.620 0.380 0.000 0.000 0.000
#> SRR2314038 5 0.2730 0.9547 0.000 0.000 0.192 0.000 0.808 0.000
#> SRR2314037 3 0.2531 0.6484 0.000 0.132 0.856 0.000 0.012 0.000
#> SRR2314036 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2314035 3 0.4260 0.1245 0.000 0.472 0.512 0.000 0.016 0.000
#> SRR2314034 3 0.3110 0.6422 0.016 0.128 0.836 0.000 0.020 0.000
#> SRR2314032 4 0.1563 0.9092 0.056 0.000 0.012 0.932 0.000 0.000
#> SRR2314031 3 0.4629 0.4183 0.156 0.000 0.732 0.012 0.092 0.008
#> SRR2314029 2 0.3890 0.2282 0.000 0.596 0.400 0.000 0.004 0.000
#> SRR2314030 5 0.2913 0.9496 0.000 0.000 0.180 0.004 0.812 0.004
#> SRR2314028 3 0.5265 0.4396 0.156 0.020 0.708 0.020 0.088 0.008
#> SRR2314027 2 0.3706 0.3260 0.000 0.620 0.380 0.000 0.000 0.000
#> SRR2314026 3 0.2709 0.6444 0.000 0.132 0.848 0.000 0.020 0.000
#> SRR2314020 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2314019 3 0.2709 0.6444 0.000 0.132 0.848 0.000 0.020 0.000
#> SRR2314021 3 0.2709 0.6444 0.000 0.132 0.848 0.000 0.020 0.000
#> SRR2314018 3 0.4260 0.1245 0.000 0.472 0.512 0.000 0.016 0.000
#> SRR2314017 5 0.2762 0.9537 0.000 0.000 0.196 0.000 0.804 0.000
#> SRR2314016 5 0.2913 0.9496 0.000 0.000 0.180 0.004 0.812 0.004
#> SRR2314014 2 0.0713 0.7528 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR2314013 2 0.0858 0.7465 0.000 0.968 0.004 0.000 0.000 0.028
#> SRR2314012 3 0.6397 -0.1770 0.408 0.000 0.428 0.040 0.116 0.008
#> SRR2314011 1 0.4290 0.4583 0.772 0.000 0.088 0.104 0.036 0.000
#> SRR2314010 2 0.1204 0.7137 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR2314009 3 0.2531 0.6484 0.000 0.132 0.856 0.000 0.012 0.000
#> SRR2314008 2 0.0713 0.7598 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR2314007 2 0.3620 0.3709 0.000 0.648 0.352 0.000 0.000 0.000
#> SRR2314005 2 0.3563 -0.0740 0.000 0.664 0.000 0.000 0.000 0.336
#> SRR2314004 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2314003 1 0.6987 0.2008 0.424 0.000 0.340 0.124 0.108 0.004
#> SRR2314006 6 0.3371 0.9843 0.000 0.292 0.000 0.000 0.000 0.708
#> SRR2314002 5 0.4204 0.8223 0.000 0.000 0.128 0.132 0.740 0.000
#> SRR2314001 2 0.0458 0.7627 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2314000 3 0.4464 0.5182 0.004 0.284 0.672 0.012 0.028 0.000
#> SRR2313998 2 0.0717 0.7625 0.000 0.976 0.016 0.000 0.000 0.008
#> SRR2313996 2 0.0458 0.7627 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2313999 1 0.6184 0.3580 0.604 0.000 0.180 0.124 0.088 0.004
#> SRR2313997 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2313992 3 0.2531 0.6484 0.000 0.132 0.856 0.000 0.012 0.000
#> SRR2313991 5 0.2730 0.9547 0.000 0.000 0.192 0.000 0.808 0.000
#> SRR2313990 2 0.0458 0.7627 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2313988 4 0.3503 0.7726 0.180 0.000 0.012 0.788 0.020 0.000
#> SRR2313987 1 0.6305 0.5211 0.636 0.000 0.068 0.120 0.044 0.132
#> SRR2313984 2 0.0458 0.7627 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2313989 6 0.3351 0.9868 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR2313983 1 0.5189 0.5368 0.628 0.000 0.000 0.056 0.036 0.280
#> SRR2313986 3 0.4486 0.4246 0.156 0.000 0.740 0.008 0.088 0.008
#> SRR2313982 1 0.4743 0.5589 0.652 0.000 0.000 0.012 0.056 0.280
#> SRR2313981 3 0.2531 0.6484 0.000 0.132 0.856 0.000 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.803 0.848 0.944 0.4937 0.503 0.503
#> 3 3 1.000 0.972 0.977 0.3177 0.654 0.420
#> 4 4 0.659 0.498 0.754 0.1225 0.975 0.928
#> 5 5 0.651 0.540 0.689 0.0614 0.749 0.362
#> 6 6 0.698 0.678 0.796 0.0491 0.896 0.588
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
#> SRR2313978 2 0.000 0.92925 0.000 1.000
#> SRR2313980 2 0.000 0.92925 0.000 1.000
#> SRR2313979 1 0.000 0.94303 1.000 0.000
#> SRR2313977 2 0.000 0.92925 0.000 1.000
#> SRR2313976 2 0.000 0.92925 0.000 1.000
#> SRR2313975 1 0.000 0.94303 1.000 0.000
#> SRR2313973 1 0.000 0.94303 1.000 0.000
#> SRR2313974 1 0.000 0.94303 1.000 0.000
#> SRR2313985 1 0.000 0.94303 1.000 0.000
#> SRR2313993 2 0.000 0.92925 0.000 1.000
#> SRR2313994 2 0.000 0.92925 0.000 1.000
#> SRR2313995 2 0.000 0.92925 0.000 1.000
#> SRR2314024 2 0.000 0.92925 0.000 1.000
#> SRR2314015 2 0.000 0.92925 0.000 1.000
#> SRR2314025 1 0.000 0.94303 1.000 0.000
#> SRR2314023 1 0.000 0.94303 1.000 0.000
#> SRR2314033 2 0.000 0.92925 0.000 1.000
#> SRR2314045 1 0.000 0.94303 1.000 0.000
#> SRR2314042 1 0.278 0.91699 0.952 0.048
#> SRR2314043 2 0.992 0.17753 0.448 0.552
#> SRR2314041 2 0.000 0.92925 0.000 1.000
#> SRR2314044 2 0.000 0.92925 0.000 1.000
#> SRR2314040 2 0.469 0.83554 0.100 0.900
#> SRR2314039 2 0.000 0.92925 0.000 1.000
#> SRR2314038 2 0.000 0.92925 0.000 1.000
#> SRR2314037 2 0.999 0.06757 0.480 0.520
#> SRR2314036 2 0.000 0.92925 0.000 1.000
#> SRR2314035 2 1.000 0.00485 0.496 0.504
#> SRR2314034 1 0.000 0.94303 1.000 0.000
#> SRR2314032 1 0.000 0.94303 1.000 0.000
#> SRR2314031 1 0.278 0.91699 0.952 0.048
#> SRR2314029 2 0.000 0.92925 0.000 1.000
#> SRR2314030 1 0.343 0.90445 0.936 0.064
#> SRR2314028 1 0.000 0.94303 1.000 0.000
#> SRR2314027 2 0.000 0.92925 0.000 1.000
#> SRR2314026 1 1.000 -0.02725 0.504 0.496
#> SRR2314020 2 0.000 0.92925 0.000 1.000
#> SRR2314019 2 0.995 0.13930 0.460 0.540
#> SRR2314021 1 0.260 0.91970 0.956 0.044
#> SRR2314018 2 0.443 0.84407 0.092 0.908
#> SRR2314017 1 0.634 0.80281 0.840 0.160
#> SRR2314016 1 0.000 0.94303 1.000 0.000
#> SRR2314014 2 0.000 0.92925 0.000 1.000
#> SRR2314013 2 0.000 0.92925 0.000 1.000
#> SRR2314012 1 0.000 0.94303 1.000 0.000
#> SRR2314011 1 0.000 0.94303 1.000 0.000
#> SRR2314010 2 0.000 0.92925 0.000 1.000
#> SRR2314009 2 0.995 0.13926 0.460 0.540
#> SRR2314008 2 0.000 0.92925 0.000 1.000
#> SRR2314007 2 0.000 0.92925 0.000 1.000
#> SRR2314005 2 0.000 0.92925 0.000 1.000
#> SRR2314004 2 0.000 0.92925 0.000 1.000
#> SRR2314003 1 0.000 0.94303 1.000 0.000
#> SRR2314006 2 0.000 0.92925 0.000 1.000
#> SRR2314002 1 0.506 0.85152 0.888 0.112
#> SRR2314001 2 0.000 0.92925 0.000 1.000
#> SRR2314000 1 0.939 0.41349 0.644 0.356
#> SRR2313998 2 0.000 0.92925 0.000 1.000
#> SRR2313996 2 0.000 0.92925 0.000 1.000
#> SRR2313999 1 0.000 0.94303 1.000 0.000
#> SRR2313997 2 0.000 0.92925 0.000 1.000
#> SRR2313992 1 0.260 0.91970 0.956 0.044
#> SRR2313991 2 0.000 0.92925 0.000 1.000
#> SRR2313990 2 0.000 0.92925 0.000 1.000
#> SRR2313988 1 0.000 0.94303 1.000 0.000
#> SRR2313987 1 0.000 0.94303 1.000 0.000
#> SRR2313984 2 0.000 0.92925 0.000 1.000
#> SRR2313989 2 0.000 0.92925 0.000 1.000
#> SRR2313983 1 0.000 0.94303 1.000 0.000
#> SRR2313986 1 0.000 0.94303 1.000 0.000
#> SRR2313982 1 0.000 0.94303 1.000 0.000
#> SRR2313981 1 0.634 0.80281 0.840 0.160
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313977 2 0.0892 0.979 0.000 0.980 0.020
#> SRR2313976 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313994 2 0.0424 0.991 0.000 0.992 0.008
#> SRR2313995 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314025 1 0.4555 0.758 0.800 0.000 0.200
#> SRR2314023 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314045 1 0.1163 0.954 0.972 0.000 0.028
#> SRR2314042 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2314043 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314041 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314044 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314040 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314039 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314038 3 0.0000 0.964 0.000 0.000 1.000
#> SRR2314037 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314036 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314035 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314034 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2314032 1 0.1163 0.954 0.972 0.000 0.028
#> SRR2314031 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2314029 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314030 3 0.0000 0.964 0.000 0.000 1.000
#> SRR2314028 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2314027 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314026 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314020 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314019 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314021 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2314018 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314017 3 0.0000 0.964 0.000 0.000 1.000
#> SRR2314016 3 0.0424 0.960 0.008 0.000 0.992
#> SRR2314014 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314013 2 0.0237 0.994 0.000 0.996 0.004
#> SRR2314012 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314009 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2314008 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314007 3 0.1643 0.976 0.000 0.044 0.956
#> SRR2314005 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314003 1 0.4452 0.768 0.808 0.000 0.192
#> SRR2314006 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2314002 3 0.0424 0.960 0.008 0.000 0.992
#> SRR2314001 2 0.0424 0.991 0.000 0.992 0.008
#> SRR2314000 3 0.1765 0.977 0.004 0.040 0.956
#> SRR2313998 2 0.0424 0.991 0.000 0.992 0.008
#> SRR2313996 2 0.0892 0.979 0.000 0.980 0.020
#> SRR2313999 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313992 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2313991 3 0.0000 0.964 0.000 0.000 1.000
#> SRR2313990 3 0.1753 0.974 0.000 0.048 0.952
#> SRR2313988 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.997 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.970 1.000 0.000 0.000
#> SRR2313986 3 0.1643 0.962 0.044 0.000 0.956
#> SRR2313982 1 0.0237 0.968 0.996 0.000 0.004
#> SRR2313981 3 0.1877 0.976 0.012 0.032 0.956
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.5696 -0.2249 0.000 0.492 0.024 0.484
#> SRR2313980 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0469 0.8901 0.988 0.000 0.000 0.012
#> SRR2313977 4 0.6599 0.1364 0.000 0.432 0.080 0.488
#> SRR2313976 2 0.0469 0.6174 0.000 0.988 0.000 0.012
#> SRR2313975 1 0.2345 0.8825 0.900 0.000 0.000 0.100
#> SRR2313973 1 0.0000 0.8907 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.2216 0.8800 0.908 0.000 0.000 0.092
#> SRR2313985 1 0.0000 0.8907 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.4972 0.0384 0.000 0.544 0.000 0.456
#> SRR2313994 2 0.5408 -0.1864 0.000 0.500 0.012 0.488
#> SRR2313995 2 0.4643 0.3068 0.000 0.656 0.000 0.344
#> SRR2314024 2 0.0469 0.6175 0.000 0.988 0.000 0.012
#> SRR2314015 2 0.4989 -0.0352 0.000 0.528 0.000 0.472
#> SRR2314025 1 0.7210 0.4741 0.540 0.000 0.276 0.184
#> SRR2314023 1 0.0000 0.8907 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.5564 0.7174 0.708 0.000 0.076 0.216
#> SRR2314042 3 0.2973 0.6631 0.000 0.000 0.856 0.144
#> SRR2314043 3 0.1302 0.6799 0.000 0.000 0.956 0.044
#> SRR2314041 3 0.4998 -0.0932 0.000 0.000 0.512 0.488
#> SRR2314044 3 0.4817 0.2605 0.000 0.000 0.612 0.388
#> SRR2314040 3 0.4164 0.4836 0.000 0.000 0.736 0.264
#> SRR2314039 3 0.4817 0.2605 0.000 0.000 0.612 0.388
#> SRR2314038 3 0.4679 0.5372 0.000 0.000 0.648 0.352
#> SRR2314037 3 0.1211 0.6814 0.000 0.000 0.960 0.040
#> SRR2314036 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.1302 0.6799 0.000 0.000 0.956 0.044
#> SRR2314034 3 0.2647 0.6688 0.000 0.000 0.880 0.120
#> SRR2314032 1 0.3528 0.7965 0.808 0.000 0.000 0.192
#> SRR2314031 3 0.3356 0.6492 0.000 0.000 0.824 0.176
#> SRR2314029 3 0.4925 0.1607 0.000 0.000 0.572 0.428
#> SRR2314030 3 0.4916 0.5022 0.000 0.000 0.576 0.424
#> SRR2314028 3 0.2760 0.6663 0.000 0.000 0.872 0.128
#> SRR2314027 3 0.4830 0.2502 0.000 0.000 0.608 0.392
#> SRR2314026 3 0.0707 0.6853 0.000 0.000 0.980 0.020
#> SRR2314020 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.1302 0.6869 0.000 0.000 0.956 0.044
#> SRR2314021 3 0.1211 0.6854 0.000 0.000 0.960 0.040
#> SRR2314018 3 0.3907 0.5245 0.000 0.000 0.768 0.232
#> SRR2314017 3 0.4697 0.5341 0.000 0.000 0.644 0.356
#> SRR2314016 3 0.5000 0.4196 0.000 0.000 0.500 0.500
#> SRR2314014 2 0.4996 -0.0953 0.000 0.516 0.000 0.484
#> SRR2314013 2 0.4996 -0.0957 0.000 0.516 0.000 0.484
#> SRR2314012 1 0.4713 0.7960 0.776 0.000 0.052 0.172
#> SRR2314011 1 0.1557 0.8869 0.944 0.000 0.000 0.056
#> SRR2314010 2 0.4977 0.0219 0.000 0.540 0.000 0.460
#> SRR2314009 3 0.1118 0.6825 0.000 0.000 0.964 0.036
#> SRR2314008 3 0.4817 0.2605 0.000 0.000 0.612 0.388
#> SRR2314007 3 0.4830 0.2502 0.000 0.000 0.608 0.392
#> SRR2314005 2 0.3486 0.5314 0.000 0.812 0.000 0.188
#> SRR2314004 2 0.2345 0.5891 0.000 0.900 0.000 0.100
#> SRR2314003 1 0.7149 0.4951 0.552 0.000 0.264 0.184
#> SRR2314006 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2314002 3 0.4790 0.5189 0.000 0.000 0.620 0.380
#> SRR2314001 4 0.5861 -0.0839 0.000 0.480 0.032 0.488
#> SRR2314000 3 0.1022 0.6854 0.000 0.000 0.968 0.032
#> SRR2313998 2 0.5607 -0.2332 0.000 0.492 0.020 0.488
#> SRR2313996 4 0.6211 -0.0741 0.000 0.052 0.460 0.488
#> SRR2313999 1 0.2149 0.8800 0.912 0.000 0.000 0.088
#> SRR2313997 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2313992 3 0.2647 0.6688 0.000 0.000 0.880 0.120
#> SRR2313991 3 0.4679 0.5372 0.000 0.000 0.648 0.352
#> SRR2313990 3 0.4998 -0.0932 0.000 0.000 0.512 0.488
#> SRR2313988 1 0.0592 0.8896 0.984 0.000 0.000 0.016
#> SRR2313987 1 0.1118 0.8897 0.964 0.000 0.000 0.036
#> SRR2313984 2 0.4605 0.3545 0.000 0.664 0.000 0.336
#> SRR2313989 2 0.0000 0.6187 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0469 0.8901 0.988 0.000 0.000 0.012
#> SRR2313986 3 0.3400 0.6471 0.000 0.000 0.820 0.180
#> SRR2313982 1 0.0469 0.8901 0.988 0.000 0.000 0.012
#> SRR2313981 3 0.0000 0.6863 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.4046 0.4477 0.000 0.296 0.696 0.000 0.008
#> SRR2313980 2 0.0404 0.8651 0.000 0.988 0.000 0.000 0.012
#> SRR2313979 1 0.0609 0.7973 0.980 0.000 0.000 0.000 0.020
#> SRR2313977 3 0.4793 0.4700 0.000 0.260 0.684 0.000 0.056
#> SRR2313976 2 0.2795 0.8078 0.000 0.880 0.056 0.000 0.064
#> SRR2313975 1 0.5717 0.6386 0.540 0.000 0.000 0.368 0.092
#> SRR2313973 1 0.0609 0.7995 0.980 0.000 0.000 0.020 0.000
#> SRR2313974 1 0.5529 0.6084 0.512 0.000 0.000 0.420 0.068
#> SRR2313985 1 0.0609 0.7995 0.980 0.000 0.000 0.020 0.000
#> SRR2313993 3 0.4934 0.3161 0.000 0.364 0.600 0.000 0.036
#> SRR2313994 3 0.4550 0.4657 0.000 0.276 0.688 0.000 0.036
#> SRR2313995 2 0.4752 0.1902 0.000 0.568 0.412 0.000 0.020
#> SRR2314024 2 0.1106 0.8562 0.000 0.964 0.024 0.000 0.012
#> SRR2314015 3 0.4540 0.4065 0.000 0.320 0.656 0.000 0.024
#> SRR2314025 4 0.4809 -0.1531 0.296 0.000 0.004 0.664 0.036
#> SRR2314023 1 0.0609 0.7995 0.980 0.000 0.000 0.020 0.000
#> SRR2314033 2 0.0794 0.8595 0.000 0.972 0.000 0.000 0.028
#> SRR2314045 1 0.5524 0.3522 0.516 0.000 0.000 0.068 0.416
#> SRR2314042 4 0.4263 0.5437 0.000 0.000 0.180 0.760 0.060
#> SRR2314043 4 0.6288 0.5826 0.000 0.000 0.372 0.472 0.156
#> SRR2314041 3 0.1818 0.5129 0.000 0.000 0.932 0.024 0.044
#> SRR2314044 3 0.4010 0.3613 0.000 0.000 0.792 0.136 0.072
#> SRR2314040 3 0.6114 -0.3467 0.000 0.000 0.536 0.312 0.152
#> SRR2314039 3 0.4934 0.1712 0.000 0.000 0.708 0.188 0.104
#> SRR2314038 5 0.3495 0.8993 0.000 0.000 0.032 0.152 0.816
#> SRR2314037 4 0.6235 0.6085 0.000 0.000 0.344 0.500 0.156
#> SRR2314036 2 0.0290 0.8647 0.000 0.992 0.000 0.000 0.008
#> SRR2314035 4 0.6308 0.5692 0.000 0.000 0.388 0.456 0.156
#> SRR2314034 4 0.5004 0.5790 0.000 0.000 0.216 0.692 0.092
#> SRR2314032 1 0.5128 0.5036 0.604 0.000 0.000 0.052 0.344
#> SRR2314031 4 0.2595 0.3800 0.000 0.000 0.080 0.888 0.032
#> SRR2314029 3 0.3608 0.4172 0.000 0.000 0.824 0.112 0.064
#> SRR2314030 5 0.3550 0.8800 0.000 0.000 0.004 0.236 0.760
#> SRR2314028 4 0.4289 0.5445 0.000 0.000 0.176 0.760 0.064
#> SRR2314027 3 0.3912 0.3759 0.000 0.000 0.804 0.108 0.088
#> SRR2314026 4 0.6234 0.6082 0.000 0.000 0.332 0.508 0.160
#> SRR2314020 2 0.0000 0.8654 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.6194 0.6107 0.000 0.000 0.328 0.516 0.156
#> SRR2314021 4 0.6103 0.6106 0.000 0.000 0.300 0.544 0.156
#> SRR2314018 3 0.6200 -0.3668 0.000 0.000 0.520 0.320 0.160
#> SRR2314017 5 0.3381 0.8987 0.000 0.000 0.016 0.176 0.808
#> SRR2314016 5 0.3816 0.7962 0.000 0.000 0.000 0.304 0.696
#> SRR2314014 3 0.5027 0.4114 0.000 0.304 0.640 0.000 0.056
#> SRR2314013 3 0.5163 0.4174 0.000 0.296 0.636 0.000 0.068
#> SRR2314012 4 0.4650 -0.5156 0.468 0.000 0.000 0.520 0.012
#> SRR2314011 1 0.3561 0.7259 0.740 0.000 0.000 0.260 0.000
#> SRR2314010 3 0.4283 0.3705 0.000 0.348 0.644 0.000 0.008
#> SRR2314009 4 0.6235 0.6060 0.000 0.000 0.344 0.500 0.156
#> SRR2314008 3 0.2983 0.4542 0.000 0.000 0.864 0.040 0.096
#> SRR2314007 3 0.3967 0.3701 0.000 0.000 0.800 0.108 0.092
#> SRR2314005 2 0.5338 0.3925 0.000 0.604 0.324 0.000 0.072
#> SRR2314004 2 0.3995 0.6958 0.000 0.776 0.180 0.000 0.044
#> SRR2314003 4 0.4919 -0.1776 0.304 0.000 0.004 0.652 0.040
#> SRR2314006 2 0.0404 0.8651 0.000 0.988 0.000 0.000 0.012
#> SRR2314002 5 0.2563 0.8790 0.000 0.000 0.008 0.120 0.872
#> SRR2314001 3 0.4576 0.4713 0.000 0.268 0.692 0.000 0.040
#> SRR2314000 4 0.6477 0.5603 0.000 0.000 0.340 0.464 0.196
#> SRR2313998 3 0.4691 0.4625 0.000 0.276 0.680 0.000 0.044
#> SRR2313996 3 0.0290 0.5563 0.000 0.008 0.992 0.000 0.000
#> SRR2313999 1 0.4470 0.6450 0.616 0.000 0.000 0.372 0.012
#> SRR2313997 2 0.0162 0.8654 0.000 0.996 0.000 0.000 0.004
#> SRR2313992 4 0.4558 0.5730 0.000 0.000 0.216 0.724 0.060
#> SRR2313991 5 0.3495 0.8993 0.000 0.000 0.032 0.152 0.816
#> SRR2313990 3 0.0794 0.5511 0.000 0.000 0.972 0.000 0.028
#> SRR2313988 1 0.2504 0.7738 0.896 0.000 0.000 0.040 0.064
#> SRR2313987 1 0.2798 0.7744 0.852 0.000 0.000 0.140 0.008
#> SRR2313984 3 0.5173 0.0252 0.000 0.460 0.500 0.000 0.040
#> SRR2313989 2 0.0510 0.8648 0.000 0.984 0.000 0.000 0.016
#> SRR2313983 1 0.0609 0.7973 0.980 0.000 0.000 0.000 0.020
#> SRR2313986 4 0.2595 0.3800 0.000 0.000 0.080 0.888 0.032
#> SRR2313982 1 0.0609 0.7973 0.980 0.000 0.000 0.000 0.020
#> SRR2313981 4 0.6145 0.6128 0.000 0.000 0.312 0.532 0.156
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.1396 0.7386 0.000 0.952 0.004 0.008 0.024 0.012
#> SRR2313980 6 0.1477 0.8869 0.000 0.048 0.000 0.004 0.008 0.940
#> SRR2313979 1 0.0000 0.7300 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.3036 0.7265 0.000 0.840 0.008 0.124 0.028 0.000
#> SRR2313976 6 0.5644 0.6833 0.000 0.172 0.000 0.156 0.040 0.632
#> SRR2313975 4 0.6204 0.2858 0.348 0.008 0.020 0.528 0.056 0.040
#> SRR2313973 1 0.1398 0.7193 0.940 0.000 0.000 0.052 0.008 0.000
#> SRR2313974 4 0.4664 0.5015 0.284 0.000 0.012 0.664 0.028 0.012
#> SRR2313985 1 0.1462 0.7176 0.936 0.000 0.000 0.056 0.008 0.000
#> SRR2313993 2 0.4048 0.6972 0.000 0.788 0.000 0.104 0.028 0.080
#> SRR2313994 2 0.3272 0.7334 0.000 0.848 0.008 0.092 0.032 0.020
#> SRR2313995 2 0.4960 0.3136 0.000 0.600 0.000 0.044 0.020 0.336
#> SRR2314024 6 0.2699 0.8755 0.000 0.068 0.000 0.032 0.020 0.880
#> SRR2314015 2 0.1616 0.7352 0.000 0.940 0.000 0.012 0.020 0.028
#> SRR2314025 4 0.5666 0.6547 0.180 0.000 0.144 0.632 0.044 0.000
#> SRR2314023 1 0.2122 0.7004 0.900 0.000 0.000 0.084 0.008 0.008
#> SRR2314033 6 0.3468 0.8408 0.000 0.072 0.000 0.072 0.024 0.832
#> SRR2314045 1 0.6647 0.1928 0.468 0.004 0.044 0.084 0.372 0.028
#> SRR2314042 3 0.2772 0.6989 0.000 0.000 0.816 0.180 0.004 0.000
#> SRR2314043 3 0.1726 0.8642 0.000 0.044 0.932 0.012 0.012 0.000
#> SRR2314041 2 0.3550 0.6725 0.000 0.788 0.176 0.012 0.024 0.000
#> SRR2314044 2 0.4753 0.4542 0.000 0.620 0.328 0.024 0.028 0.000
#> SRR2314040 3 0.3201 0.8167 0.000 0.088 0.848 0.036 0.028 0.000
#> SRR2314039 3 0.4771 0.2368 0.000 0.384 0.572 0.020 0.024 0.000
#> SRR2314038 5 0.2704 0.9208 0.000 0.016 0.140 0.000 0.844 0.000
#> SRR2314037 3 0.1226 0.8710 0.000 0.040 0.952 0.004 0.004 0.000
#> SRR2314036 6 0.1980 0.8843 0.000 0.048 0.000 0.016 0.016 0.920
#> SRR2314035 3 0.1858 0.8628 0.000 0.052 0.924 0.012 0.012 0.000
#> SRR2314034 3 0.1471 0.8208 0.000 0.000 0.932 0.064 0.004 0.000
#> SRR2314032 1 0.5611 0.4296 0.576 0.004 0.000 0.080 0.312 0.028
#> SRR2314031 4 0.4602 0.4474 0.000 0.000 0.384 0.572 0.044 0.000
#> SRR2314029 2 0.4695 0.6240 0.000 0.696 0.224 0.052 0.028 0.000
#> SRR2314030 5 0.3480 0.9109 0.000 0.000 0.140 0.044 0.808 0.008
#> SRR2314028 3 0.2558 0.7267 0.000 0.004 0.840 0.156 0.000 0.000
#> SRR2314027 2 0.4817 0.2801 0.000 0.548 0.408 0.020 0.024 0.000
#> SRR2314026 3 0.1116 0.8703 0.000 0.028 0.960 0.008 0.004 0.000
#> SRR2314020 6 0.2068 0.8874 0.000 0.048 0.000 0.020 0.016 0.916
#> SRR2314019 3 0.1218 0.8703 0.000 0.028 0.956 0.012 0.004 0.000
#> SRR2314021 3 0.0405 0.8616 0.000 0.000 0.988 0.008 0.004 0.000
#> SRR2314018 3 0.2969 0.8242 0.000 0.088 0.860 0.020 0.032 0.000
#> SRR2314017 5 0.2884 0.9205 0.000 0.004 0.164 0.008 0.824 0.000
#> SRR2314016 5 0.3528 0.8797 0.000 0.000 0.092 0.084 0.816 0.008
#> SRR2314014 2 0.3318 0.7154 0.000 0.824 0.000 0.132 0.020 0.024
#> SRR2314013 2 0.3996 0.6916 0.000 0.772 0.000 0.164 0.036 0.028
#> SRR2314012 4 0.5165 0.6202 0.244 0.000 0.068 0.652 0.036 0.000
#> SRR2314011 1 0.4546 -0.1608 0.516 0.000 0.008 0.460 0.008 0.008
#> SRR2314010 2 0.2501 0.7163 0.000 0.888 0.000 0.028 0.012 0.072
#> SRR2314009 3 0.1082 0.8703 0.000 0.040 0.956 0.004 0.000 0.000
#> SRR2314008 2 0.3972 0.6285 0.000 0.740 0.220 0.016 0.024 0.000
#> SRR2314007 2 0.4752 0.2658 0.000 0.548 0.412 0.020 0.020 0.000
#> SRR2314005 2 0.6537 0.0298 0.000 0.464 0.000 0.176 0.048 0.312
#> SRR2314004 6 0.5945 0.4398 0.000 0.312 0.000 0.108 0.040 0.540
#> SRR2314003 4 0.5491 0.6566 0.180 0.000 0.124 0.652 0.044 0.000
#> SRR2314006 6 0.1477 0.8869 0.000 0.048 0.000 0.004 0.008 0.940
#> SRR2314002 5 0.2776 0.8941 0.000 0.004 0.104 0.032 0.860 0.000
#> SRR2314001 2 0.2686 0.7370 0.000 0.880 0.008 0.080 0.024 0.008
#> SRR2314000 3 0.3375 0.8311 0.000 0.056 0.852 0.052 0.028 0.012
#> SRR2313998 2 0.3623 0.7283 0.000 0.816 0.008 0.124 0.036 0.016
#> SRR2313996 2 0.1956 0.7347 0.000 0.908 0.080 0.004 0.008 0.000
#> SRR2313999 4 0.4505 0.4655 0.348 0.000 0.016 0.620 0.008 0.008
#> SRR2313997 6 0.2401 0.8848 0.000 0.048 0.000 0.028 0.024 0.900
#> SRR2313992 3 0.2278 0.7673 0.000 0.004 0.868 0.128 0.000 0.000
#> SRR2313991 5 0.2664 0.9213 0.000 0.016 0.136 0.000 0.848 0.000
#> SRR2313990 2 0.4022 0.7361 0.000 0.788 0.104 0.084 0.024 0.000
#> SRR2313988 1 0.2985 0.6699 0.868 0.004 0.000 0.068 0.040 0.020
#> SRR2313987 1 0.3950 0.3618 0.672 0.000 0.000 0.312 0.008 0.008
#> SRR2313984 2 0.4313 0.6755 0.000 0.768 0.000 0.108 0.032 0.092
#> SRR2313989 6 0.1793 0.8867 0.000 0.048 0.000 0.012 0.012 0.928
#> SRR2313983 1 0.0000 0.7300 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.4593 0.4549 0.000 0.000 0.380 0.576 0.044 0.000
#> SRR2313982 1 0.0000 0.7300 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.1003 0.8670 0.000 0.020 0.964 0.016 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", "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 16873 rows and 72 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 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 0.995 0.998 0.5075 0.493 0.493
#> 3 3 0.928 0.851 0.940 0.2677 0.848 0.698
#> 4 4 0.908 0.853 0.930 0.0957 0.867 0.664
#> 5 5 0.789 0.708 0.866 0.0618 0.928 0.770
#> 6 6 0.763 0.688 0.829 0.0591 0.933 0.753
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
#> SRR2313978 2 0.0000 0.995 0.000 1.000
#> SRR2313980 2 0.0000 0.995 0.000 1.000
#> SRR2313979 1 0.0000 1.000 1.000 0.000
#> SRR2313977 2 0.0000 0.995 0.000 1.000
#> SRR2313976 2 0.0000 0.995 0.000 1.000
#> SRR2313975 1 0.0000 1.000 1.000 0.000
#> SRR2313973 1 0.0000 1.000 1.000 0.000
#> SRR2313974 1 0.0000 1.000 1.000 0.000
#> SRR2313985 1 0.0000 1.000 1.000 0.000
#> SRR2313993 2 0.0000 0.995 0.000 1.000
#> SRR2313994 2 0.0000 0.995 0.000 1.000
#> SRR2313995 2 0.0000 0.995 0.000 1.000
#> SRR2314024 2 0.0000 0.995 0.000 1.000
#> SRR2314015 2 0.0000 0.995 0.000 1.000
#> SRR2314025 1 0.0000 1.000 1.000 0.000
#> SRR2314023 1 0.0000 1.000 1.000 0.000
#> SRR2314033 2 0.0000 0.995 0.000 1.000
#> SRR2314045 1 0.0000 1.000 1.000 0.000
#> SRR2314042 1 0.0000 1.000 1.000 0.000
#> SRR2314043 1 0.0000 1.000 1.000 0.000
#> SRR2314041 2 0.0000 0.995 0.000 1.000
#> SRR2314044 2 0.0000 0.995 0.000 1.000
#> SRR2314040 2 0.6148 0.821 0.152 0.848
#> SRR2314039 2 0.0000 0.995 0.000 1.000
#> SRR2314038 2 0.0000 0.995 0.000 1.000
#> SRR2314037 1 0.0000 1.000 1.000 0.000
#> SRR2314036 2 0.0000 0.995 0.000 1.000
#> SRR2314035 1 0.0000 1.000 1.000 0.000
#> SRR2314034 1 0.0000 1.000 1.000 0.000
#> SRR2314032 1 0.0000 1.000 1.000 0.000
#> SRR2314031 1 0.0000 1.000 1.000 0.000
#> SRR2314029 2 0.0000 0.995 0.000 1.000
#> SRR2314030 1 0.0000 1.000 1.000 0.000
#> SRR2314028 1 0.0000 1.000 1.000 0.000
#> SRR2314027 2 0.0000 0.995 0.000 1.000
#> SRR2314026 1 0.0000 1.000 1.000 0.000
#> SRR2314020 2 0.0000 0.995 0.000 1.000
#> SRR2314019 1 0.0376 0.996 0.996 0.004
#> SRR2314021 1 0.0000 1.000 1.000 0.000
#> SRR2314018 2 0.0938 0.984 0.012 0.988
#> SRR2314017 1 0.0000 1.000 1.000 0.000
#> SRR2314016 1 0.0000 1.000 1.000 0.000
#> SRR2314014 2 0.0000 0.995 0.000 1.000
#> SRR2314013 2 0.0000 0.995 0.000 1.000
#> SRR2314012 1 0.0000 1.000 1.000 0.000
#> SRR2314011 1 0.0000 1.000 1.000 0.000
#> SRR2314010 2 0.0000 0.995 0.000 1.000
#> SRR2314009 1 0.0000 1.000 1.000 0.000
#> SRR2314008 2 0.0000 0.995 0.000 1.000
#> SRR2314007 2 0.0000 0.995 0.000 1.000
#> SRR2314005 2 0.0000 0.995 0.000 1.000
#> SRR2314004 2 0.0000 0.995 0.000 1.000
#> SRR2314003 1 0.0000 1.000 1.000 0.000
#> SRR2314006 2 0.0000 0.995 0.000 1.000
#> SRR2314002 1 0.0000 1.000 1.000 0.000
#> SRR2314001 2 0.0000 0.995 0.000 1.000
#> SRR2314000 1 0.0000 1.000 1.000 0.000
#> SRR2313998 2 0.0000 0.995 0.000 1.000
#> SRR2313996 2 0.0000 0.995 0.000 1.000
#> SRR2313999 1 0.0000 1.000 1.000 0.000
#> SRR2313997 2 0.0000 0.995 0.000 1.000
#> SRR2313992 1 0.0000 1.000 1.000 0.000
#> SRR2313991 2 0.0000 0.995 0.000 1.000
#> SRR2313990 2 0.0000 0.995 0.000 1.000
#> SRR2313988 1 0.0000 1.000 1.000 0.000
#> SRR2313987 1 0.0000 1.000 1.000 0.000
#> SRR2313984 2 0.0000 0.995 0.000 1.000
#> SRR2313989 2 0.0000 0.995 0.000 1.000
#> SRR2313983 1 0.0000 1.000 1.000 0.000
#> SRR2313986 1 0.0000 1.000 1.000 0.000
#> SRR2313982 1 0.0000 1.000 1.000 0.000
#> SRR2313981 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
#> SRR2313978 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313977 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313995 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314025 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314023 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314045 1 0.1411 0.9225 0.964 0.000 0.036
#> SRR2314042 1 0.6309 -0.1280 0.500 0.000 0.500
#> SRR2314043 3 0.1289 0.8778 0.032 0.000 0.968
#> SRR2314041 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314044 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314040 3 0.1411 0.8607 0.000 0.036 0.964
#> SRR2314039 3 0.5254 0.5643 0.000 0.264 0.736
#> SRR2314038 2 0.2663 0.8933 0.024 0.932 0.044
#> SRR2314037 3 0.1643 0.8797 0.044 0.000 0.956
#> SRR2314036 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.8618 0.000 0.000 1.000
#> SRR2314034 3 0.6225 0.2542 0.432 0.000 0.568
#> SRR2314032 1 0.1411 0.9225 0.964 0.000 0.036
#> SRR2314031 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314029 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314030 1 0.1411 0.9225 0.964 0.000 0.036
#> SRR2314028 3 0.6235 0.2421 0.436 0.000 0.564
#> SRR2314027 2 0.6126 0.3654 0.000 0.600 0.400
#> SRR2314026 3 0.1643 0.8797 0.044 0.000 0.956
#> SRR2314020 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314019 3 0.1643 0.8797 0.044 0.000 0.956
#> SRR2314021 3 0.1643 0.8797 0.044 0.000 0.956
#> SRR2314018 3 0.1411 0.8607 0.000 0.036 0.964
#> SRR2314017 1 0.1643 0.9172 0.956 0.000 0.044
#> SRR2314016 1 0.1411 0.9225 0.964 0.000 0.036
#> SRR2314014 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314009 3 0.1411 0.8786 0.036 0.000 0.964
#> SRR2314008 2 0.0424 0.9466 0.000 0.992 0.008
#> SRR2314007 2 0.6295 0.1676 0.000 0.528 0.472
#> SRR2314005 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314003 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2314006 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314002 1 0.1643 0.9172 0.956 0.000 0.044
#> SRR2314001 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2314000 1 0.2356 0.8797 0.928 0.000 0.072
#> SRR2313998 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313996 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313999 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313992 1 0.6307 -0.0838 0.512 0.000 0.488
#> SRR2313991 2 0.1643 0.9167 0.000 0.956 0.044
#> SRR2313990 2 0.6260 0.2422 0.000 0.552 0.448
#> SRR2313988 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313986 1 0.0000 0.9407 1.000 0.000 0.000
#> SRR2313982 1 0.0237 0.9390 0.996 0.000 0.004
#> SRR2313981 3 0.1643 0.8797 0.044 0.000 0.956
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.0707 0.985 0.000 0.980 0.000 0.020
#> SRR2313980 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.0817 0.983 0.000 0.976 0.000 0.024
#> SRR2313976 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2313975 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313985 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0188 0.992 0.000 0.996 0.000 0.004
#> SRR2313994 2 0.0188 0.992 0.000 0.996 0.000 0.004
#> SRR2313995 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314024 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314025 1 0.1661 0.852 0.944 0.000 0.004 0.052
#> SRR2314023 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.4804 0.297 0.616 0.000 0.000 0.384
#> SRR2314042 1 0.5925 0.590 0.648 0.000 0.284 0.068
#> SRR2314043 3 0.0657 0.807 0.004 0.000 0.984 0.012
#> SRR2314041 2 0.0707 0.985 0.000 0.980 0.000 0.020
#> SRR2314044 2 0.0817 0.983 0.000 0.976 0.000 0.024
#> SRR2314040 3 0.0376 0.807 0.004 0.000 0.992 0.004
#> SRR2314039 3 0.4898 0.608 0.000 0.260 0.716 0.024
#> SRR2314038 4 0.1474 0.912 0.000 0.052 0.000 0.948
#> SRR2314037 3 0.1452 0.793 0.008 0.000 0.956 0.036
#> SRR2314036 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.1576 0.785 0.004 0.000 0.948 0.048
#> SRR2314034 1 0.5517 0.419 0.568 0.000 0.412 0.020
#> SRR2314032 1 0.4804 0.297 0.616 0.000 0.000 0.384
#> SRR2314031 1 0.2101 0.845 0.928 0.000 0.012 0.060
#> SRR2314029 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314030 4 0.1389 0.940 0.048 0.000 0.000 0.952
#> SRR2314028 1 0.5805 0.455 0.576 0.000 0.388 0.036
#> SRR2314027 3 0.5586 0.296 0.000 0.452 0.528 0.020
#> SRR2314026 3 0.0657 0.807 0.004 0.000 0.984 0.012
#> SRR2314020 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.0469 0.807 0.000 0.000 0.988 0.012
#> SRR2314021 3 0.0804 0.807 0.008 0.000 0.980 0.012
#> SRR2314018 3 0.1356 0.800 0.000 0.008 0.960 0.032
#> SRR2314017 4 0.1743 0.944 0.056 0.000 0.004 0.940
#> SRR2314016 4 0.1792 0.941 0.068 0.000 0.000 0.932
#> SRR2314014 2 0.0817 0.983 0.000 0.976 0.000 0.024
#> SRR2314013 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314012 1 0.0592 0.867 0.984 0.000 0.000 0.016
#> SRR2314011 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2314010 2 0.0188 0.992 0.000 0.996 0.000 0.004
#> SRR2314009 3 0.0469 0.806 0.000 0.000 0.988 0.012
#> SRR2314008 2 0.1109 0.977 0.000 0.968 0.004 0.028
#> SRR2314007 3 0.5478 0.524 0.000 0.344 0.628 0.028
#> SRR2314005 2 0.0188 0.991 0.000 0.996 0.000 0.004
#> SRR2314004 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314003 1 0.1489 0.856 0.952 0.000 0.004 0.044
#> SRR2314006 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2314002 4 0.2216 0.924 0.092 0.000 0.000 0.908
#> SRR2314001 2 0.0707 0.985 0.000 0.980 0.000 0.020
#> SRR2314000 1 0.1624 0.848 0.952 0.000 0.020 0.028
#> SRR2313998 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2313996 2 0.0707 0.985 0.000 0.980 0.000 0.020
#> SRR2313999 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313997 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2313992 1 0.5639 0.554 0.636 0.000 0.324 0.040
#> SRR2313991 4 0.1867 0.900 0.000 0.072 0.000 0.928
#> SRR2313990 3 0.5420 0.514 0.000 0.352 0.624 0.024
#> SRR2313988 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.0188 0.992 0.000 0.996 0.000 0.004
#> SRR2313989 2 0.0000 0.993 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313986 1 0.1743 0.850 0.940 0.000 0.004 0.056
#> SRR2313982 1 0.0000 0.871 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.1305 0.794 0.004 0.000 0.960 0.036
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.2249 0.8958 0.000 0.896 0.096 0.000 0.008
#> SRR2313980 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.3388 0.8164 0.000 0.792 0.200 0.000 0.008
#> SRR2313976 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2313975 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313985 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313993 2 0.0671 0.9384 0.000 0.980 0.016 0.000 0.004
#> SRR2313994 2 0.0955 0.9344 0.000 0.968 0.028 0.000 0.004
#> SRR2313995 2 0.0290 0.9394 0.000 0.992 0.008 0.000 0.000
#> SRR2314024 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0162 0.9411 0.000 0.996 0.004 0.000 0.000
#> SRR2314025 1 0.3838 0.6344 0.716 0.000 0.000 0.280 0.004
#> SRR2314023 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.3752 0.5448 0.708 0.000 0.000 0.000 0.292
#> SRR2314042 4 0.2929 0.4120 0.180 0.000 0.000 0.820 0.000
#> SRR2314043 3 0.4137 0.3817 0.012 0.000 0.732 0.248 0.008
#> SRR2314041 2 0.2358 0.8919 0.000 0.888 0.104 0.000 0.008
#> SRR2314044 2 0.3170 0.8550 0.000 0.828 0.160 0.004 0.008
#> SRR2314040 3 0.2462 0.4846 0.000 0.000 0.880 0.112 0.008
#> SRR2314039 4 0.6341 0.0524 0.000 0.120 0.328 0.536 0.016
#> SRR2314038 5 0.0703 0.9614 0.000 0.024 0.000 0.000 0.976
#> SRR2314037 4 0.1704 0.3856 0.004 0.000 0.068 0.928 0.000
#> SRR2314036 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.3277 0.4579 0.008 0.000 0.832 0.148 0.012
#> SRR2314034 4 0.6159 0.3208 0.348 0.000 0.096 0.540 0.016
#> SRR2314032 1 0.3752 0.5448 0.708 0.000 0.000 0.000 0.292
#> SRR2314031 1 0.4029 0.5763 0.680 0.000 0.000 0.316 0.004
#> SRR2314029 2 0.0510 0.9369 0.000 0.984 0.016 0.000 0.000
#> SRR2314030 5 0.0613 0.9631 0.004 0.000 0.004 0.008 0.984
#> SRR2314028 4 0.5336 0.3387 0.252 0.000 0.100 0.648 0.000
#> SRR2314027 4 0.7208 -0.1291 0.000 0.344 0.292 0.348 0.016
#> SRR2314026 4 0.4311 0.2840 0.004 0.000 0.264 0.712 0.020
#> SRR2314020 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.4106 0.2958 0.000 0.000 0.256 0.724 0.020
#> SRR2314021 4 0.4301 0.3019 0.008 0.000 0.244 0.728 0.020
#> SRR2314018 3 0.0671 0.4807 0.000 0.000 0.980 0.016 0.004
#> SRR2314017 5 0.0865 0.9716 0.024 0.000 0.004 0.000 0.972
#> SRR2314016 5 0.0794 0.9705 0.028 0.000 0.000 0.000 0.972
#> SRR2314014 2 0.3318 0.8248 0.000 0.800 0.192 0.000 0.008
#> SRR2314013 2 0.0290 0.9404 0.000 0.992 0.008 0.000 0.000
#> SRR2314012 1 0.1410 0.8435 0.940 0.000 0.000 0.060 0.000
#> SRR2314011 1 0.0162 0.8766 0.996 0.000 0.000 0.004 0.000
#> SRR2314010 2 0.0671 0.9382 0.000 0.980 0.016 0.000 0.004
#> SRR2314009 3 0.4283 0.0930 0.000 0.000 0.544 0.456 0.000
#> SRR2314008 2 0.4046 0.6864 0.000 0.696 0.296 0.000 0.008
#> SRR2314007 3 0.6864 0.0217 0.000 0.196 0.420 0.372 0.012
#> SRR2314005 2 0.1608 0.9084 0.000 0.928 0.072 0.000 0.000
#> SRR2314004 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 1 0.3586 0.6576 0.736 0.000 0.000 0.264 0.000
#> SRR2314006 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.0880 0.9672 0.032 0.000 0.000 0.000 0.968
#> SRR2314001 2 0.2971 0.8566 0.000 0.836 0.156 0.000 0.008
#> SRR2314000 3 0.4451 -0.0573 0.492 0.000 0.504 0.004 0.000
#> SRR2313998 2 0.0703 0.9349 0.000 0.976 0.024 0.000 0.000
#> SRR2313996 2 0.2798 0.8674 0.000 0.852 0.140 0.000 0.008
#> SRR2313999 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313997 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.4359 0.1027 0.412 0.000 0.004 0.584 0.000
#> SRR2313991 5 0.0794 0.9591 0.000 0.028 0.000 0.000 0.972
#> SRR2313990 3 0.4082 0.3077 0.000 0.240 0.740 0.012 0.008
#> SRR2313988 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313984 2 0.0451 0.9401 0.000 0.988 0.008 0.000 0.004
#> SRR2313989 2 0.0000 0.9410 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 1 0.3861 0.6290 0.712 0.000 0.000 0.284 0.004
#> SRR2313982 1 0.0000 0.8785 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 4 0.3452 0.2359 0.000 0.000 0.244 0.756 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.3883 0.58985 0.000 0.656 0.332 0.012 0.000 0.000
#> SRR2313980 2 0.0291 0.82978 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR2313979 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.5183 0.39757 0.000 0.516 0.408 0.008 0.000 0.068
#> SRR2313976 2 0.1049 0.82730 0.000 0.960 0.032 0.000 0.000 0.008
#> SRR2313975 1 0.0146 0.94405 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR2313973 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.0146 0.94455 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313985 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313993 2 0.2048 0.80647 0.000 0.880 0.120 0.000 0.000 0.000
#> SRR2313994 2 0.2402 0.80162 0.000 0.868 0.120 0.000 0.000 0.012
#> SRR2313995 2 0.1349 0.81474 0.000 0.940 0.056 0.004 0.000 0.000
#> SRR2314024 2 0.1082 0.82339 0.000 0.956 0.040 0.004 0.000 0.000
#> SRR2314015 2 0.0777 0.83255 0.000 0.972 0.024 0.000 0.000 0.004
#> SRR2314025 4 0.3578 0.63012 0.340 0.000 0.000 0.660 0.000 0.000
#> SRR2314023 1 0.0146 0.94455 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2314033 2 0.0291 0.82959 0.000 0.992 0.004 0.000 0.000 0.004
#> SRR2314045 1 0.2092 0.82505 0.876 0.000 0.000 0.000 0.124 0.000
#> SRR2314042 4 0.1707 0.68426 0.056 0.000 0.012 0.928 0.000 0.004
#> SRR2314043 6 0.3932 0.54054 0.004 0.000 0.072 0.112 0.016 0.796
#> SRR2314041 2 0.3923 0.56419 0.000 0.620 0.372 0.008 0.000 0.000
#> SRR2314044 2 0.6032 0.27017 0.000 0.436 0.424 0.104 0.000 0.036
#> SRR2314040 6 0.1753 0.57938 0.000 0.000 0.084 0.004 0.000 0.912
#> SRR2314039 3 0.4662 0.42138 0.000 0.060 0.748 0.104 0.000 0.088
#> SRR2314038 5 0.0000 0.99782 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314037 4 0.2389 0.58575 0.000 0.000 0.060 0.888 0.000 0.052
#> SRR2314036 2 0.0000 0.83055 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314035 6 0.1121 0.59319 0.008 0.000 0.008 0.016 0.004 0.964
#> SRR2314034 4 0.6958 0.20519 0.220 0.000 0.080 0.440 0.000 0.260
#> SRR2314032 1 0.2092 0.82505 0.876 0.000 0.000 0.000 0.124 0.000
#> SRR2314031 4 0.3043 0.70666 0.200 0.000 0.000 0.792 0.008 0.000
#> SRR2314029 2 0.2420 0.79340 0.000 0.888 0.076 0.032 0.000 0.004
#> SRR2314030 5 0.0146 0.99504 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR2314028 4 0.2962 0.67793 0.068 0.000 0.000 0.848 0.000 0.084
#> SRR2314027 3 0.4296 0.41345 0.000 0.152 0.760 0.044 0.000 0.044
#> SRR2314026 3 0.5894 0.31859 0.004 0.000 0.500 0.216 0.000 0.280
#> SRR2314020 2 0.0000 0.83055 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314019 3 0.5894 0.31859 0.004 0.000 0.500 0.216 0.000 0.280
#> SRR2314021 3 0.5894 0.31859 0.004 0.000 0.500 0.216 0.000 0.280
#> SRR2314018 6 0.3109 0.46926 0.000 0.000 0.224 0.000 0.004 0.772
#> SRR2314017 5 0.0000 0.99782 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314016 5 0.0146 0.99388 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR2314014 2 0.4969 0.44797 0.000 0.544 0.396 0.008 0.000 0.052
#> SRR2314013 2 0.1265 0.82565 0.000 0.948 0.044 0.000 0.000 0.008
#> SRR2314012 1 0.2730 0.70464 0.808 0.000 0.000 0.192 0.000 0.000
#> SRR2314011 1 0.1610 0.87221 0.916 0.000 0.000 0.084 0.000 0.000
#> SRR2314010 2 0.2100 0.80241 0.000 0.884 0.112 0.004 0.000 0.000
#> SRR2314009 6 0.4962 0.16384 0.000 0.000 0.068 0.416 0.000 0.516
#> SRR2314008 3 0.6007 -0.23734 0.000 0.404 0.424 0.012 0.000 0.160
#> SRR2314007 3 0.3092 0.36777 0.000 0.036 0.860 0.040 0.000 0.064
#> SRR2314005 2 0.2383 0.79699 0.000 0.880 0.096 0.000 0.000 0.024
#> SRR2314004 2 0.0363 0.83157 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR2314003 4 0.3672 0.58223 0.368 0.000 0.000 0.632 0.000 0.000
#> SRR2314006 2 0.0405 0.82926 0.000 0.988 0.008 0.004 0.000 0.000
#> SRR2314002 5 0.0000 0.99782 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314001 2 0.4326 0.50117 0.000 0.572 0.404 0.000 0.000 0.024
#> SRR2314000 6 0.4591 0.09661 0.452 0.000 0.000 0.028 0.004 0.516
#> SRR2313998 2 0.1745 0.81812 0.000 0.920 0.068 0.000 0.000 0.012
#> SRR2313996 2 0.4208 0.46643 0.000 0.536 0.452 0.008 0.000 0.004
#> SRR2313999 1 0.1007 0.91388 0.956 0.000 0.000 0.044 0.000 0.000
#> SRR2313997 2 0.0146 0.83019 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR2313992 4 0.2389 0.71435 0.128 0.000 0.000 0.864 0.000 0.008
#> SRR2313991 5 0.0000 0.99782 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313990 3 0.5970 -0.00184 0.000 0.172 0.444 0.008 0.000 0.376
#> SRR2313988 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.0146 0.94455 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313984 2 0.1387 0.82648 0.000 0.932 0.068 0.000 0.000 0.000
#> SRR2313989 2 0.0291 0.82978 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR2313983 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.3244 0.68000 0.268 0.000 0.000 0.732 0.000 0.000
#> SRR2313982 1 0.0000 0.94549 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 4 0.3190 0.49857 0.000 0.000 0.008 0.772 0.000 0.220
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 16873 rows and 72 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 0.622 0.917 0.952 0.4895 0.503 0.503
#> 3 3 0.741 0.899 0.949 0.3192 0.678 0.452
#> 4 4 0.681 0.653 0.806 0.1241 0.900 0.727
#> 5 5 0.780 0.742 0.881 0.0739 0.840 0.519
#> 6 6 0.725 0.651 0.830 0.0410 0.972 0.873
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
#> SRR2313978 2 0.0000 0.972 0.000 1.000
#> SRR2313980 2 0.0000 0.972 0.000 1.000
#> SRR2313979 1 0.0000 0.929 1.000 0.000
#> SRR2313977 2 0.0000 0.972 0.000 1.000
#> SRR2313976 2 0.0000 0.972 0.000 1.000
#> SRR2313975 1 0.0000 0.929 1.000 0.000
#> SRR2313973 1 0.0000 0.929 1.000 0.000
#> SRR2313974 1 0.0000 0.929 1.000 0.000
#> SRR2313985 1 0.0000 0.929 1.000 0.000
#> SRR2313993 2 0.0000 0.972 0.000 1.000
#> SRR2313994 2 0.0000 0.972 0.000 1.000
#> SRR2313995 2 0.0000 0.972 0.000 1.000
#> SRR2314024 2 0.0000 0.972 0.000 1.000
#> SRR2314015 2 0.0000 0.972 0.000 1.000
#> SRR2314025 1 0.0000 0.929 1.000 0.000
#> SRR2314023 1 0.0000 0.929 1.000 0.000
#> SRR2314033 2 0.0000 0.972 0.000 1.000
#> SRR2314045 1 0.0000 0.929 1.000 0.000
#> SRR2314042 1 0.4815 0.913 0.896 0.104
#> SRR2314043 1 0.4815 0.913 0.896 0.104
#> SRR2314041 2 0.3733 0.911 0.072 0.928
#> SRR2314044 1 0.7602 0.787 0.780 0.220
#> SRR2314040 1 0.4815 0.913 0.896 0.104
#> SRR2314039 2 0.3733 0.911 0.072 0.928
#> SRR2314038 1 0.9850 0.364 0.572 0.428
#> SRR2314037 1 0.4815 0.913 0.896 0.104
#> SRR2314036 2 0.0000 0.972 0.000 1.000
#> SRR2314035 1 0.4815 0.913 0.896 0.104
#> SRR2314034 1 0.4690 0.914 0.900 0.100
#> SRR2314032 1 0.0000 0.929 1.000 0.000
#> SRR2314031 1 0.4690 0.914 0.900 0.100
#> SRR2314029 2 0.8608 0.585 0.284 0.716
#> SRR2314030 1 0.4815 0.913 0.896 0.104
#> SRR2314028 1 0.2236 0.925 0.964 0.036
#> SRR2314027 2 0.3733 0.911 0.072 0.928
#> SRR2314026 1 0.5294 0.900 0.880 0.120
#> SRR2314020 2 0.0000 0.972 0.000 1.000
#> SRR2314019 1 0.4815 0.913 0.896 0.104
#> SRR2314021 1 0.4815 0.913 0.896 0.104
#> SRR2314018 1 0.4815 0.913 0.896 0.104
#> SRR2314017 1 0.4815 0.913 0.896 0.104
#> SRR2314016 1 0.0000 0.929 1.000 0.000
#> SRR2314014 2 0.0000 0.972 0.000 1.000
#> SRR2314013 2 0.0000 0.972 0.000 1.000
#> SRR2314012 1 0.0000 0.929 1.000 0.000
#> SRR2314011 1 0.0000 0.929 1.000 0.000
#> SRR2314010 2 0.0000 0.972 0.000 1.000
#> SRR2314009 1 0.4815 0.913 0.896 0.104
#> SRR2314008 1 0.9358 0.558 0.648 0.352
#> SRR2314007 2 0.0000 0.972 0.000 1.000
#> SRR2314005 2 0.0000 0.972 0.000 1.000
#> SRR2314004 2 0.0000 0.972 0.000 1.000
#> SRR2314003 1 0.0000 0.929 1.000 0.000
#> SRR2314006 2 0.0000 0.972 0.000 1.000
#> SRR2314002 1 0.0672 0.928 0.992 0.008
#> SRR2314001 2 0.0000 0.972 0.000 1.000
#> SRR2314000 1 0.0000 0.929 1.000 0.000
#> SRR2313998 2 0.0000 0.972 0.000 1.000
#> SRR2313996 2 0.0000 0.972 0.000 1.000
#> SRR2313999 1 0.0000 0.929 1.000 0.000
#> SRR2313997 2 0.0000 0.972 0.000 1.000
#> SRR2313992 1 0.4815 0.913 0.896 0.104
#> SRR2313991 2 0.6438 0.792 0.164 0.836
#> SRR2313990 2 0.4562 0.883 0.096 0.904
#> SRR2313988 1 0.0000 0.929 1.000 0.000
#> SRR2313987 1 0.0000 0.929 1.000 0.000
#> SRR2313984 2 0.0000 0.972 0.000 1.000
#> SRR2313989 2 0.0000 0.972 0.000 1.000
#> SRR2313983 1 0.0000 0.929 1.000 0.000
#> SRR2313986 1 0.0000 0.929 1.000 0.000
#> SRR2313982 1 0.0000 0.929 1.000 0.000
#> SRR2313981 1 0.4815 0.913 0.896 0.104
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.6204 0.228 0.000 0.424 0.576
#> SRR2313980 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313977 2 0.4702 0.811 0.000 0.788 0.212
#> SRR2313976 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2313975 1 0.4346 0.764 0.816 0.000 0.184
#> SRR2313973 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313993 2 0.3116 0.909 0.000 0.892 0.108
#> SRR2313994 2 0.4062 0.864 0.000 0.836 0.164
#> SRR2313995 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314015 2 0.3116 0.909 0.000 0.892 0.108
#> SRR2314025 1 0.1529 0.951 0.960 0.000 0.040
#> SRR2314023 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314045 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2314042 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314043 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314041 3 0.0747 0.924 0.000 0.016 0.984
#> SRR2314044 3 0.2448 0.873 0.000 0.076 0.924
#> SRR2314040 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314039 3 0.0237 0.932 0.000 0.004 0.996
#> SRR2314038 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314037 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314036 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314034 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314032 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2314031 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314029 3 0.5591 0.543 0.000 0.304 0.696
#> SRR2314030 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314028 3 0.2356 0.880 0.072 0.000 0.928
#> SRR2314027 3 0.3619 0.808 0.000 0.136 0.864
#> SRR2314026 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314020 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314019 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314021 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314018 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314016 3 0.3816 0.801 0.148 0.000 0.852
#> SRR2314014 2 0.4555 0.825 0.000 0.800 0.200
#> SRR2314013 2 0.3038 0.910 0.000 0.896 0.104
#> SRR2314012 1 0.1529 0.951 0.960 0.000 0.040
#> SRR2314011 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2314010 2 0.3116 0.909 0.000 0.892 0.108
#> SRR2314009 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314008 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2314007 3 0.0237 0.932 0.000 0.004 0.996
#> SRR2314005 2 0.1289 0.921 0.000 0.968 0.032
#> SRR2314004 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314003 1 0.1529 0.951 0.960 0.000 0.040
#> SRR2314006 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2314002 3 0.3412 0.829 0.124 0.000 0.876
#> SRR2314001 2 0.4702 0.811 0.000 0.788 0.212
#> SRR2314000 3 0.3116 0.844 0.108 0.000 0.892
#> SRR2313998 2 0.3116 0.909 0.000 0.892 0.108
#> SRR2313996 3 0.5859 0.434 0.000 0.344 0.656
#> SRR2313999 1 0.0592 0.970 0.988 0.000 0.012
#> SRR2313997 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2313992 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2313991 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2313990 3 0.0000 0.934 0.000 0.000 1.000
#> SRR2313988 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313984 2 0.2959 0.911 0.000 0.900 0.100
#> SRR2313989 2 0.0000 0.922 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313986 3 0.3116 0.844 0.108 0.000 0.892
#> SRR2313982 1 0.0000 0.977 1.000 0.000 0.000
#> SRR2313981 3 0.0000 0.934 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.4907 -0.380 0.000 0.580 0.420 0.000
#> SRR2313980 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2313979 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.3907 0.395 0.000 0.768 0.232 0.000
#> SRR2313976 2 0.4977 0.664 0.000 0.540 0.000 0.460
#> SRR2313975 1 0.3486 0.697 0.812 0.000 0.188 0.000
#> SRR2313973 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.0188 0.923 0.996 0.000 0.004 0.000
#> SRR2313985 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0592 0.616 0.000 0.984 0.000 0.016
#> SRR2313994 2 0.0000 0.608 0.000 1.000 0.000 0.000
#> SRR2313995 2 0.3726 0.646 0.000 0.788 0.000 0.212
#> SRR2314024 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314015 2 0.0000 0.608 0.000 1.000 0.000 0.000
#> SRR2314025 1 0.3528 0.779 0.808 0.000 0.192 0.000
#> SRR2314023 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314045 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2314042 3 0.0336 0.659 0.000 0.008 0.992 0.000
#> SRR2314043 3 0.0707 0.657 0.000 0.020 0.980 0.000
#> SRR2314041 3 0.4977 0.504 0.000 0.460 0.540 0.000
#> SRR2314044 3 0.4977 0.503 0.000 0.460 0.540 0.000
#> SRR2314040 3 0.3486 0.607 0.000 0.188 0.812 0.000
#> SRR2314039 3 0.4916 0.535 0.000 0.424 0.576 0.000
#> SRR2314038 4 0.7149 0.715 0.000 0.156 0.316 0.528
#> SRR2314037 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2314036 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314035 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2314034 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2314032 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2314031 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2314029 3 0.4888 0.351 0.000 0.412 0.588 0.000
#> SRR2314030 4 0.6949 0.485 0.000 0.348 0.124 0.528
#> SRR2314028 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2314027 3 0.4955 0.517 0.000 0.444 0.556 0.000
#> SRR2314026 3 0.4877 0.542 0.000 0.408 0.592 0.000
#> SRR2314020 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314019 3 0.4817 0.548 0.000 0.388 0.612 0.000
#> SRR2314021 3 0.4730 0.557 0.000 0.364 0.636 0.000
#> SRR2314018 3 0.1637 0.647 0.000 0.060 0.940 0.000
#> SRR2314017 4 0.5493 0.719 0.000 0.016 0.456 0.528
#> SRR2314016 4 0.5281 0.704 0.008 0.000 0.464 0.528
#> SRR2314014 2 0.0000 0.608 0.000 1.000 0.000 0.000
#> SRR2314013 2 0.0592 0.616 0.000 0.984 0.000 0.016
#> SRR2314012 1 0.3486 0.783 0.812 0.000 0.188 0.000
#> SRR2314011 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2314010 2 0.0592 0.616 0.000 0.984 0.000 0.016
#> SRR2314009 3 0.0592 0.654 0.000 0.016 0.984 0.000
#> SRR2314008 3 0.3528 0.605 0.000 0.192 0.808 0.000
#> SRR2314007 3 0.4925 0.533 0.000 0.428 0.572 0.000
#> SRR2314005 2 0.4679 0.660 0.000 0.648 0.000 0.352
#> SRR2314004 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314003 1 0.3528 0.779 0.808 0.000 0.192 0.000
#> SRR2314006 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2314002 4 0.6044 0.736 0.000 0.044 0.428 0.528
#> SRR2314001 2 0.0000 0.608 0.000 1.000 0.000 0.000
#> SRR2314000 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2313998 2 0.0336 0.611 0.000 0.992 0.000 0.008
#> SRR2313996 2 0.4855 -0.336 0.000 0.600 0.400 0.000
#> SRR2313999 1 0.3486 0.783 0.812 0.000 0.188 0.000
#> SRR2313997 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2313992 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2313991 4 0.7344 0.666 0.000 0.224 0.248 0.528
#> SRR2313990 3 0.3975 0.581 0.000 0.240 0.760 0.000
#> SRR2313988 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.3528 0.645 0.000 0.808 0.000 0.192
#> SRR2313989 2 0.4989 0.664 0.000 0.528 0.000 0.472
#> SRR2313983 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313986 3 0.0000 0.659 0.000 0.000 1.000 0.000
#> SRR2313982 1 0.0000 0.925 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.0000 0.659 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.0865 0.7554 0.000 0.004 0.972 0.024 0.000
#> SRR2313980 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> SRR2313977 3 0.3368 0.6712 0.000 0.024 0.820 0.156 0.000
#> SRR2313976 2 0.0703 0.9705 0.000 0.976 0.024 0.000 0.000
#> SRR2313975 1 0.4325 0.6884 0.736 0.000 0.000 0.220 0.044
#> SRR2313973 1 0.1121 0.8400 0.956 0.000 0.000 0.000 0.044
#> SRR2313974 1 0.2890 0.7930 0.836 0.000 0.000 0.160 0.004
#> SRR2313985 1 0.0162 0.8401 0.996 0.000 0.000 0.000 0.004
#> SRR2313993 3 0.0794 0.7523 0.000 0.028 0.972 0.000 0.000
#> SRR2313994 3 0.0000 0.7550 0.000 0.000 1.000 0.000 0.000
#> SRR2313995 3 0.3305 0.6014 0.000 0.224 0.776 0.000 0.000
#> SRR2314024 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 3 0.0794 0.7523 0.000 0.028 0.972 0.000 0.000
#> SRR2314025 1 0.4151 0.6587 0.652 0.000 0.000 0.344 0.004
#> SRR2314023 1 0.0162 0.8401 0.996 0.000 0.000 0.000 0.004
#> SRR2314033 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.3758 0.7795 0.816 0.000 0.000 0.088 0.096
#> SRR2314042 4 0.0451 0.8656 0.000 0.000 0.008 0.988 0.004
#> SRR2314043 4 0.1121 0.8628 0.000 0.000 0.044 0.956 0.000
#> SRR2314041 3 0.1851 0.7274 0.000 0.000 0.912 0.088 0.000
#> SRR2314044 3 0.0963 0.7546 0.000 0.000 0.964 0.036 0.000
#> SRR2314040 4 0.3999 0.4228 0.000 0.000 0.344 0.656 0.000
#> SRR2314039 3 0.4307 -0.0486 0.000 0.000 0.504 0.496 0.000
#> SRR2314038 5 0.1408 0.9650 0.000 0.000 0.008 0.044 0.948
#> SRR2314037 4 0.0162 0.8673 0.000 0.000 0.000 0.996 0.004
#> SRR2314036 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 4 0.1341 0.8550 0.000 0.000 0.056 0.944 0.000
#> SRR2314034 4 0.0000 0.8676 0.000 0.000 0.000 1.000 0.000
#> SRR2314032 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> SRR2314031 4 0.0162 0.8673 0.000 0.000 0.000 0.996 0.004
#> SRR2314029 3 0.3300 0.6156 0.000 0.000 0.792 0.204 0.004
#> SRR2314030 5 0.1341 0.9180 0.000 0.000 0.056 0.000 0.944
#> SRR2314028 4 0.0000 0.8676 0.000 0.000 0.000 1.000 0.000
#> SRR2314027 3 0.4307 -0.0486 0.000 0.000 0.504 0.496 0.000
#> SRR2314026 3 0.4307 -0.0486 0.000 0.000 0.504 0.496 0.000
#> SRR2314020 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.4307 -0.0591 0.000 0.000 0.500 0.500 0.000
#> SRR2314021 4 0.4256 0.1920 0.000 0.000 0.436 0.564 0.000
#> SRR2314018 4 0.2471 0.7769 0.000 0.000 0.136 0.864 0.000
#> SRR2314017 5 0.1341 0.9619 0.000 0.000 0.000 0.056 0.944
#> SRR2314016 5 0.2068 0.9312 0.004 0.000 0.000 0.092 0.904
#> SRR2314014 3 0.0703 0.7525 0.000 0.024 0.976 0.000 0.000
#> SRR2314013 3 0.0794 0.7519 0.000 0.028 0.972 0.000 0.000
#> SRR2314012 1 0.4183 0.6773 0.668 0.000 0.000 0.324 0.008
#> SRR2314011 1 0.1282 0.8337 0.952 0.000 0.000 0.044 0.004
#> SRR2314010 3 0.0162 0.7553 0.000 0.004 0.996 0.000 0.000
#> SRR2314009 4 0.1121 0.8628 0.000 0.000 0.044 0.956 0.000
#> SRR2314008 4 0.4015 0.4157 0.000 0.000 0.348 0.652 0.000
#> SRR2314007 3 0.3816 0.4531 0.000 0.000 0.696 0.304 0.000
#> SRR2314005 3 0.4287 0.0686 0.000 0.460 0.540 0.000 0.000
#> SRR2314004 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 1 0.4235 0.6667 0.656 0.000 0.000 0.336 0.008
#> SRR2314006 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.1197 0.9630 0.000 0.000 0.000 0.048 0.952
#> SRR2314001 3 0.0000 0.7550 0.000 0.000 1.000 0.000 0.000
#> SRR2314000 4 0.0000 0.8676 0.000 0.000 0.000 1.000 0.000
#> SRR2313998 3 0.3274 0.6324 0.000 0.220 0.780 0.000 0.000
#> SRR2313996 3 0.0162 0.7556 0.000 0.000 0.996 0.004 0.000
#> SRR2313999 1 0.4235 0.6667 0.656 0.000 0.000 0.336 0.008
#> SRR2313997 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.0162 0.8673 0.000 0.000 0.000 0.996 0.004
#> SRR2313991 5 0.1568 0.9605 0.000 0.000 0.020 0.036 0.944
#> SRR2313990 3 0.2690 0.6725 0.000 0.000 0.844 0.156 0.000
#> SRR2313988 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> SRR2313987 1 0.0290 0.8402 0.992 0.000 0.000 0.000 0.008
#> SRR2313984 3 0.3612 0.5250 0.000 0.268 0.732 0.000 0.000
#> SRR2313989 2 0.0000 0.9968 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> SRR2313986 4 0.0162 0.8673 0.000 0.000 0.000 0.996 0.004
#> SRR2313982 1 0.1197 0.8399 0.952 0.000 0.000 0.000 0.048
#> SRR2313981 4 0.1121 0.8628 0.000 0.000 0.044 0.956 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.2526 0.7212 0.000 0.876 0.096 0.024 0.000 0.004
#> SRR2313980 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.2378 0.6835 0.848 0.000 0.000 0.152 0.000 0.000
#> SRR2313977 2 0.4348 0.6265 0.000 0.748 0.124 0.116 0.000 0.012
#> SRR2313976 6 0.2164 0.8903 0.000 0.032 0.000 0.068 0.000 0.900
#> SRR2313975 1 0.5144 0.2177 0.560 0.000 0.100 0.340 0.000 0.000
#> SRR2313973 1 0.2378 0.6835 0.848 0.000 0.000 0.152 0.000 0.000
#> SRR2313974 4 0.2563 0.6971 0.052 0.000 0.072 0.876 0.000 0.000
#> SRR2313985 4 0.3747 0.3598 0.396 0.000 0.000 0.604 0.000 0.000
#> SRR2313993 2 0.2458 0.7285 0.000 0.892 0.068 0.024 0.000 0.016
#> SRR2313994 2 0.1082 0.7273 0.000 0.956 0.004 0.040 0.000 0.000
#> SRR2313995 2 0.4620 0.5920 0.000 0.692 0.068 0.012 0.000 0.228
#> SRR2314024 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314015 2 0.2889 0.7287 0.000 0.868 0.048 0.068 0.000 0.016
#> SRR2314025 4 0.4319 0.4127 0.032 0.000 0.348 0.620 0.000 0.000
#> SRR2314023 4 0.3747 0.3598 0.396 0.000 0.000 0.604 0.000 0.000
#> SRR2314033 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314045 1 0.7240 -0.1192 0.360 0.000 0.240 0.304 0.096 0.000
#> SRR2314042 3 0.3266 0.5656 0.000 0.000 0.728 0.272 0.000 0.000
#> SRR2314043 3 0.0363 0.7742 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR2314041 2 0.2669 0.6840 0.000 0.836 0.156 0.008 0.000 0.000
#> SRR2314044 2 0.2609 0.7205 0.000 0.868 0.096 0.036 0.000 0.000
#> SRR2314040 3 0.4563 0.2671 0.000 0.368 0.588 0.044 0.000 0.000
#> SRR2314039 2 0.4096 0.1502 0.000 0.508 0.484 0.008 0.000 0.000
#> SRR2314038 5 0.0000 0.9311 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314037 3 0.1387 0.7696 0.000 0.000 0.932 0.068 0.000 0.000
#> SRR2314036 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.0713 0.7642 0.000 0.028 0.972 0.000 0.000 0.000
#> SRR2314034 3 0.1387 0.7696 0.000 0.000 0.932 0.068 0.000 0.000
#> SRR2314032 1 0.0000 0.7117 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2314031 3 0.3266 0.5656 0.000 0.000 0.728 0.272 0.000 0.000
#> SRR2314029 2 0.3819 0.5601 0.000 0.700 0.280 0.020 0.000 0.000
#> SRR2314030 5 0.0000 0.9311 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314028 3 0.1501 0.7657 0.000 0.000 0.924 0.076 0.000 0.000
#> SRR2314027 2 0.4096 0.1502 0.000 0.508 0.484 0.008 0.000 0.000
#> SRR2314026 2 0.4096 0.1502 0.000 0.508 0.484 0.008 0.000 0.000
#> SRR2314020 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 2 0.4097 0.1417 0.000 0.504 0.488 0.008 0.000 0.000
#> SRR2314021 3 0.3961 -0.0219 0.000 0.440 0.556 0.004 0.000 0.000
#> SRR2314018 3 0.3245 0.6621 0.000 0.172 0.800 0.028 0.000 0.000
#> SRR2314017 5 0.0000 0.9311 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314016 5 0.3608 0.5859 0.000 0.000 0.012 0.272 0.716 0.000
#> SRR2314014 2 0.2357 0.7083 0.000 0.872 0.000 0.116 0.000 0.012
#> SRR2314013 2 0.2404 0.7088 0.000 0.872 0.000 0.112 0.000 0.016
#> SRR2314012 4 0.2509 0.6996 0.036 0.000 0.088 0.876 0.000 0.000
#> SRR2314011 4 0.2312 0.6579 0.112 0.000 0.012 0.876 0.000 0.000
#> SRR2314010 2 0.1787 0.7217 0.000 0.920 0.068 0.008 0.000 0.004
#> SRR2314009 3 0.0363 0.7742 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR2314008 3 0.3819 0.2723 0.000 0.340 0.652 0.008 0.000 0.000
#> SRR2314007 2 0.3653 0.5222 0.000 0.692 0.300 0.008 0.000 0.000
#> SRR2314005 2 0.5329 0.2315 0.000 0.528 0.000 0.116 0.000 0.356
#> SRR2314004 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314003 4 0.2680 0.6885 0.032 0.000 0.108 0.860 0.000 0.000
#> SRR2314006 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 5 0.0000 0.9311 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314001 2 0.1007 0.7265 0.000 0.956 0.000 0.044 0.000 0.000
#> SRR2314000 3 0.1616 0.7706 0.000 0.020 0.932 0.048 0.000 0.000
#> SRR2313998 2 0.3600 0.6452 0.000 0.776 0.012 0.020 0.000 0.192
#> SRR2313996 2 0.1524 0.7269 0.000 0.932 0.008 0.060 0.000 0.000
#> SRR2313999 4 0.2487 0.6981 0.032 0.000 0.092 0.876 0.000 0.000
#> SRR2313997 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.1387 0.7696 0.000 0.000 0.932 0.068 0.000 0.000
#> SRR2313991 5 0.0000 0.9311 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313990 2 0.3706 0.6149 0.000 0.772 0.172 0.056 0.000 0.000
#> SRR2313988 1 0.0000 0.7117 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 4 0.3747 0.3598 0.396 0.000 0.000 0.604 0.000 0.000
#> SRR2313984 2 0.4556 0.5607 0.000 0.696 0.000 0.116 0.000 0.188
#> SRR2313989 6 0.0000 0.9884 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.2378 0.6835 0.848 0.000 0.000 0.152 0.000 0.000
#> SRR2313986 3 0.3266 0.5656 0.000 0.000 0.728 0.272 0.000 0.000
#> SRR2313982 1 0.0000 0.7117 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.0363 0.7742 0.000 0.012 0.988 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 16873 rows and 72 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.428 0.793 0.894 0.4443 0.540 0.540
#> 3 3 0.423 0.564 0.805 0.2207 0.898 0.820
#> 4 4 0.499 0.661 0.810 0.1517 0.807 0.653
#> 5 5 0.522 0.574 0.760 0.1902 0.782 0.509
#> 6 6 0.600 0.586 0.765 0.0704 0.896 0.608
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
#> SRR2313978 2 0.2236 0.886 0.036 0.964
#> SRR2313980 2 0.8144 0.689 0.252 0.748
#> SRR2313979 1 0.2948 0.851 0.948 0.052
#> SRR2313977 2 0.0000 0.890 0.000 1.000
#> SRR2313976 2 0.5294 0.850 0.120 0.880
#> SRR2313975 1 0.4562 0.846 0.904 0.096
#> SRR2313973 1 0.4431 0.848 0.908 0.092
#> SRR2313974 1 0.6438 0.805 0.836 0.164
#> SRR2313985 1 0.3431 0.853 0.936 0.064
#> SRR2313993 2 0.0938 0.890 0.012 0.988
#> SRR2313994 2 0.4562 0.864 0.096 0.904
#> SRR2313995 2 0.7528 0.737 0.216 0.784
#> SRR2314024 2 0.5178 0.852 0.116 0.884
#> SRR2314015 2 0.4939 0.857 0.108 0.892
#> SRR2314025 1 0.9000 0.607 0.684 0.316
#> SRR2314023 1 0.4298 0.849 0.912 0.088
#> SRR2314033 1 0.9983 0.149 0.524 0.476
#> SRR2314045 1 0.0000 0.832 1.000 0.000
#> SRR2314042 2 0.7453 0.723 0.212 0.788
#> SRR2314043 2 0.0938 0.890 0.012 0.988
#> SRR2314041 2 0.0000 0.890 0.000 1.000
#> SRR2314044 2 0.0000 0.890 0.000 1.000
#> SRR2314040 2 0.3114 0.878 0.056 0.944
#> SRR2314039 2 0.0000 0.890 0.000 1.000
#> SRR2314038 1 0.6973 0.728 0.812 0.188
#> SRR2314037 2 0.0000 0.890 0.000 1.000
#> SRR2314036 1 0.8608 0.582 0.716 0.284
#> SRR2314035 2 0.8267 0.555 0.260 0.740
#> SRR2314034 2 0.3584 0.853 0.068 0.932
#> SRR2314032 1 0.0000 0.832 1.000 0.000
#> SRR2314031 2 0.9909 0.147 0.444 0.556
#> SRR2314029 2 0.4690 0.860 0.100 0.900
#> SRR2314030 1 0.4022 0.822 0.920 0.080
#> SRR2314028 2 0.0000 0.890 0.000 1.000
#> SRR2314027 2 0.0000 0.890 0.000 1.000
#> SRR2314026 2 0.2043 0.879 0.032 0.968
#> SRR2314020 2 0.6343 0.814 0.160 0.840
#> SRR2314019 2 0.0000 0.890 0.000 1.000
#> SRR2314021 2 0.0672 0.889 0.008 0.992
#> SRR2314018 2 0.0000 0.890 0.000 1.000
#> SRR2314017 1 0.1843 0.827 0.972 0.028
#> SRR2314016 1 0.0672 0.833 0.992 0.008
#> SRR2314014 2 0.0000 0.890 0.000 1.000
#> SRR2314013 2 0.2043 0.887 0.032 0.968
#> SRR2314012 1 0.5178 0.839 0.884 0.116
#> SRR2314011 1 0.9248 0.533 0.660 0.340
#> SRR2314010 2 0.1843 0.888 0.028 0.972
#> SRR2314009 2 0.0000 0.890 0.000 1.000
#> SRR2314008 2 0.0376 0.890 0.004 0.996
#> SRR2314007 2 0.0000 0.890 0.000 1.000
#> SRR2314005 2 0.5294 0.850 0.120 0.880
#> SRR2314004 2 0.5294 0.850 0.120 0.880
#> SRR2314003 1 0.9833 0.340 0.576 0.424
#> SRR2314006 2 0.8144 0.689 0.252 0.748
#> SRR2314002 1 0.0000 0.832 1.000 0.000
#> SRR2314001 2 0.0000 0.890 0.000 1.000
#> SRR2314000 2 0.9427 0.474 0.360 0.640
#> SRR2313998 2 0.5294 0.850 0.120 0.880
#> SRR2313996 2 0.0000 0.890 0.000 1.000
#> SRR2313999 1 0.8081 0.726 0.752 0.248
#> SRR2313997 2 0.7453 0.752 0.212 0.788
#> SRR2313992 2 0.2043 0.879 0.032 0.968
#> SRR2313991 1 0.5842 0.779 0.860 0.140
#> SRR2313990 2 0.0000 0.890 0.000 1.000
#> SRR2313988 1 0.3733 0.853 0.928 0.072
#> SRR2313987 1 0.3733 0.853 0.928 0.072
#> SRR2313984 2 0.1633 0.887 0.024 0.976
#> SRR2313989 2 0.7056 0.778 0.192 0.808
#> SRR2313983 1 0.3431 0.853 0.936 0.064
#> SRR2313986 2 0.9608 0.335 0.384 0.616
#> SRR2313982 1 0.0000 0.832 1.000 0.000
#> SRR2313981 2 0.0000 0.890 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.1031 0.8032 0.000 0.976 0.024
#> SRR2313980 2 0.6045 0.5002 0.000 0.620 0.380
#> SRR2313979 1 0.1129 0.4917 0.976 0.004 0.020
#> SRR2313977 2 0.0661 0.8035 0.004 0.988 0.008
#> SRR2313976 2 0.5831 0.6043 0.008 0.708 0.284
#> SRR2313975 1 0.2339 0.4754 0.940 0.012 0.048
#> SRR2313973 1 0.2400 0.4793 0.932 0.004 0.064
#> SRR2313974 1 0.5158 0.5400 0.764 0.232 0.004
#> SRR2313985 1 0.0475 0.4974 0.992 0.004 0.004
#> SRR2313993 2 0.1411 0.7994 0.000 0.964 0.036
#> SRR2313994 2 0.1525 0.8012 0.004 0.964 0.032
#> SRR2313995 2 0.4682 0.7014 0.004 0.804 0.192
#> SRR2314024 2 0.5982 0.5575 0.004 0.668 0.328
#> SRR2314015 2 0.2096 0.7948 0.004 0.944 0.052
#> SRR2314025 1 0.6822 0.3029 0.508 0.480 0.012
#> SRR2314023 1 0.2945 0.5341 0.908 0.088 0.004
#> SRR2314033 2 0.7054 0.3248 0.020 0.524 0.456
#> SRR2314045 1 0.5397 0.0197 0.720 0.000 0.280
#> SRR2314042 2 0.6309 -0.3140 0.496 0.504 0.000
#> SRR2314043 2 0.1170 0.7979 0.016 0.976 0.008
#> SRR2314041 2 0.0424 0.8013 0.000 0.992 0.008
#> SRR2314044 2 0.0424 0.8013 0.000 0.992 0.008
#> SRR2314040 2 0.0237 0.8028 0.004 0.996 0.000
#> SRR2314039 2 0.0424 0.8013 0.000 0.992 0.008
#> SRR2314038 3 0.6295 0.6288 0.036 0.236 0.728
#> SRR2314037 2 0.0475 0.8013 0.004 0.992 0.004
#> SRR2314036 3 0.2383 0.5511 0.016 0.044 0.940
#> SRR2314035 2 0.6467 0.0798 0.388 0.604 0.008
#> SRR2314034 2 0.6169 0.1624 0.360 0.636 0.004
#> SRR2314032 3 0.6260 0.4976 0.448 0.000 0.552
#> SRR2314031 1 0.6309 0.2671 0.500 0.500 0.000
#> SRR2314029 2 0.3851 0.7433 0.004 0.860 0.136
#> SRR2314030 3 0.8309 0.6815 0.180 0.188 0.632
#> SRR2314028 2 0.0424 0.8013 0.000 0.992 0.008
#> SRR2314027 2 0.0424 0.8013 0.000 0.992 0.008
#> SRR2314026 2 0.4589 0.6123 0.172 0.820 0.008
#> SRR2314020 2 0.6045 0.5002 0.000 0.620 0.380
#> SRR2314019 2 0.1585 0.7878 0.028 0.964 0.008
#> SRR2314021 2 0.2280 0.7700 0.052 0.940 0.008
#> SRR2314018 2 0.0661 0.8013 0.004 0.988 0.008
#> SRR2314017 3 0.8950 0.6451 0.212 0.220 0.568
#> SRR2314016 3 0.8460 0.6265 0.148 0.244 0.608
#> SRR2314014 2 0.1525 0.8004 0.004 0.964 0.032
#> SRR2314013 2 0.1267 0.8022 0.004 0.972 0.024
#> SRR2314012 1 0.7683 0.4671 0.608 0.328 0.064
#> SRR2314011 1 0.5681 0.5293 0.748 0.236 0.016
#> SRR2314010 2 0.1411 0.7994 0.000 0.964 0.036
#> SRR2314009 2 0.2173 0.7728 0.048 0.944 0.008
#> SRR2314008 2 0.0983 0.8020 0.004 0.980 0.016
#> SRR2314007 2 0.0000 0.8024 0.000 1.000 0.000
#> SRR2314005 2 0.5247 0.6677 0.008 0.768 0.224
#> SRR2314004 2 0.3425 0.7623 0.004 0.884 0.112
#> SRR2314003 1 0.6680 0.2942 0.508 0.484 0.008
#> SRR2314006 2 0.6045 0.5002 0.000 0.620 0.380
#> SRR2314002 3 0.6735 0.5254 0.424 0.012 0.564
#> SRR2314001 2 0.0424 0.8031 0.000 0.992 0.008
#> SRR2314000 2 0.6677 0.2984 0.324 0.652 0.024
#> SRR2313998 2 0.1453 0.8021 0.008 0.968 0.024
#> SRR2313996 2 0.0000 0.8024 0.000 1.000 0.000
#> SRR2313999 1 0.6641 0.3640 0.544 0.448 0.008
#> SRR2313997 2 0.6045 0.5002 0.000 0.620 0.380
#> SRR2313992 2 0.6244 -0.1371 0.440 0.560 0.000
#> SRR2313991 3 0.4563 0.6537 0.036 0.112 0.852
#> SRR2313990 2 0.0237 0.8028 0.004 0.996 0.000
#> SRR2313988 1 0.1753 0.4663 0.952 0.000 0.048
#> SRR2313987 1 0.5365 0.5206 0.744 0.252 0.004
#> SRR2313984 2 0.1753 0.7970 0.000 0.952 0.048
#> SRR2313989 2 0.6247 0.5006 0.004 0.620 0.376
#> SRR2313983 1 0.0475 0.4974 0.992 0.004 0.004
#> SRR2313986 2 0.6309 -0.3249 0.500 0.500 0.000
#> SRR2313982 1 0.6309 -0.5287 0.504 0.000 0.496
#> SRR2313981 2 0.2096 0.7719 0.052 0.944 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.0469 0.8346 0.000 0.012 0.988 0.000
#> SRR2313980 2 0.2408 0.9014 0.000 0.896 0.104 0.000
#> SRR2313979 1 0.3649 0.4734 0.796 0.000 0.000 0.204
#> SRR2313977 3 0.1792 0.8251 0.000 0.068 0.932 0.000
#> SRR2313976 3 0.4804 0.4818 0.000 0.384 0.616 0.000
#> SRR2313975 1 0.5516 0.4863 0.732 0.056 0.012 0.200
#> SRR2313973 1 0.1022 0.5903 0.968 0.000 0.000 0.032
#> SRR2313974 1 0.7034 0.3185 0.576 0.000 0.204 0.220
#> SRR2313985 1 0.2081 0.5919 0.916 0.000 0.000 0.084
#> SRR2313993 3 0.0336 0.8351 0.000 0.008 0.992 0.000
#> SRR2313994 3 0.2149 0.8119 0.000 0.088 0.912 0.000
#> SRR2313995 3 0.4761 0.4134 0.000 0.372 0.628 0.000
#> SRR2314024 3 0.4916 0.2851 0.000 0.424 0.576 0.000
#> SRR2314015 3 0.2530 0.7846 0.000 0.112 0.888 0.000
#> SRR2314025 3 0.8291 -0.1197 0.308 0.012 0.344 0.336
#> SRR2314023 1 0.3266 0.5588 0.832 0.000 0.000 0.168
#> SRR2314033 2 0.2515 0.8489 0.004 0.912 0.072 0.012
#> SRR2314045 1 0.6139 -0.0518 0.544 0.052 0.000 0.404
#> SRR2314042 3 0.6473 0.6438 0.152 0.012 0.676 0.160
#> SRR2314043 3 0.3144 0.8290 0.000 0.044 0.884 0.072
#> SRR2314041 3 0.0469 0.8346 0.000 0.012 0.988 0.000
#> SRR2314044 3 0.1004 0.8365 0.000 0.024 0.972 0.004
#> SRR2314040 3 0.3900 0.8105 0.000 0.084 0.844 0.072
#> SRR2314039 3 0.0817 0.8360 0.000 0.024 0.976 0.000
#> SRR2314038 4 0.5057 0.6639 0.016 0.060 0.140 0.784
#> SRR2314037 3 0.2329 0.8319 0.000 0.012 0.916 0.072
#> SRR2314036 2 0.5427 0.1890 0.016 0.568 0.000 0.416
#> SRR2314035 3 0.4724 0.7907 0.000 0.136 0.788 0.076
#> SRR2314034 3 0.2775 0.8300 0.000 0.020 0.896 0.084
#> SRR2314032 4 0.5865 0.4401 0.340 0.048 0.000 0.612
#> SRR2314031 3 0.7248 0.5167 0.200 0.012 0.592 0.196
#> SRR2314029 3 0.2494 0.8325 0.000 0.036 0.916 0.048
#> SRR2314030 4 0.4093 0.6841 0.032 0.012 0.120 0.836
#> SRR2314028 3 0.2984 0.8307 0.000 0.028 0.888 0.084
#> SRR2314027 3 0.0592 0.8353 0.000 0.016 0.984 0.000
#> SRR2314026 3 0.2450 0.8334 0.000 0.016 0.912 0.072
#> SRR2314020 2 0.2408 0.9014 0.000 0.896 0.104 0.000
#> SRR2314019 3 0.2670 0.8305 0.000 0.024 0.904 0.072
#> SRR2314021 3 0.2563 0.8313 0.000 0.020 0.908 0.072
#> SRR2314018 3 0.3056 0.8295 0.000 0.040 0.888 0.072
#> SRR2314017 4 0.3597 0.6698 0.016 0.000 0.148 0.836
#> SRR2314016 4 0.1637 0.5582 0.060 0.000 0.000 0.940
#> SRR2314014 3 0.2281 0.8159 0.000 0.096 0.904 0.000
#> SRR2314013 3 0.1118 0.8311 0.000 0.036 0.964 0.000
#> SRR2314012 1 0.4500 0.4521 0.684 0.000 0.000 0.316
#> SRR2314011 1 0.8088 0.1598 0.420 0.012 0.340 0.228
#> SRR2314010 3 0.0469 0.8346 0.000 0.012 0.988 0.000
#> SRR2314009 3 0.2563 0.8326 0.000 0.020 0.908 0.072
#> SRR2314008 3 0.1978 0.8177 0.000 0.068 0.928 0.004
#> SRR2314007 3 0.0336 0.8356 0.000 0.008 0.992 0.000
#> SRR2314005 3 0.3528 0.7391 0.000 0.192 0.808 0.000
#> SRR2314004 3 0.1940 0.8141 0.000 0.076 0.924 0.000
#> SRR2314003 3 0.8105 0.1621 0.300 0.012 0.436 0.252
#> SRR2314006 2 0.2408 0.9014 0.000 0.896 0.104 0.000
#> SRR2314002 4 0.4222 0.5612 0.272 0.000 0.000 0.728
#> SRR2314001 3 0.1118 0.8311 0.000 0.036 0.964 0.000
#> SRR2314000 3 0.5615 0.7030 0.000 0.096 0.716 0.188
#> SRR2313998 3 0.1211 0.8311 0.000 0.040 0.960 0.000
#> SRR2313996 3 0.0000 0.8355 0.000 0.000 1.000 0.000
#> SRR2313999 3 0.8103 -0.1121 0.344 0.008 0.388 0.260
#> SRR2313997 2 0.2408 0.9014 0.000 0.896 0.104 0.000
#> SRR2313992 3 0.3015 0.8263 0.000 0.024 0.884 0.092
#> SRR2313991 4 0.5500 0.6550 0.016 0.080 0.148 0.756
#> SRR2313990 3 0.2081 0.8182 0.000 0.084 0.916 0.000
#> SRR2313988 1 0.4669 0.4751 0.780 0.052 0.000 0.168
#> SRR2313987 1 0.4103 0.5101 0.744 0.000 0.000 0.256
#> SRR2313984 3 0.3024 0.7482 0.000 0.148 0.852 0.000
#> SRR2313989 2 0.2408 0.9014 0.000 0.896 0.104 0.000
#> SRR2313983 1 0.2760 0.5497 0.872 0.000 0.000 0.128
#> SRR2313986 3 0.7309 0.5054 0.196 0.012 0.584 0.208
#> SRR2313982 4 0.4790 0.4384 0.380 0.000 0.000 0.620
#> SRR2313981 3 0.2635 0.8320 0.000 0.020 0.904 0.076
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.3132 0.6843 0.000 0.008 0.820 0.172 0.000
#> SRR2313980 2 0.0510 0.9466 0.000 0.984 0.016 0.000 0.000
#> SRR2313979 1 0.4219 0.6524 0.584 0.000 0.000 0.000 0.416
#> SRR2313977 3 0.4297 0.4593 0.000 0.020 0.692 0.288 0.000
#> SRR2313976 3 0.4067 0.4715 0.000 0.300 0.692 0.008 0.000
#> SRR2313975 1 0.6890 0.4865 0.456 0.000 0.028 0.148 0.368
#> SRR2313973 1 0.3883 0.7200 0.744 0.000 0.004 0.008 0.244
#> SRR2313974 1 0.6313 0.3168 0.540 0.000 0.008 0.304 0.148
#> SRR2313985 1 0.3274 0.7194 0.780 0.000 0.000 0.000 0.220
#> SRR2313993 3 0.1668 0.7222 0.000 0.028 0.940 0.032 0.000
#> SRR2313994 3 0.0898 0.7159 0.000 0.020 0.972 0.008 0.000
#> SRR2313995 3 0.5379 0.5385 0.000 0.244 0.648 0.108 0.000
#> SRR2314024 3 0.5218 0.4871 0.000 0.296 0.632 0.072 0.000
#> SRR2314015 3 0.2513 0.6888 0.000 0.116 0.876 0.008 0.000
#> SRR2314025 4 0.4604 0.2196 0.404 0.000 0.004 0.584 0.008
#> SRR2314023 1 0.3177 0.7175 0.792 0.000 0.000 0.000 0.208
#> SRR2314033 2 0.0898 0.9323 0.000 0.972 0.020 0.000 0.008
#> SRR2314045 5 0.5043 -0.3980 0.356 0.000 0.000 0.044 0.600
#> SRR2314042 4 0.2124 0.5779 0.096 0.000 0.004 0.900 0.000
#> SRR2314043 4 0.4074 0.5307 0.000 0.000 0.364 0.636 0.000
#> SRR2314041 3 0.3424 0.6298 0.000 0.000 0.760 0.240 0.000
#> SRR2314044 4 0.3876 0.5570 0.000 0.000 0.316 0.684 0.000
#> SRR2314040 4 0.4298 0.5132 0.000 0.008 0.352 0.640 0.000
#> SRR2314039 4 0.4235 0.3270 0.000 0.000 0.424 0.576 0.000
#> SRR2314038 5 0.7079 0.6074 0.008 0.064 0.148 0.204 0.576
#> SRR2314037 4 0.3177 0.6561 0.000 0.000 0.208 0.792 0.000
#> SRR2314036 2 0.3662 0.6201 0.000 0.744 0.004 0.000 0.252
#> SRR2314035 4 0.4283 0.5011 0.000 0.008 0.348 0.644 0.000
#> SRR2314034 4 0.2970 0.6610 0.004 0.000 0.168 0.828 0.000
#> SRR2314032 5 0.1907 0.4423 0.028 0.000 0.000 0.044 0.928
#> SRR2314031 4 0.3196 0.5302 0.192 0.000 0.004 0.804 0.000
#> SRR2314029 3 0.3752 0.4995 0.000 0.000 0.708 0.292 0.000
#> SRR2314030 5 0.5964 0.6613 0.040 0.016 0.064 0.204 0.676
#> SRR2314028 4 0.3010 0.6612 0.004 0.000 0.172 0.824 0.000
#> SRR2314027 3 0.3534 0.6078 0.000 0.000 0.744 0.256 0.000
#> SRR2314026 4 0.3707 0.5958 0.000 0.000 0.284 0.716 0.000
#> SRR2314020 2 0.0510 0.9466 0.000 0.984 0.016 0.000 0.000
#> SRR2314019 4 0.3336 0.6451 0.000 0.000 0.228 0.772 0.000
#> SRR2314021 4 0.3143 0.6572 0.000 0.000 0.204 0.796 0.000
#> SRR2314018 4 0.4367 0.5119 0.000 0.008 0.372 0.620 0.000
#> SRR2314017 5 0.5278 0.6528 0.008 0.008 0.064 0.232 0.688
#> SRR2314016 5 0.5264 0.4344 0.340 0.004 0.000 0.052 0.604
#> SRR2314014 3 0.3687 0.6522 0.000 0.028 0.792 0.180 0.000
#> SRR2314013 3 0.0771 0.7186 0.000 0.004 0.976 0.020 0.000
#> SRR2314012 1 0.1862 0.5780 0.932 0.000 0.004 0.048 0.016
#> SRR2314011 4 0.5983 -0.0161 0.380 0.000 0.000 0.504 0.116
#> SRR2314010 3 0.2723 0.6972 0.000 0.012 0.864 0.124 0.000
#> SRR2314009 4 0.3480 0.6321 0.000 0.000 0.248 0.752 0.000
#> SRR2314008 3 0.4713 0.5300 0.000 0.044 0.676 0.280 0.000
#> SRR2314007 3 0.4297 -0.0390 0.000 0.000 0.528 0.472 0.000
#> SRR2314005 3 0.1628 0.7033 0.000 0.056 0.936 0.008 0.000
#> SRR2314004 3 0.0794 0.7153 0.000 0.028 0.972 0.000 0.000
#> SRR2314003 4 0.4318 0.3445 0.348 0.000 0.004 0.644 0.004
#> SRR2314006 2 0.0510 0.9466 0.000 0.984 0.016 0.000 0.000
#> SRR2314002 5 0.3132 0.6430 0.000 0.008 0.000 0.172 0.820
#> SRR2314001 3 0.3194 0.6783 0.000 0.020 0.832 0.148 0.000
#> SRR2314000 4 0.4551 0.3647 0.004 0.004 0.436 0.556 0.000
#> SRR2313998 3 0.0693 0.7159 0.000 0.008 0.980 0.012 0.000
#> SRR2313996 3 0.4283 0.4221 0.000 0.008 0.644 0.348 0.000
#> SRR2313999 4 0.4520 0.3614 0.340 0.000 0.008 0.644 0.008
#> SRR2313997 2 0.0510 0.9466 0.000 0.984 0.016 0.000 0.000
#> SRR2313992 4 0.2930 0.6603 0.004 0.000 0.164 0.832 0.000
#> SRR2313991 5 0.6776 0.6313 0.008 0.116 0.072 0.188 0.616
#> SRR2313990 3 0.4651 0.2114 0.000 0.020 0.608 0.372 0.000
#> SRR2313988 1 0.5216 0.5994 0.520 0.000 0.000 0.044 0.436
#> SRR2313987 1 0.2166 0.6479 0.912 0.000 0.004 0.012 0.072
#> SRR2313984 3 0.3209 0.6456 0.000 0.180 0.812 0.008 0.000
#> SRR2313989 2 0.0510 0.9466 0.000 0.984 0.016 0.000 0.000
#> SRR2313983 1 0.4192 0.6595 0.596 0.000 0.000 0.000 0.404
#> SRR2313986 4 0.3300 0.5224 0.204 0.000 0.004 0.792 0.000
#> SRR2313982 5 0.1478 0.4560 0.064 0.000 0.000 0.000 0.936
#> SRR2313981 4 0.3177 0.6561 0.000 0.000 0.208 0.792 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.5036 0.5520 0.000 0.644 0.260 0.008 0.084 0.004
#> SRR2313980 6 0.0000 0.9384 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.0937 0.8240 0.960 0.000 0.000 0.000 0.040 0.000
#> SRR2313977 2 0.3672 0.1943 0.000 0.632 0.368 0.000 0.000 0.000
#> SRR2313976 2 0.3409 0.4864 0.000 0.780 0.000 0.000 0.028 0.192
#> SRR2313975 1 0.4903 0.7091 0.720 0.012 0.068 0.020 0.176 0.004
#> SRR2313973 1 0.2257 0.7642 0.876 0.000 0.000 0.116 0.008 0.000
#> SRR2313974 4 0.5553 0.1935 0.432 0.000 0.060 0.476 0.032 0.000
#> SRR2313985 1 0.0603 0.8219 0.980 0.000 0.000 0.016 0.004 0.000
#> SRR2313993 2 0.3371 0.6141 0.000 0.780 0.200 0.000 0.004 0.016
#> SRR2313994 2 0.0993 0.6308 0.000 0.964 0.024 0.000 0.000 0.012
#> SRR2313995 2 0.6554 0.4767 0.000 0.512 0.180 0.012 0.036 0.260
#> SRR2314024 2 0.6590 0.4110 0.000 0.456 0.216 0.012 0.020 0.296
#> SRR2314015 2 0.4584 0.5769 0.000 0.724 0.024 0.008 0.044 0.200
#> SRR2314025 4 0.2001 0.6141 0.068 0.008 0.012 0.912 0.000 0.000
#> SRR2314023 1 0.1531 0.8069 0.928 0.000 0.000 0.068 0.004 0.000
#> SRR2314033 6 0.1644 0.8877 0.000 0.040 0.000 0.000 0.028 0.932
#> SRR2314045 1 0.3606 0.6215 0.708 0.000 0.004 0.004 0.284 0.000
#> SRR2314042 4 0.3741 0.5025 0.000 0.008 0.320 0.672 0.000 0.000
#> SRR2314043 3 0.0790 0.7534 0.000 0.032 0.968 0.000 0.000 0.000
#> SRR2314041 2 0.5884 0.4095 0.000 0.480 0.328 0.004 0.188 0.000
#> SRR2314044 3 0.4035 0.5836 0.000 0.052 0.740 0.004 0.204 0.000
#> SRR2314040 3 0.3409 0.5437 0.000 0.300 0.700 0.000 0.000 0.000
#> SRR2314039 3 0.5085 0.4325 0.000 0.144 0.644 0.004 0.208 0.000
#> SRR2314038 5 0.6643 0.6560 0.076 0.108 0.188 0.012 0.596 0.020
#> SRR2314037 3 0.0520 0.7609 0.000 0.008 0.984 0.008 0.000 0.000
#> SRR2314036 6 0.3126 0.6314 0.000 0.000 0.000 0.000 0.248 0.752
#> SRR2314035 3 0.3468 0.5728 0.000 0.264 0.728 0.000 0.000 0.008
#> SRR2314034 3 0.0260 0.7604 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR2314032 5 0.3835 0.4778 0.320 0.000 0.000 0.012 0.668 0.000
#> SRR2314031 4 0.2706 0.5648 0.000 0.008 0.160 0.832 0.000 0.000
#> SRR2314029 3 0.6623 -0.1750 0.028 0.384 0.468 0.024 0.076 0.020
#> SRR2314030 5 0.5992 0.6942 0.144 0.024 0.204 0.020 0.608 0.000
#> SRR2314028 3 0.0260 0.7604 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR2314027 2 0.6006 0.2988 0.000 0.420 0.376 0.004 0.200 0.000
#> SRR2314026 3 0.0790 0.7534 0.000 0.032 0.968 0.000 0.000 0.000
#> SRR2314020 6 0.0146 0.9361 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR2314019 3 0.0146 0.7612 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR2314021 3 0.0000 0.7608 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314018 3 0.3534 0.5601 0.000 0.276 0.716 0.000 0.000 0.008
#> SRR2314017 5 0.5975 0.6735 0.112 0.032 0.240 0.016 0.600 0.000
#> SRR2314016 5 0.5848 0.5210 0.204 0.000 0.012 0.236 0.548 0.000
#> SRR2314014 2 0.3531 0.2916 0.000 0.672 0.328 0.000 0.000 0.000
#> SRR2314013 2 0.1908 0.6292 0.000 0.900 0.096 0.000 0.004 0.000
#> SRR2314012 4 0.4312 -0.0403 0.476 0.000 0.004 0.508 0.012 0.000
#> SRR2314011 4 0.3679 0.4905 0.260 0.000 0.004 0.724 0.012 0.000
#> SRR2314010 2 0.4932 0.5796 0.000 0.680 0.236 0.012 0.056 0.016
#> SRR2314009 3 0.0520 0.7609 0.000 0.008 0.984 0.008 0.000 0.000
#> SRR2314008 2 0.6891 0.2862 0.000 0.412 0.328 0.000 0.188 0.072
#> SRR2314007 3 0.4264 0.3068 0.000 0.332 0.636 0.000 0.032 0.000
#> SRR2314005 2 0.1257 0.6262 0.000 0.952 0.000 0.000 0.020 0.028
#> SRR2314004 2 0.3100 0.6418 0.000 0.840 0.124 0.008 0.004 0.024
#> SRR2314003 4 0.1914 0.6155 0.056 0.008 0.016 0.920 0.000 0.000
#> SRR2314006 6 0.0000 0.9384 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 5 0.3997 0.6105 0.216 0.000 0.044 0.004 0.736 0.000
#> SRR2314001 2 0.3390 0.3575 0.000 0.704 0.296 0.000 0.000 0.000
#> SRR2314000 3 0.4382 0.5408 0.008 0.280 0.684 0.012 0.008 0.008
#> SRR2313998 2 0.1065 0.6266 0.000 0.964 0.020 0.000 0.008 0.008
#> SRR2313996 3 0.4569 0.0801 0.000 0.396 0.564 0.000 0.040 0.000
#> SRR2313999 4 0.5395 0.5396 0.164 0.000 0.216 0.612 0.008 0.000
#> SRR2313997 6 0.0000 0.9384 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.0520 0.7609 0.000 0.008 0.984 0.008 0.000 0.000
#> SRR2313991 5 0.6688 0.6734 0.084 0.088 0.180 0.012 0.604 0.032
#> SRR2313990 2 0.3747 0.1262 0.000 0.604 0.396 0.000 0.000 0.000
#> SRR2313988 1 0.2706 0.7858 0.832 0.000 0.000 0.008 0.160 0.000
#> SRR2313987 1 0.3281 0.6658 0.784 0.000 0.004 0.200 0.012 0.000
#> SRR2313984 2 0.3481 0.5803 0.000 0.772 0.012 0.004 0.004 0.208
#> SRR2313989 6 0.0000 0.9384 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.1082 0.8253 0.956 0.000 0.000 0.004 0.040 0.000
#> SRR2313986 4 0.3707 0.5164 0.000 0.008 0.312 0.680 0.000 0.000
#> SRR2313982 5 0.3971 0.4168 0.448 0.000 0.000 0.004 0.548 0.000
#> SRR2313981 3 0.0405 0.7612 0.000 0.004 0.988 0.008 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", "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 16873 rows and 72 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 0.913 0.948 0.976 0.5046 0.493 0.493
#> 3 3 0.463 0.478 0.744 0.2613 0.856 0.718
#> 4 4 0.588 0.659 0.820 0.1621 0.683 0.336
#> 5 5 0.691 0.681 0.825 0.0652 0.888 0.617
#> 6 6 0.697 0.616 0.799 0.0347 0.923 0.675
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
#> SRR2313978 2 0.0000 0.994 0.000 1.000
#> SRR2313980 2 0.0000 0.994 0.000 1.000
#> SRR2313979 1 0.0000 0.955 1.000 0.000
#> SRR2313977 2 0.0000 0.994 0.000 1.000
#> SRR2313976 2 0.0000 0.994 0.000 1.000
#> SRR2313975 1 0.0000 0.955 1.000 0.000
#> SRR2313973 1 0.0000 0.955 1.000 0.000
#> SRR2313974 1 0.0000 0.955 1.000 0.000
#> SRR2313985 1 0.0000 0.955 1.000 0.000
#> SRR2313993 2 0.0000 0.994 0.000 1.000
#> SRR2313994 2 0.0000 0.994 0.000 1.000
#> SRR2313995 2 0.0000 0.994 0.000 1.000
#> SRR2314024 2 0.0000 0.994 0.000 1.000
#> SRR2314015 2 0.0000 0.994 0.000 1.000
#> SRR2314025 1 0.0000 0.955 1.000 0.000
#> SRR2314023 1 0.0000 0.955 1.000 0.000
#> SRR2314033 2 0.0000 0.994 0.000 1.000
#> SRR2314045 1 0.0000 0.955 1.000 0.000
#> SRR2314042 1 0.0000 0.955 1.000 0.000
#> SRR2314043 1 0.9850 0.319 0.572 0.428
#> SRR2314041 2 0.0000 0.994 0.000 1.000
#> SRR2314044 2 0.0000 0.994 0.000 1.000
#> SRR2314040 2 0.5059 0.868 0.112 0.888
#> SRR2314039 2 0.0000 0.994 0.000 1.000
#> SRR2314038 2 0.1414 0.975 0.020 0.980
#> SRR2314037 1 0.6973 0.790 0.812 0.188
#> SRR2314036 2 0.0000 0.994 0.000 1.000
#> SRR2314035 1 0.6712 0.804 0.824 0.176
#> SRR2314034 1 0.0000 0.955 1.000 0.000
#> SRR2314032 1 0.0000 0.955 1.000 0.000
#> SRR2314031 1 0.0000 0.955 1.000 0.000
#> SRR2314029 2 0.0000 0.994 0.000 1.000
#> SRR2314030 1 0.0000 0.955 1.000 0.000
#> SRR2314028 1 0.0000 0.955 1.000 0.000
#> SRR2314027 2 0.0000 0.994 0.000 1.000
#> SRR2314026 1 0.7602 0.747 0.780 0.220
#> SRR2314020 2 0.0000 0.994 0.000 1.000
#> SRR2314019 1 0.8386 0.675 0.732 0.268
#> SRR2314021 1 0.0000 0.955 1.000 0.000
#> SRR2314018 2 0.3431 0.928 0.064 0.936
#> SRR2314017 1 0.0000 0.955 1.000 0.000
#> SRR2314016 1 0.0000 0.955 1.000 0.000
#> SRR2314014 2 0.0000 0.994 0.000 1.000
#> SRR2314013 2 0.0000 0.994 0.000 1.000
#> SRR2314012 1 0.0000 0.955 1.000 0.000
#> SRR2314011 1 0.0000 0.955 1.000 0.000
#> SRR2314010 2 0.0000 0.994 0.000 1.000
#> SRR2314009 1 0.6973 0.790 0.812 0.188
#> SRR2314008 2 0.0000 0.994 0.000 1.000
#> SRR2314007 2 0.0000 0.994 0.000 1.000
#> SRR2314005 2 0.0000 0.994 0.000 1.000
#> SRR2314004 2 0.0000 0.994 0.000 1.000
#> SRR2314003 1 0.0000 0.955 1.000 0.000
#> SRR2314006 2 0.0000 0.994 0.000 1.000
#> SRR2314002 1 0.0000 0.955 1.000 0.000
#> SRR2314001 2 0.0000 0.994 0.000 1.000
#> SRR2314000 1 0.4562 0.881 0.904 0.096
#> SRR2313998 2 0.0000 0.994 0.000 1.000
#> SRR2313996 2 0.0000 0.994 0.000 1.000
#> SRR2313999 1 0.0000 0.955 1.000 0.000
#> SRR2313997 2 0.0000 0.994 0.000 1.000
#> SRR2313992 1 0.0000 0.955 1.000 0.000
#> SRR2313991 2 0.0376 0.990 0.004 0.996
#> SRR2313990 2 0.0000 0.994 0.000 1.000
#> SRR2313988 1 0.0000 0.955 1.000 0.000
#> SRR2313987 1 0.0000 0.955 1.000 0.000
#> SRR2313984 2 0.0000 0.994 0.000 1.000
#> SRR2313989 2 0.0000 0.994 0.000 1.000
#> SRR2313983 1 0.0000 0.955 1.000 0.000
#> SRR2313986 1 0.0000 0.955 1.000 0.000
#> SRR2313982 1 0.0000 0.955 1.000 0.000
#> SRR2313981 1 0.0000 0.955 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.4575 0.6995 0.004 0.812 0.184
#> SRR2313980 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2313979 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313977 2 0.1163 0.7851 0.000 0.972 0.028
#> SRR2313976 2 0.0592 0.7904 0.000 0.988 0.012
#> SRR2313975 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313973 1 0.4178 0.4175 0.828 0.000 0.172
#> SRR2313974 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313985 1 0.5621 0.3575 0.692 0.000 0.308
#> SRR2313993 2 0.3340 0.7556 0.000 0.880 0.120
#> SRR2313994 2 0.3340 0.7580 0.000 0.880 0.120
#> SRR2313995 2 0.3141 0.7682 0.020 0.912 0.068
#> SRR2314024 2 0.0237 0.7926 0.000 0.996 0.004
#> SRR2314015 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2314025 1 0.4750 0.5158 0.784 0.000 0.216
#> SRR2314023 1 0.5363 0.3777 0.724 0.000 0.276
#> SRR2314033 2 0.0237 0.7920 0.000 0.996 0.004
#> SRR2314045 3 0.6045 0.1583 0.380 0.000 0.620
#> SRR2314042 1 0.5591 0.4819 0.696 0.000 0.304
#> SRR2314043 2 0.8312 0.2721 0.324 0.576 0.100
#> SRR2314041 2 0.7559 0.4806 0.056 0.608 0.336
#> SRR2314044 2 0.7222 0.5545 0.072 0.684 0.244
#> SRR2314040 3 0.7549 -0.0879 0.040 0.436 0.524
#> SRR2314039 2 0.9880 0.0553 0.260 0.384 0.356
#> SRR2314038 2 0.6773 0.3172 0.024 0.636 0.340
#> SRR2314037 1 0.7085 0.4088 0.612 0.032 0.356
#> SRR2314036 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2314035 3 0.8042 0.3651 0.116 0.248 0.636
#> SRR2314034 1 0.5926 0.4600 0.644 0.000 0.356
#> SRR2314032 3 0.6235 0.0592 0.436 0.000 0.564
#> SRR2314031 1 0.5138 0.5045 0.748 0.000 0.252
#> SRR2314029 2 0.0829 0.7907 0.012 0.984 0.004
#> SRR2314030 3 0.4978 0.1073 0.216 0.004 0.780
#> SRR2314028 1 0.5650 0.4754 0.688 0.000 0.312
#> SRR2314027 2 0.6867 0.5277 0.028 0.636 0.336
#> SRR2314026 3 0.8019 0.0966 0.076 0.348 0.576
#> SRR2314020 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2314019 1 0.9627 0.1224 0.428 0.208 0.364
#> SRR2314021 1 0.7192 0.3499 0.560 0.028 0.412
#> SRR2314018 3 0.6280 -0.1704 0.000 0.460 0.540
#> SRR2314017 3 0.2860 0.2746 0.084 0.004 0.912
#> SRR2314016 3 0.5859 0.0128 0.344 0.000 0.656
#> SRR2314014 2 0.4346 0.7253 0.000 0.816 0.184
#> SRR2314013 2 0.0892 0.7919 0.000 0.980 0.020
#> SRR2314012 1 0.4178 0.5161 0.828 0.000 0.172
#> SRR2314011 1 0.0424 0.4724 0.992 0.000 0.008
#> SRR2314010 2 0.3686 0.7485 0.000 0.860 0.140
#> SRR2314009 1 0.9653 0.1150 0.424 0.212 0.364
#> SRR2314008 2 0.5859 0.5632 0.000 0.656 0.344
#> SRR2314007 2 0.5733 0.5922 0.000 0.676 0.324
#> SRR2314005 2 0.1753 0.7741 0.000 0.952 0.048
#> SRR2314004 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2314003 1 0.4654 0.5170 0.792 0.000 0.208
#> SRR2314006 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2314002 3 0.6275 0.1919 0.348 0.008 0.644
#> SRR2314001 2 0.5058 0.6774 0.000 0.756 0.244
#> SRR2314000 1 0.8737 -0.1403 0.464 0.108 0.428
#> SRR2313998 2 0.0592 0.7904 0.000 0.988 0.012
#> SRR2313996 2 0.6033 0.5621 0.004 0.660 0.336
#> SRR2313999 1 0.5138 0.4729 0.748 0.000 0.252
#> SRR2313997 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2313992 1 0.5785 0.4609 0.668 0.000 0.332
#> SRR2313991 2 0.6460 0.0758 0.004 0.556 0.440
#> SRR2313990 2 0.5968 0.5500 0.000 0.636 0.364
#> SRR2313988 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313987 1 0.5291 0.3823 0.732 0.000 0.268
#> SRR2313984 2 0.0892 0.7926 0.000 0.980 0.020
#> SRR2313989 2 0.0000 0.7923 0.000 1.000 0.000
#> SRR2313983 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313986 1 0.4452 0.5179 0.808 0.000 0.192
#> SRR2313982 1 0.5810 0.3423 0.664 0.000 0.336
#> SRR2313981 1 0.6667 0.4140 0.616 0.016 0.368
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.0469 0.9106 0.000 0.988 0.000 0.012
#> SRR2313980 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.1302 0.8504 0.956 0.000 0.000 0.044
#> SRR2313977 2 0.3668 0.7450 0.000 0.808 0.188 0.004
#> SRR2313976 2 0.2469 0.8573 0.000 0.892 0.108 0.000
#> SRR2313975 1 0.1302 0.8504 0.956 0.000 0.000 0.044
#> SRR2313973 1 0.2647 0.8154 0.880 0.000 0.000 0.120
#> SRR2313974 1 0.1389 0.8497 0.952 0.000 0.000 0.048
#> SRR2313985 1 0.1940 0.8408 0.924 0.000 0.000 0.076
#> SRR2313993 3 0.5050 0.4683 0.000 0.408 0.588 0.004
#> SRR2313994 3 0.4560 0.6170 0.000 0.296 0.700 0.004
#> SRR2313995 2 0.1557 0.8733 0.000 0.944 0.000 0.056
#> SRR2314024 2 0.0188 0.9136 0.000 0.996 0.004 0.000
#> SRR2314015 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR2314025 4 0.1211 0.6945 0.040 0.000 0.000 0.960
#> SRR2314023 1 0.2944 0.8072 0.868 0.000 0.004 0.128
#> SRR2314033 2 0.1792 0.8893 0.000 0.932 0.068 0.000
#> SRR2314045 1 0.4364 0.6636 0.764 0.000 0.220 0.016
#> SRR2314042 4 0.0524 0.6931 0.008 0.000 0.004 0.988
#> SRR2314043 3 0.6440 0.6381 0.068 0.224 0.676 0.032
#> SRR2314041 4 0.5705 0.4521 0.000 0.204 0.092 0.704
#> SRR2314044 4 0.5167 -0.0076 0.000 0.488 0.004 0.508
#> SRR2314040 3 0.3088 0.6930 0.008 0.128 0.864 0.000
#> SRR2314039 4 0.6042 0.0762 0.000 0.048 0.392 0.560
#> SRR2314038 2 0.8024 0.1845 0.216 0.492 0.272 0.020
#> SRR2314037 4 0.4647 0.4069 0.000 0.008 0.288 0.704
#> SRR2314036 2 0.0336 0.9134 0.000 0.992 0.008 0.000
#> SRR2314035 3 0.1929 0.6793 0.024 0.036 0.940 0.000
#> SRR2314034 3 0.5256 0.2870 0.012 0.000 0.596 0.392
#> SRR2314032 1 0.1970 0.7987 0.932 0.000 0.060 0.008
#> SRR2314031 4 0.0895 0.6970 0.020 0.000 0.004 0.976
#> SRR2314029 2 0.0336 0.9117 0.000 0.992 0.000 0.008
#> SRR2314030 3 0.6281 0.3935 0.128 0.000 0.656 0.216
#> SRR2314028 4 0.5392 -0.0172 0.012 0.000 0.460 0.528
#> SRR2314027 3 0.6356 0.4822 0.004 0.076 0.612 0.308
#> SRR2314026 3 0.2319 0.6526 0.036 0.000 0.924 0.040
#> SRR2314020 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.4652 0.5983 0.020 0.012 0.776 0.192
#> SRR2314021 3 0.3525 0.6402 0.040 0.000 0.860 0.100
#> SRR2314018 3 0.4255 0.6797 0.008 0.200 0.784 0.008
#> SRR2314017 3 0.4149 0.5660 0.152 0.000 0.812 0.036
#> SRR2314016 1 0.7547 0.3570 0.488 0.000 0.276 0.236
#> SRR2314014 3 0.4720 0.5786 0.000 0.324 0.672 0.004
#> SRR2314013 2 0.2149 0.8759 0.000 0.912 0.088 0.000
#> SRR2314012 4 0.1940 0.6803 0.076 0.000 0.000 0.924
#> SRR2314011 4 0.4936 0.2092 0.372 0.000 0.004 0.624
#> SRR2314010 2 0.1182 0.9021 0.000 0.968 0.016 0.016
#> SRR2314009 3 0.3672 0.6332 0.000 0.012 0.824 0.164
#> SRR2314008 3 0.6635 0.5955 0.008 0.176 0.652 0.164
#> SRR2314007 3 0.2910 0.6659 0.028 0.020 0.908 0.044
#> SRR2314005 2 0.2466 0.8698 0.000 0.900 0.096 0.004
#> SRR2314004 2 0.0336 0.9125 0.000 0.992 0.008 0.000
#> SRR2314003 4 0.1824 0.6878 0.060 0.000 0.004 0.936
#> SRR2314006 2 0.0188 0.9129 0.000 0.996 0.004 0.000
#> SRR2314002 1 0.5467 0.4678 0.612 0.000 0.364 0.024
#> SRR2314001 3 0.4018 0.6706 0.000 0.224 0.772 0.004
#> SRR2314000 1 0.2528 0.7959 0.908 0.004 0.080 0.008
#> SRR2313998 2 0.2266 0.8761 0.004 0.912 0.084 0.000
#> SRR2313996 3 0.6954 0.5642 0.000 0.280 0.568 0.152
#> SRR2313999 4 0.5168 -0.1834 0.496 0.000 0.004 0.500
#> SRR2313997 2 0.0188 0.9129 0.000 0.996 0.004 0.000
#> SRR2313992 4 0.2593 0.6593 0.016 0.000 0.080 0.904
#> SRR2313991 3 0.8337 0.1314 0.176 0.356 0.432 0.036
#> SRR2313990 3 0.3978 0.6833 0.000 0.192 0.796 0.012
#> SRR2313988 1 0.1211 0.8497 0.960 0.000 0.000 0.040
#> SRR2313987 1 0.4122 0.6879 0.760 0.000 0.004 0.236
#> SRR2313984 2 0.3123 0.7387 0.000 0.844 0.156 0.000
#> SRR2313989 2 0.0000 0.9137 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.1302 0.8504 0.956 0.000 0.000 0.044
#> SRR2313986 4 0.1902 0.6854 0.064 0.000 0.004 0.932
#> SRR2313982 1 0.1302 0.8504 0.956 0.000 0.000 0.044
#> SRR2313981 3 0.3626 0.6082 0.004 0.000 0.812 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.1569 0.9125 0.000 0.944 0.008 0.004 0.044
#> SRR2313980 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 0.8406 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 3 0.6503 0.0995 0.000 0.372 0.436 0.000 0.192
#> SRR2313976 2 0.3700 0.7137 0.000 0.752 0.240 0.000 0.008
#> SRR2313975 1 0.0000 0.8406 1.000 0.000 0.000 0.000 0.000
#> SRR2313973 1 0.3661 0.6240 0.724 0.000 0.000 0.276 0.000
#> SRR2313974 1 0.1197 0.8328 0.952 0.000 0.000 0.048 0.000
#> SRR2313985 1 0.2471 0.7785 0.864 0.000 0.000 0.136 0.000
#> SRR2313993 3 0.3421 0.6234 0.000 0.164 0.816 0.004 0.016
#> SRR2313994 3 0.3823 0.6353 0.000 0.048 0.808 0.004 0.140
#> SRR2313995 2 0.0727 0.9366 0.000 0.980 0.004 0.012 0.004
#> SRR2314024 2 0.0324 0.9395 0.000 0.992 0.000 0.004 0.004
#> SRR2314015 2 0.0162 0.9401 0.000 0.996 0.000 0.000 0.004
#> SRR2314025 4 0.1173 0.7210 0.012 0.000 0.004 0.964 0.020
#> SRR2314023 1 0.3819 0.6844 0.756 0.000 0.000 0.228 0.016
#> SRR2314033 2 0.1281 0.9265 0.000 0.956 0.012 0.000 0.032
#> SRR2314045 1 0.5243 0.3063 0.596 0.000 0.048 0.004 0.352
#> SRR2314042 4 0.2504 0.6800 0.000 0.000 0.040 0.896 0.064
#> SRR2314043 3 0.2606 0.6712 0.008 0.024 0.904 0.008 0.056
#> SRR2314041 3 0.8018 0.1663 0.000 0.120 0.380 0.328 0.172
#> SRR2314044 4 0.5900 0.3924 0.004 0.312 0.072 0.596 0.016
#> SRR2314040 3 0.2604 0.6601 0.004 0.004 0.880 0.004 0.108
#> SRR2314039 3 0.6307 0.4096 0.000 0.000 0.532 0.224 0.244
#> SRR2314038 5 0.5264 0.6258 0.008 0.120 0.008 0.144 0.720
#> SRR2314037 3 0.5018 0.5192 0.000 0.004 0.660 0.284 0.052
#> SRR2314036 2 0.0290 0.9397 0.000 0.992 0.000 0.000 0.008
#> SRR2314035 3 0.1478 0.6741 0.000 0.000 0.936 0.000 0.064
#> SRR2314034 3 0.4649 0.6042 0.000 0.000 0.720 0.068 0.212
#> SRR2314032 1 0.2416 0.7672 0.888 0.000 0.012 0.000 0.100
#> SRR2314031 4 0.2517 0.7079 0.008 0.000 0.004 0.884 0.104
#> SRR2314029 2 0.0162 0.9401 0.000 0.996 0.000 0.004 0.000
#> SRR2314030 5 0.5466 0.5853 0.004 0.000 0.192 0.136 0.668
#> SRR2314028 3 0.3489 0.6413 0.000 0.000 0.820 0.144 0.036
#> SRR2314027 3 0.5756 0.4222 0.000 0.000 0.576 0.112 0.312
#> SRR2314026 3 0.4268 0.3136 0.000 0.000 0.556 0.000 0.444
#> SRR2314020 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.5042 0.2212 0.000 0.000 0.508 0.032 0.460
#> SRR2314021 3 0.4557 0.3412 0.000 0.000 0.584 0.012 0.404
#> SRR2314018 3 0.3491 0.5276 0.004 0.000 0.768 0.000 0.228
#> SRR2314017 5 0.3018 0.7487 0.008 0.000 0.084 0.036 0.872
#> SRR2314016 5 0.3792 0.6920 0.020 0.000 0.008 0.180 0.792
#> SRR2314014 3 0.3003 0.6477 0.000 0.092 0.864 0.000 0.044
#> SRR2314013 2 0.2848 0.8284 0.000 0.840 0.156 0.000 0.004
#> SRR2314012 4 0.2450 0.7197 0.076 0.000 0.000 0.896 0.028
#> SRR2314011 4 0.4040 0.5048 0.276 0.000 0.000 0.712 0.012
#> SRR2314010 2 0.2283 0.8937 0.000 0.916 0.036 0.008 0.040
#> SRR2314009 3 0.3214 0.6599 0.000 0.000 0.844 0.036 0.120
#> SRR2314008 5 0.5244 0.3285 0.000 0.024 0.360 0.020 0.596
#> SRR2314007 3 0.4088 0.5442 0.000 0.004 0.712 0.008 0.276
#> SRR2314005 2 0.3692 0.8158 0.000 0.812 0.136 0.000 0.052
#> SRR2314004 2 0.0566 0.9383 0.000 0.984 0.004 0.000 0.012
#> SRR2314003 4 0.2520 0.7141 0.012 0.000 0.004 0.888 0.096
#> SRR2314006 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.3810 0.6858 0.168 0.000 0.040 0.000 0.792
#> SRR2314001 3 0.2260 0.6756 0.000 0.028 0.908 0.000 0.064
#> SRR2314000 1 0.0867 0.8340 0.976 0.008 0.008 0.000 0.008
#> SRR2313998 2 0.1952 0.8964 0.000 0.912 0.084 0.000 0.004
#> SRR2313996 3 0.3301 0.6642 0.000 0.076 0.864 0.024 0.036
#> SRR2313999 4 0.6637 0.1477 0.368 0.000 0.008 0.452 0.172
#> SRR2313997 2 0.0290 0.9397 0.000 0.992 0.000 0.000 0.008
#> SRR2313992 4 0.4482 0.4911 0.004 0.000 0.252 0.712 0.032
#> SRR2313991 5 0.3388 0.7607 0.012 0.024 0.048 0.044 0.872
#> SRR2313990 3 0.0992 0.6777 0.000 0.024 0.968 0.000 0.008
#> SRR2313988 1 0.0162 0.8398 0.996 0.000 0.000 0.000 0.004
#> SRR2313987 1 0.4491 0.5232 0.652 0.000 0.000 0.328 0.020
#> SRR2313984 2 0.1628 0.9083 0.000 0.936 0.056 0.000 0.008
#> SRR2313989 2 0.0000 0.9401 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.8406 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.3012 0.6949 0.024 0.000 0.000 0.852 0.124
#> SRR2313982 1 0.0955 0.8368 0.968 0.000 0.000 0.028 0.004
#> SRR2313981 3 0.3112 0.6648 0.000 0.000 0.856 0.044 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 6 0.2326 0.8367 0.000 0.012 0.000 0.008 0.092 0.888
#> SRR2313980 6 0.0458 0.8877 0.000 0.000 0.000 0.000 0.016 0.984
#> SRR2313979 1 0.0000 0.8226 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 5 0.3898 0.4977 0.000 0.296 0.000 0.000 0.684 0.020
#> SRR2313976 6 0.5018 0.3883 0.000 0.372 0.004 0.000 0.068 0.556
#> SRR2313975 1 0.0000 0.8226 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313973 1 0.3810 0.2766 0.572 0.000 0.000 0.428 0.000 0.000
#> SRR2313974 1 0.2100 0.7850 0.884 0.000 0.000 0.112 0.004 0.000
#> SRR2313985 1 0.2823 0.6951 0.796 0.000 0.000 0.204 0.000 0.000
#> SRR2313993 2 0.4264 0.6525 0.000 0.776 0.108 0.000 0.044 0.072
#> SRR2313994 2 0.3062 0.7007 0.000 0.824 0.144 0.000 0.000 0.032
#> SRR2313995 6 0.0405 0.8884 0.000 0.000 0.000 0.008 0.004 0.988
#> SRR2314024 6 0.1049 0.8822 0.000 0.000 0.008 0.000 0.032 0.960
#> SRR2314015 6 0.1934 0.8628 0.000 0.044 0.000 0.000 0.040 0.916
#> SRR2314025 4 0.1951 0.7276 0.000 0.000 0.076 0.908 0.016 0.000
#> SRR2314023 1 0.4389 0.2010 0.528 0.000 0.000 0.448 0.024 0.000
#> SRR2314033 6 0.1871 0.8747 0.000 0.016 0.032 0.000 0.024 0.928
#> SRR2314045 3 0.4702 0.0155 0.440 0.024 0.524 0.000 0.012 0.000
#> SRR2314042 4 0.4706 0.5605 0.000 0.020 0.232 0.688 0.060 0.000
#> SRR2314043 2 0.2728 0.7118 0.004 0.864 0.032 0.000 0.100 0.000
#> SRR2314041 2 0.7635 0.2161 0.000 0.464 0.052 0.188 0.092 0.204
#> SRR2314044 4 0.5533 0.5281 0.000 0.112 0.004 0.668 0.056 0.160
#> SRR2314040 2 0.1753 0.7497 0.000 0.912 0.084 0.000 0.004 0.000
#> SRR2314039 3 0.5989 0.3263 0.000 0.404 0.468 0.060 0.068 0.000
#> SRR2314038 5 0.3486 0.5102 0.000 0.000 0.056 0.064 0.836 0.044
#> SRR2314037 2 0.4506 0.5162 0.000 0.684 0.048 0.256 0.012 0.000
#> SRR2314036 6 0.0458 0.8873 0.000 0.000 0.000 0.000 0.016 0.984
#> SRR2314035 2 0.1408 0.7601 0.000 0.944 0.036 0.000 0.020 0.000
#> SRR2314034 2 0.4379 0.0862 0.000 0.576 0.400 0.020 0.004 0.000
#> SRR2314032 1 0.2243 0.7473 0.880 0.004 0.112 0.000 0.004 0.000
#> SRR2314031 4 0.1845 0.7361 0.000 0.000 0.052 0.920 0.028 0.000
#> SRR2314029 6 0.0405 0.8884 0.000 0.000 0.000 0.008 0.004 0.988
#> SRR2314030 3 0.2847 0.4061 0.000 0.048 0.876 0.040 0.036 0.000
#> SRR2314028 2 0.3005 0.7266 0.000 0.864 0.024 0.060 0.052 0.000
#> SRR2314027 3 0.4858 0.4559 0.000 0.368 0.584 0.016 0.028 0.004
#> SRR2314026 3 0.3351 0.5619 0.000 0.288 0.712 0.000 0.000 0.000
#> SRR2314020 6 0.0260 0.8883 0.000 0.000 0.000 0.000 0.008 0.992
#> SRR2314019 3 0.3629 0.5733 0.000 0.276 0.712 0.012 0.000 0.000
#> SRR2314021 3 0.3862 0.4367 0.000 0.388 0.608 0.004 0.000 0.000
#> SRR2314018 5 0.3854 0.1766 0.000 0.464 0.000 0.000 0.536 0.000
#> SRR2314017 5 0.4528 0.2900 0.000 0.028 0.404 0.004 0.564 0.000
#> SRR2314016 3 0.5475 0.0275 0.000 0.008 0.596 0.160 0.236 0.000
#> SRR2314014 2 0.2766 0.7000 0.000 0.852 0.004 0.000 0.124 0.020
#> SRR2314013 6 0.4204 0.6276 0.000 0.260 0.004 0.000 0.040 0.696
#> SRR2314012 4 0.1779 0.7385 0.016 0.000 0.064 0.920 0.000 0.000
#> SRR2314011 4 0.3931 0.6032 0.192 0.000 0.008 0.756 0.044 0.000
#> SRR2314010 6 0.2594 0.8498 0.000 0.048 0.016 0.004 0.040 0.892
#> SRR2314009 2 0.2527 0.7466 0.000 0.884 0.048 0.004 0.064 0.000
#> SRR2314008 5 0.3385 0.5737 0.000 0.144 0.036 0.008 0.812 0.000
#> SRR2314007 2 0.3878 0.3009 0.000 0.644 0.348 0.000 0.004 0.004
#> SRR2314005 6 0.5719 0.3290 0.000 0.248 0.000 0.000 0.232 0.520
#> SRR2314004 6 0.1219 0.8802 0.000 0.004 0.000 0.000 0.048 0.948
#> SRR2314003 4 0.2178 0.7203 0.000 0.000 0.000 0.868 0.132 0.000
#> SRR2314006 6 0.0146 0.8878 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR2314002 5 0.5513 0.2141 0.068 0.024 0.428 0.000 0.480 0.000
#> SRR2314001 2 0.1956 0.7469 0.000 0.908 0.080 0.000 0.008 0.004
#> SRR2314000 1 0.2390 0.7780 0.896 0.044 0.000 0.008 0.052 0.000
#> SRR2313998 6 0.1644 0.8646 0.000 0.076 0.004 0.000 0.000 0.920
#> SRR2313996 2 0.2322 0.7291 0.000 0.896 0.000 0.004 0.064 0.036
#> SRR2313999 4 0.4759 0.4494 0.044 0.004 0.000 0.556 0.396 0.000
#> SRR2313997 6 0.0146 0.8875 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR2313992 4 0.3777 0.5912 0.000 0.208 0.028 0.756 0.008 0.000
#> SRR2313991 3 0.4624 0.0400 0.000 0.032 0.648 0.004 0.304 0.012
#> SRR2313990 2 0.1074 0.7600 0.000 0.960 0.028 0.000 0.012 0.000
#> SRR2313988 1 0.0000 0.8226 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 4 0.4591 0.0593 0.408 0.000 0.000 0.552 0.040 0.000
#> SRR2313984 6 0.1802 0.8514 0.000 0.072 0.000 0.000 0.012 0.916
#> SRR2313989 6 0.0000 0.8873 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.0000 0.8226 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.1863 0.7280 0.000 0.000 0.000 0.896 0.104 0.000
#> SRR2313982 1 0.1285 0.8132 0.944 0.000 0.000 0.052 0.004 0.000
#> SRR2313981 2 0.2265 0.7426 0.000 0.896 0.076 0.004 0.024 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", "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 16873 rows and 72 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 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 0.419 0.673 0.847 0.4229 0.581 0.581
#> 3 3 0.520 0.763 0.864 0.4758 0.668 0.472
#> 4 4 0.508 0.691 0.803 0.0819 0.977 0.932
#> 5 5 0.609 0.499 0.768 0.0977 0.860 0.599
#> 6 6 0.634 0.517 0.672 0.0545 0.873 0.573
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
#> SRR2313978 2 0.0000 0.769 0.000 1.000
#> SRR2313980 2 0.0000 0.769 0.000 1.000
#> SRR2313979 1 0.0000 0.854 1.000 0.000
#> SRR2313977 2 0.0672 0.768 0.008 0.992
#> SRR2313976 2 0.0000 0.769 0.000 1.000
#> SRR2313975 1 0.0000 0.854 1.000 0.000
#> SRR2313973 1 0.0000 0.854 1.000 0.000
#> SRR2313974 1 0.0938 0.852 0.988 0.012
#> SRR2313985 1 0.2236 0.839 0.964 0.036
#> SRR2313993 2 0.0000 0.769 0.000 1.000
#> SRR2313994 2 0.0000 0.769 0.000 1.000
#> SRR2313995 2 0.1414 0.765 0.020 0.980
#> SRR2314024 2 0.0000 0.769 0.000 1.000
#> SRR2314015 2 0.0000 0.769 0.000 1.000
#> SRR2314025 1 0.9460 0.298 0.636 0.364
#> SRR2314023 1 0.0000 0.854 1.000 0.000
#> SRR2314033 2 0.0000 0.769 0.000 1.000
#> SRR2314045 1 0.1414 0.848 0.980 0.020
#> SRR2314042 1 0.9552 0.254 0.624 0.376
#> SRR2314043 2 0.9044 0.620 0.320 0.680
#> SRR2314041 2 0.5294 0.732 0.120 0.880
#> SRR2314044 2 0.9866 0.442 0.432 0.568
#> SRR2314040 2 0.8555 0.664 0.280 0.720
#> SRR2314039 2 0.8386 0.673 0.268 0.732
#> SRR2314038 2 0.9993 0.307 0.484 0.516
#> SRR2314037 2 0.9944 0.389 0.456 0.544
#> SRR2314036 2 0.0000 0.769 0.000 1.000
#> SRR2314035 2 0.8555 0.664 0.280 0.720
#> SRR2314034 2 0.9909 0.416 0.444 0.556
#> SRR2314032 1 0.0000 0.854 1.000 0.000
#> SRR2314031 1 0.9460 0.298 0.636 0.364
#> SRR2314029 2 0.9815 0.466 0.420 0.580
#> SRR2314030 2 0.9933 0.397 0.452 0.548
#> SRR2314028 1 0.9427 0.309 0.640 0.360
#> SRR2314027 2 0.8386 0.673 0.268 0.732
#> SRR2314026 2 0.8661 0.656 0.288 0.712
#> SRR2314020 2 0.0000 0.769 0.000 1.000
#> SRR2314019 2 0.9909 0.416 0.444 0.556
#> SRR2314021 2 0.9909 0.416 0.444 0.556
#> SRR2314018 2 0.8555 0.664 0.280 0.720
#> SRR2314017 2 0.8555 0.664 0.280 0.720
#> SRR2314016 2 0.9993 0.307 0.484 0.516
#> SRR2314014 2 0.0000 0.769 0.000 1.000
#> SRR2314013 2 0.0672 0.768 0.008 0.992
#> SRR2314012 1 0.9427 0.309 0.640 0.360
#> SRR2314011 1 0.0000 0.854 1.000 0.000
#> SRR2314010 2 0.1184 0.766 0.016 0.984
#> SRR2314009 2 0.8555 0.664 0.280 0.720
#> SRR2314008 2 0.8555 0.664 0.280 0.720
#> SRR2314007 2 0.2043 0.764 0.032 0.968
#> SRR2314005 2 0.0000 0.769 0.000 1.000
#> SRR2314004 2 0.0000 0.769 0.000 1.000
#> SRR2314003 1 0.3431 0.817 0.936 0.064
#> SRR2314006 2 0.0000 0.769 0.000 1.000
#> SRR2314002 2 0.8555 0.664 0.280 0.720
#> SRR2314001 2 0.0000 0.769 0.000 1.000
#> SRR2314000 2 0.9732 0.498 0.404 0.596
#> SRR2313998 2 0.0376 0.769 0.004 0.996
#> SRR2313996 2 0.0000 0.769 0.000 1.000
#> SRR2313999 1 0.0000 0.854 1.000 0.000
#> SRR2313997 2 0.0000 0.769 0.000 1.000
#> SRR2313992 2 0.9963 0.367 0.464 0.536
#> SRR2313991 2 0.8499 0.667 0.276 0.724
#> SRR2313990 2 0.0000 0.769 0.000 1.000
#> SRR2313988 1 0.0000 0.854 1.000 0.000
#> SRR2313987 1 0.0938 0.852 0.988 0.012
#> SRR2313984 2 0.0000 0.769 0.000 1.000
#> SRR2313989 2 0.0000 0.769 0.000 1.000
#> SRR2313983 1 0.0000 0.854 1.000 0.000
#> SRR2313986 1 0.4022 0.802 0.920 0.080
#> SRR2313982 1 0.0000 0.854 1.000 0.000
#> SRR2313981 2 0.9954 0.377 0.460 0.540
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.4399 0.817 0.000 0.812 0.188
#> SRR2313980 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2313979 1 0.0237 0.928 0.996 0.000 0.004
#> SRR2313977 2 0.4842 0.782 0.000 0.776 0.224
#> SRR2313976 2 0.0747 0.831 0.000 0.984 0.016
#> SRR2313975 1 0.0424 0.928 0.992 0.000 0.008
#> SRR2313973 1 0.0237 0.926 0.996 0.000 0.004
#> SRR2313974 1 0.2448 0.901 0.924 0.000 0.076
#> SRR2313985 1 0.2625 0.889 0.916 0.000 0.084
#> SRR2313993 2 0.3619 0.835 0.000 0.864 0.136
#> SRR2313994 2 0.5216 0.742 0.000 0.740 0.260
#> SRR2313995 2 0.4887 0.686 0.000 0.772 0.228
#> SRR2314024 2 0.0892 0.831 0.000 0.980 0.020
#> SRR2314015 2 0.3619 0.836 0.000 0.864 0.136
#> SRR2314025 3 0.4002 0.649 0.160 0.000 0.840
#> SRR2314023 1 0.0237 0.926 0.996 0.000 0.004
#> SRR2314033 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2314045 1 0.1399 0.921 0.968 0.004 0.028
#> SRR2314042 3 0.4047 0.663 0.148 0.004 0.848
#> SRR2314043 3 0.4605 0.731 0.000 0.204 0.796
#> SRR2314041 2 0.6140 0.470 0.000 0.596 0.404
#> SRR2314044 3 0.1964 0.785 0.000 0.056 0.944
#> SRR2314040 3 0.5363 0.679 0.000 0.276 0.724
#> SRR2314039 3 0.6260 0.242 0.000 0.448 0.552
#> SRR2314038 3 0.0661 0.772 0.008 0.004 0.988
#> SRR2314037 3 0.1711 0.785 0.008 0.032 0.960
#> SRR2314036 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2314035 3 0.5327 0.684 0.000 0.272 0.728
#> SRR2314034 3 0.1411 0.786 0.000 0.036 0.964
#> SRR2314032 1 0.0424 0.928 0.992 0.000 0.008
#> SRR2314031 3 0.4002 0.649 0.160 0.000 0.840
#> SRR2314029 3 0.2774 0.784 0.008 0.072 0.920
#> SRR2314030 3 0.1163 0.785 0.000 0.028 0.972
#> SRR2314028 3 0.4062 0.644 0.164 0.000 0.836
#> SRR2314027 3 0.6260 0.242 0.000 0.448 0.552
#> SRR2314026 3 0.5431 0.665 0.000 0.284 0.716
#> SRR2314020 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2314019 3 0.1411 0.786 0.000 0.036 0.964
#> SRR2314021 3 0.1411 0.786 0.000 0.036 0.964
#> SRR2314018 3 0.5327 0.684 0.000 0.272 0.728
#> SRR2314017 3 0.5327 0.685 0.000 0.272 0.728
#> SRR2314016 3 0.0661 0.772 0.008 0.004 0.988
#> SRR2314014 2 0.4062 0.825 0.000 0.836 0.164
#> SRR2314013 2 0.5529 0.666 0.000 0.704 0.296
#> SRR2314012 3 0.4062 0.644 0.164 0.000 0.836
#> SRR2314011 1 0.0747 0.927 0.984 0.000 0.016
#> SRR2314010 2 0.4842 0.793 0.000 0.776 0.224
#> SRR2314009 3 0.5327 0.684 0.000 0.272 0.728
#> SRR2314008 3 0.5327 0.684 0.000 0.272 0.728
#> SRR2314007 2 0.5529 0.684 0.000 0.704 0.296
#> SRR2314005 2 0.2165 0.837 0.000 0.936 0.064
#> SRR2314004 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2314003 1 0.5905 0.488 0.648 0.000 0.352
#> SRR2314006 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2314002 3 0.5553 0.684 0.004 0.272 0.724
#> SRR2314001 2 0.4555 0.806 0.000 0.800 0.200
#> SRR2314000 3 0.6954 0.706 0.084 0.196 0.720
#> SRR2313998 2 0.3267 0.839 0.000 0.884 0.116
#> SRR2313996 2 0.4654 0.803 0.000 0.792 0.208
#> SRR2313999 1 0.0747 0.927 0.984 0.000 0.016
#> SRR2313997 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2313992 3 0.1751 0.784 0.012 0.028 0.960
#> SRR2313991 3 0.5363 0.682 0.000 0.276 0.724
#> SRR2313990 2 0.5291 0.731 0.000 0.732 0.268
#> SRR2313988 1 0.0424 0.928 0.992 0.000 0.008
#> SRR2313987 1 0.2448 0.901 0.924 0.000 0.076
#> SRR2313984 2 0.4654 0.803 0.000 0.792 0.208
#> SRR2313989 2 0.0000 0.826 0.000 1.000 0.000
#> SRR2313983 1 0.0237 0.928 0.996 0.000 0.004
#> SRR2313986 1 0.5988 0.450 0.632 0.000 0.368
#> SRR2313982 1 0.0237 0.928 0.996 0.000 0.004
#> SRR2313981 3 0.1585 0.784 0.008 0.028 0.964
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.3528 0.798 0.000 0.808 0.192 0.000
#> SRR2313980 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2313979 1 0.0188 0.886 0.996 0.000 0.000 0.004
#> SRR2313977 2 0.4353 0.767 0.000 0.756 0.232 0.012
#> SRR2313976 2 0.1488 0.812 0.000 0.956 0.012 0.032
#> SRR2313975 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> SRR2313973 4 0.4134 0.690 0.260 0.000 0.000 0.740
#> SRR2313974 1 0.5522 0.464 0.668 0.000 0.044 0.288
#> SRR2313985 1 0.2408 0.818 0.920 0.000 0.044 0.036
#> SRR2313993 2 0.2999 0.818 0.000 0.864 0.132 0.004
#> SRR2313994 2 0.4193 0.729 0.000 0.732 0.268 0.000
#> SRR2313995 2 0.5478 0.637 0.000 0.696 0.248 0.056
#> SRR2314024 2 0.2256 0.810 0.000 0.924 0.020 0.056
#> SRR2314015 2 0.3813 0.818 0.000 0.828 0.148 0.024
#> SRR2314025 3 0.4564 0.427 0.000 0.000 0.672 0.328
#> SRR2314023 4 0.4134 0.690 0.260 0.000 0.000 0.740
#> SRR2314033 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2314045 1 0.0927 0.869 0.976 0.000 0.016 0.008
#> SRR2314042 3 0.4134 0.513 0.000 0.000 0.740 0.260
#> SRR2314043 3 0.3672 0.696 0.000 0.164 0.824 0.012
#> SRR2314041 2 0.5070 0.477 0.000 0.580 0.416 0.004
#> SRR2314044 3 0.1610 0.714 0.000 0.032 0.952 0.016
#> SRR2314040 3 0.5047 0.621 0.004 0.256 0.716 0.024
#> SRR2314039 3 0.5126 0.191 0.000 0.444 0.552 0.004
#> SRR2314038 3 0.2530 0.670 0.000 0.000 0.888 0.112
#> SRR2314037 3 0.1716 0.696 0.000 0.000 0.936 0.064
#> SRR2314036 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2314035 3 0.4632 0.639 0.004 0.244 0.740 0.012
#> SRR2314034 3 0.1305 0.708 0.000 0.004 0.960 0.036
#> SRR2314032 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> SRR2314031 3 0.4564 0.427 0.000 0.000 0.672 0.328
#> SRR2314029 3 0.2830 0.712 0.000 0.040 0.900 0.060
#> SRR2314030 3 0.2053 0.699 0.000 0.004 0.924 0.072
#> SRR2314028 3 0.4769 0.440 0.008 0.000 0.684 0.308
#> SRR2314027 3 0.5126 0.191 0.000 0.444 0.552 0.004
#> SRR2314026 3 0.4661 0.617 0.004 0.264 0.724 0.008
#> SRR2314020 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2314019 3 0.1305 0.708 0.000 0.004 0.960 0.036
#> SRR2314021 3 0.1305 0.708 0.000 0.004 0.960 0.036
#> SRR2314018 3 0.4632 0.639 0.004 0.244 0.740 0.012
#> SRR2314017 3 0.4778 0.639 0.004 0.248 0.732 0.016
#> SRR2314016 3 0.2530 0.670 0.000 0.000 0.888 0.112
#> SRR2314014 2 0.3266 0.805 0.000 0.832 0.168 0.000
#> SRR2314013 2 0.5417 0.647 0.000 0.676 0.284 0.040
#> SRR2314012 3 0.4769 0.440 0.008 0.000 0.684 0.308
#> SRR2314011 4 0.4040 0.696 0.248 0.000 0.000 0.752
#> SRR2314010 2 0.4158 0.778 0.000 0.768 0.224 0.008
#> SRR2314009 3 0.4632 0.639 0.004 0.244 0.740 0.012
#> SRR2314008 3 0.4632 0.639 0.004 0.244 0.740 0.012
#> SRR2314007 2 0.4584 0.672 0.000 0.696 0.300 0.004
#> SRR2314005 2 0.2376 0.819 0.000 0.916 0.068 0.016
#> SRR2314004 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2314003 4 0.7285 0.515 0.180 0.000 0.300 0.520
#> SRR2314006 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2314002 3 0.5155 0.625 0.008 0.252 0.716 0.024
#> SRR2314001 2 0.3649 0.787 0.000 0.796 0.204 0.000
#> SRR2314000 3 0.6331 0.665 0.088 0.160 0.712 0.040
#> SRR2313998 2 0.2469 0.821 0.000 0.892 0.108 0.000
#> SRR2313996 2 0.3726 0.784 0.000 0.788 0.212 0.000
#> SRR2313999 4 0.3975 0.695 0.240 0.000 0.000 0.760
#> SRR2313997 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2313992 3 0.2216 0.686 0.000 0.000 0.908 0.092
#> SRR2313991 3 0.5047 0.623 0.004 0.256 0.716 0.024
#> SRR2313990 2 0.4401 0.721 0.000 0.724 0.272 0.004
#> SRR2313988 1 0.0000 0.886 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.5522 0.464 0.668 0.000 0.044 0.288
#> SRR2313984 2 0.3726 0.784 0.000 0.788 0.212 0.000
#> SRR2313989 2 0.1557 0.805 0.000 0.944 0.000 0.056
#> SRR2313983 1 0.0188 0.886 0.996 0.000 0.000 0.004
#> SRR2313986 4 0.7172 0.510 0.164 0.000 0.304 0.532
#> SRR2313982 1 0.0188 0.886 0.996 0.000 0.000 0.004
#> SRR2313981 3 0.2647 0.669 0.000 0.000 0.880 0.120
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.4613 0.57367 0.000 0.580 0.408 0.008 0.004
#> SRR2313980 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0162 0.86546 0.996 0.000 0.000 0.000 0.004
#> SRR2313977 2 0.4794 0.48016 0.000 0.520 0.464 0.004 0.012
#> SRR2313976 2 0.1544 0.72101 0.000 0.932 0.068 0.000 0.000
#> SRR2313975 1 0.0000 0.86536 1.000 0.000 0.000 0.000 0.000
#> SRR2313973 5 0.3130 0.81741 0.048 0.000 0.000 0.096 0.856
#> SRR2313974 1 0.5798 0.17659 0.500 0.000 0.020 0.048 0.432
#> SRR2313985 1 0.2362 0.81135 0.916 0.000 0.032 0.024 0.028
#> SRR2313993 2 0.3913 0.65398 0.000 0.676 0.324 0.000 0.000
#> SRR2313994 3 0.4559 -0.43224 0.000 0.480 0.512 0.000 0.008
#> SRR2313995 2 0.5023 0.55180 0.000 0.708 0.192 0.096 0.004
#> SRR2314024 2 0.0609 0.72094 0.000 0.980 0.020 0.000 0.000
#> SRR2314015 2 0.4302 0.63668 0.000 0.648 0.344 0.004 0.004
#> SRR2314025 4 0.0671 0.51772 0.000 0.000 0.004 0.980 0.016
#> SRR2314023 5 0.3130 0.81741 0.048 0.000 0.000 0.096 0.856
#> SRR2314033 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.0867 0.85519 0.976 0.000 0.008 0.008 0.008
#> SRR2314042 4 0.2011 0.57838 0.000 0.000 0.088 0.908 0.004
#> SRR2314043 3 0.2787 0.49575 0.000 0.004 0.856 0.136 0.004
#> SRR2314041 3 0.6144 -0.22594 0.000 0.432 0.452 0.112 0.004
#> SRR2314044 3 0.4362 0.11153 0.000 0.004 0.632 0.360 0.004
#> SRR2314040 3 0.1612 0.57618 0.000 0.016 0.948 0.024 0.012
#> SRR2314039 3 0.4398 0.41736 0.000 0.240 0.720 0.040 0.000
#> SRR2314038 4 0.5925 0.35556 0.000 0.000 0.424 0.472 0.104
#> SRR2314037 3 0.4504 -0.12791 0.000 0.000 0.564 0.428 0.008
#> SRR2314036 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.1673 0.57820 0.000 0.016 0.944 0.032 0.008
#> SRR2314034 3 0.4299 0.00482 0.000 0.000 0.608 0.388 0.004
#> SRR2314032 1 0.0000 0.86536 1.000 0.000 0.000 0.000 0.000
#> SRR2314031 4 0.0671 0.51772 0.000 0.000 0.004 0.980 0.016
#> SRR2314029 4 0.4238 0.39667 0.000 0.000 0.368 0.628 0.004
#> SRR2314030 4 0.5583 0.34392 0.000 0.000 0.424 0.504 0.072
#> SRR2314028 4 0.1741 0.55165 0.000 0.000 0.040 0.936 0.024
#> SRR2314027 3 0.4398 0.41736 0.000 0.240 0.720 0.040 0.000
#> SRR2314026 3 0.1743 0.57640 0.000 0.028 0.940 0.028 0.004
#> SRR2314020 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.4299 0.00482 0.000 0.000 0.608 0.388 0.004
#> SRR2314021 3 0.4299 0.00482 0.000 0.000 0.608 0.388 0.004
#> SRR2314018 3 0.1673 0.57820 0.000 0.016 0.944 0.032 0.008
#> SRR2314017 3 0.2866 0.51691 0.000 0.004 0.872 0.024 0.100
#> SRR2314016 4 0.5925 0.35556 0.000 0.000 0.424 0.472 0.104
#> SRR2314014 2 0.4350 0.58515 0.000 0.588 0.408 0.000 0.004
#> SRR2314013 2 0.6043 0.48393 0.000 0.568 0.308 0.116 0.008
#> SRR2314012 4 0.1741 0.55165 0.000 0.000 0.040 0.936 0.024
#> SRR2314011 5 0.2959 0.82091 0.036 0.000 0.000 0.100 0.864
#> SRR2314010 2 0.4856 0.56093 0.000 0.584 0.388 0.028 0.000
#> SRR2314009 3 0.1673 0.57820 0.000 0.016 0.944 0.032 0.008
#> SRR2314008 3 0.1673 0.57820 0.000 0.016 0.944 0.032 0.008
#> SRR2314007 3 0.4549 -0.36911 0.000 0.464 0.528 0.008 0.000
#> SRR2314005 2 0.3659 0.69020 0.000 0.768 0.220 0.000 0.012
#> SRR2314004 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 5 0.4648 0.61190 0.012 0.000 0.000 0.464 0.524
#> SRR2314006 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 3 0.2729 0.51634 0.004 0.004 0.876 0.008 0.108
#> SRR2314001 2 0.4397 0.54898 0.000 0.564 0.432 0.000 0.004
#> SRR2314000 3 0.4264 0.46706 0.084 0.000 0.808 0.076 0.032
#> SRR2313998 2 0.4084 0.65414 0.000 0.668 0.328 0.000 0.004
#> SRR2313996 2 0.4645 0.54643 0.000 0.564 0.424 0.008 0.004
#> SRR2313999 5 0.2707 0.81918 0.024 0.000 0.000 0.100 0.876
#> SRR2313997 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 3 0.4440 -0.22784 0.000 0.000 0.528 0.468 0.004
#> SRR2313991 3 0.2805 0.52055 0.000 0.008 0.872 0.012 0.108
#> SRR2313990 3 0.4555 -0.41881 0.000 0.472 0.520 0.000 0.008
#> SRR2313988 1 0.0000 0.86536 1.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.5798 0.17659 0.500 0.000 0.020 0.048 0.432
#> SRR2313984 2 0.4645 0.54643 0.000 0.564 0.424 0.008 0.004
#> SRR2313989 2 0.0000 0.72061 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0162 0.86546 0.996 0.000 0.000 0.000 0.004
#> SRR2313986 5 0.4656 0.59220 0.012 0.000 0.000 0.480 0.508
#> SRR2313982 1 0.0162 0.86546 0.996 0.000 0.000 0.000 0.004
#> SRR2313981 4 0.4452 0.20697 0.000 0.000 0.496 0.500 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.0632 0.6430 0.000 0.976 0.000 0.000 0.024 0.000
#> SRR2313980 2 0.4086 0.6124 0.000 0.528 0.464 0.000 0.008 0.000
#> SRR2313979 1 0.0146 0.8605 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR2313977 2 0.3175 0.5777 0.000 0.808 0.028 0.000 0.164 0.000
#> SRR2313976 2 0.3672 0.6496 0.000 0.632 0.368 0.000 0.000 0.000
#> SRR2313975 1 0.0146 0.8603 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313973 6 0.0622 0.7896 0.012 0.000 0.008 0.000 0.000 0.980
#> SRR2313974 1 0.5493 0.1445 0.480 0.000 0.008 0.052 0.020 0.440
#> SRR2313985 1 0.2539 0.8093 0.900 0.000 0.012 0.020 0.040 0.028
#> SRR2313993 2 0.1806 0.6766 0.000 0.908 0.088 0.000 0.004 0.000
#> SRR2313994 2 0.2630 0.5564 0.000 0.872 0.064 0.000 0.064 0.000
#> SRR2313995 2 0.5753 0.4903 0.000 0.448 0.432 0.020 0.100 0.000
#> SRR2314024 2 0.4032 0.6365 0.000 0.572 0.420 0.000 0.008 0.000
#> SRR2314015 2 0.1753 0.6695 0.000 0.912 0.084 0.000 0.004 0.000
#> SRR2314025 4 0.0964 0.6888 0.000 0.000 0.012 0.968 0.004 0.016
#> SRR2314023 6 0.0622 0.7896 0.012 0.000 0.008 0.000 0.000 0.980
#> SRR2314033 2 0.3823 0.6320 0.000 0.564 0.436 0.000 0.000 0.000
#> SRR2314045 1 0.0909 0.8520 0.968 0.000 0.012 0.000 0.020 0.000
#> SRR2314042 4 0.3246 0.6600 0.000 0.020 0.052 0.860 0.052 0.016
#> SRR2314043 3 0.7215 0.4778 0.000 0.260 0.352 0.088 0.300 0.000
#> SRR2314041 2 0.5206 0.3881 0.000 0.696 0.108 0.060 0.136 0.000
#> SRR2314044 3 0.7298 0.4555 0.000 0.152 0.424 0.220 0.204 0.000
#> SRR2314040 3 0.5990 0.4769 0.000 0.368 0.400 0.000 0.232 0.000
#> SRR2314039 2 0.4538 -0.2115 0.000 0.612 0.340 0.000 0.048 0.000
#> SRR2314038 5 0.4598 0.1237 0.000 0.000 0.048 0.360 0.592 0.000
#> SRR2314037 3 0.6941 0.4218 0.000 0.116 0.444 0.304 0.136 0.000
#> SRR2314036 2 0.3828 0.6314 0.000 0.560 0.440 0.000 0.000 0.000
#> SRR2314035 3 0.6236 0.4764 0.000 0.296 0.352 0.004 0.348 0.000
#> SRR2314034 3 0.7082 0.4928 0.000 0.144 0.444 0.272 0.140 0.000
#> SRR2314032 1 0.0291 0.8600 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR2314031 4 0.0964 0.6888 0.000 0.000 0.012 0.968 0.004 0.016
#> SRR2314029 4 0.7084 0.1737 0.000 0.132 0.192 0.464 0.212 0.000
#> SRR2314030 5 0.6432 -0.0756 0.000 0.040 0.160 0.368 0.432 0.000
#> SRR2314028 4 0.1874 0.6970 0.000 0.000 0.016 0.928 0.028 0.028
#> SRR2314027 2 0.4538 -0.2115 0.000 0.612 0.340 0.000 0.048 0.000
#> SRR2314026 3 0.5902 0.4764 0.000 0.392 0.404 0.000 0.204 0.000
#> SRR2314020 2 0.3828 0.6314 0.000 0.560 0.440 0.000 0.000 0.000
#> SRR2314019 3 0.7082 0.4928 0.000 0.144 0.444 0.272 0.140 0.000
#> SRR2314021 3 0.7082 0.4928 0.000 0.144 0.444 0.272 0.140 0.000
#> SRR2314018 3 0.6236 0.4764 0.000 0.296 0.352 0.004 0.348 0.000
#> SRR2314017 5 0.4233 0.3529 0.000 0.216 0.060 0.004 0.720 0.000
#> SRR2314016 5 0.4598 0.1237 0.000 0.000 0.048 0.360 0.592 0.000
#> SRR2314014 2 0.1434 0.6461 0.000 0.940 0.012 0.000 0.048 0.000
#> SRR2314013 2 0.5781 0.5200 0.000 0.604 0.212 0.036 0.148 0.000
#> SRR2314012 4 0.1874 0.6970 0.000 0.000 0.016 0.928 0.028 0.028
#> SRR2314011 6 0.0000 0.7927 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314010 2 0.2365 0.6365 0.000 0.888 0.072 0.000 0.040 0.000
#> SRR2314009 3 0.6242 0.4798 0.000 0.304 0.352 0.004 0.340 0.000
#> SRR2314008 3 0.6242 0.4798 0.000 0.304 0.352 0.004 0.340 0.000
#> SRR2314007 2 0.3039 0.5179 0.000 0.848 0.088 0.004 0.060 0.000
#> SRR2314005 2 0.4278 0.6503 0.000 0.712 0.212 0.000 0.076 0.000
#> SRR2314004 2 0.3828 0.6314 0.000 0.560 0.440 0.000 0.000 0.000
#> SRR2314003 6 0.4242 0.4505 0.000 0.000 0.000 0.448 0.016 0.536
#> SRR2314006 2 0.4086 0.6124 0.000 0.528 0.464 0.000 0.008 0.000
#> SRR2314002 5 0.3231 0.4089 0.000 0.200 0.016 0.000 0.784 0.000
#> SRR2314001 2 0.1003 0.6300 0.000 0.964 0.016 0.000 0.020 0.000
#> SRR2314000 5 0.7954 -0.4546 0.072 0.216 0.324 0.036 0.340 0.012
#> SRR2313998 2 0.2383 0.6731 0.000 0.880 0.096 0.000 0.024 0.000
#> SRR2313996 2 0.1049 0.6316 0.000 0.960 0.032 0.000 0.008 0.000
#> SRR2313999 6 0.0363 0.7913 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR2313997 2 0.3828 0.6314 0.000 0.560 0.440 0.000 0.000 0.000
#> SRR2313992 3 0.6856 0.3267 0.000 0.108 0.408 0.364 0.120 0.000
#> SRR2313991 5 0.3488 0.4011 0.000 0.216 0.016 0.004 0.764 0.000
#> SRR2313990 2 0.2740 0.5495 0.000 0.864 0.060 0.000 0.076 0.000
#> SRR2313988 1 0.0291 0.8600 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR2313987 1 0.5493 0.1445 0.480 0.000 0.008 0.052 0.020 0.440
#> SRR2313984 2 0.1049 0.6316 0.000 0.960 0.032 0.000 0.008 0.000
#> SRR2313989 2 0.3828 0.6314 0.000 0.560 0.440 0.000 0.000 0.000
#> SRR2313983 1 0.0146 0.8605 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR2313986 6 0.4175 0.4245 0.000 0.000 0.000 0.464 0.012 0.524
#> SRR2313982 1 0.0146 0.8605 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR2313981 4 0.6658 -0.3916 0.000 0.096 0.388 0.412 0.104 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 0.889 0.941 0.974 0.4797 0.512 0.512
#> 3 3 0.670 0.772 0.879 0.3454 0.656 0.426
#> 4 4 0.656 0.705 0.823 0.1379 0.828 0.548
#> 5 5 0.652 0.551 0.756 0.0703 0.872 0.579
#> 6 6 0.656 0.542 0.701 0.0427 0.883 0.565
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
#> SRR2313978 2 0.0000 0.991 0.000 1.000
#> SRR2313980 2 0.0000 0.991 0.000 1.000
#> SRR2313979 1 0.0000 0.944 1.000 0.000
#> SRR2313977 2 0.0000 0.991 0.000 1.000
#> SRR2313976 2 0.0000 0.991 0.000 1.000
#> SRR2313975 1 0.0000 0.944 1.000 0.000
#> SRR2313973 1 0.0000 0.944 1.000 0.000
#> SRR2313974 1 0.0000 0.944 1.000 0.000
#> SRR2313985 1 0.0000 0.944 1.000 0.000
#> SRR2313993 2 0.0000 0.991 0.000 1.000
#> SRR2313994 2 0.0000 0.991 0.000 1.000
#> SRR2313995 2 0.0000 0.991 0.000 1.000
#> SRR2314024 2 0.0000 0.991 0.000 1.000
#> SRR2314015 2 0.0000 0.991 0.000 1.000
#> SRR2314025 1 0.0672 0.940 0.992 0.008
#> SRR2314023 1 0.0000 0.944 1.000 0.000
#> SRR2314033 2 0.0000 0.991 0.000 1.000
#> SRR2314045 1 0.0000 0.944 1.000 0.000
#> SRR2314042 1 0.5059 0.867 0.888 0.112
#> SRR2314043 2 0.0000 0.991 0.000 1.000
#> SRR2314041 2 0.0000 0.991 0.000 1.000
#> SRR2314044 2 0.0000 0.991 0.000 1.000
#> SRR2314040 2 0.0000 0.991 0.000 1.000
#> SRR2314039 2 0.0000 0.991 0.000 1.000
#> SRR2314038 2 0.8661 0.559 0.288 0.712
#> SRR2314037 1 0.5059 0.867 0.888 0.112
#> SRR2314036 2 0.0000 0.991 0.000 1.000
#> SRR2314035 2 0.0000 0.991 0.000 1.000
#> SRR2314034 1 0.4815 0.874 0.896 0.104
#> SRR2314032 1 0.0000 0.944 1.000 0.000
#> SRR2314031 1 0.0672 0.940 0.992 0.008
#> SRR2314029 2 0.0000 0.991 0.000 1.000
#> SRR2314030 1 0.9775 0.368 0.588 0.412
#> SRR2314028 1 0.0000 0.944 1.000 0.000
#> SRR2314027 2 0.0000 0.991 0.000 1.000
#> SRR2314026 2 0.0000 0.991 0.000 1.000
#> SRR2314020 2 0.0000 0.991 0.000 1.000
#> SRR2314019 2 0.0000 0.991 0.000 1.000
#> SRR2314021 1 0.9732 0.389 0.596 0.404
#> SRR2314018 2 0.0000 0.991 0.000 1.000
#> SRR2314017 2 0.1184 0.975 0.016 0.984
#> SRR2314016 1 0.0000 0.944 1.000 0.000
#> SRR2314014 2 0.0000 0.991 0.000 1.000
#> SRR2314013 2 0.0000 0.991 0.000 1.000
#> SRR2314012 1 0.0000 0.944 1.000 0.000
#> SRR2314011 1 0.0000 0.944 1.000 0.000
#> SRR2314010 2 0.0000 0.991 0.000 1.000
#> SRR2314009 2 0.0000 0.991 0.000 1.000
#> SRR2314008 2 0.0000 0.991 0.000 1.000
#> SRR2314007 2 0.0000 0.991 0.000 1.000
#> SRR2314005 2 0.0000 0.991 0.000 1.000
#> SRR2314004 2 0.0000 0.991 0.000 1.000
#> SRR2314003 1 0.0000 0.944 1.000 0.000
#> SRR2314006 2 0.0000 0.991 0.000 1.000
#> SRR2314002 2 0.2603 0.945 0.044 0.956
#> SRR2314001 2 0.0000 0.991 0.000 1.000
#> SRR2314000 1 0.8144 0.668 0.748 0.252
#> SRR2313998 2 0.0000 0.991 0.000 1.000
#> SRR2313996 2 0.0000 0.991 0.000 1.000
#> SRR2313999 1 0.0000 0.944 1.000 0.000
#> SRR2313997 2 0.0000 0.991 0.000 1.000
#> SRR2313992 1 0.0672 0.940 0.992 0.008
#> SRR2313991 2 0.0000 0.991 0.000 1.000
#> SRR2313990 2 0.0000 0.991 0.000 1.000
#> SRR2313988 1 0.0000 0.944 1.000 0.000
#> SRR2313987 1 0.0000 0.944 1.000 0.000
#> SRR2313984 2 0.0000 0.991 0.000 1.000
#> SRR2313989 2 0.0000 0.991 0.000 1.000
#> SRR2313983 1 0.0000 0.944 1.000 0.000
#> SRR2313986 1 0.0000 0.944 1.000 0.000
#> SRR2313982 1 0.0000 0.944 1.000 0.000
#> SRR2313981 1 0.5178 0.863 0.884 0.116
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2313980 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2313979 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313977 2 0.3267 0.834976 0.000 0.884 0.116
#> SRR2313976 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313973 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2313974 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2313985 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2313995 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2314024 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2314015 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314025 1 0.6274 0.502332 0.544 0.000 0.456
#> SRR2314023 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2314033 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314045 3 0.6286 0.184028 0.464 0.000 0.536
#> SRR2314042 3 0.0237 0.743254 0.004 0.000 0.996
#> SRR2314043 3 0.1964 0.772325 0.000 0.056 0.944
#> SRR2314041 3 0.5988 0.608687 0.000 0.368 0.632
#> SRR2314044 3 0.1529 0.769211 0.000 0.040 0.960
#> SRR2314040 3 0.5058 0.740437 0.000 0.244 0.756
#> SRR2314039 3 0.6154 0.540789 0.000 0.408 0.592
#> SRR2314038 3 0.1267 0.761019 0.004 0.024 0.972
#> SRR2314037 3 0.0237 0.743254 0.004 0.000 0.996
#> SRR2314036 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314035 3 0.4654 0.760009 0.000 0.208 0.792
#> SRR2314034 3 0.0237 0.743254 0.004 0.000 0.996
#> SRR2314032 1 0.6260 0.000494 0.552 0.000 0.448
#> SRR2314031 3 0.6291 -0.377801 0.468 0.000 0.532
#> SRR2314029 3 0.1529 0.769211 0.000 0.040 0.960
#> SRR2314030 3 0.1031 0.762103 0.000 0.024 0.976
#> SRR2314028 3 0.0237 0.743254 0.004 0.000 0.996
#> SRR2314027 3 0.6192 0.518651 0.000 0.420 0.580
#> SRR2314026 3 0.4974 0.746611 0.000 0.236 0.764
#> SRR2314020 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314019 3 0.1860 0.772117 0.000 0.052 0.948
#> SRR2314021 3 0.1267 0.760868 0.004 0.024 0.972
#> SRR2314018 3 0.4605 0.761086 0.000 0.204 0.796
#> SRR2314017 3 0.3816 0.769673 0.000 0.148 0.852
#> SRR2314016 1 0.6225 0.546701 0.568 0.000 0.432
#> SRR2314014 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314013 2 0.1860 0.926770 0.000 0.948 0.052
#> SRR2314012 1 0.6192 0.564948 0.580 0.000 0.420
#> SRR2314011 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2314010 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2314009 3 0.1964 0.772325 0.000 0.056 0.944
#> SRR2314008 3 0.5948 0.624394 0.000 0.360 0.640
#> SRR2314007 3 0.6154 0.548928 0.000 0.408 0.592
#> SRR2314005 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314003 1 0.6168 0.574095 0.588 0.000 0.412
#> SRR2314006 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2314002 3 0.5115 0.761043 0.016 0.188 0.796
#> SRR2314001 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2314000 3 0.2804 0.744975 0.060 0.016 0.924
#> SRR2313998 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2313996 3 0.5968 0.614572 0.000 0.364 0.636
#> SRR2313999 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2313997 2 0.0000 0.988543 0.000 1.000 0.000
#> SRR2313992 3 0.0237 0.743254 0.004 0.000 0.996
#> SRR2313991 3 0.5905 0.634448 0.000 0.352 0.648
#> SRR2313990 3 0.5968 0.619219 0.000 0.364 0.636
#> SRR2313988 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313987 1 0.1860 0.829027 0.948 0.000 0.052
#> SRR2313984 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2313989 2 0.0237 0.987441 0.000 0.996 0.004
#> SRR2313983 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313986 1 0.6180 0.569758 0.584 0.000 0.416
#> SRR2313982 1 0.0000 0.823020 1.000 0.000 0.000
#> SRR2313981 3 0.0237 0.743254 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.5050 0.0401 0.000 0.408 0.588 0.004
#> SRR2313980 2 0.1211 0.8414 0.000 0.960 0.000 0.040
#> SRR2313979 1 0.0000 0.8268 1.000 0.000 0.000 0.000
#> SRR2313977 3 0.4331 0.4221 0.000 0.288 0.712 0.000
#> SRR2313976 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2313975 1 0.0921 0.8164 0.972 0.000 0.000 0.028
#> SRR2313973 1 0.4387 0.7996 0.752 0.000 0.012 0.236
#> SRR2313974 1 0.4262 0.8002 0.756 0.000 0.008 0.236
#> SRR2313985 1 0.1022 0.8285 0.968 0.000 0.000 0.032
#> SRR2313993 2 0.1867 0.8424 0.000 0.928 0.072 0.000
#> SRR2313994 2 0.4925 0.4394 0.000 0.572 0.428 0.000
#> SRR2313995 2 0.3266 0.8356 0.000 0.876 0.084 0.040
#> SRR2314024 2 0.1211 0.8414 0.000 0.960 0.000 0.040
#> SRR2314015 2 0.2469 0.8287 0.000 0.892 0.108 0.000
#> SRR2314025 4 0.4290 0.6154 0.164 0.000 0.036 0.800
#> SRR2314023 1 0.4387 0.7996 0.752 0.000 0.012 0.236
#> SRR2314033 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.5174 0.6264 0.760 0.000 0.116 0.124
#> SRR2314042 4 0.4304 0.6852 0.000 0.000 0.284 0.716
#> SRR2314043 3 0.2281 0.7846 0.000 0.000 0.904 0.096
#> SRR2314041 3 0.1389 0.8015 0.000 0.048 0.952 0.000
#> SRR2314044 4 0.4877 0.6016 0.000 0.000 0.408 0.592
#> SRR2314040 3 0.2179 0.8011 0.000 0.012 0.924 0.064
#> SRR2314039 3 0.1576 0.8016 0.000 0.048 0.948 0.004
#> SRR2314038 3 0.5000 -0.2358 0.000 0.000 0.500 0.500
#> SRR2314037 4 0.4624 0.6344 0.000 0.000 0.340 0.660
#> SRR2314036 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.2867 0.7958 0.000 0.012 0.884 0.104
#> SRR2314034 4 0.4454 0.6716 0.000 0.000 0.308 0.692
#> SRR2314032 1 0.4764 0.6587 0.788 0.000 0.088 0.124
#> SRR2314031 4 0.4290 0.6154 0.164 0.000 0.036 0.800
#> SRR2314029 4 0.4866 0.6099 0.000 0.000 0.404 0.596
#> SRR2314030 4 0.4454 0.6690 0.000 0.000 0.308 0.692
#> SRR2314028 4 0.2760 0.7052 0.000 0.000 0.128 0.872
#> SRR2314027 3 0.1389 0.8015 0.000 0.048 0.952 0.000
#> SRR2314026 3 0.2610 0.7934 0.000 0.012 0.900 0.088
#> SRR2314020 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.4406 0.4778 0.000 0.000 0.700 0.300
#> SRR2314021 3 0.4431 0.4701 0.000 0.000 0.696 0.304
#> SRR2314018 3 0.2676 0.7994 0.000 0.012 0.896 0.092
#> SRR2314017 3 0.2976 0.7901 0.000 0.008 0.872 0.120
#> SRR2314016 4 0.4244 0.6173 0.160 0.000 0.036 0.804
#> SRR2314014 2 0.4907 0.4564 0.000 0.580 0.420 0.000
#> SRR2314013 2 0.4933 0.4268 0.000 0.568 0.432 0.000
#> SRR2314012 4 0.4197 0.6198 0.156 0.000 0.036 0.808
#> SRR2314011 1 0.4420 0.7971 0.748 0.000 0.012 0.240
#> SRR2314010 2 0.3198 0.8372 0.000 0.880 0.080 0.040
#> SRR2314009 3 0.2281 0.7846 0.000 0.000 0.904 0.096
#> SRR2314008 3 0.1389 0.8015 0.000 0.048 0.952 0.000
#> SRR2314007 3 0.1389 0.8015 0.000 0.048 0.952 0.000
#> SRR2314005 2 0.2530 0.8008 0.000 0.888 0.112 0.000
#> SRR2314004 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2314003 4 0.3545 0.5606 0.164 0.000 0.008 0.828
#> SRR2314006 2 0.1211 0.8414 0.000 0.960 0.000 0.040
#> SRR2314002 3 0.3188 0.7920 0.008 0.008 0.872 0.112
#> SRR2314001 2 0.4941 0.4209 0.000 0.564 0.436 0.000
#> SRR2314000 3 0.5604 0.6229 0.160 0.000 0.724 0.116
#> SRR2313998 2 0.4643 0.5817 0.000 0.656 0.344 0.000
#> SRR2313996 3 0.1474 0.7991 0.000 0.052 0.948 0.000
#> SRR2313999 1 0.4420 0.7971 0.748 0.000 0.012 0.240
#> SRR2313997 2 0.0000 0.8503 0.000 1.000 0.000 0.000
#> SRR2313992 4 0.4431 0.6749 0.000 0.000 0.304 0.696
#> SRR2313991 3 0.3056 0.8030 0.000 0.040 0.888 0.072
#> SRR2313990 3 0.1389 0.8015 0.000 0.048 0.952 0.000
#> SRR2313988 1 0.2281 0.7719 0.904 0.000 0.000 0.096
#> SRR2313987 1 0.4295 0.7985 0.752 0.000 0.008 0.240
#> SRR2313984 2 0.1867 0.8424 0.000 0.928 0.072 0.000
#> SRR2313989 2 0.1211 0.8414 0.000 0.960 0.000 0.040
#> SRR2313983 1 0.0000 0.8268 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.3545 0.5606 0.164 0.000 0.008 0.828
#> SRR2313982 1 0.0000 0.8268 1.000 0.000 0.000 0.000
#> SRR2313981 4 0.4661 0.6224 0.000 0.000 0.348 0.652
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.2674 0.6358 0.000 0.120 0.868 0.000 0.012
#> SRR2313980 2 0.2011 0.8236 0.000 0.908 0.000 0.004 0.088
#> SRR2313979 1 0.4249 0.4575 0.568 0.000 0.000 0.000 0.432
#> SRR2313977 3 0.4171 0.6155 0.000 0.112 0.784 0.000 0.104
#> SRR2313976 2 0.0693 0.8359 0.000 0.980 0.012 0.000 0.008
#> SRR2313975 1 0.4450 0.3238 0.508 0.000 0.004 0.000 0.488
#> SRR2313973 1 0.0000 0.6602 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.1851 0.6659 0.912 0.000 0.000 0.000 0.088
#> SRR2313985 1 0.4201 0.4829 0.592 0.000 0.000 0.000 0.408
#> SRR2313993 2 0.4297 0.6315 0.000 0.692 0.288 0.000 0.020
#> SRR2313994 3 0.4530 0.2385 0.000 0.376 0.612 0.004 0.008
#> SRR2313995 2 0.5036 0.7191 0.000 0.704 0.200 0.004 0.092
#> SRR2314024 2 0.2011 0.8236 0.000 0.908 0.000 0.004 0.088
#> SRR2314015 2 0.3992 0.6575 0.000 0.720 0.268 0.000 0.012
#> SRR2314025 4 0.3727 0.6558 0.216 0.000 0.000 0.768 0.016
#> SRR2314023 1 0.0000 0.6602 1.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.8375 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 5 0.4289 0.2969 0.192 0.000 0.024 0.020 0.764
#> SRR2314042 4 0.0693 0.7260 0.000 0.000 0.008 0.980 0.012
#> SRR2314043 3 0.6183 0.3948 0.000 0.000 0.544 0.180 0.276
#> SRR2314041 3 0.1442 0.6632 0.000 0.004 0.952 0.032 0.012
#> SRR2314044 4 0.4393 0.6617 0.000 0.000 0.168 0.756 0.076
#> SRR2314040 3 0.3460 0.6239 0.000 0.000 0.828 0.044 0.128
#> SRR2314039 3 0.2722 0.6320 0.000 0.000 0.872 0.108 0.020
#> SRR2314038 4 0.6678 0.2042 0.000 0.000 0.264 0.436 0.300
#> SRR2314037 4 0.3366 0.6948 0.000 0.000 0.140 0.828 0.032
#> SRR2314036 2 0.0290 0.8369 0.000 0.992 0.000 0.000 0.008
#> SRR2314035 3 0.4687 0.4737 0.000 0.000 0.636 0.028 0.336
#> SRR2314034 4 0.2659 0.7132 0.000 0.000 0.052 0.888 0.060
#> SRR2314032 5 0.4422 0.2484 0.232 0.000 0.016 0.020 0.732
#> SRR2314031 4 0.3727 0.6558 0.216 0.000 0.000 0.768 0.016
#> SRR2314029 4 0.4277 0.6706 0.000 0.000 0.156 0.768 0.076
#> SRR2314030 4 0.1082 0.7251 0.000 0.000 0.008 0.964 0.028
#> SRR2314028 4 0.0912 0.7233 0.016 0.000 0.000 0.972 0.012
#> SRR2314027 3 0.1956 0.6500 0.000 0.000 0.916 0.076 0.008
#> SRR2314026 3 0.4634 0.5782 0.000 0.000 0.744 0.136 0.120
#> SRR2314020 2 0.0324 0.8375 0.000 0.992 0.000 0.004 0.004
#> SRR2314019 4 0.6002 0.1940 0.000 0.000 0.392 0.492 0.116
#> SRR2314021 4 0.6054 0.2086 0.000 0.000 0.380 0.496 0.124
#> SRR2314018 3 0.4671 0.4756 0.000 0.000 0.640 0.028 0.332
#> SRR2314017 3 0.5143 0.4165 0.000 0.000 0.584 0.048 0.368
#> SRR2314016 4 0.3663 0.6614 0.208 0.000 0.000 0.776 0.016
#> SRR2314014 3 0.4494 0.2225 0.000 0.380 0.608 0.000 0.012
#> SRR2314013 3 0.5568 0.1995 0.000 0.384 0.540 0.000 0.076
#> SRR2314012 4 0.3530 0.6633 0.204 0.000 0.000 0.784 0.012
#> SRR2314011 1 0.0000 0.6602 1.000 0.000 0.000 0.000 0.000
#> SRR2314010 2 0.5844 0.5555 0.000 0.556 0.344 0.004 0.096
#> SRR2314009 3 0.4587 0.5700 0.000 0.000 0.744 0.096 0.160
#> SRR2314008 3 0.2280 0.6332 0.000 0.000 0.880 0.000 0.120
#> SRR2314007 3 0.1041 0.6637 0.000 0.000 0.964 0.032 0.004
#> SRR2314005 2 0.4923 0.5619 0.000 0.700 0.212 0.000 0.088
#> SRR2314004 2 0.0609 0.8360 0.000 0.980 0.000 0.000 0.020
#> SRR2314003 4 0.4626 0.5161 0.364 0.000 0.000 0.616 0.020
#> SRR2314006 2 0.2011 0.8236 0.000 0.908 0.000 0.004 0.088
#> SRR2314002 3 0.5167 0.3669 0.000 0.000 0.552 0.044 0.404
#> SRR2314001 3 0.4607 0.2545 0.000 0.368 0.616 0.004 0.012
#> SRR2314000 5 0.6118 -0.3628 0.020 0.000 0.448 0.072 0.460
#> SRR2313998 3 0.4781 0.0718 0.000 0.428 0.552 0.000 0.020
#> SRR2313996 3 0.1492 0.6623 0.000 0.004 0.948 0.040 0.008
#> SRR2313999 1 0.0000 0.6602 1.000 0.000 0.000 0.000 0.000
#> SRR2313997 2 0.0404 0.8365 0.000 0.988 0.000 0.000 0.012
#> SRR2313992 4 0.1300 0.7258 0.000 0.000 0.028 0.956 0.016
#> SRR2313991 3 0.4696 0.4516 0.000 0.000 0.616 0.024 0.360
#> SRR2313990 3 0.1281 0.6639 0.000 0.000 0.956 0.032 0.012
#> SRR2313988 5 0.4892 -0.4903 0.488 0.000 0.004 0.016 0.492
#> SRR2313987 1 0.1851 0.6659 0.912 0.000 0.000 0.000 0.088
#> SRR2313984 2 0.4839 0.6347 0.000 0.668 0.288 0.004 0.040
#> SRR2313989 2 0.1892 0.8254 0.000 0.916 0.000 0.004 0.080
#> SRR2313983 1 0.4256 0.4569 0.564 0.000 0.000 0.000 0.436
#> SRR2313986 4 0.4538 0.5176 0.364 0.000 0.000 0.620 0.016
#> SRR2313982 1 0.4256 0.4569 0.564 0.000 0.000 0.000 0.436
#> SRR2313981 4 0.3193 0.6974 0.000 0.000 0.132 0.840 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.3430 0.5641 0.000 0.836 0.076 0.000 0.028 0.060
#> SRR2313980 6 0.3725 0.7988 0.000 0.008 0.060 0.000 0.140 0.792
#> SRR2313979 1 0.0000 0.5785 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.5285 0.2102 0.000 0.596 0.316 0.000 0.040 0.048
#> SRR2313976 6 0.1901 0.8107 0.000 0.028 0.008 0.000 0.040 0.924
#> SRR2313975 1 0.1802 0.5854 0.916 0.000 0.072 0.000 0.012 0.000
#> SRR2313973 5 0.4250 0.9878 0.456 0.000 0.016 0.000 0.528 0.000
#> SRR2313974 1 0.4379 -0.6713 0.576 0.000 0.004 0.020 0.400 0.000
#> SRR2313985 1 0.2039 0.4747 0.904 0.000 0.020 0.000 0.076 0.000
#> SRR2313993 2 0.4467 0.2106 0.000 0.508 0.000 0.000 0.028 0.464
#> SRR2313994 2 0.3813 0.5887 0.000 0.736 0.008 0.000 0.020 0.236
#> SRR2313995 6 0.6779 0.2790 0.000 0.308 0.068 0.000 0.180 0.444
#> SRR2314024 6 0.3478 0.8055 0.000 0.008 0.060 0.000 0.116 0.816
#> SRR2314015 2 0.5168 0.1867 0.000 0.480 0.020 0.000 0.044 0.456
#> SRR2314025 4 0.3231 0.6296 0.008 0.000 0.012 0.800 0.180 0.000
#> SRR2314023 5 0.4250 0.9878 0.456 0.000 0.016 0.000 0.528 0.000
#> SRR2314033 6 0.1116 0.8323 0.000 0.004 0.008 0.000 0.028 0.960
#> SRR2314045 1 0.4674 0.4231 0.600 0.000 0.356 0.012 0.032 0.000
#> SRR2314042 4 0.1767 0.6887 0.000 0.012 0.036 0.932 0.020 0.000
#> SRR2314043 3 0.6610 0.4470 0.000 0.308 0.480 0.132 0.080 0.000
#> SRR2314041 2 0.1713 0.5549 0.000 0.928 0.044 0.000 0.028 0.000
#> SRR2314044 4 0.6180 0.5770 0.000 0.160 0.108 0.600 0.132 0.000
#> SRR2314040 2 0.4145 0.3009 0.000 0.724 0.220 0.004 0.052 0.000
#> SRR2314039 2 0.4235 0.4386 0.000 0.784 0.072 0.076 0.068 0.000
#> SRR2314038 3 0.5746 0.4570 0.000 0.092 0.612 0.236 0.060 0.000
#> SRR2314037 4 0.5545 0.6186 0.000 0.168 0.072 0.660 0.100 0.000
#> SRR2314036 6 0.0603 0.8365 0.000 0.000 0.004 0.000 0.016 0.980
#> SRR2314035 3 0.3489 0.7635 0.000 0.288 0.708 0.000 0.004 0.000
#> SRR2314034 4 0.5288 0.6370 0.000 0.104 0.092 0.696 0.108 0.000
#> SRR2314032 1 0.4570 0.4286 0.608 0.000 0.352 0.008 0.032 0.000
#> SRR2314031 4 0.3264 0.6291 0.008 0.000 0.012 0.796 0.184 0.000
#> SRR2314029 4 0.6249 0.5772 0.000 0.156 0.108 0.592 0.144 0.000
#> SRR2314030 4 0.3666 0.6742 0.000 0.048 0.060 0.824 0.068 0.000
#> SRR2314028 4 0.1204 0.6818 0.000 0.000 0.000 0.944 0.056 0.000
#> SRR2314027 2 0.2831 0.5167 0.000 0.876 0.052 0.028 0.044 0.000
#> SRR2314026 2 0.5727 0.2505 0.000 0.648 0.156 0.100 0.096 0.000
#> SRR2314020 6 0.0551 0.8378 0.000 0.004 0.008 0.000 0.004 0.984
#> SRR2314019 4 0.7094 0.2871 0.000 0.340 0.148 0.392 0.120 0.000
#> SRR2314021 4 0.7109 0.2940 0.000 0.332 0.152 0.396 0.120 0.000
#> SRR2314018 3 0.3528 0.7567 0.000 0.296 0.700 0.000 0.004 0.000
#> SRR2314017 3 0.3151 0.7798 0.000 0.252 0.748 0.000 0.000 0.000
#> SRR2314016 4 0.3187 0.6321 0.004 0.000 0.012 0.796 0.188 0.000
#> SRR2314014 2 0.4484 0.5691 0.000 0.696 0.020 0.000 0.040 0.244
#> SRR2314013 2 0.5981 0.5220 0.000 0.588 0.116 0.000 0.060 0.236
#> SRR2314012 4 0.2482 0.6419 0.004 0.000 0.000 0.848 0.148 0.000
#> SRR2314011 5 0.4246 0.9867 0.452 0.000 0.016 0.000 0.532 0.000
#> SRR2314010 2 0.6261 0.2810 0.000 0.544 0.064 0.000 0.128 0.264
#> SRR2314009 2 0.5897 -0.0824 0.000 0.568 0.288 0.060 0.084 0.000
#> SRR2314008 2 0.4159 -0.0659 0.000 0.588 0.396 0.000 0.016 0.000
#> SRR2314007 2 0.2221 0.5244 0.000 0.896 0.072 0.000 0.032 0.000
#> SRR2314005 6 0.5404 0.4802 0.000 0.156 0.128 0.000 0.048 0.668
#> SRR2314004 6 0.1334 0.8225 0.000 0.020 0.000 0.000 0.032 0.948
#> SRR2314003 4 0.4274 0.4389 0.008 0.000 0.012 0.600 0.380 0.000
#> SRR2314006 6 0.3763 0.7963 0.000 0.008 0.060 0.000 0.144 0.788
#> SRR2314002 3 0.2703 0.7561 0.000 0.172 0.824 0.000 0.004 0.000
#> SRR2314001 2 0.3998 0.5884 0.000 0.728 0.016 0.000 0.020 0.236
#> SRR2314000 3 0.3234 0.7271 0.028 0.120 0.836 0.012 0.004 0.000
#> SRR2313998 2 0.4482 0.5348 0.000 0.664 0.012 0.000 0.036 0.288
#> SRR2313996 2 0.1789 0.5568 0.000 0.924 0.044 0.000 0.032 0.000
#> SRR2313999 5 0.3975 0.9762 0.452 0.000 0.004 0.000 0.544 0.000
#> SRR2313997 6 0.0363 0.8356 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR2313992 4 0.3320 0.6851 0.000 0.032 0.048 0.844 0.076 0.000
#> SRR2313991 3 0.3266 0.7710 0.000 0.272 0.728 0.000 0.000 0.000
#> SRR2313990 2 0.1753 0.5333 0.000 0.912 0.084 0.000 0.004 0.000
#> SRR2313988 1 0.2468 0.5779 0.880 0.000 0.096 0.008 0.016 0.000
#> SRR2313987 1 0.4371 -0.6642 0.580 0.000 0.004 0.020 0.396 0.000
#> SRR2313984 2 0.5484 0.2329 0.000 0.504 0.032 0.000 0.056 0.408
#> SRR2313989 6 0.3002 0.8166 0.000 0.004 0.048 0.000 0.100 0.848
#> SRR2313983 1 0.0000 0.5785 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.4274 0.4389 0.008 0.000 0.012 0.600 0.380 0.000
#> SRR2313982 1 0.0260 0.5779 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR2313981 4 0.5568 0.6095 0.000 0.176 0.076 0.656 0.092 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 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 0.887 0.932 0.970 0.5047 0.495 0.495
#> 3 3 0.719 0.785 0.896 0.2742 0.802 0.619
#> 4 4 0.772 0.736 0.872 0.1260 0.832 0.570
#> 5 5 0.798 0.743 0.881 0.0590 0.919 0.725
#> 6 6 0.733 0.585 0.796 0.0385 0.973 0.890
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
#> SRR2313978 2 0.000 0.973 0.000 1.000
#> SRR2313980 2 0.000 0.973 0.000 1.000
#> SRR2313979 1 0.000 0.962 1.000 0.000
#> SRR2313977 2 0.000 0.973 0.000 1.000
#> SRR2313976 2 0.000 0.973 0.000 1.000
#> SRR2313975 1 0.000 0.962 1.000 0.000
#> SRR2313973 1 0.000 0.962 1.000 0.000
#> SRR2313974 1 0.000 0.962 1.000 0.000
#> SRR2313985 1 0.000 0.962 1.000 0.000
#> SRR2313993 2 0.000 0.973 0.000 1.000
#> SRR2313994 2 0.000 0.973 0.000 1.000
#> SRR2313995 2 0.000 0.973 0.000 1.000
#> SRR2314024 2 0.000 0.973 0.000 1.000
#> SRR2314015 2 0.000 0.973 0.000 1.000
#> SRR2314025 1 0.000 0.962 1.000 0.000
#> SRR2314023 1 0.000 0.962 1.000 0.000
#> SRR2314033 2 0.000 0.973 0.000 1.000
#> SRR2314045 1 0.000 0.962 1.000 0.000
#> SRR2314042 1 0.000 0.962 1.000 0.000
#> SRR2314043 1 0.745 0.755 0.788 0.212
#> SRR2314041 2 0.000 0.973 0.000 1.000
#> SRR2314044 1 0.722 0.770 0.800 0.200
#> SRR2314040 2 0.000 0.973 0.000 1.000
#> SRR2314039 2 0.000 0.973 0.000 1.000
#> SRR2314038 1 0.000 0.962 1.000 0.000
#> SRR2314037 1 0.000 0.962 1.000 0.000
#> SRR2314036 2 0.000 0.973 0.000 1.000
#> SRR2314035 2 0.242 0.939 0.040 0.960
#> SRR2314034 1 0.000 0.962 1.000 0.000
#> SRR2314032 1 0.000 0.962 1.000 0.000
#> SRR2314031 1 0.000 0.962 1.000 0.000
#> SRR2314029 1 0.738 0.762 0.792 0.208
#> SRR2314030 1 0.456 0.885 0.904 0.096
#> SRR2314028 1 0.000 0.962 1.000 0.000
#> SRR2314027 2 0.000 0.973 0.000 1.000
#> SRR2314026 2 0.000 0.973 0.000 1.000
#> SRR2314020 2 0.000 0.973 0.000 1.000
#> SRR2314019 1 0.958 0.434 0.620 0.380
#> SRR2314021 1 0.541 0.857 0.876 0.124
#> SRR2314018 2 0.295 0.927 0.052 0.948
#> SRR2314017 2 0.722 0.751 0.200 0.800
#> SRR2314016 1 0.000 0.962 1.000 0.000
#> SRR2314014 2 0.000 0.973 0.000 1.000
#> SRR2314013 2 0.000 0.973 0.000 1.000
#> SRR2314012 1 0.000 0.962 1.000 0.000
#> SRR2314011 1 0.000 0.962 1.000 0.000
#> SRR2314010 2 0.000 0.973 0.000 1.000
#> SRR2314009 2 0.985 0.180 0.428 0.572
#> SRR2314008 2 0.000 0.973 0.000 1.000
#> SRR2314007 2 0.000 0.973 0.000 1.000
#> SRR2314005 2 0.000 0.973 0.000 1.000
#> SRR2314004 2 0.000 0.973 0.000 1.000
#> SRR2314003 1 0.000 0.962 1.000 0.000
#> SRR2314006 2 0.000 0.973 0.000 1.000
#> SRR2314002 2 0.722 0.751 0.200 0.800
#> SRR2314001 2 0.000 0.973 0.000 1.000
#> SRR2314000 1 0.000 0.962 1.000 0.000
#> SRR2313998 2 0.000 0.973 0.000 1.000
#> SRR2313996 2 0.000 0.973 0.000 1.000
#> SRR2313999 1 0.000 0.962 1.000 0.000
#> SRR2313997 2 0.000 0.973 0.000 1.000
#> SRR2313992 1 0.000 0.962 1.000 0.000
#> SRR2313991 2 0.000 0.973 0.000 1.000
#> SRR2313990 2 0.000 0.973 0.000 1.000
#> SRR2313988 1 0.000 0.962 1.000 0.000
#> SRR2313987 1 0.000 0.962 1.000 0.000
#> SRR2313984 2 0.000 0.973 0.000 1.000
#> SRR2313989 2 0.000 0.973 0.000 1.000
#> SRR2313983 1 0.000 0.962 1.000 0.000
#> SRR2313986 1 0.000 0.962 1.000 0.000
#> SRR2313982 1 0.000 0.962 1.000 0.000
#> SRR2313981 1 0.000 0.962 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313979 1 0.6260 0.307 0.552 0.000 0.448
#> SRR2313977 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313975 1 0.6235 0.331 0.564 0.000 0.436
#> SRR2313973 3 0.3686 0.758 0.140 0.000 0.860
#> SRR2313974 3 0.4555 0.697 0.200 0.000 0.800
#> SRR2313985 1 0.6286 0.262 0.536 0.000 0.464
#> SRR2313993 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313994 2 0.0424 0.980 0.008 0.992 0.000
#> SRR2313995 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314025 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2314023 3 0.4555 0.697 0.200 0.000 0.800
#> SRR2314033 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314045 1 0.2537 0.670 0.920 0.000 0.080
#> SRR2314042 3 0.2066 0.823 0.060 0.000 0.940
#> SRR2314043 3 0.8373 0.223 0.388 0.088 0.524
#> SRR2314041 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314044 3 0.2625 0.784 0.000 0.084 0.916
#> SRR2314040 1 0.4555 0.575 0.800 0.200 0.000
#> SRR2314039 2 0.0892 0.969 0.020 0.980 0.000
#> SRR2314038 3 0.3412 0.772 0.124 0.000 0.876
#> SRR2314037 3 0.2165 0.821 0.064 0.000 0.936
#> SRR2314036 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314035 1 0.3038 0.641 0.896 0.104 0.000
#> SRR2314034 3 0.2537 0.813 0.080 0.000 0.920
#> SRR2314032 1 0.2537 0.670 0.920 0.000 0.080
#> SRR2314031 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2314029 3 0.3551 0.727 0.000 0.132 0.868
#> SRR2314030 3 0.4002 0.748 0.160 0.000 0.840
#> SRR2314028 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2314027 2 0.0424 0.980 0.008 0.992 0.000
#> SRR2314026 1 0.6308 -0.101 0.508 0.492 0.000
#> SRR2314020 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314019 3 0.7720 0.548 0.208 0.120 0.672
#> SRR2314021 3 0.4978 0.686 0.216 0.004 0.780
#> SRR2314018 1 0.3375 0.645 0.892 0.100 0.008
#> SRR2314017 1 0.4636 0.619 0.848 0.036 0.116
#> SRR2314016 3 0.0892 0.830 0.020 0.000 0.980
#> SRR2314014 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314012 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2314011 3 0.3686 0.758 0.140 0.000 0.860
#> SRR2314010 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314009 1 0.6144 0.574 0.780 0.132 0.088
#> SRR2314008 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314007 2 0.0424 0.980 0.008 0.992 0.000
#> SRR2314005 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314003 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2314006 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2314002 1 0.2448 0.670 0.924 0.000 0.076
#> SRR2314001 2 0.0237 0.982 0.004 0.996 0.000
#> SRR2314000 1 0.4002 0.645 0.840 0.000 0.160
#> SRR2313998 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313996 2 0.0424 0.980 0.008 0.992 0.000
#> SRR2313999 3 0.4555 0.697 0.200 0.000 0.800
#> SRR2313997 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313992 3 0.1964 0.825 0.056 0.000 0.944
#> SRR2313991 2 0.5254 0.624 0.264 0.736 0.000
#> SRR2313990 2 0.2878 0.884 0.096 0.904 0.000
#> SRR2313988 1 0.3879 0.650 0.848 0.000 0.152
#> SRR2313987 3 0.4702 0.679 0.212 0.000 0.788
#> SRR2313984 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.985 0.000 1.000 0.000
#> SRR2313983 1 0.6260 0.307 0.552 0.000 0.448
#> SRR2313986 3 0.0000 0.836 0.000 0.000 1.000
#> SRR2313982 1 0.6267 0.297 0.548 0.000 0.452
#> SRR2313981 3 0.2537 0.813 0.080 0.000 0.920
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313980 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.1867 0.7795 0.928 0.000 0.000 0.072
#> SRR2313977 2 0.1557 0.8942 0.000 0.944 0.056 0.000
#> SRR2313976 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313975 1 0.1389 0.7704 0.952 0.000 0.000 0.048
#> SRR2313973 1 0.4961 0.4243 0.552 0.000 0.000 0.448
#> SRR2313974 1 0.4072 0.7224 0.748 0.000 0.000 0.252
#> SRR2313985 1 0.2081 0.7793 0.916 0.000 0.000 0.084
#> SRR2313993 2 0.0188 0.9353 0.000 0.996 0.004 0.000
#> SRR2313994 2 0.2081 0.8714 0.000 0.916 0.084 0.000
#> SRR2313995 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314024 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314025 4 0.0336 0.9395 0.008 0.000 0.000 0.992
#> SRR2314023 1 0.4250 0.7021 0.724 0.000 0.000 0.276
#> SRR2314033 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.1356 0.7299 0.960 0.000 0.032 0.008
#> SRR2314042 4 0.0469 0.9342 0.000 0.000 0.012 0.988
#> SRR2314043 3 0.7309 0.3467 0.112 0.020 0.556 0.312
#> SRR2314041 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314044 4 0.1398 0.9095 0.000 0.040 0.004 0.956
#> SRR2314040 3 0.1489 0.6084 0.044 0.004 0.952 0.000
#> SRR2314039 2 0.5193 0.2847 0.000 0.580 0.412 0.008
#> SRR2314038 1 0.5570 0.3429 0.540 0.000 0.020 0.440
#> SRR2314037 4 0.0524 0.9371 0.004 0.000 0.008 0.988
#> SRR2314036 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.3123 0.5748 0.156 0.000 0.844 0.000
#> SRR2314034 4 0.2888 0.8420 0.004 0.000 0.124 0.872
#> SRR2314032 1 0.1356 0.7299 0.960 0.000 0.032 0.008
#> SRR2314031 4 0.0336 0.9395 0.008 0.000 0.000 0.992
#> SRR2314029 4 0.1743 0.8905 0.000 0.056 0.004 0.940
#> SRR2314030 4 0.3249 0.7865 0.008 0.000 0.140 0.852
#> SRR2314028 4 0.0336 0.9395 0.008 0.000 0.000 0.992
#> SRR2314027 2 0.4872 0.4294 0.000 0.640 0.356 0.004
#> SRR2314026 3 0.3651 0.5954 0.012 0.136 0.844 0.008
#> SRR2314020 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.5463 -0.0174 0.008 0.004 0.504 0.484
#> SRR2314021 3 0.5295 -0.0289 0.008 0.000 0.504 0.488
#> SRR2314018 3 0.3355 0.5733 0.160 0.004 0.836 0.000
#> SRR2314017 3 0.6116 0.4691 0.220 0.000 0.668 0.112
#> SRR2314016 4 0.3024 0.7607 0.148 0.000 0.000 0.852
#> SRR2314014 2 0.1022 0.9176 0.000 0.968 0.032 0.000
#> SRR2314013 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314012 4 0.0469 0.9385 0.012 0.000 0.000 0.988
#> SRR2314011 1 0.4996 0.3286 0.516 0.000 0.000 0.484
#> SRR2314010 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314009 3 0.0707 0.6080 0.020 0.000 0.980 0.000
#> SRR2314008 3 0.5511 0.0736 0.016 0.484 0.500 0.000
#> SRR2314007 2 0.4624 0.4678 0.000 0.660 0.340 0.000
#> SRR2314005 2 0.0188 0.9349 0.000 0.996 0.004 0.000
#> SRR2314004 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314003 4 0.1302 0.9130 0.044 0.000 0.000 0.956
#> SRR2314006 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2314002 1 0.4994 -0.0534 0.520 0.000 0.480 0.000
#> SRR2314001 2 0.1792 0.8894 0.000 0.932 0.068 0.000
#> SRR2314000 1 0.0592 0.7355 0.984 0.000 0.016 0.000
#> SRR2313998 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313996 2 0.1940 0.8800 0.000 0.924 0.076 0.000
#> SRR2313999 1 0.4304 0.6941 0.716 0.000 0.000 0.284
#> SRR2313997 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313992 4 0.0188 0.9390 0.004 0.000 0.000 0.996
#> SRR2313991 3 0.6442 0.2189 0.068 0.440 0.492 0.000
#> SRR2313990 3 0.4746 0.3247 0.000 0.368 0.632 0.000
#> SRR2313988 1 0.0707 0.7556 0.980 0.000 0.000 0.020
#> SRR2313987 1 0.4040 0.7251 0.752 0.000 0.000 0.248
#> SRR2313984 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313989 2 0.0000 0.9377 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.1867 0.7795 0.928 0.000 0.000 0.072
#> SRR2313986 4 0.0592 0.9366 0.016 0.000 0.000 0.984
#> SRR2313982 1 0.1867 0.7795 0.928 0.000 0.000 0.072
#> SRR2313981 4 0.1398 0.9227 0.004 0.000 0.040 0.956
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.0566 0.9349 0.000 0.984 0.012 0.000 0.004
#> SRR2313980 2 0.0290 0.9364 0.000 0.992 0.008 0.000 0.000
#> SRR2313979 1 0.0290 0.8664 0.992 0.000 0.000 0.008 0.000
#> SRR2313977 2 0.3534 0.6683 0.000 0.744 0.000 0.000 0.256
#> SRR2313976 2 0.0162 0.9368 0.000 0.996 0.000 0.000 0.004
#> SRR2313975 1 0.0290 0.8664 0.992 0.000 0.000 0.008 0.000
#> SRR2313973 4 0.4538 0.0511 0.452 0.000 0.000 0.540 0.008
#> SRR2313974 1 0.3461 0.7126 0.772 0.000 0.000 0.224 0.004
#> SRR2313985 1 0.0451 0.8655 0.988 0.000 0.000 0.008 0.004
#> SRR2313993 2 0.0404 0.9343 0.000 0.988 0.012 0.000 0.000
#> SRR2313994 2 0.2674 0.8194 0.000 0.856 0.140 0.000 0.004
#> SRR2313995 2 0.0404 0.9351 0.000 0.988 0.012 0.000 0.000
#> SRR2314024 2 0.0290 0.9364 0.000 0.992 0.008 0.000 0.000
#> SRR2314015 2 0.0162 0.9368 0.000 0.996 0.000 0.000 0.004
#> SRR2314025 4 0.0671 0.8290 0.004 0.000 0.016 0.980 0.000
#> SRR2314023 1 0.4183 0.5503 0.668 0.000 0.000 0.324 0.008
#> SRR2314033 2 0.0162 0.9368 0.000 0.996 0.000 0.000 0.004
#> SRR2314045 1 0.0771 0.8544 0.976 0.000 0.000 0.004 0.020
#> SRR2314042 4 0.1341 0.8217 0.000 0.000 0.056 0.944 0.000
#> SRR2314043 5 0.7376 0.2122 0.036 0.004 0.248 0.244 0.468
#> SRR2314041 2 0.0290 0.9364 0.000 0.992 0.008 0.000 0.000
#> SRR2314044 4 0.2740 0.7887 0.004 0.048 0.020 0.900 0.028
#> SRR2314040 3 0.4470 0.3647 0.004 0.004 0.596 0.000 0.396
#> SRR2314039 3 0.2439 0.6396 0.000 0.120 0.876 0.000 0.004
#> SRR2314038 4 0.6130 0.2563 0.088 0.000 0.016 0.524 0.372
#> SRR2314037 4 0.1788 0.8246 0.004 0.000 0.056 0.932 0.008
#> SRR2314036 2 0.0162 0.9368 0.000 0.996 0.000 0.000 0.004
#> SRR2314035 5 0.2077 0.7450 0.008 0.000 0.084 0.000 0.908
#> SRR2314034 4 0.4301 0.6822 0.020 0.000 0.244 0.728 0.008
#> SRR2314032 1 0.0703 0.8509 0.976 0.000 0.000 0.000 0.024
#> SRR2314031 4 0.0451 0.8286 0.004 0.000 0.008 0.988 0.000
#> SRR2314029 4 0.3544 0.7504 0.004 0.088 0.036 0.852 0.020
#> SRR2314030 4 0.4680 0.2755 0.004 0.000 0.448 0.540 0.008
#> SRR2314028 4 0.0671 0.8290 0.004 0.000 0.016 0.980 0.000
#> SRR2314027 3 0.3607 0.5419 0.000 0.244 0.752 0.000 0.004
#> SRR2314026 3 0.1404 0.6543 0.004 0.008 0.956 0.004 0.028
#> SRR2314020 2 0.0000 0.9371 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.1124 0.6487 0.004 0.000 0.960 0.036 0.000
#> SRR2314021 3 0.1205 0.6466 0.004 0.000 0.956 0.040 0.000
#> SRR2314018 5 0.1168 0.7741 0.008 0.000 0.032 0.000 0.960
#> SRR2314017 5 0.1074 0.7755 0.004 0.000 0.012 0.016 0.968
#> SRR2314016 4 0.2017 0.7935 0.080 0.000 0.000 0.912 0.008
#> SRR2314014 2 0.1764 0.8936 0.000 0.928 0.008 0.000 0.064
#> SRR2314013 2 0.0000 0.9371 0.000 1.000 0.000 0.000 0.000
#> SRR2314012 4 0.0912 0.8298 0.012 0.000 0.016 0.972 0.000
#> SRR2314011 4 0.4446 0.2289 0.400 0.000 0.000 0.592 0.008
#> SRR2314010 2 0.0794 0.9294 0.000 0.972 0.028 0.000 0.000
#> SRR2314009 3 0.4278 0.2458 0.000 0.000 0.548 0.000 0.452
#> SRR2314008 5 0.2624 0.6889 0.000 0.116 0.012 0.000 0.872
#> SRR2314007 2 0.5405 -0.1223 0.000 0.484 0.460 0.000 0.056
#> SRR2314005 2 0.1671 0.8876 0.000 0.924 0.000 0.000 0.076
#> SRR2314004 2 0.0000 0.9371 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 4 0.0992 0.8238 0.024 0.000 0.000 0.968 0.008
#> SRR2314006 2 0.0404 0.9351 0.000 0.988 0.012 0.000 0.000
#> SRR2314002 5 0.2516 0.7020 0.140 0.000 0.000 0.000 0.860
#> SRR2314001 2 0.3051 0.8398 0.000 0.864 0.076 0.000 0.060
#> SRR2314000 1 0.1608 0.8151 0.928 0.000 0.000 0.000 0.072
#> SRR2313998 2 0.0162 0.9368 0.000 0.996 0.000 0.000 0.004
#> SRR2313996 2 0.2280 0.8513 0.000 0.880 0.120 0.000 0.000
#> SRR2313999 1 0.4392 0.4189 0.612 0.000 0.000 0.380 0.008
#> SRR2313997 2 0.0000 0.9371 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.1357 0.8245 0.004 0.000 0.048 0.948 0.000
#> SRR2313991 5 0.1792 0.7417 0.000 0.084 0.000 0.000 0.916
#> SRR2313990 3 0.6584 0.3052 0.000 0.272 0.468 0.000 0.260
#> SRR2313988 1 0.0324 0.8627 0.992 0.000 0.000 0.004 0.004
#> SRR2313987 1 0.3388 0.7371 0.792 0.000 0.000 0.200 0.008
#> SRR2313984 2 0.0404 0.9356 0.000 0.988 0.012 0.000 0.000
#> SRR2313989 2 0.0162 0.9369 0.000 0.996 0.004 0.000 0.000
#> SRR2313983 1 0.0290 0.8664 0.992 0.000 0.000 0.008 0.000
#> SRR2313986 4 0.1012 0.8245 0.020 0.000 0.000 0.968 0.012
#> SRR2313982 1 0.0290 0.8664 0.992 0.000 0.000 0.008 0.000
#> SRR2313981 4 0.1831 0.8174 0.004 0.000 0.076 0.920 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.1327 0.8832 0.000 0.936 0.000 0.000 0.000 0.064
#> SRR2313980 2 0.1204 0.8748 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR2313979 1 0.0146 0.7998 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313977 2 0.5106 0.5919 0.000 0.652 0.012 0.000 0.220 0.116
#> SRR2313976 2 0.0508 0.8847 0.000 0.984 0.004 0.000 0.000 0.012
#> SRR2313975 1 0.0146 0.7998 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313973 4 0.5301 0.2710 0.300 0.000 0.000 0.568 0.000 0.132
#> SRR2313974 1 0.4931 0.3399 0.592 0.000 0.000 0.324 0.000 0.084
#> SRR2313985 1 0.1088 0.7806 0.960 0.000 0.000 0.016 0.000 0.024
#> SRR2313993 2 0.2051 0.8702 0.000 0.916 0.036 0.000 0.008 0.040
#> SRR2313994 2 0.4095 0.7442 0.000 0.756 0.152 0.000 0.004 0.088
#> SRR2313995 2 0.2212 0.8450 0.000 0.880 0.008 0.000 0.000 0.112
#> SRR2314024 2 0.1007 0.8785 0.000 0.956 0.000 0.000 0.000 0.044
#> SRR2314015 2 0.0547 0.8858 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR2314025 4 0.1663 0.5946 0.000 0.000 0.000 0.912 0.000 0.088
#> SRR2314023 1 0.5521 0.0266 0.468 0.000 0.000 0.400 0.000 0.132
#> SRR2314033 2 0.0458 0.8851 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR2314045 1 0.1010 0.7781 0.960 0.000 0.000 0.000 0.036 0.004
#> SRR2314042 4 0.3045 0.5700 0.000 0.000 0.060 0.840 0.000 0.100
#> SRR2314043 5 0.7492 -0.1110 0.020 0.000 0.180 0.096 0.392 0.312
#> SRR2314041 2 0.2636 0.8557 0.000 0.860 0.016 0.000 0.004 0.120
#> SRR2314044 4 0.4675 0.0852 0.004 0.028 0.000 0.552 0.004 0.412
#> SRR2314040 3 0.6015 0.1279 0.000 0.004 0.460 0.000 0.312 0.224
#> SRR2314039 3 0.2882 0.5420 0.000 0.076 0.860 0.000 0.004 0.060
#> SRR2314038 6 0.6692 0.0000 0.040 0.000 0.000 0.228 0.336 0.396
#> SRR2314037 4 0.4452 0.5193 0.004 0.000 0.064 0.708 0.004 0.220
#> SRR2314036 2 0.0000 0.8839 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314035 5 0.4087 0.6198 0.020 0.000 0.056 0.000 0.768 0.156
#> SRR2314034 4 0.5457 0.3622 0.012 0.000 0.220 0.624 0.004 0.140
#> SRR2314032 1 0.1010 0.7781 0.960 0.000 0.000 0.000 0.036 0.004
#> SRR2314031 4 0.1863 0.5928 0.000 0.000 0.000 0.896 0.000 0.104
#> SRR2314029 4 0.5362 -0.0558 0.004 0.056 0.016 0.496 0.000 0.428
#> SRR2314030 3 0.6175 -0.0739 0.000 0.000 0.436 0.408 0.040 0.116
#> SRR2314028 4 0.1500 0.6147 0.012 0.000 0.000 0.936 0.000 0.052
#> SRR2314027 3 0.4030 0.4725 0.000 0.172 0.748 0.000 0.000 0.080
#> SRR2314026 3 0.0820 0.5499 0.000 0.000 0.972 0.000 0.016 0.012
#> SRR2314020 2 0.0260 0.8838 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR2314019 3 0.2554 0.5240 0.000 0.000 0.876 0.028 0.004 0.092
#> SRR2314021 3 0.2984 0.5110 0.000 0.000 0.848 0.044 0.004 0.104
#> SRR2314018 5 0.3242 0.6298 0.004 0.000 0.032 0.000 0.816 0.148
#> SRR2314017 5 0.0725 0.5930 0.000 0.000 0.000 0.012 0.976 0.012
#> SRR2314016 4 0.3845 0.5289 0.056 0.000 0.000 0.768 0.004 0.172
#> SRR2314014 2 0.4049 0.7822 0.000 0.784 0.032 0.000 0.056 0.128
#> SRR2314013 2 0.1340 0.8811 0.000 0.948 0.008 0.000 0.004 0.040
#> SRR2314012 4 0.1391 0.6169 0.016 0.000 0.000 0.944 0.000 0.040
#> SRR2314011 4 0.5207 0.3008 0.276 0.000 0.000 0.592 0.000 0.132
#> SRR2314010 2 0.2775 0.8453 0.000 0.856 0.040 0.000 0.000 0.104
#> SRR2314009 3 0.6090 0.0135 0.000 0.000 0.404 0.004 0.368 0.224
#> SRR2314008 5 0.5578 0.5121 0.000 0.124 0.048 0.000 0.644 0.184
#> SRR2314007 2 0.6655 -0.1384 0.000 0.392 0.388 0.000 0.056 0.164
#> SRR2314005 2 0.2583 0.8495 0.000 0.884 0.008 0.000 0.056 0.052
#> SRR2314004 2 0.0665 0.8836 0.000 0.980 0.004 0.000 0.008 0.008
#> SRR2314003 4 0.2872 0.5615 0.024 0.000 0.000 0.836 0.000 0.140
#> SRR2314006 2 0.1753 0.8614 0.000 0.912 0.004 0.000 0.000 0.084
#> SRR2314002 5 0.2877 0.4515 0.168 0.000 0.000 0.000 0.820 0.012
#> SRR2314001 2 0.4689 0.7317 0.000 0.732 0.100 0.000 0.032 0.136
#> SRR2314000 1 0.1908 0.7358 0.916 0.000 0.000 0.000 0.056 0.028
#> SRR2313998 2 0.1036 0.8848 0.000 0.964 0.008 0.000 0.004 0.024
#> SRR2313996 2 0.4600 0.7151 0.000 0.708 0.136 0.000 0.004 0.152
#> SRR2313999 4 0.5527 0.0693 0.408 0.000 0.000 0.460 0.000 0.132
#> SRR2313997 2 0.0665 0.8836 0.000 0.980 0.004 0.000 0.008 0.008
#> SRR2313992 4 0.2000 0.6109 0.000 0.000 0.032 0.916 0.004 0.048
#> SRR2313991 5 0.2333 0.5514 0.000 0.092 0.000 0.000 0.884 0.024
#> SRR2313990 3 0.7433 0.1603 0.000 0.184 0.380 0.000 0.168 0.268
#> SRR2313988 1 0.0291 0.7987 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR2313987 1 0.5135 0.3095 0.572 0.000 0.000 0.324 0.000 0.104
#> SRR2313984 2 0.1633 0.8788 0.000 0.932 0.024 0.000 0.000 0.044
#> SRR2313989 2 0.0547 0.8829 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR2313983 1 0.0146 0.7998 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313986 4 0.2869 0.5606 0.020 0.000 0.000 0.832 0.000 0.148
#> SRR2313982 1 0.0146 0.7998 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313981 4 0.3013 0.5759 0.000 0.000 0.068 0.844 0.000 0.088
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 16873 rows and 72 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 0.977 0.915 0.949 0.4410 0.549 0.549
#> 3 3 0.813 0.868 0.937 0.4204 0.831 0.692
#> 4 4 0.841 0.880 0.940 0.1600 0.856 0.640
#> 5 5 0.698 0.662 0.802 0.0574 0.978 0.920
#> 6 6 0.716 0.625 0.832 0.0507 0.871 0.541
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR2313978 2 0.402 0.9516 0.080 0.920
#> SRR2313980 2 0.000 0.9368 0.000 1.000
#> SRR2313979 1 0.000 0.9536 1.000 0.000
#> SRR2313977 2 0.402 0.9516 0.080 0.920
#> SRR2313976 2 0.000 0.9368 0.000 1.000
#> SRR2313975 1 0.000 0.9536 1.000 0.000
#> SRR2313973 1 0.000 0.9536 1.000 0.000
#> SRR2313974 1 0.000 0.9536 1.000 0.000
#> SRR2313985 1 0.000 0.9536 1.000 0.000
#> SRR2313993 2 0.000 0.9368 0.000 1.000
#> SRR2313994 2 0.000 0.9368 0.000 1.000
#> SRR2313995 2 0.000 0.9368 0.000 1.000
#> SRR2314024 2 0.000 0.9368 0.000 1.000
#> SRR2314015 2 0.000 0.9368 0.000 1.000
#> SRR2314025 1 0.000 0.9536 1.000 0.000
#> SRR2314023 1 0.000 0.9536 1.000 0.000
#> SRR2314033 2 0.000 0.9368 0.000 1.000
#> SRR2314045 1 0.000 0.9536 1.000 0.000
#> SRR2314042 1 1.000 -0.0733 0.508 0.492
#> SRR2314043 2 0.416 0.9500 0.084 0.916
#> SRR2314041 2 0.402 0.9516 0.080 0.920
#> SRR2314044 2 0.416 0.9500 0.084 0.916
#> SRR2314040 2 0.402 0.9516 0.080 0.920
#> SRR2314039 2 0.402 0.9516 0.080 0.920
#> SRR2314038 2 0.722 0.8208 0.200 0.800
#> SRR2314037 2 0.416 0.9500 0.084 0.916
#> SRR2314036 2 0.000 0.9368 0.000 1.000
#> SRR2314035 2 0.402 0.9516 0.080 0.920
#> SRR2314034 1 0.981 0.2049 0.580 0.420
#> SRR2314032 1 0.000 0.9536 1.000 0.000
#> SRR2314031 1 0.141 0.9367 0.980 0.020
#> SRR2314029 2 0.416 0.9500 0.084 0.916
#> SRR2314030 2 0.416 0.9500 0.084 0.916
#> SRR2314028 1 0.000 0.9536 1.000 0.000
#> SRR2314027 2 0.402 0.9516 0.080 0.920
#> SRR2314026 2 0.402 0.9516 0.080 0.920
#> SRR2314020 2 0.000 0.9368 0.000 1.000
#> SRR2314019 2 0.416 0.9500 0.084 0.916
#> SRR2314021 2 0.416 0.9500 0.084 0.916
#> SRR2314018 2 0.416 0.9500 0.084 0.916
#> SRR2314017 2 0.416 0.9500 0.084 0.916
#> SRR2314016 1 0.000 0.9536 1.000 0.000
#> SRR2314014 2 0.388 0.9513 0.076 0.924
#> SRR2314013 2 0.000 0.9368 0.000 1.000
#> SRR2314012 1 0.000 0.9536 1.000 0.000
#> SRR2314011 1 0.000 0.9536 1.000 0.000
#> SRR2314010 2 0.000 0.9368 0.000 1.000
#> SRR2314009 2 0.416 0.9500 0.084 0.916
#> SRR2314008 2 0.402 0.9516 0.080 0.920
#> SRR2314007 2 0.402 0.9516 0.080 0.920
#> SRR2314005 2 0.000 0.9368 0.000 1.000
#> SRR2314004 2 0.000 0.9368 0.000 1.000
#> SRR2314003 1 0.000 0.9536 1.000 0.000
#> SRR2314006 2 0.000 0.9368 0.000 1.000
#> SRR2314002 2 0.402 0.9516 0.080 0.920
#> SRR2314001 2 0.358 0.9502 0.068 0.932
#> SRR2314000 2 0.929 0.5612 0.344 0.656
#> SRR2313998 2 0.000 0.9368 0.000 1.000
#> SRR2313996 2 0.402 0.9516 0.080 0.920
#> SRR2313999 1 0.000 0.9536 1.000 0.000
#> SRR2313997 2 0.000 0.9368 0.000 1.000
#> SRR2313992 1 0.242 0.9175 0.960 0.040
#> SRR2313991 2 0.402 0.9516 0.080 0.920
#> SRR2313990 2 0.402 0.9516 0.080 0.920
#> SRR2313988 1 0.000 0.9536 1.000 0.000
#> SRR2313987 1 0.000 0.9536 1.000 0.000
#> SRR2313984 2 0.000 0.9368 0.000 1.000
#> SRR2313989 2 0.000 0.9368 0.000 1.000
#> SRR2313983 1 0.000 0.9536 1.000 0.000
#> SRR2313986 1 0.000 0.9536 1.000 0.000
#> SRR2313982 1 0.000 0.9536 1.000 0.000
#> SRR2313981 2 0.416 0.9500 0.084 0.916
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2313980 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313977 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2313976 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313993 3 0.6235 0.355 0.000 0.436 0.564
#> SRR2313994 3 0.4974 0.746 0.000 0.236 0.764
#> SRR2313995 2 0.0237 0.963 0.000 0.996 0.004
#> SRR2314024 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314015 3 0.3816 0.807 0.000 0.148 0.852
#> SRR2314025 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314023 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314045 1 0.3879 0.807 0.848 0.000 0.152
#> SRR2314042 1 0.7758 0.113 0.484 0.048 0.468
#> SRR2314043 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314041 3 0.2165 0.913 0.000 0.064 0.936
#> SRR2314044 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314040 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314039 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314038 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314037 3 0.0237 0.922 0.000 0.004 0.996
#> SRR2314036 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314034 1 0.7462 0.455 0.600 0.048 0.352
#> SRR2314032 1 0.2261 0.873 0.932 0.000 0.068
#> SRR2314031 1 0.3267 0.840 0.884 0.000 0.116
#> SRR2314029 3 0.1860 0.918 0.000 0.052 0.948
#> SRR2314030 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314028 1 0.3879 0.807 0.848 0.000 0.152
#> SRR2314027 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314026 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314020 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314019 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314021 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2314018 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314016 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314014 3 0.0237 0.921 0.000 0.004 0.996
#> SRR2314013 3 0.4654 0.777 0.000 0.208 0.792
#> SRR2314012 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314010 2 0.4654 0.690 0.000 0.792 0.208
#> SRR2314009 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314008 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314007 3 0.1643 0.920 0.000 0.044 0.956
#> SRR2314005 3 0.3941 0.799 0.000 0.156 0.844
#> SRR2314004 2 0.1964 0.923 0.000 0.944 0.056
#> SRR2314003 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2314006 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2314001 3 0.0424 0.920 0.000 0.008 0.992
#> SRR2314000 3 0.0424 0.918 0.008 0.000 0.992
#> SRR2313998 3 0.5621 0.635 0.000 0.308 0.692
#> SRR2313996 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2313999 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313997 2 0.1529 0.934 0.000 0.960 0.040
#> SRR2313992 1 0.4702 0.746 0.788 0.000 0.212
#> SRR2313991 3 0.0000 0.922 0.000 0.000 1.000
#> SRR2313990 3 0.1753 0.919 0.000 0.048 0.952
#> SRR2313988 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313984 3 0.6299 0.231 0.000 0.476 0.524
#> SRR2313989 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313986 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313982 1 0.0000 0.915 1.000 0.000 0.000
#> SRR2313981 3 0.1753 0.919 0.000 0.048 0.952
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313977 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2313976 2 0.1182 0.947 0.000 0.968 0.016 0.016
#> SRR2313975 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313985 1 0.0188 0.981 0.996 0.000 0.000 0.004
#> SRR2313993 3 0.5220 0.472 0.000 0.352 0.632 0.016
#> SRR2313994 3 0.4989 0.751 0.000 0.164 0.764 0.072
#> SRR2313995 2 0.0592 0.958 0.000 0.984 0.000 0.016
#> SRR2314024 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2314015 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314025 4 0.3024 0.810 0.148 0.000 0.000 0.852
#> SRR2314023 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.3356 0.755 0.824 0.000 0.000 0.176
#> SRR2314042 4 0.0000 0.861 0.000 0.000 0.000 1.000
#> SRR2314043 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314041 3 0.0779 0.921 0.000 0.004 0.980 0.016
#> SRR2314044 3 0.4250 0.580 0.000 0.000 0.724 0.276
#> SRR2314040 3 0.1867 0.892 0.000 0.000 0.928 0.072
#> SRR2314039 3 0.3024 0.832 0.000 0.000 0.852 0.148
#> SRR2314038 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314037 4 0.0592 0.859 0.000 0.000 0.016 0.984
#> SRR2314036 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314034 4 0.0000 0.861 0.000 0.000 0.000 1.000
#> SRR2314032 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2314031 4 0.3497 0.822 0.124 0.000 0.024 0.852
#> SRR2314029 4 0.4585 0.483 0.000 0.000 0.332 0.668
#> SRR2314030 4 0.0000 0.861 0.000 0.000 0.000 1.000
#> SRR2314028 4 0.1867 0.850 0.072 0.000 0.000 0.928
#> SRR2314027 3 0.2281 0.873 0.000 0.000 0.904 0.096
#> SRR2314026 3 0.3024 0.832 0.000 0.000 0.852 0.148
#> SRR2314020 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2314019 4 0.0000 0.861 0.000 0.000 0.000 1.000
#> SRR2314021 4 0.0469 0.858 0.000 0.000 0.012 0.988
#> SRR2314018 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314017 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314016 4 0.3873 0.738 0.228 0.000 0.000 0.772
#> SRR2314014 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314013 3 0.2376 0.884 0.000 0.068 0.916 0.016
#> SRR2314012 4 0.3649 0.769 0.204 0.000 0.000 0.796
#> SRR2314011 1 0.0188 0.981 0.996 0.000 0.000 0.004
#> SRR2314010 2 0.5581 0.675 0.000 0.728 0.140 0.132
#> SRR2314009 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314008 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314007 3 0.0469 0.923 0.000 0.000 0.988 0.012
#> SRR2314005 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314004 2 0.0469 0.959 0.000 0.988 0.012 0.000
#> SRR2314003 1 0.0817 0.962 0.976 0.000 0.000 0.024
#> SRR2314006 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2314002 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314001 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2314000 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2313998 3 0.3495 0.817 0.000 0.140 0.844 0.016
#> SRR2313996 3 0.0592 0.921 0.000 0.000 0.984 0.016
#> SRR2313999 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313997 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2313992 4 0.0672 0.862 0.008 0.000 0.008 0.984
#> SRR2313991 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> SRR2313990 3 0.0707 0.920 0.000 0.000 0.980 0.020
#> SRR2313988 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0188 0.981 0.996 0.000 0.000 0.004
#> SRR2313984 3 0.5427 0.320 0.000 0.416 0.568 0.016
#> SRR2313989 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.3975 0.728 0.240 0.000 0.000 0.760
#> SRR2313982 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> SRR2313981 4 0.4040 0.620 0.000 0.000 0.248 0.752
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2313980 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.4182 0.353 0.600 0.000 0.000 0.000 0.400
#> SRR2313977 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2313976 2 0.4358 0.635 0.000 0.732 0.016 0.016 0.236
#> SRR2313975 1 0.4235 0.301 0.576 0.000 0.000 0.000 0.424
#> SRR2313973 1 0.0000 0.678 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.2654 0.688 0.884 0.000 0.000 0.084 0.032
#> SRR2313985 1 0.2561 0.685 0.884 0.000 0.000 0.096 0.020
#> SRR2313993 3 0.6954 0.280 0.000 0.196 0.404 0.016 0.384
#> SRR2313994 3 0.7525 0.346 0.000 0.164 0.444 0.072 0.320
#> SRR2313995 2 0.3098 0.748 0.000 0.836 0.000 0.016 0.148
#> SRR2314024 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314025 4 0.3359 0.800 0.072 0.000 0.000 0.844 0.084
#> SRR2314023 1 0.0000 0.678 1.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.5787 0.452 0.608 0.000 0.000 0.240 0.152
#> SRR2314042 4 0.0162 0.832 0.000 0.000 0.000 0.996 0.004
#> SRR2314043 3 0.0404 0.812 0.000 0.000 0.988 0.000 0.012
#> SRR2314041 3 0.3213 0.751 0.000 0.004 0.836 0.016 0.144
#> SRR2314044 3 0.5232 0.520 0.000 0.000 0.648 0.268 0.084
#> SRR2314040 3 0.5117 0.633 0.000 0.000 0.652 0.072 0.276
#> SRR2314039 3 0.5163 0.632 0.000 0.000 0.692 0.156 0.152
#> SRR2314038 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314037 4 0.0671 0.832 0.000 0.000 0.016 0.980 0.004
#> SRR2314036 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314034 4 0.0162 0.832 0.000 0.000 0.000 0.996 0.004
#> SRR2314032 5 0.4297 -0.357 0.472 0.000 0.000 0.000 0.528
#> SRR2314031 4 0.3644 0.806 0.048 0.000 0.024 0.844 0.084
#> SRR2314029 4 0.5464 0.572 0.000 0.000 0.224 0.648 0.128
#> SRR2314030 4 0.1478 0.816 0.000 0.000 0.000 0.936 0.064
#> SRR2314028 4 0.3301 0.802 0.072 0.000 0.000 0.848 0.080
#> SRR2314027 3 0.4609 0.680 0.000 0.000 0.744 0.104 0.152
#> SRR2314026 3 0.6308 0.442 0.000 0.000 0.456 0.156 0.388
#> SRR2314020 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.2605 0.775 0.000 0.000 0.000 0.852 0.148
#> SRR2314021 4 0.2997 0.769 0.000 0.000 0.012 0.840 0.148
#> SRR2314018 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314017 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314016 4 0.4390 0.738 0.156 0.000 0.000 0.760 0.084
#> SRR2314014 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314013 3 0.5680 0.584 0.000 0.068 0.608 0.016 0.308
#> SRR2314012 4 0.3210 0.730 0.212 0.000 0.000 0.788 0.000
#> SRR2314011 1 0.1792 0.639 0.916 0.000 0.000 0.000 0.084
#> SRR2314010 2 0.7311 0.218 0.000 0.412 0.060 0.140 0.388
#> SRR2314009 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314008 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314007 3 0.1444 0.801 0.000 0.000 0.948 0.012 0.040
#> SRR2314005 3 0.3003 0.728 0.000 0.000 0.812 0.000 0.188
#> SRR2314004 2 0.0404 0.882 0.000 0.988 0.012 0.000 0.000
#> SRR2314003 1 0.4226 0.605 0.776 0.000 0.000 0.140 0.084
#> SRR2314006 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2314001 3 0.3395 0.694 0.000 0.000 0.764 0.000 0.236
#> SRR2314000 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2313998 3 0.6536 0.454 0.000 0.140 0.508 0.016 0.336
#> SRR2313996 3 0.3011 0.755 0.000 0.000 0.844 0.016 0.140
#> SRR2313999 1 0.0290 0.678 0.992 0.000 0.000 0.000 0.008
#> SRR2313997 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.0579 0.832 0.008 0.000 0.008 0.984 0.000
#> SRR2313991 3 0.0000 0.814 0.000 0.000 1.000 0.000 0.000
#> SRR2313990 3 0.4157 0.675 0.000 0.000 0.716 0.020 0.264
#> SRR2313988 5 0.4297 -0.357 0.472 0.000 0.000 0.000 0.528
#> SRR2313987 1 0.2230 0.674 0.884 0.000 0.000 0.116 0.000
#> SRR2313984 5 0.7215 -0.342 0.000 0.316 0.312 0.016 0.356
#> SRR2313989 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.4182 0.353 0.600 0.000 0.000 0.000 0.400
#> SRR2313986 4 0.4390 0.743 0.156 0.000 0.000 0.760 0.084
#> SRR2313982 1 0.4182 0.353 0.600 0.000 0.000 0.000 0.400
#> SRR2313981 4 0.4404 0.681 0.000 0.000 0.152 0.760 0.088
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2313980 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.3862 0.4129 0.524 0.000 0.000 0.000 0.476 0.000
#> SRR2313977 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2313976 2 0.3789 0.1193 0.000 0.584 0.000 0.000 0.000 0.416
#> SRR2313975 1 0.3782 0.4501 0.588 0.000 0.000 0.000 0.412 0.000
#> SRR2313973 5 0.0000 0.6952 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313974 5 0.3543 0.6594 0.032 0.000 0.000 0.200 0.768 0.000
#> SRR2313985 5 0.3920 0.6188 0.120 0.000 0.000 0.112 0.768 0.000
#> SRR2313993 2 0.3098 0.6190 0.000 0.812 0.164 0.000 0.000 0.024
#> SRR2313994 2 0.3231 0.6078 0.000 0.784 0.200 0.000 0.000 0.016
#> SRR2313995 6 0.3684 0.3472 0.000 0.372 0.000 0.000 0.000 0.628
#> SRR2314024 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314015 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314025 4 0.0000 0.8210 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314023 5 0.0000 0.6952 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314033 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314045 1 0.5848 -0.1280 0.500 0.004 0.016 0.368 0.112 0.000
#> SRR2314042 4 0.2048 0.8313 0.000 0.120 0.000 0.880 0.000 0.000
#> SRR2314043 3 0.0547 0.8331 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR2314041 3 0.3807 0.2729 0.000 0.368 0.628 0.000 0.000 0.004
#> SRR2314044 3 0.5011 0.3126 0.000 0.112 0.616 0.272 0.000 0.000
#> SRR2314040 2 0.3409 0.5142 0.000 0.700 0.300 0.000 0.000 0.000
#> SRR2314039 2 0.3838 0.0618 0.000 0.552 0.448 0.000 0.000 0.000
#> SRR2314038 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314037 4 0.2003 0.8327 0.000 0.116 0.000 0.884 0.000 0.000
#> SRR2314036 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314034 4 0.2597 0.8103 0.000 0.176 0.000 0.824 0.000 0.000
#> SRR2314032 1 0.0000 0.4788 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2314031 4 0.0000 0.8210 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314029 4 0.4992 0.5755 0.000 0.268 0.112 0.620 0.000 0.000
#> SRR2314030 4 0.3464 0.6725 0.000 0.312 0.000 0.688 0.000 0.000
#> SRR2314028 4 0.0260 0.8240 0.000 0.008 0.000 0.992 0.000 0.000
#> SRR2314027 2 0.3868 -0.0320 0.000 0.508 0.492 0.000 0.000 0.000
#> SRR2314026 2 0.0000 0.5591 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314020 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 2 0.3727 -0.1203 0.000 0.612 0.000 0.388 0.000 0.000
#> SRR2314021 2 0.3833 -0.2656 0.000 0.556 0.000 0.444 0.000 0.000
#> SRR2314018 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314017 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314016 4 0.1444 0.7768 0.000 0.000 0.000 0.928 0.072 0.000
#> SRR2314014 3 0.0146 0.8450 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR2314013 2 0.3841 0.4052 0.000 0.616 0.380 0.000 0.000 0.004
#> SRR2314012 4 0.3050 0.6402 0.000 0.000 0.000 0.764 0.236 0.000
#> SRR2314011 5 0.1957 0.6424 0.000 0.000 0.000 0.112 0.888 0.000
#> SRR2314010 2 0.1327 0.5631 0.000 0.936 0.000 0.000 0.000 0.064
#> SRR2314009 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314008 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314007 3 0.2730 0.6182 0.000 0.192 0.808 0.000 0.000 0.000
#> SRR2314005 3 0.3175 0.4806 0.000 0.256 0.744 0.000 0.000 0.000
#> SRR2314004 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314003 5 0.3695 0.5342 0.000 0.000 0.000 0.376 0.624 0.000
#> SRR2314006 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314001 3 0.3851 -0.0607 0.000 0.460 0.540 0.000 0.000 0.000
#> SRR2314000 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2313998 2 0.3649 0.6127 0.000 0.764 0.196 0.000 0.000 0.040
#> SRR2313996 3 0.3647 0.3018 0.000 0.360 0.640 0.000 0.000 0.000
#> SRR2313999 5 0.0260 0.6964 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR2313997 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 4 0.1957 0.8325 0.000 0.112 0.000 0.888 0.000 0.000
#> SRR2313991 3 0.0000 0.8481 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2313990 2 0.3647 0.4325 0.000 0.640 0.360 0.000 0.000 0.000
#> SRR2313988 1 0.0000 0.4788 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 5 0.3023 0.6473 0.000 0.000 0.000 0.232 0.768 0.000
#> SRR2313984 2 0.3752 0.6214 0.000 0.772 0.164 0.000 0.000 0.064
#> SRR2313989 6 0.0000 0.9482 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.3862 0.4129 0.524 0.000 0.000 0.000 0.476 0.000
#> SRR2313986 4 0.1501 0.7735 0.000 0.000 0.000 0.924 0.076 0.000
#> SRR2313982 1 0.3862 0.4129 0.524 0.000 0.000 0.000 0.476 0.000
#> SRR2313981 4 0.4444 0.6997 0.000 0.184 0.108 0.708 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.711 0.924 0.951 0.4838 0.518 0.518
#> 3 3 0.518 0.540 0.806 0.3273 0.737 0.527
#> 4 4 0.511 0.509 0.680 0.0568 0.815 0.523
#> 5 5 0.784 0.795 0.921 0.1093 0.869 0.591
#> 6 6 0.701 0.586 0.783 0.0613 0.912 0.660
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
#> SRR2313978 2 0.0000 0.983 0.000 1.000
#> SRR2313980 2 0.0000 0.983 0.000 1.000
#> SRR2313979 1 0.0672 0.933 0.992 0.008
#> SRR2313977 2 0.0000 0.983 0.000 1.000
#> SRR2313976 2 0.0000 0.983 0.000 1.000
#> SRR2313975 1 0.1843 0.931 0.972 0.028
#> SRR2313973 1 0.0672 0.933 0.992 0.008
#> SRR2313974 1 0.0672 0.933 0.992 0.008
#> SRR2313985 1 0.0672 0.933 0.992 0.008
#> SRR2313993 2 0.0376 0.981 0.004 0.996
#> SRR2313994 2 0.0672 0.979 0.008 0.992
#> SRR2313995 1 0.9286 0.594 0.656 0.344
#> SRR2314024 2 0.0000 0.983 0.000 1.000
#> SRR2314015 2 0.0000 0.983 0.000 1.000
#> SRR2314025 1 0.1414 0.934 0.980 0.020
#> SRR2314023 1 0.0672 0.933 0.992 0.008
#> SRR2314033 2 0.0000 0.983 0.000 1.000
#> SRR2314045 1 0.0000 0.930 1.000 0.000
#> SRR2314042 1 0.0938 0.933 0.988 0.012
#> SRR2314043 1 0.6531 0.859 0.832 0.168
#> SRR2314041 2 0.0938 0.976 0.012 0.988
#> SRR2314044 1 0.2948 0.927 0.948 0.052
#> SRR2314040 1 0.5737 0.872 0.864 0.136
#> SRR2314039 1 0.7674 0.788 0.776 0.224
#> SRR2314038 1 0.2236 0.931 0.964 0.036
#> SRR2314037 1 0.1414 0.932 0.980 0.020
#> SRR2314036 2 0.0000 0.983 0.000 1.000
#> SRR2314035 1 0.6712 0.842 0.824 0.176
#> SRR2314034 1 0.2603 0.925 0.956 0.044
#> SRR2314032 1 0.0672 0.933 0.992 0.008
#> SRR2314031 1 0.1184 0.934 0.984 0.016
#> SRR2314029 1 0.2423 0.930 0.960 0.040
#> SRR2314030 1 0.5178 0.886 0.884 0.116
#> SRR2314028 1 0.0938 0.933 0.988 0.012
#> SRR2314027 1 0.9393 0.567 0.644 0.356
#> SRR2314026 1 0.6712 0.840 0.824 0.176
#> SRR2314020 2 0.0000 0.983 0.000 1.000
#> SRR2314019 1 0.6531 0.847 0.832 0.168
#> SRR2314021 1 0.6247 0.856 0.844 0.156
#> SRR2314018 1 0.6623 0.854 0.828 0.172
#> SRR2314017 1 0.5408 0.884 0.876 0.124
#> SRR2314016 1 0.1414 0.934 0.980 0.020
#> SRR2314014 2 0.0672 0.979 0.008 0.992
#> SRR2314013 2 0.0000 0.983 0.000 1.000
#> SRR2314012 1 0.0938 0.933 0.988 0.012
#> SRR2314011 1 0.0672 0.933 0.992 0.008
#> SRR2314010 2 0.0000 0.983 0.000 1.000
#> SRR2314009 1 0.6801 0.838 0.820 0.180
#> SRR2314008 2 0.0000 0.983 0.000 1.000
#> SRR2314007 2 0.0000 0.983 0.000 1.000
#> SRR2314005 2 0.1414 0.970 0.020 0.980
#> SRR2314004 2 0.0000 0.983 0.000 1.000
#> SRR2314003 1 0.0938 0.934 0.988 0.012
#> SRR2314006 2 0.0938 0.976 0.012 0.988
#> SRR2314002 1 0.2043 0.931 0.968 0.032
#> SRR2314001 2 0.0672 0.979 0.008 0.992
#> SRR2314000 1 0.1843 0.931 0.972 0.028
#> SRR2313998 2 0.0000 0.983 0.000 1.000
#> SRR2313996 2 0.5178 0.871 0.116 0.884
#> SRR2313999 1 0.0672 0.933 0.992 0.008
#> SRR2313997 2 0.0000 0.983 0.000 1.000
#> SRR2313992 1 0.0938 0.933 0.988 0.012
#> SRR2313991 2 0.4298 0.893 0.088 0.912
#> SRR2313990 2 0.5629 0.848 0.132 0.868
#> SRR2313988 1 0.0672 0.933 0.992 0.008
#> SRR2313987 1 0.0672 0.933 0.992 0.008
#> SRR2313984 2 0.0672 0.979 0.008 0.992
#> SRR2313989 2 0.0000 0.983 0.000 1.000
#> SRR2313983 1 0.0672 0.933 0.992 0.008
#> SRR2313986 1 0.0938 0.934 0.988 0.012
#> SRR2313982 1 0.0672 0.933 0.992 0.008
#> SRR2313981 1 0.2948 0.921 0.948 0.052
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.6180 0.4046 0.000 0.584 0.416
#> SRR2313980 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.8241 1.000 0.000 0.000
#> SRR2313977 2 0.6095 0.4557 0.000 0.608 0.392
#> SRR2313976 2 0.1753 0.7429 0.000 0.952 0.048
#> SRR2313975 1 0.0000 0.8241 1.000 0.000 0.000
#> SRR2313973 1 0.0424 0.8255 0.992 0.000 0.008
#> SRR2313974 1 0.0424 0.8255 0.992 0.000 0.008
#> SRR2313985 1 0.5465 0.6692 0.712 0.000 0.288
#> SRR2313993 2 0.2066 0.7429 0.000 0.940 0.060
#> SRR2313994 2 0.5397 0.5962 0.000 0.720 0.280
#> SRR2313995 2 0.6204 0.3912 0.000 0.576 0.424
#> SRR2314024 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2314015 2 0.1860 0.7430 0.000 0.948 0.052
#> SRR2314025 1 0.6252 0.5017 0.556 0.000 0.444
#> SRR2314023 1 0.0424 0.8255 0.992 0.000 0.008
#> SRR2314033 2 0.0892 0.7386 0.000 0.980 0.020
#> SRR2314045 1 0.6062 0.5564 0.616 0.000 0.384
#> SRR2314042 3 0.5431 0.1953 0.284 0.000 0.716
#> SRR2314043 3 0.6274 -0.1559 0.000 0.456 0.544
#> SRR2314041 2 0.6291 0.3110 0.000 0.532 0.468
#> SRR2314044 3 0.8236 -0.0875 0.076 0.416 0.508
#> SRR2314040 3 0.1411 0.6529 0.000 0.036 0.964
#> SRR2314039 3 0.2066 0.6426 0.000 0.060 0.940
#> SRR2314038 3 0.9572 -0.0269 0.196 0.388 0.416
#> SRR2314037 3 0.5062 0.5133 0.016 0.184 0.800
#> SRR2314036 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2314035 3 0.5016 0.4502 0.000 0.240 0.760
#> SRR2314034 3 0.5178 0.2691 0.256 0.000 0.744
#> SRR2314032 1 0.6062 0.5564 0.616 0.000 0.384
#> SRR2314031 1 0.5497 0.6716 0.708 0.000 0.292
#> SRR2314029 3 0.8373 -0.0238 0.088 0.388 0.524
#> SRR2314030 3 0.2165 0.6024 0.064 0.000 0.936
#> SRR2314028 3 0.6305 -0.4159 0.484 0.000 0.516
#> SRR2314027 3 0.2066 0.6426 0.000 0.060 0.940
#> SRR2314026 3 0.1411 0.6529 0.000 0.036 0.964
#> SRR2314020 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2314019 3 0.1289 0.6533 0.000 0.032 0.968
#> SRR2314021 3 0.0237 0.6488 0.000 0.004 0.996
#> SRR2314018 3 0.6676 -0.2232 0.008 0.476 0.516
#> SRR2314017 3 0.1031 0.6520 0.000 0.024 0.976
#> SRR2314016 1 0.4887 0.7311 0.772 0.000 0.228
#> SRR2314014 2 0.3267 0.7274 0.000 0.884 0.116
#> SRR2314013 2 0.5560 0.5796 0.000 0.700 0.300
#> SRR2314012 1 0.6252 0.5017 0.556 0.000 0.444
#> SRR2314011 1 0.0424 0.8255 0.992 0.000 0.008
#> SRR2314010 2 0.5905 0.5045 0.000 0.648 0.352
#> SRR2314009 3 0.1860 0.6482 0.000 0.052 0.948
#> SRR2314008 2 0.6291 0.3110 0.000 0.532 0.468
#> SRR2314007 2 0.6307 0.2647 0.000 0.512 0.488
#> SRR2314005 2 0.2537 0.7397 0.000 0.920 0.080
#> SRR2314004 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2314003 1 0.2066 0.8087 0.940 0.000 0.060
#> SRR2314006 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2314002 2 0.9706 0.0117 0.220 0.412 0.368
#> SRR2314001 2 0.4399 0.6758 0.000 0.812 0.188
#> SRR2314000 1 0.9792 -0.1602 0.436 0.288 0.276
#> SRR2313998 2 0.2448 0.7404 0.000 0.924 0.076
#> SRR2313996 3 0.5810 0.2512 0.000 0.336 0.664
#> SRR2313999 1 0.0424 0.8255 0.992 0.000 0.008
#> SRR2313997 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2313992 3 0.5733 0.0554 0.324 0.000 0.676
#> SRR2313991 2 0.6286 0.3181 0.000 0.536 0.464
#> SRR2313990 3 0.5621 0.3246 0.000 0.308 0.692
#> SRR2313988 1 0.1163 0.8195 0.972 0.000 0.028
#> SRR2313987 1 0.0237 0.8249 0.996 0.000 0.004
#> SRR2313984 2 0.3482 0.7211 0.000 0.872 0.128
#> SRR2313989 2 0.0000 0.7334 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.8241 1.000 0.000 0.000
#> SRR2313986 1 0.2066 0.8087 0.940 0.000 0.060
#> SRR2313982 1 0.0000 0.8241 1.000 0.000 0.000
#> SRR2313981 3 0.0237 0.6489 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.353 0.7273 0.000 0.808 0.192 0.000
#> SRR2313980 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.495 0.6739 0.620 0.000 0.376 0.004
#> SRR2313977 2 0.419 0.5786 0.000 0.732 0.268 0.000
#> SRR2313976 2 0.121 0.8224 0.000 0.960 0.040 0.000
#> SRR2313975 1 0.495 0.6739 0.620 0.000 0.376 0.004
#> SRR2313973 1 0.482 0.1511 0.612 0.000 0.000 0.388
#> SRR2313974 4 0.788 -0.3858 0.300 0.000 0.312 0.388
#> SRR2313985 1 0.655 0.6335 0.540 0.000 0.376 0.084
#> SRR2313993 2 0.156 0.8217 0.000 0.944 0.056 0.000
#> SRR2313994 2 0.450 0.4868 0.000 0.684 0.316 0.000
#> SRR2313995 2 0.749 0.1410 0.000 0.464 0.188 0.348
#> SRR2314024 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.208 0.8121 0.000 0.916 0.084 0.000
#> SRR2314025 4 0.277 0.3893 0.116 0.000 0.004 0.880
#> SRR2314023 1 0.482 0.1511 0.612 0.000 0.000 0.388
#> SRR2314033 2 0.130 0.8225 0.000 0.956 0.044 0.000
#> SRR2314045 1 0.510 0.6290 0.568 0.000 0.428 0.004
#> SRR2314042 4 0.480 0.2109 0.000 0.000 0.384 0.616
#> SRR2314043 3 0.482 0.6492 0.000 0.388 0.612 0.000
#> SRR2314041 3 0.492 0.6122 0.000 0.424 0.576 0.000
#> SRR2314044 3 0.510 0.6003 0.000 0.432 0.564 0.004
#> SRR2314040 3 0.495 0.6560 0.004 0.376 0.620 0.000
#> SRR2314039 3 0.494 0.3142 0.000 0.004 0.624 0.372
#> SRR2314038 3 0.679 0.5897 0.024 0.408 0.520 0.048
#> SRR2314037 3 0.721 0.3242 0.000 0.144 0.476 0.380
#> SRR2314036 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.496 0.6543 0.004 0.380 0.616 0.000
#> SRR2314034 4 0.488 0.1614 0.000 0.000 0.408 0.592
#> SRR2314032 1 0.505 0.6492 0.588 0.000 0.408 0.004
#> SRR2314031 4 0.317 0.3558 0.160 0.000 0.000 0.840
#> SRR2314029 3 0.546 0.5976 0.000 0.432 0.552 0.016
#> SRR2314030 3 0.499 0.2767 0.004 0.000 0.608 0.388
#> SRR2314028 4 0.441 0.3366 0.000 0.000 0.300 0.700
#> SRR2314027 3 0.663 0.5824 0.000 0.164 0.624 0.212
#> SRR2314026 3 0.494 0.3142 0.000 0.004 0.624 0.372
#> SRR2314020 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.479 0.2993 0.000 0.000 0.620 0.380
#> SRR2314021 3 0.482 0.2854 0.000 0.000 0.612 0.388
#> SRR2314018 3 0.489 0.6268 0.000 0.412 0.588 0.000
#> SRR2314017 3 0.446 0.6261 0.004 0.280 0.716 0.000
#> SRR2314016 4 0.259 0.3872 0.116 0.000 0.000 0.884
#> SRR2314014 2 0.302 0.7779 0.000 0.852 0.148 0.000
#> SRR2314013 2 0.436 0.5129 0.000 0.708 0.292 0.000
#> SRR2314012 4 0.292 0.3906 0.116 0.000 0.008 0.876
#> SRR2314011 1 0.482 0.1511 0.612 0.000 0.000 0.388
#> SRR2314010 2 0.336 0.7476 0.000 0.824 0.176 0.000
#> SRR2314009 3 0.542 0.6591 0.000 0.352 0.624 0.024
#> SRR2314008 3 0.494 0.5953 0.000 0.436 0.564 0.000
#> SRR2314007 3 0.484 0.6421 0.000 0.396 0.604 0.000
#> SRR2314005 2 0.156 0.8214 0.000 0.944 0.056 0.000
#> SRR2314004 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2314003 4 0.500 -0.0535 0.492 0.000 0.000 0.508
#> SRR2314006 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2314002 3 0.238 0.2299 0.068 0.016 0.916 0.000
#> SRR2314001 2 0.413 0.6257 0.000 0.740 0.260 0.000
#> SRR2314000 3 0.769 0.4451 0.220 0.364 0.416 0.000
#> SRR2313998 2 0.327 0.7533 0.000 0.832 0.168 0.000
#> SRR2313996 3 0.479 0.6548 0.000 0.380 0.620 0.000
#> SRR2313999 1 0.482 0.1511 0.612 0.000 0.000 0.388
#> SRR2313997 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2313992 4 0.480 0.2109 0.000 0.000 0.384 0.616
#> SRR2313991 3 0.494 0.5953 0.000 0.436 0.564 0.000
#> SRR2313990 3 0.479 0.6548 0.000 0.380 0.620 0.000
#> SRR2313988 1 0.495 0.6739 0.620 0.000 0.376 0.004
#> SRR2313987 4 0.782 -0.4119 0.256 0.000 0.356 0.388
#> SRR2313984 2 0.331 0.7589 0.000 0.828 0.172 0.000
#> SRR2313989 2 0.000 0.8150 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.495 0.6739 0.620 0.000 0.376 0.004
#> SRR2313986 4 0.500 -0.0535 0.492 0.000 0.000 0.508
#> SRR2313982 1 0.495 0.6739 0.620 0.000 0.376 0.004
#> SRR2313981 4 0.498 0.0348 0.000 0.000 0.460 0.540
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.2648 0.779 0.000 0.152 0.848 0.000 0.000
#> SRR2313980 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 3 0.3177 0.690 0.000 0.208 0.792 0.000 0.000
#> SRR2313976 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2313975 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2313973 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313974 1 0.4235 0.351 0.576 0.000 0.000 0.000 0.424
#> SRR2313985 1 0.0963 0.870 0.964 0.000 0.000 0.000 0.036
#> SRR2313993 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2313994 3 0.4210 0.237 0.000 0.412 0.588 0.000 0.000
#> SRR2313995 3 0.4126 0.371 0.000 0.380 0.620 0.000 0.000
#> SRR2314024 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314025 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314023 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314033 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2314042 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314043 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314041 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314044 3 0.2280 0.806 0.000 0.000 0.880 0.120 0.000
#> SRR2314040 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314039 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314038 3 0.3074 0.719 0.000 0.000 0.804 0.196 0.000
#> SRR2314037 4 0.2773 0.708 0.000 0.000 0.164 0.836 0.000
#> SRR2314036 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314034 4 0.1341 0.823 0.000 0.000 0.056 0.944 0.000
#> SRR2314032 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2314031 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314029 3 0.3039 0.724 0.000 0.000 0.808 0.192 0.000
#> SRR2314030 4 0.4304 0.056 0.000 0.000 0.484 0.516 0.000
#> SRR2314028 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314027 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314026 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314020 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314021 3 0.4182 0.270 0.000 0.000 0.600 0.400 0.000
#> SRR2314018 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314017 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314016 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314014 2 0.2605 0.758 0.000 0.852 0.148 0.000 0.000
#> SRR2314013 3 0.2471 0.784 0.000 0.136 0.864 0.000 0.000
#> SRR2314012 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2314011 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314010 2 0.3857 0.514 0.000 0.688 0.312 0.000 0.000
#> SRR2314009 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314008 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314007 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2314005 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314004 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 4 0.0963 0.845 0.000 0.000 0.000 0.964 0.036
#> SRR2314006 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 3 0.0290 0.890 0.008 0.000 0.992 0.000 0.000
#> SRR2314001 2 0.4045 0.456 0.000 0.644 0.356 0.000 0.000
#> SRR2314000 3 0.2377 0.805 0.128 0.000 0.872 0.000 0.000
#> SRR2313998 2 0.4268 0.170 0.000 0.556 0.444 0.000 0.000
#> SRR2313996 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2313999 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313997 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.0000 0.867 0.000 0.000 0.000 1.000 0.000
#> SRR2313991 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2313990 3 0.0000 0.894 0.000 0.000 1.000 0.000 0.000
#> SRR2313988 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.4262 0.314 0.560 0.000 0.000 0.000 0.440
#> SRR2313984 2 0.2230 0.792 0.000 0.884 0.116 0.000 0.000
#> SRR2313989 2 0.0000 0.887 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.0290 0.863 0.000 0.000 0.000 0.992 0.008
#> SRR2313982 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 4 0.3966 0.502 0.000 0.000 0.336 0.664 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.4751 0.58959 0.000 0.624 0.300 0.000 0.000 0.076
#> SRR2313980 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 3 0.6052 -0.18093 0.000 0.256 0.380 0.000 0.000 0.364
#> SRR2313976 6 0.1863 0.79655 0.000 0.044 0.036 0.000 0.000 0.920
#> SRR2313975 1 0.1663 0.85005 0.912 0.088 0.000 0.000 0.000 0.000
#> SRR2313973 5 0.0000 0.85536 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313974 1 0.3868 -0.16338 0.504 0.000 0.000 0.000 0.496 0.000
#> SRR2313985 1 0.0790 0.88819 0.968 0.000 0.000 0.000 0.032 0.000
#> SRR2313993 6 0.2706 0.77662 0.000 0.104 0.036 0.000 0.000 0.860
#> SRR2313994 6 0.6067 -0.02067 0.000 0.276 0.324 0.000 0.000 0.400
#> SRR2313995 2 0.4918 0.56029 0.000 0.644 0.232 0.000 0.000 0.124
#> SRR2314024 6 0.0547 0.79578 0.000 0.020 0.000 0.000 0.000 0.980
#> SRR2314015 6 0.3998 0.67880 0.000 0.248 0.040 0.000 0.000 0.712
#> SRR2314025 4 0.1958 0.86053 0.000 0.100 0.000 0.896 0.004 0.000
#> SRR2314023 5 0.0000 0.85536 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314033 6 0.1498 0.80058 0.000 0.028 0.032 0.000 0.000 0.940
#> SRR2314045 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2314042 4 0.0405 0.87569 0.000 0.008 0.004 0.988 0.000 0.000
#> SRR2314043 3 0.2969 0.29856 0.000 0.224 0.776 0.000 0.000 0.000
#> SRR2314041 2 0.3847 0.55652 0.000 0.544 0.456 0.000 0.000 0.000
#> SRR2314044 2 0.5841 0.38882 0.000 0.480 0.300 0.220 0.000 0.000
#> SRR2314040 3 0.0260 0.54199 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR2314039 2 0.3868 0.49486 0.000 0.508 0.492 0.000 0.000 0.000
#> SRR2314038 3 0.5133 -0.00192 0.000 0.336 0.564 0.100 0.000 0.000
#> SRR2314037 4 0.2872 0.78213 0.000 0.140 0.024 0.836 0.000 0.000
#> SRR2314036 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.0458 0.54302 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR2314034 4 0.1983 0.84359 0.000 0.020 0.072 0.908 0.000 0.000
#> SRR2314032 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2314031 4 0.1958 0.86053 0.000 0.100 0.000 0.896 0.004 0.000
#> SRR2314029 2 0.5756 0.38413 0.000 0.508 0.272 0.220 0.000 0.000
#> SRR2314030 4 0.4144 0.38475 0.000 0.020 0.360 0.620 0.000 0.000
#> SRR2314028 4 0.0000 0.87691 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314027 2 0.3854 0.54748 0.000 0.536 0.464 0.000 0.000 0.000
#> SRR2314026 3 0.0363 0.54094 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR2314020 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 3 0.5198 0.12866 0.000 0.204 0.616 0.180 0.000 0.000
#> SRR2314021 3 0.4300 0.12211 0.000 0.020 0.548 0.432 0.000 0.000
#> SRR2314018 3 0.0713 0.54004 0.000 0.028 0.972 0.000 0.000 0.000
#> SRR2314017 3 0.0146 0.54299 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR2314016 4 0.1082 0.87472 0.000 0.040 0.000 0.956 0.004 0.000
#> SRR2314014 6 0.4650 0.64040 0.000 0.220 0.104 0.000 0.000 0.676
#> SRR2314013 3 0.6115 -0.23437 0.000 0.324 0.368 0.000 0.000 0.308
#> SRR2314012 4 0.0508 0.87729 0.000 0.012 0.000 0.984 0.004 0.000
#> SRR2314011 5 0.0000 0.85536 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2314010 2 0.4937 0.52347 0.000 0.652 0.196 0.000 0.000 0.152
#> SRR2314009 3 0.2219 0.43546 0.000 0.136 0.864 0.000 0.000 0.000
#> SRR2314008 3 0.2883 0.31331 0.000 0.212 0.788 0.000 0.000 0.000
#> SRR2314007 3 0.3869 -0.55551 0.000 0.500 0.500 0.000 0.000 0.000
#> SRR2314005 6 0.2527 0.78355 0.000 0.084 0.040 0.000 0.000 0.876
#> SRR2314004 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314003 4 0.3985 0.75645 0.000 0.100 0.000 0.760 0.140 0.000
#> SRR2314006 6 0.0858 0.79543 0.000 0.028 0.004 0.000 0.000 0.968
#> SRR2314002 3 0.3130 0.45291 0.048 0.124 0.828 0.000 0.000 0.000
#> SRR2314001 6 0.5767 0.35204 0.000 0.260 0.232 0.000 0.000 0.508
#> SRR2314000 3 0.4792 0.30942 0.148 0.180 0.672 0.000 0.000 0.000
#> SRR2313998 6 0.5796 0.27292 0.000 0.268 0.232 0.000 0.000 0.500
#> SRR2313996 2 0.3851 0.55794 0.000 0.540 0.460 0.000 0.000 0.000
#> SRR2313999 5 0.0000 0.85536 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313997 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 4 0.0692 0.87329 0.000 0.020 0.004 0.976 0.000 0.000
#> SRR2313991 3 0.2340 0.41563 0.000 0.148 0.852 0.000 0.000 0.000
#> SRR2313990 3 0.1957 0.47714 0.000 0.112 0.888 0.000 0.000 0.000
#> SRR2313988 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 5 0.3869 -0.13557 0.500 0.000 0.000 0.000 0.500 0.000
#> SRR2313984 6 0.4834 0.61205 0.000 0.260 0.100 0.000 0.000 0.640
#> SRR2313989 6 0.0000 0.79709 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.3159 0.82348 0.000 0.100 0.000 0.832 0.068 0.000
#> SRR2313982 1 0.0000 0.91104 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 4 0.2277 0.83904 0.000 0.032 0.076 0.892 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 1.000 0.949 0.980 0.4981 0.503 0.503
#> 3 3 0.842 0.856 0.938 0.3312 0.797 0.608
#> 4 4 0.874 0.871 0.932 0.1409 0.805 0.492
#> 5 5 0.793 0.786 0.874 0.0574 0.916 0.677
#> 6 6 0.806 0.746 0.868 0.0336 0.924 0.660
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
#> SRR2313978 2 0.0000 0.982 0.000 1.000
#> SRR2313980 2 0.0000 0.982 0.000 1.000
#> SRR2313979 1 0.0000 0.977 1.000 0.000
#> SRR2313977 2 0.0000 0.982 0.000 1.000
#> SRR2313976 2 0.0000 0.982 0.000 1.000
#> SRR2313975 1 0.0000 0.977 1.000 0.000
#> SRR2313973 1 0.0000 0.977 1.000 0.000
#> SRR2313974 1 0.0000 0.977 1.000 0.000
#> SRR2313985 1 0.0000 0.977 1.000 0.000
#> SRR2313993 2 0.0000 0.982 0.000 1.000
#> SRR2313994 2 0.0000 0.982 0.000 1.000
#> SRR2313995 2 0.0000 0.982 0.000 1.000
#> SRR2314024 2 0.0000 0.982 0.000 1.000
#> SRR2314015 2 0.0000 0.982 0.000 1.000
#> SRR2314025 1 0.0000 0.977 1.000 0.000
#> SRR2314023 1 0.0000 0.977 1.000 0.000
#> SRR2314033 2 0.0000 0.982 0.000 1.000
#> SRR2314045 1 0.0000 0.977 1.000 0.000
#> SRR2314042 1 0.0000 0.977 1.000 0.000
#> SRR2314043 1 0.0000 0.977 1.000 0.000
#> SRR2314041 2 0.0376 0.978 0.004 0.996
#> SRR2314044 1 0.8499 0.619 0.724 0.276
#> SRR2314040 1 0.1843 0.954 0.972 0.028
#> SRR2314039 2 0.0672 0.975 0.008 0.992
#> SRR2314038 1 0.0000 0.977 1.000 0.000
#> SRR2314037 1 0.0000 0.977 1.000 0.000
#> SRR2314036 2 0.0000 0.982 0.000 1.000
#> SRR2314035 1 0.0000 0.977 1.000 0.000
#> SRR2314034 1 0.0000 0.977 1.000 0.000
#> SRR2314032 1 0.0000 0.977 1.000 0.000
#> SRR2314031 1 0.0000 0.977 1.000 0.000
#> SRR2314029 1 0.9944 0.160 0.544 0.456
#> SRR2314030 1 0.0000 0.977 1.000 0.000
#> SRR2314028 1 0.0000 0.977 1.000 0.000
#> SRR2314027 2 0.0000 0.982 0.000 1.000
#> SRR2314026 1 0.2948 0.931 0.948 0.052
#> SRR2314020 2 0.0000 0.982 0.000 1.000
#> SRR2314019 1 0.3584 0.914 0.932 0.068
#> SRR2314021 1 0.0000 0.977 1.000 0.000
#> SRR2314018 1 0.0000 0.977 1.000 0.000
#> SRR2314017 1 0.0000 0.977 1.000 0.000
#> SRR2314016 1 0.0000 0.977 1.000 0.000
#> SRR2314014 2 0.0000 0.982 0.000 1.000
#> SRR2314013 2 0.0000 0.982 0.000 1.000
#> SRR2314012 1 0.0000 0.977 1.000 0.000
#> SRR2314011 1 0.0000 0.977 1.000 0.000
#> SRR2314010 2 0.0000 0.982 0.000 1.000
#> SRR2314009 1 0.0000 0.977 1.000 0.000
#> SRR2314008 2 0.4161 0.895 0.084 0.916
#> SRR2314007 2 0.0000 0.982 0.000 1.000
#> SRR2314005 2 0.0000 0.982 0.000 1.000
#> SRR2314004 2 0.0000 0.982 0.000 1.000
#> SRR2314003 1 0.0000 0.977 1.000 0.000
#> SRR2314006 2 0.0000 0.982 0.000 1.000
#> SRR2314002 1 0.0000 0.977 1.000 0.000
#> SRR2314001 2 0.0000 0.982 0.000 1.000
#> SRR2314000 1 0.0000 0.977 1.000 0.000
#> SRR2313998 2 0.0000 0.982 0.000 1.000
#> SRR2313996 2 0.0000 0.982 0.000 1.000
#> SRR2313999 1 0.0000 0.977 1.000 0.000
#> SRR2313997 2 0.0000 0.982 0.000 1.000
#> SRR2313992 1 0.0000 0.977 1.000 0.000
#> SRR2313991 2 0.9866 0.209 0.432 0.568
#> SRR2313990 2 0.0000 0.982 0.000 1.000
#> SRR2313988 1 0.0000 0.977 1.000 0.000
#> SRR2313987 1 0.0000 0.977 1.000 0.000
#> SRR2313984 2 0.0000 0.982 0.000 1.000
#> SRR2313989 2 0.0000 0.982 0.000 1.000
#> SRR2313983 1 0.0000 0.977 1.000 0.000
#> SRR2313986 1 0.0000 0.977 1.000 0.000
#> SRR2313982 1 0.0000 0.977 1.000 0.000
#> SRR2313981 1 0.0000 0.977 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313979 1 0.0892 0.8749 0.980 0.000 0.020
#> SRR2313977 2 0.0747 0.9590 0.016 0.984 0.000
#> SRR2313976 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313975 1 0.0237 0.8768 0.996 0.000 0.004
#> SRR2313973 3 0.1163 0.9203 0.028 0.000 0.972
#> SRR2313974 1 0.2165 0.8534 0.936 0.000 0.064
#> SRR2313985 1 0.1031 0.8734 0.976 0.000 0.024
#> SRR2313993 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313994 2 0.0424 0.9638 0.000 0.992 0.008
#> SRR2313995 2 0.5785 0.5043 0.000 0.668 0.332
#> SRR2314024 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314025 3 0.0237 0.9321 0.004 0.000 0.996
#> SRR2314023 1 0.6045 0.4176 0.620 0.000 0.380
#> SRR2314033 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314045 1 0.0424 0.8766 0.992 0.000 0.008
#> SRR2314042 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2314043 1 0.7438 0.3400 0.568 0.040 0.392
#> SRR2314041 3 0.5810 0.4955 0.000 0.336 0.664
#> SRR2314044 3 0.0424 0.9301 0.000 0.008 0.992
#> SRR2314040 1 0.0424 0.8744 0.992 0.000 0.008
#> SRR2314039 2 0.6111 0.3524 0.000 0.604 0.396
#> SRR2314038 3 0.3213 0.8756 0.028 0.060 0.912
#> SRR2314037 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2314036 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314035 1 0.0237 0.8755 0.996 0.000 0.004
#> SRR2314034 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2314032 1 0.0237 0.8768 0.996 0.000 0.004
#> SRR2314031 3 0.0237 0.9321 0.004 0.000 0.996
#> SRR2314029 3 0.0747 0.9254 0.000 0.016 0.984
#> SRR2314030 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2314028 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2314027 2 0.1031 0.9537 0.000 0.976 0.024
#> SRR2314026 1 0.5551 0.6801 0.760 0.224 0.016
#> SRR2314020 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314019 3 0.2959 0.8397 0.000 0.100 0.900
#> SRR2314021 3 0.3502 0.8520 0.084 0.020 0.896
#> SRR2314018 1 0.0237 0.8755 0.996 0.000 0.004
#> SRR2314017 1 0.3879 0.7839 0.848 0.000 0.152
#> SRR2314016 3 0.0592 0.9295 0.012 0.000 0.988
#> SRR2314014 2 0.0661 0.9627 0.008 0.988 0.004
#> SRR2314013 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314012 3 0.0424 0.9312 0.008 0.000 0.992
#> SRR2314011 3 0.0892 0.9255 0.020 0.000 0.980
#> SRR2314010 2 0.0424 0.9636 0.000 0.992 0.008
#> SRR2314009 1 0.4575 0.7547 0.812 0.004 0.184
#> SRR2314008 2 0.0747 0.9592 0.016 0.984 0.000
#> SRR2314007 2 0.0424 0.9638 0.000 0.992 0.008
#> SRR2314005 2 0.0592 0.9613 0.012 0.988 0.000
#> SRR2314004 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2314003 3 0.0424 0.9312 0.008 0.000 0.992
#> SRR2314006 2 0.0237 0.9648 0.000 0.996 0.004
#> SRR2314002 1 0.0000 0.8759 1.000 0.000 0.000
#> SRR2314001 2 0.0848 0.9616 0.008 0.984 0.008
#> SRR2314000 1 0.0000 0.8759 1.000 0.000 0.000
#> SRR2313998 2 0.0237 0.9649 0.004 0.996 0.000
#> SRR2313996 2 0.0892 0.9572 0.000 0.980 0.020
#> SRR2313999 3 0.6308 -0.1072 0.492 0.000 0.508
#> SRR2313997 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313992 3 0.0000 0.9323 0.000 0.000 1.000
#> SRR2313991 1 0.6302 0.0942 0.520 0.480 0.000
#> SRR2313990 2 0.1015 0.9594 0.012 0.980 0.008
#> SRR2313988 1 0.0237 0.8768 0.996 0.000 0.004
#> SRR2313987 1 0.5138 0.6775 0.748 0.000 0.252
#> SRR2313984 2 0.0424 0.9638 0.000 0.992 0.008
#> SRR2313989 2 0.0000 0.9660 0.000 1.000 0.000
#> SRR2313983 1 0.0747 0.8757 0.984 0.000 0.016
#> SRR2313986 3 0.0424 0.9312 0.008 0.000 0.992
#> SRR2313982 1 0.0892 0.8749 0.980 0.000 0.020
#> SRR2313981 3 0.0000 0.9323 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1004 0.9391 0.000 0.972 0.024 0.004
#> SRR2313980 2 0.0524 0.9484 0.000 0.988 0.008 0.004
#> SRR2313979 1 0.0000 0.9228 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.1209 0.9370 0.004 0.964 0.032 0.000
#> SRR2313976 2 0.0336 0.9494 0.000 0.992 0.008 0.000
#> SRR2313975 1 0.0188 0.9227 0.996 0.000 0.000 0.004
#> SRR2313973 4 0.1716 0.8707 0.064 0.000 0.000 0.936
#> SRR2313974 1 0.0817 0.9152 0.976 0.000 0.000 0.024
#> SRR2313985 1 0.0188 0.9226 0.996 0.000 0.000 0.004
#> SRR2313993 3 0.4103 0.6835 0.000 0.256 0.744 0.000
#> SRR2313994 3 0.1022 0.9415 0.000 0.032 0.968 0.000
#> SRR2313995 2 0.4182 0.7478 0.000 0.796 0.024 0.180
#> SRR2314024 2 0.0657 0.9470 0.000 0.984 0.012 0.004
#> SRR2314015 2 0.0188 0.9500 0.000 0.996 0.004 0.000
#> SRR2314025 4 0.0376 0.8992 0.004 0.000 0.004 0.992
#> SRR2314023 1 0.1940 0.8827 0.924 0.000 0.000 0.076
#> SRR2314033 2 0.0336 0.9494 0.000 0.992 0.008 0.000
#> SRR2314045 1 0.0921 0.9089 0.972 0.000 0.028 0.000
#> SRR2314042 4 0.0469 0.8971 0.000 0.000 0.012 0.988
#> SRR2314043 4 0.7268 0.3311 0.312 0.000 0.172 0.516
#> SRR2314041 4 0.1913 0.8750 0.000 0.040 0.020 0.940
#> SRR2314044 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> SRR2314040 3 0.1302 0.9305 0.044 0.000 0.956 0.000
#> SRR2314039 3 0.1284 0.9424 0.000 0.024 0.964 0.012
#> SRR2314038 4 0.8514 0.0726 0.292 0.312 0.024 0.372
#> SRR2314037 4 0.1302 0.8827 0.000 0.000 0.044 0.956
#> SRR2314036 2 0.0188 0.9500 0.000 0.996 0.004 0.000
#> SRR2314035 3 0.1940 0.9077 0.076 0.000 0.924 0.000
#> SRR2314034 3 0.1389 0.9275 0.000 0.000 0.952 0.048
#> SRR2314032 1 0.0188 0.9217 0.996 0.000 0.004 0.000
#> SRR2314031 4 0.0376 0.8992 0.004 0.000 0.004 0.992
#> SRR2314029 4 0.2053 0.8598 0.000 0.072 0.004 0.924
#> SRR2314030 3 0.2469 0.8766 0.000 0.000 0.892 0.108
#> SRR2314028 4 0.3583 0.7439 0.004 0.000 0.180 0.816
#> SRR2314027 3 0.1151 0.9428 0.000 0.024 0.968 0.008
#> SRR2314026 3 0.1042 0.9412 0.020 0.008 0.972 0.000
#> SRR2314020 2 0.0000 0.9494 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.1059 0.9433 0.000 0.016 0.972 0.012
#> SRR2314021 3 0.0927 0.9405 0.008 0.000 0.976 0.016
#> SRR2314018 1 0.1356 0.9090 0.960 0.008 0.032 0.000
#> SRR2314017 1 0.5321 0.7076 0.740 0.004 0.064 0.192
#> SRR2314016 4 0.0707 0.8940 0.020 0.000 0.000 0.980
#> SRR2314014 3 0.3610 0.7555 0.000 0.200 0.800 0.000
#> SRR2314013 2 0.0000 0.9494 0.000 1.000 0.000 0.000
#> SRR2314012 4 0.0657 0.8981 0.004 0.000 0.012 0.984
#> SRR2314011 4 0.1637 0.8735 0.060 0.000 0.000 0.940
#> SRR2314010 2 0.2546 0.8839 0.000 0.900 0.092 0.008
#> SRR2314009 3 0.0804 0.9391 0.012 0.000 0.980 0.008
#> SRR2314008 2 0.2988 0.8708 0.012 0.876 0.112 0.000
#> SRR2314007 3 0.0707 0.9423 0.000 0.020 0.980 0.000
#> SRR2314005 2 0.0817 0.9406 0.000 0.976 0.024 0.000
#> SRR2314004 2 0.0707 0.9425 0.000 0.980 0.020 0.000
#> SRR2314003 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> SRR2314006 2 0.0657 0.9470 0.000 0.984 0.012 0.004
#> SRR2314002 1 0.0469 0.9208 0.988 0.000 0.012 0.000
#> SRR2314001 3 0.0921 0.9401 0.000 0.028 0.972 0.000
#> SRR2314000 1 0.0188 0.9227 0.996 0.000 0.000 0.004
#> SRR2313998 2 0.0188 0.9500 0.000 0.996 0.004 0.000
#> SRR2313996 3 0.1174 0.9442 0.000 0.020 0.968 0.012
#> SRR2313999 1 0.3400 0.7806 0.820 0.000 0.000 0.180
#> SRR2313997 2 0.0336 0.9472 0.000 0.992 0.008 0.000
#> SRR2313992 4 0.0524 0.8989 0.004 0.000 0.008 0.988
#> SRR2313991 1 0.5793 0.3475 0.580 0.384 0.036 0.000
#> SRR2313990 3 0.0921 0.9424 0.000 0.028 0.972 0.000
#> SRR2313988 1 0.0188 0.9217 0.996 0.000 0.004 0.000
#> SRR2313987 1 0.2760 0.8395 0.872 0.000 0.000 0.128
#> SRR2313984 2 0.4741 0.4824 0.000 0.668 0.328 0.004
#> SRR2313989 2 0.0188 0.9500 0.000 0.996 0.004 0.000
#> SRR2313983 1 0.0000 0.9228 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> SRR2313982 1 0.0000 0.9228 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.1557 0.9220 0.000 0.000 0.944 0.056
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 5 0.6055 0.253 0.000 0.408 0.000 0.120 0.472
#> SRR2313980 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0162 0.933 0.996 0.000 0.000 0.000 0.004
#> SRR2313977 5 0.4615 0.615 0.004 0.220 0.052 0.000 0.724
#> SRR2313976 2 0.0404 0.909 0.000 0.988 0.000 0.000 0.012
#> SRR2313975 1 0.0162 0.933 0.996 0.000 0.000 0.000 0.004
#> SRR2313973 4 0.1168 0.816 0.032 0.000 0.000 0.960 0.008
#> SRR2313974 1 0.1117 0.914 0.964 0.000 0.000 0.020 0.016
#> SRR2313985 1 0.0324 0.931 0.992 0.000 0.000 0.004 0.004
#> SRR2313993 3 0.3039 0.683 0.000 0.192 0.808 0.000 0.000
#> SRR2313994 3 0.0290 0.906 0.000 0.008 0.992 0.000 0.000
#> SRR2313995 2 0.2209 0.850 0.000 0.912 0.000 0.056 0.032
#> SRR2314024 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2314025 4 0.3242 0.773 0.000 0.000 0.000 0.784 0.216
#> SRR2314023 1 0.4712 0.600 0.684 0.000 0.000 0.268 0.048
#> SRR2314033 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.1493 0.897 0.948 0.000 0.024 0.000 0.028
#> SRR2314042 4 0.3521 0.762 0.000 0.000 0.004 0.764 0.232
#> SRR2314043 5 0.4718 0.651 0.000 0.000 0.092 0.180 0.728
#> SRR2314041 5 0.6522 0.375 0.000 0.008 0.156 0.360 0.476
#> SRR2314044 4 0.2179 0.775 0.000 0.004 0.000 0.896 0.100
#> SRR2314040 3 0.0290 0.906 0.000 0.000 0.992 0.000 0.008
#> SRR2314039 3 0.1043 0.902 0.000 0.000 0.960 0.000 0.040
#> SRR2314038 5 0.5138 0.618 0.016 0.080 0.000 0.192 0.712
#> SRR2314037 4 0.2863 0.762 0.000 0.000 0.060 0.876 0.064
#> SRR2314036 2 0.0162 0.912 0.000 0.996 0.000 0.000 0.004
#> SRR2314035 3 0.0703 0.903 0.000 0.000 0.976 0.000 0.024
#> SRR2314034 3 0.1568 0.896 0.000 0.000 0.944 0.020 0.036
#> SRR2314032 1 0.0290 0.930 0.992 0.000 0.000 0.000 0.008
#> SRR2314031 4 0.2329 0.812 0.000 0.000 0.000 0.876 0.124
#> SRR2314029 4 0.3661 0.573 0.000 0.276 0.000 0.724 0.000
#> SRR2314030 3 0.6565 0.129 0.000 0.000 0.472 0.280 0.248
#> SRR2314028 4 0.4558 0.735 0.000 0.000 0.060 0.724 0.216
#> SRR2314027 3 0.0963 0.901 0.000 0.000 0.964 0.000 0.036
#> SRR2314026 3 0.0963 0.900 0.000 0.000 0.964 0.000 0.036
#> SRR2314020 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.1270 0.892 0.000 0.000 0.948 0.000 0.052
#> SRR2314021 3 0.0963 0.901 0.000 0.000 0.964 0.000 0.036
#> SRR2314018 5 0.3992 0.643 0.012 0.000 0.268 0.000 0.720
#> SRR2314017 5 0.3961 0.591 0.212 0.000 0.028 0.000 0.760
#> SRR2314016 4 0.2690 0.810 0.000 0.000 0.000 0.844 0.156
#> SRR2314014 5 0.4276 0.479 0.000 0.004 0.380 0.000 0.616
#> SRR2314013 2 0.1364 0.893 0.000 0.952 0.012 0.000 0.036
#> SRR2314012 4 0.3305 0.768 0.000 0.000 0.000 0.776 0.224
#> SRR2314011 4 0.2450 0.783 0.052 0.000 0.000 0.900 0.048
#> SRR2314010 2 0.2389 0.788 0.000 0.880 0.116 0.000 0.004
#> SRR2314009 3 0.1043 0.896 0.000 0.000 0.960 0.000 0.040
#> SRR2314008 5 0.3684 0.632 0.000 0.000 0.280 0.000 0.720
#> SRR2314007 3 0.1792 0.863 0.000 0.000 0.916 0.000 0.084
#> SRR2314005 2 0.4367 0.353 0.000 0.620 0.008 0.000 0.372
#> SRR2314004 2 0.3561 0.624 0.000 0.740 0.000 0.000 0.260
#> SRR2314003 4 0.1704 0.796 0.004 0.000 0.000 0.928 0.068
#> SRR2314006 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 1 0.0290 0.931 0.992 0.000 0.000 0.000 0.008
#> SRR2314001 3 0.0510 0.905 0.000 0.000 0.984 0.000 0.016
#> SRR2314000 1 0.0162 0.933 0.996 0.000 0.000 0.000 0.004
#> SRR2313998 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2313996 3 0.0703 0.903 0.000 0.000 0.976 0.000 0.024
#> SRR2313999 4 0.5923 0.371 0.280 0.000 0.000 0.576 0.144
#> SRR2313997 2 0.1544 0.875 0.000 0.932 0.000 0.000 0.068
#> SRR2313992 4 0.0807 0.813 0.000 0.000 0.012 0.976 0.012
#> SRR2313991 5 0.5416 0.642 0.144 0.132 0.020 0.000 0.704
#> SRR2313990 3 0.0794 0.901 0.000 0.000 0.972 0.000 0.028
#> SRR2313988 1 0.0162 0.932 0.996 0.000 0.000 0.000 0.004
#> SRR2313987 1 0.4453 0.652 0.724 0.000 0.000 0.228 0.048
#> SRR2313984 2 0.2648 0.758 0.000 0.848 0.152 0.000 0.000
#> SRR2313989 2 0.0000 0.914 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.933 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.1357 0.803 0.004 0.000 0.000 0.948 0.048
#> SRR2313982 1 0.0162 0.932 0.996 0.000 0.000 0.000 0.004
#> SRR2313981 3 0.2209 0.862 0.000 0.000 0.912 0.056 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 6 0.6078 0.2063 0.000 0.220 0.008 0.000 0.304 0.468
#> SRR2313980 6 0.0458 0.8123 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR2313979 1 0.0260 0.8946 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313977 2 0.1078 0.7390 0.000 0.964 0.008 0.000 0.012 0.016
#> SRR2313976 6 0.2358 0.7872 0.000 0.108 0.000 0.016 0.000 0.876
#> SRR2313975 1 0.0260 0.8946 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313973 5 0.2328 0.7970 0.052 0.000 0.000 0.056 0.892 0.000
#> SRR2313974 1 0.1010 0.8738 0.960 0.004 0.000 0.000 0.036 0.000
#> SRR2313985 1 0.0260 0.8946 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313993 3 0.1779 0.8694 0.000 0.016 0.920 0.000 0.000 0.064
#> SRR2313994 3 0.0458 0.9213 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR2313995 6 0.0713 0.8102 0.000 0.000 0.000 0.028 0.000 0.972
#> SRR2314024 6 0.0458 0.8123 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR2314015 6 0.0363 0.8156 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR2314025 4 0.2527 0.8512 0.000 0.000 0.000 0.832 0.168 0.000
#> SRR2314023 5 0.3052 0.6795 0.216 0.004 0.000 0.000 0.780 0.000
#> SRR2314033 6 0.2066 0.8006 0.000 0.072 0.000 0.024 0.000 0.904
#> SRR2314045 1 0.2196 0.8177 0.884 0.004 0.004 0.108 0.000 0.000
#> SRR2314042 4 0.2454 0.8545 0.000 0.000 0.000 0.840 0.160 0.000
#> SRR2314043 3 0.5875 0.0408 0.000 0.264 0.480 0.000 0.256 0.000
#> SRR2314041 2 0.5946 0.4185 0.000 0.552 0.196 0.008 0.236 0.008
#> SRR2314044 5 0.3663 0.6600 0.000 0.180 0.000 0.040 0.776 0.004
#> SRR2314040 3 0.0820 0.9239 0.000 0.016 0.972 0.012 0.000 0.000
#> SRR2314039 3 0.1194 0.9161 0.000 0.004 0.956 0.032 0.000 0.008
#> SRR2314038 2 0.2737 0.6746 0.004 0.832 0.000 0.000 0.160 0.004
#> SRR2314037 5 0.3109 0.6238 0.000 0.000 0.224 0.004 0.772 0.000
#> SRR2314036 6 0.1765 0.7985 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR2314035 3 0.0909 0.9235 0.000 0.020 0.968 0.012 0.000 0.000
#> SRR2314034 3 0.1668 0.8929 0.000 0.004 0.928 0.008 0.060 0.000
#> SRR2314032 1 0.0713 0.8810 0.972 0.000 0.000 0.028 0.000 0.000
#> SRR2314031 4 0.4301 0.7509 0.000 0.064 0.000 0.696 0.240 0.000
#> SRR2314029 6 0.6448 0.1778 0.000 0.088 0.000 0.088 0.376 0.448
#> SRR2314030 4 0.0748 0.7611 0.000 0.004 0.016 0.976 0.004 0.000
#> SRR2314028 4 0.2006 0.8401 0.000 0.000 0.004 0.892 0.104 0.000
#> SRR2314027 3 0.1080 0.9169 0.000 0.004 0.960 0.032 0.000 0.004
#> SRR2314026 3 0.2149 0.8749 0.000 0.004 0.888 0.104 0.000 0.004
#> SRR2314020 6 0.0260 0.8155 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR2314019 3 0.1788 0.8971 0.000 0.004 0.916 0.076 0.000 0.004
#> SRR2314021 3 0.1285 0.9124 0.000 0.004 0.944 0.052 0.000 0.000
#> SRR2314018 2 0.2199 0.7392 0.000 0.892 0.088 0.000 0.020 0.000
#> SRR2314017 2 0.3472 0.6698 0.012 0.804 0.004 0.160 0.020 0.000
#> SRR2314016 4 0.5147 0.4301 0.000 0.096 0.000 0.548 0.356 0.000
#> SRR2314014 2 0.4041 0.3180 0.000 0.584 0.408 0.004 0.000 0.004
#> SRR2314013 6 0.4875 0.6349 0.000 0.224 0.008 0.100 0.000 0.668
#> SRR2314012 4 0.2378 0.8550 0.000 0.000 0.000 0.848 0.152 0.000
#> SRR2314011 5 0.0622 0.8273 0.012 0.000 0.000 0.008 0.980 0.000
#> SRR2314010 6 0.3298 0.5942 0.000 0.000 0.236 0.000 0.008 0.756
#> SRR2314009 3 0.0603 0.9233 0.000 0.016 0.980 0.000 0.004 0.000
#> SRR2314008 2 0.1829 0.7490 0.000 0.920 0.056 0.000 0.024 0.000
#> SRR2314007 3 0.0146 0.9239 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR2314005 2 0.3702 0.4397 0.000 0.720 0.004 0.012 0.000 0.264
#> SRR2314004 6 0.3804 0.3915 0.000 0.424 0.000 0.000 0.000 0.576
#> SRR2314003 5 0.0909 0.8220 0.000 0.012 0.000 0.020 0.968 0.000
#> SRR2314006 6 0.0363 0.8130 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR2314002 1 0.4159 0.3323 0.588 0.396 0.000 0.016 0.000 0.000
#> SRR2314001 3 0.0692 0.9233 0.000 0.020 0.976 0.004 0.000 0.000
#> SRR2314000 1 0.0260 0.8946 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313998 6 0.1010 0.8148 0.000 0.036 0.004 0.000 0.000 0.960
#> SRR2313996 3 0.0777 0.9211 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR2313999 5 0.2250 0.7867 0.092 0.020 0.000 0.000 0.888 0.000
#> SRR2313997 6 0.3266 0.6572 0.000 0.272 0.000 0.000 0.000 0.728
#> SRR2313992 5 0.2201 0.7876 0.000 0.000 0.028 0.076 0.896 0.000
#> SRR2313991 2 0.2170 0.7295 0.008 0.912 0.008 0.056 0.000 0.016
#> SRR2313990 3 0.0547 0.9226 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR2313988 1 0.0000 0.8935 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.3979 0.0547 0.540 0.004 0.000 0.000 0.456 0.000
#> SRR2313984 6 0.2631 0.6856 0.000 0.000 0.180 0.000 0.000 0.820
#> SRR2313989 6 0.0260 0.8158 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR2313983 1 0.0000 0.8935 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 5 0.0692 0.8231 0.000 0.004 0.000 0.020 0.976 0.000
#> SRR2313982 1 0.0146 0.8936 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313981 3 0.0820 0.9209 0.000 0.012 0.972 0.000 0.016 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 0.453 0.723 0.865 0.4551 0.493 0.493
#> 3 3 0.630 0.774 0.893 0.3804 0.718 0.500
#> 4 4 0.636 0.682 0.795 0.1242 0.930 0.807
#> 5 5 0.657 0.732 0.834 0.0723 0.919 0.738
#> 6 6 0.718 0.758 0.828 0.0458 0.965 0.851
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
#> SRR2313978 2 0.0000 0.894 0.000 1.000
#> SRR2313980 2 0.0000 0.894 0.000 1.000
#> SRR2313979 1 0.0000 0.752 1.000 0.000
#> SRR2313977 2 0.0000 0.894 0.000 1.000
#> SRR2313976 2 0.0000 0.894 0.000 1.000
#> SRR2313975 1 0.0376 0.752 0.996 0.004
#> SRR2313973 1 0.0000 0.752 1.000 0.000
#> SRR2313974 1 0.2603 0.755 0.956 0.044
#> SRR2313985 1 0.0000 0.752 1.000 0.000
#> SRR2313993 2 0.0000 0.894 0.000 1.000
#> SRR2313994 2 0.0376 0.891 0.004 0.996
#> SRR2313995 2 0.0000 0.894 0.000 1.000
#> SRR2314024 2 0.0000 0.894 0.000 1.000
#> SRR2314015 2 0.0000 0.894 0.000 1.000
#> SRR2314025 1 0.2423 0.755 0.960 0.040
#> SRR2314023 1 0.0000 0.752 1.000 0.000
#> SRR2314033 2 0.0000 0.894 0.000 1.000
#> SRR2314045 1 0.0000 0.752 1.000 0.000
#> SRR2314042 1 0.8327 0.691 0.736 0.264
#> SRR2314043 1 0.9983 0.379 0.524 0.476
#> SRR2314041 2 0.8909 0.428 0.308 0.692
#> SRR2314044 2 0.8909 0.428 0.308 0.692
#> SRR2314040 2 0.9552 0.202 0.376 0.624
#> SRR2314039 2 0.8267 0.549 0.260 0.740
#> SRR2314038 1 0.9866 0.500 0.568 0.432
#> SRR2314037 1 0.9635 0.593 0.612 0.388
#> SRR2314036 2 0.0000 0.894 0.000 1.000
#> SRR2314035 2 0.9552 0.202 0.376 0.624
#> SRR2314034 1 0.8955 0.664 0.688 0.312
#> SRR2314032 1 0.0000 0.752 1.000 0.000
#> SRR2314031 1 0.5059 0.745 0.888 0.112
#> SRR2314029 2 0.2423 0.860 0.040 0.960
#> SRR2314030 1 0.9608 0.601 0.616 0.384
#> SRR2314028 1 0.8081 0.699 0.752 0.248
#> SRR2314027 2 0.8267 0.549 0.260 0.740
#> SRR2314026 1 0.9522 0.617 0.628 0.372
#> SRR2314020 2 0.0000 0.894 0.000 1.000
#> SRR2314019 1 0.9522 0.617 0.628 0.372
#> SRR2314021 1 0.9522 0.617 0.628 0.372
#> SRR2314018 2 0.9552 0.202 0.376 0.624
#> SRR2314017 1 0.9491 0.622 0.632 0.368
#> SRR2314016 1 0.9491 0.622 0.632 0.368
#> SRR2314014 2 0.0000 0.894 0.000 1.000
#> SRR2314013 2 0.0000 0.894 0.000 1.000
#> SRR2314012 1 0.1414 0.754 0.980 0.020
#> SRR2314011 1 0.0000 0.752 1.000 0.000
#> SRR2314010 2 0.0000 0.894 0.000 1.000
#> SRR2314009 1 0.9635 0.593 0.612 0.388
#> SRR2314008 2 0.0000 0.894 0.000 1.000
#> SRR2314007 2 0.8207 0.557 0.256 0.744
#> SRR2314005 2 0.0000 0.894 0.000 1.000
#> SRR2314004 2 0.0000 0.894 0.000 1.000
#> SRR2314003 1 0.2236 0.755 0.964 0.036
#> SRR2314006 2 0.0000 0.894 0.000 1.000
#> SRR2314002 1 0.9491 0.622 0.632 0.368
#> SRR2314001 2 0.0000 0.894 0.000 1.000
#> SRR2314000 1 0.9983 0.379 0.524 0.476
#> SRR2313998 2 0.1633 0.874 0.024 0.976
#> SRR2313996 2 0.0000 0.894 0.000 1.000
#> SRR2313999 1 0.0000 0.752 1.000 0.000
#> SRR2313997 2 0.0000 0.894 0.000 1.000
#> SRR2313992 1 0.9635 0.593 0.612 0.388
#> SRR2313991 1 0.9491 0.622 0.632 0.368
#> SRR2313990 2 0.0000 0.894 0.000 1.000
#> SRR2313988 1 0.0000 0.752 1.000 0.000
#> SRR2313987 1 0.0000 0.752 1.000 0.000
#> SRR2313984 2 0.0000 0.894 0.000 1.000
#> SRR2313989 2 0.0000 0.894 0.000 1.000
#> SRR2313983 1 0.0000 0.752 1.000 0.000
#> SRR2313986 1 0.5059 0.745 0.888 0.112
#> SRR2313982 1 0.0000 0.752 1.000 0.000
#> SRR2313981 1 0.9635 0.593 0.612 0.388
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2313980 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.804 1.000 0.000 0.000
#> SRR2313977 2 0.1289 0.972 0.000 0.968 0.032
#> SRR2313976 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313975 1 0.5591 0.662 0.696 0.000 0.304
#> SRR2313973 1 0.0000 0.804 1.000 0.000 0.000
#> SRR2313974 3 0.5859 0.252 0.344 0.000 0.656
#> SRR2313985 1 0.0237 0.805 0.996 0.000 0.004
#> SRR2313993 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2313994 2 0.1643 0.967 0.000 0.956 0.044
#> SRR2313995 2 0.1643 0.968 0.000 0.956 0.044
#> SRR2314024 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314015 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2314025 3 0.5760 0.299 0.328 0.000 0.672
#> SRR2314023 1 0.0237 0.805 0.996 0.000 0.004
#> SRR2314033 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314045 1 0.5291 0.707 0.732 0.000 0.268
#> SRR2314042 3 0.3038 0.694 0.104 0.000 0.896
#> SRR2314043 3 0.3116 0.741 0.000 0.108 0.892
#> SRR2314041 3 0.5760 0.569 0.000 0.328 0.672
#> SRR2314044 3 0.5760 0.569 0.000 0.328 0.672
#> SRR2314040 3 0.5291 0.637 0.000 0.268 0.732
#> SRR2314039 3 0.6026 0.483 0.000 0.376 0.624
#> SRR2314038 3 0.2165 0.758 0.000 0.064 0.936
#> SRR2314037 3 0.0892 0.769 0.000 0.020 0.980
#> SRR2314036 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314035 3 0.5254 0.640 0.000 0.264 0.736
#> SRR2314034 3 0.1964 0.734 0.056 0.000 0.944
#> SRR2314032 1 0.5291 0.707 0.732 0.000 0.268
#> SRR2314031 3 0.5178 0.472 0.256 0.000 0.744
#> SRR2314029 2 0.3267 0.891 0.000 0.884 0.116
#> SRR2314030 3 0.0747 0.767 0.000 0.016 0.984
#> SRR2314028 3 0.3340 0.677 0.120 0.000 0.880
#> SRR2314027 3 0.6026 0.483 0.000 0.376 0.624
#> SRR2314026 3 0.0237 0.766 0.000 0.004 0.996
#> SRR2314020 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314019 3 0.0237 0.766 0.000 0.004 0.996
#> SRR2314021 3 0.0237 0.766 0.000 0.004 0.996
#> SRR2314018 3 0.5254 0.640 0.000 0.264 0.736
#> SRR2314017 3 0.0000 0.764 0.000 0.000 1.000
#> SRR2314016 3 0.0000 0.764 0.000 0.000 1.000
#> SRR2314014 2 0.1289 0.972 0.000 0.968 0.032
#> SRR2314013 2 0.1289 0.972 0.000 0.968 0.032
#> SRR2314012 1 0.6307 0.244 0.512 0.000 0.488
#> SRR2314011 1 0.3752 0.775 0.856 0.000 0.144
#> SRR2314010 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2314009 3 0.0892 0.769 0.000 0.020 0.980
#> SRR2314008 2 0.2959 0.908 0.000 0.900 0.100
#> SRR2314007 3 0.6045 0.474 0.000 0.380 0.620
#> SRR2314005 2 0.0747 0.970 0.000 0.984 0.016
#> SRR2314004 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314003 3 0.6079 0.116 0.388 0.000 0.612
#> SRR2314006 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.764 0.000 0.000 1.000
#> SRR2314001 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2314000 3 0.3116 0.741 0.000 0.108 0.892
#> SRR2313998 2 0.2261 0.946 0.000 0.932 0.068
#> SRR2313996 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2313999 1 0.5810 0.614 0.664 0.000 0.336
#> SRR2313997 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313992 3 0.0892 0.769 0.000 0.020 0.980
#> SRR2313991 3 0.0000 0.764 0.000 0.000 1.000
#> SRR2313990 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2313988 1 0.5291 0.707 0.732 0.000 0.268
#> SRR2313987 1 0.1031 0.801 0.976 0.000 0.024
#> SRR2313984 2 0.1411 0.972 0.000 0.964 0.036
#> SRR2313989 2 0.0000 0.966 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.804 1.000 0.000 0.000
#> SRR2313986 3 0.5178 0.472 0.256 0.000 0.744
#> SRR2313982 1 0.0000 0.804 1.000 0.000 0.000
#> SRR2313981 3 0.0892 0.769 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2313980 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2313979 1 0.0000 0.8881 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.0592 0.8461 0.000 0.984 0.000 0.016
#> SRR2313976 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2313975 4 0.6619 0.4920 0.332 0.000 0.100 0.568
#> SRR2313973 1 0.0000 0.8881 1.000 0.000 0.000 0.000
#> SRR2313974 4 0.6633 0.5919 0.084 0.000 0.416 0.500
#> SRR2313985 1 0.1022 0.8818 0.968 0.000 0.000 0.032
#> SRR2313993 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2313994 2 0.0336 0.8406 0.000 0.992 0.008 0.000
#> SRR2313995 2 0.1256 0.8385 0.000 0.964 0.008 0.028
#> SRR2314024 2 0.4103 0.7903 0.000 0.744 0.000 0.256
#> SRR2314015 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2314025 4 0.6487 0.5689 0.072 0.000 0.428 0.500
#> SRR2314023 1 0.2081 0.8374 0.916 0.000 0.000 0.084
#> SRR2314033 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2314045 4 0.6451 0.4328 0.404 0.000 0.072 0.524
#> SRR2314042 3 0.4220 0.3322 0.000 0.004 0.748 0.248
#> SRR2314043 3 0.3428 0.7088 0.000 0.144 0.844 0.012
#> SRR2314041 3 0.4889 0.5798 0.000 0.360 0.636 0.004
#> SRR2314044 3 0.4889 0.5798 0.000 0.360 0.636 0.004
#> SRR2314040 3 0.4868 0.6225 0.000 0.304 0.684 0.012
#> SRR2314039 3 0.4888 0.5218 0.000 0.412 0.588 0.000
#> SRR2314038 3 0.2413 0.7288 0.000 0.064 0.916 0.020
#> SRR2314037 3 0.1211 0.7364 0.000 0.040 0.960 0.000
#> SRR2314036 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2314035 3 0.4844 0.6249 0.000 0.300 0.688 0.012
#> SRR2314034 3 0.1902 0.6736 0.000 0.004 0.932 0.064
#> SRR2314032 4 0.6451 0.4328 0.404 0.000 0.072 0.524
#> SRR2314031 4 0.5000 0.3910 0.000 0.000 0.500 0.500
#> SRR2314029 2 0.2593 0.7750 0.000 0.904 0.080 0.016
#> SRR2314030 3 0.1637 0.7109 0.000 0.000 0.940 0.060
#> SRR2314028 3 0.4522 0.1470 0.000 0.000 0.680 0.320
#> SRR2314027 3 0.4888 0.5218 0.000 0.412 0.588 0.000
#> SRR2314026 3 0.0672 0.7242 0.000 0.008 0.984 0.008
#> SRR2314020 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2314019 3 0.0672 0.7242 0.000 0.008 0.984 0.008
#> SRR2314021 3 0.0672 0.7242 0.000 0.008 0.984 0.008
#> SRR2314018 3 0.4844 0.6249 0.000 0.300 0.688 0.012
#> SRR2314017 3 0.1118 0.7192 0.000 0.000 0.964 0.036
#> SRR2314016 3 0.1211 0.7145 0.000 0.000 0.960 0.040
#> SRR2314014 2 0.0592 0.8461 0.000 0.984 0.000 0.016
#> SRR2314013 2 0.1978 0.8405 0.000 0.928 0.004 0.068
#> SRR2314012 4 0.7500 0.6067 0.248 0.000 0.252 0.500
#> SRR2314011 1 0.5712 -0.0436 0.584 0.000 0.032 0.384
#> SRR2314010 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2314009 3 0.1211 0.7364 0.000 0.040 0.960 0.000
#> SRR2314008 2 0.1716 0.7863 0.000 0.936 0.064 0.000
#> SRR2314007 3 0.4898 0.5136 0.000 0.416 0.584 0.000
#> SRR2314005 2 0.4040 0.7992 0.000 0.752 0.000 0.248
#> SRR2314004 2 0.4661 0.7642 0.000 0.652 0.000 0.348
#> SRR2314003 4 0.6794 0.6330 0.104 0.000 0.372 0.524
#> SRR2314006 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2314002 3 0.1118 0.7192 0.000 0.000 0.964 0.036
#> SRR2314001 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2314000 3 0.3428 0.7088 0.000 0.144 0.844 0.012
#> SRR2313998 2 0.1174 0.8278 0.000 0.968 0.020 0.012
#> SRR2313996 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2313999 4 0.7143 0.4935 0.380 0.000 0.136 0.484
#> SRR2313997 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2313992 3 0.1211 0.7364 0.000 0.040 0.960 0.000
#> SRR2313991 3 0.1118 0.7192 0.000 0.000 0.964 0.036
#> SRR2313990 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2313988 4 0.6451 0.4328 0.404 0.000 0.072 0.524
#> SRR2313987 1 0.1724 0.8677 0.948 0.000 0.020 0.032
#> SRR2313984 2 0.0000 0.8444 0.000 1.000 0.000 0.000
#> SRR2313989 2 0.4679 0.7625 0.000 0.648 0.000 0.352
#> SRR2313983 1 0.0336 0.8877 0.992 0.000 0.000 0.008
#> SRR2313986 3 0.5000 -0.4698 0.000 0.000 0.504 0.496
#> SRR2313982 1 0.0000 0.8881 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.1211 0.7364 0.000 0.040 0.960 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.0162 0.931 0.000 0.996 0.000 0.000 0.004
#> SRR2313980 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2313979 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.0794 0.920 0.000 0.972 0.000 0.000 0.028
#> SRR2313976 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2313975 4 0.2472 0.506 0.012 0.000 0.036 0.908 0.044
#> SRR2313973 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 4 0.5546 0.655 0.084 0.000 0.340 0.576 0.000
#> SRR2313985 1 0.1121 0.881 0.956 0.000 0.000 0.044 0.000
#> SRR2313993 2 0.0162 0.931 0.000 0.996 0.000 0.000 0.004
#> SRR2313994 2 0.0451 0.927 0.000 0.988 0.008 0.000 0.004
#> SRR2313995 2 0.1697 0.906 0.000 0.932 0.008 0.000 0.060
#> SRR2314024 2 0.4182 0.155 0.000 0.600 0.000 0.000 0.400
#> SRR2314015 2 0.0162 0.931 0.000 0.996 0.000 0.000 0.004
#> SRR2314025 4 0.5558 0.653 0.072 0.000 0.344 0.580 0.004
#> SRR2314023 1 0.1965 0.844 0.904 0.000 0.000 0.096 0.000
#> SRR2314033 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2314045 4 0.4658 0.482 0.196 0.000 0.028 0.744 0.032
#> SRR2314042 3 0.4102 0.226 0.000 0.004 0.692 0.300 0.004
#> SRR2314043 3 0.3213 0.727 0.000 0.144 0.836 0.016 0.004
#> SRR2314041 3 0.4211 0.577 0.000 0.360 0.636 0.000 0.004
#> SRR2314044 3 0.4211 0.577 0.000 0.360 0.636 0.000 0.004
#> SRR2314040 3 0.4240 0.640 0.000 0.304 0.684 0.008 0.004
#> SRR2314039 3 0.4210 0.478 0.000 0.412 0.588 0.000 0.000
#> SRR2314038 3 0.3279 0.730 0.000 0.064 0.868 0.028 0.040
#> SRR2314037 3 0.1043 0.747 0.000 0.040 0.960 0.000 0.000
#> SRR2314036 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2314035 3 0.4220 0.644 0.000 0.300 0.688 0.008 0.004
#> SRR2314034 3 0.1704 0.688 0.000 0.004 0.928 0.068 0.000
#> SRR2314032 4 0.4658 0.482 0.196 0.000 0.028 0.744 0.032
#> SRR2314031 4 0.4367 0.551 0.000 0.000 0.416 0.580 0.004
#> SRR2314029 2 0.2233 0.858 0.000 0.904 0.080 0.000 0.016
#> SRR2314030 3 0.2659 0.700 0.000 0.000 0.888 0.052 0.060
#> SRR2314028 3 0.4321 -0.071 0.000 0.000 0.600 0.396 0.004
#> SRR2314027 3 0.4210 0.478 0.000 0.412 0.588 0.000 0.000
#> SRR2314026 3 0.0579 0.734 0.000 0.008 0.984 0.008 0.000
#> SRR2314020 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2314019 3 0.0579 0.734 0.000 0.008 0.984 0.008 0.000
#> SRR2314021 3 0.0579 0.734 0.000 0.008 0.984 0.008 0.000
#> SRR2314018 3 0.4220 0.644 0.000 0.300 0.688 0.008 0.004
#> SRR2314017 3 0.2153 0.715 0.000 0.000 0.916 0.040 0.044
#> SRR2314016 3 0.2376 0.707 0.000 0.000 0.904 0.052 0.044
#> SRR2314014 2 0.0794 0.920 0.000 0.972 0.000 0.000 0.028
#> SRR2314013 2 0.2970 0.757 0.000 0.828 0.004 0.000 0.168
#> SRR2314012 4 0.6207 0.583 0.244 0.000 0.184 0.568 0.004
#> SRR2314011 1 0.4383 0.113 0.572 0.000 0.004 0.424 0.000
#> SRR2314010 2 0.0703 0.924 0.000 0.976 0.000 0.000 0.024
#> SRR2314009 3 0.1043 0.747 0.000 0.040 0.960 0.000 0.000
#> SRR2314008 2 0.1478 0.878 0.000 0.936 0.064 0.000 0.000
#> SRR2314007 3 0.4219 0.470 0.000 0.416 0.584 0.000 0.000
#> SRR2314005 5 0.3561 0.758 0.000 0.260 0.000 0.000 0.740
#> SRR2314004 5 0.2179 0.973 0.000 0.112 0.000 0.000 0.888
#> SRR2314003 4 0.5488 0.657 0.092 0.000 0.300 0.608 0.000
#> SRR2314006 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2314002 3 0.2153 0.715 0.000 0.000 0.916 0.040 0.044
#> SRR2314001 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000
#> SRR2314000 3 0.3213 0.727 0.000 0.144 0.836 0.016 0.004
#> SRR2313998 2 0.1186 0.913 0.000 0.964 0.020 0.008 0.008
#> SRR2313996 2 0.0162 0.931 0.000 0.996 0.000 0.000 0.004
#> SRR2313999 4 0.5530 0.362 0.368 0.000 0.076 0.556 0.000
#> SRR2313997 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2313992 3 0.1043 0.747 0.000 0.040 0.960 0.000 0.000
#> SRR2313991 3 0.2153 0.715 0.000 0.000 0.916 0.040 0.044
#> SRR2313990 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000
#> SRR2313988 4 0.4658 0.482 0.196 0.000 0.028 0.744 0.032
#> SRR2313987 1 0.1725 0.873 0.936 0.000 0.020 0.044 0.000
#> SRR2313984 2 0.0162 0.931 0.000 0.996 0.000 0.000 0.004
#> SRR2313989 5 0.2127 0.977 0.000 0.108 0.000 0.000 0.892
#> SRR2313983 1 0.1251 0.874 0.956 0.000 0.000 0.036 0.008
#> SRR2313986 4 0.4383 0.545 0.000 0.000 0.424 0.572 0.004
#> SRR2313982 1 0.0000 0.885 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.1043 0.747 0.000 0.040 0.960 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.0291 0.923 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR2313980 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2313979 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.0858 0.915 0.000 0.968 0.000 0.004 0.000 0.028
#> SRR2313976 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2313975 5 0.1970 0.770 0.000 0.000 0.028 0.060 0.912 0.000
#> SRR2313973 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313974 4 0.3377 0.742 0.084 0.000 0.080 0.828 0.008 0.000
#> SRR2313985 1 0.1151 0.871 0.956 0.000 0.000 0.012 0.032 0.000
#> SRR2313993 2 0.0146 0.923 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313994 2 0.1036 0.911 0.000 0.964 0.008 0.024 0.000 0.004
#> SRR2313995 2 0.2165 0.889 0.000 0.912 0.008 0.024 0.004 0.052
#> SRR2314024 2 0.3843 0.110 0.000 0.548 0.000 0.000 0.000 0.452
#> SRR2314015 2 0.0291 0.923 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR2314025 4 0.2965 0.745 0.072 0.000 0.080 0.848 0.000 0.000
#> SRR2314023 1 0.2106 0.838 0.904 0.000 0.000 0.064 0.032 0.000
#> SRR2314033 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2314045 5 0.4414 0.914 0.184 0.000 0.028 0.052 0.736 0.000
#> SRR2314042 4 0.3828 0.239 0.000 0.000 0.440 0.560 0.000 0.000
#> SRR2314043 3 0.3920 0.707 0.000 0.112 0.768 0.120 0.000 0.000
#> SRR2314041 3 0.4344 0.587 0.000 0.356 0.612 0.032 0.000 0.000
#> SRR2314044 3 0.4344 0.587 0.000 0.356 0.612 0.032 0.000 0.000
#> SRR2314040 3 0.4066 0.674 0.000 0.272 0.692 0.036 0.000 0.000
#> SRR2314039 3 0.3915 0.535 0.000 0.412 0.584 0.004 0.000 0.000
#> SRR2314038 3 0.5139 0.653 0.000 0.064 0.740 0.092 0.068 0.036
#> SRR2314037 3 0.2058 0.721 0.000 0.036 0.908 0.056 0.000 0.000
#> SRR2314036 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2314035 3 0.4045 0.677 0.000 0.268 0.696 0.036 0.000 0.000
#> SRR2314034 3 0.2402 0.648 0.000 0.000 0.856 0.140 0.004 0.000
#> SRR2314032 5 0.4414 0.914 0.184 0.000 0.028 0.052 0.736 0.000
#> SRR2314031 4 0.1556 0.728 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR2314029 2 0.2344 0.853 0.000 0.892 0.076 0.028 0.004 0.000
#> SRR2314030 3 0.5312 0.485 0.000 0.000 0.648 0.228 0.088 0.036
#> SRR2314028 4 0.3221 0.582 0.000 0.000 0.264 0.736 0.000 0.000
#> SRR2314027 3 0.3915 0.535 0.000 0.412 0.584 0.004 0.000 0.000
#> SRR2314026 3 0.1843 0.698 0.000 0.004 0.912 0.080 0.004 0.000
#> SRR2314020 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2314019 3 0.1843 0.698 0.000 0.004 0.912 0.080 0.004 0.000
#> SRR2314021 3 0.1843 0.698 0.000 0.004 0.912 0.080 0.004 0.000
#> SRR2314018 3 0.4045 0.677 0.000 0.268 0.696 0.036 0.000 0.000
#> SRR2314017 3 0.4004 0.638 0.000 0.000 0.796 0.084 0.084 0.036
#> SRR2314016 3 0.4770 0.587 0.000 0.000 0.720 0.164 0.080 0.036
#> SRR2314014 2 0.0713 0.916 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR2314013 2 0.3385 0.752 0.000 0.796 0.004 0.028 0.000 0.172
#> SRR2314012 4 0.3488 0.546 0.244 0.000 0.008 0.744 0.004 0.000
#> SRR2314011 1 0.4445 0.264 0.572 0.000 0.000 0.396 0.032 0.000
#> SRR2314010 2 0.0713 0.917 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR2314009 3 0.2058 0.721 0.000 0.036 0.908 0.056 0.000 0.000
#> SRR2314008 2 0.1471 0.871 0.000 0.932 0.064 0.004 0.000 0.000
#> SRR2314007 3 0.3923 0.528 0.000 0.416 0.580 0.004 0.000 0.000
#> SRR2314005 6 0.3440 0.724 0.000 0.196 0.000 0.028 0.000 0.776
#> SRR2314004 6 0.0937 0.966 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR2314003 4 0.3876 0.722 0.092 0.000 0.072 0.804 0.032 0.000
#> SRR2314006 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2314002 3 0.4004 0.638 0.000 0.000 0.796 0.084 0.084 0.036
#> SRR2314001 2 0.0000 0.922 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314000 3 0.3920 0.707 0.000 0.112 0.768 0.120 0.000 0.000
#> SRR2313998 2 0.1636 0.894 0.000 0.936 0.024 0.036 0.000 0.004
#> SRR2313996 2 0.0146 0.923 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313999 4 0.4868 0.251 0.368 0.000 0.020 0.580 0.032 0.000
#> SRR2313997 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2313992 3 0.2058 0.721 0.000 0.036 0.908 0.056 0.000 0.000
#> SRR2313991 3 0.4004 0.638 0.000 0.000 0.796 0.084 0.084 0.036
#> SRR2313990 2 0.0000 0.922 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313988 5 0.4414 0.914 0.184 0.000 0.028 0.052 0.736 0.000
#> SRR2313987 1 0.1642 0.866 0.936 0.000 0.004 0.028 0.032 0.000
#> SRR2313984 2 0.0146 0.923 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313989 6 0.0865 0.971 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR2313983 1 0.1007 0.855 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR2313986 4 0.1663 0.728 0.000 0.000 0.088 0.912 0.000 0.000
#> SRR2313982 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.2058 0.721 0.000 0.036 0.908 0.056 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", "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 16873 rows and 72 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 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 0.759 0.894 0.950 0.4965 0.493 0.493
#> 3 3 1.000 0.980 0.991 0.3177 0.672 0.436
#> 4 4 0.615 0.543 0.706 0.1193 0.939 0.826
#> 5 5 0.633 0.431 0.662 0.0721 0.760 0.375
#> 6 6 0.651 0.556 0.726 0.0397 0.848 0.462
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
#> SRR2313978 2 0.000 0.986 0.000 1.000
#> SRR2313980 2 0.000 0.986 0.000 1.000
#> SRR2313979 1 0.000 0.904 1.000 0.000
#> SRR2313977 2 0.000 0.986 0.000 1.000
#> SRR2313976 2 0.000 0.986 0.000 1.000
#> SRR2313975 1 0.000 0.904 1.000 0.000
#> SRR2313973 1 0.000 0.904 1.000 0.000
#> SRR2313974 1 0.000 0.904 1.000 0.000
#> SRR2313985 1 0.000 0.904 1.000 0.000
#> SRR2313993 2 0.000 0.986 0.000 1.000
#> SRR2313994 2 0.000 0.986 0.000 1.000
#> SRR2313995 2 0.000 0.986 0.000 1.000
#> SRR2314024 2 0.000 0.986 0.000 1.000
#> SRR2314015 2 0.000 0.986 0.000 1.000
#> SRR2314025 1 0.000 0.904 1.000 0.000
#> SRR2314023 1 0.000 0.904 1.000 0.000
#> SRR2314033 2 0.000 0.986 0.000 1.000
#> SRR2314045 1 0.000 0.904 1.000 0.000
#> SRR2314042 1 0.000 0.904 1.000 0.000
#> SRR2314043 1 0.955 0.520 0.624 0.376
#> SRR2314041 2 0.000 0.986 0.000 1.000
#> SRR2314044 2 0.000 0.986 0.000 1.000
#> SRR2314040 2 0.753 0.681 0.216 0.784
#> SRR2314039 2 0.000 0.986 0.000 1.000
#> SRR2314038 2 0.000 0.986 0.000 1.000
#> SRR2314037 1 0.952 0.528 0.628 0.372
#> SRR2314036 2 0.000 0.986 0.000 1.000
#> SRR2314035 1 0.955 0.520 0.624 0.376
#> SRR2314034 1 0.000 0.904 1.000 0.000
#> SRR2314032 1 0.000 0.904 1.000 0.000
#> SRR2314031 1 0.000 0.904 1.000 0.000
#> SRR2314029 2 0.000 0.986 0.000 1.000
#> SRR2314030 1 0.000 0.904 1.000 0.000
#> SRR2314028 1 0.000 0.904 1.000 0.000
#> SRR2314027 2 0.000 0.986 0.000 1.000
#> SRR2314026 1 0.949 0.535 0.632 0.368
#> SRR2314020 2 0.000 0.986 0.000 1.000
#> SRR2314019 1 0.949 0.535 0.632 0.368
#> SRR2314021 1 0.000 0.904 1.000 0.000
#> SRR2314018 2 0.738 0.696 0.208 0.792
#> SRR2314017 1 0.738 0.747 0.792 0.208
#> SRR2314016 1 0.000 0.904 1.000 0.000
#> SRR2314014 2 0.000 0.986 0.000 1.000
#> SRR2314013 2 0.000 0.986 0.000 1.000
#> SRR2314012 1 0.000 0.904 1.000 0.000
#> SRR2314011 1 0.000 0.904 1.000 0.000
#> SRR2314010 2 0.000 0.986 0.000 1.000
#> SRR2314009 1 0.952 0.528 0.628 0.372
#> SRR2314008 2 0.000 0.986 0.000 1.000
#> SRR2314007 2 0.000 0.986 0.000 1.000
#> SRR2314005 2 0.000 0.986 0.000 1.000
#> SRR2314004 2 0.000 0.986 0.000 1.000
#> SRR2314003 1 0.000 0.904 1.000 0.000
#> SRR2314006 2 0.000 0.986 0.000 1.000
#> SRR2314002 1 0.738 0.747 0.792 0.208
#> SRR2314001 2 0.000 0.986 0.000 1.000
#> SRR2314000 1 0.955 0.520 0.624 0.376
#> SRR2313998 2 0.000 0.986 0.000 1.000
#> SRR2313996 2 0.000 0.986 0.000 1.000
#> SRR2313999 1 0.000 0.904 1.000 0.000
#> SRR2313997 2 0.000 0.986 0.000 1.000
#> SRR2313992 1 0.000 0.904 1.000 0.000
#> SRR2313991 2 0.000 0.986 0.000 1.000
#> SRR2313990 2 0.000 0.986 0.000 1.000
#> SRR2313988 1 0.000 0.904 1.000 0.000
#> SRR2313987 1 0.000 0.904 1.000 0.000
#> SRR2313984 2 0.000 0.986 0.000 1.000
#> SRR2313989 2 0.000 0.986 0.000 1.000
#> SRR2313983 1 0.000 0.904 1.000 0.000
#> SRR2313986 1 0.000 0.904 1.000 0.000
#> SRR2313982 1 0.000 0.904 1.000 0.000
#> SRR2313981 1 0.552 0.818 0.872 0.128
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313977 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313995 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314025 1 0.4555 0.764 0.800 0.000 0.200
#> SRR2314023 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314045 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2314042 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314043 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314041 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314044 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314040 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314039 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314038 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314037 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314036 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314034 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314032 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2314031 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314029 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314030 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314028 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314027 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314026 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314020 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314019 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314021 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314018 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314016 3 0.1031 0.974 0.024 0.000 0.976
#> SRR2314014 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314009 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314008 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314007 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314005 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314003 1 0.4178 0.802 0.828 0.000 0.172
#> SRR2314006 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2314001 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2314000 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2313998 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313996 2 0.4842 0.705 0.000 0.776 0.224
#> SRR2313999 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313992 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2313991 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2313990 3 0.0424 0.991 0.000 0.008 0.992
#> SRR2313988 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.989 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313986 3 0.0000 0.999 0.000 0.000 1.000
#> SRR2313982 1 0.0000 0.974 1.000 0.000 0.000
#> SRR2313981 3 0.0000 0.999 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1557 0.5297 0.000 0.944 0.056 0.000
#> SRR2313980 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2313979 1 0.0188 0.8737 0.996 0.000 0.000 0.004
#> SRR2313977 2 0.2124 0.5205 0.000 0.924 0.068 0.008
#> SRR2313976 2 0.4985 -0.8993 0.000 0.532 0.000 0.468
#> SRR2313975 1 0.5165 0.7910 0.752 0.000 0.080 0.168
#> SRR2313973 1 0.0000 0.8739 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.3208 0.8444 0.848 0.000 0.004 0.148
#> SRR2313985 1 0.0000 0.8739 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.1557 0.4859 0.000 0.944 0.000 0.056
#> SRR2313994 2 0.0188 0.5411 0.000 0.996 0.004 0.000
#> SRR2313995 2 0.3942 -0.0317 0.000 0.764 0.000 0.236
#> SRR2314024 2 0.5000 -0.9811 0.000 0.504 0.000 0.496
#> SRR2314015 2 0.0336 0.5432 0.000 0.992 0.008 0.000
#> SRR2314025 1 0.7472 0.4614 0.504 0.000 0.232 0.264
#> SRR2314023 1 0.0000 0.8739 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.4999 -0.9424 0.000 0.508 0.000 0.492
#> SRR2314045 1 0.5375 0.7460 0.744 0.000 0.116 0.140
#> SRR2314042 3 0.4193 0.6359 0.000 0.000 0.732 0.268
#> SRR2314043 3 0.1209 0.7271 0.000 0.032 0.964 0.004
#> SRR2314041 3 0.4985 0.2880 0.000 0.468 0.532 0.000
#> SRR2314044 3 0.4866 0.4205 0.000 0.404 0.596 0.000
#> SRR2314040 3 0.4283 0.5954 0.000 0.256 0.740 0.004
#> SRR2314039 3 0.5016 0.4298 0.000 0.396 0.600 0.004
#> SRR2314038 3 0.5221 0.6610 0.000 0.060 0.732 0.208
#> SRR2314037 3 0.0921 0.7277 0.000 0.028 0.972 0.000
#> SRR2314036 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2314035 3 0.1211 0.7254 0.000 0.040 0.960 0.000
#> SRR2314034 3 0.4103 0.6429 0.000 0.000 0.744 0.256
#> SRR2314032 1 0.2216 0.8470 0.908 0.000 0.000 0.092
#> SRR2314031 3 0.4222 0.6313 0.000 0.000 0.728 0.272
#> SRR2314029 3 0.5213 0.5197 0.000 0.328 0.652 0.020
#> SRR2314030 3 0.4776 0.5775 0.000 0.000 0.624 0.376
#> SRR2314028 3 0.4134 0.6363 0.000 0.000 0.740 0.260
#> SRR2314027 3 0.5028 0.4237 0.000 0.400 0.596 0.004
#> SRR2314026 3 0.1118 0.7301 0.000 0.000 0.964 0.036
#> SRR2314020 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2314019 3 0.1867 0.7243 0.000 0.000 0.928 0.072
#> SRR2314021 3 0.2868 0.7050 0.000 0.000 0.864 0.136
#> SRR2314018 3 0.3942 0.6139 0.000 0.236 0.764 0.000
#> SRR2314017 3 0.3764 0.6593 0.000 0.000 0.784 0.216
#> SRR2314016 3 0.4994 0.4687 0.000 0.000 0.520 0.480
#> SRR2314014 2 0.0672 0.5431 0.000 0.984 0.008 0.008
#> SRR2314013 2 0.0336 0.5391 0.000 0.992 0.000 0.008
#> SRR2314012 1 0.5434 0.7349 0.696 0.000 0.052 0.252
#> SRR2314011 1 0.1867 0.8650 0.928 0.000 0.000 0.072
#> SRR2314010 2 0.1474 0.4917 0.000 0.948 0.000 0.052
#> SRR2314009 3 0.0921 0.7277 0.000 0.028 0.972 0.000
#> SRR2314008 3 0.4877 0.4136 0.000 0.408 0.592 0.000
#> SRR2314007 3 0.5028 0.4237 0.000 0.400 0.596 0.004
#> SRR2314005 2 0.4040 -0.1179 0.000 0.752 0.000 0.248
#> SRR2314004 2 0.4843 -0.7227 0.000 0.604 0.000 0.396
#> SRR2314003 1 0.7450 0.4687 0.508 0.000 0.228 0.264
#> SRR2314006 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2314002 3 0.3907 0.6498 0.000 0.000 0.768 0.232
#> SRR2314001 2 0.1118 0.5406 0.000 0.964 0.036 0.000
#> SRR2314000 3 0.1798 0.7267 0.000 0.040 0.944 0.016
#> SRR2313998 2 0.0592 0.5441 0.000 0.984 0.016 0.000
#> SRR2313996 2 0.4994 -0.2284 0.000 0.520 0.480 0.000
#> SRR2313999 1 0.2868 0.8470 0.864 0.000 0.000 0.136
#> SRR2313997 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2313992 3 0.4040 0.6446 0.000 0.000 0.752 0.248
#> SRR2313991 3 0.5257 0.6598 0.000 0.060 0.728 0.212
#> SRR2313990 2 0.5168 -0.2650 0.000 0.504 0.492 0.004
#> SRR2313988 1 0.0707 0.8732 0.980 0.000 0.000 0.020
#> SRR2313987 1 0.0188 0.8741 0.996 0.000 0.000 0.004
#> SRR2313984 2 0.3172 0.2563 0.000 0.840 0.000 0.160
#> SRR2313989 4 0.5000 1.0000 0.000 0.496 0.000 0.504
#> SRR2313983 1 0.0188 0.8737 0.996 0.000 0.000 0.004
#> SRR2313986 3 0.4250 0.6300 0.000 0.000 0.724 0.276
#> SRR2313982 1 0.0188 0.8737 0.996 0.000 0.000 0.004
#> SRR2313981 3 0.0817 0.7300 0.000 0.000 0.976 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.5382 0.5047 0.000 0.104 0.644 0.000 0.252
#> SRR2313980 2 0.0162 0.8332 0.000 0.996 0.000 0.000 0.004
#> SRR2313979 1 0.0290 0.8518 0.992 0.000 0.000 0.000 0.008
#> SRR2313977 3 0.5757 0.4878 0.000 0.104 0.560 0.000 0.336
#> SRR2313976 2 0.3488 0.7365 0.000 0.808 0.024 0.000 0.168
#> SRR2313975 1 0.6394 0.5344 0.504 0.000 0.000 0.292 0.204
#> SRR2313973 1 0.0000 0.8520 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.5704 0.6492 0.620 0.000 0.000 0.232 0.148
#> SRR2313985 1 0.0000 0.8520 1.000 0.000 0.000 0.000 0.000
#> SRR2313993 3 0.6558 0.3674 0.000 0.232 0.468 0.000 0.300
#> SRR2313994 3 0.6220 0.4446 0.000 0.168 0.524 0.000 0.308
#> SRR2313995 2 0.6422 0.0166 0.000 0.460 0.360 0.000 0.180
#> SRR2314024 2 0.0510 0.8315 0.000 0.984 0.000 0.000 0.016
#> SRR2314015 3 0.5952 0.4621 0.000 0.164 0.584 0.000 0.252
#> SRR2314025 4 0.5888 0.1113 0.288 0.000 0.000 0.576 0.136
#> SRR2314023 1 0.0000 0.8520 1.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.1522 0.8111 0.000 0.944 0.012 0.000 0.044
#> SRR2314045 1 0.5693 0.5892 0.620 0.000 0.000 0.144 0.236
#> SRR2314042 4 0.2409 0.4316 0.000 0.000 0.032 0.900 0.068
#> SRR2314043 4 0.6032 -0.1516 0.000 0.000 0.424 0.460 0.116
#> SRR2314041 3 0.2464 0.4174 0.000 0.000 0.888 0.016 0.096
#> SRR2314044 3 0.2879 0.2623 0.000 0.000 0.868 0.100 0.032
#> SRR2314040 3 0.5480 -0.3607 0.000 0.000 0.560 0.368 0.072
#> SRR2314039 3 0.3398 0.0478 0.000 0.000 0.780 0.216 0.004
#> SRR2314038 5 0.6236 0.8011 0.000 0.000 0.400 0.144 0.456
#> SRR2314037 4 0.4707 0.1158 0.000 0.000 0.392 0.588 0.020
#> SRR2314036 2 0.0000 0.8329 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.5857 -0.4595 0.000 0.000 0.460 0.444 0.096
#> SRR2314034 4 0.2079 0.4161 0.000 0.000 0.064 0.916 0.020
#> SRR2314032 1 0.3690 0.7415 0.764 0.000 0.000 0.012 0.224
#> SRR2314031 4 0.3197 0.4164 0.000 0.000 0.024 0.836 0.140
#> SRR2314029 3 0.3967 0.2202 0.000 0.000 0.800 0.092 0.108
#> SRR2314030 4 0.5434 -0.1366 0.000 0.000 0.076 0.588 0.336
#> SRR2314028 4 0.1992 0.4397 0.000 0.000 0.044 0.924 0.032
#> SRR2314027 3 0.2338 0.2414 0.000 0.000 0.884 0.112 0.004
#> SRR2314026 4 0.4800 0.1357 0.000 0.000 0.368 0.604 0.028
#> SRR2314020 2 0.0404 0.8323 0.000 0.988 0.000 0.000 0.012
#> SRR2314019 4 0.4570 0.1754 0.000 0.000 0.348 0.632 0.020
#> SRR2314021 4 0.4400 0.2085 0.000 0.000 0.308 0.672 0.020
#> SRR2314018 3 0.5922 -0.4119 0.000 0.000 0.520 0.368 0.112
#> SRR2314017 5 0.6706 0.7438 0.000 0.000 0.288 0.284 0.428
#> SRR2314016 4 0.4256 0.0944 0.000 0.000 0.000 0.564 0.436
#> SRR2314014 3 0.6215 0.4396 0.000 0.156 0.508 0.000 0.336
#> SRR2314013 3 0.6360 0.4110 0.000 0.172 0.476 0.000 0.352
#> SRR2314012 4 0.5976 -0.1397 0.376 0.000 0.000 0.508 0.116
#> SRR2314011 1 0.3647 0.7740 0.816 0.000 0.000 0.132 0.052
#> SRR2314010 3 0.6307 0.4016 0.000 0.224 0.532 0.000 0.244
#> SRR2314009 4 0.5524 -0.0229 0.000 0.000 0.416 0.516 0.068
#> SRR2314008 3 0.1830 0.2655 0.000 0.000 0.932 0.028 0.040
#> SRR2314007 3 0.2124 0.2546 0.000 0.000 0.900 0.096 0.004
#> SRR2314005 2 0.6526 0.2266 0.000 0.464 0.212 0.000 0.324
#> SRR2314004 2 0.5005 0.5585 0.000 0.660 0.064 0.000 0.276
#> SRR2314003 4 0.5905 0.1065 0.292 0.000 0.000 0.572 0.136
#> SRR2314006 2 0.0162 0.8332 0.000 0.996 0.000 0.000 0.004
#> SRR2314002 5 0.6332 0.7770 0.000 0.000 0.264 0.212 0.524
#> SRR2314001 3 0.5864 0.4834 0.000 0.128 0.572 0.000 0.300
#> SRR2314000 3 0.6392 -0.5004 0.000 0.000 0.432 0.400 0.168
#> SRR2313998 3 0.6220 0.4446 0.000 0.168 0.524 0.000 0.308
#> SRR2313996 3 0.3607 0.5319 0.000 0.004 0.752 0.000 0.244
#> SRR2313999 1 0.5256 0.6600 0.672 0.000 0.000 0.212 0.116
#> SRR2313997 2 0.0290 0.8328 0.000 0.992 0.000 0.000 0.008
#> SRR2313992 4 0.1544 0.4276 0.000 0.000 0.068 0.932 0.000
#> SRR2313991 5 0.6405 0.8237 0.000 0.000 0.384 0.172 0.444
#> SRR2313990 3 0.3885 0.5263 0.000 0.000 0.724 0.008 0.268
#> SRR2313988 1 0.2069 0.8304 0.912 0.000 0.000 0.012 0.076
#> SRR2313987 1 0.0771 0.8495 0.976 0.000 0.000 0.004 0.020
#> SRR2313984 3 0.6738 0.2726 0.000 0.280 0.412 0.000 0.308
#> SRR2313989 2 0.0000 0.8329 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0290 0.8518 0.992 0.000 0.000 0.000 0.008
#> SRR2313986 4 0.3197 0.4164 0.000 0.000 0.024 0.836 0.140
#> SRR2313982 1 0.0290 0.8518 0.992 0.000 0.000 0.000 0.008
#> SRR2313981 4 0.4599 0.1670 0.000 0.000 0.356 0.624 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.4870 0.6858 0.000 0.740 0.048 0.008 0.096 0.108
#> SRR2313980 6 0.0363 0.8785 0.000 0.000 0.000 0.012 0.000 0.988
#> SRR2313979 1 0.0146 0.8158 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313977 2 0.4424 0.6619 0.000 0.776 0.012 0.036 0.068 0.108
#> SRR2313976 6 0.5413 0.5744 0.000 0.224 0.000 0.044 0.088 0.644
#> SRR2313975 4 0.7910 0.0814 0.248 0.108 0.044 0.392 0.208 0.000
#> SRR2313973 1 0.0508 0.8145 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR2313974 4 0.5725 0.2733 0.296 0.048 0.008 0.588 0.060 0.000
#> SRR2313985 1 0.0458 0.8147 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR2313993 2 0.4193 0.6308 0.000 0.740 0.008 0.032 0.012 0.208
#> SRR2313994 2 0.3002 0.6807 0.000 0.836 0.008 0.020 0.000 0.136
#> SRR2313995 2 0.6475 0.3521 0.000 0.456 0.036 0.036 0.076 0.396
#> SRR2314024 6 0.1453 0.8722 0.000 0.008 0.000 0.040 0.008 0.944
#> SRR2314015 2 0.4456 0.6847 0.000 0.748 0.028 0.000 0.080 0.144
#> SRR2314025 4 0.3229 0.6432 0.120 0.000 0.048 0.828 0.004 0.000
#> SRR2314023 1 0.0964 0.8112 0.968 0.012 0.000 0.016 0.004 0.000
#> SRR2314033 6 0.2727 0.8160 0.000 0.028 0.000 0.028 0.064 0.880
#> SRR2314045 1 0.7234 0.4364 0.492 0.088 0.080 0.064 0.276 0.000
#> SRR2314042 4 0.4264 -0.0826 0.000 0.000 0.488 0.496 0.016 0.000
#> SRR2314043 3 0.3031 0.5858 0.000 0.044 0.844 0.004 0.108 0.000
#> SRR2314041 2 0.5284 0.4499 0.000 0.596 0.296 0.012 0.096 0.000
#> SRR2314044 2 0.5840 0.1740 0.000 0.468 0.404 0.024 0.104 0.000
#> SRR2314040 3 0.3079 0.5962 0.000 0.128 0.836 0.008 0.028 0.000
#> SRR2314039 3 0.4768 0.4008 0.000 0.244 0.668 0.008 0.080 0.000
#> SRR2314038 5 0.4401 0.7325 0.000 0.060 0.168 0.028 0.744 0.000
#> SRR2314037 3 0.2432 0.6422 0.000 0.024 0.888 0.080 0.008 0.000
#> SRR2314036 6 0.0000 0.8790 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.3270 0.5590 0.000 0.060 0.820 0.000 0.120 0.000
#> SRR2314034 3 0.3670 0.4421 0.000 0.000 0.704 0.284 0.012 0.000
#> SRR2314032 1 0.5951 0.5756 0.600 0.088 0.008 0.056 0.248 0.000
#> SRR2314031 4 0.2743 0.5718 0.000 0.000 0.164 0.828 0.008 0.000
#> SRR2314029 2 0.6443 0.2547 0.000 0.448 0.336 0.036 0.180 0.000
#> SRR2314030 5 0.6091 0.4127 0.000 0.008 0.296 0.228 0.468 0.000
#> SRR2314028 3 0.4072 0.1092 0.000 0.000 0.544 0.448 0.008 0.000
#> SRR2314027 3 0.5350 0.0551 0.000 0.376 0.532 0.012 0.080 0.000
#> SRR2314026 3 0.2195 0.6374 0.000 0.016 0.904 0.068 0.012 0.000
#> SRR2314020 6 0.0922 0.8771 0.000 0.004 0.000 0.024 0.004 0.968
#> SRR2314019 3 0.2308 0.6359 0.000 0.016 0.896 0.076 0.012 0.000
#> SRR2314021 3 0.2520 0.6188 0.000 0.008 0.872 0.108 0.012 0.000
#> SRR2314018 3 0.4463 0.4952 0.000 0.096 0.732 0.012 0.160 0.000
#> SRR2314017 5 0.4540 0.7100 0.000 0.008 0.308 0.040 0.644 0.000
#> SRR2314016 4 0.4983 -0.1202 0.000 0.004 0.056 0.484 0.456 0.000
#> SRR2314014 2 0.4420 0.6398 0.000 0.756 0.000 0.036 0.072 0.136
#> SRR2314013 2 0.4939 0.6153 0.000 0.724 0.004 0.048 0.084 0.140
#> SRR2314012 4 0.2983 0.6373 0.136 0.000 0.032 0.832 0.000 0.000
#> SRR2314011 1 0.4505 0.3063 0.612 0.020 0.004 0.356 0.008 0.000
#> SRR2314010 2 0.5665 0.6441 0.000 0.656 0.040 0.024 0.076 0.204
#> SRR2314009 3 0.2100 0.6275 0.000 0.036 0.916 0.016 0.032 0.000
#> SRR2314008 2 0.5845 0.2628 0.000 0.472 0.368 0.008 0.152 0.000
#> SRR2314007 3 0.5535 -0.0262 0.000 0.392 0.500 0.012 0.096 0.000
#> SRR2314005 2 0.6047 0.0563 0.000 0.484 0.004 0.040 0.088 0.384
#> SRR2314004 6 0.5232 0.3158 0.000 0.356 0.000 0.040 0.036 0.568
#> SRR2314003 4 0.3229 0.6432 0.120 0.000 0.048 0.828 0.004 0.000
#> SRR2314006 6 0.0363 0.8785 0.000 0.000 0.000 0.012 0.000 0.988
#> SRR2314002 5 0.3473 0.7178 0.000 0.004 0.192 0.024 0.780 0.000
#> SRR2314001 2 0.2912 0.6911 0.000 0.856 0.016 0.008 0.008 0.112
#> SRR2314000 3 0.4882 0.3993 0.000 0.068 0.668 0.020 0.244 0.000
#> SRR2313998 2 0.3674 0.6785 0.000 0.812 0.016 0.024 0.016 0.132
#> SRR2313996 2 0.4420 0.6114 0.000 0.728 0.172 0.008 0.092 0.000
#> SRR2313999 4 0.4467 0.3306 0.344 0.020 0.004 0.624 0.008 0.000
#> SRR2313997 6 0.1003 0.8766 0.000 0.004 0.000 0.028 0.004 0.964
#> SRR2313992 3 0.3782 0.3268 0.000 0.000 0.636 0.360 0.004 0.000
#> SRR2313991 5 0.4288 0.7433 0.000 0.056 0.180 0.020 0.744 0.000
#> SRR2313990 2 0.3805 0.5525 0.000 0.728 0.248 0.008 0.016 0.000
#> SRR2313988 1 0.5178 0.6693 0.716 0.092 0.008 0.064 0.120 0.000
#> SRR2313987 1 0.1701 0.7786 0.920 0.008 0.000 0.072 0.000 0.000
#> SRR2313984 2 0.4454 0.5932 0.000 0.704 0.000 0.032 0.028 0.236
#> SRR2313989 6 0.0363 0.8787 0.000 0.000 0.000 0.012 0.000 0.988
#> SRR2313983 1 0.0000 0.8161 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.2743 0.5718 0.000 0.000 0.164 0.828 0.008 0.000
#> SRR2313982 1 0.0146 0.8158 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313981 3 0.2165 0.6283 0.000 0.008 0.884 0.108 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", "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 16873 rows and 72 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.5075 0.493 0.493
#> 3 3 0.826 0.917 0.956 0.2667 0.823 0.654
#> 4 4 0.792 0.804 0.895 0.0931 0.913 0.766
#> 5 5 0.721 0.682 0.823 0.0641 0.935 0.795
#> 6 6 0.744 0.687 0.822 0.0543 0.929 0.746
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
#> SRR2313978 2 0.000 0.995 0.000 1.000
#> SRR2313980 2 0.000 0.995 0.000 1.000
#> SRR2313979 1 0.000 1.000 1.000 0.000
#> SRR2313977 2 0.000 0.995 0.000 1.000
#> SRR2313976 2 0.000 0.995 0.000 1.000
#> SRR2313975 1 0.000 1.000 1.000 0.000
#> SRR2313973 1 0.000 1.000 1.000 0.000
#> SRR2313974 1 0.000 1.000 1.000 0.000
#> SRR2313985 1 0.000 1.000 1.000 0.000
#> SRR2313993 2 0.000 0.995 0.000 1.000
#> SRR2313994 2 0.000 0.995 0.000 1.000
#> SRR2313995 2 0.000 0.995 0.000 1.000
#> SRR2314024 2 0.000 0.995 0.000 1.000
#> SRR2314015 2 0.000 0.995 0.000 1.000
#> SRR2314025 1 0.000 1.000 1.000 0.000
#> SRR2314023 1 0.000 1.000 1.000 0.000
#> SRR2314033 2 0.000 0.995 0.000 1.000
#> SRR2314045 1 0.000 1.000 1.000 0.000
#> SRR2314042 1 0.000 1.000 1.000 0.000
#> SRR2314043 1 0.000 1.000 1.000 0.000
#> SRR2314041 2 0.000 0.995 0.000 1.000
#> SRR2314044 2 0.000 0.995 0.000 1.000
#> SRR2314040 2 0.644 0.805 0.164 0.836
#> SRR2314039 2 0.000 0.995 0.000 1.000
#> SRR2314038 2 0.000 0.995 0.000 1.000
#> SRR2314037 1 0.000 1.000 1.000 0.000
#> SRR2314036 2 0.000 0.995 0.000 1.000
#> SRR2314035 1 0.000 1.000 1.000 0.000
#> SRR2314034 1 0.000 1.000 1.000 0.000
#> SRR2314032 1 0.000 1.000 1.000 0.000
#> SRR2314031 1 0.000 1.000 1.000 0.000
#> SRR2314029 2 0.000 0.995 0.000 1.000
#> SRR2314030 1 0.000 1.000 1.000 0.000
#> SRR2314028 1 0.000 1.000 1.000 0.000
#> SRR2314027 2 0.000 0.995 0.000 1.000
#> SRR2314026 1 0.000 1.000 1.000 0.000
#> SRR2314020 2 0.000 0.995 0.000 1.000
#> SRR2314019 1 0.000 1.000 1.000 0.000
#> SRR2314021 1 0.000 1.000 1.000 0.000
#> SRR2314018 2 0.118 0.980 0.016 0.984
#> SRR2314017 1 0.000 1.000 1.000 0.000
#> SRR2314016 1 0.000 1.000 1.000 0.000
#> SRR2314014 2 0.000 0.995 0.000 1.000
#> SRR2314013 2 0.000 0.995 0.000 1.000
#> SRR2314012 1 0.000 1.000 1.000 0.000
#> SRR2314011 1 0.000 1.000 1.000 0.000
#> SRR2314010 2 0.000 0.995 0.000 1.000
#> SRR2314009 1 0.000 1.000 1.000 0.000
#> SRR2314008 2 0.000 0.995 0.000 1.000
#> SRR2314007 2 0.000 0.995 0.000 1.000
#> SRR2314005 2 0.000 0.995 0.000 1.000
#> SRR2314004 2 0.000 0.995 0.000 1.000
#> SRR2314003 1 0.000 1.000 1.000 0.000
#> SRR2314006 2 0.000 0.995 0.000 1.000
#> SRR2314002 1 0.000 1.000 1.000 0.000
#> SRR2314001 2 0.000 0.995 0.000 1.000
#> SRR2314000 1 0.000 1.000 1.000 0.000
#> SRR2313998 2 0.000 0.995 0.000 1.000
#> SRR2313996 2 0.000 0.995 0.000 1.000
#> SRR2313999 1 0.000 1.000 1.000 0.000
#> SRR2313997 2 0.000 0.995 0.000 1.000
#> SRR2313992 1 0.000 1.000 1.000 0.000
#> SRR2313991 2 0.000 0.995 0.000 1.000
#> SRR2313990 2 0.000 0.995 0.000 1.000
#> SRR2313988 1 0.000 1.000 1.000 0.000
#> SRR2313987 1 0.000 1.000 1.000 0.000
#> SRR2313984 2 0.000 0.995 0.000 1.000
#> SRR2313989 2 0.000 0.995 0.000 1.000
#> SRR2313983 1 0.000 1.000 1.000 0.000
#> SRR2313986 1 0.000 1.000 1.000 0.000
#> SRR2313982 1 0.000 1.000 1.000 0.000
#> SRR2313981 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
#> SRR2313978 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313977 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313995 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314025 1 0.0237 0.953 0.996 0.000 0.004
#> SRR2314023 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314045 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314042 1 0.1753 0.922 0.952 0.000 0.048
#> SRR2314043 3 0.0237 0.852 0.004 0.000 0.996
#> SRR2314041 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314044 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314040 3 0.0000 0.853 0.000 0.000 1.000
#> SRR2314039 3 0.5560 0.646 0.000 0.300 0.700
#> SRR2314038 2 0.3038 0.883 0.000 0.896 0.104
#> SRR2314037 3 0.3038 0.837 0.104 0.000 0.896
#> SRR2314036 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314035 3 0.0237 0.852 0.004 0.000 0.996
#> SRR2314034 1 0.5216 0.675 0.740 0.000 0.260
#> SRR2314032 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314031 1 0.0237 0.953 0.996 0.000 0.004
#> SRR2314029 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314030 1 0.0747 0.946 0.984 0.000 0.016
#> SRR2314028 1 0.5138 0.688 0.748 0.000 0.252
#> SRR2314027 3 0.5948 0.546 0.000 0.360 0.640
#> SRR2314026 3 0.3038 0.837 0.104 0.000 0.896
#> SRR2314020 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314019 3 0.3038 0.837 0.104 0.000 0.896
#> SRR2314021 3 0.3038 0.837 0.104 0.000 0.896
#> SRR2314018 3 0.0237 0.853 0.000 0.004 0.996
#> SRR2314017 1 0.3038 0.872 0.896 0.000 0.104
#> SRR2314016 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314014 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314009 3 0.0237 0.853 0.004 0.000 0.996
#> SRR2314008 2 0.2066 0.931 0.000 0.940 0.060
#> SRR2314007 3 0.5905 0.562 0.000 0.352 0.648
#> SRR2314005 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314003 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2314006 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314002 1 0.3038 0.872 0.896 0.000 0.104
#> SRR2314001 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2314000 1 0.3038 0.872 0.896 0.000 0.104
#> SRR2313998 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313996 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313999 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313992 1 0.5058 0.699 0.756 0.000 0.244
#> SRR2313991 2 0.3038 0.883 0.000 0.896 0.104
#> SRR2313990 3 0.4399 0.776 0.000 0.188 0.812
#> SRR2313988 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.990 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313986 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313982 1 0.0000 0.955 1.000 0.000 0.000
#> SRR2313981 3 0.3038 0.837 0.104 0.000 0.896
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1022 0.947 0.000 0.968 0.000 0.032
#> SRR2313980 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.1576 0.936 0.000 0.948 0.004 0.048
#> SRR2313976 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2313975 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313985 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0524 0.956 0.000 0.988 0.004 0.008
#> SRR2313994 2 0.0657 0.954 0.000 0.984 0.004 0.012
#> SRR2313995 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314024 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314025 1 0.3205 0.847 0.872 0.000 0.024 0.104
#> SRR2314023 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.1389 0.861 0.952 0.000 0.000 0.048
#> SRR2314042 1 0.5839 0.706 0.696 0.000 0.200 0.104
#> SRR2314043 4 0.5980 0.373 0.044 0.000 0.396 0.560
#> SRR2314041 2 0.1118 0.946 0.000 0.964 0.000 0.036
#> SRR2314044 2 0.1576 0.937 0.000 0.948 0.004 0.048
#> SRR2314040 3 0.2714 0.757 0.004 0.000 0.884 0.112
#> SRR2314039 3 0.3803 0.693 0.000 0.132 0.836 0.032
#> SRR2314038 4 0.3649 0.555 0.000 0.204 0.000 0.796
#> SRR2314037 3 0.3166 0.747 0.016 0.000 0.868 0.116
#> SRR2314036 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314035 4 0.5587 0.423 0.028 0.000 0.372 0.600
#> SRR2314034 1 0.4635 0.678 0.720 0.000 0.268 0.012
#> SRR2314032 1 0.1389 0.861 0.952 0.000 0.000 0.048
#> SRR2314031 1 0.3587 0.839 0.856 0.000 0.040 0.104
#> SRR2314029 2 0.0188 0.957 0.000 0.996 0.000 0.004
#> SRR2314030 1 0.7459 0.366 0.476 0.000 0.188 0.336
#> SRR2314028 1 0.5716 0.698 0.700 0.000 0.212 0.088
#> SRR2314027 3 0.4932 0.558 0.000 0.240 0.728 0.032
#> SRR2314026 3 0.0524 0.793 0.008 0.000 0.988 0.004
#> SRR2314020 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314019 3 0.0376 0.794 0.004 0.000 0.992 0.004
#> SRR2314021 3 0.0524 0.793 0.008 0.000 0.988 0.004
#> SRR2314018 4 0.5130 0.413 0.004 0.008 0.344 0.644
#> SRR2314017 4 0.2266 0.609 0.084 0.000 0.004 0.912
#> SRR2314016 1 0.4483 0.678 0.712 0.000 0.004 0.284
#> SRR2314014 2 0.1576 0.936 0.000 0.948 0.004 0.048
#> SRR2314013 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314012 1 0.2473 0.861 0.908 0.000 0.012 0.080
#> SRR2314011 1 0.0188 0.884 0.996 0.000 0.000 0.004
#> SRR2314010 2 0.0336 0.957 0.000 0.992 0.000 0.008
#> SRR2314009 3 0.2408 0.774 0.000 0.000 0.896 0.104
#> SRR2314008 2 0.4155 0.698 0.000 0.756 0.004 0.240
#> SRR2314007 3 0.5247 0.565 0.000 0.228 0.720 0.052
#> SRR2314005 2 0.0336 0.957 0.000 0.992 0.000 0.008
#> SRR2314004 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314003 1 0.2741 0.855 0.892 0.000 0.012 0.096
#> SRR2314006 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2314002 4 0.3764 0.595 0.216 0.000 0.000 0.784
#> SRR2314001 2 0.1489 0.939 0.000 0.952 0.004 0.044
#> SRR2314000 4 0.5126 0.325 0.444 0.000 0.004 0.552
#> SRR2313998 2 0.0524 0.956 0.000 0.988 0.004 0.008
#> SRR2313996 2 0.1489 0.939 0.000 0.952 0.004 0.044
#> SRR2313999 1 0.0188 0.884 0.996 0.000 0.004 0.000
#> SRR2313997 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2313992 1 0.5572 0.716 0.716 0.000 0.196 0.088
#> SRR2313991 4 0.3688 0.554 0.000 0.208 0.000 0.792
#> SRR2313990 2 0.6831 -0.069 0.000 0.480 0.420 0.100
#> SRR2313988 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.0188 0.958 0.000 0.996 0.000 0.004
#> SRR2313989 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313986 1 0.2987 0.851 0.880 0.000 0.016 0.104
#> SRR2313982 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.3088 0.750 0.008 0.000 0.864 0.128
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.2554 0.8878 0.000 0.892 0.072 0.000 0.036
#> SRR2313980 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.4400 0.7642 0.000 0.736 0.212 0.000 0.052
#> SRR2313976 2 0.0798 0.9213 0.000 0.976 0.016 0.000 0.008
#> SRR2313975 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313985 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313993 2 0.1386 0.9161 0.000 0.952 0.032 0.000 0.016
#> SRR2313994 2 0.1914 0.9086 0.000 0.924 0.060 0.000 0.016
#> SRR2313995 2 0.0290 0.9224 0.000 0.992 0.008 0.000 0.000
#> SRR2314024 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0162 0.9240 0.000 0.996 0.004 0.000 0.000
#> SRR2314025 1 0.5409 0.6337 0.660 0.000 0.252 0.012 0.076
#> SRR2314023 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.0404 0.9225 0.000 0.988 0.012 0.000 0.000
#> SRR2314045 1 0.1608 0.7573 0.928 0.000 0.000 0.000 0.072
#> SRR2314042 4 0.7953 -0.0452 0.296 0.000 0.280 0.348 0.076
#> SRR2314043 3 0.6897 0.5091 0.032 0.000 0.532 0.216 0.220
#> SRR2314041 2 0.2632 0.8851 0.000 0.888 0.072 0.000 0.040
#> SRR2314044 2 0.4497 0.7646 0.000 0.732 0.208 0.000 0.060
#> SRR2314040 3 0.4840 0.4456 0.000 0.000 0.640 0.320 0.040
#> SRR2314039 4 0.3504 0.5279 0.000 0.044 0.100 0.844 0.012
#> SRR2314038 5 0.2522 0.6048 0.000 0.108 0.012 0.000 0.880
#> SRR2314037 4 0.5329 0.1222 0.000 0.000 0.432 0.516 0.052
#> SRR2314036 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.6310 0.5429 0.020 0.000 0.600 0.184 0.196
#> SRR2314034 1 0.5938 0.4177 0.568 0.000 0.072 0.340 0.020
#> SRR2314032 1 0.1671 0.7531 0.924 0.000 0.000 0.000 0.076
#> SRR2314031 1 0.6375 0.5670 0.588 0.000 0.280 0.056 0.076
#> SRR2314029 2 0.0566 0.9209 0.000 0.984 0.012 0.000 0.004
#> SRR2314030 5 0.6153 0.4383 0.048 0.000 0.076 0.260 0.616
#> SRR2314028 1 0.7477 0.3231 0.440 0.000 0.320 0.180 0.060
#> SRR2314027 4 0.4990 0.4298 0.000 0.152 0.104 0.732 0.012
#> SRR2314026 4 0.0000 0.5592 0.000 0.000 0.000 1.000 0.000
#> SRR2314020 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.0000 0.5592 0.000 0.000 0.000 1.000 0.000
#> SRR2314021 4 0.0162 0.5580 0.000 0.000 0.004 0.996 0.000
#> SRR2314018 3 0.5428 0.5294 0.000 0.008 0.668 0.100 0.224
#> SRR2314017 5 0.2390 0.6105 0.044 0.000 0.044 0.004 0.908
#> SRR2314016 5 0.5386 0.3067 0.372 0.000 0.064 0.000 0.564
#> SRR2314014 2 0.4369 0.7693 0.000 0.740 0.208 0.000 0.052
#> SRR2314013 2 0.0992 0.9200 0.000 0.968 0.024 0.000 0.008
#> SRR2314012 1 0.4000 0.7241 0.800 0.000 0.132 0.004 0.064
#> SRR2314011 1 0.0671 0.8121 0.980 0.000 0.016 0.000 0.004
#> SRR2314010 2 0.1124 0.9176 0.000 0.960 0.036 0.000 0.004
#> SRR2314009 3 0.5097 0.2488 0.000 0.000 0.624 0.320 0.056
#> SRR2314008 2 0.5593 0.4780 0.000 0.572 0.340 0.000 0.088
#> SRR2314007 4 0.6309 0.3418 0.000 0.148 0.160 0.640 0.052
#> SRR2314005 2 0.2416 0.8749 0.000 0.888 0.100 0.000 0.012
#> SRR2314004 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 1 0.5137 0.6449 0.676 0.000 0.244 0.004 0.076
#> SRR2314006 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.2732 0.6052 0.160 0.000 0.000 0.000 0.840
#> SRR2314001 2 0.3409 0.8548 0.000 0.836 0.112 0.000 0.052
#> SRR2314000 3 0.6645 0.1425 0.332 0.000 0.432 0.000 0.236
#> SRR2313998 2 0.1018 0.9212 0.000 0.968 0.016 0.000 0.016
#> SRR2313996 2 0.3359 0.8578 0.000 0.840 0.108 0.000 0.052
#> SRR2313999 1 0.0579 0.8134 0.984 0.000 0.008 0.000 0.008
#> SRR2313997 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 1 0.7222 0.4288 0.496 0.000 0.292 0.152 0.060
#> SRR2313991 5 0.2732 0.5807 0.000 0.160 0.000 0.000 0.840
#> SRR2313990 3 0.7146 0.2317 0.000 0.240 0.540 0.144 0.076
#> SRR2313988 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313984 2 0.0579 0.9239 0.000 0.984 0.008 0.000 0.008
#> SRR2313989 2 0.0000 0.9237 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 1 0.5460 0.6128 0.636 0.000 0.280 0.008 0.076
#> SRR2313982 1 0.0000 0.8163 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.5459 -0.0222 0.012 0.000 0.588 0.352 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.4534 0.7458 0.000 0.764 0.136 0.020 0.036 0.044
#> SRR2313980 2 0.0405 0.8390 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR2313979 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.6640 0.4392 0.000 0.520 0.308 0.040 0.060 0.072
#> SRR2313976 2 0.2562 0.8243 0.000 0.900 0.036 0.028 0.016 0.020
#> SRR2313975 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313973 1 0.0260 0.9069 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2313974 1 0.0260 0.9069 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2313985 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313993 2 0.2732 0.8285 0.000 0.884 0.028 0.004 0.024 0.060
#> SRR2313994 2 0.4223 0.7900 0.000 0.800 0.064 0.028 0.028 0.080
#> SRR2313995 2 0.1585 0.8260 0.000 0.940 0.012 0.012 0.000 0.036
#> SRR2314024 2 0.0806 0.8365 0.000 0.972 0.000 0.008 0.000 0.020
#> SRR2314015 2 0.1235 0.8435 0.000 0.960 0.016 0.008 0.008 0.008
#> SRR2314025 4 0.3923 0.5451 0.372 0.000 0.000 0.620 0.008 0.000
#> SRR2314023 1 0.0260 0.9069 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2314033 2 0.1078 0.8385 0.000 0.964 0.012 0.016 0.000 0.008
#> SRR2314045 1 0.1010 0.8764 0.960 0.000 0.000 0.004 0.036 0.000
#> SRR2314042 4 0.3479 0.6866 0.088 0.000 0.000 0.820 0.008 0.084
#> SRR2314043 3 0.6674 0.3907 0.040 0.000 0.600 0.140 0.104 0.116
#> SRR2314041 2 0.4796 0.7397 0.000 0.748 0.116 0.020 0.028 0.088
#> SRR2314044 2 0.7932 0.1722 0.000 0.396 0.288 0.148 0.068 0.100
#> SRR2314040 3 0.3642 0.4531 0.000 0.000 0.800 0.048 0.012 0.140
#> SRR2314039 6 0.3101 0.7498 0.000 0.040 0.048 0.036 0.008 0.868
#> SRR2314038 5 0.1844 0.7616 0.000 0.048 0.024 0.004 0.924 0.000
#> SRR2314037 4 0.3208 0.5731 0.008 0.000 0.040 0.832 0.000 0.120
#> SRR2314036 2 0.0000 0.8400 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314035 3 0.4814 0.4834 0.016 0.000 0.756 0.064 0.092 0.072
#> SRR2314034 1 0.6671 -0.1262 0.436 0.000 0.020 0.292 0.012 0.240
#> SRR2314032 1 0.1010 0.8764 0.960 0.000 0.000 0.004 0.036 0.000
#> SRR2314031 4 0.3314 0.7216 0.224 0.000 0.000 0.764 0.012 0.000
#> SRR2314029 2 0.2632 0.8044 0.000 0.896 0.020 0.028 0.016 0.040
#> SRR2314030 5 0.4556 0.5967 0.004 0.000 0.004 0.068 0.696 0.228
#> SRR2314028 4 0.3024 0.7166 0.116 0.000 0.008 0.844 0.000 0.032
#> SRR2314027 6 0.3574 0.6788 0.000 0.104 0.052 0.008 0.012 0.824
#> SRR2314026 6 0.2833 0.7772 0.000 0.000 0.024 0.104 0.012 0.860
#> SRR2314020 2 0.0000 0.8400 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314019 6 0.2880 0.7770 0.000 0.000 0.024 0.108 0.012 0.856
#> SRR2314021 6 0.2844 0.7754 0.000 0.000 0.020 0.112 0.012 0.856
#> SRR2314018 3 0.2563 0.5046 0.000 0.000 0.880 0.040 0.076 0.004
#> SRR2314017 5 0.2169 0.7514 0.008 0.000 0.080 0.012 0.900 0.000
#> SRR2314016 5 0.4808 0.5294 0.272 0.000 0.000 0.092 0.636 0.000
#> SRR2314014 2 0.6522 0.4936 0.000 0.548 0.280 0.040 0.048 0.084
#> SRR2314013 2 0.2938 0.8166 0.000 0.880 0.044 0.028 0.016 0.032
#> SRR2314012 1 0.3601 0.4027 0.684 0.000 0.000 0.312 0.004 0.000
#> SRR2314011 1 0.1765 0.8243 0.904 0.000 0.000 0.096 0.000 0.000
#> SRR2314010 2 0.2704 0.8213 0.000 0.880 0.020 0.012 0.008 0.080
#> SRR2314009 4 0.5520 -0.0299 0.000 0.000 0.444 0.448 0.008 0.100
#> SRR2314008 3 0.6685 -0.1648 0.000 0.404 0.428 0.024 0.072 0.072
#> SRR2314007 6 0.5285 0.5195 0.000 0.076 0.176 0.012 0.044 0.692
#> SRR2314005 2 0.3984 0.7624 0.000 0.800 0.124 0.028 0.016 0.032
#> SRR2314004 2 0.0405 0.8404 0.000 0.988 0.004 0.008 0.000 0.000
#> SRR2314003 4 0.3975 0.5058 0.392 0.000 0.000 0.600 0.008 0.000
#> SRR2314006 2 0.0520 0.8387 0.000 0.984 0.000 0.008 0.000 0.008
#> SRR2314002 5 0.1908 0.7599 0.096 0.000 0.004 0.000 0.900 0.000
#> SRR2314001 2 0.6117 0.6399 0.000 0.636 0.180 0.040 0.048 0.096
#> SRR2314000 3 0.5953 0.2320 0.328 0.000 0.532 0.048 0.092 0.000
#> SRR2313998 2 0.3216 0.8127 0.000 0.864 0.056 0.028 0.020 0.032
#> SRR2313996 2 0.6129 0.6546 0.000 0.636 0.168 0.036 0.048 0.112
#> SRR2313999 1 0.1007 0.8807 0.956 0.000 0.000 0.044 0.000 0.000
#> SRR2313997 2 0.0405 0.8390 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR2313992 4 0.2595 0.7327 0.160 0.000 0.000 0.836 0.000 0.004
#> SRR2313991 5 0.1753 0.7525 0.000 0.084 0.004 0.000 0.912 0.000
#> SRR2313990 3 0.6581 0.2581 0.000 0.240 0.560 0.040 0.040 0.120
#> SRR2313988 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313987 1 0.0260 0.9069 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2313984 2 0.1932 0.8391 0.000 0.924 0.016 0.000 0.020 0.040
#> SRR2313989 2 0.0405 0.8390 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR2313983 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.3468 0.6922 0.264 0.000 0.000 0.728 0.008 0.000
#> SRR2313982 1 0.0000 0.9079 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 4 0.3875 0.5512 0.008 0.000 0.144 0.780 0.000 0.068
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 16873 rows and 72 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 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 0.838 0.924 0.959 0.4897 0.503 0.503
#> 3 3 0.854 0.942 0.973 0.3173 0.678 0.452
#> 4 4 0.657 0.748 0.876 0.1448 0.837 0.580
#> 5 5 0.627 0.553 0.716 0.0688 0.849 0.521
#> 6 6 0.690 0.662 0.787 0.0432 0.911 0.624
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
#> SRR2313978 2 0.0000 0.958 0.000 1.000
#> SRR2313980 2 0.0000 0.958 0.000 1.000
#> SRR2313979 1 0.0000 0.952 1.000 0.000
#> SRR2313977 2 0.0000 0.958 0.000 1.000
#> SRR2313976 2 0.0000 0.958 0.000 1.000
#> SRR2313975 1 0.0000 0.952 1.000 0.000
#> SRR2313973 1 0.0000 0.952 1.000 0.000
#> SRR2313974 1 0.0000 0.952 1.000 0.000
#> SRR2313985 1 0.0000 0.952 1.000 0.000
#> SRR2313993 2 0.0000 0.958 0.000 1.000
#> SRR2313994 2 0.0000 0.958 0.000 1.000
#> SRR2313995 2 0.0000 0.958 0.000 1.000
#> SRR2314024 2 0.0000 0.958 0.000 1.000
#> SRR2314015 2 0.0000 0.958 0.000 1.000
#> SRR2314025 1 0.0000 0.952 1.000 0.000
#> SRR2314023 1 0.0000 0.952 1.000 0.000
#> SRR2314033 2 0.0000 0.958 0.000 1.000
#> SRR2314045 1 0.0000 0.952 1.000 0.000
#> SRR2314042 1 0.3584 0.938 0.932 0.068
#> SRR2314043 1 0.3733 0.936 0.928 0.072
#> SRR2314041 2 0.5629 0.842 0.132 0.868
#> SRR2314044 1 0.4815 0.910 0.896 0.104
#> SRR2314040 1 0.3733 0.936 0.928 0.072
#> SRR2314039 2 0.5842 0.834 0.140 0.860
#> SRR2314038 1 0.8955 0.590 0.688 0.312
#> SRR2314037 1 0.3733 0.936 0.928 0.072
#> SRR2314036 2 0.0000 0.958 0.000 1.000
#> SRR2314035 1 0.3733 0.936 0.928 0.072
#> SRR2314034 1 0.0000 0.952 1.000 0.000
#> SRR2314032 1 0.0000 0.952 1.000 0.000
#> SRR2314031 1 0.0000 0.952 1.000 0.000
#> SRR2314029 2 0.9393 0.451 0.356 0.644
#> SRR2314030 1 0.3733 0.936 0.928 0.072
#> SRR2314028 1 0.0000 0.952 1.000 0.000
#> SRR2314027 2 0.5842 0.834 0.140 0.860
#> SRR2314026 1 0.7219 0.789 0.800 0.200
#> SRR2314020 2 0.0000 0.958 0.000 1.000
#> SRR2314019 1 0.3733 0.936 0.928 0.072
#> SRR2314021 1 0.3733 0.936 0.928 0.072
#> SRR2314018 1 0.3733 0.936 0.928 0.072
#> SRR2314017 1 0.3584 0.938 0.932 0.068
#> SRR2314016 1 0.0000 0.952 1.000 0.000
#> SRR2314014 2 0.0000 0.958 0.000 1.000
#> SRR2314013 2 0.0000 0.958 0.000 1.000
#> SRR2314012 1 0.0000 0.952 1.000 0.000
#> SRR2314011 1 0.0000 0.952 1.000 0.000
#> SRR2314010 2 0.0000 0.958 0.000 1.000
#> SRR2314009 1 0.3733 0.936 0.928 0.072
#> SRR2314008 1 0.7219 0.797 0.800 0.200
#> SRR2314007 2 0.0376 0.955 0.004 0.996
#> SRR2314005 2 0.0000 0.958 0.000 1.000
#> SRR2314004 2 0.0000 0.958 0.000 1.000
#> SRR2314003 1 0.0000 0.952 1.000 0.000
#> SRR2314006 2 0.0000 0.958 0.000 1.000
#> SRR2314002 1 0.3431 0.939 0.936 0.064
#> SRR2314001 2 0.0000 0.958 0.000 1.000
#> SRR2314000 1 0.0000 0.952 1.000 0.000
#> SRR2313998 2 0.0000 0.958 0.000 1.000
#> SRR2313996 2 0.0000 0.958 0.000 1.000
#> SRR2313999 1 0.0000 0.952 1.000 0.000
#> SRR2313997 2 0.0000 0.958 0.000 1.000
#> SRR2313992 1 0.3431 0.939 0.936 0.064
#> SRR2313991 2 0.8327 0.646 0.264 0.736
#> SRR2313990 2 0.5737 0.834 0.136 0.864
#> SRR2313988 1 0.0000 0.952 1.000 0.000
#> SRR2313987 1 0.0000 0.952 1.000 0.000
#> SRR2313984 2 0.0000 0.958 0.000 1.000
#> SRR2313989 2 0.0000 0.958 0.000 1.000
#> SRR2313983 1 0.0000 0.952 1.000 0.000
#> SRR2313986 1 0.0000 0.952 1.000 0.000
#> SRR2313982 1 0.0000 0.952 1.000 0.000
#> SRR2313981 1 0.3733 0.936 0.928 0.072
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.4178 0.805 0.000 0.172 0.828
#> SRR2313980 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313977 2 0.4178 0.802 0.000 0.828 0.172
#> SRR2313976 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313975 1 0.4605 0.757 0.796 0.000 0.204
#> SRR2313973 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313974 1 0.0237 0.950 0.996 0.000 0.004
#> SRR2313985 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313994 2 0.2537 0.896 0.000 0.920 0.080
#> SRR2313995 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314025 1 0.3879 0.841 0.848 0.000 0.152
#> SRR2314023 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314045 1 0.0237 0.950 0.996 0.000 0.004
#> SRR2314042 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314043 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314041 3 0.0592 0.970 0.000 0.012 0.988
#> SRR2314044 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314040 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314039 3 0.0592 0.970 0.000 0.012 0.988
#> SRR2314038 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314037 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314036 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314034 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314032 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2314031 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314029 3 0.5291 0.640 0.000 0.268 0.732
#> SRR2314030 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314028 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314027 3 0.0592 0.970 0.000 0.012 0.988
#> SRR2314026 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314020 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314019 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314021 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314018 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314016 3 0.2878 0.880 0.096 0.000 0.904
#> SRR2314014 2 0.4178 0.802 0.000 0.828 0.172
#> SRR2314013 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314012 1 0.3482 0.863 0.872 0.000 0.128
#> SRR2314011 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314009 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314008 3 0.0592 0.970 0.000 0.012 0.988
#> SRR2314007 3 0.1031 0.960 0.000 0.024 0.976
#> SRR2314005 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314003 1 0.3941 0.838 0.844 0.000 0.156
#> SRR2314006 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2314001 2 0.4178 0.802 0.000 0.828 0.172
#> SRR2314000 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2313998 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313996 3 0.2448 0.911 0.000 0.076 0.924
#> SRR2313999 1 0.1163 0.938 0.972 0.000 0.028
#> SRR2313997 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313992 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2313991 3 0.0424 0.972 0.000 0.008 0.992
#> SRR2313990 3 0.0592 0.969 0.000 0.012 0.988
#> SRR2313988 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.965 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313986 3 0.0000 0.976 0.000 0.000 1.000
#> SRR2313982 1 0.0000 0.951 1.000 0.000 0.000
#> SRR2313981 3 0.0000 0.976 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1151 0.741 0.000 0.968 0.024 0.008
#> SRR2313980 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.5977 0.597 0.000 0.688 0.192 0.120
#> SRR2313976 4 0.1940 0.900 0.000 0.076 0.000 0.924
#> SRR2313975 1 0.3831 0.741 0.792 0.004 0.204 0.000
#> SRR2313973 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.1489 0.897 0.952 0.004 0.044 0.000
#> SRR2313985 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.3726 0.671 0.000 0.788 0.000 0.212
#> SRR2313994 2 0.0188 0.742 0.000 0.996 0.000 0.004
#> SRR2313995 2 0.3801 0.647 0.000 0.780 0.000 0.220
#> SRR2314024 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314015 2 0.2704 0.719 0.000 0.876 0.000 0.124
#> SRR2314025 1 0.4372 0.732 0.728 0.004 0.268 0.000
#> SRR2314023 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2314033 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314045 1 0.0817 0.905 0.976 0.000 0.024 0.000
#> SRR2314042 3 0.0188 0.828 0.000 0.004 0.996 0.000
#> SRR2314043 3 0.0817 0.830 0.000 0.024 0.976 0.000
#> SRR2314041 2 0.3569 0.574 0.000 0.804 0.196 0.000
#> SRR2314044 2 0.2647 0.692 0.000 0.880 0.120 0.000
#> SRR2314040 3 0.3569 0.724 0.000 0.196 0.804 0.000
#> SRR2314039 3 0.5000 0.277 0.000 0.496 0.504 0.000
#> SRR2314038 3 0.3024 0.772 0.000 0.148 0.852 0.000
#> SRR2314037 3 0.0000 0.829 0.000 0.000 1.000 0.000
#> SRR2314036 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.0592 0.829 0.000 0.016 0.984 0.000
#> SRR2314034 3 0.0000 0.829 0.000 0.000 1.000 0.000
#> SRR2314032 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2314031 3 0.0188 0.828 0.000 0.004 0.996 0.000
#> SRR2314029 2 0.3837 0.599 0.000 0.776 0.224 0.000
#> SRR2314030 3 0.4585 0.529 0.000 0.332 0.668 0.000
#> SRR2314028 3 0.0188 0.828 0.000 0.004 0.996 0.000
#> SRR2314027 2 0.4955 -0.178 0.000 0.556 0.444 0.000
#> SRR2314026 3 0.4888 0.416 0.000 0.412 0.588 0.000
#> SRR2314020 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314019 3 0.4877 0.421 0.000 0.408 0.592 0.000
#> SRR2314021 3 0.4776 0.472 0.000 0.376 0.624 0.000
#> SRR2314018 3 0.2530 0.794 0.000 0.112 0.888 0.000
#> SRR2314017 3 0.2281 0.802 0.000 0.096 0.904 0.000
#> SRR2314016 3 0.1489 0.804 0.044 0.004 0.952 0.000
#> SRR2314014 2 0.2647 0.720 0.000 0.880 0.000 0.120
#> SRR2314013 2 0.3649 0.677 0.000 0.796 0.000 0.204
#> SRR2314012 1 0.4283 0.746 0.740 0.004 0.256 0.000
#> SRR2314011 1 0.0779 0.906 0.980 0.004 0.016 0.000
#> SRR2314010 2 0.2868 0.707 0.000 0.864 0.000 0.136
#> SRR2314009 3 0.1118 0.827 0.000 0.036 0.964 0.000
#> SRR2314008 3 0.4134 0.657 0.000 0.260 0.740 0.000
#> SRR2314007 2 0.4164 0.434 0.000 0.736 0.264 0.000
#> SRR2314005 2 0.4933 0.253 0.000 0.568 0.000 0.432
#> SRR2314004 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314003 1 0.4313 0.742 0.736 0.004 0.260 0.000
#> SRR2314006 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2314002 3 0.3123 0.769 0.000 0.156 0.844 0.000
#> SRR2314001 2 0.0188 0.742 0.000 0.996 0.000 0.004
#> SRR2314000 3 0.0000 0.829 0.000 0.000 1.000 0.000
#> SRR2313998 2 0.4431 0.558 0.000 0.696 0.000 0.304
#> SRR2313996 2 0.0469 0.743 0.000 0.988 0.012 0.000
#> SRR2313999 1 0.3668 0.811 0.808 0.004 0.188 0.000
#> SRR2313997 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.0000 0.829 0.000 0.000 1.000 0.000
#> SRR2313991 3 0.4356 0.643 0.000 0.292 0.708 0.000
#> SRR2313990 2 0.4406 0.475 0.000 0.700 0.300 0.000
#> SRR2313988 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.4948 0.260 0.000 0.560 0.000 0.440
#> SRR2313989 4 0.0000 0.990 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313986 3 0.0188 0.828 0.000 0.004 0.996 0.000
#> SRR2313982 1 0.0000 0.911 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.0592 0.829 0.000 0.016 0.984 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.4418 0.52509 0.000 0.000 0.652 0.016 0.332
#> SRR2313980 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 0.83481 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 5 0.7609 -0.07249 0.000 0.096 0.212 0.200 0.492
#> SRR2313976 2 0.5537 0.58475 0.000 0.648 0.160 0.192 0.000
#> SRR2313975 1 0.4183 0.44569 0.668 0.000 0.000 0.324 0.008
#> SRR2313973 1 0.0000 0.83481 1.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.4088 0.35364 0.632 0.000 0.000 0.368 0.000
#> SRR2313985 1 0.0609 0.83164 0.980 0.000 0.000 0.020 0.000
#> SRR2313993 3 0.2905 0.62701 0.000 0.096 0.868 0.036 0.000
#> SRR2313994 3 0.0404 0.67020 0.000 0.000 0.988 0.012 0.000
#> SRR2313995 3 0.3861 0.50673 0.000 0.284 0.712 0.004 0.000
#> SRR2314024 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 3 0.7958 0.32777 0.000 0.096 0.372 0.200 0.332
#> SRR2314025 4 0.4626 0.43507 0.364 0.000 0.000 0.616 0.020
#> SRR2314023 1 0.0609 0.83164 0.980 0.000 0.000 0.020 0.000
#> SRR2314033 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.4588 0.64688 0.748 0.000 0.000 0.116 0.136
#> SRR2314042 4 0.4897 0.04361 0.000 0.000 0.024 0.516 0.460
#> SRR2314043 5 0.5413 0.47835 0.000 0.000 0.172 0.164 0.664
#> SRR2314041 3 0.3395 0.63714 0.000 0.000 0.764 0.000 0.236
#> SRR2314044 3 0.3452 0.65780 0.000 0.000 0.820 0.032 0.148
#> SRR2314040 5 0.4030 0.43208 0.000 0.000 0.352 0.000 0.648
#> SRR2314039 3 0.4555 0.53850 0.000 0.000 0.636 0.020 0.344
#> SRR2314038 5 0.3835 0.42725 0.000 0.000 0.008 0.260 0.732
#> SRR2314037 5 0.4074 0.31521 0.000 0.000 0.000 0.364 0.636
#> SRR2314036 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 5 0.3013 0.56974 0.000 0.000 0.008 0.160 0.832
#> SRR2314034 5 0.4045 0.34480 0.000 0.000 0.000 0.356 0.644
#> SRR2314032 1 0.1608 0.80282 0.928 0.000 0.000 0.072 0.000
#> SRR2314031 4 0.4192 0.28910 0.000 0.000 0.000 0.596 0.404
#> SRR2314029 3 0.3732 0.61501 0.000 0.000 0.792 0.176 0.032
#> SRR2314030 3 0.5556 0.49206 0.000 0.000 0.616 0.276 0.108
#> SRR2314028 5 0.4291 -0.01650 0.000 0.000 0.000 0.464 0.536
#> SRR2314027 3 0.4252 0.57173 0.000 0.000 0.700 0.020 0.280
#> SRR2314026 3 0.5640 0.47793 0.000 0.000 0.636 0.176 0.188
#> SRR2314020 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 3 0.5642 0.47682 0.000 0.000 0.636 0.180 0.184
#> SRR2314021 3 0.5731 0.46316 0.000 0.000 0.624 0.180 0.196
#> SRR2314018 5 0.0000 0.60270 0.000 0.000 0.000 0.000 1.000
#> SRR2314017 5 0.1908 0.57397 0.000 0.000 0.000 0.092 0.908
#> SRR2314016 4 0.4152 0.33385 0.012 0.000 0.000 0.692 0.296
#> SRR2314014 3 0.7393 0.35292 0.000 0.096 0.528 0.200 0.176
#> SRR2314013 3 0.6003 0.47425 0.000 0.100 0.656 0.200 0.044
#> SRR2314012 4 0.4707 0.38540 0.392 0.000 0.000 0.588 0.020
#> SRR2314011 1 0.3983 0.45032 0.660 0.000 0.000 0.340 0.000
#> SRR2314010 3 0.0000 0.67004 0.000 0.000 1.000 0.000 0.000
#> SRR2314009 5 0.1478 0.60612 0.000 0.000 0.000 0.064 0.936
#> SRR2314008 5 0.1197 0.59777 0.000 0.000 0.048 0.000 0.952
#> SRR2314007 3 0.3612 0.61892 0.000 0.000 0.732 0.000 0.268
#> SRR2314005 2 0.7898 0.07273 0.000 0.356 0.356 0.200 0.088
#> SRR2314004 2 0.0162 0.90139 0.000 0.996 0.000 0.004 0.000
#> SRR2314003 4 0.4651 0.42347 0.372 0.000 0.000 0.608 0.020
#> SRR2314006 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.2773 0.53418 0.000 0.000 0.000 0.164 0.836
#> SRR2314001 3 0.0703 0.67002 0.000 0.000 0.976 0.024 0.000
#> SRR2314000 5 0.2891 0.55982 0.000 0.000 0.000 0.176 0.824
#> SRR2313998 3 0.2516 0.63393 0.000 0.140 0.860 0.000 0.000
#> SRR2313996 3 0.3075 0.67073 0.000 0.000 0.860 0.048 0.092
#> SRR2313999 4 0.4446 0.34772 0.400 0.000 0.000 0.592 0.008
#> SRR2313997 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 5 0.4074 0.31521 0.000 0.000 0.000 0.364 0.636
#> SRR2313991 5 0.3648 0.54588 0.000 0.000 0.084 0.092 0.824
#> SRR2313990 3 0.4481 -0.00285 0.000 0.000 0.576 0.008 0.416
#> SRR2313988 1 0.1608 0.80282 0.928 0.000 0.000 0.072 0.000
#> SRR2313987 1 0.2179 0.77381 0.888 0.000 0.000 0.112 0.000
#> SRR2313984 3 0.6225 0.22187 0.000 0.256 0.544 0.200 0.000
#> SRR2313989 2 0.0000 0.90383 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 0.83481 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.4192 0.28910 0.000 0.000 0.000 0.596 0.404
#> SRR2313982 1 0.0000 0.83481 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 5 0.5378 0.48151 0.000 0.000 0.172 0.160 0.668
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.433 0.5484 0.000 0.644 0.040 0.000 0.316 0.000
#> SRR2313980 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 1 0.000 0.8436 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 5 0.353 0.7405 0.000 0.068 0.040 0.000 0.832 0.060
#> SRR2313976 5 0.376 0.2849 0.000 0.000 0.000 0.000 0.600 0.400
#> SRR2313975 4 0.389 0.5482 0.400 0.000 0.004 0.596 0.000 0.000
#> SRR2313973 1 0.000 0.8436 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313974 4 0.358 0.5793 0.340 0.000 0.000 0.660 0.000 0.000
#> SRR2313985 1 0.161 0.8144 0.916 0.000 0.000 0.084 0.000 0.000
#> SRR2313993 2 0.490 0.2361 0.000 0.476 0.000 0.000 0.464 0.060
#> SRR2313994 2 0.370 0.5585 0.000 0.624 0.000 0.000 0.376 0.000
#> SRR2313995 2 0.550 0.5164 0.000 0.564 0.000 0.000 0.200 0.236
#> SRR2314024 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314015 5 0.399 0.7194 0.000 0.104 0.040 0.000 0.796 0.060
#> SRR2314025 4 0.367 0.7596 0.132 0.000 0.080 0.788 0.000 0.000
#> SRR2314023 1 0.161 0.8144 0.916 0.000 0.000 0.084 0.000 0.000
#> SRR2314033 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314045 1 0.642 0.2185 0.404 0.000 0.376 0.192 0.028 0.000
#> SRR2314042 4 0.433 0.5301 0.000 0.048 0.288 0.664 0.000 0.000
#> SRR2314043 3 0.309 0.6825 0.000 0.140 0.824 0.036 0.000 0.000
#> SRR2314041 2 0.327 0.7164 0.000 0.824 0.076 0.000 0.100 0.000
#> SRR2314044 2 0.403 0.6808 0.000 0.744 0.016 0.032 0.208 0.000
#> SRR2314040 3 0.415 0.6321 0.000 0.108 0.744 0.000 0.148 0.000
#> SRR2314039 2 0.242 0.6914 0.000 0.844 0.156 0.000 0.000 0.000
#> SRR2314038 3 0.711 0.1363 0.000 0.224 0.400 0.288 0.088 0.000
#> SRR2314037 3 0.297 0.5960 0.000 0.000 0.776 0.224 0.000 0.000
#> SRR2314036 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314035 3 0.307 0.6967 0.000 0.084 0.856 0.036 0.024 0.000
#> SRR2314034 3 0.312 0.6279 0.000 0.020 0.804 0.176 0.000 0.000
#> SRR2314032 1 0.348 0.7649 0.816 0.000 0.024 0.132 0.028 0.000
#> SRR2314031 4 0.300 0.6633 0.000 0.000 0.228 0.772 0.000 0.000
#> SRR2314029 2 0.557 0.6601 0.000 0.656 0.080 0.088 0.176 0.000
#> SRR2314030 2 0.429 0.5018 0.000 0.740 0.184 0.016 0.060 0.000
#> SRR2314028 3 0.385 0.0699 0.000 0.000 0.540 0.460 0.000 0.000
#> SRR2314027 2 0.277 0.6944 0.000 0.832 0.156 0.000 0.012 0.000
#> SRR2314026 2 0.300 0.6779 0.000 0.772 0.228 0.000 0.000 0.000
#> SRR2314020 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 2 0.297 0.6797 0.000 0.776 0.224 0.000 0.000 0.000
#> SRR2314021 2 0.362 0.6531 0.000 0.744 0.232 0.024 0.000 0.000
#> SRR2314018 3 0.396 0.6863 0.000 0.068 0.796 0.032 0.104 0.000
#> SRR2314017 3 0.479 0.5536 0.000 0.224 0.680 0.012 0.084 0.000
#> SRR2314016 4 0.294 0.6652 0.000 0.000 0.220 0.780 0.000 0.000
#> SRR2314014 5 0.197 0.7766 0.000 0.000 0.028 0.000 0.912 0.060
#> SRR2314013 5 0.206 0.7740 0.000 0.020 0.008 0.000 0.912 0.060
#> SRR2314012 4 0.285 0.7354 0.176 0.000 0.008 0.816 0.000 0.000
#> SRR2314011 4 0.362 0.5524 0.352 0.000 0.000 0.648 0.000 0.000
#> SRR2314010 2 0.310 0.6763 0.000 0.756 0.000 0.000 0.244 0.000
#> SRR2314009 3 0.366 0.6967 0.000 0.136 0.804 0.036 0.024 0.000
#> SRR2314008 3 0.367 0.6422 0.000 0.240 0.736 0.000 0.024 0.000
#> SRR2314007 2 0.309 0.7093 0.000 0.824 0.036 0.000 0.140 0.000
#> SRR2314005 5 0.181 0.7770 0.000 0.000 0.008 0.000 0.912 0.080
#> SRR2314004 6 0.026 0.9902 0.000 0.000 0.000 0.000 0.008 0.992
#> SRR2314003 4 0.271 0.7584 0.132 0.000 0.020 0.848 0.000 0.000
#> SRR2314006 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 3 0.648 0.4453 0.000 0.220 0.544 0.148 0.088 0.000
#> SRR2314001 2 0.381 0.4803 0.000 0.572 0.000 0.000 0.428 0.000
#> SRR2314000 3 0.280 0.6875 0.000 0.000 0.860 0.068 0.072 0.000
#> SRR2313998 2 0.501 0.5520 0.000 0.620 0.000 0.000 0.264 0.116
#> SRR2313996 5 0.385 -0.2835 0.000 0.460 0.000 0.000 0.540 0.000
#> SRR2313999 4 0.240 0.7519 0.140 0.000 0.004 0.856 0.000 0.000
#> SRR2313997 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.297 0.5960 0.000 0.000 0.776 0.224 0.000 0.000
#> SRR2313991 3 0.527 0.5029 0.000 0.328 0.576 0.012 0.084 0.000
#> SRR2313990 3 0.549 0.2896 0.000 0.136 0.504 0.000 0.360 0.000
#> SRR2313988 1 0.348 0.7649 0.816 0.000 0.024 0.132 0.028 0.000
#> SRR2313987 1 0.263 0.7273 0.820 0.000 0.000 0.180 0.000 0.000
#> SRR2313984 5 0.235 0.7551 0.000 0.036 0.000 0.000 0.888 0.076
#> SRR2313989 6 0.000 0.9988 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.000 0.8436 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.300 0.6633 0.000 0.000 0.228 0.772 0.000 0.000
#> SRR2313982 1 0.000 0.8436 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.309 0.6825 0.000 0.140 0.824 0.036 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 16873 rows and 72 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.486 0.857 0.898 0.4685 0.525 0.525
#> 3 3 0.337 0.710 0.795 0.1878 0.619 0.462
#> 4 4 0.419 0.496 0.727 0.2476 0.760 0.537
#> 5 5 0.581 0.608 0.788 0.1065 0.797 0.431
#> 6 6 0.647 0.632 0.794 0.0479 0.971 0.875
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
#> SRR2313978 2 0.5294 0.901 0.120 0.880
#> SRR2313980 2 0.5946 0.895 0.144 0.856
#> SRR2313979 1 0.0000 0.908 1.000 0.000
#> SRR2313977 2 0.3879 0.904 0.076 0.924
#> SRR2313976 2 0.5946 0.895 0.144 0.856
#> SRR2313975 1 0.1414 0.906 0.980 0.020
#> SRR2313973 1 0.0000 0.908 1.000 0.000
#> SRR2313974 1 0.2948 0.896 0.948 0.052
#> SRR2313985 1 0.0000 0.908 1.000 0.000
#> SRR2313993 2 0.4022 0.903 0.080 0.920
#> SRR2313994 2 0.4298 0.904 0.088 0.912
#> SRR2313995 2 0.4022 0.894 0.080 0.920
#> SRR2314024 2 0.4562 0.896 0.096 0.904
#> SRR2314015 2 0.5519 0.899 0.128 0.872
#> SRR2314025 1 0.7376 0.840 0.792 0.208
#> SRR2314023 1 0.0000 0.908 1.000 0.000
#> SRR2314033 2 0.6247 0.887 0.156 0.844
#> SRR2314045 1 0.0000 0.908 1.000 0.000
#> SRR2314042 1 0.7528 0.833 0.784 0.216
#> SRR2314043 2 0.8909 0.606 0.308 0.692
#> SRR2314041 2 0.1414 0.886 0.020 0.980
#> SRR2314044 2 0.1633 0.886 0.024 0.976
#> SRR2314040 2 0.4431 0.905 0.092 0.908
#> SRR2314039 2 0.0000 0.880 0.000 1.000
#> SRR2314038 2 0.8909 0.707 0.308 0.692
#> SRR2314037 2 0.0000 0.880 0.000 1.000
#> SRR2314036 2 0.7299 0.845 0.204 0.796
#> SRR2314035 1 0.8813 0.649 0.700 0.300
#> SRR2314034 2 0.9635 0.131 0.388 0.612
#> SRR2314032 1 0.0000 0.908 1.000 0.000
#> SRR2314031 1 0.7453 0.836 0.788 0.212
#> SRR2314029 2 0.3584 0.886 0.068 0.932
#> SRR2314030 1 0.3584 0.884 0.932 0.068
#> SRR2314028 2 0.0376 0.882 0.004 0.996
#> SRR2314027 2 0.0000 0.880 0.000 1.000
#> SRR2314026 2 0.7528 0.634 0.216 0.784
#> SRR2314020 2 0.5946 0.895 0.144 0.856
#> SRR2314019 2 0.0000 0.880 0.000 1.000
#> SRR2314021 2 0.2603 0.861 0.044 0.956
#> SRR2314018 2 0.3879 0.904 0.076 0.924
#> SRR2314017 1 0.3733 0.881 0.928 0.072
#> SRR2314016 1 0.1184 0.905 0.984 0.016
#> SRR2314014 2 0.3879 0.904 0.076 0.924
#> SRR2314013 2 0.5059 0.904 0.112 0.888
#> SRR2314012 1 0.2948 0.893 0.948 0.052
#> SRR2314011 1 0.7056 0.846 0.808 0.192
#> SRR2314010 2 0.2948 0.887 0.052 0.948
#> SRR2314009 2 0.3733 0.903 0.072 0.928
#> SRR2314008 2 0.4022 0.903 0.080 0.920
#> SRR2314007 2 0.0000 0.880 0.000 1.000
#> SRR2314005 2 0.5946 0.895 0.144 0.856
#> SRR2314004 2 0.5946 0.895 0.144 0.856
#> SRR2314003 1 0.7376 0.840 0.792 0.208
#> SRR2314006 2 0.5946 0.895 0.144 0.856
#> SRR2314002 1 0.0000 0.908 1.000 0.000
#> SRR2314001 2 0.3879 0.904 0.076 0.924
#> SRR2314000 1 0.5519 0.849 0.872 0.128
#> SRR2313998 2 0.5946 0.895 0.144 0.856
#> SRR2313996 2 0.0000 0.880 0.000 1.000
#> SRR2313999 1 0.5737 0.853 0.864 0.136
#> SRR2313997 2 0.5946 0.895 0.144 0.856
#> SRR2313992 1 0.8909 0.745 0.692 0.308
#> SRR2313991 2 0.9866 0.470 0.432 0.568
#> SRR2313990 2 0.3879 0.904 0.076 0.924
#> SRR2313988 1 0.0000 0.908 1.000 0.000
#> SRR2313987 1 0.0000 0.908 1.000 0.000
#> SRR2313984 2 0.4022 0.903 0.080 0.920
#> SRR2313989 2 0.5946 0.895 0.144 0.856
#> SRR2313983 1 0.0000 0.908 1.000 0.000
#> SRR2313986 1 0.7453 0.837 0.788 0.212
#> SRR2313982 1 0.0000 0.908 1.000 0.000
#> SRR2313981 2 0.0000 0.880 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.5756 0.650 0.028 0.208 0.764
#> SRR2313980 2 0.5667 0.939 0.060 0.800 0.140
#> SRR2313979 1 0.0592 0.907 0.988 0.012 0.000
#> SRR2313977 3 0.6402 0.651 0.040 0.236 0.724
#> SRR2313976 3 0.8019 0.422 0.076 0.348 0.576
#> SRR2313975 1 0.5505 0.814 0.816 0.088 0.096
#> SRR2313973 1 0.1529 0.899 0.960 0.040 0.000
#> SRR2313974 1 0.2846 0.855 0.924 0.020 0.056
#> SRR2313985 1 0.0237 0.908 0.996 0.004 0.000
#> SRR2313993 3 0.5158 0.635 0.004 0.232 0.764
#> SRR2313994 3 0.7062 0.618 0.068 0.236 0.696
#> SRR2313995 2 0.5774 0.889 0.020 0.748 0.232
#> SRR2314024 2 0.5817 0.891 0.020 0.744 0.236
#> SRR2314015 3 0.6936 0.622 0.064 0.232 0.704
#> SRR2314025 3 0.7996 0.455 0.380 0.068 0.552
#> SRR2314023 1 0.0237 0.907 0.996 0.004 0.000
#> SRR2314033 2 0.5780 0.920 0.080 0.800 0.120
#> SRR2314045 1 0.3181 0.882 0.912 0.064 0.024
#> SRR2314042 3 0.6541 0.671 0.212 0.056 0.732
#> SRR2314043 3 0.3532 0.735 0.108 0.008 0.884
#> SRR2314041 3 0.0747 0.723 0.000 0.016 0.984
#> SRR2314044 3 0.0747 0.729 0.016 0.000 0.984
#> SRR2314040 3 0.5454 0.715 0.044 0.152 0.804
#> SRR2314039 3 0.0237 0.725 0.004 0.000 0.996
#> SRR2314038 3 0.7317 0.682 0.208 0.096 0.696
#> SRR2314037 3 0.0237 0.724 0.004 0.000 0.996
#> SRR2314036 2 0.5740 0.898 0.096 0.804 0.100
#> SRR2314035 3 0.6765 0.690 0.208 0.068 0.724
#> SRR2314034 3 0.3415 0.727 0.080 0.020 0.900
#> SRR2314032 1 0.2945 0.882 0.908 0.088 0.004
#> SRR2314031 3 0.6820 0.637 0.248 0.052 0.700
#> SRR2314029 3 0.7524 0.540 0.116 0.196 0.688
#> SRR2314030 3 0.7644 0.590 0.308 0.068 0.624
#> SRR2314028 3 0.1482 0.724 0.012 0.020 0.968
#> SRR2314027 3 0.0747 0.723 0.000 0.016 0.984
#> SRR2314026 3 0.2414 0.729 0.040 0.020 0.940
#> SRR2314020 2 0.5355 0.940 0.036 0.804 0.160
#> SRR2314019 3 0.1482 0.722 0.012 0.020 0.968
#> SRR2314021 3 0.1781 0.723 0.020 0.020 0.960
#> SRR2314018 3 0.4677 0.734 0.132 0.028 0.840
#> SRR2314017 3 0.8247 0.492 0.324 0.096 0.580
#> SRR2314016 3 0.7690 0.463 0.416 0.048 0.536
#> SRR2314014 3 0.6633 0.653 0.040 0.260 0.700
#> SRR2314013 3 0.6913 0.620 0.056 0.248 0.696
#> SRR2314012 3 0.7777 0.423 0.416 0.052 0.532
#> SRR2314011 1 0.6621 0.590 0.720 0.052 0.228
#> SRR2314010 3 0.4842 0.638 0.000 0.224 0.776
#> SRR2314009 3 0.1289 0.732 0.032 0.000 0.968
#> SRR2314008 3 0.3752 0.732 0.144 0.000 0.856
#> SRR2314007 3 0.3116 0.713 0.000 0.108 0.892
#> SRR2314005 3 0.8019 0.433 0.076 0.348 0.576
#> SRR2314004 3 0.7874 0.462 0.076 0.320 0.604
#> SRR2314003 3 0.7982 0.457 0.376 0.068 0.556
#> SRR2314006 2 0.6025 0.931 0.076 0.784 0.140
#> SRR2314002 1 0.6184 0.786 0.780 0.112 0.108
#> SRR2314001 3 0.6965 0.646 0.060 0.244 0.696
#> SRR2314000 3 0.8191 0.370 0.396 0.076 0.528
#> SRR2313998 3 0.7411 0.599 0.076 0.256 0.668
#> SRR2313996 3 0.4452 0.670 0.000 0.192 0.808
#> SRR2313999 3 0.7289 0.406 0.468 0.028 0.504
#> SRR2313997 2 0.5295 0.941 0.036 0.808 0.156
#> SRR2313992 3 0.4979 0.713 0.168 0.020 0.812
#> SRR2313991 3 0.8372 0.615 0.228 0.152 0.620
#> SRR2313990 3 0.5637 0.710 0.040 0.172 0.788
#> SRR2313988 1 0.2400 0.889 0.932 0.064 0.004
#> SRR2313987 1 0.0000 0.907 1.000 0.000 0.000
#> SRR2313984 3 0.6847 0.625 0.060 0.232 0.708
#> SRR2313989 2 0.5295 0.941 0.036 0.808 0.156
#> SRR2313983 1 0.0592 0.907 0.988 0.012 0.000
#> SRR2313986 3 0.6668 0.630 0.264 0.040 0.696
#> SRR2313982 1 0.1031 0.904 0.976 0.024 0.000
#> SRR2313981 3 0.0829 0.724 0.012 0.004 0.984
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.4399 0.5539 0.000 0.016 0.760 0.224
#> SRR2313980 2 0.1749 0.8162 0.024 0.952 0.012 0.012
#> SRR2313979 1 0.0000 0.8731 1.000 0.000 0.000 0.000
#> SRR2313977 3 0.3875 0.5678 0.004 0.068 0.852 0.076
#> SRR2313976 3 0.5252 0.4030 0.020 0.280 0.692 0.008
#> SRR2313975 1 0.4762 0.7869 0.796 0.004 0.120 0.080
#> SRR2313973 1 0.1474 0.8530 0.948 0.000 0.000 0.052
#> SRR2313974 1 0.4290 0.6883 0.772 0.000 0.016 0.212
#> SRR2313985 1 0.0000 0.8731 1.000 0.000 0.000 0.000
#> SRR2313993 3 0.5585 0.5802 0.008 0.108 0.744 0.140
#> SRR2313994 3 0.2673 0.5893 0.020 0.048 0.916 0.016
#> SRR2313995 2 0.7792 -0.0800 0.012 0.412 0.412 0.164
#> SRR2314024 2 0.7469 0.2241 0.012 0.524 0.320 0.144
#> SRR2314015 3 0.4914 0.6120 0.024 0.064 0.804 0.108
#> SRR2314025 4 0.3400 0.5167 0.180 0.000 0.000 0.820
#> SRR2314023 1 0.0707 0.8658 0.980 0.000 0.000 0.020
#> SRR2314033 2 0.3820 0.7436 0.064 0.848 0.088 0.000
#> SRR2314045 1 0.5332 0.7802 0.748 0.000 0.124 0.128
#> SRR2314042 4 0.5823 0.5278 0.176 0.000 0.120 0.704
#> SRR2314043 3 0.5497 -0.0274 0.016 0.000 0.524 0.460
#> SRR2314041 3 0.4008 0.5374 0.000 0.000 0.756 0.244
#> SRR2314044 3 0.4500 0.4665 0.000 0.000 0.684 0.316
#> SRR2314040 3 0.5773 0.0409 0.004 0.032 0.612 0.352
#> SRR2314039 3 0.4730 0.3801 0.000 0.000 0.636 0.364
#> SRR2314038 3 0.8791 0.2070 0.076 0.160 0.428 0.336
#> SRR2314037 4 0.4830 0.3066 0.000 0.000 0.392 0.608
#> SRR2314036 2 0.2125 0.7674 0.076 0.920 0.004 0.000
#> SRR2314035 3 0.6361 -0.0827 0.044 0.008 0.504 0.444
#> SRR2314034 4 0.5420 0.3526 0.024 0.000 0.352 0.624
#> SRR2314032 1 0.3128 0.8365 0.884 0.000 0.076 0.040
#> SRR2314031 4 0.5811 0.5283 0.180 0.000 0.116 0.704
#> SRR2314029 3 0.7072 0.4369 0.000 0.172 0.560 0.268
#> SRR2314030 4 0.8649 0.1963 0.112 0.152 0.212 0.524
#> SRR2314028 4 0.4790 0.3259 0.000 0.000 0.380 0.620
#> SRR2314027 3 0.4605 0.4343 0.000 0.000 0.664 0.336
#> SRR2314026 4 0.4697 0.3088 0.000 0.000 0.356 0.644
#> SRR2314020 2 0.1526 0.8153 0.016 0.960 0.012 0.012
#> SRR2314019 4 0.4817 0.2998 0.000 0.000 0.388 0.612
#> SRR2314021 4 0.5038 0.3452 0.012 0.000 0.336 0.652
#> SRR2314018 3 0.5728 0.0279 0.008 0.024 0.616 0.352
#> SRR2314017 4 0.7781 0.0767 0.344 0.000 0.248 0.408
#> SRR2314016 4 0.4767 0.4244 0.256 0.020 0.000 0.724
#> SRR2314014 3 0.3861 0.5880 0.008 0.080 0.856 0.056
#> SRR2314013 3 0.3231 0.5978 0.012 0.116 0.868 0.004
#> SRR2314012 4 0.4328 0.4583 0.244 0.008 0.000 0.748
#> SRR2314011 4 0.4998 -0.0334 0.488 0.000 0.000 0.512
#> SRR2314010 3 0.5442 0.5712 0.008 0.080 0.748 0.164
#> SRR2314009 4 0.4877 0.2882 0.000 0.000 0.408 0.592
#> SRR2314008 3 0.3441 0.6010 0.024 0.000 0.856 0.120
#> SRR2314007 3 0.5060 0.2461 0.004 0.000 0.584 0.412
#> SRR2314005 3 0.5718 0.4481 0.024 0.232 0.708 0.036
#> SRR2314004 3 0.6820 0.5333 0.016 0.228 0.636 0.120
#> SRR2314003 4 0.3400 0.5167 0.180 0.000 0.000 0.820
#> SRR2314006 2 0.1749 0.8162 0.024 0.952 0.012 0.012
#> SRR2314002 1 0.6385 0.7330 0.716 0.048 0.096 0.140
#> SRR2314001 3 0.3587 0.5980 0.004 0.032 0.860 0.104
#> SRR2314000 4 0.6722 0.1329 0.068 0.008 0.424 0.500
#> SRR2313998 3 0.3353 0.5749 0.020 0.056 0.888 0.036
#> SRR2313996 3 0.4331 0.5003 0.000 0.000 0.712 0.288
#> SRR2313999 4 0.5169 0.4278 0.272 0.000 0.032 0.696
#> SRR2313997 2 0.1526 0.8153 0.016 0.960 0.012 0.012
#> SRR2313992 4 0.6677 0.3984 0.100 0.000 0.348 0.552
#> SRR2313991 3 0.8369 0.3329 0.076 0.172 0.540 0.212
#> SRR2313990 3 0.5693 0.1957 0.004 0.036 0.644 0.316
#> SRR2313988 1 0.3128 0.8365 0.884 0.000 0.076 0.040
#> SRR2313987 1 0.3219 0.7806 0.836 0.000 0.000 0.164
#> SRR2313984 3 0.5945 0.5885 0.020 0.156 0.728 0.096
#> SRR2313989 2 0.2074 0.8098 0.016 0.940 0.032 0.012
#> SRR2313983 1 0.0000 0.8731 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.5759 0.5292 0.180 0.000 0.112 0.708
#> SRR2313982 1 0.0000 0.8731 1.000 0.000 0.000 0.000
#> SRR2313981 4 0.4978 0.3251 0.004 0.000 0.384 0.612
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.3844 0.704 0.000 0.788 0.180 0.004 0.028
#> SRR2313980 5 0.0324 0.885 0.004 0.004 0.000 0.000 0.992
#> SRR2313979 1 0.0865 0.860 0.972 0.004 0.000 0.024 0.000
#> SRR2313977 2 0.3766 0.547 0.004 0.728 0.268 0.000 0.000
#> SRR2313976 2 0.4702 -0.259 0.004 0.512 0.008 0.000 0.476
#> SRR2313975 1 0.5227 0.467 0.688 0.024 0.236 0.052 0.000
#> SRR2313973 1 0.1544 0.837 0.932 0.000 0.000 0.068 0.000
#> SRR2313974 4 0.4802 -0.172 0.480 0.012 0.004 0.504 0.000
#> SRR2313985 1 0.1768 0.852 0.924 0.004 0.000 0.072 0.000
#> SRR2313993 2 0.4239 0.698 0.000 0.784 0.080 0.004 0.132
#> SRR2313994 2 0.1682 0.706 0.004 0.940 0.044 0.000 0.012
#> SRR2313995 2 0.4754 0.654 0.000 0.736 0.072 0.008 0.184
#> SRR2314024 5 0.5370 0.142 0.000 0.408 0.040 0.008 0.544
#> SRR2314015 2 0.3937 0.718 0.004 0.804 0.132 0.000 0.060
#> SRR2314025 4 0.2338 0.715 0.004 0.000 0.112 0.884 0.000
#> SRR2314023 1 0.2536 0.820 0.868 0.004 0.000 0.128 0.000
#> SRR2314033 5 0.2909 0.769 0.012 0.140 0.000 0.000 0.848
#> SRR2314045 1 0.3797 0.702 0.756 0.008 0.004 0.232 0.000
#> SRR2314042 4 0.3796 0.599 0.000 0.000 0.300 0.700 0.000
#> SRR2314043 3 0.2719 0.684 0.004 0.144 0.852 0.000 0.000
#> SRR2314041 2 0.5190 0.454 0.000 0.540 0.424 0.008 0.028
#> SRR2314044 3 0.2852 0.598 0.000 0.172 0.828 0.000 0.000
#> SRR2314040 3 0.3333 0.651 0.004 0.208 0.788 0.000 0.000
#> SRR2314039 3 0.4118 0.534 0.000 0.188 0.772 0.008 0.032
#> SRR2314038 3 0.6749 0.368 0.044 0.316 0.556 0.024 0.060
#> SRR2314037 3 0.0162 0.712 0.000 0.004 0.996 0.000 0.000
#> SRR2314036 5 0.1605 0.849 0.040 0.004 0.000 0.012 0.944
#> SRR2314035 3 0.4973 0.635 0.012 0.184 0.724 0.080 0.000
#> SRR2314034 3 0.1569 0.692 0.004 0.008 0.944 0.044 0.000
#> SRR2314032 1 0.1082 0.849 0.964 0.008 0.000 0.028 0.000
#> SRR2314031 4 0.3796 0.599 0.000 0.000 0.300 0.700 0.000
#> SRR2314029 2 0.5745 0.360 0.000 0.568 0.352 0.012 0.068
#> SRR2314030 3 0.7451 0.190 0.048 0.124 0.444 0.368 0.016
#> SRR2314028 3 0.1443 0.694 0.004 0.004 0.948 0.044 0.000
#> SRR2314027 3 0.4998 0.183 0.000 0.328 0.632 0.008 0.032
#> SRR2314026 3 0.1518 0.710 0.000 0.012 0.952 0.016 0.020
#> SRR2314020 5 0.0000 0.884 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 3 0.1357 0.704 0.000 0.000 0.948 0.048 0.004
#> SRR2314021 3 0.2177 0.684 0.004 0.008 0.908 0.080 0.000
#> SRR2314018 3 0.3266 0.655 0.004 0.200 0.796 0.000 0.000
#> SRR2314017 3 0.7105 0.346 0.160 0.060 0.532 0.248 0.000
#> SRR2314016 4 0.1408 0.640 0.044 0.000 0.008 0.948 0.000
#> SRR2314014 2 0.3989 0.552 0.004 0.728 0.260 0.000 0.008
#> SRR2314013 2 0.1357 0.705 0.000 0.948 0.048 0.000 0.004
#> SRR2314012 4 0.2726 0.671 0.064 0.000 0.052 0.884 0.000
#> SRR2314011 4 0.4920 0.350 0.308 0.000 0.048 0.644 0.000
#> SRR2314010 2 0.4347 0.694 0.000 0.780 0.076 0.008 0.136
#> SRR2314009 3 0.0162 0.712 0.000 0.004 0.996 0.000 0.000
#> SRR2314008 3 0.4238 0.405 0.004 0.368 0.628 0.000 0.000
#> SRR2314007 3 0.4523 0.407 0.000 0.252 0.712 0.008 0.028
#> SRR2314005 2 0.2408 0.645 0.004 0.892 0.008 0.000 0.096
#> SRR2314004 2 0.4269 0.689 0.004 0.780 0.076 0.000 0.140
#> SRR2314003 4 0.2536 0.717 0.004 0.000 0.128 0.868 0.000
#> SRR2314006 5 0.0451 0.884 0.008 0.004 0.000 0.000 0.988
#> SRR2314002 3 0.6717 0.157 0.412 0.056 0.464 0.064 0.004
#> SRR2314001 2 0.4937 0.502 0.004 0.604 0.364 0.000 0.028
#> SRR2314000 3 0.6780 0.464 0.028 0.216 0.544 0.212 0.000
#> SRR2313998 2 0.1443 0.703 0.004 0.948 0.044 0.000 0.004
#> SRR2313996 2 0.5089 0.446 0.000 0.536 0.432 0.004 0.028
#> SRR2313999 4 0.5283 0.646 0.144 0.016 0.128 0.712 0.000
#> SRR2313997 5 0.0000 0.884 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.1282 0.697 0.000 0.004 0.952 0.044 0.000
#> SRR2313991 3 0.7739 0.267 0.048 0.380 0.432 0.084 0.056
#> SRR2313990 2 0.3906 0.517 0.004 0.704 0.292 0.000 0.000
#> SRR2313988 1 0.0693 0.854 0.980 0.012 0.000 0.008 0.000
#> SRR2313987 1 0.3550 0.710 0.760 0.004 0.000 0.236 0.000
#> SRR2313984 2 0.4150 0.691 0.004 0.788 0.068 0.000 0.140
#> SRR2313989 5 0.0162 0.883 0.000 0.000 0.004 0.000 0.996
#> SRR2313983 1 0.0771 0.860 0.976 0.004 0.000 0.020 0.000
#> SRR2313986 4 0.3949 0.555 0.000 0.000 0.332 0.668 0.000
#> SRR2313982 1 0.0963 0.860 0.964 0.000 0.000 0.036 0.000
#> SRR2313981 3 0.0162 0.712 0.000 0.004 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.5693 0.5739 0.000 0.584 0.188 0.004 0.216 0.008
#> SRR2313980 6 0.0291 0.9237 0.004 0.000 0.000 0.000 0.004 0.992
#> SRR2313979 1 0.0260 0.7474 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2313977 2 0.1829 0.7222 0.000 0.920 0.064 0.004 0.012 0.000
#> SRR2313976 2 0.4619 0.0377 0.000 0.564 0.000 0.000 0.044 0.392
#> SRR2313975 5 0.7021 0.6052 0.316 0.004 0.156 0.092 0.432 0.000
#> SRR2313973 1 0.1411 0.7146 0.936 0.000 0.000 0.060 0.004 0.000
#> SRR2313974 4 0.5253 0.3048 0.296 0.000 0.000 0.576 0.128 0.000
#> SRR2313985 1 0.0935 0.7479 0.964 0.000 0.000 0.032 0.004 0.000
#> SRR2313993 2 0.3644 0.6930 0.000 0.784 0.020 0.004 0.012 0.180
#> SRR2313994 2 0.1149 0.7332 0.000 0.960 0.008 0.000 0.024 0.008
#> SRR2313995 2 0.5831 0.5708 0.000 0.560 0.008 0.004 0.204 0.224
#> SRR2314024 6 0.3013 0.7573 0.000 0.152 0.004 0.004 0.012 0.828
#> SRR2314015 2 0.4829 0.7082 0.004 0.748 0.048 0.008 0.124 0.068
#> SRR2314025 4 0.0790 0.7998 0.000 0.000 0.032 0.968 0.000 0.000
#> SRR2314023 1 0.1152 0.7429 0.952 0.000 0.000 0.044 0.004 0.000
#> SRR2314033 6 0.3691 0.7447 0.004 0.168 0.000 0.000 0.048 0.780
#> SRR2314045 1 0.4881 0.0234 0.496 0.000 0.004 0.048 0.452 0.000
#> SRR2314042 4 0.1958 0.7796 0.000 0.000 0.100 0.896 0.004 0.000
#> SRR2314043 3 0.1444 0.7135 0.000 0.072 0.928 0.000 0.000 0.000
#> SRR2314041 2 0.6323 0.2542 0.000 0.364 0.332 0.008 0.296 0.000
#> SRR2314044 3 0.4266 0.6333 0.000 0.088 0.736 0.004 0.172 0.000
#> SRR2314040 3 0.2882 0.6684 0.000 0.180 0.812 0.000 0.008 0.000
#> SRR2314039 3 0.4374 0.6350 0.000 0.088 0.732 0.008 0.172 0.000
#> SRR2314038 3 0.6405 0.5627 0.004 0.160 0.608 0.020 0.148 0.060
#> SRR2314037 3 0.0000 0.7110 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314036 6 0.1285 0.8972 0.004 0.000 0.000 0.000 0.052 0.944
#> SRR2314035 3 0.4012 0.6389 0.004 0.144 0.784 0.044 0.024 0.000
#> SRR2314034 3 0.2092 0.6440 0.000 0.000 0.876 0.124 0.000 0.000
#> SRR2314032 1 0.3857 0.0489 0.532 0.000 0.000 0.000 0.468 0.000
#> SRR2314031 4 0.1858 0.7844 0.000 0.000 0.092 0.904 0.004 0.000
#> SRR2314029 3 0.6269 0.2557 0.000 0.284 0.476 0.008 0.224 0.008
#> SRR2314030 3 0.6373 0.0255 0.004 0.012 0.432 0.360 0.188 0.004
#> SRR2314028 3 0.1863 0.6638 0.000 0.000 0.896 0.104 0.000 0.000
#> SRR2314027 3 0.5715 0.3773 0.000 0.160 0.540 0.008 0.292 0.000
#> SRR2314026 3 0.0665 0.7142 0.000 0.008 0.980 0.008 0.004 0.000
#> SRR2314020 6 0.0000 0.9252 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 3 0.0692 0.7108 0.000 0.000 0.976 0.020 0.004 0.000
#> SRR2314021 3 0.2234 0.6539 0.000 0.000 0.872 0.124 0.004 0.000
#> SRR2314018 3 0.2988 0.6667 0.000 0.152 0.824 0.000 0.024 0.000
#> SRR2314017 3 0.5473 0.3855 0.028 0.028 0.632 0.044 0.268 0.000
#> SRR2314016 4 0.1605 0.7710 0.016 0.000 0.004 0.936 0.044 0.000
#> SRR2314014 2 0.1745 0.7195 0.000 0.920 0.068 0.000 0.012 0.000
#> SRR2314013 2 0.0291 0.7319 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR2314012 4 0.1152 0.7837 0.044 0.000 0.004 0.952 0.000 0.000
#> SRR2314011 4 0.3890 0.3425 0.400 0.000 0.004 0.596 0.000 0.000
#> SRR2314010 2 0.5782 0.6291 0.000 0.612 0.028 0.004 0.192 0.164
#> SRR2314009 3 0.0000 0.7110 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314008 3 0.5018 0.6236 0.004 0.156 0.680 0.008 0.152 0.000
#> SRR2314007 3 0.4480 0.6091 0.000 0.192 0.716 0.008 0.084 0.000
#> SRR2314005 2 0.1984 0.7098 0.000 0.912 0.000 0.000 0.056 0.032
#> SRR2314004 2 0.3618 0.6648 0.000 0.764 0.012 0.004 0.008 0.212
#> SRR2314003 4 0.0790 0.7998 0.000 0.000 0.032 0.968 0.000 0.000
#> SRR2314006 6 0.0405 0.9234 0.004 0.000 0.000 0.000 0.008 0.988
#> SRR2314002 5 0.5252 0.6718 0.172 0.004 0.200 0.000 0.624 0.000
#> SRR2314001 2 0.2312 0.7214 0.000 0.876 0.112 0.000 0.012 0.000
#> SRR2314000 3 0.4930 0.6098 0.016 0.176 0.720 0.044 0.044 0.000
#> SRR2313998 2 0.0865 0.7253 0.000 0.964 0.000 0.000 0.036 0.000
#> SRR2313996 2 0.5961 0.4848 0.000 0.504 0.276 0.008 0.212 0.000
#> SRR2313999 4 0.3551 0.7200 0.148 0.000 0.060 0.792 0.000 0.000
#> SRR2313997 6 0.0000 0.9252 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.1610 0.6770 0.000 0.000 0.916 0.084 0.000 0.000
#> SRR2313991 3 0.6520 0.3938 0.004 0.204 0.456 0.020 0.312 0.004
#> SRR2313990 2 0.3298 0.6059 0.000 0.756 0.236 0.000 0.008 0.000
#> SRR2313988 1 0.3817 0.1220 0.568 0.000 0.000 0.000 0.432 0.000
#> SRR2313987 1 0.1531 0.7260 0.928 0.000 0.000 0.068 0.004 0.000
#> SRR2313984 2 0.3385 0.6895 0.000 0.792 0.016 0.004 0.004 0.184
#> SRR2313989 6 0.0000 0.9252 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.0260 0.7474 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR2313986 4 0.2234 0.7582 0.000 0.000 0.124 0.872 0.004 0.000
#> SRR2313982 1 0.0891 0.7397 0.968 0.000 0.000 0.008 0.024 0.000
#> SRR2313981 3 0.0000 0.7110 0.000 0.000 1.000 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", "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 16873 rows and 72 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 0.971 0.963 0.982 0.5057 0.493 0.493
#> 3 3 0.388 0.453 0.717 0.2966 0.783 0.585
#> 4 4 0.585 0.669 0.795 0.1406 0.786 0.463
#> 5 5 0.677 0.602 0.787 0.0673 0.870 0.544
#> 6 6 0.701 0.562 0.770 0.0288 0.916 0.628
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
#> SRR2313978 2 0.000 0.980 0.000 1.000
#> SRR2313980 2 0.000 0.980 0.000 1.000
#> SRR2313979 1 0.000 0.982 1.000 0.000
#> SRR2313977 2 0.000 0.980 0.000 1.000
#> SRR2313976 2 0.000 0.980 0.000 1.000
#> SRR2313975 1 0.000 0.982 1.000 0.000
#> SRR2313973 1 0.000 0.982 1.000 0.000
#> SRR2313974 1 0.000 0.982 1.000 0.000
#> SRR2313985 1 0.000 0.982 1.000 0.000
#> SRR2313993 2 0.000 0.980 0.000 1.000
#> SRR2313994 2 0.000 0.980 0.000 1.000
#> SRR2313995 2 0.000 0.980 0.000 1.000
#> SRR2314024 2 0.000 0.980 0.000 1.000
#> SRR2314015 2 0.000 0.980 0.000 1.000
#> SRR2314025 1 0.000 0.982 1.000 0.000
#> SRR2314023 1 0.000 0.982 1.000 0.000
#> SRR2314033 2 0.000 0.980 0.000 1.000
#> SRR2314045 1 0.000 0.982 1.000 0.000
#> SRR2314042 1 0.000 0.982 1.000 0.000
#> SRR2314043 1 0.605 0.840 0.852 0.148
#> SRR2314041 2 0.000 0.980 0.000 1.000
#> SRR2314044 2 0.000 0.980 0.000 1.000
#> SRR2314040 2 0.839 0.637 0.268 0.732
#> SRR2314039 2 0.000 0.980 0.000 1.000
#> SRR2314038 2 0.469 0.881 0.100 0.900
#> SRR2314037 1 0.260 0.951 0.956 0.044
#> SRR2314036 2 0.000 0.980 0.000 1.000
#> SRR2314035 1 0.373 0.927 0.928 0.072
#> SRR2314034 1 0.000 0.982 1.000 0.000
#> SRR2314032 1 0.000 0.982 1.000 0.000
#> SRR2314031 1 0.000 0.982 1.000 0.000
#> SRR2314029 2 0.000 0.980 0.000 1.000
#> SRR2314030 1 0.000 0.982 1.000 0.000
#> SRR2314028 1 0.000 0.982 1.000 0.000
#> SRR2314027 2 0.000 0.980 0.000 1.000
#> SRR2314026 1 0.634 0.824 0.840 0.160
#> SRR2314020 2 0.000 0.980 0.000 1.000
#> SRR2314019 1 0.327 0.938 0.940 0.060
#> SRR2314021 1 0.000 0.982 1.000 0.000
#> SRR2314018 2 0.850 0.622 0.276 0.724
#> SRR2314017 1 0.000 0.982 1.000 0.000
#> SRR2314016 1 0.000 0.982 1.000 0.000
#> SRR2314014 2 0.000 0.980 0.000 1.000
#> SRR2314013 2 0.000 0.980 0.000 1.000
#> SRR2314012 1 0.000 0.982 1.000 0.000
#> SRR2314011 1 0.000 0.982 1.000 0.000
#> SRR2314010 2 0.000 0.980 0.000 1.000
#> SRR2314009 1 0.260 0.951 0.956 0.044
#> SRR2314008 2 0.000 0.980 0.000 1.000
#> SRR2314007 2 0.000 0.980 0.000 1.000
#> SRR2314005 2 0.000 0.980 0.000 1.000
#> SRR2314004 2 0.000 0.980 0.000 1.000
#> SRR2314003 1 0.000 0.982 1.000 0.000
#> SRR2314006 2 0.000 0.980 0.000 1.000
#> SRR2314002 1 0.000 0.982 1.000 0.000
#> SRR2314001 2 0.000 0.980 0.000 1.000
#> SRR2314000 1 0.388 0.923 0.924 0.076
#> SRR2313998 2 0.000 0.980 0.000 1.000
#> SRR2313996 2 0.000 0.980 0.000 1.000
#> SRR2313999 1 0.000 0.982 1.000 0.000
#> SRR2313997 2 0.000 0.980 0.000 1.000
#> SRR2313992 1 0.000 0.982 1.000 0.000
#> SRR2313991 2 0.141 0.963 0.020 0.980
#> SRR2313990 2 0.000 0.980 0.000 1.000
#> SRR2313988 1 0.000 0.982 1.000 0.000
#> SRR2313987 1 0.000 0.982 1.000 0.000
#> SRR2313984 2 0.000 0.980 0.000 1.000
#> SRR2313989 2 0.000 0.980 0.000 1.000
#> SRR2313983 1 0.000 0.982 1.000 0.000
#> SRR2313986 1 0.000 0.982 1.000 0.000
#> SRR2313982 1 0.000 0.982 1.000 0.000
#> SRR2313981 1 0.000 0.982 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.2959 0.7299 0.000 0.900 0.100
#> SRR2313980 2 0.0000 0.7481 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.7114 1.000 0.000 0.000
#> SRR2313977 2 0.5760 0.5626 0.000 0.672 0.328
#> SRR2313976 2 0.4931 0.6578 0.000 0.768 0.232
#> SRR2313975 1 0.0000 0.7114 1.000 0.000 0.000
#> SRR2313973 1 0.3412 0.6964 0.876 0.000 0.124
#> SRR2313974 1 0.0237 0.7124 0.996 0.000 0.004
#> SRR2313985 1 0.1289 0.7141 0.968 0.000 0.032
#> SRR2313993 2 0.4931 0.5950 0.000 0.768 0.232
#> SRR2313994 2 0.6280 0.3672 0.000 0.540 0.460
#> SRR2313995 2 0.5529 0.4514 0.000 0.704 0.296
#> SRR2314024 2 0.0592 0.7453 0.000 0.988 0.012
#> SRR2314015 2 0.1643 0.7469 0.000 0.956 0.044
#> SRR2314025 1 0.6274 0.3881 0.544 0.000 0.456
#> SRR2314023 1 0.1860 0.7127 0.948 0.000 0.052
#> SRR2314033 2 0.3752 0.7083 0.000 0.856 0.144
#> SRR2314045 1 0.2796 0.6491 0.908 0.000 0.092
#> SRR2314042 3 0.7293 -0.3132 0.476 0.028 0.496
#> SRR2314043 1 0.9409 -0.0347 0.508 0.236 0.256
#> SRR2314041 2 0.6095 0.3043 0.000 0.608 0.392
#> SRR2314044 2 0.6880 0.5167 0.036 0.660 0.304
#> SRR2314040 3 0.8714 0.1123 0.156 0.264 0.580
#> SRR2314039 3 0.7001 0.1409 0.024 0.388 0.588
#> SRR2314038 2 0.5174 0.6330 0.128 0.824 0.048
#> SRR2314037 3 0.5810 0.0675 0.336 0.000 0.664
#> SRR2314036 2 0.1411 0.7481 0.000 0.964 0.036
#> SRR2314035 3 0.6451 0.0955 0.436 0.004 0.560
#> SRR2314034 1 0.6079 0.3179 0.612 0.000 0.388
#> SRR2314032 1 0.1411 0.6943 0.964 0.000 0.036
#> SRR2314031 1 0.6305 0.3306 0.516 0.000 0.484
#> SRR2314029 2 0.3340 0.6780 0.000 0.880 0.120
#> SRR2314030 3 0.8168 0.1319 0.280 0.108 0.612
#> SRR2314028 1 0.6286 0.2695 0.536 0.000 0.464
#> SRR2314027 3 0.6307 -0.1376 0.000 0.488 0.512
#> SRR2314026 3 0.9574 0.3489 0.244 0.276 0.480
#> SRR2314020 2 0.0000 0.7481 0.000 1.000 0.000
#> SRR2314019 3 0.8433 0.3395 0.204 0.176 0.620
#> SRR2314021 3 0.6507 0.1881 0.284 0.028 0.688
#> SRR2314018 3 0.9257 0.0841 0.196 0.284 0.520
#> SRR2314017 3 0.6305 -0.0728 0.484 0.000 0.516
#> SRR2314016 1 0.5216 0.6287 0.740 0.000 0.260
#> SRR2314014 3 0.6309 -0.3538 0.000 0.500 0.500
#> SRR2314013 2 0.3941 0.7017 0.000 0.844 0.156
#> SRR2314012 1 0.6154 0.4677 0.592 0.000 0.408
#> SRR2314011 1 0.5650 0.5699 0.688 0.000 0.312
#> SRR2314010 2 0.4235 0.6481 0.000 0.824 0.176
#> SRR2314009 3 0.8221 0.3416 0.248 0.128 0.624
#> SRR2314008 2 0.6608 0.4393 0.008 0.560 0.432
#> SRR2314007 2 0.6062 0.3788 0.000 0.616 0.384
#> SRR2314005 2 0.5529 0.5893 0.000 0.704 0.296
#> SRR2314004 2 0.1289 0.7485 0.000 0.968 0.032
#> SRR2314003 1 0.6168 0.4617 0.588 0.000 0.412
#> SRR2314006 2 0.0237 0.7474 0.000 0.996 0.004
#> SRR2314002 1 0.3879 0.5771 0.848 0.000 0.152
#> SRR2314001 3 0.6302 -0.3192 0.000 0.480 0.520
#> SRR2314000 1 0.5480 0.3911 0.732 0.004 0.264
#> SRR2313998 2 0.5058 0.6444 0.000 0.756 0.244
#> SRR2313996 2 0.6079 0.4296 0.000 0.612 0.388
#> SRR2313999 1 0.4178 0.6736 0.828 0.000 0.172
#> SRR2313997 2 0.0000 0.7481 0.000 1.000 0.000
#> SRR2313992 3 0.6168 -0.1175 0.412 0.000 0.588
#> SRR2313991 2 0.5566 0.6534 0.080 0.812 0.108
#> SRR2313990 3 0.6215 -0.2323 0.000 0.428 0.572
#> SRR2313988 1 0.0592 0.7057 0.988 0.000 0.012
#> SRR2313987 1 0.2448 0.7086 0.924 0.000 0.076
#> SRR2313984 2 0.3267 0.7023 0.000 0.884 0.116
#> SRR2313989 2 0.0000 0.7481 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.7114 1.000 0.000 0.000
#> SRR2313986 1 0.6095 0.4887 0.608 0.000 0.392
#> SRR2313982 1 0.0237 0.7124 0.996 0.000 0.004
#> SRR2313981 3 0.5158 0.2311 0.232 0.004 0.764
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1936 0.85788 0.000 0.940 0.032 0.028
#> SRR2313980 2 0.0336 0.86230 0.000 0.992 0.000 0.008
#> SRR2313979 1 0.1716 0.85105 0.936 0.000 0.000 0.064
#> SRR2313977 2 0.5389 0.63729 0.000 0.660 0.308 0.032
#> SRR2313976 2 0.5110 0.62313 0.000 0.656 0.328 0.016
#> SRR2313975 1 0.1716 0.85105 0.936 0.000 0.000 0.064
#> SRR2313973 1 0.3444 0.78220 0.816 0.000 0.000 0.184
#> SRR2313974 1 0.1867 0.85017 0.928 0.000 0.000 0.072
#> SRR2313985 1 0.2011 0.84808 0.920 0.000 0.000 0.080
#> SRR2313993 3 0.4767 0.62319 0.000 0.256 0.724 0.020
#> SRR2313994 3 0.2775 0.70504 0.000 0.084 0.896 0.020
#> SRR2313995 2 0.1940 0.82901 0.000 0.924 0.000 0.076
#> SRR2314024 2 0.0469 0.86187 0.000 0.988 0.000 0.012
#> SRR2314015 2 0.0817 0.86345 0.000 0.976 0.024 0.000
#> SRR2314025 4 0.2408 0.69910 0.104 0.000 0.000 0.896
#> SRR2314023 1 0.2281 0.84229 0.904 0.000 0.000 0.096
#> SRR2314033 2 0.3836 0.79781 0.000 0.816 0.168 0.016
#> SRR2314045 1 0.3464 0.73097 0.860 0.000 0.108 0.032
#> SRR2314042 4 0.2384 0.69170 0.016 0.040 0.016 0.928
#> SRR2314043 3 0.6395 0.49316 0.248 0.060 0.664 0.028
#> SRR2314041 4 0.5172 0.55284 0.000 0.188 0.068 0.744
#> SRR2314044 4 0.7210 0.26992 0.024 0.356 0.084 0.536
#> SRR2314040 3 0.1943 0.71263 0.032 0.016 0.944 0.008
#> SRR2314039 4 0.6731 -0.11240 0.004 0.080 0.404 0.512
#> SRR2314038 2 0.5329 0.73329 0.136 0.772 0.072 0.020
#> SRR2314037 4 0.4964 0.46411 0.032 0.000 0.244 0.724
#> SRR2314036 2 0.1174 0.86326 0.000 0.968 0.020 0.012
#> SRR2314035 3 0.1661 0.70593 0.052 0.000 0.944 0.004
#> SRR2314034 3 0.5792 0.55683 0.056 0.000 0.648 0.296
#> SRR2314032 1 0.2032 0.78397 0.936 0.000 0.036 0.028
#> SRR2314031 4 0.2345 0.69944 0.100 0.000 0.000 0.900
#> SRR2314029 2 0.0707 0.85966 0.000 0.980 0.000 0.020
#> SRR2314030 4 0.6332 0.53676 0.080 0.044 0.164 0.712
#> SRR2314028 4 0.7001 0.06674 0.116 0.000 0.420 0.464
#> SRR2314027 3 0.6602 0.44307 0.000 0.092 0.552 0.356
#> SRR2314026 3 0.5292 0.66947 0.064 0.024 0.776 0.136
#> SRR2314020 2 0.0524 0.86287 0.000 0.988 0.008 0.004
#> SRR2314019 3 0.6602 0.55358 0.056 0.028 0.624 0.292
#> SRR2314021 3 0.5567 0.65370 0.068 0.016 0.744 0.172
#> SRR2314018 3 0.4834 0.64294 0.136 0.032 0.800 0.032
#> SRR2314017 3 0.4153 0.67745 0.132 0.000 0.820 0.048
#> SRR2314016 4 0.6384 0.21880 0.400 0.000 0.068 0.532
#> SRR2314014 3 0.3659 0.65777 0.000 0.136 0.840 0.024
#> SRR2314013 2 0.4214 0.76997 0.000 0.780 0.204 0.016
#> SRR2314012 4 0.2469 0.69767 0.108 0.000 0.000 0.892
#> SRR2314011 1 0.5000 0.14055 0.500 0.000 0.000 0.500
#> SRR2314010 2 0.2124 0.83565 0.000 0.924 0.008 0.068
#> SRR2314009 3 0.4422 0.63553 0.008 0.000 0.736 0.256
#> SRR2314008 3 0.7175 -0.00622 0.000 0.404 0.460 0.136
#> SRR2314007 3 0.5764 0.67532 0.044 0.068 0.756 0.132
#> SRR2314005 2 0.4776 0.73150 0.000 0.732 0.244 0.024
#> SRR2314004 2 0.1510 0.86126 0.000 0.956 0.028 0.016
#> SRR2314003 4 0.2647 0.68510 0.120 0.000 0.000 0.880
#> SRR2314006 2 0.0469 0.86187 0.000 0.988 0.000 0.012
#> SRR2314002 1 0.3774 0.72576 0.844 0.008 0.128 0.020
#> SRR2314001 3 0.2363 0.71777 0.000 0.056 0.920 0.024
#> SRR2314000 1 0.4315 0.70649 0.816 0.012 0.144 0.028
#> SRR2313998 2 0.4214 0.76984 0.000 0.780 0.204 0.016
#> SRR2313996 3 0.6942 0.54175 0.000 0.176 0.584 0.240
#> SRR2313999 1 0.5060 0.41301 0.584 0.000 0.004 0.412
#> SRR2313997 2 0.0524 0.86287 0.000 0.988 0.008 0.004
#> SRR2313992 4 0.2565 0.70898 0.056 0.000 0.032 0.912
#> SRR2313991 2 0.7202 0.53847 0.092 0.632 0.224 0.052
#> SRR2313990 3 0.2443 0.71243 0.000 0.060 0.916 0.024
#> SRR2313988 1 0.0817 0.83569 0.976 0.000 0.000 0.024
#> SRR2313987 1 0.3311 0.79180 0.828 0.000 0.000 0.172
#> SRR2313984 2 0.3768 0.70805 0.000 0.808 0.184 0.008
#> SRR2313989 2 0.0188 0.86271 0.000 0.996 0.000 0.004
#> SRR2313983 1 0.1716 0.85105 0.936 0.000 0.000 0.064
#> SRR2313986 4 0.2868 0.66913 0.136 0.000 0.000 0.864
#> SRR2313982 1 0.1716 0.85105 0.936 0.000 0.000 0.064
#> SRR2313981 3 0.5646 0.61158 0.056 0.000 0.672 0.272
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.4691 0.6929 0.000 0.764 0.076 0.020 0.140
#> SRR2313980 2 0.0000 0.8789 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0451 0.8219 0.988 0.000 0.000 0.008 0.004
#> SRR2313977 3 0.4933 0.5361 0.004 0.060 0.688 0.000 0.248
#> SRR2313976 3 0.4747 -0.0153 0.000 0.484 0.500 0.000 0.016
#> SRR2313975 1 0.0451 0.8226 0.988 0.000 0.000 0.008 0.004
#> SRR2313973 1 0.4118 0.5236 0.660 0.000 0.000 0.336 0.004
#> SRR2313974 1 0.2110 0.8040 0.912 0.000 0.000 0.072 0.016
#> SRR2313985 1 0.1892 0.8037 0.916 0.000 0.000 0.080 0.004
#> SRR2313993 5 0.6441 0.4666 0.000 0.188 0.344 0.000 0.468
#> SRR2313994 3 0.4691 0.3238 0.000 0.044 0.680 0.000 0.276
#> SRR2313995 2 0.1082 0.8662 0.000 0.964 0.000 0.028 0.008
#> SRR2314024 2 0.0000 0.8789 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0771 0.8751 0.000 0.976 0.020 0.000 0.004
#> SRR2314025 4 0.1106 0.7077 0.024 0.000 0.000 0.964 0.012
#> SRR2314023 1 0.3906 0.6520 0.744 0.000 0.000 0.240 0.016
#> SRR2314033 2 0.2304 0.8166 0.000 0.892 0.100 0.000 0.008
#> SRR2314045 1 0.3561 0.6627 0.740 0.000 0.000 0.000 0.260
#> SRR2314042 4 0.2439 0.6219 0.000 0.000 0.004 0.876 0.120
#> SRR2314043 3 0.3694 0.5891 0.032 0.000 0.796 0.000 0.172
#> SRR2314041 4 0.7141 0.2535 0.000 0.072 0.312 0.500 0.116
#> SRR2314044 4 0.4454 0.6309 0.008 0.096 0.108 0.784 0.004
#> SRR2314040 3 0.3366 0.4673 0.004 0.000 0.784 0.000 0.212
#> SRR2314039 5 0.6684 0.5061 0.000 0.000 0.276 0.288 0.436
#> SRR2314038 2 0.8162 0.1380 0.012 0.384 0.136 0.120 0.348
#> SRR2314037 4 0.5296 0.0638 0.000 0.000 0.468 0.484 0.048
#> SRR2314036 2 0.0451 0.8772 0.000 0.988 0.004 0.000 0.008
#> SRR2314035 3 0.2471 0.5709 0.000 0.000 0.864 0.000 0.136
#> SRR2314034 5 0.6427 0.5599 0.004 0.000 0.340 0.164 0.492
#> SRR2314032 1 0.2929 0.7245 0.820 0.000 0.000 0.000 0.180
#> SRR2314031 4 0.0703 0.7099 0.024 0.000 0.000 0.976 0.000
#> SRR2314029 2 0.0162 0.8785 0.000 0.996 0.000 0.000 0.004
#> SRR2314030 5 0.4556 0.4099 0.004 0.000 0.024 0.292 0.680
#> SRR2314028 3 0.3351 0.5634 0.004 0.000 0.828 0.148 0.020
#> SRR2314027 5 0.6103 0.6143 0.000 0.000 0.292 0.160 0.548
#> SRR2314026 5 0.4325 0.6440 0.004 0.000 0.300 0.012 0.684
#> SRR2314020 2 0.0162 0.8784 0.000 0.996 0.000 0.000 0.004
#> SRR2314019 5 0.4960 0.6570 0.000 0.000 0.268 0.064 0.668
#> SRR2314021 5 0.4556 0.6496 0.004 0.000 0.292 0.024 0.680
#> SRR2314018 3 0.3689 0.5609 0.000 0.000 0.740 0.004 0.256
#> SRR2314017 5 0.5048 0.1646 0.052 0.000 0.268 0.008 0.672
#> SRR2314016 4 0.5597 0.1226 0.072 0.000 0.000 0.488 0.440
#> SRR2314014 3 0.0404 0.6257 0.000 0.000 0.988 0.000 0.012
#> SRR2314013 2 0.4542 0.0745 0.000 0.536 0.456 0.000 0.008
#> SRR2314012 4 0.1205 0.7107 0.040 0.000 0.000 0.956 0.004
#> SRR2314011 4 0.4538 0.2477 0.364 0.000 0.000 0.620 0.016
#> SRR2314010 2 0.1597 0.8563 0.000 0.948 0.020 0.008 0.024
#> SRR2314009 3 0.3694 0.6069 0.000 0.000 0.796 0.032 0.172
#> SRR2314008 3 0.4686 0.5512 0.004 0.016 0.712 0.020 0.248
#> SRR2314007 5 0.4675 0.6111 0.000 0.004 0.360 0.016 0.620
#> SRR2314005 3 0.4886 0.2712 0.000 0.372 0.596 0.000 0.032
#> SRR2314004 2 0.0992 0.8739 0.000 0.968 0.024 0.000 0.008
#> SRR2314003 4 0.3089 0.7020 0.040 0.000 0.012 0.872 0.076
#> SRR2314006 2 0.0000 0.8789 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 1 0.4074 0.4960 0.636 0.000 0.000 0.000 0.364
#> SRR2314001 3 0.3616 0.4461 0.000 0.004 0.768 0.004 0.224
#> SRR2314000 1 0.1901 0.8052 0.932 0.000 0.040 0.004 0.024
#> SRR2313998 2 0.2723 0.7864 0.000 0.864 0.124 0.000 0.012
#> SRR2313996 3 0.3076 0.6083 0.000 0.028 0.880 0.040 0.052
#> SRR2313999 4 0.7932 0.2055 0.272 0.000 0.084 0.392 0.252
#> SRR2313997 2 0.0451 0.8781 0.000 0.988 0.004 0.000 0.008
#> SRR2313992 4 0.2054 0.6861 0.004 0.000 0.072 0.916 0.008
#> SRR2313991 5 0.5075 0.1446 0.020 0.360 0.016 0.000 0.604
#> SRR2313990 3 0.2629 0.5627 0.000 0.000 0.860 0.004 0.136
#> SRR2313988 1 0.0671 0.8185 0.980 0.000 0.000 0.004 0.016
#> SRR2313987 1 0.4451 0.4979 0.644 0.000 0.000 0.340 0.016
#> SRR2313984 2 0.3551 0.7371 0.000 0.820 0.136 0.000 0.044
#> SRR2313989 2 0.0000 0.8789 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0290 0.8224 0.992 0.000 0.000 0.008 0.000
#> SRR2313986 4 0.1981 0.7091 0.048 0.000 0.000 0.924 0.028
#> SRR2313982 1 0.0912 0.8221 0.972 0.000 0.000 0.016 0.012
#> SRR2313981 3 0.4317 0.4890 0.000 0.000 0.772 0.112 0.116
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 6 0.4695 0.64254 0.000 0.040 0.004 0.032 0.220 0.704
#> SRR2313980 6 0.0146 0.92656 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR2313979 1 0.0146 0.79655 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313977 5 0.3799 0.55541 0.000 0.276 0.000 0.000 0.704 0.020
#> SRR2313976 2 0.5309 0.13970 0.000 0.492 0.020 0.000 0.056 0.432
#> SRR2313975 1 0.0146 0.79770 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313973 1 0.3695 0.46773 0.624 0.000 0.000 0.376 0.000 0.000
#> SRR2313974 1 0.2199 0.77735 0.892 0.000 0.000 0.088 0.020 0.000
#> SRR2313985 1 0.1918 0.78037 0.904 0.000 0.000 0.088 0.008 0.000
#> SRR2313993 2 0.5969 0.05183 0.000 0.536 0.304 0.000 0.032 0.128
#> SRR2313994 2 0.3164 0.55274 0.000 0.824 0.140 0.000 0.004 0.032
#> SRR2313995 6 0.0405 0.92561 0.000 0.000 0.000 0.008 0.004 0.988
#> SRR2314024 6 0.0260 0.92605 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR2314015 6 0.1320 0.91156 0.000 0.016 0.000 0.000 0.036 0.948
#> SRR2314025 4 0.1010 0.71162 0.000 0.000 0.036 0.960 0.004 0.000
#> SRR2314023 1 0.4361 0.53314 0.648 0.000 0.000 0.308 0.044 0.000
#> SRR2314033 6 0.3201 0.83262 0.000 0.088 0.036 0.000 0.028 0.848
#> SRR2314045 1 0.4874 0.19283 0.496 0.008 0.456 0.000 0.040 0.000
#> SRR2314042 4 0.3084 0.64738 0.000 0.008 0.136 0.832 0.024 0.000
#> SRR2314043 2 0.3245 0.38100 0.000 0.764 0.008 0.000 0.228 0.000
#> SRR2314041 4 0.8204 -0.00419 0.000 0.324 0.080 0.328 0.120 0.148
#> SRR2314044 4 0.5073 0.54756 0.000 0.156 0.004 0.704 0.036 0.100
#> SRR2314040 2 0.1204 0.61306 0.000 0.944 0.056 0.000 0.000 0.000
#> SRR2314039 3 0.6419 0.38810 0.000 0.340 0.456 0.164 0.040 0.000
#> SRR2314038 5 0.3206 0.50219 0.012 0.000 0.072 0.048 0.856 0.012
#> SRR2314037 2 0.5207 0.22547 0.000 0.572 0.028 0.352 0.048 0.000
#> SRR2314036 6 0.0146 0.92663 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR2314035 2 0.1010 0.61158 0.000 0.960 0.036 0.000 0.004 0.000
#> SRR2314034 2 0.5636 -0.37938 0.016 0.456 0.456 0.056 0.016 0.000
#> SRR2314032 1 0.3284 0.66393 0.800 0.000 0.168 0.000 0.032 0.000
#> SRR2314031 4 0.1320 0.71193 0.000 0.000 0.036 0.948 0.016 0.000
#> SRR2314029 6 0.0551 0.92617 0.000 0.000 0.008 0.004 0.004 0.984
#> SRR2314030 3 0.3210 0.37728 0.000 0.004 0.836 0.088 0.072 0.000
#> SRR2314028 2 0.4142 0.47670 0.000 0.764 0.024 0.160 0.052 0.000
#> SRR2314027 3 0.5485 0.50286 0.000 0.336 0.560 0.080 0.024 0.000
#> SRR2314026 3 0.3266 0.57605 0.000 0.272 0.728 0.000 0.000 0.000
#> SRR2314020 6 0.1092 0.91975 0.000 0.000 0.020 0.000 0.020 0.960
#> SRR2314019 3 0.4011 0.56675 0.000 0.304 0.672 0.024 0.000 0.000
#> SRR2314021 3 0.3464 0.56356 0.000 0.312 0.688 0.000 0.000 0.000
#> SRR2314018 5 0.3851 0.27227 0.000 0.460 0.000 0.000 0.540 0.000
#> SRR2314017 5 0.4407 0.30403 0.004 0.020 0.304 0.012 0.660 0.000
#> SRR2314016 3 0.6032 0.01158 0.016 0.000 0.496 0.172 0.316 0.000
#> SRR2314014 2 0.2527 0.53993 0.000 0.868 0.000 0.000 0.108 0.024
#> SRR2314013 2 0.5169 0.04827 0.000 0.468 0.012 0.000 0.056 0.464
#> SRR2314012 4 0.1382 0.71401 0.008 0.000 0.036 0.948 0.008 0.000
#> SRR2314011 4 0.4313 0.39106 0.284 0.000 0.000 0.668 0.048 0.000
#> SRR2314010 6 0.3617 0.78618 0.000 0.088 0.048 0.004 0.032 0.828
#> SRR2314009 2 0.3000 0.57221 0.000 0.860 0.048 0.016 0.076 0.000
#> SRR2314008 5 0.4460 0.58176 0.008 0.204 0.016 0.036 0.732 0.004
#> SRR2314007 3 0.4254 0.51903 0.000 0.352 0.624 0.004 0.020 0.000
#> SRR2314005 2 0.6014 -0.00657 0.000 0.392 0.000 0.000 0.240 0.368
#> SRR2314004 6 0.0865 0.92073 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR2314003 4 0.2402 0.68295 0.000 0.004 0.000 0.856 0.140 0.000
#> SRR2314006 6 0.0000 0.92634 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 3 0.6057 -0.14929 0.188 0.008 0.412 0.000 0.392 0.000
#> SRR2314001 2 0.1714 0.59617 0.000 0.908 0.092 0.000 0.000 0.000
#> SRR2314000 1 0.2361 0.76416 0.896 0.032 0.000 0.008 0.064 0.000
#> SRR2313998 6 0.1606 0.89613 0.000 0.056 0.008 0.000 0.004 0.932
#> SRR2313996 2 0.2340 0.57725 0.000 0.896 0.004 0.000 0.056 0.044
#> SRR2313999 4 0.5713 0.22793 0.140 0.004 0.000 0.436 0.420 0.000
#> SRR2313997 6 0.0717 0.92489 0.000 0.000 0.008 0.000 0.016 0.976
#> SRR2313992 4 0.3352 0.61501 0.000 0.172 0.016 0.800 0.012 0.000
#> SRR2313991 3 0.4632 0.10490 0.000 0.000 0.620 0.004 0.328 0.048
#> SRR2313990 2 0.0865 0.61330 0.000 0.964 0.036 0.000 0.000 0.000
#> SRR2313988 1 0.0291 0.79528 0.992 0.000 0.004 0.000 0.004 0.000
#> SRR2313987 1 0.4756 0.38267 0.564 0.000 0.000 0.380 0.056 0.000
#> SRR2313984 6 0.3798 0.79041 0.000 0.088 0.060 0.000 0.040 0.812
#> SRR2313989 6 0.0000 0.92634 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 1 0.0000 0.79708 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.2445 0.68674 0.008 0.004 0.000 0.868 0.120 0.000
#> SRR2313982 1 0.1168 0.79703 0.956 0.000 0.000 0.016 0.028 0.000
#> SRR2313981 2 0.2583 0.59261 0.000 0.884 0.056 0.052 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 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 0.815 0.917 0.955 0.4180 0.593 0.593
#> 3 3 0.709 0.844 0.913 0.5618 0.739 0.560
#> 4 4 0.709 0.775 0.833 0.1072 0.944 0.838
#> 5 5 0.738 0.713 0.841 0.0617 0.944 0.812
#> 6 6 0.726 0.590 0.781 0.0358 0.969 0.876
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
#> SRR2313978 2 0.0376 0.948 0.004 0.996
#> SRR2313980 2 0.0000 0.948 0.000 1.000
#> SRR2313979 1 0.0000 0.965 1.000 0.000
#> SRR2313977 2 0.0000 0.948 0.000 1.000
#> SRR2313976 2 0.0000 0.948 0.000 1.000
#> SRR2313975 1 0.5842 0.851 0.860 0.140
#> SRR2313973 1 0.0000 0.965 1.000 0.000
#> SRR2313974 1 0.4161 0.907 0.916 0.084
#> SRR2313985 1 0.0000 0.965 1.000 0.000
#> SRR2313993 2 0.0000 0.948 0.000 1.000
#> SRR2313994 2 0.0000 0.948 0.000 1.000
#> SRR2313995 2 0.0672 0.948 0.008 0.992
#> SRR2314024 2 0.0000 0.948 0.000 1.000
#> SRR2314015 2 0.0000 0.948 0.000 1.000
#> SRR2314025 1 0.0000 0.965 1.000 0.000
#> SRR2314023 1 0.0000 0.965 1.000 0.000
#> SRR2314033 2 0.0000 0.948 0.000 1.000
#> SRR2314045 1 0.0000 0.965 1.000 0.000
#> SRR2314042 2 0.9580 0.456 0.380 0.620
#> SRR2314043 2 0.2043 0.945 0.032 0.968
#> SRR2314041 2 0.1843 0.946 0.028 0.972
#> SRR2314044 2 0.2043 0.945 0.032 0.968
#> SRR2314040 2 0.2043 0.945 0.032 0.968
#> SRR2314039 2 0.2043 0.945 0.032 0.968
#> SRR2314038 2 0.4161 0.913 0.084 0.916
#> SRR2314037 2 0.4562 0.903 0.096 0.904
#> SRR2314036 2 0.0000 0.948 0.000 1.000
#> SRR2314035 2 0.2236 0.944 0.036 0.964
#> SRR2314034 2 0.7056 0.798 0.192 0.808
#> SRR2314032 1 0.0000 0.965 1.000 0.000
#> SRR2314031 1 0.5842 0.851 0.860 0.140
#> SRR2314029 2 0.0672 0.948 0.008 0.992
#> SRR2314030 2 0.9686 0.412 0.396 0.604
#> SRR2314028 2 0.9580 0.456 0.380 0.620
#> SRR2314027 2 0.2043 0.945 0.032 0.968
#> SRR2314026 2 0.2423 0.942 0.040 0.960
#> SRR2314020 2 0.0000 0.948 0.000 1.000
#> SRR2314019 2 0.5842 0.860 0.140 0.860
#> SRR2314021 2 0.5842 0.860 0.140 0.860
#> SRR2314018 2 0.2043 0.945 0.032 0.968
#> SRR2314017 2 0.2236 0.944 0.036 0.964
#> SRR2314016 1 0.5842 0.851 0.860 0.140
#> SRR2314014 2 0.0000 0.948 0.000 1.000
#> SRR2314013 2 0.0000 0.948 0.000 1.000
#> SRR2314012 1 0.0000 0.965 1.000 0.000
#> SRR2314011 1 0.0000 0.965 1.000 0.000
#> SRR2314010 2 0.0000 0.948 0.000 1.000
#> SRR2314009 2 0.4562 0.903 0.096 0.904
#> SRR2314008 2 0.2043 0.945 0.032 0.968
#> SRR2314007 2 0.2236 0.944 0.036 0.964
#> SRR2314005 2 0.0000 0.948 0.000 1.000
#> SRR2314004 2 0.0000 0.948 0.000 1.000
#> SRR2314003 1 0.0000 0.965 1.000 0.000
#> SRR2314006 2 0.0000 0.948 0.000 1.000
#> SRR2314002 2 0.2423 0.942 0.040 0.960
#> SRR2314001 2 0.0000 0.948 0.000 1.000
#> SRR2314000 2 0.2043 0.945 0.032 0.968
#> SRR2313998 2 0.0000 0.948 0.000 1.000
#> SRR2313996 2 0.0376 0.948 0.004 0.996
#> SRR2313999 1 0.0000 0.965 1.000 0.000
#> SRR2313997 2 0.0000 0.948 0.000 1.000
#> SRR2313992 2 0.4690 0.900 0.100 0.900
#> SRR2313991 2 0.2423 0.942 0.040 0.960
#> SRR2313990 2 0.0672 0.948 0.008 0.992
#> SRR2313988 1 0.0000 0.965 1.000 0.000
#> SRR2313987 1 0.0000 0.965 1.000 0.000
#> SRR2313984 2 0.0000 0.948 0.000 1.000
#> SRR2313989 2 0.0000 0.948 0.000 1.000
#> SRR2313983 1 0.0000 0.965 1.000 0.000
#> SRR2313986 1 0.4690 0.893 0.900 0.100
#> SRR2313982 1 0.0000 0.965 1.000 0.000
#> SRR2313981 2 0.4562 0.903 0.096 0.904
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.6291 0.0619 0.000 0.532 0.468
#> SRR2313980 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313977 2 0.5016 0.6772 0.000 0.760 0.240
#> SRR2313976 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2313975 1 0.5058 0.7792 0.756 0.000 0.244
#> SRR2313973 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313974 1 0.4002 0.8599 0.840 0.000 0.160
#> SRR2313985 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313993 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2313994 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2313995 3 0.3619 0.8568 0.000 0.136 0.864
#> SRR2314024 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2314015 2 0.4750 0.7123 0.000 0.784 0.216
#> SRR2314025 1 0.1964 0.9308 0.944 0.000 0.056
#> SRR2314023 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314045 1 0.1753 0.9327 0.952 0.000 0.048
#> SRR2314042 3 0.5363 0.5457 0.276 0.000 0.724
#> SRR2314043 3 0.2448 0.8925 0.000 0.076 0.924
#> SRR2314041 3 0.3686 0.8584 0.000 0.140 0.860
#> SRR2314044 3 0.2537 0.8920 0.000 0.080 0.920
#> SRR2314040 3 0.3340 0.8750 0.000 0.120 0.880
#> SRR2314039 3 0.2448 0.8925 0.000 0.076 0.924
#> SRR2314038 3 0.0892 0.8803 0.000 0.020 0.980
#> SRR2314037 3 0.0424 0.8753 0.000 0.008 0.992
#> SRR2314036 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314035 3 0.2878 0.8876 0.000 0.096 0.904
#> SRR2314034 3 0.2711 0.8092 0.088 0.000 0.912
#> SRR2314032 1 0.1753 0.9327 0.952 0.000 0.048
#> SRR2314031 1 0.5058 0.7792 0.756 0.000 0.244
#> SRR2314029 3 0.3619 0.8568 0.000 0.136 0.864
#> SRR2314030 3 0.5497 0.4987 0.292 0.000 0.708
#> SRR2314028 3 0.5363 0.5457 0.276 0.000 0.724
#> SRR2314027 3 0.2448 0.8925 0.000 0.076 0.924
#> SRR2314026 3 0.2261 0.8924 0.000 0.068 0.932
#> SRR2314020 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314019 3 0.1411 0.8536 0.036 0.000 0.964
#> SRR2314021 3 0.1411 0.8536 0.036 0.000 0.964
#> SRR2314018 3 0.3340 0.8750 0.000 0.120 0.880
#> SRR2314017 3 0.2878 0.8876 0.000 0.096 0.904
#> SRR2314016 1 0.5058 0.7792 0.756 0.000 0.244
#> SRR2314014 2 0.0237 0.9128 0.000 0.996 0.004
#> SRR2314013 2 0.0237 0.9128 0.000 0.996 0.004
#> SRR2314012 1 0.1964 0.9308 0.944 0.000 0.056
#> SRR2314011 1 0.0747 0.9326 0.984 0.000 0.016
#> SRR2314010 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2314009 3 0.0424 0.8753 0.000 0.008 0.992
#> SRR2314008 3 0.3551 0.8661 0.000 0.132 0.868
#> SRR2314007 3 0.2356 0.8924 0.000 0.072 0.928
#> SRR2314005 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314003 1 0.1753 0.9327 0.952 0.000 0.048
#> SRR2314006 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2314002 3 0.2796 0.8890 0.000 0.092 0.908
#> SRR2314001 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2314000 3 0.2448 0.8925 0.000 0.076 0.924
#> SRR2313998 2 0.1860 0.8990 0.000 0.948 0.052
#> SRR2313996 2 0.6140 0.3084 0.000 0.596 0.404
#> SRR2313999 1 0.0747 0.9326 0.984 0.000 0.016
#> SRR2313997 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2313992 3 0.0237 0.8729 0.000 0.004 0.996
#> SRR2313991 3 0.2796 0.8890 0.000 0.092 0.908
#> SRR2313990 3 0.6252 0.2395 0.000 0.444 0.556
#> SRR2313988 1 0.1753 0.9327 0.952 0.000 0.048
#> SRR2313987 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.9137 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313986 1 0.4346 0.8389 0.816 0.000 0.184
#> SRR2313982 1 0.0000 0.9306 1.000 0.000 0.000
#> SRR2313981 3 0.0424 0.8753 0.000 0.008 0.992
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.6586 -0.0199 0.080 0.420 0.500 0.000
#> SRR2313980 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2313979 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313977 2 0.5962 0.6436 0.080 0.660 0.260 0.000
#> SRR2313976 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2313975 4 0.4182 0.7376 0.180 0.000 0.024 0.796
#> SRR2313973 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313974 4 0.3160 0.7951 0.108 0.000 0.020 0.872
#> SRR2313985 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313993 2 0.3080 0.8810 0.024 0.880 0.096 0.000
#> SRR2313994 2 0.3080 0.8810 0.024 0.880 0.096 0.000
#> SRR2313995 3 0.3497 0.7911 0.104 0.036 0.860 0.000
#> SRR2314024 2 0.1970 0.8938 0.008 0.932 0.060 0.000
#> SRR2314015 2 0.5790 0.6823 0.080 0.684 0.236 0.000
#> SRR2314025 4 0.1209 0.8156 0.032 0.000 0.004 0.964
#> SRR2314023 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2314033 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2314045 4 0.0188 0.8232 0.000 0.000 0.004 0.996
#> SRR2314042 3 0.7889 0.2152 0.304 0.000 0.380 0.316
#> SRR2314043 3 0.0000 0.8078 0.000 0.000 1.000 0.000
#> SRR2314041 3 0.2739 0.7838 0.060 0.036 0.904 0.000
#> SRR2314044 3 0.0469 0.8067 0.012 0.000 0.988 0.000
#> SRR2314040 3 0.2142 0.7924 0.056 0.016 0.928 0.000
#> SRR2314039 3 0.0000 0.8078 0.000 0.000 1.000 0.000
#> SRR2314038 3 0.4175 0.7594 0.200 0.000 0.784 0.016
#> SRR2314037 3 0.2845 0.7939 0.076 0.000 0.896 0.028
#> SRR2314036 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2314035 3 0.1716 0.7983 0.064 0.000 0.936 0.000
#> SRR2314034 3 0.6708 0.6116 0.272 0.000 0.596 0.132
#> SRR2314032 4 0.0188 0.8232 0.000 0.000 0.004 0.996
#> SRR2314031 4 0.4182 0.7376 0.180 0.000 0.024 0.796
#> SRR2314029 3 0.4050 0.7815 0.144 0.036 0.820 0.000
#> SRR2314030 3 0.7925 0.1172 0.332 0.000 0.336 0.332
#> SRR2314028 3 0.7889 0.2152 0.304 0.000 0.380 0.316
#> SRR2314027 3 0.0000 0.8078 0.000 0.000 1.000 0.000
#> SRR2314026 3 0.3355 0.7786 0.160 0.000 0.836 0.004
#> SRR2314020 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2314019 3 0.6136 0.6591 0.288 0.000 0.632 0.080
#> SRR2314021 3 0.6136 0.6591 0.288 0.000 0.632 0.080
#> SRR2314018 3 0.2142 0.7924 0.056 0.016 0.928 0.000
#> SRR2314017 3 0.1716 0.7983 0.064 0.000 0.936 0.000
#> SRR2314016 4 0.4182 0.7376 0.180 0.000 0.024 0.796
#> SRR2314014 2 0.1406 0.9006 0.016 0.960 0.024 0.000
#> SRR2314013 2 0.2197 0.8949 0.024 0.928 0.048 0.000
#> SRR2314012 4 0.1209 0.8156 0.032 0.000 0.004 0.964
#> SRR2314011 4 0.2469 0.6661 0.108 0.000 0.000 0.892
#> SRR2314010 2 0.3080 0.8810 0.024 0.880 0.096 0.000
#> SRR2314009 3 0.2845 0.7939 0.076 0.000 0.896 0.028
#> SRR2314008 3 0.2565 0.7872 0.056 0.032 0.912 0.000
#> SRR2314007 3 0.2868 0.7884 0.136 0.000 0.864 0.000
#> SRR2314005 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2314004 2 0.0188 0.9009 0.000 0.996 0.004 0.000
#> SRR2314003 4 0.0895 0.8115 0.020 0.000 0.004 0.976
#> SRR2314006 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2314002 3 0.3494 0.7966 0.172 0.000 0.824 0.004
#> SRR2314001 2 0.3080 0.8810 0.024 0.880 0.096 0.000
#> SRR2314000 3 0.0000 0.8078 0.000 0.000 1.000 0.000
#> SRR2313998 2 0.3080 0.8810 0.024 0.880 0.096 0.000
#> SRR2313996 2 0.6599 0.2365 0.080 0.488 0.432 0.000
#> SRR2313999 4 0.2469 0.6661 0.108 0.000 0.000 0.892
#> SRR2313997 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2313992 3 0.3694 0.7725 0.124 0.000 0.844 0.032
#> SRR2313991 3 0.3494 0.7966 0.172 0.000 0.824 0.004
#> SRR2313990 3 0.6334 0.2999 0.080 0.328 0.592 0.000
#> SRR2313988 4 0.0188 0.8232 0.000 0.000 0.004 0.996
#> SRR2313987 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313984 2 0.1510 0.9001 0.016 0.956 0.028 0.000
#> SRR2313989 2 0.0188 0.9004 0.004 0.996 0.000 0.000
#> SRR2313983 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313986 4 0.3552 0.7826 0.128 0.000 0.024 0.848
#> SRR2313982 1 0.4898 1.0000 0.584 0.000 0.000 0.416
#> SRR2313981 3 0.2845 0.7939 0.076 0.000 0.896 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.6341 -0.0583 0.012 0.388 0.496 0.004 0.100
#> SRR2313980 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2313979 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313977 2 0.5880 0.6279 0.012 0.628 0.256 0.004 0.100
#> SRR2313976 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2313975 4 0.3730 0.6840 0.000 0.000 0.000 0.712 0.288
#> SRR2313973 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313974 4 0.2351 0.8322 0.016 0.000 0.000 0.896 0.088
#> SRR2313985 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313993 2 0.3606 0.8610 0.012 0.848 0.072 0.004 0.064
#> SRR2313994 2 0.3606 0.8610 0.012 0.848 0.072 0.004 0.064
#> SRR2313995 3 0.4985 0.3507 0.012 0.016 0.580 0.000 0.392
#> SRR2314024 2 0.1862 0.8797 0.004 0.932 0.048 0.000 0.016
#> SRR2314015 2 0.5641 0.6599 0.012 0.652 0.248 0.004 0.084
#> SRR2314025 4 0.1410 0.8645 0.060 0.000 0.000 0.940 0.000
#> SRR2314023 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2314033 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2314045 4 0.1800 0.8661 0.048 0.000 0.000 0.932 0.020
#> SRR2314042 5 0.5169 0.6921 0.000 0.000 0.128 0.184 0.688
#> SRR2314043 3 0.1341 0.6876 0.000 0.000 0.944 0.000 0.056
#> SRR2314041 3 0.2424 0.6579 0.004 0.024 0.908 0.004 0.060
#> SRR2314044 3 0.1270 0.6910 0.000 0.000 0.948 0.000 0.052
#> SRR2314040 3 0.0912 0.6837 0.000 0.012 0.972 0.000 0.016
#> SRR2314039 3 0.3003 0.6078 0.000 0.000 0.812 0.000 0.188
#> SRR2314038 3 0.4305 0.0194 0.000 0.000 0.512 0.000 0.488
#> SRR2314037 3 0.3163 0.6229 0.000 0.000 0.824 0.012 0.164
#> SRR2314036 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2314035 3 0.0290 0.6864 0.000 0.000 0.992 0.000 0.008
#> SRR2314034 5 0.5484 0.5974 0.000 0.000 0.336 0.080 0.584
#> SRR2314032 4 0.1800 0.8661 0.048 0.000 0.000 0.932 0.020
#> SRR2314031 4 0.4150 0.5816 0.000 0.000 0.000 0.612 0.388
#> SRR2314029 3 0.5061 0.2349 0.012 0.016 0.540 0.000 0.432
#> SRR2314030 5 0.2677 0.5646 0.000 0.000 0.016 0.112 0.872
#> SRR2314028 5 0.5169 0.6921 0.000 0.000 0.128 0.184 0.688
#> SRR2314027 3 0.2929 0.6147 0.000 0.000 0.820 0.000 0.180
#> SRR2314026 3 0.4249 0.0917 0.000 0.000 0.568 0.000 0.432
#> SRR2314020 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2314019 5 0.4570 0.5605 0.000 0.000 0.348 0.020 0.632
#> SRR2314021 5 0.4570 0.5605 0.000 0.000 0.348 0.020 0.632
#> SRR2314018 3 0.0693 0.6851 0.000 0.012 0.980 0.000 0.008
#> SRR2314017 3 0.0510 0.6867 0.000 0.000 0.984 0.000 0.016
#> SRR2314016 4 0.3837 0.6750 0.000 0.000 0.000 0.692 0.308
#> SRR2314014 2 0.1805 0.8816 0.008 0.936 0.004 0.004 0.048
#> SRR2314013 2 0.2728 0.8740 0.012 0.896 0.024 0.004 0.064
#> SRR2314012 4 0.1410 0.8645 0.060 0.000 0.000 0.940 0.000
#> SRR2314011 4 0.2690 0.8103 0.156 0.000 0.000 0.844 0.000
#> SRR2314010 2 0.3606 0.8610 0.012 0.848 0.072 0.004 0.064
#> SRR2314009 3 0.3163 0.6229 0.000 0.000 0.824 0.012 0.164
#> SRR2314008 3 0.1710 0.6719 0.000 0.016 0.940 0.004 0.040
#> SRR2314007 3 0.4150 0.2350 0.000 0.000 0.612 0.000 0.388
#> SRR2314005 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2314004 2 0.0000 0.8842 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 4 0.1478 0.8630 0.064 0.000 0.000 0.936 0.000
#> SRR2314006 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2314002 3 0.2813 0.5981 0.000 0.000 0.832 0.000 0.168
#> SRR2314001 2 0.3606 0.8610 0.012 0.848 0.072 0.004 0.064
#> SRR2314000 3 0.1341 0.6876 0.000 0.000 0.944 0.000 0.056
#> SRR2313998 2 0.3606 0.8610 0.012 0.848 0.072 0.004 0.064
#> SRR2313996 2 0.6415 0.2003 0.012 0.456 0.424 0.004 0.104
#> SRR2313999 4 0.2690 0.8103 0.156 0.000 0.000 0.844 0.000
#> SRR2313997 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2313992 3 0.3628 0.5492 0.000 0.000 0.772 0.012 0.216
#> SRR2313991 3 0.2813 0.5977 0.000 0.000 0.832 0.000 0.168
#> SRR2313990 3 0.5841 0.2773 0.012 0.296 0.608 0.004 0.080
#> SRR2313988 4 0.1800 0.8661 0.048 0.000 0.000 0.932 0.020
#> SRR2313987 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313984 2 0.1836 0.8826 0.008 0.936 0.016 0.000 0.040
#> SRR2313989 2 0.0566 0.8837 0.000 0.984 0.000 0.004 0.012
#> SRR2313983 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313986 4 0.2522 0.8194 0.012 0.000 0.000 0.880 0.108
#> SRR2313982 1 0.0404 1.0000 0.988 0.000 0.000 0.012 0.000
#> SRR2313981 3 0.3163 0.6229 0.000 0.000 0.824 0.012 0.164
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 6 0.5928 -0.1870 0.000 0.180 0.400 0.000 0.004 0.416
#> SRR2313980 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313979 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313977 2 0.5830 0.3374 0.000 0.420 0.160 0.000 0.004 0.416
#> SRR2313976 2 0.0363 0.7691 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR2313975 6 0.5667 -0.4647 0.000 0.000 0.000 0.168 0.340 0.492
#> SRR2313973 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313974 4 0.3290 0.6746 0.004 0.000 0.000 0.820 0.132 0.044
#> SRR2313985 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313993 2 0.3898 0.7180 0.000 0.652 0.012 0.000 0.000 0.336
#> SRR2313994 2 0.3898 0.7180 0.000 0.652 0.012 0.000 0.000 0.336
#> SRR2313995 3 0.5758 0.2188 0.000 0.000 0.492 0.000 0.312 0.196
#> SRR2314024 2 0.2762 0.7694 0.000 0.804 0.000 0.000 0.000 0.196
#> SRR2314015 2 0.5716 0.3863 0.000 0.444 0.164 0.000 0.000 0.392
#> SRR2314025 4 0.0692 0.7146 0.020 0.000 0.000 0.976 0.000 0.004
#> SRR2314023 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2314033 2 0.0363 0.7691 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR2314045 4 0.4378 0.4392 0.004 0.000 0.000 0.528 0.016 0.452
#> SRR2314042 5 0.4294 0.6515 0.000 0.000 0.128 0.092 0.760 0.020
#> SRR2314043 3 0.1349 0.6525 0.000 0.000 0.940 0.000 0.056 0.004
#> SRR2314041 3 0.2955 0.5908 0.000 0.004 0.816 0.000 0.008 0.172
#> SRR2314044 3 0.1367 0.6567 0.000 0.000 0.944 0.000 0.044 0.012
#> SRR2314040 3 0.1327 0.6471 0.000 0.000 0.936 0.000 0.000 0.064
#> SRR2314039 3 0.3892 0.5393 0.000 0.000 0.752 0.000 0.188 0.060
#> SRR2314038 3 0.4702 0.0056 0.000 0.000 0.496 0.000 0.460 0.044
#> SRR2314037 3 0.2562 0.5899 0.000 0.000 0.828 0.000 0.172 0.000
#> SRR2314036 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314035 3 0.0405 0.6506 0.000 0.000 0.988 0.000 0.004 0.008
#> SRR2314034 5 0.4511 0.5708 0.000 0.000 0.332 0.048 0.620 0.000
#> SRR2314032 4 0.4378 0.4392 0.004 0.000 0.000 0.528 0.016 0.452
#> SRR2314031 4 0.5061 0.4228 0.000 0.000 0.000 0.496 0.428 0.076
#> SRR2314029 3 0.5616 0.1696 0.000 0.000 0.492 0.000 0.352 0.156
#> SRR2314030 5 0.2060 0.4450 0.000 0.000 0.000 0.016 0.900 0.084
#> SRR2314028 5 0.4294 0.6515 0.000 0.000 0.128 0.092 0.760 0.020
#> SRR2314027 3 0.3796 0.5520 0.000 0.000 0.764 0.000 0.176 0.060
#> SRR2314026 3 0.4136 0.0702 0.000 0.000 0.560 0.000 0.428 0.012
#> SRR2314020 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314019 5 0.3728 0.5441 0.000 0.000 0.344 0.000 0.652 0.004
#> SRR2314021 5 0.3728 0.5441 0.000 0.000 0.344 0.000 0.652 0.004
#> SRR2314018 3 0.1204 0.6488 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR2314017 3 0.0622 0.6502 0.000 0.000 0.980 0.000 0.012 0.008
#> SRR2314016 4 0.4868 0.5280 0.000 0.000 0.000 0.592 0.332 0.076
#> SRR2314014 2 0.3175 0.7591 0.000 0.744 0.000 0.000 0.000 0.256
#> SRR2314013 2 0.3710 0.7397 0.000 0.696 0.012 0.000 0.000 0.292
#> SRR2314012 4 0.0692 0.7146 0.020 0.000 0.000 0.976 0.000 0.004
#> SRR2314011 4 0.2003 0.6940 0.116 0.000 0.000 0.884 0.000 0.000
#> SRR2314010 2 0.3898 0.7180 0.000 0.652 0.012 0.000 0.000 0.336
#> SRR2314009 3 0.2562 0.5899 0.000 0.000 0.828 0.000 0.172 0.000
#> SRR2314008 3 0.2006 0.6300 0.000 0.000 0.892 0.000 0.004 0.104
#> SRR2314007 3 0.4273 0.2046 0.000 0.000 0.596 0.000 0.380 0.024
#> SRR2314005 2 0.0363 0.7691 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR2314004 2 0.2135 0.7770 0.000 0.872 0.000 0.000 0.000 0.128
#> SRR2314003 4 0.0547 0.7132 0.020 0.000 0.000 0.980 0.000 0.000
#> SRR2314006 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314002 3 0.2743 0.5671 0.000 0.000 0.828 0.000 0.164 0.008
#> SRR2314001 2 0.3898 0.7180 0.000 0.652 0.012 0.000 0.000 0.336
#> SRR2314000 3 0.1349 0.6525 0.000 0.000 0.940 0.000 0.056 0.004
#> SRR2313998 2 0.3898 0.7180 0.000 0.652 0.012 0.000 0.000 0.336
#> SRR2313996 3 0.6342 -0.3347 0.000 0.324 0.336 0.000 0.008 0.332
#> SRR2313999 4 0.2003 0.6940 0.116 0.000 0.000 0.884 0.000 0.000
#> SRR2313997 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313992 3 0.2969 0.5169 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR2313991 3 0.2841 0.5646 0.000 0.000 0.824 0.000 0.164 0.012
#> SRR2313990 3 0.5057 -0.0627 0.000 0.088 0.560 0.000 0.000 0.352
#> SRR2313988 4 0.4378 0.4392 0.004 0.000 0.000 0.528 0.016 0.452
#> SRR2313987 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313984 2 0.3081 0.7675 0.000 0.776 0.004 0.000 0.000 0.220
#> SRR2313989 2 0.0000 0.7712 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313983 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.3048 0.6628 0.004 0.000 0.000 0.824 0.152 0.020
#> SRR2313982 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.2562 0.5899 0.000 0.000 0.828 0.000 0.172 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 0.992 0.997 0.4741 0.525 0.525
#> 3 3 1.000 0.946 0.979 0.4039 0.682 0.462
#> 4 4 0.766 0.810 0.854 0.1065 0.894 0.700
#> 5 5 0.746 0.677 0.790 0.0690 0.908 0.669
#> 6 6 0.724 0.616 0.742 0.0423 0.918 0.657
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
#> SRR2313978 2 0.000 1.000 0.000 1.000
#> SRR2313980 2 0.000 1.000 0.000 1.000
#> SRR2313979 1 0.000 0.991 1.000 0.000
#> SRR2313977 2 0.000 1.000 0.000 1.000
#> SRR2313976 2 0.000 1.000 0.000 1.000
#> SRR2313975 1 0.000 0.991 1.000 0.000
#> SRR2313973 1 0.000 0.991 1.000 0.000
#> SRR2313974 1 0.000 0.991 1.000 0.000
#> SRR2313985 1 0.000 0.991 1.000 0.000
#> SRR2313993 2 0.000 1.000 0.000 1.000
#> SRR2313994 2 0.000 1.000 0.000 1.000
#> SRR2313995 2 0.000 1.000 0.000 1.000
#> SRR2314024 2 0.000 1.000 0.000 1.000
#> SRR2314015 2 0.000 1.000 0.000 1.000
#> SRR2314025 1 0.000 0.991 1.000 0.000
#> SRR2314023 1 0.000 0.991 1.000 0.000
#> SRR2314033 2 0.000 1.000 0.000 1.000
#> SRR2314045 1 0.000 0.991 1.000 0.000
#> SRR2314042 1 0.000 0.991 1.000 0.000
#> SRR2314043 2 0.000 1.000 0.000 1.000
#> SRR2314041 2 0.000 1.000 0.000 1.000
#> SRR2314044 2 0.000 1.000 0.000 1.000
#> SRR2314040 2 0.000 1.000 0.000 1.000
#> SRR2314039 2 0.000 1.000 0.000 1.000
#> SRR2314038 2 0.000 1.000 0.000 1.000
#> SRR2314037 2 0.000 1.000 0.000 1.000
#> SRR2314036 2 0.000 1.000 0.000 1.000
#> SRR2314035 2 0.000 1.000 0.000 1.000
#> SRR2314034 1 0.000 0.991 1.000 0.000
#> SRR2314032 1 0.000 0.991 1.000 0.000
#> SRR2314031 1 0.000 0.991 1.000 0.000
#> SRR2314029 2 0.000 1.000 0.000 1.000
#> SRR2314030 1 0.000 0.991 1.000 0.000
#> SRR2314028 1 0.000 0.991 1.000 0.000
#> SRR2314027 2 0.000 1.000 0.000 1.000
#> SRR2314026 2 0.000 1.000 0.000 1.000
#> SRR2314020 2 0.000 1.000 0.000 1.000
#> SRR2314019 2 0.000 1.000 0.000 1.000
#> SRR2314021 1 0.000 0.991 1.000 0.000
#> SRR2314018 2 0.000 1.000 0.000 1.000
#> SRR2314017 2 0.000 1.000 0.000 1.000
#> SRR2314016 1 0.000 0.991 1.000 0.000
#> SRR2314014 2 0.000 1.000 0.000 1.000
#> SRR2314013 2 0.000 1.000 0.000 1.000
#> SRR2314012 1 0.000 0.991 1.000 0.000
#> SRR2314011 1 0.000 0.991 1.000 0.000
#> SRR2314010 2 0.000 1.000 0.000 1.000
#> SRR2314009 2 0.000 1.000 0.000 1.000
#> SRR2314008 2 0.000 1.000 0.000 1.000
#> SRR2314007 2 0.000 1.000 0.000 1.000
#> SRR2314005 2 0.000 1.000 0.000 1.000
#> SRR2314004 2 0.000 1.000 0.000 1.000
#> SRR2314003 1 0.000 0.991 1.000 0.000
#> SRR2314006 2 0.000 1.000 0.000 1.000
#> SRR2314002 2 0.000 1.000 0.000 1.000
#> SRR2314001 2 0.000 1.000 0.000 1.000
#> SRR2314000 2 0.000 1.000 0.000 1.000
#> SRR2313998 2 0.000 1.000 0.000 1.000
#> SRR2313996 2 0.000 1.000 0.000 1.000
#> SRR2313999 1 0.000 0.991 1.000 0.000
#> SRR2313997 2 0.000 1.000 0.000 1.000
#> SRR2313992 1 0.000 0.991 1.000 0.000
#> SRR2313991 2 0.000 1.000 0.000 1.000
#> SRR2313990 2 0.000 1.000 0.000 1.000
#> SRR2313988 1 0.000 0.991 1.000 0.000
#> SRR2313987 1 0.000 0.991 1.000 0.000
#> SRR2313984 2 0.000 1.000 0.000 1.000
#> SRR2313989 2 0.000 1.000 0.000 1.000
#> SRR2313983 1 0.000 0.991 1.000 0.000
#> SRR2313986 1 0.000 0.991 1.000 0.000
#> SRR2313982 1 0.000 0.991 1.000 0.000
#> SRR2313981 1 0.802 0.677 0.756 0.244
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.6095 0.394 0.000 0.392 0.608
#> SRR2313980 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313977 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313976 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313975 1 0.1031 0.977 0.976 0.000 0.024
#> SRR2313973 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313993 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313994 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313995 3 0.6280 0.213 0.000 0.460 0.540
#> SRR2314024 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314015 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314025 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314023 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314033 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314045 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314042 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314043 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314041 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314044 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314040 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314039 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314038 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314037 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314036 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314034 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314032 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314031 1 0.0892 0.981 0.980 0.000 0.020
#> SRR2314029 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314030 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314028 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314027 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314026 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314020 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314019 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314021 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314018 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314016 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314014 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314013 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314010 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314009 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314008 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314007 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314005 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314004 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314003 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2314006 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2314001 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2314000 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2313998 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313996 3 0.2959 0.858 0.000 0.100 0.900
#> SRR2313999 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313997 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313992 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2313991 3 0.0000 0.951 0.000 0.000 1.000
#> SRR2313990 3 0.6307 0.122 0.000 0.488 0.512
#> SRR2313988 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313984 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313989 2 0.0000 1.000 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313986 1 0.0892 0.981 0.980 0.000 0.020
#> SRR2313982 1 0.0000 0.996 1.000 0.000 0.000
#> SRR2313981 3 0.0000 0.951 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.4630 0.683 0.000 0.252 0.732 0.016
#> SRR2313980 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2313979 1 0.0000 0.853 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.1406 0.868 0.000 0.960 0.024 0.016
#> SRR2313976 2 0.4072 0.871 0.000 0.748 0.000 0.252
#> SRR2313975 4 0.4193 0.609 0.268 0.000 0.000 0.732
#> SRR2313973 1 0.0188 0.852 0.996 0.000 0.000 0.004
#> SRR2313974 4 0.4776 0.398 0.376 0.000 0.000 0.624
#> SRR2313985 1 0.0000 0.853 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> SRR2313994 2 0.2843 0.870 0.000 0.892 0.020 0.088
#> SRR2313995 3 0.4744 0.653 0.000 0.284 0.704 0.012
#> SRR2314024 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2314015 2 0.1610 0.864 0.000 0.952 0.032 0.016
#> SRR2314025 1 0.4522 0.582 0.680 0.000 0.000 0.320
#> SRR2314023 1 0.0188 0.852 0.996 0.000 0.000 0.004
#> SRR2314033 2 0.4072 0.871 0.000 0.748 0.000 0.252
#> SRR2314045 4 0.4585 0.499 0.332 0.000 0.000 0.668
#> SRR2314042 4 0.4193 0.719 0.000 0.000 0.268 0.732
#> SRR2314043 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314041 3 0.3672 0.768 0.000 0.164 0.824 0.012
#> SRR2314044 3 0.0188 0.907 0.000 0.000 0.996 0.004
#> SRR2314040 3 0.0524 0.906 0.000 0.008 0.988 0.004
#> SRR2314039 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314038 3 0.1637 0.885 0.000 0.000 0.940 0.060
#> SRR2314037 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314036 2 0.4040 0.871 0.000 0.752 0.000 0.248
#> SRR2314035 3 0.0188 0.909 0.000 0.000 0.996 0.004
#> SRR2314034 4 0.4193 0.719 0.000 0.000 0.268 0.732
#> SRR2314032 1 0.4040 0.683 0.752 0.000 0.000 0.248
#> SRR2314031 4 0.4193 0.606 0.268 0.000 0.000 0.732
#> SRR2314029 3 0.1867 0.885 0.000 0.000 0.928 0.072
#> SRR2314030 4 0.4193 0.719 0.000 0.000 0.268 0.732
#> SRR2314028 4 0.4193 0.719 0.000 0.000 0.268 0.732
#> SRR2314027 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR2314026 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314020 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2314019 3 0.2589 0.828 0.000 0.000 0.884 0.116
#> SRR2314021 4 0.4193 0.719 0.000 0.000 0.268 0.732
#> SRR2314018 3 0.0188 0.909 0.000 0.000 0.996 0.004
#> SRR2314017 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314016 4 0.4193 0.606 0.268 0.000 0.000 0.732
#> SRR2314014 2 0.2843 0.870 0.000 0.892 0.020 0.088
#> SRR2314013 2 0.2843 0.870 0.000 0.892 0.020 0.088
#> SRR2314012 1 0.4522 0.582 0.680 0.000 0.000 0.320
#> SRR2314011 1 0.1389 0.842 0.952 0.000 0.000 0.048
#> SRR2314010 2 0.1284 0.869 0.000 0.964 0.024 0.012
#> SRR2314009 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314008 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> SRR2314007 3 0.1022 0.909 0.000 0.000 0.968 0.032
#> SRR2314005 2 0.4072 0.871 0.000 0.748 0.000 0.252
#> SRR2314004 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2314003 1 0.4164 0.672 0.736 0.000 0.000 0.264
#> SRR2314006 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2314002 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2314001 2 0.1406 0.868 0.000 0.960 0.024 0.016
#> SRR2314000 3 0.0817 0.911 0.000 0.000 0.976 0.024
#> SRR2313998 2 0.1042 0.875 0.000 0.972 0.020 0.008
#> SRR2313996 3 0.4485 0.691 0.000 0.248 0.740 0.012
#> SRR2313999 1 0.1389 0.842 0.952 0.000 0.000 0.048
#> SRR2313997 2 0.3219 0.890 0.000 0.836 0.000 0.164
#> SRR2313992 4 0.4790 0.568 0.000 0.000 0.380 0.620
#> SRR2313991 3 0.0707 0.911 0.000 0.000 0.980 0.020
#> SRR2313990 3 0.4630 0.683 0.000 0.252 0.732 0.016
#> SRR2313988 1 0.4008 0.688 0.756 0.000 0.000 0.244
#> SRR2313987 1 0.0000 0.853 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.0657 0.876 0.000 0.984 0.004 0.012
#> SRR2313989 2 0.4040 0.871 0.000 0.752 0.000 0.248
#> SRR2313983 1 0.0000 0.853 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.4193 0.606 0.268 0.000 0.000 0.732
#> SRR2313982 1 0.0000 0.853 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.0817 0.911 0.000 0.000 0.976 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 5 0.4403 0.1067 0.000 0.004 0.436 0.000 0.560
#> SRR2313980 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2313979 1 0.0000 0.7858 1.000 0.000 0.000 0.000 0.000
#> SRR2313977 5 0.0609 0.4657 0.000 0.000 0.020 0.000 0.980
#> SRR2313976 2 0.4161 0.8730 0.000 0.608 0.000 0.000 0.392
#> SRR2313975 4 0.2886 0.7926 0.000 0.148 0.008 0.844 0.000
#> SRR2313973 1 0.0290 0.7860 0.992 0.008 0.000 0.000 0.000
#> SRR2313974 4 0.5572 0.5184 0.124 0.248 0.000 0.628 0.000
#> SRR2313985 1 0.1043 0.7891 0.960 0.040 0.000 0.000 0.000
#> SRR2313993 5 0.2929 0.1628 0.000 0.180 0.000 0.000 0.820
#> SRR2313994 5 0.3534 0.1292 0.000 0.256 0.000 0.000 0.744
#> SRR2313995 5 0.4339 0.3205 0.000 0.012 0.336 0.000 0.652
#> SRR2314024 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2314015 5 0.1478 0.4736 0.000 0.000 0.064 0.000 0.936
#> SRR2314025 1 0.6339 0.5406 0.508 0.188 0.000 0.304 0.000
#> SRR2314023 1 0.0290 0.7860 0.992 0.008 0.000 0.000 0.000
#> SRR2314033 2 0.4161 0.8730 0.000 0.608 0.000 0.000 0.392
#> SRR2314045 4 0.5283 0.6140 0.084 0.232 0.008 0.676 0.000
#> SRR2314042 4 0.0290 0.8327 0.000 0.000 0.008 0.992 0.000
#> SRR2314043 3 0.0404 0.8886 0.000 0.000 0.988 0.012 0.000
#> SRR2314041 3 0.4383 0.2373 0.000 0.004 0.572 0.000 0.424
#> SRR2314044 3 0.1608 0.8572 0.000 0.000 0.928 0.000 0.072
#> SRR2314040 3 0.0290 0.8864 0.000 0.000 0.992 0.000 0.008
#> SRR2314039 3 0.0162 0.8884 0.000 0.004 0.996 0.000 0.000
#> SRR2314038 3 0.5117 0.7567 0.000 0.052 0.748 0.128 0.072
#> SRR2314037 3 0.0955 0.8853 0.000 0.004 0.968 0.028 0.000
#> SRR2314036 2 0.4114 0.8797 0.000 0.624 0.000 0.000 0.376
#> SRR2314035 3 0.0992 0.8844 0.000 0.024 0.968 0.000 0.008
#> SRR2314034 4 0.0579 0.8318 0.000 0.008 0.008 0.984 0.000
#> SRR2314032 1 0.6329 0.5459 0.528 0.232 0.000 0.240 0.000
#> SRR2314031 4 0.1851 0.8151 0.000 0.088 0.000 0.912 0.000
#> SRR2314029 3 0.5906 0.6676 0.000 0.048 0.676 0.108 0.168
#> SRR2314030 4 0.1331 0.8199 0.000 0.040 0.008 0.952 0.000
#> SRR2314028 4 0.0290 0.8327 0.000 0.000 0.008 0.992 0.000
#> SRR2314027 3 0.0865 0.8829 0.000 0.004 0.972 0.000 0.024
#> SRR2314026 3 0.2592 0.8624 0.000 0.052 0.892 0.056 0.000
#> SRR2314020 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2314019 3 0.4754 0.6275 0.000 0.052 0.684 0.264 0.000
#> SRR2314021 4 0.1408 0.8174 0.000 0.044 0.008 0.948 0.000
#> SRR2314018 3 0.0290 0.8864 0.000 0.000 0.992 0.000 0.008
#> SRR2314017 3 0.1750 0.8772 0.000 0.036 0.936 0.028 0.000
#> SRR2314016 4 0.2020 0.8114 0.000 0.100 0.000 0.900 0.000
#> SRR2314014 5 0.3707 0.0790 0.000 0.284 0.000 0.000 0.716
#> SRR2314013 5 0.3661 0.1094 0.000 0.276 0.000 0.000 0.724
#> SRR2314012 1 0.6339 0.5406 0.508 0.188 0.000 0.304 0.000
#> SRR2314011 1 0.4522 0.7530 0.744 0.176 0.000 0.080 0.000
#> SRR2314010 5 0.0912 0.4556 0.000 0.016 0.012 0.000 0.972
#> SRR2314009 3 0.0955 0.8853 0.000 0.004 0.968 0.028 0.000
#> SRR2314008 3 0.0510 0.8852 0.000 0.000 0.984 0.000 0.016
#> SRR2314007 3 0.3953 0.7542 0.000 0.048 0.784 0.000 0.168
#> SRR2314005 2 0.4161 0.8730 0.000 0.608 0.000 0.000 0.392
#> SRR2314004 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2314003 1 0.5902 0.6587 0.600 0.192 0.000 0.208 0.000
#> SRR2314006 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2314002 3 0.2570 0.8626 0.000 0.084 0.888 0.028 0.000
#> SRR2314001 5 0.1282 0.4206 0.000 0.044 0.004 0.000 0.952
#> SRR2314000 3 0.0404 0.8886 0.000 0.000 0.988 0.012 0.000
#> SRR2313998 5 0.3305 0.1461 0.000 0.224 0.000 0.000 0.776
#> SRR2313996 5 0.4604 0.1127 0.000 0.012 0.428 0.000 0.560
#> SRR2313999 1 0.4627 0.7488 0.732 0.188 0.000 0.080 0.000
#> SRR2313997 2 0.4304 0.9021 0.000 0.516 0.000 0.000 0.484
#> SRR2313992 4 0.4390 0.2190 0.000 0.004 0.428 0.568 0.000
#> SRR2313991 3 0.2270 0.8712 0.000 0.076 0.904 0.000 0.020
#> SRR2313990 5 0.4306 -0.0629 0.000 0.000 0.492 0.000 0.508
#> SRR2313988 1 0.6309 0.5502 0.532 0.228 0.000 0.240 0.000
#> SRR2313987 1 0.1792 0.7868 0.916 0.084 0.000 0.000 0.000
#> SRR2313984 5 0.1732 0.3787 0.000 0.080 0.000 0.000 0.920
#> SRR2313989 2 0.4150 0.8857 0.000 0.612 0.000 0.000 0.388
#> SRR2313983 1 0.0000 0.7858 1.000 0.000 0.000 0.000 0.000
#> SRR2313986 4 0.1908 0.8139 0.000 0.092 0.000 0.908 0.000
#> SRR2313982 1 0.0000 0.7858 1.000 0.000 0.000 0.000 0.000
#> SRR2313981 3 0.1571 0.8724 0.000 0.004 0.936 0.060 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.2996 0.61367 0.000 0.772 0.228 0.000 NA 0.000
#> SRR2313980 6 0.1663 0.71368 0.000 0.088 0.000 0.000 NA 0.912
#> SRR2313979 1 0.0000 0.71304 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2313977 2 0.4526 0.42730 0.000 0.708 0.004 0.000 NA 0.188
#> SRR2313976 6 0.2871 0.67951 0.000 0.004 0.000 0.000 NA 0.804
#> SRR2313975 4 0.4478 0.67330 0.000 0.076 0.000 0.680 NA 0.000
#> SRR2313973 1 0.0458 0.71286 0.984 0.000 0.000 0.000 NA 0.000
#> SRR2313974 4 0.6002 0.40059 0.060 0.068 0.000 0.440 NA 0.000
#> SRR2313985 1 0.1757 0.71856 0.916 0.008 0.000 0.000 NA 0.000
#> SRR2313993 6 0.5249 0.32028 0.000 0.368 0.000 0.000 NA 0.528
#> SRR2313994 6 0.5933 0.39680 0.000 0.288 0.000 0.000 NA 0.460
#> SRR2313995 2 0.3150 0.60629 0.000 0.828 0.120 0.000 NA 0.052
#> SRR2314024 6 0.2712 0.70596 0.000 0.088 0.000 0.000 NA 0.864
#> SRR2314015 2 0.5046 0.51358 0.000 0.712 0.060 0.000 NA 0.128
#> SRR2314025 1 0.6178 0.43216 0.404 0.004 0.000 0.272 NA 0.000
#> SRR2314023 1 0.0458 0.71286 0.984 0.000 0.000 0.000 NA 0.000
#> SRR2314033 6 0.2871 0.67951 0.000 0.004 0.000 0.000 NA 0.804
#> SRR2314045 4 0.5503 0.51755 0.016 0.092 0.000 0.540 NA 0.000
#> SRR2314042 4 0.0405 0.79829 0.000 0.000 0.008 0.988 NA 0.000
#> SRR2314043 3 0.0405 0.82947 0.000 0.008 0.988 0.000 NA 0.000
#> SRR2314041 2 0.3862 0.38550 0.000 0.608 0.388 0.000 NA 0.000
#> SRR2314044 3 0.2454 0.72527 0.000 0.160 0.840 0.000 NA 0.000
#> SRR2314040 3 0.1349 0.81439 0.000 0.056 0.940 0.000 NA 0.000
#> SRR2314039 3 0.0692 0.82858 0.000 0.020 0.976 0.000 NA 0.000
#> SRR2314038 3 0.6478 0.41655 0.000 0.248 0.536 0.112 NA 0.000
#> SRR2314037 3 0.1088 0.82492 0.000 0.000 0.960 0.024 NA 0.000
#> SRR2314036 6 0.1663 0.70359 0.000 0.000 0.000 0.000 NA 0.912
#> SRR2314035 3 0.1616 0.82163 0.000 0.020 0.932 0.000 NA 0.000
#> SRR2314034 4 0.0603 0.79601 0.000 0.004 0.016 0.980 NA 0.000
#> SRR2314032 1 0.7149 0.29132 0.352 0.092 0.000 0.208 NA 0.000
#> SRR2314031 4 0.2988 0.75771 0.000 0.024 0.000 0.824 NA 0.000
#> SRR2314029 2 0.6096 0.07543 0.000 0.480 0.380 0.072 NA 0.000
#> SRR2314030 4 0.1755 0.77940 0.000 0.032 0.008 0.932 NA 0.000
#> SRR2314028 4 0.0405 0.79829 0.000 0.000 0.008 0.988 NA 0.000
#> SRR2314027 3 0.1958 0.78638 0.000 0.100 0.896 0.000 NA 0.000
#> SRR2314026 3 0.4699 0.70935 0.000 0.056 0.736 0.144 NA 0.000
#> SRR2314020 6 0.1663 0.71368 0.000 0.088 0.000 0.000 NA 0.912
#> SRR2314019 3 0.5512 0.53328 0.000 0.044 0.588 0.304 NA 0.000
#> SRR2314021 4 0.2042 0.76315 0.000 0.024 0.024 0.920 NA 0.000
#> SRR2314018 3 0.0935 0.82353 0.000 0.032 0.964 0.000 NA 0.000
#> SRR2314017 3 0.2811 0.80388 0.000 0.020 0.872 0.032 NA 0.000
#> SRR2314016 4 0.3098 0.75227 0.000 0.024 0.000 0.812 NA 0.000
#> SRR2314014 6 0.5896 0.43449 0.000 0.236 0.000 0.000 NA 0.468
#> SRR2314013 6 0.5911 0.39844 0.000 0.280 0.000 0.000 NA 0.468
#> SRR2314012 1 0.6178 0.43655 0.404 0.004 0.000 0.272 NA 0.000
#> SRR2314011 1 0.4725 0.65239 0.604 0.000 0.000 0.064 NA 0.000
#> SRR2314010 2 0.4503 0.41204 0.000 0.696 0.000 0.000 NA 0.204
#> SRR2314009 3 0.1088 0.82492 0.000 0.000 0.960 0.024 NA 0.000
#> SRR2314008 3 0.1082 0.82140 0.000 0.040 0.956 0.000 NA 0.000
#> SRR2314007 2 0.5694 0.00738 0.000 0.476 0.420 0.036 NA 0.000
#> SRR2314005 6 0.2902 0.67942 0.000 0.004 0.000 0.000 NA 0.800
#> SRR2314004 6 0.3006 0.69892 0.000 0.092 0.000 0.000 NA 0.844
#> SRR2314003 1 0.5625 0.57210 0.504 0.000 0.000 0.164 NA 0.000
#> SRR2314006 6 0.1663 0.71368 0.000 0.088 0.000 0.000 NA 0.912
#> SRR2314002 3 0.4789 0.72875 0.000 0.068 0.728 0.056 NA 0.000
#> SRR2314001 2 0.4675 0.35472 0.000 0.672 0.000 0.000 NA 0.224
#> SRR2314000 3 0.0520 0.83006 0.000 0.008 0.984 0.000 NA 0.000
#> SRR2313998 6 0.5984 0.34315 0.000 0.344 0.000 0.000 NA 0.420
#> SRR2313996 2 0.2854 0.61930 0.000 0.792 0.208 0.000 NA 0.000
#> SRR2313999 1 0.4872 0.64893 0.596 0.004 0.000 0.064 NA 0.000
#> SRR2313997 6 0.1663 0.71368 0.000 0.088 0.000 0.000 NA 0.912
#> SRR2313992 3 0.3990 0.55038 0.000 0.004 0.676 0.304 NA 0.000
#> SRR2313991 3 0.4886 0.65716 0.000 0.184 0.680 0.008 NA 0.000
#> SRR2313990 2 0.4443 0.57830 0.000 0.664 0.276 0.000 NA 0.000
#> SRR2313988 1 0.7149 0.29132 0.352 0.092 0.000 0.208 NA 0.000
#> SRR2313987 1 0.2442 0.71496 0.852 0.004 0.000 0.000 NA 0.000
#> SRR2313984 2 0.5313 -0.07792 0.000 0.484 0.000 0.000 NA 0.412
#> SRR2313989 6 0.0363 0.71411 0.000 0.012 0.000 0.000 NA 0.988
#> SRR2313983 1 0.0000 0.71304 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2313986 4 0.2988 0.75854 0.000 0.024 0.000 0.824 NA 0.000
#> SRR2313982 1 0.0000 0.71304 1.000 0.000 0.000 0.000 NA 0.000
#> SRR2313981 3 0.2006 0.80124 0.000 0.000 0.904 0.080 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.5057 0.495 0.495
#> 3 3 0.771 0.788 0.909 0.1907 0.907 0.814
#> 4 4 0.853 0.825 0.909 0.1141 0.868 0.690
#> 5 5 0.840 0.758 0.879 0.0546 0.957 0.866
#> 6 6 0.800 0.691 0.836 0.0415 0.930 0.771
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
#> SRR2313978 2 0.000 0.996 0.000 1.000
#> SRR2313980 2 0.000 0.996 0.000 1.000
#> SRR2313979 1 0.000 0.993 1.000 0.000
#> SRR2313977 2 0.000 0.996 0.000 1.000
#> SRR2313976 2 0.000 0.996 0.000 1.000
#> SRR2313975 1 0.000 0.993 1.000 0.000
#> SRR2313973 1 0.000 0.993 1.000 0.000
#> SRR2313974 1 0.000 0.993 1.000 0.000
#> SRR2313985 1 0.000 0.993 1.000 0.000
#> SRR2313993 2 0.000 0.996 0.000 1.000
#> SRR2313994 2 0.000 0.996 0.000 1.000
#> SRR2313995 2 0.000 0.996 0.000 1.000
#> SRR2314024 2 0.000 0.996 0.000 1.000
#> SRR2314015 2 0.000 0.996 0.000 1.000
#> SRR2314025 1 0.000 0.993 1.000 0.000
#> SRR2314023 1 0.000 0.993 1.000 0.000
#> SRR2314033 2 0.000 0.996 0.000 1.000
#> SRR2314045 1 0.000 0.993 1.000 0.000
#> SRR2314042 1 0.000 0.993 1.000 0.000
#> SRR2314043 2 0.000 0.996 0.000 1.000
#> SRR2314041 2 0.000 0.996 0.000 1.000
#> SRR2314044 2 0.000 0.996 0.000 1.000
#> SRR2314040 2 0.000 0.996 0.000 1.000
#> SRR2314039 2 0.000 0.996 0.000 1.000
#> SRR2314038 1 0.714 0.754 0.804 0.196
#> SRR2314037 1 0.000 0.993 1.000 0.000
#> SRR2314036 2 0.000 0.996 0.000 1.000
#> SRR2314035 2 0.000 0.996 0.000 1.000
#> SRR2314034 1 0.000 0.993 1.000 0.000
#> SRR2314032 1 0.000 0.993 1.000 0.000
#> SRR2314031 1 0.000 0.993 1.000 0.000
#> SRR2314029 2 0.000 0.996 0.000 1.000
#> SRR2314030 1 0.000 0.993 1.000 0.000
#> SRR2314028 1 0.000 0.993 1.000 0.000
#> SRR2314027 2 0.000 0.996 0.000 1.000
#> SRR2314026 1 0.000 0.993 1.000 0.000
#> SRR2314020 2 0.000 0.996 0.000 1.000
#> SRR2314019 1 0.000 0.993 1.000 0.000
#> SRR2314021 1 0.000 0.993 1.000 0.000
#> SRR2314018 2 0.000 0.996 0.000 1.000
#> SRR2314017 1 0.141 0.975 0.980 0.020
#> SRR2314016 1 0.000 0.993 1.000 0.000
#> SRR2314014 2 0.000 0.996 0.000 1.000
#> SRR2314013 2 0.000 0.996 0.000 1.000
#> SRR2314012 1 0.000 0.993 1.000 0.000
#> SRR2314011 1 0.000 0.993 1.000 0.000
#> SRR2314010 2 0.000 0.996 0.000 1.000
#> SRR2314009 1 0.000 0.993 1.000 0.000
#> SRR2314008 2 0.000 0.996 0.000 1.000
#> SRR2314007 2 0.000 0.996 0.000 1.000
#> SRR2314005 2 0.000 0.996 0.000 1.000
#> SRR2314004 2 0.000 0.996 0.000 1.000
#> SRR2314003 1 0.000 0.993 1.000 0.000
#> SRR2314006 2 0.000 0.996 0.000 1.000
#> SRR2314002 1 0.000 0.993 1.000 0.000
#> SRR2314001 2 0.000 0.996 0.000 1.000
#> SRR2314000 2 0.634 0.807 0.160 0.840
#> SRR2313998 2 0.000 0.996 0.000 1.000
#> SRR2313996 2 0.000 0.996 0.000 1.000
#> SRR2313999 1 0.000 0.993 1.000 0.000
#> SRR2313997 2 0.000 0.996 0.000 1.000
#> SRR2313992 1 0.000 0.993 1.000 0.000
#> SRR2313991 2 0.000 0.996 0.000 1.000
#> SRR2313990 2 0.000 0.996 0.000 1.000
#> SRR2313988 1 0.000 0.993 1.000 0.000
#> SRR2313987 1 0.000 0.993 1.000 0.000
#> SRR2313984 2 0.000 0.996 0.000 1.000
#> SRR2313989 2 0.000 0.996 0.000 1.000
#> SRR2313983 1 0.000 0.993 1.000 0.000
#> SRR2313986 1 0.000 0.993 1.000 0.000
#> SRR2313982 1 0.000 0.993 1.000 0.000
#> SRR2313981 1 0.000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313977 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313975 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313973 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313995 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314024 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314015 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314025 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314023 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314045 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314042 1 0.0237 0.9390 0.996 0.000 0.004
#> SRR2314043 3 0.5835 0.3270 0.000 0.340 0.660
#> SRR2314041 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314044 2 0.4796 0.6530 0.000 0.780 0.220
#> SRR2314040 2 0.5529 0.4944 0.000 0.704 0.296
#> SRR2314039 2 0.1860 0.8646 0.000 0.948 0.052
#> SRR2314038 3 0.7843 0.4911 0.208 0.128 0.664
#> SRR2314037 1 0.4931 0.5938 0.768 0.000 0.232
#> SRR2314036 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314035 2 0.6295 0.0128 0.000 0.528 0.472
#> SRR2314034 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314032 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314031 1 0.0237 0.9390 0.996 0.000 0.004
#> SRR2314029 2 0.6180 0.3035 0.000 0.584 0.416
#> SRR2314030 1 0.4842 0.6654 0.776 0.000 0.224
#> SRR2314028 1 0.0237 0.9390 0.996 0.000 0.004
#> SRR2314027 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314026 1 0.5905 0.4175 0.648 0.000 0.352
#> SRR2314020 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314019 1 0.5016 0.6407 0.760 0.000 0.240
#> SRR2314021 1 0.4842 0.6654 0.776 0.000 0.224
#> SRR2314018 3 0.6235 0.1224 0.000 0.436 0.564
#> SRR2314017 3 0.7517 0.3879 0.420 0.040 0.540
#> SRR2314016 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314014 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314009 3 0.6305 0.2269 0.484 0.000 0.516
#> SRR2314008 2 0.5733 0.4441 0.000 0.676 0.324
#> SRR2314007 2 0.6045 0.3757 0.000 0.620 0.380
#> SRR2314005 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314003 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2314006 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314002 3 0.5810 0.3924 0.336 0.000 0.664
#> SRR2314001 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2314000 3 0.6374 0.5876 0.100 0.132 0.768
#> SRR2313998 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313996 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313999 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313992 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313991 2 0.6307 0.0871 0.000 0.512 0.488
#> SRR2313990 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313988 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.9062 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313986 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313982 1 0.0000 0.9420 1.000 0.000 0.000
#> SRR2313981 1 0.0000 0.9420 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313980 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313976 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313975 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313973 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313974 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313985 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313994 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313995 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314024 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314025 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314023 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314045 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314042 1 0.0817 0.958 0.976 0.000 0.000 0.024
#> SRR2314043 3 0.6374 0.450 0.000 0.084 0.592 0.324
#> SRR2314041 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314044 2 0.6178 0.422 0.000 0.660 0.112 0.228
#> SRR2314040 3 0.4948 0.385 0.000 0.440 0.560 0.000
#> SRR2314039 2 0.5990 0.492 0.000 0.692 0.164 0.144
#> SRR2314038 4 0.2589 0.410 0.000 0.000 0.116 0.884
#> SRR2314037 1 0.6319 0.326 0.604 0.000 0.312 0.084
#> SRR2314036 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.3312 0.553 0.000 0.052 0.876 0.072
#> SRR2314034 1 0.0188 0.974 0.996 0.000 0.000 0.004
#> SRR2314032 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314031 1 0.0817 0.958 0.976 0.000 0.000 0.024
#> SRR2314029 4 0.4100 0.423 0.000 0.148 0.036 0.816
#> SRR2314030 4 0.4898 0.435 0.416 0.000 0.000 0.584
#> SRR2314028 1 0.0817 0.958 0.976 0.000 0.000 0.024
#> SRR2314027 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314026 4 0.4944 0.502 0.072 0.000 0.160 0.768
#> SRR2314020 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314019 4 0.5024 0.515 0.360 0.000 0.008 0.632
#> SRR2314021 4 0.4843 0.475 0.396 0.000 0.000 0.604
#> SRR2314018 3 0.4365 0.584 0.000 0.188 0.784 0.028
#> SRR2314017 3 0.3278 0.499 0.020 0.000 0.864 0.116
#> SRR2314016 1 0.0592 0.965 0.984 0.000 0.000 0.016
#> SRR2314014 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314013 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314012 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314011 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314010 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314009 3 0.3074 0.447 0.152 0.000 0.848 0.000
#> SRR2314008 3 0.4843 0.443 0.000 0.396 0.604 0.000
#> SRR2314007 4 0.4988 0.352 0.000 0.236 0.036 0.728
#> SRR2314005 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314004 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314003 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2314006 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314002 4 0.5762 0.351 0.040 0.000 0.352 0.608
#> SRR2314001 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2314000 3 0.4454 0.460 0.000 0.000 0.692 0.308
#> SRR2313998 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313996 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313999 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313997 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313992 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313991 4 0.7065 0.261 0.000 0.212 0.216 0.572
#> SRR2313990 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313988 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313987 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313984 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313989 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313986 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313982 1 0.0000 0.977 1.000 0.000 0.000 0.000
#> SRR2313981 1 0.0188 0.974 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
#> SRR2313978 2 0.0162 0.9875 0.000 0.996 0.004 0.000 0.000
#> SRR2313980 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313977 2 0.0912 0.9771 0.000 0.972 0.012 0.000 0.016
#> SRR2313976 2 0.0798 0.9794 0.000 0.976 0.008 0.000 0.016
#> SRR2313975 1 0.0404 0.9202 0.988 0.000 0.000 0.012 0.000
#> SRR2313973 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313974 1 0.0000 0.9222 1.000 0.000 0.000 0.000 0.000
#> SRR2313985 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313993 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313994 2 0.0798 0.9794 0.000 0.976 0.008 0.000 0.016
#> SRR2313995 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314024 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314025 1 0.1012 0.9168 0.968 0.000 0.020 0.012 0.000
#> SRR2314023 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2314033 2 0.0798 0.9794 0.000 0.976 0.008 0.000 0.016
#> SRR2314045 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2314042 1 0.4157 0.6747 0.716 0.000 0.020 0.264 0.000
#> SRR2314043 3 0.3819 0.5371 0.000 0.000 0.756 0.016 0.228
#> SRR2314041 2 0.0162 0.9875 0.000 0.996 0.004 0.000 0.000
#> SRR2314044 3 0.5341 0.2171 0.000 0.300 0.620 0.000 0.080
#> SRR2314040 5 0.6153 0.1237 0.000 0.416 0.084 0.016 0.484
#> SRR2314039 4 0.6800 0.1836 0.000 0.240 0.044 0.560 0.156
#> SRR2314038 3 0.3242 0.2364 0.000 0.000 0.784 0.216 0.000
#> SRR2314037 1 0.6946 0.0445 0.460 0.000 0.328 0.020 0.192
#> SRR2314036 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 5 0.0854 0.3670 0.000 0.004 0.012 0.008 0.976
#> SRR2314034 1 0.2616 0.8619 0.880 0.000 0.020 0.100 0.000
#> SRR2314032 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2314031 1 0.3999 0.7087 0.740 0.000 0.020 0.240 0.000
#> SRR2314029 4 0.6129 0.0643 0.000 0.128 0.420 0.452 0.000
#> SRR2314030 4 0.3109 0.4724 0.200 0.000 0.000 0.800 0.000
#> SRR2314028 1 0.4106 0.6866 0.724 0.000 0.020 0.256 0.000
#> SRR2314027 2 0.0451 0.9802 0.000 0.988 0.008 0.000 0.004
#> SRR2314026 4 0.1012 0.5148 0.000 0.000 0.020 0.968 0.012
#> SRR2314020 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.0609 0.5315 0.020 0.000 0.000 0.980 0.000
#> SRR2314021 4 0.2648 0.5119 0.152 0.000 0.000 0.848 0.000
#> SRR2314018 5 0.4564 0.2573 0.000 0.072 0.176 0.004 0.748
#> SRR2314017 5 0.2798 0.3741 0.000 0.000 0.140 0.008 0.852
#> SRR2314016 1 0.2616 0.8609 0.880 0.000 0.020 0.100 0.000
#> SRR2314014 2 0.0912 0.9771 0.000 0.972 0.012 0.000 0.016
#> SRR2314013 2 0.0912 0.9771 0.000 0.972 0.012 0.000 0.016
#> SRR2314012 1 0.1173 0.9182 0.964 0.000 0.020 0.012 0.004
#> SRR2314011 1 0.0771 0.9190 0.976 0.000 0.020 0.004 0.000
#> SRR2314010 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314009 5 0.6060 0.1933 0.136 0.000 0.116 0.072 0.676
#> SRR2314008 5 0.4564 0.2702 0.000 0.372 0.016 0.000 0.612
#> SRR2314007 4 0.6703 0.0667 0.000 0.244 0.360 0.396 0.000
#> SRR2314005 2 0.0912 0.9771 0.000 0.972 0.012 0.000 0.016
#> SRR2314004 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 1 0.0609 0.9197 0.980 0.000 0.020 0.000 0.000
#> SRR2314006 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 5 0.6389 0.1734 0.004 0.000 0.284 0.184 0.528
#> SRR2314001 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2314000 3 0.3586 0.5212 0.000 0.000 0.736 0.000 0.264
#> SRR2313998 2 0.0798 0.9794 0.000 0.976 0.008 0.000 0.016
#> SRR2313996 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313999 1 0.0510 0.9206 0.984 0.000 0.016 0.000 0.000
#> SRR2313997 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 1 0.1026 0.9170 0.968 0.000 0.024 0.004 0.004
#> SRR2313991 5 0.8082 0.1894 0.000 0.176 0.284 0.136 0.404
#> SRR2313990 2 0.0912 0.9771 0.000 0.972 0.012 0.000 0.016
#> SRR2313988 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313987 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313984 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313989 2 0.0000 0.9885 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313986 1 0.0898 0.9182 0.972 0.000 0.020 0.008 0.000
#> SRR2313982 1 0.0162 0.9224 0.996 0.000 0.000 0.000 0.004
#> SRR2313981 1 0.2722 0.8773 0.892 0.000 0.040 0.060 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313980 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313979 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313977 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2313976 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2313975 1 0.1477 0.8716 0.940 0.000 0.000 0.048 0.008 0.004
#> SRR2313973 1 0.0000 0.8953 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR2313974 1 0.0260 0.8962 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313985 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313993 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313994 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2313995 2 0.1124 0.9132 0.000 0.956 0.000 0.000 0.008 0.036
#> SRR2314024 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314015 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314025 1 0.2442 0.8021 0.852 0.000 0.000 0.144 0.000 0.004
#> SRR2314023 1 0.0146 0.8959 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2314033 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2314045 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2314042 4 0.3961 0.3074 0.440 0.000 0.000 0.556 0.000 0.004
#> SRR2314043 6 0.4587 0.2197 0.000 0.004 0.372 0.000 0.036 0.588
#> SRR2314041 2 0.0820 0.9367 0.000 0.972 0.016 0.000 0.000 0.012
#> SRR2314044 6 0.5873 0.1821 0.000 0.248 0.272 0.000 0.000 0.480
#> SRR2314040 3 0.4133 0.2553 0.000 0.156 0.772 0.004 0.040 0.028
#> SRR2314039 3 0.7974 0.0505 0.000 0.136 0.300 0.280 0.024 0.260
#> SRR2314038 6 0.5962 0.1971 0.000 0.000 0.036 0.128 0.284 0.552
#> SRR2314037 3 0.6456 0.1344 0.224 0.000 0.412 0.024 0.000 0.340
#> SRR2314036 2 0.0146 0.9418 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR2314035 3 0.3995 -0.1126 0.000 0.000 0.516 0.000 0.480 0.004
#> SRR2314034 1 0.3448 0.5754 0.716 0.000 0.000 0.280 0.000 0.004
#> SRR2314032 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2314031 4 0.3986 0.2339 0.464 0.000 0.000 0.532 0.000 0.004
#> SRR2314029 6 0.6552 0.3433 0.000 0.120 0.008 0.184 0.116 0.572
#> SRR2314030 4 0.2956 0.4868 0.100 0.000 0.000 0.856 0.016 0.028
#> SRR2314028 4 0.3966 0.2982 0.444 0.000 0.000 0.552 0.000 0.004
#> SRR2314027 2 0.2450 0.8527 0.000 0.896 0.024 0.004 0.012 0.064
#> SRR2314026 4 0.4122 0.2152 0.000 0.000 0.020 0.764 0.056 0.160
#> SRR2314020 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314019 4 0.2468 0.3383 0.000 0.000 0.016 0.880 0.008 0.096
#> SRR2314021 4 0.3054 0.4633 0.076 0.000 0.000 0.848 0.004 0.072
#> SRR2314018 3 0.3720 0.3351 0.000 0.036 0.812 0.000 0.108 0.044
#> SRR2314017 5 0.2416 0.6163 0.000 0.000 0.156 0.000 0.844 0.000
#> SRR2314016 1 0.3668 0.4573 0.668 0.000 0.000 0.328 0.000 0.004
#> SRR2314014 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2314013 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2314012 1 0.2146 0.8268 0.880 0.000 0.000 0.116 0.000 0.004
#> SRR2314011 1 0.1471 0.8681 0.932 0.000 0.000 0.064 0.000 0.004
#> SRR2314010 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314009 3 0.6000 0.3437 0.096 0.000 0.652 0.020 0.088 0.144
#> SRR2314008 2 0.6203 -0.3063 0.000 0.384 0.336 0.000 0.276 0.004
#> SRR2314007 6 0.7077 0.2867 0.000 0.252 0.012 0.120 0.128 0.488
#> SRR2314005 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2314004 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314003 1 0.0935 0.8841 0.964 0.000 0.000 0.032 0.000 0.004
#> SRR2314006 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314002 5 0.0891 0.7203 0.008 0.000 0.000 0.024 0.968 0.000
#> SRR2314001 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2314000 6 0.4709 0.1552 0.004 0.000 0.444 0.000 0.036 0.516
#> SRR2313998 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2313996 2 0.0363 0.9372 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR2313999 1 0.0777 0.8872 0.972 0.000 0.000 0.024 0.000 0.004
#> SRR2313997 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313992 1 0.2589 0.8307 0.888 0.000 0.024 0.028 0.000 0.060
#> SRR2313991 5 0.3153 0.6359 0.000 0.100 0.004 0.012 0.848 0.036
#> SRR2313990 2 0.1411 0.9235 0.000 0.936 0.060 0.000 0.000 0.004
#> SRR2313988 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313987 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313984 2 0.0146 0.9408 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313989 2 0.0000 0.9424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313983 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313986 1 0.2668 0.7777 0.828 0.000 0.000 0.168 0.000 0.004
#> SRR2313982 1 0.0405 0.8962 0.988 0.000 0.000 0.000 0.008 0.004
#> SRR2313981 1 0.5459 0.5655 0.680 0.000 0.084 0.156 0.004 0.076
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 16873 rows and 72 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 0.767 0.908 0.958 0.4896 0.512 0.512
#> 3 3 0.792 0.895 0.951 0.3580 0.714 0.492
#> 4 4 0.952 0.914 0.963 0.1109 0.891 0.689
#> 5 5 0.858 0.796 0.901 0.0523 0.942 0.789
#> 6 6 0.812 0.767 0.874 0.0535 0.923 0.681
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR2313978 2 0.0000 0.939 0.000 1.000
#> SRR2313980 2 0.0000 0.939 0.000 1.000
#> SRR2313979 1 0.0000 0.976 1.000 0.000
#> SRR2313977 2 0.0000 0.939 0.000 1.000
#> SRR2313976 2 0.0000 0.939 0.000 1.000
#> SRR2313975 1 0.0000 0.976 1.000 0.000
#> SRR2313973 1 0.0000 0.976 1.000 0.000
#> SRR2313974 1 0.0000 0.976 1.000 0.000
#> SRR2313985 1 0.0000 0.976 1.000 0.000
#> SRR2313993 2 0.0000 0.939 0.000 1.000
#> SRR2313994 2 0.0000 0.939 0.000 1.000
#> SRR2313995 2 0.0000 0.939 0.000 1.000
#> SRR2314024 2 0.0000 0.939 0.000 1.000
#> SRR2314015 2 0.0000 0.939 0.000 1.000
#> SRR2314025 1 0.0000 0.976 1.000 0.000
#> SRR2314023 1 0.0000 0.976 1.000 0.000
#> SRR2314033 2 0.0000 0.939 0.000 1.000
#> SRR2314045 1 0.0000 0.976 1.000 0.000
#> SRR2314042 1 0.0000 0.976 1.000 0.000
#> SRR2314043 2 0.8443 0.686 0.272 0.728
#> SRR2314041 2 0.0376 0.938 0.004 0.996
#> SRR2314044 2 0.0376 0.938 0.004 0.996
#> SRR2314040 2 0.0376 0.938 0.004 0.996
#> SRR2314039 2 0.4298 0.879 0.088 0.912
#> SRR2314038 2 0.8327 0.697 0.264 0.736
#> SRR2314037 2 0.8443 0.686 0.272 0.728
#> SRR2314036 2 0.0000 0.939 0.000 1.000
#> SRR2314035 2 0.7056 0.783 0.192 0.808
#> SRR2314034 1 0.0000 0.976 1.000 0.000
#> SRR2314032 1 0.0000 0.976 1.000 0.000
#> SRR2314031 1 0.0000 0.976 1.000 0.000
#> SRR2314029 2 0.0376 0.938 0.004 0.996
#> SRR2314030 1 0.0000 0.976 1.000 0.000
#> SRR2314028 1 0.0000 0.976 1.000 0.000
#> SRR2314027 2 0.0376 0.938 0.004 0.996
#> SRR2314026 2 0.8443 0.686 0.272 0.728
#> SRR2314020 2 0.0000 0.939 0.000 1.000
#> SRR2314019 2 0.8443 0.686 0.272 0.728
#> SRR2314021 1 0.0000 0.976 1.000 0.000
#> SRR2314018 2 0.5946 0.831 0.144 0.856
#> SRR2314017 1 0.9850 0.136 0.572 0.428
#> SRR2314016 1 0.0000 0.976 1.000 0.000
#> SRR2314014 2 0.0000 0.939 0.000 1.000
#> SRR2314013 2 0.0000 0.939 0.000 1.000
#> SRR2314012 1 0.0000 0.976 1.000 0.000
#> SRR2314011 1 0.0000 0.976 1.000 0.000
#> SRR2314010 2 0.0000 0.939 0.000 1.000
#> SRR2314009 2 0.8443 0.686 0.272 0.728
#> SRR2314008 2 0.0376 0.938 0.004 0.996
#> SRR2314007 2 0.0376 0.938 0.004 0.996
#> SRR2314005 2 0.0000 0.939 0.000 1.000
#> SRR2314004 2 0.0000 0.939 0.000 1.000
#> SRR2314003 1 0.0000 0.976 1.000 0.000
#> SRR2314006 2 0.0000 0.939 0.000 1.000
#> SRR2314002 1 0.6712 0.754 0.824 0.176
#> SRR2314001 2 0.0000 0.939 0.000 1.000
#> SRR2314000 2 0.9209 0.570 0.336 0.664
#> SRR2313998 2 0.0000 0.939 0.000 1.000
#> SRR2313996 2 0.0000 0.939 0.000 1.000
#> SRR2313999 1 0.0000 0.976 1.000 0.000
#> SRR2313997 2 0.0000 0.939 0.000 1.000
#> SRR2313992 1 0.0000 0.976 1.000 0.000
#> SRR2313991 2 0.0376 0.938 0.004 0.996
#> SRR2313990 2 0.0000 0.939 0.000 1.000
#> SRR2313988 1 0.0000 0.976 1.000 0.000
#> SRR2313987 1 0.0000 0.976 1.000 0.000
#> SRR2313984 2 0.0000 0.939 0.000 1.000
#> SRR2313989 2 0.0000 0.939 0.000 1.000
#> SRR2313983 1 0.0000 0.976 1.000 0.000
#> SRR2313986 1 0.0000 0.976 1.000 0.000
#> SRR2313982 1 0.0000 0.976 1.000 0.000
#> SRR2313981 1 0.0376 0.972 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 3 0.3267 0.840 0.000 0.116 0.884
#> SRR2313980 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313979 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313977 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313976 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313975 1 0.5882 0.605 0.652 0.000 0.348
#> SRR2313973 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313974 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313985 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313993 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313994 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313995 3 0.5882 0.465 0.000 0.348 0.652
#> SRR2314024 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314015 2 0.1411 0.960 0.000 0.964 0.036
#> SRR2314025 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314023 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314033 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314045 1 0.3192 0.833 0.888 0.000 0.112
#> SRR2314042 1 0.5882 0.605 0.652 0.000 0.348
#> SRR2314043 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314041 3 0.0592 0.935 0.000 0.012 0.988
#> SRR2314044 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314040 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314039 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314038 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314037 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314036 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314035 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314034 1 0.5882 0.605 0.652 0.000 0.348
#> SRR2314032 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314031 1 0.5706 0.641 0.680 0.000 0.320
#> SRR2314029 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314030 3 0.3340 0.817 0.120 0.000 0.880
#> SRR2314028 1 0.5882 0.605 0.652 0.000 0.348
#> SRR2314027 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314026 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314020 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314019 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314021 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314018 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314017 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314016 1 0.3192 0.833 0.888 0.000 0.112
#> SRR2314014 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314013 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314012 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314011 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314010 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314009 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314008 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314007 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314005 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314004 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314003 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2314006 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314002 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2314001 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2314000 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2313998 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313996 3 0.3267 0.841 0.000 0.116 0.884
#> SRR2313999 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313997 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313992 3 0.5733 0.396 0.324 0.000 0.676
#> SRR2313991 3 0.0000 0.944 0.000 0.000 1.000
#> SRR2313990 3 0.4555 0.742 0.000 0.200 0.800
#> SRR2313988 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313987 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313984 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313989 2 0.0000 0.998 0.000 1.000 0.000
#> SRR2313983 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313986 1 0.5859 0.611 0.656 0.000 0.344
#> SRR2313982 1 0.0000 0.889 1.000 0.000 0.000
#> SRR2313981 3 0.0000 0.944 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2313980 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313979 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2313977 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313976 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313975 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2313973 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2313974 4 0.0921 0.956 0.028 0.000 0.000 0.972
#> SRR2313985 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2313993 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313994 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313995 3 0.2973 0.794 0.000 0.144 0.856 0.000
#> SRR2314024 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314015 2 0.4661 0.468 0.000 0.652 0.348 0.000
#> SRR2314025 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2314023 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2314033 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314045 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2314042 4 0.0817 0.964 0.000 0.000 0.024 0.976
#> SRR2314043 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314041 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314044 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314040 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314039 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314038 3 0.4164 0.634 0.000 0.000 0.736 0.264
#> SRR2314037 3 0.1474 0.901 0.000 0.000 0.948 0.052
#> SRR2314036 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314035 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314034 4 0.0817 0.964 0.000 0.000 0.024 0.976
#> SRR2314032 4 0.2149 0.901 0.088 0.000 0.000 0.912
#> SRR2314031 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2314029 3 0.1211 0.911 0.000 0.000 0.960 0.040
#> SRR2314030 4 0.0707 0.964 0.000 0.000 0.020 0.980
#> SRR2314028 4 0.0817 0.964 0.000 0.000 0.024 0.976
#> SRR2314027 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314026 3 0.3942 0.686 0.000 0.000 0.764 0.236
#> SRR2314020 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314019 4 0.2011 0.905 0.000 0.000 0.080 0.920
#> SRR2314021 4 0.1389 0.941 0.000 0.000 0.048 0.952
#> SRR2314018 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314017 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314016 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2314014 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314013 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314012 4 0.0921 0.955 0.028 0.000 0.000 0.972
#> SRR2314011 1 0.2408 0.882 0.896 0.000 0.000 0.104
#> SRR2314010 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314009 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314008 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314007 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314005 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314004 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314003 4 0.0921 0.956 0.028 0.000 0.000 0.972
#> SRR2314006 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314002 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2314001 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2314000 3 0.1022 0.917 0.000 0.000 0.968 0.032
#> SRR2313998 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313996 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2313999 1 0.2081 0.896 0.916 0.000 0.000 0.084
#> SRR2313997 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313992 4 0.0817 0.964 0.000 0.000 0.024 0.976
#> SRR2313991 3 0.0000 0.938 0.000 0.000 1.000 0.000
#> SRR2313990 3 0.0188 0.935 0.000 0.004 0.996 0.000
#> SRR2313988 1 0.4855 0.352 0.600 0.000 0.000 0.400
#> SRR2313987 1 0.1389 0.914 0.952 0.000 0.000 0.048
#> SRR2313984 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313989 2 0.0000 0.979 0.000 1.000 0.000 0.000
#> SRR2313983 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2313986 4 0.0000 0.968 0.000 0.000 0.000 1.000
#> SRR2313982 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> SRR2313981 3 0.4992 0.103 0.000 0.000 0.524 0.476
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.1478 0.8329 0.064 0.000 0.936 0.000 0.000
#> SRR2313980 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313979 5 0.0000 0.9974 0.000 0.000 0.000 0.000 1.000
#> SRR2313977 2 0.1478 0.9163 0.064 0.936 0.000 0.000 0.000
#> SRR2313976 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313975 1 0.1732 0.8687 0.920 0.000 0.000 0.080 0.000
#> SRR2313973 5 0.0000 0.9974 0.000 0.000 0.000 0.000 1.000
#> SRR2313974 1 0.1608 0.8726 0.928 0.000 0.000 0.072 0.000
#> SRR2313985 5 0.0510 0.9868 0.016 0.000 0.000 0.000 0.984
#> SRR2313993 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313994 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313995 3 0.3116 0.7782 0.064 0.076 0.860 0.000 0.000
#> SRR2314024 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314015 2 0.5245 0.4401 0.064 0.608 0.328 0.000 0.000
#> SRR2314025 4 0.4088 0.3441 0.368 0.000 0.000 0.632 0.000
#> SRR2314023 5 0.0000 0.9974 0.000 0.000 0.000 0.000 1.000
#> SRR2314033 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314045 1 0.3452 0.6733 0.756 0.000 0.000 0.244 0.000
#> SRR2314042 4 0.0162 0.7306 0.004 0.000 0.000 0.996 0.000
#> SRR2314043 3 0.2516 0.7834 0.000 0.000 0.860 0.140 0.000
#> SRR2314041 3 0.1478 0.8329 0.064 0.000 0.936 0.000 0.000
#> SRR2314044 3 0.1478 0.8329 0.064 0.000 0.936 0.000 0.000
#> SRR2314040 3 0.0000 0.8417 0.000 0.000 1.000 0.000 0.000
#> SRR2314039 3 0.0510 0.8401 0.000 0.000 0.984 0.016 0.000
#> SRR2314038 4 0.5289 0.0311 0.064 0.000 0.340 0.596 0.000
#> SRR2314037 3 0.3966 0.6252 0.000 0.000 0.664 0.336 0.000
#> SRR2314036 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314035 3 0.0162 0.8413 0.000 0.000 0.996 0.004 0.000
#> SRR2314034 4 0.0162 0.7306 0.004 0.000 0.000 0.996 0.000
#> SRR2314032 1 0.1478 0.8730 0.936 0.000 0.000 0.064 0.000
#> SRR2314031 4 0.3999 0.3805 0.344 0.000 0.000 0.656 0.000
#> SRR2314029 3 0.2654 0.8137 0.064 0.000 0.888 0.048 0.000
#> SRR2314030 4 0.0162 0.7306 0.004 0.000 0.000 0.996 0.000
#> SRR2314028 4 0.0162 0.7306 0.004 0.000 0.000 0.996 0.000
#> SRR2314027 3 0.0000 0.8417 0.000 0.000 1.000 0.000 0.000
#> SRR2314026 3 0.3305 0.7178 0.000 0.000 0.776 0.224 0.000
#> SRR2314020 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314019 4 0.2763 0.6482 0.004 0.000 0.148 0.848 0.000
#> SRR2314021 4 0.0162 0.7306 0.004 0.000 0.000 0.996 0.000
#> SRR2314018 3 0.0162 0.8413 0.000 0.000 0.996 0.004 0.000
#> SRR2314017 3 0.3949 0.6287 0.000 0.000 0.668 0.332 0.000
#> SRR2314016 4 0.4045 0.3632 0.356 0.000 0.000 0.644 0.000
#> SRR2314014 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314013 2 0.0162 0.9665 0.000 0.996 0.004 0.000 0.000
#> SRR2314012 4 0.4268 0.1515 0.444 0.000 0.000 0.556 0.000
#> SRR2314011 1 0.1671 0.8626 0.924 0.000 0.000 0.000 0.076
#> SRR2314010 2 0.2104 0.8975 0.060 0.916 0.024 0.000 0.000
#> SRR2314009 3 0.3949 0.6287 0.000 0.000 0.668 0.332 0.000
#> SRR2314008 3 0.0000 0.8417 0.000 0.000 1.000 0.000 0.000
#> SRR2314007 3 0.1764 0.8318 0.064 0.000 0.928 0.008 0.000
#> SRR2314005 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314004 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314003 1 0.1608 0.8726 0.928 0.000 0.000 0.072 0.000
#> SRR2314006 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314002 3 0.3949 0.6287 0.000 0.000 0.668 0.332 0.000
#> SRR2314001 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2314000 3 0.3949 0.6287 0.000 0.000 0.668 0.332 0.000
#> SRR2313998 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313996 3 0.1478 0.8329 0.064 0.000 0.936 0.000 0.000
#> SRR2313999 1 0.1671 0.8626 0.924 0.000 0.000 0.000 0.076
#> SRR2313997 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313992 4 0.1478 0.6864 0.000 0.000 0.064 0.936 0.000
#> SRR2313991 3 0.0290 0.8417 0.008 0.000 0.992 0.000 0.000
#> SRR2313990 3 0.1478 0.8329 0.064 0.000 0.936 0.000 0.000
#> SRR2313988 1 0.1697 0.8667 0.932 0.000 0.000 0.008 0.060
#> SRR2313987 1 0.1908 0.8511 0.908 0.000 0.000 0.000 0.092
#> SRR2313984 2 0.0404 0.9609 0.012 0.988 0.000 0.000 0.000
#> SRR2313989 2 0.0000 0.9694 0.000 1.000 0.000 0.000 0.000
#> SRR2313983 5 0.0000 0.9974 0.000 0.000 0.000 0.000 1.000
#> SRR2313986 1 0.4150 0.3045 0.612 0.000 0.000 0.388 0.000
#> SRR2313982 5 0.0000 0.9974 0.000 0.000 0.000 0.000 1.000
#> SRR2313981 3 0.4302 0.3672 0.000 0.000 0.520 0.480 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.0146 0.7435 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313980 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313979 5 0.0000 0.9939 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313977 6 0.5481 0.5963 0.000 0.264 0.176 0.000 0.000 0.560
#> SRR2313976 6 0.2902 0.8366 0.000 0.004 0.196 0.000 0.000 0.800
#> SRR2313975 1 0.0790 0.8769 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR2313973 5 0.0000 0.9939 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313974 1 0.0146 0.8928 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2313985 5 0.0632 0.9774 0.024 0.000 0.000 0.000 0.976 0.000
#> SRR2313993 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313994 6 0.2902 0.8366 0.000 0.004 0.196 0.000 0.000 0.800
#> SRR2313995 2 0.0632 0.7311 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR2314024 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314015 2 0.3857 -0.0946 0.000 0.532 0.000 0.000 0.000 0.468
#> SRR2314025 4 0.3288 0.6321 0.276 0.000 0.000 0.724 0.000 0.000
#> SRR2314023 5 0.0260 0.9894 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR2314033 6 0.2902 0.8366 0.000 0.004 0.196 0.000 0.000 0.800
#> SRR2314045 1 0.3747 0.2741 0.604 0.000 0.000 0.396 0.000 0.000
#> SRR2314042 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314043 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314041 2 0.0458 0.7420 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR2314044 2 0.0260 0.7431 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR2314040 2 0.3221 0.5731 0.000 0.736 0.264 0.000 0.000 0.000
#> SRR2314039 2 0.3684 0.3368 0.000 0.628 0.372 0.000 0.000 0.000
#> SRR2314038 3 0.5509 0.3680 0.000 0.292 0.544 0.164 0.000 0.000
#> SRR2314037 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314036 6 0.0547 0.8900 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR2314035 3 0.3023 0.8437 0.000 0.232 0.768 0.000 0.000 0.000
#> SRR2314034 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314032 1 0.0260 0.8916 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2314031 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314029 2 0.0632 0.7306 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR2314030 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314028 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314027 2 0.3221 0.5731 0.000 0.736 0.264 0.000 0.000 0.000
#> SRR2314026 2 0.4337 -0.0816 0.000 0.500 0.480 0.020 0.000 0.000
#> SRR2314020 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314019 4 0.3612 0.6770 0.000 0.036 0.200 0.764 0.000 0.000
#> SRR2314021 4 0.0000 0.8503 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR2314018 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314017 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314016 4 0.3023 0.6816 0.232 0.000 0.000 0.768 0.000 0.000
#> SRR2314014 6 0.2902 0.8366 0.000 0.004 0.196 0.000 0.000 0.800
#> SRR2314013 6 0.3012 0.8346 0.000 0.008 0.196 0.000 0.000 0.796
#> SRR2314012 4 0.3833 0.2631 0.444 0.000 0.000 0.556 0.000 0.000
#> SRR2314011 1 0.0146 0.8927 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2314010 6 0.3428 0.5772 0.000 0.304 0.000 0.000 0.000 0.696
#> SRR2314009 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314008 2 0.3266 0.5628 0.000 0.728 0.272 0.000 0.000 0.000
#> SRR2314007 2 0.0146 0.7437 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR2314005 6 0.2902 0.8366 0.000 0.004 0.196 0.000 0.000 0.800
#> SRR2314004 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314003 1 0.0146 0.8928 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR2314006 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314002 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2314001 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2314000 3 0.2762 0.8927 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR2313998 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313996 2 0.0146 0.7435 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR2313999 1 0.0146 0.8927 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR2313997 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313992 3 0.2762 0.6566 0.000 0.000 0.804 0.196 0.000 0.000
#> SRR2313991 2 0.3151 0.5861 0.000 0.748 0.252 0.000 0.000 0.000
#> SRR2313990 2 0.0000 0.7431 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR2313988 1 0.0260 0.8916 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR2313987 1 0.0458 0.8865 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR2313984 6 0.2135 0.8077 0.000 0.128 0.000 0.000 0.000 0.872
#> SRR2313989 6 0.0000 0.8944 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR2313983 5 0.0000 0.9939 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313986 1 0.3592 0.3543 0.656 0.000 0.000 0.344 0.000 0.000
#> SRR2313982 5 0.0000 0.9939 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR2313981 3 0.3122 0.8725 0.000 0.176 0.804 0.020 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 16873 rows and 72 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 0.619 0.845 0.925 0.4663 0.503 0.503
#> 3 3 0.360 0.555 0.775 0.2344 0.710 0.526
#> 4 4 0.442 0.436 0.719 0.1528 0.796 0.571
#> 5 5 0.618 0.609 0.795 0.1427 0.752 0.384
#> 6 6 0.596 0.507 0.691 0.0517 0.867 0.556
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
#> SRR2313978 2 0.9358 0.618 0.352 0.648
#> SRR2313980 2 0.0000 0.851 0.000 1.000
#> SRR2313979 1 0.0000 0.960 1.000 0.000
#> SRR2313977 2 0.8608 0.717 0.284 0.716
#> SRR2313976 2 0.0672 0.854 0.008 0.992
#> SRR2313975 1 0.0000 0.960 1.000 0.000
#> SRR2313973 1 0.0000 0.960 1.000 0.000
#> SRR2313974 1 0.0000 0.960 1.000 0.000
#> SRR2313985 1 0.0000 0.960 1.000 0.000
#> SRR2313993 2 0.5178 0.835 0.116 0.884
#> SRR2313994 2 0.0672 0.854 0.008 0.992
#> SRR2313995 2 0.9866 0.451 0.432 0.568
#> SRR2314024 2 0.0672 0.853 0.008 0.992
#> SRR2314015 2 0.0000 0.851 0.000 1.000
#> SRR2314025 1 0.0000 0.960 1.000 0.000
#> SRR2314023 1 0.0000 0.960 1.000 0.000
#> SRR2314033 2 0.5178 0.835 0.116 0.884
#> SRR2314045 1 0.0000 0.960 1.000 0.000
#> SRR2314042 1 0.0000 0.960 1.000 0.000
#> SRR2314043 2 0.9909 0.419 0.444 0.556
#> SRR2314041 2 0.5059 0.833 0.112 0.888
#> SRR2314044 1 0.9580 0.198 0.620 0.380
#> SRR2314040 2 0.8499 0.726 0.276 0.724
#> SRR2314039 1 0.9933 -0.103 0.548 0.452
#> SRR2314038 1 0.0000 0.960 1.000 0.000
#> SRR2314037 1 0.0000 0.960 1.000 0.000
#> SRR2314036 2 0.0672 0.854 0.008 0.992
#> SRR2314035 1 0.0000 0.960 1.000 0.000
#> SRR2314034 1 0.0000 0.960 1.000 0.000
#> SRR2314032 1 0.0000 0.960 1.000 0.000
#> SRR2314031 1 0.0000 0.960 1.000 0.000
#> SRR2314029 1 0.0000 0.960 1.000 0.000
#> SRR2314030 1 0.0000 0.960 1.000 0.000
#> SRR2314028 1 0.0000 0.960 1.000 0.000
#> SRR2314027 2 0.9286 0.631 0.344 0.656
#> SRR2314026 1 0.0000 0.960 1.000 0.000
#> SRR2314020 2 0.0376 0.851 0.004 0.996
#> SRR2314019 1 0.0000 0.960 1.000 0.000
#> SRR2314021 1 0.0000 0.960 1.000 0.000
#> SRR2314018 2 0.9815 0.480 0.420 0.580
#> SRR2314017 1 0.0000 0.960 1.000 0.000
#> SRR2314016 1 0.0000 0.960 1.000 0.000
#> SRR2314014 2 0.0672 0.854 0.008 0.992
#> SRR2314013 2 0.0672 0.854 0.008 0.992
#> SRR2314012 1 0.0000 0.960 1.000 0.000
#> SRR2314011 1 0.0000 0.960 1.000 0.000
#> SRR2314010 2 0.0000 0.851 0.000 1.000
#> SRR2314009 1 0.5059 0.821 0.888 0.112
#> SRR2314008 2 0.7745 0.767 0.228 0.772
#> SRR2314007 1 0.1184 0.943 0.984 0.016
#> SRR2314005 2 0.7883 0.764 0.236 0.764
#> SRR2314004 2 0.0000 0.851 0.000 1.000
#> SRR2314003 1 0.0000 0.960 1.000 0.000
#> SRR2314006 2 0.3879 0.845 0.076 0.924
#> SRR2314002 1 0.0000 0.960 1.000 0.000
#> SRR2314001 2 0.4939 0.837 0.108 0.892
#> SRR2314000 1 0.9129 0.369 0.672 0.328
#> SRR2313998 2 0.0672 0.854 0.008 0.992
#> SRR2313996 2 0.8555 0.721 0.280 0.720
#> SRR2313999 1 0.0000 0.960 1.000 0.000
#> SRR2313997 2 0.0000 0.851 0.000 1.000
#> SRR2313992 1 0.0000 0.960 1.000 0.000
#> SRR2313991 1 0.0000 0.960 1.000 0.000
#> SRR2313990 2 0.7602 0.777 0.220 0.780
#> SRR2313988 1 0.0000 0.960 1.000 0.000
#> SRR2313987 1 0.0000 0.960 1.000 0.000
#> SRR2313984 2 0.1184 0.854 0.016 0.984
#> SRR2313989 2 0.0000 0.851 0.000 1.000
#> SRR2313983 1 0.0000 0.960 1.000 0.000
#> SRR2313986 1 0.0000 0.960 1.000 0.000
#> SRR2313982 1 0.0000 0.960 1.000 0.000
#> SRR2313981 1 0.0000 0.960 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.6142 0.707 0.212 0.748 0.040
#> SRR2313980 2 0.2537 0.757 0.000 0.920 0.080
#> SRR2313979 1 0.6280 0.249 0.540 0.000 0.460
#> SRR2313977 2 0.5028 0.756 0.132 0.828 0.040
#> SRR2313976 2 0.0237 0.772 0.000 0.996 0.004
#> SRR2313975 1 0.2806 0.569 0.928 0.040 0.032
#> SRR2313973 1 0.5621 0.392 0.692 0.000 0.308
#> SRR2313974 1 0.1315 0.591 0.972 0.020 0.008
#> SRR2313985 1 0.6309 0.203 0.504 0.000 0.496
#> SRR2313993 2 0.2773 0.782 0.048 0.928 0.024
#> SRR2313994 2 0.0000 0.772 0.000 1.000 0.000
#> SRR2313995 2 0.7534 0.537 0.368 0.584 0.048
#> SRR2314024 2 0.3120 0.764 0.012 0.908 0.080
#> SRR2314015 2 0.1163 0.777 0.000 0.972 0.028
#> SRR2314025 1 0.1289 0.585 0.968 0.000 0.032
#> SRR2314023 1 0.4750 0.463 0.784 0.000 0.216
#> SRR2314033 2 0.2301 0.766 0.004 0.936 0.060
#> SRR2314045 3 0.5882 0.536 0.348 0.000 0.652
#> SRR2314042 1 0.1753 0.572 0.952 0.000 0.048
#> SRR2314043 2 0.7301 0.607 0.308 0.640 0.052
#> SRR2314041 2 0.3039 0.781 0.044 0.920 0.036
#> SRR2314044 2 0.7328 0.556 0.364 0.596 0.040
#> SRR2314040 2 0.4526 0.766 0.104 0.856 0.040
#> SRR2314039 2 0.7624 0.523 0.368 0.580 0.052
#> SRR2314038 2 0.7674 0.356 0.472 0.484 0.044
#> SRR2314037 2 0.7464 0.503 0.400 0.560 0.040
#> SRR2314036 2 0.0237 0.772 0.000 0.996 0.004
#> SRR2314035 2 0.8543 0.496 0.292 0.580 0.128
#> SRR2314034 1 0.2187 0.586 0.948 0.024 0.028
#> SRR2314032 3 0.4235 0.514 0.176 0.000 0.824
#> SRR2314031 1 0.1031 0.585 0.976 0.000 0.024
#> SRR2314029 1 0.7759 -0.376 0.480 0.472 0.048
#> SRR2314030 1 0.6308 -0.426 0.508 0.000 0.492
#> SRR2314028 1 0.1031 0.585 0.976 0.000 0.024
#> SRR2314027 2 0.7260 0.595 0.316 0.636 0.048
#> SRR2314026 3 0.8800 0.550 0.396 0.116 0.488
#> SRR2314020 2 0.2796 0.752 0.000 0.908 0.092
#> SRR2314019 1 0.8439 -0.115 0.536 0.368 0.096
#> SRR2314021 1 0.6274 -0.384 0.544 0.000 0.456
#> SRR2314018 2 0.5951 0.717 0.196 0.764 0.040
#> SRR2314017 3 0.8599 0.654 0.276 0.140 0.584
#> SRR2314016 1 0.4178 0.391 0.828 0.000 0.172
#> SRR2314014 2 0.0237 0.772 0.000 0.996 0.004
#> SRR2314013 2 0.0237 0.772 0.000 0.996 0.004
#> SRR2314012 1 0.3482 0.524 0.872 0.000 0.128
#> SRR2314011 1 0.1315 0.591 0.972 0.020 0.008
#> SRR2314010 2 0.1163 0.777 0.000 0.972 0.028
#> SRR2314009 2 0.7263 0.547 0.372 0.592 0.036
#> SRR2314008 2 0.3742 0.777 0.072 0.892 0.036
#> SRR2314007 2 0.7807 0.410 0.432 0.516 0.052
#> SRR2314005 2 0.3369 0.776 0.052 0.908 0.040
#> SRR2314004 2 0.2356 0.760 0.000 0.928 0.072
#> SRR2314003 1 0.2229 0.586 0.944 0.012 0.044
#> SRR2314006 2 0.3550 0.768 0.024 0.896 0.080
#> SRR2314002 3 0.8483 0.651 0.260 0.140 0.600
#> SRR2314001 2 0.2200 0.783 0.056 0.940 0.004
#> SRR2314000 2 0.7395 0.535 0.380 0.580 0.040
#> SRR2313998 2 0.0000 0.772 0.000 1.000 0.000
#> SRR2313996 2 0.5955 0.720 0.180 0.772 0.048
#> SRR2313999 1 0.1315 0.591 0.972 0.020 0.008
#> SRR2313997 2 0.2537 0.757 0.000 0.920 0.080
#> SRR2313992 1 0.4786 0.447 0.844 0.112 0.044
#> SRR2313991 3 0.9290 0.519 0.372 0.164 0.464
#> SRR2313990 2 0.3921 0.776 0.080 0.884 0.036
#> SRR2313988 3 0.4002 0.497 0.160 0.000 0.840
#> SRR2313987 1 0.5926 0.338 0.644 0.000 0.356
#> SRR2313984 2 0.3031 0.766 0.012 0.912 0.076
#> SRR2313989 2 0.0000 0.772 0.000 1.000 0.000
#> SRR2313983 1 0.6280 0.249 0.540 0.000 0.460
#> SRR2313986 1 0.2772 0.543 0.916 0.080 0.004
#> SRR2313982 1 0.6280 0.249 0.540 0.000 0.460
#> SRR2313981 2 0.7735 0.400 0.440 0.512 0.048
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.5000 0.2802 0.000 0.504 0.496 0.000
#> SRR2313980 2 0.1807 0.7560 0.008 0.940 0.000 0.052
#> SRR2313979 1 0.0592 0.8513 0.984 0.000 0.016 0.000
#> SRR2313977 2 0.2149 0.7436 0.000 0.912 0.088 0.000
#> SRR2313976 2 0.2271 0.7468 0.008 0.916 0.000 0.076
#> SRR2313975 3 0.3765 0.2346 0.032 0.004 0.848 0.116
#> SRR2313973 1 0.2814 0.7717 0.868 0.000 0.132 0.000
#> SRR2313974 3 0.5346 0.0739 0.272 0.004 0.692 0.032
#> SRR2313985 1 0.1297 0.8446 0.964 0.000 0.016 0.020
#> SRR2313993 2 0.1042 0.7641 0.000 0.972 0.008 0.020
#> SRR2313994 2 0.2125 0.7491 0.004 0.920 0.000 0.076
#> SRR2313995 3 0.7822 0.1667 0.000 0.256 0.380 0.364
#> SRR2314024 2 0.1807 0.7560 0.008 0.940 0.000 0.052
#> SRR2314015 2 0.0817 0.7648 0.000 0.976 0.024 0.000
#> SRR2314025 4 0.7811 0.1002 0.268 0.000 0.320 0.412
#> SRR2314023 1 0.4843 0.4966 0.604 0.000 0.396 0.000
#> SRR2314033 2 0.2546 0.7462 0.008 0.900 0.000 0.092
#> SRR2314045 4 0.6371 0.2427 0.300 0.000 0.092 0.608
#> SRR2314042 3 0.5408 0.1150 0.016 0.000 0.576 0.408
#> SRR2314043 2 0.5465 0.4594 0.000 0.588 0.392 0.020
#> SRR2314041 2 0.4564 0.5545 0.000 0.672 0.328 0.000
#> SRR2314044 3 0.4981 -0.2323 0.000 0.464 0.536 0.000
#> SRR2314040 2 0.5460 0.5294 0.000 0.632 0.340 0.028
#> SRR2314039 2 0.7521 0.0477 0.000 0.420 0.396 0.184
#> SRR2314038 3 0.3595 0.3174 0.008 0.084 0.868 0.040
#> SRR2314037 3 0.3764 0.3382 0.000 0.216 0.784 0.000
#> SRR2314036 2 0.2125 0.7491 0.004 0.920 0.000 0.076
#> SRR2314035 2 0.6460 0.4308 0.012 0.548 0.392 0.048
#> SRR2314034 3 0.5376 0.1282 0.016 0.000 0.588 0.396
#> SRR2314032 4 0.6702 0.0269 0.436 0.000 0.088 0.476
#> SRR2314031 4 0.7835 0.0816 0.268 0.000 0.336 0.396
#> SRR2314029 3 0.7207 0.1582 0.008 0.116 0.512 0.364
#> SRR2314030 4 0.3032 0.4269 0.008 0.000 0.124 0.868
#> SRR2314028 3 0.5376 0.1282 0.016 0.000 0.588 0.396
#> SRR2314027 2 0.5465 0.4594 0.000 0.588 0.392 0.020
#> SRR2314026 4 0.3873 0.4180 0.008 0.016 0.144 0.832
#> SRR2314020 2 0.2140 0.7526 0.008 0.932 0.008 0.052
#> SRR2314019 4 0.7218 -0.1582 0.008 0.108 0.436 0.448
#> SRR2314021 4 0.3032 0.4269 0.008 0.000 0.124 0.868
#> SRR2314018 2 0.4697 0.5316 0.000 0.644 0.356 0.000
#> SRR2314017 4 0.8685 0.2595 0.104 0.288 0.124 0.484
#> SRR2314016 4 0.6961 0.1976 0.124 0.000 0.352 0.524
#> SRR2314014 2 0.2125 0.7491 0.004 0.920 0.000 0.076
#> SRR2314013 2 0.2125 0.7491 0.004 0.920 0.000 0.076
#> SRR2314012 4 0.7491 0.2010 0.268 0.000 0.232 0.500
#> SRR2314011 3 0.5141 0.0766 0.268 0.000 0.700 0.032
#> SRR2314010 2 0.0469 0.7642 0.000 0.988 0.012 0.000
#> SRR2314009 2 0.5050 0.4606 0.000 0.588 0.408 0.004
#> SRR2314008 2 0.4713 0.5248 0.000 0.640 0.360 0.000
#> SRR2314007 2 0.6163 0.3713 0.000 0.532 0.416 0.052
#> SRR2314005 2 0.2966 0.7432 0.008 0.896 0.020 0.076
#> SRR2314004 2 0.1807 0.7560 0.008 0.940 0.000 0.052
#> SRR2314003 3 0.5615 -0.0918 0.356 0.000 0.612 0.032
#> SRR2314006 2 0.1807 0.7560 0.008 0.940 0.000 0.052
#> SRR2314002 4 0.7857 0.2033 0.036 0.348 0.120 0.496
#> SRR2314001 2 0.0895 0.7652 0.000 0.976 0.004 0.020
#> SRR2314000 3 0.4543 0.1842 0.000 0.324 0.676 0.000
#> SRR2313998 2 0.0817 0.7639 0.000 0.976 0.024 0.000
#> SRR2313996 2 0.5313 0.4842 0.000 0.608 0.376 0.016
#> SRR2313999 3 0.5141 0.0766 0.268 0.000 0.700 0.032
#> SRR2313997 2 0.2140 0.7526 0.008 0.932 0.008 0.052
#> SRR2313992 3 0.6132 0.0879 0.016 0.376 0.580 0.028
#> SRR2313991 4 0.7339 0.1429 0.004 0.376 0.140 0.480
#> SRR2313990 2 0.1940 0.7486 0.000 0.924 0.076 0.000
#> SRR2313988 4 0.6702 0.0269 0.436 0.000 0.088 0.476
#> SRR2313987 1 0.2408 0.8046 0.896 0.000 0.104 0.000
#> SRR2313984 2 0.1722 0.7573 0.000 0.944 0.008 0.048
#> SRR2313989 2 0.1042 0.7610 0.008 0.972 0.000 0.020
#> SRR2313983 1 0.0592 0.8513 0.984 0.000 0.016 0.000
#> SRR2313986 3 0.5429 0.0836 0.264 0.008 0.696 0.032
#> SRR2313982 1 0.0592 0.8513 0.984 0.000 0.016 0.000
#> SRR2313981 3 0.5597 -0.2134 0.000 0.464 0.516 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 3 0.1082 0.809 0.000 0.008 0.964 0.028 0.000
#> SRR2313980 2 0.3597 0.866 0.000 0.800 0.180 0.008 0.012
#> SRR2313979 1 0.0510 0.859 0.984 0.000 0.000 0.016 0.000
#> SRR2313977 2 0.4726 0.807 0.016 0.684 0.280 0.020 0.000
#> SRR2313976 2 0.1721 0.781 0.016 0.944 0.020 0.000 0.020
#> SRR2313975 4 0.3961 0.497 0.000 0.000 0.016 0.736 0.248
#> SRR2313973 1 0.3578 0.753 0.784 0.000 0.008 0.204 0.004
#> SRR2313974 4 0.0290 0.665 0.000 0.000 0.008 0.992 0.000
#> SRR2313985 1 0.2783 0.812 0.868 0.000 0.004 0.116 0.012
#> SRR2313993 2 0.3530 0.862 0.000 0.784 0.204 0.000 0.012
#> SRR2313994 2 0.1503 0.784 0.008 0.952 0.020 0.000 0.020
#> SRR2313995 3 0.4348 0.659 0.000 0.008 0.744 0.032 0.216
#> SRR2314024 2 0.3705 0.868 0.000 0.788 0.192 0.008 0.012
#> SRR2314015 2 0.4291 0.445 0.000 0.536 0.464 0.000 0.000
#> SRR2314025 4 0.4182 0.350 0.000 0.000 0.000 0.600 0.400
#> SRR2314023 1 0.3756 0.719 0.744 0.000 0.008 0.248 0.000
#> SRR2314033 2 0.1989 0.778 0.016 0.932 0.020 0.000 0.032
#> SRR2314045 5 0.5579 0.243 0.224 0.000 0.012 0.104 0.660
#> SRR2314042 5 0.4702 -0.261 0.000 0.000 0.016 0.432 0.552
#> SRR2314043 3 0.0162 0.809 0.000 0.004 0.996 0.000 0.000
#> SRR2314041 3 0.0290 0.809 0.000 0.008 0.992 0.000 0.000
#> SRR2314044 3 0.0898 0.810 0.000 0.008 0.972 0.020 0.000
#> SRR2314040 3 0.2848 0.724 0.000 0.156 0.840 0.000 0.004
#> SRR2314039 3 0.1356 0.806 0.000 0.004 0.956 0.012 0.028
#> SRR2314038 3 0.4785 0.645 0.000 0.004 0.732 0.088 0.176
#> SRR2314037 3 0.0703 0.810 0.000 0.000 0.976 0.024 0.000
#> SRR2314036 2 0.0898 0.781 0.000 0.972 0.008 0.000 0.020
#> SRR2314035 3 0.4832 0.659 0.080 0.168 0.740 0.000 0.012
#> SRR2314034 4 0.6564 0.239 0.000 0.000 0.224 0.460 0.316
#> SRR2314032 5 0.6075 -0.107 0.444 0.000 0.004 0.104 0.448
#> SRR2314031 5 0.4294 -0.300 0.000 0.000 0.000 0.468 0.532
#> SRR2314029 3 0.4615 0.634 0.000 0.004 0.724 0.052 0.220
#> SRR2314030 5 0.1121 0.288 0.000 0.000 0.000 0.044 0.956
#> SRR2314028 5 0.4727 -0.284 0.000 0.000 0.016 0.452 0.532
#> SRR2314027 3 0.1314 0.807 0.000 0.012 0.960 0.016 0.012
#> SRR2314026 3 0.4976 0.250 0.000 0.000 0.504 0.028 0.468
#> SRR2314020 2 0.3597 0.866 0.000 0.800 0.180 0.008 0.012
#> SRR2314019 3 0.4977 0.604 0.000 0.008 0.684 0.052 0.256
#> SRR2314021 5 0.1121 0.288 0.000 0.000 0.000 0.044 0.956
#> SRR2314018 3 0.1792 0.778 0.000 0.084 0.916 0.000 0.000
#> SRR2314017 5 0.6908 0.067 0.220 0.012 0.324 0.000 0.444
#> SRR2314016 5 0.4291 -0.307 0.000 0.000 0.000 0.464 0.536
#> SRR2314014 2 0.1721 0.781 0.016 0.944 0.020 0.000 0.020
#> SRR2314013 2 0.1721 0.781 0.016 0.944 0.020 0.000 0.020
#> SRR2314012 4 0.4268 0.428 0.000 0.000 0.008 0.648 0.344
#> SRR2314011 4 0.1608 0.657 0.000 0.000 0.000 0.928 0.072
#> SRR2314010 2 0.3534 0.845 0.000 0.744 0.256 0.000 0.000
#> SRR2314009 3 0.0162 0.809 0.000 0.004 0.996 0.000 0.000
#> SRR2314008 3 0.0404 0.808 0.000 0.012 0.988 0.000 0.000
#> SRR2314007 3 0.4514 0.668 0.000 0.016 0.756 0.044 0.184
#> SRR2314005 2 0.2121 0.778 0.016 0.932 0.020 0.012 0.020
#> SRR2314004 2 0.3705 0.868 0.000 0.788 0.192 0.008 0.012
#> SRR2314003 4 0.1168 0.653 0.032 0.000 0.008 0.960 0.000
#> SRR2314006 2 0.3496 0.868 0.000 0.788 0.200 0.000 0.012
#> SRR2314002 3 0.5826 0.229 0.032 0.016 0.496 0.012 0.444
#> SRR2314001 2 0.3628 0.861 0.000 0.772 0.216 0.000 0.012
#> SRR2314000 3 0.0609 0.810 0.000 0.000 0.980 0.020 0.000
#> SRR2313998 2 0.3424 0.856 0.000 0.760 0.240 0.000 0.000
#> SRR2313996 3 0.0798 0.810 0.000 0.008 0.976 0.016 0.000
#> SRR2313999 4 0.0451 0.666 0.000 0.000 0.008 0.988 0.004
#> SRR2313997 2 0.3597 0.866 0.000 0.800 0.180 0.008 0.012
#> SRR2313992 3 0.5588 0.286 0.000 0.000 0.604 0.292 0.104
#> SRR2313991 3 0.5144 0.278 0.000 0.008 0.520 0.024 0.448
#> SRR2313990 3 0.0290 0.809 0.000 0.008 0.992 0.000 0.000
#> SRR2313988 5 0.6075 -0.107 0.444 0.000 0.004 0.104 0.448
#> SRR2313987 1 0.2424 0.817 0.868 0.000 0.000 0.132 0.000
#> SRR2313984 2 0.3671 0.848 0.000 0.756 0.236 0.008 0.000
#> SRR2313989 2 0.4125 0.868 0.012 0.772 0.196 0.008 0.012
#> SRR2313983 1 0.0510 0.859 0.984 0.000 0.000 0.016 0.000
#> SRR2313986 4 0.3508 0.541 0.000 0.000 0.000 0.748 0.252
#> SRR2313982 1 0.0510 0.859 0.984 0.000 0.000 0.016 0.000
#> SRR2313981 3 0.1026 0.808 0.000 0.004 0.968 0.024 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 3 0.5539 0.3994 0.004 0.180 0.652 0.036 0.000 0.128
#> SRR2313980 6 0.3109 0.7879 0.000 0.004 0.224 0.000 0.000 0.772
#> SRR2313979 1 0.2854 0.6427 0.792 0.000 0.000 0.000 0.208 0.000
#> SRR2313977 3 0.4634 0.4300 0.004 0.184 0.708 0.004 0.000 0.100
#> SRR2313976 2 0.3807 0.8680 0.000 0.628 0.004 0.000 0.000 0.368
#> SRR2313975 4 0.7410 0.4288 0.148 0.164 0.192 0.476 0.020 0.000
#> SRR2313973 1 0.3541 0.3517 0.728 0.000 0.000 0.260 0.012 0.000
#> SRR2313974 4 0.7213 0.4585 0.200 0.164 0.000 0.448 0.188 0.000
#> SRR2313985 1 0.3706 0.5342 0.620 0.000 0.000 0.000 0.380 0.000
#> SRR2313993 6 0.5468 0.6395 0.000 0.156 0.296 0.000 0.000 0.548
#> SRR2313994 2 0.3215 0.6948 0.000 0.756 0.004 0.000 0.000 0.240
#> SRR2313995 3 0.6070 0.4178 0.004 0.168 0.564 0.240 0.004 0.020
#> SRR2314024 6 0.3109 0.7879 0.000 0.004 0.224 0.000 0.000 0.772
#> SRR2314015 3 0.5714 -0.2639 0.000 0.168 0.464 0.000 0.000 0.368
#> SRR2314025 4 0.4617 0.5681 0.096 0.016 0.000 0.720 0.168 0.000
#> SRR2314023 1 0.3787 0.3267 0.720 0.012 0.000 0.260 0.008 0.000
#> SRR2314033 2 0.3807 0.8680 0.000 0.628 0.004 0.000 0.000 0.368
#> SRR2314045 5 0.2669 0.4670 0.008 0.000 0.000 0.156 0.836 0.000
#> SRR2314042 4 0.1151 0.5994 0.000 0.000 0.000 0.956 0.012 0.032
#> SRR2314043 3 0.0000 0.6307 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR2314041 3 0.0922 0.6269 0.000 0.024 0.968 0.000 0.004 0.004
#> SRR2314044 3 0.0912 0.6303 0.004 0.012 0.972 0.004 0.000 0.008
#> SRR2314040 3 0.4146 0.4645 0.000 0.232 0.720 0.000 0.040 0.008
#> SRR2314039 3 0.2685 0.6146 0.000 0.004 0.880 0.020 0.080 0.016
#> SRR2314038 3 0.6462 0.2734 0.008 0.020 0.480 0.264 0.000 0.228
#> SRR2314037 3 0.0653 0.6299 0.004 0.012 0.980 0.000 0.000 0.004
#> SRR2314036 2 0.3971 0.7851 0.000 0.548 0.004 0.000 0.000 0.448
#> SRR2314035 3 0.6320 -0.0545 0.000 0.336 0.364 0.000 0.292 0.008
#> SRR2314034 4 0.3243 0.4837 0.008 0.000 0.208 0.780 0.004 0.000
#> SRR2314032 5 0.2491 0.4547 0.164 0.000 0.000 0.000 0.836 0.000
#> SRR2314031 4 0.1390 0.6052 0.004 0.016 0.000 0.948 0.000 0.032
#> SRR2314029 3 0.4267 0.5157 0.004 0.000 0.692 0.260 0.000 0.044
#> SRR2314030 4 0.5475 0.2687 0.000 0.004 0.000 0.588 0.184 0.224
#> SRR2314028 4 0.0363 0.6082 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR2314027 3 0.2482 0.6170 0.000 0.004 0.892 0.012 0.072 0.020
#> SRR2314026 3 0.7161 0.0464 0.000 0.004 0.412 0.080 0.268 0.236
#> SRR2314020 6 0.1082 0.4515 0.000 0.004 0.040 0.000 0.000 0.956
#> SRR2314019 3 0.5432 0.4031 0.000 0.000 0.564 0.344 0.048 0.044
#> SRR2314021 4 0.5475 0.2687 0.000 0.004 0.000 0.588 0.184 0.224
#> SRR2314018 3 0.2994 0.5114 0.000 0.208 0.788 0.000 0.000 0.004
#> SRR2314017 5 0.2805 0.5042 0.000 0.004 0.184 0.000 0.812 0.000
#> SRR2314016 4 0.4864 0.4827 0.000 0.076 0.000 0.680 0.020 0.224
#> SRR2314014 2 0.3899 0.8674 0.000 0.628 0.008 0.000 0.000 0.364
#> SRR2314013 2 0.3236 0.7055 0.000 0.796 0.024 0.000 0.000 0.180
#> SRR2314012 4 0.4613 0.5556 0.116 0.000 0.000 0.688 0.196 0.000
#> SRR2314011 4 0.6695 0.5245 0.112 0.176 0.000 0.528 0.184 0.000
#> SRR2314010 3 0.5461 0.1007 0.000 0.168 0.560 0.000 0.000 0.272
#> SRR2314009 3 0.0363 0.6307 0.000 0.000 0.988 0.000 0.012 0.000
#> SRR2314008 3 0.0692 0.6295 0.000 0.000 0.976 0.000 0.020 0.004
#> SRR2314007 3 0.5778 0.2917 0.000 0.000 0.504 0.268 0.000 0.228
#> SRR2314005 2 0.4022 0.8656 0.004 0.628 0.008 0.000 0.000 0.360
#> SRR2314004 6 0.3109 0.7879 0.000 0.004 0.224 0.000 0.000 0.772
#> SRR2314003 4 0.7223 0.4324 0.232 0.144 0.000 0.436 0.188 0.000
#> SRR2314006 6 0.3163 0.7857 0.000 0.004 0.232 0.000 0.000 0.764
#> SRR2314002 5 0.3592 0.3699 0.000 0.000 0.344 0.000 0.656 0.000
#> SRR2314001 6 0.5588 0.6216 0.000 0.172 0.300 0.000 0.000 0.528
#> SRR2314000 3 0.0508 0.6305 0.004 0.012 0.984 0.000 0.000 0.000
#> SRR2313998 3 0.5737 -0.3824 0.000 0.172 0.460 0.000 0.000 0.368
#> SRR2313996 3 0.5887 0.3859 0.000 0.168 0.624 0.072 0.000 0.136
#> SRR2313999 4 0.7257 0.4604 0.200 0.176 0.000 0.440 0.184 0.000
#> SRR2313997 6 0.3109 0.7858 0.000 0.004 0.224 0.000 0.000 0.772
#> SRR2313992 3 0.4916 0.3957 0.000 0.000 0.620 0.296 0.080 0.004
#> SRR2313991 3 0.7145 0.0663 0.000 0.008 0.368 0.056 0.256 0.312
#> SRR2313990 3 0.2772 0.5269 0.000 0.180 0.816 0.000 0.000 0.004
#> SRR2313988 5 0.2491 0.4547 0.164 0.000 0.000 0.000 0.836 0.000
#> SRR2313987 1 0.3819 0.5392 0.624 0.000 0.000 0.004 0.372 0.000
#> SRR2313984 6 0.4510 0.4679 0.000 0.172 0.084 0.016 0.000 0.728
#> SRR2313989 6 0.4431 0.7033 0.000 0.080 0.228 0.000 0.000 0.692
#> SRR2313983 1 0.2854 0.6427 0.792 0.000 0.000 0.000 0.208 0.000
#> SRR2313986 4 0.3659 0.5943 0.028 0.180 0.000 0.780 0.012 0.000
#> SRR2313982 1 0.2883 0.6410 0.788 0.000 0.000 0.000 0.212 0.000
#> SRR2313981 3 0.1701 0.6204 0.000 0.000 0.920 0.008 0.072 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "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 16873 rows and 72 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 0.945 0.951 0.980 0.5017 0.496 0.496
#> 3 3 0.575 0.751 0.858 0.2647 0.788 0.598
#> 4 4 0.559 0.505 0.722 0.1220 0.842 0.607
#> 5 5 0.583 0.536 0.728 0.0644 0.898 0.685
#> 6 6 0.623 0.530 0.733 0.0554 0.898 0.635
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
#> SRR2313978 2 0.0000 0.9716 0.000 1.000
#> SRR2313980 2 0.0000 0.9716 0.000 1.000
#> SRR2313979 1 0.0000 0.9842 1.000 0.000
#> SRR2313977 2 0.0000 0.9716 0.000 1.000
#> SRR2313976 2 0.0000 0.9716 0.000 1.000
#> SRR2313975 1 0.0000 0.9842 1.000 0.000
#> SRR2313973 1 0.0000 0.9842 1.000 0.000
#> SRR2313974 1 0.0000 0.9842 1.000 0.000
#> SRR2313985 1 0.0000 0.9842 1.000 0.000
#> SRR2313993 2 0.0000 0.9716 0.000 1.000
#> SRR2313994 2 0.0000 0.9716 0.000 1.000
#> SRR2313995 2 0.0000 0.9716 0.000 1.000
#> SRR2314024 2 0.0000 0.9716 0.000 1.000
#> SRR2314015 2 0.0000 0.9716 0.000 1.000
#> SRR2314025 1 0.0000 0.9842 1.000 0.000
#> SRR2314023 1 0.0000 0.9842 1.000 0.000
#> SRR2314033 2 0.0000 0.9716 0.000 1.000
#> SRR2314045 1 0.0000 0.9842 1.000 0.000
#> SRR2314042 1 0.0000 0.9842 1.000 0.000
#> SRR2314043 1 0.1633 0.9639 0.976 0.024
#> SRR2314041 2 0.0000 0.9716 0.000 1.000
#> SRR2314044 2 0.9983 0.0923 0.476 0.524
#> SRR2314040 2 0.4815 0.8807 0.104 0.896
#> SRR2314039 1 0.5946 0.8255 0.856 0.144
#> SRR2314038 1 0.2603 0.9443 0.956 0.044
#> SRR2314037 1 0.0000 0.9842 1.000 0.000
#> SRR2314036 2 0.0000 0.9716 0.000 1.000
#> SRR2314035 1 0.0938 0.9748 0.988 0.012
#> SRR2314034 1 0.0000 0.9842 1.000 0.000
#> SRR2314032 1 0.0000 0.9842 1.000 0.000
#> SRR2314031 1 0.0000 0.9842 1.000 0.000
#> SRR2314029 2 0.2778 0.9343 0.048 0.952
#> SRR2314030 1 0.0000 0.9842 1.000 0.000
#> SRR2314028 1 0.0000 0.9842 1.000 0.000
#> SRR2314027 2 0.0938 0.9636 0.012 0.988
#> SRR2314026 1 0.0376 0.9813 0.996 0.004
#> SRR2314020 2 0.0000 0.9716 0.000 1.000
#> SRR2314019 1 0.0000 0.9842 1.000 0.000
#> SRR2314021 1 0.0000 0.9842 1.000 0.000
#> SRR2314018 1 0.9286 0.4594 0.656 0.344
#> SRR2314017 1 0.0000 0.9842 1.000 0.000
#> SRR2314016 1 0.0000 0.9842 1.000 0.000
#> SRR2314014 2 0.0000 0.9716 0.000 1.000
#> SRR2314013 2 0.0000 0.9716 0.000 1.000
#> SRR2314012 1 0.0000 0.9842 1.000 0.000
#> SRR2314011 1 0.0000 0.9842 1.000 0.000
#> SRR2314010 2 0.0000 0.9716 0.000 1.000
#> SRR2314009 1 0.0000 0.9842 1.000 0.000
#> SRR2314008 2 0.5737 0.8425 0.136 0.864
#> SRR2314007 2 0.0938 0.9636 0.012 0.988
#> SRR2314005 2 0.0000 0.9716 0.000 1.000
#> SRR2314004 2 0.0000 0.9716 0.000 1.000
#> SRR2314003 1 0.0000 0.9842 1.000 0.000
#> SRR2314006 2 0.0000 0.9716 0.000 1.000
#> SRR2314002 1 0.0000 0.9842 1.000 0.000
#> SRR2314001 2 0.0000 0.9716 0.000 1.000
#> SRR2314000 1 0.0376 0.9813 0.996 0.004
#> SRR2313998 2 0.0000 0.9716 0.000 1.000
#> SRR2313996 2 0.0000 0.9716 0.000 1.000
#> SRR2313999 1 0.0000 0.9842 1.000 0.000
#> SRR2313997 2 0.0000 0.9716 0.000 1.000
#> SRR2313992 1 0.0000 0.9842 1.000 0.000
#> SRR2313991 2 0.4690 0.8849 0.100 0.900
#> SRR2313990 2 0.0000 0.9716 0.000 1.000
#> SRR2313988 1 0.0000 0.9842 1.000 0.000
#> SRR2313987 1 0.0000 0.9842 1.000 0.000
#> SRR2313984 2 0.0000 0.9716 0.000 1.000
#> SRR2313989 2 0.0000 0.9716 0.000 1.000
#> SRR2313983 1 0.0000 0.9842 1.000 0.000
#> SRR2313986 1 0.0000 0.9842 1.000 0.000
#> SRR2313982 1 0.0000 0.9842 1.000 0.000
#> SRR2313981 1 0.0000 0.9842 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR2313978 2 0.2165 0.824 0.000 0.936 0.064
#> SRR2313980 3 0.5859 0.641 0.000 0.344 0.656
#> SRR2313979 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313977 2 0.0000 0.835 0.000 1.000 0.000
#> SRR2313976 2 0.1031 0.834 0.000 0.976 0.024
#> SRR2313975 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313973 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313974 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313985 1 0.0000 0.908 1.000 0.000 0.000
#> SRR2313993 3 0.6280 0.423 0.000 0.460 0.540
#> SRR2313994 2 0.0424 0.834 0.000 0.992 0.008
#> SRR2313995 3 0.4605 0.701 0.000 0.204 0.796
#> SRR2314024 2 0.2625 0.809 0.000 0.916 0.084
#> SRR2314015 2 0.4178 0.693 0.000 0.828 0.172
#> SRR2314025 1 0.3619 0.863 0.864 0.000 0.136
#> SRR2314023 1 0.0592 0.907 0.988 0.000 0.012
#> SRR2314033 2 0.0237 0.835 0.000 0.996 0.004
#> SRR2314045 1 0.3267 0.861 0.884 0.000 0.116
#> SRR2314042 1 0.4504 0.822 0.804 0.000 0.196
#> SRR2314043 1 0.3879 0.782 0.848 0.152 0.000
#> SRR2314041 2 0.0424 0.836 0.000 0.992 0.008
#> SRR2314044 2 0.6264 0.301 0.380 0.616 0.004
#> SRR2314040 2 0.5020 0.681 0.056 0.836 0.108
#> SRR2314039 1 0.8508 0.454 0.608 0.160 0.232
#> SRR2314038 3 0.7205 0.662 0.192 0.100 0.708
#> SRR2314037 1 0.1170 0.905 0.976 0.016 0.008
#> SRR2314036 2 0.1964 0.825 0.000 0.944 0.056
#> SRR2314035 1 0.7496 0.591 0.672 0.240 0.088
#> SRR2314034 1 0.0592 0.908 0.988 0.000 0.012
#> SRR2314032 1 0.2356 0.885 0.928 0.000 0.072
#> SRR2314031 1 0.4002 0.846 0.840 0.000 0.160
#> SRR2314029 3 0.5216 0.691 0.000 0.260 0.740
#> SRR2314030 3 0.3038 0.637 0.104 0.000 0.896
#> SRR2314028 1 0.3116 0.879 0.892 0.000 0.108
#> SRR2314027 3 0.8937 0.504 0.152 0.308 0.540
#> SRR2314026 3 0.5505 0.685 0.088 0.096 0.816
#> SRR2314020 3 0.6204 0.507 0.000 0.424 0.576
#> SRR2314019 1 0.7346 0.502 0.592 0.040 0.368
#> SRR2314021 3 0.3038 0.637 0.104 0.000 0.896
#> SRR2314018 2 0.6952 0.301 0.376 0.600 0.024
#> SRR2314017 1 0.4634 0.816 0.824 0.012 0.164
#> SRR2314016 3 0.6140 0.235 0.404 0.000 0.596
#> SRR2314014 2 0.0424 0.834 0.000 0.992 0.008
#> SRR2314013 2 0.0424 0.834 0.000 0.992 0.008
#> SRR2314012 1 0.3619 0.863 0.864 0.000 0.136
#> SRR2314011 1 0.2537 0.889 0.920 0.000 0.080
#> SRR2314010 2 0.2796 0.801 0.000 0.908 0.092
#> SRR2314009 1 0.1529 0.895 0.960 0.040 0.000
#> SRR2314008 2 0.5905 0.334 0.352 0.648 0.000
#> SRR2314007 3 0.5650 0.667 0.000 0.312 0.688
#> SRR2314005 2 0.0592 0.832 0.000 0.988 0.012
#> SRR2314004 2 0.2537 0.812 0.000 0.920 0.080
#> SRR2314003 1 0.1031 0.907 0.976 0.000 0.024
#> SRR2314006 3 0.5760 0.655 0.000 0.328 0.672
#> SRR2314002 3 0.5656 0.591 0.264 0.008 0.728
#> SRR2314001 2 0.1860 0.828 0.000 0.948 0.052
#> SRR2314000 1 0.4045 0.834 0.872 0.104 0.024
#> SRR2313998 2 0.0424 0.834 0.000 0.992 0.008
#> SRR2313996 2 0.2448 0.814 0.000 0.924 0.076
#> SRR2313999 1 0.0747 0.908 0.984 0.000 0.016
#> SRR2313997 2 0.6008 0.139 0.000 0.628 0.372
#> SRR2313992 1 0.0237 0.908 0.996 0.000 0.004
#> SRR2313991 3 0.4465 0.701 0.004 0.176 0.820
#> SRR2313990 2 0.0747 0.829 0.000 0.984 0.016
#> SRR2313988 1 0.1163 0.903 0.972 0.000 0.028
#> SRR2313987 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313984 3 0.5859 0.641 0.000 0.344 0.656
#> SRR2313989 2 0.2625 0.809 0.000 0.916 0.084
#> SRR2313983 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313986 1 0.2165 0.896 0.936 0.000 0.064
#> SRR2313982 1 0.0424 0.908 0.992 0.000 0.008
#> SRR2313981 1 0.1878 0.903 0.952 0.004 0.044
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR2313978 2 0.1406 0.8032 0.000 0.960 0.016 0.024
#> SRR2313980 2 0.4998 -0.0675 0.000 0.512 0.000 0.488
#> SRR2313979 1 0.3688 0.6240 0.792 0.000 0.208 0.000
#> SRR2313977 2 0.0188 0.8047 0.000 0.996 0.004 0.000
#> SRR2313976 2 0.0592 0.8043 0.000 0.984 0.000 0.016
#> SRR2313975 1 0.3907 0.5945 0.768 0.000 0.232 0.000
#> SRR2313973 1 0.0336 0.6909 0.992 0.000 0.008 0.000
#> SRR2313974 1 0.3649 0.6271 0.796 0.000 0.204 0.000
#> SRR2313985 1 0.3726 0.6341 0.788 0.000 0.212 0.000
#> SRR2313993 2 0.4888 0.1840 0.000 0.588 0.000 0.412
#> SRR2313994 2 0.0336 0.8040 0.000 0.992 0.008 0.000
#> SRR2313995 2 0.8649 -0.1538 0.036 0.384 0.248 0.332
#> SRR2314024 2 0.1389 0.7947 0.000 0.952 0.000 0.048
#> SRR2314015 2 0.2408 0.7552 0.000 0.896 0.000 0.104
#> SRR2314025 1 0.5927 0.5091 0.660 0.000 0.264 0.076
#> SRR2314023 1 0.1637 0.6844 0.940 0.000 0.060 0.000
#> SRR2314033 2 0.0336 0.8046 0.000 0.992 0.000 0.008
#> SRR2314045 3 0.4482 0.3242 0.264 0.000 0.728 0.008
#> SRR2314042 3 0.7259 0.1611 0.352 0.000 0.492 0.156
#> SRR2314043 2 0.5147 0.1268 0.460 0.536 0.004 0.000
#> SRR2314041 2 0.1940 0.7692 0.000 0.924 0.076 0.000
#> SRR2314044 2 0.6223 0.1813 0.384 0.556 0.060 0.000
#> SRR2314040 2 0.5846 0.0947 0.032 0.516 0.452 0.000
#> SRR2314039 3 0.7396 0.3252 0.252 0.036 0.596 0.116
#> SRR2314038 4 0.6570 0.2552 0.340 0.072 0.008 0.580
#> SRR2314037 1 0.4274 0.6390 0.808 0.044 0.148 0.000
#> SRR2314036 2 0.1118 0.7997 0.000 0.964 0.000 0.036
#> SRR2314035 3 0.6702 0.3055 0.168 0.216 0.616 0.000
#> SRR2314034 1 0.3813 0.6228 0.828 0.000 0.148 0.024
#> SRR2314032 3 0.5105 -0.0122 0.432 0.000 0.564 0.004
#> SRR2314031 1 0.6967 0.3862 0.580 0.000 0.244 0.176
#> SRR2314029 4 0.9082 0.1057 0.176 0.200 0.144 0.480
#> SRR2314030 4 0.3649 0.4064 0.000 0.000 0.204 0.796
#> SRR2314028 1 0.5933 0.2503 0.552 0.000 0.408 0.040
#> SRR2314027 3 0.7869 0.0733 0.024 0.332 0.492 0.152
#> SRR2314026 3 0.5457 -0.2119 0.008 0.004 0.516 0.472
#> SRR2314020 4 0.4999 0.0415 0.000 0.492 0.000 0.508
#> SRR2314019 3 0.7300 0.2187 0.180 0.000 0.516 0.304
#> SRR2314021 4 0.3681 0.4159 0.008 0.000 0.176 0.816
#> SRR2314018 2 0.5452 0.5546 0.156 0.736 0.108 0.000
#> SRR2314017 3 0.5713 0.3767 0.192 0.004 0.716 0.088
#> SRR2314016 4 0.4391 0.2980 0.252 0.000 0.008 0.740
#> SRR2314014 2 0.0336 0.8037 0.000 0.992 0.008 0.000
#> SRR2314013 2 0.0592 0.8019 0.000 0.984 0.016 0.000
#> SRR2314012 1 0.5528 0.5523 0.700 0.000 0.236 0.064
#> SRR2314011 1 0.3307 0.6699 0.868 0.000 0.104 0.028
#> SRR2314010 2 0.1305 0.7994 0.000 0.960 0.004 0.036
#> SRR2314009 1 0.3400 0.6608 0.872 0.064 0.064 0.000
#> SRR2314008 2 0.1042 0.7982 0.008 0.972 0.020 0.000
#> SRR2314007 4 0.3355 0.4962 0.000 0.160 0.004 0.836
#> SRR2314005 2 0.0336 0.8041 0.000 0.992 0.008 0.000
#> SRR2314004 2 0.1474 0.7925 0.000 0.948 0.000 0.052
#> SRR2314003 1 0.4323 0.6230 0.788 0.000 0.184 0.028
#> SRR2314006 2 0.2973 0.7243 0.000 0.856 0.000 0.144
#> SRR2314002 4 0.6832 0.2716 0.116 0.008 0.272 0.604
#> SRR2314001 2 0.1211 0.7926 0.000 0.960 0.040 0.000
#> SRR2314000 1 0.5837 0.4883 0.668 0.072 0.260 0.000
#> SRR2313998 2 0.0000 0.8045 0.000 1.000 0.000 0.000
#> SRR2313996 2 0.0779 0.8049 0.000 0.980 0.004 0.016
#> SRR2313999 1 0.1004 0.6909 0.972 0.000 0.024 0.004
#> SRR2313997 2 0.4761 0.2825 0.000 0.628 0.000 0.372
#> SRR2313992 1 0.4077 0.6396 0.800 0.004 0.184 0.012
#> SRR2313991 4 0.4010 0.4586 0.000 0.028 0.156 0.816
#> SRR2313990 2 0.0592 0.8018 0.000 0.984 0.016 0.000
#> SRR2313988 1 0.4925 0.2636 0.572 0.000 0.428 0.000
#> SRR2313987 1 0.2704 0.6651 0.876 0.000 0.124 0.000
#> SRR2313984 4 0.4948 0.1913 0.000 0.440 0.000 0.560
#> SRR2313989 2 0.1389 0.7947 0.000 0.952 0.000 0.048
#> SRR2313983 1 0.3688 0.6240 0.792 0.000 0.208 0.000
#> SRR2313986 1 0.3658 0.6546 0.836 0.000 0.144 0.020
#> SRR2313982 1 0.3610 0.6294 0.800 0.000 0.200 0.000
#> SRR2313981 1 0.4844 0.5980 0.748 0.012 0.224 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR2313978 2 0.2381 0.8094 0.000 0.908 0.004 0.036 0.052
#> SRR2313980 2 0.3274 0.7115 0.000 0.780 0.000 0.000 0.220
#> SRR2313979 1 0.0912 0.6853 0.972 0.000 0.012 0.016 0.000
#> SRR2313977 2 0.1314 0.8100 0.000 0.960 0.016 0.012 0.012
#> SRR2313976 2 0.0404 0.8117 0.000 0.988 0.000 0.000 0.012
#> SRR2313975 1 0.0451 0.6837 0.988 0.000 0.008 0.004 0.000
#> SRR2313973 1 0.4505 0.3186 0.620 0.000 0.008 0.368 0.004
#> SRR2313974 1 0.0162 0.6851 0.996 0.000 0.000 0.004 0.000
#> SRR2313985 1 0.2304 0.6726 0.908 0.000 0.048 0.044 0.000
#> SRR2313993 2 0.4313 0.4705 0.000 0.636 0.008 0.000 0.356
#> SRR2313994 2 0.0609 0.8090 0.000 0.980 0.020 0.000 0.000
#> SRR2313995 2 0.6661 0.2458 0.000 0.468 0.036 0.396 0.100
#> SRR2314024 2 0.1908 0.7975 0.000 0.908 0.000 0.000 0.092
#> SRR2314015 2 0.2605 0.7728 0.000 0.852 0.000 0.000 0.148
#> SRR2314025 4 0.3197 0.6272 0.152 0.000 0.012 0.832 0.004
#> SRR2314023 1 0.2690 0.6161 0.844 0.000 0.000 0.156 0.000
#> SRR2314033 2 0.0290 0.8116 0.000 0.992 0.000 0.000 0.008
#> SRR2314045 3 0.3314 0.5200 0.124 0.000 0.844 0.012 0.020
#> SRR2314042 4 0.5389 0.0962 0.040 0.000 0.300 0.636 0.024
#> SRR2314043 2 0.4029 0.5979 0.232 0.744 0.024 0.000 0.000
#> SRR2314041 2 0.4915 0.6105 0.000 0.696 0.064 0.236 0.004
#> SRR2314044 2 0.5488 0.5887 0.140 0.708 0.020 0.128 0.004
#> SRR2314040 3 0.4570 0.4885 0.000 0.216 0.732 0.044 0.008
#> SRR2314039 3 0.5194 0.4181 0.000 0.024 0.552 0.412 0.012
#> SRR2314038 5 0.5788 0.4085 0.260 0.056 0.008 0.028 0.648
#> SRR2314037 4 0.6051 0.1922 0.408 0.052 0.024 0.512 0.004
#> SRR2314036 2 0.1671 0.8025 0.000 0.924 0.000 0.000 0.076
#> SRR2314035 3 0.4371 0.5334 0.012 0.140 0.792 0.044 0.012
#> SRR2314034 3 0.6886 -0.1136 0.264 0.000 0.444 0.284 0.008
#> SRR2314032 1 0.4682 0.1771 0.564 0.000 0.420 0.000 0.016
#> SRR2314031 4 0.3595 0.6210 0.140 0.000 0.000 0.816 0.044
#> SRR2314029 2 0.6754 0.1149 0.000 0.404 0.000 0.312 0.284
#> SRR2314030 5 0.4793 0.4215 0.000 0.000 0.260 0.056 0.684
#> SRR2314028 4 0.5486 0.4551 0.100 0.000 0.204 0.680 0.016
#> SRR2314027 3 0.5965 0.4638 0.000 0.080 0.560 0.344 0.016
#> SRR2314026 3 0.4039 0.4502 0.004 0.000 0.776 0.036 0.184
#> SRR2314020 5 0.4126 0.2409 0.000 0.380 0.000 0.000 0.620
#> SRR2314019 3 0.5312 0.5091 0.008 0.000 0.628 0.308 0.056
#> SRR2314021 5 0.4678 0.3825 0.004 0.000 0.300 0.028 0.668
#> SRR2314018 2 0.5383 0.5846 0.008 0.684 0.232 0.064 0.012
#> SRR2314017 3 0.2457 0.5421 0.008 0.000 0.900 0.016 0.076
#> SRR2314016 5 0.6902 0.2821 0.280 0.000 0.068 0.108 0.544
#> SRR2314014 2 0.1059 0.8061 0.000 0.968 0.020 0.008 0.004
#> SRR2314013 2 0.1901 0.7934 0.000 0.932 0.040 0.024 0.004
#> SRR2314012 4 0.5587 0.4541 0.320 0.000 0.072 0.600 0.008
#> SRR2314011 1 0.4557 0.2265 0.584 0.000 0.000 0.404 0.012
#> SRR2314010 2 0.2069 0.8045 0.000 0.912 0.000 0.012 0.076
#> SRR2314009 1 0.8863 -0.2818 0.292 0.208 0.228 0.260 0.012
#> SRR2314008 2 0.4769 0.6324 0.000 0.720 0.216 0.056 0.008
#> SRR2314007 5 0.2977 0.5573 0.000 0.040 0.076 0.008 0.876
#> SRR2314005 2 0.0727 0.8088 0.000 0.980 0.012 0.004 0.004
#> SRR2314004 2 0.2074 0.7923 0.000 0.896 0.000 0.000 0.104
#> SRR2314003 4 0.4318 0.3852 0.348 0.000 0.004 0.644 0.004
#> SRR2314006 2 0.2127 0.7905 0.000 0.892 0.000 0.000 0.108
#> SRR2314002 5 0.5390 0.4497 0.052 0.004 0.284 0.012 0.648
#> SRR2314001 2 0.2966 0.7090 0.000 0.816 0.184 0.000 0.000
#> SRR2314000 1 0.2228 0.6274 0.908 0.076 0.012 0.004 0.000
#> SRR2313998 2 0.0451 0.8113 0.000 0.988 0.004 0.000 0.008
#> SRR2313996 2 0.1095 0.8130 0.000 0.968 0.012 0.008 0.012
#> SRR2313999 1 0.3928 0.4472 0.700 0.000 0.000 0.296 0.004
#> SRR2313997 2 0.3661 0.6282 0.000 0.724 0.000 0.000 0.276
#> SRR2313992 4 0.5104 0.5917 0.224 0.008 0.064 0.700 0.004
#> SRR2313991 5 0.4360 0.5350 0.000 0.032 0.192 0.016 0.760
#> SRR2313990 2 0.2674 0.7780 0.000 0.896 0.060 0.032 0.012
#> SRR2313988 1 0.2886 0.5834 0.844 0.000 0.148 0.008 0.000
#> SRR2313987 1 0.1965 0.6569 0.904 0.000 0.000 0.096 0.000
#> SRR2313984 5 0.4171 0.1927 0.000 0.396 0.000 0.000 0.604
#> SRR2313989 2 0.1965 0.7962 0.000 0.904 0.000 0.000 0.096
#> SRR2313983 1 0.0566 0.6844 0.984 0.000 0.012 0.004 0.000
#> SRR2313986 1 0.4450 -0.0196 0.508 0.000 0.000 0.488 0.004
#> SRR2313982 1 0.1251 0.6847 0.956 0.000 0.008 0.036 0.000
#> SRR2313981 4 0.6900 0.1370 0.136 0.016 0.356 0.480 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR2313978 2 0.5595 0.4146 0.000 0.588 0.236 0.164 0.012 0.000
#> SRR2313980 2 0.1901 0.8049 0.000 0.912 0.004 0.008 0.076 0.000
#> SRR2313979 1 0.1334 0.6957 0.948 0.000 0.000 0.032 0.000 0.020
#> SRR2313977 2 0.3489 0.6479 0.000 0.708 0.288 0.000 0.000 0.004
#> SRR2313976 2 0.0603 0.8279 0.000 0.980 0.016 0.000 0.000 0.004
#> SRR2313975 1 0.1074 0.6884 0.960 0.000 0.000 0.028 0.000 0.012
#> SRR2313973 1 0.4551 0.3238 0.608 0.000 0.000 0.344 0.000 0.048
#> SRR2313974 1 0.1757 0.6929 0.916 0.000 0.000 0.076 0.000 0.008
#> SRR2313985 1 0.3227 0.6626 0.828 0.000 0.000 0.088 0.000 0.084
#> SRR2313993 2 0.3938 0.5520 0.000 0.672 0.012 0.004 0.312 0.000
#> SRR2313994 2 0.1958 0.8102 0.000 0.896 0.100 0.000 0.000 0.004
#> SRR2313995 2 0.5163 0.5020 0.000 0.632 0.004 0.112 0.004 0.248
#> SRR2314024 2 0.0603 0.8266 0.000 0.980 0.004 0.000 0.016 0.000
#> SRR2314015 2 0.1858 0.8103 0.000 0.912 0.012 0.000 0.076 0.000
#> SRR2314025 4 0.5290 0.3334 0.116 0.000 0.000 0.544 0.000 0.340
#> SRR2314023 1 0.2994 0.6089 0.788 0.000 0.000 0.208 0.000 0.004
#> SRR2314033 2 0.0858 0.8274 0.000 0.968 0.028 0.000 0.000 0.004
#> SRR2314045 1 0.7915 0.0093 0.352 0.000 0.168 0.068 0.080 0.332
#> SRR2314042 6 0.4538 0.1282 0.028 0.000 0.000 0.364 0.008 0.600
#> SRR2314043 2 0.5384 0.5876 0.116 0.648 0.212 0.000 0.016 0.008
#> SRR2314041 3 0.7166 0.2389 0.000 0.216 0.448 0.136 0.000 0.200
#> SRR2314044 2 0.5160 0.5968 0.024 0.652 0.060 0.256 0.000 0.008
#> SRR2314040 3 0.5151 0.3037 0.000 0.040 0.596 0.016 0.012 0.336
#> SRR2314039 6 0.4537 0.3685 0.000 0.004 0.300 0.040 0.004 0.652
#> SRR2314038 5 0.7306 0.4346 0.088 0.040 0.192 0.140 0.532 0.008
#> SRR2314037 3 0.6064 0.2146 0.056 0.004 0.476 0.396 0.000 0.068
#> SRR2314036 2 0.0146 0.8266 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR2314035 3 0.3657 0.4592 0.000 0.004 0.788 0.012 0.024 0.172
#> SRR2314034 6 0.7321 0.2179 0.256 0.000 0.228 0.076 0.016 0.424
#> SRR2314032 1 0.5404 0.5215 0.720 0.000 0.052 0.080 0.048 0.100
#> SRR2314031 4 0.4402 0.4832 0.084 0.000 0.000 0.700 0.000 0.216
#> SRR2314029 2 0.5578 0.5821 0.000 0.656 0.004 0.192 0.084 0.064
#> SRR2314030 5 0.5216 0.4703 0.012 0.000 0.036 0.032 0.628 0.292
#> SRR2314028 6 0.5401 -0.1036 0.092 0.000 0.012 0.344 0.000 0.552
#> SRR2314027 6 0.4608 0.3994 0.000 0.004 0.264 0.028 0.024 0.680
#> SRR2314026 5 0.5318 0.4253 0.000 0.000 0.076 0.020 0.584 0.320
#> SRR2314020 5 0.4049 0.1734 0.000 0.412 0.004 0.004 0.580 0.000
#> SRR2314019 6 0.3946 0.4294 0.000 0.000 0.152 0.004 0.076 0.768
#> SRR2314021 5 0.2703 0.6157 0.000 0.000 0.016 0.008 0.860 0.116
#> SRR2314018 3 0.1812 0.5453 0.004 0.060 0.924 0.004 0.000 0.008
#> SRR2314017 3 0.6136 0.1836 0.016 0.000 0.540 0.008 0.196 0.240
#> SRR2314016 5 0.6660 0.3538 0.124 0.000 0.052 0.240 0.552 0.032
#> SRR2314014 2 0.2558 0.7811 0.000 0.840 0.156 0.000 0.000 0.004
#> SRR2314013 2 0.2165 0.8075 0.000 0.884 0.108 0.000 0.000 0.008
#> SRR2314012 4 0.6035 0.2647 0.372 0.000 0.000 0.380 0.000 0.248
#> SRR2314011 1 0.4512 0.1555 0.540 0.000 0.004 0.436 0.008 0.012
#> SRR2314010 2 0.1570 0.8287 0.000 0.944 0.028 0.004 0.016 0.008
#> SRR2314009 3 0.3161 0.5242 0.020 0.000 0.852 0.068 0.000 0.060
#> SRR2314008 3 0.2400 0.5368 0.000 0.036 0.908 0.016 0.020 0.020
#> SRR2314007 5 0.0984 0.6301 0.000 0.012 0.008 0.012 0.968 0.000
#> SRR2314005 2 0.2053 0.8088 0.000 0.888 0.108 0.000 0.000 0.004
#> SRR2314004 2 0.1219 0.8161 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR2314003 4 0.4819 0.5104 0.168 0.000 0.076 0.716 0.000 0.040
#> SRR2314006 2 0.1364 0.8176 0.000 0.944 0.000 0.004 0.048 0.004
#> SRR2314002 5 0.3187 0.6295 0.000 0.000 0.112 0.008 0.836 0.044
#> SRR2314001 2 0.4351 0.6808 0.000 0.728 0.196 0.000 0.012 0.064
#> SRR2314000 1 0.3588 0.6082 0.832 0.084 0.052 0.024 0.000 0.008
#> SRR2313998 2 0.0692 0.8278 0.000 0.976 0.020 0.000 0.000 0.004
#> SRR2313996 2 0.2462 0.8005 0.000 0.860 0.132 0.000 0.004 0.004
#> SRR2313999 1 0.5093 0.1170 0.528 0.000 0.084 0.388 0.000 0.000
#> SRR2313997 2 0.2668 0.7342 0.000 0.828 0.004 0.000 0.168 0.000
#> SRR2313992 3 0.6624 -0.0129 0.032 0.000 0.388 0.340 0.000 0.240
#> SRR2313991 5 0.3251 0.6293 0.000 0.000 0.124 0.008 0.828 0.040
#> SRR2313990 3 0.3859 0.3977 0.000 0.288 0.692 0.000 0.000 0.020
#> SRR2313988 1 0.3056 0.6366 0.856 0.000 0.008 0.084 0.004 0.048
#> SRR2313987 1 0.2402 0.6598 0.856 0.000 0.000 0.140 0.000 0.004
#> SRR2313984 5 0.3899 0.3324 0.000 0.364 0.008 0.000 0.628 0.000
#> SRR2313989 2 0.0458 0.8251 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR2313983 1 0.1176 0.6946 0.956 0.000 0.000 0.020 0.000 0.024
#> SRR2313986 4 0.3915 0.4928 0.272 0.000 0.020 0.704 0.000 0.004
#> SRR2313982 1 0.2350 0.6828 0.880 0.000 0.000 0.100 0.000 0.020
#> SRR2313981 3 0.4237 0.4744 0.012 0.000 0.768 0.072 0.008 0.140
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
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