Date: 2019-12-25 23:42:08 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 15949 rows and 64 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] 15949 64
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:skmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:pam | 2 | 1.000 | 0.958 | 0.985 | ** | |
| SD:mclust | 2 | 1.000 | 0.991 | 0.993 | ** | |
| SD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:hclust | 4 | 1.000 | 0.926 | 0.975 | ** | 3 |
| CV:kmeans | 2 | 1.000 | 0.994 | 0.965 | ** | |
| CV:skmeans | 2 | 1.000 | 0.998 | 0.999 | ** | |
| CV:mclust | 2 | 1.000 | 0.982 | 0.991 | ** | |
| CV:NMF | 2 | 1.000 | 0.966 | 0.988 | ** | |
| MAD:pam | 2 | 1.000 | 0.986 | 0.994 | ** | |
| MAD:mclust | 2 | 1.000 | 0.999 | 0.999 | ** | |
| ATC:hclust | 2 | 1.000 | 0.935 | 0.971 | ** | |
| ATC:skmeans | 2 | 1.000 | 0.977 | 0.990 | ** | |
| ATC:kmeans | 3 | 0.999 | 0.967 | 0.975 | ** | 2 |
| ATC:pam | 5 | 0.901 | 0.891 | 0.940 | * | 2 |
| ATC:NMF | 3 | 0.901 | 0.906 | 0.958 | * | 2 |
| MAD:skmeans | 4 | 0.864 | 0.907 | 0.940 | ||
| CV:pam | 2 | 0.766 | 0.923 | 0.958 | ||
| MAD:NMF | 3 | 0.763 | 0.853 | 0.921 | ||
| MAD:hclust | 4 | 0.686 | 0.840 | 0.848 | ||
| ATC:mclust | 3 | 0.607 | 0.855 | 0.898 | ||
| MAD:kmeans | 3 | 0.584 | 0.830 | 0.861 | ||
| SD:kmeans | 2 | 0.504 | 0.903 | 0.922 | ||
| SD:hclust | 2 | 0.497 | 0.807 | 0.870 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 1.000 1.000 1.000 0.5065 0.494 0.494
#> CV:NMF 2 1.000 0.966 0.988 0.5078 0.493 0.493
#> MAD:NMF 2 0.489 0.727 0.889 0.4974 0.504 0.504
#> ATC:NMF 2 0.967 0.950 0.974 0.4515 0.542 0.542
#> SD:skmeans 2 1.000 1.000 1.000 0.5064 0.494 0.494
#> CV:skmeans 2 1.000 0.998 0.999 0.5084 0.492 0.492
#> MAD:skmeans 2 0.500 0.880 0.914 0.5053 0.494 0.494
#> ATC:skmeans 2 1.000 0.977 0.990 0.5032 0.497 0.497
#> SD:mclust 2 1.000 0.991 0.993 0.5049 0.494 0.494
#> CV:mclust 2 1.000 0.982 0.991 0.5077 0.492 0.492
#> MAD:mclust 2 1.000 0.999 0.999 0.5059 0.494 0.494
#> ATC:mclust 2 0.492 0.820 0.846 0.4225 0.493 0.493
#> SD:kmeans 2 0.504 0.903 0.922 0.5021 0.494 0.494
#> CV:kmeans 2 1.000 0.994 0.965 0.4766 0.492 0.492
#> MAD:kmeans 2 0.467 0.647 0.789 0.4936 0.494 0.494
#> ATC:kmeans 2 1.000 0.979 0.990 0.4808 0.524 0.524
#> SD:pam 2 1.000 0.958 0.985 0.5035 0.497 0.497
#> CV:pam 2 0.766 0.923 0.958 0.4932 0.494 0.494
#> MAD:pam 2 1.000 0.986 0.994 0.5055 0.494 0.494
#> ATC:pam 2 1.000 0.974 0.988 0.4741 0.532 0.532
#> SD:hclust 2 0.497 0.807 0.870 0.5015 0.493 0.493
#> CV:hclust 2 1.000 0.984 1.000 0.0322 0.969 0.969
#> MAD:hclust 2 0.494 0.659 0.801 0.4978 0.516 0.516
#> ATC:hclust 2 1.000 0.935 0.971 0.4708 0.542 0.542
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.727 0.711 0.882 0.245 0.837 0.681
#> CV:NMF 3 0.808 0.901 0.920 0.243 0.865 0.729
#> MAD:NMF 3 0.763 0.853 0.921 0.317 0.793 0.602
#> ATC:NMF 3 0.901 0.906 0.959 0.430 0.694 0.488
#> SD:skmeans 3 0.736 0.924 0.901 0.267 0.859 0.714
#> CV:skmeans 3 0.753 0.891 0.893 0.248 0.877 0.751
#> MAD:skmeans 3 0.809 0.829 0.906 0.304 0.840 0.680
#> ATC:skmeans 3 0.774 0.882 0.913 0.297 0.805 0.619
#> SD:mclust 3 0.784 0.924 0.931 0.187 0.920 0.838
#> CV:mclust 3 0.786 0.824 0.911 0.186 0.913 0.824
#> MAD:mclust 3 0.751 0.950 0.918 0.255 0.840 0.685
#> ATC:mclust 3 0.607 0.855 0.898 0.484 0.842 0.684
#> SD:kmeans 3 0.710 0.870 0.821 0.250 0.859 0.714
#> CV:kmeans 3 0.681 0.769 0.869 0.262 0.970 0.940
#> MAD:kmeans 3 0.584 0.830 0.861 0.300 0.840 0.680
#> ATC:kmeans 3 0.999 0.967 0.975 0.406 0.760 0.558
#> SD:pam 3 0.872 0.792 0.896 0.187 0.884 0.771
#> CV:pam 3 0.760 0.872 0.931 0.198 0.906 0.810
#> MAD:pam 3 0.645 0.563 0.809 0.255 0.884 0.770
#> ATC:pam 3 0.824 0.926 0.937 0.393 0.795 0.614
#> SD:hclust 3 0.542 0.685 0.830 0.152 0.971 0.942
#> CV:hclust 3 1.000 0.945 0.983 15.107 0.508 0.492
#> MAD:hclust 3 0.395 0.357 0.671 0.225 0.572 0.416
#> ATC:hclust 3 0.899 0.901 0.949 0.419 0.786 0.604
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.668 0.724 0.839 0.1182 0.867 0.665
#> CV:NMF 4 0.728 0.702 0.861 0.1650 0.864 0.642
#> MAD:NMF 4 0.667 0.770 0.870 0.0829 0.912 0.747
#> ATC:NMF 4 0.546 0.552 0.730 0.1156 0.911 0.767
#> SD:skmeans 4 0.743 0.776 0.871 0.1637 0.889 0.685
#> CV:skmeans 4 0.729 0.695 0.851 0.1701 0.875 0.669
#> MAD:skmeans 4 0.864 0.907 0.940 0.1346 0.899 0.714
#> ATC:skmeans 4 0.754 0.672 0.831 0.1006 0.851 0.613
#> SD:mclust 4 0.825 0.953 0.939 0.1951 0.861 0.665
#> CV:mclust 4 0.696 0.864 0.899 0.0341 0.969 0.927
#> MAD:mclust 4 0.874 0.917 0.930 0.1396 0.880 0.684
#> ATC:mclust 4 0.676 0.721 0.826 0.1554 0.826 0.573
#> SD:kmeans 4 0.592 0.654 0.746 0.1383 0.906 0.734
#> CV:kmeans 4 0.574 0.629 0.716 0.1292 0.886 0.757
#> MAD:kmeans 4 0.709 0.853 0.836 0.1274 0.899 0.714
#> ATC:kmeans 4 0.676 0.630 0.772 0.0970 0.975 0.922
#> SD:pam 4 0.661 0.679 0.809 0.2043 0.847 0.625
#> CV:pam 4 0.667 0.748 0.852 0.1999 0.875 0.686
#> MAD:pam 4 0.766 0.894 0.918 0.1661 0.787 0.502
#> ATC:pam 4 0.684 0.788 0.790 0.1118 0.905 0.717
#> SD:hclust 4 0.711 0.539 0.737 0.1845 0.852 0.689
#> CV:hclust 4 1.000 0.926 0.975 0.1420 0.936 0.865
#> MAD:hclust 4 0.686 0.840 0.849 0.1714 0.675 0.409
#> ATC:hclust 4 0.798 0.807 0.848 0.0762 0.960 0.877
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.679 0.601 0.725 0.0998 0.801 0.439
#> CV:NMF 5 0.725 0.705 0.833 0.0735 0.902 0.649
#> MAD:NMF 5 0.630 0.511 0.752 0.0963 0.915 0.708
#> ATC:NMF 5 0.551 0.431 0.708 0.0675 0.853 0.589
#> SD:skmeans 5 0.752 0.647 0.807 0.0589 0.949 0.797
#> CV:skmeans 5 0.720 0.548 0.781 0.0651 0.910 0.679
#> MAD:skmeans 5 0.793 0.783 0.828 0.0563 1.000 1.000
#> ATC:skmeans 5 0.751 0.605 0.811 0.0574 0.878 0.632
#> SD:mclust 5 0.855 0.795 0.876 0.0867 0.887 0.623
#> CV:mclust 5 0.669 0.532 0.807 0.1345 0.905 0.768
#> MAD:mclust 5 0.871 0.884 0.910 0.0738 0.924 0.734
#> ATC:mclust 5 0.779 0.680 0.846 0.0830 0.870 0.591
#> SD:kmeans 5 0.618 0.604 0.728 0.0814 0.974 0.899
#> CV:kmeans 5 0.567 0.566 0.721 0.0838 0.845 0.587
#> MAD:kmeans 5 0.736 0.769 0.797 0.0687 0.984 0.938
#> ATC:kmeans 5 0.697 0.540 0.716 0.0651 0.821 0.467
#> SD:pam 5 0.671 0.587 0.819 0.0670 0.907 0.685
#> CV:pam 5 0.717 0.733 0.862 0.0833 0.911 0.698
#> MAD:pam 5 0.743 0.730 0.835 0.0476 0.948 0.800
#> ATC:pam 5 0.901 0.891 0.940 0.0750 0.939 0.765
#> SD:hclust 5 0.654 0.391 0.726 0.0765 0.890 0.691
#> CV:hclust 5 0.815 0.821 0.881 0.1116 0.898 0.764
#> MAD:hclust 5 0.728 0.714 0.792 0.0761 0.965 0.866
#> ATC:hclust 5 0.772 0.684 0.821 0.0798 0.953 0.836
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.723 0.681 0.803 0.0560 0.939 0.729
#> CV:NMF 6 0.795 0.757 0.797 0.0450 0.933 0.699
#> MAD:NMF 6 0.655 0.417 0.671 0.0501 0.858 0.492
#> ATC:NMF 6 0.595 0.544 0.763 0.0359 0.876 0.573
#> SD:skmeans 6 0.714 0.372 0.692 0.0412 0.917 0.649
#> CV:skmeans 6 0.727 0.484 0.751 0.0417 0.906 0.631
#> MAD:skmeans 6 0.762 0.636 0.768 0.0405 0.915 0.678
#> ATC:skmeans 6 0.730 0.625 0.804 0.0369 0.926 0.716
#> SD:mclust 6 0.811 0.767 0.810 0.0475 0.935 0.709
#> CV:mclust 6 0.745 0.680 0.863 0.1043 0.857 0.574
#> MAD:mclust 6 0.782 0.654 0.823 0.0481 0.973 0.878
#> ATC:mclust 6 0.773 0.597 0.769 0.0458 0.872 0.527
#> SD:kmeans 6 0.721 0.721 0.770 0.0484 0.953 0.810
#> CV:kmeans 6 0.577 0.564 0.702 0.0506 0.986 0.939
#> MAD:kmeans 6 0.758 0.662 0.749 0.0505 0.899 0.621
#> ATC:kmeans 6 0.731 0.733 0.779 0.0451 0.912 0.600
#> SD:pam 6 0.698 0.654 0.809 0.0348 0.936 0.742
#> CV:pam 6 0.814 0.757 0.903 0.0507 0.927 0.704
#> MAD:pam 6 0.772 0.708 0.858 0.0281 0.984 0.926
#> ATC:pam 6 0.892 0.857 0.932 0.0287 0.989 0.946
#> SD:hclust 6 0.667 0.693 0.800 0.0595 0.887 0.613
#> CV:hclust 6 0.777 0.711 0.898 0.0708 0.973 0.925
#> MAD:hclust 6 0.778 0.716 0.800 0.0376 0.963 0.842
#> ATC:hclust 6 0.767 0.579 0.753 0.0456 0.887 0.567
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 15949 rows and 64 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 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 0.497 0.807 0.870 0.5015 0.493 0.493
#> 3 3 0.542 0.685 0.830 0.1519 0.971 0.942
#> 4 4 0.711 0.539 0.737 0.1845 0.852 0.689
#> 5 5 0.654 0.391 0.726 0.0765 0.890 0.691
#> 6 6 0.667 0.693 0.800 0.0595 0.887 0.613
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
#> SRR925687 1 0.844 0.848 0.728 0.272
#> SRR925688 1 0.844 0.848 0.728 0.272
#> SRR925689 1 0.000 0.748 1.000 0.000
#> SRR925690 1 0.000 0.748 1.000 0.000
#> SRR925691 1 0.000 0.748 1.000 0.000
#> SRR925692 1 0.000 0.748 1.000 0.000
#> SRR925693 2 0.000 0.822 0.000 1.000
#> SRR925694 2 0.844 0.796 0.272 0.728
#> SRR925695 2 0.000 0.822 0.000 1.000
#> SRR925697 1 0.844 0.848 0.728 0.272
#> SRR925696 2 0.000 0.822 0.000 1.000
#> SRR925698 2 0.000 0.822 0.000 1.000
#> SRR925699 2 0.844 0.796 0.272 0.728
#> SRR925700 2 0.844 0.796 0.272 0.728
#> SRR925701 2 0.844 0.796 0.272 0.728
#> SRR925702 1 0.891 0.828 0.692 0.308
#> SRR925703 1 0.844 0.848 0.728 0.272
#> SRR925704 2 0.000 0.822 0.000 1.000
#> SRR925705 2 0.000 0.822 0.000 1.000
#> SRR925706 1 0.929 0.799 0.656 0.344
#> SRR925708 1 0.844 0.848 0.728 0.272
#> SRR925707 1 0.929 0.799 0.656 0.344
#> SRR925709 1 0.844 0.848 0.728 0.272
#> SRR925710 1 0.925 0.803 0.660 0.340
#> SRR925711 2 0.000 0.822 0.000 1.000
#> SRR934631 2 0.844 0.796 0.272 0.728
#> SRR934632 2 0.844 0.796 0.272 0.728
#> SRR934633 2 0.844 0.796 0.272 0.728
#> SRR925713 1 0.886 0.831 0.696 0.304
#> SRR925714 1 0.844 0.848 0.728 0.272
#> SRR925715 1 0.929 0.799 0.656 0.344
#> SRR925716 1 0.844 0.848 0.728 0.272
#> SRR925717 1 0.844 0.848 0.728 0.272
#> SRR925718 2 0.844 0.796 0.272 0.728
#> SRR925719 1 0.844 0.848 0.728 0.272
#> SRR925720 1 0.844 0.848 0.728 0.272
#> SRR925721 1 0.844 0.848 0.728 0.272
#> SRR934634 1 0.000 0.748 1.000 0.000
#> SRR934635 1 0.000 0.748 1.000 0.000
#> SRR925723 2 0.000 0.822 0.000 1.000
#> SRR925724 2 0.000 0.822 0.000 1.000
#> SRR925725 2 0.000 0.822 0.000 1.000
#> SRR925726 1 0.844 0.848 0.728 0.272
#> SRR925727 2 0.000 0.822 0.000 1.000
#> SRR925728 1 0.929 0.799 0.656 0.344
#> SRR934636 1 0.000 0.748 1.000 0.000
#> SRR934637 1 0.000 0.748 1.000 0.000
#> SRR925729 2 0.844 0.796 0.272 0.728
#> SRR934638 1 0.000 0.748 1.000 0.000
#> SRR934639 1 0.000 0.748 1.000 0.000
#> SRR934640 2 0.844 0.796 0.272 0.728
#> SRR934641 2 0.844 0.796 0.272 0.728
#> SRR925734 1 0.000 0.748 1.000 0.000
#> SRR925735 2 0.000 0.822 0.000 1.000
#> SRR925736 2 0.000 0.822 0.000 1.000
#> SRR925737 2 0.000 0.822 0.000 1.000
#> SRR934642 2 0.844 0.796 0.272 0.728
#> SRR934643 2 0.844 0.796 0.272 0.728
#> SRR934644 2 0.844 0.796 0.272 0.728
#> SRR934645 2 0.844 0.796 0.272 0.728
#> SRR925740 2 0.000 0.822 0.000 1.000
#> SRR925741 2 0.000 0.822 0.000 1.000
#> SRR925742 2 0.000 0.822 0.000 1.000
#> SRR1283038 2 0.000 0.822 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.000 0.703 1.000 0.000 0.000
#> SRR925688 1 0.000 0.703 1.000 0.000 0.000
#> SRR925689 1 0.611 0.325 0.604 0.000 0.396
#> SRR925690 1 0.611 0.325 0.604 0.000 0.396
#> SRR925691 1 0.611 0.325 0.604 0.000 0.396
#> SRR925692 1 0.611 0.325 0.604 0.000 0.396
#> SRR925693 2 0.533 0.826 0.272 0.728 0.000
#> SRR925694 2 0.000 0.789 0.000 1.000 0.000
#> SRR925695 2 0.533 0.826 0.272 0.728 0.000
#> SRR925697 1 0.597 0.340 0.636 0.000 0.364
#> SRR925696 2 0.533 0.826 0.272 0.728 0.000
#> SRR925698 2 0.533 0.826 0.272 0.728 0.000
#> SRR925699 2 0.000 0.789 0.000 1.000 0.000
#> SRR925700 2 0.000 0.789 0.000 1.000 0.000
#> SRR925701 2 0.000 0.789 0.000 1.000 0.000
#> SRR925702 1 0.162 0.697 0.964 0.024 0.012
#> SRR925703 1 0.597 0.340 0.636 0.000 0.364
#> SRR925704 2 0.533 0.826 0.272 0.728 0.000
#> SRR925705 2 0.533 0.826 0.272 0.728 0.000
#> SRR925706 1 0.277 0.686 0.928 0.024 0.048
#> SRR925708 1 0.000 0.703 1.000 0.000 0.000
#> SRR925707 1 0.277 0.686 0.928 0.024 0.048
#> SRR925709 1 0.000 0.703 1.000 0.000 0.000
#> SRR925710 1 0.266 0.688 0.932 0.024 0.044
#> SRR925711 2 0.694 0.789 0.272 0.680 0.048
#> SRR934631 2 0.000 0.789 0.000 1.000 0.000
#> SRR934632 2 0.000 0.789 0.000 1.000 0.000
#> SRR934633 2 0.000 0.789 0.000 1.000 0.000
#> SRR925713 1 0.145 0.697 0.968 0.024 0.008
#> SRR925714 1 0.597 0.340 0.636 0.000 0.364
#> SRR925715 1 0.277 0.686 0.928 0.024 0.048
#> SRR925716 1 0.597 0.340 0.636 0.000 0.364
#> SRR925717 1 0.236 0.672 0.928 0.000 0.072
#> SRR925718 2 0.000 0.789 0.000 1.000 0.000
#> SRR925719 1 0.000 0.703 1.000 0.000 0.000
#> SRR925720 1 0.000 0.703 1.000 0.000 0.000
#> SRR925721 1 0.000 0.703 1.000 0.000 0.000
#> SRR934634 1 0.611 0.325 0.604 0.000 0.396
#> SRR934635 1 0.611 0.325 0.604 0.000 0.396
#> SRR925723 2 0.533 0.826 0.272 0.728 0.000
#> SRR925724 2 0.533 0.826 0.272 0.728 0.000
#> SRR925725 2 0.533 0.826 0.272 0.728 0.000
#> SRR925726 1 0.475 0.541 0.784 0.000 0.216
#> SRR925727 2 0.533 0.826 0.272 0.728 0.000
#> SRR925728 1 0.277 0.686 0.928 0.024 0.048
#> SRR934636 3 0.175 1.000 0.048 0.000 0.952
#> SRR934637 3 0.175 1.000 0.048 0.000 0.952
#> SRR925729 2 0.000 0.789 0.000 1.000 0.000
#> SRR934638 1 0.611 0.325 0.604 0.000 0.396
#> SRR934639 1 0.611 0.325 0.604 0.000 0.396
#> SRR934640 2 0.175 0.760 0.000 0.952 0.048
#> SRR934641 2 0.175 0.760 0.000 0.952 0.048
#> SRR925734 1 0.611 0.325 0.604 0.000 0.396
#> SRR925735 2 0.555 0.823 0.272 0.724 0.004
#> SRR925736 2 0.533 0.826 0.272 0.728 0.000
#> SRR925737 2 0.533 0.826 0.272 0.728 0.000
#> SRR934642 2 0.000 0.789 0.000 1.000 0.000
#> SRR934643 2 0.000 0.789 0.000 1.000 0.000
#> SRR934644 2 0.000 0.789 0.000 1.000 0.000
#> SRR934645 2 0.000 0.789 0.000 1.000 0.000
#> SRR925740 2 0.533 0.826 0.272 0.728 0.000
#> SRR925741 2 0.533 0.826 0.272 0.728 0.000
#> SRR925742 2 0.533 0.826 0.272 0.728 0.000
#> SRR1283038 2 0.533 0.826 0.272 0.728 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.484 0.3903 0.604 0.000 0.000 0.396
#> SRR925688 1 0.484 0.3903 0.604 0.000 0.000 0.396
#> SRR925689 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925690 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925691 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925692 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925693 3 0.499 0.6764 0.000 0.476 0.524 0.000
#> SRR925694 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925695 3 0.498 0.6864 0.000 0.464 0.536 0.000
#> SRR925697 4 0.156 0.4994 0.056 0.000 0.000 0.944
#> SRR925696 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR925698 3 0.494 0.7006 0.000 0.436 0.564 0.000
#> SRR925699 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925700 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925701 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925702 4 0.789 -0.0491 0.324 0.300 0.000 0.376
#> SRR925703 4 0.156 0.4994 0.056 0.000 0.000 0.944
#> SRR925704 3 0.498 0.6864 0.000 0.464 0.536 0.000
#> SRR925705 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR925706 2 0.748 0.3850 0.204 0.488 0.000 0.308
#> SRR925708 1 0.599 0.3258 0.556 0.044 0.000 0.400
#> SRR925707 2 0.748 0.3850 0.204 0.488 0.000 0.308
#> SRR925709 1 0.606 0.3179 0.552 0.048 0.000 0.400
#> SRR925710 4 0.784 -0.0834 0.272 0.328 0.000 0.400
#> SRR925711 2 0.500 -0.7130 0.000 0.508 0.492 0.000
#> SRR934631 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934632 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934633 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925713 4 0.782 0.0385 0.340 0.260 0.000 0.400
#> SRR925714 4 0.156 0.4994 0.056 0.000 0.000 0.944
#> SRR925715 2 0.748 0.3850 0.204 0.488 0.000 0.308
#> SRR925716 4 0.156 0.4994 0.056 0.000 0.000 0.944
#> SRR925717 4 0.533 0.1169 0.380 0.016 0.000 0.604
#> SRR925718 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925719 1 0.599 0.3258 0.556 0.044 0.000 0.400
#> SRR925720 1 0.484 0.3903 0.604 0.000 0.000 0.396
#> SRR925721 1 0.484 0.3903 0.604 0.000 0.000 0.396
#> SRR934634 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR934635 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925723 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR925724 3 0.496 0.6951 0.000 0.448 0.552 0.000
#> SRR925725 3 0.498 0.6864 0.000 0.464 0.536 0.000
#> SRR925726 4 0.404 0.3932 0.248 0.000 0.000 0.752
#> SRR925727 3 0.498 0.6864 0.000 0.464 0.536 0.000
#> SRR925728 2 0.748 0.3850 0.204 0.488 0.000 0.308
#> SRR934636 4 0.717 0.1564 0.396 0.136 0.000 0.468
#> SRR934637 4 0.717 0.1564 0.396 0.136 0.000 0.468
#> SRR925729 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934638 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR934639 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR934640 3 0.187 0.6526 0.000 0.072 0.928 0.000
#> SRR934641 3 0.187 0.6526 0.000 0.072 0.928 0.000
#> SRR925734 1 0.000 0.6633 1.000 0.000 0.000 0.000
#> SRR925735 3 0.498 0.6854 0.000 0.464 0.536 0.000
#> SRR925736 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR925737 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR934642 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934643 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934644 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR934645 3 0.000 0.7130 0.000 0.000 1.000 0.000
#> SRR925740 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR925741 3 0.498 0.6864 0.000 0.464 0.536 0.000
#> SRR925742 3 0.485 0.7148 0.000 0.400 0.600 0.000
#> SRR1283038 3 0.492 0.7041 0.000 0.428 0.572 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.6969 0.221 0.544 0.060 0.000 0.260 0.136
#> SRR925688 1 0.6969 0.221 0.544 0.060 0.000 0.260 0.136
#> SRR925689 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.4162 0.530 0.000 0.056 0.768 0.176 0.000
#> SRR925694 2 0.4306 0.912 0.000 0.508 0.492 0.000 0.000
#> SRR925695 3 0.3381 0.546 0.000 0.016 0.808 0.176 0.000
#> SRR925697 5 0.0000 0.714 0.000 0.000 0.000 0.000 1.000
#> SRR925696 3 0.0290 0.529 0.000 0.008 0.992 0.000 0.000
#> SRR925698 3 0.2179 0.559 0.000 0.004 0.896 0.100 0.000
#> SRR925699 3 0.4256 -0.751 0.000 0.436 0.564 0.000 0.000
#> SRR925700 3 0.4256 -0.751 0.000 0.436 0.564 0.000 0.000
#> SRR925701 3 0.4256 -0.751 0.000 0.436 0.564 0.000 0.000
#> SRR925702 4 0.7202 0.494 0.288 0.028 0.000 0.448 0.236
#> SRR925703 5 0.0000 0.714 0.000 0.000 0.000 0.000 1.000
#> SRR925704 3 0.3381 0.546 0.000 0.016 0.808 0.176 0.000
#> SRR925705 3 0.0290 0.529 0.000 0.008 0.992 0.000 0.000
#> SRR925706 4 0.3074 0.791 0.196 0.000 0.000 0.804 0.000
#> SRR925708 1 0.6842 0.185 0.520 0.028 0.000 0.176 0.276
#> SRR925707 4 0.3074 0.791 0.196 0.000 0.000 0.804 0.000
#> SRR925709 1 0.6869 0.174 0.516 0.028 0.000 0.180 0.276
#> SRR925710 4 0.6939 0.669 0.224 0.064 0.000 0.564 0.148
#> SRR925711 3 0.4796 0.458 0.000 0.120 0.728 0.152 0.000
#> SRR934631 3 0.4287 -0.797 0.000 0.460 0.540 0.000 0.000
#> SRR934632 3 0.4287 -0.797 0.000 0.460 0.540 0.000 0.000
#> SRR934633 3 0.4287 -0.797 0.000 0.460 0.540 0.000 0.000
#> SRR925713 4 0.6903 0.572 0.292 0.040 0.000 0.520 0.148
#> SRR925714 5 0.0000 0.714 0.000 0.000 0.000 0.000 1.000
#> SRR925715 4 0.3745 0.781 0.196 0.024 0.000 0.780 0.000
#> SRR925716 5 0.0000 0.714 0.000 0.000 0.000 0.000 1.000
#> SRR925717 5 0.7224 0.315 0.124 0.236 0.000 0.100 0.540
#> SRR925718 3 0.4227 -0.722 0.000 0.420 0.580 0.000 0.000
#> SRR925719 1 0.6842 0.185 0.520 0.028 0.000 0.176 0.276
#> SRR925720 1 0.6855 0.235 0.552 0.052 0.000 0.260 0.136
#> SRR925721 1 0.6381 0.272 0.580 0.024 0.000 0.260 0.136
#> SRR934634 1 0.0703 0.667 0.976 0.024 0.000 0.000 0.000
#> SRR934635 1 0.0703 0.667 0.976 0.024 0.000 0.000 0.000
#> SRR925723 3 0.0609 0.515 0.000 0.020 0.980 0.000 0.000
#> SRR925724 3 0.2612 0.558 0.000 0.008 0.868 0.124 0.000
#> SRR925725 3 0.3810 0.542 0.000 0.036 0.788 0.176 0.000
#> SRR925726 5 0.3461 0.610 0.004 0.224 0.000 0.000 0.772
#> SRR925727 3 0.3476 0.546 0.000 0.020 0.804 0.176 0.000
#> SRR925728 4 0.3074 0.791 0.196 0.000 0.000 0.804 0.000
#> SRR934636 5 0.6938 0.348 0.396 0.176 0.000 0.020 0.408
#> SRR934637 5 0.6938 0.348 0.396 0.176 0.000 0.020 0.408
#> SRR925729 2 0.4306 0.912 0.000 0.508 0.492 0.000 0.000
#> SRR934638 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR934639 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR934640 2 0.5250 0.838 0.000 0.536 0.416 0.048 0.000
#> SRR934641 2 0.5250 0.838 0.000 0.536 0.416 0.048 0.000
#> SRR925734 1 0.0000 0.677 1.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.4138 0.535 0.000 0.072 0.780 0.148 0.000
#> SRR925736 3 0.0162 0.532 0.000 0.004 0.996 0.000 0.000
#> SRR925737 3 0.0162 0.532 0.000 0.004 0.996 0.000 0.000
#> SRR934642 3 0.4256 -0.751 0.000 0.436 0.564 0.000 0.000
#> SRR934643 3 0.4256 -0.751 0.000 0.436 0.564 0.000 0.000
#> SRR934644 2 0.4306 0.912 0.000 0.508 0.492 0.000 0.000
#> SRR934645 2 0.4306 0.912 0.000 0.508 0.492 0.000 0.000
#> SRR925740 3 0.0162 0.532 0.000 0.004 0.996 0.000 0.000
#> SRR925741 3 0.3732 0.543 0.000 0.032 0.792 0.176 0.000
#> SRR925742 3 0.0162 0.532 0.000 0.004 0.996 0.000 0.000
#> SRR1283038 3 0.1892 0.558 0.000 0.004 0.916 0.080 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.7234 0.340 0.464 0.000 0.012 0.256 0.124 0.144
#> SRR925688 1 0.7234 0.340 0.464 0.000 0.012 0.256 0.124 0.144
#> SRR925689 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.2070 0.735 0.000 0.100 0.892 0.000 0.000 0.008
#> SRR925694 2 0.0713 0.841 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR925695 3 0.1918 0.760 0.000 0.088 0.904 0.000 0.000 0.008
#> SRR925697 5 0.0000 0.754 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925696 3 0.3409 0.691 0.000 0.300 0.700 0.000 0.000 0.000
#> SRR925698 3 0.2260 0.768 0.000 0.140 0.860 0.000 0.000 0.000
#> SRR925699 2 0.2854 0.827 0.000 0.792 0.208 0.000 0.000 0.000
#> SRR925700 2 0.2854 0.827 0.000 0.792 0.208 0.000 0.000 0.000
#> SRR925701 2 0.2854 0.827 0.000 0.792 0.208 0.000 0.000 0.000
#> SRR925702 4 0.6020 0.452 0.160 0.000 0.000 0.572 0.228 0.040
#> SRR925703 5 0.0000 0.754 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925704 3 0.1918 0.760 0.000 0.088 0.904 0.000 0.000 0.008
#> SRR925705 3 0.3409 0.691 0.000 0.300 0.700 0.000 0.000 0.000
#> SRR925706 4 0.0146 0.720 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR925708 1 0.6522 0.368 0.504 0.000 0.000 0.172 0.264 0.060
#> SRR925707 4 0.0146 0.720 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR925709 1 0.6545 0.361 0.500 0.000 0.000 0.176 0.264 0.060
#> SRR925710 4 0.6635 0.526 0.168 0.024 0.012 0.600 0.136 0.060
#> SRR925711 3 0.4650 0.492 0.000 0.416 0.548 0.028 0.000 0.008
#> SRR934631 2 0.2003 0.854 0.000 0.884 0.116 0.000 0.000 0.000
#> SRR934632 2 0.2003 0.854 0.000 0.884 0.116 0.000 0.000 0.000
#> SRR934633 2 0.2003 0.854 0.000 0.884 0.116 0.000 0.000 0.000
#> SRR925713 4 0.6139 0.388 0.244 0.000 0.000 0.564 0.136 0.056
#> SRR925714 5 0.0000 0.754 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925715 4 0.1218 0.704 0.000 0.028 0.012 0.956 0.000 0.004
#> SRR925716 5 0.0000 0.754 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925717 5 0.6576 0.389 0.112 0.000 0.000 0.096 0.496 0.296
#> SRR925718 2 0.3309 0.719 0.000 0.720 0.280 0.000 0.000 0.000
#> SRR925719 1 0.6522 0.368 0.504 0.000 0.000 0.172 0.264 0.060
#> SRR925720 1 0.7122 0.361 0.480 0.000 0.012 0.256 0.124 0.128
#> SRR925721 1 0.6195 0.435 0.556 0.000 0.000 0.256 0.124 0.064
#> SRR934634 1 0.1075 0.701 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR934635 1 0.1075 0.701 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR925723 3 0.3499 0.659 0.000 0.320 0.680 0.000 0.000 0.000
#> SRR925724 3 0.1957 0.769 0.000 0.112 0.888 0.000 0.000 0.000
#> SRR925725 3 0.2118 0.749 0.000 0.104 0.888 0.000 0.000 0.008
#> SRR925726 5 0.3314 0.599 0.004 0.000 0.000 0.000 0.740 0.256
#> SRR925727 3 0.2020 0.758 0.000 0.096 0.896 0.000 0.000 0.008
#> SRR925728 4 0.0146 0.720 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR934636 6 0.4159 1.000 0.016 0.000 0.000 0.000 0.396 0.588
#> SRR934637 6 0.4159 1.000 0.016 0.000 0.000 0.000 0.396 0.588
#> SRR925729 2 0.0713 0.841 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR934638 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934639 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934640 2 0.1679 0.769 0.000 0.936 0.028 0.028 0.000 0.008
#> SRR934641 2 0.1679 0.769 0.000 0.936 0.028 0.028 0.000 0.008
#> SRR925734 1 0.0000 0.715 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.2845 0.725 0.000 0.172 0.820 0.004 0.000 0.004
#> SRR925736 3 0.3428 0.686 0.000 0.304 0.696 0.000 0.000 0.000
#> SRR925737 3 0.3428 0.686 0.000 0.304 0.696 0.000 0.000 0.000
#> SRR934642 2 0.2854 0.827 0.000 0.792 0.208 0.000 0.000 0.000
#> SRR934643 2 0.2854 0.827 0.000 0.792 0.208 0.000 0.000 0.000
#> SRR934644 2 0.0713 0.841 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR934645 2 0.0713 0.841 0.000 0.972 0.028 0.000 0.000 0.000
#> SRR925740 3 0.3409 0.691 0.000 0.300 0.700 0.000 0.000 0.000
#> SRR925741 3 0.2212 0.752 0.000 0.112 0.880 0.000 0.000 0.008
#> SRR925742 3 0.3409 0.691 0.000 0.300 0.700 0.000 0.000 0.000
#> SRR1283038 3 0.2562 0.769 0.000 0.172 0.828 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.504 0.903 0.922 0.5021 0.494 0.494
#> 3 3 0.710 0.870 0.821 0.2501 0.859 0.714
#> 4 4 0.592 0.654 0.746 0.1383 0.906 0.734
#> 5 5 0.618 0.604 0.728 0.0814 0.974 0.899
#> 6 6 0.721 0.721 0.770 0.0484 0.953 0.810
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
#> SRR925687 1 0.0376 0.932 0.996 0.004
#> SRR925688 1 0.0376 0.932 0.996 0.004
#> SRR925689 1 0.6148 0.866 0.848 0.152
#> SRR925690 1 0.6148 0.866 0.848 0.152
#> SRR925691 1 0.6148 0.866 0.848 0.152
#> SRR925692 1 0.6148 0.866 0.848 0.152
#> SRR925693 2 0.6048 0.908 0.148 0.852
#> SRR925694 2 0.1843 0.885 0.028 0.972
#> SRR925695 2 0.6148 0.908 0.152 0.848
#> SRR925697 1 0.1843 0.920 0.972 0.028
#> SRR925696 2 0.6048 0.908 0.148 0.852
#> SRR925698 2 0.6148 0.908 0.152 0.848
#> SRR925699 2 0.1843 0.885 0.028 0.972
#> SRR925700 2 0.1843 0.885 0.028 0.972
#> SRR925701 2 0.1843 0.885 0.028 0.972
#> SRR925702 1 0.0376 0.932 0.996 0.004
#> SRR925703 1 0.1843 0.920 0.972 0.028
#> SRR925704 2 0.6148 0.908 0.152 0.848
#> SRR925705 2 0.6148 0.908 0.152 0.848
#> SRR925706 1 0.2043 0.921 0.968 0.032
#> SRR925708 1 0.0376 0.932 0.996 0.004
#> SRR925707 2 0.2043 0.886 0.032 0.968
#> SRR925709 1 0.0376 0.932 0.996 0.004
#> SRR925710 1 0.2043 0.921 0.968 0.032
#> SRR925711 2 0.6048 0.908 0.148 0.852
#> SRR934631 2 0.1843 0.885 0.028 0.972
#> SRR934632 2 0.1843 0.885 0.028 0.972
#> SRR934633 2 0.0000 0.886 0.000 1.000
#> SRR925713 1 0.2043 0.921 0.968 0.032
#> SRR925714 1 0.1843 0.920 0.972 0.028
#> SRR925715 1 0.2043 0.921 0.968 0.032
#> SRR925716 1 0.0000 0.931 1.000 0.000
#> SRR925717 1 0.2043 0.921 0.968 0.032
#> SRR925718 2 0.1843 0.885 0.028 0.972
#> SRR925719 1 0.0376 0.932 0.996 0.004
#> SRR925720 1 0.0938 0.929 0.988 0.012
#> SRR925721 1 0.0376 0.932 0.996 0.004
#> SRR934634 1 0.6148 0.866 0.848 0.152
#> SRR934635 1 0.6148 0.866 0.848 0.152
#> SRR925723 2 0.6148 0.908 0.152 0.848
#> SRR925724 2 0.6148 0.908 0.152 0.848
#> SRR925725 2 0.6048 0.908 0.148 0.852
#> SRR925726 1 0.1843 0.920 0.972 0.028
#> SRR925727 2 0.6048 0.908 0.148 0.852
#> SRR925728 1 0.0376 0.932 0.996 0.004
#> SRR934636 1 0.6048 0.866 0.852 0.148
#> SRR934637 1 0.3733 0.904 0.928 0.072
#> SRR925729 2 0.6048 0.908 0.148 0.852
#> SRR934638 1 0.6148 0.866 0.848 0.152
#> SRR934639 1 0.0376 0.932 0.996 0.004
#> SRR934640 2 0.1843 0.885 0.028 0.972
#> SRR934641 2 0.1843 0.885 0.028 0.972
#> SRR925734 1 0.6148 0.866 0.848 0.152
#> SRR925735 2 0.6048 0.908 0.148 0.852
#> SRR925736 2 0.6048 0.908 0.148 0.852
#> SRR925737 2 0.6048 0.908 0.148 0.852
#> SRR934642 2 0.1843 0.885 0.028 0.972
#> SRR934643 2 0.1843 0.885 0.028 0.972
#> SRR934644 2 0.1843 0.885 0.028 0.972
#> SRR934645 2 0.1843 0.885 0.028 0.972
#> SRR925740 2 0.6048 0.908 0.148 0.852
#> SRR925741 2 0.6148 0.908 0.152 0.848
#> SRR925742 2 0.6148 0.908 0.152 0.848
#> SRR1283038 2 0.6148 0.908 0.152 0.848
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0592 0.897 0.988 0.000 0.012
#> SRR925688 1 0.0237 0.897 0.996 0.000 0.004
#> SRR925689 1 0.4087 0.872 0.880 0.052 0.068
#> SRR925690 1 0.4087 0.872 0.880 0.052 0.068
#> SRR925691 1 0.4087 0.872 0.880 0.052 0.068
#> SRR925692 1 0.4087 0.872 0.880 0.052 0.068
#> SRR925693 3 0.6832 0.904 0.020 0.376 0.604
#> SRR925694 2 0.0424 0.899 0.000 0.992 0.008
#> SRR925695 3 0.6154 0.961 0.000 0.408 0.592
#> SRR925697 1 0.5529 0.791 0.704 0.000 0.296
#> SRR925696 3 0.6180 0.963 0.000 0.416 0.584
#> SRR925698 3 0.6168 0.964 0.000 0.412 0.588
#> SRR925699 2 0.0592 0.902 0.000 0.988 0.012
#> SRR925700 2 0.0424 0.902 0.000 0.992 0.008
#> SRR925701 2 0.0592 0.902 0.000 0.988 0.012
#> SRR925702 1 0.2537 0.889 0.920 0.000 0.080
#> SRR925703 1 0.5529 0.791 0.704 0.000 0.296
#> SRR925704 3 0.6168 0.960 0.000 0.412 0.588
#> SRR925705 3 0.6180 0.963 0.000 0.416 0.584
#> SRR925706 1 0.2796 0.886 0.908 0.000 0.092
#> SRR925708 1 0.0237 0.897 0.996 0.000 0.004
#> SRR925707 2 0.6644 0.564 0.160 0.748 0.092
#> SRR925709 1 0.1753 0.890 0.952 0.000 0.048
#> SRR925710 1 0.3038 0.882 0.896 0.000 0.104
#> SRR925711 3 0.6204 0.865 0.000 0.424 0.576
#> SRR934631 2 0.0892 0.895 0.000 0.980 0.020
#> SRR934632 2 0.0892 0.895 0.000 0.980 0.020
#> SRR934633 2 0.5810 -0.280 0.000 0.664 0.336
#> SRR925713 1 0.2796 0.886 0.908 0.000 0.092
#> SRR925714 1 0.5529 0.791 0.704 0.000 0.296
#> SRR925715 1 0.3038 0.882 0.896 0.000 0.104
#> SRR925716 1 0.5497 0.792 0.708 0.000 0.292
#> SRR925717 1 0.1860 0.894 0.948 0.000 0.052
#> SRR925718 2 0.1163 0.888 0.000 0.972 0.028
#> SRR925719 1 0.0237 0.897 0.996 0.000 0.004
#> SRR925720 1 0.0592 0.897 0.988 0.000 0.012
#> SRR925721 1 0.0747 0.897 0.984 0.000 0.016
#> SRR934634 1 0.4179 0.872 0.876 0.052 0.072
#> SRR934635 1 0.2774 0.887 0.920 0.008 0.072
#> SRR925723 3 0.6168 0.964 0.000 0.412 0.588
#> SRR925724 3 0.6168 0.964 0.000 0.412 0.588
#> SRR925725 3 0.6168 0.956 0.000 0.412 0.588
#> SRR925726 1 0.5529 0.791 0.704 0.000 0.296
#> SRR925727 3 0.6154 0.961 0.000 0.408 0.592
#> SRR925728 1 0.2537 0.889 0.920 0.000 0.080
#> SRR934636 1 0.7492 0.755 0.608 0.052 0.340
#> SRR934637 1 0.5835 0.783 0.660 0.000 0.340
#> SRR925729 3 0.6308 0.840 0.000 0.492 0.508
#> SRR934638 1 0.3896 0.872 0.888 0.052 0.060
#> SRR934639 1 0.0237 0.897 0.996 0.000 0.004
#> SRR934640 2 0.2066 0.859 0.000 0.940 0.060
#> SRR934641 2 0.2066 0.859 0.000 0.940 0.060
#> SRR925734 1 0.3896 0.872 0.888 0.052 0.060
#> SRR925735 3 0.6180 0.938 0.000 0.416 0.584
#> SRR925736 3 0.6204 0.959 0.000 0.424 0.576
#> SRR925737 3 0.6204 0.959 0.000 0.424 0.576
#> SRR934642 2 0.0592 0.902 0.000 0.988 0.012
#> SRR934643 2 0.0592 0.902 0.000 0.988 0.012
#> SRR934644 2 0.0424 0.899 0.000 0.992 0.008
#> SRR934645 2 0.0424 0.899 0.000 0.992 0.008
#> SRR925740 3 0.6180 0.963 0.000 0.416 0.584
#> SRR925741 3 0.6168 0.956 0.000 0.412 0.588
#> SRR925742 3 0.6180 0.963 0.000 0.416 0.584
#> SRR1283038 3 0.6192 0.963 0.000 0.420 0.580
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.456 0.6096 0.764 0.000 0.028 0.208
#> SRR925688 1 0.449 0.6110 0.772 0.000 0.028 0.200
#> SRR925689 1 0.261 0.5799 0.920 0.020 0.040 0.020
#> SRR925690 1 0.261 0.5799 0.920 0.020 0.040 0.020
#> SRR925691 1 0.261 0.5799 0.920 0.020 0.040 0.020
#> SRR925692 1 0.261 0.5799 0.920 0.020 0.040 0.020
#> SRR925693 3 0.520 0.8720 0.000 0.160 0.752 0.088
#> SRR925694 2 0.183 0.8478 0.000 0.944 0.032 0.024
#> SRR925695 3 0.537 0.8982 0.000 0.188 0.732 0.080
#> SRR925697 4 0.398 0.6641 0.240 0.000 0.000 0.760
#> SRR925696 3 0.363 0.9139 0.000 0.184 0.812 0.004
#> SRR925698 3 0.336 0.9157 0.000 0.176 0.824 0.000
#> SRR925699 2 0.222 0.8516 0.000 0.924 0.060 0.016
#> SRR925700 2 0.222 0.8516 0.000 0.924 0.060 0.016
#> SRR925701 2 0.252 0.8494 0.000 0.908 0.076 0.016
#> SRR925702 1 0.712 0.3114 0.552 0.020 0.088 0.340
#> SRR925703 4 0.398 0.6641 0.240 0.000 0.000 0.760
#> SRR925704 3 0.547 0.8959 0.000 0.192 0.724 0.084
#> SRR925705 3 0.363 0.9139 0.000 0.184 0.812 0.004
#> SRR925706 4 0.788 -0.1620 0.424 0.036 0.112 0.428
#> SRR925708 1 0.418 0.6118 0.784 0.000 0.016 0.200
#> SRR925707 2 0.938 0.2177 0.204 0.436 0.204 0.156
#> SRR925709 1 0.673 0.4667 0.632 0.020 0.088 0.260
#> SRR925710 1 0.764 0.0673 0.444 0.028 0.104 0.424
#> SRR925711 3 0.652 0.6553 0.000 0.288 0.604 0.108
#> SRR934631 2 0.284 0.8350 0.000 0.896 0.076 0.028
#> SRR934632 2 0.284 0.8350 0.000 0.896 0.076 0.028
#> SRR934633 2 0.620 -0.2874 0.000 0.500 0.448 0.052
#> SRR925713 1 0.755 0.1042 0.464 0.024 0.104 0.408
#> SRR925714 4 0.398 0.6641 0.240 0.000 0.000 0.760
#> SRR925715 4 0.779 -0.1517 0.412 0.036 0.104 0.448
#> SRR925716 4 0.461 0.6016 0.304 0.000 0.004 0.692
#> SRR925717 1 0.652 0.2650 0.564 0.000 0.088 0.348
#> SRR925718 2 0.322 0.8172 0.000 0.864 0.120 0.016
#> SRR925719 1 0.418 0.6118 0.784 0.000 0.016 0.200
#> SRR925720 1 0.456 0.6096 0.764 0.000 0.028 0.208
#> SRR925721 1 0.422 0.6184 0.792 0.000 0.024 0.184
#> SRR934634 1 0.157 0.5928 0.956 0.004 0.028 0.012
#> SRR934635 1 0.192 0.5842 0.944 0.004 0.028 0.024
#> SRR925723 3 0.336 0.9157 0.000 0.176 0.824 0.000
#> SRR925724 3 0.354 0.9152 0.000 0.176 0.820 0.004
#> SRR925725 3 0.547 0.8959 0.000 0.192 0.724 0.084
#> SRR925726 4 0.398 0.6641 0.240 0.000 0.000 0.760
#> SRR925727 3 0.533 0.8996 0.000 0.184 0.736 0.080
#> SRR925728 1 0.767 0.2222 0.516 0.032 0.112 0.340
#> SRR934636 4 0.609 0.4429 0.440 0.016 0.020 0.524
#> SRR934637 4 0.560 0.4581 0.464 0.000 0.020 0.516
#> SRR925729 3 0.601 0.7910 0.000 0.312 0.624 0.064
#> SRR934638 1 0.138 0.5954 0.964 0.020 0.008 0.008
#> SRR934639 1 0.327 0.6264 0.832 0.000 0.000 0.168
#> SRR934640 2 0.308 0.7863 0.000 0.884 0.032 0.084
#> SRR934641 2 0.308 0.7863 0.000 0.884 0.032 0.084
#> SRR925734 1 0.130 0.5988 0.964 0.020 0.016 0.000
#> SRR925735 3 0.525 0.8660 0.000 0.196 0.736 0.068
#> SRR925736 3 0.410 0.9068 0.000 0.192 0.792 0.016
#> SRR925737 3 0.410 0.9068 0.000 0.192 0.792 0.016
#> SRR934642 2 0.247 0.8486 0.000 0.908 0.080 0.012
#> SRR934643 2 0.247 0.8486 0.000 0.908 0.080 0.012
#> SRR934644 2 0.194 0.8470 0.000 0.940 0.032 0.028
#> SRR934645 2 0.194 0.8470 0.000 0.940 0.032 0.028
#> SRR925740 3 0.401 0.9103 0.000 0.184 0.800 0.016
#> SRR925741 3 0.547 0.8959 0.000 0.192 0.724 0.084
#> SRR925742 3 0.401 0.9103 0.000 0.184 0.800 0.016
#> SRR1283038 3 0.389 0.9137 0.000 0.184 0.804 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.2139 0.5133 0.916 0.000 0.000 0.052 0.032
#> SRR925688 1 0.2036 0.5110 0.920 0.000 0.000 0.056 0.024
#> SRR925689 1 0.6047 0.5470 0.660 0.044 0.000 0.124 0.172
#> SRR925690 1 0.6047 0.5470 0.660 0.044 0.000 0.124 0.172
#> SRR925691 1 0.6047 0.5470 0.660 0.044 0.000 0.124 0.172
#> SRR925692 1 0.6047 0.5470 0.660 0.044 0.000 0.124 0.172
#> SRR925693 3 0.5509 0.7424 0.000 0.060 0.664 0.248 0.028
#> SRR925694 2 0.2790 0.8392 0.000 0.892 0.060 0.020 0.028
#> SRR925695 3 0.4763 0.7648 0.000 0.020 0.716 0.232 0.032
#> SRR925697 5 0.4520 0.8268 0.284 0.000 0.000 0.032 0.684
#> SRR925696 3 0.1173 0.8179 0.000 0.020 0.964 0.012 0.004
#> SRR925698 3 0.0566 0.8232 0.000 0.012 0.984 0.004 0.000
#> SRR925699 2 0.3415 0.8489 0.000 0.840 0.120 0.032 0.008
#> SRR925700 2 0.3463 0.8480 0.000 0.836 0.124 0.032 0.008
#> SRR925701 2 0.3689 0.8439 0.000 0.816 0.144 0.032 0.008
#> SRR925702 1 0.5480 -0.4684 0.560 0.000 0.000 0.368 0.072
#> SRR925703 5 0.4687 0.8257 0.288 0.000 0.000 0.040 0.672
#> SRR925704 3 0.5227 0.7557 0.000 0.044 0.692 0.232 0.032
#> SRR925705 3 0.0960 0.8198 0.000 0.016 0.972 0.008 0.004
#> SRR925706 4 0.6027 0.6209 0.420 0.004 0.000 0.476 0.100
#> SRR925708 1 0.2130 0.4414 0.908 0.000 0.000 0.080 0.012
#> SRR925707 4 0.8059 0.3568 0.124 0.144 0.204 0.504 0.024
#> SRR925709 1 0.4354 -0.3316 0.624 0.000 0.000 0.368 0.008
#> SRR925710 1 0.6208 -0.5858 0.492 0.012 0.000 0.396 0.100
#> SRR925711 3 0.6257 0.5197 0.000 0.148 0.588 0.248 0.016
#> SRR934631 2 0.4404 0.8212 0.000 0.796 0.096 0.080 0.028
#> SRR934632 2 0.4404 0.8212 0.000 0.796 0.096 0.080 0.028
#> SRR934633 2 0.6586 0.0545 0.000 0.440 0.428 0.104 0.028
#> SRR925713 1 0.5938 -0.5359 0.512 0.000 0.000 0.376 0.112
#> SRR925714 5 0.4687 0.8257 0.288 0.000 0.000 0.040 0.672
#> SRR925715 4 0.6100 0.6339 0.408 0.012 0.000 0.492 0.088
#> SRR925716 5 0.3774 0.8067 0.296 0.000 0.000 0.000 0.704
#> SRR925717 1 0.5796 -0.2957 0.588 0.000 0.000 0.284 0.128
#> SRR925718 2 0.4330 0.8055 0.000 0.752 0.204 0.036 0.008
#> SRR925719 1 0.2077 0.4408 0.908 0.000 0.000 0.084 0.008
#> SRR925720 1 0.2139 0.5133 0.916 0.000 0.000 0.052 0.032
#> SRR925721 1 0.2067 0.5341 0.920 0.000 0.000 0.032 0.048
#> SRR934634 1 0.5244 0.5545 0.688 0.004 0.000 0.112 0.196
#> SRR934635 1 0.5289 0.5539 0.684 0.004 0.000 0.116 0.196
#> SRR925723 3 0.0566 0.8232 0.000 0.012 0.984 0.004 0.000
#> SRR925724 3 0.0566 0.8232 0.000 0.012 0.984 0.004 0.000
#> SRR925725 3 0.5360 0.7479 0.000 0.052 0.676 0.244 0.028
#> SRR925726 5 0.4687 0.8257 0.288 0.000 0.000 0.040 0.672
#> SRR925727 3 0.4763 0.7648 0.000 0.020 0.716 0.232 0.032
#> SRR925728 4 0.5649 0.5598 0.452 0.000 0.000 0.472 0.076
#> SRR934636 5 0.3860 0.6529 0.148 0.016 0.000 0.028 0.808
#> SRR934637 5 0.3717 0.6596 0.144 0.012 0.000 0.028 0.816
#> SRR925729 3 0.6227 0.6475 0.000 0.216 0.632 0.104 0.048
#> SRR934638 1 0.5332 0.5599 0.712 0.024 0.000 0.100 0.164
#> SRR934639 1 0.1721 0.5324 0.944 0.020 0.000 0.016 0.020
#> SRR934640 2 0.4918 0.7564 0.000 0.748 0.080 0.148 0.024
#> SRR934641 2 0.4918 0.7564 0.000 0.748 0.080 0.148 0.024
#> SRR925734 1 0.4985 0.5627 0.732 0.016 0.000 0.084 0.168
#> SRR925735 3 0.4943 0.7742 0.000 0.088 0.752 0.132 0.028
#> SRR925736 3 0.2201 0.8020 0.000 0.032 0.920 0.040 0.008
#> SRR925737 3 0.2201 0.8020 0.000 0.032 0.920 0.040 0.008
#> SRR934642 2 0.4181 0.8395 0.000 0.788 0.152 0.048 0.012
#> SRR934643 2 0.4181 0.8395 0.000 0.788 0.152 0.048 0.012
#> SRR934644 2 0.3054 0.8375 0.000 0.880 0.060 0.028 0.032
#> SRR934645 2 0.3054 0.8375 0.000 0.880 0.060 0.028 0.032
#> SRR925740 3 0.1967 0.8092 0.000 0.020 0.932 0.036 0.012
#> SRR925741 3 0.5360 0.7479 0.000 0.052 0.676 0.244 0.028
#> SRR925742 3 0.1869 0.8111 0.000 0.016 0.936 0.036 0.012
#> SRR1283038 3 0.1739 0.8222 0.000 0.024 0.940 0.032 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.4461 0.652 0.732 0.004 0.000 0.192 0.052 NA
#> SRR925688 1 0.4431 0.652 0.732 0.004 0.000 0.196 0.048 NA
#> SRR925689 1 0.3769 0.680 0.780 0.000 0.000 0.040 0.012 NA
#> SRR925690 1 0.3769 0.680 0.780 0.000 0.000 0.040 0.012 NA
#> SRR925691 1 0.3769 0.680 0.780 0.000 0.000 0.040 0.012 NA
#> SRR925692 1 0.3769 0.680 0.780 0.000 0.000 0.040 0.012 NA
#> SRR925693 3 0.4662 0.708 0.000 0.032 0.604 0.012 0.000 NA
#> SRR925694 2 0.4138 0.812 0.000 0.796 0.036 0.016 0.040 NA
#> SRR925695 3 0.4210 0.724 0.000 0.016 0.644 0.008 0.000 NA
#> SRR925697 5 0.3530 0.877 0.056 0.000 0.000 0.152 0.792 NA
#> SRR925696 3 0.1419 0.779 0.000 0.012 0.952 0.016 0.016 NA
#> SRR925698 3 0.0405 0.787 0.000 0.000 0.988 0.000 0.004 NA
#> SRR925699 2 0.1411 0.820 0.000 0.936 0.060 0.004 0.000 NA
#> SRR925700 2 0.1411 0.820 0.000 0.936 0.060 0.004 0.000 NA
#> SRR925701 2 0.1531 0.819 0.000 0.928 0.068 0.004 0.000 NA
#> SRR925702 4 0.3511 0.764 0.148 0.000 0.000 0.800 0.048 NA
#> SRR925703 5 0.3530 0.877 0.056 0.000 0.000 0.152 0.792 NA
#> SRR925704 3 0.3998 0.725 0.000 0.016 0.644 0.000 0.000 NA
#> SRR925705 3 0.1204 0.780 0.000 0.004 0.960 0.016 0.016 NA
#> SRR925706 4 0.2588 0.768 0.040 0.004 0.000 0.888 0.008 NA
#> SRR925708 1 0.5263 0.434 0.580 0.012 0.000 0.344 0.052 NA
#> SRR925707 4 0.5476 0.551 0.004 0.048 0.096 0.708 0.024 NA
#> SRR925709 4 0.4113 0.675 0.212 0.012 0.000 0.740 0.032 NA
#> SRR925710 4 0.3632 0.787 0.088 0.012 0.000 0.828 0.056 NA
#> SRR925711 3 0.7644 0.120 0.000 0.096 0.352 0.328 0.024 NA
#> SRR934631 2 0.5227 0.789 0.000 0.704 0.040 0.020 0.068 NA
#> SRR934632 2 0.5227 0.789 0.000 0.704 0.040 0.020 0.068 NA
#> SRR934633 2 0.7378 0.329 0.000 0.400 0.300 0.020 0.072 NA
#> SRR925713 4 0.3150 0.787 0.088 0.000 0.000 0.844 0.060 NA
#> SRR925714 5 0.3530 0.877 0.056 0.000 0.000 0.152 0.792 NA
#> SRR925715 4 0.2762 0.776 0.048 0.012 0.000 0.880 0.004 NA
#> SRR925716 5 0.3123 0.863 0.076 0.000 0.000 0.088 0.836 NA
#> SRR925717 4 0.5163 0.616 0.184 0.012 0.000 0.680 0.112 NA
#> SRR925718 2 0.2558 0.783 0.000 0.840 0.156 0.004 0.000 NA
#> SRR925719 1 0.5231 0.423 0.576 0.012 0.000 0.352 0.048 NA
#> SRR925720 1 0.4461 0.652 0.732 0.004 0.000 0.192 0.052 NA
#> SRR925721 1 0.3910 0.687 0.784 0.004 0.000 0.156 0.036 NA
#> SRR934634 1 0.0984 0.731 0.968 0.000 0.000 0.012 0.008 NA
#> SRR934635 1 0.0984 0.731 0.968 0.000 0.000 0.012 0.008 NA
#> SRR925723 3 0.0291 0.787 0.000 0.000 0.992 0.000 0.004 NA
#> SRR925724 3 0.0405 0.787 0.000 0.000 0.988 0.000 0.004 NA
#> SRR925725 3 0.4278 0.716 0.000 0.016 0.624 0.008 0.000 NA
#> SRR925726 5 0.4051 0.841 0.056 0.000 0.000 0.184 0.752 NA
#> SRR925727 3 0.4252 0.720 0.000 0.016 0.632 0.008 0.000 NA
#> SRR925728 4 0.3716 0.764 0.092 0.004 0.000 0.812 0.012 NA
#> SRR934636 5 0.5037 0.770 0.164 0.004 0.000 0.044 0.708 NA
#> SRR934637 5 0.5037 0.770 0.164 0.004 0.000 0.044 0.708 NA
#> SRR925729 3 0.6231 0.534 0.000 0.192 0.572 0.012 0.032 NA
#> SRR934638 1 0.3132 0.719 0.856 0.008 0.000 0.072 0.008 NA
#> SRR934639 1 0.5002 0.681 0.700 0.012 0.000 0.200 0.032 NA
#> SRR934640 2 0.6265 0.667 0.000 0.580 0.020 0.124 0.040 NA
#> SRR934641 2 0.6265 0.667 0.000 0.580 0.020 0.124 0.040 NA
#> SRR925734 1 0.1923 0.734 0.928 0.008 0.000 0.036 0.008 NA
#> SRR925735 3 0.4669 0.742 0.000 0.044 0.704 0.020 0.008 NA
#> SRR925736 3 0.3421 0.742 0.000 0.060 0.852 0.024 0.024 NA
#> SRR925737 3 0.3421 0.742 0.000 0.060 0.852 0.024 0.024 NA
#> SRR934642 2 0.2829 0.811 0.000 0.876 0.076 0.008 0.020 NA
#> SRR934643 2 0.2829 0.811 0.000 0.876 0.076 0.008 0.020 NA
#> SRR934644 2 0.4634 0.806 0.000 0.756 0.036 0.020 0.048 NA
#> SRR934645 2 0.4634 0.806 0.000 0.756 0.036 0.020 0.048 NA
#> SRR925740 3 0.2082 0.769 0.000 0.024 0.924 0.016 0.016 NA
#> SRR925741 3 0.4278 0.717 0.000 0.016 0.624 0.008 0.000 NA
#> SRR925742 3 0.2082 0.769 0.000 0.024 0.924 0.016 0.016 NA
#> SRR1283038 3 0.1307 0.787 0.000 0.008 0.952 0.000 0.008 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15949 rows and 64 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5064 0.494 0.494
#> 3 3 0.736 0.924 0.901 0.2665 0.859 0.714
#> 4 4 0.743 0.776 0.871 0.1637 0.889 0.685
#> 5 5 0.752 0.647 0.807 0.0589 0.949 0.797
#> 6 6 0.714 0.372 0.692 0.0412 0.917 0.649
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
#> SRR925687 1 0 1 1 0
#> SRR925688 1 0 1 1 0
#> SRR925689 1 0 1 1 0
#> SRR925690 1 0 1 1 0
#> SRR925691 1 0 1 1 0
#> SRR925692 1 0 1 1 0
#> SRR925693 2 0 1 0 1
#> SRR925694 2 0 1 0 1
#> SRR925695 2 0 1 0 1
#> SRR925697 1 0 1 1 0
#> SRR925696 2 0 1 0 1
#> SRR925698 2 0 1 0 1
#> SRR925699 2 0 1 0 1
#> SRR925700 2 0 1 0 1
#> SRR925701 2 0 1 0 1
#> SRR925702 1 0 1 1 0
#> SRR925703 1 0 1 1 0
#> SRR925704 2 0 1 0 1
#> SRR925705 2 0 1 0 1
#> SRR925706 1 0 1 1 0
#> SRR925708 1 0 1 1 0
#> SRR925707 2 0 1 0 1
#> SRR925709 1 0 1 1 0
#> SRR925710 1 0 1 1 0
#> SRR925711 2 0 1 0 1
#> SRR934631 2 0 1 0 1
#> SRR934632 2 0 1 0 1
#> SRR934633 2 0 1 0 1
#> SRR925713 1 0 1 1 0
#> SRR925714 1 0 1 1 0
#> SRR925715 1 0 1 1 0
#> SRR925716 1 0 1 1 0
#> SRR925717 1 0 1 1 0
#> SRR925718 2 0 1 0 1
#> SRR925719 1 0 1 1 0
#> SRR925720 1 0 1 1 0
#> SRR925721 1 0 1 1 0
#> SRR934634 1 0 1 1 0
#> SRR934635 1 0 1 1 0
#> SRR925723 2 0 1 0 1
#> SRR925724 2 0 1 0 1
#> SRR925725 2 0 1 0 1
#> SRR925726 1 0 1 1 0
#> SRR925727 2 0 1 0 1
#> SRR925728 1 0 1 1 0
#> SRR934636 1 0 1 1 0
#> SRR934637 1 0 1 1 0
#> SRR925729 2 0 1 0 1
#> SRR934638 1 0 1 1 0
#> SRR934639 1 0 1 1 0
#> SRR934640 2 0 1 0 1
#> SRR934641 2 0 1 0 1
#> SRR925734 1 0 1 1 0
#> SRR925735 2 0 1 0 1
#> SRR925736 2 0 1 0 1
#> SRR925737 2 0 1 0 1
#> SRR934642 2 0 1 0 1
#> SRR934643 2 0 1 0 1
#> SRR934644 2 0 1 0 1
#> SRR934645 2 0 1 0 1
#> SRR925740 2 0 1 0 1
#> SRR925741 2 0 1 0 1
#> SRR925742 2 0 1 0 1
#> SRR1283038 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.925 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.925 1.000 0.000 0.000
#> SRR925689 1 0.1163 0.920 0.972 0.028 0.000
#> SRR925690 1 0.1163 0.920 0.972 0.028 0.000
#> SRR925691 1 0.1163 0.920 0.972 0.028 0.000
#> SRR925692 1 0.1163 0.920 0.972 0.028 0.000
#> SRR925693 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925694 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925695 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925697 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925696 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925698 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925699 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925702 1 0.3267 0.905 0.884 0.000 0.116
#> SRR925703 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925704 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925705 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925706 1 0.4796 0.865 0.780 0.000 0.220
#> SRR925708 1 0.0000 0.925 1.000 0.000 0.000
#> SRR925707 2 0.3116 0.828 0.000 0.892 0.108
#> SRR925709 1 0.0237 0.925 0.996 0.000 0.004
#> SRR925710 1 0.1163 0.924 0.972 0.000 0.028
#> SRR925711 3 0.6140 0.698 0.000 0.404 0.596
#> SRR934631 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934633 2 0.4452 0.644 0.000 0.808 0.192
#> SRR925713 1 0.3340 0.904 0.880 0.000 0.120
#> SRR925714 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925715 1 0.1529 0.923 0.960 0.000 0.040
#> SRR925716 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925717 1 0.2356 0.917 0.928 0.000 0.072
#> SRR925718 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.925 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.925 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.925 1.000 0.000 0.000
#> SRR934634 1 0.1163 0.920 0.972 0.028 0.000
#> SRR934635 1 0.0747 0.923 0.984 0.016 0.000
#> SRR925723 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925724 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925725 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925726 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925727 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925728 1 0.3686 0.897 0.860 0.000 0.140
#> SRR934636 1 0.8802 0.651 0.584 0.200 0.216
#> SRR934637 1 0.4750 0.867 0.784 0.000 0.216
#> SRR925729 3 0.5859 0.811 0.000 0.344 0.656
#> SRR934638 1 0.1163 0.920 0.972 0.028 0.000
#> SRR934639 1 0.0000 0.925 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925734 1 0.1163 0.920 0.972 0.028 0.000
#> SRR925735 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925736 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925737 3 0.4796 0.977 0.000 0.220 0.780
#> SRR934642 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.969 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.969 0.000 1.000 0.000
#> SRR925740 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925741 3 0.4796 0.977 0.000 0.220 0.780
#> SRR925742 3 0.4796 0.977 0.000 0.220 0.780
#> SRR1283038 3 0.4796 0.977 0.000 0.220 0.780
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.4866 0.516 0.596 0.000 0.000 0.404
#> SRR925688 1 0.4866 0.516 0.596 0.000 0.000 0.404
#> SRR925689 1 0.0336 0.687 0.992 0.008 0.000 0.000
#> SRR925690 1 0.0336 0.687 0.992 0.008 0.000 0.000
#> SRR925691 1 0.0336 0.687 0.992 0.008 0.000 0.000
#> SRR925692 1 0.0336 0.687 0.992 0.008 0.000 0.000
#> SRR925693 3 0.2149 0.912 0.000 0.088 0.912 0.000
#> SRR925694 2 0.1474 0.942 0.000 0.948 0.052 0.000
#> SRR925695 3 0.1389 0.927 0.000 0.048 0.952 0.000
#> SRR925697 4 0.1211 0.777 0.040 0.000 0.000 0.960
#> SRR925696 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925698 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925699 2 0.2704 0.935 0.000 0.876 0.124 0.000
#> SRR925700 2 0.2647 0.934 0.000 0.880 0.120 0.000
#> SRR925701 2 0.2868 0.931 0.000 0.864 0.136 0.000
#> SRR925702 4 0.3523 0.731 0.112 0.032 0.000 0.856
#> SRR925703 4 0.1211 0.777 0.040 0.000 0.000 0.960
#> SRR925704 3 0.1867 0.919 0.000 0.072 0.928 0.000
#> SRR925705 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925706 4 0.1305 0.756 0.004 0.036 0.000 0.960
#> SRR925708 1 0.4898 0.497 0.584 0.000 0.000 0.416
#> SRR925707 2 0.3442 0.840 0.012 0.876 0.028 0.084
#> SRR925709 1 0.5850 0.369 0.512 0.032 0.000 0.456
#> SRR925710 4 0.5136 0.561 0.224 0.048 0.000 0.728
#> SRR925711 3 0.5018 0.591 0.000 0.332 0.656 0.012
#> SRR934631 2 0.1716 0.942 0.000 0.936 0.064 0.000
#> SRR934632 2 0.1716 0.942 0.000 0.936 0.064 0.000
#> SRR934633 2 0.2345 0.921 0.000 0.900 0.100 0.000
#> SRR925713 4 0.3435 0.737 0.100 0.036 0.000 0.864
#> SRR925714 4 0.1211 0.777 0.040 0.000 0.000 0.960
#> SRR925715 4 0.5094 0.590 0.208 0.044 0.004 0.744
#> SRR925716 4 0.1211 0.777 0.040 0.000 0.000 0.960
#> SRR925717 4 0.3539 0.682 0.176 0.004 0.000 0.820
#> SRR925718 2 0.3400 0.896 0.000 0.820 0.180 0.000
#> SRR925719 1 0.4907 0.482 0.580 0.000 0.000 0.420
#> SRR925720 1 0.4877 0.510 0.592 0.000 0.000 0.408
#> SRR925721 1 0.4605 0.574 0.664 0.000 0.000 0.336
#> SRR934634 1 0.0336 0.688 0.992 0.000 0.000 0.008
#> SRR934635 1 0.0336 0.688 0.992 0.000 0.000 0.008
#> SRR925723 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925724 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925725 3 0.2011 0.915 0.000 0.080 0.920 0.000
#> SRR925726 4 0.1211 0.777 0.040 0.000 0.000 0.960
#> SRR925727 3 0.1389 0.927 0.000 0.048 0.952 0.000
#> SRR925728 4 0.4244 0.711 0.160 0.036 0.000 0.804
#> SRR934636 4 0.5263 0.271 0.448 0.008 0.000 0.544
#> SRR934637 4 0.4941 0.296 0.436 0.000 0.000 0.564
#> SRR925729 3 0.4431 0.651 0.000 0.304 0.696 0.000
#> SRR934638 1 0.0000 0.689 1.000 0.000 0.000 0.000
#> SRR934639 1 0.4454 0.588 0.692 0.000 0.000 0.308
#> SRR934640 2 0.1489 0.934 0.000 0.952 0.044 0.004
#> SRR934641 2 0.1489 0.934 0.000 0.952 0.044 0.004
#> SRR925734 1 0.0000 0.689 1.000 0.000 0.000 0.000
#> SRR925735 3 0.2469 0.900 0.000 0.108 0.892 0.000
#> SRR925736 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925737 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR934642 2 0.2921 0.929 0.000 0.860 0.140 0.000
#> SRR934643 2 0.2921 0.929 0.000 0.860 0.140 0.000
#> SRR934644 2 0.1474 0.942 0.000 0.948 0.052 0.000
#> SRR934645 2 0.1474 0.942 0.000 0.948 0.052 0.000
#> SRR925740 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR925741 3 0.1940 0.917 0.000 0.076 0.924 0.000
#> SRR925742 3 0.0188 0.932 0.000 0.004 0.996 0.000
#> SRR1283038 3 0.1118 0.930 0.000 0.036 0.964 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.6237 0.4856 0.536 0.000 0.000 0.188 0.276
#> SRR925688 1 0.6237 0.4856 0.536 0.000 0.000 0.188 0.276
#> SRR925689 1 0.1341 0.6817 0.944 0.000 0.000 0.056 0.000
#> SRR925690 1 0.1341 0.6817 0.944 0.000 0.000 0.056 0.000
#> SRR925691 1 0.1341 0.6817 0.944 0.000 0.000 0.056 0.000
#> SRR925692 1 0.1341 0.6817 0.944 0.000 0.000 0.056 0.000
#> SRR925693 3 0.3980 0.8380 0.000 0.076 0.796 0.128 0.000
#> SRR925694 2 0.0000 0.9085 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.3267 0.8589 0.000 0.044 0.844 0.112 0.000
#> SRR925697 5 0.0162 0.6681 0.004 0.000 0.000 0.000 0.996
#> SRR925696 3 0.1267 0.8696 0.000 0.012 0.960 0.024 0.004
#> SRR925698 3 0.0324 0.8762 0.000 0.004 0.992 0.004 0.000
#> SRR925699 2 0.1862 0.9082 0.000 0.932 0.048 0.016 0.004
#> SRR925700 2 0.2074 0.9054 0.000 0.920 0.060 0.016 0.004
#> SRR925701 2 0.2332 0.9020 0.000 0.904 0.076 0.016 0.004
#> SRR925702 4 0.5646 0.1116 0.076 0.000 0.000 0.480 0.444
#> SRR925703 5 0.0162 0.6681 0.004 0.000 0.000 0.000 0.996
#> SRR925704 3 0.3906 0.8451 0.000 0.084 0.804 0.112 0.000
#> SRR925705 3 0.1267 0.8696 0.000 0.012 0.960 0.024 0.004
#> SRR925706 5 0.4307 0.0205 0.000 0.000 0.000 0.500 0.500
#> SRR925708 1 0.6231 0.4677 0.532 0.000 0.000 0.180 0.288
#> SRR925707 4 0.4392 -0.0219 0.000 0.380 0.000 0.612 0.008
#> SRR925709 4 0.6627 0.1115 0.296 0.000 0.000 0.452 0.252
#> SRR925710 4 0.5892 0.2226 0.108 0.000 0.000 0.520 0.372
#> SRR925711 4 0.6756 -0.2388 0.000 0.264 0.364 0.372 0.000
#> SRR934631 2 0.1106 0.9034 0.000 0.964 0.024 0.012 0.000
#> SRR934632 2 0.1106 0.9034 0.000 0.964 0.024 0.012 0.000
#> SRR934633 2 0.3416 0.7876 0.000 0.840 0.088 0.072 0.000
#> SRR925713 5 0.5406 -0.2564 0.056 0.000 0.000 0.464 0.480
#> SRR925714 5 0.0162 0.6681 0.004 0.000 0.000 0.000 0.996
#> SRR925715 4 0.5105 0.3001 0.076 0.000 0.000 0.660 0.264
#> SRR925716 5 0.0671 0.6624 0.004 0.000 0.000 0.016 0.980
#> SRR925717 5 0.5016 0.2833 0.120 0.000 0.000 0.176 0.704
#> SRR925718 2 0.3169 0.8472 0.000 0.840 0.140 0.016 0.004
#> SRR925719 1 0.6243 0.4657 0.532 0.000 0.000 0.184 0.284
#> SRR925720 1 0.6237 0.4856 0.536 0.000 0.000 0.188 0.276
#> SRR925721 1 0.5740 0.5491 0.620 0.000 0.000 0.164 0.216
#> SRR934634 1 0.0807 0.6989 0.976 0.000 0.000 0.012 0.012
#> SRR934635 1 0.0807 0.6989 0.976 0.000 0.000 0.012 0.012
#> SRR925723 3 0.0162 0.8757 0.000 0.004 0.996 0.000 0.000
#> SRR925724 3 0.0324 0.8764 0.000 0.004 0.992 0.004 0.000
#> SRR925725 3 0.4111 0.8332 0.000 0.092 0.788 0.120 0.000
#> SRR925726 5 0.0566 0.6604 0.004 0.000 0.000 0.012 0.984
#> SRR925727 3 0.3165 0.8582 0.000 0.036 0.848 0.116 0.000
#> SRR925728 4 0.5640 0.0811 0.104 0.000 0.000 0.592 0.304
#> SRR934636 5 0.5013 0.4488 0.232 0.000 0.000 0.084 0.684
#> SRR934637 5 0.4901 0.4620 0.216 0.000 0.000 0.084 0.700
#> SRR925729 3 0.5951 0.4971 0.000 0.364 0.520 0.116 0.000
#> SRR934638 1 0.0162 0.6980 0.996 0.000 0.000 0.004 0.000
#> SRR934639 1 0.4058 0.6294 0.784 0.000 0.000 0.064 0.152
#> SRR934640 2 0.2286 0.8541 0.000 0.888 0.004 0.108 0.000
#> SRR934641 2 0.2286 0.8541 0.000 0.888 0.004 0.108 0.000
#> SRR925734 1 0.0000 0.6981 1.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.4624 0.8076 0.000 0.112 0.744 0.144 0.000
#> SRR925736 3 0.1885 0.8604 0.000 0.020 0.932 0.044 0.004
#> SRR925737 3 0.1885 0.8604 0.000 0.020 0.932 0.044 0.004
#> SRR934642 2 0.2429 0.9008 0.000 0.900 0.076 0.020 0.004
#> SRR934643 2 0.2429 0.9008 0.000 0.900 0.076 0.020 0.004
#> SRR934644 2 0.0000 0.9085 0.000 1.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.9085 0.000 1.000 0.000 0.000 0.000
#> SRR925740 3 0.1365 0.8688 0.000 0.004 0.952 0.040 0.004
#> SRR925741 3 0.4002 0.8380 0.000 0.084 0.796 0.120 0.000
#> SRR925742 3 0.1365 0.8688 0.000 0.004 0.952 0.040 0.004
#> SRR1283038 3 0.2074 0.8731 0.000 0.044 0.920 0.036 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.0405 0.6184 0.988 0.000 0.000 0.008 0.004 0.000
#> SRR925688 1 0.0260 0.6160 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR925689 5 0.3706 -0.3011 0.380 0.000 0.000 0.000 0.620 0.000
#> SRR925690 5 0.3706 -0.3011 0.380 0.000 0.000 0.000 0.620 0.000
#> SRR925691 5 0.3706 -0.3011 0.380 0.000 0.000 0.000 0.620 0.000
#> SRR925692 5 0.3706 -0.3011 0.380 0.000 0.000 0.000 0.620 0.000
#> SRR925693 3 0.1913 0.4377 0.000 0.016 0.924 0.016 0.000 0.044
#> SRR925694 2 0.0665 0.8596 0.000 0.980 0.008 0.004 0.000 0.008
#> SRR925695 3 0.0551 0.4547 0.000 0.004 0.984 0.004 0.000 0.008
#> SRR925697 5 0.7224 0.3507 0.116 0.000 0.000 0.184 0.376 0.324
#> SRR925696 3 0.3872 -0.5500 0.000 0.004 0.604 0.000 0.000 0.392
#> SRR925698 3 0.3547 -0.1899 0.000 0.004 0.696 0.000 0.000 0.300
#> SRR925699 2 0.2450 0.8630 0.000 0.868 0.016 0.000 0.000 0.116
#> SRR925700 2 0.2257 0.8628 0.000 0.876 0.008 0.000 0.000 0.116
#> SRR925701 2 0.2623 0.8595 0.000 0.852 0.016 0.000 0.000 0.132
#> SRR925702 4 0.5151 0.5612 0.296 0.000 0.000 0.620 0.044 0.040
#> SRR925703 5 0.7224 0.3507 0.116 0.000 0.000 0.184 0.376 0.324
#> SRR925704 3 0.0717 0.4576 0.000 0.016 0.976 0.000 0.000 0.008
#> SRR925705 3 0.3881 -0.5608 0.000 0.004 0.600 0.000 0.000 0.396
#> SRR925706 4 0.3406 0.5480 0.036 0.000 0.000 0.840 0.056 0.068
#> SRR925708 1 0.2373 0.5606 0.888 0.000 0.000 0.084 0.004 0.024
#> SRR925707 4 0.4163 0.4846 0.000 0.184 0.000 0.740 0.004 0.072
#> SRR925709 1 0.4631 -0.2787 0.528 0.000 0.000 0.440 0.012 0.020
#> SRR925710 4 0.5736 0.5076 0.356 0.000 0.004 0.532 0.028 0.080
#> SRR925711 4 0.7882 -0.0276 0.000 0.192 0.272 0.276 0.008 0.252
#> SRR934631 2 0.1923 0.8546 0.000 0.916 0.064 0.004 0.000 0.016
#> SRR934632 2 0.1923 0.8546 0.000 0.916 0.064 0.004 0.000 0.016
#> SRR934633 2 0.4016 0.6077 0.000 0.684 0.292 0.004 0.000 0.020
#> SRR925713 4 0.5599 0.5361 0.328 0.000 0.000 0.564 0.048 0.060
#> SRR925714 5 0.7224 0.3507 0.116 0.000 0.000 0.184 0.376 0.324
#> SRR925715 4 0.4296 0.6056 0.224 0.000 0.008 0.720 0.004 0.044
#> SRR925716 5 0.7230 0.3553 0.136 0.000 0.000 0.156 0.372 0.336
#> SRR925717 1 0.7268 -0.2177 0.412 0.000 0.000 0.196 0.260 0.132
#> SRR925718 2 0.3352 0.8132 0.000 0.792 0.032 0.000 0.000 0.176
#> SRR925719 1 0.2009 0.5676 0.904 0.000 0.000 0.084 0.004 0.008
#> SRR925720 1 0.0405 0.6184 0.988 0.000 0.000 0.008 0.004 0.000
#> SRR925721 1 0.1007 0.6164 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR934634 1 0.3841 0.4497 0.616 0.000 0.000 0.000 0.380 0.004
#> SRR934635 1 0.3923 0.4284 0.580 0.000 0.000 0.000 0.416 0.004
#> SRR925723 3 0.3652 -0.2764 0.000 0.004 0.672 0.000 0.000 0.324
#> SRR925724 3 0.3584 -0.2174 0.000 0.004 0.688 0.000 0.000 0.308
#> SRR925725 3 0.1350 0.4531 0.000 0.020 0.952 0.008 0.000 0.020
#> SRR925726 5 0.7368 0.3188 0.152 0.000 0.000 0.176 0.376 0.296
#> SRR925727 3 0.0767 0.4560 0.000 0.012 0.976 0.004 0.000 0.008
#> SRR925728 4 0.4156 0.5807 0.092 0.000 0.000 0.788 0.064 0.056
#> SRR934636 5 0.6357 0.3335 0.044 0.000 0.000 0.156 0.492 0.308
#> SRR934637 5 0.6401 0.3356 0.044 0.000 0.000 0.160 0.480 0.316
#> SRR925729 3 0.4454 0.1495 0.000 0.348 0.616 0.004 0.000 0.032
#> SRR934638 1 0.4328 0.3979 0.520 0.000 0.000 0.020 0.460 0.000
#> SRR934639 1 0.3871 0.5198 0.676 0.000 0.000 0.016 0.308 0.000
#> SRR934640 2 0.3880 0.7582 0.000 0.804 0.032 0.076 0.000 0.088
#> SRR934641 2 0.3880 0.7582 0.000 0.804 0.032 0.076 0.000 0.088
#> SRR925734 1 0.3866 0.3822 0.516 0.000 0.000 0.000 0.484 0.000
#> SRR925735 3 0.3940 0.3490 0.000 0.068 0.804 0.048 0.000 0.080
#> SRR925736 6 0.4413 1.0000 0.000 0.008 0.484 0.012 0.000 0.496
#> SRR925737 6 0.4413 1.0000 0.000 0.008 0.484 0.012 0.000 0.496
#> SRR934642 2 0.2623 0.8595 0.000 0.852 0.016 0.000 0.000 0.132
#> SRR934643 2 0.2623 0.8595 0.000 0.852 0.016 0.000 0.000 0.132
#> SRR934644 2 0.0622 0.8583 0.000 0.980 0.008 0.000 0.000 0.012
#> SRR934645 2 0.0405 0.8595 0.000 0.988 0.008 0.000 0.000 0.004
#> SRR925740 3 0.4070 -0.6783 0.000 0.004 0.568 0.004 0.000 0.424
#> SRR925741 3 0.0984 0.4589 0.000 0.012 0.968 0.008 0.000 0.012
#> SRR925742 3 0.4070 -0.6783 0.000 0.004 0.568 0.004 0.000 0.424
#> SRR1283038 3 0.3468 -0.0257 0.000 0.008 0.728 0.000 0.000 0.264
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 15949 rows and 64 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 1.000 0.958 0.985 0.5035 0.497 0.497
#> 3 3 0.872 0.792 0.896 0.1866 0.884 0.771
#> 4 4 0.661 0.679 0.809 0.2043 0.847 0.625
#> 5 5 0.671 0.587 0.819 0.0670 0.907 0.685
#> 6 6 0.698 0.654 0.809 0.0348 0.936 0.742
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
#> SRR925687 1 0.0000 0.9826 1.000 0.000
#> SRR925688 1 0.0000 0.9826 1.000 0.000
#> SRR925689 1 0.0000 0.9826 1.000 0.000
#> SRR925690 1 0.0000 0.9826 1.000 0.000
#> SRR925691 1 0.0000 0.9826 1.000 0.000
#> SRR925692 1 0.0000 0.9826 1.000 0.000
#> SRR925693 2 0.0000 0.9848 0.000 1.000
#> SRR925694 2 0.0000 0.9848 0.000 1.000
#> SRR925695 2 0.0000 0.9848 0.000 1.000
#> SRR925697 1 0.0000 0.9826 1.000 0.000
#> SRR925696 2 0.0000 0.9848 0.000 1.000
#> SRR925698 2 0.0000 0.9848 0.000 1.000
#> SRR925699 2 0.0000 0.9848 0.000 1.000
#> SRR925700 2 0.0000 0.9848 0.000 1.000
#> SRR925701 2 0.0000 0.9848 0.000 1.000
#> SRR925702 1 0.0000 0.9826 1.000 0.000
#> SRR925703 1 0.0000 0.9826 1.000 0.000
#> SRR925704 2 0.0000 0.9848 0.000 1.000
#> SRR925705 2 0.0000 0.9848 0.000 1.000
#> SRR925706 2 1.0000 -0.0439 0.500 0.500
#> SRR925708 1 0.0000 0.9826 1.000 0.000
#> SRR925707 2 0.0000 0.9848 0.000 1.000
#> SRR925709 1 0.0000 0.9826 1.000 0.000
#> SRR925710 1 0.0672 0.9761 0.992 0.008
#> SRR925711 2 0.0000 0.9848 0.000 1.000
#> SRR934631 2 0.0000 0.9848 0.000 1.000
#> SRR934632 2 0.0000 0.9848 0.000 1.000
#> SRR934633 2 0.0000 0.9848 0.000 1.000
#> SRR925713 1 0.5519 0.8465 0.872 0.128
#> SRR925714 1 0.0000 0.9826 1.000 0.000
#> SRR925715 1 0.9170 0.5027 0.668 0.332
#> SRR925716 1 0.0000 0.9826 1.000 0.000
#> SRR925717 1 0.0000 0.9826 1.000 0.000
#> SRR925718 2 0.0000 0.9848 0.000 1.000
#> SRR925719 1 0.0000 0.9826 1.000 0.000
#> SRR925720 1 0.0000 0.9826 1.000 0.000
#> SRR925721 1 0.0000 0.9826 1.000 0.000
#> SRR934634 1 0.0000 0.9826 1.000 0.000
#> SRR934635 1 0.0000 0.9826 1.000 0.000
#> SRR925723 2 0.0000 0.9848 0.000 1.000
#> SRR925724 2 0.0000 0.9848 0.000 1.000
#> SRR925725 2 0.0000 0.9848 0.000 1.000
#> SRR925726 1 0.0000 0.9826 1.000 0.000
#> SRR925727 2 0.0000 0.9848 0.000 1.000
#> SRR925728 1 0.0672 0.9760 0.992 0.008
#> SRR934636 1 0.0000 0.9826 1.000 0.000
#> SRR934637 1 0.0000 0.9826 1.000 0.000
#> SRR925729 2 0.0000 0.9848 0.000 1.000
#> SRR934638 1 0.0000 0.9826 1.000 0.000
#> SRR934639 1 0.0000 0.9826 1.000 0.000
#> SRR934640 2 0.0000 0.9848 0.000 1.000
#> SRR934641 2 0.0000 0.9848 0.000 1.000
#> SRR925734 1 0.0000 0.9826 1.000 0.000
#> SRR925735 2 0.0000 0.9848 0.000 1.000
#> SRR925736 2 0.0000 0.9848 0.000 1.000
#> SRR925737 2 0.0000 0.9848 0.000 1.000
#> SRR934642 2 0.0000 0.9848 0.000 1.000
#> SRR934643 2 0.0000 0.9848 0.000 1.000
#> SRR934644 2 0.0000 0.9848 0.000 1.000
#> SRR934645 2 0.0000 0.9848 0.000 1.000
#> SRR925740 2 0.0000 0.9848 0.000 1.000
#> SRR925741 2 0.0000 0.9848 0.000 1.000
#> SRR925742 2 0.0000 0.9848 0.000 1.000
#> SRR1283038 2 0.0000 0.9848 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925689 3 0.6140 0.808 0.404 0.000 0.596
#> SRR925690 3 0.6140 0.808 0.404 0.000 0.596
#> SRR925691 3 0.6140 0.808 0.404 0.000 0.596
#> SRR925692 3 0.6140 0.808 0.404 0.000 0.596
#> SRR925693 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925694 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925695 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925697 1 0.6111 0.466 0.604 0.000 0.396
#> SRR925696 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925698 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925699 2 0.0237 0.996 0.000 0.996 0.004
#> SRR925700 2 0.0237 0.996 0.000 0.996 0.004
#> SRR925701 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925702 1 0.0592 0.683 0.988 0.000 0.012
#> SRR925703 1 0.6111 0.466 0.604 0.000 0.396
#> SRR925704 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925705 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925706 1 0.5696 0.505 0.800 0.136 0.064
#> SRR925708 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925707 2 0.0592 0.987 0.012 0.988 0.000
#> SRR925709 3 0.6180 0.793 0.416 0.000 0.584
#> SRR925710 1 0.0237 0.690 0.996 0.004 0.000
#> SRR925711 2 0.0592 0.987 0.012 0.988 0.000
#> SRR934631 2 0.0424 0.995 0.000 0.992 0.008
#> SRR934632 2 0.0424 0.995 0.000 0.992 0.008
#> SRR934633 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925713 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925714 1 0.6111 0.466 0.604 0.000 0.396
#> SRR925715 1 0.4861 0.407 0.808 0.012 0.180
#> SRR925716 1 0.6111 0.466 0.604 0.000 0.396
#> SRR925717 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925718 2 0.0237 0.996 0.000 0.996 0.004
#> SRR925719 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.692 1.000 0.000 0.000
#> SRR925721 1 0.0592 0.684 0.988 0.000 0.012
#> SRR934634 1 0.0592 0.684 0.988 0.000 0.012
#> SRR934635 1 0.6154 -0.428 0.592 0.000 0.408
#> SRR925723 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925724 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925725 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925726 1 0.6111 0.466 0.604 0.000 0.396
#> SRR925727 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925728 1 0.9847 -0.335 0.416 0.268 0.316
#> SRR934636 3 0.0237 0.426 0.004 0.000 0.996
#> SRR934637 3 0.2537 0.369 0.080 0.000 0.920
#> SRR925729 2 0.0237 0.996 0.000 0.996 0.004
#> SRR934638 3 0.6154 0.804 0.408 0.000 0.592
#> SRR934639 1 0.4654 0.347 0.792 0.000 0.208
#> SRR934640 2 0.0424 0.995 0.000 0.992 0.008
#> SRR934641 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925734 3 0.6140 0.808 0.404 0.000 0.596
#> SRR925735 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925736 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.997 0.000 1.000 0.000
#> SRR934642 2 0.0237 0.996 0.000 0.996 0.004
#> SRR934643 2 0.0000 0.997 0.000 1.000 0.000
#> SRR934644 2 0.0424 0.995 0.000 0.992 0.008
#> SRR934645 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925740 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925741 2 0.0000 0.997 0.000 1.000 0.000
#> SRR925742 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1283038 2 0.0000 0.997 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925688 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925689 1 0.3942 0.7623 0.764 0.000 0.000 0.236
#> SRR925690 1 0.3942 0.7623 0.764 0.000 0.000 0.236
#> SRR925691 1 0.3942 0.7623 0.764 0.000 0.000 0.236
#> SRR925692 1 0.3942 0.7623 0.764 0.000 0.000 0.236
#> SRR925693 3 0.0336 0.8625 0.000 0.008 0.992 0.000
#> SRR925694 2 0.3311 0.8728 0.000 0.828 0.172 0.000
#> SRR925695 3 0.1302 0.8514 0.000 0.044 0.956 0.000
#> SRR925697 4 0.6808 0.5262 0.236 0.164 0.000 0.600
#> SRR925696 3 0.0188 0.8609 0.000 0.004 0.996 0.000
#> SRR925698 3 0.0000 0.8622 0.000 0.000 1.000 0.000
#> SRR925699 2 0.4072 0.8467 0.000 0.748 0.252 0.000
#> SRR925700 3 0.5000 -0.5160 0.000 0.500 0.500 0.000
#> SRR925701 2 0.4008 0.8502 0.000 0.756 0.244 0.000
#> SRR925702 4 0.0469 0.7024 0.012 0.000 0.000 0.988
#> SRR925703 4 0.6808 0.5262 0.236 0.164 0.000 0.600
#> SRR925704 3 0.1792 0.8372 0.000 0.068 0.932 0.000
#> SRR925705 3 0.0188 0.8609 0.000 0.004 0.996 0.000
#> SRR925706 4 0.6134 0.0816 0.048 0.000 0.444 0.508
#> SRR925708 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925707 3 0.4035 0.6494 0.000 0.020 0.804 0.176
#> SRR925709 1 0.4888 0.7092 0.588 0.000 0.000 0.412
#> SRR925710 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925711 3 0.3672 0.6709 0.000 0.012 0.824 0.164
#> SRR934631 2 0.3266 0.8695 0.000 0.832 0.168 0.000
#> SRR934632 2 0.3266 0.8695 0.000 0.832 0.168 0.000
#> SRR934633 2 0.3444 0.8626 0.000 0.816 0.184 0.000
#> SRR925713 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925714 4 0.6808 0.5262 0.236 0.164 0.000 0.600
#> SRR925715 4 0.3725 0.4263 0.180 0.000 0.008 0.812
#> SRR925716 4 0.6808 0.5262 0.236 0.164 0.000 0.600
#> SRR925717 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925718 2 0.4925 0.6380 0.000 0.572 0.428 0.000
#> SRR925719 4 0.0000 0.7123 0.000 0.000 0.000 1.000
#> SRR925720 4 0.0188 0.7106 0.004 0.000 0.000 0.996
#> SRR925721 4 0.3356 0.5823 0.176 0.000 0.000 0.824
#> SRR934634 4 0.2530 0.6422 0.112 0.000 0.000 0.888
#> SRR934635 1 0.4916 0.5238 0.576 0.000 0.000 0.424
#> SRR925723 3 0.0336 0.8625 0.000 0.008 0.992 0.000
#> SRR925724 3 0.0000 0.8622 0.000 0.000 1.000 0.000
#> SRR925725 3 0.1792 0.8372 0.000 0.068 0.932 0.000
#> SRR925726 4 0.6808 0.5262 0.236 0.164 0.000 0.600
#> SRR925727 3 0.0336 0.8625 0.000 0.008 0.992 0.000
#> SRR925728 1 0.5398 0.7044 0.580 0.000 0.016 0.404
#> SRR934636 1 0.3219 0.4884 0.836 0.164 0.000 0.000
#> SRR934637 1 0.4989 0.4281 0.764 0.164 0.000 0.072
#> SRR925729 3 0.3024 0.7486 0.000 0.148 0.852 0.000
#> SRR934638 1 0.4855 0.7204 0.600 0.000 0.000 0.400
#> SRR934639 4 0.4643 0.2003 0.344 0.000 0.000 0.656
#> SRR934640 2 0.5080 0.6423 0.000 0.576 0.420 0.004
#> SRR934641 2 0.5105 0.6172 0.000 0.564 0.432 0.004
#> SRR925734 1 0.4843 0.7230 0.604 0.000 0.000 0.396
#> SRR925735 3 0.1211 0.8517 0.000 0.040 0.960 0.000
#> SRR925736 3 0.0469 0.8574 0.000 0.012 0.988 0.000
#> SRR925737 3 0.0336 0.8595 0.000 0.008 0.992 0.000
#> SRR934642 3 0.4955 -0.3440 0.000 0.444 0.556 0.000
#> SRR934643 3 0.3311 0.6375 0.000 0.172 0.828 0.000
#> SRR934644 2 0.3311 0.8728 0.000 0.828 0.172 0.000
#> SRR934645 2 0.3311 0.8728 0.000 0.828 0.172 0.000
#> SRR925740 3 0.0000 0.8622 0.000 0.000 1.000 0.000
#> SRR925741 3 0.1792 0.8372 0.000 0.068 0.932 0.000
#> SRR925742 3 0.0336 0.8625 0.000 0.008 0.992 0.000
#> SRR1283038 3 0.2081 0.8258 0.000 0.084 0.916 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925688 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925689 1 0.0000 0.74466 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.74466 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.74466 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.74466 1.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.1732 0.79322 0.000 0.080 0.920 0.000 0.000
#> SRR925694 2 0.2424 0.69390 0.000 0.868 0.132 0.000 0.000
#> SRR925695 3 0.2127 0.78462 0.000 0.108 0.892 0.000 0.000
#> SRR925697 5 0.3177 0.75378 0.000 0.000 0.000 0.208 0.792
#> SRR925696 3 0.0162 0.81447 0.000 0.004 0.996 0.000 0.000
#> SRR925698 3 0.0000 0.81579 0.000 0.000 1.000 0.000 0.000
#> SRR925699 2 0.3242 0.68410 0.000 0.784 0.216 0.000 0.000
#> SRR925700 3 0.4297 -0.24070 0.000 0.472 0.528 0.000 0.000
#> SRR925701 2 0.3242 0.68399 0.000 0.784 0.216 0.000 0.000
#> SRR925702 4 0.0162 0.72789 0.004 0.000 0.000 0.996 0.000
#> SRR925703 5 0.4182 0.60353 0.000 0.000 0.000 0.400 0.600
#> SRR925704 3 0.2605 0.76145 0.000 0.148 0.852 0.000 0.000
#> SRR925705 3 0.0162 0.81447 0.000 0.004 0.996 0.000 0.000
#> SRR925706 4 0.4759 0.37046 0.024 0.000 0.336 0.636 0.004
#> SRR925708 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925707 4 0.6155 0.32736 0.000 0.212 0.228 0.560 0.000
#> SRR925709 4 0.4262 0.05977 0.440 0.000 0.000 0.560 0.000
#> SRR925710 4 0.0794 0.71979 0.028 0.000 0.000 0.972 0.000
#> SRR925711 3 0.4757 0.26580 0.000 0.024 0.596 0.380 0.000
#> SRR934631 2 0.3333 0.58989 0.000 0.788 0.004 0.000 0.208
#> SRR934632 2 0.3333 0.58989 0.000 0.788 0.004 0.000 0.208
#> SRR934633 2 0.3333 0.58989 0.000 0.788 0.004 0.000 0.208
#> SRR925713 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925714 5 0.4182 0.60353 0.000 0.000 0.000 0.400 0.600
#> SRR925715 4 0.1043 0.71397 0.040 0.000 0.000 0.960 0.000
#> SRR925716 5 0.3177 0.75378 0.000 0.000 0.000 0.208 0.792
#> SRR925717 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925718 2 0.4242 0.42090 0.000 0.572 0.428 0.000 0.000
#> SRR925719 4 0.0000 0.72848 0.000 0.000 0.000 1.000 0.000
#> SRR925720 4 0.0963 0.70947 0.036 0.000 0.000 0.964 0.000
#> SRR925721 1 0.4262 0.10441 0.560 0.000 0.000 0.440 0.000
#> SRR934634 4 0.4273 0.00722 0.448 0.000 0.000 0.552 0.000
#> SRR934635 1 0.1043 0.72163 0.960 0.000 0.000 0.040 0.000
#> SRR925723 3 0.0000 0.81579 0.000 0.000 1.000 0.000 0.000
#> SRR925724 3 0.0000 0.81579 0.000 0.000 1.000 0.000 0.000
#> SRR925725 3 0.3109 0.71903 0.000 0.200 0.800 0.000 0.000
#> SRR925726 4 0.4101 -0.10009 0.000 0.000 0.000 0.628 0.372
#> SRR925727 3 0.0404 0.81436 0.000 0.012 0.988 0.000 0.000
#> SRR925728 4 0.6197 0.23023 0.272 0.164 0.004 0.560 0.000
#> SRR934636 5 0.3953 0.58448 0.060 0.148 0.000 0.000 0.792
#> SRR934637 5 0.3300 0.54401 0.204 0.000 0.000 0.004 0.792
#> SRR925729 3 0.2732 0.71621 0.000 0.160 0.840 0.000 0.000
#> SRR934638 1 0.4126 0.33303 0.620 0.000 0.000 0.380 0.000
#> SRR934639 4 0.4088 0.32636 0.368 0.000 0.000 0.632 0.000
#> SRR934640 2 0.4567 0.36171 0.000 0.544 0.448 0.004 0.004
#> SRR934641 2 0.4580 0.33108 0.000 0.532 0.460 0.004 0.004
#> SRR925734 1 0.4060 0.37727 0.640 0.000 0.000 0.360 0.000
#> SRR925735 3 0.2921 0.77010 0.000 0.124 0.856 0.020 0.000
#> SRR925736 3 0.0162 0.81447 0.000 0.004 0.996 0.000 0.000
#> SRR925737 3 0.0162 0.81447 0.000 0.004 0.996 0.000 0.000
#> SRR934642 3 0.4262 -0.13657 0.000 0.440 0.560 0.000 0.000
#> SRR934643 3 0.3395 0.50731 0.000 0.236 0.764 0.000 0.000
#> SRR934644 2 0.4010 0.60808 0.000 0.760 0.032 0.000 0.208
#> SRR934645 2 0.2424 0.69390 0.000 0.868 0.132 0.000 0.000
#> SRR925740 3 0.0000 0.81579 0.000 0.000 1.000 0.000 0.000
#> SRR925741 3 0.3109 0.71903 0.000 0.200 0.800 0.000 0.000
#> SRR925742 3 0.0000 0.81579 0.000 0.000 1.000 0.000 0.000
#> SRR1283038 3 0.3210 0.71078 0.000 0.212 0.788 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.3094 0.6256 0.000 0.036 0.000 0.824 0.000 0.140
#> SRR925688 4 0.3094 0.6256 0.000 0.036 0.000 0.824 0.000 0.140
#> SRR925689 1 0.0000 0.7852 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.7852 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.7852 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.7852 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.2593 0.7637 0.000 0.148 0.844 0.000 0.000 0.008
#> SRR925694 2 0.4732 0.6378 0.000 0.680 0.148 0.000 0.000 0.172
#> SRR925695 3 0.0790 0.8488 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR925697 5 0.2454 0.7176 0.000 0.000 0.000 0.160 0.840 0.000
#> SRR925696 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925698 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925699 2 0.4721 0.6988 0.000 0.672 0.212 0.000 0.000 0.116
#> SRR925700 2 0.4366 0.6880 0.000 0.548 0.428 0.000 0.000 0.024
#> SRR925701 2 0.4681 0.7009 0.000 0.676 0.212 0.000 0.000 0.112
#> SRR925702 4 0.0000 0.6845 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925703 5 0.3756 0.4898 0.000 0.000 0.000 0.400 0.600 0.000
#> SRR925704 3 0.1471 0.8300 0.000 0.004 0.932 0.000 0.000 0.064
#> SRR925705 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925706 4 0.4180 0.2963 0.024 0.000 0.348 0.628 0.000 0.000
#> SRR925708 4 0.0000 0.6845 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925707 4 0.4423 0.1877 0.000 0.420 0.028 0.552 0.000 0.000
#> SRR925709 4 0.3838 -0.1060 0.448 0.000 0.000 0.552 0.000 0.000
#> SRR925710 4 0.0632 0.6775 0.024 0.000 0.000 0.976 0.000 0.000
#> SRR925711 3 0.5007 0.5244 0.000 0.144 0.656 0.196 0.000 0.004
#> SRR934631 6 0.2260 0.9927 0.000 0.140 0.000 0.000 0.000 0.860
#> SRR934632 6 0.2260 0.9927 0.000 0.140 0.000 0.000 0.000 0.860
#> SRR934633 6 0.2260 0.9927 0.000 0.140 0.000 0.000 0.000 0.860
#> SRR925713 4 0.0000 0.6845 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925714 5 0.3756 0.4898 0.000 0.000 0.000 0.400 0.600 0.000
#> SRR925715 4 0.1245 0.6699 0.016 0.032 0.000 0.952 0.000 0.000
#> SRR925716 5 0.0000 0.7190 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925717 4 0.0000 0.6845 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925718 2 0.4868 0.6983 0.000 0.524 0.416 0.000 0.000 0.060
#> SRR925719 4 0.0000 0.6845 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925720 4 0.3094 0.6256 0.000 0.036 0.000 0.824 0.000 0.140
#> SRR925721 4 0.6264 0.1587 0.376 0.036 0.000 0.448 0.000 0.140
#> SRR934634 4 0.6056 0.3240 0.284 0.036 0.000 0.540 0.000 0.140
#> SRR934635 1 0.4116 0.6564 0.776 0.036 0.000 0.048 0.000 0.140
#> SRR925723 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925724 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925725 3 0.2793 0.7429 0.000 0.000 0.800 0.000 0.000 0.200
#> SRR925726 4 0.5496 0.2671 0.000 0.016 0.000 0.608 0.236 0.140
#> SRR925727 3 0.0146 0.8566 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR925728 4 0.5654 0.1975 0.188 0.256 0.004 0.552 0.000 0.000
#> SRR934636 5 0.0000 0.7190 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934637 5 0.0000 0.7190 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925729 3 0.4904 0.0253 0.000 0.316 0.600 0.000 0.000 0.084
#> SRR934638 1 0.3756 0.3904 0.600 0.000 0.000 0.400 0.000 0.000
#> SRR934639 4 0.4650 0.4600 0.292 0.016 0.000 0.652 0.000 0.040
#> SRR934640 2 0.2191 0.5987 0.000 0.876 0.120 0.000 0.000 0.004
#> SRR934641 2 0.2234 0.6013 0.000 0.872 0.124 0.000 0.000 0.004
#> SRR925734 1 0.3717 0.4224 0.616 0.000 0.000 0.384 0.000 0.000
#> SRR925735 3 0.4673 0.6547 0.000 0.196 0.712 0.064 0.000 0.028
#> SRR925736 3 0.0458 0.8465 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR925737 3 0.0363 0.8496 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR934642 2 0.4157 0.6693 0.000 0.544 0.444 0.000 0.000 0.012
#> SRR934643 2 0.4067 0.6660 0.000 0.548 0.444 0.000 0.000 0.008
#> SRR934644 6 0.2593 0.9777 0.000 0.148 0.008 0.000 0.000 0.844
#> SRR934645 2 0.4728 0.6323 0.000 0.680 0.144 0.000 0.000 0.176
#> SRR925740 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925741 3 0.2902 0.7443 0.000 0.004 0.800 0.000 0.000 0.196
#> SRR925742 3 0.0000 0.8570 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1283038 3 0.2933 0.7404 0.000 0.004 0.796 0.000 0.000 0.200
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.993 0.5049 0.494 0.494
#> 3 3 0.784 0.924 0.931 0.1868 0.920 0.838
#> 4 4 0.825 0.953 0.939 0.1951 0.861 0.665
#> 5 5 0.855 0.795 0.876 0.0867 0.887 0.623
#> 6 6 0.811 0.767 0.810 0.0475 0.935 0.709
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
#> SRR925687 1 0.0376 0.994 0.996 0.004
#> SRR925688 1 0.0376 0.994 0.996 0.004
#> SRR925689 1 0.0376 0.994 0.996 0.004
#> SRR925690 1 0.0376 0.994 0.996 0.004
#> SRR925691 1 0.0376 0.994 0.996 0.004
#> SRR925692 1 0.0376 0.994 0.996 0.004
#> SRR925693 2 0.0376 0.996 0.004 0.996
#> SRR925694 2 0.0000 0.994 0.000 1.000
#> SRR925695 2 0.0376 0.996 0.004 0.996
#> SRR925697 1 0.0376 0.991 0.996 0.004
#> SRR925696 2 0.0376 0.996 0.004 0.996
#> SRR925698 2 0.0376 0.996 0.004 0.996
#> SRR925699 2 0.0000 0.994 0.000 1.000
#> SRR925700 2 0.0000 0.994 0.000 1.000
#> SRR925701 2 0.0000 0.994 0.000 1.000
#> SRR925702 1 0.0376 0.994 0.996 0.004
#> SRR925703 1 0.0376 0.991 0.996 0.004
#> SRR925704 2 0.0376 0.996 0.004 0.996
#> SRR925705 2 0.0376 0.996 0.004 0.996
#> SRR925706 1 0.0672 0.992 0.992 0.008
#> SRR925708 1 0.0376 0.994 0.996 0.004
#> SRR925707 2 0.4298 0.908 0.088 0.912
#> SRR925709 1 0.0376 0.994 0.996 0.004
#> SRR925710 1 0.2948 0.949 0.948 0.052
#> SRR925711 2 0.0376 0.996 0.004 0.996
#> SRR934631 2 0.0000 0.994 0.000 1.000
#> SRR934632 2 0.0000 0.994 0.000 1.000
#> SRR934633 2 0.0376 0.996 0.004 0.996
#> SRR925713 1 0.0376 0.994 0.996 0.004
#> SRR925714 1 0.0376 0.991 0.996 0.004
#> SRR925715 1 0.3733 0.928 0.928 0.072
#> SRR925716 1 0.0376 0.991 0.996 0.004
#> SRR925717 1 0.0376 0.994 0.996 0.004
#> SRR925718 2 0.0000 0.994 0.000 1.000
#> SRR925719 1 0.0376 0.994 0.996 0.004
#> SRR925720 1 0.0376 0.994 0.996 0.004
#> SRR925721 1 0.0376 0.994 0.996 0.004
#> SRR934634 1 0.0376 0.994 0.996 0.004
#> SRR934635 1 0.0376 0.994 0.996 0.004
#> SRR925723 2 0.0376 0.996 0.004 0.996
#> SRR925724 2 0.0376 0.996 0.004 0.996
#> SRR925725 2 0.0376 0.996 0.004 0.996
#> SRR925726 1 0.0376 0.991 0.996 0.004
#> SRR925727 2 0.0376 0.996 0.004 0.996
#> SRR925728 1 0.0376 0.994 0.996 0.004
#> SRR934636 1 0.0376 0.991 0.996 0.004
#> SRR934637 1 0.0376 0.991 0.996 0.004
#> SRR925729 2 0.0376 0.996 0.004 0.996
#> SRR934638 1 0.0376 0.994 0.996 0.004
#> SRR934639 1 0.0376 0.994 0.996 0.004
#> SRR934640 2 0.0672 0.989 0.008 0.992
#> SRR934641 2 0.0672 0.989 0.008 0.992
#> SRR925734 1 0.0376 0.994 0.996 0.004
#> SRR925735 2 0.0376 0.996 0.004 0.996
#> SRR925736 2 0.0376 0.996 0.004 0.996
#> SRR925737 2 0.0376 0.996 0.004 0.996
#> SRR934642 2 0.0000 0.994 0.000 1.000
#> SRR934643 2 0.0000 0.994 0.000 1.000
#> SRR934644 2 0.0000 0.994 0.000 1.000
#> SRR934645 2 0.0000 0.994 0.000 1.000
#> SRR925740 2 0.0376 0.996 0.004 0.996
#> SRR925741 2 0.0376 0.996 0.004 0.996
#> SRR925742 2 0.0376 0.996 0.004 0.996
#> SRR1283038 2 0.0376 0.996 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925689 1 0.2537 0.907 0.920 0.000 0.080
#> SRR925690 1 0.2537 0.907 0.920 0.000 0.080
#> SRR925691 1 0.2537 0.907 0.920 0.000 0.080
#> SRR925692 1 0.2537 0.907 0.920 0.000 0.080
#> SRR925693 2 0.1289 0.896 0.032 0.968 0.000
#> SRR925694 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925695 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925697 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925696 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925698 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925699 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925700 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925701 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925702 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925703 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925704 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925705 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925706 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925708 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925707 2 0.5988 0.467 0.368 0.632 0.000
#> SRR925709 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925710 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925711 2 0.4504 0.722 0.196 0.804 0.000
#> SRR934631 2 0.4121 0.880 0.000 0.832 0.168
#> SRR934632 2 0.4121 0.880 0.000 0.832 0.168
#> SRR934633 2 0.0424 0.913 0.000 0.992 0.008
#> SRR925713 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925714 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925715 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925716 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925717 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925718 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925719 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.982 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.982 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.982 1.000 0.000 0.000
#> SRR925723 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925724 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925725 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925726 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925727 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925728 1 0.0000 0.982 1.000 0.000 0.000
#> SRR934636 3 0.4121 1.000 0.168 0.000 0.832
#> SRR934637 3 0.4121 1.000 0.168 0.000 0.832
#> SRR925729 2 0.0000 0.914 0.000 1.000 0.000
#> SRR934638 1 0.0000 0.982 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.982 1.000 0.000 0.000
#> SRR934640 2 0.5000 0.876 0.044 0.832 0.124
#> SRR934641 2 0.5000 0.876 0.044 0.832 0.124
#> SRR925734 1 0.0237 0.979 0.996 0.000 0.004
#> SRR925735 2 0.3619 0.801 0.136 0.864 0.000
#> SRR925736 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.914 0.000 1.000 0.000
#> SRR934642 2 0.4121 0.880 0.000 0.832 0.168
#> SRR934643 2 0.4121 0.880 0.000 0.832 0.168
#> SRR934644 2 0.4121 0.880 0.000 0.832 0.168
#> SRR934645 2 0.4121 0.880 0.000 0.832 0.168
#> SRR925740 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925741 2 0.0000 0.914 0.000 1.000 0.000
#> SRR925742 2 0.0000 0.914 0.000 1.000 0.000
#> SRR1283038 2 0.0000 0.914 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR925688 1 0.0817 0.953 0.976 0.024 0.000 0
#> SRR925689 1 0.0336 0.954 0.992 0.008 0.000 0
#> SRR925690 1 0.0336 0.954 0.992 0.008 0.000 0
#> SRR925691 1 0.0336 0.954 0.992 0.008 0.000 0
#> SRR925692 1 0.0336 0.954 0.992 0.008 0.000 0
#> SRR925693 3 0.0657 0.963 0.004 0.012 0.984 0
#> SRR925694 2 0.3024 0.961 0.000 0.852 0.148 0
#> SRR925695 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925697 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925696 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925698 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925699 2 0.3024 0.961 0.000 0.852 0.148 0
#> SRR925700 2 0.3074 0.960 0.000 0.848 0.152 0
#> SRR925701 2 0.3024 0.961 0.000 0.852 0.148 0
#> SRR925702 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925703 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925704 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925705 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925706 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925708 1 0.0469 0.955 0.988 0.012 0.000 0
#> SRR925707 3 0.3384 0.838 0.024 0.116 0.860 0
#> SRR925709 1 0.1557 0.943 0.944 0.056 0.000 0
#> SRR925710 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925711 3 0.2742 0.883 0.024 0.076 0.900 0
#> SRR934631 2 0.2921 0.958 0.000 0.860 0.140 0
#> SRR934632 2 0.2921 0.958 0.000 0.860 0.140 0
#> SRR934633 2 0.3726 0.906 0.000 0.788 0.212 0
#> SRR925713 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925714 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925715 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925716 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925717 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR925718 2 0.3074 0.960 0.000 0.848 0.152 0
#> SRR925719 1 0.0336 0.956 0.992 0.008 0.000 0
#> SRR925720 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR925721 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR934634 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR934635 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR925723 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925724 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925725 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925726 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925727 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925728 1 0.2814 0.913 0.868 0.132 0.000 0
#> SRR934636 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR934637 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR925729 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR934638 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR934639 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR934640 2 0.4004 0.820 0.024 0.812 0.164 0
#> SRR934641 2 0.4004 0.820 0.024 0.812 0.164 0
#> SRR925734 1 0.0000 0.957 1.000 0.000 0.000 0
#> SRR925735 3 0.2522 0.890 0.016 0.076 0.908 0
#> SRR925736 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925737 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR934642 2 0.2921 0.958 0.000 0.860 0.140 0
#> SRR934643 2 0.2921 0.958 0.000 0.860 0.140 0
#> SRR934644 2 0.3024 0.961 0.000 0.852 0.148 0
#> SRR934645 2 0.3024 0.961 0.000 0.852 0.148 0
#> SRR925740 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925741 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR925742 3 0.0000 0.977 0.000 0.000 1.000 0
#> SRR1283038 3 0.0000 0.977 0.000 0.000 1.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.4088 0.739 0.632 0.000 0.000 0.368 0.000
#> SRR925688 1 0.4182 0.706 0.600 0.000 0.000 0.400 0.000
#> SRR925689 1 0.0290 0.684 0.992 0.008 0.000 0.000 0.000
#> SRR925690 1 0.0290 0.684 0.992 0.008 0.000 0.000 0.000
#> SRR925691 1 0.0290 0.684 0.992 0.008 0.000 0.000 0.000
#> SRR925692 1 0.0290 0.684 0.992 0.008 0.000 0.000 0.000
#> SRR925693 3 0.2951 0.829 0.000 0.028 0.860 0.112 0.000
#> SRR925694 2 0.1043 0.857 0.000 0.960 0.040 0.000 0.000
#> SRR925695 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925697 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925696 3 0.0794 0.927 0.000 0.028 0.972 0.000 0.000
#> SRR925698 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925699 2 0.1043 0.857 0.000 0.960 0.040 0.000 0.000
#> SRR925700 2 0.1205 0.856 0.000 0.956 0.040 0.004 0.000
#> SRR925701 2 0.1043 0.857 0.000 0.960 0.040 0.000 0.000
#> SRR925702 4 0.2046 0.806 0.068 0.000 0.000 0.916 0.016
#> SRR925703 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925704 3 0.0162 0.935 0.000 0.004 0.996 0.000 0.000
#> SRR925705 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925706 4 0.1549 0.836 0.040 0.000 0.000 0.944 0.016
#> SRR925708 1 0.4161 0.716 0.608 0.000 0.000 0.392 0.000
#> SRR925707 4 0.7037 -0.125 0.000 0.244 0.360 0.384 0.012
#> SRR925709 1 0.4294 0.593 0.532 0.000 0.000 0.468 0.000
#> SRR925710 4 0.1469 0.834 0.036 0.000 0.000 0.948 0.016
#> SRR925711 2 0.6074 0.325 0.000 0.500 0.372 0.128 0.000
#> SRR934631 2 0.0703 0.851 0.000 0.976 0.024 0.000 0.000
#> SRR934632 2 0.0703 0.851 0.000 0.976 0.024 0.000 0.000
#> SRR934633 3 0.3730 0.575 0.000 0.288 0.712 0.000 0.000
#> SRR925713 4 0.1549 0.836 0.040 0.000 0.000 0.944 0.016
#> SRR925714 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925715 4 0.1469 0.834 0.036 0.000 0.000 0.948 0.016
#> SRR925716 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925717 4 0.2464 0.760 0.096 0.000 0.000 0.888 0.016
#> SRR925718 2 0.1341 0.850 0.000 0.944 0.056 0.000 0.000
#> SRR925719 1 0.4101 0.736 0.628 0.000 0.000 0.372 0.000
#> SRR925720 1 0.4074 0.741 0.636 0.000 0.000 0.364 0.000
#> SRR925721 1 0.4074 0.741 0.636 0.000 0.000 0.364 0.000
#> SRR934634 1 0.3242 0.733 0.784 0.000 0.000 0.216 0.000
#> SRR934635 1 0.4508 0.736 0.648 0.000 0.000 0.332 0.020
#> SRR925723 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925724 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925725 3 0.0898 0.928 0.000 0.020 0.972 0.008 0.000
#> SRR925726 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925727 3 0.0290 0.934 0.000 0.008 0.992 0.000 0.000
#> SRR925728 4 0.1549 0.836 0.040 0.000 0.000 0.944 0.016
#> SRR934636 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934637 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR925729 3 0.3895 0.422 0.000 0.320 0.680 0.000 0.000
#> SRR934638 1 0.0703 0.697 0.976 0.000 0.000 0.024 0.000
#> SRR934639 1 0.4074 0.741 0.636 0.000 0.000 0.364 0.000
#> SRR934640 2 0.5635 0.571 0.000 0.620 0.252 0.128 0.000
#> SRR934641 2 0.5635 0.571 0.000 0.620 0.252 0.128 0.000
#> SRR925734 1 0.0703 0.697 0.976 0.000 0.000 0.024 0.000
#> SRR925735 2 0.6047 0.319 0.000 0.500 0.376 0.124 0.000
#> SRR925736 3 0.0290 0.934 0.000 0.008 0.992 0.000 0.000
#> SRR925737 3 0.1732 0.876 0.000 0.080 0.920 0.000 0.000
#> SRR934642 2 0.0703 0.851 0.000 0.976 0.024 0.000 0.000
#> SRR934643 2 0.0703 0.851 0.000 0.976 0.024 0.000 0.000
#> SRR934644 2 0.1043 0.857 0.000 0.960 0.040 0.000 0.000
#> SRR934645 2 0.1043 0.857 0.000 0.960 0.040 0.000 0.000
#> SRR925740 3 0.1121 0.917 0.000 0.044 0.956 0.000 0.000
#> SRR925741 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
#> SRR925742 3 0.0609 0.928 0.000 0.020 0.980 0.000 0.000
#> SRR1283038 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 6 0.0146 0.8218 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR925688 6 0.1663 0.7664 0.000 0.000 0.000 0.088 0.000 0.912
#> SRR925689 1 0.3076 0.8447 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR925690 1 0.3076 0.8447 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR925691 1 0.3076 0.8447 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR925692 1 0.3076 0.8447 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR925693 4 0.4097 -0.3865 0.008 0.000 0.488 0.504 0.000 0.000
#> SRR925694 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925695 3 0.2697 0.8160 0.000 0.000 0.812 0.188 0.000 0.000
#> SRR925697 5 0.0000 0.9841 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925696 3 0.0508 0.9017 0.000 0.012 0.984 0.004 0.000 0.000
#> SRR925698 3 0.0547 0.9019 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR925699 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925700 2 0.0260 0.9283 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925702 4 0.5531 0.5107 0.000 0.000 0.000 0.528 0.156 0.316
#> SRR925703 5 0.0000 0.9841 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925704 3 0.0692 0.9020 0.000 0.004 0.976 0.020 0.000 0.000
#> SRR925705 3 0.0000 0.9022 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925706 4 0.5186 0.6030 0.000 0.000 0.000 0.612 0.156 0.232
#> SRR925708 6 0.0000 0.8220 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925707 4 0.7032 0.4197 0.240 0.008 0.080 0.492 0.176 0.004
#> SRR925709 6 0.3050 0.5880 0.000 0.000 0.000 0.236 0.000 0.764
#> SRR925710 4 0.5880 0.6067 0.040 0.000 0.000 0.596 0.156 0.208
#> SRR925711 4 0.7208 0.3799 0.240 0.036 0.232 0.456 0.032 0.004
#> SRR934631 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 3 0.4948 0.3752 0.000 0.360 0.564 0.076 0.000 0.000
#> SRR925713 4 0.5269 0.5941 0.000 0.000 0.000 0.596 0.156 0.248
#> SRR925714 5 0.0000 0.9841 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925715 4 0.5880 0.6067 0.040 0.000 0.000 0.596 0.156 0.208
#> SRR925716 5 0.0790 0.9789 0.000 0.000 0.000 0.032 0.968 0.000
#> SRR925717 4 0.5543 0.5016 0.000 0.000 0.000 0.524 0.156 0.320
#> SRR925718 2 0.0458 0.9221 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR925719 6 0.0000 0.8220 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925720 6 0.0146 0.8218 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR925721 6 0.1765 0.7131 0.096 0.000 0.000 0.000 0.000 0.904
#> SRR934634 1 0.4083 0.6241 0.532 0.000 0.000 0.008 0.000 0.460
#> SRR934635 6 0.6539 0.0773 0.204 0.000 0.000 0.052 0.248 0.496
#> SRR925723 3 0.0000 0.9022 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925724 3 0.0547 0.9019 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR925725 3 0.3586 0.7546 0.000 0.012 0.720 0.268 0.000 0.000
#> SRR925726 5 0.0000 0.9841 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925727 3 0.2994 0.8073 0.000 0.004 0.788 0.208 0.000 0.000
#> SRR925728 4 0.5324 0.5962 0.000 0.000 0.000 0.592 0.172 0.236
#> SRR934636 5 0.1007 0.9752 0.000 0.000 0.000 0.044 0.956 0.000
#> SRR934637 5 0.1007 0.9752 0.000 0.000 0.000 0.044 0.956 0.000
#> SRR925729 3 0.2114 0.8716 0.008 0.012 0.904 0.076 0.000 0.000
#> SRR934638 1 0.3782 0.7477 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR934639 6 0.0692 0.8090 0.020 0.000 0.000 0.004 0.000 0.976
#> SRR934640 2 0.4424 0.5204 0.024 0.628 0.004 0.340 0.000 0.004
#> SRR934641 2 0.4424 0.5204 0.024 0.628 0.004 0.340 0.000 0.004
#> SRR925734 1 0.3782 0.7477 0.588 0.000 0.000 0.000 0.000 0.412
#> SRR925735 4 0.7542 0.3927 0.240 0.032 0.208 0.444 0.072 0.004
#> SRR925736 3 0.0405 0.9024 0.000 0.004 0.988 0.008 0.000 0.000
#> SRR925737 3 0.1296 0.8886 0.032 0.004 0.952 0.012 0.000 0.000
#> SRR934642 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.9340 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925740 3 0.0935 0.8925 0.000 0.032 0.964 0.004 0.000 0.000
#> SRR925741 3 0.2902 0.8153 0.000 0.004 0.800 0.196 0.000 0.000
#> SRR925742 3 0.0146 0.9024 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1283038 3 0.0000 0.9022 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["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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5065 0.494 0.494
#> 3 3 0.727 0.711 0.882 0.2454 0.837 0.681
#> 4 4 0.668 0.724 0.839 0.1182 0.867 0.665
#> 5 5 0.679 0.601 0.725 0.0998 0.801 0.439
#> 6 6 0.723 0.681 0.803 0.0560 0.939 0.729
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
#> SRR925687 1 0.0000 1.000 1.000 0.000
#> SRR925688 1 0.0000 1.000 1.000 0.000
#> SRR925689 1 0.0000 1.000 1.000 0.000
#> SRR925690 1 0.0000 1.000 1.000 0.000
#> SRR925691 1 0.0000 1.000 1.000 0.000
#> SRR925692 1 0.0000 1.000 1.000 0.000
#> SRR925693 2 0.0672 0.992 0.008 0.992
#> SRR925694 2 0.0000 1.000 0.000 1.000
#> SRR925695 2 0.0000 1.000 0.000 1.000
#> SRR925697 1 0.0000 1.000 1.000 0.000
#> SRR925696 2 0.0000 1.000 0.000 1.000
#> SRR925698 2 0.0000 1.000 0.000 1.000
#> SRR925699 2 0.0000 1.000 0.000 1.000
#> SRR925700 2 0.0000 1.000 0.000 1.000
#> SRR925701 2 0.0000 1.000 0.000 1.000
#> SRR925702 1 0.0000 1.000 1.000 0.000
#> SRR925703 1 0.0000 1.000 1.000 0.000
#> SRR925704 2 0.0000 1.000 0.000 1.000
#> SRR925705 2 0.0000 1.000 0.000 1.000
#> SRR925706 1 0.0000 1.000 1.000 0.000
#> SRR925708 1 0.0000 1.000 1.000 0.000
#> SRR925707 2 0.0000 1.000 0.000 1.000
#> SRR925709 1 0.0000 1.000 1.000 0.000
#> SRR925710 1 0.0000 1.000 1.000 0.000
#> SRR925711 2 0.0376 0.996 0.004 0.996
#> SRR934631 2 0.0000 1.000 0.000 1.000
#> SRR934632 2 0.0000 1.000 0.000 1.000
#> SRR934633 2 0.0000 1.000 0.000 1.000
#> SRR925713 1 0.0000 1.000 1.000 0.000
#> SRR925714 1 0.0000 1.000 1.000 0.000
#> SRR925715 1 0.0000 1.000 1.000 0.000
#> SRR925716 1 0.0000 1.000 1.000 0.000
#> SRR925717 1 0.0000 1.000 1.000 0.000
#> SRR925718 2 0.0000 1.000 0.000 1.000
#> SRR925719 1 0.0000 1.000 1.000 0.000
#> SRR925720 1 0.0000 1.000 1.000 0.000
#> SRR925721 1 0.0000 1.000 1.000 0.000
#> SRR934634 1 0.0000 1.000 1.000 0.000
#> SRR934635 1 0.0000 1.000 1.000 0.000
#> SRR925723 2 0.0000 1.000 0.000 1.000
#> SRR925724 2 0.0000 1.000 0.000 1.000
#> SRR925725 2 0.0000 1.000 0.000 1.000
#> SRR925726 1 0.0000 1.000 1.000 0.000
#> SRR925727 2 0.0000 1.000 0.000 1.000
#> SRR925728 1 0.0000 1.000 1.000 0.000
#> SRR934636 1 0.0000 1.000 1.000 0.000
#> SRR934637 1 0.0000 1.000 1.000 0.000
#> SRR925729 2 0.0000 1.000 0.000 1.000
#> SRR934638 1 0.0000 1.000 1.000 0.000
#> SRR934639 1 0.0000 1.000 1.000 0.000
#> SRR934640 2 0.0000 1.000 0.000 1.000
#> SRR934641 2 0.0000 1.000 0.000 1.000
#> SRR925734 1 0.0000 1.000 1.000 0.000
#> SRR925735 2 0.0376 0.996 0.004 0.996
#> SRR925736 2 0.0000 1.000 0.000 1.000
#> SRR925737 2 0.0000 1.000 0.000 1.000
#> SRR934642 2 0.0000 1.000 0.000 1.000
#> SRR934643 2 0.0000 1.000 0.000 1.000
#> SRR934644 2 0.0000 1.000 0.000 1.000
#> SRR934645 2 0.0000 1.000 0.000 1.000
#> SRR925740 2 0.0000 1.000 0.000 1.000
#> SRR925741 2 0.0000 1.000 0.000 1.000
#> SRR925742 2 0.0000 1.000 0.000 1.000
#> SRR1283038 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925689 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925690 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925691 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925692 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925693 3 0.6126 0.2807 0.000 0.400 0.600
#> SRR925694 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925695 3 0.6126 0.2807 0.000 0.400 0.600
#> SRR925697 3 0.3551 0.4992 0.132 0.000 0.868
#> SRR925696 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925698 3 0.6252 0.1773 0.000 0.444 0.556
#> SRR925699 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925702 1 0.3941 0.7580 0.844 0.000 0.156
#> SRR925703 3 0.0424 0.5676 0.008 0.000 0.992
#> SRR925704 2 0.4654 0.6717 0.000 0.792 0.208
#> SRR925705 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925706 3 0.5465 0.3282 0.288 0.000 0.712
#> SRR925708 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925707 2 0.0592 0.9009 0.012 0.988 0.000
#> SRR925709 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925710 3 0.6244 0.2560 0.440 0.000 0.560
#> SRR925711 2 0.1163 0.8854 0.028 0.972 0.000
#> SRR934631 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934633 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925713 3 0.6154 0.2828 0.408 0.000 0.592
#> SRR925714 3 0.0000 0.5685 0.000 0.000 1.000
#> SRR925715 1 0.6252 -0.0836 0.556 0.000 0.444
#> SRR925716 1 0.6126 0.4505 0.600 0.000 0.400
#> SRR925717 3 0.5859 0.3765 0.344 0.000 0.656
#> SRR925718 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925720 1 0.0237 0.8844 0.996 0.000 0.004
#> SRR925721 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR934635 1 0.0237 0.8844 0.996 0.000 0.004
#> SRR925723 2 0.3551 0.7769 0.000 0.868 0.132
#> SRR925724 2 0.6026 0.3393 0.000 0.624 0.376
#> SRR925725 2 0.6180 0.2251 0.000 0.584 0.416
#> SRR925726 3 0.5465 0.3142 0.288 0.000 0.712
#> SRR925727 3 0.6252 0.1784 0.000 0.444 0.556
#> SRR925728 1 0.3551 0.7825 0.868 0.000 0.132
#> SRR934636 1 0.6126 0.4505 0.600 0.000 0.400
#> SRR934637 1 0.6126 0.4505 0.600 0.000 0.400
#> SRR925729 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934638 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925734 1 0.0000 0.8866 1.000 0.000 0.000
#> SRR925735 2 0.6111 0.2820 0.000 0.604 0.396
#> SRR925736 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934642 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925740 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR925741 2 0.6180 0.2250 0.000 0.584 0.416
#> SRR925742 2 0.0000 0.9133 0.000 1.000 0.000
#> SRR1283038 2 0.0000 0.9133 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR925688 1 0.0188 0.89051 0.996 0.000 0.000 0.004
#> SRR925689 1 0.0469 0.89055 0.988 0.000 0.012 0.000
#> SRR925690 1 0.0469 0.89055 0.988 0.000 0.012 0.000
#> SRR925691 1 0.0657 0.88978 0.984 0.000 0.012 0.004
#> SRR925692 1 0.0469 0.89055 0.988 0.000 0.012 0.000
#> SRR925693 3 0.5160 0.65880 0.000 0.180 0.748 0.072
#> SRR925694 2 0.3718 0.72767 0.000 0.820 0.168 0.012
#> SRR925695 3 0.5599 0.68303 0.000 0.228 0.700 0.072
#> SRR925697 4 0.0817 0.86164 0.024 0.000 0.000 0.976
#> SRR925696 2 0.3208 0.71031 0.000 0.848 0.148 0.004
#> SRR925698 3 0.5744 0.56032 0.000 0.436 0.536 0.028
#> SRR925699 2 0.0817 0.79032 0.000 0.976 0.024 0.000
#> SRR925700 2 0.1211 0.78883 0.000 0.960 0.040 0.000
#> SRR925701 2 0.0000 0.78932 0.000 1.000 0.000 0.000
#> SRR925702 1 0.6649 0.22533 0.560 0.000 0.100 0.340
#> SRR925703 4 0.0657 0.85259 0.004 0.000 0.012 0.984
#> SRR925704 2 0.4877 0.49192 0.000 0.664 0.328 0.008
#> SRR925705 2 0.3266 0.69183 0.000 0.832 0.168 0.000
#> SRR925706 4 0.5497 0.75983 0.052 0.008 0.220 0.720
#> SRR925708 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR925707 2 0.4201 0.66015 0.012 0.788 0.196 0.004
#> SRR925709 1 0.2714 0.80422 0.884 0.000 0.112 0.004
#> SRR925710 1 0.6355 0.47038 0.576 0.000 0.348 0.076
#> SRR925711 2 0.4206 0.73168 0.048 0.816 0.136 0.000
#> SRR934631 2 0.2741 0.77270 0.000 0.892 0.096 0.012
#> SRR934632 2 0.2676 0.77402 0.000 0.896 0.092 0.012
#> SRR934633 2 0.2741 0.77270 0.000 0.892 0.096 0.012
#> SRR925713 3 0.6116 0.00868 0.320 0.000 0.612 0.068
#> SRR925714 4 0.0657 0.85259 0.004 0.000 0.012 0.984
#> SRR925715 1 0.5535 0.59377 0.656 0.000 0.304 0.040
#> SRR925716 4 0.2216 0.86524 0.092 0.000 0.000 0.908
#> SRR925717 1 0.6559 0.13288 0.468 0.000 0.456 0.076
#> SRR925718 2 0.0707 0.78395 0.000 0.980 0.020 0.000
#> SRR925719 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR925720 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR925721 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR934634 1 0.0000 0.89175 1.000 0.000 0.000 0.000
#> SRR934635 1 0.1174 0.87921 0.968 0.000 0.012 0.020
#> SRR925723 2 0.3539 0.67462 0.000 0.820 0.176 0.004
#> SRR925724 2 0.5364 0.23140 0.000 0.652 0.320 0.028
#> SRR925725 3 0.5150 0.57102 0.000 0.396 0.596 0.008
#> SRR925726 4 0.6058 0.60766 0.072 0.000 0.296 0.632
#> SRR925727 3 0.6120 0.68634 0.000 0.296 0.628 0.076
#> SRR925728 4 0.5859 0.76080 0.140 0.000 0.156 0.704
#> SRR934636 4 0.2216 0.86524 0.092 0.000 0.000 0.908
#> SRR934637 4 0.2216 0.86524 0.092 0.000 0.000 0.908
#> SRR925729 2 0.4182 0.71575 0.000 0.796 0.180 0.024
#> SRR934638 1 0.0336 0.89141 0.992 0.000 0.008 0.000
#> SRR934639 1 0.0657 0.88922 0.984 0.000 0.012 0.004
#> SRR934640 2 0.4948 0.62921 0.008 0.724 0.252 0.016
#> SRR934641 2 0.4948 0.62921 0.008 0.724 0.252 0.016
#> SRR925734 1 0.0336 0.89141 0.992 0.000 0.008 0.000
#> SRR925735 3 0.5149 0.61528 0.000 0.336 0.648 0.016
#> SRR925736 2 0.3266 0.69183 0.000 0.832 0.168 0.000
#> SRR925737 2 0.2760 0.73373 0.000 0.872 0.128 0.000
#> SRR934642 2 0.0188 0.78869 0.000 0.996 0.004 0.000
#> SRR934643 2 0.0188 0.78869 0.000 0.996 0.004 0.000
#> SRR934644 2 0.3428 0.75262 0.000 0.844 0.144 0.012
#> SRR934645 2 0.2928 0.77029 0.000 0.880 0.108 0.012
#> SRR925740 2 0.2589 0.74154 0.000 0.884 0.116 0.000
#> SRR925741 3 0.5099 0.58605 0.000 0.380 0.612 0.008
#> SRR925742 2 0.2408 0.74953 0.000 0.896 0.104 0.000
#> SRR1283038 2 0.3583 0.71597 0.000 0.816 0.180 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.1830 0.8874 0.924 0.000 0.000 0.068 0.008
#> SRR925688 1 0.1671 0.8863 0.924 0.000 0.000 0.076 0.000
#> SRR925689 1 0.1211 0.8827 0.960 0.024 0.000 0.016 0.000
#> SRR925690 1 0.1211 0.8827 0.960 0.024 0.000 0.016 0.000
#> SRR925691 1 0.1211 0.8827 0.960 0.024 0.000 0.016 0.000
#> SRR925692 1 0.1211 0.8827 0.960 0.024 0.000 0.016 0.000
#> SRR925693 2 0.6503 0.1440 0.000 0.480 0.220 0.300 0.000
#> SRR925694 2 0.4958 0.5014 0.000 0.592 0.372 0.036 0.000
#> SRR925695 3 0.6744 -0.0593 0.000 0.268 0.400 0.332 0.000
#> SRR925697 5 0.1043 0.8767 0.000 0.000 0.000 0.040 0.960
#> SRR925696 3 0.0162 0.7212 0.000 0.004 0.996 0.000 0.000
#> SRR925698 3 0.3375 0.5739 0.000 0.104 0.840 0.056 0.000
#> SRR925699 3 0.3990 0.4052 0.000 0.308 0.688 0.004 0.000
#> SRR925700 3 0.4248 0.5072 0.000 0.240 0.728 0.032 0.000
#> SRR925701 3 0.3398 0.5812 0.000 0.216 0.780 0.004 0.000
#> SRR925702 4 0.6274 0.1649 0.148 0.000 0.000 0.428 0.424
#> SRR925703 5 0.1341 0.8701 0.000 0.000 0.000 0.056 0.944
#> SRR925704 2 0.4559 0.4622 0.000 0.512 0.480 0.008 0.000
#> SRR925705 3 0.0000 0.7227 0.000 0.000 1.000 0.000 0.000
#> SRR925706 4 0.4415 0.2177 0.000 0.004 0.000 0.552 0.444
#> SRR925708 1 0.1205 0.8927 0.956 0.000 0.000 0.040 0.004
#> SRR925707 4 0.7493 0.4415 0.000 0.228 0.084 0.500 0.188
#> SRR925709 1 0.4321 0.3442 0.600 0.000 0.000 0.396 0.004
#> SRR925710 4 0.4532 0.4592 0.248 0.036 0.004 0.712 0.000
#> SRR925711 2 0.6589 0.0618 0.016 0.484 0.140 0.360 0.000
#> SRR934631 2 0.3966 0.5833 0.000 0.664 0.336 0.000 0.000
#> SRR934632 2 0.3966 0.5833 0.000 0.664 0.336 0.000 0.000
#> SRR934633 2 0.4030 0.5876 0.000 0.648 0.352 0.000 0.000
#> SRR925713 4 0.3890 0.4547 0.068 0.080 0.004 0.832 0.016
#> SRR925714 5 0.1410 0.8700 0.000 0.000 0.000 0.060 0.940
#> SRR925715 4 0.4510 0.5366 0.164 0.076 0.004 0.756 0.000
#> SRR925716 5 0.0162 0.8768 0.000 0.000 0.000 0.004 0.996
#> SRR925717 1 0.7196 0.0865 0.420 0.208 0.000 0.344 0.028
#> SRR925718 3 0.3006 0.6402 0.000 0.156 0.836 0.004 0.004
#> SRR925719 1 0.1671 0.8866 0.924 0.000 0.000 0.076 0.000
#> SRR925720 1 0.1831 0.8855 0.920 0.000 0.000 0.076 0.004
#> SRR925721 1 0.1544 0.8892 0.932 0.000 0.000 0.068 0.000
#> SRR934634 1 0.1478 0.8901 0.936 0.000 0.000 0.064 0.000
#> SRR934635 1 0.0000 0.8890 1.000 0.000 0.000 0.000 0.000
#> SRR925723 3 0.0404 0.7173 0.000 0.012 0.988 0.000 0.000
#> SRR925724 3 0.1918 0.6750 0.000 0.036 0.928 0.036 0.000
#> SRR925725 2 0.4235 0.5087 0.000 0.656 0.336 0.008 0.000
#> SRR925726 5 0.6188 0.3755 0.000 0.176 0.000 0.284 0.540
#> SRR925727 3 0.6621 -0.0106 0.000 0.224 0.428 0.348 0.000
#> SRR925728 4 0.4753 0.2983 0.004 0.008 0.004 0.580 0.404
#> SRR934636 5 0.0162 0.8749 0.000 0.000 0.000 0.004 0.996
#> SRR934637 5 0.0162 0.8749 0.000 0.000 0.000 0.004 0.996
#> SRR925729 2 0.5528 0.5784 0.000 0.644 0.216 0.140 0.000
#> SRR934638 1 0.1740 0.8925 0.932 0.012 0.000 0.056 0.000
#> SRR934639 1 0.1012 0.8844 0.968 0.020 0.000 0.012 0.000
#> SRR934640 4 0.5238 0.1664 0.000 0.476 0.044 0.480 0.000
#> SRR934641 4 0.5238 0.1664 0.000 0.476 0.044 0.480 0.000
#> SRR925734 1 0.0162 0.8900 0.996 0.000 0.000 0.004 0.000
#> SRR925735 2 0.6491 0.3417 0.000 0.492 0.244 0.264 0.000
#> SRR925736 3 0.0000 0.7227 0.000 0.000 1.000 0.000 0.000
#> SRR925737 3 0.0771 0.7220 0.000 0.020 0.976 0.004 0.000
#> SRR934642 3 0.3048 0.6246 0.000 0.176 0.820 0.004 0.000
#> SRR934643 3 0.3048 0.6261 0.000 0.176 0.820 0.004 0.000
#> SRR934644 2 0.4777 0.5904 0.000 0.664 0.292 0.044 0.000
#> SRR934645 2 0.4570 0.5511 0.000 0.632 0.348 0.020 0.000
#> SRR925740 3 0.0609 0.7227 0.000 0.020 0.980 0.000 0.000
#> SRR925741 2 0.4135 0.5046 0.000 0.656 0.340 0.004 0.000
#> SRR925742 3 0.0703 0.7228 0.000 0.024 0.976 0.000 0.000
#> SRR1283038 3 0.2488 0.5857 0.000 0.124 0.872 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.2019 0.8276 0.900 0.000 0.000 0.088 0.012 0.000
#> SRR925688 1 0.1814 0.8260 0.900 0.000 0.000 0.100 0.000 0.000
#> SRR925689 1 0.4721 0.7194 0.724 0.036 0.000 0.076 0.000 0.164
#> SRR925690 1 0.4653 0.7248 0.732 0.036 0.000 0.076 0.000 0.156
#> SRR925691 1 0.4653 0.7248 0.732 0.036 0.000 0.076 0.000 0.156
#> SRR925692 1 0.4653 0.7248 0.732 0.036 0.000 0.076 0.000 0.156
#> SRR925693 6 0.5144 0.5100 0.000 0.256 0.120 0.004 0.000 0.620
#> SRR925694 2 0.5622 0.4737 0.000 0.624 0.236 0.072 0.000 0.068
#> SRR925695 6 0.5703 0.4848 0.000 0.236 0.240 0.000 0.000 0.524
#> SRR925697 5 0.1686 0.8622 0.000 0.000 0.000 0.012 0.924 0.064
#> SRR925696 3 0.1633 0.7978 0.000 0.024 0.932 0.000 0.000 0.044
#> SRR925698 3 0.3670 0.5385 0.000 0.024 0.736 0.000 0.000 0.240
#> SRR925699 3 0.4038 0.6513 0.000 0.216 0.728 0.000 0.000 0.056
#> SRR925700 3 0.4729 0.6878 0.000 0.124 0.740 0.068 0.000 0.068
#> SRR925701 3 0.2740 0.7880 0.000 0.120 0.852 0.000 0.000 0.028
#> SRR925702 4 0.5368 0.6122 0.072 0.000 0.000 0.676 0.168 0.084
#> SRR925703 5 0.2762 0.8475 0.000 0.000 0.000 0.048 0.860 0.092
#> SRR925704 2 0.4669 0.4740 0.000 0.688 0.164 0.000 0.000 0.148
#> SRR925705 3 0.1408 0.8061 0.000 0.020 0.944 0.000 0.000 0.036
#> SRR925706 4 0.2956 0.7164 0.000 0.000 0.000 0.840 0.120 0.040
#> SRR925708 1 0.1777 0.8293 0.928 0.000 0.000 0.044 0.024 0.004
#> SRR925707 4 0.3924 0.7054 0.000 0.072 0.096 0.800 0.032 0.000
#> SRR925709 1 0.4312 0.0429 0.508 0.000 0.000 0.476 0.004 0.012
#> SRR925710 4 0.4245 0.6739 0.044 0.000 0.000 0.696 0.004 0.256
#> SRR925711 4 0.5660 0.4878 0.004 0.308 0.064 0.580 0.000 0.044
#> SRR934631 2 0.1501 0.6931 0.000 0.924 0.076 0.000 0.000 0.000
#> SRR934632 2 0.1501 0.6931 0.000 0.924 0.076 0.000 0.000 0.000
#> SRR934633 2 0.1531 0.6818 0.000 0.928 0.068 0.000 0.000 0.004
#> SRR925713 4 0.4060 0.7185 0.060 0.000 0.000 0.768 0.016 0.156
#> SRR925714 5 0.2948 0.8409 0.000 0.000 0.000 0.060 0.848 0.092
#> SRR925715 4 0.2277 0.7408 0.076 0.000 0.000 0.892 0.000 0.032
#> SRR925716 5 0.0000 0.8548 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925717 6 0.5695 0.1120 0.204 0.000 0.000 0.100 0.064 0.632
#> SRR925718 3 0.1908 0.8153 0.000 0.096 0.900 0.000 0.000 0.004
#> SRR925719 1 0.2001 0.8268 0.900 0.000 0.000 0.092 0.004 0.004
#> SRR925720 1 0.2342 0.8230 0.888 0.000 0.000 0.088 0.020 0.004
#> SRR925721 1 0.1663 0.8287 0.912 0.000 0.000 0.088 0.000 0.000
#> SRR934634 1 0.1444 0.8319 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR934635 1 0.0458 0.8296 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR925723 3 0.2432 0.7473 0.000 0.024 0.876 0.000 0.000 0.100
#> SRR925724 3 0.3168 0.6593 0.000 0.024 0.804 0.000 0.000 0.172
#> SRR925725 2 0.4613 0.4426 0.000 0.692 0.128 0.000 0.000 0.180
#> SRR925726 5 0.4985 0.4931 0.000 0.000 0.000 0.076 0.548 0.376
#> SRR925727 6 0.4683 0.4970 0.000 0.056 0.312 0.004 0.000 0.628
#> SRR925728 4 0.2507 0.7485 0.040 0.000 0.000 0.884 0.072 0.004
#> SRR934636 5 0.0972 0.8482 0.000 0.000 0.000 0.028 0.964 0.008
#> SRR934637 5 0.0972 0.8482 0.000 0.000 0.000 0.028 0.964 0.008
#> SRR925729 2 0.3869 0.6260 0.000 0.804 0.052 0.040 0.000 0.104
#> SRR934638 1 0.3477 0.8219 0.820 0.008 0.000 0.092 0.000 0.080
#> SRR934639 1 0.3880 0.7648 0.800 0.032 0.000 0.056 0.000 0.112
#> SRR934640 4 0.3490 0.6592 0.000 0.268 0.000 0.724 0.000 0.008
#> SRR934641 4 0.3490 0.6592 0.000 0.268 0.000 0.724 0.000 0.008
#> SRR925734 1 0.0891 0.8218 0.968 0.008 0.000 0.024 0.000 0.000
#> SRR925735 6 0.6173 0.2483 0.000 0.380 0.084 0.064 0.000 0.472
#> SRR925736 3 0.0692 0.8304 0.000 0.020 0.976 0.000 0.000 0.004
#> SRR925737 3 0.0937 0.8300 0.000 0.040 0.960 0.000 0.000 0.000
#> SRR934642 3 0.2006 0.8125 0.000 0.104 0.892 0.000 0.000 0.004
#> SRR934643 3 0.2070 0.8134 0.000 0.100 0.892 0.000 0.000 0.008
#> SRR934644 2 0.2828 0.6767 0.000 0.864 0.072 0.004 0.000 0.060
#> SRR934645 2 0.3840 0.6386 0.000 0.788 0.136 0.012 0.000 0.064
#> SRR925740 3 0.1492 0.8149 0.000 0.024 0.940 0.000 0.000 0.036
#> SRR925741 2 0.4552 0.4565 0.000 0.700 0.128 0.000 0.000 0.172
#> SRR925742 3 0.0820 0.8297 0.000 0.016 0.972 0.000 0.000 0.012
#> SRR1283038 2 0.5543 0.1441 0.000 0.488 0.372 0.000 0.000 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.
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 15949 rows and 64 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 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 0.984 1.000 0.0322 0.969 0.969
#> 3 3 1.000 0.945 0.983 15.1072 0.508 0.492
#> 4 4 1.000 0.926 0.975 0.1420 0.936 0.865
#> 5 5 0.815 0.821 0.881 0.1116 0.898 0.764
#> 6 6 0.777 0.711 0.898 0.0708 0.973 0.925
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] 3
There is also optional best \(k\) = 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR925687 2 0 1 0 1
#> SRR925688 2 0 1 0 1
#> SRR925689 2 0 1 0 1
#> SRR925690 2 0 1 0 1
#> SRR925691 2 0 1 0 1
#> SRR925692 2 0 1 0 1
#> SRR925693 2 0 1 0 1
#> SRR925694 2 0 1 0 1
#> SRR925695 2 0 1 0 1
#> SRR925697 2 0 1 0 1
#> SRR925696 2 0 1 0 1
#> SRR925698 2 0 1 0 1
#> SRR925699 2 0 1 0 1
#> SRR925700 2 0 1 0 1
#> SRR925701 2 0 1 0 1
#> SRR925702 2 0 1 0 1
#> SRR925703 2 0 1 0 1
#> SRR925704 2 0 1 0 1
#> SRR925705 2 0 1 0 1
#> SRR925706 2 0 1 0 1
#> SRR925708 2 0 1 0 1
#> SRR925707 2 0 1 0 1
#> SRR925709 2 0 1 0 1
#> SRR925710 2 0 1 0 1
#> SRR925711 1 0 0 1 0
#> SRR934631 2 0 1 0 1
#> SRR934632 2 0 1 0 1
#> SRR934633 2 0 1 0 1
#> SRR925713 2 0 1 0 1
#> SRR925714 2 0 1 0 1
#> SRR925715 2 0 1 0 1
#> SRR925716 2 0 1 0 1
#> SRR925717 2 0 1 0 1
#> SRR925718 2 0 1 0 1
#> SRR925719 2 0 1 0 1
#> SRR925720 2 0 1 0 1
#> SRR925721 2 0 1 0 1
#> SRR934634 2 0 1 0 1
#> SRR934635 2 0 1 0 1
#> SRR925723 2 0 1 0 1
#> SRR925724 2 0 1 0 1
#> SRR925725 2 0 1 0 1
#> SRR925726 2 0 1 0 1
#> SRR925727 2 0 1 0 1
#> SRR925728 2 0 1 0 1
#> SRR934636 2 0 1 0 1
#> SRR934637 2 0 1 0 1
#> SRR925729 2 0 1 0 1
#> SRR934638 2 0 1 0 1
#> SRR934639 2 0 1 0 1
#> SRR934640 2 0 1 0 1
#> SRR934641 2 0 1 0 1
#> SRR925734 2 0 1 0 1
#> SRR925735 2 0 1 0 1
#> SRR925736 2 0 1 0 1
#> SRR925737 2 0 1 0 1
#> SRR934642 2 0 1 0 1
#> SRR934643 2 0 1 0 1
#> SRR934644 2 0 1 0 1
#> SRR934645 2 0 1 0 1
#> SRR925740 2 0 1 0 1
#> SRR925741 2 0 1 0 1
#> SRR925742 2 0 1 0 1
#> SRR1283038 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.965 1.000 0.000 0
#> SRR925688 1 0.0000 0.965 1.000 0.000 0
#> SRR925689 1 0.0000 0.965 1.000 0.000 0
#> SRR925690 1 0.0000 0.965 1.000 0.000 0
#> SRR925691 1 0.0000 0.965 1.000 0.000 0
#> SRR925692 1 0.0000 0.965 1.000 0.000 0
#> SRR925693 2 0.0000 0.986 0.000 1.000 0
#> SRR925694 2 0.0592 0.994 0.012 0.988 0
#> SRR925695 2 0.0000 0.986 0.000 1.000 0
#> SRR925697 1 0.0000 0.965 1.000 0.000 0
#> SRR925696 2 0.0592 0.994 0.012 0.988 0
#> SRR925698 2 0.0592 0.994 0.012 0.988 0
#> SRR925699 2 0.0592 0.994 0.012 0.988 0
#> SRR925700 2 0.0592 0.994 0.012 0.988 0
#> SRR925701 2 0.0592 0.994 0.012 0.988 0
#> SRR925702 1 0.1753 0.923 0.952 0.048 0
#> SRR925703 1 0.0000 0.965 1.000 0.000 0
#> SRR925704 2 0.0000 0.986 0.000 1.000 0
#> SRR925705 2 0.0592 0.994 0.012 0.988 0
#> SRR925706 1 0.1753 0.923 0.952 0.048 0
#> SRR925708 1 0.0000 0.965 1.000 0.000 0
#> SRR925707 1 0.1753 0.923 0.952 0.048 0
#> SRR925709 1 0.0000 0.965 1.000 0.000 0
#> SRR925710 1 0.6295 0.107 0.528 0.472 0
#> SRR925711 3 0.0000 0.000 0.000 0.000 1
#> SRR934631 2 0.0592 0.994 0.012 0.988 0
#> SRR934632 2 0.0592 0.994 0.012 0.988 0
#> SRR934633 2 0.0592 0.994 0.012 0.988 0
#> SRR925713 1 0.1411 0.934 0.964 0.036 0
#> SRR925714 1 0.0000 0.965 1.000 0.000 0
#> SRR925715 1 0.1753 0.923 0.952 0.048 0
#> SRR925716 1 0.0000 0.965 1.000 0.000 0
#> SRR925717 1 0.0000 0.965 1.000 0.000 0
#> SRR925718 2 0.0592 0.994 0.012 0.988 0
#> SRR925719 1 0.0000 0.965 1.000 0.000 0
#> SRR925720 1 0.0000 0.965 1.000 0.000 0
#> SRR925721 1 0.0000 0.965 1.000 0.000 0
#> SRR934634 1 0.0000 0.965 1.000 0.000 0
#> SRR934635 1 0.0000 0.965 1.000 0.000 0
#> SRR925723 2 0.0592 0.994 0.012 0.988 0
#> SRR925724 2 0.0000 0.986 0.000 1.000 0
#> SRR925725 2 0.0000 0.986 0.000 1.000 0
#> SRR925726 1 0.0000 0.965 1.000 0.000 0
#> SRR925727 2 0.0000 0.986 0.000 1.000 0
#> SRR925728 1 0.1753 0.923 0.952 0.048 0
#> SRR934636 1 0.0000 0.965 1.000 0.000 0
#> SRR934637 1 0.0000 0.965 1.000 0.000 0
#> SRR925729 2 0.0592 0.994 0.012 0.988 0
#> SRR934638 1 0.0000 0.965 1.000 0.000 0
#> SRR934639 1 0.0000 0.965 1.000 0.000 0
#> SRR934640 2 0.1289 0.971 0.032 0.968 0
#> SRR934641 2 0.1289 0.971 0.032 0.968 0
#> SRR925734 1 0.0000 0.965 1.000 0.000 0
#> SRR925735 2 0.0892 0.986 0.020 0.980 0
#> SRR925736 2 0.0592 0.994 0.012 0.988 0
#> SRR925737 2 0.0592 0.994 0.012 0.988 0
#> SRR934642 2 0.0424 0.992 0.008 0.992 0
#> SRR934643 2 0.0424 0.992 0.008 0.992 0
#> SRR934644 2 0.0592 0.994 0.012 0.988 0
#> SRR934645 2 0.0592 0.994 0.012 0.988 0
#> SRR925740 2 0.0592 0.994 0.012 0.988 0
#> SRR925741 2 0.0000 0.986 0.000 1.000 0
#> SRR925742 2 0.0592 0.994 0.012 0.988 0
#> SRR1283038 2 0.0592 0.994 0.012 0.988 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925688 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925689 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925690 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925691 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925692 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925693 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925694 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925695 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925697 1 0.1118 0.9204 0.964 0 0.000 0.036
#> SRR925696 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925698 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925699 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925700 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925701 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925702 4 0.1474 0.9808 0.052 0 0.000 0.948
#> SRR925703 1 0.1118 0.9204 0.964 0 0.000 0.036
#> SRR925704 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925705 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925706 4 0.1489 0.9952 0.044 0 0.004 0.952
#> SRR925708 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925707 4 0.1489 0.9952 0.044 0 0.004 0.952
#> SRR925709 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925710 1 0.6081 0.0422 0.484 0 0.472 0.044
#> SRR925711 2 0.0000 0.0000 0.000 1 0.000 0.000
#> SRR934631 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR934632 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR934633 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925713 1 0.4972 0.0929 0.544 0 0.000 0.456
#> SRR925714 1 0.1118 0.9204 0.964 0 0.000 0.036
#> SRR925715 4 0.1489 0.9952 0.044 0 0.004 0.952
#> SRR925716 1 0.1118 0.9204 0.964 0 0.000 0.036
#> SRR925717 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925718 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925719 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925720 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925721 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR934634 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR934635 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925723 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925724 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925725 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925726 1 0.0336 0.9360 0.992 0 0.000 0.008
#> SRR925727 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925728 4 0.1489 0.9952 0.044 0 0.004 0.952
#> SRR934636 1 0.1452 0.9138 0.956 0 0.008 0.036
#> SRR934637 1 0.1452 0.9138 0.956 0 0.008 0.036
#> SRR925729 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR934638 1 0.0188 0.9372 0.996 0 0.000 0.004
#> SRR934639 1 0.0188 0.9372 0.996 0 0.000 0.004
#> SRR934640 3 0.0817 0.9750 0.000 0 0.976 0.024
#> SRR934641 3 0.0817 0.9750 0.000 0 0.976 0.024
#> SRR925734 1 0.0000 0.9396 1.000 0 0.000 0.000
#> SRR925735 3 0.0336 0.9882 0.008 0 0.992 0.000
#> SRR925736 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925737 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR934642 3 0.0188 0.9934 0.000 0 0.996 0.004
#> SRR934643 3 0.0188 0.9934 0.000 0 0.996 0.004
#> SRR934644 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR934645 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925740 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR925741 3 0.0469 0.9898 0.000 0 0.988 0.012
#> SRR925742 3 0.0000 0.9951 0.000 0 1.000 0.000
#> SRR1283038 3 0.0000 0.9951 0.000 0 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925688 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925689 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925690 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925691 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925692 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925693 3 0.4908 0.5833 0.356 0 0.608 0.036 0.000
#> SRR925694 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925695 3 0.3224 0.8003 0.160 0 0.824 0.016 0.000
#> SRR925697 5 0.0000 0.9035 0.000 0 0.000 0.000 1.000
#> SRR925696 3 0.0963 0.8898 0.036 0 0.964 0.000 0.000
#> SRR925698 3 0.0898 0.8886 0.020 0 0.972 0.008 0.000
#> SRR925699 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925700 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925701 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925702 4 0.1251 0.7779 0.036 0 0.000 0.956 0.008
#> SRR925703 5 0.0000 0.9035 0.000 0 0.000 0.000 1.000
#> SRR925704 3 0.3224 0.8003 0.160 0 0.824 0.016 0.000
#> SRR925705 3 0.0963 0.8898 0.036 0 0.964 0.000 0.000
#> SRR925706 4 0.0963 0.7857 0.036 0 0.000 0.964 0.000
#> SRR925708 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925707 4 0.0963 0.7857 0.036 0 0.000 0.964 0.000
#> SRR925709 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925710 3 0.7145 -0.0726 0.156 0 0.464 0.044 0.336
#> SRR925711 2 0.0000 0.0000 0.000 1 0.000 0.000 0.000
#> SRR934631 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR934632 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR934633 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925713 4 0.6506 -0.2041 0.200 0 0.000 0.456 0.344
#> SRR925714 5 0.0000 0.9035 0.000 0 0.000 0.000 1.000
#> SRR925715 4 0.0963 0.7857 0.036 0 0.000 0.964 0.000
#> SRR925716 5 0.0000 0.9035 0.000 0 0.000 0.000 1.000
#> SRR925717 1 0.4297 0.8667 0.528 0 0.000 0.000 0.472
#> SRR925718 3 0.0794 0.8916 0.028 0 0.972 0.000 0.000
#> SRR925719 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925720 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925721 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR934634 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR934635 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925723 3 0.0794 0.8916 0.028 0 0.972 0.000 0.000
#> SRR925724 3 0.5028 0.5266 0.400 0 0.564 0.036 0.000
#> SRR925725 3 0.4908 0.5833 0.356 0 0.608 0.036 0.000
#> SRR925726 5 0.3837 -0.1048 0.308 0 0.000 0.000 0.692
#> SRR925727 3 0.4908 0.5833 0.356 0 0.608 0.036 0.000
#> SRR925728 4 0.0963 0.7857 0.036 0 0.000 0.964 0.000
#> SRR934636 5 0.0290 0.8961 0.000 0 0.008 0.000 0.992
#> SRR934637 5 0.0290 0.8961 0.000 0 0.008 0.000 0.992
#> SRR925729 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR934638 1 0.4350 0.9857 0.588 0 0.000 0.004 0.408
#> SRR934639 1 0.4350 0.9857 0.588 0 0.000 0.004 0.408
#> SRR934640 3 0.0992 0.8844 0.008 0 0.968 0.024 0.000
#> SRR934641 3 0.0992 0.8844 0.008 0 0.968 0.024 0.000
#> SRR925734 1 0.4201 0.9915 0.592 0 0.000 0.000 0.408
#> SRR925735 3 0.0510 0.8913 0.016 0 0.984 0.000 0.000
#> SRR925736 3 0.0963 0.8898 0.036 0 0.964 0.000 0.000
#> SRR925737 3 0.0963 0.8898 0.036 0 0.964 0.000 0.000
#> SRR934642 3 0.2377 0.8485 0.128 0 0.872 0.000 0.000
#> SRR934643 3 0.2377 0.8485 0.128 0 0.872 0.000 0.000
#> SRR934644 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR934645 3 0.0290 0.8937 0.008 0 0.992 0.000 0.000
#> SRR925740 3 0.0794 0.8916 0.028 0 0.972 0.000 0.000
#> SRR925741 3 0.3224 0.8003 0.160 0 0.824 0.016 0.000
#> SRR925742 3 0.0794 0.8916 0.028 0 0.972 0.000 0.000
#> SRR1283038 3 0.0898 0.8886 0.020 0 0.972 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925688 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925689 1 0.0146 0.958 0.996 0.004 0.000 0.000 0.000 0
#> SRR925690 1 0.0146 0.958 0.996 0.004 0.000 0.000 0.000 0
#> SRR925691 1 0.0146 0.958 0.996 0.004 0.000 0.000 0.000 0
#> SRR925692 1 0.0146 0.958 0.996 0.004 0.000 0.000 0.000 0
#> SRR925693 3 0.3817 0.115 0.000 0.432 0.568 0.000 0.000 0
#> SRR925694 3 0.0146 0.800 0.000 0.004 0.996 0.000 0.000 0
#> SRR925695 3 0.2823 0.629 0.000 0.204 0.796 0.000 0.000 0
#> SRR925697 5 0.2300 0.806 0.144 0.000 0.000 0.000 0.856 0
#> SRR925696 3 0.2340 0.727 0.000 0.148 0.852 0.000 0.000 0
#> SRR925698 3 0.1007 0.789 0.000 0.044 0.956 0.000 0.000 0
#> SRR925699 3 0.0000 0.800 0.000 0.000 1.000 0.000 0.000 0
#> SRR925700 3 0.0000 0.800 0.000 0.000 1.000 0.000 0.000 0
#> SRR925701 3 0.0000 0.800 0.000 0.000 1.000 0.000 0.000 0
#> SRR925702 4 0.0260 0.945 0.008 0.000 0.000 0.992 0.000 0
#> SRR925703 5 0.2300 0.806 0.144 0.000 0.000 0.000 0.856 0
#> SRR925704 3 0.2823 0.629 0.000 0.204 0.796 0.000 0.000 0
#> SRR925705 3 0.2340 0.727 0.000 0.148 0.852 0.000 0.000 0
#> SRR925706 4 0.0146 0.952 0.000 0.004 0.000 0.996 0.000 0
#> SRR925708 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925707 4 0.0000 0.951 0.000 0.000 0.000 1.000 0.000 0
#> SRR925709 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925710 3 0.5278 -0.119 0.464 0.048 0.468 0.004 0.016 0
#> SRR925711 6 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 1
#> SRR934631 3 0.0260 0.800 0.000 0.008 0.992 0.000 0.000 0
#> SRR934632 3 0.0260 0.800 0.000 0.008 0.992 0.000 0.000 0
#> SRR934633 3 0.0260 0.800 0.000 0.008 0.992 0.000 0.000 0
#> SRR925713 1 0.4250 0.160 0.528 0.000 0.000 0.456 0.016 0
#> SRR925714 5 0.2300 0.806 0.144 0.000 0.000 0.000 0.856 0
#> SRR925715 4 0.2562 0.812 0.000 0.172 0.000 0.828 0.000 0
#> SRR925716 5 0.2300 0.806 0.144 0.000 0.000 0.000 0.856 0
#> SRR925717 1 0.1858 0.849 0.904 0.004 0.000 0.000 0.092 0
#> SRR925718 3 0.1765 0.768 0.000 0.096 0.904 0.000 0.000 0
#> SRR925719 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925720 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925721 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR934634 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR934635 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925723 3 0.1765 0.768 0.000 0.096 0.904 0.000 0.000 0
#> SRR925724 2 0.2664 0.000 0.000 0.816 0.184 0.000 0.000 0
#> SRR925725 3 0.3817 0.115 0.000 0.432 0.568 0.000 0.000 0
#> SRR925726 5 0.3996 0.239 0.484 0.004 0.000 0.000 0.512 0
#> SRR925727 3 0.3810 0.115 0.000 0.428 0.572 0.000 0.000 0
#> SRR925728 4 0.0146 0.952 0.000 0.004 0.000 0.996 0.000 0
#> SRR934636 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000 0
#> SRR934637 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000 0
#> SRR925729 3 0.0146 0.800 0.000 0.004 0.996 0.000 0.000 0
#> SRR934638 1 0.0146 0.957 0.996 0.000 0.000 0.004 0.000 0
#> SRR934639 1 0.0146 0.957 0.996 0.000 0.000 0.004 0.000 0
#> SRR934640 3 0.0713 0.790 0.000 0.028 0.972 0.000 0.000 0
#> SRR934641 3 0.0713 0.790 0.000 0.028 0.972 0.000 0.000 0
#> SRR925734 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0
#> SRR925735 3 0.0405 0.798 0.004 0.008 0.988 0.000 0.000 0
#> SRR925736 3 0.2178 0.742 0.000 0.132 0.868 0.000 0.000 0
#> SRR925737 3 0.2178 0.742 0.000 0.132 0.868 0.000 0.000 0
#> SRR934642 3 0.3867 -0.154 0.000 0.488 0.512 0.000 0.000 0
#> SRR934643 3 0.3867 -0.154 0.000 0.488 0.512 0.000 0.000 0
#> SRR934644 3 0.0146 0.800 0.000 0.004 0.996 0.000 0.000 0
#> SRR934645 3 0.0146 0.800 0.000 0.004 0.996 0.000 0.000 0
#> SRR925740 3 0.1714 0.771 0.000 0.092 0.908 0.000 0.000 0
#> SRR925741 3 0.2854 0.628 0.000 0.208 0.792 0.000 0.000 0
#> SRR925742 3 0.1714 0.771 0.000 0.092 0.908 0.000 0.000 0
#> SRR1283038 3 0.1007 0.789 0.000 0.044 0.956 0.000 0.000 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 15949 rows and 64 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 1.000 0.994 0.965 0.4766 0.492 0.492
#> 3 3 0.681 0.769 0.869 0.2624 0.970 0.940
#> 4 4 0.574 0.629 0.716 0.1292 0.886 0.757
#> 5 5 0.567 0.566 0.721 0.0838 0.845 0.587
#> 6 6 0.577 0.564 0.702 0.0506 0.986 0.939
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
#> SRR925687 1 0.0000 0.995 1.000 0.000
#> SRR925688 1 0.0000 0.995 1.000 0.000
#> SRR925689 1 0.0000 0.995 1.000 0.000
#> SRR925690 1 0.0000 0.995 1.000 0.000
#> SRR925691 1 0.0000 0.995 1.000 0.000
#> SRR925692 1 0.0000 0.995 1.000 0.000
#> SRR925693 2 0.3584 0.997 0.068 0.932
#> SRR925694 2 0.3733 0.997 0.072 0.928
#> SRR925695 2 0.3584 0.997 0.068 0.932
#> SRR925697 1 0.0376 0.994 0.996 0.004
#> SRR925696 2 0.3431 0.995 0.064 0.936
#> SRR925698 2 0.3584 0.997 0.068 0.932
#> SRR925699 2 0.3733 0.997 0.072 0.928
#> SRR925700 2 0.3733 0.997 0.072 0.928
#> SRR925701 2 0.3733 0.997 0.072 0.928
#> SRR925702 1 0.0376 0.994 0.996 0.004
#> SRR925703 1 0.0376 0.994 0.996 0.004
#> SRR925704 2 0.3584 0.997 0.068 0.932
#> SRR925705 2 0.3584 0.997 0.068 0.932
#> SRR925706 1 0.0376 0.994 0.996 0.004
#> SRR925708 1 0.0000 0.995 1.000 0.000
#> SRR925707 1 0.2043 0.967 0.968 0.032
#> SRR925709 1 0.0000 0.995 1.000 0.000
#> SRR925710 1 0.0000 0.995 1.000 0.000
#> SRR925711 1 0.3879 0.927 0.924 0.076
#> SRR934631 2 0.3733 0.997 0.072 0.928
#> SRR934632 2 0.3733 0.997 0.072 0.928
#> SRR934633 2 0.3733 0.997 0.072 0.928
#> SRR925713 1 0.0376 0.994 0.996 0.004
#> SRR925714 1 0.0376 0.994 0.996 0.004
#> SRR925715 1 0.0376 0.994 0.996 0.004
#> SRR925716 1 0.0376 0.994 0.996 0.004
#> SRR925717 1 0.0376 0.994 0.996 0.004
#> SRR925718 2 0.3733 0.997 0.072 0.928
#> SRR925719 1 0.0000 0.995 1.000 0.000
#> SRR925720 1 0.0000 0.995 1.000 0.000
#> SRR925721 1 0.0000 0.995 1.000 0.000
#> SRR934634 1 0.0000 0.995 1.000 0.000
#> SRR934635 1 0.0000 0.995 1.000 0.000
#> SRR925723 2 0.3584 0.997 0.068 0.932
#> SRR925724 2 0.3431 0.995 0.064 0.936
#> SRR925725 2 0.3584 0.997 0.068 0.932
#> SRR925726 1 0.0376 0.994 0.996 0.004
#> SRR925727 2 0.3584 0.997 0.068 0.932
#> SRR925728 1 0.0376 0.994 0.996 0.004
#> SRR934636 1 0.0376 0.994 0.996 0.004
#> SRR934637 1 0.0376 0.994 0.996 0.004
#> SRR925729 2 0.3733 0.997 0.072 0.928
#> SRR934638 1 0.0000 0.995 1.000 0.000
#> SRR934639 1 0.0000 0.995 1.000 0.000
#> SRR934640 2 0.3733 0.997 0.072 0.928
#> SRR934641 2 0.3733 0.997 0.072 0.928
#> SRR925734 1 0.0000 0.995 1.000 0.000
#> SRR925735 2 0.3733 0.997 0.072 0.928
#> SRR925736 2 0.3584 0.997 0.068 0.932
#> SRR925737 2 0.3584 0.997 0.068 0.932
#> SRR934642 2 0.3584 0.995 0.068 0.932
#> SRR934643 2 0.3584 0.995 0.068 0.932
#> SRR934644 2 0.3733 0.997 0.072 0.928
#> SRR934645 2 0.3733 0.997 0.072 0.928
#> SRR925740 2 0.3584 0.997 0.068 0.932
#> SRR925741 2 0.3584 0.997 0.068 0.932
#> SRR925742 2 0.3584 0.997 0.068 0.932
#> SRR1283038 2 0.3584 0.997 0.068 0.932
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.1163 0.786 0.972 0.000 0.028
#> SRR925688 1 0.1289 0.786 0.968 0.000 0.032
#> SRR925689 1 0.1753 0.785 0.952 0.000 0.048
#> SRR925690 1 0.1753 0.785 0.952 0.000 0.048
#> SRR925691 1 0.1753 0.785 0.952 0.000 0.048
#> SRR925692 1 0.1753 0.785 0.952 0.000 0.048
#> SRR925693 2 0.4121 0.872 0.000 0.832 0.168
#> SRR925694 2 0.1411 0.899 0.000 0.964 0.036
#> SRR925695 2 0.2537 0.909 0.000 0.920 0.080
#> SRR925697 1 0.5591 0.564 0.696 0.000 0.304
#> SRR925696 2 0.4555 0.872 0.000 0.800 0.200
#> SRR925698 2 0.3482 0.903 0.000 0.872 0.128
#> SRR925699 2 0.2165 0.906 0.000 0.936 0.064
#> SRR925700 2 0.2261 0.906 0.000 0.932 0.068
#> SRR925701 2 0.2356 0.906 0.000 0.928 0.072
#> SRR925702 1 0.5529 0.542 0.704 0.000 0.296
#> SRR925703 1 0.5591 0.564 0.696 0.000 0.304
#> SRR925704 2 0.2261 0.907 0.000 0.932 0.068
#> SRR925705 2 0.4346 0.881 0.000 0.816 0.184
#> SRR925706 1 0.6225 0.156 0.568 0.000 0.432
#> SRR925708 1 0.1163 0.787 0.972 0.000 0.028
#> SRR925707 3 0.9228 0.349 0.380 0.156 0.464
#> SRR925709 1 0.3116 0.733 0.892 0.000 0.108
#> SRR925710 1 0.4095 0.707 0.880 0.056 0.064
#> SRR925711 3 0.3987 0.545 0.108 0.020 0.872
#> SRR934631 2 0.1289 0.900 0.000 0.968 0.032
#> SRR934632 2 0.1289 0.900 0.000 0.968 0.032
#> SRR934633 2 0.1289 0.900 0.000 0.968 0.032
#> SRR925713 1 0.5497 0.546 0.708 0.000 0.292
#> SRR925714 1 0.5591 0.564 0.696 0.000 0.304
#> SRR925715 1 0.6095 0.226 0.608 0.000 0.392
#> SRR925716 1 0.5497 0.574 0.708 0.000 0.292
#> SRR925717 1 0.1860 0.784 0.948 0.000 0.052
#> SRR925718 2 0.2537 0.906 0.000 0.920 0.080
#> SRR925719 1 0.1289 0.788 0.968 0.000 0.032
#> SRR925720 1 0.1163 0.786 0.972 0.000 0.028
#> SRR925721 1 0.1163 0.786 0.972 0.000 0.028
#> SRR934634 1 0.1163 0.786 0.972 0.000 0.028
#> SRR934635 1 0.1163 0.786 0.972 0.000 0.028
#> SRR925723 2 0.3482 0.903 0.000 0.872 0.128
#> SRR925724 2 0.5058 0.840 0.000 0.756 0.244
#> SRR925725 2 0.3816 0.882 0.000 0.852 0.148
#> SRR925726 1 0.1643 0.785 0.956 0.000 0.044
#> SRR925727 2 0.4291 0.880 0.000 0.820 0.180
#> SRR925728 1 0.6215 0.171 0.572 0.000 0.428
#> SRR934636 1 0.5529 0.572 0.704 0.000 0.296
#> SRR934637 1 0.5529 0.572 0.704 0.000 0.296
#> SRR925729 2 0.1411 0.899 0.000 0.964 0.036
#> SRR934638 1 0.0424 0.790 0.992 0.000 0.008
#> SRR934639 1 0.0424 0.790 0.992 0.000 0.008
#> SRR934640 2 0.3816 0.803 0.000 0.852 0.148
#> SRR934641 2 0.3816 0.803 0.000 0.852 0.148
#> SRR925734 1 0.1289 0.786 0.968 0.000 0.032
#> SRR925735 2 0.1860 0.908 0.000 0.948 0.052
#> SRR925736 2 0.4062 0.889 0.000 0.836 0.164
#> SRR925737 2 0.4062 0.889 0.000 0.836 0.164
#> SRR934642 2 0.3941 0.882 0.000 0.844 0.156
#> SRR934643 2 0.3941 0.882 0.000 0.844 0.156
#> SRR934644 2 0.1411 0.899 0.000 0.964 0.036
#> SRR934645 2 0.1411 0.899 0.000 0.964 0.036
#> SRR925740 2 0.3192 0.907 0.000 0.888 0.112
#> SRR925741 2 0.2261 0.906 0.000 0.932 0.068
#> SRR925742 2 0.2959 0.909 0.000 0.900 0.100
#> SRR1283038 2 0.1753 0.909 0.000 0.952 0.048
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.5229 0.8107 0.564 0.008 0.000 0.428
#> SRR925688 1 0.5229 0.8107 0.564 0.008 0.000 0.428
#> SRR925689 1 0.5917 0.6957 0.520 0.036 0.000 0.444
#> SRR925690 1 0.5917 0.6957 0.520 0.036 0.000 0.444
#> SRR925691 1 0.5917 0.6957 0.520 0.036 0.000 0.444
#> SRR925692 1 0.5917 0.6957 0.520 0.036 0.000 0.444
#> SRR925693 3 0.3479 0.7175 0.012 0.148 0.840 0.000
#> SRR925694 3 0.4564 0.7153 0.000 0.328 0.672 0.000
#> SRR925695 3 0.0672 0.7669 0.008 0.008 0.984 0.000
#> SRR925697 4 0.0000 0.6111 0.000 0.000 0.000 1.000
#> SRR925696 3 0.4088 0.7173 0.004 0.232 0.764 0.000
#> SRR925698 3 0.2921 0.7553 0.000 0.140 0.860 0.000
#> SRR925699 3 0.4830 0.7411 0.000 0.392 0.608 0.000
#> SRR925700 3 0.4830 0.7411 0.000 0.392 0.608 0.000
#> SRR925701 3 0.4843 0.7396 0.000 0.396 0.604 0.000
#> SRR925702 4 0.6391 0.4222 0.328 0.084 0.000 0.588
#> SRR925703 4 0.0000 0.6111 0.000 0.000 0.000 1.000
#> SRR925704 3 0.1452 0.7606 0.008 0.036 0.956 0.000
#> SRR925705 3 0.3801 0.7276 0.000 0.220 0.780 0.000
#> SRR925706 4 0.7193 0.3210 0.340 0.152 0.000 0.508
#> SRR925708 1 0.5088 0.8110 0.572 0.004 0.000 0.424
#> SRR925707 4 0.8464 -0.0216 0.288 0.212 0.040 0.460
#> SRR925709 1 0.5677 0.6051 0.628 0.040 0.000 0.332
#> SRR925710 1 0.8057 0.4572 0.532 0.096 0.076 0.296
#> SRR925711 2 0.8131 0.0000 0.340 0.428 0.016 0.216
#> SRR934631 3 0.4134 0.7359 0.000 0.260 0.740 0.000
#> SRR934632 3 0.4134 0.7359 0.000 0.260 0.740 0.000
#> SRR934633 3 0.4134 0.7359 0.000 0.260 0.740 0.000
#> SRR925713 4 0.6394 0.4276 0.316 0.088 0.000 0.596
#> SRR925714 4 0.0000 0.6111 0.000 0.000 0.000 1.000
#> SRR925715 1 0.7526 -0.3097 0.468 0.200 0.000 0.332
#> SRR925716 4 0.0188 0.6078 0.004 0.000 0.000 0.996
#> SRR925717 4 0.5366 -0.6810 0.440 0.012 0.000 0.548
#> SRR925718 3 0.4500 0.7573 0.000 0.316 0.684 0.000
#> SRR925719 1 0.5097 0.8092 0.568 0.004 0.000 0.428
#> SRR925720 1 0.5229 0.8107 0.564 0.008 0.000 0.428
#> SRR925721 1 0.5229 0.8107 0.564 0.008 0.000 0.428
#> SRR934634 1 0.5220 0.8111 0.568 0.008 0.000 0.424
#> SRR934635 1 0.5220 0.8111 0.568 0.008 0.000 0.424
#> SRR925723 3 0.2921 0.7553 0.000 0.140 0.860 0.000
#> SRR925724 3 0.4831 0.6743 0.016 0.280 0.704 0.000
#> SRR925725 3 0.2988 0.7303 0.012 0.112 0.876 0.000
#> SRR925726 4 0.3893 0.1376 0.196 0.008 0.000 0.796
#> SRR925727 3 0.3105 0.7427 0.012 0.120 0.868 0.000
#> SRR925728 4 0.7122 0.3406 0.340 0.144 0.000 0.516
#> SRR934636 4 0.0937 0.6065 0.012 0.012 0.000 0.976
#> SRR934637 4 0.0937 0.6065 0.012 0.012 0.000 0.976
#> SRR925729 3 0.4564 0.7153 0.000 0.328 0.672 0.000
#> SRR934638 1 0.5250 0.7889 0.552 0.008 0.000 0.440
#> SRR934639 1 0.5250 0.7889 0.552 0.008 0.000 0.440
#> SRR934640 3 0.6648 0.5620 0.092 0.372 0.536 0.000
#> SRR934641 3 0.6648 0.5620 0.092 0.372 0.536 0.000
#> SRR925734 1 0.5203 0.8104 0.576 0.008 0.000 0.416
#> SRR925735 3 0.3448 0.7555 0.004 0.168 0.828 0.000
#> SRR925736 3 0.4343 0.7381 0.004 0.264 0.732 0.000
#> SRR925737 3 0.4343 0.7381 0.004 0.264 0.732 0.000
#> SRR934642 3 0.5217 0.7170 0.012 0.380 0.608 0.000
#> SRR934643 3 0.5217 0.7170 0.012 0.380 0.608 0.000
#> SRR934644 3 0.4477 0.7174 0.000 0.312 0.688 0.000
#> SRR934645 3 0.4477 0.7174 0.000 0.312 0.688 0.000
#> SRR925740 3 0.3649 0.7511 0.000 0.204 0.796 0.000
#> SRR925741 3 0.2473 0.7535 0.012 0.080 0.908 0.000
#> SRR925742 3 0.3356 0.7617 0.000 0.176 0.824 0.000
#> SRR1283038 3 0.1716 0.7605 0.000 0.064 0.936 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.0693 0.7753 0.980 0.008 0.000 0.000 0.012
#> SRR925688 1 0.0693 0.7753 0.980 0.008 0.000 0.000 0.012
#> SRR925689 1 0.5953 0.5647 0.660 0.048 0.000 0.088 0.204
#> SRR925690 1 0.5953 0.5647 0.660 0.048 0.000 0.088 0.204
#> SRR925691 1 0.5953 0.5647 0.660 0.048 0.000 0.088 0.204
#> SRR925692 1 0.5953 0.5647 0.660 0.048 0.000 0.088 0.204
#> SRR925693 3 0.5374 0.1442 0.000 0.376 0.568 0.004 0.052
#> SRR925694 2 0.4132 0.7298 0.000 0.720 0.260 0.000 0.020
#> SRR925695 3 0.5071 0.2307 0.000 0.340 0.616 0.004 0.040
#> SRR925697 5 0.6534 0.9421 0.328 0.000 0.000 0.212 0.460
#> SRR925696 3 0.0451 0.5617 0.000 0.000 0.988 0.004 0.008
#> SRR925698 3 0.2573 0.5679 0.000 0.104 0.880 0.000 0.016
#> SRR925699 3 0.4894 -0.0139 0.000 0.456 0.520 0.000 0.024
#> SRR925700 3 0.4894 -0.0139 0.000 0.456 0.520 0.000 0.024
#> SRR925701 3 0.4833 0.1103 0.000 0.412 0.564 0.000 0.024
#> SRR925702 4 0.3707 0.6116 0.284 0.000 0.000 0.716 0.000
#> SRR925703 5 0.6534 0.9421 0.328 0.000 0.000 0.212 0.460
#> SRR925704 3 0.5185 0.1355 0.000 0.376 0.580 0.004 0.040
#> SRR925705 3 0.0451 0.5617 0.000 0.000 0.988 0.004 0.008
#> SRR925706 4 0.2629 0.7346 0.136 0.004 0.000 0.860 0.000
#> SRR925708 1 0.0566 0.7770 0.984 0.000 0.000 0.012 0.004
#> SRR925707 4 0.3282 0.7059 0.092 0.024 0.024 0.860 0.000
#> SRR925709 1 0.4165 0.3859 0.672 0.000 0.000 0.320 0.008
#> SRR925710 1 0.6768 0.1392 0.544 0.132 0.004 0.288 0.032
#> SRR925711 4 0.6620 0.3546 0.000 0.160 0.016 0.512 0.312
#> SRR934631 2 0.4400 0.6909 0.000 0.672 0.308 0.000 0.020
#> SRR934632 2 0.4400 0.6909 0.000 0.672 0.308 0.000 0.020
#> SRR934633 2 0.4400 0.6909 0.000 0.672 0.308 0.000 0.020
#> SRR925713 4 0.3730 0.6097 0.288 0.000 0.000 0.712 0.000
#> SRR925714 5 0.6534 0.9421 0.328 0.000 0.000 0.212 0.460
#> SRR925715 4 0.5865 0.6507 0.228 0.056 0.000 0.656 0.060
#> SRR925716 5 0.6503 0.9406 0.332 0.000 0.000 0.204 0.464
#> SRR925717 1 0.4216 0.6345 0.780 0.000 0.000 0.120 0.100
#> SRR925718 3 0.4193 0.3617 0.000 0.304 0.684 0.000 0.012
#> SRR925719 1 0.0566 0.7761 0.984 0.000 0.000 0.012 0.004
#> SRR925720 1 0.0579 0.7760 0.984 0.008 0.000 0.000 0.008
#> SRR925721 1 0.0579 0.7760 0.984 0.008 0.000 0.000 0.008
#> SRR934634 1 0.0854 0.7764 0.976 0.008 0.000 0.004 0.012
#> SRR934635 1 0.0854 0.7764 0.976 0.008 0.000 0.004 0.012
#> SRR925723 3 0.2304 0.5715 0.000 0.100 0.892 0.000 0.008
#> SRR925724 3 0.1954 0.5382 0.000 0.032 0.932 0.008 0.028
#> SRR925725 3 0.5341 0.1606 0.000 0.364 0.580 0.004 0.052
#> SRR925726 5 0.6160 0.7732 0.420 0.000 0.000 0.132 0.448
#> SRR925727 3 0.4615 0.4329 0.000 0.220 0.724 0.004 0.052
#> SRR925728 4 0.2629 0.7346 0.136 0.004 0.000 0.860 0.000
#> SRR934636 5 0.6586 0.9192 0.300 0.004 0.000 0.208 0.488
#> SRR934637 5 0.6586 0.9192 0.300 0.004 0.000 0.208 0.488
#> SRR925729 2 0.4132 0.7298 0.000 0.720 0.260 0.000 0.020
#> SRR934638 1 0.2949 0.7470 0.880 0.012 0.000 0.072 0.036
#> SRR934639 1 0.2949 0.7470 0.880 0.012 0.000 0.072 0.036
#> SRR934640 2 0.7112 0.4309 0.000 0.544 0.212 0.180 0.064
#> SRR934641 2 0.7112 0.4309 0.000 0.544 0.212 0.180 0.064
#> SRR925734 1 0.0693 0.7793 0.980 0.000 0.000 0.012 0.008
#> SRR925735 2 0.4974 0.1547 0.000 0.508 0.464 0.000 0.028
#> SRR925736 3 0.2835 0.5623 0.000 0.112 0.868 0.004 0.016
#> SRR925737 3 0.2835 0.5623 0.000 0.112 0.868 0.004 0.016
#> SRR934642 3 0.3777 0.4242 0.000 0.192 0.784 0.004 0.020
#> SRR934643 3 0.3777 0.4242 0.000 0.192 0.784 0.004 0.020
#> SRR934644 2 0.4132 0.7298 0.000 0.720 0.260 0.000 0.020
#> SRR934645 2 0.4132 0.7298 0.000 0.720 0.260 0.000 0.020
#> SRR925740 3 0.2124 0.5771 0.000 0.096 0.900 0.000 0.004
#> SRR925741 3 0.5199 0.0346 0.000 0.412 0.548 0.004 0.036
#> SRR925742 3 0.2707 0.5652 0.000 0.132 0.860 0.000 0.008
#> SRR1283038 3 0.4866 0.0677 0.000 0.392 0.580 0.000 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.0405 0.7867 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR925688 1 0.0405 0.7867 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR925689 1 0.7180 0.5531 0.556 0.120 0.000 0.088 0.156 0.080
#> SRR925690 1 0.7180 0.5531 0.556 0.120 0.000 0.088 0.156 0.080
#> SRR925691 1 0.7180 0.5531 0.556 0.120 0.000 0.088 0.156 0.080
#> SRR925692 1 0.7180 0.5531 0.556 0.120 0.000 0.088 0.156 0.080
#> SRR925693 3 0.5841 0.1060 0.000 0.328 0.488 0.000 0.004 0.180
#> SRR925694 2 0.4097 0.7036 0.000 0.712 0.256 0.016 0.008 0.008
#> SRR925695 3 0.5318 0.2460 0.000 0.272 0.580 0.000 0.000 0.148
#> SRR925697 5 0.3202 0.9496 0.176 0.000 0.000 0.024 0.800 0.000
#> SRR925696 3 0.2322 0.5534 0.000 0.016 0.912 0.024 0.016 0.032
#> SRR925698 3 0.2724 0.5430 0.000 0.052 0.864 0.000 0.000 0.084
#> SRR925699 3 0.4932 0.0702 0.000 0.392 0.560 0.008 0.028 0.012
#> SRR925700 3 0.4932 0.0702 0.000 0.392 0.560 0.008 0.028 0.012
#> SRR925701 3 0.4786 0.1220 0.000 0.368 0.588 0.008 0.028 0.008
#> SRR925702 4 0.4200 0.7509 0.140 0.000 0.000 0.740 0.120 0.000
#> SRR925703 5 0.3202 0.9496 0.176 0.000 0.000 0.024 0.800 0.000
#> SRR925704 3 0.5382 0.2133 0.000 0.288 0.564 0.000 0.000 0.148
#> SRR925705 3 0.1994 0.5588 0.000 0.016 0.928 0.016 0.016 0.024
#> SRR925706 4 0.3368 0.7483 0.060 0.000 0.000 0.820 0.116 0.004
#> SRR925708 1 0.0603 0.7863 0.980 0.000 0.000 0.016 0.004 0.000
#> SRR925707 4 0.3514 0.7221 0.040 0.008 0.008 0.828 0.112 0.004
#> SRR925709 1 0.3982 -0.0650 0.536 0.000 0.000 0.460 0.004 0.000
#> SRR925710 4 0.6895 0.3416 0.336 0.172 0.008 0.440 0.028 0.016
#> SRR925711 6 0.4448 0.0000 0.000 0.008 0.000 0.252 0.052 0.688
#> SRR934631 2 0.4675 0.6543 0.000 0.628 0.324 0.008 0.004 0.036
#> SRR934632 2 0.4675 0.6543 0.000 0.628 0.324 0.008 0.004 0.036
#> SRR934633 2 0.4675 0.6543 0.000 0.628 0.324 0.008 0.004 0.036
#> SRR925713 4 0.4203 0.7492 0.136 0.000 0.000 0.740 0.124 0.000
#> SRR925714 5 0.3202 0.9496 0.176 0.000 0.000 0.024 0.800 0.000
#> SRR925715 4 0.4870 0.6347 0.124 0.076 0.000 0.744 0.036 0.020
#> SRR925716 5 0.3156 0.9484 0.180 0.000 0.000 0.020 0.800 0.000
#> SRR925717 1 0.5344 0.5096 0.644 0.020 0.000 0.144 0.192 0.000
#> SRR925718 3 0.3517 0.4220 0.000 0.204 0.772 0.000 0.012 0.012
#> SRR925719 1 0.0777 0.7835 0.972 0.000 0.000 0.024 0.004 0.000
#> SRR925720 1 0.0520 0.7870 0.984 0.000 0.000 0.008 0.000 0.008
#> SRR925721 1 0.0520 0.7870 0.984 0.000 0.000 0.008 0.000 0.008
#> SRR934634 1 0.0405 0.7871 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR934635 1 0.0405 0.7871 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR925723 3 0.1464 0.5670 0.000 0.036 0.944 0.000 0.004 0.016
#> SRR925724 3 0.3738 0.5334 0.000 0.032 0.820 0.020 0.020 0.108
#> SRR925725 3 0.5874 0.1663 0.000 0.300 0.516 0.004 0.004 0.176
#> SRR925726 5 0.3745 0.8787 0.240 0.000 0.000 0.028 0.732 0.000
#> SRR925727 3 0.4798 0.4089 0.000 0.156 0.672 0.000 0.000 0.172
#> SRR925728 4 0.3368 0.7483 0.060 0.000 0.000 0.820 0.116 0.004
#> SRR934636 5 0.4192 0.9107 0.172 0.028 0.000 0.012 0.764 0.024
#> SRR934637 5 0.4192 0.9107 0.172 0.028 0.000 0.012 0.764 0.024
#> SRR925729 2 0.4097 0.7036 0.000 0.712 0.256 0.016 0.008 0.008
#> SRR934638 1 0.3938 0.7400 0.820 0.052 0.000 0.072 0.032 0.024
#> SRR934639 1 0.3938 0.7400 0.820 0.052 0.000 0.072 0.032 0.024
#> SRR934640 2 0.6800 0.4312 0.000 0.548 0.168 0.192 0.024 0.068
#> SRR934641 2 0.6800 0.4312 0.000 0.548 0.168 0.192 0.024 0.068
#> SRR925734 1 0.0363 0.7881 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR925735 2 0.5725 0.1461 0.000 0.464 0.432 0.016 0.008 0.080
#> SRR925736 3 0.2945 0.5491 0.000 0.040 0.880 0.016 0.032 0.032
#> SRR925737 3 0.2945 0.5491 0.000 0.040 0.880 0.016 0.032 0.032
#> SRR934642 3 0.5205 0.4236 0.000 0.164 0.708 0.020 0.044 0.064
#> SRR934643 3 0.5205 0.4236 0.000 0.164 0.708 0.020 0.044 0.064
#> SRR934644 2 0.3767 0.7097 0.000 0.720 0.260 0.016 0.004 0.000
#> SRR934645 2 0.3767 0.7097 0.000 0.720 0.260 0.016 0.004 0.000
#> SRR925740 3 0.1837 0.5695 0.000 0.032 0.932 0.004 0.012 0.020
#> SRR925741 3 0.5674 0.0590 0.000 0.356 0.496 0.000 0.004 0.144
#> SRR925742 3 0.2034 0.5658 0.000 0.044 0.920 0.004 0.008 0.024
#> SRR1283038 3 0.5206 0.1636 0.000 0.312 0.572 0.000 0.000 0.116
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 15949 rows and 64 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 1.000 0.998 0.999 0.5084 0.492 0.492
#> 3 3 0.753 0.891 0.893 0.2482 0.877 0.751
#> 4 4 0.729 0.695 0.851 0.1701 0.875 0.669
#> 5 5 0.720 0.548 0.781 0.0651 0.910 0.679
#> 6 6 0.727 0.484 0.751 0.0417 0.906 0.631
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
#> SRR925687 1 0.000 0.998 1.000 0.000
#> SRR925688 1 0.000 0.998 1.000 0.000
#> SRR925689 1 0.000 0.998 1.000 0.000
#> SRR925690 1 0.000 0.998 1.000 0.000
#> SRR925691 1 0.000 0.998 1.000 0.000
#> SRR925692 1 0.000 0.998 1.000 0.000
#> SRR925693 2 0.000 1.000 0.000 1.000
#> SRR925694 2 0.000 1.000 0.000 1.000
#> SRR925695 2 0.000 1.000 0.000 1.000
#> SRR925697 1 0.000 0.998 1.000 0.000
#> SRR925696 2 0.000 1.000 0.000 1.000
#> SRR925698 2 0.000 1.000 0.000 1.000
#> SRR925699 2 0.000 1.000 0.000 1.000
#> SRR925700 2 0.000 1.000 0.000 1.000
#> SRR925701 2 0.000 1.000 0.000 1.000
#> SRR925702 1 0.000 0.998 1.000 0.000
#> SRR925703 1 0.000 0.998 1.000 0.000
#> SRR925704 2 0.000 1.000 0.000 1.000
#> SRR925705 2 0.000 1.000 0.000 1.000
#> SRR925706 1 0.000 0.998 1.000 0.000
#> SRR925708 1 0.000 0.998 1.000 0.000
#> SRR925707 1 0.358 0.927 0.932 0.068
#> SRR925709 1 0.000 0.998 1.000 0.000
#> SRR925710 1 0.000 0.998 1.000 0.000
#> SRR925711 1 0.000 0.998 1.000 0.000
#> SRR934631 2 0.000 1.000 0.000 1.000
#> SRR934632 2 0.000 1.000 0.000 1.000
#> SRR934633 2 0.000 1.000 0.000 1.000
#> SRR925713 1 0.000 0.998 1.000 0.000
#> SRR925714 1 0.000 0.998 1.000 0.000
#> SRR925715 1 0.000 0.998 1.000 0.000
#> SRR925716 1 0.000 0.998 1.000 0.000
#> SRR925717 1 0.000 0.998 1.000 0.000
#> SRR925718 2 0.000 1.000 0.000 1.000
#> SRR925719 1 0.000 0.998 1.000 0.000
#> SRR925720 1 0.000 0.998 1.000 0.000
#> SRR925721 1 0.000 0.998 1.000 0.000
#> SRR934634 1 0.000 0.998 1.000 0.000
#> SRR934635 1 0.000 0.998 1.000 0.000
#> SRR925723 2 0.000 1.000 0.000 1.000
#> SRR925724 2 0.000 1.000 0.000 1.000
#> SRR925725 2 0.000 1.000 0.000 1.000
#> SRR925726 1 0.000 0.998 1.000 0.000
#> SRR925727 2 0.000 1.000 0.000 1.000
#> SRR925728 1 0.000 0.998 1.000 0.000
#> SRR934636 1 0.000 0.998 1.000 0.000
#> SRR934637 1 0.000 0.998 1.000 0.000
#> SRR925729 2 0.000 1.000 0.000 1.000
#> SRR934638 1 0.000 0.998 1.000 0.000
#> SRR934639 1 0.000 0.998 1.000 0.000
#> SRR934640 2 0.000 1.000 0.000 1.000
#> SRR934641 2 0.000 1.000 0.000 1.000
#> SRR925734 1 0.000 0.998 1.000 0.000
#> SRR925735 2 0.000 1.000 0.000 1.000
#> SRR925736 2 0.000 1.000 0.000 1.000
#> SRR925737 2 0.000 1.000 0.000 1.000
#> SRR934642 2 0.000 1.000 0.000 1.000
#> SRR934643 2 0.000 1.000 0.000 1.000
#> SRR934644 2 0.000 1.000 0.000 1.000
#> SRR934645 2 0.000 1.000 0.000 1.000
#> SRR925740 2 0.000 1.000 0.000 1.000
#> SRR925741 2 0.000 1.000 0.000 1.000
#> SRR925742 2 0.000 1.000 0.000 1.000
#> SRR1283038 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925689 1 0.0592 0.953 0.988 0.000 0.012
#> SRR925690 1 0.0592 0.953 0.988 0.000 0.012
#> SRR925691 1 0.0592 0.953 0.988 0.000 0.012
#> SRR925692 1 0.0592 0.953 0.988 0.000 0.012
#> SRR925693 2 0.0892 0.908 0.000 0.980 0.020
#> SRR925694 2 0.4452 0.896 0.000 0.808 0.192
#> SRR925695 2 0.0592 0.910 0.000 0.988 0.012
#> SRR925697 3 0.5216 0.886 0.260 0.000 0.740
#> SRR925696 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925698 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925699 2 0.4346 0.897 0.000 0.816 0.184
#> SRR925700 2 0.4399 0.897 0.000 0.812 0.188
#> SRR925701 2 0.4399 0.897 0.000 0.812 0.188
#> SRR925702 3 0.4842 0.882 0.224 0.000 0.776
#> SRR925703 3 0.5216 0.886 0.260 0.000 0.740
#> SRR925704 2 0.0592 0.910 0.000 0.988 0.012
#> SRR925705 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925706 3 0.4750 0.880 0.216 0.000 0.784
#> SRR925708 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925707 3 0.2056 0.697 0.024 0.024 0.952
#> SRR925709 1 0.1860 0.905 0.948 0.000 0.052
#> SRR925710 1 0.1860 0.907 0.948 0.000 0.052
#> SRR925711 3 0.5331 0.687 0.024 0.184 0.792
#> SRR934631 2 0.4452 0.896 0.000 0.808 0.192
#> SRR934632 2 0.4452 0.896 0.000 0.808 0.192
#> SRR934633 2 0.4452 0.896 0.000 0.808 0.192
#> SRR925713 3 0.4796 0.881 0.220 0.000 0.780
#> SRR925714 3 0.5216 0.886 0.260 0.000 0.740
#> SRR925715 3 0.6168 0.601 0.412 0.000 0.588
#> SRR925716 3 0.5216 0.886 0.260 0.000 0.740
#> SRR925717 1 0.1964 0.909 0.944 0.000 0.056
#> SRR925718 2 0.4399 0.898 0.000 0.812 0.188
#> SRR925719 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.960 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.960 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925723 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925724 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925725 2 0.0747 0.909 0.000 0.984 0.016
#> SRR925726 1 0.5785 0.272 0.668 0.000 0.332
#> SRR925727 2 0.0424 0.909 0.000 0.992 0.008
#> SRR925728 3 0.4750 0.880 0.216 0.000 0.784
#> SRR934636 3 0.5216 0.886 0.260 0.000 0.740
#> SRR934637 3 0.5216 0.886 0.260 0.000 0.740
#> SRR925729 2 0.4452 0.896 0.000 0.808 0.192
#> SRR934638 1 0.0000 0.960 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.960 1.000 0.000 0.000
#> SRR934640 2 0.5098 0.862 0.000 0.752 0.248
#> SRR934641 2 0.5098 0.862 0.000 0.752 0.248
#> SRR925734 1 0.0000 0.960 1.000 0.000 0.000
#> SRR925735 2 0.0892 0.910 0.000 0.980 0.020
#> SRR925736 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925737 2 0.0592 0.909 0.000 0.988 0.012
#> SRR934642 2 0.4399 0.898 0.000 0.812 0.188
#> SRR934643 2 0.4399 0.898 0.000 0.812 0.188
#> SRR934644 2 0.4452 0.896 0.000 0.808 0.192
#> SRR934645 2 0.4452 0.896 0.000 0.808 0.192
#> SRR925740 2 0.0592 0.909 0.000 0.988 0.012
#> SRR925741 2 0.0747 0.910 0.000 0.984 0.016
#> SRR925742 2 0.0747 0.910 0.000 0.984 0.016
#> SRR1283038 2 0.0592 0.910 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR925688 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR925689 1 0.1297 0.93513 0.964 0.016 0.000 0.020
#> SRR925690 1 0.1297 0.93513 0.964 0.016 0.000 0.020
#> SRR925691 1 0.1297 0.93513 0.964 0.016 0.000 0.020
#> SRR925692 1 0.1297 0.93513 0.964 0.016 0.000 0.020
#> SRR925693 3 0.4941 0.13342 0.000 0.436 0.564 0.000
#> SRR925694 2 0.3649 0.82929 0.000 0.796 0.204 0.000
#> SRR925695 3 0.4382 0.38626 0.000 0.296 0.704 0.000
#> SRR925697 4 0.1022 0.85132 0.032 0.000 0.000 0.968
#> SRR925696 3 0.0000 0.67965 0.000 0.000 1.000 0.000
#> SRR925698 3 0.0469 0.67979 0.000 0.012 0.988 0.000
#> SRR925699 3 0.4916 0.14849 0.000 0.424 0.576 0.000
#> SRR925700 3 0.4877 0.18848 0.000 0.408 0.592 0.000
#> SRR925701 3 0.4843 0.20424 0.000 0.396 0.604 0.000
#> SRR925702 4 0.3813 0.84761 0.024 0.148 0.000 0.828
#> SRR925703 4 0.1022 0.85132 0.032 0.000 0.000 0.968
#> SRR925704 3 0.4746 0.22774 0.000 0.368 0.632 0.000
#> SRR925705 3 0.0000 0.67965 0.000 0.000 1.000 0.000
#> SRR925706 4 0.3450 0.84516 0.008 0.156 0.000 0.836
#> SRR925708 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR925707 4 0.3727 0.84063 0.008 0.164 0.004 0.824
#> SRR925709 1 0.3638 0.81575 0.848 0.120 0.000 0.032
#> SRR925710 1 0.5279 0.67668 0.716 0.232 0.000 0.052
#> SRR925711 4 0.4359 0.82743 0.008 0.176 0.020 0.796
#> SRR934631 2 0.3975 0.81826 0.000 0.760 0.240 0.000
#> SRR934632 2 0.3975 0.81826 0.000 0.760 0.240 0.000
#> SRR934633 2 0.3975 0.81826 0.000 0.760 0.240 0.000
#> SRR925713 4 0.3401 0.84599 0.008 0.152 0.000 0.840
#> SRR925714 4 0.1022 0.85132 0.032 0.000 0.000 0.968
#> SRR925715 4 0.7143 0.58998 0.232 0.208 0.000 0.560
#> SRR925716 4 0.1211 0.84891 0.040 0.000 0.000 0.960
#> SRR925717 1 0.4511 0.63856 0.724 0.008 0.000 0.268
#> SRR925718 3 0.3975 0.49852 0.000 0.240 0.760 0.000
#> SRR925719 1 0.0707 0.94068 0.980 0.000 0.000 0.020
#> SRR925720 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR925721 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR934634 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR934635 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR925723 3 0.0336 0.67987 0.000 0.008 0.992 0.000
#> SRR925724 3 0.0336 0.67756 0.000 0.008 0.992 0.000
#> SRR925725 3 0.4761 0.23393 0.000 0.372 0.628 0.000
#> SRR925726 4 0.5277 0.02027 0.460 0.008 0.000 0.532
#> SRR925727 3 0.2589 0.62037 0.000 0.116 0.884 0.000
#> SRR925728 4 0.3450 0.84516 0.008 0.156 0.000 0.836
#> SRR934636 4 0.1118 0.85041 0.036 0.000 0.000 0.964
#> SRR934637 4 0.1118 0.85041 0.036 0.000 0.000 0.964
#> SRR925729 2 0.3649 0.82929 0.000 0.796 0.204 0.000
#> SRR934638 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR934639 1 0.0188 0.94784 0.996 0.000 0.000 0.004
#> SRR934640 2 0.1406 0.63661 0.000 0.960 0.024 0.016
#> SRR934641 2 0.1406 0.63661 0.000 0.960 0.024 0.016
#> SRR925734 1 0.0000 0.94614 1.000 0.000 0.000 0.000
#> SRR925735 2 0.4776 0.44279 0.000 0.624 0.376 0.000
#> SRR925736 3 0.0592 0.67847 0.000 0.016 0.984 0.000
#> SRR925737 3 0.0592 0.67847 0.000 0.016 0.984 0.000
#> SRR934642 3 0.3726 0.52947 0.000 0.212 0.788 0.000
#> SRR934643 3 0.3726 0.52947 0.000 0.212 0.788 0.000
#> SRR934644 2 0.3726 0.83047 0.000 0.788 0.212 0.000
#> SRR934645 2 0.3837 0.82743 0.000 0.776 0.224 0.000
#> SRR925740 3 0.0336 0.68017 0.000 0.008 0.992 0.000
#> SRR925741 3 0.4961 0.00345 0.000 0.448 0.552 0.000
#> SRR925742 3 0.1022 0.67468 0.000 0.032 0.968 0.000
#> SRR1283038 3 0.4843 0.15033 0.000 0.396 0.604 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.0324 0.8814 0.992 0.000 0.000 0.004 0.004
#> SRR925688 1 0.0324 0.8814 0.992 0.000 0.000 0.004 0.004
#> SRR925689 1 0.4286 0.8002 0.792 0.032 0.000 0.140 0.036
#> SRR925690 1 0.4286 0.8002 0.792 0.032 0.000 0.140 0.036
#> SRR925691 1 0.4286 0.8002 0.792 0.032 0.000 0.140 0.036
#> SRR925692 1 0.4286 0.8002 0.792 0.032 0.000 0.140 0.036
#> SRR925693 3 0.6618 -0.3385 0.000 0.384 0.400 0.216 0.000
#> SRR925694 2 0.1818 0.6834 0.000 0.932 0.044 0.024 0.000
#> SRR925695 3 0.5691 -0.1929 0.000 0.400 0.516 0.084 0.000
#> SRR925697 5 0.0162 0.7019 0.004 0.000 0.000 0.000 0.996
#> SRR925696 3 0.0290 0.7112 0.000 0.000 0.992 0.008 0.000
#> SRR925698 3 0.2378 0.6733 0.000 0.048 0.904 0.048 0.000
#> SRR925699 3 0.4552 0.2858 0.000 0.468 0.524 0.008 0.000
#> SRR925700 3 0.4504 0.3552 0.000 0.428 0.564 0.008 0.000
#> SRR925701 3 0.4497 0.3599 0.000 0.424 0.568 0.008 0.000
#> SRR925702 5 0.5167 0.3186 0.044 0.000 0.000 0.404 0.552
#> SRR925703 5 0.0162 0.7019 0.004 0.000 0.000 0.000 0.996
#> SRR925704 2 0.5736 0.2882 0.000 0.468 0.448 0.084 0.000
#> SRR925705 3 0.0162 0.7110 0.000 0.000 0.996 0.004 0.000
#> SRR925706 5 0.4443 0.2466 0.004 0.000 0.000 0.472 0.524
#> SRR925708 1 0.0798 0.8762 0.976 0.000 0.000 0.016 0.008
#> SRR925707 4 0.4448 -0.3376 0.004 0.000 0.000 0.516 0.480
#> SRR925709 1 0.4173 0.5142 0.688 0.000 0.000 0.300 0.012
#> SRR925710 4 0.5913 0.0493 0.396 0.044 0.000 0.528 0.032
#> SRR925711 4 0.4684 -0.2560 0.004 0.000 0.008 0.536 0.452
#> SRR934631 2 0.1704 0.7035 0.000 0.928 0.068 0.004 0.000
#> SRR934632 2 0.1704 0.7035 0.000 0.928 0.068 0.004 0.000
#> SRR934633 2 0.1704 0.7035 0.000 0.928 0.068 0.004 0.000
#> SRR925713 5 0.4848 0.3193 0.024 0.000 0.000 0.420 0.556
#> SRR925714 5 0.0162 0.7019 0.004 0.000 0.000 0.000 0.996
#> SRR925715 4 0.4404 0.2999 0.092 0.016 0.000 0.788 0.104
#> SRR925716 5 0.0162 0.7019 0.004 0.000 0.000 0.000 0.996
#> SRR925717 1 0.6368 0.2633 0.484 0.016 0.000 0.108 0.392
#> SRR925718 3 0.3662 0.5736 0.000 0.252 0.744 0.004 0.000
#> SRR925719 1 0.1106 0.8711 0.964 0.000 0.000 0.024 0.012
#> SRR925720 1 0.0324 0.8814 0.992 0.000 0.000 0.004 0.004
#> SRR925721 1 0.0324 0.8814 0.992 0.000 0.000 0.004 0.004
#> SRR934634 1 0.0000 0.8814 1.000 0.000 0.000 0.000 0.000
#> SRR934635 1 0.0162 0.8812 0.996 0.000 0.000 0.004 0.000
#> SRR925723 3 0.1579 0.6964 0.000 0.032 0.944 0.024 0.000
#> SRR925724 3 0.1121 0.7021 0.000 0.000 0.956 0.044 0.000
#> SRR925725 2 0.6070 0.2723 0.000 0.440 0.440 0.120 0.000
#> SRR925726 5 0.4619 0.3668 0.216 0.000 0.000 0.064 0.720
#> SRR925727 3 0.4317 0.5251 0.000 0.112 0.772 0.116 0.000
#> SRR925728 5 0.4555 0.2425 0.008 0.000 0.000 0.472 0.520
#> SRR934636 5 0.0451 0.7002 0.008 0.000 0.000 0.004 0.988
#> SRR934637 5 0.0451 0.7002 0.008 0.000 0.000 0.004 0.988
#> SRR925729 2 0.1818 0.6834 0.000 0.932 0.044 0.024 0.000
#> SRR934638 1 0.1484 0.8706 0.944 0.008 0.000 0.048 0.000
#> SRR934639 1 0.1408 0.8719 0.948 0.008 0.000 0.044 0.000
#> SRR934640 4 0.4542 0.1075 0.000 0.456 0.008 0.536 0.000
#> SRR934641 4 0.4542 0.1075 0.000 0.456 0.008 0.536 0.000
#> SRR925734 1 0.0404 0.8810 0.988 0.000 0.000 0.012 0.000
#> SRR925735 2 0.6323 0.4184 0.000 0.516 0.192 0.292 0.000
#> SRR925736 3 0.0579 0.7111 0.000 0.008 0.984 0.008 0.000
#> SRR925737 3 0.0579 0.7111 0.000 0.008 0.984 0.008 0.000
#> SRR934642 3 0.3455 0.6109 0.000 0.208 0.784 0.008 0.000
#> SRR934643 3 0.3455 0.6109 0.000 0.208 0.784 0.008 0.000
#> SRR934644 2 0.1725 0.6902 0.000 0.936 0.044 0.020 0.000
#> SRR934645 2 0.1725 0.6902 0.000 0.936 0.044 0.020 0.000
#> SRR925740 3 0.0404 0.7123 0.000 0.012 0.988 0.000 0.000
#> SRR925741 2 0.5595 0.4482 0.000 0.560 0.356 0.084 0.000
#> SRR925742 3 0.1697 0.7007 0.000 0.060 0.932 0.008 0.000
#> SRR1283038 2 0.5314 0.3804 0.000 0.528 0.420 0.052 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.0806 0.7949 0.972 0.000 0.000 0.008 0.000 0.020
#> SRR925688 1 0.0806 0.7949 0.972 0.000 0.000 0.008 0.000 0.020
#> SRR925689 1 0.5100 0.6097 0.556 0.000 0.000 0.028 0.036 0.380
#> SRR925690 1 0.5100 0.6097 0.556 0.000 0.000 0.028 0.036 0.380
#> SRR925691 1 0.5100 0.6097 0.556 0.000 0.000 0.028 0.036 0.380
#> SRR925692 1 0.5100 0.6097 0.556 0.000 0.000 0.028 0.036 0.380
#> SRR925693 6 0.6911 0.0000 0.000 0.324 0.264 0.052 0.000 0.360
#> SRR925694 2 0.2186 0.4032 0.000 0.908 0.024 0.012 0.000 0.056
#> SRR925695 2 0.6205 -0.6075 0.000 0.364 0.360 0.004 0.000 0.272
#> SRR925697 5 0.0436 0.8742 0.004 0.000 0.000 0.004 0.988 0.004
#> SRR925696 3 0.0922 0.7072 0.000 0.004 0.968 0.004 0.000 0.024
#> SRR925698 3 0.3490 0.5055 0.000 0.040 0.784 0.000 0.000 0.176
#> SRR925699 2 0.5548 -0.2947 0.000 0.472 0.436 0.016 0.004 0.072
#> SRR925700 3 0.5550 0.2665 0.000 0.444 0.464 0.016 0.004 0.072
#> SRR925701 3 0.5313 0.3393 0.000 0.412 0.512 0.012 0.004 0.060
#> SRR925702 4 0.4495 0.8266 0.060 0.000 0.000 0.720 0.200 0.020
#> SRR925703 5 0.0603 0.8661 0.004 0.000 0.000 0.016 0.980 0.000
#> SRR925704 2 0.6130 -0.5988 0.000 0.432 0.292 0.004 0.000 0.272
#> SRR925705 3 0.0777 0.7054 0.000 0.000 0.972 0.004 0.000 0.024
#> SRR925706 4 0.3134 0.8556 0.004 0.000 0.000 0.784 0.208 0.004
#> SRR925708 1 0.2195 0.7798 0.904 0.000 0.000 0.016 0.012 0.068
#> SRR925707 4 0.2765 0.8526 0.004 0.000 0.004 0.840 0.148 0.004
#> SRR925709 1 0.5440 0.1477 0.496 0.000 0.000 0.408 0.012 0.084
#> SRR925710 1 0.6869 0.0886 0.392 0.028 0.000 0.320 0.012 0.248
#> SRR925711 4 0.4758 0.7813 0.020 0.000 0.024 0.744 0.140 0.072
#> SRR934631 2 0.1682 0.3985 0.000 0.928 0.020 0.000 0.000 0.052
#> SRR934632 2 0.1682 0.3985 0.000 0.928 0.020 0.000 0.000 0.052
#> SRR934633 2 0.1682 0.3985 0.000 0.928 0.020 0.000 0.000 0.052
#> SRR925713 4 0.4548 0.8031 0.032 0.000 0.000 0.688 0.252 0.028
#> SRR925714 5 0.0291 0.8741 0.004 0.000 0.000 0.004 0.992 0.000
#> SRR925715 4 0.3704 0.7017 0.036 0.008 0.000 0.816 0.024 0.116
#> SRR925716 5 0.0260 0.8726 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR925717 5 0.6453 0.3171 0.264 0.000 0.000 0.044 0.496 0.196
#> SRR925718 3 0.3987 0.6521 0.000 0.204 0.748 0.004 0.004 0.040
#> SRR925719 1 0.2577 0.7640 0.888 0.000 0.000 0.040 0.016 0.056
#> SRR925720 1 0.0291 0.7989 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR925721 1 0.0291 0.8000 0.992 0.000 0.000 0.004 0.000 0.004
#> SRR934634 1 0.0405 0.8001 0.988 0.000 0.000 0.004 0.000 0.008
#> SRR934635 1 0.0458 0.7999 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR925723 3 0.2094 0.6729 0.000 0.020 0.900 0.000 0.000 0.080
#> SRR925724 3 0.2051 0.6626 0.000 0.004 0.896 0.004 0.000 0.096
#> SRR925725 2 0.6695 -0.7914 0.000 0.364 0.280 0.032 0.000 0.324
#> SRR925726 5 0.3011 0.7557 0.100 0.000 0.000 0.012 0.852 0.036
#> SRR925727 3 0.5666 -0.1577 0.000 0.120 0.580 0.024 0.000 0.276
#> SRR925728 4 0.3104 0.8560 0.004 0.000 0.000 0.788 0.204 0.004
#> SRR934636 5 0.0551 0.8734 0.004 0.000 0.000 0.008 0.984 0.004
#> SRR934637 5 0.0551 0.8734 0.004 0.000 0.000 0.008 0.984 0.004
#> SRR925729 2 0.2076 0.4044 0.000 0.912 0.016 0.012 0.000 0.060
#> SRR934638 1 0.2408 0.7825 0.876 0.000 0.000 0.012 0.004 0.108
#> SRR934639 1 0.2408 0.7825 0.876 0.000 0.000 0.012 0.004 0.108
#> SRR934640 2 0.6516 0.0212 0.000 0.372 0.020 0.332 0.000 0.276
#> SRR934641 2 0.6516 0.0212 0.000 0.372 0.020 0.332 0.000 0.276
#> SRR925734 1 0.1010 0.7999 0.960 0.000 0.000 0.004 0.000 0.036
#> SRR925735 2 0.6691 -0.3756 0.000 0.440 0.112 0.096 0.000 0.352
#> SRR925736 3 0.2177 0.7037 0.000 0.004 0.908 0.024 0.004 0.060
#> SRR925737 3 0.2177 0.7037 0.000 0.004 0.908 0.024 0.004 0.060
#> SRR934642 3 0.3940 0.6637 0.000 0.168 0.772 0.008 0.004 0.048
#> SRR934643 3 0.3940 0.6637 0.000 0.168 0.772 0.008 0.004 0.048
#> SRR934644 2 0.1148 0.4110 0.000 0.960 0.016 0.004 0.000 0.020
#> SRR934645 2 0.1434 0.4134 0.000 0.948 0.020 0.008 0.000 0.024
#> SRR925740 3 0.1565 0.7149 0.000 0.028 0.940 0.004 0.000 0.028
#> SRR925741 2 0.5918 -0.5610 0.000 0.484 0.204 0.004 0.000 0.308
#> SRR925742 3 0.2839 0.6883 0.000 0.092 0.860 0.004 0.000 0.044
#> SRR1283038 2 0.5816 -0.5034 0.000 0.484 0.304 0.000 0.000 0.212
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 15949 rows and 64 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 0.766 0.923 0.958 0.4932 0.494 0.494
#> 3 3 0.760 0.872 0.931 0.1979 0.906 0.810
#> 4 4 0.667 0.748 0.852 0.1999 0.875 0.686
#> 5 5 0.717 0.733 0.862 0.0833 0.911 0.698
#> 6 6 0.814 0.757 0.903 0.0507 0.927 0.704
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
#> SRR925687 1 0.000 0.910 1.000 0.000
#> SRR925688 1 0.118 0.908 0.984 0.016
#> SRR925689 1 0.000 0.910 1.000 0.000
#> SRR925690 1 0.000 0.910 1.000 0.000
#> SRR925691 1 0.000 0.910 1.000 0.000
#> SRR925692 1 0.000 0.910 1.000 0.000
#> SRR925693 2 0.000 0.996 0.000 1.000
#> SRR925694 2 0.000 0.996 0.000 1.000
#> SRR925695 2 0.000 0.996 0.000 1.000
#> SRR925697 1 0.563 0.863 0.868 0.132
#> SRR925696 2 0.000 0.996 0.000 1.000
#> SRR925698 2 0.000 0.996 0.000 1.000
#> SRR925699 2 0.000 0.996 0.000 1.000
#> SRR925700 2 0.000 0.996 0.000 1.000
#> SRR925701 2 0.000 0.996 0.000 1.000
#> SRR925702 1 0.278 0.901 0.952 0.048
#> SRR925703 1 0.541 0.868 0.876 0.124
#> SRR925704 2 0.000 0.996 0.000 1.000
#> SRR925705 2 0.000 0.996 0.000 1.000
#> SRR925706 2 0.494 0.861 0.108 0.892
#> SRR925708 1 0.000 0.910 1.000 0.000
#> SRR925707 2 0.000 0.996 0.000 1.000
#> SRR925709 1 0.141 0.906 0.980 0.020
#> SRR925710 1 0.995 0.315 0.540 0.460
#> SRR925711 1 0.827 0.684 0.740 0.260
#> SRR934631 2 0.000 0.996 0.000 1.000
#> SRR934632 2 0.000 0.996 0.000 1.000
#> SRR934633 2 0.000 0.996 0.000 1.000
#> SRR925713 1 0.866 0.673 0.712 0.288
#> SRR925714 1 0.552 0.866 0.872 0.128
#> SRR925715 1 0.980 0.433 0.584 0.416
#> SRR925716 1 0.242 0.904 0.960 0.040
#> SRR925717 1 0.469 0.882 0.900 0.100
#> SRR925718 2 0.000 0.996 0.000 1.000
#> SRR925719 1 0.000 0.910 1.000 0.000
#> SRR925720 1 0.000 0.910 1.000 0.000
#> SRR925721 1 0.000 0.910 1.000 0.000
#> SRR934634 1 0.000 0.910 1.000 0.000
#> SRR934635 1 0.000 0.910 1.000 0.000
#> SRR925723 2 0.000 0.996 0.000 1.000
#> SRR925724 2 0.000 0.996 0.000 1.000
#> SRR925725 2 0.000 0.996 0.000 1.000
#> SRR925726 1 0.430 0.886 0.912 0.088
#> SRR925727 2 0.000 0.996 0.000 1.000
#> SRR925728 1 0.730 0.776 0.796 0.204
#> SRR934636 1 0.584 0.857 0.860 0.140
#> SRR934637 1 0.563 0.863 0.868 0.132
#> SRR925729 2 0.000 0.996 0.000 1.000
#> SRR934638 1 0.000 0.910 1.000 0.000
#> SRR934639 1 0.000 0.910 1.000 0.000
#> SRR934640 2 0.000 0.996 0.000 1.000
#> SRR934641 2 0.000 0.996 0.000 1.000
#> SRR925734 1 0.000 0.910 1.000 0.000
#> SRR925735 2 0.000 0.996 0.000 1.000
#> SRR925736 2 0.000 0.996 0.000 1.000
#> SRR925737 2 0.000 0.996 0.000 1.000
#> SRR934642 2 0.000 0.996 0.000 1.000
#> SRR934643 2 0.000 0.996 0.000 1.000
#> SRR934644 2 0.000 0.996 0.000 1.000
#> SRR934645 2 0.000 0.996 0.000 1.000
#> SRR925740 2 0.000 0.996 0.000 1.000
#> SRR925741 2 0.000 0.996 0.000 1.000
#> SRR925742 2 0.000 0.996 0.000 1.000
#> SRR1283038 2 0.000 0.996 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925689 3 0.1163 0.835 0.028 0.000 0.972
#> SRR925690 3 0.1163 0.835 0.028 0.000 0.972
#> SRR925691 3 0.1163 0.835 0.028 0.000 0.972
#> SRR925692 3 0.1163 0.835 0.028 0.000 0.972
#> SRR925693 2 0.0592 0.980 0.000 0.988 0.012
#> SRR925694 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925695 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925697 1 0.6920 0.708 0.736 0.132 0.132
#> SRR925696 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925698 2 0.0592 0.979 0.000 0.988 0.012
#> SRR925699 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925702 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925703 1 0.6854 0.715 0.740 0.124 0.136
#> SRR925704 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925705 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925706 2 0.6726 0.388 0.332 0.644 0.024
#> SRR925708 1 0.0237 0.853 0.996 0.000 0.004
#> SRR925707 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925709 3 0.5859 0.528 0.344 0.000 0.656
#> SRR925710 3 0.7988 0.645 0.144 0.200 0.656
#> SRR925711 3 0.1774 0.828 0.016 0.024 0.960
#> SRR934631 2 0.0747 0.978 0.000 0.984 0.016
#> SRR934632 2 0.0747 0.978 0.000 0.984 0.016
#> SRR934633 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925713 1 0.6902 0.598 0.736 0.148 0.116
#> SRR925714 1 0.7327 0.685 0.708 0.132 0.160
#> SRR925715 3 0.7507 0.569 0.068 0.288 0.644
#> SRR925716 1 0.4418 0.799 0.848 0.020 0.132
#> SRR925717 3 0.4379 0.797 0.072 0.060 0.868
#> SRR925718 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.854 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.854 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.854 1.000 0.000 0.000
#> SRR925723 2 0.0592 0.979 0.000 0.988 0.012
#> SRR925724 2 0.0424 0.980 0.000 0.992 0.008
#> SRR925725 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925726 1 0.6912 0.540 0.628 0.028 0.344
#> SRR925727 2 0.0424 0.980 0.000 0.992 0.008
#> SRR925728 1 0.1031 0.847 0.976 0.000 0.024
#> SRR934636 1 0.7485 0.690 0.696 0.132 0.172
#> SRR934637 1 0.7926 0.651 0.656 0.128 0.216
#> SRR925729 2 0.0000 0.981 0.000 1.000 0.000
#> SRR934638 1 0.1964 0.836 0.944 0.000 0.056
#> SRR934639 1 0.3879 0.796 0.848 0.000 0.152
#> SRR934640 2 0.0000 0.981 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925734 1 0.1643 0.837 0.956 0.000 0.044
#> SRR925735 2 0.0237 0.981 0.000 0.996 0.004
#> SRR925736 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.981 0.000 1.000 0.000
#> SRR934642 2 0.0000 0.981 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.981 0.000 1.000 0.000
#> SRR934644 2 0.0424 0.980 0.000 0.992 0.008
#> SRR934645 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925740 2 0.0000 0.981 0.000 1.000 0.000
#> SRR925741 2 0.0747 0.978 0.000 0.984 0.016
#> SRR925742 2 0.0237 0.981 0.000 0.996 0.004
#> SRR1283038 2 0.0747 0.978 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925688 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925689 1 0.0592 0.861 0.984 0.000 0.000 0.016
#> SRR925690 1 0.0592 0.861 0.984 0.000 0.000 0.016
#> SRR925691 1 0.0592 0.861 0.984 0.000 0.000 0.016
#> SRR925692 1 0.0592 0.861 0.984 0.000 0.000 0.016
#> SRR925693 3 0.4996 -0.350 0.000 0.484 0.516 0.000
#> SRR925694 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925695 2 0.4605 0.808 0.000 0.664 0.336 0.000
#> SRR925697 4 0.5599 0.736 0.072 0.228 0.000 0.700
#> SRR925696 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925698 2 0.4998 0.496 0.000 0.512 0.488 0.000
#> SRR925699 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925700 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925701 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925702 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925703 4 0.5782 0.740 0.068 0.220 0.008 0.704
#> SRR925704 2 0.4713 0.776 0.000 0.640 0.360 0.000
#> SRR925705 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925706 3 0.5467 0.253 0.024 0.000 0.612 0.364
#> SRR925708 4 0.0188 0.854 0.004 0.000 0.000 0.996
#> SRR925707 3 0.0188 0.849 0.000 0.004 0.996 0.000
#> SRR925709 1 0.4585 0.594 0.668 0.000 0.000 0.332
#> SRR925710 1 0.6661 0.703 0.676 0.028 0.116 0.180
#> SRR925711 1 0.3249 0.789 0.852 0.140 0.008 0.000
#> SRR934631 2 0.3873 0.881 0.000 0.772 0.228 0.000
#> SRR934632 2 0.3873 0.881 0.000 0.772 0.228 0.000
#> SRR934633 2 0.3873 0.881 0.000 0.772 0.228 0.000
#> SRR925713 4 0.5175 0.607 0.120 0.000 0.120 0.760
#> SRR925714 4 0.5599 0.736 0.072 0.228 0.000 0.700
#> SRR925715 1 0.5969 0.681 0.704 0.008 0.192 0.096
#> SRR925716 4 0.5321 0.747 0.056 0.228 0.000 0.716
#> SRR925717 1 0.2489 0.842 0.912 0.000 0.020 0.068
#> SRR925718 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925719 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925720 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925721 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR934634 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR934635 4 0.0000 0.855 0.000 0.000 0.000 1.000
#> SRR925723 3 0.5000 -0.514 0.000 0.496 0.504 0.000
#> SRR925724 3 0.2760 0.711 0.000 0.128 0.872 0.000
#> SRR925725 2 0.3873 0.881 0.000 0.772 0.228 0.000
#> SRR925726 4 0.5040 0.522 0.364 0.000 0.008 0.628
#> SRR925727 3 0.3569 0.589 0.000 0.196 0.804 0.000
#> SRR925728 4 0.0817 0.848 0.024 0.000 0.000 0.976
#> SRR934636 4 0.6229 0.713 0.116 0.228 0.000 0.656
#> SRR934637 4 0.6374 0.702 0.128 0.228 0.000 0.644
#> SRR925729 3 0.1302 0.817 0.000 0.044 0.956 0.000
#> SRR934638 4 0.1557 0.838 0.056 0.000 0.000 0.944
#> SRR934639 4 0.3123 0.795 0.156 0.000 0.000 0.844
#> SRR934640 3 0.0707 0.839 0.000 0.020 0.980 0.000
#> SRR934641 3 0.1022 0.830 0.000 0.032 0.968 0.000
#> SRR925734 4 0.1302 0.839 0.044 0.000 0.000 0.956
#> SRR925735 3 0.3688 0.562 0.000 0.208 0.792 0.000
#> SRR925736 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925737 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR934642 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR934643 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR934644 2 0.4907 0.621 0.000 0.580 0.420 0.000
#> SRR934645 2 0.4222 0.868 0.000 0.728 0.272 0.000
#> SRR925740 3 0.0000 0.851 0.000 0.000 1.000 0.000
#> SRR925741 2 0.4072 0.874 0.000 0.748 0.252 0.000
#> SRR925742 3 0.3074 0.671 0.000 0.152 0.848 0.000
#> SRR1283038 2 0.3873 0.881 0.000 0.772 0.228 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925688 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925689 1 0.0000 0.7723 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.7723 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.7723 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.7723 1.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.3906 0.5396 0.000 0.292 0.704 0.000 0.004
#> SRR925694 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.3143 0.7115 0.000 0.204 0.796 0.000 0.000
#> SRR925697 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR925696 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925698 3 0.4305 0.0268 0.000 0.488 0.512 0.000 0.000
#> SRR925699 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925703 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR925704 3 0.3039 0.7286 0.000 0.192 0.808 0.000 0.000
#> SRR925705 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925706 2 0.5405 0.5410 0.016 0.672 0.000 0.236 0.076
#> SRR925708 4 0.0162 0.8822 0.004 0.000 0.000 0.996 0.000
#> SRR925707 2 0.2171 0.8184 0.000 0.912 0.024 0.000 0.064
#> SRR925709 4 0.4249 0.1813 0.432 0.000 0.000 0.568 0.000
#> SRR925710 1 0.6444 0.4239 0.532 0.012 0.152 0.304 0.000
#> SRR925711 5 0.2020 0.3205 0.100 0.000 0.000 0.000 0.900
#> SRR934631 3 0.0000 0.8103 0.000 0.000 1.000 0.000 0.000
#> SRR934632 3 0.0000 0.8103 0.000 0.000 1.000 0.000 0.000
#> SRR934633 3 0.0000 0.8103 0.000 0.000 1.000 0.000 0.000
#> SRR925713 4 0.3012 0.7555 0.104 0.036 0.000 0.860 0.000
#> SRR925714 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR925715 1 0.7809 0.4262 0.456 0.092 0.020 0.104 0.328
#> SRR925716 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR925717 1 0.1732 0.7430 0.920 0.000 0.000 0.080 0.000
#> SRR925718 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925720 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925721 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR934634 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR934635 4 0.0000 0.8841 0.000 0.000 0.000 1.000 0.000
#> SRR925723 2 0.4306 -0.0703 0.000 0.508 0.492 0.000 0.000
#> SRR925724 2 0.2377 0.7689 0.000 0.872 0.128 0.000 0.000
#> SRR925725 3 0.0000 0.8103 0.000 0.000 1.000 0.000 0.000
#> SRR925726 1 0.4242 0.2207 0.572 0.000 0.000 0.428 0.000
#> SRR925727 2 0.3210 0.6723 0.000 0.788 0.212 0.000 0.000
#> SRR925728 4 0.1121 0.8594 0.044 0.000 0.000 0.956 0.000
#> SRR934636 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR934637 5 0.3932 0.8996 0.000 0.000 0.000 0.328 0.672
#> SRR925729 2 0.1732 0.8102 0.000 0.920 0.080 0.000 0.000
#> SRR934638 4 0.2329 0.8012 0.124 0.000 0.000 0.876 0.000
#> SRR934639 4 0.3752 0.5639 0.292 0.000 0.000 0.708 0.000
#> SRR934640 2 0.5129 0.5551 0.000 0.616 0.056 0.000 0.328
#> SRR934641 2 0.5300 0.5387 0.000 0.604 0.068 0.000 0.328
#> SRR925734 4 0.2179 0.8081 0.112 0.000 0.000 0.888 0.000
#> SRR925735 2 0.4251 0.3087 0.000 0.624 0.372 0.000 0.004
#> SRR925736 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925737 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR934642 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR934644 3 0.3039 0.6848 0.000 0.192 0.808 0.000 0.000
#> SRR934645 3 0.1908 0.7966 0.000 0.092 0.908 0.000 0.000
#> SRR925740 2 0.0000 0.8573 0.000 1.000 0.000 0.000 0.000
#> SRR925741 3 0.0609 0.8103 0.000 0.020 0.980 0.000 0.000
#> SRR925742 2 0.2424 0.7669 0.000 0.868 0.132 0.000 0.000
#> SRR1283038 3 0.0000 0.8103 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925688 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925689 1 0.0000 0.7775 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.7775 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.7775 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.7775 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 2 0.3755 0.6706 0.000 0.744 0.220 0.000 0.000 0.036
#> SRR925694 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925695 2 0.2883 0.7205 0.000 0.788 0.212 0.000 0.000 0.000
#> SRR925697 5 0.0146 0.9989 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR925696 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925698 3 0.3838 0.2105 0.000 0.448 0.552 0.000 0.000 0.000
#> SRR925699 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925700 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925701 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.0363 0.8558 0.012 0.000 0.000 0.988 0.000 0.000
#> SRR925703 5 0.0260 0.9946 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR925704 2 0.2793 0.7405 0.000 0.800 0.200 0.000 0.000 0.000
#> SRR925705 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925706 6 0.3850 0.4223 0.004 0.000 0.340 0.004 0.000 0.652
#> SRR925708 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925707 3 0.3934 0.4568 0.000 0.020 0.676 0.000 0.000 0.304
#> SRR925709 4 0.3482 0.5622 0.316 0.000 0.000 0.684 0.000 0.000
#> SRR925710 4 0.5751 0.0211 0.288 0.000 0.000 0.504 0.000 0.208
#> SRR925711 6 0.0146 0.8705 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR934631 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925713 4 0.0520 0.8509 0.008 0.000 0.008 0.984 0.000 0.000
#> SRR925714 5 0.0146 0.9989 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR925715 6 0.0000 0.8717 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925716 5 0.0146 0.9989 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR925717 1 0.2854 0.6856 0.792 0.000 0.000 0.208 0.000 0.000
#> SRR925718 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925720 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925721 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934634 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934635 4 0.0000 0.8608 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925723 3 0.3843 0.1992 0.000 0.452 0.548 0.000 0.000 0.000
#> SRR925724 3 0.2135 0.7749 0.000 0.128 0.872 0.000 0.000 0.000
#> SRR925725 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925726 1 0.3482 0.5515 0.684 0.000 0.000 0.316 0.000 0.000
#> SRR925727 3 0.2996 0.6648 0.000 0.228 0.772 0.000 0.000 0.000
#> SRR925728 4 0.2165 0.7836 0.108 0.000 0.000 0.884 0.000 0.008
#> SRR934636 5 0.0146 0.9989 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR934637 5 0.0146 0.9989 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR925729 3 0.1765 0.8030 0.000 0.096 0.904 0.000 0.000 0.000
#> SRR934638 4 0.3547 0.5359 0.332 0.000 0.000 0.668 0.000 0.000
#> SRR934639 1 0.3857 -0.0450 0.532 0.000 0.000 0.468 0.000 0.000
#> SRR934640 6 0.0000 0.8717 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934641 6 0.0000 0.8717 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925734 4 0.3446 0.5724 0.308 0.000 0.000 0.692 0.000 0.000
#> SRR925735 3 0.4305 0.0879 0.000 0.436 0.544 0.000 0.000 0.020
#> SRR925736 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925737 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR934642 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR934643 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR934644 2 0.2730 0.7107 0.000 0.808 0.192 0.000 0.000 0.000
#> SRR934645 2 0.1765 0.8388 0.000 0.904 0.096 0.000 0.000 0.000
#> SRR925740 3 0.0000 0.8580 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925741 2 0.0458 0.8658 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR925742 3 0.2003 0.7902 0.000 0.116 0.884 0.000 0.000 0.000
#> SRR1283038 2 0.0000 0.8677 0.000 1.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.982 0.991 0.5077 0.492 0.492
#> 3 3 0.786 0.824 0.911 0.1857 0.913 0.824
#> 4 4 0.696 0.864 0.899 0.0341 0.969 0.927
#> 5 5 0.669 0.532 0.807 0.1345 0.905 0.768
#> 6 6 0.745 0.680 0.863 0.1043 0.857 0.574
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
#> SRR925687 1 0.000 0.998 1.000 0.000
#> SRR925688 1 0.000 0.998 1.000 0.000
#> SRR925689 1 0.000 0.998 1.000 0.000
#> SRR925690 1 0.000 0.998 1.000 0.000
#> SRR925691 1 0.000 0.998 1.000 0.000
#> SRR925692 1 0.000 0.998 1.000 0.000
#> SRR925693 2 0.000 0.984 0.000 1.000
#> SRR925694 2 0.000 0.984 0.000 1.000
#> SRR925695 2 0.000 0.984 0.000 1.000
#> SRR925697 1 0.000 0.998 1.000 0.000
#> SRR925696 2 0.000 0.984 0.000 1.000
#> SRR925698 2 0.000 0.984 0.000 1.000
#> SRR925699 2 0.000 0.984 0.000 1.000
#> SRR925700 2 0.000 0.984 0.000 1.000
#> SRR925701 2 0.000 0.984 0.000 1.000
#> SRR925702 1 0.000 0.998 1.000 0.000
#> SRR925703 1 0.000 0.998 1.000 0.000
#> SRR925704 2 0.000 0.984 0.000 1.000
#> SRR925705 2 0.000 0.984 0.000 1.000
#> SRR925706 1 0.000 0.998 1.000 0.000
#> SRR925708 1 0.000 0.998 1.000 0.000
#> SRR925707 1 0.204 0.967 0.968 0.032
#> SRR925709 1 0.000 0.998 1.000 0.000
#> SRR925710 1 0.000 0.998 1.000 0.000
#> SRR925711 1 0.204 0.967 0.968 0.032
#> SRR934631 2 0.000 0.984 0.000 1.000
#> SRR934632 2 0.000 0.984 0.000 1.000
#> SRR934633 2 0.000 0.984 0.000 1.000
#> SRR925713 1 0.000 0.998 1.000 0.000
#> SRR925714 1 0.000 0.998 1.000 0.000
#> SRR925715 1 0.000 0.998 1.000 0.000
#> SRR925716 1 0.000 0.998 1.000 0.000
#> SRR925717 1 0.000 0.998 1.000 0.000
#> SRR925718 2 0.000 0.984 0.000 1.000
#> SRR925719 1 0.000 0.998 1.000 0.000
#> SRR925720 1 0.000 0.998 1.000 0.000
#> SRR925721 1 0.000 0.998 1.000 0.000
#> SRR934634 1 0.000 0.998 1.000 0.000
#> SRR934635 1 0.000 0.998 1.000 0.000
#> SRR925723 2 0.000 0.984 0.000 1.000
#> SRR925724 2 0.000 0.984 0.000 1.000
#> SRR925725 2 0.000 0.984 0.000 1.000
#> SRR925726 1 0.000 0.998 1.000 0.000
#> SRR925727 2 0.000 0.984 0.000 1.000
#> SRR925728 1 0.000 0.998 1.000 0.000
#> SRR934636 1 0.000 0.998 1.000 0.000
#> SRR934637 1 0.000 0.998 1.000 0.000
#> SRR925729 2 0.000 0.984 0.000 1.000
#> SRR934638 1 0.000 0.998 1.000 0.000
#> SRR934639 1 0.000 0.998 1.000 0.000
#> SRR934640 2 0.662 0.805 0.172 0.828
#> SRR934641 2 0.662 0.805 0.172 0.828
#> SRR925734 1 0.000 0.998 1.000 0.000
#> SRR925735 2 0.595 0.839 0.144 0.856
#> SRR925736 2 0.000 0.984 0.000 1.000
#> SRR925737 2 0.000 0.984 0.000 1.000
#> SRR934642 2 0.000 0.984 0.000 1.000
#> SRR934643 2 0.000 0.984 0.000 1.000
#> SRR934644 2 0.000 0.984 0.000 1.000
#> SRR934645 2 0.000 0.984 0.000 1.000
#> SRR925740 2 0.000 0.984 0.000 1.000
#> SRR925741 2 0.000 0.984 0.000 1.000
#> SRR925742 2 0.000 0.984 0.000 1.000
#> SRR1283038 2 0.000 0.984 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925689 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925690 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925691 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925692 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925693 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925694 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925695 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925697 3 0.4002 0.884 0.160 0.000 0.840
#> SRR925696 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925698 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925699 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925701 2 0.0592 0.973 0.000 0.988 0.012
#> SRR925702 1 0.5810 0.492 0.664 0.000 0.336
#> SRR925703 3 0.5216 0.775 0.260 0.000 0.740
#> SRR925704 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925705 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925706 1 0.6062 0.442 0.616 0.000 0.384
#> SRR925708 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925707 1 0.8347 0.241 0.512 0.084 0.404
#> SRR925709 1 0.5216 0.582 0.740 0.000 0.260
#> SRR925710 1 0.7770 0.315 0.560 0.056 0.384
#> SRR925711 3 0.1643 0.740 0.044 0.000 0.956
#> SRR934631 2 0.0892 0.968 0.000 0.980 0.020
#> SRR934632 2 0.0892 0.968 0.000 0.980 0.020
#> SRR934633 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925713 1 0.6062 0.442 0.616 0.000 0.384
#> SRR925714 3 0.4062 0.885 0.164 0.000 0.836
#> SRR925715 1 0.6062 0.442 0.616 0.000 0.384
#> SRR925716 3 0.4750 0.893 0.216 0.000 0.784
#> SRR925717 1 0.6062 0.442 0.616 0.000 0.384
#> SRR925718 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925719 1 0.0747 0.770 0.984 0.000 0.016
#> SRR925720 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925721 1 0.0424 0.773 0.992 0.000 0.008
#> SRR934634 1 0.0000 0.776 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925723 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925724 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925725 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925726 1 0.6008 0.456 0.628 0.000 0.372
#> SRR925727 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925728 1 0.5835 0.486 0.660 0.000 0.340
#> SRR934636 3 0.4750 0.893 0.216 0.000 0.784
#> SRR934637 3 0.4750 0.893 0.216 0.000 0.784
#> SRR925729 2 0.0000 0.979 0.000 1.000 0.000
#> SRR934638 1 0.0000 0.776 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.776 1.000 0.000 0.000
#> SRR934640 2 0.4121 0.821 0.000 0.832 0.168
#> SRR934641 2 0.4121 0.820 0.000 0.832 0.168
#> SRR925734 1 0.0000 0.776 1.000 0.000 0.000
#> SRR925735 2 0.3551 0.856 0.000 0.868 0.132
#> SRR925736 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.979 0.000 1.000 0.000
#> SRR934642 2 0.2165 0.936 0.000 0.936 0.064
#> SRR934643 2 0.2165 0.936 0.000 0.936 0.064
#> SRR934644 2 0.0000 0.979 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925740 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925741 2 0.0000 0.979 0.000 1.000 0.000
#> SRR925742 2 0.0000 0.979 0.000 1.000 0.000
#> SRR1283038 2 0.0000 0.979 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925688 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925689 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925693 3 0.4228 0.833 0.000 0.232 0.760 0.008
#> SRR925694 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR925695 3 0.3074 0.859 0.000 0.152 0.848 0.000
#> SRR925697 4 0.3610 0.995 0.200 0.000 0.000 0.800
#> SRR925696 3 0.3649 0.848 0.000 0.204 0.796 0.000
#> SRR925698 3 0.3074 0.859 0.000 0.152 0.848 0.000
#> SRR925699 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR925700 3 0.1389 0.881 0.000 0.048 0.952 0.000
#> SRR925701 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> SRR925702 1 0.3398 0.866 0.872 0.060 0.000 0.068
#> SRR925703 4 0.4175 0.976 0.200 0.016 0.000 0.784
#> SRR925704 3 0.3074 0.859 0.000 0.152 0.848 0.000
#> SRR925705 3 0.3528 0.852 0.000 0.192 0.808 0.000
#> SRR925706 1 0.3691 0.855 0.856 0.076 0.000 0.068
#> SRR925708 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925707 3 0.7736 0.302 0.252 0.108 0.580 0.060
#> SRR925709 1 0.2101 0.896 0.928 0.012 0.000 0.060
#> SRR925710 1 0.5222 0.720 0.792 0.056 0.108 0.044
#> SRR925711 2 0.4406 0.000 0.000 0.700 0.000 0.300
#> SRR934631 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR934632 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR934633 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR925713 1 0.3691 0.855 0.856 0.076 0.000 0.068
#> SRR925714 4 0.3610 0.995 0.200 0.000 0.000 0.800
#> SRR925715 1 0.3948 0.841 0.840 0.096 0.000 0.064
#> SRR925716 4 0.3610 0.995 0.200 0.000 0.000 0.800
#> SRR925717 1 0.3547 0.860 0.864 0.072 0.000 0.064
#> SRR925718 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> SRR925719 1 0.0592 0.921 0.984 0.000 0.000 0.016
#> SRR925720 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925721 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR934634 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR934635 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925723 3 0.3074 0.859 0.000 0.152 0.848 0.000
#> SRR925724 3 0.4228 0.833 0.000 0.232 0.760 0.008
#> SRR925725 3 0.4228 0.833 0.000 0.232 0.760 0.008
#> SRR925726 1 0.4552 0.789 0.800 0.072 0.000 0.128
#> SRR925727 3 0.4228 0.833 0.000 0.232 0.760 0.008
#> SRR925728 1 0.4055 0.834 0.832 0.108 0.000 0.060
#> SRR934636 4 0.3610 0.995 0.200 0.000 0.000 0.800
#> SRR934637 4 0.3610 0.995 0.200 0.000 0.000 0.800
#> SRR925729 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR934638 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR934639 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR934640 3 0.4050 0.801 0.000 0.144 0.820 0.036
#> SRR934641 3 0.4050 0.801 0.000 0.144 0.820 0.036
#> SRR925734 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR925735 3 0.3876 0.836 0.000 0.124 0.836 0.040
#> SRR925736 3 0.2814 0.875 0.000 0.132 0.868 0.000
#> SRR925737 3 0.1716 0.882 0.000 0.064 0.936 0.000
#> SRR934642 3 0.0921 0.885 0.000 0.028 0.972 0.000
#> SRR934643 3 0.0921 0.885 0.000 0.028 0.972 0.000
#> SRR934644 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR934645 3 0.1118 0.880 0.000 0.036 0.964 0.000
#> SRR925740 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> SRR925741 3 0.3074 0.859 0.000 0.152 0.848 0.000
#> SRR925742 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> SRR1283038 3 0.2973 0.862 0.000 0.144 0.856 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.0880 0.7606 0.000 0.000 0.000 0.968 0.032
#> SRR925688 4 0.1270 0.7568 0.000 0.000 0.000 0.948 0.052
#> SRR925689 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925690 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925691 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925692 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925693 3 0.4256 0.5256 0.000 0.436 0.564 0.000 0.000
#> SRR925694 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR925695 2 0.3752 0.3145 0.000 0.708 0.292 0.000 0.000
#> SRR925697 5 0.0000 0.7248 0.000 0.000 0.000 0.000 1.000
#> SRR925696 3 0.4305 0.5065 0.000 0.488 0.512 0.000 0.000
#> SRR925698 2 0.3707 0.3379 0.000 0.716 0.284 0.000 0.000
#> SRR925699 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR925700 2 0.1282 0.6705 0.004 0.952 0.044 0.000 0.000
#> SRR925701 2 0.0000 0.6926 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.7082 0.4198 0.048 0.000 0.348 0.468 0.136
#> SRR925703 5 0.0000 0.7248 0.000 0.000 0.000 0.000 1.000
#> SRR925704 2 0.3661 0.3588 0.000 0.724 0.276 0.000 0.000
#> SRR925705 2 0.4262 -0.3503 0.000 0.560 0.440 0.000 0.000
#> SRR925706 4 0.6793 0.3846 0.024 0.000 0.400 0.436 0.140
#> SRR925708 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925707 3 0.5666 -0.3222 0.008 0.064 0.720 0.072 0.136
#> SRR925709 4 0.5392 0.5764 0.004 0.000 0.216 0.668 0.112
#> SRR925710 4 0.7346 0.3522 0.008 0.028 0.360 0.420 0.184
#> SRR925711 1 0.0404 0.0000 0.988 0.000 0.012 0.000 0.000
#> SRR934631 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR934632 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR934633 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR925713 4 0.7488 0.3319 0.048 0.000 0.360 0.388 0.204
#> SRR925714 5 0.0000 0.7248 0.000 0.000 0.000 0.000 1.000
#> SRR925715 4 0.6593 0.4155 0.008 0.000 0.372 0.456 0.164
#> SRR925716 5 0.2471 0.6499 0.000 0.000 0.000 0.136 0.864
#> SRR925717 4 0.7171 0.4477 0.048 0.000 0.232 0.512 0.208
#> SRR925718 2 0.0000 0.6926 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.0404 0.7713 0.000 0.000 0.000 0.988 0.012
#> SRR925720 4 0.1341 0.7440 0.000 0.000 0.000 0.944 0.056
#> SRR925721 4 0.0162 0.7743 0.000 0.000 0.000 0.996 0.004
#> SRR934634 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR934635 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925723 2 0.3586 0.3864 0.000 0.736 0.264 0.000 0.000
#> SRR925724 3 0.4304 0.5226 0.000 0.484 0.516 0.000 0.000
#> SRR925725 3 0.4283 0.5520 0.000 0.456 0.544 0.000 0.000
#> SRR925726 5 0.5159 -0.1570 0.008 0.000 0.024 0.472 0.496
#> SRR925727 3 0.4294 0.5478 0.000 0.468 0.532 0.000 0.000
#> SRR925728 4 0.7132 0.4049 0.052 0.000 0.364 0.452 0.132
#> SRR934636 5 0.2773 0.6587 0.000 0.000 0.000 0.164 0.836
#> SRR934637 5 0.1732 0.7151 0.000 0.000 0.000 0.080 0.920
#> SRR925729 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR934638 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR934639 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR934640 2 0.3809 0.4068 0.008 0.736 0.256 0.000 0.000
#> SRR934641 2 0.3783 0.4129 0.008 0.740 0.252 0.000 0.000
#> SRR925734 4 0.0000 0.7761 0.000 0.000 0.000 1.000 0.000
#> SRR925735 2 0.2971 0.5914 0.008 0.836 0.156 0.000 0.000
#> SRR925736 2 0.3966 0.0869 0.000 0.664 0.336 0.000 0.000
#> SRR925737 2 0.3661 0.2696 0.000 0.724 0.276 0.000 0.000
#> SRR934642 2 0.3857 0.1703 0.000 0.688 0.312 0.000 0.000
#> SRR934643 2 0.3816 0.1930 0.000 0.696 0.304 0.000 0.000
#> SRR934644 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR934645 2 0.0404 0.6945 0.012 0.988 0.000 0.000 0.000
#> SRR925740 2 0.3752 0.2886 0.000 0.708 0.292 0.000 0.000
#> SRR925741 2 0.3266 0.4984 0.004 0.796 0.200 0.000 0.000
#> SRR925742 2 0.0609 0.6854 0.000 0.980 0.020 0.000 0.000
#> SRR1283038 2 0.2966 0.5221 0.000 0.816 0.184 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.1327 0.9391 0.936 0.000 0.000 0.000 0.064 0
#> SRR925688 1 0.1075 0.9507 0.952 0.000 0.000 0.000 0.048 0
#> SRR925689 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925690 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925691 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925692 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925693 3 0.2915 0.7801 0.000 0.184 0.808 0.008 0.000 0
#> SRR925694 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR925695 2 0.3975 0.3445 0.000 0.600 0.392 0.008 0.000 0
#> SRR925697 5 0.0000 0.7784 0.000 0.000 0.000 0.000 1.000 0
#> SRR925696 3 0.1610 0.7748 0.000 0.084 0.916 0.000 0.000 0
#> SRR925698 2 0.3975 0.3445 0.000 0.600 0.392 0.008 0.000 0
#> SRR925699 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR925700 2 0.3620 0.0464 0.000 0.648 0.352 0.000 0.000 0
#> SRR925701 2 0.0713 0.6895 0.000 0.972 0.028 0.000 0.000 0
#> SRR925702 4 0.0632 0.8134 0.024 0.000 0.000 0.976 0.000 0
#> SRR925703 5 0.0000 0.7784 0.000 0.000 0.000 0.000 1.000 0
#> SRR925704 2 0.3975 0.3445 0.000 0.600 0.392 0.008 0.000 0
#> SRR925705 3 0.3371 0.6297 0.000 0.292 0.708 0.000 0.000 0
#> SRR925706 4 0.0260 0.8171 0.008 0.000 0.000 0.992 0.000 0
#> SRR925708 1 0.1564 0.9372 0.936 0.000 0.000 0.040 0.024 0
#> SRR925707 4 0.0260 0.8123 0.000 0.008 0.000 0.992 0.000 0
#> SRR925709 4 0.2003 0.7332 0.116 0.000 0.000 0.884 0.000 0
#> SRR925710 4 0.4651 0.5728 0.208 0.028 0.040 0.716 0.008 0
#> SRR925711 6 0.0000 0.0000 0.000 0.000 0.000 0.000 0.000 1
#> SRR934631 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR934632 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR934633 2 0.0260 0.6951 0.000 0.992 0.008 0.000 0.000 0
#> SRR925713 4 0.0260 0.8141 0.000 0.000 0.000 0.992 0.008 0
#> SRR925714 5 0.0000 0.7784 0.000 0.000 0.000 0.000 1.000 0
#> SRR925715 4 0.1036 0.8072 0.004 0.000 0.024 0.964 0.008 0
#> SRR925716 5 0.0000 0.7784 0.000 0.000 0.000 0.000 1.000 0
#> SRR925717 4 0.5886 0.1425 0.388 0.000 0.000 0.412 0.200 0
#> SRR925718 2 0.1957 0.6452 0.000 0.888 0.112 0.000 0.000 0
#> SRR925719 1 0.1951 0.9201 0.908 0.000 0.000 0.016 0.076 0
#> SRR925720 1 0.1501 0.9282 0.924 0.000 0.000 0.000 0.076 0
#> SRR925721 1 0.0458 0.9670 0.984 0.000 0.000 0.000 0.016 0
#> SRR934634 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR934635 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925723 2 0.3706 0.3862 0.000 0.620 0.380 0.000 0.000 0
#> SRR925724 3 0.1714 0.7797 0.000 0.092 0.908 0.000 0.000 0
#> SRR925725 3 0.2473 0.7928 0.000 0.136 0.856 0.008 0.000 0
#> SRR925726 5 0.4851 0.2061 0.404 0.000 0.000 0.060 0.536 0
#> SRR925727 3 0.2212 0.7894 0.000 0.112 0.880 0.008 0.000 0
#> SRR925728 4 0.0260 0.8171 0.008 0.000 0.000 0.992 0.000 0
#> SRR934636 5 0.3284 0.6516 0.168 0.000 0.000 0.032 0.800 0
#> SRR934637 5 0.3252 0.6834 0.108 0.000 0.000 0.068 0.824 0
#> SRR925729 2 0.0260 0.6951 0.000 0.992 0.008 0.000 0.000 0
#> SRR934638 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR934639 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR934640 2 0.3868 -0.1225 0.000 0.508 0.492 0.000 0.000 0
#> SRR934641 2 0.3868 -0.1225 0.000 0.508 0.492 0.000 0.000 0
#> SRR925734 1 0.0000 0.9728 1.000 0.000 0.000 0.000 0.000 0
#> SRR925735 2 0.2793 0.5990 0.000 0.800 0.200 0.000 0.000 0
#> SRR925736 3 0.2762 0.7720 0.000 0.196 0.804 0.000 0.000 0
#> SRR925737 3 0.3634 0.6372 0.000 0.356 0.644 0.000 0.000 0
#> SRR934642 3 0.3330 0.6421 0.000 0.284 0.716 0.000 0.000 0
#> SRR934643 3 0.3330 0.6421 0.000 0.284 0.716 0.000 0.000 0
#> SRR934644 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR934645 2 0.0000 0.6955 0.000 1.000 0.000 0.000 0.000 0
#> SRR925740 3 0.3737 0.5778 0.000 0.392 0.608 0.000 0.000 0
#> SRR925741 2 0.3725 0.4752 0.000 0.676 0.316 0.008 0.000 0
#> SRR925742 2 0.2454 0.6119 0.000 0.840 0.160 0.000 0.000 0
#> SRR1283038 2 0.3446 0.4963 0.000 0.692 0.308 0.000 0.000 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 15949 rows and 64 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 0.966 0.988 0.5078 0.493 0.493
#> 3 3 0.808 0.901 0.920 0.2434 0.865 0.729
#> 4 4 0.728 0.702 0.861 0.1650 0.864 0.642
#> 5 5 0.725 0.705 0.833 0.0735 0.902 0.649
#> 6 6 0.795 0.757 0.797 0.0450 0.933 0.699
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
#> SRR925687 1 0.000 0.988 1.000 0.000
#> SRR925688 1 0.000 0.988 1.000 0.000
#> SRR925689 1 0.000 0.988 1.000 0.000
#> SRR925690 1 0.000 0.988 1.000 0.000
#> SRR925691 1 0.000 0.988 1.000 0.000
#> SRR925692 1 0.000 0.988 1.000 0.000
#> SRR925693 2 0.000 0.986 0.000 1.000
#> SRR925694 2 0.000 0.986 0.000 1.000
#> SRR925695 2 0.000 0.986 0.000 1.000
#> SRR925697 1 0.000 0.988 1.000 0.000
#> SRR925696 2 0.000 0.986 0.000 1.000
#> SRR925698 2 0.000 0.986 0.000 1.000
#> SRR925699 2 0.000 0.986 0.000 1.000
#> SRR925700 2 0.000 0.986 0.000 1.000
#> SRR925701 2 0.000 0.986 0.000 1.000
#> SRR925702 1 0.000 0.988 1.000 0.000
#> SRR925703 1 0.000 0.988 1.000 0.000
#> SRR925704 2 0.000 0.986 0.000 1.000
#> SRR925705 2 0.000 0.986 0.000 1.000
#> SRR925706 1 0.000 0.988 1.000 0.000
#> SRR925708 1 0.000 0.988 1.000 0.000
#> SRR925707 2 0.992 0.173 0.448 0.552
#> SRR925709 1 0.000 0.988 1.000 0.000
#> SRR925710 1 0.000 0.988 1.000 0.000
#> SRR925711 1 0.929 0.465 0.656 0.344
#> SRR934631 2 0.000 0.986 0.000 1.000
#> SRR934632 2 0.000 0.986 0.000 1.000
#> SRR934633 2 0.000 0.986 0.000 1.000
#> SRR925713 1 0.000 0.988 1.000 0.000
#> SRR925714 1 0.000 0.988 1.000 0.000
#> SRR925715 1 0.000 0.988 1.000 0.000
#> SRR925716 1 0.000 0.988 1.000 0.000
#> SRR925717 1 0.000 0.988 1.000 0.000
#> SRR925718 2 0.000 0.986 0.000 1.000
#> SRR925719 1 0.000 0.988 1.000 0.000
#> SRR925720 1 0.000 0.988 1.000 0.000
#> SRR925721 1 0.000 0.988 1.000 0.000
#> SRR934634 1 0.000 0.988 1.000 0.000
#> SRR934635 1 0.000 0.988 1.000 0.000
#> SRR925723 2 0.000 0.986 0.000 1.000
#> SRR925724 2 0.000 0.986 0.000 1.000
#> SRR925725 2 0.000 0.986 0.000 1.000
#> SRR925726 1 0.000 0.988 1.000 0.000
#> SRR925727 2 0.000 0.986 0.000 1.000
#> SRR925728 1 0.000 0.988 1.000 0.000
#> SRR934636 1 0.000 0.988 1.000 0.000
#> SRR934637 1 0.000 0.988 1.000 0.000
#> SRR925729 2 0.000 0.986 0.000 1.000
#> SRR934638 1 0.000 0.988 1.000 0.000
#> SRR934639 1 0.000 0.988 1.000 0.000
#> SRR934640 2 0.000 0.986 0.000 1.000
#> SRR934641 2 0.000 0.986 0.000 1.000
#> SRR925734 1 0.000 0.988 1.000 0.000
#> SRR925735 2 0.000 0.986 0.000 1.000
#> SRR925736 2 0.000 0.986 0.000 1.000
#> SRR925737 2 0.000 0.986 0.000 1.000
#> SRR934642 2 0.000 0.986 0.000 1.000
#> SRR934643 2 0.000 0.986 0.000 1.000
#> SRR934644 2 0.000 0.986 0.000 1.000
#> SRR934645 2 0.000 0.986 0.000 1.000
#> SRR925740 2 0.000 0.986 0.000 1.000
#> SRR925741 2 0.000 0.986 0.000 1.000
#> SRR925742 2 0.000 0.986 0.000 1.000
#> SRR1283038 2 0.000 0.986 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925689 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925690 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925691 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925692 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925693 2 0.2878 0.939 0.000 0.904 0.096
#> SRR925694 2 0.2796 0.937 0.000 0.908 0.092
#> SRR925695 2 0.1163 0.947 0.000 0.972 0.028
#> SRR925697 3 0.4887 0.862 0.228 0.000 0.772
#> SRR925696 2 0.0592 0.953 0.000 0.988 0.012
#> SRR925698 2 0.1411 0.943 0.000 0.964 0.036
#> SRR925699 2 0.2066 0.949 0.000 0.940 0.060
#> SRR925700 2 0.1289 0.954 0.000 0.968 0.032
#> SRR925701 2 0.0747 0.954 0.000 0.984 0.016
#> SRR925702 3 0.3686 0.822 0.140 0.000 0.860
#> SRR925703 3 0.4931 0.863 0.232 0.000 0.768
#> SRR925704 2 0.0892 0.955 0.000 0.980 0.020
#> SRR925705 2 0.0592 0.953 0.000 0.988 0.012
#> SRR925706 3 0.2448 0.830 0.076 0.000 0.924
#> SRR925708 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925707 3 0.2689 0.800 0.032 0.036 0.932
#> SRR925709 1 0.3816 0.799 0.852 0.000 0.148
#> SRR925710 1 0.3941 0.792 0.844 0.000 0.156
#> SRR925711 3 0.8650 0.555 0.136 0.292 0.572
#> SRR934631 2 0.2261 0.946 0.000 0.932 0.068
#> SRR934632 2 0.2261 0.946 0.000 0.932 0.068
#> SRR934633 2 0.2261 0.946 0.000 0.932 0.068
#> SRR925713 3 0.2537 0.831 0.080 0.000 0.920
#> SRR925714 3 0.4931 0.863 0.232 0.000 0.768
#> SRR925715 1 0.4750 0.738 0.784 0.000 0.216
#> SRR925716 3 0.5138 0.851 0.252 0.000 0.748
#> SRR925717 1 0.5363 0.464 0.724 0.000 0.276
#> SRR925718 2 0.0424 0.953 0.000 0.992 0.008
#> SRR925719 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.943 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.943 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925723 2 0.0592 0.953 0.000 0.988 0.012
#> SRR925724 2 0.1529 0.941 0.000 0.960 0.040
#> SRR925725 2 0.2261 0.948 0.000 0.932 0.068
#> SRR925726 3 0.5138 0.851 0.252 0.000 0.748
#> SRR925727 2 0.1289 0.945 0.000 0.968 0.032
#> SRR925728 3 0.3116 0.830 0.108 0.000 0.892
#> SRR934636 3 0.4974 0.861 0.236 0.000 0.764
#> SRR934637 3 0.4974 0.861 0.236 0.000 0.764
#> SRR925729 2 0.3116 0.928 0.000 0.892 0.108
#> SRR934638 1 0.0592 0.934 0.988 0.000 0.012
#> SRR934639 1 0.0000 0.943 1.000 0.000 0.000
#> SRR934640 2 0.4796 0.824 0.000 0.780 0.220
#> SRR934641 2 0.4796 0.824 0.000 0.780 0.220
#> SRR925734 1 0.0000 0.943 1.000 0.000 0.000
#> SRR925735 2 0.3879 0.893 0.000 0.848 0.152
#> SRR925736 2 0.0592 0.953 0.000 0.988 0.012
#> SRR925737 2 0.0592 0.953 0.000 0.988 0.012
#> SRR934642 2 0.0424 0.953 0.000 0.992 0.008
#> SRR934643 2 0.0424 0.953 0.000 0.992 0.008
#> SRR934644 2 0.2448 0.944 0.000 0.924 0.076
#> SRR934645 2 0.2356 0.945 0.000 0.928 0.072
#> SRR925740 2 0.0424 0.953 0.000 0.992 0.008
#> SRR925741 2 0.2261 0.946 0.000 0.932 0.068
#> SRR925742 2 0.0592 0.953 0.000 0.988 0.012
#> SRR1283038 2 0.1031 0.955 0.000 0.976 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR925688 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR925689 1 0.0921 0.956 0.972 0.028 0.000 0.000
#> SRR925690 1 0.0921 0.956 0.972 0.028 0.000 0.000
#> SRR925691 1 0.0921 0.956 0.972 0.028 0.000 0.000
#> SRR925692 1 0.0921 0.956 0.972 0.028 0.000 0.000
#> SRR925693 3 0.4819 0.301 0.000 0.344 0.652 0.004
#> SRR925694 2 0.2589 0.579 0.000 0.884 0.116 0.000
#> SRR925695 3 0.0779 0.794 0.000 0.016 0.980 0.004
#> SRR925697 4 0.0469 0.838 0.012 0.000 0.000 0.988
#> SRR925696 3 0.0469 0.802 0.000 0.012 0.988 0.000
#> SRR925698 3 0.0469 0.797 0.000 0.012 0.988 0.000
#> SRR925699 3 0.4713 0.403 0.000 0.360 0.640 0.000
#> SRR925700 3 0.4522 0.488 0.000 0.320 0.680 0.000
#> SRR925701 3 0.3528 0.698 0.000 0.192 0.808 0.000
#> SRR925702 4 0.4248 0.767 0.012 0.220 0.000 0.768
#> SRR925703 4 0.0469 0.838 0.012 0.000 0.000 0.988
#> SRR925704 3 0.1398 0.787 0.000 0.040 0.956 0.004
#> SRR925705 3 0.0000 0.802 0.000 0.000 1.000 0.000
#> SRR925706 4 0.4564 0.708 0.000 0.328 0.000 0.672
#> SRR925708 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR925707 4 0.5028 0.645 0.000 0.400 0.004 0.596
#> SRR925709 1 0.4868 0.572 0.684 0.304 0.000 0.012
#> SRR925710 2 0.5503 -0.198 0.468 0.516 0.000 0.016
#> SRR925711 4 0.3236 0.719 0.004 0.004 0.136 0.856
#> SRR934631 2 0.4955 0.185 0.000 0.556 0.444 0.000
#> SRR934632 2 0.4955 0.185 0.000 0.556 0.444 0.000
#> SRR934633 2 0.4955 0.185 0.000 0.556 0.444 0.000
#> SRR925713 4 0.5204 0.665 0.012 0.376 0.000 0.612
#> SRR925714 4 0.0469 0.838 0.012 0.000 0.000 0.988
#> SRR925715 2 0.4732 0.359 0.200 0.768 0.012 0.020
#> SRR925716 4 0.0592 0.838 0.016 0.000 0.000 0.984
#> SRR925717 1 0.3108 0.863 0.872 0.016 0.000 0.112
#> SRR925718 3 0.3486 0.703 0.000 0.188 0.812 0.000
#> SRR925719 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR925720 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR925721 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR934634 1 0.0188 0.964 0.996 0.004 0.000 0.000
#> SRR934635 1 0.0188 0.963 0.996 0.004 0.000 0.000
#> SRR925723 3 0.0000 0.802 0.000 0.000 1.000 0.000
#> SRR925724 3 0.0188 0.800 0.000 0.004 0.996 0.000
#> SRR925725 3 0.4252 0.504 0.000 0.252 0.744 0.004
#> SRR925726 4 0.0707 0.836 0.020 0.000 0.000 0.980
#> SRR925727 3 0.0779 0.794 0.000 0.016 0.980 0.004
#> SRR925728 4 0.5099 0.667 0.008 0.380 0.000 0.612
#> SRR934636 4 0.0592 0.838 0.016 0.000 0.000 0.984
#> SRR934637 4 0.0592 0.838 0.016 0.000 0.000 0.984
#> SRR925729 2 0.2011 0.587 0.000 0.920 0.080 0.000
#> SRR934638 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> SRR934639 1 0.0188 0.963 0.996 0.004 0.000 0.000
#> SRR934640 2 0.1545 0.562 0.000 0.952 0.040 0.008
#> SRR934641 2 0.1545 0.562 0.000 0.952 0.040 0.008
#> SRR925734 1 0.0188 0.963 0.996 0.004 0.000 0.000
#> SRR925735 2 0.4819 0.389 0.004 0.652 0.344 0.000
#> SRR925736 3 0.0336 0.802 0.000 0.008 0.992 0.000
#> SRR925737 3 0.1940 0.787 0.000 0.076 0.924 0.000
#> SRR934642 3 0.3486 0.703 0.000 0.188 0.812 0.000
#> SRR934643 3 0.3486 0.703 0.000 0.188 0.812 0.000
#> SRR934644 2 0.4661 0.369 0.000 0.652 0.348 0.000
#> SRR934645 2 0.4916 0.232 0.000 0.576 0.424 0.000
#> SRR925740 3 0.1716 0.793 0.000 0.064 0.936 0.000
#> SRR925741 3 0.4730 0.239 0.000 0.364 0.636 0.000
#> SRR925742 3 0.1557 0.795 0.000 0.056 0.944 0.000
#> SRR1283038 3 0.2647 0.720 0.000 0.120 0.880 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR925688 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR925689 1 0.4322 0.8072 0.768 0.088 0.000 0.144 0.000
#> SRR925690 1 0.4322 0.8072 0.768 0.088 0.000 0.144 0.000
#> SRR925691 1 0.4322 0.8072 0.768 0.088 0.000 0.144 0.000
#> SRR925692 1 0.4322 0.8072 0.768 0.088 0.000 0.144 0.000
#> SRR925693 2 0.5908 0.4566 0.000 0.512 0.380 0.108 0.000
#> SRR925694 2 0.2124 0.6667 0.000 0.900 0.004 0.096 0.000
#> SRR925695 3 0.4490 0.4124 0.000 0.224 0.724 0.052 0.000
#> SRR925697 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR925696 3 0.0880 0.7737 0.000 0.032 0.968 0.000 0.000
#> SRR925698 3 0.1408 0.7374 0.000 0.008 0.948 0.044 0.000
#> SRR925699 3 0.4291 0.2511 0.000 0.464 0.536 0.000 0.000
#> SRR925700 3 0.3983 0.4995 0.000 0.340 0.660 0.000 0.000
#> SRR925701 3 0.3796 0.5573 0.000 0.300 0.700 0.000 0.000
#> SRR925702 4 0.4329 0.5869 0.016 0.000 0.000 0.672 0.312
#> SRR925703 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR925704 3 0.5302 -0.1954 0.000 0.412 0.536 0.052 0.000
#> SRR925705 3 0.0290 0.7692 0.000 0.008 0.992 0.000 0.000
#> SRR925706 4 0.3521 0.6974 0.000 0.004 0.000 0.764 0.232
#> SRR925708 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR925707 4 0.3496 0.7195 0.000 0.012 0.000 0.788 0.200
#> SRR925709 4 0.3913 0.5095 0.324 0.000 0.000 0.676 0.000
#> SRR925710 4 0.4000 0.6845 0.164 0.044 0.004 0.788 0.000
#> SRR925711 5 0.3975 0.7074 0.000 0.000 0.144 0.064 0.792
#> SRR934631 2 0.2329 0.7292 0.000 0.876 0.124 0.000 0.000
#> SRR934632 2 0.2329 0.7292 0.000 0.876 0.124 0.000 0.000
#> SRR934633 2 0.2329 0.7292 0.000 0.876 0.124 0.000 0.000
#> SRR925713 4 0.3381 0.7254 0.016 0.000 0.000 0.808 0.176
#> SRR925714 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR925715 4 0.3193 0.6933 0.032 0.112 0.004 0.852 0.000
#> SRR925716 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR925717 1 0.6679 0.6351 0.624 0.072 0.012 0.088 0.204
#> SRR925718 3 0.2690 0.7189 0.000 0.156 0.844 0.000 0.000
#> SRR925719 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR925720 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR925721 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR934634 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> SRR934635 1 0.0290 0.8983 0.992 0.000 0.000 0.008 0.000
#> SRR925723 3 0.0000 0.7658 0.000 0.000 1.000 0.000 0.000
#> SRR925724 3 0.0162 0.7637 0.000 0.004 0.996 0.000 0.000
#> SRR925725 2 0.5341 0.3784 0.000 0.504 0.444 0.052 0.000
#> SRR925726 5 0.0162 0.9588 0.000 0.000 0.000 0.004 0.996
#> SRR925727 3 0.2054 0.7179 0.000 0.028 0.920 0.052 0.000
#> SRR925728 4 0.3333 0.7157 0.000 0.004 0.000 0.788 0.208
#> SRR934636 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR934637 5 0.0000 0.9624 0.000 0.000 0.000 0.000 1.000
#> SRR925729 2 0.2471 0.6285 0.000 0.864 0.000 0.136 0.000
#> SRR934638 1 0.2074 0.8411 0.896 0.000 0.000 0.104 0.000
#> SRR934639 1 0.0703 0.8969 0.976 0.000 0.000 0.024 0.000
#> SRR934640 4 0.4219 0.3539 0.000 0.416 0.000 0.584 0.000
#> SRR934641 4 0.4235 0.3347 0.000 0.424 0.000 0.576 0.000
#> SRR925734 1 0.0162 0.9001 0.996 0.000 0.000 0.004 0.000
#> SRR925735 2 0.6231 0.4925 0.000 0.544 0.204 0.252 0.000
#> SRR925736 3 0.0510 0.7714 0.000 0.016 0.984 0.000 0.000
#> SRR925737 3 0.1121 0.7737 0.000 0.044 0.956 0.000 0.000
#> SRR934642 3 0.2690 0.7189 0.000 0.156 0.844 0.000 0.000
#> SRR934643 3 0.2690 0.7189 0.000 0.156 0.844 0.000 0.000
#> SRR934644 2 0.2300 0.7240 0.000 0.908 0.052 0.040 0.000
#> SRR934645 2 0.1908 0.7368 0.000 0.908 0.092 0.000 0.000
#> SRR925740 3 0.1197 0.7727 0.000 0.048 0.952 0.000 0.000
#> SRR925741 2 0.5272 0.4728 0.000 0.552 0.396 0.052 0.000
#> SRR925742 3 0.1121 0.7737 0.000 0.044 0.956 0.000 0.000
#> SRR1283038 3 0.5236 -0.0916 0.000 0.380 0.568 0.052 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.3817 0.948 0.568 0.000 0.000 0.000 0.000 0.432
#> SRR925688 1 0.3817 0.948 0.568 0.000 0.000 0.000 0.000 0.432
#> SRR925689 6 0.0000 0.829 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925690 6 0.0000 0.829 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925691 6 0.0000 0.829 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925692 6 0.0000 0.829 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR925693 2 0.3370 0.571 0.008 0.828 0.092 0.072 0.000 0.000
#> SRR925694 2 0.5348 0.561 0.404 0.504 0.008 0.084 0.000 0.000
#> SRR925695 2 0.3804 0.358 0.000 0.656 0.336 0.008 0.000 0.000
#> SRR925697 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925696 3 0.0260 0.858 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925698 3 0.3428 0.528 0.000 0.304 0.696 0.000 0.000 0.000
#> SRR925699 3 0.5442 0.384 0.164 0.244 0.588 0.004 0.000 0.000
#> SRR925700 3 0.3732 0.699 0.076 0.144 0.780 0.000 0.000 0.000
#> SRR925701 3 0.2867 0.765 0.040 0.112 0.848 0.000 0.000 0.000
#> SRR925702 4 0.2070 0.801 0.008 0.000 0.000 0.892 0.100 0.000
#> SRR925703 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925704 2 0.3217 0.549 0.000 0.768 0.224 0.008 0.000 0.000
#> SRR925705 3 0.0458 0.856 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR925706 4 0.1531 0.822 0.004 0.000 0.000 0.928 0.068 0.000
#> SRR925708 1 0.3817 0.948 0.568 0.000 0.000 0.000 0.000 0.432
#> SRR925707 4 0.1387 0.822 0.000 0.000 0.000 0.932 0.068 0.000
#> SRR925709 4 0.3261 0.662 0.204 0.000 0.000 0.780 0.000 0.016
#> SRR925710 4 0.5358 0.658 0.240 0.092 0.000 0.636 0.000 0.032
#> SRR925711 5 0.3802 0.754 0.008 0.000 0.116 0.084 0.792 0.000
#> SRR934631 2 0.5513 0.643 0.288 0.600 0.056 0.056 0.000 0.000
#> SRR934632 2 0.5513 0.643 0.288 0.600 0.056 0.056 0.000 0.000
#> SRR934633 2 0.5513 0.643 0.288 0.600 0.056 0.056 0.000 0.000
#> SRR925713 4 0.1625 0.823 0.012 0.000 0.000 0.928 0.060 0.000
#> SRR925714 5 0.0146 0.962 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR925715 4 0.2457 0.796 0.084 0.036 0.000 0.880 0.000 0.000
#> SRR925716 5 0.0000 0.963 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925717 6 0.6135 0.390 0.076 0.076 0.000 0.000 0.332 0.516
#> SRR925718 3 0.1152 0.844 0.004 0.044 0.952 0.000 0.000 0.000
#> SRR925719 1 0.4123 0.931 0.568 0.000 0.000 0.000 0.012 0.420
#> SRR925720 1 0.3823 0.949 0.564 0.000 0.000 0.000 0.000 0.436
#> SRR925721 1 0.3823 0.949 0.564 0.000 0.000 0.000 0.000 0.436
#> SRR934634 1 0.3823 0.949 0.564 0.000 0.000 0.000 0.000 0.436
#> SRR934635 1 0.3823 0.949 0.564 0.000 0.000 0.000 0.000 0.436
#> SRR925723 3 0.0632 0.852 0.000 0.024 0.976 0.000 0.000 0.000
#> SRR925724 3 0.1007 0.841 0.000 0.044 0.956 0.000 0.000 0.000
#> SRR925725 2 0.2805 0.599 0.000 0.828 0.160 0.012 0.000 0.000
#> SRR925726 5 0.0146 0.962 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR925727 3 0.4072 0.208 0.000 0.448 0.544 0.008 0.000 0.000
#> SRR925728 4 0.1643 0.821 0.008 0.000 0.000 0.924 0.068 0.000
#> SRR934636 5 0.0363 0.960 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR934637 5 0.0363 0.960 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR925729 2 0.5207 0.568 0.404 0.512 0.004 0.080 0.000 0.000
#> SRR934638 1 0.5862 0.551 0.468 0.000 0.000 0.216 0.000 0.316
#> SRR934639 1 0.4080 0.917 0.536 0.000 0.000 0.008 0.000 0.456
#> SRR934640 4 0.4760 0.561 0.120 0.212 0.000 0.668 0.000 0.000
#> SRR934641 4 0.4760 0.561 0.120 0.212 0.000 0.668 0.000 0.000
#> SRR925734 1 0.3823 0.949 0.564 0.000 0.000 0.000 0.000 0.436
#> SRR925735 2 0.4848 0.509 0.124 0.728 0.052 0.096 0.000 0.000
#> SRR925736 3 0.0717 0.856 0.008 0.016 0.976 0.000 0.000 0.000
#> SRR925737 3 0.0520 0.857 0.008 0.008 0.984 0.000 0.000 0.000
#> SRR934642 3 0.1265 0.843 0.008 0.044 0.948 0.000 0.000 0.000
#> SRR934643 3 0.1265 0.843 0.008 0.044 0.948 0.000 0.000 0.000
#> SRR934644 2 0.4909 0.612 0.348 0.588 0.008 0.056 0.000 0.000
#> SRR934645 2 0.5006 0.594 0.388 0.548 0.008 0.056 0.000 0.000
#> SRR925740 3 0.0260 0.858 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925741 2 0.2699 0.614 0.008 0.864 0.108 0.020 0.000 0.000
#> SRR925742 3 0.0260 0.858 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1283038 2 0.3606 0.496 0.004 0.724 0.264 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["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 15949 rows and 64 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 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 0.494 0.659 0.801 0.4978 0.516 0.516
#> 3 3 0.395 0.357 0.671 0.2251 0.572 0.416
#> 4 4 0.686 0.840 0.849 0.1714 0.675 0.409
#> 5 5 0.728 0.714 0.792 0.0761 0.965 0.866
#> 6 6 0.778 0.716 0.800 0.0376 0.963 0.842
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
#> SRR925687 1 0.3584 0.626 0.932 0.068
#> SRR925688 1 0.3584 0.626 0.932 0.068
#> SRR925689 2 0.9686 0.627 0.396 0.604
#> SRR925690 2 0.9686 0.627 0.396 0.604
#> SRR925691 2 0.9686 0.627 0.396 0.604
#> SRR925692 2 0.9686 0.627 0.396 0.604
#> SRR925693 1 0.9686 0.659 0.604 0.396
#> SRR925694 2 0.0000 0.716 0.000 1.000
#> SRR925695 1 0.9686 0.659 0.604 0.396
#> SRR925697 1 0.0000 0.675 1.000 0.000
#> SRR925696 1 0.9686 0.659 0.604 0.396
#> SRR925698 1 0.9686 0.659 0.604 0.396
#> SRR925699 2 0.0000 0.716 0.000 1.000
#> SRR925700 2 0.0000 0.716 0.000 1.000
#> SRR925701 2 0.0000 0.716 0.000 1.000
#> SRR925702 1 0.2043 0.659 0.968 0.032
#> SRR925703 1 0.0000 0.675 1.000 0.000
#> SRR925704 1 0.9686 0.659 0.604 0.396
#> SRR925705 1 0.9686 0.659 0.604 0.396
#> SRR925706 1 0.0376 0.676 0.996 0.004
#> SRR925708 1 0.2043 0.659 0.968 0.032
#> SRR925707 1 0.7219 0.523 0.800 0.200
#> SRR925709 1 0.2043 0.659 0.968 0.032
#> SRR925710 1 0.0000 0.675 1.000 0.000
#> SRR925711 1 0.9552 0.601 0.624 0.376
#> SRR934631 2 0.0000 0.716 0.000 1.000
#> SRR934632 2 0.0000 0.716 0.000 1.000
#> SRR934633 2 0.0000 0.716 0.000 1.000
#> SRR925713 1 0.0000 0.675 1.000 0.000
#> SRR925714 1 0.0000 0.675 1.000 0.000
#> SRR925715 1 0.0376 0.676 0.996 0.004
#> SRR925716 1 0.1414 0.666 0.980 0.020
#> SRR925717 1 0.0000 0.675 1.000 0.000
#> SRR925718 2 0.0000 0.716 0.000 1.000
#> SRR925719 1 0.2043 0.659 0.968 0.032
#> SRR925720 1 0.3584 0.626 0.932 0.068
#> SRR925721 1 0.3584 0.626 0.932 0.068
#> SRR934634 2 0.9686 0.627 0.396 0.604
#> SRR934635 2 0.9686 0.627 0.396 0.604
#> SRR925723 1 0.9686 0.659 0.604 0.396
#> SRR925724 1 0.9686 0.659 0.604 0.396
#> SRR925725 1 0.9686 0.659 0.604 0.396
#> SRR925726 1 0.0000 0.675 1.000 0.000
#> SRR925727 1 0.9686 0.659 0.604 0.396
#> SRR925728 1 0.6048 0.508 0.852 0.148
#> SRR934636 2 0.9686 0.627 0.396 0.604
#> SRR934637 2 0.9686 0.627 0.396 0.604
#> SRR925729 1 1.0000 0.520 0.504 0.496
#> SRR934638 2 0.9686 0.627 0.396 0.604
#> SRR934639 2 0.9686 0.627 0.396 0.604
#> SRR934640 2 0.0000 0.716 0.000 1.000
#> SRR934641 2 0.0000 0.716 0.000 1.000
#> SRR925734 2 0.9686 0.627 0.396 0.604
#> SRR925735 1 0.9686 0.659 0.604 0.396
#> SRR925736 1 0.9686 0.659 0.604 0.396
#> SRR925737 1 0.9686 0.659 0.604 0.396
#> SRR934642 2 0.0000 0.716 0.000 1.000
#> SRR934643 2 0.0000 0.716 0.000 1.000
#> SRR934644 2 0.0000 0.716 0.000 1.000
#> SRR934645 2 0.0000 0.716 0.000 1.000
#> SRR925740 1 0.9686 0.659 0.604 0.396
#> SRR925741 1 0.9686 0.659 0.604 0.396
#> SRR925742 1 0.9686 0.659 0.604 0.396
#> SRR1283038 1 0.9686 0.659 0.604 0.396
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.2261 0.818 0.932 0.000 0.068
#> SRR925688 1 0.2261 0.818 0.932 0.000 0.068
#> SRR925689 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925690 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925691 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925692 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925693 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925694 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925695 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925697 1 0.3038 0.830 0.896 0.000 0.104
#> SRR925696 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925698 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925699 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925702 1 0.2537 0.833 0.920 0.000 0.080
#> SRR925703 1 0.3038 0.830 0.896 0.000 0.104
#> SRR925704 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925705 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925706 1 0.3941 0.787 0.844 0.000 0.156
#> SRR925708 1 0.0747 0.847 0.984 0.000 0.016
#> SRR925707 1 0.9488 0.203 0.424 0.184 0.392
#> SRR925709 1 0.0747 0.847 0.984 0.000 0.016
#> SRR925710 1 0.1031 0.853 0.976 0.000 0.024
#> SRR925711 2 0.9969 0.306 0.308 0.372 0.320
#> SRR934631 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934633 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925713 1 0.1031 0.853 0.976 0.000 0.024
#> SRR925714 1 0.3038 0.830 0.896 0.000 0.104
#> SRR925715 1 0.3879 0.788 0.848 0.000 0.152
#> SRR925716 1 0.3267 0.827 0.884 0.000 0.116
#> SRR925717 1 0.0747 0.852 0.984 0.000 0.016
#> SRR925718 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925719 1 0.0747 0.847 0.984 0.000 0.016
#> SRR925720 1 0.2261 0.818 0.932 0.000 0.068
#> SRR925721 1 0.2261 0.818 0.932 0.000 0.068
#> SRR934634 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR934635 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925723 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925724 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925725 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925726 1 0.3038 0.830 0.896 0.000 0.104
#> SRR925727 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925728 1 0.8991 0.342 0.476 0.132 0.392
#> SRR934636 3 0.8925 1.000 0.132 0.364 0.504
#> SRR934637 3 0.8925 1.000 0.132 0.364 0.504
#> SRR925729 2 0.8909 0.456 0.128 0.496 0.376
#> SRR934638 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR934639 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR934640 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925734 2 0.9956 -0.701 0.352 0.360 0.288
#> SRR925735 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925736 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925737 2 0.9757 0.446 0.228 0.392 0.380
#> SRR934642 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.240 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.240 0.000 1.000 0.000
#> SRR925740 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925741 2 0.9757 0.446 0.228 0.392 0.380
#> SRR925742 2 0.9757 0.446 0.228 0.392 0.380
#> SRR1283038 2 0.9757 0.446 0.228 0.392 0.380
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.2255 0.769 0.068 0.000 0.012 0.920
#> SRR925688 4 0.2255 0.769 0.068 0.000 0.012 0.920
#> SRR925689 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925690 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925691 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925692 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925693 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925694 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925695 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925697 4 0.3933 0.747 0.200 0.000 0.008 0.792
#> SRR925696 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925698 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925699 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925701 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925702 4 0.3542 0.778 0.028 0.000 0.120 0.852
#> SRR925703 4 0.3933 0.747 0.200 0.000 0.008 0.792
#> SRR925704 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925705 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925706 4 0.5050 0.520 0.004 0.000 0.408 0.588
#> SRR925708 4 0.0921 0.798 0.028 0.000 0.000 0.972
#> SRR925707 3 0.7400 0.289 0.072 0.128 0.644 0.156
#> SRR925709 4 0.0921 0.798 0.028 0.000 0.000 0.972
#> SRR925710 4 0.0921 0.807 0.000 0.000 0.028 0.972
#> SRR925711 3 0.6640 0.617 0.000 0.352 0.552 0.096
#> SRR934631 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934632 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934633 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925713 4 0.0921 0.807 0.000 0.000 0.028 0.972
#> SRR925714 4 0.3933 0.747 0.200 0.000 0.008 0.792
#> SRR925715 4 0.4843 0.541 0.000 0.000 0.396 0.604
#> SRR925716 4 0.3945 0.744 0.216 0.000 0.004 0.780
#> SRR925717 4 0.0188 0.806 0.000 0.000 0.004 0.996
#> SRR925718 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925719 4 0.0921 0.798 0.028 0.000 0.000 0.972
#> SRR925720 4 0.2255 0.769 0.068 0.000 0.012 0.920
#> SRR925721 4 0.2255 0.769 0.068 0.000 0.012 0.920
#> SRR934634 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR934635 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925723 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925724 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925725 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925726 4 0.3768 0.755 0.184 0.000 0.008 0.808
#> SRR925727 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925728 3 0.7180 0.217 0.072 0.076 0.644 0.208
#> SRR934636 1 0.0188 0.605 0.996 0.000 0.004 0.000
#> SRR934637 1 0.0188 0.605 0.996 0.000 0.004 0.000
#> SRR925729 3 0.5000 0.408 0.000 0.496 0.504 0.000
#> SRR934638 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR934639 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR934640 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934641 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925734 1 0.4624 0.904 0.660 0.000 0.000 0.340
#> SRR925735 3 0.3933 0.899 0.000 0.200 0.792 0.008
#> SRR925736 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925737 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR934642 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934643 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934644 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR934645 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> SRR925740 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925741 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR925742 3 0.3610 0.906 0.000 0.200 0.800 0.000
#> SRR1283038 3 0.3610 0.906 0.000 0.200 0.800 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.5295 0.6784 0.408 0.000 0.000 0.540 0.052
#> SRR925688 4 0.5295 0.6784 0.408 0.000 0.000 0.540 0.052
#> SRR925689 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.0000 0.7159 0.000 0.000 1.000 0.000 0.000
#> SRR925694 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.0290 0.7220 0.000 0.008 0.992 0.000 0.000
#> SRR925697 4 0.0000 0.5625 0.000 0.000 0.000 1.000 0.000
#> SRR925696 3 0.5148 0.7180 0.000 0.120 0.688 0.000 0.192
#> SRR925698 3 0.2230 0.7352 0.000 0.116 0.884 0.000 0.000
#> SRR925699 2 0.0609 0.9303 0.000 0.980 0.020 0.000 0.000
#> SRR925700 2 0.0609 0.9303 0.000 0.980 0.020 0.000 0.000
#> SRR925701 2 0.0609 0.9303 0.000 0.980 0.020 0.000 0.000
#> SRR925702 4 0.5866 0.6620 0.248 0.000 0.000 0.596 0.156
#> SRR925703 4 0.0000 0.5625 0.000 0.000 0.000 1.000 0.000
#> SRR925704 3 0.0290 0.7220 0.000 0.008 0.992 0.000 0.000
#> SRR925705 3 0.5408 0.6985 0.000 0.120 0.652 0.000 0.228
#> SRR925706 4 0.7375 0.1885 0.136 0.000 0.068 0.412 0.384
#> SRR925708 4 0.4946 0.7110 0.368 0.000 0.000 0.596 0.036
#> SRR925707 5 0.6235 0.9031 0.180 0.088 0.080 0.000 0.652
#> SRR925709 4 0.4946 0.7110 0.368 0.000 0.000 0.596 0.036
#> SRR925710 4 0.5530 0.7119 0.328 0.000 0.016 0.604 0.052
#> SRR925711 3 0.8120 0.0762 0.096 0.292 0.332 0.000 0.280
#> SRR934631 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR934633 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR925713 4 0.5530 0.7119 0.328 0.000 0.016 0.604 0.052
#> SRR925714 4 0.0000 0.5625 0.000 0.000 0.000 1.000 0.000
#> SRR925715 4 0.7364 0.2335 0.136 0.000 0.068 0.428 0.368
#> SRR925716 4 0.0609 0.5624 0.020 0.000 0.000 0.980 0.000
#> SRR925717 4 0.3266 0.6641 0.200 0.000 0.000 0.796 0.004
#> SRR925718 2 0.0880 0.9205 0.000 0.968 0.032 0.000 0.000
#> SRR925719 4 0.4946 0.7110 0.368 0.000 0.000 0.596 0.036
#> SRR925720 4 0.5295 0.6784 0.408 0.000 0.000 0.540 0.052
#> SRR925721 4 0.5295 0.6784 0.408 0.000 0.000 0.540 0.052
#> SRR934634 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR934635 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925723 3 0.3488 0.7171 0.000 0.168 0.808 0.000 0.024
#> SRR925724 3 0.2230 0.7352 0.000 0.116 0.884 0.000 0.000
#> SRR925725 3 0.0000 0.7159 0.000 0.000 1.000 0.000 0.000
#> SRR925726 4 0.0510 0.5733 0.016 0.000 0.000 0.984 0.000
#> SRR925727 3 0.0162 0.7191 0.000 0.004 0.996 0.000 0.000
#> SRR925728 5 0.6184 0.9015 0.180 0.036 0.080 0.028 0.676
#> SRR934636 1 0.6464 0.2704 0.476 0.000 0.000 0.200 0.324
#> SRR934637 1 0.6464 0.2704 0.476 0.000 0.000 0.200 0.324
#> SRR925729 2 0.6463 -0.1347 0.000 0.496 0.276 0.000 0.228
#> SRR934638 1 0.0703 0.8379 0.976 0.000 0.000 0.000 0.024
#> SRR934639 1 0.0703 0.8379 0.976 0.000 0.000 0.000 0.024
#> SRR934640 2 0.1197 0.8972 0.000 0.952 0.048 0.000 0.000
#> SRR934641 2 0.1197 0.8972 0.000 0.952 0.048 0.000 0.000
#> SRR925734 1 0.0000 0.8538 1.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.3612 0.6770 0.000 0.000 0.764 0.008 0.228
#> SRR925736 3 0.5941 0.6613 0.000 0.180 0.592 0.000 0.228
#> SRR925737 3 0.5941 0.6613 0.000 0.180 0.592 0.000 0.228
#> SRR934642 2 0.0609 0.9303 0.000 0.980 0.020 0.000 0.000
#> SRR934643 2 0.0609 0.9303 0.000 0.980 0.020 0.000 0.000
#> SRR934644 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.9309 0.000 1.000 0.000 0.000 0.000
#> SRR925740 3 0.5848 0.6727 0.000 0.168 0.604 0.000 0.228
#> SRR925741 3 0.0290 0.7220 0.000 0.008 0.992 0.000 0.000
#> SRR925742 3 0.5848 0.6727 0.000 0.168 0.604 0.000 0.228
#> SRR1283038 3 0.3912 0.6921 0.000 0.020 0.752 0.000 0.228
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.5436 0.6406 0.404 0.000 0.000 0.476 0.000 0.120
#> SRR925688 4 0.5436 0.6406 0.404 0.000 0.000 0.476 0.000 0.120
#> SRR925689 1 0.0000 0.9920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.9920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.9920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.9920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.0547 0.7550 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR925694 2 0.0146 0.8924 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR925695 3 0.0146 0.7607 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR925697 4 0.1082 0.4522 0.000 0.000 0.000 0.956 0.040 0.004
#> SRR925696 3 0.5114 0.7386 0.000 0.016 0.664 0.000 0.124 0.196
#> SRR925698 3 0.2446 0.7611 0.000 0.012 0.864 0.000 0.124 0.000
#> SRR925699 2 0.1913 0.8828 0.000 0.908 0.012 0.000 0.080 0.000
#> SRR925700 2 0.1913 0.8828 0.000 0.908 0.012 0.000 0.080 0.000
#> SRR925701 2 0.1913 0.8828 0.000 0.908 0.012 0.000 0.080 0.000
#> SRR925702 4 0.5587 0.6307 0.240 0.000 0.000 0.548 0.000 0.212
#> SRR925703 4 0.1082 0.4522 0.000 0.000 0.000 0.956 0.040 0.004
#> SRR925704 3 0.0146 0.7607 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR925705 3 0.5380 0.7148 0.000 0.016 0.620 0.000 0.124 0.240
#> SRR925706 6 0.3592 0.1635 0.000 0.000 0.000 0.344 0.000 0.656
#> SRR925708 4 0.5108 0.6891 0.356 0.000 0.000 0.552 0.000 0.092
#> SRR925707 6 0.2134 0.3378 0.044 0.052 0.000 0.000 0.000 0.904
#> SRR925709 4 0.5108 0.6891 0.356 0.000 0.000 0.552 0.000 0.092
#> SRR925710 4 0.5378 0.6965 0.304 0.000 0.000 0.556 0.000 0.140
#> SRR925711 6 0.6902 -0.2857 0.000 0.184 0.300 0.000 0.080 0.436
#> SRR934631 2 0.0000 0.8937 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.8937 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 2 0.0000 0.8937 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925713 4 0.5378 0.6965 0.304 0.000 0.000 0.556 0.000 0.140
#> SRR925714 4 0.1082 0.4522 0.000 0.000 0.000 0.956 0.040 0.004
#> SRR925715 6 0.3659 0.1214 0.000 0.000 0.000 0.364 0.000 0.636
#> SRR925716 4 0.1624 0.4552 0.020 0.000 0.000 0.936 0.040 0.004
#> SRR925717 4 0.3635 0.6205 0.180 0.000 0.000 0.780 0.008 0.032
#> SRR925718 2 0.2768 0.8161 0.000 0.832 0.012 0.000 0.156 0.000
#> SRR925719 4 0.5108 0.6891 0.356 0.000 0.000 0.552 0.000 0.092
#> SRR925720 4 0.5436 0.6406 0.404 0.000 0.000 0.476 0.000 0.120
#> SRR925721 4 0.5436 0.6406 0.404 0.000 0.000 0.476 0.000 0.120
#> SRR934634 1 0.0146 0.9898 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934635 1 0.0146 0.9898 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925723 3 0.3856 0.7480 0.000 0.032 0.784 0.000 0.156 0.028
#> SRR925724 3 0.2446 0.7611 0.000 0.012 0.864 0.000 0.124 0.000
#> SRR925725 3 0.0547 0.7550 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR925726 4 0.0937 0.4610 0.000 0.000 0.000 0.960 0.040 0.000
#> SRR925727 3 0.0405 0.7565 0.000 0.004 0.988 0.000 0.008 0.000
#> SRR925728 6 0.1007 0.3626 0.044 0.000 0.000 0.000 0.000 0.956
#> SRR934636 5 0.3269 1.0000 0.024 0.000 0.000 0.184 0.792 0.000
#> SRR934637 5 0.3269 1.0000 0.024 0.000 0.000 0.184 0.792 0.000
#> SRR925729 2 0.5831 -0.0429 0.000 0.492 0.264 0.000 0.000 0.244
#> SRR934638 1 0.0632 0.9765 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR934639 1 0.0632 0.9765 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR934640 2 0.1225 0.8770 0.000 0.952 0.036 0.000 0.012 0.000
#> SRR934641 2 0.1225 0.8770 0.000 0.952 0.036 0.000 0.012 0.000
#> SRR925734 1 0.0000 0.9920 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.3838 0.7145 0.000 0.000 0.732 0.008 0.020 0.240
#> SRR925736 3 0.6183 0.6440 0.000 0.108 0.568 0.000 0.080 0.244
#> SRR925737 3 0.6183 0.6440 0.000 0.108 0.568 0.000 0.080 0.244
#> SRR934642 2 0.1913 0.8828 0.000 0.908 0.012 0.000 0.080 0.000
#> SRR934643 2 0.1913 0.8828 0.000 0.908 0.012 0.000 0.080 0.000
#> SRR934644 2 0.0000 0.8937 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.8937 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925740 3 0.5927 0.6880 0.000 0.032 0.572 0.000 0.156 0.240
#> SRR925741 3 0.0146 0.7607 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR925742 3 0.5927 0.6880 0.000 0.032 0.572 0.000 0.156 0.240
#> SRR1283038 3 0.3667 0.7281 0.000 0.008 0.740 0.000 0.012 0.240
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "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 15949 rows and 64 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 0.467 0.647 0.789 0.4936 0.494 0.494
#> 3 3 0.584 0.830 0.861 0.3000 0.840 0.680
#> 4 4 0.709 0.853 0.836 0.1274 0.899 0.714
#> 5 5 0.736 0.769 0.797 0.0687 0.984 0.938
#> 6 6 0.758 0.662 0.749 0.0505 0.899 0.621
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
#> SRR925687 1 0.0672 0.753 0.992 0.008
#> SRR925688 1 0.0672 0.753 0.992 0.008
#> SRR925689 1 0.9963 0.487 0.536 0.464
#> SRR925690 1 0.9963 0.487 0.536 0.464
#> SRR925691 1 0.9963 0.487 0.536 0.464
#> SRR925692 1 0.9963 0.487 0.536 0.464
#> SRR925693 2 0.9970 0.650 0.468 0.532
#> SRR925694 2 0.0000 0.600 0.000 1.000
#> SRR925695 2 0.9970 0.650 0.468 0.532
#> SRR925697 1 0.0000 0.749 1.000 0.000
#> SRR925696 2 0.9944 0.657 0.456 0.544
#> SRR925698 2 0.9970 0.650 0.468 0.532
#> SRR925699 2 0.0000 0.600 0.000 1.000
#> SRR925700 2 0.0000 0.600 0.000 1.000
#> SRR925701 2 0.0000 0.600 0.000 1.000
#> SRR925702 1 0.0672 0.753 0.992 0.008
#> SRR925703 1 0.0000 0.749 1.000 0.000
#> SRR925704 2 0.9963 0.653 0.464 0.536
#> SRR925705 2 0.9944 0.657 0.456 0.544
#> SRR925706 1 0.0000 0.749 1.000 0.000
#> SRR925708 1 0.0672 0.753 0.992 0.008
#> SRR925707 2 0.0376 0.599 0.004 0.996
#> SRR925709 1 0.0672 0.753 0.992 0.008
#> SRR925710 1 0.0000 0.749 1.000 0.000
#> SRR925711 2 0.9944 0.657 0.456 0.544
#> SRR934631 2 0.0000 0.600 0.000 1.000
#> SRR934632 2 0.0000 0.600 0.000 1.000
#> SRR934633 2 0.8608 0.629 0.284 0.716
#> SRR925713 1 0.0000 0.749 1.000 0.000
#> SRR925714 1 0.0000 0.749 1.000 0.000
#> SRR925715 1 0.0000 0.749 1.000 0.000
#> SRR925716 1 0.0672 0.753 0.992 0.008
#> SRR925717 1 0.0000 0.749 1.000 0.000
#> SRR925718 2 0.0000 0.600 0.000 1.000
#> SRR925719 1 0.0672 0.753 0.992 0.008
#> SRR925720 1 0.0672 0.753 0.992 0.008
#> SRR925721 1 0.0672 0.753 0.992 0.008
#> SRR934634 1 0.9963 0.487 0.536 0.464
#> SRR934635 1 0.9944 0.491 0.544 0.456
#> SRR925723 2 0.9963 0.653 0.464 0.536
#> SRR925724 2 0.9970 0.650 0.468 0.532
#> SRR925725 2 0.9970 0.650 0.468 0.532
#> SRR925726 1 0.0000 0.749 1.000 0.000
#> SRR925727 2 0.9970 0.650 0.468 0.532
#> SRR925728 1 0.2043 0.740 0.968 0.032
#> SRR934636 1 0.9970 0.482 0.532 0.468
#> SRR934637 1 0.9209 0.554 0.664 0.336
#> SRR925729 2 0.9963 0.653 0.464 0.536
#> SRR934638 1 0.9963 0.487 0.536 0.464
#> SRR934639 1 0.0672 0.753 0.992 0.008
#> SRR934640 2 0.0000 0.600 0.000 1.000
#> SRR934641 2 0.0000 0.600 0.000 1.000
#> SRR925734 1 0.9963 0.487 0.536 0.464
#> SRR925735 2 0.9970 0.650 0.468 0.532
#> SRR925736 2 0.9944 0.657 0.456 0.544
#> SRR925737 2 0.9944 0.657 0.456 0.544
#> SRR934642 2 0.0000 0.600 0.000 1.000
#> SRR934643 2 0.0000 0.600 0.000 1.000
#> SRR934644 2 0.0000 0.600 0.000 1.000
#> SRR934645 2 0.0000 0.600 0.000 1.000
#> SRR925740 2 0.9881 0.658 0.436 0.564
#> SRR925741 2 0.9970 0.650 0.468 0.532
#> SRR925742 2 0.9881 0.658 0.436 0.564
#> SRR1283038 2 0.9922 0.658 0.448 0.552
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0237 0.840 0.996 0.004 0.000
#> SRR925688 1 0.0237 0.840 0.996 0.004 0.000
#> SRR925689 1 0.5650 0.654 0.688 0.312 0.000
#> SRR925690 1 0.5621 0.659 0.692 0.308 0.000
#> SRR925691 1 0.5621 0.659 0.692 0.308 0.000
#> SRR925692 1 0.5621 0.659 0.692 0.308 0.000
#> SRR925693 3 0.1525 0.922 0.032 0.004 0.964
#> SRR925694 2 0.4931 0.905 0.000 0.768 0.232
#> SRR925695 3 0.0237 0.964 0.004 0.000 0.996
#> SRR925697 1 0.6448 0.760 0.764 0.132 0.104
#> SRR925696 3 0.0747 0.967 0.000 0.016 0.984
#> SRR925698 3 0.0237 0.967 0.000 0.004 0.996
#> SRR925699 2 0.4887 0.905 0.000 0.772 0.228
#> SRR925700 2 0.4887 0.905 0.000 0.772 0.228
#> SRR925701 2 0.4887 0.905 0.000 0.772 0.228
#> SRR925702 1 0.0237 0.839 0.996 0.004 0.000
#> SRR925703 1 0.6519 0.757 0.760 0.132 0.108
#> SRR925704 3 0.0237 0.967 0.000 0.004 0.996
#> SRR925705 3 0.0747 0.967 0.000 0.016 0.984
#> SRR925706 1 0.3695 0.806 0.880 0.012 0.108
#> SRR925708 1 0.0000 0.839 1.000 0.000 0.000
#> SRR925707 2 0.9558 0.507 0.200 0.444 0.356
#> SRR925709 1 0.0000 0.839 1.000 0.000 0.000
#> SRR925710 1 0.3532 0.806 0.884 0.008 0.108
#> SRR925711 3 0.0592 0.966 0.000 0.012 0.988
#> SRR934631 2 0.4931 0.905 0.000 0.768 0.232
#> SRR934632 2 0.4931 0.905 0.000 0.768 0.232
#> SRR934633 3 0.4605 0.670 0.000 0.204 0.796
#> SRR925713 1 0.3532 0.806 0.884 0.008 0.108
#> SRR925714 1 0.6519 0.757 0.760 0.132 0.108
#> SRR925715 1 0.3532 0.806 0.884 0.008 0.108
#> SRR925716 1 0.3551 0.802 0.868 0.132 0.000
#> SRR925717 1 0.3532 0.806 0.884 0.008 0.108
#> SRR925718 2 0.4887 0.905 0.000 0.772 0.228
#> SRR925719 1 0.0000 0.839 1.000 0.000 0.000
#> SRR925720 1 0.0237 0.840 0.996 0.004 0.000
#> SRR925721 1 0.0592 0.839 0.988 0.012 0.000
#> SRR934634 1 0.5650 0.659 0.688 0.312 0.000
#> SRR934635 1 0.5058 0.722 0.756 0.244 0.000
#> SRR925723 3 0.0424 0.967 0.000 0.008 0.992
#> SRR925724 3 0.0237 0.967 0.000 0.004 0.996
#> SRR925725 3 0.0237 0.964 0.004 0.000 0.996
#> SRR925726 1 0.6448 0.760 0.764 0.132 0.104
#> SRR925727 3 0.0237 0.964 0.004 0.000 0.996
#> SRR925728 1 0.0892 0.838 0.980 0.020 0.000
#> SRR934636 2 0.6267 -0.409 0.452 0.548 0.000
#> SRR934637 1 0.4887 0.779 0.772 0.228 0.000
#> SRR925729 3 0.2796 0.868 0.000 0.092 0.908
#> SRR934638 1 0.5650 0.654 0.688 0.312 0.000
#> SRR934639 1 0.0424 0.839 0.992 0.008 0.000
#> SRR934640 2 0.5058 0.896 0.000 0.756 0.244
#> SRR934641 2 0.5058 0.896 0.000 0.756 0.244
#> SRR925734 1 0.5621 0.659 0.692 0.308 0.000
#> SRR925735 3 0.0237 0.964 0.004 0.000 0.996
#> SRR925736 3 0.0747 0.967 0.000 0.016 0.984
#> SRR925737 3 0.0747 0.967 0.000 0.016 0.984
#> SRR934642 2 0.4887 0.905 0.000 0.772 0.228
#> SRR934643 2 0.4887 0.905 0.000 0.772 0.228
#> SRR934644 2 0.4931 0.905 0.000 0.768 0.232
#> SRR934645 2 0.4931 0.905 0.000 0.768 0.232
#> SRR925740 3 0.0747 0.967 0.000 0.016 0.984
#> SRR925741 3 0.0000 0.966 0.000 0.000 1.000
#> SRR925742 3 0.0747 0.967 0.000 0.016 0.984
#> SRR1283038 3 0.0592 0.966 0.000 0.012 0.988
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.2334 0.817 0.088 0.004 0.000 0.908
#> SRR925688 4 0.2334 0.817 0.088 0.004 0.000 0.908
#> SRR925689 1 0.6412 0.915 0.592 0.088 0.000 0.320
#> SRR925690 1 0.6412 0.915 0.592 0.088 0.000 0.320
#> SRR925691 1 0.6412 0.915 0.592 0.088 0.000 0.320
#> SRR925692 1 0.6412 0.915 0.592 0.088 0.000 0.320
#> SRR925693 3 0.3134 0.916 0.100 0.012 0.880 0.008
#> SRR925694 2 0.2610 0.927 0.012 0.900 0.088 0.000
#> SRR925695 3 0.2546 0.924 0.092 0.008 0.900 0.000
#> SRR925697 4 0.4937 0.736 0.144 0.064 0.008 0.784
#> SRR925696 3 0.0524 0.941 0.004 0.008 0.988 0.000
#> SRR925698 3 0.0336 0.941 0.000 0.008 0.992 0.000
#> SRR925699 2 0.2987 0.930 0.016 0.880 0.104 0.000
#> SRR925700 2 0.2987 0.930 0.016 0.880 0.104 0.000
#> SRR925701 2 0.2987 0.930 0.016 0.880 0.104 0.000
#> SRR925702 4 0.1576 0.822 0.048 0.004 0.000 0.948
#> SRR925703 4 0.5179 0.733 0.144 0.064 0.016 0.776
#> SRR925704 3 0.2546 0.924 0.092 0.008 0.900 0.000
#> SRR925705 3 0.0524 0.941 0.004 0.008 0.988 0.000
#> SRR925706 4 0.2781 0.797 0.072 0.008 0.016 0.904
#> SRR925708 4 0.1940 0.815 0.076 0.000 0.000 0.924
#> SRR925707 2 0.9288 0.111 0.320 0.388 0.176 0.116
#> SRR925709 4 0.1902 0.820 0.064 0.004 0.000 0.932
#> SRR925710 4 0.2328 0.806 0.056 0.004 0.016 0.924
#> SRR925711 3 0.2060 0.929 0.052 0.016 0.932 0.000
#> SRR934631 2 0.3333 0.913 0.040 0.872 0.088 0.000
#> SRR934632 2 0.3333 0.913 0.040 0.872 0.088 0.000
#> SRR934633 3 0.4638 0.789 0.060 0.152 0.788 0.000
#> SRR925713 4 0.1796 0.820 0.032 0.004 0.016 0.948
#> SRR925714 4 0.5179 0.733 0.144 0.064 0.016 0.776
#> SRR925715 4 0.3036 0.783 0.080 0.008 0.020 0.892
#> SRR925716 4 0.4849 0.736 0.164 0.064 0.000 0.772
#> SRR925717 4 0.1369 0.830 0.016 0.004 0.016 0.964
#> SRR925718 2 0.3224 0.923 0.016 0.864 0.120 0.000
#> SRR925719 4 0.1940 0.815 0.076 0.000 0.000 0.924
#> SRR925720 4 0.1978 0.823 0.068 0.004 0.000 0.928
#> SRR925721 4 0.2401 0.815 0.092 0.004 0.000 0.904
#> SRR934634 1 0.6319 0.902 0.604 0.084 0.000 0.312
#> SRR934635 1 0.6153 0.886 0.604 0.068 0.000 0.328
#> SRR925723 3 0.0336 0.941 0.000 0.008 0.992 0.000
#> SRR925724 3 0.0336 0.941 0.000 0.008 0.992 0.000
#> SRR925725 3 0.2611 0.923 0.096 0.008 0.896 0.000
#> SRR925726 4 0.4937 0.736 0.144 0.064 0.008 0.784
#> SRR925727 3 0.2546 0.924 0.092 0.008 0.900 0.000
#> SRR925728 4 0.4673 0.395 0.292 0.008 0.000 0.700
#> SRR934636 1 0.6295 0.718 0.660 0.144 0.000 0.196
#> SRR934637 1 0.5663 0.631 0.676 0.060 0.000 0.264
#> SRR925729 3 0.3962 0.847 0.044 0.124 0.832 0.000
#> SRR934638 1 0.6356 0.914 0.596 0.084 0.000 0.320
#> SRR934639 4 0.2081 0.810 0.084 0.000 0.000 0.916
#> SRR934640 2 0.3858 0.904 0.056 0.844 0.100 0.000
#> SRR934641 2 0.3858 0.904 0.056 0.844 0.100 0.000
#> SRR925734 1 0.6356 0.914 0.596 0.084 0.000 0.320
#> SRR925735 3 0.2727 0.924 0.084 0.012 0.900 0.004
#> SRR925736 3 0.1284 0.936 0.024 0.012 0.964 0.000
#> SRR925737 3 0.1284 0.936 0.024 0.012 0.964 0.000
#> SRR934642 2 0.3160 0.929 0.020 0.872 0.108 0.000
#> SRR934643 2 0.3160 0.929 0.020 0.872 0.108 0.000
#> SRR934644 2 0.2730 0.926 0.016 0.896 0.088 0.000
#> SRR934645 2 0.2730 0.926 0.016 0.896 0.088 0.000
#> SRR925740 3 0.0804 0.940 0.012 0.008 0.980 0.000
#> SRR925741 3 0.2546 0.924 0.092 0.008 0.900 0.000
#> SRR925742 3 0.0804 0.940 0.012 0.008 0.980 0.000
#> SRR1283038 3 0.0336 0.941 0.000 0.008 0.992 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.4577 0.6225 0.244 0.012 0.000 0.716 NA
#> SRR925688 4 0.4577 0.6225 0.244 0.012 0.000 0.716 NA
#> SRR925689 1 0.3294 0.8593 0.844 0.024 0.000 0.124 NA
#> SRR925690 1 0.3294 0.8593 0.844 0.024 0.000 0.124 NA
#> SRR925691 1 0.3294 0.8593 0.844 0.024 0.000 0.124 NA
#> SRR925692 1 0.3294 0.8593 0.844 0.024 0.000 0.124 NA
#> SRR925693 3 0.4064 0.8142 0.000 0.004 0.716 0.008 NA
#> SRR925694 2 0.2087 0.9375 0.020 0.928 0.032 0.000 NA
#> SRR925695 3 0.3910 0.8154 0.000 0.000 0.720 0.008 NA
#> SRR925697 4 0.5080 0.5491 0.048 0.000 0.000 0.604 NA
#> SRR925696 3 0.1173 0.8779 0.004 0.012 0.964 0.000 NA
#> SRR925698 3 0.0865 0.8801 0.000 0.004 0.972 0.000 NA
#> SRR925699 2 0.1934 0.9420 0.016 0.928 0.052 0.000 NA
#> SRR925700 2 0.2005 0.9416 0.016 0.924 0.056 0.000 NA
#> SRR925701 2 0.2005 0.9416 0.016 0.924 0.056 0.000 NA
#> SRR925702 4 0.2362 0.6873 0.076 0.000 0.000 0.900 NA
#> SRR925703 4 0.5230 0.5482 0.048 0.000 0.004 0.600 NA
#> SRR925704 3 0.3790 0.8176 0.000 0.004 0.724 0.000 NA
#> SRR925705 3 0.0854 0.8758 0.004 0.012 0.976 0.000 NA
#> SRR925706 4 0.2782 0.6654 0.016 0.004 0.004 0.880 NA
#> SRR925708 4 0.3439 0.6669 0.188 0.008 0.000 0.800 NA
#> SRR925707 4 0.9630 0.0325 0.108 0.144 0.248 0.296 NA
#> SRR925709 4 0.2951 0.6771 0.112 0.000 0.000 0.860 NA
#> SRR925710 4 0.1404 0.6947 0.008 0.004 0.004 0.956 NA
#> SRR925711 3 0.2850 0.8533 0.016 0.016 0.880 0.000 NA
#> SRR934631 2 0.3097 0.9188 0.024 0.876 0.032 0.000 NA
#> SRR934632 2 0.3097 0.9188 0.024 0.876 0.032 0.000 NA
#> SRR934633 3 0.5391 0.7078 0.024 0.160 0.708 0.000 NA
#> SRR925713 4 0.0932 0.6939 0.000 0.004 0.004 0.972 NA
#> SRR925714 4 0.5230 0.5482 0.048 0.000 0.004 0.600 NA
#> SRR925715 4 0.2943 0.6652 0.016 0.004 0.004 0.868 NA
#> SRR925716 4 0.5658 0.4867 0.080 0.000 0.000 0.512 NA
#> SRR925717 4 0.2649 0.6853 0.020 0.004 0.004 0.892 NA
#> SRR925718 2 0.2577 0.9301 0.016 0.892 0.084 0.000 NA
#> SRR925719 4 0.3670 0.6659 0.188 0.008 0.000 0.792 NA
#> SRR925720 4 0.4345 0.6422 0.212 0.012 0.000 0.748 NA
#> SRR925721 4 0.4603 0.6189 0.248 0.012 0.000 0.712 NA
#> SRR934634 1 0.3985 0.8431 0.812 0.024 0.000 0.128 NA
#> SRR934635 1 0.3985 0.8431 0.812 0.024 0.000 0.128 NA
#> SRR925723 3 0.0566 0.8799 0.000 0.004 0.984 0.000 NA
#> SRR925724 3 0.0865 0.8801 0.000 0.004 0.972 0.000 NA
#> SRR925725 3 0.4064 0.8142 0.000 0.004 0.716 0.008 NA
#> SRR925726 4 0.5080 0.5491 0.048 0.000 0.000 0.604 NA
#> SRR925727 3 0.3910 0.8154 0.000 0.000 0.720 0.008 NA
#> SRR925728 4 0.5684 0.5227 0.132 0.004 0.020 0.688 NA
#> SRR934636 1 0.6155 0.5280 0.544 0.020 0.000 0.088 NA
#> SRR934637 1 0.5868 0.4765 0.516 0.004 0.000 0.088 NA
#> SRR925729 3 0.4275 0.7963 0.012 0.136 0.788 0.000 NA
#> SRR934638 1 0.3649 0.8497 0.824 0.020 0.000 0.136 NA
#> SRR934639 4 0.4096 0.6208 0.260 0.004 0.000 0.724 NA
#> SRR934640 2 0.3856 0.9040 0.028 0.828 0.040 0.000 NA
#> SRR934641 2 0.3856 0.9040 0.028 0.828 0.040 0.000 NA
#> SRR925734 1 0.3459 0.8516 0.832 0.016 0.000 0.136 NA
#> SRR925735 3 0.3139 0.8625 0.008 0.012 0.860 0.008 NA
#> SRR925736 3 0.1682 0.8658 0.004 0.012 0.940 0.000 NA
#> SRR925737 3 0.1682 0.8658 0.004 0.012 0.940 0.000 NA
#> SRR934642 2 0.2444 0.9377 0.012 0.904 0.068 0.000 NA
#> SRR934643 2 0.2444 0.9377 0.012 0.904 0.068 0.000 NA
#> SRR934644 2 0.2529 0.9339 0.024 0.908 0.032 0.000 NA
#> SRR934645 2 0.2529 0.9339 0.024 0.908 0.032 0.000 NA
#> SRR925740 3 0.1074 0.8745 0.004 0.012 0.968 0.000 NA
#> SRR925741 3 0.4064 0.8142 0.000 0.004 0.716 0.008 NA
#> SRR925742 3 0.1074 0.8745 0.004 0.012 0.968 0.000 NA
#> SRR1283038 3 0.0451 0.8801 0.000 0.004 0.988 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.5495 0.4754 0.316 0.000 0.064 0.580 0.040 0.000
#> SRR925688 4 0.5371 0.4831 0.316 0.000 0.064 0.588 0.032 0.000
#> SRR925689 1 0.1921 0.8420 0.924 0.000 0.044 0.024 0.004 0.004
#> SRR925690 1 0.1921 0.8420 0.924 0.000 0.044 0.024 0.004 0.004
#> SRR925691 1 0.1921 0.8420 0.924 0.000 0.044 0.024 0.004 0.004
#> SRR925692 1 0.1921 0.8420 0.924 0.000 0.044 0.024 0.004 0.004
#> SRR925693 6 0.0767 0.7742 0.000 0.008 0.000 0.004 0.012 0.976
#> SRR925694 2 0.1880 0.9072 0.008 0.932 0.032 0.004 0.020 0.004
#> SRR925695 6 0.0862 0.7739 0.000 0.008 0.016 0.000 0.004 0.972
#> SRR925697 5 0.4439 0.9490 0.028 0.000 0.000 0.432 0.540 0.000
#> SRR925696 3 0.4284 0.7485 0.000 0.016 0.588 0.000 0.004 0.392
#> SRR925698 3 0.4325 0.7297 0.000 0.016 0.568 0.000 0.004 0.412
#> SRR925699 2 0.0508 0.9078 0.004 0.984 0.012 0.000 0.000 0.000
#> SRR925700 2 0.0603 0.9076 0.004 0.980 0.016 0.000 0.000 0.000
#> SRR925701 2 0.0837 0.9064 0.004 0.972 0.020 0.000 0.000 0.004
#> SRR925702 4 0.2015 0.5092 0.056 0.000 0.016 0.916 0.012 0.000
#> SRR925703 5 0.4566 0.9494 0.028 0.000 0.000 0.428 0.540 0.004
#> SRR925704 6 0.1053 0.7713 0.000 0.012 0.020 0.000 0.004 0.964
#> SRR925705 3 0.4245 0.7525 0.000 0.016 0.604 0.000 0.004 0.376
#> SRR925706 4 0.4203 0.3670 0.004 0.000 0.072 0.764 0.148 0.012
#> SRR925708 4 0.4156 0.5205 0.224 0.000 0.020 0.728 0.028 0.000
#> SRR925707 3 0.7415 -0.0558 0.048 0.036 0.380 0.356 0.176 0.004
#> SRR925709 4 0.2415 0.5280 0.084 0.000 0.016 0.888 0.012 0.000
#> SRR925710 4 0.1980 0.4380 0.016 0.000 0.000 0.920 0.048 0.016
#> SRR925711 3 0.5408 0.6200 0.000 0.020 0.552 0.016 0.040 0.372
#> SRR934631 2 0.3221 0.8895 0.004 0.844 0.100 0.004 0.044 0.004
#> SRR934632 2 0.3221 0.8895 0.004 0.844 0.100 0.004 0.044 0.004
#> SRR934633 3 0.6817 0.1030 0.004 0.160 0.456 0.004 0.056 0.320
#> SRR925713 4 0.1887 0.4329 0.016 0.000 0.000 0.924 0.048 0.012
#> SRR925714 5 0.4566 0.9494 0.028 0.000 0.000 0.428 0.540 0.004
#> SRR925715 4 0.4239 0.3725 0.004 0.000 0.068 0.764 0.148 0.016
#> SRR925716 5 0.4703 0.8561 0.048 0.000 0.000 0.408 0.544 0.000
#> SRR925717 4 0.4330 0.0633 0.036 0.000 0.016 0.744 0.192 0.012
#> SRR925718 2 0.2051 0.8614 0.004 0.896 0.096 0.000 0.000 0.004
#> SRR925719 4 0.3831 0.5301 0.224 0.000 0.020 0.744 0.012 0.000
#> SRR925720 4 0.5481 0.4670 0.296 0.000 0.064 0.596 0.044 0.000
#> SRR925721 4 0.5411 0.4725 0.328 0.000 0.064 0.576 0.032 0.000
#> SRR934634 1 0.1838 0.8135 0.928 0.000 0.040 0.020 0.012 0.000
#> SRR934635 1 0.1861 0.8148 0.928 0.000 0.036 0.020 0.016 0.000
#> SRR925723 3 0.4301 0.7410 0.000 0.016 0.580 0.000 0.004 0.400
#> SRR925724 3 0.4332 0.7304 0.000 0.016 0.564 0.000 0.004 0.416
#> SRR925725 6 0.0725 0.7753 0.000 0.012 0.000 0.000 0.012 0.976
#> SRR925726 5 0.4463 0.9212 0.028 0.000 0.000 0.456 0.516 0.000
#> SRR925727 6 0.0665 0.7747 0.000 0.008 0.008 0.000 0.004 0.980
#> SRR925728 4 0.5542 0.3519 0.076 0.000 0.104 0.664 0.156 0.000
#> SRR934636 1 0.5546 0.4301 0.508 0.004 0.068 0.020 0.400 0.000
#> SRR934637 1 0.5452 0.3606 0.468 0.000 0.068 0.020 0.444 0.000
#> SRR925729 6 0.6211 -0.2948 0.000 0.148 0.396 0.000 0.028 0.428
#> SRR934638 1 0.1049 0.8328 0.960 0.000 0.000 0.032 0.008 0.000
#> SRR934639 4 0.4211 0.4770 0.364 0.000 0.004 0.616 0.016 0.000
#> SRR934640 2 0.5075 0.8243 0.004 0.716 0.088 0.016 0.156 0.020
#> SRR934641 2 0.5075 0.8243 0.004 0.716 0.088 0.016 0.156 0.020
#> SRR925734 1 0.0972 0.8343 0.964 0.000 0.000 0.028 0.008 0.000
#> SRR925735 6 0.4930 -0.1234 0.000 0.008 0.332 0.008 0.044 0.608
#> SRR925736 3 0.5059 0.7176 0.000 0.024 0.596 0.004 0.036 0.340
#> SRR925737 3 0.5059 0.7176 0.000 0.024 0.596 0.004 0.036 0.340
#> SRR934642 2 0.1514 0.9035 0.004 0.944 0.036 0.000 0.012 0.004
#> SRR934643 2 0.1514 0.9035 0.004 0.944 0.036 0.000 0.012 0.004
#> SRR934644 2 0.3313 0.8947 0.008 0.844 0.052 0.004 0.088 0.004
#> SRR934645 2 0.3313 0.8947 0.008 0.844 0.052 0.004 0.088 0.004
#> SRR925740 3 0.4497 0.7502 0.000 0.020 0.600 0.000 0.012 0.368
#> SRR925741 6 0.0984 0.7764 0.000 0.012 0.008 0.000 0.012 0.968
#> SRR925742 3 0.4486 0.7505 0.000 0.020 0.604 0.000 0.012 0.364
#> SRR1283038 3 0.4387 0.7406 0.000 0.016 0.584 0.000 0.008 0.392
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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.500 0.880 0.914 0.5053 0.494 0.494
#> 3 3 0.809 0.829 0.906 0.3042 0.840 0.680
#> 4 4 0.864 0.907 0.940 0.1346 0.899 0.714
#> 5 5 0.793 0.783 0.828 0.0563 1.000 1.000
#> 6 6 0.762 0.636 0.768 0.0405 0.915 0.678
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
#> SRR925687 1 0.000 0.910 1.000 0.000
#> SRR925688 1 0.000 0.910 1.000 0.000
#> SRR925689 1 0.714 0.840 0.804 0.196
#> SRR925690 1 0.714 0.840 0.804 0.196
#> SRR925691 1 0.714 0.840 0.804 0.196
#> SRR925692 1 0.714 0.840 0.804 0.196
#> SRR925693 2 0.714 0.874 0.196 0.804
#> SRR925694 2 0.000 0.879 0.000 1.000
#> SRR925695 2 0.714 0.874 0.196 0.804
#> SRR925697 1 0.000 0.910 1.000 0.000
#> SRR925696 2 0.714 0.874 0.196 0.804
#> SRR925698 2 0.714 0.874 0.196 0.804
#> SRR925699 2 0.000 0.879 0.000 1.000
#> SRR925700 2 0.000 0.879 0.000 1.000
#> SRR925701 2 0.000 0.879 0.000 1.000
#> SRR925702 1 0.000 0.910 1.000 0.000
#> SRR925703 1 0.000 0.910 1.000 0.000
#> SRR925704 2 0.714 0.874 0.196 0.804
#> SRR925705 2 0.714 0.874 0.196 0.804
#> SRR925706 1 0.000 0.910 1.000 0.000
#> SRR925708 1 0.000 0.910 1.000 0.000
#> SRR925707 2 0.000 0.879 0.000 1.000
#> SRR925709 1 0.000 0.910 1.000 0.000
#> SRR925710 1 0.000 0.910 1.000 0.000
#> SRR925711 2 0.714 0.874 0.196 0.804
#> SRR934631 2 0.000 0.879 0.000 1.000
#> SRR934632 2 0.000 0.879 0.000 1.000
#> SRR934633 2 0.000 0.879 0.000 1.000
#> SRR925713 1 0.000 0.910 1.000 0.000
#> SRR925714 1 0.000 0.910 1.000 0.000
#> SRR925715 1 0.000 0.910 1.000 0.000
#> SRR925716 1 0.000 0.910 1.000 0.000
#> SRR925717 1 0.000 0.910 1.000 0.000
#> SRR925718 2 0.000 0.879 0.000 1.000
#> SRR925719 1 0.000 0.910 1.000 0.000
#> SRR925720 1 0.000 0.910 1.000 0.000
#> SRR925721 1 0.000 0.910 1.000 0.000
#> SRR934634 1 0.714 0.840 0.804 0.196
#> SRR934635 1 0.714 0.840 0.804 0.196
#> SRR925723 2 0.714 0.874 0.196 0.804
#> SRR925724 2 0.714 0.874 0.196 0.804
#> SRR925725 2 0.714 0.874 0.196 0.804
#> SRR925726 1 0.000 0.910 1.000 0.000
#> SRR925727 2 0.714 0.874 0.196 0.804
#> SRR925728 1 0.714 0.840 0.804 0.196
#> SRR934636 1 0.714 0.840 0.804 0.196
#> SRR934637 1 0.714 0.840 0.804 0.196
#> SRR925729 2 0.714 0.874 0.196 0.804
#> SRR934638 1 0.714 0.840 0.804 0.196
#> SRR934639 1 0.000 0.910 1.000 0.000
#> SRR934640 2 0.000 0.879 0.000 1.000
#> SRR934641 2 0.000 0.879 0.000 1.000
#> SRR925734 1 0.714 0.840 0.804 0.196
#> SRR925735 2 0.714 0.874 0.196 0.804
#> SRR925736 2 0.714 0.874 0.196 0.804
#> SRR925737 2 0.714 0.874 0.196 0.804
#> SRR934642 2 0.000 0.879 0.000 1.000
#> SRR934643 2 0.000 0.879 0.000 1.000
#> SRR934644 2 0.000 0.879 0.000 1.000
#> SRR934645 2 0.000 0.879 0.000 1.000
#> SRR925740 2 0.000 0.879 0.000 1.000
#> SRR925741 2 0.714 0.874 0.196 0.804
#> SRR925742 2 0.000 0.879 0.000 1.000
#> SRR1283038 2 0.714 0.874 0.196 0.804
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925688 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925689 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR925690 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR925691 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR925692 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR925693 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925694 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925695 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925697 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925696 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925698 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925699 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925700 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925701 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925702 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925703 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925704 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925705 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925706 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925708 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925707 2 0.2356 0.8729 0.072 0.928 0.000
#> SRR925709 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925710 1 0.1411 0.8301 0.964 0.036 0.000
#> SRR925711 3 0.3038 0.8853 0.000 0.104 0.896
#> SRR934631 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934632 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934633 3 0.4235 0.7984 0.000 0.176 0.824
#> SRR925713 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925714 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925715 1 0.0747 0.8208 0.984 0.000 0.016
#> SRR925716 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925717 1 0.0237 0.8303 0.996 0.004 0.000
#> SRR925718 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925719 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925720 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR925721 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR934634 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR934635 1 0.6295 0.4192 0.528 0.472 0.000
#> SRR925723 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925724 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925725 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925726 1 0.0000 0.8299 1.000 0.000 0.000
#> SRR925727 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925728 1 0.0424 0.8288 0.992 0.008 0.000
#> SRR934636 2 0.6192 0.0929 0.420 0.580 0.000
#> SRR934637 1 0.0237 0.8296 0.996 0.004 0.000
#> SRR925729 3 0.3482 0.8595 0.000 0.128 0.872
#> SRR934638 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR934639 1 0.2356 0.8272 0.928 0.072 0.000
#> SRR934640 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934641 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925734 1 0.6309 0.3794 0.504 0.496 0.000
#> SRR925735 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925736 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925737 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR934642 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934643 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934644 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR934645 2 0.2356 0.9571 0.000 0.928 0.072
#> SRR925740 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925741 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR925742 3 0.0000 0.9775 0.000 0.000 1.000
#> SRR1283038 3 0.0000 0.9775 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.3649 0.848 0.204 0.000 0.000 0.796
#> SRR925688 4 0.3649 0.848 0.204 0.000 0.000 0.796
#> SRR925689 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925690 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925691 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925692 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925693 3 0.0895 0.959 0.004 0.020 0.976 0.000
#> SRR925694 2 0.0336 0.980 0.000 0.992 0.008 0.000
#> SRR925695 3 0.0779 0.961 0.004 0.016 0.980 0.000
#> SRR925697 4 0.1022 0.875 0.032 0.000 0.000 0.968
#> SRR925696 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925698 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925699 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR925700 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR925701 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR925702 4 0.0524 0.867 0.008 0.004 0.000 0.988
#> SRR925703 4 0.1022 0.875 0.032 0.000 0.000 0.968
#> SRR925704 3 0.0779 0.961 0.004 0.016 0.980 0.000
#> SRR925705 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925706 4 0.0188 0.863 0.000 0.004 0.000 0.996
#> SRR925708 4 0.3649 0.848 0.204 0.000 0.000 0.796
#> SRR925707 2 0.2814 0.845 0.000 0.868 0.000 0.132
#> SRR925709 4 0.3448 0.847 0.168 0.004 0.000 0.828
#> SRR925710 4 0.2053 0.873 0.072 0.004 0.000 0.924
#> SRR925711 3 0.2773 0.886 0.004 0.116 0.880 0.000
#> SRR934631 2 0.0336 0.980 0.000 0.992 0.008 0.000
#> SRR934632 2 0.0336 0.980 0.000 0.992 0.008 0.000
#> SRR934633 3 0.4252 0.710 0.004 0.252 0.744 0.000
#> SRR925713 4 0.0376 0.865 0.004 0.004 0.000 0.992
#> SRR925714 4 0.1022 0.875 0.032 0.000 0.000 0.968
#> SRR925715 4 0.1151 0.872 0.024 0.008 0.000 0.968
#> SRR925716 4 0.1118 0.874 0.036 0.000 0.000 0.964
#> SRR925717 4 0.1637 0.878 0.060 0.000 0.000 0.940
#> SRR925718 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR925719 4 0.3649 0.848 0.204 0.000 0.000 0.796
#> SRR925720 4 0.3649 0.848 0.204 0.000 0.000 0.796
#> SRR925721 4 0.3764 0.839 0.216 0.000 0.000 0.784
#> SRR934634 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR934635 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925723 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925724 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925725 3 0.0779 0.961 0.004 0.016 0.980 0.000
#> SRR925726 4 0.1022 0.875 0.032 0.000 0.000 0.968
#> SRR925727 3 0.0657 0.962 0.004 0.012 0.984 0.000
#> SRR925728 4 0.5028 0.148 0.400 0.004 0.000 0.596
#> SRR934636 1 0.3356 0.803 0.824 0.000 0.000 0.176
#> SRR934637 1 0.3356 0.803 0.824 0.000 0.000 0.176
#> SRR925729 3 0.3710 0.797 0.004 0.192 0.804 0.000
#> SRR934638 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR934639 4 0.3764 0.839 0.216 0.000 0.000 0.784
#> SRR934640 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> SRR934641 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> SRR925734 1 0.0188 0.954 0.996 0.000 0.000 0.004
#> SRR925735 3 0.1209 0.955 0.004 0.032 0.964 0.000
#> SRR925736 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925737 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR934642 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR934643 2 0.0817 0.979 0.000 0.976 0.024 0.000
#> SRR934644 2 0.0336 0.980 0.000 0.992 0.008 0.000
#> SRR934645 2 0.0336 0.980 0.000 0.992 0.008 0.000
#> SRR925740 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR925741 3 0.0779 0.961 0.004 0.016 0.980 0.000
#> SRR925742 3 0.0000 0.963 0.000 0.000 1.000 0.000
#> SRR1283038 3 0.0469 0.962 0.000 0.012 0.988 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.3929 0.735 0.208 0.000 0.000 0.764 NA
#> SRR925688 4 0.3929 0.735 0.208 0.000 0.000 0.764 NA
#> SRR925689 1 0.0000 0.893 1.000 0.000 0.000 0.000 NA
#> SRR925690 1 0.0000 0.893 1.000 0.000 0.000 0.000 NA
#> SRR925691 1 0.0000 0.893 1.000 0.000 0.000 0.000 NA
#> SRR925692 1 0.0000 0.893 1.000 0.000 0.000 0.000 NA
#> SRR925693 3 0.0703 0.778 0.000 0.000 0.976 0.000 NA
#> SRR925694 2 0.0162 0.954 0.000 0.996 0.004 0.000 NA
#> SRR925695 3 0.0000 0.785 0.000 0.000 1.000 0.000 NA
#> SRR925697 4 0.3661 0.720 0.000 0.000 0.000 0.724 NA
#> SRR925696 3 0.4182 0.794 0.000 0.004 0.644 0.000 NA
#> SRR925698 3 0.3579 0.808 0.000 0.004 0.756 0.000 NA
#> SRR925699 2 0.0609 0.953 0.000 0.980 0.000 0.000 NA
#> SRR925700 2 0.0609 0.953 0.000 0.980 0.000 0.000 NA
#> SRR925701 2 0.0771 0.952 0.000 0.976 0.004 0.000 NA
#> SRR925702 4 0.2077 0.764 0.008 0.000 0.000 0.908 NA
#> SRR925703 4 0.3661 0.720 0.000 0.000 0.000 0.724 NA
#> SRR925704 3 0.0162 0.785 0.000 0.004 0.996 0.000 NA
#> SRR925705 3 0.4299 0.785 0.000 0.004 0.608 0.000 NA
#> SRR925706 4 0.3774 0.660 0.000 0.000 0.000 0.704 NA
#> SRR925708 4 0.3300 0.744 0.204 0.000 0.000 0.792 NA
#> SRR925707 2 0.5870 0.565 0.000 0.584 0.000 0.140 NA
#> SRR925709 4 0.3090 0.757 0.104 0.000 0.000 0.856 NA
#> SRR925710 4 0.2221 0.773 0.052 0.000 0.000 0.912 NA
#> SRR925711 3 0.5458 0.761 0.000 0.088 0.608 0.000 NA
#> SRR934631 2 0.0162 0.954 0.000 0.996 0.004 0.000 NA
#> SRR934632 2 0.0162 0.954 0.000 0.996 0.004 0.000 NA
#> SRR934633 3 0.5119 0.376 0.000 0.360 0.592 0.000 NA
#> SRR925713 4 0.1331 0.772 0.008 0.000 0.000 0.952 NA
#> SRR925714 4 0.3661 0.720 0.000 0.000 0.000 0.724 NA
#> SRR925715 4 0.2942 0.743 0.008 0.000 0.008 0.856 NA
#> SRR925716 4 0.4697 0.659 0.032 0.000 0.000 0.648 NA
#> SRR925717 4 0.2624 0.772 0.012 0.000 0.000 0.872 NA
#> SRR925718 2 0.1571 0.926 0.000 0.936 0.004 0.000 NA
#> SRR925719 4 0.3492 0.747 0.188 0.000 0.000 0.796 NA
#> SRR925720 4 0.3929 0.735 0.208 0.000 0.000 0.764 NA
#> SRR925721 4 0.4083 0.722 0.228 0.000 0.000 0.744 NA
#> SRR934634 1 0.0451 0.892 0.988 0.000 0.000 0.004 NA
#> SRR934635 1 0.0451 0.892 0.988 0.000 0.000 0.004 NA
#> SRR925723 3 0.3715 0.808 0.000 0.004 0.736 0.000 NA
#> SRR925724 3 0.3521 0.809 0.000 0.004 0.764 0.000 NA
#> SRR925725 3 0.0324 0.784 0.000 0.004 0.992 0.000 NA
#> SRR925726 4 0.3336 0.741 0.000 0.000 0.000 0.772 NA
#> SRR925727 3 0.0000 0.785 0.000 0.000 1.000 0.000 NA
#> SRR925728 4 0.6715 0.050 0.248 0.000 0.000 0.392 NA
#> SRR934636 1 0.5751 0.527 0.552 0.000 0.000 0.100 NA
#> SRR934637 1 0.5900 0.483 0.516 0.000 0.000 0.108 NA
#> SRR925729 3 0.3919 0.665 0.000 0.188 0.776 0.000 NA
#> SRR934638 1 0.0451 0.892 0.988 0.000 0.000 0.004 NA
#> SRR934639 4 0.4297 0.677 0.288 0.000 0.000 0.692 NA
#> SRR934640 2 0.0865 0.946 0.000 0.972 0.004 0.000 NA
#> SRR934641 2 0.0865 0.946 0.000 0.972 0.004 0.000 NA
#> SRR925734 1 0.0324 0.893 0.992 0.000 0.000 0.004 NA
#> SRR925735 3 0.1753 0.762 0.000 0.032 0.936 0.000 NA
#> SRR925736 3 0.4425 0.761 0.000 0.004 0.544 0.000 NA
#> SRR925737 3 0.4425 0.761 0.000 0.004 0.544 0.000 NA
#> SRR934642 2 0.1041 0.947 0.000 0.964 0.004 0.000 NA
#> SRR934643 2 0.1041 0.947 0.000 0.964 0.004 0.000 NA
#> SRR934644 2 0.0162 0.954 0.000 0.996 0.004 0.000 NA
#> SRR934645 2 0.0162 0.954 0.000 0.996 0.004 0.000 NA
#> SRR925740 3 0.4403 0.768 0.000 0.004 0.560 0.000 NA
#> SRR925741 3 0.0324 0.784 0.000 0.004 0.992 0.000 NA
#> SRR925742 3 0.4403 0.768 0.000 0.004 0.560 0.000 NA
#> SRR1283038 3 0.4060 0.795 0.000 0.000 0.640 0.000 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.6175 0.5689 0.136 0.000 0.000 0.600 0.104 0.160
#> SRR925688 4 0.6227 0.5674 0.132 0.000 0.000 0.592 0.104 0.172
#> SRR925689 1 0.0146 0.8009 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR925690 1 0.0146 0.8009 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR925691 1 0.0146 0.8009 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR925692 1 0.0146 0.8009 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR925693 5 0.3619 0.8801 0.000 0.000 0.316 0.000 0.680 0.004
#> SRR925694 2 0.0146 0.8985 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925695 5 0.3634 0.8879 0.000 0.000 0.356 0.000 0.644 0.000
#> SRR925697 4 0.3551 0.3998 0.000 0.000 0.000 0.792 0.148 0.060
#> SRR925696 3 0.2278 0.7278 0.000 0.004 0.868 0.000 0.128 0.000
#> SRR925698 3 0.3426 0.5302 0.000 0.004 0.720 0.000 0.276 0.000
#> SRR925699 2 0.1086 0.8981 0.000 0.964 0.012 0.000 0.012 0.012
#> SRR925700 2 0.1086 0.8981 0.000 0.964 0.012 0.000 0.012 0.012
#> SRR925701 2 0.1086 0.8981 0.000 0.964 0.012 0.000 0.012 0.012
#> SRR925702 4 0.4549 0.3764 0.004 0.000 0.000 0.552 0.028 0.416
#> SRR925703 4 0.3551 0.3998 0.000 0.000 0.000 0.792 0.148 0.060
#> SRR925704 5 0.3634 0.8879 0.000 0.000 0.356 0.000 0.644 0.000
#> SRR925705 3 0.1858 0.7404 0.000 0.004 0.904 0.000 0.092 0.000
#> SRR925706 6 0.4276 0.1088 0.000 0.000 0.000 0.416 0.020 0.564
#> SRR925708 4 0.5345 0.5890 0.124 0.000 0.000 0.680 0.056 0.140
#> SRR925707 6 0.4097 0.3956 0.000 0.284 0.012 0.000 0.016 0.688
#> SRR925709 4 0.5322 0.4952 0.056 0.000 0.000 0.580 0.032 0.332
#> SRR925710 4 0.3627 0.4609 0.020 0.000 0.000 0.752 0.004 0.224
#> SRR925711 3 0.6551 0.1508 0.000 0.104 0.488 0.000 0.312 0.096
#> SRR934631 2 0.0405 0.8978 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR934632 2 0.0405 0.8978 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR934633 2 0.6003 -0.1940 0.000 0.444 0.180 0.000 0.368 0.008
#> SRR925713 4 0.3215 0.4338 0.000 0.000 0.000 0.756 0.004 0.240
#> SRR925714 4 0.3551 0.3998 0.000 0.000 0.000 0.792 0.148 0.060
#> SRR925715 4 0.4276 0.0589 0.000 0.000 0.000 0.564 0.020 0.416
#> SRR925716 4 0.5400 0.2590 0.012 0.000 0.000 0.624 0.184 0.180
#> SRR925717 4 0.1296 0.5312 0.004 0.000 0.000 0.948 0.004 0.044
#> SRR925718 2 0.2043 0.8611 0.000 0.912 0.064 0.000 0.012 0.012
#> SRR925719 4 0.5605 0.5848 0.116 0.000 0.000 0.652 0.064 0.168
#> SRR925720 4 0.6175 0.5689 0.136 0.000 0.000 0.600 0.104 0.160
#> SRR925721 4 0.6425 0.5485 0.156 0.000 0.000 0.568 0.108 0.168
#> SRR934634 1 0.2811 0.7814 0.872 0.000 0.000 0.032 0.076 0.020
#> SRR934635 1 0.2681 0.7856 0.880 0.000 0.000 0.028 0.072 0.020
#> SRR925723 3 0.2994 0.6530 0.000 0.004 0.788 0.000 0.208 0.000
#> SRR925724 3 0.3646 0.4731 0.000 0.004 0.700 0.000 0.292 0.004
#> SRR925725 5 0.3684 0.8912 0.000 0.000 0.332 0.000 0.664 0.004
#> SRR925726 4 0.2432 0.4639 0.000 0.000 0.000 0.876 0.100 0.024
#> SRR925727 5 0.3782 0.8843 0.000 0.000 0.360 0.000 0.636 0.004
#> SRR925728 6 0.3652 0.4909 0.088 0.000 0.004 0.072 0.016 0.820
#> SRR934636 1 0.7480 0.1661 0.376 0.000 0.000 0.196 0.176 0.252
#> SRR934637 1 0.7577 0.1155 0.344 0.000 0.000 0.212 0.184 0.260
#> SRR925729 5 0.5557 0.5912 0.000 0.148 0.300 0.000 0.548 0.004
#> SRR934638 1 0.2613 0.7881 0.884 0.000 0.000 0.016 0.068 0.032
#> SRR934639 4 0.6695 0.4778 0.264 0.000 0.000 0.496 0.088 0.152
#> SRR934640 2 0.2258 0.8351 0.000 0.896 0.000 0.000 0.044 0.060
#> SRR934641 2 0.2258 0.8351 0.000 0.896 0.000 0.000 0.044 0.060
#> SRR925734 1 0.1692 0.7961 0.932 0.000 0.000 0.012 0.048 0.008
#> SRR925735 5 0.4061 0.8333 0.000 0.012 0.316 0.000 0.664 0.008
#> SRR925736 3 0.1434 0.7148 0.000 0.008 0.948 0.000 0.020 0.024
#> SRR925737 3 0.1426 0.7102 0.000 0.008 0.948 0.000 0.016 0.028
#> SRR934642 2 0.1434 0.8920 0.000 0.948 0.028 0.000 0.012 0.012
#> SRR934643 2 0.1434 0.8920 0.000 0.948 0.028 0.000 0.012 0.012
#> SRR934644 2 0.0405 0.8979 0.000 0.988 0.000 0.000 0.004 0.008
#> SRR934645 2 0.0405 0.8979 0.000 0.988 0.000 0.000 0.004 0.008
#> SRR925740 3 0.0405 0.7349 0.000 0.008 0.988 0.000 0.000 0.004
#> SRR925741 5 0.3592 0.8935 0.000 0.000 0.344 0.000 0.656 0.000
#> SRR925742 3 0.0405 0.7349 0.000 0.008 0.988 0.000 0.000 0.004
#> SRR1283038 3 0.2595 0.6918 0.000 0.004 0.836 0.000 0.160 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.994 0.5055 0.494 0.494
#> 3 3 0.645 0.563 0.809 0.2554 0.884 0.770
#> 4 4 0.766 0.894 0.918 0.1661 0.787 0.502
#> 5 5 0.743 0.730 0.835 0.0476 0.948 0.800
#> 6 6 0.772 0.708 0.858 0.0281 0.984 0.926
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
#> SRR925687 1 0.0000 0.987 1.000 0.000
#> SRR925688 1 0.0000 0.987 1.000 0.000
#> SRR925689 1 0.0000 0.987 1.000 0.000
#> SRR925690 1 0.0000 0.987 1.000 0.000
#> SRR925691 1 0.0000 0.987 1.000 0.000
#> SRR925692 1 0.0000 0.987 1.000 0.000
#> SRR925693 2 0.0000 1.000 0.000 1.000
#> SRR925694 2 0.0000 1.000 0.000 1.000
#> SRR925695 2 0.0000 1.000 0.000 1.000
#> SRR925697 1 0.0000 0.987 1.000 0.000
#> SRR925696 2 0.0000 1.000 0.000 1.000
#> SRR925698 2 0.0000 1.000 0.000 1.000
#> SRR925699 2 0.0000 1.000 0.000 1.000
#> SRR925700 2 0.0000 1.000 0.000 1.000
#> SRR925701 2 0.0000 1.000 0.000 1.000
#> SRR925702 1 0.0000 0.987 1.000 0.000
#> SRR925703 1 0.0000 0.987 1.000 0.000
#> SRR925704 2 0.0000 1.000 0.000 1.000
#> SRR925705 2 0.0000 1.000 0.000 1.000
#> SRR925706 1 0.0376 0.984 0.996 0.004
#> SRR925708 1 0.0000 0.987 1.000 0.000
#> SRR925707 2 0.0000 1.000 0.000 1.000
#> SRR925709 1 0.0000 0.987 1.000 0.000
#> SRR925710 1 0.0000 0.987 1.000 0.000
#> SRR925711 2 0.0000 1.000 0.000 1.000
#> SRR934631 2 0.0000 1.000 0.000 1.000
#> SRR934632 2 0.0000 1.000 0.000 1.000
#> SRR934633 2 0.0000 1.000 0.000 1.000
#> SRR925713 1 0.0000 0.987 1.000 0.000
#> SRR925714 1 0.0000 0.987 1.000 0.000
#> SRR925715 1 0.3584 0.920 0.932 0.068
#> SRR925716 1 0.0000 0.987 1.000 0.000
#> SRR925717 1 0.0000 0.987 1.000 0.000
#> SRR925718 2 0.0000 1.000 0.000 1.000
#> SRR925719 1 0.0000 0.987 1.000 0.000
#> SRR925720 1 0.0000 0.987 1.000 0.000
#> SRR925721 1 0.0000 0.987 1.000 0.000
#> SRR934634 1 0.0000 0.987 1.000 0.000
#> SRR934635 1 0.0000 0.987 1.000 0.000
#> SRR925723 2 0.0000 1.000 0.000 1.000
#> SRR925724 2 0.0000 1.000 0.000 1.000
#> SRR925725 2 0.0000 1.000 0.000 1.000
#> SRR925726 1 0.0000 0.987 1.000 0.000
#> SRR925727 2 0.0000 1.000 0.000 1.000
#> SRR925728 1 0.8861 0.565 0.696 0.304
#> SRR934636 1 0.0000 0.987 1.000 0.000
#> SRR934637 1 0.0000 0.987 1.000 0.000
#> SRR925729 2 0.0000 1.000 0.000 1.000
#> SRR934638 1 0.0000 0.987 1.000 0.000
#> SRR934639 1 0.0000 0.987 1.000 0.000
#> SRR934640 2 0.0000 1.000 0.000 1.000
#> SRR934641 2 0.0000 1.000 0.000 1.000
#> SRR925734 1 0.0000 0.987 1.000 0.000
#> SRR925735 2 0.0000 1.000 0.000 1.000
#> SRR925736 2 0.0000 1.000 0.000 1.000
#> SRR925737 2 0.0000 1.000 0.000 1.000
#> SRR934642 2 0.0000 1.000 0.000 1.000
#> SRR934643 2 0.0000 1.000 0.000 1.000
#> SRR934644 2 0.0000 1.000 0.000 1.000
#> SRR934645 2 0.0000 1.000 0.000 1.000
#> SRR925740 2 0.0000 1.000 0.000 1.000
#> SRR925741 2 0.0000 1.000 0.000 1.000
#> SRR925742 2 0.0000 1.000 0.000 1.000
#> SRR1283038 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925689 3 0.5678 0.3502 0.316 0.000 0.684
#> SRR925690 1 0.6126 0.3672 0.600 0.000 0.400
#> SRR925691 1 0.5948 0.4612 0.640 0.000 0.360
#> SRR925692 3 0.6267 0.0342 0.452 0.000 0.548
#> SRR925693 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925694 3 0.6244 -0.2216 0.000 0.440 0.560
#> SRR925695 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925697 1 0.4178 0.7580 0.828 0.000 0.172
#> SRR925696 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925698 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925699 2 0.6280 0.3356 0.000 0.540 0.460
#> SRR925700 2 0.6308 0.2520 0.000 0.508 0.492
#> SRR925701 2 0.6280 0.3356 0.000 0.540 0.460
#> SRR925702 1 0.1163 0.7994 0.972 0.000 0.028
#> SRR925703 1 0.4178 0.7580 0.828 0.000 0.172
#> SRR925704 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925705 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925706 1 0.5817 0.7032 0.800 0.100 0.100
#> SRR925708 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925707 2 0.5948 0.4458 0.000 0.640 0.360
#> SRR925709 1 0.5835 0.4991 0.660 0.000 0.340
#> SRR925710 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925711 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR934631 2 0.6307 0.2917 0.000 0.512 0.488
#> SRR934632 2 0.6307 0.2917 0.000 0.512 0.488
#> SRR934633 2 0.2878 0.7180 0.000 0.904 0.096
#> SRR925713 1 0.3551 0.7750 0.868 0.000 0.132
#> SRR925714 1 0.4178 0.7580 0.828 0.000 0.172
#> SRR925715 1 0.0592 0.7959 0.988 0.012 0.000
#> SRR925716 1 0.4178 0.7580 0.828 0.000 0.172
#> SRR925717 1 0.3551 0.7750 0.868 0.000 0.132
#> SRR925718 2 0.6280 0.3356 0.000 0.540 0.460
#> SRR925719 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.8011 1.000 0.000 0.000
#> SRR934634 1 0.3686 0.7196 0.860 0.000 0.140
#> SRR934635 1 0.5835 0.4991 0.660 0.000 0.340
#> SRR925723 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925724 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925725 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925726 1 0.4178 0.7580 0.828 0.000 0.172
#> SRR925727 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925728 1 0.9266 -0.3058 0.424 0.156 0.420
#> SRR934636 3 0.2711 0.3658 0.088 0.000 0.912
#> SRR934637 1 0.6280 0.5452 0.540 0.000 0.460
#> SRR925729 2 0.1031 0.7556 0.000 0.976 0.024
#> SRR934638 3 0.5678 0.3502 0.316 0.000 0.684
#> SRR934639 1 0.1529 0.7870 0.960 0.000 0.040
#> SRR934640 2 0.6302 0.3060 0.000 0.520 0.480
#> SRR934641 2 0.6302 0.3060 0.000 0.520 0.480
#> SRR925734 3 0.6260 0.0488 0.448 0.000 0.552
#> SRR925735 2 0.0592 0.7613 0.012 0.988 0.000
#> SRR925736 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925737 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR934642 2 0.6280 0.3356 0.000 0.540 0.460
#> SRR934643 2 0.6280 0.3356 0.000 0.540 0.460
#> SRR934644 3 0.6295 -0.2926 0.000 0.472 0.528
#> SRR934645 3 0.6299 -0.3028 0.000 0.476 0.524
#> SRR925740 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925741 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR925742 2 0.0000 0.7706 0.000 1.000 0.000
#> SRR1283038 2 0.0000 0.7706 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR925688 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR925689 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> SRR925693 3 0.0188 0.975 0.000 0.004 0.996 0.000
#> SRR925694 2 0.0592 0.852 0.000 0.984 0.016 0.000
#> SRR925695 3 0.0592 0.970 0.000 0.016 0.984 0.000
#> SRR925697 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR925696 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925698 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925699 2 0.2921 0.887 0.000 0.860 0.140 0.000
#> SRR925700 2 0.3528 0.873 0.000 0.808 0.192 0.000
#> SRR925701 2 0.3444 0.877 0.000 0.816 0.184 0.000
#> SRR925702 4 0.3219 0.886 0.164 0.000 0.000 0.836
#> SRR925703 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR925704 3 0.0592 0.970 0.000 0.016 0.984 0.000
#> SRR925705 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925706 4 0.3903 0.817 0.076 0.000 0.080 0.844
#> SRR925708 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR925707 3 0.2266 0.893 0.004 0.084 0.912 0.000
#> SRR925709 1 0.0188 0.927 0.996 0.000 0.000 0.004
#> SRR925710 4 0.3610 0.883 0.200 0.000 0.000 0.800
#> SRR925711 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR934631 2 0.0336 0.841 0.000 0.992 0.008 0.000
#> SRR934632 2 0.0336 0.841 0.000 0.992 0.008 0.000
#> SRR934633 2 0.3311 0.768 0.000 0.828 0.172 0.000
#> SRR925713 4 0.1211 0.866 0.040 0.000 0.000 0.960
#> SRR925714 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR925715 4 0.4019 0.879 0.196 0.000 0.012 0.792
#> SRR925716 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR925717 4 0.1211 0.866 0.040 0.000 0.000 0.960
#> SRR925718 2 0.3528 0.873 0.000 0.808 0.192 0.000
#> SRR925719 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR925720 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR925721 4 0.3569 0.885 0.196 0.000 0.000 0.804
#> SRR934634 1 0.3649 0.668 0.796 0.000 0.000 0.204
#> SRR934635 1 0.0188 0.927 0.996 0.000 0.000 0.004
#> SRR925723 3 0.0336 0.974 0.000 0.008 0.992 0.000
#> SRR925724 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925725 3 0.0592 0.970 0.000 0.016 0.984 0.000
#> SRR925726 4 0.0000 0.850 0.000 0.000 0.000 1.000
#> SRR925727 3 0.0000 0.976 0.000 0.000 1.000 0.000
#> SRR925728 1 0.1004 0.912 0.972 0.024 0.000 0.004
#> SRR934636 1 0.4579 0.750 0.768 0.032 0.000 0.200
#> SRR934637 1 0.4164 0.726 0.736 0.000 0.000 0.264
#> SRR925729 3 0.3074 0.838 0.000 0.152 0.848 0.000
#> SRR934638 1 0.0188 0.927 0.996 0.000 0.000 0.004
#> SRR934639 4 0.3942 0.850 0.236 0.000 0.000 0.764
#> SRR934640 2 0.3123 0.886 0.000 0.844 0.156 0.000
#> SRR934641 2 0.3123 0.886 0.000 0.844 0.156 0.000
#> SRR925734 1 0.0188 0.927 0.996 0.000 0.000 0.004
#> SRR925735 3 0.0188 0.977 0.000 0.004 0.996 0.000
#> SRR925736 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925737 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR934642 2 0.3528 0.873 0.000 0.808 0.192 0.000
#> SRR934643 2 0.4477 0.715 0.000 0.688 0.312 0.000
#> SRR934644 2 0.0592 0.852 0.000 0.984 0.016 0.000
#> SRR934645 2 0.0707 0.854 0.000 0.980 0.020 0.000
#> SRR925740 3 0.0336 0.977 0.000 0.008 0.992 0.000
#> SRR925741 3 0.0592 0.970 0.000 0.016 0.984 0.000
#> SRR925742 3 0.0188 0.977 0.000 0.004 0.996 0.000
#> SRR1283038 3 0.0921 0.962 0.000 0.028 0.972 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.0000 0.8133 0.000 0.000 0.000 1.000 0.000
#> SRR925688 4 0.0000 0.8133 0.000 0.000 0.000 1.000 0.000
#> SRR925689 1 0.0000 0.8622 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.8622 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.8622 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.8622 1.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.0290 0.9605 0.000 0.008 0.992 0.000 0.000
#> SRR925694 2 0.0162 0.4371 0.000 0.996 0.004 0.000 0.000
#> SRR925695 3 0.0162 0.9556 0.000 0.004 0.996 0.000 0.000
#> SRR925697 4 0.4256 0.6228 0.000 0.000 0.000 0.564 0.436
#> SRR925696 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925698 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925699 2 0.2377 0.6202 0.000 0.872 0.128 0.000 0.000
#> SRR925700 2 0.2929 0.6521 0.000 0.820 0.180 0.000 0.000
#> SRR925701 2 0.2852 0.6503 0.000 0.828 0.172 0.000 0.000
#> SRR925702 4 0.0880 0.8076 0.032 0.000 0.000 0.968 0.000
#> SRR925703 4 0.4256 0.6228 0.000 0.000 0.000 0.564 0.436
#> SRR925704 3 0.0162 0.9556 0.000 0.004 0.996 0.000 0.000
#> SRR925705 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925706 4 0.3747 0.7600 0.044 0.000 0.060 0.844 0.052
#> SRR925708 4 0.0000 0.8133 0.000 0.000 0.000 1.000 0.000
#> SRR925707 2 0.6461 0.3081 0.004 0.492 0.172 0.332 0.000
#> SRR925709 1 0.3983 0.4848 0.660 0.000 0.000 0.340 0.000
#> SRR925710 4 0.2074 0.7800 0.104 0.000 0.000 0.896 0.000
#> SRR925711 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR934631 5 0.4829 0.6553 0.000 0.480 0.020 0.000 0.500
#> SRR934632 5 0.4829 0.6553 0.000 0.480 0.020 0.000 0.500
#> SRR934633 5 0.5861 0.5822 0.000 0.400 0.100 0.000 0.500
#> SRR925713 4 0.0000 0.8133 0.000 0.000 0.000 1.000 0.000
#> SRR925714 4 0.4256 0.6228 0.000 0.000 0.000 0.564 0.436
#> SRR925715 4 0.2249 0.7790 0.096 0.000 0.008 0.896 0.000
#> SRR925716 4 0.4268 0.6158 0.000 0.000 0.000 0.556 0.444
#> SRR925717 4 0.1197 0.8100 0.000 0.000 0.000 0.952 0.048
#> SRR925718 2 0.2929 0.6521 0.000 0.820 0.180 0.000 0.000
#> SRR925719 4 0.0000 0.8133 0.000 0.000 0.000 1.000 0.000
#> SRR925720 4 0.1608 0.7924 0.072 0.000 0.000 0.928 0.000
#> SRR925721 4 0.3932 0.5157 0.328 0.000 0.000 0.672 0.000
#> SRR934634 1 0.2020 0.8081 0.900 0.000 0.000 0.100 0.000
#> SRR934635 1 0.2074 0.8069 0.896 0.000 0.000 0.104 0.000
#> SRR925723 3 0.0162 0.9593 0.000 0.004 0.996 0.000 0.000
#> SRR925724 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925725 3 0.0162 0.9556 0.000 0.004 0.996 0.000 0.000
#> SRR925726 4 0.3949 0.6682 0.000 0.000 0.000 0.668 0.332
#> SRR925727 3 0.0404 0.9613 0.000 0.012 0.988 0.000 0.000
#> SRR925728 2 0.6753 0.0946 0.268 0.392 0.000 0.340 0.000
#> SRR934636 5 0.6671 -0.1128 0.232 0.372 0.000 0.000 0.396
#> SRR934637 1 0.4718 0.4054 0.540 0.000 0.000 0.016 0.444
#> SRR925729 3 0.2605 0.8061 0.000 0.148 0.852 0.000 0.000
#> SRR934638 1 0.0609 0.8571 0.980 0.000 0.000 0.020 0.000
#> SRR934639 4 0.2773 0.7597 0.164 0.000 0.000 0.836 0.000
#> SRR934640 2 0.5972 0.2617 0.000 0.560 0.140 0.000 0.300
#> SRR934641 2 0.5770 0.3588 0.000 0.604 0.140 0.000 0.256
#> SRR925734 1 0.0290 0.8611 0.992 0.000 0.000 0.008 0.000
#> SRR925735 3 0.3720 0.6463 0.000 0.012 0.760 0.228 0.000
#> SRR925736 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925737 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR934642 2 0.2929 0.6521 0.000 0.820 0.180 0.000 0.000
#> SRR934643 2 0.3796 0.5307 0.000 0.700 0.300 0.000 0.000
#> SRR934644 5 0.4452 0.6280 0.000 0.496 0.004 0.000 0.500
#> SRR934645 2 0.0290 0.4459 0.000 0.992 0.008 0.000 0.000
#> SRR925740 3 0.0609 0.9616 0.000 0.020 0.980 0.000 0.000
#> SRR925741 3 0.0162 0.9556 0.000 0.004 0.996 0.000 0.000
#> SRR925742 3 0.0510 0.9616 0.000 0.016 0.984 0.000 0.000
#> SRR1283038 3 0.0609 0.9442 0.000 0.020 0.980 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.0000 0.7829 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925688 4 0.0000 0.7829 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925689 1 0.0000 0.8819 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.8819 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.8819 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.8819 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.1321 0.9330 0.000 0.004 0.952 0.000 0.020 0.024
#> SRR925694 2 0.2362 0.6287 0.000 0.860 0.004 0.000 0.000 0.136
#> SRR925695 3 0.1092 0.9331 0.000 0.000 0.960 0.000 0.020 0.020
#> SRR925697 4 0.3810 0.3208 0.000 0.000 0.000 0.572 0.428 0.000
#> SRR925696 3 0.0260 0.9383 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925698 3 0.1262 0.9336 0.000 0.008 0.956 0.000 0.020 0.016
#> SRR925699 2 0.2680 0.7102 0.000 0.860 0.108 0.000 0.000 0.032
#> SRR925700 2 0.2260 0.7163 0.000 0.860 0.140 0.000 0.000 0.000
#> SRR925701 2 0.2431 0.7170 0.000 0.860 0.132 0.000 0.000 0.008
#> SRR925702 4 0.1204 0.7680 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR925703 4 0.3810 0.3208 0.000 0.000 0.000 0.572 0.428 0.000
#> SRR925704 3 0.1237 0.9329 0.000 0.004 0.956 0.000 0.020 0.020
#> SRR925705 3 0.0260 0.9383 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925706 4 0.4214 0.6620 0.060 0.000 0.064 0.784 0.092 0.000
#> SRR925708 4 0.0000 0.7829 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925707 2 0.4769 0.3567 0.000 0.652 0.068 0.272 0.000 0.008
#> SRR925709 1 0.3288 0.4968 0.724 0.000 0.000 0.276 0.000 0.000
#> SRR925710 4 0.2416 0.6976 0.156 0.000 0.000 0.844 0.000 0.000
#> SRR925711 3 0.1124 0.9167 0.000 0.036 0.956 0.000 0.000 0.008
#> SRR934631 6 0.1265 0.9707 0.000 0.044 0.008 0.000 0.000 0.948
#> SRR934632 6 0.1265 0.9707 0.000 0.044 0.008 0.000 0.000 0.948
#> SRR934633 6 0.1265 0.9707 0.000 0.044 0.008 0.000 0.000 0.948
#> SRR925713 4 0.0000 0.7829 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925714 4 0.3810 0.3208 0.000 0.000 0.000 0.572 0.428 0.000
#> SRR925715 4 0.3168 0.7038 0.024 0.120 0.008 0.840 0.000 0.008
#> SRR925716 5 0.3828 -0.2645 0.000 0.000 0.000 0.440 0.560 0.000
#> SRR925717 4 0.0937 0.7721 0.000 0.000 0.000 0.960 0.040 0.000
#> SRR925718 2 0.2300 0.7151 0.000 0.856 0.144 0.000 0.000 0.000
#> SRR925719 4 0.0000 0.7829 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925720 4 0.0713 0.7758 0.028 0.000 0.000 0.972 0.000 0.000
#> SRR925721 4 0.3244 0.4986 0.268 0.000 0.000 0.732 0.000 0.000
#> SRR934634 1 0.2416 0.7501 0.844 0.000 0.000 0.156 0.000 0.000
#> SRR934635 1 0.2416 0.7501 0.844 0.000 0.000 0.156 0.000 0.000
#> SRR925723 3 0.0000 0.9379 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925724 3 0.0405 0.9386 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR925725 3 0.1092 0.9331 0.000 0.000 0.960 0.000 0.020 0.020
#> SRR925726 4 0.3266 0.5271 0.000 0.000 0.000 0.728 0.272 0.000
#> SRR925727 3 0.1003 0.9340 0.000 0.000 0.964 0.000 0.020 0.016
#> SRR925728 2 0.6682 0.1179 0.280 0.408 0.036 0.276 0.000 0.000
#> SRR934636 5 0.5264 0.0792 0.104 0.376 0.000 0.000 0.520 0.000
#> SRR934637 5 0.3955 -0.0051 0.436 0.000 0.000 0.004 0.560 0.000
#> SRR925729 3 0.4328 0.5913 0.000 0.212 0.708 0.000 0.000 0.080
#> SRR934638 1 0.0260 0.8796 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR934639 4 0.2378 0.7221 0.152 0.000 0.000 0.848 0.000 0.000
#> SRR934640 2 0.5175 0.2146 0.000 0.492 0.000 0.000 0.420 0.088
#> SRR934641 2 0.5054 0.2338 0.000 0.504 0.000 0.000 0.420 0.076
#> SRR925734 1 0.0146 0.8812 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925735 3 0.5415 0.4712 0.000 0.192 0.612 0.188 0.000 0.008
#> SRR925736 3 0.0260 0.9383 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925737 3 0.0260 0.9383 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR934642 2 0.2340 0.7130 0.000 0.852 0.148 0.000 0.000 0.000
#> SRR934643 2 0.2491 0.7029 0.000 0.836 0.164 0.000 0.000 0.000
#> SRR934644 6 0.2053 0.9094 0.000 0.108 0.004 0.000 0.000 0.888
#> SRR934645 2 0.2431 0.6332 0.000 0.860 0.008 0.000 0.000 0.132
#> SRR925740 3 0.0260 0.9383 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR925741 3 0.1350 0.9315 0.000 0.008 0.952 0.000 0.020 0.020
#> SRR925742 3 0.0146 0.9384 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1283038 3 0.0632 0.9290 0.000 0.000 0.976 0.000 0.000 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.999 0.5059 0.494 0.494
#> 3 3 0.751 0.950 0.918 0.2550 0.840 0.685
#> 4 4 0.874 0.917 0.930 0.1396 0.880 0.684
#> 5 5 0.871 0.884 0.910 0.0738 0.924 0.734
#> 6 6 0.782 0.654 0.823 0.0481 0.973 0.878
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
#> SRR925687 1 0.0000 1.000 1.000 0.000
#> SRR925688 1 0.0000 1.000 1.000 0.000
#> SRR925689 1 0.0000 1.000 1.000 0.000
#> SRR925690 1 0.0000 1.000 1.000 0.000
#> SRR925691 1 0.0000 1.000 1.000 0.000
#> SRR925692 1 0.0000 1.000 1.000 0.000
#> SRR925693 2 0.0376 0.998 0.004 0.996
#> SRR925694 2 0.0000 0.997 0.000 1.000
#> SRR925695 2 0.0376 0.998 0.004 0.996
#> SRR925697 1 0.0000 1.000 1.000 0.000
#> SRR925696 2 0.0376 0.998 0.004 0.996
#> SRR925698 2 0.0376 0.998 0.004 0.996
#> SRR925699 2 0.0000 0.997 0.000 1.000
#> SRR925700 2 0.0000 0.997 0.000 1.000
#> SRR925701 2 0.0000 0.997 0.000 1.000
#> SRR925702 1 0.0000 1.000 1.000 0.000
#> SRR925703 1 0.0000 1.000 1.000 0.000
#> SRR925704 2 0.0376 0.998 0.004 0.996
#> SRR925705 2 0.0376 0.998 0.004 0.996
#> SRR925706 1 0.0000 1.000 1.000 0.000
#> SRR925708 1 0.0000 1.000 1.000 0.000
#> SRR925707 2 0.0376 0.998 0.004 0.996
#> SRR925709 1 0.0000 1.000 1.000 0.000
#> SRR925710 1 0.0000 1.000 1.000 0.000
#> SRR925711 2 0.0376 0.998 0.004 0.996
#> SRR934631 2 0.0000 0.997 0.000 1.000
#> SRR934632 2 0.0000 0.997 0.000 1.000
#> SRR934633 2 0.0376 0.998 0.004 0.996
#> SRR925713 1 0.0000 1.000 1.000 0.000
#> SRR925714 1 0.0000 1.000 1.000 0.000
#> SRR925715 1 0.0000 1.000 1.000 0.000
#> SRR925716 1 0.0000 1.000 1.000 0.000
#> SRR925717 1 0.0000 1.000 1.000 0.000
#> SRR925718 2 0.0000 0.997 0.000 1.000
#> SRR925719 1 0.0000 1.000 1.000 0.000
#> SRR925720 1 0.0000 1.000 1.000 0.000
#> SRR925721 1 0.0000 1.000 1.000 0.000
#> SRR934634 1 0.0000 1.000 1.000 0.000
#> SRR934635 1 0.0000 1.000 1.000 0.000
#> SRR925723 2 0.0376 0.998 0.004 0.996
#> SRR925724 2 0.0376 0.998 0.004 0.996
#> SRR925725 2 0.0376 0.998 0.004 0.996
#> SRR925726 1 0.0000 1.000 1.000 0.000
#> SRR925727 2 0.0376 0.998 0.004 0.996
#> SRR925728 1 0.0000 1.000 1.000 0.000
#> SRR934636 1 0.0000 1.000 1.000 0.000
#> SRR934637 1 0.0000 1.000 1.000 0.000
#> SRR925729 2 0.0376 0.998 0.004 0.996
#> SRR934638 1 0.0000 1.000 1.000 0.000
#> SRR934639 1 0.0000 1.000 1.000 0.000
#> SRR934640 2 0.0000 0.997 0.000 1.000
#> SRR934641 2 0.0000 0.997 0.000 1.000
#> SRR925734 1 0.0000 1.000 1.000 0.000
#> SRR925735 2 0.0376 0.998 0.004 0.996
#> SRR925736 2 0.0376 0.998 0.004 0.996
#> SRR925737 2 0.0376 0.998 0.004 0.996
#> SRR934642 2 0.0000 0.997 0.000 1.000
#> SRR934643 2 0.0000 0.997 0.000 1.000
#> SRR934644 2 0.0000 0.997 0.000 1.000
#> SRR934645 2 0.0000 0.997 0.000 1.000
#> SRR925740 2 0.0376 0.998 0.004 0.996
#> SRR925741 2 0.0376 0.998 0.004 0.996
#> SRR925742 2 0.0376 0.998 0.004 0.996
#> SRR1283038 2 0.0376 0.998 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.000 0.930 1.000 0.000 0.000
#> SRR925688 1 0.000 0.930 1.000 0.000 0.000
#> SRR925689 1 0.412 0.913 0.832 0.168 0.000
#> SRR925690 1 0.412 0.913 0.832 0.168 0.000
#> SRR925691 1 0.412 0.913 0.832 0.168 0.000
#> SRR925692 1 0.412 0.913 0.832 0.168 0.000
#> SRR925693 3 0.254 0.879 0.080 0.000 0.920
#> SRR925694 2 0.445 0.997 0.000 0.808 0.192
#> SRR925695 3 0.000 0.981 0.000 0.000 1.000
#> SRR925697 1 0.435 0.908 0.816 0.184 0.000
#> SRR925696 3 0.000 0.981 0.000 0.000 1.000
#> SRR925698 3 0.000 0.981 0.000 0.000 1.000
#> SRR925699 2 0.445 0.997 0.000 0.808 0.192
#> SRR925700 2 0.445 0.997 0.000 0.808 0.192
#> SRR925701 2 0.445 0.997 0.000 0.808 0.192
#> SRR925702 1 0.000 0.930 1.000 0.000 0.000
#> SRR925703 1 0.435 0.908 0.816 0.184 0.000
#> SRR925704 3 0.000 0.981 0.000 0.000 1.000
#> SRR925705 3 0.000 0.981 0.000 0.000 1.000
#> SRR925706 1 0.000 0.930 1.000 0.000 0.000
#> SRR925708 1 0.000 0.930 1.000 0.000 0.000
#> SRR925707 1 0.153 0.906 0.960 0.000 0.040
#> SRR925709 1 0.000 0.930 1.000 0.000 0.000
#> SRR925710 1 0.000 0.930 1.000 0.000 0.000
#> SRR925711 3 0.000 0.981 0.000 0.000 1.000
#> SRR934631 2 0.445 0.997 0.000 0.808 0.192
#> SRR934632 2 0.445 0.997 0.000 0.808 0.192
#> SRR934633 3 0.000 0.981 0.000 0.000 1.000
#> SRR925713 1 0.000 0.930 1.000 0.000 0.000
#> SRR925714 1 0.435 0.908 0.816 0.184 0.000
#> SRR925715 1 0.000 0.930 1.000 0.000 0.000
#> SRR925716 1 0.435 0.908 0.816 0.184 0.000
#> SRR925717 1 0.000 0.930 1.000 0.000 0.000
#> SRR925718 2 0.445 0.997 0.000 0.808 0.192
#> SRR925719 1 0.000 0.930 1.000 0.000 0.000
#> SRR925720 1 0.000 0.930 1.000 0.000 0.000
#> SRR925721 1 0.000 0.930 1.000 0.000 0.000
#> SRR934634 1 0.207 0.929 0.940 0.060 0.000
#> SRR934635 1 0.141 0.930 0.964 0.036 0.000
#> SRR925723 3 0.000 0.981 0.000 0.000 1.000
#> SRR925724 3 0.000 0.981 0.000 0.000 1.000
#> SRR925725 3 0.000 0.981 0.000 0.000 1.000
#> SRR925726 1 0.435 0.908 0.816 0.184 0.000
#> SRR925727 3 0.000 0.981 0.000 0.000 1.000
#> SRR925728 1 0.000 0.930 1.000 0.000 0.000
#> SRR934636 1 0.435 0.908 0.816 0.184 0.000
#> SRR934637 1 0.435 0.908 0.816 0.184 0.000
#> SRR925729 3 0.000 0.981 0.000 0.000 1.000
#> SRR934638 1 0.412 0.913 0.832 0.168 0.000
#> SRR934639 1 0.226 0.928 0.932 0.068 0.000
#> SRR934640 2 0.460 0.986 0.000 0.796 0.204
#> SRR934641 2 0.460 0.986 0.000 0.796 0.204
#> SRR925734 1 0.412 0.913 0.832 0.168 0.000
#> SRR925735 3 0.400 0.767 0.160 0.000 0.840
#> SRR925736 3 0.000 0.981 0.000 0.000 1.000
#> SRR925737 3 0.000 0.981 0.000 0.000 1.000
#> SRR934642 2 0.445 0.997 0.000 0.808 0.192
#> SRR934643 2 0.445 0.997 0.000 0.808 0.192
#> SRR934644 2 0.445 0.997 0.000 0.808 0.192
#> SRR934645 2 0.445 0.997 0.000 0.808 0.192
#> SRR925740 3 0.000 0.981 0.000 0.000 1.000
#> SRR925741 3 0.000 0.981 0.000 0.000 1.000
#> SRR925742 3 0.000 0.981 0.000 0.000 1.000
#> SRR1283038 3 0.000 0.981 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR925688 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR925689 1 0.4313 0.757 0.736 0.004 0.000 0.260
#> SRR925690 1 0.4313 0.757 0.736 0.004 0.000 0.260
#> SRR925691 1 0.4313 0.757 0.736 0.004 0.000 0.260
#> SRR925692 1 0.4313 0.757 0.736 0.004 0.000 0.260
#> SRR925693 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925694 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR925695 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925697 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925696 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925698 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925699 2 0.0469 0.967 0.000 0.988 0.012 0.000
#> SRR925700 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR925701 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR925702 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925703 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925704 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925705 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925706 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925708 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR925707 3 0.4343 0.541 0.264 0.004 0.732 0.000
#> SRR925709 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925710 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925711 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR934631 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR934632 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR934633 2 0.4222 0.645 0.000 0.728 0.272 0.000
#> SRR925713 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925714 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925715 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925716 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925717 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR925718 2 0.0921 0.955 0.000 0.972 0.028 0.000
#> SRR925719 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR925720 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR925721 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR934634 1 0.4134 0.759 0.740 0.000 0.000 0.260
#> SRR934635 1 0.2345 0.837 0.900 0.000 0.000 0.100
#> SRR925723 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925724 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925725 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925726 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925727 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925728 1 0.0188 0.879 0.996 0.004 0.000 0.000
#> SRR934636 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR934637 4 0.4134 1.000 0.260 0.000 0.000 0.740
#> SRR925729 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR934638 1 0.4220 0.764 0.748 0.004 0.000 0.248
#> SRR934639 1 0.0000 0.880 1.000 0.000 0.000 0.000
#> SRR934640 2 0.0921 0.956 0.000 0.972 0.028 0.000
#> SRR934641 2 0.1118 0.949 0.000 0.964 0.036 0.000
#> SRR925734 1 0.4313 0.757 0.736 0.004 0.000 0.260
#> SRR925735 3 0.0188 0.977 0.004 0.000 0.996 0.000
#> SRR925736 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925737 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR934642 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR934643 2 0.0469 0.967 0.000 0.988 0.012 0.000
#> SRR934644 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR934645 2 0.0336 0.968 0.000 0.992 0.008 0.000
#> SRR925740 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925741 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR925742 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> SRR1283038 3 0.0000 0.982 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
#> SRR925687 4 0.3013 0.824 0.008 0.000 0.000 0.832 0.160
#> SRR925688 4 0.0162 0.858 0.004 0.000 0.000 0.996 0.000
#> SRR925689 1 0.1043 0.917 0.960 0.000 0.000 0.040 0.000
#> SRR925690 1 0.1043 0.917 0.960 0.000 0.000 0.040 0.000
#> SRR925691 1 0.1043 0.917 0.960 0.000 0.000 0.040 0.000
#> SRR925692 1 0.1043 0.917 0.960 0.000 0.000 0.040 0.000
#> SRR925693 3 0.1300 0.929 0.016 0.000 0.956 0.000 0.028
#> SRR925694 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.1830 0.921 0.040 0.000 0.932 0.000 0.028
#> SRR925697 5 0.3508 0.968 0.000 0.000 0.000 0.252 0.748
#> SRR925696 3 0.0451 0.939 0.004 0.000 0.988 0.000 0.008
#> SRR925698 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000
#> SRR925699 2 0.0162 0.958 0.000 0.996 0.004 0.000 0.000
#> SRR925700 2 0.0162 0.958 0.000 0.996 0.004 0.000 0.000
#> SRR925701 2 0.0162 0.958 0.000 0.996 0.004 0.000 0.000
#> SRR925702 4 0.0609 0.856 0.000 0.000 0.000 0.980 0.020
#> SRR925703 5 0.3508 0.968 0.000 0.000 0.000 0.252 0.748
#> SRR925704 3 0.1043 0.930 0.040 0.000 0.960 0.000 0.000
#> SRR925705 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR925706 4 0.2179 0.757 0.000 0.000 0.000 0.888 0.112
#> SRR925708 4 0.3013 0.824 0.008 0.000 0.000 0.832 0.160
#> SRR925707 3 0.4967 0.533 0.000 0.012 0.692 0.248 0.048
#> SRR925709 4 0.0162 0.858 0.000 0.000 0.000 0.996 0.004
#> SRR925710 4 0.0609 0.856 0.000 0.000 0.000 0.980 0.020
#> SRR925711 3 0.0162 0.938 0.000 0.004 0.996 0.000 0.000
#> SRR934631 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> SRR934633 3 0.3932 0.490 0.000 0.328 0.672 0.000 0.000
#> SRR925713 4 0.0609 0.856 0.000 0.000 0.000 0.980 0.020
#> SRR925714 5 0.3508 0.968 0.000 0.000 0.000 0.252 0.748
#> SRR925715 4 0.0609 0.856 0.000 0.000 0.000 0.980 0.020
#> SRR925716 5 0.3305 0.961 0.000 0.000 0.000 0.224 0.776
#> SRR925717 4 0.0609 0.856 0.000 0.000 0.000 0.980 0.020
#> SRR925718 2 0.0880 0.936 0.000 0.968 0.032 0.000 0.000
#> SRR925719 4 0.3013 0.824 0.008 0.000 0.000 0.832 0.160
#> SRR925720 4 0.3013 0.824 0.008 0.000 0.000 0.832 0.160
#> SRR925721 4 0.4916 0.714 0.124 0.000 0.000 0.716 0.160
#> SRR934634 1 0.2732 0.834 0.840 0.000 0.000 0.160 0.000
#> SRR934635 1 0.6031 0.536 0.576 0.000 0.000 0.244 0.180
#> SRR925723 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR925724 3 0.0000 0.939 0.000 0.000 1.000 0.000 0.000
#> SRR925725 3 0.1830 0.921 0.040 0.000 0.932 0.000 0.028
#> SRR925726 5 0.3508 0.968 0.000 0.000 0.000 0.252 0.748
#> SRR925727 3 0.1830 0.921 0.040 0.000 0.932 0.000 0.028
#> SRR925728 4 0.2561 0.707 0.000 0.000 0.000 0.856 0.144
#> SRR934636 5 0.3074 0.944 0.000 0.000 0.000 0.196 0.804
#> SRR934637 5 0.3074 0.944 0.000 0.000 0.000 0.196 0.804
#> SRR925729 3 0.0510 0.936 0.016 0.000 0.984 0.000 0.000
#> SRR934638 1 0.1792 0.898 0.916 0.000 0.000 0.084 0.000
#> SRR934639 4 0.3224 0.821 0.016 0.000 0.000 0.824 0.160
#> SRR934640 2 0.2813 0.785 0.000 0.832 0.168 0.000 0.000
#> SRR934641 2 0.2852 0.780 0.000 0.828 0.172 0.000 0.000
#> SRR925734 1 0.1043 0.917 0.960 0.000 0.000 0.040 0.000
#> SRR925735 3 0.2942 0.811 0.004 0.004 0.864 0.116 0.012
#> SRR925736 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR925737 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR934642 2 0.0162 0.958 0.000 0.996 0.004 0.000 0.000
#> SRR934643 2 0.0162 0.958 0.000 0.996 0.004 0.000 0.000
#> SRR934644 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> SRR925740 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR925741 3 0.1830 0.921 0.040 0.000 0.932 0.000 0.028
#> SRR925742 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
#> SRR1283038 3 0.0290 0.939 0.000 0.000 0.992 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.2491 0.76524 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR925688 4 0.3699 0.76561 0.160 0.000 0.000 0.788 0.040 0.012
#> SRR925689 1 0.0146 0.93708 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925690 1 0.0146 0.93708 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925691 1 0.0146 0.93708 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925692 1 0.0146 0.93708 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925693 6 0.3804 0.22225 0.000 0.000 0.424 0.000 0.000 0.576
#> SRR925694 2 0.0000 0.96679 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925695 3 0.3756 -0.13986 0.000 0.000 0.600 0.000 0.000 0.400
#> SRR925697 5 0.3563 0.75342 0.000 0.000 0.000 0.336 0.664 0.000
#> SRR925696 3 0.2823 0.62178 0.000 0.000 0.796 0.000 0.000 0.204
#> SRR925698 3 0.0632 0.54066 0.000 0.000 0.976 0.000 0.000 0.024
#> SRR925699 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR925700 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR925701 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR925702 4 0.1219 0.75534 0.000 0.000 0.000 0.948 0.048 0.004
#> SRR925703 5 0.3563 0.75342 0.000 0.000 0.000 0.336 0.664 0.000
#> SRR925704 3 0.1765 0.48367 0.000 0.000 0.904 0.000 0.000 0.096
#> SRR925705 3 0.3244 0.63051 0.000 0.000 0.732 0.000 0.000 0.268
#> SRR925706 4 0.3714 0.53026 0.000 0.000 0.000 0.760 0.196 0.044
#> SRR925708 4 0.2491 0.76524 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR925707 6 0.7132 -0.40339 0.000 0.020 0.044 0.320 0.216 0.400
#> SRR925709 4 0.1767 0.76408 0.020 0.000 0.000 0.932 0.036 0.012
#> SRR925710 4 0.2384 0.73787 0.000 0.000 0.000 0.888 0.048 0.064
#> SRR925711 3 0.3852 0.30083 0.000 0.004 0.612 0.000 0.000 0.384
#> SRR934631 2 0.0000 0.96679 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.96679 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 3 0.4361 0.29787 0.000 0.168 0.720 0.000 0.000 0.112
#> SRR925713 4 0.2136 0.74262 0.000 0.000 0.000 0.904 0.048 0.048
#> SRR925714 5 0.3563 0.75342 0.000 0.000 0.000 0.336 0.664 0.000
#> SRR925715 4 0.3088 0.70160 0.000 0.000 0.000 0.832 0.048 0.120
#> SRR925716 5 0.3101 0.74466 0.000 0.000 0.000 0.244 0.756 0.000
#> SRR925717 4 0.2070 0.74430 0.000 0.000 0.000 0.908 0.048 0.044
#> SRR925718 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR925719 4 0.2491 0.76524 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR925720 4 0.2632 0.76597 0.164 0.000 0.000 0.832 0.004 0.000
#> SRR925721 4 0.3126 0.68328 0.248 0.000 0.000 0.752 0.000 0.000
#> SRR934634 1 0.1556 0.88215 0.920 0.000 0.000 0.080 0.000 0.000
#> SRR934635 1 0.4475 0.61382 0.692 0.000 0.000 0.088 0.220 0.000
#> SRR925723 3 0.3244 0.63051 0.000 0.000 0.732 0.000 0.000 0.268
#> SRR925724 3 0.0146 0.55346 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR925725 6 0.3867 0.12775 0.000 0.000 0.488 0.000 0.000 0.512
#> SRR925726 5 0.3563 0.75342 0.000 0.000 0.000 0.336 0.664 0.000
#> SRR925727 3 0.3782 -0.17045 0.000 0.000 0.588 0.000 0.000 0.412
#> SRR925728 4 0.4239 0.37618 0.000 0.000 0.000 0.696 0.248 0.056
#> SRR934636 5 0.0000 0.57202 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934637 5 0.0000 0.57202 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925729 3 0.3314 0.33034 0.000 0.012 0.764 0.000 0.000 0.224
#> SRR934638 1 0.0790 0.92335 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR934639 4 0.2631 0.75370 0.180 0.000 0.000 0.820 0.000 0.000
#> SRR934640 2 0.2631 0.80067 0.000 0.820 0.000 0.000 0.000 0.180
#> SRR934641 2 0.2631 0.80067 0.000 0.820 0.000 0.000 0.000 0.180
#> SRR925734 1 0.0146 0.93708 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR925735 3 0.4837 0.00164 0.000 0.000 0.564 0.032 0.016 0.388
#> SRR925736 3 0.3266 0.63084 0.000 0.000 0.728 0.000 0.000 0.272
#> SRR925737 3 0.3309 0.62542 0.000 0.000 0.720 0.000 0.000 0.280
#> SRR934642 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0146 0.96697 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.96679 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.96679 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925740 3 0.3528 0.62687 0.000 0.004 0.700 0.000 0.000 0.296
#> SRR925741 3 0.3797 -0.19253 0.000 0.000 0.580 0.000 0.000 0.420
#> SRR925742 3 0.3244 0.63051 0.000 0.000 0.732 0.000 0.000 0.268
#> SRR1283038 3 0.3244 0.63051 0.000 0.000 0.732 0.000 0.000 0.268
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 15949 rows and 64 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 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 0.489 0.727 0.889 0.4974 0.504 0.504
#> 3 3 0.763 0.853 0.921 0.3174 0.793 0.602
#> 4 4 0.667 0.770 0.870 0.0829 0.912 0.747
#> 5 5 0.630 0.511 0.752 0.0963 0.915 0.708
#> 6 6 0.655 0.417 0.671 0.0501 0.858 0.492
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
#> SRR925687 1 0.000 0.8351 1.000 0.000
#> SRR925688 1 0.000 0.8351 1.000 0.000
#> SRR925689 1 0.985 0.3259 0.572 0.428
#> SRR925690 1 0.358 0.7967 0.932 0.068
#> SRR925691 1 0.529 0.7589 0.880 0.120
#> SRR925692 1 0.795 0.6394 0.760 0.240
#> SRR925693 1 0.969 0.2946 0.604 0.396
#> SRR925694 2 0.000 0.8925 0.000 1.000
#> SRR925695 1 0.971 0.2849 0.600 0.400
#> SRR925697 1 0.000 0.8351 1.000 0.000
#> SRR925696 2 0.518 0.8154 0.116 0.884
#> SRR925698 1 0.971 0.2849 0.600 0.400
#> SRR925699 2 0.000 0.8925 0.000 1.000
#> SRR925700 2 0.000 0.8925 0.000 1.000
#> SRR925701 2 0.000 0.8925 0.000 1.000
#> SRR925702 1 0.000 0.8351 1.000 0.000
#> SRR925703 1 0.000 0.8351 1.000 0.000
#> SRR925704 2 0.871 0.5952 0.292 0.708
#> SRR925705 2 0.388 0.8458 0.076 0.924
#> SRR925706 1 0.000 0.8351 1.000 0.000
#> SRR925708 1 0.000 0.8351 1.000 0.000
#> SRR925707 2 0.000 0.8925 0.000 1.000
#> SRR925709 1 0.000 0.8351 1.000 0.000
#> SRR925710 1 0.000 0.8351 1.000 0.000
#> SRR925711 2 0.788 0.6825 0.236 0.764
#> SRR934631 2 0.000 0.8925 0.000 1.000
#> SRR934632 2 0.000 0.8925 0.000 1.000
#> SRR934633 2 0.000 0.8925 0.000 1.000
#> SRR925713 1 0.000 0.8351 1.000 0.000
#> SRR925714 1 0.000 0.8351 1.000 0.000
#> SRR925715 1 0.000 0.8351 1.000 0.000
#> SRR925716 1 0.000 0.8351 1.000 0.000
#> SRR925717 1 0.000 0.8351 1.000 0.000
#> SRR925718 2 0.000 0.8925 0.000 1.000
#> SRR925719 1 0.000 0.8351 1.000 0.000
#> SRR925720 1 0.000 0.8351 1.000 0.000
#> SRR925721 1 0.000 0.8351 1.000 0.000
#> SRR934634 1 0.494 0.7688 0.892 0.108
#> SRR934635 1 0.000 0.8351 1.000 0.000
#> SRR925723 2 0.980 0.3187 0.416 0.584
#> SRR925724 1 0.995 0.0853 0.540 0.460
#> SRR925725 1 0.978 0.2500 0.588 0.412
#> SRR925726 1 0.000 0.8351 1.000 0.000
#> SRR925727 1 0.971 0.2849 0.600 0.400
#> SRR925728 1 0.118 0.8270 0.984 0.016
#> SRR934636 1 0.971 0.3842 0.600 0.400
#> SRR934637 1 0.000 0.8351 1.000 0.000
#> SRR925729 2 0.943 0.4580 0.360 0.640
#> SRR934638 1 0.943 0.4587 0.640 0.360
#> SRR934639 1 0.000 0.8351 1.000 0.000
#> SRR934640 2 0.000 0.8925 0.000 1.000
#> SRR934641 2 0.000 0.8925 0.000 1.000
#> SRR925734 1 0.775 0.6534 0.772 0.228
#> SRR925735 1 0.971 0.2849 0.600 0.400
#> SRR925736 2 0.584 0.7940 0.140 0.860
#> SRR925737 2 0.706 0.7378 0.192 0.808
#> SRR934642 2 0.000 0.8925 0.000 1.000
#> SRR934643 2 0.000 0.8925 0.000 1.000
#> SRR934644 2 0.000 0.8925 0.000 1.000
#> SRR934645 2 0.000 0.8925 0.000 1.000
#> SRR925740 2 0.000 0.8925 0.000 1.000
#> SRR925741 2 0.985 0.2842 0.428 0.572
#> SRR925742 2 0.000 0.8925 0.000 1.000
#> SRR1283038 2 0.000 0.8925 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925688 1 0.0237 0.9556 0.996 0.000 0.004
#> SRR925689 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925690 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925691 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925692 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925693 3 0.4555 0.7690 0.000 0.200 0.800
#> SRR925694 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925695 3 0.4555 0.7690 0.000 0.200 0.800
#> SRR925697 3 0.1411 0.7683 0.036 0.000 0.964
#> SRR925696 2 0.1643 0.9170 0.000 0.956 0.044
#> SRR925698 3 0.4654 0.7654 0.000 0.208 0.792
#> SRR925699 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925702 1 0.3482 0.8809 0.872 0.000 0.128
#> SRR925703 3 0.0424 0.7779 0.008 0.000 0.992
#> SRR925704 3 0.6305 0.3022 0.000 0.484 0.516
#> SRR925705 2 0.0747 0.9432 0.000 0.984 0.016
#> SRR925706 3 0.3038 0.7276 0.104 0.000 0.896
#> SRR925708 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925707 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925709 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925710 3 0.4654 0.6944 0.208 0.000 0.792
#> SRR925711 2 0.2261 0.8882 0.000 0.932 0.068
#> SRR934631 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934633 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925713 3 0.1753 0.7775 0.048 0.000 0.952
#> SRR925714 3 0.0424 0.7779 0.008 0.000 0.992
#> SRR925715 3 0.3349 0.7609 0.108 0.004 0.888
#> SRR925716 1 0.4555 0.8279 0.800 0.000 0.200
#> SRR925717 3 0.1964 0.7744 0.056 0.000 0.944
#> SRR925718 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925719 1 0.0237 0.9566 0.996 0.000 0.004
#> SRR925720 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925721 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925723 2 0.6204 -0.0143 0.000 0.576 0.424
#> SRR925724 3 0.5835 0.6420 0.000 0.340 0.660
#> SRR925725 3 0.5988 0.5946 0.000 0.368 0.632
#> SRR925726 3 0.1529 0.7672 0.040 0.000 0.960
#> SRR925727 3 0.4750 0.7605 0.000 0.216 0.784
#> SRR925728 1 0.3752 0.8697 0.856 0.000 0.144
#> SRR934636 1 0.4555 0.8279 0.800 0.000 0.200
#> SRR934637 1 0.4555 0.8279 0.800 0.000 0.200
#> SRR925729 2 0.5397 0.4967 0.000 0.720 0.280
#> SRR934638 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925734 1 0.0000 0.9583 1.000 0.000 0.000
#> SRR925735 3 0.5810 0.6482 0.000 0.336 0.664
#> SRR925736 2 0.1289 0.9293 0.000 0.968 0.032
#> SRR925737 2 0.0424 0.9490 0.000 0.992 0.008
#> SRR934642 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925740 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR925741 3 0.5785 0.6530 0.000 0.332 0.668
#> SRR925742 2 0.0000 0.9535 0.000 1.000 0.000
#> SRR1283038 2 0.0424 0.9490 0.000 0.992 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0592 0.963 0.984 0.000 0.000 0.016
#> SRR925688 1 0.0804 0.961 0.980 0.000 0.008 0.012
#> SRR925689 1 0.0188 0.964 0.996 0.000 0.004 0.000
#> SRR925690 1 0.0188 0.964 0.996 0.000 0.004 0.000
#> SRR925691 1 0.0188 0.964 0.996 0.000 0.004 0.000
#> SRR925692 1 0.0188 0.964 0.996 0.000 0.004 0.000
#> SRR925693 3 0.0895 0.614 0.000 0.020 0.976 0.004
#> SRR925694 2 0.1411 0.885 0.000 0.960 0.020 0.020
#> SRR925695 3 0.2216 0.660 0.000 0.092 0.908 0.000
#> SRR925697 4 0.3105 0.695 0.004 0.000 0.140 0.856
#> SRR925696 2 0.2999 0.842 0.000 0.864 0.132 0.004
#> SRR925698 3 0.4228 0.655 0.000 0.232 0.760 0.008
#> SRR925699 2 0.0000 0.887 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0524 0.887 0.000 0.988 0.004 0.008
#> SRR925701 2 0.0376 0.887 0.000 0.992 0.004 0.004
#> SRR925702 1 0.5167 0.432 0.644 0.000 0.016 0.340
#> SRR925703 4 0.4746 0.508 0.000 0.000 0.368 0.632
#> SRR925704 2 0.6087 0.258 0.000 0.540 0.412 0.048
#> SRR925705 2 0.3142 0.836 0.000 0.860 0.132 0.008
#> SRR925706 4 0.5297 0.365 0.004 0.004 0.444 0.548
#> SRR925708 1 0.0469 0.964 0.988 0.000 0.000 0.012
#> SRR925707 2 0.2227 0.881 0.000 0.928 0.036 0.036
#> SRR925709 1 0.1059 0.956 0.972 0.000 0.012 0.016
#> SRR925710 3 0.5400 0.343 0.372 0.000 0.608 0.020
#> SRR925711 2 0.4015 0.839 0.000 0.832 0.116 0.052
#> SRR934631 2 0.1820 0.882 0.000 0.944 0.020 0.036
#> SRR934632 2 0.1820 0.882 0.000 0.944 0.020 0.036
#> SRR934633 2 0.3176 0.863 0.000 0.880 0.036 0.084
#> SRR925713 3 0.4514 0.499 0.136 0.000 0.800 0.064
#> SRR925714 4 0.4941 0.395 0.000 0.000 0.436 0.564
#> SRR925715 3 0.4586 0.559 0.136 0.000 0.796 0.068
#> SRR925716 4 0.2737 0.738 0.104 0.000 0.008 0.888
#> SRR925717 3 0.5100 0.477 0.168 0.000 0.756 0.076
#> SRR925718 2 0.1151 0.886 0.000 0.968 0.008 0.024
#> SRR925719 1 0.0592 0.963 0.984 0.000 0.000 0.016
#> SRR925720 1 0.0817 0.959 0.976 0.000 0.000 0.024
#> SRR925721 1 0.0336 0.965 0.992 0.000 0.000 0.008
#> SRR934634 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> SRR934635 1 0.0707 0.954 0.980 0.000 0.000 0.020
#> SRR925723 2 0.5024 0.432 0.000 0.632 0.360 0.008
#> SRR925724 3 0.4973 0.507 0.000 0.348 0.644 0.008
#> SRR925725 3 0.5900 0.616 0.000 0.260 0.664 0.076
#> SRR925726 3 0.4882 0.205 0.020 0.000 0.708 0.272
#> SRR925727 3 0.3494 0.673 0.000 0.172 0.824 0.004
#> SRR925728 4 0.2983 0.731 0.108 0.004 0.008 0.880
#> SRR934636 4 0.2647 0.730 0.120 0.000 0.000 0.880
#> SRR934637 4 0.2408 0.735 0.104 0.000 0.000 0.896
#> SRR925729 2 0.4818 0.711 0.000 0.748 0.216 0.036
#> SRR934638 1 0.0188 0.964 0.996 0.000 0.004 0.000
#> SRR934639 1 0.0469 0.964 0.988 0.000 0.000 0.012
#> SRR934640 2 0.3266 0.858 0.000 0.876 0.040 0.084
#> SRR934641 2 0.3266 0.858 0.000 0.876 0.040 0.084
#> SRR925734 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> SRR925735 3 0.4423 0.671 0.000 0.168 0.792 0.040
#> SRR925736 2 0.3249 0.832 0.000 0.852 0.140 0.008
#> SRR925737 2 0.2048 0.879 0.000 0.928 0.064 0.008
#> SRR934642 2 0.0524 0.887 0.000 0.988 0.008 0.004
#> SRR934643 2 0.0524 0.887 0.000 0.988 0.008 0.004
#> SRR934644 2 0.1833 0.881 0.000 0.944 0.024 0.032
#> SRR934645 2 0.1624 0.883 0.000 0.952 0.020 0.028
#> SRR925740 2 0.1302 0.885 0.000 0.956 0.044 0.000
#> SRR925741 3 0.5900 0.615 0.000 0.260 0.664 0.076
#> SRR925742 2 0.1576 0.884 0.000 0.948 0.048 0.004
#> SRR1283038 2 0.2647 0.850 0.000 0.880 0.120 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 1 0.3143 0.8546 0.872 0.000 0.044 0.068 0.016
#> SRR925688 1 0.3651 0.8324 0.828 0.000 0.060 0.108 0.004
#> SRR925689 1 0.0566 0.8792 0.984 0.004 0.012 0.000 0.000
#> SRR925690 1 0.0404 0.8802 0.988 0.000 0.012 0.000 0.000
#> SRR925691 1 0.0404 0.8802 0.988 0.000 0.012 0.000 0.000
#> SRR925692 1 0.0404 0.8802 0.988 0.000 0.012 0.000 0.000
#> SRR925693 4 0.4297 0.2503 0.000 0.000 0.472 0.528 0.000
#> SRR925694 2 0.4299 0.2026 0.000 0.672 0.316 0.008 0.004
#> SRR925695 4 0.5371 0.2266 0.000 0.056 0.420 0.524 0.000
#> SRR925697 5 0.3074 0.7679 0.000 0.000 0.000 0.196 0.804
#> SRR925696 2 0.4477 0.5449 0.000 0.708 0.252 0.040 0.000
#> SRR925698 4 0.6693 0.1174 0.000 0.364 0.240 0.396 0.000
#> SRR925699 2 0.1282 0.5911 0.000 0.952 0.044 0.004 0.000
#> SRR925700 2 0.0693 0.6014 0.000 0.980 0.012 0.008 0.000
#> SRR925701 2 0.0290 0.6048 0.000 0.992 0.008 0.000 0.000
#> SRR925702 1 0.7235 -0.1421 0.376 0.000 0.020 0.264 0.340
#> SRR925703 5 0.4227 0.5197 0.000 0.000 0.000 0.420 0.580
#> SRR925704 3 0.4450 0.5587 0.000 0.132 0.760 0.108 0.000
#> SRR925705 2 0.4169 0.5569 0.000 0.732 0.240 0.028 0.000
#> SRR925706 5 0.5591 0.6862 0.008 0.020 0.040 0.304 0.628
#> SRR925708 1 0.0880 0.8799 0.968 0.000 0.000 0.032 0.000
#> SRR925707 2 0.6625 0.1749 0.004 0.592 0.144 0.036 0.224
#> SRR925709 1 0.4896 0.7047 0.716 0.000 0.044 0.220 0.020
#> SRR925710 4 0.5257 0.2462 0.264 0.000 0.076 0.656 0.004
#> SRR925711 3 0.4536 0.5553 0.000 0.240 0.712 0.048 0.000
#> SRR934631 2 0.4273 -0.1696 0.000 0.552 0.448 0.000 0.000
#> SRR934632 2 0.4268 -0.1574 0.000 0.556 0.444 0.000 0.000
#> SRR934633 3 0.4150 0.4752 0.000 0.388 0.612 0.000 0.000
#> SRR925713 4 0.3353 0.3269 0.104 0.000 0.020 0.852 0.024
#> SRR925714 4 0.4273 -0.3475 0.000 0.000 0.000 0.552 0.448
#> SRR925715 4 0.5992 0.2328 0.104 0.000 0.328 0.560 0.008
#> SRR925716 5 0.1892 0.7764 0.004 0.000 0.000 0.080 0.916
#> SRR925717 4 0.3883 0.3225 0.152 0.000 0.012 0.804 0.032
#> SRR925718 2 0.1173 0.6046 0.000 0.964 0.020 0.004 0.012
#> SRR925719 1 0.4351 0.7823 0.776 0.000 0.040 0.164 0.020
#> SRR925720 1 0.4368 0.7844 0.772 0.000 0.044 0.168 0.016
#> SRR925721 1 0.2649 0.8656 0.900 0.000 0.048 0.036 0.016
#> SRR934634 1 0.1579 0.8756 0.944 0.000 0.032 0.024 0.000
#> SRR934635 1 0.0740 0.8822 0.980 0.000 0.004 0.008 0.008
#> SRR925723 2 0.5354 0.4813 0.000 0.652 0.240 0.108 0.000
#> SRR925724 4 0.6717 0.1268 0.000 0.364 0.248 0.388 0.000
#> SRR925725 3 0.4268 0.5042 0.000 0.084 0.772 0.144 0.000
#> SRR925726 4 0.3719 0.1561 0.004 0.000 0.012 0.776 0.208
#> SRR925727 4 0.5252 0.3590 0.000 0.076 0.292 0.632 0.000
#> SRR925728 5 0.6250 0.7035 0.048 0.036 0.044 0.212 0.660
#> SRR934636 5 0.0833 0.7300 0.004 0.016 0.000 0.004 0.976
#> SRR934637 5 0.0000 0.7439 0.000 0.000 0.000 0.000 1.000
#> SRR925729 3 0.6615 0.4669 0.000 0.324 0.444 0.232 0.000
#> SRR934638 1 0.0671 0.8818 0.980 0.000 0.016 0.000 0.004
#> SRR934639 1 0.0613 0.8830 0.984 0.000 0.004 0.008 0.004
#> SRR934640 3 0.4602 0.5643 0.000 0.240 0.708 0.052 0.000
#> SRR934641 3 0.4602 0.5643 0.000 0.240 0.708 0.052 0.000
#> SRR925734 1 0.0290 0.8803 0.992 0.000 0.008 0.000 0.000
#> SRR925735 4 0.5421 0.1753 0.004 0.048 0.436 0.512 0.000
#> SRR925736 2 0.4674 0.5411 0.000 0.708 0.232 0.060 0.000
#> SRR925737 2 0.3663 0.5788 0.000 0.776 0.208 0.016 0.000
#> SRR934642 2 0.0162 0.6056 0.000 0.996 0.004 0.000 0.000
#> SRR934643 2 0.0404 0.6068 0.000 0.988 0.012 0.000 0.000
#> SRR934644 2 0.4542 -0.1563 0.000 0.536 0.456 0.008 0.000
#> SRR934645 2 0.4276 0.0582 0.004 0.616 0.380 0.000 0.000
#> SRR925740 2 0.3366 0.5841 0.000 0.768 0.232 0.000 0.000
#> SRR925741 3 0.4179 0.5007 0.000 0.072 0.776 0.152 0.000
#> SRR925742 2 0.3143 0.5866 0.000 0.796 0.204 0.000 0.000
#> SRR1283038 2 0.4984 0.4919 0.000 0.640 0.308 0.052 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 1 0.4488 0.70025 0.664 0.000 0.000 0.280 0.004 0.052
#> SRR925688 1 0.5318 0.61255 0.580 0.004 0.000 0.296 0.000 0.120
#> SRR925689 1 0.1370 0.77830 0.948 0.004 0.000 0.012 0.000 0.036
#> SRR925690 1 0.0993 0.78483 0.964 0.000 0.000 0.012 0.000 0.024
#> SRR925691 1 0.0993 0.78483 0.964 0.000 0.000 0.012 0.000 0.024
#> SRR925692 1 0.1074 0.78306 0.960 0.000 0.000 0.012 0.000 0.028
#> SRR925693 3 0.4493 0.29662 0.000 0.000 0.596 0.040 0.000 0.364
#> SRR925694 2 0.6626 -0.27720 0.004 0.388 0.304 0.020 0.000 0.284
#> SRR925695 3 0.4262 0.37507 0.000 0.012 0.724 0.048 0.000 0.216
#> SRR925697 5 0.4076 0.43644 0.000 0.000 0.004 0.348 0.636 0.012
#> SRR925696 2 0.3797 0.41540 0.000 0.580 0.420 0.000 0.000 0.000
#> SRR925698 3 0.6332 -0.05541 0.000 0.352 0.472 0.052 0.000 0.124
#> SRR925699 2 0.1461 0.62068 0.000 0.940 0.016 0.000 0.000 0.044
#> SRR925700 2 0.0748 0.63991 0.000 0.976 0.004 0.004 0.000 0.016
#> SRR925701 2 0.0622 0.64146 0.000 0.980 0.012 0.000 0.000 0.008
#> SRR925702 4 0.4215 0.33520 0.044 0.000 0.004 0.720 0.228 0.004
#> SRR925703 5 0.4919 0.27506 0.000 0.000 0.012 0.412 0.536 0.040
#> SRR925704 3 0.1485 0.36607 0.000 0.004 0.944 0.024 0.000 0.028
#> SRR925705 2 0.3878 0.53391 0.000 0.668 0.320 0.004 0.000 0.008
#> SRR925706 4 0.5046 0.35537 0.000 0.020 0.008 0.688 0.200 0.084
#> SRR925708 1 0.2664 0.70312 0.816 0.000 0.000 0.184 0.000 0.000
#> SRR925707 2 0.7590 -0.43876 0.000 0.356 0.032 0.332 0.072 0.208
#> SRR925709 4 0.4237 -0.00909 0.396 0.000 0.000 0.584 0.000 0.020
#> SRR925710 4 0.5165 0.46151 0.036 0.000 0.068 0.652 0.000 0.244
#> SRR925711 3 0.4145 0.20582 0.000 0.040 0.784 0.108 0.000 0.068
#> SRR934631 3 0.6053 0.04424 0.000 0.308 0.412 0.000 0.000 0.280
#> SRR934632 3 0.6059 0.04613 0.000 0.312 0.408 0.000 0.000 0.280
#> SRR934633 3 0.5351 0.02875 0.000 0.144 0.568 0.000 0.000 0.288
#> SRR925713 4 0.3647 0.47668 0.000 0.000 0.052 0.788 0.004 0.156
#> SRR925714 4 0.5816 -0.25144 0.000 0.000 0.028 0.444 0.436 0.092
#> SRR925715 4 0.5184 -0.08086 0.016 0.000 0.108 0.644 0.000 0.232
#> SRR925716 5 0.2006 0.66452 0.000 0.000 0.000 0.104 0.892 0.004
#> SRR925717 4 0.6615 0.35043 0.108 0.000 0.036 0.492 0.032 0.332
#> SRR925718 2 0.0692 0.64295 0.000 0.976 0.004 0.000 0.000 0.020
#> SRR925719 1 0.4264 0.37644 0.492 0.000 0.000 0.492 0.000 0.016
#> SRR925720 1 0.4743 0.56850 0.564 0.000 0.000 0.388 0.004 0.044
#> SRR925721 1 0.4223 0.73709 0.720 0.000 0.000 0.204 0.000 0.076
#> SRR934634 1 0.3139 0.77925 0.812 0.000 0.000 0.160 0.000 0.028
#> SRR934635 1 0.1643 0.79902 0.924 0.000 0.000 0.068 0.008 0.000
#> SRR925723 2 0.4627 0.32808 0.000 0.532 0.436 0.012 0.000 0.020
#> SRR925724 3 0.6076 -0.07820 0.000 0.364 0.484 0.036 0.000 0.116
#> SRR925725 3 0.0837 0.36606 0.000 0.004 0.972 0.004 0.000 0.020
#> SRR925726 4 0.6459 0.18300 0.000 0.000 0.036 0.468 0.200 0.296
#> SRR925727 3 0.5674 0.32618 0.000 0.068 0.548 0.044 0.000 0.340
#> SRR925728 4 0.5283 0.20578 0.004 0.016 0.000 0.580 0.336 0.064
#> SRR934636 5 0.0000 0.66244 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934637 5 0.0000 0.66244 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925729 3 0.6866 -0.04371 0.000 0.196 0.388 0.064 0.000 0.352
#> SRR934638 1 0.3309 0.78075 0.800 0.000 0.000 0.172 0.004 0.024
#> SRR934639 1 0.1349 0.79492 0.940 0.000 0.000 0.056 0.000 0.004
#> SRR934640 6 0.6747 0.98426 0.000 0.040 0.256 0.316 0.000 0.388
#> SRR934641 6 0.6690 0.98415 0.000 0.036 0.252 0.320 0.000 0.392
#> SRR925734 1 0.0260 0.79403 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR925735 3 0.5127 0.30176 0.000 0.004 0.616 0.112 0.000 0.268
#> SRR925736 2 0.3398 0.59011 0.000 0.740 0.252 0.000 0.000 0.008
#> SRR925737 2 0.2748 0.63242 0.000 0.856 0.120 0.008 0.000 0.016
#> SRR934642 2 0.0909 0.64417 0.000 0.968 0.020 0.000 0.000 0.012
#> SRR934643 2 0.0858 0.64472 0.000 0.968 0.028 0.000 0.000 0.004
#> SRR934644 3 0.6293 0.03070 0.000 0.312 0.392 0.008 0.000 0.288
#> SRR934645 3 0.6351 0.03577 0.004 0.328 0.368 0.004 0.000 0.296
#> SRR925740 2 0.3578 0.53053 0.000 0.660 0.340 0.000 0.000 0.000
#> SRR925741 3 0.1307 0.35633 0.000 0.008 0.952 0.008 0.000 0.032
#> SRR925742 2 0.3101 0.59671 0.000 0.756 0.244 0.000 0.000 0.000
#> SRR1283038 3 0.4569 -0.15075 0.000 0.396 0.564 0.000 0.000 0.040
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.935 0.971 0.4708 0.542 0.542
#> 3 3 0.899 0.901 0.949 0.4188 0.786 0.604
#> 4 4 0.798 0.807 0.848 0.0762 0.960 0.877
#> 5 5 0.772 0.684 0.821 0.0798 0.953 0.836
#> 6 6 0.767 0.579 0.753 0.0456 0.887 0.567
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
#> SRR925687 1 0.0672 0.955 0.992 0.008
#> SRR925688 1 0.3114 0.927 0.944 0.056
#> SRR925689 2 0.0000 1.000 0.000 1.000
#> SRR925690 2 0.0000 1.000 0.000 1.000
#> SRR925691 2 0.0000 1.000 0.000 1.000
#> SRR925692 2 0.0000 1.000 0.000 1.000
#> SRR925693 1 0.0000 0.956 1.000 0.000
#> SRR925694 2 0.0000 1.000 0.000 1.000
#> SRR925695 1 0.0000 0.956 1.000 0.000
#> SRR925697 1 0.0000 0.956 1.000 0.000
#> SRR925696 1 0.0376 0.956 0.996 0.004
#> SRR925698 1 0.0000 0.956 1.000 0.000
#> SRR925699 2 0.0000 1.000 0.000 1.000
#> SRR925700 2 0.0000 1.000 0.000 1.000
#> SRR925701 2 0.0000 1.000 0.000 1.000
#> SRR925702 1 0.2236 0.940 0.964 0.036
#> SRR925703 1 0.0000 0.956 1.000 0.000
#> SRR925704 1 0.0000 0.956 1.000 0.000
#> SRR925705 1 0.0672 0.955 0.992 0.008
#> SRR925706 1 0.0000 0.956 1.000 0.000
#> SRR925708 1 0.0672 0.955 0.992 0.008
#> SRR925707 1 0.9866 0.310 0.568 0.432
#> SRR925709 1 0.2236 0.940 0.964 0.036
#> SRR925710 1 0.0000 0.956 1.000 0.000
#> SRR925711 1 0.0000 0.956 1.000 0.000
#> SRR934631 2 0.0000 1.000 0.000 1.000
#> SRR934632 2 0.0000 1.000 0.000 1.000
#> SRR934633 1 0.3114 0.927 0.944 0.056
#> SRR925713 1 0.0000 0.956 1.000 0.000
#> SRR925714 1 0.0000 0.956 1.000 0.000
#> SRR925715 1 0.0000 0.956 1.000 0.000
#> SRR925716 1 0.2423 0.938 0.960 0.040
#> SRR925717 1 0.0000 0.956 1.000 0.000
#> SRR925718 2 0.0000 1.000 0.000 1.000
#> SRR925719 1 0.0672 0.955 0.992 0.008
#> SRR925720 1 0.0672 0.955 0.992 0.008
#> SRR925721 1 0.3114 0.927 0.944 0.056
#> SRR934634 2 0.0000 1.000 0.000 1.000
#> SRR934635 2 0.0000 1.000 0.000 1.000
#> SRR925723 1 0.0376 0.956 0.996 0.004
#> SRR925724 1 0.0000 0.956 1.000 0.000
#> SRR925725 1 0.0000 0.956 1.000 0.000
#> SRR925726 1 0.0000 0.956 1.000 0.000
#> SRR925727 1 0.0000 0.956 1.000 0.000
#> SRR925728 1 0.9833 0.332 0.576 0.424
#> SRR934636 2 0.0000 1.000 0.000 1.000
#> SRR934637 1 0.9993 0.150 0.516 0.484
#> SRR925729 1 0.0672 0.955 0.992 0.008
#> SRR934638 2 0.0000 1.000 0.000 1.000
#> SRR934639 1 0.0672 0.955 0.992 0.008
#> SRR934640 2 0.0000 1.000 0.000 1.000
#> SRR934641 2 0.0000 1.000 0.000 1.000
#> SRR925734 2 0.0000 1.000 0.000 1.000
#> SRR925735 1 0.0672 0.955 0.992 0.008
#> SRR925736 1 0.0672 0.955 0.992 0.008
#> SRR925737 1 0.0672 0.955 0.992 0.008
#> SRR934642 2 0.0000 1.000 0.000 1.000
#> SRR934643 2 0.0000 1.000 0.000 1.000
#> SRR934644 2 0.0000 1.000 0.000 1.000
#> SRR934645 2 0.0000 1.000 0.000 1.000
#> SRR925740 1 0.3114 0.927 0.944 0.056
#> SRR925741 1 0.0000 0.956 1.000 0.000
#> SRR925742 1 0.3114 0.927 0.944 0.056
#> SRR1283038 1 0.0672 0.955 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.1753 0.895 0.952 0.000 0.048
#> SRR925688 1 0.0000 0.885 1.000 0.000 0.000
#> SRR925689 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925690 2 0.1860 0.963 0.052 0.948 0.000
#> SRR925691 2 0.1860 0.963 0.052 0.948 0.000
#> SRR925692 2 0.1860 0.963 0.052 0.948 0.000
#> SRR925693 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925694 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925695 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925697 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925696 1 0.5216 0.646 0.740 0.000 0.260
#> SRR925698 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925699 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925702 1 0.0892 0.892 0.980 0.000 0.020
#> SRR925703 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925704 3 0.4702 0.747 0.212 0.000 0.788
#> SRR925705 1 0.2066 0.888 0.940 0.000 0.060
#> SRR925706 3 0.2448 0.922 0.076 0.000 0.924
#> SRR925708 1 0.1753 0.895 0.952 0.000 0.048
#> SRR925707 1 0.6026 0.383 0.624 0.376 0.000
#> SRR925709 1 0.0892 0.892 0.980 0.000 0.020
#> SRR925710 3 0.2448 0.922 0.076 0.000 0.924
#> SRR925711 3 0.3038 0.895 0.104 0.000 0.896
#> SRR934631 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934633 1 0.0000 0.885 1.000 0.000 0.000
#> SRR925713 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925714 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925715 3 0.2448 0.922 0.076 0.000 0.924
#> SRR925716 1 0.0747 0.891 0.984 0.000 0.016
#> SRR925717 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925718 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925719 1 0.1753 0.895 0.952 0.000 0.048
#> SRR925720 1 0.1753 0.895 0.952 0.000 0.048
#> SRR925721 1 0.0000 0.885 1.000 0.000 0.000
#> SRR934634 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934635 2 0.1860 0.963 0.052 0.948 0.000
#> SRR925723 1 0.5216 0.646 0.740 0.000 0.260
#> SRR925724 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925725 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925726 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925727 3 0.0000 0.959 0.000 0.000 1.000
#> SRR925728 1 0.5988 0.401 0.632 0.368 0.000
#> SRR934636 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934637 1 0.6215 0.241 0.572 0.428 0.000
#> SRR925729 1 0.1753 0.895 0.952 0.000 0.048
#> SRR934638 2 0.1860 0.963 0.052 0.948 0.000
#> SRR934639 1 0.1753 0.895 0.952 0.000 0.048
#> SRR934640 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.986 0.000 1.000 0.000
#> SRR925734 2 0.1860 0.963 0.052 0.948 0.000
#> SRR925735 1 0.1860 0.893 0.948 0.000 0.052
#> SRR925736 1 0.1753 0.895 0.952 0.000 0.048
#> SRR925737 1 0.1753 0.895 0.952 0.000 0.048
#> SRR934642 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.986 0.000 1.000 0.000
#> SRR934645 2 0.0747 0.980 0.016 0.984 0.000
#> SRR925740 1 0.0000 0.885 1.000 0.000 0.000
#> SRR925741 3 0.2448 0.922 0.076 0.000 0.924
#> SRR925742 1 0.0000 0.885 1.000 0.000 0.000
#> SRR1283038 1 0.2066 0.888 0.940 0.000 0.060
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR925688 1 0.3400 0.797 0.820 0.000 0.180 0.000
#> SRR925689 2 0.1867 0.941 0.000 0.928 0.072 0.000
#> SRR925690 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR925691 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR925692 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR925693 3 0.4998 0.741 0.000 0.000 0.512 0.488
#> SRR925694 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925695 3 0.5155 0.753 0.004 0.000 0.528 0.468
#> SRR925697 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925696 1 0.4638 0.642 0.788 0.000 0.152 0.060
#> SRR925698 3 0.5151 0.754 0.004 0.000 0.532 0.464
#> SRR925699 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925701 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925702 1 0.1716 0.839 0.936 0.000 0.064 0.000
#> SRR925703 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925704 3 0.7039 0.443 0.256 0.000 0.568 0.176
#> SRR925705 1 0.0469 0.843 0.988 0.000 0.012 0.000
#> SRR925706 4 0.5012 0.660 0.116 0.000 0.112 0.772
#> SRR925708 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR925707 1 0.7733 0.316 0.440 0.304 0.256 0.000
#> SRR925709 1 0.1716 0.839 0.936 0.000 0.064 0.000
#> SRR925710 4 0.5012 0.660 0.116 0.000 0.112 0.772
#> SRR925711 3 0.5515 0.529 0.152 0.000 0.732 0.116
#> SRR934631 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934632 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934633 1 0.3356 0.799 0.824 0.000 0.176 0.000
#> SRR925713 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925714 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925715 4 0.5012 0.660 0.116 0.000 0.112 0.772
#> SRR925716 1 0.2011 0.835 0.920 0.000 0.080 0.000
#> SRR925717 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925718 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925719 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR925720 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR925721 1 0.3400 0.797 0.820 0.000 0.180 0.000
#> SRR934634 2 0.1716 0.943 0.000 0.936 0.064 0.000
#> SRR934635 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR925723 1 0.4638 0.642 0.788 0.000 0.152 0.060
#> SRR925724 3 0.5151 0.754 0.004 0.000 0.532 0.464
#> SRR925725 3 0.4998 0.741 0.000 0.000 0.512 0.488
#> SRR925726 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR925727 3 0.4998 0.741 0.000 0.000 0.512 0.488
#> SRR925728 1 0.7683 0.331 0.452 0.304 0.244 0.000
#> SRR934636 2 0.1716 0.943 0.000 0.936 0.064 0.000
#> SRR934637 1 0.7843 0.176 0.388 0.344 0.268 0.000
#> SRR925729 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR934638 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR934639 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR934640 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934641 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR925734 2 0.2868 0.916 0.000 0.864 0.136 0.000
#> SRR925735 1 0.0188 0.846 0.996 0.000 0.004 0.000
#> SRR925736 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR925737 1 0.0000 0.848 1.000 0.000 0.000 0.000
#> SRR934642 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934643 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934644 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR934645 2 0.0921 0.950 0.000 0.972 0.028 0.000
#> SRR925740 1 0.3356 0.799 0.824 0.000 0.176 0.000
#> SRR925741 3 0.6278 0.585 0.120 0.000 0.652 0.228
#> SRR925742 1 0.3356 0.799 0.824 0.000 0.176 0.000
#> SRR1283038 1 0.0469 0.843 0.988 0.000 0.012 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.1043 0.7935 0.040 0.000 0.000 0.960 0.000
#> SRR925688 1 0.5443 0.0585 0.516 0.036 0.012 0.436 0.000
#> SRR925689 2 0.1478 0.6296 0.064 0.936 0.000 0.000 0.000
#> SRR925690 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR925691 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR925692 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR925693 3 0.4150 0.7915 0.000 0.000 0.612 0.000 0.388
#> SRR925694 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925695 3 0.4238 0.8005 0.000 0.000 0.628 0.004 0.368
#> SRR925697 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925696 4 0.4400 0.5806 0.000 0.000 0.212 0.736 0.052
#> SRR925698 3 0.4225 0.8016 0.000 0.000 0.632 0.004 0.364
#> SRR925699 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925700 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925701 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925702 4 0.2526 0.7596 0.080 0.012 0.012 0.896 0.000
#> SRR925703 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925704 3 0.5180 0.5025 0.000 0.000 0.684 0.196 0.120
#> SRR925705 4 0.3039 0.6953 0.152 0.000 0.012 0.836 0.000
#> SRR925706 5 0.4234 0.7138 0.000 0.000 0.184 0.056 0.760
#> SRR925708 4 0.1043 0.7935 0.040 0.000 0.000 0.960 0.000
#> SRR925707 1 0.5628 0.6575 0.516 0.420 0.008 0.056 0.000
#> SRR925709 4 0.2526 0.7596 0.080 0.012 0.012 0.896 0.000
#> SRR925710 5 0.4234 0.7138 0.000 0.000 0.184 0.056 0.760
#> SRR925711 3 0.0566 0.5831 0.004 0.000 0.984 0.012 0.000
#> SRR934631 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934632 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934633 4 0.5372 0.1161 0.392 0.036 0.012 0.560 0.000
#> SRR925713 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925714 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925715 5 0.4234 0.7138 0.000 0.000 0.184 0.056 0.760
#> SRR925716 4 0.3653 0.6861 0.164 0.016 0.012 0.808 0.000
#> SRR925717 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925718 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925719 4 0.1043 0.7935 0.040 0.000 0.000 0.960 0.000
#> SRR925720 4 0.1043 0.7935 0.040 0.000 0.000 0.960 0.000
#> SRR925721 1 0.5443 0.0585 0.516 0.036 0.012 0.436 0.000
#> SRR934634 2 0.1608 0.6343 0.072 0.928 0.000 0.000 0.000
#> SRR934635 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR925723 4 0.4400 0.5806 0.000 0.000 0.212 0.736 0.052
#> SRR925724 3 0.4225 0.8016 0.000 0.000 0.632 0.004 0.364
#> SRR925725 3 0.4150 0.7915 0.000 0.000 0.612 0.000 0.388
#> SRR925726 5 0.0000 0.8563 0.000 0.000 0.000 0.000 1.000
#> SRR925727 3 0.4150 0.7915 0.000 0.000 0.612 0.000 0.388
#> SRR925728 1 0.5868 0.6562 0.512 0.408 0.012 0.068 0.000
#> SRR934636 2 0.1608 0.6343 0.072 0.928 0.000 0.000 0.000
#> SRR934637 1 0.4448 0.6097 0.516 0.480 0.000 0.004 0.000
#> SRR925729 4 0.0000 0.7913 0.000 0.000 0.000 1.000 0.000
#> SRR934638 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR934639 4 0.1043 0.7935 0.040 0.000 0.000 0.960 0.000
#> SRR934640 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934641 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR925734 2 0.0000 0.5859 0.000 1.000 0.000 0.000 0.000
#> SRR925735 4 0.0162 0.7908 0.000 0.000 0.004 0.996 0.000
#> SRR925736 4 0.0000 0.7913 0.000 0.000 0.000 1.000 0.000
#> SRR925737 4 0.0000 0.7913 0.000 0.000 0.000 1.000 0.000
#> SRR934642 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934643 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934644 2 0.4302 0.7884 0.480 0.520 0.000 0.000 0.000
#> SRR934645 2 0.4278 0.7775 0.452 0.548 0.000 0.000 0.000
#> SRR925740 4 0.5363 0.1298 0.388 0.036 0.012 0.564 0.000
#> SRR925741 3 0.3641 0.6443 0.000 0.000 0.820 0.060 0.120
#> SRR925742 4 0.5363 0.1298 0.388 0.036 0.012 0.564 0.000
#> SRR1283038 4 0.3039 0.6953 0.152 0.000 0.012 0.836 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.0000 0.6350 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925688 4 0.5748 0.0923 0.360 0.000 0.000 0.464 0.000 0.176
#> SRR925689 2 0.3789 -0.1119 0.416 0.584 0.000 0.000 0.000 0.000
#> SRR925690 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR925691 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR925692 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR925693 3 0.3515 0.7801 0.000 0.000 0.676 0.000 0.324 0.000
#> SRR925694 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925695 3 0.3565 0.7888 0.000 0.000 0.692 0.004 0.304 0.000
#> SRR925697 5 0.0000 0.8429 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925696 6 0.6122 0.4795 0.000 0.000 0.260 0.304 0.004 0.432
#> SRR925698 3 0.3547 0.7894 0.000 0.000 0.696 0.004 0.300 0.000
#> SRR925699 2 0.0458 0.8355 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR925700 2 0.0458 0.8355 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925702 4 0.1327 0.6112 0.000 0.000 0.000 0.936 0.000 0.064
#> SRR925703 5 0.0000 0.8429 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925704 3 0.4466 0.5052 0.000 0.000 0.760 0.108 0.044 0.088
#> SRR925705 6 0.5981 0.5065 0.156 0.000 0.012 0.396 0.000 0.436
#> SRR925706 5 0.4239 0.6781 0.000 0.000 0.248 0.056 0.696 0.000
#> SRR925708 4 0.0000 0.6350 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925707 1 0.3948 0.2383 0.748 0.000 0.000 0.064 0.000 0.188
#> SRR925709 4 0.1327 0.6112 0.000 0.000 0.000 0.936 0.000 0.064
#> SRR925710 5 0.4239 0.6781 0.000 0.000 0.248 0.056 0.696 0.000
#> SRR925711 3 0.4552 0.4083 0.040 0.000 0.572 0.000 0.000 0.388
#> SRR934631 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 6 0.5103 0.5984 0.276 0.000 0.000 0.120 0.000 0.604
#> SRR925713 5 0.0000 0.8429 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925714 5 0.0000 0.8429 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925715 5 0.4239 0.6781 0.000 0.000 0.248 0.056 0.696 0.000
#> SRR925716 4 0.3068 0.5388 0.088 0.000 0.000 0.840 0.000 0.072
#> SRR925717 5 0.0146 0.8391 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR925718 2 0.0458 0.8355 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR925719 4 0.0000 0.6350 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925720 4 0.0000 0.6350 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925721 4 0.5748 0.0923 0.360 0.000 0.000 0.464 0.000 0.176
#> SRR934634 2 0.3774 -0.0797 0.408 0.592 0.000 0.000 0.000 0.000
#> SRR934635 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR925723 6 0.6122 0.4795 0.000 0.000 0.260 0.304 0.004 0.432
#> SRR925724 3 0.3547 0.7894 0.000 0.000 0.696 0.004 0.300 0.000
#> SRR925725 3 0.3515 0.7801 0.000 0.000 0.676 0.000 0.324 0.000
#> SRR925726 5 0.0000 0.8429 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925727 3 0.3515 0.7801 0.000 0.000 0.676 0.000 0.324 0.000
#> SRR925728 1 0.4114 0.2147 0.732 0.000 0.000 0.072 0.000 0.196
#> SRR934636 2 0.3774 -0.0797 0.408 0.592 0.000 0.000 0.000 0.000
#> SRR934637 1 0.3202 0.3142 0.816 0.000 0.000 0.040 0.000 0.144
#> SRR925729 4 0.3659 0.0802 0.000 0.000 0.000 0.636 0.000 0.364
#> SRR934638 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR934639 4 0.0000 0.6350 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934640 2 0.1444 0.7955 0.072 0.928 0.000 0.000 0.000 0.000
#> SRR934641 2 0.1444 0.7955 0.072 0.928 0.000 0.000 0.000 0.000
#> SRR925734 1 0.3706 0.5699 0.620 0.380 0.000 0.000 0.000 0.000
#> SRR925735 4 0.3795 0.0709 0.000 0.000 0.004 0.632 0.000 0.364
#> SRR925736 4 0.3659 0.0802 0.000 0.000 0.000 0.636 0.000 0.364
#> SRR925737 4 0.3659 0.0802 0.000 0.000 0.000 0.636 0.000 0.364
#> SRR934642 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0458 0.8355 0.016 0.984 0.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.8358 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.2527 0.6742 0.168 0.832 0.000 0.000 0.000 0.000
#> SRR925740 6 0.5138 0.6013 0.276 0.000 0.000 0.124 0.000 0.600
#> SRR925741 3 0.2258 0.6208 0.000 0.000 0.896 0.060 0.044 0.000
#> SRR925742 6 0.5138 0.6013 0.276 0.000 0.000 0.124 0.000 0.600
#> SRR1283038 6 0.5981 0.5065 0.156 0.000 0.012 0.396 0.000 0.436
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 15949 rows and 64 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 0.979 0.990 0.4808 0.524 0.524
#> 3 3 0.999 0.967 0.975 0.4060 0.760 0.558
#> 4 4 0.676 0.630 0.772 0.0970 0.975 0.922
#> 5 5 0.697 0.540 0.716 0.0651 0.821 0.467
#> 6 6 0.731 0.733 0.779 0.0451 0.912 0.600
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
#> SRR925687 1 0.000 0.983 1.000 0.000
#> SRR925688 1 0.000 0.983 1.000 0.000
#> SRR925689 2 0.000 1.000 0.000 1.000
#> SRR925690 2 0.000 1.000 0.000 1.000
#> SRR925691 2 0.000 1.000 0.000 1.000
#> SRR925692 2 0.000 1.000 0.000 1.000
#> SRR925693 1 0.000 0.983 1.000 0.000
#> SRR925694 2 0.000 1.000 0.000 1.000
#> SRR925695 1 0.000 0.983 1.000 0.000
#> SRR925697 1 0.000 0.983 1.000 0.000
#> SRR925696 1 0.000 0.983 1.000 0.000
#> SRR925698 1 0.000 0.983 1.000 0.000
#> SRR925699 2 0.000 1.000 0.000 1.000
#> SRR925700 2 0.000 1.000 0.000 1.000
#> SRR925701 2 0.000 1.000 0.000 1.000
#> SRR925702 1 0.000 0.983 1.000 0.000
#> SRR925703 1 0.000 0.983 1.000 0.000
#> SRR925704 1 0.000 0.983 1.000 0.000
#> SRR925705 1 0.000 0.983 1.000 0.000
#> SRR925706 1 0.000 0.983 1.000 0.000
#> SRR925708 1 0.000 0.983 1.000 0.000
#> SRR925707 2 0.000 1.000 0.000 1.000
#> SRR925709 1 0.000 0.983 1.000 0.000
#> SRR925710 1 0.000 0.983 1.000 0.000
#> SRR925711 1 0.000 0.983 1.000 0.000
#> SRR934631 2 0.000 1.000 0.000 1.000
#> SRR934632 2 0.000 1.000 0.000 1.000
#> SRR934633 1 0.722 0.767 0.800 0.200
#> SRR925713 1 0.000 0.983 1.000 0.000
#> SRR925714 1 0.000 0.983 1.000 0.000
#> SRR925715 1 0.000 0.983 1.000 0.000
#> SRR925716 1 0.388 0.914 0.924 0.076
#> SRR925717 1 0.000 0.983 1.000 0.000
#> SRR925718 2 0.000 1.000 0.000 1.000
#> SRR925719 1 0.000 0.983 1.000 0.000
#> SRR925720 1 0.000 0.983 1.000 0.000
#> SRR925721 1 0.000 0.983 1.000 0.000
#> SRR934634 2 0.000 1.000 0.000 1.000
#> SRR934635 2 0.000 1.000 0.000 1.000
#> SRR925723 1 0.000 0.983 1.000 0.000
#> SRR925724 1 0.000 0.983 1.000 0.000
#> SRR925725 1 0.000 0.983 1.000 0.000
#> SRR925726 1 0.000 0.983 1.000 0.000
#> SRR925727 1 0.000 0.983 1.000 0.000
#> SRR925728 1 0.714 0.773 0.804 0.196
#> SRR934636 2 0.000 1.000 0.000 1.000
#> SRR934637 2 0.000 1.000 0.000 1.000
#> SRR925729 1 0.000 0.983 1.000 0.000
#> SRR934638 2 0.000 1.000 0.000 1.000
#> SRR934639 1 0.000 0.983 1.000 0.000
#> SRR934640 2 0.000 1.000 0.000 1.000
#> SRR934641 2 0.000 1.000 0.000 1.000
#> SRR925734 2 0.000 1.000 0.000 1.000
#> SRR925735 1 0.000 0.983 1.000 0.000
#> SRR925736 1 0.000 0.983 1.000 0.000
#> SRR925737 1 0.000 0.983 1.000 0.000
#> SRR934642 2 0.000 1.000 0.000 1.000
#> SRR934643 2 0.000 1.000 0.000 1.000
#> SRR934644 2 0.000 1.000 0.000 1.000
#> SRR934645 2 0.000 1.000 0.000 1.000
#> SRR925740 1 0.000 0.983 1.000 0.000
#> SRR925741 1 0.000 0.983 1.000 0.000
#> SRR925742 1 0.680 0.794 0.820 0.180
#> SRR1283038 1 0.000 0.983 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925688 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925689 2 0.0592 0.989 0.012 0.988 0.000
#> SRR925690 2 0.1753 0.958 0.048 0.952 0.000
#> SRR925691 2 0.1753 0.958 0.048 0.952 0.000
#> SRR925692 2 0.0592 0.989 0.012 0.988 0.000
#> SRR925693 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925694 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925695 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925697 3 0.2356 0.950 0.072 0.000 0.928
#> SRR925696 3 0.4121 0.787 0.168 0.000 0.832
#> SRR925698 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925699 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925702 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925703 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925704 3 0.0237 0.961 0.004 0.000 0.996
#> SRR925705 1 0.2165 0.958 0.936 0.000 0.064
#> SRR925706 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925708 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925707 1 0.2356 0.923 0.928 0.072 0.000
#> SRR925709 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925710 3 0.2448 0.948 0.076 0.000 0.924
#> SRR925711 3 0.0237 0.961 0.004 0.000 0.996
#> SRR934631 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934633 1 0.2096 0.960 0.944 0.004 0.052
#> SRR925713 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925714 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925715 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925716 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925717 3 0.1860 0.958 0.052 0.000 0.948
#> SRR925718 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.968 1.000 0.000 0.000
#> SRR925720 1 0.0592 0.964 0.988 0.000 0.012
#> SRR925721 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934634 2 0.0592 0.989 0.012 0.988 0.000
#> SRR934635 2 0.0592 0.989 0.012 0.988 0.000
#> SRR925723 3 0.1031 0.951 0.024 0.000 0.976
#> SRR925724 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925725 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925726 3 0.2356 0.950 0.072 0.000 0.928
#> SRR925727 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925728 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934636 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934637 1 0.1860 0.941 0.948 0.052 0.000
#> SRR925729 1 0.2066 0.960 0.940 0.000 0.060
#> SRR934638 2 0.0592 0.989 0.012 0.988 0.000
#> SRR934639 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925734 2 0.0592 0.989 0.012 0.988 0.000
#> SRR925735 1 0.2448 0.950 0.924 0.000 0.076
#> SRR925736 1 0.2066 0.960 0.940 0.000 0.060
#> SRR925737 1 0.2066 0.960 0.940 0.000 0.060
#> SRR934642 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.992 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.992 0.000 1.000 0.000
#> SRR925740 1 0.2165 0.958 0.936 0.000 0.064
#> SRR925741 3 0.0000 0.962 0.000 0.000 1.000
#> SRR925742 1 0.1753 0.961 0.952 0.000 0.048
#> SRR1283038 1 0.2165 0.958 0.936 0.000 0.064
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.1211 0.6563 0.960 0.000 0.040 0.000
#> SRR925688 1 0.0707 0.6617 0.980 0.000 0.020 0.000
#> SRR925689 2 0.4957 0.7468 0.016 0.684 0.300 0.000
#> SRR925690 2 0.6926 0.5975 0.108 0.460 0.432 0.000
#> SRR925691 2 0.6926 0.5975 0.108 0.460 0.432 0.000
#> SRR925692 2 0.6278 0.6727 0.060 0.532 0.408 0.000
#> SRR925693 4 0.2149 0.7062 0.000 0.000 0.088 0.912
#> SRR925694 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR925695 4 0.2814 0.6848 0.000 0.000 0.132 0.868
#> SRR925697 4 0.3695 0.6661 0.156 0.000 0.016 0.828
#> SRR925696 3 0.7416 0.7826 0.244 0.000 0.516 0.240
#> SRR925698 4 0.3172 0.6533 0.000 0.000 0.160 0.840
#> SRR925699 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR925701 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR925702 1 0.0592 0.6618 0.984 0.000 0.016 0.000
#> SRR925703 4 0.1792 0.7335 0.068 0.000 0.000 0.932
#> SRR925704 3 0.6627 0.5254 0.084 0.000 0.504 0.412
#> SRR925705 1 0.5898 0.4107 0.604 0.000 0.348 0.048
#> SRR925706 4 0.4374 0.6985 0.068 0.000 0.120 0.812
#> SRR925708 1 0.1389 0.6531 0.952 0.000 0.048 0.000
#> SRR925707 1 0.6957 0.2371 0.472 0.112 0.416 0.000
#> SRR925709 1 0.0592 0.6636 0.984 0.000 0.016 0.000
#> SRR925710 4 0.6488 0.4007 0.244 0.000 0.128 0.628
#> SRR925711 4 0.6337 -0.1208 0.068 0.000 0.380 0.552
#> SRR934631 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR934632 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR934633 1 0.5830 0.4765 0.620 0.000 0.332 0.048
#> SRR925713 4 0.2255 0.7287 0.068 0.000 0.012 0.920
#> SRR925714 4 0.1792 0.7335 0.068 0.000 0.000 0.932
#> SRR925715 4 0.4764 0.6766 0.088 0.000 0.124 0.788
#> SRR925716 1 0.2469 0.6305 0.892 0.000 0.108 0.000
#> SRR925717 4 0.1716 0.7338 0.064 0.000 0.000 0.936
#> SRR925718 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR925719 1 0.1302 0.6544 0.956 0.000 0.044 0.000
#> SRR925720 1 0.3439 0.5748 0.868 0.000 0.048 0.084
#> SRR925721 1 0.1022 0.6606 0.968 0.000 0.032 0.000
#> SRR934634 2 0.4609 0.7717 0.024 0.752 0.224 0.000
#> SRR934635 2 0.6278 0.6727 0.060 0.532 0.408 0.000
#> SRR925723 3 0.7387 0.8088 0.224 0.000 0.520 0.256
#> SRR925724 4 0.4382 0.4391 0.000 0.000 0.296 0.704
#> SRR925725 4 0.2281 0.7022 0.000 0.000 0.096 0.904
#> SRR925726 4 0.3695 0.6661 0.156 0.000 0.016 0.828
#> SRR925727 4 0.2216 0.7042 0.000 0.000 0.092 0.908
#> SRR925728 1 0.2281 0.6477 0.904 0.000 0.096 0.000
#> SRR934636 2 0.2589 0.8145 0.000 0.884 0.116 0.000
#> SRR934637 1 0.5360 0.3058 0.552 0.012 0.436 0.000
#> SRR925729 1 0.5865 0.4380 0.612 0.000 0.340 0.048
#> SRR934638 2 0.5691 0.6921 0.028 0.564 0.408 0.000
#> SRR934639 1 0.1211 0.6563 0.960 0.000 0.040 0.000
#> SRR934640 2 0.0817 0.8325 0.000 0.976 0.024 0.000
#> SRR934641 2 0.0817 0.8325 0.000 0.976 0.024 0.000
#> SRR925734 2 0.6278 0.6727 0.060 0.532 0.408 0.000
#> SRR925735 1 0.6134 0.1896 0.508 0.000 0.444 0.048
#> SRR925736 1 0.5712 0.4675 0.644 0.000 0.308 0.048
#> SRR925737 1 0.5712 0.4675 0.644 0.000 0.308 0.048
#> SRR934642 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR934643 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR934644 2 0.0000 0.8372 0.000 1.000 0.000 0.000
#> SRR934645 2 0.2921 0.8030 0.000 0.860 0.140 0.000
#> SRR925740 1 0.5812 0.4763 0.624 0.000 0.328 0.048
#> SRR925741 4 0.5498 -0.0316 0.020 0.000 0.404 0.576
#> SRR925742 1 0.5535 0.5179 0.656 0.000 0.304 0.040
#> SRR1283038 1 0.6114 0.2204 0.524 0.000 0.428 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.6385 0.6079 0.252 0.000 0.232 0.516 0.000
#> SRR925688 4 0.6656 0.5367 0.308 0.000 0.252 0.440 0.000
#> SRR925689 2 0.4560 -0.2324 0.484 0.508 0.000 0.008 0.000
#> SRR925690 1 0.3766 0.6250 0.728 0.268 0.000 0.004 0.000
#> SRR925691 1 0.3766 0.6250 0.728 0.268 0.000 0.004 0.000
#> SRR925692 1 0.4084 0.5912 0.668 0.328 0.000 0.004 0.000
#> SRR925693 5 0.0798 0.6886 0.000 0.000 0.008 0.016 0.976
#> SRR925694 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR925695 5 0.1357 0.6787 0.004 0.000 0.048 0.000 0.948
#> SRR925697 4 0.4304 -0.5174 0.000 0.000 0.000 0.516 0.484
#> SRR925696 3 0.3243 0.5188 0.004 0.000 0.812 0.004 0.180
#> SRR925698 5 0.1892 0.6589 0.004 0.000 0.080 0.000 0.916
#> SRR925699 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> SRR925701 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR925702 4 0.6323 0.6022 0.252 0.000 0.220 0.528 0.000
#> SRR925703 5 0.4088 0.6101 0.000 0.000 0.000 0.368 0.632
#> SRR925704 3 0.4449 0.0987 0.004 0.000 0.604 0.004 0.388
#> SRR925705 3 0.2540 0.7047 0.024 0.000 0.888 0.088 0.000
#> SRR925706 5 0.6032 0.5471 0.000 0.000 0.116 0.424 0.460
#> SRR925708 4 0.6387 0.6063 0.248 0.000 0.236 0.516 0.000
#> SRR925707 1 0.4798 0.4166 0.772 0.060 0.116 0.052 0.000
#> SRR925709 4 0.6443 0.5892 0.276 0.000 0.224 0.500 0.000
#> SRR925710 4 0.6172 -0.3728 0.004 0.000 0.152 0.556 0.288
#> SRR925711 5 0.5488 0.3155 0.032 0.000 0.396 0.020 0.552
#> SRR934631 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR934632 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR934633 3 0.4559 0.6776 0.100 0.000 0.748 0.152 0.000
#> SRR925713 5 0.4150 0.5986 0.000 0.000 0.000 0.388 0.612
#> SRR925714 5 0.4088 0.6101 0.000 0.000 0.000 0.368 0.632
#> SRR925715 5 0.6274 0.5304 0.000 0.000 0.148 0.424 0.428
#> SRR925716 4 0.6497 0.5165 0.392 0.000 0.188 0.420 0.000
#> SRR925717 5 0.4015 0.6141 0.000 0.000 0.000 0.348 0.652
#> SRR925718 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.6387 0.6063 0.248 0.000 0.236 0.516 0.000
#> SRR925720 4 0.5892 0.5323 0.204 0.000 0.160 0.628 0.008
#> SRR925721 4 0.6613 0.5488 0.332 0.000 0.228 0.440 0.000
#> SRR934634 2 0.4310 0.1247 0.392 0.604 0.000 0.004 0.000
#> SRR934635 1 0.3932 0.5920 0.672 0.328 0.000 0.000 0.000
#> SRR925723 3 0.3317 0.5036 0.004 0.000 0.804 0.004 0.188
#> SRR925724 5 0.4200 0.4680 0.004 0.000 0.320 0.004 0.672
#> SRR925725 5 0.0912 0.6886 0.000 0.000 0.016 0.012 0.972
#> SRR925726 4 0.4304 -0.5174 0.000 0.000 0.000 0.516 0.484
#> SRR925727 5 0.1018 0.6890 0.000 0.000 0.016 0.016 0.968
#> SRR925728 1 0.6662 -0.5692 0.420 0.000 0.236 0.344 0.000
#> SRR934636 2 0.4238 0.5636 0.228 0.740 0.000 0.028 0.004
#> SRR934637 1 0.2609 0.4109 0.896 0.000 0.052 0.048 0.004
#> SRR925729 3 0.4170 0.6427 0.048 0.000 0.760 0.192 0.000
#> SRR934638 1 0.3999 0.5574 0.656 0.344 0.000 0.000 0.000
#> SRR934639 4 0.6405 0.6064 0.252 0.000 0.236 0.512 0.000
#> SRR934640 2 0.2419 0.8049 0.028 0.904 0.000 0.064 0.004
#> SRR934641 2 0.2419 0.8049 0.028 0.904 0.000 0.064 0.004
#> SRR925734 1 0.3932 0.5920 0.672 0.328 0.000 0.000 0.000
#> SRR925735 3 0.2466 0.6606 0.012 0.000 0.900 0.076 0.012
#> SRR925736 3 0.4334 0.6837 0.080 0.000 0.764 0.156 0.000
#> SRR925737 3 0.4412 0.6760 0.080 0.000 0.756 0.164 0.000
#> SRR934642 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR934643 2 0.0000 0.8555 0.000 1.000 0.000 0.000 0.000
#> SRR934644 2 0.0703 0.8577 0.000 0.976 0.000 0.024 0.000
#> SRR934645 2 0.2852 0.6786 0.172 0.828 0.000 0.000 0.000
#> SRR925740 3 0.4469 0.6802 0.096 0.000 0.756 0.148 0.000
#> SRR925741 5 0.4580 0.1954 0.004 0.000 0.460 0.004 0.532
#> SRR925742 3 0.4675 0.6576 0.100 0.000 0.736 0.164 0.000
#> SRR1283038 3 0.0290 0.6834 0.000 0.000 0.992 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.1710 0.879 0.000 0.000 0.016 0.936 0.020 0.028
#> SRR925688 4 0.3031 0.832 0.016 0.000 0.032 0.852 0.000 0.100
#> SRR925689 1 0.4917 0.589 0.616 0.328 0.012 0.004 0.004 0.036
#> SRR925690 1 0.3486 0.820 0.840 0.092 0.008 0.036 0.004 0.020
#> SRR925691 1 0.3486 0.820 0.840 0.092 0.008 0.036 0.004 0.020
#> SRR925692 1 0.3504 0.820 0.828 0.120 0.008 0.020 0.004 0.020
#> SRR925693 3 0.4886 0.529 0.060 0.000 0.508 0.000 0.432 0.000
#> SRR925694 2 0.1049 0.888 0.000 0.960 0.032 0.000 0.000 0.008
#> SRR925695 3 0.4909 0.592 0.056 0.000 0.588 0.000 0.348 0.008
#> SRR925697 5 0.1501 0.802 0.000 0.000 0.000 0.076 0.924 0.000
#> SRR925696 6 0.4402 0.553 0.000 0.000 0.244 0.040 0.016 0.700
#> SRR925698 3 0.4090 0.605 0.004 0.000 0.652 0.000 0.328 0.016
#> SRR925699 2 0.0603 0.883 0.000 0.980 0.004 0.000 0.000 0.016
#> SRR925700 2 0.0458 0.884 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR925701 2 0.0935 0.888 0.000 0.964 0.032 0.000 0.000 0.004
#> SRR925702 4 0.1059 0.884 0.000 0.000 0.004 0.964 0.016 0.016
#> SRR925703 5 0.2164 0.778 0.008 0.000 0.056 0.028 0.908 0.000
#> SRR925704 3 0.4907 0.300 0.000 0.000 0.556 0.016 0.036 0.392
#> SRR925705 6 0.3150 0.787 0.000 0.000 0.052 0.120 0.000 0.828
#> SRR925706 5 0.4685 0.702 0.020 0.000 0.104 0.068 0.764 0.044
#> SRR925708 4 0.1257 0.881 0.000 0.000 0.000 0.952 0.020 0.028
#> SRR925707 1 0.5545 0.606 0.672 0.012 0.060 0.180 0.000 0.076
#> SRR925709 4 0.1364 0.874 0.004 0.000 0.004 0.944 0.000 0.048
#> SRR925710 5 0.6053 0.555 0.020 0.000 0.072 0.216 0.620 0.072
#> SRR925711 3 0.5916 0.560 0.032 0.000 0.612 0.020 0.100 0.236
#> SRR934631 2 0.1409 0.886 0.000 0.948 0.032 0.000 0.008 0.012
#> SRR934632 2 0.1409 0.886 0.000 0.948 0.032 0.000 0.008 0.012
#> SRR934633 6 0.4006 0.753 0.016 0.000 0.032 0.204 0.000 0.748
#> SRR925713 5 0.1464 0.798 0.004 0.000 0.016 0.036 0.944 0.000
#> SRR925714 5 0.2164 0.778 0.008 0.000 0.056 0.028 0.908 0.000
#> SRR925715 5 0.4964 0.681 0.020 0.000 0.108 0.072 0.744 0.056
#> SRR925716 4 0.3565 0.806 0.064 0.000 0.024 0.824 0.000 0.088
#> SRR925717 5 0.2663 0.747 0.012 0.000 0.084 0.028 0.876 0.000
#> SRR925718 2 0.0603 0.883 0.000 0.980 0.004 0.000 0.000 0.016
#> SRR925719 4 0.1710 0.879 0.000 0.000 0.016 0.936 0.020 0.028
#> SRR925720 4 0.2680 0.836 0.004 0.000 0.016 0.884 0.068 0.028
#> SRR925721 4 0.3101 0.834 0.024 0.000 0.032 0.852 0.000 0.092
#> SRR934634 1 0.4721 0.223 0.488 0.480 0.008 0.008 0.000 0.016
#> SRR934635 1 0.2581 0.823 0.860 0.120 0.000 0.020 0.000 0.000
#> SRR925723 6 0.4402 0.553 0.000 0.000 0.244 0.040 0.016 0.700
#> SRR925724 3 0.4923 0.607 0.000 0.000 0.656 0.000 0.176 0.168
#> SRR925725 3 0.5013 0.537 0.060 0.000 0.508 0.000 0.428 0.004
#> SRR925726 5 0.1501 0.802 0.000 0.000 0.000 0.076 0.924 0.000
#> SRR925727 3 0.5013 0.537 0.060 0.000 0.508 0.000 0.428 0.004
#> SRR925728 4 0.4548 0.737 0.072 0.000 0.060 0.756 0.000 0.112
#> SRR934636 2 0.4798 0.288 0.324 0.620 0.044 0.000 0.004 0.008
#> SRR934637 1 0.5161 0.619 0.696 0.000 0.060 0.176 0.004 0.064
#> SRR925729 6 0.3429 0.761 0.004 0.000 0.000 0.252 0.004 0.740
#> SRR934638 1 0.2573 0.815 0.856 0.132 0.004 0.008 0.000 0.000
#> SRR934639 4 0.1257 0.881 0.000 0.000 0.000 0.952 0.020 0.028
#> SRR934640 2 0.3875 0.778 0.028 0.800 0.136 0.000 0.020 0.016
#> SRR934641 2 0.3875 0.778 0.028 0.800 0.136 0.000 0.020 0.016
#> SRR925734 1 0.2581 0.823 0.860 0.120 0.000 0.020 0.000 0.000
#> SRR925735 6 0.4860 0.698 0.020 0.000 0.104 0.160 0.004 0.712
#> SRR925736 6 0.3183 0.787 0.008 0.000 0.000 0.200 0.004 0.788
#> SRR925737 6 0.3183 0.787 0.008 0.000 0.000 0.200 0.004 0.788
#> SRR934642 2 0.0935 0.888 0.000 0.964 0.032 0.000 0.000 0.004
#> SRR934643 2 0.0603 0.883 0.000 0.980 0.004 0.000 0.000 0.016
#> SRR934644 2 0.1049 0.888 0.000 0.960 0.032 0.000 0.000 0.008
#> SRR934645 2 0.3836 0.573 0.248 0.728 0.004 0.000 0.004 0.016
#> SRR925740 6 0.3977 0.756 0.016 0.000 0.032 0.200 0.000 0.752
#> SRR925741 3 0.5269 0.538 0.016 0.000 0.612 0.004 0.076 0.292
#> SRR925742 6 0.4091 0.740 0.016 0.000 0.032 0.216 0.000 0.736
#> SRR1283038 6 0.3068 0.754 0.000 0.000 0.072 0.088 0.000 0.840
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 15949 rows and 64 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 0.977 0.990 0.5032 0.497 0.497
#> 3 3 0.774 0.882 0.913 0.2974 0.805 0.619
#> 4 4 0.754 0.672 0.831 0.1006 0.851 0.613
#> 5 5 0.751 0.605 0.811 0.0574 0.878 0.632
#> 6 6 0.730 0.625 0.804 0.0369 0.926 0.716
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
#> SRR925687 1 0.0000 0.994 1.000 0.000
#> SRR925688 1 0.0000 0.994 1.000 0.000
#> SRR925689 2 0.0000 0.984 0.000 1.000
#> SRR925690 2 0.0000 0.984 0.000 1.000
#> SRR925691 2 0.0000 0.984 0.000 1.000
#> SRR925692 2 0.0000 0.984 0.000 1.000
#> SRR925693 1 0.0000 0.994 1.000 0.000
#> SRR925694 2 0.0000 0.984 0.000 1.000
#> SRR925695 1 0.0000 0.994 1.000 0.000
#> SRR925697 1 0.0000 0.994 1.000 0.000
#> SRR925696 1 0.0000 0.994 1.000 0.000
#> SRR925698 1 0.0000 0.994 1.000 0.000
#> SRR925699 2 0.0000 0.984 0.000 1.000
#> SRR925700 2 0.0000 0.984 0.000 1.000
#> SRR925701 2 0.0000 0.984 0.000 1.000
#> SRR925702 1 0.0000 0.994 1.000 0.000
#> SRR925703 1 0.0000 0.994 1.000 0.000
#> SRR925704 1 0.0000 0.994 1.000 0.000
#> SRR925705 1 0.0000 0.994 1.000 0.000
#> SRR925706 1 0.0000 0.994 1.000 0.000
#> SRR925708 1 0.0000 0.994 1.000 0.000
#> SRR925707 2 0.0000 0.984 0.000 1.000
#> SRR925709 1 0.0000 0.994 1.000 0.000
#> SRR925710 1 0.0000 0.994 1.000 0.000
#> SRR925711 1 0.0000 0.994 1.000 0.000
#> SRR934631 2 0.0000 0.984 0.000 1.000
#> SRR934632 2 0.0000 0.984 0.000 1.000
#> SRR934633 2 0.0000 0.984 0.000 1.000
#> SRR925713 1 0.0000 0.994 1.000 0.000
#> SRR925714 1 0.0000 0.994 1.000 0.000
#> SRR925715 1 0.0000 0.994 1.000 0.000
#> SRR925716 2 0.7219 0.750 0.200 0.800
#> SRR925717 1 0.0000 0.994 1.000 0.000
#> SRR925718 2 0.0000 0.984 0.000 1.000
#> SRR925719 1 0.0000 0.994 1.000 0.000
#> SRR925720 1 0.0000 0.994 1.000 0.000
#> SRR925721 2 0.7745 0.708 0.228 0.772
#> SRR934634 2 0.0000 0.984 0.000 1.000
#> SRR934635 2 0.0000 0.984 0.000 1.000
#> SRR925723 1 0.0000 0.994 1.000 0.000
#> SRR925724 1 0.0000 0.994 1.000 0.000
#> SRR925725 1 0.0000 0.994 1.000 0.000
#> SRR925726 1 0.0000 0.994 1.000 0.000
#> SRR925727 1 0.0000 0.994 1.000 0.000
#> SRR925728 2 0.0000 0.984 0.000 1.000
#> SRR934636 2 0.0000 0.984 0.000 1.000
#> SRR934637 2 0.0000 0.984 0.000 1.000
#> SRR925729 1 0.0000 0.994 1.000 0.000
#> SRR934638 2 0.0000 0.984 0.000 1.000
#> SRR934639 1 0.0000 0.994 1.000 0.000
#> SRR934640 2 0.0000 0.984 0.000 1.000
#> SRR934641 2 0.0000 0.984 0.000 1.000
#> SRR925734 2 0.0000 0.984 0.000 1.000
#> SRR925735 1 0.0000 0.994 1.000 0.000
#> SRR925736 1 0.0000 0.994 1.000 0.000
#> SRR925737 1 0.0000 0.994 1.000 0.000
#> SRR934642 2 0.0000 0.984 0.000 1.000
#> SRR934643 2 0.0000 0.984 0.000 1.000
#> SRR934644 2 0.0000 0.984 0.000 1.000
#> SRR934645 2 0.0000 0.984 0.000 1.000
#> SRR925740 1 0.7453 0.724 0.788 0.212
#> SRR925741 1 0.0000 0.994 1.000 0.000
#> SRR925742 2 0.0672 0.977 0.008 0.992
#> SRR1283038 1 0.0000 0.994 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925688 1 0.2878 0.751 0.904 0.000 0.096
#> SRR925689 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925690 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925691 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925692 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925693 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925694 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925695 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925697 1 0.4399 0.772 0.812 0.000 0.188
#> SRR925696 3 0.2959 0.971 0.100 0.000 0.900
#> SRR925698 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925699 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925702 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925703 1 0.5560 0.698 0.700 0.000 0.300
#> SRR925704 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925705 3 0.2711 0.961 0.088 0.000 0.912
#> SRR925706 1 0.5560 0.698 0.700 0.000 0.300
#> SRR925708 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925707 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925709 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925710 1 0.5363 0.718 0.724 0.000 0.276
#> SRR925711 3 0.3038 0.973 0.104 0.000 0.896
#> SRR934631 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934633 2 0.6291 0.282 0.000 0.532 0.468
#> SRR925713 1 0.5560 0.698 0.700 0.000 0.300
#> SRR925714 1 0.5560 0.698 0.700 0.000 0.300
#> SRR925715 1 0.5650 0.678 0.688 0.000 0.312
#> SRR925716 1 0.0661 0.812 0.988 0.004 0.008
#> SRR925717 1 0.5560 0.698 0.700 0.000 0.300
#> SRR925718 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.819 1.000 0.000 0.000
#> SRR925721 1 0.3587 0.743 0.892 0.020 0.088
#> SRR934634 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934635 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925723 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925724 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925725 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925726 1 0.4399 0.772 0.812 0.000 0.188
#> SRR925727 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925728 2 0.3482 0.844 0.128 0.872 0.000
#> SRR934636 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934637 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925729 3 0.3038 0.973 0.104 0.000 0.896
#> SRR934638 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934639 1 0.0000 0.819 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925734 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925735 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925736 3 0.0000 0.875 0.000 0.000 1.000
#> SRR925737 3 0.0237 0.879 0.004 0.000 0.996
#> SRR934642 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.964 0.000 1.000 0.000
#> SRR934645 2 0.0000 0.964 0.000 1.000 0.000
#> SRR925740 3 0.0000 0.875 0.000 0.000 1.000
#> SRR925741 3 0.3038 0.973 0.104 0.000 0.896
#> SRR925742 2 0.7044 0.517 0.032 0.620 0.348
#> SRR1283038 3 0.2959 0.971 0.100 0.000 0.900
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.0188 0.8397 0.996 0.000 0.004 0.000
#> SRR925688 1 0.1940 0.8047 0.924 0.000 0.000 0.076
#> SRR925689 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925690 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925691 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925692 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925693 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925694 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925695 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925697 1 0.4925 0.0165 0.572 0.000 0.428 0.000
#> SRR925696 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925698 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925699 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925701 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925702 1 0.0376 0.8396 0.992 0.000 0.004 0.004
#> SRR925703 3 0.4830 0.4568 0.392 0.000 0.608 0.000
#> SRR925704 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925705 4 0.4643 0.1445 0.000 0.000 0.344 0.656
#> SRR925706 3 0.4830 0.4568 0.392 0.000 0.608 0.000
#> SRR925708 1 0.0779 0.8376 0.980 0.000 0.016 0.004
#> SRR925707 2 0.0188 0.8453 0.000 0.996 0.004 0.000
#> SRR925709 1 0.0376 0.8396 0.992 0.000 0.004 0.004
#> SRR925710 3 0.4898 0.4028 0.416 0.000 0.584 0.000
#> SRR925711 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR934631 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934632 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934633 4 0.4477 0.4804 0.000 0.312 0.000 0.688
#> SRR925713 3 0.4830 0.4568 0.392 0.000 0.608 0.000
#> SRR925714 3 0.4830 0.4568 0.392 0.000 0.608 0.000
#> SRR925715 3 0.4991 0.4622 0.388 0.000 0.608 0.004
#> SRR925716 1 0.4770 0.4867 0.700 0.000 0.288 0.012
#> SRR925717 3 0.4830 0.4568 0.392 0.000 0.608 0.000
#> SRR925718 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925719 1 0.0336 0.8396 0.992 0.000 0.008 0.000
#> SRR925720 1 0.0707 0.8355 0.980 0.000 0.020 0.000
#> SRR925721 1 0.2198 0.8028 0.920 0.000 0.008 0.072
#> SRR934634 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR934635 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925723 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925724 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925725 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925726 1 0.4925 0.0165 0.572 0.000 0.428 0.000
#> SRR925727 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925728 2 0.7502 0.6509 0.140 0.552 0.288 0.020
#> SRR934636 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR934637 2 0.4988 0.7919 0.000 0.692 0.288 0.020
#> SRR925729 4 0.5503 -0.3923 0.016 0.000 0.468 0.516
#> SRR934638 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR934639 1 0.0376 0.8396 0.992 0.000 0.004 0.004
#> SRR934640 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934641 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925734 2 0.4647 0.8003 0.000 0.704 0.288 0.008
#> SRR925735 3 0.4509 0.7014 0.004 0.000 0.708 0.288
#> SRR925736 4 0.1302 0.6199 0.000 0.000 0.044 0.956
#> SRR925737 4 0.1474 0.6159 0.000 0.000 0.052 0.948
#> SRR934642 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934643 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934644 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR934645 2 0.0000 0.8458 0.000 1.000 0.000 0.000
#> SRR925740 4 0.0804 0.6230 0.000 0.008 0.012 0.980
#> SRR925741 3 0.4356 0.7048 0.000 0.000 0.708 0.292
#> SRR925742 4 0.4632 0.4869 0.000 0.308 0.004 0.688
#> SRR1283038 3 0.4992 0.3271 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
#> SRR925687 4 0.1670 0.58032 0.012 0.000 0.000 0.936 0.052
#> SRR925688 4 0.4822 0.33836 0.032 0.000 0.000 0.616 0.352
#> SRR925689 2 0.4287 0.26312 0.000 0.540 0.000 0.000 0.460
#> SRR925690 2 0.4294 0.24503 0.000 0.532 0.000 0.000 0.468
#> SRR925691 2 0.4294 0.24503 0.000 0.532 0.000 0.000 0.468
#> SRR925692 2 0.4294 0.24503 0.000 0.532 0.000 0.000 0.468
#> SRR925693 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925694 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925697 4 0.5007 0.63149 0.028 0.000 0.232 0.704 0.036
#> SRR925696 3 0.0579 0.92841 0.008 0.000 0.984 0.000 0.008
#> SRR925698 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925699 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.1571 0.58447 0.004 0.000 0.000 0.936 0.060
#> SRR925703 4 0.5764 0.48280 0.028 0.000 0.432 0.504 0.036
#> SRR925704 3 0.0290 0.93453 0.000 0.000 0.992 0.000 0.008
#> SRR925705 3 0.3999 0.57728 0.240 0.000 0.740 0.000 0.020
#> SRR925706 4 0.5764 0.48280 0.028 0.000 0.432 0.504 0.036
#> SRR925708 4 0.1282 0.58863 0.004 0.000 0.000 0.952 0.044
#> SRR925707 2 0.1270 0.69481 0.000 0.948 0.000 0.000 0.052
#> SRR925709 4 0.1704 0.57974 0.004 0.000 0.000 0.928 0.068
#> SRR925710 4 0.5648 0.54265 0.028 0.000 0.368 0.568 0.036
#> SRR925711 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR934631 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934633 1 0.3112 0.73415 0.856 0.100 0.000 0.000 0.044
#> SRR925713 4 0.5764 0.48280 0.028 0.000 0.432 0.504 0.036
#> SRR925714 4 0.5764 0.48280 0.028 0.000 0.432 0.504 0.036
#> SRR925715 4 0.5776 0.45114 0.028 0.000 0.448 0.488 0.036
#> SRR925716 5 0.4522 0.12122 0.008 0.000 0.000 0.440 0.552
#> SRR925717 4 0.5764 0.48280 0.028 0.000 0.432 0.504 0.036
#> SRR925718 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.0807 0.59698 0.012 0.000 0.000 0.976 0.012
#> SRR925720 4 0.1278 0.59933 0.016 0.000 0.004 0.960 0.020
#> SRR925721 4 0.4620 0.28838 0.016 0.000 0.000 0.592 0.392
#> SRR934634 2 0.4249 0.30929 0.000 0.568 0.000 0.000 0.432
#> SRR934635 2 0.4291 0.25388 0.000 0.536 0.000 0.000 0.464
#> SRR925723 3 0.0162 0.93603 0.000 0.000 0.996 0.000 0.004
#> SRR925724 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925725 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925726 4 0.4951 0.63490 0.028 0.000 0.224 0.712 0.036
#> SRR925727 3 0.0000 0.93705 0.000 0.000 1.000 0.000 0.000
#> SRR925728 5 0.4513 0.42216 0.008 0.192 0.000 0.052 0.748
#> SRR934636 2 0.4192 0.34529 0.000 0.596 0.000 0.000 0.404
#> SRR934637 5 0.4331 -0.07632 0.000 0.400 0.000 0.004 0.596
#> SRR925729 1 0.6712 -0.00227 0.428 0.000 0.420 0.128 0.024
#> SRR934638 2 0.4262 0.29726 0.000 0.560 0.000 0.000 0.440
#> SRR934639 4 0.1408 0.58575 0.008 0.000 0.000 0.948 0.044
#> SRR934640 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934641 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925734 2 0.4287 0.26312 0.000 0.540 0.000 0.000 0.460
#> SRR925735 3 0.3293 0.74005 0.012 0.000 0.852 0.108 0.028
#> SRR925736 1 0.2592 0.80800 0.892 0.000 0.052 0.000 0.056
#> SRR925737 1 0.2592 0.80800 0.892 0.000 0.052 0.000 0.056
#> SRR934642 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.72347 0.000 1.000 0.000 0.000 0.000
#> SRR925740 1 0.1282 0.80651 0.952 0.000 0.044 0.000 0.004
#> SRR925741 3 0.0290 0.93453 0.000 0.000 0.992 0.000 0.008
#> SRR925742 1 0.1408 0.78471 0.948 0.044 0.000 0.000 0.008
#> SRR1283038 3 0.3318 0.70829 0.180 0.000 0.808 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.4165 -0.0398 0.008 0.000 0.004 0.568 0.420 0.000
#> SRR925688 5 0.3411 0.6322 0.004 0.000 0.000 0.232 0.756 0.008
#> SRR925689 1 0.3747 0.8142 0.604 0.396 0.000 0.000 0.000 0.000
#> SRR925690 1 0.3684 0.8264 0.628 0.372 0.000 0.000 0.000 0.000
#> SRR925691 1 0.3684 0.8264 0.628 0.372 0.000 0.000 0.000 0.000
#> SRR925692 1 0.3684 0.8264 0.628 0.372 0.000 0.000 0.000 0.000
#> SRR925693 3 0.0363 0.9273 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR925694 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925695 3 0.0260 0.9298 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR925697 4 0.2520 0.6206 0.000 0.000 0.152 0.844 0.004 0.000
#> SRR925696 3 0.0951 0.9097 0.004 0.000 0.968 0.000 0.008 0.020
#> SRR925698 3 0.0260 0.9298 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR925699 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925702 4 0.5089 0.1408 0.084 0.000 0.000 0.624 0.280 0.012
#> SRR925703 4 0.3266 0.6361 0.000 0.000 0.272 0.728 0.000 0.000
#> SRR925704 3 0.0260 0.9265 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR925705 3 0.3663 0.6958 0.040 0.000 0.792 0.000 0.012 0.156
#> SRR925706 4 0.3266 0.6361 0.000 0.000 0.272 0.728 0.000 0.000
#> SRR925708 4 0.4667 0.2679 0.044 0.000 0.008 0.684 0.252 0.012
#> SRR925707 2 0.2393 0.7095 0.092 0.884 0.000 0.000 0.020 0.004
#> SRR925709 4 0.5315 0.0369 0.096 0.000 0.000 0.588 0.304 0.012
#> SRR925710 4 0.2883 0.6364 0.000 0.000 0.212 0.788 0.000 0.000
#> SRR925711 3 0.0000 0.9297 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR934631 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 6 0.4137 0.4725 0.012 0.216 0.000 0.000 0.040 0.732
#> SRR925713 4 0.3221 0.6375 0.000 0.000 0.264 0.736 0.000 0.000
#> SRR925714 4 0.3266 0.6361 0.000 0.000 0.272 0.728 0.000 0.000
#> SRR925715 4 0.3446 0.6002 0.000 0.000 0.308 0.692 0.000 0.000
#> SRR925716 5 0.5984 0.5041 0.296 0.000 0.000 0.204 0.492 0.008
#> SRR925717 4 0.3266 0.6361 0.000 0.000 0.272 0.728 0.000 0.000
#> SRR925718 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925719 4 0.3219 0.3931 0.004 0.000 0.012 0.792 0.192 0.000
#> SRR925720 4 0.3494 0.3156 0.000 0.000 0.012 0.736 0.252 0.000
#> SRR925721 5 0.2613 0.6951 0.012 0.000 0.000 0.140 0.848 0.000
#> SRR934634 2 0.3996 -0.6101 0.484 0.512 0.000 0.000 0.004 0.000
#> SRR934635 1 0.3890 0.8087 0.596 0.400 0.000 0.000 0.004 0.000
#> SRR925723 3 0.0551 0.9263 0.000 0.000 0.984 0.004 0.004 0.008
#> SRR925724 3 0.0146 0.9300 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR925725 3 0.0260 0.9298 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR925726 4 0.2482 0.6187 0.000 0.000 0.148 0.848 0.004 0.000
#> SRR925727 3 0.0260 0.9298 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR925728 1 0.5211 0.1433 0.680 0.060 0.000 0.044 0.208 0.008
#> SRR934636 2 0.4147 -0.4974 0.436 0.552 0.000 0.000 0.012 0.000
#> SRR934637 1 0.5625 0.6802 0.504 0.332 0.000 0.000 0.164 0.000
#> SRR925729 6 0.7343 0.1467 0.048 0.000 0.328 0.208 0.032 0.384
#> SRR934638 2 0.3993 -0.5886 0.476 0.520 0.000 0.000 0.004 0.000
#> SRR934639 4 0.4769 0.2254 0.040 0.000 0.008 0.656 0.284 0.012
#> SRR934640 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934641 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925734 1 0.3937 0.7698 0.572 0.424 0.000 0.000 0.004 0.000
#> SRR925735 3 0.3925 0.4596 0.004 0.000 0.700 0.280 0.004 0.012
#> SRR925736 6 0.4128 0.6764 0.164 0.000 0.028 0.000 0.044 0.764
#> SRR925737 6 0.4128 0.6764 0.164 0.000 0.028 0.000 0.044 0.764
#> SRR934642 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.0000 0.8424 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925740 6 0.0508 0.6833 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR925741 3 0.0000 0.9297 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925742 6 0.0363 0.6806 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR1283038 3 0.2531 0.8011 0.004 0.000 0.860 0.000 0.008 0.128
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 15949 rows and 64 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.988 0.4741 0.532 0.532
#> 3 3 0.824 0.926 0.937 0.3933 0.795 0.614
#> 4 4 0.684 0.788 0.790 0.1118 0.905 0.717
#> 5 5 0.901 0.891 0.940 0.0750 0.939 0.765
#> 6 6 0.892 0.857 0.932 0.0287 0.989 0.946
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR925687 1 0.0000 0.981 1.000 0.000
#> SRR925688 1 0.0000 0.981 1.000 0.000
#> SRR925689 2 0.0000 1.000 0.000 1.000
#> SRR925690 2 0.0000 1.000 0.000 1.000
#> SRR925691 2 0.0000 1.000 0.000 1.000
#> SRR925692 2 0.0000 1.000 0.000 1.000
#> SRR925693 1 0.0000 0.981 1.000 0.000
#> SRR925694 2 0.0000 1.000 0.000 1.000
#> SRR925695 1 0.0000 0.981 1.000 0.000
#> SRR925697 1 0.0000 0.981 1.000 0.000
#> SRR925696 1 0.0000 0.981 1.000 0.000
#> SRR925698 1 0.0000 0.981 1.000 0.000
#> SRR925699 2 0.0000 1.000 0.000 1.000
#> SRR925700 2 0.0000 1.000 0.000 1.000
#> SRR925701 2 0.0000 1.000 0.000 1.000
#> SRR925702 1 0.0000 0.981 1.000 0.000
#> SRR925703 1 0.0000 0.981 1.000 0.000
#> SRR925704 1 0.0000 0.981 1.000 0.000
#> SRR925705 1 0.0000 0.981 1.000 0.000
#> SRR925706 1 0.0000 0.981 1.000 0.000
#> SRR925708 1 0.0000 0.981 1.000 0.000
#> SRR925707 2 0.0376 0.996 0.004 0.996
#> SRR925709 1 0.0000 0.981 1.000 0.000
#> SRR925710 1 0.0000 0.981 1.000 0.000
#> SRR925711 1 0.0000 0.981 1.000 0.000
#> SRR934631 2 0.0000 1.000 0.000 1.000
#> SRR934632 2 0.0000 1.000 0.000 1.000
#> SRR934633 1 0.8555 0.631 0.720 0.280
#> SRR925713 1 0.0000 0.981 1.000 0.000
#> SRR925714 1 0.0000 0.981 1.000 0.000
#> SRR925715 1 0.0000 0.981 1.000 0.000
#> SRR925716 1 0.0000 0.981 1.000 0.000
#> SRR925717 1 0.0000 0.981 1.000 0.000
#> SRR925718 2 0.0000 1.000 0.000 1.000
#> SRR925719 1 0.0000 0.981 1.000 0.000
#> SRR925720 1 0.0000 0.981 1.000 0.000
#> SRR925721 1 0.0000 0.981 1.000 0.000
#> SRR934634 2 0.0000 1.000 0.000 1.000
#> SRR934635 2 0.0000 1.000 0.000 1.000
#> SRR925723 1 0.0000 0.981 1.000 0.000
#> SRR925724 1 0.0000 0.981 1.000 0.000
#> SRR925725 1 0.0000 0.981 1.000 0.000
#> SRR925726 1 0.0000 0.981 1.000 0.000
#> SRR925727 1 0.0000 0.981 1.000 0.000
#> SRR925728 1 0.0000 0.981 1.000 0.000
#> SRR934636 2 0.0000 1.000 0.000 1.000
#> SRR934637 1 0.5519 0.852 0.872 0.128
#> SRR925729 1 0.0000 0.981 1.000 0.000
#> SRR934638 2 0.0000 1.000 0.000 1.000
#> SRR934639 1 0.0000 0.981 1.000 0.000
#> SRR934640 2 0.0000 1.000 0.000 1.000
#> SRR934641 2 0.0000 1.000 0.000 1.000
#> SRR925734 2 0.0000 1.000 0.000 1.000
#> SRR925735 1 0.0000 0.981 1.000 0.000
#> SRR925736 1 0.0000 0.981 1.000 0.000
#> SRR925737 1 0.0000 0.981 1.000 0.000
#> SRR934642 2 0.0000 1.000 0.000 1.000
#> SRR934643 2 0.0000 1.000 0.000 1.000
#> SRR934644 2 0.0000 1.000 0.000 1.000
#> SRR934645 2 0.0000 1.000 0.000 1.000
#> SRR925740 1 0.6247 0.820 0.844 0.156
#> SRR925741 1 0.0000 0.981 1.000 0.000
#> SRR925742 1 0.7139 0.767 0.804 0.196
#> SRR1283038 1 0.0000 0.981 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925688 1 0.0747 0.932 0.984 0.000 0.016
#> SRR925689 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925690 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925691 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925692 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925693 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925694 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925695 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925697 1 0.0237 0.939 0.996 0.000 0.004
#> SRR925696 3 0.4796 0.894 0.220 0.000 0.780
#> SRR925698 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925699 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925702 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925703 1 0.4750 0.753 0.784 0.000 0.216
#> SRR925704 3 0.4974 0.891 0.236 0.000 0.764
#> SRR925705 3 0.4842 0.894 0.224 0.000 0.776
#> SRR925706 1 0.0237 0.939 0.996 0.000 0.004
#> SRR925708 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925707 2 0.1453 0.974 0.008 0.968 0.024
#> SRR925709 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925710 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925711 3 0.4974 0.891 0.236 0.000 0.764
#> SRR934631 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934633 3 0.4842 0.894 0.224 0.000 0.776
#> SRR925713 1 0.2878 0.870 0.904 0.000 0.096
#> SRR925714 1 0.4750 0.753 0.784 0.000 0.216
#> SRR925715 1 0.0237 0.939 0.996 0.000 0.004
#> SRR925716 1 0.0747 0.932 0.984 0.000 0.016
#> SRR925717 1 0.4750 0.753 0.784 0.000 0.216
#> SRR925718 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925720 1 0.0000 0.940 1.000 0.000 0.000
#> SRR925721 1 0.0747 0.932 0.984 0.000 0.016
#> SRR934634 2 0.0237 0.996 0.000 0.996 0.004
#> SRR934635 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925723 3 0.4974 0.891 0.236 0.000 0.764
#> SRR925724 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925725 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925726 1 0.0237 0.939 0.996 0.000 0.004
#> SRR925727 3 0.1031 0.819 0.024 0.000 0.976
#> SRR925728 1 0.0747 0.932 0.984 0.000 0.016
#> SRR934636 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934637 1 0.4483 0.796 0.848 0.128 0.024
#> SRR925729 1 0.2448 0.873 0.924 0.000 0.076
#> SRR934638 2 0.0424 0.995 0.000 0.992 0.008
#> SRR934639 1 0.0000 0.940 1.000 0.000 0.000
#> SRR934640 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934641 2 0.0000 0.996 0.000 1.000 0.000
#> SRR925734 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925735 1 0.0747 0.929 0.984 0.000 0.016
#> SRR925736 3 0.4842 0.894 0.224 0.000 0.776
#> SRR925737 3 0.4842 0.894 0.224 0.000 0.776
#> SRR934642 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.996 0.000 1.000 0.000
#> SRR934645 2 0.0424 0.995 0.000 0.992 0.008
#> SRR925740 3 0.4842 0.894 0.224 0.000 0.776
#> SRR925741 3 0.4887 0.891 0.228 0.000 0.772
#> SRR925742 3 0.4842 0.894 0.224 0.000 0.776
#> SRR1283038 3 0.4842 0.894 0.224 0.000 0.776
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925688 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925689 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925690 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925691 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925692 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925693 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925694 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR925695 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925697 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925696 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925698 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925699 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR925700 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR925701 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR925702 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925703 4 0.491 0.4826 0.000 0.000 0.420 0.580
#> SRR925704 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925705 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925706 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925708 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925707 1 0.361 0.6601 0.800 0.200 0.000 0.000
#> SRR925709 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925710 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925711 3 0.376 0.7459 0.000 0.000 0.784 0.216
#> SRR934631 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934632 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934633 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925713 4 0.228 0.8185 0.000 0.000 0.096 0.904
#> SRR925714 4 0.491 0.4826 0.000 0.000 0.420 0.580
#> SRR925715 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925716 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925717 4 0.491 0.4826 0.000 0.000 0.420 0.580
#> SRR925718 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR925719 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925720 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925721 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR934634 1 0.369 0.5125 0.792 0.208 0.000 0.000
#> SRR934635 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925723 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925724 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925725 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925726 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR925727 3 0.000 0.6821 0.000 0.000 1.000 0.000
#> SRR925728 1 0.761 0.0289 0.404 0.200 0.000 0.396
#> SRR934636 2 0.484 0.6563 0.396 0.604 0.000 0.000
#> SRR934637 1 0.461 0.6755 0.800 0.100 0.000 0.100
#> SRR925729 4 0.522 0.5597 0.000 0.200 0.064 0.736
#> SRR934638 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR934639 4 0.000 0.9039 0.000 0.000 0.000 1.000
#> SRR934640 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934641 2 0.483 0.6761 0.392 0.608 0.000 0.000
#> SRR925734 1 0.000 0.8016 1.000 0.000 0.000 0.000
#> SRR925735 4 0.118 0.8766 0.000 0.016 0.016 0.968
#> SRR925736 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925737 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR934642 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934643 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934644 2 0.361 0.9558 0.200 0.800 0.000 0.000
#> SRR934645 1 0.436 0.2662 0.708 0.292 0.000 0.000
#> SRR925740 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR925741 3 0.369 0.7460 0.000 0.000 0.792 0.208
#> SRR925742 3 0.700 0.7996 0.000 0.200 0.580 0.220
#> SRR1283038 3 0.700 0.7996 0.000 0.200 0.580 0.220
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925688 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925689 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925690 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925693 5 0.3109 0.820 0.000 0.000 0.200 0.000 0.800
#> SRR925694 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR925695 5 0.3109 0.820 0.000 0.000 0.200 0.000 0.800
#> SRR925697 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925696 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR925698 3 0.3274 0.686 0.000 0.000 0.780 0.000 0.220
#> SRR925699 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925703 5 0.3177 0.781 0.000 0.000 0.000 0.208 0.792
#> SRR925704 3 0.0290 0.963 0.000 0.000 0.992 0.008 0.000
#> SRR925705 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR925706 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925708 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925707 1 0.0162 0.885 0.996 0.000 0.004 0.000 0.000
#> SRR925709 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925710 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925711 3 0.1168 0.947 0.000 0.000 0.960 0.008 0.032
#> SRR934631 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR934633 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR925713 4 0.1965 0.878 0.000 0.000 0.000 0.904 0.096
#> SRR925714 5 0.3109 0.789 0.000 0.000 0.000 0.200 0.800
#> SRR925715 4 0.1121 0.940 0.000 0.000 0.044 0.956 0.000
#> SRR925716 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925717 5 0.3109 0.789 0.000 0.000 0.000 0.200 0.800
#> SRR925718 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925720 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925721 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR934634 1 0.3177 0.682 0.792 0.208 0.000 0.000 0.000
#> SRR934635 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925723 3 0.0290 0.963 0.000 0.000 0.992 0.008 0.000
#> SRR925724 3 0.1043 0.943 0.000 0.000 0.960 0.000 0.040
#> SRR925725 5 0.3109 0.820 0.000 0.000 0.200 0.000 0.800
#> SRR925726 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR925727 5 0.3109 0.820 0.000 0.000 0.200 0.000 0.800
#> SRR925728 1 0.5068 0.316 0.572 0.000 0.040 0.388 0.000
#> SRR934636 2 0.3074 0.739 0.196 0.804 0.000 0.000 0.000
#> SRR934637 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925729 4 0.1965 0.870 0.000 0.000 0.096 0.904 0.000
#> SRR934638 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR934639 4 0.0000 0.982 0.000 0.000 0.000 1.000 0.000
#> SRR934640 2 0.3109 0.811 0.000 0.800 0.000 0.000 0.200
#> SRR934641 2 0.5848 0.581 0.192 0.608 0.000 0.000 0.200
#> SRR925734 1 0.0000 0.888 1.000 0.000 0.000 0.000 0.000
#> SRR925735 4 0.0510 0.967 0.000 0.000 0.016 0.984 0.000
#> SRR925736 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR925737 3 0.0510 0.955 0.000 0.000 0.984 0.016 0.000
#> SRR934642 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.940 0.000 1.000 0.000 0.000 0.000
#> SRR934645 1 0.4161 0.319 0.608 0.392 0.000 0.000 0.000
#> SRR925740 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR925741 3 0.1168 0.947 0.000 0.000 0.960 0.008 0.032
#> SRR925742 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
#> SRR1283038 3 0.0000 0.967 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925688 4 0.0146 0.965 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR925689 1 0.0146 0.860 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR925690 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925691 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925692 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925694 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925695 5 0.0260 0.923 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR925697 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925696 3 0.0000 0.847 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR925698 3 0.3547 0.612 0.000 0.000 0.668 0.000 0.332 0.000
#> SRR925699 2 0.0458 0.957 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR925700 2 0.0458 0.957 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR925701 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925702 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925703 5 0.2854 0.646 0.000 0.000 0.000 0.208 0.792 0.000
#> SRR925704 3 0.0146 0.846 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR925705 3 0.0260 0.847 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR925706 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925708 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925707 1 0.1075 0.827 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR925709 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925710 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925711 3 0.2520 0.774 0.000 0.000 0.844 0.004 0.152 0.000
#> SRR934631 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 3 0.2762 0.818 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR925713 4 0.1765 0.875 0.000 0.000 0.000 0.904 0.096 0.000
#> SRR925714 5 0.1007 0.891 0.000 0.000 0.000 0.044 0.956 0.000
#> SRR925715 4 0.1141 0.925 0.000 0.000 0.052 0.948 0.000 0.000
#> SRR925716 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925717 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925718 2 0.0458 0.957 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR925719 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925720 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925721 4 0.0260 0.962 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR934634 1 0.3168 0.614 0.792 0.192 0.000 0.000 0.000 0.016
#> SRR934635 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925723 3 0.0146 0.846 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR925724 3 0.2416 0.773 0.000 0.000 0.844 0.000 0.156 0.000
#> SRR925725 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925726 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR925727 5 0.0000 0.929 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR925728 1 0.6135 0.302 0.564 0.000 0.044 0.196 0.000 0.196
#> SRR934636 2 0.2762 0.648 0.196 0.804 0.000 0.000 0.000 0.000
#> SRR934637 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925729 4 0.3104 0.739 0.000 0.000 0.184 0.800 0.000 0.016
#> SRR934638 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934639 4 0.0000 0.967 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934640 6 0.2762 0.701 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR934641 6 0.3168 0.702 0.172 0.024 0.000 0.000 0.000 0.804
#> SRR925734 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925735 4 0.2003 0.851 0.000 0.000 0.116 0.884 0.000 0.000
#> SRR925736 3 0.2762 0.818 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR925737 3 0.2762 0.818 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR934642 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0713 0.948 0.000 0.972 0.000 0.000 0.000 0.028
#> SRR934644 2 0.0000 0.961 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 1 0.3881 0.231 0.600 0.396 0.000 0.000 0.000 0.004
#> SRR925740 3 0.2762 0.818 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR925741 3 0.2520 0.774 0.000 0.000 0.844 0.004 0.152 0.000
#> SRR925742 3 0.2762 0.818 0.000 0.000 0.804 0.000 0.000 0.196
#> SRR1283038 3 0.0632 0.847 0.000 0.000 0.976 0.000 0.000 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.492 0.820 0.846 0.4225 0.493 0.493
#> 3 3 0.607 0.855 0.898 0.4844 0.842 0.684
#> 4 4 0.676 0.721 0.826 0.1554 0.826 0.573
#> 5 5 0.779 0.680 0.846 0.0830 0.870 0.591
#> 6 6 0.773 0.597 0.769 0.0458 0.872 0.527
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
#> SRR925687 1 0.0000 0.960 1.000 0.000
#> SRR925688 1 0.0000 0.960 1.000 0.000
#> SRR925689 1 0.1843 0.950 0.972 0.028
#> SRR925690 1 0.1843 0.950 0.972 0.028
#> SRR925691 1 0.1843 0.950 0.972 0.028
#> SRR925692 1 0.1843 0.950 0.972 0.028
#> SRR925693 2 0.9754 0.771 0.408 0.592
#> SRR925694 2 0.0000 0.652 0.000 1.000
#> SRR925695 2 0.9754 0.771 0.408 0.592
#> SRR925697 1 0.0000 0.960 1.000 0.000
#> SRR925696 2 0.9754 0.771 0.408 0.592
#> SRR925698 2 0.9754 0.771 0.408 0.592
#> SRR925699 2 0.0672 0.656 0.008 0.992
#> SRR925700 2 0.0672 0.656 0.008 0.992
#> SRR925701 2 0.0000 0.652 0.000 1.000
#> SRR925702 1 0.0938 0.961 0.988 0.012
#> SRR925703 1 0.0000 0.960 1.000 0.000
#> SRR925704 2 0.9754 0.771 0.408 0.592
#> SRR925705 2 0.9754 0.771 0.408 0.592
#> SRR925706 1 0.0000 0.960 1.000 0.000
#> SRR925708 1 0.0376 0.960 0.996 0.004
#> SRR925707 1 0.9833 -0.307 0.576 0.424
#> SRR925709 1 0.0938 0.961 0.988 0.012
#> SRR925710 1 0.0000 0.960 1.000 0.000
#> SRR925711 2 0.9754 0.771 0.408 0.592
#> SRR934631 2 0.0000 0.652 0.000 1.000
#> SRR934632 2 0.0000 0.652 0.000 1.000
#> SRR934633 2 0.9754 0.771 0.408 0.592
#> SRR925713 1 0.0000 0.960 1.000 0.000
#> SRR925714 1 0.0000 0.960 1.000 0.000
#> SRR925715 1 0.0672 0.953 0.992 0.008
#> SRR925716 1 0.0938 0.961 0.988 0.012
#> SRR925717 1 0.0000 0.960 1.000 0.000
#> SRR925718 2 0.0672 0.656 0.008 0.992
#> SRR925719 1 0.0938 0.961 0.988 0.012
#> SRR925720 1 0.0000 0.960 1.000 0.000
#> SRR925721 1 0.0000 0.960 1.000 0.000
#> SRR934634 1 0.1414 0.957 0.980 0.020
#> SRR934635 1 0.1414 0.957 0.980 0.020
#> SRR925723 2 0.9754 0.771 0.408 0.592
#> SRR925724 2 0.9754 0.771 0.408 0.592
#> SRR925725 2 0.9754 0.771 0.408 0.592
#> SRR925726 1 0.0000 0.960 1.000 0.000
#> SRR925727 2 0.9754 0.771 0.408 0.592
#> SRR925728 1 0.1184 0.959 0.984 0.016
#> SRR934636 1 0.5178 0.825 0.884 0.116
#> SRR934637 1 0.1184 0.959 0.984 0.016
#> SRR925729 2 0.9775 0.765 0.412 0.588
#> SRR934638 1 0.1414 0.957 0.980 0.020
#> SRR934639 1 0.0938 0.961 0.988 0.012
#> SRR934640 2 0.8016 0.726 0.244 0.756
#> SRR934641 2 0.8144 0.729 0.252 0.748
#> SRR925734 1 0.1414 0.957 0.980 0.020
#> SRR925735 2 0.9775 0.765 0.412 0.588
#> SRR925736 2 0.9754 0.771 0.408 0.592
#> SRR925737 2 0.9754 0.771 0.408 0.592
#> SRR934642 2 0.0000 0.652 0.000 1.000
#> SRR934643 2 0.0672 0.656 0.008 0.992
#> SRR934644 2 0.0000 0.652 0.000 1.000
#> SRR934645 2 0.9286 0.752 0.344 0.656
#> SRR925740 2 0.9754 0.771 0.408 0.592
#> SRR925741 2 0.9754 0.771 0.408 0.592
#> SRR925742 2 0.9754 0.771 0.408 0.592
#> SRR1283038 2 0.9754 0.771 0.408 0.592
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.3686 0.906 0.860 0.000 0.140
#> SRR925688 1 0.4291 0.872 0.820 0.000 0.180
#> SRR925689 1 0.1643 0.869 0.956 0.044 0.000
#> SRR925690 1 0.1643 0.869 0.956 0.044 0.000
#> SRR925691 1 0.1643 0.869 0.956 0.044 0.000
#> SRR925692 1 0.1643 0.869 0.956 0.044 0.000
#> SRR925693 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925694 2 0.0237 0.841 0.000 0.996 0.004
#> SRR925695 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925697 1 0.3551 0.909 0.868 0.000 0.132
#> SRR925696 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925698 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925699 2 0.0237 0.841 0.000 0.996 0.004
#> SRR925700 2 0.0237 0.841 0.000 0.996 0.004
#> SRR925701 2 0.0237 0.841 0.000 0.996 0.004
#> SRR925702 1 0.4413 0.912 0.860 0.036 0.104
#> SRR925703 1 0.3752 0.906 0.856 0.000 0.144
#> SRR925704 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925705 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925706 1 0.3816 0.904 0.852 0.000 0.148
#> SRR925708 1 0.4413 0.912 0.860 0.036 0.104
#> SRR925707 2 0.7749 0.542 0.300 0.624 0.076
#> SRR925709 1 0.4526 0.912 0.856 0.040 0.104
#> SRR925710 1 0.3686 0.906 0.860 0.000 0.140
#> SRR925711 3 0.1031 0.938 0.024 0.000 0.976
#> SRR934631 2 0.0237 0.841 0.000 0.996 0.004
#> SRR934632 2 0.0237 0.841 0.000 0.996 0.004
#> SRR934633 3 0.0424 0.960 0.008 0.000 0.992
#> SRR925713 1 0.3686 0.906 0.860 0.000 0.140
#> SRR925714 1 0.3752 0.906 0.856 0.000 0.144
#> SRR925715 1 0.4062 0.891 0.836 0.000 0.164
#> SRR925716 1 0.4413 0.912 0.860 0.036 0.104
#> SRR925717 1 0.3752 0.906 0.856 0.000 0.144
#> SRR925718 2 0.0237 0.841 0.000 0.996 0.004
#> SRR925719 1 0.4489 0.912 0.856 0.036 0.108
#> SRR925720 1 0.3816 0.902 0.852 0.000 0.148
#> SRR925721 1 0.4062 0.889 0.836 0.000 0.164
#> SRR934634 1 0.1950 0.875 0.952 0.040 0.008
#> SRR934635 1 0.1529 0.870 0.960 0.040 0.000
#> SRR925723 3 0.0424 0.959 0.008 0.000 0.992
#> SRR925724 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925725 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925726 1 0.3551 0.909 0.868 0.000 0.132
#> SRR925727 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925728 1 0.4676 0.912 0.848 0.040 0.112
#> SRR934636 1 0.3752 0.801 0.856 0.144 0.000
#> SRR934637 1 0.1950 0.875 0.952 0.040 0.008
#> SRR925729 2 0.9001 0.487 0.172 0.548 0.280
#> SRR934638 1 0.1878 0.871 0.952 0.044 0.004
#> SRR934639 1 0.4413 0.912 0.860 0.036 0.104
#> SRR934640 2 0.4485 0.768 0.020 0.844 0.136
#> SRR934641 2 0.4485 0.768 0.020 0.844 0.136
#> SRR925734 1 0.1643 0.869 0.956 0.044 0.000
#> SRR925735 2 0.9018 0.489 0.176 0.548 0.276
#> SRR925736 3 0.8067 0.336 0.100 0.284 0.616
#> SRR925737 2 0.9100 0.492 0.204 0.548 0.248
#> SRR934642 2 0.0237 0.841 0.000 0.996 0.004
#> SRR934643 2 0.0237 0.841 0.000 0.996 0.004
#> SRR934644 2 0.0237 0.841 0.000 0.996 0.004
#> SRR934645 2 0.3043 0.807 0.084 0.908 0.008
#> SRR925740 3 0.1643 0.925 0.044 0.000 0.956
#> SRR925741 3 0.0000 0.965 0.000 0.000 1.000
#> SRR925742 2 0.8625 0.476 0.124 0.560 0.316
#> SRR1283038 3 0.0000 0.965 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 4 0.4193 0.687 0.268 0.000 0.000 0.732
#> SRR925688 4 0.2623 0.644 0.064 0.000 0.028 0.908
#> SRR925689 1 0.3245 0.833 0.880 0.064 0.000 0.056
#> SRR925690 1 0.2926 0.844 0.896 0.048 0.000 0.056
#> SRR925691 1 0.3009 0.843 0.892 0.052 0.000 0.056
#> SRR925692 1 0.2926 0.844 0.896 0.048 0.000 0.056
#> SRR925693 3 0.0707 0.800 0.000 0.000 0.980 0.020
#> SRR925694 2 0.0188 0.994 0.000 0.996 0.004 0.000
#> SRR925695 3 0.0336 0.802 0.000 0.000 0.992 0.008
#> SRR925697 4 0.2149 0.723 0.088 0.000 0.000 0.912
#> SRR925696 3 0.0188 0.801 0.000 0.000 0.996 0.004
#> SRR925698 3 0.0336 0.802 0.000 0.000 0.992 0.008
#> SRR925699 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR925700 2 0.0188 0.994 0.000 0.996 0.004 0.000
#> SRR925701 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR925702 4 0.4605 0.637 0.336 0.000 0.000 0.664
#> SRR925703 4 0.1489 0.709 0.044 0.000 0.004 0.952
#> SRR925704 3 0.0000 0.800 0.000 0.000 1.000 0.000
#> SRR925705 3 0.0000 0.800 0.000 0.000 1.000 0.000
#> SRR925706 4 0.0657 0.698 0.012 0.000 0.004 0.984
#> SRR925708 4 0.4605 0.637 0.336 0.000 0.000 0.664
#> SRR925707 3 0.8785 0.393 0.164 0.272 0.476 0.088
#> SRR925709 4 0.4994 0.374 0.480 0.000 0.000 0.520
#> SRR925710 4 0.0336 0.702 0.008 0.000 0.000 0.992
#> SRR925711 3 0.0469 0.798 0.012 0.000 0.988 0.000
#> SRR934631 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR934632 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR934633 3 0.4567 0.757 0.032 0.008 0.792 0.168
#> SRR925713 4 0.0336 0.702 0.008 0.000 0.000 0.992
#> SRR925714 4 0.1209 0.709 0.032 0.000 0.004 0.964
#> SRR925715 4 0.0657 0.698 0.012 0.000 0.004 0.984
#> SRR925716 4 0.4564 0.638 0.328 0.000 0.000 0.672
#> SRR925717 4 0.0524 0.700 0.008 0.000 0.004 0.988
#> SRR925718 2 0.0188 0.994 0.000 0.996 0.004 0.000
#> SRR925719 4 0.4564 0.638 0.328 0.000 0.000 0.672
#> SRR925720 4 0.3444 0.714 0.184 0.000 0.000 0.816
#> SRR925721 4 0.3801 0.706 0.220 0.000 0.000 0.780
#> SRR934634 4 0.4933 0.491 0.432 0.000 0.000 0.568
#> SRR934635 4 0.4985 0.405 0.468 0.000 0.000 0.532
#> SRR925723 3 0.3873 0.741 0.000 0.000 0.772 0.228
#> SRR925724 3 0.0000 0.800 0.000 0.000 1.000 0.000
#> SRR925725 3 0.0469 0.799 0.000 0.000 0.988 0.012
#> SRR925726 4 0.2149 0.723 0.088 0.000 0.000 0.912
#> SRR925727 3 0.0336 0.802 0.000 0.000 0.992 0.008
#> SRR925728 1 0.7384 -0.062 0.436 0.140 0.004 0.420
#> SRR934636 4 0.7476 0.268 0.260 0.236 0.000 0.504
#> SRR934637 4 0.6055 0.497 0.372 0.052 0.000 0.576
#> SRR925729 3 0.7144 0.633 0.044 0.056 0.560 0.340
#> SRR934638 1 0.2647 0.781 0.880 0.000 0.000 0.120
#> SRR934639 4 0.4605 0.637 0.336 0.000 0.000 0.664
#> SRR934640 3 0.6152 0.278 0.008 0.464 0.496 0.032
#> SRR934641 3 0.6152 0.278 0.008 0.464 0.496 0.032
#> SRR925734 1 0.2469 0.793 0.892 0.000 0.000 0.108
#> SRR925735 3 0.7144 0.633 0.044 0.056 0.560 0.340
#> SRR925736 3 0.6865 0.662 0.040 0.052 0.600 0.308
#> SRR925737 3 0.7830 0.649 0.052 0.128 0.568 0.252
#> SRR934642 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR934643 2 0.0188 0.993 0.000 0.996 0.004 0.000
#> SRR934644 2 0.0000 0.996 0.000 1.000 0.000 0.000
#> SRR934645 2 0.0779 0.978 0.016 0.980 0.004 0.000
#> SRR925740 3 0.5684 0.715 0.032 0.020 0.696 0.252
#> SRR925741 3 0.0657 0.798 0.000 0.004 0.984 0.012
#> SRR925742 3 0.7956 0.651 0.060 0.156 0.576 0.208
#> SRR1283038 3 0.0000 0.800 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
#> SRR925687 4 0.1243 0.8604 0.008 0.000 0.004 0.960 0.028
#> SRR925688 4 0.4443 0.3664 0.000 0.000 0.004 0.524 0.472
#> SRR925689 1 0.1082 0.7006 0.964 0.008 0.000 0.000 0.028
#> SRR925690 1 0.1082 0.7006 0.964 0.008 0.000 0.000 0.028
#> SRR925691 1 0.1082 0.7006 0.964 0.008 0.000 0.000 0.028
#> SRR925692 1 0.1082 0.7006 0.964 0.008 0.000 0.000 0.028
#> SRR925693 3 0.4059 0.5052 0.004 0.000 0.700 0.004 0.292
#> SRR925694 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR925695 3 0.3766 0.5435 0.000 0.000 0.728 0.004 0.268
#> SRR925697 4 0.0451 0.8626 0.000 0.000 0.004 0.988 0.008
#> SRR925696 3 0.0324 0.7788 0.004 0.000 0.992 0.000 0.004
#> SRR925698 3 0.0324 0.7791 0.004 0.000 0.992 0.004 0.000
#> SRR925699 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR925700 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR925701 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.2879 0.7946 0.100 0.000 0.000 0.868 0.032
#> SRR925703 4 0.0693 0.8610 0.000 0.000 0.008 0.980 0.012
#> SRR925704 3 0.0290 0.7773 0.000 0.000 0.992 0.000 0.008
#> SRR925705 3 0.0162 0.7782 0.000 0.000 0.996 0.000 0.004
#> SRR925706 4 0.1942 0.8349 0.000 0.000 0.012 0.920 0.068
#> SRR925708 4 0.2879 0.7946 0.100 0.000 0.000 0.868 0.032
#> SRR925707 1 0.5966 0.1768 0.532 0.016 0.060 0.004 0.388
#> SRR925709 1 0.5178 0.0271 0.484 0.000 0.000 0.476 0.040
#> SRR925710 4 0.0671 0.8622 0.000 0.000 0.004 0.980 0.016
#> SRR925711 3 0.0566 0.7735 0.000 0.012 0.984 0.000 0.004
#> SRR934631 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR934633 3 0.3840 0.5986 0.000 0.116 0.808 0.000 0.076
#> SRR925713 4 0.0798 0.8619 0.000 0.000 0.008 0.976 0.016
#> SRR925714 4 0.0807 0.8618 0.000 0.000 0.012 0.976 0.012
#> SRR925715 4 0.2864 0.7783 0.000 0.000 0.012 0.852 0.136
#> SRR925716 4 0.3301 0.7929 0.080 0.000 0.000 0.848 0.072
#> SRR925717 4 0.0807 0.8618 0.000 0.000 0.012 0.976 0.012
#> SRR925718 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR925719 4 0.1661 0.8406 0.036 0.000 0.000 0.940 0.024
#> SRR925720 4 0.0955 0.8604 0.000 0.000 0.004 0.968 0.028
#> SRR925721 4 0.2890 0.7736 0.000 0.000 0.004 0.836 0.160
#> SRR934634 1 0.6315 0.2123 0.468 0.000 0.000 0.372 0.160
#> SRR934635 1 0.5730 0.3909 0.576 0.000 0.000 0.316 0.108
#> SRR925723 3 0.1012 0.7603 0.000 0.000 0.968 0.020 0.012
#> SRR925724 3 0.0000 0.7787 0.000 0.000 1.000 0.000 0.000
#> SRR925725 3 0.4037 0.5209 0.004 0.000 0.704 0.004 0.288
#> SRR925726 4 0.0451 0.8626 0.000 0.000 0.004 0.988 0.008
#> SRR925727 3 0.1732 0.7438 0.000 0.000 0.920 0.000 0.080
#> SRR925728 1 0.3975 0.6544 0.792 0.000 0.000 0.144 0.064
#> SRR934636 1 0.8360 0.1987 0.328 0.192 0.000 0.312 0.168
#> SRR934637 4 0.6637 -0.2083 0.392 0.004 0.000 0.416 0.188
#> SRR925729 5 0.4180 0.7782 0.000 0.000 0.220 0.036 0.744
#> SRR934638 1 0.0771 0.6971 0.976 0.000 0.000 0.004 0.020
#> SRR934639 4 0.2769 0.8009 0.092 0.000 0.000 0.876 0.032
#> SRR934640 2 0.5431 0.2344 0.000 0.516 0.060 0.000 0.424
#> SRR934641 2 0.5431 0.2344 0.000 0.516 0.060 0.000 0.424
#> SRR925734 1 0.0404 0.6982 0.988 0.000 0.000 0.000 0.012
#> SRR925735 5 0.4180 0.7782 0.000 0.000 0.220 0.036 0.744
#> SRR925736 3 0.4900 -0.5536 0.000 0.000 0.512 0.024 0.464
#> SRR925737 5 0.4798 0.7382 0.000 0.000 0.396 0.024 0.580
#> SRR934642 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.9155 0.000 1.000 0.000 0.000 0.000
#> SRR934645 2 0.0162 0.9120 0.004 0.996 0.000 0.000 0.000
#> SRR925740 3 0.2079 0.7242 0.000 0.000 0.916 0.020 0.064
#> SRR925741 3 0.4162 0.4900 0.004 0.000 0.680 0.004 0.312
#> SRR925742 5 0.4817 0.7254 0.000 0.000 0.404 0.024 0.572
#> SRR1283038 3 0.0162 0.7782 0.000 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.3888 0.7228 0.032 0.000 0.000 0.716 0.252 0.000
#> SRR925688 5 0.4595 0.3881 0.000 0.000 0.000 0.248 0.668 0.084
#> SRR925689 1 0.0922 0.6252 0.968 0.024 0.000 0.000 0.004 0.004
#> SRR925690 1 0.0520 0.6307 0.984 0.008 0.000 0.000 0.000 0.008
#> SRR925691 1 0.0520 0.6307 0.984 0.008 0.000 0.000 0.000 0.008
#> SRR925692 1 0.0520 0.6307 0.984 0.008 0.000 0.000 0.000 0.008
#> SRR925693 6 0.3620 0.8580 0.000 0.000 0.352 0.000 0.000 0.648
#> SRR925694 2 0.0146 0.9303 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925695 6 0.3833 0.8841 0.000 0.000 0.444 0.000 0.000 0.556
#> SRR925697 4 0.2941 0.7651 0.000 0.000 0.000 0.780 0.220 0.000
#> SRR925696 3 0.0865 0.5357 0.000 0.000 0.964 0.000 0.000 0.036
#> SRR925698 3 0.0937 0.5349 0.000 0.000 0.960 0.000 0.000 0.040
#> SRR925699 2 0.0146 0.9303 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925700 2 0.0146 0.9303 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925701 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR925702 1 0.5826 0.2865 0.436 0.000 0.000 0.376 0.188 0.000
#> SRR925703 4 0.1890 0.7855 0.000 0.000 0.000 0.916 0.024 0.060
#> SRR925704 3 0.1501 0.4817 0.000 0.000 0.924 0.000 0.000 0.076
#> SRR925705 3 0.0458 0.5409 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR925706 4 0.1838 0.7593 0.000 0.000 0.000 0.916 0.068 0.016
#> SRR925708 1 0.5832 0.2655 0.428 0.000 0.000 0.384 0.188 0.000
#> SRR925707 1 0.6414 0.3260 0.616 0.068 0.012 0.012 0.144 0.148
#> SRR925709 1 0.4516 0.4706 0.668 0.000 0.000 0.260 0.072 0.000
#> SRR925710 4 0.1863 0.7985 0.000 0.000 0.000 0.896 0.104 0.000
#> SRR925711 3 0.2631 0.4876 0.000 0.000 0.820 0.000 0.000 0.180
#> SRR934631 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934632 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934633 3 0.4050 0.5257 0.000 0.040 0.812 0.020 0.052 0.076
#> SRR925713 4 0.1152 0.7994 0.000 0.000 0.000 0.952 0.044 0.004
#> SRR925714 4 0.1524 0.7876 0.000 0.000 0.000 0.932 0.008 0.060
#> SRR925715 4 0.2966 0.7045 0.000 0.000 0.000 0.848 0.076 0.076
#> SRR925716 1 0.5916 0.2987 0.444 0.000 0.000 0.336 0.220 0.000
#> SRR925717 4 0.1267 0.7853 0.000 0.000 0.000 0.940 0.000 0.060
#> SRR925718 2 0.0146 0.9303 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925719 4 0.4244 0.6189 0.080 0.000 0.000 0.720 0.200 0.000
#> SRR925720 4 0.3175 0.7447 0.000 0.000 0.000 0.744 0.256 0.000
#> SRR925721 5 0.3482 0.2371 0.000 0.000 0.000 0.316 0.684 0.000
#> SRR934634 1 0.4735 -0.0503 0.520 0.000 0.000 0.048 0.432 0.000
#> SRR934635 1 0.2485 0.5961 0.892 0.000 0.000 0.040 0.056 0.012
#> SRR925723 3 0.1635 0.5590 0.000 0.000 0.940 0.020 0.020 0.020
#> SRR925724 3 0.0790 0.5357 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR925725 6 0.3774 0.9039 0.000 0.000 0.408 0.000 0.000 0.592
#> SRR925726 4 0.2912 0.7624 0.000 0.000 0.000 0.784 0.216 0.000
#> SRR925727 3 0.3854 -0.6115 0.000 0.000 0.536 0.000 0.000 0.464
#> SRR925728 1 0.2335 0.6173 0.904 0.000 0.000 0.044 0.024 0.028
#> SRR934636 5 0.6781 0.0318 0.356 0.188 0.000 0.040 0.408 0.008
#> SRR934637 1 0.4882 -0.0230 0.512 0.000 0.000 0.060 0.428 0.000
#> SRR925729 3 0.6674 0.4237 0.000 0.000 0.396 0.040 0.344 0.220
#> SRR934638 1 0.0146 0.6304 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934639 1 0.5841 0.2874 0.436 0.000 0.000 0.372 0.192 0.000
#> SRR934640 2 0.5237 0.4975 0.000 0.600 0.004 0.000 0.276 0.120
#> SRR934641 2 0.5237 0.4975 0.000 0.600 0.004 0.000 0.276 0.120
#> SRR925734 1 0.0000 0.6295 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925735 3 0.6644 0.4243 0.000 0.000 0.396 0.036 0.340 0.228
#> SRR925736 3 0.6289 0.4681 0.000 0.000 0.480 0.024 0.288 0.208
#> SRR925737 3 0.6905 0.4454 0.000 0.020 0.424 0.032 0.328 0.196
#> SRR934642 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934643 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934644 2 0.0000 0.9305 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934645 2 0.0146 0.9303 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR925740 3 0.3693 0.5506 0.000 0.000 0.812 0.020 0.072 0.096
#> SRR925741 6 0.3862 0.8884 0.000 0.000 0.388 0.000 0.004 0.608
#> SRR925742 3 0.6817 0.4462 0.000 0.020 0.424 0.024 0.328 0.204
#> SRR1283038 3 0.0547 0.5398 0.000 0.000 0.980 0.000 0.000 0.020
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 15949 rows and 64 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.967 0.950 0.974 0.4515 0.542 0.542
#> 3 3 0.901 0.906 0.959 0.4303 0.694 0.488
#> 4 4 0.546 0.552 0.730 0.1156 0.911 0.767
#> 5 5 0.551 0.431 0.708 0.0675 0.853 0.589
#> 6 6 0.595 0.544 0.763 0.0359 0.876 0.573
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
#> SRR925687 1 0.1184 0.983 0.984 0.016
#> SRR925688 1 0.0938 0.985 0.988 0.012
#> SRR925689 2 0.0000 0.948 0.000 1.000
#> SRR925690 2 0.7219 0.764 0.200 0.800
#> SRR925691 2 0.8443 0.653 0.272 0.728
#> SRR925692 2 0.0000 0.948 0.000 1.000
#> SRR925693 1 0.0000 0.985 1.000 0.000
#> SRR925694 2 0.0000 0.948 0.000 1.000
#> SRR925695 1 0.0000 0.985 1.000 0.000
#> SRR925697 1 0.0938 0.985 0.988 0.012
#> SRR925696 1 0.0000 0.985 1.000 0.000
#> SRR925698 1 0.0000 0.985 1.000 0.000
#> SRR925699 2 0.0376 0.947 0.004 0.996
#> SRR925700 2 0.0000 0.948 0.000 1.000
#> SRR925701 2 0.0938 0.945 0.012 0.988
#> SRR925702 1 0.1184 0.983 0.984 0.016
#> SRR925703 1 0.0938 0.985 0.988 0.012
#> SRR925704 1 0.0000 0.985 1.000 0.000
#> SRR925705 1 0.0000 0.985 1.000 0.000
#> SRR925706 1 0.0672 0.985 0.992 0.008
#> SRR925708 1 0.1184 0.983 0.984 0.016
#> SRR925707 1 0.8661 0.577 0.712 0.288
#> SRR925709 1 0.1184 0.983 0.984 0.016
#> SRR925710 1 0.0938 0.985 0.988 0.012
#> SRR925711 1 0.0000 0.985 1.000 0.000
#> SRR934631 2 0.0938 0.945 0.012 0.988
#> SRR934632 2 0.0938 0.945 0.012 0.988
#> SRR934633 1 0.2043 0.959 0.968 0.032
#> SRR925713 1 0.0938 0.985 0.988 0.012
#> SRR925714 1 0.0938 0.985 0.988 0.012
#> SRR925715 1 0.0376 0.985 0.996 0.004
#> SRR925716 1 0.1184 0.983 0.984 0.016
#> SRR925717 1 0.0938 0.985 0.988 0.012
#> SRR925718 2 0.0000 0.948 0.000 1.000
#> SRR925719 1 0.0938 0.985 0.988 0.012
#> SRR925720 1 0.0938 0.985 0.988 0.012
#> SRR925721 1 0.1184 0.983 0.984 0.016
#> SRR934634 2 0.0000 0.948 0.000 1.000
#> SRR934635 2 0.1414 0.939 0.020 0.980
#> SRR925723 1 0.0000 0.985 1.000 0.000
#> SRR925724 1 0.0000 0.985 1.000 0.000
#> SRR925725 1 0.0000 0.985 1.000 0.000
#> SRR925726 1 0.0938 0.985 0.988 0.012
#> SRR925727 1 0.0000 0.985 1.000 0.000
#> SRR925728 1 0.1184 0.983 0.984 0.016
#> SRR934636 2 0.0000 0.948 0.000 1.000
#> SRR934637 1 0.1414 0.981 0.980 0.020
#> SRR925729 1 0.0376 0.985 0.996 0.004
#> SRR934638 2 0.0000 0.948 0.000 1.000
#> SRR934639 1 0.1184 0.983 0.984 0.016
#> SRR934640 2 0.7299 0.770 0.204 0.796
#> SRR934641 2 0.8608 0.656 0.284 0.716
#> SRR925734 2 0.1843 0.934 0.028 0.972
#> SRR925735 1 0.0000 0.985 1.000 0.000
#> SRR925736 1 0.0000 0.985 1.000 0.000
#> SRR925737 1 0.0376 0.985 0.996 0.004
#> SRR934642 2 0.0000 0.948 0.000 1.000
#> SRR934643 2 0.1184 0.943 0.016 0.984
#> SRR934644 2 0.0672 0.946 0.008 0.992
#> SRR934645 2 0.0000 0.948 0.000 1.000
#> SRR925740 1 0.0000 0.985 1.000 0.000
#> SRR925741 1 0.0000 0.985 1.000 0.000
#> SRR925742 1 0.1184 0.979 0.984 0.016
#> SRR1283038 1 0.0000 0.985 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR925687 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925688 3 0.2448 0.922 0.076 0.000 0.924
#> SRR925689 1 0.0592 0.951 0.988 0.012 0.000
#> SRR925690 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925691 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925692 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925693 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925694 2 0.0000 0.897 0.000 1.000 0.000
#> SRR925695 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925697 1 0.3816 0.804 0.852 0.000 0.148
#> SRR925696 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925698 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925699 2 0.0000 0.897 0.000 1.000 0.000
#> SRR925700 2 0.0000 0.897 0.000 1.000 0.000
#> SRR925701 2 0.0000 0.897 0.000 1.000 0.000
#> SRR925702 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925703 3 0.1860 0.941 0.052 0.000 0.948
#> SRR925704 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925705 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925706 3 0.1289 0.956 0.032 0.000 0.968
#> SRR925708 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925707 1 0.8521 0.431 0.608 0.228 0.164
#> SRR925709 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925710 3 0.3619 0.846 0.136 0.000 0.864
#> SRR925711 3 0.0000 0.970 0.000 0.000 1.000
#> SRR934631 2 0.0000 0.897 0.000 1.000 0.000
#> SRR934632 2 0.0000 0.897 0.000 1.000 0.000
#> SRR934633 3 0.3412 0.848 0.000 0.124 0.876
#> SRR925713 3 0.2356 0.925 0.072 0.000 0.928
#> SRR925714 3 0.2261 0.928 0.068 0.000 0.932
#> SRR925715 3 0.0892 0.963 0.020 0.000 0.980
#> SRR925716 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925717 3 0.1163 0.958 0.028 0.000 0.972
#> SRR925718 2 0.0000 0.897 0.000 1.000 0.000
#> SRR925719 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925720 1 0.0592 0.952 0.988 0.000 0.012
#> SRR925721 1 0.0000 0.961 1.000 0.000 0.000
#> SRR934634 1 0.0000 0.961 1.000 0.000 0.000
#> SRR934635 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925723 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925724 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925725 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925726 1 0.2625 0.882 0.916 0.000 0.084
#> SRR925727 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925728 1 0.2356 0.893 0.928 0.000 0.072
#> SRR934636 2 0.6095 0.314 0.392 0.608 0.000
#> SRR934637 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925729 3 0.0237 0.969 0.004 0.000 0.996
#> SRR934638 1 0.0000 0.961 1.000 0.000 0.000
#> SRR934639 1 0.0000 0.961 1.000 0.000 0.000
#> SRR934640 2 0.5363 0.621 0.000 0.724 0.276
#> SRR934641 2 0.6026 0.420 0.000 0.624 0.376
#> SRR925734 1 0.0000 0.961 1.000 0.000 0.000
#> SRR925735 3 0.0237 0.969 0.004 0.000 0.996
#> SRR925736 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925737 3 0.0592 0.966 0.012 0.000 0.988
#> SRR934642 2 0.0000 0.897 0.000 1.000 0.000
#> SRR934643 2 0.0000 0.897 0.000 1.000 0.000
#> SRR934644 2 0.0000 0.897 0.000 1.000 0.000
#> SRR934645 2 0.4645 0.735 0.176 0.816 0.008
#> SRR925740 3 0.0424 0.966 0.000 0.008 0.992
#> SRR925741 3 0.0000 0.970 0.000 0.000 1.000
#> SRR925742 3 0.2584 0.918 0.008 0.064 0.928
#> SRR1283038 3 0.0000 0.970 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR925687 1 0.5077 0.6468 0.760 0.000 0.160 0.080
#> SRR925688 1 0.7837 0.0499 0.408 0.000 0.300 0.292
#> SRR925689 1 0.5157 0.6772 0.688 0.028 0.000 0.284
#> SRR925690 1 0.4584 0.6814 0.696 0.004 0.000 0.300
#> SRR925691 1 0.4608 0.6806 0.692 0.004 0.000 0.304
#> SRR925692 1 0.4608 0.6806 0.692 0.004 0.000 0.304
#> SRR925693 3 0.3123 0.4240 0.000 0.000 0.844 0.156
#> SRR925694 2 0.0000 0.9408 0.000 1.000 0.000 0.000
#> SRR925695 3 0.3610 0.3367 0.000 0.000 0.800 0.200
#> SRR925697 1 0.6466 0.3967 0.588 0.000 0.320 0.092
#> SRR925696 3 0.3649 0.3290 0.000 0.000 0.796 0.204
#> SRR925698 3 0.1118 0.5187 0.000 0.000 0.964 0.036
#> SRR925699 2 0.2131 0.9149 0.016 0.936 0.008 0.040
#> SRR925700 2 0.1471 0.9265 0.012 0.960 0.004 0.024
#> SRR925701 2 0.0000 0.9408 0.000 1.000 0.000 0.000
#> SRR925702 1 0.5374 0.5629 0.704 0.000 0.244 0.052
#> SRR925703 3 0.6546 0.4292 0.172 0.000 0.636 0.192
#> SRR925704 3 0.4134 0.1863 0.000 0.000 0.740 0.260
#> SRR925705 3 0.2234 0.5251 0.008 0.004 0.924 0.064
#> SRR925706 3 0.6844 0.3553 0.152 0.000 0.588 0.260
#> SRR925708 1 0.4791 0.6839 0.784 0.000 0.136 0.080
#> SRR925707 3 0.9842 -0.0298 0.216 0.288 0.312 0.184
#> SRR925709 1 0.5700 0.6717 0.716 0.000 0.120 0.164
#> SRR925710 3 0.6928 0.3276 0.308 0.000 0.556 0.136
#> SRR925711 3 0.4193 0.1916 0.000 0.000 0.732 0.268
#> SRR934631 2 0.0336 0.9396 0.000 0.992 0.000 0.008
#> SRR934632 2 0.0336 0.9396 0.000 0.992 0.000 0.008
#> SRR934633 3 0.6722 -0.0851 0.000 0.200 0.616 0.184
#> SRR925713 3 0.6602 0.4076 0.208 0.000 0.628 0.164
#> SRR925714 3 0.6473 0.4405 0.188 0.000 0.644 0.168
#> SRR925715 3 0.6112 0.3791 0.096 0.000 0.656 0.248
#> SRR925716 1 0.1474 0.7368 0.948 0.000 0.000 0.052
#> SRR925717 3 0.5326 0.4985 0.136 0.000 0.748 0.116
#> SRR925718 2 0.0927 0.9351 0.000 0.976 0.008 0.016
#> SRR925719 1 0.3239 0.7242 0.880 0.000 0.068 0.052
#> SRR925720 1 0.5548 0.6027 0.716 0.000 0.200 0.084
#> SRR925721 1 0.2197 0.7277 0.916 0.000 0.004 0.080
#> SRR934634 1 0.3569 0.7113 0.804 0.000 0.000 0.196
#> SRR934635 1 0.3610 0.7111 0.800 0.000 0.000 0.200
#> SRR925723 3 0.1118 0.5283 0.000 0.000 0.964 0.036
#> SRR925724 3 0.2921 0.4236 0.000 0.000 0.860 0.140
#> SRR925725 3 0.4933 -0.2981 0.000 0.000 0.568 0.432
#> SRR925726 1 0.6242 0.4314 0.612 0.000 0.308 0.080
#> SRR925727 3 0.4250 0.1456 0.000 0.000 0.724 0.276
#> SRR925728 1 0.3820 0.7210 0.848 0.000 0.088 0.064
#> SRR934636 1 0.7390 0.3725 0.512 0.284 0.000 0.204
#> SRR934637 1 0.3311 0.7198 0.828 0.000 0.000 0.172
#> SRR925729 3 0.4700 0.5278 0.124 0.000 0.792 0.084
#> SRR934638 1 0.3400 0.7130 0.820 0.000 0.000 0.180
#> SRR934639 1 0.1356 0.7405 0.960 0.000 0.008 0.032
#> SRR934640 4 0.7437 0.6993 0.000 0.248 0.240 0.512
#> SRR934641 4 0.7387 0.7140 0.000 0.224 0.256 0.520
#> SRR925734 1 0.3907 0.7064 0.768 0.000 0.000 0.232
#> SRR925735 3 0.4549 0.5296 0.100 0.000 0.804 0.096
#> SRR925736 3 0.4134 0.5376 0.060 0.012 0.844 0.084
#> SRR925737 3 0.5601 0.5102 0.132 0.020 0.756 0.092
#> SRR934642 2 0.0188 0.9404 0.000 0.996 0.000 0.004
#> SRR934643 2 0.2759 0.8721 0.000 0.904 0.052 0.044
#> SRR934644 2 0.0188 0.9403 0.000 0.996 0.000 0.004
#> SRR934645 2 0.6764 0.6351 0.068 0.696 0.104 0.132
#> SRR925740 3 0.3725 0.4725 0.004 0.076 0.860 0.060
#> SRR925741 4 0.4999 0.2744 0.000 0.000 0.492 0.508
#> SRR925742 3 0.6017 0.3665 0.020 0.180 0.716 0.084
#> SRR1283038 3 0.3355 0.4118 0.000 0.004 0.836 0.160
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR925687 4 0.1800 0.588425 0.048 0.000 0.020 0.932 0.000
#> SRR925688 4 0.4419 0.528074 0.020 0.000 0.032 0.760 0.188
#> SRR925689 1 0.1408 0.817691 0.948 0.008 0.000 0.044 0.000
#> SRR925690 1 0.1830 0.815824 0.932 0.000 0.000 0.040 0.028
#> SRR925691 1 0.1648 0.818178 0.940 0.000 0.000 0.040 0.020
#> SRR925692 1 0.1725 0.819559 0.936 0.000 0.000 0.044 0.020
#> SRR925693 3 0.4129 0.290140 0.000 0.000 0.756 0.040 0.204
#> SRR925694 2 0.0162 0.841098 0.004 0.996 0.000 0.000 0.000
#> SRR925695 3 0.2605 0.321041 0.000 0.000 0.852 0.000 0.148
#> SRR925697 4 0.2811 0.593108 0.012 0.000 0.100 0.876 0.012
#> SRR925696 3 0.1704 0.418556 0.000 0.000 0.928 0.004 0.068
#> SRR925698 3 0.2561 0.483068 0.000 0.000 0.884 0.096 0.020
#> SRR925699 2 0.2548 0.802829 0.116 0.876 0.004 0.004 0.000
#> SRR925700 2 0.2338 0.805525 0.112 0.884 0.000 0.000 0.004
#> SRR925701 2 0.0000 0.840438 0.000 1.000 0.000 0.000 0.000
#> SRR925702 4 0.3926 0.576133 0.052 0.000 0.132 0.808 0.008
#> SRR925703 3 0.6485 0.243531 0.048 0.000 0.496 0.388 0.068
#> SRR925704 3 0.2424 0.328910 0.000 0.000 0.868 0.000 0.132
#> SRR925705 3 0.3953 0.484665 0.004 0.016 0.828 0.076 0.076
#> SRR925706 4 0.6615 0.048488 0.008 0.000 0.276 0.508 0.208
#> SRR925708 4 0.5636 0.088290 0.444 0.000 0.048 0.496 0.012
#> SRR925707 2 0.8780 -0.138147 0.064 0.340 0.328 0.200 0.068
#> SRR925709 1 0.4994 0.159601 0.576 0.000 0.012 0.396 0.016
#> SRR925710 4 0.6251 -0.087895 0.032 0.000 0.400 0.500 0.068
#> SRR925711 3 0.3578 0.252998 0.008 0.000 0.784 0.004 0.204
#> SRR934631 2 0.1074 0.834092 0.004 0.968 0.000 0.016 0.012
#> SRR934632 2 0.1074 0.834092 0.004 0.968 0.000 0.016 0.012
#> SRR934633 3 0.6434 0.181981 0.004 0.232 0.616 0.048 0.100
#> SRR925713 4 0.5940 -0.068657 0.004 0.000 0.384 0.516 0.096
#> SRR925714 3 0.6162 0.277602 0.028 0.000 0.516 0.388 0.068
#> SRR925715 5 0.6910 0.062720 0.008 0.000 0.284 0.272 0.436
#> SRR925716 4 0.4994 0.384359 0.184 0.000 0.000 0.704 0.112
#> SRR925717 3 0.5289 0.388877 0.004 0.000 0.620 0.316 0.060
#> SRR925718 2 0.1446 0.835772 0.036 0.952 0.004 0.004 0.004
#> SRR925719 4 0.4856 0.489264 0.228 0.000 0.056 0.708 0.008
#> SRR925720 4 0.2054 0.595935 0.028 0.000 0.052 0.920 0.000
#> SRR925721 4 0.3579 0.529625 0.072 0.000 0.000 0.828 0.100
#> SRR934634 1 0.4730 0.584229 0.688 0.000 0.000 0.260 0.052
#> SRR934635 1 0.3695 0.733414 0.800 0.000 0.000 0.164 0.036
#> SRR925723 3 0.3442 0.492952 0.000 0.000 0.836 0.104 0.060
#> SRR925724 3 0.2488 0.360668 0.000 0.000 0.872 0.004 0.124
#> SRR925725 3 0.4367 -0.336052 0.000 0.000 0.580 0.004 0.416
#> SRR925726 4 0.3433 0.574542 0.032 0.000 0.132 0.832 0.004
#> SRR925727 3 0.3730 -0.000155 0.000 0.000 0.712 0.000 0.288
#> SRR925728 4 0.5541 0.510967 0.164 0.000 0.040 0.704 0.092
#> SRR934636 4 0.8015 -0.084513 0.308 0.104 0.000 0.384 0.204
#> SRR934637 4 0.6402 0.102284 0.288 0.000 0.000 0.504 0.208
#> SRR925729 3 0.6136 0.265380 0.008 0.008 0.488 0.420 0.076
#> SRR934638 4 0.5694 -0.211901 0.456 0.000 0.000 0.464 0.080
#> SRR934639 4 0.4813 0.228957 0.376 0.000 0.004 0.600 0.020
#> SRR934640 5 0.5387 0.620010 0.004 0.096 0.220 0.004 0.676
#> SRR934641 5 0.5183 0.627636 0.004 0.072 0.236 0.004 0.684
#> SRR925734 1 0.2293 0.809521 0.900 0.000 0.000 0.084 0.016
#> SRR925735 3 0.5819 0.351281 0.004 0.000 0.540 0.368 0.088
#> SRR925736 3 0.6484 0.407847 0.008 0.024 0.556 0.316 0.096
#> SRR925737 3 0.7288 0.221276 0.016 0.068 0.436 0.404 0.076
#> SRR934642 2 0.0324 0.839645 0.004 0.992 0.000 0.000 0.004
#> SRR934643 2 0.3762 0.726964 0.008 0.832 0.100 0.004 0.056
#> SRR934644 2 0.0324 0.840200 0.004 0.992 0.000 0.000 0.004
#> SRR934645 2 0.5447 0.394444 0.396 0.552 0.040 0.000 0.012
#> SRR925740 3 0.6540 0.392664 0.008 0.140 0.652 0.092 0.108
#> SRR925741 5 0.4446 0.376587 0.000 0.000 0.476 0.004 0.520
#> SRR925742 3 0.7790 0.299258 0.012 0.232 0.480 0.208 0.068
#> SRR1283038 3 0.2880 0.389533 0.004 0.000 0.868 0.020 0.108
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR925687 4 0.2944 5.96e-01 0.020 0.000 0.000 0.860 0.092 0.028
#> SRR925688 4 0.5043 4.27e-01 0.000 0.000 0.008 0.664 0.164 0.164
#> SRR925689 1 0.0603 7.28e-01 0.980 0.000 0.000 0.000 0.016 0.004
#> SRR925690 1 0.0291 7.31e-01 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR925691 1 0.0291 7.31e-01 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR925692 1 0.0000 7.31e-01 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR925693 3 0.2170 7.08e-01 0.000 0.000 0.908 0.016 0.016 0.060
#> SRR925694 2 0.0653 8.48e-01 0.012 0.980 0.000 0.000 0.004 0.004
#> SRR925695 3 0.1327 7.04e-01 0.000 0.000 0.936 0.000 0.000 0.064
#> SRR925697 4 0.3311 5.97e-01 0.016 0.000 0.032 0.836 0.112 0.004
#> SRR925696 3 0.2159 6.98e-01 0.000 0.000 0.904 0.012 0.012 0.072
#> SRR925698 3 0.1176 7.16e-01 0.000 0.000 0.956 0.024 0.000 0.020
#> SRR925699 2 0.3719 7.86e-01 0.120 0.820 0.020 0.008 0.016 0.016
#> SRR925700 2 0.3428 7.68e-01 0.148 0.816 0.004 0.008 0.016 0.008
#> SRR925701 2 0.0717 8.47e-01 0.016 0.976 0.000 0.000 0.008 0.000
#> SRR925702 4 0.3221 6.19e-01 0.040 0.000 0.032 0.864 0.048 0.016
#> SRR925703 3 0.6127 2.50e-01 0.020 0.000 0.528 0.340 0.076 0.036
#> SRR925704 3 0.1728 7.06e-01 0.000 0.000 0.924 0.008 0.004 0.064
#> SRR925705 3 0.2383 6.97e-01 0.000 0.016 0.908 0.016 0.020 0.040
#> SRR925706 4 0.5986 4.89e-01 0.000 0.000 0.176 0.616 0.088 0.120
#> SRR925708 4 0.4466 5.41e-01 0.236 0.000 0.016 0.708 0.032 0.008
#> SRR925707 2 0.7909 -3.95e-02 0.016 0.332 0.272 0.288 0.068 0.024
#> SRR925709 4 0.5695 2.77e-01 0.420 0.000 0.020 0.492 0.040 0.028
#> SRR925710 4 0.4495 6.09e-01 0.028 0.000 0.132 0.768 0.048 0.024
#> SRR925711 3 0.3013 6.68e-01 0.000 0.000 0.864 0.028 0.064 0.044
#> SRR934631 2 0.1074 8.39e-01 0.000 0.960 0.000 0.000 0.028 0.012
#> SRR934632 2 0.0972 8.40e-01 0.000 0.964 0.000 0.000 0.028 0.008
#> SRR934633 3 0.6549 3.77e-01 0.000 0.180 0.604 0.072 0.048 0.096
#> SRR925713 4 0.4789 5.78e-01 0.016 0.000 0.148 0.728 0.012 0.096
#> SRR925714 3 0.5852 1.17e-01 0.016 0.000 0.500 0.396 0.060 0.028
#> SRR925715 6 0.4933 3.16e-01 0.000 0.000 0.104 0.272 0.000 0.624
#> SRR925716 4 0.5144 6.88e-03 0.092 0.000 0.000 0.536 0.372 0.000
#> SRR925717 3 0.4983 2.84e-01 0.000 0.000 0.580 0.360 0.036 0.024
#> SRR925718 2 0.2663 8.29e-01 0.064 0.888 0.012 0.000 0.024 0.012
#> SRR925719 4 0.3803 5.68e-01 0.172 0.000 0.000 0.776 0.040 0.012
#> SRR925720 4 0.2780 5.98e-01 0.016 0.000 0.000 0.868 0.092 0.024
#> SRR925721 4 0.4359 4.59e-01 0.028 0.000 0.000 0.712 0.232 0.028
#> SRR934634 1 0.5635 1.26e-01 0.576 0.000 0.000 0.208 0.208 0.008
#> SRR934635 1 0.4601 4.71e-01 0.716 0.000 0.000 0.140 0.136 0.008
#> SRR925723 3 0.2629 6.89e-01 0.000 0.000 0.888 0.048 0.028 0.036
#> SRR925724 3 0.1262 7.12e-01 0.000 0.000 0.956 0.008 0.016 0.020
#> SRR925725 3 0.3163 5.64e-01 0.000 0.000 0.780 0.004 0.004 0.212
#> SRR925726 4 0.2677 6.19e-01 0.032 0.000 0.028 0.892 0.040 0.008
#> SRR925727 3 0.2196 6.75e-01 0.000 0.000 0.884 0.004 0.004 0.108
#> SRR925728 4 0.5784 2.98e-01 0.040 0.000 0.040 0.540 0.360 0.020
#> SRR934636 5 0.4301 7.48e-01 0.124 0.036 0.000 0.072 0.768 0.000
#> SRR934637 5 0.3885 7.70e-01 0.100 0.000 0.000 0.116 0.780 0.004
#> SRR925729 4 0.4932 5.61e-01 0.000 0.000 0.176 0.704 0.080 0.040
#> SRR934638 5 0.5982 5.92e-01 0.208 0.004 0.000 0.252 0.528 0.008
#> SRR934639 4 0.4731 4.92e-01 0.208 0.000 0.004 0.696 0.084 0.008
#> SRR934640 6 0.2255 6.00e-01 0.000 0.028 0.080 0.000 0.000 0.892
#> SRR934641 6 0.2176 6.00e-01 0.000 0.024 0.080 0.000 0.000 0.896
#> SRR925734 1 0.2333 6.92e-01 0.896 0.000 0.000 0.040 0.060 0.004
#> SRR925735 4 0.5467 4.67e-01 0.000 0.000 0.256 0.624 0.068 0.052
#> SRR925736 4 0.6771 2.07e-01 0.000 0.044 0.332 0.488 0.088 0.048
#> SRR925737 4 0.5627 5.44e-01 0.000 0.092 0.080 0.704 0.080 0.044
#> SRR934642 2 0.0748 8.46e-01 0.004 0.976 0.000 0.000 0.016 0.004
#> SRR934643 2 0.4479 7.18e-01 0.004 0.784 0.100 0.036 0.056 0.020
#> SRR934644 2 0.0748 8.45e-01 0.004 0.976 0.000 0.000 0.016 0.004
#> SRR934645 1 0.5745 -3.52e-02 0.504 0.404 0.044 0.004 0.036 0.008
#> SRR925740 3 0.8143 -4.07e-02 0.000 0.152 0.388 0.140 0.068 0.252
#> SRR925741 6 0.4413 -9.27e-06 0.000 0.000 0.488 0.008 0.012 0.492
#> SRR925742 4 0.7700 1.03e-01 0.000 0.268 0.252 0.372 0.068 0.040
#> SRR1283038 3 0.0858 7.15e-01 0.000 0.000 0.968 0.004 0.000 0.028
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0