Date: 2019-12-26 00:53:47 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 17231 rows and 53 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] 17231 53
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| ATC:pam | 2 | 1.000 | 0.952 | 0.980 | ** | |
| ATC:NMF | 2 | 1.000 | 0.946 | 0.979 | ** | |
| ATC:kmeans | 3 | 0.971 | 0.949 | 0.979 | ** | 2 |
| ATC:skmeans | 2 | 0.960 | 0.939 | 0.976 | ** | |
| SD:skmeans | 2 | 0.847 | 0.913 | 0.965 | ||
| MAD:skmeans | 2 | 0.847 | 0.891 | 0.958 | ||
| CV:skmeans | 3 | 0.811 | 0.876 | 0.931 | ||
| SD:pam | 5 | 0.798 | 0.787 | 0.901 | ||
| SD:NMF | 3 | 0.767 | 0.842 | 0.936 | ||
| MAD:NMF | 3 | 0.764 | 0.849 | 0.931 | ||
| ATC:hclust | 2 | 0.754 | 0.832 | 0.919 | ||
| MAD:pam | 5 | 0.739 | 0.715 | 0.872 | ||
| CV:NMF | 2 | 0.720 | 0.885 | 0.932 | ||
| CV:pam | 2 | 0.675 | 0.826 | 0.915 | ||
| MAD:kmeans | 2 | 0.620 | 0.790 | 0.899 | ||
| CV:hclust | 5 | 0.512 | 0.295 | 0.648 | ||
| SD:kmeans | 2 | 0.507 | 0.688 | 0.865 | ||
| SD:mclust | 2 | 0.473 | 0.799 | 0.852 | ||
| SD:hclust | 4 | 0.464 | 0.491 | 0.760 | ||
| CV:kmeans | 4 | 0.448 | 0.556 | 0.712 | ||
| ATC:mclust | 4 | 0.423 | 0.470 | 0.713 | ||
| MAD:hclust | 3 | 0.365 | 0.721 | 0.836 | ||
| MAD:mclust | 2 | 0.365 | 0.669 | 0.773 | ||
| CV:mclust | 3 | 0.334 | 0.661 | 0.783 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.589 0.823 0.924 0.491 0.492 0.492
#> CV:NMF 2 0.720 0.885 0.932 0.499 0.495 0.495
#> MAD:NMF 2 0.611 0.806 0.918 0.491 0.495 0.495
#> ATC:NMF 2 1.000 0.946 0.979 0.336 0.665 0.665
#> SD:skmeans 2 0.847 0.913 0.965 0.509 0.491 0.491
#> CV:skmeans 2 0.463 0.794 0.867 0.497 0.505 0.505
#> MAD:skmeans 2 0.847 0.891 0.958 0.509 0.491 0.491
#> ATC:skmeans 2 0.960 0.939 0.976 0.502 0.499 0.499
#> SD:mclust 2 0.473 0.799 0.852 0.397 0.643 0.643
#> CV:mclust 2 0.484 0.876 0.905 0.309 0.713 0.713
#> MAD:mclust 2 0.365 0.669 0.773 0.389 0.643 0.643
#> ATC:mclust 2 0.463 0.793 0.862 0.324 0.766 0.766
#> SD:kmeans 2 0.507 0.688 0.865 0.474 0.505 0.505
#> CV:kmeans 2 0.220 0.651 0.822 0.422 0.570 0.570
#> MAD:kmeans 2 0.620 0.790 0.899 0.494 0.495 0.495
#> ATC:kmeans 2 1.000 0.990 0.995 0.442 0.556 0.556
#> SD:pam 2 0.418 0.692 0.876 0.437 0.586 0.586
#> CV:pam 2 0.675 0.826 0.915 0.503 0.491 0.491
#> MAD:pam 2 0.569 0.861 0.933 0.404 0.623 0.623
#> ATC:pam 2 1.000 0.952 0.980 0.369 0.623 0.623
#> SD:hclust 2 0.496 0.857 0.910 0.272 0.713 0.713
#> CV:hclust 2 0.562 0.729 0.840 0.235 0.795 0.795
#> MAD:hclust 2 0.322 0.847 0.822 0.331 0.713 0.713
#> ATC:hclust 2 0.754 0.832 0.919 0.412 0.604 0.604
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.767 0.842 0.936 0.308 0.647 0.410
#> CV:NMF 3 0.607 0.748 0.857 0.328 0.718 0.491
#> MAD:NMF 3 0.764 0.849 0.931 0.299 0.641 0.414
#> ATC:NMF 3 0.607 0.705 0.883 0.845 0.637 0.487
#> SD:skmeans 3 0.618 0.773 0.868 0.323 0.683 0.440
#> CV:skmeans 3 0.811 0.876 0.931 0.358 0.688 0.454
#> MAD:skmeans 3 0.602 0.783 0.874 0.323 0.720 0.490
#> ATC:skmeans 3 0.715 0.616 0.845 0.286 0.808 0.630
#> SD:mclust 3 0.214 0.581 0.721 0.394 0.623 0.472
#> CV:mclust 3 0.334 0.661 0.783 0.869 0.441 0.352
#> MAD:mclust 3 0.172 0.283 0.617 0.331 0.556 0.441
#> ATC:mclust 3 0.310 0.518 0.701 0.741 0.623 0.508
#> SD:kmeans 3 0.376 0.302 0.561 0.334 0.756 0.588
#> CV:kmeans 3 0.323 0.468 0.693 0.426 0.577 0.386
#> MAD:kmeans 3 0.353 0.445 0.713 0.295 0.644 0.404
#> ATC:kmeans 3 0.971 0.949 0.979 0.404 0.690 0.507
#> SD:pam 3 0.412 0.590 0.797 0.465 0.692 0.500
#> CV:pam 3 0.433 0.551 0.818 0.225 0.749 0.549
#> MAD:pam 3 0.571 0.780 0.859 0.534 0.736 0.576
#> ATC:pam 3 0.831 0.852 0.947 0.440 0.752 0.629
#> SD:hclust 3 0.290 0.583 0.732 0.850 0.759 0.674
#> CV:hclust 3 0.591 0.133 0.615 0.613 0.522 0.452
#> MAD:hclust 3 0.365 0.721 0.836 0.642 0.708 0.590
#> ATC:hclust 3 0.624 0.673 0.877 0.408 0.805 0.689
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.566 0.700 0.838 0.1332 0.744 0.416
#> CV:NMF 4 0.577 0.712 0.829 0.1383 0.739 0.371
#> MAD:NMF 4 0.557 0.707 0.841 0.1607 0.763 0.447
#> ATC:NMF 4 0.689 0.788 0.891 0.1968 0.765 0.461
#> SD:skmeans 4 0.593 0.624 0.782 0.1240 0.819 0.514
#> CV:skmeans 4 0.642 0.610 0.799 0.1204 0.872 0.630
#> MAD:skmeans 4 0.666 0.721 0.801 0.1256 0.800 0.477
#> ATC:skmeans 4 0.871 0.860 0.929 0.1250 0.875 0.663
#> SD:mclust 4 0.434 0.569 0.762 0.0830 0.592 0.309
#> CV:mclust 4 0.376 0.696 0.754 0.1676 0.706 0.434
#> MAD:mclust 4 0.571 0.810 0.892 0.1175 0.767 0.590
#> ATC:mclust 4 0.423 0.470 0.713 0.1783 0.680 0.436
#> SD:kmeans 4 0.473 0.433 0.670 0.1402 0.691 0.385
#> CV:kmeans 4 0.448 0.556 0.712 0.1486 0.838 0.621
#> MAD:kmeans 4 0.466 0.567 0.730 0.1384 0.737 0.396
#> ATC:kmeans 4 0.593 0.543 0.742 0.1795 0.812 0.544
#> SD:pam 4 0.569 0.752 0.849 0.0948 0.906 0.744
#> CV:pam 4 0.629 0.647 0.847 0.0758 0.750 0.472
#> MAD:pam 4 0.689 0.822 0.919 0.1134 0.811 0.555
#> ATC:pam 4 0.609 0.598 0.838 0.2462 0.793 0.577
#> SD:hclust 4 0.464 0.491 0.760 0.3054 0.620 0.377
#> CV:hclust 4 0.600 0.707 0.850 0.2327 0.578 0.386
#> MAD:hclust 4 0.525 0.677 0.798 0.2737 0.848 0.637
#> ATC:hclust 4 0.527 0.677 0.796 0.1754 0.851 0.679
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.712 0.726 0.866 0.0977 0.759 0.320
#> CV:NMF 5 0.746 0.640 0.838 0.0670 0.819 0.415
#> MAD:NMF 5 0.784 0.764 0.884 0.0780 0.835 0.470
#> ATC:NMF 5 0.658 0.691 0.818 0.0825 0.866 0.541
#> SD:skmeans 5 0.673 0.663 0.812 0.0672 0.840 0.454
#> CV:skmeans 5 0.660 0.649 0.787 0.0664 0.893 0.606
#> MAD:skmeans 5 0.681 0.592 0.788 0.0654 0.896 0.608
#> ATC:skmeans 5 0.798 0.719 0.865 0.0517 0.946 0.810
#> SD:mclust 5 0.503 0.726 0.721 0.1745 0.797 0.496
#> CV:mclust 5 0.590 0.535 0.721 0.0999 0.877 0.649
#> MAD:mclust 5 0.474 0.602 0.773 0.2258 0.840 0.615
#> ATC:mclust 5 0.384 0.397 0.617 0.1172 0.623 0.251
#> SD:kmeans 5 0.578 0.584 0.671 0.0795 0.813 0.419
#> CV:kmeans 5 0.491 0.495 0.585 0.0861 0.790 0.413
#> MAD:kmeans 5 0.642 0.594 0.731 0.0786 0.833 0.496
#> ATC:kmeans 5 0.599 0.478 0.681 0.0780 0.832 0.465
#> SD:pam 5 0.798 0.787 0.901 0.1104 0.808 0.461
#> CV:pam 5 0.746 0.812 0.905 0.1314 0.822 0.523
#> MAD:pam 5 0.739 0.715 0.872 0.1166 0.877 0.621
#> ATC:pam 5 0.737 0.724 0.881 0.1197 0.901 0.695
#> SD:hclust 5 0.506 0.584 0.769 0.0801 0.936 0.804
#> CV:hclust 5 0.512 0.295 0.648 0.3435 0.546 0.279
#> MAD:hclust 5 0.594 0.690 0.816 0.0433 0.987 0.951
#> ATC:hclust 5 0.594 0.469 0.721 0.1264 0.828 0.515
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.760 0.655 0.837 0.0479 0.884 0.509
#> CV:NMF 6 0.861 0.793 0.900 0.0460 0.898 0.554
#> MAD:NMF 6 0.739 0.578 0.798 0.0494 0.890 0.544
#> ATC:NMF 6 0.722 0.627 0.753 0.0493 0.896 0.547
#> SD:skmeans 6 0.725 0.646 0.764 0.0400 0.964 0.811
#> CV:skmeans 6 0.711 0.556 0.753 0.0425 0.921 0.635
#> MAD:skmeans 6 0.719 0.558 0.700 0.0405 0.898 0.547
#> ATC:skmeans 6 0.761 0.681 0.833 0.0311 0.972 0.885
#> SD:mclust 6 0.646 0.562 0.742 0.1154 0.848 0.550
#> CV:mclust 6 0.633 0.691 0.733 0.0499 0.819 0.439
#> MAD:mclust 6 0.656 0.624 0.762 0.0995 0.959 0.856
#> ATC:mclust 6 0.552 0.461 0.720 0.0999 0.856 0.442
#> SD:kmeans 6 0.740 0.653 0.789 0.0524 0.954 0.782
#> CV:kmeans 6 0.529 0.474 0.593 0.0606 0.852 0.443
#> MAD:kmeans 6 0.767 0.699 0.822 0.0512 0.909 0.628
#> ATC:kmeans 6 0.651 0.405 0.633 0.0505 0.897 0.580
#> SD:pam 6 0.768 0.738 0.886 0.0481 0.959 0.813
#> CV:pam 6 0.806 0.816 0.913 0.0612 0.927 0.718
#> MAD:pam 6 0.776 0.701 0.879 0.0540 0.910 0.643
#> ATC:pam 6 0.692 0.722 0.848 0.0564 0.959 0.837
#> SD:hclust 6 0.683 0.658 0.765 0.0970 0.906 0.661
#> CV:hclust 6 0.544 0.685 0.765 0.1214 0.739 0.400
#> MAD:hclust 6 0.710 0.576 0.743 0.0926 0.814 0.430
#> ATC:hclust 6 0.678 0.598 0.765 0.0708 0.947 0.761
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 17231 rows and 53 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 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 0.496 0.857 0.910 0.2717 0.713 0.713
#> 3 3 0.290 0.583 0.732 0.8501 0.759 0.674
#> 4 4 0.464 0.491 0.760 0.3054 0.620 0.377
#> 5 5 0.506 0.584 0.769 0.0801 0.936 0.804
#> 6 6 0.683 0.658 0.765 0.0970 0.906 0.661
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
#> SRR1946675 1 0.3733 0.881 0.928 0.072
#> SRR1946691 2 0.9460 0.730 0.364 0.636
#> SRR1946690 2 0.9323 0.760 0.348 0.652
#> SRR1946689 2 0.0938 0.689 0.012 0.988
#> SRR1946686 1 0.3733 0.881 0.928 0.072
#> SRR1946685 1 0.4431 0.875 0.908 0.092
#> SRR1946688 1 0.5408 0.850 0.876 0.124
#> SRR1946684 1 0.0938 0.917 0.988 0.012
#> SRR1946683 1 0.0938 0.917 0.988 0.012
#> SRR1946682 1 0.2603 0.907 0.956 0.044
#> SRR1946680 2 0.0938 0.689 0.012 0.988
#> SRR1946681 2 0.9358 0.758 0.352 0.648
#> SRR1946687 1 0.3733 0.881 0.928 0.072
#> SRR1946679 1 0.4431 0.875 0.908 0.092
#> SRR1946678 1 0.0938 0.917 0.988 0.012
#> SRR1946676 1 0.2423 0.908 0.960 0.040
#> SRR1946677 1 0.0938 0.917 0.988 0.012
#> SRR1946672 1 0.5842 0.794 0.860 0.140
#> SRR1946673 1 0.0938 0.917 0.988 0.012
#> SRR1946671 1 0.0000 0.917 1.000 0.000
#> SRR1946669 1 0.0938 0.917 0.988 0.012
#> SRR1946668 1 0.0938 0.917 0.988 0.012
#> SRR1946666 1 0.3733 0.881 0.928 0.072
#> SRR1946667 2 0.0938 0.689 0.012 0.988
#> SRR1946670 1 0.5059 0.858 0.888 0.112
#> SRR1946663 1 0.2603 0.907 0.956 0.044
#> SRR1946664 2 0.9323 0.760 0.348 0.652
#> SRR1946662 1 0.0938 0.917 0.988 0.012
#> SRR1946661 1 0.0000 0.917 1.000 0.000
#> SRR1946660 1 0.5408 0.850 0.876 0.124
#> SRR1946659 1 0.3733 0.881 0.928 0.072
#> SRR1946658 1 0.5059 0.858 0.888 0.112
#> SRR1946657 1 0.4939 0.861 0.892 0.108
#> SRR1946655 1 0.8861 0.516 0.696 0.304
#> SRR1946654 1 0.6148 0.782 0.848 0.152
#> SRR1946653 1 0.3733 0.881 0.928 0.072
#> SRR1946652 1 0.4161 0.882 0.916 0.084
#> SRR1946651 1 0.4562 0.872 0.904 0.096
#> SRR1946650 1 0.0672 0.917 0.992 0.008
#> SRR1946649 1 0.0000 0.917 1.000 0.000
#> SRR1946648 1 0.4022 0.884 0.920 0.080
#> SRR1946647 1 0.0938 0.917 0.988 0.012
#> SRR1946646 1 0.4690 0.873 0.900 0.100
#> SRR1946645 1 0.0938 0.917 0.988 0.012
#> SRR1946644 1 0.5178 0.858 0.884 0.116
#> SRR1946643 2 0.9358 0.758 0.352 0.648
#> SRR1946642 1 0.0938 0.917 0.988 0.012
#> SRR1946641 1 0.0938 0.917 0.988 0.012
#> SRR1946656 2 0.9358 0.758 0.352 0.648
#> SRR1946640 1 0.0938 0.917 0.988 0.012
#> SRR1946639 1 0.0938 0.917 0.988 0.012
#> SRR1946638 1 0.0938 0.917 0.988 0.012
#> SRR1946637 1 0.0938 0.917 0.988 0.012
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946691 2 0.8637 -0.7051 0.100 0.456 0.444
#> SRR1946690 3 0.8523 0.6901 0.092 0.444 0.464
#> SRR1946689 3 0.0000 0.5547 0.000 0.000 1.000
#> SRR1946686 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946685 2 0.0237 0.6916 0.004 0.996 0.000
#> SRR1946688 2 0.2414 0.6824 0.040 0.940 0.020
#> SRR1946684 2 0.6305 0.0687 0.484 0.516 0.000
#> SRR1946683 2 0.6079 0.3644 0.388 0.612 0.000
#> SRR1946682 2 0.3454 0.7160 0.104 0.888 0.008
#> SRR1946680 3 0.0000 0.5547 0.000 0.000 1.000
#> SRR1946681 3 0.8691 0.6873 0.104 0.444 0.452
#> SRR1946687 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946679 2 0.0237 0.6916 0.004 0.996 0.000
#> SRR1946678 1 0.3686 0.9139 0.860 0.140 0.000
#> SRR1946676 2 0.2796 0.7168 0.092 0.908 0.000
#> SRR1946677 2 0.6062 0.3703 0.384 0.616 0.000
#> SRR1946672 2 0.6970 0.6406 0.276 0.676 0.048
#> SRR1946673 2 0.6305 0.0687 0.484 0.516 0.000
#> SRR1946671 2 0.3752 0.7044 0.144 0.856 0.000
#> SRR1946669 1 0.6260 0.1160 0.552 0.448 0.000
#> SRR1946668 2 0.6305 0.0687 0.484 0.516 0.000
#> SRR1946666 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946667 3 0.0000 0.5547 0.000 0.000 1.000
#> SRR1946670 2 0.1950 0.6874 0.040 0.952 0.008
#> SRR1946663 2 0.3454 0.7160 0.104 0.888 0.008
#> SRR1946664 3 0.8523 0.6901 0.092 0.444 0.464
#> SRR1946662 2 0.6305 0.0687 0.484 0.516 0.000
#> SRR1946661 2 0.3816 0.7025 0.148 0.852 0.000
#> SRR1946660 2 0.2414 0.6824 0.040 0.940 0.020
#> SRR1946659 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946658 2 0.1950 0.6874 0.040 0.952 0.008
#> SRR1946657 2 0.0592 0.6763 0.012 0.988 0.000
#> SRR1946655 2 0.6091 0.3967 0.124 0.784 0.092
#> SRR1946654 2 0.6937 0.6384 0.272 0.680 0.048
#> SRR1946653 2 0.6585 0.6652 0.244 0.712 0.044
#> SRR1946652 2 0.0747 0.7005 0.016 0.984 0.000
#> SRR1946651 2 0.0000 0.6880 0.000 1.000 0.000
#> SRR1946650 2 0.3482 0.7097 0.128 0.872 0.000
#> SRR1946649 2 0.3752 0.7041 0.144 0.856 0.000
#> SRR1946648 2 0.7476 0.2950 0.452 0.512 0.036
#> SRR1946647 2 0.6305 0.0687 0.484 0.516 0.000
#> SRR1946646 2 0.0829 0.6896 0.012 0.984 0.004
#> SRR1946645 2 0.6079 0.3644 0.388 0.612 0.000
#> SRR1946644 2 0.1129 0.6732 0.020 0.976 0.004
#> SRR1946643 3 0.8691 0.6873 0.104 0.444 0.452
#> SRR1946642 1 0.3551 0.9222 0.868 0.132 0.000
#> SRR1946641 1 0.3551 0.9222 0.868 0.132 0.000
#> SRR1946656 3 0.8691 0.6873 0.104 0.444 0.452
#> SRR1946640 1 0.3551 0.9222 0.868 0.132 0.000
#> SRR1946639 1 0.3551 0.9222 0.868 0.132 0.000
#> SRR1946638 1 0.3551 0.9222 0.868 0.132 0.000
#> SRR1946637 1 0.3551 0.9222 0.868 0.132 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946691 2 0.7803 -0.186 0.008 0.460 0.196 0.336
#> SRR1946690 2 0.8162 -0.226 0.008 0.368 0.288 0.336
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1946686 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946685 2 0.2179 0.713 0.012 0.924 0.064 0.000
#> SRR1946688 2 0.1471 0.704 0.004 0.960 0.024 0.012
#> SRR1946684 1 0.4941 0.419 0.564 0.436 0.000 0.000
#> SRR1946683 2 0.6661 -0.308 0.456 0.460 0.084 0.000
#> SRR1946682 2 0.2480 0.680 0.088 0.904 0.008 0.000
#> SRR1946680 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1946681 3 0.5180 -0.051 0.004 0.016 0.672 0.308
#> SRR1946687 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946679 2 0.2179 0.713 0.012 0.924 0.064 0.000
#> SRR1946678 1 0.0592 0.613 0.984 0.016 0.000 0.000
#> SRR1946676 2 0.2675 0.688 0.100 0.892 0.008 0.000
#> SRR1946677 2 0.6661 -0.308 0.456 0.460 0.084 0.000
#> SRR1946672 3 0.5690 0.653 0.216 0.084 0.700 0.000
#> SRR1946673 1 0.4941 0.419 0.564 0.436 0.000 0.000
#> SRR1946671 2 0.3074 0.640 0.152 0.848 0.000 0.000
#> SRR1946669 1 0.4746 0.482 0.632 0.368 0.000 0.000
#> SRR1946668 1 0.4941 0.419 0.564 0.436 0.000 0.000
#> SRR1946666 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1946670 2 0.1004 0.707 0.004 0.972 0.024 0.000
#> SRR1946663 2 0.2480 0.680 0.088 0.904 0.008 0.000
#> SRR1946664 2 0.8162 -0.226 0.008 0.368 0.288 0.336
#> SRR1946662 1 0.4941 0.419 0.564 0.436 0.000 0.000
#> SRR1946661 2 0.3172 0.632 0.160 0.840 0.000 0.000
#> SRR1946660 2 0.1471 0.704 0.004 0.960 0.024 0.012
#> SRR1946659 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946658 2 0.1004 0.707 0.004 0.972 0.024 0.000
#> SRR1946657 2 0.2342 0.709 0.008 0.912 0.080 0.000
#> SRR1946655 3 0.2081 0.484 0.000 0.084 0.916 0.000
#> SRR1946654 3 0.5690 0.650 0.196 0.096 0.708 0.000
#> SRR1946653 3 0.6162 0.647 0.304 0.076 0.620 0.000
#> SRR1946652 2 0.2197 0.714 0.024 0.928 0.048 0.000
#> SRR1946651 2 0.2048 0.712 0.008 0.928 0.064 0.000
#> SRR1946650 2 0.2814 0.660 0.132 0.868 0.000 0.000
#> SRR1946649 2 0.3074 0.641 0.152 0.848 0.000 0.000
#> SRR1946648 1 0.6568 -0.120 0.512 0.080 0.408 0.000
#> SRR1946647 1 0.4941 0.419 0.564 0.436 0.000 0.000
#> SRR1946646 2 0.3708 0.671 0.020 0.832 0.148 0.000
#> SRR1946645 2 0.6661 -0.308 0.456 0.460 0.084 0.000
#> SRR1946644 2 0.3790 0.664 0.016 0.820 0.164 0.000
#> SRR1946643 3 0.5180 -0.051 0.004 0.016 0.672 0.308
#> SRR1946642 1 0.0336 0.612 0.992 0.008 0.000 0.000
#> SRR1946641 1 0.0336 0.612 0.992 0.008 0.000 0.000
#> SRR1946656 3 0.5180 -0.051 0.004 0.016 0.672 0.308
#> SRR1946640 1 0.0336 0.612 0.992 0.008 0.000 0.000
#> SRR1946639 1 0.0336 0.612 0.992 0.008 0.000 0.000
#> SRR1946638 1 0.0336 0.612 0.992 0.008 0.000 0.000
#> SRR1946637 1 0.0336 0.612 0.992 0.008 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946691 2 0.3530 0.8775 0.000 0.784 0.012 0 0.204
#> SRR1946690 2 0.4342 0.9398 0.000 0.728 0.040 0 0.232
#> SRR1946689 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946686 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946685 5 0.0960 0.7558 0.004 0.016 0.008 0 0.972
#> SRR1946688 5 0.3720 0.7154 0.000 0.228 0.012 0 0.760
#> SRR1946684 1 0.4430 0.3287 0.540 0.004 0.000 0 0.456
#> SRR1946683 5 0.6320 -0.0962 0.388 0.008 0.124 0 0.480
#> SRR1946682 5 0.4573 0.7164 0.032 0.232 0.012 0 0.724
#> SRR1946680 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946681 3 0.4291 -0.1387 0.000 0.464 0.536 0 0.000
#> SRR1946687 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946679 5 0.0960 0.7558 0.004 0.016 0.008 0 0.972
#> SRR1946678 1 0.0693 0.5961 0.980 0.000 0.012 0 0.008
#> SRR1946676 5 0.1831 0.7534 0.076 0.004 0.000 0 0.920
#> SRR1946677 5 0.6633 -0.0605 0.368 0.024 0.124 0 0.484
#> SRR1946672 3 0.4204 0.6952 0.196 0.048 0.756 0 0.000
#> SRR1946673 1 0.4430 0.3287 0.540 0.004 0.000 0 0.456
#> SRR1946671 5 0.2733 0.7282 0.112 0.012 0.004 0 0.872
#> SRR1946669 1 0.4299 0.4164 0.608 0.004 0.000 0 0.388
#> SRR1946668 1 0.4425 0.3316 0.544 0.004 0.000 0 0.452
#> SRR1946666 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946667 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946670 5 0.3596 0.7276 0.000 0.212 0.012 0 0.776
#> SRR1946663 5 0.4573 0.7164 0.032 0.232 0.012 0 0.724
#> SRR1946664 2 0.4342 0.9398 0.000 0.728 0.040 0 0.232
#> SRR1946662 1 0.4430 0.3287 0.540 0.004 0.000 0 0.456
#> SRR1946661 5 0.2972 0.7304 0.108 0.024 0.004 0 0.864
#> SRR1946660 5 0.3720 0.7154 0.000 0.228 0.012 0 0.760
#> SRR1946659 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946658 5 0.3596 0.7276 0.000 0.212 0.012 0 0.776
#> SRR1946657 5 0.1211 0.7479 0.000 0.024 0.016 0 0.960
#> SRR1946655 3 0.1544 0.4878 0.000 0.068 0.932 0 0.000
#> SRR1946654 3 0.4476 0.6822 0.172 0.048 0.764 0 0.016
#> SRR1946653 3 0.3480 0.7178 0.248 0.000 0.752 0 0.000
#> SRR1946652 5 0.1306 0.7619 0.016 0.016 0.008 0 0.960
#> SRR1946651 5 0.0798 0.7532 0.000 0.016 0.008 0 0.976
#> SRR1946650 5 0.2645 0.7533 0.068 0.044 0.000 0 0.888
#> SRR1946649 5 0.2722 0.7245 0.120 0.008 0.004 0 0.868
#> SRR1946648 3 0.5039 0.2725 0.456 0.000 0.512 0 0.032
#> SRR1946647 1 0.4430 0.3287 0.540 0.004 0.000 0 0.456
#> SRR1946646 5 0.2804 0.7202 0.012 0.016 0.092 0 0.880
#> SRR1946645 5 0.6320 -0.0962 0.388 0.008 0.124 0 0.480
#> SRR1946644 5 0.2990 0.7092 0.008 0.024 0.100 0 0.868
#> SRR1946643 3 0.4291 -0.1387 0.000 0.464 0.536 0 0.000
#> SRR1946642 1 0.0404 0.5942 0.988 0.000 0.012 0 0.000
#> SRR1946641 1 0.0609 0.5933 0.980 0.000 0.020 0 0.000
#> SRR1946656 3 0.4291 -0.1387 0.000 0.464 0.536 0 0.000
#> SRR1946640 1 0.0609 0.5933 0.980 0.000 0.020 0 0.000
#> SRR1946639 1 0.0609 0.5933 0.980 0.000 0.020 0 0.000
#> SRR1946638 1 0.0609 0.5933 0.980 0.000 0.020 0 0.000
#> SRR1946637 1 0.0609 0.5933 0.980 0.000 0.020 0 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946691 5 0.4141 0.540 0.000 0.092 0.000 0 0.740 0.168
#> SRR1946690 5 0.3717 0.567 0.000 0.148 0.000 0 0.780 0.072
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946686 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946685 2 0.0291 0.782 0.004 0.992 0.000 0 0.004 0.000
#> SRR1946688 6 0.3200 0.947 0.000 0.196 0.000 0 0.016 0.788
#> SRR1946684 1 0.3833 0.283 0.556 0.444 0.000 0 0.000 0.000
#> SRR1946683 2 0.6088 0.130 0.308 0.464 0.220 0 0.000 0.008
#> SRR1946682 6 0.4271 0.911 0.036 0.180 0.036 0 0.000 0.748
#> SRR1946680 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946681 5 0.5198 0.614 0.000 0.000 0.204 0 0.616 0.180
#> SRR1946687 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946679 2 0.0291 0.782 0.004 0.992 0.000 0 0.004 0.000
#> SRR1946678 1 0.1643 0.604 0.924 0.008 0.068 0 0.000 0.000
#> SRR1946676 2 0.1970 0.783 0.060 0.912 0.028 0 0.000 0.000
#> SRR1946677 2 0.6130 0.147 0.320 0.464 0.204 0 0.000 0.012
#> SRR1946672 3 0.5322 0.685 0.104 0.000 0.676 0 0.052 0.168
#> SRR1946673 1 0.3833 0.283 0.556 0.444 0.000 0 0.000 0.000
#> SRR1946671 2 0.2933 0.760 0.108 0.852 0.032 0 0.000 0.008
#> SRR1946669 1 0.3695 0.372 0.624 0.376 0.000 0 0.000 0.000
#> SRR1946668 1 0.4238 0.296 0.540 0.444 0.016 0 0.000 0.000
#> SRR1946666 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946670 6 0.2762 0.951 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946663 6 0.4271 0.911 0.036 0.180 0.036 0 0.000 0.748
#> SRR1946664 5 0.3717 0.567 0.000 0.148 0.000 0 0.780 0.072
#> SRR1946662 1 0.3833 0.283 0.556 0.444 0.000 0 0.000 0.000
#> SRR1946661 2 0.3127 0.758 0.104 0.844 0.040 0 0.000 0.012
#> SRR1946660 6 0.3200 0.947 0.000 0.196 0.000 0 0.016 0.788
#> SRR1946659 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946658 6 0.2762 0.951 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946657 2 0.0820 0.770 0.000 0.972 0.000 0 0.016 0.012
#> SRR1946655 3 0.5270 0.252 0.000 0.000 0.604 0 0.216 0.180
#> SRR1946654 3 0.5646 0.665 0.092 0.016 0.668 0 0.052 0.172
#> SRR1946653 3 0.2048 0.838 0.120 0.000 0.880 0 0.000 0.000
#> SRR1946652 2 0.0363 0.785 0.012 0.988 0.000 0 0.000 0.000
#> SRR1946651 2 0.0146 0.780 0.000 0.996 0.000 0 0.004 0.000
#> SRR1946650 2 0.2924 0.765 0.084 0.864 0.028 0 0.000 0.024
#> SRR1946649 2 0.2981 0.761 0.100 0.852 0.040 0 0.000 0.008
#> SRR1946648 3 0.4234 0.500 0.324 0.032 0.644 0 0.000 0.000
#> SRR1946647 1 0.3833 0.283 0.556 0.444 0.000 0 0.000 0.000
#> SRR1946646 2 0.2110 0.741 0.012 0.900 0.084 0 0.004 0.000
#> SRR1946645 2 0.6080 0.125 0.312 0.464 0.216 0 0.000 0.008
#> SRR1946644 2 0.2670 0.727 0.008 0.880 0.084 0 0.016 0.012
#> SRR1946643 5 0.5198 0.614 0.000 0.000 0.204 0 0.616 0.180
#> SRR1946642 1 0.1387 0.603 0.932 0.000 0.068 0 0.000 0.000
#> SRR1946641 1 0.1501 0.601 0.924 0.000 0.076 0 0.000 0.000
#> SRR1946656 5 0.5198 0.614 0.000 0.000 0.204 0 0.616 0.180
#> SRR1946640 1 0.1501 0.601 0.924 0.000 0.076 0 0.000 0.000
#> SRR1946639 1 0.1501 0.601 0.924 0.000 0.076 0 0.000 0.000
#> SRR1946638 1 0.1501 0.601 0.924 0.000 0.076 0 0.000 0.000
#> SRR1946637 1 0.1501 0.601 0.924 0.000 0.076 0 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 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 0.507 0.688 0.865 0.4745 0.505 0.505
#> 3 3 0.376 0.302 0.561 0.3344 0.756 0.588
#> 4 4 0.473 0.433 0.670 0.1402 0.691 0.385
#> 5 5 0.578 0.584 0.671 0.0795 0.813 0.419
#> 6 6 0.740 0.653 0.789 0.0524 0.954 0.782
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
#> SRR1946675 1 0.946 0.4531 0.636 0.364
#> SRR1946691 2 0.388 0.8326 0.076 0.924
#> SRR1946690 2 0.388 0.8326 0.076 0.924
#> SRR1946689 2 0.000 0.8152 0.000 1.000
#> SRR1946686 1 0.311 0.8305 0.944 0.056
#> SRR1946685 1 0.991 0.0503 0.556 0.444
#> SRR1946688 2 0.430 0.8326 0.088 0.912
#> SRR1946684 1 0.000 0.8428 1.000 0.000
#> SRR1946683 1 0.000 0.8428 1.000 0.000
#> SRR1946682 1 0.992 0.0706 0.552 0.448
#> SRR1946680 2 0.000 0.8152 0.000 1.000
#> SRR1946681 2 0.358 0.8333 0.068 0.932
#> SRR1946687 1 0.969 0.3731 0.604 0.396
#> SRR1946679 2 0.991 0.3145 0.444 0.556
#> SRR1946678 1 0.141 0.8405 0.980 0.020
#> SRR1946676 1 0.991 0.0503 0.556 0.444
#> SRR1946677 1 0.000 0.8428 1.000 0.000
#> SRR1946672 2 0.997 0.0605 0.468 0.532
#> SRR1946673 1 0.000 0.8428 1.000 0.000
#> SRR1946671 1 0.000 0.8428 1.000 0.000
#> SRR1946669 1 0.000 0.8428 1.000 0.000
#> SRR1946668 1 0.000 0.8428 1.000 0.000
#> SRR1946666 1 0.311 0.8305 0.944 0.056
#> SRR1946667 2 0.000 0.8152 0.000 1.000
#> SRR1946670 2 0.430 0.8326 0.088 0.912
#> SRR1946663 1 0.788 0.6228 0.764 0.236
#> SRR1946664 2 0.388 0.8326 0.076 0.924
#> SRR1946662 1 0.000 0.8428 1.000 0.000
#> SRR1946661 1 0.000 0.8428 1.000 0.000
#> SRR1946660 2 0.430 0.8326 0.088 0.912
#> SRR1946659 1 0.311 0.8305 0.944 0.056
#> SRR1946658 2 0.430 0.8326 0.088 0.912
#> SRR1946657 2 0.788 0.7146 0.236 0.764
#> SRR1946655 2 0.141 0.8243 0.020 0.980
#> SRR1946654 2 0.943 0.3948 0.360 0.640
#> SRR1946653 2 0.998 0.0438 0.472 0.528
#> SRR1946652 1 0.991 0.0503 0.556 0.444
#> SRR1946651 2 0.949 0.5079 0.368 0.632
#> SRR1946650 1 0.844 0.5331 0.728 0.272
#> SRR1946649 1 0.000 0.8428 1.000 0.000
#> SRR1946648 1 0.767 0.6845 0.776 0.224
#> SRR1946647 1 0.000 0.8428 1.000 0.000
#> SRR1946646 2 0.714 0.7043 0.196 0.804
#> SRR1946645 1 0.000 0.8428 1.000 0.000
#> SRR1946644 2 0.327 0.8340 0.060 0.940
#> SRR1946643 2 0.141 0.8243 0.020 0.980
#> SRR1946642 1 0.141 0.8405 0.980 0.020
#> SRR1946641 1 0.311 0.8305 0.944 0.056
#> SRR1946656 2 0.141 0.8243 0.020 0.980
#> SRR1946640 1 0.311 0.8305 0.944 0.056
#> SRR1946639 1 0.311 0.8305 0.944 0.056
#> SRR1946638 1 0.311 0.8305 0.944 0.056
#> SRR1946637 1 0.311 0.8305 0.944 0.056
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.8985 0.8043 0.300 0.160 0.540
#> SRR1946691 2 0.0829 0.5299 0.012 0.984 0.004
#> SRR1946690 2 0.1999 0.5320 0.012 0.952 0.036
#> SRR1946689 2 0.6168 0.2477 0.000 0.588 0.412
#> SRR1946686 1 0.6204 0.1656 0.576 0.000 0.424
#> SRR1946685 1 0.9147 -0.1229 0.444 0.412 0.144
#> SRR1946688 2 0.4409 0.5192 0.172 0.824 0.004
#> SRR1946684 1 0.1315 0.4472 0.972 0.008 0.020
#> SRR1946683 1 0.1267 0.4376 0.972 0.004 0.024
#> SRR1946682 2 0.6373 0.2420 0.408 0.588 0.004
#> SRR1946680 2 0.6168 0.2477 0.000 0.588 0.412
#> SRR1946681 2 0.6126 0.3336 0.004 0.644 0.352
#> SRR1946687 3 0.9106 0.7228 0.336 0.156 0.508
#> SRR1946679 1 0.9147 -0.1229 0.444 0.412 0.144
#> SRR1946678 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946676 1 0.9147 -0.1229 0.444 0.412 0.144
#> SRR1946677 1 0.2050 0.4298 0.952 0.028 0.020
#> SRR1946672 3 0.8688 0.8290 0.196 0.208 0.596
#> SRR1946673 1 0.3129 0.3990 0.904 0.088 0.008
#> SRR1946671 1 0.2947 0.4217 0.920 0.020 0.060
#> SRR1946669 1 0.1529 0.4405 0.960 0.000 0.040
#> SRR1946668 1 0.0237 0.4458 0.996 0.000 0.004
#> SRR1946666 1 0.6215 0.1465 0.572 0.000 0.428
#> SRR1946667 2 0.6168 0.2477 0.000 0.588 0.412
#> SRR1946670 2 0.4465 0.5179 0.176 0.820 0.004
#> SRR1946663 2 0.6489 0.1543 0.456 0.540 0.004
#> SRR1946664 2 0.1999 0.5320 0.012 0.952 0.036
#> SRR1946662 1 0.1315 0.4472 0.972 0.008 0.020
#> SRR1946661 1 0.6651 0.1825 0.640 0.340 0.020
#> SRR1946660 2 0.4784 0.5078 0.200 0.796 0.004
#> SRR1946659 1 0.6309 0.0396 0.504 0.000 0.496
#> SRR1946658 2 0.6443 0.4710 0.240 0.720 0.040
#> SRR1946657 1 0.9108 -0.1274 0.444 0.416 0.140
#> SRR1946655 2 0.7075 0.1280 0.020 0.496 0.484
#> SRR1946654 3 0.8803 0.7518 0.180 0.240 0.580
#> SRR1946653 3 0.8834 0.8471 0.224 0.196 0.580
#> SRR1946652 1 0.9147 -0.1229 0.444 0.412 0.144
#> SRR1946651 1 0.9108 -0.1274 0.444 0.416 0.140
#> SRR1946650 1 0.7990 -0.0116 0.532 0.404 0.064
#> SRR1946649 1 0.2297 0.4293 0.944 0.036 0.020
#> SRR1946648 1 0.8373 -0.4122 0.524 0.088 0.388
#> SRR1946647 1 0.0237 0.4439 0.996 0.000 0.004
#> SRR1946646 2 0.8307 0.3763 0.192 0.632 0.176
#> SRR1946645 1 0.4345 0.3632 0.848 0.016 0.136
#> SRR1946644 2 0.6546 0.4570 0.096 0.756 0.148
#> SRR1946643 2 0.6819 0.1612 0.012 0.512 0.476
#> SRR1946642 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946641 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946656 2 0.6819 0.1612 0.012 0.512 0.476
#> SRR1946640 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946639 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946638 1 0.6168 0.2322 0.588 0.000 0.412
#> SRR1946637 1 0.6168 0.2322 0.588 0.000 0.412
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.798 0.3646 0.168 0.112 0.600 0.120
#> SRR1946691 4 0.888 0.5835 0.196 0.068 0.320 0.416
#> SRR1946690 4 0.881 0.5503 0.184 0.064 0.352 0.400
#> SRR1946689 4 0.228 0.2103 0.000 0.000 0.096 0.904
#> SRR1946686 1 0.754 0.4048 0.444 0.192 0.364 0.000
#> SRR1946685 2 0.741 0.3608 0.172 0.464 0.364 0.000
#> SRR1946688 4 0.944 0.5814 0.196 0.124 0.308 0.372
#> SRR1946684 2 0.265 0.5963 0.108 0.888 0.004 0.000
#> SRR1946683 2 0.252 0.6373 0.024 0.912 0.064 0.000
#> SRR1946682 2 0.855 0.1868 0.196 0.512 0.072 0.220
#> SRR1946680 4 0.270 0.1904 0.000 0.000 0.124 0.876
#> SRR1946681 3 0.758 -0.2242 0.128 0.016 0.472 0.384
#> SRR1946687 3 0.818 0.2878 0.212 0.096 0.568 0.124
#> SRR1946679 2 0.747 0.2198 0.176 0.424 0.400 0.000
#> SRR1946678 1 0.407 0.8080 0.748 0.252 0.000 0.000
#> SRR1946676 2 0.731 0.3951 0.168 0.496 0.336 0.000
#> SRR1946677 2 0.190 0.6500 0.004 0.932 0.064 0.000
#> SRR1946672 3 0.734 0.4264 0.144 0.076 0.652 0.128
#> SRR1946673 2 0.155 0.6554 0.040 0.952 0.008 0.000
#> SRR1946671 2 0.234 0.6413 0.008 0.912 0.080 0.000
#> SRR1946669 2 0.292 0.5467 0.140 0.860 0.000 0.000
#> SRR1946668 2 0.233 0.6070 0.088 0.908 0.004 0.000
#> SRR1946666 1 0.731 0.3195 0.440 0.152 0.408 0.000
#> SRR1946667 4 0.228 0.2103 0.000 0.000 0.096 0.904
#> SRR1946670 4 0.950 0.5738 0.196 0.132 0.308 0.364
#> SRR1946663 2 0.849 0.1944 0.196 0.516 0.068 0.220
#> SRR1946664 4 0.881 0.5503 0.184 0.064 0.352 0.400
#> SRR1946662 2 0.201 0.6186 0.080 0.920 0.000 0.000
#> SRR1946661 2 0.492 0.5656 0.208 0.752 0.036 0.004
#> SRR1946660 4 0.944 0.5814 0.196 0.124 0.308 0.372
#> SRR1946659 1 0.673 0.4652 0.560 0.092 0.344 0.004
#> SRR1946658 3 0.978 -0.5620 0.224 0.168 0.336 0.272
#> SRR1946657 3 0.747 -0.3235 0.176 0.396 0.428 0.000
#> SRR1946655 3 0.454 0.4275 0.000 0.000 0.676 0.324
#> SRR1946654 3 0.655 0.4712 0.088 0.072 0.712 0.128
#> SRR1946653 3 0.783 0.3898 0.160 0.096 0.612 0.132
#> SRR1946652 2 0.718 0.4475 0.184 0.572 0.240 0.004
#> SRR1946651 2 0.751 0.2247 0.184 0.440 0.376 0.000
#> SRR1946650 2 0.622 0.5423 0.188 0.680 0.128 0.004
#> SRR1946649 2 0.254 0.6560 0.012 0.904 0.084 0.000
#> SRR1946648 3 0.719 0.2108 0.064 0.396 0.508 0.032
#> SRR1946647 2 0.259 0.6070 0.080 0.904 0.016 0.000
#> SRR1946646 3 0.572 0.0925 0.152 0.132 0.716 0.000
#> SRR1946645 2 0.274 0.6303 0.024 0.900 0.076 0.000
#> SRR1946644 3 0.880 -0.3269 0.176 0.120 0.512 0.192
#> SRR1946643 3 0.476 0.3983 0.000 0.000 0.628 0.372
#> SRR1946642 1 0.401 0.8111 0.756 0.244 0.000 0.000
#> SRR1946641 1 0.384 0.8239 0.776 0.224 0.000 0.000
#> SRR1946656 3 0.476 0.3983 0.000 0.000 0.628 0.372
#> SRR1946640 1 0.384 0.8239 0.776 0.224 0.000 0.000
#> SRR1946639 1 0.384 0.8239 0.776 0.224 0.000 0.000
#> SRR1946638 1 0.384 0.8239 0.776 0.224 0.000 0.000
#> SRR1946637 1 0.384 0.8239 0.776 0.224 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.388 0.8005 0.100 0.028 0.828 0.000 0.044
#> SRR1946691 4 0.476 0.3779 0.000 0.288 0.012 0.676 0.024
#> SRR1946690 2 0.483 0.0507 0.000 0.560 0.016 0.420 0.004
#> SRR1946689 4 0.768 0.2800 0.084 0.232 0.148 0.520 0.016
#> SRR1946686 3 0.443 0.7339 0.192 0.000 0.744 0.000 0.064
#> SRR1946685 2 0.511 0.3939 0.000 0.620 0.056 0.000 0.324
#> SRR1946688 4 0.477 0.4018 0.000 0.276 0.004 0.680 0.040
#> SRR1946684 5 0.239 0.7438 0.056 0.004 0.008 0.020 0.912
#> SRR1946683 5 0.460 0.7405 0.004 0.116 0.100 0.008 0.772
#> SRR1946682 4 0.496 0.1892 0.000 0.028 0.000 0.520 0.452
#> SRR1946680 4 0.804 0.2499 0.096 0.260 0.164 0.464 0.016
#> SRR1946681 2 0.702 0.0269 0.012 0.484 0.216 0.280 0.008
#> SRR1946687 3 0.328 0.7996 0.116 0.008 0.848 0.000 0.028
#> SRR1946679 2 0.416 0.5216 0.000 0.704 0.016 0.000 0.280
#> SRR1946678 1 0.233 0.9612 0.876 0.000 0.000 0.000 0.124
#> SRR1946676 2 0.577 0.2093 0.000 0.532 0.096 0.000 0.372
#> SRR1946677 5 0.460 0.7411 0.004 0.116 0.100 0.008 0.772
#> SRR1946672 3 0.301 0.7905 0.056 0.060 0.876 0.000 0.008
#> SRR1946673 5 0.157 0.7624 0.032 0.008 0.012 0.000 0.948
#> SRR1946671 5 0.484 0.7292 0.004 0.120 0.116 0.008 0.752
#> SRR1946669 5 0.212 0.7498 0.076 0.004 0.008 0.000 0.912
#> SRR1946668 5 0.239 0.7496 0.044 0.004 0.016 0.020 0.916
#> SRR1946666 3 0.427 0.7414 0.196 0.000 0.752 0.000 0.052
#> SRR1946667 4 0.768 0.2800 0.084 0.232 0.148 0.520 0.016
#> SRR1946670 4 0.550 0.3902 0.000 0.280 0.004 0.628 0.088
#> SRR1946663 4 0.496 0.1892 0.000 0.028 0.000 0.520 0.452
#> SRR1946664 2 0.483 0.0507 0.000 0.560 0.016 0.420 0.004
#> SRR1946662 5 0.177 0.7580 0.048 0.008 0.008 0.000 0.936
#> SRR1946661 5 0.427 0.6589 0.000 0.104 0.000 0.120 0.776
#> SRR1946660 4 0.477 0.4018 0.000 0.276 0.004 0.680 0.040
#> SRR1946659 3 0.465 0.5931 0.328 0.000 0.644 0.000 0.028
#> SRR1946658 4 0.576 0.3238 0.000 0.332 0.004 0.572 0.092
#> SRR1946657 2 0.411 0.5396 0.000 0.724 0.020 0.000 0.256
#> SRR1946655 3 0.387 0.6976 0.012 0.096 0.832 0.052 0.008
#> SRR1946654 3 0.264 0.7657 0.012 0.088 0.888 0.000 0.012
#> SRR1946653 3 0.342 0.8046 0.096 0.020 0.852 0.000 0.032
#> SRR1946652 5 0.471 -0.0384 0.000 0.492 0.008 0.004 0.496
#> SRR1946651 2 0.411 0.5203 0.000 0.708 0.004 0.008 0.280
#> SRR1946650 5 0.504 0.5107 0.000 0.304 0.008 0.040 0.648
#> SRR1946649 5 0.476 0.6977 0.004 0.188 0.060 0.008 0.740
#> SRR1946648 3 0.483 0.6749 0.020 0.044 0.740 0.004 0.192
#> SRR1946647 5 0.267 0.7487 0.032 0.004 0.040 0.020 0.904
#> SRR1946646 2 0.507 0.4059 0.000 0.640 0.308 0.004 0.048
#> SRR1946645 5 0.502 0.7261 0.012 0.116 0.116 0.008 0.748
#> SRR1946644 2 0.562 0.4044 0.000 0.688 0.144 0.144 0.024
#> SRR1946643 3 0.554 0.5946 0.020 0.184 0.700 0.088 0.008
#> SRR1946642 1 0.228 0.9633 0.880 0.000 0.000 0.000 0.120
#> SRR1946641 1 0.225 0.9852 0.896 0.000 0.008 0.000 0.096
#> SRR1946656 3 0.554 0.5946 0.020 0.184 0.700 0.088 0.008
#> SRR1946640 1 0.225 0.9852 0.896 0.000 0.008 0.000 0.096
#> SRR1946639 1 0.225 0.9852 0.896 0.000 0.008 0.000 0.096
#> SRR1946638 1 0.225 0.9852 0.896 0.000 0.008 0.000 0.096
#> SRR1946637 1 0.225 0.9852 0.896 0.000 0.008 0.000 0.096
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.1605 0.7991 0.032 0.012 0.940 0.000 0.016 0.000
#> SRR1946691 6 0.0881 0.6625 0.008 0.008 0.000 0.012 0.000 0.972
#> SRR1946690 6 0.4519 0.3346 0.008 0.384 0.000 0.024 0.000 0.584
#> SRR1946689 4 0.6503 0.8887 0.016 0.344 0.032 0.496 0.008 0.104
#> SRR1946686 3 0.2039 0.7885 0.076 0.000 0.904 0.000 0.020 0.000
#> SRR1946685 2 0.4897 0.7503 0.000 0.568 0.020 0.000 0.380 0.032
#> SRR1946688 6 0.0405 0.6663 0.000 0.000 0.000 0.008 0.004 0.988
#> SRR1946684 5 0.4377 0.6581 0.016 0.004 0.000 0.296 0.668 0.016
#> SRR1946683 5 0.2099 0.6206 0.004 0.008 0.080 0.000 0.904 0.004
#> SRR1946682 6 0.4980 0.4482 0.000 0.000 0.000 0.168 0.184 0.648
#> SRR1946680 4 0.5192 0.7804 0.000 0.244 0.056 0.652 0.000 0.048
#> SRR1946681 6 0.7430 0.0483 0.004 0.300 0.100 0.264 0.000 0.332
#> SRR1946687 3 0.1152 0.7998 0.044 0.000 0.952 0.000 0.004 0.000
#> SRR1946679 2 0.5248 0.7918 0.000 0.568 0.008 0.000 0.336 0.088
#> SRR1946678 1 0.1398 0.9232 0.940 0.000 0.000 0.008 0.052 0.000
#> SRR1946676 2 0.5105 0.6301 0.000 0.504 0.052 0.000 0.432 0.012
#> SRR1946677 5 0.1988 0.6244 0.004 0.008 0.072 0.000 0.912 0.004
#> SRR1946672 3 0.0748 0.7897 0.004 0.016 0.976 0.000 0.000 0.004
#> SRR1946673 5 0.4159 0.6644 0.016 0.004 0.000 0.288 0.684 0.008
#> SRR1946671 5 0.2257 0.6179 0.008 0.012 0.076 0.000 0.900 0.004
#> SRR1946669 5 0.4074 0.6643 0.024 0.004 0.000 0.288 0.684 0.000
#> SRR1946668 5 0.4377 0.6581 0.016 0.004 0.000 0.296 0.668 0.016
#> SRR1946666 3 0.2039 0.7885 0.076 0.000 0.904 0.000 0.020 0.000
#> SRR1946667 4 0.6503 0.8887 0.016 0.344 0.032 0.496 0.008 0.104
#> SRR1946670 6 0.1275 0.6666 0.000 0.012 0.000 0.016 0.016 0.956
#> SRR1946663 6 0.4980 0.4482 0.000 0.000 0.000 0.168 0.184 0.648
#> SRR1946664 6 0.4519 0.3346 0.008 0.384 0.000 0.024 0.000 0.584
#> SRR1946662 5 0.4134 0.6651 0.020 0.004 0.000 0.288 0.684 0.004
#> SRR1946661 5 0.1333 0.6240 0.000 0.000 0.000 0.008 0.944 0.048
#> SRR1946660 6 0.0405 0.6663 0.000 0.000 0.000 0.008 0.004 0.988
#> SRR1946659 3 0.3309 0.6120 0.280 0.000 0.720 0.000 0.000 0.000
#> SRR1946658 6 0.2345 0.6598 0.000 0.072 0.000 0.016 0.016 0.896
#> SRR1946657 2 0.5347 0.7869 0.000 0.612 0.008 0.008 0.276 0.096
#> SRR1946655 3 0.3921 0.6122 0.000 0.036 0.736 0.224 0.000 0.004
#> SRR1946654 3 0.1010 0.7854 0.000 0.036 0.960 0.000 0.004 0.000
#> SRR1946653 3 0.1296 0.7997 0.032 0.012 0.952 0.004 0.000 0.000
#> SRR1946652 5 0.4531 -0.5887 0.000 0.464 0.000 0.000 0.504 0.032
#> SRR1946651 2 0.5203 0.7938 0.000 0.572 0.004 0.000 0.328 0.096
#> SRR1946650 5 0.3720 0.1918 0.000 0.236 0.000 0.000 0.736 0.028
#> SRR1946649 5 0.2705 0.5651 0.004 0.076 0.040 0.000 0.876 0.004
#> SRR1946648 3 0.2980 0.6854 0.000 0.012 0.808 0.000 0.180 0.000
#> SRR1946647 5 0.4514 0.6578 0.016 0.004 0.004 0.296 0.664 0.016
#> SRR1946646 2 0.5947 0.6336 0.000 0.612 0.192 0.000 0.124 0.072
#> SRR1946645 5 0.2317 0.6136 0.008 0.008 0.088 0.000 0.892 0.004
#> SRR1946644 2 0.5998 0.5316 0.000 0.604 0.068 0.008 0.084 0.236
#> SRR1946643 3 0.5628 0.3676 0.000 0.160 0.540 0.296 0.000 0.004
#> SRR1946642 1 0.0713 0.9535 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1946641 1 0.1074 0.9785 0.960 0.000 0.028 0.000 0.012 0.000
#> SRR1946656 3 0.5628 0.3676 0.000 0.160 0.540 0.296 0.000 0.004
#> SRR1946640 1 0.1074 0.9785 0.960 0.000 0.028 0.000 0.012 0.000
#> SRR1946639 1 0.1074 0.9785 0.960 0.000 0.028 0.000 0.012 0.000
#> SRR1946638 1 0.1074 0.9785 0.960 0.000 0.028 0.000 0.012 0.000
#> SRR1946637 1 0.1074 0.9785 0.960 0.000 0.028 0.000 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "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 17231 rows and 53 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 0.847 0.913 0.965 0.5093 0.491 0.491
#> 3 3 0.618 0.773 0.868 0.3232 0.683 0.440
#> 4 4 0.593 0.624 0.782 0.1240 0.819 0.514
#> 5 5 0.673 0.663 0.812 0.0672 0.840 0.454
#> 6 6 0.725 0.646 0.764 0.0400 0.964 0.811
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
#> SRR1946675 2 0.996 0.133 0.464 0.536
#> SRR1946691 2 0.000 0.963 0.000 1.000
#> SRR1946690 2 0.000 0.963 0.000 1.000
#> SRR1946689 2 0.000 0.963 0.000 1.000
#> SRR1946686 1 0.000 0.959 1.000 0.000
#> SRR1946685 2 0.000 0.963 0.000 1.000
#> SRR1946688 2 0.000 0.963 0.000 1.000
#> SRR1946684 1 0.000 0.959 1.000 0.000
#> SRR1946683 1 0.000 0.959 1.000 0.000
#> SRR1946682 1 0.827 0.652 0.740 0.260
#> SRR1946680 2 0.000 0.963 0.000 1.000
#> SRR1946681 2 0.000 0.963 0.000 1.000
#> SRR1946687 1 0.506 0.853 0.888 0.112
#> SRR1946679 2 0.000 0.963 0.000 1.000
#> SRR1946678 1 0.000 0.959 1.000 0.000
#> SRR1946676 2 0.000 0.963 0.000 1.000
#> SRR1946677 1 0.000 0.959 1.000 0.000
#> SRR1946672 2 0.722 0.739 0.200 0.800
#> SRR1946673 1 0.000 0.959 1.000 0.000
#> SRR1946671 1 0.000 0.959 1.000 0.000
#> SRR1946669 1 0.000 0.959 1.000 0.000
#> SRR1946668 1 0.000 0.959 1.000 0.000
#> SRR1946666 1 0.000 0.959 1.000 0.000
#> SRR1946667 2 0.000 0.963 0.000 1.000
#> SRR1946670 2 0.000 0.963 0.000 1.000
#> SRR1946663 1 0.141 0.943 0.980 0.020
#> SRR1946664 2 0.000 0.963 0.000 1.000
#> SRR1946662 1 0.000 0.959 1.000 0.000
#> SRR1946661 1 0.000 0.959 1.000 0.000
#> SRR1946660 2 0.000 0.963 0.000 1.000
#> SRR1946659 1 0.000 0.959 1.000 0.000
#> SRR1946658 2 0.000 0.963 0.000 1.000
#> SRR1946657 2 0.000 0.963 0.000 1.000
#> SRR1946655 2 0.000 0.963 0.000 1.000
#> SRR1946654 2 0.000 0.963 0.000 1.000
#> SRR1946653 2 0.745 0.721 0.212 0.788
#> SRR1946652 2 0.000 0.963 0.000 1.000
#> SRR1946651 2 0.000 0.963 0.000 1.000
#> SRR1946650 1 0.921 0.511 0.664 0.336
#> SRR1946649 1 0.000 0.959 1.000 0.000
#> SRR1946648 1 0.839 0.621 0.732 0.268
#> SRR1946647 1 0.000 0.959 1.000 0.000
#> SRR1946646 2 0.000 0.963 0.000 1.000
#> SRR1946645 1 0.000 0.959 1.000 0.000
#> SRR1946644 2 0.000 0.963 0.000 1.000
#> SRR1946643 2 0.000 0.963 0.000 1.000
#> SRR1946642 1 0.000 0.959 1.000 0.000
#> SRR1946641 1 0.000 0.959 1.000 0.000
#> SRR1946656 2 0.000 0.963 0.000 1.000
#> SRR1946640 1 0.000 0.959 1.000 0.000
#> SRR1946639 1 0.000 0.959 1.000 0.000
#> SRR1946638 1 0.000 0.959 1.000 0.000
#> SRR1946637 1 0.000 0.959 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.0592 0.773 0.012 0.000 0.988
#> SRR1946691 2 0.0000 0.870 0.000 1.000 0.000
#> SRR1946690 2 0.0000 0.870 0.000 1.000 0.000
#> SRR1946689 3 0.5591 0.691 0.000 0.304 0.696
#> SRR1946686 1 0.6308 0.349 0.508 0.000 0.492
#> SRR1946685 2 0.2280 0.850 0.008 0.940 0.052
#> SRR1946688 2 0.0592 0.872 0.012 0.988 0.000
#> SRR1946684 1 0.0000 0.868 1.000 0.000 0.000
#> SRR1946683 1 0.0424 0.870 0.992 0.000 0.008
#> SRR1946682 2 0.4452 0.775 0.192 0.808 0.000
#> SRR1946680 3 0.5560 0.695 0.000 0.300 0.700
#> SRR1946681 2 0.2448 0.825 0.000 0.924 0.076
#> SRR1946687 3 0.0747 0.771 0.016 0.000 0.984
#> SRR1946679 2 0.1170 0.869 0.008 0.976 0.016
#> SRR1946678 1 0.4291 0.864 0.820 0.000 0.180
#> SRR1946676 2 0.3607 0.797 0.008 0.880 0.112
#> SRR1946677 1 0.0000 0.868 1.000 0.000 0.000
#> SRR1946672 3 0.0000 0.777 0.000 0.000 1.000
#> SRR1946673 1 0.0892 0.853 0.980 0.020 0.000
#> SRR1946671 1 0.3879 0.870 0.848 0.000 0.152
#> SRR1946669 1 0.0000 0.868 1.000 0.000 0.000
#> SRR1946668 1 0.0424 0.870 0.992 0.000 0.008
#> SRR1946666 3 0.6267 -0.266 0.452 0.000 0.548
#> SRR1946667 3 0.5591 0.691 0.000 0.304 0.696
#> SRR1946670 2 0.0747 0.872 0.016 0.984 0.000
#> SRR1946663 2 0.4931 0.742 0.232 0.768 0.000
#> SRR1946664 2 0.0000 0.870 0.000 1.000 0.000
#> SRR1946662 1 0.0000 0.868 1.000 0.000 0.000
#> SRR1946661 2 0.5968 0.569 0.364 0.636 0.000
#> SRR1946660 2 0.0592 0.872 0.012 0.988 0.000
#> SRR1946659 3 0.1964 0.737 0.056 0.000 0.944
#> SRR1946658 2 0.0747 0.872 0.016 0.984 0.000
#> SRR1946657 2 0.0983 0.868 0.004 0.980 0.016
#> SRR1946655 3 0.4452 0.761 0.000 0.192 0.808
#> SRR1946654 3 0.2066 0.789 0.000 0.060 0.940
#> SRR1946653 3 0.0592 0.773 0.012 0.000 0.988
#> SRR1946652 2 0.4808 0.778 0.188 0.804 0.008
#> SRR1946651 2 0.1015 0.870 0.008 0.980 0.012
#> SRR1946650 2 0.4504 0.772 0.196 0.804 0.000
#> SRR1946649 1 0.0475 0.868 0.992 0.004 0.004
#> SRR1946648 3 0.4121 0.724 0.168 0.000 0.832
#> SRR1946647 1 0.0237 0.869 0.996 0.000 0.004
#> SRR1946646 3 0.5968 0.562 0.000 0.364 0.636
#> SRR1946645 1 0.3941 0.870 0.844 0.000 0.156
#> SRR1946644 2 0.5926 0.224 0.000 0.644 0.356
#> SRR1946643 3 0.4452 0.761 0.000 0.192 0.808
#> SRR1946642 1 0.4291 0.864 0.820 0.000 0.180
#> SRR1946641 1 0.4452 0.859 0.808 0.000 0.192
#> SRR1946656 3 0.4452 0.761 0.000 0.192 0.808
#> SRR1946640 1 0.4452 0.859 0.808 0.000 0.192
#> SRR1946639 1 0.4452 0.859 0.808 0.000 0.192
#> SRR1946638 1 0.4452 0.859 0.808 0.000 0.192
#> SRR1946637 1 0.4452 0.859 0.808 0.000 0.192
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.3172 0.7760 0.160 0.000 0.840 0.000
#> SRR1946691 4 0.0188 0.8332 0.000 0.004 0.000 0.996
#> SRR1946690 4 0.3081 0.7900 0.000 0.064 0.048 0.888
#> SRR1946689 4 0.3539 0.7590 0.000 0.004 0.176 0.820
#> SRR1946686 1 0.4776 -0.0198 0.624 0.000 0.376 0.000
#> SRR1946685 2 0.5951 0.5996 0.000 0.696 0.152 0.152
#> SRR1946688 4 0.0188 0.8332 0.000 0.004 0.000 0.996
#> SRR1946684 1 0.4946 0.6243 0.680 0.308 0.008 0.004
#> SRR1946683 1 0.4769 0.5815 0.684 0.308 0.008 0.000
#> SRR1946682 4 0.4020 0.7343 0.016 0.156 0.008 0.820
#> SRR1946680 4 0.4053 0.7190 0.000 0.004 0.228 0.768
#> SRR1946681 4 0.5968 0.6174 0.000 0.092 0.236 0.672
#> SRR1946687 3 0.3311 0.7693 0.172 0.000 0.828 0.000
#> SRR1946679 2 0.5906 0.6005 0.000 0.700 0.152 0.148
#> SRR1946678 1 0.1109 0.7326 0.968 0.028 0.004 0.000
#> SRR1946676 2 0.5812 0.6074 0.000 0.708 0.156 0.136
#> SRR1946677 2 0.4814 0.2596 0.316 0.676 0.008 0.000
#> SRR1946672 3 0.1474 0.7908 0.052 0.000 0.948 0.000
#> SRR1946673 2 0.5112 0.1631 0.340 0.648 0.008 0.004
#> SRR1946671 2 0.4991 0.1489 0.388 0.608 0.004 0.000
#> SRR1946669 1 0.4746 0.6275 0.688 0.304 0.008 0.000
#> SRR1946668 1 0.4799 0.6450 0.704 0.284 0.008 0.004
#> SRR1946666 3 0.4907 0.4901 0.420 0.000 0.580 0.000
#> SRR1946667 4 0.3583 0.7565 0.000 0.004 0.180 0.816
#> SRR1946670 4 0.0817 0.8301 0.000 0.024 0.000 0.976
#> SRR1946663 4 0.4020 0.7343 0.016 0.156 0.008 0.820
#> SRR1946664 4 0.3071 0.7892 0.000 0.068 0.044 0.888
#> SRR1946662 1 0.5070 0.5398 0.620 0.372 0.008 0.000
#> SRR1946661 2 0.5083 0.3874 0.220 0.740 0.008 0.032
#> SRR1946660 4 0.0188 0.8332 0.000 0.004 0.000 0.996
#> SRR1946659 3 0.4776 0.5648 0.376 0.000 0.624 0.000
#> SRR1946658 4 0.0707 0.8313 0.000 0.020 0.000 0.980
#> SRR1946657 2 0.5994 0.5957 0.000 0.692 0.152 0.156
#> SRR1946655 3 0.1256 0.7725 0.000 0.008 0.964 0.028
#> SRR1946654 3 0.0524 0.7789 0.000 0.008 0.988 0.004
#> SRR1946653 3 0.3172 0.7760 0.160 0.000 0.840 0.000
#> SRR1946652 2 0.3498 0.6206 0.000 0.832 0.008 0.160
#> SRR1946651 2 0.5770 0.6063 0.000 0.712 0.140 0.148
#> SRR1946650 2 0.2197 0.6058 0.004 0.916 0.000 0.080
#> SRR1946649 2 0.4406 0.3279 0.300 0.700 0.000 0.000
#> SRR1946648 3 0.4710 0.6973 0.088 0.120 0.792 0.000
#> SRR1946647 1 0.4799 0.6450 0.704 0.284 0.008 0.004
#> SRR1946646 3 0.7166 0.1959 0.000 0.280 0.544 0.176
#> SRR1946645 1 0.5132 0.1010 0.548 0.448 0.004 0.000
#> SRR1946644 4 0.6818 0.4810 0.000 0.232 0.168 0.600
#> SRR1946643 3 0.1820 0.7629 0.000 0.020 0.944 0.036
#> SRR1946642 1 0.1004 0.7330 0.972 0.024 0.004 0.000
#> SRR1946641 1 0.0817 0.7309 0.976 0.000 0.024 0.000
#> SRR1946656 3 0.1724 0.7656 0.000 0.020 0.948 0.032
#> SRR1946640 1 0.0817 0.7309 0.976 0.000 0.024 0.000
#> SRR1946639 1 0.0817 0.7309 0.976 0.000 0.024 0.000
#> SRR1946638 1 0.0817 0.7309 0.976 0.000 0.024 0.000
#> SRR1946637 1 0.0817 0.7309 0.976 0.000 0.024 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.2237 0.777 0.084 0.004 0.904 0.000 0.008
#> SRR1946691 4 0.0000 0.784 0.000 0.000 0.000 1.000 0.000
#> SRR1946690 4 0.4776 0.496 0.000 0.296 0.008 0.668 0.028
#> SRR1946689 4 0.4274 0.691 0.000 0.020 0.172 0.776 0.032
#> SRR1946686 1 0.4064 0.613 0.716 0.004 0.272 0.000 0.008
#> SRR1946685 2 0.1815 0.802 0.000 0.940 0.016 0.024 0.020
#> SRR1946688 4 0.0404 0.785 0.000 0.000 0.000 0.988 0.012
#> SRR1946684 5 0.3003 0.690 0.188 0.000 0.000 0.000 0.812
#> SRR1946683 5 0.6461 0.467 0.356 0.136 0.012 0.000 0.496
#> SRR1946682 4 0.3452 0.638 0.000 0.000 0.000 0.756 0.244
#> SRR1946680 4 0.5217 0.497 0.000 0.020 0.312 0.636 0.032
#> SRR1946681 3 0.6977 -0.105 0.000 0.148 0.432 0.388 0.032
#> SRR1946687 3 0.3439 0.674 0.188 0.004 0.800 0.000 0.008
#> SRR1946679 2 0.2116 0.803 0.000 0.924 0.008 0.040 0.028
#> SRR1946678 1 0.1270 0.780 0.948 0.000 0.000 0.000 0.052
#> SRR1946676 2 0.2349 0.763 0.000 0.900 0.012 0.004 0.084
#> SRR1946677 5 0.5149 0.581 0.060 0.232 0.016 0.000 0.692
#> SRR1946672 3 0.0609 0.804 0.020 0.000 0.980 0.000 0.000
#> SRR1946673 5 0.2889 0.695 0.084 0.044 0.000 0.000 0.872
#> SRR1946671 5 0.6311 0.537 0.152 0.252 0.016 0.000 0.580
#> SRR1946669 5 0.3210 0.686 0.212 0.000 0.000 0.000 0.788
#> SRR1946668 5 0.3177 0.678 0.208 0.000 0.000 0.000 0.792
#> SRR1946666 1 0.4613 0.359 0.580 0.004 0.408 0.000 0.008
#> SRR1946667 4 0.4312 0.687 0.000 0.020 0.176 0.772 0.032
#> SRR1946670 4 0.0880 0.782 0.000 0.000 0.000 0.968 0.032
#> SRR1946663 4 0.3480 0.634 0.000 0.000 0.000 0.752 0.248
#> SRR1946664 4 0.4735 0.485 0.000 0.304 0.008 0.664 0.024
#> SRR1946662 5 0.3280 0.699 0.176 0.012 0.000 0.000 0.812
#> SRR1946661 5 0.4029 0.616 0.004 0.116 0.008 0.060 0.812
#> SRR1946660 4 0.0404 0.785 0.000 0.000 0.000 0.988 0.012
#> SRR1946659 1 0.4317 0.531 0.668 0.004 0.320 0.000 0.008
#> SRR1946658 4 0.1741 0.770 0.000 0.040 0.000 0.936 0.024
#> SRR1946657 2 0.2433 0.790 0.000 0.908 0.024 0.056 0.012
#> SRR1946655 3 0.0798 0.806 0.000 0.016 0.976 0.000 0.008
#> SRR1946654 3 0.0404 0.807 0.000 0.012 0.988 0.000 0.000
#> SRR1946653 3 0.2660 0.748 0.128 0.000 0.864 0.000 0.008
#> SRR1946652 2 0.2856 0.742 0.000 0.872 0.008 0.016 0.104
#> SRR1946651 2 0.2267 0.803 0.000 0.916 0.008 0.048 0.028
#> SRR1946650 2 0.4839 0.428 0.000 0.672 0.012 0.028 0.288
#> SRR1946649 5 0.6478 0.416 0.144 0.336 0.012 0.000 0.508
#> SRR1946648 3 0.4853 0.643 0.060 0.024 0.744 0.000 0.172
#> SRR1946647 5 0.3231 0.681 0.196 0.000 0.000 0.004 0.800
#> SRR1946646 2 0.5666 0.569 0.000 0.680 0.188 0.104 0.028
#> SRR1946645 5 0.6918 0.372 0.360 0.192 0.016 0.000 0.432
#> SRR1946644 2 0.5964 0.380 0.000 0.604 0.076 0.292 0.028
#> SRR1946643 3 0.3488 0.754 0.000 0.072 0.856 0.040 0.032
#> SRR1946642 1 0.0963 0.800 0.964 0.000 0.000 0.000 0.036
#> SRR1946641 1 0.0162 0.828 0.996 0.000 0.000 0.000 0.004
#> SRR1946656 3 0.3411 0.757 0.000 0.072 0.860 0.036 0.032
#> SRR1946640 1 0.0162 0.828 0.996 0.000 0.000 0.000 0.004
#> SRR1946639 1 0.0162 0.828 0.996 0.000 0.000 0.000 0.004
#> SRR1946638 1 0.0162 0.828 0.996 0.000 0.000 0.000 0.004
#> SRR1946637 1 0.0162 0.828 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2826 0.692 0.052 0.000 0.876 0.056 0.012 0.004
#> SRR1946691 6 0.1148 0.692 0.000 0.016 0.000 0.020 0.004 0.960
#> SRR1946690 6 0.6897 0.269 0.000 0.320 0.016 0.144 0.060 0.460
#> SRR1946689 6 0.6863 0.476 0.000 0.032 0.148 0.196 0.072 0.552
#> SRR1946686 1 0.5176 0.417 0.564 0.000 0.364 0.056 0.012 0.004
#> SRR1946685 2 0.1555 0.754 0.000 0.932 0.004 0.060 0.000 0.004
#> SRR1946688 6 0.0260 0.693 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1946684 5 0.2135 0.952 0.128 0.000 0.000 0.000 0.872 0.000
#> SRR1946683 4 0.5388 0.685 0.192 0.004 0.000 0.604 0.200 0.000
#> SRR1946682 6 0.3284 0.563 0.000 0.000 0.000 0.020 0.196 0.784
#> SRR1946680 6 0.7595 0.132 0.000 0.032 0.316 0.192 0.080 0.380
#> SRR1946681 3 0.8013 0.173 0.000 0.116 0.440 0.168 0.084 0.192
#> SRR1946687 3 0.5063 0.519 0.168 0.004 0.700 0.104 0.020 0.004
#> SRR1946679 2 0.1245 0.763 0.000 0.952 0.000 0.032 0.000 0.016
#> SRR1946678 1 0.1007 0.787 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1946676 2 0.3383 0.524 0.000 0.728 0.000 0.268 0.004 0.000
#> SRR1946677 4 0.4714 0.763 0.032 0.052 0.000 0.700 0.216 0.000
#> SRR1946672 3 0.1138 0.727 0.024 0.000 0.960 0.012 0.004 0.000
#> SRR1946673 5 0.2714 0.878 0.060 0.004 0.000 0.064 0.872 0.000
#> SRR1946671 4 0.5561 0.779 0.072 0.088 0.000 0.652 0.188 0.000
#> SRR1946669 5 0.2442 0.944 0.144 0.000 0.000 0.004 0.852 0.000
#> SRR1946668 5 0.2340 0.937 0.148 0.000 0.000 0.000 0.852 0.000
#> SRR1946666 1 0.5579 0.257 0.484 0.000 0.420 0.076 0.016 0.004
#> SRR1946667 6 0.6863 0.476 0.000 0.032 0.148 0.196 0.072 0.552
#> SRR1946670 6 0.0717 0.691 0.000 0.008 0.000 0.000 0.016 0.976
#> SRR1946663 6 0.3345 0.554 0.000 0.000 0.000 0.020 0.204 0.776
#> SRR1946664 6 0.6836 0.198 0.000 0.356 0.016 0.140 0.052 0.436
#> SRR1946662 5 0.2398 0.939 0.104 0.000 0.000 0.020 0.876 0.000
#> SRR1946661 4 0.5233 0.457 0.000 0.032 0.000 0.508 0.424 0.036
#> SRR1946660 6 0.0405 0.693 0.000 0.008 0.000 0.000 0.004 0.988
#> SRR1946659 1 0.5006 0.535 0.648 0.000 0.272 0.056 0.020 0.004
#> SRR1946658 6 0.1528 0.679 0.000 0.048 0.000 0.000 0.016 0.936
#> SRR1946657 2 0.1605 0.747 0.000 0.940 0.000 0.032 0.012 0.016
#> SRR1946655 3 0.2138 0.716 0.000 0.000 0.908 0.052 0.036 0.004
#> SRR1946654 3 0.0291 0.734 0.000 0.000 0.992 0.000 0.004 0.004
#> SRR1946653 3 0.3583 0.681 0.068 0.004 0.832 0.076 0.016 0.004
#> SRR1946652 2 0.3859 0.587 0.000 0.756 0.000 0.204 0.016 0.024
#> SRR1946651 2 0.1074 0.763 0.000 0.960 0.000 0.028 0.000 0.012
#> SRR1946650 4 0.5838 0.485 0.000 0.308 0.000 0.556 0.092 0.044
#> SRR1946649 4 0.5410 0.772 0.072 0.120 0.000 0.680 0.128 0.000
#> SRR1946648 3 0.5283 0.508 0.024 0.000 0.640 0.252 0.080 0.004
#> SRR1946647 5 0.2346 0.951 0.124 0.000 0.000 0.008 0.868 0.000
#> SRR1946646 2 0.5632 0.575 0.000 0.688 0.120 0.116 0.044 0.032
#> SRR1946645 4 0.4969 0.731 0.192 0.020 0.000 0.684 0.104 0.000
#> SRR1946644 2 0.6746 0.375 0.000 0.584 0.056 0.164 0.056 0.140
#> SRR1946643 3 0.5516 0.567 0.000 0.040 0.680 0.176 0.080 0.024
#> SRR1946642 1 0.0713 0.801 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1946641 1 0.0000 0.820 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.5439 0.572 0.000 0.040 0.684 0.176 0.080 0.020
#> SRR1946640 1 0.0000 0.820 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.820 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.820 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.820 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "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 17231 rows and 53 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.418 0.692 0.876 0.4366 0.586 0.586
#> 3 3 0.412 0.590 0.797 0.4649 0.692 0.500
#> 4 4 0.569 0.752 0.849 0.0948 0.906 0.744
#> 5 5 0.798 0.787 0.901 0.1104 0.808 0.461
#> 6 6 0.768 0.738 0.886 0.0481 0.959 0.813
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1946675 2 0.9323 0.4727 0.348 0.652
#> SRR1946691 2 0.0000 0.8439 0.000 1.000
#> SRR1946690 2 0.0000 0.8439 0.000 1.000
#> SRR1946689 2 0.0000 0.8439 0.000 1.000
#> SRR1946686 2 0.9393 0.4581 0.356 0.644
#> SRR1946685 2 0.0000 0.8439 0.000 1.000
#> SRR1946688 2 0.0938 0.8376 0.012 0.988
#> SRR1946684 1 0.6048 0.7080 0.852 0.148
#> SRR1946683 2 0.9977 0.2182 0.472 0.528
#> SRR1946682 2 0.5294 0.7573 0.120 0.880
#> SRR1946680 2 0.0000 0.8439 0.000 1.000
#> SRR1946681 2 0.0000 0.8439 0.000 1.000
#> SRR1946687 2 0.9323 0.4727 0.348 0.652
#> SRR1946679 2 0.0000 0.8439 0.000 1.000
#> SRR1946678 1 0.0000 0.8095 1.000 0.000
#> SRR1946676 2 0.0000 0.8439 0.000 1.000
#> SRR1946677 2 0.6973 0.6921 0.188 0.812
#> SRR1946672 2 0.8608 0.5725 0.284 0.716
#> SRR1946673 2 0.6801 0.7016 0.180 0.820
#> SRR1946671 2 0.7883 0.6747 0.236 0.764
#> SRR1946669 1 0.0000 0.8095 1.000 0.000
#> SRR1946668 1 0.9866 0.0339 0.568 0.432
#> SRR1946666 2 0.9552 0.4184 0.376 0.624
#> SRR1946667 2 0.0000 0.8439 0.000 1.000
#> SRR1946670 2 0.0000 0.8439 0.000 1.000
#> SRR1946663 2 0.9866 0.3076 0.432 0.568
#> SRR1946664 2 0.0000 0.8439 0.000 1.000
#> SRR1946662 1 0.8443 0.5534 0.728 0.272
#> SRR1946661 2 0.8144 0.5965 0.252 0.748
#> SRR1946660 2 0.2778 0.8149 0.048 0.952
#> SRR1946659 1 0.4815 0.7352 0.896 0.104
#> SRR1946658 2 0.0000 0.8439 0.000 1.000
#> SRR1946657 2 0.0000 0.8439 0.000 1.000
#> SRR1946655 2 0.0000 0.8439 0.000 1.000
#> SRR1946654 2 0.0000 0.8439 0.000 1.000
#> SRR1946653 2 0.9323 0.4727 0.348 0.652
#> SRR1946652 2 0.0000 0.8439 0.000 1.000
#> SRR1946651 2 0.0000 0.8439 0.000 1.000
#> SRR1946650 2 0.6712 0.7005 0.176 0.824
#> SRR1946649 1 0.9754 0.3183 0.592 0.408
#> SRR1946648 2 0.9286 0.4794 0.344 0.656
#> SRR1946647 1 0.9988 -0.1327 0.520 0.480
#> SRR1946646 2 0.0000 0.8439 0.000 1.000
#> SRR1946645 1 0.7376 0.6115 0.792 0.208
#> SRR1946644 2 0.0000 0.8439 0.000 1.000
#> SRR1946643 2 0.0000 0.8439 0.000 1.000
#> SRR1946642 1 0.0000 0.8095 1.000 0.000
#> SRR1946641 1 0.0000 0.8095 1.000 0.000
#> SRR1946656 2 0.0000 0.8439 0.000 1.000
#> SRR1946640 1 0.0000 0.8095 1.000 0.000
#> SRR1946639 1 0.0000 0.8095 1.000 0.000
#> SRR1946638 1 0.0000 0.8095 1.000 0.000
#> SRR1946637 1 0.0000 0.8095 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.4137 0.72929 0.096 0.032 0.872
#> SRR1946691 2 0.0237 0.68972 0.000 0.996 0.004
#> SRR1946690 2 0.1529 0.67397 0.000 0.960 0.040
#> SRR1946689 3 0.6235 0.00653 0.000 0.436 0.564
#> SRR1946686 3 0.4062 0.70950 0.164 0.000 0.836
#> SRR1946685 3 0.6140 0.24113 0.000 0.404 0.596
#> SRR1946688 2 0.0000 0.69088 0.000 1.000 0.000
#> SRR1946684 1 0.3038 0.82072 0.896 0.104 0.000
#> SRR1946683 3 0.6247 0.66184 0.212 0.044 0.744
#> SRR1946682 2 0.0000 0.69088 0.000 1.000 0.000
#> SRR1946680 3 0.0000 0.69685 0.000 0.000 1.000
#> SRR1946681 3 0.5810 0.36083 0.000 0.336 0.664
#> SRR1946687 3 0.3845 0.72699 0.116 0.012 0.872
#> SRR1946679 2 0.6235 0.22731 0.000 0.564 0.436
#> SRR1946678 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946676 3 0.4178 0.67135 0.000 0.172 0.828
#> SRR1946677 3 0.7712 0.19958 0.052 0.392 0.556
#> SRR1946672 3 0.3129 0.72721 0.088 0.008 0.904
#> SRR1946673 2 0.8668 0.30746 0.132 0.564 0.304
#> SRR1946671 3 0.6811 0.59690 0.064 0.220 0.716
#> SRR1946669 1 0.1529 0.87391 0.960 0.040 0.000
#> SRR1946668 3 0.7578 0.25892 0.460 0.040 0.500
#> SRR1946666 3 0.5178 0.64131 0.256 0.000 0.744
#> SRR1946667 3 0.6235 0.00653 0.000 0.436 0.564
#> SRR1946670 2 0.6225 0.02283 0.000 0.568 0.432
#> SRR1946663 2 0.8891 0.08173 0.340 0.524 0.136
#> SRR1946664 2 0.1411 0.67487 0.000 0.964 0.036
#> SRR1946662 1 0.4750 0.68475 0.784 0.216 0.000
#> SRR1946661 2 0.3879 0.61710 0.000 0.848 0.152
#> SRR1946660 2 0.0000 0.69088 0.000 1.000 0.000
#> SRR1946659 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946658 2 0.0000 0.69088 0.000 1.000 0.000
#> SRR1946657 2 0.6267 0.21403 0.000 0.548 0.452
#> SRR1946655 3 0.2878 0.70952 0.000 0.096 0.904
#> SRR1946654 3 0.3412 0.70563 0.000 0.124 0.876
#> SRR1946653 3 0.3644 0.72432 0.124 0.004 0.872
#> SRR1946652 2 0.6244 0.22157 0.000 0.560 0.440
#> SRR1946651 2 0.4750 0.56836 0.000 0.784 0.216
#> SRR1946650 2 0.0000 0.69088 0.000 1.000 0.000
#> SRR1946649 1 0.8688 0.42276 0.596 0.208 0.196
#> SRR1946648 3 0.4137 0.72929 0.096 0.032 0.872
#> SRR1946647 3 0.6867 0.59460 0.288 0.040 0.672
#> SRR1946646 3 0.3267 0.70808 0.000 0.116 0.884
#> SRR1946645 1 0.6379 0.51041 0.712 0.032 0.256
#> SRR1946644 2 0.6299 0.19753 0.000 0.524 0.476
#> SRR1946643 3 0.2878 0.70952 0.000 0.096 0.904
#> SRR1946642 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946656 3 0.2878 0.70952 0.000 0.096 0.904
#> SRR1946640 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.89711 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.89711 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.0000 0.744 0.000 0.000 1.000 0
#> SRR1946691 2 0.0000 0.907 0.000 1.000 0.000 0
#> SRR1946690 2 0.0592 0.906 0.000 0.984 0.016 0
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946686 3 0.3123 0.721 0.156 0.000 0.844 0
#> SRR1946685 3 0.4996 0.298 0.000 0.484 0.516 0
#> SRR1946688 2 0.1022 0.906 0.000 0.968 0.032 0
#> SRR1946684 1 0.3597 0.761 0.836 0.016 0.148 0
#> SRR1946683 3 0.3768 0.641 0.184 0.008 0.808 0
#> SRR1946682 2 0.3024 0.817 0.000 0.852 0.148 0
#> SRR1946680 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946681 3 0.4961 0.391 0.000 0.448 0.552 0
#> SRR1946687 3 0.0817 0.747 0.024 0.000 0.976 0
#> SRR1946679 2 0.1637 0.897 0.000 0.940 0.060 0
#> SRR1946678 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946676 3 0.4008 0.694 0.000 0.244 0.756 0
#> SRR1946677 3 0.6206 0.171 0.056 0.404 0.540 0
#> SRR1946672 3 0.3249 0.727 0.140 0.008 0.852 0
#> SRR1946673 2 0.4114 0.819 0.060 0.828 0.112 0
#> SRR1946671 3 0.5631 0.600 0.072 0.232 0.696 0
#> SRR1946669 1 0.3597 0.761 0.836 0.016 0.148 0
#> SRR1946668 3 0.5203 0.372 0.348 0.016 0.636 0
#> SRR1946666 3 0.3610 0.695 0.200 0.000 0.800 0
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946670 3 0.1940 0.717 0.000 0.076 0.924 0
#> SRR1946663 3 0.6510 0.191 0.380 0.080 0.540 0
#> SRR1946664 2 0.0592 0.906 0.000 0.984 0.016 0
#> SRR1946662 1 0.5906 0.638 0.700 0.152 0.148 0
#> SRR1946661 2 0.2469 0.860 0.000 0.892 0.108 0
#> SRR1946660 2 0.0817 0.907 0.000 0.976 0.024 0
#> SRR1946659 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946658 2 0.2647 0.842 0.000 0.880 0.120 0
#> SRR1946657 2 0.1940 0.886 0.000 0.924 0.076 0
#> SRR1946655 3 0.3024 0.745 0.000 0.148 0.852 0
#> SRR1946654 3 0.2647 0.750 0.000 0.120 0.880 0
#> SRR1946653 3 0.2921 0.728 0.140 0.000 0.860 0
#> SRR1946652 2 0.2408 0.886 0.000 0.896 0.104 0
#> SRR1946651 2 0.1474 0.901 0.000 0.948 0.052 0
#> SRR1946650 2 0.0336 0.909 0.000 0.992 0.008 0
#> SRR1946649 1 0.6445 0.436 0.600 0.304 0.096 0
#> SRR1946648 3 0.0000 0.744 0.000 0.000 1.000 0
#> SRR1946647 3 0.3108 0.703 0.112 0.016 0.872 0
#> SRR1946646 3 0.3024 0.745 0.000 0.148 0.852 0
#> SRR1946645 1 0.4193 0.552 0.732 0.000 0.268 0
#> SRR1946644 2 0.1940 0.886 0.000 0.924 0.076 0
#> SRR1946643 3 0.3024 0.745 0.000 0.148 0.852 0
#> SRR1946642 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946641 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946656 3 0.3024 0.745 0.000 0.148 0.852 0
#> SRR1946640 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946639 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946638 1 0.0000 0.860 1.000 0.000 0.000 0
#> SRR1946637 1 0.0000 0.860 1.000 0.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.0703 0.8526 0.000 0.000 0.976 0.000 0.024
#> SRR1946691 2 0.2891 0.7546 0.000 0.824 0.000 0.000 0.176
#> SRR1946690 2 0.0290 0.8857 0.000 0.992 0.008 0.000 0.000
#> SRR1946689 4 0.0000 0.9983 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 3 0.1043 0.8533 0.040 0.000 0.960 0.000 0.000
#> SRR1946685 2 0.4291 -0.0304 0.000 0.536 0.464 0.000 0.000
#> SRR1946688 5 0.4287 0.0913 0.000 0.460 0.000 0.000 0.540
#> SRR1946684 5 0.2654 0.8547 0.040 0.044 0.016 0.000 0.900
#> SRR1946683 5 0.3861 0.6175 0.008 0.000 0.264 0.000 0.728
#> SRR1946682 5 0.0290 0.8500 0.000 0.008 0.000 0.000 0.992
#> SRR1946680 4 0.0162 0.9966 0.000 0.000 0.004 0.996 0.000
#> SRR1946681 3 0.4291 0.1321 0.000 0.464 0.536 0.000 0.000
#> SRR1946687 3 0.1648 0.8490 0.020 0.000 0.940 0.000 0.040
#> SRR1946679 2 0.0609 0.8866 0.000 0.980 0.020 0.000 0.000
#> SRR1946678 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 3 0.4219 0.6049 0.000 0.260 0.716 0.000 0.024
#> SRR1946677 5 0.2423 0.8437 0.000 0.024 0.080 0.000 0.896
#> SRR1946672 3 0.0703 0.8559 0.024 0.000 0.976 0.000 0.000
#> SRR1946673 5 0.2411 0.8358 0.000 0.108 0.008 0.000 0.884
#> SRR1946671 3 0.6832 0.3614 0.072 0.088 0.536 0.000 0.304
#> SRR1946669 5 0.2873 0.8098 0.128 0.000 0.016 0.000 0.856
#> SRR1946668 5 0.2450 0.8499 0.052 0.000 0.048 0.000 0.900
#> SRR1946666 3 0.1671 0.8304 0.076 0.000 0.924 0.000 0.000
#> SRR1946667 4 0.0000 0.9983 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 5 0.1117 0.8486 0.000 0.020 0.016 0.000 0.964
#> SRR1946663 5 0.0290 0.8500 0.000 0.008 0.000 0.000 0.992
#> SRR1946664 2 0.0290 0.8857 0.000 0.992 0.008 0.000 0.000
#> SRR1946662 5 0.2374 0.8543 0.020 0.052 0.016 0.000 0.912
#> SRR1946661 5 0.3183 0.7968 0.000 0.156 0.016 0.000 0.828
#> SRR1946660 2 0.2471 0.7871 0.000 0.864 0.000 0.000 0.136
#> SRR1946659 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946658 5 0.1341 0.8342 0.000 0.056 0.000 0.000 0.944
#> SRR1946657 2 0.0609 0.8866 0.000 0.980 0.020 0.000 0.000
#> SRR1946655 3 0.0510 0.8585 0.000 0.016 0.984 0.000 0.000
#> SRR1946654 3 0.0451 0.8585 0.000 0.008 0.988 0.000 0.004
#> SRR1946653 3 0.1043 0.8533 0.040 0.000 0.960 0.000 0.000
#> SRR1946652 2 0.1549 0.8563 0.000 0.944 0.016 0.000 0.040
#> SRR1946651 2 0.0693 0.8851 0.000 0.980 0.008 0.000 0.012
#> SRR1946650 2 0.0771 0.8830 0.000 0.976 0.004 0.000 0.020
#> SRR1946649 1 0.5915 0.3653 0.588 0.324 0.048 0.000 0.040
#> SRR1946648 3 0.3177 0.6979 0.000 0.000 0.792 0.000 0.208
#> SRR1946647 5 0.2409 0.8488 0.032 0.000 0.068 0.000 0.900
#> SRR1946646 3 0.0880 0.8559 0.000 0.032 0.968 0.000 0.000
#> SRR1946645 1 0.4398 0.5755 0.720 0.000 0.240 0.000 0.040
#> SRR1946644 2 0.0609 0.8866 0.000 0.980 0.020 0.000 0.000
#> SRR1946643 3 0.0510 0.8585 0.000 0.016 0.984 0.000 0.000
#> SRR1946642 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0510 0.8585 0.000 0.016 0.984 0.000 0.000
#> SRR1946640 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9026 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0632 0.846943 0.000 0.000 0.976 0 0.024 0.000
#> SRR1946691 6 0.0000 0.912514 0.000 0.000 0.000 0 0.000 1.000
#> SRR1946690 2 0.1910 0.787714 0.000 0.892 0.000 0 0.000 0.108
#> SRR1946689 4 0.0000 1.000000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946686 3 0.0632 0.848035 0.024 0.000 0.976 0 0.000 0.000
#> SRR1946685 2 0.3847 0.070101 0.000 0.544 0.456 0 0.000 0.000
#> SRR1946688 6 0.0000 0.912514 0.000 0.000 0.000 0 0.000 1.000
#> SRR1946684 5 0.0000 0.791046 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946683 5 0.4859 0.603866 0.016 0.104 0.188 0 0.692 0.000
#> SRR1946682 6 0.2378 0.832600 0.000 0.000 0.000 0 0.152 0.848
#> SRR1946680 4 0.0000 1.000000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946681 3 0.3860 -0.000579 0.000 0.472 0.528 0 0.000 0.000
#> SRR1946687 3 0.0820 0.849548 0.016 0.000 0.972 0 0.012 0.000
#> SRR1946679 2 0.0000 0.814235 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946678 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946676 3 0.4409 0.381355 0.000 0.380 0.588 0 0.032 0.000
#> SRR1946677 5 0.4678 0.650899 0.020 0.132 0.124 0 0.724 0.000
#> SRR1946672 3 0.0000 0.850588 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946673 5 0.2562 0.670416 0.000 0.172 0.000 0 0.828 0.000
#> SRR1946671 3 0.6440 -0.005703 0.028 0.196 0.400 0 0.376 0.000
#> SRR1946669 5 0.0790 0.781918 0.032 0.000 0.000 0 0.968 0.000
#> SRR1946668 5 0.0547 0.787346 0.020 0.000 0.000 0 0.980 0.000
#> SRR1946666 3 0.0937 0.838735 0.040 0.000 0.960 0 0.000 0.000
#> SRR1946667 4 0.0000 1.000000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946670 5 0.4366 0.352394 0.000 0.000 0.024 0 0.548 0.428
#> SRR1946663 6 0.1910 0.869508 0.000 0.000 0.000 0 0.108 0.892
#> SRR1946664 2 0.1910 0.787714 0.000 0.892 0.000 0 0.000 0.108
#> SRR1946662 5 0.0000 0.791046 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946661 5 0.1863 0.752991 0.000 0.104 0.000 0 0.896 0.000
#> SRR1946660 6 0.0000 0.912514 0.000 0.000 0.000 0 0.000 1.000
#> SRR1946659 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946658 5 0.4439 0.330347 0.000 0.028 0.000 0 0.540 0.432
#> SRR1946657 2 0.1910 0.789460 0.000 0.892 0.108 0 0.000 0.000
#> SRR1946655 3 0.0000 0.850588 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946654 3 0.0622 0.850276 0.000 0.012 0.980 0 0.008 0.000
#> SRR1946653 3 0.0632 0.848035 0.024 0.000 0.976 0 0.000 0.000
#> SRR1946652 2 0.2562 0.647164 0.000 0.828 0.000 0 0.172 0.000
#> SRR1946651 2 0.0000 0.814235 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946650 2 0.0260 0.811527 0.000 0.992 0.000 0 0.008 0.000
#> SRR1946649 1 0.5972 0.280951 0.492 0.324 0.012 0 0.172 0.000
#> SRR1946648 3 0.3221 0.611044 0.000 0.000 0.736 0 0.264 0.000
#> SRR1946647 5 0.0000 0.791046 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946646 3 0.0632 0.845892 0.000 0.024 0.976 0 0.000 0.000
#> SRR1946645 1 0.6414 0.426617 0.568 0.104 0.156 0 0.172 0.000
#> SRR1946644 2 0.1910 0.789460 0.000 0.892 0.108 0 0.000 0.000
#> SRR1946643 3 0.0000 0.850588 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946642 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946641 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946656 3 0.0000 0.850588 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946640 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946639 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946638 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946637 1 0.0000 0.880895 1.000 0.000 0.000 0 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.473 0.799 0.852 0.397 0.643 0.643
#> 3 3 0.214 0.581 0.721 0.394 0.623 0.472
#> 4 4 0.434 0.569 0.762 0.083 0.592 0.309
#> 5 5 0.503 0.726 0.721 0.175 0.797 0.496
#> 6 6 0.646 0.562 0.742 0.115 0.848 0.550
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
#> SRR1946675 1 0.808 0.794 0.752 0.248
#> SRR1946691 2 0.844 0.897 0.272 0.728
#> SRR1946690 2 0.871 0.881 0.292 0.708
#> SRR1946689 2 0.163 0.721 0.024 0.976
#> SRR1946686 1 0.808 0.794 0.752 0.248
#> SRR1946685 1 0.000 0.823 1.000 0.000
#> SRR1946688 2 0.844 0.897 0.272 0.728
#> SRR1946684 1 0.730 0.537 0.796 0.204
#> SRR1946683 1 0.000 0.823 1.000 0.000
#> SRR1946682 2 0.844 0.897 0.272 0.728
#> SRR1946680 2 0.163 0.721 0.024 0.976
#> SRR1946681 1 0.000 0.823 1.000 0.000
#> SRR1946687 1 0.808 0.794 0.752 0.248
#> SRR1946679 1 0.000 0.823 1.000 0.000
#> SRR1946678 1 0.430 0.822 0.912 0.088
#> SRR1946676 1 0.000 0.823 1.000 0.000
#> SRR1946677 1 0.000 0.823 1.000 0.000
#> SRR1946672 1 0.808 0.794 0.752 0.248
#> SRR1946673 1 0.000 0.823 1.000 0.000
#> SRR1946671 1 0.000 0.823 1.000 0.000
#> SRR1946669 1 0.000 0.823 1.000 0.000
#> SRR1946668 1 0.000 0.823 1.000 0.000
#> SRR1946666 1 0.808 0.794 0.752 0.248
#> SRR1946667 2 0.163 0.721 0.024 0.976
#> SRR1946670 2 0.844 0.897 0.272 0.728
#> SRR1946663 2 0.844 0.897 0.272 0.728
#> SRR1946664 2 0.917 0.835 0.332 0.668
#> SRR1946662 1 0.000 0.823 1.000 0.000
#> SRR1946661 1 0.430 0.736 0.912 0.088
#> SRR1946660 2 0.844 0.897 0.272 0.728
#> SRR1946659 1 0.808 0.794 0.752 0.248
#> SRR1946658 2 0.844 0.897 0.272 0.728
#> SRR1946657 1 0.000 0.823 1.000 0.000
#> SRR1946655 1 0.808 0.794 0.752 0.248
#> SRR1946654 1 0.808 0.794 0.752 0.248
#> SRR1946653 1 0.808 0.794 0.752 0.248
#> SRR1946652 1 0.000 0.823 1.000 0.000
#> SRR1946651 1 0.615 0.637 0.848 0.152
#> SRR1946650 1 0.814 0.412 0.748 0.252
#> SRR1946649 1 0.000 0.823 1.000 0.000
#> SRR1946648 1 0.662 0.811 0.828 0.172
#> SRR1946647 1 0.000 0.823 1.000 0.000
#> SRR1946646 1 0.358 0.825 0.932 0.068
#> SRR1946645 1 0.000 0.823 1.000 0.000
#> SRR1946644 1 0.184 0.801 0.972 0.028
#> SRR1946643 1 0.808 0.794 0.752 0.248
#> SRR1946642 1 0.430 0.822 0.912 0.088
#> SRR1946641 1 0.844 0.780 0.728 0.272
#> SRR1946656 1 0.808 0.794 0.752 0.248
#> SRR1946640 1 0.844 0.780 0.728 0.272
#> SRR1946639 1 0.844 0.780 0.728 0.272
#> SRR1946638 1 0.844 0.780 0.728 0.272
#> SRR1946637 1 0.844 0.780 0.728 0.272
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 1 0.4526 0.6839 0.856 0.104 0.040
#> SRR1946691 2 0.3784 0.7300 0.132 0.864 0.004
#> SRR1946690 2 0.4047 0.7304 0.148 0.848 0.004
#> SRR1946689 3 0.6854 0.7921 0.136 0.124 0.740
#> SRR1946686 1 0.4172 0.6873 0.868 0.104 0.028
#> SRR1946685 1 0.6473 0.6341 0.652 0.332 0.016
#> SRR1946688 2 0.3784 0.7300 0.132 0.864 0.004
#> SRR1946684 2 0.0983 0.6767 0.016 0.980 0.004
#> SRR1946683 1 0.5497 0.6834 0.708 0.292 0.000
#> SRR1946682 2 0.3965 0.7286 0.132 0.860 0.008
#> SRR1946680 3 0.7147 0.7928 0.156 0.124 0.720
#> SRR1946681 1 0.8600 0.4552 0.604 0.212 0.184
#> SRR1946687 1 0.8808 -0.0633 0.536 0.132 0.332
#> SRR1946679 2 0.6952 -0.2808 0.480 0.504 0.016
#> SRR1946678 1 0.4677 0.6225 0.840 0.028 0.132
#> SRR1946676 1 0.6313 0.6422 0.676 0.308 0.016
#> SRR1946677 1 0.5926 0.6412 0.644 0.356 0.000
#> SRR1946672 1 0.6111 0.6305 0.784 0.104 0.112
#> SRR1946673 1 0.6295 0.4370 0.528 0.472 0.000
#> SRR1946671 1 0.6057 0.6571 0.656 0.340 0.004
#> SRR1946669 1 0.5882 0.6487 0.652 0.348 0.000
#> SRR1946668 2 0.5404 0.4099 0.256 0.740 0.004
#> SRR1946666 1 0.4295 0.6868 0.864 0.104 0.032
#> SRR1946667 3 0.6854 0.7921 0.136 0.124 0.740
#> SRR1946670 2 0.3784 0.7300 0.132 0.864 0.004
#> SRR1946663 2 0.3784 0.7300 0.132 0.864 0.004
#> SRR1946664 2 0.6393 0.6561 0.148 0.764 0.088
#> SRR1946662 1 0.6305 0.4067 0.516 0.484 0.000
#> SRR1946661 2 0.1163 0.6782 0.028 0.972 0.000
#> SRR1946660 2 0.3784 0.7300 0.132 0.864 0.004
#> SRR1946659 1 0.4295 0.6868 0.864 0.104 0.032
#> SRR1946658 2 0.3686 0.7311 0.140 0.860 0.000
#> SRR1946657 2 0.6448 0.4934 0.328 0.656 0.016
#> SRR1946655 3 0.6081 0.6669 0.344 0.004 0.652
#> SRR1946654 1 0.5892 0.6440 0.796 0.104 0.100
#> SRR1946653 1 0.8676 -0.0929 0.520 0.112 0.368
#> SRR1946652 2 0.5621 0.4297 0.308 0.692 0.000
#> SRR1946651 2 0.6848 0.1978 0.416 0.568 0.016
#> SRR1946650 2 0.1643 0.6878 0.044 0.956 0.000
#> SRR1946649 1 0.6008 0.6228 0.628 0.372 0.000
#> SRR1946648 1 0.4324 0.6898 0.860 0.112 0.028
#> SRR1946647 2 0.6282 0.0518 0.384 0.612 0.004
#> SRR1946646 1 0.4033 0.6940 0.856 0.136 0.008
#> SRR1946645 1 0.5465 0.6847 0.712 0.288 0.000
#> SRR1946644 2 0.4883 0.7041 0.208 0.788 0.004
#> SRR1946643 3 0.6468 0.4148 0.444 0.004 0.552
#> SRR1946642 1 0.4799 0.6237 0.836 0.032 0.132
#> SRR1946641 1 0.3752 0.6077 0.856 0.000 0.144
#> SRR1946656 3 0.4293 0.7467 0.164 0.004 0.832
#> SRR1946640 1 0.3752 0.6077 0.856 0.000 0.144
#> SRR1946639 1 0.3752 0.6077 0.856 0.000 0.144
#> SRR1946638 1 0.3752 0.6077 0.856 0.000 0.144
#> SRR1946637 1 0.3752 0.6077 0.856 0.000 0.144
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.4224 0.727 0.100 0.076 0.824 0.000
#> SRR1946691 2 0.5949 0.401 0.004 0.484 0.484 0.028
#> SRR1946690 3 0.5677 -0.439 0.004 0.476 0.504 0.016
#> SRR1946689 4 0.1389 1.000 0.000 0.000 0.048 0.952
#> SRR1946686 3 0.5431 0.662 0.224 0.064 0.712 0.000
#> SRR1946685 2 0.5143 0.544 0.012 0.628 0.360 0.000
#> SRR1946688 2 0.6314 0.428 0.004 0.484 0.464 0.048
#> SRR1946684 2 0.1516 0.628 0.016 0.960 0.016 0.008
#> SRR1946683 2 0.1042 0.625 0.020 0.972 0.000 0.008
#> SRR1946682 2 0.6314 0.428 0.004 0.484 0.464 0.048
#> SRR1946680 3 0.3958 0.678 0.000 0.032 0.824 0.144
#> SRR1946681 3 0.5795 -0.305 0.016 0.460 0.516 0.008
#> SRR1946687 3 0.4058 0.693 0.016 0.028 0.840 0.116
#> SRR1946679 2 0.5007 0.548 0.008 0.636 0.356 0.000
#> SRR1946678 1 0.3688 0.669 0.792 0.208 0.000 0.000
#> SRR1946676 2 0.5143 0.544 0.012 0.628 0.360 0.000
#> SRR1946677 2 0.1042 0.625 0.020 0.972 0.000 0.008
#> SRR1946672 3 0.4007 0.729 0.068 0.056 0.856 0.020
#> SRR1946673 2 0.0188 0.632 0.004 0.996 0.000 0.000
#> SRR1946671 2 0.4082 0.626 0.020 0.820 0.152 0.008
#> SRR1946669 2 0.1389 0.611 0.048 0.952 0.000 0.000
#> SRR1946668 2 0.1739 0.628 0.024 0.952 0.016 0.008
#> SRR1946666 3 0.4282 0.724 0.124 0.060 0.816 0.000
#> SRR1946667 4 0.1389 1.000 0.000 0.000 0.048 0.952
#> SRR1946670 2 0.6248 0.424 0.004 0.484 0.468 0.044
#> SRR1946663 2 0.6314 0.428 0.004 0.484 0.464 0.048
#> SRR1946664 3 0.6021 -0.438 0.016 0.476 0.492 0.016
#> SRR1946662 2 0.0188 0.632 0.004 0.996 0.000 0.000
#> SRR1946661 2 0.1443 0.629 0.004 0.960 0.028 0.008
#> SRR1946660 2 0.6314 0.428 0.004 0.484 0.464 0.048
#> SRR1946659 3 0.4372 0.728 0.104 0.056 0.828 0.012
#> SRR1946658 3 0.5679 -0.445 0.004 0.484 0.496 0.016
#> SRR1946657 2 0.5024 0.545 0.008 0.632 0.360 0.000
#> SRR1946655 3 0.3622 0.703 0.064 0.012 0.872 0.052
#> SRR1946654 3 0.3805 0.730 0.068 0.072 0.856 0.004
#> SRR1946653 3 0.4277 0.713 0.052 0.028 0.844 0.076
#> SRR1946652 2 0.4679 0.549 0.000 0.648 0.352 0.000
#> SRR1946651 2 0.4936 0.543 0.000 0.624 0.372 0.004
#> SRR1946650 2 0.4781 0.568 0.004 0.660 0.336 0.000
#> SRR1946649 2 0.1042 0.625 0.020 0.972 0.000 0.008
#> SRR1946648 3 0.4724 0.706 0.096 0.112 0.792 0.000
#> SRR1946647 2 0.2282 0.626 0.052 0.924 0.024 0.000
#> SRR1946646 3 0.6363 0.480 0.108 0.236 0.652 0.004
#> SRR1946645 2 0.3472 0.632 0.024 0.868 0.100 0.008
#> SRR1946644 2 0.6394 0.452 0.048 0.528 0.416 0.008
#> SRR1946643 3 0.3622 0.703 0.064 0.012 0.872 0.052
#> SRR1946642 1 0.3356 0.702 0.824 0.176 0.000 0.000
#> SRR1946641 1 0.0804 0.881 0.980 0.008 0.012 0.000
#> SRR1946656 3 0.3622 0.703 0.064 0.012 0.872 0.052
#> SRR1946640 1 0.0804 0.881 0.980 0.008 0.012 0.000
#> SRR1946639 1 0.0804 0.881 0.980 0.008 0.012 0.000
#> SRR1946638 1 0.0804 0.881 0.980 0.008 0.012 0.000
#> SRR1946637 1 0.0804 0.881 0.980 0.008 0.012 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.5060 0.833 0.104 0.204 0.692 0.000 0.000
#> SRR1946691 2 0.3370 0.601 0.000 0.824 0.148 0.000 0.028
#> SRR1946690 2 0.4679 0.597 0.000 0.716 0.216 0.000 0.068
#> SRR1946689 4 0.0290 1.000 0.000 0.008 0.000 0.992 0.000
#> SRR1946686 3 0.5444 0.828 0.140 0.204 0.656 0.000 0.000
#> SRR1946685 2 0.5935 0.448 0.000 0.516 0.084 0.008 0.392
#> SRR1946688 2 0.0955 0.607 0.000 0.968 0.004 0.000 0.028
#> SRR1946684 5 0.3741 0.765 0.004 0.264 0.000 0.000 0.732
#> SRR1946683 5 0.0162 0.778 0.000 0.000 0.004 0.000 0.996
#> SRR1946682 2 0.0955 0.607 0.000 0.968 0.004 0.000 0.028
#> SRR1946680 3 0.5822 0.649 0.004 0.324 0.572 0.100 0.000
#> SRR1946681 2 0.6113 0.386 0.000 0.508 0.372 0.004 0.116
#> SRR1946687 3 0.5322 0.826 0.112 0.228 0.660 0.000 0.000
#> SRR1946679 2 0.5913 0.464 0.000 0.528 0.084 0.008 0.380
#> SRR1946678 1 0.1270 0.941 0.948 0.000 0.000 0.000 0.052
#> SRR1946676 2 0.6501 0.586 0.000 0.524 0.184 0.008 0.284
#> SRR1946677 5 0.0510 0.781 0.000 0.016 0.000 0.000 0.984
#> SRR1946672 3 0.3143 0.796 0.000 0.204 0.796 0.000 0.000
#> SRR1946673 5 0.3048 0.820 0.000 0.176 0.004 0.000 0.820
#> SRR1946671 5 0.2672 0.721 0.004 0.116 0.008 0.000 0.872
#> SRR1946669 5 0.3880 0.814 0.028 0.184 0.004 0.000 0.784
#> SRR1946668 5 0.3527 0.818 0.024 0.172 0.000 0.000 0.804
#> SRR1946666 3 0.5496 0.824 0.152 0.196 0.652 0.000 0.000
#> SRR1946667 4 0.0290 1.000 0.000 0.008 0.000 0.992 0.000
#> SRR1946670 2 0.1830 0.612 0.000 0.932 0.040 0.000 0.028
#> SRR1946663 2 0.0955 0.607 0.000 0.968 0.004 0.000 0.028
#> SRR1946664 2 0.4619 0.595 0.000 0.720 0.216 0.000 0.064
#> SRR1946662 5 0.3048 0.820 0.000 0.176 0.004 0.000 0.820
#> SRR1946661 5 0.3336 0.769 0.000 0.228 0.000 0.000 0.772
#> SRR1946660 2 0.0955 0.607 0.000 0.968 0.004 0.000 0.028
#> SRR1946659 3 0.5496 0.824 0.152 0.196 0.652 0.000 0.000
#> SRR1946658 2 0.4767 0.612 0.000 0.720 0.192 0.000 0.088
#> SRR1946657 2 0.6520 0.597 0.000 0.528 0.200 0.008 0.264
#> SRR1946655 3 0.1851 0.740 0.000 0.088 0.912 0.000 0.000
#> SRR1946654 3 0.3143 0.796 0.000 0.204 0.796 0.000 0.000
#> SRR1946653 3 0.5365 0.832 0.132 0.204 0.664 0.000 0.000
#> SRR1946652 2 0.5654 0.478 0.000 0.536 0.084 0.000 0.380
#> SRR1946651 2 0.5855 0.516 0.000 0.536 0.108 0.000 0.356
#> SRR1946650 2 0.4818 0.267 0.000 0.520 0.020 0.000 0.460
#> SRR1946649 5 0.0963 0.784 0.000 0.036 0.000 0.000 0.964
#> SRR1946648 3 0.5594 0.830 0.128 0.204 0.660 0.000 0.008
#> SRR1946647 5 0.4394 0.719 0.036 0.228 0.004 0.000 0.732
#> SRR1946646 3 0.5230 0.706 0.000 0.240 0.676 0.008 0.076
#> SRR1946645 5 0.3063 0.719 0.036 0.096 0.004 0.000 0.864
#> SRR1946644 2 0.5785 0.480 0.000 0.568 0.320 0.000 0.112
#> SRR1946643 3 0.0162 0.642 0.000 0.004 0.996 0.000 0.000
#> SRR1946642 1 0.1704 0.919 0.928 0.004 0.000 0.000 0.068
#> SRR1946641 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0162 0.642 0.000 0.004 0.996 0.000 0.000
#> SRR1946640 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0162 0.972 0.996 0.000 0.000 0.000 0.004
#> SRR1946638 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.974 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.3073 0.72394 0.204 0.000 0.788 0.000 NA 0.000
#> SRR1946691 6 0.5205 0.51637 0.000 0.224 0.124 0.000 NA 0.640
#> SRR1946690 2 0.5951 -0.33693 0.000 0.456 0.128 0.000 NA 0.396
#> SRR1946689 4 0.0000 1.00000 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1946686 3 0.2994 0.72368 0.208 0.000 0.788 0.000 NA 0.000
#> SRR1946685 2 0.3351 0.41787 0.000 0.828 0.120 0.000 NA 0.024
#> SRR1946688 6 0.0146 0.76448 0.000 0.004 0.000 0.000 NA 0.996
#> SRR1946684 2 0.4509 0.58205 0.000 0.532 0.000 0.000 NA 0.032
#> SRR1946683 2 0.3991 0.58134 0.004 0.524 0.000 0.000 NA 0.000
#> SRR1946682 6 0.0146 0.76448 0.000 0.004 0.000 0.000 NA 0.996
#> SRR1946680 3 0.7759 0.20132 0.004 0.008 0.384 0.232 NA 0.164
#> SRR1946681 2 0.7380 -0.15392 0.004 0.352 0.232 0.000 NA 0.100
#> SRR1946687 3 0.3043 0.72519 0.200 0.000 0.792 0.000 NA 0.000
#> SRR1946679 2 0.2804 0.41481 0.000 0.852 0.120 0.000 NA 0.024
#> SRR1946678 1 0.2980 0.70431 0.800 0.008 0.000 0.000 NA 0.000
#> SRR1946676 2 0.3192 0.41262 0.000 0.836 0.120 0.000 NA 0.024
#> SRR1946677 2 0.3847 0.59045 0.000 0.544 0.000 0.000 NA 0.000
#> SRR1946672 3 0.0603 0.68755 0.016 0.004 0.980 0.000 NA 0.000
#> SRR1946673 2 0.3782 0.60098 0.000 0.588 0.000 0.000 NA 0.000
#> SRR1946671 2 0.3860 0.58241 0.000 0.528 0.000 0.000 NA 0.000
#> SRR1946669 2 0.4018 0.60055 0.008 0.580 0.000 0.000 NA 0.000
#> SRR1946668 2 0.3862 0.59350 0.000 0.524 0.000 0.000 NA 0.000
#> SRR1946666 3 0.2883 0.72319 0.212 0.000 0.788 0.000 NA 0.000
#> SRR1946667 4 0.0000 1.00000 0.000 0.000 0.000 1.000 NA 0.000
#> SRR1946670 6 0.1536 0.74228 0.000 0.004 0.040 0.000 NA 0.940
#> SRR1946663 6 0.0146 0.76448 0.000 0.004 0.000 0.000 NA 0.996
#> SRR1946664 2 0.5948 -0.33221 0.000 0.460 0.128 0.000 NA 0.392
#> SRR1946662 2 0.3789 0.60114 0.000 0.584 0.000 0.000 NA 0.000
#> SRR1946661 2 0.3986 0.59041 0.000 0.532 0.000 0.000 NA 0.004
#> SRR1946660 6 0.0146 0.76448 0.000 0.004 0.000 0.000 NA 0.996
#> SRR1946659 3 0.2883 0.72319 0.212 0.000 0.788 0.000 NA 0.000
#> SRR1946658 6 0.7407 0.16993 0.000 0.292 0.128 0.000 NA 0.352
#> SRR1946657 2 0.2662 0.41190 0.000 0.856 0.120 0.000 NA 0.024
#> SRR1946655 3 0.4076 0.36233 0.000 0.004 0.564 0.000 NA 0.004
#> SRR1946654 3 0.0603 0.68755 0.016 0.004 0.980 0.000 NA 0.000
#> SRR1946653 3 0.3012 0.72556 0.196 0.000 0.796 0.000 NA 0.000
#> SRR1946652 2 0.3015 0.41633 0.000 0.844 0.120 0.000 NA 0.024
#> SRR1946651 2 0.3496 0.39708 0.000 0.820 0.120 0.000 NA 0.024
#> SRR1946650 2 0.4795 0.56600 0.000 0.588 0.024 0.000 NA 0.024
#> SRR1946649 2 0.3843 0.59236 0.000 0.548 0.000 0.000 NA 0.000
#> SRR1946648 3 0.3522 0.62568 0.044 0.000 0.784 0.000 NA 0.000
#> SRR1946647 2 0.4096 0.59201 0.000 0.508 0.008 0.000 NA 0.000
#> SRR1946646 3 0.4176 0.59078 0.004 0.196 0.748 0.000 NA 0.024
#> SRR1946645 2 0.4335 0.58753 0.000 0.508 0.020 0.000 NA 0.000
#> SRR1946644 2 0.6730 0.00958 0.000 0.508 0.244 0.000 NA 0.124
#> SRR1946643 3 0.4076 0.36233 0.000 0.004 0.564 0.000 NA 0.004
#> SRR1946642 1 0.3217 0.66061 0.768 0.008 0.000 0.000 NA 0.000
#> SRR1946641 1 0.0000 0.88198 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1946656 3 0.4076 0.36233 0.000 0.004 0.564 0.000 NA 0.004
#> SRR1946640 1 0.0000 0.88198 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1946639 1 0.0146 0.87898 0.996 0.000 0.000 0.000 NA 0.000
#> SRR1946638 1 0.0000 0.88198 1.000 0.000 0.000 0.000 NA 0.000
#> SRR1946637 1 0.0000 0.88198 1.000 0.000 0.000 0.000 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 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 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 0.589 0.823 0.924 0.4911 0.492 0.492
#> 3 3 0.767 0.842 0.936 0.3081 0.647 0.410
#> 4 4 0.566 0.700 0.838 0.1332 0.744 0.416
#> 5 5 0.712 0.726 0.866 0.0977 0.759 0.320
#> 6 6 0.760 0.655 0.837 0.0479 0.884 0.509
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
#> SRR1946675 1 0.861 0.5933 0.716 0.284
#> SRR1946691 2 0.000 0.9032 0.000 1.000
#> SRR1946690 2 0.000 0.9032 0.000 1.000
#> SRR1946689 2 0.000 0.9032 0.000 1.000
#> SRR1946686 1 0.000 0.9123 1.000 0.000
#> SRR1946685 1 0.866 0.5856 0.712 0.288
#> SRR1946688 2 0.000 0.9032 0.000 1.000
#> SRR1946684 1 0.000 0.9123 1.000 0.000
#> SRR1946683 1 0.000 0.9123 1.000 0.000
#> SRR1946682 2 0.722 0.7693 0.200 0.800
#> SRR1946680 2 0.000 0.9032 0.000 1.000
#> SRR1946681 2 0.000 0.9032 0.000 1.000
#> SRR1946687 1 0.760 0.6983 0.780 0.220
#> SRR1946679 2 0.653 0.8024 0.168 0.832
#> SRR1946678 1 0.000 0.9123 1.000 0.000
#> SRR1946676 2 0.943 0.4841 0.360 0.640
#> SRR1946677 1 0.000 0.9123 1.000 0.000
#> SRR1946672 1 0.988 0.1951 0.564 0.436
#> SRR1946673 1 0.000 0.9123 1.000 0.000
#> SRR1946671 1 0.000 0.9123 1.000 0.000
#> SRR1946669 1 0.000 0.9123 1.000 0.000
#> SRR1946668 1 0.000 0.9123 1.000 0.000
#> SRR1946666 1 0.000 0.9123 1.000 0.000
#> SRR1946667 2 0.000 0.9032 0.000 1.000
#> SRR1946670 2 0.000 0.9032 0.000 1.000
#> SRR1946663 2 0.955 0.4448 0.376 0.624
#> SRR1946664 2 0.000 0.9032 0.000 1.000
#> SRR1946662 1 0.000 0.9123 1.000 0.000
#> SRR1946661 1 0.653 0.7650 0.832 0.168
#> SRR1946660 2 0.000 0.9032 0.000 1.000
#> SRR1946659 1 0.000 0.9123 1.000 0.000
#> SRR1946658 2 0.000 0.9032 0.000 1.000
#> SRR1946657 2 0.416 0.8612 0.084 0.916
#> SRR1946655 2 0.000 0.9032 0.000 1.000
#> SRR1946654 2 0.706 0.7780 0.192 0.808
#> SRR1946653 2 0.827 0.6869 0.260 0.740
#> SRR1946652 2 0.788 0.7231 0.236 0.764
#> SRR1946651 2 0.000 0.9032 0.000 1.000
#> SRR1946650 1 0.998 0.0492 0.528 0.472
#> SRR1946649 1 0.000 0.9123 1.000 0.000
#> SRR1946648 1 0.625 0.7782 0.844 0.156
#> SRR1946647 1 0.000 0.9123 1.000 0.000
#> SRR1946646 2 0.595 0.8218 0.144 0.856
#> SRR1946645 1 0.000 0.9123 1.000 0.000
#> SRR1946644 2 0.000 0.9032 0.000 1.000
#> SRR1946643 2 0.000 0.9032 0.000 1.000
#> SRR1946642 1 0.000 0.9123 1.000 0.000
#> SRR1946641 1 0.000 0.9123 1.000 0.000
#> SRR1946656 2 0.000 0.9032 0.000 1.000
#> SRR1946640 1 0.000 0.9123 1.000 0.000
#> SRR1946639 1 0.000 0.9123 1.000 0.000
#> SRR1946638 1 0.000 0.9123 1.000 0.000
#> SRR1946637 1 0.000 0.9123 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 1 0.6280 0.0455 0.540 0.000 0.460
#> SRR1946691 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946690 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946689 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946686 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946685 2 0.1753 0.9247 0.048 0.952 0.000
#> SRR1946688 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946684 2 0.4842 0.7060 0.224 0.776 0.000
#> SRR1946683 1 0.0747 0.8674 0.984 0.016 0.000
#> SRR1946682 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946680 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946681 2 0.4178 0.8056 0.000 0.828 0.172
#> SRR1946687 3 0.5178 0.6647 0.256 0.000 0.744
#> SRR1946679 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946678 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946676 2 0.0237 0.9509 0.004 0.996 0.000
#> SRR1946677 2 0.0747 0.9450 0.016 0.984 0.000
#> SRR1946672 3 0.2625 0.8711 0.084 0.000 0.916
#> SRR1946673 2 0.2356 0.9041 0.072 0.928 0.000
#> SRR1946671 1 0.6267 0.1812 0.548 0.452 0.000
#> SRR1946669 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946668 1 0.6302 0.0874 0.520 0.480 0.000
#> SRR1946666 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946667 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946670 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946663 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946664 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946662 2 0.3482 0.8474 0.128 0.872 0.000
#> SRR1946661 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946660 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946659 1 0.0592 0.8691 0.988 0.000 0.012
#> SRR1946658 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946657 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946655 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946654 3 0.0592 0.9122 0.012 0.000 0.988
#> SRR1946653 3 0.4555 0.7482 0.200 0.000 0.800
#> SRR1946652 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946651 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946650 2 0.0000 0.9528 0.000 1.000 0.000
#> SRR1946649 2 0.3340 0.8578 0.120 0.880 0.000
#> SRR1946648 1 0.2261 0.8242 0.932 0.000 0.068
#> SRR1946647 1 0.4121 0.7223 0.832 0.168 0.000
#> SRR1946646 3 0.4915 0.7486 0.012 0.184 0.804
#> SRR1946645 1 0.0237 0.8750 0.996 0.004 0.000
#> SRR1946644 2 0.4750 0.7199 0.000 0.784 0.216
#> SRR1946643 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946642 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946656 3 0.0000 0.9160 0.000 0.000 1.000
#> SRR1946640 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.8768 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.8768 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 1 0.6858 0.2783 0.532 0.004 0.096 0.368
#> SRR1946691 2 0.3435 0.6699 0.000 0.864 0.100 0.036
#> SRR1946690 3 0.2868 0.7487 0.000 0.136 0.864 0.000
#> SRR1946689 4 0.3873 0.7673 0.000 0.228 0.000 0.772
#> SRR1946686 1 0.2530 0.7554 0.896 0.000 0.004 0.100
#> SRR1946685 3 0.0336 0.7988 0.000 0.008 0.992 0.000
#> SRR1946688 2 0.1004 0.7575 0.000 0.972 0.004 0.024
#> SRR1946684 2 0.4053 0.7818 0.228 0.768 0.004 0.000
#> SRR1946683 1 0.2053 0.7679 0.924 0.072 0.004 0.000
#> SRR1946682 2 0.0376 0.7709 0.000 0.992 0.004 0.004
#> SRR1946680 4 0.3497 0.7701 0.000 0.124 0.024 0.852
#> SRR1946681 3 0.0336 0.7938 0.000 0.000 0.992 0.008
#> SRR1946687 4 0.4361 0.6540 0.208 0.020 0.000 0.772
#> SRR1946679 3 0.1792 0.7875 0.000 0.068 0.932 0.000
#> SRR1946678 1 0.0188 0.8125 0.996 0.004 0.000 0.000
#> SRR1946676 3 0.0592 0.7993 0.000 0.016 0.984 0.000
#> SRR1946677 2 0.4719 0.7977 0.180 0.772 0.048 0.000
#> SRR1946672 1 0.7472 0.2959 0.504 0.000 0.264 0.232
#> SRR1946673 2 0.5222 0.7830 0.112 0.756 0.132 0.000
#> SRR1946671 1 0.5200 0.5958 0.744 0.184 0.072 0.000
#> SRR1946669 2 0.4697 0.6258 0.356 0.644 0.000 0.000
#> SRR1946668 2 0.3942 0.7761 0.236 0.764 0.000 0.000
#> SRR1946666 1 0.1118 0.8005 0.964 0.000 0.000 0.036
#> SRR1946667 4 0.3873 0.7673 0.000 0.228 0.000 0.772
#> SRR1946670 2 0.0188 0.7721 0.000 0.996 0.004 0.000
#> SRR1946663 2 0.1004 0.7590 0.000 0.972 0.004 0.024
#> SRR1946664 3 0.2814 0.7517 0.000 0.132 0.868 0.000
#> SRR1946662 2 0.5346 0.7799 0.192 0.732 0.076 0.000
#> SRR1946661 2 0.4181 0.8094 0.128 0.820 0.052 0.000
#> SRR1946660 2 0.1182 0.7695 0.000 0.968 0.016 0.016
#> SRR1946659 1 0.3610 0.6636 0.800 0.000 0.000 0.200
#> SRR1946658 2 0.3837 0.7207 0.000 0.776 0.224 0.000
#> SRR1946657 3 0.1022 0.7974 0.000 0.032 0.968 0.000
#> SRR1946655 3 0.4999 0.1967 0.000 0.000 0.508 0.492
#> SRR1946654 3 0.7031 0.3686 0.224 0.000 0.576 0.200
#> SRR1946653 4 0.3710 0.6462 0.192 0.000 0.004 0.804
#> SRR1946652 3 0.4605 0.4317 0.000 0.336 0.664 0.000
#> SRR1946651 3 0.3444 0.6999 0.000 0.184 0.816 0.000
#> SRR1946650 2 0.4158 0.7225 0.008 0.768 0.224 0.000
#> SRR1946649 1 0.7778 0.0902 0.420 0.256 0.324 0.000
#> SRR1946648 1 0.6389 0.6603 0.724 0.084 0.072 0.120
#> SRR1946647 2 0.4304 0.7321 0.284 0.716 0.000 0.000
#> SRR1946646 3 0.1356 0.7820 0.008 0.000 0.960 0.032
#> SRR1946645 1 0.0707 0.8057 0.980 0.020 0.000 0.000
#> SRR1946644 3 0.0336 0.7990 0.000 0.008 0.992 0.000
#> SRR1946643 3 0.3610 0.6724 0.000 0.000 0.800 0.200
#> SRR1946642 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> SRR1946656 3 0.4103 0.6111 0.000 0.000 0.744 0.256
#> SRR1946640 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.8136 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.8136 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.2329 0.6855 0.000 0.000 0.876 0.000 0.124
#> SRR1946691 2 0.4297 0.0657 0.000 0.528 0.000 0.472 0.000
#> SRR1946690 2 0.1121 0.8304 0.000 0.956 0.000 0.044 0.000
#> SRR1946689 4 0.2020 0.7201 0.000 0.000 0.100 0.900 0.000
#> SRR1946686 3 0.3895 0.5002 0.320 0.000 0.680 0.000 0.000
#> SRR1946685 2 0.0963 0.8538 0.000 0.964 0.036 0.000 0.000
#> SRR1946688 4 0.1544 0.7006 0.000 0.068 0.000 0.932 0.000
#> SRR1946684 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946683 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946682 5 0.2864 0.8002 0.000 0.012 0.000 0.136 0.852
#> SRR1946680 4 0.4045 0.5313 0.000 0.000 0.356 0.644 0.000
#> SRR1946681 3 0.4192 0.3073 0.000 0.404 0.596 0.000 0.000
#> SRR1946687 4 0.5594 0.3790 0.064 0.000 0.400 0.532 0.004
#> SRR1946679 2 0.1121 0.8502 0.000 0.956 0.044 0.000 0.000
#> SRR1946678 1 0.2424 0.8077 0.868 0.000 0.000 0.000 0.132
#> SRR1946676 2 0.1792 0.8234 0.000 0.916 0.084 0.000 0.000
#> SRR1946677 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946672 3 0.2189 0.7159 0.084 0.012 0.904 0.000 0.000
#> SRR1946673 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946671 5 0.5941 0.4757 0.168 0.244 0.000 0.000 0.588
#> SRR1946669 5 0.0162 0.8659 0.004 0.000 0.000 0.000 0.996
#> SRR1946668 5 0.0566 0.8641 0.004 0.000 0.000 0.012 0.984
#> SRR1946666 1 0.3242 0.6691 0.784 0.000 0.216 0.000 0.000
#> SRR1946667 4 0.2020 0.7201 0.000 0.000 0.100 0.900 0.000
#> SRR1946670 4 0.3141 0.6357 0.000 0.016 0.000 0.832 0.152
#> SRR1946663 5 0.2890 0.7888 0.000 0.004 0.000 0.160 0.836
#> SRR1946664 2 0.0290 0.8505 0.000 0.992 0.000 0.008 0.000
#> SRR1946662 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946661 5 0.3911 0.7782 0.004 0.084 0.000 0.100 0.812
#> SRR1946660 4 0.3837 0.3972 0.000 0.308 0.000 0.692 0.000
#> SRR1946659 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946658 5 0.5130 0.6241 0.000 0.220 0.000 0.100 0.680
#> SRR1946657 2 0.0880 0.8545 0.000 0.968 0.032 0.000 0.000
#> SRR1946655 3 0.0290 0.7034 0.000 0.008 0.992 0.000 0.000
#> SRR1946654 3 0.1704 0.7289 0.004 0.068 0.928 0.000 0.000
#> SRR1946653 3 0.6417 0.1416 0.228 0.000 0.508 0.264 0.000
#> SRR1946652 5 0.2471 0.7841 0.000 0.136 0.000 0.000 0.864
#> SRR1946651 2 0.0290 0.8555 0.000 0.992 0.008 0.000 0.000
#> SRR1946650 2 0.5158 0.5382 0.000 0.676 0.000 0.100 0.224
#> SRR1946649 2 0.4010 0.7129 0.088 0.796 0.000 0.000 0.116
#> SRR1946648 3 0.4060 0.4579 0.000 0.000 0.640 0.000 0.360
#> SRR1946647 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000
#> SRR1946646 2 0.2685 0.8028 0.092 0.880 0.028 0.000 0.000
#> SRR1946645 5 0.3895 0.5366 0.320 0.000 0.000 0.000 0.680
#> SRR1946644 2 0.0290 0.8555 0.000 0.992 0.008 0.000 0.000
#> SRR1946643 3 0.2020 0.7255 0.000 0.100 0.900 0.000 0.000
#> SRR1946642 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.1965 0.7267 0.000 0.096 0.904 0.000 0.000
#> SRR1946640 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9467 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.1080 0.779 0.000 0.000 0.960 0.004 0.032 0.004
#> SRR1946691 2 0.6679 0.149 0.000 0.452 0.004 0.208 0.040 0.296
#> SRR1946690 2 0.0291 0.857 0.000 0.992 0.004 0.000 0.000 0.004
#> SRR1946689 4 0.0363 0.719 0.000 0.000 0.000 0.988 0.000 0.012
#> SRR1946686 3 0.2805 0.643 0.160 0.000 0.828 0.000 0.012 0.000
#> SRR1946685 2 0.0260 0.856 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1946688 6 0.4265 0.487 0.000 0.004 0.004 0.236 0.044 0.712
#> SRR1946684 5 0.0713 0.745 0.000 0.000 0.000 0.000 0.972 0.028
#> SRR1946683 6 0.5065 0.245 0.000 0.000 0.080 0.000 0.396 0.524
#> SRR1946682 6 0.2812 0.635 0.000 0.000 0.000 0.048 0.096 0.856
#> SRR1946680 4 0.2340 0.710 0.000 0.000 0.148 0.852 0.000 0.000
#> SRR1946681 3 0.4172 0.205 0.000 0.424 0.564 0.000 0.004 0.008
#> SRR1946687 4 0.5137 0.600 0.136 0.000 0.228 0.632 0.004 0.000
#> SRR1946679 2 0.0653 0.853 0.000 0.980 0.004 0.000 0.004 0.012
#> SRR1946678 5 0.4026 0.364 0.376 0.000 0.000 0.000 0.612 0.012
#> SRR1946676 2 0.5228 0.129 0.000 0.524 0.100 0.000 0.000 0.376
#> SRR1946677 6 0.3566 0.585 0.000 0.000 0.020 0.000 0.236 0.744
#> SRR1946672 3 0.0547 0.786 0.020 0.000 0.980 0.000 0.000 0.000
#> SRR1946673 5 0.1204 0.741 0.000 0.000 0.000 0.000 0.944 0.056
#> SRR1946671 6 0.5198 0.587 0.064 0.032 0.016 0.000 0.200 0.688
#> SRR1946669 5 0.1471 0.738 0.004 0.000 0.000 0.000 0.932 0.064
#> SRR1946668 5 0.0603 0.737 0.004 0.000 0.000 0.000 0.980 0.016
#> SRR1946666 1 0.4274 0.423 0.640 0.000 0.336 0.008 0.004 0.012
#> SRR1946667 4 0.0260 0.720 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1946670 5 0.5869 0.264 0.000 0.000 0.004 0.208 0.504 0.284
#> SRR1946663 6 0.3575 0.607 0.000 0.000 0.000 0.128 0.076 0.796
#> SRR1946664 2 0.0405 0.855 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR1946662 5 0.1531 0.736 0.004 0.000 0.000 0.000 0.928 0.068
#> SRR1946661 6 0.1531 0.660 0.000 0.004 0.000 0.000 0.068 0.928
#> SRR1946660 6 0.4157 0.593 0.000 0.032 0.004 0.104 0.072 0.788
#> SRR1946659 1 0.0146 0.931 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946658 5 0.4048 0.461 0.000 0.012 0.012 0.000 0.684 0.292
#> SRR1946657 2 0.0000 0.857 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946655 3 0.0458 0.786 0.000 0.000 0.984 0.016 0.000 0.000
#> SRR1946654 3 0.0810 0.786 0.000 0.004 0.976 0.008 0.004 0.008
#> SRR1946653 4 0.6670 0.192 0.136 0.000 0.392 0.408 0.004 0.060
#> SRR1946652 5 0.5700 0.220 0.000 0.152 0.008 0.000 0.536 0.304
#> SRR1946651 2 0.0547 0.851 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1946650 6 0.3798 0.604 0.000 0.216 0.004 0.000 0.032 0.748
#> SRR1946649 6 0.5194 0.588 0.044 0.208 0.012 0.000 0.052 0.684
#> SRR1946648 3 0.5488 0.268 0.000 0.000 0.556 0.000 0.272 0.172
#> SRR1946647 5 0.0146 0.743 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1946646 2 0.1387 0.809 0.068 0.932 0.000 0.000 0.000 0.000
#> SRR1946645 6 0.4891 0.590 0.140 0.000 0.016 0.000 0.148 0.696
#> SRR1946644 2 0.0000 0.857 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946643 3 0.0458 0.789 0.000 0.016 0.984 0.000 0.000 0.000
#> SRR1946642 1 0.0146 0.931 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1946641 1 0.0000 0.934 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0603 0.788 0.000 0.016 0.980 0.004 0.000 0.000
#> SRR1946640 1 0.0000 0.934 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.934 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.934 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.934 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.562 0.729 0.840 0.235 0.795 0.795
#> 3 3 0.591 0.133 0.615 0.613 0.522 0.452
#> 4 4 0.600 0.707 0.850 0.233 0.578 0.386
#> 5 5 0.512 0.295 0.648 0.343 0.546 0.279
#> 6 6 0.544 0.685 0.765 0.121 0.739 0.400
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1946675 1 0.8813 0.4220 0.700 0.300
#> SRR1946691 1 0.0000 0.8709 1.000 0.000
#> SRR1946690 1 0.2603 0.8232 0.956 0.044
#> SRR1946689 2 0.0000 0.5808 0.000 1.000
#> SRR1946686 1 0.8861 0.4108 0.696 0.304
#> SRR1946685 1 0.0376 0.8675 0.996 0.004
#> SRR1946688 1 0.0000 0.8709 1.000 0.000
#> SRR1946684 1 0.0000 0.8709 1.000 0.000
#> SRR1946683 1 0.0000 0.8709 1.000 0.000
#> SRR1946682 1 0.0000 0.8709 1.000 0.000
#> SRR1946680 2 0.9795 0.6224 0.416 0.584
#> SRR1946681 1 0.8763 0.3197 0.704 0.296
#> SRR1946687 1 0.8813 0.4220 0.700 0.300
#> SRR1946679 1 0.0000 0.8709 1.000 0.000
#> SRR1946678 1 0.0000 0.8709 1.000 0.000
#> SRR1946676 1 0.0000 0.8709 1.000 0.000
#> SRR1946677 1 0.0000 0.8709 1.000 0.000
#> SRR1946672 1 0.9661 0.0691 0.608 0.392
#> SRR1946673 1 0.0000 0.8709 1.000 0.000
#> SRR1946671 1 0.0000 0.8709 1.000 0.000
#> SRR1946669 1 0.0000 0.8709 1.000 0.000
#> SRR1946668 1 0.0000 0.8709 1.000 0.000
#> SRR1946666 1 0.8813 0.4220 0.700 0.300
#> SRR1946667 2 0.0000 0.5808 0.000 1.000
#> SRR1946670 1 0.0000 0.8709 1.000 0.000
#> SRR1946663 1 0.0000 0.8709 1.000 0.000
#> SRR1946664 1 0.2603 0.8232 0.956 0.044
#> SRR1946662 1 0.0000 0.8709 1.000 0.000
#> SRR1946661 1 0.0000 0.8709 1.000 0.000
#> SRR1946660 1 0.0000 0.8709 1.000 0.000
#> SRR1946659 1 0.8813 0.4220 0.700 0.300
#> SRR1946658 1 0.0000 0.8709 1.000 0.000
#> SRR1946657 1 0.0000 0.8709 1.000 0.000
#> SRR1946655 2 0.9815 0.6200 0.420 0.580
#> SRR1946654 1 0.8861 0.4108 0.696 0.304
#> SRR1946653 1 0.8813 0.4220 0.700 0.300
#> SRR1946652 1 0.0000 0.8709 1.000 0.000
#> SRR1946651 1 0.0000 0.8709 1.000 0.000
#> SRR1946650 1 0.0000 0.8709 1.000 0.000
#> SRR1946649 1 0.0000 0.8709 1.000 0.000
#> SRR1946648 1 0.8763 0.4312 0.704 0.296
#> SRR1946647 1 0.0000 0.8709 1.000 0.000
#> SRR1946646 1 0.8813 0.4220 0.700 0.300
#> SRR1946645 1 0.0000 0.8709 1.000 0.000
#> SRR1946644 1 0.8763 0.4307 0.704 0.296
#> SRR1946643 2 0.9815 0.6200 0.420 0.580
#> SRR1946642 1 0.0000 0.8709 1.000 0.000
#> SRR1946641 1 0.0000 0.8709 1.000 0.000
#> SRR1946656 2 0.9815 0.6200 0.420 0.580
#> SRR1946640 1 0.0000 0.8709 1.000 0.000
#> SRR1946639 1 0.0000 0.8709 1.000 0.000
#> SRR1946638 1 0.0000 0.8709 1.000 0.000
#> SRR1946637 1 0.0000 0.8709 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 1 0.0000 0.313 1.000 0.000 0.000
#> SRR1946691 2 0.6816 0.778 0.472 0.516 0.012
#> SRR1946690 2 0.6984 0.476 0.420 0.560 0.020
#> SRR1946689 3 0.0892 1.000 0.020 0.000 0.980
#> SRR1946686 1 0.0424 0.313 0.992 0.008 0.000
#> SRR1946685 1 0.6305 -0.781 0.516 0.484 0.000
#> SRR1946688 2 0.6816 0.778 0.472 0.516 0.012
#> SRR1946684 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946683 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946682 2 0.6302 0.799 0.480 0.520 0.000
#> SRR1946680 1 0.7487 -0.313 0.500 0.464 0.036
#> SRR1946681 2 0.5098 -0.248 0.248 0.752 0.000
#> SRR1946687 1 0.0000 0.313 1.000 0.000 0.000
#> SRR1946679 2 0.6307 0.802 0.488 0.512 0.000
#> SRR1946678 2 0.6309 0.792 0.500 0.500 0.000
#> SRR1946676 2 0.6307 0.802 0.488 0.512 0.000
#> SRR1946677 2 0.6302 0.799 0.480 0.520 0.000
#> SRR1946672 1 0.4978 0.168 0.780 0.216 0.004
#> SRR1946673 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946671 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946669 2 0.6309 0.792 0.500 0.500 0.000
#> SRR1946668 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946666 1 0.0000 0.313 1.000 0.000 0.000
#> SRR1946667 3 0.0892 1.000 0.020 0.000 0.980
#> SRR1946670 2 0.6308 0.801 0.492 0.508 0.000
#> SRR1946663 2 0.6302 0.799 0.480 0.520 0.000
#> SRR1946664 2 0.6984 0.476 0.420 0.560 0.020
#> SRR1946662 2 0.6309 0.792 0.500 0.500 0.000
#> SRR1946661 2 0.6307 0.799 0.488 0.512 0.000
#> SRR1946660 2 0.6816 0.778 0.472 0.516 0.012
#> SRR1946659 1 0.0000 0.313 1.000 0.000 0.000
#> SRR1946658 2 0.6307 0.802 0.488 0.512 0.000
#> SRR1946657 1 0.6307 -0.787 0.512 0.488 0.000
#> SRR1946655 1 0.7286 -0.306 0.508 0.464 0.028
#> SRR1946654 1 0.0237 0.313 0.996 0.004 0.000
#> SRR1946653 1 0.0000 0.313 1.000 0.000 0.000
#> SRR1946652 2 0.6308 0.801 0.492 0.508 0.000
#> SRR1946651 2 0.6307 0.802 0.488 0.512 0.000
#> SRR1946650 2 0.6302 0.799 0.480 0.520 0.000
#> SRR1946649 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946648 1 0.0424 0.309 0.992 0.008 0.000
#> SRR1946647 1 0.6309 -0.809 0.500 0.500 0.000
#> SRR1946646 1 0.0592 0.310 0.988 0.012 0.000
#> SRR1946645 1 0.6307 -0.798 0.512 0.488 0.000
#> SRR1946644 1 0.0747 0.307 0.984 0.016 0.000
#> SRR1946643 1 0.7286 -0.306 0.508 0.464 0.028
#> SRR1946642 2 0.6309 0.792 0.500 0.500 0.000
#> SRR1946641 1 0.6307 -0.798 0.512 0.488 0.000
#> SRR1946656 1 0.7286 -0.306 0.508 0.464 0.028
#> SRR1946640 1 0.6307 -0.798 0.512 0.488 0.000
#> SRR1946639 1 0.6307 -0.798 0.512 0.488 0.000
#> SRR1946638 1 0.6307 -0.798 0.512 0.488 0.000
#> SRR1946637 1 0.6307 -0.798 0.512 0.488 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.4999 0.614 0.000 0.492 0.508 0.000
#> SRR1946691 2 0.5366 0.270 0.440 0.548 0.012 0.000
#> SRR1946690 1 0.0592 1.000 0.984 0.016 0.000 0.000
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1946686 3 0.4999 0.616 0.000 0.492 0.508 0.000
#> SRR1946685 2 0.1520 0.868 0.020 0.956 0.024 0.000
#> SRR1946688 2 0.5366 0.270 0.440 0.548 0.012 0.000
#> SRR1946684 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946683 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946682 2 0.2402 0.833 0.076 0.912 0.012 0.000
#> SRR1946680 3 0.1394 0.222 0.008 0.012 0.964 0.016
#> SRR1946681 3 0.5389 0.126 0.032 0.308 0.660 0.000
#> SRR1946687 3 0.4999 0.614 0.000 0.492 0.508 0.000
#> SRR1946679 2 0.1209 0.877 0.032 0.964 0.004 0.000
#> SRR1946678 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946676 2 0.1209 0.877 0.032 0.964 0.004 0.000
#> SRR1946677 2 0.2402 0.833 0.076 0.912 0.012 0.000
#> SRR1946672 3 0.4428 0.557 0.000 0.276 0.720 0.004
#> SRR1946673 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946671 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946669 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946668 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946666 3 0.4999 0.614 0.000 0.492 0.508 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1946670 2 0.1022 0.878 0.032 0.968 0.000 0.000
#> SRR1946663 2 0.2402 0.833 0.076 0.912 0.012 0.000
#> SRR1946664 1 0.0592 1.000 0.984 0.016 0.000 0.000
#> SRR1946662 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946661 2 0.1284 0.871 0.024 0.964 0.012 0.000
#> SRR1946660 2 0.5366 0.270 0.440 0.548 0.012 0.000
#> SRR1946659 3 0.4999 0.614 0.000 0.492 0.508 0.000
#> SRR1946658 2 0.1209 0.877 0.032 0.964 0.004 0.000
#> SRR1946657 2 0.1411 0.872 0.020 0.960 0.020 0.000
#> SRR1946655 3 0.1139 0.232 0.008 0.012 0.972 0.008
#> SRR1946654 3 0.4996 0.618 0.000 0.484 0.516 0.000
#> SRR1946653 3 0.4999 0.614 0.000 0.492 0.508 0.000
#> SRR1946652 2 0.0921 0.879 0.028 0.972 0.000 0.000
#> SRR1946651 2 0.1209 0.877 0.032 0.964 0.004 0.000
#> SRR1946650 2 0.2542 0.825 0.084 0.904 0.012 0.000
#> SRR1946649 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946648 2 0.5000 -0.642 0.000 0.500 0.500 0.000
#> SRR1946647 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946646 3 0.5168 0.609 0.004 0.492 0.504 0.000
#> SRR1946645 2 0.1297 0.879 0.020 0.964 0.016 0.000
#> SRR1946644 3 0.5168 0.602 0.004 0.496 0.500 0.000
#> SRR1946643 3 0.1139 0.232 0.008 0.012 0.972 0.008
#> SRR1946642 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1946641 2 0.0707 0.876 0.000 0.980 0.020 0.000
#> SRR1946656 3 0.1139 0.232 0.008 0.012 0.972 0.008
#> SRR1946640 2 0.0707 0.876 0.000 0.980 0.020 0.000
#> SRR1946639 2 0.0707 0.876 0.000 0.980 0.020 0.000
#> SRR1946638 2 0.0707 0.876 0.000 0.980 0.020 0.000
#> SRR1946637 2 0.0707 0.876 0.000 0.980 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.0162 0.4522 0.000 0.004 0.996 0.000 0.000
#> SRR1946691 2 0.4313 0.4387 0.356 0.636 0.008 0.000 0.000
#> SRR1946690 1 0.0290 1.0000 0.992 0.008 0.000 0.000 0.000
#> SRR1946689 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 3 0.0162 0.4506 0.000 0.000 0.996 0.000 0.004
#> SRR1946685 3 0.6469 0.3047 0.000 0.336 0.468 0.000 0.196
#> SRR1946688 2 0.4313 0.4387 0.356 0.636 0.008 0.000 0.000
#> SRR1946684 5 0.5546 0.2043 0.000 0.068 0.436 0.000 0.496
#> SRR1946683 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946682 2 0.1670 0.6827 0.000 0.936 0.052 0.000 0.012
#> SRR1946680 5 0.4815 -0.1848 0.008 0.000 0.480 0.008 0.504
#> SRR1946681 5 0.6522 -0.3262 0.000 0.300 0.224 0.000 0.476
#> SRR1946687 3 0.0290 0.4518 0.000 0.008 0.992 0.000 0.000
#> SRR1946679 3 0.6585 0.2968 0.000 0.360 0.428 0.000 0.212
#> SRR1946678 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946676 3 0.6585 0.2968 0.000 0.360 0.428 0.000 0.212
#> SRR1946677 2 0.1670 0.6827 0.000 0.936 0.052 0.000 0.012
#> SRR1946672 3 0.3395 0.1880 0.000 0.000 0.764 0.000 0.236
#> SRR1946673 5 0.5546 0.2043 0.000 0.068 0.436 0.000 0.496
#> SRR1946671 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946669 5 0.5450 0.2063 0.000 0.060 0.444 0.000 0.496
#> SRR1946668 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946666 3 0.0290 0.4518 0.000 0.008 0.992 0.000 0.000
#> SRR1946667 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 3 0.6589 0.2864 0.000 0.364 0.424 0.000 0.212
#> SRR1946663 2 0.1670 0.6827 0.000 0.936 0.052 0.000 0.012
#> SRR1946664 1 0.0290 1.0000 0.992 0.008 0.000 0.000 0.000
#> SRR1946662 5 0.5546 0.2043 0.000 0.068 0.436 0.000 0.496
#> SRR1946661 5 0.6710 0.0779 0.008 0.236 0.264 0.000 0.492
#> SRR1946660 2 0.4313 0.4387 0.356 0.636 0.008 0.000 0.000
#> SRR1946659 3 0.0290 0.4518 0.000 0.008 0.992 0.000 0.000
#> SRR1946658 3 0.6600 0.2862 0.000 0.380 0.408 0.000 0.212
#> SRR1946657 3 0.6511 0.2997 0.000 0.336 0.460 0.000 0.204
#> SRR1946655 5 0.4561 -0.1782 0.008 0.000 0.488 0.000 0.504
#> SRR1946654 3 0.0451 0.4474 0.000 0.004 0.988 0.000 0.008
#> SRR1946653 3 0.0290 0.4518 0.000 0.008 0.992 0.000 0.000
#> SRR1946652 3 0.6534 0.2869 0.000 0.328 0.460 0.000 0.212
#> SRR1946651 3 0.6585 0.2968 0.000 0.360 0.428 0.000 0.212
#> SRR1946650 2 0.1883 0.6794 0.008 0.932 0.048 0.000 0.012
#> SRR1946649 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946648 3 0.0162 0.4514 0.000 0.000 0.996 0.000 0.004
#> SRR1946647 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946646 3 0.1041 0.4408 0.000 0.032 0.964 0.000 0.004
#> SRR1946645 2 0.5401 -0.1509 0.000 0.536 0.404 0.000 0.060
#> SRR1946644 3 0.1281 0.4410 0.000 0.032 0.956 0.000 0.012
#> SRR1946643 5 0.4561 -0.1782 0.008 0.000 0.488 0.000 0.504
#> SRR1946642 5 0.5399 0.2300 0.000 0.056 0.448 0.000 0.496
#> SRR1946641 3 0.4561 -0.1510 0.000 0.008 0.504 0.000 0.488
#> SRR1946656 5 0.4561 -0.1782 0.008 0.000 0.488 0.000 0.504
#> SRR1946640 3 0.4561 -0.1510 0.000 0.008 0.504 0.000 0.488
#> SRR1946639 3 0.4561 -0.1510 0.000 0.008 0.504 0.000 0.488
#> SRR1946638 3 0.4561 -0.1510 0.000 0.008 0.504 0.000 0.488
#> SRR1946637 3 0.4561 -0.1510 0.000 0.008 0.504 0.000 0.488
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2527 0.936 0.000 0.000 0.832 0.000 0.168 0.000
#> SRR1946691 6 0.0363 0.473 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR1946690 2 0.5036 1.000 0.000 0.632 0.140 0.000 0.000 0.228
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946686 3 0.2668 0.935 0.004 0.000 0.828 0.000 0.168 0.000
#> SRR1946685 5 0.3052 0.406 0.000 0.004 0.216 0.000 0.780 0.000
#> SRR1946688 6 0.0363 0.473 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR1946684 5 0.5568 0.703 0.288 0.020 0.088 0.000 0.596 0.008
#> SRR1946683 5 0.6250 0.698 0.288 0.024 0.136 0.000 0.536 0.016
#> SRR1946682 6 0.5126 0.674 0.000 0.364 0.056 0.000 0.016 0.564
#> SRR1946680 1 0.3910 0.824 0.660 0.000 0.328 0.008 0.000 0.004
#> SRR1946681 1 0.3950 0.377 0.672 0.000 0.008 0.000 0.312 0.008
#> SRR1946687 3 0.2491 0.937 0.000 0.000 0.836 0.000 0.164 0.000
#> SRR1946679 5 0.0551 0.521 0.000 0.004 0.004 0.000 0.984 0.008
#> SRR1946678 5 0.6154 0.703 0.288 0.024 0.124 0.000 0.548 0.016
#> SRR1946676 5 0.0551 0.521 0.000 0.004 0.004 0.000 0.984 0.008
#> SRR1946677 6 0.5177 0.673 0.000 0.364 0.060 0.000 0.016 0.560
#> SRR1946672 3 0.3592 0.375 0.240 0.000 0.740 0.000 0.020 0.000
#> SRR1946673 5 0.5568 0.703 0.288 0.020 0.088 0.000 0.596 0.008
#> SRR1946671 5 0.6445 0.683 0.288 0.024 0.164 0.000 0.508 0.016
#> SRR1946669 5 0.5651 0.704 0.288 0.020 0.096 0.000 0.588 0.008
#> SRR1946668 5 0.6154 0.703 0.288 0.024 0.124 0.000 0.548 0.016
#> SRR1946666 3 0.2491 0.937 0.000 0.000 0.836 0.000 0.164 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946670 5 0.1716 0.520 0.000 0.004 0.028 0.000 0.932 0.036
#> SRR1946663 6 0.5126 0.674 0.000 0.364 0.056 0.000 0.016 0.564
#> SRR1946664 2 0.5036 1.000 0.000 0.632 0.140 0.000 0.000 0.228
#> SRR1946662 5 0.5568 0.703 0.288 0.020 0.088 0.000 0.596 0.008
#> SRR1946661 5 0.8032 0.539 0.288 0.164 0.108 0.000 0.376 0.064
#> SRR1946660 6 0.0363 0.473 0.000 0.000 0.000 0.000 0.012 0.988
#> SRR1946659 3 0.2491 0.937 0.000 0.000 0.836 0.000 0.164 0.000
#> SRR1946658 5 0.1155 0.503 0.000 0.004 0.004 0.000 0.956 0.036
#> SRR1946657 5 0.3043 0.435 0.004 0.004 0.196 0.000 0.796 0.000
#> SRR1946655 1 0.3351 0.853 0.712 0.000 0.288 0.000 0.000 0.000
#> SRR1946654 3 0.2706 0.933 0.008 0.000 0.832 0.000 0.160 0.000
#> SRR1946653 3 0.2491 0.937 0.000 0.000 0.836 0.000 0.164 0.000
#> SRR1946652 5 0.1477 0.540 0.000 0.004 0.048 0.000 0.940 0.008
#> SRR1946651 5 0.0551 0.521 0.000 0.004 0.004 0.000 0.984 0.008
#> SRR1946650 6 0.4991 0.672 0.000 0.364 0.052 0.000 0.012 0.572
#> SRR1946649 5 0.6445 0.683 0.288 0.024 0.164 0.000 0.508 0.016
#> SRR1946648 3 0.2703 0.932 0.004 0.000 0.824 0.000 0.172 0.000
#> SRR1946647 5 0.6154 0.703 0.288 0.024 0.124 0.000 0.548 0.016
#> SRR1946646 3 0.2883 0.899 0.000 0.000 0.788 0.000 0.212 0.000
#> SRR1946645 6 0.7941 0.112 0.020 0.188 0.276 0.000 0.180 0.336
#> SRR1946644 3 0.3136 0.885 0.004 0.000 0.768 0.000 0.228 0.000
#> SRR1946643 1 0.3351 0.853 0.712 0.000 0.288 0.000 0.000 0.000
#> SRR1946642 5 0.6154 0.703 0.288 0.024 0.124 0.000 0.548 0.016
#> SRR1946641 5 0.6104 0.496 0.288 0.000 0.348 0.000 0.364 0.000
#> SRR1946656 1 0.3351 0.853 0.712 0.000 0.288 0.000 0.000 0.000
#> SRR1946640 5 0.6104 0.496 0.288 0.000 0.348 0.000 0.364 0.000
#> SRR1946639 5 0.6104 0.496 0.288 0.000 0.348 0.000 0.364 0.000
#> SRR1946638 5 0.6104 0.496 0.288 0.000 0.348 0.000 0.364 0.000
#> SRR1946637 5 0.6104 0.496 0.288 0.000 0.348 0.000 0.364 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.220 0.651 0.822 0.4219 0.570 0.570
#> 3 3 0.323 0.468 0.693 0.4255 0.577 0.386
#> 4 4 0.448 0.556 0.712 0.1486 0.838 0.621
#> 5 5 0.491 0.495 0.585 0.0861 0.790 0.413
#> 6 6 0.529 0.474 0.593 0.0606 0.852 0.443
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
#> SRR1946675 2 0.9754 0.668 0.408 0.592
#> SRR1946691 1 0.9248 0.570 0.660 0.340
#> SRR1946690 2 0.9983 -0.230 0.476 0.524
#> SRR1946689 2 0.1414 0.619 0.020 0.980
#> SRR1946686 1 0.6623 0.590 0.828 0.172
#> SRR1946685 1 0.0376 0.797 0.996 0.004
#> SRR1946688 2 0.9998 -0.252 0.492 0.508
#> SRR1946684 1 0.0000 0.798 1.000 0.000
#> SRR1946683 1 0.0000 0.798 1.000 0.000
#> SRR1946682 1 0.6801 0.722 0.820 0.180
#> SRR1946680 2 0.4815 0.683 0.104 0.896
#> SRR1946681 1 0.9922 0.215 0.552 0.448
#> SRR1946687 2 0.9608 0.697 0.384 0.616
#> SRR1946679 1 0.6247 0.737 0.844 0.156
#> SRR1946678 1 0.0000 0.798 1.000 0.000
#> SRR1946676 1 0.5946 0.740 0.856 0.144
#> SRR1946677 1 0.5737 0.751 0.864 0.136
#> SRR1946672 2 0.8267 0.741 0.260 0.740
#> SRR1946673 1 0.0000 0.798 1.000 0.000
#> SRR1946671 1 0.0000 0.798 1.000 0.000
#> SRR1946669 1 0.0000 0.798 1.000 0.000
#> SRR1946668 1 0.0000 0.798 1.000 0.000
#> SRR1946666 2 0.9754 0.668 0.408 0.592
#> SRR1946667 2 0.1414 0.619 0.020 0.980
#> SRR1946670 1 0.7674 0.698 0.776 0.224
#> SRR1946663 1 0.6887 0.719 0.816 0.184
#> SRR1946664 1 0.9393 0.553 0.644 0.356
#> SRR1946662 1 0.0000 0.798 1.000 0.000
#> SRR1946661 1 0.6712 0.725 0.824 0.176
#> SRR1946660 1 0.9248 0.570 0.660 0.340
#> SRR1946659 2 0.9608 0.697 0.384 0.616
#> SRR1946658 1 0.9044 0.600 0.680 0.320
#> SRR1946657 1 0.3879 0.783 0.924 0.076
#> SRR1946655 2 0.7528 0.739 0.216 0.784
#> SRR1946654 2 0.9129 0.730 0.328 0.672
#> SRR1946653 2 0.9427 0.716 0.360 0.640
#> SRR1946652 1 0.2603 0.795 0.956 0.044
#> SRR1946651 1 0.7139 0.714 0.804 0.196
#> SRR1946650 1 0.6973 0.717 0.812 0.188
#> SRR1946649 1 0.1633 0.796 0.976 0.024
#> SRR1946648 1 0.9963 -0.439 0.536 0.464
#> SRR1946647 1 0.0000 0.798 1.000 0.000
#> SRR1946646 2 0.9170 0.727 0.332 0.668
#> SRR1946645 1 0.0000 0.798 1.000 0.000
#> SRR1946644 1 0.3584 0.786 0.932 0.068
#> SRR1946643 2 0.7528 0.739 0.216 0.784
#> SRR1946642 1 0.0000 0.798 1.000 0.000
#> SRR1946641 1 0.5178 0.683 0.884 0.116
#> SRR1946656 2 0.7528 0.739 0.216 0.784
#> SRR1946640 1 0.6247 0.619 0.844 0.156
#> SRR1946639 1 0.4298 0.719 0.912 0.088
#> SRR1946638 1 0.6247 0.619 0.844 0.156
#> SRR1946637 1 0.6247 0.619 0.844 0.156
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 1 0.653 -0.260 0.588 0.008 0.404
#> SRR1946691 2 0.445 0.680 0.000 0.808 0.192
#> SRR1946690 2 0.470 0.674 0.000 0.788 0.212
#> SRR1946689 3 0.346 0.636 0.012 0.096 0.892
#> SRR1946686 1 0.258 0.514 0.928 0.064 0.008
#> SRR1946685 2 0.583 0.365 0.340 0.660 0.000
#> SRR1946688 2 0.486 0.671 0.008 0.800 0.192
#> SRR1946684 1 0.581 0.450 0.664 0.336 0.000
#> SRR1946683 1 0.631 0.109 0.504 0.496 0.000
#> SRR1946682 2 0.263 0.762 0.084 0.916 0.000
#> SRR1946680 3 0.591 0.735 0.144 0.068 0.788
#> SRR1946681 2 0.765 0.553 0.124 0.680 0.196
#> SRR1946687 1 0.654 -0.266 0.584 0.008 0.408
#> SRR1946679 2 0.477 0.751 0.100 0.848 0.052
#> SRR1946678 1 0.546 0.488 0.712 0.288 0.000
#> SRR1946676 2 0.492 0.745 0.108 0.840 0.052
#> SRR1946677 2 0.280 0.759 0.092 0.908 0.000
#> SRR1946672 3 0.776 0.720 0.360 0.060 0.580
#> SRR1946673 1 0.583 0.445 0.660 0.340 0.000
#> SRR1946671 1 0.630 0.169 0.524 0.476 0.000
#> SRR1946669 1 0.581 0.450 0.664 0.336 0.000
#> SRR1946668 1 0.581 0.450 0.664 0.336 0.000
#> SRR1946666 1 0.654 -0.262 0.584 0.008 0.408
#> SRR1946667 3 0.346 0.636 0.012 0.096 0.892
#> SRR1946670 2 0.183 0.777 0.036 0.956 0.008
#> SRR1946663 2 0.254 0.763 0.080 0.920 0.000
#> SRR1946664 2 0.460 0.679 0.000 0.796 0.204
#> SRR1946662 1 0.583 0.445 0.660 0.340 0.000
#> SRR1946661 2 0.263 0.763 0.084 0.916 0.000
#> SRR1946660 2 0.445 0.680 0.000 0.808 0.192
#> SRR1946659 1 0.634 -0.250 0.596 0.004 0.400
#> SRR1946658 2 0.192 0.772 0.020 0.956 0.024
#> SRR1946657 2 0.447 0.697 0.164 0.828 0.008
#> SRR1946655 3 0.783 0.771 0.312 0.076 0.612
#> SRR1946654 1 0.802 -0.410 0.520 0.064 0.416
#> SRR1946653 1 0.707 -0.303 0.568 0.024 0.408
#> SRR1946652 2 0.412 0.705 0.168 0.832 0.000
#> SRR1946651 2 0.469 0.753 0.096 0.852 0.052
#> SRR1946650 2 0.153 0.775 0.040 0.960 0.000
#> SRR1946649 2 0.562 0.474 0.308 0.692 0.000
#> SRR1946648 1 0.821 0.130 0.600 0.104 0.296
#> SRR1946647 1 0.581 0.450 0.664 0.336 0.000
#> SRR1946646 1 0.813 -0.382 0.528 0.072 0.400
#> SRR1946645 1 0.630 0.143 0.516 0.484 0.000
#> SRR1946644 2 0.534 0.613 0.232 0.760 0.008
#> SRR1946643 3 0.783 0.771 0.312 0.076 0.612
#> SRR1946642 1 0.616 0.486 0.696 0.288 0.016
#> SRR1946641 1 0.375 0.549 0.884 0.096 0.020
#> SRR1946656 3 0.783 0.771 0.312 0.076 0.612
#> SRR1946640 1 0.375 0.549 0.884 0.096 0.020
#> SRR1946639 1 0.383 0.551 0.880 0.100 0.020
#> SRR1946638 1 0.375 0.549 0.884 0.096 0.020
#> SRR1946637 1 0.375 0.549 0.884 0.096 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.4462 0.631 0.256 0.004 0.736 0.004
#> SRR1946691 2 0.4428 0.586 0.004 0.720 0.000 0.276
#> SRR1946690 2 0.4946 0.601 0.008 0.776 0.052 0.164
#> SRR1946689 4 0.4936 1.000 0.000 0.012 0.316 0.672
#> SRR1946686 1 0.5523 0.463 0.704 0.012 0.248 0.036
#> SRR1946685 2 0.5576 0.217 0.444 0.536 0.020 0.000
#> SRR1946688 2 0.4509 0.577 0.004 0.708 0.000 0.288
#> SRR1946684 1 0.2197 0.709 0.916 0.080 0.000 0.004
#> SRR1946683 1 0.4883 0.407 0.696 0.288 0.000 0.016
#> SRR1946682 2 0.6341 0.639 0.136 0.652 0.000 0.212
#> SRR1946680 3 0.3853 0.141 0.000 0.020 0.820 0.160
#> SRR1946681 2 0.6922 0.400 0.052 0.600 0.304 0.044
#> SRR1946687 3 0.5532 0.624 0.232 0.004 0.708 0.056
#> SRR1946679 2 0.5218 0.640 0.200 0.736 0.064 0.000
#> SRR1946678 1 0.1796 0.711 0.948 0.032 0.004 0.016
#> SRR1946676 2 0.5328 0.629 0.212 0.724 0.064 0.000
#> SRR1946677 2 0.6756 0.634 0.188 0.612 0.000 0.200
#> SRR1946672 3 0.0707 0.409 0.000 0.000 0.980 0.020
#> SRR1946673 1 0.2530 0.697 0.896 0.100 0.000 0.004
#> SRR1946671 1 0.4630 0.482 0.732 0.252 0.000 0.016
#> SRR1946669 1 0.2197 0.709 0.916 0.080 0.000 0.004
#> SRR1946668 1 0.2197 0.709 0.916 0.080 0.000 0.004
#> SRR1946666 3 0.4822 0.632 0.240 0.004 0.736 0.020
#> SRR1946667 4 0.4936 1.000 0.000 0.012 0.316 0.672
#> SRR1946670 2 0.4756 0.694 0.144 0.784 0.000 0.072
#> SRR1946663 2 0.6341 0.639 0.136 0.652 0.000 0.212
#> SRR1946664 2 0.4225 0.608 0.008 0.832 0.052 0.108
#> SRR1946662 1 0.2773 0.687 0.880 0.116 0.000 0.004
#> SRR1946661 2 0.6805 0.627 0.220 0.604 0.000 0.176
#> SRR1946660 2 0.4483 0.581 0.004 0.712 0.000 0.284
#> SRR1946659 3 0.6310 0.566 0.236 0.004 0.656 0.104
#> SRR1946658 2 0.4330 0.699 0.112 0.828 0.012 0.048
#> SRR1946657 2 0.5298 0.600 0.244 0.708 0.048 0.000
#> SRR1946655 3 0.3052 0.264 0.000 0.004 0.860 0.136
#> SRR1946654 3 0.4240 0.622 0.200 0.012 0.784 0.004
#> SRR1946653 3 0.5532 0.624 0.232 0.004 0.708 0.056
#> SRR1946652 2 0.4746 0.558 0.304 0.688 0.008 0.000
#> SRR1946651 2 0.5180 0.642 0.196 0.740 0.064 0.000
#> SRR1946650 2 0.5072 0.662 0.052 0.740 0.000 0.208
#> SRR1946649 1 0.5937 -0.230 0.492 0.472 0.000 0.036
#> SRR1946648 3 0.6581 0.478 0.352 0.040 0.580 0.028
#> SRR1946647 1 0.2197 0.709 0.916 0.080 0.000 0.004
#> SRR1946646 3 0.6892 0.568 0.224 0.092 0.648 0.036
#> SRR1946645 1 0.5696 0.359 0.664 0.292 0.008 0.036
#> SRR1946644 2 0.5253 0.420 0.360 0.624 0.016 0.000
#> SRR1946643 3 0.3052 0.264 0.000 0.004 0.860 0.136
#> SRR1946642 1 0.1771 0.695 0.948 0.012 0.004 0.036
#> SRR1946641 1 0.5596 0.525 0.728 0.004 0.180 0.088
#> SRR1946656 3 0.3052 0.264 0.000 0.004 0.860 0.136
#> SRR1946640 1 0.5596 0.525 0.728 0.004 0.180 0.088
#> SRR1946639 1 0.5596 0.525 0.728 0.004 0.180 0.088
#> SRR1946638 1 0.5596 0.525 0.728 0.004 0.180 0.088
#> SRR1946637 1 0.5596 0.525 0.728 0.004 0.180 0.088
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.441 0.3515 0.000 0.028 0.780 0.152 0.040
#> SRR1946691 1 0.469 0.4605 0.708 0.240 0.000 0.048 0.004
#> SRR1946690 2 0.578 -0.0509 0.428 0.504 0.004 0.056 0.008
#> SRR1946689 4 0.811 0.3780 0.168 0.008 0.156 0.468 0.200
#> SRR1946686 3 0.564 0.3609 0.008 0.040 0.572 0.012 0.368
#> SRR1946685 2 0.397 0.5790 0.012 0.824 0.080 0.004 0.080
#> SRR1946688 1 0.413 0.5099 0.756 0.204 0.000 0.040 0.000
#> SRR1946684 5 0.500 0.8270 0.000 0.196 0.104 0.000 0.700
#> SRR1946683 5 0.764 0.6325 0.080 0.320 0.120 0.012 0.468
#> SRR1946682 1 0.624 0.6184 0.592 0.188 0.000 0.012 0.208
#> SRR1946680 4 0.452 0.7168 0.000 0.020 0.336 0.644 0.000
#> SRR1946681 2 0.508 0.0626 0.008 0.504 0.020 0.468 0.000
#> SRR1946687 3 0.345 0.3701 0.000 0.016 0.848 0.100 0.036
#> SRR1946679 2 0.122 0.6418 0.004 0.964 0.004 0.008 0.020
#> SRR1946678 5 0.520 0.7452 0.008 0.140 0.144 0.000 0.708
#> SRR1946676 2 0.135 0.6416 0.008 0.960 0.004 0.008 0.020
#> SRR1946677 1 0.689 0.5570 0.512 0.248 0.000 0.024 0.216
#> SRR1946672 4 0.460 0.6078 0.000 0.012 0.428 0.560 0.000
#> SRR1946673 5 0.512 0.8246 0.000 0.212 0.104 0.000 0.684
#> SRR1946671 5 0.763 0.6574 0.080 0.304 0.124 0.012 0.480
#> SRR1946669 5 0.498 0.8242 0.000 0.188 0.108 0.000 0.704
#> SRR1946668 5 0.501 0.8272 0.000 0.192 0.108 0.000 0.700
#> SRR1946666 3 0.421 0.3499 0.000 0.024 0.788 0.156 0.032
#> SRR1946667 4 0.811 0.3780 0.168 0.008 0.156 0.468 0.200
#> SRR1946670 2 0.373 0.4840 0.160 0.804 0.000 0.004 0.032
#> SRR1946663 1 0.621 0.6199 0.596 0.188 0.000 0.012 0.204
#> SRR1946664 2 0.566 0.1036 0.364 0.568 0.004 0.056 0.008
#> SRR1946662 5 0.474 0.7775 0.000 0.272 0.048 0.000 0.680
#> SRR1946661 1 0.702 0.4007 0.420 0.308 0.000 0.012 0.260
#> SRR1946660 1 0.428 0.5081 0.752 0.204 0.000 0.040 0.004
#> SRR1946659 3 0.200 0.4170 0.032 0.000 0.928 0.004 0.036
#> SRR1946658 2 0.315 0.5128 0.136 0.840 0.000 0.000 0.024
#> SRR1946657 2 0.181 0.6418 0.004 0.940 0.012 0.008 0.036
#> SRR1946655 4 0.436 0.7201 0.000 0.012 0.340 0.648 0.000
#> SRR1946654 3 0.522 0.1316 0.000 0.036 0.680 0.252 0.032
#> SRR1946653 3 0.343 0.3664 0.000 0.016 0.848 0.104 0.032
#> SRR1946652 2 0.280 0.6048 0.008 0.876 0.008 0.004 0.104
#> SRR1946651 2 0.122 0.6418 0.004 0.964 0.004 0.008 0.020
#> SRR1946650 1 0.589 0.5435 0.572 0.340 0.000 0.020 0.068
#> SRR1946649 2 0.797 -0.4650 0.096 0.408 0.124 0.016 0.356
#> SRR1946648 3 0.606 0.4171 0.008 0.076 0.680 0.064 0.172
#> SRR1946647 5 0.501 0.8272 0.000 0.192 0.108 0.000 0.700
#> SRR1946646 3 0.582 0.0437 0.000 0.424 0.508 0.040 0.028
#> SRR1946645 5 0.801 0.6062 0.088 0.316 0.148 0.016 0.432
#> SRR1946644 2 0.413 0.5872 0.012 0.816 0.096 0.008 0.068
#> SRR1946643 4 0.436 0.7201 0.000 0.012 0.340 0.648 0.000
#> SRR1946642 5 0.536 0.6186 0.016 0.092 0.200 0.000 0.692
#> SRR1946641 3 0.524 0.3691 0.048 0.000 0.544 0.000 0.408
#> SRR1946656 4 0.436 0.7201 0.000 0.012 0.340 0.648 0.000
#> SRR1946640 3 0.524 0.3691 0.048 0.000 0.544 0.000 0.408
#> SRR1946639 3 0.524 0.3594 0.048 0.000 0.540 0.000 0.412
#> SRR1946638 3 0.524 0.3691 0.048 0.000 0.544 0.000 0.408
#> SRR1946637 3 0.524 0.3691 0.048 0.000 0.544 0.000 0.408
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.3698 0.6716 0.048 0.020 0.804 0.000 0.128 0.000
#> SRR1946691 4 0.5139 -0.1910 0.000 0.084 0.000 0.492 0.000 0.424
#> SRR1946690 4 0.5601 0.2581 0.000 0.324 0.004 0.528 0.000 0.144
#> SRR1946689 4 0.7731 0.2223 0.192 0.000 0.188 0.440 0.032 0.148
#> SRR1946686 3 0.5630 0.4653 0.060 0.040 0.564 0.004 0.332 0.000
#> SRR1946685 2 0.3316 0.7514 0.004 0.844 0.040 0.012 0.096 0.004
#> SRR1946688 6 0.4983 -0.0142 0.004 0.056 0.000 0.456 0.000 0.484
#> SRR1946684 5 0.1910 0.5873 0.000 0.108 0.000 0.000 0.892 0.000
#> SRR1946683 5 0.6808 0.3163 0.016 0.248 0.036 0.012 0.524 0.164
#> SRR1946682 6 0.4071 0.6236 0.004 0.092 0.004 0.004 0.116 0.780
#> SRR1946680 1 0.4184 0.7637 0.556 0.008 0.432 0.000 0.004 0.000
#> SRR1946681 1 0.4622 -0.0745 0.492 0.480 0.016 0.008 0.000 0.004
#> SRR1946687 3 0.2678 0.6925 0.000 0.020 0.860 0.000 0.116 0.004
#> SRR1946679 2 0.0291 0.7722 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1946678 5 0.1923 0.5710 0.004 0.064 0.016 0.000 0.916 0.000
#> SRR1946676 2 0.0436 0.7752 0.000 0.988 0.004 0.000 0.004 0.004
#> SRR1946677 6 0.5303 0.5945 0.012 0.132 0.012 0.012 0.136 0.696
#> SRR1946672 1 0.4091 0.7034 0.520 0.008 0.472 0.000 0.000 0.000
#> SRR1946673 5 0.2454 0.5637 0.000 0.160 0.000 0.000 0.840 0.000
#> SRR1946671 5 0.6658 0.3358 0.016 0.240 0.032 0.012 0.544 0.156
#> SRR1946669 5 0.1863 0.5890 0.000 0.104 0.000 0.000 0.896 0.000
#> SRR1946668 5 0.1863 0.5890 0.000 0.104 0.000 0.000 0.896 0.000
#> SRR1946666 3 0.3417 0.6713 0.044 0.020 0.828 0.000 0.108 0.000
#> SRR1946667 4 0.7731 0.2223 0.192 0.000 0.188 0.440 0.032 0.148
#> SRR1946670 2 0.4514 0.5977 0.004 0.732 0.008 0.020 0.036 0.200
#> SRR1946663 6 0.3753 0.6204 0.004 0.092 0.000 0.004 0.100 0.800
#> SRR1946664 4 0.5351 0.2691 0.000 0.372 0.004 0.524 0.000 0.100
#> SRR1946662 5 0.2883 0.5297 0.000 0.212 0.000 0.000 0.788 0.000
#> SRR1946661 6 0.6558 0.4023 0.008 0.204 0.004 0.028 0.248 0.508
#> SRR1946660 6 0.4983 -0.0142 0.004 0.056 0.000 0.456 0.000 0.484
#> SRR1946659 3 0.5281 0.3634 0.260 0.000 0.636 0.012 0.080 0.012
#> SRR1946658 2 0.4233 0.6091 0.000 0.748 0.008 0.016 0.036 0.192
#> SRR1946657 2 0.1957 0.7727 0.000 0.920 0.008 0.024 0.048 0.000
#> SRR1946655 1 0.4025 0.7802 0.576 0.008 0.416 0.000 0.000 0.000
#> SRR1946654 3 0.5322 0.3781 0.168 0.052 0.676 0.000 0.104 0.000
#> SRR1946653 3 0.2678 0.6925 0.000 0.020 0.860 0.000 0.116 0.004
#> SRR1946652 2 0.2396 0.7696 0.004 0.892 0.012 0.004 0.084 0.004
#> SRR1946651 2 0.0291 0.7725 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR1946650 6 0.3550 0.5657 0.000 0.188 0.000 0.008 0.024 0.780
#> SRR1946649 5 0.7216 0.2215 0.016 0.304 0.032 0.028 0.456 0.164
#> SRR1946648 3 0.5299 0.5780 0.016 0.040 0.668 0.012 0.240 0.024
#> SRR1946647 5 0.1863 0.5890 0.000 0.104 0.000 0.000 0.896 0.000
#> SRR1946646 2 0.5577 0.1999 0.004 0.508 0.400 0.008 0.072 0.008
#> SRR1946645 5 0.7435 0.2537 0.016 0.232 0.084 0.012 0.472 0.184
#> SRR1946644 2 0.4786 0.6859 0.004 0.740 0.112 0.028 0.112 0.004
#> SRR1946643 1 0.4025 0.7802 0.576 0.008 0.416 0.000 0.000 0.000
#> SRR1946642 5 0.2554 0.5345 0.032 0.032 0.024 0.000 0.900 0.012
#> SRR1946641 5 0.6809 0.1885 0.272 0.000 0.284 0.012 0.408 0.024
#> SRR1946656 1 0.4025 0.7802 0.576 0.008 0.416 0.000 0.000 0.000
#> SRR1946640 5 0.6809 0.1885 0.272 0.000 0.284 0.012 0.408 0.024
#> SRR1946639 5 0.6809 0.1885 0.272 0.000 0.284 0.012 0.408 0.024
#> SRR1946638 5 0.6809 0.1885 0.272 0.000 0.284 0.012 0.408 0.024
#> SRR1946637 5 0.6809 0.1885 0.272 0.000 0.284 0.012 0.408 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.463 0.794 0.867 0.4967 0.505 0.505
#> 3 3 0.811 0.876 0.931 0.3579 0.688 0.454
#> 4 4 0.642 0.610 0.799 0.1204 0.872 0.630
#> 5 5 0.660 0.649 0.787 0.0664 0.893 0.606
#> 6 6 0.711 0.556 0.753 0.0425 0.921 0.635
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
#> SRR1946675 2 0.653 0.8268 0.168 0.832
#> SRR1946691 1 0.821 0.7715 0.744 0.256
#> SRR1946690 1 0.827 0.7677 0.740 0.260
#> SRR1946689 2 0.295 0.7862 0.052 0.948
#> SRR1946686 2 0.821 0.7857 0.256 0.744
#> SRR1946685 1 0.753 0.8250 0.784 0.216
#> SRR1946688 1 0.827 0.7677 0.740 0.260
#> SRR1946684 1 0.295 0.8289 0.948 0.052
#> SRR1946683 1 0.295 0.8289 0.948 0.052
#> SRR1946682 1 0.000 0.8369 1.000 0.000
#> SRR1946680 2 0.295 0.7862 0.052 0.948
#> SRR1946681 1 0.881 0.7276 0.700 0.300
#> SRR1946687 2 0.653 0.8268 0.168 0.832
#> SRR1946679 1 0.671 0.8184 0.824 0.176
#> SRR1946678 1 0.295 0.8289 0.948 0.052
#> SRR1946676 1 0.767 0.7958 0.776 0.224
#> SRR1946677 1 0.000 0.8369 1.000 0.000
#> SRR1946672 2 0.000 0.8058 0.000 1.000
#> SRR1946673 1 0.295 0.8289 0.948 0.052
#> SRR1946671 1 0.295 0.8289 0.948 0.052
#> SRR1946669 1 0.295 0.8289 0.948 0.052
#> SRR1946668 1 0.295 0.8289 0.948 0.052
#> SRR1946666 2 0.653 0.8268 0.168 0.832
#> SRR1946667 2 0.295 0.7862 0.052 0.948
#> SRR1946670 1 0.689 0.8156 0.816 0.184
#> SRR1946663 1 0.000 0.8369 1.000 0.000
#> SRR1946664 1 0.821 0.7715 0.744 0.256
#> SRR1946662 1 0.295 0.8289 0.948 0.052
#> SRR1946661 1 0.000 0.8369 1.000 0.000
#> SRR1946660 1 0.821 0.7715 0.744 0.256
#> SRR1946659 2 0.653 0.8268 0.168 0.832
#> SRR1946658 1 0.767 0.7954 0.776 0.224
#> SRR1946657 1 0.662 0.8218 0.828 0.172
#> SRR1946655 2 0.295 0.7862 0.052 0.948
#> SRR1946654 2 0.000 0.8058 0.000 1.000
#> SRR1946653 2 0.605 0.8282 0.148 0.852
#> SRR1946652 1 0.653 0.8204 0.832 0.168
#> SRR1946651 1 0.662 0.8195 0.828 0.172
#> SRR1946650 1 0.595 0.8279 0.856 0.144
#> SRR1946649 1 0.295 0.8289 0.948 0.052
#> SRR1946648 2 0.662 0.8253 0.172 0.828
#> SRR1946647 1 0.295 0.8289 0.948 0.052
#> SRR1946646 2 0.000 0.8058 0.000 1.000
#> SRR1946645 1 0.430 0.7961 0.912 0.088
#> SRR1946644 2 0.939 0.0936 0.356 0.644
#> SRR1946643 2 0.295 0.7862 0.052 0.948
#> SRR1946642 1 0.295 0.8289 0.948 0.052
#> SRR1946641 2 0.827 0.7835 0.260 0.740
#> SRR1946656 2 0.295 0.7862 0.052 0.948
#> SRR1946640 2 0.827 0.7835 0.260 0.740
#> SRR1946639 2 0.827 0.7835 0.260 0.740
#> SRR1946638 2 0.827 0.7835 0.260 0.740
#> SRR1946637 2 0.827 0.7835 0.260 0.740
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.2625 0.911 0.084 0.000 0.916
#> SRR1946691 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1946690 2 0.2356 0.904 0.000 0.928 0.072
#> SRR1946689 3 0.3192 0.888 0.000 0.112 0.888
#> SRR1946686 1 0.5882 0.431 0.652 0.000 0.348
#> SRR1946685 1 0.8157 0.343 0.596 0.308 0.096
#> SRR1946688 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1946684 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946683 1 0.0592 0.937 0.988 0.012 0.000
#> SRR1946682 2 0.0892 0.909 0.020 0.980 0.000
#> SRR1946680 3 0.0747 0.922 0.000 0.016 0.984
#> SRR1946681 2 0.4399 0.823 0.000 0.812 0.188
#> SRR1946687 3 0.2711 0.909 0.088 0.000 0.912
#> SRR1946679 2 0.2945 0.898 0.004 0.908 0.088
#> SRR1946678 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946676 2 0.3349 0.889 0.004 0.888 0.108
#> SRR1946677 2 0.1765 0.898 0.040 0.956 0.004
#> SRR1946672 3 0.0000 0.927 0.000 0.000 1.000
#> SRR1946673 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946671 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946669 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946668 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946666 3 0.2625 0.911 0.084 0.000 0.916
#> SRR1946667 3 0.3192 0.888 0.000 0.112 0.888
#> SRR1946670 2 0.0237 0.911 0.004 0.996 0.000
#> SRR1946663 2 0.0892 0.909 0.020 0.980 0.000
#> SRR1946664 2 0.2356 0.904 0.000 0.928 0.072
#> SRR1946662 1 0.0237 0.942 0.996 0.004 0.000
#> SRR1946661 2 0.0747 0.910 0.016 0.984 0.000
#> SRR1946660 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1946659 3 0.3192 0.891 0.112 0.000 0.888
#> SRR1946658 2 0.0000 0.911 0.000 1.000 0.000
#> SRR1946657 2 0.4007 0.887 0.036 0.880 0.084
#> SRR1946655 3 0.0424 0.926 0.000 0.008 0.992
#> SRR1946654 3 0.0000 0.927 0.000 0.000 1.000
#> SRR1946653 3 0.2625 0.911 0.084 0.000 0.916
#> SRR1946652 2 0.3445 0.899 0.016 0.896 0.088
#> SRR1946651 2 0.2945 0.898 0.004 0.908 0.088
#> SRR1946650 2 0.0592 0.910 0.012 0.988 0.000
#> SRR1946649 2 0.6008 0.454 0.372 0.628 0.000
#> SRR1946648 3 0.4842 0.761 0.224 0.000 0.776
#> SRR1946647 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946646 3 0.0592 0.924 0.000 0.012 0.988
#> SRR1946645 1 0.1267 0.928 0.972 0.024 0.004
#> SRR1946644 2 0.8295 0.411 0.088 0.548 0.364
#> SRR1946643 3 0.0424 0.926 0.000 0.008 0.992
#> SRR1946642 1 0.0000 0.943 1.000 0.000 0.000
#> SRR1946641 1 0.0892 0.937 0.980 0.000 0.020
#> SRR1946656 3 0.0424 0.926 0.000 0.008 0.992
#> SRR1946640 1 0.0892 0.937 0.980 0.000 0.020
#> SRR1946639 1 0.0892 0.937 0.980 0.000 0.020
#> SRR1946638 1 0.0892 0.937 0.980 0.000 0.020
#> SRR1946637 1 0.0892 0.937 0.980 0.000 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.0779 0.8872 0.016 0.004 0.980 0.000
#> SRR1946691 4 0.4916 0.0934 0.000 0.424 0.000 0.576
#> SRR1946690 2 0.3873 0.6677 0.000 0.772 0.000 0.228
#> SRR1946689 3 0.3266 0.8019 0.000 0.000 0.832 0.168
#> SRR1946686 1 0.4661 0.3552 0.652 0.000 0.348 0.000
#> SRR1946685 2 0.3039 0.7511 0.052 0.900 0.012 0.036
#> SRR1946688 4 0.4776 0.2104 0.000 0.376 0.000 0.624
#> SRR1946684 1 0.5496 0.6499 0.652 0.036 0.000 0.312
#> SRR1946683 4 0.6734 -0.2493 0.380 0.084 0.004 0.532
#> SRR1946682 4 0.0592 0.5516 0.000 0.016 0.000 0.984
#> SRR1946680 3 0.2125 0.8932 0.000 0.076 0.920 0.004
#> SRR1946681 2 0.4388 0.6974 0.000 0.808 0.132 0.060
#> SRR1946687 3 0.0469 0.8835 0.012 0.000 0.988 0.000
#> SRR1946679 2 0.0524 0.8057 0.000 0.988 0.004 0.008
#> SRR1946678 1 0.3016 0.6959 0.872 0.004 0.004 0.120
#> SRR1946676 2 0.0524 0.8053 0.000 0.988 0.004 0.008
#> SRR1946677 4 0.1716 0.5490 0.000 0.064 0.000 0.936
#> SRR1946672 3 0.1867 0.8945 0.000 0.072 0.928 0.000
#> SRR1946673 1 0.5966 0.6279 0.624 0.060 0.000 0.316
#> SRR1946671 1 0.6799 0.3593 0.464 0.096 0.000 0.440
#> SRR1946669 1 0.5411 0.6514 0.656 0.032 0.000 0.312
#> SRR1946668 1 0.5322 0.6518 0.660 0.028 0.000 0.312
#> SRR1946666 3 0.1022 0.8745 0.032 0.000 0.968 0.000
#> SRR1946667 3 0.3219 0.8059 0.000 0.000 0.836 0.164
#> SRR1946670 4 0.4981 -0.0186 0.000 0.464 0.000 0.536
#> SRR1946663 4 0.0592 0.5500 0.000 0.016 0.000 0.984
#> SRR1946664 2 0.3123 0.7379 0.000 0.844 0.000 0.156
#> SRR1946662 1 0.6835 0.5682 0.560 0.124 0.000 0.316
#> SRR1946661 4 0.1867 0.5460 0.000 0.072 0.000 0.928
#> SRR1946660 4 0.4776 0.2104 0.000 0.376 0.000 0.624
#> SRR1946659 3 0.3975 0.6828 0.240 0.000 0.760 0.000
#> SRR1946658 2 0.5155 0.0628 0.000 0.528 0.004 0.468
#> SRR1946657 2 0.0524 0.8042 0.004 0.988 0.000 0.008
#> SRR1946655 3 0.1940 0.8941 0.000 0.076 0.924 0.000
#> SRR1946654 3 0.1940 0.8941 0.000 0.076 0.924 0.000
#> SRR1946653 3 0.0336 0.8843 0.008 0.000 0.992 0.000
#> SRR1946652 2 0.3542 0.6844 0.060 0.864 0.000 0.076
#> SRR1946651 2 0.0336 0.8053 0.000 0.992 0.000 0.008
#> SRR1946650 4 0.4730 0.2957 0.000 0.364 0.000 0.636
#> SRR1946649 4 0.7216 0.1753 0.180 0.284 0.000 0.536
#> SRR1946648 3 0.5306 0.6717 0.236 0.008 0.720 0.036
#> SRR1946647 1 0.5411 0.6514 0.656 0.032 0.000 0.312
#> SRR1946646 3 0.4126 0.7785 0.004 0.216 0.776 0.004
#> SRR1946645 4 0.7598 -0.1228 0.384 0.084 0.040 0.492
#> SRR1946644 2 0.4939 0.7306 0.032 0.808 0.092 0.068
#> SRR1946643 3 0.1940 0.8941 0.000 0.076 0.924 0.000
#> SRR1946642 1 0.2256 0.6978 0.924 0.000 0.020 0.056
#> SRR1946641 1 0.1867 0.6869 0.928 0.000 0.072 0.000
#> SRR1946656 3 0.1940 0.8941 0.000 0.076 0.924 0.000
#> SRR1946640 1 0.1867 0.6869 0.928 0.000 0.072 0.000
#> SRR1946639 1 0.1867 0.6869 0.928 0.000 0.072 0.000
#> SRR1946638 1 0.1867 0.6869 0.928 0.000 0.072 0.000
#> SRR1946637 1 0.1867 0.6869 0.928 0.000 0.072 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.1444 0.784 0.040 0.000 0.948 0.000 0.012
#> SRR1946691 4 0.2445 0.672 0.004 0.108 0.004 0.884 0.000
#> SRR1946690 2 0.4576 0.291 0.004 0.536 0.004 0.456 0.000
#> SRR1946689 3 0.6225 0.582 0.092 0.028 0.572 0.308 0.000
#> SRR1946686 1 0.5490 0.479 0.592 0.000 0.324 0.000 0.084
#> SRR1946685 2 0.2285 0.768 0.024 0.916 0.004 0.004 0.052
#> SRR1946688 4 0.1787 0.695 0.016 0.044 0.004 0.936 0.000
#> SRR1946684 5 0.0693 0.748 0.012 0.008 0.000 0.000 0.980
#> SRR1946683 5 0.5532 0.600 0.144 0.036 0.004 0.100 0.716
#> SRR1946682 4 0.4727 0.709 0.096 0.012 0.000 0.756 0.136
#> SRR1946680 3 0.3023 0.771 0.028 0.012 0.872 0.088 0.000
#> SRR1946681 2 0.5124 0.492 0.004 0.628 0.320 0.048 0.000
#> SRR1946687 3 0.5078 0.706 0.232 0.024 0.704 0.036 0.004
#> SRR1946679 2 0.1179 0.790 0.000 0.964 0.004 0.016 0.016
#> SRR1946678 5 0.2605 0.608 0.148 0.000 0.000 0.000 0.852
#> SRR1946676 2 0.1087 0.788 0.000 0.968 0.016 0.008 0.008
#> SRR1946677 4 0.6160 0.585 0.124 0.024 0.004 0.632 0.216
#> SRR1946672 3 0.0671 0.790 0.004 0.016 0.980 0.000 0.000
#> SRR1946673 5 0.0992 0.747 0.008 0.024 0.000 0.000 0.968
#> SRR1946671 5 0.4648 0.671 0.140 0.028 0.004 0.052 0.776
#> SRR1946669 5 0.0693 0.748 0.012 0.008 0.000 0.000 0.980
#> SRR1946668 5 0.0566 0.747 0.012 0.004 0.000 0.000 0.984
#> SRR1946666 3 0.2787 0.737 0.136 0.000 0.856 0.004 0.004
#> SRR1946667 3 0.6225 0.582 0.092 0.028 0.572 0.308 0.000
#> SRR1946670 4 0.5013 0.584 0.004 0.228 0.004 0.700 0.064
#> SRR1946663 4 0.3817 0.732 0.084 0.008 0.000 0.824 0.084
#> SRR1946664 2 0.3662 0.621 0.000 0.744 0.004 0.252 0.000
#> SRR1946662 5 0.1365 0.743 0.004 0.040 0.000 0.004 0.952
#> SRR1946661 4 0.5979 0.596 0.104 0.028 0.000 0.636 0.232
#> SRR1946660 4 0.1638 0.705 0.000 0.064 0.004 0.932 0.000
#> SRR1946659 1 0.4557 0.443 0.720 0.004 0.240 0.004 0.032
#> SRR1946658 4 0.5615 0.275 0.008 0.404 0.024 0.544 0.020
#> SRR1946657 2 0.1117 0.790 0.000 0.964 0.000 0.016 0.020
#> SRR1946655 3 0.0609 0.790 0.000 0.020 0.980 0.000 0.000
#> SRR1946654 3 0.1372 0.790 0.024 0.016 0.956 0.004 0.000
#> SRR1946653 3 0.5078 0.706 0.232 0.024 0.704 0.036 0.004
#> SRR1946652 2 0.3067 0.726 0.012 0.856 0.000 0.012 0.120
#> SRR1946651 2 0.0912 0.789 0.000 0.972 0.000 0.016 0.012
#> SRR1946650 4 0.5393 0.686 0.092 0.164 0.000 0.712 0.032
#> SRR1946649 5 0.7642 0.379 0.132 0.180 0.004 0.160 0.524
#> SRR1946648 3 0.7375 0.562 0.168 0.024 0.560 0.052 0.196
#> SRR1946647 5 0.0451 0.748 0.008 0.004 0.000 0.000 0.988
#> SRR1946646 3 0.7915 0.328 0.100 0.316 0.420 0.160 0.004
#> SRR1946645 5 0.8131 0.259 0.332 0.040 0.036 0.208 0.384
#> SRR1946644 2 0.6358 0.557 0.064 0.644 0.040 0.224 0.028
#> SRR1946643 3 0.0609 0.790 0.000 0.020 0.980 0.000 0.000
#> SRR1946642 5 0.4273 -0.186 0.448 0.000 0.000 0.000 0.552
#> SRR1946641 1 0.3452 0.824 0.756 0.000 0.000 0.000 0.244
#> SRR1946656 3 0.0609 0.790 0.000 0.020 0.980 0.000 0.000
#> SRR1946640 1 0.3452 0.824 0.756 0.000 0.000 0.000 0.244
#> SRR1946639 1 0.3452 0.824 0.756 0.000 0.000 0.000 0.244
#> SRR1946638 1 0.3452 0.824 0.756 0.000 0.000 0.000 0.244
#> SRR1946637 1 0.3452 0.824 0.756 0.000 0.000 0.000 0.244
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2311 0.7136 0.016 0.000 0.880 0.104 0.000 0.000
#> SRR1946691 6 0.5154 0.5120 0.028 0.080 0.000 0.240 0.000 0.652
#> SRR1946690 2 0.6418 0.1395 0.020 0.428 0.000 0.252 0.000 0.300
#> SRR1946689 4 0.4827 0.4293 0.000 0.000 0.276 0.632 0.000 0.092
#> SRR1946686 1 0.5227 0.3993 0.604 0.000 0.292 0.092 0.012 0.000
#> SRR1946685 2 0.4272 0.6208 0.036 0.800 0.016 0.096 0.044 0.008
#> SRR1946688 6 0.4595 0.5049 0.020 0.040 0.000 0.264 0.000 0.676
#> SRR1946684 5 0.0547 0.8575 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1946683 5 0.6618 0.3203 0.024 0.036 0.000 0.152 0.524 0.264
#> SRR1946682 6 0.1410 0.6080 0.000 0.008 0.000 0.004 0.044 0.944
#> SRR1946680 3 0.2871 0.5858 0.000 0.004 0.804 0.192 0.000 0.000
#> SRR1946681 2 0.5592 0.1870 0.012 0.488 0.424 0.060 0.000 0.016
#> SRR1946687 3 0.4992 0.0599 0.068 0.000 0.472 0.460 0.000 0.000
#> SRR1946679 2 0.0665 0.7118 0.000 0.980 0.000 0.008 0.008 0.004
#> SRR1946678 5 0.2178 0.7506 0.132 0.000 0.000 0.000 0.868 0.000
#> SRR1946676 2 0.0622 0.7098 0.000 0.980 0.012 0.008 0.000 0.000
#> SRR1946677 6 0.4778 0.5390 0.020 0.040 0.000 0.124 0.064 0.752
#> SRR1946672 3 0.0405 0.7579 0.000 0.004 0.988 0.008 0.000 0.000
#> SRR1946673 5 0.0870 0.8518 0.012 0.012 0.000 0.000 0.972 0.004
#> SRR1946671 5 0.5621 0.5273 0.024 0.024 0.000 0.096 0.652 0.204
#> SRR1946669 5 0.0547 0.8575 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1946668 5 0.0547 0.8575 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1946666 3 0.4143 0.6128 0.116 0.000 0.756 0.124 0.000 0.004
#> SRR1946667 4 0.4827 0.4293 0.000 0.000 0.276 0.632 0.000 0.092
#> SRR1946670 6 0.6866 0.3968 0.020 0.224 0.000 0.144 0.080 0.532
#> SRR1946663 6 0.1092 0.6072 0.000 0.000 0.000 0.020 0.020 0.960
#> SRR1946664 2 0.5208 0.5318 0.020 0.664 0.000 0.156 0.000 0.160
#> SRR1946662 5 0.1036 0.8481 0.008 0.024 0.000 0.000 0.964 0.004
#> SRR1946661 6 0.4760 0.5619 0.008 0.044 0.000 0.056 0.156 0.736
#> SRR1946660 6 0.4600 0.5330 0.024 0.056 0.000 0.212 0.000 0.708
#> SRR1946659 1 0.3883 0.5800 0.768 0.000 0.144 0.088 0.000 0.000
#> SRR1946658 6 0.6720 0.1990 0.012 0.360 0.020 0.144 0.016 0.448
#> SRR1946657 2 0.2673 0.6940 0.012 0.888 0.000 0.064 0.016 0.020
#> SRR1946655 3 0.0146 0.7577 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1946654 3 0.1082 0.7520 0.000 0.004 0.956 0.040 0.000 0.000
#> SRR1946653 3 0.4903 0.0610 0.060 0.000 0.476 0.464 0.000 0.000
#> SRR1946652 2 0.3547 0.6338 0.004 0.828 0.000 0.024 0.100 0.044
#> SRR1946651 2 0.0520 0.7133 0.000 0.984 0.000 0.008 0.000 0.008
#> SRR1946650 6 0.3224 0.5769 0.000 0.128 0.000 0.036 0.008 0.828
#> SRR1946649 6 0.7777 -0.0447 0.020 0.160 0.000 0.156 0.328 0.336
#> SRR1946648 4 0.7074 0.1618 0.076 0.000 0.296 0.484 0.108 0.036
#> SRR1946647 5 0.0547 0.8575 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR1946646 4 0.6004 0.4183 0.016 0.288 0.160 0.532 0.000 0.004
#> SRR1946645 6 0.8333 0.2601 0.128 0.060 0.028 0.232 0.124 0.428
#> SRR1946644 4 0.6153 -0.1140 0.028 0.428 0.008 0.460 0.024 0.052
#> SRR1946643 3 0.0405 0.7561 0.000 0.004 0.988 0.008 0.000 0.000
#> SRR1946642 1 0.3817 0.2592 0.568 0.000 0.000 0.000 0.432 0.000
#> SRR1946641 1 0.1387 0.8286 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1946656 3 0.0405 0.7561 0.000 0.004 0.988 0.008 0.000 0.000
#> SRR1946640 1 0.1387 0.8286 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1946639 1 0.1387 0.8286 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1946638 1 0.1387 0.8286 0.932 0.000 0.000 0.000 0.068 0.000
#> SRR1946637 1 0.1387 0.8286 0.932 0.000 0.000 0.000 0.068 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17231 rows and 53 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 0.675 0.826 0.915 0.5033 0.491 0.491
#> 3 3 0.433 0.551 0.818 0.2248 0.749 0.549
#> 4 4 0.629 0.647 0.847 0.0758 0.750 0.472
#> 5 5 0.746 0.812 0.905 0.1314 0.822 0.523
#> 6 6 0.806 0.816 0.913 0.0612 0.927 0.718
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
#> SRR1946675 2 0.1414 0.905 0.020 0.980
#> SRR1946691 1 0.9850 0.149 0.572 0.428
#> SRR1946690 2 0.2423 0.904 0.040 0.960
#> SRR1946689 2 0.0672 0.904 0.008 0.992
#> SRR1946686 2 0.7299 0.755 0.204 0.796
#> SRR1946685 2 0.3584 0.901 0.068 0.932
#> SRR1946688 2 0.4815 0.881 0.104 0.896
#> SRR1946684 1 0.0938 0.913 0.988 0.012
#> SRR1946683 1 0.1414 0.907 0.980 0.020
#> SRR1946682 1 0.0376 0.912 0.996 0.004
#> SRR1946680 2 0.2778 0.903 0.048 0.952
#> SRR1946681 2 0.2778 0.903 0.048 0.952
#> SRR1946687 2 0.1414 0.905 0.020 0.980
#> SRR1946679 2 0.5519 0.859 0.128 0.872
#> SRR1946678 1 0.1843 0.910 0.972 0.028
#> SRR1946676 2 0.2778 0.903 0.048 0.952
#> SRR1946677 1 0.0672 0.912 0.992 0.008
#> SRR1946672 2 0.0376 0.899 0.004 0.996
#> SRR1946673 1 0.0000 0.912 1.000 0.000
#> SRR1946671 1 0.0672 0.912 0.992 0.008
#> SRR1946669 1 0.1843 0.910 0.972 0.028
#> SRR1946668 1 0.2043 0.910 0.968 0.032
#> SRR1946666 2 0.0000 0.901 0.000 1.000
#> SRR1946667 2 0.0000 0.901 0.000 1.000
#> SRR1946670 1 0.0672 0.912 0.992 0.008
#> SRR1946663 1 0.0672 0.912 0.992 0.008
#> SRR1946664 2 0.2778 0.903 0.048 0.952
#> SRR1946662 1 0.0376 0.912 0.996 0.004
#> SRR1946661 1 0.0376 0.912 0.996 0.004
#> SRR1946660 2 0.9833 0.354 0.424 0.576
#> SRR1946659 2 0.6973 0.758 0.188 0.812
#> SRR1946658 1 0.9881 0.106 0.564 0.436
#> SRR1946657 2 0.8016 0.736 0.244 0.756
#> SRR1946655 2 0.0672 0.904 0.008 0.992
#> SRR1946654 2 0.2043 0.906 0.032 0.968
#> SRR1946653 2 0.0000 0.901 0.000 1.000
#> SRR1946652 1 0.4562 0.841 0.904 0.096
#> SRR1946651 2 0.6712 0.813 0.176 0.824
#> SRR1946650 1 0.0672 0.912 0.992 0.008
#> SRR1946649 1 0.3114 0.881 0.944 0.056
#> SRR1946648 2 0.9170 0.528 0.332 0.668
#> SRR1946647 1 0.2043 0.910 0.968 0.032
#> SRR1946646 2 0.0376 0.899 0.004 0.996
#> SRR1946645 1 0.9996 0.039 0.512 0.488
#> SRR1946644 2 0.8144 0.727 0.252 0.748
#> SRR1946643 2 0.2236 0.904 0.036 0.964
#> SRR1946642 1 0.2423 0.906 0.960 0.040
#> SRR1946641 1 0.2778 0.903 0.952 0.048
#> SRR1946656 2 0.1414 0.904 0.020 0.980
#> SRR1946640 1 0.2778 0.903 0.952 0.048
#> SRR1946639 1 0.2778 0.903 0.952 0.048
#> SRR1946638 1 0.2778 0.903 0.952 0.048
#> SRR1946637 1 0.2778 0.903 0.952 0.048
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.1860 0.7619 0.000 0.052 0.948
#> SRR1946691 2 0.3941 0.5970 0.000 0.844 0.156
#> SRR1946690 3 0.6235 0.3803 0.000 0.436 0.564
#> SRR1946689 3 0.4702 0.6529 0.212 0.000 0.788
#> SRR1946686 1 0.9544 0.2479 0.440 0.364 0.196
#> SRR1946685 3 0.6267 0.3472 0.000 0.452 0.548
#> SRR1946688 3 0.1765 0.7656 0.004 0.040 0.956
#> SRR1946684 2 0.6045 -0.0956 0.380 0.620 0.000
#> SRR1946683 2 0.5202 0.4550 0.008 0.772 0.220
#> SRR1946682 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946680 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946681 3 0.6235 0.3803 0.000 0.436 0.564
#> SRR1946687 3 0.0892 0.7791 0.000 0.020 0.980
#> SRR1946679 3 0.6235 0.3803 0.000 0.436 0.564
#> SRR1946678 2 0.6235 -0.2512 0.436 0.564 0.000
#> SRR1946676 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946677 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946672 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946673 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946671 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946669 2 0.6235 -0.2512 0.436 0.564 0.000
#> SRR1946668 2 0.6235 -0.2512 0.436 0.564 0.000
#> SRR1946666 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946667 3 0.4702 0.6529 0.212 0.000 0.788
#> SRR1946670 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946663 2 0.1289 0.6909 0.000 0.968 0.032
#> SRR1946664 3 0.6235 0.3803 0.000 0.436 0.564
#> SRR1946662 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946661 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946660 2 0.5327 0.4173 0.000 0.728 0.272
#> SRR1946659 1 0.6012 0.6777 0.788 0.088 0.124
#> SRR1946658 2 0.3038 0.6459 0.000 0.896 0.104
#> SRR1946657 2 0.4555 0.5543 0.000 0.800 0.200
#> SRR1946655 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946654 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946653 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946652 2 0.1031 0.6990 0.000 0.976 0.024
#> SRR1946651 3 0.6235 0.3803 0.000 0.436 0.564
#> SRR1946650 2 0.0000 0.7081 0.000 1.000 0.000
#> SRR1946649 2 0.0424 0.7059 0.000 0.992 0.008
#> SRR1946648 3 0.6079 0.2416 0.000 0.388 0.612
#> SRR1946647 2 0.6235 -0.2512 0.436 0.564 0.000
#> SRR1946646 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946645 3 0.4702 0.5928 0.000 0.212 0.788
#> SRR1946644 2 0.6225 -0.0948 0.000 0.568 0.432
#> SRR1946643 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946642 1 0.6307 0.3625 0.512 0.488 0.000
#> SRR1946641 1 0.4702 0.8222 0.788 0.212 0.000
#> SRR1946656 3 0.0000 0.7861 0.000 0.000 1.000
#> SRR1946640 1 0.4702 0.8222 0.788 0.212 0.000
#> SRR1946639 1 0.4702 0.8222 0.788 0.212 0.000
#> SRR1946638 1 0.4702 0.8222 0.788 0.212 0.000
#> SRR1946637 1 0.4702 0.8222 0.788 0.212 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.1474 0.890 0.000 0.052 0.948 0.000
#> SRR1946691 2 0.2466 0.679 0.000 0.900 0.096 0.004
#> SRR1946690 2 0.5167 0.294 0.000 0.508 0.488 0.004
#> SRR1946689 4 0.0188 1.000 0.000 0.000 0.004 0.996
#> SRR1946686 1 0.5997 0.586 0.576 0.376 0.048 0.000
#> SRR1946685 2 0.5158 0.322 0.000 0.524 0.472 0.004
#> SRR1946688 3 0.1452 0.902 0.000 0.036 0.956 0.008
#> SRR1946684 2 0.4898 -0.385 0.416 0.584 0.000 0.000
#> SRR1946683 2 0.4621 0.381 0.008 0.708 0.284 0.000
#> SRR1946682 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946680 3 0.0336 0.925 0.000 0.008 0.992 0.000
#> SRR1946681 2 0.5168 0.275 0.000 0.500 0.496 0.004
#> SRR1946687 3 0.0817 0.917 0.000 0.024 0.976 0.000
#> SRR1946679 2 0.5167 0.294 0.000 0.508 0.488 0.004
#> SRR1946678 1 0.4999 0.537 0.508 0.492 0.000 0.000
#> SRR1946676 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1946677 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946672 3 0.0336 0.924 0.008 0.000 0.992 0.000
#> SRR1946673 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946671 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946669 1 0.4999 0.537 0.508 0.492 0.000 0.000
#> SRR1946668 1 0.4999 0.537 0.508 0.492 0.000 0.000
#> SRR1946666 3 0.0336 0.924 0.008 0.000 0.992 0.000
#> SRR1946667 4 0.0188 1.000 0.000 0.000 0.004 0.996
#> SRR1946670 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946663 2 0.1211 0.669 0.000 0.960 0.040 0.000
#> SRR1946664 2 0.5167 0.294 0.000 0.508 0.488 0.004
#> SRR1946662 2 0.0000 0.689 0.000 1.000 0.000 0.000
#> SRR1946661 2 0.0188 0.690 0.000 0.996 0.000 0.004
#> SRR1946660 2 0.3870 0.637 0.000 0.788 0.208 0.004
#> SRR1946659 1 0.0000 0.585 1.000 0.000 0.000 0.000
#> SRR1946658 2 0.1576 0.690 0.000 0.948 0.048 0.004
#> SRR1946657 2 0.2714 0.671 0.000 0.884 0.112 0.004
#> SRR1946655 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1946654 3 0.0336 0.925 0.000 0.008 0.992 0.000
#> SRR1946653 3 0.0524 0.924 0.008 0.004 0.988 0.000
#> SRR1946652 2 0.0895 0.693 0.000 0.976 0.020 0.004
#> SRR1946651 2 0.5167 0.294 0.000 0.508 0.488 0.004
#> SRR1946650 2 0.0524 0.690 0.000 0.988 0.008 0.004
#> SRR1946649 2 0.0336 0.691 0.000 0.992 0.008 0.000
#> SRR1946648 3 0.4817 0.271 0.000 0.388 0.612 0.000
#> SRR1946647 1 0.4999 0.537 0.508 0.492 0.000 0.000
#> SRR1946646 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1946645 3 0.3311 0.727 0.000 0.172 0.828 0.000
#> SRR1946644 2 0.4950 0.455 0.000 0.620 0.376 0.004
#> SRR1946643 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1946642 1 0.4866 0.600 0.596 0.404 0.000 0.000
#> SRR1946641 1 0.0000 0.585 1.000 0.000 0.000 0.000
#> SRR1946656 3 0.0000 0.925 0.000 0.000 1.000 0.000
#> SRR1946640 1 0.0000 0.585 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.585 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.585 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.585 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.1608 0.871 0.000 0.000 0.928 0.000 0.072
#> SRR1946691 2 0.0000 0.759 0.000 1.000 0.000 0.000 0.000
#> SRR1946690 2 0.2690 0.815 0.000 0.844 0.156 0.000 0.000
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 5 0.2171 0.820 0.064 0.000 0.024 0.000 0.912
#> SRR1946685 2 0.4014 0.743 0.000 0.728 0.256 0.000 0.016
#> SRR1946688 3 0.3475 0.752 0.000 0.180 0.804 0.004 0.012
#> SRR1946684 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946683 5 0.0451 0.869 0.000 0.008 0.004 0.000 0.988
#> SRR1946682 5 0.2424 0.787 0.000 0.132 0.000 0.000 0.868
#> SRR1946680 3 0.0000 0.912 0.000 0.000 1.000 0.000 0.000
#> SRR1946681 2 0.4210 0.547 0.000 0.588 0.412 0.000 0.000
#> SRR1946687 3 0.1121 0.895 0.000 0.000 0.956 0.000 0.044
#> SRR1946679 2 0.2773 0.813 0.000 0.836 0.164 0.000 0.000
#> SRR1946678 5 0.0609 0.865 0.020 0.000 0.000 0.000 0.980
#> SRR1946676 3 0.0703 0.906 0.000 0.024 0.976 0.000 0.000
#> SRR1946677 5 0.3636 0.679 0.000 0.272 0.000 0.000 0.728
#> SRR1946672 3 0.0000 0.912 0.000 0.000 1.000 0.000 0.000
#> SRR1946673 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946671 5 0.0609 0.866 0.000 0.020 0.000 0.000 0.980
#> SRR1946669 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946668 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946666 3 0.0609 0.909 0.020 0.000 0.980 0.000 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 5 0.0451 0.869 0.000 0.008 0.004 0.000 0.988
#> SRR1946663 5 0.2424 0.787 0.000 0.132 0.000 0.000 0.868
#> SRR1946664 2 0.2471 0.817 0.000 0.864 0.136 0.000 0.000
#> SRR1946662 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946661 2 0.4030 0.471 0.000 0.648 0.000 0.000 0.352
#> SRR1946660 2 0.0880 0.759 0.000 0.968 0.000 0.000 0.032
#> SRR1946659 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946658 2 0.3409 0.757 0.000 0.816 0.024 0.000 0.160
#> SRR1946657 2 0.3309 0.780 0.000 0.836 0.036 0.000 0.128
#> SRR1946655 3 0.0000 0.912 0.000 0.000 1.000 0.000 0.000
#> SRR1946654 3 0.0579 0.911 0.000 0.008 0.984 0.000 0.008
#> SRR1946653 3 0.0865 0.907 0.024 0.000 0.972 0.000 0.004
#> SRR1946652 5 0.4659 -0.145 0.000 0.492 0.012 0.000 0.496
#> SRR1946651 2 0.2773 0.813 0.000 0.836 0.164 0.000 0.000
#> SRR1946650 2 0.1478 0.758 0.000 0.936 0.000 0.000 0.064
#> SRR1946649 5 0.4321 0.246 0.000 0.396 0.004 0.000 0.600
#> SRR1946648 3 0.4161 0.379 0.000 0.000 0.608 0.000 0.392
#> SRR1946647 5 0.0000 0.871 0.000 0.000 0.000 0.000 1.000
#> SRR1946646 3 0.0794 0.904 0.000 0.028 0.972 0.000 0.000
#> SRR1946645 3 0.3639 0.757 0.044 0.000 0.812 0.000 0.144
#> SRR1946644 2 0.3427 0.819 0.000 0.836 0.108 0.000 0.056
#> SRR1946643 3 0.0000 0.912 0.000 0.000 1.000 0.000 0.000
#> SRR1946642 5 0.2377 0.786 0.128 0.000 0.000 0.000 0.872
#> SRR1946641 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0000 0.912 0.000 0.000 1.000 0.000 0.000
#> SRR1946640 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2260 0.8250 0.000 0.000 0.860 0 0.140 0.000
#> SRR1946691 2 0.1765 0.7555 0.000 0.904 0.000 0 0.000 0.096
#> SRR1946690 2 0.0000 0.7862 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946689 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946686 5 0.1644 0.8764 0.040 0.000 0.028 0 0.932 0.000
#> SRR1946685 2 0.2988 0.6968 0.000 0.828 0.144 0 0.028 0.000
#> SRR1946688 6 0.0520 0.9118 0.000 0.008 0.000 0 0.008 0.984
#> SRR1946684 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946683 5 0.0146 0.9310 0.000 0.000 0.004 0 0.996 0.000
#> SRR1946682 6 0.1444 0.9437 0.000 0.000 0.000 0 0.072 0.928
#> SRR1946680 3 0.0000 0.8540 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946681 2 0.4076 0.4520 0.000 0.592 0.396 0 0.000 0.012
#> SRR1946687 3 0.2219 0.8274 0.000 0.000 0.864 0 0.136 0.000
#> SRR1946679 2 0.0000 0.7862 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946678 5 0.0146 0.9319 0.004 0.000 0.000 0 0.996 0.000
#> SRR1946676 3 0.2300 0.8094 0.000 0.144 0.856 0 0.000 0.000
#> SRR1946677 6 0.1444 0.9437 0.000 0.000 0.000 0 0.072 0.928
#> SRR1946672 3 0.0622 0.8536 0.012 0.000 0.980 0 0.000 0.008
#> SRR1946673 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946671 5 0.0146 0.9316 0.000 0.004 0.000 0 0.996 0.000
#> SRR1946669 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946668 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946666 3 0.2191 0.8255 0.120 0.000 0.876 0 0.004 0.000
#> SRR1946667 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946670 5 0.0622 0.9220 0.000 0.008 0.012 0 0.980 0.000
#> SRR1946663 6 0.1444 0.9437 0.000 0.000 0.000 0 0.072 0.928
#> SRR1946664 2 0.0146 0.7857 0.000 0.996 0.000 0 0.000 0.004
#> SRR1946662 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946661 2 0.3899 0.4505 0.000 0.628 0.000 0 0.364 0.008
#> SRR1946660 6 0.0405 0.9130 0.000 0.004 0.000 0 0.008 0.988
#> SRR1946659 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946658 2 0.1625 0.7646 0.000 0.928 0.012 0 0.060 0.000
#> SRR1946657 2 0.0363 0.7876 0.000 0.988 0.000 0 0.012 0.000
#> SRR1946655 3 0.0000 0.8540 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946654 3 0.2019 0.8488 0.000 0.012 0.900 0 0.088 0.000
#> SRR1946653 3 0.2573 0.8319 0.112 0.000 0.864 0 0.024 0.000
#> SRR1946652 2 0.4185 0.0977 0.000 0.496 0.012 0 0.492 0.000
#> SRR1946651 2 0.0000 0.7862 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946650 2 0.4780 0.3016 0.000 0.552 0.000 0 0.056 0.392
#> SRR1946649 5 0.3881 0.1671 0.000 0.396 0.004 0 0.600 0.000
#> SRR1946648 3 0.3727 0.4638 0.000 0.000 0.612 0 0.388 0.000
#> SRR1946647 5 0.0000 0.9333 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946646 3 0.2572 0.8101 0.012 0.136 0.852 0 0.000 0.000
#> SRR1946645 3 0.3912 0.7168 0.044 0.000 0.732 0 0.224 0.000
#> SRR1946644 2 0.0363 0.7876 0.000 0.988 0.000 0 0.012 0.000
#> SRR1946643 3 0.0363 0.8512 0.000 0.000 0.988 0 0.000 0.012
#> SRR1946642 5 0.2092 0.7969 0.124 0.000 0.000 0 0.876 0.000
#> SRR1946641 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946656 3 0.0363 0.8512 0.000 0.000 0.988 0 0.000 0.012
#> SRR1946640 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946639 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946638 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946637 1 0.0000 1.0000 1.000 0.000 0.000 0 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.484 0.876 0.905 0.3089 0.713 0.713
#> 3 3 0.334 0.661 0.783 0.8694 0.441 0.352
#> 4 4 0.376 0.696 0.754 0.1676 0.706 0.434
#> 5 5 0.590 0.535 0.721 0.0999 0.877 0.649
#> 6 6 0.633 0.691 0.733 0.0499 0.819 0.439
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
#> SRR1946675 1 0.0376 0.904 0.996 0.004
#> SRR1946691 2 0.7815 0.865 0.232 0.768
#> SRR1946690 1 0.5737 0.847 0.864 0.136
#> SRR1946689 2 0.4161 0.895 0.084 0.916
#> SRR1946686 1 0.0376 0.904 0.996 0.004
#> SRR1946685 1 0.3114 0.888 0.944 0.056
#> SRR1946688 2 0.7674 0.874 0.224 0.776
#> SRR1946684 1 0.4161 0.874 0.916 0.084
#> SRR1946683 1 0.4161 0.874 0.916 0.084
#> SRR1946682 1 0.5737 0.847 0.864 0.136
#> SRR1946680 2 0.8207 0.808 0.256 0.744
#> SRR1946681 1 0.5629 0.849 0.868 0.132
#> SRR1946687 1 0.0376 0.904 0.996 0.004
#> SRR1946679 1 0.6531 0.813 0.832 0.168
#> SRR1946678 1 0.4161 0.874 0.916 0.084
#> SRR1946676 1 0.5946 0.840 0.856 0.144
#> SRR1946677 1 0.6887 0.844 0.816 0.184
#> SRR1946672 1 0.5737 0.848 0.864 0.136
#> SRR1946673 1 0.4161 0.874 0.916 0.084
#> SRR1946671 1 0.4161 0.874 0.916 0.084
#> SRR1946669 1 0.4161 0.874 0.916 0.084
#> SRR1946668 1 0.4161 0.874 0.916 0.084
#> SRR1946666 1 0.0376 0.904 0.996 0.004
#> SRR1946667 2 0.4161 0.895 0.084 0.916
#> SRR1946670 1 0.5737 0.847 0.864 0.136
#> SRR1946663 1 0.5737 0.847 0.864 0.136
#> SRR1946664 1 0.5737 0.847 0.864 0.136
#> SRR1946662 1 0.3879 0.878 0.924 0.076
#> SRR1946661 1 0.5946 0.849 0.856 0.144
#> SRR1946660 2 0.7674 0.874 0.224 0.776
#> SRR1946659 1 0.0376 0.904 0.996 0.004
#> SRR1946658 1 0.5946 0.840 0.856 0.144
#> SRR1946657 1 0.4690 0.868 0.900 0.100
#> SRR1946655 2 0.5178 0.913 0.116 0.884
#> SRR1946654 1 0.4431 0.874 0.908 0.092
#> SRR1946653 1 0.0376 0.904 0.996 0.004
#> SRR1946652 1 0.0938 0.904 0.988 0.012
#> SRR1946651 1 0.6343 0.823 0.840 0.160
#> SRR1946650 1 0.5737 0.847 0.864 0.136
#> SRR1946649 1 0.4161 0.874 0.916 0.084
#> SRR1946648 1 0.2423 0.894 0.960 0.040
#> SRR1946647 1 0.4161 0.874 0.916 0.084
#> SRR1946646 1 0.0376 0.904 0.996 0.004
#> SRR1946645 1 0.4161 0.874 0.916 0.084
#> SRR1946644 1 0.2236 0.894 0.964 0.036
#> SRR1946643 2 0.5178 0.913 0.116 0.884
#> SRR1946642 1 0.0000 0.904 1.000 0.000
#> SRR1946641 1 0.0376 0.904 0.996 0.004
#> SRR1946656 2 0.5178 0.913 0.116 0.884
#> SRR1946640 1 0.0376 0.904 0.996 0.004
#> SRR1946639 1 0.0376 0.904 0.996 0.004
#> SRR1946638 1 0.0376 0.904 0.996 0.004
#> SRR1946637 1 0.0376 0.904 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.5397 0.6342 0.280 0.720 0.000
#> SRR1946691 2 0.0747 0.7037 0.000 0.984 0.016
#> SRR1946690 2 0.0747 0.7073 0.016 0.984 0.000
#> SRR1946689 2 0.5397 0.6680 0.000 0.720 0.280
#> SRR1946686 2 0.5254 0.6417 0.264 0.736 0.000
#> SRR1946685 1 0.1031 0.8144 0.976 0.024 0.000
#> SRR1946688 2 0.1163 0.7048 0.000 0.972 0.028
#> SRR1946684 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946683 1 0.0237 0.8240 0.996 0.004 0.000
#> SRR1946682 2 0.6824 0.0741 0.408 0.576 0.016
#> SRR1946680 2 0.6906 0.6838 0.084 0.724 0.192
#> SRR1946681 2 0.2703 0.7124 0.056 0.928 0.016
#> SRR1946687 2 0.7163 0.6797 0.136 0.720 0.144
#> SRR1946679 2 0.5859 0.3595 0.344 0.656 0.000
#> SRR1946678 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946676 2 0.3116 0.6889 0.108 0.892 0.000
#> SRR1946677 1 0.6359 0.5118 0.628 0.364 0.008
#> SRR1946672 2 0.6662 0.6858 0.072 0.736 0.192
#> SRR1946673 1 0.0592 0.8236 0.988 0.012 0.000
#> SRR1946671 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946669 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946668 1 0.0237 0.8250 0.996 0.004 0.000
#> SRR1946666 2 0.5588 0.6358 0.276 0.720 0.004
#> SRR1946667 2 0.5397 0.6680 0.000 0.720 0.280
#> SRR1946670 2 0.6079 0.1859 0.388 0.612 0.000
#> SRR1946663 2 0.2703 0.6851 0.056 0.928 0.016
#> SRR1946664 2 0.0747 0.7073 0.016 0.984 0.000
#> SRR1946662 1 0.3816 0.7252 0.852 0.148 0.000
#> SRR1946661 1 0.6769 0.4553 0.592 0.392 0.016
#> SRR1946660 2 0.0747 0.7037 0.000 0.984 0.016
#> SRR1946659 2 0.6565 0.6506 0.232 0.720 0.048
#> SRR1946658 2 0.6018 0.4048 0.308 0.684 0.008
#> SRR1946657 2 0.6252 0.0548 0.444 0.556 0.000
#> SRR1946655 2 0.5443 0.6701 0.004 0.736 0.260
#> SRR1946654 2 0.5254 0.6417 0.264 0.736 0.000
#> SRR1946653 2 0.7155 0.6808 0.128 0.720 0.152
#> SRR1946652 1 0.6062 0.4383 0.616 0.384 0.000
#> SRR1946651 2 0.5706 0.4109 0.320 0.680 0.000
#> SRR1946650 2 0.6161 0.4029 0.288 0.696 0.016
#> SRR1946649 1 0.3412 0.7517 0.876 0.124 0.000
#> SRR1946648 2 0.5968 0.5208 0.364 0.636 0.000
#> SRR1946647 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946646 2 0.4931 0.6574 0.232 0.768 0.000
#> SRR1946645 1 0.0000 0.8250 1.000 0.000 0.000
#> SRR1946644 2 0.6309 0.2122 0.496 0.504 0.000
#> SRR1946643 2 0.5580 0.6716 0.008 0.736 0.256
#> SRR1946642 1 0.2537 0.7514 0.920 0.080 0.000
#> SRR1946641 3 0.5397 0.9982 0.280 0.000 0.720
#> SRR1946656 2 0.5443 0.6701 0.004 0.736 0.260
#> SRR1946640 3 0.5397 0.9982 0.280 0.000 0.720
#> SRR1946639 3 0.5623 0.9930 0.280 0.004 0.716
#> SRR1946638 3 0.5397 0.9982 0.280 0.000 0.720
#> SRR1946637 3 0.5397 0.9982 0.280 0.000 0.720
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.1807 0.7044 0.008 0.052 0.940 0.000
#> SRR1946691 2 0.1356 0.7426 0.008 0.960 0.032 0.000
#> SRR1946690 2 0.4287 0.7388 0.088 0.828 0.080 0.004
#> SRR1946689 3 0.6096 0.5682 0.044 0.004 0.576 0.376
#> SRR1946686 3 0.3286 0.6522 0.044 0.080 0.876 0.000
#> SRR1946685 2 0.6488 0.4122 0.244 0.628 0.128 0.000
#> SRR1946688 2 0.5353 -0.0747 0.012 0.556 0.432 0.000
#> SRR1946684 1 0.5676 0.8921 0.720 0.144 0.136 0.000
#> SRR1946683 1 0.7058 0.5491 0.520 0.344 0.136 0.000
#> SRR1946682 2 0.0336 0.7429 0.008 0.992 0.000 0.000
#> SRR1946680 3 0.7657 0.6923 0.192 0.052 0.604 0.152
#> SRR1946681 3 0.7738 0.4155 0.300 0.224 0.472 0.004
#> SRR1946687 3 0.1389 0.7038 0.000 0.048 0.952 0.000
#> SRR1946679 2 0.5288 0.7087 0.196 0.740 0.060 0.004
#> SRR1946678 1 0.5417 0.7495 0.704 0.056 0.240 0.000
#> SRR1946676 2 0.6176 0.6764 0.200 0.680 0.116 0.004
#> SRR1946677 2 0.3710 0.6445 0.192 0.804 0.004 0.000
#> SRR1946672 3 0.6929 0.7052 0.144 0.052 0.676 0.128
#> SRR1946673 1 0.5849 0.8839 0.704 0.164 0.132 0.000
#> SRR1946671 1 0.5897 0.8859 0.700 0.164 0.136 0.000
#> SRR1946669 1 0.5677 0.8909 0.720 0.140 0.140 0.000
#> SRR1946668 1 0.5722 0.8916 0.716 0.148 0.136 0.000
#> SRR1946666 3 0.1389 0.7038 0.000 0.048 0.952 0.000
#> SRR1946667 3 0.6096 0.5682 0.044 0.004 0.576 0.376
#> SRR1946670 2 0.0779 0.7520 0.004 0.980 0.016 0.000
#> SRR1946663 2 0.0592 0.7470 0.000 0.984 0.016 0.000
#> SRR1946664 2 0.4287 0.7388 0.088 0.828 0.080 0.004
#> SRR1946662 1 0.5395 0.8071 0.732 0.184 0.084 0.000
#> SRR1946661 2 0.1118 0.7237 0.036 0.964 0.000 0.000
#> SRR1946660 2 0.1488 0.7407 0.012 0.956 0.032 0.000
#> SRR1946659 3 0.1661 0.7035 0.004 0.052 0.944 0.000
#> SRR1946658 2 0.3181 0.7569 0.044 0.888 0.064 0.004
#> SRR1946657 2 0.5905 0.6707 0.200 0.700 0.096 0.004
#> SRR1946655 3 0.7332 0.6917 0.152 0.048 0.636 0.164
#> SRR1946654 3 0.2928 0.7151 0.052 0.052 0.896 0.000
#> SRR1946653 3 0.1389 0.7038 0.000 0.048 0.952 0.000
#> SRR1946652 2 0.5842 0.6353 0.220 0.688 0.092 0.000
#> SRR1946651 2 0.4277 0.7432 0.116 0.824 0.056 0.004
#> SRR1946650 2 0.0000 0.7459 0.000 1.000 0.000 0.000
#> SRR1946649 2 0.6708 0.3319 0.272 0.596 0.132 0.000
#> SRR1946648 3 0.3758 0.6544 0.104 0.048 0.848 0.000
#> SRR1946647 1 0.5724 0.8886 0.716 0.140 0.144 0.000
#> SRR1946646 3 0.4568 0.6377 0.076 0.124 0.800 0.000
#> SRR1946645 2 0.7287 -0.2049 0.384 0.464 0.152 0.000
#> SRR1946644 2 0.6352 0.5475 0.156 0.656 0.188 0.000
#> SRR1946643 3 0.7332 0.6917 0.152 0.048 0.636 0.164
#> SRR1946642 1 0.5417 0.7496 0.704 0.056 0.240 0.000
#> SRR1946641 4 0.5713 0.9968 0.040 0.000 0.340 0.620
#> SRR1946656 3 0.7332 0.6917 0.152 0.048 0.636 0.164
#> SRR1946640 4 0.5713 0.9968 0.040 0.000 0.340 0.620
#> SRR1946639 4 0.5746 0.9872 0.040 0.000 0.348 0.612
#> SRR1946638 4 0.5713 0.9968 0.040 0.000 0.340 0.620
#> SRR1946637 4 0.5713 0.9968 0.040 0.000 0.340 0.620
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.1106 0.472 0.000 0.012 0.964 0.000 0.024
#> SRR1946691 2 0.4030 0.730 0.000 0.648 0.000 0.352 0.000
#> SRR1946690 2 0.1661 0.728 0.000 0.940 0.024 0.036 0.000
#> SRR1946689 4 0.5664 1.000 0.220 0.000 0.152 0.628 0.000
#> SRR1946686 3 0.3463 0.458 0.040 0.044 0.860 0.000 0.056
#> SRR1946685 2 0.4242 0.398 0.000 0.572 0.000 0.000 0.428
#> SRR1946688 2 0.4166 0.730 0.000 0.648 0.004 0.348 0.000
#> SRR1946684 5 0.0162 0.844 0.004 0.000 0.000 0.000 0.996
#> SRR1946683 5 0.1571 0.816 0.004 0.060 0.000 0.000 0.936
#> SRR1946682 2 0.4668 0.743 0.000 0.624 0.000 0.352 0.024
#> SRR1946680 3 0.6427 0.167 0.072 0.332 0.552 0.040 0.004
#> SRR1946681 2 0.3820 0.700 0.016 0.836 0.084 0.004 0.060
#> SRR1946687 3 0.0960 0.456 0.008 0.000 0.972 0.016 0.004
#> SRR1946679 2 0.2291 0.728 0.012 0.908 0.000 0.008 0.072
#> SRR1946678 5 0.1661 0.803 0.024 0.000 0.036 0.000 0.940
#> SRR1946676 2 0.2387 0.728 0.012 0.908 0.004 0.008 0.068
#> SRR1946677 2 0.6378 0.579 0.000 0.528 0.004 0.180 0.288
#> SRR1946672 3 0.4237 0.491 0.152 0.076 0.772 0.000 0.000
#> SRR1946673 5 0.0566 0.845 0.004 0.012 0.000 0.000 0.984
#> SRR1946671 5 0.0404 0.845 0.000 0.012 0.000 0.000 0.988
#> SRR1946669 5 0.0324 0.843 0.004 0.000 0.004 0.000 0.992
#> SRR1946668 5 0.0162 0.844 0.004 0.000 0.000 0.000 0.996
#> SRR1946666 3 0.0324 0.444 0.004 0.000 0.992 0.000 0.004
#> SRR1946667 4 0.5664 1.000 0.220 0.000 0.152 0.628 0.000
#> SRR1946670 2 0.5571 0.748 0.000 0.600 0.020 0.332 0.048
#> SRR1946663 2 0.4030 0.730 0.000 0.648 0.000 0.352 0.000
#> SRR1946664 2 0.1605 0.732 0.004 0.944 0.012 0.040 0.000
#> SRR1946662 5 0.1662 0.812 0.004 0.056 0.000 0.004 0.936
#> SRR1946661 2 0.5006 0.744 0.000 0.624 0.000 0.328 0.048
#> SRR1946660 2 0.4030 0.730 0.000 0.648 0.000 0.352 0.000
#> SRR1946659 3 0.1121 0.461 0.008 0.004 0.968 0.016 0.004
#> SRR1946658 2 0.2844 0.745 0.000 0.888 0.032 0.016 0.064
#> SRR1946657 2 0.2989 0.715 0.008 0.852 0.000 0.008 0.132
#> SRR1946655 3 0.6155 0.112 0.428 0.076 0.476 0.020 0.000
#> SRR1946654 3 0.3436 0.499 0.012 0.080 0.852 0.000 0.056
#> SRR1946653 3 0.0960 0.462 0.008 0.000 0.972 0.016 0.004
#> SRR1946652 2 0.3333 0.672 0.000 0.788 0.000 0.004 0.208
#> SRR1946651 2 0.2115 0.731 0.008 0.916 0.000 0.008 0.068
#> SRR1946650 2 0.4846 0.743 0.004 0.612 0.000 0.360 0.024
#> SRR1946649 5 0.4443 -0.204 0.004 0.472 0.000 0.000 0.524
#> SRR1946648 3 0.3972 0.383 0.008 0.000 0.764 0.016 0.212
#> SRR1946647 5 0.0324 0.845 0.004 0.004 0.000 0.000 0.992
#> SRR1946646 3 0.5475 0.435 0.008 0.132 0.712 0.016 0.132
#> SRR1946645 5 0.4118 0.306 0.004 0.336 0.000 0.000 0.660
#> SRR1946644 2 0.5764 0.500 0.004 0.572 0.024 0.040 0.360
#> SRR1946643 3 0.6076 0.116 0.432 0.076 0.476 0.016 0.000
#> SRR1946642 5 0.2848 0.730 0.028 0.000 0.104 0.000 0.868
#> SRR1946641 1 0.4748 1.000 0.492 0.000 0.492 0.000 0.016
#> SRR1946656 3 0.6076 0.116 0.432 0.076 0.476 0.016 0.000
#> SRR1946640 1 0.4748 1.000 0.492 0.000 0.492 0.000 0.016
#> SRR1946639 3 0.4723 -0.899 0.448 0.000 0.536 0.000 0.016
#> SRR1946638 3 0.4748 -1.000 0.492 0.000 0.492 0.000 0.016
#> SRR1946637 3 0.4748 -1.000 0.492 0.000 0.492 0.000 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0622 0.8160 0.000 0.012 0.980 0.000 0.008 0.000
#> SRR1946691 6 0.3371 0.8540 0.000 0.292 0.000 0.000 0.000 0.708
#> SRR1946690 2 0.2191 0.7480 0.000 0.876 0.000 0.000 0.004 0.120
#> SRR1946689 1 0.6243 -0.0256 0.448 0.012 0.000 0.252 0.000 0.288
#> SRR1946686 3 0.0964 0.8148 0.004 0.012 0.968 0.000 0.016 0.000
#> SRR1946685 5 0.3601 0.4931 0.000 0.312 0.000 0.000 0.684 0.004
#> SRR1946688 6 0.3351 0.8519 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1946684 5 0.0000 0.7817 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946683 5 0.0713 0.7861 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1946682 6 0.4435 0.8300 0.000 0.264 0.000 0.000 0.064 0.672
#> SRR1946680 3 0.5644 -0.0669 0.000 0.424 0.456 0.112 0.004 0.004
#> SRR1946681 2 0.3087 0.8065 0.000 0.864 0.064 0.040 0.028 0.004
#> SRR1946687 3 0.0000 0.8100 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1946679 2 0.1327 0.8927 0.000 0.936 0.000 0.000 0.064 0.000
#> SRR1946678 5 0.4495 0.3483 0.064 0.000 0.276 0.000 0.660 0.000
#> SRR1946676 2 0.1327 0.8927 0.000 0.936 0.000 0.000 0.064 0.000
#> SRR1946677 5 0.5516 0.2397 0.000 0.244 0.000 0.000 0.560 0.196
#> SRR1946672 3 0.2730 0.6562 0.000 0.012 0.836 0.152 0.000 0.000
#> SRR1946673 5 0.0458 0.7854 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1946671 5 0.0713 0.7861 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1946669 5 0.0146 0.7792 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1946668 5 0.0000 0.7817 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946666 3 0.0000 0.8100 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1946667 1 0.6243 -0.0256 0.448 0.012 0.000 0.252 0.000 0.288
#> SRR1946670 6 0.5673 0.5548 0.000 0.356 0.000 0.000 0.164 0.480
#> SRR1946663 6 0.3390 0.8545 0.000 0.296 0.000 0.000 0.000 0.704
#> SRR1946664 2 0.2191 0.7480 0.000 0.876 0.000 0.000 0.004 0.120
#> SRR1946662 5 0.2092 0.7283 0.000 0.124 0.000 0.000 0.876 0.000
#> SRR1946661 6 0.5255 0.7443 0.000 0.272 0.000 0.000 0.140 0.588
#> SRR1946660 6 0.3351 0.8519 0.000 0.288 0.000 0.000 0.000 0.712
#> SRR1946659 3 0.0260 0.8131 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1946658 2 0.2917 0.8434 0.000 0.852 0.004 0.000 0.104 0.040
#> SRR1946657 2 0.1327 0.8927 0.000 0.936 0.000 0.000 0.064 0.000
#> SRR1946655 4 0.3265 1.0000 0.000 0.004 0.248 0.748 0.000 0.000
#> SRR1946654 3 0.1237 0.8105 0.000 0.020 0.956 0.004 0.020 0.000
#> SRR1946653 3 0.0000 0.8100 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1946652 2 0.1863 0.8624 0.000 0.896 0.000 0.000 0.104 0.000
#> SRR1946651 2 0.1531 0.8916 0.000 0.928 0.000 0.000 0.068 0.004
#> SRR1946650 6 0.4291 0.8377 0.000 0.268 0.000 0.000 0.052 0.680
#> SRR1946649 5 0.3050 0.6078 0.000 0.236 0.000 0.000 0.764 0.000
#> SRR1946648 3 0.2883 0.5801 0.000 0.000 0.788 0.000 0.212 0.000
#> SRR1946647 5 0.0260 0.7838 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1946646 3 0.2309 0.7508 0.000 0.028 0.888 0.000 0.084 0.000
#> SRR1946645 5 0.1957 0.7415 0.000 0.112 0.000 0.000 0.888 0.000
#> SRR1946644 5 0.5424 0.3711 0.000 0.264 0.024 0.000 0.612 0.100
#> SRR1946643 4 0.3265 1.0000 0.000 0.004 0.248 0.748 0.000 0.000
#> SRR1946642 5 0.4587 0.3078 0.064 0.000 0.296 0.000 0.640 0.000
#> SRR1946641 1 0.4062 0.5632 0.552 0.000 0.440 0.000 0.008 0.000
#> SRR1946656 4 0.3265 1.0000 0.000 0.004 0.248 0.748 0.000 0.000
#> SRR1946640 1 0.4062 0.5632 0.552 0.000 0.440 0.000 0.008 0.000
#> SRR1946639 1 0.4062 0.5632 0.552 0.000 0.440 0.000 0.008 0.000
#> SRR1946638 1 0.4062 0.5632 0.552 0.000 0.440 0.000 0.008 0.000
#> SRR1946637 1 0.4062 0.5632 0.552 0.000 0.440 0.000 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.720 0.885 0.932 0.499 0.495 0.495
#> 3 3 0.607 0.748 0.857 0.328 0.718 0.491
#> 4 4 0.577 0.712 0.829 0.138 0.739 0.371
#> 5 5 0.746 0.640 0.838 0.067 0.819 0.415
#> 6 6 0.861 0.793 0.900 0.046 0.898 0.554
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
#> SRR1946675 1 0.2603 0.924 0.956 0.044
#> SRR1946691 2 0.2236 0.925 0.036 0.964
#> SRR1946690 2 0.0376 0.921 0.004 0.996
#> SRR1946689 2 0.4298 0.898 0.088 0.912
#> SRR1946686 1 0.2603 0.924 0.956 0.044
#> SRR1946685 1 0.9933 0.174 0.548 0.452
#> SRR1946688 2 0.2423 0.925 0.040 0.960
#> SRR1946684 2 0.4939 0.915 0.108 0.892
#> SRR1946683 2 0.6438 0.843 0.164 0.836
#> SRR1946682 2 0.2603 0.924 0.044 0.956
#> SRR1946680 2 0.6623 0.825 0.172 0.828
#> SRR1946681 2 0.4562 0.901 0.096 0.904
#> SRR1946687 1 0.0376 0.932 0.996 0.004
#> SRR1946679 2 0.3114 0.921 0.056 0.944
#> SRR1946678 1 0.0376 0.932 0.996 0.004
#> SRR1946676 2 0.4431 0.903 0.092 0.908
#> SRR1946677 2 0.2603 0.924 0.044 0.956
#> SRR1946672 1 0.2603 0.924 0.956 0.044
#> SRR1946673 2 0.4815 0.916 0.104 0.896
#> SRR1946671 2 0.8016 0.773 0.244 0.756
#> SRR1946669 1 0.7674 0.685 0.776 0.224
#> SRR1946668 2 0.4298 0.910 0.088 0.912
#> SRR1946666 1 0.0376 0.932 0.996 0.004
#> SRR1946667 2 0.8763 0.590 0.296 0.704
#> SRR1946670 2 0.0672 0.924 0.008 0.992
#> SRR1946663 2 0.2603 0.924 0.044 0.956
#> SRR1946664 2 0.0376 0.921 0.004 0.996
#> SRR1946662 2 0.3879 0.919 0.076 0.924
#> SRR1946661 2 0.2603 0.924 0.044 0.956
#> SRR1946660 2 0.2423 0.925 0.040 0.960
#> SRR1946659 1 0.0000 0.932 1.000 0.000
#> SRR1946658 2 0.0376 0.921 0.004 0.996
#> SRR1946657 2 0.3114 0.921 0.056 0.944
#> SRR1946655 1 0.2603 0.924 0.956 0.044
#> SRR1946654 1 0.2603 0.924 0.956 0.044
#> SRR1946653 1 0.0000 0.932 1.000 0.000
#> SRR1946652 2 0.1633 0.925 0.024 0.976
#> SRR1946651 2 0.1843 0.925 0.028 0.972
#> SRR1946650 2 0.2603 0.924 0.044 0.956
#> SRR1946649 2 0.4562 0.910 0.096 0.904
#> SRR1946648 1 0.0376 0.932 0.996 0.004
#> SRR1946647 1 0.3879 0.881 0.924 0.076
#> SRR1946646 1 0.2603 0.924 0.956 0.044
#> SRR1946645 1 0.8386 0.600 0.732 0.268
#> SRR1946644 2 0.2236 0.926 0.036 0.964
#> SRR1946643 1 0.2603 0.924 0.956 0.044
#> SRR1946642 1 0.0376 0.932 0.996 0.004
#> SRR1946641 1 0.0376 0.932 0.996 0.004
#> SRR1946656 1 0.2603 0.924 0.956 0.044
#> SRR1946640 1 0.0376 0.932 0.996 0.004
#> SRR1946639 1 0.0376 0.932 0.996 0.004
#> SRR1946638 1 0.0376 0.932 0.996 0.004
#> SRR1946637 1 0.0376 0.932 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.4605 0.7270 0.204 0.000 0.796
#> SRR1946691 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946690 2 0.4235 0.8101 0.000 0.824 0.176
#> SRR1946689 3 0.5254 0.6657 0.000 0.264 0.736
#> SRR1946686 1 0.4654 0.6599 0.792 0.000 0.208
#> SRR1946685 3 0.7337 -0.2427 0.032 0.428 0.540
#> SRR1946688 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946684 1 0.8159 0.2980 0.588 0.320 0.092
#> SRR1946683 2 0.7542 0.7508 0.120 0.688 0.192
#> SRR1946682 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946680 3 0.2846 0.7816 0.020 0.056 0.924
#> SRR1946681 3 0.5988 0.0337 0.000 0.368 0.632
#> SRR1946687 3 0.5327 0.6583 0.272 0.000 0.728
#> SRR1946679 2 0.5254 0.7938 0.000 0.736 0.264
#> SRR1946678 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946676 2 0.5327 0.7878 0.000 0.728 0.272
#> SRR1946677 2 0.0000 0.8163 0.000 1.000 0.000
#> SRR1946672 3 0.3816 0.7637 0.148 0.000 0.852
#> SRR1946673 2 0.7884 0.6777 0.224 0.656 0.120
#> SRR1946671 2 0.7919 0.4088 0.380 0.556 0.064
#> SRR1946669 1 0.2651 0.8686 0.928 0.012 0.060
#> SRR1946668 1 0.4953 0.7620 0.808 0.176 0.016
#> SRR1946666 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946667 3 0.5254 0.6657 0.000 0.264 0.736
#> SRR1946670 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946663 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946664 2 0.5178 0.7977 0.000 0.744 0.256
#> SRR1946662 2 0.6722 0.7874 0.060 0.720 0.220
#> SRR1946661 2 0.0237 0.8159 0.000 0.996 0.004
#> SRR1946660 2 0.0424 0.8149 0.000 0.992 0.008
#> SRR1946659 1 0.0892 0.8955 0.980 0.000 0.020
#> SRR1946658 2 0.2448 0.8209 0.000 0.924 0.076
#> SRR1946657 2 0.5254 0.7938 0.000 0.736 0.264
#> SRR1946655 3 0.2165 0.7896 0.064 0.000 0.936
#> SRR1946654 3 0.2711 0.7863 0.088 0.000 0.912
#> SRR1946653 3 0.5327 0.6583 0.272 0.000 0.728
#> SRR1946652 2 0.5254 0.7938 0.000 0.736 0.264
#> SRR1946651 2 0.5254 0.7938 0.000 0.736 0.264
#> SRR1946650 2 0.0237 0.8159 0.000 0.996 0.004
#> SRR1946649 2 0.5698 0.7960 0.012 0.736 0.252
#> SRR1946648 3 0.5678 0.6039 0.316 0.000 0.684
#> SRR1946647 1 0.2173 0.8811 0.944 0.008 0.048
#> SRR1946646 3 0.2165 0.7893 0.064 0.000 0.936
#> SRR1946645 1 0.2903 0.8764 0.924 0.048 0.028
#> SRR1946644 3 0.0592 0.7595 0.000 0.012 0.988
#> SRR1946643 3 0.0747 0.7742 0.016 0.000 0.984
#> SRR1946642 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946656 3 0.2066 0.7887 0.060 0.000 0.940
#> SRR1946640 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9094 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9094 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.3577 0.814 0.012 0.156 0.832 0.000
#> SRR1946691 4 0.0336 0.788 0.008 0.000 0.000 0.992
#> SRR1946690 4 0.4998 -0.203 0.000 0.488 0.000 0.512
#> SRR1946689 4 0.3400 0.665 0.000 0.000 0.180 0.820
#> SRR1946686 3 0.3708 0.813 0.020 0.148 0.832 0.000
#> SRR1946685 2 0.0000 0.766 0.000 1.000 0.000 0.000
#> SRR1946688 4 0.1716 0.755 0.000 0.000 0.064 0.936
#> SRR1946684 1 0.3123 0.713 0.844 0.000 0.000 0.156
#> SRR1946683 1 0.6340 0.607 0.712 0.056 0.064 0.168
#> SRR1946682 4 0.2760 0.768 0.128 0.000 0.000 0.872
#> SRR1946680 3 0.4827 0.781 0.000 0.124 0.784 0.092
#> SRR1946681 2 0.1792 0.704 0.000 0.932 0.068 0.000
#> SRR1946687 3 0.1389 0.780 0.000 0.000 0.952 0.048
#> SRR1946679 2 0.0469 0.774 0.000 0.988 0.000 0.012
#> SRR1946678 1 0.2149 0.816 0.912 0.000 0.088 0.000
#> SRR1946676 2 0.0000 0.766 0.000 1.000 0.000 0.000
#> SRR1946677 4 0.4454 0.571 0.308 0.000 0.000 0.692
#> SRR1946672 3 0.3311 0.814 0.000 0.172 0.828 0.000
#> SRR1946673 1 0.6400 0.476 0.652 0.180 0.000 0.168
#> SRR1946671 1 0.4004 0.687 0.812 0.024 0.000 0.164
#> SRR1946669 1 0.0000 0.814 1.000 0.000 0.000 0.000
#> SRR1946668 1 0.2081 0.777 0.916 0.000 0.000 0.084
#> SRR1946666 3 0.3486 0.704 0.188 0.000 0.812 0.000
#> SRR1946667 4 0.4605 0.469 0.000 0.000 0.336 0.664
#> SRR1946670 4 0.0188 0.786 0.004 0.000 0.000 0.996
#> SRR1946663 4 0.2149 0.785 0.088 0.000 0.000 0.912
#> SRR1946664 2 0.3266 0.767 0.000 0.832 0.000 0.168
#> SRR1946662 2 0.6503 0.596 0.196 0.640 0.000 0.164
#> SRR1946661 4 0.2345 0.782 0.100 0.000 0.000 0.900
#> SRR1946660 4 0.1302 0.792 0.044 0.000 0.000 0.956
#> SRR1946659 3 0.4356 0.548 0.292 0.000 0.708 0.000
#> SRR1946658 2 0.4989 0.244 0.000 0.528 0.000 0.472
#> SRR1946657 2 0.2530 0.790 0.000 0.888 0.000 0.112
#> SRR1946655 3 0.4356 0.773 0.000 0.292 0.708 0.000
#> SRR1946654 3 0.4040 0.797 0.000 0.248 0.752 0.000
#> SRR1946653 3 0.1716 0.773 0.000 0.000 0.936 0.064
#> SRR1946652 2 0.3024 0.781 0.000 0.852 0.000 0.148
#> SRR1946651 2 0.2973 0.783 0.000 0.856 0.000 0.144
#> SRR1946650 4 0.4300 0.724 0.092 0.088 0.000 0.820
#> SRR1946649 2 0.6473 0.601 0.188 0.644 0.000 0.168
#> SRR1946648 3 0.4160 0.734 0.168 0.008 0.808 0.016
#> SRR1946647 1 0.0000 0.814 1.000 0.000 0.000 0.000
#> SRR1946646 3 0.4292 0.743 0.000 0.100 0.820 0.080
#> SRR1946645 1 0.2282 0.793 0.924 0.000 0.024 0.052
#> SRR1946644 2 0.4707 0.671 0.000 0.760 0.204 0.036
#> SRR1946643 3 0.4661 0.718 0.000 0.348 0.652 0.000
#> SRR1946642 1 0.2081 0.816 0.916 0.000 0.084 0.000
#> SRR1946641 1 0.3266 0.792 0.832 0.000 0.168 0.000
#> SRR1946656 3 0.4500 0.752 0.000 0.316 0.684 0.000
#> SRR1946640 1 0.3266 0.792 0.832 0.000 0.168 0.000
#> SRR1946639 1 0.3266 0.792 0.832 0.000 0.168 0.000
#> SRR1946638 1 0.3266 0.792 0.832 0.000 0.168 0.000
#> SRR1946637 1 0.3266 0.792 0.832 0.000 0.168 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.0162 0.8298 0.004 0.000 0.996 0.000 0.000
#> SRR1946691 4 0.6182 0.2899 0.000 0.324 0.000 0.520 0.156
#> SRR1946690 2 0.1800 0.8124 0.000 0.932 0.000 0.048 0.020
#> SRR1946689 4 0.0880 0.6588 0.000 0.000 0.032 0.968 0.000
#> SRR1946686 3 0.0794 0.8295 0.028 0.000 0.972 0.000 0.000
#> SRR1946685 2 0.0693 0.8488 0.012 0.980 0.008 0.000 0.000
#> SRR1946688 4 0.2997 0.6021 0.000 0.012 0.000 0.840 0.148
#> SRR1946684 5 0.3532 0.6806 0.128 0.048 0.000 0.000 0.824
#> SRR1946683 5 0.0880 0.6988 0.000 0.000 0.032 0.000 0.968
#> SRR1946682 5 0.4455 0.2325 0.000 0.008 0.000 0.404 0.588
#> SRR1946680 3 0.0880 0.8115 0.000 0.000 0.968 0.032 0.000
#> SRR1946681 3 0.4273 0.1659 0.000 0.448 0.552 0.000 0.000
#> SRR1946687 3 0.4803 0.1030 0.020 0.000 0.536 0.444 0.000
#> SRR1946679 2 0.0609 0.8485 0.000 0.980 0.020 0.000 0.000
#> SRR1946678 5 0.4306 0.0507 0.492 0.000 0.000 0.000 0.508
#> SRR1946676 2 0.0609 0.8482 0.000 0.980 0.020 0.000 0.000
#> SRR1946677 5 0.0162 0.6988 0.000 0.000 0.000 0.004 0.996
#> SRR1946672 3 0.0992 0.8324 0.024 0.008 0.968 0.000 0.000
#> SRR1946673 5 0.3656 0.6423 0.032 0.168 0.000 0.000 0.800
#> SRR1946671 5 0.0771 0.7065 0.020 0.000 0.004 0.000 0.976
#> SRR1946669 5 0.3109 0.6408 0.200 0.000 0.000 0.000 0.800
#> SRR1946668 5 0.1197 0.7080 0.048 0.000 0.000 0.000 0.952
#> SRR1946666 3 0.1792 0.7966 0.084 0.000 0.916 0.000 0.000
#> SRR1946667 4 0.0880 0.6588 0.000 0.000 0.032 0.968 0.000
#> SRR1946670 4 0.2585 0.6492 0.000 0.024 0.008 0.896 0.072
#> SRR1946663 5 0.4440 0.0908 0.000 0.004 0.000 0.468 0.528
#> SRR1946664 2 0.0404 0.8470 0.000 0.988 0.000 0.012 0.000
#> SRR1946662 5 0.3430 0.5913 0.000 0.220 0.004 0.000 0.776
#> SRR1946661 5 0.4624 0.3290 0.000 0.024 0.000 0.340 0.636
#> SRR1946660 4 0.4402 0.5333 0.000 0.056 0.000 0.740 0.204
#> SRR1946659 1 0.0290 0.9847 0.992 0.000 0.000 0.008 0.000
#> SRR1946658 2 0.6462 0.3392 0.000 0.568 0.016 0.196 0.220
#> SRR1946657 2 0.0162 0.8512 0.000 0.996 0.004 0.000 0.000
#> SRR1946655 3 0.0794 0.8346 0.000 0.028 0.972 0.000 0.000
#> SRR1946654 3 0.0955 0.8345 0.004 0.028 0.968 0.000 0.000
#> SRR1946653 4 0.5236 -0.1217 0.044 0.000 0.464 0.492 0.000
#> SRR1946652 2 0.0955 0.8428 0.000 0.968 0.004 0.000 0.028
#> SRR1946651 2 0.0000 0.8509 0.000 1.000 0.000 0.000 0.000
#> SRR1946650 5 0.6610 -0.0574 0.000 0.220 0.000 0.352 0.428
#> SRR1946649 2 0.4829 0.0836 0.000 0.496 0.000 0.020 0.484
#> SRR1946648 3 0.5333 0.4412 0.004 0.000 0.628 0.068 0.300
#> SRR1946647 5 0.2561 0.6852 0.144 0.000 0.000 0.000 0.856
#> SRR1946646 4 0.6920 0.2299 0.016 0.300 0.216 0.468 0.000
#> SRR1946645 5 0.0671 0.7030 0.016 0.000 0.000 0.004 0.980
#> SRR1946644 2 0.2660 0.7411 0.000 0.864 0.008 0.128 0.000
#> SRR1946643 3 0.0794 0.8346 0.000 0.028 0.972 0.000 0.000
#> SRR1946642 1 0.0290 0.9930 0.992 0.000 0.000 0.000 0.008
#> SRR1946641 1 0.0162 0.9967 0.996 0.000 0.000 0.000 0.004
#> SRR1946656 3 0.0794 0.8346 0.000 0.028 0.972 0.000 0.000
#> SRR1946640 1 0.0162 0.9967 0.996 0.000 0.000 0.000 0.004
#> SRR1946639 1 0.0162 0.9967 0.996 0.000 0.000 0.000 0.004
#> SRR1946638 1 0.0162 0.9967 0.996 0.000 0.000 0.000 0.004
#> SRR1946637 1 0.0162 0.9967 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0632 0.9062 0.000 0.000 0.976 0.024 0.000 0.000
#> SRR1946691 6 0.2633 0.7611 0.000 0.104 0.000 0.032 0.000 0.864
#> SRR1946690 2 0.3841 0.5583 0.000 0.716 0.000 0.028 0.000 0.256
#> SRR1946689 4 0.1524 0.8482 0.000 0.000 0.008 0.932 0.000 0.060
#> SRR1946686 3 0.0767 0.9038 0.008 0.004 0.976 0.012 0.000 0.000
#> SRR1946685 2 0.0508 0.8596 0.000 0.984 0.004 0.012 0.000 0.000
#> SRR1946688 6 0.1141 0.8343 0.000 0.000 0.000 0.052 0.000 0.948
#> SRR1946684 5 0.0000 0.8570 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946683 5 0.4252 0.6473 0.000 0.004 0.052 0.008 0.736 0.200
#> SRR1946682 6 0.0692 0.8525 0.000 0.000 0.000 0.004 0.020 0.976
#> SRR1946680 3 0.3737 0.2988 0.000 0.000 0.608 0.392 0.000 0.000
#> SRR1946681 3 0.3277 0.7188 0.000 0.188 0.792 0.016 0.004 0.000
#> SRR1946687 4 0.1753 0.8277 0.004 0.000 0.084 0.912 0.000 0.000
#> SRR1946679 2 0.1059 0.8544 0.000 0.964 0.016 0.016 0.004 0.000
#> SRR1946678 5 0.0363 0.8518 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR1946676 2 0.0820 0.8572 0.000 0.972 0.012 0.016 0.000 0.000
#> SRR1946677 6 0.2163 0.8066 0.000 0.000 0.004 0.008 0.096 0.892
#> SRR1946672 3 0.0291 0.9104 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1946673 5 0.0000 0.8570 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946671 5 0.4256 0.4578 0.000 0.008 0.008 0.008 0.648 0.328
#> SRR1946669 5 0.0146 0.8566 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1946668 5 0.0146 0.8566 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1946666 3 0.1232 0.9004 0.016 0.000 0.956 0.024 0.000 0.004
#> SRR1946667 4 0.1500 0.8508 0.000 0.000 0.012 0.936 0.000 0.052
#> SRR1946670 4 0.4691 0.6602 0.000 0.012 0.004 0.684 0.056 0.244
#> SRR1946663 6 0.0458 0.8491 0.000 0.000 0.000 0.016 0.000 0.984
#> SRR1946664 2 0.0632 0.8564 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1946662 5 0.0146 0.8549 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1946661 6 0.0622 0.8528 0.000 0.008 0.000 0.000 0.012 0.980
#> SRR1946660 6 0.0458 0.8491 0.000 0.000 0.000 0.016 0.000 0.984
#> SRR1946659 1 0.0146 0.9911 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1946658 2 0.6217 0.3881 0.000 0.524 0.024 0.092 0.332 0.028
#> SRR1946657 2 0.0632 0.8564 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR1946655 3 0.0146 0.9117 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1946654 3 0.0777 0.9075 0.000 0.004 0.972 0.024 0.000 0.000
#> SRR1946653 4 0.2350 0.8266 0.036 0.000 0.076 0.888 0.000 0.000
#> SRR1946652 2 0.3354 0.7274 0.000 0.792 0.008 0.016 0.184 0.000
#> SRR1946651 2 0.0000 0.8597 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946650 6 0.0790 0.8487 0.000 0.032 0.000 0.000 0.000 0.968
#> SRR1946649 6 0.5828 0.0342 0.000 0.436 0.004 0.012 0.112 0.436
#> SRR1946648 5 0.6246 0.0187 0.000 0.004 0.156 0.380 0.440 0.020
#> SRR1946647 5 0.0000 0.8570 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946646 4 0.3534 0.6368 0.000 0.244 0.016 0.740 0.000 0.000
#> SRR1946645 6 0.3523 0.6625 0.004 0.004 0.004 0.008 0.208 0.772
#> SRR1946644 2 0.1610 0.8220 0.000 0.916 0.000 0.084 0.000 0.000
#> SRR1946643 3 0.0291 0.9122 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1946642 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946641 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946656 3 0.0291 0.9122 0.000 0.004 0.992 0.004 0.000 0.000
#> SRR1946640 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946639 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946638 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946637 1 0.0146 0.9985 0.996 0.000 0.000 0.000 0.004 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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.322 0.847 0.822 0.3312 0.713 0.713
#> 3 3 0.365 0.721 0.836 0.6424 0.708 0.590
#> 4 4 0.525 0.677 0.798 0.2737 0.848 0.637
#> 5 5 0.594 0.690 0.816 0.0433 0.987 0.951
#> 6 6 0.710 0.576 0.743 0.0926 0.814 0.430
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
#> SRR1946675 1 0.584 0.839 0.860 0.140
#> SRR1946691 2 0.563 0.952 0.132 0.868
#> SRR1946690 2 0.563 0.952 0.132 0.868
#> SRR1946689 2 0.416 0.930 0.084 0.916
#> SRR1946686 1 0.584 0.839 0.860 0.140
#> SRR1946685 1 0.714 0.821 0.804 0.196
#> SRR1946688 1 0.662 0.837 0.828 0.172
#> SRR1946684 1 0.311 0.831 0.944 0.056
#> SRR1946683 1 0.163 0.856 0.976 0.024
#> SRR1946682 1 0.662 0.837 0.828 0.172
#> SRR1946680 2 0.416 0.930 0.084 0.916
#> SRR1946681 2 0.662 0.927 0.172 0.828
#> SRR1946687 1 0.584 0.839 0.860 0.140
#> SRR1946679 1 0.714 0.821 0.804 0.196
#> SRR1946678 1 0.416 0.814 0.916 0.084
#> SRR1946676 1 0.506 0.859 0.888 0.112
#> SRR1946677 1 0.163 0.856 0.976 0.024
#> SRR1946672 1 0.595 0.837 0.856 0.144
#> SRR1946673 1 0.311 0.831 0.944 0.056
#> SRR1946671 1 0.224 0.857 0.964 0.036
#> SRR1946669 1 0.416 0.814 0.916 0.084
#> SRR1946668 1 0.311 0.831 0.944 0.056
#> SRR1946666 1 0.584 0.839 0.860 0.140
#> SRR1946667 2 0.416 0.930 0.084 0.916
#> SRR1946670 1 0.662 0.837 0.828 0.172
#> SRR1946663 1 0.662 0.837 0.828 0.172
#> SRR1946664 2 0.563 0.952 0.132 0.868
#> SRR1946662 1 0.311 0.831 0.944 0.056
#> SRR1946661 1 0.311 0.852 0.944 0.056
#> SRR1946660 1 0.662 0.837 0.828 0.172
#> SRR1946659 1 0.584 0.839 0.860 0.140
#> SRR1946658 1 0.662 0.837 0.828 0.172
#> SRR1946657 1 0.714 0.821 0.804 0.196
#> SRR1946655 1 0.925 0.613 0.660 0.340
#> SRR1946654 1 0.706 0.806 0.808 0.192
#> SRR1946653 1 0.584 0.839 0.860 0.140
#> SRR1946652 1 0.714 0.821 0.804 0.196
#> SRR1946651 1 0.714 0.821 0.804 0.196
#> SRR1946650 1 0.443 0.853 0.908 0.092
#> SRR1946649 1 0.278 0.858 0.952 0.048
#> SRR1946648 1 0.416 0.858 0.916 0.084
#> SRR1946647 1 0.311 0.831 0.944 0.056
#> SRR1946646 1 0.714 0.821 0.804 0.196
#> SRR1946645 1 0.163 0.856 0.976 0.024
#> SRR1946644 1 0.714 0.821 0.804 0.196
#> SRR1946643 2 0.662 0.927 0.172 0.828
#> SRR1946642 1 0.416 0.814 0.916 0.084
#> SRR1946641 1 0.416 0.814 0.916 0.084
#> SRR1946656 2 0.662 0.927 0.172 0.828
#> SRR1946640 1 0.416 0.814 0.916 0.084
#> SRR1946639 1 0.416 0.814 0.916 0.084
#> SRR1946638 1 0.416 0.814 0.916 0.084
#> SRR1946637 1 0.416 0.814 0.916 0.084
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946691 3 0.5678 0.819 0.000 0.316 0.684
#> SRR1946690 3 0.5678 0.819 0.000 0.316 0.684
#> SRR1946689 3 0.0424 0.733 0.000 0.008 0.992
#> SRR1946686 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946685 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946688 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946684 1 0.6095 0.413 0.608 0.392 0.000
#> SRR1946683 2 0.5363 0.672 0.276 0.724 0.000
#> SRR1946682 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946680 3 0.0424 0.733 0.000 0.008 0.992
#> SRR1946681 3 0.5363 0.821 0.000 0.276 0.724
#> SRR1946687 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946679 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946678 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946676 2 0.3193 0.807 0.100 0.896 0.004
#> SRR1946677 2 0.5363 0.666 0.276 0.724 0.000
#> SRR1946672 2 0.7169 0.723 0.208 0.704 0.088
#> SRR1946673 1 0.6111 0.402 0.604 0.396 0.000
#> SRR1946671 2 0.4346 0.747 0.184 0.816 0.000
#> SRR1946669 1 0.3267 0.710 0.884 0.116 0.000
#> SRR1946668 1 0.6095 0.413 0.608 0.392 0.000
#> SRR1946666 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946667 3 0.0424 0.733 0.000 0.008 0.992
#> SRR1946670 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946663 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946664 3 0.5678 0.819 0.000 0.316 0.684
#> SRR1946662 1 0.6111 0.402 0.604 0.396 0.000
#> SRR1946661 2 0.6045 0.396 0.380 0.620 0.000
#> SRR1946660 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946659 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946658 2 0.1031 0.815 0.024 0.976 0.000
#> SRR1946657 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946655 2 0.4974 0.651 0.000 0.764 0.236
#> SRR1946654 2 0.6910 0.744 0.144 0.736 0.120
#> SRR1946653 2 0.7136 0.722 0.212 0.704 0.084
#> SRR1946652 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946651 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946650 2 0.3192 0.785 0.112 0.888 0.000
#> SRR1946649 2 0.4002 0.764 0.160 0.840 0.000
#> SRR1946648 2 0.6067 0.717 0.236 0.736 0.028
#> SRR1946647 1 0.6095 0.413 0.608 0.392 0.000
#> SRR1946646 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946645 2 0.5397 0.665 0.280 0.720 0.000
#> SRR1946644 2 0.0424 0.805 0.000 0.992 0.008
#> SRR1946643 3 0.5363 0.821 0.000 0.276 0.724
#> SRR1946642 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946656 3 0.5363 0.821 0.000 0.276 0.724
#> SRR1946640 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.734 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.734 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946691 4 0.6690 0.74179 0.000 0.248 0.144 0.608
#> SRR1946690 4 0.6690 0.74179 0.000 0.248 0.144 0.608
#> SRR1946689 4 0.0000 0.73194 0.000 0.000 0.000 1.000
#> SRR1946686 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946685 2 0.2011 0.77020 0.000 0.920 0.080 0.000
#> SRR1946688 2 0.1389 0.78795 0.000 0.952 0.048 0.000
#> SRR1946684 1 0.4866 0.45763 0.596 0.404 0.000 0.000
#> SRR1946683 2 0.7776 -0.03105 0.248 0.412 0.340 0.000
#> SRR1946682 2 0.1389 0.78795 0.000 0.952 0.048 0.000
#> SRR1946680 4 0.0000 0.73194 0.000 0.000 0.000 1.000
#> SRR1946681 4 0.5971 0.72032 0.000 0.048 0.368 0.584
#> SRR1946687 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946679 2 0.2011 0.77020 0.000 0.920 0.080 0.000
#> SRR1946678 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946676 2 0.3828 0.71776 0.084 0.848 0.068 0.000
#> SRR1946677 2 0.7771 0.00884 0.252 0.420 0.328 0.000
#> SRR1946672 3 0.4553 0.90083 0.180 0.040 0.780 0.000
#> SRR1946673 1 0.4877 0.44947 0.592 0.408 0.000 0.000
#> SRR1946671 2 0.3311 0.66580 0.172 0.828 0.000 0.000
#> SRR1946669 1 0.2760 0.68280 0.872 0.128 0.000 0.000
#> SRR1946668 1 0.4877 0.45299 0.592 0.408 0.000 0.000
#> SRR1946666 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946667 4 0.0000 0.73194 0.000 0.000 0.000 1.000
#> SRR1946670 2 0.1389 0.78795 0.000 0.952 0.048 0.000
#> SRR1946663 2 0.1389 0.78795 0.000 0.952 0.048 0.000
#> SRR1946664 4 0.6690 0.74179 0.000 0.248 0.144 0.608
#> SRR1946662 1 0.4877 0.44947 0.592 0.408 0.000 0.000
#> SRR1946661 2 0.4730 0.25173 0.364 0.636 0.000 0.000
#> SRR1946660 2 0.1389 0.78795 0.000 0.952 0.048 0.000
#> SRR1946659 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946658 2 0.1302 0.78808 0.000 0.956 0.044 0.000
#> SRR1946657 2 0.2011 0.77020 0.000 0.920 0.080 0.000
#> SRR1946655 3 0.2926 0.62928 0.000 0.048 0.896 0.056
#> SRR1946654 3 0.4484 0.84269 0.120 0.064 0.812 0.004
#> SRR1946653 3 0.4595 0.90319 0.184 0.040 0.776 0.000
#> SRR1946652 2 0.1474 0.78016 0.000 0.948 0.052 0.000
#> SRR1946651 2 0.1474 0.78016 0.000 0.948 0.052 0.000
#> SRR1946650 2 0.2281 0.73927 0.096 0.904 0.000 0.000
#> SRR1946649 2 0.3024 0.69440 0.148 0.852 0.000 0.000
#> SRR1946648 3 0.7493 0.40678 0.208 0.304 0.488 0.000
#> SRR1946647 1 0.4866 0.45763 0.596 0.404 0.000 0.000
#> SRR1946646 2 0.2011 0.77020 0.000 0.920 0.080 0.000
#> SRR1946645 2 0.7784 -0.02420 0.252 0.412 0.336 0.000
#> SRR1946644 2 0.2011 0.77020 0.000 0.920 0.080 0.000
#> SRR1946643 4 0.5971 0.72032 0.000 0.048 0.368 0.584
#> SRR1946642 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946641 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946656 4 0.5971 0.72032 0.000 0.048 0.368 0.584
#> SRR1946640 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946639 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946638 1 0.0336 0.71014 0.992 0.000 0.008 0.000
#> SRR1946637 1 0.0336 0.71014 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946691 2 0.0992 0.7624 0.000 0.968 0.000 0.008 0.024
#> SRR1946690 2 0.0992 0.7624 0.000 0.968 0.000 0.008 0.024
#> SRR1946689 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946685 5 0.3266 0.7482 0.000 0.200 0.004 0.000 0.796
#> SRR1946688 5 0.0162 0.7558 0.000 0.004 0.000 0.000 0.996
#> SRR1946684 1 0.4161 0.4491 0.608 0.000 0.000 0.000 0.392
#> SRR1946683 5 0.6685 0.0188 0.236 0.000 0.380 0.000 0.384
#> SRR1946682 5 0.0162 0.7558 0.000 0.004 0.000 0.000 0.996
#> SRR1946680 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000
#> SRR1946681 2 0.4109 0.7637 0.000 0.700 0.288 0.012 0.000
#> SRR1946687 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946679 5 0.3266 0.7482 0.000 0.200 0.004 0.000 0.796
#> SRR1946678 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946676 5 0.4144 0.7126 0.084 0.032 0.068 0.000 0.816
#> SRR1946677 5 0.6695 0.0597 0.240 0.000 0.368 0.000 0.392
#> SRR1946672 3 0.2723 0.8909 0.124 0.012 0.864 0.000 0.000
#> SRR1946673 1 0.4171 0.4408 0.604 0.000 0.000 0.000 0.396
#> SRR1946671 5 0.2966 0.6629 0.184 0.000 0.000 0.000 0.816
#> SRR1946669 1 0.2230 0.6961 0.884 0.000 0.000 0.000 0.116
#> SRR1946668 1 0.4171 0.4443 0.604 0.000 0.000 0.000 0.396
#> SRR1946666 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946667 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 5 0.0162 0.7558 0.000 0.004 0.000 0.000 0.996
#> SRR1946663 5 0.0162 0.7558 0.000 0.004 0.000 0.000 0.996
#> SRR1946664 2 0.0992 0.7624 0.000 0.968 0.000 0.008 0.024
#> SRR1946662 1 0.4171 0.4408 0.604 0.000 0.000 0.000 0.396
#> SRR1946661 5 0.4114 0.2633 0.376 0.000 0.000 0.000 0.624
#> SRR1946660 5 0.0162 0.7558 0.000 0.004 0.000 0.000 0.996
#> SRR1946659 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946658 5 0.0290 0.7564 0.000 0.008 0.000 0.000 0.992
#> SRR1946657 5 0.3266 0.7482 0.000 0.200 0.004 0.000 0.796
#> SRR1946655 3 0.2305 0.6149 0.000 0.092 0.896 0.012 0.000
#> SRR1946654 3 0.2805 0.8306 0.072 0.020 0.888 0.000 0.020
#> SRR1946653 3 0.2329 0.8976 0.124 0.000 0.876 0.000 0.000
#> SRR1946652 5 0.2929 0.7555 0.000 0.180 0.000 0.000 0.820
#> SRR1946651 5 0.2929 0.7555 0.000 0.180 0.000 0.000 0.820
#> SRR1946650 5 0.3493 0.7271 0.108 0.060 0.000 0.000 0.832
#> SRR1946649 5 0.3224 0.6887 0.160 0.016 0.000 0.000 0.824
#> SRR1946648 3 0.5991 0.3808 0.148 0.000 0.564 0.000 0.288
#> SRR1946647 1 0.4161 0.4491 0.608 0.000 0.000 0.000 0.392
#> SRR1946646 5 0.3266 0.7482 0.000 0.200 0.004 0.000 0.796
#> SRR1946645 5 0.6697 0.0257 0.240 0.000 0.376 0.000 0.384
#> SRR1946644 5 0.3266 0.7482 0.000 0.200 0.004 0.000 0.796
#> SRR1946643 2 0.4109 0.7637 0.000 0.700 0.288 0.012 0.000
#> SRR1946642 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946641 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946656 2 0.4109 0.7637 0.000 0.700 0.288 0.012 0.000
#> SRR1946640 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946639 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946638 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
#> SRR1946637 1 0.0703 0.7191 0.976 0.000 0.024 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946691 1 0.2562 0.754 0.828 0.172 0.000 0 0.000 0.000
#> SRR1946690 1 0.2562 0.754 0.828 0.172 0.000 0 0.000 0.000
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946686 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946685 2 0.4992 0.984 0.040 0.572 0.000 0 0.368 0.020
#> SRR1946688 6 0.2762 0.998 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946684 5 0.0000 0.409 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946683 3 0.5778 0.181 0.000 0.056 0.452 0 0.440 0.052
#> SRR1946682 6 0.2762 0.998 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946680 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946681 1 0.3250 0.752 0.788 0.004 0.012 0 0.000 0.196
#> SRR1946687 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946679 2 0.4992 0.984 0.040 0.572 0.000 0 0.368 0.020
#> SRR1946678 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946676 5 0.6627 -0.703 0.012 0.396 0.116 0 0.424 0.052
#> SRR1946677 5 0.5866 -0.235 0.000 0.064 0.428 0 0.456 0.052
#> SRR1946672 3 0.1225 0.758 0.000 0.000 0.952 0 0.012 0.036
#> SRR1946673 5 0.0146 0.407 0.000 0.004 0.000 0 0.996 0.000
#> SRR1946671 5 0.5391 -0.586 0.000 0.376 0.036 0 0.540 0.048
#> SRR1946669 5 0.3288 0.496 0.000 0.276 0.000 0 0.724 0.000
#> SRR1946668 5 0.0146 0.407 0.000 0.000 0.000 0 0.996 0.004
#> SRR1946666 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946670 6 0.2762 0.998 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946663 6 0.2762 0.998 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946664 1 0.2562 0.754 0.828 0.172 0.000 0 0.000 0.000
#> SRR1946662 5 0.0146 0.407 0.000 0.004 0.000 0 0.996 0.000
#> SRR1946661 5 0.4323 -0.108 0.000 0.188 0.028 0 0.740 0.044
#> SRR1946660 6 0.2762 0.998 0.000 0.196 0.000 0 0.000 0.804
#> SRR1946659 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946658 6 0.2823 0.989 0.000 0.204 0.000 0 0.000 0.796
#> SRR1946657 2 0.4992 0.984 0.040 0.572 0.000 0 0.368 0.020
#> SRR1946655 3 0.5283 0.390 0.180 0.004 0.620 0 0.000 0.196
#> SRR1946654 3 0.3334 0.690 0.040 0.024 0.844 0 0.004 0.088
#> SRR1946653 3 0.0363 0.774 0.000 0.000 0.988 0 0.012 0.000
#> SRR1946652 2 0.4638 0.960 0.020 0.572 0.000 0 0.392 0.016
#> SRR1946651 2 0.4638 0.960 0.020 0.572 0.000 0 0.392 0.016
#> SRR1946650 5 0.4928 -0.717 0.004 0.444 0.000 0 0.500 0.052
#> SRR1946649 5 0.5432 -0.635 0.000 0.400 0.036 0 0.516 0.048
#> SRR1946648 3 0.3871 0.501 0.000 0.000 0.676 0 0.308 0.016
#> SRR1946647 5 0.0000 0.409 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946646 2 0.4992 0.984 0.040 0.572 0.000 0 0.368 0.020
#> SRR1946645 3 0.5779 0.172 0.000 0.056 0.448 0 0.444 0.052
#> SRR1946644 2 0.4992 0.984 0.040 0.572 0.000 0 0.368 0.020
#> SRR1946643 1 0.3250 0.752 0.788 0.004 0.012 0 0.000 0.196
#> SRR1946642 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946641 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946656 1 0.3250 0.752 0.788 0.004 0.012 0 0.000 0.196
#> SRR1946640 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946639 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946638 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
#> SRR1946637 5 0.4666 0.468 0.000 0.420 0.044 0 0.536 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.620 0.790 0.899 0.4935 0.495 0.495
#> 3 3 0.353 0.445 0.713 0.2955 0.644 0.404
#> 4 4 0.466 0.567 0.730 0.1384 0.737 0.396
#> 5 5 0.642 0.594 0.731 0.0786 0.833 0.496
#> 6 6 0.767 0.699 0.822 0.0512 0.909 0.628
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1946675 2 0.9970 0.3225 0.468 0.532
#> SRR1946691 2 0.0000 0.8554 0.000 1.000
#> SRR1946690 2 0.0000 0.8554 0.000 1.000
#> SRR1946689 2 0.2236 0.8500 0.036 0.964
#> SRR1946686 1 0.0000 0.9206 1.000 0.000
#> SRR1946685 2 0.7602 0.7395 0.220 0.780
#> SRR1946688 2 0.0672 0.8569 0.008 0.992
#> SRR1946684 1 0.2236 0.9223 0.964 0.036
#> SRR1946683 1 0.2043 0.9233 0.968 0.032
#> SRR1946682 2 0.7950 0.7048 0.240 0.760
#> SRR1946680 2 0.2043 0.8511 0.032 0.968
#> SRR1946681 2 0.0000 0.8554 0.000 1.000
#> SRR1946687 2 1.0000 0.2351 0.496 0.504
#> SRR1946679 2 0.7602 0.7395 0.220 0.780
#> SRR1946678 1 0.0938 0.9234 0.988 0.012
#> SRR1946676 2 0.7745 0.7310 0.228 0.772
#> SRR1946677 1 0.2778 0.9193 0.952 0.048
#> SRR1946672 2 0.9393 0.5678 0.356 0.644
#> SRR1946673 1 0.4431 0.8803 0.908 0.092
#> SRR1946671 1 0.2778 0.9193 0.952 0.048
#> SRR1946669 1 0.2236 0.9223 0.964 0.036
#> SRR1946668 1 0.2236 0.9223 0.964 0.036
#> SRR1946666 1 0.0000 0.9206 1.000 0.000
#> SRR1946667 2 0.2236 0.8500 0.036 0.964
#> SRR1946670 2 0.0672 0.8569 0.008 0.992
#> SRR1946663 1 0.9983 -0.0718 0.524 0.476
#> SRR1946664 2 0.0000 0.8554 0.000 1.000
#> SRR1946662 1 0.2236 0.9223 0.964 0.036
#> SRR1946661 1 0.4431 0.8803 0.908 0.092
#> SRR1946660 2 0.0672 0.8569 0.008 0.992
#> SRR1946659 1 0.0000 0.9206 1.000 0.000
#> SRR1946658 2 0.0672 0.8569 0.008 0.992
#> SRR1946657 2 0.0938 0.8567 0.012 0.988
#> SRR1946655 2 0.2236 0.8500 0.036 0.964
#> SRR1946654 2 0.6531 0.8006 0.168 0.832
#> SRR1946653 2 0.9286 0.5867 0.344 0.656
#> SRR1946652 2 0.7602 0.7395 0.220 0.780
#> SRR1946651 2 0.3733 0.8352 0.072 0.928
#> SRR1946650 2 0.9775 0.3914 0.412 0.588
#> SRR1946649 1 0.2778 0.9193 0.952 0.048
#> SRR1946648 1 0.9970 -0.1787 0.532 0.468
#> SRR1946647 1 0.2236 0.9235 0.964 0.036
#> SRR1946646 2 0.2948 0.8532 0.052 0.948
#> SRR1946645 1 0.2423 0.9223 0.960 0.040
#> SRR1946644 2 0.0000 0.8554 0.000 1.000
#> SRR1946643 2 0.2236 0.8500 0.036 0.964
#> SRR1946642 1 0.0938 0.9234 0.988 0.012
#> SRR1946641 1 0.0000 0.9206 1.000 0.000
#> SRR1946656 2 0.2236 0.8500 0.036 0.964
#> SRR1946640 1 0.0000 0.9206 1.000 0.000
#> SRR1946639 1 0.0000 0.9206 1.000 0.000
#> SRR1946638 1 0.0000 0.9206 1.000 0.000
#> SRR1946637 1 0.0000 0.9206 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.9872 0.3469 0.272 0.320 0.408
#> SRR1946691 3 0.5988 0.3131 0.000 0.368 0.632
#> SRR1946690 3 0.6008 0.3134 0.000 0.372 0.628
#> SRR1946689 3 0.1163 0.5904 0.000 0.028 0.972
#> SRR1946686 1 0.3310 0.7517 0.908 0.064 0.028
#> SRR1946685 2 0.0983 0.6000 0.004 0.980 0.016
#> SRR1946688 2 0.6225 0.1496 0.000 0.568 0.432
#> SRR1946684 1 0.6280 0.1912 0.540 0.460 0.000
#> SRR1946683 2 0.6302 -0.1118 0.480 0.520 0.000
#> SRR1946682 2 0.5061 0.5062 0.008 0.784 0.208
#> SRR1946680 3 0.1163 0.5904 0.000 0.028 0.972
#> SRR1946681 3 0.4750 0.5991 0.000 0.216 0.784
#> SRR1946687 3 0.8837 0.2158 0.424 0.116 0.460
#> SRR1946679 2 0.0237 0.6036 0.004 0.996 0.000
#> SRR1946678 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946676 2 0.1163 0.5955 0.000 0.972 0.028
#> SRR1946677 2 0.5733 0.3361 0.324 0.676 0.000
#> SRR1946672 3 0.9680 0.4487 0.244 0.300 0.456
#> SRR1946673 2 0.5098 0.4630 0.248 0.752 0.000
#> SRR1946671 2 0.5835 0.3020 0.340 0.660 0.000
#> SRR1946669 1 0.6154 0.3110 0.592 0.408 0.000
#> SRR1946668 1 0.6308 0.1031 0.508 0.492 0.000
#> SRR1946666 1 0.3649 0.7301 0.896 0.068 0.036
#> SRR1946667 3 0.1163 0.5904 0.000 0.028 0.972
#> SRR1946670 2 0.6225 0.1496 0.000 0.568 0.432
#> SRR1946663 2 0.5987 0.5171 0.036 0.756 0.208
#> SRR1946664 3 0.6045 0.2970 0.000 0.380 0.620
#> SRR1946662 1 0.6280 0.1912 0.540 0.460 0.000
#> SRR1946661 2 0.3482 0.5946 0.128 0.872 0.000
#> SRR1946660 2 0.5988 0.2879 0.000 0.632 0.368
#> SRR1946659 1 0.2689 0.7413 0.932 0.032 0.036
#> SRR1946658 2 0.5760 0.3047 0.000 0.672 0.328
#> SRR1946657 2 0.0000 0.6022 0.000 1.000 0.000
#> SRR1946655 3 0.5493 0.6077 0.012 0.232 0.756
#> SRR1946654 3 0.9355 0.4758 0.188 0.320 0.492
#> SRR1946653 3 0.9624 0.4603 0.256 0.272 0.472
#> SRR1946652 2 0.0661 0.6040 0.004 0.988 0.008
#> SRR1946651 2 0.0592 0.6015 0.000 0.988 0.012
#> SRR1946650 2 0.1399 0.6085 0.028 0.968 0.004
#> SRR1946649 2 0.5497 0.3951 0.292 0.708 0.000
#> SRR1946648 2 0.9386 0.0612 0.204 0.500 0.296
#> SRR1946647 2 0.6307 -0.1435 0.488 0.512 0.000
#> SRR1946646 2 0.6113 0.0859 0.012 0.688 0.300
#> SRR1946645 2 0.6168 0.1559 0.412 0.588 0.000
#> SRR1946644 2 0.6647 -0.2930 0.008 0.540 0.452
#> SRR1946643 3 0.5450 0.6117 0.012 0.228 0.760
#> SRR1946642 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946641 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946656 3 0.5450 0.6117 0.012 0.228 0.760
#> SRR1946640 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946639 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946638 1 0.0592 0.7888 0.988 0.012 0.000
#> SRR1946637 1 0.0592 0.7888 0.988 0.012 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.587 0.6832 0.144 0.108 0.732 0.016
#> SRR1946691 4 0.241 0.6729 0.000 0.044 0.036 0.920
#> SRR1946690 4 0.403 0.6730 0.000 0.092 0.072 0.836
#> SRR1946689 4 0.654 0.3356 0.064 0.004 0.432 0.500
#> SRR1946686 1 0.703 0.0875 0.496 0.124 0.380 0.000
#> SRR1946685 2 0.642 0.4963 0.000 0.632 0.120 0.248
#> SRR1946688 4 0.302 0.6085 0.000 0.148 0.000 0.852
#> SRR1946684 2 0.518 0.6056 0.196 0.740 0.000 0.064
#> SRR1946683 2 0.388 0.6752 0.076 0.852 0.068 0.004
#> SRR1946682 2 0.495 0.3149 0.000 0.560 0.000 0.440
#> SRR1946680 4 0.654 0.3356 0.064 0.004 0.432 0.500
#> SRR1946681 4 0.566 0.4797 0.000 0.032 0.368 0.600
#> SRR1946687 3 0.583 0.6267 0.196 0.076 0.716 0.012
#> SRR1946679 2 0.579 0.4761 0.000 0.640 0.052 0.308
#> SRR1946678 1 0.201 0.8354 0.920 0.080 0.000 0.000
#> SRR1946676 2 0.596 0.5604 0.000 0.688 0.116 0.196
#> SRR1946677 2 0.351 0.6855 0.064 0.872 0.060 0.004
#> SRR1946672 3 0.570 0.6957 0.124 0.092 0.756 0.028
#> SRR1946673 2 0.301 0.7003 0.056 0.892 0.000 0.052
#> SRR1946671 2 0.380 0.6805 0.064 0.860 0.068 0.008
#> SRR1946669 2 0.496 0.4958 0.300 0.684 0.000 0.016
#> SRR1946668 2 0.468 0.6288 0.176 0.776 0.000 0.048
#> SRR1946666 3 0.699 0.0798 0.412 0.116 0.472 0.000
#> SRR1946667 4 0.654 0.3356 0.064 0.004 0.432 0.500
#> SRR1946670 4 0.307 0.6079 0.000 0.152 0.000 0.848
#> SRR1946663 2 0.495 0.3149 0.000 0.560 0.000 0.440
#> SRR1946664 4 0.409 0.6718 0.000 0.096 0.072 0.832
#> SRR1946662 2 0.418 0.6428 0.180 0.796 0.000 0.024
#> SRR1946661 2 0.381 0.6572 0.012 0.812 0.000 0.176
#> SRR1946660 4 0.302 0.6085 0.000 0.148 0.000 0.852
#> SRR1946659 1 0.611 0.0125 0.524 0.048 0.428 0.000
#> SRR1946658 4 0.451 0.4827 0.000 0.224 0.020 0.756
#> SRR1946657 2 0.622 0.4324 0.000 0.608 0.076 0.316
#> SRR1946655 3 0.201 0.5868 0.000 0.000 0.920 0.080
#> SRR1946654 3 0.555 0.6960 0.076 0.112 0.772 0.040
#> SRR1946653 3 0.581 0.6871 0.144 0.096 0.740 0.020
#> SRR1946652 2 0.502 0.5747 0.000 0.724 0.036 0.240
#> SRR1946651 2 0.585 0.4588 0.000 0.628 0.052 0.320
#> SRR1946650 2 0.414 0.6317 0.000 0.800 0.024 0.176
#> SRR1946649 2 0.269 0.7031 0.036 0.916 0.036 0.012
#> SRR1946648 3 0.600 0.5635 0.048 0.304 0.640 0.008
#> SRR1946647 2 0.482 0.6342 0.160 0.784 0.008 0.048
#> SRR1946646 3 0.781 0.0775 0.000 0.296 0.420 0.284
#> SRR1946645 2 0.415 0.6632 0.084 0.836 0.076 0.004
#> SRR1946644 4 0.662 0.4817 0.000 0.180 0.192 0.628
#> SRR1946643 3 0.340 0.4980 0.004 0.000 0.832 0.164
#> SRR1946642 1 0.201 0.8354 0.920 0.080 0.000 0.000
#> SRR1946641 1 0.190 0.8477 0.932 0.064 0.004 0.000
#> SRR1946656 3 0.340 0.4980 0.004 0.000 0.832 0.164
#> SRR1946640 1 0.190 0.8477 0.932 0.064 0.004 0.000
#> SRR1946639 1 0.190 0.8477 0.932 0.064 0.004 0.000
#> SRR1946638 1 0.190 0.8477 0.932 0.064 0.004 0.000
#> SRR1946637 1 0.190 0.8477 0.932 0.064 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.202 0.7717 0.080 0.000 0.912 0.000 0.008
#> SRR1946691 2 0.641 0.3408 0.036 0.560 0.012 0.332 0.060
#> SRR1946690 2 0.454 0.4060 0.008 0.700 0.024 0.268 0.000
#> SRR1946689 4 0.260 0.9911 0.000 0.004 0.120 0.872 0.004
#> SRR1946686 3 0.466 0.6303 0.260 0.000 0.692 0.000 0.048
#> SRR1946685 2 0.517 0.3481 0.000 0.648 0.076 0.000 0.276
#> SRR1946688 2 0.665 0.3645 0.036 0.564 0.012 0.300 0.088
#> SRR1946684 5 0.243 0.7118 0.040 0.028 0.000 0.020 0.912
#> SRR1946683 5 0.526 0.6776 0.036 0.104 0.128 0.000 0.732
#> SRR1946682 5 0.673 0.1950 0.036 0.280 0.000 0.140 0.544
#> SRR1946680 4 0.249 0.9822 0.000 0.004 0.124 0.872 0.000
#> SRR1946681 2 0.671 -0.0871 0.012 0.452 0.144 0.388 0.004
#> SRR1946687 3 0.225 0.7702 0.088 0.000 0.900 0.000 0.012
#> SRR1946679 2 0.476 0.3745 0.000 0.676 0.048 0.000 0.276
#> SRR1946678 1 0.127 0.9658 0.948 0.000 0.000 0.000 0.052
#> SRR1946676 2 0.607 0.0995 0.000 0.512 0.132 0.000 0.356
#> SRR1946677 5 0.510 0.6867 0.032 0.112 0.112 0.000 0.744
#> SRR1946672 3 0.191 0.7656 0.056 0.008 0.928 0.000 0.008
#> SRR1946673 5 0.166 0.7314 0.024 0.016 0.008 0.004 0.948
#> SRR1946671 5 0.516 0.6816 0.028 0.116 0.120 0.000 0.736
#> SRR1946669 5 0.239 0.7163 0.104 0.000 0.004 0.004 0.888
#> SRR1946668 5 0.283 0.7181 0.040 0.028 0.012 0.020 0.900
#> SRR1946666 3 0.362 0.7255 0.192 0.000 0.788 0.000 0.020
#> SRR1946667 4 0.260 0.9911 0.000 0.004 0.120 0.872 0.004
#> SRR1946670 2 0.695 0.3592 0.036 0.548 0.016 0.288 0.112
#> SRR1946663 5 0.673 0.1950 0.036 0.280 0.000 0.140 0.544
#> SRR1946664 2 0.452 0.4082 0.008 0.704 0.024 0.264 0.000
#> SRR1946662 5 0.207 0.7309 0.056 0.008 0.008 0.004 0.924
#> SRR1946661 5 0.176 0.7096 0.008 0.064 0.000 0.000 0.928
#> SRR1946660 2 0.665 0.3645 0.036 0.564 0.012 0.300 0.088
#> SRR1946659 3 0.361 0.6609 0.268 0.000 0.732 0.000 0.000
#> SRR1946658 2 0.646 0.4202 0.036 0.640 0.016 0.188 0.120
#> SRR1946657 2 0.459 0.4070 0.004 0.716 0.044 0.000 0.236
#> SRR1946655 3 0.393 0.5455 0.008 0.016 0.784 0.188 0.004
#> SRR1946654 3 0.148 0.7276 0.000 0.048 0.944 0.000 0.008
#> SRR1946653 3 0.189 0.7717 0.080 0.000 0.916 0.000 0.004
#> SRR1946652 2 0.497 0.1122 0.000 0.564 0.032 0.000 0.404
#> SRR1946651 2 0.467 0.3791 0.000 0.684 0.044 0.000 0.272
#> SRR1946650 5 0.485 0.2721 0.000 0.424 0.024 0.000 0.552
#> SRR1946649 5 0.532 0.6068 0.016 0.216 0.080 0.000 0.688
#> SRR1946648 3 0.339 0.6748 0.008 0.020 0.832 0.000 0.140
#> SRR1946647 5 0.295 0.7177 0.036 0.024 0.024 0.020 0.896
#> SRR1946646 2 0.466 0.3854 0.000 0.668 0.296 0.000 0.036
#> SRR1946645 5 0.535 0.6753 0.036 0.112 0.128 0.000 0.724
#> SRR1946644 2 0.485 0.4335 0.004 0.756 0.144 0.080 0.016
#> SRR1946643 3 0.559 0.2275 0.012 0.052 0.604 0.328 0.004
#> SRR1946642 1 0.127 0.9658 0.948 0.000 0.000 0.000 0.052
#> SRR1946641 1 0.149 0.9864 0.948 0.000 0.024 0.000 0.028
#> SRR1946656 3 0.559 0.2275 0.012 0.052 0.604 0.328 0.004
#> SRR1946640 1 0.149 0.9864 0.948 0.000 0.024 0.000 0.028
#> SRR1946639 1 0.149 0.9864 0.948 0.000 0.024 0.000 0.028
#> SRR1946638 1 0.149 0.9864 0.948 0.000 0.024 0.000 0.028
#> SRR1946637 1 0.149 0.9864 0.948 0.000 0.024 0.000 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.1176 0.8198 0.024 0.020 0.956 0.000 0.000 0.000
#> SRR1946691 6 0.1707 0.6573 0.004 0.056 0.000 0.012 0.000 0.928
#> SRR1946690 6 0.5105 0.2609 0.004 0.444 0.008 0.048 0.000 0.496
#> SRR1946689 4 0.2608 0.9656 0.000 0.000 0.048 0.872 0.000 0.080
#> SRR1946686 3 0.3339 0.7493 0.120 0.008 0.824 0.000 0.048 0.000
#> SRR1946685 2 0.2039 0.7841 0.000 0.908 0.016 0.000 0.072 0.004
#> SRR1946688 6 0.1382 0.6611 0.000 0.036 0.000 0.008 0.008 0.948
#> SRR1946684 5 0.1485 0.7240 0.028 0.000 0.000 0.004 0.944 0.024
#> SRR1946683 5 0.6005 0.6286 0.008 0.268 0.056 0.064 0.596 0.008
#> SRR1946682 6 0.4004 0.4526 0.000 0.004 0.000 0.012 0.328 0.656
#> SRR1946680 4 0.3323 0.9315 0.004 0.008 0.056 0.844 0.004 0.084
#> SRR1946681 6 0.7535 0.0873 0.008 0.300 0.088 0.192 0.012 0.400
#> SRR1946687 3 0.1257 0.8194 0.028 0.020 0.952 0.000 0.000 0.000
#> SRR1946679 2 0.2294 0.7877 0.000 0.892 0.000 0.000 0.072 0.036
#> SRR1946678 1 0.0363 0.9808 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1946676 2 0.4276 0.6605 0.000 0.752 0.020 0.048 0.176 0.004
#> SRR1946677 5 0.5969 0.6288 0.008 0.272 0.052 0.064 0.596 0.008
#> SRR1946672 3 0.0363 0.8135 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR1946673 5 0.1464 0.7385 0.016 0.036 0.000 0.000 0.944 0.004
#> SRR1946671 5 0.5856 0.6315 0.008 0.272 0.044 0.064 0.604 0.008
#> SRR1946669 5 0.1297 0.7360 0.040 0.012 0.000 0.000 0.948 0.000
#> SRR1946668 5 0.1401 0.7263 0.028 0.000 0.000 0.004 0.948 0.020
#> SRR1946666 3 0.2225 0.7942 0.092 0.008 0.892 0.000 0.008 0.000
#> SRR1946667 4 0.2608 0.9656 0.000 0.000 0.048 0.872 0.000 0.080
#> SRR1946670 6 0.1821 0.6605 0.000 0.040 0.000 0.008 0.024 0.928
#> SRR1946663 6 0.4004 0.4526 0.000 0.004 0.000 0.012 0.328 0.656
#> SRR1946664 6 0.5105 0.2609 0.004 0.444 0.008 0.048 0.000 0.496
#> SRR1946662 5 0.1168 0.7385 0.028 0.016 0.000 0.000 0.956 0.000
#> SRR1946661 5 0.4490 0.6934 0.000 0.172 0.000 0.064 0.736 0.028
#> SRR1946660 6 0.1382 0.6611 0.000 0.036 0.000 0.008 0.008 0.948
#> SRR1946659 3 0.2178 0.7749 0.132 0.000 0.868 0.000 0.000 0.000
#> SRR1946658 6 0.2526 0.6567 0.000 0.096 0.000 0.004 0.024 0.876
#> SRR1946657 2 0.2295 0.7324 0.000 0.908 0.004 0.024 0.016 0.048
#> SRR1946655 3 0.3776 0.6975 0.008 0.032 0.832 0.080 0.012 0.036
#> SRR1946654 3 0.0547 0.8124 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1946653 3 0.1088 0.8199 0.024 0.016 0.960 0.000 0.000 0.000
#> SRR1946652 2 0.3849 0.6485 0.000 0.752 0.000 0.032 0.208 0.008
#> SRR1946651 2 0.2784 0.7808 0.000 0.876 0.000 0.020 0.064 0.040
#> SRR1946650 2 0.4769 0.4609 0.000 0.652 0.000 0.068 0.272 0.008
#> SRR1946649 5 0.5417 0.5318 0.008 0.344 0.004 0.068 0.568 0.008
#> SRR1946648 3 0.2798 0.7673 0.000 0.048 0.876 0.020 0.056 0.000
#> SRR1946647 5 0.1743 0.7189 0.028 0.000 0.008 0.004 0.936 0.024
#> SRR1946646 2 0.2844 0.6919 0.000 0.864 0.092 0.004 0.004 0.036
#> SRR1946645 5 0.6074 0.6212 0.008 0.272 0.060 0.064 0.588 0.008
#> SRR1946644 2 0.4203 0.5305 0.004 0.772 0.040 0.036 0.000 0.148
#> SRR1946643 3 0.6622 0.1810 0.008 0.116 0.508 0.312 0.012 0.044
#> SRR1946642 1 0.0363 0.9808 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1946641 1 0.0717 0.9924 0.976 0.000 0.016 0.000 0.008 0.000
#> SRR1946656 3 0.6622 0.1810 0.008 0.116 0.508 0.312 0.012 0.044
#> SRR1946640 1 0.0717 0.9924 0.976 0.000 0.016 0.000 0.008 0.000
#> SRR1946639 1 0.0717 0.9924 0.976 0.000 0.016 0.000 0.008 0.000
#> SRR1946638 1 0.0717 0.9924 0.976 0.000 0.016 0.000 0.008 0.000
#> SRR1946637 1 0.0717 0.9924 0.976 0.000 0.016 0.000 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.847 0.891 0.958 0.5093 0.491 0.491
#> 3 3 0.602 0.783 0.874 0.3229 0.720 0.490
#> 4 4 0.666 0.721 0.801 0.1256 0.800 0.477
#> 5 5 0.681 0.592 0.788 0.0654 0.896 0.608
#> 6 6 0.719 0.558 0.700 0.0405 0.898 0.547
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
#> SRR1946675 2 0.961 0.3836 0.384 0.616
#> SRR1946691 2 0.000 0.9512 0.000 1.000
#> SRR1946690 2 0.000 0.9512 0.000 1.000
#> SRR1946689 2 0.000 0.9512 0.000 1.000
#> SRR1946686 1 0.000 0.9546 1.000 0.000
#> SRR1946685 2 0.000 0.9512 0.000 1.000
#> SRR1946688 2 0.000 0.9512 0.000 1.000
#> SRR1946684 1 0.000 0.9546 1.000 0.000
#> SRR1946683 1 0.000 0.9546 1.000 0.000
#> SRR1946682 1 0.958 0.3762 0.620 0.380
#> SRR1946680 2 0.000 0.9512 0.000 1.000
#> SRR1946681 2 0.000 0.9512 0.000 1.000
#> SRR1946687 1 0.388 0.8827 0.924 0.076
#> SRR1946679 2 0.000 0.9512 0.000 1.000
#> SRR1946678 1 0.000 0.9546 1.000 0.000
#> SRR1946676 2 0.000 0.9512 0.000 1.000
#> SRR1946677 1 0.000 0.9546 1.000 0.000
#> SRR1946672 2 0.738 0.7278 0.208 0.792
#> SRR1946673 1 0.000 0.9546 1.000 0.000
#> SRR1946671 1 0.000 0.9546 1.000 0.000
#> SRR1946669 1 0.000 0.9546 1.000 0.000
#> SRR1946668 1 0.000 0.9546 1.000 0.000
#> SRR1946666 1 0.000 0.9546 1.000 0.000
#> SRR1946667 2 0.000 0.9512 0.000 1.000
#> SRR1946670 2 0.000 0.9512 0.000 1.000
#> SRR1946663 1 0.518 0.8374 0.884 0.116
#> SRR1946664 2 0.000 0.9512 0.000 1.000
#> SRR1946662 1 0.000 0.9546 1.000 0.000
#> SRR1946661 1 0.000 0.9546 1.000 0.000
#> SRR1946660 2 0.000 0.9512 0.000 1.000
#> SRR1946659 1 0.000 0.9546 1.000 0.000
#> SRR1946658 2 0.000 0.9512 0.000 1.000
#> SRR1946657 2 0.000 0.9512 0.000 1.000
#> SRR1946655 2 0.000 0.9512 0.000 1.000
#> SRR1946654 2 0.000 0.9512 0.000 1.000
#> SRR1946653 2 0.745 0.7221 0.212 0.788
#> SRR1946652 2 0.000 0.9512 0.000 1.000
#> SRR1946651 2 0.000 0.9512 0.000 1.000
#> SRR1946650 2 0.949 0.3881 0.368 0.632
#> SRR1946649 1 0.000 0.9546 1.000 0.000
#> SRR1946648 1 0.998 0.0291 0.528 0.472
#> SRR1946647 1 0.000 0.9546 1.000 0.000
#> SRR1946646 2 0.000 0.9512 0.000 1.000
#> SRR1946645 1 0.000 0.9546 1.000 0.000
#> SRR1946644 2 0.000 0.9512 0.000 1.000
#> SRR1946643 2 0.000 0.9512 0.000 1.000
#> SRR1946642 1 0.000 0.9546 1.000 0.000
#> SRR1946641 1 0.000 0.9546 1.000 0.000
#> SRR1946656 2 0.000 0.9512 0.000 1.000
#> SRR1946640 1 0.000 0.9546 1.000 0.000
#> SRR1946639 1 0.000 0.9546 1.000 0.000
#> SRR1946638 1 0.000 0.9546 1.000 0.000
#> SRR1946637 1 0.000 0.9546 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.0424 0.819 0.008 0.000 0.992
#> SRR1946691 2 0.1031 0.857 0.000 0.976 0.024
#> SRR1946690 2 0.1163 0.855 0.000 0.972 0.028
#> SRR1946689 3 0.5431 0.718 0.000 0.284 0.716
#> SRR1946686 1 0.6079 0.593 0.612 0.000 0.388
#> SRR1946685 2 0.2056 0.859 0.024 0.952 0.024
#> SRR1946688 2 0.1129 0.861 0.004 0.976 0.020
#> SRR1946684 1 0.0424 0.864 0.992 0.008 0.000
#> SRR1946683 1 0.1647 0.871 0.960 0.004 0.036
#> SRR1946682 2 0.4178 0.785 0.172 0.828 0.000
#> SRR1946680 3 0.5431 0.718 0.000 0.284 0.716
#> SRR1946681 2 0.3116 0.790 0.000 0.892 0.108
#> SRR1946687 3 0.1289 0.800 0.032 0.000 0.968
#> SRR1946679 2 0.1711 0.859 0.032 0.960 0.008
#> SRR1946678 1 0.3752 0.863 0.856 0.000 0.144
#> SRR1946676 2 0.5028 0.767 0.040 0.828 0.132
#> SRR1946677 1 0.1031 0.857 0.976 0.024 0.000
#> SRR1946672 3 0.0424 0.819 0.008 0.000 0.992
#> SRR1946673 1 0.4555 0.648 0.800 0.200 0.000
#> SRR1946671 1 0.1636 0.866 0.964 0.020 0.016
#> SRR1946669 1 0.0424 0.864 0.992 0.008 0.000
#> SRR1946668 1 0.0237 0.866 0.996 0.004 0.000
#> SRR1946666 1 0.6244 0.492 0.560 0.000 0.440
#> SRR1946667 3 0.5431 0.718 0.000 0.284 0.716
#> SRR1946670 2 0.1129 0.861 0.004 0.976 0.020
#> SRR1946663 2 0.4504 0.770 0.196 0.804 0.000
#> SRR1946664 2 0.1031 0.857 0.000 0.976 0.024
#> SRR1946662 1 0.0592 0.863 0.988 0.012 0.000
#> SRR1946661 2 0.5988 0.518 0.368 0.632 0.000
#> SRR1946660 2 0.0661 0.862 0.004 0.988 0.008
#> SRR1946659 3 0.2537 0.749 0.080 0.000 0.920
#> SRR1946658 2 0.1129 0.861 0.004 0.976 0.020
#> SRR1946657 2 0.1015 0.862 0.008 0.980 0.012
#> SRR1946655 3 0.4121 0.799 0.000 0.168 0.832
#> SRR1946654 3 0.0892 0.825 0.000 0.020 0.980
#> SRR1946653 3 0.0424 0.819 0.008 0.000 0.992
#> SRR1946652 2 0.4228 0.801 0.148 0.844 0.008
#> SRR1946651 2 0.1163 0.860 0.028 0.972 0.000
#> SRR1946650 2 0.4399 0.773 0.188 0.812 0.000
#> SRR1946649 1 0.1163 0.855 0.972 0.028 0.000
#> SRR1946648 3 0.1163 0.817 0.028 0.000 0.972
#> SRR1946647 1 0.1267 0.870 0.972 0.004 0.024
#> SRR1946646 3 0.6274 0.348 0.000 0.456 0.544
#> SRR1946645 1 0.4345 0.865 0.848 0.016 0.136
#> SRR1946644 2 0.6244 -0.106 0.000 0.560 0.440
#> SRR1946643 3 0.4121 0.799 0.000 0.168 0.832
#> SRR1946642 1 0.3752 0.863 0.856 0.000 0.144
#> SRR1946641 1 0.4121 0.855 0.832 0.000 0.168
#> SRR1946656 3 0.4121 0.799 0.000 0.168 0.832
#> SRR1946640 1 0.4121 0.855 0.832 0.000 0.168
#> SRR1946639 1 0.4121 0.855 0.832 0.000 0.168
#> SRR1946638 1 0.4121 0.855 0.832 0.000 0.168
#> SRR1946637 1 0.4121 0.855 0.832 0.000 0.168
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.1557 0.843 0.056 0.000 0.944 0.000
#> SRR1946691 4 0.0188 0.848 0.000 0.004 0.000 0.996
#> SRR1946690 4 0.2036 0.836 0.000 0.032 0.032 0.936
#> SRR1946689 4 0.4193 0.724 0.000 0.000 0.268 0.732
#> SRR1946686 1 0.4543 0.341 0.676 0.000 0.324 0.000
#> SRR1946685 2 0.4307 0.758 0.000 0.808 0.048 0.144
#> SRR1946688 4 0.0188 0.847 0.000 0.004 0.000 0.996
#> SRR1946684 1 0.4655 0.735 0.760 0.208 0.000 0.032
#> SRR1946683 1 0.3257 0.766 0.844 0.152 0.004 0.000
#> SRR1946682 4 0.3806 0.749 0.020 0.156 0.000 0.824
#> SRR1946680 4 0.4222 0.720 0.000 0.000 0.272 0.728
#> SRR1946681 4 0.3895 0.790 0.000 0.012 0.184 0.804
#> SRR1946687 3 0.1792 0.840 0.068 0.000 0.932 0.000
#> SRR1946679 2 0.3946 0.760 0.000 0.812 0.020 0.168
#> SRR1946678 1 0.0779 0.814 0.980 0.016 0.004 0.000
#> SRR1946676 2 0.4374 0.759 0.000 0.812 0.068 0.120
#> SRR1946677 2 0.4382 0.516 0.296 0.704 0.000 0.000
#> SRR1946672 3 0.0921 0.843 0.028 0.000 0.972 0.000
#> SRR1946673 2 0.4245 0.605 0.196 0.784 0.000 0.020
#> SRR1946671 2 0.4477 0.510 0.312 0.688 0.000 0.000
#> SRR1946669 1 0.3649 0.753 0.796 0.204 0.000 0.000
#> SRR1946668 1 0.4466 0.754 0.784 0.180 0.000 0.036
#> SRR1946666 3 0.4866 0.414 0.404 0.000 0.596 0.000
#> SRR1946667 4 0.4193 0.724 0.000 0.000 0.268 0.732
#> SRR1946670 4 0.0817 0.843 0.000 0.024 0.000 0.976
#> SRR1946663 4 0.3806 0.749 0.020 0.156 0.000 0.824
#> SRR1946664 4 0.1929 0.835 0.000 0.036 0.024 0.940
#> SRR1946662 1 0.4250 0.689 0.724 0.276 0.000 0.000
#> SRR1946661 2 0.4656 0.628 0.160 0.784 0.000 0.056
#> SRR1946660 4 0.0188 0.847 0.000 0.004 0.000 0.996
#> SRR1946659 3 0.4564 0.563 0.328 0.000 0.672 0.000
#> SRR1946658 4 0.0469 0.846 0.000 0.012 0.000 0.988
#> SRR1946657 2 0.4467 0.745 0.000 0.788 0.040 0.172
#> SRR1946655 3 0.0779 0.830 0.000 0.004 0.980 0.016
#> SRR1946654 3 0.0188 0.837 0.000 0.004 0.996 0.000
#> SRR1946653 3 0.1557 0.843 0.056 0.000 0.944 0.000
#> SRR1946652 2 0.2921 0.774 0.000 0.860 0.000 0.140
#> SRR1946651 2 0.3925 0.757 0.000 0.808 0.016 0.176
#> SRR1946650 2 0.2401 0.771 0.004 0.904 0.000 0.092
#> SRR1946649 2 0.3649 0.649 0.204 0.796 0.000 0.000
#> SRR1946648 3 0.2830 0.826 0.060 0.040 0.900 0.000
#> SRR1946647 1 0.4423 0.756 0.788 0.176 0.000 0.036
#> SRR1946646 3 0.7434 0.223 0.000 0.232 0.512 0.256
#> SRR1946645 1 0.5168 -0.219 0.500 0.496 0.004 0.000
#> SRR1946644 4 0.6284 0.640 0.000 0.164 0.172 0.664
#> SRR1946643 3 0.1305 0.819 0.000 0.004 0.960 0.036
#> SRR1946642 1 0.0779 0.814 0.980 0.016 0.004 0.000
#> SRR1946641 1 0.0707 0.814 0.980 0.000 0.020 0.000
#> SRR1946656 3 0.1209 0.822 0.000 0.004 0.964 0.032
#> SRR1946640 1 0.0707 0.814 0.980 0.000 0.020 0.000
#> SRR1946639 1 0.0707 0.814 0.980 0.000 0.020 0.000
#> SRR1946638 1 0.0707 0.814 0.980 0.000 0.020 0.000
#> SRR1946637 1 0.0707 0.814 0.980 0.000 0.020 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.1399 0.7946 0.028 0.000 0.952 0.000 0.020
#> SRR1946691 4 0.1809 0.7740 0.000 0.012 0.000 0.928 0.060
#> SRR1946690 4 0.2389 0.7133 0.000 0.116 0.000 0.880 0.004
#> SRR1946689 4 0.3521 0.7104 0.000 0.008 0.172 0.808 0.012
#> SRR1946686 1 0.4114 0.4292 0.712 0.000 0.272 0.000 0.016
#> SRR1946685 2 0.1331 0.7047 0.000 0.952 0.000 0.040 0.008
#> SRR1946688 4 0.2361 0.7720 0.000 0.012 0.000 0.892 0.096
#> SRR1946684 5 0.4269 0.6002 0.300 0.000 0.000 0.016 0.684
#> SRR1946683 1 0.5838 0.0740 0.552 0.112 0.000 0.000 0.336
#> SRR1946682 4 0.4331 0.4875 0.000 0.004 0.000 0.596 0.400
#> SRR1946680 4 0.3873 0.6726 0.000 0.008 0.212 0.768 0.012
#> SRR1946681 4 0.5309 0.5778 0.000 0.080 0.228 0.680 0.012
#> SRR1946687 3 0.3146 0.7374 0.128 0.000 0.844 0.000 0.028
#> SRR1946679 2 0.1809 0.7027 0.000 0.928 0.000 0.060 0.012
#> SRR1946678 1 0.0609 0.7759 0.980 0.000 0.000 0.000 0.020
#> SRR1946676 2 0.3328 0.6167 0.000 0.812 0.008 0.004 0.176
#> SRR1946677 5 0.6120 0.2734 0.140 0.300 0.000 0.004 0.556
#> SRR1946672 3 0.0613 0.7951 0.004 0.008 0.984 0.000 0.004
#> SRR1946673 5 0.3536 0.5437 0.032 0.156 0.000 0.000 0.812
#> SRR1946671 5 0.6619 0.1994 0.212 0.304 0.000 0.004 0.480
#> SRR1946669 5 0.4088 0.5591 0.368 0.000 0.000 0.000 0.632
#> SRR1946668 5 0.4511 0.5621 0.356 0.000 0.000 0.016 0.628
#> SRR1946666 3 0.4827 0.1743 0.476 0.000 0.504 0.000 0.020
#> SRR1946667 4 0.3521 0.7104 0.000 0.008 0.172 0.808 0.012
#> SRR1946670 4 0.2411 0.7718 0.000 0.008 0.000 0.884 0.108
#> SRR1946663 4 0.4341 0.4802 0.000 0.004 0.000 0.592 0.404
#> SRR1946664 4 0.2719 0.6920 0.000 0.144 0.000 0.852 0.004
#> SRR1946662 5 0.4822 0.6156 0.288 0.048 0.000 0.000 0.664
#> SRR1946661 5 0.3488 0.4773 0.000 0.168 0.000 0.024 0.808
#> SRR1946660 4 0.2361 0.7720 0.000 0.012 0.000 0.892 0.096
#> SRR1946659 3 0.4829 0.1713 0.480 0.000 0.500 0.000 0.020
#> SRR1946658 4 0.3112 0.7616 0.000 0.044 0.000 0.856 0.100
#> SRR1946657 2 0.2170 0.6933 0.000 0.904 0.004 0.088 0.004
#> SRR1946655 3 0.0486 0.7928 0.000 0.004 0.988 0.004 0.004
#> SRR1946654 3 0.0324 0.7936 0.000 0.004 0.992 0.000 0.004
#> SRR1946653 3 0.1041 0.7966 0.032 0.000 0.964 0.000 0.004
#> SRR1946652 2 0.2358 0.6635 0.000 0.888 0.000 0.008 0.104
#> SRR1946651 2 0.2193 0.7040 0.000 0.912 0.000 0.060 0.028
#> SRR1946650 2 0.3550 0.5602 0.000 0.760 0.000 0.004 0.236
#> SRR1946649 2 0.6473 0.0243 0.164 0.468 0.000 0.004 0.364
#> SRR1946648 3 0.2786 0.7615 0.020 0.012 0.884 0.000 0.084
#> SRR1946647 5 0.4380 0.5923 0.292 0.000 0.004 0.016 0.688
#> SRR1946646 2 0.6551 0.3575 0.000 0.552 0.196 0.236 0.016
#> SRR1946645 1 0.6716 -0.1247 0.420 0.208 0.000 0.004 0.368
#> SRR1946644 2 0.6116 0.0846 0.000 0.480 0.080 0.424 0.016
#> SRR1946643 3 0.4341 0.6041 0.000 0.048 0.764 0.180 0.008
#> SRR1946642 1 0.0510 0.7797 0.984 0.000 0.000 0.000 0.016
#> SRR1946641 1 0.0000 0.7900 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.4160 0.6255 0.000 0.044 0.780 0.168 0.008
#> SRR1946640 1 0.0000 0.7900 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.7900 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.7900 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.7900 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.4716 0.6381 0.032 0.004 0.600 0.008 0.356 0.000
#> SRR1946691 6 0.3103 0.6629 0.000 0.048 0.080 0.004 0.012 0.856
#> SRR1946690 6 0.6090 0.4689 0.000 0.188 0.264 0.004 0.016 0.528
#> SRR1946689 6 0.5377 0.3353 0.000 0.040 0.460 0.004 0.028 0.468
#> SRR1946686 1 0.4810 0.5223 0.624 0.000 0.084 0.000 0.292 0.000
#> SRR1946685 2 0.2056 0.6766 0.000 0.904 0.012 0.080 0.000 0.004
#> SRR1946688 6 0.0508 0.6821 0.000 0.004 0.000 0.000 0.012 0.984
#> SRR1946684 5 0.5712 0.8937 0.100 0.000 0.000 0.284 0.580 0.036
#> SRR1946683 4 0.4919 0.4779 0.348 0.016 0.000 0.592 0.044 0.000
#> SRR1946682 6 0.4025 0.4569 0.000 0.000 0.000 0.048 0.232 0.720
#> SRR1946680 3 0.5350 -0.3555 0.000 0.040 0.512 0.004 0.028 0.416
#> SRR1946681 3 0.5417 -0.1252 0.000 0.148 0.584 0.000 0.004 0.264
#> SRR1946687 3 0.6017 0.4274 0.180 0.008 0.428 0.000 0.384 0.000
#> SRR1946679 2 0.2220 0.6830 0.000 0.908 0.000 0.044 0.012 0.036
#> SRR1946678 1 0.0622 0.7887 0.980 0.000 0.000 0.008 0.012 0.000
#> SRR1946676 2 0.3843 0.0723 0.000 0.548 0.000 0.452 0.000 0.000
#> SRR1946677 4 0.2642 0.6238 0.064 0.032 0.000 0.884 0.020 0.000
#> SRR1946672 3 0.4058 0.6576 0.016 0.004 0.660 0.000 0.320 0.000
#> SRR1946673 5 0.4878 0.7805 0.012 0.024 0.000 0.400 0.556 0.008
#> SRR1946671 4 0.3778 0.6679 0.128 0.048 0.000 0.800 0.024 0.000
#> SRR1946669 5 0.5495 0.8500 0.164 0.000 0.000 0.288 0.548 0.000
#> SRR1946668 5 0.5764 0.8816 0.140 0.000 0.000 0.260 0.576 0.024
#> SRR1946666 1 0.5808 0.2510 0.472 0.004 0.164 0.000 0.360 0.000
#> SRR1946667 6 0.5377 0.3353 0.000 0.040 0.460 0.004 0.028 0.468
#> SRR1946670 6 0.1476 0.6795 0.000 0.008 0.004 0.012 0.028 0.948
#> SRR1946663 6 0.4237 0.4341 0.000 0.004 0.000 0.048 0.244 0.704
#> SRR1946664 6 0.6244 0.4098 0.000 0.256 0.224 0.004 0.016 0.500
#> SRR1946662 5 0.5421 0.8793 0.092 0.008 0.000 0.336 0.560 0.004
#> SRR1946661 4 0.4865 -0.1412 0.000 0.028 0.000 0.660 0.264 0.048
#> SRR1946660 6 0.0767 0.6812 0.000 0.008 0.000 0.004 0.012 0.976
#> SRR1946659 1 0.5329 0.4268 0.564 0.004 0.112 0.000 0.320 0.000
#> SRR1946658 6 0.2392 0.6651 0.000 0.064 0.016 0.008 0.012 0.900
#> SRR1946657 2 0.2189 0.6817 0.000 0.916 0.028 0.028 0.004 0.024
#> SRR1946655 3 0.2996 0.6628 0.000 0.000 0.772 0.000 0.228 0.000
#> SRR1946654 3 0.3468 0.6690 0.000 0.000 0.712 0.004 0.284 0.000
#> SRR1946653 3 0.4570 0.6431 0.032 0.008 0.608 0.000 0.352 0.000
#> SRR1946652 2 0.4078 0.4570 0.000 0.700 0.000 0.268 0.008 0.024
#> SRR1946651 2 0.2265 0.6756 0.000 0.896 0.000 0.076 0.004 0.024
#> SRR1946650 4 0.4118 0.2047 0.000 0.396 0.000 0.592 0.004 0.008
#> SRR1946649 4 0.4299 0.5782 0.092 0.188 0.000 0.720 0.000 0.000
#> SRR1946648 3 0.5881 0.6020 0.016 0.008 0.528 0.112 0.336 0.000
#> SRR1946647 5 0.5722 0.8752 0.092 0.000 0.000 0.264 0.596 0.048
#> SRR1946646 2 0.4905 0.5007 0.000 0.652 0.280 0.012 0.012 0.044
#> SRR1946645 4 0.3807 0.6368 0.228 0.028 0.000 0.740 0.004 0.000
#> SRR1946644 2 0.6053 0.2541 0.000 0.544 0.260 0.004 0.020 0.172
#> SRR1946643 3 0.2250 0.4342 0.000 0.064 0.896 0.000 0.000 0.040
#> SRR1946642 1 0.0622 0.7887 0.980 0.000 0.000 0.008 0.012 0.000
#> SRR1946641 1 0.0000 0.8040 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.2058 0.4786 0.000 0.048 0.916 0.000 0.012 0.024
#> SRR1946640 1 0.0000 0.8040 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.8040 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.8040 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.8040 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.569 0.861 0.933 0.404 0.623 0.623
#> 3 3 0.571 0.780 0.859 0.534 0.736 0.576
#> 4 4 0.689 0.822 0.919 0.113 0.811 0.555
#> 5 5 0.739 0.715 0.872 0.117 0.877 0.621
#> 6 6 0.776 0.701 0.879 0.054 0.910 0.643
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1946675 2 0.7056 0.797 0.192 0.808
#> SRR1946691 2 0.0000 0.919 0.000 1.000
#> SRR1946690 2 0.0000 0.919 0.000 1.000
#> SRR1946689 2 0.0000 0.919 0.000 1.000
#> SRR1946686 2 0.7139 0.793 0.196 0.804
#> SRR1946685 2 0.0000 0.919 0.000 1.000
#> SRR1946688 2 0.0000 0.919 0.000 1.000
#> SRR1946684 1 0.2043 0.900 0.968 0.032
#> SRR1946683 2 0.7056 0.799 0.192 0.808
#> SRR1946682 2 0.0376 0.917 0.004 0.996
#> SRR1946680 2 0.0000 0.919 0.000 1.000
#> SRR1946681 2 0.0000 0.919 0.000 1.000
#> SRR1946687 2 0.7139 0.793 0.196 0.804
#> SRR1946679 2 0.0000 0.919 0.000 1.000
#> SRR1946678 1 0.0000 0.919 1.000 0.000
#> SRR1946676 2 0.0000 0.919 0.000 1.000
#> SRR1946677 2 0.7376 0.715 0.208 0.792
#> SRR1946672 2 0.6887 0.804 0.184 0.816
#> SRR1946673 2 0.1843 0.903 0.028 0.972
#> SRR1946671 2 0.0672 0.915 0.008 0.992
#> SRR1946669 1 0.0000 0.919 1.000 0.000
#> SRR1946668 2 0.8144 0.736 0.252 0.748
#> SRR1946666 2 0.7139 0.793 0.196 0.804
#> SRR1946667 2 0.0000 0.919 0.000 1.000
#> SRR1946670 2 0.0000 0.919 0.000 1.000
#> SRR1946663 2 0.5519 0.818 0.128 0.872
#> SRR1946664 2 0.0000 0.919 0.000 1.000
#> SRR1946662 1 0.7056 0.750 0.808 0.192
#> SRR1946661 2 0.6801 0.756 0.180 0.820
#> SRR1946660 2 0.0000 0.919 0.000 1.000
#> SRR1946659 1 0.0376 0.917 0.996 0.004
#> SRR1946658 2 0.0000 0.919 0.000 1.000
#> SRR1946657 2 0.0000 0.919 0.000 1.000
#> SRR1946655 2 0.0000 0.919 0.000 1.000
#> SRR1946654 2 0.0000 0.919 0.000 1.000
#> SRR1946653 2 0.7139 0.793 0.196 0.804
#> SRR1946652 2 0.0000 0.919 0.000 1.000
#> SRR1946651 2 0.0000 0.919 0.000 1.000
#> SRR1946650 2 0.7139 0.734 0.196 0.804
#> SRR1946649 1 0.7139 0.745 0.804 0.196
#> SRR1946648 2 0.6973 0.801 0.188 0.812
#> SRR1946647 2 0.7139 0.794 0.196 0.804
#> SRR1946646 2 0.0000 0.919 0.000 1.000
#> SRR1946645 1 0.9710 0.162 0.600 0.400
#> SRR1946644 2 0.0000 0.919 0.000 1.000
#> SRR1946643 2 0.0000 0.919 0.000 1.000
#> SRR1946642 1 0.0000 0.919 1.000 0.000
#> SRR1946641 1 0.0000 0.919 1.000 0.000
#> SRR1946656 2 0.0000 0.919 0.000 1.000
#> SRR1946640 1 0.0000 0.919 1.000 0.000
#> SRR1946639 1 0.0000 0.919 1.000 0.000
#> SRR1946638 1 0.0000 0.919 1.000 0.000
#> SRR1946637 1 0.0000 0.919 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946691 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946690 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946689 2 0.455 0.634 0.000 0.800 0.200
#> SRR1946686 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946685 3 0.382 0.746 0.000 0.148 0.852
#> SRR1946688 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946684 1 0.171 0.882 0.960 0.032 0.008
#> SRR1946683 3 0.245 0.819 0.076 0.000 0.924
#> SRR1946682 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946680 3 0.412 0.715 0.000 0.168 0.832
#> SRR1946681 3 0.440 0.712 0.000 0.188 0.812
#> SRR1946687 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946679 3 0.497 0.660 0.000 0.236 0.764
#> SRR1946678 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946676 3 0.296 0.779 0.000 0.100 0.900
#> SRR1946677 3 0.773 0.574 0.192 0.132 0.676
#> SRR1946672 3 0.236 0.820 0.072 0.000 0.928
#> SRR1946673 3 0.640 0.629 0.040 0.236 0.724
#> SRR1946671 3 0.359 0.792 0.028 0.076 0.896
#> SRR1946669 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946668 3 0.514 0.702 0.252 0.000 0.748
#> SRR1946666 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946667 2 0.497 0.589 0.000 0.764 0.236
#> SRR1946670 2 0.611 0.652 0.000 0.604 0.396
#> SRR1946663 2 0.656 0.794 0.040 0.708 0.252
#> SRR1946664 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946662 1 0.541 0.726 0.820 0.104 0.076
#> SRR1946661 2 0.502 0.799 0.000 0.760 0.240
#> SRR1946660 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946659 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946658 2 0.400 0.875 0.000 0.840 0.160
#> SRR1946657 3 0.497 0.660 0.000 0.236 0.764
#> SRR1946655 3 0.000 0.819 0.000 0.000 1.000
#> SRR1946654 3 0.000 0.819 0.000 0.000 1.000
#> SRR1946653 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946652 3 0.518 0.634 0.000 0.256 0.744
#> SRR1946651 2 0.406 0.872 0.000 0.836 0.164
#> SRR1946650 2 0.556 0.701 0.000 0.700 0.300
#> SRR1946649 1 0.611 0.673 0.780 0.140 0.080
#> SRR1946648 3 0.245 0.819 0.076 0.000 0.924
#> SRR1946647 3 0.254 0.818 0.080 0.000 0.920
#> SRR1946646 3 0.000 0.819 0.000 0.000 1.000
#> SRR1946645 1 0.613 0.145 0.600 0.000 0.400
#> SRR1946644 3 0.583 0.467 0.000 0.340 0.660
#> SRR1946643 3 0.000 0.819 0.000 0.000 1.000
#> SRR1946642 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946641 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946656 3 0.000 0.819 0.000 0.000 1.000
#> SRR1946640 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946639 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946638 1 0.000 0.910 1.000 0.000 0.000
#> SRR1946637 1 0.000 0.910 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.0000 0.868 0.000 0.000 1.000 0
#> SRR1946691 2 0.0336 0.904 0.000 0.992 0.008 0
#> SRR1946690 2 0.0000 0.903 0.000 1.000 0.000 0
#> SRR1946689 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946686 3 0.0336 0.867 0.008 0.000 0.992 0
#> SRR1946685 3 0.4431 0.587 0.000 0.304 0.696 0
#> SRR1946688 2 0.0188 0.903 0.000 0.996 0.004 0
#> SRR1946684 1 0.1545 0.849 0.952 0.040 0.008 0
#> SRR1946683 3 0.0000 0.868 0.000 0.000 1.000 0
#> SRR1946682 2 0.0336 0.901 0.000 0.992 0.008 0
#> SRR1946680 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946681 3 0.4916 0.312 0.000 0.424 0.576 0
#> SRR1946687 3 0.0000 0.868 0.000 0.000 1.000 0
#> SRR1946679 2 0.2921 0.874 0.000 0.860 0.140 0
#> SRR1946678 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946676 3 0.3764 0.713 0.000 0.216 0.784 0
#> SRR1946677 3 0.6731 0.517 0.148 0.248 0.604 0
#> SRR1946672 3 0.0376 0.868 0.004 0.004 0.992 0
#> SRR1946673 2 0.3024 0.873 0.000 0.852 0.148 0
#> SRR1946671 3 0.4050 0.772 0.036 0.144 0.820 0
#> SRR1946669 1 0.0336 0.878 0.992 0.000 0.008 0
#> SRR1946668 3 0.4008 0.671 0.244 0.000 0.756 0
#> SRR1946666 3 0.0336 0.867 0.008 0.000 0.992 0
#> SRR1946667 4 0.0000 1.000 0.000 0.000 0.000 1
#> SRR1946670 3 0.2469 0.775 0.000 0.108 0.892 0
#> SRR1946663 3 0.6868 0.462 0.120 0.336 0.544 0
#> SRR1946664 2 0.0000 0.903 0.000 1.000 0.000 0
#> SRR1946662 1 0.3933 0.668 0.792 0.200 0.008 0
#> SRR1946661 2 0.2973 0.876 0.000 0.856 0.144 0
#> SRR1946660 2 0.0188 0.903 0.000 0.996 0.004 0
#> SRR1946659 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946658 2 0.0469 0.902 0.000 0.988 0.012 0
#> SRR1946657 2 0.2921 0.874 0.000 0.860 0.140 0
#> SRR1946655 3 0.0336 0.868 0.000 0.008 0.992 0
#> SRR1946654 3 0.0336 0.868 0.000 0.008 0.992 0
#> SRR1946653 3 0.0336 0.867 0.008 0.000 0.992 0
#> SRR1946652 2 0.2921 0.874 0.000 0.860 0.140 0
#> SRR1946651 2 0.1557 0.901 0.000 0.944 0.056 0
#> SRR1946650 2 0.0336 0.906 0.000 0.992 0.008 0
#> SRR1946649 1 0.4222 0.580 0.728 0.272 0.000 0
#> SRR1946648 3 0.0000 0.868 0.000 0.000 1.000 0
#> SRR1946647 3 0.0188 0.867 0.004 0.000 0.996 0
#> SRR1946646 3 0.0336 0.868 0.000 0.008 0.992 0
#> SRR1946645 1 0.4855 0.204 0.600 0.000 0.400 0
#> SRR1946644 2 0.2868 0.877 0.000 0.864 0.136 0
#> SRR1946643 3 0.0336 0.868 0.000 0.008 0.992 0
#> SRR1946642 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946641 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946656 3 0.0336 0.868 0.000 0.008 0.992 0
#> SRR1946640 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946639 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946638 1 0.0000 0.884 1.000 0.000 0.000 0
#> SRR1946637 1 0.0000 0.884 1.000 0.000 0.000 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946691 2 0.445 0.3691 0.000 0.500 0.004 0 0.496
#> SRR1946690 2 0.256 0.7671 0.000 0.856 0.000 0 0.144
#> SRR1946689 4 0.000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946686 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946685 3 0.382 0.6122 0.000 0.304 0.696 0 0.000
#> SRR1946688 2 0.431 0.3563 0.000 0.504 0.000 0 0.496
#> SRR1946684 5 0.407 0.5467 0.300 0.008 0.000 0 0.692
#> SRR1946683 3 0.389 0.4791 0.000 0.000 0.680 0 0.320
#> SRR1946682 5 0.000 0.6918 0.000 0.000 0.000 0 1.000
#> SRR1946680 4 0.000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946681 3 0.423 0.3875 0.000 0.424 0.576 0 0.000
#> SRR1946687 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946679 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946678 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946676 3 0.321 0.7229 0.000 0.212 0.788 0 0.000
#> SRR1946677 3 0.599 -0.0290 0.016 0.068 0.476 0 0.440
#> SRR1946672 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946673 5 0.402 0.5477 0.000 0.348 0.000 0 0.652
#> SRR1946671 3 0.383 0.7609 0.028 0.136 0.816 0 0.020
#> SRR1946669 1 0.426 0.0528 0.564 0.000 0.000 0 0.436
#> SRR1946668 5 0.478 0.6055 0.060 0.000 0.248 0 0.692
#> SRR1946666 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946667 4 0.000 1.0000 0.000 0.000 0.000 1 0.000
#> SRR1946670 5 0.173 0.6798 0.000 0.000 0.080 0 0.920
#> SRR1946663 5 0.000 0.6918 0.000 0.000 0.000 0 1.000
#> SRR1946664 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946662 5 0.478 0.6003 0.248 0.060 0.000 0 0.692
#> SRR1946661 5 0.382 0.5940 0.000 0.304 0.000 0 0.696
#> SRR1946660 2 0.414 0.5576 0.000 0.616 0.000 0 0.384
#> SRR1946659 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946658 5 0.297 0.5944 0.000 0.168 0.004 0 0.828
#> SRR1946657 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946655 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946654 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946653 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946652 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946651 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946650 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946649 1 0.366 0.5300 0.724 0.276 0.000 0 0.000
#> SRR1946648 3 0.134 0.8316 0.000 0.000 0.944 0 0.056
#> SRR1946647 5 0.384 0.5419 0.000 0.000 0.308 0 0.692
#> SRR1946646 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946645 1 0.418 0.2242 0.600 0.000 0.400 0 0.000
#> SRR1946644 2 0.000 0.8398 0.000 1.000 0.000 0 0.000
#> SRR1946643 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946642 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946641 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946656 3 0.000 0.8658 0.000 0.000 1.000 0 0.000
#> SRR1946640 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946639 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946638 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
#> SRR1946637 1 0.000 0.8476 1.000 0.000 0.000 0 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946691 6 0.0146 0.6793 0.000 0.000 0.000 0 0.004 0.996
#> SRR1946690 6 0.3867 -0.0930 0.000 0.488 0.000 0 0.000 0.512
#> SRR1946689 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946686 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946685 3 0.3446 0.5346 0.000 0.308 0.692 0 0.000 0.000
#> SRR1946688 6 0.0000 0.6785 0.000 0.000 0.000 0 0.000 1.000
#> SRR1946684 5 0.0000 0.8142 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946683 3 0.4868 0.3822 0.000 0.076 0.592 0 0.332 0.000
#> SRR1946682 6 0.3446 0.4961 0.000 0.000 0.000 0 0.308 0.692
#> SRR1946680 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946681 3 0.3774 0.3120 0.000 0.408 0.592 0 0.000 0.000
#> SRR1946687 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946679 2 0.0000 0.7489 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946678 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946676 3 0.3843 0.3236 0.000 0.452 0.548 0 0.000 0.000
#> SRR1946677 5 0.6306 0.0651 0.012 0.252 0.320 0 0.416 0.000
#> SRR1946672 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946673 5 0.2178 0.7025 0.000 0.132 0.000 0 0.868 0.000
#> SRR1946671 3 0.4821 0.4091 0.028 0.376 0.576 0 0.020 0.000
#> SRR1946669 5 0.2762 0.6012 0.196 0.000 0.000 0 0.804 0.000
#> SRR1946668 5 0.0146 0.8122 0.004 0.000 0.000 0 0.996 0.000
#> SRR1946666 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946667 4 0.0000 1.0000 0.000 0.000 0.000 1 0.000 0.000
#> SRR1946670 6 0.3897 0.5213 0.000 0.000 0.024 0 0.280 0.696
#> SRR1946663 6 0.3076 0.5715 0.000 0.000 0.000 0 0.240 0.760
#> SRR1946664 2 0.3101 0.5149 0.000 0.756 0.000 0 0.000 0.244
#> SRR1946662 5 0.0000 0.8142 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946661 5 0.0000 0.8142 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946660 6 0.0000 0.6785 0.000 0.000 0.000 0 0.000 1.000
#> SRR1946659 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946658 6 0.5104 0.4487 0.000 0.108 0.000 0 0.304 0.588
#> SRR1946657 2 0.3023 0.6136 0.000 0.768 0.232 0 0.000 0.000
#> SRR1946655 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946654 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946653 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946652 2 0.0146 0.7469 0.000 0.996 0.000 0 0.004 0.000
#> SRR1946651 2 0.0000 0.7489 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946650 2 0.0000 0.7489 0.000 1.000 0.000 0 0.000 0.000
#> SRR1946649 2 0.3996 -0.0693 0.484 0.512 0.000 0 0.004 0.000
#> SRR1946648 3 0.1814 0.7924 0.000 0.000 0.900 0 0.100 0.000
#> SRR1946647 5 0.0000 0.8142 0.000 0.000 0.000 0 1.000 0.000
#> SRR1946646 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946645 1 0.5330 0.3908 0.592 0.232 0.176 0 0.000 0.000
#> SRR1946644 2 0.3101 0.6024 0.000 0.756 0.244 0 0.000 0.000
#> SRR1946643 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946642 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946641 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946656 3 0.0000 0.8550 0.000 0.000 1.000 0 0.000 0.000
#> SRR1946640 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946639 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946638 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
#> SRR1946637 1 0.0000 0.9398 1.000 0.000 0.000 0 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.365 0.669 0.773 0.3891 0.643 0.643
#> 3 3 0.172 0.283 0.617 0.3306 0.556 0.441
#> 4 4 0.571 0.810 0.892 0.1175 0.767 0.590
#> 5 5 0.474 0.602 0.773 0.2258 0.840 0.615
#> 6 6 0.656 0.624 0.762 0.0995 0.959 0.856
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
#> SRR1946675 1 0.9580 0.613 0.620 0.380
#> SRR1946691 2 0.9954 0.824 0.460 0.540
#> SRR1946690 2 0.9993 0.803 0.484 0.516
#> SRR1946689 2 0.5178 0.518 0.116 0.884
#> SRR1946686 1 0.9580 0.613 0.620 0.380
#> SRR1946685 1 0.0000 0.699 1.000 0.000
#> SRR1946688 2 0.9954 0.824 0.460 0.540
#> SRR1946684 1 0.6048 0.425 0.852 0.148
#> SRR1946683 1 0.0000 0.699 1.000 0.000
#> SRR1946682 2 0.9954 0.824 0.460 0.540
#> SRR1946680 2 0.5294 0.517 0.120 0.880
#> SRR1946681 1 0.0938 0.700 0.988 0.012
#> SRR1946687 1 0.9323 0.628 0.652 0.348
#> SRR1946679 1 0.0000 0.699 1.000 0.000
#> SRR1946678 1 0.3879 0.653 0.924 0.076
#> SRR1946676 1 0.0000 0.699 1.000 0.000
#> SRR1946677 1 0.0000 0.699 1.000 0.000
#> SRR1946672 1 0.9286 0.630 0.656 0.344
#> SRR1946673 1 0.0000 0.699 1.000 0.000
#> SRR1946671 1 0.0000 0.699 1.000 0.000
#> SRR1946669 1 0.0376 0.698 0.996 0.004
#> SRR1946668 1 0.0672 0.696 0.992 0.008
#> SRR1946666 1 0.9580 0.613 0.620 0.380
#> SRR1946667 2 0.5178 0.518 0.116 0.884
#> SRR1946670 2 0.9954 0.824 0.460 0.540
#> SRR1946663 2 0.9954 0.824 0.460 0.540
#> SRR1946664 2 0.9993 0.803 0.484 0.516
#> SRR1946662 1 0.0000 0.699 1.000 0.000
#> SRR1946661 1 0.0938 0.691 0.988 0.012
#> SRR1946660 2 0.9954 0.824 0.460 0.540
#> SRR1946659 1 0.9552 0.615 0.624 0.376
#> SRR1946658 2 0.9988 0.805 0.480 0.520
#> SRR1946657 1 0.0000 0.699 1.000 0.000
#> SRR1946655 1 0.9286 0.630 0.656 0.344
#> SRR1946654 1 0.9286 0.630 0.656 0.344
#> SRR1946653 1 0.9580 0.613 0.620 0.380
#> SRR1946652 1 0.0000 0.699 1.000 0.000
#> SRR1946651 1 0.2778 0.636 0.952 0.048
#> SRR1946650 1 0.1184 0.682 0.984 0.016
#> SRR1946649 1 0.0000 0.699 1.000 0.000
#> SRR1946648 1 0.8909 0.640 0.692 0.308
#> SRR1946647 1 0.0672 0.696 0.992 0.008
#> SRR1946646 1 0.2236 0.698 0.964 0.036
#> SRR1946645 1 0.0000 0.699 1.000 0.000
#> SRR1946644 1 0.3431 0.614 0.936 0.064
#> SRR1946643 1 0.9286 0.630 0.656 0.344
#> SRR1946642 1 0.3879 0.653 0.924 0.076
#> SRR1946641 1 0.9944 0.552 0.544 0.456
#> SRR1946656 1 0.9286 0.630 0.656 0.344
#> SRR1946640 1 0.9909 0.566 0.556 0.444
#> SRR1946639 1 0.9815 0.581 0.580 0.420
#> SRR1946638 1 0.9944 0.552 0.544 0.456
#> SRR1946637 1 0.9944 0.552 0.544 0.456
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.9424 -0.13549 0.352 0.464 0.184
#> SRR1946691 3 0.6495 -0.19646 0.004 0.460 0.536
#> SRR1946690 2 0.6252 0.13472 0.000 0.556 0.444
#> SRR1946689 3 0.5335 0.22258 0.232 0.008 0.760
#> SRR1946686 1 0.9111 0.29177 0.472 0.384 0.144
#> SRR1946685 2 0.0237 0.57638 0.004 0.996 0.000
#> SRR1946688 3 0.6309 -0.25431 0.000 0.496 0.504
#> SRR1946684 2 0.8230 0.29408 0.088 0.564 0.348
#> SRR1946683 2 0.2878 0.53793 0.096 0.904 0.000
#> SRR1946682 2 0.6295 0.08771 0.000 0.528 0.472
#> SRR1946680 1 0.9191 -0.03096 0.432 0.148 0.420
#> SRR1946681 2 0.5327 0.44806 0.000 0.728 0.272
#> SRR1946687 1 0.9714 0.14193 0.420 0.224 0.356
#> SRR1946679 2 0.0000 0.57813 0.000 1.000 0.000
#> SRR1946678 1 0.6291 0.20741 0.532 0.468 0.000
#> SRR1946676 2 0.0475 0.57811 0.004 0.992 0.004
#> SRR1946677 2 0.2448 0.55287 0.076 0.924 0.000
#> SRR1946672 2 0.9203 -0.09667 0.248 0.536 0.216
#> SRR1946673 2 0.2356 0.55545 0.072 0.928 0.000
#> SRR1946671 2 0.0424 0.57431 0.008 0.992 0.000
#> SRR1946669 2 0.7394 0.21522 0.284 0.652 0.064
#> SRR1946668 2 0.8067 0.36029 0.100 0.616 0.284
#> SRR1946666 2 0.9579 -0.17025 0.352 0.444 0.204
#> SRR1946667 3 0.5335 0.22258 0.232 0.008 0.760
#> SRR1946670 2 0.6305 0.05266 0.000 0.516 0.484
#> SRR1946663 2 0.6299 0.07854 0.000 0.524 0.476
#> SRR1946664 2 0.6267 0.11817 0.000 0.548 0.452
#> SRR1946662 2 0.4768 0.54373 0.100 0.848 0.052
#> SRR1946661 2 0.7485 0.40027 0.096 0.680 0.224
#> SRR1946660 2 0.6299 0.07854 0.000 0.524 0.476
#> SRR1946659 2 0.9674 -0.27323 0.392 0.396 0.212
#> SRR1946658 2 0.7890 0.26852 0.064 0.564 0.372
#> SRR1946657 2 0.2711 0.57185 0.000 0.912 0.088
#> SRR1946655 1 0.9648 0.19576 0.460 0.304 0.236
#> SRR1946654 2 0.9203 -0.09667 0.248 0.536 0.216
#> SRR1946653 1 0.9651 0.21533 0.436 0.348 0.216
#> SRR1946652 2 0.0237 0.57948 0.000 0.996 0.004
#> SRR1946651 2 0.1411 0.58560 0.000 0.964 0.036
#> SRR1946650 2 0.0747 0.58152 0.000 0.984 0.016
#> SRR1946649 2 0.0237 0.57638 0.004 0.996 0.000
#> SRR1946648 2 0.8955 -0.00933 0.332 0.524 0.144
#> SRR1946647 2 0.8390 0.31004 0.100 0.560 0.340
#> SRR1946646 2 0.5507 0.50607 0.056 0.808 0.136
#> SRR1946645 2 0.1860 0.56461 0.052 0.948 0.000
#> SRR1946644 2 0.6008 0.25396 0.000 0.628 0.372
#> SRR1946643 1 0.9722 0.20345 0.444 0.312 0.244
#> SRR1946642 1 0.6302 0.18164 0.520 0.480 0.000
#> SRR1946641 1 0.4931 0.53364 0.768 0.232 0.000
#> SRR1946656 1 0.9671 0.17520 0.460 0.292 0.248
#> SRR1946640 1 0.4931 0.53364 0.768 0.232 0.000
#> SRR1946639 1 0.4974 0.53191 0.764 0.236 0.000
#> SRR1946638 1 0.4931 0.53364 0.768 0.232 0.000
#> SRR1946637 1 0.4931 0.53364 0.768 0.232 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.3879 0.8339 0.024 0.128 0.840 0.008
#> SRR1946691 2 0.2853 0.9004 0.008 0.900 0.076 0.016
#> SRR1946690 2 0.2597 0.9007 0.008 0.904 0.084 0.004
#> SRR1946689 4 0.1867 0.8555 0.000 0.000 0.072 0.928
#> SRR1946686 1 0.7075 -0.0177 0.488 0.128 0.384 0.000
#> SRR1946685 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946688 2 0.2732 0.9024 0.008 0.904 0.076 0.012
#> SRR1946684 2 0.2441 0.9077 0.012 0.916 0.068 0.004
#> SRR1946683 2 0.1356 0.9211 0.008 0.960 0.032 0.000
#> SRR1946682 2 0.2732 0.9027 0.012 0.904 0.076 0.008
#> SRR1946680 4 0.5610 0.6536 0.004 0.068 0.216 0.712
#> SRR1946681 2 0.2944 0.8986 0.000 0.868 0.128 0.004
#> SRR1946687 3 0.4854 0.8177 0.052 0.136 0.796 0.016
#> SRR1946679 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946678 1 0.4250 0.5088 0.724 0.276 0.000 0.000
#> SRR1946676 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946677 2 0.1356 0.9211 0.008 0.960 0.032 0.000
#> SRR1946672 3 0.3142 0.8318 0.000 0.132 0.860 0.008
#> SRR1946673 2 0.1356 0.9211 0.008 0.960 0.032 0.000
#> SRR1946671 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946669 2 0.4632 0.4365 0.308 0.688 0.004 0.000
#> SRR1946668 2 0.0779 0.9167 0.016 0.980 0.004 0.000
#> SRR1946666 3 0.4621 0.8161 0.076 0.128 0.796 0.000
#> SRR1946667 4 0.1867 0.8555 0.000 0.000 0.072 0.928
#> SRR1946670 2 0.2732 0.9024 0.008 0.904 0.076 0.012
#> SRR1946663 2 0.2732 0.9024 0.008 0.904 0.076 0.012
#> SRR1946664 2 0.2597 0.9007 0.008 0.904 0.084 0.004
#> SRR1946662 2 0.0524 0.9172 0.008 0.988 0.004 0.000
#> SRR1946661 2 0.0376 0.9171 0.004 0.992 0.004 0.000
#> SRR1946660 2 0.2708 0.9029 0.016 0.904 0.076 0.004
#> SRR1946659 3 0.4336 0.8253 0.060 0.128 0.812 0.000
#> SRR1946658 2 0.2790 0.9032 0.012 0.904 0.072 0.012
#> SRR1946657 2 0.1305 0.9222 0.004 0.960 0.036 0.000
#> SRR1946655 3 0.2530 0.6332 0.000 0.004 0.896 0.100
#> SRR1946654 3 0.3271 0.8308 0.000 0.132 0.856 0.012
#> SRR1946653 3 0.3447 0.8340 0.020 0.128 0.852 0.000
#> SRR1946652 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946651 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946650 2 0.1209 0.9215 0.004 0.964 0.032 0.000
#> SRR1946649 2 0.1109 0.9218 0.004 0.968 0.028 0.000
#> SRR1946648 3 0.4589 0.7505 0.024 0.188 0.780 0.008
#> SRR1946647 2 0.1975 0.9123 0.048 0.936 0.016 0.000
#> SRR1946646 2 0.4277 0.6877 0.000 0.720 0.280 0.000
#> SRR1946645 2 0.1356 0.9211 0.008 0.960 0.032 0.000
#> SRR1946644 2 0.2831 0.9015 0.000 0.876 0.120 0.004
#> SRR1946643 3 0.2466 0.6367 0.000 0.004 0.900 0.096
#> SRR1946642 1 0.4543 0.4509 0.676 0.324 0.000 0.000
#> SRR1946641 1 0.0336 0.7191 0.992 0.000 0.008 0.000
#> SRR1946656 3 0.2530 0.6332 0.000 0.004 0.896 0.100
#> SRR1946640 1 0.0469 0.7155 0.988 0.000 0.012 0.000
#> SRR1946639 1 0.0927 0.7149 0.976 0.016 0.008 0.000
#> SRR1946638 1 0.0524 0.7192 0.988 0.004 0.008 0.000
#> SRR1946637 1 0.0336 0.7191 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.6350 0.670727 0.192 0.048 0.628 0.000 0.132
#> SRR1946691 2 0.3421 0.904939 0.000 0.788 0.008 0.000 0.204
#> SRR1946690 5 0.5108 -0.137998 0.000 0.420 0.008 0.024 0.548
#> SRR1946689 4 0.0510 0.682309 0.000 0.016 0.000 0.984 0.000
#> SRR1946686 3 0.7035 0.597795 0.376 0.044 0.448 0.000 0.132
#> SRR1946685 5 0.1243 0.706630 0.000 0.028 0.004 0.008 0.960
#> SRR1946688 2 0.3266 0.912719 0.000 0.796 0.000 0.004 0.200
#> SRR1946684 5 0.5718 -0.000667 0.028 0.396 0.028 0.004 0.544
#> SRR1946683 5 0.3430 0.615485 0.000 0.220 0.000 0.004 0.776
#> SRR1946682 2 0.3333 0.912085 0.000 0.788 0.000 0.004 0.208
#> SRR1946680 4 0.8031 0.060032 0.000 0.200 0.236 0.432 0.132
#> SRR1946681 5 0.5492 0.153837 0.000 0.324 0.040 0.024 0.612
#> SRR1946687 3 0.7400 0.575377 0.384 0.052 0.424 0.008 0.132
#> SRR1946679 5 0.1168 0.707316 0.000 0.032 0.000 0.008 0.960
#> SRR1946678 1 0.1124 0.934798 0.960 0.004 0.000 0.000 0.036
#> SRR1946676 5 0.1618 0.702478 0.000 0.040 0.008 0.008 0.944
#> SRR1946677 5 0.3300 0.607584 0.000 0.204 0.000 0.004 0.792
#> SRR1946672 3 0.3578 0.599138 0.000 0.048 0.820 0.000 0.132
#> SRR1946673 5 0.0955 0.699856 0.000 0.028 0.000 0.004 0.968
#> SRR1946671 5 0.3266 0.608084 0.000 0.200 0.000 0.004 0.796
#> SRR1946669 5 0.2548 0.647829 0.004 0.116 0.000 0.004 0.876
#> SRR1946668 5 0.5504 0.146265 0.028 0.344 0.032 0.000 0.596
#> SRR1946666 3 0.7043 0.590240 0.384 0.044 0.440 0.000 0.132
#> SRR1946667 4 0.0510 0.682309 0.000 0.016 0.000 0.984 0.000
#> SRR1946670 2 0.3266 0.912719 0.000 0.796 0.000 0.004 0.200
#> SRR1946663 2 0.3333 0.912085 0.000 0.788 0.000 0.004 0.208
#> SRR1946664 2 0.4698 0.321998 0.000 0.520 0.008 0.004 0.468
#> SRR1946662 5 0.1571 0.700628 0.000 0.060 0.000 0.004 0.936
#> SRR1946661 5 0.1731 0.693526 0.004 0.060 0.000 0.004 0.932
#> SRR1946660 2 0.3266 0.912719 0.000 0.796 0.000 0.004 0.200
#> SRR1946659 3 0.7043 0.590240 0.384 0.044 0.440 0.000 0.132
#> SRR1946658 5 0.5092 -0.245737 0.000 0.464 0.012 0.016 0.508
#> SRR1946657 5 0.1914 0.690217 0.000 0.056 0.008 0.008 0.928
#> SRR1946655 3 0.0613 0.453823 0.004 0.008 0.984 0.004 0.000
#> SRR1946654 3 0.3735 0.600046 0.004 0.048 0.816 0.000 0.132
#> SRR1946653 3 0.6515 0.670695 0.216 0.048 0.604 0.000 0.132
#> SRR1946652 5 0.1490 0.704227 0.004 0.032 0.004 0.008 0.952
#> SRR1946651 5 0.1638 0.689332 0.000 0.064 0.004 0.000 0.932
#> SRR1946650 5 0.0703 0.706795 0.000 0.024 0.000 0.000 0.976
#> SRR1946649 5 0.2377 0.650523 0.000 0.128 0.000 0.000 0.872
#> SRR1946648 3 0.6569 0.669534 0.200 0.056 0.608 0.000 0.136
#> SRR1946647 5 0.6558 0.083425 0.088 0.320 0.048 0.000 0.544
#> SRR1946646 3 0.5693 0.232214 0.004 0.068 0.488 0.000 0.440
#> SRR1946645 5 0.3352 0.614115 0.004 0.192 0.000 0.004 0.800
#> SRR1946644 5 0.5503 0.244043 0.000 0.272 0.092 0.004 0.632
#> SRR1946643 3 0.0693 0.449171 0.000 0.008 0.980 0.012 0.000
#> SRR1946642 1 0.2830 0.839207 0.876 0.080 0.000 0.000 0.044
#> SRR1946641 1 0.0162 0.958788 0.996 0.000 0.000 0.000 0.004
#> SRR1946656 3 0.0693 0.449171 0.000 0.008 0.980 0.012 0.000
#> SRR1946640 1 0.0290 0.951858 0.992 0.000 0.008 0.000 0.000
#> SRR1946639 1 0.0671 0.953520 0.980 0.004 0.000 0.000 0.016
#> SRR1946638 1 0.0162 0.958788 0.996 0.000 0.000 0.000 0.004
#> SRR1946637 1 0.0162 0.958788 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.3603 0.6238 0.072 NA 0.808 0.000 0.008 0.000
#> SRR1946691 6 0.3136 0.7525 0.000 NA 0.020 0.000 0.108 0.844
#> SRR1946690 6 0.6638 0.5024 0.000 NA 0.044 0.000 0.272 0.452
#> SRR1946689 4 0.0000 0.7645 0.000 NA 0.000 1.000 0.000 0.000
#> SRR1946686 3 0.5968 0.4153 0.356 NA 0.460 0.000 0.008 0.000
#> SRR1946685 5 0.2398 0.7331 0.000 NA 0.020 0.000 0.876 0.000
#> SRR1946688 6 0.1663 0.7707 0.000 NA 0.000 0.000 0.088 0.912
#> SRR1946684 5 0.6464 0.1206 0.004 NA 0.040 0.000 0.460 0.348
#> SRR1946683 5 0.3930 0.7130 0.000 NA 0.016 0.000 0.788 0.076
#> SRR1946682 6 0.1753 0.7677 0.000 NA 0.000 0.000 0.084 0.912
#> SRR1946680 4 0.7615 0.3870 0.000 NA 0.192 0.452 0.068 0.060
#> SRR1946681 5 0.7068 0.2181 0.000 NA 0.136 0.016 0.456 0.084
#> SRR1946687 3 0.6144 0.4374 0.320 NA 0.444 0.000 0.008 0.000
#> SRR1946679 5 0.1625 0.7384 0.000 NA 0.012 0.000 0.928 0.000
#> SRR1946678 1 0.3423 0.7791 0.812 NA 0.000 0.000 0.088 0.000
#> SRR1946676 5 0.2398 0.7331 0.000 NA 0.020 0.000 0.876 0.000
#> SRR1946677 5 0.3361 0.7152 0.000 NA 0.000 0.000 0.816 0.076
#> SRR1946672 3 0.0520 0.6174 0.000 NA 0.984 0.000 0.008 0.000
#> SRR1946673 5 0.1957 0.7389 0.000 NA 0.000 0.000 0.888 0.000
#> SRR1946671 5 0.3219 0.7205 0.000 NA 0.008 0.000 0.840 0.076
#> SRR1946669 5 0.3643 0.6919 0.008 NA 0.024 0.000 0.768 0.000
#> SRR1946668 5 0.6816 0.3122 0.016 NA 0.040 0.000 0.484 0.244
#> SRR1946666 3 0.6004 0.4176 0.352 NA 0.456 0.000 0.008 0.000
#> SRR1946667 4 0.0000 0.7645 0.000 NA 0.000 1.000 0.000 0.000
#> SRR1946670 6 0.1970 0.7713 0.008 NA 0.000 0.000 0.092 0.900
#> SRR1946663 6 0.2001 0.7714 0.000 NA 0.004 0.000 0.092 0.900
#> SRR1946664 6 0.6503 0.5262 0.000 NA 0.044 0.000 0.212 0.488
#> SRR1946662 5 0.3301 0.6999 0.000 NA 0.024 0.000 0.788 0.000
#> SRR1946661 5 0.3202 0.7065 0.000 NA 0.024 0.000 0.800 0.000
#> SRR1946660 6 0.1918 0.7697 0.008 NA 0.000 0.000 0.088 0.904
#> SRR1946659 3 0.6004 0.4176 0.352 NA 0.456 0.000 0.008 0.000
#> SRR1946658 6 0.6719 0.3216 0.000 NA 0.032 0.000 0.300 0.336
#> SRR1946657 5 0.2063 0.7345 0.000 NA 0.020 0.000 0.912 0.008
#> SRR1946655 3 0.1957 0.5747 0.000 NA 0.888 0.000 0.000 0.000
#> SRR1946654 3 0.0547 0.6152 0.000 NA 0.980 0.000 0.020 0.000
#> SRR1946653 3 0.3802 0.6222 0.084 NA 0.792 0.000 0.008 0.000
#> SRR1946652 5 0.2039 0.7348 0.000 NA 0.020 0.000 0.904 0.000
#> SRR1946651 5 0.3411 0.7075 0.000 NA 0.024 0.000 0.804 0.012
#> SRR1946650 5 0.2709 0.7199 0.000 NA 0.020 0.000 0.848 0.000
#> SRR1946649 5 0.2434 0.7343 0.000 NA 0.008 0.000 0.892 0.064
#> SRR1946648 3 0.5132 0.5727 0.100 NA 0.716 0.000 0.068 0.004
#> SRR1946647 5 0.7589 0.2459 0.052 NA 0.068 0.000 0.444 0.200
#> SRR1946646 3 0.6176 -0.0269 0.000 NA 0.440 0.000 0.360 0.016
#> SRR1946645 5 0.3850 0.7176 0.004 NA 0.016 0.000 0.804 0.076
#> SRR1946644 5 0.6845 0.2293 0.000 NA 0.140 0.000 0.472 0.108
#> SRR1946643 3 0.2053 0.5735 0.000 NA 0.888 0.004 0.000 0.000
#> SRR1946642 1 0.3372 0.7845 0.816 NA 0.000 0.000 0.084 0.000
#> SRR1946641 1 0.0000 0.9104 1.000 NA 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.2053 0.5735 0.000 NA 0.888 0.004 0.000 0.000
#> SRR1946640 1 0.0547 0.8983 0.980 NA 0.020 0.000 0.000 0.000
#> SRR1946639 1 0.0363 0.9066 0.988 NA 0.000 0.000 0.012 0.000
#> SRR1946638 1 0.0000 0.9104 1.000 NA 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9104 1.000 NA 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "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 17231 rows and 53 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 0.611 0.806 0.918 0.4911 0.495 0.495
#> 3 3 0.764 0.849 0.931 0.2994 0.641 0.414
#> 4 4 0.557 0.707 0.841 0.1607 0.763 0.447
#> 5 5 0.784 0.764 0.884 0.0780 0.835 0.470
#> 6 6 0.739 0.578 0.798 0.0494 0.890 0.544
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
#> SRR1946675 2 0.990 0.318 0.440 0.560
#> SRR1946691 2 0.000 0.880 0.000 1.000
#> SRR1946690 2 0.000 0.880 0.000 1.000
#> SRR1946689 2 0.000 0.880 0.000 1.000
#> SRR1946686 1 0.000 0.927 1.000 0.000
#> SRR1946685 2 0.946 0.509 0.364 0.636
#> SRR1946688 2 0.000 0.880 0.000 1.000
#> SRR1946684 1 0.000 0.927 1.000 0.000
#> SRR1946683 1 0.000 0.927 1.000 0.000
#> SRR1946682 2 0.625 0.786 0.156 0.844
#> SRR1946680 2 0.000 0.880 0.000 1.000
#> SRR1946681 2 0.000 0.880 0.000 1.000
#> SRR1946687 1 0.963 0.277 0.612 0.388
#> SRR1946679 2 0.886 0.618 0.304 0.696
#> SRR1946678 1 0.000 0.927 1.000 0.000
#> SRR1946676 2 0.745 0.735 0.212 0.788
#> SRR1946677 1 0.000 0.927 1.000 0.000
#> SRR1946672 2 0.917 0.572 0.332 0.668
#> SRR1946673 1 0.730 0.696 0.796 0.204
#> SRR1946671 1 0.000 0.927 1.000 0.000
#> SRR1946669 1 0.000 0.927 1.000 0.000
#> SRR1946668 1 0.000 0.927 1.000 0.000
#> SRR1946666 1 0.000 0.927 1.000 0.000
#> SRR1946667 2 0.000 0.880 0.000 1.000
#> SRR1946670 2 0.000 0.880 0.000 1.000
#> SRR1946663 2 0.921 0.565 0.336 0.664
#> SRR1946664 2 0.000 0.880 0.000 1.000
#> SRR1946662 1 0.000 0.927 1.000 0.000
#> SRR1946661 1 0.833 0.591 0.736 0.264
#> SRR1946660 2 0.000 0.880 0.000 1.000
#> SRR1946659 1 0.000 0.927 1.000 0.000
#> SRR1946658 2 0.000 0.880 0.000 1.000
#> SRR1946657 2 0.000 0.880 0.000 1.000
#> SRR1946655 2 0.000 0.880 0.000 1.000
#> SRR1946654 2 0.224 0.866 0.036 0.964
#> SRR1946653 2 0.781 0.714 0.232 0.768
#> SRR1946652 2 0.311 0.857 0.056 0.944
#> SRR1946651 2 0.000 0.880 0.000 1.000
#> SRR1946650 2 0.997 0.229 0.468 0.532
#> SRR1946649 1 0.000 0.927 1.000 0.000
#> SRR1946648 1 0.983 0.146 0.576 0.424
#> SRR1946647 1 0.494 0.824 0.892 0.108
#> SRR1946646 2 0.242 0.865 0.040 0.960
#> SRR1946645 1 0.000 0.927 1.000 0.000
#> SRR1946644 2 0.000 0.880 0.000 1.000
#> SRR1946643 2 0.000 0.880 0.000 1.000
#> SRR1946642 1 0.000 0.927 1.000 0.000
#> SRR1946641 1 0.000 0.927 1.000 0.000
#> SRR1946656 2 0.000 0.880 0.000 1.000
#> SRR1946640 1 0.000 0.927 1.000 0.000
#> SRR1946639 1 0.000 0.927 1.000 0.000
#> SRR1946638 1 0.000 0.927 1.000 0.000
#> SRR1946637 1 0.000 0.927 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.5760 0.504 0.328 0.000 0.672
#> SRR1946691 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946690 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946689 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946686 1 0.0237 0.948 0.996 0.000 0.004
#> SRR1946685 2 0.0592 0.896 0.012 0.988 0.000
#> SRR1946688 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946684 2 0.5859 0.538 0.344 0.656 0.000
#> SRR1946683 1 0.1860 0.915 0.948 0.052 0.000
#> SRR1946682 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946680 3 0.0592 0.932 0.000 0.012 0.988
#> SRR1946681 2 0.0592 0.892 0.000 0.988 0.012
#> SRR1946687 3 0.3116 0.864 0.108 0.000 0.892
#> SRR1946679 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946678 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946676 2 0.0237 0.897 0.004 0.996 0.000
#> SRR1946677 2 0.4555 0.757 0.200 0.800 0.000
#> SRR1946672 3 0.1031 0.929 0.024 0.000 0.976
#> SRR1946673 2 0.2796 0.853 0.092 0.908 0.000
#> SRR1946671 2 0.6244 0.317 0.440 0.560 0.000
#> SRR1946669 1 0.0237 0.949 0.996 0.004 0.000
#> SRR1946668 2 0.6286 0.243 0.464 0.536 0.000
#> SRR1946666 1 0.0892 0.938 0.980 0.000 0.020
#> SRR1946667 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946670 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946663 2 0.1163 0.890 0.028 0.972 0.000
#> SRR1946664 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946662 2 0.4555 0.757 0.200 0.800 0.000
#> SRR1946661 2 0.0892 0.893 0.020 0.980 0.000
#> SRR1946660 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946659 1 0.2625 0.885 0.916 0.000 0.084
#> SRR1946658 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946657 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946655 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946654 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946653 3 0.2625 0.887 0.084 0.000 0.916
#> SRR1946652 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946651 2 0.0000 0.898 0.000 1.000 0.000
#> SRR1946650 2 0.0747 0.895 0.016 0.984 0.000
#> SRR1946649 2 0.4002 0.799 0.160 0.840 0.000
#> SRR1946648 1 0.6303 0.629 0.720 0.032 0.248
#> SRR1946647 1 0.3267 0.841 0.884 0.116 0.000
#> SRR1946646 2 0.6434 0.377 0.008 0.612 0.380
#> SRR1946645 1 0.1643 0.922 0.956 0.044 0.000
#> SRR1946644 2 0.3340 0.808 0.000 0.880 0.120
#> SRR1946643 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946642 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946656 3 0.0000 0.939 0.000 0.000 1.000
#> SRR1946640 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.950 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.950 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.5397 0.5347 0.212 0.068 0.720 0.000
#> SRR1946691 4 0.5989 0.4726 0.000 0.264 0.080 0.656
#> SRR1946690 2 0.2921 0.7625 0.000 0.860 0.000 0.140
#> SRR1946689 3 0.3801 0.7098 0.000 0.000 0.780 0.220
#> SRR1946686 1 0.2589 0.8105 0.884 0.000 0.116 0.000
#> SRR1946685 2 0.0000 0.8052 0.000 1.000 0.000 0.000
#> SRR1946688 4 0.0000 0.7652 0.000 0.000 0.000 1.000
#> SRR1946684 4 0.3982 0.7775 0.220 0.004 0.000 0.776
#> SRR1946683 1 0.2089 0.8298 0.932 0.020 0.000 0.048
#> SRR1946682 4 0.0376 0.7698 0.004 0.004 0.000 0.992
#> SRR1946680 3 0.3764 0.7132 0.000 0.000 0.784 0.216
#> SRR1946681 2 0.0188 0.8035 0.000 0.996 0.004 0.000
#> SRR1946687 3 0.2813 0.7388 0.024 0.000 0.896 0.080
#> SRR1946679 2 0.0336 0.8057 0.000 0.992 0.000 0.008
#> SRR1946678 1 0.0000 0.8684 1.000 0.000 0.000 0.000
#> SRR1946676 2 0.0000 0.8052 0.000 1.000 0.000 0.000
#> SRR1946677 4 0.7285 0.4866 0.176 0.308 0.000 0.516
#> SRR1946672 1 0.7137 0.3762 0.544 0.168 0.288 0.000
#> SRR1946673 4 0.5159 0.7656 0.088 0.156 0.000 0.756
#> SRR1946671 1 0.5110 0.6656 0.764 0.104 0.000 0.132
#> SRR1946669 4 0.4800 0.6404 0.340 0.004 0.000 0.656
#> SRR1946668 4 0.3569 0.7863 0.196 0.000 0.000 0.804
#> SRR1946666 1 0.2408 0.8210 0.896 0.000 0.104 0.000
#> SRR1946667 3 0.3764 0.7132 0.000 0.000 0.784 0.216
#> SRR1946670 4 0.0000 0.7652 0.000 0.000 0.000 1.000
#> SRR1946663 4 0.0188 0.7677 0.004 0.000 0.000 0.996
#> SRR1946664 2 0.3074 0.7541 0.000 0.848 0.000 0.152
#> SRR1946662 4 0.4574 0.7744 0.220 0.024 0.000 0.756
#> SRR1946661 4 0.4662 0.7914 0.092 0.112 0.000 0.796
#> SRR1946660 4 0.1940 0.7762 0.000 0.076 0.000 0.924
#> SRR1946659 1 0.3801 0.7094 0.780 0.000 0.220 0.000
#> SRR1946658 4 0.3764 0.7105 0.000 0.216 0.000 0.784
#> SRR1946657 2 0.0000 0.8052 0.000 1.000 0.000 0.000
#> SRR1946655 3 0.3400 0.6283 0.000 0.180 0.820 0.000
#> SRR1946654 2 0.7315 0.2126 0.252 0.532 0.216 0.000
#> SRR1946653 3 0.2589 0.6665 0.116 0.000 0.884 0.000
#> SRR1946652 2 0.3074 0.7557 0.000 0.848 0.000 0.152
#> SRR1946651 2 0.2469 0.7805 0.000 0.892 0.000 0.108
#> SRR1946650 2 0.3873 0.6490 0.000 0.772 0.000 0.228
#> SRR1946649 2 0.6936 0.4640 0.284 0.568 0.000 0.148
#> SRR1946648 1 0.7775 0.5373 0.604 0.144 0.184 0.068
#> SRR1946647 4 0.4155 0.7544 0.240 0.004 0.000 0.756
#> SRR1946646 2 0.2412 0.7378 0.008 0.908 0.084 0.000
#> SRR1946645 1 0.1118 0.8559 0.964 0.036 0.000 0.000
#> SRR1946644 2 0.0188 0.8047 0.000 0.996 0.000 0.004
#> SRR1946643 2 0.4661 0.3532 0.000 0.652 0.348 0.000
#> SRR1946642 1 0.0000 0.8684 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.8684 1.000 0.000 0.000 0.000
#> SRR1946656 3 0.4998 -0.0383 0.000 0.488 0.512 0.000
#> SRR1946640 1 0.0592 0.8647 0.984 0.000 0.016 0.000
#> SRR1946639 1 0.0000 0.8684 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.8684 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.8684 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.1670 0.8187 0.000 0.000 0.936 0.012 0.052
#> SRR1946691 2 0.4415 0.3580 0.000 0.604 0.000 0.388 0.008
#> SRR1946690 2 0.1041 0.7998 0.000 0.964 0.000 0.032 0.004
#> SRR1946689 4 0.1671 0.8556 0.000 0.000 0.076 0.924 0.000
#> SRR1946686 1 0.3586 0.6448 0.736 0.000 0.264 0.000 0.000
#> SRR1946685 2 0.1197 0.8057 0.000 0.952 0.048 0.000 0.000
#> SRR1946688 4 0.3966 0.6495 0.000 0.132 0.000 0.796 0.072
#> SRR1946684 5 0.0451 0.8807 0.008 0.000 0.000 0.004 0.988
#> SRR1946683 5 0.1195 0.8708 0.028 0.000 0.012 0.000 0.960
#> SRR1946682 5 0.2563 0.8430 0.000 0.008 0.000 0.120 0.872
#> SRR1946680 4 0.1732 0.8543 0.000 0.000 0.080 0.920 0.000
#> SRR1946681 2 0.4452 0.0824 0.000 0.500 0.496 0.000 0.004
#> SRR1946687 4 0.4014 0.6462 0.016 0.000 0.256 0.728 0.000
#> SRR1946679 2 0.1282 0.8071 0.000 0.952 0.044 0.000 0.004
#> SRR1946678 1 0.1704 0.8920 0.928 0.000 0.004 0.000 0.068
#> SRR1946676 2 0.2536 0.7595 0.000 0.868 0.128 0.000 0.004
#> SRR1946677 5 0.1124 0.8687 0.004 0.000 0.036 0.000 0.960
#> SRR1946672 3 0.1205 0.8276 0.040 0.000 0.956 0.004 0.000
#> SRR1946673 5 0.0451 0.8790 0.008 0.000 0.004 0.000 0.988
#> SRR1946671 2 0.5047 0.1038 0.472 0.496 0.000 0.000 0.032
#> SRR1946669 5 0.0771 0.8777 0.020 0.000 0.004 0.000 0.976
#> SRR1946668 5 0.1943 0.8715 0.020 0.000 0.000 0.056 0.924
#> SRR1946666 1 0.1792 0.8864 0.916 0.000 0.084 0.000 0.000
#> SRR1946667 4 0.1671 0.8556 0.000 0.000 0.076 0.924 0.000
#> SRR1946670 5 0.4768 0.4718 0.000 0.024 0.000 0.384 0.592
#> SRR1946663 5 0.3205 0.8062 0.004 0.004 0.000 0.176 0.816
#> SRR1946664 2 0.0324 0.8096 0.000 0.992 0.000 0.004 0.004
#> SRR1946662 5 0.0566 0.8785 0.012 0.000 0.004 0.000 0.984
#> SRR1946661 5 0.4661 0.7458 0.012 0.156 0.000 0.076 0.756
#> SRR1946660 2 0.5304 0.4505 0.000 0.628 0.000 0.292 0.080
#> SRR1946659 1 0.0609 0.9288 0.980 0.000 0.020 0.000 0.000
#> SRR1946658 5 0.2770 0.8508 0.000 0.044 0.000 0.076 0.880
#> SRR1946657 2 0.0609 0.8118 0.000 0.980 0.020 0.000 0.000
#> SRR1946655 3 0.0451 0.8413 0.000 0.000 0.988 0.008 0.004
#> SRR1946654 3 0.0451 0.8449 0.004 0.008 0.988 0.000 0.000
#> SRR1946653 3 0.6506 0.0463 0.208 0.000 0.468 0.324 0.000
#> SRR1946652 5 0.4495 0.6661 0.000 0.200 0.064 0.000 0.736
#> SRR1946651 2 0.0162 0.8117 0.000 0.996 0.004 0.000 0.000
#> SRR1946650 2 0.0290 0.8108 0.000 0.992 0.000 0.000 0.008
#> SRR1946649 2 0.2522 0.7589 0.108 0.880 0.000 0.000 0.012
#> SRR1946648 3 0.3395 0.6378 0.000 0.000 0.764 0.000 0.236
#> SRR1946647 5 0.0451 0.8797 0.000 0.000 0.004 0.008 0.988
#> SRR1946646 2 0.3477 0.7474 0.112 0.832 0.056 0.000 0.000
#> SRR1946645 1 0.2983 0.8438 0.868 0.004 0.032 0.000 0.096
#> SRR1946644 2 0.0404 0.8123 0.000 0.988 0.012 0.000 0.000
#> SRR1946643 3 0.0771 0.8401 0.000 0.020 0.976 0.004 0.000
#> SRR1946642 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0290 0.8442 0.000 0.008 0.992 0.000 0.000
#> SRR1946640 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9374 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0976 0.85394 0.000 0.000 0.968 0.008 0.008 0.016
#> SRR1946691 2 0.5909 0.00221 0.000 0.420 0.000 0.208 0.000 0.372
#> SRR1946690 2 0.1555 0.75717 0.000 0.940 0.000 0.012 0.040 0.008
#> SRR1946689 4 0.0146 0.78255 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1946686 1 0.3853 0.57815 0.680 0.000 0.304 0.000 0.000 0.016
#> SRR1946685 2 0.1644 0.74186 0.000 0.920 0.004 0.000 0.076 0.000
#> SRR1946688 6 0.6376 0.15742 0.000 0.016 0.000 0.368 0.236 0.380
#> SRR1946684 6 0.3746 0.43765 0.004 0.000 0.012 0.000 0.272 0.712
#> SRR1946683 5 0.1934 0.56138 0.000 0.000 0.044 0.000 0.916 0.040
#> SRR1946682 6 0.4704 0.44147 0.000 0.000 0.000 0.072 0.300 0.628
#> SRR1946680 4 0.0146 0.78255 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1946681 2 0.4877 0.17310 0.000 0.528 0.424 0.000 0.012 0.036
#> SRR1946687 4 0.5283 0.55775 0.196 0.000 0.180 0.620 0.004 0.000
#> SRR1946679 2 0.1655 0.75468 0.000 0.932 0.008 0.000 0.008 0.052
#> SRR1946678 1 0.3927 0.58533 0.712 0.000 0.004 0.000 0.260 0.024
#> SRR1946676 5 0.4798 0.31695 0.000 0.376 0.060 0.000 0.564 0.000
#> SRR1946677 5 0.1572 0.57111 0.000 0.000 0.028 0.000 0.936 0.036
#> SRR1946672 3 0.0727 0.86401 0.004 0.004 0.980 0.004 0.004 0.004
#> SRR1946673 6 0.4381 0.14261 0.000 0.004 0.016 0.000 0.456 0.524
#> SRR1946671 5 0.4357 0.58724 0.108 0.156 0.004 0.000 0.732 0.000
#> SRR1946669 5 0.4576 -0.24784 0.016 0.000 0.012 0.000 0.504 0.468
#> SRR1946668 6 0.1036 0.56606 0.008 0.000 0.004 0.000 0.024 0.964
#> SRR1946666 1 0.2664 0.75189 0.816 0.000 0.184 0.000 0.000 0.000
#> SRR1946667 4 0.0146 0.78255 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1946670 6 0.1542 0.56694 0.000 0.004 0.000 0.052 0.008 0.936
#> SRR1946663 6 0.5255 0.33821 0.000 0.000 0.000 0.096 0.428 0.476
#> SRR1946664 2 0.0603 0.77072 0.000 0.980 0.000 0.004 0.000 0.016
#> SRR1946662 6 0.4297 0.15517 0.004 0.000 0.012 0.000 0.452 0.532
#> SRR1946661 6 0.5011 0.32736 0.000 0.080 0.000 0.000 0.368 0.552
#> SRR1946660 6 0.6947 0.25198 0.000 0.120 0.000 0.128 0.320 0.432
#> SRR1946659 1 0.0508 0.88790 0.984 0.000 0.012 0.004 0.000 0.000
#> SRR1946658 6 0.1788 0.55091 0.000 0.076 0.004 0.000 0.004 0.916
#> SRR1946657 2 0.0260 0.77160 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1946655 3 0.0603 0.85951 0.000 0.000 0.980 0.016 0.004 0.000
#> SRR1946654 3 0.1116 0.85434 0.000 0.004 0.960 0.008 0.028 0.000
#> SRR1946653 4 0.4968 0.32314 0.020 0.000 0.384 0.560 0.036 0.000
#> SRR1946652 5 0.4717 0.55518 0.000 0.124 0.056 0.000 0.740 0.080
#> SRR1946651 2 0.1010 0.76598 0.000 0.960 0.000 0.000 0.036 0.004
#> SRR1946650 2 0.3867 -0.15777 0.000 0.512 0.000 0.000 0.488 0.000
#> SRR1946649 5 0.3606 0.56786 0.016 0.256 0.000 0.000 0.728 0.000
#> SRR1946648 3 0.4095 0.05968 0.000 0.000 0.512 0.000 0.480 0.008
#> SRR1946647 6 0.2531 0.53353 0.000 0.000 0.012 0.000 0.132 0.856
#> SRR1946646 2 0.2467 0.72104 0.088 0.884 0.012 0.000 0.016 0.000
#> SRR1946645 5 0.3000 0.56125 0.156 0.004 0.016 0.000 0.824 0.000
#> SRR1946644 2 0.0363 0.77110 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1946643 3 0.0692 0.86008 0.000 0.020 0.976 0.000 0.004 0.000
#> SRR1946642 1 0.0260 0.89177 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1946641 1 0.0146 0.89337 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1946656 3 0.0363 0.86437 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR1946640 1 0.0000 0.89437 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.89437 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.89437 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.89437 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.754 0.832 0.919 0.4117 0.604 0.604
#> 3 3 0.624 0.673 0.877 0.4076 0.805 0.689
#> 4 4 0.527 0.677 0.796 0.1754 0.851 0.679
#> 5 5 0.594 0.469 0.721 0.1264 0.828 0.515
#> 6 6 0.678 0.598 0.765 0.0708 0.947 0.761
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
#> SRR1946675 2 0.5178 0.884 0.116 0.884
#> SRR1946691 2 0.1184 0.905 0.016 0.984
#> SRR1946690 2 0.0000 0.901 0.000 1.000
#> SRR1946689 2 0.0000 0.901 0.000 1.000
#> SRR1946686 1 0.1414 0.922 0.980 0.020
#> SRR1946685 2 0.1184 0.905 0.016 0.984
#> SRR1946688 2 0.0000 0.901 0.000 1.000
#> SRR1946684 1 0.1184 0.924 0.984 0.016
#> SRR1946683 2 0.9996 0.166 0.488 0.512
#> SRR1946682 2 0.4562 0.897 0.096 0.904
#> SRR1946680 2 0.0000 0.901 0.000 1.000
#> SRR1946681 2 0.0000 0.901 0.000 1.000
#> SRR1946687 2 1.0000 0.122 0.500 0.500
#> SRR1946679 2 0.9323 0.541 0.348 0.652
#> SRR1946678 1 0.0000 0.932 1.000 0.000
#> SRR1946676 2 0.2603 0.904 0.044 0.956
#> SRR1946677 2 0.4562 0.897 0.096 0.904
#> SRR1946672 2 0.5178 0.884 0.116 0.884
#> SRR1946673 2 0.4562 0.897 0.096 0.904
#> SRR1946671 2 0.5059 0.888 0.112 0.888
#> SRR1946669 1 0.0000 0.932 1.000 0.000
#> SRR1946668 1 0.1414 0.922 0.980 0.020
#> SRR1946666 2 1.0000 0.122 0.500 0.500
#> SRR1946667 2 0.0000 0.901 0.000 1.000
#> SRR1946670 2 0.4161 0.900 0.084 0.916
#> SRR1946663 2 0.4562 0.897 0.096 0.904
#> SRR1946664 2 0.0000 0.901 0.000 1.000
#> SRR1946662 1 0.0000 0.932 1.000 0.000
#> SRR1946661 2 0.4690 0.895 0.100 0.900
#> SRR1946660 2 0.0000 0.901 0.000 1.000
#> SRR1946659 1 1.0000 -0.205 0.500 0.500
#> SRR1946658 2 0.0672 0.904 0.008 0.992
#> SRR1946657 2 0.0672 0.904 0.008 0.992
#> SRR1946655 2 0.2603 0.906 0.044 0.956
#> SRR1946654 2 0.1633 0.906 0.024 0.976
#> SRR1946653 2 0.3274 0.905 0.060 0.940
#> SRR1946652 2 0.4562 0.897 0.096 0.904
#> SRR1946651 2 0.4562 0.897 0.096 0.904
#> SRR1946650 2 0.3274 0.905 0.060 0.940
#> SRR1946649 2 0.5059 0.888 0.112 0.888
#> SRR1946648 2 0.4562 0.897 0.096 0.904
#> SRR1946647 1 0.6973 0.708 0.812 0.188
#> SRR1946646 2 0.1184 0.905 0.016 0.984
#> SRR1946645 2 0.4562 0.897 0.096 0.904
#> SRR1946644 2 0.0376 0.902 0.004 0.996
#> SRR1946643 2 0.0000 0.901 0.000 1.000
#> SRR1946642 1 0.0000 0.932 1.000 0.000
#> SRR1946641 1 0.0000 0.932 1.000 0.000
#> SRR1946656 2 0.0000 0.901 0.000 1.000
#> SRR1946640 1 0.0000 0.932 1.000 0.000
#> SRR1946639 1 0.0000 0.932 1.000 0.000
#> SRR1946638 1 0.0000 0.932 1.000 0.000
#> SRR1946637 1 0.0000 0.932 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.1267 0.7710 0.024 0.972 0.004
#> SRR1946691 2 0.2625 0.7450 0.000 0.916 0.084
#> SRR1946690 3 0.4062 0.7349 0.000 0.164 0.836
#> SRR1946689 3 0.0000 0.7304 0.000 0.000 1.000
#> SRR1946686 1 0.0892 0.9474 0.980 0.020 0.000
#> SRR1946685 2 0.5465 0.4500 0.000 0.712 0.288
#> SRR1946688 3 0.6244 0.3199 0.000 0.440 0.560
#> SRR1946684 1 0.0747 0.9507 0.984 0.016 0.000
#> SRR1946683 2 0.6095 0.3116 0.392 0.608 0.000
#> SRR1946682 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946680 3 0.0000 0.7304 0.000 0.000 1.000
#> SRR1946681 2 0.6280 -0.0816 0.000 0.540 0.460
#> SRR1946687 2 0.6140 0.2830 0.404 0.596 0.000
#> SRR1946679 2 0.5138 0.5411 0.252 0.748 0.000
#> SRR1946678 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946676 2 0.2998 0.7556 0.016 0.916 0.068
#> SRR1946677 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946672 2 0.1267 0.7710 0.024 0.972 0.004
#> SRR1946673 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946671 2 0.0747 0.7753 0.016 0.984 0.000
#> SRR1946669 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946668 1 0.1163 0.9394 0.972 0.028 0.000
#> SRR1946666 2 0.6140 0.2830 0.404 0.596 0.000
#> SRR1946667 3 0.0000 0.7304 0.000 0.000 1.000
#> SRR1946670 2 0.0592 0.7779 0.000 0.988 0.012
#> SRR1946663 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946664 3 0.4062 0.7349 0.000 0.164 0.836
#> SRR1946662 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946661 2 0.0237 0.7788 0.004 0.996 0.000
#> SRR1946660 3 0.6244 0.3199 0.000 0.440 0.560
#> SRR1946659 2 0.6140 0.2830 0.404 0.596 0.000
#> SRR1946658 2 0.2796 0.7385 0.000 0.908 0.092
#> SRR1946657 2 0.4235 0.6486 0.000 0.824 0.176
#> SRR1946655 2 0.1860 0.7643 0.000 0.948 0.052
#> SRR1946654 2 0.2356 0.7520 0.000 0.928 0.072
#> SRR1946653 2 0.2339 0.7667 0.012 0.940 0.048
#> SRR1946652 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946651 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946650 2 0.1411 0.7707 0.000 0.964 0.036
#> SRR1946649 2 0.0747 0.7753 0.016 0.984 0.000
#> SRR1946648 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946647 1 0.5397 0.5728 0.720 0.280 0.000
#> SRR1946646 2 0.5465 0.4500 0.000 0.712 0.288
#> SRR1946645 2 0.0000 0.7793 0.000 1.000 0.000
#> SRR1946644 2 0.6140 0.1388 0.000 0.596 0.404
#> SRR1946643 2 0.6280 -0.0816 0.000 0.540 0.460
#> SRR1946642 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946656 2 0.6280 -0.0816 0.000 0.540 0.460
#> SRR1946640 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9613 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9613 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 2 0.3325 0.692 0.024 0.864 0.112 0.000
#> SRR1946691 2 0.4746 0.521 0.000 0.688 0.304 0.008
#> SRR1946690 4 0.5147 0.453 0.000 0.004 0.460 0.536
#> SRR1946689 4 0.0000 0.752 0.000 0.000 0.000 1.000
#> SRR1946686 1 0.0707 0.859 0.980 0.020 0.000 0.000
#> SRR1946685 3 0.4643 0.625 0.000 0.344 0.656 0.000
#> SRR1946688 3 0.6104 0.648 0.000 0.140 0.680 0.180
#> SRR1946684 1 0.0779 0.859 0.980 0.016 0.004 0.000
#> SRR1946683 2 0.5560 0.355 0.392 0.584 0.024 0.000
#> SRR1946682 2 0.2281 0.718 0.000 0.904 0.096 0.000
#> SRR1946680 4 0.0000 0.752 0.000 0.000 0.000 1.000
#> SRR1946681 3 0.5208 0.770 0.000 0.172 0.748 0.080
#> SRR1946687 2 0.5571 0.329 0.396 0.580 0.024 0.000
#> SRR1946679 2 0.5025 0.518 0.252 0.716 0.032 0.000
#> SRR1946678 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946676 2 0.4136 0.647 0.016 0.788 0.196 0.000
#> SRR1946677 2 0.1792 0.710 0.000 0.932 0.068 0.000
#> SRR1946672 2 0.3325 0.692 0.024 0.864 0.112 0.000
#> SRR1946673 2 0.1389 0.721 0.000 0.952 0.048 0.000
#> SRR1946671 2 0.2924 0.718 0.016 0.884 0.100 0.000
#> SRR1946669 1 0.0000 0.866 1.000 0.000 0.000 0.000
#> SRR1946668 1 0.1256 0.849 0.964 0.028 0.008 0.000
#> SRR1946666 2 0.5571 0.329 0.396 0.580 0.024 0.000
#> SRR1946667 4 0.0000 0.752 0.000 0.000 0.000 1.000
#> SRR1946670 2 0.2760 0.702 0.000 0.872 0.128 0.000
#> SRR1946663 2 0.2281 0.718 0.000 0.904 0.096 0.000
#> SRR1946664 4 0.5147 0.453 0.000 0.004 0.460 0.536
#> SRR1946662 1 0.0000 0.866 1.000 0.000 0.000 0.000
#> SRR1946661 2 0.1398 0.720 0.004 0.956 0.040 0.000
#> SRR1946660 3 0.6104 0.648 0.000 0.140 0.680 0.180
#> SRR1946659 2 0.5571 0.329 0.396 0.580 0.024 0.000
#> SRR1946658 2 0.5161 0.315 0.000 0.592 0.400 0.008
#> SRR1946657 3 0.4972 0.302 0.000 0.456 0.544 0.000
#> SRR1946655 2 0.4331 0.579 0.000 0.712 0.288 0.000
#> SRR1946654 2 0.4406 0.560 0.000 0.700 0.300 0.000
#> SRR1946653 2 0.4053 0.626 0.004 0.768 0.228 0.000
#> SRR1946652 2 0.2647 0.711 0.000 0.880 0.120 0.000
#> SRR1946651 2 0.2647 0.710 0.000 0.880 0.120 0.000
#> SRR1946650 2 0.3569 0.663 0.000 0.804 0.196 0.000
#> SRR1946649 2 0.2924 0.718 0.016 0.884 0.100 0.000
#> SRR1946648 2 0.1867 0.709 0.000 0.928 0.072 0.000
#> SRR1946647 1 0.4927 0.547 0.712 0.264 0.024 0.000
#> SRR1946646 3 0.4643 0.625 0.000 0.344 0.656 0.000
#> SRR1946645 2 0.1792 0.710 0.000 0.932 0.068 0.000
#> SRR1946644 3 0.4225 0.765 0.000 0.184 0.792 0.024
#> SRR1946643 3 0.5208 0.770 0.000 0.172 0.748 0.080
#> SRR1946642 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946641 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946656 3 0.5208 0.770 0.000 0.172 0.748 0.080
#> SRR1946640 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946639 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946638 1 0.3074 0.889 0.848 0.000 0.152 0.000
#> SRR1946637 1 0.3074 0.889 0.848 0.000 0.152 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.556 0.012520 0.000 0.084 0.608 0.004 0.304
#> SRR1946691 3 0.744 0.027515 0.000 0.276 0.424 0.040 0.260
#> SRR1946690 2 0.442 0.000035 0.000 0.552 0.004 0.444 0.000
#> SRR1946689 4 0.104 1.000000 0.000 0.040 0.000 0.960 0.000
#> SRR1946686 1 0.413 0.722033 0.656 0.000 0.340 0.000 0.004
#> SRR1946685 2 0.397 0.576334 0.000 0.752 0.024 0.000 0.224
#> SRR1946688 2 0.189 0.651288 0.000 0.916 0.004 0.080 0.000
#> SRR1946684 1 0.413 0.721857 0.656 0.000 0.340 0.000 0.004
#> SRR1946683 3 0.545 0.152797 0.068 0.000 0.556 0.000 0.376
#> SRR1946682 5 0.331 0.543681 0.000 0.000 0.224 0.000 0.776
#> SRR1946680 4 0.104 1.000000 0.000 0.040 0.000 0.960 0.000
#> SRR1946681 2 0.127 0.692925 0.000 0.960 0.024 0.012 0.004
#> SRR1946687 3 0.449 0.301557 0.080 0.000 0.748 0.000 0.172
#> SRR1946679 3 0.531 0.016884 0.004 0.040 0.584 0.004 0.368
#> SRR1946678 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 5 0.391 0.448907 0.000 0.164 0.048 0.000 0.788
#> SRR1946677 5 0.501 0.415393 0.000 0.084 0.232 0.000 0.684
#> SRR1946672 3 0.556 0.012520 0.000 0.084 0.608 0.004 0.304
#> SRR1946673 5 0.517 0.446887 0.000 0.048 0.332 0.004 0.616
#> SRR1946671 5 0.104 0.515407 0.000 0.000 0.040 0.000 0.960
#> SRR1946669 1 0.391 0.735485 0.676 0.000 0.324 0.000 0.000
#> SRR1946668 1 0.420 0.706598 0.640 0.000 0.356 0.000 0.004
#> SRR1946666 3 0.449 0.301557 0.080 0.000 0.748 0.000 0.172
#> SRR1946667 4 0.104 1.000000 0.000 0.040 0.000 0.960 0.000
#> SRR1946670 3 0.636 -0.078050 0.000 0.136 0.504 0.008 0.352
#> SRR1946663 5 0.346 0.544265 0.000 0.004 0.224 0.000 0.772
#> SRR1946664 2 0.442 0.000035 0.000 0.552 0.004 0.444 0.000
#> SRR1946662 1 0.391 0.735485 0.676 0.000 0.324 0.000 0.000
#> SRR1946661 5 0.511 0.440228 0.000 0.040 0.352 0.004 0.604
#> SRR1946660 2 0.189 0.651288 0.000 0.916 0.004 0.080 0.000
#> SRR1946659 3 0.449 0.301557 0.080 0.000 0.748 0.000 0.172
#> SRR1946658 2 0.726 -0.178435 0.000 0.396 0.388 0.040 0.176
#> SRR1946657 2 0.548 0.380169 0.000 0.656 0.172 0.000 0.172
#> SRR1946655 3 0.731 0.039269 0.000 0.236 0.464 0.040 0.260
#> SRR1946654 3 0.700 -0.051680 0.000 0.272 0.388 0.008 0.332
#> SRR1946653 5 0.740 0.049989 0.000 0.208 0.372 0.040 0.380
#> SRR1946652 5 0.448 0.534279 0.000 0.044 0.220 0.004 0.732
#> SRR1946651 5 0.435 0.537921 0.000 0.040 0.212 0.004 0.744
#> SRR1946650 5 0.498 0.514619 0.000 0.088 0.220 0.000 0.692
#> SRR1946649 5 0.104 0.515407 0.000 0.000 0.040 0.000 0.960
#> SRR1946648 5 0.511 0.409097 0.000 0.092 0.232 0.000 0.676
#> SRR1946647 3 0.500 -0.290815 0.388 0.000 0.576 0.000 0.036
#> SRR1946646 2 0.397 0.576334 0.000 0.752 0.024 0.000 0.224
#> SRR1946645 5 0.501 0.415393 0.000 0.084 0.232 0.000 0.684
#> SRR1946644 2 0.201 0.685968 0.000 0.920 0.020 0.000 0.060
#> SRR1946643 2 0.127 0.692925 0.000 0.960 0.024 0.012 0.004
#> SRR1946642 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 2 0.127 0.692925 0.000 0.960 0.024 0.012 0.004
#> SRR1946640 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.000 0.822925 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.4860 0.499 0.000 0.008 0.516 0.000 0.040 0.436
#> SRR1946691 3 0.2415 0.596 0.000 0.084 0.888 0.000 0.012 0.016
#> SRR1946690 2 0.4372 0.192 0.000 0.544 0.000 0.432 0.000 0.024
#> SRR1946689 4 0.0146 1.000 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1946686 1 0.3810 0.515 0.572 0.000 0.000 0.000 0.000 0.428
#> SRR1946685 2 0.3659 0.639 0.000 0.752 0.012 0.000 0.224 0.012
#> SRR1946688 2 0.1765 0.737 0.000 0.924 0.000 0.052 0.000 0.024
#> SRR1946684 1 0.3810 0.515 0.572 0.000 0.000 0.000 0.000 0.428
#> SRR1946683 6 0.3050 0.560 0.000 0.000 0.000 0.000 0.236 0.764
#> SRR1946682 5 0.3431 0.618 0.000 0.000 0.228 0.000 0.756 0.016
#> SRR1946680 4 0.0146 1.000 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1946681 2 0.2045 0.763 0.000 0.920 0.028 0.028 0.024 0.000
#> SRR1946687 6 0.0935 0.675 0.000 0.000 0.004 0.000 0.032 0.964
#> SRR1946679 6 0.6110 -0.128 0.000 0.000 0.268 0.004 0.288 0.440
#> SRR1946678 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946676 5 0.5028 0.471 0.000 0.176 0.004 0.000 0.656 0.164
#> SRR1946677 5 0.5155 0.450 0.000 0.068 0.028 0.000 0.636 0.268
#> SRR1946672 3 0.4860 0.499 0.000 0.008 0.516 0.000 0.040 0.436
#> SRR1946673 5 0.5563 0.563 0.000 0.000 0.332 0.004 0.528 0.136
#> SRR1946671 5 0.2340 0.553 0.000 0.000 0.000 0.000 0.852 0.148
#> SRR1946669 1 0.3774 0.540 0.592 0.000 0.000 0.000 0.000 0.408
#> SRR1946668 1 0.3833 0.488 0.556 0.000 0.000 0.000 0.000 0.444
#> SRR1946666 6 0.0935 0.675 0.000 0.000 0.004 0.000 0.032 0.964
#> SRR1946667 4 0.0146 1.000 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1946670 3 0.3655 0.497 0.000 0.000 0.800 0.004 0.088 0.108
#> SRR1946663 5 0.3570 0.619 0.000 0.004 0.228 0.000 0.752 0.016
#> SRR1946664 2 0.4372 0.192 0.000 0.544 0.000 0.432 0.000 0.024
#> SRR1946662 1 0.3774 0.540 0.592 0.000 0.000 0.000 0.000 0.408
#> SRR1946661 5 0.5739 0.566 0.000 0.000 0.284 0.004 0.528 0.184
#> SRR1946660 2 0.1765 0.737 0.000 0.924 0.000 0.052 0.000 0.024
#> SRR1946659 6 0.0935 0.675 0.000 0.000 0.004 0.000 0.032 0.964
#> SRR1946658 3 0.3470 0.475 0.000 0.248 0.740 0.000 0.012 0.000
#> SRR1946657 2 0.5254 0.447 0.000 0.668 0.192 0.000 0.104 0.036
#> SRR1946655 3 0.3003 0.636 0.000 0.016 0.812 0.000 0.000 0.172
#> SRR1946654 3 0.6295 0.609 0.000 0.184 0.568 0.000 0.072 0.176
#> SRR1946653 3 0.4552 0.536 0.000 0.012 0.636 0.000 0.032 0.320
#> SRR1946652 5 0.4823 0.602 0.000 0.016 0.316 0.004 0.628 0.036
#> SRR1946651 5 0.4741 0.608 0.000 0.016 0.296 0.004 0.648 0.036
#> SRR1946650 5 0.4358 0.598 0.000 0.100 0.184 0.000 0.716 0.000
#> SRR1946649 5 0.2340 0.553 0.000 0.000 0.000 0.000 0.852 0.148
#> SRR1946648 5 0.5288 0.444 0.000 0.068 0.036 0.000 0.628 0.268
#> SRR1946647 6 0.3428 0.228 0.304 0.000 0.000 0.000 0.000 0.696
#> SRR1946646 2 0.3659 0.639 0.000 0.752 0.012 0.000 0.224 0.012
#> SRR1946645 5 0.5155 0.450 0.000 0.068 0.028 0.000 0.636 0.268
#> SRR1946644 2 0.1779 0.750 0.000 0.920 0.016 0.000 0.064 0.000
#> SRR1946643 2 0.2045 0.763 0.000 0.920 0.028 0.028 0.024 0.000
#> SRR1946642 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 2 0.2045 0.763 0.000 0.920 0.028 0.028 0.024 0.000
#> SRR1946640 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.763 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 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 0.990 0.995 0.4418 0.556 0.556
#> 3 3 0.971 0.949 0.979 0.4043 0.690 0.507
#> 4 4 0.593 0.543 0.742 0.1795 0.812 0.544
#> 5 5 0.599 0.478 0.681 0.0780 0.832 0.465
#> 6 6 0.651 0.405 0.633 0.0505 0.897 0.580
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
#> SRR1946675 2 0.0376 0.998 0.004 0.996
#> SRR1946691 2 0.0000 0.998 0.000 1.000
#> SRR1946690 2 0.0000 0.998 0.000 1.000
#> SRR1946689 2 0.0000 0.998 0.000 1.000
#> SRR1946686 1 0.0000 0.987 1.000 0.000
#> SRR1946685 2 0.0000 0.998 0.000 1.000
#> SRR1946688 2 0.0000 0.998 0.000 1.000
#> SRR1946684 1 0.0000 0.987 1.000 0.000
#> SRR1946683 1 0.0000 0.987 1.000 0.000
#> SRR1946682 2 0.0376 0.998 0.004 0.996
#> SRR1946680 2 0.0000 0.998 0.000 1.000
#> SRR1946681 2 0.0000 0.998 0.000 1.000
#> SRR1946687 2 0.0376 0.998 0.004 0.996
#> SRR1946679 2 0.0376 0.998 0.004 0.996
#> SRR1946678 1 0.0000 0.987 1.000 0.000
#> SRR1946676 2 0.0376 0.998 0.004 0.996
#> SRR1946677 2 0.0376 0.998 0.004 0.996
#> SRR1946672 2 0.0376 0.998 0.004 0.996
#> SRR1946673 2 0.0376 0.998 0.004 0.996
#> SRR1946671 1 0.7299 0.743 0.796 0.204
#> SRR1946669 1 0.0000 0.987 1.000 0.000
#> SRR1946668 1 0.0000 0.987 1.000 0.000
#> SRR1946666 1 0.0000 0.987 1.000 0.000
#> SRR1946667 2 0.0000 0.998 0.000 1.000
#> SRR1946670 2 0.0376 0.998 0.004 0.996
#> SRR1946663 2 0.0000 0.998 0.000 1.000
#> SRR1946664 2 0.0000 0.998 0.000 1.000
#> SRR1946662 1 0.0000 0.987 1.000 0.000
#> SRR1946661 2 0.0376 0.998 0.004 0.996
#> SRR1946660 2 0.0000 0.998 0.000 1.000
#> SRR1946659 1 0.0000 0.987 1.000 0.000
#> SRR1946658 2 0.0000 0.998 0.000 1.000
#> SRR1946657 2 0.0000 0.998 0.000 1.000
#> SRR1946655 2 0.0000 0.998 0.000 1.000
#> SRR1946654 2 0.0000 0.998 0.000 1.000
#> SRR1946653 2 0.0376 0.998 0.004 0.996
#> SRR1946652 2 0.0376 0.998 0.004 0.996
#> SRR1946651 2 0.0376 0.998 0.004 0.996
#> SRR1946650 2 0.0000 0.998 0.000 1.000
#> SRR1946649 2 0.0376 0.998 0.004 0.996
#> SRR1946648 2 0.0000 0.998 0.000 1.000
#> SRR1946647 1 0.0000 0.987 1.000 0.000
#> SRR1946646 2 0.0000 0.998 0.000 1.000
#> SRR1946645 2 0.0376 0.998 0.004 0.996
#> SRR1946644 2 0.0000 0.998 0.000 1.000
#> SRR1946643 2 0.0000 0.998 0.000 1.000
#> SRR1946642 1 0.0000 0.987 1.000 0.000
#> SRR1946641 1 0.0000 0.987 1.000 0.000
#> SRR1946656 2 0.0000 0.998 0.000 1.000
#> SRR1946640 1 0.0000 0.987 1.000 0.000
#> SRR1946639 1 0.0000 0.987 1.000 0.000
#> SRR1946638 1 0.0000 0.987 1.000 0.000
#> SRR1946637 1 0.0000 0.987 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946691 2 0.355 0.836 0.000 0.868 0.132
#> SRR1946690 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946689 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946686 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946685 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946688 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946684 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946683 2 0.226 0.902 0.068 0.932 0.000
#> SRR1946682 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946680 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946681 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946687 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946679 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946678 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946676 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946677 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946672 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946673 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946671 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946669 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946668 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946666 2 0.571 0.535 0.320 0.680 0.000
#> SRR1946667 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946670 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946663 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946664 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946662 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946661 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946660 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946659 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946658 2 0.626 0.211 0.000 0.552 0.448
#> SRR1946657 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946655 2 0.296 0.871 0.000 0.900 0.100
#> SRR1946654 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946653 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946652 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946651 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946650 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946649 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946648 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946647 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946646 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946645 2 0.000 0.961 0.000 1.000 0.000
#> SRR1946644 3 0.186 0.933 0.000 0.052 0.948
#> SRR1946643 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946642 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946641 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946656 3 0.000 0.993 0.000 0.000 1.000
#> SRR1946640 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946639 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946638 1 0.000 1.000 1.000 0.000 0.000
#> SRR1946637 1 0.000 1.000 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.404 0.43058 0.000 0.248 0.752 0.000
#> SRR1946691 2 0.464 0.38277 0.000 0.740 0.240 0.020
#> SRR1946690 4 0.139 0.86938 0.000 0.048 0.000 0.952
#> SRR1946689 4 0.000 0.85957 0.000 0.000 0.000 1.000
#> SRR1946686 1 0.287 0.90859 0.864 0.000 0.136 0.000
#> SRR1946685 2 0.391 0.48331 0.000 0.768 0.232 0.000
#> SRR1946688 4 0.433 0.80712 0.000 0.288 0.000 0.712
#> SRR1946684 1 0.297 0.90503 0.856 0.000 0.144 0.000
#> SRR1946683 3 0.355 0.44774 0.020 0.136 0.844 0.000
#> SRR1946682 2 0.466 0.28058 0.000 0.652 0.348 0.000
#> SRR1946680 4 0.000 0.85957 0.000 0.000 0.000 1.000
#> SRR1946681 4 0.441 0.79733 0.000 0.300 0.000 0.700
#> SRR1946687 3 0.187 0.47857 0.000 0.072 0.928 0.000
#> SRR1946679 3 0.425 0.41918 0.000 0.276 0.724 0.000
#> SRR1946678 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946676 2 0.497 0.12061 0.000 0.548 0.452 0.000
#> SRR1946677 3 0.500 0.06846 0.000 0.492 0.508 0.000
#> SRR1946672 3 0.349 0.45812 0.000 0.188 0.812 0.000
#> SRR1946673 3 0.492 0.21413 0.000 0.424 0.576 0.000
#> SRR1946671 3 0.361 0.43870 0.000 0.200 0.800 0.000
#> SRR1946669 1 0.281 0.90989 0.868 0.000 0.132 0.000
#> SRR1946668 1 0.353 0.86644 0.808 0.000 0.192 0.000
#> SRR1946666 3 0.174 0.45190 0.056 0.004 0.940 0.000
#> SRR1946667 4 0.000 0.85957 0.000 0.000 0.000 1.000
#> SRR1946670 3 0.479 0.21103 0.000 0.380 0.620 0.000
#> SRR1946663 2 0.419 0.42293 0.000 0.732 0.268 0.000
#> SRR1946664 4 0.130 0.86904 0.000 0.044 0.000 0.956
#> SRR1946662 1 0.297 0.90503 0.856 0.000 0.144 0.000
#> SRR1946661 3 0.496 0.14144 0.000 0.448 0.552 0.000
#> SRR1946660 4 0.433 0.80712 0.000 0.288 0.000 0.712
#> SRR1946659 3 0.491 -0.21050 0.420 0.000 0.580 0.000
#> SRR1946658 2 0.367 0.45738 0.000 0.852 0.044 0.104
#> SRR1946657 2 0.228 0.53857 0.000 0.904 0.096 0.000
#> SRR1946655 2 0.506 0.25307 0.000 0.648 0.340 0.012
#> SRR1946654 3 0.500 -0.12406 0.000 0.500 0.500 0.000
#> SRR1946653 3 0.480 0.22445 0.000 0.384 0.616 0.000
#> SRR1946652 2 0.471 0.31967 0.000 0.640 0.360 0.000
#> SRR1946651 2 0.452 0.35552 0.000 0.680 0.320 0.000
#> SRR1946650 2 0.281 0.53657 0.000 0.868 0.132 0.000
#> SRR1946649 3 0.498 0.09330 0.000 0.460 0.540 0.000
#> SRR1946648 2 0.498 0.00732 0.000 0.536 0.464 0.000
#> SRR1946647 3 0.130 0.46265 0.000 0.044 0.956 0.000
#> SRR1946646 2 0.365 0.49644 0.000 0.796 0.204 0.000
#> SRR1946645 3 0.500 0.05699 0.000 0.496 0.504 0.000
#> SRR1946644 2 0.401 0.42724 0.000 0.800 0.016 0.184
#> SRR1946643 4 0.353 0.85235 0.000 0.192 0.000 0.808
#> SRR1946642 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946656 4 0.413 0.81895 0.000 0.260 0.000 0.740
#> SRR1946640 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.000 0.93602 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.000 0.93602 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.613 0.4357 0.000 0.208 0.564 0.000 0.228
#> SRR1946691 2 0.433 0.4024 0.000 0.768 0.092 0.000 0.140
#> SRR1946690 4 0.302 0.7401 0.000 0.132 0.020 0.848 0.000
#> SRR1946689 4 0.000 0.7910 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 1 0.418 0.7263 0.668 0.008 0.324 0.000 0.000
#> SRR1946685 5 0.489 0.4432 0.000 0.316 0.044 0.000 0.640
#> SRR1946688 2 0.639 0.0608 0.000 0.484 0.040 0.408 0.068
#> SRR1946684 1 0.420 0.7235 0.664 0.008 0.328 0.000 0.000
#> SRR1946683 3 0.384 0.3838 0.000 0.008 0.732 0.000 0.260
#> SRR1946682 5 0.462 0.4755 0.000 0.224 0.060 0.000 0.716
#> SRR1946680 4 0.000 0.7910 0.000 0.000 0.000 1.000 0.000
#> SRR1946681 2 0.438 0.2008 0.000 0.616 0.008 0.376 0.000
#> SRR1946687 3 0.455 0.4899 0.000 0.044 0.704 0.000 0.252
#> SRR1946679 5 0.555 -0.0687 0.000 0.068 0.448 0.000 0.484
#> SRR1946678 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 5 0.251 0.5628 0.000 0.028 0.080 0.000 0.892
#> SRR1946677 5 0.433 0.4782 0.000 0.044 0.220 0.000 0.736
#> SRR1946672 3 0.600 0.4386 0.000 0.168 0.576 0.000 0.256
#> SRR1946673 5 0.618 0.1481 0.000 0.168 0.296 0.000 0.536
#> SRR1946671 5 0.431 -0.1323 0.000 0.000 0.492 0.000 0.508
#> SRR1946669 1 0.413 0.7327 0.680 0.008 0.312 0.000 0.000
#> SRR1946668 1 0.453 0.5544 0.544 0.008 0.448 0.000 0.000
#> SRR1946666 3 0.219 0.5528 0.012 0.000 0.904 0.000 0.084
#> SRR1946667 4 0.000 0.7910 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 3 0.674 0.2560 0.000 0.288 0.412 0.000 0.300
#> SRR1946663 5 0.500 0.4295 0.000 0.256 0.072 0.000 0.672
#> SRR1946664 4 0.256 0.7565 0.000 0.120 0.008 0.872 0.000
#> SRR1946662 1 0.420 0.7235 0.664 0.008 0.328 0.000 0.000
#> SRR1946661 5 0.301 0.5156 0.000 0.004 0.172 0.000 0.824
#> SRR1946660 2 0.639 0.0608 0.000 0.484 0.040 0.408 0.068
#> SRR1946659 3 0.265 0.4403 0.152 0.000 0.848 0.000 0.000
#> SRR1946658 2 0.236 0.4799 0.000 0.904 0.036 0.000 0.060
#> SRR1946657 5 0.491 0.2101 0.000 0.480 0.024 0.000 0.496
#> SRR1946655 2 0.545 0.2061 0.000 0.636 0.256 0.000 0.108
#> SRR1946654 5 0.663 0.1235 0.000 0.380 0.220 0.000 0.400
#> SRR1946653 3 0.661 0.3154 0.000 0.252 0.460 0.000 0.288
#> SRR1946652 5 0.516 0.4437 0.000 0.256 0.084 0.000 0.660
#> SRR1946651 5 0.367 0.5473 0.000 0.112 0.068 0.000 0.820
#> SRR1946650 5 0.349 0.5234 0.000 0.160 0.028 0.000 0.812
#> SRR1946649 5 0.276 0.5540 0.000 0.024 0.104 0.000 0.872
#> SRR1946648 5 0.619 0.3818 0.000 0.292 0.172 0.000 0.536
#> SRR1946647 3 0.189 0.5584 0.000 0.008 0.920 0.000 0.072
#> SRR1946646 5 0.506 0.3667 0.000 0.384 0.040 0.000 0.576
#> SRR1946645 5 0.459 0.4830 0.000 0.064 0.212 0.000 0.724
#> SRR1946644 2 0.549 0.3061 0.000 0.648 0.032 0.044 0.276
#> SRR1946643 4 0.445 -0.0837 0.000 0.480 0.004 0.516 0.000
#> SRR1946642 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 2 0.445 0.1761 0.000 0.592 0.008 0.400 0.000
#> SRR1946640 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.000 0.8330 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 5 0.6614 0.35196 0.000 0.108 0.124 0.000 0.520 0.248
#> SRR1946691 6 0.4657 -0.12817 0.000 0.016 0.472 0.000 0.016 0.496
#> SRR1946690 4 0.4364 0.66081 0.000 0.004 0.192 0.732 0.008 0.064
#> SRR1946689 4 0.0000 0.85122 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946686 1 0.5428 0.52243 0.520 0.000 0.024 0.000 0.392 0.064
#> SRR1946685 2 0.4520 0.40926 0.000 0.704 0.224 0.000 0.016 0.056
#> SRR1946688 3 0.6867 0.19513 0.000 0.048 0.448 0.284 0.008 0.212
#> SRR1946684 1 0.5495 0.50140 0.500 0.000 0.024 0.000 0.408 0.068
#> SRR1946683 5 0.3860 0.47694 0.000 0.164 0.000 0.000 0.764 0.072
#> SRR1946682 6 0.5266 0.24114 0.000 0.336 0.100 0.000 0.004 0.560
#> SRR1946680 4 0.0000 0.85122 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946681 3 0.3383 0.44885 0.000 0.000 0.728 0.268 0.000 0.004
#> SRR1946687 5 0.5324 0.51746 0.000 0.184 0.056 0.000 0.672 0.088
#> SRR1946679 5 0.5849 0.25618 0.000 0.384 0.028 0.000 0.488 0.100
#> SRR1946678 1 0.0000 0.78733 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946676 2 0.3401 0.38377 0.000 0.776 0.004 0.000 0.016 0.204
#> SRR1946677 2 0.3458 0.43518 0.000 0.816 0.044 0.000 0.128 0.012
#> SRR1946672 5 0.6226 0.44689 0.000 0.156 0.088 0.000 0.588 0.168
#> SRR1946673 2 0.6695 -0.01012 0.000 0.380 0.044 0.000 0.208 0.368
#> SRR1946671 5 0.5282 0.17201 0.000 0.416 0.000 0.000 0.484 0.100
#> SRR1946669 1 0.5407 0.53342 0.532 0.000 0.024 0.000 0.380 0.064
#> SRR1946668 5 0.5331 -0.17410 0.316 0.000 0.024 0.000 0.588 0.072
#> SRR1946666 5 0.1760 0.56861 0.004 0.028 0.020 0.000 0.936 0.012
#> SRR1946667 4 0.0000 0.85122 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946670 6 0.6386 -0.05726 0.000 0.104 0.080 0.000 0.308 0.508
#> SRR1946663 6 0.5288 0.23488 0.000 0.344 0.100 0.000 0.004 0.552
#> SRR1946664 4 0.3555 0.75510 0.000 0.004 0.140 0.808 0.008 0.040
#> SRR1946662 1 0.5495 0.50140 0.500 0.000 0.024 0.000 0.408 0.068
#> SRR1946661 2 0.4275 0.36941 0.000 0.728 0.004 0.000 0.076 0.192
#> SRR1946660 3 0.6870 0.18618 0.000 0.048 0.444 0.292 0.008 0.208
#> SRR1946659 5 0.2076 0.55493 0.040 0.004 0.020 0.000 0.920 0.016
#> SRR1946658 3 0.3583 0.26488 0.000 0.008 0.728 0.000 0.004 0.260
#> SRR1946657 2 0.6085 0.16065 0.000 0.444 0.304 0.000 0.004 0.248
#> SRR1946655 3 0.5944 0.08154 0.000 0.036 0.568 0.000 0.140 0.256
#> SRR1946654 2 0.7548 -0.00582 0.000 0.328 0.256 0.000 0.152 0.264
#> SRR1946653 5 0.7174 0.15653 0.000 0.152 0.136 0.000 0.408 0.304
#> SRR1946652 6 0.5451 -0.17772 0.000 0.440 0.068 0.000 0.020 0.472
#> SRR1946651 2 0.4570 0.22877 0.000 0.600 0.016 0.000 0.020 0.364
#> SRR1946650 2 0.4234 0.24896 0.000 0.644 0.032 0.000 0.000 0.324
#> SRR1946649 2 0.3370 0.36943 0.000 0.772 0.004 0.000 0.012 0.212
#> SRR1946648 2 0.6231 0.32573 0.000 0.564 0.244 0.000 0.104 0.088
#> SRR1946647 5 0.0603 0.55931 0.000 0.016 0.000 0.000 0.980 0.004
#> SRR1946646 2 0.5189 0.34252 0.000 0.592 0.324 0.000 0.020 0.064
#> SRR1946645 2 0.3718 0.43423 0.000 0.796 0.068 0.000 0.128 0.008
#> SRR1946644 3 0.5614 0.24020 0.000 0.256 0.600 0.008 0.012 0.124
#> SRR1946643 3 0.3668 0.37982 0.000 0.000 0.668 0.328 0.000 0.004
#> SRR1946642 1 0.0000 0.78733 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0146 0.78735 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1946656 3 0.3697 0.46321 0.000 0.000 0.732 0.248 0.004 0.016
#> SRR1946640 1 0.0000 0.78733 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0146 0.78735 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1946638 1 0.0146 0.78735 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1946637 1 0.0146 0.78735 0.996 0.000 0.004 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["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 17231 rows and 53 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 0.960 0.939 0.976 0.5023 0.499 0.499
#> 3 3 0.715 0.616 0.845 0.2861 0.808 0.630
#> 4 4 0.871 0.860 0.929 0.1250 0.875 0.663
#> 5 5 0.798 0.719 0.865 0.0517 0.946 0.810
#> 6 6 0.761 0.681 0.833 0.0311 0.972 0.885
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
#> SRR1946675 2 0.9608 0.372 0.384 0.616
#> SRR1946691 2 0.0000 0.972 0.000 1.000
#> SRR1946690 2 0.0000 0.972 0.000 1.000
#> SRR1946689 2 0.0000 0.972 0.000 1.000
#> SRR1946686 1 0.0000 0.977 1.000 0.000
#> SRR1946685 2 0.0000 0.972 0.000 1.000
#> SRR1946688 2 0.0000 0.972 0.000 1.000
#> SRR1946684 1 0.0000 0.977 1.000 0.000
#> SRR1946683 1 0.0000 0.977 1.000 0.000
#> SRR1946682 2 0.0000 0.972 0.000 1.000
#> SRR1946680 2 0.0000 0.972 0.000 1.000
#> SRR1946681 2 0.0000 0.972 0.000 1.000
#> SRR1946687 1 0.0000 0.977 1.000 0.000
#> SRR1946679 1 0.0000 0.977 1.000 0.000
#> SRR1946678 1 0.0000 0.977 1.000 0.000
#> SRR1946676 2 0.0000 0.972 0.000 1.000
#> SRR1946677 2 0.0376 0.969 0.004 0.996
#> SRR1946672 1 0.0000 0.977 1.000 0.000
#> SRR1946673 1 0.4939 0.864 0.892 0.108
#> SRR1946671 1 0.0000 0.977 1.000 0.000
#> SRR1946669 1 0.0000 0.977 1.000 0.000
#> SRR1946668 1 0.0000 0.977 1.000 0.000
#> SRR1946666 1 0.0000 0.977 1.000 0.000
#> SRR1946667 2 0.0000 0.972 0.000 1.000
#> SRR1946670 2 0.9286 0.467 0.344 0.656
#> SRR1946663 2 0.0000 0.972 0.000 1.000
#> SRR1946664 2 0.0000 0.972 0.000 1.000
#> SRR1946662 1 0.0000 0.977 1.000 0.000
#> SRR1946661 1 0.9491 0.400 0.632 0.368
#> SRR1946660 2 0.0000 0.972 0.000 1.000
#> SRR1946659 1 0.0000 0.977 1.000 0.000
#> SRR1946658 2 0.0000 0.972 0.000 1.000
#> SRR1946657 2 0.0000 0.972 0.000 1.000
#> SRR1946655 2 0.0000 0.972 0.000 1.000
#> SRR1946654 2 0.0000 0.972 0.000 1.000
#> SRR1946653 2 0.2423 0.935 0.040 0.960
#> SRR1946652 2 0.0000 0.972 0.000 1.000
#> SRR1946651 2 0.0000 0.972 0.000 1.000
#> SRR1946650 2 0.0000 0.972 0.000 1.000
#> SRR1946649 1 0.0672 0.970 0.992 0.008
#> SRR1946648 2 0.0000 0.972 0.000 1.000
#> SRR1946647 1 0.0000 0.977 1.000 0.000
#> SRR1946646 2 0.0000 0.972 0.000 1.000
#> SRR1946645 2 0.0376 0.969 0.004 0.996
#> SRR1946644 2 0.0000 0.972 0.000 1.000
#> SRR1946643 2 0.0000 0.972 0.000 1.000
#> SRR1946642 1 0.0000 0.977 1.000 0.000
#> SRR1946641 1 0.0000 0.977 1.000 0.000
#> SRR1946656 2 0.0000 0.972 0.000 1.000
#> SRR1946640 1 0.0000 0.977 1.000 0.000
#> SRR1946639 1 0.0000 0.977 1.000 0.000
#> SRR1946638 1 0.0000 0.977 1.000 0.000
#> SRR1946637 1 0.0000 0.977 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.8714 0.15024 0.108 0.484 0.408
#> SRR1946691 3 0.2448 0.68144 0.000 0.076 0.924
#> SRR1946690 3 0.0424 0.74674 0.000 0.008 0.992
#> SRR1946689 3 0.0000 0.74690 0.000 0.000 1.000
#> SRR1946686 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946685 3 0.6026 0.23195 0.000 0.376 0.624
#> SRR1946688 3 0.0592 0.74509 0.000 0.012 0.988
#> SRR1946684 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946683 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946682 2 0.6267 0.01872 0.000 0.548 0.452
#> SRR1946680 3 0.0000 0.74690 0.000 0.000 1.000
#> SRR1946681 3 0.0000 0.74690 0.000 0.000 1.000
#> SRR1946687 1 0.1182 0.97423 0.976 0.012 0.012
#> SRR1946679 1 0.0592 0.98682 0.988 0.012 0.000
#> SRR1946678 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946676 3 0.6307 0.00805 0.000 0.488 0.512
#> SRR1946677 2 0.6302 -0.07837 0.000 0.520 0.480
#> SRR1946672 2 0.9144 0.09066 0.408 0.448 0.144
#> SRR1946673 2 0.1163 0.42251 0.028 0.972 0.000
#> SRR1946671 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946669 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946668 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946666 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946667 3 0.0000 0.74690 0.000 0.000 1.000
#> SRR1946670 2 0.8814 0.26369 0.140 0.548 0.312
#> SRR1946663 3 0.6291 0.04322 0.000 0.468 0.532
#> SRR1946664 3 0.0424 0.74674 0.000 0.008 0.992
#> SRR1946662 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946661 2 0.8071 0.17062 0.076 0.564 0.360
#> SRR1946660 3 0.0892 0.74065 0.000 0.020 0.980
#> SRR1946659 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946658 3 0.0892 0.73672 0.000 0.020 0.980
#> SRR1946657 3 0.2066 0.71164 0.000 0.060 0.940
#> SRR1946655 3 0.6295 -0.05104 0.000 0.472 0.528
#> SRR1946654 3 0.6126 0.09028 0.000 0.400 0.600
#> SRR1946653 2 0.6267 0.07088 0.000 0.548 0.452
#> SRR1946652 2 0.1163 0.41323 0.000 0.972 0.028
#> SRR1946651 2 0.6140 0.10402 0.000 0.596 0.404
#> SRR1946650 3 0.6307 0.00380 0.000 0.488 0.512
#> SRR1946649 2 0.6676 -0.01790 0.476 0.516 0.008
#> SRR1946648 3 0.3267 0.63205 0.000 0.116 0.884
#> SRR1946647 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946646 3 0.0237 0.74607 0.000 0.004 0.996
#> SRR1946645 3 0.6274 0.08235 0.000 0.456 0.544
#> SRR1946644 3 0.0424 0.74674 0.000 0.008 0.992
#> SRR1946643 3 0.0000 0.74690 0.000 0.000 1.000
#> SRR1946642 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946656 3 0.0592 0.74093 0.000 0.012 0.988
#> SRR1946640 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.99785 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.99785 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.0336 0.824 0.000 0.008 0.992 0.000
#> SRR1946691 4 0.1833 0.898 0.000 0.032 0.024 0.944
#> SRR1946690 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946689 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946686 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946685 4 0.4585 0.477 0.000 0.332 0.000 0.668
#> SRR1946688 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946684 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946683 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946682 2 0.5355 0.552 0.000 0.620 0.020 0.360
#> SRR1946680 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946681 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946687 1 0.3975 0.697 0.760 0.000 0.240 0.000
#> SRR1946679 1 0.1677 0.940 0.948 0.040 0.012 0.000
#> SRR1946678 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946676 2 0.2053 0.789 0.000 0.924 0.004 0.072
#> SRR1946677 2 0.2227 0.771 0.000 0.928 0.036 0.036
#> SRR1946672 3 0.2311 0.782 0.076 0.004 0.916 0.004
#> SRR1946673 3 0.4356 0.638 0.000 0.292 0.708 0.000
#> SRR1946671 1 0.0592 0.970 0.984 0.016 0.000 0.000
#> SRR1946669 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946668 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946666 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946667 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946670 3 0.2782 0.821 0.004 0.068 0.904 0.024
#> SRR1946663 2 0.5088 0.450 0.000 0.572 0.004 0.424
#> SRR1946664 4 0.0000 0.935 0.000 0.000 0.000 1.000
#> SRR1946662 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946661 2 0.0188 0.759 0.000 0.996 0.004 0.000
#> SRR1946660 4 0.0336 0.931 0.000 0.008 0.000 0.992
#> SRR1946659 1 0.0592 0.970 0.984 0.000 0.016 0.000
#> SRR1946658 4 0.0188 0.933 0.000 0.004 0.000 0.996
#> SRR1946657 4 0.2281 0.859 0.000 0.096 0.000 0.904
#> SRR1946655 3 0.1474 0.821 0.000 0.000 0.948 0.052
#> SRR1946654 3 0.3688 0.667 0.000 0.000 0.792 0.208
#> SRR1946653 3 0.1022 0.828 0.000 0.032 0.968 0.000
#> SRR1946652 3 0.4406 0.638 0.000 0.300 0.700 0.000
#> SRR1946651 2 0.4139 0.739 0.000 0.816 0.040 0.144
#> SRR1946650 2 0.3539 0.757 0.000 0.820 0.004 0.176
#> SRR1946649 2 0.1256 0.754 0.028 0.964 0.008 0.000
#> SRR1946648 4 0.5823 0.620 0.000 0.120 0.176 0.704
#> SRR1946647 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946646 4 0.1807 0.897 0.000 0.052 0.008 0.940
#> SRR1946645 2 0.2797 0.770 0.000 0.900 0.032 0.068
#> SRR1946644 4 0.0188 0.934 0.000 0.000 0.004 0.996
#> SRR1946643 4 0.0188 0.934 0.000 0.000 0.004 0.996
#> SRR1946642 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946656 4 0.1474 0.904 0.000 0.000 0.052 0.948
#> SRR1946640 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.982 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.0727 0.78128 0.000 0.004 0.980 0.012 0.004
#> SRR1946691 2 0.3044 0.76361 0.000 0.840 0.004 0.008 0.148
#> SRR1946690 2 0.0000 0.91344 0.000 1.000 0.000 0.000 0.000
#> SRR1946689 2 0.0000 0.91344 0.000 1.000 0.000 0.000 0.000
#> SRR1946686 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946685 4 0.3741 0.57788 0.000 0.264 0.000 0.732 0.004
#> SRR1946688 2 0.0162 0.91239 0.000 0.996 0.000 0.004 0.000
#> SRR1946684 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946683 1 0.0162 0.94794 0.996 0.000 0.000 0.004 0.000
#> SRR1946682 5 0.5824 0.36742 0.000 0.248 0.016 0.104 0.632
#> SRR1946680 2 0.0000 0.91344 0.000 1.000 0.000 0.000 0.000
#> SRR1946681 2 0.0451 0.91020 0.000 0.988 0.004 0.008 0.000
#> SRR1946687 1 0.5251 0.46868 0.632 0.000 0.308 0.052 0.008
#> SRR1946679 1 0.5184 0.54839 0.668 0.000 0.008 0.260 0.064
#> SRR1946678 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 5 0.4420 0.31564 0.000 0.004 0.000 0.448 0.548
#> SRR1946677 4 0.1331 0.59909 0.000 0.008 0.000 0.952 0.040
#> SRR1946672 3 0.3038 0.71431 0.080 0.000 0.872 0.040 0.008
#> SRR1946673 5 0.5819 -0.13203 0.000 0.000 0.452 0.092 0.456
#> SRR1946671 1 0.2864 0.82912 0.864 0.000 0.000 0.024 0.112
#> SRR1946669 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946668 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946666 1 0.0671 0.93842 0.980 0.000 0.016 0.000 0.004
#> SRR1946667 2 0.0000 0.91344 0.000 1.000 0.000 0.000 0.000
#> SRR1946670 3 0.4594 0.37979 0.000 0.008 0.624 0.008 0.360
#> SRR1946663 5 0.6244 0.22886 0.000 0.388 0.004 0.128 0.480
#> SRR1946664 2 0.0000 0.91344 0.000 1.000 0.000 0.000 0.000
#> SRR1946662 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946661 5 0.4182 0.39985 0.000 0.000 0.004 0.352 0.644
#> SRR1946660 2 0.0162 0.91222 0.000 0.996 0.000 0.000 0.004
#> SRR1946659 1 0.0955 0.92991 0.968 0.000 0.028 0.000 0.004
#> SRR1946658 2 0.0992 0.89907 0.000 0.968 0.008 0.000 0.024
#> SRR1946657 2 0.4861 0.62601 0.000 0.732 0.012 0.072 0.184
#> SRR1946655 3 0.1205 0.77965 0.000 0.040 0.956 0.004 0.000
#> SRR1946654 3 0.3659 0.57362 0.000 0.220 0.768 0.012 0.000
#> SRR1946653 3 0.0865 0.77561 0.000 0.004 0.972 0.000 0.024
#> SRR1946652 5 0.5157 -0.06475 0.000 0.000 0.440 0.040 0.520
#> SRR1946651 5 0.2802 0.48252 0.000 0.008 0.016 0.100 0.876
#> SRR1946650 5 0.5754 0.42589 0.000 0.136 0.000 0.260 0.604
#> SRR1946649 5 0.4060 0.41334 0.000 0.000 0.000 0.360 0.640
#> SRR1946648 4 0.5450 0.56532 0.000 0.228 0.124 0.648 0.000
#> SRR1946647 1 0.0162 0.94813 0.996 0.000 0.000 0.000 0.004
#> SRR1946646 2 0.4670 -0.00303 0.000 0.548 0.004 0.440 0.008
#> SRR1946645 4 0.1012 0.61569 0.000 0.012 0.000 0.968 0.020
#> SRR1946644 2 0.0510 0.90862 0.000 0.984 0.000 0.016 0.000
#> SRR1946643 2 0.0566 0.90848 0.000 0.984 0.004 0.012 0.000
#> SRR1946642 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 2 0.1809 0.86654 0.000 0.928 0.060 0.012 0.000
#> SRR1946640 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.94998 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.0862 0.76605 0.000 0.008 0.972 0.000 0.004 0.016
#> SRR1946691 4 0.3673 0.64186 0.000 0.024 0.008 0.764 0.000 0.204
#> SRR1946690 4 0.0260 0.90826 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1946689 4 0.0000 0.90868 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946686 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946685 5 0.3393 0.58715 0.000 0.020 0.000 0.192 0.784 0.004
#> SRR1946688 4 0.0717 0.90388 0.000 0.000 0.000 0.976 0.008 0.016
#> SRR1946684 1 0.0146 0.90718 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1946683 1 0.0891 0.89753 0.968 0.000 0.000 0.000 0.008 0.024
#> SRR1946682 6 0.5702 0.37028 0.000 0.156 0.012 0.124 0.048 0.660
#> SRR1946680 4 0.0000 0.90868 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946681 4 0.1313 0.89328 0.000 0.000 0.004 0.952 0.016 0.028
#> SRR1946687 1 0.6433 0.31505 0.524 0.008 0.292 0.000 0.056 0.120
#> SRR1946679 1 0.7343 0.26546 0.496 0.120 0.028 0.000 0.192 0.164
#> SRR1946678 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946676 2 0.5776 0.41594 0.000 0.536 0.000 0.012 0.300 0.152
#> SRR1946677 5 0.2750 0.45688 0.000 0.048 0.004 0.000 0.868 0.080
#> SRR1946672 3 0.3551 0.68310 0.060 0.008 0.828 0.000 0.012 0.092
#> SRR1946673 2 0.6310 0.09347 0.000 0.496 0.296 0.000 0.036 0.172
#> SRR1946671 1 0.4669 0.64514 0.728 0.160 0.000 0.000 0.032 0.080
#> SRR1946669 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946668 1 0.0777 0.89837 0.972 0.004 0.000 0.000 0.000 0.024
#> SRR1946666 1 0.2786 0.84068 0.876 0.008 0.060 0.000 0.004 0.052
#> SRR1946667 4 0.0000 0.90868 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946670 6 0.5915 0.00947 0.000 0.212 0.360 0.000 0.000 0.428
#> SRR1946663 6 0.6077 0.36370 0.000 0.124 0.000 0.252 0.056 0.568
#> SRR1946664 4 0.0260 0.90826 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1946662 1 0.0291 0.90618 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR1946661 2 0.5575 0.31397 0.000 0.532 0.000 0.000 0.172 0.296
#> SRR1946660 4 0.0725 0.90418 0.000 0.000 0.000 0.976 0.012 0.012
#> SRR1946659 1 0.2895 0.83408 0.868 0.008 0.072 0.000 0.004 0.048
#> SRR1946658 4 0.1364 0.89308 0.000 0.004 0.004 0.944 0.000 0.048
#> SRR1946657 4 0.5757 0.46648 0.000 0.236 0.004 0.620 0.072 0.068
#> SRR1946655 3 0.2203 0.75578 0.000 0.004 0.896 0.084 0.000 0.016
#> SRR1946654 3 0.4397 0.54792 0.000 0.012 0.724 0.220 0.024 0.020
#> SRR1946653 3 0.1480 0.75227 0.000 0.040 0.940 0.000 0.000 0.020
#> SRR1946652 2 0.5057 0.17764 0.000 0.612 0.288 0.000 0.004 0.096
#> SRR1946651 2 0.2371 0.39212 0.000 0.900 0.000 0.016 0.032 0.052
#> SRR1946650 2 0.6377 0.35821 0.000 0.576 0.000 0.116 0.140 0.168
#> SRR1946649 2 0.5145 0.45190 0.000 0.624 0.000 0.000 0.200 0.176
#> SRR1946648 5 0.4826 0.55521 0.000 0.004 0.100 0.152 0.720 0.024
#> SRR1946647 1 0.2730 0.84303 0.872 0.012 0.012 0.000 0.008 0.096
#> SRR1946646 5 0.4827 0.15624 0.000 0.008 0.004 0.464 0.496 0.028
#> SRR1946645 5 0.1334 0.51002 0.000 0.032 0.000 0.000 0.948 0.020
#> SRR1946644 4 0.2182 0.86341 0.000 0.008 0.000 0.904 0.068 0.020
#> SRR1946643 4 0.1313 0.89328 0.000 0.000 0.004 0.952 0.016 0.028
#> SRR1946642 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 4 0.2703 0.83243 0.000 0.000 0.080 0.876 0.016 0.028
#> SRR1946640 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.90824 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 17231 rows and 53 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 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 0.952 0.980 0.3688 0.623 0.623
#> 3 3 0.831 0.852 0.947 0.4405 0.752 0.629
#> 4 4 0.609 0.598 0.838 0.2462 0.793 0.577
#> 5 5 0.737 0.724 0.881 0.1197 0.901 0.695
#> 6 6 0.692 0.722 0.848 0.0564 0.959 0.837
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
#> SRR1946675 2 0.000 0.991 0.000 1.000
#> SRR1946691 2 0.000 0.991 0.000 1.000
#> SRR1946690 2 0.000 0.991 0.000 1.000
#> SRR1946689 2 0.000 0.991 0.000 1.000
#> SRR1946686 1 0.000 0.938 1.000 0.000
#> SRR1946685 2 0.000 0.991 0.000 1.000
#> SRR1946688 2 0.000 0.991 0.000 1.000
#> SRR1946684 1 0.000 0.938 1.000 0.000
#> SRR1946683 2 0.184 0.963 0.028 0.972
#> SRR1946682 2 0.000 0.991 0.000 1.000
#> SRR1946680 2 0.000 0.991 0.000 1.000
#> SRR1946681 2 0.000 0.991 0.000 1.000
#> SRR1946687 2 0.000 0.991 0.000 1.000
#> SRR1946679 2 0.000 0.991 0.000 1.000
#> SRR1946678 1 0.000 0.938 1.000 0.000
#> SRR1946676 2 0.000 0.991 0.000 1.000
#> SRR1946677 2 0.000 0.991 0.000 1.000
#> SRR1946672 2 0.000 0.991 0.000 1.000
#> SRR1946673 2 0.000 0.991 0.000 1.000
#> SRR1946671 2 0.000 0.991 0.000 1.000
#> SRR1946669 1 0.000 0.938 1.000 0.000
#> SRR1946668 1 0.943 0.471 0.640 0.360
#> SRR1946666 2 0.844 0.593 0.272 0.728
#> SRR1946667 2 0.000 0.991 0.000 1.000
#> SRR1946670 2 0.000 0.991 0.000 1.000
#> SRR1946663 2 0.000 0.991 0.000 1.000
#> SRR1946664 2 0.000 0.991 0.000 1.000
#> SRR1946662 1 0.000 0.938 1.000 0.000
#> SRR1946661 2 0.000 0.991 0.000 1.000
#> SRR1946660 2 0.000 0.991 0.000 1.000
#> SRR1946659 1 0.943 0.471 0.640 0.360
#> SRR1946658 2 0.000 0.991 0.000 1.000
#> SRR1946657 2 0.000 0.991 0.000 1.000
#> SRR1946655 2 0.000 0.991 0.000 1.000
#> SRR1946654 2 0.000 0.991 0.000 1.000
#> SRR1946653 2 0.000 0.991 0.000 1.000
#> SRR1946652 2 0.000 0.991 0.000 1.000
#> SRR1946651 2 0.000 0.991 0.000 1.000
#> SRR1946650 2 0.000 0.991 0.000 1.000
#> SRR1946649 2 0.000 0.991 0.000 1.000
#> SRR1946648 2 0.000 0.991 0.000 1.000
#> SRR1946647 2 0.163 0.967 0.024 0.976
#> SRR1946646 2 0.000 0.991 0.000 1.000
#> SRR1946645 2 0.000 0.991 0.000 1.000
#> SRR1946644 2 0.000 0.991 0.000 1.000
#> SRR1946643 2 0.000 0.991 0.000 1.000
#> SRR1946642 1 0.000 0.938 1.000 0.000
#> SRR1946641 1 0.000 0.938 1.000 0.000
#> SRR1946656 2 0.000 0.991 0.000 1.000
#> SRR1946640 1 0.000 0.938 1.000 0.000
#> SRR1946639 1 0.000 0.938 1.000 0.000
#> SRR1946638 1 0.000 0.938 1.000 0.000
#> SRR1946637 1 0.000 0.938 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946691 2 0.3192 0.8191 0.000 0.888 0.112
#> SRR1946690 3 0.0000 0.7221 0.000 0.000 1.000
#> SRR1946689 3 0.0000 0.7221 0.000 0.000 1.000
#> SRR1946686 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946685 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946688 3 0.6140 0.4328 0.000 0.404 0.596
#> SRR1946684 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946683 2 0.0237 0.9389 0.004 0.996 0.000
#> SRR1946682 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946680 3 0.0000 0.7221 0.000 0.000 1.000
#> SRR1946681 2 0.6192 0.0915 0.000 0.580 0.420
#> SRR1946687 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946679 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946678 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946676 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946677 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946672 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946673 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946671 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946669 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946668 2 0.0424 0.9358 0.008 0.992 0.000
#> SRR1946666 2 0.0237 0.9389 0.004 0.996 0.000
#> SRR1946667 3 0.0000 0.7221 0.000 0.000 1.000
#> SRR1946670 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946663 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946664 3 0.0000 0.7221 0.000 0.000 1.000
#> SRR1946662 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946661 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946660 3 0.6215 0.3842 0.000 0.428 0.572
#> SRR1946659 2 0.0237 0.9389 0.004 0.996 0.000
#> SRR1946658 2 0.1643 0.8995 0.000 0.956 0.044
#> SRR1946657 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946655 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946654 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946653 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946652 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946651 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946650 2 0.4346 0.7122 0.000 0.816 0.184
#> SRR1946649 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946648 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946647 2 0.0237 0.9389 0.004 0.996 0.000
#> SRR1946646 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946645 2 0.0000 0.9418 0.000 1.000 0.000
#> SRR1946644 2 0.6192 0.0915 0.000 0.580 0.420
#> SRR1946643 3 0.6215 0.3842 0.000 0.428 0.572
#> SRR1946642 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946641 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946656 2 0.5760 0.3896 0.000 0.672 0.328
#> SRR1946640 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946639 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946638 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1946637 1 0.0000 1.0000 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.0921 0.6935 0.000 0.028 0.972 0.000
#> SRR1946691 3 0.4843 0.0876 0.000 0.396 0.604 0.000
#> SRR1946690 4 0.4999 0.5968 0.000 0.492 0.000 0.508
#> SRR1946689 4 0.0000 0.8744 0.000 0.000 0.000 1.000
#> SRR1946686 1 0.0188 0.9905 0.996 0.000 0.004 0.000
#> SRR1946685 3 0.4730 0.1845 0.000 0.364 0.636 0.000
#> SRR1946688 2 0.0000 0.4530 0.000 1.000 0.000 0.000
#> SRR1946684 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> SRR1946683 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946682 3 0.4406 0.3141 0.000 0.300 0.700 0.000
#> SRR1946680 4 0.0000 0.8744 0.000 0.000 0.000 1.000
#> SRR1946681 2 0.4933 0.3174 0.000 0.568 0.432 0.000
#> SRR1946687 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946679 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946678 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946676 2 0.4522 0.5960 0.000 0.680 0.320 0.000
#> SRR1946677 3 0.4746 0.1481 0.000 0.368 0.632 0.000
#> SRR1946672 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946673 3 0.0921 0.6935 0.000 0.028 0.972 0.000
#> SRR1946671 3 0.0188 0.6974 0.000 0.004 0.996 0.000
#> SRR1946669 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946668 3 0.0188 0.6975 0.004 0.000 0.996 0.000
#> SRR1946666 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946667 4 0.0000 0.8744 0.000 0.000 0.000 1.000
#> SRR1946670 3 0.1022 0.6919 0.000 0.032 0.968 0.000
#> SRR1946663 2 0.3024 0.5703 0.000 0.852 0.148 0.000
#> SRR1946664 4 0.3311 0.8302 0.000 0.172 0.000 0.828
#> SRR1946662 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> SRR1946661 3 0.0707 0.6958 0.000 0.020 0.980 0.000
#> SRR1946660 2 0.0000 0.4530 0.000 1.000 0.000 0.000
#> SRR1946659 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946658 3 0.4907 0.0208 0.000 0.420 0.580 0.000
#> SRR1946657 3 0.4907 0.0208 0.000 0.420 0.580 0.000
#> SRR1946655 3 0.4888 0.0460 0.000 0.412 0.588 0.000
#> SRR1946654 3 0.4907 0.0208 0.000 0.420 0.580 0.000
#> SRR1946653 3 0.4907 0.0208 0.000 0.420 0.580 0.000
#> SRR1946652 2 0.4522 0.5960 0.000 0.680 0.320 0.000
#> SRR1946651 2 0.4522 0.5960 0.000 0.680 0.320 0.000
#> SRR1946650 2 0.4522 0.5960 0.000 0.680 0.320 0.000
#> SRR1946649 2 0.4522 0.5960 0.000 0.680 0.320 0.000
#> SRR1946648 3 0.1118 0.6893 0.000 0.036 0.964 0.000
#> SRR1946647 3 0.0000 0.6992 0.000 0.000 1.000 0.000
#> SRR1946646 3 0.4877 0.0572 0.000 0.408 0.592 0.000
#> SRR1946645 2 0.4999 0.2132 0.000 0.508 0.492 0.000
#> SRR1946644 2 0.3873 0.4512 0.000 0.772 0.228 0.000
#> SRR1946643 2 0.4933 0.3174 0.000 0.568 0.432 0.000
#> SRR1946642 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946656 2 0.4933 0.3174 0.000 0.568 0.432 0.000
#> SRR1946640 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9930 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9930 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.0404 0.8429 0.000 0.000 0.988 0.000 0.012
#> SRR1946691 3 0.3274 0.6924 0.000 0.000 0.780 0.000 0.220
#> SRR1946690 2 0.4109 -0.2178 0.000 0.700 0.000 0.288 0.012
#> SRR1946689 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000
#> SRR1946686 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946685 5 0.3636 0.4815 0.000 0.000 0.272 0.000 0.728
#> SRR1946688 2 0.3932 0.1736 0.000 0.672 0.000 0.000 0.328
#> SRR1946684 1 0.0404 0.9871 0.988 0.000 0.012 0.000 0.000
#> SRR1946683 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946682 5 0.3816 0.5617 0.000 0.000 0.304 0.000 0.696
#> SRR1946680 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000
#> SRR1946681 2 0.4150 0.3810 0.000 0.612 0.388 0.000 0.000
#> SRR1946687 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946679 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946678 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 5 0.0404 0.8243 0.000 0.000 0.012 0.000 0.988
#> SRR1946677 5 0.3242 0.6592 0.000 0.000 0.216 0.000 0.784
#> SRR1946672 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946673 3 0.0404 0.8429 0.000 0.000 0.988 0.000 0.012
#> SRR1946671 3 0.0404 0.8381 0.000 0.000 0.988 0.000 0.012
#> SRR1946669 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946668 3 0.0162 0.8423 0.004 0.000 0.996 0.000 0.000
#> SRR1946666 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946667 4 0.0000 0.8747 0.000 0.000 0.000 1.000 0.000
#> SRR1946670 3 0.0510 0.8421 0.000 0.000 0.984 0.000 0.016
#> SRR1946663 5 0.0162 0.8129 0.000 0.004 0.000 0.000 0.996
#> SRR1946664 4 0.4150 0.5499 0.000 0.388 0.000 0.612 0.000
#> SRR1946662 1 0.0404 0.9871 0.988 0.000 0.012 0.000 0.000
#> SRR1946661 3 0.0963 0.8249 0.000 0.000 0.964 0.000 0.036
#> SRR1946660 2 0.3949 0.1632 0.000 0.668 0.000 0.000 0.332
#> SRR1946659 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946658 3 0.6511 0.0146 0.000 0.336 0.460 0.000 0.204
#> SRR1946657 3 0.3274 0.6924 0.000 0.000 0.780 0.000 0.220
#> SRR1946655 3 0.6503 0.0288 0.000 0.332 0.464 0.000 0.204
#> SRR1946654 3 0.3508 0.6546 0.000 0.000 0.748 0.000 0.252
#> SRR1946653 3 0.3274 0.6924 0.000 0.000 0.780 0.000 0.220
#> SRR1946652 5 0.0404 0.8243 0.000 0.000 0.012 0.000 0.988
#> SRR1946651 5 0.0404 0.8243 0.000 0.000 0.012 0.000 0.988
#> SRR1946650 5 0.0000 0.8143 0.000 0.000 0.000 0.000 1.000
#> SRR1946649 5 0.0404 0.8243 0.000 0.000 0.012 0.000 0.988
#> SRR1946648 3 0.0404 0.8429 0.000 0.000 0.988 0.000 0.012
#> SRR1946647 3 0.0000 0.8437 0.000 0.000 1.000 0.000 0.000
#> SRR1946646 3 0.3305 0.6890 0.000 0.000 0.776 0.000 0.224
#> SRR1946645 5 0.2929 0.7029 0.000 0.000 0.180 0.000 0.820
#> SRR1946644 2 0.5927 0.3903 0.000 0.592 0.172 0.000 0.236
#> SRR1946643 2 0.4150 0.3810 0.000 0.612 0.388 0.000 0.000
#> SRR1946642 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 2 0.4150 0.3810 0.000 0.612 0.388 0.000 0.000
#> SRR1946640 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.9972 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 5 0.0713 0.827 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1946691 5 0.3357 0.687 0.000 0.224 0.004 0.000 0.764 0.008
#> SRR1946690 6 0.5313 0.591 0.000 0.000 0.324 0.124 0.000 0.552
#> SRR1946689 4 0.0000 0.787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946686 1 0.3360 0.782 0.732 0.000 0.000 0.000 0.004 0.264
#> SRR1946685 2 0.3126 0.543 0.000 0.752 0.000 0.000 0.248 0.000
#> SRR1946688 6 0.4109 0.694 0.000 0.012 0.412 0.000 0.000 0.576
#> SRR1946684 1 0.0713 0.603 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1946683 5 0.3151 0.640 0.252 0.000 0.000 0.000 0.748 0.000
#> SRR1946682 2 0.4375 0.613 0.016 0.680 0.000 0.000 0.276 0.028
#> SRR1946680 4 0.0000 0.787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946681 3 0.2793 0.805 0.000 0.000 0.800 0.000 0.200 0.000
#> SRR1946687 5 0.0146 0.826 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1946679 5 0.0000 0.826 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946678 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946676 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946677 2 0.2793 0.687 0.000 0.800 0.000 0.000 0.200 0.000
#> SRR1946672 5 0.0000 0.826 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1946673 5 0.0713 0.827 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1946671 5 0.0692 0.824 0.004 0.020 0.000 0.000 0.976 0.000
#> SRR1946669 1 0.0146 0.629 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1946668 5 0.3804 0.420 0.424 0.000 0.000 0.000 0.576 0.000
#> SRR1946666 5 0.0260 0.826 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1946667 4 0.0000 0.787 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1946670 5 0.0790 0.827 0.000 0.032 0.000 0.000 0.968 0.000
#> SRR1946663 2 0.2933 0.638 0.000 0.796 0.004 0.000 0.000 0.200
#> SRR1946664 4 0.5880 -0.200 0.000 0.000 0.200 0.424 0.000 0.376
#> SRR1946662 1 0.0713 0.603 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1946661 5 0.1075 0.818 0.000 0.048 0.000 0.000 0.952 0.000
#> SRR1946660 6 0.3804 0.689 0.000 0.000 0.424 0.000 0.000 0.576
#> SRR1946659 5 0.0260 0.826 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1946658 3 0.5370 0.728 0.000 0.192 0.588 0.000 0.220 0.000
#> SRR1946657 5 0.3050 0.691 0.000 0.236 0.000 0.000 0.764 0.000
#> SRR1946655 3 0.5392 0.725 0.000 0.192 0.584 0.000 0.224 0.000
#> SRR1946654 5 0.3175 0.663 0.000 0.256 0.000 0.000 0.744 0.000
#> SRR1946653 5 0.2941 0.699 0.000 0.220 0.000 0.000 0.780 0.000
#> SRR1946652 2 0.0260 0.817 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1946651 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946650 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946649 2 0.0000 0.820 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1946648 5 0.0713 0.827 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1946647 5 0.2912 0.621 0.216 0.000 0.000 0.000 0.784 0.000
#> SRR1946646 5 0.3050 0.691 0.000 0.236 0.000 0.000 0.764 0.000
#> SRR1946645 2 0.2454 0.729 0.000 0.840 0.000 0.000 0.160 0.000
#> SRR1946644 6 0.6578 0.175 0.000 0.224 0.080 0.000 0.172 0.524
#> SRR1946643 3 0.2793 0.805 0.000 0.000 0.800 0.000 0.200 0.000
#> SRR1946642 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946641 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946656 3 0.2793 0.805 0.000 0.000 0.800 0.000 0.200 0.000
#> SRR1946640 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946639 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946638 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
#> SRR1946637 1 0.3804 0.851 0.576 0.000 0.000 0.000 0.000 0.424
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 17231 rows and 53 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 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 0.463 0.793 0.862 0.3241 0.766 0.766
#> 3 3 0.310 0.518 0.701 0.7411 0.623 0.508
#> 4 4 0.423 0.470 0.713 0.1783 0.680 0.436
#> 5 5 0.384 0.397 0.617 0.1172 0.623 0.251
#> 6 6 0.552 0.461 0.720 0.0999 0.856 0.442
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
#> SRR1946675 2 0.9209 0.656 0.336 0.664
#> SRR1946691 2 0.4431 0.820 0.092 0.908
#> SRR1946690 2 0.0938 0.832 0.012 0.988
#> SRR1946689 2 0.9358 0.631 0.352 0.648
#> SRR1946686 2 0.9209 0.656 0.336 0.664
#> SRR1946685 2 0.0938 0.832 0.012 0.988
#> SRR1946688 2 0.0938 0.832 0.012 0.988
#> SRR1946684 2 0.3733 0.822 0.072 0.928
#> SRR1946683 2 0.2043 0.831 0.032 0.968
#> SRR1946682 2 0.2948 0.819 0.052 0.948
#> SRR1946680 2 0.9129 0.655 0.328 0.672
#> SRR1946681 2 0.3274 0.829 0.060 0.940
#> SRR1946687 2 0.9209 0.656 0.336 0.664
#> SRR1946679 2 0.2423 0.830 0.040 0.960
#> SRR1946678 1 0.5294 0.965 0.880 0.120
#> SRR1946676 2 0.0938 0.832 0.012 0.988
#> SRR1946677 2 0.0938 0.832 0.012 0.988
#> SRR1946672 2 0.9209 0.656 0.336 0.664
#> SRR1946673 2 0.3584 0.821 0.068 0.932
#> SRR1946671 2 0.0938 0.832 0.012 0.988
#> SRR1946669 2 0.6247 0.726 0.156 0.844
#> SRR1946668 2 0.4022 0.822 0.080 0.920
#> SRR1946666 2 0.9209 0.656 0.336 0.664
#> SRR1946667 2 0.9358 0.631 0.352 0.648
#> SRR1946670 2 0.4298 0.821 0.088 0.912
#> SRR1946663 2 0.2043 0.819 0.032 0.968
#> SRR1946664 2 0.0938 0.827 0.012 0.988
#> SRR1946662 2 0.2948 0.828 0.052 0.948
#> SRR1946661 2 0.1414 0.824 0.020 0.980
#> SRR1946660 2 0.0938 0.832 0.012 0.988
#> SRR1946659 2 0.9209 0.656 0.336 0.664
#> SRR1946658 2 0.4298 0.821 0.088 0.912
#> SRR1946657 2 0.0672 0.831 0.008 0.992
#> SRR1946655 2 0.9209 0.656 0.336 0.664
#> SRR1946654 2 0.9209 0.656 0.336 0.664
#> SRR1946653 2 0.9209 0.656 0.336 0.664
#> SRR1946652 2 0.3584 0.821 0.068 0.932
#> SRR1946651 2 0.2603 0.811 0.044 0.956
#> SRR1946650 2 0.1414 0.824 0.020 0.980
#> SRR1946649 2 0.0938 0.832 0.012 0.988
#> SRR1946648 2 0.8763 0.679 0.296 0.704
#> SRR1946647 2 0.4298 0.822 0.088 0.912
#> SRR1946646 2 0.8327 0.699 0.264 0.736
#> SRR1946645 2 0.0938 0.832 0.012 0.988
#> SRR1946644 2 0.0938 0.832 0.012 0.988
#> SRR1946643 2 0.9087 0.658 0.324 0.676
#> SRR1946642 1 0.5178 0.970 0.884 0.116
#> SRR1946641 1 0.4562 0.984 0.904 0.096
#> SRR1946656 2 0.9209 0.656 0.336 0.664
#> SRR1946640 1 0.4562 0.984 0.904 0.096
#> SRR1946639 1 0.4562 0.984 0.904 0.096
#> SRR1946638 1 0.4298 0.979 0.912 0.088
#> SRR1946637 1 0.4022 0.971 0.920 0.080
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.5873 0.5765 0.004 0.312 0.684
#> SRR1946691 3 0.6192 0.2578 0.000 0.420 0.580
#> SRR1946690 2 0.2486 0.6715 0.008 0.932 0.060
#> SRR1946689 3 0.6410 0.4988 0.004 0.420 0.576
#> SRR1946686 3 0.8896 0.4255 0.264 0.172 0.564
#> SRR1946685 2 0.1337 0.6945 0.016 0.972 0.012
#> SRR1946688 2 0.2280 0.6755 0.008 0.940 0.052
#> SRR1946684 3 0.7874 0.0581 0.064 0.368 0.568
#> SRR1946683 2 0.4897 0.6011 0.016 0.812 0.172
#> SRR1946682 2 0.5327 0.4946 0.000 0.728 0.272
#> SRR1946680 3 0.6410 0.4988 0.004 0.420 0.576
#> SRR1946681 2 0.6434 -0.1077 0.008 0.612 0.380
#> SRR1946687 3 0.6669 0.5177 0.008 0.468 0.524
#> SRR1946679 2 0.5992 0.4124 0.016 0.716 0.268
#> SRR1946678 1 0.0424 0.9350 0.992 0.008 0.000
#> SRR1946676 2 0.0661 0.6958 0.008 0.988 0.004
#> SRR1946677 2 0.1491 0.6937 0.016 0.968 0.016
#> SRR1946672 3 0.6416 0.5675 0.008 0.376 0.616
#> SRR1946673 3 0.6308 -0.0848 0.000 0.492 0.508
#> SRR1946671 2 0.3896 0.6406 0.008 0.864 0.128
#> SRR1946669 2 0.8623 0.3450 0.224 0.600 0.176
#> SRR1946668 3 0.7164 0.3115 0.064 0.256 0.680
#> SRR1946666 3 0.6027 0.5722 0.016 0.272 0.712
#> SRR1946667 3 0.6386 0.5087 0.004 0.412 0.584
#> SRR1946670 3 0.5905 0.3020 0.000 0.352 0.648
#> SRR1946663 2 0.2200 0.6850 0.004 0.940 0.056
#> SRR1946664 2 0.2261 0.6745 0.000 0.932 0.068
#> SRR1946662 2 0.7903 0.3240 0.068 0.576 0.356
#> SRR1946661 2 0.3715 0.6386 0.004 0.868 0.128
#> SRR1946660 2 0.2165 0.6757 0.000 0.936 0.064
#> SRR1946659 3 0.6138 0.5403 0.060 0.172 0.768
#> SRR1946658 2 0.5948 -0.0623 0.000 0.640 0.360
#> SRR1946657 2 0.2537 0.6427 0.000 0.920 0.080
#> SRR1946655 3 0.6460 0.5419 0.004 0.440 0.556
#> SRR1946654 3 0.6509 0.5176 0.004 0.472 0.524
#> SRR1946653 3 0.5623 0.5741 0.004 0.280 0.716
#> SRR1946652 2 0.6309 0.0420 0.000 0.500 0.500
#> SRR1946651 2 0.5058 0.5289 0.000 0.756 0.244
#> SRR1946650 2 0.1129 0.6937 0.004 0.976 0.020
#> SRR1946649 2 0.1170 0.6952 0.008 0.976 0.016
#> SRR1946648 2 0.6664 -0.4649 0.008 0.528 0.464
#> SRR1946647 3 0.5928 0.3320 0.008 0.296 0.696
#> SRR1946646 2 0.5122 0.3958 0.012 0.788 0.200
#> SRR1946645 2 0.1636 0.6923 0.016 0.964 0.020
#> SRR1946644 2 0.1182 0.6946 0.012 0.976 0.012
#> SRR1946643 3 0.6577 0.5216 0.008 0.420 0.572
#> SRR1946642 1 0.0661 0.9349 0.988 0.008 0.004
#> SRR1946641 1 0.0661 0.9423 0.988 0.004 0.008
#> SRR1946656 3 0.6330 0.5417 0.004 0.396 0.600
#> SRR1946640 1 0.0661 0.9423 0.988 0.004 0.008
#> SRR1946639 1 0.0661 0.9423 0.988 0.004 0.008
#> SRR1946638 1 0.0661 0.9423 0.988 0.004 0.008
#> SRR1946637 1 0.5480 0.5858 0.732 0.004 0.264
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 2 0.4955 0.4237 0.000 0.556 0.444 0.000
#> SRR1946691 2 0.6677 0.4734 0.000 0.552 0.100 0.348
#> SRR1946690 2 0.4977 -0.2669 0.000 0.540 0.000 0.460
#> SRR1946689 4 0.5742 0.7314 0.000 0.036 0.368 0.596
#> SRR1946686 3 0.7034 0.2776 0.364 0.112 0.520 0.004
#> SRR1946685 2 0.3697 0.4918 0.000 0.852 0.100 0.048
#> SRR1946688 2 0.4072 0.2676 0.000 0.748 0.000 0.252
#> SRR1946684 3 0.7899 0.5713 0.040 0.120 0.504 0.336
#> SRR1946683 2 0.5827 -0.2665 0.032 0.532 0.436 0.000
#> SRR1946682 2 0.5324 0.4993 0.004 0.644 0.016 0.336
#> SRR1946680 4 0.5742 0.7314 0.000 0.036 0.368 0.596
#> SRR1946681 2 0.7583 -0.3468 0.004 0.432 0.168 0.396
#> SRR1946687 2 0.4972 0.4153 0.000 0.544 0.456 0.000
#> SRR1946679 2 0.2651 0.5397 0.004 0.896 0.096 0.004
#> SRR1946678 1 0.0804 0.9759 0.980 0.008 0.012 0.000
#> SRR1946676 2 0.0336 0.5470 0.000 0.992 0.000 0.008
#> SRR1946677 2 0.3697 0.4918 0.000 0.852 0.100 0.048
#> SRR1946672 2 0.5244 0.4232 0.008 0.556 0.436 0.000
#> SRR1946673 2 0.6747 0.4729 0.004 0.556 0.092 0.348
#> SRR1946671 2 0.0336 0.5465 0.000 0.992 0.008 0.000
#> SRR1946669 3 0.8701 0.4858 0.180 0.068 0.464 0.288
#> SRR1946668 3 0.7910 0.5703 0.040 0.120 0.500 0.340
#> SRR1946666 3 0.3508 0.2945 0.012 0.136 0.848 0.004
#> SRR1946667 4 0.5742 0.7314 0.000 0.036 0.368 0.596
#> SRR1946670 2 0.6640 0.4718 0.000 0.552 0.096 0.352
#> SRR1946663 2 0.4697 0.5195 0.000 0.696 0.008 0.296
#> SRR1946664 4 0.4008 0.1430 0.000 0.244 0.000 0.756
#> SRR1946662 3 0.8150 0.5698 0.052 0.128 0.496 0.324
#> SRR1946661 2 0.4509 0.5219 0.000 0.708 0.004 0.288
#> SRR1946660 2 0.5866 0.0369 0.000 0.624 0.052 0.324
#> SRR1946659 3 0.2589 0.2995 0.000 0.116 0.884 0.000
#> SRR1946658 2 0.6778 0.4752 0.000 0.552 0.112 0.336
#> SRR1946657 2 0.5594 0.5205 0.004 0.672 0.040 0.284
#> SRR1946655 2 0.4955 0.4237 0.000 0.556 0.444 0.000
#> SRR1946654 2 0.5097 0.4293 0.004 0.568 0.428 0.000
#> SRR1946653 2 0.5550 0.4262 0.000 0.552 0.428 0.020
#> SRR1946652 2 0.6640 0.4718 0.000 0.552 0.096 0.352
#> SRR1946651 2 0.4875 0.5169 0.004 0.692 0.008 0.296
#> SRR1946650 2 0.4722 0.5196 0.000 0.692 0.008 0.300
#> SRR1946649 2 0.1545 0.5495 0.000 0.952 0.008 0.040
#> SRR1946648 2 0.4989 0.3996 0.000 0.528 0.472 0.000
#> SRR1946647 2 0.7253 0.1134 0.000 0.520 0.308 0.172
#> SRR1946646 2 0.5673 0.4247 0.000 0.596 0.372 0.032
#> SRR1946645 2 0.3758 0.4922 0.000 0.848 0.104 0.048
#> SRR1946644 2 0.1489 0.5318 0.000 0.952 0.004 0.044
#> SRR1946643 4 0.7216 0.5691 0.000 0.140 0.412 0.448
#> SRR1946642 1 0.0937 0.9739 0.976 0.012 0.012 0.000
#> SRR1946641 1 0.0188 0.9806 0.996 0.000 0.004 0.000
#> SRR1946656 3 0.7686 -0.4637 0.000 0.228 0.436 0.336
#> SRR1946640 1 0.0188 0.9806 0.996 0.000 0.004 0.000
#> SRR1946639 1 0.0000 0.9805 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.9805 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.1398 0.9518 0.956 0.000 0.040 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.4410 0.3664 0.000 0.004 0.556 0.000 0.440
#> SRR1946691 5 0.0579 0.4777 0.000 0.008 0.008 0.000 0.984
#> SRR1946690 2 0.6119 0.5004 0.000 0.672 0.080 0.140 0.108
#> SRR1946689 3 0.7075 0.3290 0.000 0.080 0.500 0.324 0.096
#> SRR1946686 1 0.5576 0.4445 0.536 0.000 0.388 0.000 0.076
#> SRR1946685 4 0.5811 0.5471 0.000 0.140 0.000 0.596 0.264
#> SRR1946688 2 0.5479 0.5234 0.000 0.724 0.080 0.068 0.128
#> SRR1946684 5 0.6961 0.2724 0.128 0.052 0.304 0.000 0.516
#> SRR1946683 2 0.9776 0.0115 0.224 0.252 0.248 0.124 0.152
#> SRR1946682 5 0.4886 0.0583 0.000 0.448 0.000 0.024 0.528
#> SRR1946680 3 0.7193 0.2378 0.000 0.080 0.412 0.412 0.096
#> SRR1946681 3 0.8195 0.2531 0.000 0.168 0.384 0.160 0.288
#> SRR1946687 3 0.6751 0.0692 0.000 0.004 0.424 0.220 0.352
#> SRR1946679 5 0.8430 -0.2248 0.104 0.328 0.068 0.084 0.416
#> SRR1946678 1 0.2086 0.8631 0.924 0.008 0.048 0.020 0.000
#> SRR1946676 2 0.5210 0.5574 0.000 0.652 0.000 0.084 0.264
#> SRR1946677 4 0.5770 0.5487 0.000 0.140 0.000 0.604 0.256
#> SRR1946672 3 0.4383 0.3714 0.000 0.004 0.572 0.000 0.424
#> SRR1946673 5 0.3496 0.4303 0.000 0.200 0.012 0.000 0.788
#> SRR1946671 2 0.5340 0.6036 0.000 0.692 0.056 0.032 0.220
#> SRR1946669 3 0.8157 -0.2598 0.300 0.276 0.348 0.012 0.064
#> SRR1946668 5 0.6548 0.2832 0.124 0.032 0.288 0.000 0.556
#> SRR1946666 3 0.5993 0.2232 0.248 0.000 0.580 0.000 0.172
#> SRR1946667 3 0.7075 0.3290 0.000 0.080 0.500 0.324 0.096
#> SRR1946670 5 0.0693 0.4803 0.000 0.000 0.008 0.012 0.980
#> SRR1946663 2 0.3885 0.6066 0.000 0.724 0.000 0.008 0.268
#> SRR1946664 2 0.6254 0.5031 0.000 0.660 0.080 0.136 0.124
#> SRR1946662 3 0.8401 -0.2226 0.204 0.264 0.352 0.000 0.180
#> SRR1946661 2 0.3582 0.5890 0.000 0.768 0.000 0.008 0.224
#> SRR1946660 2 0.6236 0.4571 0.000 0.660 0.076 0.144 0.120
#> SRR1946659 3 0.4026 0.3391 0.020 0.000 0.736 0.000 0.244
#> SRR1946658 5 0.3699 0.3993 0.000 0.028 0.104 0.032 0.836
#> SRR1946657 2 0.5161 0.3416 0.000 0.488 0.024 0.008 0.480
#> SRR1946655 3 0.5495 0.3871 0.000 0.008 0.536 0.048 0.408
#> SRR1946654 3 0.5065 0.3633 0.000 0.000 0.544 0.036 0.420
#> SRR1946653 5 0.4658 -0.4032 0.000 0.000 0.484 0.012 0.504
#> SRR1946652 5 0.4125 0.4723 0.000 0.224 0.004 0.024 0.748
#> SRR1946651 2 0.4183 0.4171 0.000 0.668 0.000 0.008 0.324
#> SRR1946650 2 0.3910 0.6068 0.000 0.720 0.000 0.008 0.272
#> SRR1946649 2 0.4815 0.5932 0.000 0.692 0.000 0.064 0.244
#> SRR1946648 4 0.7167 0.3623 0.000 0.024 0.220 0.420 0.336
#> SRR1946647 5 0.4236 0.4378 0.012 0.020 0.164 0.016 0.788
#> SRR1946646 4 0.7519 0.5501 0.000 0.108 0.112 0.452 0.328
#> SRR1946645 4 0.5535 0.5534 0.000 0.116 0.000 0.628 0.256
#> SRR1946644 2 0.7762 0.0567 0.000 0.392 0.064 0.296 0.248
#> SRR1946643 4 0.7250 -0.1269 0.000 0.048 0.272 0.488 0.192
#> SRR1946642 1 0.3056 0.8406 0.860 0.008 0.112 0.020 0.000
#> SRR1946641 1 0.0162 0.8749 0.996 0.000 0.000 0.000 0.004
#> SRR1946656 3 0.7086 0.3926 0.000 0.040 0.500 0.180 0.280
#> SRR1946640 1 0.0162 0.8749 0.996 0.000 0.000 0.000 0.004
#> SRR1946639 1 0.0000 0.8732 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0162 0.8749 0.996 0.000 0.000 0.000 0.004
#> SRR1946637 1 0.4010 0.7354 0.784 0.000 0.160 0.000 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.2344 0.6082 0.028 0.000 0.896 0.000 0.008 0.068
#> SRR1946691 5 0.4573 0.5972 0.000 0.040 0.068 0.004 0.752 0.136
#> SRR1946690 2 0.6839 -0.1463 0.000 0.384 0.052 0.188 0.004 0.372
#> SRR1946689 4 0.2260 0.7597 0.000 0.000 0.140 0.860 0.000 0.000
#> SRR1946686 1 0.3506 0.7315 0.792 0.000 0.052 0.000 0.156 0.000
#> SRR1946685 2 0.2312 0.5758 0.000 0.876 0.012 0.000 0.000 0.112
#> SRR1946688 6 0.6539 0.0712 0.000 0.336 0.048 0.168 0.000 0.448
#> SRR1946684 5 0.2375 0.6121 0.060 0.008 0.000 0.000 0.896 0.036
#> SRR1946683 1 0.8314 0.0867 0.392 0.200 0.028 0.028 0.132 0.220
#> SRR1946682 6 0.4386 -0.2118 0.000 0.004 0.016 0.000 0.464 0.516
#> SRR1946680 4 0.1621 0.7378 0.004 0.004 0.048 0.936 0.000 0.008
#> SRR1946681 3 0.6365 0.1202 0.000 0.336 0.468 0.156 0.000 0.040
#> SRR1946687 3 0.5645 0.4879 0.012 0.196 0.660 0.040 0.004 0.088
#> SRR1946679 6 0.8586 0.0374 0.140 0.228 0.208 0.020 0.052 0.352
#> SRR1946678 1 0.0891 0.8669 0.968 0.008 0.000 0.024 0.000 0.000
#> SRR1946676 6 0.4427 0.4291 0.000 0.236 0.020 0.016 0.016 0.712
#> SRR1946677 2 0.2313 0.5760 0.000 0.884 0.012 0.004 0.000 0.100
#> SRR1946672 3 0.3474 0.6068 0.036 0.004 0.844 0.036 0.004 0.076
#> SRR1946673 5 0.4568 0.5513 0.000 0.032 0.036 0.000 0.700 0.232
#> SRR1946671 6 0.5276 0.4567 0.004 0.168 0.032 0.028 0.060 0.708
#> SRR1946669 5 0.6453 0.0300 0.384 0.044 0.000 0.028 0.468 0.076
#> SRR1946668 5 0.1562 0.6211 0.024 0.000 0.004 0.000 0.940 0.032
#> SRR1946666 3 0.5335 0.3986 0.276 0.004 0.600 0.000 0.116 0.004
#> SRR1946667 4 0.2340 0.7551 0.000 0.000 0.148 0.852 0.000 0.000
#> SRR1946670 5 0.4514 0.6077 0.000 0.032 0.080 0.004 0.756 0.128
#> SRR1946663 6 0.1956 0.5417 0.000 0.008 0.004 0.000 0.080 0.908
#> SRR1946664 6 0.6451 0.2612 0.000 0.176 0.048 0.192 0.016 0.568
#> SRR1946662 5 0.5567 0.2775 0.316 0.008 0.000 0.024 0.580 0.072
#> SRR1946661 6 0.2592 0.5307 0.000 0.004 0.016 0.000 0.116 0.864
#> SRR1946660 6 0.6156 -0.0236 0.000 0.420 0.048 0.100 0.000 0.432
#> SRR1946659 3 0.2723 0.5710 0.016 0.000 0.852 0.000 0.128 0.004
#> SRR1946658 3 0.7239 0.0384 0.000 0.104 0.372 0.000 0.308 0.216
#> SRR1946657 6 0.4485 0.5018 0.000 0.084 0.028 0.064 0.040 0.784
#> SRR1946655 3 0.3029 0.5433 0.000 0.008 0.852 0.088 0.000 0.052
#> SRR1946654 3 0.5183 0.5303 0.004 0.032 0.692 0.148 0.000 0.124
#> SRR1946653 3 0.4233 0.5039 0.004 0.000 0.720 0.000 0.216 0.060
#> SRR1946652 5 0.4513 0.3711 0.000 0.004 0.032 0.000 0.596 0.368
#> SRR1946651 6 0.3518 0.3453 0.000 0.000 0.012 0.000 0.256 0.732
#> SRR1946650 6 0.1624 0.5424 0.000 0.008 0.012 0.000 0.044 0.936
#> SRR1946649 6 0.4267 0.5177 0.000 0.136 0.024 0.012 0.052 0.776
#> SRR1946648 2 0.7197 0.2082 0.004 0.452 0.248 0.168 0.000 0.128
#> SRR1946647 5 0.4381 0.6185 0.004 0.040 0.068 0.004 0.780 0.104
#> SRR1946646 2 0.6465 0.4205 0.004 0.580 0.156 0.128 0.000 0.132
#> SRR1946645 2 0.2375 0.5788 0.000 0.888 0.012 0.012 0.000 0.088
#> SRR1946644 2 0.6360 0.1361 0.000 0.540 0.048 0.160 0.004 0.248
#> SRR1946643 4 0.5974 0.3178 0.000 0.344 0.132 0.500 0.000 0.024
#> SRR1946642 1 0.1518 0.8539 0.944 0.008 0.000 0.024 0.024 0.000
#> SRR1946641 1 0.0260 0.8734 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1946656 3 0.5203 -0.0449 0.000 0.056 0.560 0.364 0.000 0.020
#> SRR1946640 1 0.0363 0.8723 0.988 0.000 0.012 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.8723 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0146 0.8734 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1946637 1 0.1152 0.8560 0.952 0.000 0.044 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17231 rows and 53 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.946 0.979 0.3360 0.665 0.665
#> 3 3 0.607 0.705 0.883 0.8451 0.637 0.487
#> 4 4 0.689 0.788 0.891 0.1968 0.765 0.461
#> 5 5 0.658 0.691 0.818 0.0825 0.866 0.541
#> 6 6 0.722 0.627 0.753 0.0493 0.896 0.547
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
#> SRR1946675 2 0.0000 0.985 0.000 1.000
#> SRR1946691 2 0.0000 0.985 0.000 1.000
#> SRR1946690 2 0.0000 0.985 0.000 1.000
#> SRR1946689 2 0.0000 0.985 0.000 1.000
#> SRR1946686 1 0.0376 0.944 0.996 0.004
#> SRR1946685 2 0.0000 0.985 0.000 1.000
#> SRR1946688 2 0.0000 0.985 0.000 1.000
#> SRR1946684 2 0.9977 0.021 0.472 0.528
#> SRR1946683 2 0.0000 0.985 0.000 1.000
#> SRR1946682 2 0.0000 0.985 0.000 1.000
#> SRR1946680 2 0.0000 0.985 0.000 1.000
#> SRR1946681 2 0.0000 0.985 0.000 1.000
#> SRR1946687 2 0.0000 0.985 0.000 1.000
#> SRR1946679 2 0.0000 0.985 0.000 1.000
#> SRR1946678 1 0.0000 0.946 1.000 0.000
#> SRR1946676 2 0.0000 0.985 0.000 1.000
#> SRR1946677 2 0.0000 0.985 0.000 1.000
#> SRR1946672 2 0.0000 0.985 0.000 1.000
#> SRR1946673 2 0.0000 0.985 0.000 1.000
#> SRR1946671 2 0.0000 0.985 0.000 1.000
#> SRR1946669 1 0.0000 0.946 1.000 0.000
#> SRR1946668 2 0.2236 0.949 0.036 0.964
#> SRR1946666 2 0.3274 0.923 0.060 0.940
#> SRR1946667 2 0.0000 0.985 0.000 1.000
#> SRR1946670 2 0.0000 0.985 0.000 1.000
#> SRR1946663 2 0.0000 0.985 0.000 1.000
#> SRR1946664 2 0.0000 0.985 0.000 1.000
#> SRR1946662 1 0.7453 0.737 0.788 0.212
#> SRR1946661 2 0.0000 0.985 0.000 1.000
#> SRR1946660 2 0.0000 0.985 0.000 1.000
#> SRR1946659 1 0.8909 0.575 0.692 0.308
#> SRR1946658 2 0.0000 0.985 0.000 1.000
#> SRR1946657 2 0.0000 0.985 0.000 1.000
#> SRR1946655 2 0.0000 0.985 0.000 1.000
#> SRR1946654 2 0.0000 0.985 0.000 1.000
#> SRR1946653 2 0.0000 0.985 0.000 1.000
#> SRR1946652 2 0.0000 0.985 0.000 1.000
#> SRR1946651 2 0.0000 0.985 0.000 1.000
#> SRR1946650 2 0.0000 0.985 0.000 1.000
#> SRR1946649 2 0.0000 0.985 0.000 1.000
#> SRR1946648 2 0.0000 0.985 0.000 1.000
#> SRR1946647 2 0.0000 0.985 0.000 1.000
#> SRR1946646 2 0.0000 0.985 0.000 1.000
#> SRR1946645 2 0.0000 0.985 0.000 1.000
#> SRR1946644 2 0.0000 0.985 0.000 1.000
#> SRR1946643 2 0.0000 0.985 0.000 1.000
#> SRR1946642 1 0.0000 0.946 1.000 0.000
#> SRR1946641 1 0.0000 0.946 1.000 0.000
#> SRR1946656 2 0.0000 0.985 0.000 1.000
#> SRR1946640 1 0.0000 0.946 1.000 0.000
#> SRR1946639 1 0.0000 0.946 1.000 0.000
#> SRR1946638 1 0.0000 0.946 1.000 0.000
#> SRR1946637 1 0.0000 0.946 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1946675 3 0.0000 0.83362 0.000 0.000 1.000
#> SRR1946691 3 0.4121 0.73443 0.000 0.168 0.832
#> SRR1946690 2 0.1860 0.80098 0.000 0.948 0.052
#> SRR1946689 2 0.6309 -0.00626 0.000 0.504 0.496
#> SRR1946686 3 0.2878 0.76652 0.096 0.000 0.904
#> SRR1946685 2 0.0237 0.82858 0.000 0.996 0.004
#> SRR1946688 2 0.0237 0.82858 0.000 0.996 0.004
#> SRR1946684 2 0.9606 0.12123 0.352 0.440 0.208
#> SRR1946683 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946682 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946680 2 0.5859 0.38762 0.000 0.656 0.344
#> SRR1946681 3 0.5650 0.54253 0.000 0.312 0.688
#> SRR1946687 3 0.0237 0.83314 0.000 0.004 0.996
#> SRR1946679 2 0.6111 0.25387 0.000 0.604 0.396
#> SRR1946678 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946676 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946677 2 0.0424 0.82809 0.000 0.992 0.008
#> SRR1946672 3 0.0000 0.83362 0.000 0.000 1.000
#> SRR1946673 2 0.6225 0.14011 0.000 0.568 0.432
#> SRR1946671 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946669 1 0.5529 0.52601 0.704 0.296 0.000
#> SRR1946668 2 0.9464 -0.08999 0.180 0.412 0.408
#> SRR1946666 3 0.0237 0.83199 0.004 0.000 0.996
#> SRR1946667 3 0.5254 0.56142 0.000 0.264 0.736
#> SRR1946670 3 0.6140 0.30725 0.000 0.404 0.596
#> SRR1946663 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946664 2 0.0892 0.82172 0.000 0.980 0.020
#> SRR1946662 2 0.5465 0.53171 0.288 0.712 0.000
#> SRR1946661 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946660 2 0.0237 0.82858 0.000 0.996 0.004
#> SRR1946659 3 0.0237 0.83199 0.004 0.000 0.996
#> SRR1946658 3 0.6062 0.35512 0.000 0.384 0.616
#> SRR1946657 2 0.0237 0.82816 0.000 0.996 0.004
#> SRR1946655 3 0.0000 0.83362 0.000 0.000 1.000
#> SRR1946654 3 0.1031 0.82811 0.000 0.024 0.976
#> SRR1946653 3 0.0000 0.83362 0.000 0.000 1.000
#> SRR1946652 2 0.4750 0.61629 0.000 0.784 0.216
#> SRR1946651 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946650 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946649 2 0.0000 0.82917 0.000 1.000 0.000
#> SRR1946648 3 0.5650 0.54107 0.000 0.312 0.688
#> SRR1946647 3 0.0592 0.83153 0.000 0.012 0.988
#> SRR1946646 2 0.5098 0.56341 0.000 0.752 0.248
#> SRR1946645 2 0.0592 0.82651 0.000 0.988 0.012
#> SRR1946644 2 0.2066 0.79489 0.000 0.940 0.060
#> SRR1946643 3 0.4605 0.68477 0.000 0.204 0.796
#> SRR1946642 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946641 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946656 3 0.0000 0.83362 0.000 0.000 1.000
#> SRR1946640 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946639 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946638 1 0.0000 0.94773 1.000 0.000 0.000
#> SRR1946637 1 0.0000 0.94773 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1946675 3 0.2530 0.765 0.000 0.000 0.888 0.112
#> SRR1946691 4 0.1022 0.871 0.000 0.000 0.032 0.968
#> SRR1946690 2 0.0804 0.885 0.000 0.980 0.012 0.008
#> SRR1946689 3 0.4872 0.435 0.000 0.356 0.640 0.004
#> SRR1946686 1 0.5619 0.666 0.724 0.000 0.124 0.152
#> SRR1946685 2 0.1389 0.873 0.000 0.952 0.048 0.000
#> SRR1946688 2 0.0336 0.884 0.000 0.992 0.000 0.008
#> SRR1946684 4 0.0524 0.880 0.004 0.000 0.008 0.988
#> SRR1946683 2 0.1762 0.869 0.004 0.944 0.004 0.048
#> SRR1946682 4 0.2469 0.848 0.000 0.108 0.000 0.892
#> SRR1946680 2 0.3801 0.679 0.000 0.780 0.220 0.000
#> SRR1946681 3 0.5055 0.405 0.000 0.368 0.624 0.008
#> SRR1946687 3 0.0336 0.779 0.000 0.008 0.992 0.000
#> SRR1946679 2 0.7740 0.197 0.000 0.432 0.248 0.320
#> SRR1946678 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946676 2 0.0188 0.884 0.000 0.996 0.000 0.004
#> SRR1946677 2 0.1637 0.868 0.000 0.940 0.060 0.000
#> SRR1946672 3 0.1211 0.784 0.000 0.000 0.960 0.040
#> SRR1946673 4 0.0672 0.882 0.000 0.008 0.008 0.984
#> SRR1946671 2 0.1867 0.858 0.000 0.928 0.000 0.072
#> SRR1946669 4 0.3808 0.776 0.176 0.012 0.000 0.812
#> SRR1946668 4 0.0188 0.881 0.000 0.000 0.004 0.996
#> SRR1946666 3 0.0817 0.785 0.000 0.000 0.976 0.024
#> SRR1946667 3 0.3591 0.708 0.000 0.168 0.824 0.008
#> SRR1946670 4 0.0592 0.878 0.000 0.000 0.016 0.984
#> SRR1946663 4 0.4356 0.639 0.000 0.292 0.000 0.708
#> SRR1946664 2 0.0937 0.885 0.000 0.976 0.012 0.012
#> SRR1946662 4 0.3421 0.839 0.088 0.044 0.000 0.868
#> SRR1946661 4 0.3688 0.771 0.000 0.208 0.000 0.792
#> SRR1946660 2 0.0469 0.884 0.000 0.988 0.012 0.000
#> SRR1946659 3 0.3307 0.753 0.028 0.000 0.868 0.104
#> SRR1946658 4 0.3266 0.728 0.000 0.000 0.168 0.832
#> SRR1946657 2 0.3837 0.678 0.000 0.776 0.000 0.224
#> SRR1946655 3 0.2647 0.759 0.000 0.000 0.880 0.120
#> SRR1946654 3 0.1042 0.785 0.000 0.008 0.972 0.020
#> SRR1946653 3 0.4040 0.648 0.000 0.000 0.752 0.248
#> SRR1946652 4 0.0657 0.882 0.000 0.012 0.004 0.984
#> SRR1946651 4 0.3528 0.790 0.000 0.192 0.000 0.808
#> SRR1946650 2 0.2973 0.795 0.000 0.856 0.000 0.144
#> SRR1946649 2 0.0592 0.882 0.000 0.984 0.000 0.016
#> SRR1946648 3 0.4855 0.322 0.000 0.400 0.600 0.000
#> SRR1946647 4 0.0817 0.876 0.000 0.000 0.024 0.976
#> SRR1946646 2 0.2530 0.824 0.000 0.888 0.112 0.000
#> SRR1946645 2 0.1867 0.860 0.000 0.928 0.072 0.000
#> SRR1946644 2 0.0469 0.884 0.000 0.988 0.012 0.000
#> SRR1946643 3 0.4746 0.405 0.000 0.368 0.632 0.000
#> SRR1946642 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946656 3 0.0895 0.786 0.000 0.004 0.976 0.020
#> SRR1946640 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.962 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1946675 3 0.2414 0.783 0.000 0.012 0.900 0.008 0.080
#> SRR1946691 5 0.3971 0.754 0.000 0.008 0.136 0.052 0.804
#> SRR1946690 4 0.1205 0.770 0.000 0.040 0.000 0.956 0.004
#> SRR1946689 4 0.2228 0.770 0.000 0.008 0.068 0.912 0.012
#> SRR1946686 1 0.3929 0.745 0.788 0.000 0.036 0.004 0.172
#> SRR1946685 2 0.2707 0.627 0.000 0.860 0.008 0.132 0.000
#> SRR1946688 4 0.0992 0.778 0.000 0.024 0.000 0.968 0.008
#> SRR1946684 5 0.0162 0.840 0.004 0.000 0.000 0.000 0.996
#> SRR1946683 2 0.4733 0.624 0.008 0.752 0.000 0.116 0.124
#> SRR1946682 5 0.3246 0.702 0.000 0.008 0.000 0.184 0.808
#> SRR1946680 4 0.1106 0.782 0.000 0.012 0.024 0.964 0.000
#> SRR1946681 3 0.4717 0.252 0.000 0.396 0.584 0.020 0.000
#> SRR1946687 3 0.5271 0.602 0.000 0.152 0.680 0.168 0.000
#> SRR1946679 2 0.3387 0.616 0.000 0.836 0.024 0.008 0.132
#> SRR1946678 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946676 2 0.3715 0.626 0.000 0.736 0.000 0.260 0.004
#> SRR1946677 2 0.3676 0.571 0.000 0.760 0.004 0.232 0.004
#> SRR1946672 3 0.2574 0.774 0.000 0.112 0.876 0.000 0.012
#> SRR1946673 5 0.3632 0.766 0.000 0.176 0.020 0.004 0.800
#> SRR1946671 2 0.4341 0.556 0.000 0.628 0.000 0.364 0.008
#> SRR1946669 5 0.4355 0.747 0.164 0.076 0.000 0.000 0.760
#> SRR1946668 5 0.0162 0.840 0.000 0.000 0.000 0.004 0.996
#> SRR1946666 3 0.3672 0.756 0.072 0.056 0.848 0.020 0.004
#> SRR1946667 4 0.4905 0.405 0.000 0.008 0.344 0.624 0.024
#> SRR1946670 5 0.1168 0.840 0.000 0.008 0.032 0.000 0.960
#> SRR1946663 4 0.4283 0.456 0.000 0.008 0.000 0.644 0.348
#> SRR1946664 4 0.2236 0.747 0.000 0.068 0.000 0.908 0.024
#> SRR1946662 5 0.3506 0.811 0.064 0.104 0.000 0.000 0.832
#> SRR1946661 5 0.2592 0.812 0.000 0.056 0.000 0.052 0.892
#> SRR1946660 4 0.2848 0.672 0.000 0.156 0.000 0.840 0.004
#> SRR1946659 3 0.1788 0.787 0.000 0.008 0.932 0.004 0.056
#> SRR1946658 5 0.3224 0.764 0.000 0.016 0.160 0.000 0.824
#> SRR1946657 2 0.4198 0.640 0.000 0.792 0.016 0.144 0.048
#> SRR1946655 3 0.1845 0.787 0.000 0.016 0.928 0.000 0.056
#> SRR1946654 2 0.4709 0.229 0.000 0.584 0.400 0.008 0.008
#> SRR1946653 3 0.3392 0.750 0.000 0.064 0.848 0.004 0.084
#> SRR1946652 5 0.4920 0.493 0.000 0.348 0.024 0.008 0.620
#> SRR1946651 2 0.5470 0.568 0.000 0.680 0.008 0.152 0.160
#> SRR1946650 2 0.4371 0.567 0.000 0.644 0.000 0.344 0.012
#> SRR1946649 2 0.4341 0.525 0.000 0.592 0.000 0.404 0.004
#> SRR1946648 2 0.5793 0.215 0.000 0.584 0.292 0.124 0.000
#> SRR1946647 5 0.0794 0.840 0.000 0.000 0.028 0.000 0.972
#> SRR1946646 2 0.4192 0.576 0.000 0.736 0.032 0.232 0.000
#> SRR1946645 2 0.4505 0.325 0.000 0.604 0.012 0.384 0.000
#> SRR1946644 2 0.4251 0.572 0.000 0.624 0.004 0.372 0.000
#> SRR1946643 3 0.5927 0.400 0.000 0.340 0.540 0.120 0.000
#> SRR1946642 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0451 0.787 0.000 0.008 0.988 0.000 0.004
#> SRR1946640 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 0.969 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1946675 3 0.1332 0.7497 0.000 0.008 0.952 0.000 0.012 0.028
#> SRR1946691 6 0.5952 0.4812 0.000 0.056 0.108 0.256 0.000 0.580
#> SRR1946690 4 0.2003 0.7000 0.000 0.116 0.000 0.884 0.000 0.000
#> SRR1946689 4 0.2453 0.7491 0.000 0.000 0.044 0.896 0.044 0.016
#> SRR1946686 6 0.4899 0.2810 0.392 0.004 0.036 0.004 0.004 0.560
#> SRR1946685 5 0.3608 0.7548 0.000 0.248 0.000 0.012 0.736 0.004
#> SRR1946688 4 0.1605 0.7599 0.000 0.016 0.000 0.936 0.044 0.004
#> SRR1946684 6 0.0914 0.7445 0.000 0.016 0.000 0.000 0.016 0.968
#> SRR1946683 5 0.4764 0.7042 0.000 0.232 0.000 0.000 0.660 0.108
#> SRR1946682 6 0.3189 0.6207 0.000 0.004 0.000 0.236 0.000 0.760
#> SRR1946680 4 0.2279 0.7618 0.000 0.024 0.016 0.904 0.056 0.000
#> SRR1946681 3 0.5775 0.0739 0.000 0.104 0.468 0.020 0.408 0.000
#> SRR1946687 3 0.5742 0.2013 0.000 0.028 0.456 0.084 0.432 0.000
#> SRR1946679 5 0.4952 0.6662 0.000 0.252 0.000 0.000 0.632 0.116
#> SRR1946678 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946676 2 0.3718 0.5849 0.000 0.784 0.000 0.132 0.084 0.000
#> SRR1946677 5 0.2668 0.7959 0.000 0.168 0.000 0.000 0.828 0.004
#> SRR1946672 3 0.3217 0.6313 0.000 0.008 0.768 0.000 0.224 0.000
#> SRR1946673 2 0.5034 -0.0532 0.000 0.532 0.056 0.000 0.008 0.404
#> SRR1946671 2 0.4212 0.3310 0.000 0.560 0.000 0.424 0.016 0.000
#> SRR1946669 6 0.3610 0.7041 0.052 0.088 0.000 0.000 0.036 0.824
#> SRR1946668 6 0.0405 0.7467 0.000 0.008 0.000 0.004 0.000 0.988
#> SRR1946666 3 0.5487 0.5764 0.204 0.016 0.660 0.016 0.100 0.004
#> SRR1946667 4 0.3652 0.6350 0.000 0.000 0.212 0.760 0.008 0.020
#> SRR1946670 6 0.2220 0.7416 0.000 0.052 0.020 0.020 0.000 0.908
#> SRR1946663 6 0.4097 0.0959 0.000 0.008 0.000 0.492 0.000 0.500
#> SRR1946664 4 0.2946 0.6125 0.000 0.184 0.000 0.808 0.004 0.004
#> SRR1946662 6 0.4508 0.5400 0.040 0.280 0.000 0.000 0.012 0.668
#> SRR1946661 6 0.2643 0.7131 0.000 0.128 0.000 0.008 0.008 0.856
#> SRR1946660 4 0.4282 0.3082 0.000 0.020 0.000 0.560 0.420 0.000
#> SRR1946659 3 0.1088 0.7511 0.016 0.024 0.960 0.000 0.000 0.000
#> SRR1946658 6 0.4556 0.6003 0.000 0.056 0.220 0.008 0.008 0.708
#> SRR1946657 2 0.2949 0.4988 0.000 0.848 0.028 0.000 0.116 0.008
#> SRR1946655 3 0.0713 0.7520 0.000 0.028 0.972 0.000 0.000 0.000
#> SRR1946654 2 0.4499 0.0241 0.000 0.540 0.428 0.000 0.032 0.000
#> SRR1946653 3 0.2402 0.6841 0.004 0.140 0.856 0.000 0.000 0.000
#> SRR1946652 2 0.3348 0.5224 0.000 0.836 0.036 0.000 0.028 0.100
#> SRR1946651 2 0.1946 0.6020 0.000 0.912 0.000 0.072 0.004 0.012
#> SRR1946650 2 0.4109 0.3508 0.000 0.576 0.000 0.412 0.012 0.000
#> SRR1946649 2 0.4322 0.2767 0.000 0.528 0.000 0.452 0.020 0.000
#> SRR1946648 5 0.1975 0.7522 0.000 0.020 0.028 0.012 0.928 0.012
#> SRR1946647 6 0.0508 0.7465 0.000 0.000 0.000 0.012 0.004 0.984
#> SRR1946646 5 0.3050 0.7876 0.000 0.136 0.004 0.028 0.832 0.000
#> SRR1946645 5 0.1720 0.7594 0.000 0.032 0.000 0.040 0.928 0.000
#> SRR1946644 2 0.4475 0.5342 0.000 0.692 0.000 0.220 0.088 0.000
#> SRR1946643 5 0.3373 0.6024 0.000 0.012 0.140 0.032 0.816 0.000
#> SRR1946642 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946641 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946656 3 0.0767 0.7548 0.000 0.012 0.976 0.004 0.008 0.000
#> SRR1946640 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946639 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946638 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1946637 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
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
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