Date: 2019-12-25 21:08:29 CET, cola version: 1.3.2
Document is loading...
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 21168 rows and 103 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, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 21168 103
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, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:kmeans | 2 | 1.000 | 0.996 | 0.998 | ** | |
| SD:skmeans | 2 | 1.000 | 0.998 | 0.999 | ** | |
| CV:kmeans | 2 | 1.000 | 0.982 | 0.994 | ** | |
| CV:skmeans | 2 | 1.000 | 0.983 | 0.993 | ** | |
| CV:NMF | 2 | 1.000 | 0.967 | 0.987 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.998 | 0.999 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.999 | 0.999 | ** | |
| MAD:NMF | 2 | 1.000 | 0.965 | 0.986 | ** | |
| ATC:kmeans | 3 | 1.000 | 0.989 | 0.995 | ** | |
| ATC:pam | 3 | 1.000 | 0.953 | 0.982 | ** | |
| SD:NMF | 2 | 0.999 | 0.954 | 0.981 | ** | |
| ATC:skmeans | 3 | 0.982 | 0.931 | 0.971 | ** | 2 |
| ATC:NMF | 2 | 0.939 | 0.923 | 0.968 | * | |
| CV:mclust | 4 | 0.930 | 0.935 | 0.964 | * | 2,3 |
| SD:mclust | 2 | 0.917 | 0.968 | 0.979 | * | |
| MAD:mclust | 6 | 0.911 | 0.893 | 0.939 | * | |
| ATC:mclust | 2 | 0.864 | 0.931 | 0.969 | ||
| MAD:pam | 2 | 0.859 | 0.913 | 0.963 | ||
| ATC:hclust | 4 | 0.822 | 0.818 | 0.917 | ||
| SD:pam | 2 | 0.734 | 0.859 | 0.937 | ||
| CV:pam | 2 | 0.732 | 0.854 | 0.936 | ||
| MAD:hclust | 2 | 0.652 | 0.843 | 0.926 | ||
| SD:hclust | 2 | 0.482 | 0.855 | 0.908 | ||
| CV:hclust | 2 | 0.456 | 0.868 | 0.909 |
**: 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 0.999 0.954 0.981 0.501 0.499 0.499
#> CV:NMF 2 1.000 0.967 0.987 0.504 0.496 0.496
#> MAD:NMF 2 1.000 0.965 0.986 0.503 0.496 0.496
#> ATC:NMF 2 0.939 0.923 0.968 0.494 0.512 0.512
#> SD:skmeans 2 1.000 0.998 0.999 0.504 0.496 0.496
#> CV:skmeans 2 1.000 0.983 0.993 0.505 0.496 0.496
#> MAD:skmeans 2 1.000 0.999 0.999 0.504 0.496 0.496
#> ATC:skmeans 2 1.000 0.984 0.992 0.504 0.496 0.496
#> SD:mclust 2 0.917 0.968 0.979 0.503 0.495 0.495
#> CV:mclust 2 1.000 0.957 0.981 0.504 0.496 0.496
#> MAD:mclust 2 0.777 0.918 0.960 0.498 0.496 0.496
#> ATC:mclust 2 0.864 0.931 0.969 0.493 0.503 0.503
#> SD:kmeans 2 1.000 0.996 0.998 0.504 0.496 0.496
#> CV:kmeans 2 1.000 0.982 0.994 0.505 0.496 0.496
#> MAD:kmeans 2 1.000 0.998 0.999 0.504 0.496 0.496
#> ATC:kmeans 2 0.834 0.934 0.970 0.480 0.520 0.520
#> SD:pam 2 0.734 0.859 0.937 0.457 0.567 0.567
#> CV:pam 2 0.732 0.854 0.936 0.445 0.567 0.567
#> MAD:pam 2 0.859 0.913 0.963 0.473 0.525 0.525
#> ATC:pam 2 0.806 0.873 0.948 0.468 0.530 0.530
#> SD:hclust 2 0.482 0.855 0.908 0.450 0.506 0.506
#> CV:hclust 2 0.456 0.868 0.909 0.463 0.497 0.497
#> MAD:hclust 2 0.652 0.843 0.926 0.493 0.497 0.497
#> ATC:hclust 2 0.784 0.915 0.959 0.286 0.722 0.722
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.845 0.837 0.924 0.279 0.809 0.637
#> CV:NMF 3 0.824 0.849 0.921 0.284 0.791 0.604
#> MAD:NMF 3 0.793 0.822 0.917 0.280 0.829 0.669
#> ATC:NMF 3 0.864 0.851 0.942 0.295 0.787 0.606
#> SD:skmeans 3 0.805 0.791 0.899 0.281 0.807 0.628
#> CV:skmeans 3 0.715 0.748 0.872 0.274 0.855 0.712
#> MAD:skmeans 3 0.821 0.882 0.940 0.294 0.800 0.615
#> ATC:skmeans 3 0.982 0.931 0.971 0.215 0.880 0.760
#> SD:mclust 3 0.855 0.812 0.929 0.268 0.816 0.645
#> CV:mclust 3 0.907 0.934 0.964 0.296 0.766 0.562
#> MAD:mclust 3 0.882 0.879 0.939 0.299 0.775 0.576
#> ATC:mclust 3 0.608 0.599 0.764 0.273 0.798 0.614
#> SD:kmeans 3 0.756 0.810 0.892 0.262 0.874 0.748
#> CV:kmeans 3 0.738 0.822 0.894 0.260 0.883 0.763
#> MAD:kmeans 3 0.766 0.476 0.688 0.275 0.915 0.831
#> ATC:kmeans 3 1.000 0.989 0.995 0.260 0.767 0.595
#> SD:pam 3 0.601 0.770 0.879 0.428 0.706 0.516
#> CV:pam 3 0.493 0.438 0.696 0.437 0.677 0.487
#> MAD:pam 3 0.641 0.827 0.888 0.371 0.760 0.570
#> ATC:pam 3 1.000 0.953 0.982 0.302 0.725 0.540
#> SD:hclust 3 0.610 0.699 0.864 0.260 0.910 0.821
#> CV:hclust 3 0.569 0.749 0.857 0.301 0.875 0.752
#> MAD:hclust 3 0.554 0.701 0.842 0.231 0.876 0.756
#> ATC:hclust 3 0.473 0.598 0.808 0.739 0.615 0.502
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.819 0.827 0.914 0.1628 0.816 0.538
#> CV:NMF 4 0.766 0.817 0.907 0.1579 0.863 0.629
#> MAD:NMF 4 0.806 0.815 0.905 0.1570 0.799 0.506
#> ATC:NMF 4 0.682 0.771 0.869 0.1392 0.832 0.572
#> SD:skmeans 4 0.829 0.868 0.930 0.1400 0.879 0.674
#> CV:skmeans 4 0.780 0.798 0.904 0.1492 0.878 0.678
#> MAD:skmeans 4 0.830 0.861 0.926 0.1230 0.878 0.671
#> ATC:skmeans 4 0.788 0.787 0.905 0.0949 0.911 0.780
#> SD:mclust 4 0.791 0.849 0.922 0.1176 0.881 0.689
#> CV:mclust 4 0.930 0.935 0.964 0.0919 0.939 0.823
#> MAD:mclust 4 0.822 0.848 0.914 0.1128 0.915 0.761
#> ATC:mclust 4 0.677 0.774 0.861 0.1125 0.779 0.480
#> SD:kmeans 4 0.785 0.878 0.909 0.1489 0.857 0.641
#> CV:kmeans 4 0.765 0.856 0.902 0.1499 0.842 0.607
#> MAD:kmeans 4 0.691 0.732 0.810 0.1455 0.779 0.516
#> ATC:kmeans 4 0.698 0.614 0.789 0.1605 0.880 0.706
#> SD:pam 4 0.629 0.684 0.813 0.1076 0.908 0.753
#> CV:pam 4 0.584 0.660 0.766 0.1372 0.855 0.640
#> MAD:pam 4 0.611 0.590 0.792 0.1342 0.780 0.472
#> ATC:pam 4 0.759 0.785 0.854 0.1542 0.875 0.690
#> SD:hclust 4 0.703 0.776 0.871 0.1286 0.935 0.847
#> CV:hclust 4 0.629 0.763 0.848 0.0914 0.958 0.891
#> MAD:hclust 4 0.665 0.676 0.832 0.1022 0.951 0.877
#> ATC:hclust 4 0.822 0.818 0.917 0.3006 0.822 0.633
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.766 0.739 0.870 0.0603 0.926 0.726
#> CV:NMF 5 0.738 0.713 0.844 0.0591 0.902 0.642
#> MAD:NMF 5 0.729 0.716 0.854 0.0582 0.888 0.610
#> ATC:NMF 5 0.694 0.637 0.815 0.0505 0.957 0.846
#> SD:skmeans 5 0.768 0.701 0.857 0.0739 0.917 0.708
#> CV:skmeans 5 0.684 0.535 0.710 0.0644 0.905 0.672
#> MAD:skmeans 5 0.755 0.714 0.861 0.0741 0.902 0.663
#> ATC:skmeans 5 0.788 0.785 0.865 0.0771 0.904 0.728
#> SD:mclust 5 0.698 0.572 0.778 0.0831 0.939 0.805
#> CV:mclust 5 0.698 0.644 0.775 0.0865 0.966 0.881
#> MAD:mclust 5 0.665 0.624 0.783 0.0793 0.846 0.528
#> ATC:mclust 5 0.675 0.656 0.804 0.0693 0.953 0.838
#> SD:kmeans 5 0.783 0.811 0.868 0.0735 0.923 0.727
#> CV:kmeans 5 0.728 0.687 0.791 0.0720 0.923 0.722
#> MAD:kmeans 5 0.798 0.828 0.882 0.0677 0.905 0.669
#> ATC:kmeans 5 0.726 0.689 0.809 0.0810 0.858 0.568
#> SD:pam 5 0.863 0.830 0.913 0.0978 0.864 0.574
#> CV:pam 5 0.764 0.811 0.884 0.0873 0.869 0.583
#> MAD:pam 5 0.871 0.839 0.931 0.0847 0.891 0.619
#> ATC:pam 5 0.840 0.773 0.892 0.0845 0.909 0.700
#> SD:hclust 5 0.741 0.735 0.853 0.0402 0.994 0.983
#> CV:hclust 5 0.704 0.735 0.845 0.0467 0.993 0.980
#> MAD:hclust 5 0.685 0.641 0.795 0.0418 0.980 0.945
#> ATC:hclust 5 0.805 0.684 0.849 0.0622 0.905 0.752
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.734 0.651 0.814 0.0445 0.928 0.685
#> CV:NMF 6 0.722 0.619 0.801 0.0421 0.931 0.690
#> MAD:NMF 6 0.699 0.515 0.737 0.0456 0.919 0.664
#> ATC:NMF 6 0.710 0.665 0.804 0.0280 0.961 0.849
#> SD:skmeans 6 0.739 0.645 0.758 0.0347 0.969 0.857
#> CV:skmeans 6 0.665 0.521 0.740 0.0383 0.910 0.639
#> MAD:skmeans 6 0.724 0.620 0.784 0.0369 0.956 0.804
#> ATC:skmeans 6 0.752 0.723 0.850 0.0451 0.976 0.909
#> SD:mclust 6 0.722 0.546 0.727 0.0588 0.832 0.456
#> CV:mclust 6 0.752 0.585 0.773 0.0570 0.868 0.533
#> MAD:mclust 6 0.911 0.893 0.939 0.0608 0.911 0.626
#> ATC:mclust 6 0.730 0.607 0.769 0.0448 0.922 0.717
#> SD:kmeans 6 0.743 0.621 0.769 0.0442 0.958 0.811
#> CV:kmeans 6 0.712 0.617 0.791 0.0430 0.958 0.811
#> MAD:kmeans 6 0.752 0.603 0.749 0.0445 0.935 0.713
#> ATC:kmeans 6 0.745 0.744 0.834 0.0599 0.941 0.746
#> SD:pam 6 0.821 0.724 0.865 0.0313 0.983 0.918
#> CV:pam 6 0.761 0.699 0.846 0.0332 0.974 0.875
#> MAD:pam 6 0.830 0.773 0.889 0.0256 0.951 0.771
#> ATC:pam 6 0.886 0.797 0.895 0.0362 0.958 0.824
#> SD:hclust 6 0.701 0.715 0.819 0.0394 0.995 0.985
#> CV:hclust 6 0.725 0.653 0.807 0.0304 0.990 0.971
#> MAD:hclust 6 0.615 0.571 0.753 0.0468 0.941 0.829
#> ATC:hclust 6 0.841 0.737 0.864 0.0420 0.929 0.787
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 101 0.0265 1.84e-05 0.0111 0.001893 2
#> CV:NMF 102 0.0253 1.70e-05 0.0638 0.003847 2
#> MAD:NMF 101 0.0303 1.56e-05 0.0322 0.002815 2
#> ATC:NMF 97 0.3991 2.66e-06 0.5641 0.004550 2
#> SD:skmeans 103 0.0230 1.72e-05 0.0536 0.004798 2
#> CV:skmeans 102 0.0336 2.28e-05 0.0933 0.002490 2
#> MAD:skmeans 103 0.0230 1.72e-05 0.0536 0.004798 2
#> ATC:skmeans 103 0.1082 1.63e-05 0.1804 0.000624 2
#> SD:mclust 103 0.0131 8.80e-05 0.1561 0.001279 2
#> CV:mclust 102 0.0155 1.21e-04 0.1796 0.001943 2
#> MAD:mclust 101 0.0174 1.66e-04 0.2058 0.001488 2
#> ATC:mclust 99 0.0392 2.86e-04 0.4167 0.002768 2
#> SD:kmeans 103 0.0230 1.72e-05 0.0536 0.004798 2
#> CV:kmeans 102 0.0253 1.70e-05 0.0638 0.003847 2
#> MAD:kmeans 103 0.0230 1.72e-05 0.0536 0.004798 2
#> ATC:kmeans 101 0.6320 1.86e-05 0.8772 0.009359 2
#> SD:pam 94 0.0941 4.49e-05 0.1690 0.035763 2
#> CV:pam 93 0.1397 1.28e-05 0.3038 0.041575 2
#> MAD:pam 101 0.0862 2.55e-06 0.3983 0.042353 2
#> ATC:pam 98 0.5197 2.46e-05 0.8262 0.017480 2
#> SD:hclust 101 0.0304 2.56e-05 0.1565 0.020942 2
#> CV:hclust 99 0.0118 5.39e-05 0.1124 0.005424 2
#> MAD:hclust 97 0.0270 8.31e-06 0.2359 0.012107 2
#> ATC:hclust 100 0.3786 8.50e-04 0.4134 0.114708 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 94 0.01180 1.13e-06 2.77e-04 0.01352 3
#> CV:NMF 98 0.00679 3.92e-06 3.10e-03 0.01181 3
#> MAD:NMF 93 0.01795 8.19e-06 4.88e-04 0.00988 3
#> ATC:NMF 93 0.15545 2.06e-03 1.59e-01 0.01414 3
#> SD:skmeans 97 0.20009 1.93e-04 1.97e-03 0.01775 3
#> CV:skmeans 93 0.06525 5.50e-05 4.19e-05 0.00543 3
#> MAD:skmeans 100 0.25359 1.13e-04 4.48e-03 0.01893 3
#> ATC:skmeans 98 0.02929 2.27e-04 2.70e-02 0.02393 3
#> SD:mclust 91 0.32652 3.04e-04 7.91e-04 0.00231 3
#> CV:mclust 101 0.32760 4.23e-04 4.56e-04 0.01108 3
#> MAD:mclust 99 0.28614 2.89e-04 8.89e-04 0.01015 3
#> ATC:mclust 74 0.08191 2.34e-04 1.01e-02 0.00367 3
#> SD:kmeans 101 0.08943 1.60e-06 1.40e-04 0.01183 3
#> CV:kmeans 102 0.04262 1.96e-06 8.99e-05 0.00838 3
#> MAD:kmeans 41 NA NA NA NA 3
#> ATC:kmeans 103 0.11388 1.63e-05 2.15e-01 0.00862 3
#> SD:pam 98 0.28614 4.54e-05 2.10e-01 0.03341 3
#> CV:pam 46 NA NA NA NA 3
#> MAD:pam 100 0.26917 4.77e-05 1.87e-01 0.01690 3
#> ATC:pam 100 0.09710 7.42e-05 2.98e-01 0.02088 3
#> SD:hclust 83 0.02623 1.84e-06 6.71e-03 0.22578 3
#> CV:hclust 91 0.01809 5.05e-06 3.39e-03 0.07621 3
#> MAD:hclust 90 0.02263 3.66e-06 2.38e-03 0.07559 3
#> ATC:hclust 75 0.04318 1.91e-06 9.11e-01 0.27237 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 96 0.1810 1.16e-05 0.011889 0.01151 4
#> CV:NMF 95 0.2821 1.35e-04 0.022170 0.03191 4
#> MAD:NMF 95 0.2920 2.72e-05 0.069494 0.00593 4
#> ATC:NMF 98 0.3314 5.86e-03 0.027291 0.02103 4
#> SD:skmeans 100 0.4837 1.97e-05 0.001591 0.07063 4
#> CV:skmeans 91 0.3566 5.27e-05 0.013234 0.11266 4
#> MAD:skmeans 98 0.3562 4.21e-05 0.001850 0.06701 4
#> ATC:skmeans 91 0.1294 4.99e-03 0.128535 0.05384 4
#> SD:mclust 101 0.5038 1.18e-05 0.000835 0.00744 4
#> CV:mclust 103 0.3056 6.58e-06 0.000455 0.00687 4
#> MAD:mclust 101 0.2312 1.13e-05 0.000295 0.01346 4
#> ATC:mclust 96 0.2538 6.68e-04 0.473417 0.05020 4
#> SD:kmeans 102 0.3636 3.16e-05 0.000877 0.03888 4
#> CV:kmeans 97 0.2749 2.14e-04 0.005221 0.05687 4
#> MAD:kmeans 91 0.3553 2.07e-04 0.003369 0.11417 4
#> ATC:kmeans 73 0.0442 9.98e-06 0.241808 0.00829 4
#> SD:pam 95 0.3397 1.48e-04 0.000574 0.17560 4
#> CV:pam 93 0.2162 1.31e-05 0.005271 0.10266 4
#> MAD:pam 73 0.3417 1.65e-05 0.013931 0.46803 4
#> ATC:pam 97 0.3844 1.65e-04 0.165171 0.02511 4
#> SD:hclust 91 0.0314 1.58e-06 0.004650 0.07314 4
#> CV:hclust 91 0.0288 3.93e-06 0.016068 0.06811 4
#> MAD:hclust 79 0.0859 1.30e-04 0.015371 0.02023 4
#> ATC:hclust 96 0.0978 7.07e-06 0.391517 0.01633 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 90 0.2371 6.82e-05 0.01635 0.06837 5
#> CV:NMF 87 0.2927 5.17e-04 0.02598 0.01389 5
#> MAD:NMF 87 0.4619 1.67e-04 0.10046 0.01116 5
#> ATC:NMF 81 0.3848 5.14e-04 0.09135 0.03383 5
#> SD:skmeans 90 0.6313 8.60e-04 0.00268 0.26602 5
#> CV:skmeans 63 0.7114 1.86e-04 0.00345 0.00363 5
#> MAD:skmeans 88 0.4508 2.78e-04 0.00322 0.10260 5
#> ATC:skmeans 89 0.3078 1.04e-02 0.08178 0.11425 5
#> SD:mclust 69 0.5891 5.30e-05 0.06205 0.19518 5
#> CV:mclust 86 0.7504 1.77e-04 0.00145 0.06903 5
#> MAD:mclust 78 0.5721 1.38e-04 0.03604 0.20844 5
#> ATC:mclust 89 0.0160 2.49e-03 0.17891 0.26737 5
#> SD:kmeans 95 0.4167 7.16e-04 0.00159 0.09799 5
#> CV:kmeans 85 0.2519 3.23e-03 0.01475 0.11342 5
#> MAD:kmeans 99 0.4842 3.18e-04 0.00278 0.05365 5
#> ATC:kmeans 81 0.0875 7.59e-04 0.12038 0.19817 5
#> SD:pam 93 0.3555 2.84e-05 0.00357 0.01825 5
#> CV:pam 93 0.2375 4.73e-07 0.01197 0.02728 5
#> MAD:pam 95 0.1875 1.52e-05 0.00391 0.02425 5
#> ATC:pam 89 0.4036 4.85e-03 0.22838 0.03442 5
#> SD:hclust 90 0.1585 7.44e-05 0.01154 0.05450 5
#> CV:hclust 92 0.0904 4.97e-05 0.05658 0.06735 5
#> MAD:hclust 68 0.1674 4.22e-04 0.00715 0.02805 5
#> ATC:hclust 75 0.0293 7.75e-06 0.05707 0.09307 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 85 0.3847 1.63e-03 1.78e-03 0.02574 6
#> CV:NMF 75 0.4161 1.14e-03 1.51e-03 0.11868 6
#> MAD:NMF 61 0.4986 2.72e-04 8.66e-03 0.07384 6
#> ATC:NMF 84 0.1385 1.02e-03 7.17e-02 0.09806 6
#> SD:skmeans 81 0.5889 1.17e-03 1.12e-02 0.06547 6
#> CV:skmeans 68 0.7795 3.72e-03 5.71e-02 0.24729 6
#> MAD:skmeans 78 0.7174 7.20e-05 7.65e-03 0.19579 6
#> ATC:skmeans 88 0.5382 3.69e-04 1.45e-01 0.21260 6
#> SD:mclust 71 0.5338 6.80e-05 6.63e-03 0.10925 6
#> CV:mclust 71 0.2112 2.25e-04 3.40e-02 0.00777 6
#> MAD:mclust 101 0.7063 1.12e-04 1.07e-02 0.06549 6
#> ATC:mclust 68 0.0392 1.05e-03 2.46e-02 0.62787 6
#> SD:kmeans 76 0.0717 1.51e-02 3.56e-05 0.00330 6
#> CV:kmeans 75 0.1607 2.93e-03 8.47e-02 0.17243 6
#> MAD:kmeans 79 0.3951 1.70e-02 1.43e-02 0.16220 6
#> ATC:kmeans 89 0.1001 4.48e-04 2.44e-02 0.44023 6
#> SD:pam 89 0.3101 8.17e-06 1.27e-02 0.04023 6
#> CV:pam 86 0.2572 3.44e-06 4.85e-02 0.04661 6
#> MAD:pam 91 0.4196 8.17e-05 6.20e-03 0.16150 6
#> ATC:pam 93 0.4040 3.38e-04 5.40e-02 0.03288 6
#> SD:hclust 91 0.0323 3.13e-05 5.07e-02 0.10245 6
#> CV:hclust 90 0.0427 2.25e-05 2.52e-02 0.07104 6
#> MAD:hclust 76 0.3255 1.55e-04 8.14e-03 0.06878 6
#> ATC:hclust 82 0.0164 1.27e-06 1.12e-02 0.03817 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.482 0.855 0.908 0.4502 0.506 0.506
#> 3 3 0.610 0.699 0.864 0.2604 0.910 0.821
#> 4 4 0.703 0.776 0.871 0.1286 0.935 0.847
#> 5 5 0.741 0.735 0.853 0.0402 0.994 0.983
#> 6 6 0.701 0.715 0.819 0.0394 0.995 0.985
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
#> GSM549289 1 0.8608 0.528 0.716 0.284
#> GSM549291 2 0.9427 0.659 0.360 0.640
#> GSM549274 2 0.5946 0.873 0.144 0.856
#> GSM750738 2 0.6438 0.865 0.164 0.836
#> GSM750748 1 0.0000 0.942 1.000 0.000
#> GSM549240 1 0.0376 0.940 0.996 0.004
#> GSM549279 1 0.5842 0.812 0.860 0.140
#> GSM549294 2 0.5629 0.876 0.132 0.868
#> GSM549300 2 0.1633 0.840 0.024 0.976
#> GSM549303 2 0.0000 0.826 0.000 1.000
#> GSM549309 2 0.0376 0.826 0.004 0.996
#> GSM750753 2 0.6048 0.871 0.148 0.852
#> GSM750752 2 0.9209 0.700 0.336 0.664
#> GSM549304 2 0.6438 0.864 0.164 0.836
#> GSM549305 2 0.5294 0.876 0.120 0.880
#> GSM549307 2 0.1633 0.840 0.024 0.976
#> GSM549306 2 0.0376 0.828 0.004 0.996
#> GSM549308 2 0.0000 0.826 0.000 1.000
#> GSM549233 1 0.0000 0.942 1.000 0.000
#> GSM549234 1 0.3274 0.904 0.940 0.060
#> GSM549250 1 0.0000 0.942 1.000 0.000
#> GSM549287 2 0.9286 0.688 0.344 0.656
#> GSM750735 1 0.0000 0.942 1.000 0.000
#> GSM750736 1 0.0000 0.942 1.000 0.000
#> GSM750749 1 0.0938 0.936 0.988 0.012
#> GSM549230 1 0.0000 0.942 1.000 0.000
#> GSM549231 1 0.0000 0.942 1.000 0.000
#> GSM549237 1 0.0000 0.942 1.000 0.000
#> GSM549254 1 0.8763 0.501 0.704 0.296
#> GSM750734 1 0.0000 0.942 1.000 0.000
#> GSM549271 2 0.9209 0.698 0.336 0.664
#> GSM549232 1 0.3274 0.904 0.940 0.060
#> GSM549246 1 0.1184 0.935 0.984 0.016
#> GSM549248 1 0.0000 0.942 1.000 0.000
#> GSM549255 1 0.3274 0.904 0.940 0.060
#> GSM750746 1 0.0000 0.942 1.000 0.000
#> GSM549259 1 0.0000 0.942 1.000 0.000
#> GSM549269 2 0.5408 0.876 0.124 0.876
#> GSM549273 2 0.0000 0.826 0.000 1.000
#> GSM549299 2 0.6438 0.864 0.164 0.836
#> GSM549301 2 0.0000 0.826 0.000 1.000
#> GSM549310 2 0.9209 0.700 0.336 0.664
#> GSM549311 2 0.0000 0.826 0.000 1.000
#> GSM549302 2 0.5946 0.873 0.144 0.856
#> GSM549235 1 0.0000 0.942 1.000 0.000
#> GSM549245 1 0.3274 0.904 0.940 0.060
#> GSM549265 1 0.2603 0.918 0.956 0.044
#> GSM549282 2 0.8267 0.785 0.260 0.740
#> GSM549296 2 0.9209 0.700 0.336 0.664
#> GSM750739 1 0.0000 0.942 1.000 0.000
#> GSM750742 1 0.0000 0.942 1.000 0.000
#> GSM750744 1 0.0000 0.942 1.000 0.000
#> GSM750750 2 0.8267 0.785 0.260 0.740
#> GSM549242 1 0.0000 0.942 1.000 0.000
#> GSM549252 1 0.2423 0.920 0.960 0.040
#> GSM549253 1 0.0000 0.942 1.000 0.000
#> GSM549256 1 0.0000 0.942 1.000 0.000
#> GSM549257 1 0.3274 0.904 0.940 0.060
#> GSM549263 1 0.0000 0.942 1.000 0.000
#> GSM549267 2 0.9286 0.688 0.344 0.656
#> GSM750745 1 0.0000 0.942 1.000 0.000
#> GSM549239 1 0.0000 0.942 1.000 0.000
#> GSM549244 1 0.2603 0.917 0.956 0.044
#> GSM549249 1 0.1843 0.928 0.972 0.028
#> GSM549260 1 0.0000 0.942 1.000 0.000
#> GSM549266 1 0.4939 0.852 0.892 0.108
#> GSM549293 2 0.5946 0.873 0.144 0.856
#> GSM549236 1 0.0000 0.942 1.000 0.000
#> GSM549238 1 0.1843 0.928 0.972 0.028
#> GSM549251 1 0.0000 0.942 1.000 0.000
#> GSM549258 1 0.0000 0.942 1.000 0.000
#> GSM549264 1 0.0000 0.942 1.000 0.000
#> GSM549243 1 0.0000 0.942 1.000 0.000
#> GSM549262 1 0.0000 0.942 1.000 0.000
#> GSM549278 1 0.9552 0.236 0.624 0.376
#> GSM549283 1 0.9358 0.346 0.648 0.352
#> GSM549298 2 0.0000 0.826 0.000 1.000
#> GSM750741 1 0.0000 0.942 1.000 0.000
#> GSM549286 2 0.5408 0.876 0.124 0.876
#> GSM549241 1 0.0000 0.942 1.000 0.000
#> GSM549247 1 0.0376 0.940 0.996 0.004
#> GSM549261 1 0.0000 0.942 1.000 0.000
#> GSM549270 2 0.5178 0.876 0.116 0.884
#> GSM549277 2 0.4161 0.867 0.084 0.916
#> GSM549280 2 0.4815 0.873 0.104 0.896
#> GSM549281 1 0.6887 0.745 0.816 0.184
#> GSM549285 2 0.9460 0.629 0.364 0.636
#> GSM549288 2 0.4161 0.867 0.084 0.916
#> GSM549292 2 0.5408 0.876 0.124 0.876
#> GSM549295 2 0.1843 0.842 0.028 0.972
#> GSM549297 2 0.4161 0.867 0.084 0.916
#> GSM750743 1 0.0000 0.942 1.000 0.000
#> GSM549268 1 0.6887 0.745 0.816 0.184
#> GSM549290 2 0.9286 0.689 0.344 0.656
#> GSM549272 2 0.5408 0.876 0.124 0.876
#> GSM549276 2 0.5178 0.876 0.116 0.884
#> GSM549275 1 0.3584 0.895 0.932 0.068
#> GSM549284 2 0.6438 0.865 0.164 0.836
#> GSM750737 1 0.7528 0.678 0.784 0.216
#> GSM750740 1 0.0000 0.942 1.000 0.000
#> GSM750747 1 0.0000 0.942 1.000 0.000
#> GSM750751 2 0.5519 0.876 0.128 0.872
#> GSM750754 2 0.9393 0.667 0.356 0.644
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.7880 0.522 0.668 0.168 0.164
#> GSM549291 3 0.9751 0.311 0.252 0.308 0.440
#> GSM549274 2 0.0892 0.746 0.020 0.980 0.000
#> GSM750738 2 0.2173 0.726 0.048 0.944 0.008
#> GSM750748 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549240 1 0.0424 0.923 0.992 0.008 0.000
#> GSM549279 1 0.4654 0.726 0.792 0.208 0.000
#> GSM549294 2 0.1170 0.748 0.008 0.976 0.016
#> GSM549300 3 0.5859 0.308 0.000 0.344 0.656
#> GSM549303 3 0.1411 0.572 0.000 0.036 0.964
#> GSM549309 3 0.1163 0.571 0.000 0.028 0.972
#> GSM750753 2 0.4196 0.693 0.024 0.864 0.112
#> GSM750752 2 0.9776 -0.256 0.232 0.388 0.380
#> GSM549304 2 0.2550 0.734 0.040 0.936 0.024
#> GSM549305 2 0.0892 0.744 0.000 0.980 0.020
#> GSM549307 3 0.5905 0.288 0.000 0.352 0.648
#> GSM549306 3 0.3482 0.531 0.000 0.128 0.872
#> GSM549308 3 0.1643 0.572 0.000 0.044 0.956
#> GSM549233 1 0.0237 0.925 0.996 0.004 0.000
#> GSM549234 1 0.3649 0.867 0.896 0.068 0.036
#> GSM549250 1 0.0983 0.919 0.980 0.004 0.016
#> GSM549287 3 0.9700 0.329 0.240 0.312 0.448
#> GSM750735 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750749 1 0.1315 0.913 0.972 0.020 0.008
#> GSM549230 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549254 1 0.8038 0.408 0.620 0.280 0.100
#> GSM750734 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549271 3 0.9714 0.321 0.236 0.324 0.440
#> GSM549232 1 0.3649 0.867 0.896 0.068 0.036
#> GSM549246 1 0.1491 0.913 0.968 0.016 0.016
#> GSM549248 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549255 1 0.3649 0.867 0.896 0.068 0.036
#> GSM750746 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.743 0.000 1.000 0.000
#> GSM549273 3 0.1411 0.572 0.000 0.036 0.964
#> GSM549299 2 0.2550 0.734 0.040 0.936 0.024
#> GSM549301 3 0.1860 0.570 0.000 0.052 0.948
#> GSM549310 2 0.9776 -0.256 0.232 0.388 0.380
#> GSM549311 3 0.1289 0.573 0.000 0.032 0.968
#> GSM549302 2 0.0892 0.746 0.020 0.980 0.000
#> GSM549235 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549245 1 0.3649 0.867 0.896 0.068 0.036
#> GSM549265 1 0.2926 0.890 0.924 0.040 0.036
#> GSM549282 3 0.9211 0.363 0.176 0.312 0.512
#> GSM549296 2 0.9776 -0.256 0.232 0.388 0.380
#> GSM750739 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750750 3 0.9211 0.363 0.176 0.312 0.512
#> GSM549242 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549252 1 0.3134 0.883 0.916 0.052 0.032
#> GSM549253 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549257 1 0.3649 0.867 0.896 0.068 0.036
#> GSM549263 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549267 3 0.9700 0.329 0.240 0.312 0.448
#> GSM750745 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549244 1 0.3253 0.880 0.912 0.052 0.036
#> GSM549249 1 0.2793 0.891 0.928 0.044 0.028
#> GSM549260 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549266 1 0.4121 0.780 0.832 0.168 0.000
#> GSM549293 2 0.0892 0.746 0.020 0.980 0.000
#> GSM549236 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549238 1 0.2663 0.893 0.932 0.044 0.024
#> GSM549251 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549264 1 0.0237 0.925 0.996 0.004 0.000
#> GSM549243 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549278 1 0.9153 0.124 0.524 0.300 0.176
#> GSM549283 1 0.8694 0.303 0.580 0.268 0.152
#> GSM549298 3 0.1860 0.571 0.000 0.052 0.948
#> GSM750741 1 0.0237 0.924 0.996 0.004 0.000
#> GSM549286 2 0.0000 0.743 0.000 1.000 0.000
#> GSM549241 1 0.0237 0.924 0.996 0.004 0.000
#> GSM549247 1 0.0424 0.923 0.992 0.008 0.000
#> GSM549261 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549270 2 0.2711 0.711 0.000 0.912 0.088
#> GSM549277 2 0.6075 0.441 0.008 0.676 0.316
#> GSM549280 2 0.6473 0.399 0.016 0.652 0.332
#> GSM549281 1 0.5581 0.724 0.788 0.176 0.036
#> GSM549285 2 0.9931 -0.177 0.288 0.388 0.324
#> GSM549288 2 0.5656 0.525 0.008 0.728 0.264
#> GSM549292 2 0.0000 0.743 0.000 1.000 0.000
#> GSM549295 3 0.6244 0.124 0.000 0.440 0.560
#> GSM549297 2 0.5541 0.543 0.008 0.740 0.252
#> GSM750743 1 0.0000 0.926 1.000 0.000 0.000
#> GSM549268 1 0.5581 0.724 0.788 0.176 0.036
#> GSM549290 3 0.9764 0.327 0.252 0.312 0.436
#> GSM549272 2 0.0000 0.743 0.000 1.000 0.000
#> GSM549276 2 0.2448 0.717 0.000 0.924 0.076
#> GSM549275 1 0.2772 0.874 0.916 0.080 0.004
#> GSM549284 2 0.4058 0.683 0.044 0.880 0.076
#> GSM750737 1 0.6511 0.662 0.748 0.180 0.072
#> GSM750740 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.926 1.000 0.000 0.000
#> GSM750751 2 0.0829 0.747 0.004 0.984 0.012
#> GSM750754 3 0.9731 0.315 0.248 0.308 0.444
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 1 0.4907 0.276 0.580 0.000 0.000 0.420
#> GSM549291 4 0.3761 0.741 0.080 0.000 0.068 0.852
#> GSM549274 2 0.0707 0.868 0.000 0.980 0.000 0.020
#> GSM750738 2 0.2465 0.841 0.020 0.924 0.012 0.044
#> GSM750748 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549240 1 0.1575 0.893 0.956 0.004 0.012 0.028
#> GSM549279 1 0.6414 0.594 0.700 0.180 0.040 0.080
#> GSM549294 2 0.1297 0.870 0.000 0.964 0.020 0.016
#> GSM549300 3 0.5906 0.522 0.000 0.292 0.644 0.064
#> GSM549303 3 0.3610 0.731 0.000 0.000 0.800 0.200
#> GSM549309 3 0.3873 0.722 0.000 0.000 0.772 0.228
#> GSM750753 2 0.4001 0.808 0.004 0.840 0.108 0.048
#> GSM750752 4 0.4511 0.722 0.072 0.068 0.028 0.832
#> GSM549304 2 0.2497 0.857 0.016 0.924 0.020 0.040
#> GSM549305 2 0.0817 0.869 0.000 0.976 0.024 0.000
#> GSM549307 3 0.5657 0.492 0.000 0.312 0.644 0.044
#> GSM549306 3 0.3959 0.740 0.000 0.068 0.840 0.092
#> GSM549308 3 0.2345 0.755 0.000 0.000 0.900 0.100
#> GSM549233 1 0.0707 0.908 0.980 0.000 0.000 0.020
#> GSM549234 1 0.3219 0.806 0.836 0.000 0.000 0.164
#> GSM549250 1 0.1389 0.896 0.952 0.000 0.000 0.048
#> GSM549287 4 0.4215 0.736 0.072 0.000 0.104 0.824
#> GSM750735 1 0.0469 0.908 0.988 0.000 0.000 0.012
#> GSM750736 1 0.0469 0.908 0.988 0.000 0.000 0.012
#> GSM750749 1 0.1305 0.895 0.960 0.004 0.000 0.036
#> GSM549230 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549231 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549237 1 0.0336 0.909 0.992 0.000 0.000 0.008
#> GSM549254 4 0.5404 0.105 0.476 0.012 0.000 0.512
#> GSM750734 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM549271 4 0.4752 0.725 0.068 0.008 0.124 0.800
#> GSM549232 1 0.3219 0.806 0.836 0.000 0.000 0.164
#> GSM549246 1 0.1637 0.889 0.940 0.000 0.000 0.060
#> GSM549248 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549255 1 0.3219 0.806 0.836 0.000 0.000 0.164
#> GSM750746 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549259 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549269 2 0.0376 0.868 0.000 0.992 0.004 0.004
#> GSM549273 3 0.3528 0.732 0.000 0.000 0.808 0.192
#> GSM549299 2 0.2497 0.857 0.016 0.924 0.020 0.040
#> GSM549301 3 0.2197 0.756 0.000 0.004 0.916 0.080
#> GSM549310 4 0.4511 0.722 0.072 0.068 0.028 0.832
#> GSM549311 3 0.3649 0.727 0.000 0.000 0.796 0.204
#> GSM549302 2 0.0707 0.868 0.000 0.980 0.000 0.020
#> GSM549235 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549245 1 0.3219 0.806 0.836 0.000 0.000 0.164
#> GSM549265 1 0.2647 0.851 0.880 0.000 0.000 0.120
#> GSM549282 4 0.5542 0.457 0.016 0.012 0.328 0.644
#> GSM549296 4 0.4511 0.722 0.072 0.068 0.028 0.832
#> GSM750739 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM750742 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM750744 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM750750 4 0.5542 0.457 0.016 0.012 0.328 0.644
#> GSM549242 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549252 1 0.2868 0.833 0.864 0.000 0.000 0.136
#> GSM549253 1 0.0336 0.910 0.992 0.000 0.000 0.008
#> GSM549256 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549257 1 0.3219 0.806 0.836 0.000 0.000 0.164
#> GSM549263 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549267 4 0.4215 0.736 0.072 0.000 0.104 0.824
#> GSM750745 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM549244 1 0.2921 0.830 0.860 0.000 0.000 0.140
#> GSM549249 1 0.2704 0.844 0.876 0.000 0.000 0.124
#> GSM549260 1 0.0376 0.909 0.992 0.000 0.004 0.004
#> GSM549266 1 0.5587 0.692 0.764 0.136 0.040 0.060
#> GSM549293 2 0.0707 0.868 0.000 0.980 0.000 0.020
#> GSM549236 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549238 1 0.2647 0.847 0.880 0.000 0.000 0.120
#> GSM549251 1 0.0336 0.910 0.992 0.000 0.000 0.008
#> GSM549258 1 0.0376 0.909 0.992 0.000 0.004 0.004
#> GSM549264 1 0.0592 0.909 0.984 0.000 0.000 0.016
#> GSM549243 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549262 1 0.0469 0.910 0.988 0.000 0.000 0.012
#> GSM549278 4 0.4889 0.460 0.360 0.004 0.000 0.636
#> GSM549283 1 0.9001 0.116 0.488 0.212 0.156 0.144
#> GSM549298 3 0.2401 0.757 0.000 0.004 0.904 0.092
#> GSM750741 1 0.0967 0.905 0.976 0.004 0.004 0.016
#> GSM549286 2 0.0376 0.868 0.000 0.992 0.004 0.004
#> GSM549241 1 0.0564 0.909 0.988 0.004 0.004 0.004
#> GSM549247 1 0.1575 0.893 0.956 0.004 0.012 0.028
#> GSM549261 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM549270 2 0.2915 0.830 0.000 0.892 0.080 0.028
#> GSM549277 2 0.6548 0.439 0.000 0.592 0.304 0.104
#> GSM549280 2 0.6240 0.435 0.000 0.604 0.320 0.076
#> GSM549281 1 0.6582 0.606 0.708 0.108 0.060 0.124
#> GSM549285 4 0.8238 0.343 0.084 0.108 0.280 0.528
#> GSM549288 2 0.5968 0.564 0.000 0.664 0.252 0.084
#> GSM549292 2 0.0376 0.868 0.000 0.992 0.004 0.004
#> GSM549295 3 0.5847 0.261 0.000 0.404 0.560 0.036
#> GSM549297 2 0.5881 0.582 0.000 0.676 0.240 0.084
#> GSM750743 1 0.0000 0.910 1.000 0.000 0.000 0.000
#> GSM549268 1 0.6582 0.606 0.708 0.108 0.060 0.124
#> GSM549290 4 0.4946 0.719 0.088 0.004 0.124 0.784
#> GSM549272 2 0.0376 0.868 0.000 0.992 0.004 0.004
#> GSM549276 2 0.2450 0.841 0.000 0.912 0.072 0.016
#> GSM549275 1 0.4314 0.798 0.844 0.064 0.032 0.060
#> GSM549284 2 0.3796 0.791 0.020 0.864 0.036 0.080
#> GSM750737 1 0.4957 0.482 0.668 0.012 0.000 0.320
#> GSM750740 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM750747 1 0.0188 0.910 0.996 0.000 0.004 0.000
#> GSM750751 2 0.1059 0.871 0.000 0.972 0.016 0.012
#> GSM750754 4 0.3764 0.742 0.076 0.000 0.072 0.852
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 1 0.4882 0.220 0.532 0.000 0.000 0.444 0.024
#> GSM549291 4 0.3052 0.591 0.008 0.000 0.032 0.868 0.092
#> GSM549274 2 0.0992 0.853 0.000 0.968 0.000 0.008 0.024
#> GSM750738 2 0.2987 0.814 0.020 0.888 0.004 0.044 0.044
#> GSM750748 1 0.0162 0.901 0.996 0.000 0.000 0.000 0.004
#> GSM549240 1 0.1365 0.884 0.952 0.004 0.000 0.004 0.040
#> GSM549279 1 0.5708 0.605 0.692 0.164 0.004 0.028 0.112
#> GSM549294 2 0.1419 0.855 0.000 0.956 0.016 0.012 0.016
#> GSM549300 3 0.4170 0.384 0.000 0.272 0.712 0.004 0.012
#> GSM549303 3 0.5052 0.519 0.000 0.000 0.612 0.048 0.340
#> GSM549309 3 0.5811 0.469 0.000 0.000 0.568 0.116 0.316
#> GSM750753 2 0.3982 0.785 0.004 0.812 0.136 0.020 0.028
#> GSM750752 4 0.2104 0.581 0.000 0.044 0.008 0.924 0.024
#> GSM549304 2 0.2574 0.843 0.016 0.912 0.020 0.016 0.036
#> GSM549305 2 0.1106 0.855 0.000 0.964 0.024 0.000 0.012
#> GSM549307 3 0.4283 0.380 0.000 0.292 0.692 0.004 0.012
#> GSM549306 3 0.1872 0.525 0.000 0.052 0.928 0.020 0.000
#> GSM549308 3 0.1992 0.555 0.000 0.000 0.924 0.032 0.044
#> GSM549233 1 0.1012 0.899 0.968 0.000 0.000 0.012 0.020
#> GSM549234 1 0.3565 0.782 0.800 0.000 0.000 0.176 0.024
#> GSM549250 1 0.1741 0.886 0.936 0.000 0.000 0.040 0.024
#> GSM549287 4 0.4136 0.504 0.000 0.000 0.048 0.764 0.188
#> GSM750735 1 0.0404 0.900 0.988 0.000 0.000 0.012 0.000
#> GSM750736 1 0.0404 0.900 0.988 0.000 0.000 0.012 0.000
#> GSM750749 1 0.1386 0.887 0.952 0.000 0.000 0.016 0.032
#> GSM549230 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549231 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549237 1 0.0579 0.901 0.984 0.000 0.000 0.008 0.008
#> GSM549254 4 0.4666 0.188 0.412 0.000 0.000 0.572 0.016
#> GSM750734 1 0.0000 0.901 1.000 0.000 0.000 0.000 0.000
#> GSM549271 4 0.4767 0.478 0.004 0.004 0.116 0.752 0.124
#> GSM549232 1 0.3565 0.782 0.800 0.000 0.000 0.176 0.024
#> GSM549246 1 0.2104 0.876 0.916 0.000 0.000 0.060 0.024
#> GSM549248 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549255 1 0.3565 0.782 0.800 0.000 0.000 0.176 0.024
#> GSM750746 1 0.0162 0.901 0.996 0.000 0.000 0.000 0.004
#> GSM549259 1 0.0162 0.901 0.996 0.000 0.000 0.000 0.004
#> GSM549269 2 0.0955 0.851 0.000 0.968 0.004 0.000 0.028
#> GSM549273 3 0.4836 0.522 0.000 0.000 0.612 0.032 0.356
#> GSM549299 2 0.2574 0.843 0.016 0.912 0.020 0.016 0.036
#> GSM549301 3 0.2103 0.555 0.000 0.004 0.920 0.020 0.056
#> GSM549310 4 0.2104 0.581 0.000 0.044 0.008 0.924 0.024
#> GSM549311 3 0.4849 0.518 0.000 0.000 0.608 0.032 0.360
#> GSM549302 2 0.0898 0.854 0.000 0.972 0.000 0.008 0.020
#> GSM549235 1 0.0290 0.900 0.992 0.000 0.000 0.000 0.008
#> GSM549245 1 0.3565 0.782 0.800 0.000 0.000 0.176 0.024
#> GSM549265 1 0.3146 0.827 0.844 0.000 0.000 0.128 0.028
#> GSM549282 5 0.6620 0.771 0.000 0.004 0.312 0.208 0.476
#> GSM549296 4 0.2104 0.581 0.000 0.044 0.008 0.924 0.024
#> GSM750739 1 0.0290 0.901 0.992 0.000 0.000 0.000 0.008
#> GSM750742 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM750744 1 0.0000 0.901 1.000 0.000 0.000 0.000 0.000
#> GSM750750 5 0.6630 0.773 0.000 0.004 0.316 0.208 0.472
#> GSM549242 1 0.0566 0.901 0.984 0.000 0.000 0.004 0.012
#> GSM549252 1 0.3284 0.809 0.828 0.000 0.000 0.148 0.024
#> GSM549253 1 0.0703 0.900 0.976 0.000 0.000 0.000 0.024
#> GSM549256 1 0.0566 0.901 0.984 0.000 0.000 0.004 0.012
#> GSM549257 1 0.3565 0.782 0.800 0.000 0.000 0.176 0.024
#> GSM549263 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549267 4 0.4101 0.510 0.000 0.000 0.048 0.768 0.184
#> GSM750745 1 0.0000 0.901 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.901 1.000 0.000 0.000 0.000 0.000
#> GSM549244 1 0.3326 0.805 0.824 0.000 0.000 0.152 0.024
#> GSM549249 1 0.3152 0.820 0.840 0.000 0.000 0.136 0.024
#> GSM549260 1 0.0404 0.899 0.988 0.000 0.000 0.000 0.012
#> GSM549266 1 0.4843 0.700 0.760 0.128 0.004 0.016 0.092
#> GSM549293 2 0.0898 0.854 0.000 0.972 0.000 0.008 0.020
#> GSM549236 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549238 1 0.3193 0.821 0.840 0.000 0.000 0.132 0.028
#> GSM549251 1 0.0703 0.900 0.976 0.000 0.000 0.000 0.024
#> GSM549258 1 0.0404 0.899 0.988 0.000 0.000 0.000 0.012
#> GSM549264 1 0.0955 0.899 0.968 0.000 0.000 0.004 0.028
#> GSM549243 1 0.0404 0.902 0.988 0.000 0.000 0.000 0.012
#> GSM549262 1 0.0865 0.900 0.972 0.000 0.000 0.004 0.024
#> GSM549278 4 0.4206 0.324 0.288 0.000 0.000 0.696 0.016
#> GSM549283 1 0.8354 0.115 0.480 0.188 0.136 0.032 0.164
#> GSM549298 3 0.1493 0.555 0.000 0.000 0.948 0.024 0.028
#> GSM750741 1 0.0960 0.895 0.972 0.004 0.000 0.008 0.016
#> GSM549286 2 0.0955 0.851 0.000 0.968 0.004 0.000 0.028
#> GSM549241 1 0.0566 0.899 0.984 0.004 0.000 0.000 0.012
#> GSM549247 1 0.1365 0.884 0.952 0.004 0.000 0.004 0.040
#> GSM549261 1 0.0290 0.900 0.992 0.000 0.000 0.000 0.008
#> GSM549270 2 0.2733 0.812 0.000 0.872 0.112 0.012 0.004
#> GSM549277 2 0.5916 0.427 0.000 0.564 0.344 0.016 0.076
#> GSM549280 2 0.5489 0.438 0.000 0.580 0.364 0.024 0.032
#> GSM549281 1 0.5950 0.616 0.700 0.096 0.028 0.028 0.148
#> GSM549285 5 0.7790 0.552 0.060 0.080 0.296 0.064 0.500
#> GSM549288 2 0.5349 0.545 0.000 0.636 0.300 0.016 0.048
#> GSM549292 2 0.0955 0.851 0.000 0.968 0.004 0.000 0.028
#> GSM549295 3 0.4299 0.221 0.000 0.388 0.608 0.004 0.000
#> GSM549297 2 0.5334 0.565 0.000 0.648 0.284 0.016 0.052
#> GSM750743 1 0.0000 0.901 1.000 0.000 0.000 0.000 0.000
#> GSM549268 1 0.5950 0.616 0.700 0.096 0.028 0.028 0.148
#> GSM549290 4 0.5338 0.187 0.016 0.000 0.044 0.628 0.312
#> GSM549272 2 0.0955 0.851 0.000 0.968 0.004 0.000 0.028
#> GSM549276 2 0.2463 0.822 0.000 0.888 0.100 0.004 0.008
#> GSM549275 1 0.3620 0.788 0.832 0.048 0.000 0.008 0.112
#> GSM549284 2 0.3978 0.772 0.020 0.840 0.020 0.056 0.064
#> GSM750737 1 0.4451 0.479 0.644 0.000 0.000 0.340 0.016
#> GSM750740 1 0.0290 0.900 0.992 0.000 0.000 0.000 0.008
#> GSM750747 1 0.0290 0.900 0.992 0.000 0.000 0.000 0.008
#> GSM750751 2 0.1087 0.855 0.000 0.968 0.016 0.008 0.008
#> GSM750754 4 0.3222 0.583 0.004 0.000 0.036 0.852 0.108
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 1 0.5066 0.1865 0.496 0.000 0.004 0.436 0.000 0.064
#> GSM549291 4 0.3011 0.6182 0.004 0.000 0.000 0.800 0.004 0.192
#> GSM549274 2 0.1151 0.7856 0.000 0.956 0.032 0.000 0.000 0.012
#> GSM750738 2 0.4316 0.6935 0.012 0.792 0.096 0.060 0.004 0.036
#> GSM750748 1 0.0520 0.8670 0.984 0.000 0.008 0.000 0.000 0.008
#> GSM549240 1 0.2265 0.8335 0.896 0.000 0.052 0.000 0.000 0.052
#> GSM549279 1 0.6170 0.4867 0.592 0.124 0.192 0.000 0.000 0.092
#> GSM549294 2 0.1866 0.7823 0.000 0.908 0.084 0.000 0.000 0.008
#> GSM549300 3 0.5634 0.6081 0.000 0.216 0.608 0.000 0.152 0.024
#> GSM549303 5 0.1405 0.9224 0.000 0.000 0.004 0.024 0.948 0.024
#> GSM549309 5 0.2944 0.8425 0.000 0.000 0.004 0.072 0.856 0.068
#> GSM750753 2 0.3534 0.6858 0.000 0.740 0.244 0.000 0.000 0.016
#> GSM750752 4 0.0912 0.6143 0.000 0.004 0.008 0.972 0.012 0.004
#> GSM549304 2 0.2882 0.7669 0.004 0.848 0.120 0.000 0.000 0.028
#> GSM549305 2 0.1444 0.7855 0.000 0.928 0.072 0.000 0.000 0.000
#> GSM549307 3 0.5541 0.5994 0.000 0.236 0.608 0.000 0.136 0.020
#> GSM549306 3 0.5151 0.6248 0.000 0.044 0.620 0.000 0.296 0.040
#> GSM549308 3 0.5014 0.5228 0.000 0.000 0.544 0.008 0.392 0.056
#> GSM549233 1 0.1434 0.8639 0.940 0.000 0.000 0.012 0.000 0.048
#> GSM549234 1 0.3925 0.7495 0.764 0.000 0.004 0.168 0.000 0.064
#> GSM549250 1 0.2240 0.8518 0.904 0.000 0.008 0.032 0.000 0.056
#> GSM549287 4 0.3804 0.5185 0.000 0.000 0.000 0.656 0.008 0.336
#> GSM750735 1 0.1176 0.8639 0.956 0.000 0.024 0.000 0.000 0.020
#> GSM750736 1 0.1176 0.8639 0.956 0.000 0.024 0.000 0.000 0.020
#> GSM750749 1 0.2594 0.8295 0.880 0.000 0.056 0.004 0.000 0.060
#> GSM549230 1 0.1333 0.8642 0.944 0.000 0.008 0.000 0.000 0.048
#> GSM549231 1 0.1398 0.8632 0.940 0.000 0.008 0.000 0.000 0.052
#> GSM549237 1 0.0820 0.8686 0.972 0.000 0.016 0.000 0.000 0.012
#> GSM549254 4 0.4573 0.2570 0.372 0.000 0.000 0.584 0.000 0.044
#> GSM750734 1 0.0820 0.8662 0.972 0.000 0.012 0.000 0.000 0.016
#> GSM549271 4 0.4572 0.5190 0.000 0.000 0.032 0.692 0.032 0.244
#> GSM549232 1 0.3925 0.7495 0.764 0.000 0.004 0.168 0.000 0.064
#> GSM549246 1 0.2649 0.8413 0.876 0.000 0.004 0.052 0.000 0.068
#> GSM549248 1 0.1398 0.8632 0.940 0.000 0.008 0.000 0.000 0.052
#> GSM549255 1 0.3925 0.7495 0.764 0.000 0.004 0.168 0.000 0.064
#> GSM750746 1 0.0520 0.8670 0.984 0.000 0.008 0.000 0.000 0.008
#> GSM549259 1 0.0520 0.8670 0.984 0.000 0.008 0.000 0.000 0.008
#> GSM549269 2 0.1707 0.7721 0.000 0.928 0.056 0.000 0.004 0.012
#> GSM549273 5 0.0146 0.9168 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM549299 2 0.2882 0.7669 0.004 0.848 0.120 0.000 0.000 0.028
#> GSM549301 3 0.4951 0.5456 0.000 0.004 0.568 0.004 0.372 0.052
#> GSM549310 4 0.0912 0.6143 0.000 0.004 0.008 0.972 0.012 0.004
#> GSM549311 5 0.0603 0.9228 0.000 0.000 0.000 0.004 0.980 0.016
#> GSM549302 2 0.1074 0.7863 0.000 0.960 0.028 0.000 0.000 0.012
#> GSM549235 1 0.0717 0.8662 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM549245 1 0.3925 0.7495 0.764 0.000 0.004 0.168 0.000 0.064
#> GSM549265 1 0.3436 0.7982 0.812 0.000 0.004 0.128 0.000 0.056
#> GSM549282 6 0.3818 0.7937 0.000 0.000 0.132 0.024 0.048 0.796
#> GSM549296 4 0.0912 0.6143 0.000 0.004 0.008 0.972 0.012 0.004
#> GSM750739 1 0.0508 0.8684 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM750742 1 0.1333 0.8642 0.944 0.000 0.008 0.000 0.000 0.048
#> GSM750744 1 0.0909 0.8659 0.968 0.000 0.012 0.000 0.000 0.020
#> GSM750750 6 0.3858 0.7946 0.000 0.000 0.136 0.024 0.048 0.792
#> GSM549242 1 0.0858 0.8682 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM549252 1 0.3672 0.7752 0.792 0.000 0.004 0.140 0.000 0.064
#> GSM549253 1 0.1333 0.8641 0.944 0.000 0.008 0.000 0.000 0.048
#> GSM549256 1 0.0858 0.8682 0.968 0.000 0.000 0.004 0.000 0.028
#> GSM549257 1 0.3925 0.7495 0.764 0.000 0.004 0.168 0.000 0.064
#> GSM549263 1 0.1333 0.8642 0.944 0.000 0.008 0.000 0.000 0.048
#> GSM549267 4 0.3789 0.5238 0.000 0.000 0.000 0.660 0.008 0.332
#> GSM750745 1 0.0820 0.8662 0.972 0.000 0.012 0.000 0.000 0.016
#> GSM549239 1 0.0820 0.8662 0.972 0.000 0.012 0.000 0.000 0.016
#> GSM549244 1 0.3710 0.7729 0.788 0.000 0.004 0.144 0.000 0.064
#> GSM549249 1 0.3495 0.7881 0.808 0.000 0.004 0.128 0.000 0.060
#> GSM549260 1 0.1442 0.8588 0.944 0.000 0.012 0.004 0.000 0.040
#> GSM549266 1 0.5652 0.5907 0.656 0.100 0.152 0.000 0.000 0.092
#> GSM549293 2 0.1074 0.7863 0.000 0.960 0.028 0.000 0.000 0.012
#> GSM549236 1 0.1462 0.8624 0.936 0.000 0.008 0.000 0.000 0.056
#> GSM549238 1 0.3564 0.7896 0.808 0.000 0.008 0.124 0.000 0.060
#> GSM549251 1 0.1152 0.8653 0.952 0.000 0.004 0.000 0.000 0.044
#> GSM549258 1 0.1564 0.8539 0.936 0.000 0.024 0.000 0.000 0.040
#> GSM549264 1 0.1500 0.8626 0.936 0.000 0.012 0.000 0.000 0.052
#> GSM549243 1 0.0725 0.8689 0.976 0.000 0.012 0.000 0.000 0.012
#> GSM549262 1 0.1398 0.8632 0.940 0.000 0.008 0.000 0.000 0.052
#> GSM549278 4 0.4233 0.3768 0.268 0.000 0.000 0.684 0.000 0.048
#> GSM549283 1 0.7072 -0.0532 0.400 0.140 0.336 0.000 0.000 0.124
#> GSM549298 3 0.4753 0.5662 0.000 0.000 0.580 0.004 0.368 0.048
#> GSM750741 1 0.1930 0.8461 0.916 0.000 0.036 0.000 0.000 0.048
#> GSM549286 2 0.1769 0.7705 0.000 0.924 0.060 0.000 0.004 0.012
#> GSM549241 1 0.1644 0.8531 0.932 0.000 0.028 0.000 0.000 0.040
#> GSM549247 1 0.2265 0.8335 0.896 0.000 0.052 0.000 0.000 0.052
#> GSM549261 1 0.0717 0.8662 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM549270 2 0.2871 0.7139 0.000 0.804 0.192 0.004 0.000 0.000
#> GSM549277 2 0.5693 0.1922 0.000 0.472 0.432 0.004 0.036 0.056
#> GSM549280 2 0.5502 0.2315 0.000 0.508 0.408 0.004 0.052 0.028
#> GSM549281 1 0.6251 0.5000 0.604 0.084 0.176 0.008 0.000 0.128
#> GSM549285 6 0.5269 0.5741 0.032 0.052 0.332 0.000 0.000 0.584
#> GSM549288 2 0.5232 0.3642 0.000 0.548 0.384 0.004 0.024 0.040
#> GSM549292 2 0.1769 0.7705 0.000 0.924 0.060 0.000 0.004 0.012
#> GSM549295 3 0.5588 0.4419 0.000 0.316 0.544 0.000 0.132 0.008
#> GSM549297 2 0.5156 0.3899 0.000 0.560 0.376 0.004 0.024 0.036
#> GSM750743 1 0.0909 0.8659 0.968 0.000 0.012 0.000 0.000 0.020
#> GSM549268 1 0.6251 0.5000 0.604 0.084 0.176 0.008 0.000 0.128
#> GSM549290 4 0.4394 0.1086 0.000 0.000 0.004 0.496 0.016 0.484
#> GSM549272 2 0.1707 0.7721 0.000 0.928 0.056 0.000 0.004 0.012
#> GSM549276 2 0.2631 0.7267 0.000 0.820 0.180 0.000 0.000 0.000
#> GSM549275 1 0.4640 0.6737 0.720 0.016 0.160 0.000 0.000 0.104
#> GSM549284 2 0.3955 0.6880 0.012 0.784 0.092 0.000 0.000 0.112
#> GSM750737 1 0.4867 0.4264 0.600 0.000 0.012 0.340 0.000 0.048
#> GSM750740 1 0.0717 0.8662 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM750747 1 0.0717 0.8662 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM750751 2 0.1644 0.7833 0.000 0.920 0.076 0.000 0.000 0.004
#> GSM750754 4 0.3023 0.6099 0.000 0.000 0.000 0.784 0.004 0.212
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:hclust 101 0.0304 2.56e-05 0.15646 0.0209 2
#> SD:hclust 83 0.0262 1.84e-06 0.00671 0.2258 3
#> SD:hclust 91 0.0314 1.58e-06 0.00465 0.0731 4
#> SD:hclust 90 0.1585 7.44e-05 0.01154 0.0545 5
#> SD:hclust 91 0.0323 3.13e-05 0.05072 0.1024 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.5039 0.496 0.496
#> 3 3 0.756 0.810 0.892 0.2618 0.874 0.748
#> 4 4 0.785 0.878 0.909 0.1489 0.857 0.641
#> 5 5 0.783 0.811 0.868 0.0735 0.923 0.727
#> 6 6 0.743 0.621 0.769 0.0442 0.958 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
#> GSM549289 1 0.0000 1.000 1.000 0.000
#> GSM549291 2 0.1843 0.973 0.028 0.972
#> GSM549274 2 0.0000 0.996 0.000 1.000
#> GSM750738 2 0.0000 0.996 0.000 1.000
#> GSM750748 1 0.0000 1.000 1.000 0.000
#> GSM549240 1 0.0000 1.000 1.000 0.000
#> GSM549279 2 0.2603 0.958 0.044 0.956
#> GSM549294 2 0.0000 0.996 0.000 1.000
#> GSM549300 2 0.0000 0.996 0.000 1.000
#> GSM549303 2 0.0000 0.996 0.000 1.000
#> GSM549309 2 0.0000 0.996 0.000 1.000
#> GSM750753 2 0.0000 0.996 0.000 1.000
#> GSM750752 2 0.0000 0.996 0.000 1.000
#> GSM549304 2 0.0000 0.996 0.000 1.000
#> GSM549305 2 0.0000 0.996 0.000 1.000
#> GSM549307 2 0.0000 0.996 0.000 1.000
#> GSM549306 2 0.0000 0.996 0.000 1.000
#> GSM549308 2 0.0000 0.996 0.000 1.000
#> GSM549233 1 0.0000 1.000 1.000 0.000
#> GSM549234 1 0.0000 1.000 1.000 0.000
#> GSM549250 1 0.0000 1.000 1.000 0.000
#> GSM549287 2 0.0000 0.996 0.000 1.000
#> GSM750735 1 0.0000 1.000 1.000 0.000
#> GSM750736 1 0.0000 1.000 1.000 0.000
#> GSM750749 1 0.0000 1.000 1.000 0.000
#> GSM549230 1 0.0000 1.000 1.000 0.000
#> GSM549231 1 0.0000 1.000 1.000 0.000
#> GSM549237 1 0.0000 1.000 1.000 0.000
#> GSM549254 1 0.0000 1.000 1.000 0.000
#> GSM750734 1 0.0000 1.000 1.000 0.000
#> GSM549271 2 0.0000 0.996 0.000 1.000
#> GSM549232 1 0.0000 1.000 1.000 0.000
#> GSM549246 1 0.0000 1.000 1.000 0.000
#> GSM549248 1 0.0000 1.000 1.000 0.000
#> GSM549255 1 0.0000 1.000 1.000 0.000
#> GSM750746 1 0.0000 1.000 1.000 0.000
#> GSM549259 1 0.0000 1.000 1.000 0.000
#> GSM549269 2 0.0000 0.996 0.000 1.000
#> GSM549273 2 0.0000 0.996 0.000 1.000
#> GSM549299 2 0.0000 0.996 0.000 1.000
#> GSM549301 2 0.0000 0.996 0.000 1.000
#> GSM549310 2 0.0000 0.996 0.000 1.000
#> GSM549311 2 0.0000 0.996 0.000 1.000
#> GSM549302 2 0.0000 0.996 0.000 1.000
#> GSM549235 1 0.0000 1.000 1.000 0.000
#> GSM549245 1 0.0000 1.000 1.000 0.000
#> GSM549265 1 0.0000 1.000 1.000 0.000
#> GSM549282 2 0.0000 0.996 0.000 1.000
#> GSM549296 2 0.0000 0.996 0.000 1.000
#> GSM750739 1 0.0000 1.000 1.000 0.000
#> GSM750742 1 0.0000 1.000 1.000 0.000
#> GSM750744 1 0.0000 1.000 1.000 0.000
#> GSM750750 2 0.0000 0.996 0.000 1.000
#> GSM549242 1 0.0000 1.000 1.000 0.000
#> GSM549252 1 0.0000 1.000 1.000 0.000
#> GSM549253 1 0.0000 1.000 1.000 0.000
#> GSM549256 1 0.0000 1.000 1.000 0.000
#> GSM549257 1 0.0000 1.000 1.000 0.000
#> GSM549263 1 0.0000 1.000 1.000 0.000
#> GSM549267 2 0.0000 0.996 0.000 1.000
#> GSM750745 1 0.0000 1.000 1.000 0.000
#> GSM549239 1 0.0000 1.000 1.000 0.000
#> GSM549244 1 0.0000 1.000 1.000 0.000
#> GSM549249 1 0.0000 1.000 1.000 0.000
#> GSM549260 1 0.0000 1.000 1.000 0.000
#> GSM549266 2 0.2423 0.961 0.040 0.960
#> GSM549293 2 0.0000 0.996 0.000 1.000
#> GSM549236 1 0.0000 1.000 1.000 0.000
#> GSM549238 1 0.0000 1.000 1.000 0.000
#> GSM549251 1 0.0000 1.000 1.000 0.000
#> GSM549258 1 0.0000 1.000 1.000 0.000
#> GSM549264 1 0.0000 1.000 1.000 0.000
#> GSM549243 1 0.0000 1.000 1.000 0.000
#> GSM549262 1 0.0000 1.000 1.000 0.000
#> GSM549278 1 0.0376 0.996 0.996 0.004
#> GSM549283 2 0.0000 0.996 0.000 1.000
#> GSM549298 2 0.0000 0.996 0.000 1.000
#> GSM750741 1 0.0000 1.000 1.000 0.000
#> GSM549286 2 0.0000 0.996 0.000 1.000
#> GSM549241 1 0.0000 1.000 1.000 0.000
#> GSM549247 1 0.0000 1.000 1.000 0.000
#> GSM549261 1 0.0000 1.000 1.000 0.000
#> GSM549270 2 0.0000 0.996 0.000 1.000
#> GSM549277 2 0.0000 0.996 0.000 1.000
#> GSM549280 2 0.0000 0.996 0.000 1.000
#> GSM549281 2 0.2603 0.958 0.044 0.956
#> GSM549285 2 0.0000 0.996 0.000 1.000
#> GSM549288 2 0.0000 0.996 0.000 1.000
#> GSM549292 2 0.0000 0.996 0.000 1.000
#> GSM549295 2 0.0000 0.996 0.000 1.000
#> GSM549297 2 0.0000 0.996 0.000 1.000
#> GSM750743 1 0.0000 1.000 1.000 0.000
#> GSM549268 2 0.2043 0.969 0.032 0.968
#> GSM549290 2 0.0000 0.996 0.000 1.000
#> GSM549272 2 0.0000 0.996 0.000 1.000
#> GSM549276 2 0.0000 0.996 0.000 1.000
#> GSM549275 1 0.0000 1.000 1.000 0.000
#> GSM549284 2 0.0000 0.996 0.000 1.000
#> GSM750737 1 0.0000 1.000 1.000 0.000
#> GSM750740 1 0.0000 1.000 1.000 0.000
#> GSM750747 1 0.0000 1.000 1.000 0.000
#> GSM750751 2 0.0000 0.996 0.000 1.000
#> GSM750754 2 0.0000 0.996 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.6432 0.521 0.568 0.004 0.428
#> GSM549291 3 0.0592 0.704 0.000 0.012 0.988
#> GSM549274 2 0.0424 0.977 0.000 0.992 0.008
#> GSM750738 2 0.3482 0.784 0.000 0.872 0.128
#> GSM750748 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549240 1 0.1129 0.884 0.976 0.004 0.020
#> GSM549279 2 0.1315 0.958 0.008 0.972 0.020
#> GSM549294 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549300 3 0.6286 0.458 0.000 0.464 0.536
#> GSM549303 3 0.5529 0.674 0.000 0.296 0.704
#> GSM549309 3 0.4062 0.710 0.000 0.164 0.836
#> GSM750753 2 0.0237 0.979 0.000 0.996 0.004
#> GSM750752 3 0.4235 0.653 0.000 0.176 0.824
#> GSM549304 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549305 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549307 2 0.1860 0.921 0.000 0.948 0.052
#> GSM549306 3 0.6244 0.510 0.000 0.440 0.560
#> GSM549308 3 0.6168 0.555 0.000 0.412 0.588
#> GSM549233 1 0.0424 0.890 0.992 0.000 0.008
#> GSM549234 1 0.6330 0.564 0.600 0.004 0.396
#> GSM549250 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549287 3 0.1031 0.709 0.000 0.024 0.976
#> GSM750735 1 0.0424 0.888 0.992 0.000 0.008
#> GSM750736 1 0.1129 0.884 0.976 0.004 0.020
#> GSM750749 1 0.1129 0.884 0.976 0.004 0.020
#> GSM549230 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549231 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549254 1 0.6442 0.523 0.564 0.004 0.432
#> GSM750734 1 0.0237 0.889 0.996 0.000 0.004
#> GSM549271 3 0.1031 0.709 0.000 0.024 0.976
#> GSM549232 1 0.6410 0.534 0.576 0.004 0.420
#> GSM549246 1 0.5722 0.679 0.704 0.004 0.292
#> GSM549248 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549255 1 0.6421 0.528 0.572 0.004 0.424
#> GSM750746 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549269 2 0.0424 0.977 0.000 0.992 0.008
#> GSM549273 3 0.6180 0.550 0.000 0.416 0.584
#> GSM549299 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549301 3 0.6235 0.517 0.000 0.436 0.564
#> GSM549310 3 0.3879 0.669 0.000 0.152 0.848
#> GSM549311 3 0.5497 0.676 0.000 0.292 0.708
#> GSM549302 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549235 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549245 1 0.6421 0.528 0.572 0.004 0.424
#> GSM549265 1 0.6398 0.537 0.580 0.004 0.416
#> GSM549282 3 0.5465 0.678 0.000 0.288 0.712
#> GSM549296 3 0.4062 0.650 0.000 0.164 0.836
#> GSM750739 1 0.0000 0.890 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.890 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.890 1.000 0.000 0.000
#> GSM750750 3 0.5591 0.668 0.000 0.304 0.696
#> GSM549242 1 0.0424 0.890 0.992 0.000 0.008
#> GSM549252 1 0.6345 0.559 0.596 0.004 0.400
#> GSM549253 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549256 1 0.0424 0.890 0.992 0.000 0.008
#> GSM549257 1 0.6386 0.546 0.584 0.004 0.412
#> GSM549263 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549267 3 0.0747 0.706 0.000 0.016 0.984
#> GSM750745 1 0.0237 0.889 0.996 0.000 0.004
#> GSM549239 1 0.0237 0.889 0.996 0.000 0.004
#> GSM549244 1 0.6421 0.528 0.572 0.004 0.424
#> GSM549249 1 0.6345 0.559 0.596 0.004 0.400
#> GSM549260 1 0.0424 0.889 0.992 0.000 0.008
#> GSM549266 2 0.1129 0.962 0.004 0.976 0.020
#> GSM549293 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549236 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549238 1 0.2878 0.836 0.904 0.000 0.096
#> GSM549251 1 0.0237 0.890 0.996 0.000 0.004
#> GSM549258 1 0.0747 0.887 0.984 0.000 0.016
#> GSM549264 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549278 3 0.5588 0.274 0.276 0.004 0.720
#> GSM549283 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549298 3 0.6235 0.517 0.000 0.436 0.564
#> GSM750741 1 0.0747 0.887 0.984 0.000 0.016
#> GSM549286 2 0.0000 0.980 0.000 1.000 0.000
#> GSM549241 1 0.0592 0.887 0.988 0.000 0.012
#> GSM549247 1 0.1129 0.884 0.976 0.004 0.020
#> GSM549261 1 0.0000 0.890 1.000 0.000 0.000
#> GSM549270 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549277 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549280 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549281 2 0.1170 0.962 0.008 0.976 0.016
#> GSM549285 3 0.6192 0.544 0.000 0.420 0.580
#> GSM549288 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549292 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549295 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549297 2 0.0237 0.979 0.000 0.996 0.004
#> GSM750743 1 0.0237 0.889 0.996 0.000 0.004
#> GSM549268 2 0.1170 0.962 0.008 0.976 0.016
#> GSM549290 3 0.0747 0.706 0.000 0.016 0.984
#> GSM549272 2 0.0000 0.980 0.000 1.000 0.000
#> GSM549276 2 0.0237 0.979 0.000 0.996 0.004
#> GSM549275 1 0.1129 0.884 0.976 0.004 0.020
#> GSM549284 2 0.0237 0.979 0.000 0.996 0.004
#> GSM750737 1 0.5722 0.684 0.704 0.004 0.292
#> GSM750740 1 0.0000 0.890 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.890 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.980 0.000 1.000 0.000
#> GSM750754 3 0.0747 0.707 0.000 0.016 0.984
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2053 0.894 0.072 0.000 0.004 0.924
#> GSM549291 4 0.2814 0.813 0.000 0.000 0.132 0.868
#> GSM549274 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM750738 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM750748 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM549240 1 0.5383 0.781 0.744 0.000 0.128 0.128
#> GSM549279 2 0.6347 0.700 0.048 0.720 0.132 0.100
#> GSM549294 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> GSM549300 3 0.3311 0.863 0.000 0.172 0.828 0.000
#> GSM549303 3 0.3439 0.913 0.000 0.048 0.868 0.084
#> GSM549309 3 0.2868 0.875 0.000 0.000 0.864 0.136
#> GSM750753 2 0.0188 0.929 0.000 0.996 0.004 0.000
#> GSM750752 4 0.2976 0.814 0.000 0.008 0.120 0.872
#> GSM549304 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549305 2 0.0188 0.931 0.000 0.996 0.000 0.004
#> GSM549307 2 0.4431 0.495 0.000 0.696 0.304 0.000
#> GSM549306 3 0.2973 0.886 0.000 0.144 0.856 0.000
#> GSM549308 3 0.3266 0.907 0.000 0.108 0.868 0.024
#> GSM549233 1 0.2081 0.891 0.916 0.000 0.000 0.084
#> GSM549234 4 0.2469 0.880 0.108 0.000 0.000 0.892
#> GSM549250 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549287 3 0.2868 0.875 0.000 0.000 0.864 0.136
#> GSM750735 1 0.4181 0.840 0.820 0.000 0.128 0.052
#> GSM750736 1 0.4940 0.809 0.776 0.000 0.128 0.096
#> GSM750749 1 0.5121 0.807 0.772 0.004 0.128 0.096
#> GSM549230 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549231 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549237 1 0.0921 0.919 0.972 0.000 0.000 0.028
#> GSM549254 4 0.0921 0.874 0.028 0.000 0.000 0.972
#> GSM750734 1 0.1520 0.910 0.956 0.000 0.024 0.020
#> GSM549271 3 0.2973 0.868 0.000 0.000 0.856 0.144
#> GSM549232 4 0.1867 0.896 0.072 0.000 0.000 0.928
#> GSM549246 4 0.2921 0.851 0.140 0.000 0.000 0.860
#> GSM549248 1 0.1022 0.918 0.968 0.000 0.000 0.032
#> GSM549255 4 0.1867 0.896 0.072 0.000 0.000 0.928
#> GSM750746 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0336 0.920 0.992 0.000 0.000 0.008
#> GSM549269 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549273 3 0.3032 0.901 0.000 0.124 0.868 0.008
#> GSM549299 2 0.0188 0.929 0.000 0.996 0.004 0.000
#> GSM549301 3 0.2814 0.895 0.000 0.132 0.868 0.000
#> GSM549310 4 0.3498 0.778 0.000 0.008 0.160 0.832
#> GSM549311 3 0.3463 0.908 0.000 0.040 0.864 0.096
#> GSM549302 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549235 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM549245 4 0.1867 0.896 0.072 0.000 0.000 0.928
#> GSM549265 4 0.2011 0.893 0.080 0.000 0.000 0.920
#> GSM549282 3 0.3486 0.911 0.000 0.044 0.864 0.092
#> GSM549296 4 0.2799 0.822 0.000 0.008 0.108 0.884
#> GSM750739 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM750742 1 0.1022 0.918 0.968 0.000 0.000 0.032
#> GSM750744 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM750750 3 0.3453 0.914 0.000 0.052 0.868 0.080
#> GSM549242 1 0.1557 0.910 0.944 0.000 0.000 0.056
#> GSM549252 4 0.2469 0.880 0.108 0.000 0.000 0.892
#> GSM549253 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549256 1 0.2081 0.891 0.916 0.000 0.000 0.084
#> GSM549257 4 0.1867 0.896 0.072 0.000 0.000 0.928
#> GSM549263 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549267 4 0.3649 0.737 0.000 0.000 0.204 0.796
#> GSM750745 1 0.2466 0.894 0.916 0.000 0.056 0.028
#> GSM549239 1 0.2363 0.896 0.920 0.000 0.056 0.024
#> GSM549244 4 0.1867 0.896 0.072 0.000 0.000 0.928
#> GSM549249 4 0.2469 0.880 0.108 0.000 0.000 0.892
#> GSM549260 1 0.1118 0.920 0.964 0.000 0.000 0.036
#> GSM549266 2 0.6230 0.707 0.048 0.728 0.132 0.092
#> GSM549293 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549236 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549238 4 0.4103 0.719 0.256 0.000 0.000 0.744
#> GSM549251 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM549258 1 0.4181 0.842 0.820 0.000 0.128 0.052
#> GSM549264 1 0.0921 0.919 0.972 0.000 0.000 0.028
#> GSM549243 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM549262 1 0.1022 0.918 0.968 0.000 0.000 0.032
#> GSM549278 4 0.2214 0.874 0.028 0.000 0.044 0.928
#> GSM549283 2 0.0804 0.924 0.000 0.980 0.008 0.012
#> GSM549298 3 0.2868 0.893 0.000 0.136 0.864 0.000
#> GSM750741 1 0.4940 0.809 0.776 0.000 0.128 0.096
#> GSM549286 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549241 1 0.4336 0.836 0.812 0.000 0.128 0.060
#> GSM549247 1 0.5383 0.781 0.744 0.000 0.128 0.128
#> GSM549261 1 0.0336 0.920 0.992 0.000 0.000 0.008
#> GSM549270 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> GSM549277 2 0.0921 0.919 0.000 0.972 0.028 0.000
#> GSM549280 2 0.0592 0.926 0.000 0.984 0.016 0.000
#> GSM549281 2 0.6347 0.700 0.048 0.720 0.132 0.100
#> GSM549285 3 0.3421 0.912 0.000 0.088 0.868 0.044
#> GSM549288 2 0.0817 0.921 0.000 0.976 0.024 0.000
#> GSM549292 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549295 2 0.0817 0.920 0.000 0.976 0.024 0.000
#> GSM549297 2 0.0188 0.929 0.000 0.996 0.004 0.000
#> GSM750743 1 0.2197 0.899 0.928 0.000 0.048 0.024
#> GSM549268 2 0.6347 0.700 0.048 0.720 0.132 0.100
#> GSM549290 4 0.3649 0.737 0.000 0.000 0.204 0.796
#> GSM549272 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM549276 2 0.0336 0.931 0.000 0.992 0.000 0.008
#> GSM549275 1 0.4940 0.809 0.776 0.000 0.128 0.096
#> GSM549284 2 0.0707 0.931 0.000 0.980 0.000 0.020
#> GSM750737 4 0.3653 0.770 0.028 0.000 0.128 0.844
#> GSM750740 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM750751 2 0.0469 0.931 0.000 0.988 0.000 0.012
#> GSM750754 3 0.3444 0.825 0.000 0.000 0.816 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1522 0.883 0.012 0.000 0.000 0.944 0.044
#> GSM549291 4 0.2344 0.855 0.000 0.000 0.032 0.904 0.064
#> GSM549274 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM750738 2 0.0671 0.895 0.000 0.980 0.000 0.004 0.016
#> GSM750748 1 0.1965 0.870 0.904 0.000 0.000 0.000 0.096
#> GSM549240 5 0.5216 0.711 0.248 0.000 0.004 0.080 0.668
#> GSM549279 5 0.4070 0.480 0.004 0.256 0.000 0.012 0.728
#> GSM549294 2 0.1965 0.883 0.000 0.904 0.000 0.000 0.096
#> GSM549300 3 0.3309 0.803 0.000 0.036 0.836 0.000 0.128
#> GSM549303 3 0.0854 0.899 0.000 0.004 0.976 0.008 0.012
#> GSM549309 3 0.2669 0.893 0.000 0.000 0.876 0.020 0.104
#> GSM750753 2 0.2338 0.879 0.000 0.884 0.004 0.000 0.112
#> GSM750752 4 0.2676 0.846 0.000 0.000 0.036 0.884 0.080
#> GSM549304 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549305 2 0.0703 0.901 0.000 0.976 0.000 0.000 0.024
#> GSM549307 2 0.5938 0.498 0.000 0.552 0.320 0.000 0.128
#> GSM549306 3 0.1800 0.878 0.000 0.020 0.932 0.000 0.048
#> GSM549308 3 0.0865 0.893 0.000 0.004 0.972 0.000 0.024
#> GSM549233 1 0.2648 0.735 0.848 0.000 0.000 0.152 0.000
#> GSM549234 4 0.1469 0.894 0.036 0.000 0.000 0.948 0.016
#> GSM549250 1 0.1270 0.847 0.948 0.000 0.000 0.052 0.000
#> GSM549287 3 0.3681 0.862 0.000 0.000 0.808 0.044 0.148
#> GSM750735 5 0.3966 0.681 0.336 0.000 0.000 0.000 0.664
#> GSM750736 5 0.4251 0.707 0.316 0.000 0.000 0.012 0.672
#> GSM750749 5 0.3890 0.725 0.252 0.000 0.000 0.012 0.736
#> GSM549230 1 0.0510 0.870 0.984 0.000 0.000 0.016 0.000
#> GSM549231 1 0.0404 0.872 0.988 0.000 0.000 0.012 0.000
#> GSM549237 1 0.1341 0.879 0.944 0.000 0.000 0.000 0.056
#> GSM549254 4 0.1124 0.890 0.004 0.000 0.000 0.960 0.036
#> GSM750734 1 0.2179 0.856 0.888 0.000 0.000 0.000 0.112
#> GSM549271 3 0.4254 0.832 0.000 0.000 0.772 0.080 0.148
#> GSM549232 4 0.1211 0.896 0.024 0.000 0.000 0.960 0.016
#> GSM549246 4 0.1522 0.891 0.044 0.000 0.000 0.944 0.012
#> GSM549248 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM549255 4 0.1117 0.896 0.020 0.000 0.000 0.964 0.016
#> GSM750746 1 0.1965 0.870 0.904 0.000 0.000 0.000 0.096
#> GSM549259 1 0.2074 0.865 0.896 0.000 0.000 0.000 0.104
#> GSM549269 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549273 3 0.1243 0.897 0.000 0.004 0.960 0.008 0.028
#> GSM549299 2 0.2488 0.870 0.000 0.872 0.004 0.000 0.124
#> GSM549301 3 0.1408 0.886 0.000 0.008 0.948 0.000 0.044
#> GSM549310 4 0.3992 0.765 0.000 0.000 0.124 0.796 0.080
#> GSM549311 3 0.2575 0.896 0.000 0.004 0.884 0.012 0.100
#> GSM549302 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549235 1 0.1965 0.870 0.904 0.000 0.000 0.000 0.096
#> GSM549245 4 0.1117 0.896 0.020 0.000 0.000 0.964 0.016
#> GSM549265 4 0.1444 0.893 0.040 0.000 0.000 0.948 0.012
#> GSM549282 3 0.2352 0.898 0.000 0.004 0.896 0.008 0.092
#> GSM549296 4 0.2300 0.858 0.000 0.000 0.024 0.904 0.072
#> GSM750739 1 0.1851 0.872 0.912 0.000 0.000 0.000 0.088
#> GSM750742 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM750744 1 0.1197 0.879 0.952 0.000 0.000 0.000 0.048
#> GSM750750 3 0.2068 0.899 0.000 0.004 0.904 0.000 0.092
#> GSM549242 1 0.1732 0.822 0.920 0.000 0.000 0.080 0.000
#> GSM549252 4 0.1444 0.893 0.040 0.000 0.000 0.948 0.012
#> GSM549253 1 0.1043 0.857 0.960 0.000 0.000 0.040 0.000
#> GSM549256 1 0.2648 0.735 0.848 0.000 0.000 0.152 0.000
#> GSM549257 4 0.1211 0.896 0.024 0.000 0.000 0.960 0.016
#> GSM549263 1 0.0510 0.870 0.984 0.000 0.000 0.016 0.000
#> GSM549267 4 0.5162 0.635 0.000 0.000 0.160 0.692 0.148
#> GSM750745 1 0.3932 0.454 0.672 0.000 0.000 0.000 0.328
#> GSM549239 1 0.3586 0.615 0.736 0.000 0.000 0.000 0.264
#> GSM549244 4 0.1281 0.895 0.032 0.000 0.000 0.956 0.012
#> GSM549249 4 0.1444 0.893 0.040 0.000 0.000 0.948 0.012
#> GSM549260 1 0.2685 0.868 0.880 0.000 0.000 0.028 0.092
#> GSM549266 5 0.4096 0.471 0.004 0.260 0.000 0.012 0.724
#> GSM549293 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549236 1 0.1197 0.851 0.952 0.000 0.000 0.048 0.000
#> GSM549238 4 0.3913 0.555 0.324 0.000 0.000 0.676 0.000
#> GSM549251 1 0.0794 0.865 0.972 0.000 0.000 0.028 0.000
#> GSM549258 5 0.4288 0.579 0.384 0.000 0.000 0.004 0.612
#> GSM549264 1 0.0451 0.874 0.988 0.000 0.000 0.008 0.004
#> GSM549243 1 0.1851 0.872 0.912 0.000 0.000 0.000 0.088
#> GSM549262 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM549278 4 0.1518 0.879 0.004 0.000 0.004 0.944 0.048
#> GSM549283 2 0.4350 0.458 0.000 0.588 0.004 0.000 0.408
#> GSM549298 3 0.1701 0.880 0.000 0.016 0.936 0.000 0.048
#> GSM750741 5 0.4232 0.708 0.312 0.000 0.000 0.012 0.676
#> GSM549286 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549241 5 0.4047 0.699 0.320 0.000 0.000 0.004 0.676
#> GSM549247 5 0.5343 0.712 0.244 0.004 0.004 0.080 0.668
#> GSM549261 1 0.2074 0.865 0.896 0.000 0.000 0.000 0.104
#> GSM549270 2 0.1908 0.886 0.000 0.908 0.000 0.000 0.092
#> GSM549277 2 0.4879 0.781 0.000 0.720 0.124 0.000 0.156
#> GSM549280 2 0.4035 0.831 0.000 0.784 0.060 0.000 0.156
#> GSM549281 5 0.4044 0.480 0.004 0.252 0.000 0.012 0.732
#> GSM549285 3 0.3205 0.875 0.000 0.004 0.816 0.004 0.176
#> GSM549288 2 0.4069 0.833 0.000 0.788 0.076 0.000 0.136
#> GSM549292 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549295 2 0.4717 0.786 0.000 0.736 0.144 0.000 0.120
#> GSM549297 2 0.2921 0.870 0.000 0.856 0.020 0.000 0.124
#> GSM750743 1 0.3932 0.454 0.672 0.000 0.000 0.000 0.328
#> GSM549268 5 0.4044 0.480 0.004 0.252 0.000 0.012 0.732
#> GSM549290 4 0.5198 0.628 0.000 0.000 0.164 0.688 0.148
#> GSM549272 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM549276 2 0.0510 0.901 0.000 0.984 0.000 0.000 0.016
#> GSM549275 5 0.4380 0.722 0.288 0.008 0.000 0.012 0.692
#> GSM549284 2 0.0162 0.902 0.000 0.996 0.000 0.004 0.000
#> GSM750737 4 0.3355 0.747 0.012 0.000 0.000 0.804 0.184
#> GSM750740 1 0.2074 0.865 0.896 0.000 0.000 0.000 0.104
#> GSM750747 1 0.1965 0.870 0.904 0.000 0.000 0.000 0.096
#> GSM750751 2 0.0865 0.901 0.000 0.972 0.000 0.004 0.024
#> GSM750754 3 0.5334 0.707 0.000 0.000 0.672 0.180 0.148
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.2703 0.7386 0.004 0.000 0.000 0.824 0.000 0.172
#> GSM549291 4 0.3565 0.6154 0.004 0.000 0.004 0.716 0.000 0.276
#> GSM549274 2 0.0405 0.8505 0.000 0.988 0.008 0.000 0.000 0.004
#> GSM750738 2 0.0862 0.8369 0.008 0.972 0.000 0.004 0.000 0.016
#> GSM750748 5 0.4008 0.7640 0.128 0.000 0.004 0.000 0.768 0.100
#> GSM549240 1 0.3868 0.7437 0.812 0.000 0.004 0.076 0.076 0.032
#> GSM549279 1 0.4993 0.6542 0.728 0.080 0.072 0.000 0.004 0.116
#> GSM549294 2 0.3782 0.7784 0.072 0.784 0.140 0.000 0.000 0.004
#> GSM549300 3 0.2361 0.4025 0.064 0.032 0.896 0.000 0.000 0.008
#> GSM549303 3 0.4213 0.2895 0.020 0.000 0.636 0.004 0.000 0.340
#> GSM549309 6 0.4466 -0.1792 0.020 0.000 0.476 0.004 0.000 0.500
#> GSM750753 2 0.4465 0.7287 0.080 0.704 0.212 0.000 0.000 0.004
#> GSM750752 4 0.3697 0.6250 0.016 0.000 0.004 0.732 0.000 0.248
#> GSM549304 2 0.0405 0.8505 0.000 0.988 0.008 0.000 0.000 0.004
#> GSM549305 2 0.2527 0.8215 0.024 0.868 0.108 0.000 0.000 0.000
#> GSM549307 3 0.4827 0.0314 0.064 0.296 0.632 0.000 0.000 0.008
#> GSM549306 3 0.2653 0.4671 0.000 0.012 0.844 0.000 0.000 0.144
#> GSM549308 3 0.3052 0.4412 0.000 0.004 0.780 0.000 0.000 0.216
#> GSM549233 5 0.3394 0.6033 0.000 0.000 0.000 0.236 0.752 0.012
#> GSM549234 4 0.1003 0.8207 0.000 0.000 0.000 0.964 0.016 0.020
#> GSM549250 5 0.1297 0.7812 0.000 0.000 0.000 0.040 0.948 0.012
#> GSM549287 6 0.3916 0.3491 0.000 0.000 0.300 0.020 0.000 0.680
#> GSM750735 1 0.3585 0.7135 0.792 0.000 0.000 0.004 0.156 0.048
#> GSM750736 1 0.3343 0.7218 0.812 0.000 0.000 0.004 0.144 0.040
#> GSM750749 1 0.3891 0.7535 0.788 0.000 0.004 0.004 0.096 0.108
#> GSM549230 5 0.0964 0.7927 0.000 0.000 0.004 0.016 0.968 0.012
#> GSM549231 5 0.0725 0.7932 0.000 0.000 0.000 0.012 0.976 0.012
#> GSM549237 5 0.1802 0.7962 0.072 0.000 0.000 0.000 0.916 0.012
#> GSM549254 4 0.0909 0.8146 0.012 0.000 0.000 0.968 0.000 0.020
#> GSM750734 5 0.4606 0.6299 0.268 0.000 0.000 0.000 0.656 0.076
#> GSM549271 6 0.4729 0.4522 0.000 0.000 0.284 0.080 0.000 0.636
#> GSM549232 4 0.0665 0.8224 0.008 0.000 0.000 0.980 0.008 0.004
#> GSM549246 4 0.1794 0.8048 0.000 0.000 0.000 0.924 0.040 0.036
#> GSM549248 5 0.0692 0.7947 0.000 0.000 0.000 0.004 0.976 0.020
#> GSM549255 4 0.0779 0.8222 0.008 0.000 0.000 0.976 0.008 0.008
#> GSM750746 5 0.4048 0.7624 0.132 0.000 0.004 0.000 0.764 0.100
#> GSM549259 5 0.4338 0.7427 0.164 0.000 0.004 0.000 0.732 0.100
#> GSM549269 2 0.0000 0.8503 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549273 3 0.3977 0.3705 0.020 0.004 0.692 0.000 0.000 0.284
#> GSM549299 2 0.5160 0.6994 0.080 0.672 0.208 0.000 0.000 0.040
#> GSM549301 3 0.2772 0.4605 0.000 0.004 0.816 0.000 0.000 0.180
#> GSM549310 4 0.4656 0.4848 0.016 0.000 0.044 0.660 0.000 0.280
#> GSM549311 3 0.4468 -0.0212 0.020 0.000 0.488 0.004 0.000 0.488
#> GSM549302 2 0.0146 0.8500 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549235 5 0.3968 0.7661 0.124 0.000 0.004 0.000 0.772 0.100
#> GSM549245 4 0.0779 0.8222 0.008 0.000 0.000 0.976 0.008 0.008
#> GSM549265 4 0.1418 0.8171 0.000 0.000 0.000 0.944 0.024 0.032
#> GSM549282 3 0.4126 0.0499 0.004 0.000 0.512 0.004 0.000 0.480
#> GSM549296 4 0.3457 0.6545 0.016 0.000 0.000 0.752 0.000 0.232
#> GSM750739 5 0.4046 0.7410 0.168 0.000 0.000 0.000 0.748 0.084
#> GSM750742 5 0.0405 0.7953 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM750744 5 0.3344 0.7380 0.152 0.000 0.000 0.000 0.804 0.044
#> GSM750750 3 0.4103 0.1267 0.004 0.000 0.544 0.004 0.000 0.448
#> GSM549242 5 0.2872 0.6985 0.004 0.000 0.000 0.152 0.832 0.012
#> GSM549252 4 0.1498 0.8153 0.000 0.000 0.000 0.940 0.028 0.032
#> GSM549253 5 0.1049 0.7871 0.000 0.000 0.000 0.032 0.960 0.008
#> GSM549256 5 0.3518 0.5875 0.000 0.000 0.000 0.256 0.732 0.012
#> GSM549257 4 0.0520 0.8222 0.008 0.000 0.000 0.984 0.008 0.000
#> GSM549263 5 0.0862 0.7921 0.000 0.000 0.004 0.016 0.972 0.008
#> GSM549267 6 0.4916 0.2348 0.000 0.000 0.064 0.416 0.000 0.520
#> GSM750745 5 0.5105 0.2935 0.432 0.000 0.000 0.000 0.488 0.080
#> GSM549239 5 0.5059 0.3921 0.392 0.000 0.000 0.000 0.528 0.080
#> GSM549244 4 0.1074 0.8207 0.000 0.000 0.000 0.960 0.012 0.028
#> GSM549249 4 0.1498 0.8153 0.000 0.000 0.000 0.940 0.028 0.032
#> GSM549260 5 0.3940 0.7803 0.132 0.000 0.000 0.024 0.788 0.056
#> GSM549266 1 0.5006 0.6518 0.728 0.088 0.072 0.000 0.004 0.108
#> GSM549293 2 0.0146 0.8500 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549236 5 0.1480 0.7818 0.000 0.000 0.000 0.040 0.940 0.020
#> GSM549238 4 0.4475 0.2733 0.000 0.000 0.000 0.556 0.412 0.032
#> GSM549251 5 0.0993 0.7909 0.000 0.000 0.000 0.024 0.964 0.012
#> GSM549258 1 0.3825 0.5878 0.744 0.000 0.004 0.000 0.220 0.032
#> GSM549264 5 0.1555 0.7889 0.008 0.000 0.000 0.012 0.940 0.040
#> GSM549243 5 0.3248 0.7808 0.116 0.000 0.004 0.000 0.828 0.052
#> GSM549262 5 0.0405 0.7953 0.000 0.000 0.000 0.004 0.988 0.008
#> GSM549278 4 0.3046 0.7158 0.012 0.000 0.000 0.800 0.000 0.188
#> GSM549283 1 0.6912 0.2154 0.464 0.280 0.136 0.000 0.000 0.120
#> GSM549298 3 0.2768 0.4676 0.000 0.012 0.832 0.000 0.000 0.156
#> GSM750741 1 0.2260 0.7319 0.860 0.000 0.000 0.000 0.140 0.000
#> GSM549286 2 0.0000 0.8503 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.3475 0.6968 0.800 0.000 0.000 0.000 0.140 0.060
#> GSM549247 1 0.4011 0.7459 0.812 0.008 0.004 0.076 0.068 0.032
#> GSM549261 5 0.4234 0.7504 0.152 0.000 0.004 0.000 0.744 0.100
#> GSM549270 2 0.3790 0.7727 0.072 0.772 0.156 0.000 0.000 0.000
#> GSM549277 3 0.5909 -0.2058 0.092 0.356 0.512 0.000 0.000 0.040
#> GSM549280 3 0.6118 -0.3413 0.096 0.408 0.448 0.000 0.000 0.048
#> GSM549281 1 0.5425 0.6247 0.692 0.096 0.096 0.000 0.004 0.112
#> GSM549285 3 0.4855 0.1380 0.056 0.000 0.484 0.000 0.000 0.460
#> GSM549288 2 0.5243 0.3474 0.080 0.460 0.456 0.000 0.000 0.004
#> GSM549292 2 0.0000 0.8503 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 2 0.4853 0.3803 0.056 0.488 0.456 0.000 0.000 0.000
#> GSM549297 2 0.4790 0.6706 0.080 0.648 0.268 0.000 0.000 0.004
#> GSM750743 5 0.5099 0.3045 0.424 0.000 0.000 0.000 0.496 0.080
#> GSM549268 1 0.5425 0.6247 0.692 0.096 0.096 0.000 0.004 0.112
#> GSM549290 6 0.5047 0.2355 0.004 0.000 0.064 0.416 0.000 0.516
#> GSM549272 2 0.0000 0.8503 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549276 2 0.1594 0.8422 0.016 0.932 0.052 0.000 0.000 0.000
#> GSM549275 1 0.3689 0.7505 0.812 0.012 0.008 0.000 0.120 0.048
#> GSM549284 2 0.0551 0.8495 0.004 0.984 0.008 0.000 0.000 0.004
#> GSM750737 4 0.2905 0.6927 0.144 0.000 0.000 0.836 0.008 0.012
#> GSM750740 5 0.4234 0.7504 0.152 0.000 0.004 0.000 0.744 0.100
#> GSM750747 5 0.4087 0.7603 0.136 0.000 0.004 0.000 0.760 0.100
#> GSM750751 2 0.1333 0.8453 0.008 0.944 0.048 0.000 0.000 0.000
#> GSM750754 6 0.4949 0.5098 0.000 0.000 0.208 0.144 0.000 0.648
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:kmeans 103 0.0230 1.72e-05 5.36e-02 0.0048 2
#> SD:kmeans 101 0.0894 1.60e-06 1.40e-04 0.0118 3
#> SD:kmeans 102 0.3636 3.16e-05 8.77e-04 0.0389 4
#> SD:kmeans 95 0.4167 7.16e-04 1.59e-03 0.0980 5
#> SD:kmeans 76 0.0717 1.51e-02 3.56e-05 0.0033 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.998 0.999 0.5043 0.496 0.496
#> 3 3 0.805 0.791 0.899 0.2814 0.807 0.628
#> 4 4 0.829 0.868 0.930 0.1400 0.879 0.674
#> 5 5 0.768 0.701 0.857 0.0739 0.917 0.708
#> 6 6 0.739 0.645 0.758 0.0347 0.969 0.857
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
#> GSM549289 1 0.000 0.998 1.00 0.00
#> GSM549291 2 0.000 1.000 0.00 1.00
#> GSM549274 2 0.000 1.000 0.00 1.00
#> GSM750738 2 0.000 1.000 0.00 1.00
#> GSM750748 1 0.000 0.998 1.00 0.00
#> GSM549240 1 0.000 0.998 1.00 0.00
#> GSM549279 2 0.000 1.000 0.00 1.00
#> GSM549294 2 0.000 1.000 0.00 1.00
#> GSM549300 2 0.000 1.000 0.00 1.00
#> GSM549303 2 0.000 1.000 0.00 1.00
#> GSM549309 2 0.000 1.000 0.00 1.00
#> GSM750753 2 0.000 1.000 0.00 1.00
#> GSM750752 2 0.000 1.000 0.00 1.00
#> GSM549304 2 0.000 1.000 0.00 1.00
#> GSM549305 2 0.000 1.000 0.00 1.00
#> GSM549307 2 0.000 1.000 0.00 1.00
#> GSM549306 2 0.000 1.000 0.00 1.00
#> GSM549308 2 0.000 1.000 0.00 1.00
#> GSM549233 1 0.000 0.998 1.00 0.00
#> GSM549234 1 0.000 0.998 1.00 0.00
#> GSM549250 1 0.000 0.998 1.00 0.00
#> GSM549287 2 0.000 1.000 0.00 1.00
#> GSM750735 1 0.000 0.998 1.00 0.00
#> GSM750736 1 0.000 0.998 1.00 0.00
#> GSM750749 1 0.000 0.998 1.00 0.00
#> GSM549230 1 0.000 0.998 1.00 0.00
#> GSM549231 1 0.000 0.998 1.00 0.00
#> GSM549237 1 0.000 0.998 1.00 0.00
#> GSM549254 1 0.000 0.998 1.00 0.00
#> GSM750734 1 0.000 0.998 1.00 0.00
#> GSM549271 2 0.000 1.000 0.00 1.00
#> GSM549232 1 0.000 0.998 1.00 0.00
#> GSM549246 1 0.000 0.998 1.00 0.00
#> GSM549248 1 0.000 0.998 1.00 0.00
#> GSM549255 1 0.000 0.998 1.00 0.00
#> GSM750746 1 0.000 0.998 1.00 0.00
#> GSM549259 1 0.000 0.998 1.00 0.00
#> GSM549269 2 0.000 1.000 0.00 1.00
#> GSM549273 2 0.000 1.000 0.00 1.00
#> GSM549299 2 0.000 1.000 0.00 1.00
#> GSM549301 2 0.000 1.000 0.00 1.00
#> GSM549310 2 0.000 1.000 0.00 1.00
#> GSM549311 2 0.000 1.000 0.00 1.00
#> GSM549302 2 0.000 1.000 0.00 1.00
#> GSM549235 1 0.000 0.998 1.00 0.00
#> GSM549245 1 0.000 0.998 1.00 0.00
#> GSM549265 1 0.000 0.998 1.00 0.00
#> GSM549282 2 0.000 1.000 0.00 1.00
#> GSM549296 2 0.000 1.000 0.00 1.00
#> GSM750739 1 0.000 0.998 1.00 0.00
#> GSM750742 1 0.000 0.998 1.00 0.00
#> GSM750744 1 0.000 0.998 1.00 0.00
#> GSM750750 2 0.000 1.000 0.00 1.00
#> GSM549242 1 0.000 0.998 1.00 0.00
#> GSM549252 1 0.000 0.998 1.00 0.00
#> GSM549253 1 0.000 0.998 1.00 0.00
#> GSM549256 1 0.000 0.998 1.00 0.00
#> GSM549257 1 0.000 0.998 1.00 0.00
#> GSM549263 1 0.000 0.998 1.00 0.00
#> GSM549267 2 0.000 1.000 0.00 1.00
#> GSM750745 1 0.000 0.998 1.00 0.00
#> GSM549239 1 0.000 0.998 1.00 0.00
#> GSM549244 1 0.000 0.998 1.00 0.00
#> GSM549249 1 0.000 0.998 1.00 0.00
#> GSM549260 1 0.000 0.998 1.00 0.00
#> GSM549266 2 0.000 1.000 0.00 1.00
#> GSM549293 2 0.000 1.000 0.00 1.00
#> GSM549236 1 0.000 0.998 1.00 0.00
#> GSM549238 1 0.000 0.998 1.00 0.00
#> GSM549251 1 0.000 0.998 1.00 0.00
#> GSM549258 1 0.000 0.998 1.00 0.00
#> GSM549264 1 0.000 0.998 1.00 0.00
#> GSM549243 1 0.000 0.998 1.00 0.00
#> GSM549262 1 0.000 0.998 1.00 0.00
#> GSM549278 1 0.529 0.864 0.88 0.12
#> GSM549283 2 0.000 1.000 0.00 1.00
#> GSM549298 2 0.000 1.000 0.00 1.00
#> GSM750741 1 0.000 0.998 1.00 0.00
#> GSM549286 2 0.000 1.000 0.00 1.00
#> GSM549241 1 0.000 0.998 1.00 0.00
#> GSM549247 1 0.000 0.998 1.00 0.00
#> GSM549261 1 0.000 0.998 1.00 0.00
#> GSM549270 2 0.000 1.000 0.00 1.00
#> GSM549277 2 0.000 1.000 0.00 1.00
#> GSM549280 2 0.000 1.000 0.00 1.00
#> GSM549281 2 0.000 1.000 0.00 1.00
#> GSM549285 2 0.000 1.000 0.00 1.00
#> GSM549288 2 0.000 1.000 0.00 1.00
#> GSM549292 2 0.000 1.000 0.00 1.00
#> GSM549295 2 0.000 1.000 0.00 1.00
#> GSM549297 2 0.000 1.000 0.00 1.00
#> GSM750743 1 0.000 0.998 1.00 0.00
#> GSM549268 2 0.000 1.000 0.00 1.00
#> GSM549290 2 0.000 1.000 0.00 1.00
#> GSM549272 2 0.000 1.000 0.00 1.00
#> GSM549276 2 0.000 1.000 0.00 1.00
#> GSM549275 1 0.000 0.998 1.00 0.00
#> GSM549284 2 0.000 1.000 0.00 1.00
#> GSM750737 1 0.000 0.998 1.00 0.00
#> GSM750740 1 0.000 0.998 1.00 0.00
#> GSM750747 1 0.000 0.998 1.00 0.00
#> GSM750751 2 0.000 1.000 0.00 1.00
#> GSM750754 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.2066 0.7100 0.060 0.000 0.940
#> GSM549291 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM549274 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM750738 2 0.0424 0.8688 0.000 0.992 0.008
#> GSM750748 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549279 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549300 2 0.5988 0.6337 0.000 0.632 0.368
#> GSM549303 2 0.6260 0.5251 0.000 0.552 0.448
#> GSM549309 3 0.5706 0.0689 0.000 0.320 0.680
#> GSM750753 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM750752 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM549304 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549307 2 0.2448 0.8409 0.000 0.924 0.076
#> GSM549306 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM549308 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM549233 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549234 3 0.6095 0.5538 0.392 0.000 0.608
#> GSM549250 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549287 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM750735 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750749 1 0.3445 0.8245 0.896 0.088 0.016
#> GSM549230 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549254 3 0.5859 0.5973 0.344 0.000 0.656
#> GSM750734 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549271 3 0.4062 0.4761 0.000 0.164 0.836
#> GSM549232 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549246 1 0.6026 0.1872 0.624 0.000 0.376
#> GSM549248 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549255 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM750746 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549273 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM549299 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549301 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM549310 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM549311 2 0.6302 0.4645 0.000 0.520 0.480
#> GSM549302 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549245 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549265 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549282 2 0.6204 0.5648 0.000 0.576 0.424
#> GSM549296 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM750739 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750750 2 0.6095 0.6108 0.000 0.608 0.392
#> GSM549242 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549252 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549253 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549257 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549263 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549267 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549244 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549249 3 0.6079 0.5621 0.388 0.000 0.612
#> GSM549260 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549238 1 0.5926 0.2580 0.644 0.000 0.356
#> GSM549251 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549278 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM549283 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549298 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM750741 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549247 1 0.1643 0.9029 0.956 0.044 0.000
#> GSM549261 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549277 2 0.1163 0.8646 0.000 0.972 0.028
#> GSM549280 2 0.1643 0.8576 0.000 0.956 0.044
#> GSM549281 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549285 2 0.6079 0.6160 0.000 0.612 0.388
#> GSM549288 2 0.0892 0.8677 0.000 0.980 0.020
#> GSM549292 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549295 2 0.0892 0.8677 0.000 0.980 0.020
#> GSM549297 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM549268 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549290 3 0.0000 0.7027 0.000 0.000 1.000
#> GSM549272 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM549275 1 0.2796 0.8386 0.908 0.092 0.000
#> GSM549284 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM750737 1 0.6235 -0.0626 0.564 0.000 0.436
#> GSM750740 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.9551 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.8739 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.7027 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.0592 0.875 0.000 0.000 0.016 0.984
#> GSM549291 4 0.4972 0.259 0.000 0.000 0.456 0.544
#> GSM549274 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM750738 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM750748 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM549240 1 0.1635 0.903 0.948 0.000 0.008 0.044
#> GSM549279 2 0.0859 0.932 0.008 0.980 0.008 0.004
#> GSM549294 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549300 3 0.3123 0.791 0.000 0.156 0.844 0.000
#> GSM549303 3 0.0336 0.948 0.000 0.008 0.992 0.000
#> GSM549309 3 0.0336 0.944 0.000 0.000 0.992 0.008
#> GSM750753 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM750752 4 0.4331 0.614 0.000 0.000 0.288 0.712
#> GSM549304 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549305 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549307 2 0.4804 0.448 0.000 0.616 0.384 0.000
#> GSM549306 3 0.1637 0.908 0.000 0.060 0.940 0.000
#> GSM549308 3 0.0592 0.948 0.000 0.016 0.984 0.000
#> GSM549233 1 0.4543 0.652 0.676 0.000 0.000 0.324
#> GSM549234 4 0.0188 0.878 0.004 0.000 0.000 0.996
#> GSM549250 1 0.3975 0.771 0.760 0.000 0.000 0.240
#> GSM549287 3 0.0336 0.944 0.000 0.000 0.992 0.008
#> GSM750735 1 0.0524 0.918 0.988 0.000 0.008 0.004
#> GSM750736 1 0.0524 0.918 0.988 0.000 0.008 0.004
#> GSM750749 1 0.2629 0.873 0.912 0.024 0.060 0.004
#> GSM549230 1 0.3123 0.851 0.844 0.000 0.000 0.156
#> GSM549231 1 0.3024 0.857 0.852 0.000 0.000 0.148
#> GSM549237 1 0.0921 0.918 0.972 0.000 0.000 0.028
#> GSM549254 4 0.0657 0.875 0.004 0.000 0.012 0.984
#> GSM750734 1 0.0336 0.920 0.992 0.000 0.008 0.000
#> GSM549271 3 0.0336 0.944 0.000 0.000 0.992 0.008
#> GSM549232 4 0.0376 0.879 0.004 0.000 0.004 0.992
#> GSM549246 4 0.2760 0.764 0.128 0.000 0.000 0.872
#> GSM549248 1 0.1211 0.914 0.960 0.000 0.000 0.040
#> GSM549255 4 0.0524 0.879 0.004 0.000 0.008 0.988
#> GSM750746 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM549259 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0592 0.948 0.000 0.016 0.984 0.000
#> GSM549299 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549301 3 0.0707 0.946 0.000 0.020 0.980 0.000
#> GSM549310 4 0.4877 0.387 0.000 0.000 0.408 0.592
#> GSM549311 3 0.0336 0.948 0.000 0.008 0.992 0.000
#> GSM549302 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM549245 4 0.0524 0.879 0.004 0.000 0.008 0.988
#> GSM549265 4 0.0188 0.878 0.004 0.000 0.000 0.996
#> GSM549282 3 0.0469 0.948 0.000 0.012 0.988 0.000
#> GSM549296 4 0.4008 0.672 0.000 0.000 0.244 0.756
#> GSM750739 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0921 0.918 0.972 0.000 0.000 0.028
#> GSM750744 1 0.0592 0.920 0.984 0.000 0.000 0.016
#> GSM750750 3 0.0469 0.948 0.000 0.012 0.988 0.000
#> GSM549242 1 0.3764 0.798 0.784 0.000 0.000 0.216
#> GSM549252 4 0.0188 0.878 0.004 0.000 0.000 0.996
#> GSM549253 1 0.3528 0.821 0.808 0.000 0.000 0.192
#> GSM549256 1 0.4585 0.639 0.668 0.000 0.000 0.332
#> GSM549257 4 0.0376 0.879 0.004 0.000 0.004 0.992
#> GSM549263 1 0.3219 0.845 0.836 0.000 0.000 0.164
#> GSM549267 3 0.3219 0.763 0.000 0.000 0.836 0.164
#> GSM750745 1 0.0336 0.920 0.992 0.000 0.008 0.000
#> GSM549239 1 0.0336 0.920 0.992 0.000 0.008 0.000
#> GSM549244 4 0.0524 0.879 0.004 0.000 0.008 0.988
#> GSM549249 4 0.0188 0.878 0.004 0.000 0.000 0.996
#> GSM549260 1 0.2149 0.890 0.912 0.000 0.000 0.088
#> GSM549266 2 0.0859 0.932 0.008 0.980 0.008 0.004
#> GSM549293 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549236 1 0.4072 0.756 0.748 0.000 0.000 0.252
#> GSM549238 4 0.1867 0.825 0.072 0.000 0.000 0.928
#> GSM549251 1 0.3311 0.838 0.828 0.000 0.000 0.172
#> GSM549258 1 0.0524 0.918 0.988 0.000 0.008 0.004
#> GSM549264 1 0.1716 0.905 0.936 0.000 0.000 0.064
#> GSM549243 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM549262 1 0.0921 0.918 0.972 0.000 0.000 0.028
#> GSM549278 4 0.4040 0.680 0.000 0.000 0.248 0.752
#> GSM549283 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549298 3 0.0921 0.940 0.000 0.028 0.972 0.000
#> GSM750741 1 0.0524 0.918 0.988 0.000 0.008 0.004
#> GSM549286 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0524 0.918 0.988 0.000 0.008 0.004
#> GSM549247 1 0.4447 0.773 0.812 0.136 0.008 0.044
#> GSM549261 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM549270 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549277 2 0.4382 0.632 0.000 0.704 0.296 0.000
#> GSM549280 2 0.4103 0.696 0.000 0.744 0.256 0.000
#> GSM549281 2 0.1229 0.931 0.008 0.968 0.020 0.004
#> GSM549285 3 0.0592 0.948 0.000 0.016 0.984 0.000
#> GSM549288 2 0.3610 0.769 0.000 0.800 0.200 0.000
#> GSM549292 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549295 2 0.3486 0.783 0.000 0.812 0.188 0.000
#> GSM549297 2 0.0921 0.929 0.000 0.972 0.028 0.000
#> GSM750743 1 0.0336 0.920 0.992 0.000 0.008 0.000
#> GSM549268 2 0.1296 0.930 0.004 0.964 0.028 0.004
#> GSM549290 3 0.3528 0.717 0.000 0.000 0.808 0.192
#> GSM549272 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM549275 1 0.2310 0.874 0.920 0.068 0.008 0.004
#> GSM549284 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM750737 4 0.1452 0.857 0.036 0.000 0.008 0.956
#> GSM750740 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM750747 1 0.0188 0.921 0.996 0.000 0.000 0.004
#> GSM750751 2 0.0000 0.945 0.000 1.000 0.000 0.000
#> GSM750754 3 0.0336 0.944 0.000 0.000 0.992 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.0992 0.8585 0.008 0.000 0.000 0.968 0.024
#> GSM549291 4 0.4770 0.5102 0.000 0.000 0.320 0.644 0.036
#> GSM549274 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM750738 2 0.0693 0.8585 0.000 0.980 0.000 0.012 0.008
#> GSM750748 1 0.3636 0.6228 0.728 0.000 0.000 0.000 0.272
#> GSM549240 5 0.2903 0.7309 0.080 0.000 0.000 0.048 0.872
#> GSM549279 2 0.4306 0.2314 0.000 0.508 0.000 0.000 0.492
#> GSM549294 2 0.0162 0.8643 0.000 0.996 0.000 0.000 0.004
#> GSM549300 3 0.3366 0.6413 0.000 0.232 0.768 0.000 0.000
#> GSM549303 3 0.0324 0.9241 0.000 0.000 0.992 0.004 0.004
#> GSM549309 3 0.0324 0.9241 0.000 0.000 0.992 0.004 0.004
#> GSM750753 2 0.0324 0.8639 0.000 0.992 0.004 0.000 0.004
#> GSM750752 4 0.3309 0.7780 0.000 0.000 0.128 0.836 0.036
#> GSM549304 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549305 2 0.0162 0.8643 0.000 0.996 0.000 0.000 0.004
#> GSM549307 2 0.4210 0.3786 0.000 0.588 0.412 0.000 0.000
#> GSM549306 3 0.1792 0.8577 0.000 0.084 0.916 0.000 0.000
#> GSM549308 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM549233 1 0.2179 0.6665 0.888 0.000 0.000 0.112 0.000
#> GSM549234 4 0.1410 0.8551 0.060 0.000 0.000 0.940 0.000
#> GSM549250 1 0.0703 0.7331 0.976 0.000 0.000 0.024 0.000
#> GSM549287 3 0.1195 0.9109 0.000 0.000 0.960 0.012 0.028
#> GSM750735 5 0.3395 0.6354 0.236 0.000 0.000 0.000 0.764
#> GSM750736 5 0.2286 0.7456 0.108 0.000 0.000 0.004 0.888
#> GSM750749 5 0.2420 0.7314 0.088 0.008 0.008 0.000 0.896
#> GSM549230 1 0.0404 0.7389 0.988 0.000 0.000 0.012 0.000
#> GSM549231 1 0.0324 0.7406 0.992 0.000 0.000 0.004 0.004
#> GSM549237 1 0.2074 0.7208 0.896 0.000 0.000 0.000 0.104
#> GSM549254 4 0.0771 0.8565 0.000 0.000 0.004 0.976 0.020
#> GSM750734 1 0.4235 0.3012 0.576 0.000 0.000 0.000 0.424
#> GSM549271 3 0.0807 0.9182 0.000 0.000 0.976 0.012 0.012
#> GSM549232 4 0.0404 0.8608 0.012 0.000 0.000 0.988 0.000
#> GSM549246 1 0.4436 0.1744 0.596 0.000 0.000 0.396 0.008
#> GSM549248 1 0.0609 0.7416 0.980 0.000 0.000 0.000 0.020
#> GSM549255 4 0.0510 0.8610 0.016 0.000 0.000 0.984 0.000
#> GSM750746 1 0.3774 0.5967 0.704 0.000 0.000 0.000 0.296
#> GSM549259 1 0.4060 0.4922 0.640 0.000 0.000 0.000 0.360
#> GSM549269 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549273 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM549299 2 0.0404 0.8623 0.000 0.988 0.000 0.000 0.012
#> GSM549301 3 0.0290 0.9214 0.000 0.008 0.992 0.000 0.000
#> GSM549310 4 0.4789 0.5509 0.000 0.004 0.292 0.668 0.036
#> GSM549311 3 0.0324 0.9241 0.000 0.000 0.992 0.004 0.004
#> GSM549302 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549235 1 0.3534 0.6366 0.744 0.000 0.000 0.000 0.256
#> GSM549245 4 0.0510 0.8613 0.016 0.000 0.000 0.984 0.000
#> GSM549265 4 0.3728 0.7278 0.244 0.000 0.000 0.748 0.008
#> GSM549282 3 0.0162 0.9243 0.000 0.000 0.996 0.000 0.004
#> GSM549296 4 0.1836 0.8419 0.000 0.000 0.032 0.932 0.036
#> GSM750739 1 0.3752 0.5920 0.708 0.000 0.000 0.000 0.292
#> GSM750742 1 0.0609 0.7415 0.980 0.000 0.000 0.000 0.020
#> GSM750744 1 0.3074 0.6533 0.804 0.000 0.000 0.000 0.196
#> GSM750750 3 0.0000 0.9240 0.000 0.000 1.000 0.000 0.000
#> GSM549242 1 0.1469 0.7327 0.948 0.000 0.000 0.036 0.016
#> GSM549252 4 0.2929 0.7877 0.180 0.000 0.000 0.820 0.000
#> GSM549253 1 0.0510 0.7373 0.984 0.000 0.000 0.016 0.000
#> GSM549256 1 0.2516 0.6409 0.860 0.000 0.000 0.140 0.000
#> GSM549257 4 0.0880 0.8600 0.032 0.000 0.000 0.968 0.000
#> GSM549263 1 0.0404 0.7389 0.988 0.000 0.000 0.012 0.000
#> GSM549267 3 0.4269 0.6277 0.000 0.000 0.732 0.232 0.036
#> GSM750745 5 0.4242 0.1434 0.428 0.000 0.000 0.000 0.572
#> GSM549239 1 0.4304 0.0856 0.516 0.000 0.000 0.000 0.484
#> GSM549244 4 0.2179 0.8332 0.112 0.000 0.000 0.888 0.000
#> GSM549249 4 0.2605 0.8132 0.148 0.000 0.000 0.852 0.000
#> GSM549260 1 0.3906 0.6030 0.704 0.000 0.000 0.004 0.292
#> GSM549266 5 0.4307 -0.2900 0.000 0.500 0.000 0.000 0.500
#> GSM549293 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549236 1 0.0510 0.7378 0.984 0.000 0.000 0.016 0.000
#> GSM549238 1 0.4182 0.1359 0.600 0.000 0.000 0.400 0.000
#> GSM549251 1 0.0404 0.7389 0.988 0.000 0.000 0.012 0.000
#> GSM549258 5 0.3210 0.6590 0.212 0.000 0.000 0.000 0.788
#> GSM549264 1 0.0404 0.7414 0.988 0.000 0.000 0.000 0.012
#> GSM549243 1 0.3508 0.6394 0.748 0.000 0.000 0.000 0.252
#> GSM549262 1 0.0609 0.7416 0.980 0.000 0.000 0.000 0.020
#> GSM549278 4 0.3649 0.7601 0.000 0.000 0.152 0.808 0.040
#> GSM549283 2 0.1965 0.8355 0.000 0.924 0.024 0.000 0.052
#> GSM549298 3 0.1121 0.8958 0.000 0.044 0.956 0.000 0.000
#> GSM750741 5 0.1851 0.7446 0.088 0.000 0.000 0.000 0.912
#> GSM549286 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549241 5 0.2377 0.7368 0.128 0.000 0.000 0.000 0.872
#> GSM549247 5 0.3016 0.7107 0.032 0.040 0.000 0.044 0.884
#> GSM549261 1 0.3876 0.5706 0.684 0.000 0.000 0.000 0.316
#> GSM549270 2 0.0324 0.8639 0.000 0.992 0.004 0.000 0.004
#> GSM549277 2 0.4171 0.4135 0.000 0.604 0.396 0.000 0.000
#> GSM549280 2 0.4151 0.5173 0.000 0.652 0.344 0.000 0.004
#> GSM549281 2 0.4549 0.2964 0.000 0.528 0.008 0.000 0.464
#> GSM549285 3 0.0324 0.9222 0.000 0.004 0.992 0.000 0.004
#> GSM549288 2 0.3534 0.6596 0.000 0.744 0.256 0.000 0.000
#> GSM549292 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549295 2 0.3336 0.6928 0.000 0.772 0.228 0.000 0.000
#> GSM549297 2 0.1571 0.8370 0.000 0.936 0.060 0.000 0.004
#> GSM750743 5 0.4306 -0.0952 0.492 0.000 0.000 0.000 0.508
#> GSM549268 2 0.5042 0.2887 0.000 0.508 0.032 0.000 0.460
#> GSM549290 3 0.4240 0.6369 0.000 0.000 0.736 0.228 0.036
#> GSM549272 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM549276 2 0.0000 0.8649 0.000 1.000 0.000 0.000 0.000
#> GSM549275 5 0.2694 0.7351 0.076 0.040 0.000 0.000 0.884
#> GSM549284 2 0.0162 0.8653 0.000 0.996 0.000 0.000 0.004
#> GSM750737 4 0.3455 0.6887 0.008 0.000 0.000 0.784 0.208
#> GSM750740 1 0.3816 0.5873 0.696 0.000 0.000 0.000 0.304
#> GSM750747 1 0.3816 0.5873 0.696 0.000 0.000 0.000 0.304
#> GSM750751 2 0.0000 0.8649 0.000 1.000 0.000 0.000 0.000
#> GSM750754 3 0.1493 0.9021 0.000 0.000 0.948 0.028 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.3473 0.744 0.004 0.000 0.000 0.780 0.024 0.192
#> GSM549291 4 0.6014 0.387 0.004 0.000 0.276 0.472 0.000 0.248
#> GSM549274 2 0.0260 0.798 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM750738 2 0.1092 0.776 0.000 0.960 0.000 0.020 0.000 0.020
#> GSM750748 5 0.4420 0.513 0.308 0.000 0.000 0.000 0.644 0.048
#> GSM549240 1 0.5317 0.431 0.628 0.008 0.000 0.036 0.048 0.280
#> GSM549279 6 0.5901 0.855 0.240 0.292 0.000 0.000 0.000 0.468
#> GSM549294 2 0.1349 0.787 0.004 0.940 0.000 0.000 0.000 0.056
#> GSM549300 3 0.4085 0.598 0.000 0.156 0.748 0.000 0.000 0.096
#> GSM549303 3 0.0632 0.859 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM549309 3 0.1327 0.850 0.000 0.000 0.936 0.000 0.000 0.064
#> GSM750753 2 0.1787 0.780 0.004 0.920 0.008 0.000 0.000 0.068
#> GSM750752 4 0.4065 0.686 0.000 0.000 0.056 0.724 0.000 0.220
#> GSM549304 2 0.0508 0.796 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM549305 2 0.1152 0.792 0.004 0.952 0.000 0.000 0.000 0.044
#> GSM549307 2 0.5249 0.244 0.000 0.484 0.420 0.000 0.000 0.096
#> GSM549306 3 0.2740 0.773 0.000 0.060 0.864 0.000 0.000 0.076
#> GSM549308 3 0.0937 0.849 0.000 0.000 0.960 0.000 0.000 0.040
#> GSM549233 5 0.2982 0.607 0.004 0.000 0.000 0.164 0.820 0.012
#> GSM549234 4 0.2060 0.750 0.000 0.000 0.000 0.900 0.084 0.016
#> GSM549250 5 0.1829 0.681 0.004 0.000 0.000 0.064 0.920 0.012
#> GSM549287 3 0.2378 0.807 0.000 0.000 0.848 0.000 0.000 0.152
#> GSM750735 1 0.4196 0.606 0.740 0.000 0.000 0.000 0.116 0.144
#> GSM750736 1 0.3347 0.623 0.824 0.000 0.000 0.004 0.068 0.104
#> GSM750749 1 0.4953 0.251 0.600 0.008 0.000 0.004 0.052 0.336
#> GSM549230 5 0.0717 0.706 0.008 0.000 0.000 0.000 0.976 0.016
#> GSM549231 5 0.0508 0.704 0.012 0.000 0.000 0.000 0.984 0.004
#> GSM549237 5 0.2575 0.683 0.100 0.000 0.000 0.004 0.872 0.024
#> GSM549254 4 0.2312 0.750 0.012 0.000 0.000 0.876 0.000 0.112
#> GSM750734 1 0.4437 0.328 0.576 0.000 0.000 0.000 0.392 0.032
#> GSM549271 3 0.2191 0.825 0.000 0.000 0.876 0.004 0.000 0.120
#> GSM549232 4 0.0603 0.767 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM549246 5 0.5477 0.196 0.020 0.000 0.000 0.344 0.552 0.084
#> GSM549248 5 0.1349 0.698 0.056 0.000 0.000 0.000 0.940 0.004
#> GSM549255 4 0.0508 0.766 0.000 0.000 0.000 0.984 0.004 0.012
#> GSM750746 5 0.4386 0.517 0.300 0.000 0.000 0.000 0.652 0.048
#> GSM549259 5 0.4720 0.360 0.388 0.000 0.000 0.000 0.560 0.052
#> GSM549269 2 0.0260 0.798 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549273 3 0.1082 0.857 0.000 0.004 0.956 0.000 0.000 0.040
#> GSM549299 2 0.2114 0.771 0.008 0.904 0.012 0.000 0.000 0.076
#> GSM549301 3 0.1411 0.837 0.000 0.004 0.936 0.000 0.000 0.060
#> GSM549310 4 0.5834 0.457 0.000 0.004 0.236 0.520 0.000 0.240
#> GSM549311 3 0.0937 0.856 0.000 0.000 0.960 0.000 0.000 0.040
#> GSM549302 2 0.0260 0.798 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549235 5 0.4233 0.554 0.268 0.000 0.000 0.000 0.684 0.048
#> GSM549245 4 0.0692 0.766 0.000 0.000 0.000 0.976 0.004 0.020
#> GSM549265 4 0.5177 0.624 0.032 0.000 0.000 0.660 0.224 0.084
#> GSM549282 3 0.0632 0.858 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM549296 4 0.3539 0.703 0.000 0.000 0.024 0.756 0.000 0.220
#> GSM750739 5 0.4276 0.253 0.416 0.000 0.000 0.000 0.564 0.020
#> GSM750742 5 0.1196 0.703 0.040 0.000 0.000 0.000 0.952 0.008
#> GSM750744 5 0.4333 0.247 0.376 0.000 0.000 0.000 0.596 0.028
#> GSM750750 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549242 5 0.2883 0.683 0.036 0.000 0.000 0.076 0.868 0.020
#> GSM549252 4 0.3806 0.669 0.000 0.000 0.000 0.752 0.200 0.048
#> GSM549253 5 0.0820 0.701 0.000 0.000 0.000 0.016 0.972 0.012
#> GSM549256 5 0.3354 0.596 0.016 0.000 0.000 0.184 0.792 0.008
#> GSM549257 4 0.0725 0.765 0.000 0.000 0.000 0.976 0.012 0.012
#> GSM549263 5 0.0260 0.704 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM549267 3 0.5173 0.514 0.000 0.000 0.616 0.160 0.000 0.224
#> GSM750745 1 0.3534 0.579 0.740 0.000 0.000 0.000 0.244 0.016
#> GSM549239 1 0.3979 0.394 0.628 0.000 0.000 0.000 0.360 0.012
#> GSM549244 4 0.2867 0.733 0.000 0.000 0.000 0.848 0.112 0.040
#> GSM549249 4 0.3453 0.700 0.000 0.000 0.000 0.792 0.164 0.044
#> GSM549260 5 0.5216 0.441 0.320 0.000 0.000 0.028 0.596 0.056
#> GSM549266 6 0.6158 0.820 0.284 0.292 0.004 0.000 0.000 0.420
#> GSM549293 2 0.0260 0.798 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549236 5 0.1644 0.687 0.004 0.000 0.000 0.052 0.932 0.012
#> GSM549238 5 0.4400 0.227 0.000 0.000 0.000 0.376 0.592 0.032
#> GSM549251 5 0.0767 0.702 0.004 0.000 0.000 0.012 0.976 0.008
#> GSM549258 1 0.4387 0.654 0.720 0.000 0.000 0.000 0.152 0.128
#> GSM549264 5 0.2573 0.658 0.112 0.000 0.000 0.000 0.864 0.024
#> GSM549243 5 0.3989 0.573 0.236 0.000 0.000 0.000 0.720 0.044
#> GSM549262 5 0.1411 0.700 0.060 0.000 0.000 0.000 0.936 0.004
#> GSM549278 4 0.5374 0.593 0.000 0.000 0.168 0.580 0.000 0.252
#> GSM549283 2 0.3853 0.622 0.028 0.788 0.036 0.000 0.000 0.148
#> GSM549298 3 0.2145 0.811 0.000 0.028 0.900 0.000 0.000 0.072
#> GSM750741 1 0.3049 0.617 0.844 0.000 0.000 0.004 0.048 0.104
#> GSM549286 2 0.0405 0.799 0.004 0.988 0.000 0.000 0.000 0.008
#> GSM549241 1 0.4085 0.621 0.748 0.000 0.000 0.000 0.096 0.156
#> GSM549247 1 0.5260 0.344 0.632 0.028 0.000 0.040 0.016 0.284
#> GSM549261 5 0.4635 0.461 0.336 0.000 0.000 0.000 0.608 0.056
#> GSM549270 2 0.1787 0.779 0.004 0.920 0.008 0.000 0.000 0.068
#> GSM549277 2 0.5359 0.204 0.000 0.460 0.432 0.000 0.000 0.108
#> GSM549280 2 0.5336 0.336 0.000 0.544 0.332 0.000 0.000 0.124
#> GSM549281 6 0.6182 0.876 0.208 0.256 0.024 0.000 0.000 0.512
#> GSM549285 3 0.1196 0.848 0.000 0.008 0.952 0.000 0.000 0.040
#> GSM549288 2 0.5016 0.406 0.000 0.592 0.312 0.000 0.000 0.096
#> GSM549292 2 0.0260 0.798 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549295 2 0.4771 0.490 0.000 0.652 0.248 0.000 0.000 0.100
#> GSM549297 2 0.3468 0.692 0.004 0.816 0.088 0.000 0.000 0.092
#> GSM750743 1 0.4229 0.537 0.668 0.000 0.000 0.000 0.292 0.040
#> GSM549268 6 0.6446 0.843 0.200 0.220 0.056 0.000 0.000 0.524
#> GSM549290 3 0.5473 0.418 0.000 0.000 0.568 0.192 0.000 0.240
#> GSM549272 2 0.0291 0.798 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM549276 2 0.0692 0.797 0.004 0.976 0.000 0.000 0.000 0.020
#> GSM549275 1 0.4493 0.555 0.728 0.036 0.000 0.000 0.044 0.192
#> GSM549284 2 0.0405 0.798 0.004 0.988 0.000 0.000 0.000 0.008
#> GSM750737 4 0.4970 0.500 0.252 0.000 0.000 0.640 0.004 0.104
#> GSM750740 5 0.4593 0.481 0.324 0.000 0.000 0.000 0.620 0.056
#> GSM750747 5 0.4593 0.481 0.324 0.000 0.000 0.000 0.620 0.056
#> GSM750751 2 0.0508 0.798 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM750754 3 0.2703 0.793 0.000 0.000 0.824 0.004 0.000 0.172
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:skmeans 103 0.023 1.72e-05 0.05361 0.0048 2
#> SD:skmeans 97 0.200 1.93e-04 0.00197 0.0178 3
#> SD:skmeans 100 0.484 1.97e-05 0.00159 0.0706 4
#> SD:skmeans 90 0.631 8.60e-04 0.00268 0.2660 5
#> SD:skmeans 81 0.589 1.17e-03 0.01123 0.0655 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.734 0.859 0.937 0.4566 0.567 0.567
#> 3 3 0.601 0.770 0.879 0.4276 0.706 0.516
#> 4 4 0.629 0.684 0.813 0.1076 0.908 0.753
#> 5 5 0.863 0.830 0.913 0.0978 0.864 0.574
#> 6 6 0.821 0.724 0.865 0.0313 0.983 0.918
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
#> GSM549289 1 0.0000 0.913 1.000 0.000
#> GSM549291 1 0.3879 0.860 0.924 0.076
#> GSM549274 1 0.9775 0.416 0.588 0.412
#> GSM750738 1 0.9795 0.407 0.584 0.416
#> GSM750748 1 0.0000 0.913 1.000 0.000
#> GSM549240 1 0.0000 0.913 1.000 0.000
#> GSM549279 1 0.8443 0.661 0.728 0.272
#> GSM549294 2 0.0672 0.966 0.008 0.992
#> GSM549300 2 0.0000 0.972 0.000 1.000
#> GSM549303 2 0.0000 0.972 0.000 1.000
#> GSM549309 2 0.0376 0.969 0.004 0.996
#> GSM750753 2 0.0000 0.972 0.000 1.000
#> GSM750752 1 0.9775 0.414 0.588 0.412
#> GSM549304 1 0.9795 0.407 0.584 0.416
#> GSM549305 2 0.0000 0.972 0.000 1.000
#> GSM549307 2 0.0000 0.972 0.000 1.000
#> GSM549306 2 0.0000 0.972 0.000 1.000
#> GSM549308 2 0.0000 0.972 0.000 1.000
#> GSM549233 1 0.0000 0.913 1.000 0.000
#> GSM549234 1 0.0000 0.913 1.000 0.000
#> GSM549250 1 0.0000 0.913 1.000 0.000
#> GSM549287 2 0.0672 0.966 0.008 0.992
#> GSM750735 1 0.0000 0.913 1.000 0.000
#> GSM750736 1 0.0000 0.913 1.000 0.000
#> GSM750749 1 0.0000 0.913 1.000 0.000
#> GSM549230 1 0.0000 0.913 1.000 0.000
#> GSM549231 1 0.0000 0.913 1.000 0.000
#> GSM549237 1 0.0000 0.913 1.000 0.000
#> GSM549254 1 0.1184 0.903 0.984 0.016
#> GSM750734 1 0.0000 0.913 1.000 0.000
#> GSM549271 2 0.0672 0.966 0.008 0.992
#> GSM549232 1 0.0000 0.913 1.000 0.000
#> GSM549246 1 0.0000 0.913 1.000 0.000
#> GSM549248 1 0.0000 0.913 1.000 0.000
#> GSM549255 1 0.0000 0.913 1.000 0.000
#> GSM750746 1 0.0000 0.913 1.000 0.000
#> GSM549259 1 0.0000 0.913 1.000 0.000
#> GSM549269 1 0.9754 0.425 0.592 0.408
#> GSM549273 2 0.0000 0.972 0.000 1.000
#> GSM549299 2 0.9710 0.195 0.400 0.600
#> GSM549301 2 0.0000 0.972 0.000 1.000
#> GSM549310 2 0.0000 0.972 0.000 1.000
#> GSM549311 2 0.0000 0.972 0.000 1.000
#> GSM549302 2 0.0000 0.972 0.000 1.000
#> GSM549235 1 0.0000 0.913 1.000 0.000
#> GSM549245 1 0.0000 0.913 1.000 0.000
#> GSM549265 1 0.0000 0.913 1.000 0.000
#> GSM549282 2 0.0938 0.963 0.012 0.988
#> GSM549296 1 0.9608 0.475 0.616 0.384
#> GSM750739 1 0.0000 0.913 1.000 0.000
#> GSM750742 1 0.0000 0.913 1.000 0.000
#> GSM750744 1 0.0000 0.913 1.000 0.000
#> GSM750750 2 0.0938 0.963 0.012 0.988
#> GSM549242 1 0.0000 0.913 1.000 0.000
#> GSM549252 1 0.0000 0.913 1.000 0.000
#> GSM549253 1 0.0000 0.913 1.000 0.000
#> GSM549256 1 0.0000 0.913 1.000 0.000
#> GSM549257 1 0.0000 0.913 1.000 0.000
#> GSM549263 1 0.0000 0.913 1.000 0.000
#> GSM549267 2 0.6343 0.795 0.160 0.840
#> GSM750745 1 0.0000 0.913 1.000 0.000
#> GSM549239 1 0.0000 0.913 1.000 0.000
#> GSM549244 1 0.0000 0.913 1.000 0.000
#> GSM549249 1 0.0000 0.913 1.000 0.000
#> GSM549260 1 0.0000 0.913 1.000 0.000
#> GSM549266 1 0.5946 0.803 0.856 0.144
#> GSM549293 1 0.9795 0.407 0.584 0.416
#> GSM549236 1 0.0000 0.913 1.000 0.000
#> GSM549238 1 0.0000 0.913 1.000 0.000
#> GSM549251 1 0.0000 0.913 1.000 0.000
#> GSM549258 1 0.0000 0.913 1.000 0.000
#> GSM549264 1 0.0000 0.913 1.000 0.000
#> GSM549243 1 0.0000 0.913 1.000 0.000
#> GSM549262 1 0.0000 0.913 1.000 0.000
#> GSM549278 1 0.0000 0.913 1.000 0.000
#> GSM549283 1 0.9323 0.542 0.652 0.348
#> GSM549298 2 0.0000 0.972 0.000 1.000
#> GSM750741 1 0.0000 0.913 1.000 0.000
#> GSM549286 2 0.0000 0.972 0.000 1.000
#> GSM549241 1 0.0000 0.913 1.000 0.000
#> GSM549247 1 0.0376 0.911 0.996 0.004
#> GSM549261 1 0.0000 0.913 1.000 0.000
#> GSM549270 2 0.0000 0.972 0.000 1.000
#> GSM549277 2 0.0000 0.972 0.000 1.000
#> GSM549280 2 0.0000 0.972 0.000 1.000
#> GSM549281 1 0.8081 0.693 0.752 0.248
#> GSM549285 1 0.8267 0.680 0.740 0.260
#> GSM549288 2 0.0000 0.972 0.000 1.000
#> GSM549292 2 0.6048 0.794 0.148 0.852
#> GSM549295 2 0.0000 0.972 0.000 1.000
#> GSM549297 2 0.0000 0.972 0.000 1.000
#> GSM750743 1 0.0000 0.913 1.000 0.000
#> GSM549268 1 0.8555 0.651 0.720 0.280
#> GSM549290 1 0.8443 0.635 0.728 0.272
#> GSM549272 2 0.0000 0.972 0.000 1.000
#> GSM549276 2 0.0000 0.972 0.000 1.000
#> GSM549275 1 0.5946 0.803 0.856 0.144
#> GSM549284 1 0.9909 0.334 0.556 0.444
#> GSM750737 1 0.0000 0.913 1.000 0.000
#> GSM750740 1 0.0000 0.913 1.000 0.000
#> GSM750747 1 0.0000 0.913 1.000 0.000
#> GSM750751 2 0.0000 0.972 0.000 1.000
#> GSM750754 1 0.9209 0.537 0.664 0.336
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.0000 0.8434 0.000 0.000 1.000
#> GSM549291 3 0.0000 0.8434 0.000 0.000 1.000
#> GSM549274 2 0.9947 0.0168 0.316 0.384 0.300
#> GSM750738 3 0.8427 0.5831 0.208 0.172 0.620
#> GSM750748 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549240 1 0.0592 0.8561 0.988 0.000 0.012
#> GSM549279 1 0.1411 0.8424 0.964 0.036 0.000
#> GSM549294 2 0.0424 0.8502 0.008 0.992 0.000
#> GSM549300 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549303 2 0.2878 0.8029 0.000 0.904 0.096
#> GSM549309 2 0.4750 0.6818 0.000 0.784 0.216
#> GSM750753 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM750752 3 0.4755 0.7784 0.184 0.008 0.808
#> GSM549304 2 0.9820 0.1270 0.312 0.424 0.264
#> GSM549305 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549307 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549306 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549308 2 0.1163 0.8443 0.000 0.972 0.028
#> GSM549233 1 0.5678 0.6906 0.684 0.000 0.316
#> GSM549234 3 0.3619 0.8272 0.136 0.000 0.864
#> GSM549250 1 0.4931 0.7847 0.768 0.000 0.232
#> GSM549287 2 0.5465 0.5781 0.000 0.712 0.288
#> GSM750735 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM750749 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549230 1 0.4504 0.8099 0.804 0.000 0.196
#> GSM549231 1 0.4452 0.8121 0.808 0.000 0.192
#> GSM549237 1 0.4399 0.8144 0.812 0.000 0.188
#> GSM549254 3 0.4750 0.7801 0.216 0.000 0.784
#> GSM750734 1 0.1031 0.8595 0.976 0.000 0.024
#> GSM549271 3 0.2066 0.8123 0.000 0.060 0.940
#> GSM549232 3 0.2261 0.8456 0.068 0.000 0.932
#> GSM549246 1 0.6140 0.5286 0.596 0.000 0.404
#> GSM549248 1 0.4452 0.8121 0.808 0.000 0.192
#> GSM549255 3 0.4750 0.7801 0.216 0.000 0.784
#> GSM750746 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549269 2 0.7773 0.4871 0.316 0.612 0.072
#> GSM549273 2 0.1289 0.8427 0.000 0.968 0.032
#> GSM549299 2 0.5363 0.6134 0.276 0.724 0.000
#> GSM549301 2 0.1643 0.8367 0.000 0.956 0.044
#> GSM549310 3 0.4504 0.6628 0.000 0.196 0.804
#> GSM549311 2 0.4121 0.7385 0.000 0.832 0.168
#> GSM549302 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549235 1 0.4399 0.8135 0.812 0.000 0.188
#> GSM549245 3 0.3038 0.8385 0.104 0.000 0.896
#> GSM549265 3 0.0237 0.8439 0.004 0.000 0.996
#> GSM549282 2 0.5431 0.6120 0.000 0.716 0.284
#> GSM549296 3 0.4755 0.7784 0.184 0.008 0.808
#> GSM750739 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM750742 1 0.4452 0.8121 0.808 0.000 0.192
#> GSM750744 1 0.4062 0.8244 0.836 0.000 0.164
#> GSM750750 2 0.3192 0.7904 0.000 0.888 0.112
#> GSM549242 1 0.4654 0.8027 0.792 0.000 0.208
#> GSM549252 3 0.1163 0.8414 0.028 0.000 0.972
#> GSM549253 1 0.4702 0.7994 0.788 0.000 0.212
#> GSM549256 1 0.5497 0.7032 0.708 0.000 0.292
#> GSM549257 3 0.4750 0.7801 0.216 0.000 0.784
#> GSM549263 1 0.4654 0.8024 0.792 0.000 0.208
#> GSM549267 3 0.0000 0.8434 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549244 3 0.0000 0.8434 0.000 0.000 1.000
#> GSM549249 3 0.1163 0.8414 0.028 0.000 0.972
#> GSM549260 1 0.1753 0.8546 0.952 0.000 0.048
#> GSM549266 1 0.2878 0.7939 0.904 0.096 0.000
#> GSM549293 2 0.9882 0.0842 0.312 0.408 0.280
#> GSM549236 1 0.4796 0.7940 0.780 0.000 0.220
#> GSM549238 3 0.6252 -0.1182 0.444 0.000 0.556
#> GSM549251 1 0.4702 0.7994 0.788 0.000 0.212
#> GSM549258 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549264 1 0.4452 0.8121 0.808 0.000 0.192
#> GSM549243 1 0.0237 0.8605 0.996 0.000 0.004
#> GSM549262 1 0.4452 0.8121 0.808 0.000 0.192
#> GSM549278 3 0.1163 0.8463 0.028 0.000 0.972
#> GSM549283 1 0.5797 0.5287 0.712 0.280 0.008
#> GSM549298 2 0.1163 0.8443 0.000 0.972 0.028
#> GSM750741 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549247 1 0.4002 0.7055 0.840 0.000 0.160
#> GSM549261 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549281 1 0.1964 0.8283 0.944 0.056 0.000
#> GSM549285 1 0.7548 0.7236 0.684 0.112 0.204
#> GSM549288 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549292 2 0.7595 0.5829 0.136 0.688 0.176
#> GSM549295 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM549268 1 0.3192 0.7823 0.888 0.112 0.000
#> GSM549290 3 0.0000 0.8434 0.000 0.000 1.000
#> GSM549272 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM549275 1 0.2625 0.8051 0.916 0.084 0.000
#> GSM549284 2 0.6769 0.5356 0.320 0.652 0.028
#> GSM750737 3 0.5291 0.7363 0.268 0.000 0.732
#> GSM750740 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.8604 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.8537 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.8434 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.4313 0.77334 0.260 0.000 0.004 0.736
#> GSM549291 4 0.5083 0.76596 0.248 0.000 0.036 0.716
#> GSM549274 2 0.0000 0.79657 0.000 1.000 0.000 0.000
#> GSM750738 2 0.3975 0.64670 0.000 0.760 0.000 0.240
#> GSM750748 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549240 1 0.6936 0.72597 0.548 0.132 0.000 0.320
#> GSM549279 1 0.6680 0.75347 0.604 0.136 0.000 0.260
#> GSM549294 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549300 3 0.3873 0.57335 0.000 0.228 0.772 0.000
#> GSM549303 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549309 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM750753 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM750752 4 0.5491 0.56721 0.000 0.260 0.052 0.688
#> GSM549304 2 0.0336 0.80427 0.000 0.992 0.008 0.000
#> GSM549305 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549307 3 0.4776 0.19340 0.000 0.376 0.624 0.000
#> GSM549306 3 0.1389 0.80288 0.000 0.048 0.952 0.000
#> GSM549308 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549233 1 0.3726 0.31263 0.788 0.000 0.000 0.212
#> GSM549234 4 0.2973 0.75792 0.144 0.000 0.000 0.856
#> GSM549250 1 0.2469 0.51868 0.892 0.000 0.000 0.108
#> GSM549287 3 0.3501 0.71217 0.132 0.000 0.848 0.020
#> GSM750735 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM750736 1 0.6708 0.75160 0.596 0.132 0.000 0.272
#> GSM750749 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549230 1 0.0592 0.61334 0.984 0.000 0.000 0.016
#> GSM549231 1 0.0336 0.61828 0.992 0.000 0.000 0.008
#> GSM549237 1 0.0927 0.62402 0.976 0.008 0.000 0.016
#> GSM549254 4 0.0336 0.65422 0.008 0.000 0.000 0.992
#> GSM750734 1 0.6308 0.74495 0.648 0.120 0.000 0.232
#> GSM549271 4 0.7344 0.57426 0.168 0.012 0.248 0.572
#> GSM549232 4 0.3688 0.77760 0.208 0.000 0.000 0.792
#> GSM549246 1 0.4323 0.38338 0.776 0.020 0.000 0.204
#> GSM549248 1 0.0469 0.61596 0.988 0.000 0.000 0.012
#> GSM549255 4 0.0000 0.65437 0.000 0.000 0.000 1.000
#> GSM750746 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549259 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549269 2 0.0000 0.79657 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549299 2 0.1118 0.82202 0.000 0.964 0.036 0.000
#> GSM549301 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549310 4 0.6100 0.52533 0.000 0.272 0.084 0.644
#> GSM549311 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549302 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549235 1 0.0707 0.63196 0.980 0.000 0.000 0.020
#> GSM549245 4 0.3123 0.76380 0.156 0.000 0.000 0.844
#> GSM549265 4 0.4222 0.76882 0.272 0.000 0.000 0.728
#> GSM549282 3 0.4134 0.56975 0.260 0.000 0.740 0.000
#> GSM549296 4 0.5144 0.60784 0.000 0.216 0.052 0.732
#> GSM750739 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM750742 1 0.0336 0.61828 0.992 0.000 0.000 0.008
#> GSM750744 1 0.3687 0.67745 0.856 0.080 0.000 0.064
#> GSM750750 3 0.0000 0.84231 0.000 0.000 1.000 0.000
#> GSM549242 1 0.2412 0.56820 0.908 0.008 0.000 0.084
#> GSM549252 4 0.4134 0.77330 0.260 0.000 0.000 0.740
#> GSM549253 1 0.2011 0.55746 0.920 0.000 0.000 0.080
#> GSM549256 1 0.4290 0.47483 0.772 0.016 0.000 0.212
#> GSM549257 4 0.0469 0.66362 0.012 0.000 0.000 0.988
#> GSM549263 1 0.0921 0.60564 0.972 0.000 0.000 0.028
#> GSM549267 4 0.6532 0.66937 0.368 0.000 0.084 0.548
#> GSM750745 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549239 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549244 4 0.4134 0.77330 0.260 0.000 0.000 0.740
#> GSM549249 4 0.4830 0.68195 0.392 0.000 0.000 0.608
#> GSM549260 1 0.6783 0.71817 0.572 0.124 0.000 0.304
#> GSM549266 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549293 2 0.0524 0.80593 0.000 0.988 0.008 0.004
#> GSM549236 1 0.1792 0.57075 0.932 0.000 0.000 0.068
#> GSM549238 1 0.4746 -0.22191 0.632 0.000 0.000 0.368
#> GSM549251 1 0.1389 0.59089 0.952 0.000 0.000 0.048
#> GSM549258 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549264 1 0.0336 0.61908 0.992 0.000 0.000 0.008
#> GSM549243 1 0.6442 0.75347 0.632 0.124 0.000 0.244
#> GSM549262 1 0.0336 0.61828 0.992 0.000 0.000 0.008
#> GSM549278 4 0.4188 0.77750 0.244 0.000 0.004 0.752
#> GSM549283 2 0.7149 -0.00892 0.184 0.552 0.000 0.264
#> GSM549298 3 0.0188 0.83997 0.000 0.004 0.996 0.000
#> GSM750741 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549286 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549241 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549247 1 0.7113 0.66676 0.484 0.132 0.000 0.384
#> GSM549261 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549270 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549277 2 0.4643 0.60977 0.000 0.656 0.344 0.000
#> GSM549280 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549281 1 0.6680 0.75322 0.604 0.136 0.000 0.260
#> GSM549285 1 0.4605 0.13302 0.664 0.000 0.336 0.000
#> GSM549288 2 0.3123 0.84475 0.000 0.844 0.156 0.000
#> GSM549292 2 0.3453 0.81594 0.000 0.868 0.052 0.080
#> GSM549295 2 0.4193 0.73234 0.000 0.732 0.268 0.000
#> GSM549297 2 0.4008 0.76102 0.000 0.756 0.244 0.000
#> GSM750743 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549268 1 0.7081 0.72183 0.588 0.188 0.004 0.220
#> GSM549290 4 0.6489 0.66909 0.372 0.000 0.080 0.548
#> GSM549272 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549276 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM549275 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM549284 2 0.0469 0.80738 0.000 0.988 0.012 0.000
#> GSM750737 4 0.2124 0.57780 0.008 0.068 0.000 0.924
#> GSM750740 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM750747 1 0.6637 0.75546 0.608 0.132 0.000 0.260
#> GSM750751 2 0.2814 0.85977 0.000 0.868 0.132 0.000
#> GSM750754 3 0.7884 -0.26238 0.308 0.000 0.384 0.308
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.0609 0.9272 0.000 0.000 0.000 0.980 0.020
#> GSM549291 4 0.0404 0.9248 0.000 0.000 0.000 0.988 0.012
#> GSM549274 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM750738 2 0.4273 0.1738 0.000 0.552 0.000 0.448 0.000
#> GSM750748 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.1364 0.9172 0.952 0.000 0.000 0.036 0.012
#> GSM549279 1 0.0404 0.9511 0.988 0.000 0.000 0.000 0.012
#> GSM549294 2 0.0162 0.9362 0.000 0.996 0.000 0.000 0.004
#> GSM549300 3 0.4283 0.3734 0.000 0.348 0.644 0.000 0.008
#> GSM549303 3 0.0807 0.8650 0.000 0.000 0.976 0.012 0.012
#> GSM549309 3 0.1522 0.8560 0.000 0.000 0.944 0.012 0.044
#> GSM750753 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM750752 4 0.1243 0.9024 0.000 0.008 0.004 0.960 0.028
#> GSM549304 2 0.0162 0.9362 0.000 0.996 0.000 0.000 0.004
#> GSM549305 2 0.0324 0.9353 0.000 0.992 0.004 0.000 0.004
#> GSM549307 2 0.4555 0.1119 0.000 0.520 0.472 0.000 0.008
#> GSM549306 3 0.0579 0.8617 0.000 0.008 0.984 0.000 0.008
#> GSM549308 3 0.0000 0.8662 0.000 0.000 1.000 0.000 0.000
#> GSM549233 5 0.1956 0.8200 0.008 0.000 0.000 0.076 0.916
#> GSM549234 4 0.0609 0.9264 0.000 0.000 0.000 0.980 0.020
#> GSM549250 5 0.1697 0.8242 0.008 0.000 0.000 0.060 0.932
#> GSM549287 3 0.3278 0.7741 0.000 0.000 0.824 0.020 0.156
#> GSM750735 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM750736 1 0.0162 0.9540 0.996 0.000 0.000 0.004 0.000
#> GSM750749 1 0.0290 0.9523 0.992 0.000 0.000 0.000 0.008
#> GSM549230 5 0.1732 0.8372 0.080 0.000 0.000 0.000 0.920
#> GSM549231 5 0.1732 0.8372 0.080 0.000 0.000 0.000 0.920
#> GSM549237 5 0.2536 0.8202 0.128 0.000 0.000 0.004 0.868
#> GSM549254 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM750734 1 0.3210 0.6984 0.788 0.000 0.000 0.000 0.212
#> GSM549271 4 0.2661 0.8481 0.000 0.000 0.056 0.888 0.056
#> GSM549232 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM549246 5 0.2989 0.8238 0.060 0.000 0.000 0.072 0.868
#> GSM549248 5 0.2127 0.8297 0.108 0.000 0.000 0.000 0.892
#> GSM549255 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM750746 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.0693 0.8657 0.000 0.000 0.980 0.012 0.008
#> GSM549299 2 0.0693 0.9307 0.000 0.980 0.012 0.000 0.008
#> GSM549301 3 0.0162 0.8655 0.000 0.000 0.996 0.000 0.004
#> GSM549310 4 0.2381 0.8657 0.000 0.004 0.036 0.908 0.052
#> GSM549311 3 0.1597 0.8544 0.000 0.000 0.940 0.012 0.048
#> GSM549302 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549235 5 0.4192 0.4656 0.404 0.000 0.000 0.000 0.596
#> GSM549245 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM549265 4 0.3932 0.4679 0.000 0.000 0.000 0.672 0.328
#> GSM549282 3 0.4166 0.4606 0.000 0.000 0.648 0.004 0.348
#> GSM549296 4 0.0566 0.9179 0.000 0.000 0.004 0.984 0.012
#> GSM750739 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM750742 5 0.1732 0.8372 0.080 0.000 0.000 0.000 0.920
#> GSM750744 5 0.4150 0.4554 0.388 0.000 0.000 0.000 0.612
#> GSM750750 3 0.0162 0.8664 0.000 0.000 0.996 0.000 0.004
#> GSM549242 5 0.3269 0.8154 0.096 0.000 0.000 0.056 0.848
#> GSM549252 4 0.1197 0.9069 0.000 0.000 0.000 0.952 0.048
#> GSM549253 5 0.1740 0.8263 0.012 0.000 0.000 0.056 0.932
#> GSM549256 5 0.3226 0.8104 0.088 0.000 0.000 0.060 0.852
#> GSM549257 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM549263 5 0.1704 0.8394 0.068 0.000 0.000 0.004 0.928
#> GSM549267 5 0.5039 0.0657 0.000 0.000 0.032 0.456 0.512
#> GSM750745 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM549249 5 0.2280 0.7962 0.000 0.000 0.000 0.120 0.880
#> GSM549260 1 0.2922 0.8517 0.872 0.000 0.000 0.056 0.072
#> GSM549266 1 0.0404 0.9511 0.988 0.000 0.000 0.000 0.012
#> GSM549293 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549236 5 0.1830 0.8375 0.040 0.000 0.000 0.028 0.932
#> GSM549238 5 0.1697 0.8242 0.008 0.000 0.000 0.060 0.932
#> GSM549251 5 0.1740 0.8400 0.056 0.000 0.000 0.012 0.932
#> GSM549258 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549264 5 0.2763 0.8041 0.148 0.000 0.000 0.004 0.848
#> GSM549243 1 0.0609 0.9417 0.980 0.000 0.000 0.000 0.020
#> GSM549262 5 0.1851 0.8365 0.088 0.000 0.000 0.000 0.912
#> GSM549278 4 0.0404 0.9298 0.000 0.000 0.000 0.988 0.012
#> GSM549283 1 0.4462 0.5324 0.672 0.308 0.000 0.004 0.016
#> GSM549298 3 0.0451 0.8635 0.000 0.004 0.988 0.000 0.008
#> GSM750741 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549286 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549241 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.2006 0.8835 0.916 0.000 0.000 0.072 0.012
#> GSM549261 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM549270 2 0.0162 0.9361 0.000 0.996 0.000 0.000 0.004
#> GSM549277 2 0.2929 0.7919 0.000 0.840 0.152 0.000 0.008
#> GSM549280 2 0.0693 0.9307 0.000 0.980 0.012 0.000 0.008
#> GSM549281 1 0.0404 0.9507 0.988 0.000 0.000 0.000 0.012
#> GSM549285 5 0.4622 0.5262 0.040 0.000 0.276 0.000 0.684
#> GSM549288 2 0.1041 0.9196 0.000 0.964 0.032 0.000 0.004
#> GSM549292 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549295 2 0.1502 0.8998 0.000 0.940 0.056 0.000 0.004
#> GSM549297 2 0.1331 0.9119 0.000 0.952 0.040 0.000 0.008
#> GSM750743 1 0.0162 0.9538 0.996 0.000 0.000 0.000 0.004
#> GSM549268 1 0.2777 0.8310 0.864 0.120 0.000 0.000 0.016
#> GSM549290 5 0.4817 0.2504 0.000 0.000 0.024 0.404 0.572
#> GSM549272 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM549275 1 0.0162 0.9545 0.996 0.000 0.000 0.000 0.004
#> GSM549284 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM750737 4 0.3967 0.6104 0.264 0.000 0.000 0.724 0.012
#> GSM750740 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9559 1.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.0000 0.9373 0.000 1.000 0.000 0.000 0.000
#> GSM750754 3 0.5731 0.1820 0.000 0.000 0.480 0.084 0.436
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.0779 0.9064 0.000 0.000 0.008 0.976 0.008 0.008
#> GSM549291 4 0.0820 0.9030 0.000 0.000 0.016 0.972 0.000 0.012
#> GSM549274 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750738 2 0.3221 0.4682 0.000 0.736 0.000 0.264 0.000 0.000
#> GSM750748 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549240 1 0.0870 0.9239 0.972 0.000 0.000 0.012 0.012 0.004
#> GSM549279 1 0.1802 0.8925 0.916 0.000 0.000 0.000 0.012 0.072
#> GSM549294 2 0.3769 0.5100 0.004 0.640 0.000 0.000 0.000 0.356
#> GSM549300 6 0.2679 0.3248 0.000 0.040 0.096 0.000 0.000 0.864
#> GSM549303 3 0.2092 0.6721 0.000 0.000 0.876 0.000 0.000 0.124
#> GSM549309 3 0.0547 0.6666 0.000 0.000 0.980 0.000 0.000 0.020
#> GSM750753 2 0.1556 0.7640 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM750752 4 0.0547 0.9033 0.000 0.000 0.020 0.980 0.000 0.000
#> GSM549304 2 0.1863 0.7490 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM549305 2 0.3330 0.6045 0.000 0.716 0.000 0.000 0.000 0.284
#> GSM549307 6 0.3819 0.4907 0.000 0.172 0.064 0.000 0.000 0.764
#> GSM549306 6 0.3868 -0.5572 0.000 0.000 0.496 0.000 0.000 0.504
#> GSM549308 3 0.3782 0.5460 0.000 0.000 0.588 0.000 0.000 0.412
#> GSM549233 5 0.1588 0.8228 0.004 0.000 0.000 0.072 0.924 0.000
#> GSM549234 4 0.0260 0.9093 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM549250 5 0.0508 0.8437 0.004 0.000 0.000 0.012 0.984 0.000
#> GSM549287 3 0.0291 0.6598 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM750735 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM750736 1 0.0260 0.9291 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM750749 1 0.2312 0.8669 0.876 0.000 0.000 0.000 0.012 0.112
#> GSM549230 5 0.0458 0.8444 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM549231 5 0.0458 0.8444 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM549237 5 0.1444 0.8278 0.072 0.000 0.000 0.000 0.928 0.000
#> GSM549254 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM750734 1 0.2941 0.7103 0.780 0.000 0.000 0.000 0.220 0.000
#> GSM549271 4 0.3969 0.5731 0.000 0.000 0.312 0.668 0.000 0.020
#> GSM549232 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549246 5 0.2265 0.8234 0.052 0.000 0.000 0.052 0.896 0.000
#> GSM549248 5 0.1007 0.8400 0.044 0.000 0.000 0.000 0.956 0.000
#> GSM549255 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM750746 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549259 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549269 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549273 3 0.2697 0.6557 0.000 0.000 0.812 0.000 0.000 0.188
#> GSM549299 2 0.2823 0.6867 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM549301 3 0.3828 0.5093 0.000 0.000 0.560 0.000 0.000 0.440
#> GSM549310 4 0.2730 0.7519 0.000 0.000 0.192 0.808 0.000 0.000
#> GSM549311 3 0.0260 0.6664 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM549302 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 5 0.3737 0.4347 0.392 0.000 0.000 0.000 0.608 0.000
#> GSM549245 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549265 4 0.3468 0.5603 0.000 0.000 0.004 0.712 0.284 0.000
#> GSM549282 3 0.5878 0.2877 0.000 0.000 0.468 0.000 0.308 0.224
#> GSM549296 4 0.0363 0.9076 0.000 0.000 0.012 0.988 0.000 0.000
#> GSM750739 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM750742 5 0.0458 0.8444 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM750744 5 0.3672 0.4350 0.368 0.000 0.000 0.000 0.632 0.000
#> GSM750750 3 0.3774 0.5509 0.000 0.000 0.592 0.000 0.000 0.408
#> GSM549242 5 0.2752 0.7931 0.108 0.000 0.000 0.036 0.856 0.000
#> GSM549252 4 0.0790 0.8928 0.000 0.000 0.000 0.968 0.032 0.000
#> GSM549253 5 0.0508 0.8437 0.004 0.000 0.000 0.012 0.984 0.000
#> GSM549256 5 0.2404 0.8070 0.080 0.000 0.000 0.036 0.884 0.000
#> GSM549257 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549263 5 0.0508 0.8447 0.012 0.000 0.000 0.004 0.984 0.000
#> GSM549267 5 0.6288 0.0771 0.000 0.000 0.360 0.232 0.396 0.012
#> GSM750745 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549239 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549244 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549249 5 0.2178 0.7775 0.000 0.000 0.000 0.132 0.868 0.000
#> GSM549260 1 0.2350 0.8609 0.888 0.000 0.000 0.036 0.076 0.000
#> GSM549266 1 0.3200 0.7910 0.788 0.000 0.000 0.000 0.016 0.196
#> GSM549293 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 5 0.0508 0.8437 0.004 0.000 0.000 0.012 0.984 0.000
#> GSM549238 5 0.1010 0.8368 0.004 0.000 0.000 0.036 0.960 0.000
#> GSM549251 5 0.0520 0.8444 0.008 0.000 0.000 0.008 0.984 0.000
#> GSM549258 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549264 5 0.1610 0.8182 0.084 0.000 0.000 0.000 0.916 0.000
#> GSM549243 1 0.0713 0.9207 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM549262 5 0.0632 0.8447 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM549278 4 0.0000 0.9121 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549283 1 0.5151 0.5540 0.648 0.152 0.000 0.000 0.008 0.192
#> GSM549298 3 0.3838 0.4962 0.000 0.000 0.552 0.000 0.000 0.448
#> GSM750741 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549286 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.0146 0.9298 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549247 1 0.1296 0.9138 0.952 0.000 0.000 0.032 0.012 0.004
#> GSM549261 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549270 2 0.2912 0.6542 0.000 0.784 0.000 0.000 0.000 0.216
#> GSM549277 6 0.3584 0.2634 0.000 0.308 0.000 0.000 0.004 0.688
#> GSM549280 2 0.3684 0.5024 0.000 0.664 0.000 0.000 0.004 0.332
#> GSM549281 1 0.2730 0.8364 0.836 0.000 0.000 0.000 0.012 0.152
#> GSM549285 5 0.4600 0.5210 0.012 0.000 0.040 0.000 0.648 0.300
#> GSM549288 2 0.3993 0.3291 0.000 0.592 0.008 0.000 0.000 0.400
#> GSM549292 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 2 0.3872 0.3508 0.000 0.604 0.004 0.000 0.000 0.392
#> GSM549297 6 0.3843 -0.2058 0.000 0.452 0.000 0.000 0.000 0.548
#> GSM750743 1 0.0713 0.9194 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM549268 1 0.4797 0.5912 0.648 0.060 0.000 0.000 0.012 0.280
#> GSM549290 5 0.6152 0.1932 0.000 0.000 0.332 0.204 0.452 0.012
#> GSM549272 2 0.1863 0.7543 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM549276 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549275 1 0.1588 0.8911 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM549284 2 0.0000 0.7954 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750737 4 0.3244 0.5726 0.268 0.000 0.000 0.732 0.000 0.000
#> GSM750740 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM750747 1 0.0260 0.9300 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM750751 2 0.2562 0.6900 0.000 0.828 0.000 0.000 0.000 0.172
#> GSM750754 3 0.3260 0.5443 0.000 0.000 0.824 0.028 0.136 0.012
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:pam 94 0.0941 4.49e-05 0.169035 0.0358 2
#> SD:pam 98 0.2861 4.54e-05 0.210210 0.0334 3
#> SD:pam 95 0.3397 1.48e-04 0.000574 0.1756 4
#> SD:pam 93 0.3555 2.84e-05 0.003565 0.0182 5
#> SD:pam 89 0.3101 8.17e-06 0.012730 0.0402 6
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 21168 rows and 103 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 0.917 0.968 0.979 0.5030 0.495 0.495
#> 3 3 0.855 0.812 0.929 0.2683 0.816 0.645
#> 4 4 0.791 0.849 0.922 0.1176 0.881 0.689
#> 5 5 0.698 0.572 0.778 0.0831 0.939 0.805
#> 6 6 0.722 0.546 0.727 0.0588 0.832 0.456
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
#> GSM549289 2 0.6148 0.860 0.152 0.848
#> GSM549291 2 0.2236 0.960 0.036 0.964
#> GSM549274 2 0.0000 0.973 0.000 1.000
#> GSM750738 2 0.2423 0.957 0.040 0.960
#> GSM750748 1 0.1184 0.987 0.984 0.016
#> GSM549240 1 0.1184 0.987 0.984 0.016
#> GSM549279 2 0.4690 0.902 0.100 0.900
#> GSM549294 2 0.0000 0.973 0.000 1.000
#> GSM549300 2 0.0000 0.973 0.000 1.000
#> GSM549303 2 0.1184 0.967 0.016 0.984
#> GSM549309 2 0.1184 0.967 0.016 0.984
#> GSM750753 2 0.0000 0.973 0.000 1.000
#> GSM750752 2 0.5178 0.899 0.116 0.884
#> GSM549304 2 0.0000 0.973 0.000 1.000
#> GSM549305 2 0.0000 0.973 0.000 1.000
#> GSM549307 2 0.0000 0.973 0.000 1.000
#> GSM549306 2 0.0000 0.973 0.000 1.000
#> GSM549308 2 0.0000 0.973 0.000 1.000
#> GSM549233 1 0.0000 0.985 1.000 0.000
#> GSM549234 1 0.0000 0.985 1.000 0.000
#> GSM549250 1 0.0000 0.985 1.000 0.000
#> GSM549287 2 0.1184 0.967 0.016 0.984
#> GSM750735 1 0.1184 0.987 0.984 0.016
#> GSM750736 1 0.1184 0.987 0.984 0.016
#> GSM750749 2 0.6712 0.813 0.176 0.824
#> GSM549230 1 0.0000 0.985 1.000 0.000
#> GSM549231 1 0.0938 0.987 0.988 0.012
#> GSM549237 1 0.1184 0.987 0.984 0.016
#> GSM549254 1 0.5946 0.826 0.856 0.144
#> GSM750734 1 0.1184 0.987 0.984 0.016
#> GSM549271 2 0.1184 0.967 0.016 0.984
#> GSM549232 1 0.0000 0.985 1.000 0.000
#> GSM549246 1 0.0672 0.982 0.992 0.008
#> GSM549248 1 0.1184 0.987 0.984 0.016
#> GSM549255 1 0.0000 0.985 1.000 0.000
#> GSM750746 1 0.1184 0.987 0.984 0.016
#> GSM549259 1 0.1184 0.987 0.984 0.016
#> GSM549269 2 0.0000 0.973 0.000 1.000
#> GSM549273 2 0.1184 0.967 0.016 0.984
#> GSM549299 2 0.0000 0.973 0.000 1.000
#> GSM549301 2 0.0000 0.973 0.000 1.000
#> GSM549310 2 0.4161 0.927 0.084 0.916
#> GSM549311 2 0.1184 0.967 0.016 0.984
#> GSM549302 2 0.0000 0.973 0.000 1.000
#> GSM549235 1 0.1184 0.987 0.984 0.016
#> GSM549245 1 0.0376 0.984 0.996 0.004
#> GSM549265 1 0.4815 0.880 0.896 0.104
#> GSM549282 2 0.0000 0.973 0.000 1.000
#> GSM549296 2 0.5178 0.899 0.116 0.884
#> GSM750739 1 0.1184 0.987 0.984 0.016
#> GSM750742 1 0.1184 0.987 0.984 0.016
#> GSM750744 1 0.1184 0.987 0.984 0.016
#> GSM750750 2 0.0000 0.973 0.000 1.000
#> GSM549242 1 0.0000 0.985 1.000 0.000
#> GSM549252 1 0.0000 0.985 1.000 0.000
#> GSM549253 1 0.0000 0.985 1.000 0.000
#> GSM549256 1 0.0000 0.985 1.000 0.000
#> GSM549257 1 0.0000 0.985 1.000 0.000
#> GSM549263 1 0.0000 0.985 1.000 0.000
#> GSM549267 2 0.1184 0.967 0.016 0.984
#> GSM750745 1 0.1184 0.987 0.984 0.016
#> GSM549239 1 0.1184 0.987 0.984 0.016
#> GSM549244 1 0.0000 0.985 1.000 0.000
#> GSM549249 1 0.0000 0.985 1.000 0.000
#> GSM549260 1 0.0000 0.985 1.000 0.000
#> GSM549266 2 0.4690 0.902 0.100 0.900
#> GSM549293 2 0.0000 0.973 0.000 1.000
#> GSM549236 1 0.0000 0.985 1.000 0.000
#> GSM549238 1 0.0000 0.985 1.000 0.000
#> GSM549251 1 0.0000 0.985 1.000 0.000
#> GSM549258 1 0.1184 0.987 0.984 0.016
#> GSM549264 1 0.1184 0.987 0.984 0.016
#> GSM549243 1 0.1184 0.987 0.984 0.016
#> GSM549262 1 0.1184 0.987 0.984 0.016
#> GSM549278 2 0.6623 0.835 0.172 0.828
#> GSM549283 2 0.0000 0.973 0.000 1.000
#> GSM549298 2 0.0000 0.973 0.000 1.000
#> GSM750741 1 0.1184 0.987 0.984 0.016
#> GSM549286 2 0.0000 0.973 0.000 1.000
#> GSM549241 1 0.1184 0.987 0.984 0.016
#> GSM549247 1 0.1184 0.987 0.984 0.016
#> GSM549261 1 0.1184 0.987 0.984 0.016
#> GSM549270 2 0.0000 0.973 0.000 1.000
#> GSM549277 2 0.0000 0.973 0.000 1.000
#> GSM549280 2 0.0000 0.973 0.000 1.000
#> GSM549281 2 0.4562 0.906 0.096 0.904
#> GSM549285 2 0.0000 0.973 0.000 1.000
#> GSM549288 2 0.0000 0.973 0.000 1.000
#> GSM549292 2 0.0000 0.973 0.000 1.000
#> GSM549295 2 0.0000 0.973 0.000 1.000
#> GSM549297 2 0.0000 0.973 0.000 1.000
#> GSM750743 1 0.1184 0.987 0.984 0.016
#> GSM549268 2 0.3879 0.923 0.076 0.924
#> GSM549290 2 0.1184 0.967 0.016 0.984
#> GSM549272 2 0.0000 0.973 0.000 1.000
#> GSM549276 2 0.0000 0.973 0.000 1.000
#> GSM549275 1 0.2236 0.972 0.964 0.036
#> GSM549284 2 0.0000 0.973 0.000 1.000
#> GSM750737 1 0.0000 0.985 1.000 0.000
#> GSM750740 1 0.1184 0.987 0.984 0.016
#> GSM750747 1 0.1184 0.987 0.984 0.016
#> GSM750751 2 0.0000 0.973 0.000 1.000
#> GSM750754 2 0.1184 0.967 0.016 0.984
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549291 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549274 2 0.0892 0.9749 0.020 0.980 0.000
#> GSM750738 1 0.4609 0.8037 0.856 0.052 0.092
#> GSM750748 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549240 1 0.0237 0.9050 0.996 0.000 0.004
#> GSM549279 2 0.1031 0.9722 0.024 0.976 0.000
#> GSM549294 2 0.0237 0.9833 0.004 0.996 0.000
#> GSM549300 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549303 3 0.0892 0.8091 0.000 0.020 0.980
#> GSM549309 3 0.0892 0.8091 0.000 0.020 0.980
#> GSM750753 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM750752 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549304 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549307 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549306 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549308 2 0.0747 0.9737 0.000 0.984 0.016
#> GSM549233 1 0.3551 0.8121 0.868 0.000 0.132
#> GSM549234 3 0.6309 -0.0369 0.500 0.000 0.500
#> GSM549250 1 0.1643 0.8895 0.956 0.000 0.044
#> GSM549287 3 0.0592 0.8117 0.000 0.012 0.988
#> GSM750735 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750749 2 0.3551 0.8418 0.132 0.868 0.000
#> GSM549230 1 0.1529 0.8919 0.960 0.000 0.040
#> GSM549231 1 0.1031 0.8979 0.976 0.000 0.024
#> GSM549237 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549254 1 0.6008 0.3881 0.628 0.000 0.372
#> GSM750734 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549271 3 0.0892 0.8091 0.000 0.020 0.980
#> GSM549232 3 0.6305 0.0266 0.484 0.000 0.516
#> GSM549246 1 0.5859 0.4501 0.656 0.000 0.344
#> GSM549248 1 0.0424 0.9036 0.992 0.000 0.008
#> GSM549255 1 0.6309 -0.0312 0.500 0.000 0.500
#> GSM750746 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549269 2 0.0892 0.9749 0.020 0.980 0.000
#> GSM549273 3 0.6274 0.0480 0.000 0.456 0.544
#> GSM549299 2 0.0592 0.9795 0.012 0.988 0.000
#> GSM549301 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549310 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549311 3 0.0892 0.8091 0.000 0.020 0.980
#> GSM549302 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549245 3 0.6305 0.0266 0.484 0.000 0.516
#> GSM549265 3 0.6235 0.1726 0.436 0.000 0.564
#> GSM549282 3 0.4605 0.6070 0.000 0.204 0.796
#> GSM549296 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM750739 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750750 2 0.3340 0.8633 0.000 0.880 0.120
#> GSM549242 1 0.1643 0.8895 0.956 0.000 0.044
#> GSM549252 1 0.6309 -0.0312 0.500 0.000 0.500
#> GSM549253 1 0.1529 0.8919 0.960 0.000 0.040
#> GSM549256 1 0.2261 0.8730 0.932 0.000 0.068
#> GSM549257 1 0.6309 -0.0151 0.504 0.000 0.496
#> GSM549263 1 0.1529 0.8919 0.960 0.000 0.040
#> GSM549267 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549244 3 0.6305 0.0265 0.484 0.000 0.516
#> GSM549249 1 0.6309 -0.0151 0.504 0.000 0.496
#> GSM549260 1 0.1289 0.8956 0.968 0.000 0.032
#> GSM549266 2 0.1031 0.9722 0.024 0.976 0.000
#> GSM549293 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549236 1 0.1529 0.8919 0.960 0.000 0.040
#> GSM549238 1 0.4121 0.7672 0.832 0.000 0.168
#> GSM549251 1 0.1529 0.8919 0.960 0.000 0.040
#> GSM549258 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549278 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549283 2 0.0892 0.9749 0.020 0.980 0.000
#> GSM549298 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM750741 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549247 1 0.0237 0.9050 0.996 0.000 0.004
#> GSM549261 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549281 2 0.1031 0.9722 0.024 0.976 0.000
#> GSM549285 2 0.0892 0.9749 0.020 0.980 0.000
#> GSM549288 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549292 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549295 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM549268 2 0.1031 0.9722 0.024 0.976 0.000
#> GSM549290 3 0.0000 0.8143 0.000 0.000 1.000
#> GSM549272 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM549275 1 0.0424 0.9005 0.992 0.008 0.000
#> GSM549284 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM750737 1 0.3038 0.8398 0.896 0.000 0.104
#> GSM750740 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.9058 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.9850 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.8143 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549291 4 0.0469 0.859 0.000 0.000 0.012 0.988
#> GSM549274 2 0.0592 0.945 0.000 0.984 0.016 0.000
#> GSM750738 1 0.6847 0.535 0.644 0.244 0.048 0.064
#> GSM750748 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549279 2 0.2489 0.897 0.068 0.912 0.020 0.000
#> GSM549294 2 0.0469 0.946 0.000 0.988 0.012 0.000
#> GSM549300 3 0.4679 0.469 0.000 0.352 0.648 0.000
#> GSM549303 3 0.4277 0.606 0.000 0.000 0.720 0.280
#> GSM549309 3 0.4406 0.575 0.000 0.000 0.700 0.300
#> GSM750753 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> GSM750752 4 0.0188 0.863 0.000 0.000 0.004 0.996
#> GSM549304 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549305 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549307 2 0.3688 0.738 0.000 0.792 0.208 0.000
#> GSM549306 3 0.2973 0.791 0.000 0.144 0.856 0.000
#> GSM549308 3 0.1940 0.799 0.000 0.076 0.924 0.000
#> GSM549233 1 0.3649 0.807 0.796 0.000 0.000 0.204
#> GSM549234 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549250 1 0.3444 0.826 0.816 0.000 0.000 0.184
#> GSM549287 4 0.3649 0.683 0.000 0.000 0.204 0.796
#> GSM750735 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750736 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750749 2 0.3447 0.818 0.128 0.852 0.020 0.000
#> GSM549230 1 0.3311 0.836 0.828 0.000 0.000 0.172
#> GSM549231 1 0.0707 0.919 0.980 0.000 0.000 0.020
#> GSM549237 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549254 4 0.3528 0.627 0.192 0.000 0.000 0.808
#> GSM750734 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549271 4 0.3649 0.683 0.000 0.000 0.204 0.796
#> GSM549232 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549246 4 0.5000 -0.177 0.496 0.000 0.000 0.504
#> GSM549248 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549255 4 0.0469 0.855 0.012 0.000 0.000 0.988
#> GSM750746 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549273 3 0.1118 0.782 0.000 0.036 0.964 0.000
#> GSM549299 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549301 3 0.2868 0.796 0.000 0.136 0.864 0.000
#> GSM549310 4 0.0469 0.859 0.000 0.000 0.012 0.988
#> GSM549311 3 0.4304 0.601 0.000 0.000 0.716 0.284
#> GSM549302 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549235 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549245 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549265 4 0.0336 0.859 0.008 0.000 0.000 0.992
#> GSM549282 3 0.4764 0.687 0.000 0.032 0.748 0.220
#> GSM549296 4 0.0188 0.863 0.000 0.000 0.004 0.996
#> GSM750739 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750750 3 0.2469 0.802 0.000 0.108 0.892 0.000
#> GSM549242 1 0.3356 0.833 0.824 0.000 0.000 0.176
#> GSM549252 4 0.0188 0.861 0.004 0.000 0.000 0.996
#> GSM549253 1 0.3356 0.833 0.824 0.000 0.000 0.176
#> GSM549256 1 0.3649 0.807 0.796 0.000 0.000 0.204
#> GSM549257 4 0.2868 0.706 0.136 0.000 0.000 0.864
#> GSM549263 1 0.3266 0.839 0.832 0.000 0.000 0.168
#> GSM549267 4 0.3649 0.683 0.000 0.000 0.204 0.796
#> GSM750745 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549249 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549260 1 0.3172 0.844 0.840 0.000 0.000 0.160
#> GSM549266 2 0.2413 0.901 0.064 0.916 0.020 0.000
#> GSM549293 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549236 1 0.3444 0.826 0.816 0.000 0.000 0.184
#> GSM549238 1 0.4134 0.733 0.740 0.000 0.000 0.260
#> GSM549251 1 0.3356 0.833 0.824 0.000 0.000 0.176
#> GSM549258 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549264 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549278 4 0.0000 0.863 0.000 0.000 0.000 1.000
#> GSM549283 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549298 3 0.2647 0.801 0.000 0.120 0.880 0.000
#> GSM750741 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549286 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549241 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549247 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549261 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549270 2 0.0921 0.944 0.000 0.972 0.028 0.000
#> GSM549277 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549280 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549281 2 0.2413 0.901 0.064 0.916 0.020 0.000
#> GSM549285 2 0.1867 0.911 0.000 0.928 0.072 0.000
#> GSM549288 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM549292 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549295 2 0.1022 0.940 0.000 0.968 0.032 0.000
#> GSM549297 2 0.0707 0.944 0.000 0.980 0.020 0.000
#> GSM750743 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM549268 2 0.2335 0.905 0.060 0.920 0.020 0.000
#> GSM549290 4 0.3649 0.683 0.000 0.000 0.204 0.796
#> GSM549272 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549276 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM549275 1 0.0592 0.917 0.984 0.016 0.000 0.000
#> GSM549284 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM750737 1 0.3649 0.807 0.796 0.000 0.000 0.204
#> GSM750740 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> GSM750751 2 0.1118 0.942 0.000 0.964 0.036 0.000
#> GSM750754 4 0.3649 0.683 0.000 0.000 0.204 0.796
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.0566 0.7979 0.000 0.000 0.012 0.984 0.004
#> GSM549291 4 0.2329 0.7221 0.000 0.000 0.124 0.876 0.000
#> GSM549274 2 0.3790 0.4281 0.000 0.724 0.004 0.000 0.272
#> GSM750738 5 0.6494 0.0000 0.024 0.164 0.112 0.044 0.656
#> GSM750748 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.3706 0.6981 0.756 0.004 0.000 0.004 0.236
#> GSM549279 2 0.4944 0.3664 0.044 0.700 0.016 0.000 0.240
#> GSM549294 2 0.2891 0.4997 0.000 0.824 0.000 0.000 0.176
#> GSM549300 2 0.2848 0.4695 0.000 0.840 0.156 0.000 0.004
#> GSM549303 3 0.0609 0.6955 0.000 0.000 0.980 0.020 0.000
#> GSM549309 3 0.0609 0.6955 0.000 0.000 0.980 0.020 0.000
#> GSM750753 2 0.2561 0.5123 0.000 0.856 0.000 0.000 0.144
#> GSM750752 4 0.0880 0.7896 0.000 0.000 0.032 0.968 0.000
#> GSM549304 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549305 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549307 2 0.0794 0.5462 0.000 0.972 0.028 0.000 0.000
#> GSM549306 2 0.4440 -0.0702 0.000 0.528 0.468 0.000 0.004
#> GSM549308 3 0.2891 0.6449 0.000 0.176 0.824 0.000 0.000
#> GSM549233 4 0.7204 -0.0491 0.312 0.000 0.020 0.404 0.264
#> GSM549234 4 0.1608 0.8040 0.000 0.000 0.000 0.928 0.072
#> GSM549250 1 0.6661 0.5135 0.504 0.000 0.020 0.148 0.328
#> GSM549287 3 0.4249 0.1526 0.000 0.000 0.568 0.432 0.000
#> GSM750735 1 0.1357 0.7820 0.948 0.000 0.004 0.000 0.048
#> GSM750736 1 0.3550 0.6464 0.760 0.004 0.000 0.000 0.236
#> GSM750749 2 0.6183 0.1432 0.216 0.576 0.004 0.000 0.204
#> GSM549230 1 0.5284 0.6426 0.620 0.000 0.020 0.032 0.328
#> GSM549231 1 0.3882 0.7323 0.756 0.000 0.020 0.000 0.224
#> GSM549237 1 0.3196 0.7581 0.804 0.000 0.004 0.000 0.192
#> GSM549254 4 0.1732 0.8003 0.000 0.000 0.000 0.920 0.080
#> GSM750734 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM549271 4 0.3983 0.4258 0.000 0.000 0.340 0.660 0.000
#> GSM549232 4 0.0794 0.8079 0.000 0.000 0.000 0.972 0.028
#> GSM549246 4 0.4197 0.6819 0.032 0.000 0.004 0.752 0.212
#> GSM549248 1 0.3690 0.7459 0.780 0.000 0.020 0.000 0.200
#> GSM549255 4 0.1478 0.8062 0.000 0.000 0.000 0.936 0.064
#> GSM750746 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0290 0.7952 0.992 0.000 0.000 0.000 0.008
#> GSM549269 2 0.2074 0.5298 0.000 0.896 0.000 0.000 0.104
#> GSM549273 3 0.2648 0.6341 0.000 0.152 0.848 0.000 0.000
#> GSM549299 2 0.0912 0.5467 0.000 0.972 0.016 0.000 0.012
#> GSM549301 3 0.3662 0.5298 0.000 0.252 0.744 0.000 0.004
#> GSM549310 4 0.0963 0.7874 0.000 0.000 0.036 0.964 0.000
#> GSM549311 3 0.0609 0.6955 0.000 0.000 0.980 0.020 0.000
#> GSM549302 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549235 1 0.0162 0.7961 0.996 0.000 0.000 0.000 0.004
#> GSM549245 4 0.1121 0.8100 0.000 0.000 0.000 0.956 0.044
#> GSM549265 4 0.2286 0.7718 0.000 0.000 0.004 0.888 0.108
#> GSM549282 3 0.2969 0.6753 0.000 0.128 0.852 0.020 0.000
#> GSM549296 4 0.0880 0.7896 0.000 0.000 0.032 0.968 0.000
#> GSM750739 1 0.0162 0.7957 0.996 0.000 0.004 0.000 0.000
#> GSM750742 1 0.3757 0.7412 0.772 0.000 0.020 0.000 0.208
#> GSM750744 1 0.0963 0.7949 0.964 0.000 0.000 0.000 0.036
#> GSM750750 3 0.2929 0.6408 0.000 0.180 0.820 0.000 0.000
#> GSM549242 1 0.6344 0.5770 0.556 0.000 0.020 0.120 0.304
#> GSM549252 4 0.1965 0.7928 0.000 0.000 0.000 0.904 0.096
#> GSM549253 1 0.5492 0.6332 0.608 0.000 0.020 0.044 0.328
#> GSM549256 1 0.7310 0.2813 0.388 0.000 0.024 0.300 0.288
#> GSM549257 4 0.2020 0.7873 0.000 0.000 0.000 0.900 0.100
#> GSM549263 1 0.5356 0.6398 0.616 0.000 0.020 0.036 0.328
#> GSM549267 4 0.4015 0.3620 0.000 0.000 0.348 0.652 0.000
#> GSM750745 1 0.1121 0.7838 0.956 0.000 0.000 0.000 0.044
#> GSM549239 1 0.0162 0.7958 0.996 0.000 0.000 0.000 0.004
#> GSM549244 4 0.0963 0.8092 0.000 0.000 0.000 0.964 0.036
#> GSM549249 4 0.1341 0.8092 0.000 0.000 0.000 0.944 0.056
#> GSM549260 1 0.4879 0.7088 0.720 0.000 0.016 0.052 0.212
#> GSM549266 2 0.4554 0.4125 0.032 0.736 0.016 0.000 0.216
#> GSM549293 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549236 1 0.5618 0.6282 0.600 0.000 0.020 0.052 0.328
#> GSM549238 4 0.6440 0.3622 0.168 0.000 0.020 0.576 0.236
#> GSM549251 1 0.5492 0.6332 0.608 0.000 0.020 0.044 0.328
#> GSM549258 1 0.2074 0.7586 0.896 0.000 0.000 0.000 0.104
#> GSM549264 1 0.2629 0.7755 0.860 0.000 0.004 0.000 0.136
#> GSM549243 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.3690 0.7456 0.780 0.000 0.020 0.000 0.200
#> GSM549278 4 0.2570 0.7765 0.000 0.000 0.028 0.888 0.084
#> GSM549283 2 0.3574 0.4706 0.000 0.804 0.028 0.000 0.168
#> GSM549298 2 0.4437 -0.0656 0.000 0.532 0.464 0.000 0.004
#> GSM750741 1 0.3430 0.6611 0.776 0.004 0.000 0.000 0.220
#> GSM549286 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549241 1 0.1270 0.7797 0.948 0.000 0.000 0.000 0.052
#> GSM549247 1 0.4735 0.6442 0.680 0.012 0.016 0.004 0.288
#> GSM549261 1 0.0880 0.7879 0.968 0.000 0.000 0.000 0.032
#> GSM549270 2 0.4192 0.2934 0.000 0.596 0.000 0.000 0.404
#> GSM549277 2 0.2139 0.5309 0.000 0.916 0.032 0.000 0.052
#> GSM549280 2 0.0794 0.5462 0.000 0.972 0.028 0.000 0.000
#> GSM549281 2 0.4181 0.4137 0.008 0.736 0.016 0.000 0.240
#> GSM549285 2 0.3994 0.4680 0.000 0.792 0.068 0.000 0.140
#> GSM549288 2 0.0794 0.5462 0.000 0.972 0.028 0.000 0.000
#> GSM549292 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549295 2 0.1907 0.5440 0.000 0.928 0.028 0.000 0.044
#> GSM549297 2 0.3562 0.4909 0.000 0.788 0.016 0.000 0.196
#> GSM750743 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM549268 2 0.4061 0.4179 0.004 0.740 0.016 0.000 0.240
#> GSM549290 4 0.4242 0.2058 0.000 0.000 0.428 0.572 0.000
#> GSM549272 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549276 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM549275 1 0.3970 0.6316 0.752 0.024 0.000 0.000 0.224
#> GSM549284 2 0.4924 0.2741 0.000 0.552 0.028 0.000 0.420
#> GSM750737 1 0.6482 0.4800 0.492 0.000 0.000 0.232 0.276
#> GSM750740 1 0.0162 0.7958 0.996 0.000 0.000 0.000 0.004
#> GSM750747 1 0.0000 0.7961 1.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.4227 0.2768 0.000 0.580 0.000 0.000 0.420
#> GSM750754 3 0.4242 0.1636 0.000 0.000 0.572 0.428 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4868 0.7148 0.000 0.000 0.008 0.588 0.052 0.352
#> GSM549291 4 0.3884 0.6406 0.000 0.000 0.036 0.724 0.000 0.240
#> GSM549274 2 0.2070 0.8009 0.000 0.892 0.008 0.000 0.000 0.100
#> GSM750738 2 0.5469 0.4579 0.036 0.692 0.020 0.008 0.068 0.176
#> GSM750748 1 0.3706 0.7127 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM549240 1 0.0603 0.6144 0.980 0.000 0.000 0.000 0.004 0.016
#> GSM549279 6 0.5563 0.4042 0.184 0.272 0.000 0.000 0.000 0.544
#> GSM549294 2 0.2219 0.7742 0.000 0.864 0.000 0.000 0.000 0.136
#> GSM549300 6 0.4326 0.1510 0.000 0.024 0.404 0.000 0.000 0.572
#> GSM549303 3 0.3647 0.6383 0.000 0.000 0.640 0.360 0.000 0.000
#> GSM549309 3 0.3659 0.6355 0.000 0.000 0.636 0.364 0.000 0.000
#> GSM750753 2 0.3647 0.3020 0.000 0.640 0.000 0.000 0.000 0.360
#> GSM750752 4 0.4455 0.7060 0.000 0.000 0.016 0.616 0.016 0.352
#> GSM549304 2 0.0000 0.8454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549305 2 0.1075 0.8497 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM549307 6 0.5255 0.2885 0.000 0.112 0.340 0.000 0.000 0.548
#> GSM549306 3 0.3315 0.5727 0.000 0.020 0.780 0.000 0.000 0.200
#> GSM549308 3 0.0692 0.7177 0.000 0.020 0.976 0.000 0.000 0.004
#> GSM549233 5 0.2798 0.6370 0.000 0.000 0.000 0.036 0.852 0.112
#> GSM549234 4 0.6775 0.6902 0.108 0.000 0.000 0.412 0.108 0.372
#> GSM549250 5 0.0508 0.6960 0.004 0.000 0.000 0.000 0.984 0.012
#> GSM549287 4 0.2562 0.2155 0.000 0.000 0.172 0.828 0.000 0.000
#> GSM750735 1 0.0508 0.6243 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM750736 1 0.1930 0.6401 0.916 0.000 0.000 0.000 0.036 0.048
#> GSM750749 6 0.4091 0.0917 0.472 0.008 0.000 0.000 0.000 0.520
#> GSM549230 5 0.0937 0.6798 0.040 0.000 0.000 0.000 0.960 0.000
#> GSM549231 5 0.2664 0.4852 0.184 0.000 0.000 0.000 0.816 0.000
#> GSM549237 1 0.3810 0.0153 0.572 0.000 0.000 0.000 0.428 0.000
#> GSM549254 6 0.7224 -0.7061 0.164 0.004 0.000 0.352 0.108 0.372
#> GSM750734 1 0.3706 0.7127 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM549271 4 0.1863 0.3277 0.000 0.000 0.104 0.896 0.000 0.000
#> GSM549232 4 0.6612 0.6947 0.088 0.000 0.000 0.432 0.108 0.372
#> GSM549246 5 0.7119 -0.3810 0.160 0.000 0.004 0.356 0.384 0.096
#> GSM549248 5 0.2969 0.4032 0.224 0.000 0.000 0.000 0.776 0.000
#> GSM549255 4 0.6681 0.6939 0.096 0.000 0.000 0.424 0.108 0.372
#> GSM750746 1 0.3684 0.7195 0.628 0.000 0.000 0.000 0.372 0.000
#> GSM549259 1 0.3547 0.7364 0.668 0.000 0.000 0.000 0.332 0.000
#> GSM549269 2 0.1863 0.7899 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM549273 3 0.4663 0.6872 0.000 0.000 0.664 0.244 0.000 0.092
#> GSM549299 6 0.3833 0.2473 0.000 0.444 0.000 0.000 0.000 0.556
#> GSM549301 3 0.1092 0.7127 0.000 0.020 0.960 0.000 0.000 0.020
#> GSM549310 4 0.4443 0.7050 0.000 0.000 0.016 0.620 0.016 0.348
#> GSM549311 3 0.3695 0.6285 0.000 0.000 0.624 0.376 0.000 0.000
#> GSM549302 2 0.0146 0.8471 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549235 1 0.3684 0.7196 0.628 0.000 0.000 0.000 0.372 0.000
#> GSM549245 6 0.7026 -0.7147 0.148 0.000 0.000 0.372 0.108 0.372
#> GSM549265 4 0.7259 0.6085 0.120 0.000 0.000 0.408 0.220 0.252
#> GSM549282 3 0.3455 0.7119 0.000 0.020 0.776 0.200 0.000 0.004
#> GSM549296 4 0.4455 0.7060 0.000 0.000 0.016 0.616 0.016 0.352
#> GSM750739 1 0.3647 0.7257 0.640 0.000 0.000 0.000 0.360 0.000
#> GSM750742 5 0.3221 0.2983 0.264 0.000 0.000 0.000 0.736 0.000
#> GSM750744 1 0.3854 0.5712 0.536 0.000 0.000 0.000 0.464 0.000
#> GSM750750 3 0.1092 0.7208 0.000 0.020 0.960 0.020 0.000 0.000
#> GSM549242 5 0.0964 0.6934 0.004 0.000 0.000 0.016 0.968 0.012
#> GSM549252 4 0.7005 0.6709 0.144 0.000 0.000 0.376 0.108 0.372
#> GSM549253 5 0.0820 0.6964 0.016 0.000 0.000 0.000 0.972 0.012
#> GSM549256 5 0.1933 0.6734 0.004 0.000 0.000 0.032 0.920 0.044
#> GSM549257 4 0.6983 0.6735 0.140 0.000 0.000 0.380 0.108 0.372
#> GSM549263 5 0.0865 0.6830 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM549267 4 0.1958 0.3582 0.000 0.000 0.100 0.896 0.000 0.004
#> GSM750745 1 0.3684 0.7359 0.664 0.000 0.000 0.000 0.332 0.004
#> GSM549239 1 0.3672 0.7226 0.632 0.000 0.000 0.000 0.368 0.000
#> GSM549244 4 0.6775 0.6902 0.108 0.000 0.000 0.412 0.108 0.372
#> GSM549249 4 0.6842 0.6869 0.100 0.000 0.000 0.416 0.128 0.356
#> GSM549260 5 0.2473 0.6260 0.104 0.000 0.000 0.008 0.876 0.012
#> GSM549266 6 0.5369 0.3941 0.128 0.332 0.000 0.000 0.000 0.540
#> GSM549293 2 0.0000 0.8454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 5 0.0725 0.6968 0.012 0.000 0.000 0.000 0.976 0.012
#> GSM549238 5 0.6119 -0.2910 0.004 0.000 0.000 0.268 0.436 0.292
#> GSM549251 5 0.1074 0.6923 0.028 0.000 0.000 0.000 0.960 0.012
#> GSM549258 1 0.3253 0.7140 0.788 0.000 0.000 0.000 0.192 0.020
#> GSM549264 1 0.3023 0.5150 0.768 0.000 0.000 0.000 0.232 0.000
#> GSM549243 1 0.3706 0.7127 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM549262 5 0.3309 0.2483 0.280 0.000 0.000 0.000 0.720 0.000
#> GSM549278 4 0.5817 0.6792 0.000 0.000 0.032 0.568 0.120 0.280
#> GSM549283 6 0.4357 0.2928 0.012 0.420 0.008 0.000 0.000 0.560
#> GSM549298 3 0.3592 0.5229 0.000 0.020 0.740 0.000 0.000 0.240
#> GSM750741 1 0.1204 0.6157 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM549286 2 0.0865 0.8520 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM549241 1 0.3230 0.7179 0.776 0.000 0.000 0.000 0.212 0.012
#> GSM549247 1 0.0458 0.6118 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM549261 1 0.3023 0.7295 0.768 0.000 0.000 0.000 0.232 0.000
#> GSM549270 2 0.1610 0.8305 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM549277 6 0.5528 0.4591 0.004 0.196 0.220 0.000 0.000 0.580
#> GSM549280 6 0.4246 0.3195 0.000 0.400 0.020 0.000 0.000 0.580
#> GSM549281 6 0.5233 0.4004 0.112 0.332 0.000 0.000 0.000 0.556
#> GSM549285 3 0.4227 0.3310 0.004 0.020 0.632 0.000 0.000 0.344
#> GSM549288 6 0.5396 0.3818 0.000 0.152 0.284 0.000 0.000 0.564
#> GSM549292 2 0.0000 0.8454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 6 0.5569 0.4134 0.000 0.280 0.180 0.000 0.000 0.540
#> GSM549297 2 0.3955 0.0293 0.000 0.560 0.004 0.000 0.000 0.436
#> GSM750743 1 0.3446 0.7400 0.692 0.000 0.000 0.000 0.308 0.000
#> GSM549268 6 0.5042 0.4033 0.092 0.332 0.000 0.000 0.000 0.576
#> GSM549290 4 0.2053 0.3513 0.000 0.000 0.108 0.888 0.000 0.004
#> GSM549272 2 0.0547 0.8514 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549276 2 0.1007 0.8503 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM549275 1 0.1444 0.6066 0.928 0.000 0.000 0.000 0.000 0.072
#> GSM549284 2 0.1074 0.8327 0.000 0.960 0.012 0.000 0.000 0.028
#> GSM750737 5 0.7282 -0.1543 0.292 0.004 0.000 0.084 0.368 0.252
#> GSM750740 1 0.3371 0.7396 0.708 0.000 0.000 0.000 0.292 0.000
#> GSM750747 1 0.3684 0.7202 0.628 0.000 0.000 0.000 0.372 0.000
#> GSM750751 2 0.0865 0.8520 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM750754 4 0.2631 0.1990 0.000 0.000 0.180 0.820 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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:mclust 103 0.0131 8.80e-05 0.156105 0.00128 2
#> SD:mclust 91 0.3265 3.04e-04 0.000791 0.00231 3
#> SD:mclust 101 0.5038 1.18e-05 0.000835 0.00744 4
#> SD:mclust 69 0.5891 5.30e-05 0.062047 0.19518 5
#> SD:mclust 71 0.5338 6.80e-05 0.006630 0.10925 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.999 0.954 0.981 0.5006 0.499 0.499
#> 3 3 0.845 0.837 0.924 0.2794 0.809 0.637
#> 4 4 0.819 0.827 0.914 0.1628 0.816 0.538
#> 5 5 0.766 0.739 0.870 0.0603 0.926 0.726
#> 6 6 0.734 0.651 0.814 0.0445 0.928 0.685
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
#> GSM549289 1 0.0000 0.9833 1.000 0.000
#> GSM549291 1 0.8016 0.6708 0.756 0.244
#> GSM549274 2 0.0000 0.9759 0.000 1.000
#> GSM750738 2 0.0000 0.9759 0.000 1.000
#> GSM750748 1 0.0000 0.9833 1.000 0.000
#> GSM549240 1 0.0000 0.9833 1.000 0.000
#> GSM549279 2 0.0938 0.9665 0.012 0.988
#> GSM549294 2 0.0000 0.9759 0.000 1.000
#> GSM549300 2 0.0000 0.9759 0.000 1.000
#> GSM549303 2 0.0000 0.9759 0.000 1.000
#> GSM549309 2 0.3114 0.9286 0.056 0.944
#> GSM750753 2 0.0000 0.9759 0.000 1.000
#> GSM750752 2 0.0376 0.9729 0.004 0.996
#> GSM549304 2 0.0000 0.9759 0.000 1.000
#> GSM549305 2 0.0000 0.9759 0.000 1.000
#> GSM549307 2 0.0000 0.9759 0.000 1.000
#> GSM549306 2 0.0000 0.9759 0.000 1.000
#> GSM549308 2 0.0000 0.9759 0.000 1.000
#> GSM549233 1 0.0000 0.9833 1.000 0.000
#> GSM549234 1 0.0000 0.9833 1.000 0.000
#> GSM549250 1 0.0000 0.9833 1.000 0.000
#> GSM549287 2 0.3114 0.9287 0.056 0.944
#> GSM750735 1 0.0000 0.9833 1.000 0.000
#> GSM750736 1 0.0000 0.9833 1.000 0.000
#> GSM750749 1 0.0000 0.9833 1.000 0.000
#> GSM549230 1 0.0000 0.9833 1.000 0.000
#> GSM549231 1 0.0000 0.9833 1.000 0.000
#> GSM549237 1 0.0000 0.9833 1.000 0.000
#> GSM549254 1 0.0000 0.9833 1.000 0.000
#> GSM750734 1 0.0000 0.9833 1.000 0.000
#> GSM549271 2 0.0000 0.9759 0.000 1.000
#> GSM549232 1 0.0000 0.9833 1.000 0.000
#> GSM549246 1 0.0000 0.9833 1.000 0.000
#> GSM549248 1 0.0000 0.9833 1.000 0.000
#> GSM549255 1 0.0000 0.9833 1.000 0.000
#> GSM750746 1 0.0000 0.9833 1.000 0.000
#> GSM549259 1 0.0000 0.9833 1.000 0.000
#> GSM549269 2 0.0000 0.9759 0.000 1.000
#> GSM549273 2 0.0000 0.9759 0.000 1.000
#> GSM549299 2 0.0000 0.9759 0.000 1.000
#> GSM549301 2 0.0000 0.9759 0.000 1.000
#> GSM549310 2 0.0000 0.9759 0.000 1.000
#> GSM549311 2 0.0000 0.9759 0.000 1.000
#> GSM549302 2 0.0000 0.9759 0.000 1.000
#> GSM549235 1 0.0000 0.9833 1.000 0.000
#> GSM549245 1 0.0000 0.9833 1.000 0.000
#> GSM549265 1 0.0000 0.9833 1.000 0.000
#> GSM549282 2 0.3584 0.9167 0.068 0.932
#> GSM549296 2 0.6531 0.7979 0.168 0.832
#> GSM750739 1 0.0000 0.9833 1.000 0.000
#> GSM750742 1 0.0000 0.9833 1.000 0.000
#> GSM750744 1 0.0000 0.9833 1.000 0.000
#> GSM750750 2 0.0000 0.9759 0.000 1.000
#> GSM549242 1 0.0000 0.9833 1.000 0.000
#> GSM549252 1 0.0000 0.9833 1.000 0.000
#> GSM549253 1 0.0000 0.9833 1.000 0.000
#> GSM549256 1 0.0000 0.9833 1.000 0.000
#> GSM549257 1 0.0000 0.9833 1.000 0.000
#> GSM549263 1 0.0000 0.9833 1.000 0.000
#> GSM549267 2 0.7745 0.7094 0.228 0.772
#> GSM750745 1 0.0000 0.9833 1.000 0.000
#> GSM549239 1 0.0000 0.9833 1.000 0.000
#> GSM549244 1 0.0000 0.9833 1.000 0.000
#> GSM549249 1 0.0000 0.9833 1.000 0.000
#> GSM549260 1 0.0000 0.9833 1.000 0.000
#> GSM549266 2 0.0000 0.9759 0.000 1.000
#> GSM549293 2 0.0000 0.9759 0.000 1.000
#> GSM549236 1 0.0000 0.9833 1.000 0.000
#> GSM549238 1 0.0000 0.9833 1.000 0.000
#> GSM549251 1 0.0000 0.9833 1.000 0.000
#> GSM549258 1 0.0000 0.9833 1.000 0.000
#> GSM549264 1 0.0000 0.9833 1.000 0.000
#> GSM549243 1 0.0000 0.9833 1.000 0.000
#> GSM549262 1 0.0000 0.9833 1.000 0.000
#> GSM549278 1 0.1414 0.9648 0.980 0.020
#> GSM549283 2 0.0000 0.9759 0.000 1.000
#> GSM549298 2 0.0000 0.9759 0.000 1.000
#> GSM750741 1 0.0000 0.9833 1.000 0.000
#> GSM549286 2 0.0000 0.9759 0.000 1.000
#> GSM549241 1 0.0000 0.9833 1.000 0.000
#> GSM549247 1 0.3584 0.9167 0.932 0.068
#> GSM549261 1 0.0000 0.9833 1.000 0.000
#> GSM549270 2 0.0000 0.9759 0.000 1.000
#> GSM549277 2 0.0000 0.9759 0.000 1.000
#> GSM549280 2 0.0000 0.9759 0.000 1.000
#> GSM549281 2 0.0000 0.9759 0.000 1.000
#> GSM549285 2 0.0000 0.9759 0.000 1.000
#> GSM549288 2 0.0000 0.9759 0.000 1.000
#> GSM549292 2 0.0000 0.9759 0.000 1.000
#> GSM549295 2 0.0000 0.9759 0.000 1.000
#> GSM549297 2 0.0000 0.9759 0.000 1.000
#> GSM750743 1 0.0000 0.9833 1.000 0.000
#> GSM549268 2 0.0000 0.9759 0.000 1.000
#> GSM549290 1 0.7299 0.7380 0.796 0.204
#> GSM549272 2 0.0000 0.9759 0.000 1.000
#> GSM549276 2 0.0000 0.9759 0.000 1.000
#> GSM549275 1 0.9358 0.4566 0.648 0.352
#> GSM549284 2 0.0000 0.9759 0.000 1.000
#> GSM750737 1 0.0000 0.9833 1.000 0.000
#> GSM750740 1 0.0000 0.9833 1.000 0.000
#> GSM750747 1 0.0000 0.9833 1.000 0.000
#> GSM750751 2 0.0000 0.9759 0.000 1.000
#> GSM750754 2 0.9998 0.0394 0.492 0.508
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.5733 0.540 0.676 0.000 0.324
#> GSM549291 3 0.2711 0.796 0.088 0.000 0.912
#> GSM549274 2 0.0237 0.913 0.004 0.996 0.000
#> GSM750738 2 0.0237 0.913 0.004 0.996 0.000
#> GSM750748 1 0.0000 0.943 1.000 0.000 0.000
#> GSM549240 1 0.6267 0.192 0.548 0.452 0.000
#> GSM549279 2 0.1643 0.890 0.044 0.956 0.000
#> GSM549294 2 0.0592 0.914 0.000 0.988 0.012
#> GSM549300 3 0.4062 0.766 0.000 0.164 0.836
#> GSM549303 3 0.0592 0.852 0.000 0.012 0.988
#> GSM549309 3 0.0000 0.851 0.000 0.000 1.000
#> GSM750753 2 0.2625 0.870 0.000 0.916 0.084
#> GSM750752 3 0.0237 0.852 0.000 0.004 0.996
#> GSM549304 2 0.0424 0.914 0.000 0.992 0.008
#> GSM549305 2 0.2165 0.887 0.000 0.936 0.064
#> GSM549307 3 0.6062 0.469 0.000 0.384 0.616
#> GSM549306 3 0.3038 0.814 0.000 0.104 0.896
#> GSM549308 3 0.0892 0.851 0.000 0.020 0.980
#> GSM549233 1 0.0747 0.942 0.984 0.000 0.016
#> GSM549234 1 0.1289 0.936 0.968 0.000 0.032
#> GSM549250 1 0.1163 0.938 0.972 0.000 0.028
#> GSM549287 3 0.0237 0.850 0.004 0.000 0.996
#> GSM750735 1 0.1753 0.920 0.952 0.048 0.000
#> GSM750736 1 0.6267 0.192 0.548 0.452 0.000
#> GSM750749 1 0.0892 0.937 0.980 0.020 0.000
#> GSM549230 1 0.0892 0.942 0.980 0.000 0.020
#> GSM549231 1 0.0892 0.942 0.980 0.000 0.020
#> GSM549237 1 0.0424 0.943 0.992 0.000 0.008
#> GSM549254 1 0.0661 0.943 0.988 0.008 0.004
#> GSM750734 1 0.0000 0.943 1.000 0.000 0.000
#> GSM549271 3 0.0237 0.852 0.000 0.004 0.996
#> GSM549232 1 0.1860 0.925 0.948 0.000 0.052
#> GSM549246 1 0.0892 0.942 0.980 0.000 0.020
#> GSM549248 1 0.0424 0.943 0.992 0.000 0.008
#> GSM549255 1 0.2261 0.912 0.932 0.000 0.068
#> GSM750746 1 0.0592 0.940 0.988 0.012 0.000
#> GSM549259 1 0.2165 0.907 0.936 0.064 0.000
#> GSM549269 2 0.0237 0.913 0.004 0.996 0.000
#> GSM549273 3 0.1289 0.848 0.000 0.032 0.968
#> GSM549299 2 0.2537 0.874 0.000 0.920 0.080
#> GSM549301 3 0.2356 0.832 0.000 0.072 0.928
#> GSM549310 3 0.0747 0.852 0.000 0.016 0.984
#> GSM549311 3 0.0000 0.851 0.000 0.000 1.000
#> GSM549302 2 0.0592 0.914 0.000 0.988 0.012
#> GSM549235 1 0.0237 0.943 0.996 0.000 0.004
#> GSM549245 1 0.1015 0.943 0.980 0.008 0.012
#> GSM549265 1 0.2165 0.916 0.936 0.000 0.064
#> GSM549282 3 0.0000 0.851 0.000 0.000 1.000
#> GSM549296 3 0.1529 0.834 0.040 0.000 0.960
#> GSM750739 1 0.0237 0.942 0.996 0.004 0.000
#> GSM750742 1 0.0592 0.943 0.988 0.000 0.012
#> GSM750744 1 0.0000 0.943 1.000 0.000 0.000
#> GSM750750 3 0.0747 0.852 0.000 0.016 0.984
#> GSM549242 1 0.0424 0.943 0.992 0.000 0.008
#> GSM549252 1 0.1753 0.927 0.952 0.000 0.048
#> GSM549253 1 0.0747 0.942 0.984 0.000 0.016
#> GSM549256 1 0.0424 0.943 0.992 0.000 0.008
#> GSM549257 1 0.1031 0.940 0.976 0.000 0.024
#> GSM549263 1 0.0892 0.942 0.980 0.000 0.020
#> GSM549267 3 0.1529 0.833 0.040 0.000 0.960
#> GSM750745 1 0.2165 0.907 0.936 0.064 0.000
#> GSM549239 1 0.1031 0.935 0.976 0.024 0.000
#> GSM549244 1 0.2165 0.916 0.936 0.000 0.064
#> GSM549249 1 0.1964 0.922 0.944 0.000 0.056
#> GSM549260 1 0.0237 0.943 0.996 0.000 0.004
#> GSM549266 2 0.1289 0.899 0.032 0.968 0.000
#> GSM549293 2 0.0000 0.913 0.000 1.000 0.000
#> GSM549236 1 0.1031 0.940 0.976 0.000 0.024
#> GSM549238 1 0.1529 0.932 0.960 0.000 0.040
#> GSM549251 1 0.0747 0.942 0.984 0.000 0.016
#> GSM549258 1 0.2959 0.874 0.900 0.100 0.000
#> GSM549264 1 0.0237 0.942 0.996 0.004 0.000
#> GSM549243 1 0.0000 0.943 1.000 0.000 0.000
#> GSM549262 1 0.0424 0.943 0.992 0.000 0.008
#> GSM549278 3 0.6244 0.141 0.440 0.000 0.560
#> GSM549283 2 0.1031 0.911 0.000 0.976 0.024
#> GSM549298 3 0.3038 0.814 0.000 0.104 0.896
#> GSM750741 1 0.5733 0.531 0.676 0.324 0.000
#> GSM549286 2 0.1163 0.910 0.000 0.972 0.028
#> GSM549241 2 0.6274 0.106 0.456 0.544 0.000
#> GSM549247 2 0.4346 0.727 0.184 0.816 0.000
#> GSM549261 1 0.2625 0.890 0.916 0.084 0.000
#> GSM549270 2 0.2625 0.870 0.000 0.916 0.084
#> GSM549277 3 0.6180 0.404 0.000 0.416 0.584
#> GSM549280 3 0.6235 0.354 0.000 0.436 0.564
#> GSM549281 2 0.1964 0.879 0.056 0.944 0.000
#> GSM549285 3 0.1529 0.846 0.000 0.040 0.960
#> GSM549288 3 0.6008 0.491 0.000 0.372 0.628
#> GSM549292 2 0.0237 0.914 0.000 0.996 0.004
#> GSM549295 3 0.6299 0.242 0.000 0.476 0.524
#> GSM549297 2 0.4235 0.744 0.000 0.824 0.176
#> GSM750743 1 0.0892 0.937 0.980 0.020 0.000
#> GSM549268 2 0.2918 0.896 0.044 0.924 0.032
#> GSM549290 3 0.2711 0.796 0.088 0.000 0.912
#> GSM549272 2 0.0237 0.914 0.000 0.996 0.004
#> GSM549276 2 0.1753 0.898 0.000 0.952 0.048
#> GSM549275 2 0.3340 0.810 0.120 0.880 0.000
#> GSM549284 2 0.1411 0.903 0.000 0.964 0.036
#> GSM750737 1 0.1163 0.933 0.972 0.028 0.000
#> GSM750740 1 0.0892 0.937 0.980 0.020 0.000
#> GSM750747 1 0.0592 0.940 0.988 0.012 0.000
#> GSM750751 2 0.1031 0.911 0.000 0.976 0.024
#> GSM750754 3 0.1753 0.828 0.048 0.000 0.952
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2281 0.837 0.000 0.000 0.096 0.904
#> GSM549291 4 0.3400 0.771 0.000 0.000 0.180 0.820
#> GSM549274 2 0.0000 0.912 0.000 1.000 0.000 0.000
#> GSM750738 2 0.4992 0.164 0.000 0.524 0.000 0.476
#> GSM750748 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM549240 2 0.3734 0.799 0.108 0.848 0.000 0.044
#> GSM549279 2 0.1042 0.907 0.020 0.972 0.008 0.000
#> GSM549294 2 0.0592 0.911 0.000 0.984 0.016 0.000
#> GSM549300 3 0.2149 0.863 0.000 0.088 0.912 0.000
#> GSM549303 3 0.0927 0.870 0.000 0.008 0.976 0.016
#> GSM549309 3 0.0707 0.865 0.000 0.000 0.980 0.020
#> GSM750753 2 0.1792 0.889 0.000 0.932 0.068 0.000
#> GSM750752 4 0.0336 0.872 0.000 0.000 0.008 0.992
#> GSM549304 2 0.0000 0.912 0.000 1.000 0.000 0.000
#> GSM549305 2 0.1118 0.905 0.000 0.964 0.036 0.000
#> GSM549307 3 0.3726 0.762 0.000 0.212 0.788 0.000
#> GSM549306 3 0.1557 0.875 0.000 0.056 0.944 0.000
#> GSM549308 3 0.0707 0.875 0.000 0.020 0.980 0.000
#> GSM549233 4 0.4018 0.702 0.224 0.000 0.004 0.772
#> GSM549234 4 0.0000 0.873 0.000 0.000 0.000 1.000
#> GSM549250 1 0.5167 0.436 0.644 0.000 0.016 0.340
#> GSM549287 3 0.2921 0.760 0.000 0.000 0.860 0.140
#> GSM750735 1 0.0779 0.931 0.980 0.016 0.000 0.004
#> GSM750736 2 0.5442 0.426 0.336 0.636 0.000 0.028
#> GSM750749 1 0.0524 0.932 0.988 0.008 0.004 0.000
#> GSM549230 1 0.1042 0.928 0.972 0.000 0.008 0.020
#> GSM549231 1 0.0707 0.928 0.980 0.000 0.020 0.000
#> GSM549237 1 0.0524 0.932 0.988 0.000 0.008 0.004
#> GSM549254 4 0.0524 0.870 0.008 0.004 0.000 0.988
#> GSM750734 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM549271 3 0.3444 0.701 0.000 0.000 0.816 0.184
#> GSM549232 4 0.0000 0.873 0.000 0.000 0.000 1.000
#> GSM549246 4 0.5482 0.416 0.368 0.000 0.024 0.608
#> GSM549248 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM549255 4 0.0000 0.873 0.000 0.000 0.000 1.000
#> GSM750746 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM549259 1 0.1109 0.924 0.968 0.028 0.000 0.004
#> GSM549269 2 0.0000 0.912 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0927 0.875 0.000 0.016 0.976 0.008
#> GSM549299 2 0.1637 0.894 0.000 0.940 0.060 0.000
#> GSM549301 3 0.1118 0.877 0.000 0.036 0.964 0.000
#> GSM549310 4 0.0592 0.871 0.000 0.000 0.016 0.984
#> GSM549311 3 0.0921 0.861 0.000 0.000 0.972 0.028
#> GSM549302 2 0.0657 0.911 0.000 0.984 0.012 0.004
#> GSM549235 1 0.0000 0.934 1.000 0.000 0.000 0.000
#> GSM549245 4 0.0336 0.870 0.000 0.008 0.000 0.992
#> GSM549265 4 0.3037 0.837 0.076 0.000 0.036 0.888
#> GSM549282 3 0.0895 0.863 0.004 0.000 0.976 0.020
#> GSM549296 4 0.0336 0.872 0.000 0.000 0.008 0.992
#> GSM750739 1 0.0376 0.934 0.992 0.004 0.000 0.004
#> GSM750742 1 0.0336 0.932 0.992 0.000 0.008 0.000
#> GSM750744 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM750750 3 0.0188 0.872 0.000 0.004 0.996 0.000
#> GSM549242 1 0.4428 0.596 0.720 0.000 0.004 0.276
#> GSM549252 4 0.0336 0.873 0.000 0.000 0.008 0.992
#> GSM549253 1 0.1978 0.889 0.928 0.000 0.004 0.068
#> GSM549256 4 0.4632 0.564 0.308 0.000 0.004 0.688
#> GSM549257 4 0.0336 0.873 0.000 0.000 0.008 0.992
#> GSM549263 1 0.1182 0.923 0.968 0.000 0.016 0.016
#> GSM549267 4 0.4356 0.637 0.000 0.000 0.292 0.708
#> GSM750745 1 0.0895 0.929 0.976 0.020 0.000 0.004
#> GSM549239 1 0.0188 0.934 0.996 0.004 0.000 0.000
#> GSM549244 4 0.0000 0.873 0.000 0.000 0.000 1.000
#> GSM549249 4 0.0524 0.873 0.004 0.000 0.008 0.988
#> GSM549260 1 0.0336 0.934 0.992 0.000 0.000 0.008
#> GSM549266 2 0.1059 0.909 0.016 0.972 0.012 0.000
#> GSM549293 2 0.0707 0.905 0.000 0.980 0.000 0.020
#> GSM549236 1 0.2973 0.847 0.884 0.000 0.020 0.096
#> GSM549238 4 0.2473 0.837 0.080 0.000 0.012 0.908
#> GSM549251 1 0.1042 0.927 0.972 0.000 0.008 0.020
#> GSM549258 1 0.1978 0.892 0.928 0.068 0.000 0.004
#> GSM549264 1 0.0992 0.932 0.976 0.008 0.004 0.012
#> GSM549243 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM549262 1 0.0469 0.931 0.988 0.000 0.012 0.000
#> GSM549278 4 0.4972 0.258 0.000 0.000 0.456 0.544
#> GSM549283 2 0.1637 0.895 0.000 0.940 0.060 0.000
#> GSM549298 3 0.1716 0.872 0.000 0.064 0.936 0.000
#> GSM750741 1 0.4677 0.539 0.680 0.316 0.000 0.004
#> GSM549286 2 0.0592 0.911 0.000 0.984 0.016 0.000
#> GSM549241 1 0.5105 0.239 0.564 0.432 0.000 0.004
#> GSM549247 2 0.2500 0.868 0.040 0.916 0.000 0.044
#> GSM549261 1 0.0895 0.929 0.976 0.020 0.000 0.004
#> GSM549270 2 0.1792 0.889 0.000 0.932 0.068 0.000
#> GSM549277 3 0.3444 0.793 0.000 0.184 0.816 0.000
#> GSM549280 3 0.4304 0.655 0.000 0.284 0.716 0.000
#> GSM549281 2 0.2670 0.887 0.040 0.908 0.052 0.000
#> GSM549285 3 0.1302 0.877 0.000 0.044 0.956 0.000
#> GSM549288 3 0.3356 0.801 0.000 0.176 0.824 0.000
#> GSM549292 2 0.0336 0.910 0.000 0.992 0.000 0.008
#> GSM549295 3 0.4972 0.246 0.000 0.456 0.544 0.000
#> GSM549297 2 0.3649 0.722 0.000 0.796 0.204 0.000
#> GSM750743 1 0.0524 0.933 0.988 0.008 0.000 0.004
#> GSM549268 2 0.3612 0.845 0.044 0.856 0.100 0.000
#> GSM549290 4 0.4431 0.626 0.000 0.000 0.304 0.696
#> GSM549272 2 0.0000 0.912 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0817 0.910 0.000 0.976 0.024 0.000
#> GSM549275 2 0.1389 0.888 0.048 0.952 0.000 0.000
#> GSM549284 2 0.2408 0.885 0.000 0.920 0.036 0.044
#> GSM750737 4 0.1510 0.860 0.028 0.016 0.000 0.956
#> GSM750740 1 0.0524 0.933 0.988 0.008 0.000 0.004
#> GSM750747 1 0.0188 0.934 0.996 0.000 0.000 0.004
#> GSM750751 2 0.0469 0.912 0.000 0.988 0.012 0.000
#> GSM750754 3 0.2647 0.781 0.000 0.000 0.880 0.120
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1331 0.86134 0.000 0.000 0.040 0.952 0.008
#> GSM549291 4 0.1704 0.84425 0.000 0.000 0.068 0.928 0.004
#> GSM549274 2 0.0324 0.86076 0.000 0.992 0.004 0.000 0.004
#> GSM750738 2 0.4108 0.52174 0.000 0.684 0.000 0.308 0.008
#> GSM750748 1 0.1571 0.80444 0.936 0.000 0.000 0.004 0.060
#> GSM549240 2 0.4682 0.40055 0.356 0.620 0.000 0.024 0.000
#> GSM549279 2 0.5831 0.53786 0.304 0.616 0.036 0.008 0.036
#> GSM549294 2 0.1525 0.85537 0.012 0.948 0.036 0.000 0.004
#> GSM549300 3 0.0566 0.89452 0.000 0.012 0.984 0.000 0.004
#> GSM549303 3 0.1211 0.89258 0.000 0.000 0.960 0.024 0.016
#> GSM549309 3 0.1399 0.88983 0.000 0.000 0.952 0.020 0.028
#> GSM750753 2 0.1952 0.83679 0.000 0.912 0.084 0.000 0.004
#> GSM750752 4 0.0451 0.87103 0.000 0.008 0.000 0.988 0.004
#> GSM549304 2 0.0451 0.86061 0.000 0.988 0.004 0.000 0.008
#> GSM549305 2 0.1124 0.85703 0.004 0.960 0.036 0.000 0.000
#> GSM549307 3 0.1502 0.87674 0.000 0.056 0.940 0.000 0.004
#> GSM549306 3 0.0000 0.89652 0.000 0.000 1.000 0.000 0.000
#> GSM549308 3 0.0703 0.89569 0.000 0.000 0.976 0.000 0.024
#> GSM549233 4 0.3410 0.78018 0.068 0.000 0.000 0.840 0.092
#> GSM549234 4 0.0693 0.87055 0.000 0.008 0.000 0.980 0.012
#> GSM549250 5 0.2795 0.76514 0.056 0.000 0.000 0.064 0.880
#> GSM549287 3 0.2974 0.83518 0.000 0.000 0.868 0.080 0.052
#> GSM750735 1 0.1357 0.79996 0.948 0.004 0.000 0.000 0.048
#> GSM750736 1 0.4946 0.45440 0.656 0.300 0.000 0.008 0.036
#> GSM750749 1 0.1569 0.78973 0.944 0.004 0.008 0.000 0.044
#> GSM549230 1 0.3969 0.52604 0.692 0.000 0.000 0.004 0.304
#> GSM549231 5 0.2690 0.77212 0.156 0.000 0.000 0.000 0.844
#> GSM549237 1 0.3010 0.72499 0.824 0.000 0.000 0.004 0.172
#> GSM549254 4 0.1082 0.86504 0.008 0.000 0.000 0.964 0.028
#> GSM750734 1 0.0771 0.81156 0.976 0.000 0.000 0.004 0.020
#> GSM549271 3 0.3690 0.68972 0.000 0.000 0.764 0.224 0.012
#> GSM549232 4 0.0000 0.87105 0.000 0.000 0.000 1.000 0.000
#> GSM549246 4 0.4465 0.57652 0.212 0.000 0.000 0.732 0.056
#> GSM549248 1 0.4278 0.14976 0.548 0.000 0.000 0.000 0.452
#> GSM549255 4 0.0000 0.87105 0.000 0.000 0.000 1.000 0.000
#> GSM750746 1 0.0794 0.81236 0.972 0.000 0.000 0.000 0.028
#> GSM549259 1 0.0880 0.81224 0.968 0.000 0.000 0.000 0.032
#> GSM549269 2 0.0324 0.86087 0.004 0.992 0.004 0.000 0.000
#> GSM549273 3 0.1106 0.89374 0.000 0.000 0.964 0.024 0.012
#> GSM549299 2 0.3815 0.71253 0.012 0.764 0.220 0.000 0.004
#> GSM549301 3 0.0162 0.89675 0.000 0.000 0.996 0.000 0.004
#> GSM549310 4 0.0898 0.86821 0.000 0.000 0.020 0.972 0.008
#> GSM549311 3 0.1522 0.88716 0.000 0.000 0.944 0.012 0.044
#> GSM549302 2 0.0451 0.86061 0.000 0.988 0.004 0.000 0.008
#> GSM549235 1 0.2020 0.78164 0.900 0.000 0.000 0.000 0.100
#> GSM549245 4 0.0451 0.87068 0.000 0.008 0.000 0.988 0.004
#> GSM549265 4 0.4596 0.14401 0.004 0.004 0.000 0.500 0.492
#> GSM549282 5 0.3086 0.58352 0.000 0.000 0.180 0.004 0.816
#> GSM549296 4 0.0162 0.87102 0.000 0.000 0.000 0.996 0.004
#> GSM750739 1 0.1430 0.80783 0.944 0.000 0.000 0.004 0.052
#> GSM750742 5 0.3913 0.54772 0.324 0.000 0.000 0.000 0.676
#> GSM750744 1 0.4126 0.37457 0.620 0.000 0.000 0.000 0.380
#> GSM750750 3 0.0703 0.89569 0.000 0.000 0.976 0.000 0.024
#> GSM549242 1 0.4836 0.33486 0.612 0.000 0.000 0.356 0.032
#> GSM549252 4 0.1788 0.85623 0.004 0.008 0.000 0.932 0.056
#> GSM549253 5 0.4108 0.59953 0.308 0.000 0.000 0.008 0.684
#> GSM549256 4 0.1831 0.83102 0.076 0.000 0.000 0.920 0.004
#> GSM549257 4 0.0162 0.87125 0.000 0.000 0.000 0.996 0.004
#> GSM549263 5 0.3242 0.73241 0.216 0.000 0.000 0.000 0.784
#> GSM549267 4 0.3631 0.78076 0.000 0.000 0.104 0.824 0.072
#> GSM750745 1 0.0865 0.80613 0.972 0.000 0.000 0.004 0.024
#> GSM549239 1 0.1043 0.81113 0.960 0.000 0.000 0.000 0.040
#> GSM549244 4 0.1444 0.86379 0.000 0.012 0.000 0.948 0.040
#> GSM549249 4 0.2052 0.84344 0.004 0.004 0.000 0.912 0.080
#> GSM549260 1 0.0912 0.80891 0.972 0.000 0.000 0.012 0.016
#> GSM549266 2 0.5333 0.41760 0.376 0.576 0.036 0.000 0.012
#> GSM549293 2 0.0290 0.85943 0.000 0.992 0.000 0.000 0.008
#> GSM549236 5 0.2660 0.78334 0.128 0.000 0.000 0.008 0.864
#> GSM549238 4 0.4444 0.44276 0.012 0.000 0.000 0.624 0.364
#> GSM549251 1 0.3132 0.71524 0.820 0.000 0.000 0.008 0.172
#> GSM549258 1 0.0324 0.80807 0.992 0.004 0.000 0.000 0.004
#> GSM549264 5 0.2054 0.77559 0.072 0.008 0.000 0.004 0.916
#> GSM549243 1 0.1638 0.80279 0.932 0.000 0.000 0.004 0.064
#> GSM549262 1 0.3837 0.52546 0.692 0.000 0.000 0.000 0.308
#> GSM549278 4 0.4599 0.35260 0.000 0.000 0.384 0.600 0.016
#> GSM549283 2 0.4074 0.69864 0.012 0.752 0.224 0.000 0.012
#> GSM549298 3 0.0324 0.89649 0.000 0.004 0.992 0.000 0.004
#> GSM750741 1 0.1251 0.79082 0.956 0.008 0.000 0.000 0.036
#> GSM549286 2 0.0324 0.86076 0.000 0.992 0.004 0.000 0.004
#> GSM549241 1 0.1300 0.79027 0.956 0.028 0.000 0.000 0.016
#> GSM549247 2 0.1356 0.84711 0.028 0.956 0.000 0.012 0.004
#> GSM549261 1 0.1626 0.80919 0.940 0.016 0.000 0.000 0.044
#> GSM549270 2 0.2439 0.81261 0.000 0.876 0.120 0.000 0.004
#> GSM549277 3 0.2006 0.86686 0.000 0.072 0.916 0.000 0.012
#> GSM549280 3 0.1571 0.87297 0.000 0.060 0.936 0.000 0.004
#> GSM549281 1 0.6724 0.32647 0.568 0.200 0.196 0.000 0.036
#> GSM549285 3 0.3635 0.69012 0.000 0.004 0.748 0.000 0.248
#> GSM549288 3 0.3143 0.72604 0.000 0.204 0.796 0.000 0.000
#> GSM549292 2 0.0162 0.85957 0.000 0.996 0.000 0.000 0.004
#> GSM549295 3 0.3969 0.54711 0.000 0.304 0.692 0.000 0.004
#> GSM549297 2 0.4299 0.37202 0.000 0.608 0.388 0.000 0.004
#> GSM750743 1 0.1478 0.80637 0.936 0.000 0.000 0.000 0.064
#> GSM549268 1 0.6749 -0.00376 0.440 0.108 0.416 0.000 0.036
#> GSM549290 5 0.4990 0.29037 0.000 0.000 0.048 0.324 0.628
#> GSM549272 2 0.0486 0.86079 0.004 0.988 0.004 0.000 0.004
#> GSM549276 2 0.0955 0.85883 0.000 0.968 0.028 0.000 0.004
#> GSM549275 2 0.2513 0.80020 0.116 0.876 0.000 0.000 0.008
#> GSM549284 2 0.0727 0.85621 0.000 0.980 0.004 0.004 0.012
#> GSM750737 4 0.1485 0.85634 0.020 0.000 0.000 0.948 0.032
#> GSM750740 1 0.0510 0.81236 0.984 0.000 0.000 0.000 0.016
#> GSM750747 1 0.0510 0.81236 0.984 0.000 0.000 0.000 0.016
#> GSM750751 2 0.0771 0.85987 0.004 0.976 0.020 0.000 0.000
#> GSM750754 3 0.2569 0.85604 0.000 0.000 0.892 0.068 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.0603 0.8949 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM549291 4 0.1780 0.8714 0.000 0.000 0.048 0.924 0.000 0.028
#> GSM549274 2 0.0713 0.8445 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM750738 2 0.6101 0.0986 0.000 0.404 0.000 0.364 0.004 0.228
#> GSM750748 1 0.0260 0.7112 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549240 2 0.4831 0.3485 0.380 0.572 0.000 0.028 0.000 0.020
#> GSM549279 6 0.5735 0.5318 0.112 0.108 0.128 0.000 0.000 0.652
#> GSM549294 2 0.1390 0.8386 0.004 0.948 0.016 0.000 0.000 0.032
#> GSM549300 3 0.1226 0.8120 0.000 0.004 0.952 0.000 0.004 0.040
#> GSM549303 3 0.3526 0.7730 0.000 0.000 0.792 0.016 0.020 0.172
#> GSM549309 3 0.2695 0.7903 0.000 0.000 0.844 0.004 0.008 0.144
#> GSM750753 2 0.5009 0.5421 0.000 0.624 0.256 0.000 0.000 0.120
#> GSM750752 4 0.0603 0.8956 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM549304 2 0.2306 0.8099 0.000 0.888 0.016 0.004 0.000 0.092
#> GSM549305 2 0.0405 0.8461 0.000 0.988 0.008 0.000 0.000 0.004
#> GSM549307 3 0.0909 0.8151 0.000 0.012 0.968 0.000 0.000 0.020
#> GSM549306 3 0.0603 0.8162 0.000 0.004 0.980 0.000 0.000 0.016
#> GSM549308 3 0.0260 0.8169 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM549233 4 0.3453 0.8120 0.068 0.000 0.000 0.836 0.064 0.032
#> GSM549234 4 0.1196 0.8889 0.000 0.000 0.000 0.952 0.008 0.040
#> GSM549250 5 0.2604 0.7296 0.096 0.000 0.000 0.028 0.872 0.004
#> GSM549287 3 0.4788 0.7264 0.000 0.000 0.720 0.096 0.032 0.152
#> GSM750735 6 0.3713 0.6662 0.284 0.008 0.000 0.000 0.004 0.704
#> GSM750736 6 0.4189 0.6335 0.152 0.096 0.000 0.000 0.004 0.748
#> GSM750749 6 0.4130 0.6722 0.264 0.008 0.028 0.000 0.000 0.700
#> GSM549230 1 0.2980 0.5440 0.800 0.000 0.000 0.000 0.192 0.008
#> GSM549231 5 0.2821 0.7159 0.152 0.000 0.000 0.000 0.832 0.016
#> GSM549237 1 0.5238 0.1403 0.604 0.000 0.000 0.000 0.160 0.236
#> GSM549254 4 0.0547 0.8968 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM750734 1 0.3869 -0.4872 0.500 0.000 0.000 0.000 0.000 0.500
#> GSM549271 3 0.2814 0.7030 0.000 0.000 0.820 0.172 0.000 0.008
#> GSM549232 4 0.0291 0.8967 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549246 4 0.4089 0.7050 0.176 0.000 0.000 0.760 0.040 0.024
#> GSM549248 5 0.4846 0.3033 0.356 0.000 0.000 0.000 0.576 0.068
#> GSM549255 4 0.0146 0.8963 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM750746 1 0.0547 0.7106 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM549259 1 0.0260 0.7122 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM549269 2 0.0146 0.8457 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549273 3 0.3560 0.7721 0.000 0.000 0.788 0.016 0.020 0.176
#> GSM549299 2 0.4832 0.4750 0.008 0.612 0.324 0.000 0.000 0.056
#> GSM549301 3 0.0551 0.8183 0.000 0.004 0.984 0.000 0.008 0.004
#> GSM549310 4 0.0858 0.8915 0.000 0.000 0.004 0.968 0.000 0.028
#> GSM549311 3 0.3973 0.7677 0.004 0.000 0.768 0.020 0.028 0.180
#> GSM549302 2 0.0146 0.8458 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549235 1 0.1082 0.7012 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM549245 4 0.0458 0.8959 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM549265 5 0.5605 0.3946 0.000 0.000 0.000 0.212 0.544 0.244
#> GSM549282 5 0.1204 0.6878 0.000 0.000 0.056 0.000 0.944 0.000
#> GSM549296 4 0.0405 0.8960 0.000 0.000 0.004 0.988 0.000 0.008
#> GSM750739 6 0.3975 0.5215 0.452 0.000 0.000 0.000 0.004 0.544
#> GSM750742 5 0.3819 0.5397 0.316 0.000 0.000 0.000 0.672 0.012
#> GSM750744 6 0.5296 0.5674 0.260 0.000 0.000 0.000 0.152 0.588
#> GSM750750 3 0.0603 0.8167 0.000 0.000 0.980 0.000 0.016 0.004
#> GSM549242 1 0.4319 0.2103 0.576 0.000 0.000 0.400 0.000 0.024
#> GSM549252 4 0.1863 0.8783 0.000 0.000 0.000 0.920 0.044 0.036
#> GSM549253 1 0.4310 -0.2410 0.512 0.000 0.000 0.004 0.472 0.012
#> GSM549256 4 0.2631 0.7494 0.180 0.000 0.000 0.820 0.000 0.000
#> GSM549257 4 0.0146 0.8967 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM549263 5 0.4086 0.2732 0.464 0.000 0.000 0.000 0.528 0.008
#> GSM549267 4 0.3288 0.8202 0.000 0.000 0.036 0.848 0.064 0.052
#> GSM750745 6 0.3857 0.4949 0.468 0.000 0.000 0.000 0.000 0.532
#> GSM549239 1 0.3634 -0.0353 0.644 0.000 0.000 0.000 0.000 0.356
#> GSM549244 4 0.1003 0.8932 0.000 0.000 0.000 0.964 0.020 0.016
#> GSM549249 4 0.1429 0.8824 0.004 0.000 0.000 0.940 0.052 0.004
#> GSM549260 1 0.1082 0.6990 0.956 0.000 0.000 0.004 0.000 0.040
#> GSM549266 2 0.4659 0.5129 0.304 0.644 0.004 0.000 0.008 0.040
#> GSM549293 2 0.0603 0.8449 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM549236 5 0.3000 0.7130 0.156 0.000 0.000 0.004 0.824 0.016
#> GSM549238 4 0.4165 0.2614 0.008 0.000 0.000 0.568 0.420 0.004
#> GSM549251 1 0.2066 0.6834 0.904 0.000 0.000 0.000 0.072 0.024
#> GSM549258 1 0.1556 0.6754 0.920 0.000 0.000 0.000 0.000 0.080
#> GSM549264 5 0.1579 0.7124 0.024 0.008 0.000 0.004 0.944 0.020
#> GSM549243 1 0.0891 0.7107 0.968 0.000 0.000 0.000 0.008 0.024
#> GSM549262 1 0.5033 -0.0542 0.476 0.000 0.000 0.000 0.452 0.072
#> GSM549278 4 0.3841 0.6318 0.000 0.000 0.244 0.724 0.000 0.032
#> GSM549283 3 0.5818 0.1046 0.000 0.352 0.456 0.000 0.000 0.192
#> GSM549298 3 0.0603 0.8170 0.000 0.000 0.980 0.000 0.004 0.016
#> GSM750741 6 0.3833 0.5404 0.444 0.000 0.000 0.000 0.000 0.556
#> GSM549286 2 0.0363 0.8448 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM549241 1 0.2178 0.5914 0.868 0.000 0.000 0.000 0.000 0.132
#> GSM549247 2 0.1364 0.8370 0.016 0.952 0.000 0.012 0.000 0.020
#> GSM549261 1 0.1908 0.6364 0.900 0.096 0.000 0.000 0.004 0.000
#> GSM549270 2 0.2586 0.7921 0.000 0.880 0.080 0.000 0.008 0.032
#> GSM549277 3 0.4407 0.7246 0.008 0.164 0.744 0.000 0.008 0.076
#> GSM549280 3 0.2058 0.7982 0.000 0.036 0.908 0.000 0.000 0.056
#> GSM549281 6 0.5566 0.6148 0.220 0.064 0.068 0.000 0.004 0.644
#> GSM549285 3 0.4233 0.6699 0.032 0.008 0.756 0.000 0.180 0.024
#> GSM549288 3 0.5060 0.4664 0.000 0.336 0.580 0.000 0.004 0.080
#> GSM549292 2 0.0458 0.8442 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM549295 3 0.5480 0.3327 0.000 0.368 0.520 0.000 0.008 0.104
#> GSM549297 2 0.4525 0.5597 0.000 0.696 0.228 0.000 0.008 0.068
#> GSM750743 6 0.3830 0.6141 0.376 0.000 0.000 0.000 0.004 0.620
#> GSM549268 6 0.6334 0.5040 0.140 0.080 0.180 0.000 0.008 0.592
#> GSM549290 5 0.3449 0.5961 0.000 0.000 0.016 0.196 0.780 0.008
#> GSM549272 2 0.0146 0.8457 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549276 2 0.0717 0.8453 0.000 0.976 0.008 0.000 0.000 0.016
#> GSM549275 2 0.4598 0.6941 0.132 0.732 0.020 0.000 0.000 0.116
#> GSM549284 2 0.1074 0.8412 0.000 0.960 0.000 0.000 0.012 0.028
#> GSM750737 6 0.3833 0.3308 0.008 0.000 0.000 0.344 0.000 0.648
#> GSM750740 1 0.0260 0.7116 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM750747 1 0.0260 0.7116 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM750751 2 0.0458 0.8464 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM750754 3 0.2633 0.8012 0.000 0.000 0.888 0.028 0.040 0.044
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> SD:NMF 101 0.0265 1.84e-05 0.011073 0.00189 2
#> SD:NMF 94 0.0118 1.13e-06 0.000277 0.01352 3
#> SD:NMF 96 0.1810 1.16e-05 0.011889 0.01151 4
#> SD:NMF 90 0.2371 6.82e-05 0.016348 0.06837 5
#> SD:NMF 85 0.3847 1.63e-03 0.001784 0.02574 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.456 0.868 0.909 0.4629 0.497 0.497
#> 3 3 0.569 0.749 0.857 0.3012 0.875 0.752
#> 4 4 0.629 0.763 0.848 0.0914 0.958 0.891
#> 5 5 0.704 0.735 0.845 0.0467 0.993 0.980
#> 6 6 0.725 0.653 0.807 0.0304 0.990 0.971
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
#> GSM549289 1 0.9933 -0.0821 0.548 0.452
#> GSM549291 2 0.8327 0.7976 0.264 0.736
#> GSM549274 2 0.5946 0.8964 0.144 0.856
#> GSM750738 2 0.7056 0.8749 0.192 0.808
#> GSM750748 1 0.0000 0.9446 1.000 0.000
#> GSM549240 1 0.2043 0.9298 0.968 0.032
#> GSM549279 2 0.9393 0.6534 0.356 0.644
#> GSM549294 2 0.5946 0.8963 0.144 0.856
#> GSM549300 2 0.2043 0.8636 0.032 0.968
#> GSM549303 2 0.0376 0.8463 0.004 0.996
#> GSM549309 2 0.1843 0.8521 0.028 0.972
#> GSM750753 2 0.5946 0.8972 0.144 0.856
#> GSM750752 2 0.7453 0.8522 0.212 0.788
#> GSM549304 2 0.7453 0.8592 0.212 0.788
#> GSM549305 2 0.5842 0.8973 0.140 0.860
#> GSM549307 2 0.1414 0.8573 0.020 0.980
#> GSM549306 2 0.0000 0.8449 0.000 1.000
#> GSM549308 2 0.0000 0.8449 0.000 1.000
#> GSM549233 1 0.0376 0.9434 0.996 0.004
#> GSM549234 1 0.2948 0.9167 0.948 0.052
#> GSM549250 1 0.0000 0.9446 1.000 0.000
#> GSM549287 2 0.7376 0.8549 0.208 0.792
#> GSM750735 1 0.0672 0.9422 0.992 0.008
#> GSM750736 1 0.0672 0.9422 0.992 0.008
#> GSM750749 1 0.3431 0.9043 0.936 0.064
#> GSM549230 1 0.0000 0.9446 1.000 0.000
#> GSM549231 1 0.0000 0.9446 1.000 0.000
#> GSM549237 1 0.0000 0.9446 1.000 0.000
#> GSM549254 1 0.9710 0.1420 0.600 0.400
#> GSM750734 1 0.0000 0.9446 1.000 0.000
#> GSM549271 2 0.7219 0.8612 0.200 0.800
#> GSM549232 1 0.3584 0.9017 0.932 0.068
#> GSM549246 1 0.3114 0.9150 0.944 0.056
#> GSM549248 1 0.0000 0.9446 1.000 0.000
#> GSM549255 1 0.3879 0.8937 0.924 0.076
#> GSM750746 1 0.0000 0.9446 1.000 0.000
#> GSM549259 1 0.0000 0.9446 1.000 0.000
#> GSM549269 2 0.5842 0.8973 0.140 0.860
#> GSM549273 2 0.0376 0.8463 0.004 0.996
#> GSM549299 2 0.7453 0.8592 0.212 0.788
#> GSM549301 2 0.0000 0.8449 0.000 1.000
#> GSM549310 2 0.7453 0.8520 0.212 0.788
#> GSM549311 2 0.0376 0.8463 0.004 0.996
#> GSM549302 2 0.5946 0.8964 0.144 0.856
#> GSM549235 1 0.0000 0.9446 1.000 0.000
#> GSM549245 1 0.3879 0.8937 0.924 0.076
#> GSM549265 1 0.3114 0.9139 0.944 0.056
#> GSM549282 2 0.5519 0.8932 0.128 0.872
#> GSM549296 2 0.7453 0.8522 0.212 0.788
#> GSM750739 1 0.0000 0.9446 1.000 0.000
#> GSM750742 1 0.0000 0.9446 1.000 0.000
#> GSM750744 1 0.0000 0.9446 1.000 0.000
#> GSM750750 2 0.5519 0.8932 0.128 0.872
#> GSM549242 1 0.0000 0.9446 1.000 0.000
#> GSM549252 1 0.1843 0.9328 0.972 0.028
#> GSM549253 1 0.0000 0.9446 1.000 0.000
#> GSM549256 1 0.0000 0.9446 1.000 0.000
#> GSM549257 1 0.3733 0.8978 0.928 0.072
#> GSM549263 1 0.0000 0.9446 1.000 0.000
#> GSM549267 2 0.7674 0.8401 0.224 0.776
#> GSM750745 1 0.0000 0.9446 1.000 0.000
#> GSM549239 1 0.0000 0.9446 1.000 0.000
#> GSM549244 1 0.2603 0.9224 0.956 0.044
#> GSM549249 1 0.1633 0.9351 0.976 0.024
#> GSM549260 1 0.0000 0.9446 1.000 0.000
#> GSM549266 2 0.9732 0.5403 0.404 0.596
#> GSM549293 2 0.5946 0.8964 0.144 0.856
#> GSM549236 1 0.0000 0.9446 1.000 0.000
#> GSM549238 1 0.1633 0.9351 0.976 0.024
#> GSM549251 1 0.0000 0.9446 1.000 0.000
#> GSM549258 1 0.1414 0.9368 0.980 0.020
#> GSM549264 1 0.0000 0.9446 1.000 0.000
#> GSM549243 1 0.0000 0.9446 1.000 0.000
#> GSM549262 1 0.0000 0.9446 1.000 0.000
#> GSM549278 2 0.9393 0.6426 0.356 0.644
#> GSM549283 2 0.8763 0.7612 0.296 0.704
#> GSM549298 2 0.0000 0.8449 0.000 1.000
#> GSM750741 1 0.1414 0.9368 0.980 0.020
#> GSM549286 2 0.5842 0.8973 0.140 0.860
#> GSM549241 1 0.1414 0.9368 0.980 0.020
#> GSM549247 1 0.2043 0.9298 0.968 0.032
#> GSM549261 1 0.0000 0.9446 1.000 0.000
#> GSM549270 2 0.5294 0.8963 0.120 0.880
#> GSM549277 2 0.4690 0.8928 0.100 0.900
#> GSM549280 2 0.4690 0.8933 0.100 0.900
#> GSM549281 1 0.9209 0.4065 0.664 0.336
#> GSM549285 2 0.6148 0.8895 0.152 0.848
#> GSM549288 2 0.4690 0.8928 0.100 0.900
#> GSM549292 2 0.5842 0.8973 0.140 0.860
#> GSM549295 2 0.0000 0.8449 0.000 1.000
#> GSM549297 2 0.4690 0.8928 0.100 0.900
#> GSM750743 1 0.0000 0.9446 1.000 0.000
#> GSM549268 1 0.9209 0.4065 0.664 0.336
#> GSM549290 2 0.7528 0.8526 0.216 0.784
#> GSM549272 2 0.5842 0.8973 0.140 0.860
#> GSM549276 2 0.5519 0.8970 0.128 0.872
#> GSM549275 1 0.3733 0.8984 0.928 0.072
#> GSM549284 2 0.7219 0.8712 0.200 0.800
#> GSM750737 1 0.4562 0.8694 0.904 0.096
#> GSM750740 1 0.0000 0.9446 1.000 0.000
#> GSM750747 1 0.0000 0.9446 1.000 0.000
#> GSM750751 2 0.5842 0.8971 0.140 0.860
#> GSM750754 2 0.8081 0.8142 0.248 0.752
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.9377 0.246 0.380 0.172 0.448
#> GSM549291 3 0.7664 0.603 0.104 0.228 0.668
#> GSM549274 2 0.1129 0.792 0.004 0.976 0.020
#> GSM750738 2 0.2527 0.766 0.044 0.936 0.020
#> GSM750748 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549240 1 0.1905 0.908 0.956 0.028 0.016
#> GSM549279 2 0.7762 0.502 0.212 0.668 0.120
#> GSM549294 2 0.3670 0.778 0.020 0.888 0.092
#> GSM549300 3 0.5988 0.375 0.000 0.368 0.632
#> GSM549303 3 0.3551 0.636 0.000 0.132 0.868
#> GSM549309 3 0.3193 0.638 0.004 0.100 0.896
#> GSM750753 2 0.5200 0.696 0.020 0.796 0.184
#> GSM750752 3 0.7394 0.612 0.064 0.284 0.652
#> GSM549304 2 0.4609 0.761 0.052 0.856 0.092
#> GSM549305 2 0.1031 0.792 0.000 0.976 0.024
#> GSM549307 3 0.5529 0.507 0.000 0.296 0.704
#> GSM549306 3 0.4702 0.580 0.000 0.212 0.788
#> GSM549308 3 0.4002 0.620 0.000 0.160 0.840
#> GSM549233 1 0.1170 0.917 0.976 0.016 0.008
#> GSM549234 1 0.5538 0.812 0.812 0.072 0.116
#> GSM549250 1 0.1711 0.911 0.960 0.008 0.032
#> GSM549287 3 0.7295 0.639 0.072 0.252 0.676
#> GSM750735 1 0.0747 0.919 0.984 0.016 0.000
#> GSM750736 1 0.0747 0.919 0.984 0.016 0.000
#> GSM750749 1 0.3791 0.869 0.892 0.060 0.048
#> GSM549230 1 0.0237 0.922 0.996 0.000 0.004
#> GSM549231 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549254 1 0.9509 0.036 0.484 0.220 0.296
#> GSM750734 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549271 3 0.7384 0.628 0.068 0.272 0.660
#> GSM549232 1 0.5851 0.790 0.792 0.068 0.140
#> GSM549246 1 0.3983 0.871 0.884 0.048 0.068
#> GSM549248 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549255 1 0.5939 0.787 0.788 0.072 0.140
#> GSM750746 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.786 0.000 1.000 0.000
#> GSM549273 3 0.3412 0.633 0.000 0.124 0.876
#> GSM549299 2 0.4609 0.761 0.052 0.856 0.092
#> GSM549301 3 0.4002 0.620 0.000 0.160 0.840
#> GSM549310 3 0.7363 0.616 0.064 0.280 0.656
#> GSM549311 3 0.3412 0.633 0.000 0.124 0.876
#> GSM549302 2 0.0983 0.793 0.004 0.980 0.016
#> GSM549235 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549245 1 0.6087 0.778 0.780 0.076 0.144
#> GSM549265 1 0.5393 0.819 0.820 0.072 0.108
#> GSM549282 3 0.6322 0.623 0.024 0.276 0.700
#> GSM549296 3 0.7394 0.612 0.064 0.284 0.652
#> GSM750739 1 0.0000 0.922 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.922 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.922 1.000 0.000 0.000
#> GSM750750 3 0.6287 0.626 0.024 0.272 0.704
#> GSM549242 1 0.0475 0.922 0.992 0.004 0.004
#> GSM549252 1 0.4902 0.840 0.844 0.064 0.092
#> GSM549253 1 0.0237 0.922 0.996 0.004 0.000
#> GSM549256 1 0.0829 0.920 0.984 0.004 0.012
#> GSM549257 1 0.5913 0.786 0.788 0.068 0.144
#> GSM549263 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549267 3 0.7106 0.635 0.072 0.232 0.696
#> GSM750745 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549244 1 0.5467 0.815 0.816 0.072 0.112
#> GSM549249 1 0.5004 0.836 0.840 0.072 0.088
#> GSM549260 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549266 2 0.8350 0.367 0.280 0.600 0.120
#> GSM549293 2 0.0983 0.793 0.004 0.980 0.016
#> GSM549236 1 0.0424 0.922 0.992 0.000 0.008
#> GSM549238 1 0.4921 0.839 0.844 0.072 0.084
#> GSM549251 1 0.0237 0.922 0.996 0.000 0.004
#> GSM549258 1 0.1170 0.916 0.976 0.016 0.008
#> GSM549264 1 0.0475 0.922 0.992 0.004 0.004
#> GSM549243 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549278 3 0.8726 0.483 0.196 0.212 0.592
#> GSM549283 2 0.6634 0.629 0.144 0.752 0.104
#> GSM549298 3 0.4178 0.612 0.000 0.172 0.828
#> GSM750741 1 0.1170 0.916 0.976 0.016 0.008
#> GSM549286 2 0.0000 0.786 0.000 1.000 0.000
#> GSM549241 1 0.1170 0.916 0.976 0.016 0.008
#> GSM549247 1 0.1905 0.908 0.956 0.028 0.016
#> GSM549261 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549270 2 0.4291 0.698 0.000 0.820 0.180
#> GSM549277 2 0.5706 0.468 0.000 0.680 0.320
#> GSM549280 2 0.6104 0.392 0.004 0.648 0.348
#> GSM549281 1 0.8160 0.430 0.608 0.288 0.104
#> GSM549285 3 0.8157 0.382 0.072 0.412 0.516
#> GSM549288 2 0.5968 0.356 0.000 0.636 0.364
#> GSM549292 2 0.0000 0.786 0.000 1.000 0.000
#> GSM549295 3 0.5529 0.488 0.000 0.296 0.704
#> GSM549297 2 0.5216 0.586 0.000 0.740 0.260
#> GSM750743 1 0.0000 0.922 1.000 0.000 0.000
#> GSM549268 1 0.8160 0.430 0.608 0.288 0.104
#> GSM549290 3 0.7376 0.633 0.076 0.252 0.672
#> GSM549272 2 0.0000 0.786 0.000 1.000 0.000
#> GSM549276 2 0.3340 0.755 0.000 0.880 0.120
#> GSM549275 1 0.4136 0.834 0.864 0.116 0.020
#> GSM549284 2 0.4848 0.712 0.036 0.836 0.128
#> GSM750737 1 0.4097 0.861 0.880 0.060 0.060
#> GSM750740 1 0.0000 0.922 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.922 1.000 0.000 0.000
#> GSM750751 2 0.2063 0.794 0.008 0.948 0.044
#> GSM750754 3 0.7339 0.621 0.088 0.224 0.688
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.5442 0.443 0.336 0.000 0.028 0.636
#> GSM549291 4 0.3667 0.722 0.056 0.000 0.088 0.856
#> GSM549274 2 0.2189 0.755 0.004 0.932 0.020 0.044
#> GSM750738 2 0.3302 0.718 0.032 0.888 0.016 0.064
#> GSM750748 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549240 1 0.1993 0.895 0.944 0.016 0.024 0.016
#> GSM549279 2 0.8898 0.492 0.180 0.500 0.124 0.196
#> GSM549294 2 0.5505 0.739 0.016 0.752 0.072 0.160
#> GSM549300 3 0.5417 0.565 0.000 0.284 0.676 0.040
#> GSM549303 3 0.2882 0.820 0.000 0.024 0.892 0.084
#> GSM549309 3 0.3450 0.749 0.000 0.008 0.836 0.156
#> GSM750753 2 0.6743 0.672 0.016 0.656 0.176 0.152
#> GSM750752 4 0.4340 0.713 0.024 0.044 0.096 0.836
#> GSM549304 2 0.6053 0.734 0.032 0.728 0.084 0.156
#> GSM549305 2 0.2845 0.760 0.000 0.896 0.028 0.076
#> GSM549307 3 0.4562 0.732 0.000 0.208 0.764 0.028
#> GSM549306 3 0.3205 0.836 0.000 0.104 0.872 0.024
#> GSM549308 3 0.2565 0.853 0.000 0.056 0.912 0.032
#> GSM549233 1 0.1118 0.909 0.964 0.000 0.000 0.036
#> GSM549234 1 0.3801 0.761 0.780 0.000 0.000 0.220
#> GSM549250 1 0.1302 0.903 0.956 0.000 0.000 0.044
#> GSM549287 4 0.4567 0.710 0.032 0.008 0.168 0.792
#> GSM750735 1 0.0707 0.913 0.980 0.000 0.000 0.020
#> GSM750736 1 0.0707 0.913 0.980 0.000 0.000 0.020
#> GSM750749 1 0.3292 0.855 0.880 0.004 0.036 0.080
#> GSM549230 1 0.0188 0.917 0.996 0.000 0.000 0.004
#> GSM549231 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0188 0.918 0.996 0.000 0.000 0.004
#> GSM549254 4 0.5893 0.136 0.444 0.012 0.016 0.528
#> GSM750734 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549271 4 0.4840 0.714 0.032 0.028 0.144 0.796
#> GSM549232 1 0.4122 0.735 0.760 0.000 0.004 0.236
#> GSM549246 1 0.2999 0.847 0.864 0.000 0.004 0.132
#> GSM549248 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549255 1 0.4155 0.731 0.756 0.000 0.004 0.240
#> GSM750746 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549269 2 0.1004 0.734 0.000 0.972 0.004 0.024
#> GSM549273 3 0.2882 0.820 0.000 0.024 0.892 0.084
#> GSM549299 2 0.6097 0.732 0.032 0.724 0.084 0.160
#> GSM549301 3 0.2565 0.853 0.000 0.056 0.912 0.032
#> GSM549310 4 0.4370 0.715 0.024 0.032 0.116 0.828
#> GSM549311 3 0.2882 0.820 0.000 0.024 0.892 0.084
#> GSM549302 2 0.2099 0.755 0.004 0.936 0.020 0.040
#> GSM549235 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549245 1 0.4220 0.720 0.748 0.000 0.004 0.248
#> GSM549265 1 0.3726 0.772 0.788 0.000 0.000 0.212
#> GSM549282 4 0.6099 0.394 0.024 0.016 0.396 0.564
#> GSM549296 4 0.4254 0.714 0.024 0.040 0.096 0.840
#> GSM750739 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM750750 4 0.6109 0.386 0.024 0.016 0.400 0.560
#> GSM549242 1 0.0469 0.917 0.988 0.000 0.000 0.012
#> GSM549252 1 0.3444 0.800 0.816 0.000 0.000 0.184
#> GSM549253 1 0.0188 0.917 0.996 0.000 0.000 0.004
#> GSM549256 1 0.0707 0.915 0.980 0.000 0.000 0.020
#> GSM549257 1 0.4155 0.730 0.756 0.000 0.004 0.240
#> GSM549263 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549267 4 0.3958 0.719 0.032 0.000 0.144 0.824
#> GSM750745 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549244 1 0.3870 0.770 0.788 0.000 0.004 0.208
#> GSM549249 1 0.3400 0.803 0.820 0.000 0.000 0.180
#> GSM549260 1 0.0376 0.916 0.992 0.000 0.004 0.004
#> GSM549266 2 0.9167 0.335 0.260 0.440 0.112 0.188
#> GSM549293 2 0.2099 0.755 0.004 0.936 0.020 0.040
#> GSM549236 1 0.0469 0.916 0.988 0.000 0.000 0.012
#> GSM549238 1 0.3356 0.807 0.824 0.000 0.000 0.176
#> GSM549251 1 0.0336 0.917 0.992 0.000 0.000 0.008
#> GSM549258 1 0.1394 0.905 0.964 0.008 0.012 0.016
#> GSM549264 1 0.0592 0.915 0.984 0.000 0.000 0.016
#> GSM549243 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549278 4 0.4758 0.652 0.156 0.000 0.064 0.780
#> GSM549283 2 0.7942 0.638 0.104 0.596 0.108 0.192
#> GSM549298 3 0.2521 0.852 0.000 0.064 0.912 0.024
#> GSM750741 1 0.1394 0.905 0.964 0.008 0.012 0.016
#> GSM549286 2 0.0657 0.738 0.000 0.984 0.004 0.012
#> GSM549241 1 0.1394 0.905 0.964 0.008 0.012 0.016
#> GSM549247 1 0.1993 0.895 0.944 0.016 0.024 0.016
#> GSM549261 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM549270 2 0.5855 0.689 0.000 0.704 0.160 0.136
#> GSM549277 2 0.7198 0.460 0.000 0.520 0.320 0.160
#> GSM549280 2 0.7396 0.404 0.004 0.500 0.340 0.156
#> GSM549281 1 0.8037 0.390 0.592 0.180 0.116 0.112
#> GSM549285 4 0.8321 0.253 0.056 0.136 0.336 0.472
#> GSM549288 2 0.7286 0.362 0.000 0.480 0.364 0.156
#> GSM549292 2 0.0895 0.734 0.000 0.976 0.004 0.020
#> GSM549295 3 0.4054 0.765 0.000 0.188 0.796 0.016
#> GSM549297 2 0.6509 0.607 0.000 0.632 0.228 0.140
#> GSM750743 1 0.0188 0.917 0.996 0.000 0.000 0.004
#> GSM549268 1 0.8037 0.390 0.592 0.180 0.116 0.112
#> GSM549290 4 0.5172 0.673 0.036 0.008 0.220 0.736
#> GSM549272 2 0.1004 0.734 0.000 0.972 0.004 0.024
#> GSM549276 2 0.4982 0.731 0.000 0.772 0.092 0.136
#> GSM549275 1 0.4805 0.784 0.820 0.052 0.052 0.076
#> GSM549284 2 0.5182 0.679 0.024 0.776 0.048 0.152
#> GSM750737 1 0.3016 0.847 0.872 0.004 0.004 0.120
#> GSM750740 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.918 1.000 0.000 0.000 0.000
#> GSM750751 2 0.4001 0.761 0.004 0.840 0.048 0.108
#> GSM750754 4 0.3850 0.726 0.044 0.000 0.116 0.840
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.4380 0.375 0.304 0.000 0.000 0.676 0.020
#> GSM549291 4 0.2099 0.701 0.024 0.000 0.024 0.928 0.024
#> GSM549274 2 0.1443 0.711 0.004 0.948 0.004 0.000 0.044
#> GSM750738 2 0.3246 0.646 0.024 0.848 0.000 0.008 0.120
#> GSM750748 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549240 1 0.2177 0.868 0.908 0.004 0.000 0.008 0.080
#> GSM549279 2 0.7571 0.347 0.124 0.436 0.036 0.032 0.372
#> GSM549294 2 0.4822 0.686 0.012 0.740 0.020 0.028 0.200
#> GSM549300 3 0.6120 0.438 0.000 0.224 0.596 0.008 0.172
#> GSM549303 3 0.1568 0.710 0.000 0.000 0.944 0.036 0.020
#> GSM549309 3 0.3115 0.614 0.000 0.000 0.852 0.112 0.036
#> GSM750753 2 0.6196 0.572 0.004 0.592 0.124 0.012 0.268
#> GSM750752 4 0.2777 0.685 0.000 0.028 0.040 0.896 0.036
#> GSM549304 2 0.4845 0.659 0.012 0.684 0.024 0.004 0.276
#> GSM549305 2 0.2519 0.713 0.000 0.884 0.016 0.000 0.100
#> GSM549307 3 0.5359 0.611 0.000 0.148 0.692 0.008 0.152
#> GSM549306 3 0.3902 0.732 0.000 0.048 0.808 0.008 0.136
#> GSM549308 3 0.2732 0.766 0.000 0.020 0.884 0.008 0.088
#> GSM549233 1 0.1168 0.901 0.960 0.000 0.000 0.032 0.008
#> GSM549234 1 0.3877 0.745 0.764 0.000 0.000 0.212 0.024
#> GSM549250 1 0.1408 0.893 0.948 0.000 0.000 0.044 0.008
#> GSM549287 4 0.3464 0.661 0.000 0.000 0.096 0.836 0.068
#> GSM750735 1 0.0609 0.906 0.980 0.000 0.000 0.020 0.000
#> GSM750736 1 0.0609 0.906 0.980 0.000 0.000 0.020 0.000
#> GSM750749 1 0.3473 0.835 0.852 0.004 0.008 0.052 0.084
#> GSM549230 1 0.0451 0.908 0.988 0.000 0.000 0.008 0.004
#> GSM549231 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549237 1 0.0324 0.909 0.992 0.000 0.000 0.004 0.004
#> GSM549254 4 0.5482 0.198 0.416 0.004 0.004 0.532 0.044
#> GSM750734 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> GSM549271 4 0.3320 0.669 0.000 0.012 0.068 0.860 0.060
#> GSM549232 1 0.4083 0.720 0.744 0.000 0.000 0.228 0.028
#> GSM549246 1 0.3099 0.836 0.848 0.000 0.000 0.124 0.028
#> GSM549248 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549255 1 0.4083 0.720 0.744 0.000 0.000 0.228 0.028
#> GSM750746 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549259 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549269 2 0.1197 0.683 0.000 0.952 0.000 0.000 0.048
#> GSM549273 3 0.1582 0.708 0.000 0.000 0.944 0.028 0.028
#> GSM549299 2 0.4867 0.657 0.012 0.680 0.024 0.004 0.280
#> GSM549301 3 0.2732 0.766 0.000 0.020 0.884 0.008 0.088
#> GSM549310 4 0.2897 0.684 0.000 0.020 0.072 0.884 0.024
#> GSM549311 3 0.1582 0.708 0.000 0.000 0.944 0.028 0.028
#> GSM549302 2 0.1492 0.711 0.004 0.948 0.008 0.000 0.040
#> GSM549235 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549245 1 0.4169 0.705 0.732 0.000 0.000 0.240 0.028
#> GSM549265 1 0.3812 0.758 0.772 0.000 0.000 0.204 0.024
#> GSM549282 5 0.6171 0.805 0.004 0.016 0.204 0.152 0.624
#> GSM549296 4 0.2689 0.687 0.000 0.024 0.040 0.900 0.036
#> GSM750739 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM750744 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> GSM750750 5 0.6198 0.804 0.004 0.016 0.208 0.152 0.620
#> GSM549242 1 0.0566 0.908 0.984 0.000 0.000 0.012 0.004
#> GSM549252 1 0.3565 0.784 0.800 0.000 0.000 0.176 0.024
#> GSM549253 1 0.0324 0.908 0.992 0.000 0.000 0.004 0.004
#> GSM549256 1 0.0771 0.907 0.976 0.000 0.000 0.020 0.004
#> GSM549257 1 0.4113 0.714 0.740 0.000 0.000 0.232 0.028
#> GSM549263 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549267 4 0.2914 0.673 0.000 0.000 0.052 0.872 0.076
#> GSM750745 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549244 1 0.3863 0.756 0.772 0.000 0.000 0.200 0.028
#> GSM549249 1 0.3527 0.788 0.804 0.000 0.000 0.172 0.024
#> GSM549260 1 0.0798 0.903 0.976 0.000 0.000 0.008 0.016
#> GSM549266 2 0.8028 0.203 0.212 0.388 0.032 0.036 0.332
#> GSM549293 2 0.1492 0.711 0.004 0.948 0.008 0.000 0.040
#> GSM549236 1 0.0693 0.906 0.980 0.000 0.000 0.012 0.008
#> GSM549238 1 0.3488 0.792 0.808 0.000 0.000 0.168 0.024
#> GSM549251 1 0.0451 0.908 0.988 0.000 0.000 0.008 0.004
#> GSM549258 1 0.1557 0.885 0.940 0.000 0.000 0.008 0.052
#> GSM549264 1 0.0693 0.906 0.980 0.000 0.000 0.008 0.012
#> GSM549243 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549278 4 0.3344 0.624 0.112 0.000 0.012 0.848 0.028
#> GSM549283 2 0.6249 0.524 0.044 0.536 0.028 0.016 0.376
#> GSM549298 3 0.2761 0.762 0.000 0.024 0.872 0.000 0.104
#> GSM750741 1 0.1557 0.885 0.940 0.000 0.000 0.008 0.052
#> GSM549286 2 0.1270 0.690 0.000 0.948 0.000 0.000 0.052
#> GSM549241 1 0.1557 0.885 0.940 0.000 0.000 0.008 0.052
#> GSM549247 1 0.2177 0.868 0.908 0.004 0.000 0.008 0.080
#> GSM549261 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM549270 2 0.5094 0.640 0.000 0.696 0.076 0.008 0.220
#> GSM549277 2 0.6975 0.369 0.000 0.460 0.236 0.016 0.288
#> GSM549280 2 0.7166 0.330 0.000 0.444 0.248 0.024 0.284
#> GSM549281 1 0.7667 0.380 0.560 0.132 0.052 0.060 0.196
#> GSM549285 5 0.6328 0.623 0.020 0.092 0.124 0.080 0.684
#> GSM549288 2 0.7121 0.273 0.000 0.416 0.284 0.016 0.284
#> GSM549292 2 0.1121 0.684 0.000 0.956 0.000 0.000 0.044
#> GSM549295 3 0.4955 0.660 0.000 0.132 0.732 0.008 0.128
#> GSM549297 2 0.5847 0.561 0.000 0.624 0.132 0.008 0.236
#> GSM750743 1 0.0162 0.908 0.996 0.000 0.000 0.004 0.000
#> GSM549268 1 0.7667 0.380 0.560 0.132 0.052 0.060 0.196
#> GSM549290 4 0.5592 0.379 0.008 0.000 0.140 0.664 0.188
#> GSM549272 2 0.1043 0.687 0.000 0.960 0.000 0.000 0.040
#> GSM549276 2 0.4056 0.682 0.000 0.768 0.024 0.008 0.200
#> GSM549275 1 0.4185 0.713 0.752 0.024 0.000 0.008 0.216
#> GSM549284 2 0.5144 0.589 0.016 0.756 0.032 0.060 0.136
#> GSM750737 1 0.3035 0.833 0.856 0.000 0.000 0.112 0.032
#> GSM750740 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM750747 1 0.0162 0.908 0.996 0.000 0.000 0.000 0.004
#> GSM750751 2 0.3313 0.714 0.004 0.844 0.016 0.008 0.128
#> GSM750754 4 0.2688 0.693 0.012 0.000 0.036 0.896 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4311 0.163 0.296 0.000 0.000 0.668 0.024 0.012
#> GSM549291 4 0.1873 0.722 0.020 0.000 0.008 0.924 0.000 0.048
#> GSM549274 2 0.1511 0.665 0.000 0.940 0.012 0.000 0.044 0.004
#> GSM750738 2 0.4129 0.568 0.004 0.720 0.000 0.008 0.240 0.028
#> GSM750748 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549240 1 0.2402 0.723 0.856 0.004 0.000 0.000 0.140 0.000
#> GSM549279 2 0.7969 0.342 0.076 0.416 0.052 0.028 0.312 0.116
#> GSM549294 2 0.4864 0.645 0.000 0.748 0.040 0.020 0.080 0.112
#> GSM549300 3 0.5292 0.482 0.000 0.188 0.680 0.004 0.076 0.052
#> GSM549303 3 0.4167 0.672 0.000 0.000 0.772 0.032 0.056 0.140
#> GSM549309 3 0.5337 0.583 0.000 0.000 0.680 0.116 0.056 0.148
#> GSM750753 2 0.6609 0.525 0.000 0.564 0.188 0.008 0.128 0.112
#> GSM750752 4 0.2528 0.704 0.000 0.016 0.012 0.900 0.032 0.040
#> GSM549304 2 0.5398 0.607 0.000 0.668 0.040 0.004 0.184 0.104
#> GSM549305 2 0.3547 0.667 0.000 0.828 0.036 0.000 0.088 0.048
#> GSM549307 3 0.4193 0.619 0.000 0.140 0.776 0.004 0.044 0.036
#> GSM549306 3 0.2471 0.718 0.000 0.044 0.900 0.004 0.032 0.020
#> GSM549308 3 0.0665 0.747 0.000 0.008 0.980 0.004 0.000 0.008
#> GSM549233 1 0.1636 0.825 0.936 0.000 0.000 0.024 0.036 0.004
#> GSM549234 1 0.3888 0.594 0.752 0.000 0.000 0.208 0.024 0.016
#> GSM549250 1 0.1367 0.820 0.944 0.000 0.000 0.044 0.012 0.000
#> GSM549287 4 0.3068 0.692 0.000 0.000 0.032 0.840 0.008 0.120
#> GSM750735 1 0.1151 0.830 0.956 0.000 0.000 0.012 0.032 0.000
#> GSM750736 1 0.1151 0.830 0.956 0.000 0.000 0.012 0.032 0.000
#> GSM750749 1 0.3728 0.681 0.816 0.000 0.008 0.040 0.112 0.024
#> GSM549230 1 0.0767 0.841 0.976 0.000 0.000 0.008 0.012 0.004
#> GSM549231 1 0.0260 0.840 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549237 1 0.0777 0.839 0.972 0.000 0.000 0.004 0.024 0.000
#> GSM549254 4 0.5535 -0.178 0.376 0.000 0.000 0.524 0.076 0.024
#> GSM750734 1 0.0363 0.840 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549271 4 0.2994 0.699 0.000 0.008 0.024 0.868 0.024 0.076
#> GSM549232 1 0.4073 0.561 0.732 0.000 0.000 0.224 0.028 0.016
#> GSM549246 1 0.3263 0.720 0.832 0.000 0.000 0.116 0.040 0.012
#> GSM549248 1 0.0260 0.840 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549255 1 0.4073 0.560 0.732 0.000 0.000 0.224 0.028 0.016
#> GSM750746 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549259 1 0.0458 0.839 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549269 2 0.2669 0.613 0.000 0.836 0.000 0.000 0.156 0.008
#> GSM549273 3 0.4223 0.665 0.000 0.000 0.760 0.024 0.060 0.156
#> GSM549299 2 0.5439 0.605 0.000 0.664 0.040 0.004 0.184 0.108
#> GSM549301 3 0.0810 0.747 0.000 0.008 0.976 0.004 0.004 0.008
#> GSM549310 4 0.2732 0.702 0.000 0.008 0.032 0.888 0.024 0.048
#> GSM549311 3 0.4223 0.665 0.000 0.000 0.760 0.024 0.060 0.156
#> GSM549302 2 0.1320 0.666 0.000 0.948 0.016 0.000 0.036 0.000
#> GSM549235 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549245 1 0.4150 0.539 0.720 0.000 0.000 0.236 0.028 0.016
#> GSM549265 1 0.3799 0.617 0.764 0.000 0.000 0.196 0.024 0.016
#> GSM549282 6 0.3590 0.828 0.000 0.012 0.112 0.064 0.000 0.812
#> GSM549296 4 0.2403 0.706 0.000 0.008 0.012 0.904 0.032 0.044
#> GSM750739 1 0.0146 0.839 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM750742 1 0.0260 0.840 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM750744 1 0.0363 0.840 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM750750 6 0.3634 0.828 0.000 0.012 0.116 0.064 0.000 0.808
#> GSM549242 1 0.1116 0.836 0.960 0.000 0.000 0.008 0.028 0.004
#> GSM549252 1 0.3606 0.653 0.788 0.000 0.000 0.172 0.024 0.016
#> GSM549253 1 0.0508 0.840 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM549256 1 0.1313 0.834 0.952 0.000 0.000 0.016 0.028 0.004
#> GSM549257 1 0.4099 0.553 0.728 0.000 0.000 0.228 0.028 0.016
#> GSM549263 1 0.0260 0.840 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549267 4 0.2544 0.699 0.000 0.000 0.012 0.864 0.004 0.120
#> GSM750745 1 0.0363 0.840 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549239 1 0.0363 0.839 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549244 1 0.3875 0.612 0.760 0.000 0.000 0.196 0.028 0.016
#> GSM549249 1 0.3572 0.660 0.792 0.000 0.000 0.168 0.024 0.016
#> GSM549260 1 0.1584 0.813 0.928 0.000 0.000 0.000 0.064 0.008
#> GSM549266 2 0.8392 0.187 0.172 0.376 0.048 0.020 0.260 0.124
#> GSM549293 2 0.1320 0.666 0.000 0.948 0.016 0.000 0.036 0.000
#> GSM549236 1 0.0717 0.837 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549238 1 0.3537 0.665 0.796 0.000 0.000 0.164 0.024 0.016
#> GSM549251 1 0.0748 0.841 0.976 0.000 0.000 0.004 0.016 0.004
#> GSM549258 1 0.1765 0.779 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM549264 1 0.0692 0.838 0.976 0.000 0.000 0.004 0.020 0.000
#> GSM549243 1 0.0146 0.840 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549262 1 0.0260 0.840 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549278 4 0.3467 0.660 0.092 0.000 0.004 0.836 0.032 0.036
#> GSM549283 2 0.6547 0.474 0.004 0.512 0.040 0.016 0.312 0.116
#> GSM549298 3 0.1078 0.745 0.000 0.016 0.964 0.000 0.012 0.008
#> GSM750741 1 0.1765 0.780 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM549286 2 0.2489 0.626 0.000 0.860 0.000 0.000 0.128 0.012
#> GSM549241 1 0.1765 0.779 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM549247 1 0.2402 0.723 0.856 0.004 0.000 0.000 0.140 0.000
#> GSM549261 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549270 2 0.5492 0.597 0.000 0.676 0.100 0.000 0.100 0.124
#> GSM549277 2 0.7284 0.336 0.000 0.428 0.268 0.004 0.132 0.168
#> GSM549280 2 0.7275 0.313 0.000 0.412 0.308 0.008 0.108 0.164
#> GSM549281 1 0.7671 -0.301 0.524 0.092 0.068 0.040 0.212 0.064
#> GSM549285 6 0.6034 0.648 0.000 0.068 0.140 0.016 0.136 0.640
#> GSM549288 2 0.7071 0.287 0.000 0.408 0.336 0.004 0.092 0.160
#> GSM549292 2 0.2593 0.615 0.000 0.844 0.000 0.000 0.148 0.008
#> GSM549295 3 0.3824 0.660 0.000 0.124 0.804 0.004 0.040 0.028
#> GSM549297 2 0.6160 0.519 0.000 0.600 0.160 0.000 0.096 0.144
#> GSM750743 1 0.0458 0.839 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549268 1 0.7671 -0.301 0.524 0.092 0.068 0.040 0.212 0.064
#> GSM549290 4 0.5139 0.374 0.008 0.000 0.032 0.616 0.032 0.312
#> GSM549272 2 0.2482 0.619 0.000 0.848 0.000 0.000 0.148 0.004
#> GSM549276 2 0.4368 0.638 0.000 0.764 0.040 0.000 0.072 0.124
#> GSM549275 5 0.4651 0.000 0.372 0.012 0.000 0.000 0.588 0.028
#> GSM549284 2 0.5819 0.511 0.000 0.632 0.020 0.032 0.212 0.104
#> GSM750737 1 0.3655 0.671 0.812 0.000 0.000 0.100 0.072 0.016
#> GSM750740 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM750747 1 0.0547 0.838 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM750751 2 0.3807 0.671 0.000 0.816 0.032 0.004 0.084 0.064
#> GSM750754 4 0.2412 0.716 0.012 0.000 0.012 0.892 0.004 0.080
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:hclust 99 0.0118 5.39e-05 0.11238 0.00542 2
#> CV:hclust 91 0.0181 5.05e-06 0.00339 0.07621 3
#> CV:hclust 91 0.0288 3.93e-06 0.01607 0.06811 4
#> CV:hclust 92 0.0904 4.97e-05 0.05658 0.06735 5
#> CV:hclust 90 0.0427 2.25e-05 0.02519 0.07104 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.982 0.994 0.505 0.496 0.496
#> 3 3 0.738 0.822 0.894 0.260 0.883 0.763
#> 4 4 0.765 0.856 0.902 0.150 0.842 0.607
#> 5 5 0.728 0.687 0.791 0.072 0.923 0.722
#> 6 6 0.712 0.617 0.791 0.043 0.958 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
#> GSM549289 1 0.000 0.99679 1.000 0.000
#> GSM549291 2 0.000 0.98977 0.000 1.000
#> GSM549274 2 0.000 0.98977 0.000 1.000
#> GSM750738 2 0.000 0.98977 0.000 1.000
#> GSM750748 1 0.000 0.99679 1.000 0.000
#> GSM549240 1 0.000 0.99679 1.000 0.000
#> GSM549279 2 0.000 0.98977 0.000 1.000
#> GSM549294 2 0.000 0.98977 0.000 1.000
#> GSM549300 2 0.000 0.98977 0.000 1.000
#> GSM549303 2 0.000 0.98977 0.000 1.000
#> GSM549309 2 0.000 0.98977 0.000 1.000
#> GSM750753 2 0.000 0.98977 0.000 1.000
#> GSM750752 2 0.000 0.98977 0.000 1.000
#> GSM549304 2 0.000 0.98977 0.000 1.000
#> GSM549305 2 0.000 0.98977 0.000 1.000
#> GSM549307 2 0.000 0.98977 0.000 1.000
#> GSM549306 2 0.000 0.98977 0.000 1.000
#> GSM549308 2 0.000 0.98977 0.000 1.000
#> GSM549233 1 0.000 0.99679 1.000 0.000
#> GSM549234 1 0.000 0.99679 1.000 0.000
#> GSM549250 1 0.000 0.99679 1.000 0.000
#> GSM549287 2 0.000 0.98977 0.000 1.000
#> GSM750735 1 0.000 0.99679 1.000 0.000
#> GSM750736 1 0.000 0.99679 1.000 0.000
#> GSM750749 1 0.644 0.80063 0.836 0.164
#> GSM549230 1 0.000 0.99679 1.000 0.000
#> GSM549231 1 0.000 0.99679 1.000 0.000
#> GSM549237 1 0.000 0.99679 1.000 0.000
#> GSM549254 1 0.000 0.99679 1.000 0.000
#> GSM750734 1 0.000 0.99679 1.000 0.000
#> GSM549271 2 0.000 0.98977 0.000 1.000
#> GSM549232 1 0.000 0.99679 1.000 0.000
#> GSM549246 1 0.000 0.99679 1.000 0.000
#> GSM549248 1 0.000 0.99679 1.000 0.000
#> GSM549255 1 0.000 0.99679 1.000 0.000
#> GSM750746 1 0.000 0.99679 1.000 0.000
#> GSM549259 1 0.000 0.99679 1.000 0.000
#> GSM549269 2 0.000 0.98977 0.000 1.000
#> GSM549273 2 0.000 0.98977 0.000 1.000
#> GSM549299 2 0.000 0.98977 0.000 1.000
#> GSM549301 2 0.000 0.98977 0.000 1.000
#> GSM549310 2 0.000 0.98977 0.000 1.000
#> GSM549311 2 0.000 0.98977 0.000 1.000
#> GSM549302 2 0.000 0.98977 0.000 1.000
#> GSM549235 1 0.000 0.99679 1.000 0.000
#> GSM549245 1 0.000 0.99679 1.000 0.000
#> GSM549265 1 0.000 0.99679 1.000 0.000
#> GSM549282 2 0.000 0.98977 0.000 1.000
#> GSM549296 2 0.000 0.98977 0.000 1.000
#> GSM750739 1 0.000 0.99679 1.000 0.000
#> GSM750742 1 0.000 0.99679 1.000 0.000
#> GSM750744 1 0.000 0.99679 1.000 0.000
#> GSM750750 2 0.000 0.98977 0.000 1.000
#> GSM549242 1 0.000 0.99679 1.000 0.000
#> GSM549252 1 0.000 0.99679 1.000 0.000
#> GSM549253 1 0.000 0.99679 1.000 0.000
#> GSM549256 1 0.000 0.99679 1.000 0.000
#> GSM549257 1 0.000 0.99679 1.000 0.000
#> GSM549263 1 0.000 0.99679 1.000 0.000
#> GSM549267 2 0.000 0.98977 0.000 1.000
#> GSM750745 1 0.000 0.99679 1.000 0.000
#> GSM549239 1 0.000 0.99679 1.000 0.000
#> GSM549244 1 0.000 0.99679 1.000 0.000
#> GSM549249 1 0.000 0.99679 1.000 0.000
#> GSM549260 1 0.000 0.99679 1.000 0.000
#> GSM549266 2 0.000 0.98977 0.000 1.000
#> GSM549293 2 0.000 0.98977 0.000 1.000
#> GSM549236 1 0.000 0.99679 1.000 0.000
#> GSM549238 1 0.000 0.99679 1.000 0.000
#> GSM549251 1 0.000 0.99679 1.000 0.000
#> GSM549258 1 0.000 0.99679 1.000 0.000
#> GSM549264 1 0.000 0.99679 1.000 0.000
#> GSM549243 1 0.000 0.99679 1.000 0.000
#> GSM549262 1 0.000 0.99679 1.000 0.000
#> GSM549278 2 1.000 0.00582 0.496 0.504
#> GSM549283 2 0.000 0.98977 0.000 1.000
#> GSM549298 2 0.000 0.98977 0.000 1.000
#> GSM750741 1 0.000 0.99679 1.000 0.000
#> GSM549286 2 0.000 0.98977 0.000 1.000
#> GSM549241 1 0.000 0.99679 1.000 0.000
#> GSM549247 1 0.000 0.99679 1.000 0.000
#> GSM549261 1 0.000 0.99679 1.000 0.000
#> GSM549270 2 0.000 0.98977 0.000 1.000
#> GSM549277 2 0.000 0.98977 0.000 1.000
#> GSM549280 2 0.000 0.98977 0.000 1.000
#> GSM549281 2 0.000 0.98977 0.000 1.000
#> GSM549285 2 0.000 0.98977 0.000 1.000
#> GSM549288 2 0.000 0.98977 0.000 1.000
#> GSM549292 2 0.000 0.98977 0.000 1.000
#> GSM549295 2 0.000 0.98977 0.000 1.000
#> GSM549297 2 0.000 0.98977 0.000 1.000
#> GSM750743 1 0.000 0.99679 1.000 0.000
#> GSM549268 2 0.000 0.98977 0.000 1.000
#> GSM549290 2 0.000 0.98977 0.000 1.000
#> GSM549272 2 0.000 0.98977 0.000 1.000
#> GSM549276 2 0.000 0.98977 0.000 1.000
#> GSM549275 1 0.000 0.99679 1.000 0.000
#> GSM549284 2 0.000 0.98977 0.000 1.000
#> GSM750737 1 0.000 0.99679 1.000 0.000
#> GSM750740 1 0.000 0.99679 1.000 0.000
#> GSM750747 1 0.000 0.99679 1.000 0.000
#> GSM750751 2 0.000 0.98977 0.000 1.000
#> GSM750754 2 0.000 0.98977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.6308 0.407 0.508 0.000 0.492
#> GSM549291 3 0.0592 0.719 0.000 0.012 0.988
#> GSM549274 2 0.0592 0.933 0.000 0.988 0.012
#> GSM750738 2 0.4796 0.604 0.000 0.780 0.220
#> GSM750748 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549240 1 0.1031 0.901 0.976 0.000 0.024
#> GSM549279 2 0.0892 0.926 0.000 0.980 0.020
#> GSM549294 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549300 3 0.6215 0.551 0.000 0.428 0.572
#> GSM549303 3 0.5678 0.718 0.000 0.316 0.684
#> GSM549309 3 0.5291 0.740 0.000 0.268 0.732
#> GSM750753 2 0.0000 0.936 0.000 1.000 0.000
#> GSM750752 3 0.4062 0.640 0.000 0.164 0.836
#> GSM549304 2 0.0424 0.935 0.000 0.992 0.008
#> GSM549305 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549307 2 0.5291 0.513 0.000 0.732 0.268
#> GSM549306 3 0.6111 0.619 0.000 0.396 0.604
#> GSM549308 3 0.5926 0.680 0.000 0.356 0.644
#> GSM549233 1 0.0237 0.906 0.996 0.000 0.004
#> GSM549234 1 0.5650 0.706 0.688 0.000 0.312
#> GSM549250 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549287 3 0.2448 0.738 0.000 0.076 0.924
#> GSM750735 1 0.0592 0.904 0.988 0.000 0.012
#> GSM750736 1 0.0892 0.902 0.980 0.000 0.020
#> GSM750749 1 0.4413 0.835 0.852 0.024 0.124
#> GSM549230 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549254 1 0.5926 0.664 0.644 0.000 0.356
#> GSM750734 1 0.0424 0.905 0.992 0.000 0.008
#> GSM549271 3 0.4062 0.747 0.000 0.164 0.836
#> GSM549232 1 0.5859 0.672 0.656 0.000 0.344
#> GSM549246 1 0.5621 0.709 0.692 0.000 0.308
#> GSM549248 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549255 1 0.5859 0.672 0.656 0.000 0.344
#> GSM750746 1 0.0237 0.906 0.996 0.000 0.004
#> GSM549259 1 0.0237 0.906 0.996 0.000 0.004
#> GSM549269 2 0.0592 0.933 0.000 0.988 0.012
#> GSM549273 3 0.5785 0.704 0.000 0.332 0.668
#> GSM549299 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549301 3 0.5968 0.671 0.000 0.364 0.636
#> GSM549310 3 0.1289 0.719 0.000 0.032 0.968
#> GSM549311 3 0.5497 0.732 0.000 0.292 0.708
#> GSM549302 2 0.0424 0.935 0.000 0.992 0.008
#> GSM549235 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549245 1 0.5859 0.672 0.656 0.000 0.344
#> GSM549265 1 0.5706 0.698 0.680 0.000 0.320
#> GSM549282 3 0.5363 0.738 0.000 0.276 0.724
#> GSM549296 3 0.4062 0.640 0.000 0.164 0.836
#> GSM750739 1 0.0000 0.906 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.906 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.906 1.000 0.000 0.000
#> GSM750750 3 0.5678 0.718 0.000 0.316 0.684
#> GSM549242 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549252 1 0.5650 0.706 0.688 0.000 0.312
#> GSM549253 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549256 1 0.0237 0.906 0.996 0.000 0.004
#> GSM549257 1 0.5859 0.672 0.656 0.000 0.344
#> GSM549263 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549267 3 0.0592 0.719 0.000 0.012 0.988
#> GSM750745 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549239 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549244 1 0.5859 0.672 0.656 0.000 0.344
#> GSM549249 1 0.5650 0.706 0.688 0.000 0.312
#> GSM549260 1 0.0424 0.905 0.992 0.000 0.008
#> GSM549266 2 0.0892 0.926 0.000 0.980 0.020
#> GSM549293 2 0.0424 0.935 0.000 0.992 0.008
#> GSM549236 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549238 1 0.3879 0.825 0.848 0.000 0.152
#> GSM549251 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549258 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549264 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.906 1.000 0.000 0.000
#> GSM549278 3 0.1163 0.697 0.028 0.000 0.972
#> GSM549283 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549298 3 0.5968 0.671 0.000 0.364 0.636
#> GSM750741 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549286 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549241 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549247 1 0.2773 0.867 0.928 0.048 0.024
#> GSM549261 1 0.0237 0.906 0.996 0.000 0.004
#> GSM549270 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549277 2 0.3941 0.752 0.000 0.844 0.156
#> GSM549280 2 0.2448 0.861 0.000 0.924 0.076
#> GSM549281 2 0.0592 0.930 0.000 0.988 0.012
#> GSM549285 3 0.6026 0.653 0.000 0.376 0.624
#> GSM549288 2 0.3752 0.771 0.000 0.856 0.144
#> GSM549292 2 0.0424 0.935 0.000 0.992 0.008
#> GSM549295 2 0.4702 0.648 0.000 0.788 0.212
#> GSM549297 2 0.0000 0.936 0.000 1.000 0.000
#> GSM750743 1 0.0592 0.904 0.988 0.000 0.012
#> GSM549268 2 0.0592 0.930 0.000 0.988 0.012
#> GSM549290 3 0.0592 0.719 0.000 0.012 0.988
#> GSM549272 2 0.0237 0.936 0.000 0.996 0.004
#> GSM549276 2 0.0000 0.936 0.000 1.000 0.000
#> GSM549275 1 0.1636 0.893 0.964 0.016 0.020
#> GSM549284 2 0.0424 0.935 0.000 0.992 0.008
#> GSM750737 1 0.5327 0.745 0.728 0.000 0.272
#> GSM750740 1 0.0237 0.906 0.996 0.000 0.004
#> GSM750747 1 0.0237 0.906 0.996 0.000 0.004
#> GSM750751 2 0.0000 0.936 0.000 1.000 0.000
#> GSM750754 3 0.0892 0.721 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2101 0.877 0.060 0.000 0.012 0.928
#> GSM549291 4 0.2999 0.806 0.000 0.004 0.132 0.864
#> GSM549274 2 0.0592 0.915 0.000 0.984 0.000 0.016
#> GSM750738 2 0.1938 0.883 0.000 0.936 0.012 0.052
#> GSM750748 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM549240 1 0.4774 0.850 0.804 0.016 0.056 0.124
#> GSM549279 2 0.3497 0.840 0.008 0.876 0.056 0.060
#> GSM549294 2 0.0469 0.917 0.000 0.988 0.012 0.000
#> GSM549300 3 0.2868 0.838 0.000 0.136 0.864 0.000
#> GSM549303 3 0.2546 0.891 0.000 0.060 0.912 0.028
#> GSM549309 3 0.2578 0.886 0.000 0.052 0.912 0.036
#> GSM750753 2 0.0707 0.915 0.000 0.980 0.020 0.000
#> GSM750752 4 0.3088 0.804 0.000 0.008 0.128 0.864
#> GSM549304 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549305 2 0.0469 0.917 0.000 0.988 0.012 0.000
#> GSM549307 3 0.4661 0.497 0.000 0.348 0.652 0.000
#> GSM549306 3 0.2011 0.886 0.000 0.080 0.920 0.000
#> GSM549308 3 0.2053 0.891 0.000 0.072 0.924 0.004
#> GSM549233 1 0.3271 0.852 0.856 0.000 0.012 0.132
#> GSM549234 4 0.2530 0.886 0.112 0.000 0.000 0.888
#> GSM549250 1 0.1938 0.917 0.936 0.000 0.012 0.052
#> GSM549287 3 0.3037 0.833 0.000 0.020 0.880 0.100
#> GSM750735 1 0.3174 0.898 0.892 0.008 0.048 0.052
#> GSM750736 1 0.3411 0.893 0.880 0.008 0.048 0.064
#> GSM750749 1 0.5969 0.750 0.732 0.052 0.048 0.168
#> GSM549230 1 0.1767 0.921 0.944 0.000 0.012 0.044
#> GSM549231 1 0.1677 0.922 0.948 0.000 0.012 0.040
#> GSM549237 1 0.0336 0.933 0.992 0.000 0.000 0.008
#> GSM549254 4 0.1356 0.866 0.032 0.000 0.008 0.960
#> GSM750734 1 0.2408 0.914 0.920 0.000 0.044 0.036
#> GSM549271 3 0.3552 0.808 0.000 0.024 0.848 0.128
#> GSM549232 4 0.2466 0.890 0.096 0.000 0.004 0.900
#> GSM549246 4 0.2469 0.887 0.108 0.000 0.000 0.892
#> GSM549248 1 0.0657 0.932 0.984 0.000 0.012 0.004
#> GSM549255 4 0.2530 0.890 0.100 0.000 0.004 0.896
#> GSM750746 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM549259 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM549269 2 0.0592 0.915 0.000 0.984 0.000 0.016
#> GSM549273 3 0.2563 0.892 0.000 0.072 0.908 0.020
#> GSM549299 2 0.0817 0.913 0.000 0.976 0.024 0.000
#> GSM549301 3 0.1867 0.890 0.000 0.072 0.928 0.000
#> GSM549310 4 0.4353 0.695 0.000 0.012 0.232 0.756
#> GSM549311 3 0.2546 0.891 0.000 0.060 0.912 0.028
#> GSM549302 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549235 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM549245 4 0.2593 0.890 0.104 0.000 0.004 0.892
#> GSM549265 4 0.2530 0.888 0.112 0.000 0.000 0.888
#> GSM549282 3 0.2670 0.884 0.000 0.052 0.908 0.040
#> GSM549296 4 0.2799 0.817 0.000 0.008 0.108 0.884
#> GSM750739 1 0.0188 0.933 0.996 0.000 0.000 0.004
#> GSM750742 1 0.0804 0.931 0.980 0.000 0.012 0.008
#> GSM750744 1 0.0657 0.933 0.984 0.000 0.012 0.004
#> GSM750750 3 0.2443 0.891 0.000 0.060 0.916 0.024
#> GSM549242 1 0.2101 0.914 0.928 0.000 0.012 0.060
#> GSM549252 4 0.2647 0.882 0.120 0.000 0.000 0.880
#> GSM549253 1 0.1854 0.919 0.940 0.000 0.012 0.048
#> GSM549256 1 0.3324 0.848 0.852 0.000 0.012 0.136
#> GSM549257 4 0.2593 0.890 0.104 0.000 0.004 0.892
#> GSM549263 1 0.1767 0.921 0.944 0.000 0.012 0.044
#> GSM549267 4 0.4122 0.700 0.000 0.004 0.236 0.760
#> GSM750745 1 0.2313 0.913 0.924 0.000 0.044 0.032
#> GSM549239 1 0.2313 0.913 0.924 0.000 0.044 0.032
#> GSM549244 4 0.2593 0.890 0.104 0.000 0.004 0.892
#> GSM549249 4 0.2760 0.876 0.128 0.000 0.000 0.872
#> GSM549260 1 0.2908 0.915 0.896 0.000 0.040 0.064
#> GSM549266 2 0.3497 0.840 0.008 0.876 0.056 0.060
#> GSM549293 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549236 1 0.1938 0.917 0.936 0.000 0.012 0.052
#> GSM549238 4 0.4137 0.784 0.208 0.000 0.012 0.780
#> GSM549251 1 0.1767 0.921 0.944 0.000 0.012 0.044
#> GSM549258 1 0.3497 0.895 0.876 0.008 0.056 0.060
#> GSM549264 1 0.1059 0.931 0.972 0.000 0.016 0.012
#> GSM549243 1 0.0524 0.933 0.988 0.000 0.008 0.004
#> GSM549262 1 0.0657 0.932 0.984 0.000 0.012 0.004
#> GSM549278 4 0.2011 0.839 0.000 0.000 0.080 0.920
#> GSM549283 2 0.0707 0.912 0.000 0.980 0.020 0.000
#> GSM549298 3 0.1867 0.890 0.000 0.072 0.928 0.000
#> GSM750741 1 0.3574 0.892 0.872 0.008 0.056 0.064
#> GSM549286 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549241 1 0.3497 0.893 0.876 0.008 0.056 0.060
#> GSM549247 1 0.6668 0.731 0.700 0.120 0.056 0.124
#> GSM549261 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM549270 2 0.0592 0.916 0.000 0.984 0.016 0.000
#> GSM549277 2 0.4776 0.357 0.000 0.624 0.376 0.000
#> GSM549280 2 0.4522 0.494 0.000 0.680 0.320 0.000
#> GSM549281 2 0.2936 0.862 0.004 0.900 0.040 0.056
#> GSM549285 3 0.2342 0.889 0.000 0.080 0.912 0.008
#> GSM549288 2 0.4843 0.299 0.000 0.604 0.396 0.000
#> GSM549292 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549295 3 0.4992 0.129 0.000 0.476 0.524 0.000
#> GSM549297 2 0.1118 0.907 0.000 0.964 0.036 0.000
#> GSM750743 1 0.2319 0.916 0.924 0.000 0.040 0.036
#> GSM549268 2 0.2936 0.862 0.004 0.900 0.040 0.056
#> GSM549290 4 0.4053 0.710 0.000 0.004 0.228 0.768
#> GSM549272 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM549276 2 0.0524 0.917 0.000 0.988 0.008 0.004
#> GSM549275 1 0.4238 0.876 0.848 0.032 0.060 0.060
#> GSM549284 2 0.0927 0.917 0.000 0.976 0.008 0.016
#> GSM750737 4 0.2982 0.847 0.068 0.004 0.032 0.896
#> GSM750740 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM750747 1 0.0376 0.933 0.992 0.000 0.004 0.004
#> GSM750751 2 0.0336 0.917 0.000 0.992 0.008 0.000
#> GSM750754 3 0.4655 0.498 0.000 0.004 0.684 0.312
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.3365 0.7963 0.008 0.000 0.004 0.808 0.180
#> GSM549291 4 0.4315 0.7287 0.000 0.000 0.024 0.700 0.276
#> GSM549274 2 0.0162 0.8792 0.000 0.996 0.000 0.000 0.004
#> GSM750738 2 0.1364 0.8485 0.000 0.952 0.000 0.036 0.012
#> GSM750748 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM549240 5 0.6468 0.4457 0.332 0.012 0.000 0.144 0.512
#> GSM549279 5 0.4811 -0.0538 0.000 0.452 0.000 0.020 0.528
#> GSM549294 2 0.2411 0.8581 0.000 0.884 0.008 0.000 0.108
#> GSM549300 3 0.2712 0.7528 0.000 0.088 0.880 0.000 0.032
#> GSM549303 3 0.1682 0.8081 0.000 0.012 0.944 0.012 0.032
#> GSM549309 3 0.3007 0.7928 0.000 0.004 0.864 0.028 0.104
#> GSM750753 2 0.2470 0.8605 0.000 0.884 0.012 0.000 0.104
#> GSM750752 4 0.3675 0.7782 0.000 0.000 0.024 0.788 0.188
#> GSM549304 2 0.0404 0.8773 0.000 0.988 0.000 0.000 0.012
#> GSM549305 2 0.1956 0.8710 0.000 0.916 0.008 0.000 0.076
#> GSM549307 3 0.4679 0.5496 0.000 0.216 0.716 0.000 0.068
#> GSM549306 3 0.0865 0.8034 0.000 0.024 0.972 0.000 0.004
#> GSM549308 3 0.0609 0.8054 0.000 0.020 0.980 0.000 0.000
#> GSM549233 1 0.3074 0.6721 0.804 0.000 0.000 0.196 0.000
#> GSM549234 4 0.1883 0.8308 0.048 0.000 0.008 0.932 0.012
#> GSM549250 1 0.1908 0.7842 0.908 0.000 0.000 0.092 0.000
#> GSM549287 3 0.5009 0.6494 0.000 0.000 0.652 0.060 0.288
#> GSM750735 5 0.5972 0.3599 0.420 0.000 0.016 0.068 0.496
#> GSM750736 5 0.5967 0.3627 0.416 0.000 0.016 0.068 0.500
#> GSM750749 5 0.5830 0.5035 0.236 0.008 0.016 0.088 0.652
#> GSM549230 1 0.1270 0.8129 0.948 0.000 0.000 0.052 0.000
#> GSM549231 1 0.1197 0.8145 0.952 0.000 0.000 0.048 0.000
#> GSM549237 1 0.1197 0.8301 0.952 0.000 0.000 0.000 0.048
#> GSM549254 4 0.1792 0.8236 0.000 0.000 0.000 0.916 0.084
#> GSM750734 1 0.4095 0.6398 0.748 0.000 0.016 0.008 0.228
#> GSM549271 3 0.5498 0.6023 0.000 0.000 0.612 0.096 0.292
#> GSM549232 4 0.0794 0.8398 0.028 0.000 0.000 0.972 0.000
#> GSM549246 4 0.1942 0.8252 0.068 0.000 0.000 0.920 0.012
#> GSM549248 1 0.0510 0.8251 0.984 0.000 0.000 0.016 0.000
#> GSM549255 4 0.0880 0.8397 0.032 0.000 0.000 0.968 0.000
#> GSM750746 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM549259 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM549269 2 0.0324 0.8796 0.000 0.992 0.004 0.000 0.004
#> GSM549273 3 0.1569 0.8083 0.000 0.012 0.948 0.008 0.032
#> GSM549299 2 0.2536 0.8421 0.000 0.868 0.000 0.004 0.128
#> GSM549301 3 0.0609 0.8054 0.000 0.020 0.980 0.000 0.000
#> GSM549310 4 0.5137 0.6957 0.000 0.000 0.108 0.684 0.208
#> GSM549311 3 0.2756 0.8009 0.000 0.012 0.880 0.012 0.096
#> GSM549302 2 0.0451 0.8803 0.000 0.988 0.008 0.000 0.004
#> GSM549235 1 0.1952 0.8228 0.912 0.000 0.004 0.000 0.084
#> GSM549245 4 0.0880 0.8397 0.032 0.000 0.000 0.968 0.000
#> GSM549265 4 0.1756 0.8300 0.036 0.000 0.008 0.940 0.016
#> GSM549282 3 0.3768 0.7759 0.000 0.008 0.812 0.036 0.144
#> GSM549296 4 0.3675 0.7782 0.000 0.000 0.024 0.788 0.188
#> GSM750739 1 0.2457 0.8144 0.900 0.000 0.016 0.008 0.076
#> GSM750742 1 0.0510 0.8251 0.984 0.000 0.000 0.016 0.000
#> GSM750744 1 0.2850 0.7952 0.880 0.000 0.016 0.016 0.088
#> GSM750750 3 0.3319 0.7952 0.000 0.016 0.848 0.020 0.116
#> GSM549242 1 0.2561 0.7352 0.856 0.000 0.000 0.144 0.000
#> GSM549252 4 0.1956 0.8293 0.052 0.000 0.008 0.928 0.012
#> GSM549253 1 0.1908 0.7842 0.908 0.000 0.000 0.092 0.000
#> GSM549256 1 0.3143 0.6596 0.796 0.000 0.000 0.204 0.000
#> GSM549257 4 0.0880 0.8397 0.032 0.000 0.000 0.968 0.000
#> GSM549263 1 0.1270 0.8129 0.948 0.000 0.000 0.052 0.000
#> GSM549267 4 0.5691 0.6067 0.000 0.000 0.112 0.592 0.296
#> GSM750745 1 0.4236 0.6030 0.728 0.000 0.016 0.008 0.248
#> GSM549239 1 0.4236 0.6030 0.728 0.000 0.016 0.008 0.248
#> GSM549244 4 0.0880 0.8397 0.032 0.000 0.000 0.968 0.000
#> GSM549249 4 0.1877 0.8261 0.064 0.000 0.000 0.924 0.012
#> GSM549260 1 0.3011 0.7759 0.844 0.000 0.000 0.016 0.140
#> GSM549266 5 0.4740 -0.0887 0.000 0.468 0.000 0.016 0.516
#> GSM549293 2 0.0162 0.8792 0.000 0.996 0.000 0.000 0.004
#> GSM549236 1 0.2074 0.7732 0.896 0.000 0.000 0.104 0.000
#> GSM549238 4 0.4306 0.4915 0.328 0.000 0.000 0.660 0.012
#> GSM549251 1 0.1270 0.8129 0.948 0.000 0.000 0.052 0.000
#> GSM549258 1 0.4383 0.0908 0.572 0.000 0.000 0.004 0.424
#> GSM549264 1 0.2649 0.8121 0.900 0.000 0.016 0.036 0.048
#> GSM549243 1 0.1478 0.8265 0.936 0.000 0.000 0.000 0.064
#> GSM549262 1 0.0671 0.8255 0.980 0.000 0.000 0.016 0.004
#> GSM549278 4 0.3519 0.7754 0.000 0.000 0.008 0.776 0.216
#> GSM549283 2 0.3123 0.7967 0.000 0.812 0.000 0.004 0.184
#> GSM549298 3 0.0771 0.8047 0.000 0.020 0.976 0.000 0.004
#> GSM750741 5 0.5153 0.3250 0.436 0.000 0.000 0.040 0.524
#> GSM549286 2 0.0451 0.8800 0.000 0.988 0.008 0.000 0.004
#> GSM549241 5 0.4451 0.1503 0.492 0.000 0.000 0.004 0.504
#> GSM549247 5 0.7008 0.4789 0.296 0.048 0.000 0.144 0.512
#> GSM549261 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM549270 2 0.2304 0.8621 0.000 0.892 0.008 0.000 0.100
#> GSM549277 2 0.6069 0.1028 0.000 0.448 0.432 0.000 0.120
#> GSM549280 2 0.6260 0.2190 0.000 0.476 0.372 0.000 0.152
#> GSM549281 5 0.4731 -0.0576 0.000 0.456 0.000 0.016 0.528
#> GSM549285 3 0.4554 0.7573 0.000 0.032 0.736 0.016 0.216
#> GSM549288 3 0.5948 -0.0223 0.000 0.408 0.484 0.000 0.108
#> GSM549292 2 0.0451 0.8803 0.000 0.988 0.008 0.000 0.004
#> GSM549295 3 0.5390 0.3099 0.000 0.324 0.600 0.000 0.076
#> GSM549297 2 0.4704 0.7391 0.000 0.736 0.152 0.000 0.112
#> GSM750743 1 0.4181 0.6178 0.736 0.000 0.016 0.008 0.240
#> GSM549268 5 0.4731 -0.0576 0.000 0.456 0.000 0.016 0.528
#> GSM549290 4 0.5649 0.6118 0.000 0.000 0.108 0.596 0.296
#> GSM549272 2 0.0451 0.8803 0.000 0.988 0.008 0.000 0.004
#> GSM549276 2 0.1251 0.8789 0.000 0.956 0.008 0.000 0.036
#> GSM549275 5 0.5588 0.4393 0.372 0.028 0.000 0.032 0.568
#> GSM549284 2 0.0162 0.8792 0.000 0.996 0.000 0.000 0.004
#> GSM750737 4 0.4065 0.6862 0.032 0.000 0.016 0.792 0.160
#> GSM750740 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM750747 1 0.2011 0.8214 0.908 0.000 0.004 0.000 0.088
#> GSM750751 2 0.1831 0.8698 0.000 0.920 0.004 0.000 0.076
#> GSM750754 3 0.6575 0.3052 0.000 0.000 0.464 0.236 0.300
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4209 0.192 0.020 0.000 0.000 0.596 0.000 0.384
#> GSM549291 6 0.4355 0.265 0.024 0.000 0.000 0.420 0.000 0.556
#> GSM549274 2 0.0260 0.884 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM750738 2 0.1053 0.870 0.020 0.964 0.000 0.012 0.000 0.004
#> GSM750748 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM549240 1 0.5465 0.637 0.684 0.012 0.000 0.116 0.144 0.044
#> GSM549279 1 0.5172 0.433 0.644 0.200 0.008 0.000 0.000 0.148
#> GSM549294 2 0.3176 0.841 0.084 0.832 0.000 0.000 0.000 0.084
#> GSM549300 3 0.3382 0.631 0.040 0.040 0.840 0.000 0.000 0.080
#> GSM549303 3 0.2605 0.605 0.028 0.000 0.864 0.000 0.000 0.108
#> GSM549309 3 0.4170 0.397 0.032 0.000 0.660 0.000 0.000 0.308
#> GSM750753 2 0.4368 0.800 0.080 0.768 0.044 0.000 0.000 0.108
#> GSM750752 4 0.4417 0.045 0.028 0.000 0.000 0.556 0.000 0.416
#> GSM549304 2 0.0622 0.882 0.008 0.980 0.000 0.000 0.000 0.012
#> GSM549305 2 0.2965 0.849 0.072 0.848 0.000 0.000 0.000 0.080
#> GSM549307 3 0.4593 0.597 0.044 0.120 0.748 0.000 0.000 0.088
#> GSM549306 3 0.0551 0.652 0.004 0.004 0.984 0.000 0.000 0.008
#> GSM549308 3 0.0547 0.650 0.000 0.000 0.980 0.000 0.000 0.020
#> GSM549233 5 0.3945 0.411 0.000 0.000 0.000 0.380 0.612 0.008
#> GSM549234 4 0.0725 0.748 0.000 0.000 0.000 0.976 0.012 0.012
#> GSM549250 5 0.1958 0.729 0.000 0.000 0.000 0.100 0.896 0.004
#> GSM549287 6 0.4071 0.463 0.020 0.000 0.304 0.004 0.000 0.672
#> GSM750735 1 0.4329 0.613 0.712 0.000 0.000 0.016 0.232 0.040
#> GSM750736 1 0.4207 0.610 0.728 0.000 0.000 0.024 0.220 0.028
#> GSM750749 1 0.4133 0.643 0.768 0.000 0.000 0.012 0.100 0.120
#> GSM549230 5 0.1471 0.750 0.000 0.000 0.000 0.064 0.932 0.004
#> GSM549231 5 0.1285 0.754 0.000 0.000 0.000 0.052 0.944 0.004
#> GSM549237 5 0.1788 0.767 0.076 0.000 0.000 0.004 0.916 0.004
#> GSM549254 4 0.2350 0.670 0.036 0.000 0.000 0.888 0.000 0.076
#> GSM750734 5 0.4092 0.495 0.344 0.000 0.000 0.000 0.636 0.020
#> GSM549271 6 0.4373 0.580 0.008 0.000 0.260 0.044 0.000 0.688
#> GSM549232 4 0.0291 0.748 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549246 4 0.1232 0.746 0.004 0.000 0.000 0.956 0.024 0.016
#> GSM549248 5 0.0260 0.765 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM549255 4 0.0291 0.748 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM750746 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM549259 5 0.3163 0.745 0.140 0.000 0.000 0.000 0.820 0.040
#> GSM549269 2 0.0260 0.884 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM549273 3 0.2605 0.605 0.028 0.000 0.864 0.000 0.000 0.108
#> GSM549299 2 0.4853 0.760 0.124 0.720 0.036 0.000 0.000 0.120
#> GSM549301 3 0.0458 0.651 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM549310 4 0.5249 -0.270 0.028 0.000 0.040 0.472 0.000 0.460
#> GSM549311 3 0.4022 0.481 0.040 0.000 0.708 0.000 0.000 0.252
#> GSM549302 2 0.0146 0.884 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM549235 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM549245 4 0.0291 0.748 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549265 4 0.1088 0.745 0.000 0.000 0.000 0.960 0.024 0.016
#> GSM549282 3 0.4139 0.386 0.024 0.000 0.640 0.000 0.000 0.336
#> GSM549296 4 0.4353 0.147 0.028 0.000 0.000 0.588 0.000 0.384
#> GSM750739 5 0.2949 0.743 0.140 0.000 0.000 0.000 0.832 0.028
#> GSM750742 5 0.0260 0.765 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM750744 5 0.2996 0.713 0.144 0.000 0.000 0.008 0.832 0.016
#> GSM750750 3 0.3993 0.443 0.024 0.000 0.676 0.000 0.000 0.300
#> GSM549242 5 0.3575 0.549 0.000 0.000 0.000 0.284 0.708 0.008
#> GSM549252 4 0.1334 0.739 0.000 0.000 0.000 0.948 0.032 0.020
#> GSM549253 5 0.1858 0.734 0.000 0.000 0.000 0.092 0.904 0.004
#> GSM549256 5 0.4010 0.358 0.000 0.000 0.000 0.408 0.584 0.008
#> GSM549257 4 0.0291 0.748 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549263 5 0.1411 0.752 0.000 0.000 0.000 0.060 0.936 0.004
#> GSM549267 6 0.4371 0.629 0.000 0.000 0.052 0.284 0.000 0.664
#> GSM750745 5 0.4167 0.464 0.368 0.000 0.000 0.000 0.612 0.020
#> GSM549239 5 0.4167 0.464 0.368 0.000 0.000 0.000 0.612 0.020
#> GSM549244 4 0.0820 0.748 0.000 0.000 0.000 0.972 0.012 0.016
#> GSM549249 4 0.1334 0.739 0.000 0.000 0.000 0.948 0.032 0.020
#> GSM549260 5 0.4617 0.593 0.272 0.000 0.000 0.044 0.668 0.016
#> GSM549266 1 0.5275 0.397 0.624 0.228 0.008 0.000 0.000 0.140
#> GSM549293 2 0.0260 0.884 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM549236 5 0.2234 0.714 0.000 0.000 0.000 0.124 0.872 0.004
#> GSM549238 4 0.3756 0.462 0.000 0.000 0.000 0.712 0.268 0.020
#> GSM549251 5 0.1471 0.750 0.000 0.000 0.000 0.064 0.932 0.004
#> GSM549258 1 0.4411 0.186 0.576 0.000 0.000 0.012 0.400 0.012
#> GSM549264 5 0.2426 0.754 0.048 0.000 0.000 0.044 0.896 0.012
#> GSM549243 5 0.2494 0.756 0.120 0.000 0.000 0.000 0.864 0.016
#> GSM549262 5 0.0260 0.765 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM549278 4 0.4473 -0.172 0.028 0.000 0.000 0.492 0.000 0.480
#> GSM549283 2 0.5793 0.598 0.220 0.596 0.032 0.000 0.000 0.152
#> GSM549298 3 0.0146 0.652 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM750741 1 0.3761 0.600 0.744 0.000 0.000 0.008 0.228 0.020
#> GSM549286 2 0.0260 0.883 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM549241 1 0.4065 0.465 0.672 0.000 0.000 0.000 0.300 0.028
#> GSM549247 1 0.5558 0.641 0.684 0.020 0.000 0.112 0.140 0.044
#> GSM549261 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM549270 2 0.3479 0.835 0.084 0.820 0.008 0.000 0.000 0.088
#> GSM549277 3 0.6737 0.255 0.100 0.296 0.476 0.000 0.000 0.128
#> GSM549280 3 0.6991 0.210 0.124 0.300 0.440 0.000 0.000 0.136
#> GSM549281 1 0.5539 0.381 0.604 0.232 0.016 0.000 0.000 0.148
#> GSM549285 3 0.5460 0.374 0.116 0.004 0.524 0.000 0.000 0.356
#> GSM549288 3 0.6358 0.363 0.092 0.264 0.540 0.000 0.000 0.104
#> GSM549292 2 0.0260 0.884 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM549295 3 0.5255 0.542 0.048 0.204 0.668 0.000 0.000 0.080
#> GSM549297 2 0.6027 0.543 0.092 0.600 0.212 0.000 0.000 0.096
#> GSM750743 5 0.4241 0.480 0.348 0.000 0.000 0.004 0.628 0.020
#> GSM549268 1 0.5539 0.381 0.604 0.232 0.016 0.000 0.000 0.148
#> GSM549290 6 0.4332 0.623 0.000 0.000 0.048 0.288 0.000 0.664
#> GSM549272 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549276 2 0.2046 0.871 0.032 0.908 0.000 0.000 0.000 0.060
#> GSM549275 1 0.4396 0.656 0.752 0.012 0.004 0.004 0.160 0.068
#> GSM549284 2 0.0405 0.884 0.008 0.988 0.000 0.000 0.000 0.004
#> GSM750737 4 0.2656 0.645 0.120 0.000 0.000 0.860 0.012 0.008
#> GSM750740 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM750747 5 0.3123 0.747 0.136 0.000 0.000 0.000 0.824 0.040
#> GSM750751 2 0.2389 0.865 0.052 0.888 0.000 0.000 0.000 0.060
#> GSM750754 6 0.4601 0.671 0.000 0.000 0.200 0.112 0.000 0.688
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:kmeans 102 0.0253 1.70e-05 6.38e-02 0.00385 2
#> CV:kmeans 102 0.0426 1.96e-06 8.99e-05 0.00838 3
#> CV:kmeans 97 0.2749 2.14e-04 5.22e-03 0.05687 4
#> CV:kmeans 85 0.2519 3.23e-03 1.47e-02 0.11342 5
#> CV:kmeans 75 0.1607 2.93e-03 8.47e-02 0.17243 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.993 0.5049 0.496 0.496
#> 3 3 0.715 0.748 0.872 0.2740 0.855 0.712
#> 4 4 0.780 0.798 0.904 0.1492 0.878 0.678
#> 5 5 0.684 0.535 0.710 0.0644 0.905 0.672
#> 6 6 0.665 0.521 0.740 0.0383 0.910 0.639
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
#> GSM549289 1 0.0000 0.992 1.000 0.000
#> GSM549291 2 0.0000 0.994 0.000 1.000
#> GSM549274 2 0.0000 0.994 0.000 1.000
#> GSM750738 2 0.0000 0.994 0.000 1.000
#> GSM750748 1 0.0000 0.992 1.000 0.000
#> GSM549240 1 0.0000 0.992 1.000 0.000
#> GSM549279 2 0.0000 0.994 0.000 1.000
#> GSM549294 2 0.0000 0.994 0.000 1.000
#> GSM549300 2 0.0000 0.994 0.000 1.000
#> GSM549303 2 0.0000 0.994 0.000 1.000
#> GSM549309 2 0.0000 0.994 0.000 1.000
#> GSM750753 2 0.0000 0.994 0.000 1.000
#> GSM750752 2 0.0000 0.994 0.000 1.000
#> GSM549304 2 0.0000 0.994 0.000 1.000
#> GSM549305 2 0.0000 0.994 0.000 1.000
#> GSM549307 2 0.0000 0.994 0.000 1.000
#> GSM549306 2 0.0000 0.994 0.000 1.000
#> GSM549308 2 0.0000 0.994 0.000 1.000
#> GSM549233 1 0.0000 0.992 1.000 0.000
#> GSM549234 1 0.0000 0.992 1.000 0.000
#> GSM549250 1 0.0000 0.992 1.000 0.000
#> GSM549287 2 0.0000 0.994 0.000 1.000
#> GSM750735 1 0.0000 0.992 1.000 0.000
#> GSM750736 1 0.0000 0.992 1.000 0.000
#> GSM750749 1 0.9608 0.368 0.616 0.384
#> GSM549230 1 0.0000 0.992 1.000 0.000
#> GSM549231 1 0.0000 0.992 1.000 0.000
#> GSM549237 1 0.0000 0.992 1.000 0.000
#> GSM549254 1 0.0376 0.989 0.996 0.004
#> GSM750734 1 0.0000 0.992 1.000 0.000
#> GSM549271 2 0.0000 0.994 0.000 1.000
#> GSM549232 1 0.0000 0.992 1.000 0.000
#> GSM549246 1 0.0000 0.992 1.000 0.000
#> GSM549248 1 0.0000 0.992 1.000 0.000
#> GSM549255 1 0.0000 0.992 1.000 0.000
#> GSM750746 1 0.0000 0.992 1.000 0.000
#> GSM549259 1 0.0000 0.992 1.000 0.000
#> GSM549269 2 0.0000 0.994 0.000 1.000
#> GSM549273 2 0.0000 0.994 0.000 1.000
#> GSM549299 2 0.0000 0.994 0.000 1.000
#> GSM549301 2 0.0000 0.994 0.000 1.000
#> GSM549310 2 0.0000 0.994 0.000 1.000
#> GSM549311 2 0.0000 0.994 0.000 1.000
#> GSM549302 2 0.0000 0.994 0.000 1.000
#> GSM549235 1 0.0000 0.992 1.000 0.000
#> GSM549245 1 0.0000 0.992 1.000 0.000
#> GSM549265 1 0.0000 0.992 1.000 0.000
#> GSM549282 2 0.0000 0.994 0.000 1.000
#> GSM549296 2 0.0000 0.994 0.000 1.000
#> GSM750739 1 0.0000 0.992 1.000 0.000
#> GSM750742 1 0.0000 0.992 1.000 0.000
#> GSM750744 1 0.0000 0.992 1.000 0.000
#> GSM750750 2 0.0000 0.994 0.000 1.000
#> GSM549242 1 0.0000 0.992 1.000 0.000
#> GSM549252 1 0.0000 0.992 1.000 0.000
#> GSM549253 1 0.0000 0.992 1.000 0.000
#> GSM549256 1 0.0000 0.992 1.000 0.000
#> GSM549257 1 0.0000 0.992 1.000 0.000
#> GSM549263 1 0.0000 0.992 1.000 0.000
#> GSM549267 2 0.0000 0.994 0.000 1.000
#> GSM750745 1 0.0000 0.992 1.000 0.000
#> GSM549239 1 0.0000 0.992 1.000 0.000
#> GSM549244 1 0.0000 0.992 1.000 0.000
#> GSM549249 1 0.0000 0.992 1.000 0.000
#> GSM549260 1 0.0000 0.992 1.000 0.000
#> GSM549266 2 0.0000 0.994 0.000 1.000
#> GSM549293 2 0.0000 0.994 0.000 1.000
#> GSM549236 1 0.0000 0.992 1.000 0.000
#> GSM549238 1 0.0000 0.992 1.000 0.000
#> GSM549251 1 0.0000 0.992 1.000 0.000
#> GSM549258 1 0.0000 0.992 1.000 0.000
#> GSM549264 1 0.0000 0.992 1.000 0.000
#> GSM549243 1 0.0000 0.992 1.000 0.000
#> GSM549262 1 0.0000 0.992 1.000 0.000
#> GSM549278 2 0.8608 0.597 0.284 0.716
#> GSM549283 2 0.0000 0.994 0.000 1.000
#> GSM549298 2 0.0000 0.994 0.000 1.000
#> GSM750741 1 0.0000 0.992 1.000 0.000
#> GSM549286 2 0.0000 0.994 0.000 1.000
#> GSM549241 1 0.0000 0.992 1.000 0.000
#> GSM549247 1 0.0000 0.992 1.000 0.000
#> GSM549261 1 0.0000 0.992 1.000 0.000
#> GSM549270 2 0.0000 0.994 0.000 1.000
#> GSM549277 2 0.0000 0.994 0.000 1.000
#> GSM549280 2 0.0000 0.994 0.000 1.000
#> GSM549281 2 0.0000 0.994 0.000 1.000
#> GSM549285 2 0.0000 0.994 0.000 1.000
#> GSM549288 2 0.0000 0.994 0.000 1.000
#> GSM549292 2 0.0000 0.994 0.000 1.000
#> GSM549295 2 0.0000 0.994 0.000 1.000
#> GSM549297 2 0.0000 0.994 0.000 1.000
#> GSM750743 1 0.0000 0.992 1.000 0.000
#> GSM549268 2 0.0000 0.994 0.000 1.000
#> GSM549290 2 0.0000 0.994 0.000 1.000
#> GSM549272 2 0.0000 0.994 0.000 1.000
#> GSM549276 2 0.0000 0.994 0.000 1.000
#> GSM549275 1 0.0000 0.992 1.000 0.000
#> GSM549284 2 0.0000 0.994 0.000 1.000
#> GSM750737 1 0.0000 0.992 1.000 0.000
#> GSM750740 1 0.0000 0.992 1.000 0.000
#> GSM750747 1 0.0000 0.992 1.000 0.000
#> GSM750751 2 0.0000 0.994 0.000 1.000
#> GSM750754 2 0.0000 0.994 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.3192 0.545 0.112 0.000 0.888
#> GSM549291 3 0.0237 0.646 0.000 0.004 0.996
#> GSM549274 2 0.0000 0.938 0.000 1.000 0.000
#> GSM750738 2 0.2537 0.829 0.000 0.920 0.080
#> GSM750748 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549240 1 0.0983 0.866 0.980 0.016 0.004
#> GSM549279 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549300 3 0.6305 0.381 0.000 0.484 0.516
#> GSM549303 3 0.6168 0.520 0.000 0.412 0.588
#> GSM549309 3 0.5706 0.579 0.000 0.320 0.680
#> GSM750753 2 0.0000 0.938 0.000 1.000 0.000
#> GSM750752 3 0.0892 0.637 0.000 0.020 0.980
#> GSM549304 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549307 2 0.5291 0.524 0.000 0.732 0.268
#> GSM549306 3 0.6280 0.444 0.000 0.460 0.540
#> GSM549308 3 0.6252 0.476 0.000 0.444 0.556
#> GSM549233 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549234 1 0.6192 0.534 0.580 0.000 0.420
#> GSM549250 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549287 3 0.3267 0.636 0.000 0.116 0.884
#> GSM750735 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750749 1 0.9173 0.177 0.536 0.200 0.264
#> GSM549230 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549254 3 0.6386 -0.225 0.412 0.004 0.584
#> GSM750734 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549271 3 0.5810 0.572 0.000 0.336 0.664
#> GSM549232 1 0.6252 0.501 0.556 0.000 0.444
#> GSM549246 1 0.5988 0.593 0.632 0.000 0.368
#> GSM549248 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549255 1 0.6252 0.501 0.556 0.000 0.444
#> GSM750746 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549273 3 0.6225 0.495 0.000 0.432 0.568
#> GSM549299 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549301 3 0.6267 0.461 0.000 0.452 0.548
#> GSM549310 3 0.0237 0.646 0.000 0.004 0.996
#> GSM549311 3 0.6154 0.524 0.000 0.408 0.592
#> GSM549302 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549245 1 0.6252 0.501 0.556 0.000 0.444
#> GSM549265 1 0.6215 0.524 0.572 0.000 0.428
#> GSM549282 3 0.6154 0.524 0.000 0.408 0.592
#> GSM549296 3 0.0592 0.641 0.000 0.012 0.988
#> GSM750739 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750750 3 0.6225 0.495 0.000 0.432 0.568
#> GSM549242 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549252 1 0.6204 0.529 0.576 0.000 0.424
#> GSM549253 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549257 1 0.6252 0.501 0.556 0.000 0.444
#> GSM549263 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549267 3 0.0237 0.646 0.000 0.004 0.996
#> GSM750745 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549244 1 0.6252 0.501 0.556 0.000 0.444
#> GSM549249 1 0.6192 0.534 0.580 0.000 0.420
#> GSM549260 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549238 1 0.5733 0.638 0.676 0.000 0.324
#> GSM549251 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549278 3 0.0000 0.643 0.000 0.000 1.000
#> GSM549283 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549298 3 0.6260 0.469 0.000 0.448 0.552
#> GSM750741 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549247 1 0.4521 0.706 0.816 0.180 0.004
#> GSM549261 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549277 2 0.3816 0.779 0.000 0.852 0.148
#> GSM549280 2 0.4399 0.712 0.000 0.812 0.188
#> GSM549281 2 0.0892 0.924 0.000 0.980 0.020
#> GSM549285 3 0.6280 0.444 0.000 0.460 0.540
#> GSM549288 2 0.3879 0.773 0.000 0.848 0.152
#> GSM549292 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549295 2 0.4555 0.689 0.000 0.800 0.200
#> GSM549297 2 0.2165 0.885 0.000 0.936 0.064
#> GSM750743 1 0.0000 0.879 1.000 0.000 0.000
#> GSM549268 2 0.1860 0.897 0.000 0.948 0.052
#> GSM549290 3 0.0237 0.646 0.000 0.004 0.996
#> GSM549272 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.938 0.000 1.000 0.000
#> GSM549275 1 0.5058 0.628 0.756 0.244 0.000
#> GSM549284 2 0.0000 0.938 0.000 1.000 0.000
#> GSM750737 1 0.5397 0.680 0.720 0.000 0.280
#> GSM750740 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.879 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.938 0.000 1.000 0.000
#> GSM750754 3 0.0237 0.646 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.0921 0.895 0.000 0.000 0.028 0.972
#> GSM549291 3 0.4356 0.589 0.000 0.000 0.708 0.292
#> GSM549274 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM750738 2 0.1022 0.877 0.000 0.968 0.000 0.032
#> GSM750748 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549240 1 0.3366 0.824 0.872 0.028 0.004 0.096
#> GSM549279 2 0.0188 0.893 0.000 0.996 0.004 0.000
#> GSM549294 2 0.0188 0.895 0.000 0.996 0.004 0.000
#> GSM549300 3 0.3074 0.754 0.000 0.152 0.848 0.000
#> GSM549303 3 0.0336 0.883 0.000 0.008 0.992 0.000
#> GSM549309 3 0.0376 0.883 0.000 0.004 0.992 0.004
#> GSM750753 2 0.1022 0.884 0.000 0.968 0.032 0.000
#> GSM750752 4 0.4920 0.358 0.000 0.004 0.368 0.628
#> GSM549304 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549305 2 0.0188 0.895 0.000 0.996 0.004 0.000
#> GSM549307 3 0.4830 0.213 0.000 0.392 0.608 0.000
#> GSM549306 3 0.1716 0.856 0.000 0.064 0.936 0.000
#> GSM549308 3 0.0592 0.882 0.000 0.016 0.984 0.000
#> GSM549233 1 0.4898 0.436 0.584 0.000 0.000 0.416
#> GSM549234 4 0.0188 0.906 0.004 0.000 0.000 0.996
#> GSM549250 1 0.4040 0.729 0.752 0.000 0.000 0.248
#> GSM549287 3 0.0336 0.881 0.000 0.000 0.992 0.008
#> GSM750735 1 0.0672 0.889 0.984 0.000 0.008 0.008
#> GSM750736 1 0.0992 0.885 0.976 0.004 0.008 0.012
#> GSM750749 1 0.6888 0.382 0.584 0.096 0.308 0.012
#> GSM549230 1 0.2704 0.843 0.876 0.000 0.000 0.124
#> GSM549231 1 0.2814 0.838 0.868 0.000 0.000 0.132
#> GSM549237 1 0.1118 0.886 0.964 0.000 0.000 0.036
#> GSM549254 4 0.0804 0.901 0.008 0.000 0.012 0.980
#> GSM750734 1 0.0524 0.890 0.988 0.000 0.004 0.008
#> GSM549271 3 0.0376 0.883 0.000 0.004 0.992 0.004
#> GSM549232 4 0.0188 0.907 0.000 0.000 0.004 0.996
#> GSM549246 4 0.3074 0.769 0.152 0.000 0.000 0.848
#> GSM549248 1 0.1474 0.880 0.948 0.000 0.000 0.052
#> GSM549255 4 0.0336 0.906 0.000 0.000 0.008 0.992
#> GSM750746 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0469 0.883 0.000 0.012 0.988 0.000
#> GSM549299 2 0.0921 0.885 0.000 0.972 0.028 0.000
#> GSM549301 3 0.1118 0.874 0.000 0.036 0.964 0.000
#> GSM549310 3 0.4277 0.606 0.000 0.000 0.720 0.280
#> GSM549311 3 0.0376 0.883 0.000 0.004 0.992 0.004
#> GSM549302 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549245 4 0.0336 0.906 0.000 0.000 0.008 0.992
#> GSM549265 4 0.0376 0.906 0.004 0.000 0.004 0.992
#> GSM549282 3 0.0376 0.883 0.000 0.004 0.992 0.004
#> GSM549296 4 0.4522 0.473 0.000 0.000 0.320 0.680
#> GSM750739 1 0.0376 0.890 0.992 0.000 0.004 0.004
#> GSM750742 1 0.1022 0.886 0.968 0.000 0.000 0.032
#> GSM750744 1 0.0895 0.890 0.976 0.000 0.004 0.020
#> GSM750750 3 0.0592 0.882 0.000 0.016 0.984 0.000
#> GSM549242 1 0.4164 0.711 0.736 0.000 0.000 0.264
#> GSM549252 4 0.0188 0.906 0.004 0.000 0.000 0.996
#> GSM549253 1 0.3528 0.788 0.808 0.000 0.000 0.192
#> GSM549256 1 0.4948 0.378 0.560 0.000 0.000 0.440
#> GSM549257 4 0.0188 0.907 0.000 0.000 0.004 0.996
#> GSM549263 1 0.2973 0.829 0.856 0.000 0.000 0.144
#> GSM549267 3 0.2868 0.790 0.000 0.000 0.864 0.136
#> GSM750745 1 0.0524 0.889 0.988 0.000 0.008 0.004
#> GSM549239 1 0.0524 0.889 0.988 0.000 0.008 0.004
#> GSM549244 4 0.0336 0.906 0.000 0.000 0.008 0.992
#> GSM549249 4 0.0188 0.906 0.004 0.000 0.000 0.996
#> GSM549260 1 0.1637 0.876 0.940 0.000 0.000 0.060
#> GSM549266 2 0.0336 0.894 0.000 0.992 0.008 0.000
#> GSM549293 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549236 1 0.4250 0.694 0.724 0.000 0.000 0.276
#> GSM549238 4 0.3172 0.755 0.160 0.000 0.000 0.840
#> GSM549251 1 0.3172 0.817 0.840 0.000 0.000 0.160
#> GSM549258 1 0.0524 0.889 0.988 0.000 0.008 0.004
#> GSM549264 1 0.2216 0.866 0.908 0.000 0.000 0.092
#> GSM549243 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549262 1 0.1022 0.886 0.968 0.000 0.000 0.032
#> GSM549278 3 0.4746 0.433 0.000 0.000 0.632 0.368
#> GSM549283 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> GSM549298 3 0.0817 0.880 0.000 0.024 0.976 0.000
#> GSM750741 1 0.0524 0.889 0.988 0.000 0.008 0.004
#> GSM549286 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0524 0.889 0.988 0.000 0.008 0.004
#> GSM549247 1 0.6798 0.344 0.552 0.348 0.004 0.096
#> GSM549261 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM549270 2 0.0336 0.895 0.000 0.992 0.008 0.000
#> GSM549277 2 0.4981 0.251 0.000 0.536 0.464 0.000
#> GSM549280 2 0.4996 0.183 0.000 0.516 0.484 0.000
#> GSM549281 2 0.2530 0.828 0.000 0.888 0.112 0.000
#> GSM549285 3 0.1557 0.863 0.000 0.056 0.944 0.000
#> GSM549288 2 0.4992 0.212 0.000 0.524 0.476 0.000
#> GSM549292 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549295 2 0.4985 0.237 0.000 0.532 0.468 0.000
#> GSM549297 2 0.3486 0.751 0.000 0.812 0.188 0.000
#> GSM750743 1 0.0672 0.889 0.984 0.000 0.008 0.008
#> GSM549268 2 0.3610 0.742 0.000 0.800 0.200 0.000
#> GSM549290 3 0.3266 0.759 0.000 0.000 0.832 0.168
#> GSM549272 2 0.0000 0.895 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0188 0.895 0.000 0.996 0.004 0.000
#> GSM549275 1 0.3992 0.726 0.800 0.188 0.008 0.004
#> GSM549284 2 0.0188 0.895 0.000 0.996 0.004 0.000
#> GSM750737 4 0.2271 0.854 0.076 0.000 0.008 0.916
#> GSM750740 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.890 1.000 0.000 0.000 0.000
#> GSM750751 2 0.0188 0.895 0.000 0.996 0.004 0.000
#> GSM750754 3 0.0921 0.872 0.000 0.000 0.972 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.4038 0.7111 0.000 0.000 0.080 0.792 0.128
#> GSM549291 3 0.5925 0.2934 0.000 0.000 0.556 0.316 0.128
#> GSM549274 2 0.0609 0.8991 0.000 0.980 0.000 0.000 0.020
#> GSM750738 2 0.1915 0.8774 0.000 0.928 0.000 0.032 0.040
#> GSM750748 1 0.3983 0.1514 0.660 0.000 0.000 0.000 0.340
#> GSM549240 1 0.5933 0.3461 0.632 0.048 0.000 0.060 0.260
#> GSM549279 2 0.5517 0.6954 0.068 0.688 0.020 0.008 0.216
#> GSM549294 2 0.0579 0.8973 0.000 0.984 0.008 0.000 0.008
#> GSM549300 3 0.3203 0.6952 0.000 0.168 0.820 0.000 0.012
#> GSM549303 3 0.0794 0.7555 0.000 0.000 0.972 0.000 0.028
#> GSM549309 3 0.0880 0.7546 0.000 0.000 0.968 0.000 0.032
#> GSM750753 2 0.1764 0.8708 0.000 0.928 0.064 0.000 0.008
#> GSM750752 4 0.5137 0.5980 0.000 0.016 0.152 0.724 0.108
#> GSM549304 2 0.0510 0.8996 0.000 0.984 0.000 0.000 0.016
#> GSM549305 2 0.0566 0.8979 0.000 0.984 0.012 0.000 0.004
#> GSM549307 3 0.4430 0.4241 0.000 0.360 0.628 0.000 0.012
#> GSM549306 3 0.2522 0.7403 0.000 0.108 0.880 0.000 0.012
#> GSM549308 3 0.0579 0.7589 0.000 0.008 0.984 0.000 0.008
#> GSM549233 5 0.6598 0.5959 0.276 0.000 0.000 0.260 0.464
#> GSM549234 4 0.1851 0.7726 0.000 0.000 0.000 0.912 0.088
#> GSM549250 5 0.6019 0.7483 0.380 0.000 0.000 0.120 0.500
#> GSM549287 3 0.2069 0.7351 0.000 0.000 0.912 0.012 0.076
#> GSM750735 1 0.2843 0.4369 0.848 0.000 0.000 0.008 0.144
#> GSM750736 1 0.3476 0.4264 0.804 0.000 0.000 0.020 0.176
#> GSM750749 1 0.7390 0.2532 0.532 0.036 0.116 0.040 0.276
#> GSM549230 5 0.5092 0.6830 0.440 0.000 0.000 0.036 0.524
#> GSM549231 5 0.5036 0.6804 0.452 0.000 0.000 0.032 0.516
#> GSM549237 1 0.4622 -0.3064 0.548 0.000 0.000 0.012 0.440
#> GSM549254 4 0.2332 0.7527 0.016 0.004 0.000 0.904 0.076
#> GSM750734 1 0.2179 0.4296 0.888 0.000 0.000 0.000 0.112
#> GSM549271 3 0.1943 0.7413 0.000 0.000 0.924 0.020 0.056
#> GSM549232 4 0.1043 0.7784 0.000 0.000 0.000 0.960 0.040
#> GSM549246 4 0.6164 -0.0552 0.136 0.000 0.000 0.476 0.388
#> GSM549248 1 0.4446 -0.5010 0.520 0.000 0.000 0.004 0.476
#> GSM549255 4 0.0963 0.7775 0.000 0.000 0.000 0.964 0.036
#> GSM750746 1 0.3895 0.1923 0.680 0.000 0.000 0.000 0.320
#> GSM549259 1 0.3534 0.3200 0.744 0.000 0.000 0.000 0.256
#> GSM549269 2 0.0609 0.8991 0.000 0.980 0.000 0.000 0.020
#> GSM549273 3 0.0798 0.7595 0.000 0.008 0.976 0.000 0.016
#> GSM549299 2 0.1943 0.8725 0.000 0.924 0.056 0.000 0.020
#> GSM549301 3 0.2130 0.7506 0.000 0.080 0.908 0.000 0.012
#> GSM549310 3 0.6207 0.2258 0.000 0.008 0.524 0.348 0.120
#> GSM549311 3 0.0794 0.7555 0.000 0.000 0.972 0.000 0.028
#> GSM549302 2 0.0609 0.8991 0.000 0.980 0.000 0.000 0.020
#> GSM549235 1 0.4060 0.0877 0.640 0.000 0.000 0.000 0.360
#> GSM549245 4 0.0880 0.7775 0.000 0.000 0.000 0.968 0.032
#> GSM549265 4 0.4305 0.6947 0.052 0.000 0.000 0.748 0.200
#> GSM549282 3 0.0794 0.7568 0.000 0.000 0.972 0.000 0.028
#> GSM549296 4 0.4850 0.5979 0.000 0.004 0.156 0.732 0.108
#> GSM750739 1 0.3707 0.2115 0.716 0.000 0.000 0.000 0.284
#> GSM750742 1 0.4300 -0.4509 0.524 0.000 0.000 0.000 0.476
#> GSM750744 1 0.3707 0.2066 0.716 0.000 0.000 0.000 0.284
#> GSM750750 3 0.0451 0.7591 0.000 0.004 0.988 0.000 0.008
#> GSM549242 5 0.6347 0.6808 0.376 0.000 0.000 0.164 0.460
#> GSM549252 4 0.3086 0.7269 0.004 0.000 0.000 0.816 0.180
#> GSM549253 5 0.5524 0.7447 0.416 0.000 0.000 0.068 0.516
#> GSM549256 5 0.6631 0.5005 0.236 0.000 0.000 0.324 0.440
#> GSM549257 4 0.1544 0.7762 0.000 0.000 0.000 0.932 0.068
#> GSM549263 5 0.5216 0.7142 0.436 0.000 0.000 0.044 0.520
#> GSM549267 3 0.4704 0.5905 0.000 0.000 0.736 0.152 0.112
#> GSM750745 1 0.1124 0.4608 0.960 0.000 0.000 0.004 0.036
#> GSM549239 1 0.2020 0.4447 0.900 0.000 0.000 0.000 0.100
#> GSM549244 4 0.2074 0.7664 0.000 0.000 0.000 0.896 0.104
#> GSM549249 4 0.3160 0.7227 0.004 0.000 0.000 0.808 0.188
#> GSM549260 1 0.4908 0.1708 0.636 0.000 0.000 0.044 0.320
#> GSM549266 2 0.4902 0.7364 0.036 0.728 0.024 0.004 0.208
#> GSM549293 2 0.0703 0.8985 0.000 0.976 0.000 0.000 0.024
#> GSM549236 5 0.6012 0.7449 0.376 0.000 0.000 0.120 0.504
#> GSM549238 4 0.5942 0.0985 0.116 0.000 0.000 0.524 0.360
#> GSM549251 5 0.5381 0.7328 0.428 0.000 0.000 0.056 0.516
#> GSM549258 1 0.2338 0.4556 0.884 0.000 0.000 0.004 0.112
#> GSM549264 1 0.4632 -0.4594 0.540 0.000 0.000 0.012 0.448
#> GSM549243 1 0.4088 0.0619 0.632 0.000 0.000 0.000 0.368
#> GSM549262 1 0.4294 -0.4292 0.532 0.000 0.000 0.000 0.468
#> GSM549278 4 0.6328 0.0224 0.004 0.000 0.412 0.448 0.136
#> GSM549283 2 0.2903 0.8445 0.000 0.872 0.080 0.000 0.048
#> GSM549298 3 0.1942 0.7536 0.000 0.068 0.920 0.000 0.012
#> GSM750741 1 0.3282 0.4265 0.804 0.000 0.000 0.008 0.188
#> GSM549286 2 0.0510 0.8994 0.000 0.984 0.000 0.000 0.016
#> GSM549241 1 0.2286 0.4580 0.888 0.000 0.000 0.004 0.108
#> GSM549247 1 0.7239 0.2671 0.520 0.164 0.000 0.068 0.248
#> GSM549261 1 0.3684 0.2760 0.720 0.000 0.000 0.000 0.280
#> GSM549270 2 0.1408 0.8817 0.000 0.948 0.044 0.000 0.008
#> GSM549277 3 0.4900 0.1465 0.000 0.464 0.512 0.000 0.024
#> GSM549280 3 0.4867 0.2415 0.000 0.432 0.544 0.000 0.024
#> GSM549281 2 0.6619 0.5771 0.028 0.592 0.144 0.008 0.228
#> GSM549285 3 0.2448 0.7472 0.000 0.088 0.892 0.000 0.020
#> GSM549288 3 0.4747 0.1018 0.000 0.484 0.500 0.000 0.016
#> GSM549292 2 0.0609 0.8991 0.000 0.980 0.000 0.000 0.020
#> GSM549295 3 0.4659 0.0887 0.000 0.488 0.500 0.000 0.012
#> GSM549297 2 0.3934 0.6213 0.000 0.740 0.244 0.000 0.016
#> GSM750743 1 0.1792 0.4446 0.916 0.000 0.000 0.000 0.084
#> GSM549268 2 0.6585 0.4669 0.016 0.548 0.224 0.000 0.212
#> GSM549290 3 0.4914 0.5596 0.000 0.000 0.712 0.180 0.108
#> GSM549272 2 0.0609 0.8991 0.000 0.980 0.000 0.000 0.020
#> GSM549276 2 0.0290 0.8977 0.000 0.992 0.008 0.000 0.000
#> GSM549275 1 0.5493 0.3636 0.672 0.124 0.000 0.008 0.196
#> GSM549284 2 0.1082 0.8968 0.000 0.964 0.008 0.000 0.028
#> GSM750737 4 0.4982 0.6154 0.220 0.000 0.000 0.692 0.088
#> GSM750740 1 0.3876 0.2231 0.684 0.000 0.000 0.000 0.316
#> GSM750747 1 0.3932 0.1840 0.672 0.000 0.000 0.000 0.328
#> GSM750751 2 0.0579 0.8989 0.000 0.984 0.008 0.000 0.008
#> GSM750754 3 0.2761 0.7126 0.000 0.000 0.872 0.024 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.5437 0.2851 0.004 0.000 0.040 0.608 0.056 0.292
#> GSM549291 6 0.6250 0.8233 0.008 0.000 0.344 0.252 0.000 0.396
#> GSM549274 2 0.1082 0.8330 0.004 0.956 0.000 0.000 0.000 0.040
#> GSM750738 2 0.2550 0.8039 0.024 0.892 0.000 0.036 0.000 0.048
#> GSM750748 5 0.4145 0.5049 0.252 0.000 0.000 0.000 0.700 0.048
#> GSM549240 1 0.6316 0.5323 0.560 0.020 0.000 0.032 0.136 0.252
#> GSM549279 2 0.6585 0.4381 0.216 0.448 0.040 0.000 0.000 0.296
#> GSM549294 2 0.2728 0.8183 0.008 0.872 0.040 0.000 0.000 0.080
#> GSM549300 3 0.2907 0.5368 0.000 0.152 0.828 0.000 0.000 0.020
#> GSM549303 3 0.1765 0.5262 0.000 0.000 0.904 0.000 0.000 0.096
#> GSM549309 3 0.2402 0.4788 0.000 0.000 0.856 0.004 0.000 0.140
#> GSM750753 2 0.2934 0.7932 0.000 0.844 0.112 0.000 0.000 0.044
#> GSM750752 4 0.5902 -0.2135 0.004 0.024 0.104 0.512 0.000 0.356
#> GSM549304 2 0.1080 0.8292 0.004 0.960 0.004 0.000 0.000 0.032
#> GSM549305 2 0.2129 0.8201 0.000 0.904 0.056 0.000 0.000 0.040
#> GSM549307 3 0.4146 0.4478 0.000 0.288 0.676 0.000 0.000 0.036
#> GSM549306 3 0.1895 0.5762 0.000 0.072 0.912 0.000 0.000 0.016
#> GSM549308 3 0.0146 0.5756 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM549233 5 0.4260 0.4625 0.016 0.000 0.000 0.268 0.692 0.024
#> GSM549234 4 0.2547 0.6720 0.004 0.000 0.000 0.880 0.080 0.036
#> GSM549250 5 0.2673 0.6003 0.004 0.000 0.000 0.132 0.852 0.012
#> GSM549287 3 0.3468 0.2277 0.000 0.000 0.728 0.008 0.000 0.264
#> GSM750735 1 0.5132 0.5973 0.684 0.000 0.000 0.032 0.168 0.116
#> GSM750736 1 0.4409 0.6398 0.748 0.000 0.000 0.028 0.156 0.068
#> GSM750749 1 0.6269 0.4635 0.588 0.016 0.048 0.012 0.076 0.260
#> GSM549230 5 0.0909 0.6502 0.020 0.000 0.000 0.012 0.968 0.000
#> GSM549231 5 0.1485 0.6450 0.024 0.000 0.000 0.028 0.944 0.004
#> GSM549237 5 0.3960 0.5405 0.224 0.000 0.000 0.008 0.736 0.032
#> GSM549254 4 0.4087 0.4958 0.064 0.004 0.000 0.744 0.000 0.188
#> GSM750734 1 0.4168 0.3711 0.584 0.000 0.000 0.000 0.400 0.016
#> GSM549271 3 0.3281 0.3786 0.004 0.000 0.784 0.012 0.000 0.200
#> GSM549232 4 0.1578 0.6530 0.004 0.000 0.000 0.936 0.012 0.048
#> GSM549246 5 0.6063 -0.0935 0.056 0.000 0.000 0.400 0.464 0.080
#> GSM549248 5 0.2488 0.6177 0.124 0.000 0.000 0.008 0.864 0.004
#> GSM549255 4 0.1622 0.6562 0.016 0.000 0.000 0.940 0.016 0.028
#> GSM750746 5 0.4274 0.4729 0.276 0.000 0.000 0.000 0.676 0.048
#> GSM549259 5 0.4497 0.3772 0.328 0.000 0.000 0.000 0.624 0.048
#> GSM549269 2 0.0891 0.8325 0.008 0.968 0.000 0.000 0.000 0.024
#> GSM549273 3 0.1297 0.5713 0.000 0.012 0.948 0.000 0.000 0.040
#> GSM549299 2 0.4023 0.7628 0.016 0.780 0.124 0.000 0.000 0.080
#> GSM549301 3 0.1320 0.5840 0.000 0.036 0.948 0.000 0.000 0.016
#> GSM549310 6 0.6437 0.8009 0.004 0.012 0.368 0.240 0.000 0.376
#> GSM549311 3 0.2100 0.5106 0.000 0.000 0.884 0.004 0.000 0.112
#> GSM549302 2 0.0547 0.8302 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549235 5 0.4038 0.5168 0.244 0.000 0.000 0.000 0.712 0.044
#> GSM549245 4 0.1448 0.6630 0.012 0.000 0.000 0.948 0.016 0.024
#> GSM549265 4 0.5646 0.5566 0.080 0.000 0.000 0.640 0.200 0.080
#> GSM549282 3 0.2402 0.4845 0.000 0.000 0.856 0.004 0.000 0.140
#> GSM549296 4 0.5288 -0.1402 0.004 0.000 0.100 0.552 0.000 0.344
#> GSM750739 5 0.3996 0.3027 0.388 0.000 0.000 0.004 0.604 0.004
#> GSM750742 5 0.1471 0.6447 0.064 0.000 0.000 0.000 0.932 0.004
#> GSM750744 5 0.4419 0.1175 0.404 0.000 0.000 0.012 0.572 0.012
#> GSM750750 3 0.1471 0.5564 0.000 0.004 0.932 0.000 0.000 0.064
#> GSM549242 5 0.4178 0.5661 0.052 0.000 0.000 0.184 0.748 0.016
#> GSM549252 4 0.3388 0.6448 0.004 0.000 0.000 0.804 0.156 0.036
#> GSM549253 5 0.1643 0.6371 0.000 0.000 0.000 0.068 0.924 0.008
#> GSM549256 5 0.4578 0.4263 0.024 0.000 0.000 0.320 0.636 0.020
#> GSM549257 4 0.1719 0.6657 0.004 0.000 0.000 0.932 0.032 0.032
#> GSM549263 5 0.0713 0.6490 0.000 0.000 0.000 0.028 0.972 0.000
#> GSM549267 3 0.5220 -0.4826 0.000 0.000 0.528 0.100 0.000 0.372
#> GSM750745 1 0.3521 0.5695 0.724 0.000 0.000 0.004 0.268 0.004
#> GSM549239 1 0.3992 0.4261 0.624 0.000 0.000 0.000 0.364 0.012
#> GSM549244 4 0.2487 0.6668 0.000 0.000 0.000 0.876 0.092 0.032
#> GSM549249 4 0.3512 0.6212 0.000 0.000 0.000 0.772 0.196 0.032
#> GSM549260 5 0.5569 0.3057 0.304 0.000 0.000 0.048 0.584 0.064
#> GSM549266 2 0.6132 0.5409 0.176 0.524 0.028 0.000 0.000 0.272
#> GSM549293 2 0.0865 0.8285 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM549236 5 0.3016 0.5922 0.012 0.000 0.000 0.136 0.836 0.016
#> GSM549238 4 0.4393 0.1474 0.004 0.000 0.000 0.500 0.480 0.016
#> GSM549251 5 0.1364 0.6457 0.004 0.000 0.000 0.048 0.944 0.004
#> GSM549258 1 0.4905 0.5785 0.640 0.000 0.000 0.004 0.264 0.092
#> GSM549264 5 0.3905 0.5257 0.200 0.000 0.000 0.028 0.756 0.016
#> GSM549243 5 0.3641 0.5427 0.224 0.000 0.000 0.000 0.748 0.028
#> GSM549262 5 0.2149 0.6299 0.104 0.000 0.000 0.004 0.888 0.004
#> GSM549278 6 0.6343 0.8057 0.012 0.000 0.300 0.280 0.000 0.408
#> GSM549283 2 0.4957 0.7464 0.056 0.720 0.116 0.000 0.000 0.108
#> GSM549298 3 0.1461 0.5825 0.000 0.044 0.940 0.000 0.000 0.016
#> GSM750741 1 0.3782 0.6523 0.780 0.000 0.000 0.000 0.124 0.096
#> GSM549286 2 0.0363 0.8319 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM549241 1 0.4926 0.5886 0.640 0.000 0.000 0.000 0.240 0.120
#> GSM549247 1 0.6723 0.5106 0.544 0.096 0.000 0.032 0.072 0.256
#> GSM549261 5 0.4332 0.4678 0.288 0.000 0.000 0.000 0.664 0.048
#> GSM549270 2 0.3561 0.7715 0.012 0.812 0.120 0.000 0.000 0.056
#> GSM549277 3 0.5063 0.2215 0.008 0.368 0.560 0.000 0.000 0.064
#> GSM549280 3 0.5116 0.2612 0.016 0.360 0.568 0.000 0.000 0.056
#> GSM549281 2 0.7368 0.3702 0.152 0.376 0.176 0.000 0.000 0.296
#> GSM549285 3 0.3029 0.5650 0.008 0.052 0.852 0.000 0.000 0.088
#> GSM549288 3 0.4788 0.2403 0.000 0.372 0.568 0.000 0.000 0.060
#> GSM549292 2 0.0891 0.8305 0.008 0.968 0.000 0.000 0.000 0.024
#> GSM549295 3 0.4735 0.1566 0.004 0.416 0.540 0.000 0.000 0.040
#> GSM549297 2 0.4688 0.4636 0.004 0.616 0.328 0.000 0.000 0.052
#> GSM750743 1 0.4504 0.5031 0.628 0.000 0.000 0.008 0.332 0.032
#> GSM549268 2 0.7463 0.2855 0.140 0.348 0.228 0.000 0.000 0.284
#> GSM549290 3 0.5673 -0.6048 0.000 0.000 0.484 0.140 0.004 0.372
#> GSM549272 2 0.0777 0.8330 0.004 0.972 0.000 0.000 0.000 0.024
#> GSM549276 2 0.1492 0.8271 0.000 0.940 0.036 0.000 0.000 0.024
#> GSM549275 1 0.6468 0.5529 0.576 0.112 0.000 0.008 0.104 0.200
#> GSM549284 2 0.1049 0.8298 0.000 0.960 0.008 0.000 0.000 0.032
#> GSM750737 4 0.5895 0.3100 0.324 0.000 0.000 0.532 0.032 0.112
#> GSM750740 5 0.4204 0.5008 0.252 0.000 0.000 0.000 0.696 0.052
#> GSM750747 5 0.4190 0.4960 0.260 0.000 0.000 0.000 0.692 0.048
#> GSM750751 2 0.1871 0.8295 0.016 0.928 0.024 0.000 0.000 0.032
#> GSM750754 3 0.4183 0.0346 0.000 0.000 0.668 0.036 0.000 0.296
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:skmeans 102 0.0336 2.28e-05 9.33e-02 0.00249 2
#> CV:skmeans 93 0.0653 5.50e-05 4.19e-05 0.00543 3
#> CV:skmeans 91 0.3566 5.27e-05 1.32e-02 0.11266 4
#> CV:skmeans 63 0.7114 1.86e-04 3.45e-03 0.00363 5
#> CV:skmeans 68 0.7795 3.72e-03 5.71e-02 0.24729 6
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 21168 rows and 103 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 0.732 0.854 0.936 0.4451 0.567 0.567
#> 3 3 0.493 0.438 0.696 0.4366 0.677 0.487
#> 4 4 0.584 0.660 0.766 0.1372 0.855 0.640
#> 5 5 0.764 0.811 0.884 0.0873 0.869 0.583
#> 6 6 0.761 0.699 0.846 0.0332 0.974 0.875
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM549289 1 0.0000 0.926 1.000 0.000
#> GSM549291 1 0.2948 0.889 0.948 0.052
#> GSM549274 1 0.9427 0.478 0.640 0.360
#> GSM750738 1 0.9209 0.525 0.664 0.336
#> GSM750748 1 0.0000 0.926 1.000 0.000
#> GSM549240 1 0.0000 0.926 1.000 0.000
#> GSM549279 1 0.6623 0.769 0.828 0.172
#> GSM549294 2 0.5059 0.872 0.112 0.888
#> GSM549300 2 0.0000 0.931 0.000 1.000
#> GSM549303 2 0.0000 0.931 0.000 1.000
#> GSM549309 2 0.0672 0.929 0.008 0.992
#> GSM750753 2 0.4298 0.895 0.088 0.912
#> GSM750752 1 0.9491 0.464 0.632 0.368
#> GSM549304 1 0.9552 0.443 0.624 0.376
#> GSM549305 2 0.3733 0.905 0.072 0.928
#> GSM549307 2 0.0000 0.931 0.000 1.000
#> GSM549306 2 0.0000 0.931 0.000 1.000
#> GSM549308 2 0.0000 0.931 0.000 1.000
#> GSM549233 1 0.0000 0.926 1.000 0.000
#> GSM549234 1 0.0000 0.926 1.000 0.000
#> GSM549250 1 0.0000 0.926 1.000 0.000
#> GSM549287 2 0.0376 0.930 0.004 0.996
#> GSM750735 1 0.0000 0.926 1.000 0.000
#> GSM750736 1 0.0000 0.926 1.000 0.000
#> GSM750749 1 0.0000 0.926 1.000 0.000
#> GSM549230 1 0.0000 0.926 1.000 0.000
#> GSM549231 1 0.0000 0.926 1.000 0.000
#> GSM549237 1 0.0000 0.926 1.000 0.000
#> GSM549254 1 0.0376 0.923 0.996 0.004
#> GSM750734 1 0.0000 0.926 1.000 0.000
#> GSM549271 2 0.0672 0.930 0.008 0.992
#> GSM549232 1 0.0000 0.926 1.000 0.000
#> GSM549246 1 0.0000 0.926 1.000 0.000
#> GSM549248 1 0.0000 0.926 1.000 0.000
#> GSM549255 1 0.0000 0.926 1.000 0.000
#> GSM750746 1 0.0000 0.926 1.000 0.000
#> GSM549259 1 0.0000 0.926 1.000 0.000
#> GSM549269 1 0.9358 0.495 0.648 0.352
#> GSM549273 2 0.0000 0.931 0.000 1.000
#> GSM549299 2 0.9491 0.394 0.368 0.632
#> GSM549301 2 0.0000 0.931 0.000 1.000
#> GSM549310 2 0.0672 0.930 0.008 0.992
#> GSM549311 2 0.0000 0.931 0.000 1.000
#> GSM549302 2 0.4939 0.876 0.108 0.892
#> GSM549235 1 0.0000 0.926 1.000 0.000
#> GSM549245 1 0.0000 0.926 1.000 0.000
#> GSM549265 1 0.0000 0.926 1.000 0.000
#> GSM549282 2 0.3431 0.898 0.064 0.936
#> GSM549296 1 0.9775 0.358 0.588 0.412
#> GSM750739 1 0.0000 0.926 1.000 0.000
#> GSM750742 1 0.0000 0.926 1.000 0.000
#> GSM750744 1 0.0000 0.926 1.000 0.000
#> GSM750750 2 0.5059 0.854 0.112 0.888
#> GSM549242 1 0.0000 0.926 1.000 0.000
#> GSM549252 1 0.0000 0.926 1.000 0.000
#> GSM549253 1 0.0000 0.926 1.000 0.000
#> GSM549256 1 0.0000 0.926 1.000 0.000
#> GSM549257 1 0.0000 0.926 1.000 0.000
#> GSM549263 1 0.0000 0.926 1.000 0.000
#> GSM549267 2 0.7139 0.764 0.196 0.804
#> GSM750745 1 0.0000 0.926 1.000 0.000
#> GSM549239 1 0.0000 0.926 1.000 0.000
#> GSM549244 1 0.0000 0.926 1.000 0.000
#> GSM549249 1 0.0000 0.926 1.000 0.000
#> GSM549260 1 0.0000 0.926 1.000 0.000
#> GSM549266 1 0.5294 0.825 0.880 0.120
#> GSM549293 1 0.9522 0.452 0.628 0.372
#> GSM549236 1 0.0000 0.926 1.000 0.000
#> GSM549238 1 0.0000 0.926 1.000 0.000
#> GSM549251 1 0.0000 0.926 1.000 0.000
#> GSM549258 1 0.0000 0.926 1.000 0.000
#> GSM549264 1 0.0000 0.926 1.000 0.000
#> GSM549243 1 0.0000 0.926 1.000 0.000
#> GSM549262 1 0.0000 0.926 1.000 0.000
#> GSM549278 1 0.0000 0.926 1.000 0.000
#> GSM549283 1 0.7602 0.710 0.780 0.220
#> GSM549298 2 0.0000 0.931 0.000 1.000
#> GSM750741 1 0.0000 0.926 1.000 0.000
#> GSM549286 2 0.4022 0.900 0.080 0.920
#> GSM549241 1 0.0000 0.926 1.000 0.000
#> GSM549247 1 0.0000 0.926 1.000 0.000
#> GSM549261 1 0.0000 0.926 1.000 0.000
#> GSM549270 2 0.0000 0.931 0.000 1.000
#> GSM549277 2 0.0000 0.931 0.000 1.000
#> GSM549280 2 0.2043 0.923 0.032 0.968
#> GSM549281 1 0.4022 0.864 0.920 0.080
#> GSM549285 1 0.1184 0.915 0.984 0.016
#> GSM549288 2 0.0000 0.931 0.000 1.000
#> GSM549292 1 0.9988 0.131 0.520 0.480
#> GSM549295 2 0.0000 0.931 0.000 1.000
#> GSM549297 2 0.0000 0.931 0.000 1.000
#> GSM750743 1 0.0000 0.926 1.000 0.000
#> GSM549268 1 0.8327 0.648 0.736 0.264
#> GSM549290 1 0.8861 0.547 0.696 0.304
#> GSM549272 2 0.4298 0.895 0.088 0.912
#> GSM549276 2 0.3879 0.903 0.076 0.924
#> GSM549275 1 0.0000 0.926 1.000 0.000
#> GSM549284 1 0.9754 0.363 0.592 0.408
#> GSM750737 1 0.0000 0.926 1.000 0.000
#> GSM750740 1 0.0000 0.926 1.000 0.000
#> GSM750747 1 0.0000 0.926 1.000 0.000
#> GSM750751 2 0.4298 0.895 0.088 0.912
#> GSM750754 2 0.9775 0.317 0.412 0.588
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.9318 0.26979 0.172 0.352 0.476
#> GSM549291 3 0.8784 0.26678 0.124 0.352 0.524
#> GSM549274 2 0.6933 0.19093 0.208 0.716 0.076
#> GSM750738 2 0.8440 0.08992 0.184 0.620 0.196
#> GSM750748 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549240 1 0.0237 0.81013 0.996 0.000 0.004
#> GSM549279 1 0.4291 0.63722 0.820 0.180 0.000
#> GSM549294 2 0.6579 0.43985 0.020 0.652 0.328
#> GSM549300 2 0.6291 0.27779 0.000 0.532 0.468
#> GSM549303 3 0.6026 -0.05730 0.000 0.376 0.624
#> GSM549309 3 0.5968 -0.04801 0.000 0.364 0.636
#> GSM750753 2 0.5810 0.43791 0.000 0.664 0.336
#> GSM750752 2 0.9229 -0.03151 0.168 0.496 0.336
#> GSM549304 2 0.6449 0.26118 0.204 0.740 0.056
#> GSM549305 2 0.5859 0.43446 0.000 0.656 0.344
#> GSM549307 3 0.6302 -0.24692 0.000 0.480 0.520
#> GSM549306 3 0.6260 -0.18974 0.000 0.448 0.552
#> GSM549308 3 0.6045 -0.06108 0.000 0.380 0.620
#> GSM549233 1 0.5072 0.78046 0.792 0.012 0.196
#> GSM549234 2 0.9986 -0.27664 0.308 0.352 0.340
#> GSM549250 1 0.4399 0.79028 0.812 0.000 0.188
#> GSM549287 3 0.5621 -0.03257 0.000 0.308 0.692
#> GSM750735 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM750736 1 0.0424 0.80933 0.992 0.000 0.008
#> GSM750749 1 0.0424 0.80596 0.992 0.008 0.000
#> GSM549230 1 0.4291 0.79359 0.820 0.000 0.180
#> GSM549231 1 0.4291 0.79292 0.820 0.000 0.180
#> GSM549237 1 0.4178 0.79703 0.828 0.000 0.172
#> GSM549254 1 0.9587 -0.06018 0.440 0.356 0.204
#> GSM750734 1 0.2711 0.80972 0.912 0.000 0.088
#> GSM549271 3 0.6308 0.13837 0.000 0.492 0.508
#> GSM549232 3 0.9829 0.19777 0.248 0.352 0.400
#> GSM549246 1 0.6253 0.73182 0.732 0.036 0.232
#> GSM549248 1 0.4346 0.79172 0.816 0.000 0.184
#> GSM549255 1 0.9550 -0.04466 0.448 0.352 0.200
#> GSM750746 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549269 2 0.5633 0.23042 0.208 0.768 0.024
#> GSM549273 3 0.6045 -0.06108 0.000 0.380 0.620
#> GSM549299 2 0.9174 0.38743 0.164 0.504 0.332
#> GSM549301 3 0.6045 -0.06108 0.000 0.380 0.620
#> GSM549310 2 0.6045 -0.08913 0.000 0.620 0.380
#> GSM549311 3 0.5926 -0.04499 0.000 0.356 0.644
#> GSM549302 2 0.6482 0.42887 0.024 0.680 0.296
#> GSM549235 1 0.4235 0.79414 0.824 0.000 0.176
#> GSM549245 2 0.9986 -0.27697 0.308 0.352 0.340
#> GSM549265 3 0.9816 0.20404 0.244 0.356 0.400
#> GSM549282 3 0.4861 -0.00329 0.008 0.192 0.800
#> GSM549296 2 0.9424 -0.05133 0.184 0.464 0.352
#> GSM750739 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM750742 1 0.4291 0.79292 0.820 0.000 0.180
#> GSM750744 1 0.4291 0.79292 0.820 0.000 0.180
#> GSM750750 3 0.6379 -0.05413 0.008 0.368 0.624
#> GSM549242 1 0.4399 0.79083 0.812 0.000 0.188
#> GSM549252 3 0.9904 0.15746 0.268 0.352 0.380
#> GSM549253 1 0.4452 0.78833 0.808 0.000 0.192
#> GSM549256 1 0.4235 0.79663 0.824 0.000 0.176
#> GSM549257 1 0.9579 -0.05027 0.444 0.352 0.204
#> GSM549263 1 0.4346 0.79172 0.816 0.000 0.184
#> GSM549267 3 0.6379 0.22956 0.008 0.368 0.624
#> GSM750745 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549244 3 0.9550 0.26847 0.200 0.352 0.448
#> GSM549249 3 0.9904 0.15746 0.268 0.352 0.380
#> GSM549260 1 0.2261 0.81143 0.932 0.000 0.068
#> GSM549266 1 0.3686 0.68889 0.860 0.140 0.000
#> GSM549293 2 0.7199 0.17858 0.204 0.704 0.092
#> GSM549236 1 0.4452 0.78833 0.808 0.000 0.192
#> GSM549238 1 0.8404 0.55055 0.592 0.120 0.288
#> GSM549251 1 0.4452 0.78833 0.808 0.000 0.192
#> GSM549258 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549264 1 0.4291 0.79292 0.820 0.000 0.180
#> GSM549243 1 0.4121 0.79707 0.832 0.000 0.168
#> GSM549262 1 0.4291 0.79292 0.820 0.000 0.180
#> GSM549278 3 0.9792 0.21663 0.240 0.352 0.408
#> GSM549283 1 0.5774 0.54329 0.748 0.232 0.020
#> GSM549298 3 0.6045 -0.06108 0.000 0.380 0.620
#> GSM750741 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549286 2 0.5760 0.43981 0.000 0.672 0.328
#> GSM549241 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549247 1 0.3644 0.72287 0.872 0.124 0.004
#> GSM549261 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549270 2 0.5905 0.42851 0.000 0.648 0.352
#> GSM549277 2 0.5905 0.42851 0.000 0.648 0.352
#> GSM549280 2 0.5859 0.43446 0.000 0.656 0.344
#> GSM549281 1 0.2537 0.75532 0.920 0.080 0.000
#> GSM549285 1 0.5420 0.74931 0.752 0.008 0.240
#> GSM549288 2 0.6225 0.33226 0.000 0.568 0.432
#> GSM549292 2 0.5792 0.26834 0.192 0.772 0.036
#> GSM549295 2 0.6154 0.36861 0.000 0.592 0.408
#> GSM549297 2 0.5905 0.42851 0.000 0.648 0.352
#> GSM750743 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549268 1 0.7411 0.37201 0.668 0.256 0.076
#> GSM549290 3 0.8314 0.26116 0.092 0.352 0.556
#> GSM549272 2 0.5706 0.43883 0.000 0.680 0.320
#> GSM549276 2 0.5785 0.43921 0.000 0.668 0.332
#> GSM549275 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM549284 2 0.9034 0.35153 0.200 0.556 0.244
#> GSM750737 1 0.9424 -0.01102 0.464 0.352 0.184
#> GSM750740 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.81075 1.000 0.000 0.000
#> GSM750751 2 0.5760 0.43981 0.000 0.672 0.328
#> GSM750754 3 0.5905 0.22895 0.000 0.352 0.648
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.3991 0.7870 0.172 0.000 0.020 0.808
#> GSM549291 4 0.4004 0.7865 0.164 0.000 0.024 0.812
#> GSM549274 2 0.4164 0.6409 0.000 0.736 0.264 0.000
#> GSM750738 4 0.4925 0.2687 0.000 0.428 0.000 0.572
#> GSM750748 1 0.7084 0.7191 0.560 0.000 0.264 0.176
#> GSM549240 1 0.7325 0.7096 0.528 0.000 0.264 0.208
#> GSM549279 1 0.9404 0.5712 0.412 0.140 0.264 0.184
#> GSM549294 2 0.0188 0.7303 0.000 0.996 0.004 0.000
#> GSM549300 3 0.4999 0.5601 0.000 0.492 0.508 0.000
#> GSM549303 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549309 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM750753 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM750752 4 0.4238 0.6927 0.000 0.176 0.028 0.796
#> GSM549304 2 0.4313 0.6419 0.000 0.736 0.260 0.004
#> GSM549305 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM549307 3 0.4855 0.7400 0.000 0.400 0.600 0.000
#> GSM549306 3 0.4304 0.8759 0.000 0.284 0.716 0.000
#> GSM549308 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549233 1 0.3311 0.4402 0.828 0.000 0.000 0.172
#> GSM549234 4 0.2589 0.7847 0.116 0.000 0.000 0.884
#> GSM549250 1 0.0921 0.6225 0.972 0.000 0.000 0.028
#> GSM549287 3 0.4452 0.8882 0.008 0.260 0.732 0.000
#> GSM750735 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM750736 1 0.7377 0.7043 0.520 0.000 0.264 0.216
#> GSM750749 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549230 1 0.0336 0.6344 0.992 0.000 0.000 0.008
#> GSM549231 1 0.0000 0.6388 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0657 0.6438 0.984 0.000 0.004 0.012
#> GSM549254 4 0.0927 0.7303 0.008 0.016 0.000 0.976
#> GSM750734 1 0.5875 0.7112 0.692 0.000 0.204 0.104
#> GSM549271 4 0.5808 0.6634 0.020 0.084 0.160 0.736
#> GSM549232 4 0.3400 0.7875 0.180 0.000 0.000 0.820
#> GSM549246 1 0.2999 0.5127 0.864 0.000 0.004 0.132
#> GSM549248 1 0.0469 0.6325 0.988 0.000 0.000 0.012
#> GSM549255 4 0.0000 0.7197 0.000 0.000 0.000 1.000
#> GSM750746 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549259 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549269 2 0.4164 0.6409 0.000 0.736 0.264 0.000
#> GSM549273 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549299 2 0.2760 0.7003 0.000 0.872 0.128 0.000
#> GSM549301 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549310 4 0.6316 0.5751 0.000 0.156 0.184 0.660
#> GSM549311 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549302 2 0.1118 0.7168 0.000 0.964 0.000 0.036
#> GSM549235 1 0.1733 0.6593 0.948 0.000 0.028 0.024
#> GSM549245 4 0.2868 0.7897 0.136 0.000 0.000 0.864
#> GSM549265 4 0.4356 0.7270 0.292 0.000 0.000 0.708
#> GSM549282 3 0.7054 0.6321 0.232 0.196 0.572 0.000
#> GSM549296 4 0.4057 0.7086 0.000 0.152 0.032 0.816
#> GSM750739 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM750742 1 0.0000 0.6388 1.000 0.000 0.000 0.000
#> GSM750744 1 0.1545 0.6587 0.952 0.000 0.040 0.008
#> GSM750750 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM549242 1 0.2149 0.5678 0.912 0.000 0.000 0.088
#> GSM549252 4 0.3356 0.7889 0.176 0.000 0.000 0.824
#> GSM549253 1 0.2149 0.5678 0.912 0.000 0.000 0.088
#> GSM549256 1 0.4514 0.6294 0.800 0.000 0.064 0.136
#> GSM549257 4 0.0188 0.7228 0.004 0.000 0.000 0.996
#> GSM549263 1 0.0336 0.6344 0.992 0.000 0.000 0.008
#> GSM549267 4 0.7647 0.4546 0.388 0.000 0.208 0.404
#> GSM750745 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549239 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549244 4 0.3400 0.7875 0.180 0.000 0.000 0.820
#> GSM549249 4 0.4967 0.5692 0.452 0.000 0.000 0.548
#> GSM549260 1 0.7297 0.6811 0.536 0.000 0.244 0.220
#> GSM549266 1 0.9099 0.6080 0.452 0.120 0.264 0.164
#> GSM549293 2 0.4164 0.6409 0.000 0.736 0.264 0.000
#> GSM549236 1 0.1867 0.5850 0.928 0.000 0.000 0.072
#> GSM549238 1 0.4406 0.0821 0.700 0.000 0.000 0.300
#> GSM549251 1 0.0817 0.6249 0.976 0.000 0.000 0.024
#> GSM549258 1 0.7402 0.7033 0.516 0.000 0.264 0.220
#> GSM549264 1 0.0000 0.6388 1.000 0.000 0.000 0.000
#> GSM549243 1 0.3048 0.6780 0.876 0.000 0.108 0.016
#> GSM549262 1 0.0000 0.6388 1.000 0.000 0.000 0.000
#> GSM549278 4 0.4121 0.7867 0.184 0.000 0.020 0.796
#> GSM549283 2 0.9874 -0.2601 0.260 0.300 0.264 0.176
#> GSM549298 3 0.4164 0.8942 0.000 0.264 0.736 0.000
#> GSM750741 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549286 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM549241 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549247 1 0.7752 0.6395 0.436 0.000 0.264 0.300
#> GSM549261 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549270 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM549277 2 0.1211 0.6962 0.000 0.960 0.040 0.000
#> GSM549280 2 0.0188 0.7272 0.000 0.996 0.004 0.000
#> GSM549281 1 0.9272 0.4942 0.420 0.204 0.264 0.112
#> GSM549285 1 0.3324 0.5282 0.852 0.000 0.136 0.012
#> GSM549288 2 0.3726 0.3720 0.000 0.788 0.212 0.000
#> GSM549292 2 0.5077 0.6250 0.000 0.760 0.080 0.160
#> GSM549295 2 0.4331 0.1516 0.000 0.712 0.288 0.000
#> GSM549297 2 0.0921 0.7065 0.000 0.972 0.028 0.000
#> GSM750743 1 0.7084 0.7192 0.560 0.000 0.264 0.176
#> GSM549268 2 0.8331 0.0401 0.316 0.452 0.200 0.032
#> GSM549290 4 0.6094 0.6024 0.416 0.000 0.048 0.536
#> GSM549272 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM549275 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM549284 2 0.4155 0.6527 0.000 0.756 0.240 0.004
#> GSM750737 4 0.3074 0.5694 0.000 0.000 0.152 0.848
#> GSM750740 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM750747 1 0.7117 0.7190 0.556 0.000 0.264 0.180
#> GSM750751 2 0.0000 0.7298 0.000 1.000 0.000 0.000
#> GSM750754 3 0.6548 0.4128 0.176 0.000 0.636 0.188
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1270 0.905 0.000 0.000 0.000 0.948 0.052
#> GSM549291 4 0.0404 0.919 0.000 0.000 0.000 0.988 0.012
#> GSM549274 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM750738 4 0.3642 0.678 0.000 0.232 0.000 0.760 0.008
#> GSM750748 1 0.1608 0.856 0.928 0.000 0.000 0.000 0.072
#> GSM549240 1 0.1485 0.869 0.948 0.000 0.000 0.020 0.032
#> GSM549279 1 0.3449 0.790 0.844 0.064 0.000 0.004 0.088
#> GSM549294 2 0.1608 0.899 0.000 0.928 0.000 0.000 0.072
#> GSM549300 3 0.4326 0.615 0.000 0.264 0.708 0.000 0.028
#> GSM549303 3 0.0324 0.942 0.000 0.000 0.992 0.004 0.004
#> GSM549309 3 0.0162 0.943 0.000 0.000 0.996 0.000 0.004
#> GSM750753 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM750752 4 0.0865 0.911 0.000 0.004 0.000 0.972 0.024
#> GSM549304 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549305 2 0.0162 0.927 0.000 0.996 0.000 0.000 0.004
#> GSM549307 3 0.3224 0.798 0.000 0.160 0.824 0.000 0.016
#> GSM549306 3 0.0290 0.941 0.000 0.000 0.992 0.000 0.008
#> GSM549308 3 0.0000 0.943 0.000 0.000 1.000 0.000 0.000
#> GSM549233 5 0.3759 0.812 0.092 0.000 0.000 0.092 0.816
#> GSM549234 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549250 5 0.2886 0.830 0.148 0.000 0.000 0.008 0.844
#> GSM549287 3 0.1106 0.932 0.000 0.000 0.964 0.012 0.024
#> GSM750735 1 0.0000 0.874 1.000 0.000 0.000 0.000 0.000
#> GSM750736 1 0.0451 0.872 0.988 0.000 0.000 0.008 0.004
#> GSM750749 1 0.1965 0.816 0.904 0.000 0.000 0.000 0.096
#> GSM549230 5 0.2773 0.827 0.164 0.000 0.000 0.000 0.836
#> GSM549231 5 0.2690 0.828 0.156 0.000 0.000 0.000 0.844
#> GSM549237 5 0.3741 0.747 0.264 0.000 0.000 0.004 0.732
#> GSM549254 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM750734 1 0.2074 0.802 0.896 0.000 0.000 0.000 0.104
#> GSM549271 4 0.1403 0.897 0.000 0.000 0.024 0.952 0.024
#> GSM549232 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549246 5 0.5232 0.701 0.268 0.000 0.000 0.084 0.648
#> GSM549248 5 0.4397 0.395 0.432 0.000 0.000 0.004 0.564
#> GSM549255 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM750746 1 0.1270 0.866 0.948 0.000 0.000 0.000 0.052
#> GSM549259 1 0.1197 0.867 0.952 0.000 0.000 0.000 0.048
#> GSM549269 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.0671 0.939 0.000 0.000 0.980 0.004 0.016
#> GSM549299 2 0.1478 0.903 0.000 0.936 0.000 0.000 0.064
#> GSM549301 3 0.0000 0.943 0.000 0.000 1.000 0.000 0.000
#> GSM549310 4 0.3246 0.810 0.000 0.008 0.120 0.848 0.024
#> GSM549311 3 0.0771 0.938 0.000 0.000 0.976 0.004 0.020
#> GSM549302 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549235 1 0.3983 0.363 0.660 0.000 0.000 0.000 0.340
#> GSM549245 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549265 4 0.4151 0.417 0.004 0.000 0.000 0.652 0.344
#> GSM549282 5 0.4446 0.118 0.000 0.000 0.476 0.004 0.520
#> GSM549296 4 0.0609 0.914 0.000 0.000 0.000 0.980 0.020
#> GSM750739 1 0.0404 0.874 0.988 0.000 0.000 0.000 0.012
#> GSM750742 5 0.2690 0.828 0.156 0.000 0.000 0.000 0.844
#> GSM750744 5 0.4201 0.570 0.408 0.000 0.000 0.000 0.592
#> GSM750750 3 0.0000 0.943 0.000 0.000 1.000 0.000 0.000
#> GSM549242 5 0.3289 0.826 0.108 0.000 0.000 0.048 0.844
#> GSM549252 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549253 5 0.3237 0.825 0.104 0.000 0.000 0.048 0.848
#> GSM549256 5 0.4333 0.766 0.212 0.000 0.000 0.048 0.740
#> GSM549257 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549263 5 0.2690 0.828 0.156 0.000 0.000 0.000 0.844
#> GSM549267 5 0.5983 0.439 0.000 0.000 0.168 0.252 0.580
#> GSM750745 1 0.0000 0.874 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.874 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0290 0.923 0.000 0.000 0.000 0.992 0.008
#> GSM549249 5 0.3160 0.701 0.004 0.000 0.000 0.188 0.808
#> GSM549260 1 0.3090 0.819 0.860 0.000 0.000 0.052 0.088
#> GSM549266 1 0.4411 0.737 0.764 0.116 0.000 0.000 0.120
#> GSM549293 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549236 5 0.3242 0.828 0.116 0.000 0.000 0.040 0.844
#> GSM549238 5 0.3543 0.791 0.060 0.000 0.000 0.112 0.828
#> GSM549251 5 0.2997 0.830 0.148 0.000 0.000 0.012 0.840
#> GSM549258 1 0.0992 0.862 0.968 0.000 0.000 0.024 0.008
#> GSM549264 5 0.3177 0.804 0.208 0.000 0.000 0.000 0.792
#> GSM549243 1 0.3586 0.581 0.736 0.000 0.000 0.000 0.264
#> GSM549262 5 0.2813 0.825 0.168 0.000 0.000 0.000 0.832
#> GSM549278 4 0.0693 0.914 0.012 0.000 0.000 0.980 0.008
#> GSM549283 1 0.5468 0.440 0.600 0.332 0.000 0.008 0.060
#> GSM549298 3 0.0000 0.943 0.000 0.000 1.000 0.000 0.000
#> GSM750741 1 0.0000 0.874 1.000 0.000 0.000 0.000 0.000
#> GSM549286 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549241 1 0.0000 0.874 1.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.3995 0.715 0.776 0.000 0.000 0.180 0.044
#> GSM549261 1 0.1341 0.864 0.944 0.000 0.000 0.000 0.056
#> GSM549270 2 0.0162 0.927 0.000 0.996 0.000 0.000 0.004
#> GSM549277 2 0.1493 0.911 0.000 0.948 0.024 0.000 0.028
#> GSM549280 2 0.2068 0.892 0.000 0.904 0.004 0.000 0.092
#> GSM549281 1 0.5906 0.463 0.576 0.284 0.000 0.000 0.140
#> GSM549285 5 0.5507 0.700 0.188 0.000 0.160 0.000 0.652
#> GSM549288 2 0.4528 0.662 0.000 0.728 0.212 0.000 0.060
#> GSM549292 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549295 2 0.4590 0.230 0.000 0.568 0.420 0.000 0.012
#> GSM549297 2 0.1740 0.906 0.000 0.932 0.012 0.000 0.056
#> GSM750743 1 0.0510 0.872 0.984 0.000 0.000 0.000 0.016
#> GSM549268 2 0.5691 0.446 0.296 0.592 0.000 0.000 0.112
#> GSM549290 5 0.4166 0.451 0.000 0.000 0.004 0.348 0.648
#> GSM549272 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM549276 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM549275 1 0.0162 0.874 0.996 0.000 0.000 0.000 0.004
#> GSM549284 2 0.0290 0.928 0.000 0.992 0.000 0.000 0.008
#> GSM750737 4 0.3783 0.661 0.252 0.000 0.000 0.740 0.008
#> GSM750740 1 0.1341 0.864 0.944 0.000 0.000 0.000 0.056
#> GSM750747 1 0.1341 0.864 0.944 0.000 0.000 0.000 0.056
#> GSM750751 2 0.0510 0.924 0.000 0.984 0.000 0.000 0.016
#> GSM750754 3 0.2905 0.853 0.000 0.000 0.868 0.096 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.2213 0.87007 0.000 0.000 0.004 0.904 0.048 0.044
#> GSM549291 4 0.1196 0.89019 0.000 0.000 0.000 0.952 0.008 0.040
#> GSM549274 2 0.0000 0.81454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750738 4 0.3464 0.50968 0.000 0.312 0.000 0.688 0.000 0.000
#> GSM750748 1 0.1814 0.82316 0.900 0.000 0.000 0.000 0.100 0.000
#> GSM549240 1 0.3639 0.77963 0.816 0.000 0.000 0.020 0.068 0.096
#> GSM549279 1 0.3394 0.65454 0.752 0.012 0.000 0.000 0.000 0.236
#> GSM549294 2 0.3620 0.41045 0.000 0.648 0.000 0.000 0.000 0.352
#> GSM549300 6 0.5537 -0.22257 0.000 0.136 0.388 0.000 0.000 0.476
#> GSM549303 3 0.0363 0.81829 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM549309 3 0.0363 0.81842 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM750753 2 0.0363 0.81329 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM750752 4 0.1148 0.88712 0.000 0.000 0.004 0.960 0.020 0.016
#> GSM549304 2 0.0146 0.81441 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549305 2 0.2562 0.70059 0.000 0.828 0.000 0.000 0.000 0.172
#> GSM549307 3 0.5508 0.14859 0.000 0.128 0.444 0.000 0.000 0.428
#> GSM549306 3 0.2697 0.76115 0.000 0.000 0.812 0.000 0.000 0.188
#> GSM549308 3 0.1910 0.81586 0.000 0.000 0.892 0.000 0.000 0.108
#> GSM549233 5 0.2255 0.82121 0.016 0.000 0.000 0.088 0.892 0.004
#> GSM549234 4 0.0291 0.89957 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549250 5 0.1155 0.84655 0.036 0.000 0.000 0.004 0.956 0.004
#> GSM549287 3 0.1708 0.79163 0.000 0.000 0.932 0.004 0.024 0.040
#> GSM750735 1 0.0000 0.83539 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750736 1 0.0363 0.83390 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM750749 1 0.3727 0.39580 0.612 0.000 0.000 0.000 0.000 0.388
#> GSM549230 5 0.1387 0.84424 0.068 0.000 0.000 0.000 0.932 0.000
#> GSM549231 5 0.1082 0.84627 0.040 0.000 0.000 0.000 0.956 0.004
#> GSM549237 5 0.2664 0.76623 0.184 0.000 0.000 0.000 0.816 0.000
#> GSM549254 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM750734 1 0.1765 0.79253 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM549271 4 0.2016 0.86984 0.000 0.000 0.016 0.920 0.024 0.040
#> GSM549232 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549246 5 0.4349 0.69800 0.208 0.000 0.000 0.084 0.708 0.000
#> GSM549248 5 0.3737 0.35897 0.392 0.000 0.000 0.000 0.608 0.000
#> GSM549255 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM750746 1 0.1556 0.83055 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM549259 1 0.1501 0.83148 0.924 0.000 0.000 0.000 0.076 0.000
#> GSM549269 2 0.0508 0.81291 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM549273 3 0.0363 0.81795 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM549299 2 0.3101 0.53725 0.000 0.756 0.000 0.000 0.000 0.244
#> GSM549301 3 0.1910 0.81586 0.000 0.000 0.892 0.000 0.000 0.108
#> GSM549310 4 0.3705 0.77920 0.000 0.004 0.120 0.812 0.024 0.040
#> GSM549311 3 0.0914 0.81144 0.000 0.000 0.968 0.000 0.016 0.016
#> GSM549302 2 0.0000 0.81454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 1 0.3647 0.42154 0.640 0.000 0.000 0.000 0.360 0.000
#> GSM549245 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549265 4 0.3965 0.31087 0.004 0.000 0.000 0.616 0.376 0.004
#> GSM549282 3 0.5937 0.21602 0.000 0.000 0.416 0.000 0.368 0.216
#> GSM549296 4 0.1232 0.88519 0.000 0.000 0.004 0.956 0.024 0.016
#> GSM750739 1 0.0458 0.83907 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM750742 5 0.1075 0.84652 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM750744 5 0.3684 0.55097 0.372 0.000 0.000 0.000 0.628 0.000
#> GSM750750 3 0.2003 0.81521 0.000 0.000 0.884 0.000 0.000 0.116
#> GSM549242 5 0.1682 0.84018 0.020 0.000 0.000 0.052 0.928 0.000
#> GSM549252 4 0.0291 0.89957 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549253 5 0.1391 0.84134 0.016 0.000 0.000 0.040 0.944 0.000
#> GSM549256 5 0.3193 0.78376 0.124 0.000 0.000 0.052 0.824 0.000
#> GSM549257 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549263 5 0.1007 0.84636 0.044 0.000 0.000 0.000 0.956 0.000
#> GSM549267 5 0.5737 0.50572 0.000 0.000 0.128 0.212 0.616 0.044
#> GSM750745 1 0.0000 0.83539 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.83539 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0146 0.90029 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549249 5 0.2006 0.80182 0.000 0.000 0.000 0.104 0.892 0.004
#> GSM549260 1 0.3032 0.79188 0.840 0.000 0.000 0.056 0.104 0.000
#> GSM549266 1 0.4589 0.20214 0.504 0.036 0.000 0.000 0.000 0.460
#> GSM549293 2 0.0000 0.81454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 5 0.1642 0.84232 0.028 0.000 0.000 0.032 0.936 0.004
#> GSM549238 5 0.1728 0.83005 0.008 0.000 0.000 0.064 0.924 0.004
#> GSM549251 5 0.1082 0.84687 0.040 0.000 0.000 0.004 0.956 0.000
#> GSM549258 1 0.0603 0.82876 0.980 0.000 0.000 0.016 0.004 0.000
#> GSM549264 5 0.1814 0.83164 0.100 0.000 0.000 0.000 0.900 0.000
#> GSM549243 1 0.3371 0.59785 0.708 0.000 0.000 0.000 0.292 0.000
#> GSM549262 5 0.1501 0.84209 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM549278 4 0.0665 0.89383 0.008 0.000 0.004 0.980 0.008 0.000
#> GSM549283 1 0.5557 0.16140 0.552 0.248 0.000 0.000 0.000 0.200
#> GSM549298 3 0.1957 0.81485 0.000 0.000 0.888 0.000 0.000 0.112
#> GSM750741 1 0.0363 0.83868 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549286 2 0.0000 0.81454 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.0000 0.83539 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.5494 0.61193 0.676 0.004 0.000 0.156 0.068 0.096
#> GSM549261 1 0.1556 0.83055 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM549270 2 0.1663 0.77261 0.000 0.912 0.000 0.000 0.000 0.088
#> GSM549277 2 0.3872 0.38693 0.000 0.604 0.004 0.000 0.000 0.392
#> GSM549280 6 0.3864 -0.10532 0.000 0.480 0.000 0.000 0.000 0.520
#> GSM549281 6 0.5954 -0.00984 0.400 0.128 0.000 0.000 0.020 0.452
#> GSM549285 5 0.5518 0.64087 0.168 0.000 0.144 0.000 0.648 0.040
#> GSM549288 6 0.5794 0.05175 0.000 0.384 0.180 0.000 0.000 0.436
#> GSM549292 2 0.0260 0.81172 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549295 2 0.5925 -0.04162 0.000 0.456 0.236 0.000 0.000 0.308
#> GSM549297 2 0.3950 0.30960 0.000 0.564 0.004 0.000 0.000 0.432
#> GSM750743 1 0.0458 0.83586 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549268 6 0.5876 0.33447 0.180 0.276 0.000 0.000 0.012 0.532
#> GSM549290 5 0.4455 0.56514 0.000 0.000 0.008 0.264 0.680 0.048
#> GSM549272 2 0.0865 0.80435 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM549276 2 0.0458 0.81295 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM549275 1 0.0260 0.83782 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549284 2 0.0146 0.81386 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM750737 4 0.3426 0.58213 0.276 0.000 0.000 0.720 0.004 0.000
#> GSM750740 1 0.1556 0.83055 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM750747 1 0.1610 0.82939 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM750751 2 0.2491 0.69532 0.000 0.836 0.000 0.000 0.000 0.164
#> GSM750754 3 0.3234 0.72412 0.000 0.000 0.848 0.080 0.028 0.044
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:pam 93 0.140 1.28e-05 0.30380 0.0416 2
#> CV:pam 46 NA NA NA NA 3
#> CV:pam 93 0.216 1.31e-05 0.00527 0.1027 4
#> CV:pam 93 0.238 4.73e-07 0.01197 0.0273 5
#> CV:pam 86 0.257 3.44e-06 0.04846 0.0466 6
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 21168 rows and 103 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.957 0.981 0.5039 0.496 0.496
#> 3 3 0.907 0.934 0.964 0.2961 0.766 0.562
#> 4 4 0.930 0.935 0.964 0.0919 0.939 0.823
#> 5 5 0.698 0.644 0.775 0.0865 0.966 0.881
#> 6 6 0.752 0.585 0.773 0.0570 0.868 0.533
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM549289 2 0.1184 0.9739 0.016 0.984
#> GSM549291 2 0.0376 0.9821 0.004 0.996
#> GSM549274 2 0.0000 0.9839 0.000 1.000
#> GSM750738 2 0.3584 0.9214 0.068 0.932
#> GSM750748 1 0.0376 0.9774 0.996 0.004
#> GSM549240 1 0.0376 0.9774 0.996 0.004
#> GSM549279 2 0.0000 0.9839 0.000 1.000
#> GSM549294 2 0.0000 0.9839 0.000 1.000
#> GSM549300 2 0.0000 0.9839 0.000 1.000
#> GSM549303 2 0.0376 0.9821 0.004 0.996
#> GSM549309 2 0.0376 0.9821 0.004 0.996
#> GSM750753 2 0.0000 0.9839 0.000 1.000
#> GSM750752 2 0.3274 0.9330 0.060 0.940
#> GSM549304 2 0.0000 0.9839 0.000 1.000
#> GSM549305 2 0.0000 0.9839 0.000 1.000
#> GSM549307 2 0.0000 0.9839 0.000 1.000
#> GSM549306 2 0.0000 0.9839 0.000 1.000
#> GSM549308 2 0.0000 0.9839 0.000 1.000
#> GSM549233 1 0.0000 0.9770 1.000 0.000
#> GSM549234 1 0.0000 0.9770 1.000 0.000
#> GSM549250 1 0.0000 0.9770 1.000 0.000
#> GSM549287 2 0.0376 0.9821 0.004 0.996
#> GSM750735 1 0.0376 0.9774 0.996 0.004
#> GSM750736 1 0.0376 0.9774 0.996 0.004
#> GSM750749 2 0.3733 0.9173 0.072 0.928
#> GSM549230 1 0.0000 0.9770 1.000 0.000
#> GSM549231 1 0.0000 0.9770 1.000 0.000
#> GSM549237 1 0.0376 0.9774 0.996 0.004
#> GSM549254 2 0.9983 0.0745 0.476 0.524
#> GSM750734 1 0.0376 0.9774 0.996 0.004
#> GSM549271 2 0.0376 0.9821 0.004 0.996
#> GSM549232 1 0.0000 0.9770 1.000 0.000
#> GSM549246 1 0.8608 0.6130 0.716 0.284
#> GSM549248 1 0.0376 0.9774 0.996 0.004
#> GSM549255 1 0.0000 0.9770 1.000 0.000
#> GSM750746 1 0.0376 0.9774 0.996 0.004
#> GSM549259 1 0.0376 0.9774 0.996 0.004
#> GSM549269 2 0.0000 0.9839 0.000 1.000
#> GSM549273 2 0.0376 0.9821 0.004 0.996
#> GSM549299 2 0.0000 0.9839 0.000 1.000
#> GSM549301 2 0.0000 0.9839 0.000 1.000
#> GSM549310 2 0.0376 0.9821 0.004 0.996
#> GSM549311 2 0.0376 0.9821 0.004 0.996
#> GSM549302 2 0.0000 0.9839 0.000 1.000
#> GSM549235 1 0.0376 0.9774 0.996 0.004
#> GSM549245 1 0.0000 0.9770 1.000 0.000
#> GSM549265 1 0.8713 0.5979 0.708 0.292
#> GSM549282 2 0.0000 0.9839 0.000 1.000
#> GSM549296 2 0.4022 0.9118 0.080 0.920
#> GSM750739 1 0.0376 0.9774 0.996 0.004
#> GSM750742 1 0.0376 0.9774 0.996 0.004
#> GSM750744 1 0.0376 0.9774 0.996 0.004
#> GSM750750 2 0.0000 0.9839 0.000 1.000
#> GSM549242 1 0.0000 0.9770 1.000 0.000
#> GSM549252 1 0.0000 0.9770 1.000 0.000
#> GSM549253 1 0.0000 0.9770 1.000 0.000
#> GSM549256 1 0.0000 0.9770 1.000 0.000
#> GSM549257 1 0.0000 0.9770 1.000 0.000
#> GSM549263 1 0.0000 0.9770 1.000 0.000
#> GSM549267 2 0.0376 0.9821 0.004 0.996
#> GSM750745 1 0.0376 0.9774 0.996 0.004
#> GSM549239 1 0.0376 0.9774 0.996 0.004
#> GSM549244 1 0.0000 0.9770 1.000 0.000
#> GSM549249 1 0.0000 0.9770 1.000 0.000
#> GSM549260 1 0.0000 0.9770 1.000 0.000
#> GSM549266 2 0.0000 0.9839 0.000 1.000
#> GSM549293 2 0.0000 0.9839 0.000 1.000
#> GSM549236 1 0.0000 0.9770 1.000 0.000
#> GSM549238 1 0.0000 0.9770 1.000 0.000
#> GSM549251 1 0.0000 0.9770 1.000 0.000
#> GSM549258 1 0.0376 0.9774 0.996 0.004
#> GSM549264 1 0.0376 0.9774 0.996 0.004
#> GSM549243 1 0.0376 0.9774 0.996 0.004
#> GSM549262 1 0.0376 0.9774 0.996 0.004
#> GSM549278 2 0.0672 0.9800 0.008 0.992
#> GSM549283 2 0.0000 0.9839 0.000 1.000
#> GSM549298 2 0.0000 0.9839 0.000 1.000
#> GSM750741 1 0.0376 0.9774 0.996 0.004
#> GSM549286 2 0.0000 0.9839 0.000 1.000
#> GSM549241 1 0.0376 0.9774 0.996 0.004
#> GSM549247 1 0.7139 0.7635 0.804 0.196
#> GSM549261 1 0.0376 0.9774 0.996 0.004
#> GSM549270 2 0.0000 0.9839 0.000 1.000
#> GSM549277 2 0.0000 0.9839 0.000 1.000
#> GSM549280 2 0.0000 0.9839 0.000 1.000
#> GSM549281 2 0.0000 0.9839 0.000 1.000
#> GSM549285 2 0.0000 0.9839 0.000 1.000
#> GSM549288 2 0.0000 0.9839 0.000 1.000
#> GSM549292 2 0.0000 0.9839 0.000 1.000
#> GSM549295 2 0.0000 0.9839 0.000 1.000
#> GSM549297 2 0.0000 0.9839 0.000 1.000
#> GSM750743 1 0.0376 0.9774 0.996 0.004
#> GSM549268 2 0.0000 0.9839 0.000 1.000
#> GSM549290 2 0.0376 0.9821 0.004 0.996
#> GSM549272 2 0.0000 0.9839 0.000 1.000
#> GSM549276 2 0.0000 0.9839 0.000 1.000
#> GSM549275 1 0.7745 0.7163 0.772 0.228
#> GSM549284 2 0.0000 0.9839 0.000 1.000
#> GSM750737 1 0.0000 0.9770 1.000 0.000
#> GSM750740 1 0.0376 0.9774 0.996 0.004
#> GSM750747 1 0.0376 0.9774 0.996 0.004
#> GSM750751 2 0.0000 0.9839 0.000 1.000
#> GSM750754 2 0.0376 0.9821 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549291 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549274 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750738 1 0.2096 0.927 0.944 0.004 0.052
#> GSM750748 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549240 1 0.0424 0.975 0.992 0.008 0.000
#> GSM549279 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549300 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549303 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549309 3 0.0000 0.890 0.000 0.000 1.000
#> GSM750753 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750752 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549304 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549307 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549306 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549308 2 0.0424 0.979 0.000 0.992 0.008
#> GSM549233 1 0.0892 0.963 0.980 0.000 0.020
#> GSM549234 3 0.5016 0.798 0.240 0.000 0.760
#> GSM549250 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549287 3 0.0000 0.890 0.000 0.000 1.000
#> GSM750735 1 0.0237 0.979 0.996 0.004 0.000
#> GSM750736 1 0.0237 0.979 0.996 0.004 0.000
#> GSM750749 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549230 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549237 1 0.0237 0.979 0.996 0.004 0.000
#> GSM549254 3 0.4750 0.819 0.216 0.000 0.784
#> GSM750734 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549271 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549232 3 0.4842 0.814 0.224 0.000 0.776
#> GSM549246 3 0.5216 0.773 0.260 0.000 0.740
#> GSM549248 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549255 3 0.4887 0.811 0.228 0.000 0.772
#> GSM750746 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549273 2 0.6299 0.163 0.000 0.524 0.476
#> GSM549299 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549301 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549310 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549311 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549302 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549245 3 0.4887 0.811 0.228 0.000 0.772
#> GSM549265 3 0.4062 0.843 0.164 0.000 0.836
#> GSM549282 3 0.2448 0.837 0.000 0.076 0.924
#> GSM549296 3 0.0000 0.890 0.000 0.000 1.000
#> GSM750739 1 0.0000 0.981 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.981 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.981 1.000 0.000 0.000
#> GSM750750 2 0.0747 0.972 0.000 0.984 0.016
#> GSM549242 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549252 3 0.4931 0.807 0.232 0.000 0.768
#> GSM549253 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549257 3 0.4887 0.811 0.228 0.000 0.772
#> GSM549263 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549267 3 0.0000 0.890 0.000 0.000 1.000
#> GSM750745 1 0.0237 0.979 0.996 0.004 0.000
#> GSM549239 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549244 3 0.4654 0.824 0.208 0.000 0.792
#> GSM549249 3 0.5178 0.778 0.256 0.000 0.744
#> GSM549260 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549238 1 0.5497 0.499 0.708 0.000 0.292
#> GSM549251 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549258 1 0.0237 0.979 0.996 0.004 0.000
#> GSM549264 1 0.0237 0.979 0.996 0.004 0.000
#> GSM549243 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549278 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549283 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549298 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750741 1 0.0237 0.979 0.996 0.004 0.000
#> GSM549286 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549247 1 0.0747 0.967 0.984 0.016 0.000
#> GSM549261 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549281 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549285 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549288 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549292 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549295 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.981 1.000 0.000 0.000
#> GSM549268 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549290 3 0.0000 0.890 0.000 0.000 1.000
#> GSM549272 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.986 0.000 1.000 0.000
#> GSM549275 1 0.4702 0.703 0.788 0.212 0.000
#> GSM549284 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750737 1 0.0237 0.979 0.996 0.000 0.004
#> GSM750740 1 0.0000 0.981 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.981 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.986 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.890 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.0592 0.946 0.000 0.000 0.016 0.984
#> GSM549291 4 0.1118 0.940 0.000 0.000 0.036 0.964
#> GSM549274 2 0.0188 0.987 0.000 0.996 0.004 0.000
#> GSM750738 1 0.5763 0.657 0.708 0.084 0.004 0.204
#> GSM750748 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549240 1 0.1305 0.937 0.960 0.036 0.000 0.004
#> GSM549279 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549294 2 0.0188 0.987 0.000 0.996 0.004 0.000
#> GSM549300 3 0.3907 0.731 0.000 0.232 0.768 0.000
#> GSM549303 3 0.2589 0.816 0.000 0.000 0.884 0.116
#> GSM549309 3 0.2760 0.806 0.000 0.000 0.872 0.128
#> GSM750753 2 0.0188 0.987 0.000 0.996 0.004 0.000
#> GSM750752 4 0.0707 0.945 0.000 0.000 0.020 0.980
#> GSM549304 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549305 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549307 2 0.2973 0.839 0.000 0.856 0.144 0.000
#> GSM549306 3 0.3172 0.824 0.000 0.160 0.840 0.000
#> GSM549308 3 0.1716 0.867 0.000 0.064 0.936 0.000
#> GSM549233 1 0.1637 0.928 0.940 0.000 0.000 0.060
#> GSM549234 4 0.0188 0.945 0.004 0.000 0.000 0.996
#> GSM549250 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549287 4 0.3266 0.847 0.000 0.000 0.168 0.832
#> GSM750735 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750736 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM750749 2 0.0657 0.981 0.004 0.984 0.012 0.000
#> GSM549230 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549231 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549254 4 0.0188 0.945 0.004 0.000 0.000 0.996
#> GSM750734 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549271 4 0.3074 0.863 0.000 0.000 0.152 0.848
#> GSM549232 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM549246 4 0.3208 0.751 0.148 0.000 0.004 0.848
#> GSM549248 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549255 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM750746 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0188 0.987 0.000 0.996 0.004 0.000
#> GSM549273 3 0.0188 0.857 0.000 0.000 0.996 0.004
#> GSM549299 2 0.0188 0.987 0.000 0.996 0.004 0.000
#> GSM549301 3 0.3172 0.824 0.000 0.160 0.840 0.000
#> GSM549310 4 0.1792 0.924 0.000 0.000 0.068 0.932
#> GSM549311 3 0.2704 0.810 0.000 0.000 0.876 0.124
#> GSM549302 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549245 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM549265 4 0.0524 0.945 0.008 0.000 0.004 0.988
#> GSM549282 3 0.1792 0.843 0.000 0.000 0.932 0.068
#> GSM549296 4 0.0592 0.946 0.000 0.000 0.016 0.984
#> GSM750739 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750750 3 0.0817 0.866 0.000 0.024 0.976 0.000
#> GSM549242 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> GSM549252 4 0.0188 0.945 0.004 0.000 0.000 0.996
#> GSM549253 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549256 1 0.1474 0.934 0.948 0.000 0.000 0.052
#> GSM549257 4 0.0188 0.945 0.004 0.000 0.000 0.996
#> GSM549263 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549267 4 0.2704 0.887 0.000 0.000 0.124 0.876
#> GSM750745 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549239 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.946 0.000 0.000 0.000 1.000
#> GSM549249 4 0.0336 0.944 0.008 0.000 0.000 0.992
#> GSM549260 1 0.0336 0.967 0.992 0.000 0.000 0.008
#> GSM549266 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549293 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549236 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549238 1 0.2589 0.871 0.884 0.000 0.000 0.116
#> GSM549251 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549258 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549264 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549278 4 0.0707 0.945 0.000 0.000 0.020 0.980
#> GSM549283 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549298 3 0.2216 0.861 0.000 0.092 0.908 0.000
#> GSM750741 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549286 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0188 0.968 0.996 0.000 0.000 0.004
#> GSM549247 1 0.3933 0.728 0.792 0.200 0.000 0.008
#> GSM549261 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549270 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549277 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549280 2 0.0592 0.981 0.000 0.984 0.016 0.000
#> GSM549281 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549285 2 0.0592 0.981 0.000 0.984 0.016 0.000
#> GSM549288 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549292 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549295 2 0.2408 0.887 0.000 0.896 0.104 0.000
#> GSM549297 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM750743 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM549268 2 0.0336 0.986 0.000 0.992 0.008 0.000
#> GSM549290 4 0.2408 0.902 0.000 0.000 0.104 0.896
#> GSM549272 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM549275 1 0.3975 0.671 0.760 0.240 0.000 0.000
#> GSM549284 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM750737 1 0.2011 0.910 0.920 0.000 0.000 0.080
#> GSM750740 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM750751 2 0.0000 0.987 0.000 1.000 0.000 0.000
#> GSM750754 4 0.3024 0.867 0.000 0.000 0.148 0.852
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.2723 0.74636 0.000 0.000 0.124 0.864 0.012
#> GSM549291 4 0.3074 0.71271 0.000 0.000 0.196 0.804 0.000
#> GSM549274 2 0.3297 0.80551 0.000 0.848 0.068 0.000 0.084
#> GSM750738 5 0.7159 0.21554 0.044 0.312 0.036 0.076 0.532
#> GSM750748 1 0.0290 0.71333 0.992 0.000 0.000 0.000 0.008
#> GSM549240 1 0.3689 0.55105 0.740 0.004 0.000 0.000 0.256
#> GSM549279 2 0.5456 0.72914 0.004 0.608 0.072 0.000 0.316
#> GSM549294 2 0.2409 0.80521 0.000 0.900 0.032 0.000 0.068
#> GSM549300 3 0.6507 0.06807 0.000 0.316 0.472 0.000 0.212
#> GSM549303 3 0.1671 0.73228 0.000 0.000 0.924 0.076 0.000
#> GSM549309 3 0.1671 0.73228 0.000 0.000 0.924 0.076 0.000
#> GSM750753 2 0.2777 0.80899 0.000 0.864 0.016 0.000 0.120
#> GSM750752 4 0.1251 0.75493 0.000 0.000 0.036 0.956 0.008
#> GSM549304 2 0.1270 0.76877 0.000 0.948 0.000 0.000 0.052
#> GSM549305 2 0.0404 0.78453 0.000 0.988 0.000 0.000 0.012
#> GSM549307 2 0.5673 0.70826 0.000 0.628 0.156 0.000 0.216
#> GSM549306 3 0.5163 0.62396 0.000 0.152 0.692 0.000 0.156
#> GSM549308 3 0.2046 0.79974 0.000 0.068 0.916 0.000 0.016
#> GSM549233 5 0.6708 0.68588 0.244 0.000 0.000 0.376 0.380
#> GSM549234 4 0.3305 0.57999 0.000 0.000 0.000 0.776 0.224
#> GSM549250 1 0.5314 0.00525 0.528 0.000 0.000 0.052 0.420
#> GSM549287 4 0.4161 0.48958 0.000 0.000 0.392 0.608 0.000
#> GSM750735 1 0.2020 0.67093 0.900 0.000 0.000 0.000 0.100
#> GSM750736 1 0.3395 0.55965 0.764 0.000 0.000 0.000 0.236
#> GSM750749 2 0.7007 0.57703 0.092 0.484 0.072 0.000 0.352
#> GSM549230 1 0.4354 0.35278 0.624 0.000 0.000 0.008 0.368
#> GSM549231 1 0.4166 0.39555 0.648 0.000 0.000 0.004 0.348
#> GSM549237 1 0.3048 0.64798 0.820 0.000 0.000 0.004 0.176
#> GSM549254 4 0.1732 0.74371 0.000 0.000 0.000 0.920 0.080
#> GSM750734 1 0.0510 0.71215 0.984 0.000 0.000 0.000 0.016
#> GSM549271 4 0.3661 0.64525 0.000 0.000 0.276 0.724 0.000
#> GSM549232 4 0.1965 0.73422 0.000 0.000 0.000 0.904 0.096
#> GSM549246 4 0.3141 0.72389 0.000 0.000 0.016 0.832 0.152
#> GSM549248 1 0.3837 0.47151 0.692 0.000 0.000 0.000 0.308
#> GSM549255 4 0.2179 0.72649 0.000 0.000 0.000 0.888 0.112
#> GSM750746 1 0.0162 0.71362 0.996 0.000 0.000 0.000 0.004
#> GSM549259 1 0.0162 0.71362 0.996 0.000 0.000 0.000 0.004
#> GSM549269 2 0.3991 0.78082 0.000 0.780 0.048 0.000 0.172
#> GSM549273 3 0.1560 0.79023 0.000 0.020 0.948 0.004 0.028
#> GSM549299 2 0.4237 0.78843 0.000 0.752 0.048 0.000 0.200
#> GSM549301 3 0.2824 0.77758 0.000 0.116 0.864 0.000 0.020
#> GSM549310 4 0.1830 0.75303 0.000 0.000 0.068 0.924 0.008
#> GSM549311 3 0.1732 0.72817 0.000 0.000 0.920 0.080 0.000
#> GSM549302 2 0.0963 0.77610 0.000 0.964 0.000 0.000 0.036
#> GSM549235 1 0.0510 0.71181 0.984 0.000 0.000 0.000 0.016
#> GSM549245 4 0.2516 0.70552 0.000 0.000 0.000 0.860 0.140
#> GSM549265 4 0.2625 0.74298 0.000 0.000 0.016 0.876 0.108
#> GSM549282 3 0.2026 0.75528 0.000 0.012 0.924 0.056 0.008
#> GSM549296 4 0.1251 0.75493 0.000 0.000 0.036 0.956 0.008
#> GSM750739 1 0.0404 0.71149 0.988 0.000 0.000 0.000 0.012
#> GSM750742 1 0.3707 0.50230 0.716 0.000 0.000 0.000 0.284
#> GSM750744 1 0.1410 0.69666 0.940 0.000 0.000 0.000 0.060
#> GSM750750 3 0.1914 0.80000 0.000 0.060 0.924 0.000 0.016
#> GSM549242 1 0.5529 -0.05818 0.512 0.000 0.000 0.068 0.420
#> GSM549252 4 0.3452 0.53722 0.000 0.000 0.000 0.756 0.244
#> GSM549253 1 0.4658 0.20993 0.576 0.000 0.000 0.016 0.408
#> GSM549256 5 0.6769 0.67007 0.288 0.000 0.000 0.316 0.396
#> GSM549257 4 0.2891 0.66563 0.000 0.000 0.000 0.824 0.176
#> GSM549263 1 0.4367 0.34292 0.620 0.000 0.000 0.008 0.372
#> GSM549267 4 0.4060 0.53897 0.000 0.000 0.360 0.640 0.000
#> GSM750745 1 0.2329 0.65364 0.876 0.000 0.000 0.000 0.124
#> GSM549239 1 0.0000 0.71377 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.2280 0.72152 0.000 0.000 0.000 0.880 0.120
#> GSM549249 4 0.2930 0.67420 0.004 0.000 0.000 0.832 0.164
#> GSM549260 1 0.4329 0.44630 0.672 0.000 0.000 0.016 0.312
#> GSM549266 2 0.5387 0.73988 0.004 0.624 0.072 0.000 0.300
#> GSM549293 2 0.2179 0.72649 0.000 0.888 0.000 0.000 0.112
#> GSM549236 1 0.4682 0.16592 0.564 0.000 0.000 0.016 0.420
#> GSM549238 5 0.6769 0.68640 0.272 0.000 0.000 0.352 0.376
#> GSM549251 1 0.4482 0.32157 0.612 0.000 0.000 0.012 0.376
#> GSM549258 1 0.3074 0.59790 0.804 0.000 0.000 0.000 0.196
#> GSM549264 1 0.2891 0.66217 0.824 0.000 0.000 0.000 0.176
#> GSM549243 1 0.0000 0.71377 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.3684 0.50855 0.720 0.000 0.000 0.000 0.280
#> GSM549278 4 0.2516 0.73757 0.000 0.000 0.140 0.860 0.000
#> GSM549283 2 0.4850 0.77507 0.000 0.696 0.072 0.000 0.232
#> GSM549298 3 0.4916 0.66212 0.000 0.124 0.716 0.000 0.160
#> GSM750741 1 0.3662 0.53735 0.744 0.004 0.000 0.000 0.252
#> GSM549286 2 0.0609 0.78121 0.000 0.980 0.000 0.000 0.020
#> GSM549241 1 0.1732 0.68248 0.920 0.000 0.000 0.000 0.080
#> GSM549247 1 0.4400 0.48536 0.672 0.020 0.000 0.000 0.308
#> GSM549261 1 0.0162 0.71369 0.996 0.000 0.000 0.000 0.004
#> GSM549270 2 0.0579 0.79070 0.000 0.984 0.008 0.000 0.008
#> GSM549277 2 0.4732 0.77588 0.000 0.716 0.076 0.000 0.208
#> GSM549280 2 0.4707 0.77594 0.000 0.716 0.072 0.000 0.212
#> GSM549281 2 0.5387 0.73982 0.004 0.624 0.072 0.000 0.300
#> GSM549285 2 0.5391 0.73135 0.000 0.616 0.084 0.000 0.300
#> GSM549288 2 0.5195 0.75324 0.000 0.676 0.108 0.000 0.216
#> GSM549292 2 0.1608 0.75658 0.000 0.928 0.000 0.000 0.072
#> GSM549295 2 0.5638 0.71326 0.000 0.632 0.152 0.000 0.216
#> GSM549297 2 0.3321 0.80668 0.000 0.832 0.032 0.000 0.136
#> GSM750743 1 0.0162 0.71402 0.996 0.000 0.000 0.000 0.004
#> GSM549268 2 0.5387 0.73982 0.004 0.624 0.072 0.000 0.300
#> GSM549290 4 0.4060 0.54169 0.000 0.000 0.360 0.640 0.000
#> GSM549272 2 0.1121 0.77129 0.000 0.956 0.000 0.000 0.044
#> GSM549276 2 0.0510 0.78298 0.000 0.984 0.000 0.000 0.016
#> GSM549275 1 0.5001 0.45627 0.700 0.080 0.004 0.000 0.216
#> GSM549284 2 0.3152 0.74516 0.000 0.840 0.024 0.000 0.136
#> GSM750737 5 0.6600 0.44321 0.380 0.000 0.000 0.212 0.408
#> GSM750740 1 0.0290 0.71289 0.992 0.000 0.000 0.000 0.008
#> GSM750747 1 0.0000 0.71377 1.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.0609 0.78263 0.000 0.980 0.000 0.000 0.020
#> GSM750754 4 0.4161 0.48958 0.000 0.000 0.392 0.608 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.3606 0.3962 0.000 0.000 0.004 0.724 0.008 0.264
#> GSM549291 6 0.3997 0.1732 0.000 0.000 0.004 0.488 0.000 0.508
#> GSM549274 2 0.1655 0.7686 0.008 0.932 0.052 0.000 0.000 0.008
#> GSM750738 4 0.8156 0.1642 0.040 0.284 0.212 0.348 0.108 0.008
#> GSM750748 1 0.2300 0.8676 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM549240 1 0.0405 0.8481 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM549279 2 0.4013 0.6869 0.040 0.728 0.228 0.000 0.000 0.004
#> GSM549294 2 0.0632 0.7714 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM549300 3 0.4343 0.5813 0.000 0.120 0.724 0.000 0.000 0.156
#> GSM549303 6 0.2260 0.2944 0.000 0.000 0.140 0.000 0.000 0.860
#> GSM549309 6 0.1267 0.3646 0.000 0.000 0.060 0.000 0.000 0.940
#> GSM750753 2 0.2491 0.7425 0.000 0.836 0.164 0.000 0.000 0.000
#> GSM750752 4 0.2597 0.5211 0.000 0.000 0.000 0.824 0.000 0.176
#> GSM549304 2 0.2743 0.6882 0.000 0.828 0.164 0.000 0.008 0.000
#> GSM549305 2 0.1141 0.7645 0.000 0.948 0.052 0.000 0.000 0.000
#> GSM549307 3 0.3323 0.5433 0.000 0.240 0.752 0.000 0.000 0.008
#> GSM549306 3 0.4682 0.4356 0.000 0.048 0.556 0.000 0.000 0.396
#> GSM549308 6 0.3998 -0.3560 0.000 0.004 0.492 0.000 0.000 0.504
#> GSM549233 5 0.3819 0.3699 0.008 0.000 0.000 0.340 0.652 0.000
#> GSM549234 4 0.1910 0.7290 0.000 0.000 0.000 0.892 0.108 0.000
#> GSM549250 5 0.0405 0.7641 0.008 0.000 0.000 0.004 0.988 0.000
#> GSM549287 6 0.3782 0.3176 0.000 0.000 0.000 0.412 0.000 0.588
#> GSM750735 1 0.0508 0.8468 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM750736 1 0.0291 0.8461 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM750749 1 0.6094 -0.2364 0.396 0.376 0.224 0.000 0.000 0.004
#> GSM549230 5 0.1075 0.7879 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM549231 5 0.1141 0.7876 0.052 0.000 0.000 0.000 0.948 0.000
#> GSM549237 5 0.4015 0.5488 0.372 0.000 0.012 0.000 0.616 0.000
#> GSM549254 4 0.1471 0.7189 0.000 0.000 0.004 0.932 0.064 0.000
#> GSM750734 1 0.2340 0.8645 0.852 0.000 0.000 0.000 0.148 0.000
#> GSM549271 6 0.3843 0.2587 0.000 0.000 0.000 0.452 0.000 0.548
#> GSM549232 4 0.1471 0.7189 0.000 0.000 0.004 0.932 0.064 0.000
#> GSM549246 4 0.3955 0.5464 0.000 0.000 0.004 0.668 0.316 0.012
#> GSM549248 5 0.2562 0.7244 0.172 0.000 0.000 0.000 0.828 0.000
#> GSM549255 4 0.1858 0.7301 0.000 0.000 0.004 0.904 0.092 0.000
#> GSM750746 1 0.2219 0.8722 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM549259 1 0.1814 0.8777 0.900 0.000 0.000 0.000 0.100 0.000
#> GSM549269 2 0.3545 0.6467 0.008 0.748 0.236 0.000 0.008 0.000
#> GSM549273 6 0.3563 -0.0484 0.000 0.000 0.336 0.000 0.000 0.664
#> GSM549299 2 0.3076 0.7038 0.000 0.760 0.240 0.000 0.000 0.000
#> GSM549301 3 0.4333 0.2884 0.000 0.020 0.512 0.000 0.000 0.468
#> GSM549310 4 0.3804 -0.0392 0.000 0.000 0.000 0.576 0.000 0.424
#> GSM549311 6 0.1267 0.3646 0.000 0.000 0.060 0.000 0.000 0.940
#> GSM549302 2 0.1957 0.7268 0.000 0.888 0.112 0.000 0.000 0.000
#> GSM549235 1 0.2219 0.8721 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM549245 4 0.1958 0.7304 0.000 0.000 0.004 0.896 0.100 0.000
#> GSM549265 4 0.2361 0.7082 0.000 0.000 0.004 0.880 0.104 0.012
#> GSM549282 6 0.3076 0.1485 0.000 0.000 0.240 0.000 0.000 0.760
#> GSM549296 4 0.2562 0.5263 0.000 0.000 0.000 0.828 0.000 0.172
#> GSM750739 1 0.1714 0.8769 0.908 0.000 0.000 0.000 0.092 0.000
#> GSM750742 5 0.3717 0.3991 0.384 0.000 0.000 0.000 0.616 0.000
#> GSM750744 5 0.3866 0.0540 0.484 0.000 0.000 0.000 0.516 0.000
#> GSM750750 6 0.3864 -0.3193 0.000 0.000 0.480 0.000 0.000 0.520
#> GSM549242 5 0.1398 0.7210 0.008 0.000 0.000 0.052 0.940 0.000
#> GSM549252 4 0.1957 0.7273 0.000 0.000 0.000 0.888 0.112 0.000
#> GSM549253 5 0.0363 0.7696 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM549256 5 0.3693 0.4737 0.008 0.000 0.004 0.280 0.708 0.000
#> GSM549257 4 0.1858 0.7301 0.000 0.000 0.004 0.904 0.092 0.000
#> GSM549263 5 0.1075 0.7879 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM549267 6 0.3828 0.2815 0.000 0.000 0.000 0.440 0.000 0.560
#> GSM750745 1 0.1556 0.8744 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM549239 1 0.2300 0.8676 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM549244 4 0.2006 0.7298 0.000 0.000 0.004 0.892 0.104 0.000
#> GSM549249 4 0.2196 0.7285 0.000 0.000 0.004 0.884 0.108 0.004
#> GSM549260 5 0.2146 0.7669 0.116 0.000 0.000 0.004 0.880 0.000
#> GSM549266 2 0.3930 0.6875 0.032 0.728 0.236 0.000 0.000 0.004
#> GSM549293 2 0.3103 0.6501 0.000 0.784 0.208 0.000 0.008 0.000
#> GSM549236 5 0.0972 0.7574 0.008 0.000 0.000 0.028 0.964 0.000
#> GSM549238 4 0.4144 0.2403 0.008 0.000 0.004 0.580 0.408 0.000
#> GSM549251 5 0.1075 0.7879 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM549258 1 0.0858 0.8581 0.968 0.000 0.004 0.000 0.028 0.000
#> GSM549264 1 0.3575 0.4095 0.708 0.000 0.008 0.000 0.284 0.000
#> GSM549243 1 0.2300 0.8676 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM549262 5 0.3659 0.4443 0.364 0.000 0.000 0.000 0.636 0.000
#> GSM549278 4 0.3636 0.2631 0.000 0.000 0.004 0.676 0.000 0.320
#> GSM549283 2 0.3624 0.7024 0.016 0.756 0.220 0.000 0.000 0.008
#> GSM549298 3 0.4649 0.4543 0.000 0.048 0.572 0.000 0.000 0.380
#> GSM750741 1 0.0547 0.8426 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM549286 2 0.0146 0.7692 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM549241 1 0.2191 0.8766 0.876 0.000 0.004 0.000 0.120 0.000
#> GSM549247 1 0.0291 0.8461 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM549261 1 0.2219 0.8720 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM549270 2 0.1327 0.7630 0.000 0.936 0.064 0.000 0.000 0.000
#> GSM549277 2 0.3820 0.6591 0.008 0.700 0.284 0.000 0.000 0.008
#> GSM549280 2 0.3725 0.6236 0.000 0.676 0.316 0.000 0.000 0.008
#> GSM549281 2 0.3858 0.6904 0.028 0.732 0.236 0.000 0.000 0.004
#> GSM549285 3 0.4916 -0.1332 0.032 0.444 0.508 0.000 0.000 0.016
#> GSM549288 2 0.4080 0.2799 0.000 0.536 0.456 0.000 0.000 0.008
#> GSM549292 2 0.2730 0.6715 0.000 0.808 0.192 0.000 0.000 0.000
#> GSM549295 3 0.3653 0.4775 0.000 0.300 0.692 0.000 0.000 0.008
#> GSM549297 2 0.2730 0.7301 0.000 0.808 0.192 0.000 0.000 0.000
#> GSM750743 1 0.1863 0.8780 0.896 0.000 0.000 0.000 0.104 0.000
#> GSM549268 2 0.3941 0.6900 0.028 0.732 0.232 0.000 0.000 0.008
#> GSM549290 6 0.3833 0.2748 0.000 0.000 0.000 0.444 0.000 0.556
#> GSM549272 2 0.1910 0.7291 0.000 0.892 0.108 0.000 0.000 0.000
#> GSM549276 2 0.0363 0.7701 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM549275 1 0.0837 0.8433 0.972 0.004 0.020 0.000 0.004 0.000
#> GSM549284 2 0.3936 0.6511 0.008 0.716 0.260 0.000 0.008 0.008
#> GSM750737 4 0.4705 0.0691 0.044 0.000 0.000 0.484 0.472 0.000
#> GSM750740 1 0.1957 0.8781 0.888 0.000 0.000 0.000 0.112 0.000
#> GSM750747 1 0.2260 0.8701 0.860 0.000 0.000 0.000 0.140 0.000
#> GSM750751 2 0.0146 0.7692 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM750754 6 0.3782 0.3176 0.000 0.000 0.000 0.412 0.000 0.588
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:mclust 102 0.0155 1.21e-04 0.179607 0.00194 2
#> CV:mclust 101 0.3276 4.23e-04 0.000456 0.01108 3
#> CV:mclust 103 0.3056 6.58e-06 0.000455 0.00687 4
#> CV:mclust 86 0.7504 1.77e-04 0.001453 0.06903 5
#> CV:mclust 71 0.2112 2.25e-04 0.034026 0.00777 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.967 0.987 0.5043 0.496 0.496
#> 3 3 0.824 0.849 0.921 0.2843 0.791 0.604
#> 4 4 0.766 0.817 0.907 0.1579 0.863 0.629
#> 5 5 0.738 0.713 0.844 0.0591 0.902 0.642
#> 6 6 0.722 0.619 0.801 0.0421 0.931 0.690
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
#> GSM549289 1 0.000 0.9832 1.000 0.000
#> GSM549291 2 0.697 0.7707 0.188 0.812
#> GSM549274 2 0.000 0.9896 0.000 1.000
#> GSM750738 2 0.118 0.9757 0.016 0.984
#> GSM750748 1 0.000 0.9832 1.000 0.000
#> GSM549240 1 0.000 0.9832 1.000 0.000
#> GSM549279 2 0.141 0.9721 0.020 0.980
#> GSM549294 2 0.000 0.9896 0.000 1.000
#> GSM549300 2 0.000 0.9896 0.000 1.000
#> GSM549303 2 0.000 0.9896 0.000 1.000
#> GSM549309 2 0.000 0.9896 0.000 1.000
#> GSM750753 2 0.000 0.9896 0.000 1.000
#> GSM750752 2 0.000 0.9896 0.000 1.000
#> GSM549304 2 0.000 0.9896 0.000 1.000
#> GSM549305 2 0.000 0.9896 0.000 1.000
#> GSM549307 2 0.000 0.9896 0.000 1.000
#> GSM549306 2 0.000 0.9896 0.000 1.000
#> GSM549308 2 0.000 0.9896 0.000 1.000
#> GSM549233 1 0.000 0.9832 1.000 0.000
#> GSM549234 1 0.000 0.9832 1.000 0.000
#> GSM549250 1 0.000 0.9832 1.000 0.000
#> GSM549287 2 0.000 0.9896 0.000 1.000
#> GSM750735 1 0.000 0.9832 1.000 0.000
#> GSM750736 1 0.000 0.9832 1.000 0.000
#> GSM750749 1 0.402 0.9033 0.920 0.080
#> GSM549230 1 0.000 0.9832 1.000 0.000
#> GSM549231 1 0.000 0.9832 1.000 0.000
#> GSM549237 1 0.000 0.9832 1.000 0.000
#> GSM549254 1 0.000 0.9832 1.000 0.000
#> GSM750734 1 0.000 0.9832 1.000 0.000
#> GSM549271 2 0.000 0.9896 0.000 1.000
#> GSM549232 1 0.000 0.9832 1.000 0.000
#> GSM549246 1 0.000 0.9832 1.000 0.000
#> GSM549248 1 0.000 0.9832 1.000 0.000
#> GSM549255 1 0.000 0.9832 1.000 0.000
#> GSM750746 1 0.000 0.9832 1.000 0.000
#> GSM549259 1 0.000 0.9832 1.000 0.000
#> GSM549269 2 0.000 0.9896 0.000 1.000
#> GSM549273 2 0.000 0.9896 0.000 1.000
#> GSM549299 2 0.000 0.9896 0.000 1.000
#> GSM549301 2 0.000 0.9896 0.000 1.000
#> GSM549310 2 0.000 0.9896 0.000 1.000
#> GSM549311 2 0.000 0.9896 0.000 1.000
#> GSM549302 2 0.000 0.9896 0.000 1.000
#> GSM549235 1 0.000 0.9832 1.000 0.000
#> GSM549245 1 0.000 0.9832 1.000 0.000
#> GSM549265 1 0.000 0.9832 1.000 0.000
#> GSM549282 2 0.000 0.9896 0.000 1.000
#> GSM549296 2 0.000 0.9896 0.000 1.000
#> GSM750739 1 0.000 0.9832 1.000 0.000
#> GSM750742 1 0.000 0.9832 1.000 0.000
#> GSM750744 1 0.000 0.9832 1.000 0.000
#> GSM750750 2 0.000 0.9896 0.000 1.000
#> GSM549242 1 0.000 0.9832 1.000 0.000
#> GSM549252 1 0.000 0.9832 1.000 0.000
#> GSM549253 1 0.000 0.9832 1.000 0.000
#> GSM549256 1 0.000 0.9832 1.000 0.000
#> GSM549257 1 0.000 0.9832 1.000 0.000
#> GSM549263 1 0.000 0.9832 1.000 0.000
#> GSM549267 2 0.000 0.9896 0.000 1.000
#> GSM750745 1 0.000 0.9832 1.000 0.000
#> GSM549239 1 0.000 0.9832 1.000 0.000
#> GSM549244 1 0.000 0.9832 1.000 0.000
#> GSM549249 1 0.000 0.9832 1.000 0.000
#> GSM549260 1 0.000 0.9832 1.000 0.000
#> GSM549266 2 0.000 0.9896 0.000 1.000
#> GSM549293 2 0.000 0.9896 0.000 1.000
#> GSM549236 1 0.000 0.9832 1.000 0.000
#> GSM549238 1 0.000 0.9832 1.000 0.000
#> GSM549251 1 0.000 0.9832 1.000 0.000
#> GSM549258 1 0.000 0.9832 1.000 0.000
#> GSM549264 1 0.000 0.9832 1.000 0.000
#> GSM549243 1 0.000 0.9832 1.000 0.000
#> GSM549262 1 0.000 0.9832 1.000 0.000
#> GSM549278 1 0.999 0.0453 0.516 0.484
#> GSM549283 2 0.000 0.9896 0.000 1.000
#> GSM549298 2 0.000 0.9896 0.000 1.000
#> GSM750741 1 0.000 0.9832 1.000 0.000
#> GSM549286 2 0.000 0.9896 0.000 1.000
#> GSM549241 1 0.000 0.9832 1.000 0.000
#> GSM549247 1 0.482 0.8778 0.896 0.104
#> GSM549261 1 0.000 0.9832 1.000 0.000
#> GSM549270 2 0.000 0.9896 0.000 1.000
#> GSM549277 2 0.000 0.9896 0.000 1.000
#> GSM549280 2 0.000 0.9896 0.000 1.000
#> GSM549281 2 0.000 0.9896 0.000 1.000
#> GSM549285 2 0.000 0.9896 0.000 1.000
#> GSM549288 2 0.000 0.9896 0.000 1.000
#> GSM549292 2 0.000 0.9896 0.000 1.000
#> GSM549295 2 0.000 0.9896 0.000 1.000
#> GSM549297 2 0.000 0.9896 0.000 1.000
#> GSM750743 1 0.000 0.9832 1.000 0.000
#> GSM549268 2 0.000 0.9896 0.000 1.000
#> GSM549290 2 0.775 0.7082 0.228 0.772
#> GSM549272 2 0.000 0.9896 0.000 1.000
#> GSM549276 2 0.000 0.9896 0.000 1.000
#> GSM549275 1 0.738 0.7386 0.792 0.208
#> GSM549284 2 0.000 0.9896 0.000 1.000
#> GSM750737 1 0.000 0.9832 1.000 0.000
#> GSM750740 1 0.000 0.9832 1.000 0.000
#> GSM750747 1 0.000 0.9832 1.000 0.000
#> GSM750751 2 0.000 0.9896 0.000 1.000
#> GSM750754 2 0.242 0.9523 0.040 0.960
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.5988 0.489 0.632 0.000 0.368
#> GSM549291 3 0.1860 0.851 0.052 0.000 0.948
#> GSM549274 2 0.0237 0.848 0.000 0.996 0.004
#> GSM750738 2 0.0829 0.845 0.012 0.984 0.004
#> GSM750748 1 0.0237 0.953 0.996 0.004 0.000
#> GSM549240 2 0.5363 0.601 0.276 0.724 0.000
#> GSM549279 2 0.0829 0.845 0.012 0.984 0.004
#> GSM549294 2 0.1163 0.851 0.000 0.972 0.028
#> GSM549300 3 0.3816 0.829 0.000 0.148 0.852
#> GSM549303 3 0.1163 0.891 0.000 0.028 0.972
#> GSM549309 3 0.0237 0.887 0.004 0.000 0.996
#> GSM750753 2 0.4555 0.712 0.000 0.800 0.200
#> GSM750752 3 0.0237 0.890 0.000 0.004 0.996
#> GSM549304 2 0.1031 0.851 0.000 0.976 0.024
#> GSM549305 2 0.3116 0.811 0.000 0.892 0.108
#> GSM549307 3 0.5327 0.692 0.000 0.272 0.728
#> GSM549306 3 0.3116 0.860 0.000 0.108 0.892
#> GSM549308 3 0.1529 0.889 0.000 0.040 0.960
#> GSM549233 1 0.1031 0.955 0.976 0.000 0.024
#> GSM549234 1 0.1753 0.948 0.952 0.000 0.048
#> GSM549250 1 0.1289 0.954 0.968 0.000 0.032
#> GSM549287 3 0.0237 0.887 0.004 0.000 0.996
#> GSM750735 1 0.1753 0.928 0.952 0.048 0.000
#> GSM750736 2 0.6126 0.347 0.400 0.600 0.000
#> GSM750749 1 0.1031 0.945 0.976 0.024 0.000
#> GSM549230 1 0.1163 0.954 0.972 0.000 0.028
#> GSM549231 1 0.1289 0.954 0.968 0.000 0.032
#> GSM549237 1 0.0424 0.955 0.992 0.000 0.008
#> GSM549254 1 0.2261 0.935 0.932 0.000 0.068
#> GSM750734 1 0.0424 0.952 0.992 0.008 0.000
#> GSM549271 3 0.0747 0.891 0.000 0.016 0.984
#> GSM549232 1 0.2625 0.925 0.916 0.000 0.084
#> GSM549246 1 0.1964 0.944 0.944 0.000 0.056
#> GSM549248 1 0.0424 0.955 0.992 0.000 0.008
#> GSM549255 1 0.1964 0.944 0.944 0.000 0.056
#> GSM750746 1 0.0592 0.950 0.988 0.012 0.000
#> GSM549259 1 0.2625 0.896 0.916 0.084 0.000
#> GSM549269 2 0.0424 0.849 0.000 0.992 0.008
#> GSM549273 3 0.1860 0.885 0.000 0.052 0.948
#> GSM549299 2 0.3686 0.781 0.000 0.860 0.140
#> GSM549301 3 0.2448 0.877 0.000 0.076 0.924
#> GSM549310 3 0.0892 0.892 0.000 0.020 0.980
#> GSM549311 3 0.0424 0.891 0.000 0.008 0.992
#> GSM549302 2 0.1529 0.850 0.000 0.960 0.040
#> GSM549235 1 0.0237 0.953 0.996 0.004 0.000
#> GSM549245 1 0.1989 0.947 0.948 0.004 0.048
#> GSM549265 1 0.2448 0.931 0.924 0.000 0.076
#> GSM549282 3 0.0237 0.887 0.004 0.000 0.996
#> GSM549296 3 0.0237 0.887 0.004 0.000 0.996
#> GSM750739 1 0.0237 0.953 0.996 0.004 0.000
#> GSM750742 1 0.0592 0.955 0.988 0.000 0.012
#> GSM750744 1 0.0237 0.953 0.996 0.004 0.000
#> GSM750750 3 0.1163 0.891 0.000 0.028 0.972
#> GSM549242 1 0.0747 0.956 0.984 0.000 0.016
#> GSM549252 1 0.1860 0.947 0.948 0.000 0.052
#> GSM549253 1 0.1163 0.954 0.972 0.000 0.028
#> GSM549256 1 0.0892 0.955 0.980 0.000 0.020
#> GSM549257 1 0.1964 0.944 0.944 0.000 0.056
#> GSM549263 1 0.1163 0.954 0.972 0.000 0.028
#> GSM549267 3 0.0747 0.880 0.016 0.000 0.984
#> GSM750745 1 0.2066 0.918 0.940 0.060 0.000
#> GSM549239 1 0.1031 0.945 0.976 0.024 0.000
#> GSM549244 1 0.2165 0.940 0.936 0.000 0.064
#> GSM549249 1 0.1860 0.947 0.948 0.000 0.052
#> GSM549260 1 0.0237 0.955 0.996 0.000 0.004
#> GSM549266 2 0.0424 0.845 0.008 0.992 0.000
#> GSM549293 2 0.0592 0.850 0.000 0.988 0.012
#> GSM549236 1 0.1289 0.954 0.968 0.000 0.032
#> GSM549238 1 0.1860 0.947 0.948 0.000 0.052
#> GSM549251 1 0.1289 0.954 0.968 0.000 0.032
#> GSM549258 1 0.5859 0.459 0.656 0.344 0.000
#> GSM549264 1 0.0237 0.953 0.996 0.004 0.000
#> GSM549243 1 0.0424 0.952 0.992 0.008 0.000
#> GSM549262 1 0.0592 0.955 0.988 0.000 0.012
#> GSM549278 3 0.4002 0.724 0.160 0.000 0.840
#> GSM549283 2 0.2448 0.836 0.000 0.924 0.076
#> GSM549298 3 0.2878 0.867 0.000 0.096 0.904
#> GSM750741 2 0.6252 0.219 0.444 0.556 0.000
#> GSM549286 2 0.1753 0.848 0.000 0.952 0.048
#> GSM549241 2 0.5733 0.513 0.324 0.676 0.000
#> GSM549247 2 0.2448 0.802 0.076 0.924 0.000
#> GSM549261 1 0.3192 0.865 0.888 0.112 0.000
#> GSM549270 2 0.4887 0.669 0.000 0.772 0.228
#> GSM549277 3 0.5733 0.598 0.000 0.324 0.676
#> GSM549280 3 0.5363 0.686 0.000 0.276 0.724
#> GSM549281 2 0.1860 0.846 0.000 0.948 0.052
#> GSM549285 3 0.2796 0.870 0.000 0.092 0.908
#> GSM549288 3 0.5291 0.697 0.000 0.268 0.732
#> GSM549292 2 0.0747 0.851 0.000 0.984 0.016
#> GSM549295 3 0.5363 0.686 0.000 0.276 0.724
#> GSM549297 2 0.6235 0.130 0.000 0.564 0.436
#> GSM750743 1 0.0747 0.949 0.984 0.016 0.000
#> GSM549268 2 0.4654 0.696 0.000 0.792 0.208
#> GSM549290 3 0.2261 0.834 0.068 0.000 0.932
#> GSM549272 2 0.0892 0.851 0.000 0.980 0.020
#> GSM549276 2 0.2625 0.829 0.000 0.916 0.084
#> GSM549275 2 0.1964 0.817 0.056 0.944 0.000
#> GSM549284 2 0.1860 0.847 0.000 0.948 0.052
#> GSM750737 1 0.0848 0.955 0.984 0.008 0.008
#> GSM750740 1 0.0237 0.953 0.996 0.004 0.000
#> GSM750747 1 0.0424 0.952 0.992 0.008 0.000
#> GSM750751 2 0.1753 0.848 0.000 0.952 0.048
#> GSM750754 3 0.0892 0.877 0.020 0.000 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2593 0.8381 0.004 0.000 0.104 0.892
#> GSM549291 4 0.4819 0.5205 0.004 0.000 0.344 0.652
#> GSM549274 2 0.0188 0.8633 0.000 0.996 0.004 0.000
#> GSM750738 2 0.4998 0.0817 0.000 0.512 0.000 0.488
#> GSM750748 1 0.0524 0.9279 0.988 0.008 0.000 0.004
#> GSM549240 2 0.1722 0.8391 0.008 0.944 0.000 0.048
#> GSM549279 2 0.1356 0.8643 0.008 0.960 0.032 0.000
#> GSM549294 2 0.1211 0.8658 0.000 0.960 0.040 0.000
#> GSM549300 3 0.1211 0.9040 0.000 0.040 0.960 0.000
#> GSM549303 3 0.0469 0.9098 0.000 0.000 0.988 0.012
#> GSM549309 3 0.0817 0.9048 0.000 0.000 0.976 0.024
#> GSM750753 2 0.3942 0.7284 0.000 0.764 0.236 0.000
#> GSM750752 4 0.0672 0.8917 0.000 0.008 0.008 0.984
#> GSM549304 2 0.0817 0.8667 0.000 0.976 0.024 0.000
#> GSM549305 2 0.2408 0.8404 0.000 0.896 0.104 0.000
#> GSM549307 3 0.2081 0.8764 0.000 0.084 0.916 0.000
#> GSM549306 3 0.0592 0.9140 0.000 0.016 0.984 0.000
#> GSM549308 3 0.0000 0.9132 0.000 0.000 1.000 0.000
#> GSM549233 4 0.3157 0.7975 0.144 0.004 0.000 0.852
#> GSM549234 4 0.0000 0.8930 0.000 0.000 0.000 1.000
#> GSM549250 1 0.3801 0.7096 0.780 0.000 0.000 0.220
#> GSM549287 3 0.2654 0.8328 0.004 0.000 0.888 0.108
#> GSM750735 1 0.2216 0.8868 0.908 0.092 0.000 0.000
#> GSM750736 2 0.3850 0.7649 0.116 0.840 0.000 0.044
#> GSM750749 1 0.2546 0.8797 0.912 0.060 0.028 0.000
#> GSM549230 1 0.0469 0.9259 0.988 0.000 0.000 0.012
#> GSM549231 1 0.0469 0.9259 0.988 0.000 0.000 0.012
#> GSM549237 1 0.0188 0.9273 0.996 0.000 0.000 0.004
#> GSM549254 4 0.0000 0.8930 0.000 0.000 0.000 1.000
#> GSM750734 1 0.0524 0.9279 0.988 0.008 0.000 0.004
#> GSM549271 3 0.2760 0.8134 0.000 0.000 0.872 0.128
#> GSM549232 4 0.0336 0.8924 0.000 0.000 0.008 0.992
#> GSM549246 4 0.4831 0.5978 0.280 0.000 0.016 0.704
#> GSM549248 1 0.0336 0.9269 0.992 0.000 0.000 0.008
#> GSM549255 4 0.0000 0.8930 0.000 0.000 0.000 1.000
#> GSM750746 1 0.0469 0.9268 0.988 0.012 0.000 0.000
#> GSM549259 1 0.2408 0.8769 0.896 0.104 0.000 0.000
#> GSM549269 2 0.0188 0.8633 0.000 0.996 0.004 0.000
#> GSM549273 3 0.0336 0.9143 0.000 0.008 0.992 0.000
#> GSM549299 2 0.3311 0.7936 0.000 0.828 0.172 0.000
#> GSM549301 3 0.0469 0.9144 0.000 0.012 0.988 0.000
#> GSM549310 4 0.1118 0.8823 0.000 0.000 0.036 0.964
#> GSM549311 3 0.0817 0.9048 0.000 0.000 0.976 0.024
#> GSM549302 2 0.1004 0.8666 0.000 0.972 0.024 0.004
#> GSM549235 1 0.0188 0.9271 0.996 0.004 0.000 0.000
#> GSM549245 4 0.0469 0.8887 0.000 0.012 0.000 0.988
#> GSM549265 4 0.2988 0.8314 0.112 0.000 0.012 0.876
#> GSM549282 3 0.0927 0.9055 0.008 0.000 0.976 0.016
#> GSM549296 4 0.0524 0.8919 0.000 0.004 0.008 0.988
#> GSM750739 1 0.0524 0.9279 0.988 0.008 0.000 0.004
#> GSM750742 1 0.0188 0.9273 0.996 0.000 0.000 0.004
#> GSM750744 1 0.0657 0.9271 0.984 0.004 0.000 0.012
#> GSM750750 3 0.0000 0.9132 0.000 0.000 1.000 0.000
#> GSM549242 1 0.4905 0.4400 0.632 0.004 0.000 0.364
#> GSM549252 4 0.0524 0.8921 0.004 0.000 0.008 0.988
#> GSM549253 1 0.2469 0.8538 0.892 0.000 0.000 0.108
#> GSM549256 4 0.2773 0.8248 0.116 0.004 0.000 0.880
#> GSM549257 4 0.0000 0.8930 0.000 0.000 0.000 1.000
#> GSM549263 1 0.0592 0.9244 0.984 0.000 0.000 0.016
#> GSM549267 4 0.5028 0.3984 0.004 0.000 0.400 0.596
#> GSM750745 1 0.1940 0.8986 0.924 0.076 0.000 0.000
#> GSM549239 1 0.0921 0.9228 0.972 0.028 0.000 0.000
#> GSM549244 4 0.0000 0.8930 0.000 0.000 0.000 1.000
#> GSM549249 4 0.0937 0.8905 0.012 0.000 0.012 0.976
#> GSM549260 1 0.2773 0.8532 0.880 0.004 0.000 0.116
#> GSM549266 2 0.1256 0.8639 0.008 0.964 0.028 0.000
#> GSM549293 2 0.1297 0.8631 0.000 0.964 0.016 0.020
#> GSM549236 1 0.3355 0.7929 0.836 0.000 0.004 0.160
#> GSM549238 4 0.1151 0.8873 0.024 0.000 0.008 0.968
#> GSM549251 1 0.0817 0.9220 0.976 0.000 0.000 0.024
#> GSM549258 1 0.4981 0.1566 0.536 0.464 0.000 0.000
#> GSM549264 1 0.1182 0.9264 0.968 0.016 0.000 0.016
#> GSM549243 1 0.0336 0.9272 0.992 0.008 0.000 0.000
#> GSM549262 1 0.0188 0.9273 0.996 0.000 0.000 0.004
#> GSM549278 3 0.5126 0.0931 0.004 0.000 0.552 0.444
#> GSM549283 2 0.2973 0.8175 0.000 0.856 0.144 0.000
#> GSM549298 3 0.0469 0.9144 0.000 0.012 0.988 0.000
#> GSM750741 2 0.4477 0.5171 0.312 0.688 0.000 0.000
#> GSM549286 2 0.1022 0.8663 0.000 0.968 0.032 0.000
#> GSM549241 2 0.4222 0.5929 0.272 0.728 0.000 0.000
#> GSM549247 2 0.0817 0.8544 0.000 0.976 0.000 0.024
#> GSM549261 1 0.1940 0.8975 0.924 0.076 0.000 0.000
#> GSM549270 2 0.4164 0.6937 0.000 0.736 0.264 0.000
#> GSM549277 3 0.2589 0.8441 0.000 0.116 0.884 0.000
#> GSM549280 3 0.2345 0.8623 0.000 0.100 0.900 0.000
#> GSM549281 2 0.3791 0.7669 0.004 0.796 0.200 0.000
#> GSM549285 3 0.0592 0.9139 0.000 0.016 0.984 0.000
#> GSM549288 3 0.2149 0.8730 0.000 0.088 0.912 0.000
#> GSM549292 2 0.0657 0.8655 0.000 0.984 0.012 0.004
#> GSM549295 3 0.2647 0.8399 0.000 0.120 0.880 0.000
#> GSM549297 2 0.4992 0.2240 0.000 0.524 0.476 0.000
#> GSM750743 1 0.1004 0.9256 0.972 0.024 0.000 0.004
#> GSM549268 2 0.4776 0.4954 0.000 0.624 0.376 0.000
#> GSM549290 4 0.5203 0.3546 0.008 0.000 0.416 0.576
#> GSM549272 2 0.0817 0.8668 0.000 0.976 0.024 0.000
#> GSM549276 2 0.1867 0.8556 0.000 0.928 0.072 0.000
#> GSM549275 2 0.0592 0.8596 0.016 0.984 0.000 0.000
#> GSM549284 2 0.1624 0.8648 0.000 0.952 0.028 0.020
#> GSM750737 4 0.0592 0.8875 0.000 0.016 0.000 0.984
#> GSM750740 1 0.0592 0.9264 0.984 0.016 0.000 0.000
#> GSM750747 1 0.0469 0.9268 0.988 0.012 0.000 0.000
#> GSM750751 2 0.1211 0.8653 0.000 0.960 0.040 0.000
#> GSM750754 3 0.2737 0.8340 0.008 0.000 0.888 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1568 0.8872 0.000 0.000 0.036 0.944 0.020
#> GSM549291 4 0.3789 0.6942 0.000 0.000 0.224 0.760 0.016
#> GSM549274 2 0.0290 0.8425 0.000 0.992 0.000 0.000 0.008
#> GSM750738 2 0.4794 0.4213 0.000 0.624 0.000 0.344 0.032
#> GSM750748 1 0.1908 0.7900 0.908 0.000 0.000 0.000 0.092
#> GSM549240 2 0.2844 0.7798 0.092 0.876 0.000 0.028 0.004
#> GSM549279 2 0.6301 0.5983 0.256 0.616 0.060 0.004 0.064
#> GSM549294 2 0.3050 0.8135 0.024 0.876 0.076 0.000 0.024
#> GSM549300 3 0.1117 0.8617 0.000 0.016 0.964 0.000 0.020
#> GSM549303 3 0.1670 0.8549 0.000 0.000 0.936 0.012 0.052
#> GSM549309 3 0.1082 0.8574 0.000 0.000 0.964 0.008 0.028
#> GSM750753 2 0.3333 0.7170 0.000 0.788 0.208 0.000 0.004
#> GSM750752 4 0.0324 0.8944 0.000 0.004 0.000 0.992 0.004
#> GSM549304 2 0.0404 0.8419 0.000 0.988 0.000 0.000 0.012
#> GSM549305 2 0.2006 0.8234 0.000 0.916 0.072 0.000 0.012
#> GSM549307 3 0.1331 0.8570 0.000 0.040 0.952 0.000 0.008
#> GSM549306 3 0.0807 0.8621 0.000 0.012 0.976 0.000 0.012
#> GSM549308 3 0.1041 0.8594 0.000 0.004 0.964 0.000 0.032
#> GSM549233 4 0.3526 0.8004 0.072 0.000 0.000 0.832 0.096
#> GSM549234 4 0.1059 0.8927 0.004 0.008 0.000 0.968 0.020
#> GSM549250 5 0.3779 0.6474 0.144 0.000 0.000 0.052 0.804
#> GSM549287 3 0.1907 0.8458 0.000 0.000 0.928 0.028 0.044
#> GSM750735 1 0.3242 0.6681 0.784 0.000 0.000 0.000 0.216
#> GSM750736 2 0.6106 0.4127 0.336 0.560 0.000 0.024 0.080
#> GSM750749 1 0.4925 0.5679 0.708 0.024 0.036 0.000 0.232
#> GSM549230 1 0.4030 0.3344 0.648 0.000 0.000 0.000 0.352
#> GSM549231 5 0.3366 0.6310 0.232 0.000 0.000 0.000 0.768
#> GSM549237 1 0.3561 0.6426 0.740 0.000 0.000 0.000 0.260
#> GSM549254 4 0.1869 0.8842 0.012 0.000 0.016 0.936 0.036
#> GSM750734 1 0.0963 0.7987 0.964 0.000 0.000 0.000 0.036
#> GSM549271 3 0.2920 0.7724 0.000 0.000 0.852 0.132 0.016
#> GSM549232 4 0.0162 0.8945 0.000 0.000 0.000 0.996 0.004
#> GSM549246 4 0.3009 0.8469 0.064 0.000 0.008 0.876 0.052
#> GSM549248 5 0.4171 0.3888 0.396 0.000 0.000 0.000 0.604
#> GSM549255 4 0.0162 0.8941 0.000 0.000 0.004 0.996 0.000
#> GSM750746 1 0.1341 0.8058 0.944 0.000 0.000 0.000 0.056
#> GSM549259 1 0.1430 0.8086 0.944 0.004 0.000 0.000 0.052
#> GSM549269 2 0.0290 0.8428 0.000 0.992 0.000 0.000 0.008
#> GSM549273 3 0.1809 0.8543 0.000 0.000 0.928 0.012 0.060
#> GSM549299 2 0.4863 0.3853 0.008 0.592 0.384 0.000 0.016
#> GSM549301 3 0.0162 0.8623 0.000 0.004 0.996 0.000 0.000
#> GSM549310 4 0.1750 0.8823 0.000 0.000 0.028 0.936 0.036
#> GSM549311 3 0.1597 0.8564 0.000 0.000 0.940 0.012 0.048
#> GSM549302 2 0.0000 0.8423 0.000 1.000 0.000 0.000 0.000
#> GSM549235 1 0.2074 0.7841 0.896 0.000 0.000 0.000 0.104
#> GSM549245 4 0.0451 0.8937 0.004 0.008 0.000 0.988 0.000
#> GSM549265 5 0.4758 0.0252 0.008 0.008 0.000 0.424 0.560
#> GSM549282 5 0.4150 0.1502 0.000 0.000 0.388 0.000 0.612
#> GSM549296 4 0.1211 0.8891 0.000 0.000 0.016 0.960 0.024
#> GSM750739 1 0.2230 0.7853 0.884 0.000 0.000 0.000 0.116
#> GSM750742 5 0.4227 0.3910 0.420 0.000 0.000 0.000 0.580
#> GSM750744 5 0.4249 0.2605 0.432 0.000 0.000 0.000 0.568
#> GSM750750 3 0.1041 0.8587 0.000 0.004 0.964 0.000 0.032
#> GSM549242 1 0.5000 0.1943 0.576 0.000 0.000 0.388 0.036
#> GSM549252 4 0.1732 0.8752 0.000 0.000 0.000 0.920 0.080
#> GSM549253 5 0.4639 0.5357 0.344 0.000 0.000 0.024 0.632
#> GSM549256 4 0.2233 0.8531 0.080 0.000 0.000 0.904 0.016
#> GSM549257 4 0.0324 0.8946 0.004 0.000 0.004 0.992 0.000
#> GSM549263 5 0.3857 0.5796 0.312 0.000 0.000 0.000 0.688
#> GSM549267 4 0.4960 0.5908 0.000 0.000 0.268 0.668 0.064
#> GSM750745 1 0.0794 0.7953 0.972 0.000 0.000 0.000 0.028
#> GSM549239 1 0.1043 0.8084 0.960 0.000 0.000 0.000 0.040
#> GSM549244 4 0.1557 0.8846 0.000 0.008 0.000 0.940 0.052
#> GSM549249 4 0.2629 0.8360 0.004 0.000 0.000 0.860 0.136
#> GSM549260 1 0.1750 0.7863 0.936 0.000 0.000 0.036 0.028
#> GSM549266 2 0.5800 0.6534 0.220 0.664 0.068 0.000 0.048
#> GSM549293 2 0.0451 0.8409 0.000 0.988 0.000 0.004 0.008
#> GSM549236 5 0.3995 0.6492 0.180 0.000 0.000 0.044 0.776
#> GSM549238 4 0.4063 0.6373 0.012 0.000 0.000 0.708 0.280
#> GSM549251 1 0.2852 0.7150 0.828 0.000 0.000 0.000 0.172
#> GSM549258 1 0.1364 0.7800 0.952 0.036 0.000 0.000 0.012
#> GSM549264 5 0.3250 0.6400 0.128 0.020 0.000 0.008 0.844
#> GSM549243 1 0.2074 0.7824 0.896 0.000 0.000 0.000 0.104
#> GSM549262 1 0.4171 0.2700 0.604 0.000 0.000 0.000 0.396
#> GSM549278 3 0.5092 0.1228 0.000 0.000 0.524 0.440 0.036
#> GSM549283 2 0.4772 0.4703 0.012 0.624 0.352 0.000 0.012
#> GSM549298 3 0.0566 0.8620 0.000 0.004 0.984 0.000 0.012
#> GSM750741 1 0.2972 0.7214 0.880 0.048 0.004 0.004 0.064
#> GSM549286 2 0.0162 0.8428 0.000 0.996 0.000 0.000 0.004
#> GSM549241 1 0.2139 0.7493 0.916 0.052 0.000 0.000 0.032
#> GSM549247 2 0.0992 0.8370 0.000 0.968 0.000 0.024 0.008
#> GSM549261 1 0.2208 0.8033 0.908 0.020 0.000 0.000 0.072
#> GSM549270 2 0.4473 0.5183 0.000 0.656 0.324 0.000 0.020
#> GSM549277 3 0.2511 0.8342 0.000 0.080 0.892 0.000 0.028
#> GSM549280 3 0.1701 0.8532 0.000 0.048 0.936 0.000 0.016
#> GSM549281 3 0.7100 0.4448 0.140 0.212 0.560 0.000 0.088
#> GSM549285 3 0.2825 0.8175 0.000 0.016 0.860 0.000 0.124
#> GSM549288 3 0.2069 0.8452 0.000 0.076 0.912 0.000 0.012
#> GSM549292 2 0.0404 0.8412 0.000 0.988 0.000 0.000 0.012
#> GSM549295 3 0.2844 0.8298 0.000 0.092 0.876 0.004 0.028
#> GSM549297 3 0.4937 0.1637 0.000 0.428 0.544 0.000 0.028
#> GSM750743 1 0.3366 0.6749 0.768 0.000 0.000 0.000 0.232
#> GSM549268 3 0.6776 0.5134 0.112 0.196 0.600 0.000 0.092
#> GSM549290 5 0.6309 0.2605 0.000 0.000 0.236 0.232 0.532
#> GSM549272 2 0.0000 0.8423 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.0880 0.8403 0.000 0.968 0.032 0.000 0.000
#> GSM549275 2 0.2069 0.8213 0.076 0.912 0.000 0.000 0.012
#> GSM549284 2 0.1571 0.8236 0.000 0.936 0.000 0.004 0.060
#> GSM750737 4 0.2300 0.8609 0.040 0.000 0.000 0.908 0.052
#> GSM750740 1 0.1282 0.8079 0.952 0.004 0.000 0.000 0.044
#> GSM750747 1 0.1341 0.8083 0.944 0.000 0.000 0.000 0.056
#> GSM750751 2 0.1597 0.8390 0.008 0.948 0.020 0.000 0.024
#> GSM750754 3 0.2300 0.8324 0.000 0.000 0.908 0.052 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.1116 0.89252 0.000 0.000 0.008 0.960 0.004 0.028
#> GSM549291 4 0.3686 0.66465 0.000 0.000 0.220 0.748 0.000 0.032
#> GSM549274 2 0.0363 0.82024 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM750738 2 0.5914 0.20663 0.000 0.464 0.000 0.344 0.004 0.188
#> GSM750748 1 0.0713 0.71504 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM549240 2 0.3347 0.69013 0.152 0.812 0.000 0.004 0.004 0.028
#> GSM549279 6 0.6892 0.42701 0.128 0.200 0.152 0.004 0.000 0.516
#> GSM549294 2 0.2088 0.79239 0.000 0.904 0.028 0.000 0.000 0.068
#> GSM549300 3 0.1410 0.76131 0.000 0.004 0.944 0.000 0.008 0.044
#> GSM549303 3 0.3714 0.70591 0.000 0.000 0.720 0.008 0.008 0.264
#> GSM549309 3 0.2703 0.74973 0.000 0.000 0.824 0.000 0.004 0.172
#> GSM750753 2 0.5161 0.13102 0.000 0.472 0.452 0.000 0.004 0.072
#> GSM750752 4 0.1010 0.88913 0.000 0.000 0.000 0.960 0.004 0.036
#> GSM549304 2 0.3295 0.75440 0.004 0.844 0.036 0.012 0.004 0.100
#> GSM549305 2 0.1644 0.80973 0.000 0.932 0.028 0.000 0.000 0.040
#> GSM549307 3 0.1074 0.77059 0.000 0.012 0.960 0.000 0.000 0.028
#> GSM549306 3 0.0146 0.77620 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549308 3 0.0891 0.77790 0.000 0.000 0.968 0.000 0.008 0.024
#> GSM549233 4 0.2796 0.84908 0.044 0.000 0.000 0.868 0.080 0.008
#> GSM549234 4 0.1391 0.88750 0.000 0.000 0.000 0.944 0.016 0.040
#> GSM549250 5 0.2030 0.71549 0.064 0.000 0.000 0.028 0.908 0.000
#> GSM549287 3 0.5312 0.65565 0.000 0.000 0.648 0.120 0.024 0.208
#> GSM750735 6 0.4943 0.23928 0.376 0.016 0.000 0.004 0.032 0.572
#> GSM750736 6 0.5647 0.44681 0.152 0.204 0.000 0.016 0.008 0.620
#> GSM750749 6 0.5626 0.39530 0.276 0.008 0.068 0.000 0.040 0.608
#> GSM549230 1 0.3309 0.32082 0.720 0.000 0.000 0.000 0.280 0.000
#> GSM549231 5 0.2020 0.72081 0.096 0.000 0.000 0.000 0.896 0.008
#> GSM549237 1 0.5010 0.43477 0.644 0.000 0.000 0.000 0.172 0.184
#> GSM549254 4 0.1155 0.89118 0.004 0.000 0.004 0.956 0.000 0.036
#> GSM750734 1 0.3782 0.20626 0.588 0.000 0.000 0.000 0.000 0.412
#> GSM549271 3 0.2039 0.74506 0.000 0.000 0.904 0.076 0.000 0.020
#> GSM549232 4 0.0603 0.89309 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM549246 4 0.2823 0.84930 0.068 0.000 0.000 0.872 0.044 0.016
#> GSM549248 5 0.4054 0.62118 0.188 0.000 0.000 0.000 0.740 0.072
#> GSM549255 4 0.0146 0.89402 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM750746 1 0.0363 0.71803 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549259 1 0.0725 0.71543 0.976 0.012 0.000 0.000 0.012 0.000
#> GSM549269 2 0.0260 0.82028 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM549273 3 0.4103 0.68738 0.000 0.000 0.684 0.020 0.008 0.288
#> GSM549299 3 0.5089 0.09852 0.000 0.384 0.540 0.000 0.004 0.072
#> GSM549301 3 0.1531 0.77473 0.000 0.000 0.928 0.000 0.004 0.068
#> GSM549310 4 0.1625 0.87875 0.000 0.000 0.012 0.928 0.000 0.060
#> GSM549311 3 0.4334 0.68171 0.000 0.004 0.676 0.020 0.012 0.288
#> GSM549302 2 0.0547 0.81793 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549235 1 0.1007 0.70783 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM549245 4 0.0260 0.89412 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM549265 5 0.5066 0.45640 0.000 0.000 0.000 0.176 0.636 0.188
#> GSM549282 5 0.2346 0.62284 0.000 0.000 0.124 0.000 0.868 0.008
#> GSM549296 4 0.0692 0.89268 0.000 0.000 0.004 0.976 0.000 0.020
#> GSM750739 1 0.4439 0.09191 0.540 0.000 0.000 0.000 0.028 0.432
#> GSM750742 5 0.3615 0.64281 0.292 0.000 0.000 0.000 0.700 0.008
#> GSM750744 6 0.6013 0.01337 0.200 0.000 0.000 0.004 0.396 0.400
#> GSM750750 3 0.1225 0.77768 0.000 0.000 0.952 0.000 0.012 0.036
#> GSM549242 1 0.4214 0.00844 0.528 0.000 0.000 0.460 0.004 0.008
#> GSM549252 4 0.2170 0.85931 0.000 0.000 0.000 0.888 0.100 0.012
#> GSM549253 5 0.4045 0.44772 0.428 0.000 0.000 0.008 0.564 0.000
#> GSM549256 4 0.2163 0.84745 0.096 0.000 0.000 0.892 0.004 0.008
#> GSM549257 4 0.0000 0.89388 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549263 5 0.3578 0.59442 0.340 0.000 0.000 0.000 0.660 0.000
#> GSM549267 4 0.4669 0.73913 0.000 0.000 0.104 0.748 0.084 0.064
#> GSM750745 1 0.3823 0.14962 0.564 0.000 0.000 0.000 0.000 0.436
#> GSM549239 1 0.3531 0.37440 0.672 0.000 0.000 0.000 0.000 0.328
#> GSM549244 4 0.1074 0.89343 0.000 0.000 0.000 0.960 0.028 0.012
#> GSM549249 4 0.2730 0.78537 0.000 0.000 0.000 0.808 0.192 0.000
#> GSM549260 1 0.0972 0.70457 0.964 0.000 0.000 0.008 0.000 0.028
#> GSM549266 2 0.5197 0.59379 0.096 0.672 0.036 0.000 0.000 0.196
#> GSM549293 2 0.0937 0.81197 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM549236 5 0.2250 0.72178 0.092 0.000 0.000 0.020 0.888 0.000
#> GSM549238 4 0.3923 0.43701 0.008 0.000 0.000 0.620 0.372 0.000
#> GSM549251 1 0.1897 0.67628 0.908 0.000 0.000 0.004 0.084 0.004
#> GSM549258 1 0.1753 0.67379 0.912 0.004 0.000 0.000 0.000 0.084
#> GSM549264 5 0.1719 0.68506 0.016 0.000 0.000 0.000 0.924 0.060
#> GSM549243 1 0.0547 0.71778 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549262 5 0.5316 0.13937 0.416 0.000 0.000 0.000 0.480 0.104
#> GSM549278 3 0.4780 0.03346 0.000 0.000 0.480 0.476 0.004 0.040
#> GSM549283 3 0.5080 0.46159 0.000 0.224 0.640 0.000 0.004 0.132
#> GSM549298 3 0.0260 0.77523 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM750741 1 0.3838 0.10154 0.552 0.000 0.000 0.000 0.000 0.448
#> GSM549286 2 0.0937 0.81699 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM549241 1 0.2491 0.64495 0.868 0.020 0.000 0.000 0.000 0.112
#> GSM549247 2 0.0858 0.81802 0.004 0.968 0.000 0.000 0.000 0.028
#> GSM549261 1 0.1480 0.69887 0.940 0.040 0.000 0.000 0.020 0.000
#> GSM549270 2 0.4569 0.61849 0.000 0.700 0.156 0.000 0.000 0.144
#> GSM549277 3 0.4932 0.65074 0.000 0.152 0.668 0.000 0.004 0.176
#> GSM549280 3 0.1682 0.76671 0.000 0.020 0.928 0.000 0.000 0.052
#> GSM549281 6 0.5472 0.36481 0.044 0.108 0.180 0.000 0.004 0.664
#> GSM549285 3 0.2507 0.73913 0.000 0.004 0.884 0.000 0.072 0.040
#> GSM549288 3 0.5254 0.62653 0.000 0.156 0.620 0.000 0.004 0.220
#> GSM549292 2 0.0547 0.82037 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549295 3 0.5273 0.62871 0.000 0.132 0.620 0.000 0.008 0.240
#> GSM549297 2 0.5807 0.23127 0.000 0.516 0.284 0.000 0.004 0.196
#> GSM750743 6 0.4886 0.06595 0.432 0.000 0.000 0.000 0.060 0.508
#> GSM549268 6 0.5165 0.16380 0.004 0.108 0.256 0.000 0.004 0.628
#> GSM549290 5 0.4087 0.58908 0.000 0.000 0.044 0.168 0.764 0.024
#> GSM549272 2 0.0146 0.82001 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549276 2 0.0405 0.82084 0.000 0.988 0.004 0.000 0.000 0.008
#> GSM549275 2 0.6514 0.47459 0.080 0.588 0.108 0.016 0.004 0.204
#> GSM549284 2 0.1116 0.81587 0.000 0.960 0.004 0.000 0.008 0.028
#> GSM750737 6 0.4328 0.02050 0.020 0.000 0.000 0.460 0.000 0.520
#> GSM750740 1 0.0458 0.71808 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM750747 1 0.0547 0.71778 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM750751 2 0.1471 0.80776 0.000 0.932 0.004 0.000 0.000 0.064
#> GSM750754 3 0.2487 0.76691 0.000 0.000 0.892 0.024 0.020 0.064
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> CV:NMF 102 0.02528 1.70e-05 0.06376 0.00385 2
#> CV:NMF 98 0.00679 3.92e-06 0.00310 0.01181 3
#> CV:NMF 95 0.28211 1.35e-04 0.02217 0.03191 4
#> CV:NMF 87 0.29269 5.17e-04 0.02598 0.01389 5
#> CV:NMF 75 0.41610 1.14e-03 0.00151 0.11868 6
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 21168 rows and 103 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 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 0.652 0.843 0.926 0.4931 0.497 0.497
#> 3 3 0.554 0.701 0.842 0.2314 0.876 0.756
#> 4 4 0.665 0.676 0.832 0.1022 0.951 0.877
#> 5 5 0.685 0.641 0.795 0.0418 0.980 0.945
#> 6 6 0.615 0.571 0.753 0.0468 0.941 0.829
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
#> GSM549289 2 0.9866 0.222 0.432 0.568
#> GSM549291 2 0.6531 0.802 0.168 0.832
#> GSM549274 2 0.0672 0.928 0.008 0.992
#> GSM750738 2 0.1414 0.925 0.020 0.980
#> GSM750748 1 0.0000 0.909 1.000 0.000
#> GSM549240 1 0.0938 0.905 0.988 0.012
#> GSM549279 1 0.9170 0.536 0.668 0.332
#> GSM549294 2 0.2043 0.922 0.032 0.968
#> GSM549300 2 0.0672 0.928 0.008 0.992
#> GSM549303 2 0.0376 0.927 0.004 0.996
#> GSM549309 2 0.0938 0.928 0.012 0.988
#> GSM750753 2 0.0938 0.928 0.012 0.988
#> GSM750752 2 0.4161 0.889 0.084 0.916
#> GSM549304 2 0.2778 0.913 0.048 0.952
#> GSM549305 2 0.0672 0.928 0.008 0.992
#> GSM549307 2 0.0376 0.927 0.004 0.996
#> GSM549306 2 0.0376 0.927 0.004 0.996
#> GSM549308 2 0.0376 0.927 0.004 0.996
#> GSM549233 1 0.0000 0.909 1.000 0.000
#> GSM549234 1 0.7883 0.720 0.764 0.236
#> GSM549250 1 0.0000 0.909 1.000 0.000
#> GSM549287 2 0.4939 0.871 0.108 0.892
#> GSM750735 1 0.0376 0.908 0.996 0.004
#> GSM750736 1 0.0376 0.908 0.996 0.004
#> GSM750749 1 0.2778 0.885 0.952 0.048
#> GSM549230 1 0.0000 0.909 1.000 0.000
#> GSM549231 1 0.0000 0.909 1.000 0.000
#> GSM549237 1 0.0000 0.909 1.000 0.000
#> GSM549254 2 0.9944 0.127 0.456 0.544
#> GSM750734 1 0.0000 0.909 1.000 0.000
#> GSM549271 2 0.4690 0.876 0.100 0.900
#> GSM549232 1 0.8016 0.708 0.756 0.244
#> GSM549246 1 0.4562 0.854 0.904 0.096
#> GSM549248 1 0.0000 0.909 1.000 0.000
#> GSM549255 1 0.7950 0.714 0.760 0.240
#> GSM750746 1 0.0000 0.909 1.000 0.000
#> GSM549259 1 0.0000 0.909 1.000 0.000
#> GSM549269 2 0.0672 0.928 0.008 0.992
#> GSM549273 2 0.0376 0.927 0.004 0.996
#> GSM549299 2 0.2778 0.913 0.048 0.952
#> GSM549301 2 0.0376 0.927 0.004 0.996
#> GSM549310 2 0.4161 0.889 0.084 0.916
#> GSM549311 2 0.0376 0.927 0.004 0.996
#> GSM549302 2 0.0672 0.928 0.008 0.992
#> GSM549235 1 0.0000 0.909 1.000 0.000
#> GSM549245 1 0.7950 0.714 0.760 0.240
#> GSM549265 1 0.7528 0.745 0.784 0.216
#> GSM549282 2 0.1843 0.924 0.028 0.972
#> GSM549296 2 0.4161 0.889 0.084 0.916
#> GSM750739 1 0.0000 0.909 1.000 0.000
#> GSM750742 1 0.0000 0.909 1.000 0.000
#> GSM750744 1 0.0376 0.908 0.996 0.004
#> GSM750750 2 0.1843 0.924 0.028 0.972
#> GSM549242 1 0.0376 0.908 0.996 0.004
#> GSM549252 1 0.7453 0.748 0.788 0.212
#> GSM549253 1 0.0000 0.909 1.000 0.000
#> GSM549256 1 0.0376 0.908 0.996 0.004
#> GSM549257 1 0.8016 0.708 0.756 0.244
#> GSM549263 1 0.0000 0.909 1.000 0.000
#> GSM549267 2 0.4939 0.870 0.108 0.892
#> GSM750745 1 0.0000 0.909 1.000 0.000
#> GSM549239 1 0.0000 0.909 1.000 0.000
#> GSM549244 1 0.7815 0.725 0.768 0.232
#> GSM549249 1 0.7528 0.744 0.784 0.216
#> GSM549260 1 0.0000 0.909 1.000 0.000
#> GSM549266 1 0.8661 0.615 0.712 0.288
#> GSM549293 2 0.0672 0.928 0.008 0.992
#> GSM549236 1 0.0000 0.909 1.000 0.000
#> GSM549238 1 0.7453 0.748 0.788 0.212
#> GSM549251 1 0.0000 0.909 1.000 0.000
#> GSM549258 1 0.0000 0.909 1.000 0.000
#> GSM549264 1 0.0000 0.909 1.000 0.000
#> GSM549243 1 0.0000 0.909 1.000 0.000
#> GSM549262 1 0.0000 0.909 1.000 0.000
#> GSM549278 2 0.9129 0.510 0.328 0.672
#> GSM549283 2 0.9909 0.188 0.444 0.556
#> GSM549298 2 0.0376 0.927 0.004 0.996
#> GSM750741 1 0.0000 0.909 1.000 0.000
#> GSM549286 2 0.0672 0.928 0.008 0.992
#> GSM549241 1 0.0000 0.909 1.000 0.000
#> GSM549247 1 0.0938 0.905 0.988 0.012
#> GSM549261 1 0.0000 0.909 1.000 0.000
#> GSM549270 2 0.0376 0.927 0.004 0.996
#> GSM549277 2 0.0938 0.928 0.012 0.988
#> GSM549280 2 0.0672 0.928 0.008 0.992
#> GSM549281 1 0.9552 0.432 0.624 0.376
#> GSM549285 2 0.6438 0.805 0.164 0.836
#> GSM549288 2 0.0938 0.928 0.012 0.988
#> GSM549292 2 0.0672 0.928 0.008 0.992
#> GSM549295 2 0.0376 0.927 0.004 0.996
#> GSM549297 2 0.0672 0.928 0.008 0.992
#> GSM750743 1 0.0376 0.908 0.996 0.004
#> GSM549268 1 0.9552 0.432 0.624 0.376
#> GSM549290 2 0.3879 0.897 0.076 0.924
#> GSM549272 2 0.0672 0.928 0.008 0.992
#> GSM549276 2 0.0376 0.927 0.004 0.996
#> GSM549275 1 0.3584 0.872 0.932 0.068
#> GSM549284 2 0.0938 0.927 0.012 0.988
#> GSM750737 1 0.9954 0.166 0.540 0.460
#> GSM750740 1 0.0000 0.909 1.000 0.000
#> GSM750747 1 0.0000 0.909 1.000 0.000
#> GSM750751 2 0.1184 0.928 0.016 0.984
#> GSM750754 2 0.6343 0.813 0.160 0.840
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.9014 0.1863 0.380 0.136 0.484
#> GSM549291 3 0.6529 0.5705 0.116 0.124 0.760
#> GSM549274 2 0.0592 0.8255 0.000 0.988 0.012
#> GSM750738 2 0.1015 0.8129 0.012 0.980 0.008
#> GSM750748 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549240 1 0.1315 0.8777 0.972 0.020 0.008
#> GSM549279 1 0.8047 0.4950 0.632 0.256 0.112
#> GSM549294 2 0.3445 0.8064 0.016 0.896 0.088
#> GSM549300 2 0.6008 0.5320 0.004 0.664 0.332
#> GSM549303 3 0.4062 0.6002 0.000 0.164 0.836
#> GSM549309 3 0.3816 0.6043 0.000 0.148 0.852
#> GSM750753 2 0.5365 0.6652 0.004 0.744 0.252
#> GSM750752 3 0.6929 0.5382 0.052 0.260 0.688
#> GSM549304 2 0.4335 0.7884 0.036 0.864 0.100
#> GSM549305 2 0.1643 0.8268 0.000 0.956 0.044
#> GSM549307 3 0.5905 0.3850 0.000 0.352 0.648
#> GSM549306 3 0.5254 0.5181 0.000 0.264 0.736
#> GSM549308 3 0.4931 0.5535 0.000 0.232 0.768
#> GSM549233 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549234 1 0.7202 0.6721 0.716 0.124 0.160
#> GSM549250 1 0.0237 0.8849 0.996 0.000 0.004
#> GSM549287 3 0.6174 0.6197 0.064 0.168 0.768
#> GSM750735 1 0.0829 0.8834 0.984 0.012 0.004
#> GSM750736 1 0.0829 0.8834 0.984 0.012 0.004
#> GSM750749 1 0.2550 0.8601 0.936 0.024 0.040
#> GSM549230 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549237 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549254 3 0.9464 0.0802 0.408 0.180 0.412
#> GSM750734 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549271 3 0.6348 0.6029 0.060 0.188 0.752
#> GSM549232 1 0.7309 0.6625 0.708 0.124 0.168
#> GSM549246 1 0.3618 0.8233 0.884 0.012 0.104
#> GSM549248 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549255 1 0.7256 0.6675 0.712 0.124 0.164
#> GSM750746 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549259 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549269 2 0.0000 0.8201 0.000 1.000 0.000
#> GSM549273 3 0.4062 0.6002 0.000 0.164 0.836
#> GSM549299 2 0.4335 0.7884 0.036 0.864 0.100
#> GSM549301 3 0.5327 0.5134 0.000 0.272 0.728
#> GSM549310 3 0.7032 0.5255 0.052 0.272 0.676
#> GSM549311 3 0.4062 0.6002 0.000 0.164 0.836
#> GSM549302 2 0.0592 0.8255 0.000 0.988 0.012
#> GSM549235 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549245 1 0.7256 0.6675 0.712 0.124 0.164
#> GSM549265 1 0.6902 0.6964 0.736 0.116 0.148
#> GSM549282 3 0.5903 0.5998 0.024 0.232 0.744
#> GSM549296 3 0.7032 0.5255 0.052 0.272 0.676
#> GSM750739 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM750742 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM750744 1 0.0424 0.8853 0.992 0.008 0.000
#> GSM750750 3 0.5903 0.5998 0.024 0.232 0.744
#> GSM549242 1 0.0424 0.8856 0.992 0.008 0.000
#> GSM549252 1 0.6843 0.6993 0.740 0.116 0.144
#> GSM549253 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549256 1 0.0848 0.8835 0.984 0.008 0.008
#> GSM549257 1 0.7309 0.6625 0.708 0.124 0.168
#> GSM549263 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549267 3 0.6174 0.6196 0.064 0.168 0.768
#> GSM750745 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549239 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549244 1 0.7147 0.6779 0.720 0.124 0.156
#> GSM549249 1 0.6915 0.6946 0.736 0.124 0.140
#> GSM549260 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549266 1 0.7380 0.5728 0.684 0.228 0.088
#> GSM549293 2 0.0592 0.8255 0.000 0.988 0.012
#> GSM549236 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549238 1 0.6854 0.6983 0.740 0.124 0.136
#> GSM549251 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549258 1 0.0475 0.8851 0.992 0.004 0.004
#> GSM549264 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549243 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549262 1 0.0000 0.8857 1.000 0.000 0.000
#> GSM549278 3 0.8571 0.3987 0.272 0.140 0.588
#> GSM549283 1 0.9785 -0.1438 0.420 0.336 0.244
#> GSM549298 3 0.4974 0.5502 0.000 0.236 0.764
#> GSM750741 1 0.0661 0.8847 0.988 0.008 0.004
#> GSM549286 2 0.0000 0.8201 0.000 1.000 0.000
#> GSM549241 1 0.0848 0.8835 0.984 0.008 0.008
#> GSM549247 1 0.1315 0.8777 0.972 0.020 0.008
#> GSM549261 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM549270 2 0.2165 0.8225 0.000 0.936 0.064
#> GSM549277 2 0.6659 0.1109 0.008 0.532 0.460
#> GSM549280 2 0.5754 0.5920 0.004 0.700 0.296
#> GSM549281 1 0.8609 0.4122 0.596 0.244 0.160
#> GSM549285 3 0.9109 0.2654 0.148 0.364 0.488
#> GSM549288 2 0.6275 0.4740 0.008 0.644 0.348
#> GSM549292 2 0.0000 0.8201 0.000 1.000 0.000
#> GSM549295 3 0.6274 0.0988 0.000 0.456 0.544
#> GSM549297 2 0.5929 0.5504 0.004 0.676 0.320
#> GSM750743 1 0.0424 0.8853 0.992 0.008 0.000
#> GSM549268 1 0.8609 0.4122 0.596 0.244 0.160
#> GSM549290 3 0.6034 0.6158 0.036 0.212 0.752
#> GSM549272 2 0.0000 0.8201 0.000 1.000 0.000
#> GSM549276 2 0.2165 0.8225 0.000 0.936 0.064
#> GSM549275 1 0.3461 0.8315 0.900 0.076 0.024
#> GSM549284 2 0.4047 0.7316 0.004 0.848 0.148
#> GSM750737 1 0.9174 0.1697 0.504 0.164 0.332
#> GSM750740 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM750747 1 0.0237 0.8860 0.996 0.004 0.000
#> GSM750751 2 0.1832 0.8262 0.008 0.956 0.036
#> GSM750754 3 0.6455 0.5777 0.108 0.128 0.764
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.5779 0.4997 0.292 0.008 0.040 0.660
#> GSM549291 4 0.3991 0.6088 0.020 0.000 0.172 0.808
#> GSM549274 2 0.0592 0.8198 0.000 0.984 0.016 0.000
#> GSM750738 2 0.1404 0.7983 0.012 0.964 0.012 0.012
#> GSM750748 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549240 1 0.1488 0.8439 0.956 0.012 0.000 0.032
#> GSM549279 1 0.8040 0.4080 0.588 0.176 0.088 0.148
#> GSM549294 2 0.3889 0.7970 0.004 0.844 0.112 0.040
#> GSM549300 2 0.5452 0.3838 0.000 0.556 0.428 0.016
#> GSM549303 3 0.3626 0.6430 0.000 0.004 0.812 0.184
#> GSM549309 3 0.3907 0.6079 0.000 0.000 0.768 0.232
#> GSM750753 2 0.5193 0.5863 0.000 0.656 0.324 0.020
#> GSM750752 4 0.5080 0.5843 0.000 0.092 0.144 0.764
#> GSM549304 2 0.4536 0.7715 0.032 0.812 0.136 0.020
#> GSM549305 2 0.1940 0.8191 0.000 0.924 0.076 0.000
#> GSM549307 3 0.3908 0.6102 0.000 0.212 0.784 0.004
#> GSM549306 3 0.2799 0.7225 0.000 0.108 0.884 0.008
#> GSM549308 3 0.2255 0.7271 0.000 0.068 0.920 0.012
#> GSM549233 1 0.0817 0.8512 0.976 0.000 0.000 0.024
#> GSM549234 1 0.4905 0.4729 0.632 0.004 0.000 0.364
#> GSM549250 1 0.0817 0.8510 0.976 0.000 0.000 0.024
#> GSM549287 4 0.4522 0.4846 0.000 0.000 0.320 0.680
#> GSM750735 1 0.1109 0.8491 0.968 0.004 0.000 0.028
#> GSM750736 1 0.1109 0.8491 0.968 0.004 0.000 0.028
#> GSM750749 1 0.2587 0.8161 0.908 0.004 0.012 0.076
#> GSM549230 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549231 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549237 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549254 4 0.5910 0.4389 0.316 0.040 0.008 0.636
#> GSM750734 1 0.0336 0.8550 0.992 0.000 0.000 0.008
#> GSM549271 4 0.4535 0.5597 0.000 0.016 0.240 0.744
#> GSM549232 1 0.4950 0.4500 0.620 0.004 0.000 0.376
#> GSM549246 1 0.3306 0.7544 0.840 0.004 0.000 0.156
#> GSM549248 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549255 1 0.4936 0.4574 0.624 0.004 0.000 0.372
#> GSM750746 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549259 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549269 2 0.0000 0.8157 0.000 1.000 0.000 0.000
#> GSM549273 3 0.3626 0.6430 0.000 0.004 0.812 0.184
#> GSM549299 2 0.4536 0.7715 0.032 0.812 0.136 0.020
#> GSM549301 3 0.3143 0.7210 0.000 0.100 0.876 0.024
#> GSM549310 4 0.5151 0.5810 0.000 0.100 0.140 0.760
#> GSM549311 3 0.3626 0.6430 0.000 0.004 0.812 0.184
#> GSM549302 2 0.0469 0.8191 0.000 0.988 0.012 0.000
#> GSM549235 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549245 1 0.4936 0.4574 0.624 0.004 0.000 0.372
#> GSM549265 1 0.4781 0.5214 0.660 0.004 0.000 0.336
#> GSM549282 3 0.4319 0.6006 0.000 0.012 0.760 0.228
#> GSM549296 4 0.5151 0.5810 0.000 0.100 0.140 0.760
#> GSM750739 1 0.0000 0.8557 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM750744 1 0.0469 0.8548 0.988 0.000 0.000 0.012
#> GSM750750 3 0.4319 0.6006 0.000 0.012 0.760 0.228
#> GSM549242 1 0.0592 0.8558 0.984 0.000 0.000 0.016
#> GSM549252 1 0.4800 0.5148 0.656 0.004 0.000 0.340
#> GSM549253 1 0.0469 0.8549 0.988 0.000 0.000 0.012
#> GSM549256 1 0.0817 0.8534 0.976 0.000 0.000 0.024
#> GSM549257 1 0.4950 0.4500 0.620 0.004 0.000 0.376
#> GSM549263 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549267 4 0.4543 0.4813 0.000 0.000 0.324 0.676
#> GSM750745 1 0.0469 0.8543 0.988 0.000 0.000 0.012
#> GSM549239 1 0.0592 0.8535 0.984 0.000 0.000 0.016
#> GSM549244 1 0.4905 0.4744 0.632 0.004 0.000 0.364
#> GSM549249 1 0.4819 0.5081 0.652 0.004 0.000 0.344
#> GSM549260 1 0.0707 0.8523 0.980 0.000 0.000 0.020
#> GSM549266 1 0.7357 0.4986 0.644 0.160 0.064 0.132
#> GSM549293 2 0.0469 0.8191 0.000 0.988 0.012 0.000
#> GSM549236 1 0.0469 0.8549 0.988 0.000 0.000 0.012
#> GSM549238 1 0.4800 0.5137 0.656 0.004 0.000 0.340
#> GSM549251 1 0.0469 0.8549 0.988 0.000 0.000 0.012
#> GSM549258 1 0.0817 0.8508 0.976 0.000 0.000 0.024
#> GSM549264 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549243 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549262 1 0.0336 0.8555 0.992 0.000 0.000 0.008
#> GSM549278 4 0.5424 0.5692 0.176 0.012 0.064 0.748
#> GSM549283 1 0.9700 -0.1081 0.372 0.192 0.248 0.188
#> GSM549298 3 0.2402 0.7279 0.000 0.076 0.912 0.012
#> GSM750741 1 0.0895 0.8517 0.976 0.004 0.000 0.020
#> GSM549286 2 0.0000 0.8157 0.000 1.000 0.000 0.000
#> GSM549241 1 0.1109 0.8489 0.968 0.004 0.000 0.028
#> GSM549247 1 0.1488 0.8439 0.956 0.012 0.000 0.032
#> GSM549261 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM549270 2 0.2469 0.8126 0.000 0.892 0.108 0.000
#> GSM549277 3 0.6698 0.1539 0.000 0.372 0.532 0.096
#> GSM549280 2 0.5781 0.4595 0.000 0.584 0.380 0.036
#> GSM549281 1 0.8535 0.3286 0.548 0.156 0.132 0.164
#> GSM549285 3 0.8539 0.4250 0.096 0.164 0.532 0.208
#> GSM549288 2 0.5957 0.3430 0.000 0.540 0.420 0.040
#> GSM549292 2 0.0000 0.8157 0.000 1.000 0.000 0.000
#> GSM549295 3 0.4699 0.4129 0.000 0.320 0.676 0.004
#> GSM549297 2 0.5723 0.4519 0.000 0.580 0.388 0.032
#> GSM750743 1 0.0469 0.8548 0.988 0.000 0.000 0.012
#> GSM549268 1 0.8535 0.3286 0.548 0.156 0.132 0.164
#> GSM549290 4 0.5511 0.0308 0.000 0.016 0.484 0.500
#> GSM549272 2 0.0000 0.8157 0.000 1.000 0.000 0.000
#> GSM549276 2 0.2469 0.8126 0.000 0.892 0.108 0.000
#> GSM549275 1 0.3670 0.7713 0.860 0.044 0.004 0.092
#> GSM549284 2 0.4406 0.6919 0.004 0.788 0.184 0.024
#> GSM750737 4 0.6165 0.1076 0.448 0.040 0.004 0.508
#> GSM750740 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM750747 1 0.0188 0.8560 0.996 0.000 0.000 0.004
#> GSM750751 2 0.2101 0.8212 0.000 0.928 0.060 0.012
#> GSM750754 4 0.3937 0.6015 0.012 0.000 0.188 0.800
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.4016 0.49075 0.272 0.000 0.000 0.716 0.012
#> GSM549291 4 0.3080 0.60255 0.008 0.000 0.060 0.872 0.060
#> GSM549274 2 0.0880 0.77852 0.000 0.968 0.032 0.000 0.000
#> GSM750738 2 0.1685 0.74331 0.004 0.948 0.016 0.016 0.016
#> GSM750748 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549240 1 0.2199 0.82710 0.916 0.000 0.060 0.016 0.008
#> GSM549279 1 0.7152 0.30948 0.508 0.068 0.344 0.036 0.044
#> GSM549294 2 0.4028 0.72726 0.004 0.764 0.212 0.008 0.012
#> GSM549300 2 0.5988 0.29707 0.000 0.480 0.420 0.004 0.096
#> GSM549303 5 0.1908 0.95742 0.000 0.000 0.000 0.092 0.908
#> GSM549309 5 0.3132 0.88348 0.000 0.000 0.008 0.172 0.820
#> GSM750753 2 0.5058 0.51067 0.000 0.584 0.380 0.004 0.032
#> GSM750752 4 0.3901 0.57974 0.000 0.060 0.032 0.832 0.076
#> GSM549304 2 0.4400 0.70644 0.024 0.740 0.224 0.004 0.008
#> GSM549305 2 0.2127 0.77674 0.000 0.892 0.108 0.000 0.000
#> GSM549307 3 0.6545 0.45486 0.000 0.168 0.492 0.008 0.332
#> GSM549306 3 0.6218 0.43903 0.000 0.072 0.508 0.028 0.392
#> GSM549308 3 0.5805 0.41039 0.000 0.036 0.524 0.032 0.408
#> GSM549233 1 0.1168 0.84124 0.960 0.000 0.008 0.032 0.000
#> GSM549234 1 0.4161 0.43187 0.608 0.000 0.000 0.392 0.000
#> GSM549250 1 0.0794 0.84036 0.972 0.000 0.000 0.028 0.000
#> GSM549287 4 0.4810 0.47768 0.000 0.000 0.084 0.712 0.204
#> GSM750735 1 0.2053 0.83030 0.928 0.000 0.040 0.016 0.016
#> GSM750736 1 0.2053 0.83030 0.928 0.000 0.040 0.016 0.016
#> GSM750749 1 0.3456 0.78353 0.852 0.000 0.092 0.028 0.028
#> GSM549230 1 0.0404 0.84440 0.988 0.000 0.000 0.012 0.000
#> GSM549231 1 0.0404 0.84440 0.988 0.000 0.000 0.012 0.000
#> GSM549237 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549254 4 0.5267 0.47839 0.276 0.008 0.040 0.664 0.012
#> GSM750734 1 0.0898 0.84319 0.972 0.000 0.020 0.000 0.008
#> GSM549271 4 0.4158 0.55241 0.000 0.000 0.092 0.784 0.124
#> GSM549232 1 0.4192 0.41061 0.596 0.000 0.000 0.404 0.000
#> GSM549246 1 0.3365 0.73271 0.808 0.000 0.004 0.180 0.008
#> GSM549248 1 0.0290 0.84496 0.992 0.000 0.000 0.008 0.000
#> GSM549255 1 0.4182 0.41852 0.600 0.000 0.000 0.400 0.000
#> GSM750746 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549259 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549269 2 0.0324 0.76820 0.000 0.992 0.004 0.004 0.000
#> GSM549273 5 0.2068 0.95885 0.000 0.000 0.004 0.092 0.904
#> GSM549299 2 0.4400 0.70644 0.024 0.740 0.224 0.004 0.008
#> GSM549301 3 0.5638 0.44186 0.000 0.040 0.568 0.024 0.368
#> GSM549310 4 0.3920 0.57933 0.000 0.060 0.036 0.832 0.072
#> GSM549311 5 0.2068 0.95885 0.000 0.000 0.004 0.092 0.904
#> GSM549302 2 0.0703 0.77739 0.000 0.976 0.024 0.000 0.000
#> GSM549235 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549245 1 0.4182 0.41852 0.600 0.000 0.000 0.400 0.000
#> GSM549265 1 0.4060 0.48991 0.640 0.000 0.000 0.360 0.000
#> GSM549282 3 0.5876 -0.00642 0.000 0.000 0.512 0.104 0.384
#> GSM549296 4 0.3920 0.57933 0.000 0.060 0.036 0.832 0.072
#> GSM750739 1 0.0000 0.84527 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0404 0.84440 0.988 0.000 0.000 0.012 0.000
#> GSM750744 1 0.1267 0.84032 0.960 0.000 0.024 0.004 0.012
#> GSM750750 3 0.5876 -0.00642 0.000 0.000 0.512 0.104 0.384
#> GSM549242 1 0.1173 0.84494 0.964 0.000 0.012 0.020 0.004
#> GSM549252 1 0.4074 0.48223 0.636 0.000 0.000 0.364 0.000
#> GSM549253 1 0.0510 0.84383 0.984 0.000 0.000 0.016 0.000
#> GSM549256 1 0.1356 0.84298 0.956 0.000 0.012 0.028 0.004
#> GSM549257 1 0.4192 0.41061 0.596 0.000 0.000 0.404 0.000
#> GSM549263 1 0.0404 0.84440 0.988 0.000 0.000 0.012 0.000
#> GSM549267 4 0.5037 0.47191 0.000 0.000 0.088 0.684 0.228
#> GSM750745 1 0.1012 0.84287 0.968 0.000 0.020 0.000 0.012
#> GSM549239 1 0.1483 0.84068 0.952 0.000 0.028 0.008 0.012
#> GSM549244 1 0.4150 0.44233 0.612 0.000 0.000 0.388 0.000
#> GSM549249 1 0.4088 0.47514 0.632 0.000 0.000 0.368 0.000
#> GSM549260 1 0.1673 0.83771 0.944 0.000 0.032 0.016 0.008
#> GSM549266 1 0.6523 0.43840 0.572 0.044 0.316 0.028 0.040
#> GSM549293 2 0.0703 0.77739 0.000 0.976 0.024 0.000 0.000
#> GSM549236 1 0.0510 0.84383 0.984 0.000 0.000 0.016 0.000
#> GSM549238 1 0.4060 0.48450 0.640 0.000 0.000 0.360 0.000
#> GSM549251 1 0.0510 0.84383 0.984 0.000 0.000 0.016 0.000
#> GSM549258 1 0.1836 0.83522 0.936 0.000 0.040 0.016 0.008
#> GSM549264 1 0.0727 0.84570 0.980 0.000 0.004 0.012 0.004
#> GSM549243 1 0.0324 0.84568 0.992 0.000 0.004 0.000 0.004
#> GSM549262 1 0.0290 0.84496 0.992 0.000 0.000 0.008 0.000
#> GSM549278 4 0.4156 0.56407 0.168 0.000 0.028 0.784 0.020
#> GSM549283 3 0.7432 0.13945 0.316 0.064 0.512 0.044 0.064
#> GSM549298 3 0.5926 0.42131 0.000 0.044 0.520 0.032 0.404
#> GSM750741 1 0.1808 0.83357 0.936 0.000 0.044 0.012 0.008
#> GSM549286 2 0.0324 0.76820 0.000 0.992 0.004 0.004 0.000
#> GSM549241 1 0.2100 0.83076 0.924 0.000 0.048 0.016 0.012
#> GSM549247 1 0.2199 0.82710 0.916 0.000 0.060 0.016 0.008
#> GSM549261 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM549270 2 0.2536 0.77191 0.000 0.868 0.128 0.000 0.004
#> GSM549277 3 0.5587 0.26156 0.000 0.256 0.644 0.012 0.088
#> GSM549280 2 0.5867 0.37159 0.000 0.508 0.408 0.008 0.076
#> GSM549281 1 0.7215 0.23688 0.484 0.064 0.368 0.040 0.044
#> GSM549285 3 0.4879 0.33891 0.080 0.024 0.788 0.032 0.076
#> GSM549288 2 0.6359 0.23332 0.000 0.456 0.424 0.016 0.104
#> GSM549292 2 0.0324 0.76820 0.000 0.992 0.004 0.004 0.000
#> GSM549295 3 0.7406 0.34098 0.000 0.288 0.356 0.028 0.328
#> GSM549297 2 0.5942 0.36582 0.000 0.512 0.396 0.008 0.084
#> GSM750743 1 0.1267 0.84032 0.960 0.000 0.024 0.004 0.012
#> GSM549268 1 0.7215 0.23688 0.484 0.064 0.368 0.040 0.044
#> GSM549290 4 0.6579 -0.04728 0.000 0.000 0.220 0.448 0.332
#> GSM549272 2 0.0324 0.76820 0.000 0.992 0.004 0.004 0.000
#> GSM549276 2 0.2583 0.77061 0.000 0.864 0.132 0.000 0.004
#> GSM549275 1 0.4666 0.71838 0.780 0.016 0.140 0.020 0.044
#> GSM549284 2 0.4919 0.65545 0.004 0.768 0.112 0.036 0.080
#> GSM750737 4 0.5910 0.19808 0.408 0.008 0.052 0.520 0.012
#> GSM750740 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM750747 1 0.0451 0.84583 0.988 0.000 0.008 0.000 0.004
#> GSM750751 2 0.2249 0.77969 0.000 0.896 0.096 0.008 0.000
#> GSM750754 4 0.3268 0.59537 0.004 0.000 0.060 0.856 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4472 0.48611 0.228 0.000 0.000 0.708 0.036 0.028
#> GSM549291 4 0.3082 0.63996 0.008 0.000 0.020 0.828 0.000 0.144
#> GSM549274 2 0.1196 0.76000 0.000 0.952 0.040 0.000 0.008 0.000
#> GSM750738 2 0.2316 0.70534 0.000 0.912 0.012 0.032 0.024 0.020
#> GSM750748 1 0.0935 0.73717 0.964 0.000 0.000 0.000 0.032 0.004
#> GSM549240 1 0.3546 0.59776 0.788 0.000 0.004 0.020 0.180 0.008
#> GSM549279 5 0.6343 0.77314 0.348 0.048 0.080 0.008 0.508 0.008
#> GSM549294 2 0.4692 0.66155 0.000 0.716 0.152 0.008 0.120 0.004
#> GSM549300 3 0.5491 -0.02930 0.000 0.420 0.484 0.000 0.080 0.016
#> GSM549303 6 0.6775 0.57599 0.000 0.000 0.260 0.040 0.348 0.352
#> GSM549309 6 0.7349 0.55993 0.000 0.000 0.216 0.120 0.316 0.348
#> GSM750753 2 0.5464 0.27692 0.000 0.524 0.372 0.000 0.092 0.012
#> GSM750752 4 0.3285 0.62110 0.000 0.024 0.052 0.860 0.020 0.044
#> GSM549304 2 0.5024 0.61668 0.000 0.672 0.180 0.000 0.136 0.012
#> GSM549305 2 0.2913 0.74188 0.000 0.848 0.116 0.000 0.032 0.004
#> GSM549307 3 0.2219 0.59693 0.000 0.136 0.864 0.000 0.000 0.000
#> GSM549306 3 0.1950 0.56358 0.000 0.044 0.924 0.020 0.004 0.008
#> GSM549308 3 0.2034 0.52167 0.000 0.012 0.924 0.024 0.008 0.032
#> GSM549233 1 0.1515 0.73645 0.944 0.000 0.000 0.028 0.020 0.008
#> GSM549234 1 0.4718 0.32311 0.572 0.000 0.000 0.384 0.036 0.008
#> GSM549250 1 0.1167 0.73304 0.960 0.000 0.000 0.020 0.008 0.012
#> GSM549287 4 0.4514 0.50830 0.000 0.000 0.044 0.660 0.008 0.288
#> GSM750735 1 0.2814 0.62365 0.820 0.000 0.000 0.008 0.172 0.000
#> GSM750736 1 0.2848 0.61866 0.816 0.000 0.000 0.008 0.176 0.000
#> GSM750749 1 0.3848 0.35814 0.692 0.000 0.000 0.012 0.292 0.004
#> GSM549230 1 0.0767 0.73899 0.976 0.000 0.000 0.004 0.008 0.012
#> GSM549231 1 0.0767 0.73899 0.976 0.000 0.000 0.004 0.008 0.012
#> GSM549237 1 0.1152 0.73699 0.952 0.000 0.000 0.000 0.044 0.004
#> GSM549254 4 0.4843 0.47851 0.192 0.000 0.004 0.692 0.104 0.008
#> GSM750734 1 0.1814 0.69943 0.900 0.000 0.000 0.000 0.100 0.000
#> GSM549271 4 0.4140 0.59040 0.000 0.000 0.056 0.756 0.016 0.172
#> GSM549232 1 0.4751 0.30185 0.556 0.000 0.000 0.400 0.036 0.008
#> GSM549246 1 0.3987 0.56377 0.760 0.000 0.000 0.176 0.056 0.008
#> GSM549248 1 0.0622 0.73977 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM549255 1 0.4743 0.30812 0.560 0.000 0.000 0.396 0.036 0.008
#> GSM750746 1 0.0935 0.73717 0.964 0.000 0.000 0.000 0.032 0.004
#> GSM549259 1 0.0935 0.73717 0.964 0.000 0.000 0.000 0.032 0.004
#> GSM549269 2 0.0508 0.74790 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM549273 6 0.6775 0.57583 0.000 0.000 0.260 0.040 0.348 0.352
#> GSM549299 2 0.5024 0.61668 0.000 0.672 0.180 0.000 0.136 0.012
#> GSM549301 3 0.2669 0.53268 0.000 0.016 0.892 0.012 0.044 0.036
#> GSM549310 4 0.3136 0.62091 0.000 0.020 0.052 0.868 0.020 0.040
#> GSM549311 6 0.6775 0.57583 0.000 0.000 0.260 0.040 0.348 0.352
#> GSM549302 2 0.0972 0.75930 0.000 0.964 0.028 0.000 0.008 0.000
#> GSM549235 1 0.1010 0.73605 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM549245 1 0.4743 0.30812 0.560 0.000 0.000 0.396 0.036 0.008
#> GSM549265 1 0.4582 0.36526 0.612 0.000 0.000 0.348 0.028 0.012
#> GSM549282 6 0.4125 0.32598 0.000 0.000 0.244 0.024 0.016 0.716
#> GSM549296 4 0.3136 0.62091 0.000 0.020 0.052 0.868 0.020 0.040
#> GSM750739 1 0.0458 0.74170 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM750742 1 0.0767 0.73899 0.976 0.000 0.000 0.004 0.008 0.012
#> GSM750744 1 0.2048 0.68325 0.880 0.000 0.000 0.000 0.120 0.000
#> GSM750750 6 0.4125 0.32598 0.000 0.000 0.244 0.024 0.016 0.716
#> GSM549242 1 0.1873 0.73168 0.924 0.000 0.000 0.020 0.048 0.008
#> GSM549252 1 0.4527 0.36187 0.604 0.000 0.000 0.360 0.028 0.008
#> GSM549253 1 0.0881 0.73796 0.972 0.000 0.000 0.008 0.008 0.012
#> GSM549256 1 0.2036 0.73015 0.916 0.000 0.000 0.028 0.048 0.008
#> GSM549257 1 0.4751 0.30185 0.556 0.000 0.000 0.400 0.036 0.008
#> GSM549263 1 0.0767 0.73899 0.976 0.000 0.000 0.004 0.008 0.012
#> GSM549267 4 0.4302 0.48399 0.000 0.000 0.028 0.644 0.004 0.324
#> GSM750745 1 0.1863 0.69744 0.896 0.000 0.000 0.000 0.104 0.000
#> GSM549239 1 0.2234 0.68301 0.872 0.000 0.000 0.004 0.124 0.000
#> GSM549244 1 0.4598 0.32770 0.576 0.000 0.000 0.388 0.028 0.008
#> GSM549249 1 0.4538 0.35556 0.600 0.000 0.000 0.364 0.028 0.008
#> GSM549260 1 0.3144 0.65626 0.832 0.000 0.004 0.020 0.136 0.008
#> GSM549266 5 0.5997 0.63178 0.436 0.032 0.068 0.004 0.452 0.008
#> GSM549293 2 0.0972 0.75930 0.000 0.964 0.028 0.000 0.008 0.000
#> GSM549236 1 0.0881 0.73796 0.972 0.000 0.000 0.008 0.008 0.012
#> GSM549238 1 0.4515 0.36323 0.608 0.000 0.000 0.356 0.028 0.008
#> GSM549251 1 0.0881 0.73796 0.972 0.000 0.000 0.008 0.008 0.012
#> GSM549258 1 0.3337 0.63303 0.812 0.000 0.004 0.020 0.156 0.008
#> GSM549264 1 0.0964 0.74068 0.968 0.000 0.000 0.004 0.016 0.012
#> GSM549243 1 0.0363 0.74091 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549262 1 0.0622 0.73977 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM549278 4 0.4106 0.60479 0.116 0.000 0.004 0.788 0.064 0.028
#> GSM549283 5 0.7558 0.45108 0.192 0.032 0.232 0.004 0.456 0.084
#> GSM549298 3 0.1976 0.53722 0.000 0.020 0.928 0.024 0.008 0.020
#> GSM750741 1 0.3159 0.63158 0.812 0.000 0.004 0.012 0.168 0.004
#> GSM549286 2 0.0405 0.74921 0.000 0.988 0.000 0.000 0.004 0.008
#> GSM549241 1 0.3513 0.60649 0.792 0.000 0.004 0.020 0.176 0.008
#> GSM549247 1 0.3546 0.59776 0.788 0.000 0.004 0.020 0.180 0.008
#> GSM549261 1 0.1010 0.73605 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM549270 2 0.3264 0.73074 0.000 0.820 0.136 0.000 0.040 0.004
#> GSM549277 3 0.6590 0.44913 0.000 0.196 0.540 0.000 0.160 0.104
#> GSM549280 2 0.5787 0.00144 0.000 0.444 0.436 0.000 0.096 0.024
#> GSM549281 5 0.6764 0.80736 0.316 0.036 0.108 0.012 0.504 0.024
#> GSM549285 3 0.7038 0.11778 0.040 0.008 0.404 0.004 0.300 0.244
#> GSM549288 3 0.6059 0.05629 0.000 0.396 0.468 0.008 0.104 0.024
#> GSM549292 2 0.0508 0.74790 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM549295 3 0.4416 0.52407 0.000 0.232 0.712 0.020 0.032 0.004
#> GSM549297 2 0.5843 0.03863 0.000 0.460 0.420 0.004 0.096 0.020
#> GSM750743 1 0.2048 0.68325 0.880 0.000 0.000 0.000 0.120 0.000
#> GSM549268 5 0.6764 0.80736 0.316 0.036 0.108 0.012 0.504 0.024
#> GSM549290 6 0.4900 0.00537 0.000 0.000 0.044 0.372 0.012 0.572
#> GSM549272 2 0.0508 0.74790 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM549276 2 0.3304 0.72854 0.000 0.816 0.140 0.000 0.040 0.004
#> GSM549275 1 0.4212 -0.02891 0.592 0.000 0.008 0.008 0.392 0.000
#> GSM549284 2 0.4474 0.60437 0.000 0.756 0.080 0.008 0.020 0.136
#> GSM750737 4 0.5569 0.13392 0.332 0.000 0.000 0.536 0.124 0.008
#> GSM750740 1 0.1010 0.73605 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM750747 1 0.1010 0.73605 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM750751 2 0.2914 0.74819 0.000 0.860 0.092 0.004 0.040 0.004
#> GSM750754 4 0.3384 0.63322 0.004 0.000 0.028 0.808 0.004 0.156
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:hclust 97 0.0270 8.31e-06 0.23588 0.0121 2
#> MAD:hclust 90 0.0226 3.66e-06 0.00238 0.0756 3
#> MAD:hclust 79 0.0859 1.30e-04 0.01537 0.0202 4
#> MAD:hclust 68 0.1674 4.22e-04 0.00715 0.0280 5
#> MAD:hclust 76 0.3255 1.55e-04 0.00814 0.0688 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.998 0.999 0.5041 0.496 0.496
#> 3 3 0.766 0.476 0.688 0.2745 0.915 0.831
#> 4 4 0.691 0.732 0.810 0.1455 0.779 0.516
#> 5 5 0.798 0.828 0.882 0.0677 0.905 0.669
#> 6 6 0.752 0.603 0.749 0.0445 0.935 0.713
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
#> GSM549289 1 0.0000 1.000 1.000 0.000
#> GSM549291 2 0.0672 0.992 0.008 0.992
#> GSM549274 2 0.0000 0.998 0.000 1.000
#> GSM750738 2 0.0000 0.998 0.000 1.000
#> GSM750748 1 0.0000 1.000 1.000 0.000
#> GSM549240 1 0.0000 1.000 1.000 0.000
#> GSM549279 2 0.1414 0.981 0.020 0.980
#> GSM549294 2 0.0000 0.998 0.000 1.000
#> GSM549300 2 0.0000 0.998 0.000 1.000
#> GSM549303 2 0.0000 0.998 0.000 1.000
#> GSM549309 2 0.0000 0.998 0.000 1.000
#> GSM750753 2 0.0000 0.998 0.000 1.000
#> GSM750752 2 0.0000 0.998 0.000 1.000
#> GSM549304 2 0.0000 0.998 0.000 1.000
#> GSM549305 2 0.0000 0.998 0.000 1.000
#> GSM549307 2 0.0000 0.998 0.000 1.000
#> GSM549306 2 0.0000 0.998 0.000 1.000
#> GSM549308 2 0.0000 0.998 0.000 1.000
#> GSM549233 1 0.0000 1.000 1.000 0.000
#> GSM549234 1 0.0000 1.000 1.000 0.000
#> GSM549250 1 0.0000 1.000 1.000 0.000
#> GSM549287 2 0.0000 0.998 0.000 1.000
#> GSM750735 1 0.0000 1.000 1.000 0.000
#> GSM750736 1 0.0000 1.000 1.000 0.000
#> GSM750749 1 0.0000 1.000 1.000 0.000
#> GSM549230 1 0.0000 1.000 1.000 0.000
#> GSM549231 1 0.0000 1.000 1.000 0.000
#> GSM549237 1 0.0000 1.000 1.000 0.000
#> GSM549254 1 0.0376 0.996 0.996 0.004
#> GSM750734 1 0.0000 1.000 1.000 0.000
#> GSM549271 2 0.0000 0.998 0.000 1.000
#> GSM549232 1 0.0000 1.000 1.000 0.000
#> GSM549246 1 0.0000 1.000 1.000 0.000
#> GSM549248 1 0.0000 1.000 1.000 0.000
#> GSM549255 1 0.0000 1.000 1.000 0.000
#> GSM750746 1 0.0000 1.000 1.000 0.000
#> GSM549259 1 0.0000 1.000 1.000 0.000
#> GSM549269 2 0.0000 0.998 0.000 1.000
#> GSM549273 2 0.0000 0.998 0.000 1.000
#> GSM549299 2 0.0000 0.998 0.000 1.000
#> GSM549301 2 0.0000 0.998 0.000 1.000
#> GSM549310 2 0.0000 0.998 0.000 1.000
#> GSM549311 2 0.0000 0.998 0.000 1.000
#> GSM549302 2 0.0000 0.998 0.000 1.000
#> GSM549235 1 0.0000 1.000 1.000 0.000
#> GSM549245 1 0.0000 1.000 1.000 0.000
#> GSM549265 1 0.0000 1.000 1.000 0.000
#> GSM549282 2 0.0000 0.998 0.000 1.000
#> GSM549296 2 0.0000 0.998 0.000 1.000
#> GSM750739 1 0.0000 1.000 1.000 0.000
#> GSM750742 1 0.0000 1.000 1.000 0.000
#> GSM750744 1 0.0000 1.000 1.000 0.000
#> GSM750750 2 0.0000 0.998 0.000 1.000
#> GSM549242 1 0.0000 1.000 1.000 0.000
#> GSM549252 1 0.0000 1.000 1.000 0.000
#> GSM549253 1 0.0000 1.000 1.000 0.000
#> GSM549256 1 0.0000 1.000 1.000 0.000
#> GSM549257 1 0.0000 1.000 1.000 0.000
#> GSM549263 1 0.0000 1.000 1.000 0.000
#> GSM549267 2 0.0000 0.998 0.000 1.000
#> GSM750745 1 0.0000 1.000 1.000 0.000
#> GSM549239 1 0.0000 1.000 1.000 0.000
#> GSM549244 1 0.0000 1.000 1.000 0.000
#> GSM549249 1 0.0000 1.000 1.000 0.000
#> GSM549260 1 0.0000 1.000 1.000 0.000
#> GSM549266 2 0.1414 0.981 0.020 0.980
#> GSM549293 2 0.0000 0.998 0.000 1.000
#> GSM549236 1 0.0000 1.000 1.000 0.000
#> GSM549238 1 0.0000 1.000 1.000 0.000
#> GSM549251 1 0.0000 1.000 1.000 0.000
#> GSM549258 1 0.0000 1.000 1.000 0.000
#> GSM549264 1 0.0000 1.000 1.000 0.000
#> GSM549243 1 0.0000 1.000 1.000 0.000
#> GSM549262 1 0.0000 1.000 1.000 0.000
#> GSM549278 1 0.0000 1.000 1.000 0.000
#> GSM549283 2 0.0000 0.998 0.000 1.000
#> GSM549298 2 0.0000 0.998 0.000 1.000
#> GSM750741 1 0.0000 1.000 1.000 0.000
#> GSM549286 2 0.0000 0.998 0.000 1.000
#> GSM549241 1 0.0000 1.000 1.000 0.000
#> GSM549247 1 0.0000 1.000 1.000 0.000
#> GSM549261 1 0.0000 1.000 1.000 0.000
#> GSM549270 2 0.0000 0.998 0.000 1.000
#> GSM549277 2 0.0000 0.998 0.000 1.000
#> GSM549280 2 0.0000 0.998 0.000 1.000
#> GSM549281 2 0.1633 0.977 0.024 0.976
#> GSM549285 2 0.0000 0.998 0.000 1.000
#> GSM549288 2 0.0000 0.998 0.000 1.000
#> GSM549292 2 0.0000 0.998 0.000 1.000
#> GSM549295 2 0.0000 0.998 0.000 1.000
#> GSM549297 2 0.0000 0.998 0.000 1.000
#> GSM750743 1 0.0000 1.000 1.000 0.000
#> GSM549268 2 0.1414 0.981 0.020 0.980
#> GSM549290 2 0.0000 0.998 0.000 1.000
#> GSM549272 2 0.0000 0.998 0.000 1.000
#> GSM549276 2 0.0000 0.998 0.000 1.000
#> GSM549275 1 0.0000 1.000 1.000 0.000
#> GSM549284 2 0.0000 0.998 0.000 1.000
#> GSM750737 1 0.0000 1.000 1.000 0.000
#> GSM750740 1 0.0000 1.000 1.000 0.000
#> GSM750747 1 0.0000 1.000 1.000 0.000
#> GSM750751 2 0.0000 0.998 0.000 1.000
#> GSM750754 2 0.0000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.7824 -0.1653 0.444 0.052 0.504
#> GSM549291 2 0.6309 0.0763 0.000 0.500 0.500
#> GSM549274 2 0.6305 0.4269 0.000 0.516 0.484
#> GSM750738 2 0.6307 0.4260 0.000 0.512 0.488
#> GSM750748 1 0.0000 0.8672 1.000 0.000 0.000
#> GSM549240 1 0.1289 0.8599 0.968 0.000 0.032
#> GSM549279 3 0.7480 -0.4068 0.036 0.456 0.508
#> GSM549294 2 0.6307 0.4215 0.000 0.512 0.488
#> GSM549300 2 0.3879 0.3965 0.000 0.848 0.152
#> GSM549303 2 0.5291 0.2540 0.000 0.732 0.268
#> GSM549309 2 0.6260 0.1335 0.000 0.552 0.448
#> GSM750753 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM750752 2 0.6280 0.1286 0.000 0.540 0.460
#> GSM549304 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549305 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549307 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549306 2 0.1643 0.3888 0.000 0.956 0.044
#> GSM549308 2 0.0424 0.3777 0.000 0.992 0.008
#> GSM549233 1 0.1753 0.8589 0.952 0.000 0.048
#> GSM549234 1 0.6309 0.1633 0.504 0.000 0.496
#> GSM549250 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549287 2 0.6274 0.1276 0.000 0.544 0.456
#> GSM750735 1 0.0747 0.8628 0.984 0.000 0.016
#> GSM750736 1 0.1289 0.8599 0.968 0.000 0.032
#> GSM750749 1 0.1163 0.8607 0.972 0.000 0.028
#> GSM549230 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549231 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549237 1 0.1031 0.8656 0.976 0.000 0.024
#> GSM549254 3 0.6672 -0.2331 0.472 0.008 0.520
#> GSM750734 1 0.0424 0.8659 0.992 0.000 0.008
#> GSM549271 2 0.6260 0.1337 0.000 0.552 0.448
#> GSM549232 1 0.6309 0.1522 0.500 0.000 0.500
#> GSM549246 1 0.6111 0.3926 0.604 0.000 0.396
#> GSM549248 1 0.1031 0.8656 0.976 0.000 0.024
#> GSM549255 1 0.6309 0.1522 0.500 0.000 0.500
#> GSM750746 1 0.0237 0.8669 0.996 0.000 0.004
#> GSM549259 1 0.0237 0.8669 0.996 0.000 0.004
#> GSM549269 2 0.6307 0.4215 0.000 0.512 0.488
#> GSM549273 2 0.0424 0.3777 0.000 0.992 0.008
#> GSM549299 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549301 2 0.0747 0.3836 0.000 0.984 0.016
#> GSM549310 2 0.6280 0.1286 0.000 0.540 0.460
#> GSM549311 2 0.5291 0.2540 0.000 0.732 0.268
#> GSM549302 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549235 1 0.0000 0.8672 1.000 0.000 0.000
#> GSM549245 3 0.6309 -0.2618 0.500 0.000 0.500
#> GSM549265 1 0.6309 0.1522 0.500 0.000 0.500
#> GSM549282 2 0.5397 0.2461 0.000 0.720 0.280
#> GSM549296 2 0.6295 0.1168 0.000 0.528 0.472
#> GSM750739 1 0.0000 0.8672 1.000 0.000 0.000
#> GSM750742 1 0.1031 0.8656 0.976 0.000 0.024
#> GSM750744 1 0.0237 0.8674 0.996 0.000 0.004
#> GSM750750 2 0.5178 0.2601 0.000 0.744 0.256
#> GSM549242 1 0.1753 0.8589 0.952 0.000 0.048
#> GSM549252 1 0.6309 0.1633 0.504 0.000 0.496
#> GSM549253 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549256 1 0.1753 0.8589 0.952 0.000 0.048
#> GSM549257 1 0.6308 0.1733 0.508 0.000 0.492
#> GSM549263 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549267 2 0.6302 0.1015 0.000 0.520 0.480
#> GSM750745 1 0.0424 0.8659 0.992 0.000 0.008
#> GSM549239 1 0.0424 0.8659 0.992 0.000 0.008
#> GSM549244 3 0.6309 -0.2618 0.500 0.000 0.500
#> GSM549249 1 0.6309 0.1633 0.504 0.000 0.496
#> GSM549260 1 0.0747 0.8661 0.984 0.000 0.016
#> GSM549266 3 0.7480 -0.4068 0.036 0.456 0.508
#> GSM549293 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549236 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549238 1 0.4452 0.7100 0.808 0.000 0.192
#> GSM549251 1 0.1411 0.8619 0.964 0.000 0.036
#> GSM549258 1 0.1163 0.8620 0.972 0.000 0.028
#> GSM549264 1 0.1031 0.8656 0.976 0.000 0.024
#> GSM549243 1 0.0000 0.8672 1.000 0.000 0.000
#> GSM549262 1 0.1031 0.8656 0.976 0.000 0.024
#> GSM549278 3 0.7283 -0.1485 0.028 0.460 0.512
#> GSM549283 2 0.6302 0.4320 0.000 0.520 0.480
#> GSM549298 2 0.1643 0.3888 0.000 0.956 0.044
#> GSM750741 1 0.1289 0.8599 0.968 0.000 0.032
#> GSM549286 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549241 1 0.0892 0.8607 0.980 0.000 0.020
#> GSM549247 1 0.1289 0.8599 0.968 0.000 0.032
#> GSM549261 1 0.0237 0.8669 0.996 0.000 0.004
#> GSM549270 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549277 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549280 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549281 3 0.7480 -0.4068 0.036 0.456 0.508
#> GSM549285 2 0.0424 0.3774 0.000 0.992 0.008
#> GSM549288 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549292 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549295 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549297 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM750743 1 0.0424 0.8659 0.992 0.000 0.008
#> GSM549268 3 0.7480 -0.4068 0.036 0.456 0.508
#> GSM549290 2 0.6302 0.1015 0.000 0.520 0.480
#> GSM549272 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549276 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM549275 1 0.1289 0.8599 0.968 0.000 0.032
#> GSM549284 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM750737 1 0.5835 0.5220 0.660 0.000 0.340
#> GSM750740 1 0.0237 0.8669 0.996 0.000 0.004
#> GSM750747 1 0.0237 0.8669 0.996 0.000 0.004
#> GSM750751 2 0.6299 0.4367 0.000 0.524 0.476
#> GSM750754 2 0.6309 0.0814 0.000 0.504 0.496
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.5788 0.809 0.084 0.000 0.228 0.688
#> GSM549291 4 0.5000 0.407 0.000 0.000 0.496 0.504
#> GSM549274 2 0.0336 0.908 0.000 0.992 0.000 0.008
#> GSM750738 2 0.0592 0.905 0.000 0.984 0.000 0.016
#> GSM750748 1 0.0000 0.831 1.000 0.000 0.000 0.000
#> GSM549240 1 0.4907 0.590 0.580 0.000 0.000 0.420
#> GSM549279 2 0.6744 0.443 0.084 0.528 0.004 0.384
#> GSM549294 2 0.0000 0.910 0.000 1.000 0.000 0.000
#> GSM549300 3 0.5295 0.140 0.000 0.488 0.504 0.008
#> GSM549303 3 0.1722 0.728 0.000 0.048 0.944 0.008
#> GSM549309 3 0.0524 0.698 0.000 0.004 0.988 0.008
#> GSM750753 2 0.0188 0.909 0.000 0.996 0.000 0.004
#> GSM750752 4 0.4996 0.430 0.000 0.000 0.484 0.516
#> GSM549304 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549305 2 0.0000 0.910 0.000 1.000 0.000 0.000
#> GSM549307 2 0.2271 0.841 0.000 0.916 0.076 0.008
#> GSM549306 3 0.4511 0.630 0.000 0.268 0.724 0.008
#> GSM549308 3 0.3893 0.695 0.000 0.196 0.796 0.008
#> GSM549233 1 0.3355 0.776 0.836 0.000 0.004 0.160
#> GSM549234 4 0.6027 0.821 0.124 0.000 0.192 0.684
#> GSM549250 1 0.2831 0.797 0.876 0.000 0.004 0.120
#> GSM549287 3 0.0336 0.694 0.000 0.000 0.992 0.008
#> GSM750735 1 0.4331 0.705 0.712 0.000 0.000 0.288
#> GSM750736 1 0.4817 0.623 0.612 0.000 0.000 0.388
#> GSM750749 1 0.4790 0.629 0.620 0.000 0.000 0.380
#> GSM549230 1 0.2714 0.801 0.884 0.000 0.004 0.112
#> GSM549231 1 0.2654 0.803 0.888 0.000 0.004 0.108
#> GSM549237 1 0.1557 0.824 0.944 0.000 0.000 0.056
#> GSM549254 4 0.4387 0.725 0.024 0.000 0.200 0.776
#> GSM750734 1 0.1474 0.822 0.948 0.000 0.000 0.052
#> GSM549271 3 0.0336 0.694 0.000 0.000 0.992 0.008
#> GSM549232 4 0.5820 0.825 0.100 0.000 0.204 0.696
#> GSM549246 4 0.6078 0.795 0.152 0.000 0.164 0.684
#> GSM549248 1 0.2197 0.816 0.916 0.000 0.004 0.080
#> GSM549255 4 0.5820 0.825 0.100 0.000 0.204 0.696
#> GSM750746 1 0.0469 0.830 0.988 0.000 0.000 0.012
#> GSM549259 1 0.1302 0.827 0.956 0.000 0.000 0.044
#> GSM549269 2 0.0336 0.908 0.000 0.992 0.000 0.008
#> GSM549273 3 0.3972 0.693 0.000 0.204 0.788 0.008
#> GSM549299 2 0.0188 0.909 0.000 0.996 0.000 0.004
#> GSM549301 3 0.4011 0.689 0.000 0.208 0.784 0.008
#> GSM549310 3 0.4961 -0.324 0.000 0.000 0.552 0.448
#> GSM549311 3 0.1576 0.727 0.000 0.048 0.948 0.004
#> GSM549302 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549235 1 0.0000 0.831 1.000 0.000 0.000 0.000
#> GSM549245 4 0.5820 0.825 0.100 0.000 0.204 0.696
#> GSM549265 4 0.5998 0.823 0.116 0.000 0.200 0.684
#> GSM549282 3 0.1118 0.722 0.000 0.036 0.964 0.000
#> GSM549296 4 0.4981 0.471 0.000 0.000 0.464 0.536
#> GSM750739 1 0.0000 0.831 1.000 0.000 0.000 0.000
#> GSM750742 1 0.2197 0.816 0.916 0.000 0.004 0.080
#> GSM750744 1 0.1302 0.826 0.956 0.000 0.000 0.044
#> GSM750750 3 0.1389 0.727 0.000 0.048 0.952 0.000
#> GSM549242 1 0.3257 0.783 0.844 0.000 0.004 0.152
#> GSM549252 4 0.6027 0.821 0.124 0.000 0.192 0.684
#> GSM549253 1 0.2831 0.797 0.876 0.000 0.004 0.120
#> GSM549256 1 0.3402 0.773 0.832 0.000 0.004 0.164
#> GSM549257 4 0.5820 0.824 0.108 0.000 0.192 0.700
#> GSM549263 1 0.2714 0.801 0.884 0.000 0.004 0.112
#> GSM549267 3 0.4776 -0.107 0.000 0.000 0.624 0.376
#> GSM750745 1 0.3764 0.751 0.784 0.000 0.000 0.216
#> GSM549239 1 0.3074 0.783 0.848 0.000 0.000 0.152
#> GSM549244 4 0.5839 0.826 0.104 0.000 0.200 0.696
#> GSM549249 4 0.6027 0.821 0.124 0.000 0.192 0.684
#> GSM549260 1 0.2011 0.820 0.920 0.000 0.000 0.080
#> GSM549266 2 0.6724 0.453 0.084 0.536 0.004 0.376
#> GSM549293 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549236 1 0.2831 0.797 0.876 0.000 0.004 0.120
#> GSM549238 4 0.5016 0.421 0.396 0.000 0.004 0.600
#> GSM549251 1 0.2714 0.801 0.884 0.000 0.004 0.112
#> GSM549258 1 0.4454 0.697 0.692 0.000 0.000 0.308
#> GSM549264 1 0.2334 0.813 0.908 0.000 0.004 0.088
#> GSM549243 1 0.0000 0.831 1.000 0.000 0.000 0.000
#> GSM549262 1 0.2197 0.816 0.916 0.000 0.004 0.080
#> GSM549278 4 0.4804 0.604 0.000 0.000 0.384 0.616
#> GSM549283 2 0.0657 0.905 0.000 0.984 0.004 0.012
#> GSM549298 3 0.4539 0.625 0.000 0.272 0.720 0.008
#> GSM750741 1 0.4817 0.623 0.612 0.000 0.000 0.388
#> GSM549286 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549241 1 0.4522 0.685 0.680 0.000 0.000 0.320
#> GSM549247 1 0.4907 0.590 0.580 0.000 0.000 0.420
#> GSM549261 1 0.1302 0.827 0.956 0.000 0.000 0.044
#> GSM549270 2 0.0000 0.910 0.000 1.000 0.000 0.000
#> GSM549277 2 0.1256 0.891 0.000 0.964 0.028 0.008
#> GSM549280 2 0.1109 0.893 0.000 0.968 0.028 0.004
#> GSM549281 2 0.6557 0.467 0.072 0.548 0.004 0.376
#> GSM549285 3 0.2530 0.723 0.000 0.100 0.896 0.004
#> GSM549288 2 0.1356 0.887 0.000 0.960 0.032 0.008
#> GSM549292 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549295 2 0.1151 0.893 0.000 0.968 0.024 0.008
#> GSM549297 2 0.0188 0.909 0.000 0.996 0.000 0.004
#> GSM750743 1 0.3444 0.768 0.816 0.000 0.000 0.184
#> GSM549268 2 0.6557 0.467 0.072 0.548 0.004 0.376
#> GSM549290 3 0.4855 -0.182 0.000 0.000 0.600 0.400
#> GSM549272 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM549276 2 0.0000 0.910 0.000 1.000 0.000 0.000
#> GSM549275 1 0.4817 0.623 0.612 0.000 0.000 0.388
#> GSM549284 2 0.0188 0.910 0.000 0.996 0.000 0.004
#> GSM750737 4 0.1576 0.542 0.048 0.000 0.004 0.948
#> GSM750740 1 0.1302 0.827 0.956 0.000 0.000 0.044
#> GSM750747 1 0.0469 0.830 0.988 0.000 0.000 0.012
#> GSM750751 2 0.0000 0.910 0.000 1.000 0.000 0.000
#> GSM750754 3 0.1022 0.671 0.000 0.000 0.968 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1041 0.877 0.000 0.000 0.004 0.964 0.032
#> GSM549291 4 0.3262 0.809 0.000 0.000 0.124 0.840 0.036
#> GSM549274 2 0.0162 0.902 0.000 0.996 0.000 0.000 0.004
#> GSM750738 2 0.0290 0.901 0.000 0.992 0.000 0.000 0.008
#> GSM750748 1 0.0671 0.916 0.980 0.000 0.004 0.000 0.016
#> GSM549240 5 0.5142 0.796 0.192 0.000 0.008 0.096 0.704
#> GSM549279 5 0.4041 0.671 0.012 0.164 0.012 0.016 0.796
#> GSM549294 2 0.1732 0.890 0.000 0.920 0.000 0.000 0.080
#> GSM549300 3 0.5930 0.539 0.000 0.196 0.596 0.000 0.208
#> GSM549303 3 0.1907 0.881 0.000 0.000 0.928 0.044 0.028
#> GSM549309 3 0.1205 0.876 0.000 0.000 0.956 0.040 0.004
#> GSM750753 2 0.2448 0.884 0.000 0.892 0.020 0.000 0.088
#> GSM750752 4 0.3037 0.823 0.000 0.000 0.100 0.860 0.040
#> GSM549304 2 0.0162 0.902 0.000 0.996 0.000 0.000 0.004
#> GSM549305 2 0.0963 0.901 0.000 0.964 0.000 0.000 0.036
#> GSM549307 2 0.5954 0.596 0.000 0.592 0.192 0.000 0.216
#> GSM549306 3 0.3921 0.821 0.000 0.044 0.784 0.000 0.172
#> GSM549308 3 0.2771 0.859 0.000 0.012 0.860 0.000 0.128
#> GSM549233 1 0.1732 0.861 0.920 0.000 0.000 0.080 0.000
#> GSM549234 4 0.1124 0.887 0.036 0.000 0.000 0.960 0.004
#> GSM549250 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549287 3 0.1915 0.865 0.000 0.000 0.928 0.040 0.032
#> GSM750735 5 0.4161 0.789 0.280 0.000 0.000 0.016 0.704
#> GSM750736 5 0.4090 0.800 0.268 0.000 0.000 0.016 0.716
#> GSM750749 5 0.4208 0.810 0.248 0.000 0.004 0.020 0.728
#> GSM549230 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549231 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549237 1 0.0566 0.918 0.984 0.000 0.000 0.004 0.012
#> GSM549254 4 0.1121 0.879 0.000 0.000 0.000 0.956 0.044
#> GSM750734 1 0.1478 0.888 0.936 0.000 0.000 0.000 0.064
#> GSM549271 3 0.2074 0.863 0.000 0.000 0.920 0.044 0.036
#> GSM549232 4 0.1117 0.888 0.020 0.000 0.000 0.964 0.016
#> GSM549246 4 0.1043 0.885 0.040 0.000 0.000 0.960 0.000
#> GSM549248 1 0.0290 0.918 0.992 0.000 0.000 0.008 0.000
#> GSM549255 4 0.1216 0.888 0.020 0.000 0.000 0.960 0.020
#> GSM750746 1 0.0671 0.916 0.980 0.000 0.004 0.000 0.016
#> GSM549259 1 0.2124 0.854 0.900 0.000 0.004 0.000 0.096
#> GSM549269 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.2930 0.878 0.000 0.012 0.880 0.032 0.076
#> GSM549299 2 0.2864 0.870 0.000 0.864 0.024 0.000 0.112
#> GSM549301 3 0.3438 0.836 0.000 0.020 0.808 0.000 0.172
#> GSM549310 4 0.4552 0.639 0.000 0.000 0.264 0.696 0.040
#> GSM549311 3 0.1408 0.879 0.000 0.000 0.948 0.044 0.008
#> GSM549302 2 0.0162 0.902 0.000 0.996 0.000 0.000 0.004
#> GSM549235 1 0.0566 0.917 0.984 0.000 0.004 0.000 0.012
#> GSM549245 4 0.1216 0.888 0.020 0.000 0.000 0.960 0.020
#> GSM549265 4 0.1202 0.888 0.032 0.000 0.004 0.960 0.004
#> GSM549282 3 0.1469 0.881 0.000 0.000 0.948 0.036 0.016
#> GSM549296 4 0.2569 0.843 0.000 0.000 0.068 0.892 0.040
#> GSM750739 1 0.0609 0.915 0.980 0.000 0.000 0.000 0.020
#> GSM750742 1 0.0290 0.918 0.992 0.000 0.000 0.008 0.000
#> GSM750744 1 0.0703 0.914 0.976 0.000 0.000 0.000 0.024
#> GSM750750 3 0.1753 0.883 0.000 0.000 0.936 0.032 0.032
#> GSM549242 1 0.1270 0.890 0.948 0.000 0.000 0.052 0.000
#> GSM549252 4 0.1124 0.887 0.036 0.000 0.000 0.960 0.004
#> GSM549253 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549256 1 0.2179 0.823 0.888 0.000 0.000 0.112 0.000
#> GSM549257 4 0.1117 0.888 0.020 0.000 0.000 0.964 0.016
#> GSM549263 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549267 4 0.4958 0.464 0.000 0.000 0.372 0.592 0.036
#> GSM750745 1 0.4126 0.230 0.620 0.000 0.000 0.000 0.380
#> GSM549239 1 0.3210 0.683 0.788 0.000 0.000 0.000 0.212
#> GSM549244 4 0.1082 0.888 0.028 0.000 0.000 0.964 0.008
#> GSM549249 4 0.1124 0.887 0.036 0.000 0.000 0.960 0.004
#> GSM549260 1 0.1571 0.891 0.936 0.000 0.000 0.004 0.060
#> GSM549266 5 0.4002 0.675 0.012 0.160 0.012 0.016 0.800
#> GSM549293 2 0.0162 0.902 0.000 0.996 0.000 0.000 0.004
#> GSM549236 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549238 4 0.3231 0.725 0.196 0.000 0.000 0.800 0.004
#> GSM549251 1 0.0510 0.916 0.984 0.000 0.000 0.016 0.000
#> GSM549258 5 0.4151 0.682 0.344 0.000 0.000 0.004 0.652
#> GSM549264 1 0.0290 0.918 0.992 0.000 0.000 0.008 0.000
#> GSM549243 1 0.0404 0.917 0.988 0.000 0.000 0.000 0.012
#> GSM549262 1 0.0290 0.918 0.992 0.000 0.000 0.008 0.000
#> GSM549278 4 0.1626 0.872 0.000 0.000 0.016 0.940 0.044
#> GSM549283 2 0.4248 0.751 0.000 0.728 0.032 0.000 0.240
#> GSM549298 3 0.3921 0.821 0.000 0.044 0.784 0.000 0.172
#> GSM750741 5 0.4090 0.800 0.268 0.000 0.000 0.016 0.716
#> GSM549286 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> GSM549241 5 0.3838 0.787 0.280 0.000 0.000 0.004 0.716
#> GSM549247 5 0.5142 0.796 0.192 0.000 0.008 0.096 0.704
#> GSM549261 1 0.2068 0.857 0.904 0.000 0.004 0.000 0.092
#> GSM549270 2 0.0963 0.901 0.000 0.964 0.000 0.000 0.036
#> GSM549277 2 0.5871 0.621 0.000 0.604 0.184 0.000 0.212
#> GSM549280 2 0.5201 0.731 0.000 0.684 0.128 0.000 0.188
#> GSM549281 5 0.3932 0.667 0.008 0.164 0.012 0.016 0.800
#> GSM549285 3 0.2597 0.856 0.000 0.004 0.872 0.004 0.120
#> GSM549288 2 0.5122 0.734 0.000 0.688 0.112 0.000 0.200
#> GSM549292 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> GSM549295 2 0.3958 0.811 0.000 0.776 0.040 0.000 0.184
#> GSM549297 2 0.2722 0.874 0.000 0.872 0.020 0.000 0.108
#> GSM750743 1 0.4060 0.302 0.640 0.000 0.000 0.000 0.360
#> GSM549268 5 0.3932 0.667 0.008 0.164 0.012 0.016 0.800
#> GSM549290 4 0.4908 0.498 0.000 0.000 0.356 0.608 0.036
#> GSM549272 2 0.0000 0.902 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.0963 0.901 0.000 0.964 0.000 0.000 0.036
#> GSM549275 5 0.4363 0.811 0.244 0.008 0.004 0.016 0.728
#> GSM549284 2 0.0451 0.901 0.000 0.988 0.008 0.000 0.004
#> GSM750737 4 0.2612 0.809 0.008 0.000 0.000 0.868 0.124
#> GSM750740 1 0.2068 0.857 0.904 0.000 0.004 0.000 0.092
#> GSM750747 1 0.0771 0.914 0.976 0.000 0.004 0.000 0.020
#> GSM750751 2 0.0963 0.901 0.000 0.964 0.000 0.000 0.036
#> GSM750754 3 0.3012 0.799 0.000 0.000 0.860 0.104 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.3317 0.8166 0.024 0.104 0.000 0.836 0.000 0.036
#> GSM549291 4 0.5322 0.6079 0.024 0.104 0.000 0.636 0.000 0.236
#> GSM549274 2 0.3838 0.9799 0.000 0.552 0.448 0.000 0.000 0.000
#> GSM750738 2 0.4082 0.9392 0.004 0.560 0.432 0.004 0.000 0.000
#> GSM750748 5 0.3611 0.8108 0.096 0.108 0.000 0.000 0.796 0.000
#> GSM549240 1 0.3476 0.7635 0.840 0.056 0.000 0.068 0.032 0.004
#> GSM549279 1 0.4064 0.6864 0.740 0.200 0.056 0.004 0.000 0.000
#> GSM549294 3 0.3944 -0.6257 0.004 0.428 0.568 0.000 0.000 0.000
#> GSM549300 3 0.3969 -0.1071 0.016 0.000 0.652 0.000 0.000 0.332
#> GSM549303 6 0.2216 0.7380 0.024 0.016 0.052 0.000 0.000 0.908
#> GSM549309 6 0.0653 0.7399 0.012 0.000 0.004 0.004 0.000 0.980
#> GSM750753 3 0.3428 -0.2882 0.000 0.304 0.696 0.000 0.000 0.000
#> GSM750752 4 0.4733 0.6695 0.020 0.088 0.000 0.708 0.000 0.184
#> GSM549304 2 0.3843 0.9848 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549305 3 0.3828 -0.7074 0.000 0.440 0.560 0.000 0.000 0.000
#> GSM549307 3 0.2623 0.3807 0.016 0.000 0.852 0.000 0.000 0.132
#> GSM549306 6 0.4580 0.3981 0.016 0.012 0.484 0.000 0.000 0.488
#> GSM549308 6 0.4429 0.5500 0.016 0.012 0.372 0.000 0.000 0.600
#> GSM549233 5 0.2558 0.7265 0.000 0.004 0.000 0.156 0.840 0.000
#> GSM549234 4 0.1124 0.8675 0.008 0.036 0.000 0.956 0.000 0.000
#> GSM549250 5 0.0603 0.8374 0.000 0.004 0.000 0.016 0.980 0.000
#> GSM549287 6 0.1957 0.7194 0.008 0.072 0.000 0.008 0.000 0.912
#> GSM750735 1 0.2651 0.7703 0.872 0.036 0.000 0.004 0.088 0.000
#> GSM750736 1 0.2535 0.7777 0.888 0.036 0.000 0.012 0.064 0.000
#> GSM750749 1 0.3782 0.7654 0.788 0.140 0.000 0.008 0.064 0.000
#> GSM549230 5 0.0146 0.8454 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM549231 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549237 5 0.1983 0.8418 0.072 0.020 0.000 0.000 0.908 0.000
#> GSM549254 4 0.1232 0.8628 0.016 0.024 0.000 0.956 0.000 0.004
#> GSM750734 5 0.4228 0.7234 0.212 0.072 0.000 0.000 0.716 0.000
#> GSM549271 6 0.2345 0.7156 0.016 0.072 0.000 0.016 0.000 0.896
#> GSM549232 4 0.0405 0.8675 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM549246 4 0.1464 0.8660 0.016 0.036 0.000 0.944 0.004 0.000
#> GSM549248 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549255 4 0.0405 0.8669 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM750746 5 0.3657 0.8087 0.100 0.108 0.000 0.000 0.792 0.000
#> GSM549259 5 0.4444 0.7397 0.184 0.108 0.000 0.000 0.708 0.000
#> GSM549269 2 0.3843 0.9762 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549273 6 0.3737 0.6825 0.024 0.016 0.188 0.000 0.000 0.772
#> GSM549299 3 0.3819 -0.2485 0.012 0.316 0.672 0.000 0.000 0.000
#> GSM549301 6 0.4563 0.4603 0.016 0.012 0.448 0.000 0.000 0.524
#> GSM549310 4 0.5555 0.4436 0.020 0.088 0.004 0.576 0.000 0.312
#> GSM549311 6 0.1622 0.7406 0.016 0.016 0.028 0.000 0.000 0.940
#> GSM549302 2 0.3843 0.9848 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549235 5 0.3563 0.8128 0.092 0.108 0.000 0.000 0.800 0.000
#> GSM549245 4 0.0405 0.8669 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM549265 4 0.1461 0.8659 0.016 0.044 0.000 0.940 0.000 0.000
#> GSM549282 6 0.1204 0.7413 0.000 0.000 0.056 0.000 0.000 0.944
#> GSM549296 4 0.4206 0.7372 0.020 0.088 0.000 0.768 0.000 0.124
#> GSM750739 5 0.2997 0.8255 0.096 0.060 0.000 0.000 0.844 0.000
#> GSM750742 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM750744 5 0.2798 0.8196 0.112 0.036 0.000 0.000 0.852 0.000
#> GSM750750 6 0.2302 0.7231 0.008 0.000 0.120 0.000 0.000 0.872
#> GSM549242 5 0.3214 0.7734 0.032 0.016 0.000 0.116 0.836 0.000
#> GSM549252 4 0.1461 0.8659 0.016 0.044 0.000 0.940 0.000 0.000
#> GSM549253 5 0.0291 0.8429 0.000 0.004 0.000 0.004 0.992 0.000
#> GSM549256 5 0.3622 0.6189 0.004 0.016 0.000 0.236 0.744 0.000
#> GSM549257 4 0.0405 0.8669 0.008 0.004 0.000 0.988 0.000 0.000
#> GSM549263 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549267 6 0.5476 0.2214 0.020 0.096 0.000 0.304 0.000 0.580
#> GSM750745 1 0.5069 -0.0930 0.484 0.076 0.000 0.000 0.440 0.000
#> GSM549239 5 0.4938 0.4507 0.356 0.076 0.000 0.000 0.568 0.000
#> GSM549244 4 0.1245 0.8670 0.016 0.032 0.000 0.952 0.000 0.000
#> GSM549249 4 0.1391 0.8663 0.016 0.040 0.000 0.944 0.000 0.000
#> GSM549260 5 0.4457 0.7159 0.228 0.056 0.000 0.012 0.704 0.000
#> GSM549266 1 0.4121 0.6843 0.736 0.200 0.060 0.004 0.000 0.000
#> GSM549293 2 0.3843 0.9848 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549236 5 0.0405 0.8417 0.000 0.004 0.000 0.008 0.988 0.000
#> GSM549238 4 0.4540 0.5271 0.008 0.040 0.000 0.644 0.308 0.000
#> GSM549251 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549258 1 0.3573 0.6908 0.796 0.052 0.000 0.004 0.148 0.000
#> GSM549264 5 0.1088 0.8401 0.016 0.024 0.000 0.000 0.960 0.000
#> GSM549243 5 0.2826 0.8278 0.092 0.052 0.000 0.000 0.856 0.000
#> GSM549262 5 0.0000 0.8454 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549278 4 0.2947 0.8210 0.024 0.080 0.000 0.864 0.000 0.032
#> GSM549283 3 0.5976 0.1551 0.244 0.320 0.436 0.000 0.000 0.000
#> GSM549298 3 0.4580 -0.4673 0.016 0.012 0.488 0.000 0.000 0.484
#> GSM750741 1 0.1686 0.7806 0.924 0.000 0.000 0.012 0.064 0.000
#> GSM549286 2 0.3843 0.9848 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549241 1 0.2724 0.7549 0.864 0.052 0.000 0.000 0.084 0.000
#> GSM549247 1 0.3476 0.7635 0.840 0.056 0.000 0.068 0.032 0.004
#> GSM549261 5 0.4444 0.7397 0.184 0.108 0.000 0.000 0.708 0.000
#> GSM549270 3 0.3823 -0.7033 0.000 0.436 0.564 0.000 0.000 0.000
#> GSM549277 3 0.3767 0.3850 0.016 0.064 0.800 0.000 0.000 0.120
#> GSM549280 3 0.3189 0.3846 0.016 0.072 0.848 0.000 0.000 0.064
#> GSM549281 1 0.4402 0.6644 0.716 0.196 0.084 0.004 0.000 0.000
#> GSM549285 6 0.5012 0.6118 0.024 0.076 0.236 0.000 0.000 0.664
#> GSM549288 3 0.1616 0.3529 0.000 0.020 0.932 0.000 0.000 0.048
#> GSM549292 2 0.3843 0.9848 0.000 0.548 0.452 0.000 0.000 0.000
#> GSM549295 3 0.1398 0.2863 0.008 0.052 0.940 0.000 0.000 0.000
#> GSM549297 3 0.3126 -0.1287 0.000 0.248 0.752 0.000 0.000 0.000
#> GSM750743 1 0.5069 -0.0834 0.484 0.076 0.000 0.000 0.440 0.000
#> GSM549268 1 0.4402 0.6644 0.716 0.196 0.084 0.004 0.000 0.000
#> GSM549290 6 0.5542 0.1637 0.020 0.096 0.000 0.324 0.000 0.560
#> GSM549272 2 0.3847 0.9815 0.000 0.544 0.456 0.000 0.000 0.000
#> GSM549276 3 0.3857 -0.7879 0.000 0.468 0.532 0.000 0.000 0.000
#> GSM549275 1 0.2854 0.7749 0.860 0.088 0.000 0.004 0.048 0.000
#> GSM549284 2 0.3854 0.9622 0.000 0.536 0.464 0.000 0.000 0.000
#> GSM750737 4 0.2009 0.8273 0.068 0.024 0.000 0.908 0.000 0.000
#> GSM750740 5 0.4444 0.7397 0.184 0.108 0.000 0.000 0.708 0.000
#> GSM750747 5 0.3958 0.7906 0.128 0.108 0.000 0.000 0.764 0.000
#> GSM750751 3 0.3864 -0.8180 0.000 0.480 0.520 0.000 0.000 0.000
#> GSM750754 6 0.3306 0.6746 0.020 0.088 0.000 0.052 0.000 0.840
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:kmeans 103 0.023 1.72e-05 0.05361 0.0048 2
#> MAD:kmeans 41 NA NA NA NA 3
#> MAD:kmeans 91 0.355 2.07e-04 0.00337 0.1142 4
#> MAD:kmeans 99 0.484 3.18e-04 0.00278 0.0536 5
#> MAD:kmeans 79 0.395 1.70e-02 0.01434 0.1622 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.999 0.5043 0.496 0.496
#> 3 3 0.821 0.882 0.940 0.2937 0.800 0.615
#> 4 4 0.830 0.861 0.926 0.1230 0.878 0.671
#> 5 5 0.755 0.714 0.861 0.0741 0.902 0.663
#> 6 6 0.724 0.620 0.784 0.0369 0.956 0.804
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
#> GSM549289 1 0.000 0.999 1.000 0.000
#> GSM549291 2 0.000 1.000 0.000 1.000
#> GSM549274 2 0.000 1.000 0.000 1.000
#> GSM750738 2 0.000 1.000 0.000 1.000
#> GSM750748 1 0.000 0.999 1.000 0.000
#> GSM549240 1 0.000 0.999 1.000 0.000
#> GSM549279 2 0.000 1.000 0.000 1.000
#> GSM549294 2 0.000 1.000 0.000 1.000
#> GSM549300 2 0.000 1.000 0.000 1.000
#> GSM549303 2 0.000 1.000 0.000 1.000
#> GSM549309 2 0.000 1.000 0.000 1.000
#> GSM750753 2 0.000 1.000 0.000 1.000
#> GSM750752 2 0.000 1.000 0.000 1.000
#> GSM549304 2 0.000 1.000 0.000 1.000
#> GSM549305 2 0.000 1.000 0.000 1.000
#> GSM549307 2 0.000 1.000 0.000 1.000
#> GSM549306 2 0.000 1.000 0.000 1.000
#> GSM549308 2 0.000 1.000 0.000 1.000
#> GSM549233 1 0.000 0.999 1.000 0.000
#> GSM549234 1 0.000 0.999 1.000 0.000
#> GSM549250 1 0.000 0.999 1.000 0.000
#> GSM549287 2 0.000 1.000 0.000 1.000
#> GSM750735 1 0.000 0.999 1.000 0.000
#> GSM750736 1 0.000 0.999 1.000 0.000
#> GSM750749 1 0.000 0.999 1.000 0.000
#> GSM549230 1 0.000 0.999 1.000 0.000
#> GSM549231 1 0.000 0.999 1.000 0.000
#> GSM549237 1 0.000 0.999 1.000 0.000
#> GSM549254 1 0.295 0.946 0.948 0.052
#> GSM750734 1 0.000 0.999 1.000 0.000
#> GSM549271 2 0.000 1.000 0.000 1.000
#> GSM549232 1 0.000 0.999 1.000 0.000
#> GSM549246 1 0.000 0.999 1.000 0.000
#> GSM549248 1 0.000 0.999 1.000 0.000
#> GSM549255 1 0.000 0.999 1.000 0.000
#> GSM750746 1 0.000 0.999 1.000 0.000
#> GSM549259 1 0.000 0.999 1.000 0.000
#> GSM549269 2 0.000 1.000 0.000 1.000
#> GSM549273 2 0.000 1.000 0.000 1.000
#> GSM549299 2 0.000 1.000 0.000 1.000
#> GSM549301 2 0.000 1.000 0.000 1.000
#> GSM549310 2 0.000 1.000 0.000 1.000
#> GSM549311 2 0.000 1.000 0.000 1.000
#> GSM549302 2 0.000 1.000 0.000 1.000
#> GSM549235 1 0.000 0.999 1.000 0.000
#> GSM549245 1 0.000 0.999 1.000 0.000
#> GSM549265 1 0.000 0.999 1.000 0.000
#> GSM549282 2 0.000 1.000 0.000 1.000
#> GSM549296 2 0.000 1.000 0.000 1.000
#> GSM750739 1 0.000 0.999 1.000 0.000
#> GSM750742 1 0.000 0.999 1.000 0.000
#> GSM750744 1 0.000 0.999 1.000 0.000
#> GSM750750 2 0.000 1.000 0.000 1.000
#> GSM549242 1 0.000 0.999 1.000 0.000
#> GSM549252 1 0.000 0.999 1.000 0.000
#> GSM549253 1 0.000 0.999 1.000 0.000
#> GSM549256 1 0.000 0.999 1.000 0.000
#> GSM549257 1 0.000 0.999 1.000 0.000
#> GSM549263 1 0.000 0.999 1.000 0.000
#> GSM549267 2 0.000 1.000 0.000 1.000
#> GSM750745 1 0.000 0.999 1.000 0.000
#> GSM549239 1 0.000 0.999 1.000 0.000
#> GSM549244 1 0.000 0.999 1.000 0.000
#> GSM549249 1 0.000 0.999 1.000 0.000
#> GSM549260 1 0.000 0.999 1.000 0.000
#> GSM549266 2 0.000 1.000 0.000 1.000
#> GSM549293 2 0.000 1.000 0.000 1.000
#> GSM549236 1 0.000 0.999 1.000 0.000
#> GSM549238 1 0.000 0.999 1.000 0.000
#> GSM549251 1 0.000 0.999 1.000 0.000
#> GSM549258 1 0.000 0.999 1.000 0.000
#> GSM549264 1 0.000 0.999 1.000 0.000
#> GSM549243 1 0.000 0.999 1.000 0.000
#> GSM549262 1 0.000 0.999 1.000 0.000
#> GSM549278 1 0.163 0.975 0.976 0.024
#> GSM549283 2 0.000 1.000 0.000 1.000
#> GSM549298 2 0.000 1.000 0.000 1.000
#> GSM750741 1 0.000 0.999 1.000 0.000
#> GSM549286 2 0.000 1.000 0.000 1.000
#> GSM549241 1 0.000 0.999 1.000 0.000
#> GSM549247 1 0.000 0.999 1.000 0.000
#> GSM549261 1 0.000 0.999 1.000 0.000
#> GSM549270 2 0.000 1.000 0.000 1.000
#> GSM549277 2 0.000 1.000 0.000 1.000
#> GSM549280 2 0.000 1.000 0.000 1.000
#> GSM549281 2 0.000 1.000 0.000 1.000
#> GSM549285 2 0.000 1.000 0.000 1.000
#> GSM549288 2 0.000 1.000 0.000 1.000
#> GSM549292 2 0.000 1.000 0.000 1.000
#> GSM549295 2 0.000 1.000 0.000 1.000
#> GSM549297 2 0.000 1.000 0.000 1.000
#> GSM750743 1 0.000 0.999 1.000 0.000
#> GSM549268 2 0.000 1.000 0.000 1.000
#> GSM549290 2 0.000 1.000 0.000 1.000
#> GSM549272 2 0.000 1.000 0.000 1.000
#> GSM549276 2 0.000 1.000 0.000 1.000
#> GSM549275 1 0.000 0.999 1.000 0.000
#> GSM549284 2 0.000 1.000 0.000 1.000
#> GSM750737 1 0.000 0.999 1.000 0.000
#> GSM750740 1 0.000 0.999 1.000 0.000
#> GSM750747 1 0.000 0.999 1.000 0.000
#> GSM750751 2 0.000 1.000 0.000 1.000
#> GSM750754 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.0592 0.8531 0.012 0.000 0.988
#> GSM549291 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM549274 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750738 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750748 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549279 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549300 2 0.3340 0.8667 0.000 0.880 0.120
#> GSM549303 2 0.6062 0.5570 0.000 0.616 0.384
#> GSM549309 3 0.1964 0.8059 0.000 0.056 0.944
#> GSM750753 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750752 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM549304 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549307 2 0.0237 0.9303 0.000 0.996 0.004
#> GSM549306 2 0.4178 0.8327 0.000 0.828 0.172
#> GSM549308 2 0.4750 0.7971 0.000 0.784 0.216
#> GSM549233 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549234 3 0.4974 0.7714 0.236 0.000 0.764
#> GSM549250 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549287 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM750735 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750749 1 0.0424 0.9654 0.992 0.008 0.000
#> GSM549230 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549254 3 0.1289 0.8532 0.032 0.000 0.968
#> GSM750734 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549271 3 0.3551 0.7145 0.000 0.132 0.868
#> GSM549232 3 0.4555 0.8047 0.200 0.000 0.800
#> GSM549246 1 0.6225 0.0441 0.568 0.000 0.432
#> GSM549248 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549255 3 0.4504 0.8075 0.196 0.000 0.804
#> GSM750746 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549273 2 0.4750 0.7970 0.000 0.784 0.216
#> GSM549299 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549301 2 0.4504 0.8145 0.000 0.804 0.196
#> GSM549310 3 0.0237 0.8492 0.000 0.004 0.996
#> GSM549311 2 0.6095 0.5411 0.000 0.608 0.392
#> GSM549302 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549245 3 0.4504 0.8075 0.196 0.000 0.804
#> GSM549265 3 0.4605 0.8016 0.204 0.000 0.796
#> GSM549282 2 0.5706 0.6677 0.000 0.680 0.320
#> GSM549296 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM750739 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750750 2 0.5058 0.7679 0.000 0.756 0.244
#> GSM549242 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549252 3 0.4974 0.7714 0.236 0.000 0.764
#> GSM549253 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549257 3 0.5098 0.7555 0.248 0.000 0.752
#> GSM549263 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549267 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549244 3 0.4504 0.8075 0.196 0.000 0.804
#> GSM549249 3 0.4974 0.7714 0.236 0.000 0.764
#> GSM549260 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549238 1 0.5733 0.4184 0.676 0.000 0.324
#> GSM549251 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549278 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM549283 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549298 2 0.4291 0.8269 0.000 0.820 0.180
#> GSM750741 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549247 1 0.0424 0.9654 0.992 0.008 0.000
#> GSM549261 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549281 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549285 2 0.4555 0.8112 0.000 0.800 0.200
#> GSM549288 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549292 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549295 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM549268 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549290 3 0.0000 0.8514 0.000 0.000 1.000
#> GSM549272 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM549275 1 0.2878 0.8524 0.904 0.096 0.000
#> GSM549284 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750737 3 0.6308 0.2047 0.492 0.000 0.508
#> GSM750740 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.9742 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.9322 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.8514 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2255 0.8523 0.012 0.000 0.068 0.920
#> GSM549291 3 0.3726 0.6871 0.000 0.000 0.788 0.212
#> GSM549274 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM750738 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM750748 1 0.0336 0.9271 0.992 0.000 0.000 0.008
#> GSM549240 1 0.2563 0.8919 0.908 0.000 0.020 0.072
#> GSM549279 2 0.1762 0.9340 0.016 0.952 0.020 0.012
#> GSM549294 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549300 3 0.4998 0.0242 0.000 0.488 0.512 0.000
#> GSM549303 3 0.0707 0.8666 0.000 0.020 0.980 0.000
#> GSM549309 3 0.0707 0.8613 0.000 0.000 0.980 0.020
#> GSM750753 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM750752 4 0.5132 0.1349 0.000 0.004 0.448 0.548
#> GSM549304 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549305 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549307 2 0.3873 0.7172 0.000 0.772 0.228 0.000
#> GSM549306 3 0.3486 0.7356 0.000 0.188 0.812 0.000
#> GSM549308 3 0.0707 0.8666 0.000 0.020 0.980 0.000
#> GSM549233 1 0.4040 0.7531 0.752 0.000 0.000 0.248
#> GSM549234 4 0.0817 0.8921 0.024 0.000 0.000 0.976
#> GSM549250 1 0.3266 0.8472 0.832 0.000 0.000 0.168
#> GSM549287 3 0.0707 0.8613 0.000 0.000 0.980 0.020
#> GSM750735 1 0.1059 0.9200 0.972 0.000 0.012 0.016
#> GSM750736 1 0.1520 0.9145 0.956 0.000 0.020 0.024
#> GSM750749 1 0.1648 0.9157 0.956 0.012 0.016 0.016
#> GSM549230 1 0.2647 0.8865 0.880 0.000 0.000 0.120
#> GSM549231 1 0.2530 0.8913 0.888 0.000 0.000 0.112
#> GSM549237 1 0.1211 0.9233 0.960 0.000 0.000 0.040
#> GSM549254 4 0.2207 0.8417 0.012 0.004 0.056 0.928
#> GSM750734 1 0.0524 0.9246 0.988 0.000 0.004 0.008
#> GSM549271 3 0.0779 0.8630 0.000 0.004 0.980 0.016
#> GSM549232 4 0.0779 0.8921 0.016 0.000 0.004 0.980
#> GSM549246 4 0.3801 0.6763 0.220 0.000 0.000 0.780
#> GSM549248 1 0.1940 0.9102 0.924 0.000 0.000 0.076
#> GSM549255 4 0.0779 0.8921 0.016 0.000 0.004 0.980
#> GSM750746 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM549259 1 0.0336 0.9266 0.992 0.000 0.000 0.008
#> GSM549269 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0707 0.8666 0.000 0.020 0.980 0.000
#> GSM549299 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549301 3 0.1867 0.8399 0.000 0.072 0.928 0.000
#> GSM549310 3 0.4564 0.4830 0.000 0.000 0.672 0.328
#> GSM549311 3 0.0707 0.8666 0.000 0.020 0.980 0.000
#> GSM549302 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0336 0.9271 0.992 0.000 0.000 0.008
#> GSM549245 4 0.0672 0.8885 0.008 0.000 0.008 0.984
#> GSM549265 4 0.0707 0.8919 0.020 0.000 0.000 0.980
#> GSM549282 3 0.0779 0.8661 0.000 0.016 0.980 0.004
#> GSM549296 4 0.4819 0.4248 0.000 0.004 0.344 0.652
#> GSM750739 1 0.0469 0.9271 0.988 0.000 0.000 0.012
#> GSM750742 1 0.1716 0.9153 0.936 0.000 0.000 0.064
#> GSM750744 1 0.1022 0.9261 0.968 0.000 0.000 0.032
#> GSM750750 3 0.0707 0.8666 0.000 0.020 0.980 0.000
#> GSM549242 1 0.3311 0.8473 0.828 0.000 0.000 0.172
#> GSM549252 4 0.0817 0.8921 0.024 0.000 0.000 0.976
#> GSM549253 1 0.2973 0.8688 0.856 0.000 0.000 0.144
#> GSM549256 1 0.4697 0.5610 0.644 0.000 0.000 0.356
#> GSM549257 4 0.0707 0.8924 0.020 0.000 0.000 0.980
#> GSM549263 1 0.2647 0.8865 0.880 0.000 0.000 0.120
#> GSM549267 3 0.1637 0.8395 0.000 0.000 0.940 0.060
#> GSM750745 1 0.1059 0.9200 0.972 0.000 0.012 0.016
#> GSM549239 1 0.0804 0.9226 0.980 0.000 0.008 0.012
#> GSM549244 4 0.0707 0.8924 0.020 0.000 0.000 0.980
#> GSM549249 4 0.0817 0.8921 0.024 0.000 0.000 0.976
#> GSM549260 1 0.1118 0.9269 0.964 0.000 0.000 0.036
#> GSM549266 2 0.1998 0.9278 0.020 0.944 0.020 0.016
#> GSM549293 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549236 1 0.3074 0.8619 0.848 0.000 0.000 0.152
#> GSM549238 4 0.2281 0.8324 0.096 0.000 0.000 0.904
#> GSM549251 1 0.2647 0.8865 0.880 0.000 0.000 0.120
#> GSM549258 1 0.1520 0.9145 0.956 0.000 0.020 0.024
#> GSM549264 1 0.1867 0.9121 0.928 0.000 0.000 0.072
#> GSM549243 1 0.0336 0.9271 0.992 0.000 0.000 0.008
#> GSM549262 1 0.1557 0.9181 0.944 0.000 0.000 0.056
#> GSM549278 3 0.4877 0.2882 0.000 0.000 0.592 0.408
#> GSM549283 2 0.0188 0.9631 0.000 0.996 0.004 0.000
#> GSM549298 3 0.3219 0.7607 0.000 0.164 0.836 0.000
#> GSM750741 1 0.1520 0.9145 0.956 0.000 0.020 0.024
#> GSM549286 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549241 1 0.1520 0.9145 0.956 0.000 0.020 0.024
#> GSM549247 1 0.4256 0.8313 0.840 0.048 0.020 0.092
#> GSM549261 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM549270 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549277 2 0.3528 0.7725 0.000 0.808 0.192 0.000
#> GSM549280 2 0.2704 0.8618 0.000 0.876 0.124 0.000
#> GSM549281 2 0.1471 0.9426 0.004 0.960 0.024 0.012
#> GSM549285 3 0.1716 0.8451 0.000 0.064 0.936 0.000
#> GSM549288 2 0.2345 0.8867 0.000 0.900 0.100 0.000
#> GSM549292 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549295 2 0.1557 0.9270 0.000 0.944 0.056 0.000
#> GSM549297 2 0.0188 0.9631 0.000 0.996 0.004 0.000
#> GSM750743 1 0.0804 0.9230 0.980 0.000 0.012 0.008
#> GSM549268 2 0.1471 0.9426 0.004 0.960 0.024 0.012
#> GSM549290 3 0.2408 0.8062 0.000 0.000 0.896 0.104
#> GSM549272 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM549275 1 0.3769 0.8334 0.860 0.096 0.020 0.024
#> GSM549284 2 0.0336 0.9609 0.000 0.992 0.008 0.000
#> GSM750737 4 0.2142 0.8523 0.056 0.000 0.016 0.928
#> GSM750740 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM750747 1 0.0188 0.9270 0.996 0.000 0.000 0.004
#> GSM750751 2 0.0000 0.9648 0.000 1.000 0.000 0.000
#> GSM750754 3 0.0707 0.8613 0.000 0.000 0.980 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.2747 0.8004 0.012 0.000 0.088 0.884 0.016
#> GSM549291 3 0.4090 0.5884 0.000 0.000 0.716 0.268 0.016
#> GSM549274 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM750738 2 0.1195 0.8978 0.000 0.960 0.000 0.028 0.012
#> GSM750748 1 0.3039 0.7672 0.808 0.000 0.000 0.000 0.192
#> GSM549240 5 0.2946 0.6283 0.088 0.000 0.000 0.044 0.868
#> GSM549279 5 0.4256 0.2121 0.000 0.436 0.000 0.000 0.564
#> GSM549294 2 0.0162 0.9180 0.000 0.996 0.000 0.000 0.004
#> GSM549300 2 0.4798 0.2146 0.000 0.540 0.440 0.000 0.020
#> GSM549303 3 0.0000 0.8604 0.000 0.000 1.000 0.000 0.000
#> GSM549309 3 0.0000 0.8604 0.000 0.000 1.000 0.000 0.000
#> GSM750753 2 0.0162 0.9180 0.000 0.996 0.000 0.000 0.004
#> GSM750752 4 0.4774 0.4104 0.000 0.012 0.328 0.644 0.016
#> GSM549304 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549305 2 0.0000 0.9187 0.000 1.000 0.000 0.000 0.000
#> GSM549307 2 0.3882 0.7090 0.000 0.756 0.224 0.000 0.020
#> GSM549306 3 0.4026 0.6193 0.000 0.244 0.736 0.000 0.020
#> GSM549308 3 0.0609 0.8571 0.000 0.000 0.980 0.000 0.020
#> GSM549233 1 0.2127 0.7417 0.892 0.000 0.000 0.108 0.000
#> GSM549234 4 0.1331 0.8491 0.040 0.000 0.000 0.952 0.008
#> GSM549250 1 0.0880 0.8005 0.968 0.000 0.000 0.032 0.000
#> GSM549287 3 0.0451 0.8578 0.000 0.000 0.988 0.008 0.004
#> GSM750735 5 0.4126 0.2132 0.380 0.000 0.000 0.000 0.620
#> GSM750736 5 0.2674 0.6185 0.140 0.000 0.000 0.004 0.856
#> GSM750749 5 0.3318 0.5665 0.192 0.000 0.008 0.000 0.800
#> GSM549230 1 0.0162 0.8127 0.996 0.000 0.000 0.004 0.000
#> GSM549231 1 0.0162 0.8127 0.996 0.000 0.000 0.004 0.000
#> GSM549237 1 0.1608 0.8120 0.928 0.000 0.000 0.000 0.072
#> GSM549254 4 0.1704 0.8271 0.000 0.000 0.004 0.928 0.068
#> GSM750734 1 0.3730 0.6421 0.712 0.000 0.000 0.000 0.288
#> GSM549271 3 0.0162 0.8598 0.000 0.000 0.996 0.000 0.004
#> GSM549232 4 0.0290 0.8484 0.008 0.000 0.000 0.992 0.000
#> GSM549246 1 0.4473 0.1533 0.580 0.000 0.000 0.412 0.008
#> GSM549248 1 0.0290 0.8145 0.992 0.000 0.000 0.000 0.008
#> GSM549255 4 0.0162 0.8454 0.000 0.000 0.000 0.996 0.004
#> GSM750746 1 0.3143 0.7599 0.796 0.000 0.000 0.000 0.204
#> GSM549259 1 0.3534 0.7153 0.744 0.000 0.000 0.000 0.256
#> GSM549269 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549273 3 0.0671 0.8575 0.000 0.004 0.980 0.000 0.016
#> GSM549299 2 0.0404 0.9181 0.000 0.988 0.000 0.000 0.012
#> GSM549301 3 0.2390 0.8059 0.000 0.084 0.896 0.000 0.020
#> GSM549310 3 0.5010 0.3557 0.000 0.008 0.592 0.376 0.024
#> GSM549311 3 0.0000 0.8604 0.000 0.000 1.000 0.000 0.000
#> GSM549302 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549235 1 0.2773 0.7833 0.836 0.000 0.000 0.000 0.164
#> GSM549245 4 0.0324 0.8472 0.004 0.000 0.000 0.992 0.004
#> GSM549265 4 0.3308 0.7930 0.144 0.000 0.004 0.832 0.020
#> GSM549282 3 0.0162 0.8602 0.000 0.000 0.996 0.000 0.004
#> GSM549296 4 0.4054 0.5815 0.000 0.000 0.248 0.732 0.020
#> GSM750739 1 0.2561 0.7898 0.856 0.000 0.000 0.000 0.144
#> GSM750742 1 0.0290 0.8147 0.992 0.000 0.000 0.000 0.008
#> GSM750744 1 0.2127 0.7937 0.892 0.000 0.000 0.000 0.108
#> GSM750750 3 0.0404 0.8591 0.000 0.000 0.988 0.000 0.012
#> GSM549242 1 0.2124 0.7910 0.916 0.000 0.000 0.056 0.028
#> GSM549252 4 0.2233 0.8256 0.104 0.000 0.000 0.892 0.004
#> GSM549253 1 0.0510 0.8081 0.984 0.000 0.000 0.016 0.000
#> GSM549256 1 0.3455 0.6289 0.784 0.000 0.000 0.208 0.008
#> GSM549257 4 0.0693 0.8495 0.012 0.000 0.000 0.980 0.008
#> GSM549263 1 0.0290 0.8113 0.992 0.000 0.000 0.008 0.000
#> GSM549267 3 0.2136 0.8073 0.000 0.000 0.904 0.088 0.008
#> GSM750745 5 0.4307 -0.2504 0.500 0.000 0.000 0.000 0.500
#> GSM549239 1 0.4161 0.4642 0.608 0.000 0.000 0.000 0.392
#> GSM549244 4 0.1478 0.8447 0.064 0.000 0.000 0.936 0.000
#> GSM549249 4 0.1952 0.8369 0.084 0.000 0.000 0.912 0.004
#> GSM549260 1 0.3796 0.6589 0.700 0.000 0.000 0.000 0.300
#> GSM549266 5 0.4256 0.2169 0.000 0.436 0.000 0.000 0.564
#> GSM549293 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549236 1 0.0963 0.7981 0.964 0.000 0.000 0.036 0.000
#> GSM549238 4 0.4434 0.2372 0.460 0.000 0.000 0.536 0.004
#> GSM549251 1 0.0162 0.8127 0.996 0.000 0.000 0.004 0.000
#> GSM549258 5 0.3774 0.3918 0.296 0.000 0.000 0.000 0.704
#> GSM549264 1 0.0609 0.8144 0.980 0.000 0.000 0.000 0.020
#> GSM549243 1 0.2813 0.7806 0.832 0.000 0.000 0.000 0.168
#> GSM549262 1 0.0290 0.8145 0.992 0.000 0.000 0.000 0.008
#> GSM549278 3 0.5045 0.0680 0.004 0.000 0.508 0.464 0.024
#> GSM549283 2 0.1704 0.8726 0.000 0.928 0.004 0.000 0.068
#> GSM549298 3 0.4026 0.6190 0.000 0.244 0.736 0.000 0.020
#> GSM750741 5 0.2074 0.6328 0.104 0.000 0.000 0.000 0.896
#> GSM549286 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549241 5 0.2605 0.6097 0.148 0.000 0.000 0.000 0.852
#> GSM549247 5 0.2897 0.6315 0.052 0.020 0.000 0.040 0.888
#> GSM549261 1 0.3561 0.7116 0.740 0.000 0.000 0.000 0.260
#> GSM549270 2 0.0000 0.9187 0.000 1.000 0.000 0.000 0.000
#> GSM549277 2 0.3970 0.6931 0.000 0.744 0.236 0.000 0.020
#> GSM549280 2 0.3527 0.7648 0.000 0.804 0.172 0.000 0.024
#> GSM549281 5 0.4302 0.1011 0.000 0.480 0.000 0.000 0.520
#> GSM549285 3 0.1943 0.8278 0.000 0.056 0.924 0.000 0.020
#> GSM549288 2 0.3194 0.7919 0.000 0.832 0.148 0.000 0.020
#> GSM549292 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549295 2 0.1740 0.8788 0.000 0.932 0.056 0.000 0.012
#> GSM549297 2 0.0579 0.9131 0.000 0.984 0.008 0.000 0.008
#> GSM750743 1 0.4273 0.3213 0.552 0.000 0.000 0.000 0.448
#> GSM549268 5 0.4307 0.0522 0.000 0.496 0.000 0.000 0.504
#> GSM549290 3 0.2624 0.7826 0.000 0.000 0.872 0.116 0.012
#> GSM549272 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM549276 2 0.0000 0.9187 0.000 1.000 0.000 0.000 0.000
#> GSM549275 5 0.2984 0.6354 0.108 0.032 0.000 0.000 0.860
#> GSM549284 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM750737 4 0.3496 0.7042 0.012 0.000 0.000 0.788 0.200
#> GSM750740 1 0.3395 0.7348 0.764 0.000 0.000 0.000 0.236
#> GSM750747 1 0.3274 0.7483 0.780 0.000 0.000 0.000 0.220
#> GSM750751 2 0.0290 0.9197 0.000 0.992 0.000 0.000 0.008
#> GSM750754 3 0.0451 0.8581 0.000 0.000 0.988 0.004 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.5041 0.6921 0.000 0.000 0.092 0.696 0.040 0.172
#> GSM549291 3 0.5563 0.2791 0.000 0.000 0.548 0.260 0.000 0.192
#> GSM549274 2 0.0547 0.8572 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM750738 2 0.1492 0.8315 0.000 0.940 0.000 0.024 0.000 0.036
#> GSM750748 5 0.4392 0.4834 0.332 0.000 0.000 0.000 0.628 0.040
#> GSM549240 1 0.4369 0.4761 0.744 0.000 0.000 0.036 0.044 0.176
#> GSM549279 6 0.6039 0.8519 0.344 0.216 0.000 0.004 0.000 0.436
#> GSM549294 2 0.1444 0.8317 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM549300 3 0.5716 0.0629 0.000 0.392 0.444 0.000 0.000 0.164
#> GSM549303 3 0.0363 0.7592 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM549309 3 0.1204 0.7477 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM750753 2 0.1349 0.8449 0.000 0.940 0.004 0.000 0.000 0.056
#> GSM750752 4 0.5641 0.4104 0.000 0.008 0.272 0.560 0.000 0.160
#> GSM549304 2 0.0458 0.8583 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM549305 2 0.0547 0.8589 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549307 2 0.5277 0.4262 0.000 0.592 0.256 0.000 0.000 0.152
#> GSM549306 3 0.4831 0.5505 0.000 0.168 0.668 0.000 0.000 0.164
#> GSM549308 3 0.2402 0.7280 0.000 0.004 0.856 0.000 0.000 0.140
#> GSM549233 5 0.2809 0.6138 0.004 0.000 0.000 0.128 0.848 0.020
#> GSM549234 4 0.2325 0.7623 0.000 0.000 0.000 0.892 0.060 0.048
#> GSM549250 5 0.1088 0.6863 0.000 0.000 0.000 0.024 0.960 0.016
#> GSM549287 3 0.1913 0.7373 0.000 0.000 0.908 0.012 0.000 0.080
#> GSM750735 1 0.5111 0.5616 0.624 0.000 0.000 0.000 0.224 0.152
#> GSM750736 1 0.3417 0.5684 0.812 0.000 0.000 0.004 0.052 0.132
#> GSM750749 1 0.5238 0.2018 0.560 0.000 0.000 0.004 0.096 0.340
#> GSM549230 5 0.0520 0.6999 0.008 0.000 0.000 0.000 0.984 0.008
#> GSM549231 5 0.0260 0.6971 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM549237 5 0.2679 0.6759 0.096 0.000 0.000 0.004 0.868 0.032
#> GSM549254 4 0.2766 0.7345 0.028 0.000 0.012 0.868 0.000 0.092
#> GSM750734 5 0.4377 0.1434 0.436 0.000 0.000 0.000 0.540 0.024
#> GSM549271 3 0.2264 0.7428 0.004 0.000 0.888 0.012 0.000 0.096
#> GSM549232 4 0.0993 0.7674 0.000 0.000 0.000 0.964 0.012 0.024
#> GSM549246 5 0.5156 0.3092 0.016 0.000 0.004 0.300 0.616 0.064
#> GSM549248 5 0.0891 0.6987 0.024 0.000 0.000 0.000 0.968 0.008
#> GSM549255 4 0.0935 0.7629 0.000 0.000 0.000 0.964 0.004 0.032
#> GSM750746 5 0.4493 0.4610 0.344 0.000 0.000 0.000 0.612 0.044
#> GSM549259 5 0.4591 0.3504 0.408 0.000 0.000 0.000 0.552 0.040
#> GSM549269 2 0.0713 0.8559 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM549273 3 0.1657 0.7539 0.000 0.016 0.928 0.000 0.000 0.056
#> GSM549299 2 0.1531 0.8419 0.000 0.928 0.004 0.000 0.000 0.068
#> GSM549301 3 0.3772 0.6680 0.000 0.068 0.772 0.000 0.000 0.160
#> GSM549310 3 0.6104 -0.0494 0.000 0.020 0.436 0.392 0.000 0.152
#> GSM549311 3 0.0632 0.7591 0.000 0.000 0.976 0.000 0.000 0.024
#> GSM549302 2 0.0458 0.8573 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM549235 5 0.4253 0.5339 0.284 0.000 0.000 0.000 0.672 0.044
#> GSM549245 4 0.0692 0.7657 0.000 0.000 0.000 0.976 0.004 0.020
#> GSM549265 4 0.4710 0.6278 0.004 0.000 0.000 0.672 0.236 0.088
#> GSM549282 3 0.1349 0.7571 0.004 0.000 0.940 0.000 0.000 0.056
#> GSM549296 4 0.5285 0.5097 0.000 0.004 0.220 0.616 0.000 0.160
#> GSM750739 5 0.3778 0.5352 0.288 0.000 0.000 0.000 0.696 0.016
#> GSM750742 5 0.1151 0.6976 0.032 0.000 0.000 0.000 0.956 0.012
#> GSM750744 5 0.3782 0.5616 0.224 0.000 0.000 0.000 0.740 0.036
#> GSM750750 3 0.2278 0.7354 0.004 0.000 0.868 0.000 0.000 0.128
#> GSM549242 5 0.3341 0.6615 0.088 0.000 0.000 0.060 0.836 0.016
#> GSM549252 4 0.3739 0.6979 0.000 0.000 0.000 0.768 0.176 0.056
#> GSM549253 5 0.0717 0.6925 0.000 0.000 0.000 0.016 0.976 0.008
#> GSM549256 5 0.3750 0.5466 0.020 0.000 0.000 0.200 0.764 0.016
#> GSM549257 4 0.1245 0.7683 0.000 0.000 0.000 0.952 0.032 0.016
#> GSM549263 5 0.0520 0.6953 0.000 0.000 0.000 0.008 0.984 0.008
#> GSM549267 3 0.4131 0.6099 0.000 0.000 0.744 0.100 0.000 0.156
#> GSM750745 1 0.3879 0.4605 0.688 0.000 0.000 0.000 0.292 0.020
#> GSM549239 1 0.4520 0.0942 0.520 0.000 0.000 0.000 0.448 0.032
#> GSM549244 4 0.2867 0.7426 0.000 0.000 0.000 0.848 0.112 0.040
#> GSM549249 4 0.3612 0.7076 0.000 0.000 0.000 0.780 0.168 0.052
#> GSM549260 5 0.4792 0.2860 0.408 0.000 0.000 0.012 0.548 0.032
#> GSM549266 6 0.6004 0.8282 0.352 0.240 0.000 0.000 0.000 0.408
#> GSM549293 2 0.0632 0.8554 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM549236 5 0.0993 0.6873 0.000 0.000 0.000 0.024 0.964 0.012
#> GSM549238 5 0.4804 -0.1393 0.000 0.000 0.000 0.456 0.492 0.052
#> GSM549251 5 0.0436 0.6984 0.004 0.000 0.000 0.004 0.988 0.004
#> GSM549258 1 0.3172 0.6259 0.816 0.000 0.000 0.000 0.148 0.036
#> GSM549264 5 0.2189 0.6809 0.060 0.000 0.000 0.004 0.904 0.032
#> GSM549243 5 0.3518 0.5780 0.256 0.000 0.000 0.000 0.732 0.012
#> GSM549262 5 0.1124 0.6977 0.036 0.000 0.000 0.000 0.956 0.008
#> GSM549278 4 0.5961 0.1786 0.004 0.000 0.364 0.440 0.000 0.192
#> GSM549283 2 0.3737 0.6795 0.016 0.772 0.024 0.000 0.000 0.188
#> GSM549298 3 0.4952 0.5285 0.000 0.180 0.652 0.000 0.000 0.168
#> GSM750741 1 0.2772 0.5784 0.864 0.000 0.000 0.004 0.040 0.092
#> GSM549286 2 0.0547 0.8574 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM549241 1 0.2263 0.5960 0.896 0.000 0.000 0.000 0.048 0.056
#> GSM549247 1 0.4441 0.4033 0.728 0.004 0.000 0.040 0.024 0.204
#> GSM549261 5 0.4634 0.3635 0.400 0.000 0.000 0.000 0.556 0.044
#> GSM549270 2 0.1007 0.8535 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM549277 2 0.5446 0.3877 0.000 0.568 0.256 0.000 0.000 0.176
#> GSM549280 2 0.5058 0.4967 0.000 0.636 0.200 0.000 0.000 0.164
#> GSM549281 6 0.5835 0.8822 0.280 0.232 0.000 0.000 0.000 0.488
#> GSM549285 3 0.3166 0.7207 0.004 0.024 0.816 0.000 0.000 0.156
#> GSM549288 2 0.4570 0.6023 0.000 0.700 0.148 0.000 0.000 0.152
#> GSM549292 2 0.0632 0.8566 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM549295 2 0.3118 0.7558 0.000 0.836 0.092 0.000 0.000 0.072
#> GSM549297 2 0.1802 0.8345 0.000 0.916 0.012 0.000 0.000 0.072
#> GSM750743 1 0.4952 0.2265 0.524 0.000 0.000 0.000 0.408 0.068
#> GSM549268 6 0.6011 0.8703 0.264 0.232 0.008 0.000 0.000 0.496
#> GSM549290 3 0.4488 0.5691 0.000 0.000 0.708 0.128 0.000 0.164
#> GSM549272 2 0.0632 0.8566 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM549276 2 0.0458 0.8583 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM549275 1 0.4226 0.4559 0.744 0.032 0.000 0.000 0.032 0.192
#> GSM549284 2 0.0993 0.8581 0.000 0.964 0.012 0.000 0.000 0.024
#> GSM750737 4 0.4343 0.5952 0.188 0.000 0.000 0.724 0.004 0.084
#> GSM750740 5 0.4593 0.4014 0.380 0.000 0.000 0.000 0.576 0.044
#> GSM750747 5 0.4583 0.4091 0.376 0.000 0.000 0.000 0.580 0.044
#> GSM750751 2 0.0713 0.8589 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM750754 3 0.2212 0.7233 0.000 0.000 0.880 0.008 0.000 0.112
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:skmeans 103 0.023 1.72e-05 0.05361 0.0048 2
#> MAD:skmeans 100 0.254 1.13e-04 0.00448 0.0189 3
#> MAD:skmeans 98 0.356 4.21e-05 0.00185 0.0670 4
#> MAD:skmeans 88 0.451 2.78e-04 0.00322 0.1026 5
#> MAD:skmeans 78 0.717 7.20e-05 0.00765 0.1958 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.859 0.913 0.963 0.4731 0.525 0.525
#> 3 3 0.641 0.827 0.888 0.3710 0.760 0.570
#> 4 4 0.611 0.590 0.792 0.1342 0.780 0.472
#> 5 5 0.871 0.839 0.931 0.0847 0.891 0.619
#> 6 6 0.830 0.773 0.889 0.0256 0.951 0.771
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM549289 1 0.0672 0.9607 0.992 0.008
#> GSM549291 1 0.9977 0.0332 0.528 0.472
#> GSM549274 1 0.8555 0.6074 0.720 0.280
#> GSM750738 1 0.9635 0.3512 0.612 0.388
#> GSM750748 1 0.0000 0.9668 1.000 0.000
#> GSM549240 1 0.0000 0.9668 1.000 0.000
#> GSM549279 1 0.2043 0.9410 0.968 0.032
#> GSM549294 2 0.0000 0.9480 0.000 1.000
#> GSM549300 2 0.0000 0.9480 0.000 1.000
#> GSM549303 2 0.0000 0.9480 0.000 1.000
#> GSM549309 2 0.0376 0.9458 0.004 0.996
#> GSM750753 2 0.0000 0.9480 0.000 1.000
#> GSM750752 2 0.3879 0.8934 0.076 0.924
#> GSM549304 2 0.8661 0.6269 0.288 0.712
#> GSM549305 2 0.0000 0.9480 0.000 1.000
#> GSM549307 2 0.0000 0.9480 0.000 1.000
#> GSM549306 2 0.0000 0.9480 0.000 1.000
#> GSM549308 2 0.0000 0.9480 0.000 1.000
#> GSM549233 1 0.0000 0.9668 1.000 0.000
#> GSM549234 1 0.0000 0.9668 1.000 0.000
#> GSM549250 1 0.0000 0.9668 1.000 0.000
#> GSM549287 2 0.0000 0.9480 0.000 1.000
#> GSM750735 1 0.0000 0.9668 1.000 0.000
#> GSM750736 1 0.0000 0.9668 1.000 0.000
#> GSM750749 1 0.0000 0.9668 1.000 0.000
#> GSM549230 1 0.0000 0.9668 1.000 0.000
#> GSM549231 1 0.0000 0.9668 1.000 0.000
#> GSM549237 1 0.0000 0.9668 1.000 0.000
#> GSM549254 1 0.0376 0.9639 0.996 0.004
#> GSM750734 1 0.0000 0.9668 1.000 0.000
#> GSM549271 2 0.0000 0.9480 0.000 1.000
#> GSM549232 1 0.0000 0.9668 1.000 0.000
#> GSM549246 1 0.0000 0.9668 1.000 0.000
#> GSM549248 1 0.0000 0.9668 1.000 0.000
#> GSM549255 1 0.0000 0.9668 1.000 0.000
#> GSM750746 1 0.0000 0.9668 1.000 0.000
#> GSM549259 1 0.0000 0.9668 1.000 0.000
#> GSM549269 1 0.8499 0.6146 0.724 0.276
#> GSM549273 2 0.0000 0.9480 0.000 1.000
#> GSM549299 2 0.5294 0.8520 0.120 0.880
#> GSM549301 2 0.0000 0.9480 0.000 1.000
#> GSM549310 2 0.0000 0.9480 0.000 1.000
#> GSM549311 2 0.0000 0.9480 0.000 1.000
#> GSM549302 2 0.0000 0.9480 0.000 1.000
#> GSM549235 1 0.0000 0.9668 1.000 0.000
#> GSM549245 1 0.0000 0.9668 1.000 0.000
#> GSM549265 1 0.0000 0.9668 1.000 0.000
#> GSM549282 2 0.0672 0.9434 0.008 0.992
#> GSM549296 2 0.8267 0.6749 0.260 0.740
#> GSM750739 1 0.0000 0.9668 1.000 0.000
#> GSM750742 1 0.0000 0.9668 1.000 0.000
#> GSM750744 1 0.0000 0.9668 1.000 0.000
#> GSM750750 2 0.1843 0.9303 0.028 0.972
#> GSM549242 1 0.0000 0.9668 1.000 0.000
#> GSM549252 1 0.0000 0.9668 1.000 0.000
#> GSM549253 1 0.0000 0.9668 1.000 0.000
#> GSM549256 1 0.0000 0.9668 1.000 0.000
#> GSM549257 1 0.0000 0.9668 1.000 0.000
#> GSM549263 1 0.0000 0.9668 1.000 0.000
#> GSM549267 2 0.1633 0.9331 0.024 0.976
#> GSM750745 1 0.0000 0.9668 1.000 0.000
#> GSM549239 1 0.0000 0.9668 1.000 0.000
#> GSM549244 1 0.0000 0.9668 1.000 0.000
#> GSM549249 1 0.0000 0.9668 1.000 0.000
#> GSM549260 1 0.0000 0.9668 1.000 0.000
#> GSM549266 1 0.1843 0.9443 0.972 0.028
#> GSM549293 2 0.8861 0.5957 0.304 0.696
#> GSM549236 1 0.0000 0.9668 1.000 0.000
#> GSM549238 1 0.0000 0.9668 1.000 0.000
#> GSM549251 1 0.0000 0.9668 1.000 0.000
#> GSM549258 1 0.0000 0.9668 1.000 0.000
#> GSM549264 1 0.0000 0.9668 1.000 0.000
#> GSM549243 1 0.0000 0.9668 1.000 0.000
#> GSM549262 1 0.0000 0.9668 1.000 0.000
#> GSM549278 1 0.0376 0.9639 0.996 0.004
#> GSM549283 1 0.7883 0.6858 0.764 0.236
#> GSM549298 2 0.0000 0.9480 0.000 1.000
#> GSM750741 1 0.0000 0.9668 1.000 0.000
#> GSM549286 2 0.0000 0.9480 0.000 1.000
#> GSM549241 1 0.0000 0.9668 1.000 0.000
#> GSM549247 1 0.0000 0.9668 1.000 0.000
#> GSM549261 1 0.0000 0.9668 1.000 0.000
#> GSM549270 2 0.0000 0.9480 0.000 1.000
#> GSM549277 2 0.0000 0.9480 0.000 1.000
#> GSM549280 2 0.0000 0.9480 0.000 1.000
#> GSM549281 1 0.1843 0.9444 0.972 0.028
#> GSM549285 1 0.3879 0.8960 0.924 0.076
#> GSM549288 2 0.0000 0.9480 0.000 1.000
#> GSM549292 2 0.0000 0.9480 0.000 1.000
#> GSM549295 2 0.0000 0.9480 0.000 1.000
#> GSM549297 2 0.0000 0.9480 0.000 1.000
#> GSM750743 1 0.0000 0.9668 1.000 0.000
#> GSM549268 1 0.5294 0.8515 0.880 0.120
#> GSM549290 2 0.6623 0.8022 0.172 0.828
#> GSM549272 2 0.0000 0.9480 0.000 1.000
#> GSM549276 2 0.0000 0.9480 0.000 1.000
#> GSM549275 1 0.0000 0.9668 1.000 0.000
#> GSM549284 2 0.8267 0.6751 0.260 0.740
#> GSM750737 1 0.0000 0.9668 1.000 0.000
#> GSM750740 1 0.0000 0.9668 1.000 0.000
#> GSM750747 1 0.0000 0.9668 1.000 0.000
#> GSM750751 2 0.0000 0.9480 0.000 1.000
#> GSM750754 2 0.9209 0.5412 0.336 0.664
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.1753 0.871 0.048 0.000 0.952
#> GSM549291 3 0.0000 0.853 0.000 0.000 1.000
#> GSM549274 2 0.8740 0.145 0.432 0.460 0.108
#> GSM750738 3 0.6809 0.767 0.156 0.104 0.740
#> GSM750748 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549279 1 0.0892 0.885 0.980 0.020 0.000
#> GSM549294 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549300 2 0.0424 0.890 0.000 0.992 0.008
#> GSM549303 2 0.2625 0.861 0.000 0.916 0.084
#> GSM549309 2 0.5529 0.634 0.000 0.704 0.296
#> GSM750753 2 0.0000 0.891 0.000 1.000 0.000
#> GSM750752 3 0.4934 0.768 0.024 0.156 0.820
#> GSM549304 2 0.6529 0.691 0.152 0.756 0.092
#> GSM549305 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549307 2 0.0237 0.890 0.000 0.996 0.004
#> GSM549306 2 0.1753 0.878 0.000 0.952 0.048
#> GSM549308 2 0.2625 0.861 0.000 0.916 0.084
#> GSM549233 1 0.4452 0.834 0.808 0.000 0.192
#> GSM549234 3 0.3340 0.870 0.120 0.000 0.880
#> GSM549250 1 0.4235 0.845 0.824 0.000 0.176
#> GSM549287 2 0.5760 0.577 0.000 0.672 0.328
#> GSM750735 1 0.0000 0.895 1.000 0.000 0.000
#> GSM750736 1 0.0592 0.891 0.988 0.000 0.012
#> GSM750749 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549230 1 0.3816 0.862 0.852 0.000 0.148
#> GSM549231 1 0.3752 0.864 0.856 0.000 0.144
#> GSM549237 1 0.3412 0.874 0.876 0.000 0.124
#> GSM549254 3 0.4887 0.817 0.228 0.000 0.772
#> GSM750734 1 0.1031 0.894 0.976 0.000 0.024
#> GSM549271 3 0.4062 0.728 0.000 0.164 0.836
#> GSM549232 3 0.3340 0.870 0.120 0.000 0.880
#> GSM549246 1 0.5591 0.682 0.696 0.000 0.304
#> GSM549248 1 0.3752 0.864 0.856 0.000 0.144
#> GSM549255 3 0.4887 0.817 0.228 0.000 0.772
#> GSM750746 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549269 2 0.6587 0.304 0.424 0.568 0.008
#> GSM549273 2 0.2066 0.873 0.000 0.940 0.060
#> GSM549299 2 0.1529 0.869 0.040 0.960 0.000
#> GSM549301 2 0.1753 0.878 0.000 0.952 0.048
#> GSM549310 3 0.4346 0.733 0.000 0.184 0.816
#> GSM549311 2 0.5291 0.677 0.000 0.732 0.268
#> GSM549302 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549235 1 0.3412 0.872 0.876 0.000 0.124
#> GSM549245 3 0.3038 0.872 0.104 0.000 0.896
#> GSM549265 3 0.1860 0.872 0.052 0.000 0.948
#> GSM549282 2 0.5178 0.717 0.000 0.744 0.256
#> GSM549296 3 0.5263 0.806 0.088 0.084 0.828
#> GSM750739 1 0.0000 0.895 1.000 0.000 0.000
#> GSM750742 1 0.3752 0.864 0.856 0.000 0.144
#> GSM750744 1 0.3551 0.870 0.868 0.000 0.132
#> GSM750750 2 0.4953 0.780 0.016 0.808 0.176
#> GSM549242 1 0.3686 0.866 0.860 0.000 0.140
#> GSM549252 3 0.2625 0.869 0.084 0.000 0.916
#> GSM549253 1 0.4235 0.845 0.824 0.000 0.176
#> GSM549256 1 0.4399 0.829 0.812 0.000 0.188
#> GSM549257 3 0.4796 0.823 0.220 0.000 0.780
#> GSM549263 1 0.4235 0.845 0.824 0.000 0.176
#> GSM549267 3 0.0000 0.853 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549244 3 0.2625 0.869 0.084 0.000 0.916
#> GSM549249 3 0.2625 0.869 0.084 0.000 0.916
#> GSM549260 1 0.2066 0.886 0.940 0.000 0.060
#> GSM549266 1 0.1643 0.868 0.956 0.044 0.000
#> GSM549293 2 0.7059 0.650 0.164 0.724 0.112
#> GSM549236 1 0.4235 0.845 0.824 0.000 0.176
#> GSM549238 3 0.5560 0.545 0.300 0.000 0.700
#> GSM549251 1 0.4235 0.845 0.824 0.000 0.176
#> GSM549258 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549264 1 0.3619 0.867 0.864 0.000 0.136
#> GSM549243 1 0.0592 0.895 0.988 0.000 0.012
#> GSM549262 1 0.3752 0.864 0.856 0.000 0.144
#> GSM549278 3 0.2625 0.876 0.084 0.000 0.916
#> GSM549283 1 0.6062 0.302 0.616 0.384 0.000
#> GSM549298 2 0.1753 0.878 0.000 0.952 0.048
#> GSM750741 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549247 1 0.4605 0.662 0.796 0.000 0.204
#> GSM549261 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549281 1 0.0747 0.887 0.984 0.016 0.000
#> GSM549285 1 0.3983 0.864 0.852 0.004 0.144
#> GSM549288 2 0.0237 0.890 0.000 0.996 0.004
#> GSM549292 2 0.0237 0.890 0.000 0.996 0.004
#> GSM549295 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.891 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549268 1 0.5058 0.627 0.756 0.244 0.000
#> GSM549290 3 0.0237 0.852 0.000 0.004 0.996
#> GSM549272 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.891 0.000 1.000 0.000
#> GSM549275 1 0.0000 0.895 1.000 0.000 0.000
#> GSM549284 2 0.3619 0.787 0.136 0.864 0.000
#> GSM750737 3 0.4931 0.814 0.232 0.000 0.768
#> GSM750740 1 0.0000 0.895 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.895 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.891 0.000 1.000 0.000
#> GSM750754 3 0.0000 0.853 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.1743 0.5170 0.004 0.000 0.056 0.940
#> GSM549291 4 0.2345 0.4878 0.000 0.000 0.100 0.900
#> GSM549274 2 0.3123 0.7100 0.156 0.844 0.000 0.000
#> GSM750738 2 0.4454 0.5149 0.000 0.692 0.000 0.308
#> GSM750748 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0336 0.8548 0.992 0.000 0.000 0.008
#> GSM549279 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549294 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549300 3 0.4624 0.6766 0.000 0.340 0.660 0.000
#> GSM549303 3 0.3569 0.7863 0.000 0.196 0.804 0.000
#> GSM549309 3 0.3810 0.7867 0.000 0.188 0.804 0.008
#> GSM750753 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM750752 4 0.6694 -0.0454 0.000 0.392 0.092 0.516
#> GSM549304 2 0.1854 0.8285 0.048 0.940 0.000 0.012
#> GSM549305 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549307 3 0.4585 0.6890 0.000 0.332 0.668 0.000
#> GSM549306 3 0.4356 0.7397 0.000 0.292 0.708 0.000
#> GSM549308 3 0.3569 0.7863 0.000 0.196 0.804 0.000
#> GSM549233 4 0.7080 0.5103 0.236 0.000 0.196 0.568
#> GSM549234 4 0.2345 0.5360 0.100 0.000 0.000 0.900
#> GSM549250 4 0.7080 0.5094 0.236 0.000 0.196 0.568
#> GSM549287 3 0.4225 0.7817 0.000 0.184 0.792 0.024
#> GSM750735 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM750736 1 0.0592 0.8489 0.984 0.000 0.000 0.016
#> GSM750749 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549230 4 0.7479 0.4192 0.324 0.000 0.196 0.480
#> GSM549231 4 0.7479 0.4192 0.324 0.000 0.196 0.480
#> GSM549237 1 0.7172 -0.1390 0.484 0.000 0.140 0.376
#> GSM549254 4 0.4989 0.0487 0.472 0.000 0.000 0.528
#> GSM750734 1 0.4552 0.6120 0.784 0.000 0.044 0.172
#> GSM549271 3 0.4991 0.3914 0.000 0.004 0.608 0.388
#> GSM549232 4 0.2149 0.5392 0.088 0.000 0.000 0.912
#> GSM549246 4 0.7152 0.4795 0.284 0.000 0.172 0.544
#> GSM549248 4 0.7468 0.4244 0.320 0.000 0.196 0.484
#> GSM549255 4 0.4994 0.0338 0.480 0.000 0.000 0.520
#> GSM750746 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549269 2 0.2973 0.7261 0.144 0.856 0.000 0.000
#> GSM549273 3 0.4193 0.7571 0.000 0.268 0.732 0.000
#> GSM549299 2 0.0921 0.8475 0.028 0.972 0.000 0.000
#> GSM549301 3 0.4331 0.7431 0.000 0.288 0.712 0.000
#> GSM549310 4 0.7006 -0.1565 0.000 0.428 0.116 0.456
#> GSM549311 3 0.3810 0.7867 0.000 0.188 0.804 0.008
#> GSM549302 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549235 1 0.7302 -0.1306 0.500 0.000 0.168 0.332
#> GSM549245 4 0.3074 0.5213 0.152 0.000 0.000 0.848
#> GSM549265 4 0.2081 0.5567 0.000 0.000 0.084 0.916
#> GSM549282 3 0.4337 0.7248 0.000 0.140 0.808 0.052
#> GSM549296 4 0.6727 -0.0298 0.000 0.384 0.096 0.520
#> GSM750739 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM750742 4 0.7479 0.4192 0.324 0.000 0.196 0.480
#> GSM750744 1 0.7254 -0.1872 0.468 0.000 0.148 0.384
#> GSM750750 3 0.3810 0.7867 0.000 0.188 0.804 0.008
#> GSM549242 1 0.7024 -0.0625 0.512 0.000 0.128 0.360
#> GSM549252 4 0.0000 0.5409 0.000 0.000 0.000 1.000
#> GSM549253 4 0.7028 0.5131 0.228 0.000 0.196 0.576
#> GSM549256 4 0.7344 0.3204 0.380 0.000 0.160 0.460
#> GSM549257 4 0.4981 0.0647 0.464 0.000 0.000 0.536
#> GSM549263 4 0.7261 0.4823 0.268 0.000 0.196 0.536
#> GSM549267 4 0.4222 0.4214 0.000 0.000 0.272 0.728
#> GSM750745 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.5409 0.000 0.000 0.000 1.000
#> GSM549249 4 0.3569 0.5592 0.000 0.000 0.196 0.804
#> GSM549260 1 0.4057 0.6669 0.816 0.000 0.032 0.152
#> GSM549266 1 0.0592 0.8491 0.984 0.016 0.000 0.000
#> GSM549293 2 0.1635 0.8343 0.044 0.948 0.000 0.008
#> GSM549236 4 0.7080 0.5090 0.236 0.000 0.196 0.568
#> GSM549238 4 0.5035 0.5758 0.056 0.000 0.196 0.748
#> GSM549251 4 0.7176 0.4970 0.252 0.000 0.196 0.552
#> GSM549258 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549264 4 0.7479 0.4192 0.324 0.000 0.196 0.480
#> GSM549243 1 0.0921 0.8385 0.972 0.000 0.000 0.028
#> GSM549262 4 0.7479 0.4192 0.324 0.000 0.196 0.480
#> GSM549278 4 0.3581 0.5130 0.116 0.000 0.032 0.852
#> GSM549283 1 0.5057 0.3898 0.648 0.340 0.012 0.000
#> GSM549298 3 0.4356 0.7397 0.000 0.292 0.708 0.000
#> GSM750741 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549286 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549247 1 0.2530 0.7339 0.888 0.000 0.000 0.112
#> GSM549261 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549270 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549277 2 0.4877 0.0157 0.000 0.592 0.408 0.000
#> GSM549280 2 0.0188 0.8587 0.000 0.996 0.004 0.000
#> GSM549281 1 0.0188 0.8575 0.996 0.004 0.000 0.000
#> GSM549285 3 0.6980 0.1157 0.164 0.000 0.572 0.264
#> GSM549288 2 0.4989 -0.2531 0.000 0.528 0.472 0.000
#> GSM549292 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549295 2 0.3074 0.6957 0.000 0.848 0.152 0.000
#> GSM549297 2 0.3400 0.6533 0.000 0.820 0.180 0.000
#> GSM750743 1 0.1211 0.8295 0.960 0.000 0.000 0.040
#> GSM549268 1 0.2081 0.7847 0.916 0.084 0.000 0.000
#> GSM549290 4 0.4406 0.4528 0.000 0.000 0.300 0.700
#> GSM549272 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM549275 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM549284 2 0.1807 0.8303 0.052 0.940 0.008 0.000
#> GSM750737 4 0.4998 0.0167 0.488 0.000 0.000 0.512
#> GSM750740 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM750751 2 0.0000 0.8610 0.000 1.000 0.000 0.000
#> GSM750754 3 0.4916 0.0509 0.000 0.000 0.576 0.424
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549291 4 0.0162 0.9465 0.000 0.000 0.004 0.996 0.000
#> GSM549274 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM750738 4 0.4307 0.0101 0.000 0.500 0.000 0.500 0.000
#> GSM750748 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549279 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549294 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549300 3 0.2074 0.8277 0.000 0.104 0.896 0.000 0.000
#> GSM549303 3 0.0000 0.8835 0.000 0.000 1.000 0.000 0.000
#> GSM549309 3 0.0000 0.8835 0.000 0.000 1.000 0.000 0.000
#> GSM750753 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM750752 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549304 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549305 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549307 3 0.2127 0.8247 0.000 0.108 0.892 0.000 0.000
#> GSM549306 3 0.0794 0.8802 0.000 0.028 0.972 0.000 0.000
#> GSM549308 3 0.0000 0.8835 0.000 0.000 1.000 0.000 0.000
#> GSM549233 5 0.0404 0.8348 0.012 0.000 0.000 0.000 0.988
#> GSM549234 4 0.0162 0.9463 0.000 0.000 0.000 0.996 0.004
#> GSM549250 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549287 3 0.0404 0.8794 0.000 0.000 0.988 0.000 0.012
#> GSM750735 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM750736 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM750749 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549230 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549231 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549237 5 0.3752 0.6045 0.292 0.000 0.000 0.000 0.708
#> GSM549254 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM750734 1 0.3857 0.5135 0.688 0.000 0.000 0.000 0.312
#> GSM549271 4 0.2127 0.8559 0.000 0.000 0.108 0.892 0.000
#> GSM549232 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549246 5 0.3276 0.7591 0.132 0.000 0.000 0.032 0.836
#> GSM549248 5 0.0162 0.8378 0.004 0.000 0.000 0.000 0.996
#> GSM549255 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM750746 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.0290 0.8837 0.000 0.008 0.992 0.000 0.000
#> GSM549299 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549301 3 0.0794 0.8802 0.000 0.028 0.972 0.000 0.000
#> GSM549310 4 0.0880 0.9279 0.000 0.000 0.032 0.968 0.000
#> GSM549311 3 0.0000 0.8835 0.000 0.000 1.000 0.000 0.000
#> GSM549302 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549235 5 0.3774 0.6088 0.296 0.000 0.000 0.000 0.704
#> GSM549245 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549265 5 0.4210 0.3039 0.000 0.000 0.000 0.412 0.588
#> GSM549282 3 0.1544 0.8453 0.000 0.000 0.932 0.000 0.068
#> GSM549296 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM750739 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM750742 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM750744 5 0.3534 0.6648 0.256 0.000 0.000 0.000 0.744
#> GSM750750 3 0.0000 0.8835 0.000 0.000 1.000 0.000 0.000
#> GSM549242 5 0.4242 0.3328 0.428 0.000 0.000 0.000 0.572
#> GSM549252 4 0.0404 0.9407 0.000 0.000 0.000 0.988 0.012
#> GSM549253 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549256 5 0.3534 0.6541 0.256 0.000 0.000 0.000 0.744
#> GSM549257 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549263 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549267 5 0.6219 0.0953 0.000 0.000 0.140 0.424 0.436
#> GSM750745 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549249 5 0.0290 0.8356 0.000 0.000 0.000 0.008 0.992
#> GSM549260 1 0.1851 0.8783 0.912 0.000 0.000 0.000 0.088
#> GSM549266 1 0.0404 0.9526 0.988 0.012 0.000 0.000 0.000
#> GSM549293 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549236 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549238 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549251 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549258 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549264 5 0.0162 0.8379 0.004 0.000 0.000 0.000 0.996
#> GSM549243 1 0.0703 0.9424 0.976 0.000 0.000 0.000 0.024
#> GSM549262 5 0.0000 0.8387 0.000 0.000 0.000 0.000 1.000
#> GSM549278 4 0.0000 0.9485 0.000 0.000 0.000 1.000 0.000
#> GSM549283 1 0.4067 0.5599 0.692 0.300 0.008 0.000 0.000
#> GSM549298 3 0.0794 0.8802 0.000 0.028 0.972 0.000 0.000
#> GSM750741 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549286 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549241 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.0609 0.9458 0.980 0.000 0.000 0.020 0.000
#> GSM549261 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549270 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549277 2 0.4242 0.2272 0.000 0.572 0.428 0.000 0.000
#> GSM549280 2 0.0609 0.9436 0.000 0.980 0.020 0.000 0.000
#> GSM549281 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549285 3 0.5148 0.1917 0.040 0.000 0.528 0.000 0.432
#> GSM549288 3 0.4256 0.2152 0.000 0.436 0.564 0.000 0.000
#> GSM549292 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549295 2 0.2424 0.8314 0.000 0.868 0.132 0.000 0.000
#> GSM549297 2 0.2471 0.8271 0.000 0.864 0.136 0.000 0.000
#> GSM750743 1 0.0703 0.9433 0.976 0.000 0.000 0.000 0.024
#> GSM549268 1 0.2127 0.8567 0.892 0.108 0.000 0.000 0.000
#> GSM549290 5 0.5446 0.5095 0.000 0.000 0.100 0.272 0.628
#> GSM549272 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM549275 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM549284 2 0.0290 0.9527 0.000 0.992 0.008 0.000 0.000
#> GSM750737 4 0.1732 0.8669 0.080 0.000 0.000 0.920 0.000
#> GSM750740 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9611 1.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.0000 0.9587 0.000 1.000 0.000 0.000 0.000
#> GSM750754 3 0.5002 0.4507 0.000 0.000 0.636 0.052 0.312
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549291 4 0.2340 0.8092 0.000 0.000 0.000 0.852 0.000 0.148
#> GSM549274 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750738 2 0.3789 0.2446 0.000 0.584 0.000 0.416 0.000 0.000
#> GSM750748 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549279 1 0.2048 0.8717 0.880 0.000 0.120 0.000 0.000 0.000
#> GSM549294 2 0.3221 0.7335 0.000 0.736 0.264 0.000 0.000 0.000
#> GSM549300 3 0.3418 0.7334 0.000 0.032 0.784 0.000 0.000 0.184
#> GSM549303 6 0.2454 0.5262 0.000 0.000 0.160 0.000 0.000 0.840
#> GSM549309 6 0.0000 0.6800 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM750753 2 0.0632 0.8796 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM750752 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549304 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549305 2 0.2491 0.8118 0.000 0.836 0.164 0.000 0.000 0.000
#> GSM549307 3 0.3247 0.7244 0.000 0.036 0.808 0.000 0.000 0.156
#> GSM549306 3 0.3261 0.7301 0.000 0.016 0.780 0.000 0.000 0.204
#> GSM549308 3 0.3221 0.6615 0.000 0.000 0.736 0.000 0.000 0.264
#> GSM549233 5 0.0508 0.8311 0.012 0.000 0.000 0.004 0.984 0.000
#> GSM549234 4 0.0146 0.9670 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549250 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549287 6 0.0000 0.6800 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM750735 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750736 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750749 1 0.2378 0.8516 0.848 0.000 0.152 0.000 0.000 0.000
#> GSM549230 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549231 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549237 5 0.3371 0.5975 0.292 0.000 0.000 0.000 0.708 0.000
#> GSM549254 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM750734 1 0.3464 0.5292 0.688 0.000 0.000 0.000 0.312 0.000
#> GSM549271 6 0.3998 -0.0165 0.000 0.000 0.004 0.492 0.000 0.504
#> GSM549232 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549246 5 0.3014 0.7378 0.132 0.000 0.000 0.036 0.832 0.000
#> GSM549248 5 0.0146 0.8357 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549255 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM750746 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0260 0.8850 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM549273 6 0.3714 0.0928 0.000 0.004 0.340 0.000 0.000 0.656
#> GSM549299 2 0.2527 0.8068 0.000 0.832 0.168 0.000 0.000 0.000
#> GSM549301 3 0.3348 0.7212 0.000 0.016 0.768 0.000 0.000 0.216
#> GSM549310 4 0.1714 0.8834 0.000 0.000 0.000 0.908 0.000 0.092
#> GSM549311 6 0.0000 0.6800 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM549302 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 5 0.3390 0.5898 0.296 0.000 0.000 0.000 0.704 0.000
#> GSM549245 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549265 5 0.3789 0.2639 0.000 0.000 0.000 0.416 0.584 0.000
#> GSM549282 6 0.2882 0.5500 0.000 0.000 0.180 0.000 0.008 0.812
#> GSM549296 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM750739 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750742 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM750744 5 0.3175 0.6429 0.256 0.000 0.000 0.000 0.744 0.000
#> GSM750750 3 0.3747 0.4283 0.000 0.000 0.604 0.000 0.000 0.396
#> GSM549242 5 0.3944 0.3412 0.428 0.000 0.000 0.004 0.568 0.000
#> GSM549252 4 0.0363 0.9596 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM549253 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549256 5 0.3314 0.6316 0.256 0.000 0.000 0.004 0.740 0.000
#> GSM549257 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549263 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549267 6 0.4769 0.5306 0.000 0.000 0.000 0.104 0.240 0.656
#> GSM750745 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549249 5 0.0260 0.8326 0.000 0.000 0.000 0.008 0.992 0.000
#> GSM549260 1 0.1806 0.8645 0.908 0.000 0.000 0.004 0.088 0.000
#> GSM549266 1 0.2454 0.8461 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM549293 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549238 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549251 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549258 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549264 5 0.0146 0.8358 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549243 1 0.0632 0.9189 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM549262 5 0.0000 0.8368 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM549278 4 0.0000 0.9700 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549283 1 0.5319 0.4432 0.596 0.220 0.184 0.000 0.000 0.000
#> GSM549298 3 0.3261 0.7301 0.000 0.016 0.780 0.000 0.000 0.204
#> GSM750741 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549286 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.0547 0.9212 0.980 0.000 0.000 0.020 0.000 0.000
#> GSM549261 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549270 2 0.1714 0.8585 0.000 0.908 0.092 0.000 0.000 0.000
#> GSM549277 3 0.3615 0.4983 0.000 0.292 0.700 0.000 0.000 0.008
#> GSM549280 2 0.2558 0.7962 0.000 0.840 0.156 0.000 0.000 0.004
#> GSM549281 1 0.2378 0.8516 0.848 0.000 0.152 0.000 0.000 0.000
#> GSM549285 5 0.6953 0.1872 0.140 0.000 0.200 0.000 0.492 0.168
#> GSM549288 3 0.4827 0.5886 0.000 0.236 0.652 0.000 0.000 0.112
#> GSM549292 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 2 0.4004 0.4383 0.000 0.620 0.368 0.000 0.000 0.012
#> GSM549297 3 0.3804 0.1578 0.000 0.424 0.576 0.000 0.000 0.000
#> GSM750743 1 0.0632 0.9205 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM549268 1 0.4134 0.7070 0.708 0.052 0.240 0.000 0.000 0.000
#> GSM549290 6 0.5185 0.4121 0.000 0.000 0.000 0.108 0.328 0.564
#> GSM549272 2 0.1663 0.8599 0.000 0.912 0.088 0.000 0.000 0.000
#> GSM549276 2 0.0146 0.8855 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM549275 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549284 2 0.0000 0.8857 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750737 4 0.1556 0.8657 0.080 0.000 0.000 0.920 0.000 0.000
#> GSM750740 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9332 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.1556 0.8644 0.000 0.920 0.080 0.000 0.000 0.000
#> GSM750754 6 0.0603 0.6788 0.000 0.000 0.000 0.004 0.016 0.980
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:pam 101 0.0862 2.55e-06 0.39832 0.0424 2
#> MAD:pam 100 0.2692 4.77e-05 0.18665 0.0169 3
#> MAD:pam 73 0.3417 1.65e-05 0.01393 0.4680 4
#> MAD:pam 95 0.1875 1.52e-05 0.00391 0.0242 5
#> MAD:pam 91 0.4196 8.17e-05 0.00620 0.1615 6
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 21168 rows and 103 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.777 0.918 0.960 0.4975 0.496 0.496
#> 3 3 0.882 0.879 0.939 0.2986 0.775 0.576
#> 4 4 0.822 0.848 0.914 0.1128 0.915 0.761
#> 5 5 0.665 0.624 0.783 0.0793 0.846 0.528
#> 6 6 0.911 0.893 0.939 0.0608 0.911 0.626
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> GSM549289 2 0.5946 0.831 0.144 0.856
#> GSM549291 2 0.0376 0.971 0.004 0.996
#> GSM549274 2 0.0000 0.973 0.000 1.000
#> GSM750738 2 0.2236 0.949 0.036 0.964
#> GSM750748 1 0.0376 0.940 0.996 0.004
#> GSM549240 1 0.0376 0.940 0.996 0.004
#> GSM549279 2 0.2043 0.950 0.032 0.968
#> GSM549294 2 0.0000 0.973 0.000 1.000
#> GSM549300 2 0.0000 0.973 0.000 1.000
#> GSM549303 2 0.0376 0.971 0.004 0.996
#> GSM549309 2 0.0376 0.971 0.004 0.996
#> GSM750753 2 0.0000 0.973 0.000 1.000
#> GSM750752 2 0.4562 0.889 0.096 0.904
#> GSM549304 2 0.0000 0.973 0.000 1.000
#> GSM549305 2 0.0000 0.973 0.000 1.000
#> GSM549307 2 0.0000 0.973 0.000 1.000
#> GSM549306 2 0.0000 0.973 0.000 1.000
#> GSM549308 2 0.0000 0.973 0.000 1.000
#> GSM549233 1 0.0000 0.939 1.000 0.000
#> GSM549234 1 0.6712 0.817 0.824 0.176
#> GSM549250 1 0.0000 0.939 1.000 0.000
#> GSM549287 2 0.0376 0.971 0.004 0.996
#> GSM750735 1 0.0376 0.940 0.996 0.004
#> GSM750736 1 0.0938 0.936 0.988 0.012
#> GSM750749 2 0.6973 0.761 0.188 0.812
#> GSM549230 1 0.0000 0.939 1.000 0.000
#> GSM549231 1 0.0000 0.939 1.000 0.000
#> GSM549237 1 0.0376 0.940 0.996 0.004
#> GSM549254 2 0.9896 0.148 0.440 0.560
#> GSM750734 1 0.0376 0.940 0.996 0.004
#> GSM549271 2 0.0376 0.971 0.004 0.996
#> GSM549232 1 0.6712 0.817 0.824 0.176
#> GSM549246 1 0.7883 0.737 0.764 0.236
#> GSM549248 1 0.0376 0.940 0.996 0.004
#> GSM549255 1 0.6712 0.817 0.824 0.176
#> GSM750746 1 0.0376 0.940 0.996 0.004
#> GSM549259 1 0.0376 0.940 0.996 0.004
#> GSM549269 2 0.0000 0.973 0.000 1.000
#> GSM549273 2 0.0376 0.971 0.004 0.996
#> GSM549299 2 0.0000 0.973 0.000 1.000
#> GSM549301 2 0.0000 0.973 0.000 1.000
#> GSM549310 2 0.0672 0.970 0.008 0.992
#> GSM549311 2 0.0376 0.971 0.004 0.996
#> GSM549302 2 0.0000 0.973 0.000 1.000
#> GSM549235 1 0.0376 0.940 0.996 0.004
#> GSM549245 1 0.6712 0.817 0.824 0.176
#> GSM549265 1 0.9922 0.255 0.552 0.448
#> GSM549282 2 0.0376 0.971 0.004 0.996
#> GSM549296 2 0.5408 0.857 0.124 0.876
#> GSM750739 1 0.0376 0.940 0.996 0.004
#> GSM750742 1 0.0376 0.940 0.996 0.004
#> GSM750744 1 0.0376 0.940 0.996 0.004
#> GSM750750 2 0.0000 0.973 0.000 1.000
#> GSM549242 1 0.0000 0.939 1.000 0.000
#> GSM549252 1 0.6712 0.817 0.824 0.176
#> GSM549253 1 0.0000 0.939 1.000 0.000
#> GSM549256 1 0.0000 0.939 1.000 0.000
#> GSM549257 1 0.6712 0.817 0.824 0.176
#> GSM549263 1 0.0000 0.939 1.000 0.000
#> GSM549267 2 0.0376 0.971 0.004 0.996
#> GSM750745 1 0.0376 0.940 0.996 0.004
#> GSM549239 1 0.0376 0.940 0.996 0.004
#> GSM549244 1 0.6712 0.817 0.824 0.176
#> GSM549249 1 0.6712 0.817 0.824 0.176
#> GSM549260 1 0.0000 0.939 1.000 0.000
#> GSM549266 2 0.2603 0.939 0.044 0.956
#> GSM549293 2 0.0000 0.973 0.000 1.000
#> GSM549236 1 0.0000 0.939 1.000 0.000
#> GSM549238 1 0.0672 0.936 0.992 0.008
#> GSM549251 1 0.0000 0.939 1.000 0.000
#> GSM549258 1 0.0376 0.940 0.996 0.004
#> GSM549264 1 0.0376 0.940 0.996 0.004
#> GSM549243 1 0.0376 0.940 0.996 0.004
#> GSM549262 1 0.0376 0.940 0.996 0.004
#> GSM549278 2 0.5294 0.862 0.120 0.880
#> GSM549283 2 0.0000 0.973 0.000 1.000
#> GSM549298 2 0.0000 0.973 0.000 1.000
#> GSM750741 1 0.0376 0.940 0.996 0.004
#> GSM549286 2 0.0000 0.973 0.000 1.000
#> GSM549241 1 0.0376 0.940 0.996 0.004
#> GSM549247 1 0.6801 0.817 0.820 0.180
#> GSM549261 1 0.0376 0.940 0.996 0.004
#> GSM549270 2 0.0000 0.973 0.000 1.000
#> GSM549277 2 0.0000 0.973 0.000 1.000
#> GSM549280 2 0.0000 0.973 0.000 1.000
#> GSM549281 2 0.2043 0.950 0.032 0.968
#> GSM549285 2 0.0000 0.973 0.000 1.000
#> GSM549288 2 0.0000 0.973 0.000 1.000
#> GSM549292 2 0.0000 0.973 0.000 1.000
#> GSM549295 2 0.0000 0.973 0.000 1.000
#> GSM549297 2 0.0000 0.973 0.000 1.000
#> GSM750743 1 0.0376 0.940 0.996 0.004
#> GSM549268 2 0.0672 0.968 0.008 0.992
#> GSM549290 2 0.0376 0.971 0.004 0.996
#> GSM549272 2 0.0000 0.973 0.000 1.000
#> GSM549276 2 0.0000 0.973 0.000 1.000
#> GSM549275 1 0.8081 0.697 0.752 0.248
#> GSM549284 2 0.0000 0.973 0.000 1.000
#> GSM750737 1 0.6712 0.817 0.824 0.176
#> GSM750740 1 0.0376 0.940 0.996 0.004
#> GSM750747 1 0.0376 0.940 0.996 0.004
#> GSM750751 2 0.0000 0.973 0.000 1.000
#> GSM750754 2 0.0376 0.971 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.1765 0.8354 0.004 0.040 0.956
#> GSM549291 3 0.1753 0.8364 0.000 0.048 0.952
#> GSM549274 2 0.1031 0.9582 0.024 0.976 0.000
#> GSM750738 1 0.8118 0.4342 0.648 0.164 0.188
#> GSM750748 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549279 2 0.1163 0.9555 0.028 0.972 0.000
#> GSM549294 2 0.0424 0.9670 0.008 0.992 0.000
#> GSM549300 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549303 3 0.2356 0.8297 0.000 0.072 0.928
#> GSM549309 3 0.2356 0.8297 0.000 0.072 0.928
#> GSM750753 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM750752 3 0.1753 0.8364 0.000 0.048 0.952
#> GSM549304 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549307 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549306 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549308 2 0.1860 0.9253 0.000 0.948 0.052
#> GSM549233 1 0.1529 0.9389 0.960 0.000 0.040
#> GSM549234 3 0.5529 0.6465 0.296 0.000 0.704
#> GSM549250 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549287 3 0.2356 0.8297 0.000 0.072 0.928
#> GSM750735 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750749 2 0.3340 0.8468 0.120 0.880 0.000
#> GSM549230 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549231 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549237 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549254 3 0.7164 0.3036 0.452 0.024 0.524
#> GSM750734 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549271 3 0.2356 0.8297 0.000 0.072 0.928
#> GSM549232 3 0.5254 0.6835 0.264 0.000 0.736
#> GSM549246 1 0.6819 -0.1673 0.512 0.012 0.476
#> GSM549248 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549255 3 0.5465 0.6566 0.288 0.000 0.712
#> GSM750746 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549269 2 0.1031 0.9582 0.024 0.976 0.000
#> GSM549273 2 0.6302 0.0425 0.000 0.520 0.480
#> GSM549299 2 0.0237 0.9689 0.004 0.996 0.000
#> GSM549301 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549310 3 0.1753 0.8364 0.000 0.048 0.952
#> GSM549311 3 0.2356 0.8297 0.000 0.072 0.928
#> GSM549302 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549245 3 0.5016 0.7046 0.240 0.000 0.760
#> GSM549265 3 0.2902 0.8196 0.064 0.016 0.920
#> GSM549282 3 0.4555 0.6929 0.000 0.200 0.800
#> GSM549296 3 0.1753 0.8364 0.000 0.048 0.952
#> GSM750739 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750750 2 0.1964 0.9217 0.000 0.944 0.056
#> GSM549242 1 0.1289 0.9443 0.968 0.000 0.032
#> GSM549252 3 0.5859 0.5670 0.344 0.000 0.656
#> GSM549253 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549256 1 0.1753 0.9320 0.952 0.000 0.048
#> GSM549257 3 0.5859 0.5669 0.344 0.000 0.656
#> GSM549263 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549267 3 0.2165 0.8328 0.000 0.064 0.936
#> GSM750745 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549244 3 0.5291 0.6795 0.268 0.000 0.732
#> GSM549249 3 0.5560 0.6407 0.300 0.000 0.700
#> GSM549260 1 0.0747 0.9528 0.984 0.000 0.016
#> GSM549266 2 0.1163 0.9555 0.028 0.972 0.000
#> GSM549293 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549236 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549238 1 0.2625 0.8985 0.916 0.000 0.084
#> GSM549251 1 0.1031 0.9490 0.976 0.000 0.024
#> GSM549258 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549278 3 0.1753 0.8364 0.000 0.048 0.952
#> GSM549283 2 0.1031 0.9582 0.024 0.976 0.000
#> GSM549298 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM750741 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549247 1 0.1031 0.9377 0.976 0.024 0.000
#> GSM549261 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549270 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549277 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549280 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549281 2 0.1163 0.9555 0.028 0.972 0.000
#> GSM549285 2 0.1031 0.9582 0.024 0.976 0.000
#> GSM549288 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549292 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549295 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549297 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM549268 2 0.1163 0.9555 0.028 0.972 0.000
#> GSM549290 3 0.2066 0.8340 0.000 0.060 0.940
#> GSM549272 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM549275 1 0.3192 0.8276 0.888 0.112 0.000
#> GSM549284 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM750737 1 0.2878 0.8895 0.904 0.000 0.096
#> GSM750740 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.9592 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.9706 0.000 1.000 0.000
#> GSM750754 3 0.2356 0.8297 0.000 0.072 0.928
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.2011 0.8483 0.000 0.000 0.080 0.920
#> GSM549291 4 0.3569 0.7947 0.000 0.000 0.196 0.804
#> GSM549274 2 0.1305 0.9106 0.000 0.960 0.036 0.004
#> GSM750738 2 0.9271 0.0572 0.248 0.392 0.092 0.268
#> GSM750748 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0524 0.9368 0.988 0.000 0.008 0.004
#> GSM549279 2 0.2441 0.8957 0.020 0.920 0.056 0.004
#> GSM549294 2 0.0188 0.9130 0.000 0.996 0.004 0.000
#> GSM549300 3 0.4356 0.6831 0.000 0.292 0.708 0.000
#> GSM549303 3 0.1637 0.7720 0.000 0.000 0.940 0.060
#> GSM549309 3 0.1867 0.7616 0.000 0.000 0.928 0.072
#> GSM750753 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM750752 4 0.2216 0.8430 0.000 0.000 0.092 0.908
#> GSM549304 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549305 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549307 2 0.3942 0.6826 0.000 0.764 0.236 0.000
#> GSM549306 3 0.3907 0.7782 0.000 0.232 0.768 0.000
#> GSM549308 3 0.2760 0.8403 0.000 0.128 0.872 0.000
#> GSM549233 1 0.4072 0.7370 0.748 0.000 0.000 0.252
#> GSM549234 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549250 1 0.2973 0.8585 0.856 0.000 0.000 0.144
#> GSM549287 4 0.4804 0.6037 0.000 0.000 0.384 0.616
#> GSM750735 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750736 1 0.0376 0.9382 0.992 0.000 0.004 0.004
#> GSM750749 2 0.5096 0.6488 0.184 0.756 0.056 0.004
#> GSM549230 1 0.2011 0.9058 0.920 0.000 0.000 0.080
#> GSM549231 1 0.1211 0.9254 0.960 0.000 0.000 0.040
#> GSM549237 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549254 4 0.1833 0.8382 0.032 0.000 0.024 0.944
#> GSM750734 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549271 4 0.4790 0.6095 0.000 0.000 0.380 0.620
#> GSM549232 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549246 4 0.4046 0.7319 0.124 0.000 0.048 0.828
#> GSM549248 1 0.0336 0.9382 0.992 0.000 0.000 0.008
#> GSM549255 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM750746 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0592 0.9136 0.000 0.984 0.016 0.000
#> GSM549273 3 0.1545 0.8256 0.000 0.040 0.952 0.008
#> GSM549299 2 0.0921 0.9119 0.000 0.972 0.028 0.000
#> GSM549301 3 0.3569 0.8112 0.000 0.196 0.804 0.000
#> GSM549310 4 0.2921 0.8232 0.000 0.000 0.140 0.860
#> GSM549311 3 0.1792 0.7658 0.000 0.000 0.932 0.068
#> GSM549302 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549245 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549265 4 0.1807 0.8501 0.008 0.000 0.052 0.940
#> GSM549282 3 0.2816 0.8287 0.000 0.064 0.900 0.036
#> GSM549296 4 0.2081 0.8450 0.000 0.000 0.084 0.916
#> GSM750739 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750750 3 0.2081 0.8411 0.000 0.084 0.916 0.000
#> GSM549242 1 0.2647 0.8798 0.880 0.000 0.000 0.120
#> GSM549252 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549253 1 0.2469 0.8878 0.892 0.000 0.000 0.108
#> GSM549256 1 0.4072 0.7381 0.748 0.000 0.000 0.252
#> GSM549257 4 0.0469 0.8472 0.012 0.000 0.000 0.988
#> GSM549263 1 0.2081 0.9034 0.916 0.000 0.000 0.084
#> GSM549267 4 0.4761 0.6206 0.000 0.000 0.372 0.628
#> GSM750745 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549249 4 0.0000 0.8547 0.000 0.000 0.000 1.000
#> GSM549260 1 0.1557 0.9190 0.944 0.000 0.000 0.056
#> GSM549266 2 0.2441 0.8957 0.020 0.920 0.056 0.004
#> GSM549293 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549236 1 0.2647 0.8790 0.880 0.000 0.000 0.120
#> GSM549238 1 0.4843 0.4928 0.604 0.000 0.000 0.396
#> GSM549251 1 0.2149 0.9010 0.912 0.000 0.000 0.088
#> GSM549258 1 0.0376 0.9382 0.992 0.000 0.004 0.004
#> GSM549264 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549278 4 0.2345 0.8452 0.000 0.000 0.100 0.900
#> GSM549283 2 0.1474 0.9030 0.000 0.948 0.052 0.000
#> GSM549298 3 0.3873 0.7828 0.000 0.228 0.772 0.000
#> GSM750741 1 0.0524 0.9368 0.988 0.000 0.008 0.004
#> GSM549286 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0376 0.9382 0.992 0.000 0.004 0.004
#> GSM549247 1 0.0657 0.9349 0.984 0.000 0.012 0.004
#> GSM549261 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549270 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549277 2 0.1474 0.9030 0.000 0.948 0.052 0.000
#> GSM549280 2 0.1474 0.9030 0.000 0.948 0.052 0.000
#> GSM549281 2 0.2441 0.8957 0.020 0.920 0.056 0.004
#> GSM549285 2 0.4454 0.5556 0.000 0.692 0.308 0.000
#> GSM549288 2 0.2011 0.8891 0.000 0.920 0.080 0.000
#> GSM549292 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549295 2 0.2149 0.8825 0.000 0.912 0.088 0.000
#> GSM549297 2 0.0707 0.9129 0.000 0.980 0.020 0.000
#> GSM750743 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM549268 2 0.2328 0.8980 0.016 0.924 0.056 0.004
#> GSM549290 4 0.4730 0.6293 0.000 0.000 0.364 0.636
#> GSM549272 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549276 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM549275 1 0.3292 0.8203 0.868 0.112 0.016 0.004
#> GSM549284 2 0.1792 0.8956 0.000 0.932 0.068 0.000
#> GSM750737 1 0.4661 0.5924 0.652 0.000 0.000 0.348
#> GSM750740 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9401 1.000 0.000 0.000 0.000
#> GSM750751 2 0.0000 0.9132 0.000 1.000 0.000 0.000
#> GSM750754 4 0.4804 0.6037 0.000 0.000 0.384 0.616
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.4351 0.5525 0.000 0.000 0.132 0.768 0.100
#> GSM549291 3 0.5373 0.5618 0.000 0.000 0.632 0.276 0.092
#> GSM549274 2 0.3291 0.6727 0.000 0.840 0.120 0.000 0.040
#> GSM750738 3 0.8446 0.1939 0.004 0.252 0.376 0.196 0.172
#> GSM750748 1 0.1121 0.8197 0.956 0.000 0.000 0.000 0.044
#> GSM549240 1 0.2561 0.7591 0.856 0.000 0.000 0.000 0.144
#> GSM549279 2 0.7672 0.3015 0.060 0.404 0.308 0.000 0.228
#> GSM549294 2 0.0404 0.7132 0.000 0.988 0.000 0.000 0.012
#> GSM549300 3 0.5691 -0.1248 0.000 0.444 0.476 0.000 0.080
#> GSM549303 3 0.0162 0.6654 0.000 0.000 0.996 0.004 0.000
#> GSM549309 3 0.0451 0.6661 0.000 0.000 0.988 0.004 0.008
#> GSM750753 2 0.0451 0.7134 0.000 0.988 0.008 0.000 0.004
#> GSM750752 4 0.5768 -0.3038 0.000 0.000 0.428 0.484 0.088
#> GSM549304 2 0.0609 0.7095 0.000 0.980 0.000 0.000 0.020
#> GSM549305 2 0.0290 0.7110 0.000 0.992 0.000 0.000 0.008
#> GSM549307 2 0.5547 0.3902 0.000 0.564 0.356 0.000 0.080
#> GSM549306 3 0.4876 0.4705 0.000 0.220 0.700 0.000 0.080
#> GSM549308 3 0.3459 0.6112 0.000 0.116 0.832 0.000 0.052
#> GSM549233 4 0.5354 0.3804 0.108 0.000 0.000 0.652 0.240
#> GSM549234 4 0.1544 0.7626 0.000 0.000 0.000 0.932 0.068
#> GSM549250 5 0.5761 0.7828 0.184 0.000 0.000 0.196 0.620
#> GSM549287 3 0.4851 0.6312 0.000 0.000 0.712 0.196 0.092
#> GSM750735 1 0.0880 0.8282 0.968 0.000 0.000 0.000 0.032
#> GSM750736 1 0.2773 0.7424 0.836 0.000 0.000 0.000 0.164
#> GSM750749 1 0.6911 0.3110 0.520 0.232 0.028 0.000 0.220
#> GSM549230 5 0.5579 0.8992 0.264 0.000 0.000 0.116 0.620
#> GSM549231 5 0.5751 0.8577 0.348 0.000 0.000 0.100 0.552
#> GSM549237 1 0.4421 0.4188 0.748 0.000 0.000 0.068 0.184
#> GSM549254 4 0.1990 0.7701 0.004 0.000 0.028 0.928 0.040
#> GSM750734 1 0.1121 0.8197 0.956 0.000 0.000 0.000 0.044
#> GSM549271 3 0.4914 0.6274 0.000 0.000 0.704 0.204 0.092
#> GSM549232 4 0.0671 0.7629 0.000 0.000 0.004 0.980 0.016
#> GSM549246 4 0.2177 0.7525 0.004 0.000 0.008 0.908 0.080
#> GSM549248 5 0.5717 0.8389 0.368 0.000 0.000 0.092 0.540
#> GSM549255 4 0.0703 0.7757 0.000 0.000 0.000 0.976 0.024
#> GSM750746 1 0.1043 0.8216 0.960 0.000 0.000 0.000 0.040
#> GSM549259 1 0.0794 0.8295 0.972 0.000 0.000 0.000 0.028
#> GSM549269 2 0.0693 0.7133 0.000 0.980 0.008 0.000 0.012
#> GSM549273 3 0.2370 0.6464 0.000 0.056 0.904 0.000 0.040
#> GSM549299 2 0.4646 0.5967 0.000 0.712 0.228 0.000 0.060
#> GSM549301 3 0.3888 0.5878 0.000 0.136 0.800 0.000 0.064
#> GSM549310 3 0.5816 0.3234 0.000 0.000 0.468 0.440 0.092
#> GSM549311 3 0.0324 0.6655 0.000 0.000 0.992 0.004 0.004
#> GSM549302 2 0.0510 0.7107 0.000 0.984 0.000 0.000 0.016
#> GSM549235 1 0.1121 0.8197 0.956 0.000 0.000 0.000 0.044
#> GSM549245 4 0.0880 0.7762 0.000 0.000 0.000 0.968 0.032
#> GSM549265 4 0.2411 0.7299 0.000 0.000 0.008 0.884 0.108
#> GSM549282 3 0.2554 0.6424 0.000 0.072 0.892 0.000 0.036
#> GSM549296 4 0.5747 -0.2670 0.000 0.000 0.408 0.504 0.088
#> GSM750739 1 0.1197 0.8215 0.952 0.000 0.000 0.000 0.048
#> GSM750742 5 0.5760 0.8390 0.368 0.000 0.000 0.096 0.536
#> GSM750744 1 0.1197 0.8165 0.952 0.000 0.000 0.000 0.048
#> GSM750750 3 0.3112 0.6208 0.000 0.100 0.856 0.000 0.044
#> GSM549242 1 0.6581 -0.3289 0.468 0.000 0.000 0.252 0.280
#> GSM549252 4 0.2020 0.7413 0.000 0.000 0.000 0.900 0.100
#> GSM549253 5 0.5666 0.8874 0.244 0.000 0.000 0.136 0.620
#> GSM549256 4 0.5240 0.3987 0.092 0.000 0.000 0.656 0.252
#> GSM549257 4 0.1792 0.7535 0.000 0.000 0.000 0.916 0.084
#> GSM549263 5 0.5599 0.9000 0.260 0.000 0.000 0.120 0.620
#> GSM549267 3 0.5032 0.6168 0.000 0.000 0.688 0.220 0.092
#> GSM750745 1 0.0794 0.8289 0.972 0.000 0.000 0.000 0.028
#> GSM549239 1 0.0609 0.8279 0.980 0.000 0.000 0.000 0.020
#> GSM549244 4 0.0290 0.7739 0.000 0.000 0.000 0.992 0.008
#> GSM549249 4 0.0510 0.7755 0.000 0.000 0.000 0.984 0.016
#> GSM549260 1 0.3304 0.6447 0.816 0.000 0.000 0.016 0.168
#> GSM549266 2 0.7757 0.2960 0.068 0.400 0.304 0.000 0.228
#> GSM549293 2 0.0609 0.7095 0.000 0.980 0.000 0.000 0.020
#> GSM549236 5 0.5666 0.8874 0.244 0.000 0.000 0.136 0.620
#> GSM549238 4 0.3616 0.6402 0.032 0.000 0.000 0.804 0.164
#> GSM549251 5 0.5599 0.9000 0.260 0.000 0.000 0.120 0.620
#> GSM549258 1 0.1341 0.8170 0.944 0.000 0.000 0.000 0.056
#> GSM549264 1 0.2909 0.6688 0.848 0.000 0.000 0.012 0.140
#> GSM549243 1 0.1121 0.8197 0.956 0.000 0.000 0.000 0.044
#> GSM549262 5 0.5663 0.8113 0.384 0.000 0.000 0.084 0.532
#> GSM549278 3 0.6207 0.3234 0.000 0.000 0.460 0.400 0.140
#> GSM549283 2 0.6262 0.4028 0.000 0.504 0.332 0.000 0.164
#> GSM549298 3 0.4905 0.4635 0.000 0.224 0.696 0.000 0.080
#> GSM750741 1 0.2891 0.7304 0.824 0.000 0.000 0.000 0.176
#> GSM549286 2 0.0000 0.7125 0.000 1.000 0.000 0.000 0.000
#> GSM549241 1 0.1270 0.8188 0.948 0.000 0.000 0.000 0.052
#> GSM549247 1 0.2848 0.7504 0.840 0.000 0.004 0.000 0.156
#> GSM549261 1 0.0703 0.8291 0.976 0.000 0.000 0.000 0.024
#> GSM549270 2 0.0290 0.7110 0.000 0.992 0.000 0.000 0.008
#> GSM549277 2 0.6202 0.3436 0.000 0.496 0.356 0.000 0.148
#> GSM549280 2 0.4820 0.4856 0.000 0.632 0.332 0.000 0.036
#> GSM549281 2 0.7566 0.3215 0.052 0.416 0.304 0.000 0.228
#> GSM549285 3 0.6205 0.0796 0.000 0.332 0.512 0.000 0.156
#> GSM549288 2 0.5396 0.4369 0.000 0.588 0.340 0.000 0.072
#> GSM549292 2 0.0510 0.7107 0.000 0.984 0.000 0.000 0.016
#> GSM549295 2 0.5382 0.4443 0.000 0.592 0.336 0.000 0.072
#> GSM549297 2 0.3421 0.6256 0.000 0.788 0.204 0.000 0.008
#> GSM750743 1 0.0609 0.8275 0.980 0.000 0.000 0.000 0.020
#> GSM549268 2 0.7356 0.3266 0.036 0.424 0.312 0.000 0.228
#> GSM549290 3 0.5115 0.6027 0.000 0.000 0.676 0.232 0.092
#> GSM549272 2 0.0000 0.7125 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.0162 0.7119 0.000 0.996 0.000 0.000 0.004
#> GSM549275 1 0.3282 0.7088 0.804 0.008 0.000 0.000 0.188
#> GSM549284 2 0.4820 0.4943 0.000 0.632 0.332 0.000 0.036
#> GSM750737 4 0.4455 0.5631 0.068 0.000 0.000 0.744 0.188
#> GSM750740 1 0.0703 0.8292 0.976 0.000 0.000 0.000 0.024
#> GSM750747 1 0.0794 0.8256 0.972 0.000 0.000 0.000 0.028
#> GSM750751 2 0.0162 0.7124 0.000 0.996 0.000 0.000 0.004
#> GSM750754 3 0.4851 0.6312 0.000 0.000 0.712 0.196 0.092
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.1124 0.915 0.000 0.000 0.000 0.956 0.036 0.008
#> GSM549291 6 0.3990 0.512 0.000 0.000 0.016 0.304 0.004 0.676
#> GSM549274 2 0.0790 0.950 0.000 0.968 0.032 0.000 0.000 0.000
#> GSM750738 4 0.4002 0.673 0.000 0.212 0.016 0.748 0.008 0.016
#> GSM750748 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM549240 1 0.0622 0.951 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM549279 2 0.2756 0.888 0.084 0.872 0.028 0.000 0.000 0.016
#> GSM549294 2 0.0146 0.959 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM549300 3 0.0632 0.917 0.000 0.024 0.976 0.000 0.000 0.000
#> GSM549303 6 0.0632 0.928 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM549309 6 0.0547 0.931 0.000 0.000 0.020 0.000 0.000 0.980
#> GSM750753 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750752 4 0.1693 0.889 0.000 0.000 0.020 0.932 0.004 0.044
#> GSM549304 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549305 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549307 3 0.0790 0.916 0.000 0.032 0.968 0.000 0.000 0.000
#> GSM549306 3 0.0806 0.918 0.000 0.020 0.972 0.000 0.000 0.008
#> GSM549308 3 0.1594 0.902 0.000 0.016 0.932 0.000 0.000 0.052
#> GSM549233 5 0.2730 0.749 0.000 0.000 0.000 0.192 0.808 0.000
#> GSM549234 4 0.0291 0.927 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549250 5 0.0146 0.887 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM549287 6 0.0603 0.932 0.000 0.000 0.016 0.004 0.000 0.980
#> GSM750735 1 0.0547 0.960 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM750736 1 0.0363 0.950 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM750749 1 0.1026 0.944 0.968 0.008 0.012 0.000 0.004 0.008
#> GSM549230 5 0.0146 0.890 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549231 5 0.1327 0.876 0.064 0.000 0.000 0.000 0.936 0.000
#> GSM549237 5 0.3843 0.184 0.452 0.000 0.000 0.000 0.548 0.000
#> GSM549254 4 0.0291 0.926 0.004 0.000 0.000 0.992 0.004 0.000
#> GSM750734 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM549271 6 0.0717 0.931 0.000 0.000 0.016 0.008 0.000 0.976
#> GSM549232 4 0.0146 0.927 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549246 4 0.3215 0.711 0.000 0.000 0.000 0.756 0.240 0.004
#> GSM549248 5 0.1501 0.870 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM549255 4 0.0146 0.927 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM750746 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM549259 1 0.0713 0.962 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM549269 2 0.0632 0.951 0.000 0.976 0.024 0.000 0.000 0.000
#> GSM549273 3 0.3578 0.493 0.000 0.000 0.660 0.000 0.000 0.340
#> GSM549299 2 0.0146 0.959 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM549301 3 0.1003 0.917 0.000 0.020 0.964 0.000 0.000 0.016
#> GSM549310 4 0.3936 0.574 0.000 0.000 0.020 0.700 0.004 0.276
#> GSM549311 6 0.0547 0.931 0.000 0.000 0.020 0.000 0.000 0.980
#> GSM549302 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 1 0.1141 0.958 0.948 0.000 0.000 0.000 0.052 0.000
#> GSM549245 4 0.0146 0.927 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM549265 4 0.1411 0.903 0.000 0.000 0.000 0.936 0.060 0.004
#> GSM549282 6 0.2653 0.788 0.000 0.012 0.144 0.000 0.000 0.844
#> GSM549296 4 0.1478 0.898 0.000 0.000 0.020 0.944 0.004 0.032
#> GSM750739 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM750742 5 0.1814 0.854 0.100 0.000 0.000 0.000 0.900 0.000
#> GSM750744 1 0.3288 0.641 0.724 0.000 0.000 0.000 0.276 0.000
#> GSM750750 3 0.1719 0.897 0.000 0.016 0.924 0.000 0.000 0.060
#> GSM549242 5 0.0692 0.883 0.004 0.000 0.000 0.020 0.976 0.000
#> GSM549252 4 0.0291 0.927 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549253 5 0.0146 0.890 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549256 5 0.2823 0.729 0.000 0.000 0.000 0.204 0.796 0.000
#> GSM549257 4 0.0291 0.927 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549263 5 0.0146 0.890 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549267 6 0.0717 0.931 0.000 0.000 0.016 0.008 0.000 0.976
#> GSM750745 1 0.0937 0.961 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM549239 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM549244 4 0.0291 0.927 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549249 4 0.0291 0.927 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM549260 5 0.1501 0.864 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM549266 2 0.2756 0.888 0.084 0.872 0.028 0.000 0.000 0.016
#> GSM549293 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 5 0.0146 0.890 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549238 4 0.0458 0.925 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM549251 5 0.0146 0.890 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM549258 1 0.0508 0.952 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM549264 1 0.1267 0.951 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM549243 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM549262 5 0.1814 0.854 0.100 0.000 0.000 0.000 0.900 0.000
#> GSM549278 4 0.3125 0.829 0.000 0.000 0.000 0.836 0.084 0.080
#> GSM549283 2 0.0858 0.948 0.004 0.968 0.028 0.000 0.000 0.000
#> GSM549298 3 0.0806 0.918 0.000 0.020 0.972 0.000 0.000 0.008
#> GSM750741 1 0.0363 0.950 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM549286 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.0508 0.952 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM549247 1 0.0508 0.952 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM549261 1 0.0458 0.959 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549270 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549277 3 0.2442 0.821 0.004 0.144 0.852 0.000 0.000 0.000
#> GSM549280 2 0.2416 0.833 0.000 0.844 0.156 0.000 0.000 0.000
#> GSM549281 2 0.2756 0.888 0.084 0.872 0.028 0.000 0.000 0.016
#> GSM549285 3 0.0870 0.914 0.004 0.012 0.972 0.000 0.000 0.012
#> GSM549288 3 0.2135 0.842 0.000 0.128 0.872 0.000 0.000 0.000
#> GSM549292 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 3 0.1663 0.886 0.000 0.088 0.912 0.000 0.000 0.000
#> GSM549297 2 0.0260 0.958 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM750743 1 0.0937 0.961 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM549268 2 0.2649 0.895 0.076 0.880 0.028 0.000 0.000 0.016
#> GSM549290 6 0.1003 0.924 0.000 0.000 0.016 0.020 0.000 0.964
#> GSM549272 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549276 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549275 1 0.0717 0.943 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM549284 2 0.2320 0.847 0.000 0.864 0.132 0.000 0.000 0.004
#> GSM750737 4 0.1010 0.917 0.004 0.000 0.000 0.960 0.036 0.000
#> GSM750740 1 0.0713 0.962 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM750747 1 0.1075 0.960 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM750751 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750754 6 0.0603 0.932 0.000 0.000 0.016 0.004 0.000 0.980
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:mclust 101 0.0174 1.66e-04 0.205841 0.00149 2
#> MAD:mclust 99 0.2861 2.89e-04 0.000889 0.01015 3
#> MAD:mclust 101 0.2312 1.13e-05 0.000295 0.01346 4
#> MAD:mclust 78 0.5721 1.38e-04 0.036036 0.20844 5
#> MAD:mclust 101 0.7063 1.12e-04 0.010675 0.06549 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.965 0.986 0.5028 0.496 0.496
#> 3 3 0.793 0.822 0.917 0.2798 0.829 0.669
#> 4 4 0.806 0.815 0.905 0.1570 0.799 0.506
#> 5 5 0.729 0.716 0.854 0.0582 0.888 0.610
#> 6 6 0.699 0.515 0.737 0.0456 0.919 0.664
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
#> GSM549289 1 0.0000 0.995 1.000 0.000
#> GSM549291 2 0.9909 0.230 0.444 0.556
#> GSM549274 2 0.0000 0.975 0.000 1.000
#> GSM750738 2 0.0000 0.975 0.000 1.000
#> GSM750748 1 0.0000 0.995 1.000 0.000
#> GSM549240 1 0.0000 0.995 1.000 0.000
#> GSM549279 2 0.0938 0.965 0.012 0.988
#> GSM549294 2 0.0000 0.975 0.000 1.000
#> GSM549300 2 0.0000 0.975 0.000 1.000
#> GSM549303 2 0.0000 0.975 0.000 1.000
#> GSM549309 2 0.0000 0.975 0.000 1.000
#> GSM750753 2 0.0000 0.975 0.000 1.000
#> GSM750752 2 0.0000 0.975 0.000 1.000
#> GSM549304 2 0.0000 0.975 0.000 1.000
#> GSM549305 2 0.0000 0.975 0.000 1.000
#> GSM549307 2 0.0000 0.975 0.000 1.000
#> GSM549306 2 0.0000 0.975 0.000 1.000
#> GSM549308 2 0.0000 0.975 0.000 1.000
#> GSM549233 1 0.0000 0.995 1.000 0.000
#> GSM549234 1 0.0000 0.995 1.000 0.000
#> GSM549250 1 0.0000 0.995 1.000 0.000
#> GSM549287 2 0.0000 0.975 0.000 1.000
#> GSM750735 1 0.0000 0.995 1.000 0.000
#> GSM750736 1 0.0000 0.995 1.000 0.000
#> GSM750749 1 0.0000 0.995 1.000 0.000
#> GSM549230 1 0.0000 0.995 1.000 0.000
#> GSM549231 1 0.0000 0.995 1.000 0.000
#> GSM549237 1 0.0000 0.995 1.000 0.000
#> GSM549254 1 0.0000 0.995 1.000 0.000
#> GSM750734 1 0.0000 0.995 1.000 0.000
#> GSM549271 2 0.0000 0.975 0.000 1.000
#> GSM549232 1 0.0000 0.995 1.000 0.000
#> GSM549246 1 0.0000 0.995 1.000 0.000
#> GSM549248 1 0.0000 0.995 1.000 0.000
#> GSM549255 1 0.0000 0.995 1.000 0.000
#> GSM750746 1 0.0000 0.995 1.000 0.000
#> GSM549259 1 0.0000 0.995 1.000 0.000
#> GSM549269 2 0.0000 0.975 0.000 1.000
#> GSM549273 2 0.0000 0.975 0.000 1.000
#> GSM549299 2 0.0000 0.975 0.000 1.000
#> GSM549301 2 0.0000 0.975 0.000 1.000
#> GSM549310 2 0.0000 0.975 0.000 1.000
#> GSM549311 2 0.0000 0.975 0.000 1.000
#> GSM549302 2 0.0000 0.975 0.000 1.000
#> GSM549235 1 0.0000 0.995 1.000 0.000
#> GSM549245 1 0.0000 0.995 1.000 0.000
#> GSM549265 1 0.0000 0.995 1.000 0.000
#> GSM549282 2 0.0000 0.975 0.000 1.000
#> GSM549296 2 0.0000 0.975 0.000 1.000
#> GSM750739 1 0.0000 0.995 1.000 0.000
#> GSM750742 1 0.0000 0.995 1.000 0.000
#> GSM750744 1 0.0000 0.995 1.000 0.000
#> GSM750750 2 0.0000 0.975 0.000 1.000
#> GSM549242 1 0.0000 0.995 1.000 0.000
#> GSM549252 1 0.0000 0.995 1.000 0.000
#> GSM549253 1 0.0000 0.995 1.000 0.000
#> GSM549256 1 0.0000 0.995 1.000 0.000
#> GSM549257 1 0.0000 0.995 1.000 0.000
#> GSM549263 1 0.0000 0.995 1.000 0.000
#> GSM549267 2 0.2043 0.947 0.032 0.968
#> GSM750745 1 0.0000 0.995 1.000 0.000
#> GSM549239 1 0.0000 0.995 1.000 0.000
#> GSM549244 1 0.0000 0.995 1.000 0.000
#> GSM549249 1 0.0000 0.995 1.000 0.000
#> GSM549260 1 0.0000 0.995 1.000 0.000
#> GSM549266 2 0.0376 0.972 0.004 0.996
#> GSM549293 2 0.0000 0.975 0.000 1.000
#> GSM549236 1 0.0000 0.995 1.000 0.000
#> GSM549238 1 0.0000 0.995 1.000 0.000
#> GSM549251 1 0.0000 0.995 1.000 0.000
#> GSM549258 1 0.0000 0.995 1.000 0.000
#> GSM549264 1 0.0000 0.995 1.000 0.000
#> GSM549243 1 0.0000 0.995 1.000 0.000
#> GSM549262 1 0.0000 0.995 1.000 0.000
#> GSM549278 1 0.1633 0.971 0.976 0.024
#> GSM549283 2 0.0000 0.975 0.000 1.000
#> GSM549298 2 0.0000 0.975 0.000 1.000
#> GSM750741 1 0.0000 0.995 1.000 0.000
#> GSM549286 2 0.0000 0.975 0.000 1.000
#> GSM549241 1 0.0000 0.995 1.000 0.000
#> GSM549247 1 0.0000 0.995 1.000 0.000
#> GSM549261 1 0.0000 0.995 1.000 0.000
#> GSM549270 2 0.0000 0.975 0.000 1.000
#> GSM549277 2 0.0000 0.975 0.000 1.000
#> GSM549280 2 0.0000 0.975 0.000 1.000
#> GSM549281 2 0.0376 0.972 0.004 0.996
#> GSM549285 2 0.0376 0.972 0.004 0.996
#> GSM549288 2 0.0000 0.975 0.000 1.000
#> GSM549292 2 0.0000 0.975 0.000 1.000
#> GSM549295 2 0.0000 0.975 0.000 1.000
#> GSM549297 2 0.0000 0.975 0.000 1.000
#> GSM750743 1 0.0000 0.995 1.000 0.000
#> GSM549268 2 0.0000 0.975 0.000 1.000
#> GSM549290 2 0.9754 0.333 0.408 0.592
#> GSM549272 2 0.0000 0.975 0.000 1.000
#> GSM549276 2 0.0000 0.975 0.000 1.000
#> GSM549275 1 0.7674 0.703 0.776 0.224
#> GSM549284 2 0.0000 0.975 0.000 1.000
#> GSM750737 1 0.0000 0.995 1.000 0.000
#> GSM750740 1 0.0000 0.995 1.000 0.000
#> GSM750747 1 0.0000 0.995 1.000 0.000
#> GSM750751 2 0.0000 0.975 0.000 1.000
#> GSM750754 2 0.8386 0.640 0.268 0.732
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.5948 0.3382 0.360 0.000 0.640
#> GSM549291 3 0.0747 0.8745 0.016 0.000 0.984
#> GSM549274 2 0.0000 0.8736 0.000 1.000 0.000
#> GSM750738 2 0.0237 0.8752 0.000 0.996 0.004
#> GSM750748 1 0.0000 0.9312 1.000 0.000 0.000
#> GSM549240 1 0.4750 0.7281 0.784 0.216 0.000
#> GSM549279 2 0.1411 0.8503 0.036 0.964 0.000
#> GSM549294 2 0.0424 0.8765 0.000 0.992 0.008
#> GSM549300 3 0.5882 0.4475 0.000 0.348 0.652
#> GSM549303 3 0.1031 0.8807 0.000 0.024 0.976
#> GSM549309 3 0.0000 0.8835 0.000 0.000 1.000
#> GSM750753 2 0.2066 0.8615 0.000 0.940 0.060
#> GSM750752 3 0.0892 0.8822 0.000 0.020 0.980
#> GSM549304 2 0.1031 0.8782 0.000 0.976 0.024
#> GSM549305 2 0.1411 0.8751 0.000 0.964 0.036
#> GSM549307 2 0.6299 0.0715 0.000 0.524 0.476
#> GSM549306 3 0.4504 0.7225 0.000 0.196 0.804
#> GSM549308 3 0.1411 0.8754 0.000 0.036 0.964
#> GSM549233 1 0.1163 0.9315 0.972 0.000 0.028
#> GSM549234 1 0.2448 0.9113 0.924 0.000 0.076
#> GSM549250 1 0.1753 0.9257 0.952 0.000 0.048
#> GSM549287 3 0.0000 0.8835 0.000 0.000 1.000
#> GSM750735 1 0.0747 0.9272 0.984 0.016 0.000
#> GSM750736 1 0.6095 0.3937 0.608 0.392 0.000
#> GSM750749 1 0.1289 0.9207 0.968 0.032 0.000
#> GSM549230 1 0.1289 0.9306 0.968 0.000 0.032
#> GSM549231 1 0.1529 0.9286 0.960 0.000 0.040
#> GSM549237 1 0.0892 0.9324 0.980 0.000 0.020
#> GSM549254 1 0.1964 0.9214 0.944 0.000 0.056
#> GSM750734 1 0.0000 0.9312 1.000 0.000 0.000
#> GSM549271 3 0.0424 0.8848 0.000 0.008 0.992
#> GSM549232 1 0.3879 0.8463 0.848 0.000 0.152
#> GSM549246 1 0.2959 0.8947 0.900 0.000 0.100
#> GSM549248 1 0.0424 0.9323 0.992 0.000 0.008
#> GSM549255 1 0.2711 0.9036 0.912 0.000 0.088
#> GSM750746 1 0.0424 0.9297 0.992 0.008 0.000
#> GSM549259 1 0.1643 0.9135 0.956 0.044 0.000
#> GSM549269 2 0.0237 0.8719 0.004 0.996 0.000
#> GSM549273 3 0.2165 0.8576 0.000 0.064 0.936
#> GSM549299 2 0.1643 0.8713 0.000 0.956 0.044
#> GSM549301 3 0.3412 0.8045 0.000 0.124 0.876
#> GSM549310 3 0.1289 0.8783 0.000 0.032 0.968
#> GSM549311 3 0.0237 0.8844 0.000 0.004 0.996
#> GSM549302 2 0.0892 0.8783 0.000 0.980 0.020
#> GSM549235 1 0.0237 0.9319 0.996 0.000 0.004
#> GSM549245 1 0.1964 0.9226 0.944 0.000 0.056
#> GSM549265 1 0.4654 0.7780 0.792 0.000 0.208
#> GSM549282 3 0.0000 0.8835 0.000 0.000 1.000
#> GSM549296 3 0.0424 0.8848 0.000 0.008 0.992
#> GSM750739 1 0.0424 0.9297 0.992 0.008 0.000
#> GSM750742 1 0.1031 0.9318 0.976 0.000 0.024
#> GSM750744 1 0.0424 0.9323 0.992 0.000 0.008
#> GSM750750 3 0.0424 0.8848 0.000 0.008 0.992
#> GSM549242 1 0.0747 0.9325 0.984 0.000 0.016
#> GSM549252 1 0.3038 0.8909 0.896 0.000 0.104
#> GSM549253 1 0.1411 0.9296 0.964 0.000 0.036
#> GSM549256 1 0.0892 0.9323 0.980 0.000 0.020
#> GSM549257 1 0.1643 0.9273 0.956 0.000 0.044
#> GSM549263 1 0.1529 0.9286 0.960 0.000 0.040
#> GSM549267 3 0.0424 0.8795 0.008 0.000 0.992
#> GSM750745 1 0.1411 0.9184 0.964 0.036 0.000
#> GSM549239 1 0.0592 0.9285 0.988 0.012 0.000
#> GSM549244 1 0.3752 0.8547 0.856 0.000 0.144
#> GSM549249 1 0.3482 0.8701 0.872 0.000 0.128
#> GSM549260 1 0.0237 0.9319 0.996 0.000 0.004
#> GSM549266 2 0.1163 0.8564 0.028 0.972 0.000
#> GSM549293 2 0.0592 0.8778 0.000 0.988 0.012
#> GSM549236 1 0.1643 0.9273 0.956 0.000 0.044
#> GSM549238 1 0.2261 0.9162 0.932 0.000 0.068
#> GSM549251 1 0.1289 0.9306 0.968 0.000 0.032
#> GSM549258 1 0.2448 0.8907 0.924 0.076 0.000
#> GSM549264 1 0.0424 0.9325 0.992 0.000 0.008
#> GSM549243 1 0.0237 0.9305 0.996 0.004 0.000
#> GSM549262 1 0.0892 0.9323 0.980 0.000 0.020
#> GSM549278 3 0.2796 0.7988 0.092 0.000 0.908
#> GSM549283 2 0.1163 0.8776 0.000 0.972 0.028
#> GSM549298 3 0.5138 0.6397 0.000 0.252 0.748
#> GSM750741 1 0.5859 0.5034 0.656 0.344 0.000
#> GSM549286 2 0.1163 0.8775 0.000 0.972 0.028
#> GSM549241 1 0.6274 0.2156 0.544 0.456 0.000
#> GSM549247 2 0.5859 0.4120 0.344 0.656 0.000
#> GSM549261 1 0.1964 0.9053 0.944 0.056 0.000
#> GSM549270 2 0.1964 0.8641 0.000 0.944 0.056
#> GSM549277 2 0.6154 0.2930 0.000 0.592 0.408
#> GSM549280 2 0.6235 0.2126 0.000 0.564 0.436
#> GSM549281 2 0.0983 0.8680 0.016 0.980 0.004
#> GSM549285 3 0.1860 0.8670 0.000 0.052 0.948
#> GSM549288 3 0.6305 0.0147 0.000 0.484 0.516
#> GSM549292 2 0.0592 0.8778 0.000 0.988 0.012
#> GSM549295 2 0.6126 0.3179 0.000 0.600 0.400
#> GSM549297 2 0.2448 0.8488 0.000 0.924 0.076
#> GSM750743 1 0.0424 0.9297 0.992 0.008 0.000
#> GSM549268 2 0.1182 0.8743 0.012 0.976 0.012
#> GSM549290 3 0.0747 0.8742 0.016 0.000 0.984
#> GSM549272 2 0.0747 0.8781 0.000 0.984 0.016
#> GSM549276 2 0.1411 0.8751 0.000 0.964 0.036
#> GSM549275 2 0.4346 0.6855 0.184 0.816 0.000
#> GSM549284 2 0.3619 0.7958 0.000 0.864 0.136
#> GSM750737 1 0.0424 0.9297 0.992 0.008 0.000
#> GSM750740 1 0.0592 0.9285 0.988 0.012 0.000
#> GSM750747 1 0.0424 0.9297 0.992 0.008 0.000
#> GSM750751 2 0.1031 0.8782 0.000 0.976 0.024
#> GSM750754 3 0.0592 0.8771 0.012 0.000 0.988
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.4175 0.6772 0.012 0.000 0.212 0.776
#> GSM549291 3 0.4877 0.3162 0.000 0.000 0.592 0.408
#> GSM549274 2 0.0376 0.9100 0.000 0.992 0.004 0.004
#> GSM750738 4 0.4406 0.5145 0.000 0.300 0.000 0.700
#> GSM750748 1 0.0376 0.9323 0.992 0.000 0.004 0.004
#> GSM549240 2 0.5665 0.6443 0.176 0.716 0.000 0.108
#> GSM549279 2 0.1545 0.8995 0.040 0.952 0.008 0.000
#> GSM549294 2 0.0817 0.9121 0.000 0.976 0.024 0.000
#> GSM549300 3 0.2814 0.8001 0.000 0.132 0.868 0.000
#> GSM549303 3 0.1042 0.8394 0.000 0.008 0.972 0.020
#> GSM549309 3 0.0817 0.8360 0.000 0.000 0.976 0.024
#> GSM750753 2 0.1302 0.9071 0.000 0.956 0.044 0.000
#> GSM750752 4 0.1174 0.8662 0.000 0.020 0.012 0.968
#> GSM549304 2 0.0779 0.9115 0.000 0.980 0.016 0.004
#> GSM549305 2 0.1022 0.9110 0.000 0.968 0.032 0.000
#> GSM549307 3 0.3764 0.7264 0.000 0.216 0.784 0.000
#> GSM549306 3 0.1792 0.8331 0.000 0.068 0.932 0.000
#> GSM549308 3 0.0817 0.8411 0.000 0.024 0.976 0.000
#> GSM549233 4 0.5105 0.2445 0.432 0.000 0.004 0.564
#> GSM549234 4 0.0524 0.8769 0.008 0.000 0.004 0.988
#> GSM549250 1 0.4050 0.7907 0.808 0.000 0.024 0.168
#> GSM549287 3 0.1302 0.8281 0.000 0.000 0.956 0.044
#> GSM750735 1 0.1398 0.9194 0.956 0.040 0.000 0.004
#> GSM750736 2 0.5646 0.5173 0.296 0.656 0.000 0.048
#> GSM750749 1 0.2124 0.8902 0.924 0.068 0.008 0.000
#> GSM549230 1 0.1677 0.9259 0.948 0.000 0.012 0.040
#> GSM549231 1 0.2224 0.9159 0.928 0.000 0.032 0.040
#> GSM549237 1 0.1151 0.9318 0.968 0.000 0.008 0.024
#> GSM549254 4 0.0779 0.8716 0.004 0.016 0.000 0.980
#> GSM750734 1 0.0469 0.9331 0.988 0.000 0.000 0.012
#> GSM549271 3 0.1824 0.8238 0.000 0.004 0.936 0.060
#> GSM549232 4 0.0188 0.8760 0.000 0.000 0.004 0.996
#> GSM549246 4 0.5571 0.3298 0.396 0.000 0.024 0.580
#> GSM549248 1 0.1109 0.9315 0.968 0.000 0.004 0.028
#> GSM549255 4 0.0188 0.8760 0.000 0.000 0.004 0.996
#> GSM750746 1 0.0188 0.9318 0.996 0.000 0.000 0.004
#> GSM549259 1 0.1305 0.9195 0.960 0.036 0.000 0.004
#> GSM549269 2 0.0188 0.9088 0.000 0.996 0.000 0.004
#> GSM549273 3 0.1452 0.8411 0.000 0.036 0.956 0.008
#> GSM549299 2 0.1557 0.9014 0.000 0.944 0.056 0.000
#> GSM549301 3 0.1118 0.8403 0.000 0.036 0.964 0.000
#> GSM549310 4 0.1510 0.8557 0.000 0.016 0.028 0.956
#> GSM549311 3 0.0921 0.8360 0.000 0.000 0.972 0.028
#> GSM549302 2 0.0927 0.9110 0.000 0.976 0.016 0.008
#> GSM549235 1 0.0188 0.9315 0.996 0.000 0.004 0.000
#> GSM549245 4 0.0592 0.8713 0.000 0.016 0.000 0.984
#> GSM549265 4 0.2300 0.8547 0.028 0.000 0.048 0.924
#> GSM549282 3 0.0469 0.8359 0.000 0.000 0.988 0.012
#> GSM549296 4 0.0524 0.8735 0.000 0.004 0.008 0.988
#> GSM750739 1 0.0707 0.9333 0.980 0.000 0.000 0.020
#> GSM750742 1 0.1297 0.9303 0.964 0.000 0.020 0.016
#> GSM750744 1 0.1109 0.9315 0.968 0.000 0.004 0.028
#> GSM750750 3 0.0524 0.8397 0.000 0.008 0.988 0.004
#> GSM549242 1 0.3945 0.7334 0.780 0.000 0.004 0.216
#> GSM549252 4 0.0804 0.8756 0.012 0.000 0.008 0.980
#> GSM549253 1 0.2342 0.9007 0.912 0.000 0.008 0.080
#> GSM549256 4 0.4088 0.6795 0.232 0.000 0.004 0.764
#> GSM549257 4 0.0524 0.8765 0.004 0.000 0.008 0.988
#> GSM549263 1 0.2282 0.9130 0.924 0.000 0.024 0.052
#> GSM549267 3 0.4624 0.4757 0.000 0.000 0.660 0.340
#> GSM750745 1 0.1151 0.9247 0.968 0.024 0.000 0.008
#> GSM549239 1 0.0376 0.9312 0.992 0.004 0.000 0.004
#> GSM549244 4 0.0336 0.8754 0.000 0.000 0.008 0.992
#> GSM549249 4 0.0937 0.8745 0.012 0.000 0.012 0.976
#> GSM549260 1 0.1118 0.9316 0.964 0.000 0.000 0.036
#> GSM549266 2 0.1584 0.9021 0.036 0.952 0.012 0.000
#> GSM549293 2 0.0895 0.9056 0.000 0.976 0.004 0.020
#> GSM549236 1 0.3404 0.8636 0.864 0.000 0.032 0.104
#> GSM549238 4 0.2773 0.8329 0.072 0.000 0.028 0.900
#> GSM549251 1 0.1722 0.9237 0.944 0.000 0.008 0.048
#> GSM549258 1 0.2480 0.8726 0.904 0.088 0.000 0.008
#> GSM549264 1 0.1677 0.9266 0.948 0.000 0.012 0.040
#> GSM549243 1 0.0336 0.9326 0.992 0.000 0.000 0.008
#> GSM549262 1 0.1406 0.9301 0.960 0.000 0.016 0.024
#> GSM549278 3 0.3763 0.7386 0.024 0.000 0.832 0.144
#> GSM549283 2 0.1557 0.9017 0.000 0.944 0.056 0.000
#> GSM549298 3 0.2149 0.8261 0.000 0.088 0.912 0.000
#> GSM750741 1 0.5295 -0.0503 0.504 0.488 0.000 0.008
#> GSM549286 2 0.0817 0.9117 0.000 0.976 0.024 0.000
#> GSM549241 2 0.5150 0.3657 0.396 0.596 0.000 0.008
#> GSM549247 2 0.2928 0.8474 0.052 0.896 0.000 0.052
#> GSM549261 1 0.1211 0.9181 0.960 0.040 0.000 0.000
#> GSM549270 2 0.1211 0.9087 0.000 0.960 0.040 0.000
#> GSM549277 3 0.3837 0.7174 0.000 0.224 0.776 0.000
#> GSM549280 3 0.4564 0.5532 0.000 0.328 0.672 0.000
#> GSM549281 2 0.2124 0.9018 0.028 0.932 0.040 0.000
#> GSM549285 3 0.0804 0.8393 0.008 0.012 0.980 0.000
#> GSM549288 3 0.3688 0.7348 0.000 0.208 0.792 0.000
#> GSM549292 2 0.0927 0.9083 0.000 0.976 0.008 0.016
#> GSM549295 3 0.4985 0.1964 0.000 0.468 0.532 0.000
#> GSM549297 2 0.2868 0.8212 0.000 0.864 0.136 0.000
#> GSM750743 1 0.0937 0.9324 0.976 0.012 0.000 0.012
#> GSM549268 2 0.2483 0.8953 0.032 0.916 0.052 0.000
#> GSM549290 3 0.4877 0.3078 0.000 0.000 0.592 0.408
#> GSM549272 2 0.0657 0.9116 0.000 0.984 0.012 0.004
#> GSM549276 2 0.1022 0.9110 0.000 0.968 0.032 0.000
#> GSM549275 2 0.1940 0.8742 0.076 0.924 0.000 0.000
#> GSM549284 2 0.3443 0.8040 0.000 0.848 0.136 0.016
#> GSM750737 4 0.1297 0.8687 0.016 0.020 0.000 0.964
#> GSM750740 1 0.0895 0.9267 0.976 0.020 0.000 0.004
#> GSM750747 1 0.0188 0.9318 0.996 0.000 0.000 0.004
#> GSM750751 2 0.1022 0.9110 0.000 0.968 0.032 0.000
#> GSM750754 3 0.1302 0.8268 0.000 0.000 0.956 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.2909 0.76890 0.000 0.000 0.140 0.848 0.012
#> GSM549291 4 0.4590 0.33351 0.000 0.000 0.420 0.568 0.012
#> GSM549274 2 0.0000 0.88156 0.000 1.000 0.000 0.000 0.000
#> GSM750738 4 0.4273 0.20634 0.000 0.448 0.000 0.552 0.000
#> GSM750748 1 0.1544 0.80765 0.932 0.000 0.000 0.000 0.068
#> GSM549240 1 0.5148 0.53011 0.692 0.220 0.000 0.080 0.008
#> GSM549279 2 0.5858 0.54125 0.284 0.624 0.028 0.004 0.060
#> GSM549294 2 0.1862 0.87422 0.004 0.932 0.048 0.000 0.016
#> GSM549300 3 0.1408 0.84055 0.000 0.044 0.948 0.000 0.008
#> GSM549303 3 0.1012 0.83870 0.000 0.000 0.968 0.012 0.020
#> GSM549309 3 0.0955 0.83534 0.000 0.000 0.968 0.004 0.028
#> GSM750753 2 0.2497 0.83704 0.004 0.880 0.112 0.000 0.004
#> GSM750752 4 0.0613 0.83985 0.000 0.004 0.008 0.984 0.004
#> GSM549304 2 0.0162 0.88297 0.000 0.996 0.004 0.000 0.000
#> GSM549305 2 0.1717 0.87350 0.004 0.936 0.052 0.000 0.008
#> GSM549307 3 0.1851 0.82455 0.000 0.088 0.912 0.000 0.000
#> GSM549306 3 0.0703 0.84462 0.000 0.024 0.976 0.000 0.000
#> GSM549308 3 0.1124 0.84000 0.000 0.004 0.960 0.000 0.036
#> GSM549233 4 0.4083 0.69385 0.132 0.000 0.000 0.788 0.080
#> GSM549234 4 0.0771 0.83786 0.000 0.004 0.000 0.976 0.020
#> GSM549250 5 0.3741 0.71906 0.108 0.000 0.000 0.076 0.816
#> GSM549287 3 0.2104 0.81643 0.000 0.000 0.916 0.024 0.060
#> GSM750735 1 0.1671 0.80574 0.924 0.000 0.000 0.000 0.076
#> GSM750736 1 0.5733 0.32110 0.552 0.380 0.000 0.024 0.044
#> GSM750749 1 0.2177 0.77916 0.908 0.004 0.008 0.000 0.080
#> GSM549230 1 0.3452 0.62824 0.756 0.000 0.000 0.000 0.244
#> GSM549231 5 0.2891 0.72759 0.176 0.000 0.000 0.000 0.824
#> GSM549237 1 0.2074 0.79280 0.896 0.000 0.000 0.000 0.104
#> GSM549254 4 0.2201 0.82648 0.008 0.000 0.032 0.920 0.040
#> GSM750734 1 0.1121 0.80897 0.956 0.000 0.000 0.000 0.044
#> GSM549271 3 0.2130 0.79780 0.000 0.000 0.908 0.080 0.012
#> GSM549232 4 0.0162 0.83883 0.000 0.004 0.000 0.996 0.000
#> GSM549246 4 0.3395 0.76075 0.108 0.000 0.016 0.848 0.028
#> GSM549248 1 0.4294 0.04598 0.532 0.000 0.000 0.000 0.468
#> GSM549255 4 0.0162 0.83890 0.000 0.000 0.004 0.996 0.000
#> GSM750746 1 0.0963 0.81521 0.964 0.000 0.000 0.000 0.036
#> GSM549259 1 0.1522 0.81456 0.944 0.012 0.000 0.000 0.044
#> GSM549269 2 0.0451 0.88340 0.004 0.988 0.008 0.000 0.000
#> GSM549273 3 0.0798 0.84049 0.000 0.000 0.976 0.008 0.016
#> GSM549299 2 0.3883 0.68511 0.004 0.744 0.244 0.000 0.008
#> GSM549301 3 0.0771 0.84476 0.000 0.020 0.976 0.000 0.004
#> GSM549310 4 0.1682 0.83158 0.000 0.004 0.044 0.940 0.012
#> GSM549311 3 0.1638 0.82948 0.000 0.000 0.932 0.004 0.064
#> GSM549302 2 0.0162 0.88297 0.000 0.996 0.004 0.000 0.000
#> GSM549235 1 0.1792 0.79952 0.916 0.000 0.000 0.000 0.084
#> GSM549245 4 0.0451 0.83850 0.000 0.004 0.000 0.988 0.008
#> GSM549265 4 0.4564 0.42083 0.008 0.004 0.000 0.600 0.388
#> GSM549282 5 0.3586 0.40421 0.000 0.000 0.264 0.000 0.736
#> GSM549296 4 0.1281 0.83395 0.000 0.000 0.032 0.956 0.012
#> GSM750739 1 0.1544 0.81068 0.932 0.000 0.000 0.000 0.068
#> GSM750742 5 0.4088 0.47741 0.368 0.000 0.000 0.000 0.632
#> GSM750744 1 0.3999 0.45078 0.656 0.000 0.000 0.000 0.344
#> GSM750750 3 0.1270 0.83545 0.000 0.000 0.948 0.000 0.052
#> GSM549242 1 0.4009 0.43379 0.684 0.000 0.000 0.312 0.004
#> GSM549252 4 0.1502 0.82918 0.000 0.004 0.000 0.940 0.056
#> GSM549253 5 0.4436 0.42181 0.396 0.000 0.000 0.008 0.596
#> GSM549256 4 0.2179 0.78616 0.100 0.000 0.000 0.896 0.004
#> GSM549257 4 0.0324 0.83950 0.004 0.000 0.004 0.992 0.000
#> GSM549263 5 0.3366 0.69629 0.232 0.000 0.000 0.000 0.768
#> GSM549267 4 0.5475 0.49945 0.000 0.000 0.308 0.604 0.088
#> GSM750745 1 0.1043 0.80427 0.960 0.000 0.000 0.000 0.040
#> GSM549239 1 0.0609 0.81615 0.980 0.000 0.000 0.000 0.020
#> GSM549244 4 0.0794 0.83697 0.000 0.000 0.000 0.972 0.028
#> GSM549249 4 0.1478 0.82706 0.000 0.000 0.000 0.936 0.064
#> GSM549260 1 0.1082 0.80519 0.964 0.000 0.000 0.008 0.028
#> GSM549266 1 0.6466 -0.03103 0.480 0.408 0.060 0.000 0.052
#> GSM549293 2 0.0290 0.87737 0.000 0.992 0.000 0.008 0.000
#> GSM549236 5 0.3427 0.72579 0.192 0.000 0.000 0.012 0.796
#> GSM549238 4 0.3885 0.62477 0.008 0.000 0.000 0.724 0.268
#> GSM549251 1 0.2424 0.76073 0.868 0.000 0.000 0.000 0.132
#> GSM549258 1 0.0609 0.80839 0.980 0.000 0.000 0.000 0.020
#> GSM549264 5 0.2429 0.71580 0.076 0.020 0.000 0.004 0.900
#> GSM549243 1 0.1341 0.81027 0.944 0.000 0.000 0.000 0.056
#> GSM549262 1 0.3684 0.57852 0.720 0.000 0.000 0.000 0.280
#> GSM549278 3 0.3795 0.68394 0.004 0.000 0.788 0.184 0.024
#> GSM549283 2 0.3762 0.68853 0.004 0.748 0.244 0.000 0.004
#> GSM549298 3 0.0898 0.84475 0.000 0.020 0.972 0.000 0.008
#> GSM750741 1 0.1864 0.78011 0.924 0.004 0.000 0.004 0.068
#> GSM549286 2 0.0162 0.88297 0.000 0.996 0.004 0.000 0.000
#> GSM549241 1 0.1597 0.78898 0.940 0.012 0.000 0.000 0.048
#> GSM549247 2 0.4799 0.59944 0.220 0.716 0.000 0.056 0.008
#> GSM549261 1 0.1893 0.80996 0.928 0.024 0.000 0.000 0.048
#> GSM549270 2 0.3001 0.80853 0.004 0.844 0.144 0.000 0.008
#> GSM549277 3 0.2389 0.80844 0.000 0.116 0.880 0.000 0.004
#> GSM549280 3 0.2727 0.80387 0.000 0.116 0.868 0.000 0.016
#> GSM549281 3 0.7876 0.07147 0.216 0.320 0.392 0.004 0.068
#> GSM549285 3 0.4568 0.58601 0.008 0.020 0.684 0.000 0.288
#> GSM549288 3 0.3366 0.68681 0.000 0.232 0.768 0.000 0.000
#> GSM549292 2 0.0000 0.88156 0.000 1.000 0.000 0.000 0.000
#> GSM549295 3 0.3521 0.67981 0.000 0.232 0.764 0.000 0.004
#> GSM549297 2 0.4491 0.42495 0.004 0.624 0.364 0.000 0.008
#> GSM750743 1 0.1732 0.80810 0.920 0.000 0.000 0.000 0.080
#> GSM549268 3 0.7476 0.28379 0.168 0.276 0.480 0.000 0.076
#> GSM549290 5 0.6349 0.00462 0.000 0.000 0.168 0.360 0.472
#> GSM549272 2 0.0290 0.88340 0.000 0.992 0.008 0.000 0.000
#> GSM549276 2 0.0955 0.88231 0.000 0.968 0.028 0.000 0.004
#> GSM549275 2 0.2249 0.82146 0.096 0.896 0.000 0.000 0.008
#> GSM549284 2 0.1018 0.86428 0.000 0.968 0.000 0.016 0.016
#> GSM750737 4 0.2462 0.81979 0.016 0.004 0.020 0.912 0.048
#> GSM750740 1 0.0771 0.81545 0.976 0.004 0.000 0.000 0.020
#> GSM750747 1 0.0794 0.81574 0.972 0.000 0.000 0.000 0.028
#> GSM750751 2 0.1059 0.88215 0.004 0.968 0.020 0.000 0.008
#> GSM750754 3 0.1774 0.82225 0.000 0.000 0.932 0.016 0.052
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.2462 0.80979 0.012 0.000 0.032 0.892 0.000 0.064
#> GSM549291 4 0.5018 0.40591 0.012 0.000 0.328 0.604 0.004 0.052
#> GSM549274 2 0.1562 0.82013 0.024 0.940 0.000 0.000 0.004 0.032
#> GSM750738 2 0.5887 0.02979 0.000 0.428 0.000 0.396 0.004 0.172
#> GSM750748 1 0.4176 -0.01538 0.580 0.000 0.000 0.000 0.016 0.404
#> GSM549240 6 0.6843 0.32762 0.292 0.192 0.000 0.060 0.004 0.452
#> GSM549279 1 0.7206 0.09135 0.448 0.256 0.132 0.000 0.004 0.160
#> GSM549294 2 0.2095 0.81622 0.016 0.916 0.028 0.000 0.000 0.040
#> GSM549300 3 0.1464 0.81227 0.000 0.016 0.944 0.000 0.004 0.036
#> GSM549303 3 0.3343 0.78112 0.000 0.000 0.796 0.024 0.004 0.176
#> GSM549309 3 0.2834 0.79826 0.000 0.000 0.848 0.016 0.008 0.128
#> GSM750753 2 0.4247 0.60697 0.000 0.688 0.268 0.000 0.004 0.040
#> GSM750752 4 0.0858 0.83548 0.000 0.000 0.004 0.968 0.000 0.028
#> GSM549304 2 0.2721 0.80138 0.000 0.868 0.040 0.000 0.004 0.088
#> GSM549305 2 0.1334 0.82209 0.000 0.948 0.032 0.000 0.000 0.020
#> GSM549307 3 0.1951 0.80497 0.000 0.060 0.916 0.000 0.004 0.020
#> GSM549306 3 0.0547 0.81583 0.000 0.000 0.980 0.000 0.000 0.020
#> GSM549308 3 0.0291 0.81737 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM549233 4 0.5266 0.59571 0.076 0.000 0.000 0.692 0.088 0.144
#> GSM549234 4 0.2002 0.81611 0.000 0.004 0.000 0.908 0.012 0.076
#> GSM549250 5 0.2560 0.65795 0.016 0.000 0.000 0.016 0.880 0.088
#> GSM549287 3 0.4010 0.76631 0.000 0.000 0.772 0.068 0.012 0.148
#> GSM750735 1 0.3799 0.30214 0.756 0.024 0.000 0.000 0.012 0.208
#> GSM750736 1 0.5835 0.18832 0.564 0.164 0.000 0.008 0.008 0.256
#> GSM750749 1 0.4299 0.29955 0.748 0.028 0.028 0.000 0.008 0.188
#> GSM549230 6 0.6095 0.22018 0.380 0.000 0.000 0.004 0.224 0.392
#> GSM549231 5 0.2954 0.65447 0.048 0.000 0.000 0.000 0.844 0.108
#> GSM549237 1 0.4539 0.09159 0.688 0.000 0.000 0.000 0.096 0.216
#> GSM549254 4 0.1285 0.83039 0.000 0.000 0.004 0.944 0.000 0.052
#> GSM750734 1 0.0458 0.34954 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM549271 3 0.2011 0.79884 0.000 0.000 0.912 0.064 0.004 0.020
#> GSM549232 4 0.0547 0.83494 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM549246 4 0.3994 0.70333 0.056 0.000 0.000 0.792 0.036 0.116
#> GSM549248 5 0.3834 0.48873 0.268 0.000 0.000 0.000 0.708 0.024
#> GSM549255 4 0.0260 0.83407 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM750746 1 0.4084 -0.00175 0.588 0.000 0.000 0.000 0.012 0.400
#> GSM549259 1 0.4329 -0.02806 0.576 0.008 0.000 0.000 0.012 0.404
#> GSM549269 2 0.0935 0.82752 0.000 0.964 0.004 0.000 0.000 0.032
#> GSM549273 3 0.3730 0.77316 0.000 0.004 0.772 0.032 0.004 0.188
#> GSM549299 2 0.4544 0.51698 0.000 0.632 0.320 0.000 0.004 0.044
#> GSM549301 3 0.0603 0.82000 0.000 0.004 0.980 0.000 0.000 0.016
#> GSM549310 4 0.1719 0.82191 0.000 0.000 0.016 0.924 0.000 0.060
#> GSM549311 3 0.3452 0.77843 0.000 0.000 0.788 0.016 0.012 0.184
#> GSM549302 2 0.0922 0.82383 0.000 0.968 0.000 0.004 0.004 0.024
#> GSM549235 1 0.4597 -0.08678 0.548 0.000 0.000 0.000 0.040 0.412
#> GSM549245 4 0.0748 0.83308 0.000 0.004 0.000 0.976 0.004 0.016
#> GSM549265 5 0.6495 0.38328 0.084 0.000 0.004 0.236 0.548 0.128
#> GSM549282 5 0.2214 0.60888 0.000 0.000 0.096 0.000 0.888 0.016
#> GSM549296 4 0.0858 0.83302 0.000 0.000 0.004 0.968 0.000 0.028
#> GSM750739 1 0.2257 0.32689 0.876 0.000 0.000 0.000 0.008 0.116
#> GSM750742 5 0.4276 0.57717 0.104 0.000 0.000 0.000 0.728 0.168
#> GSM750744 1 0.5100 0.14426 0.612 0.000 0.000 0.000 0.260 0.128
#> GSM750750 3 0.0622 0.81907 0.000 0.000 0.980 0.000 0.012 0.008
#> GSM549242 6 0.6114 0.34183 0.304 0.000 0.000 0.328 0.000 0.368
#> GSM549252 4 0.2129 0.81753 0.000 0.000 0.000 0.904 0.040 0.056
#> GSM549253 5 0.5646 0.06888 0.120 0.000 0.000 0.008 0.484 0.388
#> GSM549256 4 0.3477 0.69701 0.056 0.000 0.000 0.808 0.004 0.132
#> GSM549257 4 0.0458 0.83496 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM549263 5 0.4427 0.47877 0.044 0.000 0.000 0.004 0.660 0.292
#> GSM549267 4 0.5042 0.65966 0.000 0.000 0.116 0.716 0.072 0.096
#> GSM750745 1 0.0547 0.34572 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM549239 1 0.1588 0.31860 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM549244 4 0.1565 0.82811 0.000 0.004 0.000 0.940 0.028 0.028
#> GSM549249 4 0.1471 0.82160 0.000 0.000 0.000 0.932 0.064 0.004
#> GSM549260 1 0.4101 -0.04331 0.580 0.000 0.000 0.012 0.000 0.408
#> GSM549266 2 0.6179 -0.10180 0.368 0.420 0.012 0.000 0.000 0.200
#> GSM549293 2 0.1296 0.81955 0.000 0.948 0.000 0.004 0.004 0.044
#> GSM549236 5 0.3473 0.62628 0.024 0.000 0.000 0.004 0.780 0.192
#> GSM549238 4 0.3976 0.38549 0.004 0.000 0.000 0.612 0.380 0.004
#> GSM549251 1 0.5308 -0.26763 0.484 0.000 0.000 0.004 0.088 0.424
#> GSM549258 1 0.3737 0.00163 0.608 0.000 0.000 0.000 0.000 0.392
#> GSM549264 5 0.1434 0.63969 0.020 0.008 0.000 0.000 0.948 0.024
#> GSM549243 1 0.4176 -0.02199 0.580 0.000 0.000 0.000 0.016 0.404
#> GSM549262 5 0.4570 0.06462 0.436 0.000 0.000 0.000 0.528 0.036
#> GSM549278 3 0.4360 0.58035 0.000 0.000 0.680 0.260 0.000 0.060
#> GSM549283 3 0.5163 -0.10598 0.000 0.460 0.464 0.000 0.004 0.072
#> GSM549298 3 0.0508 0.81818 0.000 0.004 0.984 0.000 0.000 0.012
#> GSM750741 1 0.1349 0.34723 0.940 0.000 0.000 0.000 0.004 0.056
#> GSM549286 2 0.0865 0.82600 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM549241 1 0.3265 0.17475 0.748 0.004 0.000 0.000 0.000 0.248
#> GSM549247 2 0.3926 0.66168 0.052 0.768 0.000 0.004 0.004 0.172
#> GSM549261 1 0.5039 -0.11757 0.540 0.044 0.000 0.000 0.016 0.400
#> GSM549270 2 0.3168 0.75159 0.000 0.828 0.116 0.000 0.000 0.056
#> GSM549277 3 0.3946 0.72498 0.000 0.168 0.756 0.000 0.000 0.076
#> GSM549280 3 0.2915 0.78491 0.004 0.064 0.864 0.000 0.004 0.064
#> GSM549281 1 0.6532 0.18582 0.548 0.136 0.112 0.000 0.000 0.204
#> GSM549285 3 0.4197 0.66940 0.000 0.016 0.752 0.000 0.172 0.060
#> GSM549288 3 0.5335 0.37366 0.000 0.364 0.532 0.000 0.004 0.100
#> GSM549292 2 0.1152 0.82147 0.000 0.952 0.000 0.000 0.004 0.044
#> GSM549295 3 0.5110 0.58585 0.000 0.248 0.616 0.000 0.000 0.136
#> GSM549297 2 0.4573 0.53196 0.000 0.672 0.244 0.000 0.000 0.084
#> GSM750743 1 0.2704 0.32852 0.844 0.000 0.000 0.000 0.016 0.140
#> GSM549268 1 0.7112 0.06556 0.452 0.124 0.196 0.000 0.000 0.228
#> GSM549290 5 0.5154 0.31371 0.000 0.000 0.040 0.312 0.608 0.040
#> GSM549272 2 0.0405 0.82694 0.000 0.988 0.004 0.000 0.000 0.008
#> GSM549276 2 0.1257 0.82469 0.000 0.952 0.028 0.000 0.000 0.020
#> GSM549275 2 0.5106 0.63878 0.100 0.700 0.036 0.000 0.004 0.160
#> GSM549284 2 0.1151 0.82404 0.000 0.956 0.000 0.000 0.012 0.032
#> GSM750737 4 0.5736 0.28885 0.340 0.000 0.000 0.480 0.000 0.180
#> GSM750740 1 0.4084 -0.00175 0.588 0.000 0.000 0.000 0.012 0.400
#> GSM750747 1 0.4093 -0.00805 0.584 0.000 0.000 0.000 0.012 0.404
#> GSM750751 2 0.1320 0.82524 0.000 0.948 0.016 0.000 0.000 0.036
#> GSM750754 3 0.2594 0.80652 0.000 0.000 0.888 0.036 0.020 0.056
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> MAD:NMF 101 0.0303 1.56e-05 0.032233 0.00282 2
#> MAD:NMF 93 0.0179 8.19e-06 0.000488 0.00988 3
#> MAD:NMF 95 0.2920 2.72e-05 0.069494 0.00593 4
#> MAD:NMF 87 0.4619 1.67e-04 0.100457 0.01116 5
#> MAD:NMF 61 0.4986 2.72e-04 0.008657 0.07384 6
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 21168 rows and 103 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 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 0.784 0.915 0.959 0.2862 0.722 0.722
#> 3 3 0.473 0.598 0.808 0.7389 0.615 0.502
#> 4 4 0.822 0.818 0.917 0.3006 0.822 0.633
#> 5 5 0.805 0.684 0.849 0.0622 0.905 0.752
#> 6 6 0.841 0.737 0.864 0.0420 0.929 0.787
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
#> GSM549289 1 0.0000 0.965 1.000 0.000
#> GSM549291 1 0.0672 0.961 0.992 0.008
#> GSM549274 1 0.0000 0.965 1.000 0.000
#> GSM750738 1 0.0000 0.965 1.000 0.000
#> GSM750748 1 0.0000 0.965 1.000 0.000
#> GSM549240 1 0.0000 0.965 1.000 0.000
#> GSM549279 1 0.0938 0.957 0.988 0.012
#> GSM549294 1 0.6531 0.788 0.832 0.168
#> GSM549300 2 0.4298 0.906 0.088 0.912
#> GSM549303 2 0.0000 0.892 0.000 1.000
#> GSM549309 2 0.0000 0.892 0.000 1.000
#> GSM750753 1 0.6531 0.787 0.832 0.168
#> GSM750752 1 0.0000 0.965 1.000 0.000
#> GSM549304 1 0.0000 0.965 1.000 0.000
#> GSM549305 1 0.6531 0.787 0.832 0.168
#> GSM549307 2 0.4431 0.905 0.092 0.908
#> GSM549306 2 0.2236 0.900 0.036 0.964
#> GSM549308 2 0.0000 0.892 0.000 1.000
#> GSM549233 1 0.0000 0.965 1.000 0.000
#> GSM549234 1 0.0000 0.965 1.000 0.000
#> GSM549250 1 0.0000 0.965 1.000 0.000
#> GSM549287 1 0.7299 0.732 0.796 0.204
#> GSM750735 1 0.0000 0.965 1.000 0.000
#> GSM750736 1 0.0000 0.965 1.000 0.000
#> GSM750749 1 0.2043 0.943 0.968 0.032
#> GSM549230 1 0.0000 0.965 1.000 0.000
#> GSM549231 1 0.0000 0.965 1.000 0.000
#> GSM549237 1 0.0000 0.965 1.000 0.000
#> GSM549254 1 0.0000 0.965 1.000 0.000
#> GSM750734 1 0.0000 0.965 1.000 0.000
#> GSM549271 1 0.9795 0.218 0.584 0.416
#> GSM549232 1 0.0000 0.965 1.000 0.000
#> GSM549246 1 0.0000 0.965 1.000 0.000
#> GSM549248 1 0.0000 0.965 1.000 0.000
#> GSM549255 1 0.0000 0.965 1.000 0.000
#> GSM750746 1 0.0000 0.965 1.000 0.000
#> GSM549259 1 0.0000 0.965 1.000 0.000
#> GSM549269 1 0.0376 0.963 0.996 0.004
#> GSM549273 2 0.0000 0.892 0.000 1.000
#> GSM549299 1 0.2043 0.942 0.968 0.032
#> GSM549301 2 0.0000 0.892 0.000 1.000
#> GSM549310 1 0.0376 0.963 0.996 0.004
#> GSM549311 2 0.4815 0.901 0.104 0.896
#> GSM549302 1 0.0000 0.965 1.000 0.000
#> GSM549235 1 0.0000 0.965 1.000 0.000
#> GSM549245 1 0.0000 0.965 1.000 0.000
#> GSM549265 1 0.0000 0.965 1.000 0.000
#> GSM549282 1 0.9754 0.248 0.592 0.408
#> GSM549296 1 0.0376 0.963 0.996 0.004
#> GSM750739 1 0.0000 0.965 1.000 0.000
#> GSM750742 1 0.0000 0.965 1.000 0.000
#> GSM750744 1 0.0000 0.965 1.000 0.000
#> GSM750750 2 0.4815 0.901 0.104 0.896
#> GSM549242 1 0.0000 0.965 1.000 0.000
#> GSM549252 1 0.0000 0.965 1.000 0.000
#> GSM549253 1 0.0000 0.965 1.000 0.000
#> GSM549256 1 0.0000 0.965 1.000 0.000
#> GSM549257 1 0.0000 0.965 1.000 0.000
#> GSM549263 1 0.0000 0.965 1.000 0.000
#> GSM549267 1 0.6048 0.815 0.852 0.148
#> GSM750745 1 0.0000 0.965 1.000 0.000
#> GSM549239 1 0.0000 0.965 1.000 0.000
#> GSM549244 1 0.0000 0.965 1.000 0.000
#> GSM549249 1 0.0000 0.965 1.000 0.000
#> GSM549260 1 0.0000 0.965 1.000 0.000
#> GSM549266 1 0.0938 0.957 0.988 0.012
#> GSM549293 1 0.0000 0.965 1.000 0.000
#> GSM549236 1 0.0000 0.965 1.000 0.000
#> GSM549238 1 0.0000 0.965 1.000 0.000
#> GSM549251 1 0.0000 0.965 1.000 0.000
#> GSM549258 1 0.0000 0.965 1.000 0.000
#> GSM549264 1 0.0000 0.965 1.000 0.000
#> GSM549243 1 0.0000 0.965 1.000 0.000
#> GSM549262 1 0.0000 0.965 1.000 0.000
#> GSM549278 1 0.0000 0.965 1.000 0.000
#> GSM549283 1 0.1184 0.955 0.984 0.016
#> GSM549298 2 0.0000 0.892 0.000 1.000
#> GSM750741 1 0.0000 0.965 1.000 0.000
#> GSM549286 1 0.1414 0.952 0.980 0.020
#> GSM549241 1 0.0000 0.965 1.000 0.000
#> GSM549247 1 0.0000 0.965 1.000 0.000
#> GSM549261 1 0.0000 0.965 1.000 0.000
#> GSM549270 2 0.8909 0.649 0.308 0.692
#> GSM549277 2 0.6148 0.870 0.152 0.848
#> GSM549280 2 0.8955 0.641 0.312 0.688
#> GSM549281 1 0.2043 0.943 0.968 0.032
#> GSM549285 1 0.5842 0.821 0.860 0.140
#> GSM549288 2 0.6623 0.850 0.172 0.828
#> GSM549292 1 0.0000 0.965 1.000 0.000
#> GSM549295 2 0.4298 0.906 0.088 0.912
#> GSM549297 2 0.6148 0.870 0.152 0.848
#> GSM750743 1 0.0000 0.965 1.000 0.000
#> GSM549268 1 0.2043 0.943 0.968 0.032
#> GSM549290 1 0.1414 0.952 0.980 0.020
#> GSM549272 1 0.0376 0.963 0.996 0.004
#> GSM549276 1 0.6048 0.818 0.852 0.148
#> GSM549275 1 0.0000 0.965 1.000 0.000
#> GSM549284 1 0.0000 0.965 1.000 0.000
#> GSM750737 1 0.0000 0.965 1.000 0.000
#> GSM750740 1 0.0000 0.965 1.000 0.000
#> GSM750747 1 0.0000 0.965 1.000 0.000
#> GSM750751 1 0.4562 0.877 0.904 0.096
#> GSM750754 1 0.9286 0.435 0.656 0.344
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.5591 0.320 0.696 0.304 0.000
#> GSM549291 2 0.6291 0.482 0.468 0.532 0.000
#> GSM549274 2 0.6291 0.485 0.468 0.532 0.000
#> GSM750738 2 0.6299 0.468 0.476 0.524 0.000
#> GSM750748 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549279 1 0.6295 -0.327 0.528 0.472 0.000
#> GSM549294 2 0.5397 0.589 0.280 0.720 0.000
#> GSM549300 2 0.6305 -0.568 0.000 0.516 0.484
#> GSM549303 3 0.0000 0.927 0.000 0.000 1.000
#> GSM549309 3 0.0000 0.927 0.000 0.000 1.000
#> GSM750753 2 0.5864 0.588 0.288 0.704 0.008
#> GSM750752 1 0.6308 -0.407 0.508 0.492 0.000
#> GSM549304 2 0.6291 0.485 0.468 0.532 0.000
#> GSM549305 2 0.5397 0.589 0.280 0.720 0.000
#> GSM549307 2 0.6299 -0.560 0.000 0.524 0.476
#> GSM549306 3 0.3551 0.852 0.000 0.132 0.868
#> GSM549308 3 0.0000 0.927 0.000 0.000 1.000
#> GSM549233 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549234 1 0.0424 0.902 0.992 0.008 0.000
#> GSM549250 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549287 2 0.5325 0.584 0.248 0.748 0.004
#> GSM750735 1 0.5138 0.475 0.748 0.252 0.000
#> GSM750736 1 0.0237 0.905 0.996 0.004 0.000
#> GSM750749 2 0.6215 0.533 0.428 0.572 0.000
#> GSM549230 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549237 1 0.0592 0.898 0.988 0.012 0.000
#> GSM549254 1 0.4504 0.617 0.804 0.196 0.000
#> GSM750734 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549271 2 0.2269 0.285 0.040 0.944 0.016
#> GSM549232 1 0.2959 0.790 0.900 0.100 0.000
#> GSM549246 1 0.4504 0.617 0.804 0.196 0.000
#> GSM549248 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549255 1 0.0892 0.891 0.980 0.020 0.000
#> GSM750746 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549269 2 0.6274 0.503 0.456 0.544 0.000
#> GSM549273 3 0.0000 0.927 0.000 0.000 1.000
#> GSM549299 2 0.6180 0.543 0.416 0.584 0.000
#> GSM549301 3 0.0000 0.927 0.000 0.000 1.000
#> GSM549310 2 0.6286 0.491 0.464 0.536 0.000
#> GSM549311 2 0.6280 -0.546 0.000 0.540 0.460
#> GSM549302 2 0.6291 0.485 0.468 0.532 0.000
#> GSM549235 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549245 1 0.0424 0.902 0.992 0.008 0.000
#> GSM549265 1 0.0892 0.891 0.980 0.020 0.000
#> GSM549282 2 0.1647 0.293 0.036 0.960 0.004
#> GSM549296 2 0.6286 0.491 0.464 0.536 0.000
#> GSM750739 1 0.0000 0.907 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.907 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.907 1.000 0.000 0.000
#> GSM750750 2 0.6291 -0.554 0.000 0.532 0.468
#> GSM549242 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549252 1 0.0892 0.891 0.980 0.020 0.000
#> GSM549253 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549257 1 0.0892 0.891 0.980 0.020 0.000
#> GSM549263 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549267 2 0.5560 0.586 0.300 0.700 0.000
#> GSM750745 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549244 1 0.0237 0.905 0.996 0.004 0.000
#> GSM549249 1 0.0237 0.905 0.996 0.004 0.000
#> GSM549260 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549266 1 0.6308 -0.387 0.508 0.492 0.000
#> GSM549293 2 0.6291 0.485 0.468 0.532 0.000
#> GSM549236 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549238 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549251 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549278 1 0.6307 -0.393 0.512 0.488 0.000
#> GSM549283 2 0.6235 0.524 0.436 0.564 0.000
#> GSM549298 3 0.0000 0.927 0.000 0.000 1.000
#> GSM750741 1 0.2165 0.838 0.936 0.064 0.000
#> GSM549286 2 0.6215 0.534 0.428 0.572 0.000
#> GSM549241 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549247 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549261 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549270 2 0.5138 -0.233 0.000 0.748 0.252
#> GSM549277 2 0.6154 -0.476 0.000 0.592 0.408
#> GSM549280 2 0.5098 -0.225 0.000 0.752 0.248
#> GSM549281 2 0.6215 0.533 0.428 0.572 0.000
#> GSM549285 2 0.5591 0.577 0.304 0.696 0.000
#> GSM549288 2 0.6079 -0.450 0.000 0.612 0.388
#> GSM549292 2 0.6291 0.485 0.468 0.532 0.000
#> GSM549295 3 0.6305 0.536 0.000 0.484 0.516
#> GSM549297 2 0.6168 -0.479 0.000 0.588 0.412
#> GSM750743 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549268 2 0.6215 0.533 0.428 0.572 0.000
#> GSM549290 2 0.6225 0.530 0.432 0.568 0.000
#> GSM549272 2 0.6274 0.503 0.456 0.544 0.000
#> GSM549276 2 0.5497 0.589 0.292 0.708 0.000
#> GSM549275 1 0.0000 0.907 1.000 0.000 0.000
#> GSM549284 2 0.6299 0.468 0.476 0.524 0.000
#> GSM750737 1 0.4291 0.650 0.820 0.180 0.000
#> GSM750740 1 0.0000 0.907 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.907 1.000 0.000 0.000
#> GSM750751 2 0.5905 0.572 0.352 0.648 0.000
#> GSM750754 2 0.5159 0.427 0.140 0.820 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 2 0.4250 0.540281 0.276 0.724 0.000 0.000
#> GSM549291 2 0.1297 0.814486 0.020 0.964 0.000 0.016
#> GSM549274 2 0.0937 0.810266 0.012 0.976 0.000 0.012
#> GSM750738 2 0.1488 0.806794 0.032 0.956 0.000 0.012
#> GSM750748 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549279 2 0.3907 0.737188 0.120 0.836 0.000 0.044
#> GSM549294 2 0.3801 0.686814 0.000 0.780 0.000 0.220
#> GSM549300 4 0.2589 0.728231 0.000 0.000 0.116 0.884
#> GSM549303 3 0.0188 0.967847 0.000 0.000 0.996 0.004
#> GSM549309 3 0.0188 0.967847 0.000 0.000 0.996 0.004
#> GSM750753 2 0.3801 0.684514 0.000 0.780 0.000 0.220
#> GSM750752 2 0.2255 0.785671 0.068 0.920 0.000 0.012
#> GSM549304 2 0.1059 0.810295 0.016 0.972 0.000 0.012
#> GSM549305 2 0.3801 0.685267 0.000 0.780 0.000 0.220
#> GSM549307 4 0.2408 0.735942 0.000 0.000 0.104 0.896
#> GSM549306 3 0.3172 0.793127 0.000 0.000 0.840 0.160
#> GSM549308 3 0.0000 0.969035 0.000 0.000 1.000 0.000
#> GSM549233 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549234 1 0.1118 0.945417 0.964 0.036 0.000 0.000
#> GSM549250 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549287 2 0.4454 0.558981 0.000 0.692 0.000 0.308
#> GSM750735 1 0.5281 -0.000291 0.528 0.464 0.000 0.008
#> GSM750736 1 0.0188 0.968896 0.996 0.004 0.000 0.000
#> GSM750749 2 0.2730 0.800375 0.016 0.896 0.000 0.088
#> GSM549230 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549231 1 0.0188 0.969114 0.996 0.004 0.000 0.000
#> GSM549237 1 0.0921 0.952520 0.972 0.028 0.000 0.000
#> GSM549254 2 0.4977 0.202850 0.460 0.540 0.000 0.000
#> GSM750734 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549271 4 0.4925 0.190013 0.000 0.428 0.000 0.572
#> GSM549232 1 0.3528 0.750695 0.808 0.192 0.000 0.000
#> GSM549246 2 0.4977 0.202850 0.460 0.540 0.000 0.000
#> GSM549248 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549255 1 0.1557 0.927559 0.944 0.056 0.000 0.000
#> GSM750746 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0707 0.807972 0.000 0.980 0.000 0.020
#> GSM549273 3 0.0000 0.969035 0.000 0.000 1.000 0.000
#> GSM549299 2 0.1716 0.801195 0.000 0.936 0.000 0.064
#> GSM549301 3 0.0000 0.969035 0.000 0.000 1.000 0.000
#> GSM549310 2 0.0804 0.812300 0.008 0.980 0.000 0.012
#> GSM549311 4 0.2149 0.744239 0.000 0.000 0.088 0.912
#> GSM549302 2 0.0937 0.810266 0.012 0.976 0.000 0.012
#> GSM549235 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549245 1 0.1118 0.945417 0.964 0.036 0.000 0.000
#> GSM549265 1 0.1557 0.927715 0.944 0.056 0.000 0.000
#> GSM549282 4 0.4941 0.152888 0.000 0.436 0.000 0.564
#> GSM549296 2 0.0804 0.812300 0.008 0.980 0.000 0.012
#> GSM750739 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM750750 4 0.2281 0.740471 0.000 0.000 0.096 0.904
#> GSM549242 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549252 1 0.1637 0.923617 0.940 0.060 0.000 0.000
#> GSM549253 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549257 1 0.1637 0.923617 0.940 0.060 0.000 0.000
#> GSM549263 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549267 2 0.3942 0.659251 0.000 0.764 0.000 0.236
#> GSM750745 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549244 1 0.0469 0.963782 0.988 0.012 0.000 0.000
#> GSM549249 1 0.0469 0.963782 0.988 0.012 0.000 0.000
#> GSM549260 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549266 2 0.3796 0.762429 0.096 0.848 0.000 0.056
#> GSM549293 2 0.0937 0.810266 0.012 0.976 0.000 0.012
#> GSM549236 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549238 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549251 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549264 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549278 2 0.2450 0.791391 0.072 0.912 0.000 0.016
#> GSM549283 2 0.2021 0.808539 0.012 0.932 0.000 0.056
#> GSM549298 3 0.0000 0.969035 0.000 0.000 1.000 0.000
#> GSM750741 1 0.2704 0.843085 0.876 0.124 0.000 0.000
#> GSM549286 2 0.1118 0.807215 0.000 0.964 0.000 0.036
#> GSM549241 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549247 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549261 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549270 4 0.3539 0.675258 0.000 0.176 0.004 0.820
#> GSM549277 4 0.1584 0.757149 0.000 0.012 0.036 0.952
#> GSM549280 4 0.3583 0.672190 0.000 0.180 0.004 0.816
#> GSM549281 2 0.2730 0.800375 0.016 0.896 0.000 0.088
#> GSM549285 2 0.3873 0.653560 0.000 0.772 0.000 0.228
#> GSM549288 4 0.1297 0.753970 0.000 0.016 0.020 0.964
#> GSM549292 2 0.0937 0.810266 0.012 0.976 0.000 0.012
#> GSM549295 4 0.3610 0.634372 0.000 0.000 0.200 0.800
#> GSM549297 4 0.1677 0.757235 0.000 0.012 0.040 0.948
#> GSM750743 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549268 2 0.2730 0.800375 0.016 0.896 0.000 0.088
#> GSM549290 2 0.1557 0.801556 0.000 0.944 0.000 0.056
#> GSM549272 2 0.0707 0.807972 0.000 0.980 0.000 0.020
#> GSM549276 2 0.3610 0.708331 0.000 0.800 0.000 0.200
#> GSM549275 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM549284 2 0.1488 0.806794 0.032 0.956 0.000 0.012
#> GSM750737 2 0.5000 0.064015 0.500 0.500 0.000 0.000
#> GSM750740 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.971490 1.000 0.000 0.000 0.000
#> GSM750751 2 0.2760 0.763860 0.000 0.872 0.000 0.128
#> GSM750754 2 0.4955 0.203640 0.000 0.556 0.000 0.444
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 2 0.6376 0.0512 0.264 0.516 0.000 0.220 0.000
#> GSM549291 2 0.4961 -0.0252 0.020 0.520 0.000 0.456 0.004
#> GSM549274 2 0.0000 0.6246 0.000 1.000 0.000 0.000 0.000
#> GSM750738 2 0.0898 0.6135 0.020 0.972 0.000 0.008 0.000
#> GSM750748 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549279 4 0.5968 0.1470 0.108 0.444 0.000 0.448 0.000
#> GSM549294 2 0.5368 0.3032 0.000 0.596 0.000 0.332 0.072
#> GSM549300 5 0.1430 0.8185 0.000 0.000 0.052 0.004 0.944
#> GSM549303 3 0.0510 0.9606 0.000 0.000 0.984 0.000 0.016
#> GSM549309 3 0.0510 0.9606 0.000 0.000 0.984 0.000 0.016
#> GSM750753 2 0.5420 0.2908 0.000 0.592 0.000 0.332 0.076
#> GSM750752 2 0.3323 0.5053 0.056 0.844 0.000 0.100 0.000
#> GSM549304 2 0.0162 0.6233 0.004 0.996 0.000 0.000 0.000
#> GSM549305 2 0.5375 0.3131 0.000 0.604 0.000 0.320 0.076
#> GSM549307 5 0.1331 0.8225 0.000 0.000 0.040 0.008 0.952
#> GSM549306 3 0.2852 0.7920 0.000 0.000 0.828 0.000 0.172
#> GSM549308 3 0.0000 0.9648 0.000 0.000 1.000 0.000 0.000
#> GSM549233 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549234 1 0.1106 0.9176 0.964 0.012 0.000 0.024 0.000
#> GSM549250 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549287 4 0.4747 0.4653 0.000 0.196 0.000 0.720 0.084
#> GSM750735 1 0.6385 0.1209 0.516 0.232 0.000 0.252 0.000
#> GSM750736 1 0.0162 0.9372 0.996 0.004 0.000 0.000 0.000
#> GSM750749 4 0.4425 0.3542 0.008 0.392 0.000 0.600 0.000
#> GSM549230 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549231 1 0.0162 0.9375 0.996 0.000 0.000 0.004 0.000
#> GSM549237 1 0.0955 0.9198 0.968 0.028 0.000 0.004 0.000
#> GSM549254 1 0.6188 -0.1051 0.448 0.416 0.000 0.136 0.000
#> GSM750734 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549271 4 0.4180 0.2018 0.000 0.036 0.000 0.744 0.220
#> GSM549232 1 0.3863 0.7364 0.796 0.152 0.000 0.052 0.000
#> GSM549246 1 0.6188 -0.1051 0.448 0.416 0.000 0.136 0.000
#> GSM549248 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549255 1 0.1668 0.8999 0.940 0.032 0.000 0.028 0.000
#> GSM750746 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0609 0.6226 0.000 0.980 0.000 0.020 0.000
#> GSM549273 3 0.0162 0.9633 0.000 0.000 0.996 0.000 0.004
#> GSM549299 2 0.4221 0.4712 0.000 0.732 0.000 0.236 0.032
#> GSM549301 3 0.0000 0.9648 0.000 0.000 1.000 0.000 0.000
#> GSM549310 2 0.4425 0.1777 0.008 0.600 0.000 0.392 0.000
#> GSM549311 5 0.3496 0.7583 0.000 0.000 0.012 0.200 0.788
#> GSM549302 2 0.0000 0.6246 0.000 1.000 0.000 0.000 0.000
#> GSM549235 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549245 1 0.1106 0.9176 0.964 0.012 0.000 0.024 0.000
#> GSM549265 1 0.1661 0.8995 0.940 0.036 0.000 0.024 0.000
#> GSM549282 4 0.3710 0.2148 0.000 0.024 0.000 0.784 0.192
#> GSM549296 2 0.4436 0.1710 0.008 0.596 0.000 0.396 0.000
#> GSM750739 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM750750 5 0.3656 0.7712 0.000 0.000 0.032 0.168 0.800
#> GSM549242 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549252 1 0.1750 0.8962 0.936 0.036 0.000 0.028 0.000
#> GSM549253 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549257 1 0.1750 0.8962 0.936 0.036 0.000 0.028 0.000
#> GSM549263 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549267 4 0.4817 0.4385 0.000 0.264 0.000 0.680 0.056
#> GSM750745 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549244 1 0.0451 0.9330 0.988 0.004 0.000 0.008 0.000
#> GSM549249 1 0.0451 0.9330 0.988 0.004 0.000 0.008 0.000
#> GSM549260 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549266 4 0.5725 0.2135 0.084 0.428 0.000 0.488 0.000
#> GSM549293 2 0.0000 0.6246 0.000 1.000 0.000 0.000 0.000
#> GSM549236 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549238 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549251 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549264 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549278 2 0.5450 -0.1201 0.060 0.496 0.000 0.444 0.000
#> GSM549283 2 0.4522 -0.0488 0.008 0.552 0.000 0.440 0.000
#> GSM549298 3 0.0000 0.9648 0.000 0.000 1.000 0.000 0.000
#> GSM750741 1 0.2677 0.8201 0.872 0.112 0.000 0.016 0.000
#> GSM549286 2 0.2248 0.5937 0.000 0.900 0.000 0.088 0.012
#> GSM549241 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549261 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549270 5 0.5074 0.6096 0.000 0.072 0.000 0.268 0.660
#> GSM549277 5 0.1478 0.8371 0.000 0.000 0.000 0.064 0.936
#> GSM549280 5 0.5096 0.6035 0.000 0.072 0.000 0.272 0.656
#> GSM549281 4 0.4425 0.3542 0.008 0.392 0.000 0.600 0.000
#> GSM549285 4 0.3074 0.4707 0.000 0.196 0.000 0.804 0.000
#> GSM549288 5 0.2179 0.8261 0.000 0.000 0.000 0.112 0.888
#> GSM549292 2 0.0000 0.6246 0.000 1.000 0.000 0.000 0.000
#> GSM549295 5 0.2424 0.7557 0.000 0.000 0.132 0.000 0.868
#> GSM549297 5 0.1478 0.8373 0.000 0.000 0.000 0.064 0.936
#> GSM750743 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549268 4 0.4425 0.3542 0.008 0.392 0.000 0.600 0.000
#> GSM549290 2 0.4287 0.0193 0.000 0.540 0.000 0.460 0.000
#> GSM549272 2 0.0609 0.6226 0.000 0.980 0.000 0.020 0.000
#> GSM549276 2 0.5203 0.3683 0.000 0.648 0.000 0.272 0.080
#> GSM549275 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM549284 2 0.0898 0.6135 0.020 0.972 0.000 0.008 0.000
#> GSM750737 1 0.6121 0.0374 0.488 0.380 0.000 0.132 0.000
#> GSM750740 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9395 1.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.4398 0.4494 0.000 0.720 0.000 0.240 0.040
#> GSM750754 4 0.4412 0.4262 0.000 0.080 0.000 0.756 0.164
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4616 0.344 0.228 0.084 0.000 0.684 0.000 0.004
#> GSM549291 4 0.5075 0.251 0.012 0.228 0.000 0.652 0.000 0.108
#> GSM549274 2 0.0458 0.799 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM750738 2 0.1334 0.779 0.020 0.948 0.000 0.032 0.000 0.000
#> GSM750748 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549279 4 0.4841 0.364 0.080 0.076 0.012 0.752 0.000 0.080
#> GSM549294 2 0.5203 0.534 0.000 0.588 0.052 0.028 0.000 0.332
#> GSM549300 3 0.1564 0.756 0.000 0.000 0.936 0.000 0.040 0.024
#> GSM549303 5 0.0717 0.954 0.000 0.000 0.008 0.000 0.976 0.016
#> GSM549309 5 0.0717 0.954 0.000 0.000 0.008 0.000 0.976 0.016
#> GSM750753 2 0.5428 0.525 0.000 0.592 0.048 0.052 0.000 0.308
#> GSM750752 2 0.4296 0.418 0.052 0.700 0.000 0.244 0.000 0.004
#> GSM549304 2 0.0777 0.798 0.004 0.972 0.000 0.024 0.000 0.000
#> GSM549305 2 0.5004 0.546 0.000 0.600 0.048 0.020 0.000 0.332
#> GSM549307 3 0.1257 0.759 0.000 0.000 0.952 0.000 0.028 0.020
#> GSM549306 5 0.2981 0.778 0.000 0.000 0.160 0.000 0.820 0.020
#> GSM549308 5 0.0146 0.958 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM549233 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549234 1 0.0972 0.941 0.964 0.008 0.000 0.028 0.000 0.000
#> GSM549250 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549287 6 0.4569 0.579 0.000 0.024 0.008 0.408 0.000 0.560
#> GSM750735 1 0.5193 -0.163 0.484 0.032 0.000 0.452 0.000 0.032
#> GSM750736 1 0.0146 0.964 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM750749 4 0.3525 0.223 0.000 0.032 0.012 0.800 0.000 0.156
#> GSM549230 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549231 1 0.0458 0.957 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM549237 1 0.1225 0.932 0.952 0.012 0.000 0.036 0.000 0.000
#> GSM549254 4 0.5083 0.290 0.408 0.068 0.000 0.520 0.000 0.004
#> GSM750734 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549271 6 0.4028 0.642 0.000 0.012 0.044 0.192 0.000 0.752
#> GSM549232 1 0.3683 0.686 0.764 0.044 0.000 0.192 0.000 0.000
#> GSM549246 4 0.5083 0.290 0.408 0.068 0.000 0.520 0.000 0.004
#> GSM549248 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549255 1 0.1757 0.900 0.916 0.008 0.000 0.076 0.000 0.000
#> GSM750746 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0405 0.798 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM549273 5 0.0520 0.955 0.000 0.000 0.000 0.008 0.984 0.008
#> GSM549299 2 0.5437 0.589 0.000 0.648 0.032 0.184 0.000 0.136
#> GSM549301 5 0.0146 0.958 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM549310 4 0.4481 0.265 0.000 0.296 0.000 0.648 0.000 0.056
#> GSM549311 3 0.3993 0.406 0.000 0.000 0.520 0.004 0.000 0.476
#> GSM549302 2 0.0458 0.799 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM549235 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549245 1 0.0972 0.941 0.964 0.008 0.000 0.028 0.000 0.000
#> GSM549265 1 0.1757 0.900 0.916 0.008 0.000 0.076 0.000 0.000
#> GSM549282 6 0.2982 0.655 0.000 0.004 0.012 0.164 0.000 0.820
#> GSM549296 4 0.4517 0.266 0.000 0.292 0.000 0.648 0.000 0.060
#> GSM750739 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750750 3 0.4412 0.477 0.000 0.000 0.572 0.008 0.016 0.404
#> GSM549242 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549252 1 0.1812 0.896 0.912 0.008 0.000 0.080 0.000 0.000
#> GSM549253 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549257 1 0.1812 0.896 0.912 0.008 0.000 0.080 0.000 0.000
#> GSM549263 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549267 6 0.5209 0.493 0.000 0.092 0.000 0.416 0.000 0.492
#> GSM750745 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549244 1 0.0405 0.959 0.988 0.004 0.000 0.008 0.000 0.000
#> GSM549249 1 0.0405 0.959 0.988 0.004 0.000 0.008 0.000 0.000
#> GSM549260 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549266 4 0.4343 0.337 0.056 0.044 0.012 0.784 0.000 0.104
#> GSM549293 2 0.0458 0.799 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM549236 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549238 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549251 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549264 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549278 4 0.2803 0.360 0.032 0.064 0.000 0.876 0.000 0.028
#> GSM549283 4 0.5235 0.205 0.000 0.284 0.012 0.608 0.000 0.096
#> GSM549298 5 0.0146 0.958 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM750741 1 0.2581 0.815 0.856 0.016 0.000 0.128 0.000 0.000
#> GSM549286 2 0.1956 0.776 0.000 0.908 0.004 0.008 0.000 0.080
#> GSM549241 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549261 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549270 3 0.4778 0.530 0.000 0.060 0.652 0.012 0.000 0.276
#> GSM549277 3 0.1297 0.763 0.000 0.000 0.948 0.012 0.000 0.040
#> GSM549280 3 0.4797 0.523 0.000 0.060 0.648 0.012 0.000 0.280
#> GSM549281 4 0.3525 0.223 0.000 0.032 0.012 0.800 0.000 0.156
#> GSM549285 4 0.4996 -0.354 0.000 0.064 0.004 0.548 0.000 0.384
#> GSM549288 3 0.2266 0.741 0.000 0.000 0.880 0.012 0.000 0.108
#> GSM549292 2 0.0458 0.799 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM549295 3 0.2538 0.712 0.000 0.000 0.860 0.000 0.124 0.016
#> GSM549297 3 0.1297 0.763 0.000 0.000 0.948 0.012 0.000 0.040
#> GSM750743 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549268 4 0.3525 0.223 0.000 0.032 0.012 0.800 0.000 0.156
#> GSM549290 4 0.4749 0.215 0.000 0.260 0.000 0.648 0.000 0.092
#> GSM549272 2 0.0405 0.798 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM549276 2 0.5026 0.600 0.000 0.640 0.076 0.016 0.000 0.268
#> GSM549275 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549284 2 0.1334 0.779 0.020 0.948 0.000 0.032 0.000 0.000
#> GSM750737 4 0.4979 0.233 0.448 0.056 0.000 0.492 0.000 0.004
#> GSM750740 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.967 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750751 2 0.3934 0.661 0.000 0.716 0.020 0.008 0.000 0.256
#> GSM750754 6 0.4916 0.572 0.000 0.000 0.064 0.416 0.000 0.520
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:hclust 100 0.3786 8.50e-04 0.4134 0.1147 2
#> ATC:hclust 75 0.0432 1.91e-06 0.9114 0.2724 3
#> ATC:hclust 96 0.0978 7.07e-06 0.3915 0.0163 4
#> ATC:hclust 75 0.0293 7.75e-06 0.0571 0.0931 5
#> ATC:hclust 82 0.0164 1.27e-06 0.0112 0.0382 6
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 21168 rows and 103 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 0.834 0.934 0.970 0.4799 0.520 0.520
#> 3 3 1.000 0.989 0.995 0.2602 0.767 0.595
#> 4 4 0.698 0.614 0.789 0.1605 0.880 0.706
#> 5 5 0.726 0.689 0.809 0.0810 0.858 0.568
#> 6 6 0.745 0.744 0.834 0.0599 0.941 0.746
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
#> GSM549289 1 0.000 0.9665 1.000 0.000
#> GSM549291 2 0.373 0.9197 0.072 0.928
#> GSM549274 1 0.697 0.7839 0.812 0.188
#> GSM750738 1 0.000 0.9665 1.000 0.000
#> GSM750748 1 0.000 0.9665 1.000 0.000
#> GSM549240 1 0.000 0.9665 1.000 0.000
#> GSM549279 1 0.697 0.7839 0.812 0.188
#> GSM549294 2 0.000 0.9702 0.000 1.000
#> GSM549300 2 0.000 0.9702 0.000 1.000
#> GSM549303 2 0.000 0.9702 0.000 1.000
#> GSM549309 2 0.000 0.9702 0.000 1.000
#> GSM750753 2 0.000 0.9702 0.000 1.000
#> GSM750752 1 0.605 0.8315 0.852 0.148
#> GSM549304 1 0.689 0.7890 0.816 0.184
#> GSM549305 2 0.000 0.9702 0.000 1.000
#> GSM549307 2 0.000 0.9702 0.000 1.000
#> GSM549306 2 0.000 0.9702 0.000 1.000
#> GSM549308 2 0.000 0.9702 0.000 1.000
#> GSM549233 1 0.000 0.9665 1.000 0.000
#> GSM549234 1 0.000 0.9665 1.000 0.000
#> GSM549250 1 0.000 0.9665 1.000 0.000
#> GSM549287 2 0.000 0.9702 0.000 1.000
#> GSM750735 1 0.000 0.9665 1.000 0.000
#> GSM750736 1 0.000 0.9665 1.000 0.000
#> GSM750749 2 0.343 0.9271 0.064 0.936
#> GSM549230 1 0.000 0.9665 1.000 0.000
#> GSM549231 1 0.000 0.9665 1.000 0.000
#> GSM549237 1 0.000 0.9665 1.000 0.000
#> GSM549254 1 0.000 0.9665 1.000 0.000
#> GSM750734 1 0.000 0.9665 1.000 0.000
#> GSM549271 2 0.000 0.9702 0.000 1.000
#> GSM549232 1 0.000 0.9665 1.000 0.000
#> GSM549246 1 0.000 0.9665 1.000 0.000
#> GSM549248 1 0.000 0.9665 1.000 0.000
#> GSM549255 1 0.000 0.9665 1.000 0.000
#> GSM750746 1 0.000 0.9665 1.000 0.000
#> GSM549259 1 0.000 0.9665 1.000 0.000
#> GSM549269 1 0.955 0.4277 0.624 0.376
#> GSM549273 2 0.000 0.9702 0.000 1.000
#> GSM549299 2 0.295 0.9362 0.052 0.948
#> GSM549301 2 0.000 0.9702 0.000 1.000
#> GSM549310 2 0.343 0.9271 0.064 0.936
#> GSM549311 2 0.000 0.9702 0.000 1.000
#> GSM549302 2 0.456 0.8947 0.096 0.904
#> GSM549235 1 0.000 0.9665 1.000 0.000
#> GSM549245 1 0.000 0.9665 1.000 0.000
#> GSM549265 1 0.000 0.9665 1.000 0.000
#> GSM549282 2 0.000 0.9702 0.000 1.000
#> GSM549296 2 0.552 0.8558 0.128 0.872
#> GSM750739 1 0.000 0.9665 1.000 0.000
#> GSM750742 1 0.000 0.9665 1.000 0.000
#> GSM750744 1 0.000 0.9665 1.000 0.000
#> GSM750750 2 0.000 0.9702 0.000 1.000
#> GSM549242 1 0.000 0.9665 1.000 0.000
#> GSM549252 1 0.000 0.9665 1.000 0.000
#> GSM549253 1 0.000 0.9665 1.000 0.000
#> GSM549256 1 0.000 0.9665 1.000 0.000
#> GSM549257 1 0.000 0.9665 1.000 0.000
#> GSM549263 1 0.000 0.9665 1.000 0.000
#> GSM549267 2 0.118 0.9606 0.016 0.984
#> GSM750745 1 0.000 0.9665 1.000 0.000
#> GSM549239 1 0.000 0.9665 1.000 0.000
#> GSM549244 1 0.000 0.9665 1.000 0.000
#> GSM549249 1 0.000 0.9665 1.000 0.000
#> GSM549260 1 0.000 0.9665 1.000 0.000
#> GSM549266 1 0.697 0.7839 0.812 0.188
#> GSM549293 1 0.697 0.7839 0.812 0.188
#> GSM549236 1 0.000 0.9665 1.000 0.000
#> GSM549238 1 0.000 0.9665 1.000 0.000
#> GSM549251 1 0.000 0.9665 1.000 0.000
#> GSM549258 1 0.000 0.9665 1.000 0.000
#> GSM549264 1 0.000 0.9665 1.000 0.000
#> GSM549243 1 0.000 0.9665 1.000 0.000
#> GSM549262 1 0.000 0.9665 1.000 0.000
#> GSM549278 1 0.494 0.8735 0.892 0.108
#> GSM549283 2 0.373 0.9197 0.072 0.928
#> GSM549298 2 0.000 0.9702 0.000 1.000
#> GSM750741 1 0.000 0.9665 1.000 0.000
#> GSM549286 2 0.000 0.9702 0.000 1.000
#> GSM549241 1 0.000 0.9665 1.000 0.000
#> GSM549247 1 0.000 0.9665 1.000 0.000
#> GSM549261 1 0.000 0.9665 1.000 0.000
#> GSM549270 2 0.000 0.9702 0.000 1.000
#> GSM549277 2 0.000 0.9702 0.000 1.000
#> GSM549280 2 0.000 0.9702 0.000 1.000
#> GSM549281 2 0.000 0.9702 0.000 1.000
#> GSM549285 2 0.343 0.9271 0.064 0.936
#> GSM549288 2 0.000 0.9702 0.000 1.000
#> GSM549292 1 0.839 0.6589 0.732 0.268
#> GSM549295 2 0.000 0.9702 0.000 1.000
#> GSM549297 2 0.000 0.9702 0.000 1.000
#> GSM750743 1 0.000 0.9665 1.000 0.000
#> GSM549268 2 0.000 0.9702 0.000 1.000
#> GSM549290 2 0.998 0.0653 0.472 0.528
#> GSM549272 2 0.000 0.9702 0.000 1.000
#> GSM549276 2 0.000 0.9702 0.000 1.000
#> GSM549275 1 0.000 0.9665 1.000 0.000
#> GSM549284 1 0.574 0.8445 0.864 0.136
#> GSM750737 1 0.000 0.9665 1.000 0.000
#> GSM750740 1 0.000 0.9665 1.000 0.000
#> GSM750747 1 0.000 0.9665 1.000 0.000
#> GSM750751 2 0.000 0.9702 0.000 1.000
#> GSM750754 2 0.000 0.9702 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.0237 0.995 0.996 0.004 0.000
#> GSM549291 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549274 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750738 2 0.3816 0.787 0.148 0.852 0.000
#> GSM750748 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549279 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549300 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549303 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549309 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750753 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750752 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549304 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549305 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549307 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549306 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549308 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549233 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549234 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549250 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549287 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750735 1 0.0237 0.995 0.996 0.004 0.000
#> GSM750736 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750749 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549230 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549254 1 0.1163 0.967 0.972 0.028 0.000
#> GSM750734 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549271 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549232 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549246 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549248 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549255 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750746 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549273 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549299 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549301 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549310 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549311 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549302 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549245 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549265 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549282 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549296 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750739 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750750 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549242 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549252 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549253 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549257 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549263 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549267 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750745 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549244 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549249 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549260 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549238 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549251 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549278 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549283 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549298 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750741 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549247 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549261 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549270 2 0.4121 0.805 0.000 0.832 0.168
#> GSM549277 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549280 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549281 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549285 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549288 2 0.4062 0.810 0.000 0.836 0.164
#> GSM549292 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549295 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549297 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750743 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549268 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549290 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549272 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.985 0.000 1.000 0.000
#> GSM549275 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549284 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750737 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750740 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.985 0.000 1.000 0.000
#> GSM750754 2 0.0000 0.985 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.6323 0.2376 0.100 0.272 0.000 0.628
#> GSM549291 2 0.4843 0.3408 0.000 0.604 0.000 0.396
#> GSM549274 4 0.4830 -0.2448 0.000 0.392 0.000 0.608
#> GSM750738 4 0.0707 0.1651 0.000 0.020 0.000 0.980
#> GSM750748 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0921 0.8872 0.972 0.000 0.000 0.028
#> GSM549279 2 0.4999 0.3580 0.000 0.508 0.000 0.492
#> GSM549294 2 0.4382 0.5742 0.000 0.704 0.000 0.296
#> GSM549300 3 0.1743 0.9395 0.000 0.056 0.940 0.004
#> GSM549303 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM549309 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM750753 2 0.4072 0.5727 0.000 0.748 0.000 0.252
#> GSM750752 4 0.4624 -0.1027 0.000 0.340 0.000 0.660
#> GSM549304 4 0.4830 -0.2448 0.000 0.392 0.000 0.608
#> GSM549305 2 0.4564 0.5473 0.000 0.672 0.000 0.328
#> GSM549307 3 0.2466 0.9159 0.000 0.096 0.900 0.004
#> GSM549306 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM549308 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM549233 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549234 1 0.4776 0.5358 0.624 0.000 0.000 0.376
#> GSM549250 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549287 2 0.2081 0.5421 0.000 0.916 0.000 0.084
#> GSM750735 4 0.6003 -0.1081 0.456 0.040 0.000 0.504
#> GSM750736 1 0.3873 0.7322 0.772 0.000 0.000 0.228
#> GSM750749 2 0.2647 0.5864 0.000 0.880 0.000 0.120
#> GSM549230 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549231 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549237 1 0.3801 0.7398 0.780 0.000 0.000 0.220
#> GSM549254 4 0.6015 0.2204 0.080 0.268 0.000 0.652
#> GSM750734 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549271 2 0.0336 0.5939 0.000 0.992 0.000 0.008
#> GSM549232 4 0.5966 0.2665 0.316 0.060 0.000 0.624
#> GSM549246 4 0.6646 0.2717 0.304 0.112 0.000 0.584
#> GSM549248 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549255 1 0.4898 0.4570 0.584 0.000 0.000 0.416
#> GSM750746 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549269 4 0.4898 -0.2789 0.000 0.416 0.000 0.584
#> GSM549273 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM549299 2 0.4972 0.4315 0.000 0.544 0.000 0.456
#> GSM549301 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM549310 2 0.4877 0.3234 0.000 0.592 0.000 0.408
#> GSM549311 3 0.1474 0.9422 0.000 0.052 0.948 0.000
#> GSM549302 4 0.4888 -0.2711 0.000 0.412 0.000 0.588
#> GSM549235 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549245 1 0.4790 0.5292 0.620 0.000 0.000 0.380
#> GSM549265 4 0.4941 -0.0753 0.436 0.000 0.000 0.564
#> GSM549282 2 0.0469 0.5935 0.000 0.988 0.000 0.012
#> GSM549296 2 0.4888 0.3214 0.000 0.588 0.000 0.412
#> GSM750739 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM750750 3 0.1474 0.9422 0.000 0.052 0.948 0.000
#> GSM549242 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549252 1 0.4843 0.4997 0.604 0.000 0.000 0.396
#> GSM549253 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549257 1 0.4790 0.5292 0.620 0.000 0.000 0.380
#> GSM549263 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549267 2 0.4222 0.4518 0.000 0.728 0.000 0.272
#> GSM750745 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549244 1 0.4382 0.6519 0.704 0.000 0.000 0.296
#> GSM549249 1 0.4790 0.5292 0.620 0.000 0.000 0.380
#> GSM549260 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549266 2 0.4855 0.4545 0.000 0.600 0.000 0.400
#> GSM549293 4 0.4790 -0.2335 0.000 0.380 0.000 0.620
#> GSM549236 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549238 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549251 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549264 1 0.3266 0.7849 0.832 0.000 0.000 0.168
#> GSM549243 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549278 2 0.4996 0.1914 0.000 0.516 0.000 0.484
#> GSM549283 2 0.4955 0.4681 0.000 0.556 0.000 0.444
#> GSM549298 3 0.0000 0.9501 0.000 0.000 1.000 0.000
#> GSM750741 1 0.3873 0.7322 0.772 0.000 0.000 0.228
#> GSM549286 2 0.4916 0.4897 0.000 0.576 0.000 0.424
#> GSM549241 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549247 1 0.3873 0.7322 0.772 0.000 0.000 0.228
#> GSM549261 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549270 2 0.6027 0.5179 0.000 0.660 0.088 0.252
#> GSM549277 3 0.4283 0.7460 0.000 0.256 0.740 0.004
#> GSM549280 2 0.3123 0.6031 0.000 0.844 0.000 0.156
#> GSM549281 2 0.3172 0.6173 0.000 0.840 0.000 0.160
#> GSM549285 2 0.2704 0.5846 0.000 0.876 0.000 0.124
#> GSM549288 2 0.5159 0.5546 0.000 0.756 0.088 0.156
#> GSM549292 4 0.4877 -0.2632 0.000 0.408 0.000 0.592
#> GSM549295 3 0.0895 0.9482 0.000 0.020 0.976 0.004
#> GSM549297 3 0.3626 0.8362 0.000 0.184 0.812 0.004
#> GSM750743 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549268 2 0.3172 0.6173 0.000 0.840 0.000 0.160
#> GSM549290 2 0.4925 0.2951 0.000 0.572 0.000 0.428
#> GSM549272 2 0.4916 0.4897 0.000 0.576 0.000 0.424
#> GSM549276 2 0.4624 0.5465 0.000 0.660 0.000 0.340
#> GSM549275 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM549284 4 0.4697 -0.2203 0.000 0.356 0.000 0.644
#> GSM750737 4 0.6280 0.1949 0.344 0.072 0.000 0.584
#> GSM750740 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> GSM750751 2 0.4643 0.5459 0.000 0.656 0.000 0.344
#> GSM750754 2 0.2345 0.5326 0.000 0.900 0.000 0.100
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1202 0.4550 0.004 0.032 0.000 0.960 0.004
#> GSM549291 5 0.6266 0.5190 0.000 0.152 0.000 0.376 0.472
#> GSM549274 2 0.0794 0.6981 0.000 0.972 0.000 0.028 0.000
#> GSM750738 2 0.3999 0.4040 0.000 0.656 0.000 0.344 0.000
#> GSM750748 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.3844 0.6354 0.792 0.000 0.000 0.164 0.044
#> GSM549279 2 0.5957 0.2413 0.000 0.572 0.000 0.280 0.148
#> GSM549294 5 0.4430 0.0747 0.000 0.456 0.000 0.004 0.540
#> GSM549300 3 0.3438 0.8165 0.000 0.000 0.808 0.020 0.172
#> GSM549303 3 0.0290 0.8538 0.000 0.000 0.992 0.008 0.000
#> GSM549309 3 0.0290 0.8538 0.000 0.000 0.992 0.008 0.000
#> GSM750753 5 0.4074 0.2292 0.000 0.364 0.000 0.000 0.636
#> GSM750752 2 0.4238 0.3713 0.000 0.628 0.000 0.368 0.004
#> GSM549304 2 0.0794 0.6972 0.000 0.972 0.000 0.028 0.000
#> GSM549305 5 0.4307 -0.0411 0.000 0.496 0.000 0.000 0.504
#> GSM549307 3 0.4626 0.6622 0.000 0.000 0.616 0.020 0.364
#> GSM549306 3 0.0000 0.8547 0.000 0.000 1.000 0.000 0.000
#> GSM549308 3 0.0000 0.8547 0.000 0.000 1.000 0.000 0.000
#> GSM549233 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549234 4 0.4225 0.7572 0.364 0.004 0.000 0.632 0.000
#> GSM549250 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549287 5 0.4049 0.5932 0.000 0.056 0.000 0.164 0.780
#> GSM750735 4 0.4725 0.7306 0.196 0.024 0.000 0.740 0.040
#> GSM750736 4 0.5407 0.6506 0.424 0.004 0.000 0.524 0.048
#> GSM750749 5 0.5681 0.5792 0.000 0.124 0.000 0.268 0.608
#> GSM549230 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549231 1 0.0290 0.9571 0.992 0.000 0.000 0.000 0.008
#> GSM549237 4 0.5280 0.6172 0.440 0.000 0.000 0.512 0.048
#> GSM549254 4 0.1202 0.4550 0.004 0.032 0.000 0.960 0.004
#> GSM750734 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549271 5 0.3056 0.5749 0.000 0.068 0.000 0.068 0.864
#> GSM549232 4 0.3489 0.6921 0.144 0.036 0.000 0.820 0.000
#> GSM549246 4 0.2331 0.5720 0.064 0.024 0.000 0.908 0.004
#> GSM549248 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549255 4 0.4025 0.7793 0.292 0.008 0.000 0.700 0.000
#> GSM750746 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0880 0.9439 0.968 0.000 0.000 0.000 0.032
#> GSM549269 2 0.0404 0.6975 0.000 0.988 0.000 0.012 0.000
#> GSM549273 3 0.0290 0.8538 0.000 0.000 0.992 0.008 0.000
#> GSM549299 2 0.5733 0.1984 0.000 0.608 0.000 0.136 0.256
#> GSM549301 3 0.0000 0.8547 0.000 0.000 1.000 0.000 0.000
#> GSM549310 5 0.6500 0.4727 0.000 0.188 0.000 0.400 0.412
#> GSM549311 3 0.3977 0.7933 0.000 0.000 0.764 0.032 0.204
#> GSM549302 2 0.0404 0.6975 0.000 0.988 0.000 0.012 0.000
#> GSM549235 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.4182 0.7655 0.352 0.004 0.000 0.644 0.000
#> GSM549265 4 0.4169 0.7722 0.240 0.028 0.000 0.732 0.000
#> GSM549282 5 0.3464 0.5797 0.000 0.068 0.000 0.096 0.836
#> GSM549296 5 0.6480 0.4774 0.000 0.184 0.000 0.400 0.416
#> GSM750739 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM750744 1 0.0404 0.9563 0.988 0.000 0.000 0.000 0.012
#> GSM750750 3 0.3284 0.8212 0.000 0.000 0.828 0.024 0.148
#> GSM549242 1 0.0290 0.9561 0.992 0.000 0.000 0.000 0.008
#> GSM549252 4 0.4127 0.7780 0.312 0.008 0.000 0.680 0.000
#> GSM549253 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549256 1 0.0404 0.9563 0.988 0.000 0.000 0.000 0.012
#> GSM549257 4 0.4166 0.7674 0.348 0.004 0.000 0.648 0.000
#> GSM549263 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549267 5 0.6206 0.5307 0.000 0.152 0.000 0.344 0.504
#> GSM750745 1 0.0609 0.9497 0.980 0.000 0.000 0.000 0.020
#> GSM549239 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.4350 0.7068 0.408 0.000 0.000 0.588 0.004
#> GSM549249 4 0.4211 0.7605 0.360 0.004 0.000 0.636 0.000
#> GSM549260 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549266 2 0.6562 -0.1751 0.000 0.464 0.000 0.228 0.308
#> GSM549293 2 0.0794 0.6972 0.000 0.972 0.000 0.028 0.000
#> GSM549236 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549238 1 0.0912 0.9445 0.972 0.000 0.000 0.016 0.012
#> GSM549251 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549258 1 0.1043 0.9390 0.960 0.000 0.000 0.000 0.040
#> GSM549264 1 0.4977 -0.1240 0.604 0.000 0.000 0.356 0.040
#> GSM549243 1 0.0000 0.9581 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0162 0.9577 0.996 0.000 0.000 0.000 0.004
#> GSM549278 5 0.6188 0.4947 0.000 0.136 0.000 0.416 0.448
#> GSM549283 2 0.3789 0.4772 0.000 0.760 0.000 0.016 0.224
#> GSM549298 3 0.0000 0.8547 0.000 0.000 1.000 0.000 0.000
#> GSM750741 4 0.5250 0.6620 0.416 0.000 0.000 0.536 0.048
#> GSM549286 2 0.0794 0.6827 0.000 0.972 0.000 0.000 0.028
#> GSM549241 1 0.1197 0.9327 0.952 0.000 0.000 0.000 0.048
#> GSM549247 4 0.5291 0.5858 0.456 0.000 0.000 0.496 0.048
#> GSM549261 1 0.1121 0.9356 0.956 0.000 0.000 0.000 0.044
#> GSM549270 5 0.4659 0.2560 0.000 0.332 0.004 0.020 0.644
#> GSM549277 3 0.5349 0.4841 0.000 0.020 0.488 0.020 0.472
#> GSM549280 5 0.2966 0.4713 0.000 0.184 0.000 0.000 0.816
#> GSM549281 5 0.5787 0.5100 0.000 0.240 0.000 0.152 0.608
#> GSM549285 5 0.6021 0.5498 0.000 0.188 0.000 0.232 0.580
#> GSM549288 5 0.3670 0.4618 0.000 0.180 0.004 0.020 0.796
#> GSM549292 2 0.0510 0.6981 0.000 0.984 0.000 0.016 0.000
#> GSM549295 3 0.2561 0.8386 0.000 0.000 0.884 0.020 0.096
#> GSM549297 3 0.5231 0.5620 0.000 0.016 0.536 0.020 0.428
#> GSM750743 1 0.1197 0.9327 0.952 0.000 0.000 0.000 0.048
#> GSM549268 5 0.5787 0.5100 0.000 0.240 0.000 0.152 0.608
#> GSM549290 5 0.6428 0.4994 0.000 0.180 0.000 0.364 0.456
#> GSM549272 2 0.0794 0.6827 0.000 0.972 0.000 0.000 0.028
#> GSM549276 2 0.4278 0.0600 0.000 0.548 0.000 0.000 0.452
#> GSM549275 1 0.1121 0.9356 0.956 0.000 0.000 0.000 0.044
#> GSM549284 2 0.1197 0.6836 0.000 0.952 0.000 0.048 0.000
#> GSM750737 4 0.3106 0.6762 0.132 0.024 0.000 0.844 0.000
#> GSM750740 1 0.1197 0.9327 0.952 0.000 0.000 0.000 0.048
#> GSM750747 1 0.0794 0.9456 0.972 0.000 0.000 0.000 0.028
#> GSM750751 2 0.4268 0.0610 0.000 0.556 0.000 0.000 0.444
#> GSM750754 5 0.4125 0.5942 0.000 0.056 0.000 0.172 0.772
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.2867 0.7081 0.000 0.000 0.000 0.848 0.040 0.112
#> GSM549291 6 0.2518 0.6623 0.000 0.016 0.000 0.092 0.012 0.880
#> GSM549274 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750738 2 0.2697 0.6930 0.000 0.812 0.000 0.188 0.000 0.000
#> GSM750748 1 0.0458 0.9283 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549240 1 0.5691 0.3488 0.568 0.000 0.000 0.256 0.164 0.012
#> GSM549279 6 0.6883 0.2931 0.000 0.340 0.000 0.104 0.132 0.424
#> GSM549294 5 0.5647 0.5615 0.000 0.260 0.000 0.004 0.552 0.184
#> GSM549300 3 0.3595 0.6788 0.000 0.000 0.704 0.008 0.288 0.000
#> GSM549303 3 0.0146 0.8905 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM549309 3 0.0146 0.8905 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM750753 5 0.4801 0.6369 0.000 0.196 0.000 0.000 0.668 0.136
#> GSM750752 2 0.3702 0.5930 0.000 0.720 0.000 0.264 0.004 0.012
#> GSM549304 2 0.0146 0.8777 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM549305 5 0.5142 0.6068 0.000 0.304 0.000 0.000 0.584 0.112
#> GSM549307 5 0.4284 0.1254 0.000 0.000 0.384 0.008 0.596 0.012
#> GSM549306 3 0.0000 0.8912 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549308 3 0.0000 0.8912 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549233 1 0.1049 0.9247 0.960 0.000 0.000 0.008 0.032 0.000
#> GSM549234 4 0.2311 0.8457 0.104 0.000 0.000 0.880 0.016 0.000
#> GSM549250 1 0.1049 0.9247 0.960 0.000 0.000 0.008 0.032 0.000
#> GSM549287 6 0.3073 0.5450 0.000 0.000 0.000 0.008 0.204 0.788
#> GSM750735 4 0.4034 0.7438 0.024 0.000 0.000 0.776 0.148 0.052
#> GSM750736 4 0.5136 0.7531 0.168 0.000 0.000 0.660 0.160 0.012
#> GSM750749 6 0.4367 0.6135 0.000 0.024 0.000 0.076 0.148 0.752
#> GSM549230 1 0.0972 0.9273 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM549231 1 0.0972 0.9277 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM549237 4 0.5368 0.7289 0.208 0.000 0.000 0.632 0.144 0.016
#> GSM549254 4 0.2250 0.7506 0.000 0.000 0.000 0.896 0.040 0.064
#> GSM750734 1 0.0363 0.9287 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549271 6 0.3578 0.3605 0.000 0.000 0.000 0.000 0.340 0.660
#> GSM549232 4 0.1155 0.8147 0.036 0.000 0.000 0.956 0.004 0.004
#> GSM549246 4 0.2538 0.7771 0.020 0.000 0.000 0.892 0.040 0.048
#> GSM549248 1 0.0717 0.9269 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549255 4 0.1788 0.8405 0.076 0.000 0.000 0.916 0.004 0.004
#> GSM750746 1 0.0363 0.9286 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM549259 1 0.2191 0.8796 0.876 0.000 0.000 0.000 0.120 0.004
#> GSM549269 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549273 3 0.0146 0.8905 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM549299 6 0.5540 0.1666 0.000 0.440 0.000 0.016 0.084 0.460
#> GSM549301 3 0.0000 0.8912 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549310 6 0.3542 0.6401 0.000 0.028 0.000 0.156 0.016 0.800
#> GSM549311 3 0.4700 0.7087 0.000 0.000 0.700 0.008 0.180 0.112
#> GSM549302 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 1 0.0458 0.9283 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM549245 4 0.2311 0.8457 0.104 0.000 0.000 0.880 0.016 0.000
#> GSM549265 4 0.1444 0.8405 0.072 0.000 0.000 0.928 0.000 0.000
#> GSM549282 6 0.3428 0.3990 0.000 0.000 0.000 0.000 0.304 0.696
#> GSM549296 6 0.3516 0.6330 0.000 0.024 0.000 0.172 0.012 0.792
#> GSM750739 1 0.0146 0.9291 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM750742 1 0.0891 0.9276 0.968 0.000 0.000 0.008 0.024 0.000
#> GSM750744 1 0.0972 0.9261 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM750750 3 0.3254 0.7914 0.000 0.000 0.804 0.008 0.172 0.016
#> GSM549242 1 0.0717 0.9291 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549252 4 0.1765 0.8466 0.096 0.000 0.000 0.904 0.000 0.000
#> GSM549253 1 0.0717 0.9269 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549256 1 0.1644 0.9079 0.932 0.000 0.000 0.040 0.028 0.000
#> GSM549257 4 0.1814 0.8467 0.100 0.000 0.000 0.900 0.000 0.000
#> GSM549263 1 0.0891 0.9276 0.968 0.000 0.000 0.008 0.024 0.000
#> GSM549267 6 0.2016 0.6565 0.000 0.016 0.000 0.040 0.024 0.920
#> GSM750745 1 0.1327 0.9089 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM549239 1 0.0146 0.9290 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549244 4 0.2624 0.8359 0.124 0.000 0.000 0.856 0.020 0.000
#> GSM549249 4 0.2311 0.8457 0.104 0.000 0.000 0.880 0.016 0.000
#> GSM549260 1 0.0260 0.9289 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM549266 6 0.6647 0.3850 0.000 0.280 0.000 0.092 0.132 0.496
#> GSM549293 2 0.0146 0.8777 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM549236 1 0.0806 0.9271 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM549238 1 0.3083 0.8007 0.828 0.000 0.000 0.132 0.040 0.000
#> GSM549251 1 0.0806 0.9271 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM549258 1 0.2320 0.8739 0.864 0.000 0.000 0.000 0.132 0.004
#> GSM549264 4 0.5530 0.6454 0.260 0.000 0.000 0.588 0.140 0.012
#> GSM549243 1 0.0146 0.9293 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549262 1 0.0717 0.9269 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549278 6 0.3307 0.6506 0.000 0.012 0.000 0.120 0.040 0.828
#> GSM549283 2 0.5627 0.0858 0.000 0.544 0.000 0.016 0.112 0.328
#> GSM549298 3 0.0000 0.8912 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM750741 4 0.5426 0.7390 0.188 0.000 0.000 0.628 0.168 0.016
#> GSM549286 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.2669 0.8555 0.836 0.000 0.000 0.000 0.156 0.008
#> GSM549247 4 0.5338 0.7349 0.192 0.000 0.000 0.632 0.164 0.012
#> GSM549261 1 0.2593 0.8611 0.844 0.000 0.000 0.000 0.148 0.008
#> GSM549270 5 0.4297 0.6465 0.000 0.176 0.000 0.000 0.724 0.100
#> GSM549277 5 0.4178 0.4349 0.000 0.000 0.260 0.008 0.700 0.032
#> GSM549280 5 0.4307 0.5515 0.000 0.072 0.000 0.000 0.704 0.224
#> GSM549281 6 0.5863 0.2957 0.000 0.092 0.000 0.040 0.336 0.532
#> GSM549285 6 0.1785 0.6489 0.000 0.016 0.000 0.008 0.048 0.928
#> GSM549288 5 0.4432 0.5737 0.000 0.080 0.000 0.008 0.720 0.192
#> GSM549292 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 3 0.3398 0.7204 0.000 0.000 0.740 0.008 0.252 0.000
#> GSM549297 5 0.4253 0.3552 0.000 0.000 0.304 0.008 0.664 0.024
#> GSM750743 1 0.3020 0.8426 0.824 0.000 0.000 0.012 0.156 0.008
#> GSM549268 6 0.5863 0.2957 0.000 0.092 0.000 0.040 0.336 0.532
#> GSM549290 6 0.2400 0.6593 0.000 0.024 0.000 0.064 0.016 0.896
#> GSM549272 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549276 5 0.5353 0.4780 0.000 0.420 0.000 0.000 0.472 0.108
#> GSM549275 1 0.2841 0.8428 0.824 0.000 0.000 0.000 0.164 0.012
#> GSM549284 2 0.0458 0.8688 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM750737 4 0.2494 0.7880 0.028 0.000 0.000 0.896 0.040 0.036
#> GSM750740 1 0.2593 0.8598 0.844 0.000 0.000 0.000 0.148 0.008
#> GSM750747 1 0.2006 0.8903 0.892 0.000 0.000 0.000 0.104 0.004
#> GSM750751 5 0.5419 0.4644 0.000 0.424 0.000 0.000 0.460 0.116
#> GSM750754 6 0.2980 0.5571 0.000 0.000 0.000 0.008 0.192 0.800
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:kmeans 101 0.6320 1.86e-05 0.8772 0.00936 2
#> ATC:kmeans 103 0.1139 1.63e-05 0.2149 0.00862 3
#> ATC:kmeans 73 0.0442 9.98e-06 0.2418 0.00829 4
#> ATC:kmeans 81 0.0875 7.59e-04 0.1204 0.19817 5
#> ATC:kmeans 89 0.1001 4.48e-04 0.0244 0.44023 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.984 0.992 0.5039 0.496 0.496
#> 3 3 0.982 0.931 0.971 0.2148 0.880 0.760
#> 4 4 0.788 0.787 0.905 0.0949 0.911 0.780
#> 5 5 0.788 0.785 0.865 0.0771 0.904 0.728
#> 6 6 0.752 0.723 0.850 0.0451 0.976 0.909
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
#> GSM549289 1 0.000 1.000 1.000 0.000
#> GSM549291 2 0.000 0.984 0.000 1.000
#> GSM549274 2 0.000 0.984 0.000 1.000
#> GSM750738 1 0.000 1.000 1.000 0.000
#> GSM750748 1 0.000 1.000 1.000 0.000
#> GSM549240 1 0.000 1.000 1.000 0.000
#> GSM549279 2 0.697 0.785 0.188 0.812
#> GSM549294 2 0.000 0.984 0.000 1.000
#> GSM549300 2 0.000 0.984 0.000 1.000
#> GSM549303 2 0.000 0.984 0.000 1.000
#> GSM549309 2 0.000 0.984 0.000 1.000
#> GSM750753 2 0.000 0.984 0.000 1.000
#> GSM750752 2 0.416 0.908 0.084 0.916
#> GSM549304 2 0.706 0.779 0.192 0.808
#> GSM549305 2 0.000 0.984 0.000 1.000
#> GSM549307 2 0.000 0.984 0.000 1.000
#> GSM549306 2 0.000 0.984 0.000 1.000
#> GSM549308 2 0.000 0.984 0.000 1.000
#> GSM549233 1 0.000 1.000 1.000 0.000
#> GSM549234 1 0.000 1.000 1.000 0.000
#> GSM549250 1 0.000 1.000 1.000 0.000
#> GSM549287 2 0.000 0.984 0.000 1.000
#> GSM750735 1 0.000 1.000 1.000 0.000
#> GSM750736 1 0.000 1.000 1.000 0.000
#> GSM750749 2 0.000 0.984 0.000 1.000
#> GSM549230 1 0.000 1.000 1.000 0.000
#> GSM549231 1 0.000 1.000 1.000 0.000
#> GSM549237 1 0.000 1.000 1.000 0.000
#> GSM549254 1 0.000 1.000 1.000 0.000
#> GSM750734 1 0.000 1.000 1.000 0.000
#> GSM549271 2 0.000 0.984 0.000 1.000
#> GSM549232 1 0.000 1.000 1.000 0.000
#> GSM549246 1 0.000 1.000 1.000 0.000
#> GSM549248 1 0.000 1.000 1.000 0.000
#> GSM549255 1 0.000 1.000 1.000 0.000
#> GSM750746 1 0.000 1.000 1.000 0.000
#> GSM549259 1 0.000 1.000 1.000 0.000
#> GSM549269 2 0.000 0.984 0.000 1.000
#> GSM549273 2 0.000 0.984 0.000 1.000
#> GSM549299 2 0.000 0.984 0.000 1.000
#> GSM549301 2 0.000 0.984 0.000 1.000
#> GSM549310 2 0.000 0.984 0.000 1.000
#> GSM549311 2 0.000 0.984 0.000 1.000
#> GSM549302 2 0.000 0.984 0.000 1.000
#> GSM549235 1 0.000 1.000 1.000 0.000
#> GSM549245 1 0.000 1.000 1.000 0.000
#> GSM549265 1 0.000 1.000 1.000 0.000
#> GSM549282 2 0.000 0.984 0.000 1.000
#> GSM549296 2 0.000 0.984 0.000 1.000
#> GSM750739 1 0.000 1.000 1.000 0.000
#> GSM750742 1 0.000 1.000 1.000 0.000
#> GSM750744 1 0.000 1.000 1.000 0.000
#> GSM750750 2 0.000 0.984 0.000 1.000
#> GSM549242 1 0.000 1.000 1.000 0.000
#> GSM549252 1 0.000 1.000 1.000 0.000
#> GSM549253 1 0.000 1.000 1.000 0.000
#> GSM549256 1 0.000 1.000 1.000 0.000
#> GSM549257 1 0.000 1.000 1.000 0.000
#> GSM549263 1 0.000 1.000 1.000 0.000
#> GSM549267 2 0.000 0.984 0.000 1.000
#> GSM750745 1 0.000 1.000 1.000 0.000
#> GSM549239 1 0.000 1.000 1.000 0.000
#> GSM549244 1 0.000 1.000 1.000 0.000
#> GSM549249 1 0.000 1.000 1.000 0.000
#> GSM549260 1 0.000 1.000 1.000 0.000
#> GSM549266 2 0.000 0.984 0.000 1.000
#> GSM549293 2 0.706 0.779 0.192 0.808
#> GSM549236 1 0.000 1.000 1.000 0.000
#> GSM549238 1 0.000 1.000 1.000 0.000
#> GSM549251 1 0.000 1.000 1.000 0.000
#> GSM549258 1 0.000 1.000 1.000 0.000
#> GSM549264 1 0.000 1.000 1.000 0.000
#> GSM549243 1 0.000 1.000 1.000 0.000
#> GSM549262 1 0.000 1.000 1.000 0.000
#> GSM549278 2 0.118 0.971 0.016 0.984
#> GSM549283 2 0.000 0.984 0.000 1.000
#> GSM549298 2 0.000 0.984 0.000 1.000
#> GSM750741 1 0.000 1.000 1.000 0.000
#> GSM549286 2 0.000 0.984 0.000 1.000
#> GSM549241 1 0.000 1.000 1.000 0.000
#> GSM549247 1 0.000 1.000 1.000 0.000
#> GSM549261 1 0.000 1.000 1.000 0.000
#> GSM549270 2 0.000 0.984 0.000 1.000
#> GSM549277 2 0.000 0.984 0.000 1.000
#> GSM549280 2 0.000 0.984 0.000 1.000
#> GSM549281 2 0.000 0.984 0.000 1.000
#> GSM549285 2 0.000 0.984 0.000 1.000
#> GSM549288 2 0.000 0.984 0.000 1.000
#> GSM549292 2 0.000 0.984 0.000 1.000
#> GSM549295 2 0.000 0.984 0.000 1.000
#> GSM549297 2 0.000 0.984 0.000 1.000
#> GSM750743 1 0.000 1.000 1.000 0.000
#> GSM549268 2 0.000 0.984 0.000 1.000
#> GSM549290 2 0.000 0.984 0.000 1.000
#> GSM549272 2 0.000 0.984 0.000 1.000
#> GSM549276 2 0.000 0.984 0.000 1.000
#> GSM549275 1 0.000 1.000 1.000 0.000
#> GSM549284 2 0.494 0.883 0.108 0.892
#> GSM750737 1 0.000 1.000 1.000 0.000
#> GSM750740 1 0.000 1.000 1.000 0.000
#> GSM750747 1 0.000 1.000 1.000 0.000
#> GSM750751 2 0.000 0.984 0.000 1.000
#> GSM750754 2 0.000 0.984 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549291 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549274 2 0.0237 0.862 0.000 0.996 0.004
#> GSM750738 2 0.0000 0.861 0.000 1.000 0.000
#> GSM750748 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549279 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549294 3 0.5859 0.364 0.000 0.344 0.656
#> GSM549300 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549303 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549309 3 0.0000 0.972 0.000 0.000 1.000
#> GSM750753 3 0.4291 0.739 0.000 0.180 0.820
#> GSM750752 2 0.0000 0.861 0.000 1.000 0.000
#> GSM549304 2 0.0000 0.861 0.000 1.000 0.000
#> GSM549305 2 0.6215 0.377 0.000 0.572 0.428
#> GSM549307 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549306 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549308 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549233 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549234 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549250 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549287 3 0.0000 0.972 0.000 0.000 1.000
#> GSM750735 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750736 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750749 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549230 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549254 1 0.0237 0.997 0.996 0.004 0.000
#> GSM750734 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549271 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549232 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549246 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549248 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549255 1 0.0237 0.997 0.996 0.004 0.000
#> GSM750746 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549269 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549273 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549299 2 0.6225 0.368 0.000 0.568 0.432
#> GSM549301 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549310 3 0.0892 0.955 0.000 0.020 0.980
#> GSM549311 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549302 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549235 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549245 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549265 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549282 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549296 3 0.0892 0.955 0.000 0.020 0.980
#> GSM750739 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750750 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549242 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549252 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549253 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549257 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549263 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549267 3 0.0000 0.972 0.000 0.000 1.000
#> GSM750745 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549244 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549249 1 0.0237 0.997 0.996 0.004 0.000
#> GSM549260 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549266 2 0.6308 0.190 0.000 0.508 0.492
#> GSM549293 2 0.0000 0.861 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549238 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549251 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549278 3 0.0237 0.968 0.000 0.004 0.996
#> GSM549283 2 0.0424 0.860 0.000 0.992 0.008
#> GSM549298 3 0.0000 0.972 0.000 0.000 1.000
#> GSM750741 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549286 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549241 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549247 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549261 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549270 3 0.4235 0.745 0.000 0.176 0.824
#> GSM549277 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549280 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549281 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549285 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549288 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549292 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549295 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549297 3 0.0000 0.972 0.000 0.000 1.000
#> GSM750743 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549268 3 0.0000 0.972 0.000 0.000 1.000
#> GSM549290 3 0.0237 0.968 0.000 0.004 0.996
#> GSM549272 2 0.0237 0.862 0.000 0.996 0.004
#> GSM549276 2 0.5905 0.517 0.000 0.648 0.352
#> GSM549275 1 0.0000 0.999 1.000 0.000 0.000
#> GSM549284 2 0.0000 0.861 0.000 1.000 0.000
#> GSM750737 1 0.0237 0.997 0.996 0.004 0.000
#> GSM750740 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.999 1.000 0.000 0.000
#> GSM750751 2 0.6225 0.368 0.000 0.568 0.432
#> GSM750754 3 0.0000 0.972 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.3801 0.4930 0.220 0.000 0.000 0.780
#> GSM549291 4 0.4998 0.2784 0.000 0.000 0.488 0.512
#> GSM549274 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM750738 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM750748 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549279 2 0.4122 0.7287 0.000 0.760 0.004 0.236
#> GSM549294 3 0.4193 0.5826 0.000 0.268 0.732 0.000
#> GSM549300 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549303 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549309 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM750753 3 0.2530 0.7664 0.000 0.112 0.888 0.000
#> GSM750752 2 0.3610 0.6652 0.000 0.800 0.000 0.200
#> GSM549304 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549305 3 0.4967 0.2202 0.000 0.452 0.548 0.000
#> GSM549307 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549306 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549308 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549233 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549234 1 0.3873 0.7347 0.772 0.000 0.000 0.228
#> GSM549250 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549287 3 0.0707 0.8420 0.000 0.000 0.980 0.020
#> GSM750735 1 0.3528 0.7150 0.808 0.000 0.000 0.192
#> GSM750736 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750749 3 0.3528 0.6749 0.000 0.000 0.808 0.192
#> GSM549230 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549231 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549254 4 0.3801 0.4912 0.220 0.000 0.000 0.780
#> GSM750734 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549271 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549232 1 0.3942 0.7243 0.764 0.000 0.000 0.236
#> GSM549246 4 0.4898 0.0906 0.416 0.000 0.000 0.584
#> GSM549248 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549255 1 0.3975 0.7188 0.760 0.000 0.000 0.240
#> GSM750746 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549299 3 0.5183 0.3282 0.000 0.408 0.584 0.008
#> GSM549301 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549310 4 0.4741 0.5443 0.000 0.004 0.328 0.668
#> GSM549311 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549302 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549245 1 0.3873 0.7347 0.772 0.000 0.000 0.228
#> GSM549265 1 0.3873 0.7347 0.772 0.000 0.000 0.228
#> GSM549282 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549296 4 0.4697 0.5599 0.000 0.008 0.296 0.696
#> GSM750739 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750750 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549242 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549252 1 0.3873 0.7347 0.772 0.000 0.000 0.228
#> GSM549253 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549257 1 0.3801 0.7437 0.780 0.000 0.000 0.220
#> GSM549263 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549267 3 0.4040 0.5017 0.000 0.000 0.752 0.248
#> GSM750745 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549244 1 0.3726 0.7525 0.788 0.000 0.000 0.212
#> GSM549249 1 0.3873 0.7347 0.772 0.000 0.000 0.228
#> GSM549260 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549266 3 0.7551 0.1209 0.000 0.356 0.448 0.196
#> GSM549293 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549236 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549238 1 0.0469 0.9274 0.988 0.000 0.000 0.012
#> GSM549251 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549264 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549243 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549278 4 0.4564 0.4823 0.000 0.000 0.328 0.672
#> GSM549283 2 0.4446 0.6643 0.000 0.776 0.196 0.028
#> GSM549298 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM750741 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549286 2 0.0188 0.8914 0.000 0.996 0.004 0.000
#> GSM549241 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549247 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549261 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549270 3 0.2408 0.7741 0.000 0.104 0.896 0.000
#> GSM549277 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549280 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549281 3 0.3486 0.6801 0.000 0.000 0.812 0.188
#> GSM549285 3 0.0188 0.8539 0.000 0.000 0.996 0.004
#> GSM549288 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549292 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549295 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM549297 3 0.0000 0.8565 0.000 0.000 1.000 0.000
#> GSM750743 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549268 3 0.3311 0.6990 0.000 0.000 0.828 0.172
#> GSM549290 4 0.4925 0.4210 0.000 0.000 0.428 0.572
#> GSM549272 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM549276 2 0.4961 0.0616 0.000 0.552 0.448 0.000
#> GSM549275 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM549284 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> GSM750737 1 0.4985 0.2137 0.532 0.000 0.000 0.468
#> GSM750740 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9355 1.000 0.000 0.000 0.000
#> GSM750751 3 0.4967 0.2198 0.000 0.452 0.548 0.000
#> GSM750754 3 0.0921 0.8353 0.000 0.000 0.972 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 5 0.5878 0.2302 0.116 0.000 0.000 0.336 0.548
#> GSM549291 5 0.4046 0.7484 0.000 0.000 0.296 0.008 0.696
#> GSM549274 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM750738 2 0.1282 0.8639 0.000 0.952 0.000 0.044 0.004
#> GSM750748 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.1270 0.9137 0.948 0.000 0.000 0.052 0.000
#> GSM549279 2 0.6561 0.4989 0.000 0.552 0.016 0.224 0.208
#> GSM549294 3 0.3827 0.6579 0.000 0.144 0.812 0.020 0.024
#> GSM549300 3 0.0290 0.8036 0.000 0.000 0.992 0.000 0.008
#> GSM549303 3 0.0609 0.8026 0.000 0.000 0.980 0.000 0.020
#> GSM549309 3 0.0609 0.8026 0.000 0.000 0.980 0.000 0.020
#> GSM750753 3 0.1948 0.7697 0.000 0.036 0.932 0.008 0.024
#> GSM750752 2 0.4062 0.6623 0.000 0.764 0.000 0.196 0.040
#> GSM549304 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM549305 3 0.5169 0.3711 0.000 0.364 0.596 0.016 0.024
#> GSM549307 3 0.0000 0.8030 0.000 0.000 1.000 0.000 0.000
#> GSM549306 3 0.0510 0.8034 0.000 0.000 0.984 0.000 0.016
#> GSM549308 3 0.0510 0.8034 0.000 0.000 0.984 0.000 0.016
#> GSM549233 1 0.1043 0.9314 0.960 0.000 0.000 0.040 0.000
#> GSM549234 4 0.4030 0.8427 0.352 0.000 0.000 0.648 0.000
#> GSM549250 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549287 3 0.3366 0.5631 0.000 0.000 0.784 0.004 0.212
#> GSM750735 1 0.5200 0.4246 0.688 0.000 0.000 0.152 0.160
#> GSM750736 1 0.2471 0.7791 0.864 0.000 0.000 0.136 0.000
#> GSM750749 3 0.5774 0.3918 0.000 0.000 0.612 0.156 0.232
#> GSM549230 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549231 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549237 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549254 4 0.5458 -0.0206 0.068 0.000 0.000 0.552 0.380
#> GSM750734 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549271 3 0.0609 0.8026 0.000 0.000 0.980 0.000 0.020
#> GSM549232 4 0.3999 0.8447 0.344 0.000 0.000 0.656 0.000
#> GSM549246 4 0.5689 0.3327 0.136 0.000 0.000 0.616 0.248
#> GSM549248 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549255 4 0.4015 0.8441 0.348 0.000 0.000 0.652 0.000
#> GSM750746 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.0510 0.8034 0.000 0.000 0.984 0.000 0.016
#> GSM549299 3 0.5466 0.3772 0.000 0.336 0.600 0.012 0.052
#> GSM549301 3 0.0510 0.8034 0.000 0.000 0.984 0.000 0.016
#> GSM549310 5 0.4133 0.7983 0.000 0.012 0.232 0.012 0.744
#> GSM549311 3 0.0880 0.7959 0.000 0.000 0.968 0.000 0.032
#> GSM549302 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM549235 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.4015 0.8451 0.348 0.000 0.000 0.652 0.000
#> GSM549265 4 0.4045 0.8392 0.356 0.000 0.000 0.644 0.000
#> GSM549282 3 0.1043 0.7904 0.000 0.000 0.960 0.000 0.040
#> GSM549296 5 0.4391 0.7947 0.000 0.016 0.216 0.024 0.744
#> GSM750739 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM750744 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM750750 3 0.0609 0.8026 0.000 0.000 0.980 0.000 0.020
#> GSM549242 1 0.0290 0.9606 0.992 0.000 0.000 0.008 0.000
#> GSM549252 4 0.3999 0.8447 0.344 0.000 0.000 0.656 0.000
#> GSM549253 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549256 1 0.1478 0.8995 0.936 0.000 0.000 0.064 0.000
#> GSM549257 4 0.4182 0.7728 0.400 0.000 0.000 0.600 0.000
#> GSM549263 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549267 5 0.4440 0.3856 0.000 0.000 0.468 0.004 0.528
#> GSM750745 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.4045 0.8391 0.356 0.000 0.000 0.644 0.000
#> GSM549249 4 0.4015 0.8451 0.348 0.000 0.000 0.652 0.000
#> GSM549260 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549266 3 0.8417 0.0167 0.000 0.256 0.340 0.164 0.240
#> GSM549293 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM549236 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549238 1 0.3039 0.6596 0.808 0.000 0.000 0.192 0.000
#> GSM549251 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549258 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549264 1 0.0963 0.9357 0.964 0.000 0.000 0.036 0.000
#> GSM549243 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0162 0.9630 0.996 0.000 0.000 0.004 0.000
#> GSM549278 5 0.3759 0.7314 0.000 0.000 0.136 0.056 0.808
#> GSM549283 2 0.5962 0.4281 0.000 0.620 0.272 0.036 0.072
#> GSM549298 3 0.0510 0.8034 0.000 0.000 0.984 0.000 0.016
#> GSM750741 1 0.0510 0.9476 0.984 0.000 0.000 0.016 0.000
#> GSM549286 2 0.0162 0.8978 0.000 0.996 0.000 0.004 0.000
#> GSM549241 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549247 1 0.1544 0.8930 0.932 0.000 0.000 0.068 0.000
#> GSM549261 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549270 3 0.2060 0.7669 0.000 0.036 0.928 0.012 0.024
#> GSM549277 3 0.0000 0.8030 0.000 0.000 1.000 0.000 0.000
#> GSM549280 3 0.0451 0.7987 0.000 0.000 0.988 0.004 0.008
#> GSM549281 3 0.5592 0.4339 0.000 0.000 0.636 0.144 0.220
#> GSM549285 3 0.1638 0.7743 0.000 0.000 0.932 0.004 0.064
#> GSM549288 3 0.0000 0.8030 0.000 0.000 1.000 0.000 0.000
#> GSM549292 2 0.0000 0.8991 0.000 1.000 0.000 0.000 0.000
#> GSM549295 3 0.0000 0.8030 0.000 0.000 1.000 0.000 0.000
#> GSM549297 3 0.0000 0.8030 0.000 0.000 1.000 0.000 0.000
#> GSM750743 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM549268 3 0.5167 0.5027 0.000 0.000 0.684 0.116 0.200
#> GSM549290 5 0.3452 0.7925 0.000 0.000 0.244 0.000 0.756
#> GSM549272 2 0.0162 0.8978 0.000 0.996 0.000 0.004 0.000
#> GSM549276 3 0.5342 0.1587 0.000 0.464 0.496 0.016 0.024
#> GSM549275 1 0.0404 0.9550 0.988 0.000 0.000 0.012 0.000
#> GSM549284 2 0.0324 0.8952 0.000 0.992 0.000 0.004 0.004
#> GSM750737 4 0.5115 0.5064 0.168 0.000 0.000 0.696 0.136
#> GSM750740 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9633 1.000 0.000 0.000 0.000 0.000
#> GSM750751 3 0.5181 0.3673 0.000 0.368 0.592 0.016 0.024
#> GSM750754 3 0.2891 0.6315 0.000 0.000 0.824 0.000 0.176
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 6 0.6461 0.2918 0.060 0.000 0.000 0.268 0.160 0.512
#> GSM549291 6 0.4133 0.6116 0.000 0.000 0.252 0.008 0.032 0.708
#> GSM549274 2 0.0260 0.8768 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM750738 2 0.1082 0.8494 0.000 0.956 0.000 0.040 0.004 0.000
#> GSM750748 1 0.0436 0.9226 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM549240 1 0.2978 0.8323 0.860 0.000 0.000 0.072 0.056 0.012
#> GSM549279 5 0.4568 0.2311 0.000 0.276 0.016 0.016 0.676 0.016
#> GSM549294 3 0.5126 0.4349 0.000 0.112 0.684 0.008 0.180 0.016
#> GSM549300 3 0.0291 0.7991 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM549303 3 0.0260 0.7999 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM549309 3 0.0260 0.7999 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM750753 3 0.3629 0.6509 0.000 0.044 0.820 0.008 0.112 0.016
#> GSM750752 2 0.4114 0.6182 0.000 0.740 0.000 0.200 0.008 0.052
#> GSM549304 2 0.0260 0.8763 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM549305 3 0.6065 0.1707 0.000 0.296 0.536 0.008 0.140 0.020
#> GSM549307 3 0.0603 0.7955 0.000 0.000 0.980 0.000 0.016 0.004
#> GSM549306 3 0.0146 0.8006 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549308 3 0.0260 0.7999 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM549233 1 0.2744 0.8034 0.840 0.000 0.000 0.144 0.016 0.000
#> GSM549234 4 0.2793 0.7822 0.200 0.000 0.000 0.800 0.000 0.000
#> GSM549250 1 0.1528 0.9011 0.936 0.000 0.000 0.048 0.016 0.000
#> GSM549287 3 0.3695 0.4684 0.000 0.000 0.712 0.000 0.016 0.272
#> GSM750735 1 0.5196 0.1571 0.520 0.000 0.000 0.080 0.396 0.004
#> GSM750736 1 0.4320 0.5647 0.704 0.000 0.000 0.240 0.048 0.008
#> GSM750749 5 0.3737 0.6416 0.000 0.000 0.392 0.000 0.608 0.000
#> GSM549230 1 0.0820 0.9206 0.972 0.000 0.000 0.012 0.016 0.000
#> GSM549231 1 0.0914 0.9182 0.968 0.000 0.000 0.016 0.016 0.000
#> GSM549237 1 0.0862 0.9215 0.972 0.000 0.000 0.004 0.016 0.008
#> GSM549254 4 0.6209 -0.2014 0.016 0.000 0.000 0.420 0.188 0.376
#> GSM750734 1 0.0291 0.9229 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM549271 3 0.0260 0.7999 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM549232 4 0.3087 0.7609 0.176 0.004 0.000 0.808 0.012 0.000
#> GSM549246 4 0.6901 0.1428 0.092 0.000 0.000 0.472 0.200 0.236
#> GSM549248 1 0.0914 0.9182 0.968 0.000 0.000 0.016 0.016 0.000
#> GSM549255 4 0.3401 0.7700 0.204 0.000 0.000 0.776 0.016 0.004
#> GSM750746 1 0.0146 0.9231 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549259 1 0.0405 0.9225 0.988 0.000 0.000 0.000 0.008 0.004
#> GSM549269 2 0.1218 0.8631 0.000 0.956 0.000 0.012 0.028 0.004
#> GSM549273 3 0.0146 0.8006 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549299 3 0.6971 0.0388 0.000 0.276 0.480 0.012 0.152 0.080
#> GSM549301 3 0.0146 0.8006 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549310 6 0.3781 0.7086 0.000 0.008 0.116 0.028 0.036 0.812
#> GSM549311 3 0.0692 0.7905 0.000 0.000 0.976 0.000 0.004 0.020
#> GSM549302 2 0.0551 0.8743 0.000 0.984 0.000 0.004 0.008 0.004
#> GSM549235 1 0.0291 0.9228 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM549245 4 0.2933 0.7824 0.200 0.000 0.000 0.796 0.004 0.000
#> GSM549265 4 0.2964 0.7813 0.204 0.004 0.000 0.792 0.000 0.000
#> GSM549282 3 0.1719 0.7536 0.000 0.000 0.924 0.000 0.016 0.060
#> GSM549296 6 0.4304 0.7057 0.000 0.008 0.140 0.040 0.040 0.772
#> GSM750739 1 0.0260 0.9234 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM750742 1 0.0717 0.9207 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM750744 1 0.0806 0.9236 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM750750 3 0.0260 0.7999 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM549242 1 0.0806 0.9229 0.972 0.000 0.000 0.020 0.008 0.000
#> GSM549252 4 0.2793 0.7822 0.200 0.000 0.000 0.800 0.000 0.000
#> GSM549253 1 0.1168 0.9150 0.956 0.000 0.000 0.028 0.016 0.000
#> GSM549256 1 0.2613 0.8092 0.848 0.000 0.000 0.140 0.012 0.000
#> GSM549257 4 0.3647 0.6066 0.360 0.000 0.000 0.640 0.000 0.000
#> GSM549263 1 0.0914 0.9182 0.968 0.000 0.000 0.016 0.016 0.000
#> GSM549267 6 0.4318 0.2603 0.000 0.000 0.448 0.000 0.020 0.532
#> GSM750745 1 0.0547 0.9206 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549239 1 0.0291 0.9232 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM549244 4 0.3373 0.7479 0.248 0.000 0.000 0.744 0.008 0.000
#> GSM549249 4 0.2854 0.7814 0.208 0.000 0.000 0.792 0.000 0.000
#> GSM549260 1 0.0291 0.9229 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM549266 5 0.4992 0.5451 0.000 0.140 0.160 0.000 0.684 0.016
#> GSM549293 2 0.0146 0.8764 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM549236 1 0.1088 0.9168 0.960 0.000 0.000 0.024 0.016 0.000
#> GSM549238 1 0.3871 0.4718 0.676 0.000 0.000 0.308 0.016 0.000
#> GSM549251 1 0.0914 0.9196 0.968 0.000 0.000 0.016 0.016 0.000
#> GSM549258 1 0.0547 0.9206 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM549264 1 0.1983 0.8837 0.908 0.000 0.000 0.072 0.020 0.000
#> GSM549243 1 0.0146 0.9228 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM549262 1 0.0717 0.9207 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM549278 6 0.4074 0.6682 0.000 0.000 0.096 0.044 0.068 0.792
#> GSM549283 2 0.7179 -0.2206 0.000 0.384 0.276 0.020 0.280 0.040
#> GSM549298 3 0.0146 0.8006 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM750741 1 0.2386 0.8691 0.896 0.000 0.000 0.028 0.064 0.012
#> GSM549286 2 0.2257 0.8381 0.000 0.912 0.008 0.016 0.044 0.020
#> GSM549241 1 0.1578 0.9002 0.936 0.000 0.000 0.004 0.048 0.012
#> GSM549247 1 0.3190 0.8137 0.844 0.000 0.000 0.088 0.056 0.012
#> GSM549261 1 0.0909 0.9161 0.968 0.000 0.000 0.000 0.020 0.012
#> GSM549270 3 0.3325 0.6619 0.000 0.028 0.832 0.004 0.120 0.016
#> GSM549277 3 0.0363 0.7981 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM549280 3 0.1265 0.7752 0.000 0.000 0.948 0.000 0.044 0.008
#> GSM549281 5 0.3930 0.6172 0.000 0.000 0.420 0.000 0.576 0.004
#> GSM549285 3 0.3592 0.6358 0.000 0.000 0.808 0.008 0.068 0.116
#> GSM549288 3 0.0547 0.7959 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM549292 2 0.0000 0.8769 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 3 0.0603 0.7955 0.000 0.000 0.980 0.000 0.016 0.004
#> GSM549297 3 0.0777 0.7917 0.000 0.000 0.972 0.000 0.024 0.004
#> GSM750743 1 0.0922 0.9179 0.968 0.000 0.000 0.004 0.024 0.004
#> GSM549268 5 0.3982 0.5414 0.000 0.000 0.460 0.000 0.536 0.004
#> GSM549290 6 0.2752 0.6995 0.000 0.012 0.104 0.000 0.020 0.864
#> GSM549272 2 0.2288 0.8267 0.000 0.900 0.000 0.016 0.068 0.016
#> GSM549276 3 0.6409 -0.0406 0.000 0.384 0.440 0.016 0.140 0.020
#> GSM549275 1 0.1887 0.8945 0.924 0.000 0.000 0.016 0.048 0.012
#> GSM549284 2 0.0146 0.8764 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM750737 4 0.6518 0.2644 0.096 0.000 0.000 0.548 0.192 0.164
#> GSM750740 1 0.0520 0.9219 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM750747 1 0.0146 0.9233 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM750751 3 0.6167 0.1552 0.000 0.300 0.528 0.012 0.140 0.020
#> GSM750754 3 0.2814 0.6272 0.000 0.000 0.820 0.000 0.008 0.172
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:skmeans 103 0.1082 1.63e-05 0.1804 0.000624 2
#> ATC:skmeans 98 0.0293 2.27e-04 0.0270 0.023931 3
#> ATC:skmeans 91 0.1294 4.99e-03 0.1285 0.053841 4
#> ATC:skmeans 89 0.3078 1.04e-02 0.0818 0.114250 5
#> ATC:skmeans 88 0.5382 3.69e-04 0.1451 0.212604 6
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 21168 rows and 103 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 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 0.806 0.873 0.948 0.4684 0.530 0.530
#> 3 3 1.000 0.953 0.982 0.3017 0.725 0.540
#> 4 4 0.759 0.785 0.854 0.1542 0.875 0.690
#> 5 5 0.840 0.773 0.892 0.0845 0.909 0.700
#> 6 6 0.886 0.797 0.895 0.0362 0.958 0.824
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
#> GSM549289 1 0.000 0.9474 1.000 0.000
#> GSM549291 2 0.871 0.5949 0.292 0.708
#> GSM549274 1 0.802 0.6756 0.756 0.244
#> GSM750738 1 0.141 0.9320 0.980 0.020
#> GSM750748 1 0.000 0.9474 1.000 0.000
#> GSM549240 1 0.000 0.9474 1.000 0.000
#> GSM549279 1 0.802 0.6756 0.756 0.244
#> GSM549294 2 0.000 0.9333 0.000 1.000
#> GSM549300 2 0.000 0.9333 0.000 1.000
#> GSM549303 2 0.000 0.9333 0.000 1.000
#> GSM549309 2 0.000 0.9333 0.000 1.000
#> GSM750753 2 0.000 0.9333 0.000 1.000
#> GSM750752 1 0.416 0.8752 0.916 0.084
#> GSM549304 1 0.745 0.7226 0.788 0.212
#> GSM549305 2 0.000 0.9333 0.000 1.000
#> GSM549307 2 0.000 0.9333 0.000 1.000
#> GSM549306 2 0.000 0.9333 0.000 1.000
#> GSM549308 2 0.000 0.9333 0.000 1.000
#> GSM549233 1 0.000 0.9474 1.000 0.000
#> GSM549234 1 0.000 0.9474 1.000 0.000
#> GSM549250 1 0.000 0.9474 1.000 0.000
#> GSM549287 2 0.000 0.9333 0.000 1.000
#> GSM750735 1 0.000 0.9474 1.000 0.000
#> GSM750736 1 0.000 0.9474 1.000 0.000
#> GSM750749 2 0.983 0.2657 0.424 0.576
#> GSM549230 1 0.000 0.9474 1.000 0.000
#> GSM549231 1 0.000 0.9474 1.000 0.000
#> GSM549237 1 0.000 0.9474 1.000 0.000
#> GSM549254 1 0.000 0.9474 1.000 0.000
#> GSM750734 1 0.000 0.9474 1.000 0.000
#> GSM549271 2 0.000 0.9333 0.000 1.000
#> GSM549232 1 0.000 0.9474 1.000 0.000
#> GSM549246 1 0.000 0.9474 1.000 0.000
#> GSM549248 1 0.000 0.9474 1.000 0.000
#> GSM549255 1 0.000 0.9474 1.000 0.000
#> GSM750746 1 0.000 0.9474 1.000 0.000
#> GSM549259 1 0.000 0.9474 1.000 0.000
#> GSM549269 1 0.921 0.5017 0.664 0.336
#> GSM549273 2 0.000 0.9333 0.000 1.000
#> GSM549299 2 0.402 0.8684 0.080 0.920
#> GSM549301 2 0.000 0.9333 0.000 1.000
#> GSM549310 2 0.760 0.7107 0.220 0.780
#> GSM549311 2 0.000 0.9333 0.000 1.000
#> GSM549302 2 0.833 0.6439 0.264 0.736
#> GSM549235 1 0.000 0.9474 1.000 0.000
#> GSM549245 1 0.000 0.9474 1.000 0.000
#> GSM549265 1 0.000 0.9474 1.000 0.000
#> GSM549282 2 0.000 0.9333 0.000 1.000
#> GSM549296 2 0.900 0.5487 0.316 0.684
#> GSM750739 1 0.000 0.9474 1.000 0.000
#> GSM750742 1 0.000 0.9474 1.000 0.000
#> GSM750744 1 0.000 0.9474 1.000 0.000
#> GSM750750 2 0.000 0.9333 0.000 1.000
#> GSM549242 1 0.000 0.9474 1.000 0.000
#> GSM549252 1 0.000 0.9474 1.000 0.000
#> GSM549253 1 0.000 0.9474 1.000 0.000
#> GSM549256 1 0.000 0.9474 1.000 0.000
#> GSM549257 1 0.000 0.9474 1.000 0.000
#> GSM549263 1 0.000 0.9474 1.000 0.000
#> GSM549267 2 0.653 0.7766 0.168 0.832
#> GSM750745 1 0.000 0.9474 1.000 0.000
#> GSM549239 1 0.000 0.9474 1.000 0.000
#> GSM549244 1 0.000 0.9474 1.000 0.000
#> GSM549249 1 0.000 0.9474 1.000 0.000
#> GSM549260 1 0.000 0.9474 1.000 0.000
#> GSM549266 1 0.802 0.6756 0.756 0.244
#> GSM549293 1 0.788 0.6879 0.764 0.236
#> GSM549236 1 0.000 0.9474 1.000 0.000
#> GSM549238 1 0.000 0.9474 1.000 0.000
#> GSM549251 1 0.000 0.9474 1.000 0.000
#> GSM549258 1 0.000 0.9474 1.000 0.000
#> GSM549264 1 0.000 0.9474 1.000 0.000
#> GSM549243 1 0.000 0.9474 1.000 0.000
#> GSM549262 1 0.000 0.9474 1.000 0.000
#> GSM549278 1 0.311 0.9017 0.944 0.056
#> GSM549283 1 0.981 0.2737 0.580 0.420
#> GSM549298 2 0.000 0.9333 0.000 1.000
#> GSM750741 1 0.000 0.9474 1.000 0.000
#> GSM549286 2 0.000 0.9333 0.000 1.000
#> GSM549241 1 0.000 0.9474 1.000 0.000
#> GSM549247 1 0.000 0.9474 1.000 0.000
#> GSM549261 1 0.000 0.9474 1.000 0.000
#> GSM549270 2 0.000 0.9333 0.000 1.000
#> GSM549277 2 0.000 0.9333 0.000 1.000
#> GSM549280 2 0.000 0.9333 0.000 1.000
#> GSM549281 2 0.000 0.9333 0.000 1.000
#> GSM549285 2 1.000 0.0561 0.488 0.512
#> GSM549288 2 0.000 0.9333 0.000 1.000
#> GSM549292 1 0.973 0.3264 0.596 0.404
#> GSM549295 2 0.000 0.9333 0.000 1.000
#> GSM549297 2 0.000 0.9333 0.000 1.000
#> GSM750743 1 0.000 0.9474 1.000 0.000
#> GSM549268 2 0.000 0.9333 0.000 1.000
#> GSM549290 1 0.999 0.0257 0.520 0.480
#> GSM549272 2 0.000 0.9333 0.000 1.000
#> GSM549276 2 0.000 0.9333 0.000 1.000
#> GSM549275 1 0.000 0.9474 1.000 0.000
#> GSM549284 1 0.402 0.8793 0.920 0.080
#> GSM750737 1 0.000 0.9474 1.000 0.000
#> GSM750740 1 0.000 0.9474 1.000 0.000
#> GSM750747 1 0.000 0.9474 1.000 0.000
#> GSM750751 2 0.000 0.9333 0.000 1.000
#> GSM750754 2 0.000 0.9333 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.5968 0.427 0.636 0.364 0.000
#> GSM549291 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549274 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750738 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750748 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549240 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549279 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549294 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549300 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549303 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549309 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750753 2 0.0237 0.970 0.000 0.996 0.004
#> GSM750752 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549304 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549305 2 0.0237 0.970 0.000 0.996 0.004
#> GSM549307 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549306 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549308 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549233 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549234 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549250 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549287 2 0.0237 0.970 0.000 0.996 0.004
#> GSM750735 1 0.4974 0.683 0.764 0.236 0.000
#> GSM750736 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750749 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549230 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549254 2 0.3192 0.839 0.112 0.888 0.000
#> GSM750734 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549271 2 0.0237 0.970 0.000 0.996 0.004
#> GSM549232 1 0.6062 0.369 0.616 0.384 0.000
#> GSM549246 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549248 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549255 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750746 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549259 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549269 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549273 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549299 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549301 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549310 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549311 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549302 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549245 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549265 2 0.5835 0.486 0.340 0.660 0.000
#> GSM549282 2 0.0237 0.970 0.000 0.996 0.004
#> GSM549296 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750739 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750750 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549242 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549252 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549253 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549257 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549263 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549267 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750745 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549244 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549249 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549260 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549266 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549293 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549238 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549251 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549278 2 0.5058 0.648 0.244 0.756 0.000
#> GSM549283 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549298 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750741 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549286 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549241 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549247 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549261 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549270 2 0.2356 0.908 0.000 0.928 0.072
#> GSM549277 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549280 2 0.0237 0.970 0.000 0.996 0.004
#> GSM549281 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549285 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549288 2 0.2261 0.912 0.000 0.932 0.068
#> GSM549292 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549295 3 0.0000 1.000 0.000 0.000 1.000
#> GSM549297 3 0.0000 1.000 0.000 0.000 1.000
#> GSM750743 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549268 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549290 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549272 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549276 2 0.0000 0.972 0.000 1.000 0.000
#> GSM549275 1 0.0000 0.976 1.000 0.000 0.000
#> GSM549284 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750737 1 0.1031 0.951 0.976 0.024 0.000
#> GSM750740 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.976 1.000 0.000 0.000
#> GSM750751 2 0.0000 0.972 0.000 1.000 0.000
#> GSM750754 2 0.0237 0.970 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.6655 0.668 0.192 0.184 0.000 0.624
#> GSM549291 2 0.5000 0.522 0.000 0.504 0.000 0.496
#> GSM549274 2 0.0188 0.774 0.000 0.996 0.000 0.004
#> GSM750738 2 0.2647 0.681 0.000 0.880 0.000 0.120
#> GSM750748 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549279 2 0.0469 0.772 0.000 0.988 0.000 0.012
#> GSM549294 2 0.4406 0.744 0.000 0.700 0.000 0.300
#> GSM549300 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549303 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549309 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM750753 2 0.4776 0.712 0.000 0.624 0.000 0.376
#> GSM750752 4 0.4941 0.260 0.000 0.436 0.000 0.564
#> GSM549304 2 0.0188 0.774 0.000 0.996 0.000 0.004
#> GSM549305 2 0.4776 0.712 0.000 0.624 0.000 0.376
#> GSM549307 3 0.2593 0.891 0.000 0.004 0.892 0.104
#> GSM549306 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549308 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549233 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549234 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549250 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549287 4 0.5000 -0.664 0.000 0.500 0.000 0.500
#> GSM750735 1 0.4122 0.534 0.760 0.236 0.000 0.004
#> GSM750736 1 0.3356 0.679 0.824 0.000 0.000 0.176
#> GSM750749 2 0.4814 0.740 0.008 0.676 0.000 0.316
#> GSM549230 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549231 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0469 0.948 0.988 0.000 0.000 0.012
#> GSM549254 4 0.6100 0.493 0.072 0.304 0.000 0.624
#> GSM750734 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549271 2 0.4817 0.710 0.000 0.612 0.000 0.388
#> GSM549232 4 0.6452 0.722 0.268 0.112 0.000 0.620
#> GSM549246 4 0.4817 0.703 0.388 0.000 0.000 0.612
#> GSM549248 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549255 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM750746 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549273 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549299 2 0.0188 0.776 0.000 0.996 0.000 0.004
#> GSM549301 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549310 2 0.3486 0.617 0.000 0.812 0.000 0.188
#> GSM549311 3 0.1940 0.909 0.000 0.000 0.924 0.076
#> GSM549302 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549235 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549245 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549265 4 0.6521 0.720 0.256 0.124 0.000 0.620
#> GSM549282 2 0.4855 0.706 0.000 0.600 0.000 0.400
#> GSM549296 2 0.4103 0.516 0.000 0.744 0.000 0.256
#> GSM750739 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM750750 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM549242 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549252 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549253 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549257 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549263 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549267 2 0.4072 0.603 0.000 0.748 0.000 0.252
#> GSM750745 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549244 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549249 4 0.4790 0.716 0.380 0.000 0.000 0.620
#> GSM549260 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549266 2 0.0804 0.769 0.008 0.980 0.000 0.012
#> GSM549293 2 0.0188 0.774 0.000 0.996 0.000 0.004
#> GSM549236 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549238 1 0.1867 0.871 0.928 0.000 0.000 0.072
#> GSM549251 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549264 1 0.0921 0.931 0.972 0.000 0.000 0.028
#> GSM549243 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549278 4 0.4855 0.310 0.000 0.400 0.000 0.600
#> GSM549283 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549298 3 0.0000 0.936 0.000 0.000 1.000 0.000
#> GSM750741 1 0.0469 0.948 0.988 0.000 0.000 0.012
#> GSM549286 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549241 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549247 1 0.4977 -0.342 0.540 0.000 0.000 0.460
#> GSM549261 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549270 2 0.4776 0.712 0.000 0.624 0.000 0.376
#> GSM549277 3 0.4889 0.650 0.000 0.004 0.636 0.360
#> GSM549280 2 0.4776 0.712 0.000 0.624 0.000 0.376
#> GSM549281 2 0.4500 0.743 0.000 0.684 0.000 0.316
#> GSM549285 2 0.1059 0.767 0.012 0.972 0.000 0.016
#> GSM549288 2 0.4776 0.712 0.000 0.624 0.000 0.376
#> GSM549292 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549295 3 0.1557 0.918 0.000 0.000 0.944 0.056
#> GSM549297 3 0.4632 0.713 0.000 0.004 0.688 0.308
#> GSM750743 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549268 2 0.4522 0.742 0.000 0.680 0.000 0.320
#> GSM549290 2 0.4855 0.190 0.000 0.600 0.000 0.400
#> GSM549272 2 0.0000 0.776 0.000 1.000 0.000 0.000
#> GSM549276 2 0.4406 0.744 0.000 0.700 0.000 0.300
#> GSM549275 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM549284 2 0.0188 0.774 0.000 0.996 0.000 0.004
#> GSM750737 4 0.5436 0.721 0.356 0.024 0.000 0.620
#> GSM750740 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.962 1.000 0.000 0.000 0.000
#> GSM750751 2 0.4477 0.741 0.000 0.688 0.000 0.312
#> GSM750754 2 0.4999 0.639 0.000 0.508 0.000 0.492
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.1403 0.8723 0.024 0.024 0.000 0.952 0.000
#> GSM549291 2 0.6519 0.1628 0.000 0.436 0.000 0.196 0.368
#> GSM549274 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM750738 2 0.4291 0.1627 0.000 0.536 0.000 0.464 0.000
#> GSM750748 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549279 2 0.1043 0.7869 0.000 0.960 0.000 0.000 0.040
#> GSM549294 5 0.4297 0.3134 0.000 0.472 0.000 0.000 0.528
#> GSM549300 3 0.1197 0.8875 0.000 0.000 0.952 0.048 0.000
#> GSM549303 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM549309 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM750753 5 0.3039 0.6694 0.000 0.192 0.000 0.000 0.808
#> GSM750752 4 0.3395 0.6176 0.000 0.236 0.000 0.764 0.000
#> GSM549304 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549305 5 0.4201 0.4659 0.000 0.408 0.000 0.000 0.592
#> GSM549307 5 0.5271 -0.1328 0.000 0.000 0.432 0.048 0.520
#> GSM549306 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM549308 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM549233 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549234 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549250 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549287 5 0.0290 0.6626 0.000 0.000 0.000 0.008 0.992
#> GSM750735 1 0.3109 0.7433 0.800 0.200 0.000 0.000 0.000
#> GSM750736 4 0.4306 0.0589 0.492 0.000 0.000 0.508 0.000
#> GSM750749 5 0.4440 0.3152 0.004 0.468 0.000 0.000 0.528
#> GSM549230 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549231 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549237 1 0.0703 0.9592 0.976 0.000 0.000 0.024 0.000
#> GSM549254 4 0.1197 0.8423 0.000 0.048 0.000 0.952 0.000
#> GSM750734 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549271 5 0.0000 0.6644 0.000 0.000 0.000 0.000 1.000
#> GSM549232 4 0.1282 0.8918 0.044 0.004 0.000 0.952 0.000
#> GSM549246 4 0.1410 0.8849 0.060 0.000 0.000 0.940 0.000
#> GSM549248 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549255 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM750746 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549269 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549273 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM549299 2 0.0162 0.8030 0.000 0.996 0.000 0.000 0.004
#> GSM549301 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM549310 2 0.2969 0.7276 0.000 0.852 0.000 0.128 0.020
#> GSM549311 3 0.5296 0.3052 0.000 0.000 0.480 0.048 0.472
#> GSM549302 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549235 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549265 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549282 5 0.1792 0.6475 0.000 0.084 0.000 0.000 0.916
#> GSM549296 2 0.3596 0.6672 0.000 0.784 0.000 0.200 0.016
#> GSM750739 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM750744 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM750750 3 0.0703 0.9002 0.000 0.000 0.976 0.024 0.000
#> GSM549242 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549252 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549253 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549256 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549257 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549263 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549267 2 0.5236 0.6030 0.000 0.684 0.000 0.164 0.152
#> GSM750745 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549249 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM549260 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549266 2 0.2338 0.7332 0.004 0.884 0.000 0.000 0.112
#> GSM549293 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549236 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549238 1 0.3143 0.7356 0.796 0.000 0.000 0.204 0.000
#> GSM549251 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549264 1 0.2891 0.7751 0.824 0.000 0.000 0.176 0.000
#> GSM549243 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549278 4 0.4798 0.2027 0.000 0.396 0.000 0.580 0.024
#> GSM549283 2 0.2179 0.7332 0.000 0.888 0.000 0.000 0.112
#> GSM549298 3 0.0000 0.9096 0.000 0.000 1.000 0.000 0.000
#> GSM750741 1 0.1410 0.9242 0.940 0.000 0.000 0.060 0.000
#> GSM549286 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549241 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549247 4 0.2329 0.8154 0.124 0.000 0.000 0.876 0.000
#> GSM549261 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549270 5 0.3710 0.6832 0.000 0.144 0.000 0.048 0.808
#> GSM549277 5 0.3532 0.5822 0.000 0.000 0.128 0.048 0.824
#> GSM549280 5 0.2179 0.6937 0.000 0.112 0.000 0.000 0.888
#> GSM549281 5 0.4297 0.3134 0.000 0.472 0.000 0.000 0.528
#> GSM549285 2 0.3012 0.6967 0.104 0.860 0.000 0.000 0.036
#> GSM549288 5 0.2520 0.6591 0.000 0.056 0.000 0.048 0.896
#> GSM549292 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549295 3 0.4822 0.5743 0.000 0.000 0.664 0.048 0.288
#> GSM549297 5 0.3991 0.5257 0.000 0.000 0.172 0.048 0.780
#> GSM750743 1 0.0404 0.9697 0.988 0.000 0.000 0.012 0.000
#> GSM549268 5 0.4249 0.3878 0.000 0.432 0.000 0.000 0.568
#> GSM549290 2 0.5666 0.4557 0.000 0.592 0.000 0.300 0.108
#> GSM549272 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM549276 2 0.4305 -0.3380 0.000 0.512 0.000 0.000 0.488
#> GSM549275 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM549284 2 0.0000 0.8052 0.000 1.000 0.000 0.000 0.000
#> GSM750737 4 0.1197 0.8946 0.048 0.000 0.000 0.952 0.000
#> GSM750740 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9795 1.000 0.000 0.000 0.000 0.000
#> GSM750751 5 0.4307 0.3062 0.000 0.496 0.000 0.000 0.504
#> GSM750754 5 0.0000 0.6644 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.0865 0.866 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM549291 6 0.1806 0.661 0.000 0.088 0.000 0.000 0.004 0.908
#> GSM549274 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750738 4 0.3866 0.068 0.000 0.484 0.000 0.516 0.000 0.000
#> GSM750748 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549279 2 0.1765 0.800 0.000 0.904 0.000 0.000 0.096 0.000
#> GSM549294 5 0.3543 0.738 0.000 0.032 0.000 0.000 0.768 0.200
#> GSM549300 3 0.2823 0.795 0.000 0.000 0.796 0.000 0.204 0.000
#> GSM549303 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549309 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM750753 5 0.5138 0.710 0.000 0.128 0.000 0.000 0.604 0.268
#> GSM750752 4 0.2823 0.641 0.000 0.204 0.000 0.796 0.000 0.000
#> GSM549304 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549305 5 0.4873 0.713 0.000 0.100 0.000 0.000 0.632 0.268
#> GSM549307 5 0.4634 0.330 0.000 0.000 0.284 0.000 0.644 0.072
#> GSM549306 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549308 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549233 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549234 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549250 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549287 6 0.0363 0.658 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM750735 1 0.2793 0.747 0.800 0.200 0.000 0.000 0.000 0.000
#> GSM750736 4 0.3833 0.101 0.444 0.000 0.000 0.556 0.000 0.000
#> GSM750749 5 0.3470 0.739 0.000 0.028 0.000 0.000 0.772 0.200
#> GSM549230 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549231 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549237 1 0.0790 0.944 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM549254 4 0.0865 0.866 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM750734 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549271 6 0.3409 0.180 0.000 0.000 0.000 0.000 0.300 0.700
#> GSM549232 4 0.0146 0.880 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM549246 4 0.1141 0.859 0.052 0.000 0.000 0.948 0.000 0.000
#> GSM549248 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549255 4 0.0547 0.875 0.020 0.000 0.000 0.980 0.000 0.000
#> GSM750746 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0458 0.963 0.984 0.000 0.000 0.016 0.000 0.000
#> GSM549269 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549273 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549299 2 0.0146 0.861 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM549301 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549310 2 0.4455 0.548 0.000 0.688 0.000 0.080 0.000 0.232
#> GSM549311 6 0.4475 0.473 0.000 0.000 0.088 0.000 0.220 0.692
#> GSM549302 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549235 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549245 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549265 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549282 6 0.0000 0.658 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM549296 2 0.4971 0.370 0.000 0.604 0.000 0.096 0.000 0.300
#> GSM750739 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM750742 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750744 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM750750 3 0.1387 0.898 0.000 0.000 0.932 0.000 0.068 0.000
#> GSM549242 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549252 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549253 1 0.0632 0.963 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM549256 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549257 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549263 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549267 6 0.3023 0.575 0.000 0.232 0.000 0.000 0.000 0.768
#> GSM750745 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549239 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549244 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549249 4 0.0000 0.882 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM549260 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549266 2 0.3151 0.660 0.000 0.748 0.000 0.000 0.252 0.000
#> GSM549293 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549236 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549238 1 0.2883 0.775 0.788 0.000 0.000 0.212 0.000 0.000
#> GSM549251 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549258 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549264 1 0.2941 0.761 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM549243 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549262 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549278 6 0.6125 0.144 0.000 0.312 0.000 0.336 0.000 0.352
#> GSM549283 2 0.3151 0.660 0.000 0.748 0.000 0.000 0.252 0.000
#> GSM549298 3 0.0000 0.933 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM750741 1 0.1387 0.913 0.932 0.000 0.000 0.068 0.000 0.000
#> GSM549286 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549241 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549247 4 0.0790 0.855 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM549261 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549270 5 0.2625 0.664 0.000 0.056 0.000 0.000 0.872 0.072
#> GSM549277 5 0.1444 0.667 0.000 0.000 0.000 0.000 0.928 0.072
#> GSM549280 5 0.2823 0.732 0.000 0.000 0.000 0.000 0.796 0.204
#> GSM549281 5 0.3470 0.739 0.000 0.028 0.000 0.000 0.772 0.200
#> GSM549285 2 0.3789 0.237 0.000 0.584 0.000 0.000 0.000 0.416
#> GSM549288 5 0.4530 0.287 0.000 0.044 0.000 0.000 0.600 0.356
#> GSM549292 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549295 3 0.4456 0.639 0.000 0.000 0.668 0.000 0.268 0.064
#> GSM549297 5 0.1802 0.665 0.000 0.000 0.012 0.000 0.916 0.072
#> GSM750743 1 0.1075 0.957 0.952 0.000 0.000 0.048 0.000 0.000
#> GSM549268 5 0.3470 0.739 0.000 0.028 0.000 0.000 0.772 0.200
#> GSM549290 6 0.3464 0.444 0.000 0.312 0.000 0.000 0.000 0.688
#> GSM549272 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM549276 5 0.5788 0.533 0.000 0.316 0.000 0.000 0.484 0.200
#> GSM549275 1 0.0865 0.963 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM549284 2 0.0000 0.864 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM750737 4 0.0865 0.866 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM750740 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750751 5 0.5543 0.630 0.000 0.240 0.000 0.000 0.556 0.204
#> GSM750754 6 0.0000 0.658 0.000 0.000 0.000 0.000 0.000 1.000
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:pam 98 0.5197 2.46e-05 0.826 0.0175 2
#> ATC:pam 100 0.0971 7.42e-05 0.298 0.0209 3
#> ATC:pam 97 0.3844 1.65e-04 0.165 0.0251 4
#> ATC:pam 89 0.4036 4.85e-03 0.228 0.0344 5
#> ATC:pam 93 0.4040 3.38e-04 0.054 0.0329 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.864 0.931 0.969 0.4934 0.503 0.503
#> 3 3 0.608 0.599 0.764 0.2735 0.798 0.614
#> 4 4 0.677 0.774 0.861 0.1125 0.779 0.480
#> 5 5 0.675 0.656 0.804 0.0693 0.953 0.838
#> 6 6 0.730 0.607 0.769 0.0448 0.922 0.717
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
#> GSM549289 2 0.2043 0.949 0.032 0.968
#> GSM549291 2 0.0376 0.970 0.004 0.996
#> GSM549274 2 0.0376 0.970 0.004 0.996
#> GSM750738 2 0.0000 0.971 0.000 1.000
#> GSM750748 1 0.0000 0.960 1.000 0.000
#> GSM549240 1 0.0000 0.960 1.000 0.000
#> GSM549279 2 0.1184 0.963 0.016 0.984
#> GSM549294 2 0.0376 0.970 0.004 0.996
#> GSM549300 2 0.0000 0.971 0.000 1.000
#> GSM549303 2 0.0000 0.971 0.000 1.000
#> GSM549309 2 0.0000 0.971 0.000 1.000
#> GSM750753 2 0.0000 0.971 0.000 1.000
#> GSM750752 2 0.0000 0.971 0.000 1.000
#> GSM549304 2 0.0000 0.971 0.000 1.000
#> GSM549305 2 0.0000 0.971 0.000 1.000
#> GSM549307 2 0.0000 0.971 0.000 1.000
#> GSM549306 2 0.0000 0.971 0.000 1.000
#> GSM549308 2 0.0000 0.971 0.000 1.000
#> GSM549233 1 0.0000 0.960 1.000 0.000
#> GSM549234 1 0.5059 0.882 0.888 0.112
#> GSM549250 1 0.0000 0.960 1.000 0.000
#> GSM549287 2 0.0000 0.971 0.000 1.000
#> GSM750735 2 0.1184 0.963 0.016 0.984
#> GSM750736 1 0.7602 0.732 0.780 0.220
#> GSM750749 2 0.1184 0.963 0.016 0.984
#> GSM549230 1 0.0000 0.960 1.000 0.000
#> GSM549231 1 0.0000 0.960 1.000 0.000
#> GSM549237 1 0.1843 0.944 0.972 0.028
#> GSM549254 2 0.3879 0.904 0.076 0.924
#> GSM750734 1 0.0000 0.960 1.000 0.000
#> GSM549271 2 0.0000 0.971 0.000 1.000
#> GSM549232 2 0.9286 0.473 0.344 0.656
#> GSM549246 2 0.7883 0.687 0.236 0.764
#> GSM549248 1 0.0000 0.960 1.000 0.000
#> GSM549255 1 0.9850 0.270 0.572 0.428
#> GSM750746 1 0.0000 0.960 1.000 0.000
#> GSM549259 1 0.0000 0.960 1.000 0.000
#> GSM549269 2 0.0376 0.970 0.004 0.996
#> GSM549273 2 0.0000 0.971 0.000 1.000
#> GSM549299 2 0.0376 0.970 0.004 0.996
#> GSM549301 2 0.0000 0.971 0.000 1.000
#> GSM549310 2 0.0000 0.971 0.000 1.000
#> GSM549311 2 0.0000 0.971 0.000 1.000
#> GSM549302 2 0.0000 0.971 0.000 1.000
#> GSM549235 1 0.0000 0.960 1.000 0.000
#> GSM549245 1 0.5178 0.878 0.884 0.116
#> GSM549265 2 0.9323 0.464 0.348 0.652
#> GSM549282 2 0.0000 0.971 0.000 1.000
#> GSM549296 2 0.0000 0.971 0.000 1.000
#> GSM750739 1 0.0000 0.960 1.000 0.000
#> GSM750742 1 0.0000 0.960 1.000 0.000
#> GSM750744 1 0.0000 0.960 1.000 0.000
#> GSM750750 2 0.0000 0.971 0.000 1.000
#> GSM549242 1 0.0000 0.960 1.000 0.000
#> GSM549252 1 0.6048 0.841 0.852 0.148
#> GSM549253 1 0.0000 0.960 1.000 0.000
#> GSM549256 1 0.0000 0.960 1.000 0.000
#> GSM549257 1 0.4939 0.885 0.892 0.108
#> GSM549263 1 0.0000 0.960 1.000 0.000
#> GSM549267 2 0.0000 0.971 0.000 1.000
#> GSM750745 1 0.0000 0.960 1.000 0.000
#> GSM549239 1 0.0000 0.960 1.000 0.000
#> GSM549244 1 0.5059 0.882 0.888 0.112
#> GSM549249 1 0.5629 0.861 0.868 0.132
#> GSM549260 1 0.0000 0.960 1.000 0.000
#> GSM549266 2 0.1184 0.963 0.016 0.984
#> GSM549293 2 0.0000 0.971 0.000 1.000
#> GSM549236 1 0.0000 0.960 1.000 0.000
#> GSM549238 1 0.0000 0.960 1.000 0.000
#> GSM549251 1 0.0000 0.960 1.000 0.000
#> GSM549258 1 0.0000 0.960 1.000 0.000
#> GSM549264 1 0.0376 0.958 0.996 0.004
#> GSM549243 1 0.0000 0.960 1.000 0.000
#> GSM549262 1 0.0000 0.960 1.000 0.000
#> GSM549278 2 0.1184 0.963 0.016 0.984
#> GSM549283 2 0.1184 0.963 0.016 0.984
#> GSM549298 2 0.0000 0.971 0.000 1.000
#> GSM750741 1 0.4690 0.892 0.900 0.100
#> GSM549286 2 0.0000 0.971 0.000 1.000
#> GSM549241 1 0.0000 0.960 1.000 0.000
#> GSM549247 1 0.3733 0.914 0.928 0.072
#> GSM549261 1 0.0000 0.960 1.000 0.000
#> GSM549270 2 0.0000 0.971 0.000 1.000
#> GSM549277 2 0.0000 0.971 0.000 1.000
#> GSM549280 2 0.0000 0.971 0.000 1.000
#> GSM549281 2 0.1184 0.963 0.016 0.984
#> GSM549285 2 0.1184 0.963 0.016 0.984
#> GSM549288 2 0.0000 0.971 0.000 1.000
#> GSM549292 2 0.0000 0.971 0.000 1.000
#> GSM549295 2 0.0000 0.971 0.000 1.000
#> GSM549297 2 0.0000 0.971 0.000 1.000
#> GSM750743 1 0.0000 0.960 1.000 0.000
#> GSM549268 2 0.1184 0.963 0.016 0.984
#> GSM549290 2 0.0000 0.971 0.000 1.000
#> GSM549272 2 0.0000 0.971 0.000 1.000
#> GSM549276 2 0.0000 0.971 0.000 1.000
#> GSM549275 1 0.4298 0.895 0.912 0.088
#> GSM549284 2 0.0000 0.971 0.000 1.000
#> GSM750737 2 0.9248 0.483 0.340 0.660
#> GSM750740 1 0.0000 0.960 1.000 0.000
#> GSM750747 1 0.0000 0.960 1.000 0.000
#> GSM750751 2 0.0000 0.971 0.000 1.000
#> GSM750754 2 0.0000 0.971 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 3 0.2261 0.36132 0.000 0.068 0.932
#> GSM549291 3 0.5327 0.15310 0.000 0.272 0.728
#> GSM549274 2 0.6026 0.70712 0.000 0.624 0.376
#> GSM750738 2 0.6839 0.69265 0.024 0.624 0.352
#> GSM750748 1 0.0237 0.92883 0.996 0.000 0.004
#> GSM549240 1 0.4654 0.77691 0.792 0.000 0.208
#> GSM549279 2 0.6888 0.65362 0.016 0.552 0.432
#> GSM549294 2 0.6026 0.70712 0.000 0.624 0.376
#> GSM549300 2 0.0237 0.55222 0.000 0.996 0.004
#> GSM549303 2 0.5431 0.15720 0.000 0.716 0.284
#> GSM549309 2 0.5621 0.10729 0.000 0.692 0.308
#> GSM750753 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM750752 3 0.5968 -0.00868 0.000 0.364 0.636
#> GSM549304 2 0.6057 0.72061 0.004 0.656 0.340
#> GSM549305 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549307 2 0.1163 0.57410 0.000 0.972 0.028
#> GSM549306 2 0.0592 0.55132 0.000 0.988 0.012
#> GSM549308 2 0.1753 0.53524 0.000 0.952 0.048
#> GSM549233 1 0.0892 0.92831 0.980 0.000 0.020
#> GSM549234 3 0.6180 0.01167 0.416 0.000 0.584
#> GSM549250 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549287 3 0.5785 0.08038 0.000 0.332 0.668
#> GSM750735 3 0.8714 -0.27013 0.108 0.408 0.484
#> GSM750736 1 0.6519 0.66774 0.760 0.132 0.108
#> GSM750749 2 0.6905 0.64230 0.016 0.544 0.440
#> GSM549230 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549231 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549237 1 0.4796 0.76509 0.780 0.000 0.220
#> GSM549254 3 0.3607 0.34439 0.008 0.112 0.880
#> GSM750734 1 0.0000 0.92816 1.000 0.000 0.000
#> GSM549271 3 0.5968 -0.03103 0.000 0.364 0.636
#> GSM549232 3 0.4397 0.35458 0.028 0.116 0.856
#> GSM549246 3 0.7062 0.44379 0.236 0.068 0.696
#> GSM549248 1 0.0892 0.92831 0.980 0.000 0.020
#> GSM549255 3 0.6513 0.05813 0.400 0.008 0.592
#> GSM750746 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549259 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549269 2 0.6026 0.70712 0.000 0.624 0.376
#> GSM549273 2 0.1753 0.53524 0.000 0.952 0.048
#> GSM549299 2 0.6111 0.69377 0.000 0.604 0.396
#> GSM549301 2 0.1753 0.53524 0.000 0.952 0.048
#> GSM549310 3 0.5591 0.13858 0.000 0.304 0.696
#> GSM549311 2 0.5621 0.10729 0.000 0.692 0.308
#> GSM549302 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549235 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549245 3 0.6180 0.01167 0.416 0.000 0.584
#> GSM549265 3 0.4931 0.43374 0.212 0.004 0.784
#> GSM549282 3 0.5882 0.03398 0.000 0.348 0.652
#> GSM549296 3 0.5650 0.13186 0.000 0.312 0.688
#> GSM750739 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM750742 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM750744 1 0.0592 0.92885 0.988 0.000 0.012
#> GSM750750 2 0.3038 0.47817 0.000 0.896 0.104
#> GSM549242 1 0.0892 0.92734 0.980 0.000 0.020
#> GSM549252 3 0.6225 -0.02067 0.432 0.000 0.568
#> GSM549253 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549256 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549257 1 0.6280 0.22072 0.540 0.000 0.460
#> GSM549263 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549267 3 0.5706 0.11535 0.000 0.320 0.680
#> GSM750745 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549239 1 0.0000 0.92816 1.000 0.000 0.000
#> GSM549244 3 0.6192 -0.00242 0.420 0.000 0.580
#> GSM549249 3 0.6180 0.01167 0.416 0.000 0.584
#> GSM549260 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549266 2 0.6888 0.65362 0.016 0.552 0.432
#> GSM549293 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549236 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549238 1 0.5058 0.75206 0.756 0.000 0.244
#> GSM549251 1 0.0747 0.92854 0.984 0.000 0.016
#> GSM549258 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549264 1 0.5178 0.73707 0.744 0.000 0.256
#> GSM549243 1 0.0000 0.92816 1.000 0.000 0.000
#> GSM549262 1 0.0892 0.92831 0.980 0.000 0.020
#> GSM549278 3 0.4750 0.23304 0.000 0.216 0.784
#> GSM549283 2 0.6721 0.69535 0.016 0.604 0.380
#> GSM549298 2 0.0592 0.55132 0.000 0.988 0.012
#> GSM750741 1 0.4629 0.77622 0.808 0.004 0.188
#> GSM549286 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549241 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549247 1 0.4750 0.76886 0.784 0.000 0.216
#> GSM549261 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM549270 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549277 2 0.2165 0.59398 0.000 0.936 0.064
#> GSM549280 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549281 2 0.6888 0.65362 0.016 0.552 0.432
#> GSM549285 2 0.6823 0.58160 0.012 0.504 0.484
#> GSM549288 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549292 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549295 2 0.0000 0.55359 0.000 1.000 0.000
#> GSM549297 2 0.1529 0.58126 0.000 0.960 0.040
#> GSM750743 1 0.0424 0.92755 0.992 0.000 0.008
#> GSM549268 2 0.6888 0.65362 0.016 0.552 0.432
#> GSM549290 3 0.5621 0.13031 0.000 0.308 0.692
#> GSM549272 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549276 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM549275 1 0.5219 0.77215 0.788 0.016 0.196
#> GSM549284 2 0.6839 0.69265 0.024 0.624 0.352
#> GSM750737 3 0.7777 0.21704 0.364 0.060 0.576
#> GSM750740 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM750747 1 0.0237 0.92783 0.996 0.000 0.004
#> GSM750751 2 0.5835 0.72307 0.000 0.660 0.340
#> GSM750754 3 0.5706 0.11223 0.000 0.320 0.680
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.5040 0.2244 0.008 0.364 0.000 0.628
#> GSM549291 2 0.5102 0.7540 0.000 0.732 0.048 0.220
#> GSM549274 2 0.1584 0.8443 0.000 0.952 0.036 0.012
#> GSM750738 2 0.3058 0.8050 0.056 0.900 0.024 0.020
#> GSM750748 1 0.0592 0.9462 0.984 0.000 0.000 0.016
#> GSM549240 4 0.4790 0.5649 0.380 0.000 0.000 0.620
#> GSM549279 2 0.3558 0.8376 0.044 0.880 0.052 0.024
#> GSM549294 2 0.1557 0.8396 0.000 0.944 0.056 0.000
#> GSM549300 3 0.5311 0.5929 0.000 0.328 0.648 0.024
#> GSM549303 3 0.0707 0.8348 0.000 0.020 0.980 0.000
#> GSM549309 3 0.0707 0.8348 0.000 0.020 0.980 0.000
#> GSM750753 2 0.1302 0.8421 0.000 0.956 0.044 0.000
#> GSM750752 2 0.5312 0.7443 0.000 0.712 0.052 0.236
#> GSM549304 2 0.1520 0.8291 0.000 0.956 0.024 0.020
#> GSM549305 2 0.0188 0.8422 0.000 0.996 0.004 0.000
#> GSM549307 3 0.5628 0.4024 0.000 0.420 0.556 0.024
#> GSM549306 3 0.1389 0.8311 0.000 0.048 0.952 0.000
#> GSM549308 3 0.0707 0.8348 0.000 0.020 0.980 0.000
#> GSM549233 1 0.1211 0.9430 0.960 0.000 0.000 0.040
#> GSM549234 4 0.2589 0.7377 0.116 0.000 0.000 0.884
#> GSM549250 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549287 2 0.5240 0.7582 0.000 0.740 0.072 0.188
#> GSM750735 2 0.4689 0.8116 0.084 0.824 0.052 0.040
#> GSM750736 4 0.7469 0.5465 0.368 0.180 0.000 0.452
#> GSM750749 2 0.3756 0.8366 0.044 0.872 0.052 0.032
#> GSM549230 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549231 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549237 4 0.4985 0.3538 0.468 0.000 0.000 0.532
#> GSM549254 4 0.4999 0.3275 0.012 0.328 0.000 0.660
#> GSM750734 1 0.0469 0.9452 0.988 0.000 0.000 0.012
#> GSM549271 2 0.5434 0.7707 0.000 0.740 0.128 0.132
#> GSM549232 4 0.4361 0.5580 0.020 0.208 0.000 0.772
#> GSM549246 4 0.4267 0.5787 0.024 0.188 0.000 0.788
#> GSM549248 1 0.1211 0.9430 0.960 0.000 0.000 0.040
#> GSM549255 4 0.3219 0.7363 0.112 0.020 0.000 0.868
#> GSM750746 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549269 2 0.1807 0.8426 0.000 0.940 0.052 0.008
#> GSM549273 3 0.0707 0.8348 0.000 0.020 0.980 0.000
#> GSM549299 2 0.2549 0.8425 0.024 0.916 0.056 0.004
#> GSM549301 3 0.0707 0.8348 0.000 0.020 0.980 0.000
#> GSM549310 2 0.4956 0.7491 0.000 0.732 0.036 0.232
#> GSM549311 3 0.5770 0.6949 0.000 0.148 0.712 0.140
#> GSM549302 2 0.1520 0.8291 0.000 0.956 0.024 0.020
#> GSM549235 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549245 4 0.2589 0.7377 0.116 0.000 0.000 0.884
#> GSM549265 4 0.2578 0.6916 0.052 0.036 0.000 0.912
#> GSM549282 2 0.5314 0.7605 0.000 0.740 0.084 0.176
#> GSM549296 2 0.4956 0.7491 0.000 0.732 0.036 0.232
#> GSM750739 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM750742 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM750744 1 0.0817 0.9468 0.976 0.000 0.000 0.024
#> GSM750750 3 0.2489 0.8221 0.000 0.068 0.912 0.020
#> GSM549242 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549252 4 0.2814 0.7372 0.132 0.000 0.000 0.868
#> GSM549253 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549256 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549257 4 0.4304 0.6589 0.284 0.000 0.000 0.716
#> GSM549263 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549267 2 0.5318 0.7547 0.000 0.732 0.072 0.196
#> GSM750745 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0336 0.9437 0.992 0.000 0.000 0.008
#> GSM549244 4 0.2589 0.7377 0.116 0.000 0.000 0.884
#> GSM549249 4 0.2589 0.7377 0.116 0.000 0.000 0.884
#> GSM549260 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549266 2 0.3558 0.8376 0.044 0.880 0.052 0.024
#> GSM549293 2 0.1520 0.8291 0.000 0.956 0.024 0.020
#> GSM549236 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549238 4 0.3837 0.7047 0.224 0.000 0.000 0.776
#> GSM549251 1 0.1389 0.9433 0.952 0.000 0.000 0.048
#> GSM549258 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549264 4 0.4624 0.5877 0.340 0.000 0.000 0.660
#> GSM549243 1 0.0592 0.9462 0.984 0.000 0.000 0.016
#> GSM549262 1 0.1211 0.9430 0.960 0.000 0.000 0.040
#> GSM549278 2 0.4343 0.7418 0.000 0.732 0.004 0.264
#> GSM549283 2 0.3000 0.8406 0.040 0.900 0.052 0.008
#> GSM549298 3 0.1389 0.8311 0.000 0.048 0.952 0.000
#> GSM750741 1 0.6636 -0.4075 0.476 0.032 0.028 0.464
#> GSM549286 2 0.0779 0.8372 0.000 0.980 0.016 0.004
#> GSM549241 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549247 4 0.4730 0.5894 0.364 0.000 0.000 0.636
#> GSM549261 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549270 2 0.2060 0.8373 0.000 0.932 0.052 0.016
#> GSM549277 2 0.5914 0.0721 0.008 0.556 0.412 0.024
#> GSM549280 2 0.2412 0.8318 0.000 0.908 0.084 0.008
#> GSM549281 2 0.3451 0.8373 0.044 0.884 0.052 0.020
#> GSM549285 2 0.4499 0.8315 0.044 0.836 0.052 0.068
#> GSM549288 2 0.2973 0.8225 0.000 0.884 0.096 0.020
#> GSM549292 2 0.1520 0.8291 0.000 0.956 0.024 0.020
#> GSM549295 3 0.5467 0.5421 0.000 0.364 0.612 0.024
#> GSM549297 2 0.5636 0.0184 0.000 0.552 0.424 0.024
#> GSM750743 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM549268 2 0.3451 0.8373 0.044 0.884 0.052 0.020
#> GSM549290 2 0.5318 0.7547 0.000 0.732 0.072 0.196
#> GSM549272 2 0.0376 0.8413 0.000 0.992 0.004 0.004
#> GSM549276 2 0.0188 0.8422 0.000 0.996 0.004 0.000
#> GSM549275 4 0.5560 0.5554 0.392 0.024 0.000 0.584
#> GSM549284 2 0.3058 0.8050 0.056 0.900 0.024 0.020
#> GSM750737 4 0.5926 0.6390 0.116 0.192 0.000 0.692
#> GSM750740 1 0.0000 0.9432 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0336 0.9437 0.992 0.000 0.000 0.008
#> GSM750751 2 0.0188 0.8422 0.000 0.996 0.004 0.000
#> GSM750754 2 0.5332 0.7564 0.000 0.736 0.080 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 4 0.3476 0.5375 0.000 0.176 0.000 0.804 0.020
#> GSM549291 2 0.6799 0.4855 0.000 0.560 0.076 0.272 0.092
#> GSM549274 2 0.3774 -0.2306 0.000 0.704 0.000 0.000 0.296
#> GSM750738 5 0.4297 0.8561 0.000 0.472 0.000 0.000 0.528
#> GSM750748 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549240 4 0.6219 0.5702 0.292 0.000 0.000 0.532 0.176
#> GSM549279 2 0.4275 0.5388 0.000 0.696 0.000 0.020 0.284
#> GSM549294 2 0.0000 0.5539 0.000 1.000 0.000 0.000 0.000
#> GSM549300 3 0.4747 0.1517 0.000 0.484 0.500 0.000 0.016
#> GSM549303 3 0.0000 0.7639 0.000 0.000 1.000 0.000 0.000
#> GSM549309 3 0.0000 0.7639 0.000 0.000 1.000 0.000 0.000
#> GSM750753 2 0.0000 0.5539 0.000 1.000 0.000 0.000 0.000
#> GSM750752 5 0.8221 0.0741 0.000 0.308 0.172 0.156 0.364
#> GSM549304 5 0.4297 0.8561 0.000 0.472 0.000 0.000 0.528
#> GSM549305 2 0.0880 0.5385 0.000 0.968 0.000 0.000 0.032
#> GSM549307 2 0.4620 0.1868 0.000 0.592 0.392 0.000 0.016
#> GSM549306 3 0.1043 0.7557 0.000 0.040 0.960 0.000 0.000
#> GSM549308 3 0.0000 0.7639 0.000 0.000 1.000 0.000 0.000
#> GSM549233 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549234 4 0.2020 0.7398 0.100 0.000 0.000 0.900 0.000
#> GSM549250 1 0.0290 0.9589 0.992 0.000 0.000 0.008 0.000
#> GSM549287 2 0.7178 0.5142 0.000 0.560 0.172 0.172 0.096
#> GSM750735 2 0.6846 0.3595 0.012 0.528 0.020 0.136 0.304
#> GSM750736 4 0.8032 0.5541 0.264 0.120 0.000 0.416 0.200
#> GSM750749 2 0.4790 0.5299 0.000 0.672 0.020 0.016 0.292
#> GSM549230 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549231 1 0.0609 0.9494 0.980 0.000 0.000 0.020 0.000
#> GSM549237 4 0.5049 0.2840 0.480 0.000 0.000 0.488 0.032
#> GSM549254 4 0.3438 0.5448 0.000 0.172 0.000 0.808 0.020
#> GSM750734 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549271 2 0.6037 0.5421 0.000 0.636 0.172 0.172 0.020
#> GSM549232 4 0.2677 0.6482 0.016 0.112 0.000 0.872 0.000
#> GSM549246 4 0.2773 0.6197 0.000 0.112 0.000 0.868 0.020
#> GSM549248 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549255 4 0.0880 0.7069 0.032 0.000 0.000 0.968 0.000
#> GSM750746 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.1121 0.9363 0.956 0.000 0.000 0.000 0.044
#> GSM549269 2 0.1792 0.4795 0.000 0.916 0.000 0.000 0.084
#> GSM549273 3 0.0000 0.7639 0.000 0.000 1.000 0.000 0.000
#> GSM549299 2 0.0794 0.5616 0.000 0.972 0.000 0.000 0.028
#> GSM549301 3 0.0000 0.7639 0.000 0.000 1.000 0.000 0.000
#> GSM549310 2 0.7057 0.5204 0.000 0.572 0.172 0.168 0.088
#> GSM549311 3 0.5912 0.4066 0.000 0.284 0.616 0.064 0.036
#> GSM549302 2 0.4302 -0.8119 0.000 0.520 0.000 0.000 0.480
#> GSM549235 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.1792 0.7361 0.084 0.000 0.000 0.916 0.000
#> GSM549265 4 0.0671 0.6942 0.016 0.004 0.000 0.980 0.000
#> GSM549282 2 0.6992 0.5221 0.000 0.576 0.172 0.172 0.080
#> GSM549296 2 0.7103 0.5184 0.000 0.568 0.172 0.168 0.092
#> GSM750739 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM750744 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM750750 3 0.4299 0.5907 0.000 0.220 0.744 0.008 0.028
#> GSM549242 1 0.0703 0.9467 0.976 0.000 0.000 0.024 0.000
#> GSM549252 4 0.1851 0.7368 0.088 0.000 0.000 0.912 0.000
#> GSM549253 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549256 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549257 4 0.3534 0.6882 0.256 0.000 0.000 0.744 0.000
#> GSM549263 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549267 2 0.7178 0.5139 0.000 0.560 0.172 0.172 0.096
#> GSM750745 1 0.0404 0.9563 0.988 0.000 0.000 0.000 0.012
#> GSM549239 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549244 4 0.2377 0.7374 0.128 0.000 0.000 0.872 0.000
#> GSM549249 4 0.2074 0.7401 0.104 0.000 0.000 0.896 0.000
#> GSM549260 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549266 2 0.4227 0.5356 0.000 0.692 0.000 0.016 0.292
#> GSM549293 5 0.4297 0.8561 0.000 0.472 0.000 0.000 0.528
#> GSM549236 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549238 4 0.3508 0.6903 0.252 0.000 0.000 0.748 0.000
#> GSM549251 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549258 1 0.1740 0.9183 0.932 0.000 0.000 0.012 0.056
#> GSM549264 4 0.4894 0.5486 0.352 0.000 0.000 0.612 0.036
#> GSM549243 1 0.0162 0.9609 0.996 0.000 0.000 0.004 0.000
#> GSM549262 1 0.0000 0.9603 1.000 0.000 0.000 0.000 0.000
#> GSM549278 2 0.4551 0.4646 0.000 0.616 0.000 0.368 0.016
#> GSM549283 2 0.2777 0.5655 0.000 0.864 0.000 0.016 0.120
#> GSM549298 3 0.1043 0.7557 0.000 0.040 0.960 0.000 0.000
#> GSM750741 1 0.6951 -0.3152 0.424 0.012 0.000 0.340 0.224
#> GSM549286 2 0.1197 0.5189 0.000 0.952 0.000 0.000 0.048
#> GSM549241 1 0.1502 0.9246 0.940 0.000 0.000 0.004 0.056
#> GSM549247 4 0.6301 0.5984 0.252 0.000 0.000 0.532 0.216
#> GSM549261 1 0.1502 0.9246 0.940 0.000 0.000 0.004 0.056
#> GSM549270 2 0.0794 0.5453 0.000 0.972 0.000 0.000 0.028
#> GSM549277 2 0.2172 0.5109 0.000 0.908 0.076 0.000 0.016
#> GSM549280 2 0.0000 0.5539 0.000 1.000 0.000 0.000 0.000
#> GSM549281 2 0.4227 0.5356 0.000 0.692 0.000 0.016 0.292
#> GSM549285 2 0.5687 0.5364 0.000 0.676 0.020 0.144 0.160
#> GSM549288 2 0.2624 0.5701 0.000 0.872 0.116 0.000 0.012
#> GSM549292 5 0.4300 0.8512 0.000 0.476 0.000 0.000 0.524
#> GSM549295 3 0.4747 0.1383 0.000 0.488 0.496 0.000 0.016
#> GSM549297 2 0.3381 0.3692 0.000 0.808 0.176 0.000 0.016
#> GSM750743 1 0.1943 0.9099 0.924 0.000 0.000 0.020 0.056
#> GSM549268 2 0.4227 0.5356 0.000 0.692 0.000 0.016 0.292
#> GSM549290 2 0.7178 0.5139 0.000 0.560 0.172 0.172 0.096
#> GSM549272 2 0.0963 0.5342 0.000 0.964 0.000 0.000 0.036
#> GSM549276 2 0.0880 0.5385 0.000 0.968 0.000 0.000 0.032
#> GSM549275 4 0.6235 0.5398 0.324 0.004 0.000 0.528 0.144
#> GSM549284 5 0.4297 0.8561 0.000 0.472 0.000 0.000 0.528
#> GSM750737 4 0.3010 0.6543 0.020 0.100 0.000 0.868 0.012
#> GSM750740 1 0.1341 0.9276 0.944 0.000 0.000 0.000 0.056
#> GSM750747 1 0.0579 0.9549 0.984 0.000 0.000 0.008 0.008
#> GSM750751 2 0.0880 0.5385 0.000 0.968 0.000 0.000 0.032
#> GSM750754 2 0.7263 0.5083 0.000 0.552 0.172 0.172 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 4 0.4003 0.51942 0.020 0.020 0.000 0.736 0.000 0.224
#> GSM549291 2 0.6471 -0.77297 0.000 0.368 0.016 0.312 0.000 0.304
#> GSM549274 5 0.3864 0.02586 0.000 0.480 0.000 0.000 0.520 0.000
#> GSM750738 5 0.0777 0.83058 0.000 0.024 0.000 0.004 0.972 0.000
#> GSM750748 1 0.0363 0.91746 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM549240 4 0.5488 0.34722 0.372 0.012 0.000 0.536 0.008 0.072
#> GSM549279 2 0.3245 0.44864 0.000 0.764 0.000 0.000 0.008 0.228
#> GSM549294 2 0.2003 0.52313 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM549300 2 0.6114 0.15082 0.000 0.504 0.256 0.000 0.016 0.224
#> GSM549303 3 0.1327 0.81936 0.000 0.000 0.936 0.000 0.000 0.064
#> GSM549309 3 0.1387 0.81863 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM750753 2 0.2135 0.52173 0.000 0.872 0.000 0.000 0.128 0.000
#> GSM750752 5 0.6777 0.12902 0.000 0.188 0.008 0.124 0.544 0.136
#> GSM549304 5 0.0632 0.83353 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM549305 2 0.3515 0.48020 0.000 0.676 0.000 0.000 0.324 0.000
#> GSM549307 2 0.6021 0.32062 0.000 0.568 0.172 0.000 0.036 0.224
#> GSM549306 3 0.2100 0.79386 0.000 0.112 0.884 0.000 0.000 0.004
#> GSM549308 3 0.0000 0.83590 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549233 1 0.0508 0.91547 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM549234 4 0.1003 0.69323 0.020 0.000 0.000 0.964 0.000 0.016
#> GSM549250 1 0.1296 0.90212 0.952 0.000 0.000 0.032 0.004 0.012
#> GSM549287 6 0.6317 0.88173 0.000 0.372 0.016 0.216 0.000 0.396
#> GSM750735 2 0.5924 0.30783 0.056 0.632 0.004 0.096 0.008 0.204
#> GSM750736 1 0.7133 -0.28780 0.368 0.188 0.000 0.364 0.008 0.072
#> GSM750749 2 0.3437 0.43422 0.000 0.752 0.004 0.000 0.008 0.236
#> GSM549230 1 0.0870 0.91477 0.972 0.000 0.000 0.012 0.004 0.012
#> GSM549231 1 0.0603 0.91374 0.980 0.000 0.000 0.016 0.004 0.000
#> GSM549237 1 0.4924 -0.00292 0.524 0.004 0.000 0.428 0.008 0.036
#> GSM549254 4 0.4074 0.52451 0.020 0.028 0.000 0.740 0.000 0.212
#> GSM750734 1 0.0000 0.91614 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549271 2 0.5696 -0.01116 0.000 0.632 0.076 0.208 0.000 0.084
#> GSM549232 4 0.3839 0.50630 0.020 0.172 0.000 0.776 0.000 0.032
#> GSM549246 4 0.3514 0.57050 0.020 0.000 0.000 0.768 0.004 0.208
#> GSM549248 1 0.0146 0.91616 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM549255 4 0.1478 0.68957 0.020 0.000 0.000 0.944 0.004 0.032
#> GSM750746 1 0.0000 0.91614 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.1333 0.89136 0.944 0.000 0.000 0.000 0.008 0.048
#> GSM549269 2 0.3266 0.49217 0.000 0.728 0.000 0.000 0.272 0.000
#> GSM549273 3 0.0146 0.83611 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM549299 2 0.4680 0.23231 0.000 0.680 0.000 0.000 0.120 0.200
#> GSM549301 3 0.0000 0.83590 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM549310 6 0.7037 0.80792 0.000 0.344 0.008 0.136 0.092 0.420
#> GSM549311 3 0.6953 0.23662 0.000 0.172 0.468 0.108 0.000 0.252
#> GSM549302 5 0.1444 0.79084 0.000 0.072 0.000 0.000 0.928 0.000
#> GSM549235 1 0.0000 0.91614 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.0909 0.69320 0.020 0.000 0.000 0.968 0.000 0.012
#> GSM549265 4 0.1478 0.68929 0.020 0.000 0.000 0.944 0.004 0.032
#> GSM549282 2 0.5955 -0.43400 0.000 0.540 0.016 0.216 0.000 0.228
#> GSM549296 6 0.6984 0.84002 0.000 0.340 0.008 0.152 0.076 0.424
#> GSM750739 1 0.0000 0.91614 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0870 0.91477 0.972 0.000 0.000 0.012 0.004 0.012
#> GSM750744 1 0.0260 0.91663 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM750750 3 0.4455 0.62301 0.000 0.160 0.712 0.000 0.000 0.128
#> GSM549242 1 0.0870 0.91477 0.972 0.000 0.000 0.012 0.004 0.012
#> GSM549252 4 0.1924 0.69357 0.048 0.000 0.000 0.920 0.004 0.028
#> GSM549253 1 0.0870 0.91477 0.972 0.000 0.000 0.012 0.004 0.012
#> GSM549256 1 0.0964 0.91312 0.968 0.000 0.000 0.016 0.004 0.012
#> GSM549257 4 0.3154 0.62751 0.184 0.000 0.000 0.800 0.004 0.012
#> GSM549263 1 0.0870 0.91477 0.972 0.000 0.000 0.012 0.004 0.012
#> GSM549267 6 0.6286 0.89350 0.000 0.336 0.016 0.216 0.000 0.432
#> GSM750745 1 0.0260 0.91534 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM549239 1 0.0363 0.91746 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM549244 4 0.1003 0.69323 0.020 0.000 0.000 0.964 0.000 0.016
#> GSM549249 4 0.0909 0.69320 0.020 0.000 0.000 0.968 0.000 0.012
#> GSM549260 1 0.0363 0.91746 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM549266 2 0.3431 0.44379 0.000 0.756 0.000 0.000 0.016 0.228
#> GSM549293 5 0.0632 0.83353 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM549236 1 0.0508 0.91716 0.984 0.000 0.000 0.012 0.004 0.000
#> GSM549238 4 0.4092 0.45568 0.344 0.000 0.000 0.636 0.000 0.020
#> GSM549251 1 0.0508 0.91716 0.984 0.000 0.000 0.012 0.004 0.000
#> GSM549258 1 0.2123 0.86713 0.908 0.000 0.000 0.020 0.008 0.064
#> GSM549264 4 0.4896 0.37821 0.372 0.004 0.000 0.572 0.004 0.048
#> GSM549243 1 0.0508 0.91736 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM549262 1 0.0000 0.91614 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549278 2 0.6088 -0.72759 0.000 0.380 0.000 0.340 0.000 0.280
#> GSM549283 2 0.3566 0.49574 0.000 0.788 0.000 0.000 0.056 0.156
#> GSM549298 3 0.2100 0.79386 0.000 0.112 0.884 0.000 0.000 0.004
#> GSM750741 1 0.6076 0.10419 0.524 0.052 0.000 0.348 0.012 0.064
#> GSM549286 2 0.3847 0.33228 0.000 0.544 0.000 0.000 0.456 0.000
#> GSM549241 1 0.2036 0.87010 0.912 0.000 0.000 0.016 0.008 0.064
#> GSM549247 4 0.5524 0.38759 0.352 0.016 0.000 0.552 0.008 0.072
#> GSM549261 1 0.1841 0.87556 0.920 0.000 0.000 0.008 0.008 0.064
#> GSM549270 2 0.4253 0.51876 0.000 0.704 0.000 0.000 0.232 0.064
#> GSM549277 2 0.5371 0.41569 0.000 0.636 0.024 0.000 0.116 0.224
#> GSM549280 2 0.2288 0.51975 0.000 0.876 0.004 0.000 0.116 0.004
#> GSM549281 2 0.3431 0.44379 0.000 0.756 0.000 0.000 0.016 0.228
#> GSM549285 2 0.4542 0.39699 0.000 0.684 0.004 0.072 0.000 0.240
#> GSM549288 2 0.4468 0.47960 0.000 0.760 0.060 0.000 0.060 0.120
#> GSM549292 5 0.0632 0.83353 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM549295 2 0.6314 0.21775 0.000 0.516 0.224 0.000 0.036 0.224
#> GSM549297 2 0.6111 0.35997 0.000 0.584 0.076 0.000 0.116 0.224
#> GSM750743 1 0.2206 0.86369 0.904 0.000 0.000 0.024 0.008 0.064
#> GSM549268 2 0.3431 0.44379 0.000 0.756 0.000 0.000 0.016 0.228
#> GSM549290 6 0.6280 0.89095 0.000 0.332 0.016 0.216 0.000 0.436
#> GSM549272 2 0.3782 0.41377 0.000 0.588 0.000 0.000 0.412 0.000
#> GSM549276 2 0.3482 0.48498 0.000 0.684 0.000 0.000 0.316 0.000
#> GSM549275 4 0.5451 0.34278 0.376 0.008 0.000 0.532 0.008 0.076
#> GSM549284 5 0.0632 0.83353 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM750737 4 0.4172 0.62315 0.092 0.004 0.000 0.760 0.004 0.140
#> GSM750740 1 0.1841 0.87556 0.920 0.000 0.000 0.008 0.008 0.064
#> GSM750747 1 0.0436 0.91508 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM750751 2 0.3175 0.50925 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM750754 6 0.6317 0.88015 0.000 0.372 0.016 0.216 0.000 0.396
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:mclust 99 0.0392 0.000286 0.4167 0.00277 2
#> ATC:mclust 74 0.0819 0.000234 0.0101 0.00367 3
#> ATC:mclust 96 0.2538 0.000668 0.4734 0.05020 4
#> ATC:mclust 89 0.0160 0.002489 0.1789 0.26737 5
#> ATC:mclust 68 0.0392 0.001049 0.0246 0.62787 6
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 21168 rows and 103 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.939 0.923 0.968 0.4944 0.512 0.512
#> 3 3 0.864 0.851 0.942 0.2955 0.787 0.606
#> 4 4 0.682 0.771 0.869 0.1392 0.832 0.572
#> 5 5 0.694 0.637 0.815 0.0505 0.957 0.846
#> 6 6 0.710 0.665 0.804 0.0280 0.961 0.849
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
#> GSM549289 1 0.0000 0.948 1.000 0.000
#> GSM549291 2 0.0000 0.994 0.000 1.000
#> GSM549274 1 0.9795 0.352 0.584 0.416
#> GSM750738 1 0.0000 0.948 1.000 0.000
#> GSM750748 1 0.0000 0.948 1.000 0.000
#> GSM549240 1 0.0000 0.948 1.000 0.000
#> GSM549279 1 0.7815 0.698 0.768 0.232
#> GSM549294 2 0.0000 0.994 0.000 1.000
#> GSM549300 2 0.0000 0.994 0.000 1.000
#> GSM549303 2 0.0000 0.994 0.000 1.000
#> GSM549309 2 0.0000 0.994 0.000 1.000
#> GSM750753 2 0.0000 0.994 0.000 1.000
#> GSM750752 1 0.9608 0.429 0.616 0.384
#> GSM549304 1 0.9933 0.251 0.548 0.452
#> GSM549305 2 0.0000 0.994 0.000 1.000
#> GSM549307 2 0.0000 0.994 0.000 1.000
#> GSM549306 2 0.0000 0.994 0.000 1.000
#> GSM549308 2 0.0000 0.994 0.000 1.000
#> GSM549233 1 0.0000 0.948 1.000 0.000
#> GSM549234 1 0.0000 0.948 1.000 0.000
#> GSM549250 1 0.0000 0.948 1.000 0.000
#> GSM549287 2 0.0000 0.994 0.000 1.000
#> GSM750735 1 0.0000 0.948 1.000 0.000
#> GSM750736 1 0.0000 0.948 1.000 0.000
#> GSM750749 2 0.0000 0.994 0.000 1.000
#> GSM549230 1 0.0000 0.948 1.000 0.000
#> GSM549231 1 0.0000 0.948 1.000 0.000
#> GSM549237 1 0.0000 0.948 1.000 0.000
#> GSM549254 1 0.0000 0.948 1.000 0.000
#> GSM750734 1 0.0000 0.948 1.000 0.000
#> GSM549271 2 0.0000 0.994 0.000 1.000
#> GSM549232 1 0.0000 0.948 1.000 0.000
#> GSM549246 1 0.0000 0.948 1.000 0.000
#> GSM549248 1 0.0000 0.948 1.000 0.000
#> GSM549255 1 0.0000 0.948 1.000 0.000
#> GSM750746 1 0.0000 0.948 1.000 0.000
#> GSM549259 1 0.0000 0.948 1.000 0.000
#> GSM549269 2 0.2423 0.954 0.040 0.960
#> GSM549273 2 0.0000 0.994 0.000 1.000
#> GSM549299 2 0.0000 0.994 0.000 1.000
#> GSM549301 2 0.0000 0.994 0.000 1.000
#> GSM549310 2 0.0000 0.994 0.000 1.000
#> GSM549311 2 0.0000 0.994 0.000 1.000
#> GSM549302 2 0.0000 0.994 0.000 1.000
#> GSM549235 1 0.0000 0.948 1.000 0.000
#> GSM549245 1 0.0000 0.948 1.000 0.000
#> GSM549265 1 0.0000 0.948 1.000 0.000
#> GSM549282 2 0.0000 0.994 0.000 1.000
#> GSM549296 2 0.0376 0.990 0.004 0.996
#> GSM750739 1 0.0000 0.948 1.000 0.000
#> GSM750742 1 0.0000 0.948 1.000 0.000
#> GSM750744 1 0.0000 0.948 1.000 0.000
#> GSM750750 2 0.0000 0.994 0.000 1.000
#> GSM549242 1 0.0000 0.948 1.000 0.000
#> GSM549252 1 0.0000 0.948 1.000 0.000
#> GSM549253 1 0.0000 0.948 1.000 0.000
#> GSM549256 1 0.0000 0.948 1.000 0.000
#> GSM549257 1 0.0000 0.948 1.000 0.000
#> GSM549263 1 0.0000 0.948 1.000 0.000
#> GSM549267 2 0.0000 0.994 0.000 1.000
#> GSM750745 1 0.0000 0.948 1.000 0.000
#> GSM549239 1 0.0000 0.948 1.000 0.000
#> GSM549244 1 0.0000 0.948 1.000 0.000
#> GSM549249 1 0.0000 0.948 1.000 0.000
#> GSM549260 1 0.0000 0.948 1.000 0.000
#> GSM549266 1 0.8861 0.588 0.696 0.304
#> GSM549293 1 0.9996 0.135 0.512 0.488
#> GSM549236 1 0.0000 0.948 1.000 0.000
#> GSM549238 1 0.0000 0.948 1.000 0.000
#> GSM549251 1 0.0000 0.948 1.000 0.000
#> GSM549258 1 0.0000 0.948 1.000 0.000
#> GSM549264 1 0.0000 0.948 1.000 0.000
#> GSM549243 1 0.0000 0.948 1.000 0.000
#> GSM549262 1 0.0000 0.948 1.000 0.000
#> GSM549278 1 0.9754 0.373 0.592 0.408
#> GSM549283 2 0.0000 0.994 0.000 1.000
#> GSM549298 2 0.0000 0.994 0.000 1.000
#> GSM750741 1 0.0000 0.948 1.000 0.000
#> GSM549286 2 0.0000 0.994 0.000 1.000
#> GSM549241 1 0.0000 0.948 1.000 0.000
#> GSM549247 1 0.0000 0.948 1.000 0.000
#> GSM549261 1 0.0000 0.948 1.000 0.000
#> GSM549270 2 0.0000 0.994 0.000 1.000
#> GSM549277 2 0.0000 0.994 0.000 1.000
#> GSM549280 2 0.0000 0.994 0.000 1.000
#> GSM549281 2 0.0000 0.994 0.000 1.000
#> GSM549285 2 0.0000 0.994 0.000 1.000
#> GSM549288 2 0.0000 0.994 0.000 1.000
#> GSM549292 2 0.3584 0.922 0.068 0.932
#> GSM549295 2 0.0000 0.994 0.000 1.000
#> GSM549297 2 0.0000 0.994 0.000 1.000
#> GSM750743 1 0.0000 0.948 1.000 0.000
#> GSM549268 2 0.0000 0.994 0.000 1.000
#> GSM549290 2 0.5059 0.866 0.112 0.888
#> GSM549272 2 0.0000 0.994 0.000 1.000
#> GSM549276 2 0.0000 0.994 0.000 1.000
#> GSM549275 1 0.0000 0.948 1.000 0.000
#> GSM549284 1 0.9460 0.473 0.636 0.364
#> GSM750737 1 0.0000 0.948 1.000 0.000
#> GSM750740 1 0.0000 0.948 1.000 0.000
#> GSM750747 1 0.0000 0.948 1.000 0.000
#> GSM750751 2 0.0000 0.994 0.000 1.000
#> GSM750754 2 0.0000 0.994 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM549289 1 0.0237 0.9551 0.996 0.000 0.004
#> GSM549291 3 0.0592 0.9405 0.012 0.000 0.988
#> GSM549274 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM750738 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM750748 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549240 1 0.5291 0.6092 0.732 0.268 0.000
#> GSM549279 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549294 2 0.6215 0.2281 0.000 0.572 0.428
#> GSM549300 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549303 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549309 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM750753 3 0.6309 -0.0309 0.000 0.496 0.504
#> GSM750752 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549304 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549305 2 0.1289 0.8427 0.000 0.968 0.032
#> GSM549307 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549306 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549308 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549233 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549234 1 0.5178 0.6461 0.744 0.256 0.000
#> GSM549250 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549287 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM750735 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750736 2 0.1753 0.8283 0.048 0.952 0.000
#> GSM750749 3 0.0592 0.9404 0.012 0.000 0.988
#> GSM549230 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549231 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549237 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549254 1 0.0424 0.9533 0.992 0.008 0.000
#> GSM750734 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549271 3 0.1289 0.9257 0.000 0.032 0.968
#> GSM549232 2 0.6305 0.0154 0.484 0.516 0.000
#> GSM549246 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549248 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549255 1 0.0747 0.9476 0.984 0.016 0.000
#> GSM750746 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549259 1 0.0237 0.9558 0.996 0.004 0.000
#> GSM549269 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549273 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549299 2 0.6062 0.3484 0.000 0.616 0.384
#> GSM549301 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549310 2 0.4555 0.6908 0.000 0.800 0.200
#> GSM549311 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549302 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549235 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549245 2 0.6062 0.3391 0.384 0.616 0.000
#> GSM549265 1 0.6062 0.3689 0.616 0.384 0.000
#> GSM549282 3 0.0424 0.9456 0.000 0.008 0.992
#> GSM549296 2 0.4178 0.7278 0.000 0.828 0.172
#> GSM750739 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750742 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750744 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750750 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549242 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549252 1 0.0747 0.9468 0.984 0.016 0.000
#> GSM549253 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549256 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549257 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549263 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549267 3 0.0424 0.9459 0.000 0.008 0.992
#> GSM750745 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549239 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549244 1 0.2878 0.8698 0.904 0.096 0.000
#> GSM549249 1 0.0424 0.9537 0.992 0.008 0.000
#> GSM549260 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549266 2 0.8972 0.4713 0.200 0.564 0.236
#> GSM549293 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549236 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549238 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549251 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549258 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549264 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549243 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549262 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549278 1 0.6295 0.0984 0.528 0.000 0.472
#> GSM549283 2 0.0237 0.8559 0.000 0.996 0.004
#> GSM549298 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM750741 1 0.0592 0.9507 0.988 0.012 0.000
#> GSM549286 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549241 1 0.1289 0.9329 0.968 0.032 0.000
#> GSM549247 2 0.5810 0.4927 0.336 0.664 0.000
#> GSM549261 1 0.0424 0.9533 0.992 0.008 0.000
#> GSM549270 3 0.6260 0.1420 0.000 0.448 0.552
#> GSM549277 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549280 3 0.0424 0.9456 0.000 0.008 0.992
#> GSM549281 3 0.1289 0.9265 0.000 0.032 0.968
#> GSM549285 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549288 3 0.1411 0.9236 0.000 0.036 0.964
#> GSM549292 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549295 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549297 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM750743 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM549268 3 0.0000 0.9498 0.000 0.000 1.000
#> GSM549290 3 0.3619 0.7784 0.136 0.000 0.864
#> GSM549272 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM549276 2 0.0592 0.8524 0.000 0.988 0.012
#> GSM549275 1 0.5016 0.6563 0.760 0.240 0.000
#> GSM549284 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM750737 1 0.0237 0.9558 0.996 0.004 0.000
#> GSM750740 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750747 1 0.0000 0.9579 1.000 0.000 0.000
#> GSM750751 2 0.1643 0.8350 0.000 0.956 0.044
#> GSM750754 3 0.0000 0.9498 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM549289 4 0.4469 0.7107 0.080 0.000 0.112 0.808
#> GSM549291 4 0.5000 -0.0479 0.000 0.000 0.496 0.504
#> GSM549274 2 0.1474 0.7944 0.000 0.948 0.000 0.052
#> GSM750738 2 0.3569 0.6732 0.000 0.804 0.000 0.196
#> GSM750748 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549240 1 0.3606 0.7969 0.840 0.140 0.000 0.020
#> GSM549279 2 0.3791 0.7899 0.016 0.844 0.128 0.012
#> GSM549294 2 0.4018 0.7149 0.000 0.772 0.224 0.004
#> GSM549300 3 0.0592 0.8718 0.000 0.016 0.984 0.000
#> GSM549303 3 0.1716 0.8520 0.000 0.000 0.936 0.064
#> GSM549309 3 0.2281 0.8306 0.000 0.000 0.904 0.096
#> GSM750753 2 0.4193 0.6670 0.000 0.732 0.268 0.000
#> GSM750752 4 0.4543 0.5284 0.000 0.324 0.000 0.676
#> GSM549304 2 0.2149 0.7840 0.000 0.912 0.000 0.088
#> GSM549305 2 0.2676 0.8095 0.000 0.896 0.092 0.012
#> GSM549307 3 0.1004 0.8717 0.000 0.024 0.972 0.004
#> GSM549306 3 0.0657 0.8725 0.000 0.004 0.984 0.012
#> GSM549308 3 0.0817 0.8700 0.000 0.000 0.976 0.024
#> GSM549233 1 0.3942 0.6788 0.764 0.000 0.000 0.236
#> GSM549234 4 0.5470 0.7106 0.168 0.100 0.000 0.732
#> GSM549250 1 0.4134 0.6286 0.740 0.000 0.000 0.260
#> GSM549287 4 0.4992 0.0441 0.000 0.000 0.476 0.524
#> GSM750735 1 0.1953 0.8880 0.940 0.044 0.012 0.004
#> GSM750736 2 0.4769 0.5075 0.308 0.684 0.000 0.008
#> GSM750749 3 0.5733 0.6122 0.208 0.064 0.716 0.012
#> GSM549230 1 0.0469 0.9345 0.988 0.000 0.000 0.012
#> GSM549231 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549237 1 0.0469 0.9346 0.988 0.000 0.000 0.012
#> GSM549254 4 0.3301 0.7359 0.048 0.000 0.076 0.876
#> GSM750734 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549271 3 0.3550 0.8352 0.000 0.096 0.860 0.044
#> GSM549232 4 0.5764 0.5848 0.024 0.292 0.020 0.664
#> GSM549246 4 0.4088 0.7330 0.140 0.000 0.040 0.820
#> GSM549248 1 0.0336 0.9354 0.992 0.000 0.000 0.008
#> GSM549255 4 0.3464 0.7469 0.108 0.032 0.000 0.860
#> GSM750746 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549259 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549269 2 0.1209 0.8081 0.000 0.964 0.032 0.004
#> GSM549273 3 0.0707 0.8710 0.000 0.000 0.980 0.020
#> GSM549299 2 0.5994 0.5524 0.000 0.636 0.296 0.068
#> GSM549301 3 0.0592 0.8717 0.000 0.000 0.984 0.016
#> GSM549310 4 0.2722 0.7245 0.000 0.032 0.064 0.904
#> GSM549311 3 0.3172 0.7767 0.000 0.000 0.840 0.160
#> GSM549302 2 0.1474 0.7944 0.000 0.948 0.000 0.052
#> GSM549235 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549245 4 0.5392 0.6710 0.072 0.204 0.000 0.724
#> GSM549265 4 0.4323 0.6840 0.028 0.184 0.000 0.788
#> GSM549282 3 0.4452 0.6218 0.000 0.008 0.732 0.260
#> GSM549296 4 0.3693 0.7231 0.000 0.072 0.072 0.856
#> GSM750739 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM750742 1 0.0336 0.9354 0.992 0.000 0.000 0.008
#> GSM750744 1 0.0469 0.9339 0.988 0.000 0.000 0.012
#> GSM750750 3 0.1118 0.8657 0.000 0.000 0.964 0.036
#> GSM549242 1 0.2081 0.8926 0.916 0.000 0.000 0.084
#> GSM549252 4 0.4387 0.6896 0.236 0.012 0.000 0.752
#> GSM549253 1 0.2530 0.8560 0.888 0.000 0.000 0.112
#> GSM549256 1 0.4643 0.4379 0.656 0.000 0.000 0.344
#> GSM549257 4 0.4967 0.2508 0.452 0.000 0.000 0.548
#> GSM549263 1 0.0817 0.9275 0.976 0.000 0.000 0.024
#> GSM549267 4 0.4137 0.6267 0.000 0.012 0.208 0.780
#> GSM750745 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549239 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549244 4 0.4829 0.7266 0.156 0.068 0.000 0.776
#> GSM549249 4 0.4174 0.7388 0.140 0.044 0.000 0.816
#> GSM549260 1 0.0188 0.9362 0.996 0.000 0.000 0.004
#> GSM549266 2 0.6927 0.6071 0.160 0.628 0.200 0.012
#> GSM549293 2 0.2081 0.7817 0.000 0.916 0.000 0.084
#> GSM549236 1 0.1792 0.8978 0.932 0.000 0.000 0.068
#> GSM549238 4 0.4500 0.5657 0.316 0.000 0.000 0.684
#> GSM549251 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549258 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549264 1 0.2345 0.8663 0.900 0.000 0.000 0.100
#> GSM549243 1 0.0188 0.9362 0.996 0.000 0.000 0.004
#> GSM549262 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM549278 3 0.4868 0.5492 0.012 0.000 0.684 0.304
#> GSM549283 2 0.2831 0.7981 0.000 0.876 0.120 0.004
#> GSM549298 3 0.0707 0.8710 0.000 0.020 0.980 0.000
#> GSM750741 1 0.0524 0.9323 0.988 0.008 0.000 0.004
#> GSM549286 2 0.1557 0.7940 0.000 0.944 0.000 0.056
#> GSM549241 1 0.1042 0.9226 0.972 0.020 0.000 0.008
#> GSM549247 2 0.5778 0.4040 0.356 0.604 0.000 0.040
#> GSM549261 1 0.0188 0.9359 0.996 0.004 0.000 0.000
#> GSM549270 2 0.4220 0.6891 0.000 0.748 0.248 0.004
#> GSM549277 3 0.1661 0.8589 0.000 0.052 0.944 0.004
#> GSM549280 3 0.2530 0.8191 0.000 0.112 0.888 0.000
#> GSM549281 3 0.4891 0.5122 0.000 0.308 0.680 0.012
#> GSM549285 3 0.1909 0.8688 0.004 0.008 0.940 0.048
#> GSM549288 3 0.1042 0.8731 0.000 0.020 0.972 0.008
#> GSM549292 2 0.1792 0.7897 0.000 0.932 0.000 0.068
#> GSM549295 3 0.1661 0.8588 0.000 0.052 0.944 0.004
#> GSM549297 3 0.2944 0.8011 0.000 0.128 0.868 0.004
#> GSM750743 1 0.0188 0.9364 0.996 0.000 0.000 0.004
#> GSM549268 3 0.4248 0.6767 0.000 0.220 0.768 0.012
#> GSM549290 4 0.2715 0.7131 0.004 0.004 0.100 0.892
#> GSM549272 2 0.1004 0.8071 0.000 0.972 0.024 0.004
#> GSM549276 2 0.2546 0.8075 0.000 0.900 0.092 0.008
#> GSM549275 1 0.4724 0.7533 0.792 0.096 0.000 0.112
#> GSM549284 2 0.3975 0.6046 0.000 0.760 0.000 0.240
#> GSM750737 1 0.3982 0.7007 0.776 0.000 0.004 0.220
#> GSM750740 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM750747 1 0.0000 0.9369 1.000 0.000 0.000 0.000
#> GSM750751 2 0.2466 0.8084 0.000 0.900 0.096 0.004
#> GSM750754 3 0.3311 0.7685 0.000 0.000 0.828 0.172
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM549289 5 0.6140 0.81701 0.024 0.000 0.068 0.432 0.476
#> GSM549291 3 0.6785 -0.37533 0.000 0.000 0.364 0.280 0.356
#> GSM549274 2 0.0771 0.77300 0.000 0.976 0.000 0.020 0.004
#> GSM750738 2 0.4977 0.33263 0.000 0.604 0.000 0.356 0.040
#> GSM750748 1 0.0290 0.89978 0.992 0.000 0.000 0.000 0.008
#> GSM549240 1 0.2125 0.86635 0.920 0.052 0.000 0.004 0.024
#> GSM549279 2 0.4472 0.75979 0.032 0.800 0.072 0.004 0.092
#> GSM549294 2 0.4774 0.65222 0.000 0.688 0.264 0.004 0.044
#> GSM549300 3 0.1270 0.79337 0.000 0.000 0.948 0.000 0.052
#> GSM549303 3 0.2011 0.77424 0.000 0.000 0.908 0.004 0.088
#> GSM549309 3 0.2563 0.75690 0.000 0.000 0.872 0.008 0.120
#> GSM750753 2 0.4768 0.61544 0.000 0.672 0.288 0.004 0.036
#> GSM750752 4 0.3883 0.42479 0.000 0.184 0.000 0.780 0.036
#> GSM549304 2 0.2707 0.74324 0.000 0.876 0.000 0.024 0.100
#> GSM549305 2 0.2017 0.78516 0.000 0.912 0.080 0.000 0.008
#> GSM549307 3 0.0566 0.79326 0.000 0.004 0.984 0.000 0.012
#> GSM549306 3 0.1608 0.78492 0.000 0.000 0.928 0.000 0.072
#> GSM549308 3 0.1671 0.78662 0.000 0.000 0.924 0.000 0.076
#> GSM549233 1 0.5901 0.26439 0.540 0.000 0.000 0.344 0.116
#> GSM549234 4 0.3120 0.53138 0.032 0.064 0.000 0.876 0.028
#> GSM549250 1 0.5896 0.43733 0.596 0.000 0.000 0.236 0.168
#> GSM549287 3 0.6756 0.09678 0.000 0.000 0.404 0.308 0.288
#> GSM750735 1 0.3089 0.82861 0.884 0.032 0.032 0.004 0.048
#> GSM750736 1 0.6139 0.18647 0.528 0.380 0.000 0.044 0.048
#> GSM750749 3 0.5141 0.63093 0.136 0.028 0.744 0.004 0.088
#> GSM549230 1 0.0404 0.89922 0.988 0.000 0.000 0.012 0.000
#> GSM549231 1 0.0671 0.89719 0.980 0.000 0.000 0.016 0.004
#> GSM549237 1 0.0324 0.90036 0.992 0.000 0.000 0.004 0.004
#> GSM549254 5 0.5268 0.80775 0.004 0.004 0.028 0.480 0.484
#> GSM750734 1 0.0000 0.90079 1.000 0.000 0.000 0.000 0.000
#> GSM549271 3 0.3546 0.75707 0.000 0.060 0.852 0.024 0.064
#> GSM549232 4 0.4457 0.50451 0.016 0.104 0.000 0.784 0.096
#> GSM549246 4 0.6414 -0.74468 0.080 0.000 0.032 0.464 0.424
#> GSM549248 1 0.0290 0.90002 0.992 0.000 0.000 0.008 0.000
#> GSM549255 4 0.3842 0.17340 0.028 0.012 0.000 0.804 0.156
#> GSM750746 1 0.0000 0.90079 1.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0162 0.90065 0.996 0.000 0.000 0.000 0.004
#> GSM549269 2 0.2798 0.78510 0.000 0.888 0.060 0.008 0.044
#> GSM549273 3 0.1197 0.79385 0.000 0.000 0.952 0.000 0.048
#> GSM549299 2 0.5593 0.46190 0.000 0.588 0.068 0.008 0.336
#> GSM549301 3 0.0963 0.79060 0.000 0.000 0.964 0.000 0.036
#> GSM549310 4 0.5483 -0.80790 0.000 0.012 0.040 0.532 0.416
#> GSM549311 3 0.4588 0.67306 0.000 0.000 0.720 0.060 0.220
#> GSM549302 2 0.1106 0.77088 0.000 0.964 0.000 0.024 0.012
#> GSM549235 1 0.0000 0.90079 1.000 0.000 0.000 0.000 0.000
#> GSM549245 4 0.3126 0.51150 0.016 0.088 0.000 0.868 0.028
#> GSM549265 4 0.3573 0.53537 0.012 0.072 0.000 0.844 0.072
#> GSM549282 3 0.6955 0.00967 0.000 0.004 0.352 0.324 0.320
#> GSM549296 4 0.5214 -0.18562 0.000 0.024 0.048 0.684 0.244
#> GSM750739 1 0.0000 0.90079 1.000 0.000 0.000 0.000 0.000
#> GSM750742 1 0.0510 0.89782 0.984 0.000 0.000 0.016 0.000
#> GSM750744 1 0.0609 0.89641 0.980 0.000 0.000 0.020 0.000
#> GSM750750 3 0.1410 0.79247 0.000 0.000 0.940 0.000 0.060
#> GSM549242 1 0.2992 0.82638 0.868 0.000 0.000 0.068 0.064
#> GSM549252 4 0.3633 0.53714 0.064 0.028 0.000 0.848 0.060
#> GSM549253 1 0.2236 0.85686 0.908 0.000 0.000 0.068 0.024
#> GSM549256 1 0.3913 0.52505 0.676 0.000 0.000 0.324 0.000
#> GSM549257 4 0.4275 0.29555 0.284 0.000 0.000 0.696 0.020
#> GSM549263 1 0.3180 0.81591 0.856 0.000 0.000 0.068 0.076
#> GSM549267 4 0.5165 0.33899 0.000 0.004 0.080 0.676 0.240
#> GSM750745 1 0.0404 0.89953 0.988 0.000 0.000 0.000 0.012
#> GSM549239 1 0.0451 0.89910 0.988 0.000 0.000 0.004 0.008
#> GSM549244 4 0.3518 0.54274 0.036 0.044 0.000 0.856 0.064
#> GSM549249 4 0.1787 0.51872 0.016 0.032 0.000 0.940 0.012
#> GSM549260 1 0.0566 0.90024 0.984 0.000 0.000 0.004 0.012
#> GSM549266 2 0.6556 0.54759 0.200 0.572 0.204 0.000 0.024
#> GSM549293 2 0.1907 0.76297 0.000 0.928 0.000 0.044 0.028
#> GSM549236 1 0.4498 0.71145 0.756 0.000 0.000 0.112 0.132
#> GSM549238 4 0.4487 0.44759 0.140 0.000 0.000 0.756 0.104
#> GSM549251 1 0.0290 0.90002 0.992 0.000 0.000 0.008 0.000
#> GSM549258 1 0.0162 0.90065 0.996 0.000 0.000 0.000 0.004
#> GSM549264 1 0.4169 0.74375 0.784 0.000 0.000 0.116 0.100
#> GSM549243 1 0.0324 0.90090 0.992 0.000 0.000 0.004 0.004
#> GSM549262 1 0.0290 0.90003 0.992 0.000 0.000 0.008 0.000
#> GSM549278 3 0.5808 0.42568 0.008 0.000 0.600 0.100 0.292
#> GSM549283 2 0.4583 0.72975 0.000 0.748 0.192 0.016 0.044
#> GSM549298 3 0.0451 0.79245 0.000 0.004 0.988 0.000 0.008
#> GSM750741 1 0.2116 0.86109 0.912 0.008 0.000 0.004 0.076
#> GSM549286 2 0.2326 0.77507 0.000 0.916 0.020 0.044 0.020
#> GSM549241 1 0.1173 0.89015 0.964 0.012 0.000 0.004 0.020
#> GSM549247 2 0.6015 0.32444 0.356 0.552 0.000 0.024 0.068
#> GSM549261 1 0.0579 0.89813 0.984 0.008 0.000 0.000 0.008
#> GSM549270 2 0.5203 0.55836 0.000 0.620 0.324 0.004 0.052
#> GSM549277 3 0.1981 0.78390 0.000 0.048 0.924 0.000 0.028
#> GSM549280 3 0.1728 0.78937 0.000 0.020 0.940 0.004 0.036
#> GSM549281 3 0.4250 0.68015 0.000 0.128 0.784 0.004 0.084
#> GSM549285 3 0.6216 0.40060 0.012 0.024 0.496 0.048 0.420
#> GSM549288 3 0.3060 0.73169 0.000 0.128 0.848 0.000 0.024
#> GSM549292 2 0.1800 0.76344 0.000 0.932 0.000 0.048 0.020
#> GSM549295 3 0.1300 0.78879 0.000 0.016 0.956 0.000 0.028
#> GSM549297 3 0.2012 0.77679 0.000 0.060 0.920 0.000 0.020
#> GSM750743 1 0.0162 0.90048 0.996 0.000 0.000 0.004 0.000
#> GSM549268 3 0.3012 0.76187 0.000 0.060 0.876 0.008 0.056
#> GSM549290 4 0.4464 0.27147 0.000 0.000 0.028 0.684 0.288
#> GSM549272 2 0.2546 0.78215 0.000 0.904 0.036 0.012 0.048
#> GSM549276 2 0.2497 0.77920 0.000 0.880 0.112 0.004 0.004
#> GSM549275 1 0.6781 0.37344 0.552 0.208 0.000 0.032 0.208
#> GSM549284 2 0.3863 0.59063 0.000 0.740 0.000 0.248 0.012
#> GSM750737 4 0.7209 -0.23498 0.264 0.004 0.012 0.388 0.332
#> GSM750740 1 0.0404 0.89953 0.988 0.000 0.000 0.000 0.012
#> GSM750747 1 0.0162 0.90065 0.996 0.000 0.000 0.000 0.004
#> GSM750751 2 0.2727 0.77742 0.000 0.868 0.116 0.000 0.016
#> GSM750754 3 0.4527 0.58545 0.000 0.000 0.692 0.036 0.272
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM549289 5 0.4465 0.7406 0.024 0.000 0.044 0.128 0.772 0.032
#> GSM549291 5 0.4688 0.5409 0.000 0.000 0.208 0.084 0.696 0.012
#> GSM549274 2 0.1007 0.7308 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM750738 4 0.4696 0.4355 0.000 0.276 0.000 0.660 0.016 0.048
#> GSM750748 1 0.0000 0.8903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549240 1 0.2468 0.8291 0.884 0.092 0.000 0.012 0.008 0.004
#> GSM549279 2 0.6782 0.5727 0.120 0.624 0.052 0.024 0.060 0.120
#> GSM549294 2 0.4976 0.6017 0.000 0.676 0.228 0.004 0.020 0.072
#> GSM549300 3 0.2527 0.7614 0.000 0.004 0.884 0.000 0.064 0.048
#> GSM549303 3 0.2214 0.7569 0.000 0.000 0.888 0.000 0.096 0.016
#> GSM549309 3 0.2798 0.7415 0.000 0.000 0.852 0.000 0.112 0.036
#> GSM750753 2 0.5062 0.5497 0.000 0.648 0.248 0.000 0.016 0.088
#> GSM750752 4 0.3077 0.6692 0.000 0.068 0.000 0.860 0.040 0.032
#> GSM549304 2 0.3246 0.6899 0.000 0.848 0.000 0.028 0.048 0.076
#> GSM549305 2 0.1799 0.7326 0.000 0.928 0.052 0.004 0.008 0.008
#> GSM549307 3 0.1720 0.7709 0.000 0.000 0.928 0.000 0.040 0.032
#> GSM549306 3 0.1829 0.7657 0.000 0.000 0.920 0.000 0.056 0.024
#> GSM549308 3 0.1984 0.7615 0.000 0.000 0.912 0.000 0.032 0.056
#> GSM549233 1 0.5057 0.1247 0.504 0.000 0.000 0.436 0.012 0.048
#> GSM549234 4 0.2302 0.7034 0.024 0.036 0.000 0.912 0.016 0.012
#> GSM549250 1 0.5091 0.5373 0.632 0.000 0.000 0.172 0.000 0.196
#> GSM549287 6 0.6394 0.5204 0.000 0.000 0.336 0.152 0.044 0.468
#> GSM750735 1 0.6533 0.5674 0.656 0.076 0.044 0.044 0.060 0.120
#> GSM750736 1 0.7450 0.0785 0.440 0.220 0.000 0.228 0.024 0.088
#> GSM750749 3 0.6884 0.4001 0.140 0.072 0.580 0.004 0.044 0.160
#> GSM549230 1 0.0717 0.8882 0.976 0.000 0.000 0.008 0.000 0.016
#> GSM549231 1 0.0692 0.8888 0.976 0.000 0.000 0.020 0.000 0.004
#> GSM549237 1 0.0291 0.8898 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM549254 5 0.4083 0.7697 0.004 0.000 0.024 0.216 0.740 0.016
#> GSM750734 1 0.0146 0.8903 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM549271 3 0.6046 0.4867 0.000 0.024 0.648 0.108 0.072 0.148
#> GSM549232 4 0.4386 0.6133 0.004 0.108 0.004 0.780 0.052 0.052
#> GSM549246 5 0.4371 0.7793 0.016 0.000 0.032 0.184 0.748 0.020
#> GSM549248 1 0.0260 0.8901 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM549255 4 0.4302 0.4410 0.016 0.016 0.000 0.700 0.260 0.008
#> GSM750746 1 0.0000 0.8903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549259 1 0.0146 0.8903 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM549269 2 0.3787 0.7211 0.000 0.828 0.024 0.052 0.024 0.072
#> GSM549273 3 0.2608 0.7525 0.000 0.000 0.872 0.000 0.080 0.048
#> GSM549299 2 0.6638 0.2220 0.000 0.460 0.048 0.004 0.320 0.168
#> GSM549301 3 0.1418 0.7674 0.000 0.000 0.944 0.000 0.024 0.032
#> GSM549310 5 0.3855 0.7226 0.000 0.000 0.024 0.272 0.704 0.000
#> GSM549311 3 0.5087 0.3560 0.000 0.000 0.620 0.028 0.052 0.300
#> GSM549302 2 0.1785 0.7269 0.000 0.928 0.000 0.048 0.008 0.016
#> GSM549235 1 0.0405 0.8904 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM549245 4 0.2257 0.6903 0.004 0.060 0.000 0.904 0.028 0.004
#> GSM549265 4 0.1686 0.6855 0.008 0.004 0.000 0.932 0.004 0.052
#> GSM549282 6 0.5135 0.6701 0.000 0.000 0.240 0.144 0.000 0.616
#> GSM549296 4 0.5317 -0.0121 0.000 0.012 0.040 0.560 0.368 0.020
#> GSM750739 1 0.0508 0.8897 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM750742 1 0.0972 0.8838 0.964 0.000 0.000 0.008 0.000 0.028
#> GSM750744 1 0.0632 0.8872 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM750750 3 0.1918 0.7552 0.000 0.000 0.904 0.000 0.008 0.088
#> GSM549242 1 0.1983 0.8521 0.908 0.000 0.000 0.020 0.072 0.000
#> GSM549252 4 0.1693 0.6980 0.032 0.000 0.000 0.936 0.020 0.012
#> GSM549253 1 0.1780 0.8646 0.924 0.000 0.000 0.028 0.000 0.048
#> GSM549256 1 0.2941 0.7091 0.780 0.000 0.000 0.220 0.000 0.000
#> GSM549257 4 0.4498 0.3201 0.320 0.000 0.000 0.640 0.024 0.016
#> GSM549263 1 0.3139 0.7757 0.812 0.000 0.000 0.028 0.000 0.160
#> GSM549267 6 0.5335 0.5293 0.000 0.000 0.100 0.364 0.004 0.532
#> GSM750745 1 0.0260 0.8900 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM549239 1 0.0291 0.8898 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM549244 4 0.2764 0.6582 0.020 0.000 0.000 0.872 0.024 0.084
#> GSM549249 4 0.2945 0.6651 0.016 0.000 0.000 0.864 0.072 0.048
#> GSM549260 1 0.0551 0.8899 0.984 0.000 0.000 0.004 0.008 0.004
#> GSM549266 2 0.6362 0.4148 0.268 0.524 0.168 0.000 0.008 0.032
#> GSM549293 2 0.2784 0.7158 0.000 0.868 0.000 0.092 0.020 0.020
#> GSM549236 1 0.4195 0.6785 0.724 0.000 0.000 0.076 0.000 0.200
#> GSM549238 4 0.5132 0.4191 0.136 0.000 0.000 0.672 0.020 0.172
#> GSM549251 1 0.0146 0.8902 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM549258 1 0.0405 0.8905 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM549264 1 0.3316 0.7782 0.812 0.000 0.000 0.052 0.000 0.136
#> GSM549243 1 0.0146 0.8902 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM549262 1 0.0508 0.8897 0.984 0.000 0.000 0.004 0.000 0.012
#> GSM549278 3 0.6350 0.0229 0.004 0.000 0.424 0.032 0.400 0.140
#> GSM549283 2 0.5780 0.6199 0.000 0.660 0.164 0.028 0.036 0.112
#> GSM549298 3 0.0405 0.7712 0.000 0.000 0.988 0.000 0.004 0.008
#> GSM750741 1 0.2605 0.8101 0.864 0.000 0.000 0.000 0.108 0.028
#> GSM549286 2 0.2756 0.7275 0.000 0.880 0.016 0.076 0.016 0.012
#> GSM549241 1 0.0951 0.8836 0.968 0.004 0.000 0.000 0.008 0.020
#> GSM549247 2 0.4787 0.0727 0.456 0.508 0.000 0.008 0.020 0.008
#> GSM549261 1 0.0000 0.8903 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM549270 2 0.5683 0.4662 0.000 0.572 0.308 0.004 0.028 0.088
#> GSM549277 3 0.1850 0.7683 0.000 0.016 0.924 0.000 0.008 0.052
#> GSM549280 3 0.3452 0.7221 0.000 0.052 0.828 0.000 0.020 0.100
#> GSM549281 3 0.5481 0.5688 0.004 0.100 0.684 0.004 0.056 0.152
#> GSM549285 6 0.4255 0.5601 0.008 0.008 0.228 0.008 0.020 0.728
#> GSM549288 3 0.4031 0.6305 0.000 0.168 0.768 0.000 0.028 0.036
#> GSM549292 2 0.2362 0.7213 0.000 0.892 0.000 0.080 0.012 0.016
#> GSM549295 3 0.1887 0.7618 0.000 0.012 0.924 0.000 0.016 0.048
#> GSM549297 3 0.1829 0.7606 0.000 0.028 0.928 0.000 0.008 0.036
#> GSM750743 1 0.0622 0.8894 0.980 0.000 0.000 0.012 0.000 0.008
#> GSM549268 3 0.4111 0.6991 0.004 0.064 0.796 0.000 0.044 0.092
#> GSM549290 6 0.5924 0.4160 0.000 0.000 0.036 0.376 0.096 0.492
#> GSM549272 2 0.3848 0.7159 0.000 0.816 0.012 0.092 0.024 0.056
#> GSM549276 2 0.3363 0.7214 0.000 0.852 0.064 0.020 0.016 0.048
#> GSM549275 1 0.7150 -0.1081 0.380 0.312 0.000 0.008 0.240 0.060
#> GSM549284 2 0.4675 0.3646 0.000 0.584 0.000 0.376 0.024 0.016
#> GSM750737 5 0.6668 0.5081 0.112 0.004 0.012 0.288 0.520 0.064
#> GSM750740 1 0.0508 0.8889 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM750747 1 0.0260 0.8904 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM750751 2 0.2831 0.7294 0.000 0.876 0.072 0.012 0.008 0.032
#> GSM750754 3 0.4815 0.3582 0.000 0.000 0.556 0.000 0.384 0.060
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)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) specimen(p) genotype/variation(p) other(p) k
#> ATC:NMF 97 0.399 2.66e-06 0.5641 0.00455 2
#> ATC:NMF 93 0.155 2.06e-03 0.1589 0.01414 3
#> ATC:NMF 98 0.331 5.86e-03 0.0273 0.02103 4
#> ATC:NMF 81 0.385 5.14e-04 0.0913 0.03383 5
#> ATC:NMF 84 0.138 1.02e-03 0.0717 0.09806 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0