Date: 2019-12-25 20:47:11 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 25171 rows and 96 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] 25171 96
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:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:pam | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:kmeans | 2 | 1.000 | 0.988 | 0.993 | ** | |
| CV:NMF | 2 | 1.000 | 0.978 | 0.991 | ** | |
| MAD:hclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:skmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:pam | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| CV:mclust | 3 | 0.980 | 0.934 | 0.959 | ** | 2 |
| ATC:NMF | 3 | 0.968 | 0.950 | 0.976 | ** | 2 |
| CV:skmeans | 3 | 0.951 | 0.910 | 0.945 | ** | 2 |
| ATC:hclust | 5 | 0.938 | 0.914 | 0.957 | * | 2,4 |
| SD:skmeans | 5 | 0.911 | 0.891 | 0.931 | * | 2 |
| ATC:skmeans | 4 | 0.908 | 0.934 | 0.963 | * | 2,3 |
| ATC:pam | 6 | 0.906 | 0.917 | 0.949 | * | 2,3 |
| CV:pam | 4 | 0.900 | 0.893 | 0.950 | * | 2,3 |
| CV:hclust | 5 | 0.810 | 0.868 | 0.898 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:NMF 2 1.000 0.978 0.991 0.506 0.495 0.495
#> MAD:NMF 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:NMF 2 1.000 1.000 1.000 0.506 0.495 0.495
#> SD:skmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:skmeans 2 1.000 0.996 0.998 0.506 0.495 0.495
#> MAD:skmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:skmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> SD:mclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:mclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> MAD:mclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:mclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> SD:kmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:kmeans 2 1.000 0.988 0.993 0.505 0.495 0.495
#> MAD:kmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:kmeans 2 1.000 1.000 1.000 0.506 0.495 0.495
#> SD:pam 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:pam 2 0.978 0.931 0.970 0.500 0.503 0.503
#> MAD:pam 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:pam 2 1.000 1.000 1.000 0.506 0.495 0.495
#> SD:hclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> CV:hclust 2 0.434 0.795 0.870 0.470 0.509 0.509
#> MAD:hclust 2 1.000 1.000 1.000 0.506 0.495 0.495
#> ATC:hclust 2 1.000 1.000 1.000 0.506 0.495 0.495
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.842 0.851 0.900 0.1962 0.879 0.756
#> CV:NMF 3 0.811 0.855 0.926 0.2722 0.858 0.716
#> MAD:NMF 3 0.840 0.894 0.915 0.1749 0.900 0.798
#> ATC:NMF 3 0.968 0.950 0.976 0.2407 0.877 0.752
#> SD:skmeans 3 0.807 0.970 0.927 0.2322 0.874 0.745
#> CV:skmeans 3 0.951 0.910 0.945 0.2500 0.859 0.717
#> MAD:skmeans 3 0.814 0.972 0.927 0.2268 0.874 0.745
#> ATC:skmeans 3 1.000 0.969 0.981 0.2254 0.882 0.761
#> SD:mclust 3 0.698 0.763 0.850 0.1979 0.945 0.888
#> CV:mclust 3 0.980 0.934 0.959 0.2403 0.876 0.749
#> MAD:mclust 3 0.829 0.940 0.886 0.1963 0.884 0.766
#> ATC:mclust 3 0.888 0.906 0.933 0.2222 0.876 0.749
#> SD:kmeans 3 0.695 0.875 0.809 0.2362 0.874 0.745
#> CV:kmeans 3 0.676 0.832 0.829 0.2385 0.877 0.752
#> MAD:kmeans 3 0.693 0.866 0.798 0.2369 0.874 0.745
#> ATC:kmeans 3 0.747 0.850 0.825 0.2328 0.882 0.761
#> SD:pam 3 0.753 0.947 0.897 0.2485 0.874 0.745
#> CV:pam 3 0.965 0.937 0.970 0.2664 0.797 0.620
#> MAD:pam 3 0.750 0.910 0.901 0.2480 0.874 0.745
#> ATC:pam 3 1.000 0.984 0.992 0.2460 0.876 0.749
#> SD:hclust 3 0.766 0.834 0.870 0.1753 0.937 0.873
#> CV:hclust 3 0.440 0.598 0.710 0.2986 0.654 0.446
#> MAD:hclust 3 0.787 0.905 0.932 0.1611 0.937 0.873
#> ATC:hclust 3 1.000 0.968 0.988 0.0387 0.990 0.979
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.685 0.794 0.829 0.1043 0.930 0.827
#> CV:NMF 4 0.778 0.785 0.877 0.0895 0.893 0.719
#> MAD:NMF 4 0.681 0.673 0.791 0.1084 0.896 0.750
#> ATC:NMF 4 0.807 0.784 0.888 0.0666 0.984 0.956
#> SD:skmeans 4 0.870 0.799 0.867 0.1777 0.896 0.717
#> CV:skmeans 4 0.872 0.855 0.931 0.1751 0.876 0.663
#> MAD:skmeans 4 0.842 0.758 0.861 0.1697 0.923 0.791
#> ATC:skmeans 4 0.908 0.934 0.963 0.1351 0.911 0.763
#> SD:mclust 4 0.730 0.809 0.852 0.1731 0.801 0.565
#> CV:mclust 4 0.862 0.851 0.938 0.1713 0.880 0.684
#> MAD:mclust 4 0.766 0.895 0.882 0.1830 0.884 0.695
#> ATC:mclust 4 0.685 0.735 0.841 0.1564 0.862 0.644
#> SD:kmeans 4 0.616 0.688 0.658 0.1114 0.864 0.642
#> CV:kmeans 4 0.662 0.641 0.807 0.1337 0.873 0.676
#> MAD:kmeans 4 0.622 0.626 0.680 0.1109 0.910 0.761
#> ATC:kmeans 4 0.644 0.820 0.820 0.1237 0.881 0.687
#> SD:pam 4 0.776 0.793 0.899 0.1852 0.876 0.662
#> CV:pam 4 0.900 0.893 0.950 0.1221 0.911 0.761
#> MAD:pam 4 0.772 0.673 0.847 0.1859 0.874 0.663
#> ATC:pam 4 0.874 0.820 0.925 0.1713 0.888 0.697
#> SD:hclust 4 0.762 0.819 0.899 0.1734 0.836 0.625
#> CV:hclust 4 0.751 0.873 0.886 0.1571 0.833 0.599
#> MAD:hclust 4 0.824 0.842 0.921 0.1914 0.864 0.685
#> ATC:hclust 4 0.912 0.909 0.962 0.1900 0.900 0.794
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.767 0.795 0.883 0.09348 0.868 0.652
#> CV:NMF 5 0.621 0.543 0.756 0.08732 0.928 0.775
#> MAD:NMF 5 0.701 0.765 0.854 0.08878 0.869 0.647
#> ATC:NMF 5 0.749 0.728 0.855 0.07159 0.919 0.779
#> SD:skmeans 5 0.911 0.891 0.931 0.06231 0.902 0.664
#> CV:skmeans 5 0.787 0.685 0.844 0.04791 0.950 0.815
#> MAD:skmeans 5 0.872 0.897 0.928 0.07233 0.911 0.705
#> ATC:skmeans 5 0.832 0.831 0.908 0.05869 0.975 0.912
#> SD:mclust 5 0.597 0.575 0.700 0.06609 0.933 0.774
#> CV:mclust 5 0.808 0.681 0.871 0.00937 0.898 0.682
#> MAD:mclust 5 0.678 0.761 0.821 0.07041 0.948 0.805
#> ATC:mclust 5 0.667 0.758 0.826 0.06243 0.940 0.784
#> SD:kmeans 5 0.586 0.654 0.737 0.07820 0.870 0.571
#> CV:kmeans 5 0.664 0.582 0.756 0.07771 0.932 0.768
#> MAD:kmeans 5 0.581 0.680 0.722 0.07624 0.910 0.703
#> ATC:kmeans 5 0.612 0.685 0.772 0.07685 0.961 0.867
#> SD:pam 5 0.818 0.854 0.891 0.04887 0.946 0.787
#> CV:pam 5 0.766 0.700 0.824 0.08590 0.946 0.814
#> MAD:pam 5 0.849 0.791 0.907 0.05607 0.913 0.682
#> ATC:pam 5 0.842 0.898 0.909 0.07249 0.914 0.689
#> SD:hclust 5 0.767 0.705 0.847 0.05055 0.981 0.933
#> CV:hclust 5 0.810 0.868 0.898 0.06047 0.976 0.911
#> MAD:hclust 5 0.835 0.808 0.901 0.04003 0.951 0.838
#> ATC:hclust 5 0.938 0.914 0.957 0.05803 0.967 0.914
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.876 0.871 0.920 0.0340 0.973 0.902
#> CV:NMF 6 0.653 0.580 0.751 0.0583 0.895 0.651
#> MAD:NMF 6 0.768 0.833 0.888 0.0406 0.953 0.838
#> ATC:NMF 6 0.645 0.743 0.823 0.0464 0.961 0.872
#> SD:skmeans 6 0.818 0.647 0.854 0.0360 0.986 0.938
#> CV:skmeans 6 0.782 0.656 0.801 0.0458 0.934 0.737
#> MAD:skmeans 6 0.826 0.769 0.862 0.0347 0.976 0.898
#> ATC:skmeans 6 0.793 0.707 0.858 0.0407 0.987 0.952
#> SD:mclust 6 0.706 0.621 0.732 0.0668 0.872 0.547
#> CV:mclust 6 0.811 0.638 0.866 0.0534 0.971 0.895
#> MAD:mclust 6 0.694 0.639 0.752 0.0543 0.895 0.587
#> ATC:mclust 6 0.710 0.686 0.800 0.0454 0.931 0.728
#> SD:kmeans 6 0.583 0.584 0.707 0.0551 0.937 0.738
#> CV:kmeans 6 0.722 0.595 0.693 0.0534 0.900 0.610
#> MAD:kmeans 6 0.584 0.591 0.729 0.0581 0.974 0.882
#> ATC:kmeans 6 0.651 0.523 0.747 0.0457 0.906 0.672
#> SD:pam 6 0.843 0.757 0.849 0.0394 0.956 0.801
#> CV:pam 6 0.807 0.742 0.875 0.0553 0.936 0.741
#> MAD:pam 6 0.844 0.776 0.836 0.0336 0.938 0.727
#> ATC:pam 6 0.906 0.917 0.949 0.0541 0.946 0.747
#> SD:hclust 6 0.774 0.685 0.808 0.0354 0.988 0.953
#> CV:hclust 6 0.783 0.843 0.846 0.0331 0.958 0.830
#> MAD:hclust 6 0.750 0.758 0.868 0.0328 0.996 0.985
#> ATC:hclust 6 0.753 0.727 0.862 0.1070 0.946 0.847
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 cell.type(p) protocol(p) individual(p) k
#> SD:NMF 96 8.49e-22 1.00 1.00 2
#> CV:NMF 95 1.41e-21 1.00 1.00 2
#> MAD:NMF 96 8.49e-22 1.00 1.00 2
#> ATC:NMF 96 8.49e-22 1.00 1.00 2
#> SD:skmeans 96 8.49e-22 1.00 1.00 2
#> CV:skmeans 96 8.49e-22 1.00 1.00 2
#> MAD:skmeans 96 8.49e-22 1.00 1.00 2
#> ATC:skmeans 96 8.49e-22 1.00 1.00 2
#> SD:mclust 96 8.49e-22 1.00 1.00 2
#> CV:mclust 96 8.49e-22 1.00 1.00 2
#> MAD:mclust 96 8.49e-22 1.00 1.00 2
#> ATC:mclust 96 8.49e-22 1.00 1.00 2
#> SD:kmeans 96 8.49e-22 1.00 1.00 2
#> CV:kmeans 96 8.49e-22 1.00 1.00 2
#> MAD:kmeans 96 8.49e-22 1.00 1.00 2
#> ATC:kmeans 96 8.49e-22 1.00 1.00 2
#> SD:pam 96 8.49e-22 1.00 1.00 2
#> CV:pam 91 7.48e-20 1.00 1.00 2
#> MAD:pam 96 8.49e-22 1.00 1.00 2
#> ATC:pam 96 8.49e-22 1.00 1.00 2
#> SD:hclust 96 8.49e-22 1.00 1.00 2
#> CV:hclust 96 8.38e-05 0.35 0.81 2
#> MAD:hclust 96 8.49e-22 1.00 1.00 2
#> ATC:hclust 96 8.49e-22 1.00 1.00 2
test_to_known_factors(res_list, k = 3)
#> n cell.type(p) protocol(p) individual(p) k
#> SD:NMF 93 6.39e-21 0.665 1.000 3
#> CV:NMF 91 1.74e-20 0.772 1.000 3
#> MAD:NMF 95 2.35e-21 0.614 1.000 3
#> ATC:NMF 94 3.87e-21 0.484 1.000 3
#> SD:skmeans 96 1.43e-21 0.691 1.000 3
#> CV:skmeans 93 6.39e-21 0.791 1.000 3
#> MAD:skmeans 96 1.43e-21 0.691 1.000 3
#> ATC:skmeans 94 3.87e-21 0.161 1.000 3
#> SD:mclust 90 2.86e-20 0.626 1.000 3
#> CV:mclust 92 1.05e-20 0.793 1.000 3
#> MAD:mclust 96 1.43e-21 0.519 1.000 3
#> ATC:mclust 94 3.87e-21 0.797 1.000 3
#> SD:kmeans 94 3.87e-21 0.539 1.000 3
#> CV:kmeans 92 1.05e-20 0.751 1.000 3
#> MAD:kmeans 94 3.87e-21 0.539 1.000 3
#> ATC:kmeans 93 6.39e-21 0.329 1.000 3
#> SD:pam 96 1.43e-21 0.364 1.000 3
#> CV:pam 94 3.87e-21 0.642 1.000 3
#> MAD:pam 93 6.39e-21 0.713 1.000 3
#> ATC:pam 96 1.43e-21 0.508 1.000 3
#> SD:hclust 91 1.74e-20 0.898 1.000 3
#> CV:hclust 77 1.90e-17 0.309 0.993 3
#> MAD:hclust 92 1.05e-20 0.898 1.000 3
#> ATC:hclust 95 1.41e-21 1.000 1.000 3
test_to_known_factors(res_list, k = 4)
#> n cell.type(p) protocol(p) individual(p) k
#> SD:NMF 92 1.05e-20 0.365 1.000 4
#> CV:NMF 85 1.06e-16 0.667 0.942 4
#> MAD:NMF 75 5.18e-17 0.786 0.999 4
#> ATC:NMF 87 1.28e-19 0.445 1.000 4
#> SD:skmeans 91 1.34e-19 0.863 0.996 4
#> CV:skmeans 89 3.59e-19 0.407 0.993 4
#> MAD:skmeans 78 8.24e-17 0.746 0.984 4
#> ATC:skmeans 94 3.03e-20 0.295 0.998 4
#> SD:mclust 94 3.03e-20 0.146 0.998 4
#> CV:mclust 89 3.59e-19 0.217 0.994 4
#> MAD:mclust 95 1.85e-20 0.500 0.998 4
#> ATC:mclust 84 4.25e-18 0.333 0.992 4
#> SD:kmeans 63 2.09e-14 0.401 0.909 4
#> CV:kmeans 80 3.07e-17 0.375 0.979 4
#> MAD:kmeans 72 1.59e-15 0.834 0.983 4
#> ATC:kmeans 90 2.19e-19 0.481 0.996 4
#> SD:pam 88 5.89e-19 0.571 0.994 4
#> CV:pam 92 8.15e-20 0.699 0.992 4
#> MAD:pam 73 9.72e-16 0.883 0.966 4
#> ATC:pam 83 6.97e-18 0.505 0.990 4
#> SD:hclust 87 9.66e-19 0.538 0.994 4
#> CV:hclust 95 3.63e-16 0.497 0.945 4
#> MAD:hclust 89 3.59e-19 0.417 0.996 4
#> ATC:hclust 95 2.35e-21 0.189 1.000 4
test_to_known_factors(res_list, k = 5)
#> n cell.type(p) protocol(p) individual(p) k
#> SD:NMF 90 1.32e-18 0.630 0.991 5
#> CV:NMF 67 1.87e-14 0.415 0.489 5
#> MAD:NMF 86 9.31e-18 0.685 0.926 5
#> ATC:NMF 87 5.71e-18 0.436 0.928 5
#> SD:skmeans 93 3.03e-19 0.639 0.991 5
#> CV:skmeans 80 3.07e-17 0.641 0.944 5
#> MAD:skmeans 92 4.95e-19 0.623 0.990 5
#> ATC:skmeans 92 4.95e-19 0.113 0.991 5
#> SD:mclust 73 5.28e-15 0.236 0.853 5
#> CV:mclust 70 2.27e-14 0.211 0.858 5
#> MAD:mclust 85 1.52e-17 0.246 0.988 5
#> ATC:mclust 87 5.71e-18 0.356 0.989 5
#> SD:kmeans 83 4.03e-17 0.518 0.980 5
#> CV:kmeans 73 5.28e-15 0.593 0.981 5
#> MAD:kmeans 83 4.03e-17 0.591 0.982 5
#> ATC:kmeans 84 4.25e-18 0.279 0.987 5
#> SD:pam 93 3.03e-19 0.496 0.991 5
#> CV:pam 81 1.07e-16 0.823 0.979 5
#> MAD:pam 86 9.31e-18 0.931 0.983 5
#> ATC:pam 95 1.14e-19 0.773 0.991 5
#> SD:hclust 80 1.74e-16 0.593 0.873 5
#> CV:hclust 95 1.14e-19 0.610 0.993 5
#> MAD:hclust 89 2.15e-18 0.471 0.990 5
#> ATC:hclust 93 4.97e-20 0.109 0.997 5
test_to_known_factors(res_list, k = 6)
#> n cell.type(p) protocol(p) individual(p) k
#> SD:NMF 93 3.03e-19 0.434 0.989 6
#> CV:NMF 71 1.40e-14 0.615 0.955 6
#> MAD:NMF 91 8.07e-19 0.399 0.985 6
#> ATC:NMF 91 8.07e-19 0.707 0.972 6
#> SD:skmeans 82 6.56e-17 0.640 0.937 6
#> CV:skmeans 80 8.39e-16 0.420 0.879 6
#> MAD:skmeans 93 3.03e-19 0.639 0.991 6
#> ATC:skmeans 76 5.75e-15 0.471 0.808 6
#> SD:mclust 72 8.58e-15 0.803 0.915 6
#> CV:mclust 71 1.40e-14 0.705 0.861 6
#> MAD:mclust 78 2.20e-15 0.433 0.843 6
#> ATC:mclust 84 1.22e-16 0.297 0.915 6
#> SD:kmeans 70 2.27e-14 0.203 0.860 6
#> CV:kmeans 64 1.81e-12 0.399 0.850 6
#> MAD:kmeans 74 1.50e-14 0.480 0.777 6
#> ATC:kmeans 68 2.67e-13 0.639 0.937 6
#> SD:pam 87 2.87e-17 0.285 0.985 6
#> CV:pam 85 7.53e-17 0.958 0.929 6
#> MAD:pam 87 2.87e-17 0.674 0.961 6
#> ATC:pam 95 5.97e-19 0.643 0.981 6
#> SD:hclust 80 1.74e-16 0.605 0.929 6
#> CV:hclust 92 2.55e-18 0.457 0.976 6
#> MAD:hclust 83 4.03e-17 0.503 0.986 6
#> ATC:hclust 84 2.47e-17 0.168 0.957 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 25171 rows and 96 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 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.766 0.834 0.870 0.1753 0.937 0.873
#> 4 4 0.762 0.819 0.899 0.1734 0.836 0.625
#> 5 5 0.767 0.705 0.847 0.0506 0.981 0.933
#> 6 6 0.774 0.685 0.808 0.0354 0.988 0.953
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 3 0.5706 0.8481 0.320 0.000 0.680
#> GSM257888 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257890 1 0.0424 0.9023 0.992 0.000 0.008
#> GSM257892 3 0.5706 0.8481 0.320 0.000 0.680
#> GSM257894 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257898 3 0.6225 0.8855 0.432 0.000 0.568
#> GSM257900 1 0.5706 0.0586 0.680 0.000 0.320
#> GSM257902 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257904 3 0.6225 0.8855 0.432 0.000 0.568
#> GSM257906 3 0.6225 0.8855 0.432 0.000 0.568
#> GSM257908 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257917 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257919 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257921 1 0.1643 0.8606 0.956 0.000 0.044
#> GSM257923 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257927 1 0.4842 0.4744 0.776 0.000 0.224
#> GSM257929 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257937 1 0.0237 0.9053 0.996 0.000 0.004
#> GSM257939 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257941 1 0.5591 0.1444 0.696 0.000 0.304
#> GSM257943 1 0.5706 0.0586 0.680 0.000 0.320
#> GSM257945 1 0.5706 0.0615 0.680 0.000 0.320
#> GSM257947 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257953 1 0.1163 0.8828 0.972 0.000 0.028
#> GSM257955 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257960 1 0.4702 0.5113 0.788 0.000 0.212
#> GSM257962 1 0.4702 0.5113 0.788 0.000 0.212
#> GSM257964 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257966 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257968 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257972 1 0.1753 0.8557 0.952 0.000 0.048
#> GSM257977 1 0.0237 0.9053 0.996 0.000 0.004
#> GSM257982 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.9083 1.000 0.000 0.000
#> GSM257990 1 0.0747 0.8943 0.984 0.000 0.016
#> GSM257992 3 0.5968 0.8779 0.364 0.000 0.636
#> GSM257996 1 0.1289 0.8768 0.968 0.000 0.032
#> GSM258006 3 0.6291 0.8167 0.468 0.000 0.532
#> GSM257887 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257889 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257891 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257893 2 0.2796 0.8659 0.000 0.908 0.092
#> GSM257895 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257897 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257899 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257901 2 0.1289 0.8814 0.000 0.968 0.032
#> GSM257903 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257905 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257907 2 0.1289 0.8814 0.000 0.968 0.032
#> GSM257909 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257911 2 0.1289 0.8814 0.000 0.968 0.032
#> GSM257913 2 0.1031 0.8823 0.000 0.976 0.024
#> GSM257916 2 0.2165 0.8802 0.000 0.936 0.064
#> GSM257918 2 0.2165 0.8802 0.000 0.936 0.064
#> GSM257920 2 0.1031 0.8823 0.000 0.976 0.024
#> GSM257922 2 0.3551 0.8682 0.000 0.868 0.132
#> GSM257924 2 0.1031 0.8823 0.000 0.976 0.024
#> GSM257926 2 0.1031 0.8823 0.000 0.976 0.024
#> GSM257928 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257930 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257938 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257940 2 0.1289 0.8814 0.000 0.968 0.032
#> GSM257942 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257944 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257946 2 0.2878 0.8645 0.000 0.904 0.096
#> GSM257948 2 0.1031 0.8823 0.000 0.976 0.024
#> GSM257950 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257952 2 0.1031 0.8832 0.000 0.976 0.024
#> GSM257954 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257956 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257959 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257961 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257963 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257965 2 0.1289 0.8814 0.000 0.968 0.032
#> GSM257967 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257969 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257971 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257973 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257981 2 0.1163 0.8831 0.000 0.972 0.028
#> GSM257983 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257985 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257988 2 0.3038 0.8617 0.000 0.896 0.104
#> GSM257991 2 0.1753 0.8815 0.000 0.952 0.048
#> GSM257993 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257994 2 0.4750 0.8548 0.000 0.784 0.216
#> GSM257989 2 0.3038 0.8617 0.000 0.896 0.104
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.1118 0.6926 0.000 0.000 0.036 0.964
#> GSM257888 1 0.0592 0.9423 0.984 0.000 0.000 0.016
#> GSM257890 1 0.0817 0.9394 0.976 0.000 0.000 0.024
#> GSM257892 4 0.1118 0.6926 0.000 0.000 0.036 0.964
#> GSM257894 1 0.0188 0.9520 0.996 0.000 0.000 0.004
#> GSM257896 1 0.0336 0.9492 0.992 0.000 0.000 0.008
#> GSM257898 4 0.2469 0.7766 0.108 0.000 0.000 0.892
#> GSM257900 4 0.4955 0.4616 0.444 0.000 0.000 0.556
#> GSM257902 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257904 4 0.2469 0.7766 0.108 0.000 0.000 0.892
#> GSM257906 4 0.2469 0.7766 0.108 0.000 0.000 0.892
#> GSM257908 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257921 1 0.1867 0.8863 0.928 0.000 0.000 0.072
#> GSM257923 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257927 1 0.4543 0.3561 0.676 0.000 0.000 0.324
#> GSM257929 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257937 1 0.0188 0.9522 0.996 0.000 0.000 0.004
#> GSM257939 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257941 4 0.4989 0.3994 0.472 0.000 0.000 0.528
#> GSM257943 4 0.4972 0.4382 0.456 0.000 0.000 0.544
#> GSM257945 4 0.4989 0.3947 0.472 0.000 0.000 0.528
#> GSM257947 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257953 1 0.1474 0.9133 0.948 0.000 0.000 0.052
#> GSM257955 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257960 1 0.4222 0.5126 0.728 0.000 0.000 0.272
#> GSM257962 1 0.4222 0.5126 0.728 0.000 0.000 0.272
#> GSM257964 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257966 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257968 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257970 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257972 1 0.1940 0.8816 0.924 0.000 0.000 0.076
#> GSM257977 1 0.0469 0.9487 0.988 0.000 0.000 0.012
#> GSM257982 1 0.0336 0.9492 0.992 0.000 0.000 0.008
#> GSM257984 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9546 1.000 0.000 0.000 0.000
#> GSM257990 1 0.1211 0.9223 0.960 0.000 0.000 0.040
#> GSM257992 4 0.1022 0.7289 0.032 0.000 0.000 0.968
#> GSM257996 1 0.1637 0.9021 0.940 0.000 0.000 0.060
#> GSM258006 4 0.2760 0.7690 0.128 0.000 0.000 0.872
#> GSM257887 2 0.0188 0.9046 0.000 0.996 0.004 0.000
#> GSM257889 3 0.1867 0.8575 0.000 0.072 0.928 0.000
#> GSM257891 3 0.1118 0.8375 0.000 0.036 0.964 0.000
#> GSM257893 3 0.3024 0.8678 0.000 0.148 0.852 0.000
#> GSM257895 2 0.0188 0.9046 0.000 0.996 0.004 0.000
#> GSM257897 3 0.1118 0.8375 0.000 0.036 0.964 0.000
#> GSM257899 3 0.1118 0.8375 0.000 0.036 0.964 0.000
#> GSM257901 3 0.3486 0.8492 0.000 0.188 0.812 0.000
#> GSM257903 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257905 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257907 3 0.3486 0.8492 0.000 0.188 0.812 0.000
#> GSM257909 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257911 3 0.3975 0.8234 0.000 0.240 0.760 0.000
#> GSM257913 3 0.3942 0.8306 0.000 0.236 0.764 0.000
#> GSM257916 2 0.4855 0.0685 0.000 0.600 0.400 0.000
#> GSM257918 2 0.4855 0.0685 0.000 0.600 0.400 0.000
#> GSM257920 3 0.3942 0.8306 0.000 0.236 0.764 0.000
#> GSM257922 3 0.4866 0.4205 0.000 0.404 0.596 0.000
#> GSM257924 3 0.3942 0.8306 0.000 0.236 0.764 0.000
#> GSM257926 3 0.3942 0.8306 0.000 0.236 0.764 0.000
#> GSM257928 2 0.0000 0.9027 0.000 1.000 0.000 0.000
#> GSM257930 2 0.0000 0.9027 0.000 1.000 0.000 0.000
#> GSM257938 2 0.0000 0.9027 0.000 1.000 0.000 0.000
#> GSM257940 3 0.3764 0.8404 0.000 0.216 0.784 0.000
#> GSM257942 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257944 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257946 3 0.3024 0.8670 0.000 0.148 0.852 0.000
#> GSM257948 3 0.3907 0.8332 0.000 0.232 0.768 0.000
#> GSM257950 3 0.2704 0.8703 0.000 0.124 0.876 0.000
#> GSM257952 3 0.4605 0.6989 0.000 0.336 0.664 0.000
#> GSM257954 2 0.0592 0.9017 0.000 0.984 0.016 0.000
#> GSM257956 2 0.1557 0.8701 0.000 0.944 0.056 0.000
#> GSM257959 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257961 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257963 2 0.0469 0.9067 0.000 0.988 0.012 0.000
#> GSM257965 3 0.3975 0.8234 0.000 0.240 0.760 0.000
#> GSM257967 2 0.1211 0.8851 0.000 0.960 0.040 0.000
#> GSM257969 2 0.1557 0.8701 0.000 0.944 0.056 0.000
#> GSM257971 3 0.2149 0.8628 0.000 0.088 0.912 0.000
#> GSM257973 3 0.2345 0.8681 0.000 0.100 0.900 0.000
#> GSM257981 3 0.4898 0.5362 0.000 0.416 0.584 0.000
#> GSM257983 3 0.1118 0.8375 0.000 0.036 0.964 0.000
#> GSM257985 3 0.2345 0.8681 0.000 0.100 0.900 0.000
#> GSM257988 3 0.2345 0.8681 0.000 0.100 0.900 0.000
#> GSM257991 2 0.4985 -0.2188 0.000 0.532 0.468 0.000
#> GSM257993 2 0.0000 0.9027 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0000 0.9027 0.000 1.000 0.000 0.000
#> GSM257989 3 0.2345 0.8681 0.000 0.100 0.900 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.3177 0.5860 0.000 0.000 0.000 0.792 0.208
#> GSM257888 1 0.0579 0.9376 0.984 0.000 0.000 0.008 0.008
#> GSM257890 1 0.0898 0.9327 0.972 0.000 0.000 0.020 0.008
#> GSM257892 4 0.3177 0.5860 0.000 0.000 0.000 0.792 0.208
#> GSM257894 1 0.0162 0.9456 0.996 0.000 0.000 0.000 0.004
#> GSM257896 1 0.0290 0.9435 0.992 0.000 0.000 0.000 0.008
#> GSM257898 4 0.0880 0.7037 0.032 0.000 0.000 0.968 0.000
#> GSM257900 4 0.4088 0.5346 0.368 0.000 0.000 0.632 0.000
#> GSM257902 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257904 4 0.0880 0.7037 0.032 0.000 0.000 0.968 0.000
#> GSM257906 4 0.0880 0.7037 0.032 0.000 0.000 0.968 0.000
#> GSM257908 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257921 1 0.1732 0.8796 0.920 0.000 0.000 0.080 0.000
#> GSM257923 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.4161 0.2281 0.608 0.000 0.000 0.392 0.000
#> GSM257929 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257937 1 0.0290 0.9433 0.992 0.000 0.000 0.008 0.000
#> GSM257939 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257941 4 0.4171 0.4838 0.396 0.000 0.000 0.604 0.000
#> GSM257943 4 0.4126 0.5152 0.380 0.000 0.000 0.620 0.000
#> GSM257945 4 0.4171 0.4780 0.396 0.000 0.000 0.604 0.000
#> GSM257947 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.2074 0.8556 0.896 0.000 0.000 0.104 0.000
#> GSM257955 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0162 0.9455 0.996 0.000 0.000 0.004 0.000
#> GSM257960 1 0.3999 0.3875 0.656 0.000 0.000 0.344 0.000
#> GSM257962 1 0.3999 0.3875 0.656 0.000 0.000 0.344 0.000
#> GSM257964 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257968 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257970 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.1792 0.8756 0.916 0.000 0.000 0.084 0.000
#> GSM257977 1 0.0579 0.9404 0.984 0.000 0.000 0.008 0.008
#> GSM257982 1 0.0290 0.9435 0.992 0.000 0.000 0.000 0.008
#> GSM257984 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9473 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.1270 0.9080 0.948 0.000 0.000 0.052 0.000
#> GSM257992 4 0.1557 0.6698 0.008 0.000 0.000 0.940 0.052
#> GSM257996 1 0.1608 0.8883 0.928 0.000 0.000 0.072 0.000
#> GSM258006 4 0.1914 0.7001 0.060 0.000 0.000 0.924 0.016
#> GSM257887 2 0.4305 -0.3665 0.000 0.512 0.000 0.000 0.488
#> GSM257889 3 0.1197 0.8075 0.000 0.048 0.952 0.000 0.000
#> GSM257891 3 0.0290 0.7836 0.000 0.000 0.992 0.000 0.008
#> GSM257893 3 0.2561 0.8166 0.000 0.144 0.856 0.000 0.000
#> GSM257895 2 0.4305 -0.3665 0.000 0.512 0.000 0.000 0.488
#> GSM257897 3 0.0451 0.7843 0.000 0.004 0.988 0.000 0.008
#> GSM257899 3 0.0451 0.7843 0.000 0.004 0.988 0.000 0.008
#> GSM257901 3 0.3689 0.7877 0.000 0.256 0.740 0.000 0.004
#> GSM257903 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257905 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257907 3 0.3689 0.7877 0.000 0.256 0.740 0.000 0.004
#> GSM257909 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257911 3 0.4009 0.7592 0.000 0.312 0.684 0.000 0.004
#> GSM257913 3 0.3857 0.7720 0.000 0.312 0.688 0.000 0.000
#> GSM257916 2 0.4029 0.0843 0.000 0.680 0.316 0.000 0.004
#> GSM257918 2 0.4029 0.0843 0.000 0.680 0.316 0.000 0.004
#> GSM257920 3 0.3857 0.7720 0.000 0.312 0.688 0.000 0.000
#> GSM257922 3 0.5341 0.4517 0.000 0.080 0.620 0.000 0.300
#> GSM257924 3 0.3857 0.7720 0.000 0.312 0.688 0.000 0.000
#> GSM257926 3 0.3857 0.7720 0.000 0.312 0.688 0.000 0.000
#> GSM257928 5 0.3274 0.8754 0.000 0.220 0.000 0.000 0.780
#> GSM257930 5 0.3274 0.8754 0.000 0.220 0.000 0.000 0.780
#> GSM257938 5 0.3274 0.8754 0.000 0.220 0.000 0.000 0.780
#> GSM257940 3 0.3861 0.7799 0.000 0.284 0.712 0.000 0.004
#> GSM257942 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257944 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257946 3 0.2516 0.8148 0.000 0.140 0.860 0.000 0.000
#> GSM257948 3 0.3837 0.7744 0.000 0.308 0.692 0.000 0.000
#> GSM257950 3 0.2286 0.8202 0.000 0.108 0.888 0.000 0.004
#> GSM257952 3 0.4557 0.6430 0.000 0.404 0.584 0.000 0.012
#> GSM257954 2 0.4467 0.1921 0.000 0.640 0.016 0.000 0.344
#> GSM257956 2 0.5507 -0.1936 0.000 0.480 0.064 0.000 0.456
#> GSM257959 2 0.1792 0.5950 0.000 0.916 0.000 0.000 0.084
#> GSM257961 2 0.1965 0.5867 0.000 0.904 0.000 0.000 0.096
#> GSM257963 2 0.1965 0.5867 0.000 0.904 0.000 0.000 0.096
#> GSM257965 3 0.4009 0.7592 0.000 0.312 0.684 0.000 0.004
#> GSM257967 2 0.4822 0.3602 0.000 0.664 0.048 0.000 0.288
#> GSM257969 2 0.5229 0.2882 0.000 0.612 0.064 0.000 0.324
#> GSM257971 3 0.2966 0.8174 0.000 0.136 0.848 0.000 0.016
#> GSM257973 3 0.1831 0.8166 0.000 0.076 0.920 0.000 0.004
#> GSM257981 3 0.4743 0.5043 0.000 0.472 0.512 0.000 0.016
#> GSM257983 3 0.0451 0.7843 0.000 0.004 0.988 0.000 0.008
#> GSM257985 3 0.1831 0.8166 0.000 0.076 0.920 0.000 0.004
#> GSM257988 3 0.1831 0.8166 0.000 0.076 0.920 0.000 0.004
#> GSM257991 2 0.4310 -0.2128 0.000 0.604 0.392 0.000 0.004
#> GSM257993 5 0.4305 0.2761 0.000 0.488 0.000 0.000 0.512
#> GSM257994 5 0.3274 0.8754 0.000 0.220 0.000 0.000 0.780
#> GSM257989 3 0.1831 0.8166 0.000 0.076 0.920 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.5566 0.3613 0.000 0.028 0.000 NA 0.068 0.492
#> GSM257888 1 0.1204 0.9145 0.944 0.000 0.000 NA 0.000 0.000
#> GSM257890 1 0.1563 0.9110 0.932 0.000 0.000 NA 0.000 0.012
#> GSM257892 6 0.5566 0.3613 0.000 0.028 0.000 NA 0.068 0.492
#> GSM257894 1 0.0547 0.9262 0.980 0.000 0.000 NA 0.000 0.000
#> GSM257896 1 0.1219 0.9174 0.948 0.000 0.000 NA 0.000 0.004
#> GSM257898 6 0.0363 0.6402 0.012 0.000 0.000 NA 0.000 0.988
#> GSM257900 6 0.3607 0.5331 0.348 0.000 0.000 NA 0.000 0.652
#> GSM257902 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257904 6 0.0363 0.6402 0.012 0.000 0.000 NA 0.000 0.988
#> GSM257906 6 0.0363 0.6402 0.012 0.000 0.000 NA 0.000 0.988
#> GSM257908 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257910 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257912 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257914 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257917 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257919 1 0.0937 0.9206 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257921 1 0.1866 0.8682 0.908 0.000 0.000 NA 0.000 0.084
#> GSM257923 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257925 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257927 1 0.3907 0.1889 0.588 0.000 0.000 NA 0.000 0.408
#> GSM257929 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257937 1 0.1124 0.9221 0.956 0.000 0.000 NA 0.000 0.008
#> GSM257939 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257941 6 0.3819 0.4887 0.372 0.000 0.000 NA 0.000 0.624
#> GSM257943 6 0.3647 0.5148 0.360 0.000 0.000 NA 0.000 0.640
#> GSM257945 6 0.3819 0.4835 0.372 0.000 0.000 NA 0.000 0.624
#> GSM257947 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257949 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257951 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257953 1 0.2053 0.8410 0.888 0.000 0.000 NA 0.000 0.108
#> GSM257955 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257958 1 0.0291 0.9256 0.992 0.000 0.000 NA 0.000 0.004
#> GSM257960 1 0.3782 0.3478 0.636 0.000 0.000 NA 0.000 0.360
#> GSM257962 1 0.3782 0.3478 0.636 0.000 0.000 NA 0.000 0.360
#> GSM257964 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257966 1 0.0865 0.9219 0.964 0.000 0.000 NA 0.000 0.000
#> GSM257968 1 0.0458 0.9261 0.984 0.000 0.000 NA 0.000 0.000
#> GSM257970 1 0.0146 0.9270 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257972 1 0.1663 0.8632 0.912 0.000 0.000 NA 0.000 0.088
#> GSM257977 1 0.1434 0.9159 0.940 0.000 0.000 NA 0.000 0.012
#> GSM257982 1 0.1219 0.9174 0.948 0.000 0.000 NA 0.000 0.004
#> GSM257984 1 0.0000 0.9270 1.000 0.000 0.000 NA 0.000 0.000
#> GSM257986 1 0.0000 0.9270 1.000 0.000 0.000 NA 0.000 0.000
#> GSM257990 1 0.1285 0.8947 0.944 0.000 0.000 NA 0.000 0.052
#> GSM257992 6 0.1649 0.6125 0.000 0.008 0.000 NA 0.016 0.936
#> GSM257996 1 0.1588 0.8767 0.924 0.000 0.000 NA 0.000 0.072
#> GSM258006 6 0.3504 0.6161 0.052 0.000 0.000 NA 0.016 0.820
#> GSM257887 5 0.4131 0.6077 0.000 0.384 0.000 NA 0.600 0.000
#> GSM257889 3 0.3065 0.6844 0.000 0.028 0.820 NA 0.000 0.000
#> GSM257891 3 0.2996 0.6456 0.000 0.000 0.772 NA 0.000 0.000
#> GSM257893 3 0.2537 0.7112 0.000 0.096 0.872 NA 0.000 0.000
#> GSM257895 5 0.4131 0.6077 0.000 0.384 0.000 NA 0.600 0.000
#> GSM257897 3 0.3023 0.6438 0.000 0.000 0.768 NA 0.000 0.000
#> GSM257899 3 0.3023 0.6438 0.000 0.000 0.768 NA 0.000 0.000
#> GSM257901 3 0.4634 0.6519 0.000 0.080 0.656 NA 0.000 0.000
#> GSM257903 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257905 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257907 3 0.4634 0.6519 0.000 0.080 0.656 NA 0.000 0.000
#> GSM257909 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257911 3 0.5055 0.6232 0.000 0.132 0.624 NA 0.000 0.000
#> GSM257913 3 0.4662 0.6585 0.000 0.172 0.688 NA 0.000 0.000
#> GSM257916 2 0.5499 0.2656 0.000 0.560 0.292 NA 0.004 0.000
#> GSM257918 2 0.5499 0.2656 0.000 0.560 0.292 NA 0.004 0.000
#> GSM257920 3 0.4697 0.6587 0.000 0.172 0.684 NA 0.000 0.000
#> GSM257922 3 0.5796 0.3483 0.000 0.024 0.544 NA 0.312 0.000
#> GSM257924 3 0.4697 0.6587 0.000 0.172 0.684 NA 0.000 0.000
#> GSM257926 3 0.4697 0.6587 0.000 0.172 0.684 NA 0.000 0.000
#> GSM257928 5 0.1387 0.7366 0.000 0.068 0.000 NA 0.932 0.000
#> GSM257930 5 0.1387 0.7366 0.000 0.068 0.000 NA 0.932 0.000
#> GSM257938 5 0.1387 0.7366 0.000 0.068 0.000 NA 0.932 0.000
#> GSM257940 3 0.4791 0.6450 0.000 0.104 0.652 NA 0.000 0.000
#> GSM257942 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257944 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257946 3 0.2020 0.7097 0.000 0.096 0.896 NA 0.000 0.000
#> GSM257948 3 0.4631 0.6613 0.000 0.168 0.692 NA 0.000 0.000
#> GSM257950 3 0.2030 0.7158 0.000 0.064 0.908 NA 0.000 0.000
#> GSM257952 3 0.6095 0.5089 0.000 0.256 0.508 NA 0.016 0.000
#> GSM257954 2 0.4688 -0.2604 0.000 0.544 0.020 NA 0.420 0.000
#> GSM257956 5 0.5530 0.3010 0.000 0.420 0.084 NA 0.480 0.000
#> GSM257959 2 0.0713 0.6987 0.000 0.972 0.028 NA 0.000 0.000
#> GSM257961 2 0.1074 0.6891 0.000 0.960 0.028 NA 0.012 0.000
#> GSM257963 2 0.1074 0.6891 0.000 0.960 0.028 NA 0.012 0.000
#> GSM257965 3 0.5055 0.6232 0.000 0.132 0.624 NA 0.000 0.000
#> GSM257967 2 0.4867 0.2951 0.000 0.660 0.068 NA 0.256 0.000
#> GSM257969 2 0.5354 0.0955 0.000 0.572 0.084 NA 0.328 0.000
#> GSM257971 3 0.3659 0.7068 0.000 0.012 0.752 NA 0.012 0.000
#> GSM257973 3 0.2930 0.6993 0.000 0.036 0.840 NA 0.000 0.000
#> GSM257981 3 0.6181 0.3658 0.000 0.316 0.468 NA 0.016 0.000
#> GSM257983 3 0.2941 0.6462 0.000 0.000 0.780 NA 0.000 0.000
#> GSM257985 3 0.2972 0.6978 0.000 0.036 0.836 NA 0.000 0.000
#> GSM257988 3 0.2930 0.6993 0.000 0.036 0.840 NA 0.000 0.000
#> GSM257991 2 0.5931 -0.0866 0.000 0.424 0.360 NA 0.000 0.000
#> GSM257993 5 0.4039 0.6370 0.000 0.352 0.000 NA 0.632 0.000
#> GSM257994 5 0.1387 0.7366 0.000 0.068 0.000 NA 0.932 0.000
#> GSM257989 3 0.2930 0.6993 0.000 0.036 0.840 NA 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 cell.type(p) protocol(p) individual(p) k
#> SD:hclust 96 8.49e-22 1.000 1.000 2
#> SD:hclust 91 1.74e-20 0.898 1.000 3
#> SD:hclust 87 9.66e-19 0.538 0.994 4
#> SD:hclust 80 1.74e-16 0.593 0.873 5
#> SD:hclust 80 1.74e-16 0.605 0.929 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 25171 rows and 96 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 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.695 0.875 0.809 0.2362 0.874 0.745
#> 4 4 0.616 0.688 0.658 0.1114 0.864 0.642
#> 5 5 0.586 0.654 0.737 0.0782 0.870 0.571
#> 6 6 0.583 0.584 0.707 0.0551 0.937 0.738
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.6126 0.818 0.600 0.400 0.000
#> GSM257888 1 0.4504 0.870 0.804 0.196 0.000
#> GSM257890 1 0.5785 0.848 0.668 0.332 0.000
#> GSM257892 1 0.6126 0.818 0.600 0.400 0.000
#> GSM257894 1 0.4121 0.874 0.832 0.168 0.000
#> GSM257896 1 0.4974 0.871 0.764 0.236 0.000
#> GSM257898 1 0.5138 0.854 0.748 0.252 0.000
#> GSM257900 1 0.4121 0.878 0.832 0.168 0.000
#> GSM257902 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257904 1 0.5560 0.850 0.700 0.300 0.000
#> GSM257906 1 0.5560 0.850 0.700 0.300 0.000
#> GSM257908 1 0.3879 0.877 0.848 0.152 0.000
#> GSM257910 1 0.3879 0.877 0.848 0.152 0.000
#> GSM257912 1 0.5216 0.871 0.740 0.260 0.000
#> GSM257914 1 0.5216 0.871 0.740 0.260 0.000
#> GSM257917 1 0.5254 0.871 0.736 0.264 0.000
#> GSM257919 1 0.5216 0.871 0.740 0.260 0.000
#> GSM257921 1 0.4178 0.894 0.828 0.172 0.000
#> GSM257923 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257925 1 0.0237 0.897 0.996 0.004 0.000
#> GSM257927 1 0.3686 0.885 0.860 0.140 0.000
#> GSM257929 1 0.0237 0.897 0.996 0.004 0.000
#> GSM257937 1 0.5327 0.867 0.728 0.272 0.000
#> GSM257939 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257941 1 0.4002 0.879 0.840 0.160 0.000
#> GSM257943 1 0.4750 0.859 0.784 0.216 0.000
#> GSM257945 1 0.4235 0.874 0.824 0.176 0.000
#> GSM257947 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257953 1 0.0237 0.897 0.996 0.004 0.000
#> GSM257955 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257958 1 0.0237 0.897 0.996 0.004 0.000
#> GSM257960 1 0.4002 0.880 0.840 0.160 0.000
#> GSM257962 1 0.3686 0.885 0.860 0.140 0.000
#> GSM257964 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257966 1 0.4452 0.871 0.808 0.192 0.000
#> GSM257968 1 0.3340 0.884 0.880 0.120 0.000
#> GSM257970 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257977 1 0.5178 0.869 0.744 0.256 0.000
#> GSM257982 1 0.4235 0.873 0.824 0.176 0.000
#> GSM257984 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.897 1.000 0.000 0.000
#> GSM257990 1 0.0424 0.897 0.992 0.008 0.000
#> GSM257992 1 0.5138 0.854 0.748 0.252 0.000
#> GSM257996 1 0.2711 0.895 0.912 0.088 0.000
#> GSM258006 1 0.5621 0.848 0.692 0.308 0.000
#> GSM257887 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257889 3 0.0747 0.910 0.000 0.016 0.984
#> GSM257891 3 0.0424 0.915 0.000 0.008 0.992
#> GSM257893 3 0.1163 0.900 0.000 0.028 0.972
#> GSM257895 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257897 3 0.1163 0.900 0.000 0.028 0.972
#> GSM257899 3 0.1163 0.900 0.000 0.028 0.972
#> GSM257901 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257903 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257905 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257907 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257909 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257911 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257913 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257916 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257918 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257920 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257922 3 0.1163 0.900 0.000 0.028 0.972
#> GSM257924 3 0.1411 0.881 0.000 0.036 0.964
#> GSM257926 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257928 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257930 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257938 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257940 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257942 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257944 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257946 3 0.0424 0.915 0.000 0.008 0.992
#> GSM257948 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257950 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257952 3 0.6274 -0.731 0.000 0.456 0.544
#> GSM257954 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257956 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257959 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257961 2 0.6215 0.974 0.000 0.572 0.428
#> GSM257963 2 0.6215 0.974 0.000 0.572 0.428
#> GSM257965 2 0.6235 0.969 0.000 0.564 0.436
#> GSM257967 2 0.6225 0.974 0.000 0.568 0.432
#> GSM257969 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257971 3 0.1163 0.900 0.000 0.028 0.972
#> GSM257973 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257981 3 0.6260 -0.719 0.000 0.448 0.552
#> GSM257983 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257985 3 0.0424 0.915 0.000 0.008 0.992
#> GSM257988 3 0.0000 0.918 0.000 0.000 1.000
#> GSM257991 2 0.6280 0.930 0.000 0.540 0.460
#> GSM257993 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257994 2 0.6140 0.969 0.000 0.596 0.404
#> GSM257989 3 0.0000 0.918 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.6523 0.4481 0.628 0.000 0.236 0.136
#> GSM257888 1 0.2675 0.4157 0.892 0.000 0.008 0.100
#> GSM257890 1 0.2060 0.4974 0.932 0.000 0.016 0.052
#> GSM257892 1 0.6523 0.4481 0.628 0.000 0.236 0.136
#> GSM257894 1 0.3196 0.3532 0.856 0.000 0.008 0.136
#> GSM257896 1 0.2048 0.4579 0.928 0.000 0.008 0.064
#> GSM257898 1 0.7753 0.3138 0.432 0.000 0.256 0.312
#> GSM257900 1 0.7081 -0.0834 0.452 0.000 0.124 0.424
#> GSM257902 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257904 1 0.7478 0.3950 0.504 0.000 0.256 0.240
#> GSM257906 1 0.7476 0.3953 0.504 0.000 0.260 0.236
#> GSM257908 1 0.3694 0.3809 0.844 0.000 0.032 0.124
#> GSM257910 1 0.3694 0.3809 0.844 0.000 0.032 0.124
#> GSM257912 1 0.1929 0.4871 0.940 0.000 0.036 0.024
#> GSM257914 1 0.1929 0.4871 0.940 0.000 0.036 0.024
#> GSM257917 1 0.2227 0.4903 0.928 0.000 0.036 0.036
#> GSM257919 1 0.1929 0.4871 0.940 0.000 0.036 0.024
#> GSM257921 1 0.3725 0.3101 0.812 0.000 0.008 0.180
#> GSM257923 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257925 4 0.4955 0.9829 0.444 0.000 0.000 0.556
#> GSM257927 1 0.7030 -0.1788 0.472 0.000 0.120 0.408
#> GSM257929 4 0.4955 0.9829 0.444 0.000 0.000 0.556
#> GSM257937 1 0.0804 0.4919 0.980 0.000 0.008 0.012
#> GSM257939 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257941 1 0.7113 -0.1077 0.456 0.000 0.128 0.416
#> GSM257943 1 0.7806 0.2562 0.392 0.000 0.252 0.356
#> GSM257945 1 0.7188 -0.0305 0.432 0.000 0.136 0.432
#> GSM257947 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257949 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257951 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257953 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257955 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257958 4 0.4955 0.9829 0.444 0.000 0.000 0.556
#> GSM257960 1 0.7110 -0.1183 0.460 0.000 0.128 0.412
#> GSM257962 1 0.7030 -0.1788 0.472 0.000 0.120 0.408
#> GSM257964 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257966 1 0.3056 0.4398 0.888 0.000 0.040 0.072
#> GSM257968 1 0.4699 -0.2259 0.676 0.000 0.004 0.320
#> GSM257970 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257972 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257977 1 0.1452 0.4800 0.956 0.000 0.008 0.036
#> GSM257982 1 0.3032 0.3697 0.868 0.000 0.008 0.124
#> GSM257984 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257986 4 0.4961 0.9890 0.448 0.000 0.000 0.552
#> GSM257990 4 0.5821 0.8454 0.432 0.000 0.032 0.536
#> GSM257992 1 0.7758 0.3145 0.432 0.000 0.260 0.308
#> GSM257996 1 0.5837 -0.5439 0.564 0.000 0.036 0.400
#> GSM258006 1 0.7371 0.4074 0.520 0.000 0.268 0.212
#> GSM257887 2 0.3166 0.8511 0.000 0.868 0.016 0.116
#> GSM257889 3 0.4746 0.9339 0.000 0.304 0.688 0.008
#> GSM257891 3 0.4792 0.9373 0.000 0.312 0.680 0.008
#> GSM257893 3 0.5161 0.9225 0.000 0.300 0.676 0.024
#> GSM257895 2 0.3647 0.8361 0.000 0.832 0.016 0.152
#> GSM257897 3 0.4844 0.9297 0.000 0.300 0.688 0.012
#> GSM257899 3 0.5161 0.9240 0.000 0.300 0.676 0.024
#> GSM257901 3 0.6746 0.8931 0.000 0.316 0.568 0.116
#> GSM257903 2 0.0817 0.8647 0.000 0.976 0.000 0.024
#> GSM257905 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257907 3 0.6746 0.8931 0.000 0.316 0.568 0.116
#> GSM257909 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257911 3 0.6778 0.8760 0.000 0.336 0.552 0.112
#> GSM257913 3 0.6123 0.9121 0.000 0.336 0.600 0.064
#> GSM257916 2 0.0817 0.8647 0.000 0.976 0.000 0.024
#> GSM257918 2 0.0817 0.8647 0.000 0.976 0.000 0.024
#> GSM257920 3 0.5917 0.9272 0.000 0.320 0.624 0.056
#> GSM257922 3 0.5814 0.8965 0.000 0.300 0.644 0.056
#> GSM257924 3 0.4624 0.9257 0.000 0.340 0.660 0.000
#> GSM257926 3 0.4677 0.9390 0.000 0.316 0.680 0.004
#> GSM257928 2 0.5160 0.7688 0.000 0.748 0.072 0.180
#> GSM257930 2 0.4035 0.8195 0.000 0.804 0.020 0.176
#> GSM257938 2 0.3925 0.8219 0.000 0.808 0.016 0.176
#> GSM257940 3 0.6700 0.8934 0.000 0.316 0.572 0.112
#> GSM257942 2 0.0817 0.8647 0.000 0.976 0.000 0.024
#> GSM257944 2 0.0817 0.8647 0.000 0.976 0.000 0.024
#> GSM257946 3 0.4477 0.9383 0.000 0.312 0.688 0.000
#> GSM257948 3 0.5917 0.9272 0.000 0.320 0.624 0.056
#> GSM257950 3 0.5152 0.9389 0.000 0.316 0.664 0.020
#> GSM257952 2 0.6147 0.3599 0.000 0.664 0.224 0.112
#> GSM257954 2 0.3280 0.8483 0.000 0.860 0.016 0.124
#> GSM257956 2 0.2987 0.8548 0.000 0.880 0.016 0.104
#> GSM257959 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257965 2 0.3464 0.7770 0.000 0.860 0.032 0.108
#> GSM257967 2 0.0000 0.8707 0.000 1.000 0.000 0.000
#> GSM257969 2 0.2593 0.8602 0.000 0.904 0.016 0.080
#> GSM257971 3 0.6425 0.8852 0.000 0.300 0.604 0.096
#> GSM257973 3 0.5517 0.9358 0.000 0.316 0.648 0.036
#> GSM257981 2 0.6064 0.3755 0.000 0.672 0.220 0.108
#> GSM257983 3 0.4814 0.9381 0.000 0.316 0.676 0.008
#> GSM257985 3 0.4792 0.9373 0.000 0.312 0.680 0.008
#> GSM257988 3 0.5754 0.9317 0.000 0.316 0.636 0.048
#> GSM257991 2 0.3219 0.7762 0.000 0.868 0.020 0.112
#> GSM257993 2 0.3443 0.8437 0.000 0.848 0.016 0.136
#> GSM257994 2 0.3925 0.8219 0.000 0.808 0.016 0.176
#> GSM257989 3 0.5047 0.9392 0.000 0.316 0.668 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.7308 0.61530 0.156 0.112 0.000 0.184 0.548
#> GSM257888 4 0.6956 0.75641 0.392 0.120 0.000 0.444 0.044
#> GSM257890 4 0.7793 0.65861 0.228 0.132 0.000 0.476 0.164
#> GSM257892 5 0.7308 0.61530 0.156 0.112 0.000 0.184 0.548
#> GSM257894 1 0.6464 -0.72488 0.444 0.116 0.000 0.424 0.016
#> GSM257896 4 0.7150 0.77944 0.372 0.120 0.000 0.448 0.060
#> GSM257898 5 0.5184 0.79569 0.280 0.044 0.000 0.016 0.660
#> GSM257900 1 0.4533 0.00123 0.544 0.000 0.000 0.008 0.448
#> GSM257902 1 0.0162 0.70432 0.996 0.004 0.000 0.000 0.000
#> GSM257904 5 0.5757 0.82798 0.232 0.044 0.000 0.064 0.660
#> GSM257906 5 0.5823 0.82829 0.232 0.048 0.000 0.064 0.656
#> GSM257908 4 0.4225 0.81796 0.364 0.000 0.000 0.632 0.004
#> GSM257910 4 0.4225 0.81796 0.364 0.000 0.000 0.632 0.004
#> GSM257912 4 0.5104 0.82958 0.308 0.000 0.000 0.632 0.060
#> GSM257914 4 0.5104 0.82958 0.308 0.000 0.000 0.632 0.060
#> GSM257917 4 0.5285 0.80227 0.288 0.000 0.000 0.632 0.080
#> GSM257919 4 0.5104 0.82958 0.308 0.000 0.000 0.632 0.060
#> GSM257921 1 0.6555 -0.24893 0.460 0.000 0.000 0.320 0.220
#> GSM257923 1 0.0000 0.70424 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0794 0.70022 0.972 0.000 0.000 0.000 0.028
#> GSM257927 1 0.4510 0.05804 0.560 0.000 0.000 0.008 0.432
#> GSM257929 1 0.0794 0.70022 0.972 0.000 0.000 0.000 0.028
#> GSM257937 4 0.7386 0.79467 0.304 0.112 0.000 0.484 0.100
#> GSM257939 1 0.0000 0.70424 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.4533 0.00123 0.544 0.000 0.000 0.008 0.448
#> GSM257943 5 0.3932 0.66058 0.328 0.000 0.000 0.000 0.672
#> GSM257945 1 0.4302 -0.11779 0.520 0.000 0.000 0.000 0.480
#> GSM257947 1 0.0000 0.70424 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0162 0.70267 0.996 0.004 0.000 0.000 0.000
#> GSM257951 1 0.0162 0.70432 0.996 0.004 0.000 0.000 0.000
#> GSM257953 1 0.0404 0.70399 0.988 0.000 0.000 0.000 0.012
#> GSM257955 1 0.0162 0.70432 0.996 0.004 0.000 0.000 0.000
#> GSM257958 1 0.0794 0.70022 0.972 0.000 0.000 0.000 0.028
#> GSM257960 1 0.4533 0.00123 0.544 0.000 0.000 0.008 0.448
#> GSM257962 1 0.4510 0.05804 0.560 0.000 0.000 0.008 0.432
#> GSM257964 1 0.0000 0.70424 1.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.5243 0.83168 0.352 0.048 0.000 0.596 0.004
#> GSM257968 1 0.4967 0.16779 0.732 0.080 0.000 0.172 0.016
#> GSM257970 1 0.0162 0.70432 0.996 0.004 0.000 0.000 0.000
#> GSM257972 1 0.0912 0.69863 0.972 0.016 0.000 0.000 0.012
#> GSM257977 4 0.7311 0.78468 0.352 0.120 0.000 0.452 0.076
#> GSM257982 1 0.6934 -0.74119 0.424 0.116 0.000 0.416 0.044
#> GSM257984 1 0.0162 0.70267 0.996 0.004 0.000 0.000 0.000
#> GSM257986 1 0.0162 0.70267 0.996 0.004 0.000 0.000 0.000
#> GSM257990 1 0.2719 0.62308 0.852 0.000 0.000 0.004 0.144
#> GSM257992 5 0.5548 0.80092 0.280 0.056 0.000 0.024 0.640
#> GSM257996 1 0.4681 0.43056 0.696 0.000 0.000 0.052 0.252
#> GSM258006 5 0.6344 0.79930 0.216 0.076 0.000 0.080 0.628
#> GSM257887 2 0.4263 0.78113 0.000 0.760 0.180 0.000 0.060
#> GSM257889 3 0.3019 0.83460 0.000 0.016 0.880 0.056 0.048
#> GSM257891 3 0.2153 0.85037 0.000 0.000 0.916 0.044 0.040
#> GSM257893 3 0.3110 0.82593 0.000 0.020 0.876 0.060 0.044
#> GSM257895 2 0.5322 0.75768 0.000 0.708 0.180 0.024 0.088
#> GSM257897 3 0.3255 0.83168 0.000 0.020 0.868 0.056 0.056
#> GSM257899 3 0.3388 0.82669 0.000 0.020 0.860 0.064 0.056
#> GSM257901 3 0.3804 0.78491 0.000 0.000 0.796 0.044 0.160
#> GSM257903 2 0.6181 0.81007 0.000 0.576 0.200 0.220 0.004
#> GSM257905 2 0.5904 0.81743 0.000 0.600 0.200 0.200 0.000
#> GSM257907 3 0.3804 0.78491 0.000 0.000 0.796 0.044 0.160
#> GSM257909 2 0.5931 0.81655 0.000 0.596 0.200 0.204 0.000
#> GSM257911 3 0.3911 0.77161 0.000 0.000 0.796 0.060 0.144
#> GSM257913 3 0.2522 0.82990 0.000 0.000 0.896 0.052 0.052
#> GSM257916 2 0.6181 0.81007 0.000 0.576 0.200 0.220 0.004
#> GSM257918 2 0.6181 0.81007 0.000 0.576 0.200 0.220 0.004
#> GSM257920 3 0.2067 0.84055 0.000 0.000 0.920 0.032 0.048
#> GSM257922 3 0.3375 0.81669 0.000 0.020 0.860 0.072 0.048
#> GSM257924 3 0.2745 0.83618 0.000 0.028 0.896 0.052 0.024
#> GSM257926 3 0.0992 0.85573 0.000 0.000 0.968 0.024 0.008
#> GSM257928 2 0.6645 0.66393 0.000 0.600 0.220 0.072 0.108
#> GSM257930 2 0.6041 0.73104 0.000 0.664 0.180 0.056 0.100
#> GSM257938 2 0.5866 0.74019 0.000 0.676 0.180 0.048 0.096
#> GSM257940 3 0.3565 0.78461 0.000 0.000 0.816 0.040 0.144
#> GSM257942 2 0.6181 0.81007 0.000 0.576 0.200 0.220 0.004
#> GSM257944 2 0.6181 0.81007 0.000 0.576 0.200 0.220 0.004
#> GSM257946 3 0.1018 0.85446 0.000 0.000 0.968 0.016 0.016
#> GSM257948 3 0.2067 0.84055 0.000 0.000 0.920 0.032 0.048
#> GSM257950 3 0.0162 0.85543 0.000 0.000 0.996 0.004 0.000
#> GSM257952 3 0.7268 -0.10663 0.000 0.328 0.460 0.052 0.160
#> GSM257954 2 0.4661 0.77542 0.000 0.744 0.180 0.008 0.068
#> GSM257956 2 0.4199 0.78228 0.000 0.764 0.180 0.000 0.056
#> GSM257959 2 0.5904 0.81743 0.000 0.600 0.200 0.200 0.000
#> GSM257961 2 0.5904 0.81743 0.000 0.600 0.200 0.200 0.000
#> GSM257963 2 0.5904 0.81743 0.000 0.600 0.200 0.200 0.000
#> GSM257965 2 0.7719 0.63122 0.000 0.468 0.268 0.124 0.140
#> GSM257967 2 0.5877 0.81770 0.000 0.604 0.200 0.196 0.000
#> GSM257969 2 0.5379 0.79443 0.000 0.712 0.180 0.052 0.056
#> GSM257971 3 0.4853 0.78536 0.000 0.020 0.748 0.076 0.156
#> GSM257973 3 0.1012 0.85238 0.000 0.000 0.968 0.012 0.020
#> GSM257981 3 0.7029 -0.11226 0.000 0.336 0.476 0.040 0.148
#> GSM257983 3 0.2077 0.85129 0.000 0.000 0.920 0.040 0.040
#> GSM257985 3 0.1582 0.85434 0.000 0.000 0.944 0.028 0.028
#> GSM257988 3 0.1281 0.85184 0.000 0.000 0.956 0.012 0.032
#> GSM257991 2 0.7843 0.60217 0.000 0.440 0.288 0.140 0.132
#> GSM257993 2 0.4719 0.77388 0.000 0.740 0.180 0.008 0.072
#> GSM257994 2 0.5866 0.74019 0.000 0.676 0.180 0.048 0.096
#> GSM257989 3 0.0324 0.85578 0.000 0.000 0.992 0.004 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.7523 0.3640 0.092 0.088 0.000 0.128 0.180 0.512
#> GSM257888 4 0.6552 0.7456 0.280 0.000 0.000 0.452 0.232 0.036
#> GSM257890 4 0.6851 0.6955 0.136 0.000 0.000 0.492 0.244 0.128
#> GSM257892 6 0.7523 0.3640 0.092 0.088 0.000 0.128 0.180 0.512
#> GSM257894 4 0.6431 0.7095 0.328 0.004 0.000 0.428 0.224 0.016
#> GSM257896 4 0.6802 0.7535 0.248 0.000 0.000 0.456 0.232 0.064
#> GSM257898 6 0.5295 0.6190 0.172 0.036 0.000 0.020 0.076 0.696
#> GSM257900 6 0.4141 0.4796 0.432 0.000 0.000 0.012 0.000 0.556
#> GSM257902 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257904 6 0.5563 0.5984 0.140 0.036 0.000 0.052 0.076 0.696
#> GSM257906 6 0.5920 0.5911 0.140 0.060 0.000 0.052 0.076 0.672
#> GSM257908 4 0.3323 0.7536 0.240 0.000 0.000 0.752 0.000 0.008
#> GSM257910 4 0.3323 0.7536 0.240 0.000 0.000 0.752 0.000 0.008
#> GSM257912 4 0.3874 0.7499 0.172 0.000 0.000 0.760 0.000 0.068
#> GSM257914 4 0.3874 0.7499 0.172 0.000 0.000 0.760 0.000 0.068
#> GSM257917 4 0.3928 0.7358 0.160 0.000 0.000 0.760 0.000 0.080
#> GSM257919 4 0.3874 0.7499 0.172 0.000 0.000 0.760 0.000 0.068
#> GSM257921 6 0.6129 0.0314 0.336 0.000 0.000 0.320 0.000 0.344
#> GSM257923 1 0.0000 0.8883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.1327 0.8505 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM257927 6 0.4175 0.4216 0.464 0.000 0.000 0.012 0.000 0.524
#> GSM257929 1 0.1327 0.8505 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM257937 4 0.6781 0.7477 0.180 0.000 0.000 0.496 0.232 0.092
#> GSM257939 1 0.0000 0.8883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 6 0.4147 0.4760 0.436 0.000 0.000 0.012 0.000 0.552
#> GSM257943 6 0.2964 0.6319 0.204 0.000 0.000 0.004 0.000 0.792
#> GSM257945 6 0.4158 0.5006 0.416 0.004 0.000 0.008 0.000 0.572
#> GSM257947 1 0.0000 0.8883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257951 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257953 1 0.0937 0.8684 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM257955 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257958 1 0.1327 0.8505 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM257960 6 0.4141 0.4822 0.432 0.000 0.000 0.012 0.000 0.556
#> GSM257962 6 0.4175 0.4216 0.464 0.000 0.000 0.012 0.000 0.524
#> GSM257964 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257966 4 0.4763 0.7832 0.220 0.000 0.000 0.680 0.092 0.008
#> GSM257968 1 0.5231 0.2564 0.668 0.004 0.000 0.152 0.160 0.016
#> GSM257970 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257972 1 0.1937 0.8537 0.924 0.004 0.000 0.004 0.032 0.036
#> GSM257977 4 0.6802 0.7529 0.236 0.000 0.000 0.464 0.232 0.068
#> GSM257982 4 0.6663 0.7272 0.304 0.004 0.000 0.432 0.228 0.032
#> GSM257984 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257986 1 0.0291 0.8893 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM257990 1 0.3668 0.4850 0.728 0.008 0.000 0.008 0.000 0.256
#> GSM257992 6 0.5965 0.6085 0.172 0.064 0.000 0.024 0.092 0.648
#> GSM257996 1 0.4845 -0.1035 0.560 0.008 0.000 0.044 0.000 0.388
#> GSM258006 6 0.6829 0.5129 0.124 0.084 0.000 0.072 0.124 0.596
#> GSM257887 2 0.7155 0.5097 0.000 0.528 0.108 0.148 0.184 0.032
#> GSM257889 3 0.3777 0.6860 0.000 0.008 0.812 0.012 0.088 0.080
#> GSM257891 3 0.3039 0.7029 0.000 0.000 0.852 0.008 0.056 0.084
#> GSM257893 3 0.3672 0.6771 0.000 0.012 0.820 0.012 0.104 0.052
#> GSM257895 2 0.7562 0.4595 0.000 0.444 0.108 0.164 0.252 0.032
#> GSM257897 3 0.4023 0.6712 0.000 0.012 0.796 0.012 0.096 0.084
#> GSM257899 3 0.4070 0.6652 0.000 0.012 0.792 0.012 0.100 0.084
#> GSM257901 3 0.3547 0.4269 0.000 0.004 0.696 0.000 0.300 0.000
#> GSM257903 2 0.2696 0.5800 0.000 0.856 0.116 0.000 0.028 0.000
#> GSM257905 2 0.2003 0.6004 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM257907 3 0.3584 0.4266 0.000 0.004 0.688 0.000 0.308 0.000
#> GSM257909 2 0.2146 0.5983 0.000 0.880 0.116 0.000 0.004 0.000
#> GSM257911 3 0.4302 0.3407 0.000 0.036 0.668 0.000 0.292 0.004
#> GSM257913 3 0.3183 0.6210 0.000 0.040 0.828 0.000 0.128 0.004
#> GSM257916 2 0.3185 0.5768 0.000 0.832 0.116 0.000 0.048 0.004
#> GSM257918 2 0.2985 0.5777 0.000 0.844 0.116 0.000 0.036 0.004
#> GSM257920 3 0.2544 0.6650 0.000 0.012 0.864 0.000 0.120 0.004
#> GSM257922 3 0.4831 0.5819 0.000 0.012 0.744 0.040 0.116 0.088
#> GSM257924 3 0.3001 0.6860 0.000 0.048 0.868 0.004 0.060 0.020
#> GSM257926 3 0.1974 0.7225 0.000 0.012 0.920 0.000 0.048 0.020
#> GSM257928 2 0.8518 0.2447 0.000 0.284 0.160 0.204 0.276 0.076
#> GSM257930 2 0.8150 0.3955 0.000 0.372 0.108 0.200 0.252 0.068
#> GSM257938 2 0.8141 0.4004 0.000 0.376 0.108 0.200 0.248 0.068
#> GSM257940 3 0.3626 0.4243 0.000 0.004 0.704 0.000 0.288 0.004
#> GSM257942 2 0.2771 0.5777 0.000 0.852 0.116 0.000 0.032 0.000
#> GSM257944 2 0.2771 0.5777 0.000 0.852 0.116 0.000 0.032 0.000
#> GSM257946 3 0.1261 0.7246 0.000 0.000 0.952 0.000 0.024 0.024
#> GSM257948 3 0.2566 0.6625 0.000 0.012 0.868 0.000 0.112 0.008
#> GSM257950 3 0.0862 0.7208 0.000 0.004 0.972 0.000 0.016 0.008
#> GSM257952 5 0.6075 0.0000 0.000 0.268 0.360 0.000 0.372 0.000
#> GSM257954 2 0.7407 0.4885 0.000 0.484 0.108 0.164 0.212 0.032
#> GSM257956 2 0.7108 0.5069 0.000 0.528 0.108 0.144 0.192 0.028
#> GSM257959 2 0.2003 0.6004 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM257961 2 0.2003 0.6004 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM257963 2 0.2003 0.6004 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM257965 2 0.5912 -0.3763 0.000 0.472 0.192 0.000 0.332 0.004
#> GSM257967 2 0.2257 0.5998 0.000 0.876 0.116 0.000 0.008 0.000
#> GSM257969 2 0.6561 0.5276 0.000 0.600 0.108 0.104 0.160 0.028
#> GSM257971 3 0.5012 0.5154 0.000 0.012 0.652 0.008 0.264 0.064
#> GSM257973 3 0.1296 0.7114 0.000 0.004 0.948 0.000 0.044 0.004
#> GSM257981 3 0.6285 -0.9485 0.000 0.272 0.388 0.000 0.332 0.008
#> GSM257983 3 0.2762 0.7141 0.000 0.004 0.876 0.008 0.040 0.072
#> GSM257985 3 0.1700 0.7242 0.000 0.000 0.928 0.000 0.024 0.048
#> GSM257988 3 0.1787 0.6989 0.000 0.004 0.920 0.000 0.068 0.008
#> GSM257991 2 0.5756 -0.2070 0.000 0.544 0.180 0.000 0.268 0.008
#> GSM257993 2 0.7407 0.4885 0.000 0.484 0.108 0.164 0.212 0.032
#> GSM257994 2 0.8141 0.4004 0.000 0.376 0.108 0.200 0.248 0.068
#> GSM257989 3 0.0653 0.7239 0.000 0.004 0.980 0.000 0.004 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 cell.type(p) protocol(p) individual(p) k
#> SD:kmeans 96 8.49e-22 1.000 1.000 2
#> SD:kmeans 94 3.87e-21 0.539 1.000 3
#> SD:kmeans 63 2.09e-14 0.401 0.909 4
#> SD:kmeans 83 4.03e-17 0.518 0.980 5
#> SD:kmeans 70 2.27e-14 0.203 0.860 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 25171 rows and 96 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.807 0.970 0.927 0.2322 0.874 0.745
#> 4 4 0.870 0.799 0.867 0.1777 0.896 0.717
#> 5 5 0.911 0.891 0.931 0.0623 0.902 0.664
#> 6 6 0.818 0.647 0.854 0.0360 0.986 0.938
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.4654 0.861 0.792 0.000 0.208
#> GSM257888 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257890 1 0.0592 0.961 0.988 0.000 0.012
#> GSM257892 1 0.4654 0.861 0.792 0.000 0.208
#> GSM257894 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257896 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257898 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257900 1 0.2537 0.931 0.920 0.000 0.080
#> GSM257902 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257904 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257906 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257908 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257910 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257912 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257914 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257917 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257919 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257921 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257927 1 0.1643 0.947 0.956 0.000 0.044
#> GSM257929 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257937 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257939 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257941 1 0.2537 0.931 0.920 0.000 0.080
#> GSM257943 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257945 1 0.3619 0.901 0.864 0.000 0.136
#> GSM257947 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257960 1 0.2537 0.931 0.920 0.000 0.080
#> GSM257962 1 0.1643 0.947 0.956 0.000 0.044
#> GSM257964 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257966 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257968 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257970 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257977 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257982 1 0.0424 0.962 0.992 0.000 0.008
#> GSM257984 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.963 1.000 0.000 0.000
#> GSM257990 1 0.0237 0.962 0.996 0.000 0.004
#> GSM257992 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257996 1 0.0000 0.963 1.000 0.000 0.000
#> GSM258006 1 0.4555 0.862 0.800 0.000 0.200
#> GSM257887 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257889 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257891 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257893 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257895 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257897 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257899 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257901 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257903 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257907 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257909 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257911 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257913 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257916 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257920 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257922 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257924 3 0.4702 0.995 0.000 0.212 0.788
#> GSM257926 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257928 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257930 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257938 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257940 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257942 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257946 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257948 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257950 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257952 2 0.0237 0.995 0.000 0.996 0.004
#> GSM257954 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257965 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257971 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257973 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257981 2 0.0424 0.990 0.000 0.992 0.008
#> GSM257983 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257985 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257988 3 0.4654 1.000 0.000 0.208 0.792
#> GSM257991 2 0.0237 0.995 0.000 0.996 0.004
#> GSM257993 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257994 2 0.0000 0.999 0.000 1.000 0.000
#> GSM257989 3 0.4654 1.000 0.000 0.208 0.792
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.5220 0.320 0.424 0.000 0.008 0.568
#> GSM257888 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257890 1 0.1557 0.545 0.944 0.000 0.000 0.056
#> GSM257892 4 0.5220 0.320 0.424 0.000 0.008 0.568
#> GSM257894 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257896 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257898 4 0.0672 0.750 0.008 0.000 0.008 0.984
#> GSM257900 4 0.3444 0.640 0.184 0.000 0.000 0.816
#> GSM257902 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257904 4 0.0672 0.750 0.008 0.000 0.008 0.984
#> GSM257906 4 0.0672 0.750 0.008 0.000 0.008 0.984
#> GSM257908 1 0.0188 0.610 0.996 0.000 0.000 0.004
#> GSM257910 1 0.0188 0.610 0.996 0.000 0.000 0.004
#> GSM257912 1 0.0188 0.610 0.996 0.000 0.000 0.004
#> GSM257914 1 0.0188 0.610 0.996 0.000 0.000 0.004
#> GSM257917 1 0.0336 0.608 0.992 0.000 0.000 0.008
#> GSM257919 1 0.0188 0.610 0.996 0.000 0.000 0.004
#> GSM257921 1 0.4925 0.596 0.572 0.000 0.000 0.428
#> GSM257923 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257925 1 0.4925 0.596 0.572 0.000 0.000 0.428
#> GSM257927 4 0.4072 0.496 0.252 0.000 0.000 0.748
#> GSM257929 1 0.4925 0.596 0.572 0.000 0.000 0.428
#> GSM257937 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257939 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257941 4 0.3356 0.650 0.176 0.000 0.000 0.824
#> GSM257943 4 0.0672 0.750 0.008 0.000 0.008 0.984
#> GSM257945 4 0.2647 0.698 0.120 0.000 0.000 0.880
#> GSM257947 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257949 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257951 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257953 1 0.4925 0.596 0.572 0.000 0.000 0.428
#> GSM257955 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257958 1 0.4925 0.596 0.572 0.000 0.000 0.428
#> GSM257960 4 0.3444 0.640 0.184 0.000 0.000 0.816
#> GSM257962 4 0.4072 0.496 0.252 0.000 0.000 0.748
#> GSM257964 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257966 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257968 1 0.0592 0.610 0.984 0.000 0.000 0.016
#> GSM257970 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257972 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257977 1 0.0000 0.608 1.000 0.000 0.000 0.000
#> GSM257982 1 0.0469 0.610 0.988 0.000 0.000 0.012
#> GSM257984 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257986 1 0.4916 0.601 0.576 0.000 0.000 0.424
#> GSM257990 1 0.4998 0.460 0.512 0.000 0.000 0.488
#> GSM257992 4 0.0672 0.750 0.008 0.000 0.008 0.984
#> GSM257996 1 0.4933 0.590 0.568 0.000 0.000 0.432
#> GSM258006 4 0.0804 0.747 0.012 0.000 0.008 0.980
#> GSM257887 2 0.0188 0.992 0.000 0.996 0.000 0.004
#> GSM257889 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257891 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257893 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257895 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257897 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257899 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257901 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257903 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257907 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257909 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257911 3 0.0592 0.991 0.000 0.016 0.984 0.000
#> GSM257913 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257916 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257920 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257922 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257924 3 0.1211 0.964 0.000 0.040 0.960 0.000
#> GSM257926 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257928 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257930 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257938 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257940 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257942 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257948 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257950 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257952 2 0.1474 0.944 0.000 0.948 0.052 0.000
#> GSM257954 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257956 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257959 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 0.992 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257971 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257973 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257981 2 0.1637 0.935 0.000 0.940 0.060 0.000
#> GSM257983 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257985 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257988 3 0.0336 0.998 0.000 0.008 0.992 0.000
#> GSM257991 2 0.0188 0.990 0.000 0.996 0.004 0.000
#> GSM257993 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257994 2 0.0336 0.991 0.000 0.992 0.000 0.008
#> GSM257989 3 0.0336 0.998 0.000 0.008 0.992 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.1792 0.8066 0.000 0.000 0.000 0.084 0.916
#> GSM257888 4 0.3805 0.8654 0.184 0.000 0.000 0.784 0.032
#> GSM257890 4 0.2514 0.9168 0.060 0.000 0.000 0.896 0.044
#> GSM257892 5 0.1792 0.8066 0.000 0.000 0.000 0.084 0.916
#> GSM257894 4 0.3910 0.8565 0.196 0.000 0.000 0.772 0.032
#> GSM257896 4 0.3876 0.8610 0.192 0.000 0.000 0.776 0.032
#> GSM257898 5 0.1965 0.8966 0.096 0.000 0.000 0.000 0.904
#> GSM257900 1 0.3160 0.7459 0.808 0.000 0.000 0.004 0.188
#> GSM257902 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257904 5 0.1965 0.8966 0.096 0.000 0.000 0.000 0.904
#> GSM257906 5 0.1965 0.8966 0.096 0.000 0.000 0.000 0.904
#> GSM257908 4 0.1671 0.9309 0.076 0.000 0.000 0.924 0.000
#> GSM257910 4 0.1671 0.9309 0.076 0.000 0.000 0.924 0.000
#> GSM257912 4 0.1704 0.9297 0.068 0.000 0.000 0.928 0.004
#> GSM257914 4 0.1704 0.9297 0.068 0.000 0.000 0.928 0.004
#> GSM257917 4 0.1764 0.9266 0.064 0.000 0.000 0.928 0.008
#> GSM257919 4 0.1704 0.9297 0.068 0.000 0.000 0.928 0.004
#> GSM257921 1 0.4573 0.6023 0.700 0.000 0.000 0.256 0.044
#> GSM257923 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257925 1 0.0000 0.8806 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.2848 0.7782 0.840 0.000 0.000 0.004 0.156
#> GSM257929 1 0.0000 0.8806 1.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.2426 0.9220 0.064 0.000 0.000 0.900 0.036
#> GSM257939 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257941 1 0.3160 0.7441 0.808 0.000 0.000 0.004 0.188
#> GSM257943 5 0.4211 0.4670 0.360 0.000 0.000 0.004 0.636
#> GSM257945 1 0.3266 0.7270 0.796 0.000 0.000 0.004 0.200
#> GSM257947 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257949 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257951 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257953 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257955 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257958 1 0.0000 0.8806 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.3086 0.7544 0.816 0.000 0.000 0.004 0.180
#> GSM257962 1 0.2848 0.7782 0.840 0.000 0.000 0.004 0.156
#> GSM257964 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257966 4 0.1671 0.9309 0.076 0.000 0.000 0.924 0.000
#> GSM257968 1 0.4898 0.1275 0.592 0.000 0.000 0.376 0.032
#> GSM257970 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257972 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257977 4 0.3841 0.8623 0.188 0.000 0.000 0.780 0.032
#> GSM257982 1 0.4958 0.0309 0.568 0.000 0.000 0.400 0.032
#> GSM257984 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257986 1 0.0162 0.8820 0.996 0.000 0.000 0.004 0.000
#> GSM257990 1 0.1357 0.8560 0.948 0.000 0.000 0.004 0.048
#> GSM257992 5 0.1965 0.8966 0.096 0.000 0.000 0.000 0.904
#> GSM257996 1 0.3749 0.7631 0.816 0.000 0.000 0.104 0.080
#> GSM258006 5 0.1638 0.8821 0.064 0.000 0.000 0.004 0.932
#> GSM257887 2 0.1281 0.9537 0.000 0.956 0.000 0.012 0.032
#> GSM257889 3 0.0566 0.9823 0.000 0.000 0.984 0.012 0.004
#> GSM257891 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.1117 0.9737 0.000 0.000 0.964 0.020 0.016
#> GSM257895 2 0.2359 0.9361 0.000 0.904 0.000 0.060 0.036
#> GSM257897 3 0.0693 0.9812 0.000 0.000 0.980 0.012 0.008
#> GSM257899 3 0.0807 0.9769 0.000 0.000 0.976 0.012 0.012
#> GSM257901 3 0.0290 0.9828 0.000 0.000 0.992 0.000 0.008
#> GSM257903 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257907 3 0.0290 0.9828 0.000 0.000 0.992 0.000 0.008
#> GSM257909 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257911 3 0.1557 0.9402 0.000 0.052 0.940 0.000 0.008
#> GSM257913 3 0.0798 0.9763 0.000 0.016 0.976 0.008 0.000
#> GSM257916 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257920 3 0.0290 0.9837 0.000 0.000 0.992 0.008 0.000
#> GSM257922 3 0.1310 0.9646 0.000 0.000 0.956 0.024 0.020
#> GSM257924 3 0.1605 0.9507 0.000 0.040 0.944 0.012 0.004
#> GSM257926 3 0.0290 0.9837 0.000 0.000 0.992 0.008 0.000
#> GSM257928 2 0.2859 0.9214 0.000 0.876 0.000 0.068 0.056
#> GSM257930 2 0.2729 0.9257 0.000 0.884 0.000 0.060 0.056
#> GSM257938 2 0.2659 0.9280 0.000 0.888 0.000 0.060 0.052
#> GSM257940 3 0.0290 0.9828 0.000 0.000 0.992 0.000 0.008
#> GSM257942 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.0290 0.9837 0.000 0.000 0.992 0.008 0.000
#> GSM257948 3 0.0290 0.9837 0.000 0.000 0.992 0.008 0.000
#> GSM257950 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.2302 0.8942 0.000 0.904 0.080 0.008 0.008
#> GSM257954 2 0.2074 0.9428 0.000 0.920 0.000 0.044 0.036
#> GSM257956 2 0.1485 0.9519 0.000 0.948 0.000 0.020 0.032
#> GSM257959 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0290 0.9579 0.000 0.992 0.000 0.000 0.008
#> GSM257967 2 0.0000 0.9608 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.1082 0.9558 0.000 0.964 0.000 0.008 0.028
#> GSM257971 3 0.1399 0.9645 0.000 0.000 0.952 0.028 0.020
#> GSM257973 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.2077 0.8932 0.000 0.908 0.084 0.000 0.008
#> GSM257983 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
#> GSM257991 2 0.0798 0.9492 0.000 0.976 0.016 0.000 0.008
#> GSM257993 2 0.2074 0.9428 0.000 0.920 0.000 0.044 0.036
#> GSM257994 2 0.2659 0.9280 0.000 0.888 0.000 0.060 0.052
#> GSM257989 3 0.0000 0.9846 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.2618 0.760 0.000 0.000 0.000 0.116 0.024 0.860
#> GSM257888 4 0.3206 0.651 0.152 0.000 0.000 0.816 0.004 0.028
#> GSM257890 4 0.1829 0.663 0.004 0.000 0.000 0.920 0.012 0.064
#> GSM257892 6 0.2618 0.760 0.000 0.000 0.000 0.116 0.024 0.860
#> GSM257894 4 0.3614 0.620 0.220 0.000 0.000 0.752 0.000 0.028
#> GSM257896 4 0.3139 0.647 0.160 0.000 0.000 0.812 0.000 0.028
#> GSM257898 6 0.1196 0.848 0.040 0.000 0.000 0.000 0.008 0.952
#> GSM257900 1 0.4311 0.711 0.716 0.000 0.000 0.008 0.056 0.220
#> GSM257902 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257904 6 0.1340 0.846 0.040 0.000 0.000 0.004 0.008 0.948
#> GSM257906 6 0.0937 0.850 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM257908 4 0.3954 0.711 0.012 0.000 0.000 0.636 0.352 0.000
#> GSM257910 4 0.4039 0.711 0.016 0.000 0.000 0.632 0.352 0.000
#> GSM257912 4 0.3892 0.710 0.004 0.000 0.000 0.640 0.352 0.004
#> GSM257914 4 0.3892 0.710 0.004 0.000 0.000 0.640 0.352 0.004
#> GSM257917 4 0.3892 0.710 0.004 0.000 0.000 0.640 0.352 0.004
#> GSM257919 4 0.3892 0.710 0.004 0.000 0.000 0.640 0.352 0.004
#> GSM257921 1 0.5973 0.574 0.608 0.000 0.000 0.196 0.072 0.124
#> GSM257923 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.1075 0.853 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM257927 1 0.4179 0.741 0.744 0.000 0.000 0.012 0.056 0.188
#> GSM257929 1 0.1075 0.853 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM257937 4 0.1503 0.682 0.008 0.000 0.000 0.944 0.016 0.032
#> GSM257939 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.4352 0.718 0.720 0.000 0.000 0.012 0.056 0.212
#> GSM257943 6 0.4909 0.230 0.348 0.000 0.000 0.008 0.056 0.588
#> GSM257945 1 0.4325 0.722 0.724 0.000 0.000 0.012 0.056 0.208
#> GSM257947 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0146 0.861 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257955 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.1075 0.853 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM257960 1 0.4297 0.726 0.728 0.000 0.000 0.012 0.056 0.204
#> GSM257962 1 0.4209 0.737 0.740 0.000 0.000 0.012 0.056 0.192
#> GSM257964 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.3171 0.717 0.012 0.000 0.000 0.784 0.204 0.000
#> GSM257968 1 0.4449 -0.089 0.532 0.000 0.000 0.440 0.000 0.028
#> GSM257970 1 0.0000 0.861 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0458 0.860 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM257977 4 0.3279 0.651 0.148 0.000 0.000 0.816 0.008 0.028
#> GSM257982 4 0.4469 0.153 0.468 0.000 0.000 0.504 0.000 0.028
#> GSM257984 1 0.0146 0.859 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257986 1 0.0146 0.859 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257990 1 0.3429 0.796 0.824 0.000 0.000 0.012 0.056 0.108
#> GSM257992 6 0.1391 0.849 0.040 0.000 0.000 0.000 0.016 0.944
#> GSM257996 1 0.4789 0.732 0.728 0.000 0.000 0.068 0.056 0.148
#> GSM258006 6 0.1555 0.824 0.008 0.000 0.000 0.040 0.012 0.940
#> GSM257887 2 0.2883 0.289 0.000 0.788 0.000 0.000 0.212 0.000
#> GSM257889 3 0.1411 0.923 0.000 0.000 0.936 0.000 0.060 0.004
#> GSM257891 3 0.0692 0.934 0.000 0.000 0.976 0.000 0.020 0.004
#> GSM257893 3 0.2362 0.881 0.000 0.000 0.860 0.000 0.136 0.004
#> GSM257895 2 0.3774 -0.618 0.000 0.592 0.000 0.000 0.408 0.000
#> GSM257897 3 0.1806 0.911 0.000 0.000 0.908 0.000 0.088 0.004
#> GSM257899 3 0.2234 0.891 0.000 0.000 0.872 0.000 0.124 0.004
#> GSM257901 3 0.1590 0.920 0.000 0.000 0.936 0.008 0.048 0.008
#> GSM257903 2 0.0767 0.653 0.000 0.976 0.008 0.000 0.012 0.004
#> GSM257905 2 0.0146 0.652 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257907 3 0.1523 0.921 0.000 0.000 0.940 0.008 0.044 0.008
#> GSM257909 2 0.0000 0.652 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 3 0.4429 0.662 0.000 0.228 0.712 0.008 0.044 0.008
#> GSM257913 3 0.2002 0.888 0.000 0.076 0.908 0.000 0.012 0.004
#> GSM257916 2 0.0767 0.653 0.000 0.976 0.008 0.000 0.012 0.004
#> GSM257918 2 0.0767 0.653 0.000 0.976 0.008 0.000 0.012 0.004
#> GSM257920 3 0.0551 0.934 0.000 0.004 0.984 0.000 0.008 0.004
#> GSM257922 3 0.2703 0.851 0.000 0.000 0.824 0.000 0.172 0.004
#> GSM257924 3 0.2197 0.903 0.000 0.056 0.900 0.000 0.044 0.000
#> GSM257926 3 0.0260 0.936 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM257928 5 0.3996 0.000 0.000 0.484 0.004 0.000 0.512 0.000
#> GSM257930 2 0.3868 -0.937 0.000 0.504 0.000 0.000 0.496 0.000
#> GSM257938 2 0.3868 -0.937 0.000 0.504 0.000 0.000 0.496 0.000
#> GSM257940 3 0.1523 0.919 0.000 0.000 0.940 0.008 0.044 0.008
#> GSM257942 2 0.0767 0.653 0.000 0.976 0.008 0.000 0.012 0.004
#> GSM257944 2 0.0767 0.653 0.000 0.976 0.008 0.000 0.012 0.004
#> GSM257946 3 0.0260 0.936 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM257948 3 0.0291 0.935 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM257950 3 0.0000 0.936 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257952 2 0.3927 0.433 0.000 0.792 0.124 0.008 0.068 0.008
#> GSM257954 2 0.3578 -0.294 0.000 0.660 0.000 0.000 0.340 0.000
#> GSM257956 2 0.2996 0.232 0.000 0.772 0.000 0.000 0.228 0.000
#> GSM257959 2 0.0146 0.652 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257961 2 0.0146 0.652 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257963 2 0.0146 0.652 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257965 2 0.1975 0.612 0.000 0.924 0.016 0.008 0.044 0.008
#> GSM257967 2 0.0146 0.652 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257969 2 0.2793 0.321 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM257971 3 0.3133 0.855 0.000 0.000 0.804 0.008 0.180 0.008
#> GSM257973 3 0.0260 0.936 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM257981 2 0.3231 0.519 0.000 0.848 0.084 0.008 0.052 0.008
#> GSM257983 3 0.0692 0.935 0.000 0.000 0.976 0.000 0.020 0.004
#> GSM257985 3 0.0146 0.936 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257988 3 0.0260 0.936 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM257991 2 0.2226 0.599 0.000 0.912 0.028 0.008 0.044 0.008
#> GSM257993 2 0.3592 -0.313 0.000 0.656 0.000 0.000 0.344 0.000
#> GSM257994 2 0.3868 -0.937 0.000 0.504 0.000 0.000 0.496 0.000
#> GSM257989 3 0.0146 0.936 0.000 0.000 0.996 0.000 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> SD:skmeans 96 8.49e-22 1.000 1.000 2
#> SD:skmeans 96 1.43e-21 0.691 1.000 3
#> SD:skmeans 91 1.34e-19 0.863 0.996 4
#> SD:skmeans 93 3.03e-19 0.639 0.991 5
#> SD:skmeans 82 6.56e-17 0.640 0.937 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.753 0.947 0.897 0.2485 0.874 0.745
#> 4 4 0.776 0.793 0.899 0.1852 0.876 0.662
#> 5 5 0.818 0.854 0.891 0.0489 0.946 0.787
#> 6 6 0.843 0.757 0.849 0.0394 0.956 0.801
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257888 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257890 1 0.3816 0.933 0.852 0.000 0.148
#> GSM257892 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257894 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257896 1 0.3619 0.933 0.864 0.000 0.136
#> GSM257898 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257900 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257902 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257904 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257906 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257908 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257910 1 0.0892 0.923 0.980 0.000 0.020
#> GSM257912 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257914 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257917 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257919 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257921 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257923 1 0.1031 0.922 0.976 0.000 0.024
#> GSM257925 1 0.0000 0.926 1.000 0.000 0.000
#> GSM257927 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257929 1 0.0424 0.925 0.992 0.000 0.008
#> GSM257937 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257939 1 0.1031 0.922 0.976 0.000 0.024
#> GSM257941 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257943 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257945 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257947 1 0.1031 0.922 0.976 0.000 0.024
#> GSM257949 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257951 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257953 1 0.0424 0.925 0.992 0.000 0.008
#> GSM257955 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257958 1 0.0424 0.925 0.992 0.000 0.008
#> GSM257960 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257962 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257964 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257966 1 0.0892 0.923 0.980 0.000 0.020
#> GSM257968 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257970 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257972 1 0.3619 0.933 0.864 0.000 0.136
#> GSM257977 1 0.3816 0.933 0.852 0.000 0.148
#> GSM257982 1 0.0892 0.923 0.980 0.000 0.020
#> GSM257984 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257986 1 0.1163 0.921 0.972 0.000 0.028
#> GSM257990 1 0.1860 0.930 0.948 0.000 0.052
#> GSM257992 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257996 1 0.3752 0.933 0.856 0.000 0.144
#> GSM258006 1 0.3752 0.933 0.856 0.000 0.144
#> GSM257887 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257889 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257891 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257893 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257895 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257897 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257899 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257901 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257903 2 0.3482 0.821 0.000 0.872 0.128
#> GSM257905 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257907 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257909 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257911 3 0.4291 0.986 0.000 0.180 0.820
#> GSM257913 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257916 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257920 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257922 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257924 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257926 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257928 2 0.2261 0.908 0.000 0.932 0.068
#> GSM257930 2 0.1643 0.934 0.000 0.956 0.044
#> GSM257938 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257940 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257942 2 0.1529 0.936 0.000 0.960 0.040
#> GSM257944 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257946 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257948 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257950 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257952 2 0.0237 0.969 0.000 0.996 0.004
#> GSM257954 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257956 2 0.0237 0.969 0.000 0.996 0.004
#> GSM257959 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257965 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257971 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257973 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257981 2 0.5098 0.586 0.000 0.752 0.248
#> GSM257983 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257985 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257988 3 0.4178 0.995 0.000 0.172 0.828
#> GSM257991 3 0.5138 0.893 0.000 0.252 0.748
#> GSM257993 2 0.0000 0.971 0.000 1.000 0.000
#> GSM257994 2 0.0237 0.969 0.000 0.996 0.004
#> GSM257989 3 0.4178 0.995 0.000 0.172 0.828
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257888 4 0.3528 0.561 0.192 0.000 0.000 0.808
#> GSM257890 1 0.4907 0.314 0.580 0.000 0.000 0.420
#> GSM257892 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257894 4 0.0000 0.679 0.000 0.000 0.000 1.000
#> GSM257896 1 0.4933 0.293 0.568 0.000 0.000 0.432
#> GSM257898 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257900 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257902 4 0.0000 0.679 0.000 0.000 0.000 1.000
#> GSM257904 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257906 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257908 4 0.4134 0.463 0.260 0.000 0.000 0.740
#> GSM257910 4 0.2589 0.635 0.116 0.000 0.000 0.884
#> GSM257912 1 0.2011 0.789 0.920 0.000 0.000 0.080
#> GSM257914 1 0.2408 0.766 0.896 0.000 0.000 0.104
#> GSM257917 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257919 1 0.2868 0.731 0.864 0.000 0.000 0.136
#> GSM257921 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257923 4 0.4898 0.548 0.416 0.000 0.000 0.584
#> GSM257925 1 0.4989 -0.391 0.528 0.000 0.000 0.472
#> GSM257927 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257929 4 0.4977 0.484 0.460 0.000 0.000 0.540
#> GSM257937 1 0.4500 0.479 0.684 0.000 0.000 0.316
#> GSM257939 4 0.4907 0.543 0.420 0.000 0.000 0.580
#> GSM257941 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257947 4 0.4898 0.548 0.416 0.000 0.000 0.584
#> GSM257949 4 0.2868 0.684 0.136 0.000 0.000 0.864
#> GSM257951 4 0.4661 0.615 0.348 0.000 0.000 0.652
#> GSM257953 4 0.4948 0.513 0.440 0.000 0.000 0.560
#> GSM257955 4 0.4624 0.620 0.340 0.000 0.000 0.660
#> GSM257958 4 0.4955 0.507 0.444 0.000 0.000 0.556
#> GSM257960 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257964 4 0.4643 0.617 0.344 0.000 0.000 0.656
#> GSM257966 4 0.4382 0.395 0.296 0.000 0.000 0.704
#> GSM257968 4 0.0000 0.679 0.000 0.000 0.000 1.000
#> GSM257970 4 0.3610 0.675 0.200 0.000 0.000 0.800
#> GSM257972 1 0.2704 0.700 0.876 0.000 0.000 0.124
#> GSM257977 1 0.4925 0.300 0.572 0.000 0.000 0.428
#> GSM257982 4 0.2149 0.651 0.088 0.000 0.000 0.912
#> GSM257984 4 0.0000 0.679 0.000 0.000 0.000 1.000
#> GSM257986 4 0.0000 0.679 0.000 0.000 0.000 1.000
#> GSM257990 1 0.2408 0.722 0.896 0.000 0.000 0.104
#> GSM257992 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257996 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM258006 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257887 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257895 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257901 3 0.0592 0.975 0.000 0.016 0.984 0.000
#> GSM257903 2 0.4250 0.719 0.000 0.724 0.276 0.000
#> GSM257905 2 0.3024 0.875 0.000 0.852 0.148 0.000
#> GSM257907 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257909 2 0.2530 0.892 0.000 0.888 0.112 0.000
#> GSM257911 3 0.0336 0.980 0.000 0.008 0.992 0.000
#> GSM257913 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257916 2 0.3024 0.875 0.000 0.852 0.148 0.000
#> GSM257918 2 0.3024 0.875 0.000 0.852 0.148 0.000
#> GSM257920 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257922 3 0.1022 0.962 0.000 0.032 0.968 0.000
#> GSM257924 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257926 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257928 2 0.2081 0.880 0.000 0.916 0.084 0.000
#> GSM257930 2 0.1302 0.903 0.000 0.956 0.044 0.000
#> GSM257938 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257940 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257942 2 0.3486 0.837 0.000 0.812 0.188 0.000
#> GSM257944 2 0.3024 0.875 0.000 0.852 0.148 0.000
#> GSM257946 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257950 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257952 2 0.0188 0.914 0.000 0.996 0.004 0.000
#> GSM257954 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0188 0.914 0.000 0.996 0.004 0.000
#> GSM257959 2 0.1716 0.910 0.000 0.936 0.064 0.000
#> GSM257961 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257963 2 0.1389 0.914 0.000 0.952 0.048 0.000
#> GSM257965 2 0.1716 0.910 0.000 0.936 0.064 0.000
#> GSM257967 2 0.1389 0.913 0.000 0.952 0.048 0.000
#> GSM257969 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257971 3 0.1792 0.927 0.000 0.068 0.932 0.000
#> GSM257973 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257981 2 0.4543 0.564 0.000 0.676 0.324 0.000
#> GSM257983 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257985 3 0.1716 0.931 0.000 0.064 0.936 0.000
#> GSM257988 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> GSM257991 3 0.2281 0.883 0.000 0.096 0.904 0.000
#> GSM257993 2 0.0000 0.913 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0188 0.914 0.000 0.996 0.004 0.000
#> GSM257989 3 0.0000 0.986 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.3074 0.687 0.804 0.000 0.000 0.196 0.000
#> GSM257888 4 0.0963 0.821 0.000 0.000 0.000 0.964 0.036
#> GSM257890 4 0.1043 0.816 0.040 0.000 0.000 0.960 0.000
#> GSM257892 1 0.3074 0.687 0.804 0.000 0.000 0.196 0.000
#> GSM257894 4 0.0963 0.821 0.000 0.000 0.000 0.964 0.036
#> GSM257896 4 0.1043 0.816 0.040 0.000 0.000 0.960 0.000
#> GSM257898 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257900 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257902 4 0.3700 0.712 0.008 0.000 0.000 0.752 0.240
#> GSM257904 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257906 1 0.0162 0.851 0.996 0.000 0.000 0.004 0.000
#> GSM257908 4 0.5998 0.572 0.188 0.000 0.000 0.584 0.228
#> GSM257910 4 0.6021 0.570 0.188 0.000 0.000 0.580 0.232
#> GSM257912 1 0.4134 0.681 0.744 0.000 0.000 0.032 0.224
#> GSM257914 1 0.4134 0.681 0.744 0.000 0.000 0.032 0.224
#> GSM257917 1 0.4134 0.681 0.744 0.000 0.000 0.032 0.224
#> GSM257919 1 0.4134 0.681 0.744 0.000 0.000 0.032 0.224
#> GSM257921 1 0.0162 0.851 0.996 0.000 0.000 0.004 0.000
#> GSM257923 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257925 5 0.4256 0.617 0.436 0.000 0.000 0.000 0.564
#> GSM257927 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257929 5 0.3837 0.862 0.308 0.000 0.000 0.000 0.692
#> GSM257937 1 0.4273 0.253 0.552 0.000 0.000 0.448 0.000
#> GSM257939 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257941 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257947 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257949 5 0.4024 0.935 0.220 0.000 0.000 0.028 0.752
#> GSM257951 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257953 5 0.3336 0.954 0.228 0.000 0.000 0.000 0.772
#> GSM257955 5 0.3551 0.953 0.220 0.000 0.000 0.008 0.772
#> GSM257958 5 0.3395 0.949 0.236 0.000 0.000 0.000 0.764
#> GSM257960 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.1043 0.819 0.960 0.000 0.000 0.000 0.040
#> GSM257964 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257966 4 0.3003 0.748 0.000 0.000 0.000 0.812 0.188
#> GSM257968 4 0.2605 0.783 0.000 0.000 0.000 0.852 0.148
#> GSM257970 5 0.3461 0.956 0.224 0.000 0.000 0.004 0.772
#> GSM257972 1 0.3876 0.257 0.684 0.000 0.000 0.000 0.316
#> GSM257977 4 0.1043 0.816 0.040 0.000 0.000 0.960 0.000
#> GSM257982 4 0.1082 0.821 0.008 0.000 0.000 0.964 0.028
#> GSM257984 4 0.3582 0.726 0.008 0.000 0.000 0.768 0.224
#> GSM257986 4 0.4425 0.479 0.008 0.000 0.000 0.600 0.392
#> GSM257990 1 0.2648 0.671 0.848 0.000 0.000 0.000 0.152
#> GSM257992 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM257996 1 0.0000 0.853 1.000 0.000 0.000 0.000 0.000
#> GSM258006 1 0.0290 0.849 0.992 0.000 0.000 0.008 0.000
#> GSM257887 2 0.0162 0.906 0.000 0.996 0.000 0.004 0.000
#> GSM257889 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257891 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257893 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257895 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> GSM257897 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257899 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257901 3 0.0510 0.974 0.000 0.016 0.984 0.000 0.000
#> GSM257903 2 0.3790 0.724 0.000 0.724 0.272 0.004 0.000
#> GSM257905 2 0.2719 0.868 0.000 0.852 0.144 0.004 0.000
#> GSM257907 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257909 2 0.2286 0.884 0.000 0.888 0.108 0.004 0.000
#> GSM257911 3 0.0290 0.978 0.000 0.008 0.992 0.000 0.000
#> GSM257913 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257916 2 0.2561 0.869 0.000 0.856 0.144 0.000 0.000
#> GSM257918 2 0.2719 0.868 0.000 0.852 0.144 0.004 0.000
#> GSM257920 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257922 3 0.0955 0.963 0.000 0.028 0.968 0.000 0.004
#> GSM257924 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257926 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257928 2 0.1952 0.867 0.000 0.912 0.084 0.000 0.004
#> GSM257930 2 0.1121 0.894 0.000 0.956 0.044 0.000 0.000
#> GSM257938 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> GSM257940 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257942 2 0.3123 0.832 0.000 0.812 0.184 0.004 0.000
#> GSM257944 2 0.2719 0.868 0.000 0.852 0.144 0.004 0.000
#> GSM257946 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257948 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257950 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.0162 0.907 0.000 0.996 0.004 0.000 0.000
#> GSM257954 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> GSM257956 2 0.0162 0.907 0.000 0.996 0.004 0.000 0.000
#> GSM257959 2 0.1410 0.903 0.000 0.940 0.060 0.000 0.000
#> GSM257961 2 0.0162 0.906 0.000 0.996 0.000 0.004 0.000
#> GSM257963 2 0.1357 0.907 0.000 0.948 0.048 0.004 0.000
#> GSM257965 2 0.1410 0.903 0.000 0.940 0.060 0.000 0.000
#> GSM257967 2 0.1357 0.906 0.000 0.948 0.048 0.004 0.000
#> GSM257969 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> GSM257971 3 0.1638 0.927 0.000 0.064 0.932 0.000 0.004
#> GSM257973 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.3932 0.556 0.000 0.672 0.328 0.000 0.000
#> GSM257983 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
#> GSM257985 3 0.1571 0.930 0.000 0.060 0.936 0.000 0.004
#> GSM257988 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM257991 3 0.2124 0.877 0.000 0.096 0.900 0.004 0.000
#> GSM257993 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> GSM257994 2 0.0162 0.906 0.000 0.996 0.004 0.000 0.000
#> GSM257989 3 0.0162 0.984 0.000 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.3867 -0.0574 0.000 0.000 0.000 0.000 0.488 0.512
#> GSM257888 5 0.0865 0.8098 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM257890 5 0.0865 0.8147 0.000 0.000 0.000 0.000 0.964 0.036
#> GSM257892 6 0.3867 -0.0574 0.000 0.000 0.000 0.000 0.488 0.512
#> GSM257894 5 0.0865 0.8098 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM257896 5 0.0865 0.8147 0.000 0.000 0.000 0.000 0.964 0.036
#> GSM257898 6 0.0000 0.8080 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257900 6 0.1765 0.8358 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM257902 1 0.3857 0.1023 0.532 0.000 0.000 0.000 0.468 0.000
#> GSM257904 6 0.0000 0.8080 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257906 6 0.0000 0.8080 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257908 4 0.3710 0.5767 0.000 0.000 0.000 0.696 0.292 0.012
#> GSM257910 4 0.3828 0.5788 0.004 0.000 0.000 0.696 0.288 0.012
#> GSM257912 4 0.3428 0.7250 0.000 0.000 0.000 0.696 0.000 0.304
#> GSM257914 4 0.3428 0.7250 0.000 0.000 0.000 0.696 0.000 0.304
#> GSM257917 4 0.3428 0.7250 0.000 0.000 0.000 0.696 0.000 0.304
#> GSM257919 4 0.3428 0.7250 0.000 0.000 0.000 0.696 0.000 0.304
#> GSM257921 6 0.2006 0.7869 0.016 0.000 0.000 0.080 0.000 0.904
#> GSM257923 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.3851 -0.0124 0.540 0.000 0.000 0.000 0.000 0.460
#> GSM257927 6 0.1765 0.8358 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM257929 1 0.3672 0.2691 0.632 0.000 0.000 0.000 0.000 0.368
#> GSM257937 5 0.3992 0.2581 0.000 0.000 0.000 0.012 0.624 0.364
#> GSM257939 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 6 0.1765 0.8358 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM257943 6 0.1610 0.8354 0.084 0.000 0.000 0.000 0.000 0.916
#> GSM257945 6 0.1714 0.8360 0.092 0.000 0.000 0.000 0.000 0.908
#> GSM257947 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0865 0.7746 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM257951 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0146 0.7980 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257960 6 0.1765 0.8358 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM257962 6 0.2048 0.8191 0.120 0.000 0.000 0.000 0.000 0.880
#> GSM257964 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.3515 0.5332 0.000 0.000 0.000 0.676 0.324 0.000
#> GSM257968 5 0.3634 0.3047 0.356 0.000 0.000 0.000 0.644 0.000
#> GSM257970 1 0.0000 0.8009 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 6 0.3592 0.5321 0.344 0.000 0.000 0.000 0.000 0.656
#> GSM257977 5 0.0865 0.8147 0.000 0.000 0.000 0.000 0.964 0.036
#> GSM257982 5 0.0993 0.8150 0.024 0.000 0.000 0.000 0.964 0.012
#> GSM257984 1 0.3862 0.0797 0.524 0.000 0.000 0.000 0.476 0.000
#> GSM257986 1 0.3706 0.3129 0.620 0.000 0.000 0.000 0.380 0.000
#> GSM257990 6 0.2730 0.7457 0.192 0.000 0.000 0.000 0.000 0.808
#> GSM257992 6 0.0000 0.8080 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257996 6 0.1765 0.8358 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM258006 6 0.0000 0.8080 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257887 2 0.1257 0.8390 0.000 0.952 0.000 0.028 0.020 0.000
#> GSM257889 3 0.0820 0.9528 0.000 0.000 0.972 0.016 0.012 0.000
#> GSM257891 3 0.1074 0.9471 0.000 0.000 0.960 0.028 0.012 0.000
#> GSM257893 3 0.0508 0.9534 0.000 0.000 0.984 0.004 0.012 0.000
#> GSM257895 2 0.0665 0.8343 0.000 0.980 0.004 0.008 0.008 0.000
#> GSM257897 3 0.1151 0.9481 0.000 0.000 0.956 0.032 0.012 0.000
#> GSM257899 3 0.1074 0.9471 0.000 0.000 0.960 0.028 0.012 0.000
#> GSM257901 3 0.1036 0.9474 0.000 0.008 0.964 0.024 0.004 0.000
#> GSM257903 2 0.6116 0.6318 0.000 0.516 0.212 0.252 0.020 0.000
#> GSM257905 2 0.5223 0.7675 0.000 0.652 0.116 0.212 0.020 0.000
#> GSM257907 3 0.1268 0.9463 0.000 0.008 0.952 0.036 0.004 0.000
#> GSM257909 2 0.5051 0.7634 0.000 0.652 0.080 0.248 0.020 0.000
#> GSM257911 3 0.2454 0.8930 0.000 0.008 0.884 0.088 0.020 0.000
#> GSM257913 3 0.1010 0.9480 0.000 0.000 0.960 0.036 0.004 0.000
#> GSM257916 2 0.3396 0.8122 0.000 0.828 0.108 0.048 0.016 0.000
#> GSM257918 2 0.4915 0.7856 0.000 0.696 0.100 0.180 0.024 0.000
#> GSM257920 3 0.0632 0.9526 0.000 0.000 0.976 0.024 0.000 0.000
#> GSM257922 3 0.1269 0.9402 0.000 0.012 0.956 0.020 0.012 0.000
#> GSM257924 3 0.0725 0.9531 0.000 0.000 0.976 0.012 0.012 0.000
#> GSM257926 3 0.0547 0.9527 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM257928 2 0.2114 0.8029 0.000 0.904 0.076 0.008 0.012 0.000
#> GSM257930 2 0.1542 0.8156 0.000 0.936 0.052 0.008 0.004 0.000
#> GSM257938 2 0.0405 0.8336 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM257940 3 0.1010 0.9448 0.000 0.000 0.960 0.036 0.004 0.000
#> GSM257942 2 0.5554 0.7258 0.000 0.600 0.128 0.252 0.020 0.000
#> GSM257944 2 0.5270 0.7517 0.000 0.632 0.100 0.248 0.020 0.000
#> GSM257946 3 0.0363 0.9530 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM257948 3 0.0777 0.9497 0.000 0.000 0.972 0.024 0.004 0.000
#> GSM257950 3 0.0260 0.9533 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM257952 2 0.0777 0.8318 0.000 0.972 0.000 0.024 0.004 0.000
#> GSM257954 2 0.0146 0.8357 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM257956 2 0.0146 0.8352 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM257959 2 0.3752 0.8087 0.000 0.760 0.036 0.200 0.004 0.000
#> GSM257961 2 0.3543 0.8045 0.000 0.764 0.004 0.212 0.020 0.000
#> GSM257963 2 0.4046 0.8019 0.000 0.744 0.028 0.208 0.020 0.000
#> GSM257965 2 0.1749 0.8364 0.000 0.932 0.036 0.024 0.008 0.000
#> GSM257967 2 0.3429 0.8247 0.000 0.812 0.028 0.144 0.016 0.000
#> GSM257969 2 0.0000 0.8350 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257971 3 0.2265 0.9004 0.000 0.056 0.904 0.028 0.012 0.000
#> GSM257973 3 0.0547 0.9520 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM257981 2 0.4286 0.5640 0.000 0.648 0.320 0.028 0.004 0.000
#> GSM257983 3 0.0891 0.9486 0.000 0.000 0.968 0.024 0.008 0.000
#> GSM257985 3 0.1922 0.9142 0.000 0.040 0.924 0.024 0.012 0.000
#> GSM257988 3 0.0777 0.9491 0.000 0.000 0.972 0.024 0.004 0.000
#> GSM257991 3 0.4926 0.5788 0.000 0.056 0.652 0.268 0.024 0.000
#> GSM257993 2 0.0146 0.8346 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM257994 2 0.0951 0.8298 0.000 0.968 0.020 0.008 0.004 0.000
#> GSM257989 3 0.0363 0.9530 0.000 0.000 0.988 0.000 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> SD:pam 96 8.49e-22 1.000 1.000 2
#> SD:pam 96 1.43e-21 0.364 1.000 3
#> SD:pam 88 5.89e-19 0.571 0.994 4
#> SD:pam 93 3.03e-19 0.496 0.991 5
#> SD:pam 87 2.87e-17 0.285 0.985 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.698 0.763 0.850 0.1979 0.945 0.888
#> 4 4 0.730 0.809 0.852 0.1731 0.801 0.565
#> 5 5 0.597 0.575 0.700 0.0661 0.933 0.774
#> 6 6 0.706 0.621 0.732 0.0668 0.872 0.547
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.5785 0.8278 0.668 0.000 0.332
#> GSM257888 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257890 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257892 1 0.5810 0.8260 0.664 0.000 0.336
#> GSM257894 1 0.5529 0.8347 0.704 0.000 0.296
#> GSM257896 1 0.5497 0.8395 0.708 0.000 0.292
#> GSM257898 1 0.2878 0.8564 0.904 0.000 0.096
#> GSM257900 1 0.2066 0.8610 0.940 0.000 0.060
#> GSM257902 1 0.2448 0.8762 0.924 0.000 0.076
#> GSM257904 1 0.3941 0.8680 0.844 0.000 0.156
#> GSM257906 1 0.2878 0.8564 0.904 0.000 0.096
#> GSM257908 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257910 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257912 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257914 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257917 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257919 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257921 1 0.3412 0.8724 0.876 0.000 0.124
#> GSM257923 1 0.1964 0.8664 0.944 0.000 0.056
#> GSM257925 1 0.1529 0.8655 0.960 0.000 0.040
#> GSM257927 1 0.1529 0.8666 0.960 0.000 0.040
#> GSM257929 1 0.1643 0.8657 0.956 0.000 0.044
#> GSM257937 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257939 1 0.1964 0.8664 0.944 0.000 0.056
#> GSM257941 1 0.2356 0.8573 0.928 0.000 0.072
#> GSM257943 1 0.3038 0.8543 0.896 0.000 0.104
#> GSM257945 1 0.3116 0.8531 0.892 0.000 0.108
#> GSM257947 1 0.1753 0.8660 0.952 0.000 0.048
#> GSM257949 1 0.4235 0.8651 0.824 0.000 0.176
#> GSM257951 1 0.1411 0.8651 0.964 0.000 0.036
#> GSM257953 1 0.2261 0.8666 0.932 0.000 0.068
#> GSM257955 1 0.1411 0.8651 0.964 0.000 0.036
#> GSM257958 1 0.1529 0.8645 0.960 0.000 0.040
#> GSM257960 1 0.2356 0.8646 0.928 0.000 0.072
#> GSM257962 1 0.1411 0.8675 0.964 0.000 0.036
#> GSM257964 1 0.2625 0.8746 0.916 0.000 0.084
#> GSM257966 1 0.5706 0.8235 0.680 0.000 0.320
#> GSM257968 1 0.5650 0.8286 0.688 0.000 0.312
#> GSM257970 1 0.1411 0.8651 0.964 0.000 0.036
#> GSM257972 1 0.2537 0.8759 0.920 0.000 0.080
#> GSM257977 1 0.5560 0.8349 0.700 0.000 0.300
#> GSM257982 1 0.5497 0.8395 0.708 0.000 0.292
#> GSM257984 1 0.3482 0.8709 0.872 0.000 0.128
#> GSM257986 1 0.3412 0.8716 0.876 0.000 0.124
#> GSM257990 1 0.2066 0.8619 0.940 0.000 0.060
#> GSM257992 1 0.3879 0.8567 0.848 0.000 0.152
#> GSM257996 1 0.3941 0.8675 0.844 0.000 0.156
#> GSM258006 1 0.3879 0.8567 0.848 0.000 0.152
#> GSM257887 2 0.0237 0.8350 0.000 0.996 0.004
#> GSM257889 2 0.5988 -0.3284 0.000 0.632 0.368
#> GSM257891 3 0.6267 0.9598 0.000 0.452 0.548
#> GSM257893 2 0.2959 0.7615 0.000 0.900 0.100
#> GSM257895 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257897 3 0.6225 0.9680 0.000 0.432 0.568
#> GSM257899 3 0.6225 0.9680 0.000 0.432 0.568
#> GSM257901 2 0.2959 0.7595 0.000 0.900 0.100
#> GSM257903 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257907 2 0.3752 0.6827 0.000 0.856 0.144
#> GSM257909 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257911 2 0.2537 0.7779 0.000 0.920 0.080
#> GSM257913 2 0.2625 0.7735 0.000 0.916 0.084
#> GSM257916 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257918 2 0.0237 0.8350 0.000 0.996 0.004
#> GSM257920 2 0.5529 0.0977 0.000 0.704 0.296
#> GSM257922 2 0.6267 -0.7055 0.000 0.548 0.452
#> GSM257924 2 0.2537 0.7776 0.000 0.920 0.080
#> GSM257926 2 0.2878 0.7611 0.000 0.904 0.096
#> GSM257928 2 0.0592 0.8289 0.000 0.988 0.012
#> GSM257930 2 0.0237 0.8350 0.000 0.996 0.004
#> GSM257938 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257940 2 0.5810 -0.1777 0.000 0.664 0.336
#> GSM257942 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257946 2 0.4654 0.5021 0.000 0.792 0.208
#> GSM257948 2 0.3551 0.7022 0.000 0.868 0.132
#> GSM257950 3 0.6267 0.9615 0.000 0.452 0.548
#> GSM257952 2 0.0892 0.8275 0.000 0.980 0.020
#> GSM257954 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257961 2 0.0237 0.8350 0.000 0.996 0.004
#> GSM257963 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257965 2 0.0237 0.8356 0.000 0.996 0.004
#> GSM257967 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257971 2 0.2625 0.7799 0.000 0.916 0.084
#> GSM257973 2 0.3116 0.7432 0.000 0.892 0.108
#> GSM257981 2 0.0592 0.8330 0.000 0.988 0.012
#> GSM257983 3 0.6225 0.9680 0.000 0.432 0.568
#> GSM257985 2 0.6111 -0.4804 0.000 0.604 0.396
#> GSM257988 3 0.6286 0.9404 0.000 0.464 0.536
#> GSM257991 2 0.0592 0.8330 0.000 0.988 0.012
#> GSM257993 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257994 2 0.0000 0.8364 0.000 1.000 0.000
#> GSM257989 2 0.6280 -0.7270 0.000 0.540 0.460
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.5028 0.5729 0.596 0.000 0.004 0.400
#> GSM257888 4 0.4830 0.9879 0.392 0.000 0.000 0.608
#> GSM257890 4 0.4843 0.9861 0.396 0.000 0.000 0.604
#> GSM257892 1 0.5028 0.5729 0.596 0.000 0.004 0.400
#> GSM257894 4 0.4855 0.9830 0.400 0.000 0.000 0.600
#> GSM257896 1 0.4040 0.6608 0.752 0.000 0.000 0.248
#> GSM257898 1 0.4991 0.5829 0.608 0.000 0.004 0.388
#> GSM257900 1 0.0817 0.7876 0.976 0.000 0.000 0.024
#> GSM257902 1 0.0469 0.7821 0.988 0.000 0.000 0.012
#> GSM257904 1 0.4991 0.5829 0.608 0.000 0.004 0.388
#> GSM257906 1 0.4991 0.5829 0.608 0.000 0.004 0.388
#> GSM257908 4 0.4790 0.9880 0.380 0.000 0.000 0.620
#> GSM257910 4 0.4790 0.9880 0.380 0.000 0.000 0.620
#> GSM257912 4 0.4790 0.9880 0.380 0.000 0.000 0.620
#> GSM257914 4 0.4790 0.9880 0.380 0.000 0.000 0.620
#> GSM257917 4 0.4817 0.9879 0.388 0.000 0.000 0.612
#> GSM257919 4 0.4790 0.9880 0.380 0.000 0.000 0.620
#> GSM257921 1 0.0592 0.7856 0.984 0.000 0.000 0.016
#> GSM257923 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0188 0.7883 0.996 0.000 0.000 0.004
#> GSM257927 1 0.0336 0.7884 0.992 0.000 0.000 0.008
#> GSM257929 1 0.0336 0.7871 0.992 0.000 0.000 0.008
#> GSM257937 4 0.4843 0.9861 0.396 0.000 0.000 0.604
#> GSM257939 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257941 1 0.1302 0.7791 0.956 0.000 0.000 0.044
#> GSM257943 1 0.4830 0.5842 0.608 0.000 0.000 0.392
#> GSM257945 1 0.4843 0.5827 0.604 0.000 0.000 0.396
#> GSM257947 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0336 0.7871 0.992 0.000 0.000 0.008
#> GSM257953 1 0.0592 0.7858 0.984 0.000 0.000 0.016
#> GSM257955 1 0.0336 0.7854 0.992 0.000 0.000 0.008
#> GSM257958 1 0.0469 0.7884 0.988 0.000 0.000 0.012
#> GSM257960 1 0.1118 0.7828 0.964 0.000 0.000 0.036
#> GSM257962 1 0.0592 0.7887 0.984 0.000 0.000 0.016
#> GSM257964 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257966 4 0.4830 0.9879 0.392 0.000 0.000 0.608
#> GSM257968 4 0.4866 0.9805 0.404 0.000 0.000 0.596
#> GSM257970 1 0.0188 0.7873 0.996 0.000 0.000 0.004
#> GSM257972 1 0.0188 0.7871 0.996 0.000 0.000 0.004
#> GSM257977 1 0.4999 -0.7599 0.508 0.000 0.000 0.492
#> GSM257982 1 0.3024 0.7203 0.852 0.000 0.000 0.148
#> GSM257984 1 0.0000 0.7881 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0592 0.7820 0.984 0.000 0.000 0.016
#> GSM257990 1 0.0336 0.7884 0.992 0.000 0.000 0.008
#> GSM257992 1 0.4978 0.5834 0.612 0.000 0.004 0.384
#> GSM257996 1 0.1637 0.7174 0.940 0.000 0.000 0.060
#> GSM258006 1 0.4817 0.5849 0.612 0.000 0.000 0.388
#> GSM257887 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257889 3 0.3219 0.8963 0.000 0.164 0.836 0.000
#> GSM257891 3 0.0921 0.8513 0.000 0.028 0.972 0.000
#> GSM257893 3 0.4164 0.8144 0.000 0.264 0.736 0.000
#> GSM257895 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0188 0.8351 0.000 0.004 0.996 0.000
#> GSM257899 3 0.0188 0.8351 0.000 0.004 0.996 0.000
#> GSM257901 3 0.3444 0.8927 0.000 0.184 0.816 0.000
#> GSM257903 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257907 3 0.3123 0.8960 0.000 0.156 0.844 0.000
#> GSM257909 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257911 2 0.4008 0.6409 0.000 0.756 0.244 0.000
#> GSM257913 2 0.3975 0.6482 0.000 0.760 0.240 0.000
#> GSM257916 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257920 3 0.3726 0.8782 0.000 0.212 0.788 0.000
#> GSM257922 3 0.3024 0.8946 0.000 0.148 0.852 0.000
#> GSM257924 2 0.4955 -0.0107 0.000 0.556 0.444 0.000
#> GSM257926 3 0.4304 0.7831 0.000 0.284 0.716 0.000
#> GSM257928 2 0.0188 0.9265 0.000 0.996 0.004 0.000
#> GSM257930 2 0.0188 0.9265 0.000 0.996 0.004 0.000
#> GSM257938 2 0.0188 0.9265 0.000 0.996 0.004 0.000
#> GSM257940 3 0.3486 0.8914 0.000 0.188 0.812 0.000
#> GSM257942 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257946 3 0.3610 0.8856 0.000 0.200 0.800 0.000
#> GSM257948 3 0.3688 0.8809 0.000 0.208 0.792 0.000
#> GSM257950 3 0.0921 0.8512 0.000 0.028 0.972 0.000
#> GSM257952 2 0.3837 0.6772 0.000 0.776 0.224 0.000
#> GSM257954 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257959 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0817 0.9116 0.000 0.976 0.024 0.000
#> GSM257967 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257971 3 0.3801 0.8709 0.000 0.220 0.780 0.000
#> GSM257973 3 0.3764 0.8749 0.000 0.216 0.784 0.000
#> GSM257981 2 0.3074 0.7815 0.000 0.848 0.152 0.000
#> GSM257983 3 0.0188 0.8351 0.000 0.004 0.996 0.000
#> GSM257985 3 0.3266 0.8961 0.000 0.168 0.832 0.000
#> GSM257988 3 0.0817 0.8489 0.000 0.024 0.976 0.000
#> GSM257991 2 0.3486 0.7324 0.000 0.812 0.188 0.000
#> GSM257993 2 0.0000 0.9292 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0188 0.9265 0.000 0.996 0.004 0.000
#> GSM257989 3 0.2281 0.8794 0.000 0.096 0.904 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.6202 0.2985 0.484 0.000 0.000 0.372 0.144
#> GSM257888 4 0.3183 0.8574 0.156 0.000 0.000 0.828 0.016
#> GSM257890 4 0.3399 0.8472 0.168 0.000 0.000 0.812 0.020
#> GSM257892 1 0.6232 0.2880 0.480 0.000 0.000 0.372 0.148
#> GSM257894 4 0.3160 0.8346 0.188 0.000 0.000 0.808 0.004
#> GSM257896 1 0.4734 0.3220 0.604 0.000 0.000 0.372 0.024
#> GSM257898 1 0.5475 0.5670 0.644 0.000 0.000 0.232 0.124
#> GSM257900 1 0.0510 0.7684 0.984 0.000 0.000 0.016 0.000
#> GSM257902 1 0.2471 0.7671 0.864 0.000 0.000 0.136 0.000
#> GSM257904 1 0.5500 0.5645 0.640 0.000 0.000 0.236 0.124
#> GSM257906 1 0.5500 0.5645 0.640 0.000 0.000 0.236 0.124
#> GSM257908 4 0.2708 0.8711 0.072 0.000 0.000 0.884 0.044
#> GSM257910 4 0.2645 0.8707 0.068 0.000 0.000 0.888 0.044
#> GSM257912 4 0.2645 0.8707 0.068 0.000 0.000 0.888 0.044
#> GSM257914 4 0.2645 0.8707 0.068 0.000 0.000 0.888 0.044
#> GSM257917 4 0.2645 0.8707 0.068 0.000 0.000 0.888 0.044
#> GSM257919 4 0.2645 0.8707 0.068 0.000 0.000 0.888 0.044
#> GSM257921 1 0.4096 0.7017 0.760 0.000 0.000 0.200 0.040
#> GSM257923 1 0.1965 0.7751 0.904 0.000 0.000 0.096 0.000
#> GSM257925 1 0.1908 0.7762 0.908 0.000 0.000 0.092 0.000
#> GSM257927 1 0.0510 0.7698 0.984 0.000 0.000 0.016 0.000
#> GSM257929 1 0.1908 0.7762 0.908 0.000 0.000 0.092 0.000
#> GSM257937 4 0.3319 0.8543 0.160 0.000 0.000 0.820 0.020
#> GSM257939 1 0.1908 0.7760 0.908 0.000 0.000 0.092 0.000
#> GSM257941 1 0.1121 0.7645 0.956 0.000 0.000 0.044 0.000
#> GSM257943 1 0.4666 0.6412 0.732 0.000 0.000 0.180 0.088
#> GSM257945 1 0.5072 0.6156 0.696 0.000 0.000 0.188 0.116
#> GSM257947 1 0.1908 0.7760 0.908 0.000 0.000 0.092 0.000
#> GSM257949 1 0.2230 0.7700 0.884 0.000 0.000 0.116 0.000
#> GSM257951 1 0.1965 0.7766 0.904 0.000 0.000 0.096 0.000
#> GSM257953 1 0.0955 0.7727 0.968 0.000 0.000 0.028 0.004
#> GSM257955 1 0.1965 0.7776 0.904 0.000 0.000 0.096 0.000
#> GSM257958 1 0.1908 0.7762 0.908 0.000 0.000 0.092 0.000
#> GSM257960 1 0.2124 0.7410 0.900 0.000 0.000 0.096 0.004
#> GSM257962 1 0.0880 0.7674 0.968 0.000 0.000 0.032 0.000
#> GSM257964 1 0.2389 0.7711 0.880 0.000 0.000 0.116 0.004
#> GSM257966 4 0.2920 0.8646 0.132 0.000 0.000 0.852 0.016
#> GSM257968 4 0.3882 0.7978 0.224 0.000 0.000 0.756 0.020
#> GSM257970 1 0.2020 0.7755 0.900 0.000 0.000 0.100 0.000
#> GSM257972 1 0.2074 0.7757 0.896 0.000 0.000 0.104 0.000
#> GSM257977 4 0.4815 0.1518 0.456 0.000 0.000 0.524 0.020
#> GSM257982 1 0.4709 0.3401 0.612 0.000 0.000 0.364 0.024
#> GSM257984 1 0.3521 0.7300 0.820 0.000 0.000 0.140 0.040
#> GSM257986 1 0.3531 0.7301 0.816 0.000 0.000 0.148 0.036
#> GSM257990 1 0.1043 0.7763 0.960 0.000 0.000 0.040 0.000
#> GSM257992 1 0.5516 0.5640 0.640 0.000 0.000 0.232 0.128
#> GSM257996 1 0.3848 0.6888 0.788 0.000 0.000 0.172 0.040
#> GSM258006 1 0.5475 0.5670 0.644 0.000 0.000 0.232 0.124
#> GSM257887 2 0.4548 0.6238 0.000 0.752 0.120 0.000 0.128
#> GSM257889 3 0.6188 -0.3680 0.000 0.144 0.492 0.000 0.364
#> GSM257891 3 0.3409 0.2037 0.000 0.024 0.816 0.000 0.160
#> GSM257893 3 0.6531 0.1650 0.000 0.336 0.456 0.000 0.208
#> GSM257895 2 0.5210 0.5332 0.000 0.652 0.084 0.000 0.264
#> GSM257897 5 0.4283 0.8672 0.000 0.000 0.456 0.000 0.544
#> GSM257899 5 0.4283 0.8672 0.000 0.000 0.456 0.000 0.544
#> GSM257901 3 0.2293 0.5680 0.000 0.084 0.900 0.000 0.016
#> GSM257903 2 0.3707 0.5477 0.000 0.716 0.284 0.000 0.000
#> GSM257905 2 0.3177 0.6092 0.000 0.792 0.208 0.000 0.000
#> GSM257907 3 0.2362 0.5626 0.000 0.076 0.900 0.000 0.024
#> GSM257909 2 0.3661 0.5562 0.000 0.724 0.276 0.000 0.000
#> GSM257911 3 0.3884 0.4617 0.000 0.288 0.708 0.000 0.004
#> GSM257913 3 0.3421 0.5634 0.000 0.204 0.788 0.000 0.008
#> GSM257916 2 0.3550 0.5951 0.000 0.760 0.236 0.000 0.004
#> GSM257918 2 0.3398 0.6040 0.000 0.780 0.216 0.000 0.004
#> GSM257920 3 0.2616 0.5689 0.000 0.100 0.880 0.000 0.020
#> GSM257922 5 0.6095 0.4542 0.000 0.124 0.416 0.000 0.460
#> GSM257924 3 0.4841 0.3333 0.000 0.416 0.560 0.000 0.024
#> GSM257926 3 0.3602 0.5666 0.000 0.180 0.796 0.000 0.024
#> GSM257928 2 0.5547 0.4701 0.000 0.644 0.148 0.000 0.208
#> GSM257930 2 0.5187 0.5327 0.000 0.656 0.084 0.000 0.260
#> GSM257938 2 0.5082 0.5385 0.000 0.664 0.076 0.000 0.260
#> GSM257940 3 0.3622 0.5559 0.000 0.124 0.820 0.000 0.056
#> GSM257942 2 0.3906 0.5391 0.000 0.704 0.292 0.000 0.004
#> GSM257944 2 0.4138 0.3717 0.000 0.616 0.384 0.000 0.000
#> GSM257946 3 0.4852 0.4306 0.000 0.184 0.716 0.000 0.100
#> GSM257948 3 0.3051 0.5807 0.000 0.120 0.852 0.000 0.028
#> GSM257950 3 0.4348 -0.4130 0.000 0.016 0.668 0.000 0.316
#> GSM257952 3 0.4886 0.1082 0.000 0.448 0.528 0.000 0.024
#> GSM257954 2 0.3720 0.5913 0.000 0.760 0.012 0.000 0.228
#> GSM257956 2 0.3912 0.5931 0.000 0.752 0.020 0.000 0.228
#> GSM257959 2 0.3242 0.6035 0.000 0.784 0.216 0.000 0.000
#> GSM257961 2 0.0671 0.6237 0.000 0.980 0.016 0.000 0.004
#> GSM257963 2 0.1341 0.6289 0.000 0.944 0.056 0.000 0.000
#> GSM257965 2 0.4138 0.4013 0.000 0.616 0.384 0.000 0.000
#> GSM257967 2 0.3534 0.5794 0.000 0.744 0.256 0.000 0.000
#> GSM257969 2 0.4627 0.5706 0.000 0.732 0.080 0.000 0.188
#> GSM257971 3 0.6645 0.1315 0.000 0.316 0.440 0.000 0.244
#> GSM257973 3 0.3477 0.5532 0.000 0.112 0.832 0.000 0.056
#> GSM257981 3 0.4283 0.0229 0.000 0.456 0.544 0.000 0.000
#> GSM257983 5 0.4283 0.8672 0.000 0.000 0.456 0.000 0.544
#> GSM257985 3 0.5348 0.2186 0.000 0.112 0.656 0.000 0.232
#> GSM257988 3 0.4467 -0.4928 0.000 0.016 0.640 0.000 0.344
#> GSM257991 2 0.4451 0.1126 0.000 0.504 0.492 0.000 0.004
#> GSM257993 2 0.3671 0.5889 0.000 0.756 0.008 0.000 0.236
#> GSM257994 2 0.5082 0.5385 0.000 0.664 0.076 0.000 0.260
#> GSM257989 3 0.3164 0.3850 0.000 0.044 0.852 0.000 0.104
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.4118 0.1049 0.004 0.000 0.000 0.396 0.008 0.592
#> GSM257888 4 0.5875 0.3273 0.188 0.000 0.000 0.516 0.008 0.288
#> GSM257890 4 0.5898 0.3258 0.192 0.000 0.000 0.512 0.008 0.288
#> GSM257892 6 0.4118 0.1049 0.004 0.000 0.000 0.396 0.008 0.592
#> GSM257894 1 0.5873 0.0992 0.444 0.000 0.000 0.204 0.000 0.352
#> GSM257896 4 0.5201 0.1392 0.092 0.000 0.000 0.500 0.000 0.408
#> GSM257898 6 0.3890 0.7889 0.400 0.000 0.000 0.004 0.000 0.596
#> GSM257900 1 0.0622 0.7633 0.980 0.000 0.000 0.008 0.000 0.012
#> GSM257902 1 0.1321 0.7628 0.952 0.000 0.000 0.020 0.004 0.024
#> GSM257904 6 0.3765 0.7890 0.404 0.000 0.000 0.000 0.000 0.596
#> GSM257906 6 0.3765 0.7890 0.404 0.000 0.000 0.000 0.000 0.596
#> GSM257908 4 0.4403 0.4634 0.024 0.000 0.000 0.508 0.000 0.468
#> GSM257910 4 0.4403 0.4634 0.024 0.000 0.000 0.508 0.000 0.468
#> GSM257912 4 0.4533 0.4627 0.024 0.000 0.000 0.504 0.004 0.468
#> GSM257914 4 0.4533 0.4627 0.024 0.000 0.000 0.504 0.004 0.468
#> GSM257917 4 0.4834 0.4587 0.044 0.000 0.000 0.484 0.004 0.468
#> GSM257919 4 0.4533 0.4627 0.024 0.000 0.000 0.504 0.004 0.468
#> GSM257921 1 0.4083 0.4041 0.532 0.000 0.000 0.008 0.000 0.460
#> GSM257923 1 0.2838 0.7145 0.808 0.000 0.000 0.000 0.004 0.188
#> GSM257925 1 0.0146 0.7684 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257927 1 0.0520 0.7638 0.984 0.000 0.000 0.008 0.000 0.008
#> GSM257929 1 0.0508 0.7718 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM257937 4 0.5940 0.3207 0.200 0.000 0.000 0.504 0.008 0.288
#> GSM257939 1 0.2558 0.7309 0.840 0.000 0.000 0.000 0.004 0.156
#> GSM257941 1 0.1584 0.7094 0.928 0.000 0.000 0.008 0.000 0.064
#> GSM257943 6 0.4328 0.7152 0.460 0.000 0.000 0.020 0.000 0.520
#> GSM257945 6 0.3915 0.7840 0.412 0.000 0.000 0.004 0.000 0.584
#> GSM257947 1 0.0405 0.7711 0.988 0.000 0.000 0.000 0.004 0.008
#> GSM257949 1 0.3265 0.6751 0.748 0.000 0.000 0.000 0.004 0.248
#> GSM257951 1 0.0146 0.7682 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257953 1 0.1010 0.7478 0.960 0.000 0.000 0.036 0.000 0.004
#> GSM257955 1 0.0146 0.7682 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257958 1 0.0146 0.7706 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257960 1 0.2357 0.6200 0.872 0.000 0.000 0.012 0.000 0.116
#> GSM257962 1 0.0520 0.7638 0.984 0.000 0.000 0.008 0.000 0.008
#> GSM257964 1 0.3795 0.5464 0.632 0.000 0.000 0.000 0.004 0.364
#> GSM257966 4 0.5715 0.3245 0.160 0.000 0.000 0.540 0.008 0.292
#> GSM257968 4 0.6261 0.2313 0.296 0.000 0.000 0.412 0.008 0.284
#> GSM257970 1 0.1219 0.7689 0.948 0.000 0.000 0.000 0.004 0.048
#> GSM257972 1 0.2740 0.7420 0.852 0.000 0.000 0.028 0.000 0.120
#> GSM257977 4 0.6101 0.2793 0.228 0.000 0.000 0.464 0.008 0.300
#> GSM257982 4 0.5494 0.2268 0.136 0.000 0.000 0.504 0.000 0.360
#> GSM257984 1 0.3986 0.5229 0.608 0.000 0.000 0.004 0.004 0.384
#> GSM257986 1 0.3986 0.5181 0.608 0.000 0.000 0.004 0.004 0.384
#> GSM257990 1 0.0405 0.7662 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM257992 6 0.4238 0.7867 0.404 0.000 0.000 0.008 0.008 0.580
#> GSM257996 1 0.3955 0.5220 0.608 0.000 0.000 0.008 0.000 0.384
#> GSM258006 6 0.4524 0.7683 0.376 0.000 0.000 0.040 0.000 0.584
#> GSM257887 2 0.3860 0.0892 0.000 0.528 0.000 0.000 0.472 0.000
#> GSM257889 3 0.4214 0.7246 0.000 0.060 0.800 0.076 0.048 0.016
#> GSM257891 3 0.4084 0.7179 0.000 0.040 0.808 0.076 0.060 0.016
#> GSM257893 3 0.4040 0.7180 0.000 0.028 0.808 0.076 0.072 0.016
#> GSM257895 5 0.1745 0.9601 0.000 0.068 0.012 0.000 0.920 0.000
#> GSM257897 3 0.3281 0.6876 0.000 0.000 0.840 0.088 0.056 0.016
#> GSM257899 3 0.3281 0.6876 0.000 0.000 0.840 0.088 0.056 0.016
#> GSM257901 3 0.3862 0.5845 0.000 0.388 0.608 0.000 0.000 0.004
#> GSM257903 2 0.0632 0.7404 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM257905 2 0.0790 0.7421 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM257907 3 0.3807 0.5996 0.000 0.368 0.628 0.000 0.000 0.004
#> GSM257909 2 0.0790 0.7421 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM257911 3 0.3979 0.5136 0.000 0.456 0.540 0.000 0.000 0.004
#> GSM257913 3 0.3857 0.4946 0.000 0.468 0.532 0.000 0.000 0.000
#> GSM257916 2 0.2996 0.5973 0.000 0.772 0.000 0.000 0.228 0.000
#> GSM257918 2 0.3351 0.5086 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM257920 3 0.3636 0.6337 0.000 0.320 0.676 0.000 0.000 0.004
#> GSM257922 3 0.3812 0.7129 0.000 0.012 0.816 0.076 0.080 0.016
#> GSM257924 3 0.4434 0.5375 0.000 0.428 0.544 0.000 0.028 0.000
#> GSM257926 3 0.4093 0.5779 0.000 0.404 0.584 0.000 0.012 0.000
#> GSM257928 5 0.2752 0.9078 0.000 0.052 0.024 0.044 0.880 0.000
#> GSM257930 5 0.1745 0.9601 0.000 0.068 0.012 0.000 0.920 0.000
#> GSM257938 5 0.1745 0.9601 0.000 0.068 0.012 0.000 0.920 0.000
#> GSM257940 3 0.1644 0.7464 0.000 0.076 0.920 0.000 0.000 0.004
#> GSM257942 2 0.0000 0.7308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0790 0.7421 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM257946 3 0.1858 0.7470 0.000 0.092 0.904 0.000 0.004 0.000
#> GSM257948 3 0.3830 0.5937 0.000 0.376 0.620 0.000 0.000 0.004
#> GSM257950 3 0.0951 0.7407 0.000 0.020 0.968 0.000 0.008 0.004
#> GSM257952 3 0.4403 0.4632 0.000 0.468 0.508 0.000 0.024 0.000
#> GSM257954 5 0.1866 0.9558 0.000 0.084 0.008 0.000 0.908 0.000
#> GSM257956 5 0.2266 0.9365 0.000 0.108 0.012 0.000 0.880 0.000
#> GSM257959 2 0.2823 0.6379 0.000 0.796 0.000 0.000 0.204 0.000
#> GSM257961 2 0.3659 0.3953 0.000 0.636 0.000 0.000 0.364 0.000
#> GSM257963 2 0.3592 0.4297 0.000 0.656 0.000 0.000 0.344 0.000
#> GSM257965 2 0.2631 0.5703 0.000 0.840 0.152 0.000 0.008 0.000
#> GSM257967 2 0.0790 0.7421 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM257969 5 0.2877 0.8432 0.000 0.168 0.012 0.000 0.820 0.000
#> GSM257971 3 0.4294 0.7004 0.000 0.028 0.788 0.076 0.092 0.016
#> GSM257973 3 0.2838 0.7225 0.000 0.188 0.808 0.000 0.000 0.004
#> GSM257981 2 0.4423 -0.2971 0.000 0.552 0.420 0.000 0.028 0.000
#> GSM257983 3 0.3024 0.6909 0.000 0.000 0.856 0.088 0.040 0.016
#> GSM257985 3 0.2263 0.7447 0.000 0.100 0.884 0.000 0.016 0.000
#> GSM257988 3 0.0951 0.7407 0.000 0.020 0.968 0.000 0.008 0.004
#> GSM257991 2 0.1863 0.6278 0.000 0.896 0.104 0.000 0.000 0.000
#> GSM257993 5 0.1866 0.9558 0.000 0.084 0.008 0.000 0.908 0.000
#> GSM257994 5 0.1745 0.9601 0.000 0.068 0.012 0.000 0.920 0.000
#> GSM257989 3 0.1908 0.7454 0.000 0.096 0.900 0.000 0.000 0.004
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 cell.type(p) protocol(p) individual(p) k
#> SD:mclust 96 8.49e-22 1.000 1.000 2
#> SD:mclust 90 2.86e-20 0.626 1.000 3
#> SD:mclust 94 3.03e-20 0.146 0.998 4
#> SD:mclust 73 5.28e-15 0.236 0.853 5
#> SD:mclust 72 8.58e-15 0.803 0.915 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.842 0.851 0.900 0.1962 0.879 0.756
#> 4 4 0.685 0.794 0.829 0.1043 0.930 0.827
#> 5 5 0.767 0.795 0.883 0.0935 0.868 0.652
#> 6 6 0.876 0.871 0.920 0.0340 0.973 0.902
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257888 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257890 1 0.0237 0.987 0.996 0.000 0.004
#> GSM257892 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257894 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257898 1 0.2796 0.934 0.908 0.000 0.092
#> GSM257900 1 0.0592 0.984 0.988 0.000 0.012
#> GSM257902 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257904 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257906 1 0.2261 0.955 0.932 0.000 0.068
#> GSM257908 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257917 1 0.0237 0.987 0.996 0.000 0.004
#> GSM257919 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257921 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257927 1 0.0237 0.987 0.996 0.000 0.004
#> GSM257929 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257937 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257939 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257941 1 0.1529 0.971 0.960 0.000 0.040
#> GSM257943 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257945 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257947 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257960 1 0.1529 0.971 0.960 0.000 0.040
#> GSM257962 1 0.0424 0.986 0.992 0.000 0.008
#> GSM257964 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257966 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257968 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257977 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257982 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.989 1.000 0.000 0.000
#> GSM257992 1 0.2261 0.955 0.932 0.000 0.068
#> GSM257996 1 0.0000 0.989 1.000 0.000 0.000
#> GSM258006 1 0.1860 0.965 0.948 0.000 0.052
#> GSM257887 2 0.0237 0.837 0.000 0.996 0.004
#> GSM257889 3 0.4702 0.824 0.000 0.212 0.788
#> GSM257891 3 0.4235 0.781 0.000 0.176 0.824
#> GSM257893 3 0.5465 0.857 0.000 0.288 0.712
#> GSM257895 2 0.1860 0.824 0.000 0.948 0.052
#> GSM257897 3 0.4750 0.829 0.000 0.216 0.784
#> GSM257899 3 0.5254 0.862 0.000 0.264 0.736
#> GSM257901 3 0.6307 0.587 0.000 0.488 0.512
#> GSM257903 2 0.3038 0.768 0.000 0.896 0.104
#> GSM257905 2 0.0237 0.839 0.000 0.996 0.004
#> GSM257907 3 0.6026 0.784 0.000 0.376 0.624
#> GSM257909 2 0.3267 0.750 0.000 0.884 0.116
#> GSM257911 2 0.3686 0.739 0.000 0.860 0.140
#> GSM257913 2 0.3752 0.714 0.000 0.856 0.144
#> GSM257916 2 0.0237 0.839 0.000 0.996 0.004
#> GSM257918 2 0.1163 0.825 0.000 0.972 0.028
#> GSM257920 2 0.6299 -0.515 0.000 0.524 0.476
#> GSM257922 3 0.4842 0.832 0.000 0.224 0.776
#> GSM257924 2 0.6111 -0.218 0.000 0.604 0.396
#> GSM257926 3 0.6260 0.685 0.000 0.448 0.552
#> GSM257928 3 0.5926 0.804 0.000 0.356 0.644
#> GSM257930 2 0.4121 0.661 0.000 0.832 0.168
#> GSM257938 2 0.1753 0.827 0.000 0.952 0.048
#> GSM257940 3 0.6274 0.670 0.000 0.456 0.544
#> GSM257942 2 0.1964 0.810 0.000 0.944 0.056
#> GSM257944 2 0.3482 0.742 0.000 0.872 0.128
#> GSM257946 3 0.5327 0.862 0.000 0.272 0.728
#> GSM257948 3 0.6274 0.670 0.000 0.456 0.544
#> GSM257950 3 0.5560 0.851 0.000 0.300 0.700
#> GSM257952 2 0.3879 0.699 0.000 0.848 0.152
#> GSM257954 2 0.1163 0.837 0.000 0.972 0.028
#> GSM257956 2 0.1163 0.837 0.000 0.972 0.028
#> GSM257959 2 0.2796 0.776 0.000 0.908 0.092
#> GSM257961 2 0.0237 0.839 0.000 0.996 0.004
#> GSM257963 2 0.0592 0.834 0.000 0.988 0.012
#> GSM257965 2 0.1411 0.835 0.000 0.964 0.036
#> GSM257967 2 0.2537 0.786 0.000 0.920 0.080
#> GSM257969 2 0.1289 0.835 0.000 0.968 0.032
#> GSM257971 3 0.5216 0.860 0.000 0.260 0.740
#> GSM257973 2 0.6291 -0.489 0.000 0.532 0.468
#> GSM257981 2 0.1964 0.822 0.000 0.944 0.056
#> GSM257983 3 0.5254 0.862 0.000 0.264 0.736
#> GSM257985 3 0.5254 0.862 0.000 0.264 0.736
#> GSM257988 3 0.6286 0.652 0.000 0.464 0.536
#> GSM257991 2 0.1163 0.839 0.000 0.972 0.028
#> GSM257993 2 0.0000 0.838 0.000 1.000 0.000
#> GSM257994 2 0.1753 0.827 0.000 0.952 0.048
#> GSM257989 3 0.5291 0.862 0.000 0.268 0.732
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.4522 0.7751 0.680 0.000 0.000 NA
#> GSM257888 1 0.0817 0.9160 0.976 0.000 0.000 NA
#> GSM257890 1 0.4134 0.8164 0.740 0.000 0.000 NA
#> GSM257892 1 0.4522 0.7751 0.680 0.000 0.000 NA
#> GSM257894 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257896 1 0.0188 0.9196 0.996 0.000 0.000 NA
#> GSM257898 1 0.5193 0.6688 0.580 0.000 0.008 NA
#> GSM257900 1 0.2647 0.8823 0.880 0.000 0.000 NA
#> GSM257902 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257904 1 0.4522 0.7751 0.680 0.000 0.000 NA
#> GSM257906 1 0.4564 0.7690 0.672 0.000 0.000 NA
#> GSM257908 1 0.0592 0.9171 0.984 0.000 0.000 NA
#> GSM257910 1 0.0592 0.9171 0.984 0.000 0.000 NA
#> GSM257912 1 0.1867 0.9034 0.928 0.000 0.000 NA
#> GSM257914 1 0.1211 0.9123 0.960 0.000 0.000 NA
#> GSM257917 1 0.4072 0.8225 0.748 0.000 0.000 NA
#> GSM257919 1 0.1716 0.9063 0.936 0.000 0.000 NA
#> GSM257921 1 0.2760 0.8856 0.872 0.000 0.000 NA
#> GSM257923 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257925 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257927 1 0.1118 0.9131 0.964 0.000 0.000 NA
#> GSM257929 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257937 1 0.1557 0.9085 0.944 0.000 0.000 NA
#> GSM257939 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257941 1 0.3311 0.8577 0.828 0.000 0.000 NA
#> GSM257943 1 0.4406 0.7800 0.700 0.000 0.000 NA
#> GSM257945 1 0.3764 0.8339 0.784 0.000 0.000 NA
#> GSM257947 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257949 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257951 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257953 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257955 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257958 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257960 1 0.3726 0.8372 0.788 0.000 0.000 NA
#> GSM257962 1 0.1302 0.9109 0.956 0.000 0.000 NA
#> GSM257964 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257966 1 0.1118 0.9136 0.964 0.000 0.000 NA
#> GSM257968 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257970 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257972 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257977 1 0.0921 0.9165 0.972 0.000 0.000 NA
#> GSM257982 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257984 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257986 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257990 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM257992 1 0.5163 0.5893 0.516 0.000 0.004 NA
#> GSM257996 1 0.0000 0.9202 1.000 0.000 0.000 NA
#> GSM258006 1 0.4454 0.7792 0.692 0.000 0.000 NA
#> GSM257887 2 0.2281 0.8255 0.000 0.904 0.096 NA
#> GSM257889 3 0.3355 0.7268 0.000 0.004 0.836 NA
#> GSM257891 3 0.1978 0.7673 0.000 0.004 0.928 NA
#> GSM257893 3 0.6690 0.6042 0.000 0.144 0.608 NA
#> GSM257895 2 0.3105 0.8131 0.000 0.868 0.120 NA
#> GSM257897 3 0.3908 0.6893 0.000 0.004 0.784 NA
#> GSM257899 3 0.4920 0.7365 0.000 0.052 0.756 NA
#> GSM257901 3 0.3497 0.8064 0.000 0.104 0.860 NA
#> GSM257903 2 0.6875 0.5600 0.000 0.520 0.112 NA
#> GSM257905 2 0.3037 0.8230 0.000 0.880 0.100 NA
#> GSM257907 3 0.3015 0.8111 0.000 0.092 0.884 NA
#> GSM257909 2 0.5767 0.6704 0.000 0.660 0.060 NA
#> GSM257911 3 0.7138 0.4088 0.000 0.164 0.540 NA
#> GSM257913 3 0.5857 0.6446 0.000 0.196 0.696 NA
#> GSM257916 2 0.4843 0.7875 0.000 0.784 0.112 NA
#> GSM257918 2 0.5747 0.7405 0.000 0.704 0.100 NA
#> GSM257920 3 0.3427 0.8040 0.000 0.112 0.860 NA
#> GSM257922 3 0.4744 0.6790 0.000 0.024 0.736 NA
#> GSM257924 3 0.4995 0.6857 0.000 0.248 0.720 NA
#> GSM257926 3 0.2647 0.8071 0.000 0.120 0.880 NA
#> GSM257928 2 0.6879 0.5331 0.000 0.596 0.216 NA
#> GSM257930 2 0.3984 0.7890 0.000 0.828 0.132 NA
#> GSM257938 2 0.3099 0.8201 0.000 0.876 0.104 NA
#> GSM257940 3 0.3464 0.8050 0.000 0.108 0.860 NA
#> GSM257942 2 0.7494 0.5007 0.000 0.460 0.188 NA
#> GSM257944 2 0.5947 0.5094 0.000 0.572 0.044 NA
#> GSM257946 3 0.3004 0.8053 0.000 0.060 0.892 NA
#> GSM257948 3 0.3464 0.8050 0.000 0.108 0.860 NA
#> GSM257950 3 0.1867 0.8133 0.000 0.072 0.928 NA
#> GSM257952 3 0.4671 0.6960 0.000 0.220 0.752 NA
#> GSM257954 2 0.2675 0.8245 0.000 0.892 0.100 NA
#> GSM257956 2 0.2675 0.8245 0.000 0.892 0.100 NA
#> GSM257959 2 0.2915 0.8189 0.000 0.892 0.080 NA
#> GSM257961 2 0.2281 0.8255 0.000 0.904 0.096 NA
#> GSM257963 2 0.2466 0.8255 0.000 0.900 0.096 NA
#> GSM257965 2 0.7873 0.2948 0.000 0.388 0.320 NA
#> GSM257967 2 0.3399 0.8177 0.000 0.868 0.092 NA
#> GSM257969 2 0.2675 0.8245 0.000 0.892 0.100 NA
#> GSM257971 3 0.4485 0.7533 0.000 0.052 0.796 NA
#> GSM257973 3 0.3598 0.7967 0.000 0.124 0.848 NA
#> GSM257981 2 0.6928 0.1325 0.000 0.456 0.436 NA
#> GSM257983 3 0.1854 0.8098 0.000 0.048 0.940 NA
#> GSM257985 3 0.2483 0.8066 0.000 0.052 0.916 NA
#> GSM257988 3 0.3934 0.8013 0.000 0.116 0.836 NA
#> GSM257991 3 0.7796 0.0119 0.000 0.248 0.392 NA
#> GSM257993 2 0.2611 0.8246 0.000 0.896 0.096 NA
#> GSM257994 2 0.3205 0.8188 0.000 0.872 0.104 NA
#> GSM257989 3 0.1890 0.8120 0.000 0.056 0.936 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.2813 0.872 0.168 0.000 0.000 0.832 0.000
#> GSM257888 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257890 4 0.3366 0.839 0.232 0.000 0.000 0.768 0.000
#> GSM257892 4 0.2813 0.872 0.168 0.000 0.000 0.832 0.000
#> GSM257894 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257896 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257898 4 0.2732 0.867 0.160 0.000 0.000 0.840 0.000
#> GSM257900 4 0.4304 0.426 0.484 0.000 0.000 0.516 0.000
#> GSM257902 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257904 4 0.2813 0.872 0.168 0.000 0.000 0.832 0.000
#> GSM257906 4 0.2732 0.867 0.160 0.000 0.000 0.840 0.000
#> GSM257908 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257912 1 0.0609 0.923 0.980 0.000 0.000 0.020 0.000
#> GSM257914 1 0.0162 0.937 0.996 0.000 0.000 0.004 0.000
#> GSM257917 4 0.4201 0.614 0.408 0.000 0.000 0.592 0.000
#> GSM257919 1 0.0290 0.934 0.992 0.000 0.000 0.008 0.000
#> GSM257921 1 0.3074 0.664 0.804 0.000 0.000 0.196 0.000
#> GSM257923 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.2280 0.794 0.880 0.000 0.000 0.120 0.000
#> GSM257929 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257937 1 0.1732 0.857 0.920 0.000 0.000 0.080 0.000
#> GSM257939 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257941 4 0.4350 0.614 0.408 0.000 0.000 0.588 0.004
#> GSM257943 4 0.2773 0.870 0.164 0.000 0.000 0.836 0.000
#> GSM257945 1 0.4542 -0.289 0.536 0.000 0.000 0.456 0.008
#> GSM257947 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0290 0.933 0.992 0.000 0.000 0.008 0.000
#> GSM257958 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.4305 -0.386 0.512 0.000 0.000 0.488 0.000
#> GSM257962 1 0.1410 0.878 0.940 0.000 0.000 0.060 0.000
#> GSM257964 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257968 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257970 1 0.0162 0.936 0.996 0.000 0.000 0.004 0.000
#> GSM257972 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257977 1 0.2329 0.795 0.876 0.000 0.000 0.124 0.000
#> GSM257982 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257984 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM257992 4 0.2561 0.845 0.144 0.000 0.000 0.856 0.000
#> GSM257996 1 0.0000 0.940 1.000 0.000 0.000 0.000 0.000
#> GSM258006 4 0.2852 0.872 0.172 0.000 0.000 0.828 0.000
#> GSM257887 2 0.1331 0.894 0.000 0.952 0.040 0.000 0.008
#> GSM257889 3 0.3527 0.740 0.000 0.000 0.804 0.024 0.172
#> GSM257891 3 0.1461 0.831 0.000 0.004 0.952 0.028 0.016
#> GSM257893 3 0.6123 0.535 0.000 0.164 0.616 0.016 0.204
#> GSM257895 2 0.1341 0.883 0.000 0.944 0.056 0.000 0.000
#> GSM257897 3 0.3812 0.720 0.000 0.004 0.780 0.020 0.196
#> GSM257899 3 0.3921 0.759 0.000 0.044 0.784 0.000 0.172
#> GSM257901 3 0.1668 0.843 0.000 0.032 0.940 0.000 0.028
#> GSM257903 5 0.5114 0.583 0.000 0.340 0.052 0.000 0.608
#> GSM257905 2 0.2074 0.884 0.000 0.920 0.036 0.000 0.044
#> GSM257907 3 0.1168 0.848 0.000 0.032 0.960 0.000 0.008
#> GSM257909 2 0.4718 0.283 0.000 0.628 0.028 0.000 0.344
#> GSM257911 3 0.3477 0.759 0.000 0.040 0.824 0.000 0.136
#> GSM257913 3 0.2946 0.806 0.000 0.044 0.868 0.000 0.088
#> GSM257916 2 0.3297 0.809 0.000 0.848 0.084 0.000 0.068
#> GSM257918 2 0.4049 0.724 0.000 0.780 0.056 0.000 0.164
#> GSM257920 3 0.1267 0.851 0.000 0.024 0.960 0.004 0.012
#> GSM257922 3 0.4534 0.697 0.000 0.016 0.732 0.028 0.224
#> GSM257924 3 0.2921 0.759 0.000 0.148 0.844 0.004 0.004
#> GSM257926 3 0.0992 0.850 0.000 0.024 0.968 0.000 0.008
#> GSM257928 2 0.3853 0.677 0.000 0.804 0.008 0.036 0.152
#> GSM257930 2 0.1547 0.886 0.000 0.948 0.032 0.016 0.004
#> GSM257938 2 0.0798 0.886 0.000 0.976 0.016 0.008 0.000
#> GSM257940 3 0.1399 0.847 0.000 0.020 0.952 0.000 0.028
#> GSM257942 5 0.5832 0.669 0.000 0.248 0.152 0.000 0.600
#> GSM257944 5 0.4455 0.611 0.000 0.260 0.036 0.000 0.704
#> GSM257946 3 0.1623 0.847 0.000 0.016 0.948 0.020 0.016
#> GSM257948 3 0.1787 0.847 0.000 0.016 0.940 0.012 0.032
#> GSM257950 3 0.1413 0.847 0.000 0.012 0.956 0.020 0.012
#> GSM257952 3 0.3304 0.757 0.000 0.128 0.840 0.004 0.028
#> GSM257954 2 0.0963 0.893 0.000 0.964 0.036 0.000 0.000
#> GSM257956 2 0.1430 0.889 0.000 0.944 0.052 0.000 0.004
#> GSM257959 2 0.2171 0.869 0.000 0.912 0.024 0.000 0.064
#> GSM257961 2 0.1568 0.896 0.000 0.944 0.036 0.000 0.020
#> GSM257963 2 0.1725 0.883 0.000 0.936 0.020 0.000 0.044
#> GSM257965 3 0.6884 -0.420 0.000 0.324 0.400 0.004 0.272
#> GSM257967 2 0.2843 0.854 0.000 0.876 0.048 0.000 0.076
#> GSM257969 2 0.1121 0.893 0.000 0.956 0.044 0.000 0.000
#> GSM257971 3 0.3281 0.812 0.000 0.060 0.848 0.000 0.092
#> GSM257973 3 0.1012 0.850 0.000 0.020 0.968 0.000 0.012
#> GSM257981 3 0.4095 0.607 0.000 0.220 0.752 0.004 0.024
#> GSM257983 3 0.0324 0.847 0.000 0.000 0.992 0.004 0.004
#> GSM257985 3 0.1300 0.849 0.000 0.028 0.956 0.000 0.016
#> GSM257988 3 0.1041 0.848 0.000 0.004 0.964 0.000 0.032
#> GSM257991 5 0.5901 0.280 0.000 0.104 0.400 0.000 0.496
#> GSM257993 2 0.0609 0.892 0.000 0.980 0.020 0.000 0.000
#> GSM257994 2 0.1179 0.879 0.000 0.964 0.016 0.016 0.004
#> GSM257989 3 0.0693 0.843 0.000 0.000 0.980 0.012 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.0405 0.912 0.004 0.000 0.000 0.000 NA 0.988
#> GSM257888 1 0.1082 0.919 0.956 0.000 0.000 0.000 NA 0.004
#> GSM257890 6 0.2433 0.858 0.072 0.000 0.000 0.000 NA 0.884
#> GSM257892 6 0.0405 0.912 0.004 0.000 0.000 0.000 NA 0.988
#> GSM257894 1 0.0713 0.923 0.972 0.000 0.000 0.000 NA 0.000
#> GSM257896 1 0.0865 0.921 0.964 0.000 0.000 0.000 NA 0.000
#> GSM257898 6 0.0146 0.911 0.000 0.000 0.000 0.000 NA 0.996
#> GSM257900 6 0.3261 0.690 0.204 0.000 0.000 0.000 NA 0.780
#> GSM257902 1 0.0632 0.928 0.976 0.000 0.000 0.000 NA 0.000
#> GSM257904 6 0.0146 0.913 0.004 0.000 0.000 0.000 NA 0.996
#> GSM257906 6 0.0000 0.911 0.000 0.000 0.000 0.000 NA 1.000
#> GSM257908 1 0.0547 0.926 0.980 0.000 0.000 0.000 NA 0.000
#> GSM257910 1 0.0547 0.926 0.980 0.000 0.000 0.000 NA 0.000
#> GSM257912 1 0.3024 0.833 0.844 0.000 0.000 0.116 NA 0.008
#> GSM257914 1 0.1168 0.919 0.956 0.000 0.000 0.016 NA 0.000
#> GSM257917 6 0.2088 0.874 0.068 0.000 0.000 0.000 NA 0.904
#> GSM257919 1 0.2030 0.892 0.908 0.000 0.000 0.064 NA 0.000
#> GSM257921 1 0.4144 0.425 0.620 0.000 0.000 0.000 NA 0.360
#> GSM257923 1 0.0632 0.928 0.976 0.000 0.000 0.000 NA 0.000
#> GSM257925 1 0.0865 0.925 0.964 0.000 0.000 0.000 NA 0.000
#> GSM257927 1 0.4602 0.318 0.572 0.000 0.000 0.000 NA 0.384
#> GSM257929 1 0.0790 0.926 0.968 0.000 0.000 0.000 NA 0.000
#> GSM257937 1 0.1720 0.905 0.928 0.000 0.000 0.000 NA 0.032
#> GSM257939 1 0.0632 0.928 0.976 0.000 0.000 0.000 NA 0.000
#> GSM257941 6 0.2660 0.847 0.084 0.000 0.000 0.000 NA 0.868
#> GSM257943 6 0.0458 0.910 0.000 0.000 0.000 0.000 NA 0.984
#> GSM257945 6 0.3978 0.762 0.064 0.000 0.000 0.000 NA 0.744
#> GSM257947 1 0.0713 0.927 0.972 0.000 0.000 0.000 NA 0.000
#> GSM257949 1 0.0260 0.929 0.992 0.000 0.000 0.000 NA 0.000
#> GSM257951 1 0.0713 0.927 0.972 0.000 0.000 0.000 NA 0.000
#> GSM257953 1 0.0713 0.927 0.972 0.000 0.000 0.000 NA 0.000
#> GSM257955 1 0.0713 0.927 0.972 0.000 0.000 0.000 NA 0.000
#> GSM257958 1 0.1007 0.922 0.956 0.000 0.000 0.000 NA 0.000
#> GSM257960 6 0.1908 0.881 0.056 0.000 0.000 0.000 NA 0.916
#> GSM257962 1 0.5254 0.355 0.564 0.000 0.000 0.000 NA 0.316
#> GSM257964 1 0.0260 0.929 0.992 0.000 0.000 0.000 NA 0.000
#> GSM257966 1 0.0937 0.920 0.960 0.000 0.000 0.000 NA 0.000
#> GSM257968 1 0.0458 0.926 0.984 0.000 0.000 0.000 NA 0.000
#> GSM257970 1 0.0632 0.928 0.976 0.000 0.000 0.000 NA 0.000
#> GSM257972 1 0.0291 0.929 0.992 0.000 0.000 0.000 NA 0.004
#> GSM257977 1 0.1461 0.912 0.940 0.000 0.000 0.000 NA 0.016
#> GSM257982 1 0.0632 0.925 0.976 0.000 0.000 0.000 NA 0.000
#> GSM257984 1 0.0000 0.928 1.000 0.000 0.000 0.000 NA 0.000
#> GSM257986 1 0.0146 0.928 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257990 1 0.2003 0.877 0.884 0.000 0.000 0.000 NA 0.000
#> GSM257992 6 0.0146 0.911 0.000 0.000 0.000 0.000 NA 0.996
#> GSM257996 1 0.1434 0.918 0.940 0.000 0.000 0.000 NA 0.012
#> GSM258006 6 0.0146 0.913 0.004 0.000 0.000 0.000 NA 0.996
#> GSM257887 2 0.1584 0.894 0.000 0.928 0.064 0.008 NA 0.000
#> GSM257889 3 0.1370 0.915 0.000 0.012 0.948 0.004 NA 0.000
#> GSM257891 3 0.0937 0.905 0.000 0.000 0.960 0.000 NA 0.000
#> GSM257893 3 0.4207 0.647 0.000 0.232 0.720 0.020 NA 0.000
#> GSM257895 2 0.2651 0.838 0.000 0.860 0.112 0.028 NA 0.000
#> GSM257897 3 0.1845 0.910 0.000 0.028 0.920 0.000 NA 0.000
#> GSM257899 3 0.1296 0.920 0.000 0.032 0.952 0.004 NA 0.000
#> GSM257901 3 0.0405 0.923 0.000 0.008 0.988 0.004 NA 0.000
#> GSM257903 4 0.2964 0.887 0.000 0.204 0.004 0.792 NA 0.000
#> GSM257905 2 0.2046 0.892 0.000 0.908 0.060 0.032 NA 0.000
#> GSM257907 3 0.0622 0.924 0.000 0.012 0.980 0.008 NA 0.000
#> GSM257909 2 0.2358 0.841 0.000 0.876 0.016 0.108 NA 0.000
#> GSM257911 3 0.0993 0.923 0.000 0.024 0.964 0.012 NA 0.000
#> GSM257913 3 0.1970 0.897 0.000 0.060 0.912 0.028 NA 0.000
#> GSM257916 2 0.2706 0.824 0.000 0.852 0.124 0.024 NA 0.000
#> GSM257918 2 0.2747 0.859 0.000 0.860 0.096 0.044 NA 0.000
#> GSM257920 3 0.0767 0.923 0.000 0.012 0.976 0.004 NA 0.000
#> GSM257922 3 0.2402 0.903 0.000 0.020 0.904 0.032 NA 0.004
#> GSM257924 3 0.3221 0.648 0.000 0.264 0.736 0.000 NA 0.000
#> GSM257926 3 0.0632 0.923 0.000 0.024 0.976 0.000 NA 0.000
#> GSM257928 2 0.2518 0.837 0.000 0.892 0.008 0.036 NA 0.004
#> GSM257930 2 0.1492 0.881 0.000 0.940 0.024 0.036 NA 0.000
#> GSM257938 2 0.1225 0.880 0.000 0.952 0.012 0.036 NA 0.000
#> GSM257940 3 0.0665 0.921 0.000 0.004 0.980 0.008 NA 0.000
#> GSM257942 4 0.4151 0.798 0.000 0.276 0.040 0.684 NA 0.000
#> GSM257944 4 0.2703 0.873 0.000 0.172 0.004 0.824 NA 0.000
#> GSM257946 3 0.0914 0.923 0.000 0.016 0.968 0.000 NA 0.000
#> GSM257948 3 0.1269 0.918 0.000 0.012 0.956 0.020 NA 0.000
#> GSM257950 3 0.1124 0.914 0.000 0.008 0.956 0.000 NA 0.000
#> GSM257952 3 0.1923 0.903 0.000 0.064 0.916 0.016 NA 0.000
#> GSM257954 2 0.0891 0.895 0.000 0.968 0.024 0.008 NA 0.000
#> GSM257956 2 0.1753 0.883 0.000 0.912 0.084 0.004 NA 0.000
#> GSM257959 2 0.1444 0.858 0.000 0.928 0.000 0.072 NA 0.000
#> GSM257961 2 0.1408 0.889 0.000 0.944 0.020 0.036 NA 0.000
#> GSM257963 2 0.1219 0.871 0.000 0.948 0.004 0.048 NA 0.000
#> GSM257965 3 0.3929 0.600 0.000 0.272 0.700 0.028 NA 0.000
#> GSM257967 2 0.2554 0.879 0.000 0.876 0.076 0.048 NA 0.000
#> GSM257969 2 0.1556 0.885 0.000 0.920 0.080 0.000 NA 0.000
#> GSM257971 3 0.1225 0.918 0.000 0.036 0.952 0.012 NA 0.000
#> GSM257973 3 0.0603 0.924 0.000 0.016 0.980 0.004 NA 0.000
#> GSM257981 3 0.1829 0.905 0.000 0.064 0.920 0.012 NA 0.000
#> GSM257983 3 0.0291 0.923 0.000 0.000 0.992 0.004 NA 0.000
#> GSM257985 3 0.0858 0.921 0.000 0.028 0.968 0.004 NA 0.000
#> GSM257988 3 0.0717 0.920 0.000 0.000 0.976 0.008 NA 0.000
#> GSM257991 3 0.4307 0.663 0.000 0.072 0.704 0.224 NA 0.000
#> GSM257993 2 0.0717 0.887 0.000 0.976 0.008 0.016 NA 0.000
#> GSM257994 2 0.1268 0.877 0.000 0.952 0.008 0.036 NA 0.004
#> GSM257989 3 0.0363 0.920 0.000 0.000 0.988 0.000 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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 cell.type(p) protocol(p) individual(p) k
#> SD:NMF 96 8.49e-22 1.000 1.000 2
#> SD:NMF 93 6.39e-21 0.665 1.000 3
#> SD:NMF 92 1.05e-20 0.365 1.000 4
#> SD:NMF 90 1.32e-18 0.630 0.991 5
#> SD:NMF 93 3.03e-19 0.434 0.989 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.434 0.795 0.870 0.4702 0.509 0.509
#> 3 3 0.440 0.598 0.710 0.2986 0.654 0.446
#> 4 4 0.751 0.873 0.886 0.1571 0.833 0.599
#> 5 5 0.810 0.868 0.898 0.0605 0.976 0.911
#> 6 6 0.783 0.843 0.846 0.0331 0.958 0.830
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM257886 2 0.9710 0.674 0.400 0.600
#> GSM257888 2 0.9661 0.678 0.392 0.608
#> GSM257890 2 0.9661 0.678 0.392 0.608
#> GSM257892 2 0.9710 0.674 0.400 0.600
#> GSM257894 2 0.9661 0.678 0.392 0.608
#> GSM257896 2 0.9661 0.678 0.392 0.608
#> GSM257898 1 0.0000 0.881 1.000 0.000
#> GSM257900 1 0.0000 0.881 1.000 0.000
#> GSM257902 1 0.1184 0.878 0.984 0.016
#> GSM257904 1 0.0672 0.881 0.992 0.008
#> GSM257906 1 0.0672 0.881 0.992 0.008
#> GSM257908 1 0.4161 0.844 0.916 0.084
#> GSM257910 1 0.4161 0.844 0.916 0.084
#> GSM257912 1 0.4161 0.844 0.916 0.084
#> GSM257914 1 0.4161 0.844 0.916 0.084
#> GSM257917 1 0.4161 0.844 0.916 0.084
#> GSM257919 1 0.4161 0.844 0.916 0.084
#> GSM257921 1 0.3274 0.859 0.940 0.060
#> GSM257923 1 0.0000 0.881 1.000 0.000
#> GSM257925 1 0.0000 0.881 1.000 0.000
#> GSM257927 1 0.0000 0.881 1.000 0.000
#> GSM257929 1 0.0000 0.881 1.000 0.000
#> GSM257937 2 0.9933 0.532 0.452 0.548
#> GSM257939 1 0.0376 0.880 0.996 0.004
#> GSM257941 1 0.0376 0.881 0.996 0.004
#> GSM257943 1 0.0000 0.881 1.000 0.000
#> GSM257945 1 0.0376 0.881 0.996 0.004
#> GSM257947 1 0.0938 0.877 0.988 0.012
#> GSM257949 1 0.0938 0.877 0.988 0.012
#> GSM257951 1 0.0376 0.880 0.996 0.004
#> GSM257953 1 0.0938 0.877 0.988 0.012
#> GSM257955 1 0.0000 0.881 1.000 0.000
#> GSM257958 1 0.0376 0.881 0.996 0.004
#> GSM257960 1 0.0376 0.881 0.996 0.004
#> GSM257962 1 0.0376 0.881 0.996 0.004
#> GSM257964 1 0.0000 0.881 1.000 0.000
#> GSM257966 2 0.9608 0.634 0.384 0.616
#> GSM257968 2 0.9661 0.678 0.392 0.608
#> GSM257970 1 0.0000 0.881 1.000 0.000
#> GSM257972 1 0.0938 0.877 0.988 0.012
#> GSM257977 2 0.9661 0.678 0.392 0.608
#> GSM257982 1 0.1414 0.872 0.980 0.020
#> GSM257984 1 0.2043 0.871 0.968 0.032
#> GSM257986 1 0.2043 0.871 0.968 0.032
#> GSM257990 1 0.0938 0.880 0.988 0.012
#> GSM257992 1 0.0000 0.881 1.000 0.000
#> GSM257996 1 0.3879 0.847 0.924 0.076
#> GSM258006 1 0.0000 0.881 1.000 0.000
#> GSM257887 2 0.9044 0.728 0.320 0.680
#> GSM257889 1 0.7528 0.771 0.784 0.216
#> GSM257891 1 0.7528 0.771 0.784 0.216
#> GSM257893 1 0.7528 0.771 0.784 0.216
#> GSM257895 2 0.8955 0.731 0.312 0.688
#> GSM257897 1 0.7528 0.771 0.784 0.216
#> GSM257899 1 0.7528 0.771 0.784 0.216
#> GSM257901 2 0.0000 0.791 0.000 1.000
#> GSM257903 2 0.0000 0.791 0.000 1.000
#> GSM257905 2 0.0000 0.791 0.000 1.000
#> GSM257907 2 0.0000 0.791 0.000 1.000
#> GSM257909 2 0.0000 0.791 0.000 1.000
#> GSM257911 2 0.0376 0.791 0.004 0.996
#> GSM257913 1 0.8443 0.710 0.728 0.272
#> GSM257916 2 0.0938 0.791 0.012 0.988
#> GSM257918 2 0.0938 0.791 0.012 0.988
#> GSM257920 1 0.7745 0.761 0.772 0.228
#> GSM257922 1 0.7745 0.755 0.772 0.228
#> GSM257924 1 0.7453 0.774 0.788 0.212
#> GSM257926 1 0.7602 0.768 0.780 0.220
#> GSM257928 2 0.9580 0.689 0.380 0.620
#> GSM257930 2 0.9580 0.689 0.380 0.620
#> GSM257938 2 0.9580 0.689 0.380 0.620
#> GSM257940 2 0.0376 0.791 0.004 0.996
#> GSM257942 2 0.0000 0.791 0.000 1.000
#> GSM257944 2 0.0000 0.791 0.000 1.000
#> GSM257946 1 0.7602 0.768 0.780 0.220
#> GSM257948 1 0.7745 0.761 0.772 0.228
#> GSM257950 1 0.7883 0.752 0.764 0.236
#> GSM257952 2 0.2236 0.790 0.036 0.964
#> GSM257954 2 0.7219 0.760 0.200 0.800
#> GSM257956 2 0.9323 0.708 0.348 0.652
#> GSM257959 2 0.0000 0.791 0.000 1.000
#> GSM257961 2 0.0000 0.791 0.000 1.000
#> GSM257963 2 0.0000 0.791 0.000 1.000
#> GSM257965 2 0.0376 0.791 0.004 0.996
#> GSM257967 2 0.0000 0.791 0.000 1.000
#> GSM257969 2 0.7219 0.760 0.200 0.800
#> GSM257971 2 0.7883 0.728 0.236 0.764
#> GSM257973 1 0.7883 0.752 0.764 0.236
#> GSM257981 2 0.2043 0.790 0.032 0.968
#> GSM257983 1 0.7815 0.756 0.768 0.232
#> GSM257985 1 0.7815 0.756 0.768 0.232
#> GSM257988 1 0.7883 0.752 0.764 0.236
#> GSM257991 2 0.0000 0.791 0.000 1.000
#> GSM257993 2 0.9129 0.725 0.328 0.672
#> GSM257994 2 0.9580 0.689 0.380 0.620
#> GSM257989 1 0.7815 0.756 0.768 0.232
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.8128 -0.2209 0.492 0.440 0.068
#> GSM257888 1 0.6869 -0.1394 0.560 0.424 0.016
#> GSM257890 1 0.6869 -0.1394 0.560 0.424 0.016
#> GSM257892 1 0.8128 -0.2209 0.492 0.440 0.068
#> GSM257894 1 0.6869 -0.1394 0.560 0.424 0.016
#> GSM257896 1 0.6869 -0.1394 0.560 0.424 0.016
#> GSM257898 1 0.6715 0.6230 0.660 0.028 0.312
#> GSM257900 1 0.6512 0.6377 0.676 0.024 0.300
#> GSM257902 1 0.5098 0.6698 0.752 0.000 0.248
#> GSM257904 1 0.5919 0.6685 0.724 0.016 0.260
#> GSM257906 1 0.5919 0.6685 0.724 0.016 0.260
#> GSM257908 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257910 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257912 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257914 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257917 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257919 1 0.8835 0.5309 0.576 0.180 0.244
#> GSM257921 1 0.6465 0.6483 0.724 0.044 0.232
#> GSM257923 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257925 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257927 1 0.6357 0.6438 0.684 0.020 0.296
#> GSM257929 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257937 1 0.5926 0.0485 0.644 0.356 0.000
#> GSM257939 1 0.5178 0.6727 0.744 0.000 0.256
#> GSM257941 1 0.5986 0.6567 0.704 0.012 0.284
#> GSM257943 1 0.6541 0.6345 0.672 0.024 0.304
#> GSM257945 1 0.5919 0.6615 0.712 0.012 0.276
#> GSM257947 1 0.5098 0.6732 0.752 0.000 0.248
#> GSM257949 1 0.5098 0.6732 0.752 0.000 0.248
#> GSM257951 1 0.5178 0.6727 0.744 0.000 0.256
#> GSM257953 1 0.5098 0.6732 0.752 0.000 0.248
#> GSM257955 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257958 1 0.5216 0.6713 0.740 0.000 0.260
#> GSM257960 1 0.5884 0.6641 0.716 0.012 0.272
#> GSM257962 1 0.5884 0.6641 0.716 0.012 0.272
#> GSM257964 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257966 2 0.6302 0.1949 0.480 0.520 0.000
#> GSM257968 1 0.6735 -0.1369 0.564 0.424 0.012
#> GSM257970 1 0.5216 0.6717 0.740 0.000 0.260
#> GSM257972 1 0.5098 0.6732 0.752 0.000 0.248
#> GSM257977 1 0.6869 -0.1394 0.560 0.424 0.016
#> GSM257982 1 0.5502 0.6721 0.744 0.008 0.248
#> GSM257984 1 0.5578 0.6690 0.748 0.012 0.240
#> GSM257986 1 0.5578 0.6690 0.748 0.012 0.240
#> GSM257990 1 0.5541 0.6709 0.740 0.008 0.252
#> GSM257992 1 0.6715 0.6230 0.660 0.028 0.312
#> GSM257996 1 0.8748 0.5388 0.584 0.172 0.244
#> GSM258006 1 0.6715 0.6230 0.660 0.028 0.312
#> GSM257887 2 0.9527 0.4173 0.372 0.436 0.192
#> GSM257889 3 0.0747 0.9764 0.016 0.000 0.984
#> GSM257891 3 0.0747 0.9764 0.016 0.000 0.984
#> GSM257893 3 0.0747 0.9764 0.016 0.000 0.984
#> GSM257895 2 0.9573 0.4303 0.364 0.436 0.200
#> GSM257897 3 0.0747 0.9764 0.016 0.000 0.984
#> GSM257899 3 0.0747 0.9764 0.016 0.000 0.984
#> GSM257901 2 0.4399 0.8296 0.000 0.812 0.188
#> GSM257903 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257905 2 0.4291 0.8338 0.000 0.820 0.180
#> GSM257907 2 0.4399 0.8296 0.000 0.812 0.188
#> GSM257909 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257911 2 0.4504 0.8269 0.000 0.804 0.196
#> GSM257913 3 0.2550 0.9271 0.012 0.056 0.932
#> GSM257916 2 0.4399 0.8318 0.000 0.812 0.188
#> GSM257918 2 0.4399 0.8318 0.000 0.812 0.188
#> GSM257920 3 0.1182 0.9754 0.012 0.012 0.976
#> GSM257922 3 0.3583 0.8619 0.056 0.044 0.900
#> GSM257924 3 0.0892 0.9729 0.020 0.000 0.980
#> GSM257926 3 0.0829 0.9772 0.012 0.004 0.984
#> GSM257928 1 0.9264 -0.3371 0.432 0.412 0.156
#> GSM257930 1 0.9264 -0.3371 0.432 0.412 0.156
#> GSM257938 1 0.9264 -0.3371 0.432 0.412 0.156
#> GSM257940 2 0.4504 0.8269 0.000 0.804 0.196
#> GSM257942 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257944 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257946 3 0.0592 0.9773 0.012 0.000 0.988
#> GSM257948 3 0.1182 0.9754 0.012 0.012 0.976
#> GSM257950 3 0.1337 0.9745 0.012 0.016 0.972
#> GSM257952 2 0.5202 0.8106 0.008 0.772 0.220
#> GSM257954 2 0.9605 0.5634 0.264 0.476 0.260
#> GSM257956 1 0.9507 -0.3892 0.432 0.380 0.188
#> GSM257959 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257961 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257963 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257965 2 0.4504 0.8269 0.000 0.804 0.196
#> GSM257967 2 0.4235 0.8342 0.000 0.824 0.176
#> GSM257969 2 0.9605 0.5634 0.264 0.476 0.260
#> GSM257971 2 0.9842 0.4353 0.248 0.384 0.368
#> GSM257973 3 0.1337 0.9745 0.012 0.016 0.972
#> GSM257981 2 0.5070 0.8118 0.004 0.772 0.224
#> GSM257983 3 0.1182 0.9762 0.012 0.012 0.976
#> GSM257985 3 0.1182 0.9762 0.012 0.012 0.976
#> GSM257988 3 0.1337 0.9745 0.012 0.016 0.972
#> GSM257991 2 0.4291 0.8319 0.000 0.820 0.180
#> GSM257993 2 0.9476 0.4012 0.380 0.436 0.184
#> GSM257994 1 0.9264 -0.3371 0.432 0.412 0.156
#> GSM257989 3 0.1182 0.9762 0.012 0.012 0.976
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.6033 0.775 0.316 0.064 0.000 0.620
#> GSM257888 4 0.6253 0.747 0.372 0.064 0.000 0.564
#> GSM257890 4 0.6253 0.747 0.372 0.064 0.000 0.564
#> GSM257892 4 0.6033 0.775 0.316 0.064 0.000 0.620
#> GSM257894 4 0.6253 0.747 0.372 0.064 0.000 0.564
#> GSM257896 4 0.6253 0.747 0.372 0.064 0.000 0.564
#> GSM257898 1 0.2011 0.866 0.920 0.000 0.000 0.080
#> GSM257900 1 0.1716 0.882 0.936 0.000 0.000 0.064
#> GSM257902 1 0.0707 0.914 0.980 0.000 0.000 0.020
#> GSM257904 1 0.1022 0.913 0.968 0.000 0.000 0.032
#> GSM257906 1 0.1022 0.913 0.968 0.000 0.000 0.032
#> GSM257908 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257910 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257912 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257914 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257917 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257919 1 0.3933 0.755 0.792 0.008 0.000 0.200
#> GSM257921 1 0.1978 0.882 0.928 0.004 0.000 0.068
#> GSM257923 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257927 1 0.1557 0.889 0.944 0.000 0.000 0.056
#> GSM257929 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257937 4 0.6315 0.572 0.432 0.060 0.000 0.508
#> GSM257939 1 0.0188 0.916 0.996 0.000 0.000 0.004
#> GSM257941 1 0.1302 0.903 0.956 0.000 0.000 0.044
#> GSM257943 1 0.1792 0.878 0.932 0.000 0.000 0.068
#> GSM257945 1 0.1118 0.908 0.964 0.000 0.000 0.036
#> GSM257947 1 0.0469 0.913 0.988 0.000 0.000 0.012
#> GSM257949 1 0.0469 0.913 0.988 0.000 0.000 0.012
#> GSM257951 1 0.0188 0.916 0.996 0.000 0.000 0.004
#> GSM257953 1 0.0469 0.913 0.988 0.000 0.000 0.012
#> GSM257955 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0336 0.916 0.992 0.000 0.000 0.008
#> GSM257960 1 0.1022 0.910 0.968 0.000 0.000 0.032
#> GSM257962 1 0.1022 0.910 0.968 0.000 0.000 0.032
#> GSM257964 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257966 4 0.5636 0.600 0.260 0.060 0.000 0.680
#> GSM257968 4 0.6276 0.741 0.380 0.064 0.000 0.556
#> GSM257970 1 0.0000 0.916 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0469 0.913 0.988 0.000 0.000 0.012
#> GSM257977 4 0.6253 0.747 0.372 0.064 0.000 0.564
#> GSM257982 1 0.0779 0.908 0.980 0.004 0.000 0.016
#> GSM257984 1 0.1022 0.906 0.968 0.000 0.000 0.032
#> GSM257986 1 0.1022 0.906 0.968 0.000 0.000 0.032
#> GSM257990 1 0.0817 0.915 0.976 0.000 0.000 0.024
#> GSM257992 1 0.2011 0.866 0.920 0.000 0.000 0.080
#> GSM257996 1 0.3710 0.765 0.804 0.004 0.000 0.192
#> GSM258006 1 0.2011 0.866 0.920 0.000 0.000 0.080
#> GSM257887 4 0.6117 0.790 0.188 0.120 0.004 0.688
#> GSM257889 3 0.0000 0.975 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.975 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0188 0.975 0.000 0.000 0.996 0.004
#> GSM257895 4 0.6389 0.787 0.184 0.124 0.012 0.680
#> GSM257897 3 0.0000 0.975 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.975 0.000 0.000 1.000 0.000
#> GSM257901 2 0.1743 0.947 0.000 0.940 0.056 0.004
#> GSM257903 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0188 0.955 0.000 0.996 0.000 0.004
#> GSM257907 2 0.1743 0.947 0.000 0.940 0.056 0.004
#> GSM257909 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257911 2 0.2053 0.938 0.000 0.924 0.072 0.004
#> GSM257913 3 0.1940 0.932 0.000 0.076 0.924 0.000
#> GSM257916 2 0.1042 0.955 0.000 0.972 0.020 0.008
#> GSM257918 2 0.1042 0.955 0.000 0.972 0.020 0.008
#> GSM257920 3 0.0817 0.977 0.000 0.024 0.976 0.000
#> GSM257922 3 0.2469 0.894 0.000 0.000 0.892 0.108
#> GSM257924 3 0.0712 0.974 0.004 0.004 0.984 0.008
#> GSM257926 3 0.0779 0.977 0.000 0.016 0.980 0.004
#> GSM257928 4 0.3893 0.789 0.196 0.000 0.008 0.796
#> GSM257930 4 0.3893 0.789 0.196 0.000 0.008 0.796
#> GSM257938 4 0.3893 0.789 0.196 0.000 0.008 0.796
#> GSM257940 2 0.2053 0.938 0.000 0.924 0.072 0.004
#> GSM257942 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0657 0.978 0.000 0.012 0.984 0.004
#> GSM257948 3 0.0817 0.977 0.000 0.024 0.976 0.000
#> GSM257950 3 0.0921 0.976 0.000 0.028 0.972 0.000
#> GSM257952 2 0.3257 0.865 0.004 0.844 0.152 0.000
#> GSM257954 4 0.5907 0.639 0.080 0.252 0.000 0.668
#> GSM257956 4 0.5215 0.795 0.196 0.056 0.004 0.744
#> GSM257959 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257965 2 0.2053 0.938 0.000 0.924 0.072 0.004
#> GSM257967 2 0.0000 0.957 0.000 1.000 0.000 0.000
#> GSM257969 4 0.5907 0.639 0.080 0.252 0.000 0.668
#> GSM257971 4 0.4713 0.303 0.000 0.000 0.360 0.640
#> GSM257973 3 0.1022 0.974 0.000 0.032 0.968 0.000
#> GSM257981 2 0.3289 0.872 0.004 0.852 0.140 0.004
#> GSM257983 3 0.0921 0.976 0.000 0.028 0.972 0.000
#> GSM257985 3 0.0921 0.976 0.000 0.028 0.972 0.000
#> GSM257988 3 0.1022 0.974 0.000 0.032 0.968 0.000
#> GSM257991 2 0.1489 0.950 0.000 0.952 0.044 0.004
#> GSM257993 4 0.6088 0.795 0.196 0.112 0.004 0.688
#> GSM257994 4 0.3893 0.789 0.196 0.000 0.008 0.796
#> GSM257989 3 0.0817 0.977 0.000 0.024 0.976 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.4318 0.799 0.292 0.000 0.000 0.688 0.020
#> GSM257888 4 0.3999 0.895 0.344 0.000 0.000 0.656 0.000
#> GSM257890 4 0.3999 0.895 0.344 0.000 0.000 0.656 0.000
#> GSM257892 4 0.4318 0.799 0.292 0.000 0.000 0.688 0.020
#> GSM257894 4 0.3999 0.895 0.344 0.000 0.000 0.656 0.000
#> GSM257896 4 0.3999 0.895 0.344 0.000 0.000 0.656 0.000
#> GSM257898 1 0.2304 0.809 0.892 0.000 0.000 0.100 0.008
#> GSM257900 1 0.2077 0.825 0.908 0.000 0.000 0.084 0.008
#> GSM257902 1 0.0880 0.869 0.968 0.000 0.000 0.032 0.000
#> GSM257904 1 0.1357 0.863 0.948 0.000 0.000 0.048 0.004
#> GSM257906 1 0.1357 0.863 0.948 0.000 0.000 0.048 0.004
#> GSM257908 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257910 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257912 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257914 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257917 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257919 1 0.3957 0.612 0.712 0.008 0.000 0.280 0.000
#> GSM257921 1 0.2488 0.794 0.872 0.004 0.000 0.124 0.000
#> GSM257923 1 0.0162 0.873 0.996 0.000 0.000 0.004 0.000
#> GSM257925 1 0.0162 0.873 0.996 0.000 0.000 0.004 0.000
#> GSM257927 1 0.1764 0.842 0.928 0.000 0.000 0.064 0.008
#> GSM257929 1 0.0162 0.873 0.996 0.000 0.000 0.004 0.000
#> GSM257937 4 0.4367 0.745 0.372 0.008 0.000 0.620 0.000
#> GSM257939 1 0.0290 0.872 0.992 0.000 0.000 0.008 0.000
#> GSM257941 1 0.1571 0.857 0.936 0.000 0.000 0.060 0.004
#> GSM257943 1 0.2136 0.821 0.904 0.000 0.000 0.088 0.008
#> GSM257945 1 0.1282 0.864 0.952 0.000 0.000 0.044 0.004
#> GSM257947 1 0.0510 0.869 0.984 0.000 0.000 0.016 0.000
#> GSM257949 1 0.0510 0.869 0.984 0.000 0.000 0.016 0.000
#> GSM257951 1 0.0162 0.873 0.996 0.000 0.000 0.004 0.000
#> GSM257953 1 0.0510 0.869 0.984 0.000 0.000 0.016 0.000
#> GSM257955 1 0.0000 0.873 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0404 0.874 0.988 0.000 0.000 0.012 0.000
#> GSM257960 1 0.1282 0.866 0.952 0.000 0.000 0.044 0.004
#> GSM257962 1 0.1282 0.866 0.952 0.000 0.000 0.044 0.004
#> GSM257964 1 0.0162 0.873 0.996 0.000 0.000 0.004 0.000
#> GSM257966 4 0.3209 0.607 0.180 0.008 0.000 0.812 0.000
#> GSM257968 4 0.4060 0.882 0.360 0.000 0.000 0.640 0.000
#> GSM257970 1 0.0000 0.873 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0510 0.869 0.984 0.000 0.000 0.016 0.000
#> GSM257977 4 0.3999 0.895 0.344 0.000 0.000 0.656 0.000
#> GSM257982 1 0.0703 0.865 0.976 0.000 0.000 0.024 0.000
#> GSM257984 1 0.1043 0.859 0.960 0.000 0.000 0.040 0.000
#> GSM257986 1 0.1043 0.859 0.960 0.000 0.000 0.040 0.000
#> GSM257990 1 0.1124 0.871 0.960 0.000 0.000 0.036 0.004
#> GSM257992 1 0.2304 0.809 0.892 0.000 0.000 0.100 0.008
#> GSM257996 1 0.3715 0.640 0.736 0.004 0.000 0.260 0.000
#> GSM258006 1 0.2304 0.809 0.892 0.000 0.000 0.100 0.008
#> GSM257887 5 0.2782 0.869 0.000 0.072 0.000 0.048 0.880
#> GSM257889 3 0.0000 0.971 0.000 0.000 1.000 0.000 0.000
#> GSM257891 3 0.0000 0.971 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.0162 0.971 0.000 0.000 0.996 0.000 0.004
#> GSM257895 5 0.3126 0.868 0.000 0.076 0.008 0.048 0.868
#> GSM257897 3 0.0000 0.971 0.000 0.000 1.000 0.000 0.000
#> GSM257899 3 0.0000 0.971 0.000 0.000 1.000 0.000 0.000
#> GSM257901 2 0.1739 0.944 0.000 0.940 0.032 0.024 0.004
#> GSM257903 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257905 2 0.0290 0.953 0.000 0.992 0.000 0.000 0.008
#> GSM257907 2 0.1739 0.944 0.000 0.940 0.032 0.024 0.004
#> GSM257909 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257911 2 0.2053 0.936 0.000 0.924 0.048 0.024 0.004
#> GSM257913 3 0.1671 0.923 0.000 0.076 0.924 0.000 0.000
#> GSM257916 2 0.0798 0.953 0.000 0.976 0.016 0.000 0.008
#> GSM257918 2 0.0798 0.953 0.000 0.976 0.016 0.000 0.008
#> GSM257920 3 0.0703 0.973 0.000 0.024 0.976 0.000 0.000
#> GSM257922 3 0.2233 0.877 0.000 0.000 0.892 0.004 0.104
#> GSM257924 3 0.0566 0.970 0.000 0.004 0.984 0.000 0.012
#> GSM257926 3 0.0671 0.974 0.000 0.016 0.980 0.000 0.004
#> GSM257928 5 0.0324 0.872 0.000 0.000 0.004 0.004 0.992
#> GSM257930 5 0.0324 0.872 0.000 0.000 0.004 0.004 0.992
#> GSM257938 5 0.0324 0.872 0.000 0.000 0.004 0.004 0.992
#> GSM257940 2 0.2053 0.936 0.000 0.924 0.048 0.024 0.004
#> GSM257942 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257944 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257946 3 0.0566 0.974 0.000 0.012 0.984 0.000 0.004
#> GSM257948 3 0.0703 0.973 0.000 0.024 0.976 0.000 0.000
#> GSM257950 3 0.0992 0.972 0.000 0.024 0.968 0.008 0.000
#> GSM257952 2 0.3022 0.856 0.000 0.848 0.136 0.012 0.004
#> GSM257954 5 0.4270 0.787 0.000 0.204 0.000 0.048 0.748
#> GSM257956 5 0.1270 0.876 0.000 0.052 0.000 0.000 0.948
#> GSM257959 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257961 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257963 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257965 2 0.2053 0.936 0.000 0.924 0.048 0.024 0.004
#> GSM257967 2 0.0162 0.955 0.000 0.996 0.000 0.000 0.004
#> GSM257969 5 0.4270 0.787 0.000 0.204 0.000 0.048 0.748
#> GSM257971 5 0.4211 0.431 0.000 0.000 0.360 0.004 0.636
#> GSM257973 3 0.1082 0.970 0.000 0.028 0.964 0.008 0.000
#> GSM257981 2 0.3005 0.863 0.000 0.856 0.124 0.012 0.008
#> GSM257983 3 0.0992 0.971 0.000 0.024 0.968 0.008 0.000
#> GSM257985 3 0.0992 0.971 0.000 0.024 0.968 0.008 0.000
#> GSM257988 3 0.1082 0.970 0.000 0.028 0.964 0.008 0.000
#> GSM257991 2 0.1372 0.948 0.000 0.956 0.024 0.016 0.004
#> GSM257993 5 0.2654 0.870 0.000 0.064 0.000 0.048 0.888
#> GSM257994 5 0.0324 0.872 0.000 0.000 0.004 0.004 0.992
#> GSM257989 3 0.0898 0.973 0.000 0.020 0.972 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.3630 0.753 0.212 0.000 0.000 0.032 0.000 0.756
#> GSM257888 6 0.3390 0.862 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM257890 6 0.3390 0.862 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM257892 6 0.3630 0.753 0.212 0.000 0.000 0.032 0.000 0.756
#> GSM257894 6 0.3390 0.862 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM257896 6 0.3390 0.862 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM257898 1 0.2822 0.768 0.852 0.000 0.000 0.040 0.000 0.108
#> GSM257900 1 0.2487 0.800 0.876 0.000 0.000 0.032 0.000 0.092
#> GSM257902 1 0.1588 0.813 0.924 0.000 0.000 0.072 0.000 0.004
#> GSM257904 1 0.2647 0.801 0.868 0.000 0.000 0.088 0.000 0.044
#> GSM257906 1 0.2647 0.801 0.868 0.000 0.000 0.088 0.000 0.044
#> GSM257908 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257910 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257912 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257914 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257917 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257919 4 0.4852 1.000 0.452 0.000 0.000 0.492 0.000 0.056
#> GSM257921 1 0.2846 0.680 0.856 0.000 0.000 0.084 0.000 0.060
#> GSM257923 1 0.0146 0.872 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257925 1 0.0146 0.872 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257927 1 0.2039 0.826 0.904 0.000 0.000 0.020 0.000 0.076
#> GSM257929 1 0.0146 0.872 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257937 6 0.5219 0.641 0.340 0.000 0.000 0.108 0.000 0.552
#> GSM257939 1 0.0260 0.871 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257941 1 0.2129 0.836 0.904 0.000 0.000 0.040 0.000 0.056
#> GSM257943 1 0.2609 0.790 0.868 0.000 0.000 0.036 0.000 0.096
#> GSM257945 1 0.1713 0.851 0.928 0.000 0.000 0.028 0.000 0.044
#> GSM257947 1 0.0547 0.866 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257949 1 0.0547 0.866 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257951 1 0.0146 0.873 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257953 1 0.0692 0.867 0.976 0.000 0.000 0.004 0.000 0.020
#> GSM257955 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0363 0.871 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257960 1 0.1649 0.855 0.932 0.000 0.000 0.032 0.000 0.036
#> GSM257962 1 0.1649 0.855 0.932 0.000 0.000 0.032 0.000 0.036
#> GSM257964 1 0.0146 0.872 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257966 6 0.5283 0.455 0.148 0.000 0.000 0.264 0.000 0.588
#> GSM257968 6 0.3742 0.810 0.348 0.000 0.000 0.004 0.000 0.648
#> GSM257970 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0547 0.866 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257977 6 0.3390 0.862 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM257982 1 0.0790 0.859 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM257984 1 0.1082 0.850 0.956 0.000 0.000 0.004 0.000 0.040
#> GSM257986 1 0.1082 0.850 0.956 0.000 0.000 0.004 0.000 0.040
#> GSM257990 1 0.1151 0.863 0.956 0.000 0.000 0.032 0.000 0.012
#> GSM257992 1 0.2822 0.768 0.852 0.000 0.000 0.040 0.000 0.108
#> GSM257996 1 0.4808 -0.950 0.476 0.000 0.000 0.472 0.000 0.052
#> GSM258006 1 0.2445 0.781 0.872 0.000 0.000 0.020 0.000 0.108
#> GSM257887 5 0.2554 0.873 0.000 0.076 0.000 0.000 0.876 0.048
#> GSM257889 3 0.0405 0.954 0.000 0.000 0.988 0.004 0.000 0.008
#> GSM257891 3 0.0405 0.954 0.000 0.000 0.988 0.004 0.000 0.008
#> GSM257893 3 0.0551 0.954 0.000 0.000 0.984 0.004 0.004 0.008
#> GSM257895 5 0.2908 0.872 0.000 0.076 0.012 0.000 0.864 0.048
#> GSM257897 3 0.0520 0.954 0.000 0.000 0.984 0.008 0.000 0.008
#> GSM257899 3 0.0520 0.954 0.000 0.000 0.984 0.008 0.000 0.008
#> GSM257901 2 0.4229 0.840 0.000 0.744 0.012 0.064 0.000 0.180
#> GSM257903 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0146 0.894 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257907 2 0.4229 0.840 0.000 0.744 0.012 0.064 0.000 0.180
#> GSM257909 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.4401 0.841 0.000 0.744 0.028 0.060 0.000 0.168
#> GSM257913 3 0.1493 0.908 0.000 0.056 0.936 0.004 0.000 0.004
#> GSM257916 2 0.0976 0.892 0.000 0.968 0.008 0.016 0.008 0.000
#> GSM257918 2 0.0976 0.892 0.000 0.968 0.008 0.016 0.008 0.000
#> GSM257920 3 0.0405 0.956 0.000 0.000 0.988 0.004 0.000 0.008
#> GSM257922 3 0.5296 0.426 0.000 0.000 0.516 0.396 0.080 0.008
#> GSM257924 3 0.0653 0.953 0.000 0.000 0.980 0.004 0.012 0.004
#> GSM257926 3 0.0291 0.956 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM257928 5 0.0000 0.876 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257930 5 0.0000 0.876 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257938 5 0.0000 0.876 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257940 2 0.4401 0.841 0.000 0.744 0.028 0.060 0.000 0.168
#> GSM257942 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.0146 0.956 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257948 3 0.0405 0.956 0.000 0.000 0.988 0.004 0.000 0.008
#> GSM257950 3 0.0717 0.954 0.000 0.000 0.976 0.008 0.000 0.016
#> GSM257952 2 0.4578 0.818 0.000 0.756 0.116 0.040 0.004 0.084
#> GSM257954 5 0.3864 0.798 0.000 0.208 0.000 0.000 0.744 0.048
#> GSM257956 5 0.1204 0.880 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM257959 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.4401 0.841 0.000 0.744 0.028 0.060 0.000 0.168
#> GSM257967 2 0.0000 0.895 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 5 0.3864 0.798 0.000 0.208 0.000 0.000 0.744 0.048
#> GSM257971 5 0.4509 0.465 0.000 0.000 0.316 0.036 0.640 0.008
#> GSM257973 3 0.0820 0.952 0.000 0.000 0.972 0.012 0.000 0.016
#> GSM257981 2 0.4620 0.819 0.000 0.756 0.116 0.036 0.008 0.084
#> GSM257983 3 0.0725 0.953 0.000 0.000 0.976 0.012 0.000 0.012
#> GSM257985 3 0.0725 0.953 0.000 0.000 0.976 0.012 0.000 0.012
#> GSM257988 3 0.0820 0.952 0.000 0.000 0.972 0.012 0.000 0.016
#> GSM257991 2 0.3897 0.850 0.000 0.772 0.012 0.048 0.000 0.168
#> GSM257993 5 0.2442 0.874 0.000 0.068 0.000 0.000 0.884 0.048
#> GSM257994 5 0.0000 0.876 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257989 3 0.0622 0.955 0.000 0.000 0.980 0.008 0.000 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 cell.type(p) protocol(p) individual(p) k
#> CV:hclust 96 8.38e-05 0.350 0.810 2
#> CV:hclust 77 1.90e-17 0.309 0.993 3
#> CV:hclust 95 3.63e-16 0.497 0.945 4
#> CV:hclust 95 1.14e-19 0.610 0.993 5
#> CV:hclust 92 2.55e-18 0.457 0.976 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 25171 rows and 96 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 0.988 0.993 0.5050 0.495 0.495
#> 3 3 0.676 0.832 0.829 0.2385 0.877 0.752
#> 4 4 0.662 0.641 0.807 0.1337 0.873 0.676
#> 5 5 0.664 0.582 0.756 0.0777 0.932 0.768
#> 6 6 0.722 0.595 0.693 0.0534 0.900 0.610
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
#> GSM257886 1 0.0000 0.994 1.000 0.000
#> GSM257888 1 0.0000 0.994 1.000 0.000
#> GSM257890 1 0.0000 0.994 1.000 0.000
#> GSM257892 1 0.7219 0.755 0.800 0.200
#> GSM257894 1 0.0000 0.994 1.000 0.000
#> GSM257896 1 0.0000 0.994 1.000 0.000
#> GSM257898 1 0.0376 0.993 0.996 0.004
#> GSM257900 1 0.0376 0.993 0.996 0.004
#> GSM257902 1 0.0376 0.993 0.996 0.004
#> GSM257904 1 0.0376 0.993 0.996 0.004
#> GSM257906 1 0.0376 0.993 0.996 0.004
#> GSM257908 1 0.0000 0.994 1.000 0.000
#> GSM257910 1 0.0000 0.994 1.000 0.000
#> GSM257912 1 0.0000 0.994 1.000 0.000
#> GSM257914 1 0.0000 0.994 1.000 0.000
#> GSM257917 1 0.0000 0.994 1.000 0.000
#> GSM257919 1 0.0000 0.994 1.000 0.000
#> GSM257921 1 0.0000 0.994 1.000 0.000
#> GSM257923 1 0.0000 0.994 1.000 0.000
#> GSM257925 1 0.0000 0.994 1.000 0.000
#> GSM257927 1 0.0000 0.994 1.000 0.000
#> GSM257929 1 0.0000 0.994 1.000 0.000
#> GSM257937 1 0.0000 0.994 1.000 0.000
#> GSM257939 1 0.0000 0.994 1.000 0.000
#> GSM257941 1 0.0376 0.993 0.996 0.004
#> GSM257943 1 0.0376 0.993 0.996 0.004
#> GSM257945 1 0.0376 0.993 0.996 0.004
#> GSM257947 1 0.0000 0.994 1.000 0.000
#> GSM257949 1 0.0000 0.994 1.000 0.000
#> GSM257951 1 0.0000 0.994 1.000 0.000
#> GSM257953 1 0.0000 0.994 1.000 0.000
#> GSM257955 1 0.0376 0.993 0.996 0.004
#> GSM257958 1 0.0376 0.993 0.996 0.004
#> GSM257960 1 0.0376 0.993 0.996 0.004
#> GSM257962 1 0.0376 0.993 0.996 0.004
#> GSM257964 1 0.0000 0.994 1.000 0.000
#> GSM257966 1 0.0000 0.994 1.000 0.000
#> GSM257968 1 0.0000 0.994 1.000 0.000
#> GSM257970 1 0.0000 0.994 1.000 0.000
#> GSM257972 1 0.0000 0.994 1.000 0.000
#> GSM257977 1 0.0000 0.994 1.000 0.000
#> GSM257982 1 0.0000 0.994 1.000 0.000
#> GSM257984 1 0.0000 0.994 1.000 0.000
#> GSM257986 1 0.0000 0.994 1.000 0.000
#> GSM257990 1 0.0376 0.993 0.996 0.004
#> GSM257992 1 0.0376 0.993 0.996 0.004
#> GSM257996 1 0.0376 0.993 0.996 0.004
#> GSM258006 1 0.0376 0.993 0.996 0.004
#> GSM257887 2 0.0376 0.992 0.004 0.996
#> GSM257889 2 0.2236 0.970 0.036 0.964
#> GSM257891 2 0.2236 0.970 0.036 0.964
#> GSM257893 2 0.0938 0.987 0.012 0.988
#> GSM257895 2 0.0376 0.992 0.004 0.996
#> GSM257897 2 0.2236 0.970 0.036 0.964
#> GSM257899 2 0.2236 0.970 0.036 0.964
#> GSM257901 2 0.0000 0.991 0.000 1.000
#> GSM257903 2 0.0376 0.992 0.004 0.996
#> GSM257905 2 0.0376 0.992 0.004 0.996
#> GSM257907 2 0.0000 0.991 0.000 1.000
#> GSM257909 2 0.0376 0.992 0.004 0.996
#> GSM257911 2 0.0376 0.992 0.004 0.996
#> GSM257913 2 0.0000 0.991 0.000 1.000
#> GSM257916 2 0.0376 0.992 0.004 0.996
#> GSM257918 2 0.0376 0.992 0.004 0.996
#> GSM257920 2 0.0938 0.987 0.012 0.988
#> GSM257922 2 0.2236 0.970 0.036 0.964
#> GSM257924 2 0.0938 0.987 0.012 0.988
#> GSM257926 2 0.0000 0.991 0.000 1.000
#> GSM257928 2 0.0376 0.990 0.004 0.996
#> GSM257930 2 0.0000 0.991 0.000 1.000
#> GSM257938 2 0.0376 0.992 0.004 0.996
#> GSM257940 2 0.0000 0.991 0.000 1.000
#> GSM257942 2 0.0376 0.992 0.004 0.996
#> GSM257944 2 0.0376 0.992 0.004 0.996
#> GSM257946 2 0.0938 0.987 0.012 0.988
#> GSM257948 2 0.0000 0.991 0.000 1.000
#> GSM257950 2 0.2236 0.970 0.036 0.964
#> GSM257952 2 0.0376 0.992 0.004 0.996
#> GSM257954 2 0.0376 0.992 0.004 0.996
#> GSM257956 2 0.0376 0.992 0.004 0.996
#> GSM257959 2 0.0376 0.992 0.004 0.996
#> GSM257961 2 0.0376 0.992 0.004 0.996
#> GSM257963 2 0.0376 0.992 0.004 0.996
#> GSM257965 2 0.0376 0.992 0.004 0.996
#> GSM257967 2 0.0376 0.992 0.004 0.996
#> GSM257969 2 0.0376 0.992 0.004 0.996
#> GSM257971 2 0.0000 0.991 0.000 1.000
#> GSM257973 2 0.0938 0.987 0.012 0.988
#> GSM257981 2 0.0376 0.992 0.004 0.996
#> GSM257983 2 0.2236 0.970 0.036 0.964
#> GSM257985 2 0.0938 0.987 0.012 0.988
#> GSM257988 2 0.0938 0.987 0.012 0.988
#> GSM257991 2 0.0376 0.992 0.004 0.996
#> GSM257993 2 0.0376 0.992 0.004 0.996
#> GSM257994 2 0.0376 0.992 0.004 0.996
#> GSM257989 2 0.0938 0.987 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.4796 0.8619 0.780 0.000 0.220
#> GSM257888 1 0.4605 0.8628 0.796 0.000 0.204
#> GSM257890 1 0.4702 0.8633 0.788 0.000 0.212
#> GSM257892 1 0.9326 0.1985 0.440 0.396 0.164
#> GSM257894 1 0.3340 0.8952 0.880 0.000 0.120
#> GSM257896 1 0.3116 0.8994 0.892 0.000 0.108
#> GSM257898 1 0.1163 0.9180 0.972 0.000 0.028
#> GSM257900 1 0.0747 0.9192 0.984 0.000 0.016
#> GSM257902 1 0.0237 0.9219 0.996 0.000 0.004
#> GSM257904 1 0.3879 0.8872 0.848 0.000 0.152
#> GSM257906 1 0.1031 0.9192 0.976 0.000 0.024
#> GSM257908 1 0.6416 0.8163 0.708 0.032 0.260
#> GSM257910 1 0.6341 0.8199 0.716 0.032 0.252
#> GSM257912 1 0.6341 0.8199 0.716 0.032 0.252
#> GSM257914 1 0.6341 0.8199 0.716 0.032 0.252
#> GSM257917 1 0.6341 0.8199 0.716 0.032 0.252
#> GSM257919 1 0.6341 0.8199 0.716 0.032 0.252
#> GSM257921 1 0.4291 0.8738 0.820 0.000 0.180
#> GSM257923 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257927 1 0.0237 0.9216 0.996 0.000 0.004
#> GSM257929 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257937 1 0.5216 0.8354 0.740 0.000 0.260
#> GSM257939 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257941 1 0.1031 0.9192 0.976 0.000 0.024
#> GSM257943 1 0.1031 0.9192 0.976 0.000 0.024
#> GSM257945 1 0.0592 0.9215 0.988 0.000 0.012
#> GSM257947 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257949 1 0.0237 0.9217 0.996 0.000 0.004
#> GSM257951 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257953 1 0.0237 0.9216 0.996 0.000 0.004
#> GSM257955 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257958 1 0.0237 0.9219 0.996 0.000 0.004
#> GSM257960 1 0.0424 0.9216 0.992 0.000 0.008
#> GSM257962 1 0.0424 0.9216 0.992 0.000 0.008
#> GSM257964 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257966 1 0.6379 0.8158 0.712 0.032 0.256
#> GSM257968 1 0.3038 0.8995 0.896 0.000 0.104
#> GSM257970 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257972 1 0.0424 0.9214 0.992 0.000 0.008
#> GSM257977 1 0.3116 0.8994 0.892 0.000 0.108
#> GSM257982 1 0.1031 0.9191 0.976 0.000 0.024
#> GSM257984 1 0.0000 0.9220 1.000 0.000 0.000
#> GSM257986 1 0.0237 0.9217 0.996 0.000 0.004
#> GSM257990 1 0.0424 0.9216 0.992 0.000 0.008
#> GSM257992 1 0.1031 0.9192 0.976 0.000 0.024
#> GSM257996 1 0.4796 0.8544 0.780 0.000 0.220
#> GSM258006 1 0.1031 0.9192 0.976 0.000 0.024
#> GSM257887 2 0.1163 0.8040 0.000 0.972 0.028
#> GSM257889 3 0.6339 0.8271 0.008 0.360 0.632
#> GSM257891 3 0.5728 0.9133 0.008 0.272 0.720
#> GSM257893 3 0.6008 0.8232 0.000 0.372 0.628
#> GSM257895 2 0.1964 0.7915 0.000 0.944 0.056
#> GSM257897 3 0.7378 0.7632 0.052 0.320 0.628
#> GSM257899 3 0.6359 0.8217 0.008 0.364 0.628
#> GSM257901 2 0.6305 -0.3210 0.000 0.516 0.484
#> GSM257903 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257905 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257907 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257909 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257911 2 0.4931 0.7046 0.000 0.768 0.232
#> GSM257913 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257916 2 0.3340 0.8186 0.000 0.880 0.120
#> GSM257918 2 0.3038 0.8244 0.000 0.896 0.104
#> GSM257920 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257922 3 0.6359 0.8217 0.008 0.364 0.628
#> GSM257924 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257926 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257928 2 0.5905 -0.0198 0.000 0.648 0.352
#> GSM257930 2 0.2066 0.7882 0.000 0.940 0.060
#> GSM257938 2 0.2066 0.7882 0.000 0.940 0.060
#> GSM257940 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257942 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257944 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257946 3 0.5497 0.9218 0.000 0.292 0.708
#> GSM257948 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257950 3 0.5958 0.9189 0.008 0.300 0.692
#> GSM257952 2 0.4842 0.7186 0.000 0.776 0.224
#> GSM257954 2 0.1529 0.7987 0.000 0.960 0.040
#> GSM257956 2 0.1964 0.7915 0.000 0.944 0.056
#> GSM257959 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257961 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257963 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257965 2 0.4842 0.7186 0.000 0.776 0.224
#> GSM257967 2 0.2796 0.8262 0.000 0.908 0.092
#> GSM257969 2 0.1163 0.8040 0.000 0.972 0.028
#> GSM257971 2 0.5678 0.2688 0.000 0.684 0.316
#> GSM257973 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257981 2 0.4842 0.7186 0.000 0.776 0.224
#> GSM257983 3 0.5728 0.9133 0.008 0.272 0.720
#> GSM257985 3 0.5431 0.9186 0.000 0.284 0.716
#> GSM257988 3 0.5621 0.9231 0.000 0.308 0.692
#> GSM257991 2 0.4399 0.7409 0.000 0.812 0.188
#> GSM257993 2 0.1753 0.7945 0.000 0.952 0.048
#> GSM257994 2 0.2066 0.7882 0.000 0.940 0.060
#> GSM257989 3 0.5690 0.9215 0.004 0.288 0.708
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.6197 0.5523 0.440 0.052 0.000 0.508
#> GSM257888 1 0.5755 -0.6209 0.528 0.028 0.000 0.444
#> GSM257890 1 0.6200 -0.5962 0.504 0.052 0.000 0.444
#> GSM257892 2 0.7758 -0.2412 0.308 0.432 0.000 0.260
#> GSM257894 1 0.5228 -0.0781 0.664 0.024 0.000 0.312
#> GSM257896 1 0.4964 0.2101 0.716 0.028 0.000 0.256
#> GSM257898 1 0.3367 0.7161 0.864 0.028 0.000 0.108
#> GSM257900 1 0.2699 0.7481 0.904 0.028 0.000 0.068
#> GSM257902 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257904 1 0.5538 0.1027 0.644 0.036 0.000 0.320
#> GSM257906 1 0.3497 0.7150 0.860 0.036 0.000 0.104
#> GSM257908 4 0.4907 0.8950 0.420 0.000 0.000 0.580
#> GSM257910 4 0.4933 0.8980 0.432 0.000 0.000 0.568
#> GSM257912 4 0.4925 0.8994 0.428 0.000 0.000 0.572
#> GSM257914 4 0.4925 0.8994 0.428 0.000 0.000 0.572
#> GSM257917 4 0.4933 0.8980 0.432 0.000 0.000 0.568
#> GSM257919 4 0.4925 0.8994 0.428 0.000 0.000 0.572
#> GSM257921 1 0.5630 -0.4396 0.608 0.032 0.000 0.360
#> GSM257923 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257927 1 0.1004 0.7914 0.972 0.004 0.000 0.024
#> GSM257929 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257937 4 0.5660 0.8299 0.396 0.028 0.000 0.576
#> GSM257939 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257941 1 0.2984 0.7377 0.888 0.028 0.000 0.084
#> GSM257943 1 0.3182 0.7289 0.876 0.028 0.000 0.096
#> GSM257945 1 0.1635 0.7811 0.948 0.008 0.000 0.044
#> GSM257947 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0336 0.7972 0.992 0.000 0.000 0.008
#> GSM257955 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0469 0.7959 0.988 0.000 0.000 0.012
#> GSM257960 1 0.1677 0.7815 0.948 0.012 0.000 0.040
#> GSM257962 1 0.1545 0.7826 0.952 0.008 0.000 0.040
#> GSM257964 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257966 4 0.5290 0.8319 0.404 0.012 0.000 0.584
#> GSM257968 1 0.4661 0.2409 0.728 0.016 0.000 0.256
#> GSM257970 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0188 0.7971 0.996 0.000 0.000 0.004
#> GSM257977 1 0.4993 0.2064 0.712 0.028 0.000 0.260
#> GSM257982 1 0.2861 0.6666 0.888 0.016 0.000 0.096
#> GSM257984 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.7983 1.000 0.000 0.000 0.000
#> GSM257990 1 0.1635 0.7811 0.948 0.008 0.000 0.044
#> GSM257992 1 0.3367 0.7161 0.864 0.028 0.000 0.108
#> GSM257996 4 0.5000 0.7398 0.500 0.000 0.000 0.500
#> GSM258006 1 0.3245 0.7248 0.872 0.028 0.000 0.100
#> GSM257887 2 0.1637 0.7353 0.000 0.940 0.060 0.000
#> GSM257889 3 0.2775 0.7817 0.000 0.084 0.896 0.020
#> GSM257891 3 0.0524 0.8393 0.000 0.008 0.988 0.004
#> GSM257893 3 0.4483 0.7261 0.000 0.104 0.808 0.088
#> GSM257895 2 0.2751 0.7221 0.000 0.904 0.056 0.040
#> GSM257897 3 0.4999 0.7175 0.012 0.100 0.792 0.096
#> GSM257899 3 0.4731 0.7223 0.004 0.100 0.800 0.096
#> GSM257901 3 0.6065 0.5037 0.000 0.140 0.684 0.176
#> GSM257903 2 0.5770 0.7517 0.000 0.712 0.140 0.148
#> GSM257905 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257907 3 0.3494 0.7330 0.000 0.004 0.824 0.172
#> GSM257909 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257911 3 0.7534 -0.2647 0.000 0.380 0.432 0.188
#> GSM257913 3 0.2654 0.7742 0.000 0.004 0.888 0.108
#> GSM257916 2 0.5480 0.7555 0.000 0.736 0.140 0.124
#> GSM257918 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257920 3 0.0376 0.8421 0.000 0.004 0.992 0.004
#> GSM257922 3 0.4908 0.7142 0.004 0.100 0.788 0.108
#> GSM257924 3 0.0188 0.8423 0.000 0.004 0.996 0.000
#> GSM257926 3 0.0188 0.8423 0.000 0.004 0.996 0.000
#> GSM257928 2 0.6654 0.3215 0.000 0.588 0.296 0.116
#> GSM257930 2 0.4188 0.6800 0.000 0.824 0.064 0.112
#> GSM257938 2 0.3935 0.6892 0.000 0.840 0.060 0.100
#> GSM257940 3 0.3494 0.7330 0.000 0.004 0.824 0.172
#> GSM257942 2 0.5770 0.7517 0.000 0.712 0.140 0.148
#> GSM257944 2 0.5770 0.7517 0.000 0.712 0.140 0.148
#> GSM257946 3 0.0000 0.8420 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0376 0.8421 0.000 0.004 0.992 0.004
#> GSM257950 3 0.0376 0.8421 0.000 0.004 0.992 0.004
#> GSM257952 2 0.7476 0.2849 0.000 0.416 0.408 0.176
#> GSM257954 2 0.2142 0.7287 0.000 0.928 0.056 0.016
#> GSM257956 2 0.2660 0.7236 0.000 0.908 0.056 0.036
#> GSM257959 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257961 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257963 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257965 2 0.7500 0.2846 0.000 0.412 0.408 0.180
#> GSM257967 2 0.5630 0.7563 0.000 0.724 0.140 0.136
#> GSM257969 2 0.1824 0.7347 0.000 0.936 0.060 0.004
#> GSM257971 2 0.7443 -0.0312 0.000 0.436 0.392 0.172
#> GSM257973 3 0.0376 0.8421 0.000 0.004 0.992 0.004
#> GSM257981 3 0.7476 -0.3238 0.000 0.408 0.416 0.176
#> GSM257983 3 0.0188 0.8411 0.000 0.004 0.996 0.000
#> GSM257985 3 0.0336 0.8396 0.000 0.008 0.992 0.000
#> GSM257988 3 0.0376 0.8421 0.000 0.004 0.992 0.004
#> GSM257991 2 0.7586 0.4368 0.000 0.460 0.328 0.212
#> GSM257993 2 0.2466 0.7256 0.000 0.916 0.056 0.028
#> GSM257994 2 0.3996 0.6875 0.000 0.836 0.060 0.104
#> GSM257989 3 0.0188 0.8423 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.7000 0.4529 0.232 0.052 0.000 0.544 0.172
#> GSM257888 4 0.6051 0.4369 0.384 0.020 0.000 0.524 0.072
#> GSM257890 4 0.6898 0.4875 0.296 0.052 0.000 0.528 0.124
#> GSM257892 5 0.8342 -0.2232 0.204 0.156 0.000 0.304 0.336
#> GSM257894 1 0.6008 -0.2111 0.492 0.020 0.000 0.424 0.064
#> GSM257896 1 0.6162 -0.1596 0.500 0.024 0.000 0.404 0.072
#> GSM257898 1 0.5267 0.6533 0.724 0.032 0.000 0.088 0.156
#> GSM257900 1 0.4745 0.6813 0.764 0.032 0.000 0.060 0.144
#> GSM257902 1 0.0566 0.7740 0.984 0.004 0.000 0.012 0.000
#> GSM257904 1 0.7052 0.3266 0.532 0.052 0.000 0.248 0.168
#> GSM257906 1 0.5677 0.6290 0.696 0.052 0.000 0.084 0.168
#> GSM257908 4 0.5174 0.8069 0.196 0.008 0.000 0.700 0.096
#> GSM257910 4 0.5266 0.8100 0.208 0.008 0.000 0.688 0.096
#> GSM257912 4 0.5266 0.8100 0.208 0.008 0.000 0.688 0.096
#> GSM257914 4 0.5266 0.8100 0.208 0.008 0.000 0.688 0.096
#> GSM257917 4 0.5266 0.8100 0.208 0.008 0.000 0.688 0.096
#> GSM257919 4 0.5266 0.8100 0.208 0.008 0.000 0.688 0.096
#> GSM257921 1 0.6374 -0.3018 0.460 0.032 0.000 0.432 0.076
#> GSM257923 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257925 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257927 1 0.2012 0.7563 0.920 0.000 0.000 0.020 0.060
#> GSM257929 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257937 4 0.4043 0.7438 0.160 0.012 0.000 0.792 0.036
#> GSM257939 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257941 1 0.4901 0.6719 0.756 0.032 0.000 0.076 0.136
#> GSM257943 1 0.5175 0.6591 0.732 0.032 0.000 0.084 0.152
#> GSM257945 1 0.3043 0.7369 0.864 0.000 0.000 0.056 0.080
#> GSM257947 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257949 1 0.0510 0.7741 0.984 0.000 0.000 0.016 0.000
#> GSM257951 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257953 1 0.0404 0.7730 0.988 0.000 0.000 0.012 0.000
#> GSM257955 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257958 1 0.0290 0.7729 0.992 0.000 0.000 0.008 0.000
#> GSM257960 1 0.3635 0.7231 0.836 0.016 0.000 0.040 0.108
#> GSM257962 1 0.2754 0.7420 0.880 0.000 0.000 0.040 0.080
#> GSM257964 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257966 4 0.3132 0.7545 0.172 0.008 0.000 0.820 0.000
#> GSM257968 1 0.5142 -0.0207 0.564 0.000 0.000 0.392 0.044
#> GSM257970 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257972 1 0.0609 0.7732 0.980 0.000 0.000 0.020 0.000
#> GSM257977 1 0.6084 -0.1537 0.504 0.020 0.000 0.404 0.072
#> GSM257982 1 0.4062 0.5258 0.764 0.000 0.000 0.196 0.040
#> GSM257984 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257986 1 0.0404 0.7750 0.988 0.000 0.000 0.012 0.000
#> GSM257990 1 0.3047 0.7389 0.868 0.004 0.000 0.044 0.084
#> GSM257992 1 0.5214 0.6564 0.728 0.032 0.000 0.084 0.156
#> GSM257996 4 0.5600 0.7202 0.264 0.004 0.000 0.628 0.104
#> GSM258006 1 0.5214 0.6564 0.728 0.032 0.000 0.084 0.156
#> GSM257887 2 0.4415 -0.1355 0.000 0.604 0.008 0.000 0.388
#> GSM257889 3 0.2505 0.7706 0.000 0.000 0.888 0.020 0.092
#> GSM257891 3 0.0794 0.8373 0.000 0.000 0.972 0.028 0.000
#> GSM257893 3 0.4181 0.6078 0.000 0.000 0.712 0.020 0.268
#> GSM257895 5 0.4824 0.4143 0.000 0.468 0.020 0.000 0.512
#> GSM257897 3 0.4360 0.5923 0.000 0.000 0.692 0.024 0.284
#> GSM257899 3 0.4360 0.5923 0.000 0.000 0.692 0.024 0.284
#> GSM257901 3 0.6467 0.3721 0.000 0.264 0.592 0.072 0.072
#> GSM257903 2 0.1502 0.6590 0.000 0.940 0.056 0.000 0.004
#> GSM257905 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257907 3 0.5849 0.5788 0.000 0.172 0.684 0.072 0.072
#> GSM257909 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257911 2 0.6855 0.3322 0.000 0.488 0.364 0.072 0.076
#> GSM257913 3 0.3167 0.7042 0.000 0.172 0.820 0.004 0.004
#> GSM257916 2 0.2036 0.6516 0.000 0.920 0.056 0.000 0.024
#> GSM257918 2 0.1341 0.6598 0.000 0.944 0.056 0.000 0.000
#> GSM257920 3 0.0566 0.8470 0.000 0.012 0.984 0.004 0.000
#> GSM257922 3 0.4437 0.5562 0.000 0.000 0.664 0.020 0.316
#> GSM257924 3 0.0693 0.8468 0.000 0.012 0.980 0.008 0.000
#> GSM257926 3 0.0404 0.8473 0.000 0.012 0.988 0.000 0.000
#> GSM257928 5 0.5578 0.4885 0.000 0.176 0.180 0.000 0.644
#> GSM257930 5 0.4585 0.5869 0.000 0.352 0.020 0.000 0.628
#> GSM257938 5 0.4585 0.5869 0.000 0.352 0.020 0.000 0.628
#> GSM257940 3 0.5849 0.5788 0.000 0.172 0.684 0.072 0.072
#> GSM257942 2 0.1502 0.6590 0.000 0.940 0.056 0.000 0.004
#> GSM257944 2 0.1502 0.6590 0.000 0.940 0.056 0.000 0.004
#> GSM257946 3 0.0693 0.8468 0.000 0.012 0.980 0.008 0.000
#> GSM257948 3 0.0566 0.8470 0.000 0.012 0.984 0.004 0.000
#> GSM257950 3 0.0693 0.8473 0.000 0.012 0.980 0.008 0.000
#> GSM257952 2 0.7047 0.3369 0.000 0.480 0.352 0.072 0.096
#> GSM257954 2 0.4787 -0.3045 0.000 0.548 0.020 0.000 0.432
#> GSM257956 5 0.4824 0.4143 0.000 0.468 0.020 0.000 0.512
#> GSM257959 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257961 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257963 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257965 2 0.7047 0.3369 0.000 0.480 0.352 0.072 0.096
#> GSM257967 2 0.1740 0.6600 0.000 0.932 0.056 0.000 0.012
#> GSM257969 2 0.4446 -0.1706 0.000 0.592 0.008 0.000 0.400
#> GSM257971 5 0.6992 0.3168 0.000 0.148 0.244 0.060 0.548
#> GSM257973 3 0.0566 0.8470 0.000 0.012 0.984 0.004 0.000
#> GSM257981 2 0.6974 0.3357 0.000 0.480 0.360 0.068 0.092
#> GSM257983 3 0.0162 0.8457 0.000 0.004 0.996 0.000 0.000
#> GSM257985 3 0.0162 0.8430 0.000 0.000 0.996 0.004 0.000
#> GSM257988 3 0.0566 0.8470 0.000 0.012 0.984 0.004 0.000
#> GSM257991 2 0.5403 0.5042 0.000 0.720 0.156 0.052 0.072
#> GSM257993 2 0.4824 -0.4028 0.000 0.512 0.020 0.000 0.468
#> GSM257994 5 0.4585 0.5869 0.000 0.352 0.020 0.000 0.628
#> GSM257989 3 0.0404 0.8473 0.000 0.012 0.988 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.3881 0.3396 0.000 0.000 0.000 0.600 0.004 0.396
#> GSM257888 4 0.4459 0.4797 0.204 0.000 0.000 0.700 0.000 0.096
#> GSM257890 4 0.4559 0.4359 0.060 0.000 0.000 0.664 0.004 0.272
#> GSM257892 4 0.6711 0.2627 0.008 0.056 0.000 0.476 0.152 0.308
#> GSM257894 4 0.5138 0.3581 0.268 0.000 0.000 0.604 0.000 0.128
#> GSM257896 4 0.5303 0.3803 0.232 0.000 0.000 0.596 0.000 0.172
#> GSM257898 6 0.0146 0.7610 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM257900 6 0.0508 0.7568 0.012 0.000 0.000 0.004 0.000 0.984
#> GSM257902 1 0.3993 0.9345 0.520 0.000 0.000 0.000 0.004 0.476
#> GSM257904 6 0.2544 0.5967 0.004 0.000 0.000 0.140 0.004 0.852
#> GSM257906 6 0.1674 0.6873 0.004 0.000 0.000 0.068 0.004 0.924
#> GSM257908 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257910 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257912 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257914 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257917 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257919 4 0.6890 0.5979 0.252 0.020 0.000 0.492 0.188 0.048
#> GSM257921 4 0.6686 0.3071 0.112 0.000 0.000 0.444 0.096 0.348
#> GSM257923 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257925 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257927 6 0.3309 0.1469 0.280 0.000 0.000 0.000 0.000 0.720
#> GSM257929 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257937 4 0.3727 0.5846 0.076 0.000 0.000 0.816 0.076 0.032
#> GSM257939 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257941 6 0.1007 0.7378 0.044 0.000 0.000 0.000 0.000 0.956
#> GSM257943 6 0.0146 0.7610 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM257945 6 0.2793 0.4949 0.200 0.000 0.000 0.000 0.000 0.800
#> GSM257947 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257949 1 0.3862 0.9415 0.524 0.000 0.000 0.000 0.000 0.476
#> GSM257951 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257953 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257955 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257958 1 0.3866 0.9371 0.516 0.000 0.000 0.000 0.000 0.484
#> GSM257960 6 0.2527 0.5761 0.168 0.000 0.000 0.000 0.000 0.832
#> GSM257962 6 0.2969 0.4187 0.224 0.000 0.000 0.000 0.000 0.776
#> GSM257964 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257966 4 0.5593 0.5938 0.136 0.020 0.000 0.672 0.140 0.032
#> GSM257968 4 0.5791 0.0458 0.336 0.000 0.000 0.472 0.000 0.192
#> GSM257970 1 0.3864 0.9460 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257972 1 0.3993 0.9353 0.520 0.000 0.000 0.004 0.000 0.476
#> GSM257977 4 0.5279 0.3727 0.244 0.000 0.000 0.596 0.000 0.160
#> GSM257982 1 0.6078 0.2860 0.396 0.000 0.000 0.320 0.000 0.284
#> GSM257984 1 0.3995 0.9412 0.516 0.000 0.000 0.004 0.000 0.480
#> GSM257986 1 0.3862 0.9415 0.524 0.000 0.000 0.000 0.000 0.476
#> GSM257990 6 0.2964 0.4970 0.204 0.000 0.000 0.000 0.004 0.792
#> GSM257992 6 0.0146 0.7610 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM257996 4 0.7592 0.5519 0.252 0.016 0.000 0.412 0.188 0.132
#> GSM258006 6 0.0291 0.7588 0.004 0.000 0.000 0.000 0.004 0.992
#> GSM257887 2 0.4275 -0.1362 0.016 0.592 0.000 0.004 0.388 0.000
#> GSM257889 3 0.2196 0.7643 0.016 0.000 0.908 0.020 0.056 0.000
#> GSM257891 3 0.1320 0.8011 0.016 0.000 0.948 0.036 0.000 0.000
#> GSM257893 3 0.4482 0.5002 0.016 0.000 0.644 0.024 0.316 0.000
#> GSM257895 5 0.4389 0.3777 0.016 0.468 0.004 0.000 0.512 0.000
#> GSM257897 3 0.4900 0.4853 0.016 0.000 0.628 0.024 0.316 0.016
#> GSM257899 3 0.4900 0.4853 0.016 0.000 0.628 0.024 0.316 0.016
#> GSM257901 3 0.6780 0.3572 0.236 0.180 0.516 0.020 0.048 0.000
#> GSM257903 2 0.0748 0.6357 0.000 0.976 0.016 0.004 0.004 0.000
#> GSM257905 2 0.0748 0.6345 0.004 0.976 0.016 0.004 0.000 0.000
#> GSM257907 3 0.6380 0.4742 0.236 0.124 0.572 0.020 0.048 0.000
#> GSM257909 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257911 2 0.7214 0.2018 0.240 0.384 0.308 0.016 0.052 0.000
#> GSM257913 3 0.3142 0.7179 0.032 0.124 0.836 0.004 0.004 0.000
#> GSM257916 2 0.1078 0.6256 0.012 0.964 0.016 0.000 0.008 0.000
#> GSM257918 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257920 3 0.0777 0.8152 0.024 0.004 0.972 0.000 0.000 0.000
#> GSM257922 3 0.4937 0.3873 0.016 0.000 0.564 0.024 0.388 0.008
#> GSM257924 3 0.0146 0.8164 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM257926 3 0.0146 0.8164 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM257928 5 0.4350 0.6087 0.000 0.116 0.120 0.004 0.752 0.008
#> GSM257930 5 0.3354 0.6970 0.000 0.240 0.004 0.004 0.752 0.000
#> GSM257938 5 0.3240 0.6985 0.000 0.244 0.004 0.000 0.752 0.000
#> GSM257940 3 0.6341 0.4697 0.244 0.124 0.568 0.016 0.048 0.000
#> GSM257942 2 0.0603 0.6364 0.000 0.980 0.016 0.000 0.004 0.000
#> GSM257944 2 0.0603 0.6364 0.000 0.980 0.016 0.000 0.004 0.000
#> GSM257946 3 0.0146 0.8164 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM257948 3 0.0777 0.8152 0.024 0.004 0.972 0.000 0.000 0.000
#> GSM257950 3 0.0777 0.8152 0.024 0.004 0.972 0.000 0.000 0.000
#> GSM257952 2 0.7333 0.2514 0.240 0.396 0.284 0.020 0.060 0.000
#> GSM257954 2 0.4268 -0.2504 0.012 0.556 0.004 0.000 0.428 0.000
#> GSM257956 5 0.4310 0.3696 0.012 0.472 0.004 0.000 0.512 0.000
#> GSM257959 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257961 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257963 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257965 2 0.7290 0.2549 0.240 0.400 0.284 0.020 0.056 0.000
#> GSM257967 2 0.0458 0.6369 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257969 2 0.4109 -0.1891 0.012 0.576 0.000 0.000 0.412 0.000
#> GSM257971 5 0.6774 0.2607 0.184 0.044 0.200 0.032 0.540 0.000
#> GSM257973 3 0.1080 0.8131 0.032 0.004 0.960 0.004 0.000 0.000
#> GSM257981 2 0.7041 0.2244 0.232 0.396 0.312 0.008 0.052 0.000
#> GSM257983 3 0.0603 0.8146 0.016 0.000 0.980 0.004 0.000 0.000
#> GSM257985 3 0.0508 0.8155 0.012 0.000 0.984 0.004 0.000 0.000
#> GSM257988 3 0.1080 0.8131 0.032 0.004 0.960 0.004 0.000 0.000
#> GSM257991 2 0.5829 0.4070 0.200 0.640 0.096 0.016 0.048 0.000
#> GSM257993 2 0.4315 -0.4230 0.012 0.496 0.004 0.000 0.488 0.000
#> GSM257994 5 0.3240 0.6985 0.000 0.244 0.004 0.000 0.752 0.000
#> GSM257989 3 0.0436 0.8168 0.004 0.004 0.988 0.004 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 cell.type(p) protocol(p) individual(p) k
#> CV:kmeans 96 8.49e-22 1.000 1.000 2
#> CV:kmeans 92 1.05e-20 0.751 1.000 3
#> CV:kmeans 80 3.07e-17 0.375 0.979 4
#> CV:kmeans 73 5.28e-15 0.593 0.981 5
#> CV:kmeans 64 1.81e-12 0.399 0.850 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.5057 0.495 0.495
#> 3 3 0.951 0.910 0.945 0.2500 0.859 0.717
#> 4 4 0.872 0.855 0.931 0.1751 0.876 0.663
#> 5 5 0.787 0.685 0.844 0.0479 0.950 0.815
#> 6 6 0.782 0.656 0.801 0.0458 0.934 0.737
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
#> GSM257886 1 0.000 0.997 1.00 0.00
#> GSM257888 1 0.000 0.997 1.00 0.00
#> GSM257890 1 0.000 0.997 1.00 0.00
#> GSM257892 1 0.634 0.810 0.84 0.16
#> GSM257894 1 0.000 0.997 1.00 0.00
#> GSM257896 1 0.000 0.997 1.00 0.00
#> GSM257898 1 0.000 0.997 1.00 0.00
#> GSM257900 1 0.000 0.997 1.00 0.00
#> GSM257902 1 0.000 0.997 1.00 0.00
#> GSM257904 1 0.000 0.997 1.00 0.00
#> GSM257906 1 0.000 0.997 1.00 0.00
#> GSM257908 1 0.000 0.997 1.00 0.00
#> GSM257910 1 0.000 0.997 1.00 0.00
#> GSM257912 1 0.000 0.997 1.00 0.00
#> GSM257914 1 0.000 0.997 1.00 0.00
#> GSM257917 1 0.000 0.997 1.00 0.00
#> GSM257919 1 0.000 0.997 1.00 0.00
#> GSM257921 1 0.000 0.997 1.00 0.00
#> GSM257923 1 0.000 0.997 1.00 0.00
#> GSM257925 1 0.000 0.997 1.00 0.00
#> GSM257927 1 0.000 0.997 1.00 0.00
#> GSM257929 1 0.000 0.997 1.00 0.00
#> GSM257937 1 0.000 0.997 1.00 0.00
#> GSM257939 1 0.000 0.997 1.00 0.00
#> GSM257941 1 0.000 0.997 1.00 0.00
#> GSM257943 1 0.000 0.997 1.00 0.00
#> GSM257945 1 0.000 0.997 1.00 0.00
#> GSM257947 1 0.000 0.997 1.00 0.00
#> GSM257949 1 0.000 0.997 1.00 0.00
#> GSM257951 1 0.000 0.997 1.00 0.00
#> GSM257953 1 0.000 0.997 1.00 0.00
#> GSM257955 1 0.000 0.997 1.00 0.00
#> GSM257958 1 0.000 0.997 1.00 0.00
#> GSM257960 1 0.000 0.997 1.00 0.00
#> GSM257962 1 0.000 0.997 1.00 0.00
#> GSM257964 1 0.000 0.997 1.00 0.00
#> GSM257966 1 0.000 0.997 1.00 0.00
#> GSM257968 1 0.000 0.997 1.00 0.00
#> GSM257970 1 0.000 0.997 1.00 0.00
#> GSM257972 1 0.000 0.997 1.00 0.00
#> GSM257977 1 0.000 0.997 1.00 0.00
#> GSM257982 1 0.000 0.997 1.00 0.00
#> GSM257984 1 0.000 0.997 1.00 0.00
#> GSM257986 1 0.000 0.997 1.00 0.00
#> GSM257990 1 0.000 0.997 1.00 0.00
#> GSM257992 1 0.000 0.997 1.00 0.00
#> GSM257996 1 0.000 0.997 1.00 0.00
#> GSM258006 1 0.000 0.997 1.00 0.00
#> GSM257887 2 0.000 1.000 0.00 1.00
#> GSM257889 2 0.000 1.000 0.00 1.00
#> GSM257891 2 0.000 1.000 0.00 1.00
#> GSM257893 2 0.000 1.000 0.00 1.00
#> GSM257895 2 0.000 1.000 0.00 1.00
#> GSM257897 2 0.000 1.000 0.00 1.00
#> GSM257899 2 0.000 1.000 0.00 1.00
#> GSM257901 2 0.000 1.000 0.00 1.00
#> GSM257903 2 0.000 1.000 0.00 1.00
#> GSM257905 2 0.000 1.000 0.00 1.00
#> GSM257907 2 0.000 1.000 0.00 1.00
#> GSM257909 2 0.000 1.000 0.00 1.00
#> GSM257911 2 0.000 1.000 0.00 1.00
#> GSM257913 2 0.000 1.000 0.00 1.00
#> GSM257916 2 0.000 1.000 0.00 1.00
#> GSM257918 2 0.000 1.000 0.00 1.00
#> GSM257920 2 0.000 1.000 0.00 1.00
#> GSM257922 2 0.000 1.000 0.00 1.00
#> GSM257924 2 0.000 1.000 0.00 1.00
#> GSM257926 2 0.000 1.000 0.00 1.00
#> GSM257928 2 0.000 1.000 0.00 1.00
#> GSM257930 2 0.000 1.000 0.00 1.00
#> GSM257938 2 0.000 1.000 0.00 1.00
#> GSM257940 2 0.000 1.000 0.00 1.00
#> GSM257942 2 0.000 1.000 0.00 1.00
#> GSM257944 2 0.000 1.000 0.00 1.00
#> GSM257946 2 0.000 1.000 0.00 1.00
#> GSM257948 2 0.000 1.000 0.00 1.00
#> GSM257950 2 0.000 1.000 0.00 1.00
#> GSM257952 2 0.000 1.000 0.00 1.00
#> GSM257954 2 0.000 1.000 0.00 1.00
#> GSM257956 2 0.000 1.000 0.00 1.00
#> GSM257959 2 0.000 1.000 0.00 1.00
#> GSM257961 2 0.000 1.000 0.00 1.00
#> GSM257963 2 0.000 1.000 0.00 1.00
#> GSM257965 2 0.000 1.000 0.00 1.00
#> GSM257967 2 0.000 1.000 0.00 1.00
#> GSM257969 2 0.000 1.000 0.00 1.00
#> GSM257971 2 0.000 1.000 0.00 1.00
#> GSM257973 2 0.000 1.000 0.00 1.00
#> GSM257981 2 0.000 1.000 0.00 1.00
#> GSM257983 2 0.000 1.000 0.00 1.00
#> GSM257985 2 0.000 1.000 0.00 1.00
#> GSM257988 2 0.000 1.000 0.00 1.00
#> GSM257991 2 0.000 1.000 0.00 1.00
#> GSM257993 2 0.000 1.000 0.00 1.00
#> GSM257994 2 0.000 1.000 0.00 1.00
#> GSM257989 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
#> GSM257886 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257888 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257890 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257892 2 0.5859 0.393 0.344 0.656 0.000
#> GSM257894 1 0.1529 0.971 0.960 0.040 0.000
#> GSM257896 1 0.0237 0.983 0.996 0.004 0.000
#> GSM257898 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257904 1 0.1964 0.965 0.944 0.056 0.000
#> GSM257906 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257908 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257910 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257912 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257914 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257917 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257919 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257921 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257923 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257937 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257939 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257966 1 0.2066 0.964 0.940 0.060 0.000
#> GSM257968 1 0.0237 0.983 0.996 0.004 0.000
#> GSM257970 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257977 1 0.0237 0.983 0.996 0.004 0.000
#> GSM257982 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257992 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257996 1 0.2066 0.964 0.940 0.060 0.000
#> GSM258006 1 0.0000 0.984 1.000 0.000 0.000
#> GSM257887 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257889 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257891 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257893 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257895 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257897 3 0.0237 0.958 0.004 0.000 0.996
#> GSM257899 3 0.0237 0.958 0.004 0.000 0.996
#> GSM257901 3 0.4887 0.626 0.000 0.228 0.772
#> GSM257903 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257905 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257907 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257909 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257911 2 0.6140 0.473 0.000 0.596 0.404
#> GSM257913 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257916 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257918 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257920 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257922 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257924 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257926 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257928 2 0.5926 0.503 0.000 0.644 0.356
#> GSM257930 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257938 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257940 3 0.0237 0.959 0.000 0.004 0.996
#> GSM257942 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257944 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257946 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257948 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257950 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257952 2 0.6045 0.526 0.000 0.620 0.380
#> GSM257954 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257956 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257959 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257961 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257963 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257965 2 0.6045 0.526 0.000 0.620 0.380
#> GSM257967 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257969 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257971 3 0.6111 0.161 0.000 0.396 0.604
#> GSM257973 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257981 2 0.6026 0.533 0.000 0.624 0.376
#> GSM257983 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257985 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257988 3 0.0000 0.963 0.000 0.000 1.000
#> GSM257991 2 0.5968 0.554 0.000 0.636 0.364
#> GSM257993 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257994 2 0.2066 0.893 0.000 0.940 0.060
#> GSM257989 3 0.0000 0.963 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.0592 0.830 0.016 0.000 0.000 0.984
#> GSM257888 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257890 4 0.0469 0.832 0.012 0.000 0.000 0.988
#> GSM257892 4 0.5279 0.189 0.012 0.400 0.000 0.588
#> GSM257894 4 0.3649 0.716 0.204 0.000 0.000 0.796
#> GSM257896 4 0.4877 0.414 0.408 0.000 0.000 0.592
#> GSM257898 1 0.0188 0.988 0.996 0.000 0.000 0.004
#> GSM257900 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257902 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257904 4 0.4961 0.304 0.448 0.000 0.000 0.552
#> GSM257906 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257908 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257910 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257912 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257914 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257917 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257919 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257921 4 0.3074 0.762 0.152 0.000 0.000 0.848
#> GSM257923 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257925 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257927 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257929 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257937 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257939 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257941 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257947 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257949 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257951 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257953 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257955 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257958 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257960 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257964 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257966 4 0.0336 0.832 0.008 0.000 0.000 0.992
#> GSM257968 4 0.4866 0.424 0.404 0.000 0.000 0.596
#> GSM257970 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257972 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257977 4 0.4866 0.424 0.404 0.000 0.000 0.596
#> GSM257982 1 0.2647 0.840 0.880 0.000 0.000 0.120
#> GSM257984 1 0.0188 0.992 0.996 0.000 0.000 0.004
#> GSM257986 1 0.0336 0.989 0.992 0.000 0.000 0.008
#> GSM257990 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM257992 1 0.0188 0.988 0.996 0.000 0.000 0.004
#> GSM257996 4 0.4193 0.647 0.268 0.000 0.000 0.732
#> GSM258006 1 0.0188 0.988 0.996 0.000 0.000 0.004
#> GSM257887 2 0.0000 0.883 0.000 1.000 0.000 0.000
#> GSM257889 3 0.1211 0.922 0.000 0.040 0.960 0.000
#> GSM257891 3 0.0000 0.945 0.000 0.000 1.000 0.000
#> GSM257893 3 0.1398 0.921 0.000 0.040 0.956 0.004
#> GSM257895 2 0.0376 0.881 0.000 0.992 0.004 0.004
#> GSM257897 3 0.1398 0.921 0.000 0.040 0.956 0.004
#> GSM257899 3 0.1398 0.921 0.000 0.040 0.956 0.004
#> GSM257901 3 0.4040 0.603 0.000 0.248 0.752 0.000
#> GSM257903 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257905 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257907 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257909 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257911 2 0.4830 0.465 0.000 0.608 0.392 0.000
#> GSM257913 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257916 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257918 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257920 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257922 3 0.1398 0.921 0.000 0.040 0.956 0.004
#> GSM257924 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257926 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257928 2 0.4594 0.558 0.000 0.712 0.280 0.008
#> GSM257930 2 0.0672 0.878 0.000 0.984 0.008 0.008
#> GSM257938 2 0.0672 0.878 0.000 0.984 0.008 0.008
#> GSM257940 3 0.0469 0.944 0.000 0.012 0.988 0.000
#> GSM257942 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257944 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257946 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> GSM257948 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257950 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257952 2 0.4713 0.534 0.000 0.640 0.360 0.000
#> GSM257954 2 0.0188 0.883 0.000 0.996 0.000 0.004
#> GSM257956 2 0.0376 0.881 0.000 0.992 0.004 0.004
#> GSM257959 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257961 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257963 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257965 2 0.4713 0.534 0.000 0.640 0.360 0.000
#> GSM257967 2 0.1211 0.892 0.000 0.960 0.040 0.000
#> GSM257969 2 0.0000 0.883 0.000 1.000 0.000 0.000
#> GSM257971 3 0.5257 0.117 0.000 0.444 0.548 0.008
#> GSM257973 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257981 2 0.4697 0.541 0.000 0.644 0.356 0.000
#> GSM257983 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> GSM257985 3 0.0000 0.945 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0336 0.947 0.000 0.008 0.992 0.000
#> GSM257991 2 0.4643 0.562 0.000 0.656 0.344 0.000
#> GSM257993 2 0.0376 0.881 0.000 0.992 0.004 0.004
#> GSM257994 2 0.0672 0.878 0.000 0.984 0.008 0.008
#> GSM257989 3 0.0188 0.946 0.000 0.004 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.4300 -0.38286 0.000 0.000 0.000 0.476 0.524
#> GSM257888 4 0.4774 0.40622 0.028 0.000 0.000 0.612 0.360
#> GSM257890 4 0.4510 0.33556 0.008 0.000 0.000 0.560 0.432
#> GSM257892 5 0.6194 -0.10461 0.028 0.088 0.000 0.312 0.572
#> GSM257894 5 0.6805 -0.13944 0.296 0.000 0.000 0.344 0.360
#> GSM257896 1 0.6453 -0.04489 0.432 0.000 0.000 0.180 0.388
#> GSM257898 1 0.2929 0.80727 0.820 0.000 0.000 0.000 0.180
#> GSM257900 1 0.2929 0.80727 0.820 0.000 0.000 0.000 0.180
#> GSM257902 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257904 4 0.5917 0.29264 0.224 0.000 0.000 0.596 0.180
#> GSM257906 1 0.3086 0.80410 0.816 0.000 0.000 0.004 0.180
#> GSM257908 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257910 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257912 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257914 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257917 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257919 4 0.0000 0.79787 0.000 0.000 0.000 1.000 0.000
#> GSM257921 4 0.1331 0.76412 0.040 0.000 0.000 0.952 0.008
#> GSM257923 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.1908 0.84762 0.908 0.000 0.000 0.000 0.092
#> GSM257929 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.2891 0.68710 0.000 0.000 0.000 0.824 0.176
#> GSM257939 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.2561 0.82520 0.856 0.000 0.000 0.000 0.144
#> GSM257943 1 0.2929 0.80727 0.820 0.000 0.000 0.000 0.180
#> GSM257945 1 0.2020 0.84553 0.900 0.000 0.000 0.000 0.100
#> GSM257947 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.2127 0.84250 0.892 0.000 0.000 0.000 0.108
#> GSM257962 1 0.1965 0.84663 0.904 0.000 0.000 0.000 0.096
#> GSM257964 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.2929 0.68357 0.000 0.000 0.000 0.820 0.180
#> GSM257968 1 0.6445 -0.00894 0.456 0.000 0.000 0.184 0.360
#> GSM257970 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257977 1 0.6453 -0.04489 0.432 0.000 0.000 0.180 0.388
#> GSM257982 1 0.4774 0.36452 0.612 0.000 0.000 0.028 0.360
#> GSM257984 1 0.0000 0.86711 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0162 0.86484 0.996 0.000 0.000 0.004 0.000
#> GSM257990 1 0.2020 0.84553 0.900 0.000 0.000 0.000 0.100
#> GSM257992 1 0.2929 0.80727 0.820 0.000 0.000 0.000 0.180
#> GSM257996 4 0.2036 0.72430 0.056 0.000 0.000 0.920 0.024
#> GSM258006 1 0.2929 0.80727 0.820 0.000 0.000 0.000 0.180
#> GSM257887 2 0.2516 0.75074 0.000 0.860 0.000 0.000 0.140
#> GSM257889 3 0.1965 0.82537 0.000 0.000 0.904 0.000 0.096
#> GSM257891 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.3730 0.66076 0.000 0.000 0.712 0.000 0.288
#> GSM257895 2 0.2852 0.73696 0.000 0.828 0.000 0.000 0.172
#> GSM257897 3 0.3707 0.66533 0.000 0.000 0.716 0.000 0.284
#> GSM257899 3 0.3707 0.66533 0.000 0.000 0.716 0.000 0.284
#> GSM257901 3 0.4382 0.50321 0.000 0.288 0.688 0.000 0.024
#> GSM257903 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257907 3 0.2653 0.79903 0.000 0.096 0.880 0.000 0.024
#> GSM257909 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257911 2 0.4798 0.37870 0.000 0.580 0.396 0.000 0.024
#> GSM257913 3 0.1965 0.81507 0.000 0.096 0.904 0.000 0.000
#> GSM257916 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257920 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257922 3 0.3730 0.66098 0.000 0.000 0.712 0.000 0.288
#> GSM257924 3 0.0162 0.88181 0.000 0.004 0.996 0.000 0.000
#> GSM257926 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257928 5 0.6519 -0.19984 0.000 0.368 0.196 0.000 0.436
#> GSM257930 2 0.4256 0.45799 0.000 0.564 0.000 0.000 0.436
#> GSM257938 2 0.4256 0.45799 0.000 0.564 0.000 0.000 0.436
#> GSM257940 3 0.3060 0.76329 0.000 0.128 0.848 0.000 0.024
#> GSM257942 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257950 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.4503 0.50367 0.000 0.664 0.312 0.000 0.024
#> GSM257954 2 0.2648 0.74564 0.000 0.848 0.000 0.000 0.152
#> GSM257956 2 0.2891 0.73511 0.000 0.824 0.000 0.000 0.176
#> GSM257959 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.4484 0.50885 0.000 0.668 0.308 0.000 0.024
#> GSM257967 2 0.0000 0.79659 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.2329 0.75539 0.000 0.876 0.000 0.000 0.124
#> GSM257971 5 0.6728 -0.18715 0.000 0.336 0.260 0.000 0.404
#> GSM257973 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.4404 0.52691 0.000 0.684 0.292 0.000 0.024
#> GSM257983 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
#> GSM257991 2 0.3877 0.60602 0.000 0.764 0.212 0.000 0.024
#> GSM257993 2 0.2773 0.74085 0.000 0.836 0.000 0.000 0.164
#> GSM257994 2 0.4256 0.45799 0.000 0.564 0.000 0.000 0.436
#> GSM257989 3 0.0000 0.88394 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.1606 0.53938 0.004 0.000 0.000 0.056 0.008 0.932
#> GSM257888 6 0.4378 0.34067 0.040 0.000 0.000 0.328 0.000 0.632
#> GSM257890 6 0.2793 0.47021 0.000 0.000 0.000 0.200 0.000 0.800
#> GSM257892 6 0.2374 0.54173 0.004 0.016 0.000 0.028 0.048 0.904
#> GSM257894 6 0.4524 0.66333 0.320 0.000 0.000 0.052 0.000 0.628
#> GSM257896 6 0.4201 0.67707 0.300 0.000 0.000 0.036 0.000 0.664
#> GSM257898 1 0.5276 0.52818 0.540 0.000 0.000 0.000 0.112 0.348
#> GSM257900 1 0.5197 0.54861 0.560 0.000 0.000 0.000 0.108 0.332
#> GSM257902 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257904 6 0.7156 0.00533 0.172 0.000 0.000 0.336 0.112 0.380
#> GSM257906 1 0.5334 0.48595 0.512 0.000 0.000 0.000 0.112 0.376
#> GSM257908 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257910 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257912 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257914 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257917 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257919 4 0.0000 0.92007 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257921 4 0.1151 0.88924 0.012 0.000 0.000 0.956 0.000 0.032
#> GSM257923 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.2537 0.76663 0.872 0.000 0.000 0.000 0.032 0.096
#> GSM257929 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.3198 0.63168 0.000 0.000 0.000 0.740 0.000 0.260
#> GSM257939 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.4913 0.59663 0.612 0.000 0.000 0.000 0.092 0.296
#> GSM257943 1 0.5276 0.52818 0.540 0.000 0.000 0.000 0.112 0.348
#> GSM257945 1 0.3492 0.73997 0.804 0.000 0.000 0.000 0.076 0.120
#> GSM257947 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0146 0.80349 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257951 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.4030 0.70521 0.748 0.000 0.000 0.000 0.080 0.172
#> GSM257962 1 0.3544 0.73811 0.800 0.000 0.000 0.000 0.080 0.120
#> GSM257964 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.3023 0.67541 0.000 0.000 0.000 0.768 0.000 0.232
#> GSM257968 6 0.4199 0.61813 0.380 0.000 0.000 0.020 0.000 0.600
#> GSM257970 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0146 0.80349 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257977 6 0.4201 0.67707 0.300 0.000 0.000 0.036 0.000 0.664
#> GSM257982 6 0.3797 0.55971 0.420 0.000 0.000 0.000 0.000 0.580
#> GSM257984 1 0.0000 0.80662 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0458 0.79196 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM257990 1 0.3595 0.73635 0.796 0.000 0.000 0.000 0.084 0.120
#> GSM257992 1 0.5276 0.52818 0.540 0.000 0.000 0.000 0.112 0.348
#> GSM257996 4 0.1080 0.88552 0.004 0.000 0.000 0.960 0.004 0.032
#> GSM258006 1 0.5285 0.52284 0.536 0.000 0.000 0.000 0.112 0.352
#> GSM257887 2 0.3405 0.55748 0.000 0.724 0.000 0.000 0.272 0.004
#> GSM257889 3 0.2302 0.66776 0.000 0.000 0.872 0.000 0.120 0.008
#> GSM257891 3 0.0405 0.80412 0.000 0.000 0.988 0.000 0.004 0.008
#> GSM257893 3 0.4093 -0.22142 0.000 0.000 0.516 0.000 0.476 0.008
#> GSM257895 2 0.3699 0.48288 0.000 0.660 0.000 0.000 0.336 0.004
#> GSM257897 3 0.4097 -0.24567 0.000 0.000 0.504 0.000 0.488 0.008
#> GSM257899 5 0.4097 0.11093 0.000 0.000 0.492 0.000 0.500 0.008
#> GSM257901 3 0.5253 0.35451 0.000 0.324 0.584 0.000 0.076 0.016
#> GSM257903 2 0.0260 0.75798 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM257905 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257907 3 0.4732 0.55288 0.000 0.216 0.692 0.000 0.076 0.016
#> GSM257909 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.5396 0.24290 0.000 0.528 0.380 0.000 0.076 0.016
#> GSM257913 3 0.3539 0.60105 0.000 0.220 0.756 0.000 0.024 0.000
#> GSM257916 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257922 5 0.4184 0.12215 0.000 0.000 0.488 0.000 0.500 0.012
#> GSM257924 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257926 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257928 5 0.3367 0.68868 0.000 0.104 0.080 0.000 0.816 0.000
#> GSM257930 5 0.2697 0.66043 0.000 0.188 0.000 0.000 0.812 0.000
#> GSM257938 5 0.2902 0.65186 0.000 0.196 0.000 0.000 0.800 0.004
#> GSM257940 3 0.4944 0.51097 0.000 0.252 0.656 0.000 0.076 0.016
#> GSM257942 2 0.0260 0.75798 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM257944 2 0.0260 0.75798 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM257946 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257948 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257950 3 0.0146 0.80920 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257952 2 0.5134 0.44934 0.000 0.616 0.292 0.000 0.076 0.016
#> GSM257954 2 0.3636 0.50404 0.000 0.676 0.000 0.000 0.320 0.004
#> GSM257956 2 0.3769 0.44930 0.000 0.640 0.000 0.000 0.356 0.004
#> GSM257959 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.5151 0.43914 0.000 0.612 0.296 0.000 0.076 0.016
#> GSM257967 2 0.0000 0.76028 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 2 0.3468 0.53854 0.000 0.712 0.000 0.000 0.284 0.004
#> GSM257971 5 0.3645 0.65108 0.000 0.092 0.072 0.000 0.816 0.020
#> GSM257973 3 0.0146 0.80849 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM257981 2 0.5082 0.46746 0.000 0.628 0.280 0.000 0.076 0.016
#> GSM257983 3 0.0146 0.80849 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM257985 3 0.0146 0.80849 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM257988 3 0.0146 0.80849 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM257991 2 0.4322 0.56947 0.000 0.732 0.184 0.000 0.076 0.008
#> GSM257993 2 0.3668 0.49421 0.000 0.668 0.000 0.000 0.328 0.004
#> GSM257994 5 0.2762 0.65430 0.000 0.196 0.000 0.000 0.804 0.000
#> GSM257989 3 0.0000 0.80897 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> CV:skmeans 96 8.49e-22 1.000 1.000 2
#> CV:skmeans 93 6.39e-21 0.791 1.000 3
#> CV:skmeans 89 3.59e-19 0.407 0.993 4
#> CV:skmeans 80 3.07e-17 0.641 0.944 5
#> CV:skmeans 80 8.39e-16 0.420 0.879 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 25171 rows and 96 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 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 0.978 0.931 0.970 0.4999 0.503 0.503
#> 3 3 0.965 0.937 0.970 0.2664 0.797 0.620
#> 4 4 0.900 0.893 0.950 0.1221 0.911 0.761
#> 5 5 0.766 0.700 0.824 0.0859 0.946 0.814
#> 6 6 0.807 0.742 0.875 0.0553 0.936 0.741
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
#> GSM257886 1 0.0000 0.954 1.000 0.000
#> GSM257888 1 0.1843 0.931 0.972 0.028
#> GSM257890 1 0.0000 0.954 1.000 0.000
#> GSM257892 1 0.3114 0.907 0.944 0.056
#> GSM257894 1 0.0000 0.954 1.000 0.000
#> GSM257896 1 0.0000 0.954 1.000 0.000
#> GSM257898 1 0.0000 0.954 1.000 0.000
#> GSM257900 1 0.0000 0.954 1.000 0.000
#> GSM257902 1 0.0000 0.954 1.000 0.000
#> GSM257904 1 0.0000 0.954 1.000 0.000
#> GSM257906 1 0.0000 0.954 1.000 0.000
#> GSM257908 1 0.0000 0.954 1.000 0.000
#> GSM257910 1 0.0000 0.954 1.000 0.000
#> GSM257912 1 0.1843 0.931 0.972 0.028
#> GSM257914 1 0.0000 0.954 1.000 0.000
#> GSM257917 1 0.0000 0.954 1.000 0.000
#> GSM257919 1 0.0672 0.948 0.992 0.008
#> GSM257921 1 0.0000 0.954 1.000 0.000
#> GSM257923 1 0.0000 0.954 1.000 0.000
#> GSM257925 1 0.0000 0.954 1.000 0.000
#> GSM257927 1 0.0000 0.954 1.000 0.000
#> GSM257929 1 0.0000 0.954 1.000 0.000
#> GSM257937 1 0.0000 0.954 1.000 0.000
#> GSM257939 1 0.0000 0.954 1.000 0.000
#> GSM257941 1 0.0000 0.954 1.000 0.000
#> GSM257943 1 0.0000 0.954 1.000 0.000
#> GSM257945 1 0.0000 0.954 1.000 0.000
#> GSM257947 1 0.0000 0.954 1.000 0.000
#> GSM257949 1 0.0000 0.954 1.000 0.000
#> GSM257951 1 0.0000 0.954 1.000 0.000
#> GSM257953 1 0.0000 0.954 1.000 0.000
#> GSM257955 1 0.0000 0.954 1.000 0.000
#> GSM257958 1 0.0000 0.954 1.000 0.000
#> GSM257960 1 0.0000 0.954 1.000 0.000
#> GSM257962 1 0.0000 0.954 1.000 0.000
#> GSM257964 1 0.0000 0.954 1.000 0.000
#> GSM257966 1 0.0000 0.954 1.000 0.000
#> GSM257968 1 0.0000 0.954 1.000 0.000
#> GSM257970 1 0.0000 0.954 1.000 0.000
#> GSM257972 1 0.0000 0.954 1.000 0.000
#> GSM257977 1 0.0000 0.954 1.000 0.000
#> GSM257982 1 0.0000 0.954 1.000 0.000
#> GSM257984 1 0.0000 0.954 1.000 0.000
#> GSM257986 1 0.0000 0.954 1.000 0.000
#> GSM257990 1 0.0000 0.954 1.000 0.000
#> GSM257992 1 0.0000 0.954 1.000 0.000
#> GSM257996 1 0.0000 0.954 1.000 0.000
#> GSM258006 1 0.0000 0.954 1.000 0.000
#> GSM257887 2 0.0000 0.987 0.000 1.000
#> GSM257889 2 0.3114 0.948 0.056 0.944
#> GSM257891 2 0.3114 0.948 0.056 0.944
#> GSM257893 2 0.3114 0.948 0.056 0.944
#> GSM257895 2 0.0000 0.987 0.000 1.000
#> GSM257897 1 0.9522 0.439 0.628 0.372
#> GSM257899 1 0.9635 0.401 0.612 0.388
#> GSM257901 2 0.0000 0.987 0.000 1.000
#> GSM257903 2 0.0000 0.987 0.000 1.000
#> GSM257905 2 0.0000 0.987 0.000 1.000
#> GSM257907 2 0.0000 0.987 0.000 1.000
#> GSM257909 2 0.0000 0.987 0.000 1.000
#> GSM257911 2 0.0000 0.987 0.000 1.000
#> GSM257913 2 0.0000 0.987 0.000 1.000
#> GSM257916 2 0.0000 0.987 0.000 1.000
#> GSM257918 2 0.0000 0.987 0.000 1.000
#> GSM257920 2 0.0938 0.981 0.012 0.988
#> GSM257922 1 0.9635 0.401 0.612 0.388
#> GSM257924 2 0.2948 0.952 0.052 0.948
#> GSM257926 2 0.0376 0.986 0.004 0.996
#> GSM257928 1 0.9460 0.460 0.636 0.364
#> GSM257930 2 0.3879 0.927 0.076 0.924
#> GSM257938 1 0.9954 0.230 0.540 0.460
#> GSM257940 2 0.0000 0.987 0.000 1.000
#> GSM257942 2 0.0000 0.987 0.000 1.000
#> GSM257944 2 0.0000 0.987 0.000 1.000
#> GSM257946 2 0.2948 0.952 0.052 0.948
#> GSM257948 2 0.0376 0.986 0.004 0.996
#> GSM257950 2 0.3114 0.948 0.056 0.944
#> GSM257952 2 0.0000 0.987 0.000 1.000
#> GSM257954 2 0.0000 0.987 0.000 1.000
#> GSM257956 2 0.0938 0.980 0.012 0.988
#> GSM257959 2 0.0000 0.987 0.000 1.000
#> GSM257961 2 0.0000 0.987 0.000 1.000
#> GSM257963 2 0.0000 0.987 0.000 1.000
#> GSM257965 2 0.0000 0.987 0.000 1.000
#> GSM257967 2 0.0000 0.987 0.000 1.000
#> GSM257969 2 0.0000 0.987 0.000 1.000
#> GSM257971 2 0.0000 0.987 0.000 1.000
#> GSM257973 2 0.0376 0.986 0.004 0.996
#> GSM257981 2 0.0000 0.987 0.000 1.000
#> GSM257983 2 0.3114 0.948 0.056 0.944
#> GSM257985 2 0.0376 0.986 0.004 0.996
#> GSM257988 2 0.0376 0.986 0.004 0.996
#> GSM257991 2 0.0000 0.987 0.000 1.000
#> GSM257993 2 0.0000 0.987 0.000 1.000
#> GSM257994 1 0.8443 0.651 0.728 0.272
#> GSM257989 2 0.0376 0.986 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257888 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257890 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257892 2 0.6299 0.140 0.476 0.524 0.000
#> GSM257894 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257898 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257904 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257906 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257908 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257910 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257912 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257914 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257917 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257919 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257921 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257937 1 0.0237 0.996 0.996 0.000 0.004
#> GSM257939 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257966 1 0.1647 0.959 0.960 0.036 0.004
#> GSM257968 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257977 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257982 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257992 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257996 1 0.0237 0.996 0.996 0.000 0.004
#> GSM258006 1 0.0000 0.999 1.000 0.000 0.000
#> GSM257887 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257889 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257891 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257893 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257895 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257897 3 0.0237 0.988 0.004 0.000 0.996
#> GSM257899 3 0.0237 0.988 0.004 0.000 0.996
#> GSM257901 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257903 2 0.5760 0.566 0.000 0.672 0.328
#> GSM257905 2 0.4555 0.741 0.000 0.800 0.200
#> GSM257907 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257909 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257911 2 0.6299 0.242 0.000 0.524 0.476
#> GSM257913 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257916 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257920 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257922 3 0.0237 0.988 0.004 0.000 0.996
#> GSM257924 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257926 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257928 2 0.2774 0.858 0.008 0.920 0.072
#> GSM257930 2 0.2448 0.857 0.000 0.924 0.076
#> GSM257938 2 0.0237 0.892 0.004 0.996 0.000
#> GSM257940 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257942 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257946 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257948 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257950 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257952 2 0.2878 0.844 0.000 0.904 0.096
#> GSM257954 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257965 2 0.3116 0.837 0.000 0.892 0.108
#> GSM257967 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257971 2 0.5785 0.578 0.000 0.668 0.332
#> GSM257973 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257981 2 0.5650 0.598 0.000 0.688 0.312
#> GSM257983 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257985 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257988 3 0.0237 0.992 0.000 0.004 0.996
#> GSM257991 3 0.3686 0.829 0.000 0.140 0.860
#> GSM257993 2 0.0000 0.894 0.000 1.000 0.000
#> GSM257994 2 0.3042 0.856 0.040 0.920 0.040
#> GSM257989 3 0.0237 0.992 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.1389 0.944 0.952 0.000 0.000 0.048
#> GSM257888 1 0.4406 0.596 0.700 0.000 0.000 0.300
#> GSM257890 1 0.1557 0.940 0.944 0.000 0.000 0.056
#> GSM257892 1 0.6070 0.265 0.548 0.404 0.000 0.048
#> GSM257894 1 0.1211 0.946 0.960 0.000 0.000 0.040
#> GSM257896 1 0.1211 0.946 0.960 0.000 0.000 0.040
#> GSM257898 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257900 1 0.1389 0.944 0.952 0.000 0.000 0.048
#> GSM257902 1 0.0707 0.950 0.980 0.000 0.000 0.020
#> GSM257904 1 0.4817 0.352 0.612 0.000 0.000 0.388
#> GSM257906 1 0.1022 0.948 0.968 0.000 0.000 0.032
#> GSM257908 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> GSM257910 4 0.0707 0.915 0.020 0.000 0.000 0.980
#> GSM257912 4 0.0592 0.916 0.016 0.000 0.000 0.984
#> GSM257914 4 0.0592 0.916 0.016 0.000 0.000 0.984
#> GSM257917 4 0.0707 0.915 0.020 0.000 0.000 0.980
#> GSM257919 4 0.0592 0.916 0.016 0.000 0.000 0.984
#> GSM257921 1 0.1118 0.945 0.964 0.000 0.000 0.036
#> GSM257923 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0188 0.950 0.996 0.000 0.000 0.004
#> GSM257927 1 0.0336 0.950 0.992 0.000 0.000 0.008
#> GSM257929 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257937 4 0.4761 0.357 0.372 0.000 0.000 0.628
#> GSM257939 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257941 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257943 1 0.0817 0.950 0.976 0.000 0.000 0.024
#> GSM257945 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257947 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0592 0.945 0.984 0.000 0.000 0.016
#> GSM257951 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0707 0.951 0.980 0.000 0.000 0.020
#> GSM257960 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257962 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257964 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257966 4 0.0000 0.907 0.000 0.000 0.000 1.000
#> GSM257968 1 0.0707 0.944 0.980 0.000 0.000 0.020
#> GSM257970 1 0.0000 0.949 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0707 0.944 0.980 0.000 0.000 0.020
#> GSM257977 1 0.1211 0.946 0.960 0.000 0.000 0.040
#> GSM257982 1 0.0707 0.944 0.980 0.000 0.000 0.020
#> GSM257984 1 0.0707 0.950 0.980 0.000 0.000 0.020
#> GSM257986 1 0.0592 0.945 0.984 0.000 0.000 0.016
#> GSM257990 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257992 1 0.0921 0.949 0.972 0.000 0.000 0.028
#> GSM257996 4 0.2814 0.828 0.132 0.000 0.000 0.868
#> GSM258006 1 0.0469 0.950 0.988 0.000 0.000 0.012
#> GSM257887 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257895 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0469 0.973 0.012 0.000 0.988 0.000
#> GSM257901 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257903 2 0.4382 0.634 0.000 0.704 0.296 0.000
#> GSM257905 2 0.3356 0.777 0.000 0.824 0.176 0.000
#> GSM257907 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257909 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257911 2 0.4994 0.245 0.000 0.520 0.480 0.000
#> GSM257913 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257916 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257920 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257922 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257924 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257926 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257928 2 0.3320 0.821 0.068 0.876 0.056 0.000
#> GSM257930 2 0.2704 0.821 0.000 0.876 0.124 0.000
#> GSM257938 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257940 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257942 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257950 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257952 2 0.2216 0.850 0.000 0.908 0.092 0.000
#> GSM257954 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257959 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257965 2 0.2345 0.846 0.000 0.900 0.100 0.000
#> GSM257967 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257971 2 0.5527 0.520 0.028 0.616 0.356 0.000
#> GSM257973 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257981 2 0.4193 0.681 0.000 0.732 0.268 0.000
#> GSM257983 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257985 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0000 0.989 0.000 0.000 1.000 0.000
#> GSM257991 3 0.3444 0.754 0.000 0.184 0.816 0.000
#> GSM257993 2 0.0000 0.898 0.000 1.000 0.000 0.000
#> GSM257994 2 0.2704 0.775 0.124 0.876 0.000 0.000
#> GSM257989 3 0.0000 0.989 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.4305 0.04898 0.512 0.000 0.000 0.488 0.000
#> GSM257888 4 0.3671 0.86466 0.236 0.000 0.000 0.756 0.008
#> GSM257890 4 0.3336 0.86881 0.228 0.000 0.000 0.772 0.000
#> GSM257892 4 0.3700 0.85102 0.240 0.008 0.000 0.752 0.000
#> GSM257894 4 0.3895 0.80220 0.320 0.000 0.000 0.680 0.000
#> GSM257896 4 0.3336 0.86881 0.228 0.000 0.000 0.772 0.000
#> GSM257898 1 0.4273 0.17551 0.552 0.000 0.000 0.448 0.000
#> GSM257900 1 0.4297 0.10474 0.528 0.000 0.000 0.472 0.000
#> GSM257902 4 0.4138 0.73805 0.384 0.000 0.000 0.616 0.000
#> GSM257904 1 0.4961 0.11205 0.524 0.000 0.000 0.448 0.028
#> GSM257906 1 0.4297 0.10474 0.528 0.000 0.000 0.472 0.000
#> GSM257908 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257910 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257912 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257914 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257917 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257919 5 0.0000 0.88537 0.000 0.000 0.000 0.000 1.000
#> GSM257921 1 0.3336 0.54431 0.772 0.000 0.000 0.228 0.000
#> GSM257923 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.1121 0.63588 0.956 0.000 0.000 0.044 0.000
#> GSM257927 1 0.2516 0.60941 0.860 0.000 0.000 0.140 0.000
#> GSM257929 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257937 5 0.6281 -0.01962 0.152 0.000 0.000 0.388 0.460
#> GSM257939 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.2966 0.58384 0.816 0.000 0.000 0.184 0.000
#> GSM257943 1 0.4273 0.17551 0.552 0.000 0.000 0.448 0.000
#> GSM257945 1 0.4030 0.35981 0.648 0.000 0.000 0.352 0.000
#> GSM257947 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0404 0.62832 0.988 0.000 0.000 0.012 0.000
#> GSM257951 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.1965 0.62621 0.904 0.000 0.000 0.096 0.000
#> GSM257960 1 0.4262 0.19234 0.560 0.000 0.000 0.440 0.000
#> GSM257962 1 0.2516 0.60941 0.860 0.000 0.000 0.140 0.000
#> GSM257964 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257966 5 0.2329 0.81152 0.000 0.000 0.000 0.124 0.876
#> GSM257968 1 0.4150 -0.13821 0.612 0.000 0.000 0.388 0.000
#> GSM257970 1 0.0000 0.63650 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.1965 0.59831 0.904 0.000 0.000 0.096 0.000
#> GSM257977 4 0.3336 0.86881 0.228 0.000 0.000 0.772 0.000
#> GSM257982 1 0.3999 0.00249 0.656 0.000 0.000 0.344 0.000
#> GSM257984 4 0.4268 0.64247 0.444 0.000 0.000 0.556 0.000
#> GSM257986 1 0.3774 0.10346 0.704 0.000 0.000 0.296 0.000
#> GSM257990 1 0.2516 0.60941 0.860 0.000 0.000 0.140 0.000
#> GSM257992 1 0.4273 0.17551 0.552 0.000 0.000 0.448 0.000
#> GSM257996 5 0.4054 0.66714 0.072 0.000 0.000 0.140 0.788
#> GSM258006 1 0.4201 0.25868 0.592 0.000 0.000 0.408 0.000
#> GSM257887 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257889 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257891 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257895 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257897 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257899 3 0.0404 0.96197 0.000 0.000 0.988 0.012 0.000
#> GSM257901 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257903 2 0.5911 0.68446 0.000 0.596 0.176 0.228 0.000
#> GSM257905 2 0.4201 0.74112 0.000 0.752 0.204 0.044 0.000
#> GSM257907 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257909 2 0.3336 0.80976 0.000 0.772 0.000 0.228 0.000
#> GSM257911 2 0.5178 0.24493 0.000 0.480 0.480 0.040 0.000
#> GSM257913 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257916 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.3336 0.80976 0.000 0.772 0.000 0.228 0.000
#> GSM257920 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257922 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257924 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257926 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257928 2 0.1661 0.82190 0.036 0.940 0.024 0.000 0.000
#> GSM257930 2 0.1410 0.82361 0.000 0.940 0.060 0.000 0.000
#> GSM257938 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257940 3 0.2813 0.80434 0.000 0.000 0.832 0.168 0.000
#> GSM257942 2 0.3336 0.80976 0.000 0.772 0.000 0.228 0.000
#> GSM257944 2 0.3336 0.80976 0.000 0.772 0.000 0.228 0.000
#> GSM257946 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257950 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.2179 0.81169 0.000 0.888 0.112 0.000 0.000
#> GSM257954 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257956 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257959 2 0.3336 0.80976 0.000 0.772 0.000 0.228 0.000
#> GSM257961 2 0.3109 0.81872 0.000 0.800 0.000 0.200 0.000
#> GSM257963 2 0.3003 0.82178 0.000 0.812 0.000 0.188 0.000
#> GSM257965 2 0.2329 0.80489 0.000 0.876 0.124 0.000 0.000
#> GSM257967 2 0.2929 0.82253 0.000 0.820 0.000 0.180 0.000
#> GSM257969 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257971 2 0.4805 0.59848 0.000 0.648 0.312 0.040 0.000
#> GSM257973 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.3895 0.62044 0.000 0.680 0.320 0.000 0.000
#> GSM257983 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
#> GSM257991 3 0.5408 0.54835 0.000 0.120 0.652 0.228 0.000
#> GSM257993 2 0.0000 0.84112 0.000 1.000 0.000 0.000 0.000
#> GSM257994 2 0.1648 0.82087 0.040 0.940 0.020 0.000 0.000
#> GSM257989 3 0.0000 0.97389 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 1 0.3868 0.477 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM257888 6 0.0458 0.754 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM257890 6 0.0000 0.750 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257892 6 0.2234 0.606 0.124 0.004 0.000 0.000 0.000 0.872
#> GSM257894 6 0.1814 0.745 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM257896 6 0.0000 0.750 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257898 1 0.3847 0.530 0.544 0.000 0.000 0.000 0.000 0.456
#> GSM257900 1 0.3864 0.500 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257902 6 0.2597 0.718 0.176 0.000 0.000 0.000 0.000 0.824
#> GSM257904 1 0.4463 0.502 0.516 0.000 0.000 0.028 0.000 0.456
#> GSM257906 1 0.3864 0.500 0.520 0.000 0.000 0.000 0.000 0.480
#> GSM257908 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257910 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257912 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257914 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257917 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257919 4 0.0000 0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257921 1 0.3050 0.687 0.764 0.000 0.000 0.000 0.000 0.236
#> GSM257923 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.1204 0.715 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM257927 1 0.2491 0.709 0.836 0.000 0.000 0.000 0.000 0.164
#> GSM257929 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257937 6 0.3979 0.252 0.012 0.000 0.000 0.360 0.000 0.628
#> GSM257939 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.2793 0.701 0.800 0.000 0.000 0.000 0.000 0.200
#> GSM257943 1 0.3847 0.530 0.544 0.000 0.000 0.000 0.000 0.456
#> GSM257945 1 0.3659 0.606 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM257947 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0458 0.696 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM257951 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.2048 0.715 0.880 0.000 0.000 0.000 0.000 0.120
#> GSM257960 1 0.3838 0.537 0.552 0.000 0.000 0.000 0.000 0.448
#> GSM257962 1 0.2491 0.709 0.836 0.000 0.000 0.000 0.000 0.164
#> GSM257964 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.2092 0.820 0.000 0.000 0.000 0.876 0.000 0.124
#> GSM257968 6 0.3747 0.485 0.396 0.000 0.000 0.000 0.000 0.604
#> GSM257970 1 0.0000 0.706 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.1610 0.688 0.916 0.000 0.000 0.000 0.000 0.084
#> GSM257977 6 0.0000 0.750 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257982 1 0.3868 -0.370 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM257984 6 0.3101 0.666 0.244 0.000 0.000 0.000 0.000 0.756
#> GSM257986 6 0.3866 0.355 0.484 0.000 0.000 0.000 0.000 0.516
#> GSM257990 1 0.2491 0.709 0.836 0.000 0.000 0.000 0.000 0.164
#> GSM257992 1 0.3847 0.530 0.544 0.000 0.000 0.000 0.000 0.456
#> GSM257996 4 0.4108 0.574 0.092 0.000 0.000 0.744 0.000 0.164
#> GSM258006 1 0.3782 0.570 0.588 0.000 0.000 0.000 0.000 0.412
#> GSM257887 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257889 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257891 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257893 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257895 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257897 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257899 3 0.0363 0.972 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM257901 3 0.2996 0.665 0.000 0.000 0.772 0.000 0.228 0.000
#> GSM257903 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257905 5 0.5528 0.470 0.000 0.252 0.192 0.000 0.556 0.000
#> GSM257907 3 0.0260 0.978 0.000 0.000 0.992 0.000 0.008 0.000
#> GSM257909 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257911 5 0.3756 0.351 0.000 0.000 0.400 0.000 0.600 0.000
#> GSM257913 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257916 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257918 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257920 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257922 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257924 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257926 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257928 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257930 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257938 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257940 5 0.2793 0.692 0.000 0.000 0.200 0.000 0.800 0.000
#> GSM257942 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257944 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257946 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257948 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257950 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257952 2 0.2697 0.713 0.000 0.812 0.188 0.000 0.000 0.000
#> GSM257954 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257956 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257959 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257961 2 0.3823 0.379 0.000 0.564 0.000 0.000 0.436 0.000
#> GSM257963 2 0.3717 0.478 0.000 0.616 0.000 0.000 0.384 0.000
#> GSM257965 2 0.2793 0.702 0.000 0.800 0.200 0.000 0.000 0.000
#> GSM257967 2 0.3672 0.445 0.000 0.632 0.000 0.000 0.368 0.000
#> GSM257969 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257971 2 0.4219 0.570 0.000 0.660 0.304 0.000 0.000 0.036
#> GSM257973 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257981 2 0.3717 0.458 0.000 0.616 0.384 0.000 0.000 0.000
#> GSM257983 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257985 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257988 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257991 5 0.0000 0.853 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257993 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257994 2 0.0000 0.843 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257989 3 0.0000 0.985 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> CV:pam 91 7.48e-20 1.000 1.000 2
#> CV:pam 94 3.87e-21 0.642 1.000 3
#> CV:pam 92 8.15e-20 0.699 0.992 4
#> CV:pam 81 1.07e-16 0.823 0.979 5
#> CV:pam 85 7.53e-17 0.958 0.929 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.50576 0.495 0.495
#> 3 3 0.980 0.934 0.959 0.24029 0.876 0.749
#> 4 4 0.862 0.851 0.938 0.17127 0.880 0.684
#> 5 5 0.808 0.681 0.871 0.00937 0.898 0.682
#> 6 6 0.811 0.638 0.866 0.05341 0.971 0.895
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257888 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257890 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257892 1 0.1015 0.982 0.980 0.012 0.008
#> GSM257894 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257896 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257898 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257900 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257904 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257906 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257908 1 0.1529 0.972 0.960 0.000 0.040
#> GSM257910 1 0.1529 0.972 0.960 0.000 0.040
#> GSM257912 1 0.1643 0.969 0.956 0.000 0.044
#> GSM257914 1 0.1643 0.969 0.956 0.000 0.044
#> GSM257917 1 0.1529 0.972 0.960 0.000 0.040
#> GSM257919 1 0.1643 0.969 0.956 0.000 0.044
#> GSM257921 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257937 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257939 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257966 1 0.1529 0.972 0.960 0.000 0.040
#> GSM257968 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257977 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257982 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257984 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.994 1.000 0.000 0.000
#> GSM257992 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257996 1 0.0000 0.994 1.000 0.000 0.000
#> GSM258006 1 0.0237 0.993 0.996 0.000 0.004
#> GSM257887 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257889 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257891 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257893 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257895 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257897 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257899 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257901 3 0.5882 0.445 0.000 0.348 0.652
#> GSM257903 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257907 3 0.1753 0.976 0.000 0.048 0.952
#> GSM257909 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257911 2 0.5926 0.487 0.000 0.644 0.356
#> GSM257913 3 0.1753 0.976 0.000 0.048 0.952
#> GSM257916 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257920 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257922 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257924 3 0.2356 0.950 0.000 0.072 0.928
#> GSM257926 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257928 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257930 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257938 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257940 3 0.1753 0.976 0.000 0.048 0.952
#> GSM257942 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257946 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257948 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257950 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257952 2 0.5926 0.487 0.000 0.644 0.356
#> GSM257954 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257965 2 0.4702 0.716 0.000 0.788 0.212
#> GSM257967 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257971 2 0.6295 0.151 0.000 0.528 0.472
#> GSM257973 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257981 2 0.5706 0.557 0.000 0.680 0.320
#> GSM257983 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257985 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257988 3 0.1643 0.979 0.000 0.044 0.956
#> GSM257991 2 0.5591 0.584 0.000 0.696 0.304
#> GSM257993 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257994 2 0.0000 0.914 0.000 1.000 0.000
#> GSM257989 3 0.1643 0.979 0.000 0.044 0.956
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.1211 0.90601 0.960 0.000 0.000 0.040
#> GSM257888 4 0.0469 0.93414 0.012 0.000 0.000 0.988
#> GSM257890 4 0.3266 0.80891 0.168 0.000 0.000 0.832
#> GSM257892 1 0.3764 0.72252 0.784 0.000 0.000 0.216
#> GSM257894 4 0.0592 0.93529 0.016 0.000 0.000 0.984
#> GSM257896 1 0.3024 0.80766 0.852 0.000 0.000 0.148
#> GSM257898 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257900 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257902 1 0.4907 0.23788 0.580 0.000 0.000 0.420
#> GSM257904 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257906 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257908 4 0.0921 0.94238 0.028 0.000 0.000 0.972
#> GSM257910 4 0.1389 0.94612 0.048 0.000 0.000 0.952
#> GSM257912 4 0.1389 0.94612 0.048 0.000 0.000 0.952
#> GSM257914 4 0.1389 0.94612 0.048 0.000 0.000 0.952
#> GSM257917 4 0.1716 0.93749 0.064 0.000 0.000 0.936
#> GSM257919 4 0.1389 0.94612 0.048 0.000 0.000 0.952
#> GSM257921 1 0.4996 0.00608 0.516 0.000 0.000 0.484
#> GSM257923 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257927 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257929 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257937 4 0.0592 0.93529 0.016 0.000 0.000 0.984
#> GSM257939 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257941 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257947 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257964 1 0.0188 0.93320 0.996 0.000 0.000 0.004
#> GSM257966 4 0.0000 0.92735 0.000 0.000 0.000 1.000
#> GSM257968 4 0.3356 0.79497 0.176 0.000 0.000 0.824
#> GSM257970 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257977 1 0.3764 0.72252 0.784 0.000 0.000 0.216
#> GSM257982 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257984 1 0.4331 0.57550 0.712 0.000 0.000 0.288
#> GSM257986 1 0.3311 0.76484 0.828 0.000 0.000 0.172
#> GSM257990 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257992 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257996 4 0.1940 0.92844 0.076 0.000 0.000 0.924
#> GSM258006 1 0.0000 0.93604 1.000 0.000 0.000 0.000
#> GSM257887 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257895 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257901 3 0.2647 0.84617 0.000 0.120 0.880 0.000
#> GSM257903 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257907 3 0.1792 0.89921 0.000 0.068 0.932 0.000
#> GSM257909 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257911 3 0.4999 -0.05608 0.000 0.492 0.508 0.000
#> GSM257913 3 0.2011 0.88938 0.000 0.080 0.920 0.000
#> GSM257916 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257920 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257922 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257924 3 0.0336 0.93839 0.000 0.008 0.992 0.000
#> GSM257926 3 0.0336 0.93961 0.000 0.008 0.992 0.000
#> GSM257928 2 0.4500 0.52496 0.000 0.684 0.316 0.000
#> GSM257930 2 0.3172 0.75654 0.000 0.840 0.160 0.000
#> GSM257938 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257940 3 0.2011 0.88938 0.000 0.080 0.920 0.000
#> GSM257942 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0336 0.93961 0.000 0.008 0.992 0.000
#> GSM257950 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257952 2 0.4948 0.23106 0.000 0.560 0.440 0.000
#> GSM257954 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0336 0.89070 0.000 0.992 0.008 0.000
#> GSM257959 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257965 2 0.4855 0.34328 0.000 0.600 0.400 0.000
#> GSM257967 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257971 3 0.3942 0.66246 0.000 0.236 0.764 0.000
#> GSM257973 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257981 2 0.4855 0.34328 0.000 0.600 0.400 0.000
#> GSM257983 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257985 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0000 0.94323 0.000 0.000 1.000 0.000
#> GSM257991 2 0.4855 0.34328 0.000 0.600 0.400 0.000
#> GSM257993 2 0.0000 0.89592 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0336 0.89070 0.000 0.992 0.008 0.000
#> GSM257989 3 0.0000 0.94323 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.4252 0.4961 0.652 0.000 0.000 0.340 0.008
#> GSM257888 1 0.4273 0.2882 0.552 0.000 0.000 0.448 0.000
#> GSM257890 1 0.4262 0.3058 0.560 0.000 0.000 0.440 0.000
#> GSM257892 1 0.5588 0.4421 0.604 0.000 0.000 0.292 0.104
#> GSM257894 1 0.4273 0.2882 0.552 0.000 0.000 0.448 0.000
#> GSM257896 1 0.4511 0.4582 0.628 0.000 0.000 0.356 0.016
#> GSM257898 1 0.0794 0.8620 0.972 0.000 0.000 0.000 0.028
#> GSM257900 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257902 1 0.0963 0.8555 0.964 0.000 0.000 0.036 0.000
#> GSM257904 1 0.0162 0.8725 0.996 0.000 0.000 0.000 0.004
#> GSM257906 1 0.0794 0.8620 0.972 0.000 0.000 0.000 0.028
#> GSM257908 4 0.1341 0.8311 0.056 0.000 0.000 0.944 0.000
#> GSM257910 4 0.0404 0.8646 0.012 0.000 0.000 0.988 0.000
#> GSM257912 4 0.0000 0.8657 0.000 0.000 0.000 1.000 0.000
#> GSM257914 4 0.0000 0.8657 0.000 0.000 0.000 1.000 0.000
#> GSM257917 4 0.0703 0.8564 0.024 0.000 0.000 0.976 0.000
#> GSM257919 4 0.0000 0.8657 0.000 0.000 0.000 1.000 0.000
#> GSM257921 1 0.1197 0.8467 0.952 0.000 0.000 0.048 0.000
#> GSM257923 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257929 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257937 1 0.4268 0.2971 0.556 0.000 0.000 0.444 0.000
#> GSM257939 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257943 1 0.0404 0.8693 0.988 0.000 0.000 0.000 0.012
#> GSM257945 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257947 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257964 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.4114 0.2224 0.376 0.000 0.000 0.624 0.000
#> GSM257968 1 0.4268 0.2971 0.556 0.000 0.000 0.444 0.000
#> GSM257970 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257977 1 0.4211 0.4609 0.636 0.000 0.000 0.360 0.004
#> GSM257982 1 0.1117 0.8611 0.964 0.000 0.000 0.016 0.020
#> GSM257984 1 0.0880 0.8573 0.968 0.000 0.000 0.032 0.000
#> GSM257986 1 0.0510 0.8669 0.984 0.000 0.000 0.016 0.000
#> GSM257990 1 0.0000 0.8739 1.000 0.000 0.000 0.000 0.000
#> GSM257992 1 0.0794 0.8620 0.972 0.000 0.000 0.000 0.028
#> GSM257996 1 0.4150 0.3739 0.612 0.000 0.000 0.388 0.000
#> GSM258006 1 0.0794 0.8620 0.972 0.000 0.000 0.000 0.028
#> GSM257887 2 0.4235 -0.1275 0.000 0.576 0.000 0.000 0.424
#> GSM257889 3 0.0609 0.8366 0.000 0.000 0.980 0.000 0.020
#> GSM257891 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.3210 0.7113 0.000 0.000 0.788 0.000 0.212
#> GSM257895 5 0.4268 0.4108 0.000 0.444 0.000 0.000 0.556
#> GSM257897 3 0.3366 0.6959 0.000 0.000 0.768 0.000 0.232
#> GSM257899 3 0.3366 0.6959 0.000 0.000 0.768 0.000 0.232
#> GSM257901 3 0.4182 0.2137 0.000 0.400 0.600 0.000 0.000
#> GSM257903 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257907 3 0.4088 0.3060 0.000 0.368 0.632 0.000 0.000
#> GSM257909 2 0.0162 0.7335 0.000 0.996 0.000 0.000 0.004
#> GSM257911 2 0.4242 0.3090 0.000 0.572 0.428 0.000 0.000
#> GSM257913 3 0.4138 0.2614 0.000 0.384 0.616 0.000 0.000
#> GSM257916 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257920 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257922 3 0.3366 0.6959 0.000 0.000 0.768 0.000 0.232
#> GSM257924 3 0.0290 0.8414 0.000 0.008 0.992 0.000 0.000
#> GSM257926 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257928 5 0.0880 0.6591 0.000 0.032 0.000 0.000 0.968
#> GSM257930 5 0.0963 0.6611 0.000 0.036 0.000 0.000 0.964
#> GSM257938 5 0.1270 0.6648 0.000 0.052 0.000 0.000 0.948
#> GSM257940 3 0.4101 0.2956 0.000 0.372 0.628 0.000 0.000
#> GSM257942 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257950 3 0.0162 0.8446 0.000 0.000 0.996 0.000 0.004
#> GSM257952 2 0.4242 0.3090 0.000 0.572 0.428 0.000 0.000
#> GSM257954 5 0.4242 0.4251 0.000 0.428 0.000 0.000 0.572
#> GSM257956 5 0.3074 0.6303 0.000 0.196 0.000 0.000 0.804
#> GSM257959 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.4171 0.3771 0.000 0.604 0.396 0.000 0.000
#> GSM257967 2 0.0000 0.7365 0.000 1.000 0.000 0.000 0.000
#> GSM257969 5 0.4297 0.3462 0.000 0.472 0.000 0.000 0.528
#> GSM257971 5 0.4331 -0.0708 0.000 0.004 0.400 0.000 0.596
#> GSM257973 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.4649 0.3448 0.000 0.580 0.404 0.000 0.016
#> GSM257983 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0290 0.8429 0.000 0.000 0.992 0.000 0.008
#> GSM257991 2 0.4620 0.3654 0.000 0.592 0.392 0.000 0.016
#> GSM257993 5 0.4171 0.4592 0.000 0.396 0.000 0.000 0.604
#> GSM257994 5 0.1121 0.6644 0.000 0.044 0.000 0.000 0.956
#> GSM257989 3 0.0000 0.8459 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 1 0.3756 0.1597 0.600 0.000 0.000 0.000 0.000 0.400
#> GSM257888 6 0.4594 -0.0680 0.476 0.000 0.000 0.036 0.000 0.488
#> GSM257890 1 0.3867 -0.1904 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM257892 6 0.2401 0.2439 0.036 0.044 0.000 0.000 0.020 0.900
#> GSM257894 1 0.3867 -0.1904 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM257896 1 0.3868 -0.1581 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM257898 1 0.3868 0.1847 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM257900 1 0.0146 0.7840 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257902 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257904 1 0.1204 0.7498 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM257906 1 0.3868 0.1847 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM257908 4 0.1408 0.9025 0.020 0.000 0.000 0.944 0.000 0.036
#> GSM257910 4 0.0547 0.9271 0.020 0.000 0.000 0.980 0.000 0.000
#> GSM257912 4 0.0000 0.9385 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257914 4 0.0000 0.9385 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257917 4 0.0000 0.9385 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257919 4 0.0000 0.9385 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257921 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257923 1 0.0260 0.7833 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257925 1 0.0547 0.7779 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257927 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257929 1 0.0547 0.7779 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257937 1 0.4184 -0.2261 0.500 0.000 0.000 0.012 0.000 0.488
#> GSM257939 1 0.0363 0.7817 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM257941 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257943 1 0.2562 0.6369 0.828 0.000 0.000 0.000 0.000 0.172
#> GSM257945 1 0.0260 0.7823 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257947 1 0.0547 0.7779 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257949 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0547 0.7779 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257953 1 0.0547 0.7779 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257955 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257964 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 6 0.4594 -0.1518 0.036 0.000 0.000 0.476 0.000 0.488
#> GSM257968 1 0.3867 -0.1904 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM257970 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0260 0.7833 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257977 1 0.3847 -0.0976 0.544 0.000 0.000 0.000 0.000 0.456
#> GSM257982 1 0.2631 0.6383 0.820 0.000 0.000 0.000 0.000 0.180
#> GSM257984 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.7854 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257992 1 0.3868 0.1847 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM257996 4 0.2260 0.7272 0.140 0.000 0.000 0.860 0.000 0.000
#> GSM258006 1 0.3868 0.1847 0.508 0.000 0.000 0.000 0.000 0.492
#> GSM257887 2 0.3810 0.1150 0.000 0.572 0.000 0.000 0.428 0.000
#> GSM257889 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257891 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257893 3 0.2378 0.7743 0.000 0.000 0.848 0.000 0.152 0.000
#> GSM257895 5 0.2048 0.8425 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM257897 3 0.2793 0.7500 0.000 0.000 0.800 0.000 0.200 0.000
#> GSM257899 3 0.2793 0.7500 0.000 0.000 0.800 0.000 0.200 0.000
#> GSM257901 3 0.3789 0.2353 0.000 0.416 0.584 0.000 0.000 0.000
#> GSM257903 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257907 3 0.3578 0.4330 0.000 0.340 0.660 0.000 0.000 0.000
#> GSM257909 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.3797 0.2756 0.000 0.580 0.420 0.000 0.000 0.000
#> GSM257913 3 0.3672 0.3692 0.000 0.368 0.632 0.000 0.000 0.000
#> GSM257916 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257922 3 0.2793 0.7500 0.000 0.000 0.800 0.000 0.200 0.000
#> GSM257924 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257926 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257928 5 0.1007 0.8363 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM257930 5 0.1007 0.8363 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM257938 5 0.1007 0.8363 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM257940 3 0.3592 0.4252 0.000 0.344 0.656 0.000 0.000 0.000
#> GSM257942 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257948 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257950 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257952 2 0.3756 0.3271 0.000 0.600 0.400 0.000 0.000 0.000
#> GSM257954 5 0.2048 0.8425 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM257956 5 0.2003 0.8442 0.000 0.116 0.000 0.000 0.884 0.000
#> GSM257959 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.3756 0.3271 0.000 0.600 0.400 0.000 0.000 0.000
#> GSM257967 2 0.0000 0.8175 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 5 0.3659 0.4371 0.000 0.364 0.000 0.000 0.636 0.000
#> GSM257971 5 0.4634 0.0847 0.000 0.044 0.400 0.000 0.556 0.000
#> GSM257973 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257981 2 0.3756 0.3271 0.000 0.600 0.400 0.000 0.000 0.000
#> GSM257983 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257985 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257988 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257991 2 0.0547 0.8048 0.000 0.980 0.020 0.000 0.000 0.000
#> GSM257993 5 0.2003 0.8441 0.000 0.116 0.000 0.000 0.884 0.000
#> GSM257994 5 0.1007 0.8363 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM257989 3 0.0000 0.8692 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> CV:mclust 96 8.49e-22 1.000 1.000 2
#> CV:mclust 92 1.05e-20 0.793 1.000 3
#> CV:mclust 89 3.59e-19 0.217 0.994 4
#> CV:mclust 70 2.27e-14 0.211 0.858 5
#> CV:mclust 71 1.40e-14 0.705 0.861 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 25171 rows and 96 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 0.978 0.991 0.5057 0.495 0.495
#> 3 3 0.811 0.855 0.926 0.2722 0.858 0.716
#> 4 4 0.778 0.785 0.877 0.0895 0.893 0.719
#> 5 5 0.621 0.543 0.756 0.0873 0.928 0.775
#> 6 6 0.653 0.580 0.751 0.0583 0.895 0.651
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
#> GSM257886 1 0.0000 0.993 1.000 0.000
#> GSM257888 1 0.0000 0.993 1.000 0.000
#> GSM257890 1 0.0000 0.993 1.000 0.000
#> GSM257892 1 0.9209 0.490 0.664 0.336
#> GSM257894 1 0.0000 0.993 1.000 0.000
#> GSM257896 1 0.0000 0.993 1.000 0.000
#> GSM257898 1 0.0000 0.993 1.000 0.000
#> GSM257900 1 0.0000 0.993 1.000 0.000
#> GSM257902 1 0.0000 0.993 1.000 0.000
#> GSM257904 1 0.0000 0.993 1.000 0.000
#> GSM257906 1 0.0000 0.993 1.000 0.000
#> GSM257908 1 0.0000 0.993 1.000 0.000
#> GSM257910 1 0.0000 0.993 1.000 0.000
#> GSM257912 1 0.0000 0.993 1.000 0.000
#> GSM257914 1 0.0000 0.993 1.000 0.000
#> GSM257917 1 0.0000 0.993 1.000 0.000
#> GSM257919 1 0.0000 0.993 1.000 0.000
#> GSM257921 1 0.0000 0.993 1.000 0.000
#> GSM257923 1 0.0000 0.993 1.000 0.000
#> GSM257925 1 0.0000 0.993 1.000 0.000
#> GSM257927 1 0.0000 0.993 1.000 0.000
#> GSM257929 1 0.0000 0.993 1.000 0.000
#> GSM257937 1 0.0000 0.993 1.000 0.000
#> GSM257939 1 0.0000 0.993 1.000 0.000
#> GSM257941 1 0.0000 0.993 1.000 0.000
#> GSM257943 1 0.0000 0.993 1.000 0.000
#> GSM257945 1 0.0000 0.993 1.000 0.000
#> GSM257947 1 0.0000 0.993 1.000 0.000
#> GSM257949 1 0.0000 0.993 1.000 0.000
#> GSM257951 1 0.0000 0.993 1.000 0.000
#> GSM257953 1 0.0000 0.993 1.000 0.000
#> GSM257955 1 0.0000 0.993 1.000 0.000
#> GSM257958 1 0.0000 0.993 1.000 0.000
#> GSM257960 1 0.0000 0.993 1.000 0.000
#> GSM257962 1 0.0000 0.993 1.000 0.000
#> GSM257964 1 0.0000 0.993 1.000 0.000
#> GSM257966 1 0.0000 0.993 1.000 0.000
#> GSM257968 1 0.0000 0.993 1.000 0.000
#> GSM257970 1 0.0000 0.993 1.000 0.000
#> GSM257972 1 0.0000 0.993 1.000 0.000
#> GSM257977 1 0.0000 0.993 1.000 0.000
#> GSM257982 1 0.0000 0.993 1.000 0.000
#> GSM257984 1 0.0000 0.993 1.000 0.000
#> GSM257986 1 0.0000 0.993 1.000 0.000
#> GSM257990 1 0.0000 0.993 1.000 0.000
#> GSM257992 1 0.0000 0.993 1.000 0.000
#> GSM257996 1 0.0000 0.993 1.000 0.000
#> GSM258006 1 0.0000 0.993 1.000 0.000
#> GSM257887 2 0.0000 0.988 0.000 1.000
#> GSM257889 2 0.0000 0.988 0.000 1.000
#> GSM257891 2 0.0000 0.988 0.000 1.000
#> GSM257893 2 0.0000 0.988 0.000 1.000
#> GSM257895 2 0.0000 0.988 0.000 1.000
#> GSM257897 2 0.9129 0.518 0.328 0.672
#> GSM257899 2 0.1633 0.966 0.024 0.976
#> GSM257901 2 0.0000 0.988 0.000 1.000
#> GSM257903 2 0.0000 0.988 0.000 1.000
#> GSM257905 2 0.0000 0.988 0.000 1.000
#> GSM257907 2 0.0000 0.988 0.000 1.000
#> GSM257909 2 0.0000 0.988 0.000 1.000
#> GSM257911 2 0.0000 0.988 0.000 1.000
#> GSM257913 2 0.0000 0.988 0.000 1.000
#> GSM257916 2 0.0000 0.988 0.000 1.000
#> GSM257918 2 0.0000 0.988 0.000 1.000
#> GSM257920 2 0.0000 0.988 0.000 1.000
#> GSM257922 2 0.7056 0.762 0.192 0.808
#> GSM257924 2 0.0000 0.988 0.000 1.000
#> GSM257926 2 0.0000 0.988 0.000 1.000
#> GSM257928 2 0.0376 0.985 0.004 0.996
#> GSM257930 2 0.0000 0.988 0.000 1.000
#> GSM257938 2 0.0000 0.988 0.000 1.000
#> GSM257940 2 0.0000 0.988 0.000 1.000
#> GSM257942 2 0.0000 0.988 0.000 1.000
#> GSM257944 2 0.0000 0.988 0.000 1.000
#> GSM257946 2 0.0000 0.988 0.000 1.000
#> GSM257948 2 0.0000 0.988 0.000 1.000
#> GSM257950 2 0.0000 0.988 0.000 1.000
#> GSM257952 2 0.0000 0.988 0.000 1.000
#> GSM257954 2 0.0000 0.988 0.000 1.000
#> GSM257956 2 0.0000 0.988 0.000 1.000
#> GSM257959 2 0.0000 0.988 0.000 1.000
#> GSM257961 2 0.0000 0.988 0.000 1.000
#> GSM257963 2 0.0000 0.988 0.000 1.000
#> GSM257965 2 0.0000 0.988 0.000 1.000
#> GSM257967 2 0.0000 0.988 0.000 1.000
#> GSM257969 2 0.0000 0.988 0.000 1.000
#> GSM257971 2 0.0000 0.988 0.000 1.000
#> GSM257973 2 0.0000 0.988 0.000 1.000
#> GSM257981 2 0.0000 0.988 0.000 1.000
#> GSM257983 2 0.0000 0.988 0.000 1.000
#> GSM257985 2 0.0000 0.988 0.000 1.000
#> GSM257988 2 0.0000 0.988 0.000 1.000
#> GSM257991 2 0.0000 0.988 0.000 1.000
#> GSM257993 2 0.0000 0.988 0.000 1.000
#> GSM257994 2 0.0000 0.988 0.000 1.000
#> GSM257989 2 0.0000 0.988 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257888 1 0.6026 0.396 0.624 0.376 0.000
#> GSM257890 1 0.5678 0.534 0.684 0.316 0.000
#> GSM257892 2 0.6373 0.264 0.408 0.588 0.004
#> GSM257894 1 0.0237 0.941 0.996 0.004 0.000
#> GSM257896 1 0.0592 0.939 0.988 0.012 0.000
#> GSM257898 1 0.2590 0.898 0.924 0.004 0.072
#> GSM257900 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257902 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257904 1 0.0747 0.936 0.984 0.000 0.016
#> GSM257906 1 0.2878 0.878 0.904 0.000 0.096
#> GSM257908 1 0.0592 0.938 0.988 0.012 0.000
#> GSM257910 1 0.0661 0.938 0.988 0.004 0.008
#> GSM257912 1 0.5219 0.739 0.788 0.016 0.196
#> GSM257914 1 0.4063 0.840 0.868 0.020 0.112
#> GSM257917 1 0.2590 0.892 0.924 0.004 0.072
#> GSM257919 1 0.6541 0.544 0.672 0.024 0.304
#> GSM257921 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257923 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257925 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257927 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257929 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257937 1 0.0892 0.933 0.980 0.020 0.000
#> GSM257939 1 0.0475 0.942 0.992 0.004 0.004
#> GSM257941 1 0.0424 0.941 0.992 0.000 0.008
#> GSM257943 1 0.0592 0.940 0.988 0.000 0.012
#> GSM257945 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257947 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257949 1 0.0475 0.942 0.992 0.004 0.004
#> GSM257951 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257953 1 0.0475 0.942 0.992 0.004 0.004
#> GSM257955 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257958 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257966 1 0.6280 0.164 0.540 0.460 0.000
#> GSM257968 1 0.4842 0.706 0.776 0.224 0.000
#> GSM257970 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257972 1 0.0237 0.942 0.996 0.000 0.004
#> GSM257977 1 0.1753 0.913 0.952 0.048 0.000
#> GSM257982 1 0.0661 0.940 0.988 0.008 0.004
#> GSM257984 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.942 1.000 0.000 0.000
#> GSM257992 1 0.2096 0.913 0.944 0.004 0.052
#> GSM257996 1 0.0000 0.942 1.000 0.000 0.000
#> GSM258006 1 0.0747 0.938 0.984 0.000 0.016
#> GSM257887 2 0.0424 0.896 0.000 0.992 0.008
#> GSM257889 3 0.1163 0.898 0.000 0.028 0.972
#> GSM257891 3 0.0747 0.907 0.000 0.016 0.984
#> GSM257893 3 0.3116 0.852 0.000 0.108 0.892
#> GSM257895 2 0.1031 0.893 0.000 0.976 0.024
#> GSM257897 3 0.2878 0.862 0.000 0.096 0.904
#> GSM257899 3 0.2711 0.868 0.000 0.088 0.912
#> GSM257901 3 0.2448 0.901 0.000 0.076 0.924
#> GSM257903 2 0.3816 0.805 0.000 0.852 0.148
#> GSM257905 2 0.1163 0.894 0.000 0.972 0.028
#> GSM257907 3 0.1753 0.913 0.000 0.048 0.952
#> GSM257909 2 0.0747 0.896 0.000 0.984 0.016
#> GSM257911 3 0.6260 0.131 0.000 0.448 0.552
#> GSM257913 3 0.3267 0.874 0.000 0.116 0.884
#> GSM257916 2 0.0747 0.898 0.000 0.984 0.016
#> GSM257918 2 0.1289 0.894 0.000 0.968 0.032
#> GSM257920 3 0.1753 0.912 0.000 0.048 0.952
#> GSM257922 3 0.1964 0.888 0.000 0.056 0.944
#> GSM257924 3 0.4291 0.815 0.000 0.180 0.820
#> GSM257926 3 0.2165 0.912 0.000 0.064 0.936
#> GSM257928 2 0.3941 0.818 0.000 0.844 0.156
#> GSM257930 2 0.3116 0.854 0.000 0.892 0.108
#> GSM257938 2 0.1753 0.882 0.000 0.952 0.048
#> GSM257940 3 0.2165 0.907 0.000 0.064 0.936
#> GSM257942 2 0.2796 0.859 0.000 0.908 0.092
#> GSM257944 2 0.2165 0.877 0.000 0.936 0.064
#> GSM257946 3 0.1031 0.908 0.000 0.024 0.976
#> GSM257948 3 0.1860 0.911 0.000 0.052 0.948
#> GSM257950 3 0.1289 0.912 0.000 0.032 0.968
#> GSM257952 2 0.5397 0.635 0.000 0.720 0.280
#> GSM257954 2 0.0237 0.894 0.000 0.996 0.004
#> GSM257956 2 0.0237 0.896 0.000 0.996 0.004
#> GSM257959 2 0.0892 0.896 0.000 0.980 0.020
#> GSM257961 2 0.0592 0.897 0.000 0.988 0.012
#> GSM257963 2 0.0592 0.897 0.000 0.988 0.012
#> GSM257965 2 0.4654 0.739 0.000 0.792 0.208
#> GSM257967 2 0.0592 0.897 0.000 0.988 0.012
#> GSM257969 2 0.0000 0.895 0.000 1.000 0.000
#> GSM257971 2 0.5650 0.612 0.000 0.688 0.312
#> GSM257973 3 0.1643 0.912 0.000 0.044 0.956
#> GSM257981 2 0.5327 0.635 0.000 0.728 0.272
#> GSM257983 3 0.0892 0.908 0.000 0.020 0.980
#> GSM257985 3 0.0892 0.908 0.000 0.020 0.980
#> GSM257988 3 0.1643 0.912 0.000 0.044 0.956
#> GSM257991 3 0.6079 0.446 0.000 0.388 0.612
#> GSM257993 2 0.0424 0.893 0.000 0.992 0.008
#> GSM257994 2 0.2165 0.873 0.000 0.936 0.064
#> GSM257989 3 0.1163 0.911 0.000 0.028 0.972
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.0524 0.962 0.988 0.000 0.004 0.008
#> GSM257888 1 0.0672 0.961 0.984 0.008 0.000 0.008
#> GSM257890 1 0.0712 0.961 0.984 0.004 0.004 0.008
#> GSM257892 2 0.6212 0.409 0.304 0.624 0.004 0.068
#> GSM257894 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM257896 1 0.0336 0.962 0.992 0.000 0.000 0.008
#> GSM257898 4 0.2593 0.609 0.104 0.000 0.004 0.892
#> GSM257900 1 0.0592 0.961 0.984 0.000 0.000 0.016
#> GSM257902 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM257904 1 0.0336 0.962 0.992 0.000 0.000 0.008
#> GSM257906 4 0.4459 0.577 0.188 0.000 0.032 0.780
#> GSM257908 1 0.0469 0.961 0.988 0.000 0.012 0.000
#> GSM257910 1 0.0336 0.963 0.992 0.000 0.008 0.000
#> GSM257912 1 0.3142 0.836 0.860 0.000 0.132 0.008
#> GSM257914 1 0.3401 0.810 0.840 0.000 0.152 0.008
#> GSM257917 1 0.1151 0.950 0.968 0.000 0.024 0.008
#> GSM257919 1 0.4973 0.484 0.644 0.000 0.348 0.008
#> GSM257921 1 0.0336 0.962 0.992 0.000 0.000 0.008
#> GSM257923 1 0.0336 0.964 0.992 0.000 0.000 0.008
#> GSM257925 1 0.0336 0.964 0.992 0.000 0.000 0.008
#> GSM257927 1 0.0707 0.959 0.980 0.000 0.000 0.020
#> GSM257929 1 0.0469 0.963 0.988 0.000 0.000 0.012
#> GSM257937 1 0.0336 0.962 0.992 0.000 0.000 0.008
#> GSM257939 1 0.2469 0.879 0.892 0.000 0.000 0.108
#> GSM257941 1 0.2868 0.823 0.864 0.000 0.000 0.136
#> GSM257943 4 0.4955 0.242 0.444 0.000 0.000 0.556
#> GSM257945 1 0.1022 0.949 0.968 0.000 0.000 0.032
#> GSM257947 1 0.0469 0.963 0.988 0.000 0.000 0.012
#> GSM257949 1 0.0817 0.958 0.976 0.000 0.000 0.024
#> GSM257951 1 0.0707 0.960 0.980 0.000 0.000 0.020
#> GSM257953 1 0.2081 0.905 0.916 0.000 0.000 0.084
#> GSM257955 1 0.0336 0.964 0.992 0.000 0.000 0.008
#> GSM257958 1 0.0336 0.964 0.992 0.000 0.000 0.008
#> GSM257960 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0469 0.963 0.988 0.000 0.000 0.012
#> GSM257964 1 0.0188 0.963 0.996 0.000 0.000 0.004
#> GSM257966 1 0.1297 0.946 0.964 0.016 0.020 0.000
#> GSM257968 1 0.0524 0.963 0.988 0.008 0.000 0.004
#> GSM257970 1 0.0469 0.963 0.988 0.000 0.000 0.012
#> GSM257972 1 0.0592 0.961 0.984 0.000 0.000 0.016
#> GSM257977 1 0.0524 0.962 0.988 0.004 0.000 0.008
#> GSM257982 1 0.0336 0.963 0.992 0.000 0.000 0.008
#> GSM257984 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM257990 1 0.0336 0.964 0.992 0.000 0.000 0.008
#> GSM257992 4 0.4277 0.528 0.280 0.000 0.000 0.720
#> GSM257996 1 0.0000 0.963 1.000 0.000 0.000 0.000
#> GSM258006 4 0.4998 0.116 0.488 0.000 0.000 0.512
#> GSM257887 2 0.0336 0.882 0.000 0.992 0.000 0.008
#> GSM257889 4 0.2973 0.496 0.000 0.000 0.144 0.856
#> GSM257891 3 0.4877 0.636 0.000 0.000 0.592 0.408
#> GSM257893 4 0.3320 0.579 0.000 0.068 0.056 0.876
#> GSM257895 2 0.0592 0.881 0.000 0.984 0.000 0.016
#> GSM257897 4 0.1557 0.588 0.000 0.000 0.056 0.944
#> GSM257899 4 0.2011 0.576 0.000 0.000 0.080 0.920
#> GSM257901 3 0.2814 0.754 0.000 0.000 0.868 0.132
#> GSM257903 3 0.4406 0.364 0.000 0.300 0.700 0.000
#> GSM257905 2 0.1305 0.881 0.000 0.960 0.036 0.004
#> GSM257907 3 0.3975 0.776 0.000 0.000 0.760 0.240
#> GSM257909 2 0.2081 0.868 0.000 0.916 0.084 0.000
#> GSM257911 3 0.7064 0.499 0.000 0.280 0.556 0.164
#> GSM257913 3 0.3836 0.761 0.000 0.016 0.816 0.168
#> GSM257916 2 0.0817 0.883 0.000 0.976 0.024 0.000
#> GSM257918 2 0.3444 0.809 0.000 0.816 0.184 0.000
#> GSM257920 3 0.3610 0.776 0.000 0.000 0.800 0.200
#> GSM257922 4 0.2408 0.555 0.000 0.000 0.104 0.896
#> GSM257924 3 0.5950 0.670 0.000 0.156 0.696 0.148
#> GSM257926 3 0.4331 0.762 0.000 0.000 0.712 0.288
#> GSM257928 4 0.4511 0.429 0.000 0.268 0.008 0.724
#> GSM257930 2 0.4391 0.661 0.000 0.740 0.008 0.252
#> GSM257938 2 0.1867 0.855 0.000 0.928 0.000 0.072
#> GSM257940 3 0.2216 0.726 0.000 0.000 0.908 0.092
#> GSM257942 3 0.4730 0.118 0.000 0.364 0.636 0.000
#> GSM257944 2 0.4522 0.675 0.000 0.680 0.320 0.000
#> GSM257946 3 0.4888 0.634 0.000 0.000 0.588 0.412
#> GSM257948 3 0.3837 0.778 0.000 0.000 0.776 0.224
#> GSM257950 3 0.4406 0.754 0.000 0.000 0.700 0.300
#> GSM257952 2 0.6547 0.488 0.000 0.616 0.260 0.124
#> GSM257954 2 0.0469 0.882 0.000 0.988 0.000 0.012
#> GSM257956 2 0.0336 0.882 0.000 0.992 0.000 0.008
#> GSM257959 2 0.1940 0.871 0.000 0.924 0.076 0.000
#> GSM257961 2 0.1022 0.882 0.000 0.968 0.032 0.000
#> GSM257963 2 0.0817 0.883 0.000 0.976 0.024 0.000
#> GSM257965 2 0.4644 0.757 0.000 0.748 0.228 0.024
#> GSM257967 2 0.1557 0.877 0.000 0.944 0.056 0.000
#> GSM257969 2 0.0188 0.882 0.000 0.996 0.000 0.004
#> GSM257971 4 0.2742 0.573 0.000 0.024 0.076 0.900
#> GSM257973 3 0.4193 0.769 0.000 0.000 0.732 0.268
#> GSM257981 2 0.3647 0.811 0.000 0.852 0.108 0.040
#> GSM257983 4 0.4790 -0.176 0.000 0.000 0.380 0.620
#> GSM257985 3 0.4679 0.708 0.000 0.000 0.648 0.352
#> GSM257988 3 0.3688 0.777 0.000 0.000 0.792 0.208
#> GSM257991 3 0.2149 0.597 0.000 0.088 0.912 0.000
#> GSM257993 2 0.0921 0.877 0.000 0.972 0.000 0.028
#> GSM257994 2 0.2469 0.832 0.000 0.892 0.000 0.108
#> GSM257989 3 0.4304 0.763 0.000 0.000 0.716 0.284
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.6875 -0.12379 0.344 0.004 0.000 0.392 0.260
#> GSM257888 1 0.4985 0.65965 0.748 0.032 0.000 0.144 0.076
#> GSM257890 1 0.7188 0.10211 0.424 0.024 0.000 0.320 0.232
#> GSM257892 4 0.7359 0.14504 0.020 0.316 0.004 0.392 0.268
#> GSM257894 1 0.1012 0.79681 0.968 0.000 0.000 0.020 0.012
#> GSM257896 1 0.3700 0.73474 0.832 0.008 0.000 0.076 0.084
#> GSM257898 5 0.4616 0.52051 0.028 0.000 0.152 0.052 0.768
#> GSM257900 1 0.4840 0.62057 0.724 0.000 0.000 0.152 0.124
#> GSM257902 1 0.0000 0.79859 1.000 0.000 0.000 0.000 0.000
#> GSM257904 1 0.7621 0.26667 0.512 0.000 0.140 0.176 0.172
#> GSM257906 5 0.7909 0.26068 0.084 0.000 0.316 0.220 0.380
#> GSM257908 1 0.2890 0.73607 0.836 0.004 0.000 0.160 0.000
#> GSM257910 1 0.0703 0.79926 0.976 0.000 0.000 0.024 0.000
#> GSM257912 1 0.4567 0.51536 0.628 0.000 0.012 0.356 0.004
#> GSM257914 1 0.4444 0.50413 0.624 0.000 0.012 0.364 0.000
#> GSM257917 1 0.3743 0.73216 0.824 0.000 0.004 0.076 0.096
#> GSM257919 1 0.5238 0.22272 0.480 0.000 0.044 0.476 0.000
#> GSM257921 1 0.3267 0.73690 0.844 0.000 0.000 0.044 0.112
#> GSM257923 1 0.3333 0.70977 0.788 0.000 0.000 0.004 0.208
#> GSM257925 1 0.3661 0.64256 0.724 0.000 0.000 0.000 0.276
#> GSM257927 1 0.2813 0.74569 0.832 0.000 0.000 0.000 0.168
#> GSM257929 1 0.3913 0.57878 0.676 0.000 0.000 0.000 0.324
#> GSM257937 1 0.1469 0.79117 0.948 0.000 0.000 0.036 0.016
#> GSM257939 1 0.4321 0.45865 0.600 0.004 0.000 0.000 0.396
#> GSM257941 1 0.4193 0.53009 0.684 0.000 0.000 0.012 0.304
#> GSM257943 5 0.5892 0.07238 0.372 0.000 0.000 0.108 0.520
#> GSM257945 1 0.3003 0.72416 0.812 0.000 0.000 0.000 0.188
#> GSM257947 1 0.2852 0.74325 0.828 0.000 0.000 0.000 0.172
#> GSM257949 1 0.1121 0.79780 0.956 0.000 0.000 0.000 0.044
#> GSM257951 1 0.2424 0.76724 0.868 0.000 0.000 0.000 0.132
#> GSM257953 1 0.2179 0.77569 0.888 0.000 0.000 0.000 0.112
#> GSM257955 1 0.1410 0.79477 0.940 0.000 0.000 0.000 0.060
#> GSM257958 1 0.0703 0.79838 0.976 0.000 0.000 0.000 0.024
#> GSM257960 1 0.0000 0.79859 1.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.0703 0.79838 0.976 0.000 0.000 0.000 0.024
#> GSM257964 1 0.0290 0.79874 0.992 0.000 0.000 0.000 0.008
#> GSM257966 1 0.1787 0.79338 0.936 0.016 0.000 0.044 0.004
#> GSM257968 1 0.0324 0.79882 0.992 0.004 0.000 0.004 0.000
#> GSM257970 1 0.2605 0.75659 0.852 0.000 0.000 0.000 0.148
#> GSM257972 1 0.0162 0.79960 0.996 0.000 0.000 0.000 0.004
#> GSM257977 1 0.6249 0.35766 0.556 0.016 0.000 0.312 0.116
#> GSM257982 1 0.0613 0.79924 0.984 0.004 0.000 0.004 0.008
#> GSM257984 1 0.0162 0.79842 0.996 0.000 0.000 0.004 0.000
#> GSM257986 1 0.0162 0.79877 0.996 0.000 0.000 0.000 0.004
#> GSM257990 1 0.2280 0.77608 0.880 0.000 0.000 0.000 0.120
#> GSM257992 5 0.3904 0.47903 0.116 0.000 0.020 0.044 0.820
#> GSM257996 1 0.0162 0.79842 0.996 0.000 0.000 0.004 0.000
#> GSM258006 1 0.6320 -0.00411 0.440 0.000 0.000 0.156 0.404
#> GSM257887 2 0.1444 0.75642 0.000 0.948 0.000 0.040 0.012
#> GSM257889 3 0.4425 0.09911 0.000 0.000 0.600 0.008 0.392
#> GSM257891 3 0.2139 0.64900 0.000 0.000 0.916 0.032 0.052
#> GSM257893 5 0.4380 0.39419 0.004 0.016 0.292 0.000 0.688
#> GSM257895 2 0.4184 0.53836 0.000 0.764 0.004 0.192 0.040
#> GSM257897 5 0.4397 0.23599 0.000 0.000 0.432 0.004 0.564
#> GSM257899 3 0.5071 -0.07479 0.000 0.000 0.540 0.036 0.424
#> GSM257901 3 0.4586 0.51244 0.000 0.004 0.644 0.336 0.016
#> GSM257903 4 0.6066 0.12457 0.000 0.368 0.128 0.504 0.000
#> GSM257905 2 0.3759 0.60011 0.000 0.764 0.016 0.220 0.000
#> GSM257907 3 0.2813 0.64225 0.000 0.000 0.832 0.168 0.000
#> GSM257909 2 0.3266 0.66566 0.000 0.796 0.004 0.200 0.000
#> GSM257911 3 0.7362 0.05730 0.000 0.240 0.400 0.328 0.032
#> GSM257913 3 0.5616 0.31077 0.000 0.084 0.552 0.364 0.000
#> GSM257916 2 0.1329 0.76283 0.000 0.956 0.004 0.032 0.008
#> GSM257918 2 0.3492 0.65887 0.000 0.796 0.016 0.188 0.000
#> GSM257920 3 0.3715 0.57722 0.000 0.004 0.736 0.260 0.000
#> GSM257922 5 0.5450 0.14678 0.000 0.000 0.444 0.060 0.496
#> GSM257924 3 0.5602 0.41740 0.000 0.096 0.636 0.260 0.008
#> GSM257926 3 0.3639 0.62237 0.000 0.020 0.808 0.164 0.008
#> GSM257928 5 0.4270 0.22381 0.000 0.320 0.012 0.000 0.668
#> GSM257930 2 0.4478 0.43280 0.000 0.628 0.008 0.004 0.360
#> GSM257938 2 0.2446 0.73931 0.000 0.900 0.000 0.044 0.056
#> GSM257940 3 0.4234 0.59198 0.000 0.004 0.724 0.252 0.020
#> GSM257942 4 0.5884 0.15966 0.000 0.352 0.112 0.536 0.000
#> GSM257944 4 0.5243 0.03371 0.000 0.412 0.048 0.540 0.000
#> GSM257946 3 0.1990 0.65648 0.000 0.004 0.920 0.008 0.068
#> GSM257948 3 0.3734 0.59621 0.000 0.004 0.752 0.240 0.004
#> GSM257950 3 0.1877 0.67620 0.000 0.000 0.924 0.064 0.012
#> GSM257952 4 0.7901 0.15744 0.000 0.292 0.200 0.412 0.096
#> GSM257954 2 0.0693 0.76951 0.000 0.980 0.000 0.008 0.012
#> GSM257956 2 0.0579 0.76941 0.000 0.984 0.000 0.008 0.008
#> GSM257959 2 0.3491 0.63102 0.000 0.768 0.004 0.228 0.000
#> GSM257961 2 0.1831 0.75407 0.000 0.920 0.004 0.076 0.000
#> GSM257963 2 0.2488 0.72878 0.000 0.872 0.004 0.124 0.000
#> GSM257965 4 0.7099 0.08511 0.000 0.380 0.088 0.452 0.080
#> GSM257967 2 0.2338 0.73883 0.000 0.884 0.004 0.112 0.000
#> GSM257969 2 0.0404 0.76951 0.000 0.988 0.000 0.012 0.000
#> GSM257971 3 0.6942 0.19417 0.000 0.052 0.468 0.372 0.108
#> GSM257973 3 0.1205 0.67874 0.000 0.004 0.956 0.040 0.000
#> GSM257981 2 0.7146 -0.09976 0.000 0.448 0.188 0.332 0.032
#> GSM257983 3 0.2654 0.63659 0.000 0.000 0.888 0.048 0.064
#> GSM257985 3 0.2171 0.65248 0.000 0.000 0.912 0.064 0.024
#> GSM257988 3 0.1851 0.67572 0.000 0.000 0.912 0.088 0.000
#> GSM257991 4 0.4982 -0.14822 0.000 0.032 0.412 0.556 0.000
#> GSM257993 2 0.1568 0.75755 0.000 0.944 0.000 0.036 0.020
#> GSM257994 2 0.3596 0.63416 0.000 0.776 0.000 0.012 0.212
#> GSM257989 3 0.0566 0.67225 0.000 0.000 0.984 0.004 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.4932 0.5845 0.040 0.000 0.000 0.080 0.176 0.704
#> GSM257888 1 0.5052 0.5674 0.648 0.020 0.000 0.012 0.276 0.044
#> GSM257890 6 0.6711 0.4598 0.140 0.028 0.000 0.032 0.320 0.480
#> GSM257892 6 0.5523 0.3303 0.000 0.116 0.000 0.008 0.340 0.536
#> GSM257894 1 0.1949 0.7802 0.904 0.000 0.000 0.004 0.088 0.004
#> GSM257896 1 0.3664 0.7165 0.792 0.012 0.000 0.004 0.164 0.028
#> GSM257898 6 0.3736 0.5354 0.016 0.000 0.028 0.028 0.108 0.820
#> GSM257900 6 0.4724 0.4249 0.332 0.000 0.000 0.012 0.040 0.616
#> GSM257902 1 0.0767 0.7900 0.976 0.000 0.000 0.004 0.008 0.012
#> GSM257904 6 0.5513 0.5798 0.176 0.000 0.048 0.076 0.020 0.680
#> GSM257906 6 0.4194 0.6116 0.032 0.000 0.080 0.052 0.032 0.804
#> GSM257908 1 0.4470 0.5212 0.656 0.012 0.000 0.300 0.000 0.032
#> GSM257910 1 0.4216 0.6266 0.720 0.000 0.000 0.228 0.012 0.040
#> GSM257912 4 0.5095 0.5392 0.164 0.000 0.012 0.700 0.020 0.104
#> GSM257914 4 0.4644 0.5532 0.160 0.000 0.004 0.728 0.016 0.092
#> GSM257917 4 0.6133 0.0976 0.172 0.000 0.000 0.480 0.020 0.328
#> GSM257919 4 0.4697 0.5769 0.136 0.004 0.016 0.752 0.020 0.072
#> GSM257921 1 0.5577 0.2979 0.568 0.000 0.000 0.088 0.028 0.316
#> GSM257923 1 0.2009 0.7855 0.908 0.000 0.000 0.024 0.068 0.000
#> GSM257925 1 0.2864 0.7690 0.860 0.000 0.000 0.028 0.100 0.012
#> GSM257927 1 0.5924 0.4346 0.580 0.000 0.000 0.032 0.208 0.180
#> GSM257929 1 0.3140 0.7589 0.840 0.000 0.000 0.028 0.116 0.016
#> GSM257937 1 0.4671 0.6210 0.720 0.000 0.000 0.084 0.024 0.172
#> GSM257939 1 0.3993 0.7256 0.788 0.000 0.000 0.060 0.124 0.028
#> GSM257941 6 0.5027 0.5601 0.160 0.000 0.000 0.016 0.144 0.680
#> GSM257943 6 0.2190 0.6303 0.060 0.000 0.000 0.000 0.040 0.900
#> GSM257945 1 0.6276 0.2883 0.524 0.000 0.000 0.036 0.224 0.216
#> GSM257947 1 0.2046 0.7857 0.908 0.000 0.000 0.032 0.060 0.000
#> GSM257949 1 0.1562 0.7946 0.940 0.000 0.000 0.032 0.024 0.004
#> GSM257951 1 0.2136 0.7852 0.904 0.000 0.000 0.048 0.048 0.000
#> GSM257953 1 0.2216 0.7898 0.908 0.000 0.000 0.052 0.024 0.016
#> GSM257955 1 0.1564 0.7917 0.936 0.000 0.000 0.040 0.024 0.000
#> GSM257958 1 0.0779 0.7938 0.976 0.000 0.000 0.008 0.008 0.008
#> GSM257960 1 0.4265 0.5373 0.680 0.000 0.000 0.020 0.016 0.284
#> GSM257962 1 0.5008 0.6066 0.692 0.000 0.000 0.024 0.128 0.156
#> GSM257964 1 0.0000 0.7914 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.3395 0.7559 0.852 0.032 0.000 0.064 0.016 0.036
#> GSM257968 1 0.1490 0.7903 0.948 0.016 0.000 0.004 0.024 0.008
#> GSM257970 1 0.2250 0.7823 0.896 0.000 0.000 0.040 0.064 0.000
#> GSM257972 1 0.0806 0.7902 0.972 0.000 0.000 0.000 0.020 0.008
#> GSM257977 1 0.5179 0.4992 0.612 0.036 0.000 0.000 0.304 0.048
#> GSM257982 1 0.1644 0.7852 0.920 0.004 0.000 0.000 0.076 0.000
#> GSM257984 1 0.0984 0.7892 0.968 0.000 0.000 0.008 0.012 0.012
#> GSM257986 1 0.0653 0.7915 0.980 0.000 0.000 0.004 0.012 0.004
#> GSM257990 1 0.5417 0.5322 0.628 0.000 0.000 0.024 0.232 0.116
#> GSM257992 6 0.2338 0.5831 0.016 0.000 0.004 0.012 0.068 0.900
#> GSM257996 1 0.5518 0.3480 0.564 0.000 0.000 0.116 0.012 0.308
#> GSM258006 6 0.3350 0.6389 0.156 0.000 0.004 0.012 0.016 0.812
#> GSM257887 2 0.1814 0.7390 0.000 0.900 0.000 0.000 0.100 0.000
#> GSM257889 3 0.3536 0.6633 0.000 0.000 0.832 0.052 0.072 0.044
#> GSM257891 3 0.0972 0.7340 0.000 0.000 0.964 0.000 0.008 0.028
#> GSM257893 5 0.7536 0.0466 0.008 0.024 0.336 0.104 0.408 0.120
#> GSM257895 2 0.3861 0.4146 0.000 0.672 0.004 0.000 0.316 0.008
#> GSM257897 3 0.7253 -0.2138 0.008 0.000 0.364 0.092 0.360 0.176
#> GSM257899 3 0.6459 0.1581 0.000 0.000 0.536 0.072 0.164 0.228
#> GSM257901 3 0.2342 0.7051 0.000 0.000 0.888 0.020 0.088 0.004
#> GSM257903 4 0.4161 0.2982 0.000 0.372 0.008 0.612 0.008 0.000
#> GSM257905 2 0.3141 0.6894 0.000 0.788 0.000 0.200 0.012 0.000
#> GSM257907 3 0.1434 0.7349 0.000 0.000 0.948 0.020 0.024 0.008
#> GSM257909 2 0.2941 0.6833 0.000 0.780 0.000 0.220 0.000 0.000
#> GSM257911 3 0.4348 0.5731 0.000 0.064 0.748 0.024 0.164 0.000
#> GSM257913 3 0.4255 0.5782 0.000 0.064 0.732 0.196 0.008 0.000
#> GSM257916 2 0.1643 0.7600 0.000 0.924 0.000 0.008 0.068 0.000
#> GSM257918 2 0.2265 0.7728 0.000 0.896 0.004 0.076 0.024 0.000
#> GSM257920 3 0.3438 0.6280 0.000 0.000 0.764 0.220 0.008 0.008
#> GSM257922 6 0.7311 -0.2960 0.000 0.000 0.268 0.100 0.296 0.336
#> GSM257924 3 0.6710 0.1722 0.000 0.124 0.484 0.304 0.084 0.004
#> GSM257926 3 0.2278 0.7247 0.000 0.008 0.908 0.052 0.024 0.008
#> GSM257928 5 0.7614 0.0894 0.012 0.276 0.004 0.104 0.388 0.216
#> GSM257930 2 0.5847 0.3840 0.000 0.604 0.000 0.068 0.236 0.092
#> GSM257938 2 0.4489 0.6172 0.000 0.744 0.000 0.024 0.140 0.092
#> GSM257940 3 0.4440 0.6173 0.000 0.000 0.752 0.132 0.088 0.028
#> GSM257942 4 0.3714 0.3773 0.000 0.340 0.004 0.656 0.000 0.000
#> GSM257944 4 0.3330 0.4593 0.000 0.284 0.000 0.716 0.000 0.000
#> GSM257946 3 0.1649 0.7266 0.000 0.000 0.936 0.008 0.040 0.016
#> GSM257948 3 0.2558 0.6974 0.000 0.000 0.840 0.156 0.004 0.000
#> GSM257950 3 0.3533 0.5931 0.000 0.000 0.748 0.236 0.004 0.012
#> GSM257952 5 0.7138 0.3329 0.000 0.256 0.260 0.008 0.412 0.064
#> GSM257954 2 0.0935 0.7723 0.000 0.964 0.000 0.004 0.032 0.000
#> GSM257956 2 0.0547 0.7743 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM257959 2 0.3742 0.4389 0.000 0.648 0.000 0.348 0.004 0.000
#> GSM257961 2 0.2092 0.7582 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM257963 2 0.2491 0.7358 0.000 0.836 0.000 0.164 0.000 0.000
#> GSM257965 5 0.6917 0.3333 0.000 0.304 0.272 0.024 0.384 0.016
#> GSM257967 2 0.1765 0.7680 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM257969 2 0.0820 0.7773 0.000 0.972 0.000 0.016 0.012 0.000
#> GSM257971 3 0.6268 0.0713 0.000 0.052 0.504 0.004 0.336 0.104
#> GSM257973 3 0.0551 0.7373 0.000 0.000 0.984 0.008 0.004 0.004
#> GSM257981 3 0.4482 0.5096 0.000 0.096 0.712 0.000 0.188 0.004
#> GSM257983 3 0.0508 0.7363 0.000 0.000 0.984 0.004 0.000 0.012
#> GSM257985 3 0.0291 0.7367 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM257988 3 0.0458 0.7378 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM257991 4 0.4229 0.4447 0.000 0.048 0.192 0.744 0.012 0.004
#> GSM257993 2 0.2113 0.7397 0.000 0.896 0.000 0.004 0.092 0.008
#> GSM257994 2 0.5371 0.5295 0.000 0.676 0.000 0.056 0.152 0.116
#> GSM257989 3 0.0551 0.7381 0.000 0.000 0.984 0.008 0.004 0.004
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 cell.type(p) protocol(p) individual(p) k
#> CV:NMF 95 1.41e-21 1.000 1.000 2
#> CV:NMF 91 1.74e-20 0.772 1.000 3
#> CV:NMF 85 1.06e-16 0.667 0.942 4
#> CV:NMF 67 1.87e-14 0.415 0.489 5
#> CV:NMF 71 1.40e-14 0.615 0.955 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 25171 rows and 96 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 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.787 0.905 0.932 0.1611 0.937 0.873
#> 4 4 0.824 0.842 0.921 0.1914 0.864 0.685
#> 5 5 0.835 0.808 0.901 0.0400 0.951 0.838
#> 6 6 0.750 0.758 0.868 0.0328 0.996 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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 3 0.3941 0.946 0.156 0.000 0.844
#> GSM257888 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257890 1 0.0237 0.938 0.996 0.000 0.004
#> GSM257892 3 0.3941 0.946 0.156 0.000 0.844
#> GSM257894 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257898 3 0.4750 0.955 0.216 0.000 0.784
#> GSM257900 1 0.5926 0.314 0.644 0.000 0.356
#> GSM257902 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257904 3 0.4796 0.952 0.220 0.000 0.780
#> GSM257906 3 0.4796 0.952 0.220 0.000 0.780
#> GSM257908 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257917 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257919 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257921 1 0.0424 0.935 0.992 0.000 0.008
#> GSM257923 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257927 1 0.4178 0.736 0.828 0.000 0.172
#> GSM257929 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257937 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257939 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257941 1 0.5835 0.365 0.660 0.000 0.340
#> GSM257943 1 0.5926 0.314 0.644 0.000 0.356
#> GSM257945 1 0.5810 0.376 0.664 0.000 0.336
#> GSM257947 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257953 1 0.0747 0.928 0.984 0.000 0.016
#> GSM257955 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257960 1 0.3340 0.812 0.880 0.000 0.120
#> GSM257962 1 0.3340 0.812 0.880 0.000 0.120
#> GSM257964 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257966 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257968 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257972 1 0.0237 0.938 0.996 0.000 0.004
#> GSM257977 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257982 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.941 1.000 0.000 0.000
#> GSM257990 1 0.0237 0.938 0.996 0.000 0.004
#> GSM257992 3 0.4235 0.957 0.176 0.000 0.824
#> GSM257996 1 0.0237 0.938 0.996 0.000 0.004
#> GSM258006 3 0.4504 0.960 0.196 0.000 0.804
#> GSM257887 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257889 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257891 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257893 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257895 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257897 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257899 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257901 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257903 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257905 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257907 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257909 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257911 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257913 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257916 2 0.3686 0.923 0.000 0.860 0.140
#> GSM257918 2 0.3686 0.923 0.000 0.860 0.140
#> GSM257920 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257922 2 0.3340 0.926 0.000 0.880 0.120
#> GSM257924 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257926 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257928 2 0.3619 0.924 0.000 0.864 0.136
#> GSM257930 2 0.3619 0.924 0.000 0.864 0.136
#> GSM257938 2 0.3619 0.924 0.000 0.864 0.136
#> GSM257940 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257942 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257944 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257946 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257948 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257950 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257952 2 0.0747 0.934 0.000 0.984 0.016
#> GSM257954 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257956 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257959 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257961 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257963 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257965 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257967 2 0.3816 0.921 0.000 0.852 0.148
#> GSM257969 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257971 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257973 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257981 2 0.0747 0.934 0.000 0.984 0.016
#> GSM257983 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257985 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257988 2 0.0237 0.934 0.000 0.996 0.004
#> GSM257991 2 0.1964 0.932 0.000 0.944 0.056
#> GSM257993 2 0.3879 0.919 0.000 0.848 0.152
#> GSM257994 2 0.3619 0.924 0.000 0.864 0.136
#> GSM257989 2 0.0237 0.934 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.0707 0.8593 0.000 0.000 0.020 0.980
#> GSM257888 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257890 1 0.0336 0.9378 0.992 0.000 0.000 0.008
#> GSM257892 4 0.0707 0.8593 0.000 0.000 0.020 0.980
#> GSM257894 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257896 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257898 4 0.1637 0.8790 0.060 0.000 0.000 0.940
#> GSM257900 1 0.4999 -0.0576 0.508 0.000 0.000 0.492
#> GSM257902 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257904 4 0.1792 0.8750 0.068 0.000 0.000 0.932
#> GSM257906 4 0.1716 0.8777 0.064 0.000 0.000 0.936
#> GSM257908 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257921 1 0.0469 0.9341 0.988 0.000 0.000 0.012
#> GSM257923 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257927 1 0.3528 0.7307 0.808 0.000 0.000 0.192
#> GSM257929 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257937 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257939 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257941 1 0.4999 -0.0576 0.508 0.000 0.000 0.492
#> GSM257943 4 0.4999 -0.0293 0.492 0.000 0.000 0.508
#> GSM257945 1 0.4994 -0.0109 0.520 0.000 0.000 0.480
#> GSM257947 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0921 0.9221 0.972 0.000 0.000 0.028
#> GSM257955 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257960 1 0.2814 0.8128 0.868 0.000 0.000 0.132
#> GSM257962 1 0.2814 0.8128 0.868 0.000 0.000 0.132
#> GSM257964 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257966 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257968 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257970 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0336 0.9365 0.992 0.000 0.000 0.008
#> GSM257977 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257982 1 0.0188 0.9392 0.996 0.000 0.000 0.004
#> GSM257984 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9406 1.000 0.000 0.000 0.000
#> GSM257990 1 0.0592 0.9312 0.984 0.000 0.000 0.016
#> GSM257992 4 0.0469 0.8710 0.012 0.000 0.000 0.988
#> GSM257996 1 0.0592 0.9312 0.984 0.000 0.000 0.016
#> GSM258006 4 0.1022 0.8784 0.032 0.000 0.000 0.968
#> GSM257887 2 0.0000 0.9175 0.000 1.000 0.000 0.000
#> GSM257889 3 0.2408 0.8833 0.000 0.104 0.896 0.000
#> GSM257891 3 0.0707 0.8745 0.000 0.020 0.980 0.000
#> GSM257893 3 0.2760 0.8697 0.000 0.128 0.872 0.000
#> GSM257895 2 0.0000 0.9175 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0707 0.8745 0.000 0.020 0.980 0.000
#> GSM257899 3 0.0707 0.8745 0.000 0.020 0.980 0.000
#> GSM257901 3 0.3123 0.8476 0.000 0.156 0.844 0.000
#> GSM257903 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257905 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257907 3 0.3123 0.8476 0.000 0.156 0.844 0.000
#> GSM257909 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257911 3 0.4661 0.6118 0.000 0.348 0.652 0.000
#> GSM257913 3 0.2345 0.8896 0.000 0.100 0.900 0.000
#> GSM257916 2 0.3486 0.7543 0.000 0.812 0.188 0.000
#> GSM257918 2 0.3486 0.7543 0.000 0.812 0.188 0.000
#> GSM257920 3 0.1792 0.8943 0.000 0.068 0.932 0.000
#> GSM257922 2 0.4624 0.4763 0.000 0.660 0.340 0.000
#> GSM257924 3 0.2469 0.8817 0.000 0.108 0.892 0.000
#> GSM257926 3 0.2345 0.8896 0.000 0.100 0.900 0.000
#> GSM257928 2 0.0707 0.9191 0.000 0.980 0.020 0.000
#> GSM257930 2 0.0707 0.9191 0.000 0.980 0.020 0.000
#> GSM257938 2 0.0707 0.9191 0.000 0.980 0.020 0.000
#> GSM257940 3 0.3266 0.8442 0.000 0.168 0.832 0.000
#> GSM257942 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257944 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257946 3 0.1474 0.8905 0.000 0.052 0.948 0.000
#> GSM257948 3 0.1867 0.8943 0.000 0.072 0.928 0.000
#> GSM257950 3 0.1716 0.8943 0.000 0.064 0.936 0.000
#> GSM257952 3 0.4866 0.4454 0.000 0.404 0.596 0.000
#> GSM257954 2 0.0336 0.9218 0.000 0.992 0.008 0.000
#> GSM257956 2 0.0188 0.9199 0.000 0.996 0.004 0.000
#> GSM257959 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257961 2 0.1118 0.9259 0.000 0.964 0.036 0.000
#> GSM257963 2 0.1118 0.9259 0.000 0.964 0.036 0.000
#> GSM257965 3 0.4661 0.6118 0.000 0.348 0.652 0.000
#> GSM257967 2 0.1211 0.9254 0.000 0.960 0.040 0.000
#> GSM257969 2 0.0336 0.9218 0.000 0.992 0.008 0.000
#> GSM257971 3 0.3528 0.8162 0.000 0.192 0.808 0.000
#> GSM257973 3 0.1716 0.8943 0.000 0.064 0.936 0.000
#> GSM257981 3 0.4697 0.5628 0.000 0.356 0.644 0.000
#> GSM257983 3 0.0707 0.8745 0.000 0.020 0.980 0.000
#> GSM257985 3 0.1389 0.8890 0.000 0.048 0.952 0.000
#> GSM257988 3 0.1716 0.8943 0.000 0.064 0.936 0.000
#> GSM257991 2 0.4605 0.4309 0.000 0.664 0.336 0.000
#> GSM257993 2 0.0000 0.9175 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0707 0.9191 0.000 0.980 0.020 0.000
#> GSM257989 3 0.1716 0.8943 0.000 0.064 0.936 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.2605 0.527 0.000 0.000 0.000 0.852 0.148
#> GSM257888 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257890 1 0.0290 0.973 0.992 0.000 0.000 0.008 0.000
#> GSM257892 4 0.2648 0.524 0.000 0.000 0.000 0.848 0.152
#> GSM257894 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257896 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257898 4 0.0880 0.617 0.032 0.000 0.000 0.968 0.000
#> GSM257900 4 0.4302 0.316 0.480 0.000 0.000 0.520 0.000
#> GSM257902 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257904 4 0.1043 0.618 0.040 0.000 0.000 0.960 0.000
#> GSM257906 4 0.0963 0.618 0.036 0.000 0.000 0.964 0.000
#> GSM257908 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257921 1 0.0404 0.968 0.988 0.000 0.000 0.012 0.000
#> GSM257923 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.3274 0.657 0.780 0.000 0.000 0.220 0.000
#> GSM257929 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257937 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257939 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257941 4 0.4302 0.317 0.480 0.000 0.000 0.520 0.000
#> GSM257943 4 0.4291 0.352 0.464 0.000 0.000 0.536 0.000
#> GSM257945 4 0.4306 0.281 0.492 0.000 0.000 0.508 0.000
#> GSM257947 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.1121 0.935 0.956 0.000 0.000 0.044 0.000
#> GSM257955 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.2732 0.773 0.840 0.000 0.000 0.160 0.000
#> GSM257962 1 0.2732 0.773 0.840 0.000 0.000 0.160 0.000
#> GSM257964 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257968 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257970 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0290 0.971 0.992 0.000 0.000 0.008 0.000
#> GSM257977 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257982 1 0.0162 0.974 0.996 0.000 0.000 0.004 0.000
#> GSM257984 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.976 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.0703 0.957 0.976 0.000 0.000 0.024 0.000
#> GSM257992 4 0.0771 0.590 0.004 0.000 0.000 0.976 0.020
#> GSM257996 1 0.0609 0.960 0.980 0.000 0.000 0.020 0.000
#> GSM258006 4 0.0693 0.601 0.012 0.000 0.000 0.980 0.008
#> GSM257887 2 0.3305 0.676 0.000 0.776 0.000 0.000 0.224
#> GSM257889 3 0.1952 0.853 0.000 0.084 0.912 0.000 0.004
#> GSM257891 3 0.0404 0.839 0.000 0.000 0.988 0.000 0.012
#> GSM257893 3 0.2470 0.835 0.000 0.104 0.884 0.000 0.012
#> GSM257895 2 0.3305 0.676 0.000 0.776 0.000 0.000 0.224
#> GSM257897 3 0.1121 0.830 0.000 0.000 0.956 0.000 0.044
#> GSM257899 3 0.1121 0.830 0.000 0.000 0.956 0.000 0.044
#> GSM257901 3 0.2813 0.804 0.000 0.168 0.832 0.000 0.000
#> GSM257903 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257905 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257907 3 0.2813 0.804 0.000 0.168 0.832 0.000 0.000
#> GSM257909 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257911 3 0.4114 0.569 0.000 0.376 0.624 0.000 0.000
#> GSM257913 3 0.1792 0.861 0.000 0.084 0.916 0.000 0.000
#> GSM257916 2 0.2648 0.660 0.000 0.848 0.152 0.000 0.000
#> GSM257918 2 0.2648 0.660 0.000 0.848 0.152 0.000 0.000
#> GSM257920 3 0.1197 0.866 0.000 0.048 0.952 0.000 0.000
#> GSM257922 5 0.5552 0.446 0.000 0.088 0.328 0.000 0.584
#> GSM257924 3 0.2011 0.851 0.000 0.088 0.908 0.000 0.004
#> GSM257926 3 0.1792 0.861 0.000 0.084 0.916 0.000 0.000
#> GSM257928 5 0.3109 0.859 0.000 0.200 0.000 0.000 0.800
#> GSM257930 5 0.3109 0.859 0.000 0.200 0.000 0.000 0.800
#> GSM257938 5 0.3109 0.859 0.000 0.200 0.000 0.000 0.800
#> GSM257940 3 0.2929 0.802 0.000 0.180 0.820 0.000 0.000
#> GSM257942 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257944 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257946 3 0.0880 0.861 0.000 0.032 0.968 0.000 0.000
#> GSM257948 3 0.1270 0.866 0.000 0.052 0.948 0.000 0.000
#> GSM257950 3 0.1121 0.866 0.000 0.044 0.956 0.000 0.000
#> GSM257952 3 0.4582 0.434 0.000 0.416 0.572 0.000 0.012
#> GSM257954 2 0.3039 0.715 0.000 0.808 0.000 0.000 0.192
#> GSM257956 2 0.3109 0.707 0.000 0.800 0.000 0.000 0.200
#> GSM257959 2 0.0162 0.824 0.000 0.996 0.004 0.000 0.000
#> GSM257961 2 0.0566 0.824 0.000 0.984 0.004 0.000 0.012
#> GSM257963 2 0.0566 0.824 0.000 0.984 0.004 0.000 0.012
#> GSM257965 3 0.4114 0.569 0.000 0.376 0.624 0.000 0.000
#> GSM257967 2 0.0671 0.823 0.000 0.980 0.004 0.000 0.016
#> GSM257969 2 0.3039 0.715 0.000 0.808 0.000 0.000 0.192
#> GSM257971 3 0.3946 0.769 0.000 0.120 0.800 0.000 0.080
#> GSM257973 3 0.1121 0.866 0.000 0.044 0.956 0.000 0.000
#> GSM257981 3 0.4088 0.562 0.000 0.368 0.632 0.000 0.000
#> GSM257983 3 0.1121 0.830 0.000 0.000 0.956 0.000 0.044
#> GSM257985 3 0.0794 0.859 0.000 0.028 0.972 0.000 0.000
#> GSM257988 3 0.1121 0.866 0.000 0.044 0.956 0.000 0.000
#> GSM257991 2 0.3837 0.398 0.000 0.692 0.308 0.000 0.000
#> GSM257993 2 0.3508 0.631 0.000 0.748 0.000 0.000 0.252
#> GSM257994 5 0.3109 0.859 0.000 0.200 0.000 0.000 0.800
#> GSM257989 3 0.1121 0.866 0.000 0.044 0.956 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.3881 0.9939 0.000 0.000 0.000 0.600 0.004 0.396
#> GSM257888 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257890 1 0.2006 0.8989 0.892 0.000 0.000 0.104 0.000 0.004
#> GSM257892 4 0.3872 0.9939 0.000 0.000 0.000 0.604 0.004 0.392
#> GSM257894 1 0.1765 0.9038 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM257896 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257898 6 0.0363 0.1878 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM257900 6 0.3979 0.4578 0.456 0.000 0.000 0.004 0.000 0.540
#> GSM257902 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257904 6 0.0547 0.2023 0.020 0.000 0.000 0.000 0.000 0.980
#> GSM257906 6 0.0458 0.1978 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM257908 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257910 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257912 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257914 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257917 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257919 1 0.1814 0.9022 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM257921 1 0.1151 0.9173 0.956 0.000 0.000 0.032 0.000 0.012
#> GSM257923 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257925 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257927 1 0.3163 0.5777 0.764 0.000 0.000 0.004 0.000 0.232
#> GSM257929 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257937 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257939 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257941 6 0.3979 0.4582 0.456 0.000 0.000 0.004 0.000 0.540
#> GSM257943 6 0.3961 0.4839 0.440 0.000 0.000 0.004 0.000 0.556
#> GSM257945 6 0.3991 0.4195 0.472 0.000 0.000 0.004 0.000 0.524
#> GSM257947 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257949 1 0.0146 0.9202 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257951 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257953 1 0.1152 0.8894 0.952 0.000 0.000 0.004 0.000 0.044
#> GSM257955 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257958 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257960 1 0.2703 0.7064 0.824 0.000 0.000 0.004 0.000 0.172
#> GSM257962 1 0.2703 0.7064 0.824 0.000 0.000 0.004 0.000 0.172
#> GSM257964 1 0.0146 0.9202 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257966 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257968 1 0.0790 0.9184 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM257970 1 0.0146 0.9192 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257972 1 0.0405 0.9187 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM257977 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257982 1 0.1863 0.9004 0.896 0.000 0.000 0.104 0.000 0.000
#> GSM257984 1 0.0146 0.9202 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257986 1 0.0146 0.9202 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257990 1 0.0935 0.8995 0.964 0.000 0.000 0.004 0.000 0.032
#> GSM257992 6 0.0865 0.0771 0.000 0.000 0.000 0.036 0.000 0.964
#> GSM257996 1 0.1010 0.8964 0.960 0.000 0.000 0.004 0.000 0.036
#> GSM258006 6 0.2212 -0.0893 0.008 0.000 0.000 0.112 0.000 0.880
#> GSM257887 2 0.3175 0.6623 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM257889 3 0.2136 0.8136 0.000 0.064 0.908 0.012 0.016 0.000
#> GSM257891 3 0.1320 0.8054 0.000 0.000 0.948 0.036 0.016 0.000
#> GSM257893 3 0.2663 0.7971 0.000 0.084 0.876 0.012 0.028 0.000
#> GSM257895 2 0.3175 0.6623 0.000 0.744 0.000 0.000 0.256 0.000
#> GSM257897 3 0.3206 0.7477 0.000 0.000 0.828 0.068 0.104 0.000
#> GSM257899 3 0.3206 0.7477 0.000 0.000 0.828 0.068 0.104 0.000
#> GSM257901 3 0.4094 0.7105 0.000 0.080 0.740 0.180 0.000 0.000
#> GSM257903 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257905 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257907 3 0.4094 0.7105 0.000 0.080 0.740 0.180 0.000 0.000
#> GSM257909 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257911 3 0.5600 0.5064 0.000 0.296 0.528 0.176 0.000 0.000
#> GSM257913 3 0.1349 0.8227 0.000 0.056 0.940 0.000 0.004 0.000
#> GSM257916 2 0.3396 0.6646 0.000 0.812 0.116 0.072 0.000 0.000
#> GSM257918 2 0.3396 0.6646 0.000 0.812 0.116 0.072 0.000 0.000
#> GSM257920 3 0.0632 0.8269 0.000 0.024 0.976 0.000 0.000 0.000
#> GSM257922 5 0.5415 0.4025 0.000 0.044 0.280 0.064 0.612 0.000
#> GSM257924 3 0.2195 0.8121 0.000 0.068 0.904 0.012 0.016 0.000
#> GSM257926 3 0.1349 0.8227 0.000 0.056 0.940 0.000 0.004 0.000
#> GSM257928 5 0.1957 0.8621 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257930 5 0.1957 0.8621 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257938 5 0.1957 0.8621 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257940 3 0.4238 0.7126 0.000 0.092 0.728 0.180 0.000 0.000
#> GSM257942 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257944 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257946 3 0.0964 0.8237 0.000 0.016 0.968 0.012 0.004 0.000
#> GSM257948 3 0.0777 0.8267 0.000 0.024 0.972 0.000 0.004 0.000
#> GSM257950 3 0.0603 0.8263 0.000 0.016 0.980 0.004 0.000 0.000
#> GSM257952 3 0.5799 0.3574 0.000 0.372 0.496 0.112 0.020 0.000
#> GSM257954 2 0.2854 0.7159 0.000 0.792 0.000 0.000 0.208 0.000
#> GSM257956 2 0.2941 0.7048 0.000 0.780 0.000 0.000 0.220 0.000
#> GSM257959 2 0.0260 0.8236 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257961 2 0.0508 0.8226 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM257963 2 0.0508 0.8226 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM257965 3 0.5600 0.5064 0.000 0.296 0.528 0.176 0.000 0.000
#> GSM257967 2 0.0692 0.8208 0.000 0.976 0.004 0.000 0.020 0.000
#> GSM257969 2 0.2854 0.7159 0.000 0.792 0.000 0.000 0.208 0.000
#> GSM257971 3 0.5227 0.6776 0.000 0.060 0.692 0.148 0.100 0.000
#> GSM257973 3 0.0603 0.8263 0.000 0.016 0.980 0.004 0.000 0.000
#> GSM257981 3 0.5209 0.4828 0.000 0.324 0.564 0.112 0.000 0.000
#> GSM257983 3 0.3206 0.7477 0.000 0.000 0.828 0.068 0.104 0.000
#> GSM257985 3 0.0820 0.8238 0.000 0.016 0.972 0.012 0.000 0.000
#> GSM257988 3 0.0603 0.8263 0.000 0.016 0.980 0.004 0.000 0.000
#> GSM257991 2 0.4849 0.4194 0.000 0.648 0.240 0.112 0.000 0.000
#> GSM257993 2 0.3482 0.5628 0.000 0.684 0.000 0.000 0.316 0.000
#> GSM257994 5 0.1957 0.8621 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257989 3 0.0603 0.8263 0.000 0.016 0.980 0.004 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 cell.type(p) protocol(p) individual(p) k
#> MAD:hclust 96 8.49e-22 1.000 1.000 2
#> MAD:hclust 92 1.05e-20 0.898 1.000 3
#> MAD:hclust 89 3.59e-19 0.417 0.996 4
#> MAD:hclust 89 2.15e-18 0.471 0.990 5
#> MAD:hclust 83 4.03e-17 0.503 0.986 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 25171 rows and 96 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 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.693 0.866 0.798 0.2369 0.874 0.745
#> 4 4 0.622 0.626 0.680 0.1109 0.910 0.761
#> 5 5 0.581 0.680 0.722 0.0762 0.910 0.703
#> 6 6 0.584 0.591 0.729 0.0581 0.974 0.882
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.6168 0.812 0.588 0.412 0.000
#> GSM257888 1 0.4702 0.865 0.788 0.212 0.000
#> GSM257890 1 0.6126 0.817 0.600 0.400 0.000
#> GSM257892 1 0.6168 0.812 0.588 0.412 0.000
#> GSM257894 1 0.4452 0.867 0.808 0.192 0.000
#> GSM257896 1 0.5098 0.866 0.752 0.248 0.000
#> GSM257898 1 0.5016 0.851 0.760 0.240 0.000
#> GSM257900 1 0.3686 0.882 0.860 0.140 0.000
#> GSM257902 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257904 1 0.5785 0.840 0.668 0.332 0.000
#> GSM257906 1 0.5785 0.840 0.668 0.332 0.000
#> GSM257908 1 0.4346 0.868 0.816 0.184 0.000
#> GSM257910 1 0.4346 0.868 0.816 0.184 0.000
#> GSM257912 1 0.5497 0.862 0.708 0.292 0.000
#> GSM257914 1 0.5465 0.862 0.712 0.288 0.000
#> GSM257917 1 0.5497 0.862 0.708 0.292 0.000
#> GSM257919 1 0.5497 0.862 0.708 0.292 0.000
#> GSM257921 1 0.4796 0.882 0.780 0.220 0.000
#> GSM257923 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257927 1 0.3340 0.883 0.880 0.120 0.000
#> GSM257929 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257937 1 0.5560 0.859 0.700 0.300 0.000
#> GSM257939 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257941 1 0.3412 0.883 0.876 0.124 0.000
#> GSM257943 1 0.4750 0.852 0.784 0.216 0.000
#> GSM257945 1 0.3686 0.879 0.860 0.140 0.000
#> GSM257947 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257960 1 0.3551 0.882 0.868 0.132 0.000
#> GSM257962 1 0.3340 0.883 0.880 0.120 0.000
#> GSM257964 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257966 1 0.4974 0.867 0.764 0.236 0.000
#> GSM257968 1 0.3941 0.874 0.844 0.156 0.000
#> GSM257970 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257972 1 0.0237 0.890 0.996 0.004 0.000
#> GSM257977 1 0.5291 0.864 0.732 0.268 0.000
#> GSM257982 1 0.4605 0.865 0.796 0.204 0.000
#> GSM257984 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.890 1.000 0.000 0.000
#> GSM257990 1 0.0237 0.890 0.996 0.004 0.000
#> GSM257992 1 0.5016 0.851 0.760 0.240 0.000
#> GSM257996 1 0.2711 0.888 0.912 0.088 0.000
#> GSM258006 1 0.5785 0.840 0.668 0.332 0.000
#> GSM257887 2 0.6180 0.962 0.000 0.584 0.416
#> GSM257889 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257891 3 0.0592 0.905 0.000 0.012 0.988
#> GSM257893 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257895 2 0.6180 0.957 0.000 0.584 0.416
#> GSM257897 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257899 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257901 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257903 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257905 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257907 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257909 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257911 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257913 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257916 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257918 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257920 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257922 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257924 3 0.1031 0.897 0.000 0.024 0.976
#> GSM257926 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257928 2 0.6260 0.913 0.000 0.552 0.448
#> GSM257930 2 0.6180 0.957 0.000 0.584 0.416
#> GSM257938 2 0.6180 0.957 0.000 0.584 0.416
#> GSM257940 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257942 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257944 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257946 3 0.0592 0.905 0.000 0.012 0.988
#> GSM257948 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257950 3 0.0000 0.907 0.000 0.000 1.000
#> GSM257952 3 0.6302 -0.827 0.000 0.480 0.520
#> GSM257954 2 0.6168 0.960 0.000 0.588 0.412
#> GSM257956 2 0.6168 0.960 0.000 0.588 0.412
#> GSM257959 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257961 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257963 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257965 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257967 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257969 2 0.6168 0.960 0.000 0.588 0.412
#> GSM257971 3 0.1289 0.891 0.000 0.032 0.968
#> GSM257973 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257981 3 0.6286 -0.787 0.000 0.464 0.536
#> GSM257983 3 0.0237 0.906 0.000 0.004 0.996
#> GSM257985 3 0.0592 0.905 0.000 0.012 0.988
#> GSM257988 3 0.0237 0.907 0.000 0.004 0.996
#> GSM257991 2 0.6252 0.971 0.000 0.556 0.444
#> GSM257993 2 0.6168 0.960 0.000 0.588 0.412
#> GSM257994 2 0.6180 0.957 0.000 0.584 0.416
#> GSM257989 3 0.0000 0.907 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.5522 0.6814 0.288 0.000 0.044 0.668
#> GSM257888 1 0.7427 0.2765 0.500 0.000 0.300 0.200
#> GSM257890 1 0.7910 -0.0494 0.352 0.000 0.304 0.344
#> GSM257892 4 0.5522 0.6814 0.288 0.000 0.044 0.668
#> GSM257894 1 0.7205 0.3013 0.532 0.000 0.296 0.172
#> GSM257896 1 0.7606 0.2471 0.468 0.000 0.304 0.228
#> GSM257898 4 0.4948 0.7837 0.440 0.000 0.000 0.560
#> GSM257900 1 0.4914 -0.1263 0.676 0.000 0.012 0.312
#> GSM257902 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257904 4 0.4790 0.8280 0.380 0.000 0.000 0.620
#> GSM257906 4 0.4790 0.8280 0.380 0.000 0.000 0.620
#> GSM257908 1 0.7222 0.3046 0.528 0.000 0.300 0.172
#> GSM257910 1 0.7222 0.3046 0.528 0.000 0.300 0.172
#> GSM257912 1 0.7689 0.2237 0.452 0.000 0.300 0.248
#> GSM257914 1 0.7671 0.2304 0.456 0.000 0.300 0.244
#> GSM257917 1 0.7722 0.2067 0.444 0.000 0.300 0.256
#> GSM257919 1 0.7689 0.2237 0.452 0.000 0.300 0.248
#> GSM257921 1 0.7093 0.2185 0.556 0.000 0.272 0.172
#> GSM257923 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0188 0.5186 0.996 0.000 0.004 0.000
#> GSM257927 1 0.4606 0.0454 0.724 0.000 0.012 0.264
#> GSM257929 1 0.0188 0.5186 0.996 0.000 0.004 0.000
#> GSM257937 1 0.7700 0.2164 0.448 0.000 0.304 0.248
#> GSM257939 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257941 1 0.4891 -0.1150 0.680 0.000 0.012 0.308
#> GSM257943 4 0.5161 0.7037 0.476 0.000 0.004 0.520
#> GSM257945 1 0.4999 -0.1976 0.660 0.000 0.012 0.328
#> GSM257947 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0188 0.5186 0.996 0.000 0.004 0.000
#> GSM257955 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0188 0.5186 0.996 0.000 0.004 0.000
#> GSM257960 1 0.4868 -0.0993 0.684 0.000 0.012 0.304
#> GSM257962 1 0.4606 0.0454 0.724 0.000 0.012 0.264
#> GSM257964 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257966 1 0.7531 0.2605 0.476 0.000 0.316 0.208
#> GSM257968 1 0.5247 0.3880 0.752 0.000 0.100 0.148
#> GSM257970 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0469 0.5165 0.988 0.000 0.012 0.000
#> GSM257977 1 0.7626 0.2415 0.464 0.000 0.304 0.232
#> GSM257982 1 0.7344 0.2854 0.512 0.000 0.300 0.188
#> GSM257984 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.5212 1.000 0.000 0.000 0.000
#> GSM257990 1 0.2048 0.4504 0.928 0.000 0.008 0.064
#> GSM257992 4 0.4948 0.7837 0.440 0.000 0.000 0.560
#> GSM257996 1 0.3552 0.3715 0.848 0.000 0.024 0.128
#> GSM258006 4 0.5189 0.8197 0.372 0.000 0.012 0.616
#> GSM257887 2 0.2021 0.8714 0.000 0.936 0.024 0.040
#> GSM257889 3 0.4957 0.9084 0.000 0.320 0.668 0.012
#> GSM257891 3 0.4917 0.9185 0.000 0.336 0.656 0.008
#> GSM257893 3 0.5619 0.8924 0.000 0.320 0.640 0.040
#> GSM257895 2 0.3384 0.8452 0.000 0.860 0.024 0.116
#> GSM257897 3 0.5698 0.8916 0.000 0.320 0.636 0.044
#> GSM257899 3 0.5698 0.8916 0.000 0.320 0.636 0.044
#> GSM257901 3 0.6773 0.8826 0.000 0.348 0.544 0.108
#> GSM257903 2 0.0469 0.8806 0.000 0.988 0.000 0.012
#> GSM257905 2 0.0000 0.8835 0.000 1.000 0.000 0.000
#> GSM257907 3 0.6773 0.8826 0.000 0.348 0.544 0.108
#> GSM257909 2 0.0188 0.8828 0.000 0.996 0.000 0.004
#> GSM257911 3 0.6888 0.7355 0.000 0.448 0.448 0.104
#> GSM257913 3 0.6426 0.8622 0.000 0.392 0.536 0.072
#> GSM257916 2 0.0336 0.8819 0.000 0.992 0.000 0.008
#> GSM257918 2 0.0336 0.8819 0.000 0.992 0.000 0.008
#> GSM257920 3 0.6171 0.9056 0.000 0.348 0.588 0.064
#> GSM257922 3 0.6179 0.8668 0.000 0.320 0.608 0.072
#> GSM257924 3 0.4761 0.9176 0.000 0.332 0.664 0.004
#> GSM257926 3 0.5093 0.9204 0.000 0.348 0.640 0.012
#> GSM257928 2 0.6058 0.6427 0.000 0.684 0.136 0.180
#> GSM257930 2 0.4238 0.7981 0.000 0.796 0.028 0.176
#> GSM257938 2 0.4238 0.7981 0.000 0.796 0.028 0.176
#> GSM257940 3 0.6725 0.8825 0.000 0.348 0.548 0.104
#> GSM257942 2 0.0469 0.8806 0.000 0.988 0.000 0.012
#> GSM257944 2 0.0469 0.8806 0.000 0.988 0.000 0.012
#> GSM257946 3 0.4605 0.9195 0.000 0.336 0.664 0.000
#> GSM257948 3 0.6171 0.9056 0.000 0.348 0.588 0.064
#> GSM257950 3 0.5075 0.9209 0.000 0.344 0.644 0.012
#> GSM257952 2 0.4424 0.6873 0.000 0.812 0.088 0.100
#> GSM257954 2 0.3384 0.8452 0.000 0.860 0.024 0.116
#> GSM257956 2 0.3015 0.8559 0.000 0.884 0.024 0.092
#> GSM257959 2 0.0000 0.8835 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.8835 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.8835 0.000 1.000 0.000 0.000
#> GSM257965 2 0.2281 0.8166 0.000 0.904 0.000 0.096
#> GSM257967 2 0.0000 0.8835 0.000 1.000 0.000 0.000
#> GSM257969 2 0.3015 0.8559 0.000 0.884 0.024 0.092
#> GSM257971 3 0.6519 0.8682 0.000 0.320 0.584 0.096
#> GSM257973 3 0.5823 0.9145 0.000 0.348 0.608 0.044
#> GSM257981 2 0.4608 0.6609 0.000 0.800 0.104 0.096
#> GSM257983 3 0.5075 0.9206 0.000 0.344 0.644 0.012
#> GSM257985 3 0.4917 0.9203 0.000 0.336 0.656 0.008
#> GSM257988 3 0.6171 0.9065 0.000 0.348 0.588 0.064
#> GSM257991 2 0.2593 0.8045 0.000 0.892 0.004 0.104
#> GSM257993 2 0.3384 0.8452 0.000 0.860 0.024 0.116
#> GSM257994 2 0.4238 0.7981 0.000 0.796 0.028 0.176
#> GSM257989 3 0.5186 0.9210 0.000 0.344 0.640 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.6943 0.3801 0.164 0.028 0.000 0.336 0.472
#> GSM257888 4 0.4366 0.8235 0.320 0.000 0.000 0.664 0.016
#> GSM257890 4 0.5950 0.6530 0.192 0.020 0.000 0.644 0.144
#> GSM257892 5 0.6943 0.3801 0.164 0.028 0.000 0.336 0.472
#> GSM257894 4 0.4444 0.7894 0.364 0.000 0.000 0.624 0.012
#> GSM257896 4 0.4546 0.8288 0.304 0.000 0.000 0.668 0.028
#> GSM257898 5 0.4823 0.7087 0.316 0.000 0.000 0.040 0.644
#> GSM257900 1 0.5456 0.0981 0.556 0.048 0.000 0.008 0.388
#> GSM257902 1 0.0451 0.7445 0.988 0.004 0.000 0.008 0.000
#> GSM257904 5 0.5334 0.7658 0.244 0.000 0.000 0.104 0.652
#> GSM257906 5 0.5379 0.7664 0.244 0.000 0.000 0.108 0.648
#> GSM257908 4 0.6552 0.7986 0.332 0.124 0.000 0.520 0.024
#> GSM257910 4 0.6552 0.7986 0.332 0.124 0.000 0.520 0.024
#> GSM257912 4 0.7216 0.8001 0.256 0.132 0.000 0.528 0.084
#> GSM257914 4 0.7216 0.8001 0.256 0.132 0.000 0.528 0.084
#> GSM257917 4 0.7216 0.8001 0.256 0.132 0.000 0.528 0.084
#> GSM257919 4 0.7216 0.8001 0.256 0.132 0.000 0.528 0.084
#> GSM257921 1 0.7399 -0.3749 0.420 0.060 0.000 0.364 0.156
#> GSM257923 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257925 1 0.0794 0.7369 0.972 0.028 0.000 0.000 0.000
#> GSM257927 1 0.5292 0.1801 0.580 0.048 0.000 0.004 0.368
#> GSM257929 1 0.0290 0.7420 0.992 0.008 0.000 0.000 0.000
#> GSM257937 4 0.5156 0.8014 0.256 0.004 0.000 0.668 0.072
#> GSM257939 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257941 1 0.5324 0.1422 0.568 0.048 0.000 0.004 0.380
#> GSM257943 5 0.5303 0.5243 0.372 0.048 0.000 0.004 0.576
#> GSM257945 1 0.5343 0.1128 0.560 0.048 0.000 0.004 0.388
#> GSM257947 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257949 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257951 1 0.0451 0.7445 0.988 0.004 0.000 0.008 0.000
#> GSM257953 1 0.0794 0.7369 0.972 0.028 0.000 0.000 0.000
#> GSM257955 1 0.0451 0.7445 0.988 0.004 0.000 0.008 0.000
#> GSM257958 1 0.0794 0.7369 0.972 0.028 0.000 0.000 0.000
#> GSM257960 1 0.5437 0.1281 0.564 0.048 0.000 0.008 0.380
#> GSM257962 1 0.5292 0.1801 0.580 0.048 0.000 0.004 0.368
#> GSM257964 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257966 4 0.5246 0.8412 0.300 0.024 0.000 0.644 0.032
#> GSM257968 1 0.4182 -0.0947 0.644 0.000 0.000 0.352 0.004
#> GSM257970 1 0.0451 0.7445 0.988 0.004 0.000 0.008 0.000
#> GSM257972 1 0.1124 0.7321 0.960 0.036 0.000 0.004 0.000
#> GSM257977 4 0.4853 0.8243 0.296 0.008 0.000 0.664 0.032
#> GSM257982 4 0.4339 0.8132 0.336 0.000 0.000 0.652 0.012
#> GSM257984 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257986 1 0.0290 0.7449 0.992 0.000 0.000 0.008 0.000
#> GSM257990 1 0.4184 0.5518 0.772 0.048 0.000 0.004 0.176
#> GSM257992 5 0.4956 0.7158 0.312 0.004 0.000 0.040 0.644
#> GSM257996 1 0.5444 0.3426 0.644 0.048 0.000 0.024 0.284
#> GSM258006 5 0.6093 0.7309 0.224 0.012 0.000 0.156 0.608
#> GSM257887 2 0.5354 0.8066 0.000 0.696 0.208 0.068 0.028
#> GSM257889 3 0.2869 0.8158 0.000 0.016 0.888 0.040 0.056
#> GSM257891 3 0.2278 0.8277 0.000 0.000 0.908 0.032 0.060
#> GSM257893 3 0.3700 0.7901 0.000 0.020 0.840 0.080 0.060
#> GSM257895 2 0.7082 0.7532 0.000 0.540 0.212 0.192 0.056
#> GSM257897 3 0.3624 0.7939 0.000 0.020 0.844 0.052 0.084
#> GSM257899 3 0.3692 0.7914 0.000 0.020 0.840 0.056 0.084
#> GSM257901 3 0.4234 0.7501 0.000 0.012 0.776 0.040 0.172
#> GSM257903 2 0.3858 0.8177 0.000 0.760 0.224 0.008 0.008
#> GSM257905 2 0.3305 0.8219 0.000 0.776 0.224 0.000 0.000
#> GSM257907 3 0.4234 0.7501 0.000 0.012 0.776 0.040 0.172
#> GSM257909 2 0.3461 0.8212 0.000 0.772 0.224 0.000 0.004
#> GSM257911 3 0.5474 0.6605 0.000 0.084 0.708 0.040 0.168
#> GSM257913 3 0.3674 0.7888 0.000 0.064 0.844 0.024 0.068
#> GSM257916 2 0.3966 0.8168 0.000 0.756 0.224 0.008 0.012
#> GSM257918 2 0.3966 0.8168 0.000 0.756 0.224 0.008 0.012
#> GSM257920 3 0.2696 0.8171 0.000 0.012 0.892 0.024 0.072
#> GSM257922 3 0.4383 0.7583 0.000 0.020 0.792 0.108 0.080
#> GSM257924 3 0.2523 0.8341 0.000 0.024 0.908 0.028 0.040
#> GSM257926 3 0.1299 0.8410 0.000 0.012 0.960 0.008 0.020
#> GSM257928 2 0.8112 0.5215 0.000 0.336 0.300 0.268 0.096
#> GSM257930 2 0.7720 0.6959 0.000 0.460 0.212 0.240 0.088
#> GSM257938 2 0.7720 0.6959 0.000 0.460 0.212 0.240 0.088
#> GSM257940 3 0.4196 0.7501 0.000 0.012 0.780 0.040 0.168
#> GSM257942 2 0.4025 0.8123 0.000 0.748 0.232 0.008 0.012
#> GSM257944 2 0.3996 0.8146 0.000 0.752 0.228 0.008 0.012
#> GSM257946 3 0.1106 0.8408 0.000 0.000 0.964 0.012 0.024
#> GSM257948 3 0.2633 0.8184 0.000 0.012 0.896 0.024 0.068
#> GSM257950 3 0.0451 0.8411 0.000 0.008 0.988 0.004 0.000
#> GSM257952 2 0.7296 0.3916 0.000 0.404 0.372 0.040 0.184
#> GSM257954 2 0.6887 0.7665 0.000 0.564 0.212 0.172 0.052
#> GSM257956 2 0.6425 0.7863 0.000 0.612 0.212 0.132 0.044
#> GSM257959 2 0.3305 0.8219 0.000 0.776 0.224 0.000 0.000
#> GSM257961 2 0.3305 0.8219 0.000 0.776 0.224 0.000 0.000
#> GSM257963 2 0.3305 0.8219 0.000 0.776 0.224 0.000 0.000
#> GSM257965 2 0.6840 0.6636 0.000 0.544 0.244 0.036 0.176
#> GSM257967 2 0.3461 0.8220 0.000 0.772 0.224 0.000 0.004
#> GSM257969 2 0.6425 0.7863 0.000 0.612 0.212 0.132 0.044
#> GSM257971 3 0.5341 0.7360 0.000 0.020 0.708 0.108 0.164
#> GSM257973 3 0.1483 0.8367 0.000 0.008 0.952 0.012 0.028
#> GSM257981 3 0.7218 -0.3786 0.000 0.384 0.408 0.040 0.168
#> GSM257983 3 0.2299 0.8311 0.000 0.004 0.912 0.032 0.052
#> GSM257985 3 0.1117 0.8403 0.000 0.000 0.964 0.016 0.020
#> GSM257988 3 0.1836 0.8323 0.000 0.008 0.936 0.016 0.040
#> GSM257991 2 0.6847 0.6371 0.000 0.536 0.264 0.036 0.164
#> GSM257993 2 0.6916 0.7657 0.000 0.560 0.212 0.176 0.052
#> GSM257994 2 0.7720 0.6959 0.000 0.460 0.212 0.240 0.088
#> GSM257989 3 0.0579 0.8411 0.000 0.008 0.984 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.6441 0.4608 0.080 0.052 0.000 0.288 0.032 0.548
#> GSM257888 4 0.3590 0.7791 0.188 0.004 0.000 0.776 0.000 0.032
#> GSM257890 4 0.4287 0.7200 0.104 0.008 0.000 0.764 0.008 0.116
#> GSM257892 6 0.6441 0.4608 0.080 0.052 0.000 0.288 0.032 0.548
#> GSM257894 4 0.3852 0.7482 0.240 0.012 0.000 0.732 0.000 0.016
#> GSM257896 4 0.3637 0.7838 0.164 0.000 0.000 0.780 0.000 0.056
#> GSM257898 6 0.3103 0.7567 0.208 0.000 0.000 0.008 0.000 0.784
#> GSM257900 1 0.5754 0.0779 0.456 0.000 0.000 0.012 0.120 0.412
#> GSM257902 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257904 6 0.3765 0.7910 0.164 0.000 0.000 0.048 0.008 0.780
#> GSM257906 6 0.4298 0.7951 0.164 0.012 0.000 0.048 0.016 0.760
#> GSM257908 4 0.6040 0.7610 0.208 0.016 0.000 0.572 0.192 0.012
#> GSM257910 4 0.6040 0.7610 0.208 0.016 0.000 0.572 0.192 0.012
#> GSM257912 4 0.6534 0.7709 0.148 0.016 0.000 0.572 0.196 0.068
#> GSM257914 4 0.6534 0.7709 0.148 0.016 0.000 0.572 0.196 0.068
#> GSM257917 4 0.6550 0.7672 0.144 0.016 0.000 0.572 0.196 0.072
#> GSM257919 4 0.6534 0.7709 0.148 0.016 0.000 0.572 0.196 0.068
#> GSM257921 4 0.7486 0.0701 0.304 0.000 0.000 0.336 0.152 0.208
#> GSM257923 1 0.0405 0.7432 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM257925 1 0.1196 0.7363 0.952 0.008 0.000 0.000 0.040 0.000
#> GSM257927 1 0.5641 0.1653 0.488 0.000 0.000 0.008 0.120 0.384
#> GSM257929 1 0.0806 0.7409 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM257937 4 0.4274 0.7817 0.144 0.008 0.000 0.764 0.012 0.072
#> GSM257939 1 0.0405 0.7432 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM257941 1 0.5655 0.1321 0.476 0.000 0.000 0.008 0.120 0.396
#> GSM257943 6 0.4956 0.6015 0.228 0.000 0.000 0.004 0.116 0.652
#> GSM257945 1 0.5703 0.0582 0.452 0.004 0.000 0.004 0.120 0.420
#> GSM257947 1 0.0405 0.7432 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM257949 1 0.0405 0.7431 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM257951 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257953 1 0.1196 0.7363 0.952 0.008 0.000 0.000 0.040 0.000
#> GSM257955 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257958 1 0.1196 0.7363 0.952 0.008 0.000 0.000 0.040 0.000
#> GSM257960 1 0.5741 0.1196 0.472 0.000 0.000 0.012 0.120 0.396
#> GSM257962 1 0.5641 0.1653 0.488 0.000 0.000 0.008 0.120 0.384
#> GSM257964 1 0.0508 0.7432 0.984 0.012 0.000 0.004 0.000 0.000
#> GSM257966 4 0.4954 0.8017 0.164 0.016 0.000 0.724 0.052 0.044
#> GSM257968 1 0.4417 -0.0314 0.588 0.024 0.000 0.384 0.000 0.004
#> GSM257970 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257972 1 0.2026 0.7229 0.916 0.008 0.000 0.012 0.060 0.004
#> GSM257977 4 0.3961 0.7790 0.156 0.004 0.000 0.772 0.004 0.064
#> GSM257982 4 0.3374 0.7700 0.208 0.000 0.000 0.772 0.000 0.020
#> GSM257984 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257986 1 0.0692 0.7431 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM257990 1 0.5088 0.4956 0.672 0.012 0.000 0.004 0.116 0.196
#> GSM257992 6 0.3939 0.7728 0.204 0.012 0.000 0.008 0.020 0.756
#> GSM257996 1 0.6132 0.2709 0.528 0.004 0.000 0.032 0.132 0.304
#> GSM258006 6 0.5288 0.7668 0.156 0.032 0.000 0.092 0.020 0.700
#> GSM257887 2 0.4441 0.4811 0.000 0.768 0.108 0.008 0.088 0.028
#> GSM257889 3 0.3476 0.7604 0.000 0.016 0.844 0.020 0.048 0.072
#> GSM257891 3 0.3216 0.7661 0.000 0.000 0.852 0.032 0.052 0.064
#> GSM257893 3 0.3748 0.7410 0.000 0.016 0.824 0.020 0.088 0.052
#> GSM257895 2 0.6245 -0.4024 0.000 0.508 0.112 0.012 0.336 0.032
#> GSM257897 3 0.4498 0.7178 0.000 0.016 0.772 0.032 0.108 0.072
#> GSM257899 3 0.4444 0.7184 0.000 0.016 0.776 0.032 0.108 0.068
#> GSM257901 3 0.5876 0.6444 0.000 0.012 0.656 0.144 0.100 0.088
#> GSM257903 2 0.2346 0.6571 0.000 0.868 0.124 0.000 0.008 0.000
#> GSM257905 2 0.2092 0.6571 0.000 0.876 0.124 0.000 0.000 0.000
#> GSM257907 3 0.5917 0.6440 0.000 0.012 0.652 0.144 0.104 0.088
#> GSM257909 2 0.2092 0.6571 0.000 0.876 0.124 0.000 0.000 0.000
#> GSM257911 3 0.7053 0.5466 0.000 0.092 0.572 0.152 0.100 0.084
#> GSM257913 3 0.4416 0.7288 0.000 0.060 0.792 0.060 0.060 0.028
#> GSM257916 2 0.2791 0.6539 0.000 0.852 0.124 0.016 0.008 0.000
#> GSM257918 2 0.2791 0.6539 0.000 0.852 0.124 0.016 0.008 0.000
#> GSM257920 3 0.3663 0.7539 0.000 0.016 0.836 0.056 0.060 0.032
#> GSM257922 3 0.4669 0.6648 0.000 0.016 0.720 0.016 0.200 0.048
#> GSM257924 3 0.2460 0.7859 0.000 0.020 0.904 0.012 0.040 0.024
#> GSM257926 3 0.1325 0.7911 0.000 0.012 0.956 0.016 0.004 0.012
#> GSM257928 5 0.5756 0.6834 0.000 0.264 0.200 0.000 0.532 0.004
#> GSM257930 5 0.5341 0.8756 0.000 0.380 0.112 0.000 0.508 0.000
#> GSM257938 5 0.5341 0.8756 0.000 0.380 0.112 0.000 0.508 0.000
#> GSM257940 3 0.5909 0.6422 0.000 0.012 0.652 0.148 0.100 0.088
#> GSM257942 2 0.2643 0.6533 0.000 0.856 0.128 0.008 0.008 0.000
#> GSM257944 2 0.2643 0.6533 0.000 0.856 0.128 0.008 0.008 0.000
#> GSM257946 3 0.1148 0.7890 0.000 0.000 0.960 0.004 0.016 0.020
#> GSM257948 3 0.3663 0.7539 0.000 0.016 0.836 0.056 0.060 0.032
#> GSM257950 3 0.0603 0.7899 0.000 0.000 0.980 0.004 0.000 0.016
#> GSM257952 2 0.8357 0.0562 0.000 0.328 0.300 0.156 0.108 0.108
#> GSM257954 2 0.5938 -0.3216 0.000 0.532 0.108 0.004 0.328 0.028
#> GSM257956 2 0.5852 0.0347 0.000 0.608 0.108 0.012 0.240 0.032
#> GSM257959 2 0.2092 0.6571 0.000 0.876 0.124 0.000 0.000 0.000
#> GSM257961 2 0.2346 0.6557 0.000 0.868 0.124 0.000 0.000 0.008
#> GSM257963 2 0.2346 0.6557 0.000 0.868 0.124 0.000 0.000 0.008
#> GSM257965 2 0.7694 0.2513 0.000 0.496 0.144 0.164 0.100 0.096
#> GSM257967 2 0.2346 0.6557 0.000 0.868 0.124 0.000 0.000 0.008
#> GSM257969 2 0.5852 0.0347 0.000 0.608 0.108 0.012 0.240 0.032
#> GSM257971 3 0.6531 0.5976 0.000 0.016 0.588 0.100 0.172 0.124
#> GSM257973 3 0.1307 0.7888 0.000 0.000 0.952 0.008 0.032 0.008
#> GSM257981 3 0.8248 -0.1373 0.000 0.308 0.340 0.148 0.100 0.104
#> GSM257983 3 0.3225 0.7676 0.000 0.000 0.852 0.036 0.048 0.064
#> GSM257985 3 0.1448 0.7897 0.000 0.000 0.948 0.012 0.016 0.024
#> GSM257988 3 0.2136 0.7804 0.000 0.000 0.908 0.016 0.064 0.012
#> GSM257991 2 0.7604 0.2533 0.000 0.504 0.168 0.144 0.100 0.084
#> GSM257993 2 0.5938 -0.3216 0.000 0.532 0.108 0.004 0.328 0.028
#> GSM257994 5 0.5341 0.8756 0.000 0.380 0.112 0.000 0.508 0.000
#> GSM257989 3 0.0508 0.7901 0.000 0.000 0.984 0.004 0.000 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 cell.type(p) protocol(p) individual(p) k
#> MAD:kmeans 96 8.49e-22 1.000 1.000 2
#> MAD:kmeans 94 3.87e-21 0.539 1.000 3
#> MAD:kmeans 72 1.59e-15 0.834 0.983 4
#> MAD:kmeans 83 4.03e-17 0.591 0.982 5
#> MAD:kmeans 74 1.50e-14 0.480 0.777 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 25171 rows and 96 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 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.814 0.972 0.927 0.2268 0.874 0.745
#> 4 4 0.842 0.758 0.861 0.1697 0.923 0.791
#> 5 5 0.872 0.897 0.928 0.0723 0.911 0.705
#> 6 6 0.826 0.769 0.862 0.0347 0.976 0.898
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.4750 0.841 0.784 0.000 0.216
#> GSM257888 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257890 1 0.1860 0.947 0.948 0.000 0.052
#> GSM257892 1 0.4750 0.841 0.784 0.000 0.216
#> GSM257894 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257896 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257898 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257900 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257904 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257906 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257908 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257910 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257912 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257914 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257917 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257919 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257921 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257937 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257939 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257943 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257945 1 0.0592 0.963 0.988 0.000 0.012
#> GSM257947 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257966 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257968 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257970 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257977 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257982 1 0.0592 0.965 0.988 0.000 0.012
#> GSM257984 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.967 1.000 0.000 0.000
#> GSM257992 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257996 1 0.0000 0.967 1.000 0.000 0.000
#> GSM258006 1 0.4605 0.843 0.796 0.000 0.204
#> GSM257887 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257889 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257891 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257893 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257895 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257897 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257899 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257901 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257903 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257905 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257907 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257909 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257911 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257913 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257916 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257918 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257920 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257922 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257924 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257926 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257928 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257930 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257938 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257940 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257942 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257944 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257946 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257948 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257950 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257952 2 0.0237 0.995 0.000 0.996 0.004
#> GSM257954 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257956 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257959 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257961 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257963 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257965 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257967 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257969 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257971 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257973 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257981 2 0.0237 0.995 0.000 0.996 0.004
#> GSM257983 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257985 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257988 3 0.4750 1.000 0.000 0.216 0.784
#> GSM257991 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257993 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257994 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257989 3 0.4750 1.000 0.000 0.216 0.784
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.1042 0.374 0.020 0.000 0.008 0.972
#> GSM257888 1 0.4977 0.441 0.540 0.000 0.000 0.460
#> GSM257890 4 0.5088 -0.332 0.424 0.000 0.004 0.572
#> GSM257892 4 0.1042 0.374 0.020 0.000 0.008 0.972
#> GSM257894 1 0.4977 0.441 0.540 0.000 0.000 0.460
#> GSM257896 1 0.4977 0.441 0.540 0.000 0.000 0.460
#> GSM257898 4 0.5288 0.649 0.472 0.000 0.008 0.520
#> GSM257900 1 0.2530 0.516 0.888 0.000 0.000 0.112
#> GSM257902 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257904 4 0.5288 0.649 0.472 0.000 0.008 0.520
#> GSM257906 4 0.5288 0.649 0.472 0.000 0.008 0.520
#> GSM257908 1 0.5137 0.446 0.544 0.000 0.004 0.452
#> GSM257910 1 0.5137 0.446 0.544 0.000 0.004 0.452
#> GSM257912 1 0.5143 0.444 0.540 0.000 0.004 0.456
#> GSM257914 1 0.5143 0.444 0.540 0.000 0.004 0.456
#> GSM257917 1 0.5143 0.444 0.540 0.000 0.004 0.456
#> GSM257919 1 0.5143 0.444 0.540 0.000 0.004 0.456
#> GSM257921 1 0.1209 0.640 0.964 0.000 0.004 0.032
#> GSM257923 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257927 1 0.2469 0.523 0.892 0.000 0.000 0.108
#> GSM257929 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257937 1 0.5151 0.437 0.532 0.000 0.004 0.464
#> GSM257939 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257941 1 0.2530 0.516 0.888 0.000 0.000 0.112
#> GSM257943 4 0.5161 0.644 0.476 0.000 0.004 0.520
#> GSM257945 1 0.2868 0.467 0.864 0.000 0.000 0.136
#> GSM257947 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257960 1 0.2469 0.523 0.892 0.000 0.000 0.108
#> GSM257962 1 0.2469 0.523 0.892 0.000 0.000 0.108
#> GSM257964 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257966 1 0.5147 0.439 0.536 0.000 0.004 0.460
#> GSM257968 1 0.4955 0.449 0.556 0.000 0.000 0.444
#> GSM257970 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257977 1 0.4977 0.441 0.540 0.000 0.000 0.460
#> GSM257982 1 0.4955 0.449 0.556 0.000 0.000 0.444
#> GSM257984 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.653 1.000 0.000 0.000 0.000
#> GSM257990 1 0.2011 0.560 0.920 0.000 0.000 0.080
#> GSM257992 4 0.5288 0.649 0.472 0.000 0.008 0.520
#> GSM257996 1 0.0895 0.634 0.976 0.000 0.004 0.020
#> GSM258006 4 0.5281 0.646 0.464 0.000 0.008 0.528
#> GSM257887 2 0.0000 0.977 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0817 0.987 0.000 0.024 0.976 0.000
#> GSM257891 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257893 3 0.1004 0.986 0.000 0.024 0.972 0.004
#> GSM257895 2 0.0469 0.975 0.000 0.988 0.000 0.012
#> GSM257897 3 0.1004 0.986 0.000 0.024 0.972 0.004
#> GSM257899 3 0.1004 0.986 0.000 0.024 0.972 0.004
#> GSM257901 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257903 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257905 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257907 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257909 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257911 3 0.1109 0.980 0.000 0.028 0.968 0.004
#> GSM257913 3 0.1305 0.972 0.000 0.036 0.960 0.004
#> GSM257916 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257918 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257920 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257922 3 0.1004 0.986 0.000 0.024 0.972 0.004
#> GSM257924 3 0.1022 0.983 0.000 0.032 0.968 0.000
#> GSM257926 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257928 2 0.0779 0.972 0.000 0.980 0.004 0.016
#> GSM257930 2 0.0592 0.973 0.000 0.984 0.000 0.016
#> GSM257938 2 0.0592 0.973 0.000 0.984 0.000 0.016
#> GSM257940 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257942 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257944 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257946 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257948 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257950 3 0.0469 0.992 0.000 0.012 0.988 0.000
#> GSM257952 2 0.2999 0.856 0.000 0.864 0.132 0.004
#> GSM257954 2 0.0469 0.975 0.000 0.988 0.000 0.012
#> GSM257956 2 0.0188 0.976 0.000 0.996 0.000 0.004
#> GSM257959 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257961 2 0.0188 0.978 0.000 0.996 0.004 0.000
#> GSM257963 2 0.0336 0.978 0.000 0.992 0.008 0.000
#> GSM257965 2 0.0657 0.977 0.000 0.984 0.012 0.004
#> GSM257967 2 0.0469 0.978 0.000 0.988 0.012 0.000
#> GSM257969 2 0.0188 0.976 0.000 0.996 0.000 0.004
#> GSM257971 3 0.1151 0.986 0.000 0.024 0.968 0.008
#> GSM257973 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257981 2 0.3157 0.842 0.000 0.852 0.144 0.004
#> GSM257983 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257985 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257988 3 0.0657 0.991 0.000 0.012 0.984 0.004
#> GSM257991 2 0.0895 0.972 0.000 0.976 0.020 0.004
#> GSM257993 2 0.0469 0.975 0.000 0.988 0.000 0.012
#> GSM257994 2 0.0592 0.973 0.000 0.984 0.000 0.016
#> GSM257989 3 0.0469 0.992 0.000 0.012 0.988 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.2127 0.8184 0.000 0.000 0.000 0.108 0.892
#> GSM257888 4 0.3565 0.8559 0.144 0.000 0.000 0.816 0.040
#> GSM257890 4 0.2719 0.8466 0.048 0.000 0.000 0.884 0.068
#> GSM257892 5 0.2074 0.8226 0.000 0.000 0.000 0.104 0.896
#> GSM257894 4 0.3922 0.8382 0.180 0.000 0.000 0.780 0.040
#> GSM257896 4 0.3771 0.8467 0.164 0.000 0.000 0.796 0.040
#> GSM257898 5 0.2127 0.9227 0.108 0.000 0.000 0.000 0.892
#> GSM257900 1 0.2900 0.8590 0.864 0.000 0.000 0.028 0.108
#> GSM257902 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257904 5 0.2286 0.9204 0.108 0.000 0.000 0.004 0.888
#> GSM257906 5 0.2127 0.9227 0.108 0.000 0.000 0.000 0.892
#> GSM257908 4 0.1908 0.8748 0.092 0.000 0.000 0.908 0.000
#> GSM257910 4 0.1908 0.8748 0.092 0.000 0.000 0.908 0.000
#> GSM257912 4 0.1768 0.8715 0.072 0.000 0.000 0.924 0.004
#> GSM257914 4 0.1768 0.8715 0.072 0.000 0.000 0.924 0.004
#> GSM257917 4 0.1768 0.8715 0.072 0.000 0.000 0.924 0.004
#> GSM257919 4 0.1768 0.8715 0.072 0.000 0.000 0.924 0.004
#> GSM257921 1 0.4384 0.4930 0.660 0.000 0.000 0.324 0.016
#> GSM257923 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0324 0.9164 0.992 0.000 0.000 0.004 0.004
#> GSM257927 1 0.2740 0.8665 0.876 0.000 0.000 0.028 0.096
#> GSM257929 1 0.0162 0.9167 0.996 0.000 0.000 0.000 0.004
#> GSM257937 4 0.2569 0.8670 0.068 0.000 0.000 0.892 0.040
#> GSM257939 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.2900 0.8582 0.864 0.000 0.000 0.028 0.108
#> GSM257943 1 0.4829 0.0265 0.500 0.000 0.000 0.020 0.480
#> GSM257945 1 0.3002 0.8506 0.856 0.000 0.000 0.028 0.116
#> GSM257947 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0162 0.9167 0.996 0.000 0.000 0.000 0.004
#> GSM257955 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0451 0.9153 0.988 0.000 0.000 0.008 0.004
#> GSM257960 1 0.2848 0.8611 0.868 0.000 0.000 0.028 0.104
#> GSM257962 1 0.2740 0.8665 0.876 0.000 0.000 0.028 0.096
#> GSM257964 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.2136 0.8755 0.088 0.000 0.000 0.904 0.008
#> GSM257968 4 0.5103 0.4497 0.452 0.000 0.000 0.512 0.036
#> GSM257970 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0290 0.9158 0.992 0.000 0.000 0.008 0.000
#> GSM257977 4 0.3810 0.8444 0.168 0.000 0.000 0.792 0.040
#> GSM257982 4 0.5161 0.4609 0.444 0.000 0.000 0.516 0.040
#> GSM257984 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9174 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.2124 0.8879 0.916 0.000 0.000 0.028 0.056
#> GSM257992 5 0.2127 0.9227 0.108 0.000 0.000 0.000 0.892
#> GSM257996 1 0.3471 0.8322 0.836 0.000 0.000 0.092 0.072
#> GSM258006 5 0.1608 0.9107 0.072 0.000 0.000 0.000 0.928
#> GSM257887 2 0.0566 0.9548 0.000 0.984 0.000 0.012 0.004
#> GSM257889 3 0.0324 0.9756 0.000 0.000 0.992 0.004 0.004
#> GSM257891 3 0.0162 0.9763 0.000 0.000 0.996 0.004 0.000
#> GSM257893 3 0.1710 0.9528 0.000 0.012 0.944 0.020 0.024
#> GSM257895 2 0.2278 0.9316 0.000 0.908 0.000 0.060 0.032
#> GSM257897 3 0.0693 0.9722 0.000 0.000 0.980 0.008 0.012
#> GSM257899 3 0.0693 0.9722 0.000 0.000 0.980 0.008 0.012
#> GSM257901 3 0.0451 0.9757 0.000 0.004 0.988 0.000 0.008
#> GSM257903 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257907 3 0.0451 0.9757 0.000 0.004 0.988 0.000 0.008
#> GSM257909 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257911 3 0.2077 0.9094 0.000 0.084 0.908 0.000 0.008
#> GSM257913 3 0.2074 0.8904 0.000 0.104 0.896 0.000 0.000
#> GSM257916 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257920 3 0.0404 0.9753 0.000 0.012 0.988 0.000 0.000
#> GSM257922 3 0.1493 0.9543 0.000 0.000 0.948 0.024 0.028
#> GSM257924 3 0.1365 0.9537 0.000 0.040 0.952 0.004 0.004
#> GSM257926 3 0.0404 0.9753 0.000 0.012 0.988 0.000 0.000
#> GSM257928 2 0.3145 0.9102 0.000 0.868 0.008 0.064 0.060
#> GSM257930 2 0.2863 0.9147 0.000 0.876 0.000 0.064 0.060
#> GSM257938 2 0.2863 0.9147 0.000 0.876 0.000 0.064 0.060
#> GSM257940 3 0.0451 0.9757 0.000 0.004 0.988 0.000 0.008
#> GSM257942 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.0000 0.9767 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0404 0.9753 0.000 0.012 0.988 0.000 0.000
#> GSM257950 3 0.0000 0.9767 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.2352 0.8780 0.000 0.896 0.092 0.004 0.008
#> GSM257954 2 0.1915 0.9394 0.000 0.928 0.000 0.040 0.032
#> GSM257956 2 0.1493 0.9464 0.000 0.948 0.000 0.024 0.028
#> GSM257959 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0290 0.9544 0.000 0.992 0.000 0.000 0.008
#> GSM257967 2 0.0000 0.9572 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.1493 0.9464 0.000 0.948 0.000 0.024 0.028
#> GSM257971 3 0.1471 0.9609 0.000 0.004 0.952 0.024 0.020
#> GSM257973 3 0.0162 0.9767 0.000 0.004 0.996 0.000 0.000
#> GSM257981 2 0.2660 0.8350 0.000 0.864 0.128 0.000 0.008
#> GSM257983 3 0.0162 0.9763 0.000 0.000 0.996 0.004 0.000
#> GSM257985 3 0.0000 0.9767 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0162 0.9767 0.000 0.004 0.996 0.000 0.000
#> GSM257991 2 0.0451 0.9525 0.000 0.988 0.004 0.000 0.008
#> GSM257993 2 0.2067 0.9365 0.000 0.920 0.000 0.048 0.032
#> GSM257994 2 0.2863 0.9147 0.000 0.876 0.000 0.064 0.060
#> GSM257989 3 0.0000 0.9767 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.2704 0.747 0.000 0.000 0.000 0.016 NA 0.844
#> GSM257888 4 0.6053 0.681 0.168 0.000 0.000 0.480 NA 0.016
#> GSM257890 4 0.4405 0.724 0.008 0.000 0.000 0.604 NA 0.020
#> GSM257892 6 0.2744 0.744 0.000 0.000 0.000 0.016 NA 0.840
#> GSM257894 4 0.6189 0.659 0.200 0.000 0.000 0.464 NA 0.016
#> GSM257896 4 0.6126 0.671 0.184 0.000 0.000 0.472 NA 0.016
#> GSM257898 6 0.0993 0.832 0.024 0.000 0.000 0.000 NA 0.964
#> GSM257900 1 0.3529 0.713 0.764 0.000 0.000 0.000 NA 0.208
#> GSM257902 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257904 6 0.1088 0.830 0.024 0.000 0.000 0.000 NA 0.960
#> GSM257906 6 0.0632 0.834 0.024 0.000 0.000 0.000 NA 0.976
#> GSM257908 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257910 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257912 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257914 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257917 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257919 4 0.0363 0.749 0.012 0.000 0.000 0.988 NA 0.000
#> GSM257921 1 0.4734 0.564 0.660 0.000 0.000 0.272 NA 0.052
#> GSM257923 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257925 1 0.0458 0.843 0.984 0.000 0.000 0.000 NA 0.000
#> GSM257927 1 0.3377 0.732 0.784 0.000 0.000 0.000 NA 0.188
#> GSM257929 1 0.0363 0.844 0.988 0.000 0.000 0.000 NA 0.000
#> GSM257937 4 0.4179 0.739 0.008 0.000 0.000 0.652 NA 0.016
#> GSM257939 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257941 1 0.3500 0.717 0.768 0.000 0.000 0.000 NA 0.204
#> GSM257943 6 0.4473 -0.178 0.480 0.000 0.000 0.000 NA 0.492
#> GSM257945 1 0.3440 0.725 0.776 0.000 0.000 0.000 NA 0.196
#> GSM257947 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257949 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257951 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257953 1 0.0260 0.845 0.992 0.000 0.000 0.000 NA 0.000
#> GSM257955 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257958 1 0.0458 0.843 0.984 0.000 0.000 0.000 NA 0.000
#> GSM257960 1 0.3440 0.725 0.776 0.000 0.000 0.000 NA 0.196
#> GSM257962 1 0.3377 0.732 0.784 0.000 0.000 0.000 NA 0.188
#> GSM257964 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257966 4 0.3820 0.747 0.008 0.000 0.000 0.700 NA 0.008
#> GSM257968 1 0.6439 -0.396 0.392 0.000 0.000 0.288 NA 0.016
#> GSM257970 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257972 1 0.0000 0.846 1.000 0.000 0.000 0.000 NA 0.000
#> GSM257977 4 0.6075 0.675 0.168 0.000 0.000 0.468 NA 0.016
#> GSM257982 1 0.6484 -0.478 0.352 0.000 0.000 0.320 NA 0.016
#> GSM257984 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257986 1 0.0146 0.846 0.996 0.000 0.000 0.000 NA 0.000
#> GSM257990 1 0.2740 0.780 0.852 0.000 0.000 0.000 NA 0.120
#> GSM257992 6 0.1633 0.829 0.024 0.000 0.000 0.000 NA 0.932
#> GSM257996 1 0.4617 0.689 0.736 0.000 0.000 0.100 NA 0.136
#> GSM258006 6 0.0820 0.831 0.012 0.000 0.000 0.000 NA 0.972
#> GSM257887 2 0.1141 0.853 0.000 0.948 0.000 0.000 NA 0.000
#> GSM257889 3 0.0937 0.912 0.000 0.000 0.960 0.000 NA 0.000
#> GSM257891 3 0.0458 0.915 0.000 0.000 0.984 0.000 NA 0.000
#> GSM257893 3 0.3110 0.820 0.000 0.012 0.792 0.000 NA 0.000
#> GSM257895 2 0.3464 0.739 0.000 0.688 0.000 0.000 NA 0.000
#> GSM257897 3 0.2146 0.881 0.000 0.000 0.880 0.004 NA 0.000
#> GSM257899 3 0.2482 0.862 0.000 0.000 0.848 0.004 NA 0.000
#> GSM257901 3 0.1628 0.904 0.000 0.012 0.940 0.004 NA 0.008
#> GSM257903 2 0.0260 0.859 0.000 0.992 0.000 0.000 NA 0.000
#> GSM257905 2 0.0146 0.860 0.000 0.996 0.000 0.000 NA 0.000
#> GSM257907 3 0.1526 0.906 0.000 0.008 0.944 0.004 NA 0.008
#> GSM257909 2 0.0000 0.860 0.000 1.000 0.000 0.000 NA 0.000
#> GSM257911 3 0.4467 0.624 0.000 0.276 0.676 0.004 NA 0.008
#> GSM257913 3 0.3393 0.761 0.000 0.192 0.784 0.000 NA 0.004
#> GSM257916 2 0.0260 0.859 0.000 0.992 0.000 0.000 NA 0.000
#> GSM257918 2 0.0260 0.859 0.000 0.992 0.000 0.000 NA 0.000
#> GSM257920 3 0.0870 0.916 0.000 0.012 0.972 0.000 NA 0.004
#> GSM257922 3 0.3198 0.771 0.000 0.000 0.740 0.000 NA 0.000
#> GSM257924 3 0.1789 0.907 0.000 0.032 0.924 0.000 NA 0.000
#> GSM257926 3 0.0909 0.917 0.000 0.012 0.968 0.000 NA 0.000
#> GSM257928 2 0.4338 0.579 0.000 0.496 0.020 0.000 NA 0.000
#> GSM257930 2 0.3860 0.609 0.000 0.528 0.000 0.000 NA 0.000
#> GSM257938 2 0.3854 0.618 0.000 0.536 0.000 0.000 NA 0.000
#> GSM257940 3 0.1628 0.904 0.000 0.012 0.940 0.004 NA 0.008
#> GSM257942 2 0.0520 0.857 0.000 0.984 0.008 0.000 NA 0.000
#> GSM257944 2 0.0520 0.857 0.000 0.984 0.008 0.000 NA 0.000
#> GSM257946 3 0.0777 0.916 0.000 0.004 0.972 0.000 NA 0.000
#> GSM257948 3 0.1138 0.913 0.000 0.024 0.960 0.000 NA 0.004
#> GSM257950 3 0.0405 0.916 0.000 0.004 0.988 0.000 NA 0.000
#> GSM257952 2 0.3170 0.781 0.000 0.844 0.104 0.004 NA 0.008
#> GSM257954 2 0.3198 0.770 0.000 0.740 0.000 0.000 NA 0.000
#> GSM257956 2 0.2340 0.824 0.000 0.852 0.000 0.000 NA 0.000
#> GSM257959 2 0.0146 0.860 0.000 0.996 0.000 0.000 NA 0.000
#> GSM257961 2 0.0260 0.860 0.000 0.992 0.000 0.000 NA 0.000
#> GSM257963 2 0.0260 0.860 0.000 0.992 0.000 0.000 NA 0.000
#> GSM257965 2 0.1628 0.839 0.000 0.940 0.012 0.004 NA 0.008
#> GSM257967 2 0.0146 0.860 0.000 0.996 0.000 0.000 NA 0.000
#> GSM257969 2 0.2300 0.826 0.000 0.856 0.000 0.000 NA 0.000
#> GSM257971 3 0.3512 0.788 0.000 0.000 0.740 0.004 NA 0.008
#> GSM257973 3 0.0508 0.916 0.000 0.004 0.984 0.000 NA 0.000
#> GSM257981 2 0.3392 0.755 0.000 0.824 0.124 0.004 NA 0.008
#> GSM257983 3 0.1010 0.909 0.000 0.000 0.960 0.004 NA 0.000
#> GSM257985 3 0.0508 0.917 0.000 0.004 0.984 0.000 NA 0.000
#> GSM257988 3 0.0603 0.916 0.000 0.000 0.980 0.000 NA 0.004
#> GSM257991 2 0.2050 0.827 0.000 0.920 0.032 0.004 NA 0.008
#> GSM257993 2 0.3266 0.763 0.000 0.728 0.000 0.000 NA 0.000
#> GSM257994 2 0.3854 0.618 0.000 0.536 0.000 0.000 NA 0.000
#> GSM257989 3 0.0405 0.916 0.000 0.004 0.988 0.000 NA 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> MAD:skmeans 96 8.49e-22 1.000 1.000 2
#> MAD:skmeans 96 1.43e-21 0.691 1.000 3
#> MAD:skmeans 78 8.24e-17 0.746 0.984 4
#> MAD:skmeans 92 4.95e-19 0.623 0.990 5
#> MAD:skmeans 93 3.03e-19 0.639 0.991 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.750 0.910 0.901 0.2480 0.874 0.745
#> 4 4 0.772 0.673 0.847 0.1859 0.874 0.663
#> 5 5 0.849 0.791 0.907 0.0561 0.913 0.682
#> 6 6 0.844 0.776 0.836 0.0336 0.938 0.727
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257888 1 0.4235 0.919 0.824 0.176 0.000
#> GSM257890 1 0.3482 0.924 0.872 0.128 0.000
#> GSM257892 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257894 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257896 1 0.3482 0.924 0.872 0.128 0.000
#> GSM257898 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257902 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257904 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257906 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257908 1 0.4121 0.920 0.832 0.168 0.000
#> GSM257910 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257912 1 0.2448 0.935 0.924 0.076 0.000
#> GSM257914 1 0.3192 0.929 0.888 0.112 0.000
#> GSM257917 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257919 1 0.2959 0.931 0.900 0.100 0.000
#> GSM257921 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257923 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257925 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257927 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257929 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257937 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257939 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257941 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257947 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257949 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257951 1 0.4002 0.924 0.840 0.160 0.000
#> GSM257953 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257955 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257958 1 0.1860 0.937 0.948 0.052 0.000
#> GSM257960 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257964 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257966 1 0.4062 0.921 0.836 0.164 0.000
#> GSM257968 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257970 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257972 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257977 1 0.3482 0.924 0.872 0.128 0.000
#> GSM257982 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257984 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257986 1 0.4291 0.918 0.820 0.180 0.000
#> GSM257990 1 0.0747 0.939 0.984 0.016 0.000
#> GSM257992 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257996 1 0.0000 0.939 1.000 0.000 0.000
#> GSM258006 1 0.0000 0.939 1.000 0.000 0.000
#> GSM257887 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257889 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257891 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257893 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257895 2 0.4654 0.943 0.000 0.792 0.208
#> GSM257897 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257899 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257901 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257903 2 0.5254 0.870 0.000 0.736 0.264
#> GSM257905 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257907 3 0.1163 0.910 0.000 0.028 0.972
#> GSM257909 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257911 3 0.6140 -0.014 0.000 0.404 0.596
#> GSM257913 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257916 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257918 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257920 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257922 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257924 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257926 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257928 3 0.5363 0.481 0.000 0.276 0.724
#> GSM257930 3 0.6140 0.059 0.000 0.404 0.596
#> GSM257938 2 0.4504 0.954 0.000 0.804 0.196
#> GSM257940 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257942 2 0.4346 0.964 0.000 0.816 0.184
#> GSM257944 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257946 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257948 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257950 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257952 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257954 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257956 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257959 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257961 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257963 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257965 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257967 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257969 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257971 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257973 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257981 2 0.5733 0.774 0.000 0.676 0.324
#> GSM257983 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257985 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257988 3 0.0000 0.940 0.000 0.000 1.000
#> GSM257991 2 0.6215 0.547 0.000 0.572 0.428
#> GSM257993 2 0.4291 0.966 0.000 0.820 0.180
#> GSM257994 2 0.4654 0.943 0.000 0.792 0.208
#> GSM257989 3 0.0000 0.940 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257888 4 0.3219 0.5660 0.164 0.000 0.000 0.836
#> GSM257890 4 0.0000 0.5127 0.000 0.000 0.000 1.000
#> GSM257892 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257894 4 0.4992 0.4940 0.476 0.000 0.000 0.524
#> GSM257896 4 0.0000 0.5127 0.000 0.000 0.000 1.000
#> GSM257898 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257900 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257902 4 0.4996 0.4893 0.484 0.000 0.000 0.516
#> GSM257904 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257906 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257908 4 0.2149 0.5582 0.088 0.000 0.000 0.912
#> GSM257910 4 0.4134 0.5561 0.260 0.000 0.000 0.740
#> GSM257912 4 0.4040 -0.0208 0.248 0.000 0.000 0.752
#> GSM257914 4 0.3528 0.1599 0.192 0.000 0.000 0.808
#> GSM257917 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257919 4 0.3610 0.1388 0.200 0.000 0.000 0.800
#> GSM257921 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257923 1 0.0000 0.3730 1.000 0.000 0.000 0.000
#> GSM257925 1 0.3172 0.4373 0.840 0.000 0.000 0.160
#> GSM257927 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257929 1 0.0000 0.3730 1.000 0.000 0.000 0.000
#> GSM257937 4 0.2868 0.2991 0.136 0.000 0.000 0.864
#> GSM257939 1 0.0000 0.3730 1.000 0.000 0.000 0.000
#> GSM257941 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257943 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257945 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257947 1 0.0000 0.3730 1.000 0.000 0.000 0.000
#> GSM257949 1 0.4454 -0.2294 0.692 0.000 0.000 0.308
#> GSM257951 1 0.2647 0.2022 0.880 0.000 0.000 0.120
#> GSM257953 1 0.0817 0.3893 0.976 0.000 0.000 0.024
#> GSM257955 1 0.2868 0.1741 0.864 0.000 0.000 0.136
#> GSM257958 1 0.0817 0.3893 0.976 0.000 0.000 0.024
#> GSM257960 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257962 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257964 1 0.2868 0.1741 0.864 0.000 0.000 0.136
#> GSM257966 4 0.1867 0.5530 0.072 0.000 0.000 0.928
#> GSM257968 4 0.4996 0.4893 0.484 0.000 0.000 0.516
#> GSM257970 1 0.2868 0.1741 0.864 0.000 0.000 0.136
#> GSM257972 1 0.4830 0.5844 0.608 0.000 0.000 0.392
#> GSM257977 4 0.0000 0.5127 0.000 0.000 0.000 1.000
#> GSM257982 4 0.4643 0.5336 0.344 0.000 0.000 0.656
#> GSM257984 4 0.4992 0.4940 0.476 0.000 0.000 0.524
#> GSM257986 4 0.4996 0.4893 0.484 0.000 0.000 0.516
#> GSM257990 1 0.4981 0.6270 0.536 0.000 0.000 0.464
#> GSM257992 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257996 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM258006 1 0.4996 0.6397 0.516 0.000 0.000 0.484
#> GSM257887 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257895 2 0.3074 0.8114 0.000 0.848 0.152 0.000
#> GSM257897 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257901 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257903 2 0.2149 0.8662 0.000 0.912 0.088 0.000
#> GSM257905 2 0.0188 0.9239 0.000 0.996 0.004 0.000
#> GSM257907 3 0.3074 0.7894 0.000 0.152 0.848 0.000
#> GSM257909 2 0.0188 0.9239 0.000 0.996 0.004 0.000
#> GSM257911 2 0.4994 0.1149 0.000 0.520 0.480 0.000
#> GSM257913 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257916 2 0.0188 0.9239 0.000 0.996 0.004 0.000
#> GSM257918 2 0.0188 0.9239 0.000 0.996 0.004 0.000
#> GSM257920 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257922 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257924 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257926 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257928 3 0.4277 0.5733 0.000 0.280 0.720 0.000
#> GSM257930 3 0.4977 0.0941 0.000 0.460 0.540 0.000
#> GSM257938 2 0.2345 0.8602 0.000 0.900 0.100 0.000
#> GSM257940 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257942 2 0.0336 0.9223 0.000 0.992 0.008 0.000
#> GSM257944 2 0.0188 0.9239 0.000 0.996 0.004 0.000
#> GSM257946 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257950 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257952 2 0.0188 0.9234 0.000 0.996 0.004 0.000
#> GSM257954 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0188 0.9234 0.000 0.996 0.004 0.000
#> GSM257959 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257971 3 0.0188 0.9529 0.000 0.004 0.996 0.000
#> GSM257973 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257981 2 0.4250 0.6408 0.000 0.724 0.276 0.000
#> GSM257983 3 0.0336 0.9496 0.000 0.008 0.992 0.000
#> GSM257985 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0000 0.9563 0.000 0.000 1.000 0.000
#> GSM257991 2 0.4406 0.5986 0.000 0.700 0.300 0.000
#> GSM257993 2 0.0000 0.9238 0.000 1.000 0.000 0.000
#> GSM257994 2 0.3074 0.8114 0.000 0.848 0.152 0.000
#> GSM257989 3 0.0000 0.9563 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.4420 0.0384 0.548 0.000 0.000 0.448 0.004
#> GSM257888 4 0.1908 0.7027 0.000 0.000 0.000 0.908 0.092
#> GSM257890 4 0.2488 0.7329 0.124 0.000 0.000 0.872 0.004
#> GSM257892 1 0.4420 0.0384 0.548 0.000 0.000 0.448 0.004
#> GSM257894 4 0.2280 0.6856 0.000 0.000 0.000 0.880 0.120
#> GSM257896 4 0.1557 0.7381 0.052 0.000 0.000 0.940 0.008
#> GSM257898 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257900 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257902 5 0.4150 0.3691 0.000 0.000 0.000 0.388 0.612
#> GSM257904 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257906 1 0.0162 0.8868 0.996 0.000 0.000 0.000 0.004
#> GSM257908 4 0.5286 0.1006 0.448 0.000 0.000 0.504 0.048
#> GSM257910 4 0.5492 0.1341 0.432 0.000 0.000 0.504 0.064
#> GSM257912 1 0.3242 0.7718 0.844 0.000 0.000 0.116 0.040
#> GSM257914 1 0.3386 0.7593 0.832 0.000 0.000 0.128 0.040
#> GSM257917 1 0.3192 0.7745 0.848 0.000 0.000 0.112 0.040
#> GSM257919 1 0.3339 0.7639 0.836 0.000 0.000 0.124 0.040
#> GSM257921 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257923 5 0.1410 0.8607 0.060 0.000 0.000 0.000 0.940
#> GSM257925 5 0.4210 0.3690 0.412 0.000 0.000 0.000 0.588
#> GSM257927 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257929 5 0.2648 0.7710 0.152 0.000 0.000 0.000 0.848
#> GSM257937 4 0.4192 0.3046 0.404 0.000 0.000 0.596 0.000
#> GSM257939 5 0.1410 0.8607 0.060 0.000 0.000 0.000 0.940
#> GSM257941 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257947 5 0.1410 0.8607 0.060 0.000 0.000 0.000 0.940
#> GSM257949 5 0.1549 0.8606 0.040 0.000 0.000 0.016 0.944
#> GSM257951 5 0.1626 0.8613 0.044 0.000 0.000 0.016 0.940
#> GSM257953 5 0.1732 0.8476 0.080 0.000 0.000 0.000 0.920
#> GSM257955 5 0.1549 0.8606 0.040 0.000 0.000 0.016 0.944
#> GSM257958 5 0.1410 0.8607 0.060 0.000 0.000 0.000 0.940
#> GSM257960 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257964 5 0.1549 0.8606 0.040 0.000 0.000 0.016 0.944
#> GSM257966 4 0.0703 0.7099 0.000 0.000 0.000 0.976 0.024
#> GSM257968 4 0.3305 0.5684 0.000 0.000 0.000 0.776 0.224
#> GSM257970 5 0.1549 0.8606 0.040 0.000 0.000 0.016 0.944
#> GSM257972 1 0.3274 0.6161 0.780 0.000 0.000 0.000 0.220
#> GSM257977 4 0.2389 0.7353 0.116 0.000 0.000 0.880 0.004
#> GSM257982 4 0.2685 0.7347 0.092 0.000 0.000 0.880 0.028
#> GSM257984 5 0.4504 0.2615 0.008 0.000 0.000 0.428 0.564
#> GSM257986 5 0.2648 0.7445 0.000 0.000 0.000 0.152 0.848
#> GSM257990 1 0.0963 0.8600 0.964 0.000 0.000 0.000 0.036
#> GSM257992 1 0.0162 0.8868 0.996 0.000 0.000 0.000 0.004
#> GSM257996 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM258006 1 0.0000 0.8886 1.000 0.000 0.000 0.000 0.000
#> GSM257887 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> GSM257889 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257891 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257895 2 0.3080 0.8120 0.000 0.844 0.140 0.008 0.008
#> GSM257897 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257899 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257901 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257903 2 0.1851 0.8536 0.000 0.912 0.088 0.000 0.000
#> GSM257905 2 0.0324 0.9145 0.000 0.992 0.004 0.004 0.000
#> GSM257907 3 0.2516 0.8002 0.000 0.140 0.860 0.000 0.000
#> GSM257909 2 0.0162 0.9140 0.000 0.996 0.004 0.000 0.000
#> GSM257911 2 0.4300 0.1248 0.000 0.524 0.476 0.000 0.000
#> GSM257913 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257916 2 0.0162 0.9140 0.000 0.996 0.004 0.000 0.000
#> GSM257918 2 0.0162 0.9140 0.000 0.996 0.004 0.000 0.000
#> GSM257920 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257922 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257924 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257926 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257928 3 0.4336 0.5423 0.000 0.280 0.700 0.008 0.012
#> GSM257930 3 0.4895 0.0684 0.000 0.452 0.528 0.008 0.012
#> GSM257938 2 0.2532 0.8569 0.000 0.892 0.088 0.008 0.012
#> GSM257940 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257942 2 0.0290 0.9128 0.000 0.992 0.008 0.000 0.000
#> GSM257944 2 0.0162 0.9140 0.000 0.996 0.004 0.000 0.000
#> GSM257946 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257950 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.0854 0.9120 0.000 0.976 0.004 0.008 0.012
#> GSM257954 2 0.0693 0.9120 0.000 0.980 0.000 0.008 0.012
#> GSM257956 2 0.0854 0.9120 0.000 0.976 0.004 0.008 0.012
#> GSM257959 2 0.0579 0.9128 0.000 0.984 0.000 0.008 0.008
#> GSM257961 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0566 0.9129 0.000 0.984 0.000 0.004 0.012
#> GSM257967 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.0693 0.9120 0.000 0.980 0.000 0.008 0.012
#> GSM257971 3 0.0162 0.9488 0.000 0.004 0.996 0.000 0.000
#> GSM257973 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.3661 0.6437 0.000 0.724 0.276 0.000 0.000
#> GSM257983 3 0.0290 0.9454 0.000 0.008 0.992 0.000 0.000
#> GSM257985 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
#> GSM257991 2 0.3752 0.6070 0.000 0.708 0.292 0.000 0.000
#> GSM257993 2 0.0693 0.9120 0.000 0.980 0.000 0.008 0.012
#> GSM257994 2 0.3190 0.8109 0.000 0.840 0.140 0.008 0.012
#> GSM257989 3 0.0000 0.9525 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.4255 0.450 0.004 0.000 0.000 0.712 0.228 0.056
#> GSM257888 4 0.2728 0.685 0.100 0.000 0.000 0.860 0.040 0.000
#> GSM257890 4 0.0653 0.712 0.004 0.000 0.000 0.980 0.012 0.004
#> GSM257892 4 0.4255 0.450 0.004 0.000 0.000 0.712 0.228 0.056
#> GSM257894 4 0.2260 0.675 0.140 0.000 0.000 0.860 0.000 0.000
#> GSM257896 4 0.2377 0.701 0.008 0.000 0.000 0.892 0.076 0.024
#> GSM257898 6 0.4993 0.960 0.004 0.000 0.000 0.072 0.344 0.580
#> GSM257900 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257902 1 0.3050 0.609 0.764 0.000 0.000 0.236 0.000 0.000
#> GSM257904 6 0.4993 0.960 0.004 0.000 0.000 0.072 0.344 0.580
#> GSM257906 6 0.5593 0.884 0.004 0.000 0.000 0.140 0.332 0.524
#> GSM257908 5 0.3189 0.587 0.004 0.000 0.000 0.236 0.760 0.000
#> GSM257910 5 0.3298 0.582 0.008 0.000 0.000 0.236 0.756 0.000
#> GSM257912 5 0.0000 0.816 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257914 5 0.0146 0.819 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM257917 5 0.0000 0.816 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257919 5 0.0146 0.819 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM257921 6 0.5128 0.961 0.008 0.000 0.000 0.072 0.356 0.564
#> GSM257923 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257925 1 0.5868 -0.161 0.448 0.000 0.000 0.052 0.064 0.436
#> GSM257927 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257929 1 0.4882 0.159 0.540 0.000 0.000 0.052 0.004 0.404
#> GSM257937 4 0.4650 0.493 0.004 0.000 0.000 0.688 0.212 0.096
#> GSM257939 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257941 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257943 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257945 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257947 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257949 1 0.0146 0.844 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257951 1 0.0000 0.844 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0922 0.825 0.968 0.000 0.000 0.004 0.004 0.024
#> GSM257955 1 0.0146 0.844 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257958 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM257960 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257962 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM257964 1 0.0146 0.844 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257966 4 0.3290 0.539 0.004 0.000 0.000 0.744 0.252 0.000
#> GSM257968 4 0.3634 0.378 0.356 0.000 0.000 0.644 0.000 0.000
#> GSM257970 1 0.0146 0.844 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM257972 6 0.5979 0.906 0.072 0.000 0.000 0.072 0.308 0.548
#> GSM257977 4 0.1555 0.711 0.004 0.000 0.000 0.932 0.004 0.060
#> GSM257982 4 0.2052 0.718 0.028 0.000 0.000 0.912 0.004 0.056
#> GSM257984 1 0.3468 0.530 0.712 0.000 0.000 0.284 0.000 0.004
#> GSM257986 1 0.1765 0.776 0.904 0.000 0.000 0.096 0.000 0.000
#> GSM257990 6 0.5738 0.933 0.052 0.000 0.000 0.068 0.324 0.556
#> GSM257992 6 0.5593 0.884 0.004 0.000 0.000 0.140 0.332 0.524
#> GSM257996 6 0.5345 0.968 0.020 0.000 0.000 0.072 0.344 0.564
#> GSM258006 6 0.4993 0.960 0.004 0.000 0.000 0.072 0.344 0.580
#> GSM257887 2 0.2664 0.781 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM257889 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257891 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257893 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257895 2 0.2618 0.720 0.000 0.872 0.076 0.000 0.000 0.052
#> GSM257897 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257899 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257901 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257903 2 0.5086 0.678 0.000 0.532 0.084 0.000 0.000 0.384
#> GSM257905 2 0.3023 0.781 0.000 0.784 0.004 0.000 0.000 0.212
#> GSM257907 3 0.1757 0.866 0.000 0.076 0.916 0.000 0.000 0.008
#> GSM257909 2 0.3862 0.736 0.000 0.608 0.004 0.000 0.000 0.388
#> GSM257911 3 0.6125 -0.349 0.000 0.320 0.356 0.000 0.000 0.324
#> GSM257913 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257916 2 0.3769 0.748 0.000 0.640 0.004 0.000 0.000 0.356
#> GSM257918 2 0.3862 0.736 0.000 0.608 0.004 0.000 0.000 0.388
#> GSM257920 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257922 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257924 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257926 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257928 2 0.4241 0.293 0.000 0.608 0.368 0.000 0.000 0.024
#> GSM257930 2 0.4045 0.418 0.000 0.664 0.312 0.000 0.000 0.024
#> GSM257938 2 0.1633 0.729 0.000 0.932 0.044 0.000 0.000 0.024
#> GSM257940 3 0.2378 0.808 0.000 0.000 0.848 0.000 0.000 0.152
#> GSM257942 2 0.3862 0.736 0.000 0.608 0.004 0.000 0.000 0.388
#> GSM257944 2 0.3862 0.736 0.000 0.608 0.004 0.000 0.000 0.388
#> GSM257946 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257948 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257950 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257952 2 0.0935 0.766 0.000 0.964 0.004 0.000 0.000 0.032
#> GSM257954 2 0.0146 0.758 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM257956 2 0.0935 0.766 0.000 0.964 0.004 0.000 0.000 0.032
#> GSM257959 2 0.3101 0.775 0.000 0.756 0.000 0.000 0.000 0.244
#> GSM257961 2 0.3023 0.780 0.000 0.768 0.000 0.000 0.000 0.232
#> GSM257963 2 0.3151 0.777 0.000 0.748 0.000 0.000 0.000 0.252
#> GSM257965 2 0.1141 0.769 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM257967 2 0.3428 0.767 0.000 0.696 0.000 0.000 0.000 0.304
#> GSM257969 2 0.0146 0.758 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM257971 3 0.0146 0.952 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM257973 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257981 2 0.4170 0.579 0.000 0.660 0.308 0.000 0.000 0.032
#> GSM257983 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257985 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257988 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257991 2 0.5930 0.540 0.000 0.404 0.212 0.000 0.000 0.384
#> GSM257993 2 0.0000 0.757 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257994 2 0.2122 0.707 0.000 0.900 0.076 0.000 0.000 0.024
#> GSM257989 3 0.0000 0.956 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> MAD:pam 96 8.49e-22 1.000 1.000 2
#> MAD:pam 93 6.39e-21 0.713 1.000 3
#> MAD:pam 73 9.72e-16 0.883 0.966 4
#> MAD:pam 86 9.31e-18 0.931 0.983 5
#> MAD:pam 87 2.87e-17 0.674 0.961 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.829 0.940 0.886 0.1963 0.884 0.766
#> 4 4 0.766 0.895 0.882 0.1830 0.884 0.695
#> 5 5 0.678 0.761 0.821 0.0704 0.948 0.805
#> 6 6 0.694 0.639 0.752 0.0543 0.895 0.587
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 3 0.6140 0.970 0.404 0.000 0.596
#> GSM257888 3 0.6079 0.987 0.388 0.000 0.612
#> GSM257890 3 0.6154 0.969 0.408 0.000 0.592
#> GSM257892 3 0.6140 0.970 0.404 0.000 0.596
#> GSM257894 3 0.6111 0.982 0.396 0.000 0.604
#> GSM257896 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257898 1 0.0237 0.969 0.996 0.000 0.004
#> GSM257900 1 0.0892 0.967 0.980 0.000 0.020
#> GSM257902 1 0.1753 0.943 0.952 0.000 0.048
#> GSM257904 1 0.1860 0.943 0.948 0.000 0.052
#> GSM257906 1 0.0892 0.969 0.980 0.000 0.020
#> GSM257908 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257910 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257912 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257914 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257917 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257919 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257921 1 0.1860 0.938 0.948 0.000 0.052
#> GSM257923 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257927 1 0.0424 0.972 0.992 0.000 0.008
#> GSM257929 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257937 3 0.6079 0.987 0.388 0.000 0.612
#> GSM257939 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257943 1 0.0237 0.969 0.996 0.000 0.004
#> GSM257945 1 0.0237 0.969 0.996 0.000 0.004
#> GSM257947 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257949 1 0.1860 0.938 0.948 0.000 0.052
#> GSM257951 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257953 1 0.0237 0.969 0.996 0.000 0.004
#> GSM257955 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257960 1 0.0892 0.967 0.980 0.000 0.020
#> GSM257962 1 0.0592 0.971 0.988 0.000 0.012
#> GSM257964 1 0.0892 0.967 0.980 0.000 0.020
#> GSM257966 3 0.6079 0.987 0.388 0.000 0.612
#> GSM257968 3 0.6154 0.969 0.408 0.000 0.592
#> GSM257970 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257972 1 0.1643 0.947 0.956 0.000 0.044
#> GSM257977 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257982 3 0.6062 0.986 0.384 0.000 0.616
#> GSM257984 1 0.1860 0.938 0.948 0.000 0.052
#> GSM257986 1 0.1753 0.943 0.952 0.000 0.048
#> GSM257990 1 0.0000 0.972 1.000 0.000 0.000
#> GSM257992 1 0.0747 0.970 0.984 0.000 0.016
#> GSM257996 1 0.1860 0.938 0.948 0.000 0.052
#> GSM258006 1 0.0747 0.970 0.984 0.000 0.016
#> GSM257887 2 0.1964 0.935 0.000 0.944 0.056
#> GSM257889 2 0.2959 0.926 0.000 0.900 0.100
#> GSM257891 2 0.5785 0.762 0.000 0.668 0.332
#> GSM257893 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257895 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257897 2 0.6062 0.725 0.000 0.616 0.384
#> GSM257899 2 0.6062 0.725 0.000 0.616 0.384
#> GSM257901 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257903 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257905 2 0.1031 0.939 0.000 0.976 0.024
#> GSM257907 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257909 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257911 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257913 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257916 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257920 2 0.0592 0.938 0.000 0.988 0.012
#> GSM257922 2 0.4796 0.861 0.000 0.780 0.220
#> GSM257924 2 0.1411 0.938 0.000 0.964 0.036
#> GSM257926 2 0.0237 0.940 0.000 0.996 0.004
#> GSM257928 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257930 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257938 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257940 2 0.2261 0.917 0.000 0.932 0.068
#> GSM257942 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257946 2 0.1031 0.939 0.000 0.976 0.024
#> GSM257948 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257950 2 0.5138 0.815 0.000 0.748 0.252
#> GSM257952 2 0.0237 0.940 0.000 0.996 0.004
#> GSM257954 2 0.2448 0.932 0.000 0.924 0.076
#> GSM257956 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257959 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257961 2 0.2165 0.934 0.000 0.936 0.064
#> GSM257963 2 0.0892 0.939 0.000 0.980 0.020
#> GSM257965 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257969 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257971 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257973 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257981 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257983 2 0.5988 0.727 0.000 0.632 0.368
#> GSM257985 2 0.2711 0.907 0.000 0.912 0.088
#> GSM257988 2 0.5058 0.816 0.000 0.756 0.244
#> GSM257991 2 0.0000 0.940 0.000 1.000 0.000
#> GSM257993 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257994 2 0.2537 0.931 0.000 0.920 0.080
#> GSM257989 2 0.4121 0.857 0.000 0.832 0.168
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.2868 0.878 0.136 0.000 0.000 0.864
#> GSM257888 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257890 4 0.2704 0.889 0.124 0.000 0.000 0.876
#> GSM257892 4 0.2868 0.878 0.136 0.000 0.000 0.864
#> GSM257894 4 0.1211 0.933 0.040 0.000 0.000 0.960
#> GSM257896 4 0.0817 0.952 0.024 0.000 0.000 0.976
#> GSM257898 1 0.2281 0.917 0.904 0.000 0.000 0.096
#> GSM257900 1 0.1474 0.928 0.948 0.000 0.000 0.052
#> GSM257902 1 0.2281 0.916 0.904 0.000 0.000 0.096
#> GSM257904 1 0.2281 0.917 0.904 0.000 0.000 0.096
#> GSM257906 1 0.2281 0.917 0.904 0.000 0.000 0.096
#> GSM257908 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257910 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257912 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257914 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257917 4 0.1557 0.927 0.056 0.000 0.000 0.944
#> GSM257919 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257921 1 0.2081 0.921 0.916 0.000 0.000 0.084
#> GSM257923 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257925 1 0.2345 0.926 0.900 0.000 0.000 0.100
#> GSM257927 1 0.1302 0.928 0.956 0.000 0.000 0.044
#> GSM257929 1 0.2011 0.922 0.920 0.000 0.000 0.080
#> GSM257937 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257939 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257941 1 0.1211 0.927 0.960 0.000 0.000 0.040
#> GSM257943 1 0.1867 0.923 0.928 0.000 0.000 0.072
#> GSM257945 1 0.2149 0.921 0.912 0.000 0.000 0.088
#> GSM257947 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257949 1 0.2345 0.916 0.900 0.000 0.000 0.100
#> GSM257951 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257953 1 0.3074 0.914 0.848 0.000 0.000 0.152
#> GSM257955 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257958 1 0.1022 0.926 0.968 0.000 0.000 0.032
#> GSM257960 1 0.1867 0.926 0.928 0.000 0.000 0.072
#> GSM257962 1 0.1474 0.928 0.948 0.000 0.000 0.052
#> GSM257964 1 0.2281 0.916 0.904 0.000 0.000 0.096
#> GSM257966 4 0.0188 0.959 0.004 0.000 0.000 0.996
#> GSM257968 4 0.2345 0.899 0.100 0.000 0.000 0.900
#> GSM257970 1 0.2081 0.915 0.916 0.000 0.000 0.084
#> GSM257972 1 0.2216 0.931 0.908 0.000 0.000 0.092
#> GSM257977 4 0.0336 0.958 0.008 0.000 0.000 0.992
#> GSM257982 4 0.1118 0.944 0.036 0.000 0.000 0.964
#> GSM257984 1 0.2408 0.917 0.896 0.000 0.000 0.104
#> GSM257986 1 0.2345 0.916 0.900 0.000 0.000 0.100
#> GSM257990 1 0.1211 0.927 0.960 0.000 0.000 0.040
#> GSM257992 1 0.2281 0.917 0.904 0.000 0.000 0.096
#> GSM257996 1 0.2081 0.921 0.916 0.000 0.000 0.084
#> GSM258006 1 0.2281 0.917 0.904 0.000 0.000 0.096
#> GSM257887 2 0.0469 0.955 0.000 0.988 0.012 0.000
#> GSM257889 3 0.4356 0.825 0.000 0.292 0.708 0.000
#> GSM257891 3 0.2345 0.768 0.000 0.100 0.900 0.000
#> GSM257893 2 0.2647 0.826 0.000 0.880 0.120 0.000
#> GSM257895 2 0.0336 0.954 0.000 0.992 0.008 0.000
#> GSM257897 3 0.0188 0.679 0.000 0.004 0.996 0.000
#> GSM257899 3 0.0188 0.679 0.000 0.004 0.996 0.000
#> GSM257901 3 0.4790 0.775 0.000 0.380 0.620 0.000
#> GSM257903 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257905 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257907 3 0.4790 0.775 0.000 0.380 0.620 0.000
#> GSM257909 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257911 2 0.1389 0.912 0.000 0.952 0.048 0.000
#> GSM257913 2 0.0921 0.939 0.000 0.972 0.028 0.000
#> GSM257916 2 0.0188 0.956 0.000 0.996 0.004 0.000
#> GSM257918 2 0.0188 0.956 0.000 0.996 0.004 0.000
#> GSM257920 3 0.4776 0.780 0.000 0.376 0.624 0.000
#> GSM257922 3 0.3649 0.818 0.000 0.204 0.796 0.000
#> GSM257924 2 0.2345 0.841 0.000 0.900 0.100 0.000
#> GSM257926 2 0.4661 0.122 0.000 0.652 0.348 0.000
#> GSM257928 2 0.1118 0.936 0.000 0.964 0.036 0.000
#> GSM257930 2 0.0707 0.948 0.000 0.980 0.020 0.000
#> GSM257938 2 0.0707 0.948 0.000 0.980 0.020 0.000
#> GSM257940 3 0.4761 0.784 0.000 0.372 0.628 0.000
#> GSM257942 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257944 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257946 3 0.4500 0.820 0.000 0.316 0.684 0.000
#> GSM257948 3 0.4790 0.775 0.000 0.380 0.620 0.000
#> GSM257950 3 0.3569 0.816 0.000 0.196 0.804 0.000
#> GSM257952 2 0.0188 0.955 0.000 0.996 0.004 0.000
#> GSM257954 2 0.0188 0.955 0.000 0.996 0.004 0.000
#> GSM257956 2 0.0469 0.953 0.000 0.988 0.012 0.000
#> GSM257959 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257961 2 0.0336 0.956 0.000 0.992 0.008 0.000
#> GSM257963 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257965 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0336 0.955 0.000 0.992 0.008 0.000
#> GSM257969 2 0.0336 0.954 0.000 0.992 0.008 0.000
#> GSM257971 2 0.2814 0.820 0.000 0.868 0.132 0.000
#> GSM257973 3 0.4761 0.780 0.000 0.372 0.628 0.000
#> GSM257981 2 0.0188 0.956 0.000 0.996 0.004 0.000
#> GSM257983 3 0.0000 0.674 0.000 0.000 1.000 0.000
#> GSM257985 3 0.4431 0.824 0.000 0.304 0.696 0.000
#> GSM257988 3 0.3649 0.817 0.000 0.204 0.796 0.000
#> GSM257991 2 0.0000 0.955 0.000 1.000 0.000 0.000
#> GSM257993 2 0.0469 0.953 0.000 0.988 0.012 0.000
#> GSM257994 2 0.0707 0.948 0.000 0.980 0.020 0.000
#> GSM257989 3 0.4304 0.827 0.000 0.284 0.716 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.4013 0.8368 0.084 0.000 0.004 0.804 0.108
#> GSM257888 4 0.0865 0.9199 0.004 0.000 0.000 0.972 0.024
#> GSM257890 4 0.2992 0.8669 0.068 0.000 0.000 0.868 0.064
#> GSM257892 4 0.4006 0.8388 0.080 0.000 0.004 0.804 0.112
#> GSM257894 4 0.2370 0.8933 0.040 0.000 0.000 0.904 0.056
#> GSM257896 4 0.1211 0.9188 0.016 0.000 0.000 0.960 0.024
#> GSM257898 1 0.4425 0.8175 0.772 0.000 0.004 0.116 0.108
#> GSM257900 1 0.2654 0.8739 0.888 0.000 0.000 0.048 0.064
#> GSM257902 1 0.1894 0.8685 0.920 0.000 0.000 0.008 0.072
#> GSM257904 1 0.4425 0.8175 0.772 0.000 0.004 0.116 0.108
#> GSM257906 1 0.4425 0.8175 0.772 0.000 0.004 0.116 0.108
#> GSM257908 4 0.2020 0.9128 0.000 0.000 0.000 0.900 0.100
#> GSM257910 4 0.2020 0.9128 0.000 0.000 0.000 0.900 0.100
#> GSM257912 4 0.1851 0.9125 0.000 0.000 0.000 0.912 0.088
#> GSM257914 4 0.1851 0.9125 0.000 0.000 0.000 0.912 0.088
#> GSM257917 4 0.1965 0.9120 0.000 0.000 0.000 0.904 0.096
#> GSM257919 4 0.1851 0.9125 0.000 0.000 0.000 0.912 0.088
#> GSM257921 1 0.5198 0.7615 0.688 0.000 0.000 0.164 0.148
#> GSM257923 1 0.1894 0.8685 0.920 0.000 0.000 0.008 0.072
#> GSM257925 1 0.0865 0.8834 0.972 0.000 0.000 0.024 0.004
#> GSM257927 1 0.2580 0.8747 0.892 0.000 0.000 0.044 0.064
#> GSM257929 1 0.0798 0.8808 0.976 0.000 0.000 0.008 0.016
#> GSM257937 4 0.0290 0.9203 0.008 0.000 0.000 0.992 0.000
#> GSM257939 1 0.1768 0.8698 0.924 0.000 0.000 0.004 0.072
#> GSM257941 1 0.3234 0.8636 0.852 0.000 0.000 0.084 0.064
#> GSM257943 1 0.4295 0.8232 0.780 0.000 0.004 0.132 0.084
#> GSM257945 1 0.3649 0.8344 0.808 0.000 0.000 0.152 0.040
#> GSM257947 1 0.1768 0.8698 0.924 0.000 0.000 0.004 0.072
#> GSM257949 1 0.1894 0.8685 0.920 0.000 0.000 0.008 0.072
#> GSM257951 1 0.1830 0.8699 0.924 0.000 0.000 0.008 0.068
#> GSM257953 1 0.1740 0.8842 0.932 0.000 0.000 0.056 0.012
#> GSM257955 1 0.1894 0.8685 0.920 0.000 0.000 0.008 0.072
#> GSM257958 1 0.0992 0.8839 0.968 0.000 0.000 0.024 0.008
#> GSM257960 1 0.3558 0.8522 0.828 0.000 0.000 0.108 0.064
#> GSM257962 1 0.2853 0.8722 0.876 0.000 0.000 0.072 0.052
#> GSM257964 1 0.1894 0.8685 0.920 0.000 0.000 0.008 0.072
#> GSM257966 4 0.0290 0.9202 0.000 0.000 0.000 0.992 0.008
#> GSM257968 4 0.3828 0.8192 0.120 0.000 0.000 0.808 0.072
#> GSM257970 1 0.1764 0.8714 0.928 0.000 0.000 0.008 0.064
#> GSM257972 1 0.1205 0.8838 0.956 0.000 0.000 0.040 0.004
#> GSM257977 4 0.0693 0.9199 0.012 0.000 0.000 0.980 0.008
#> GSM257982 4 0.1403 0.9178 0.024 0.000 0.000 0.952 0.024
#> GSM257984 1 0.2971 0.8255 0.836 0.000 0.000 0.008 0.156
#> GSM257986 1 0.2660 0.8439 0.864 0.000 0.000 0.008 0.128
#> GSM257990 1 0.1997 0.8805 0.924 0.000 0.000 0.040 0.036
#> GSM257992 1 0.4473 0.8164 0.768 0.000 0.004 0.116 0.112
#> GSM257996 1 0.4017 0.8378 0.788 0.000 0.000 0.064 0.148
#> GSM258006 1 0.3997 0.8327 0.804 0.000 0.004 0.116 0.076
#> GSM257887 2 0.2300 0.8462 0.000 0.904 0.024 0.000 0.072
#> GSM257889 5 0.6456 0.3751 0.000 0.184 0.368 0.000 0.448
#> GSM257891 3 0.2130 0.6223 0.000 0.080 0.908 0.000 0.012
#> GSM257893 5 0.6455 0.4063 0.000 0.188 0.352 0.000 0.460
#> GSM257895 5 0.4651 0.6109 0.000 0.372 0.020 0.000 0.608
#> GSM257897 3 0.0880 0.5527 0.000 0.000 0.968 0.000 0.032
#> GSM257899 3 0.0880 0.5527 0.000 0.000 0.968 0.000 0.032
#> GSM257901 3 0.5697 0.3530 0.000 0.404 0.512 0.000 0.084
#> GSM257903 2 0.0162 0.8972 0.000 0.996 0.004 0.000 0.000
#> GSM257905 2 0.0324 0.8970 0.000 0.992 0.004 0.000 0.004
#> GSM257907 3 0.6030 0.4562 0.000 0.224 0.580 0.000 0.196
#> GSM257909 2 0.0324 0.8970 0.000 0.992 0.004 0.000 0.004
#> GSM257911 2 0.2660 0.7710 0.000 0.864 0.128 0.000 0.008
#> GSM257913 2 0.4693 0.4304 0.000 0.700 0.244 0.000 0.056
#> GSM257916 2 0.0451 0.8961 0.000 0.988 0.004 0.000 0.008
#> GSM257918 2 0.0451 0.8961 0.000 0.988 0.004 0.000 0.008
#> GSM257920 3 0.5958 0.4611 0.000 0.208 0.592 0.000 0.200
#> GSM257922 3 0.4162 0.6669 0.000 0.176 0.768 0.000 0.056
#> GSM257924 5 0.6543 0.3765 0.000 0.212 0.332 0.000 0.456
#> GSM257926 5 0.6543 0.3765 0.000 0.212 0.332 0.000 0.456
#> GSM257928 5 0.4452 0.6643 0.000 0.272 0.032 0.000 0.696
#> GSM257930 5 0.4275 0.6693 0.000 0.284 0.020 0.000 0.696
#> GSM257938 5 0.4275 0.6693 0.000 0.284 0.020 0.000 0.696
#> GSM257940 3 0.4585 0.5252 0.000 0.352 0.628 0.000 0.020
#> GSM257942 2 0.0771 0.8908 0.000 0.976 0.020 0.000 0.004
#> GSM257944 2 0.0324 0.8970 0.000 0.992 0.004 0.000 0.004
#> GSM257946 3 0.6570 -0.0792 0.000 0.212 0.440 0.000 0.348
#> GSM257948 3 0.6575 -0.1417 0.000 0.208 0.424 0.000 0.368
#> GSM257950 3 0.3171 0.6807 0.000 0.176 0.816 0.000 0.008
#> GSM257952 2 0.2676 0.8086 0.000 0.884 0.080 0.000 0.036
#> GSM257954 2 0.3264 0.7639 0.000 0.820 0.016 0.000 0.164
#> GSM257956 2 0.3016 0.7940 0.000 0.848 0.020 0.000 0.132
#> GSM257959 2 0.0324 0.8970 0.000 0.992 0.004 0.000 0.004
#> GSM257961 2 0.2583 0.7482 0.000 0.864 0.004 0.000 0.132
#> GSM257963 2 0.0324 0.8975 0.000 0.992 0.004 0.000 0.004
#> GSM257965 2 0.0451 0.8973 0.000 0.988 0.004 0.000 0.008
#> GSM257967 2 0.0324 0.8970 0.000 0.992 0.004 0.000 0.004
#> GSM257969 5 0.4686 0.5980 0.000 0.384 0.020 0.000 0.596
#> GSM257971 5 0.6439 0.4098 0.000 0.184 0.356 0.000 0.460
#> GSM257973 3 0.3993 0.6732 0.000 0.216 0.756 0.000 0.028
#> GSM257981 2 0.1764 0.8564 0.000 0.928 0.064 0.000 0.008
#> GSM257983 3 0.0771 0.5565 0.000 0.004 0.976 0.000 0.020
#> GSM257985 3 0.4042 0.6711 0.000 0.212 0.756 0.000 0.032
#> GSM257988 3 0.3123 0.6808 0.000 0.184 0.812 0.000 0.004
#> GSM257991 2 0.0771 0.8880 0.000 0.976 0.020 0.000 0.004
#> GSM257993 2 0.3194 0.7781 0.000 0.832 0.020 0.000 0.148
#> GSM257994 5 0.4275 0.6693 0.000 0.284 0.020 0.000 0.696
#> GSM257989 3 0.3977 0.6766 0.000 0.204 0.764 0.000 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.3595 0.7938 0.004 0.000 0.124 0.808 0.060 0.004
#> GSM257888 4 0.1265 0.8694 0.008 0.000 0.000 0.948 0.000 0.044
#> GSM257890 4 0.2925 0.8322 0.104 0.000 0.000 0.856 0.016 0.024
#> GSM257892 4 0.3595 0.7938 0.004 0.000 0.124 0.808 0.060 0.004
#> GSM257894 4 0.2309 0.8312 0.028 0.000 0.000 0.888 0.000 0.084
#> GSM257896 4 0.0603 0.8730 0.016 0.000 0.000 0.980 0.000 0.004
#> GSM257898 1 0.4493 0.7048 0.760 0.000 0.124 0.076 0.036 0.004
#> GSM257900 1 0.1515 0.7904 0.944 0.000 0.000 0.008 0.020 0.028
#> GSM257902 1 0.3163 0.7605 0.764 0.000 0.000 0.004 0.000 0.232
#> GSM257904 1 0.4542 0.7092 0.760 0.000 0.116 0.076 0.044 0.004
#> GSM257906 1 0.4811 0.6955 0.740 0.000 0.124 0.072 0.060 0.004
#> GSM257908 6 0.3714 0.6009 0.000 0.000 0.000 0.340 0.004 0.656
#> GSM257910 6 0.3699 0.6030 0.000 0.000 0.000 0.336 0.004 0.660
#> GSM257912 6 0.3887 0.6064 0.008 0.000 0.000 0.360 0.000 0.632
#> GSM257914 6 0.3887 0.6064 0.008 0.000 0.000 0.360 0.000 0.632
#> GSM257917 6 0.3887 0.6064 0.008 0.000 0.000 0.360 0.000 0.632
#> GSM257919 6 0.3887 0.6064 0.008 0.000 0.000 0.360 0.000 0.632
#> GSM257921 6 0.5521 0.3752 0.384 0.000 0.000 0.080 0.020 0.516
#> GSM257923 1 0.3714 0.6563 0.656 0.000 0.000 0.004 0.000 0.340
#> GSM257925 1 0.2346 0.7731 0.868 0.000 0.000 0.008 0.000 0.124
#> GSM257927 1 0.0260 0.7855 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM257929 1 0.3136 0.7567 0.768 0.000 0.000 0.004 0.000 0.228
#> GSM257937 4 0.1176 0.8698 0.024 0.000 0.000 0.956 0.000 0.020
#> GSM257939 1 0.3489 0.7242 0.708 0.000 0.000 0.004 0.000 0.288
#> GSM257941 1 0.0622 0.7855 0.980 0.000 0.000 0.008 0.012 0.000
#> GSM257943 1 0.3416 0.7379 0.840 0.000 0.072 0.064 0.020 0.004
#> GSM257945 1 0.2451 0.7405 0.892 0.000 0.016 0.076 0.012 0.004
#> GSM257947 1 0.3426 0.7331 0.720 0.000 0.000 0.004 0.000 0.276
#> GSM257949 1 0.3468 0.7293 0.712 0.000 0.000 0.004 0.000 0.284
#> GSM257951 1 0.3314 0.7467 0.740 0.000 0.000 0.004 0.000 0.256
#> GSM257953 1 0.2513 0.7869 0.896 0.000 0.016 0.020 0.008 0.060
#> GSM257955 1 0.3265 0.7530 0.748 0.000 0.000 0.004 0.000 0.248
#> GSM257958 1 0.1584 0.7942 0.928 0.000 0.000 0.008 0.000 0.064
#> GSM257960 1 0.1498 0.7780 0.940 0.000 0.000 0.032 0.028 0.000
#> GSM257962 1 0.0260 0.7855 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM257964 1 0.3986 0.3720 0.532 0.000 0.000 0.004 0.000 0.464
#> GSM257966 4 0.1349 0.8617 0.000 0.000 0.000 0.940 0.004 0.056
#> GSM257968 4 0.3422 0.7260 0.040 0.000 0.000 0.792 0.000 0.168
#> GSM257970 1 0.3426 0.7319 0.720 0.000 0.000 0.004 0.000 0.276
#> GSM257972 1 0.2389 0.7615 0.864 0.000 0.000 0.008 0.000 0.128
#> GSM257977 4 0.1588 0.8560 0.072 0.000 0.000 0.924 0.000 0.004
#> GSM257982 4 0.0725 0.8764 0.012 0.000 0.000 0.976 0.000 0.012
#> GSM257984 6 0.3668 0.2884 0.328 0.000 0.000 0.004 0.000 0.668
#> GSM257986 6 0.3684 0.2776 0.332 0.000 0.000 0.004 0.000 0.664
#> GSM257990 1 0.0260 0.7855 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM257992 1 0.4856 0.6906 0.736 0.000 0.124 0.080 0.056 0.004
#> GSM257996 6 0.5169 0.3358 0.416 0.000 0.000 0.052 0.016 0.516
#> GSM258006 1 0.4220 0.7059 0.776 0.000 0.112 0.088 0.020 0.004
#> GSM257887 2 0.3838 -0.0527 0.000 0.552 0.000 0.000 0.448 0.000
#> GSM257889 3 0.6498 0.6756 0.000 0.104 0.552 0.000 0.144 0.200
#> GSM257891 3 0.5533 0.6853 0.000 0.060 0.648 0.000 0.092 0.200
#> GSM257893 3 0.7035 0.6065 0.000 0.120 0.468 0.000 0.212 0.200
#> GSM257895 5 0.2048 0.9226 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM257897 3 0.4991 0.6448 0.000 0.004 0.656 0.000 0.136 0.204
#> GSM257899 3 0.4991 0.6448 0.000 0.004 0.656 0.000 0.136 0.204
#> GSM257901 2 0.3756 0.3789 0.000 0.644 0.352 0.000 0.004 0.000
#> GSM257903 2 0.0363 0.6796 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM257905 2 0.1141 0.6646 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM257907 2 0.3911 0.3502 0.000 0.624 0.368 0.000 0.008 0.000
#> GSM257909 2 0.0547 0.6796 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM257911 2 0.2805 0.5782 0.000 0.812 0.184 0.000 0.004 0.000
#> GSM257913 2 0.3426 0.4879 0.000 0.720 0.276 0.000 0.004 0.000
#> GSM257916 2 0.3717 0.1797 0.000 0.616 0.000 0.000 0.384 0.000
#> GSM257918 2 0.3774 0.1145 0.000 0.592 0.000 0.000 0.408 0.000
#> GSM257920 3 0.4080 0.0730 0.000 0.456 0.536 0.000 0.008 0.000
#> GSM257922 3 0.6243 0.6909 0.000 0.092 0.580 0.000 0.124 0.204
#> GSM257924 2 0.5604 0.1402 0.000 0.524 0.368 0.000 0.084 0.024
#> GSM257926 2 0.4529 0.0345 0.000 0.508 0.460 0.000 0.032 0.000
#> GSM257928 5 0.4026 0.7790 0.000 0.088 0.000 0.000 0.752 0.160
#> GSM257930 5 0.1556 0.9223 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM257938 5 0.1610 0.9229 0.000 0.084 0.000 0.000 0.916 0.000
#> GSM257940 3 0.2969 0.6337 0.000 0.224 0.776 0.000 0.000 0.000
#> GSM257942 2 0.0363 0.6792 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM257944 2 0.0363 0.6798 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM257946 3 0.5101 0.6726 0.000 0.220 0.664 0.000 0.024 0.092
#> GSM257948 2 0.4004 0.3436 0.000 0.620 0.368 0.000 0.012 0.000
#> GSM257950 3 0.3219 0.6691 0.000 0.192 0.792 0.000 0.004 0.012
#> GSM257952 2 0.2896 0.5992 0.000 0.824 0.160 0.000 0.016 0.000
#> GSM257954 5 0.2135 0.9222 0.000 0.128 0.000 0.000 0.872 0.000
#> GSM257956 5 0.2941 0.8110 0.000 0.220 0.000 0.000 0.780 0.000
#> GSM257959 2 0.3482 0.3387 0.000 0.684 0.000 0.000 0.316 0.000
#> GSM257961 2 0.3867 -0.1154 0.000 0.512 0.000 0.000 0.488 0.000
#> GSM257963 2 0.3804 0.0784 0.000 0.576 0.000 0.000 0.424 0.000
#> GSM257965 2 0.0146 0.6769 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM257967 2 0.0547 0.6796 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM257969 5 0.2219 0.9158 0.000 0.136 0.000 0.000 0.864 0.000
#> GSM257971 3 0.7149 0.6075 0.000 0.144 0.456 0.000 0.200 0.200
#> GSM257973 3 0.3126 0.6142 0.000 0.248 0.752 0.000 0.000 0.000
#> GSM257981 2 0.1204 0.6598 0.000 0.944 0.056 0.000 0.000 0.000
#> GSM257983 3 0.5025 0.6607 0.000 0.016 0.672 0.000 0.112 0.200
#> GSM257985 3 0.3827 0.6667 0.000 0.212 0.752 0.000 0.012 0.024
#> GSM257988 3 0.2762 0.6591 0.000 0.196 0.804 0.000 0.000 0.000
#> GSM257991 2 0.0363 0.6792 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM257993 5 0.2048 0.9241 0.000 0.120 0.000 0.000 0.880 0.000
#> GSM257994 5 0.1556 0.9223 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM257989 3 0.2854 0.6526 0.000 0.208 0.792 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> MAD:mclust 96 8.49e-22 1.000 1.000 2
#> MAD:mclust 96 1.43e-21 0.519 1.000 3
#> MAD:mclust 95 1.85e-20 0.500 0.998 4
#> MAD:mclust 85 1.52e-17 0.246 0.988 5
#> MAD:mclust 78 2.20e-15 0.433 0.843 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 25171 rows and 96 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 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.840 0.894 0.915 0.1749 0.900 0.798
#> 4 4 0.681 0.673 0.791 0.1084 0.896 0.750
#> 5 5 0.701 0.765 0.854 0.0888 0.869 0.647
#> 6 6 0.768 0.833 0.888 0.0406 0.953 0.838
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.2625 0.943 0.916 0.000 0.084
#> GSM257888 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257890 1 0.1031 0.974 0.976 0.000 0.024
#> GSM257892 1 0.2878 0.935 0.904 0.000 0.096
#> GSM257894 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257898 1 0.3752 0.888 0.856 0.000 0.144
#> GSM257900 1 0.0424 0.979 0.992 0.000 0.008
#> GSM257902 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257904 1 0.2448 0.946 0.924 0.000 0.076
#> GSM257906 1 0.3116 0.924 0.892 0.000 0.108
#> GSM257908 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257912 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257914 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257917 1 0.0747 0.977 0.984 0.000 0.016
#> GSM257919 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257921 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257927 1 0.0424 0.979 0.992 0.000 0.008
#> GSM257929 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257937 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257939 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257941 1 0.1964 0.956 0.944 0.000 0.056
#> GSM257943 1 0.2356 0.947 0.928 0.000 0.072
#> GSM257945 1 0.2165 0.952 0.936 0.000 0.064
#> GSM257947 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257960 1 0.1643 0.963 0.956 0.000 0.044
#> GSM257962 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257964 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257966 1 0.0592 0.978 0.988 0.000 0.012
#> GSM257968 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257977 1 0.0237 0.980 0.996 0.000 0.004
#> GSM257982 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.981 1.000 0.000 0.000
#> GSM257992 1 0.3686 0.897 0.860 0.000 0.140
#> GSM257996 1 0.0000 0.981 1.000 0.000 0.000
#> GSM258006 1 0.2356 0.947 0.928 0.000 0.072
#> GSM257887 2 0.0424 0.866 0.000 0.992 0.008
#> GSM257889 3 0.5397 0.906 0.000 0.280 0.720
#> GSM257891 3 0.4178 0.775 0.000 0.172 0.828
#> GSM257893 3 0.5968 0.877 0.000 0.364 0.636
#> GSM257895 2 0.1529 0.866 0.000 0.960 0.040
#> GSM257897 3 0.5397 0.906 0.000 0.280 0.720
#> GSM257899 3 0.5560 0.912 0.000 0.300 0.700
#> GSM257901 2 0.3816 0.772 0.000 0.852 0.148
#> GSM257903 2 0.1753 0.841 0.000 0.952 0.048
#> GSM257905 2 0.0000 0.868 0.000 1.000 0.000
#> GSM257907 2 0.4702 0.677 0.000 0.788 0.212
#> GSM257909 2 0.1753 0.836 0.000 0.952 0.048
#> GSM257911 2 0.2066 0.863 0.000 0.940 0.060
#> GSM257913 2 0.2537 0.843 0.000 0.920 0.080
#> GSM257916 2 0.0747 0.864 0.000 0.984 0.016
#> GSM257918 2 0.1289 0.854 0.000 0.968 0.032
#> GSM257920 2 0.4974 0.630 0.000 0.764 0.236
#> GSM257922 3 0.5291 0.888 0.000 0.268 0.732
#> GSM257924 2 0.5216 0.564 0.000 0.740 0.260
#> GSM257926 2 0.5706 0.370 0.000 0.680 0.320
#> GSM257928 3 0.5835 0.892 0.000 0.340 0.660
#> GSM257930 2 0.2878 0.829 0.000 0.904 0.096
#> GSM257938 2 0.1529 0.866 0.000 0.960 0.040
#> GSM257940 2 0.5058 0.612 0.000 0.756 0.244
#> GSM257942 2 0.1411 0.856 0.000 0.964 0.036
#> GSM257944 2 0.2448 0.809 0.000 0.924 0.076
#> GSM257946 3 0.5785 0.907 0.000 0.332 0.668
#> GSM257948 2 0.5058 0.611 0.000 0.756 0.244
#> GSM257950 3 0.6140 0.792 0.000 0.404 0.596
#> GSM257952 2 0.2165 0.854 0.000 0.936 0.064
#> GSM257954 2 0.0892 0.869 0.000 0.980 0.020
#> GSM257956 2 0.0747 0.869 0.000 0.984 0.016
#> GSM257959 2 0.1529 0.843 0.000 0.960 0.040
#> GSM257961 2 0.0237 0.867 0.000 0.996 0.004
#> GSM257963 2 0.0747 0.862 0.000 0.984 0.016
#> GSM257965 2 0.0892 0.870 0.000 0.980 0.020
#> GSM257967 2 0.1643 0.840 0.000 0.956 0.044
#> GSM257969 2 0.1163 0.869 0.000 0.972 0.028
#> GSM257971 3 0.5497 0.908 0.000 0.292 0.708
#> GSM257973 2 0.4750 0.670 0.000 0.784 0.216
#> GSM257981 2 0.1753 0.863 0.000 0.952 0.048
#> GSM257983 3 0.5882 0.887 0.000 0.348 0.652
#> GSM257985 3 0.5760 0.909 0.000 0.328 0.672
#> GSM257988 2 0.4842 0.664 0.000 0.776 0.224
#> GSM257991 2 0.0892 0.869 0.000 0.980 0.020
#> GSM257993 2 0.0237 0.867 0.000 0.996 0.004
#> GSM257994 2 0.1643 0.865 0.000 0.956 0.044
#> GSM257989 3 0.5926 0.881 0.000 0.356 0.644
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.5528 0.7593 0.700 0.000 0.064 0.236
#> GSM257888 1 0.1151 0.9209 0.968 0.000 0.024 0.008
#> GSM257890 1 0.4410 0.8438 0.808 0.000 0.064 0.128
#> GSM257892 1 0.5528 0.7593 0.700 0.000 0.064 0.236
#> GSM257894 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257896 1 0.0188 0.9277 0.996 0.000 0.004 0.000
#> GSM257898 1 0.5821 0.5859 0.592 0.000 0.040 0.368
#> GSM257900 1 0.2342 0.8976 0.912 0.000 0.008 0.080
#> GSM257902 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257904 1 0.5489 0.7582 0.700 0.000 0.060 0.240
#> GSM257906 1 0.5463 0.7484 0.692 0.000 0.052 0.256
#> GSM257908 1 0.0188 0.9274 0.996 0.000 0.004 0.000
#> GSM257910 1 0.0592 0.9247 0.984 0.000 0.016 0.000
#> GSM257912 1 0.2385 0.9046 0.920 0.000 0.052 0.028
#> GSM257914 1 0.2002 0.9109 0.936 0.000 0.044 0.020
#> GSM257917 1 0.4804 0.8223 0.776 0.000 0.064 0.160
#> GSM257919 1 0.2002 0.9109 0.936 0.000 0.044 0.020
#> GSM257921 1 0.2224 0.9113 0.928 0.000 0.040 0.032
#> GSM257923 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257927 1 0.0921 0.9217 0.972 0.000 0.000 0.028
#> GSM257929 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257937 1 0.2089 0.9094 0.932 0.000 0.048 0.020
#> GSM257939 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257941 1 0.2921 0.8661 0.860 0.000 0.000 0.140
#> GSM257943 1 0.4818 0.7862 0.748 0.000 0.036 0.216
#> GSM257945 1 0.3266 0.8475 0.832 0.000 0.000 0.168
#> GSM257947 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257960 1 0.3498 0.8495 0.832 0.000 0.008 0.160
#> GSM257962 1 0.1302 0.9161 0.956 0.000 0.000 0.044
#> GSM257964 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257966 1 0.1406 0.9182 0.960 0.000 0.024 0.016
#> GSM257968 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257970 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257977 1 0.1406 0.9190 0.960 0.000 0.024 0.016
#> GSM257982 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257984 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM257992 4 0.6079 -0.4329 0.464 0.000 0.044 0.492
#> GSM257996 1 0.0000 0.9281 1.000 0.000 0.000 0.000
#> GSM258006 1 0.5056 0.7768 0.732 0.000 0.044 0.224
#> GSM257887 2 0.0000 0.7696 0.000 1.000 0.000 0.000
#> GSM257889 4 0.7005 0.2648 0.000 0.172 0.256 0.572
#> GSM257891 3 0.6705 0.0425 0.000 0.088 0.472 0.440
#> GSM257893 4 0.5947 0.2199 0.000 0.384 0.044 0.572
#> GSM257895 2 0.0188 0.7684 0.000 0.996 0.004 0.000
#> GSM257897 4 0.6267 0.3870 0.000 0.188 0.148 0.664
#> GSM257899 4 0.6780 0.3293 0.000 0.232 0.164 0.604
#> GSM257901 3 0.7044 0.6678 0.000 0.276 0.560 0.164
#> GSM257903 2 0.4964 0.4297 0.000 0.616 0.380 0.004
#> GSM257905 2 0.1302 0.7588 0.000 0.956 0.044 0.000
#> GSM257907 3 0.7344 0.6748 0.000 0.268 0.524 0.208
#> GSM257909 2 0.4193 0.6153 0.000 0.732 0.268 0.000
#> GSM257911 3 0.4647 0.5447 0.000 0.288 0.704 0.008
#> GSM257913 3 0.6500 0.5822 0.000 0.376 0.544 0.080
#> GSM257916 2 0.3688 0.6382 0.000 0.792 0.208 0.000
#> GSM257918 2 0.4134 0.5949 0.000 0.740 0.260 0.000
#> GSM257920 3 0.7458 0.6702 0.000 0.288 0.500 0.212
#> GSM257922 4 0.5833 0.3933 0.000 0.212 0.096 0.692
#> GSM257924 2 0.7764 -0.6023 0.000 0.424 0.324 0.252
#> GSM257926 3 0.7689 0.6255 0.000 0.300 0.452 0.248
#> GSM257928 2 0.5411 0.3257 0.000 0.656 0.032 0.312
#> GSM257930 2 0.1297 0.7512 0.000 0.964 0.016 0.020
#> GSM257938 2 0.0524 0.7648 0.000 0.988 0.008 0.004
#> GSM257940 3 0.7269 0.6753 0.000 0.264 0.536 0.200
#> GSM257942 2 0.5132 0.1874 0.000 0.548 0.448 0.004
#> GSM257944 2 0.5607 0.3433 0.000 0.492 0.488 0.020
#> GSM257946 4 0.7699 -0.3861 0.000 0.220 0.380 0.400
#> GSM257948 3 0.7442 0.6725 0.000 0.284 0.504 0.212
#> GSM257950 3 0.7586 0.5243 0.000 0.212 0.460 0.328
#> GSM257952 2 0.6960 -0.5070 0.000 0.468 0.420 0.112
#> GSM257954 2 0.0000 0.7696 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0336 0.7684 0.000 0.992 0.008 0.000
#> GSM257959 2 0.1637 0.7576 0.000 0.940 0.060 0.000
#> GSM257961 2 0.0000 0.7696 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0707 0.7672 0.000 0.980 0.020 0.000
#> GSM257965 3 0.5167 0.1043 0.000 0.488 0.508 0.004
#> GSM257967 2 0.2469 0.7300 0.000 0.892 0.108 0.000
#> GSM257969 2 0.0000 0.7696 0.000 1.000 0.000 0.000
#> GSM257971 4 0.7168 0.2677 0.000 0.256 0.192 0.552
#> GSM257973 3 0.7408 0.6737 0.000 0.276 0.512 0.212
#> GSM257981 3 0.5292 0.2643 0.000 0.480 0.512 0.008
#> GSM257983 3 0.7486 0.4665 0.000 0.188 0.464 0.348
#> GSM257985 3 0.7710 0.4182 0.000 0.224 0.408 0.368
#> GSM257988 3 0.6912 0.6252 0.000 0.216 0.592 0.192
#> GSM257991 3 0.4661 0.4756 0.000 0.348 0.652 0.000
#> GSM257993 2 0.0188 0.7684 0.000 0.996 0.004 0.000
#> GSM257994 2 0.0927 0.7588 0.000 0.976 0.008 0.016
#> GSM257989 3 0.7520 0.4877 0.000 0.196 0.464 0.340
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.2966 0.9676 0.184 0.000 0.000 0.816 0.000
#> GSM257888 1 0.0510 0.9189 0.984 0.000 0.000 0.016 0.000
#> GSM257890 4 0.3480 0.8874 0.248 0.000 0.000 0.752 0.000
#> GSM257892 4 0.2929 0.9708 0.180 0.000 0.000 0.820 0.000
#> GSM257894 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM257896 1 0.0404 0.9210 0.988 0.000 0.000 0.012 0.000
#> GSM257898 4 0.3163 0.9584 0.164 0.000 0.000 0.824 0.012
#> GSM257900 1 0.4138 0.2281 0.616 0.000 0.000 0.384 0.000
#> GSM257902 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257904 4 0.3048 0.9710 0.176 0.000 0.000 0.820 0.004
#> GSM257906 4 0.3048 0.9710 0.176 0.000 0.000 0.820 0.004
#> GSM257908 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257912 1 0.1041 0.9061 0.964 0.000 0.000 0.032 0.004
#> GSM257914 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM257917 1 0.4201 0.1432 0.592 0.000 0.000 0.408 0.000
#> GSM257919 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM257921 1 0.1608 0.8749 0.928 0.000 0.000 0.072 0.000
#> GSM257923 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257927 1 0.1851 0.8539 0.912 0.000 0.000 0.088 0.000
#> GSM257929 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257937 1 0.2377 0.8092 0.872 0.000 0.000 0.128 0.000
#> GSM257939 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257941 1 0.4718 -0.1004 0.540 0.000 0.000 0.444 0.016
#> GSM257943 4 0.2970 0.9659 0.168 0.000 0.000 0.828 0.004
#> GSM257945 1 0.4769 0.4743 0.688 0.000 0.000 0.256 0.056
#> GSM257947 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257949 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257953 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257955 1 0.0579 0.9169 0.984 0.000 0.000 0.008 0.008
#> GSM257958 1 0.0162 0.9244 0.996 0.000 0.000 0.000 0.004
#> GSM257960 1 0.3452 0.6213 0.756 0.000 0.000 0.244 0.000
#> GSM257962 1 0.0963 0.9047 0.964 0.000 0.000 0.036 0.000
#> GSM257964 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.0290 0.9231 0.992 0.000 0.000 0.008 0.000
#> GSM257968 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257970 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM257977 1 0.1671 0.8693 0.924 0.000 0.000 0.076 0.000
#> GSM257982 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM257984 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.9249 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.0324 0.9224 0.992 0.000 0.000 0.004 0.004
#> GSM257992 4 0.2886 0.9403 0.148 0.000 0.000 0.844 0.008
#> GSM257996 1 0.0162 0.9244 0.996 0.000 0.000 0.004 0.000
#> GSM258006 4 0.2929 0.9708 0.180 0.000 0.000 0.820 0.000
#> GSM257887 2 0.1082 0.8637 0.000 0.964 0.028 0.000 0.008
#> GSM257889 3 0.4745 0.6065 0.000 0.048 0.724 0.012 0.216
#> GSM257891 3 0.2131 0.6747 0.000 0.008 0.920 0.016 0.056
#> GSM257893 3 0.7504 0.1693 0.000 0.308 0.372 0.036 0.284
#> GSM257895 2 0.1282 0.8475 0.000 0.952 0.044 0.004 0.000
#> GSM257897 3 0.5815 0.5432 0.000 0.072 0.616 0.024 0.288
#> GSM257899 3 0.5895 0.6240 0.000 0.148 0.624 0.008 0.220
#> GSM257901 3 0.3493 0.7523 0.000 0.108 0.832 0.000 0.060
#> GSM257903 5 0.6191 0.7259 0.000 0.424 0.136 0.000 0.440
#> GSM257905 2 0.2074 0.8414 0.000 0.920 0.036 0.000 0.044
#> GSM257907 3 0.3192 0.7551 0.000 0.112 0.848 0.000 0.040
#> GSM257909 2 0.4163 0.4879 0.000 0.740 0.032 0.000 0.228
#> GSM257911 3 0.5006 0.6524 0.000 0.116 0.704 0.000 0.180
#> GSM257913 3 0.5295 0.6201 0.000 0.200 0.672 0.000 0.128
#> GSM257916 2 0.4277 0.5668 0.000 0.784 0.112 0.004 0.100
#> GSM257918 2 0.4119 0.6036 0.000 0.780 0.068 0.000 0.152
#> GSM257920 3 0.3289 0.7547 0.000 0.108 0.844 0.000 0.048
#> GSM257922 3 0.6996 0.4496 0.000 0.124 0.496 0.052 0.328
#> GSM257924 3 0.4558 0.6888 0.000 0.224 0.728 0.008 0.040
#> GSM257926 3 0.2707 0.7540 0.000 0.132 0.860 0.000 0.008
#> GSM257928 2 0.3764 0.5972 0.000 0.808 0.004 0.040 0.148
#> GSM257930 2 0.1095 0.8537 0.000 0.968 0.012 0.008 0.012
#> GSM257938 2 0.0727 0.8483 0.000 0.980 0.004 0.012 0.004
#> GSM257940 3 0.3215 0.7533 0.000 0.092 0.852 0.000 0.056
#> GSM257942 5 0.6667 0.6975 0.000 0.364 0.232 0.000 0.404
#> GSM257944 5 0.5798 0.6876 0.000 0.336 0.108 0.000 0.556
#> GSM257946 3 0.3482 0.7488 0.000 0.096 0.844 0.008 0.052
#> GSM257948 3 0.3454 0.7528 0.000 0.100 0.836 0.000 0.064
#> GSM257950 3 0.2349 0.7547 0.000 0.084 0.900 0.012 0.004
#> GSM257952 3 0.4533 0.6156 0.000 0.260 0.704 0.004 0.032
#> GSM257954 2 0.0609 0.8622 0.000 0.980 0.020 0.000 0.000
#> GSM257956 2 0.1124 0.8612 0.000 0.960 0.036 0.004 0.000
#> GSM257959 2 0.2448 0.8011 0.000 0.892 0.020 0.000 0.088
#> GSM257961 2 0.1310 0.8630 0.000 0.956 0.024 0.000 0.020
#> GSM257963 2 0.1579 0.8550 0.000 0.944 0.024 0.000 0.032
#> GSM257965 3 0.6778 -0.3266 0.000 0.364 0.408 0.004 0.224
#> GSM257967 2 0.3043 0.7734 0.000 0.864 0.056 0.000 0.080
#> GSM257969 2 0.0880 0.8629 0.000 0.968 0.032 0.000 0.000
#> GSM257971 3 0.5304 0.6924 0.000 0.168 0.696 0.008 0.128
#> GSM257973 3 0.3115 0.7543 0.000 0.112 0.852 0.000 0.036
#> GSM257981 3 0.5231 0.4595 0.000 0.316 0.624 0.004 0.056
#> GSM257983 3 0.1270 0.7459 0.000 0.052 0.948 0.000 0.000
#> GSM257985 3 0.2731 0.7581 0.000 0.104 0.876 0.004 0.016
#> GSM257988 3 0.3141 0.7252 0.000 0.040 0.852 0.000 0.108
#> GSM257991 3 0.6524 -0.0688 0.000 0.200 0.444 0.000 0.356
#> GSM257993 2 0.0290 0.8556 0.000 0.992 0.008 0.000 0.000
#> GSM257994 2 0.0727 0.8483 0.000 0.980 0.004 0.012 0.004
#> GSM257989 3 0.1798 0.7482 0.000 0.064 0.928 0.004 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.0622 0.850 0.000 0.000 0.000 NA 0.012 0.980
#> GSM257888 1 0.1478 0.920 0.944 0.000 0.000 NA 0.032 0.004
#> GSM257890 6 0.2467 0.822 0.048 0.000 0.000 NA 0.036 0.896
#> GSM257892 6 0.0520 0.850 0.000 0.000 0.000 NA 0.008 0.984
#> GSM257894 1 0.1088 0.926 0.960 0.000 0.000 NA 0.024 0.000
#> GSM257896 1 0.1334 0.922 0.948 0.000 0.000 NA 0.032 0.000
#> GSM257898 6 0.0291 0.853 0.000 0.000 0.000 NA 0.004 0.992
#> GSM257900 6 0.4150 0.441 0.372 0.000 0.000 NA 0.004 0.612
#> GSM257902 1 0.0146 0.932 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257904 6 0.0146 0.853 0.000 0.000 0.000 NA 0.004 0.996
#> GSM257906 6 0.0000 0.853 0.000 0.000 0.000 NA 0.000 1.000
#> GSM257908 1 0.0858 0.927 0.968 0.000 0.000 NA 0.028 0.000
#> GSM257910 1 0.0858 0.927 0.968 0.000 0.000 NA 0.028 0.000
#> GSM257912 1 0.3304 0.817 0.804 0.000 0.000 NA 0.168 0.020
#> GSM257914 1 0.1511 0.921 0.940 0.000 0.000 NA 0.044 0.012
#> GSM257917 6 0.3969 0.684 0.212 0.000 0.000 NA 0.044 0.740
#> GSM257919 1 0.2002 0.903 0.908 0.000 0.000 NA 0.076 0.012
#> GSM257921 1 0.3724 0.741 0.772 0.000 0.000 NA 0.028 0.188
#> GSM257923 1 0.0260 0.932 0.992 0.000 0.000 NA 0.000 0.000
#> GSM257925 1 0.0551 0.931 0.984 0.000 0.000 NA 0.004 0.004
#> GSM257927 1 0.4589 0.252 0.580 0.000 0.000 NA 0.008 0.384
#> GSM257929 1 0.0405 0.931 0.988 0.000 0.000 NA 0.000 0.004
#> GSM257937 1 0.2664 0.887 0.884 0.000 0.000 NA 0.040 0.056
#> GSM257939 1 0.0363 0.932 0.988 0.000 0.000 NA 0.000 0.000
#> GSM257941 6 0.3111 0.772 0.156 0.000 0.000 NA 0.008 0.820
#> GSM257943 6 0.0405 0.852 0.000 0.000 0.000 NA 0.004 0.988
#> GSM257945 6 0.4280 0.735 0.092 0.000 0.000 NA 0.004 0.736
#> GSM257947 1 0.0363 0.932 0.988 0.000 0.000 NA 0.000 0.000
#> GSM257949 1 0.0146 0.932 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257951 1 0.0260 0.932 0.992 0.000 0.000 NA 0.000 0.000
#> GSM257953 1 0.0405 0.931 0.988 0.000 0.000 NA 0.000 0.004
#> GSM257955 1 0.0260 0.932 0.992 0.000 0.000 NA 0.000 0.000
#> GSM257958 1 0.0508 0.931 0.984 0.000 0.000 NA 0.004 0.000
#> GSM257960 6 0.3393 0.736 0.192 0.000 0.000 NA 0.004 0.784
#> GSM257962 1 0.5635 0.179 0.532 0.000 0.000 NA 0.012 0.336
#> GSM257964 1 0.0000 0.932 1.000 0.000 0.000 NA 0.000 0.000
#> GSM257966 1 0.1555 0.920 0.940 0.000 0.000 NA 0.040 0.008
#> GSM257968 1 0.0820 0.928 0.972 0.000 0.000 NA 0.016 0.000
#> GSM257970 1 0.0260 0.932 0.992 0.000 0.000 NA 0.000 0.000
#> GSM257972 1 0.0551 0.932 0.984 0.000 0.000 NA 0.004 0.008
#> GSM257977 1 0.2271 0.902 0.908 0.000 0.000 NA 0.036 0.032
#> GSM257982 1 0.0909 0.928 0.968 0.000 0.000 NA 0.020 0.000
#> GSM257984 1 0.0146 0.932 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257986 1 0.0146 0.932 0.996 0.000 0.000 NA 0.000 0.000
#> GSM257990 1 0.2872 0.820 0.832 0.000 0.000 NA 0.004 0.012
#> GSM257992 6 0.0291 0.853 0.000 0.000 0.000 NA 0.004 0.992
#> GSM257996 1 0.1262 0.923 0.956 0.000 0.000 NA 0.008 0.020
#> GSM258006 6 0.0260 0.853 0.000 0.000 0.000 NA 0.008 0.992
#> GSM257887 2 0.2121 0.858 0.000 0.892 0.096 NA 0.012 0.000
#> GSM257889 3 0.1536 0.869 0.000 0.016 0.940 NA 0.004 0.000
#> GSM257891 3 0.1779 0.827 0.000 0.000 0.920 NA 0.016 0.000
#> GSM257893 3 0.4948 0.475 0.000 0.316 0.604 NA 0.004 0.000
#> GSM257895 2 0.3252 0.798 0.000 0.828 0.128 NA 0.012 0.000
#> GSM257897 3 0.2492 0.853 0.000 0.020 0.876 NA 0.004 0.000
#> GSM257899 3 0.2269 0.892 0.000 0.080 0.896 NA 0.012 0.000
#> GSM257901 3 0.1349 0.896 0.000 0.056 0.940 NA 0.004 0.000
#> GSM257903 5 0.4518 0.764 0.000 0.352 0.044 NA 0.604 0.000
#> GSM257905 2 0.2728 0.847 0.000 0.860 0.100 NA 0.040 0.000
#> GSM257907 3 0.1411 0.895 0.000 0.060 0.936 NA 0.004 0.000
#> GSM257909 2 0.3118 0.799 0.000 0.836 0.072 NA 0.092 0.000
#> GSM257911 3 0.2094 0.889 0.000 0.080 0.900 NA 0.020 0.000
#> GSM257913 3 0.3370 0.824 0.000 0.148 0.804 NA 0.048 0.000
#> GSM257916 2 0.2744 0.794 0.000 0.840 0.144 NA 0.016 0.000
#> GSM257918 2 0.3044 0.835 0.000 0.836 0.116 NA 0.048 0.000
#> GSM257920 3 0.1682 0.896 0.000 0.052 0.928 NA 0.020 0.000
#> GSM257922 3 0.3370 0.831 0.000 0.048 0.804 NA 0.000 0.000
#> GSM257924 3 0.2902 0.804 0.000 0.196 0.800 NA 0.004 0.000
#> GSM257926 3 0.1444 0.894 0.000 0.072 0.928 NA 0.000 0.000
#> GSM257928 2 0.3094 0.689 0.000 0.824 0.036 NA 0.000 0.000
#> GSM257930 2 0.2914 0.823 0.000 0.860 0.084 NA 0.008 0.000
#> GSM257938 2 0.2106 0.765 0.000 0.904 0.032 NA 0.000 0.000
#> GSM257940 3 0.1265 0.895 0.000 0.044 0.948 NA 0.008 0.000
#> GSM257942 5 0.5329 0.509 0.000 0.444 0.104 NA 0.452 0.000
#> GSM257944 5 0.3898 0.726 0.000 0.296 0.020 NA 0.684 0.000
#> GSM257946 3 0.1464 0.895 0.000 0.036 0.944 NA 0.004 0.000
#> GSM257948 3 0.1864 0.893 0.000 0.040 0.924 NA 0.032 0.000
#> GSM257950 3 0.1616 0.879 0.000 0.020 0.940 NA 0.012 0.000
#> GSM257952 3 0.2755 0.857 0.000 0.140 0.844 NA 0.012 0.000
#> GSM257954 2 0.1588 0.856 0.000 0.924 0.072 NA 0.000 0.000
#> GSM257956 2 0.2053 0.848 0.000 0.888 0.108 NA 0.000 0.000
#> GSM257959 2 0.2629 0.752 0.000 0.868 0.040 NA 0.092 0.000
#> GSM257961 2 0.2404 0.854 0.000 0.884 0.080 NA 0.036 0.000
#> GSM257963 2 0.2442 0.837 0.000 0.884 0.068 NA 0.048 0.000
#> GSM257965 3 0.4148 0.493 0.000 0.344 0.636 NA 0.016 0.000
#> GSM257967 2 0.2954 0.839 0.000 0.844 0.108 NA 0.048 0.000
#> GSM257969 2 0.2053 0.847 0.000 0.888 0.108 NA 0.000 0.000
#> GSM257971 3 0.2554 0.886 0.000 0.088 0.880 NA 0.012 0.000
#> GSM257973 3 0.1563 0.896 0.000 0.056 0.932 NA 0.012 0.000
#> GSM257981 3 0.2673 0.863 0.000 0.132 0.852 NA 0.012 0.000
#> GSM257983 3 0.0405 0.883 0.000 0.000 0.988 NA 0.008 0.000
#> GSM257985 3 0.1555 0.895 0.000 0.060 0.932 NA 0.004 0.000
#> GSM257988 3 0.1088 0.889 0.000 0.016 0.960 NA 0.024 0.000
#> GSM257991 3 0.4414 0.720 0.000 0.108 0.712 NA 0.180 0.000
#> GSM257993 2 0.1624 0.837 0.000 0.936 0.044 NA 0.008 0.000
#> GSM257994 2 0.2249 0.765 0.000 0.900 0.032 NA 0.004 0.000
#> GSM257989 3 0.0881 0.879 0.000 0.008 0.972 NA 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> MAD:NMF 96 8.49e-22 1.000 1.000 2
#> MAD:NMF 95 2.35e-21 0.614 1.000 3
#> MAD:NMF 75 5.18e-17 0.786 0.999 4
#> MAD:NMF 86 9.31e-18 0.685 0.926 5
#> MAD:NMF 91 8.07e-19 0.399 0.985 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 25171 rows and 96 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 1.000 0.968 0.988 0.0387 0.990 0.979
#> 4 4 0.912 0.909 0.962 0.1900 0.900 0.794
#> 5 5 0.938 0.914 0.957 0.0580 0.967 0.914
#> 6 6 0.753 0.727 0.862 0.1070 0.946 0.847
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 4
There is also optional best \(k\) = 2 4 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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.4887 0.708 0.772 0.000 0.228
#> GSM257888 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257890 1 0.4750 0.733 0.784 0.000 0.216
#> GSM257892 3 0.1411 0.000 0.036 0.000 0.964
#> GSM257894 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257896 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257898 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257904 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257906 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257908 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257917 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257919 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257921 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257937 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257939 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257966 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257968 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257970 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257977 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257982 1 0.0892 0.973 0.980 0.000 0.020
#> GSM257984 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.985 1.000 0.000 0.000
#> GSM257992 1 0.1529 0.952 0.960 0.000 0.040
#> GSM257996 1 0.0000 0.985 1.000 0.000 0.000
#> GSM258006 1 0.1529 0.952 0.960 0.000 0.040
#> GSM257887 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257889 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257891 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257893 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257895 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257897 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257899 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257901 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257903 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257907 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257909 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257911 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257913 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257916 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257920 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257922 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257924 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257926 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257928 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257930 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257938 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257940 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257942 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257946 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257948 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257950 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257952 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257954 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257965 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257971 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257973 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257981 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257983 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257985 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257988 2 0.1411 0.973 0.000 0.964 0.036
#> GSM257991 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257993 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257994 2 0.0000 0.990 0.000 1.000 0.000
#> GSM257989 2 0.1411 0.973 0.000 0.964 0.036
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.4134 0.669 0.740 0.000 0.000 0.260
#> GSM257888 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257890 1 0.4008 0.701 0.756 0.000 0.000 0.244
#> GSM257892 4 0.0000 0.000 0.000 0.000 0.000 1.000
#> GSM257894 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257896 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257898 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257900 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257902 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257904 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257906 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257908 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257910 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257921 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257923 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257927 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257929 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257937 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257939 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257941 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257947 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257964 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257966 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257968 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257970 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257977 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257982 1 0.0817 0.969 0.976 0.000 0.000 0.024
#> GSM257984 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM257992 1 0.1211 0.952 0.960 0.000 0.000 0.040
#> GSM257996 1 0.0000 0.983 1.000 0.000 0.000 0.000
#> GSM258006 1 0.1211 0.952 0.960 0.000 0.000 0.040
#> GSM257887 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0707 0.893 0.000 0.020 0.980 0.000
#> GSM257891 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> GSM257893 3 0.2149 0.903 0.000 0.088 0.912 0.000
#> GSM257895 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> GSM257901 2 0.0707 0.922 0.000 0.980 0.020 0.000
#> GSM257903 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257907 2 0.0707 0.922 0.000 0.980 0.020 0.000
#> GSM257909 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257911 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257913 2 0.0817 0.920 0.000 0.976 0.024 0.000
#> GSM257916 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257920 2 0.1867 0.893 0.000 0.928 0.072 0.000
#> GSM257922 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> GSM257924 2 0.1867 0.893 0.000 0.928 0.072 0.000
#> GSM257926 2 0.1867 0.893 0.000 0.928 0.072 0.000
#> GSM257928 2 0.4605 0.544 0.000 0.664 0.336 0.000
#> GSM257930 2 0.4605 0.544 0.000 0.664 0.336 0.000
#> GSM257938 2 0.4605 0.544 0.000 0.664 0.336 0.000
#> GSM257940 2 0.0817 0.921 0.000 0.976 0.024 0.000
#> GSM257942 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257946 3 0.2149 0.903 0.000 0.088 0.912 0.000
#> GSM257948 2 0.1867 0.893 0.000 0.928 0.072 0.000
#> GSM257950 3 0.2149 0.903 0.000 0.088 0.912 0.000
#> GSM257952 2 0.0817 0.921 0.000 0.976 0.024 0.000
#> GSM257954 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257959 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257971 2 0.4661 0.519 0.000 0.652 0.348 0.000
#> GSM257973 3 0.3266 0.799 0.000 0.168 0.832 0.000
#> GSM257981 2 0.1302 0.911 0.000 0.956 0.044 0.000
#> GSM257983 3 0.0000 0.885 0.000 0.000 1.000 0.000
#> GSM257985 3 0.2216 0.899 0.000 0.092 0.908 0.000
#> GSM257988 3 0.2921 0.842 0.000 0.140 0.860 0.000
#> GSM257991 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257993 2 0.0000 0.928 0.000 1.000 0.000 0.000
#> GSM257994 2 0.4605 0.544 0.000 0.664 0.336 0.000
#> GSM257989 3 0.2149 0.903 0.000 0.088 0.912 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 1 0.3814 0.656 0.720 0.000 0.000 0.276 0.004
#> GSM257888 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257890 1 0.3715 0.689 0.736 0.000 0.000 0.260 0.004
#> GSM257892 4 0.0510 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM257894 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257896 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257898 1 0.0671 0.972 0.980 0.000 0.000 0.016 0.004
#> GSM257900 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257902 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257904 1 0.0671 0.972 0.980 0.000 0.000 0.016 0.004
#> GSM257906 1 0.0671 0.972 0.980 0.000 0.000 0.016 0.004
#> GSM257908 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257910 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257912 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257914 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257917 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257919 1 0.0404 0.975 0.988 0.000 0.000 0.012 0.000
#> GSM257921 1 0.0162 0.977 0.996 0.000 0.000 0.004 0.000
#> GSM257923 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257929 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257937 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257939 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257943 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257945 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257947 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257964 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257968 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257970 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257977 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257982 1 0.1043 0.962 0.960 0.000 0.000 0.040 0.000
#> GSM257984 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM257992 1 0.1502 0.944 0.940 0.000 0.000 0.056 0.004
#> GSM257996 1 0.0000 0.977 1.000 0.000 0.000 0.000 0.000
#> GSM258006 1 0.1502 0.944 0.940 0.000 0.000 0.056 0.004
#> GSM257887 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257889 3 0.1205 0.880 0.000 0.004 0.956 0.000 0.040
#> GSM257891 3 0.0000 0.862 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.2338 0.891 0.000 0.004 0.884 0.000 0.112
#> GSM257895 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257897 3 0.0000 0.862 0.000 0.000 1.000 0.000 0.000
#> GSM257899 3 0.0000 0.862 0.000 0.000 1.000 0.000 0.000
#> GSM257901 2 0.0963 0.933 0.000 0.964 0.000 0.000 0.036
#> GSM257903 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257907 2 0.0963 0.933 0.000 0.964 0.000 0.000 0.036
#> GSM257909 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257911 2 0.0510 0.940 0.000 0.984 0.000 0.000 0.016
#> GSM257913 2 0.2561 0.871 0.000 0.884 0.020 0.000 0.096
#> GSM257916 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257920 2 0.3460 0.820 0.000 0.828 0.044 0.000 0.128
#> GSM257922 3 0.3966 0.495 0.000 0.000 0.664 0.000 0.336
#> GSM257924 2 0.3460 0.820 0.000 0.828 0.044 0.000 0.128
#> GSM257926 2 0.3460 0.820 0.000 0.828 0.044 0.000 0.128
#> GSM257928 5 0.0609 0.967 0.000 0.020 0.000 0.000 0.980
#> GSM257930 5 0.0609 0.967 0.000 0.020 0.000 0.000 0.980
#> GSM257938 5 0.0609 0.967 0.000 0.020 0.000 0.000 0.980
#> GSM257940 2 0.1043 0.931 0.000 0.960 0.000 0.000 0.040
#> GSM257942 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.2338 0.891 0.000 0.004 0.884 0.000 0.112
#> GSM257948 2 0.3460 0.820 0.000 0.828 0.044 0.000 0.128
#> GSM257950 3 0.2338 0.891 0.000 0.004 0.884 0.000 0.112
#> GSM257952 2 0.1121 0.929 0.000 0.956 0.000 0.000 0.044
#> GSM257954 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257956 2 0.4242 0.239 0.000 0.572 0.000 0.000 0.428
#> GSM257959 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0510 0.940 0.000 0.984 0.000 0.000 0.016
#> GSM257967 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257971 5 0.2390 0.863 0.000 0.020 0.084 0.000 0.896
#> GSM257973 3 0.3898 0.807 0.000 0.080 0.804 0.000 0.116
#> GSM257981 2 0.1800 0.915 0.000 0.932 0.020 0.000 0.048
#> GSM257983 3 0.0000 0.862 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.2389 0.889 0.000 0.004 0.880 0.000 0.116
#> GSM257988 3 0.3459 0.845 0.000 0.052 0.832 0.000 0.116
#> GSM257991 2 0.0162 0.944 0.000 0.996 0.000 0.000 0.004
#> GSM257993 2 0.0000 0.945 0.000 1.000 0.000 0.000 0.000
#> GSM257994 5 0.0609 0.967 0.000 0.020 0.000 0.000 0.980
#> GSM257989 3 0.2338 0.891 0.000 0.004 0.884 0.000 0.112
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.5124 0.4857 0.148 0.000 0.000 0.232 0.000 0.620
#> GSM257888 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257890 1 0.5440 0.2422 0.576 0.000 0.000 0.224 0.000 0.200
#> GSM257892 4 0.0632 0.0000 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM257894 1 0.3364 0.6866 0.780 0.000 0.000 0.024 0.000 0.196
#> GSM257896 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257898 6 0.3592 0.8508 0.344 0.000 0.000 0.000 0.000 0.656
#> GSM257900 1 0.3828 -0.2320 0.560 0.000 0.000 0.000 0.000 0.440
#> GSM257902 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257904 6 0.3592 0.8493 0.344 0.000 0.000 0.000 0.000 0.656
#> GSM257906 6 0.3592 0.8493 0.344 0.000 0.000 0.000 0.000 0.656
#> GSM257908 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257910 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257912 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257914 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257917 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257919 1 0.2260 0.7409 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM257921 1 0.2135 0.7135 0.872 0.000 0.000 0.000 0.000 0.128
#> GSM257923 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257925 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257927 1 0.3774 -0.0993 0.592 0.000 0.000 0.000 0.000 0.408
#> GSM257929 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257937 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257939 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257941 1 0.3531 0.1997 0.672 0.000 0.000 0.000 0.000 0.328
#> GSM257943 1 0.3838 -0.2536 0.552 0.000 0.000 0.000 0.000 0.448
#> GSM257945 1 0.3672 0.0852 0.632 0.000 0.000 0.000 0.000 0.368
#> GSM257947 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257949 1 0.0508 0.7700 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM257951 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257953 1 0.0260 0.7706 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257955 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257958 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257960 1 0.3563 0.1783 0.664 0.000 0.000 0.000 0.000 0.336
#> GSM257962 1 0.3563 0.1783 0.664 0.000 0.000 0.000 0.000 0.336
#> GSM257964 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257966 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257968 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257970 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257972 1 0.0547 0.7709 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257977 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257982 1 0.3394 0.6827 0.776 0.000 0.000 0.024 0.000 0.200
#> GSM257984 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257986 1 0.0363 0.7703 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM257990 1 0.0547 0.7699 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257992 6 0.3710 0.8617 0.292 0.000 0.000 0.012 0.000 0.696
#> GSM257996 1 0.0790 0.7687 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM258006 6 0.3710 0.8617 0.292 0.000 0.000 0.012 0.000 0.696
#> GSM257887 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257889 3 0.1686 0.8758 0.000 0.000 0.924 0.000 0.012 0.064
#> GSM257891 3 0.1908 0.8597 0.000 0.000 0.900 0.004 0.000 0.096
#> GSM257893 3 0.0713 0.8863 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM257895 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257897 3 0.2170 0.8545 0.000 0.000 0.888 0.012 0.000 0.100
#> GSM257899 3 0.2170 0.8545 0.000 0.000 0.888 0.012 0.000 0.100
#> GSM257901 2 0.3436 0.8233 0.000 0.796 0.020 0.000 0.012 0.172
#> GSM257903 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257907 2 0.3436 0.8233 0.000 0.796 0.020 0.000 0.012 0.172
#> GSM257909 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.2703 0.8328 0.000 0.824 0.000 0.000 0.004 0.172
#> GSM257913 2 0.4420 0.7772 0.000 0.728 0.092 0.000 0.008 0.172
#> GSM257916 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 2 0.5380 0.7166 0.000 0.660 0.132 0.000 0.036 0.172
#> GSM257922 3 0.4881 0.4772 0.000 0.000 0.588 0.000 0.336 0.076
#> GSM257924 2 0.5380 0.7166 0.000 0.660 0.132 0.000 0.036 0.172
#> GSM257926 2 0.5380 0.7166 0.000 0.660 0.132 0.000 0.036 0.172
#> GSM257928 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257930 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257938 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257940 2 0.3526 0.8214 0.000 0.792 0.020 0.000 0.016 0.172
#> GSM257942 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.0713 0.8863 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM257948 2 0.5380 0.7166 0.000 0.660 0.132 0.000 0.036 0.172
#> GSM257950 3 0.0713 0.8863 0.000 0.000 0.972 0.000 0.028 0.000
#> GSM257952 2 0.3606 0.8194 0.000 0.788 0.024 0.000 0.016 0.172
#> GSM257954 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257956 2 0.3810 0.3391 0.000 0.572 0.000 0.000 0.428 0.000
#> GSM257959 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.2703 0.8328 0.000 0.824 0.000 0.000 0.004 0.172
#> GSM257967 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257971 5 0.1806 0.8533 0.000 0.000 0.088 0.000 0.908 0.004
#> GSM257973 3 0.2380 0.8052 0.000 0.068 0.892 0.000 0.036 0.004
#> GSM257981 2 0.4041 0.8066 0.000 0.764 0.044 0.000 0.020 0.172
#> GSM257983 3 0.2170 0.8545 0.000 0.000 0.888 0.012 0.000 0.100
#> GSM257985 3 0.0858 0.8842 0.000 0.000 0.968 0.000 0.028 0.004
#> GSM257988 3 0.1938 0.8429 0.000 0.040 0.920 0.000 0.036 0.004
#> GSM257991 2 0.0458 0.8762 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM257993 2 0.0000 0.8791 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257994 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM257989 3 0.0713 0.8863 0.000 0.000 0.972 0.000 0.028 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 cell.type(p) protocol(p) individual(p) k
#> ATC:hclust 96 8.49e-22 1.000 1.000 2
#> ATC:hclust 95 1.41e-21 1.000 1.000 3
#> ATC:hclust 95 2.35e-21 0.189 1.000 4
#> ATC:hclust 93 4.97e-20 0.109 0.997 5
#> ATC:hclust 84 2.47e-17 0.168 0.957 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 25171 rows and 96 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.747 0.850 0.825 0.2328 0.882 0.761
#> 4 4 0.644 0.820 0.820 0.1237 0.881 0.687
#> 5 5 0.612 0.685 0.772 0.0768 0.961 0.867
#> 6 6 0.651 0.523 0.747 0.0457 0.906 0.672
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.6280 0.718 0.540 0.000 0.460
#> GSM257888 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257890 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257892 1 0.6280 0.718 0.540 0.000 0.460
#> GSM257894 1 0.5968 0.757 0.636 0.000 0.364
#> GSM257896 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257898 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257900 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257902 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257904 1 0.0592 0.866 0.988 0.000 0.012
#> GSM257906 1 0.4750 0.813 0.784 0.000 0.216
#> GSM257908 1 0.6126 0.744 0.600 0.000 0.400
#> GSM257910 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257912 1 0.6244 0.728 0.560 0.000 0.440
#> GSM257914 1 0.6062 0.751 0.616 0.000 0.384
#> GSM257917 1 0.3551 0.836 0.868 0.000 0.132
#> GSM257919 1 0.6244 0.728 0.560 0.000 0.440
#> GSM257921 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257927 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257929 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257937 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257939 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257941 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257943 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257945 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257947 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257960 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257962 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257964 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257966 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257968 1 0.6252 0.725 0.556 0.000 0.444
#> GSM257970 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257977 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257982 1 0.6274 0.719 0.544 0.000 0.456
#> GSM257984 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.868 1.000 0.000 0.000
#> GSM257990 1 0.0237 0.867 0.996 0.000 0.004
#> GSM257992 1 0.4750 0.813 0.784 0.000 0.216
#> GSM257996 1 0.0000 0.868 1.000 0.000 0.000
#> GSM258006 1 0.5591 0.784 0.696 0.000 0.304
#> GSM257887 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257889 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257891 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257893 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257895 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257897 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257899 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257901 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257903 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257907 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257909 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257911 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257913 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257916 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257920 2 0.6274 -0.805 0.000 0.544 0.456
#> GSM257922 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257924 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257926 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257928 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257930 2 0.5650 -0.235 0.000 0.688 0.312
#> GSM257938 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257940 3 0.6286 0.992 0.000 0.464 0.536
#> GSM257942 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257946 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257948 2 0.5138 0.142 0.000 0.748 0.252
#> GSM257950 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257952 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257954 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257956 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257959 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257965 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257971 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257973 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257981 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257983 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257985 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257988 3 0.6280 1.000 0.000 0.460 0.540
#> GSM257991 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257993 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257994 2 0.0000 0.934 0.000 1.000 0.000
#> GSM257989 3 0.6280 1.000 0.000 0.460 0.540
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.5732 0.911 0.264 0.000 0.064 0.672
#> GSM257888 4 0.4343 0.941 0.264 0.000 0.004 0.732
#> GSM257890 4 0.4164 0.941 0.264 0.000 0.000 0.736
#> GSM257892 4 0.5732 0.911 0.264 0.000 0.064 0.672
#> GSM257894 4 0.5018 0.885 0.332 0.000 0.012 0.656
#> GSM257896 4 0.4343 0.941 0.264 0.000 0.004 0.732
#> GSM257898 1 0.3873 0.770 0.772 0.000 0.228 0.000
#> GSM257900 1 0.3444 0.803 0.816 0.000 0.184 0.000
#> GSM257902 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257904 1 0.4053 0.765 0.768 0.000 0.228 0.004
#> GSM257906 1 0.7271 0.268 0.540 0.000 0.244 0.216
#> GSM257908 4 0.5668 0.897 0.300 0.000 0.048 0.652
#> GSM257910 1 0.1624 0.827 0.952 0.000 0.020 0.028
#> GSM257912 4 0.6323 0.879 0.272 0.000 0.100 0.628
#> GSM257914 4 0.6711 0.815 0.308 0.000 0.116 0.576
#> GSM257917 1 0.6548 0.488 0.636 0.000 0.176 0.188
#> GSM257919 4 0.6323 0.879 0.272 0.000 0.100 0.628
#> GSM257921 1 0.3074 0.819 0.848 0.000 0.152 0.000
#> GSM257923 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257927 1 0.3172 0.816 0.840 0.000 0.160 0.000
#> GSM257929 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257937 4 0.4164 0.941 0.264 0.000 0.000 0.736
#> GSM257939 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257941 1 0.3172 0.816 0.840 0.000 0.160 0.000
#> GSM257943 1 0.3801 0.776 0.780 0.000 0.220 0.000
#> GSM257945 1 0.3172 0.816 0.840 0.000 0.160 0.000
#> GSM257947 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257960 1 0.3172 0.816 0.840 0.000 0.160 0.000
#> GSM257962 1 0.3172 0.816 0.840 0.000 0.160 0.000
#> GSM257964 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257966 4 0.4164 0.941 0.264 0.000 0.000 0.736
#> GSM257968 4 0.4428 0.936 0.276 0.000 0.004 0.720
#> GSM257970 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257972 1 0.1118 0.847 0.964 0.000 0.036 0.000
#> GSM257977 4 0.4343 0.941 0.264 0.000 0.004 0.732
#> GSM257982 4 0.4343 0.941 0.264 0.000 0.004 0.732
#> GSM257984 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.851 1.000 0.000 0.000 0.000
#> GSM257990 1 0.1792 0.842 0.932 0.000 0.068 0.000
#> GSM257992 1 0.7271 0.268 0.540 0.000 0.244 0.216
#> GSM257996 1 0.3356 0.809 0.824 0.000 0.176 0.000
#> GSM258006 1 0.7494 -0.243 0.460 0.000 0.188 0.352
#> GSM257887 2 0.0188 0.898 0.000 0.996 0.000 0.004
#> GSM257889 3 0.4606 0.923 0.000 0.264 0.724 0.012
#> GSM257891 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257893 3 0.4606 0.923 0.000 0.264 0.724 0.012
#> GSM257895 2 0.0469 0.895 0.000 0.988 0.000 0.012
#> GSM257897 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257899 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257901 2 0.3444 0.777 0.000 0.816 0.000 0.184
#> GSM257903 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257907 2 0.3444 0.777 0.000 0.816 0.000 0.184
#> GSM257909 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257911 2 0.2760 0.823 0.000 0.872 0.000 0.128
#> GSM257913 2 0.3444 0.777 0.000 0.816 0.000 0.184
#> GSM257916 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257920 3 0.7432 0.715 0.000 0.336 0.480 0.184
#> GSM257922 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257924 3 0.7149 0.821 0.000 0.264 0.552 0.184
#> GSM257926 3 0.7149 0.821 0.000 0.264 0.552 0.184
#> GSM257928 3 0.5732 0.899 0.000 0.264 0.672 0.064
#> GSM257930 2 0.7539 -0.133 0.000 0.492 0.256 0.252
#> GSM257938 2 0.2589 0.838 0.000 0.884 0.000 0.116
#> GSM257940 3 0.7373 0.786 0.000 0.280 0.516 0.204
#> GSM257942 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257946 3 0.4606 0.923 0.000 0.264 0.724 0.012
#> GSM257948 2 0.7110 0.110 0.000 0.564 0.236 0.200
#> GSM257950 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257952 2 0.3528 0.772 0.000 0.808 0.000 0.192
#> GSM257954 2 0.0592 0.894 0.000 0.984 0.000 0.016
#> GSM257956 2 0.1118 0.888 0.000 0.964 0.000 0.036
#> GSM257959 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0592 0.895 0.000 0.984 0.000 0.016
#> GSM257967 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0188 0.898 0.000 0.996 0.000 0.004
#> GSM257971 3 0.7450 0.792 0.000 0.264 0.508 0.228
#> GSM257973 3 0.5927 0.896 0.000 0.264 0.660 0.076
#> GSM257981 2 0.3908 0.746 0.000 0.784 0.004 0.212
#> GSM257983 3 0.4343 0.924 0.000 0.264 0.732 0.004
#> GSM257985 3 0.4606 0.923 0.000 0.264 0.724 0.012
#> GSM257988 3 0.4164 0.924 0.000 0.264 0.736 0.000
#> GSM257991 2 0.0336 0.897 0.000 0.992 0.000 0.008
#> GSM257993 2 0.0469 0.895 0.000 0.988 0.000 0.012
#> GSM257994 2 0.2773 0.834 0.000 0.880 0.004 0.116
#> GSM257989 3 0.4164 0.924 0.000 0.264 0.736 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.5735 0.8461 0.156 0.000 0.056 0.696 NA
#> GSM257888 4 0.2806 0.9148 0.152 0.000 0.000 0.844 NA
#> GSM257890 4 0.3087 0.9120 0.152 0.000 0.008 0.836 NA
#> GSM257892 4 0.5735 0.8474 0.156 0.000 0.056 0.696 NA
#> GSM257894 4 0.3796 0.8767 0.216 0.000 0.008 0.768 NA
#> GSM257896 4 0.2806 0.9148 0.152 0.000 0.000 0.844 NA
#> GSM257898 1 0.2358 0.6695 0.888 0.000 0.000 0.008 NA
#> GSM257900 1 0.1544 0.7041 0.932 0.000 0.000 0.000 NA
#> GSM257902 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257904 1 0.2681 0.6591 0.876 0.000 0.004 0.012 NA
#> GSM257906 1 0.5634 0.2446 0.656 0.000 0.008 0.204 NA
#> GSM257908 4 0.5187 0.8552 0.216 0.000 0.064 0.700 NA
#> GSM257910 1 0.5887 0.7221 0.652 0.000 0.064 0.052 NA
#> GSM257912 4 0.5497 0.8184 0.264 0.000 0.064 0.652 NA
#> GSM257914 4 0.5745 0.7560 0.316 0.000 0.064 0.600 NA
#> GSM257917 1 0.4915 0.4372 0.740 0.000 0.064 0.172 NA
#> GSM257919 4 0.5497 0.8184 0.264 0.000 0.064 0.652 NA
#> GSM257921 1 0.2419 0.7312 0.904 0.000 0.064 0.004 NA
#> GSM257923 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257925 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257927 1 0.0000 0.7413 1.000 0.000 0.000 0.000 NA
#> GSM257929 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257937 4 0.2648 0.9146 0.152 0.000 0.000 0.848 NA
#> GSM257939 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257941 1 0.0000 0.7413 1.000 0.000 0.000 0.000 NA
#> GSM257943 1 0.1965 0.6835 0.904 0.000 0.000 0.000 NA
#> GSM257945 1 0.0000 0.7413 1.000 0.000 0.000 0.000 NA
#> GSM257947 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257949 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257951 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257953 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257955 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257958 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257960 1 0.0000 0.7413 1.000 0.000 0.000 0.000 NA
#> GSM257962 1 0.0000 0.7413 1.000 0.000 0.000 0.000 NA
#> GSM257964 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257966 4 0.2648 0.9146 0.152 0.000 0.000 0.848 NA
#> GSM257968 4 0.3450 0.9038 0.176 0.000 0.008 0.808 NA
#> GSM257970 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257972 1 0.3706 0.7883 0.756 0.000 0.004 0.004 NA
#> GSM257977 4 0.2806 0.9148 0.152 0.000 0.000 0.844 NA
#> GSM257982 4 0.2806 0.9148 0.152 0.000 0.000 0.844 NA
#> GSM257984 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257986 1 0.3790 0.7919 0.724 0.000 0.000 0.004 NA
#> GSM257990 1 0.1792 0.7646 0.916 0.000 0.000 0.000 NA
#> GSM257992 1 0.6007 0.2289 0.640 0.000 0.024 0.204 NA
#> GSM257996 1 0.1893 0.7139 0.928 0.000 0.048 0.000 NA
#> GSM258006 1 0.6504 -0.0736 0.548 0.000 0.024 0.296 NA
#> GSM257887 2 0.4278 0.7377 0.000 0.548 0.000 0.000 NA
#> GSM257889 3 0.2929 0.9217 0.000 0.128 0.856 0.012 NA
#> GSM257891 3 0.3332 0.9205 0.000 0.120 0.844 0.028 NA
#> GSM257893 3 0.3163 0.9217 0.000 0.128 0.848 0.012 NA
#> GSM257895 2 0.4622 0.7301 0.000 0.548 0.000 0.012 NA
#> GSM257897 3 0.3707 0.9176 0.000 0.120 0.828 0.036 NA
#> GSM257899 3 0.3707 0.9176 0.000 0.120 0.828 0.036 NA
#> GSM257901 2 0.0162 0.5180 0.000 0.996 0.000 0.000 NA
#> GSM257903 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257905 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257907 2 0.0451 0.5107 0.000 0.988 0.008 0.000 NA
#> GSM257909 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257911 2 0.2690 0.6159 0.000 0.844 0.000 0.000 NA
#> GSM257913 2 0.0693 0.5075 0.000 0.980 0.008 0.012 NA
#> GSM257916 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257918 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257920 2 0.4696 -0.3912 0.000 0.584 0.400 0.012 NA
#> GSM257922 3 0.3463 0.9205 0.000 0.120 0.840 0.020 NA
#> GSM257924 2 0.4973 -0.5339 0.000 0.496 0.480 0.020 NA
#> GSM257926 2 0.4791 -0.5025 0.000 0.524 0.460 0.012 NA
#> GSM257928 3 0.5637 0.8355 0.000 0.128 0.712 0.092 NA
#> GSM257930 2 0.6650 0.0384 0.000 0.616 0.184 0.104 NA
#> GSM257938 2 0.5613 0.6283 0.000 0.576 0.000 0.092 NA
#> GSM257940 2 0.4480 -0.4140 0.000 0.592 0.400 0.004 NA
#> GSM257942 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257944 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257946 3 0.3031 0.9208 0.000 0.128 0.852 0.016 NA
#> GSM257948 2 0.3845 0.0678 0.000 0.760 0.224 0.012 NA
#> GSM257950 3 0.3031 0.9226 0.000 0.120 0.856 0.020 NA
#> GSM257952 2 0.1661 0.5116 0.000 0.940 0.000 0.024 NA
#> GSM257954 2 0.4622 0.7301 0.000 0.548 0.000 0.012 NA
#> GSM257956 2 0.5118 0.7135 0.000 0.548 0.000 0.040 NA
#> GSM257959 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257961 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257963 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257965 2 0.4210 0.7354 0.000 0.588 0.000 0.000 NA
#> GSM257967 2 0.4268 0.7396 0.000 0.556 0.000 0.000 NA
#> GSM257969 2 0.4278 0.7377 0.000 0.548 0.000 0.000 NA
#> GSM257971 3 0.7115 0.4870 0.000 0.412 0.420 0.092 NA
#> GSM257973 3 0.4178 0.7849 0.000 0.292 0.696 0.008 NA
#> GSM257981 2 0.1787 0.4807 0.000 0.940 0.016 0.012 NA
#> GSM257983 3 0.3495 0.9191 0.000 0.120 0.836 0.036 NA
#> GSM257985 3 0.3031 0.9208 0.000 0.128 0.852 0.016 NA
#> GSM257988 3 0.2612 0.9238 0.000 0.124 0.868 0.008 NA
#> GSM257991 2 0.4249 0.7380 0.000 0.568 0.000 0.000 NA
#> GSM257993 2 0.4437 0.7332 0.000 0.532 0.000 0.004 NA
#> GSM257994 2 0.5836 0.6142 0.000 0.568 0.004 0.100 NA
#> GSM257989 3 0.2488 0.9239 0.000 0.124 0.872 0.004 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.6605 0.641 0.084 0.000 0.052 0.612 0.136 0.116
#> GSM257888 4 0.1610 0.844 0.084 0.000 0.000 0.916 0.000 0.000
#> GSM257890 4 0.2230 0.836 0.084 0.000 0.000 0.892 0.024 0.000
#> GSM257892 4 0.6660 0.652 0.084 0.000 0.080 0.616 0.120 0.100
#> GSM257894 4 0.3098 0.796 0.140 0.000 0.004 0.832 0.008 0.016
#> GSM257896 4 0.1610 0.844 0.084 0.000 0.000 0.916 0.000 0.000
#> GSM257898 6 0.4459 0.633 0.384 0.000 0.012 0.000 0.016 0.588
#> GSM257900 6 0.3979 0.448 0.456 0.000 0.000 0.000 0.004 0.540
#> GSM257902 1 0.0603 0.714 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM257904 6 0.4047 0.631 0.384 0.000 0.012 0.000 0.000 0.604
#> GSM257906 6 0.6137 0.665 0.244 0.000 0.020 0.112 0.036 0.588
#> GSM257908 4 0.5673 0.694 0.108 0.000 0.000 0.636 0.196 0.060
#> GSM257910 1 0.4823 0.343 0.700 0.000 0.000 0.056 0.204 0.040
#> GSM257912 4 0.6288 0.625 0.100 0.000 0.000 0.576 0.204 0.120
#> GSM257914 4 0.6603 0.565 0.124 0.000 0.000 0.540 0.204 0.132
#> GSM257917 6 0.7445 0.420 0.320 0.000 0.000 0.144 0.204 0.332
#> GSM257919 4 0.6288 0.625 0.100 0.000 0.000 0.576 0.204 0.120
#> GSM257921 1 0.5671 -0.142 0.544 0.000 0.004 0.000 0.180 0.272
#> GSM257923 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257927 1 0.4177 -0.314 0.520 0.000 0.000 0.000 0.012 0.468
#> GSM257929 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.1610 0.844 0.084 0.000 0.000 0.916 0.000 0.000
#> GSM257939 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.4089 -0.308 0.524 0.000 0.000 0.000 0.008 0.468
#> GSM257943 6 0.3747 0.606 0.396 0.000 0.000 0.000 0.000 0.604
#> GSM257945 1 0.4177 -0.314 0.520 0.000 0.000 0.000 0.012 0.468
#> GSM257947 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0405 0.713 0.988 0.000 0.008 0.000 0.000 0.004
#> GSM257951 1 0.0603 0.714 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM257953 1 0.0260 0.715 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM257955 1 0.0603 0.714 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM257958 1 0.0603 0.714 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM257960 1 0.4177 -0.314 0.520 0.000 0.000 0.000 0.012 0.468
#> GSM257962 1 0.4177 -0.314 0.520 0.000 0.000 0.000 0.012 0.468
#> GSM257964 1 0.0000 0.717 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.1610 0.844 0.084 0.000 0.000 0.916 0.000 0.000
#> GSM257968 4 0.2153 0.842 0.084 0.000 0.004 0.900 0.008 0.004
#> GSM257970 1 0.0603 0.714 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM257972 1 0.2013 0.650 0.908 0.000 0.008 0.000 0.008 0.076
#> GSM257977 4 0.1610 0.844 0.084 0.000 0.000 0.916 0.000 0.000
#> GSM257982 4 0.1897 0.843 0.084 0.000 0.004 0.908 0.000 0.004
#> GSM257984 1 0.0551 0.714 0.984 0.000 0.008 0.000 0.004 0.004
#> GSM257986 1 0.0551 0.714 0.984 0.000 0.008 0.000 0.004 0.004
#> GSM257990 1 0.3957 0.261 0.696 0.000 0.004 0.000 0.020 0.280
#> GSM257992 6 0.6592 0.654 0.244 0.000 0.024 0.112 0.064 0.556
#> GSM257996 1 0.5595 -0.361 0.464 0.000 0.000 0.000 0.144 0.392
#> GSM258006 6 0.7079 0.481 0.192 0.000 0.024 0.228 0.064 0.492
#> GSM257887 2 0.1065 0.759 0.000 0.964 0.000 0.020 0.008 0.008
#> GSM257889 3 0.2765 0.740 0.000 0.104 0.864 0.004 0.024 0.004
#> GSM257891 3 0.3527 0.739 0.000 0.104 0.828 0.024 0.004 0.040
#> GSM257893 3 0.2986 0.734 0.000 0.104 0.852 0.000 0.032 0.012
#> GSM257895 2 0.2485 0.725 0.000 0.884 0.000 0.024 0.084 0.008
#> GSM257897 3 0.3658 0.735 0.000 0.104 0.820 0.024 0.004 0.048
#> GSM257899 3 0.3658 0.735 0.000 0.104 0.820 0.024 0.004 0.048
#> GSM257901 2 0.6375 -0.139 0.000 0.452 0.000 0.024 0.308 0.216
#> GSM257903 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0458 0.765 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM257907 2 0.6375 -0.139 0.000 0.452 0.000 0.024 0.308 0.216
#> GSM257909 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.5621 0.306 0.000 0.620 0.000 0.028 0.188 0.164
#> GSM257913 2 0.6081 -0.183 0.000 0.448 0.004 0.000 0.296 0.252
#> GSM257916 2 0.0146 0.767 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM257918 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 3 0.7553 -0.547 0.000 0.156 0.312 0.000 0.300 0.232
#> GSM257922 3 0.3089 0.743 0.000 0.104 0.848 0.020 0.000 0.028
#> GSM257924 3 0.7201 -0.367 0.000 0.104 0.404 0.000 0.264 0.228
#> GSM257926 3 0.7259 -0.417 0.000 0.104 0.376 0.000 0.288 0.232
#> GSM257928 3 0.5302 0.445 0.000 0.104 0.628 0.004 0.252 0.012
#> GSM257930 5 0.5579 0.499 0.000 0.248 0.148 0.004 0.592 0.008
#> GSM257938 2 0.3915 0.332 0.000 0.584 0.000 0.004 0.412 0.000
#> GSM257940 5 0.7884 0.426 0.000 0.128 0.300 0.024 0.328 0.220
#> GSM257942 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.2986 0.734 0.000 0.104 0.852 0.000 0.032 0.012
#> GSM257948 5 0.7533 0.521 0.000 0.288 0.144 0.000 0.312 0.256
#> GSM257950 3 0.3219 0.741 0.000 0.104 0.840 0.016 0.000 0.040
#> GSM257952 2 0.6440 -0.162 0.000 0.420 0.000 0.032 0.364 0.184
#> GSM257954 2 0.2537 0.723 0.000 0.880 0.000 0.024 0.088 0.008
#> GSM257956 2 0.3419 0.657 0.000 0.796 0.000 0.024 0.172 0.008
#> GSM257959 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.2480 0.717 0.000 0.896 0.000 0.028 0.048 0.028
#> GSM257967 2 0.0000 0.767 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 2 0.1065 0.759 0.000 0.964 0.000 0.020 0.008 0.008
#> GSM257971 5 0.5683 0.287 0.000 0.104 0.348 0.000 0.528 0.020
#> GSM257973 3 0.5657 0.457 0.000 0.104 0.660 0.000 0.112 0.124
#> GSM257981 2 0.6535 -0.274 0.000 0.388 0.004 0.024 0.380 0.204
#> GSM257983 3 0.3781 0.735 0.000 0.104 0.812 0.024 0.004 0.056
#> GSM257985 3 0.3003 0.737 0.000 0.104 0.852 0.000 0.028 0.016
#> GSM257988 3 0.2622 0.745 0.000 0.104 0.868 0.000 0.004 0.024
#> GSM257991 2 0.1714 0.738 0.000 0.936 0.000 0.016 0.024 0.024
#> GSM257993 2 0.2206 0.734 0.000 0.904 0.000 0.024 0.064 0.008
#> GSM257994 2 0.4165 0.294 0.000 0.568 0.008 0.004 0.420 0.000
#> GSM257989 3 0.2261 0.745 0.000 0.104 0.884 0.000 0.004 0.008
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 cell.type(p) protocol(p) individual(p) k
#> ATC:kmeans 96 8.49e-22 1.000 1.000 2
#> ATC:kmeans 93 6.39e-21 0.329 1.000 3
#> ATC:kmeans 90 2.19e-19 0.481 0.996 4
#> ATC:kmeans 84 4.25e-18 0.279 0.987 5
#> ATC:kmeans 68 2.67e-13 0.639 0.937 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 25171 rows and 96 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 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 1.000 1.000 0.5058 0.495 0.495
#> 3 3 1.000 0.969 0.981 0.2254 0.882 0.761
#> 4 4 0.908 0.934 0.963 0.1351 0.911 0.763
#> 5 5 0.832 0.831 0.908 0.0587 0.975 0.912
#> 6 6 0.793 0.707 0.858 0.0407 0.987 0.952
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.1031 0.984 0.976 0.000 0.024
#> GSM257888 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257890 1 0.1031 0.984 0.976 0.000 0.024
#> GSM257892 1 0.1031 0.984 0.976 0.000 0.024
#> GSM257894 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257896 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257898 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257904 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257906 1 0.0237 0.994 0.996 0.000 0.004
#> GSM257908 1 0.0237 0.994 0.996 0.000 0.004
#> GSM257910 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257917 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257919 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257921 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257937 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257939 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257966 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257968 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257970 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257977 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257982 1 0.0892 0.986 0.980 0.000 0.020
#> GSM257984 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.996 1.000 0.000 0.000
#> GSM257992 1 0.0237 0.994 0.996 0.000 0.004
#> GSM257996 1 0.0000 0.996 1.000 0.000 0.000
#> GSM258006 1 0.0237 0.994 0.996 0.000 0.004
#> GSM257887 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257889 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257891 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257893 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257895 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257897 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257899 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257901 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257903 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257905 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257907 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257909 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257911 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257913 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257916 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257918 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257920 3 0.6154 0.417 0.000 0.408 0.592
#> GSM257922 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257924 3 0.3551 0.844 0.000 0.132 0.868
#> GSM257926 3 0.6126 0.436 0.000 0.400 0.600
#> GSM257928 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257930 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257938 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257940 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257942 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257944 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257946 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257948 2 0.0237 0.995 0.000 0.996 0.004
#> GSM257950 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257952 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257954 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257956 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257959 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257961 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257963 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257965 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257967 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257969 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257971 3 0.5465 0.638 0.000 0.288 0.712
#> GSM257973 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257981 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257983 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257985 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257988 3 0.1031 0.930 0.000 0.024 0.976
#> GSM257991 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257993 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257994 2 0.0000 1.000 0.000 1.000 0.000
#> GSM257989 3 0.1031 0.930 0.000 0.024 0.976
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.0000 0.770 0.000 0.000 0.000 1.000
#> GSM257888 4 0.3486 0.896 0.188 0.000 0.000 0.812
#> GSM257890 4 0.0000 0.770 0.000 0.000 0.000 1.000
#> GSM257892 4 0.0000 0.770 0.000 0.000 0.000 1.000
#> GSM257894 4 0.3942 0.878 0.236 0.000 0.000 0.764
#> GSM257896 4 0.3907 0.881 0.232 0.000 0.000 0.768
#> GSM257898 1 0.0336 0.969 0.992 0.000 0.000 0.008
#> GSM257900 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257902 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257904 1 0.0336 0.969 0.992 0.000 0.000 0.008
#> GSM257906 1 0.3610 0.742 0.800 0.000 0.000 0.200
#> GSM257908 1 0.1940 0.887 0.924 0.000 0.000 0.076
#> GSM257910 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257912 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257914 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257917 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257919 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257921 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257923 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257927 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257929 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257937 4 0.3356 0.897 0.176 0.000 0.000 0.824
#> GSM257939 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257941 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257943 1 0.0336 0.969 0.992 0.000 0.000 0.008
#> GSM257945 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257947 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257960 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257962 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257964 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257966 4 0.3400 0.897 0.180 0.000 0.000 0.820
#> GSM257968 4 0.3873 0.883 0.228 0.000 0.000 0.772
#> GSM257970 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257977 4 0.3356 0.897 0.176 0.000 0.000 0.824
#> GSM257982 4 0.3942 0.878 0.236 0.000 0.000 0.764
#> GSM257984 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257990 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM257992 1 0.3801 0.715 0.780 0.000 0.000 0.220
#> GSM257996 1 0.0000 0.975 1.000 0.000 0.000 0.000
#> GSM258006 1 0.3907 0.699 0.768 0.000 0.000 0.232
#> GSM257887 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257895 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257901 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257903 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257907 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257909 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257911 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257913 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257916 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257920 3 0.4817 0.432 0.000 0.388 0.612 0.000
#> GSM257922 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257924 3 0.2704 0.797 0.000 0.124 0.876 0.000
#> GSM257926 3 0.4776 0.460 0.000 0.376 0.624 0.000
#> GSM257928 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257930 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257938 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257940 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257942 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257948 2 0.0188 0.996 0.000 0.996 0.004 0.000
#> GSM257950 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257952 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257954 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257959 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257971 3 0.4222 0.616 0.000 0.272 0.728 0.000
#> GSM257973 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257981 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257983 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257985 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM257991 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257993 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257994 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> GSM257989 3 0.0000 0.908 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 5 0.4283 -0.298 0.000 0.000 0.000 0.456 0.544
#> GSM257888 4 0.1043 0.891 0.040 0.000 0.000 0.960 0.000
#> GSM257890 4 0.0703 0.843 0.000 0.000 0.000 0.976 0.024
#> GSM257892 4 0.4227 0.310 0.000 0.000 0.000 0.580 0.420
#> GSM257894 4 0.2127 0.869 0.108 0.000 0.000 0.892 0.000
#> GSM257896 4 0.2074 0.873 0.104 0.000 0.000 0.896 0.000
#> GSM257898 1 0.3424 0.631 0.760 0.000 0.000 0.000 0.240
#> GSM257900 1 0.2179 0.832 0.888 0.000 0.000 0.000 0.112
#> GSM257902 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257904 1 0.3395 0.640 0.764 0.000 0.000 0.000 0.236
#> GSM257906 5 0.4088 0.704 0.368 0.000 0.000 0.000 0.632
#> GSM257908 1 0.4751 0.555 0.732 0.000 0.000 0.152 0.116
#> GSM257910 1 0.2230 0.797 0.884 0.000 0.000 0.000 0.116
#> GSM257912 1 0.3780 0.725 0.808 0.000 0.000 0.060 0.132
#> GSM257914 1 0.3780 0.725 0.808 0.000 0.000 0.060 0.132
#> GSM257917 1 0.2516 0.797 0.860 0.000 0.000 0.000 0.140
#> GSM257919 1 0.3844 0.719 0.804 0.000 0.000 0.064 0.132
#> GSM257921 1 0.0404 0.888 0.988 0.000 0.000 0.000 0.012
#> GSM257923 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0290 0.888 0.992 0.000 0.000 0.000 0.008
#> GSM257927 1 0.1792 0.854 0.916 0.000 0.000 0.000 0.084
#> GSM257929 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.1043 0.891 0.040 0.000 0.000 0.960 0.000
#> GSM257939 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.1908 0.849 0.908 0.000 0.000 0.000 0.092
#> GSM257943 1 0.3395 0.640 0.764 0.000 0.000 0.000 0.236
#> GSM257945 1 0.1908 0.849 0.908 0.000 0.000 0.000 0.092
#> GSM257947 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0510 0.886 0.984 0.000 0.000 0.000 0.016
#> GSM257955 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0609 0.884 0.980 0.000 0.000 0.000 0.020
#> GSM257960 1 0.1851 0.852 0.912 0.000 0.000 0.000 0.088
#> GSM257962 1 0.1851 0.852 0.912 0.000 0.000 0.000 0.088
#> GSM257964 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.1043 0.891 0.040 0.000 0.000 0.960 0.000
#> GSM257968 4 0.2020 0.875 0.100 0.000 0.000 0.900 0.000
#> GSM257970 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257977 4 0.1043 0.891 0.040 0.000 0.000 0.960 0.000
#> GSM257982 4 0.2230 0.859 0.116 0.000 0.000 0.884 0.000
#> GSM257984 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.890 1.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.1908 0.849 0.908 0.000 0.000 0.000 0.092
#> GSM257992 5 0.4211 0.717 0.360 0.000 0.000 0.004 0.636
#> GSM257996 1 0.2561 0.830 0.856 0.000 0.000 0.000 0.144
#> GSM258006 5 0.4327 0.716 0.360 0.000 0.000 0.008 0.632
#> GSM257887 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257889 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000
#> GSM257891 3 0.0510 0.849 0.000 0.000 0.984 0.000 0.016
#> GSM257893 3 0.0162 0.850 0.000 0.000 0.996 0.004 0.000
#> GSM257895 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257897 3 0.1012 0.845 0.000 0.000 0.968 0.012 0.020
#> GSM257899 3 0.0898 0.846 0.000 0.000 0.972 0.008 0.020
#> GSM257901 2 0.2813 0.847 0.000 0.832 0.000 0.000 0.168
#> GSM257903 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257907 2 0.2813 0.847 0.000 0.832 0.000 0.000 0.168
#> GSM257909 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257911 2 0.1478 0.928 0.000 0.936 0.000 0.000 0.064
#> GSM257913 2 0.2813 0.847 0.000 0.832 0.000 0.000 0.168
#> GSM257916 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257920 3 0.6356 0.311 0.000 0.364 0.468 0.000 0.168
#> GSM257922 3 0.0771 0.848 0.000 0.000 0.976 0.004 0.020
#> GSM257924 3 0.5155 0.669 0.000 0.140 0.692 0.000 0.168
#> GSM257926 3 0.6349 0.322 0.000 0.360 0.472 0.000 0.168
#> GSM257928 3 0.1357 0.833 0.000 0.000 0.948 0.004 0.048
#> GSM257930 2 0.1518 0.928 0.000 0.944 0.004 0.004 0.048
#> GSM257938 2 0.1357 0.931 0.000 0.948 0.000 0.004 0.048
#> GSM257940 2 0.3203 0.836 0.000 0.820 0.012 0.000 0.168
#> GSM257942 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.0609 0.848 0.000 0.000 0.980 0.000 0.020
#> GSM257948 2 0.3656 0.813 0.000 0.800 0.032 0.000 0.168
#> GSM257950 3 0.0290 0.850 0.000 0.000 0.992 0.000 0.008
#> GSM257952 2 0.0162 0.959 0.000 0.996 0.000 0.000 0.004
#> GSM257954 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257956 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257959 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257967 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257971 3 0.6107 0.568 0.000 0.200 0.588 0.004 0.208
#> GSM257973 3 0.2813 0.784 0.000 0.000 0.832 0.000 0.168
#> GSM257981 2 0.1478 0.927 0.000 0.936 0.000 0.000 0.064
#> GSM257983 3 0.1012 0.845 0.000 0.000 0.968 0.012 0.020
#> GSM257985 3 0.1197 0.840 0.000 0.000 0.952 0.000 0.048
#> GSM257988 3 0.2648 0.793 0.000 0.000 0.848 0.000 0.152
#> GSM257991 2 0.0162 0.959 0.000 0.996 0.000 0.000 0.004
#> GSM257993 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM257994 2 0.1518 0.928 0.000 0.944 0.004 0.004 0.048
#> GSM257989 3 0.0000 0.850 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.4664 0.37190 0.000 0.000 0.000 0.280 0.076 0.644
#> GSM257888 4 0.0260 0.97025 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM257890 4 0.0363 0.95382 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM257892 6 0.5422 0.00192 0.000 0.000 0.000 0.436 0.116 0.448
#> GSM257894 4 0.0937 0.95497 0.040 0.000 0.000 0.960 0.000 0.000
#> GSM257896 4 0.0865 0.95927 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM257898 1 0.3810 0.27840 0.572 0.000 0.000 0.000 0.000 0.428
#> GSM257900 1 0.2219 0.77948 0.864 0.000 0.000 0.000 0.000 0.136
#> GSM257902 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257904 1 0.3797 0.30252 0.580 0.000 0.000 0.000 0.000 0.420
#> GSM257906 6 0.3050 0.67460 0.236 0.000 0.000 0.000 0.000 0.764
#> GSM257908 1 0.6624 0.32546 0.528 0.000 0.000 0.220 0.152 0.100
#> GSM257910 1 0.4562 0.62965 0.728 0.000 0.000 0.016 0.156 0.100
#> GSM257912 1 0.6362 0.45318 0.580 0.000 0.000 0.140 0.156 0.124
#> GSM257914 1 0.6297 0.46612 0.588 0.000 0.000 0.132 0.156 0.124
#> GSM257917 1 0.4666 0.63202 0.708 0.000 0.000 0.008 0.156 0.128
#> GSM257919 1 0.6362 0.45318 0.580 0.000 0.000 0.140 0.156 0.124
#> GSM257921 1 0.0777 0.83813 0.972 0.000 0.000 0.000 0.004 0.024
#> GSM257923 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0146 0.84376 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257927 1 0.1556 0.81767 0.920 0.000 0.000 0.000 0.000 0.080
#> GSM257929 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257937 4 0.0260 0.97025 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM257939 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 1 0.2003 0.79539 0.884 0.000 0.000 0.000 0.000 0.116
#> GSM257943 1 0.3774 0.33536 0.592 0.000 0.000 0.000 0.000 0.408
#> GSM257945 1 0.1863 0.80405 0.896 0.000 0.000 0.000 0.000 0.104
#> GSM257947 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0146 0.84376 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257955 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0146 0.84376 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM257960 1 0.1663 0.81371 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM257962 1 0.1765 0.80911 0.904 0.000 0.000 0.000 0.000 0.096
#> GSM257964 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.0260 0.97025 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM257968 4 0.0632 0.96685 0.024 0.000 0.000 0.976 0.000 0.000
#> GSM257970 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257977 4 0.0260 0.97025 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM257982 4 0.1267 0.92784 0.060 0.000 0.000 0.940 0.000 0.000
#> GSM257984 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.84461 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257990 1 0.1663 0.81389 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM257992 6 0.2883 0.70358 0.212 0.000 0.000 0.000 0.000 0.788
#> GSM257996 1 0.3206 0.77548 0.828 0.000 0.000 0.000 0.068 0.104
#> GSM258006 6 0.3052 0.70148 0.216 0.000 0.000 0.004 0.000 0.780
#> GSM257887 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257889 3 0.0937 0.75333 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM257891 3 0.0937 0.74923 0.000 0.000 0.960 0.000 0.040 0.000
#> GSM257893 3 0.1806 0.73890 0.000 0.000 0.908 0.000 0.088 0.004
#> GSM257895 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257897 3 0.2288 0.71507 0.000 0.000 0.876 0.004 0.116 0.004
#> GSM257899 3 0.2101 0.72519 0.000 0.000 0.892 0.004 0.100 0.004
#> GSM257901 2 0.3547 0.42244 0.000 0.668 0.000 0.000 0.332 0.000
#> GSM257903 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257907 2 0.3714 0.39427 0.000 0.656 0.004 0.000 0.340 0.000
#> GSM257909 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.2300 0.73529 0.000 0.856 0.000 0.000 0.144 0.000
#> GSM257913 2 0.3592 0.39561 0.000 0.656 0.000 0.000 0.344 0.000
#> GSM257916 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 5 0.6101 0.67053 0.000 0.288 0.340 0.000 0.372 0.000
#> GSM257922 3 0.2738 0.66384 0.000 0.000 0.820 0.000 0.176 0.004
#> GSM257924 3 0.5418 -0.42639 0.000 0.120 0.492 0.000 0.388 0.000
#> GSM257926 5 0.6053 0.66919 0.000 0.256 0.368 0.000 0.376 0.000
#> GSM257928 3 0.4414 0.47505 0.000 0.000 0.676 0.000 0.260 0.064
#> GSM257930 2 0.4537 0.47222 0.000 0.684 0.008 0.000 0.248 0.060
#> GSM257938 2 0.4121 0.53659 0.000 0.720 0.000 0.000 0.220 0.060
#> GSM257940 2 0.4396 0.26480 0.000 0.612 0.036 0.000 0.352 0.000
#> GSM257942 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.2048 0.71738 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM257948 2 0.4885 0.05674 0.000 0.560 0.068 0.000 0.372 0.000
#> GSM257950 3 0.0146 0.75591 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257952 2 0.0790 0.83961 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM257954 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257956 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257959 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257967 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257969 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257971 5 0.6253 0.30244 0.000 0.120 0.328 0.000 0.500 0.052
#> GSM257973 3 0.4002 0.27067 0.000 0.020 0.660 0.000 0.320 0.000
#> GSM257981 2 0.1910 0.77269 0.000 0.892 0.000 0.000 0.108 0.000
#> GSM257983 3 0.2362 0.70337 0.000 0.000 0.860 0.004 0.136 0.000
#> GSM257985 3 0.2048 0.70388 0.000 0.000 0.880 0.000 0.120 0.000
#> GSM257988 3 0.3175 0.49962 0.000 0.000 0.744 0.000 0.256 0.000
#> GSM257991 2 0.0632 0.84493 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM257993 2 0.0000 0.85922 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257994 2 0.4198 0.51583 0.000 0.708 0.000 0.000 0.232 0.060
#> GSM257989 3 0.0260 0.75634 0.000 0.000 0.992 0.000 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> ATC:skmeans 96 8.49e-22 1.000 1.000 2
#> ATC:skmeans 94 3.87e-21 0.161 1.000 3
#> ATC:skmeans 94 3.03e-20 0.295 0.998 4
#> ATC:skmeans 92 4.95e-19 0.113 0.991 5
#> ATC:skmeans 76 5.75e-15 0.471 0.808 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 25171 rows and 96 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 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 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 1.000 0.984 0.992 0.2460 0.876 0.749
#> 4 4 0.874 0.820 0.925 0.1713 0.888 0.697
#> 5 5 0.842 0.898 0.909 0.0725 0.914 0.689
#> 6 6 0.906 0.917 0.949 0.0541 0.946 0.747
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> 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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0000 1.000 1 0.000 0.000
#> GSM257888 1 0.0000 1.000 1 0.000 0.000
#> GSM257890 1 0.0000 1.000 1 0.000 0.000
#> GSM257892 1 0.0000 1.000 1 0.000 0.000
#> GSM257894 1 0.0000 1.000 1 0.000 0.000
#> GSM257896 1 0.0000 1.000 1 0.000 0.000
#> GSM257898 1 0.0000 1.000 1 0.000 0.000
#> GSM257900 1 0.0000 1.000 1 0.000 0.000
#> GSM257902 1 0.0000 1.000 1 0.000 0.000
#> GSM257904 1 0.0000 1.000 1 0.000 0.000
#> GSM257906 1 0.0000 1.000 1 0.000 0.000
#> GSM257908 1 0.0000 1.000 1 0.000 0.000
#> GSM257910 1 0.0000 1.000 1 0.000 0.000
#> GSM257912 1 0.0000 1.000 1 0.000 0.000
#> GSM257914 1 0.0000 1.000 1 0.000 0.000
#> GSM257917 1 0.0000 1.000 1 0.000 0.000
#> GSM257919 1 0.0000 1.000 1 0.000 0.000
#> GSM257921 1 0.0000 1.000 1 0.000 0.000
#> GSM257923 1 0.0000 1.000 1 0.000 0.000
#> GSM257925 1 0.0000 1.000 1 0.000 0.000
#> GSM257927 1 0.0000 1.000 1 0.000 0.000
#> GSM257929 1 0.0000 1.000 1 0.000 0.000
#> GSM257937 1 0.0000 1.000 1 0.000 0.000
#> GSM257939 1 0.0000 1.000 1 0.000 0.000
#> GSM257941 1 0.0000 1.000 1 0.000 0.000
#> GSM257943 1 0.0000 1.000 1 0.000 0.000
#> GSM257945 1 0.0000 1.000 1 0.000 0.000
#> GSM257947 1 0.0000 1.000 1 0.000 0.000
#> GSM257949 1 0.0000 1.000 1 0.000 0.000
#> GSM257951 1 0.0000 1.000 1 0.000 0.000
#> GSM257953 1 0.0000 1.000 1 0.000 0.000
#> GSM257955 1 0.0000 1.000 1 0.000 0.000
#> GSM257958 1 0.0000 1.000 1 0.000 0.000
#> GSM257960 1 0.0000 1.000 1 0.000 0.000
#> GSM257962 1 0.0000 1.000 1 0.000 0.000
#> GSM257964 1 0.0000 1.000 1 0.000 0.000
#> GSM257966 1 0.0000 1.000 1 0.000 0.000
#> GSM257968 1 0.0000 1.000 1 0.000 0.000
#> GSM257970 1 0.0000 1.000 1 0.000 0.000
#> GSM257972 1 0.0000 1.000 1 0.000 0.000
#> GSM257977 1 0.0000 1.000 1 0.000 0.000
#> GSM257982 1 0.0000 1.000 1 0.000 0.000
#> GSM257984 1 0.0000 1.000 1 0.000 0.000
#> GSM257986 1 0.0000 1.000 1 0.000 0.000
#> GSM257990 1 0.0000 1.000 1 0.000 0.000
#> GSM257992 1 0.0000 1.000 1 0.000 0.000
#> GSM257996 1 0.0000 1.000 1 0.000 0.000
#> GSM258006 1 0.0000 1.000 1 0.000 0.000
#> GSM257887 2 0.0000 0.982 0 1.000 0.000
#> GSM257889 3 0.0000 0.987 0 0.000 1.000
#> GSM257891 3 0.0000 0.987 0 0.000 1.000
#> GSM257893 3 0.0000 0.987 0 0.000 1.000
#> GSM257895 2 0.0000 0.982 0 1.000 0.000
#> GSM257897 3 0.0000 0.987 0 0.000 1.000
#> GSM257899 3 0.0000 0.987 0 0.000 1.000
#> GSM257901 2 0.2796 0.899 0 0.908 0.092
#> GSM257903 2 0.0000 0.982 0 1.000 0.000
#> GSM257905 2 0.0000 0.982 0 1.000 0.000
#> GSM257907 2 0.4842 0.721 0 0.776 0.224
#> GSM257909 2 0.0000 0.982 0 1.000 0.000
#> GSM257911 2 0.0000 0.982 0 1.000 0.000
#> GSM257913 2 0.0892 0.968 0 0.980 0.020
#> GSM257916 2 0.0000 0.982 0 1.000 0.000
#> GSM257918 2 0.0000 0.982 0 1.000 0.000
#> GSM257920 3 0.0000 0.987 0 0.000 1.000
#> GSM257922 3 0.0000 0.987 0 0.000 1.000
#> GSM257924 3 0.0000 0.987 0 0.000 1.000
#> GSM257926 3 0.0000 0.987 0 0.000 1.000
#> GSM257928 3 0.0000 0.987 0 0.000 1.000
#> GSM257930 3 0.5016 0.681 0 0.240 0.760
#> GSM257938 2 0.0237 0.979 0 0.996 0.004
#> GSM257940 3 0.0424 0.980 0 0.008 0.992
#> GSM257942 2 0.0000 0.982 0 1.000 0.000
#> GSM257944 2 0.0000 0.982 0 1.000 0.000
#> GSM257946 3 0.0000 0.987 0 0.000 1.000
#> GSM257948 3 0.0424 0.980 0 0.008 0.992
#> GSM257950 3 0.0000 0.987 0 0.000 1.000
#> GSM257952 2 0.0237 0.979 0 0.996 0.004
#> GSM257954 2 0.0000 0.982 0 1.000 0.000
#> GSM257956 2 0.0000 0.982 0 1.000 0.000
#> GSM257959 2 0.0000 0.982 0 1.000 0.000
#> GSM257961 2 0.0000 0.982 0 1.000 0.000
#> GSM257963 2 0.0000 0.982 0 1.000 0.000
#> GSM257965 2 0.0000 0.982 0 1.000 0.000
#> GSM257967 2 0.0000 0.982 0 1.000 0.000
#> GSM257969 2 0.0000 0.982 0 1.000 0.000
#> GSM257971 3 0.0000 0.987 0 0.000 1.000
#> GSM257973 3 0.0000 0.987 0 0.000 1.000
#> GSM257981 2 0.1163 0.961 0 0.972 0.028
#> GSM257983 3 0.0000 0.987 0 0.000 1.000
#> GSM257985 3 0.0000 0.987 0 0.000 1.000
#> GSM257988 3 0.0000 0.987 0 0.000 1.000
#> GSM257991 2 0.0000 0.982 0 1.000 0.000
#> GSM257993 2 0.0000 0.982 0 1.000 0.000
#> GSM257994 2 0.2878 0.894 0 0.904 0.096
#> GSM257989 3 0.0000 0.987 0 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.1716 0.776 0.064 0.000 0.000 0.936
#> GSM257888 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257890 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257892 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257894 4 0.1792 0.781 0.068 0.000 0.000 0.932
#> GSM257896 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257898 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM257900 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM257902 1 0.0336 0.817 0.992 0.000 0.000 0.008
#> GSM257904 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM257906 4 0.4948 0.209 0.440 0.000 0.000 0.560
#> GSM257908 4 0.4790 0.340 0.380 0.000 0.000 0.620
#> GSM257910 1 0.0188 0.819 0.996 0.000 0.000 0.004
#> GSM257912 4 0.4948 0.209 0.440 0.000 0.000 0.560
#> GSM257914 1 0.4985 0.126 0.532 0.000 0.000 0.468
#> GSM257917 1 0.2345 0.777 0.900 0.000 0.000 0.100
#> GSM257919 4 0.4948 0.209 0.440 0.000 0.000 0.560
#> GSM257921 1 0.4040 0.630 0.752 0.000 0.000 0.248
#> GSM257923 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257927 1 0.0921 0.817 0.972 0.000 0.000 0.028
#> GSM257929 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257937 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257939 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257941 1 0.0921 0.817 0.972 0.000 0.000 0.028
#> GSM257943 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM257945 1 0.0921 0.817 0.972 0.000 0.000 0.028
#> GSM257947 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257949 1 0.1637 0.779 0.940 0.000 0.000 0.060
#> GSM257951 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257953 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257955 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257960 1 0.2281 0.780 0.904 0.000 0.000 0.096
#> GSM257962 1 0.0921 0.817 0.972 0.000 0.000 0.028
#> GSM257964 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257966 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257968 4 0.2868 0.722 0.136 0.000 0.000 0.864
#> GSM257970 1 0.0000 0.820 1.000 0.000 0.000 0.000
#> GSM257972 1 0.4907 0.348 0.580 0.000 0.000 0.420
#> GSM257977 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257982 4 0.0000 0.804 0.000 0.000 0.000 1.000
#> GSM257984 1 0.4761 0.432 0.628 0.000 0.000 0.372
#> GSM257986 1 0.1716 0.775 0.936 0.000 0.000 0.064
#> GSM257990 1 0.0817 0.817 0.976 0.000 0.000 0.024
#> GSM257992 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM257996 1 0.4761 0.442 0.628 0.000 0.000 0.372
#> GSM258006 4 0.4222 0.573 0.272 0.000 0.000 0.728
#> GSM257887 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257889 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257891 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257893 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257895 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257897 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257899 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257901 2 0.2216 0.899 0.000 0.908 0.092 0.000
#> GSM257903 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257907 2 0.3837 0.721 0.000 0.776 0.224 0.000
#> GSM257909 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257911 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257913 2 0.0707 0.967 0.000 0.980 0.020 0.000
#> GSM257916 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257920 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257922 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257924 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257926 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257928 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257930 3 0.3975 0.681 0.000 0.240 0.760 0.000
#> GSM257938 2 0.0188 0.979 0.000 0.996 0.004 0.000
#> GSM257940 3 0.0336 0.978 0.000 0.008 0.992 0.000
#> GSM257942 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257946 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257948 3 0.0336 0.978 0.000 0.008 0.992 0.000
#> GSM257950 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257952 2 0.0188 0.979 0.000 0.996 0.004 0.000
#> GSM257954 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257956 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257959 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257971 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257973 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257981 2 0.0921 0.960 0.000 0.972 0.028 0.000
#> GSM257983 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257985 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257988 3 0.0000 0.985 0.000 0.000 1.000 0.000
#> GSM257991 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257993 2 0.0000 0.981 0.000 1.000 0.000 0.000
#> GSM257994 2 0.2281 0.894 0.000 0.904 0.096 0.000
#> GSM257989 3 0.0000 0.985 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.2280 0.953 0.120 0.000 0.000 0.880 0.000
#> GSM257888 4 0.2020 0.964 0.100 0.000 0.000 0.900 0.000
#> GSM257890 4 0.2020 0.964 0.100 0.000 0.000 0.900 0.000
#> GSM257892 4 0.2020 0.964 0.100 0.000 0.000 0.900 0.000
#> GSM257894 4 0.3655 0.894 0.160 0.000 0.000 0.804 0.036
#> GSM257896 4 0.2074 0.964 0.104 0.000 0.000 0.896 0.000
#> GSM257898 1 0.2570 0.877 0.888 0.000 0.000 0.028 0.084
#> GSM257900 1 0.2570 0.877 0.888 0.000 0.000 0.028 0.084
#> GSM257902 5 0.0451 0.973 0.008 0.000 0.000 0.004 0.988
#> GSM257904 1 0.2570 0.877 0.888 0.000 0.000 0.028 0.084
#> GSM257906 1 0.2645 0.846 0.888 0.000 0.000 0.068 0.044
#> GSM257908 1 0.4031 0.700 0.772 0.000 0.000 0.184 0.044
#> GSM257910 5 0.1768 0.931 0.072 0.000 0.000 0.004 0.924
#> GSM257912 1 0.2153 0.852 0.916 0.000 0.000 0.040 0.044
#> GSM257914 1 0.2046 0.870 0.916 0.000 0.000 0.016 0.068
#> GSM257917 1 0.2813 0.857 0.832 0.000 0.000 0.000 0.168
#> GSM257919 1 0.2153 0.852 0.916 0.000 0.000 0.040 0.044
#> GSM257921 1 0.2583 0.873 0.864 0.000 0.000 0.004 0.132
#> GSM257923 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257925 5 0.1121 0.952 0.044 0.000 0.000 0.000 0.956
#> GSM257927 1 0.3508 0.799 0.748 0.000 0.000 0.000 0.252
#> GSM257929 5 0.0963 0.960 0.036 0.000 0.000 0.000 0.964
#> GSM257937 4 0.2377 0.950 0.128 0.000 0.000 0.872 0.000
#> GSM257939 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257941 1 0.3242 0.830 0.784 0.000 0.000 0.000 0.216
#> GSM257943 1 0.2570 0.877 0.888 0.000 0.000 0.028 0.084
#> GSM257945 1 0.3242 0.830 0.784 0.000 0.000 0.000 0.216
#> GSM257947 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257949 5 0.0451 0.970 0.004 0.000 0.000 0.008 0.988
#> GSM257951 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257953 5 0.0880 0.963 0.032 0.000 0.000 0.000 0.968
#> GSM257955 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257958 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257960 1 0.3527 0.848 0.792 0.000 0.000 0.016 0.192
#> GSM257962 1 0.3242 0.830 0.784 0.000 0.000 0.000 0.216
#> GSM257964 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257966 4 0.2377 0.950 0.128 0.000 0.000 0.872 0.000
#> GSM257968 4 0.3789 0.818 0.212 0.000 0.000 0.768 0.020
#> GSM257970 5 0.0404 0.975 0.012 0.000 0.000 0.000 0.988
#> GSM257972 1 0.4342 0.776 0.728 0.000 0.000 0.040 0.232
#> GSM257977 4 0.2020 0.964 0.100 0.000 0.000 0.900 0.000
#> GSM257982 4 0.2074 0.964 0.104 0.000 0.000 0.896 0.000
#> GSM257984 5 0.3112 0.812 0.100 0.000 0.000 0.044 0.856
#> GSM257986 5 0.0404 0.966 0.000 0.000 0.000 0.012 0.988
#> GSM257990 1 0.4074 0.637 0.636 0.000 0.000 0.000 0.364
#> GSM257992 1 0.2570 0.877 0.888 0.000 0.000 0.028 0.084
#> GSM257996 1 0.1792 0.875 0.916 0.000 0.000 0.000 0.084
#> GSM258006 1 0.4350 0.575 0.704 0.000 0.000 0.268 0.028
#> GSM257887 2 0.0968 0.889 0.004 0.972 0.000 0.012 0.012
#> GSM257889 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257891 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257893 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257895 2 0.1314 0.885 0.016 0.960 0.000 0.012 0.012
#> GSM257897 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257899 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257901 2 0.4998 0.845 0.068 0.764 0.080 0.088 0.000
#> GSM257903 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257905 2 0.0968 0.889 0.004 0.972 0.000 0.012 0.012
#> GSM257907 2 0.6415 0.695 0.080 0.628 0.204 0.088 0.000
#> GSM257909 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257911 2 0.3291 0.902 0.064 0.848 0.000 0.088 0.000
#> GSM257913 2 0.3485 0.900 0.060 0.852 0.016 0.072 0.000
#> GSM257916 2 0.3051 0.903 0.060 0.864 0.000 0.076 0.000
#> GSM257918 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257920 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> GSM257922 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257924 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257926 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257928 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257930 3 0.5285 0.409 0.016 0.368 0.592 0.012 0.012
#> GSM257938 2 0.1475 0.884 0.016 0.956 0.004 0.012 0.012
#> GSM257940 3 0.4437 0.761 0.064 0.048 0.800 0.088 0.000
#> GSM257942 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257944 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257946 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257948 3 0.0703 0.942 0.000 0.024 0.976 0.000 0.000
#> GSM257950 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257952 2 0.1475 0.884 0.016 0.956 0.004 0.012 0.012
#> GSM257954 2 0.1314 0.885 0.016 0.960 0.000 0.012 0.012
#> GSM257956 2 0.1314 0.885 0.016 0.960 0.000 0.012 0.012
#> GSM257959 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257961 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257963 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257965 2 0.1074 0.890 0.016 0.968 0.000 0.004 0.012
#> GSM257967 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257969 2 0.1314 0.885 0.016 0.960 0.000 0.012 0.012
#> GSM257971 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257973 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257981 2 0.2100 0.872 0.016 0.932 0.028 0.012 0.012
#> GSM257983 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257985 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257988 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
#> GSM257991 2 0.3354 0.902 0.068 0.844 0.000 0.088 0.000
#> GSM257993 2 0.1314 0.885 0.016 0.960 0.000 0.012 0.012
#> GSM257994 2 0.3360 0.805 0.016 0.856 0.104 0.012 0.012
#> GSM257989 3 0.0000 0.966 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.0260 0.968 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM257888 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257890 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257892 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257894 4 0.2046 0.904 0.032 0.000 0.000 0.908 0.000 0.060
#> GSM257896 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257898 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257900 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257902 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257904 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257906 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257908 6 0.2597 0.785 0.000 0.000 0.000 0.176 0.000 0.824
#> GSM257910 1 0.1075 0.954 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM257912 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257914 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257917 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257919 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257921 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM257923 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257925 1 0.0865 0.963 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM257927 6 0.1387 0.904 0.068 0.000 0.000 0.000 0.000 0.932
#> GSM257929 1 0.0713 0.970 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM257937 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM257939 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257941 6 0.0146 0.951 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM257943 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257945 6 0.0146 0.951 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM257947 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257949 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.0865 0.961 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM257955 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257958 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257960 6 0.0146 0.951 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM257962 6 0.0146 0.951 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM257964 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257966 4 0.0146 0.971 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM257968 4 0.2178 0.840 0.000 0.000 0.000 0.868 0.000 0.132
#> GSM257970 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257972 6 0.3073 0.758 0.204 0.000 0.000 0.008 0.000 0.788
#> GSM257977 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257982 4 0.0000 0.973 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM257984 1 0.0972 0.962 0.964 0.000 0.000 0.008 0.000 0.028
#> GSM257986 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM257990 6 0.2854 0.758 0.208 0.000 0.000 0.000 0.000 0.792
#> GSM257992 6 0.0146 0.951 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM257996 6 0.0000 0.951 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM258006 6 0.2854 0.734 0.000 0.000 0.000 0.208 0.000 0.792
#> GSM257887 5 0.3221 0.775 0.000 0.264 0.000 0.000 0.736 0.000
#> GSM257889 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257891 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257893 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257895 5 0.1957 0.923 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257897 3 0.0146 0.964 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257899 3 0.0146 0.964 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257901 2 0.1531 0.859 0.000 0.928 0.004 0.000 0.068 0.000
#> GSM257903 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 5 0.3175 0.786 0.000 0.256 0.000 0.000 0.744 0.000
#> GSM257907 2 0.3163 0.736 0.000 0.764 0.004 0.000 0.232 0.000
#> GSM257909 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257911 2 0.0363 0.901 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM257913 2 0.3828 0.398 0.000 0.560 0.000 0.000 0.440 0.000
#> GSM257916 2 0.2912 0.633 0.000 0.784 0.000 0.000 0.216 0.000
#> GSM257918 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257920 3 0.1957 0.922 0.000 0.000 0.888 0.000 0.112 0.000
#> GSM257922 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257924 3 0.1910 0.924 0.000 0.000 0.892 0.000 0.108 0.000
#> GSM257926 3 0.1910 0.924 0.000 0.000 0.892 0.000 0.108 0.000
#> GSM257928 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257930 5 0.2003 0.842 0.000 0.000 0.116 0.000 0.884 0.000
#> GSM257938 5 0.1204 0.894 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM257940 2 0.4474 0.641 0.000 0.704 0.188 0.000 0.108 0.000
#> GSM257942 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257948 3 0.2165 0.919 0.000 0.008 0.884 0.000 0.108 0.000
#> GSM257950 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257952 5 0.1863 0.921 0.000 0.104 0.000 0.000 0.896 0.000
#> GSM257954 5 0.1957 0.923 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257956 5 0.1957 0.923 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257959 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257965 5 0.2219 0.908 0.000 0.136 0.000 0.000 0.864 0.000
#> GSM257967 2 0.0260 0.902 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM257969 5 0.1957 0.923 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257971 3 0.0363 0.962 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM257973 3 0.1910 0.924 0.000 0.000 0.892 0.000 0.108 0.000
#> GSM257981 5 0.0146 0.850 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM257983 3 0.0146 0.964 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM257985 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM257988 3 0.1814 0.928 0.000 0.000 0.900 0.000 0.100 0.000
#> GSM257991 2 0.0000 0.907 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257993 5 0.1957 0.923 0.000 0.112 0.000 0.000 0.888 0.000
#> GSM257994 5 0.1958 0.852 0.000 0.004 0.100 0.000 0.896 0.000
#> GSM257989 3 0.0000 0.965 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> ATC:pam 96 8.49e-22 1.000 1.000 2
#> ATC:pam 96 1.43e-21 0.508 1.000 3
#> ATC:pam 83 6.97e-18 0.505 0.990 4
#> ATC:pam 95 1.14e-19 0.773 0.991 5
#> ATC:pam 95 5.97e-19 0.643 0.981 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 25171 rows and 96 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 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.888 0.906 0.933 0.2222 0.876 0.749
#> 4 4 0.685 0.735 0.841 0.1564 0.862 0.644
#> 5 5 0.667 0.758 0.826 0.0624 0.940 0.784
#> 6 6 0.710 0.686 0.800 0.0454 0.931 0.728
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0592 0.980 0.988 0.000 0.012
#> GSM257888 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257890 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257892 1 0.1163 0.971 0.972 0.000 0.028
#> GSM257894 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257896 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257898 1 0.0000 0.983 1.000 0.000 0.000
#> GSM257900 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257902 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257904 1 0.0000 0.983 1.000 0.000 0.000
#> GSM257906 1 0.0237 0.982 0.996 0.000 0.004
#> GSM257908 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257910 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257912 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257914 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257917 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257919 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257921 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257923 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257925 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257927 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257929 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257937 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257939 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257941 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257943 1 0.0000 0.983 1.000 0.000 0.000
#> GSM257945 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257947 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257949 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257951 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257953 1 0.1289 0.982 0.968 0.000 0.032
#> GSM257955 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257958 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257960 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257962 1 0.1163 0.983 0.972 0.000 0.028
#> GSM257964 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257966 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257968 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257970 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257972 1 0.1289 0.982 0.968 0.000 0.032
#> GSM257977 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257982 1 0.0424 0.982 0.992 0.000 0.008
#> GSM257984 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257986 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257990 1 0.1411 0.982 0.964 0.000 0.036
#> GSM257992 1 0.0237 0.982 0.996 0.000 0.004
#> GSM257996 1 0.0424 0.982 0.992 0.000 0.008
#> GSM258006 1 0.0237 0.982 0.996 0.000 0.004
#> GSM257887 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257889 3 0.2356 0.825 0.000 0.072 0.928
#> GSM257891 3 0.2261 0.824 0.000 0.068 0.932
#> GSM257893 3 0.3879 0.817 0.000 0.152 0.848
#> GSM257895 2 0.1411 0.918 0.000 0.964 0.036
#> GSM257897 3 0.1753 0.810 0.000 0.048 0.952
#> GSM257899 3 0.2066 0.819 0.000 0.060 0.940
#> GSM257901 3 0.6008 0.684 0.000 0.372 0.628
#> GSM257903 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257905 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257907 3 0.5926 0.706 0.000 0.356 0.644
#> GSM257909 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257911 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257913 3 0.6045 0.672 0.000 0.380 0.620
#> GSM257916 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257920 3 0.5760 0.732 0.000 0.328 0.672
#> GSM257922 3 0.2356 0.825 0.000 0.072 0.928
#> GSM257924 3 0.5882 0.714 0.000 0.348 0.652
#> GSM257926 3 0.5926 0.706 0.000 0.356 0.644
#> GSM257928 2 0.5497 0.490 0.000 0.708 0.292
#> GSM257930 2 0.3038 0.865 0.000 0.896 0.104
#> GSM257938 2 0.2959 0.870 0.000 0.900 0.100
#> GSM257940 3 0.5926 0.706 0.000 0.356 0.644
#> GSM257942 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257944 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257946 3 0.3192 0.826 0.000 0.112 0.888
#> GSM257948 3 0.5926 0.706 0.000 0.356 0.644
#> GSM257950 3 0.2261 0.824 0.000 0.068 0.932
#> GSM257952 2 0.2625 0.884 0.000 0.916 0.084
#> GSM257954 2 0.1411 0.918 0.000 0.964 0.036
#> GSM257956 2 0.2625 0.884 0.000 0.916 0.084
#> GSM257959 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257965 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257967 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257969 2 0.0000 0.931 0.000 1.000 0.000
#> GSM257971 2 0.5988 0.222 0.000 0.632 0.368
#> GSM257973 3 0.5621 0.746 0.000 0.308 0.692
#> GSM257981 2 0.3038 0.865 0.000 0.896 0.104
#> GSM257983 3 0.1753 0.810 0.000 0.048 0.952
#> GSM257985 3 0.3267 0.825 0.000 0.116 0.884
#> GSM257988 3 0.2261 0.824 0.000 0.068 0.932
#> GSM257991 2 0.0424 0.929 0.000 0.992 0.008
#> GSM257993 2 0.1411 0.918 0.000 0.964 0.036
#> GSM257994 2 0.2959 0.870 0.000 0.900 0.100
#> GSM257989 3 0.2448 0.826 0.000 0.076 0.924
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 4 0.1557 0.74142 0.056 0.000 0.000 0.944
#> GSM257888 4 0.3610 0.88113 0.200 0.000 0.000 0.800
#> GSM257890 4 0.3444 0.86939 0.184 0.000 0.000 0.816
#> GSM257892 4 0.1118 0.72998 0.036 0.000 0.000 0.964
#> GSM257894 4 0.4040 0.87479 0.248 0.000 0.000 0.752
#> GSM257896 4 0.4454 0.84555 0.308 0.000 0.000 0.692
#> GSM257898 1 0.4866 0.58135 0.596 0.000 0.000 0.404
#> GSM257900 1 0.3172 0.77890 0.840 0.000 0.000 0.160
#> GSM257902 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257904 1 0.4585 0.64581 0.668 0.000 0.000 0.332
#> GSM257906 1 0.4866 0.58135 0.596 0.000 0.000 0.404
#> GSM257908 4 0.4477 0.84463 0.312 0.000 0.000 0.688
#> GSM257910 1 0.4830 0.20750 0.608 0.000 0.000 0.392
#> GSM257912 4 0.4522 0.83518 0.320 0.000 0.000 0.680
#> GSM257914 4 0.4522 0.83518 0.320 0.000 0.000 0.680
#> GSM257917 1 0.4522 0.46362 0.680 0.000 0.000 0.320
#> GSM257919 4 0.4500 0.83975 0.316 0.000 0.000 0.684
#> GSM257921 1 0.1302 0.80593 0.956 0.000 0.000 0.044
#> GSM257923 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257925 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257927 1 0.2345 0.79780 0.900 0.000 0.000 0.100
#> GSM257929 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257937 4 0.3610 0.88113 0.200 0.000 0.000 0.800
#> GSM257939 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257941 1 0.4193 0.69848 0.732 0.000 0.000 0.268
#> GSM257943 1 0.4585 0.64977 0.668 0.000 0.000 0.332
#> GSM257945 1 0.4356 0.67576 0.708 0.000 0.000 0.292
#> GSM257947 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257949 1 0.0188 0.81417 0.996 0.000 0.000 0.004
#> GSM257951 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257953 1 0.1792 0.79423 0.932 0.000 0.000 0.068
#> GSM257955 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257958 1 0.0188 0.81449 0.996 0.000 0.000 0.004
#> GSM257960 1 0.4103 0.71110 0.744 0.000 0.000 0.256
#> GSM257962 1 0.4304 0.67965 0.716 0.000 0.000 0.284
#> GSM257964 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257966 4 0.3610 0.88113 0.200 0.000 0.000 0.800
#> GSM257968 4 0.3610 0.88113 0.200 0.000 0.000 0.800
#> GSM257970 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257972 1 0.2216 0.78732 0.908 0.000 0.000 0.092
#> GSM257977 4 0.3726 0.88131 0.212 0.000 0.000 0.788
#> GSM257982 4 0.4431 0.84690 0.304 0.000 0.000 0.696
#> GSM257984 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257986 1 0.0000 0.81499 1.000 0.000 0.000 0.000
#> GSM257990 1 0.1867 0.79805 0.928 0.000 0.000 0.072
#> GSM257992 1 0.4877 0.57567 0.592 0.000 0.000 0.408
#> GSM257996 1 0.3356 0.68032 0.824 0.000 0.000 0.176
#> GSM258006 1 0.4877 0.57567 0.592 0.000 0.000 0.408
#> GSM257887 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257889 3 0.1940 0.78607 0.000 0.076 0.924 0.000
#> GSM257891 3 0.1940 0.78593 0.000 0.076 0.924 0.000
#> GSM257893 3 0.3726 0.70272 0.000 0.212 0.788 0.000
#> GSM257895 2 0.1004 0.86174 0.000 0.972 0.024 0.004
#> GSM257897 3 0.0817 0.76292 0.000 0.000 0.976 0.024
#> GSM257899 3 0.0817 0.76292 0.000 0.000 0.976 0.024
#> GSM257901 2 0.4843 0.24873 0.000 0.604 0.396 0.000
#> GSM257903 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257905 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257907 2 0.4972 -0.00902 0.000 0.544 0.456 0.000
#> GSM257909 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257911 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257913 2 0.4730 0.35475 0.000 0.636 0.364 0.000
#> GSM257916 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257918 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257920 3 0.4761 0.49899 0.000 0.372 0.628 0.000
#> GSM257922 3 0.2813 0.78250 0.000 0.080 0.896 0.024
#> GSM257924 3 0.4941 0.36061 0.000 0.436 0.564 0.000
#> GSM257926 3 0.4981 0.28172 0.000 0.464 0.536 0.000
#> GSM257928 2 0.5492 0.39729 0.000 0.640 0.328 0.032
#> GSM257930 2 0.4728 0.63648 0.000 0.752 0.216 0.032
#> GSM257938 2 0.3694 0.76326 0.000 0.844 0.124 0.032
#> GSM257940 3 0.4981 0.28172 0.000 0.464 0.536 0.000
#> GSM257942 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257944 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257946 3 0.1557 0.78579 0.000 0.056 0.944 0.000
#> GSM257948 3 0.4977 0.29441 0.000 0.460 0.540 0.000
#> GSM257950 3 0.0592 0.77554 0.000 0.016 0.984 0.000
#> GSM257952 2 0.0817 0.86260 0.000 0.976 0.024 0.000
#> GSM257954 2 0.1284 0.85916 0.000 0.964 0.024 0.012
#> GSM257956 2 0.1488 0.85453 0.000 0.956 0.032 0.012
#> GSM257959 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257961 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257963 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257965 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257967 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257969 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257971 2 0.5423 0.39156 0.000 0.640 0.332 0.028
#> GSM257973 3 0.4477 0.59387 0.000 0.312 0.688 0.000
#> GSM257981 2 0.3610 0.69079 0.000 0.800 0.200 0.000
#> GSM257983 3 0.0707 0.76386 0.000 0.000 0.980 0.020
#> GSM257985 3 0.2149 0.78228 0.000 0.088 0.912 0.000
#> GSM257988 3 0.0469 0.77358 0.000 0.012 0.988 0.000
#> GSM257991 2 0.0000 0.87039 0.000 1.000 0.000 0.000
#> GSM257993 2 0.1284 0.85916 0.000 0.964 0.024 0.012
#> GSM257994 2 0.3694 0.76326 0.000 0.844 0.124 0.032
#> GSM257989 3 0.1022 0.78113 0.000 0.032 0.968 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.2873 0.781 0.016 0.000 0.000 0.856 0.128
#> GSM257888 4 0.1608 0.921 0.072 0.000 0.000 0.928 0.000
#> GSM257890 4 0.2278 0.906 0.060 0.000 0.000 0.908 0.032
#> GSM257892 4 0.2424 0.777 0.000 0.000 0.000 0.868 0.132
#> GSM257894 4 0.2017 0.919 0.080 0.000 0.000 0.912 0.008
#> GSM257896 4 0.3828 0.890 0.072 0.000 0.000 0.808 0.120
#> GSM257898 1 0.4793 0.759 0.708 0.000 0.000 0.216 0.076
#> GSM257900 1 0.2773 0.831 0.836 0.000 0.000 0.164 0.000
#> GSM257902 1 0.0324 0.843 0.992 0.000 0.000 0.004 0.004
#> GSM257904 1 0.4073 0.795 0.752 0.000 0.000 0.216 0.032
#> GSM257906 1 0.5004 0.746 0.692 0.000 0.000 0.216 0.092
#> GSM257908 4 0.2293 0.919 0.084 0.000 0.000 0.900 0.016
#> GSM257910 1 0.6024 0.390 0.512 0.000 0.000 0.364 0.124
#> GSM257912 4 0.3992 0.888 0.080 0.000 0.000 0.796 0.124
#> GSM257914 4 0.4049 0.887 0.084 0.000 0.000 0.792 0.124
#> GSM257917 1 0.5835 0.545 0.568 0.000 0.000 0.312 0.120
#> GSM257919 4 0.3992 0.888 0.080 0.000 0.000 0.796 0.124
#> GSM257921 1 0.4717 0.786 0.736 0.000 0.000 0.144 0.120
#> GSM257923 1 0.0865 0.842 0.972 0.000 0.000 0.004 0.024
#> GSM257925 1 0.0794 0.848 0.972 0.000 0.000 0.028 0.000
#> GSM257927 1 0.2424 0.842 0.868 0.000 0.000 0.132 0.000
#> GSM257929 1 0.0451 0.844 0.988 0.000 0.000 0.004 0.008
#> GSM257937 4 0.1544 0.920 0.068 0.000 0.000 0.932 0.000
#> GSM257939 1 0.0671 0.844 0.980 0.000 0.000 0.004 0.016
#> GSM257941 1 0.3086 0.821 0.816 0.000 0.000 0.180 0.004
#> GSM257943 1 0.4134 0.792 0.744 0.000 0.000 0.224 0.032
#> GSM257945 1 0.3690 0.801 0.780 0.000 0.000 0.200 0.020
#> GSM257947 1 0.0566 0.844 0.984 0.000 0.000 0.004 0.012
#> GSM257949 1 0.2790 0.836 0.880 0.000 0.000 0.052 0.068
#> GSM257951 1 0.0671 0.843 0.980 0.000 0.000 0.004 0.016
#> GSM257953 1 0.1270 0.849 0.948 0.000 0.000 0.052 0.000
#> GSM257955 1 0.0162 0.843 0.996 0.000 0.000 0.004 0.000
#> GSM257958 1 0.0898 0.845 0.972 0.000 0.000 0.008 0.020
#> GSM257960 1 0.2852 0.827 0.828 0.000 0.000 0.172 0.000
#> GSM257962 1 0.3160 0.816 0.808 0.000 0.000 0.188 0.004
#> GSM257964 1 0.0566 0.844 0.984 0.000 0.000 0.004 0.012
#> GSM257966 4 0.1608 0.921 0.072 0.000 0.000 0.928 0.000
#> GSM257968 4 0.1608 0.921 0.072 0.000 0.000 0.928 0.000
#> GSM257970 1 0.0955 0.841 0.968 0.000 0.000 0.004 0.028
#> GSM257972 1 0.3035 0.846 0.856 0.000 0.000 0.112 0.032
#> GSM257977 4 0.1544 0.920 0.068 0.000 0.000 0.932 0.000
#> GSM257982 4 0.4049 0.877 0.084 0.000 0.000 0.792 0.124
#> GSM257984 1 0.2439 0.795 0.876 0.000 0.000 0.004 0.120
#> GSM257986 1 0.2439 0.795 0.876 0.000 0.000 0.004 0.120
#> GSM257990 1 0.2329 0.844 0.876 0.000 0.000 0.124 0.000
#> GSM257992 1 0.5149 0.734 0.680 0.000 0.000 0.216 0.104
#> GSM257996 1 0.3621 0.811 0.788 0.000 0.000 0.192 0.020
#> GSM258006 1 0.5240 0.726 0.672 0.000 0.000 0.216 0.112
#> GSM257887 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257889 3 0.3904 0.622 0.000 0.052 0.792 0.000 0.156
#> GSM257891 3 0.2853 0.707 0.000 0.052 0.876 0.000 0.072
#> GSM257893 5 0.5894 0.451 0.000 0.112 0.356 0.000 0.532
#> GSM257895 5 0.4644 0.372 0.000 0.460 0.012 0.000 0.528
#> GSM257897 3 0.2648 0.664 0.000 0.000 0.848 0.000 0.152
#> GSM257899 3 0.2648 0.664 0.000 0.000 0.848 0.000 0.152
#> GSM257901 2 0.4088 0.470 0.000 0.688 0.304 0.000 0.008
#> GSM257903 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257905 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257907 2 0.5376 0.261 0.000 0.612 0.308 0.000 0.080
#> GSM257909 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257911 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257913 2 0.3906 0.504 0.000 0.704 0.292 0.000 0.004
#> GSM257916 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257918 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257920 5 0.6582 0.568 0.000 0.208 0.376 0.000 0.416
#> GSM257922 3 0.5370 0.389 0.000 0.068 0.584 0.000 0.348
#> GSM257924 5 0.6527 0.565 0.000 0.196 0.376 0.000 0.428
#> GSM257926 5 0.6585 0.584 0.000 0.212 0.360 0.000 0.428
#> GSM257928 5 0.5296 0.679 0.000 0.180 0.144 0.000 0.676
#> GSM257930 5 0.4757 0.671 0.000 0.204 0.080 0.000 0.716
#> GSM257938 5 0.4649 0.666 0.000 0.212 0.068 0.000 0.720
#> GSM257940 2 0.4309 0.450 0.000 0.676 0.308 0.000 0.016
#> GSM257942 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257946 3 0.4950 0.187 0.000 0.040 0.612 0.000 0.348
#> GSM257948 5 0.6646 0.582 0.000 0.228 0.356 0.000 0.416
#> GSM257950 3 0.0703 0.717 0.000 0.024 0.976 0.000 0.000
#> GSM257952 2 0.1364 0.853 0.000 0.952 0.036 0.000 0.012
#> GSM257954 2 0.3163 0.742 0.000 0.824 0.012 0.000 0.164
#> GSM257956 2 0.3343 0.731 0.000 0.812 0.016 0.000 0.172
#> GSM257959 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257961 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257963 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257965 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257967 2 0.0000 0.882 0.000 1.000 0.000 0.000 0.000
#> GSM257969 2 0.0912 0.869 0.000 0.972 0.012 0.000 0.016
#> GSM257971 5 0.5334 0.679 0.000 0.180 0.148 0.000 0.672
#> GSM257973 3 0.3690 0.444 0.000 0.200 0.780 0.000 0.020
#> GSM257981 2 0.2864 0.742 0.000 0.852 0.136 0.000 0.012
#> GSM257983 3 0.2074 0.689 0.000 0.000 0.896 0.000 0.104
#> GSM257985 3 0.4313 0.542 0.000 0.040 0.732 0.000 0.228
#> GSM257988 3 0.0880 0.718 0.000 0.032 0.968 0.000 0.000
#> GSM257991 2 0.0290 0.878 0.000 0.992 0.008 0.000 0.000
#> GSM257993 2 0.3123 0.747 0.000 0.828 0.012 0.000 0.160
#> GSM257994 5 0.4649 0.666 0.000 0.212 0.068 0.000 0.720
#> GSM257989 3 0.2813 0.708 0.000 0.040 0.876 0.000 0.084
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 4 0.5329 0.502 0.128 0.000 0.000 0.684 0.060 0.128
#> GSM257888 4 0.2944 0.684 0.068 0.000 0.000 0.856 0.004 0.072
#> GSM257890 4 0.2257 0.703 0.040 0.000 0.000 0.904 0.008 0.048
#> GSM257892 4 0.3522 0.562 0.000 0.000 0.000 0.800 0.072 0.128
#> GSM257894 4 0.5380 0.345 0.232 0.000 0.000 0.600 0.004 0.164
#> GSM257896 4 0.4569 0.455 0.156 0.000 0.000 0.700 0.000 0.144
#> GSM257898 1 0.5265 0.664 0.676 0.000 0.000 0.176 0.044 0.104
#> GSM257900 1 0.2003 0.857 0.912 0.000 0.000 0.044 0.000 0.044
#> GSM257902 1 0.0547 0.862 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257904 1 0.4840 0.699 0.700 0.000 0.000 0.200 0.036 0.064
#> GSM257906 1 0.5460 0.634 0.652 0.000 0.000 0.192 0.044 0.112
#> GSM257908 6 0.4664 0.620 0.052 0.000 0.000 0.364 0.000 0.584
#> GSM257910 6 0.4339 0.765 0.060 0.000 0.000 0.256 0.000 0.684
#> GSM257912 6 0.4009 0.762 0.028 0.000 0.000 0.288 0.000 0.684
#> GSM257914 6 0.4270 0.766 0.052 0.000 0.000 0.264 0.000 0.684
#> GSM257917 6 0.4619 0.681 0.088 0.000 0.000 0.244 0.000 0.668
#> GSM257919 6 0.3990 0.762 0.028 0.000 0.000 0.284 0.000 0.688
#> GSM257921 1 0.3758 0.728 0.772 0.000 0.000 0.048 0.004 0.176
#> GSM257923 1 0.0790 0.860 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM257925 1 0.1075 0.861 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM257927 1 0.1934 0.858 0.916 0.000 0.000 0.040 0.000 0.044
#> GSM257929 1 0.0547 0.861 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257937 4 0.2670 0.677 0.040 0.000 0.000 0.872 0.004 0.084
#> GSM257939 1 0.0458 0.863 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM257941 1 0.2328 0.856 0.892 0.000 0.000 0.052 0.000 0.056
#> GSM257943 1 0.4672 0.716 0.716 0.000 0.000 0.192 0.036 0.056
#> GSM257945 1 0.4125 0.740 0.736 0.000 0.000 0.184 0.000 0.080
#> GSM257947 1 0.0363 0.862 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM257949 1 0.1528 0.850 0.936 0.000 0.000 0.016 0.000 0.048
#> GSM257951 1 0.0547 0.862 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257953 1 0.1789 0.858 0.924 0.000 0.000 0.032 0.000 0.044
#> GSM257955 1 0.0547 0.862 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM257958 1 0.1663 0.857 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM257960 1 0.2660 0.842 0.868 0.000 0.000 0.084 0.000 0.048
#> GSM257962 1 0.2747 0.834 0.860 0.000 0.000 0.096 0.000 0.044
#> GSM257964 1 0.0632 0.861 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM257966 4 0.3204 0.667 0.068 0.000 0.000 0.836 0.004 0.092
#> GSM257968 4 0.2380 0.700 0.068 0.000 0.000 0.892 0.004 0.036
#> GSM257970 1 0.0865 0.858 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM257972 1 0.2119 0.860 0.904 0.000 0.000 0.036 0.000 0.060
#> GSM257977 4 0.1082 0.699 0.040 0.000 0.000 0.956 0.000 0.004
#> GSM257982 4 0.4526 0.454 0.164 0.000 0.000 0.704 0.000 0.132
#> GSM257984 1 0.2003 0.806 0.884 0.000 0.000 0.000 0.000 0.116
#> GSM257986 1 0.2752 0.798 0.856 0.000 0.000 0.036 0.000 0.108
#> GSM257990 1 0.2046 0.859 0.908 0.000 0.000 0.032 0.000 0.060
#> GSM257992 1 0.5474 0.641 0.656 0.000 0.000 0.180 0.048 0.116
#> GSM257996 6 0.5052 0.322 0.388 0.000 0.000 0.080 0.000 0.532
#> GSM258006 1 0.5596 0.633 0.648 0.000 0.000 0.176 0.056 0.120
#> GSM257887 2 0.0260 0.847 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM257889 3 0.3078 0.612 0.000 0.012 0.796 0.000 0.192 0.000
#> GSM257891 3 0.1644 0.648 0.000 0.004 0.920 0.000 0.076 0.000
#> GSM257893 3 0.4508 0.153 0.000 0.024 0.536 0.000 0.436 0.004
#> GSM257895 5 0.3969 0.478 0.000 0.332 0.016 0.000 0.652 0.000
#> GSM257897 3 0.2165 0.584 0.000 0.000 0.884 0.000 0.108 0.008
#> GSM257899 3 0.2165 0.584 0.000 0.000 0.884 0.000 0.108 0.008
#> GSM257901 2 0.6205 -0.259 0.000 0.428 0.412 0.000 0.040 0.120
#> GSM257903 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257905 2 0.0363 0.850 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM257907 3 0.6468 0.448 0.000 0.304 0.500 0.000 0.076 0.120
#> GSM257909 2 0.0146 0.848 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257911 2 0.0458 0.849 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM257913 2 0.6306 -0.269 0.000 0.420 0.412 0.000 0.048 0.120
#> GSM257916 2 0.0363 0.850 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM257918 2 0.0363 0.850 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM257920 3 0.6649 0.542 0.000 0.120 0.520 0.000 0.240 0.120
#> GSM257922 3 0.3833 0.237 0.000 0.000 0.648 0.000 0.344 0.008
#> GSM257924 3 0.6050 0.567 0.000 0.112 0.588 0.000 0.228 0.072
#> GSM257926 3 0.6363 0.539 0.000 0.076 0.532 0.000 0.272 0.120
#> GSM257928 5 0.3827 0.654 0.000 0.020 0.256 0.000 0.720 0.004
#> GSM257930 5 0.3167 0.796 0.000 0.072 0.096 0.000 0.832 0.000
#> GSM257938 5 0.3225 0.799 0.000 0.080 0.092 0.000 0.828 0.000
#> GSM257940 3 0.6378 0.464 0.000 0.276 0.528 0.000 0.076 0.120
#> GSM257942 2 0.0363 0.850 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM257944 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257946 3 0.3683 0.616 0.000 0.044 0.764 0.000 0.192 0.000
#> GSM257948 3 0.6683 0.535 0.000 0.120 0.512 0.000 0.248 0.120
#> GSM257950 3 0.1074 0.666 0.000 0.028 0.960 0.000 0.012 0.000
#> GSM257952 2 0.3110 0.757 0.000 0.836 0.016 0.000 0.128 0.020
#> GSM257954 2 0.3674 0.615 0.000 0.716 0.016 0.000 0.268 0.000
#> GSM257956 2 0.4253 0.547 0.000 0.672 0.044 0.000 0.284 0.000
#> GSM257959 2 0.0000 0.848 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM257961 2 0.0260 0.850 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM257963 2 0.0146 0.848 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257965 2 0.1151 0.836 0.000 0.956 0.012 0.000 0.032 0.000
#> GSM257967 2 0.0146 0.848 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM257969 2 0.2450 0.770 0.000 0.868 0.016 0.000 0.116 0.000
#> GSM257971 5 0.3850 0.651 0.000 0.020 0.260 0.000 0.716 0.004
#> GSM257973 3 0.5585 0.572 0.000 0.196 0.640 0.000 0.048 0.116
#> GSM257981 2 0.3351 0.680 0.000 0.800 0.168 0.000 0.004 0.028
#> GSM257983 3 0.2213 0.587 0.000 0.004 0.888 0.000 0.100 0.008
#> GSM257985 3 0.2258 0.675 0.000 0.060 0.896 0.000 0.044 0.000
#> GSM257988 3 0.1895 0.668 0.000 0.072 0.912 0.000 0.016 0.000
#> GSM257991 2 0.1007 0.832 0.000 0.956 0.044 0.000 0.000 0.000
#> GSM257993 2 0.4026 0.475 0.000 0.636 0.016 0.000 0.348 0.000
#> GSM257994 5 0.3225 0.799 0.000 0.080 0.092 0.000 0.828 0.000
#> GSM257989 3 0.1391 0.671 0.000 0.040 0.944 0.000 0.016 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 cell.type(p) protocol(p) individual(p) k
#> ATC:mclust 96 8.49e-22 1.000 1.000 2
#> ATC:mclust 94 3.87e-21 0.797 1.000 3
#> ATC:mclust 84 4.25e-18 0.333 0.992 4
#> ATC:mclust 87 5.71e-18 0.356 0.989 5
#> ATC:mclust 84 1.22e-16 0.297 0.915 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 25171 rows and 96 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5058 0.495 0.495
#> 3 3 0.968 0.950 0.976 0.2407 0.877 0.752
#> 4 4 0.807 0.784 0.888 0.0666 0.984 0.956
#> 5 5 0.749 0.728 0.855 0.0716 0.919 0.779
#> 6 6 0.645 0.743 0.823 0.0464 0.961 0.872
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
#> GSM257886 1 0 1 1 0
#> GSM257888 1 0 1 1 0
#> GSM257890 1 0 1 1 0
#> GSM257892 1 0 1 1 0
#> GSM257894 1 0 1 1 0
#> GSM257896 1 0 1 1 0
#> GSM257898 1 0 1 1 0
#> GSM257900 1 0 1 1 0
#> GSM257902 1 0 1 1 0
#> GSM257904 1 0 1 1 0
#> GSM257906 1 0 1 1 0
#> GSM257908 1 0 1 1 0
#> GSM257910 1 0 1 1 0
#> GSM257912 1 0 1 1 0
#> GSM257914 1 0 1 1 0
#> GSM257917 1 0 1 1 0
#> GSM257919 1 0 1 1 0
#> GSM257921 1 0 1 1 0
#> GSM257923 1 0 1 1 0
#> GSM257925 1 0 1 1 0
#> GSM257927 1 0 1 1 0
#> GSM257929 1 0 1 1 0
#> GSM257937 1 0 1 1 0
#> GSM257939 1 0 1 1 0
#> GSM257941 1 0 1 1 0
#> GSM257943 1 0 1 1 0
#> GSM257945 1 0 1 1 0
#> GSM257947 1 0 1 1 0
#> GSM257949 1 0 1 1 0
#> GSM257951 1 0 1 1 0
#> GSM257953 1 0 1 1 0
#> GSM257955 1 0 1 1 0
#> GSM257958 1 0 1 1 0
#> GSM257960 1 0 1 1 0
#> GSM257962 1 0 1 1 0
#> GSM257964 1 0 1 1 0
#> GSM257966 1 0 1 1 0
#> GSM257968 1 0 1 1 0
#> GSM257970 1 0 1 1 0
#> GSM257972 1 0 1 1 0
#> GSM257977 1 0 1 1 0
#> GSM257982 1 0 1 1 0
#> GSM257984 1 0 1 1 0
#> GSM257986 1 0 1 1 0
#> GSM257990 1 0 1 1 0
#> GSM257992 1 0 1 1 0
#> GSM257996 1 0 1 1 0
#> GSM258006 1 0 1 1 0
#> GSM257887 2 0 1 0 1
#> GSM257889 2 0 1 0 1
#> GSM257891 2 0 1 0 1
#> GSM257893 2 0 1 0 1
#> GSM257895 2 0 1 0 1
#> GSM257897 2 0 1 0 1
#> GSM257899 2 0 1 0 1
#> GSM257901 2 0 1 0 1
#> GSM257903 2 0 1 0 1
#> GSM257905 2 0 1 0 1
#> GSM257907 2 0 1 0 1
#> GSM257909 2 0 1 0 1
#> GSM257911 2 0 1 0 1
#> GSM257913 2 0 1 0 1
#> GSM257916 2 0 1 0 1
#> GSM257918 2 0 1 0 1
#> GSM257920 2 0 1 0 1
#> GSM257922 2 0 1 0 1
#> GSM257924 2 0 1 0 1
#> GSM257926 2 0 1 0 1
#> GSM257928 2 0 1 0 1
#> GSM257930 2 0 1 0 1
#> GSM257938 2 0 1 0 1
#> GSM257940 2 0 1 0 1
#> GSM257942 2 0 1 0 1
#> GSM257944 2 0 1 0 1
#> GSM257946 2 0 1 0 1
#> GSM257948 2 0 1 0 1
#> GSM257950 2 0 1 0 1
#> GSM257952 2 0 1 0 1
#> GSM257954 2 0 1 0 1
#> GSM257956 2 0 1 0 1
#> GSM257959 2 0 1 0 1
#> GSM257961 2 0 1 0 1
#> GSM257963 2 0 1 0 1
#> GSM257965 2 0 1 0 1
#> GSM257967 2 0 1 0 1
#> GSM257969 2 0 1 0 1
#> GSM257971 2 0 1 0 1
#> GSM257973 2 0 1 0 1
#> GSM257981 2 0 1 0 1
#> GSM257983 2 0 1 0 1
#> GSM257985 2 0 1 0 1
#> GSM257988 2 0 1 0 1
#> GSM257991 2 0 1 0 1
#> GSM257993 2 0 1 0 1
#> GSM257994 2 0 1 0 1
#> GSM257989 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM257886 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257888 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257890 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257892 1 0.3192 0.875 0.888 0.112 0.000
#> GSM257894 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257896 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257898 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257900 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257902 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257904 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257906 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257908 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257910 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257912 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257914 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257917 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257919 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257921 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257923 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257925 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257927 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257929 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257937 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257939 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257941 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257943 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257945 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257947 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257949 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257951 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257953 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257955 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257958 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257960 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257962 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257964 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257966 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257968 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257970 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257972 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257977 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257982 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257984 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257986 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257990 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257992 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257996 1 0.0000 0.998 1.000 0.000 0.000
#> GSM258006 1 0.0000 0.998 1.000 0.000 0.000
#> GSM257887 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257889 3 0.0000 0.949 0.000 0.000 1.000
#> GSM257891 3 0.0000 0.949 0.000 0.000 1.000
#> GSM257893 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257895 2 0.0592 0.950 0.000 0.988 0.012
#> GSM257897 3 0.0000 0.949 0.000 0.000 1.000
#> GSM257899 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257901 2 0.2878 0.883 0.000 0.904 0.096
#> GSM257903 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257905 2 0.0237 0.952 0.000 0.996 0.004
#> GSM257907 2 0.6126 0.313 0.000 0.600 0.400
#> GSM257909 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257911 2 0.0892 0.947 0.000 0.980 0.020
#> GSM257913 2 0.2625 0.895 0.000 0.916 0.084
#> GSM257916 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257918 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257920 3 0.4399 0.803 0.000 0.188 0.812
#> GSM257922 3 0.0000 0.949 0.000 0.000 1.000
#> GSM257924 3 0.0592 0.947 0.000 0.012 0.988
#> GSM257926 3 0.3619 0.861 0.000 0.136 0.864
#> GSM257928 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257930 2 0.6235 0.236 0.000 0.564 0.436
#> GSM257938 2 0.1163 0.942 0.000 0.972 0.028
#> GSM257940 3 0.2261 0.916 0.000 0.068 0.932
#> GSM257942 2 0.0424 0.951 0.000 0.992 0.008
#> GSM257944 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257946 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257948 3 0.4796 0.756 0.000 0.220 0.780
#> GSM257950 3 0.0000 0.949 0.000 0.000 1.000
#> GSM257952 2 0.1031 0.945 0.000 0.976 0.024
#> GSM257954 2 0.0237 0.952 0.000 0.996 0.004
#> GSM257956 2 0.0237 0.952 0.000 0.996 0.004
#> GSM257959 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257961 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257963 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257965 2 0.0237 0.952 0.000 0.996 0.004
#> GSM257967 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257969 2 0.0237 0.952 0.000 0.996 0.004
#> GSM257971 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257973 3 0.4121 0.827 0.000 0.168 0.832
#> GSM257981 2 0.2066 0.917 0.000 0.940 0.060
#> GSM257983 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257985 3 0.0237 0.950 0.000 0.004 0.996
#> GSM257988 3 0.2878 0.896 0.000 0.096 0.904
#> GSM257991 2 0.1031 0.945 0.000 0.976 0.024
#> GSM257993 2 0.0000 0.952 0.000 1.000 0.000
#> GSM257994 2 0.1289 0.940 0.000 0.968 0.032
#> GSM257989 3 0.0000 0.949 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM257886 1 0.4500 0.594 0.684 0.000 0.000 0.316
#> GSM257888 1 0.2149 0.898 0.912 0.000 0.000 0.088
#> GSM257890 1 0.4477 0.604 0.688 0.000 0.000 0.312
#> GSM257892 4 0.6990 0.210 0.304 0.144 0.000 0.552
#> GSM257894 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257896 1 0.0188 0.942 0.996 0.000 0.000 0.004
#> GSM257898 1 0.2868 0.840 0.864 0.000 0.000 0.136
#> GSM257900 1 0.0921 0.934 0.972 0.000 0.000 0.028
#> GSM257902 1 0.0469 0.942 0.988 0.000 0.000 0.012
#> GSM257904 1 0.2081 0.891 0.916 0.000 0.000 0.084
#> GSM257906 1 0.4008 0.701 0.756 0.000 0.000 0.244
#> GSM257908 1 0.0592 0.942 0.984 0.000 0.000 0.016
#> GSM257910 1 0.0469 0.942 0.988 0.000 0.000 0.012
#> GSM257912 1 0.1474 0.921 0.948 0.000 0.000 0.052
#> GSM257914 1 0.1474 0.924 0.948 0.000 0.000 0.052
#> GSM257917 1 0.0469 0.942 0.988 0.000 0.000 0.012
#> GSM257919 1 0.1637 0.916 0.940 0.000 0.000 0.060
#> GSM257921 1 0.0000 0.943 1.000 0.000 0.000 0.000
#> GSM257923 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257925 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257927 1 0.0188 0.942 0.996 0.000 0.000 0.004
#> GSM257929 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257937 1 0.2149 0.898 0.912 0.000 0.000 0.088
#> GSM257939 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257941 1 0.0000 0.943 1.000 0.000 0.000 0.000
#> GSM257943 1 0.3024 0.827 0.852 0.000 0.000 0.148
#> GSM257945 1 0.0000 0.943 1.000 0.000 0.000 0.000
#> GSM257947 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257949 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257951 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257953 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257955 1 0.0592 0.942 0.984 0.000 0.000 0.016
#> GSM257958 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257960 1 0.0188 0.942 0.996 0.000 0.000 0.004
#> GSM257962 1 0.0188 0.942 0.996 0.000 0.000 0.004
#> GSM257964 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257966 1 0.2149 0.895 0.912 0.000 0.000 0.088
#> GSM257968 1 0.0817 0.940 0.976 0.000 0.000 0.024
#> GSM257970 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257972 1 0.0592 0.942 0.984 0.000 0.000 0.016
#> GSM257977 1 0.1474 0.923 0.948 0.000 0.000 0.052
#> GSM257982 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257984 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257986 1 0.0469 0.943 0.988 0.000 0.000 0.012
#> GSM257990 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM257992 1 0.4040 0.691 0.752 0.000 0.000 0.248
#> GSM257996 1 0.0336 0.943 0.992 0.000 0.000 0.008
#> GSM258006 1 0.3172 0.813 0.840 0.000 0.000 0.160
#> GSM257887 2 0.1824 0.815 0.000 0.936 0.004 0.060
#> GSM257889 3 0.1004 0.769 0.000 0.004 0.972 0.024
#> GSM257891 3 0.1004 0.769 0.000 0.004 0.972 0.024
#> GSM257893 3 0.4123 0.645 0.000 0.008 0.772 0.220
#> GSM257895 2 0.3217 0.776 0.000 0.860 0.012 0.128
#> GSM257897 3 0.2053 0.753 0.000 0.004 0.924 0.072
#> GSM257899 3 0.2412 0.748 0.000 0.008 0.908 0.084
#> GSM257901 2 0.6039 0.366 0.000 0.596 0.348 0.056
#> GSM257903 2 0.1474 0.819 0.000 0.948 0.000 0.052
#> GSM257905 2 0.1042 0.835 0.000 0.972 0.008 0.020
#> GSM257907 2 0.6310 0.113 0.000 0.512 0.428 0.060
#> GSM257909 2 0.0336 0.833 0.000 0.992 0.000 0.008
#> GSM257911 2 0.3903 0.750 0.000 0.844 0.076 0.080
#> GSM257913 2 0.5180 0.623 0.000 0.740 0.196 0.064
#> GSM257916 2 0.0376 0.835 0.000 0.992 0.004 0.004
#> GSM257918 2 0.0524 0.834 0.000 0.988 0.004 0.008
#> GSM257920 3 0.5648 0.561 0.000 0.252 0.684 0.064
#> GSM257922 3 0.4431 0.554 0.000 0.000 0.696 0.304
#> GSM257924 3 0.1888 0.770 0.000 0.016 0.940 0.044
#> GSM257926 3 0.5147 0.626 0.000 0.200 0.740 0.060
#> GSM257928 3 0.6170 0.412 0.000 0.068 0.600 0.332
#> GSM257930 4 0.7752 -0.156 0.000 0.300 0.264 0.436
#> GSM257938 2 0.5523 0.361 0.000 0.596 0.024 0.380
#> GSM257940 3 0.5218 0.623 0.000 0.200 0.736 0.064
#> GSM257942 2 0.2255 0.810 0.000 0.920 0.012 0.068
#> GSM257944 2 0.1867 0.812 0.000 0.928 0.000 0.072
#> GSM257946 3 0.1004 0.769 0.000 0.004 0.972 0.024
#> GSM257948 3 0.6083 0.377 0.000 0.360 0.584 0.056
#> GSM257950 3 0.0188 0.771 0.000 0.004 0.996 0.000
#> GSM257952 2 0.1520 0.834 0.000 0.956 0.020 0.024
#> GSM257954 2 0.3032 0.782 0.000 0.868 0.008 0.124
#> GSM257956 2 0.2859 0.792 0.000 0.880 0.008 0.112
#> GSM257959 2 0.0592 0.832 0.000 0.984 0.000 0.016
#> GSM257961 2 0.1545 0.826 0.000 0.952 0.008 0.040
#> GSM257963 2 0.0336 0.833 0.000 0.992 0.000 0.008
#> GSM257965 2 0.0657 0.835 0.000 0.984 0.012 0.004
#> GSM257967 2 0.0336 0.833 0.000 0.992 0.000 0.008
#> GSM257969 2 0.2342 0.809 0.000 0.912 0.008 0.080
#> GSM257971 3 0.5754 0.468 0.000 0.048 0.636 0.316
#> GSM257973 3 0.5218 0.628 0.000 0.200 0.736 0.064
#> GSM257981 2 0.4332 0.727 0.000 0.816 0.112 0.072
#> GSM257983 3 0.1798 0.762 0.000 0.016 0.944 0.040
#> GSM257985 3 0.0524 0.772 0.000 0.008 0.988 0.004
#> GSM257988 3 0.5062 0.644 0.000 0.184 0.752 0.064
#> GSM257991 2 0.2335 0.810 0.000 0.920 0.020 0.060
#> GSM257993 2 0.2831 0.783 0.000 0.876 0.004 0.120
#> GSM257994 2 0.5650 0.242 0.000 0.544 0.024 0.432
#> GSM257989 3 0.0657 0.770 0.000 0.004 0.984 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM257886 4 0.3274 0.825 0.220 0.000 0.000 0.780 0.000
#> GSM257888 1 0.2583 0.777 0.864 0.000 0.000 0.132 0.004
#> GSM257890 4 0.3961 0.811 0.248 0.000 0.000 0.736 0.016
#> GSM257892 4 0.3937 0.551 0.052 0.072 0.000 0.832 0.044
#> GSM257894 1 0.0290 0.897 0.992 0.000 0.000 0.008 0.000
#> GSM257896 1 0.0703 0.895 0.976 0.000 0.000 0.024 0.000
#> GSM257898 1 0.5176 -0.354 0.492 0.000 0.000 0.468 0.040
#> GSM257900 1 0.3123 0.696 0.812 0.000 0.000 0.184 0.004
#> GSM257902 1 0.0324 0.898 0.992 0.000 0.000 0.004 0.004
#> GSM257904 1 0.4505 0.101 0.604 0.000 0.000 0.384 0.012
#> GSM257906 4 0.4260 0.776 0.308 0.000 0.008 0.680 0.004
#> GSM257908 1 0.0162 0.898 0.996 0.000 0.000 0.004 0.000
#> GSM257910 1 0.0000 0.898 1.000 0.000 0.000 0.000 0.000
#> GSM257912 1 0.1331 0.886 0.952 0.000 0.000 0.040 0.008
#> GSM257914 1 0.0693 0.898 0.980 0.000 0.000 0.012 0.008
#> GSM257917 1 0.1251 0.886 0.956 0.000 0.000 0.036 0.008
#> GSM257919 1 0.1041 0.891 0.964 0.000 0.000 0.032 0.004
#> GSM257921 1 0.0693 0.895 0.980 0.000 0.000 0.012 0.008
#> GSM257923 1 0.0451 0.895 0.988 0.000 0.000 0.008 0.004
#> GSM257925 1 0.0324 0.897 0.992 0.000 0.000 0.004 0.004
#> GSM257927 1 0.1469 0.881 0.948 0.000 0.000 0.036 0.016
#> GSM257929 1 0.0162 0.898 0.996 0.000 0.000 0.004 0.000
#> GSM257937 1 0.3671 0.632 0.756 0.000 0.000 0.236 0.008
#> GSM257939 1 0.0324 0.898 0.992 0.000 0.000 0.004 0.004
#> GSM257941 1 0.2046 0.851 0.916 0.000 0.000 0.068 0.016
#> GSM257943 1 0.4562 -0.350 0.496 0.000 0.000 0.496 0.008
#> GSM257945 1 0.0798 0.895 0.976 0.000 0.000 0.016 0.008
#> GSM257947 1 0.0324 0.897 0.992 0.000 0.000 0.004 0.004
#> GSM257949 1 0.0000 0.898 1.000 0.000 0.000 0.000 0.000
#> GSM257951 1 0.0000 0.898 1.000 0.000 0.000 0.000 0.000
#> GSM257953 1 0.1117 0.892 0.964 0.000 0.000 0.020 0.016
#> GSM257955 1 0.0451 0.895 0.988 0.000 0.000 0.008 0.004
#> GSM257958 1 0.0404 0.898 0.988 0.000 0.000 0.000 0.012
#> GSM257960 1 0.0992 0.892 0.968 0.000 0.000 0.024 0.008
#> GSM257962 1 0.1251 0.886 0.956 0.000 0.000 0.036 0.008
#> GSM257964 1 0.0000 0.898 1.000 0.000 0.000 0.000 0.000
#> GSM257966 1 0.2886 0.761 0.844 0.000 0.000 0.148 0.008
#> GSM257968 1 0.0404 0.896 0.988 0.000 0.000 0.012 0.000
#> GSM257970 1 0.0162 0.898 0.996 0.000 0.000 0.000 0.004
#> GSM257972 1 0.0898 0.894 0.972 0.000 0.000 0.020 0.008
#> GSM257977 1 0.2929 0.769 0.840 0.000 0.000 0.152 0.008
#> GSM257982 1 0.0290 0.897 0.992 0.000 0.000 0.008 0.000
#> GSM257984 1 0.0000 0.898 1.000 0.000 0.000 0.000 0.000
#> GSM257986 1 0.0162 0.898 0.996 0.000 0.000 0.004 0.000
#> GSM257990 1 0.0807 0.896 0.976 0.000 0.000 0.012 0.012
#> GSM257992 4 0.5303 0.792 0.232 0.000 0.000 0.660 0.108
#> GSM257996 1 0.0898 0.894 0.972 0.000 0.000 0.020 0.008
#> GSM258006 1 0.4559 -0.298 0.512 0.000 0.000 0.480 0.008
#> GSM257887 2 0.2833 0.802 0.000 0.864 0.004 0.012 0.120
#> GSM257889 3 0.2331 0.712 0.000 0.004 0.908 0.024 0.064
#> GSM257891 3 0.1911 0.718 0.000 0.004 0.932 0.036 0.028
#> GSM257893 3 0.5225 0.404 0.000 0.028 0.660 0.032 0.280
#> GSM257895 2 0.4872 0.627 0.000 0.692 0.024 0.024 0.260
#> GSM257897 3 0.2713 0.697 0.000 0.004 0.888 0.036 0.072
#> GSM257899 3 0.3781 0.669 0.000 0.020 0.828 0.040 0.112
#> GSM257901 3 0.5693 0.207 0.000 0.456 0.484 0.020 0.040
#> GSM257903 2 0.2605 0.819 0.000 0.900 0.016 0.024 0.060
#> GSM257905 2 0.3022 0.826 0.000 0.880 0.036 0.020 0.064
#> GSM257907 3 0.5557 0.430 0.000 0.376 0.564 0.016 0.044
#> GSM257909 2 0.1117 0.836 0.000 0.964 0.000 0.016 0.020
#> GSM257911 2 0.3981 0.776 0.000 0.824 0.076 0.024 0.076
#> GSM257913 2 0.5549 0.641 0.000 0.692 0.188 0.032 0.088
#> GSM257916 2 0.1074 0.844 0.000 0.968 0.012 0.004 0.016
#> GSM257918 2 0.1074 0.844 0.000 0.968 0.012 0.004 0.016
#> GSM257920 3 0.5380 0.616 0.000 0.224 0.688 0.036 0.052
#> GSM257922 5 0.5131 0.285 0.000 0.000 0.420 0.040 0.540
#> GSM257924 3 0.2966 0.727 0.000 0.040 0.884 0.020 0.056
#> GSM257926 3 0.4443 0.689 0.000 0.152 0.776 0.020 0.052
#> GSM257928 5 0.5169 0.581 0.000 0.056 0.260 0.012 0.672
#> GSM257930 5 0.4251 0.621 0.000 0.200 0.040 0.004 0.756
#> GSM257938 5 0.5029 0.320 0.000 0.376 0.012 0.020 0.592
#> GSM257940 3 0.4791 0.678 0.000 0.168 0.752 0.036 0.044
#> GSM257942 2 0.3175 0.805 0.000 0.872 0.044 0.020 0.064
#> GSM257944 2 0.2609 0.816 0.000 0.896 0.008 0.028 0.068
#> GSM257946 3 0.2518 0.714 0.000 0.008 0.896 0.016 0.080
#> GSM257948 3 0.5653 0.511 0.000 0.316 0.608 0.024 0.052
#> GSM257950 3 0.1483 0.729 0.000 0.008 0.952 0.028 0.012
#> GSM257952 2 0.4316 0.797 0.000 0.808 0.068 0.044 0.080
#> GSM257954 2 0.4080 0.721 0.000 0.760 0.016 0.012 0.212
#> GSM257956 2 0.4672 0.743 0.000 0.752 0.040 0.028 0.180
#> GSM257959 2 0.0992 0.838 0.000 0.968 0.000 0.008 0.024
#> GSM257961 2 0.1956 0.828 0.000 0.916 0.000 0.008 0.076
#> GSM257963 2 0.0880 0.839 0.000 0.968 0.000 0.000 0.032
#> GSM257965 2 0.2722 0.834 0.000 0.896 0.020 0.028 0.056
#> GSM257967 2 0.1278 0.836 0.000 0.960 0.004 0.016 0.020
#> GSM257969 2 0.3280 0.775 0.000 0.824 0.004 0.012 0.160
#> GSM257971 5 0.5390 0.527 0.000 0.036 0.316 0.024 0.624
#> GSM257973 3 0.3783 0.714 0.000 0.120 0.824 0.016 0.040
#> GSM257981 2 0.5414 0.681 0.000 0.708 0.164 0.028 0.100
#> GSM257983 3 0.1701 0.738 0.000 0.016 0.944 0.012 0.028
#> GSM257985 3 0.2467 0.726 0.000 0.016 0.908 0.024 0.052
#> GSM257988 3 0.3907 0.712 0.000 0.108 0.824 0.028 0.040
#> GSM257991 2 0.3538 0.805 0.000 0.848 0.056 0.016 0.080
#> GSM257993 2 0.3550 0.738 0.000 0.796 0.000 0.020 0.184
#> GSM257994 5 0.4679 0.516 0.000 0.288 0.016 0.016 0.680
#> GSM257989 3 0.1243 0.731 0.000 0.008 0.960 0.028 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM257886 6 0.3611 0.730 0.108 0.000 0.000 NA 0.000 0.796
#> GSM257888 1 0.4495 0.414 0.580 0.000 0.000 NA 0.004 0.028
#> GSM257890 6 0.5721 0.551 0.128 0.000 0.000 NA 0.008 0.460
#> GSM257892 6 0.5443 0.543 0.036 0.028 0.000 NA 0.036 0.640
#> GSM257894 1 0.2562 0.775 0.828 0.000 0.000 NA 0.000 0.000
#> GSM257896 1 0.3470 0.738 0.772 0.000 0.000 NA 0.000 0.028
#> GSM257898 6 0.3298 0.761 0.236 0.000 0.000 NA 0.008 0.756
#> GSM257900 1 0.3816 0.511 0.688 0.000 0.000 NA 0.000 0.296
#> GSM257902 1 0.0937 0.852 0.960 0.000 0.000 NA 0.000 0.000
#> GSM257904 6 0.3772 0.716 0.296 0.000 0.008 NA 0.000 0.692
#> GSM257906 6 0.2051 0.727 0.096 0.000 0.004 NA 0.000 0.896
#> GSM257908 1 0.1349 0.850 0.940 0.000 0.000 NA 0.000 0.004
#> GSM257910 1 0.1267 0.848 0.940 0.000 0.000 NA 0.000 0.000
#> GSM257912 1 0.2776 0.823 0.860 0.000 0.000 NA 0.000 0.088
#> GSM257914 1 0.2136 0.844 0.904 0.000 0.000 NA 0.000 0.048
#> GSM257917 1 0.3139 0.770 0.812 0.000 0.000 NA 0.000 0.160
#> GSM257919 1 0.2660 0.828 0.868 0.000 0.000 NA 0.000 0.084
#> GSM257921 1 0.1528 0.847 0.936 0.000 0.000 NA 0.000 0.048
#> GSM257923 1 0.1268 0.848 0.952 0.000 0.000 NA 0.004 0.008
#> GSM257925 1 0.0984 0.852 0.968 0.000 0.000 NA 0.008 0.012
#> GSM257927 1 0.2312 0.805 0.876 0.000 0.000 NA 0.000 0.112
#> GSM257929 1 0.0870 0.851 0.972 0.000 0.000 NA 0.004 0.012
#> GSM257937 1 0.5341 0.468 0.592 0.000 0.000 NA 0.000 0.184
#> GSM257939 1 0.1268 0.850 0.952 0.000 0.000 NA 0.004 0.008
#> GSM257941 1 0.3404 0.659 0.760 0.000 0.000 NA 0.000 0.224
#> GSM257943 6 0.3445 0.759 0.260 0.000 0.000 NA 0.008 0.732
#> GSM257945 1 0.2214 0.815 0.888 0.000 0.000 NA 0.000 0.096
#> GSM257947 1 0.0922 0.849 0.968 0.000 0.000 NA 0.004 0.004
#> GSM257949 1 0.0790 0.853 0.968 0.000 0.000 NA 0.000 0.000
#> GSM257951 1 0.1003 0.849 0.964 0.000 0.000 NA 0.004 0.004
#> GSM257953 1 0.0951 0.850 0.968 0.000 0.000 NA 0.004 0.020
#> GSM257955 1 0.1410 0.847 0.944 0.000 0.000 NA 0.004 0.008
#> GSM257958 1 0.1321 0.849 0.952 0.000 0.000 NA 0.004 0.024
#> GSM257960 1 0.2489 0.791 0.860 0.000 0.000 NA 0.000 0.128
#> GSM257962 1 0.2896 0.755 0.824 0.000 0.000 NA 0.000 0.160
#> GSM257964 1 0.0790 0.852 0.968 0.000 0.000 NA 0.000 0.000
#> GSM257966 1 0.4812 0.564 0.640 0.000 0.000 NA 0.000 0.096
#> GSM257968 1 0.2823 0.744 0.796 0.000 0.000 NA 0.000 0.000
#> GSM257970 1 0.0922 0.850 0.968 0.000 0.000 NA 0.004 0.004
#> GSM257972 1 0.1682 0.851 0.928 0.000 0.000 NA 0.000 0.020
#> GSM257977 1 0.5602 0.267 0.500 0.000 0.000 NA 0.004 0.132
#> GSM257982 1 0.2454 0.782 0.840 0.000 0.000 NA 0.000 0.000
#> GSM257984 1 0.0790 0.852 0.968 0.000 0.000 NA 0.000 0.000
#> GSM257986 1 0.1531 0.844 0.928 0.000 0.000 NA 0.000 0.004
#> GSM257990 1 0.2288 0.826 0.896 0.000 0.000 NA 0.004 0.072
#> GSM257992 6 0.2791 0.722 0.096 0.000 0.000 NA 0.032 0.864
#> GSM257996 1 0.2404 0.805 0.872 0.000 0.000 NA 0.000 0.112
#> GSM258006 6 0.3725 0.690 0.316 0.000 0.000 NA 0.000 0.676
#> GSM257887 2 0.2421 0.823 0.000 0.900 0.040 NA 0.028 0.000
#> GSM257889 3 0.2918 0.771 0.000 0.016 0.872 NA 0.056 0.004
#> GSM257891 3 0.2839 0.696 0.000 0.000 0.860 NA 0.004 0.044
#> GSM257893 3 0.4497 0.615 0.000 0.064 0.696 NA 0.232 0.000
#> GSM257895 2 0.4330 0.741 0.000 0.764 0.056 NA 0.136 0.000
#> GSM257897 3 0.3066 0.755 0.000 0.012 0.868 NA 0.056 0.016
#> GSM257899 3 0.3882 0.763 0.000 0.044 0.824 NA 0.072 0.024
#> GSM257901 3 0.4816 0.600 0.000 0.264 0.664 NA 0.036 0.000
#> GSM257903 2 0.2854 0.795 0.000 0.872 0.020 NA 0.024 0.004
#> GSM257905 2 0.2680 0.818 0.000 0.856 0.124 NA 0.016 0.000
#> GSM257907 3 0.4429 0.652 0.000 0.244 0.700 NA 0.032 0.000
#> GSM257909 2 0.1003 0.822 0.000 0.964 0.016 NA 0.000 0.000
#> GSM257911 2 0.4698 0.767 0.000 0.740 0.132 NA 0.060 0.000
#> GSM257913 2 0.4695 0.706 0.000 0.704 0.220 NA 0.040 0.004
#> GSM257916 2 0.1812 0.834 0.000 0.912 0.080 NA 0.008 0.000
#> GSM257918 2 0.2367 0.835 0.000 0.888 0.088 NA 0.008 0.000
#> GSM257920 3 0.4522 0.718 0.000 0.180 0.736 NA 0.028 0.004
#> GSM257922 5 0.4731 0.575 0.000 0.008 0.220 NA 0.704 0.040
#> GSM257924 3 0.3270 0.787 0.000 0.092 0.840 NA 0.052 0.000
#> GSM257926 3 0.3053 0.754 0.000 0.168 0.812 NA 0.020 0.000
#> GSM257928 5 0.4513 0.703 0.000 0.116 0.152 NA 0.724 0.000
#> GSM257930 5 0.3947 0.724 0.000 0.220 0.048 NA 0.732 0.000
#> GSM257938 5 0.4058 0.593 0.000 0.308 0.004 NA 0.672 0.012
#> GSM257940 3 0.5851 0.638 0.000 0.200 0.648 NA 0.048 0.032
#> GSM257942 2 0.3378 0.809 0.000 0.840 0.068 NA 0.028 0.000
#> GSM257944 2 0.3987 0.745 0.000 0.792 0.028 NA 0.036 0.008
#> GSM257946 3 0.3018 0.779 0.000 0.024 0.868 NA 0.060 0.004
#> GSM257948 3 0.5392 0.579 0.000 0.272 0.624 NA 0.036 0.004
#> GSM257950 3 0.1741 0.775 0.000 0.008 0.936 NA 0.012 0.008
#> GSM257952 2 0.4681 0.694 0.000 0.700 0.228 NA 0.032 0.004
#> GSM257954 2 0.3879 0.765 0.000 0.784 0.064 NA 0.140 0.000
#> GSM257956 2 0.4053 0.786 0.000 0.788 0.100 NA 0.084 0.000
#> GSM257959 2 0.1168 0.824 0.000 0.956 0.016 NA 0.000 0.000
#> GSM257961 2 0.1116 0.814 0.000 0.960 0.008 NA 0.004 0.000
#> GSM257963 2 0.0993 0.821 0.000 0.964 0.012 NA 0.000 0.000
#> GSM257965 2 0.3042 0.830 0.000 0.852 0.100 NA 0.020 0.000
#> GSM257967 2 0.1176 0.829 0.000 0.956 0.024 NA 0.000 0.000
#> GSM257969 2 0.2939 0.808 0.000 0.872 0.036 NA 0.064 0.004
#> GSM257971 5 0.5506 0.318 0.000 0.044 0.400 NA 0.520 0.024
#> GSM257973 3 0.2871 0.781 0.000 0.116 0.852 NA 0.024 0.000
#> GSM257981 2 0.4925 0.122 0.000 0.492 0.460 NA 0.032 0.000
#> GSM257983 3 0.1706 0.796 0.000 0.032 0.936 NA 0.004 0.004
#> GSM257985 3 0.2259 0.793 0.000 0.044 0.904 NA 0.044 0.000
#> GSM257988 3 0.2434 0.795 0.000 0.056 0.896 NA 0.016 0.000
#> GSM257991 2 0.4327 0.798 0.000 0.772 0.108 NA 0.048 0.000
#> GSM257993 2 0.3392 0.770 0.000 0.844 0.020 NA 0.080 0.008
#> GSM257994 5 0.3702 0.703 0.000 0.224 0.012 NA 0.752 0.008
#> GSM257989 3 0.1768 0.780 0.000 0.012 0.936 NA 0.012 0.008
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 cell.type(p) protocol(p) individual(p) k
#> ATC:NMF 96 8.49e-22 1.000 1.000 2
#> ATC:NMF 94 3.87e-21 0.484 1.000 3
#> ATC:NMF 87 1.28e-19 0.445 1.000 4
#> ATC:NMF 87 5.71e-18 0.436 0.928 5
#> ATC:NMF 91 8.07e-19 0.707 0.972 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