Date: 2019-12-25 20:40:25 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 21168 rows and 116 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 21168 116
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:kmeans | 3 | 1.000 | 0.955 | 0.957 | ** | |
| MAD:hclust | 2 | 1.000 | 0.973 | 0.986 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.999 | 1.000 | ** | |
| MAD:mclust | 2 | 1.000 | 0.993 | 0.997 | ** | |
| ATC:kmeans | 3 | 1.000 | 0.980 | 0.991 | ** | 2 |
| ATC:mclust | 2 | 1.000 | 0.991 | 0.997 | ** | |
| CV:skmeans | 4 | 0.988 | 0.947 | 0.968 | ** | 2,3 |
| CV:NMF | 2 | 0.963 | 0.942 | 0.976 | ** | |
| MAD:skmeans | 4 | 0.954 | 0.931 | 0.950 | ** | 2,3 |
| CV:hclust | 4 | 0.954 | 0.920 | 0.953 | ** | |
| MAD:NMF | 2 | 0.946 | 0.943 | 0.977 | * | |
| ATC:skmeans | 4 | 0.938 | 0.876 | 0.947 | * | 2,3 |
| ATC:NMF | 3 | 0.933 | 0.923 | 0.964 | * | 2 |
| CV:pam | 2 | 0.928 | 0.936 | 0.974 | * | |
| SD:skmeans | 4 | 0.927 | 0.887 | 0.930 | * | 2,3 |
| ATC:pam | 4 | 0.927 | 0.907 | 0.962 | * | 2 |
| ATC:hclust | 3 | 0.920 | 0.917 | 0.956 | * | |
| SD:mclust | 5 | 0.919 | 0.937 | 0.952 | * | 2 |
| CV:kmeans | 3 | 0.916 | 0.961 | 0.960 | * | 2 |
| SD:NMF | 2 | 0.894 | 0.928 | 0.970 | ||
| MAD:pam | 2 | 0.893 | 0.926 | 0.969 | ||
| SD:pam | 2 | 0.842 | 0.934 | 0.970 | ||
| SD:hclust | 3 | 0.811 | 0.939 | 0.943 | ||
| CV:mclust | 2 | 0.803 | 0.937 | 0.966 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.894 0.928 0.970 0.502 0.496 0.496
#> CV:NMF 2 0.963 0.942 0.976 0.504 0.496 0.496
#> MAD:NMF 2 0.946 0.943 0.977 0.503 0.496 0.496
#> ATC:NMF 2 1.000 0.981 0.992 0.503 0.498 0.498
#> SD:skmeans 2 1.000 0.981 0.991 0.503 0.498 0.498
#> CV:skmeans 2 1.000 0.990 0.995 0.501 0.499 0.499
#> MAD:skmeans 2 1.000 0.992 0.997 0.501 0.499 0.499
#> ATC:skmeans 2 1.000 0.974 0.991 0.503 0.496 0.496
#> SD:mclust 2 0.922 0.967 0.973 0.478 0.511 0.511
#> CV:mclust 2 0.803 0.937 0.966 0.492 0.511 0.511
#> MAD:mclust 2 1.000 0.993 0.997 0.490 0.511 0.511
#> ATC:mclust 2 1.000 0.991 0.997 0.485 0.514 0.514
#> SD:kmeans 2 0.899 0.964 0.982 0.499 0.503 0.503
#> CV:kmeans 2 1.000 0.991 0.995 0.499 0.501 0.501
#> MAD:kmeans 2 1.000 0.999 1.000 0.497 0.503 0.503
#> ATC:kmeans 2 1.000 0.987 0.994 0.502 0.499 0.499
#> SD:pam 2 0.842 0.934 0.970 0.503 0.496 0.496
#> CV:pam 2 0.928 0.936 0.974 0.503 0.496 0.496
#> MAD:pam 2 0.893 0.926 0.969 0.503 0.496 0.496
#> ATC:pam 2 1.000 0.992 0.997 0.501 0.499 0.499
#> SD:hclust 2 0.611 0.873 0.911 0.475 0.505 0.505
#> CV:hclust 2 0.658 0.913 0.943 0.484 0.505 0.505
#> MAD:hclust 2 1.000 0.973 0.986 0.494 0.505 0.505
#> ATC:hclust 2 0.805 0.923 0.958 0.434 0.568 0.568
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.750 0.832 0.914 0.309 0.794 0.605
#> CV:NMF 3 0.799 0.837 0.917 0.287 0.820 0.651
#> MAD:NMF 3 0.797 0.869 0.931 0.287 0.806 0.627
#> ATC:NMF 3 0.933 0.923 0.964 0.308 0.799 0.612
#> SD:skmeans 3 0.960 0.955 0.980 0.289 0.822 0.654
#> CV:skmeans 3 0.914 0.935 0.969 0.253 0.863 0.731
#> MAD:skmeans 3 0.922 0.967 0.982 0.284 0.829 0.667
#> ATC:skmeans 3 0.969 0.948 0.977 0.257 0.835 0.677
#> SD:mclust 3 0.679 0.785 0.771 0.316 0.829 0.665
#> CV:mclust 3 0.828 0.830 0.920 0.311 0.839 0.684
#> MAD:mclust 3 0.682 0.808 0.790 0.292 0.830 0.668
#> ATC:mclust 3 0.723 0.753 0.840 0.296 0.841 0.691
#> SD:kmeans 3 1.000 0.955 0.957 0.319 0.806 0.624
#> CV:kmeans 3 0.916 0.961 0.960 0.318 0.800 0.615
#> MAD:kmeans 3 0.798 0.949 0.938 0.317 0.806 0.626
#> ATC:kmeans 3 1.000 0.980 0.991 0.324 0.794 0.606
#> SD:pam 3 0.851 0.878 0.945 0.302 0.797 0.611
#> CV:pam 3 0.872 0.887 0.948 0.299 0.804 0.621
#> MAD:pam 3 0.833 0.845 0.924 0.299 0.797 0.610
#> ATC:pam 3 0.873 0.872 0.951 0.314 0.781 0.585
#> SD:hclust 3 0.811 0.939 0.943 0.353 0.837 0.677
#> CV:hclust 3 0.882 0.916 0.936 0.327 0.838 0.680
#> MAD:hclust 3 0.829 0.929 0.933 0.307 0.837 0.677
#> ATC:hclust 3 0.920 0.917 0.956 0.511 0.770 0.595
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.596 0.631 0.794 0.1193 0.887 0.689
#> CV:NMF 4 0.621 0.596 0.783 0.1141 0.891 0.713
#> MAD:NMF 4 0.637 0.655 0.820 0.1187 0.895 0.712
#> ATC:NMF 4 0.789 0.748 0.876 0.0672 0.982 0.947
#> SD:skmeans 4 0.927 0.887 0.930 0.0769 0.947 0.852
#> CV:skmeans 4 0.988 0.947 0.968 0.1126 0.922 0.796
#> MAD:skmeans 4 0.954 0.931 0.950 0.0796 0.941 0.837
#> ATC:skmeans 4 0.938 0.876 0.947 0.0662 0.927 0.807
#> SD:mclust 4 0.721 0.790 0.819 0.1502 0.825 0.547
#> CV:mclust 4 0.693 0.761 0.812 0.1030 0.939 0.829
#> MAD:mclust 4 0.691 0.837 0.868 0.1572 0.845 0.589
#> ATC:mclust 4 0.681 0.770 0.801 0.1104 0.850 0.626
#> SD:kmeans 4 0.723 0.695 0.817 0.1072 0.995 0.986
#> CV:kmeans 4 0.775 0.584 0.814 0.1000 0.993 0.979
#> MAD:kmeans 4 0.735 0.724 0.800 0.1046 0.981 0.943
#> ATC:kmeans 4 0.851 0.796 0.883 0.0884 0.939 0.819
#> SD:pam 4 0.799 0.838 0.911 0.1389 0.885 0.678
#> CV:pam 4 0.808 0.842 0.917 0.1381 0.895 0.701
#> MAD:pam 4 0.667 0.731 0.856 0.1369 0.877 0.656
#> ATC:pam 4 0.927 0.907 0.962 0.0694 0.934 0.811
#> SD:hclust 4 0.744 0.884 0.888 0.0763 1.000 1.000
#> CV:hclust 4 0.954 0.920 0.953 0.0430 0.966 0.905
#> MAD:hclust 4 0.829 0.865 0.915 0.0713 0.991 0.974
#> ATC:hclust 4 0.877 0.837 0.859 0.0672 1.000 1.000
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.658 0.588 0.780 0.0528 0.911 0.699
#> CV:NMF 5 0.676 0.638 0.789 0.0483 0.921 0.752
#> MAD:NMF 5 0.647 0.609 0.764 0.0572 0.933 0.770
#> ATC:NMF 5 0.704 0.611 0.795 0.0520 0.965 0.895
#> SD:skmeans 5 0.776 0.742 0.827 0.0895 0.897 0.677
#> CV:skmeans 5 0.792 0.723 0.868 0.0922 0.939 0.800
#> MAD:skmeans 5 0.799 0.769 0.824 0.0881 0.917 0.737
#> ATC:skmeans 5 0.886 0.852 0.914 0.0349 0.981 0.940
#> SD:mclust 5 0.919 0.937 0.952 0.1014 0.872 0.559
#> CV:mclust 5 0.634 0.656 0.780 0.0761 0.901 0.682
#> MAD:mclust 5 0.860 0.889 0.916 0.0892 0.889 0.605
#> ATC:mclust 5 0.806 0.859 0.885 0.0879 0.889 0.655
#> SD:kmeans 5 0.688 0.660 0.721 0.0628 0.861 0.590
#> CV:kmeans 5 0.724 0.714 0.766 0.0641 0.849 0.558
#> MAD:kmeans 5 0.706 0.607 0.735 0.0695 0.884 0.641
#> ATC:kmeans 5 0.787 0.797 0.856 0.0502 0.940 0.788
#> SD:pam 5 0.815 0.756 0.866 0.0544 0.957 0.832
#> CV:pam 5 0.821 0.831 0.918 0.0549 0.946 0.795
#> MAD:pam 5 0.807 0.761 0.875 0.0598 0.927 0.731
#> ATC:pam 5 0.777 0.710 0.805 0.0899 0.976 0.922
#> SD:hclust 5 0.725 0.536 0.808 0.0597 0.969 0.909
#> CV:hclust 5 0.787 0.818 0.859 0.0828 0.991 0.973
#> MAD:hclust 5 0.766 0.726 0.856 0.0659 0.945 0.834
#> ATC:hclust 5 0.876 0.837 0.876 0.0242 0.933 0.804
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.686 0.573 0.748 0.0320 0.949 0.793
#> CV:NMF 6 0.671 0.597 0.773 0.0453 0.939 0.778
#> MAD:NMF 6 0.695 0.578 0.771 0.0292 0.909 0.687
#> ATC:NMF 6 0.703 0.622 0.783 0.0283 0.964 0.883
#> SD:skmeans 6 0.730 0.663 0.802 0.0483 0.981 0.916
#> CV:skmeans 6 0.752 0.641 0.804 0.0368 0.961 0.845
#> MAD:skmeans 6 0.732 0.659 0.808 0.0508 0.941 0.766
#> ATC:skmeans 6 0.883 0.814 0.899 0.0302 0.996 0.987
#> SD:mclust 6 0.881 0.836 0.898 0.0319 0.964 0.820
#> CV:mclust 6 0.654 0.588 0.740 0.0417 0.964 0.851
#> MAD:mclust 6 0.829 0.792 0.850 0.0292 0.968 0.838
#> ATC:mclust 6 0.884 0.864 0.890 0.0551 0.942 0.759
#> SD:kmeans 6 0.659 0.485 0.684 0.0415 0.917 0.644
#> CV:kmeans 6 0.695 0.679 0.764 0.0400 0.993 0.966
#> MAD:kmeans 6 0.691 0.670 0.727 0.0406 0.952 0.792
#> ATC:kmeans 6 0.728 0.821 0.820 0.0427 0.963 0.846
#> SD:pam 6 0.792 0.701 0.836 0.0424 0.964 0.833
#> CV:pam 6 0.803 0.752 0.851 0.0453 0.959 0.810
#> MAD:pam 6 0.778 0.570 0.790 0.0474 0.927 0.688
#> ATC:pam 6 0.785 0.765 0.860 0.0607 0.876 0.577
#> SD:hclust 6 0.723 0.787 0.825 0.0528 0.904 0.696
#> CV:hclust 6 0.791 0.531 0.808 0.0386 0.963 0.886
#> MAD:hclust 6 0.761 0.710 0.833 0.0437 0.939 0.789
#> ATC:hclust 6 0.830 0.778 0.870 0.0288 0.968 0.892
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 agent(p) individual(p) k
#> SD:NMF 111 1.000 1.13e-04 2
#> CV:NMF 113 0.778 1.13e-04 2
#> MAD:NMF 113 1.000 4.12e-05 2
#> ATC:NMF 116 1.000 1.90e-05 2
#> SD:skmeans 116 1.000 1.90e-05 2
#> CV:skmeans 116 1.000 1.12e-05 2
#> MAD:skmeans 116 1.000 1.12e-05 2
#> ATC:skmeans 114 1.000 3.19e-05 2
#> SD:mclust 116 1.000 6.52e-06 2
#> CV:mclust 116 1.000 6.52e-06 2
#> MAD:mclust 116 1.000 6.52e-06 2
#> ATC:mclust 115 1.000 1.49e-05 2
#> SD:kmeans 115 0.934 1.46e-05 2
#> CV:kmeans 116 0.852 1.91e-05 2
#> MAD:kmeans 116 1.000 1.12e-05 2
#> ATC:kmeans 116 1.000 3.22e-05 2
#> SD:pam 115 1.000 6.83e-05 2
#> CV:pam 113 1.000 2.89e-05 2
#> MAD:pam 113 1.000 4.84e-05 2
#> ATC:pam 115 0.918 2.48e-05 2
#> SD:hclust 116 1.000 6.52e-06 2
#> CV:hclust 114 1.000 7.66e-06 2
#> MAD:hclust 115 1.000 8.53e-06 2
#> ATC:hclust 115 1.000 9.46e-05 2
test_to_known_factors(res_list, k = 3)
#> n agent(p) individual(p) k
#> SD:NMF 110 0.101 8.84e-06 3
#> CV:NMF 108 0.350 5.57e-06 3
#> MAD:NMF 111 0.186 1.08e-05 3
#> ATC:NMF 113 0.991 1.71e-07 3
#> SD:skmeans 114 0.695 1.33e-07 3
#> CV:skmeans 116 0.962 1.49e-09 3
#> MAD:skmeans 115 0.789 9.02e-08 3
#> ATC:skmeans 111 0.950 2.16e-07 3
#> SD:mclust 108 0.815 1.81e-07 3
#> CV:mclust 102 0.976 5.06e-08 3
#> MAD:mclust 110 0.968 5.70e-08 3
#> ATC:mclust 100 0.739 4.72e-06 3
#> SD:kmeans 115 0.830 2.51e-08 3
#> CV:kmeans 115 0.933 1.07e-08 3
#> MAD:kmeans 115 0.826 2.49e-08 3
#> ATC:kmeans 114 0.974 3.76e-08 3
#> SD:pam 108 0.777 2.04e-06 3
#> CV:pam 109 0.963 1.55e-06 3
#> MAD:pam 105 0.871 2.78e-06 3
#> ATC:pam 108 0.879 6.53e-07 3
#> SD:hclust 116 1.000 2.82e-09 3
#> CV:hclust 111 0.989 8.88e-09 3
#> MAD:hclust 115 0.994 4.62e-09 3
#> ATC:hclust 113 0.988 3.59e-08 3
test_to_known_factors(res_list, k = 4)
#> n agent(p) individual(p) k
#> SD:NMF 87 0.304 7.78e-06 4
#> CV:NMF 76 1.000 1.02e-03 4
#> MAD:NMF 88 0.544 1.24e-06 4
#> ATC:NMF 102 0.928 2.49e-06 4
#> SD:skmeans 113 0.950 1.96e-10 4
#> CV:skmeans 115 0.997 1.96e-11 4
#> MAD:skmeans 116 0.933 1.12e-10 4
#> ATC:skmeans 107 0.937 2.21e-11 4
#> SD:mclust 105 0.716 2.62e-05 4
#> CV:mclust 110 0.985 1.80e-11 4
#> MAD:mclust 111 0.690 1.18e-06 4
#> ATC:mclust 109 0.531 1.61e-08 4
#> SD:kmeans 101 0.805 2.80e-07 4
#> CV:kmeans 87 0.884 1.89e-06 4
#> MAD:kmeans 100 0.370 7.98e-08 4
#> ATC:kmeans 109 0.986 6.55e-09 4
#> SD:pam 108 0.281 1.06e-05 4
#> CV:pam 108 0.292 5.35e-06 4
#> MAD:pam 99 0.313 1.66e-05 4
#> ATC:pam 111 0.699 6.67e-09 4
#> SD:hclust 116 1.000 2.82e-09 4
#> CV:hclust 113 0.999 7.89e-13 4
#> MAD:hclust 110 1.000 4.34e-13 4
#> ATC:hclust 110 1.000 4.55e-08 4
test_to_known_factors(res_list, k = 5)
#> n agent(p) individual(p) k
#> SD:NMF 78 0.110 1.68e-05 5
#> CV:NMF 87 0.599 8.94e-05 5
#> MAD:NMF 84 0.130 4.62e-05 5
#> ATC:NMF 76 0.616 4.24e-06 5
#> SD:skmeans 102 0.971 7.44e-10 5
#> CV:skmeans 99 0.978 4.56e-11 5
#> MAD:skmeans 105 0.943 1.90e-09 5
#> ATC:skmeans 105 0.971 1.45e-11 5
#> SD:mclust 114 0.316 4.20e-06 5
#> CV:mclust 95 0.891 2.21e-08 5
#> MAD:mclust 114 0.387 7.53e-07 5
#> ATC:mclust 113 0.998 2.41e-10 5
#> SD:kmeans 98 0.999 3.30e-09 5
#> CV:kmeans 96 0.808 6.59e-11 5
#> MAD:kmeans 97 0.924 7.87e-08 5
#> ATC:kmeans 109 0.963 1.24e-10 5
#> SD:pam 105 0.476 2.58e-08 5
#> CV:pam 110 0.426 8.96e-09 5
#> MAD:pam 106 0.590 4.15e-08 5
#> ATC:pam 108 0.893 8.71e-09 5
#> SD:hclust 88 0.744 5.18e-12 5
#> CV:hclust 115 1.000 5.47e-17 5
#> MAD:hclust 101 0.867 1.75e-12 5
#> ATC:hclust 110 0.930 9.45e-13 5
test_to_known_factors(res_list, k = 6)
#> n agent(p) individual(p) k
#> SD:NMF 76 0.2979 7.38e-09 6
#> CV:NMF 81 0.0605 1.32e-05 6
#> MAD:NMF 81 0.0235 3.01e-05 6
#> ATC:NMF 87 0.9920 1.52e-07 6
#> SD:skmeans 99 0.9832 3.58e-09 6
#> CV:skmeans 90 0.9286 1.24e-09 6
#> MAD:skmeans 101 0.9938 3.09e-09 6
#> ATC:skmeans 108 0.9943 4.97e-13 6
#> SD:mclust 106 0.3948 3.42e-06 6
#> CV:mclust 84 0.8168 4.91e-06 6
#> MAD:mclust 105 0.3279 1.62e-07 6
#> ATC:mclust 111 0.9925 2.84e-11 6
#> SD:kmeans 74 0.9896 3.76e-07 6
#> CV:kmeans 96 0.8692 6.24e-11 6
#> MAD:kmeans 104 0.9122 2.93e-10 6
#> ATC:kmeans 115 0.9182 1.10e-12 6
#> SD:pam 96 0.5525 2.11e-09 6
#> CV:pam 107 0.5321 9.03e-10 6
#> MAD:pam 71 0.4643 9.00e-05 6
#> ATC:pam 106 0.8048 1.12e-09 6
#> SD:hclust 111 0.9998 1.43e-17 6
#> CV:hclust 84 0.9999 2.62e-14 6
#> MAD:hclust 98 0.9833 1.05e-15 6
#> ATC:hclust 105 0.9841 1.52e-17 6
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.611 0.873 0.911 0.4754 0.505 0.505
#> 3 3 0.811 0.939 0.943 0.3527 0.837 0.677
#> 4 4 0.744 0.884 0.888 0.0763 1.000 1.000
#> 5 5 0.725 0.536 0.808 0.0597 0.969 0.909
#> 6 6 0.723 0.787 0.825 0.0528 0.904 0.696
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM125123 1 0.0000 0.998 1.000 0.000
#> GSM125125 1 0.0000 0.998 1.000 0.000
#> GSM125127 1 0.0000 0.998 1.000 0.000
#> GSM125129 1 0.0000 0.998 1.000 0.000
#> GSM125131 1 0.0000 0.998 1.000 0.000
#> GSM125133 1 0.0000 0.998 1.000 0.000
#> GSM125135 1 0.0000 0.998 1.000 0.000
#> GSM125137 1 0.0000 0.998 1.000 0.000
#> GSM125139 1 0.0000 0.998 1.000 0.000
#> GSM125141 1 0.0000 0.998 1.000 0.000
#> GSM125143 1 0.0000 0.998 1.000 0.000
#> GSM125145 1 0.0000 0.998 1.000 0.000
#> GSM125147 1 0.0000 0.998 1.000 0.000
#> GSM125149 1 0.0000 0.998 1.000 0.000
#> GSM125151 1 0.0000 0.998 1.000 0.000
#> GSM125153 1 0.0000 0.998 1.000 0.000
#> GSM125155 1 0.0000 0.998 1.000 0.000
#> GSM125157 1 0.0000 0.998 1.000 0.000
#> GSM125159 2 0.0000 0.828 0.000 1.000
#> GSM125161 1 0.0000 0.998 1.000 0.000
#> GSM125163 2 0.0000 0.828 0.000 1.000
#> GSM125165 2 0.8443 0.766 0.272 0.728
#> GSM125167 2 0.0000 0.828 0.000 1.000
#> GSM125169 2 0.0000 0.828 0.000 1.000
#> GSM125171 2 0.0000 0.828 0.000 1.000
#> GSM125173 2 0.9393 0.697 0.356 0.644
#> GSM125175 2 0.0000 0.828 0.000 1.000
#> GSM125177 2 0.9248 0.717 0.340 0.660
#> GSM125179 2 0.9209 0.721 0.336 0.664
#> GSM125181 2 0.7674 0.782 0.224 0.776
#> GSM125183 2 0.9209 0.721 0.336 0.664
#> GSM125185 2 0.9209 0.721 0.336 0.664
#> GSM125187 2 0.8861 0.746 0.304 0.696
#> GSM125189 2 0.0000 0.828 0.000 1.000
#> GSM125191 2 0.3733 0.816 0.072 0.928
#> GSM125193 2 0.9358 0.701 0.352 0.648
#> GSM125195 2 0.9393 0.697 0.356 0.644
#> GSM125197 2 0.0000 0.828 0.000 1.000
#> GSM125199 1 0.0000 0.998 1.000 0.000
#> GSM125201 2 0.0000 0.828 0.000 1.000
#> GSM125203 2 0.9248 0.717 0.340 0.660
#> GSM125205 2 0.0000 0.828 0.000 1.000
#> GSM125207 2 0.9000 0.738 0.316 0.684
#> GSM125209 2 0.8386 0.766 0.268 0.732
#> GSM125211 2 0.9209 0.716 0.336 0.664
#> GSM125213 2 0.0000 0.828 0.000 1.000
#> GSM125215 2 0.0000 0.828 0.000 1.000
#> GSM125217 2 0.0000 0.828 0.000 1.000
#> GSM125219 1 0.0000 0.998 1.000 0.000
#> GSM125221 2 0.8661 0.757 0.288 0.712
#> GSM125223 2 0.0000 0.828 0.000 1.000
#> GSM125225 2 0.0000 0.828 0.000 1.000
#> GSM125227 2 0.0000 0.828 0.000 1.000
#> GSM125229 2 0.9209 0.716 0.336 0.664
#> GSM125231 1 0.3733 0.899 0.928 0.072
#> GSM125233 1 0.0000 0.998 1.000 0.000
#> GSM125235 1 0.0000 0.998 1.000 0.000
#> GSM125237 1 0.0000 0.998 1.000 0.000
#> GSM125124 1 0.0000 0.998 1.000 0.000
#> GSM125126 1 0.0000 0.998 1.000 0.000
#> GSM125128 1 0.0000 0.998 1.000 0.000
#> GSM125130 1 0.0000 0.998 1.000 0.000
#> GSM125132 1 0.0000 0.998 1.000 0.000
#> GSM125134 1 0.0000 0.998 1.000 0.000
#> GSM125136 1 0.0000 0.998 1.000 0.000
#> GSM125138 1 0.0000 0.998 1.000 0.000
#> GSM125140 1 0.0000 0.998 1.000 0.000
#> GSM125142 1 0.0000 0.998 1.000 0.000
#> GSM125144 1 0.0000 0.998 1.000 0.000
#> GSM125146 1 0.0000 0.998 1.000 0.000
#> GSM125148 1 0.0000 0.998 1.000 0.000
#> GSM125150 1 0.0000 0.998 1.000 0.000
#> GSM125152 1 0.0000 0.998 1.000 0.000
#> GSM125154 1 0.0000 0.998 1.000 0.000
#> GSM125156 1 0.0000 0.998 1.000 0.000
#> GSM125158 1 0.0000 0.998 1.000 0.000
#> GSM125160 2 0.0000 0.828 0.000 1.000
#> GSM125162 1 0.0000 0.998 1.000 0.000
#> GSM125164 2 0.0000 0.828 0.000 1.000
#> GSM125166 2 0.0000 0.828 0.000 1.000
#> GSM125168 2 0.0000 0.828 0.000 1.000
#> GSM125170 2 0.0000 0.828 0.000 1.000
#> GSM125172 2 0.0000 0.828 0.000 1.000
#> GSM125174 2 0.9393 0.697 0.356 0.644
#> GSM125176 2 0.0000 0.828 0.000 1.000
#> GSM125178 2 0.9248 0.717 0.340 0.660
#> GSM125180 2 0.9209 0.721 0.336 0.664
#> GSM125182 2 0.7674 0.782 0.224 0.776
#> GSM125184 2 0.9209 0.721 0.336 0.664
#> GSM125186 2 0.9209 0.721 0.336 0.664
#> GSM125188 2 0.7883 0.778 0.236 0.764
#> GSM125190 2 0.0000 0.828 0.000 1.000
#> GSM125192 2 0.0000 0.828 0.000 1.000
#> GSM125194 2 0.9358 0.701 0.352 0.648
#> GSM125196 2 0.9393 0.697 0.356 0.644
#> GSM125198 2 0.0000 0.828 0.000 1.000
#> GSM125200 1 0.0000 0.998 1.000 0.000
#> GSM125202 2 0.0000 0.828 0.000 1.000
#> GSM125204 2 0.9248 0.717 0.340 0.660
#> GSM125206 2 0.9393 0.697 0.356 0.644
#> GSM125208 2 0.9000 0.738 0.316 0.684
#> GSM125210 2 0.8386 0.766 0.268 0.732
#> GSM125212 2 0.9209 0.716 0.336 0.664
#> GSM125214 2 0.0000 0.828 0.000 1.000
#> GSM125216 2 0.0000 0.828 0.000 1.000
#> GSM125218 2 0.0000 0.828 0.000 1.000
#> GSM125220 1 0.0000 0.998 1.000 0.000
#> GSM125222 2 0.8661 0.757 0.288 0.712
#> GSM125224 2 0.0000 0.828 0.000 1.000
#> GSM125226 2 0.0000 0.828 0.000 1.000
#> GSM125228 2 0.0000 0.828 0.000 1.000
#> GSM125230 2 0.9209 0.716 0.336 0.664
#> GSM125232 1 0.0376 0.993 0.996 0.004
#> GSM125234 1 0.0938 0.983 0.988 0.012
#> GSM125236 1 0.0000 0.998 1.000 0.000
#> GSM125238 1 0.0000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125127 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125129 1 0.0592 0.984 0.988 0.000 0.012
#> GSM125131 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125133 1 0.1289 0.969 0.968 0.000 0.032
#> GSM125135 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125137 1 0.1031 0.973 0.976 0.000 0.024
#> GSM125139 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125143 1 0.0592 0.984 0.988 0.000 0.012
#> GSM125145 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125147 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125151 1 0.0424 0.986 0.992 0.000 0.008
#> GSM125153 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125155 1 0.0237 0.987 0.996 0.000 0.004
#> GSM125157 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125159 2 0.4002 0.895 0.000 0.840 0.160
#> GSM125161 1 0.1289 0.969 0.968 0.000 0.032
#> GSM125163 2 0.3412 0.906 0.000 0.876 0.124
#> GSM125165 3 0.4339 0.906 0.048 0.084 0.868
#> GSM125167 2 0.4291 0.879 0.000 0.820 0.180
#> GSM125169 2 0.4291 0.879 0.000 0.820 0.180
#> GSM125171 2 0.0892 0.906 0.000 0.980 0.020
#> GSM125173 3 0.3846 0.928 0.108 0.016 0.876
#> GSM125175 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125177 3 0.3587 0.942 0.088 0.020 0.892
#> GSM125179 3 0.3415 0.943 0.080 0.020 0.900
#> GSM125181 3 0.2537 0.887 0.000 0.080 0.920
#> GSM125183 3 0.4015 0.937 0.096 0.028 0.876
#> GSM125185 3 0.3415 0.943 0.080 0.020 0.900
#> GSM125187 3 0.3009 0.940 0.052 0.028 0.920
#> GSM125189 2 0.4002 0.895 0.000 0.840 0.160
#> GSM125191 2 0.6275 0.568 0.008 0.644 0.348
#> GSM125193 3 0.4121 0.921 0.108 0.024 0.868
#> GSM125195 3 0.3528 0.939 0.092 0.016 0.892
#> GSM125197 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125203 3 0.3415 0.944 0.080 0.020 0.900
#> GSM125205 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125207 3 0.2743 0.940 0.052 0.020 0.928
#> GSM125209 3 0.2269 0.919 0.016 0.040 0.944
#> GSM125211 3 0.1647 0.918 0.036 0.004 0.960
#> GSM125213 2 0.2878 0.910 0.000 0.904 0.096
#> GSM125215 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125217 2 0.4002 0.894 0.000 0.840 0.160
#> GSM125219 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125221 3 0.3888 0.923 0.048 0.064 0.888
#> GSM125223 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125225 2 0.3816 0.900 0.000 0.852 0.148
#> GSM125227 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125229 3 0.1647 0.918 0.036 0.004 0.960
#> GSM125231 1 0.3816 0.823 0.852 0.000 0.148
#> GSM125233 1 0.0424 0.986 0.992 0.000 0.008
#> GSM125235 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125237 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125124 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125126 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125128 1 0.1289 0.969 0.968 0.000 0.032
#> GSM125130 1 0.0592 0.984 0.988 0.000 0.012
#> GSM125132 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125134 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125136 1 0.1289 0.969 0.968 0.000 0.032
#> GSM125138 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125140 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125144 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125146 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125148 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125152 1 0.0424 0.986 0.992 0.000 0.008
#> GSM125154 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125156 1 0.0237 0.987 0.996 0.000 0.004
#> GSM125158 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125160 2 0.4002 0.895 0.000 0.840 0.160
#> GSM125162 1 0.1289 0.969 0.968 0.000 0.032
#> GSM125164 2 0.3412 0.906 0.000 0.876 0.124
#> GSM125166 2 0.3192 0.909 0.000 0.888 0.112
#> GSM125168 2 0.4452 0.867 0.000 0.808 0.192
#> GSM125170 2 0.4452 0.867 0.000 0.808 0.192
#> GSM125172 2 0.0892 0.906 0.000 0.980 0.020
#> GSM125174 3 0.3846 0.928 0.108 0.016 0.876
#> GSM125176 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125178 3 0.3587 0.942 0.088 0.020 0.892
#> GSM125180 3 0.3415 0.943 0.080 0.020 0.900
#> GSM125182 3 0.2537 0.887 0.000 0.080 0.920
#> GSM125184 3 0.4015 0.937 0.096 0.028 0.876
#> GSM125186 3 0.3415 0.943 0.080 0.020 0.900
#> GSM125188 3 0.2496 0.898 0.004 0.068 0.928
#> GSM125190 2 0.4002 0.895 0.000 0.840 0.160
#> GSM125192 2 0.3192 0.909 0.000 0.888 0.112
#> GSM125194 3 0.4121 0.921 0.108 0.024 0.868
#> GSM125196 3 0.3528 0.939 0.092 0.016 0.892
#> GSM125198 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.988 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125204 3 0.3415 0.944 0.080 0.020 0.900
#> GSM125206 3 0.3528 0.939 0.092 0.016 0.892
#> GSM125208 3 0.2743 0.940 0.052 0.020 0.928
#> GSM125210 3 0.2269 0.919 0.016 0.040 0.944
#> GSM125212 3 0.1647 0.918 0.036 0.004 0.960
#> GSM125214 2 0.2878 0.910 0.000 0.904 0.096
#> GSM125216 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125218 2 0.4002 0.894 0.000 0.840 0.160
#> GSM125220 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125222 3 0.3888 0.923 0.048 0.064 0.888
#> GSM125224 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125226 2 0.3816 0.900 0.000 0.852 0.148
#> GSM125228 2 0.0000 0.905 0.000 1.000 0.000
#> GSM125230 3 0.1647 0.918 0.036 0.004 0.960
#> GSM125232 1 0.2537 0.914 0.920 0.000 0.080
#> GSM125234 1 0.1753 0.950 0.952 0.000 0.048
#> GSM125236 1 0.0237 0.988 0.996 0.000 0.004
#> GSM125238 1 0.0000 0.988 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.1938 0.926 0.936 0.000 0.012 NA
#> GSM125125 1 0.1938 0.926 0.936 0.000 0.012 NA
#> GSM125127 1 0.2999 0.913 0.864 0.000 0.004 NA
#> GSM125129 1 0.3991 0.894 0.808 0.000 0.020 NA
#> GSM125131 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125133 1 0.1716 0.901 0.936 0.000 0.000 NA
#> GSM125135 1 0.2589 0.916 0.884 0.000 0.000 NA
#> GSM125137 1 0.1716 0.902 0.936 0.000 0.000 NA
#> GSM125139 1 0.2021 0.926 0.932 0.000 0.012 NA
#> GSM125141 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125143 1 0.3946 0.895 0.812 0.000 0.020 NA
#> GSM125145 1 0.3969 0.891 0.804 0.000 0.016 NA
#> GSM125147 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125149 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125151 1 0.2662 0.923 0.900 0.000 0.016 NA
#> GSM125153 1 0.3597 0.905 0.836 0.000 0.016 NA
#> GSM125155 1 0.0817 0.922 0.976 0.000 0.000 NA
#> GSM125157 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125159 2 0.3505 0.899 0.000 0.864 0.088 NA
#> GSM125161 1 0.2011 0.893 0.920 0.000 0.000 NA
#> GSM125163 2 0.2706 0.908 0.000 0.900 0.080 NA
#> GSM125165 3 0.4148 0.854 0.012 0.072 0.844 NA
#> GSM125167 2 0.3959 0.886 0.000 0.840 0.092 NA
#> GSM125169 2 0.3959 0.886 0.000 0.840 0.092 NA
#> GSM125171 2 0.1209 0.907 0.000 0.964 0.004 NA
#> GSM125173 3 0.4985 0.627 0.000 0.000 0.532 NA
#> GSM125175 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125177 3 0.2329 0.887 0.000 0.012 0.916 NA
#> GSM125179 3 0.2101 0.885 0.012 0.000 0.928 NA
#> GSM125181 3 0.3392 0.846 0.000 0.072 0.872 NA
#> GSM125183 3 0.2310 0.885 0.008 0.004 0.920 NA
#> GSM125185 3 0.2101 0.885 0.012 0.000 0.928 NA
#> GSM125187 3 0.1762 0.886 0.004 0.004 0.944 NA
#> GSM125189 2 0.3601 0.898 0.000 0.860 0.084 NA
#> GSM125191 2 0.5300 0.581 0.000 0.664 0.308 NA
#> GSM125193 3 0.3229 0.869 0.072 0.000 0.880 NA
#> GSM125195 3 0.2647 0.873 0.000 0.000 0.880 NA
#> GSM125197 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125199 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125201 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125203 3 0.2048 0.888 0.000 0.008 0.928 NA
#> GSM125205 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125207 3 0.0921 0.886 0.000 0.000 0.972 NA
#> GSM125209 3 0.1724 0.878 0.000 0.020 0.948 NA
#> GSM125211 3 0.5511 0.717 0.000 0.028 0.620 NA
#> GSM125213 2 0.2376 0.911 0.000 0.916 0.068 NA
#> GSM125215 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125217 2 0.3601 0.897 0.000 0.860 0.084 NA
#> GSM125219 1 0.2843 0.924 0.892 0.000 0.020 NA
#> GSM125221 3 0.3909 0.864 0.012 0.052 0.856 NA
#> GSM125223 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125225 2 0.3383 0.902 0.000 0.872 0.076 NA
#> GSM125227 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125229 3 0.5511 0.717 0.000 0.028 0.620 NA
#> GSM125231 1 0.6469 0.753 0.668 0.012 0.116 NA
#> GSM125233 1 0.3659 0.908 0.840 0.000 0.024 NA
#> GSM125235 1 0.2610 0.923 0.900 0.000 0.012 NA
#> GSM125237 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125124 1 0.4035 0.890 0.804 0.000 0.020 NA
#> GSM125126 1 0.1938 0.926 0.936 0.000 0.012 NA
#> GSM125128 1 0.1940 0.898 0.924 0.000 0.000 NA
#> GSM125130 1 0.3991 0.894 0.808 0.000 0.020 NA
#> GSM125132 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125134 1 0.3925 0.894 0.808 0.000 0.016 NA
#> GSM125136 1 0.2011 0.893 0.920 0.000 0.000 NA
#> GSM125138 1 0.4035 0.890 0.804 0.000 0.020 NA
#> GSM125140 1 0.2021 0.926 0.932 0.000 0.012 NA
#> GSM125142 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125144 1 0.3695 0.902 0.828 0.000 0.016 NA
#> GSM125146 1 0.3969 0.891 0.804 0.000 0.016 NA
#> GSM125148 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125150 1 0.0469 0.922 0.988 0.000 0.000 NA
#> GSM125152 1 0.2662 0.923 0.900 0.000 0.016 NA
#> GSM125154 1 0.3335 0.912 0.856 0.000 0.016 NA
#> GSM125156 1 0.0817 0.922 0.976 0.000 0.000 NA
#> GSM125158 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125160 2 0.3505 0.899 0.000 0.864 0.088 NA
#> GSM125162 1 0.2011 0.893 0.920 0.000 0.000 NA
#> GSM125164 2 0.2706 0.908 0.000 0.900 0.080 NA
#> GSM125166 2 0.2271 0.910 0.000 0.916 0.076 NA
#> GSM125168 2 0.4144 0.876 0.000 0.828 0.104 NA
#> GSM125170 2 0.4144 0.876 0.000 0.828 0.104 NA
#> GSM125172 2 0.1209 0.907 0.000 0.964 0.004 NA
#> GSM125174 3 0.4985 0.627 0.000 0.000 0.532 NA
#> GSM125176 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125178 3 0.2329 0.887 0.000 0.012 0.916 NA
#> GSM125180 3 0.2101 0.885 0.012 0.000 0.928 NA
#> GSM125182 3 0.3392 0.846 0.000 0.072 0.872 NA
#> GSM125184 3 0.2310 0.885 0.008 0.004 0.920 NA
#> GSM125186 3 0.2101 0.885 0.012 0.000 0.928 NA
#> GSM125188 3 0.3245 0.855 0.000 0.056 0.880 NA
#> GSM125190 2 0.3601 0.898 0.000 0.860 0.084 NA
#> GSM125192 2 0.2271 0.910 0.000 0.916 0.076 NA
#> GSM125194 3 0.3229 0.869 0.072 0.000 0.880 NA
#> GSM125196 3 0.2647 0.873 0.000 0.000 0.880 NA
#> GSM125198 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125200 1 0.0188 0.923 0.996 0.000 0.000 NA
#> GSM125202 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125204 3 0.2048 0.888 0.000 0.008 0.928 NA
#> GSM125206 3 0.2647 0.873 0.000 0.000 0.880 NA
#> GSM125208 3 0.0921 0.886 0.000 0.000 0.972 NA
#> GSM125210 3 0.1724 0.878 0.000 0.020 0.948 NA
#> GSM125212 3 0.5511 0.717 0.000 0.028 0.620 NA
#> GSM125214 2 0.2376 0.911 0.000 0.916 0.068 NA
#> GSM125216 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125218 2 0.3601 0.897 0.000 0.860 0.084 NA
#> GSM125220 1 0.2300 0.927 0.920 0.000 0.016 NA
#> GSM125222 3 0.3909 0.864 0.012 0.052 0.856 NA
#> GSM125224 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125226 2 0.3383 0.902 0.000 0.872 0.076 NA
#> GSM125228 2 0.1211 0.906 0.000 0.960 0.000 NA
#> GSM125230 3 0.5511 0.717 0.000 0.028 0.620 NA
#> GSM125232 1 0.5500 0.816 0.708 0.000 0.068 NA
#> GSM125234 1 0.4951 0.848 0.744 0.000 0.044 NA
#> GSM125236 1 0.2610 0.923 0.900 0.000 0.012 NA
#> GSM125238 1 0.0469 0.922 0.988 0.000 0.000 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2561 0.48780 0.856 0.000 0.000 0.000 0.144
#> GSM125125 1 0.2561 0.48780 0.856 0.000 0.000 0.000 0.144
#> GSM125127 1 0.4150 -0.32442 0.612 0.000 0.000 0.000 0.388
#> GSM125129 1 0.4549 -0.65773 0.528 0.000 0.008 0.000 0.464
#> GSM125131 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125133 1 0.2795 0.53190 0.880 0.000 0.000 0.056 0.064
#> GSM125135 1 0.3983 -0.17944 0.660 0.000 0.000 0.000 0.340
#> GSM125137 1 0.1981 0.53995 0.924 0.000 0.000 0.048 0.028
#> GSM125139 1 0.2773 0.46329 0.836 0.000 0.000 0.000 0.164
#> GSM125141 1 0.0404 0.58563 0.988 0.000 0.000 0.000 0.012
#> GSM125143 1 0.4546 -0.65015 0.532 0.000 0.008 0.000 0.460
#> GSM125145 1 0.4305 -0.71278 0.512 0.000 0.000 0.000 0.488
#> GSM125147 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
#> GSM125149 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
#> GSM125151 1 0.3999 -0.09293 0.656 0.000 0.000 0.000 0.344
#> GSM125153 1 0.4242 -0.51595 0.572 0.000 0.000 0.000 0.428
#> GSM125155 1 0.1124 0.58089 0.960 0.000 0.000 0.004 0.036
#> GSM125157 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125159 2 0.3337 0.86169 0.000 0.856 0.072 0.064 0.008
#> GSM125161 1 0.2171 0.52204 0.912 0.000 0.000 0.064 0.024
#> GSM125163 2 0.2650 0.87065 0.000 0.892 0.068 0.036 0.004
#> GSM125165 3 0.3775 0.75138 0.012 0.060 0.844 0.072 0.012
#> GSM125167 2 0.3807 0.84733 0.000 0.828 0.072 0.088 0.012
#> GSM125169 2 0.3750 0.84933 0.000 0.832 0.072 0.084 0.012
#> GSM125171 2 0.2450 0.86217 0.000 0.896 0.000 0.028 0.076
#> GSM125173 3 0.6480 0.00948 0.000 0.000 0.412 0.184 0.404
#> GSM125175 2 0.2172 0.86235 0.000 0.908 0.000 0.016 0.076
#> GSM125177 3 0.2740 0.81344 0.000 0.004 0.888 0.064 0.044
#> GSM125179 3 0.2144 0.81134 0.000 0.000 0.912 0.020 0.068
#> GSM125181 3 0.3523 0.71356 0.000 0.076 0.844 0.072 0.008
#> GSM125183 3 0.2227 0.81490 0.000 0.004 0.916 0.048 0.032
#> GSM125185 3 0.2144 0.81134 0.000 0.000 0.912 0.020 0.068
#> GSM125187 3 0.1877 0.81649 0.004 0.004 0.932 0.052 0.008
#> GSM125189 2 0.3511 0.85880 0.000 0.848 0.072 0.068 0.012
#> GSM125191 2 0.4907 0.52828 0.000 0.656 0.292 0.052 0.000
#> GSM125193 3 0.3073 0.77083 0.068 0.000 0.872 0.052 0.008
#> GSM125195 3 0.3359 0.74368 0.000 0.000 0.840 0.108 0.052
#> GSM125197 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125199 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125201 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125203 3 0.2529 0.81471 0.000 0.004 0.900 0.056 0.040
#> GSM125205 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125207 3 0.1493 0.81644 0.000 0.000 0.948 0.024 0.028
#> GSM125209 3 0.1885 0.80024 0.000 0.020 0.932 0.044 0.004
#> GSM125211 4 0.3662 1.00000 0.000 0.004 0.252 0.744 0.000
#> GSM125213 2 0.1970 0.87456 0.000 0.924 0.060 0.012 0.004
#> GSM125215 2 0.1942 0.86426 0.000 0.920 0.000 0.012 0.068
#> GSM125217 2 0.3511 0.85885 0.000 0.848 0.068 0.072 0.012
#> GSM125219 1 0.3895 0.25521 0.728 0.000 0.004 0.004 0.264
#> GSM125221 3 0.3490 0.75888 0.008 0.040 0.856 0.084 0.012
#> GSM125223 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125225 2 0.3320 0.86363 0.000 0.860 0.068 0.060 0.012
#> GSM125227 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125229 4 0.3662 1.00000 0.000 0.004 0.252 0.744 0.000
#> GSM125231 1 0.7206 -0.70773 0.436 0.004 0.092 0.072 0.396
#> GSM125233 1 0.4350 -0.40866 0.588 0.000 0.004 0.000 0.408
#> GSM125235 1 0.3895 0.04887 0.680 0.000 0.000 0.000 0.320
#> GSM125237 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
#> GSM125124 1 0.4437 -0.67435 0.532 0.000 0.004 0.000 0.464
#> GSM125126 1 0.2561 0.48780 0.856 0.000 0.000 0.000 0.144
#> GSM125128 1 0.2726 0.52703 0.884 0.000 0.000 0.064 0.052
#> GSM125130 1 0.4549 -0.65773 0.528 0.000 0.008 0.000 0.464
#> GSM125132 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125134 1 0.4283 -0.57680 0.544 0.000 0.000 0.000 0.456
#> GSM125136 1 0.2171 0.52204 0.912 0.000 0.000 0.064 0.024
#> GSM125138 1 0.4437 -0.67435 0.532 0.000 0.004 0.000 0.464
#> GSM125140 1 0.2773 0.46329 0.836 0.000 0.000 0.000 0.164
#> GSM125142 1 0.0404 0.58563 0.988 0.000 0.000 0.000 0.012
#> GSM125144 1 0.4403 -0.58015 0.560 0.000 0.004 0.000 0.436
#> GSM125146 1 0.4305 -0.71278 0.512 0.000 0.000 0.000 0.488
#> GSM125148 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
#> GSM125150 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
#> GSM125152 1 0.3999 -0.09293 0.656 0.000 0.000 0.000 0.344
#> GSM125154 1 0.4114 -0.33261 0.624 0.000 0.000 0.000 0.376
#> GSM125156 1 0.1124 0.58089 0.960 0.000 0.000 0.004 0.036
#> GSM125158 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125160 2 0.3337 0.86169 0.000 0.856 0.072 0.064 0.008
#> GSM125162 1 0.2171 0.52204 0.912 0.000 0.000 0.064 0.024
#> GSM125164 2 0.2650 0.87065 0.000 0.892 0.068 0.036 0.004
#> GSM125166 2 0.2353 0.87356 0.000 0.908 0.060 0.028 0.004
#> GSM125168 2 0.3982 0.83748 0.000 0.816 0.084 0.088 0.012
#> GSM125170 2 0.3926 0.83952 0.000 0.820 0.084 0.084 0.012
#> GSM125172 2 0.2450 0.86217 0.000 0.896 0.000 0.028 0.076
#> GSM125174 3 0.6480 0.00948 0.000 0.000 0.412 0.184 0.404
#> GSM125176 2 0.2172 0.86235 0.000 0.908 0.000 0.016 0.076
#> GSM125178 3 0.2740 0.81344 0.000 0.004 0.888 0.064 0.044
#> GSM125180 3 0.2144 0.81134 0.000 0.000 0.912 0.020 0.068
#> GSM125182 3 0.3523 0.71356 0.000 0.076 0.844 0.072 0.008
#> GSM125184 3 0.2227 0.81490 0.000 0.004 0.916 0.048 0.032
#> GSM125186 3 0.2144 0.81134 0.000 0.000 0.912 0.020 0.068
#> GSM125188 3 0.3272 0.73948 0.000 0.060 0.860 0.072 0.008
#> GSM125190 2 0.3511 0.85880 0.000 0.848 0.072 0.068 0.012
#> GSM125192 2 0.2353 0.87356 0.000 0.908 0.060 0.028 0.004
#> GSM125194 3 0.3073 0.77083 0.068 0.000 0.872 0.052 0.008
#> GSM125196 3 0.3359 0.74368 0.000 0.000 0.840 0.108 0.052
#> GSM125198 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125200 1 0.0290 0.58669 0.992 0.000 0.000 0.000 0.008
#> GSM125202 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125204 3 0.2529 0.81471 0.000 0.004 0.900 0.056 0.040
#> GSM125206 3 0.3359 0.74368 0.000 0.000 0.840 0.108 0.052
#> GSM125208 3 0.1493 0.81644 0.000 0.000 0.948 0.024 0.028
#> GSM125210 3 0.1885 0.80024 0.000 0.020 0.932 0.044 0.004
#> GSM125212 4 0.3662 1.00000 0.000 0.004 0.252 0.744 0.000
#> GSM125214 2 0.1970 0.87456 0.000 0.924 0.060 0.012 0.004
#> GSM125216 2 0.1942 0.86426 0.000 0.920 0.000 0.012 0.068
#> GSM125218 2 0.3511 0.85885 0.000 0.848 0.068 0.072 0.012
#> GSM125220 1 0.3461 0.36049 0.772 0.000 0.000 0.004 0.224
#> GSM125222 3 0.3490 0.75888 0.008 0.040 0.856 0.084 0.012
#> GSM125224 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125226 2 0.3320 0.86363 0.000 0.860 0.068 0.060 0.012
#> GSM125228 2 0.2069 0.86242 0.000 0.912 0.000 0.012 0.076
#> GSM125230 4 0.3662 1.00000 0.000 0.004 0.252 0.744 0.000
#> GSM125232 5 0.5879 0.84206 0.436 0.000 0.048 0.024 0.492
#> GSM125234 5 0.4971 0.83331 0.460 0.000 0.028 0.000 0.512
#> GSM125236 1 0.3895 0.04887 0.680 0.000 0.000 0.000 0.320
#> GSM125238 1 0.0162 0.58611 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2491 0.705 0.836 0.000 0.000 0.000 0.164 0.000
#> GSM125125 1 0.2491 0.705 0.836 0.000 0.000 0.000 0.164 0.000
#> GSM125127 5 0.3819 0.723 0.372 0.000 0.000 0.000 0.624 0.004
#> GSM125129 5 0.3240 0.834 0.244 0.000 0.000 0.000 0.752 0.004
#> GSM125131 1 0.0547 0.859 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM125133 1 0.3072 0.779 0.836 0.000 0.036 0.000 0.124 0.004
#> GSM125135 5 0.3706 0.762 0.380 0.000 0.000 0.000 0.620 0.000
#> GSM125137 1 0.2039 0.804 0.904 0.000 0.020 0.000 0.076 0.000
#> GSM125139 1 0.2697 0.664 0.812 0.000 0.000 0.000 0.188 0.000
#> GSM125141 1 0.0260 0.859 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125143 5 0.3265 0.835 0.248 0.000 0.000 0.000 0.748 0.004
#> GSM125145 5 0.3050 0.832 0.236 0.000 0.000 0.000 0.764 0.000
#> GSM125147 1 0.0260 0.861 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125149 1 0.0260 0.861 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125151 5 0.3727 0.746 0.388 0.000 0.000 0.000 0.612 0.000
#> GSM125153 5 0.3428 0.829 0.304 0.000 0.000 0.000 0.696 0.000
#> GSM125155 1 0.1204 0.844 0.944 0.000 0.000 0.000 0.056 0.000
#> GSM125157 1 0.0632 0.857 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM125159 2 0.3792 0.782 0.000 0.816 0.020 0.068 0.008 0.088
#> GSM125161 1 0.2237 0.787 0.896 0.000 0.036 0.000 0.068 0.000
#> GSM125163 2 0.2538 0.800 0.000 0.892 0.020 0.068 0.008 0.012
#> GSM125165 4 0.3715 0.801 0.008 0.064 0.028 0.832 0.004 0.064
#> GSM125167 2 0.4467 0.764 0.000 0.772 0.048 0.064 0.008 0.108
#> GSM125169 2 0.4565 0.766 0.000 0.768 0.048 0.064 0.012 0.108
#> GSM125171 2 0.3544 0.787 0.000 0.820 0.020 0.000 0.052 0.108
#> GSM125173 6 0.3514 1.000 0.000 0.000 0.000 0.228 0.020 0.752
#> GSM125175 2 0.3589 0.787 0.000 0.816 0.020 0.000 0.052 0.112
#> GSM125177 4 0.2896 0.853 0.000 0.012 0.032 0.880 0.024 0.052
#> GSM125179 4 0.2146 0.847 0.000 0.000 0.008 0.908 0.060 0.024
#> GSM125181 4 0.3529 0.757 0.000 0.088 0.024 0.832 0.004 0.052
#> GSM125183 4 0.2034 0.848 0.000 0.004 0.000 0.912 0.024 0.060
#> GSM125185 4 0.2146 0.847 0.000 0.000 0.008 0.908 0.060 0.024
#> GSM125187 4 0.1694 0.860 0.004 0.004 0.024 0.940 0.004 0.024
#> GSM125189 2 0.4230 0.771 0.000 0.784 0.040 0.064 0.004 0.108
#> GSM125191 2 0.4053 0.499 0.000 0.676 0.020 0.300 0.000 0.004
#> GSM125193 4 0.3009 0.819 0.064 0.000 0.024 0.868 0.004 0.040
#> GSM125195 4 0.3748 0.746 0.000 0.000 0.040 0.816 0.084 0.060
#> GSM125197 2 0.3462 0.786 0.000 0.824 0.020 0.000 0.044 0.112
#> GSM125199 1 0.0632 0.857 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM125201 2 0.3589 0.783 0.000 0.816 0.020 0.000 0.052 0.112
#> GSM125203 4 0.2649 0.855 0.000 0.008 0.032 0.892 0.020 0.048
#> GSM125205 2 0.3589 0.783 0.000 0.816 0.020 0.000 0.052 0.112
#> GSM125207 4 0.1536 0.857 0.000 0.000 0.024 0.944 0.020 0.012
#> GSM125209 4 0.1966 0.845 0.000 0.028 0.024 0.924 0.000 0.024
#> GSM125211 3 0.1663 1.000 0.000 0.000 0.912 0.088 0.000 0.000
#> GSM125213 2 0.2177 0.805 0.000 0.908 0.004 0.060 0.004 0.024
#> GSM125215 2 0.3396 0.789 0.000 0.828 0.020 0.000 0.040 0.112
#> GSM125217 2 0.4245 0.770 0.000 0.784 0.032 0.064 0.008 0.112
#> GSM125219 1 0.3860 -0.283 0.528 0.000 0.000 0.000 0.472 0.000
#> GSM125221 4 0.3618 0.808 0.004 0.032 0.036 0.836 0.008 0.084
#> GSM125223 2 0.3507 0.785 0.000 0.820 0.020 0.000 0.044 0.116
#> GSM125225 2 0.4047 0.776 0.000 0.796 0.032 0.064 0.004 0.104
#> GSM125227 2 0.3417 0.789 0.000 0.828 0.020 0.000 0.044 0.108
#> GSM125229 3 0.1663 1.000 0.000 0.000 0.912 0.088 0.000 0.000
#> GSM125231 5 0.6174 0.647 0.168 0.012 0.016 0.084 0.644 0.076
#> GSM125233 5 0.3499 0.808 0.320 0.000 0.000 0.000 0.680 0.000
#> GSM125235 5 0.3866 0.499 0.484 0.000 0.000 0.000 0.516 0.000
#> GSM125237 1 0.0260 0.861 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125124 5 0.3371 0.825 0.292 0.000 0.000 0.000 0.708 0.000
#> GSM125126 1 0.2491 0.705 0.836 0.000 0.000 0.000 0.164 0.000
#> GSM125128 1 0.2848 0.786 0.856 0.000 0.036 0.000 0.104 0.004
#> GSM125130 5 0.3240 0.834 0.244 0.000 0.000 0.000 0.752 0.004
#> GSM125132 1 0.0547 0.859 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM125134 5 0.3464 0.796 0.312 0.000 0.000 0.000 0.688 0.000
#> GSM125136 1 0.2237 0.787 0.896 0.000 0.036 0.000 0.068 0.000
#> GSM125138 5 0.3371 0.825 0.292 0.000 0.000 0.000 0.708 0.000
#> GSM125140 1 0.2697 0.664 0.812 0.000 0.000 0.000 0.188 0.000
#> GSM125142 1 0.0260 0.859 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125144 5 0.3499 0.819 0.320 0.000 0.000 0.000 0.680 0.000
#> GSM125146 5 0.3050 0.832 0.236 0.000 0.000 0.000 0.764 0.000
#> GSM125148 1 0.0260 0.861 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125150 1 0.0260 0.861 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125152 5 0.3727 0.746 0.388 0.000 0.000 0.000 0.612 0.000
#> GSM125154 5 0.3634 0.796 0.356 0.000 0.000 0.000 0.644 0.000
#> GSM125156 1 0.1204 0.844 0.944 0.000 0.000 0.000 0.056 0.000
#> GSM125158 1 0.0632 0.857 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM125160 2 0.3792 0.782 0.000 0.816 0.020 0.068 0.008 0.088
#> GSM125162 1 0.2237 0.787 0.896 0.000 0.036 0.000 0.068 0.000
#> GSM125164 2 0.2538 0.800 0.000 0.892 0.020 0.068 0.008 0.012
#> GSM125166 2 0.2138 0.804 0.000 0.912 0.012 0.060 0.008 0.008
#> GSM125168 2 0.4531 0.759 0.000 0.768 0.048 0.072 0.008 0.104
#> GSM125170 2 0.4629 0.761 0.000 0.764 0.048 0.072 0.012 0.104
#> GSM125172 2 0.3544 0.787 0.000 0.820 0.020 0.000 0.052 0.108
#> GSM125174 6 0.3514 1.000 0.000 0.000 0.000 0.228 0.020 0.752
#> GSM125176 2 0.3589 0.787 0.000 0.816 0.020 0.000 0.052 0.112
#> GSM125178 4 0.2896 0.853 0.000 0.012 0.032 0.880 0.024 0.052
#> GSM125180 4 0.2146 0.847 0.000 0.000 0.008 0.908 0.060 0.024
#> GSM125182 4 0.3529 0.757 0.000 0.088 0.024 0.832 0.004 0.052
#> GSM125184 4 0.2034 0.848 0.000 0.004 0.000 0.912 0.024 0.060
#> GSM125186 4 0.2146 0.847 0.000 0.000 0.008 0.908 0.060 0.024
#> GSM125188 4 0.3277 0.788 0.000 0.068 0.028 0.852 0.004 0.048
#> GSM125190 2 0.4230 0.771 0.000 0.784 0.040 0.064 0.004 0.108
#> GSM125192 2 0.2138 0.804 0.000 0.912 0.012 0.060 0.008 0.008
#> GSM125194 4 0.3009 0.819 0.064 0.000 0.024 0.868 0.004 0.040
#> GSM125196 4 0.3748 0.746 0.000 0.000 0.040 0.816 0.084 0.060
#> GSM125198 2 0.3462 0.786 0.000 0.824 0.020 0.000 0.044 0.112
#> GSM125200 1 0.0632 0.857 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM125202 2 0.3589 0.783 0.000 0.816 0.020 0.000 0.052 0.112
#> GSM125204 4 0.2649 0.855 0.000 0.008 0.032 0.892 0.020 0.048
#> GSM125206 4 0.3748 0.746 0.000 0.000 0.040 0.816 0.084 0.060
#> GSM125208 4 0.1536 0.857 0.000 0.000 0.024 0.944 0.020 0.012
#> GSM125210 4 0.1966 0.845 0.000 0.028 0.024 0.924 0.000 0.024
#> GSM125212 3 0.1663 1.000 0.000 0.000 0.912 0.088 0.000 0.000
#> GSM125214 2 0.2177 0.805 0.000 0.908 0.004 0.060 0.004 0.024
#> GSM125216 2 0.3396 0.789 0.000 0.828 0.020 0.000 0.040 0.112
#> GSM125218 2 0.4245 0.770 0.000 0.784 0.032 0.064 0.008 0.112
#> GSM125220 1 0.3810 -0.117 0.572 0.000 0.000 0.000 0.428 0.000
#> GSM125222 4 0.3618 0.808 0.004 0.032 0.036 0.836 0.008 0.084
#> GSM125224 2 0.3507 0.785 0.000 0.820 0.020 0.000 0.044 0.116
#> GSM125226 2 0.4047 0.776 0.000 0.796 0.032 0.064 0.004 0.104
#> GSM125228 2 0.3417 0.789 0.000 0.828 0.020 0.000 0.044 0.108
#> GSM125230 3 0.1663 1.000 0.000 0.000 0.912 0.088 0.000 0.000
#> GSM125232 5 0.4302 0.758 0.168 0.000 0.004 0.044 0.756 0.028
#> GSM125234 5 0.3288 0.795 0.176 0.000 0.000 0.016 0.800 0.008
#> GSM125236 5 0.3866 0.499 0.484 0.000 0.000 0.000 0.516 0.000
#> GSM125238 1 0.0260 0.861 0.992 0.000 0.000 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 agent(p) individual(p) k
#> SD:hclust 116 1.000 6.52e-06 2
#> SD:hclust 116 1.000 2.82e-09 3
#> SD:hclust 116 1.000 2.82e-09 4
#> SD:hclust 88 0.744 5.18e-12 5
#> SD:hclust 111 1.000 1.43e-17 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.899 0.964 0.982 0.4991 0.503 0.503
#> 3 3 1.000 0.955 0.957 0.3190 0.806 0.624
#> 4 4 0.723 0.695 0.817 0.1072 0.995 0.986
#> 5 5 0.688 0.660 0.721 0.0628 0.861 0.590
#> 6 6 0.659 0.485 0.684 0.0415 0.917 0.644
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM125123 1 0.0000 1.000 1.000 0.000
#> GSM125125 1 0.0000 1.000 1.000 0.000
#> GSM125127 1 0.0000 1.000 1.000 0.000
#> GSM125129 1 0.0000 1.000 1.000 0.000
#> GSM125131 1 0.0000 1.000 1.000 0.000
#> GSM125133 1 0.0000 1.000 1.000 0.000
#> GSM125135 1 0.0000 1.000 1.000 0.000
#> GSM125137 1 0.0000 1.000 1.000 0.000
#> GSM125139 1 0.0000 1.000 1.000 0.000
#> GSM125141 1 0.0000 1.000 1.000 0.000
#> GSM125143 1 0.0000 1.000 1.000 0.000
#> GSM125145 1 0.0000 1.000 1.000 0.000
#> GSM125147 1 0.0000 1.000 1.000 0.000
#> GSM125149 1 0.0000 1.000 1.000 0.000
#> GSM125151 1 0.0000 1.000 1.000 0.000
#> GSM125153 1 0.0000 1.000 1.000 0.000
#> GSM125155 1 0.0000 1.000 1.000 0.000
#> GSM125157 1 0.0000 1.000 1.000 0.000
#> GSM125159 2 0.0000 0.968 0.000 1.000
#> GSM125161 1 0.0000 1.000 1.000 0.000
#> GSM125163 2 0.0000 0.968 0.000 1.000
#> GSM125165 2 0.0000 0.968 0.000 1.000
#> GSM125167 2 0.0000 0.968 0.000 1.000
#> GSM125169 2 0.0000 0.968 0.000 1.000
#> GSM125171 2 0.0000 0.968 0.000 1.000
#> GSM125173 2 0.0000 0.968 0.000 1.000
#> GSM125175 2 0.0000 0.968 0.000 1.000
#> GSM125177 2 0.0000 0.968 0.000 1.000
#> GSM125179 2 0.6247 0.836 0.156 0.844
#> GSM125181 2 0.0000 0.968 0.000 1.000
#> GSM125183 2 0.6343 0.832 0.160 0.840
#> GSM125185 2 0.5178 0.877 0.116 0.884
#> GSM125187 2 0.8081 0.710 0.248 0.752
#> GSM125189 2 0.0000 0.968 0.000 1.000
#> GSM125191 2 0.0000 0.968 0.000 1.000
#> GSM125193 2 0.6887 0.802 0.184 0.816
#> GSM125195 2 0.0672 0.964 0.008 0.992
#> GSM125197 2 0.0000 0.968 0.000 1.000
#> GSM125199 1 0.0000 1.000 1.000 0.000
#> GSM125201 2 0.0000 0.968 0.000 1.000
#> GSM125203 2 0.0376 0.966 0.004 0.996
#> GSM125205 2 0.0000 0.968 0.000 1.000
#> GSM125207 2 0.0672 0.964 0.008 0.992
#> GSM125209 2 0.0000 0.968 0.000 1.000
#> GSM125211 2 0.0000 0.968 0.000 1.000
#> GSM125213 2 0.0000 0.968 0.000 1.000
#> GSM125215 2 0.0000 0.968 0.000 1.000
#> GSM125217 2 0.0000 0.968 0.000 1.000
#> GSM125219 1 0.0000 1.000 1.000 0.000
#> GSM125221 2 0.0376 0.966 0.004 0.996
#> GSM125223 2 0.0000 0.968 0.000 1.000
#> GSM125225 2 0.0000 0.968 0.000 1.000
#> GSM125227 2 0.0000 0.968 0.000 1.000
#> GSM125229 2 0.0000 0.968 0.000 1.000
#> GSM125231 1 0.0376 0.996 0.996 0.004
#> GSM125233 1 0.0000 1.000 1.000 0.000
#> GSM125235 1 0.0000 1.000 1.000 0.000
#> GSM125237 1 0.0000 1.000 1.000 0.000
#> GSM125124 1 0.0000 1.000 1.000 0.000
#> GSM125126 1 0.0000 1.000 1.000 0.000
#> GSM125128 1 0.0000 1.000 1.000 0.000
#> GSM125130 1 0.0000 1.000 1.000 0.000
#> GSM125132 1 0.0000 1.000 1.000 0.000
#> GSM125134 1 0.0000 1.000 1.000 0.000
#> GSM125136 1 0.0000 1.000 1.000 0.000
#> GSM125138 1 0.0000 1.000 1.000 0.000
#> GSM125140 1 0.0000 1.000 1.000 0.000
#> GSM125142 1 0.0000 1.000 1.000 0.000
#> GSM125144 1 0.0000 1.000 1.000 0.000
#> GSM125146 1 0.0000 1.000 1.000 0.000
#> GSM125148 1 0.0000 1.000 1.000 0.000
#> GSM125150 1 0.0000 1.000 1.000 0.000
#> GSM125152 1 0.0000 1.000 1.000 0.000
#> GSM125154 1 0.0000 1.000 1.000 0.000
#> GSM125156 1 0.0000 1.000 1.000 0.000
#> GSM125158 1 0.0000 1.000 1.000 0.000
#> GSM125160 2 0.0000 0.968 0.000 1.000
#> GSM125162 1 0.0000 1.000 1.000 0.000
#> GSM125164 2 0.0000 0.968 0.000 1.000
#> GSM125166 2 0.0000 0.968 0.000 1.000
#> GSM125168 2 0.0000 0.968 0.000 1.000
#> GSM125170 2 0.0000 0.968 0.000 1.000
#> GSM125172 2 0.0000 0.968 0.000 1.000
#> GSM125174 2 0.5519 0.865 0.128 0.872
#> GSM125176 2 0.0000 0.968 0.000 1.000
#> GSM125178 2 0.1414 0.956 0.020 0.980
#> GSM125180 2 0.6247 0.836 0.156 0.844
#> GSM125182 2 0.0000 0.968 0.000 1.000
#> GSM125184 2 0.0000 0.968 0.000 1.000
#> GSM125186 2 0.6247 0.836 0.156 0.844
#> GSM125188 2 0.0000 0.968 0.000 1.000
#> GSM125190 2 0.0000 0.968 0.000 1.000
#> GSM125192 2 0.0000 0.968 0.000 1.000
#> GSM125194 1 0.0000 1.000 1.000 0.000
#> GSM125196 2 0.0672 0.964 0.008 0.992
#> GSM125198 2 0.0000 0.968 0.000 1.000
#> GSM125200 1 0.0000 1.000 1.000 0.000
#> GSM125202 2 0.0000 0.968 0.000 1.000
#> GSM125204 2 0.4161 0.905 0.084 0.916
#> GSM125206 2 0.0376 0.966 0.004 0.996
#> GSM125208 2 0.5737 0.857 0.136 0.864
#> GSM125210 2 0.0000 0.968 0.000 1.000
#> GSM125212 2 0.0000 0.968 0.000 1.000
#> GSM125214 2 0.0000 0.968 0.000 1.000
#> GSM125216 2 0.0000 0.968 0.000 1.000
#> GSM125218 2 0.0000 0.968 0.000 1.000
#> GSM125220 1 0.0000 1.000 1.000 0.000
#> GSM125222 2 0.0376 0.966 0.004 0.996
#> GSM125224 2 0.0000 0.968 0.000 1.000
#> GSM125226 2 0.0000 0.968 0.000 1.000
#> GSM125228 2 0.0000 0.968 0.000 1.000
#> GSM125230 2 0.9944 0.235 0.456 0.544
#> GSM125232 1 0.0000 1.000 1.000 0.000
#> GSM125234 1 0.0000 1.000 1.000 0.000
#> GSM125236 1 0.0000 1.000 1.000 0.000
#> GSM125238 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125125 1 0.0237 0.9717 0.996 0.000 0.004
#> GSM125127 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125129 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125131 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125133 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125135 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125137 1 0.0424 0.9700 0.992 0.000 0.008
#> GSM125139 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125141 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125143 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125145 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125147 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125149 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125151 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125153 1 0.1964 0.9692 0.944 0.000 0.056
#> GSM125155 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125157 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125159 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125161 1 0.0592 0.9698 0.988 0.000 0.012
#> GSM125163 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125165 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125167 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125173 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125175 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125177 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125179 3 0.2301 0.9741 0.004 0.060 0.936
#> GSM125181 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125183 3 0.2496 0.9785 0.004 0.068 0.928
#> GSM125185 3 0.2496 0.9785 0.004 0.068 0.928
#> GSM125187 3 0.2301 0.9741 0.004 0.060 0.936
#> GSM125189 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125191 2 0.4346 0.7523 0.000 0.816 0.184
#> GSM125193 3 0.2066 0.9740 0.000 0.060 0.940
#> GSM125195 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125197 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125199 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125201 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125203 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125205 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125207 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125209 2 0.6274 0.0704 0.000 0.544 0.456
#> GSM125211 3 0.2261 0.9785 0.000 0.068 0.932
#> GSM125213 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125219 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125221 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125223 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125229 3 0.5178 0.7343 0.000 0.256 0.744
#> GSM125231 3 0.0592 0.9150 0.012 0.000 0.988
#> GSM125233 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125235 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125237 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125124 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125126 1 0.0237 0.9717 0.996 0.000 0.004
#> GSM125128 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125130 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125132 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125134 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125136 1 0.0592 0.9698 0.988 0.000 0.012
#> GSM125138 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125140 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125142 1 0.0592 0.9722 0.988 0.000 0.012
#> GSM125144 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125146 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125148 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125150 1 0.0237 0.9712 0.996 0.000 0.004
#> GSM125152 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125154 1 0.2066 0.9685 0.940 0.000 0.060
#> GSM125156 1 0.0892 0.9719 0.980 0.000 0.020
#> GSM125158 1 0.0892 0.9719 0.980 0.000 0.020
#> GSM125160 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125162 1 0.0592 0.9698 0.988 0.000 0.012
#> GSM125164 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125168 3 0.2625 0.9707 0.000 0.084 0.916
#> GSM125170 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125172 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125174 3 0.2496 0.9785 0.004 0.068 0.928
#> GSM125176 2 0.4452 0.7402 0.000 0.808 0.192
#> GSM125178 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125180 3 0.2301 0.9741 0.004 0.060 0.936
#> GSM125182 3 0.2711 0.9670 0.000 0.088 0.912
#> GSM125184 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125186 3 0.2301 0.9741 0.004 0.060 0.936
#> GSM125188 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125190 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125194 3 0.0237 0.9163 0.004 0.000 0.996
#> GSM125196 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125198 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.9716 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125204 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125206 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125208 3 0.2496 0.9785 0.004 0.068 0.928
#> GSM125210 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125212 3 0.2537 0.9715 0.000 0.080 0.920
#> GSM125214 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125220 1 0.0424 0.9711 0.992 0.000 0.008
#> GSM125222 3 0.2356 0.9797 0.000 0.072 0.928
#> GSM125224 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.9715 0.000 1.000 0.000
#> GSM125230 3 0.1031 0.9407 0.000 0.024 0.976
#> GSM125232 3 0.0592 0.9150 0.012 0.000 0.988
#> GSM125234 1 0.4842 0.7913 0.776 0.000 0.224
#> GSM125236 1 0.2165 0.9685 0.936 0.000 0.064
#> GSM125238 1 0.0237 0.9712 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0000 0.76575 1.000 0.000 0.000 0.000
#> GSM125125 1 0.4790 0.82368 0.620 0.000 0.000 0.380
#> GSM125127 1 0.0921 0.74643 0.972 0.000 0.028 0.000
#> GSM125129 1 0.0000 0.76575 1.000 0.000 0.000 0.000
#> GSM125131 1 0.4955 0.81576 0.556 0.000 0.000 0.444
#> GSM125133 1 0.4643 0.80250 0.656 0.000 0.000 0.344
#> GSM125135 1 0.1474 0.78564 0.948 0.000 0.000 0.052
#> GSM125137 1 0.4972 0.81290 0.544 0.000 0.000 0.456
#> GSM125139 1 0.2408 0.79159 0.896 0.000 0.000 0.104
#> GSM125141 1 0.4961 0.81550 0.552 0.000 0.000 0.448
#> GSM125143 1 0.0469 0.75828 0.988 0.000 0.012 0.000
#> GSM125145 1 0.0817 0.77443 0.976 0.000 0.000 0.024
#> GSM125147 1 0.4961 0.81550 0.552 0.000 0.000 0.448
#> GSM125149 1 0.4961 0.81550 0.552 0.000 0.000 0.448
#> GSM125151 1 0.2345 0.79194 0.900 0.000 0.000 0.100
#> GSM125153 1 0.3688 0.81485 0.792 0.000 0.000 0.208
#> GSM125155 1 0.4948 0.81761 0.560 0.000 0.000 0.440
#> GSM125157 1 0.4955 0.81576 0.556 0.000 0.000 0.444
#> GSM125159 2 0.3975 0.75702 0.000 0.760 0.000 0.240
#> GSM125161 1 0.4925 0.81195 0.572 0.000 0.000 0.428
#> GSM125163 2 0.2345 0.84267 0.000 0.900 0.000 0.100
#> GSM125165 3 0.4907 0.14216 0.000 0.000 0.580 0.420
#> GSM125167 2 0.4072 0.74882 0.000 0.748 0.000 0.252
#> GSM125169 2 0.4511 0.72144 0.000 0.724 0.008 0.268
#> GSM125171 2 0.1022 0.85693 0.000 0.968 0.000 0.032
#> GSM125173 3 0.4624 0.43959 0.000 0.000 0.660 0.340
#> GSM125175 2 0.0921 0.85719 0.000 0.972 0.000 0.028
#> GSM125177 3 0.0336 0.66682 0.000 0.000 0.992 0.008
#> GSM125179 3 0.3982 0.63544 0.004 0.000 0.776 0.220
#> GSM125181 3 0.4898 0.11178 0.000 0.000 0.584 0.416
#> GSM125183 3 0.3649 0.64091 0.000 0.000 0.796 0.204
#> GSM125185 3 0.3908 0.63604 0.004 0.000 0.784 0.212
#> GSM125187 3 0.3688 0.63868 0.000 0.000 0.792 0.208
#> GSM125189 2 0.4072 0.74882 0.000 0.748 0.000 0.252
#> GSM125191 2 0.7026 -0.16351 0.000 0.476 0.120 0.404
#> GSM125193 3 0.2011 0.63183 0.000 0.000 0.920 0.080
#> GSM125195 3 0.1118 0.66754 0.000 0.000 0.964 0.036
#> GSM125197 2 0.0707 0.85081 0.000 0.980 0.000 0.020
#> GSM125199 1 0.4955 0.81576 0.556 0.000 0.000 0.444
#> GSM125201 2 0.0817 0.84961 0.000 0.976 0.000 0.024
#> GSM125203 3 0.0817 0.66707 0.000 0.000 0.976 0.024
#> GSM125205 2 0.0817 0.84961 0.000 0.976 0.000 0.024
#> GSM125207 3 0.1118 0.66792 0.000 0.000 0.964 0.036
#> GSM125209 4 0.7663 0.00000 0.000 0.212 0.380 0.408
#> GSM125211 3 0.3870 0.47821 0.000 0.004 0.788 0.208
#> GSM125213 2 0.2081 0.84654 0.000 0.916 0.000 0.084
#> GSM125215 2 0.0000 0.85688 0.000 1.000 0.000 0.000
#> GSM125217 2 0.4040 0.75213 0.000 0.752 0.000 0.248
#> GSM125219 1 0.0000 0.76575 1.000 0.000 0.000 0.000
#> GSM125221 3 0.4331 0.54493 0.000 0.000 0.712 0.288
#> GSM125223 2 0.0469 0.85366 0.000 0.988 0.000 0.012
#> GSM125225 2 0.0188 0.85717 0.000 0.996 0.000 0.004
#> GSM125227 2 0.0000 0.85688 0.000 1.000 0.000 0.000
#> GSM125229 3 0.5494 0.25625 0.000 0.076 0.716 0.208
#> GSM125231 3 0.4417 0.46241 0.160 0.000 0.796 0.044
#> GSM125233 1 0.0000 0.76575 1.000 0.000 0.000 0.000
#> GSM125235 1 0.4643 0.80250 0.656 0.000 0.000 0.344
#> GSM125237 1 0.4955 0.81576 0.556 0.000 0.000 0.444
#> GSM125124 1 0.2408 0.79159 0.896 0.000 0.000 0.104
#> GSM125126 1 0.4898 0.82046 0.584 0.000 0.000 0.416
#> GSM125128 1 0.4643 0.80250 0.656 0.000 0.000 0.344
#> GSM125130 1 0.0895 0.75007 0.976 0.000 0.020 0.004
#> GSM125132 1 0.4955 0.81576 0.556 0.000 0.000 0.444
#> GSM125134 1 0.2973 0.80182 0.856 0.000 0.000 0.144
#> GSM125136 1 0.4697 0.80055 0.644 0.000 0.000 0.356
#> GSM125138 1 0.2408 0.79159 0.896 0.000 0.000 0.104
#> GSM125140 1 0.2408 0.79159 0.896 0.000 0.000 0.104
#> GSM125142 1 0.4866 0.82404 0.596 0.000 0.000 0.404
#> GSM125144 1 0.2408 0.79159 0.896 0.000 0.000 0.104
#> GSM125146 1 0.1792 0.78980 0.932 0.000 0.000 0.068
#> GSM125148 1 0.4961 0.81550 0.552 0.000 0.000 0.448
#> GSM125150 1 0.4961 0.81550 0.552 0.000 0.000 0.448
#> GSM125152 1 0.2345 0.79194 0.900 0.000 0.000 0.100
#> GSM125154 1 0.3356 0.80864 0.824 0.000 0.000 0.176
#> GSM125156 1 0.4477 0.82568 0.688 0.000 0.000 0.312
#> GSM125158 1 0.4406 0.82582 0.700 0.000 0.000 0.300
#> GSM125160 2 0.3688 0.78306 0.000 0.792 0.000 0.208
#> GSM125162 1 0.4925 0.81195 0.572 0.000 0.000 0.428
#> GSM125164 2 0.2408 0.84157 0.000 0.896 0.000 0.104
#> GSM125166 2 0.2469 0.84148 0.000 0.892 0.000 0.108
#> GSM125168 3 0.5586 -0.07775 0.000 0.020 0.528 0.452
#> GSM125170 3 0.5273 0.00285 0.000 0.008 0.536 0.456
#> GSM125172 2 0.1022 0.85693 0.000 0.968 0.000 0.032
#> GSM125174 3 0.3688 0.64030 0.000 0.000 0.792 0.208
#> GSM125176 2 0.6104 0.51515 0.000 0.680 0.140 0.180
#> GSM125178 3 0.0336 0.66682 0.000 0.000 0.992 0.008
#> GSM125180 3 0.3982 0.63544 0.004 0.000 0.776 0.220
#> GSM125182 3 0.5526 -0.02476 0.000 0.020 0.564 0.416
#> GSM125184 3 0.3688 0.64030 0.000 0.000 0.792 0.208
#> GSM125186 3 0.3908 0.63604 0.004 0.000 0.784 0.212
#> GSM125188 3 0.4877 0.15433 0.000 0.000 0.592 0.408
#> GSM125190 2 0.4391 0.73937 0.000 0.740 0.008 0.252
#> GSM125192 2 0.2216 0.84510 0.000 0.908 0.000 0.092
#> GSM125194 3 0.0707 0.66633 0.000 0.000 0.980 0.020
#> GSM125196 3 0.1118 0.66754 0.000 0.000 0.964 0.036
#> GSM125198 2 0.0707 0.85081 0.000 0.980 0.000 0.020
#> GSM125200 1 0.4643 0.82733 0.656 0.000 0.000 0.344
#> GSM125202 2 0.0817 0.84961 0.000 0.976 0.000 0.024
#> GSM125204 3 0.0817 0.66707 0.000 0.000 0.976 0.024
#> GSM125206 3 0.0188 0.66722 0.000 0.000 0.996 0.004
#> GSM125208 3 0.1118 0.66792 0.000 0.000 0.964 0.036
#> GSM125210 3 0.3726 0.63705 0.000 0.000 0.788 0.212
#> GSM125212 3 0.4011 0.47003 0.000 0.008 0.784 0.208
#> GSM125214 2 0.0000 0.85688 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.85688 0.000 1.000 0.000 0.000
#> GSM125218 2 0.4040 0.75213 0.000 0.752 0.000 0.248
#> GSM125220 1 0.4624 0.80286 0.660 0.000 0.000 0.340
#> GSM125222 3 0.4250 0.56355 0.000 0.000 0.724 0.276
#> GSM125224 2 0.0336 0.85490 0.000 0.992 0.000 0.008
#> GSM125226 2 0.4072 0.74882 0.000 0.748 0.000 0.252
#> GSM125228 2 0.0000 0.85688 0.000 1.000 0.000 0.000
#> GSM125230 3 0.1792 0.63994 0.000 0.000 0.932 0.068
#> GSM125232 3 0.5533 0.35858 0.220 0.000 0.708 0.072
#> GSM125234 1 0.4589 0.48944 0.784 0.000 0.168 0.048
#> GSM125236 1 0.0000 0.76575 1.000 0.000 0.000 0.000
#> GSM125238 1 0.4961 0.81550 0.552 0.000 0.000 0.448
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.4249 0.802 0.296 0.000 0.016 0.000 0.688
#> GSM125125 1 0.3151 0.649 0.836 0.000 0.020 0.000 0.144
#> GSM125127 5 0.4184 0.802 0.284 0.000 0.016 0.000 0.700
#> GSM125129 5 0.4025 0.802 0.292 0.000 0.008 0.000 0.700
#> GSM125131 1 0.0898 0.771 0.972 0.000 0.008 0.000 0.020
#> GSM125133 1 0.4017 0.634 0.788 0.000 0.064 0.000 0.148
#> GSM125135 5 0.4610 0.692 0.388 0.000 0.016 0.000 0.596
#> GSM125137 1 0.1281 0.760 0.956 0.000 0.032 0.000 0.012
#> GSM125139 5 0.5396 0.691 0.444 0.000 0.056 0.000 0.500
#> GSM125141 1 0.0000 0.772 1.000 0.000 0.000 0.000 0.000
#> GSM125143 5 0.3884 0.803 0.288 0.000 0.004 0.000 0.708
#> GSM125145 5 0.5114 0.763 0.340 0.000 0.052 0.000 0.608
#> GSM125147 1 0.0162 0.772 0.996 0.000 0.000 0.000 0.004
#> GSM125149 1 0.0000 0.772 1.000 0.000 0.000 0.000 0.000
#> GSM125151 5 0.5308 0.723 0.416 0.000 0.052 0.000 0.532
#> GSM125153 1 0.5091 0.133 0.676 0.000 0.088 0.000 0.236
#> GSM125155 1 0.2260 0.722 0.908 0.000 0.064 0.000 0.028
#> GSM125157 1 0.0451 0.772 0.988 0.000 0.004 0.000 0.008
#> GSM125159 2 0.5632 0.777 0.000 0.676 0.024 0.200 0.100
#> GSM125161 1 0.3590 0.692 0.828 0.000 0.080 0.000 0.092
#> GSM125163 2 0.4013 0.830 0.000 0.804 0.004 0.108 0.084
#> GSM125165 4 0.3134 0.533 0.000 0.000 0.120 0.848 0.032
#> GSM125167 2 0.6220 0.750 0.000 0.616 0.028 0.224 0.132
#> GSM125169 2 0.6857 0.665 0.000 0.528 0.040 0.288 0.144
#> GSM125171 2 0.3744 0.837 0.000 0.832 0.024 0.036 0.108
#> GSM125173 4 0.3876 0.522 0.000 0.000 0.192 0.776 0.032
#> GSM125175 2 0.3278 0.840 0.000 0.860 0.020 0.028 0.092
#> GSM125177 3 0.3913 0.733 0.000 0.000 0.676 0.324 0.000
#> GSM125179 4 0.4329 0.498 0.000 0.000 0.252 0.716 0.032
#> GSM125181 4 0.3146 0.490 0.000 0.000 0.092 0.856 0.052
#> GSM125183 4 0.4003 0.490 0.000 0.000 0.288 0.704 0.008
#> GSM125185 4 0.4141 0.501 0.000 0.000 0.236 0.736 0.028
#> GSM125187 4 0.4040 0.483 0.000 0.000 0.260 0.724 0.016
#> GSM125189 2 0.6448 0.747 0.000 0.604 0.040 0.220 0.136
#> GSM125191 4 0.5381 0.140 0.000 0.288 0.012 0.640 0.060
#> GSM125193 3 0.4588 0.707 0.000 0.000 0.604 0.380 0.016
#> GSM125195 3 0.4963 0.718 0.000 0.000 0.608 0.352 0.040
#> GSM125197 2 0.1124 0.833 0.000 0.960 0.004 0.000 0.036
#> GSM125199 1 0.0451 0.772 0.988 0.000 0.004 0.000 0.008
#> GSM125201 2 0.1444 0.832 0.000 0.948 0.012 0.000 0.040
#> GSM125203 3 0.4511 0.735 0.000 0.000 0.628 0.356 0.016
#> GSM125205 2 0.1408 0.830 0.000 0.948 0.008 0.000 0.044
#> GSM125207 3 0.4321 0.698 0.000 0.000 0.600 0.396 0.004
#> GSM125209 4 0.4147 0.437 0.000 0.116 0.016 0.804 0.064
#> GSM125211 3 0.4957 0.560 0.000 0.000 0.624 0.332 0.044
#> GSM125213 2 0.2511 0.840 0.000 0.892 0.000 0.080 0.028
#> GSM125215 2 0.0671 0.838 0.000 0.980 0.004 0.000 0.016
#> GSM125217 2 0.6497 0.744 0.000 0.596 0.040 0.228 0.136
#> GSM125219 5 0.4464 0.789 0.288 0.000 0.028 0.000 0.684
#> GSM125221 4 0.3430 0.535 0.000 0.000 0.220 0.776 0.004
#> GSM125223 2 0.1082 0.836 0.000 0.964 0.008 0.000 0.028
#> GSM125225 2 0.0771 0.839 0.000 0.976 0.004 0.000 0.020
#> GSM125227 2 0.0992 0.837 0.000 0.968 0.008 0.000 0.024
#> GSM125229 3 0.5769 0.504 0.000 0.044 0.632 0.276 0.048
#> GSM125231 3 0.5446 0.522 0.000 0.000 0.628 0.272 0.100
#> GSM125233 5 0.4046 0.804 0.296 0.000 0.008 0.000 0.696
#> GSM125235 1 0.3193 0.673 0.840 0.000 0.028 0.000 0.132
#> GSM125237 1 0.0290 0.772 0.992 0.000 0.000 0.000 0.008
#> GSM125124 5 0.5761 0.704 0.420 0.000 0.088 0.000 0.492
#> GSM125126 1 0.2079 0.740 0.916 0.000 0.020 0.000 0.064
#> GSM125128 1 0.4489 0.582 0.740 0.000 0.068 0.000 0.192
#> GSM125130 5 0.3957 0.802 0.280 0.000 0.008 0.000 0.712
#> GSM125132 1 0.0579 0.772 0.984 0.000 0.008 0.000 0.008
#> GSM125134 1 0.5742 -0.541 0.508 0.000 0.088 0.000 0.404
#> GSM125136 1 0.4197 0.631 0.776 0.000 0.076 0.000 0.148
#> GSM125138 5 0.5803 0.701 0.420 0.000 0.092 0.000 0.488
#> GSM125140 5 0.5396 0.691 0.444 0.000 0.056 0.000 0.500
#> GSM125142 1 0.3535 0.618 0.832 0.000 0.088 0.000 0.080
#> GSM125144 5 0.5761 0.704 0.420 0.000 0.088 0.000 0.492
#> GSM125146 5 0.5341 0.650 0.444 0.000 0.052 0.000 0.504
#> GSM125148 1 0.0162 0.772 0.996 0.000 0.000 0.000 0.004
#> GSM125150 1 0.0324 0.769 0.992 0.000 0.004 0.000 0.004
#> GSM125152 5 0.5308 0.723 0.416 0.000 0.052 0.000 0.532
#> GSM125154 1 0.5440 -0.185 0.612 0.000 0.088 0.000 0.300
#> GSM125156 1 0.4678 0.301 0.712 0.000 0.064 0.000 0.224
#> GSM125158 1 0.4639 0.298 0.708 0.000 0.056 0.000 0.236
#> GSM125160 2 0.5419 0.789 0.000 0.696 0.020 0.184 0.100
#> GSM125162 1 0.3590 0.692 0.828 0.000 0.080 0.000 0.092
#> GSM125164 2 0.4063 0.829 0.000 0.800 0.004 0.112 0.084
#> GSM125166 2 0.4458 0.825 0.000 0.784 0.016 0.100 0.100
#> GSM125168 4 0.3911 0.493 0.000 0.008 0.072 0.816 0.104
#> GSM125170 4 0.3854 0.502 0.000 0.004 0.080 0.816 0.100
#> GSM125172 2 0.3613 0.839 0.000 0.840 0.024 0.032 0.104
#> GSM125174 4 0.4275 0.499 0.000 0.000 0.284 0.696 0.020
#> GSM125176 2 0.6631 0.561 0.000 0.528 0.036 0.324 0.112
#> GSM125178 3 0.3895 0.733 0.000 0.000 0.680 0.320 0.000
#> GSM125180 4 0.4329 0.498 0.000 0.000 0.252 0.716 0.032
#> GSM125182 4 0.3765 0.477 0.000 0.020 0.080 0.836 0.064
#> GSM125184 4 0.3884 0.498 0.000 0.000 0.288 0.708 0.004
#> GSM125186 4 0.4141 0.501 0.000 0.000 0.236 0.736 0.028
#> GSM125188 4 0.3112 0.494 0.000 0.000 0.100 0.856 0.044
#> GSM125190 2 0.6776 0.703 0.000 0.560 0.048 0.256 0.136
#> GSM125192 2 0.3743 0.834 0.000 0.824 0.004 0.096 0.076
#> GSM125194 3 0.4418 0.732 0.000 0.000 0.652 0.332 0.016
#> GSM125196 3 0.4977 0.715 0.000 0.000 0.604 0.356 0.040
#> GSM125198 2 0.1124 0.833 0.000 0.960 0.004 0.000 0.036
#> GSM125200 1 0.3944 0.542 0.788 0.000 0.052 0.000 0.160
#> GSM125202 2 0.1444 0.832 0.000 0.948 0.012 0.000 0.040
#> GSM125204 3 0.4511 0.735 0.000 0.000 0.628 0.356 0.016
#> GSM125206 3 0.4734 0.738 0.000 0.000 0.652 0.312 0.036
#> GSM125208 3 0.4321 0.698 0.000 0.000 0.600 0.396 0.004
#> GSM125210 4 0.3720 0.518 0.000 0.000 0.228 0.760 0.012
#> GSM125212 3 0.5107 0.558 0.000 0.004 0.620 0.332 0.044
#> GSM125214 2 0.0324 0.840 0.000 0.992 0.000 0.004 0.004
#> GSM125216 2 0.0671 0.838 0.000 0.980 0.004 0.000 0.016
#> GSM125218 2 0.6497 0.744 0.000 0.596 0.040 0.228 0.136
#> GSM125220 1 0.4199 0.621 0.772 0.000 0.068 0.000 0.160
#> GSM125222 4 0.3521 0.532 0.000 0.000 0.232 0.764 0.004
#> GSM125224 2 0.1082 0.836 0.000 0.964 0.008 0.000 0.028
#> GSM125226 2 0.6379 0.749 0.000 0.608 0.036 0.220 0.136
#> GSM125228 2 0.0992 0.837 0.000 0.968 0.008 0.000 0.024
#> GSM125230 3 0.4193 0.695 0.000 0.000 0.720 0.256 0.024
#> GSM125232 3 0.6436 0.187 0.000 0.000 0.504 0.232 0.264
#> GSM125234 5 0.5242 0.698 0.204 0.000 0.044 0.044 0.708
#> GSM125236 5 0.4297 0.799 0.288 0.000 0.020 0.000 0.692
#> GSM125238 1 0.0000 0.772 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.3354 0.7672 0.780 0.000 0.004 0.004 0.204 0.008
#> GSM125125 5 0.3562 0.5505 0.224 0.000 0.008 0.000 0.756 0.012
#> GSM125127 1 0.4167 0.7616 0.760 0.000 0.012 0.016 0.180 0.032
#> GSM125129 1 0.3244 0.7694 0.784 0.000 0.004 0.004 0.204 0.004
#> GSM125131 5 0.0881 0.7531 0.012 0.000 0.008 0.000 0.972 0.008
#> GSM125133 5 0.4921 0.5858 0.156 0.000 0.076 0.004 0.720 0.044
#> GSM125135 1 0.4402 0.6852 0.664 0.000 0.020 0.000 0.296 0.020
#> GSM125137 5 0.1844 0.7385 0.024 0.000 0.048 0.000 0.924 0.004
#> GSM125139 1 0.5838 0.6014 0.496 0.000 0.028 0.000 0.376 0.100
#> GSM125141 5 0.0291 0.7532 0.004 0.000 0.000 0.000 0.992 0.004
#> GSM125143 1 0.3371 0.7680 0.788 0.000 0.008 0.008 0.192 0.004
#> GSM125145 1 0.5260 0.7293 0.652 0.000 0.040 0.000 0.232 0.076
#> GSM125147 5 0.0291 0.7535 0.004 0.000 0.000 0.000 0.992 0.004
#> GSM125149 5 0.0260 0.7538 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125151 1 0.5486 0.6718 0.560 0.000 0.020 0.000 0.332 0.088
#> GSM125153 5 0.5723 0.1929 0.204 0.000 0.044 0.000 0.620 0.132
#> GSM125155 5 0.3201 0.6764 0.036 0.000 0.028 0.000 0.848 0.088
#> GSM125157 5 0.0665 0.7536 0.008 0.000 0.004 0.000 0.980 0.008
#> GSM125159 2 0.4439 0.5089 0.040 0.760 0.084 0.000 0.000 0.116
#> GSM125161 5 0.4083 0.6698 0.060 0.000 0.100 0.004 0.796 0.040
#> GSM125163 2 0.3987 0.3756 0.024 0.728 0.012 0.000 0.000 0.236
#> GSM125165 4 0.5995 0.2439 0.024 0.404 0.124 0.448 0.000 0.000
#> GSM125167 2 0.1223 0.6020 0.004 0.960 0.012 0.008 0.000 0.016
#> GSM125169 2 0.1552 0.5960 0.004 0.940 0.020 0.036 0.000 0.000
#> GSM125171 2 0.5493 -0.2747 0.040 0.528 0.040 0.004 0.000 0.388
#> GSM125173 4 0.6080 0.2862 0.032 0.248 0.140 0.572 0.000 0.008
#> GSM125175 2 0.5111 -0.2803 0.032 0.552 0.032 0.000 0.000 0.384
#> GSM125177 3 0.4538 0.5918 0.004 0.012 0.508 0.468 0.000 0.008
#> GSM125179 4 0.1003 0.4385 0.020 0.016 0.000 0.964 0.000 0.000
#> GSM125181 4 0.6406 0.1839 0.052 0.400 0.128 0.420 0.000 0.000
#> GSM125183 4 0.2270 0.4197 0.004 0.020 0.072 0.900 0.000 0.004
#> GSM125185 4 0.1173 0.4365 0.016 0.008 0.016 0.960 0.000 0.000
#> GSM125187 4 0.1503 0.4281 0.016 0.008 0.032 0.944 0.000 0.000
#> GSM125189 2 0.0881 0.6011 0.012 0.972 0.008 0.000 0.000 0.008
#> GSM125191 2 0.6145 0.2700 0.056 0.536 0.080 0.320 0.000 0.008
#> GSM125193 3 0.5056 0.5877 0.020 0.024 0.536 0.412 0.000 0.008
#> GSM125195 4 0.5115 -0.5375 0.036 0.004 0.464 0.480 0.000 0.016
#> GSM125197 6 0.3855 0.9305 0.016 0.276 0.004 0.000 0.000 0.704
#> GSM125199 5 0.0779 0.7528 0.008 0.000 0.008 0.000 0.976 0.008
#> GSM125201 6 0.4687 0.9101 0.044 0.276 0.012 0.004 0.000 0.664
#> GSM125203 4 0.4878 -0.5511 0.020 0.008 0.464 0.496 0.000 0.012
#> GSM125205 6 0.4204 0.9204 0.028 0.272 0.004 0.004 0.000 0.692
#> GSM125207 4 0.4350 -0.4901 0.016 0.004 0.428 0.552 0.000 0.000
#> GSM125209 4 0.6327 0.0339 0.064 0.424 0.084 0.424 0.000 0.004
#> GSM125211 3 0.6041 0.5621 0.028 0.096 0.612 0.228 0.000 0.036
#> GSM125213 2 0.5481 -0.2129 0.044 0.520 0.044 0.000 0.000 0.392
#> GSM125215 6 0.4042 0.9204 0.016 0.316 0.004 0.000 0.000 0.664
#> GSM125217 2 0.1579 0.5977 0.008 0.944 0.020 0.004 0.000 0.024
#> GSM125219 1 0.3597 0.7562 0.776 0.000 0.012 0.004 0.196 0.012
#> GSM125221 4 0.3996 0.3847 0.008 0.112 0.104 0.776 0.000 0.000
#> GSM125223 6 0.3555 0.9340 0.008 0.280 0.000 0.000 0.000 0.712
#> GSM125225 6 0.3955 0.9038 0.008 0.340 0.004 0.000 0.000 0.648
#> GSM125227 6 0.3565 0.9354 0.004 0.304 0.000 0.000 0.000 0.692
#> GSM125229 3 0.5971 0.5129 0.024 0.108 0.640 0.180 0.000 0.048
#> GSM125231 3 0.6040 0.4959 0.048 0.008 0.456 0.424 0.000 0.064
#> GSM125233 1 0.3104 0.7679 0.788 0.000 0.000 0.004 0.204 0.004
#> GSM125235 5 0.3529 0.6277 0.176 0.000 0.028 0.000 0.788 0.008
#> GSM125237 5 0.0146 0.7537 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM125124 1 0.6488 0.6082 0.464 0.000 0.052 0.000 0.332 0.152
#> GSM125126 5 0.2222 0.7142 0.084 0.000 0.008 0.000 0.896 0.012
#> GSM125128 5 0.5458 0.5008 0.224 0.000 0.080 0.004 0.648 0.044
#> GSM125130 1 0.3483 0.7589 0.792 0.000 0.004 0.024 0.176 0.004
#> GSM125132 5 0.0665 0.7529 0.004 0.000 0.008 0.000 0.980 0.008
#> GSM125134 5 0.6509 -0.4356 0.364 0.000 0.052 0.000 0.436 0.148
#> GSM125136 5 0.5188 0.5782 0.140 0.000 0.112 0.004 0.700 0.044
#> GSM125138 1 0.6495 0.6026 0.460 0.000 0.052 0.000 0.336 0.152
#> GSM125140 1 0.5854 0.5804 0.484 0.000 0.028 0.000 0.388 0.100
#> GSM125142 5 0.4139 0.6020 0.036 0.000 0.052 0.000 0.776 0.136
#> GSM125144 1 0.6477 0.6060 0.460 0.000 0.052 0.000 0.340 0.148
#> GSM125146 1 0.5827 0.5931 0.528 0.000 0.044 0.000 0.348 0.080
#> GSM125148 5 0.0436 0.7533 0.004 0.000 0.004 0.000 0.988 0.004
#> GSM125150 5 0.0767 0.7501 0.008 0.000 0.012 0.000 0.976 0.004
#> GSM125152 1 0.5474 0.6748 0.564 0.000 0.020 0.000 0.328 0.088
#> GSM125154 5 0.6280 -0.1235 0.268 0.000 0.052 0.000 0.532 0.148
#> GSM125156 5 0.5053 0.3560 0.208 0.000 0.028 0.000 0.676 0.088
#> GSM125158 5 0.4907 0.3692 0.204 0.000 0.024 0.000 0.688 0.084
#> GSM125160 2 0.4675 0.4875 0.040 0.736 0.084 0.000 0.000 0.140
#> GSM125162 5 0.4083 0.6698 0.060 0.000 0.100 0.004 0.796 0.040
#> GSM125164 2 0.3962 0.3826 0.024 0.732 0.012 0.000 0.000 0.232
#> GSM125166 2 0.4081 0.3738 0.024 0.732 0.020 0.000 0.000 0.224
#> GSM125168 2 0.4603 0.1689 0.008 0.628 0.040 0.324 0.000 0.000
#> GSM125170 2 0.4690 0.1021 0.008 0.584 0.036 0.372 0.000 0.000
#> GSM125172 2 0.5486 -0.2512 0.040 0.532 0.040 0.004 0.000 0.384
#> GSM125174 4 0.3065 0.4134 0.024 0.020 0.080 0.864 0.000 0.012
#> GSM125176 2 0.5509 0.5053 0.032 0.680 0.032 0.180 0.000 0.076
#> GSM125178 3 0.4542 0.5896 0.004 0.012 0.496 0.480 0.000 0.008
#> GSM125180 4 0.1003 0.4385 0.020 0.016 0.000 0.964 0.000 0.000
#> GSM125182 2 0.6305 -0.1982 0.048 0.424 0.120 0.408 0.000 0.000
#> GSM125184 4 0.2082 0.4242 0.008 0.020 0.052 0.916 0.000 0.004
#> GSM125186 4 0.1173 0.4365 0.016 0.008 0.016 0.960 0.000 0.000
#> GSM125188 4 0.6438 0.2496 0.056 0.336 0.136 0.472 0.000 0.000
#> GSM125190 2 0.1173 0.6010 0.008 0.960 0.016 0.016 0.000 0.000
#> GSM125192 2 0.4329 0.2276 0.024 0.664 0.012 0.000 0.000 0.300
#> GSM125194 3 0.4778 0.5911 0.020 0.008 0.520 0.444 0.000 0.008
#> GSM125196 4 0.5115 -0.5375 0.036 0.004 0.464 0.480 0.000 0.016
#> GSM125198 6 0.3855 0.9305 0.016 0.276 0.004 0.000 0.000 0.704
#> GSM125200 5 0.4392 0.5113 0.176 0.000 0.024 0.000 0.740 0.060
#> GSM125202 6 0.4687 0.9101 0.044 0.276 0.012 0.004 0.000 0.664
#> GSM125204 4 0.4878 -0.5511 0.020 0.008 0.464 0.496 0.000 0.012
#> GSM125206 3 0.4991 0.5486 0.028 0.004 0.496 0.456 0.000 0.016
#> GSM125208 4 0.4350 -0.4901 0.016 0.004 0.428 0.552 0.000 0.000
#> GSM125210 4 0.1275 0.4365 0.016 0.012 0.016 0.956 0.000 0.000
#> GSM125212 3 0.6041 0.5621 0.028 0.096 0.612 0.228 0.000 0.036
#> GSM125214 6 0.4379 0.8749 0.024 0.336 0.008 0.000 0.000 0.632
#> GSM125216 6 0.4042 0.9204 0.016 0.316 0.004 0.000 0.000 0.664
#> GSM125218 2 0.1490 0.5976 0.008 0.948 0.016 0.004 0.000 0.024
#> GSM125220 5 0.5310 0.5287 0.208 0.000 0.076 0.004 0.668 0.044
#> GSM125222 4 0.3862 0.3872 0.008 0.104 0.100 0.788 0.000 0.000
#> GSM125224 6 0.3489 0.9365 0.004 0.288 0.000 0.000 0.000 0.708
#> GSM125226 2 0.0665 0.6017 0.008 0.980 0.000 0.004 0.000 0.008
#> GSM125228 6 0.3565 0.9354 0.004 0.304 0.000 0.000 0.000 0.692
#> GSM125230 3 0.5057 0.6175 0.016 0.016 0.628 0.304 0.000 0.036
#> GSM125232 4 0.6698 -0.0409 0.136 0.004 0.176 0.548 0.000 0.136
#> GSM125234 1 0.3590 0.7007 0.804 0.000 0.000 0.076 0.116 0.004
#> GSM125236 1 0.3183 0.7689 0.792 0.000 0.004 0.004 0.196 0.004
#> GSM125238 5 0.0146 0.7537 0.004 0.000 0.000 0.000 0.996 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 agent(p) individual(p) k
#> SD:kmeans 115 0.934 1.46e-05 2
#> SD:kmeans 115 0.830 2.51e-08 3
#> SD:kmeans 101 0.805 2.80e-07 4
#> SD:kmeans 98 0.999 3.30e-09 5
#> SD:kmeans 74 0.990 3.76e-07 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.981 0.991 0.5026 0.498 0.498
#> 3 3 0.960 0.955 0.980 0.2890 0.822 0.654
#> 4 4 0.927 0.887 0.930 0.0769 0.947 0.852
#> 5 5 0.776 0.742 0.827 0.0895 0.897 0.677
#> 6 6 0.730 0.663 0.802 0.0483 0.981 0.916
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
#> GSM125123 1 0.0000 0.996 1.000 0.000
#> GSM125125 1 0.0000 0.996 1.000 0.000
#> GSM125127 1 0.0000 0.996 1.000 0.000
#> GSM125129 1 0.0000 0.996 1.000 0.000
#> GSM125131 1 0.0000 0.996 1.000 0.000
#> GSM125133 1 0.0000 0.996 1.000 0.000
#> GSM125135 1 0.0000 0.996 1.000 0.000
#> GSM125137 1 0.0000 0.996 1.000 0.000
#> GSM125139 1 0.0000 0.996 1.000 0.000
#> GSM125141 1 0.0000 0.996 1.000 0.000
#> GSM125143 1 0.0000 0.996 1.000 0.000
#> GSM125145 1 0.0000 0.996 1.000 0.000
#> GSM125147 1 0.0000 0.996 1.000 0.000
#> GSM125149 1 0.0000 0.996 1.000 0.000
#> GSM125151 1 0.0000 0.996 1.000 0.000
#> GSM125153 1 0.0000 0.996 1.000 0.000
#> GSM125155 1 0.0000 0.996 1.000 0.000
#> GSM125157 1 0.0000 0.996 1.000 0.000
#> GSM125159 2 0.0000 0.986 0.000 1.000
#> GSM125161 1 0.0000 0.996 1.000 0.000
#> GSM125163 2 0.0000 0.986 0.000 1.000
#> GSM125165 2 0.0000 0.986 0.000 1.000
#> GSM125167 2 0.0000 0.986 0.000 1.000
#> GSM125169 2 0.0000 0.986 0.000 1.000
#> GSM125171 2 0.0000 0.986 0.000 1.000
#> GSM125173 2 0.0000 0.986 0.000 1.000
#> GSM125175 2 0.0000 0.986 0.000 1.000
#> GSM125177 2 0.0000 0.986 0.000 1.000
#> GSM125179 2 0.7056 0.775 0.192 0.808
#> GSM125181 2 0.0000 0.986 0.000 1.000
#> GSM125183 2 0.6887 0.786 0.184 0.816
#> GSM125185 2 0.0000 0.986 0.000 1.000
#> GSM125187 1 0.4690 0.887 0.900 0.100
#> GSM125189 2 0.0000 0.986 0.000 1.000
#> GSM125191 2 0.0000 0.986 0.000 1.000
#> GSM125193 1 0.5178 0.869 0.884 0.116
#> GSM125195 2 0.0000 0.986 0.000 1.000
#> GSM125197 2 0.0000 0.986 0.000 1.000
#> GSM125199 1 0.0000 0.996 1.000 0.000
#> GSM125201 2 0.0000 0.986 0.000 1.000
#> GSM125203 2 0.0000 0.986 0.000 1.000
#> GSM125205 2 0.0000 0.986 0.000 1.000
#> GSM125207 2 0.0000 0.986 0.000 1.000
#> GSM125209 2 0.0000 0.986 0.000 1.000
#> GSM125211 2 0.0000 0.986 0.000 1.000
#> GSM125213 2 0.0000 0.986 0.000 1.000
#> GSM125215 2 0.0000 0.986 0.000 1.000
#> GSM125217 2 0.0000 0.986 0.000 1.000
#> GSM125219 1 0.0000 0.996 1.000 0.000
#> GSM125221 2 0.0000 0.986 0.000 1.000
#> GSM125223 2 0.0000 0.986 0.000 1.000
#> GSM125225 2 0.0000 0.986 0.000 1.000
#> GSM125227 2 0.0000 0.986 0.000 1.000
#> GSM125229 2 0.0000 0.986 0.000 1.000
#> GSM125231 1 0.0000 0.996 1.000 0.000
#> GSM125233 1 0.0000 0.996 1.000 0.000
#> GSM125235 1 0.0000 0.996 1.000 0.000
#> GSM125237 1 0.0000 0.996 1.000 0.000
#> GSM125124 1 0.0000 0.996 1.000 0.000
#> GSM125126 1 0.0000 0.996 1.000 0.000
#> GSM125128 1 0.0000 0.996 1.000 0.000
#> GSM125130 1 0.0000 0.996 1.000 0.000
#> GSM125132 1 0.0000 0.996 1.000 0.000
#> GSM125134 1 0.0000 0.996 1.000 0.000
#> GSM125136 1 0.0000 0.996 1.000 0.000
#> GSM125138 1 0.0000 0.996 1.000 0.000
#> GSM125140 1 0.0000 0.996 1.000 0.000
#> GSM125142 1 0.0000 0.996 1.000 0.000
#> GSM125144 1 0.0000 0.996 1.000 0.000
#> GSM125146 1 0.0000 0.996 1.000 0.000
#> GSM125148 1 0.0000 0.996 1.000 0.000
#> GSM125150 1 0.0000 0.996 1.000 0.000
#> GSM125152 1 0.0000 0.996 1.000 0.000
#> GSM125154 1 0.0000 0.996 1.000 0.000
#> GSM125156 1 0.0000 0.996 1.000 0.000
#> GSM125158 1 0.0000 0.996 1.000 0.000
#> GSM125160 2 0.0000 0.986 0.000 1.000
#> GSM125162 1 0.0000 0.996 1.000 0.000
#> GSM125164 2 0.0000 0.986 0.000 1.000
#> GSM125166 2 0.0000 0.986 0.000 1.000
#> GSM125168 2 0.0000 0.986 0.000 1.000
#> GSM125170 2 0.0000 0.986 0.000 1.000
#> GSM125172 2 0.0000 0.986 0.000 1.000
#> GSM125174 2 0.4022 0.910 0.080 0.920
#> GSM125176 2 0.0000 0.986 0.000 1.000
#> GSM125178 2 0.0000 0.986 0.000 1.000
#> GSM125180 2 0.6973 0.781 0.188 0.812
#> GSM125182 2 0.0000 0.986 0.000 1.000
#> GSM125184 2 0.0000 0.986 0.000 1.000
#> GSM125186 2 0.7139 0.769 0.196 0.804
#> GSM125188 2 0.0000 0.986 0.000 1.000
#> GSM125190 2 0.0000 0.986 0.000 1.000
#> GSM125192 2 0.0000 0.986 0.000 1.000
#> GSM125194 1 0.0000 0.996 1.000 0.000
#> GSM125196 2 0.0000 0.986 0.000 1.000
#> GSM125198 2 0.0000 0.986 0.000 1.000
#> GSM125200 1 0.0000 0.996 1.000 0.000
#> GSM125202 2 0.0000 0.986 0.000 1.000
#> GSM125204 2 0.0000 0.986 0.000 1.000
#> GSM125206 2 0.0000 0.986 0.000 1.000
#> GSM125208 2 0.0376 0.983 0.004 0.996
#> GSM125210 2 0.0000 0.986 0.000 1.000
#> GSM125212 2 0.0000 0.986 0.000 1.000
#> GSM125214 2 0.0000 0.986 0.000 1.000
#> GSM125216 2 0.0000 0.986 0.000 1.000
#> GSM125218 2 0.0000 0.986 0.000 1.000
#> GSM125220 1 0.0000 0.996 1.000 0.000
#> GSM125222 2 0.0000 0.986 0.000 1.000
#> GSM125224 2 0.0000 0.986 0.000 1.000
#> GSM125226 2 0.0000 0.986 0.000 1.000
#> GSM125228 2 0.0000 0.986 0.000 1.000
#> GSM125230 1 0.0000 0.996 1.000 0.000
#> GSM125232 1 0.0000 0.996 1.000 0.000
#> GSM125234 1 0.0000 0.996 1.000 0.000
#> GSM125236 1 0.0000 0.996 1.000 0.000
#> GSM125238 1 0.0000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125127 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125129 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125131 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125135 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125137 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125139 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125143 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125145 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125147 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125153 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125165 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125167 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125173 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125175 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125177 3 0.3816 0.828 0.000 0.148 0.852
#> GSM125179 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125181 2 0.0237 0.971 0.000 0.996 0.004
#> GSM125183 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125185 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125187 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125189 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125191 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125193 3 0.5763 0.690 0.244 0.016 0.740
#> GSM125195 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125197 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125203 3 0.3267 0.863 0.000 0.116 0.884
#> GSM125205 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125207 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125209 2 0.0237 0.971 0.000 0.996 0.004
#> GSM125211 2 0.5882 0.454 0.000 0.652 0.348
#> GSM125213 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125219 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125221 2 0.6045 0.362 0.000 0.620 0.380
#> GSM125223 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125229 2 0.0424 0.967 0.000 0.992 0.008
#> GSM125231 3 0.0424 0.947 0.008 0.000 0.992
#> GSM125233 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125124 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125126 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125130 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125132 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125138 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125144 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125146 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125168 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125170 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125172 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125174 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125176 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125178 3 0.0424 0.948 0.000 0.008 0.992
#> GSM125180 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125182 2 0.0237 0.971 0.000 0.996 0.004
#> GSM125184 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125186 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125188 2 0.1031 0.953 0.000 0.976 0.024
#> GSM125190 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125194 3 0.4796 0.732 0.220 0.000 0.780
#> GSM125196 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125198 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125204 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125206 3 0.3116 0.871 0.000 0.108 0.892
#> GSM125208 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125210 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125212 2 0.5465 0.583 0.000 0.712 0.288
#> GSM125214 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125220 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125222 3 0.4291 0.782 0.000 0.180 0.820
#> GSM125224 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.974 0.000 1.000 0.000
#> GSM125230 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125232 3 0.0000 0.953 0.000 0.000 1.000
#> GSM125234 1 0.4504 0.756 0.804 0.000 0.196
#> GSM125236 1 0.0000 0.996 1.000 0.000 0.000
#> GSM125238 1 0.0000 0.996 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125125 1 0.0336 0.9731 0.992 0.000 0.008 0.000
#> GSM125127 1 0.1256 0.9671 0.964 0.000 0.028 0.008
#> GSM125129 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125131 1 0.0817 0.9715 0.976 0.000 0.024 0.000
#> GSM125133 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125135 1 0.1004 0.9703 0.972 0.000 0.024 0.004
#> GSM125137 1 0.1022 0.9693 0.968 0.000 0.032 0.000
#> GSM125139 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125141 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125143 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125145 1 0.1256 0.9671 0.964 0.000 0.028 0.008
#> GSM125147 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125149 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125151 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125153 1 0.0707 0.9730 0.980 0.000 0.020 0.000
#> GSM125155 1 0.0707 0.9721 0.980 0.000 0.020 0.000
#> GSM125157 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125159 2 0.0188 0.9299 0.000 0.996 0.000 0.004
#> GSM125161 1 0.1022 0.9693 0.968 0.000 0.032 0.000
#> GSM125163 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125165 2 0.7440 -0.0527 0.000 0.440 0.172 0.388
#> GSM125167 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125169 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125171 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125173 2 0.5012 0.7200 0.000 0.772 0.112 0.116
#> GSM125175 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125177 3 0.3611 0.8346 0.000 0.060 0.860 0.080
#> GSM125179 4 0.0188 0.8780 0.000 0.000 0.004 0.996
#> GSM125181 2 0.7026 0.1187 0.000 0.476 0.120 0.404
#> GSM125183 4 0.2921 0.8487 0.000 0.000 0.140 0.860
#> GSM125185 4 0.1022 0.8726 0.000 0.000 0.032 0.968
#> GSM125187 4 0.1389 0.8690 0.000 0.000 0.048 0.952
#> GSM125189 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125191 2 0.1209 0.9086 0.000 0.964 0.004 0.032
#> GSM125193 3 0.2224 0.8036 0.032 0.000 0.928 0.040
#> GSM125195 3 0.3837 0.8194 0.000 0.000 0.776 0.224
#> GSM125197 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125201 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125203 3 0.4035 0.8343 0.000 0.020 0.804 0.176
#> GSM125205 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125207 3 0.4040 0.8076 0.000 0.000 0.752 0.248
#> GSM125209 2 0.4661 0.6577 0.000 0.728 0.016 0.256
#> GSM125211 3 0.3198 0.7904 0.000 0.080 0.880 0.040
#> GSM125213 2 0.0188 0.9299 0.000 0.996 0.000 0.004
#> GSM125215 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125217 2 0.0188 0.9299 0.000 0.996 0.000 0.004
#> GSM125219 1 0.0895 0.9709 0.976 0.000 0.020 0.004
#> GSM125221 4 0.4801 0.7836 0.000 0.048 0.188 0.764
#> GSM125223 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125229 3 0.4220 0.6171 0.000 0.248 0.748 0.004
#> GSM125231 3 0.3672 0.7836 0.012 0.000 0.824 0.164
#> GSM125233 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125235 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125237 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125124 1 0.1256 0.9671 0.964 0.000 0.028 0.008
#> GSM125126 1 0.0188 0.9730 0.996 0.000 0.004 0.000
#> GSM125128 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125130 1 0.1109 0.9688 0.968 0.000 0.028 0.004
#> GSM125132 1 0.0817 0.9715 0.976 0.000 0.024 0.000
#> GSM125134 1 0.1151 0.9688 0.968 0.000 0.024 0.008
#> GSM125136 1 0.1022 0.9693 0.968 0.000 0.032 0.000
#> GSM125138 1 0.1256 0.9671 0.964 0.000 0.028 0.008
#> GSM125140 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125142 1 0.1118 0.9712 0.964 0.000 0.036 0.000
#> GSM125144 1 0.1256 0.9671 0.964 0.000 0.028 0.008
#> GSM125146 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125148 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125150 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125152 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125154 1 0.0895 0.9722 0.976 0.000 0.020 0.004
#> GSM125156 1 0.0336 0.9730 0.992 0.000 0.008 0.000
#> GSM125158 1 0.0469 0.9727 0.988 0.000 0.012 0.000
#> GSM125160 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125162 1 0.1022 0.9693 0.968 0.000 0.032 0.000
#> GSM125164 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125168 2 0.2334 0.8623 0.000 0.908 0.004 0.088
#> GSM125170 2 0.2973 0.8108 0.000 0.856 0.000 0.144
#> GSM125172 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125174 4 0.2589 0.8620 0.000 0.000 0.116 0.884
#> GSM125176 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125178 3 0.2799 0.8308 0.000 0.008 0.884 0.108
#> GSM125180 4 0.0188 0.8780 0.000 0.000 0.004 0.996
#> GSM125182 2 0.5147 0.6812 0.000 0.740 0.060 0.200
#> GSM125184 4 0.2469 0.8632 0.000 0.000 0.108 0.892
#> GSM125186 4 0.1022 0.8726 0.000 0.000 0.032 0.968
#> GSM125188 2 0.7107 0.0810 0.000 0.464 0.128 0.408
#> GSM125190 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125194 3 0.1629 0.8073 0.024 0.000 0.952 0.024
#> GSM125196 3 0.3873 0.8168 0.000 0.000 0.772 0.228
#> GSM125198 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0469 0.9727 0.988 0.000 0.012 0.000
#> GSM125202 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125204 3 0.3569 0.8293 0.000 0.000 0.804 0.196
#> GSM125206 3 0.4462 0.8298 0.000 0.064 0.804 0.132
#> GSM125208 3 0.4008 0.8103 0.000 0.000 0.756 0.244
#> GSM125210 4 0.1022 0.8726 0.000 0.000 0.032 0.968
#> GSM125212 3 0.3117 0.7807 0.000 0.092 0.880 0.028
#> GSM125214 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125220 1 0.0921 0.9707 0.972 0.000 0.028 0.000
#> GSM125222 4 0.4323 0.8073 0.000 0.028 0.184 0.788
#> GSM125224 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125226 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125228 2 0.0000 0.9321 0.000 1.000 0.000 0.000
#> GSM125230 3 0.2466 0.8220 0.004 0.000 0.900 0.096
#> GSM125232 4 0.4511 0.6705 0.008 0.000 0.268 0.724
#> GSM125234 1 0.4365 0.7636 0.784 0.000 0.028 0.188
#> GSM125236 1 0.1004 0.9701 0.972 0.000 0.024 0.004
#> GSM125238 1 0.0921 0.9707 0.972 0.000 0.028 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.4114 0.87050 0.376 0.000 0.000 0.000 0.624
#> GSM125125 1 0.3561 0.40270 0.740 0.000 0.000 0.000 0.260
#> GSM125127 5 0.3990 0.85625 0.308 0.000 0.000 0.004 0.688
#> GSM125129 5 0.4060 0.88552 0.360 0.000 0.000 0.000 0.640
#> GSM125131 1 0.0404 0.78415 0.988 0.000 0.000 0.000 0.012
#> GSM125133 1 0.0963 0.76675 0.964 0.000 0.000 0.000 0.036
#> GSM125135 5 0.4283 0.72726 0.456 0.000 0.000 0.000 0.544
#> GSM125137 1 0.0510 0.77611 0.984 0.000 0.000 0.000 0.016
#> GSM125139 5 0.4249 0.83526 0.432 0.000 0.000 0.000 0.568
#> GSM125141 1 0.0290 0.78455 0.992 0.000 0.000 0.000 0.008
#> GSM125143 5 0.4238 0.88330 0.368 0.000 0.000 0.004 0.628
#> GSM125145 5 0.4256 0.75704 0.436 0.000 0.000 0.000 0.564
#> GSM125147 1 0.0290 0.78474 0.992 0.000 0.000 0.000 0.008
#> GSM125149 1 0.0162 0.78231 0.996 0.000 0.000 0.000 0.004
#> GSM125151 5 0.4171 0.87625 0.396 0.000 0.000 0.000 0.604
#> GSM125153 1 0.3774 0.32920 0.704 0.000 0.000 0.000 0.296
#> GSM125155 1 0.2329 0.68915 0.876 0.000 0.000 0.000 0.124
#> GSM125157 1 0.0000 0.78373 1.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.1124 0.92129 0.000 0.960 0.004 0.000 0.036
#> GSM125161 1 0.0609 0.77336 0.980 0.000 0.000 0.000 0.020
#> GSM125163 2 0.0290 0.93033 0.000 0.992 0.000 0.000 0.008
#> GSM125165 4 0.7389 0.34520 0.000 0.328 0.052 0.440 0.180
#> GSM125167 2 0.1704 0.90709 0.000 0.928 0.004 0.000 0.068
#> GSM125169 2 0.1831 0.90415 0.000 0.920 0.004 0.000 0.076
#> GSM125171 2 0.0404 0.93011 0.000 0.988 0.000 0.000 0.012
#> GSM125173 2 0.5834 0.54327 0.000 0.656 0.020 0.192 0.132
#> GSM125175 2 0.0290 0.93119 0.000 0.992 0.000 0.000 0.008
#> GSM125177 3 0.2737 0.81964 0.000 0.052 0.896 0.032 0.020
#> GSM125179 4 0.1485 0.74978 0.000 0.000 0.020 0.948 0.032
#> GSM125181 4 0.8028 0.35870 0.000 0.312 0.116 0.388 0.184
#> GSM125183 4 0.2139 0.73484 0.000 0.000 0.032 0.916 0.052
#> GSM125185 4 0.2110 0.74080 0.000 0.000 0.072 0.912 0.016
#> GSM125187 4 0.3055 0.73596 0.000 0.000 0.072 0.864 0.064
#> GSM125189 2 0.1502 0.91393 0.000 0.940 0.004 0.000 0.056
#> GSM125191 2 0.2910 0.85963 0.000 0.888 0.036 0.052 0.024
#> GSM125193 3 0.5056 0.73953 0.088 0.000 0.732 0.020 0.160
#> GSM125195 3 0.1893 0.82863 0.000 0.000 0.928 0.048 0.024
#> GSM125197 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125199 1 0.0404 0.78415 0.988 0.000 0.000 0.000 0.012
#> GSM125201 2 0.0290 0.93099 0.000 0.992 0.000 0.000 0.008
#> GSM125203 3 0.1492 0.83165 0.000 0.004 0.948 0.040 0.008
#> GSM125205 2 0.0404 0.92994 0.000 0.988 0.000 0.000 0.012
#> GSM125207 3 0.2448 0.81507 0.000 0.000 0.892 0.088 0.020
#> GSM125209 2 0.6721 0.30583 0.000 0.564 0.088 0.276 0.072
#> GSM125211 3 0.4703 0.78725 0.000 0.028 0.768 0.068 0.136
#> GSM125213 2 0.0162 0.93144 0.000 0.996 0.000 0.000 0.004
#> GSM125215 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125217 2 0.1768 0.90492 0.000 0.924 0.004 0.000 0.072
#> GSM125219 5 0.4249 0.76142 0.432 0.000 0.000 0.000 0.568
#> GSM125221 4 0.4106 0.70127 0.000 0.020 0.040 0.800 0.140
#> GSM125223 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125225 2 0.0000 0.93156 0.000 1.000 0.000 0.000 0.000
#> GSM125227 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125229 3 0.4724 0.69306 0.000 0.164 0.732 0.000 0.104
#> GSM125231 3 0.6313 0.41045 0.008 0.000 0.512 0.132 0.348
#> GSM125233 5 0.4045 0.87957 0.356 0.000 0.000 0.000 0.644
#> GSM125235 1 0.0703 0.78306 0.976 0.000 0.000 0.000 0.024
#> GSM125237 1 0.0162 0.78475 0.996 0.000 0.000 0.000 0.004
#> GSM125124 5 0.4211 0.86893 0.360 0.000 0.000 0.004 0.636
#> GSM125126 1 0.2424 0.68111 0.868 0.000 0.000 0.000 0.132
#> GSM125128 1 0.1544 0.73699 0.932 0.000 0.000 0.000 0.068
#> GSM125130 5 0.4127 0.85922 0.312 0.000 0.000 0.008 0.680
#> GSM125132 1 0.1121 0.76736 0.956 0.000 0.000 0.000 0.044
#> GSM125134 1 0.4302 -0.54437 0.520 0.000 0.000 0.000 0.480
#> GSM125136 1 0.0880 0.76838 0.968 0.000 0.000 0.000 0.032
#> GSM125138 5 0.4264 0.86195 0.376 0.000 0.000 0.004 0.620
#> GSM125140 5 0.4242 0.84001 0.428 0.000 0.000 0.000 0.572
#> GSM125142 1 0.3039 0.59058 0.808 0.000 0.000 0.000 0.192
#> GSM125144 5 0.4225 0.86884 0.364 0.000 0.000 0.004 0.632
#> GSM125146 1 0.4283 -0.44792 0.544 0.000 0.000 0.000 0.456
#> GSM125148 1 0.0609 0.78342 0.980 0.000 0.000 0.000 0.020
#> GSM125150 1 0.1671 0.74302 0.924 0.000 0.000 0.000 0.076
#> GSM125152 5 0.4138 0.88313 0.384 0.000 0.000 0.000 0.616
#> GSM125154 1 0.4074 0.00765 0.636 0.000 0.000 0.000 0.364
#> GSM125156 1 0.3966 0.04976 0.664 0.000 0.000 0.000 0.336
#> GSM125158 1 0.4030 -0.04589 0.648 0.000 0.000 0.000 0.352
#> GSM125160 2 0.0703 0.92696 0.000 0.976 0.000 0.000 0.024
#> GSM125162 1 0.0609 0.77336 0.980 0.000 0.000 0.000 0.020
#> GSM125164 2 0.0290 0.93089 0.000 0.992 0.000 0.000 0.008
#> GSM125166 2 0.0290 0.93116 0.000 0.992 0.000 0.000 0.008
#> GSM125168 2 0.3798 0.79566 0.000 0.816 0.004 0.120 0.060
#> GSM125170 2 0.5107 0.58379 0.000 0.676 0.004 0.248 0.072
#> GSM125172 2 0.0290 0.93099 0.000 0.992 0.000 0.000 0.008
#> GSM125174 4 0.1725 0.73661 0.000 0.000 0.020 0.936 0.044
#> GSM125176 2 0.0807 0.92681 0.000 0.976 0.000 0.012 0.012
#> GSM125178 3 0.2535 0.81583 0.000 0.000 0.892 0.076 0.032
#> GSM125180 4 0.1485 0.74978 0.000 0.000 0.020 0.948 0.032
#> GSM125182 2 0.6651 0.50592 0.000 0.624 0.104 0.152 0.120
#> GSM125184 4 0.1493 0.73838 0.000 0.000 0.028 0.948 0.024
#> GSM125186 4 0.2144 0.74196 0.000 0.000 0.068 0.912 0.020
#> GSM125188 4 0.8261 0.35013 0.000 0.300 0.152 0.360 0.188
#> GSM125190 2 0.1768 0.90636 0.000 0.924 0.004 0.000 0.072
#> GSM125192 2 0.0000 0.93156 0.000 1.000 0.000 0.000 0.000
#> GSM125194 3 0.5546 0.74155 0.068 0.000 0.708 0.060 0.164
#> GSM125196 3 0.1800 0.82902 0.000 0.000 0.932 0.048 0.020
#> GSM125198 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125200 1 0.3661 0.33381 0.724 0.000 0.000 0.000 0.276
#> GSM125202 2 0.0290 0.93099 0.000 0.992 0.000 0.000 0.008
#> GSM125204 3 0.1522 0.83069 0.000 0.000 0.944 0.044 0.012
#> GSM125206 3 0.1891 0.83333 0.000 0.032 0.936 0.016 0.016
#> GSM125208 3 0.2390 0.81693 0.000 0.000 0.896 0.084 0.020
#> GSM125210 4 0.2270 0.73973 0.000 0.000 0.076 0.904 0.020
#> GSM125212 3 0.4800 0.78490 0.000 0.036 0.764 0.064 0.136
#> GSM125214 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125216 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125218 2 0.1831 0.90331 0.000 0.920 0.004 0.000 0.076
#> GSM125220 1 0.1341 0.75076 0.944 0.000 0.000 0.000 0.056
#> GSM125222 4 0.3619 0.71147 0.000 0.008 0.040 0.828 0.124
#> GSM125224 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125226 2 0.1638 0.90921 0.000 0.932 0.004 0.000 0.064
#> GSM125228 2 0.0162 0.93175 0.000 0.996 0.000 0.000 0.004
#> GSM125230 3 0.3691 0.80481 0.000 0.000 0.820 0.076 0.104
#> GSM125232 4 0.5538 0.37454 0.000 0.000 0.088 0.588 0.324
#> GSM125234 5 0.4872 0.76969 0.248 0.000 0.004 0.056 0.692
#> GSM125236 5 0.4074 0.87853 0.364 0.000 0.000 0.000 0.636
#> GSM125238 1 0.0162 0.78475 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.3620 0.7618 0.772 0.000 0.000 0.000 0.184 0.044
#> GSM125125 5 0.4099 0.2993 0.372 0.000 0.000 0.000 0.612 0.016
#> GSM125127 1 0.3213 0.7693 0.836 0.000 0.000 0.004 0.084 0.076
#> GSM125129 1 0.3014 0.7838 0.832 0.000 0.000 0.000 0.132 0.036
#> GSM125131 5 0.1151 0.7900 0.032 0.000 0.000 0.000 0.956 0.012
#> GSM125133 5 0.2058 0.7510 0.056 0.000 0.000 0.000 0.908 0.036
#> GSM125135 1 0.4570 0.6709 0.644 0.000 0.000 0.000 0.292 0.064
#> GSM125137 5 0.0806 0.7830 0.008 0.000 0.000 0.000 0.972 0.020
#> GSM125139 1 0.3690 0.7232 0.700 0.000 0.000 0.000 0.288 0.012
#> GSM125141 5 0.0632 0.7894 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM125143 1 0.3837 0.7741 0.752 0.000 0.000 0.000 0.196 0.052
#> GSM125145 1 0.4970 0.6837 0.640 0.000 0.000 0.004 0.252 0.104
#> GSM125147 5 0.0891 0.7890 0.024 0.000 0.000 0.000 0.968 0.008
#> GSM125149 5 0.0363 0.7878 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM125151 1 0.3445 0.7639 0.744 0.000 0.000 0.000 0.244 0.012
#> GSM125153 5 0.5081 0.2519 0.308 0.000 0.000 0.000 0.588 0.104
#> GSM125155 5 0.3110 0.6493 0.196 0.000 0.000 0.000 0.792 0.012
#> GSM125157 5 0.0717 0.7881 0.016 0.000 0.000 0.000 0.976 0.008
#> GSM125159 2 0.2442 0.7929 0.004 0.852 0.000 0.000 0.000 0.144
#> GSM125161 5 0.1196 0.7691 0.008 0.000 0.000 0.000 0.952 0.040
#> GSM125163 2 0.0937 0.8484 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM125165 6 0.5861 0.6308 0.000 0.156 0.020 0.272 0.000 0.552
#> GSM125167 2 0.3175 0.7000 0.000 0.744 0.000 0.000 0.000 0.256
#> GSM125169 2 0.3314 0.6973 0.004 0.740 0.000 0.000 0.000 0.256
#> GSM125171 2 0.0260 0.8534 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125173 2 0.6271 0.0931 0.020 0.536 0.028 0.116 0.000 0.300
#> GSM125175 2 0.0603 0.8521 0.004 0.980 0.000 0.000 0.000 0.016
#> GSM125177 3 0.3541 0.7132 0.020 0.024 0.832 0.020 0.000 0.104
#> GSM125179 4 0.0458 0.7450 0.000 0.000 0.016 0.984 0.000 0.000
#> GSM125181 6 0.6788 0.7694 0.004 0.128 0.088 0.308 0.000 0.472
#> GSM125183 4 0.2793 0.6993 0.004 0.000 0.028 0.856 0.000 0.112
#> GSM125185 4 0.1700 0.7311 0.000 0.000 0.048 0.928 0.000 0.024
#> GSM125187 4 0.3133 0.6863 0.016 0.000 0.064 0.852 0.000 0.068
#> GSM125189 2 0.2482 0.7947 0.004 0.848 0.000 0.000 0.000 0.148
#> GSM125191 2 0.3539 0.7553 0.000 0.828 0.032 0.052 0.000 0.088
#> GSM125193 3 0.5795 0.4730 0.036 0.000 0.500 0.004 0.068 0.392
#> GSM125195 3 0.2318 0.7009 0.020 0.000 0.904 0.048 0.000 0.028
#> GSM125197 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125199 5 0.0632 0.7894 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM125201 2 0.0291 0.8524 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM125203 3 0.1453 0.7134 0.008 0.000 0.944 0.040 0.000 0.008
#> GSM125205 2 0.0146 0.8540 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125207 3 0.3930 0.6780 0.016 0.000 0.780 0.148 0.000 0.056
#> GSM125209 2 0.6849 -0.3080 0.000 0.440 0.064 0.268 0.000 0.228
#> GSM125211 3 0.5472 0.5741 0.048 0.024 0.520 0.008 0.000 0.400
#> GSM125213 2 0.1204 0.8420 0.000 0.944 0.000 0.000 0.000 0.056
#> GSM125215 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125217 2 0.3290 0.7020 0.004 0.744 0.000 0.000 0.000 0.252
#> GSM125219 1 0.3896 0.7083 0.744 0.000 0.000 0.000 0.204 0.052
#> GSM125221 4 0.4792 0.2404 0.004 0.008 0.036 0.592 0.000 0.360
#> GSM125223 2 0.0146 0.8541 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125225 2 0.0632 0.8535 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125227 2 0.0146 0.8541 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125229 3 0.6037 0.5449 0.044 0.108 0.524 0.000 0.000 0.324
#> GSM125231 3 0.7185 0.3388 0.244 0.000 0.404 0.080 0.004 0.268
#> GSM125233 1 0.3202 0.7752 0.816 0.000 0.000 0.000 0.144 0.040
#> GSM125235 5 0.2201 0.7638 0.076 0.000 0.000 0.000 0.896 0.028
#> GSM125237 5 0.0891 0.7890 0.024 0.000 0.000 0.000 0.968 0.008
#> GSM125124 1 0.4556 0.7517 0.704 0.000 0.000 0.004 0.192 0.100
#> GSM125126 5 0.3376 0.6293 0.220 0.000 0.000 0.000 0.764 0.016
#> GSM125128 5 0.3062 0.6881 0.112 0.000 0.000 0.000 0.836 0.052
#> GSM125130 1 0.2609 0.7743 0.868 0.000 0.000 0.000 0.096 0.036
#> GSM125132 5 0.1219 0.7865 0.048 0.000 0.000 0.000 0.948 0.004
#> GSM125134 1 0.5345 0.5214 0.540 0.000 0.000 0.004 0.352 0.104
#> GSM125136 5 0.1564 0.7644 0.024 0.000 0.000 0.000 0.936 0.040
#> GSM125138 1 0.4828 0.7286 0.668 0.000 0.000 0.004 0.220 0.108
#> GSM125140 1 0.3748 0.7095 0.688 0.000 0.000 0.000 0.300 0.012
#> GSM125142 5 0.4396 0.5311 0.208 0.000 0.000 0.000 0.704 0.088
#> GSM125144 1 0.4599 0.7496 0.700 0.000 0.000 0.004 0.192 0.104
#> GSM125146 1 0.5443 0.4235 0.504 0.000 0.000 0.004 0.384 0.108
#> GSM125148 5 0.1498 0.7818 0.032 0.000 0.000 0.000 0.940 0.028
#> GSM125150 5 0.2302 0.7301 0.120 0.000 0.000 0.000 0.872 0.008
#> GSM125152 1 0.3348 0.7776 0.768 0.000 0.000 0.000 0.216 0.016
#> GSM125154 5 0.5319 -0.2026 0.420 0.000 0.000 0.000 0.476 0.104
#> GSM125156 5 0.4333 -0.1999 0.468 0.000 0.000 0.000 0.512 0.020
#> GSM125158 5 0.4172 -0.1342 0.460 0.000 0.000 0.000 0.528 0.012
#> GSM125160 2 0.1700 0.8310 0.004 0.916 0.000 0.000 0.000 0.080
#> GSM125162 5 0.1196 0.7691 0.008 0.000 0.000 0.000 0.952 0.040
#> GSM125164 2 0.0865 0.8492 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM125166 2 0.0935 0.8512 0.004 0.964 0.000 0.000 0.000 0.032
#> GSM125168 2 0.4707 0.5483 0.000 0.656 0.000 0.092 0.000 0.252
#> GSM125170 2 0.5882 0.1344 0.004 0.508 0.000 0.256 0.000 0.232
#> GSM125172 2 0.0146 0.8540 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125174 4 0.2766 0.7015 0.012 0.000 0.028 0.868 0.000 0.092
#> GSM125176 2 0.1390 0.8383 0.004 0.948 0.000 0.032 0.000 0.016
#> GSM125178 3 0.3467 0.7130 0.024 0.000 0.820 0.032 0.000 0.124
#> GSM125180 4 0.0458 0.7450 0.000 0.000 0.016 0.984 0.000 0.000
#> GSM125182 2 0.6954 -0.2173 0.004 0.436 0.096 0.136 0.000 0.328
#> GSM125184 4 0.1933 0.7215 0.004 0.000 0.032 0.920 0.000 0.044
#> GSM125186 4 0.1700 0.7311 0.000 0.000 0.048 0.928 0.000 0.024
#> GSM125188 6 0.7126 0.7414 0.012 0.116 0.116 0.312 0.000 0.444
#> GSM125190 2 0.3081 0.7352 0.004 0.776 0.000 0.000 0.000 0.220
#> GSM125192 2 0.0146 0.8543 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125194 3 0.6341 0.4824 0.056 0.000 0.504 0.016 0.076 0.348
#> GSM125196 3 0.2220 0.7027 0.020 0.000 0.908 0.052 0.000 0.020
#> GSM125198 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125200 5 0.3782 0.1002 0.412 0.000 0.000 0.000 0.588 0.000
#> GSM125202 2 0.0146 0.8535 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125204 3 0.1523 0.7107 0.008 0.000 0.940 0.044 0.000 0.008
#> GSM125206 3 0.2472 0.7047 0.020 0.032 0.904 0.012 0.000 0.032
#> GSM125208 3 0.3950 0.6802 0.016 0.000 0.780 0.144 0.000 0.060
#> GSM125210 4 0.1616 0.7322 0.000 0.000 0.048 0.932 0.000 0.020
#> GSM125212 3 0.5554 0.5646 0.044 0.032 0.512 0.008 0.000 0.404
#> GSM125214 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125216 2 0.0000 0.8538 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125218 2 0.3109 0.7315 0.004 0.772 0.000 0.000 0.000 0.224
#> GSM125220 5 0.2625 0.7286 0.072 0.000 0.000 0.000 0.872 0.056
#> GSM125222 4 0.4495 0.3877 0.004 0.008 0.028 0.648 0.000 0.312
#> GSM125224 2 0.0146 0.8541 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125226 2 0.2838 0.7645 0.004 0.808 0.000 0.000 0.000 0.188
#> GSM125228 2 0.0146 0.8541 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125230 3 0.4768 0.6551 0.048 0.000 0.628 0.012 0.000 0.312
#> GSM125232 4 0.6728 0.2475 0.248 0.000 0.080 0.492 0.000 0.180
#> GSM125234 1 0.2957 0.7488 0.872 0.000 0.004 0.024 0.056 0.044
#> GSM125236 1 0.3455 0.7702 0.800 0.000 0.000 0.000 0.144 0.056
#> GSM125238 5 0.0891 0.7890 0.024 0.000 0.000 0.000 0.968 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 agent(p) individual(p) k
#> SD:skmeans 116 1.000 1.90e-05 2
#> SD:skmeans 114 0.695 1.33e-07 3
#> SD:skmeans 113 0.950 1.96e-10 4
#> SD:skmeans 102 0.971 7.44e-10 5
#> SD:skmeans 99 0.983 3.58e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.842 0.934 0.970 0.5033 0.496 0.496
#> 3 3 0.851 0.878 0.945 0.3016 0.797 0.611
#> 4 4 0.799 0.838 0.911 0.1389 0.885 0.678
#> 5 5 0.815 0.756 0.866 0.0544 0.957 0.832
#> 6 6 0.792 0.701 0.836 0.0424 0.964 0.833
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
#> GSM125123 1 0.0000 0.967 1.000 0.000
#> GSM125125 1 0.0000 0.967 1.000 0.000
#> GSM125127 1 0.0000 0.967 1.000 0.000
#> GSM125129 1 0.0000 0.967 1.000 0.000
#> GSM125131 1 0.0000 0.967 1.000 0.000
#> GSM125133 1 0.0000 0.967 1.000 0.000
#> GSM125135 1 0.0000 0.967 1.000 0.000
#> GSM125137 1 0.0000 0.967 1.000 0.000
#> GSM125139 1 0.0000 0.967 1.000 0.000
#> GSM125141 1 0.0000 0.967 1.000 0.000
#> GSM125143 1 0.0000 0.967 1.000 0.000
#> GSM125145 1 0.0000 0.967 1.000 0.000
#> GSM125147 1 0.0000 0.967 1.000 0.000
#> GSM125149 1 0.0000 0.967 1.000 0.000
#> GSM125151 1 0.0000 0.967 1.000 0.000
#> GSM125153 1 0.0000 0.967 1.000 0.000
#> GSM125155 1 0.0000 0.967 1.000 0.000
#> GSM125157 1 0.0000 0.967 1.000 0.000
#> GSM125159 2 0.0000 0.970 0.000 1.000
#> GSM125161 1 0.0000 0.967 1.000 0.000
#> GSM125163 2 0.0000 0.970 0.000 1.000
#> GSM125165 2 0.1184 0.959 0.016 0.984
#> GSM125167 2 0.0000 0.970 0.000 1.000
#> GSM125169 2 0.0000 0.970 0.000 1.000
#> GSM125171 2 0.0000 0.970 0.000 1.000
#> GSM125173 2 0.0000 0.970 0.000 1.000
#> GSM125175 2 0.0000 0.970 0.000 1.000
#> GSM125177 2 0.0000 0.970 0.000 1.000
#> GSM125179 2 0.6973 0.769 0.188 0.812
#> GSM125181 2 0.2043 0.946 0.032 0.968
#> GSM125183 2 0.9896 0.195 0.440 0.560
#> GSM125185 2 0.0376 0.967 0.004 0.996
#> GSM125187 1 0.6343 0.811 0.840 0.160
#> GSM125189 2 0.0000 0.970 0.000 1.000
#> GSM125191 2 0.0000 0.970 0.000 1.000
#> GSM125193 1 0.5629 0.846 0.868 0.132
#> GSM125195 1 0.8661 0.613 0.712 0.288
#> GSM125197 2 0.0000 0.970 0.000 1.000
#> GSM125199 1 0.0000 0.967 1.000 0.000
#> GSM125201 2 0.0000 0.970 0.000 1.000
#> GSM125203 1 0.8861 0.594 0.696 0.304
#> GSM125205 2 0.0000 0.970 0.000 1.000
#> GSM125207 2 0.5519 0.850 0.128 0.872
#> GSM125209 2 0.0000 0.970 0.000 1.000
#> GSM125211 2 0.4298 0.893 0.088 0.912
#> GSM125213 2 0.0000 0.970 0.000 1.000
#> GSM125215 2 0.0000 0.970 0.000 1.000
#> GSM125217 2 0.0000 0.970 0.000 1.000
#> GSM125219 1 0.0000 0.967 1.000 0.000
#> GSM125221 2 0.3431 0.919 0.064 0.936
#> GSM125223 2 0.0000 0.970 0.000 1.000
#> GSM125225 2 0.0000 0.970 0.000 1.000
#> GSM125227 2 0.0000 0.970 0.000 1.000
#> GSM125229 2 0.0000 0.970 0.000 1.000
#> GSM125231 1 0.5294 0.858 0.880 0.120
#> GSM125233 1 0.0000 0.967 1.000 0.000
#> GSM125235 1 0.0000 0.967 1.000 0.000
#> GSM125237 1 0.0000 0.967 1.000 0.000
#> GSM125124 1 0.0000 0.967 1.000 0.000
#> GSM125126 1 0.0000 0.967 1.000 0.000
#> GSM125128 1 0.0000 0.967 1.000 0.000
#> GSM125130 1 0.0000 0.967 1.000 0.000
#> GSM125132 1 0.0000 0.967 1.000 0.000
#> GSM125134 1 0.0000 0.967 1.000 0.000
#> GSM125136 1 0.0000 0.967 1.000 0.000
#> GSM125138 1 0.0000 0.967 1.000 0.000
#> GSM125140 1 0.0000 0.967 1.000 0.000
#> GSM125142 1 0.0000 0.967 1.000 0.000
#> GSM125144 1 0.0000 0.967 1.000 0.000
#> GSM125146 1 0.0000 0.967 1.000 0.000
#> GSM125148 1 0.0000 0.967 1.000 0.000
#> GSM125150 1 0.0000 0.967 1.000 0.000
#> GSM125152 1 0.0000 0.967 1.000 0.000
#> GSM125154 1 0.0000 0.967 1.000 0.000
#> GSM125156 1 0.0000 0.967 1.000 0.000
#> GSM125158 1 0.0000 0.967 1.000 0.000
#> GSM125160 2 0.0000 0.970 0.000 1.000
#> GSM125162 1 0.0000 0.967 1.000 0.000
#> GSM125164 2 0.0000 0.970 0.000 1.000
#> GSM125166 2 0.0000 0.970 0.000 1.000
#> GSM125168 2 0.0000 0.970 0.000 1.000
#> GSM125170 2 0.0000 0.970 0.000 1.000
#> GSM125172 2 0.0000 0.970 0.000 1.000
#> GSM125174 2 0.3733 0.912 0.072 0.928
#> GSM125176 2 0.0000 0.970 0.000 1.000
#> GSM125178 2 0.6148 0.820 0.152 0.848
#> GSM125180 2 0.6148 0.823 0.152 0.848
#> GSM125182 2 0.0000 0.970 0.000 1.000
#> GSM125184 2 0.0000 0.970 0.000 1.000
#> GSM125186 2 0.8608 0.604 0.284 0.716
#> GSM125188 2 0.0000 0.970 0.000 1.000
#> GSM125190 2 0.0000 0.970 0.000 1.000
#> GSM125192 2 0.0000 0.970 0.000 1.000
#> GSM125194 1 0.0000 0.967 1.000 0.000
#> GSM125196 2 0.0000 0.970 0.000 1.000
#> GSM125198 2 0.0000 0.970 0.000 1.000
#> GSM125200 1 0.0000 0.967 1.000 0.000
#> GSM125202 2 0.0000 0.970 0.000 1.000
#> GSM125204 1 0.8081 0.690 0.752 0.248
#> GSM125206 2 0.0000 0.970 0.000 1.000
#> GSM125208 1 0.8813 0.599 0.700 0.300
#> GSM125210 2 0.0376 0.967 0.004 0.996
#> GSM125212 2 0.0000 0.970 0.000 1.000
#> GSM125214 2 0.0000 0.970 0.000 1.000
#> GSM125216 2 0.0000 0.970 0.000 1.000
#> GSM125218 2 0.0000 0.970 0.000 1.000
#> GSM125220 1 0.0000 0.967 1.000 0.000
#> GSM125222 2 0.1184 0.959 0.016 0.984
#> GSM125224 2 0.0000 0.970 0.000 1.000
#> GSM125226 2 0.0000 0.970 0.000 1.000
#> GSM125228 2 0.0000 0.970 0.000 1.000
#> GSM125230 1 0.5294 0.858 0.880 0.120
#> GSM125232 1 0.5294 0.858 0.880 0.120
#> GSM125234 1 0.0376 0.964 0.996 0.004
#> GSM125236 1 0.0000 0.967 1.000 0.000
#> GSM125238 1 0.0000 0.967 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125125 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125127 1 0.1031 0.972 0.976 0.000 0.024
#> GSM125129 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125131 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125133 1 0.0424 0.977 0.992 0.000 0.008
#> GSM125135 1 0.0747 0.976 0.984 0.000 0.016
#> GSM125137 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125139 1 0.0747 0.976 0.984 0.000 0.016
#> GSM125141 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125143 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125145 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125147 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125151 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125153 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125165 3 0.4121 0.777 0.000 0.168 0.832
#> GSM125167 2 0.0237 0.922 0.000 0.996 0.004
#> GSM125169 2 0.0237 0.922 0.000 0.996 0.004
#> GSM125171 2 0.0424 0.920 0.000 0.992 0.008
#> GSM125173 3 0.0424 0.875 0.000 0.008 0.992
#> GSM125175 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125177 2 0.1753 0.891 0.000 0.952 0.048
#> GSM125179 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125181 3 0.0237 0.877 0.000 0.004 0.996
#> GSM125183 3 0.0237 0.877 0.004 0.000 0.996
#> GSM125185 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125187 3 0.0237 0.877 0.004 0.000 0.996
#> GSM125189 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125191 2 0.3116 0.836 0.000 0.892 0.108
#> GSM125193 1 0.2959 0.890 0.900 0.000 0.100
#> GSM125195 3 0.0424 0.876 0.000 0.008 0.992
#> GSM125197 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125203 3 0.6473 0.493 0.332 0.016 0.652
#> GSM125205 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125207 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125209 2 0.4931 0.688 0.000 0.768 0.232
#> GSM125211 3 0.4164 0.798 0.008 0.144 0.848
#> GSM125213 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125217 2 0.5431 0.559 0.000 0.716 0.284
#> GSM125219 1 0.1031 0.972 0.976 0.000 0.024
#> GSM125221 3 0.4094 0.826 0.028 0.100 0.872
#> GSM125223 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125229 2 0.0592 0.918 0.000 0.988 0.012
#> GSM125231 3 0.0592 0.874 0.012 0.000 0.988
#> GSM125233 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125235 1 0.0237 0.980 0.996 0.000 0.004
#> GSM125237 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125124 1 0.1411 0.963 0.964 0.000 0.036
#> GSM125126 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125128 1 0.0747 0.976 0.984 0.000 0.016
#> GSM125130 1 0.1031 0.972 0.976 0.000 0.024
#> GSM125132 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125138 1 0.2356 0.913 0.928 0.000 0.072
#> GSM125140 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125144 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125146 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125152 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125154 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125166 2 0.0424 0.920 0.000 0.992 0.008
#> GSM125168 3 0.5706 0.459 0.000 0.320 0.680
#> GSM125170 2 0.6225 0.190 0.000 0.568 0.432
#> GSM125172 2 0.0237 0.922 0.000 0.996 0.004
#> GSM125174 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125176 2 0.0424 0.920 0.000 0.992 0.008
#> GSM125178 2 0.5926 0.397 0.000 0.644 0.356
#> GSM125180 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125182 2 0.4796 0.702 0.000 0.780 0.220
#> GSM125184 3 0.1529 0.864 0.000 0.040 0.960
#> GSM125186 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125188 3 0.5058 0.690 0.000 0.244 0.756
#> GSM125190 2 0.0747 0.914 0.000 0.984 0.016
#> GSM125192 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125194 3 0.5905 0.489 0.352 0.000 0.648
#> GSM125196 2 0.6295 0.179 0.000 0.528 0.472
#> GSM125198 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125204 3 0.5072 0.726 0.196 0.012 0.792
#> GSM125206 3 0.5397 0.638 0.000 0.280 0.720
#> GSM125208 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125210 3 0.0000 0.877 0.000 0.000 1.000
#> GSM125212 3 0.5397 0.639 0.000 0.280 0.720
#> GSM125214 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125220 1 0.0237 0.980 0.996 0.000 0.004
#> GSM125222 3 0.3816 0.796 0.000 0.148 0.852
#> GSM125224 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125226 2 0.6026 0.348 0.000 0.624 0.376
#> GSM125228 2 0.0000 0.923 0.000 1.000 0.000
#> GSM125230 3 0.3686 0.792 0.140 0.000 0.860
#> GSM125232 3 0.0592 0.874 0.012 0.000 0.988
#> GSM125234 1 0.6008 0.424 0.628 0.000 0.372
#> GSM125236 1 0.1031 0.972 0.976 0.000 0.024
#> GSM125238 1 0.0000 0.981 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 4 0.3219 0.837 0.164 0.000 0.000 0.836
#> GSM125125 1 0.1022 0.945 0.968 0.000 0.000 0.032
#> GSM125127 4 0.2376 0.911 0.068 0.000 0.016 0.916
#> GSM125129 1 0.2408 0.878 0.896 0.000 0.000 0.104
#> GSM125131 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125133 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125135 1 0.4454 0.521 0.692 0.000 0.000 0.308
#> GSM125137 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125139 4 0.2011 0.904 0.080 0.000 0.000 0.920
#> GSM125141 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125143 4 0.1302 0.914 0.044 0.000 0.000 0.956
#> GSM125145 4 0.1716 0.914 0.064 0.000 0.000 0.936
#> GSM125147 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125151 4 0.1022 0.911 0.032 0.000 0.000 0.968
#> GSM125153 4 0.2011 0.910 0.080 0.000 0.000 0.920
#> GSM125155 1 0.0707 0.956 0.980 0.000 0.000 0.020
#> GSM125157 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0188 0.916 0.000 0.996 0.004 0.000
#> GSM125161 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125163 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125165 3 0.2888 0.808 0.000 0.124 0.872 0.004
#> GSM125167 2 0.2281 0.860 0.000 0.904 0.096 0.000
#> GSM125169 2 0.2412 0.864 0.000 0.908 0.084 0.008
#> GSM125171 2 0.0817 0.907 0.000 0.976 0.024 0.000
#> GSM125173 3 0.1118 0.850 0.000 0.036 0.964 0.000
#> GSM125175 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125177 2 0.1767 0.891 0.000 0.944 0.044 0.012
#> GSM125179 3 0.2589 0.810 0.000 0.000 0.884 0.116
#> GSM125181 3 0.1022 0.847 0.000 0.000 0.968 0.032
#> GSM125183 3 0.0817 0.846 0.024 0.000 0.976 0.000
#> GSM125185 3 0.1637 0.842 0.000 0.000 0.940 0.060
#> GSM125187 3 0.3157 0.802 0.004 0.000 0.852 0.144
#> GSM125189 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125191 2 0.2593 0.848 0.000 0.892 0.104 0.004
#> GSM125193 1 0.3182 0.821 0.860 0.004 0.132 0.004
#> GSM125195 3 0.1256 0.848 0.000 0.008 0.964 0.028
#> GSM125197 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125201 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125203 3 0.5564 0.519 0.312 0.012 0.656 0.020
#> GSM125205 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125207 3 0.1022 0.846 0.000 0.000 0.968 0.032
#> GSM125209 2 0.5085 0.643 0.000 0.708 0.260 0.032
#> GSM125211 3 0.2888 0.812 0.000 0.124 0.872 0.004
#> GSM125213 2 0.0592 0.911 0.000 0.984 0.016 0.000
#> GSM125215 2 0.0592 0.911 0.000 0.984 0.016 0.000
#> GSM125217 2 0.4790 0.353 0.000 0.620 0.380 0.000
#> GSM125219 4 0.1978 0.903 0.068 0.000 0.004 0.928
#> GSM125221 3 0.2287 0.844 0.012 0.060 0.924 0.004
#> GSM125223 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0188 0.916 0.000 0.996 0.004 0.000
#> GSM125227 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125229 2 0.2048 0.883 0.000 0.928 0.064 0.008
#> GSM125231 4 0.4776 0.428 0.000 0.000 0.376 0.624
#> GSM125233 4 0.3311 0.830 0.172 0.000 0.000 0.828
#> GSM125235 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125237 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125124 4 0.1022 0.911 0.032 0.000 0.000 0.968
#> GSM125126 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125128 1 0.0188 0.966 0.996 0.000 0.000 0.004
#> GSM125130 4 0.0817 0.907 0.024 0.000 0.000 0.976
#> GSM125132 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125134 4 0.2011 0.910 0.080 0.000 0.000 0.920
#> GSM125136 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125138 4 0.1824 0.913 0.060 0.000 0.004 0.936
#> GSM125140 4 0.1118 0.912 0.036 0.000 0.000 0.964
#> GSM125142 4 0.2149 0.908 0.088 0.000 0.000 0.912
#> GSM125144 4 0.1022 0.911 0.032 0.000 0.000 0.968
#> GSM125146 4 0.2469 0.900 0.108 0.000 0.000 0.892
#> GSM125148 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125150 1 0.1557 0.919 0.944 0.000 0.000 0.056
#> GSM125152 4 0.1022 0.911 0.032 0.000 0.000 0.968
#> GSM125154 4 0.2011 0.910 0.080 0.000 0.000 0.920
#> GSM125156 4 0.2149 0.901 0.088 0.000 0.000 0.912
#> GSM125158 4 0.4624 0.572 0.340 0.000 0.000 0.660
#> GSM125160 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0000 0.969 1.000 0.000 0.000 0.000
#> GSM125164 2 0.0524 0.914 0.000 0.988 0.004 0.008
#> GSM125166 2 0.0336 0.914 0.000 0.992 0.008 0.000
#> GSM125168 3 0.4781 0.432 0.000 0.336 0.660 0.004
#> GSM125170 2 0.4972 0.106 0.000 0.544 0.456 0.000
#> GSM125172 2 0.1867 0.875 0.000 0.928 0.072 0.000
#> GSM125174 3 0.0779 0.847 0.000 0.004 0.980 0.016
#> GSM125176 2 0.0592 0.911 0.000 0.984 0.016 0.000
#> GSM125178 2 0.5024 0.349 0.000 0.632 0.360 0.008
#> GSM125180 3 0.3444 0.749 0.000 0.000 0.816 0.184
#> GSM125182 2 0.5022 0.643 0.000 0.708 0.264 0.028
#> GSM125184 3 0.1474 0.849 0.000 0.052 0.948 0.000
#> GSM125186 3 0.1940 0.837 0.000 0.000 0.924 0.076
#> GSM125188 3 0.4692 0.739 0.000 0.212 0.756 0.032
#> GSM125190 2 0.0817 0.908 0.000 0.976 0.024 0.000
#> GSM125192 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125194 3 0.4608 0.577 0.304 0.000 0.692 0.004
#> GSM125196 2 0.5611 0.344 0.000 0.564 0.412 0.024
#> GSM125198 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125200 4 0.1867 0.914 0.072 0.000 0.000 0.928
#> GSM125202 2 0.0469 0.913 0.000 0.988 0.012 0.000
#> GSM125204 3 0.5023 0.735 0.164 0.008 0.772 0.056
#> GSM125206 3 0.4594 0.630 0.000 0.280 0.712 0.008
#> GSM125208 3 0.1474 0.845 0.000 0.000 0.948 0.052
#> GSM125210 3 0.1022 0.846 0.000 0.000 0.968 0.032
#> GSM125212 3 0.3688 0.740 0.000 0.208 0.792 0.000
#> GSM125214 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125220 1 0.0336 0.962 0.992 0.000 0.000 0.008
#> GSM125222 3 0.2654 0.821 0.000 0.108 0.888 0.004
#> GSM125224 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125226 3 0.4994 0.101 0.000 0.480 0.520 0.000
#> GSM125228 2 0.0000 0.916 0.000 1.000 0.000 0.000
#> GSM125230 3 0.3160 0.808 0.108 0.000 0.872 0.020
#> GSM125232 4 0.1867 0.873 0.000 0.000 0.072 0.928
#> GSM125234 4 0.2589 0.810 0.000 0.000 0.116 0.884
#> GSM125236 4 0.4978 0.457 0.384 0.000 0.004 0.612
#> GSM125238 1 0.0000 0.969 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.2852 0.787 0.172 0.000 0.000 0.000 0.828
#> GSM125125 1 0.0510 0.954 0.984 0.000 0.000 0.000 0.016
#> GSM125127 5 0.1251 0.896 0.036 0.000 0.000 0.008 0.956
#> GSM125129 1 0.1965 0.879 0.904 0.000 0.000 0.000 0.096
#> GSM125131 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125135 1 0.3752 0.564 0.708 0.000 0.000 0.000 0.292
#> GSM125137 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125139 5 0.1478 0.878 0.064 0.000 0.000 0.000 0.936
#> GSM125141 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125143 5 0.0609 0.897 0.020 0.000 0.000 0.000 0.980
#> GSM125145 5 0.0880 0.897 0.032 0.000 0.000 0.000 0.968
#> GSM125147 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125151 5 0.0162 0.893 0.004 0.000 0.000 0.000 0.996
#> GSM125153 5 0.1043 0.896 0.040 0.000 0.000 0.000 0.960
#> GSM125155 1 0.0404 0.958 0.988 0.000 0.000 0.000 0.012
#> GSM125157 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.1740 0.852 0.000 0.932 0.056 0.012 0.000
#> GSM125161 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125163 2 0.1597 0.856 0.000 0.940 0.048 0.012 0.000
#> GSM125165 4 0.1644 0.652 0.000 0.048 0.008 0.940 0.004
#> GSM125167 2 0.5675 0.581 0.000 0.608 0.100 0.288 0.004
#> GSM125169 2 0.4823 0.597 0.000 0.672 0.052 0.276 0.000
#> GSM125171 2 0.1952 0.826 0.000 0.912 0.004 0.084 0.000
#> GSM125173 4 0.0771 0.663 0.000 0.020 0.004 0.976 0.000
#> GSM125175 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000
#> GSM125177 3 0.5008 0.384 0.000 0.428 0.544 0.024 0.004
#> GSM125179 4 0.4713 0.568 0.000 0.000 0.280 0.676 0.044
#> GSM125181 4 0.3999 0.552 0.000 0.000 0.344 0.656 0.000
#> GSM125183 4 0.1012 0.657 0.020 0.000 0.012 0.968 0.000
#> GSM125185 4 0.4613 0.521 0.000 0.000 0.360 0.620 0.020
#> GSM125187 4 0.4866 0.535 0.000 0.000 0.344 0.620 0.036
#> GSM125189 2 0.0162 0.862 0.000 0.996 0.004 0.000 0.000
#> GSM125191 2 0.3116 0.815 0.000 0.860 0.076 0.064 0.000
#> GSM125193 1 0.4018 0.778 0.812 0.004 0.092 0.088 0.004
#> GSM125195 3 0.3707 0.581 0.000 0.000 0.716 0.284 0.000
#> GSM125197 2 0.1768 0.851 0.000 0.924 0.072 0.000 0.004
#> GSM125199 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125201 2 0.2289 0.852 0.000 0.904 0.080 0.012 0.004
#> GSM125203 3 0.4592 0.609 0.140 0.000 0.756 0.100 0.004
#> GSM125205 2 0.1768 0.851 0.000 0.924 0.072 0.000 0.004
#> GSM125207 3 0.2020 0.620 0.000 0.000 0.900 0.100 0.000
#> GSM125209 2 0.5952 0.376 0.000 0.548 0.324 0.128 0.000
#> GSM125211 4 0.1934 0.648 0.000 0.052 0.016 0.928 0.004
#> GSM125213 2 0.2208 0.849 0.000 0.908 0.072 0.020 0.000
#> GSM125215 2 0.2349 0.847 0.000 0.900 0.084 0.012 0.004
#> GSM125217 2 0.5470 0.455 0.000 0.564 0.072 0.364 0.000
#> GSM125219 5 0.4290 0.700 0.044 0.000 0.196 0.004 0.756
#> GSM125221 4 0.1412 0.660 0.000 0.036 0.008 0.952 0.004
#> GSM125223 2 0.1638 0.851 0.000 0.932 0.064 0.000 0.004
#> GSM125225 2 0.1768 0.853 0.000 0.924 0.072 0.000 0.004
#> GSM125227 2 0.0703 0.861 0.000 0.976 0.024 0.000 0.000
#> GSM125229 3 0.6033 0.557 0.000 0.220 0.580 0.200 0.000
#> GSM125231 5 0.5895 0.417 0.000 0.000 0.152 0.260 0.588
#> GSM125233 5 0.2813 0.792 0.168 0.000 0.000 0.000 0.832
#> GSM125235 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125237 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125124 5 0.0162 0.893 0.004 0.000 0.000 0.000 0.996
#> GSM125126 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.0162 0.962 0.996 0.000 0.000 0.000 0.004
#> GSM125130 5 0.0162 0.893 0.004 0.000 0.000 0.000 0.996
#> GSM125132 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125134 5 0.1043 0.896 0.040 0.000 0.000 0.000 0.960
#> GSM125136 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125138 5 0.0703 0.896 0.024 0.000 0.000 0.000 0.976
#> GSM125140 5 0.0290 0.894 0.008 0.000 0.000 0.000 0.992
#> GSM125142 5 0.1121 0.896 0.044 0.000 0.000 0.000 0.956
#> GSM125144 5 0.0162 0.893 0.004 0.000 0.000 0.000 0.996
#> GSM125146 5 0.1608 0.884 0.072 0.000 0.000 0.000 0.928
#> GSM125148 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125150 1 0.1341 0.915 0.944 0.000 0.000 0.000 0.056
#> GSM125152 5 0.0162 0.893 0.004 0.000 0.000 0.000 0.996
#> GSM125154 5 0.1043 0.896 0.040 0.000 0.000 0.000 0.960
#> GSM125156 5 0.1908 0.861 0.092 0.000 0.000 0.000 0.908
#> GSM125158 5 0.3999 0.535 0.344 0.000 0.000 0.000 0.656
#> GSM125160 2 0.1124 0.858 0.000 0.960 0.036 0.004 0.000
#> GSM125162 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
#> GSM125164 2 0.1549 0.855 0.000 0.944 0.040 0.016 0.000
#> GSM125166 2 0.0579 0.862 0.000 0.984 0.008 0.008 0.000
#> GSM125168 4 0.4559 -0.251 0.000 0.480 0.008 0.512 0.000
#> GSM125170 2 0.4249 0.366 0.000 0.568 0.000 0.432 0.000
#> GSM125172 2 0.3366 0.669 0.000 0.768 0.000 0.232 0.000
#> GSM125174 4 0.1211 0.657 0.000 0.000 0.016 0.960 0.024
#> GSM125176 2 0.1469 0.856 0.000 0.948 0.036 0.016 0.000
#> GSM125178 3 0.5571 0.479 0.000 0.388 0.544 0.064 0.004
#> GSM125180 4 0.4887 0.561 0.000 0.000 0.288 0.660 0.052
#> GSM125182 2 0.5851 0.464 0.000 0.548 0.112 0.340 0.000
#> GSM125184 4 0.1124 0.661 0.000 0.036 0.004 0.960 0.000
#> GSM125186 4 0.4657 0.561 0.000 0.000 0.296 0.668 0.036
#> GSM125188 4 0.5501 0.516 0.000 0.064 0.360 0.572 0.004
#> GSM125190 2 0.1341 0.848 0.000 0.944 0.000 0.056 0.000
#> GSM125192 2 0.0609 0.862 0.000 0.980 0.020 0.000 0.000
#> GSM125194 4 0.4264 0.325 0.376 0.000 0.000 0.620 0.004
#> GSM125196 3 0.2970 0.651 0.000 0.004 0.828 0.168 0.000
#> GSM125198 2 0.1638 0.851 0.000 0.932 0.064 0.000 0.004
#> GSM125200 5 0.0880 0.898 0.032 0.000 0.000 0.000 0.968
#> GSM125202 2 0.2238 0.852 0.000 0.912 0.064 0.020 0.004
#> GSM125204 3 0.3248 0.644 0.032 0.000 0.864 0.084 0.020
#> GSM125206 3 0.5590 0.513 0.000 0.080 0.592 0.324 0.004
#> GSM125208 3 0.2144 0.638 0.000 0.000 0.912 0.068 0.020
#> GSM125210 4 0.3966 0.551 0.000 0.000 0.336 0.664 0.000
#> GSM125212 4 0.2653 0.624 0.000 0.096 0.024 0.880 0.000
#> GSM125214 2 0.1444 0.861 0.000 0.948 0.040 0.012 0.000
#> GSM125216 2 0.0290 0.862 0.000 0.992 0.008 0.000 0.000
#> GSM125218 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000
#> GSM125220 1 0.1478 0.913 0.936 0.000 0.064 0.000 0.000
#> GSM125222 4 0.2313 0.649 0.000 0.044 0.040 0.912 0.004
#> GSM125224 2 0.1638 0.851 0.000 0.932 0.064 0.000 0.004
#> GSM125226 4 0.4659 -0.183 0.000 0.492 0.012 0.496 0.000
#> GSM125228 2 0.0000 0.861 0.000 1.000 0.000 0.000 0.000
#> GSM125230 4 0.2193 0.646 0.028 0.000 0.044 0.920 0.008
#> GSM125232 5 0.0794 0.885 0.000 0.000 0.000 0.028 0.972
#> GSM125234 5 0.2659 0.826 0.000 0.000 0.060 0.052 0.888
#> GSM125236 5 0.4438 0.428 0.384 0.000 0.004 0.004 0.608
#> GSM125238 1 0.0000 0.966 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2631 0.7747 0.820 0.000 0.000 0.000 0.180 0.000
#> GSM125125 5 0.0547 0.9508 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125127 1 0.0935 0.8942 0.964 0.000 0.004 0.000 0.032 0.000
#> GSM125129 5 0.1714 0.8846 0.092 0.000 0.000 0.000 0.908 0.000
#> GSM125131 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125133 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125135 5 0.3330 0.5790 0.284 0.000 0.000 0.000 0.716 0.000
#> GSM125137 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125139 1 0.1327 0.8734 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM125141 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125143 1 0.0458 0.8948 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM125145 1 0.0713 0.8956 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125147 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125149 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125151 1 0.0146 0.8917 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125153 1 0.0790 0.8948 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125155 5 0.0363 0.9568 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM125157 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125159 2 0.1010 0.6913 0.000 0.960 0.000 0.004 0.000 0.036
#> GSM125161 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125163 2 0.0777 0.6902 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM125165 4 0.1524 0.6402 0.000 0.060 0.008 0.932 0.000 0.000
#> GSM125167 2 0.5045 0.4920 0.004 0.624 0.008 0.292 0.000 0.072
#> GSM125169 2 0.3820 0.5322 0.000 0.700 0.008 0.284 0.000 0.008
#> GSM125171 2 0.4560 0.6495 0.000 0.744 0.032 0.092 0.000 0.132
#> GSM125173 4 0.1989 0.6535 0.000 0.028 0.052 0.916 0.000 0.004
#> GSM125175 2 0.2730 0.6260 0.000 0.808 0.000 0.000 0.000 0.192
#> GSM125177 3 0.4513 0.4599 0.000 0.372 0.596 0.016 0.000 0.016
#> GSM125179 4 0.4748 0.5605 0.052 0.000 0.316 0.624 0.000 0.008
#> GSM125181 4 0.6276 0.4538 0.004 0.168 0.348 0.460 0.000 0.020
#> GSM125183 4 0.1745 0.6495 0.000 0.000 0.056 0.924 0.020 0.000
#> GSM125185 4 0.6529 0.4362 0.016 0.164 0.360 0.440 0.000 0.020
#> GSM125187 4 0.4325 0.5120 0.016 0.000 0.412 0.568 0.000 0.004
#> GSM125189 2 0.2664 0.6351 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM125191 2 0.2610 0.6679 0.004 0.892 0.048 0.020 0.000 0.036
#> GSM125193 5 0.3464 0.7762 0.000 0.000 0.108 0.084 0.808 0.000
#> GSM125195 3 0.2558 0.6832 0.004 0.000 0.840 0.156 0.000 0.000
#> GSM125197 6 0.1863 0.8619 0.000 0.104 0.000 0.000 0.000 0.896
#> GSM125199 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125201 6 0.2454 0.7993 0.000 0.160 0.000 0.000 0.000 0.840
#> GSM125203 3 0.3483 0.6715 0.000 0.000 0.820 0.040 0.120 0.020
#> GSM125205 6 0.1863 0.8619 0.000 0.104 0.000 0.000 0.000 0.896
#> GSM125207 3 0.1237 0.6636 0.000 0.020 0.956 0.020 0.000 0.004
#> GSM125209 2 0.5583 0.3443 0.004 0.584 0.304 0.080 0.000 0.028
#> GSM125211 4 0.2114 0.6305 0.000 0.008 0.012 0.904 0.000 0.076
#> GSM125213 2 0.2467 0.6542 0.004 0.880 0.008 0.008 0.000 0.100
#> GSM125215 6 0.1714 0.8552 0.000 0.092 0.000 0.000 0.000 0.908
#> GSM125217 2 0.4617 0.4901 0.000 0.636 0.008 0.312 0.000 0.044
#> GSM125219 1 0.3912 0.6597 0.732 0.000 0.224 0.000 0.044 0.000
#> GSM125221 4 0.1789 0.6495 0.000 0.032 0.044 0.924 0.000 0.000
#> GSM125223 6 0.1910 0.8621 0.000 0.108 0.000 0.000 0.000 0.892
#> GSM125225 6 0.3996 -0.0636 0.000 0.484 0.000 0.004 0.000 0.512
#> GSM125227 2 0.3531 0.4535 0.000 0.672 0.000 0.000 0.000 0.328
#> GSM125229 3 0.5940 0.6356 0.000 0.128 0.620 0.172 0.000 0.080
#> GSM125231 1 0.5554 0.3113 0.544 0.000 0.276 0.180 0.000 0.000
#> GSM125233 1 0.2562 0.7834 0.828 0.000 0.000 0.000 0.172 0.000
#> GSM125235 5 0.0146 0.9620 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM125237 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125124 1 0.0146 0.8917 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125126 5 0.0146 0.9620 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM125128 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125130 1 0.0146 0.8917 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125132 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125134 1 0.0790 0.8948 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125136 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125138 1 0.0632 0.8950 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM125140 1 0.0260 0.8928 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125142 1 0.0865 0.8948 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM125144 1 0.0260 0.8928 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125146 1 0.1387 0.8813 0.932 0.000 0.000 0.000 0.068 0.000
#> GSM125148 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125150 5 0.1267 0.9096 0.060 0.000 0.000 0.000 0.940 0.000
#> GSM125152 1 0.0146 0.8917 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125154 1 0.0790 0.8948 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125156 1 0.1765 0.8535 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM125158 1 0.3607 0.5247 0.652 0.000 0.000 0.000 0.348 0.000
#> GSM125160 2 0.0692 0.6959 0.000 0.976 0.000 0.004 0.000 0.020
#> GSM125162 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125164 2 0.0547 0.6948 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125166 2 0.2278 0.6676 0.000 0.868 0.000 0.004 0.000 0.128
#> GSM125168 4 0.5032 -0.2400 0.000 0.456 0.052 0.484 0.000 0.008
#> GSM125170 2 0.4086 0.3161 0.000 0.528 0.000 0.464 0.000 0.008
#> GSM125172 2 0.5454 0.5442 0.000 0.568 0.000 0.252 0.000 0.180
#> GSM125174 4 0.1829 0.6513 0.024 0.000 0.056 0.920 0.000 0.000
#> GSM125176 2 0.0777 0.6955 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM125178 3 0.4326 0.4809 0.000 0.368 0.608 0.016 0.000 0.008
#> GSM125180 4 0.4906 0.5542 0.064 0.000 0.316 0.612 0.000 0.008
#> GSM125182 2 0.5604 0.4054 0.004 0.584 0.100 0.292 0.000 0.020
#> GSM125184 4 0.2001 0.6524 0.000 0.040 0.048 0.912 0.000 0.000
#> GSM125186 4 0.4780 0.5539 0.040 0.004 0.336 0.612 0.000 0.008
#> GSM125188 4 0.6549 0.4192 0.004 0.224 0.316 0.432 0.000 0.024
#> GSM125190 2 0.3158 0.6346 0.000 0.812 0.004 0.164 0.000 0.020
#> GSM125192 2 0.2278 0.6678 0.000 0.868 0.004 0.000 0.000 0.128
#> GSM125194 4 0.3717 0.3385 0.000 0.000 0.000 0.616 0.384 0.000
#> GSM125196 3 0.1714 0.7108 0.000 0.000 0.908 0.092 0.000 0.000
#> GSM125198 6 0.1910 0.8621 0.000 0.108 0.000 0.000 0.000 0.892
#> GSM125200 1 0.0713 0.8964 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125202 6 0.2872 0.8298 0.000 0.140 0.000 0.024 0.000 0.836
#> GSM125204 3 0.1320 0.7063 0.000 0.000 0.948 0.016 0.036 0.000
#> GSM125206 3 0.4487 0.5935 0.000 0.068 0.668 0.264 0.000 0.000
#> GSM125208 3 0.0603 0.6966 0.004 0.000 0.980 0.016 0.000 0.000
#> GSM125210 4 0.6239 0.4665 0.004 0.168 0.328 0.480 0.000 0.020
#> GSM125212 4 0.2828 0.6219 0.000 0.040 0.012 0.868 0.000 0.080
#> GSM125214 2 0.3866 -0.2974 0.000 0.516 0.000 0.000 0.000 0.484
#> GSM125216 6 0.3765 0.4120 0.000 0.404 0.000 0.000 0.000 0.596
#> GSM125218 2 0.2743 0.6510 0.000 0.828 0.000 0.008 0.000 0.164
#> GSM125220 5 0.1663 0.8883 0.000 0.000 0.088 0.000 0.912 0.000
#> GSM125222 4 0.2376 0.6405 0.000 0.044 0.068 0.888 0.000 0.000
#> GSM125224 6 0.1910 0.8621 0.000 0.108 0.000 0.000 0.000 0.892
#> GSM125226 4 0.5712 -0.3384 0.004 0.436 0.008 0.444 0.000 0.108
#> GSM125228 2 0.2697 0.6277 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM125230 4 0.2856 0.6154 0.000 0.000 0.068 0.856 0.000 0.076
#> GSM125232 1 0.0632 0.8853 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM125234 1 0.2100 0.8201 0.884 0.000 0.112 0.004 0.000 0.000
#> GSM125236 1 0.3965 0.4163 0.604 0.000 0.008 0.000 0.388 0.000
#> GSM125238 5 0.0000 0.9645 0.000 0.000 0.000 0.000 1.000 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 agent(p) individual(p) k
#> SD:pam 115 1.000 6.83e-05 2
#> SD:pam 108 0.777 2.04e-06 3
#> SD:pam 108 0.281 1.06e-05 4
#> SD:pam 105 0.476 2.58e-08 5
#> SD:pam 96 0.552 2.11e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 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 0.922 0.967 0.973 0.4782 0.511 0.511
#> 3 3 0.679 0.785 0.771 0.3164 0.829 0.665
#> 4 4 0.721 0.790 0.819 0.1502 0.825 0.547
#> 5 5 0.919 0.937 0.952 0.1014 0.872 0.559
#> 6 6 0.881 0.836 0.898 0.0319 0.964 0.820
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
#> GSM125123 1 0.0376 0.967 0.996 0.004
#> GSM125125 1 0.0000 0.969 1.000 0.000
#> GSM125127 1 0.6048 0.849 0.852 0.148
#> GSM125129 1 0.0000 0.969 1.000 0.000
#> GSM125131 1 0.0000 0.969 1.000 0.000
#> GSM125133 1 0.4690 0.898 0.900 0.100
#> GSM125135 1 0.0000 0.969 1.000 0.000
#> GSM125137 1 0.0000 0.969 1.000 0.000
#> GSM125139 1 0.0000 0.969 1.000 0.000
#> GSM125141 1 0.0000 0.969 1.000 0.000
#> GSM125143 1 0.6247 0.840 0.844 0.156
#> GSM125145 1 0.0938 0.962 0.988 0.012
#> GSM125147 1 0.0000 0.969 1.000 0.000
#> GSM125149 1 0.0000 0.969 1.000 0.000
#> GSM125151 1 0.0000 0.969 1.000 0.000
#> GSM125153 1 0.0000 0.969 1.000 0.000
#> GSM125155 1 0.0000 0.969 1.000 0.000
#> GSM125157 1 0.0000 0.969 1.000 0.000
#> GSM125159 2 0.1414 0.988 0.020 0.980
#> GSM125161 1 0.0000 0.969 1.000 0.000
#> GSM125163 2 0.1414 0.988 0.020 0.980
#> GSM125165 2 0.1414 0.988 0.020 0.980
#> GSM125167 2 0.1414 0.988 0.020 0.980
#> GSM125169 2 0.1414 0.988 0.020 0.980
#> GSM125171 2 0.1414 0.988 0.020 0.980
#> GSM125173 2 0.1414 0.988 0.020 0.980
#> GSM125175 2 0.1184 0.986 0.016 0.984
#> GSM125177 2 0.1414 0.988 0.020 0.980
#> GSM125179 2 0.2603 0.976 0.044 0.956
#> GSM125181 2 0.1414 0.988 0.020 0.980
#> GSM125183 2 0.2603 0.976 0.044 0.956
#> GSM125185 2 0.2236 0.982 0.036 0.964
#> GSM125187 2 0.2603 0.977 0.044 0.956
#> GSM125189 2 0.1414 0.988 0.020 0.980
#> GSM125191 2 0.1414 0.988 0.020 0.980
#> GSM125193 2 0.2423 0.979 0.040 0.960
#> GSM125195 2 0.2043 0.984 0.032 0.968
#> GSM125197 2 0.0000 0.976 0.000 1.000
#> GSM125199 1 0.0000 0.969 1.000 0.000
#> GSM125201 2 0.0000 0.976 0.000 1.000
#> GSM125203 2 0.1633 0.987 0.024 0.976
#> GSM125205 2 0.0000 0.976 0.000 1.000
#> GSM125207 2 0.2043 0.984 0.032 0.968
#> GSM125209 2 0.1414 0.988 0.020 0.980
#> GSM125211 2 0.1414 0.988 0.020 0.980
#> GSM125213 2 0.1414 0.988 0.020 0.980
#> GSM125215 2 0.1414 0.988 0.020 0.980
#> GSM125217 2 0.1414 0.988 0.020 0.980
#> GSM125219 1 0.5178 0.884 0.884 0.116
#> GSM125221 2 0.2236 0.982 0.036 0.964
#> GSM125223 2 0.0000 0.976 0.000 1.000
#> GSM125225 2 0.1414 0.988 0.020 0.980
#> GSM125227 2 0.0000 0.976 0.000 1.000
#> GSM125229 2 0.1414 0.988 0.020 0.980
#> GSM125231 2 0.4690 0.917 0.100 0.900
#> GSM125233 1 0.0000 0.969 1.000 0.000
#> GSM125235 1 0.0000 0.969 1.000 0.000
#> GSM125237 1 0.0000 0.969 1.000 0.000
#> GSM125124 1 0.0000 0.969 1.000 0.000
#> GSM125126 1 0.0000 0.969 1.000 0.000
#> GSM125128 1 0.4815 0.895 0.896 0.104
#> GSM125130 1 0.5408 0.876 0.876 0.124
#> GSM125132 1 0.0000 0.969 1.000 0.000
#> GSM125134 1 0.0000 0.969 1.000 0.000
#> GSM125136 1 0.1633 0.955 0.976 0.024
#> GSM125138 1 0.0000 0.969 1.000 0.000
#> GSM125140 1 0.0000 0.969 1.000 0.000
#> GSM125142 1 0.0000 0.969 1.000 0.000
#> GSM125144 1 0.0000 0.969 1.000 0.000
#> GSM125146 1 0.4161 0.911 0.916 0.084
#> GSM125148 1 0.0000 0.969 1.000 0.000
#> GSM125150 1 0.0000 0.969 1.000 0.000
#> GSM125152 1 0.0000 0.969 1.000 0.000
#> GSM125154 1 0.0000 0.969 1.000 0.000
#> GSM125156 1 0.0000 0.969 1.000 0.000
#> GSM125158 1 0.0000 0.969 1.000 0.000
#> GSM125160 2 0.1414 0.988 0.020 0.980
#> GSM125162 1 0.0000 0.969 1.000 0.000
#> GSM125164 2 0.1414 0.988 0.020 0.980
#> GSM125166 2 0.0000 0.976 0.000 1.000
#> GSM125168 2 0.1414 0.988 0.020 0.980
#> GSM125170 2 0.1414 0.988 0.020 0.980
#> GSM125172 2 0.0000 0.976 0.000 1.000
#> GSM125174 2 0.2603 0.976 0.044 0.956
#> GSM125176 2 0.1414 0.988 0.020 0.980
#> GSM125178 2 0.2236 0.982 0.036 0.964
#> GSM125180 2 0.2603 0.976 0.044 0.956
#> GSM125182 2 0.1414 0.988 0.020 0.980
#> GSM125184 2 0.1633 0.987 0.024 0.976
#> GSM125186 2 0.2423 0.979 0.040 0.960
#> GSM125188 2 0.1414 0.988 0.020 0.980
#> GSM125190 2 0.1414 0.988 0.020 0.980
#> GSM125192 2 0.0000 0.976 0.000 1.000
#> GSM125194 2 0.2603 0.977 0.044 0.956
#> GSM125196 2 0.1633 0.987 0.024 0.976
#> GSM125198 2 0.0000 0.976 0.000 1.000
#> GSM125200 1 0.0000 0.969 1.000 0.000
#> GSM125202 2 0.1184 0.986 0.016 0.984
#> GSM125204 2 0.2423 0.979 0.040 0.960
#> GSM125206 2 0.1633 0.987 0.024 0.976
#> GSM125208 2 0.2423 0.979 0.040 0.960
#> GSM125210 2 0.1633 0.987 0.024 0.976
#> GSM125212 2 0.1414 0.988 0.020 0.980
#> GSM125214 2 0.1414 0.988 0.020 0.980
#> GSM125216 2 0.1414 0.988 0.020 0.980
#> GSM125218 2 0.1414 0.988 0.020 0.980
#> GSM125220 1 0.6048 0.849 0.852 0.148
#> GSM125222 2 0.2236 0.982 0.036 0.964
#> GSM125224 2 0.0000 0.976 0.000 1.000
#> GSM125226 2 0.1414 0.988 0.020 0.980
#> GSM125228 2 0.0000 0.976 0.000 1.000
#> GSM125230 2 0.2778 0.973 0.048 0.952
#> GSM125232 2 0.4939 0.908 0.108 0.892
#> GSM125234 1 0.8081 0.701 0.752 0.248
#> GSM125236 1 0.5629 0.867 0.868 0.132
#> GSM125238 1 0.0000 0.969 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125125 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125127 1 0.0475 0.8427 0.992 0.004 0.004
#> GSM125129 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125131 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125133 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125135 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125137 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125139 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125141 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125143 1 0.0829 0.8382 0.984 0.004 0.012
#> GSM125145 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125147 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125149 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125151 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125153 1 0.1163 0.8499 0.972 0.000 0.028
#> GSM125155 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125157 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125159 2 0.1163 0.8449 0.000 0.972 0.028
#> GSM125161 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125163 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125165 3 0.6302 0.7028 0.000 0.480 0.520
#> GSM125167 2 0.0892 0.8500 0.000 0.980 0.020
#> GSM125169 2 0.3192 0.7412 0.000 0.888 0.112
#> GSM125171 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125173 3 0.6280 0.7475 0.000 0.460 0.540
#> GSM125175 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125177 3 0.5810 0.9171 0.000 0.336 0.664
#> GSM125179 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125181 3 0.6302 0.7028 0.000 0.480 0.520
#> GSM125183 3 0.5810 0.9171 0.000 0.336 0.664
#> GSM125185 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125187 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125189 2 0.1289 0.8418 0.000 0.968 0.032
#> GSM125191 2 0.4605 0.5357 0.000 0.796 0.204
#> GSM125193 3 0.6291 0.7307 0.000 0.468 0.532
#> GSM125195 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125197 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125199 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125201 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125203 3 0.5810 0.9171 0.000 0.336 0.664
#> GSM125205 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125207 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125209 2 0.6215 -0.4472 0.000 0.572 0.428
#> GSM125211 2 0.5926 -0.0656 0.000 0.644 0.356
#> GSM125213 2 0.0592 0.8539 0.000 0.988 0.012
#> GSM125215 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125217 2 0.1411 0.8387 0.000 0.964 0.036
#> GSM125219 1 0.0237 0.8444 0.996 0.004 0.000
#> GSM125221 3 0.6204 0.8118 0.000 0.424 0.576
#> GSM125223 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125225 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125229 2 0.5785 0.0742 0.000 0.668 0.332
#> GSM125231 3 0.7622 0.8271 0.060 0.332 0.608
#> GSM125233 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125235 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125237 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125124 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125126 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125128 1 0.5982 0.8591 0.668 0.004 0.328
#> GSM125130 1 0.2200 0.8052 0.940 0.004 0.056
#> GSM125132 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125134 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125136 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125138 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125142 1 0.4452 0.8611 0.808 0.000 0.192
#> GSM125144 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125146 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125148 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125150 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125152 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.8465 1.000 0.000 0.000
#> GSM125156 1 0.3752 0.8592 0.856 0.000 0.144
#> GSM125158 1 0.4346 0.8609 0.816 0.000 0.184
#> GSM125160 2 0.0592 0.8539 0.000 0.988 0.012
#> GSM125162 1 0.5760 0.8612 0.672 0.000 0.328
#> GSM125164 2 0.0237 0.8571 0.000 0.996 0.004
#> GSM125166 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125168 2 0.5138 0.3923 0.000 0.748 0.252
#> GSM125170 3 0.6225 0.7990 0.000 0.432 0.568
#> GSM125172 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125174 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125176 2 0.5948 -0.1618 0.000 0.640 0.360
#> GSM125178 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125180 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125182 2 0.6295 -0.5992 0.000 0.528 0.472
#> GSM125184 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125186 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125188 3 0.6302 0.7028 0.000 0.480 0.520
#> GSM125190 2 0.2261 0.8032 0.000 0.932 0.068
#> GSM125192 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125194 3 0.6033 0.9149 0.004 0.336 0.660
#> GSM125196 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125198 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125200 1 0.5706 0.8615 0.680 0.000 0.320
#> GSM125202 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125204 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125206 3 0.5810 0.9171 0.000 0.336 0.664
#> GSM125208 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125210 3 0.5785 0.9183 0.000 0.332 0.668
#> GSM125212 2 0.5760 0.0949 0.000 0.672 0.328
#> GSM125214 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.8581 0.000 1.000 0.000
#> GSM125218 2 0.1289 0.8418 0.000 0.968 0.032
#> GSM125220 1 0.6057 0.8530 0.656 0.004 0.340
#> GSM125222 3 0.5810 0.9171 0.000 0.336 0.664
#> GSM125224 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125226 2 0.1411 0.8386 0.000 0.964 0.036
#> GSM125228 2 0.0237 0.8565 0.000 0.996 0.004
#> GSM125230 3 0.6738 0.8859 0.020 0.356 0.624
#> GSM125232 3 0.9585 0.5309 0.212 0.332 0.456
#> GSM125234 1 0.9424 -0.1571 0.472 0.188 0.340
#> GSM125236 1 0.0237 0.8444 0.996 0.004 0.000
#> GSM125238 1 0.5760 0.8612 0.672 0.000 0.328
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125125 4 0.5000 -0.4920 0.500 0.000 0.000 0.500
#> GSM125127 1 0.4595 0.8966 0.776 0.000 0.040 0.184
#> GSM125129 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125131 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125133 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125135 1 0.4072 0.9034 0.748 0.000 0.000 0.252
#> GSM125137 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125139 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125141 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125143 1 0.4348 0.9133 0.780 0.000 0.024 0.196
#> GSM125145 1 0.4158 0.9238 0.768 0.000 0.008 0.224
#> GSM125147 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125149 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125151 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125153 1 0.4252 0.9039 0.744 0.000 0.004 0.252
#> GSM125155 4 0.0921 0.8990 0.028 0.000 0.000 0.972
#> GSM125157 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125159 2 0.7002 0.4137 0.128 0.520 0.352 0.000
#> GSM125161 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125163 2 0.2928 0.7661 0.012 0.880 0.108 0.000
#> GSM125165 3 0.4590 0.8070 0.148 0.060 0.792 0.000
#> GSM125167 2 0.6876 0.4266 0.116 0.532 0.352 0.000
#> GSM125169 3 0.7242 0.0292 0.148 0.376 0.476 0.000
#> GSM125171 2 0.3428 0.7410 0.012 0.844 0.144 0.000
#> GSM125173 3 0.3858 0.8414 0.100 0.056 0.844 0.000
#> GSM125175 2 0.1004 0.7988 0.004 0.972 0.024 0.000
#> GSM125177 3 0.2197 0.8767 0.024 0.048 0.928 0.000
#> GSM125179 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125181 3 0.3787 0.8406 0.124 0.036 0.840 0.000
#> GSM125183 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125185 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125187 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125189 2 0.7155 0.3912 0.144 0.504 0.352 0.000
#> GSM125191 3 0.5551 0.7174 0.112 0.160 0.728 0.000
#> GSM125193 3 0.2882 0.8670 0.084 0.024 0.892 0.000
#> GSM125195 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125197 2 0.1867 0.7658 0.072 0.928 0.000 0.000
#> GSM125199 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125201 2 0.0188 0.7955 0.000 0.996 0.004 0.000
#> GSM125203 3 0.2224 0.8778 0.040 0.032 0.928 0.000
#> GSM125205 2 0.0188 0.7955 0.000 0.996 0.004 0.000
#> GSM125207 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125209 3 0.4188 0.8299 0.112 0.064 0.824 0.000
#> GSM125211 3 0.5247 0.7651 0.148 0.100 0.752 0.000
#> GSM125213 2 0.6324 0.4557 0.072 0.572 0.356 0.000
#> GSM125215 2 0.1042 0.7993 0.008 0.972 0.020 0.000
#> GSM125217 2 0.7175 0.3726 0.144 0.496 0.360 0.000
#> GSM125219 1 0.4284 0.9178 0.780 0.000 0.020 0.200
#> GSM125221 3 0.3198 0.8609 0.080 0.040 0.880 0.000
#> GSM125223 2 0.1867 0.7658 0.072 0.928 0.000 0.000
#> GSM125225 2 0.1305 0.7946 0.036 0.960 0.004 0.000
#> GSM125227 2 0.1211 0.7799 0.040 0.960 0.000 0.000
#> GSM125229 3 0.5416 0.7506 0.148 0.112 0.740 0.000
#> GSM125231 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125233 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125235 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125237 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125124 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125126 4 0.0592 0.9131 0.016 0.000 0.000 0.984
#> GSM125128 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125130 1 0.4888 0.7747 0.780 0.000 0.124 0.096
#> GSM125132 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125134 1 0.4123 0.9254 0.772 0.000 0.008 0.220
#> GSM125136 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125138 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125140 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125142 1 0.4746 0.7530 0.632 0.000 0.000 0.368
#> GSM125144 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125146 1 0.4262 0.9170 0.756 0.000 0.008 0.236
#> GSM125148 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125150 4 0.4985 -0.3956 0.468 0.000 0.000 0.532
#> GSM125152 1 0.4049 0.9278 0.780 0.000 0.008 0.212
#> GSM125154 1 0.4088 0.9185 0.764 0.000 0.004 0.232
#> GSM125156 1 0.4977 0.5535 0.540 0.000 0.000 0.460
#> GSM125158 1 0.4877 0.6747 0.592 0.000 0.000 0.408
#> GSM125160 2 0.6280 0.5311 0.084 0.612 0.304 0.000
#> GSM125162 4 0.0000 0.9288 0.000 0.000 0.000 1.000
#> GSM125164 2 0.3925 0.7178 0.016 0.808 0.176 0.000
#> GSM125166 2 0.1940 0.7823 0.000 0.924 0.076 0.000
#> GSM125168 3 0.4852 0.7693 0.072 0.152 0.776 0.000
#> GSM125170 3 0.0779 0.8888 0.004 0.016 0.980 0.000
#> GSM125172 2 0.0188 0.7955 0.000 0.996 0.004 0.000
#> GSM125174 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125176 3 0.4040 0.5839 0.000 0.248 0.752 0.000
#> GSM125178 3 0.0188 0.8911 0.004 0.000 0.996 0.000
#> GSM125180 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125182 3 0.4037 0.8362 0.112 0.056 0.832 0.000
#> GSM125184 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125186 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125188 3 0.3962 0.8353 0.124 0.044 0.832 0.000
#> GSM125190 2 0.7049 0.3090 0.124 0.484 0.392 0.000
#> GSM125192 2 0.0336 0.7972 0.000 0.992 0.008 0.000
#> GSM125194 3 0.0895 0.8838 0.004 0.000 0.976 0.020
#> GSM125196 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125198 2 0.1867 0.7658 0.072 0.928 0.000 0.000
#> GSM125200 1 0.4985 0.5348 0.532 0.000 0.000 0.468
#> GSM125202 2 0.0817 0.7988 0.000 0.976 0.024 0.000
#> GSM125204 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125206 3 0.0804 0.8890 0.008 0.012 0.980 0.000
#> GSM125208 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125210 3 0.0188 0.8914 0.004 0.000 0.996 0.000
#> GSM125212 3 0.5416 0.7506 0.148 0.112 0.740 0.000
#> GSM125214 2 0.0817 0.7988 0.000 0.976 0.024 0.000
#> GSM125216 2 0.1004 0.7987 0.004 0.972 0.024 0.000
#> GSM125218 2 0.7081 0.4032 0.136 0.512 0.352 0.000
#> GSM125220 4 0.1398 0.8718 0.004 0.000 0.040 0.956
#> GSM125222 3 0.0469 0.8909 0.012 0.000 0.988 0.000
#> GSM125224 2 0.1867 0.7658 0.072 0.928 0.000 0.000
#> GSM125226 2 0.7165 0.3829 0.144 0.500 0.356 0.000
#> GSM125228 2 0.1637 0.7710 0.060 0.940 0.000 0.000
#> GSM125230 3 0.1677 0.8725 0.012 0.000 0.948 0.040
#> GSM125232 3 0.2179 0.8446 0.064 0.000 0.924 0.012
#> GSM125234 3 0.3991 0.6961 0.172 0.000 0.808 0.020
#> GSM125236 1 0.4387 0.9152 0.776 0.000 0.024 0.200
#> GSM125238 4 0.0000 0.9288 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125125 5 0.0290 0.970 0.008 0.000 0.000 0.000 0.992
#> GSM125127 5 0.0290 0.968 0.000 0.000 0.008 0.000 0.992
#> GSM125129 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125131 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125133 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125135 5 0.0510 0.965 0.016 0.000 0.000 0.000 0.984
#> GSM125137 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125139 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125141 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125143 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125145 5 0.0162 0.972 0.004 0.000 0.000 0.000 0.996
#> GSM125147 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125149 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125151 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125153 5 0.1197 0.936 0.048 0.000 0.000 0.000 0.952
#> GSM125155 1 0.2690 0.860 0.844 0.000 0.000 0.000 0.156
#> GSM125157 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125159 4 0.1270 0.901 0.000 0.052 0.000 0.948 0.000
#> GSM125161 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125163 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125165 4 0.1732 0.907 0.000 0.000 0.080 0.920 0.000
#> GSM125167 4 0.1908 0.880 0.000 0.092 0.000 0.908 0.000
#> GSM125169 4 0.0000 0.913 0.000 0.000 0.000 1.000 0.000
#> GSM125171 2 0.2974 0.897 0.000 0.868 0.052 0.080 0.000
#> GSM125173 4 0.2424 0.887 0.000 0.000 0.132 0.868 0.000
#> GSM125175 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125177 3 0.0880 0.965 0.000 0.000 0.968 0.032 0.000
#> GSM125179 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125181 4 0.3276 0.875 0.032 0.000 0.132 0.836 0.000
#> GSM125183 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125185 3 0.0510 0.979 0.016 0.000 0.984 0.000 0.000
#> GSM125187 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125189 4 0.0880 0.908 0.000 0.032 0.000 0.968 0.000
#> GSM125191 4 0.2362 0.907 0.000 0.024 0.076 0.900 0.000
#> GSM125193 4 0.2389 0.888 0.000 0.000 0.116 0.880 0.004
#> GSM125195 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125197 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> GSM125199 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125201 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125203 3 0.1282 0.952 0.004 0.000 0.952 0.044 0.000
#> GSM125205 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125207 3 0.0703 0.975 0.024 0.000 0.976 0.000 0.000
#> GSM125209 4 0.3035 0.891 0.032 0.000 0.112 0.856 0.000
#> GSM125211 4 0.0162 0.913 0.000 0.000 0.004 0.996 0.000
#> GSM125213 4 0.2179 0.865 0.000 0.112 0.000 0.888 0.000
#> GSM125215 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125217 4 0.0162 0.912 0.000 0.004 0.000 0.996 0.000
#> GSM125219 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125221 4 0.3305 0.773 0.000 0.000 0.224 0.776 0.000
#> GSM125223 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125227 2 0.1121 0.946 0.000 0.956 0.000 0.044 0.000
#> GSM125229 4 0.0000 0.913 0.000 0.000 0.000 1.000 0.000
#> GSM125231 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125233 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125235 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125237 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125124 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125126 1 0.2516 0.879 0.860 0.000 0.000 0.000 0.140
#> GSM125128 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125130 5 0.0880 0.945 0.000 0.000 0.032 0.000 0.968
#> GSM125132 1 0.1043 0.974 0.960 0.000 0.000 0.000 0.040
#> GSM125134 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125136 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125138 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125140 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125142 5 0.0404 0.968 0.012 0.000 0.000 0.000 0.988
#> GSM125144 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125146 5 0.0290 0.970 0.008 0.000 0.000 0.000 0.992
#> GSM125148 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125150 5 0.2377 0.842 0.128 0.000 0.000 0.000 0.872
#> GSM125152 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125154 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125156 5 0.0162 0.972 0.004 0.000 0.000 0.000 0.996
#> GSM125158 5 0.0162 0.972 0.004 0.000 0.000 0.000 0.996
#> GSM125160 4 0.2424 0.846 0.000 0.132 0.000 0.868 0.000
#> GSM125162 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
#> GSM125164 2 0.2130 0.938 0.000 0.908 0.012 0.080 0.000
#> GSM125166 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125168 4 0.3846 0.843 0.000 0.056 0.144 0.800 0.000
#> GSM125170 3 0.0880 0.960 0.000 0.000 0.968 0.032 0.000
#> GSM125172 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125174 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125176 2 0.4201 0.326 0.000 0.592 0.408 0.000 0.000
#> GSM125178 3 0.0162 0.982 0.000 0.000 0.996 0.004 0.000
#> GSM125180 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125182 4 0.3182 0.882 0.032 0.000 0.124 0.844 0.000
#> GSM125184 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125186 3 0.0290 0.982 0.008 0.000 0.992 0.000 0.000
#> GSM125188 4 0.3229 0.878 0.032 0.000 0.128 0.840 0.000
#> GSM125190 4 0.1300 0.916 0.000 0.016 0.028 0.956 0.000
#> GSM125192 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125194 3 0.0404 0.976 0.000 0.000 0.988 0.000 0.012
#> GSM125196 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125198 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> GSM125200 5 0.0162 0.972 0.004 0.000 0.000 0.000 0.996
#> GSM125202 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125204 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125206 3 0.0000 0.984 0.000 0.000 1.000 0.000 0.000
#> GSM125208 3 0.0703 0.975 0.024 0.000 0.976 0.000 0.000
#> GSM125210 3 0.0162 0.983 0.004 0.000 0.996 0.000 0.000
#> GSM125212 4 0.0000 0.913 0.000 0.000 0.000 1.000 0.000
#> GSM125214 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125216 2 0.1544 0.954 0.000 0.932 0.000 0.068 0.000
#> GSM125218 4 0.0162 0.912 0.000 0.004 0.000 0.996 0.000
#> GSM125220 1 0.2984 0.864 0.860 0.000 0.108 0.000 0.032
#> GSM125222 3 0.0162 0.982 0.000 0.000 0.996 0.004 0.000
#> GSM125224 2 0.0000 0.925 0.000 1.000 0.000 0.000 0.000
#> GSM125226 4 0.0609 0.911 0.000 0.020 0.000 0.980 0.000
#> GSM125228 2 0.0703 0.937 0.000 0.976 0.000 0.024 0.000
#> GSM125230 3 0.1074 0.967 0.004 0.000 0.968 0.016 0.012
#> GSM125232 3 0.2280 0.853 0.000 0.000 0.880 0.000 0.120
#> GSM125234 5 0.4138 0.379 0.000 0.000 0.384 0.000 0.616
#> GSM125236 5 0.0000 0.974 0.000 0.000 0.000 0.000 1.000
#> GSM125238 1 0.0880 0.980 0.968 0.000 0.000 0.000 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.0692 0.9432 0.976 0.000 0.000 0.004 0.020 0.000
#> GSM125125 1 0.1398 0.9408 0.940 0.000 0.000 0.008 0.052 0.000
#> GSM125127 1 0.1686 0.9092 0.924 0.000 0.064 0.012 0.000 0.000
#> GSM125129 1 0.0972 0.9448 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM125131 5 0.0713 0.9594 0.028 0.000 0.000 0.000 0.972 0.000
#> GSM125133 5 0.0547 0.9605 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125135 1 0.1625 0.9265 0.928 0.000 0.000 0.012 0.060 0.000
#> GSM125137 5 0.0547 0.9609 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM125139 1 0.0806 0.9429 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM125141 5 0.0260 0.9645 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125143 1 0.1334 0.9340 0.948 0.000 0.032 0.000 0.020 0.000
#> GSM125145 1 0.1176 0.9412 0.956 0.000 0.000 0.020 0.024 0.000
#> GSM125147 5 0.0547 0.9605 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125149 5 0.0458 0.9627 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM125151 1 0.0806 0.9429 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM125153 1 0.2170 0.8882 0.888 0.000 0.000 0.012 0.100 0.000
#> GSM125155 5 0.1957 0.8620 0.112 0.000 0.000 0.000 0.888 0.000
#> GSM125157 5 0.0146 0.9659 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM125159 6 0.1838 0.7800 0.000 0.068 0.000 0.016 0.000 0.916
#> GSM125161 5 0.0547 0.9609 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM125163 6 0.3833 0.2440 0.000 0.444 0.000 0.000 0.000 0.556
#> GSM125165 4 0.4325 0.6941 0.000 0.000 0.064 0.692 0.000 0.244
#> GSM125167 6 0.2312 0.7691 0.000 0.112 0.000 0.012 0.000 0.876
#> GSM125169 6 0.0458 0.7604 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM125171 2 0.2230 0.8582 0.000 0.892 0.084 0.000 0.000 0.024
#> GSM125173 4 0.5416 0.6788 0.000 0.000 0.224 0.580 0.000 0.196
#> GSM125175 2 0.0790 0.9227 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM125177 3 0.0405 0.9150 0.000 0.000 0.988 0.004 0.000 0.008
#> GSM125179 3 0.0146 0.9174 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM125181 4 0.3321 0.7293 0.000 0.000 0.080 0.820 0.000 0.100
#> GSM125183 3 0.0790 0.9046 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM125185 3 0.1957 0.8692 0.000 0.000 0.888 0.112 0.000 0.000
#> GSM125187 3 0.0909 0.9100 0.000 0.000 0.968 0.012 0.020 0.000
#> GSM125189 6 0.1367 0.7801 0.000 0.044 0.000 0.012 0.000 0.944
#> GSM125191 6 0.3757 0.7459 0.000 0.084 0.028 0.076 0.000 0.812
#> GSM125193 4 0.4199 0.7428 0.000 0.000 0.100 0.736 0.000 0.164
#> GSM125195 3 0.0632 0.9161 0.000 0.000 0.976 0.024 0.000 0.000
#> GSM125197 2 0.0937 0.9052 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125199 5 0.0000 0.9664 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125201 2 0.0713 0.9232 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM125203 3 0.4403 0.0709 0.000 0.000 0.564 0.408 0.000 0.028
#> GSM125205 2 0.0713 0.9232 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM125207 3 0.1957 0.8692 0.000 0.000 0.888 0.112 0.000 0.000
#> GSM125209 6 0.4301 0.5737 0.000 0.000 0.064 0.240 0.000 0.696
#> GSM125211 4 0.3446 0.6065 0.000 0.000 0.000 0.692 0.000 0.308
#> GSM125213 6 0.2744 0.7465 0.000 0.144 0.000 0.016 0.000 0.840
#> GSM125215 2 0.1141 0.9149 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM125217 6 0.0547 0.7589 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM125219 1 0.1148 0.9411 0.960 0.000 0.016 0.004 0.020 0.000
#> GSM125221 4 0.4486 0.7462 0.000 0.000 0.184 0.704 0.000 0.112
#> GSM125223 2 0.0937 0.9052 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125225 2 0.1501 0.8994 0.000 0.924 0.000 0.000 0.000 0.076
#> GSM125227 2 0.0891 0.9151 0.000 0.968 0.000 0.024 0.000 0.008
#> GSM125229 6 0.3672 0.1305 0.000 0.000 0.000 0.368 0.000 0.632
#> GSM125231 3 0.1204 0.8895 0.000 0.000 0.944 0.056 0.000 0.000
#> GSM125233 1 0.0806 0.9429 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM125235 5 0.0000 0.9664 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125237 5 0.0000 0.9664 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125124 1 0.0260 0.9421 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM125126 5 0.1663 0.8910 0.088 0.000 0.000 0.000 0.912 0.000
#> GSM125128 5 0.0000 0.9664 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125130 1 0.2081 0.9210 0.916 0.000 0.036 0.036 0.012 0.000
#> GSM125132 5 0.0865 0.9560 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM125134 1 0.1092 0.9418 0.960 0.000 0.000 0.020 0.020 0.000
#> GSM125136 5 0.0458 0.9627 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM125138 1 0.0405 0.9428 0.988 0.000 0.000 0.008 0.004 0.000
#> GSM125140 1 0.0806 0.9429 0.972 0.000 0.000 0.008 0.020 0.000
#> GSM125142 1 0.1524 0.9244 0.932 0.000 0.000 0.008 0.060 0.000
#> GSM125144 1 0.0260 0.9421 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM125146 1 0.1297 0.9349 0.948 0.000 0.000 0.012 0.040 0.000
#> GSM125148 5 0.0790 0.9581 0.032 0.000 0.000 0.000 0.968 0.000
#> GSM125150 1 0.2558 0.8303 0.840 0.000 0.000 0.004 0.156 0.000
#> GSM125152 1 0.0909 0.9424 0.968 0.000 0.000 0.012 0.020 0.000
#> GSM125154 1 0.1176 0.9412 0.956 0.000 0.000 0.020 0.024 0.000
#> GSM125156 1 0.0937 0.9443 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125158 1 0.0692 0.9428 0.976 0.000 0.000 0.004 0.020 0.000
#> GSM125160 6 0.2692 0.7447 0.000 0.148 0.000 0.012 0.000 0.840
#> GSM125162 5 0.0547 0.9609 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM125164 2 0.2573 0.8340 0.000 0.856 0.008 0.004 0.000 0.132
#> GSM125166 2 0.1863 0.8698 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM125168 6 0.6264 0.3400 0.000 0.128 0.328 0.048 0.000 0.496
#> GSM125170 3 0.0603 0.9131 0.000 0.000 0.980 0.016 0.000 0.004
#> GSM125172 2 0.0713 0.9232 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM125174 3 0.0146 0.9174 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM125176 2 0.4642 0.1601 0.000 0.508 0.452 0.000 0.000 0.040
#> GSM125178 3 0.0146 0.9167 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM125180 3 0.0260 0.9168 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM125182 6 0.4638 0.4794 0.000 0.000 0.068 0.296 0.000 0.636
#> GSM125184 3 0.0146 0.9174 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM125186 3 0.1910 0.8723 0.000 0.000 0.892 0.108 0.000 0.000
#> GSM125188 4 0.3307 0.7254 0.000 0.000 0.072 0.820 0.000 0.108
#> GSM125190 6 0.2001 0.7751 0.000 0.044 0.020 0.016 0.000 0.920
#> GSM125192 2 0.1007 0.9189 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM125194 4 0.4510 0.4673 0.008 0.000 0.416 0.556 0.020 0.000
#> GSM125196 3 0.0363 0.9179 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM125198 2 0.0937 0.9052 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125200 1 0.0692 0.9428 0.976 0.000 0.000 0.004 0.020 0.000
#> GSM125202 2 0.0713 0.9232 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM125204 3 0.1196 0.9106 0.000 0.000 0.952 0.040 0.008 0.000
#> GSM125206 3 0.0146 0.9167 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM125208 3 0.2260 0.8475 0.000 0.000 0.860 0.140 0.000 0.000
#> GSM125210 3 0.1387 0.8976 0.000 0.000 0.932 0.068 0.000 0.000
#> GSM125212 4 0.3797 0.4344 0.000 0.000 0.000 0.580 0.000 0.420
#> GSM125214 2 0.1267 0.9126 0.000 0.940 0.000 0.000 0.000 0.060
#> GSM125216 2 0.0790 0.9227 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM125218 6 0.0547 0.7589 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM125220 5 0.1556 0.8796 0.000 0.000 0.080 0.000 0.920 0.000
#> GSM125222 4 0.3817 0.4770 0.000 0.000 0.432 0.568 0.000 0.000
#> GSM125224 2 0.0937 0.9052 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125226 6 0.1461 0.7775 0.000 0.044 0.000 0.016 0.000 0.940
#> GSM125228 2 0.0972 0.9138 0.000 0.964 0.000 0.028 0.000 0.008
#> GSM125230 4 0.4391 0.6365 0.000 0.000 0.320 0.644 0.028 0.008
#> GSM125232 3 0.3032 0.7746 0.104 0.000 0.840 0.056 0.000 0.000
#> GSM125234 1 0.4893 0.3862 0.572 0.000 0.356 0.072 0.000 0.000
#> GSM125236 1 0.1478 0.9344 0.944 0.000 0.032 0.004 0.020 0.000
#> GSM125238 5 0.0547 0.9605 0.020 0.000 0.000 0.000 0.980 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 agent(p) individual(p) k
#> SD:mclust 116 1.000 6.52e-06 2
#> SD:mclust 108 0.815 1.81e-07 3
#> SD:mclust 105 0.716 2.62e-05 4
#> SD:mclust 114 0.316 4.20e-06 5
#> SD:mclust 106 0.395 3.42e-06 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.894 0.928 0.970 0.5023 0.496 0.496
#> 3 3 0.750 0.832 0.914 0.3090 0.794 0.605
#> 4 4 0.596 0.631 0.794 0.1193 0.887 0.689
#> 5 5 0.658 0.588 0.780 0.0528 0.911 0.699
#> 6 6 0.686 0.573 0.748 0.0320 0.949 0.793
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
#> GSM125123 1 0.0000 0.982 1.000 0.000
#> GSM125125 1 0.0000 0.982 1.000 0.000
#> GSM125127 1 0.0000 0.982 1.000 0.000
#> GSM125129 1 0.0000 0.982 1.000 0.000
#> GSM125131 1 0.0000 0.982 1.000 0.000
#> GSM125133 1 0.0000 0.982 1.000 0.000
#> GSM125135 1 0.0000 0.982 1.000 0.000
#> GSM125137 1 0.0000 0.982 1.000 0.000
#> GSM125139 1 0.0000 0.982 1.000 0.000
#> GSM125141 1 0.0000 0.982 1.000 0.000
#> GSM125143 1 0.0000 0.982 1.000 0.000
#> GSM125145 1 0.0000 0.982 1.000 0.000
#> GSM125147 1 0.0000 0.982 1.000 0.000
#> GSM125149 1 0.0000 0.982 1.000 0.000
#> GSM125151 1 0.0000 0.982 1.000 0.000
#> GSM125153 1 0.0000 0.982 1.000 0.000
#> GSM125155 1 0.0000 0.982 1.000 0.000
#> GSM125157 1 0.0000 0.982 1.000 0.000
#> GSM125159 2 0.0000 0.954 0.000 1.000
#> GSM125161 1 0.0000 0.982 1.000 0.000
#> GSM125163 2 0.0000 0.954 0.000 1.000
#> GSM125165 2 0.0000 0.954 0.000 1.000
#> GSM125167 2 0.0000 0.954 0.000 1.000
#> GSM125169 2 0.0376 0.951 0.004 0.996
#> GSM125171 2 0.0000 0.954 0.000 1.000
#> GSM125173 2 0.0000 0.954 0.000 1.000
#> GSM125175 2 0.0000 0.954 0.000 1.000
#> GSM125177 2 0.0000 0.954 0.000 1.000
#> GSM125179 2 0.9977 0.155 0.472 0.528
#> GSM125181 2 0.0000 0.954 0.000 1.000
#> GSM125183 2 0.9393 0.484 0.356 0.644
#> GSM125185 2 0.0000 0.954 0.000 1.000
#> GSM125187 1 0.1184 0.967 0.984 0.016
#> GSM125189 2 0.0000 0.954 0.000 1.000
#> GSM125191 2 0.0000 0.954 0.000 1.000
#> GSM125193 1 0.0376 0.978 0.996 0.004
#> GSM125195 1 0.7219 0.738 0.800 0.200
#> GSM125197 2 0.0000 0.954 0.000 1.000
#> GSM125199 1 0.0000 0.982 1.000 0.000
#> GSM125201 2 0.0000 0.954 0.000 1.000
#> GSM125203 2 0.9522 0.446 0.372 0.628
#> GSM125205 2 0.0000 0.954 0.000 1.000
#> GSM125207 2 0.0000 0.954 0.000 1.000
#> GSM125209 2 0.0000 0.954 0.000 1.000
#> GSM125211 2 0.0000 0.954 0.000 1.000
#> GSM125213 2 0.0000 0.954 0.000 1.000
#> GSM125215 2 0.0000 0.954 0.000 1.000
#> GSM125217 2 0.0000 0.954 0.000 1.000
#> GSM125219 1 0.0000 0.982 1.000 0.000
#> GSM125221 2 0.7299 0.750 0.204 0.796
#> GSM125223 2 0.0000 0.954 0.000 1.000
#> GSM125225 2 0.0000 0.954 0.000 1.000
#> GSM125227 2 0.0000 0.954 0.000 1.000
#> GSM125229 2 0.0000 0.954 0.000 1.000
#> GSM125231 1 0.0000 0.982 1.000 0.000
#> GSM125233 1 0.0000 0.982 1.000 0.000
#> GSM125235 1 0.0000 0.982 1.000 0.000
#> GSM125237 1 0.0000 0.982 1.000 0.000
#> GSM125124 1 0.0000 0.982 1.000 0.000
#> GSM125126 1 0.0000 0.982 1.000 0.000
#> GSM125128 1 0.0000 0.982 1.000 0.000
#> GSM125130 1 0.0000 0.982 1.000 0.000
#> GSM125132 1 0.0000 0.982 1.000 0.000
#> GSM125134 1 0.0000 0.982 1.000 0.000
#> GSM125136 1 0.0000 0.982 1.000 0.000
#> GSM125138 1 0.0000 0.982 1.000 0.000
#> GSM125140 1 0.0000 0.982 1.000 0.000
#> GSM125142 1 0.0000 0.982 1.000 0.000
#> GSM125144 1 0.0000 0.982 1.000 0.000
#> GSM125146 1 0.0000 0.982 1.000 0.000
#> GSM125148 1 0.0000 0.982 1.000 0.000
#> GSM125150 1 0.0000 0.982 1.000 0.000
#> GSM125152 1 0.0000 0.982 1.000 0.000
#> GSM125154 1 0.0000 0.982 1.000 0.000
#> GSM125156 1 0.0000 0.982 1.000 0.000
#> GSM125158 1 0.0000 0.982 1.000 0.000
#> GSM125160 2 0.0000 0.954 0.000 1.000
#> GSM125162 1 0.0000 0.982 1.000 0.000
#> GSM125164 2 0.0000 0.954 0.000 1.000
#> GSM125166 2 0.0000 0.954 0.000 1.000
#> GSM125168 2 0.0000 0.954 0.000 1.000
#> GSM125170 2 0.0000 0.954 0.000 1.000
#> GSM125172 2 0.0000 0.954 0.000 1.000
#> GSM125174 2 0.2043 0.929 0.032 0.968
#> GSM125176 2 0.0000 0.954 0.000 1.000
#> GSM125178 1 0.9209 0.465 0.664 0.336
#> GSM125180 1 0.6148 0.809 0.848 0.152
#> GSM125182 2 0.0000 0.954 0.000 1.000
#> GSM125184 2 0.0000 0.954 0.000 1.000
#> GSM125186 2 0.9608 0.410 0.384 0.616
#> GSM125188 2 0.0000 0.954 0.000 1.000
#> GSM125190 2 0.0000 0.954 0.000 1.000
#> GSM125192 2 0.0000 0.954 0.000 1.000
#> GSM125194 1 0.0000 0.982 1.000 0.000
#> GSM125196 2 0.7139 0.760 0.196 0.804
#> GSM125198 2 0.0000 0.954 0.000 1.000
#> GSM125200 1 0.0000 0.982 1.000 0.000
#> GSM125202 2 0.0000 0.954 0.000 1.000
#> GSM125204 1 0.7602 0.705 0.780 0.220
#> GSM125206 2 0.6531 0.796 0.168 0.832
#> GSM125208 2 0.8608 0.622 0.284 0.716
#> GSM125210 2 0.0000 0.954 0.000 1.000
#> GSM125212 2 0.0000 0.954 0.000 1.000
#> GSM125214 2 0.0000 0.954 0.000 1.000
#> GSM125216 2 0.0000 0.954 0.000 1.000
#> GSM125218 2 0.0000 0.954 0.000 1.000
#> GSM125220 1 0.0000 0.982 1.000 0.000
#> GSM125222 2 0.4298 0.880 0.088 0.912
#> GSM125224 2 0.0000 0.954 0.000 1.000
#> GSM125226 2 0.0000 0.954 0.000 1.000
#> GSM125228 2 0.0000 0.954 0.000 1.000
#> GSM125230 1 0.2236 0.948 0.964 0.036
#> GSM125232 1 0.0000 0.982 1.000 0.000
#> GSM125234 1 0.0000 0.982 1.000 0.000
#> GSM125236 1 0.0000 0.982 1.000 0.000
#> GSM125238 1 0.0000 0.982 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.4974 0.7363 0.764 0.000 0.236
#> GSM125125 1 0.1163 0.9186 0.972 0.000 0.028
#> GSM125127 1 0.5733 0.5764 0.676 0.000 0.324
#> GSM125129 1 0.3686 0.8461 0.860 0.000 0.140
#> GSM125131 1 0.0000 0.9193 1.000 0.000 0.000
#> GSM125133 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125135 1 0.1411 0.9163 0.964 0.000 0.036
#> GSM125137 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125139 1 0.5254 0.6935 0.736 0.000 0.264
#> GSM125141 1 0.0237 0.9201 0.996 0.000 0.004
#> GSM125143 1 0.4887 0.7459 0.772 0.000 0.228
#> GSM125145 1 0.2448 0.8965 0.924 0.000 0.076
#> GSM125147 1 0.0000 0.9193 1.000 0.000 0.000
#> GSM125149 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125151 3 0.6111 0.3205 0.396 0.000 0.604
#> GSM125153 1 0.1163 0.9182 0.972 0.000 0.028
#> GSM125155 1 0.0747 0.9199 0.984 0.000 0.016
#> GSM125157 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125159 2 0.0829 0.9178 0.004 0.984 0.012
#> GSM125161 1 0.0475 0.9156 0.992 0.004 0.004
#> GSM125163 2 0.0237 0.9195 0.000 0.996 0.004
#> GSM125165 2 0.1877 0.9125 0.012 0.956 0.032
#> GSM125167 2 0.0829 0.9178 0.004 0.984 0.012
#> GSM125169 2 0.5541 0.6823 0.252 0.740 0.008
#> GSM125171 2 0.0424 0.9193 0.000 0.992 0.008
#> GSM125173 2 0.0747 0.9180 0.000 0.984 0.016
#> GSM125175 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125177 3 0.6307 -0.0681 0.000 0.488 0.512
#> GSM125179 3 0.0424 0.8658 0.008 0.000 0.992
#> GSM125181 2 0.4233 0.8231 0.004 0.836 0.160
#> GSM125183 3 0.4290 0.8301 0.064 0.064 0.872
#> GSM125185 3 0.1031 0.8593 0.000 0.024 0.976
#> GSM125187 3 0.0424 0.8655 0.008 0.000 0.992
#> GSM125189 2 0.0661 0.9173 0.004 0.988 0.008
#> GSM125191 2 0.2625 0.8848 0.000 0.916 0.084
#> GSM125193 1 0.0661 0.9129 0.988 0.004 0.008
#> GSM125195 3 0.0592 0.8652 0.012 0.000 0.988
#> GSM125197 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125199 1 0.0237 0.9201 0.996 0.000 0.004
#> GSM125201 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125203 2 0.8886 0.4264 0.188 0.572 0.240
#> GSM125205 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125207 3 0.1163 0.8577 0.000 0.028 0.972
#> GSM125209 2 0.4654 0.7720 0.000 0.792 0.208
#> GSM125211 2 0.2804 0.8872 0.060 0.924 0.016
#> GSM125213 2 0.0892 0.9171 0.000 0.980 0.020
#> GSM125215 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125217 2 0.2280 0.8925 0.052 0.940 0.008
#> GSM125219 1 0.4062 0.8222 0.836 0.000 0.164
#> GSM125221 2 0.6702 0.5614 0.328 0.648 0.024
#> GSM125223 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125225 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM125227 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125229 2 0.4808 0.7626 0.188 0.804 0.008
#> GSM125231 3 0.4346 0.7471 0.184 0.000 0.816
#> GSM125233 1 0.5058 0.7234 0.756 0.000 0.244
#> GSM125235 1 0.0000 0.9193 1.000 0.000 0.000
#> GSM125237 1 0.0237 0.9201 0.996 0.000 0.004
#> GSM125124 3 0.2165 0.8436 0.064 0.000 0.936
#> GSM125126 1 0.0747 0.9201 0.984 0.000 0.016
#> GSM125128 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125130 3 0.2796 0.8248 0.092 0.000 0.908
#> GSM125132 1 0.0237 0.9201 0.996 0.000 0.004
#> GSM125134 1 0.2878 0.8832 0.904 0.000 0.096
#> GSM125136 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125138 3 0.6111 0.3347 0.396 0.000 0.604
#> GSM125140 1 0.5431 0.6600 0.716 0.000 0.284
#> GSM125142 1 0.1411 0.9161 0.964 0.000 0.036
#> GSM125144 3 0.5016 0.6648 0.240 0.000 0.760
#> GSM125146 1 0.1964 0.9081 0.944 0.000 0.056
#> GSM125148 1 0.0237 0.9201 0.996 0.000 0.004
#> GSM125150 1 0.0747 0.9199 0.984 0.000 0.016
#> GSM125152 3 0.4654 0.7081 0.208 0.000 0.792
#> GSM125154 1 0.3192 0.8720 0.888 0.000 0.112
#> GSM125156 1 0.1643 0.9137 0.956 0.000 0.044
#> GSM125158 1 0.1643 0.9133 0.956 0.000 0.044
#> GSM125160 2 0.0424 0.9191 0.000 0.992 0.008
#> GSM125162 1 0.0475 0.9156 0.992 0.004 0.004
#> GSM125164 2 0.1289 0.9133 0.000 0.968 0.032
#> GSM125166 2 0.0424 0.9194 0.000 0.992 0.008
#> GSM125168 2 0.3551 0.8450 0.000 0.868 0.132
#> GSM125170 2 0.1860 0.9034 0.000 0.948 0.052
#> GSM125172 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125174 3 0.1411 0.8548 0.000 0.036 0.964
#> GSM125176 2 0.5560 0.6298 0.000 0.700 0.300
#> GSM125178 3 0.4473 0.7665 0.164 0.008 0.828
#> GSM125180 3 0.0424 0.8658 0.008 0.000 0.992
#> GSM125182 2 0.5678 0.6205 0.000 0.684 0.316
#> GSM125184 3 0.1411 0.8538 0.000 0.036 0.964
#> GSM125186 3 0.0237 0.8654 0.004 0.000 0.996
#> GSM125188 2 0.5845 0.6288 0.004 0.688 0.308
#> GSM125190 2 0.1015 0.9153 0.012 0.980 0.008
#> GSM125192 2 0.0747 0.9181 0.000 0.984 0.016
#> GSM125194 1 0.6228 0.3945 0.624 0.004 0.372
#> GSM125196 3 0.0661 0.8650 0.004 0.008 0.988
#> GSM125198 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125200 1 0.1529 0.9148 0.960 0.000 0.040
#> GSM125202 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125204 3 0.0848 0.8662 0.008 0.008 0.984
#> GSM125206 3 0.2492 0.8531 0.016 0.048 0.936
#> GSM125208 3 0.0475 0.8658 0.004 0.004 0.992
#> GSM125210 3 0.1753 0.8446 0.000 0.048 0.952
#> GSM125212 2 0.1781 0.9119 0.020 0.960 0.020
#> GSM125214 2 0.0892 0.9172 0.000 0.980 0.020
#> GSM125216 2 0.1163 0.9153 0.000 0.972 0.028
#> GSM125218 2 0.2384 0.8898 0.056 0.936 0.008
#> GSM125220 1 0.0237 0.9180 0.996 0.000 0.004
#> GSM125222 2 0.7717 0.6091 0.112 0.668 0.220
#> GSM125224 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125226 2 0.0661 0.9173 0.004 0.988 0.008
#> GSM125228 2 0.0237 0.9196 0.000 0.996 0.004
#> GSM125230 3 0.6950 0.1133 0.476 0.016 0.508
#> GSM125232 3 0.0747 0.8646 0.016 0.000 0.984
#> GSM125234 3 0.1289 0.8586 0.032 0.000 0.968
#> GSM125236 1 0.2796 0.8849 0.908 0.000 0.092
#> GSM125238 1 0.0000 0.9193 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.4827 0.72998 0.784 0.000 0.124 0.092
#> GSM125125 1 0.0524 0.84170 0.988 0.000 0.008 0.004
#> GSM125127 1 0.5271 0.49203 0.640 0.000 0.340 0.020
#> GSM125129 1 0.3658 0.77379 0.836 0.000 0.144 0.020
#> GSM125131 1 0.1211 0.84061 0.960 0.000 0.000 0.040
#> GSM125133 1 0.1867 0.83290 0.928 0.000 0.000 0.072
#> GSM125135 1 0.1890 0.82846 0.936 0.000 0.056 0.008
#> GSM125137 1 0.4730 0.51962 0.636 0.000 0.000 0.364
#> GSM125139 1 0.3182 0.80585 0.876 0.000 0.096 0.028
#> GSM125141 1 0.2281 0.82431 0.904 0.000 0.000 0.096
#> GSM125143 1 0.2796 0.81811 0.892 0.000 0.092 0.016
#> GSM125145 1 0.4204 0.72670 0.788 0.000 0.192 0.020
#> GSM125147 1 0.1474 0.83709 0.948 0.000 0.000 0.052
#> GSM125149 1 0.3528 0.75584 0.808 0.000 0.000 0.192
#> GSM125151 1 0.6626 0.14147 0.528 0.000 0.384 0.088
#> GSM125153 1 0.1854 0.83186 0.940 0.000 0.048 0.012
#> GSM125155 1 0.1867 0.83404 0.928 0.000 0.000 0.072
#> GSM125157 1 0.3172 0.78342 0.840 0.000 0.000 0.160
#> GSM125159 4 0.4819 0.37826 0.000 0.344 0.004 0.652
#> GSM125161 1 0.4776 0.49440 0.624 0.000 0.000 0.376
#> GSM125163 2 0.0921 0.80183 0.000 0.972 0.000 0.028
#> GSM125165 4 0.4050 0.65593 0.000 0.168 0.024 0.808
#> GSM125167 2 0.4477 0.53640 0.000 0.688 0.000 0.312
#> GSM125169 2 0.6850 0.37849 0.188 0.600 0.000 0.212
#> GSM125171 2 0.3972 0.65607 0.004 0.816 0.164 0.016
#> GSM125173 2 0.5353 0.25159 0.000 0.556 0.012 0.432
#> GSM125175 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125177 3 0.7205 0.10431 0.000 0.172 0.532 0.296
#> GSM125179 3 0.1902 0.63587 0.004 0.000 0.932 0.064
#> GSM125181 4 0.2775 0.64394 0.000 0.020 0.084 0.896
#> GSM125183 4 0.5530 0.40255 0.020 0.004 0.360 0.616
#> GSM125185 3 0.4661 0.40774 0.000 0.000 0.652 0.348
#> GSM125187 4 0.4992 0.01792 0.000 0.000 0.476 0.524
#> GSM125189 2 0.4679 0.47964 0.000 0.648 0.000 0.352
#> GSM125191 2 0.6801 -0.01952 0.000 0.456 0.096 0.448
#> GSM125193 4 0.3400 0.58663 0.180 0.000 0.000 0.820
#> GSM125195 3 0.3853 0.61563 0.020 0.000 0.820 0.160
#> GSM125197 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125199 1 0.2081 0.82884 0.916 0.000 0.000 0.084
#> GSM125201 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125203 4 0.6795 0.53285 0.016 0.140 0.196 0.648
#> GSM125205 2 0.1182 0.78955 0.000 0.968 0.016 0.016
#> GSM125207 3 0.4817 0.33193 0.000 0.000 0.612 0.388
#> GSM125209 4 0.7176 0.42014 0.000 0.196 0.252 0.552
#> GSM125211 4 0.3324 0.67152 0.012 0.136 0.000 0.852
#> GSM125213 2 0.5420 0.42896 0.000 0.624 0.024 0.352
#> GSM125215 2 0.0188 0.80683 0.000 0.996 0.000 0.004
#> GSM125217 4 0.4677 0.41153 0.004 0.316 0.000 0.680
#> GSM125219 1 0.4171 0.77313 0.828 0.000 0.084 0.088
#> GSM125221 4 0.4664 0.64427 0.128 0.068 0.004 0.800
#> GSM125223 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0921 0.80183 0.000 0.972 0.000 0.028
#> GSM125227 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125229 4 0.5428 0.60077 0.140 0.120 0.000 0.740
#> GSM125231 3 0.4833 0.55947 0.228 0.000 0.740 0.032
#> GSM125233 1 0.4205 0.76384 0.820 0.000 0.124 0.056
#> GSM125235 1 0.2149 0.82822 0.912 0.000 0.000 0.088
#> GSM125237 1 0.2408 0.81970 0.896 0.000 0.000 0.104
#> GSM125124 3 0.4019 0.58545 0.196 0.000 0.792 0.012
#> GSM125126 1 0.0336 0.84193 0.992 0.000 0.000 0.008
#> GSM125128 1 0.2149 0.83014 0.912 0.000 0.000 0.088
#> GSM125130 3 0.6037 0.46324 0.304 0.000 0.628 0.068
#> GSM125132 1 0.0592 0.84183 0.984 0.000 0.000 0.016
#> GSM125134 1 0.4775 0.66846 0.740 0.000 0.232 0.028
#> GSM125136 1 0.3764 0.73277 0.784 0.000 0.000 0.216
#> GSM125138 3 0.5708 0.17583 0.416 0.000 0.556 0.028
#> GSM125140 1 0.2868 0.79545 0.864 0.000 0.136 0.000
#> GSM125142 1 0.0779 0.84062 0.980 0.000 0.016 0.004
#> GSM125144 3 0.5643 0.13938 0.428 0.000 0.548 0.024
#> GSM125146 1 0.3991 0.74609 0.808 0.000 0.172 0.020
#> GSM125148 1 0.0000 0.84158 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.84158 1.000 0.000 0.000 0.000
#> GSM125152 3 0.6000 0.37963 0.356 0.000 0.592 0.052
#> GSM125154 1 0.4163 0.73060 0.792 0.000 0.188 0.020
#> GSM125156 1 0.0376 0.84228 0.992 0.000 0.004 0.004
#> GSM125158 1 0.1118 0.83588 0.964 0.000 0.036 0.000
#> GSM125160 2 0.4661 0.46925 0.000 0.652 0.000 0.348
#> GSM125162 1 0.4624 0.56043 0.660 0.000 0.000 0.340
#> GSM125164 2 0.1970 0.78923 0.000 0.932 0.008 0.060
#> GSM125166 2 0.1022 0.80051 0.000 0.968 0.000 0.032
#> GSM125168 2 0.7015 0.02408 0.000 0.484 0.120 0.396
#> GSM125170 2 0.4638 0.70192 0.000 0.788 0.060 0.152
#> GSM125172 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125174 3 0.2611 0.62880 0.008 0.000 0.896 0.096
#> GSM125176 2 0.3810 0.65322 0.000 0.804 0.188 0.008
#> GSM125178 3 0.5769 0.26199 0.036 0.000 0.588 0.376
#> GSM125180 3 0.1356 0.63839 0.008 0.000 0.960 0.032
#> GSM125182 4 0.6488 0.41904 0.000 0.104 0.292 0.604
#> GSM125184 3 0.3340 0.59872 0.004 0.004 0.848 0.144
#> GSM125186 3 0.4431 0.47984 0.000 0.000 0.696 0.304
#> GSM125188 4 0.3392 0.62233 0.000 0.020 0.124 0.856
#> GSM125190 2 0.3528 0.69738 0.000 0.808 0.000 0.192
#> GSM125192 2 0.0657 0.80577 0.000 0.984 0.004 0.012
#> GSM125194 4 0.3991 0.63792 0.120 0.000 0.048 0.832
#> GSM125196 3 0.3219 0.60457 0.000 0.000 0.836 0.164
#> GSM125198 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125200 1 0.1004 0.83963 0.972 0.000 0.024 0.004
#> GSM125202 2 0.0524 0.80127 0.000 0.988 0.004 0.008
#> GSM125204 3 0.4677 0.46669 0.000 0.004 0.680 0.316
#> GSM125206 3 0.4266 0.61581 0.040 0.056 0.848 0.056
#> GSM125208 4 0.4992 0.00885 0.000 0.000 0.476 0.524
#> GSM125210 3 0.4164 0.52696 0.000 0.000 0.736 0.264
#> GSM125212 4 0.3538 0.65957 0.004 0.160 0.004 0.832
#> GSM125214 2 0.0376 0.80659 0.000 0.992 0.004 0.004
#> GSM125216 2 0.0336 0.80554 0.000 0.992 0.008 0.000
#> GSM125218 2 0.5155 0.16795 0.004 0.528 0.000 0.468
#> GSM125220 1 0.3649 0.74824 0.796 0.000 0.000 0.204
#> GSM125222 4 0.5692 0.67729 0.024 0.136 0.088 0.752
#> GSM125224 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125226 2 0.4679 0.47821 0.000 0.648 0.000 0.352
#> GSM125228 2 0.0000 0.80684 0.000 1.000 0.000 0.000
#> GSM125230 4 0.4785 0.65478 0.080 0.020 0.088 0.812
#> GSM125232 3 0.2443 0.63738 0.024 0.000 0.916 0.060
#> GSM125234 3 0.5435 0.57493 0.204 0.004 0.728 0.064
#> GSM125236 1 0.3160 0.79933 0.872 0.000 0.108 0.020
#> GSM125238 1 0.2216 0.82581 0.908 0.000 0.000 0.092
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2538 0.84597 0.900 0.000 0.048 0.004 0.048
#> GSM125125 1 0.0960 0.86397 0.972 0.000 0.008 0.004 0.016
#> GSM125127 1 0.6098 0.41599 0.592 0.008 0.104 0.008 0.288
#> GSM125129 1 0.3065 0.83715 0.872 0.000 0.072 0.008 0.048
#> GSM125131 1 0.0404 0.86547 0.988 0.000 0.000 0.012 0.000
#> GSM125133 1 0.0703 0.86429 0.976 0.000 0.000 0.024 0.000
#> GSM125135 1 0.2938 0.84473 0.880 0.000 0.064 0.008 0.048
#> GSM125137 1 0.4366 0.57990 0.664 0.000 0.016 0.320 0.000
#> GSM125139 1 0.2419 0.85207 0.904 0.000 0.028 0.004 0.064
#> GSM125141 1 0.2172 0.85070 0.908 0.000 0.016 0.076 0.000
#> GSM125143 1 0.3961 0.78239 0.792 0.000 0.160 0.004 0.044
#> GSM125145 1 0.3292 0.81183 0.844 0.000 0.032 0.004 0.120
#> GSM125147 1 0.1205 0.86249 0.956 0.000 0.004 0.040 0.000
#> GSM125149 1 0.1851 0.84584 0.912 0.000 0.000 0.088 0.000
#> GSM125151 1 0.5411 0.58885 0.664 0.000 0.160 0.000 0.176
#> GSM125153 1 0.3558 0.78404 0.816 0.000 0.020 0.008 0.156
#> GSM125155 1 0.1282 0.86450 0.952 0.000 0.000 0.044 0.004
#> GSM125157 1 0.1851 0.84584 0.912 0.000 0.000 0.088 0.000
#> GSM125159 4 0.4386 0.59637 0.000 0.096 0.140 0.764 0.000
#> GSM125161 1 0.5051 0.58380 0.664 0.000 0.072 0.264 0.000
#> GSM125163 2 0.1671 0.76587 0.000 0.924 0.000 0.076 0.000
#> GSM125165 4 0.3070 0.59851 0.004 0.088 0.008 0.872 0.028
#> GSM125167 2 0.4689 0.34115 0.000 0.592 0.008 0.392 0.008
#> GSM125169 2 0.6908 0.26093 0.160 0.516 0.020 0.296 0.008
#> GSM125171 2 0.1743 0.77010 0.004 0.940 0.028 0.000 0.028
#> GSM125173 4 0.5705 0.42240 0.000 0.288 0.024 0.624 0.064
#> GSM125175 2 0.0290 0.78549 0.000 0.992 0.000 0.008 0.000
#> GSM125177 3 0.6083 0.36463 0.000 0.024 0.608 0.104 0.264
#> GSM125179 5 0.2863 0.49533 0.000 0.000 0.064 0.060 0.876
#> GSM125181 4 0.3437 0.51730 0.000 0.012 0.176 0.808 0.004
#> GSM125183 5 0.5267 0.09762 0.000 0.008 0.032 0.428 0.532
#> GSM125185 3 0.6262 0.50008 0.000 0.000 0.520 0.176 0.304
#> GSM125187 3 0.6530 0.20722 0.000 0.000 0.424 0.380 0.196
#> GSM125189 2 0.4452 0.04190 0.000 0.500 0.004 0.496 0.000
#> GSM125191 2 0.6873 -0.13302 0.000 0.400 0.220 0.372 0.008
#> GSM125193 4 0.4101 0.53262 0.048 0.000 0.184 0.768 0.000
#> GSM125195 3 0.3459 0.60039 0.000 0.016 0.832 0.016 0.136
#> GSM125197 2 0.1544 0.76017 0.000 0.932 0.068 0.000 0.000
#> GSM125199 1 0.1121 0.86163 0.956 0.000 0.000 0.044 0.000
#> GSM125201 2 0.2707 0.70596 0.000 0.860 0.132 0.008 0.000
#> GSM125203 3 0.3269 0.48978 0.016 0.020 0.852 0.112 0.000
#> GSM125205 2 0.3475 0.65205 0.000 0.804 0.180 0.004 0.012
#> GSM125207 3 0.4930 0.56694 0.000 0.000 0.716 0.144 0.140
#> GSM125209 4 0.6849 0.13474 0.000 0.148 0.404 0.424 0.024
#> GSM125211 4 0.4000 0.53430 0.004 0.004 0.224 0.756 0.012
#> GSM125213 2 0.6270 0.09664 0.000 0.496 0.136 0.364 0.004
#> GSM125215 2 0.1281 0.78444 0.000 0.956 0.032 0.012 0.000
#> GSM125217 4 0.3639 0.59698 0.008 0.164 0.020 0.808 0.000
#> GSM125219 1 0.3346 0.82263 0.848 0.000 0.108 0.008 0.036
#> GSM125221 4 0.3412 0.58994 0.024 0.080 0.012 0.864 0.020
#> GSM125223 2 0.0703 0.78058 0.000 0.976 0.024 0.000 0.000
#> GSM125225 2 0.1544 0.77038 0.000 0.932 0.000 0.068 0.000
#> GSM125227 2 0.0693 0.78590 0.000 0.980 0.008 0.012 0.000
#> GSM125229 4 0.5101 0.43916 0.040 0.012 0.296 0.652 0.000
#> GSM125231 5 0.3491 0.49386 0.028 0.000 0.124 0.012 0.836
#> GSM125233 1 0.3880 0.78378 0.800 0.000 0.152 0.004 0.044
#> GSM125235 1 0.0963 0.86299 0.964 0.000 0.000 0.036 0.000
#> GSM125237 1 0.1478 0.85571 0.936 0.000 0.000 0.064 0.000
#> GSM125124 5 0.1918 0.52729 0.036 0.000 0.036 0.000 0.928
#> GSM125126 1 0.0324 0.86502 0.992 0.000 0.000 0.004 0.004
#> GSM125128 1 0.1915 0.85791 0.928 0.000 0.040 0.032 0.000
#> GSM125130 3 0.6635 0.22961 0.220 0.000 0.480 0.004 0.296
#> GSM125132 1 0.0162 0.86520 0.996 0.000 0.000 0.000 0.004
#> GSM125134 5 0.4971 0.00364 0.472 0.000 0.020 0.004 0.504
#> GSM125136 1 0.2448 0.83713 0.892 0.000 0.020 0.088 0.000
#> GSM125138 5 0.3863 0.50786 0.176 0.000 0.020 0.012 0.792
#> GSM125140 1 0.3037 0.83085 0.864 0.000 0.032 0.004 0.100
#> GSM125142 1 0.3503 0.80070 0.828 0.000 0.016 0.016 0.140
#> GSM125144 5 0.4113 0.46294 0.232 0.000 0.028 0.000 0.740
#> GSM125146 1 0.3935 0.73056 0.772 0.000 0.024 0.004 0.200
#> GSM125148 1 0.0854 0.86594 0.976 0.000 0.008 0.004 0.012
#> GSM125150 1 0.0671 0.86451 0.980 0.000 0.004 0.000 0.016
#> GSM125152 1 0.6225 0.11891 0.484 0.000 0.148 0.000 0.368
#> GSM125154 5 0.5278 0.21477 0.408 0.000 0.024 0.016 0.552
#> GSM125156 1 0.1278 0.86781 0.960 0.000 0.004 0.016 0.020
#> GSM125158 1 0.1285 0.85915 0.956 0.000 0.004 0.004 0.036
#> GSM125160 4 0.5741 0.34241 0.000 0.360 0.096 0.544 0.000
#> GSM125162 1 0.4496 0.67575 0.728 0.000 0.056 0.216 0.000
#> GSM125164 2 0.2894 0.73008 0.000 0.860 0.008 0.124 0.008
#> GSM125166 2 0.3323 0.72496 0.000 0.844 0.004 0.116 0.036
#> GSM125168 4 0.7216 0.17882 0.000 0.304 0.016 0.352 0.328
#> GSM125170 5 0.6918 -0.10507 0.000 0.388 0.016 0.188 0.408
#> GSM125172 2 0.0968 0.78631 0.000 0.972 0.012 0.012 0.004
#> GSM125174 5 0.1894 0.52118 0.000 0.000 0.008 0.072 0.920
#> GSM125176 2 0.4003 0.68002 0.000 0.796 0.012 0.036 0.156
#> GSM125178 3 0.6386 0.23809 0.000 0.000 0.480 0.180 0.340
#> GSM125180 5 0.3037 0.46470 0.000 0.000 0.100 0.040 0.860
#> GSM125182 4 0.5700 0.06381 0.000 0.052 0.460 0.476 0.012
#> GSM125184 5 0.2625 0.49465 0.000 0.000 0.016 0.108 0.876
#> GSM125186 3 0.6099 0.48202 0.000 0.000 0.512 0.136 0.352
#> GSM125188 4 0.4227 0.20956 0.000 0.000 0.420 0.580 0.000
#> GSM125190 2 0.6054 0.19011 0.000 0.500 0.016 0.408 0.076
#> GSM125192 2 0.1764 0.77061 0.000 0.928 0.000 0.064 0.008
#> GSM125194 4 0.3224 0.55312 0.016 0.000 0.160 0.824 0.000
#> GSM125196 3 0.3548 0.59254 0.000 0.012 0.796 0.004 0.188
#> GSM125198 2 0.1270 0.76856 0.000 0.948 0.052 0.000 0.000
#> GSM125200 1 0.0880 0.86214 0.968 0.000 0.000 0.000 0.032
#> GSM125202 2 0.2036 0.75919 0.000 0.920 0.056 0.000 0.024
#> GSM125204 3 0.2798 0.56039 0.008 0.000 0.888 0.060 0.044
#> GSM125206 3 0.5511 0.35667 0.004 0.056 0.632 0.012 0.296
#> GSM125208 3 0.4221 0.44104 0.000 0.000 0.732 0.236 0.032
#> GSM125210 3 0.6190 0.40424 0.000 0.000 0.444 0.136 0.420
#> GSM125212 4 0.4190 0.51816 0.000 0.008 0.256 0.724 0.012
#> GSM125214 2 0.0671 0.78567 0.000 0.980 0.004 0.016 0.000
#> GSM125216 2 0.0579 0.78621 0.000 0.984 0.008 0.008 0.000
#> GSM125218 4 0.4321 0.21654 0.000 0.396 0.004 0.600 0.000
#> GSM125220 1 0.2361 0.83804 0.892 0.000 0.012 0.096 0.000
#> GSM125222 4 0.5932 0.46289 0.008 0.100 0.020 0.656 0.216
#> GSM125224 2 0.0671 0.78396 0.000 0.980 0.016 0.004 0.000
#> GSM125226 2 0.5008 0.12461 0.000 0.500 0.012 0.476 0.012
#> GSM125228 2 0.0510 0.78473 0.000 0.984 0.000 0.016 0.000
#> GSM125230 4 0.4513 0.47446 0.000 0.004 0.284 0.688 0.024
#> GSM125232 5 0.1836 0.51915 0.000 0.000 0.036 0.032 0.932
#> GSM125234 3 0.6140 0.29116 0.096 0.004 0.460 0.004 0.436
#> GSM125236 1 0.2589 0.84545 0.900 0.000 0.044 0.008 0.048
#> GSM125238 1 0.1830 0.85521 0.924 0.000 0.008 0.068 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.1949 0.8380 0.904 0.000 0.000 0.004 0.004 0.088
#> GSM125125 1 0.0363 0.8607 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM125127 1 0.4973 0.6684 0.708 0.000 0.004 0.172 0.080 0.036
#> GSM125129 1 0.2271 0.8417 0.904 0.000 0.004 0.004 0.032 0.056
#> GSM125131 1 0.0000 0.8607 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.0405 0.8609 0.988 0.000 0.004 0.000 0.008 0.000
#> GSM125135 1 0.2503 0.8432 0.900 0.000 0.044 0.012 0.032 0.012
#> GSM125137 1 0.5849 0.4149 0.552 0.000 0.280 0.008 0.152 0.008
#> GSM125139 1 0.1370 0.8599 0.948 0.000 0.000 0.012 0.004 0.036
#> GSM125141 1 0.3075 0.8079 0.840 0.000 0.004 0.016 0.128 0.012
#> GSM125143 1 0.3952 0.7340 0.756 0.000 0.012 0.012 0.016 0.204
#> GSM125145 1 0.2532 0.8277 0.884 0.000 0.000 0.080 0.024 0.012
#> GSM125147 1 0.0870 0.8615 0.972 0.000 0.000 0.004 0.012 0.012
#> GSM125149 1 0.2122 0.8398 0.900 0.000 0.008 0.000 0.084 0.008
#> GSM125151 1 0.3833 0.5802 0.648 0.000 0.000 0.008 0.000 0.344
#> GSM125153 1 0.4537 0.3473 0.572 0.000 0.004 0.400 0.016 0.008
#> GSM125155 1 0.0976 0.8623 0.968 0.000 0.016 0.000 0.008 0.008
#> GSM125157 1 0.1226 0.8562 0.952 0.000 0.004 0.000 0.040 0.004
#> GSM125159 3 0.4282 0.4534 0.000 0.072 0.724 0.000 0.200 0.004
#> GSM125161 1 0.5190 0.3023 0.524 0.000 0.392 0.000 0.080 0.004
#> GSM125163 2 0.2730 0.5491 0.000 0.808 0.000 0.000 0.192 0.000
#> GSM125165 5 0.4479 0.4473 0.000 0.064 0.180 0.024 0.732 0.000
#> GSM125167 2 0.3854 -0.2516 0.000 0.536 0.000 0.000 0.464 0.000
#> GSM125169 5 0.4870 0.4033 0.048 0.436 0.000 0.000 0.512 0.004
#> GSM125171 2 0.2400 0.6759 0.004 0.904 0.008 0.032 0.048 0.004
#> GSM125173 3 0.7247 0.0580 0.000 0.112 0.360 0.196 0.332 0.000
#> GSM125175 2 0.1010 0.6997 0.000 0.960 0.000 0.004 0.036 0.000
#> GSM125177 3 0.5904 0.4586 0.000 0.060 0.668 0.052 0.156 0.064
#> GSM125179 4 0.4008 0.7052 0.000 0.000 0.004 0.768 0.100 0.128
#> GSM125181 5 0.5909 0.2441 0.000 0.028 0.188 0.000 0.576 0.208
#> GSM125183 4 0.4533 0.2295 0.000 0.004 0.024 0.504 0.468 0.000
#> GSM125185 6 0.2882 0.5838 0.000 0.000 0.004 0.028 0.120 0.848
#> GSM125187 6 0.4210 0.4377 0.000 0.004 0.008 0.008 0.344 0.636
#> GSM125189 2 0.4179 -0.3124 0.000 0.516 0.012 0.000 0.472 0.000
#> GSM125191 2 0.6254 -0.3739 0.000 0.400 0.012 0.000 0.368 0.220
#> GSM125193 3 0.5605 0.4030 0.028 0.000 0.568 0.000 0.312 0.092
#> GSM125195 6 0.5794 0.4434 0.004 0.028 0.136 0.020 0.156 0.656
#> GSM125197 2 0.2306 0.6540 0.000 0.888 0.016 0.000 0.092 0.004
#> GSM125199 1 0.0603 0.8612 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM125201 2 0.4724 0.4995 0.000 0.724 0.084 0.008 0.168 0.016
#> GSM125203 6 0.6850 0.0398 0.020 0.040 0.392 0.000 0.152 0.396
#> GSM125205 2 0.5480 0.4338 0.000 0.668 0.088 0.024 0.196 0.024
#> GSM125207 6 0.3859 0.3998 0.000 0.000 0.292 0.008 0.008 0.692
#> GSM125209 6 0.5943 0.1489 0.000 0.136 0.016 0.004 0.324 0.520
#> GSM125211 3 0.0951 0.5916 0.004 0.000 0.968 0.008 0.020 0.000
#> GSM125213 2 0.5992 -0.0978 0.000 0.532 0.024 0.000 0.288 0.156
#> GSM125215 2 0.0363 0.7072 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM125217 5 0.6165 0.3780 0.004 0.220 0.332 0.000 0.440 0.004
#> GSM125219 1 0.3073 0.7478 0.788 0.000 0.000 0.000 0.008 0.204
#> GSM125221 5 0.4967 0.5781 0.020 0.144 0.104 0.008 0.720 0.004
#> GSM125223 2 0.0632 0.7029 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM125225 2 0.2378 0.6066 0.000 0.848 0.000 0.000 0.152 0.000
#> GSM125227 2 0.0508 0.7072 0.000 0.984 0.004 0.000 0.012 0.000
#> GSM125229 3 0.1396 0.5858 0.008 0.012 0.952 0.000 0.024 0.004
#> GSM125231 4 0.2854 0.7425 0.000 0.004 0.048 0.876 0.056 0.016
#> GSM125233 1 0.3758 0.6000 0.668 0.000 0.000 0.000 0.008 0.324
#> GSM125235 1 0.0551 0.8611 0.984 0.000 0.004 0.000 0.008 0.004
#> GSM125237 1 0.0653 0.8614 0.980 0.000 0.004 0.000 0.012 0.004
#> GSM125124 4 0.1982 0.7869 0.016 0.000 0.000 0.912 0.004 0.068
#> GSM125126 1 0.0291 0.8614 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM125128 1 0.0717 0.8631 0.976 0.000 0.016 0.000 0.008 0.000
#> GSM125130 6 0.3876 0.3668 0.244 0.000 0.000 0.016 0.012 0.728
#> GSM125132 1 0.0146 0.8609 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM125134 4 0.3830 0.5205 0.280 0.000 0.004 0.704 0.004 0.008
#> GSM125136 1 0.2128 0.8428 0.908 0.000 0.056 0.000 0.032 0.004
#> GSM125138 4 0.1148 0.7910 0.020 0.000 0.000 0.960 0.016 0.004
#> GSM125140 1 0.1275 0.8609 0.956 0.000 0.000 0.012 0.016 0.016
#> GSM125142 1 0.4915 0.5568 0.652 0.000 0.004 0.264 0.072 0.008
#> GSM125144 4 0.2361 0.7485 0.104 0.000 0.000 0.880 0.004 0.012
#> GSM125146 1 0.4502 0.3933 0.588 0.000 0.004 0.384 0.016 0.008
#> GSM125148 1 0.1729 0.8526 0.936 0.000 0.004 0.036 0.012 0.012
#> GSM125150 1 0.0291 0.8614 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM125152 1 0.4555 0.5231 0.616 0.000 0.000 0.040 0.004 0.340
#> GSM125154 4 0.3698 0.7090 0.108 0.000 0.008 0.812 0.064 0.008
#> GSM125156 1 0.1010 0.8613 0.960 0.000 0.036 0.000 0.000 0.004
#> GSM125158 1 0.0405 0.8610 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM125160 3 0.5771 -0.0783 0.000 0.352 0.484 0.000 0.160 0.004
#> GSM125162 1 0.4704 0.5272 0.632 0.000 0.304 0.000 0.060 0.004
#> GSM125164 2 0.3499 0.2727 0.000 0.680 0.000 0.000 0.320 0.000
#> GSM125166 2 0.3629 0.3995 0.000 0.724 0.000 0.016 0.260 0.000
#> GSM125168 5 0.6359 0.4541 0.000 0.296 0.012 0.292 0.400 0.000
#> GSM125170 5 0.5925 0.5012 0.004 0.352 0.000 0.164 0.476 0.004
#> GSM125172 2 0.1116 0.7025 0.000 0.960 0.008 0.004 0.028 0.000
#> GSM125174 4 0.1643 0.7847 0.000 0.000 0.000 0.924 0.068 0.008
#> GSM125176 2 0.4301 0.5443 0.000 0.760 0.000 0.124 0.096 0.020
#> GSM125178 3 0.5214 0.4735 0.000 0.008 0.680 0.204 0.072 0.036
#> GSM125180 4 0.3596 0.6638 0.000 0.000 0.004 0.748 0.016 0.232
#> GSM125182 6 0.5715 0.3685 0.000 0.040 0.068 0.004 0.328 0.560
#> GSM125184 4 0.2485 0.7775 0.000 0.000 0.008 0.884 0.084 0.024
#> GSM125186 6 0.2964 0.5816 0.000 0.000 0.004 0.040 0.108 0.848
#> GSM125188 6 0.5650 0.3516 0.000 0.000 0.168 0.000 0.332 0.500
#> GSM125190 5 0.4452 0.5729 0.004 0.328 0.000 0.028 0.636 0.004
#> GSM125192 2 0.2053 0.6522 0.000 0.888 0.000 0.004 0.108 0.000
#> GSM125194 3 0.5414 0.3151 0.004 0.000 0.500 0.028 0.424 0.044
#> GSM125196 6 0.4845 0.4850 0.000 0.008 0.120 0.024 0.120 0.728
#> GSM125198 2 0.2162 0.6599 0.000 0.896 0.012 0.000 0.088 0.004
#> GSM125200 1 0.0260 0.8607 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM125202 2 0.3551 0.5901 0.000 0.812 0.028 0.012 0.140 0.008
#> GSM125204 6 0.4922 0.4600 0.004 0.008 0.196 0.004 0.096 0.692
#> GSM125206 3 0.8661 0.1592 0.000 0.120 0.292 0.256 0.200 0.132
#> GSM125208 3 0.4325 0.0333 0.000 0.000 0.524 0.000 0.020 0.456
#> GSM125210 6 0.4199 0.5601 0.000 0.004 0.004 0.088 0.148 0.756
#> GSM125212 3 0.1026 0.5918 0.000 0.004 0.968 0.008 0.012 0.008
#> GSM125214 2 0.1007 0.6968 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM125216 2 0.0458 0.7059 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM125218 5 0.4468 0.3133 0.004 0.484 0.020 0.000 0.492 0.000
#> GSM125220 1 0.1812 0.8453 0.912 0.000 0.008 0.000 0.080 0.000
#> GSM125222 5 0.5267 0.5636 0.004 0.132 0.016 0.156 0.684 0.008
#> GSM125224 2 0.0260 0.7069 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM125226 5 0.3930 0.4609 0.000 0.420 0.004 0.000 0.576 0.000
#> GSM125228 2 0.0790 0.7040 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM125230 3 0.1434 0.5910 0.000 0.000 0.948 0.020 0.024 0.008
#> GSM125232 4 0.1180 0.7877 0.000 0.000 0.012 0.960 0.012 0.016
#> GSM125234 6 0.4425 0.4635 0.124 0.004 0.000 0.088 0.024 0.760
#> GSM125236 1 0.1448 0.8565 0.948 0.000 0.000 0.012 0.016 0.024
#> GSM125238 1 0.2014 0.8509 0.920 0.000 0.008 0.008 0.052 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 agent(p) individual(p) k
#> SD:NMF 111 1.000 1.13e-04 2
#> SD:NMF 110 0.101 8.84e-06 3
#> SD:NMF 87 0.304 7.78e-06 4
#> SD:NMF 78 0.110 1.68e-05 5
#> SD:NMF 76 0.298 7.38e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.658 0.913 0.943 0.4843 0.505 0.505
#> 3 3 0.882 0.916 0.936 0.3266 0.838 0.680
#> 4 4 0.954 0.920 0.953 0.0430 0.966 0.905
#> 5 5 0.787 0.818 0.859 0.0828 0.991 0.973
#> 6 6 0.791 0.531 0.808 0.0386 0.963 0.886
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM125123 1 0.0000 0.984 1.000 0.000
#> GSM125125 1 0.0000 0.984 1.000 0.000
#> GSM125127 1 0.0000 0.984 1.000 0.000
#> GSM125129 1 0.0000 0.984 1.000 0.000
#> GSM125131 1 0.0000 0.984 1.000 0.000
#> GSM125133 1 0.0000 0.984 1.000 0.000
#> GSM125135 1 0.0000 0.984 1.000 0.000
#> GSM125137 1 0.0000 0.984 1.000 0.000
#> GSM125139 1 0.0000 0.984 1.000 0.000
#> GSM125141 1 0.0000 0.984 1.000 0.000
#> GSM125143 1 0.0000 0.984 1.000 0.000
#> GSM125145 1 0.0000 0.984 1.000 0.000
#> GSM125147 1 0.0000 0.984 1.000 0.000
#> GSM125149 1 0.0000 0.984 1.000 0.000
#> GSM125151 1 0.0000 0.984 1.000 0.000
#> GSM125153 1 0.0000 0.984 1.000 0.000
#> GSM125155 1 0.0000 0.984 1.000 0.000
#> GSM125157 1 0.0000 0.984 1.000 0.000
#> GSM125159 2 0.0000 0.905 0.000 1.000
#> GSM125161 1 0.0000 0.984 1.000 0.000
#> GSM125163 2 0.0000 0.905 0.000 1.000
#> GSM125165 2 0.5737 0.882 0.136 0.864
#> GSM125167 2 0.0376 0.905 0.004 0.996
#> GSM125169 2 0.0000 0.905 0.000 1.000
#> GSM125171 2 0.0000 0.905 0.000 1.000
#> GSM125173 2 0.4431 0.893 0.092 0.908
#> GSM125175 2 0.0000 0.905 0.000 1.000
#> GSM125177 2 0.7674 0.827 0.224 0.776
#> GSM125179 2 0.6712 0.864 0.176 0.824
#> GSM125181 2 0.4562 0.893 0.096 0.904
#> GSM125183 2 0.6531 0.868 0.168 0.832
#> GSM125185 2 0.6712 0.864 0.176 0.824
#> GSM125187 2 0.6623 0.866 0.172 0.828
#> GSM125189 2 0.0000 0.905 0.000 1.000
#> GSM125191 2 0.2603 0.901 0.044 0.956
#> GSM125193 2 0.7674 0.827 0.224 0.776
#> GSM125195 2 0.7883 0.815 0.236 0.764
#> GSM125197 2 0.0000 0.905 0.000 1.000
#> GSM125199 1 0.0000 0.984 1.000 0.000
#> GSM125201 2 0.0000 0.905 0.000 1.000
#> GSM125203 2 0.7674 0.827 0.224 0.776
#> GSM125205 2 0.0000 0.905 0.000 1.000
#> GSM125207 2 0.7815 0.819 0.232 0.768
#> GSM125209 2 0.5946 0.879 0.144 0.856
#> GSM125211 2 0.7815 0.819 0.232 0.768
#> GSM125213 2 0.0000 0.905 0.000 1.000
#> GSM125215 2 0.0000 0.905 0.000 1.000
#> GSM125217 2 0.0000 0.905 0.000 1.000
#> GSM125219 1 0.0000 0.984 1.000 0.000
#> GSM125221 2 0.6048 0.877 0.148 0.852
#> GSM125223 2 0.0000 0.905 0.000 1.000
#> GSM125225 2 0.0000 0.905 0.000 1.000
#> GSM125227 2 0.0000 0.905 0.000 1.000
#> GSM125229 2 0.7815 0.819 0.232 0.768
#> GSM125231 1 0.9248 0.363 0.660 0.340
#> GSM125233 1 0.0000 0.984 1.000 0.000
#> GSM125235 1 0.0000 0.984 1.000 0.000
#> GSM125237 1 0.0000 0.984 1.000 0.000
#> GSM125124 1 0.0000 0.984 1.000 0.000
#> GSM125126 1 0.0000 0.984 1.000 0.000
#> GSM125128 1 0.0000 0.984 1.000 0.000
#> GSM125130 1 0.0000 0.984 1.000 0.000
#> GSM125132 1 0.0000 0.984 1.000 0.000
#> GSM125134 1 0.0000 0.984 1.000 0.000
#> GSM125136 1 0.0000 0.984 1.000 0.000
#> GSM125138 1 0.0000 0.984 1.000 0.000
#> GSM125140 1 0.0000 0.984 1.000 0.000
#> GSM125142 1 0.0000 0.984 1.000 0.000
#> GSM125144 1 0.0000 0.984 1.000 0.000
#> GSM125146 1 0.0000 0.984 1.000 0.000
#> GSM125148 1 0.0000 0.984 1.000 0.000
#> GSM125150 1 0.0000 0.984 1.000 0.000
#> GSM125152 1 0.0000 0.984 1.000 0.000
#> GSM125154 1 0.0000 0.984 1.000 0.000
#> GSM125156 1 0.0000 0.984 1.000 0.000
#> GSM125158 1 0.0000 0.984 1.000 0.000
#> GSM125160 2 0.0000 0.905 0.000 1.000
#> GSM125162 1 0.0000 0.984 1.000 0.000
#> GSM125164 2 0.0000 0.905 0.000 1.000
#> GSM125166 2 0.0000 0.905 0.000 1.000
#> GSM125168 2 0.0376 0.905 0.004 0.996
#> GSM125170 2 0.0000 0.905 0.000 1.000
#> GSM125172 2 0.0000 0.905 0.000 1.000
#> GSM125174 2 0.4431 0.893 0.092 0.908
#> GSM125176 2 0.0000 0.905 0.000 1.000
#> GSM125178 2 0.7674 0.827 0.224 0.776
#> GSM125180 2 0.6712 0.864 0.176 0.824
#> GSM125182 2 0.4562 0.893 0.096 0.904
#> GSM125184 2 0.6531 0.868 0.168 0.832
#> GSM125186 2 0.6712 0.864 0.176 0.824
#> GSM125188 2 0.5178 0.888 0.116 0.884
#> GSM125190 2 0.0000 0.905 0.000 1.000
#> GSM125192 2 0.0000 0.905 0.000 1.000
#> GSM125194 2 0.7674 0.827 0.224 0.776
#> GSM125196 2 0.7883 0.815 0.236 0.764
#> GSM125198 2 0.0000 0.905 0.000 1.000
#> GSM125200 1 0.0000 0.984 1.000 0.000
#> GSM125202 2 0.0000 0.905 0.000 1.000
#> GSM125204 2 0.7674 0.827 0.224 0.776
#> GSM125206 2 0.7883 0.815 0.236 0.764
#> GSM125208 2 0.7815 0.819 0.232 0.768
#> GSM125210 2 0.5946 0.879 0.144 0.856
#> GSM125212 2 0.7815 0.819 0.232 0.768
#> GSM125214 2 0.0000 0.905 0.000 1.000
#> GSM125216 2 0.0000 0.905 0.000 1.000
#> GSM125218 2 0.0000 0.905 0.000 1.000
#> GSM125220 1 0.0000 0.984 1.000 0.000
#> GSM125222 2 0.6048 0.877 0.148 0.852
#> GSM125224 2 0.0000 0.905 0.000 1.000
#> GSM125226 2 0.0672 0.905 0.008 0.992
#> GSM125228 2 0.0000 0.905 0.000 1.000
#> GSM125230 2 0.8016 0.805 0.244 0.756
#> GSM125232 1 0.9170 0.387 0.668 0.332
#> GSM125234 1 0.0000 0.984 1.000 0.000
#> GSM125236 1 0.0000 0.984 1.000 0.000
#> GSM125238 1 0.0000 0.984 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0237 0.9696 0.996 0.000 0.004
#> GSM125125 1 0.0237 0.9696 0.996 0.000 0.004
#> GSM125127 1 0.1289 0.9591 0.968 0.000 0.032
#> GSM125129 1 0.0892 0.9641 0.980 0.000 0.020
#> GSM125131 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125133 1 0.0892 0.9660 0.980 0.000 0.020
#> GSM125135 1 0.1411 0.9587 0.964 0.000 0.036
#> GSM125137 1 0.0892 0.9614 0.980 0.000 0.020
#> GSM125139 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125143 1 0.0892 0.9641 0.980 0.000 0.020
#> GSM125145 1 0.1289 0.9605 0.968 0.000 0.032
#> GSM125147 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125153 1 0.0237 0.9695 0.996 0.000 0.004
#> GSM125155 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125159 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125161 1 0.1163 0.9569 0.972 0.000 0.028
#> GSM125163 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125165 3 0.5610 0.8661 0.028 0.196 0.776
#> GSM125167 2 0.1643 0.9244 0.000 0.956 0.044
#> GSM125169 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125173 2 0.6912 0.2368 0.016 0.540 0.444
#> GSM125175 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125177 3 0.4087 0.9302 0.068 0.052 0.880
#> GSM125179 3 0.5067 0.9301 0.052 0.116 0.832
#> GSM125181 3 0.4784 0.8545 0.004 0.200 0.796
#> GSM125183 3 0.4930 0.9293 0.044 0.120 0.836
#> GSM125185 3 0.5067 0.9301 0.052 0.116 0.832
#> GSM125187 3 0.5105 0.9278 0.048 0.124 0.828
#> GSM125189 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125191 2 0.6445 0.4390 0.020 0.672 0.308
#> GSM125193 3 0.4189 0.9304 0.068 0.056 0.876
#> GSM125195 3 0.3764 0.9249 0.068 0.040 0.892
#> GSM125197 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125203 3 0.4087 0.9302 0.068 0.052 0.880
#> GSM125205 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125207 3 0.4165 0.9262 0.076 0.048 0.876
#> GSM125209 3 0.4995 0.9158 0.032 0.144 0.824
#> GSM125211 3 0.3155 0.9209 0.044 0.040 0.916
#> GSM125213 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125215 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125219 1 0.1411 0.9545 0.964 0.000 0.036
#> GSM125221 3 0.4931 0.9182 0.032 0.140 0.828
#> GSM125223 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125229 3 0.3267 0.9222 0.044 0.044 0.912
#> GSM125231 1 0.6302 0.0693 0.520 0.000 0.480
#> GSM125233 1 0.1031 0.9625 0.976 0.000 0.024
#> GSM125235 1 0.1031 0.9632 0.976 0.000 0.024
#> GSM125237 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125124 1 0.0237 0.9695 0.996 0.000 0.004
#> GSM125126 1 0.0237 0.9696 0.996 0.000 0.004
#> GSM125128 1 0.0424 0.9682 0.992 0.000 0.008
#> GSM125130 1 0.0892 0.9641 0.980 0.000 0.020
#> GSM125132 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125134 1 0.0592 0.9674 0.988 0.000 0.012
#> GSM125136 1 0.1163 0.9569 0.972 0.000 0.028
#> GSM125138 1 0.0237 0.9695 0.996 0.000 0.004
#> GSM125140 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125144 1 0.0424 0.9686 0.992 0.000 0.008
#> GSM125146 1 0.1289 0.9605 0.968 0.000 0.032
#> GSM125148 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125154 1 0.0237 0.9695 0.996 0.000 0.004
#> GSM125156 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125160 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125162 1 0.1163 0.9569 0.972 0.000 0.028
#> GSM125164 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125166 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125168 2 0.1643 0.9244 0.000 0.956 0.044
#> GSM125170 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125172 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125174 2 0.6912 0.2368 0.016 0.540 0.444
#> GSM125176 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125178 3 0.4087 0.9302 0.068 0.052 0.880
#> GSM125180 3 0.5067 0.9301 0.052 0.116 0.832
#> GSM125182 3 0.4784 0.8545 0.004 0.200 0.796
#> GSM125184 3 0.4930 0.9293 0.044 0.120 0.836
#> GSM125186 3 0.5067 0.9301 0.052 0.116 0.832
#> GSM125188 3 0.4645 0.8811 0.008 0.176 0.816
#> GSM125190 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125192 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125194 3 0.4189 0.9304 0.068 0.056 0.876
#> GSM125196 3 0.3764 0.9249 0.068 0.040 0.892
#> GSM125198 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.9701 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125204 3 0.4087 0.9302 0.068 0.052 0.880
#> GSM125206 3 0.3764 0.9249 0.068 0.040 0.892
#> GSM125208 3 0.4165 0.9262 0.076 0.048 0.876
#> GSM125210 3 0.4995 0.9158 0.032 0.144 0.824
#> GSM125212 3 0.3155 0.9209 0.044 0.040 0.916
#> GSM125214 2 0.1031 0.9415 0.000 0.976 0.024
#> GSM125216 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125220 1 0.1163 0.9610 0.972 0.000 0.028
#> GSM125222 3 0.4931 0.9182 0.032 0.140 0.828
#> GSM125224 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125226 2 0.2165 0.8993 0.000 0.936 0.064
#> GSM125228 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125230 3 0.2492 0.9089 0.048 0.016 0.936
#> GSM125232 1 0.6295 0.1003 0.528 0.000 0.472
#> GSM125234 1 0.1411 0.9545 0.964 0.000 0.036
#> GSM125236 1 0.1031 0.9632 0.976 0.000 0.024
#> GSM125238 1 0.0000 0.9701 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0188 0.9845 0.996 0.000 0.000 0.004
#> GSM125125 1 0.0188 0.9845 0.996 0.000 0.000 0.004
#> GSM125127 1 0.1356 0.9724 0.960 0.000 0.008 0.032
#> GSM125129 1 0.1109 0.9754 0.968 0.000 0.004 0.028
#> GSM125131 1 0.0000 0.9843 1.000 0.000 0.000 0.000
#> GSM125133 1 0.0895 0.9802 0.976 0.000 0.004 0.020
#> GSM125135 1 0.1452 0.9711 0.956 0.000 0.008 0.036
#> GSM125137 1 0.0927 0.9766 0.976 0.000 0.008 0.016
#> GSM125139 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125141 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125143 1 0.1109 0.9754 0.968 0.000 0.004 0.028
#> GSM125145 1 0.1489 0.9686 0.952 0.000 0.004 0.044
#> GSM125147 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125149 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125151 1 0.0336 0.9840 0.992 0.000 0.000 0.008
#> GSM125153 1 0.0469 0.9837 0.988 0.000 0.000 0.012
#> GSM125155 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125157 1 0.0000 0.9843 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125161 1 0.1042 0.9723 0.972 0.000 0.008 0.020
#> GSM125163 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125165 3 0.4237 0.7852 0.000 0.152 0.808 0.040
#> GSM125167 2 0.1302 0.9385 0.000 0.956 0.044 0.000
#> GSM125169 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125171 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125173 4 0.1211 1.0000 0.000 0.000 0.040 0.960
#> GSM125175 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125177 3 0.1739 0.8587 0.016 0.024 0.952 0.008
#> GSM125179 3 0.3455 0.8586 0.012 0.064 0.880 0.044
#> GSM125181 3 0.4188 0.7898 0.000 0.148 0.812 0.040
#> GSM125183 3 0.3312 0.8593 0.008 0.068 0.884 0.040
#> GSM125185 3 0.3455 0.8586 0.012 0.064 0.880 0.044
#> GSM125187 3 0.3515 0.8579 0.012 0.072 0.876 0.040
#> GSM125189 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125191 2 0.4999 0.4350 0.000 0.660 0.328 0.012
#> GSM125193 3 0.1843 0.8592 0.016 0.028 0.948 0.008
#> GSM125195 3 0.1526 0.8505 0.016 0.012 0.960 0.012
#> GSM125197 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125201 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125203 3 0.1739 0.8587 0.016 0.024 0.952 0.008
#> GSM125205 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125207 3 0.2221 0.8501 0.020 0.020 0.936 0.024
#> GSM125209 3 0.3399 0.8470 0.000 0.092 0.868 0.040
#> GSM125211 3 0.1733 0.8488 0.000 0.024 0.948 0.028
#> GSM125213 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125215 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125217 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125219 1 0.1584 0.9659 0.952 0.000 0.012 0.036
#> GSM125221 3 0.3333 0.8493 0.000 0.088 0.872 0.040
#> GSM125223 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125229 3 0.1837 0.8504 0.000 0.028 0.944 0.028
#> GSM125231 3 0.6557 0.0279 0.448 0.000 0.476 0.076
#> GSM125233 1 0.1209 0.9744 0.964 0.000 0.004 0.032
#> GSM125235 1 0.1256 0.9741 0.964 0.000 0.008 0.028
#> GSM125237 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125124 1 0.0376 0.9842 0.992 0.000 0.004 0.004
#> GSM125126 1 0.0188 0.9845 0.996 0.000 0.000 0.004
#> GSM125128 1 0.0469 0.9833 0.988 0.000 0.000 0.012
#> GSM125130 1 0.1109 0.9754 0.968 0.000 0.004 0.028
#> GSM125132 1 0.0000 0.9843 1.000 0.000 0.000 0.000
#> GSM125134 1 0.0672 0.9825 0.984 0.000 0.008 0.008
#> GSM125136 1 0.1042 0.9723 0.972 0.000 0.008 0.020
#> GSM125138 1 0.0376 0.9842 0.992 0.000 0.004 0.004
#> GSM125140 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125142 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125144 1 0.0592 0.9826 0.984 0.000 0.000 0.016
#> GSM125146 1 0.1489 0.9686 0.952 0.000 0.004 0.044
#> GSM125148 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125150 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125152 1 0.0336 0.9840 0.992 0.000 0.000 0.008
#> GSM125154 1 0.0524 0.9833 0.988 0.000 0.004 0.008
#> GSM125156 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125158 1 0.0000 0.9843 1.000 0.000 0.000 0.000
#> GSM125160 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125162 1 0.1042 0.9723 0.972 0.000 0.008 0.020
#> GSM125164 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125166 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125168 2 0.1302 0.9385 0.000 0.956 0.044 0.000
#> GSM125170 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125172 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125174 4 0.1211 1.0000 0.000 0.000 0.040 0.960
#> GSM125176 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125178 3 0.1739 0.8587 0.016 0.024 0.952 0.008
#> GSM125180 3 0.3455 0.8586 0.012 0.064 0.880 0.044
#> GSM125182 3 0.4188 0.7898 0.000 0.148 0.812 0.040
#> GSM125184 3 0.3312 0.8593 0.008 0.068 0.884 0.040
#> GSM125186 3 0.3455 0.8586 0.012 0.064 0.880 0.044
#> GSM125188 3 0.3876 0.8174 0.000 0.124 0.836 0.040
#> GSM125190 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125194 3 0.1843 0.8592 0.016 0.028 0.948 0.008
#> GSM125196 3 0.1526 0.8505 0.016 0.012 0.960 0.012
#> GSM125198 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0188 0.9846 0.996 0.000 0.004 0.000
#> GSM125202 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125204 3 0.1739 0.8587 0.016 0.024 0.952 0.008
#> GSM125206 3 0.1526 0.8505 0.016 0.012 0.960 0.012
#> GSM125208 3 0.2221 0.8501 0.020 0.020 0.936 0.024
#> GSM125210 3 0.3399 0.8470 0.000 0.092 0.868 0.040
#> GSM125212 3 0.1733 0.8488 0.000 0.024 0.948 0.028
#> GSM125214 2 0.0817 0.9598 0.000 0.976 0.024 0.000
#> GSM125216 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125220 1 0.1388 0.9715 0.960 0.000 0.012 0.028
#> GSM125222 3 0.3333 0.8493 0.000 0.088 0.872 0.040
#> GSM125224 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125226 2 0.2048 0.8986 0.000 0.928 0.064 0.008
#> GSM125228 2 0.0000 0.9733 0.000 1.000 0.000 0.000
#> GSM125230 3 0.1022 0.8324 0.000 0.000 0.968 0.032
#> GSM125232 3 0.6559 0.0197 0.456 0.000 0.468 0.076
#> GSM125234 1 0.1584 0.9659 0.952 0.000 0.012 0.036
#> GSM125236 1 0.1256 0.9741 0.964 0.000 0.008 0.028
#> GSM125238 1 0.0188 0.9846 0.996 0.000 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2377 0.818 0.872 0.000 0.000 0.000 0.128
#> GSM125125 1 0.2377 0.818 0.872 0.000 0.000 0.000 0.128
#> GSM125127 1 0.4003 0.714 0.704 0.000 0.000 0.008 0.288
#> GSM125129 1 0.3928 0.694 0.700 0.000 0.000 0.004 0.296
#> GSM125131 1 0.1608 0.813 0.928 0.000 0.000 0.000 0.072
#> GSM125133 1 0.3430 0.737 0.776 0.000 0.000 0.004 0.220
#> GSM125135 1 0.4046 0.709 0.696 0.000 0.000 0.008 0.296
#> GSM125137 1 0.3696 0.691 0.772 0.000 0.000 0.016 0.212
#> GSM125139 1 0.1341 0.825 0.944 0.000 0.000 0.000 0.056
#> GSM125141 1 0.0794 0.826 0.972 0.000 0.000 0.000 0.028
#> GSM125143 1 0.3814 0.711 0.720 0.000 0.000 0.004 0.276
#> GSM125145 1 0.3861 0.725 0.728 0.000 0.000 0.008 0.264
#> GSM125147 1 0.0794 0.825 0.972 0.000 0.000 0.000 0.028
#> GSM125149 1 0.0794 0.822 0.972 0.000 0.000 0.000 0.028
#> GSM125151 1 0.2719 0.804 0.852 0.000 0.000 0.004 0.144
#> GSM125153 1 0.2488 0.813 0.872 0.000 0.000 0.004 0.124
#> GSM125155 1 0.1195 0.824 0.960 0.000 0.000 0.012 0.028
#> GSM125157 1 0.0404 0.827 0.988 0.000 0.000 0.000 0.012
#> GSM125159 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125161 1 0.3988 0.650 0.732 0.000 0.000 0.016 0.252
#> GSM125163 2 0.0290 0.967 0.000 0.992 0.008 0.000 0.000
#> GSM125165 3 0.2116 0.734 0.000 0.076 0.912 0.004 0.008
#> GSM125167 2 0.1571 0.931 0.000 0.936 0.060 0.000 0.004
#> GSM125169 2 0.0510 0.965 0.000 0.984 0.016 0.000 0.000
#> GSM125171 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125173 4 0.0880 1.000 0.000 0.000 0.032 0.968 0.000
#> GSM125175 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125177 3 0.4166 0.669 0.004 0.000 0.648 0.000 0.348
#> GSM125179 3 0.0833 0.784 0.004 0.000 0.976 0.004 0.016
#> GSM125181 3 0.2504 0.737 0.000 0.040 0.896 0.000 0.064
#> GSM125183 3 0.0613 0.785 0.004 0.000 0.984 0.004 0.008
#> GSM125185 3 0.0833 0.784 0.004 0.000 0.976 0.004 0.016
#> GSM125187 3 0.0727 0.785 0.004 0.004 0.980 0.000 0.012
#> GSM125189 2 0.0404 0.966 0.000 0.988 0.012 0.000 0.000
#> GSM125191 2 0.4354 0.415 0.000 0.624 0.368 0.000 0.008
#> GSM125193 3 0.4166 0.668 0.004 0.000 0.648 0.000 0.348
#> GSM125195 3 0.4276 0.636 0.004 0.000 0.616 0.000 0.380
#> GSM125197 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125199 1 0.0794 0.827 0.972 0.000 0.000 0.000 0.028
#> GSM125201 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125203 3 0.4166 0.669 0.004 0.000 0.648 0.000 0.348
#> GSM125205 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125207 3 0.4819 0.646 0.004 0.000 0.620 0.024 0.352
#> GSM125209 3 0.0798 0.780 0.000 0.016 0.976 0.000 0.008
#> GSM125211 3 0.3840 0.738 0.000 0.008 0.772 0.012 0.208
#> GSM125213 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125215 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125217 2 0.0510 0.965 0.000 0.984 0.016 0.000 0.000
#> GSM125219 1 0.3990 0.684 0.688 0.000 0.000 0.004 0.308
#> GSM125221 3 0.0671 0.782 0.000 0.016 0.980 0.004 0.000
#> GSM125223 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.0162 0.966 0.000 0.996 0.004 0.000 0.000
#> GSM125227 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125229 3 0.3670 0.743 0.000 0.008 0.792 0.012 0.188
#> GSM125231 5 0.6002 0.981 0.228 0.000 0.084 0.044 0.644
#> GSM125233 1 0.3461 0.754 0.772 0.000 0.000 0.004 0.224
#> GSM125235 1 0.3885 0.739 0.724 0.000 0.000 0.008 0.268
#> GSM125237 1 0.0609 0.824 0.980 0.000 0.000 0.000 0.020
#> GSM125124 1 0.2561 0.801 0.856 0.000 0.000 0.000 0.144
#> GSM125126 1 0.2377 0.818 0.872 0.000 0.000 0.000 0.128
#> GSM125128 1 0.3612 0.698 0.764 0.000 0.000 0.008 0.228
#> GSM125130 1 0.3928 0.694 0.700 0.000 0.000 0.004 0.296
#> GSM125132 1 0.1608 0.813 0.928 0.000 0.000 0.000 0.072
#> GSM125134 1 0.3013 0.804 0.832 0.000 0.000 0.008 0.160
#> GSM125136 1 0.3988 0.650 0.732 0.000 0.000 0.016 0.252
#> GSM125138 1 0.2561 0.801 0.856 0.000 0.000 0.000 0.144
#> GSM125140 1 0.1341 0.825 0.944 0.000 0.000 0.000 0.056
#> GSM125142 1 0.0794 0.826 0.972 0.000 0.000 0.000 0.028
#> GSM125144 1 0.2966 0.779 0.816 0.000 0.000 0.000 0.184
#> GSM125146 1 0.3861 0.725 0.728 0.000 0.000 0.008 0.264
#> GSM125148 1 0.0794 0.825 0.972 0.000 0.000 0.000 0.028
#> GSM125150 1 0.0794 0.822 0.972 0.000 0.000 0.000 0.028
#> GSM125152 1 0.2719 0.804 0.852 0.000 0.000 0.004 0.144
#> GSM125154 1 0.2389 0.814 0.880 0.000 0.000 0.004 0.116
#> GSM125156 1 0.1195 0.824 0.960 0.000 0.000 0.012 0.028
#> GSM125158 1 0.0404 0.827 0.988 0.000 0.000 0.000 0.012
#> GSM125160 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125162 1 0.3988 0.650 0.732 0.000 0.000 0.016 0.252
#> GSM125164 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125166 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125168 2 0.1571 0.931 0.000 0.936 0.060 0.000 0.004
#> GSM125170 2 0.0510 0.965 0.000 0.984 0.016 0.000 0.000
#> GSM125172 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125174 4 0.0880 1.000 0.000 0.000 0.032 0.968 0.000
#> GSM125176 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125178 3 0.4166 0.669 0.004 0.000 0.648 0.000 0.348
#> GSM125180 3 0.0833 0.784 0.004 0.000 0.976 0.004 0.016
#> GSM125182 3 0.2504 0.737 0.000 0.040 0.896 0.000 0.064
#> GSM125184 3 0.0613 0.785 0.004 0.000 0.984 0.004 0.008
#> GSM125186 3 0.0833 0.784 0.004 0.000 0.976 0.004 0.016
#> GSM125188 3 0.1981 0.755 0.000 0.016 0.920 0.000 0.064
#> GSM125190 2 0.0404 0.966 0.000 0.988 0.012 0.000 0.000
#> GSM125192 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125194 3 0.4166 0.668 0.004 0.000 0.648 0.000 0.348
#> GSM125196 3 0.4276 0.636 0.004 0.000 0.616 0.000 0.380
#> GSM125198 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125200 1 0.0794 0.827 0.972 0.000 0.000 0.000 0.028
#> GSM125202 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125204 3 0.4166 0.669 0.004 0.000 0.648 0.000 0.348
#> GSM125206 3 0.4276 0.636 0.004 0.000 0.616 0.000 0.380
#> GSM125208 3 0.4819 0.646 0.004 0.000 0.620 0.024 0.352
#> GSM125210 3 0.0798 0.780 0.000 0.016 0.976 0.000 0.008
#> GSM125212 3 0.3840 0.738 0.000 0.008 0.772 0.012 0.208
#> GSM125214 2 0.1041 0.956 0.000 0.964 0.032 0.000 0.004
#> GSM125216 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125218 2 0.0510 0.965 0.000 0.984 0.016 0.000 0.000
#> GSM125220 1 0.3783 0.750 0.740 0.000 0.000 0.008 0.252
#> GSM125222 3 0.0671 0.782 0.000 0.016 0.980 0.004 0.000
#> GSM125224 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125226 2 0.1965 0.878 0.000 0.904 0.096 0.000 0.000
#> GSM125228 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000
#> GSM125230 3 0.4161 0.708 0.000 0.000 0.704 0.016 0.280
#> GSM125232 5 0.6001 0.981 0.236 0.000 0.080 0.044 0.640
#> GSM125234 1 0.4029 0.665 0.680 0.000 0.000 0.004 0.316
#> GSM125236 1 0.3885 0.739 0.724 0.000 0.000 0.008 0.268
#> GSM125238 1 0.0609 0.824 0.980 0.000 0.000 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2946 0.5111 0.824 0.000 0.012 0.004 0.160 0.000
#> GSM125125 1 0.2946 0.5111 0.824 0.000 0.012 0.004 0.160 0.000
#> GSM125127 1 0.4354 -0.8105 0.508 0.000 0.008 0.004 0.476 0.004
#> GSM125129 1 0.3999 -0.8599 0.500 0.000 0.000 0.004 0.496 0.000
#> GSM125131 1 0.1967 0.5598 0.904 0.000 0.012 0.000 0.084 0.000
#> GSM125133 1 0.4158 -0.0135 0.572 0.000 0.008 0.004 0.416 0.000
#> GSM125135 1 0.4389 -0.7472 0.512 0.000 0.016 0.004 0.468 0.000
#> GSM125137 1 0.4065 0.3251 0.672 0.000 0.028 0.000 0.300 0.000
#> GSM125139 1 0.1007 0.5617 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM125141 1 0.0508 0.5822 0.984 0.000 0.004 0.000 0.012 0.000
#> GSM125143 1 0.3989 -0.8116 0.528 0.000 0.000 0.004 0.468 0.000
#> GSM125145 1 0.4268 -0.6634 0.556 0.000 0.012 0.004 0.428 0.000
#> GSM125147 1 0.0713 0.5837 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125149 1 0.0790 0.5830 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125151 1 0.3076 0.1838 0.760 0.000 0.000 0.000 0.240 0.000
#> GSM125153 1 0.2302 0.4915 0.872 0.000 0.008 0.000 0.120 0.000
#> GSM125155 1 0.1124 0.5757 0.956 0.000 0.008 0.000 0.036 0.000
#> GSM125157 1 0.0603 0.5837 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM125159 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125161 1 0.4606 0.2765 0.604 0.000 0.052 0.000 0.344 0.000
#> GSM125163 2 0.0291 0.9653 0.000 0.992 0.004 0.004 0.000 0.000
#> GSM125165 4 0.5061 0.5337 0.000 0.076 0.352 0.568 0.000 0.004
#> GSM125167 2 0.1564 0.9316 0.000 0.936 0.040 0.024 0.000 0.000
#> GSM125169 2 0.0508 0.9642 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM125171 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125173 6 0.0363 1.0000 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM125175 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125177 4 0.0458 0.5067 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM125179 4 0.4102 0.6087 0.000 0.000 0.356 0.628 0.012 0.004
#> GSM125181 4 0.4788 0.5383 0.000 0.036 0.424 0.532 0.008 0.000
#> GSM125183 4 0.3905 0.6102 0.000 0.000 0.356 0.636 0.004 0.004
#> GSM125185 4 0.4102 0.6087 0.000 0.000 0.356 0.628 0.012 0.004
#> GSM125187 4 0.4009 0.6101 0.000 0.004 0.356 0.632 0.008 0.000
#> GSM125189 2 0.0405 0.9650 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM125191 2 0.5068 0.3889 0.000 0.624 0.240 0.136 0.000 0.000
#> GSM125193 4 0.0748 0.5038 0.004 0.000 0.004 0.976 0.016 0.000
#> GSM125195 4 0.3413 0.4149 0.000 0.000 0.080 0.812 0.108 0.000
#> GSM125197 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125199 1 0.0777 0.5813 0.972 0.000 0.004 0.000 0.024 0.000
#> GSM125201 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125203 4 0.0458 0.5067 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM125205 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125207 4 0.1176 0.4815 0.000 0.000 0.000 0.956 0.020 0.024
#> GSM125209 4 0.4099 0.6007 0.000 0.016 0.372 0.612 0.000 0.000
#> GSM125211 3 0.5038 0.9479 0.000 0.004 0.628 0.292 0.064 0.012
#> GSM125213 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125215 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125217 2 0.0508 0.9642 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM125219 5 0.3993 0.9205 0.476 0.000 0.000 0.004 0.520 0.000
#> GSM125221 4 0.4211 0.6040 0.000 0.016 0.364 0.616 0.000 0.004
#> GSM125223 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125225 2 0.0146 0.9659 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM125227 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125229 3 0.4978 0.9263 0.000 0.004 0.648 0.268 0.068 0.012
#> GSM125231 4 0.7373 -0.2135 0.232 0.000 0.044 0.420 0.264 0.040
#> GSM125233 1 0.3699 -0.2567 0.660 0.000 0.000 0.004 0.336 0.000
#> GSM125235 1 0.4126 -0.7321 0.512 0.000 0.004 0.004 0.480 0.000
#> GSM125237 1 0.0547 0.5835 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM125124 1 0.2219 0.4525 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM125126 1 0.2946 0.5111 0.824 0.000 0.012 0.004 0.160 0.000
#> GSM125128 1 0.4184 0.1982 0.576 0.000 0.016 0.000 0.408 0.000
#> GSM125130 1 0.3999 -0.8599 0.500 0.000 0.000 0.004 0.496 0.000
#> GSM125132 1 0.1967 0.5598 0.904 0.000 0.012 0.000 0.084 0.000
#> GSM125134 1 0.3043 0.3304 0.796 0.000 0.004 0.000 0.196 0.004
#> GSM125136 1 0.4606 0.2765 0.604 0.000 0.052 0.000 0.344 0.000
#> GSM125138 1 0.2219 0.4525 0.864 0.000 0.000 0.000 0.136 0.000
#> GSM125140 1 0.1007 0.5617 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM125142 1 0.0508 0.5822 0.984 0.000 0.004 0.000 0.012 0.000
#> GSM125144 1 0.2762 0.3373 0.804 0.000 0.000 0.000 0.196 0.000
#> GSM125146 1 0.4268 -0.6634 0.556 0.000 0.012 0.004 0.428 0.000
#> GSM125148 1 0.0713 0.5837 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125150 1 0.0790 0.5830 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125152 1 0.3076 0.1838 0.760 0.000 0.000 0.000 0.240 0.000
#> GSM125154 1 0.2053 0.5031 0.888 0.000 0.004 0.000 0.108 0.000
#> GSM125156 1 0.1124 0.5757 0.956 0.000 0.008 0.000 0.036 0.000
#> GSM125158 1 0.0603 0.5837 0.980 0.000 0.004 0.000 0.016 0.000
#> GSM125160 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125162 1 0.4606 0.2765 0.604 0.000 0.052 0.000 0.344 0.000
#> GSM125164 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125166 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125168 2 0.1564 0.9316 0.000 0.936 0.040 0.024 0.000 0.000
#> GSM125170 2 0.0508 0.9642 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM125172 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125174 6 0.0363 1.0000 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM125176 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125178 4 0.0458 0.5067 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM125180 4 0.4102 0.6087 0.000 0.000 0.356 0.628 0.012 0.004
#> GSM125182 4 0.4788 0.5383 0.000 0.036 0.424 0.532 0.008 0.000
#> GSM125184 4 0.3905 0.6102 0.000 0.000 0.356 0.636 0.004 0.004
#> GSM125186 4 0.4102 0.6087 0.000 0.000 0.356 0.628 0.012 0.004
#> GSM125188 4 0.4364 0.5609 0.000 0.012 0.424 0.556 0.008 0.000
#> GSM125190 2 0.0405 0.9650 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM125192 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125194 4 0.0748 0.5038 0.004 0.000 0.004 0.976 0.016 0.000
#> GSM125196 4 0.3413 0.4149 0.000 0.000 0.080 0.812 0.108 0.000
#> GSM125198 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125200 1 0.0777 0.5813 0.972 0.000 0.004 0.000 0.024 0.000
#> GSM125202 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125204 4 0.0458 0.5067 0.000 0.000 0.000 0.984 0.016 0.000
#> GSM125206 4 0.3413 0.4149 0.000 0.000 0.080 0.812 0.108 0.000
#> GSM125208 4 0.1176 0.4815 0.000 0.000 0.000 0.956 0.020 0.024
#> GSM125210 4 0.4099 0.6007 0.000 0.016 0.372 0.612 0.000 0.000
#> GSM125212 3 0.5038 0.9479 0.000 0.004 0.628 0.292 0.064 0.012
#> GSM125214 2 0.0935 0.9546 0.000 0.964 0.032 0.004 0.000 0.000
#> GSM125216 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125218 2 0.0508 0.9642 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM125220 1 0.4211 -0.7028 0.532 0.000 0.008 0.004 0.456 0.000
#> GSM125222 4 0.4211 0.6040 0.000 0.016 0.364 0.616 0.000 0.004
#> GSM125224 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125226 2 0.2039 0.8814 0.000 0.904 0.020 0.076 0.000 0.000
#> GSM125228 2 0.0146 0.9664 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125230 3 0.4988 0.8684 0.004 0.000 0.620 0.312 0.048 0.016
#> GSM125232 4 0.7398 -0.2109 0.240 0.000 0.044 0.412 0.264 0.040
#> GSM125234 5 0.3989 0.9228 0.468 0.000 0.000 0.004 0.528 0.000
#> GSM125236 1 0.4126 -0.7321 0.512 0.000 0.004 0.004 0.480 0.000
#> GSM125238 1 0.0547 0.5835 0.980 0.000 0.000 0.000 0.020 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 agent(p) individual(p) k
#> CV:hclust 114 1.000 7.66e-06 2
#> CV:hclust 111 0.989 8.88e-09 3
#> CV:hclust 113 0.999 7.89e-13 4
#> CV:hclust 115 1.000 5.47e-17 5
#> CV:hclust 84 1.000 2.62e-14 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 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 0.991 0.995 0.4991 0.501 0.501
#> 3 3 0.916 0.961 0.960 0.3181 0.800 0.615
#> 4 4 0.775 0.584 0.814 0.1000 0.993 0.979
#> 5 5 0.724 0.714 0.766 0.0641 0.849 0.558
#> 6 6 0.695 0.679 0.764 0.0400 0.993 0.966
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
#> GSM125123 1 0.000 0.998 1.000 0.000
#> GSM125125 1 0.000 0.998 1.000 0.000
#> GSM125127 1 0.000 0.998 1.000 0.000
#> GSM125129 1 0.000 0.998 1.000 0.000
#> GSM125131 1 0.000 0.998 1.000 0.000
#> GSM125133 1 0.000 0.998 1.000 0.000
#> GSM125135 1 0.000 0.998 1.000 0.000
#> GSM125137 1 0.000 0.998 1.000 0.000
#> GSM125139 1 0.000 0.998 1.000 0.000
#> GSM125141 1 0.000 0.998 1.000 0.000
#> GSM125143 1 0.000 0.998 1.000 0.000
#> GSM125145 1 0.000 0.998 1.000 0.000
#> GSM125147 1 0.000 0.998 1.000 0.000
#> GSM125149 1 0.000 0.998 1.000 0.000
#> GSM125151 1 0.000 0.998 1.000 0.000
#> GSM125153 1 0.000 0.998 1.000 0.000
#> GSM125155 1 0.000 0.998 1.000 0.000
#> GSM125157 1 0.000 0.998 1.000 0.000
#> GSM125159 2 0.000 0.992 0.000 1.000
#> GSM125161 1 0.000 0.998 1.000 0.000
#> GSM125163 2 0.000 0.992 0.000 1.000
#> GSM125165 2 0.000 0.992 0.000 1.000
#> GSM125167 2 0.000 0.992 0.000 1.000
#> GSM125169 2 0.000 0.992 0.000 1.000
#> GSM125171 2 0.000 0.992 0.000 1.000
#> GSM125173 2 0.000 0.992 0.000 1.000
#> GSM125175 2 0.000 0.992 0.000 1.000
#> GSM125177 2 0.000 0.992 0.000 1.000
#> GSM125179 2 0.163 0.979 0.024 0.976
#> GSM125181 2 0.000 0.992 0.000 1.000
#> GSM125183 2 0.163 0.979 0.024 0.976
#> GSM125185 2 0.163 0.979 0.024 0.976
#> GSM125187 2 0.204 0.974 0.032 0.968
#> GSM125189 2 0.000 0.992 0.000 1.000
#> GSM125191 2 0.000 0.992 0.000 1.000
#> GSM125193 2 0.416 0.920 0.084 0.916
#> GSM125195 2 0.204 0.974 0.032 0.968
#> GSM125197 2 0.000 0.992 0.000 1.000
#> GSM125199 1 0.000 0.998 1.000 0.000
#> GSM125201 2 0.000 0.992 0.000 1.000
#> GSM125203 2 0.204 0.974 0.032 0.968
#> GSM125205 2 0.000 0.992 0.000 1.000
#> GSM125207 2 0.204 0.974 0.032 0.968
#> GSM125209 2 0.000 0.992 0.000 1.000
#> GSM125211 2 0.000 0.992 0.000 1.000
#> GSM125213 2 0.000 0.992 0.000 1.000
#> GSM125215 2 0.000 0.992 0.000 1.000
#> GSM125217 2 0.000 0.992 0.000 1.000
#> GSM125219 1 0.000 0.998 1.000 0.000
#> GSM125221 2 0.000 0.992 0.000 1.000
#> GSM125223 2 0.000 0.992 0.000 1.000
#> GSM125225 2 0.000 0.992 0.000 1.000
#> GSM125227 2 0.000 0.992 0.000 1.000
#> GSM125229 2 0.000 0.992 0.000 1.000
#> GSM125231 1 0.000 0.998 1.000 0.000
#> GSM125233 1 0.000 0.998 1.000 0.000
#> GSM125235 1 0.000 0.998 1.000 0.000
#> GSM125237 1 0.000 0.998 1.000 0.000
#> GSM125124 1 0.000 0.998 1.000 0.000
#> GSM125126 1 0.000 0.998 1.000 0.000
#> GSM125128 1 0.000 0.998 1.000 0.000
#> GSM125130 1 0.000 0.998 1.000 0.000
#> GSM125132 1 0.000 0.998 1.000 0.000
#> GSM125134 1 0.000 0.998 1.000 0.000
#> GSM125136 1 0.000 0.998 1.000 0.000
#> GSM125138 1 0.000 0.998 1.000 0.000
#> GSM125140 1 0.000 0.998 1.000 0.000
#> GSM125142 1 0.000 0.998 1.000 0.000
#> GSM125144 1 0.000 0.998 1.000 0.000
#> GSM125146 1 0.000 0.998 1.000 0.000
#> GSM125148 1 0.000 0.998 1.000 0.000
#> GSM125150 1 0.000 0.998 1.000 0.000
#> GSM125152 1 0.000 0.998 1.000 0.000
#> GSM125154 1 0.000 0.998 1.000 0.000
#> GSM125156 1 0.000 0.998 1.000 0.000
#> GSM125158 1 0.000 0.998 1.000 0.000
#> GSM125160 2 0.000 0.992 0.000 1.000
#> GSM125162 1 0.000 0.998 1.000 0.000
#> GSM125164 2 0.000 0.992 0.000 1.000
#> GSM125166 2 0.000 0.992 0.000 1.000
#> GSM125168 2 0.000 0.992 0.000 1.000
#> GSM125170 2 0.000 0.992 0.000 1.000
#> GSM125172 2 0.000 0.992 0.000 1.000
#> GSM125174 2 0.141 0.981 0.020 0.980
#> GSM125176 2 0.000 0.992 0.000 1.000
#> GSM125178 2 0.204 0.974 0.032 0.968
#> GSM125180 2 0.184 0.976 0.028 0.972
#> GSM125182 2 0.000 0.992 0.000 1.000
#> GSM125184 2 0.000 0.992 0.000 1.000
#> GSM125186 2 0.184 0.976 0.028 0.972
#> GSM125188 2 0.000 0.992 0.000 1.000
#> GSM125190 2 0.000 0.992 0.000 1.000
#> GSM125192 2 0.000 0.992 0.000 1.000
#> GSM125194 1 0.000 0.998 1.000 0.000
#> GSM125196 2 0.204 0.974 0.032 0.968
#> GSM125198 2 0.000 0.992 0.000 1.000
#> GSM125200 1 0.000 0.998 1.000 0.000
#> GSM125202 2 0.000 0.992 0.000 1.000
#> GSM125204 2 0.204 0.974 0.032 0.968
#> GSM125206 2 0.204 0.974 0.032 0.968
#> GSM125208 2 0.204 0.974 0.032 0.968
#> GSM125210 2 0.000 0.992 0.000 1.000
#> GSM125212 2 0.000 0.992 0.000 1.000
#> GSM125214 2 0.000 0.992 0.000 1.000
#> GSM125216 2 0.000 0.992 0.000 1.000
#> GSM125218 2 0.000 0.992 0.000 1.000
#> GSM125220 1 0.000 0.998 1.000 0.000
#> GSM125222 2 0.000 0.992 0.000 1.000
#> GSM125224 2 0.000 0.992 0.000 1.000
#> GSM125226 2 0.000 0.992 0.000 1.000
#> GSM125228 2 0.000 0.992 0.000 1.000
#> GSM125230 1 0.469 0.887 0.900 0.100
#> GSM125232 1 0.000 0.998 1.000 0.000
#> GSM125234 1 0.000 0.998 1.000 0.000
#> GSM125236 1 0.000 0.998 1.000 0.000
#> GSM125238 1 0.000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.1860 0.972 0.948 0.000 0.052
#> GSM125125 1 0.0592 0.976 0.988 0.000 0.012
#> GSM125127 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125129 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125131 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125133 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125135 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125137 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125139 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125141 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125143 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125145 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125147 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125149 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125151 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125153 1 0.1860 0.973 0.948 0.000 0.052
#> GSM125155 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125157 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125159 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125161 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125163 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125165 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125167 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125173 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125175 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125177 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125179 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125181 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125183 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125185 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125187 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125189 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125191 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125193 3 0.1860 0.945 0.000 0.052 0.948
#> GSM125195 3 0.1860 0.945 0.000 0.052 0.948
#> GSM125197 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125199 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125201 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125203 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125205 2 0.1163 0.967 0.000 0.972 0.028
#> GSM125207 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125209 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125211 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125213 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125219 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125221 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125223 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125229 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125231 3 0.0237 0.903 0.004 0.000 0.996
#> GSM125233 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125235 1 0.0747 0.976 0.984 0.000 0.016
#> GSM125237 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125124 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125126 1 0.0000 0.975 1.000 0.000 0.000
#> GSM125128 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125130 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125132 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125134 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125136 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125138 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125140 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125142 1 0.0892 0.975 0.980 0.000 0.020
#> GSM125144 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125146 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125148 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125150 1 0.0000 0.975 1.000 0.000 0.000
#> GSM125152 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125154 1 0.1964 0.972 0.944 0.000 0.056
#> GSM125156 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125158 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125160 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125162 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125164 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125168 3 0.6309 0.183 0.000 0.500 0.500
#> GSM125170 2 0.1860 0.938 0.000 0.948 0.052
#> GSM125172 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125174 3 0.2878 0.950 0.000 0.096 0.904
#> GSM125176 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125178 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125180 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125182 3 0.5327 0.737 0.000 0.272 0.728
#> GSM125184 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125186 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125188 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125190 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125194 3 0.0237 0.903 0.004 0.000 0.996
#> GSM125196 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125198 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125200 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125202 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125204 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125206 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125208 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125210 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125212 3 0.2066 0.950 0.000 0.060 0.940
#> GSM125214 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125220 1 0.0237 0.975 0.996 0.000 0.004
#> GSM125222 3 0.2959 0.949 0.000 0.100 0.900
#> GSM125224 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.997 0.000 1.000 0.000
#> GSM125230 3 0.0237 0.903 0.004 0.000 0.996
#> GSM125232 3 0.0237 0.903 0.004 0.000 0.996
#> GSM125234 1 0.4750 0.788 0.784 0.000 0.216
#> GSM125236 1 0.1860 0.972 0.948 0.000 0.052
#> GSM125238 1 0.0237 0.975 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.4972 -0.449 0.544 0.000 0.000 0.456
#> GSM125125 1 0.3873 0.381 0.772 0.000 0.000 0.228
#> GSM125127 1 0.4992 -0.534 0.524 0.000 0.000 0.476
#> GSM125129 1 0.4981 -0.473 0.536 0.000 0.000 0.464
#> GSM125131 1 0.1557 0.552 0.944 0.000 0.000 0.056
#> GSM125133 1 0.3726 0.324 0.788 0.000 0.000 0.212
#> GSM125135 1 0.4843 -0.252 0.604 0.000 0.000 0.396
#> GSM125137 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125139 1 0.4594 0.233 0.712 0.000 0.008 0.280
#> GSM125141 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125143 1 0.4992 -0.534 0.524 0.000 0.000 0.476
#> GSM125145 1 0.4941 -0.402 0.564 0.000 0.000 0.436
#> GSM125147 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125151 1 0.4594 0.233 0.712 0.000 0.008 0.280
#> GSM125153 1 0.3032 0.514 0.868 0.000 0.008 0.124
#> GSM125155 1 0.1256 0.580 0.964 0.000 0.008 0.028
#> GSM125157 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0707 0.937 0.000 0.980 0.000 0.020
#> GSM125161 1 0.2408 0.506 0.896 0.000 0.000 0.104
#> GSM125163 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> GSM125165 3 0.3828 0.750 0.000 0.084 0.848 0.068
#> GSM125167 2 0.1807 0.923 0.000 0.940 0.008 0.052
#> GSM125169 2 0.2048 0.921 0.000 0.928 0.008 0.064
#> GSM125171 2 0.1637 0.934 0.000 0.940 0.000 0.060
#> GSM125173 3 0.3239 0.771 0.000 0.052 0.880 0.068
#> GSM125175 2 0.1389 0.938 0.000 0.952 0.000 0.048
#> GSM125177 3 0.4814 0.787 0.000 0.008 0.676 0.316
#> GSM125179 3 0.2319 0.781 0.000 0.040 0.924 0.036
#> GSM125181 3 0.3421 0.751 0.000 0.088 0.868 0.044
#> GSM125183 3 0.2363 0.781 0.000 0.056 0.920 0.024
#> GSM125185 3 0.2142 0.782 0.000 0.056 0.928 0.016
#> GSM125187 3 0.2224 0.781 0.000 0.040 0.928 0.032
#> GSM125189 2 0.1661 0.926 0.000 0.944 0.004 0.052
#> GSM125191 2 0.5168 0.663 0.000 0.712 0.248 0.040
#> GSM125193 3 0.4643 0.780 0.000 0.000 0.656 0.344
#> GSM125195 3 0.4661 0.776 0.000 0.000 0.652 0.348
#> GSM125197 2 0.1022 0.939 0.000 0.968 0.000 0.032
#> GSM125199 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125201 2 0.1302 0.936 0.000 0.956 0.000 0.044
#> GSM125203 3 0.4897 0.782 0.000 0.008 0.660 0.332
#> GSM125205 2 0.2586 0.903 0.000 0.912 0.040 0.048
#> GSM125207 3 0.4814 0.788 0.000 0.008 0.676 0.316
#> GSM125209 2 0.5678 0.551 0.000 0.640 0.316 0.044
#> GSM125211 3 0.5125 0.766 0.000 0.008 0.604 0.388
#> GSM125213 2 0.0592 0.938 0.000 0.984 0.000 0.016
#> GSM125215 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125217 2 0.1970 0.922 0.000 0.932 0.008 0.060
#> GSM125219 1 0.5163 -0.565 0.516 0.000 0.004 0.480
#> GSM125221 3 0.3320 0.762 0.000 0.068 0.876 0.056
#> GSM125223 2 0.1022 0.939 0.000 0.968 0.000 0.032
#> GSM125225 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125227 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125229 3 0.5256 0.764 0.000 0.012 0.596 0.392
#> GSM125231 3 0.4713 0.774 0.000 0.000 0.640 0.360
#> GSM125233 1 0.4972 -0.449 0.544 0.000 0.000 0.456
#> GSM125235 1 0.4193 0.251 0.732 0.000 0.000 0.268
#> GSM125237 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125124 1 0.4647 0.210 0.704 0.000 0.008 0.288
#> GSM125126 1 0.1022 0.580 0.968 0.000 0.000 0.032
#> GSM125128 1 0.3801 0.311 0.780 0.000 0.000 0.220
#> GSM125130 1 0.4992 -0.534 0.524 0.000 0.000 0.476
#> GSM125132 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125134 1 0.4049 0.387 0.780 0.000 0.008 0.212
#> GSM125136 1 0.3486 0.365 0.812 0.000 0.000 0.188
#> GSM125138 1 0.4621 0.222 0.708 0.000 0.008 0.284
#> GSM125140 1 0.4452 0.284 0.732 0.000 0.008 0.260
#> GSM125142 1 0.2048 0.564 0.928 0.000 0.008 0.064
#> GSM125144 1 0.4647 0.210 0.704 0.000 0.008 0.288
#> GSM125146 1 0.4817 -0.222 0.612 0.000 0.000 0.388
#> GSM125148 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.582 1.000 0.000 0.000 0.000
#> GSM125152 1 0.4594 0.233 0.712 0.000 0.008 0.280
#> GSM125154 1 0.3300 0.490 0.848 0.000 0.008 0.144
#> GSM125156 1 0.1890 0.570 0.936 0.000 0.008 0.056
#> GSM125158 1 0.1302 0.578 0.956 0.000 0.000 0.044
#> GSM125160 2 0.0707 0.937 0.000 0.980 0.000 0.020
#> GSM125162 1 0.2408 0.506 0.896 0.000 0.000 0.104
#> GSM125164 2 0.0188 0.940 0.000 0.996 0.000 0.004
#> GSM125166 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> GSM125168 3 0.6203 0.293 0.000 0.340 0.592 0.068
#> GSM125170 2 0.6171 0.474 0.000 0.588 0.348 0.064
#> GSM125172 2 0.1557 0.936 0.000 0.944 0.000 0.056
#> GSM125174 3 0.2670 0.783 0.000 0.052 0.908 0.040
#> GSM125176 2 0.1520 0.932 0.000 0.956 0.020 0.024
#> GSM125178 3 0.4814 0.787 0.000 0.008 0.676 0.316
#> GSM125180 3 0.2319 0.781 0.000 0.040 0.924 0.036
#> GSM125182 3 0.4951 0.594 0.000 0.212 0.744 0.044
#> GSM125184 3 0.2363 0.781 0.000 0.056 0.920 0.024
#> GSM125186 3 0.2224 0.781 0.000 0.040 0.928 0.032
#> GSM125188 3 0.3354 0.753 0.000 0.084 0.872 0.044
#> GSM125190 2 0.1807 0.924 0.000 0.940 0.008 0.052
#> GSM125192 2 0.0000 0.940 0.000 1.000 0.000 0.000
#> GSM125194 3 0.4661 0.780 0.000 0.000 0.652 0.348
#> GSM125196 3 0.4936 0.781 0.000 0.008 0.652 0.340
#> GSM125198 2 0.1022 0.939 0.000 0.968 0.000 0.032
#> GSM125200 1 0.1118 0.573 0.964 0.000 0.000 0.036
#> GSM125202 2 0.1302 0.936 0.000 0.956 0.000 0.044
#> GSM125204 3 0.4897 0.782 0.000 0.008 0.660 0.332
#> GSM125206 3 0.4936 0.781 0.000 0.008 0.652 0.340
#> GSM125208 3 0.4814 0.788 0.000 0.008 0.676 0.316
#> GSM125210 3 0.1890 0.781 0.000 0.056 0.936 0.008
#> GSM125212 3 0.5125 0.766 0.000 0.008 0.604 0.388
#> GSM125214 2 0.0817 0.940 0.000 0.976 0.000 0.024
#> GSM125216 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125218 2 0.1824 0.924 0.000 0.936 0.004 0.060
#> GSM125220 1 0.4283 0.215 0.740 0.000 0.004 0.256
#> GSM125222 3 0.3320 0.762 0.000 0.068 0.876 0.056
#> GSM125224 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125226 2 0.1807 0.924 0.000 0.940 0.008 0.052
#> GSM125228 2 0.0921 0.940 0.000 0.972 0.000 0.028
#> GSM125230 3 0.4817 0.763 0.000 0.000 0.612 0.388
#> GSM125232 3 0.4804 0.768 0.000 0.000 0.616 0.384
#> GSM125234 4 0.6187 0.000 0.432 0.000 0.052 0.516
#> GSM125236 1 0.4985 -0.489 0.532 0.000 0.000 0.468
#> GSM125238 1 0.0000 0.582 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.0451 0.6116 0.004 0.000 0.000 0.008 0.988
#> GSM125125 5 0.4288 -0.1706 0.384 0.000 0.000 0.004 0.612
#> GSM125127 5 0.0510 0.6100 0.000 0.000 0.016 0.000 0.984
#> GSM125129 5 0.0162 0.6113 0.004 0.000 0.000 0.000 0.996
#> GSM125131 1 0.4397 0.6400 0.564 0.000 0.000 0.004 0.432
#> GSM125133 5 0.4594 0.1960 0.284 0.000 0.000 0.036 0.680
#> GSM125135 5 0.2519 0.5406 0.100 0.000 0.000 0.016 0.884
#> GSM125137 1 0.4642 0.7993 0.660 0.000 0.000 0.032 0.308
#> GSM125139 5 0.5661 0.2244 0.272 0.000 0.000 0.120 0.608
#> GSM125141 1 0.3876 0.8139 0.684 0.000 0.000 0.000 0.316
#> GSM125143 5 0.0510 0.6100 0.000 0.000 0.016 0.000 0.984
#> GSM125145 5 0.1251 0.6057 0.036 0.000 0.000 0.008 0.956
#> GSM125147 1 0.3949 0.8172 0.668 0.000 0.000 0.000 0.332
#> GSM125149 1 0.3932 0.8186 0.672 0.000 0.000 0.000 0.328
#> GSM125151 5 0.5558 0.2293 0.268 0.000 0.000 0.112 0.620
#> GSM125153 1 0.5932 0.4327 0.456 0.000 0.000 0.104 0.440
#> GSM125155 1 0.5523 0.7100 0.572 0.000 0.000 0.080 0.348
#> GSM125157 1 0.4306 0.8143 0.660 0.000 0.000 0.012 0.328
#> GSM125159 2 0.2426 0.9110 0.036 0.900 0.000 0.064 0.000
#> GSM125161 1 0.5594 0.4548 0.492 0.000 0.000 0.072 0.436
#> GSM125163 2 0.1549 0.9210 0.016 0.944 0.000 0.040 0.000
#> GSM125165 4 0.5216 0.7811 0.036 0.032 0.252 0.680 0.000
#> GSM125167 2 0.3593 0.8707 0.060 0.824 0.000 0.116 0.000
#> GSM125169 2 0.3657 0.8706 0.064 0.820 0.000 0.116 0.000
#> GSM125171 2 0.1701 0.9193 0.048 0.936 0.000 0.016 0.000
#> GSM125173 4 0.5648 0.7775 0.060 0.024 0.288 0.628 0.000
#> GSM125175 2 0.1469 0.9205 0.036 0.948 0.000 0.016 0.000
#> GSM125177 3 0.0000 0.9347 0.000 0.000 1.000 0.000 0.000
#> GSM125179 4 0.5733 0.7825 0.036 0.024 0.336 0.596 0.008
#> GSM125181 4 0.5190 0.7838 0.032 0.032 0.260 0.676 0.000
#> GSM125183 4 0.5439 0.7858 0.036 0.024 0.328 0.612 0.000
#> GSM125185 4 0.5662 0.7840 0.032 0.024 0.336 0.600 0.008
#> GSM125187 4 0.5662 0.7840 0.032 0.024 0.336 0.600 0.008
#> GSM125189 2 0.3558 0.8766 0.064 0.828 0.000 0.108 0.000
#> GSM125191 4 0.5000 0.2773 0.036 0.388 0.000 0.576 0.000
#> GSM125193 3 0.0693 0.9356 0.000 0.000 0.980 0.008 0.012
#> GSM125195 3 0.1393 0.9280 0.024 0.000 0.956 0.012 0.008
#> GSM125197 2 0.1357 0.9146 0.048 0.948 0.000 0.004 0.000
#> GSM125199 1 0.4147 0.8143 0.676 0.000 0.000 0.008 0.316
#> GSM125201 2 0.1638 0.9089 0.064 0.932 0.000 0.004 0.000
#> GSM125203 3 0.0566 0.9363 0.004 0.000 0.984 0.000 0.012
#> GSM125205 2 0.2238 0.8943 0.064 0.912 0.020 0.004 0.000
#> GSM125207 3 0.0290 0.9341 0.008 0.000 0.992 0.000 0.000
#> GSM125209 4 0.4752 0.4551 0.036 0.316 0.000 0.648 0.000
#> GSM125211 3 0.2300 0.9031 0.052 0.000 0.908 0.040 0.000
#> GSM125213 2 0.2304 0.9207 0.044 0.908 0.000 0.048 0.000
#> GSM125215 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125217 2 0.3543 0.8757 0.060 0.828 0.000 0.112 0.000
#> GSM125219 5 0.1806 0.5996 0.016 0.000 0.016 0.028 0.940
#> GSM125221 4 0.4420 0.7985 0.000 0.028 0.280 0.692 0.000
#> GSM125223 2 0.1205 0.9183 0.040 0.956 0.000 0.004 0.000
#> GSM125225 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125227 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125229 3 0.2376 0.9001 0.052 0.000 0.904 0.044 0.000
#> GSM125231 3 0.0771 0.9316 0.000 0.000 0.976 0.020 0.004
#> GSM125233 5 0.0693 0.6112 0.008 0.000 0.000 0.012 0.980
#> GSM125235 5 0.3366 0.3729 0.212 0.000 0.000 0.004 0.784
#> GSM125237 1 0.3932 0.8186 0.672 0.000 0.000 0.000 0.328
#> GSM125124 5 0.5851 0.2058 0.288 0.000 0.000 0.132 0.580
#> GSM125126 1 0.4114 0.7789 0.624 0.000 0.000 0.000 0.376
#> GSM125128 5 0.4640 0.2479 0.256 0.000 0.000 0.048 0.696
#> GSM125130 5 0.0671 0.6097 0.000 0.000 0.016 0.004 0.980
#> GSM125132 1 0.3932 0.8186 0.672 0.000 0.000 0.000 0.328
#> GSM125134 5 0.5922 -0.0816 0.352 0.000 0.000 0.116 0.532
#> GSM125136 5 0.5459 -0.0857 0.360 0.000 0.000 0.072 0.568
#> GSM125138 5 0.5885 0.1863 0.296 0.000 0.000 0.132 0.572
#> GSM125140 5 0.5835 0.1075 0.312 0.000 0.000 0.120 0.568
#> GSM125142 1 0.5970 0.5848 0.524 0.000 0.000 0.120 0.356
#> GSM125144 5 0.5834 0.2044 0.284 0.000 0.000 0.132 0.584
#> GSM125146 5 0.2921 0.5403 0.124 0.000 0.000 0.020 0.856
#> GSM125148 1 0.4165 0.8092 0.672 0.000 0.000 0.008 0.320
#> GSM125150 1 0.3932 0.8186 0.672 0.000 0.000 0.000 0.328
#> GSM125152 5 0.5558 0.2293 0.268 0.000 0.000 0.112 0.620
#> GSM125154 1 0.6069 0.3555 0.448 0.000 0.000 0.120 0.432
#> GSM125156 1 0.5579 0.6758 0.552 0.000 0.000 0.080 0.368
#> GSM125158 1 0.5240 0.7159 0.584 0.000 0.000 0.056 0.360
#> GSM125160 2 0.2426 0.9110 0.036 0.900 0.000 0.064 0.000
#> GSM125162 1 0.5594 0.4548 0.492 0.000 0.000 0.072 0.436
#> GSM125164 2 0.1549 0.9210 0.016 0.944 0.000 0.040 0.000
#> GSM125166 2 0.1648 0.9205 0.020 0.940 0.000 0.040 0.000
#> GSM125168 4 0.6500 0.6697 0.060 0.156 0.160 0.624 0.000
#> GSM125170 4 0.5942 0.5066 0.064 0.276 0.040 0.620 0.000
#> GSM125172 2 0.1626 0.9195 0.044 0.940 0.000 0.016 0.000
#> GSM125174 4 0.5935 0.7649 0.072 0.024 0.316 0.588 0.000
#> GSM125176 2 0.2914 0.9032 0.052 0.872 0.000 0.076 0.000
#> GSM125178 3 0.0000 0.9347 0.000 0.000 1.000 0.000 0.000
#> GSM125180 4 0.5733 0.7825 0.036 0.024 0.336 0.596 0.008
#> GSM125182 4 0.5762 0.7599 0.032 0.076 0.240 0.652 0.000
#> GSM125184 4 0.5517 0.7888 0.036 0.028 0.328 0.608 0.000
#> GSM125186 4 0.5662 0.7840 0.032 0.024 0.336 0.600 0.008
#> GSM125188 4 0.5058 0.7882 0.028 0.028 0.264 0.680 0.000
#> GSM125190 2 0.3657 0.8706 0.064 0.820 0.000 0.116 0.000
#> GSM125192 2 0.1549 0.9210 0.016 0.944 0.000 0.040 0.000
#> GSM125194 3 0.0693 0.9356 0.000 0.000 0.980 0.008 0.012
#> GSM125196 3 0.1393 0.9280 0.024 0.000 0.956 0.012 0.008
#> GSM125198 2 0.1357 0.9146 0.048 0.948 0.000 0.004 0.000
#> GSM125200 1 0.4761 0.7609 0.616 0.000 0.000 0.028 0.356
#> GSM125202 2 0.1638 0.9089 0.064 0.932 0.000 0.004 0.000
#> GSM125204 3 0.0566 0.9363 0.004 0.000 0.984 0.000 0.012
#> GSM125206 3 0.1393 0.9280 0.024 0.000 0.956 0.012 0.008
#> GSM125208 3 0.0290 0.9341 0.008 0.000 0.992 0.000 0.000
#> GSM125210 4 0.5610 0.7886 0.032 0.028 0.332 0.604 0.004
#> GSM125212 3 0.2300 0.9031 0.052 0.000 0.908 0.040 0.000
#> GSM125214 2 0.1124 0.9197 0.036 0.960 0.000 0.004 0.000
#> GSM125216 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125218 2 0.3543 0.8757 0.060 0.828 0.000 0.112 0.000
#> GSM125220 5 0.4554 0.3579 0.216 0.000 0.016 0.032 0.736
#> GSM125222 4 0.4442 0.7987 0.000 0.028 0.284 0.688 0.000
#> GSM125224 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125226 2 0.3657 0.8706 0.064 0.820 0.000 0.116 0.000
#> GSM125228 2 0.1041 0.9203 0.032 0.964 0.000 0.004 0.000
#> GSM125230 3 0.2228 0.9051 0.048 0.000 0.912 0.040 0.000
#> GSM125232 3 0.5889 0.5944 0.068 0.000 0.688 0.148 0.096
#> GSM125234 5 0.2217 0.5731 0.024 0.000 0.044 0.012 0.920
#> GSM125236 5 0.0162 0.6120 0.000 0.000 0.004 0.000 0.996
#> GSM125238 1 0.3932 0.8186 0.672 0.000 0.000 0.000 0.328
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 5 0.1448 0.623382 0.012 0.000 0.000 0.016 0.948 NA
#> GSM125125 5 0.4730 -0.268650 0.468 0.000 0.000 0.016 0.496 NA
#> GSM125127 5 0.0665 0.624366 0.000 0.000 0.008 0.004 0.980 NA
#> GSM125129 5 0.0717 0.624880 0.008 0.000 0.000 0.000 0.976 NA
#> GSM125131 1 0.4281 0.692124 0.688 0.000 0.000 0.016 0.272 NA
#> GSM125133 5 0.4851 0.362993 0.268 0.000 0.000 0.020 0.656 NA
#> GSM125135 5 0.1989 0.606291 0.052 0.000 0.000 0.004 0.916 NA
#> GSM125137 1 0.4515 0.758314 0.724 0.000 0.000 0.024 0.192 NA
#> GSM125139 5 0.6019 -0.013206 0.300 0.000 0.000 0.004 0.464 NA
#> GSM125141 1 0.3200 0.780896 0.788 0.000 0.000 0.000 0.196 NA
#> GSM125143 5 0.0653 0.624937 0.004 0.000 0.012 0.000 0.980 NA
#> GSM125145 5 0.2563 0.600869 0.032 0.000 0.004 0.004 0.884 NA
#> GSM125147 1 0.2964 0.780569 0.792 0.000 0.000 0.000 0.204 NA
#> GSM125149 1 0.2933 0.780017 0.796 0.000 0.000 0.000 0.200 NA
#> GSM125151 5 0.6058 0.029858 0.284 0.000 0.000 0.008 0.480 NA
#> GSM125153 1 0.5918 0.481945 0.496 0.000 0.004 0.000 0.276 NA
#> GSM125155 1 0.5576 0.643940 0.572 0.000 0.000 0.004 0.220 NA
#> GSM125157 1 0.4006 0.772372 0.748 0.000 0.000 0.016 0.204 NA
#> GSM125159 2 0.3237 0.852331 0.008 0.836 0.000 0.056 0.000 NA
#> GSM125161 1 0.5928 0.476594 0.564 0.000 0.000 0.044 0.280 NA
#> GSM125163 2 0.2631 0.862693 0.004 0.876 0.000 0.044 0.000 NA
#> GSM125165 4 0.4933 0.771763 0.020 0.020 0.096 0.728 0.000 NA
#> GSM125167 2 0.4684 0.784076 0.012 0.704 0.000 0.096 0.000 NA
#> GSM125169 2 0.4682 0.783481 0.004 0.680 0.000 0.092 0.000 NA
#> GSM125171 2 0.3503 0.842027 0.016 0.808 0.000 0.032 0.000 NA
#> GSM125173 4 0.5768 0.729177 0.044 0.016 0.096 0.660 0.004 NA
#> GSM125175 2 0.2404 0.860986 0.000 0.872 0.000 0.016 0.000 NA
#> GSM125177 3 0.0790 0.892532 0.000 0.000 0.968 0.032 0.000 NA
#> GSM125179 4 0.3557 0.777611 0.004 0.016 0.188 0.784 0.004 NA
#> GSM125181 4 0.5099 0.774259 0.020 0.024 0.104 0.716 0.000 NA
#> GSM125183 4 0.3277 0.781061 0.000 0.016 0.188 0.792 0.000 NA
#> GSM125185 4 0.3764 0.777572 0.004 0.016 0.188 0.776 0.004 NA
#> GSM125187 4 0.3667 0.777678 0.004 0.016 0.188 0.780 0.004 NA
#> GSM125189 2 0.4402 0.794492 0.000 0.700 0.000 0.084 0.000 NA
#> GSM125191 4 0.5708 0.433538 0.016 0.300 0.000 0.552 0.000 NA
#> GSM125193 3 0.2247 0.886972 0.008 0.000 0.912 0.044 0.012 NA
#> GSM125195 3 0.2842 0.864805 0.032 0.000 0.876 0.020 0.004 NA
#> GSM125197 2 0.2149 0.839839 0.016 0.900 0.000 0.004 0.000 NA
#> GSM125199 1 0.4046 0.772857 0.748 0.000 0.000 0.016 0.200 NA
#> GSM125201 2 0.2365 0.842284 0.012 0.892 0.004 0.008 0.000 NA
#> GSM125203 3 0.1452 0.893313 0.004 0.000 0.948 0.032 0.008 NA
#> GSM125205 2 0.3331 0.808987 0.016 0.840 0.032 0.008 0.000 NA
#> GSM125207 3 0.1745 0.886417 0.000 0.000 0.924 0.056 0.000 NA
#> GSM125209 4 0.5391 0.617217 0.016 0.212 0.004 0.640 0.000 NA
#> GSM125211 3 0.3823 0.838203 0.060 0.000 0.812 0.028 0.004 NA
#> GSM125213 2 0.2862 0.858537 0.008 0.864 0.000 0.048 0.000 NA
#> GSM125215 2 0.1333 0.858323 0.008 0.944 0.000 0.000 0.000 NA
#> GSM125217 2 0.4549 0.787256 0.000 0.680 0.000 0.088 0.000 NA
#> GSM125219 5 0.2005 0.611041 0.020 0.000 0.016 0.004 0.924 NA
#> GSM125221 4 0.4233 0.793856 0.016 0.020 0.120 0.784 0.000 NA
#> GSM125223 2 0.1668 0.853880 0.008 0.928 0.000 0.004 0.000 NA
#> GSM125225 2 0.1333 0.858323 0.008 0.944 0.000 0.000 0.000 NA
#> GSM125227 2 0.1398 0.857353 0.008 0.940 0.000 0.000 0.000 NA
#> GSM125229 3 0.3929 0.833684 0.056 0.000 0.808 0.040 0.004 NA
#> GSM125231 3 0.2341 0.874719 0.008 0.000 0.908 0.016 0.024 NA
#> GSM125233 5 0.1149 0.623687 0.008 0.000 0.000 0.008 0.960 NA
#> GSM125235 5 0.3450 0.480784 0.208 0.000 0.000 0.012 0.772 NA
#> GSM125237 1 0.2793 0.780607 0.800 0.000 0.000 0.000 0.200 NA
#> GSM125124 5 0.6150 -0.030893 0.284 0.000 0.004 0.000 0.424 NA
#> GSM125126 1 0.3852 0.740992 0.720 0.000 0.000 0.016 0.256 NA
#> GSM125128 5 0.4861 0.404558 0.224 0.000 0.000 0.028 0.684 NA
#> GSM125130 5 0.0725 0.623773 0.000 0.000 0.012 0.000 0.976 NA
#> GSM125132 1 0.3691 0.774657 0.764 0.000 0.000 0.012 0.204 NA
#> GSM125134 1 0.6176 0.190442 0.372 0.000 0.004 0.000 0.368 NA
#> GSM125136 5 0.6227 -0.000105 0.380 0.000 0.000 0.056 0.464 NA
#> GSM125138 5 0.6181 -0.067388 0.300 0.000 0.004 0.000 0.408 NA
#> GSM125140 5 0.6076 -0.100453 0.328 0.000 0.000 0.004 0.436 NA
#> GSM125142 1 0.5908 0.514586 0.500 0.000 0.004 0.000 0.228 NA
#> GSM125144 5 0.6150 -0.030893 0.284 0.000 0.004 0.000 0.424 NA
#> GSM125146 5 0.3847 0.529767 0.136 0.000 0.004 0.000 0.780 NA
#> GSM125148 1 0.3534 0.771359 0.772 0.000 0.004 0.000 0.200 NA
#> GSM125150 1 0.3043 0.780697 0.792 0.000 0.000 0.000 0.200 NA
#> GSM125152 5 0.6058 0.029858 0.284 0.000 0.000 0.008 0.480 NA
#> GSM125154 1 0.6086 0.392934 0.448 0.000 0.004 0.000 0.288 NA
#> GSM125156 1 0.5675 0.617032 0.552 0.000 0.000 0.004 0.240 NA
#> GSM125158 1 0.5475 0.674556 0.596 0.000 0.000 0.008 0.236 NA
#> GSM125160 2 0.3237 0.852331 0.008 0.836 0.000 0.056 0.000 NA
#> GSM125162 1 0.5928 0.476594 0.564 0.000 0.000 0.044 0.280 NA
#> GSM125164 2 0.2631 0.861846 0.004 0.876 0.000 0.044 0.000 NA
#> GSM125166 2 0.2493 0.863256 0.004 0.884 0.000 0.036 0.000 NA
#> GSM125168 4 0.5779 0.667128 0.012 0.136 0.036 0.640 0.000 NA
#> GSM125170 4 0.5302 0.567081 0.004 0.172 0.000 0.616 0.000 NA
#> GSM125172 2 0.3809 0.840906 0.016 0.788 0.000 0.048 0.000 NA
#> GSM125174 4 0.5213 0.730109 0.044 0.016 0.152 0.716 0.004 NA
#> GSM125176 2 0.4074 0.826028 0.000 0.748 0.000 0.092 0.000 NA
#> GSM125178 3 0.0790 0.892532 0.000 0.000 0.968 0.032 0.000 NA
#> GSM125180 4 0.3557 0.777611 0.004 0.016 0.188 0.784 0.004 NA
#> GSM125182 4 0.5652 0.752789 0.020 0.072 0.088 0.684 0.000 NA
#> GSM125184 4 0.3329 0.783566 0.000 0.020 0.184 0.792 0.000 NA
#> GSM125186 4 0.3764 0.777572 0.004 0.016 0.188 0.776 0.004 NA
#> GSM125188 4 0.5027 0.777892 0.020 0.020 0.108 0.720 0.000 NA
#> GSM125190 2 0.4494 0.789954 0.000 0.692 0.000 0.092 0.000 NA
#> GSM125192 2 0.2263 0.865062 0.004 0.900 0.000 0.036 0.000 NA
#> GSM125194 3 0.2247 0.886972 0.008 0.000 0.912 0.044 0.012 NA
#> GSM125196 3 0.2842 0.864805 0.032 0.000 0.876 0.020 0.004 NA
#> GSM125198 2 0.2149 0.839839 0.016 0.900 0.000 0.004 0.000 NA
#> GSM125200 1 0.4903 0.738050 0.668 0.000 0.000 0.016 0.236 NA
#> GSM125202 2 0.2365 0.842284 0.012 0.892 0.004 0.008 0.000 NA
#> GSM125204 3 0.1452 0.893313 0.004 0.000 0.948 0.032 0.008 NA
#> GSM125206 3 0.2755 0.865021 0.032 0.000 0.880 0.016 0.004 NA
#> GSM125208 3 0.1745 0.886417 0.000 0.000 0.924 0.056 0.000 NA
#> GSM125210 4 0.3592 0.780529 0.000 0.016 0.184 0.784 0.004 NA
#> GSM125212 3 0.3823 0.838203 0.060 0.000 0.812 0.028 0.004 NA
#> GSM125214 2 0.0692 0.864796 0.000 0.976 0.000 0.004 0.000 NA
#> GSM125216 2 0.1333 0.858323 0.008 0.944 0.000 0.000 0.000 NA
#> GSM125218 2 0.4449 0.794425 0.000 0.696 0.000 0.088 0.000 NA
#> GSM125220 5 0.4750 0.471803 0.184 0.000 0.016 0.020 0.724 NA
#> GSM125222 4 0.4274 0.793667 0.016 0.020 0.124 0.780 0.000 NA
#> GSM125224 2 0.1398 0.857353 0.008 0.940 0.000 0.000 0.000 NA
#> GSM125226 2 0.4494 0.789954 0.000 0.692 0.000 0.092 0.000 NA
#> GSM125228 2 0.1398 0.857353 0.008 0.940 0.000 0.000 0.000 NA
#> GSM125230 3 0.3259 0.854874 0.052 0.000 0.848 0.016 0.004 NA
#> GSM125232 3 0.6770 0.450743 0.012 0.000 0.516 0.124 0.084 NA
#> GSM125234 5 0.2016 0.595387 0.000 0.000 0.040 0.016 0.920 NA
#> GSM125236 5 0.0984 0.624782 0.012 0.000 0.000 0.012 0.968 NA
#> GSM125238 1 0.2793 0.780607 0.800 0.000 0.000 0.000 0.200 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> CV:kmeans 116 0.852 1.91e-05 2
#> CV:kmeans 115 0.933 1.07e-08 3
#> CV:kmeans 87 0.884 1.89e-06 4
#> CV:kmeans 96 0.808 6.59e-11 5
#> CV:kmeans 96 0.869 6.24e-11 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.990 0.995 0.5015 0.499 0.499
#> 3 3 0.914 0.935 0.969 0.2527 0.863 0.731
#> 4 4 0.988 0.947 0.968 0.1126 0.922 0.796
#> 5 5 0.792 0.723 0.868 0.0922 0.939 0.800
#> 6 6 0.752 0.641 0.804 0.0368 0.961 0.845
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
#> GSM125123 1 0.0000 0.998 1.000 0.000
#> GSM125125 1 0.0000 0.998 1.000 0.000
#> GSM125127 1 0.0000 0.998 1.000 0.000
#> GSM125129 1 0.0000 0.998 1.000 0.000
#> GSM125131 1 0.0000 0.998 1.000 0.000
#> GSM125133 1 0.0000 0.998 1.000 0.000
#> GSM125135 1 0.0000 0.998 1.000 0.000
#> GSM125137 1 0.0000 0.998 1.000 0.000
#> GSM125139 1 0.0000 0.998 1.000 0.000
#> GSM125141 1 0.0000 0.998 1.000 0.000
#> GSM125143 1 0.0000 0.998 1.000 0.000
#> GSM125145 1 0.0000 0.998 1.000 0.000
#> GSM125147 1 0.0000 0.998 1.000 0.000
#> GSM125149 1 0.0000 0.998 1.000 0.000
#> GSM125151 1 0.0000 0.998 1.000 0.000
#> GSM125153 1 0.0000 0.998 1.000 0.000
#> GSM125155 1 0.0000 0.998 1.000 0.000
#> GSM125157 1 0.0000 0.998 1.000 0.000
#> GSM125159 2 0.0000 0.993 0.000 1.000
#> GSM125161 1 0.0000 0.998 1.000 0.000
#> GSM125163 2 0.0000 0.993 0.000 1.000
#> GSM125165 2 0.0000 0.993 0.000 1.000
#> GSM125167 2 0.0000 0.993 0.000 1.000
#> GSM125169 2 0.0000 0.993 0.000 1.000
#> GSM125171 2 0.0000 0.993 0.000 1.000
#> GSM125173 2 0.0000 0.993 0.000 1.000
#> GSM125175 2 0.0000 0.993 0.000 1.000
#> GSM125177 2 0.0000 0.993 0.000 1.000
#> GSM125179 2 0.0000 0.993 0.000 1.000
#> GSM125181 2 0.0000 0.993 0.000 1.000
#> GSM125183 2 0.0000 0.993 0.000 1.000
#> GSM125185 2 0.0000 0.993 0.000 1.000
#> GSM125187 2 0.5842 0.839 0.140 0.860
#> GSM125189 2 0.0000 0.993 0.000 1.000
#> GSM125191 2 0.0000 0.993 0.000 1.000
#> GSM125193 1 0.5059 0.873 0.888 0.112
#> GSM125195 2 0.7453 0.735 0.212 0.788
#> GSM125197 2 0.0000 0.993 0.000 1.000
#> GSM125199 1 0.0000 0.998 1.000 0.000
#> GSM125201 2 0.0000 0.993 0.000 1.000
#> GSM125203 2 0.0376 0.990 0.004 0.996
#> GSM125205 2 0.0000 0.993 0.000 1.000
#> GSM125207 2 0.0000 0.993 0.000 1.000
#> GSM125209 2 0.0000 0.993 0.000 1.000
#> GSM125211 2 0.0000 0.993 0.000 1.000
#> GSM125213 2 0.0000 0.993 0.000 1.000
#> GSM125215 2 0.0000 0.993 0.000 1.000
#> GSM125217 2 0.0000 0.993 0.000 1.000
#> GSM125219 1 0.0000 0.998 1.000 0.000
#> GSM125221 2 0.0000 0.993 0.000 1.000
#> GSM125223 2 0.0000 0.993 0.000 1.000
#> GSM125225 2 0.0000 0.993 0.000 1.000
#> GSM125227 2 0.0000 0.993 0.000 1.000
#> GSM125229 2 0.0000 0.993 0.000 1.000
#> GSM125231 1 0.0000 0.998 1.000 0.000
#> GSM125233 1 0.0000 0.998 1.000 0.000
#> GSM125235 1 0.0000 0.998 1.000 0.000
#> GSM125237 1 0.0000 0.998 1.000 0.000
#> GSM125124 1 0.0000 0.998 1.000 0.000
#> GSM125126 1 0.0000 0.998 1.000 0.000
#> GSM125128 1 0.0000 0.998 1.000 0.000
#> GSM125130 1 0.0000 0.998 1.000 0.000
#> GSM125132 1 0.0000 0.998 1.000 0.000
#> GSM125134 1 0.0000 0.998 1.000 0.000
#> GSM125136 1 0.0000 0.998 1.000 0.000
#> GSM125138 1 0.0000 0.998 1.000 0.000
#> GSM125140 1 0.0000 0.998 1.000 0.000
#> GSM125142 1 0.0000 0.998 1.000 0.000
#> GSM125144 1 0.0000 0.998 1.000 0.000
#> GSM125146 1 0.0000 0.998 1.000 0.000
#> GSM125148 1 0.0000 0.998 1.000 0.000
#> GSM125150 1 0.0000 0.998 1.000 0.000
#> GSM125152 1 0.0000 0.998 1.000 0.000
#> GSM125154 1 0.0000 0.998 1.000 0.000
#> GSM125156 1 0.0000 0.998 1.000 0.000
#> GSM125158 1 0.0000 0.998 1.000 0.000
#> GSM125160 2 0.0000 0.993 0.000 1.000
#> GSM125162 1 0.0000 0.998 1.000 0.000
#> GSM125164 2 0.0000 0.993 0.000 1.000
#> GSM125166 2 0.0000 0.993 0.000 1.000
#> GSM125168 2 0.0000 0.993 0.000 1.000
#> GSM125170 2 0.0000 0.993 0.000 1.000
#> GSM125172 2 0.0000 0.993 0.000 1.000
#> GSM125174 2 0.0000 0.993 0.000 1.000
#> GSM125176 2 0.0000 0.993 0.000 1.000
#> GSM125178 2 0.0000 0.993 0.000 1.000
#> GSM125180 2 0.0000 0.993 0.000 1.000
#> GSM125182 2 0.0000 0.993 0.000 1.000
#> GSM125184 2 0.0000 0.993 0.000 1.000
#> GSM125186 2 0.0000 0.993 0.000 1.000
#> GSM125188 2 0.0000 0.993 0.000 1.000
#> GSM125190 2 0.0000 0.993 0.000 1.000
#> GSM125192 2 0.0000 0.993 0.000 1.000
#> GSM125194 1 0.0000 0.998 1.000 0.000
#> GSM125196 2 0.0376 0.990 0.004 0.996
#> GSM125198 2 0.0000 0.993 0.000 1.000
#> GSM125200 1 0.0000 0.998 1.000 0.000
#> GSM125202 2 0.0000 0.993 0.000 1.000
#> GSM125204 2 0.1843 0.968 0.028 0.972
#> GSM125206 2 0.0000 0.993 0.000 1.000
#> GSM125208 2 0.2603 0.952 0.044 0.956
#> GSM125210 2 0.0000 0.993 0.000 1.000
#> GSM125212 2 0.0000 0.993 0.000 1.000
#> GSM125214 2 0.0000 0.993 0.000 1.000
#> GSM125216 2 0.0000 0.993 0.000 1.000
#> GSM125218 2 0.0000 0.993 0.000 1.000
#> GSM125220 1 0.0000 0.998 1.000 0.000
#> GSM125222 2 0.0000 0.993 0.000 1.000
#> GSM125224 2 0.0000 0.993 0.000 1.000
#> GSM125226 2 0.0000 0.993 0.000 1.000
#> GSM125228 2 0.0000 0.993 0.000 1.000
#> GSM125230 1 0.0000 0.998 1.000 0.000
#> GSM125232 1 0.0000 0.998 1.000 0.000
#> GSM125234 1 0.0000 0.998 1.000 0.000
#> GSM125236 1 0.0000 0.998 1.000 0.000
#> GSM125238 1 0.0000 0.998 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.000 0.998 1.000 0.000 0.000
#> GSM125125 1 0.000 0.998 1.000 0.000 0.000
#> GSM125127 1 0.000 0.998 1.000 0.000 0.000
#> GSM125129 1 0.000 0.998 1.000 0.000 0.000
#> GSM125131 1 0.000 0.998 1.000 0.000 0.000
#> GSM125133 1 0.000 0.998 1.000 0.000 0.000
#> GSM125135 1 0.000 0.998 1.000 0.000 0.000
#> GSM125137 1 0.000 0.998 1.000 0.000 0.000
#> GSM125139 1 0.000 0.998 1.000 0.000 0.000
#> GSM125141 1 0.000 0.998 1.000 0.000 0.000
#> GSM125143 1 0.000 0.998 1.000 0.000 0.000
#> GSM125145 1 0.000 0.998 1.000 0.000 0.000
#> GSM125147 1 0.000 0.998 1.000 0.000 0.000
#> GSM125149 1 0.000 0.998 1.000 0.000 0.000
#> GSM125151 1 0.000 0.998 1.000 0.000 0.000
#> GSM125153 1 0.000 0.998 1.000 0.000 0.000
#> GSM125155 1 0.000 0.998 1.000 0.000 0.000
#> GSM125157 1 0.000 0.998 1.000 0.000 0.000
#> GSM125159 2 0.000 0.940 0.000 1.000 0.000
#> GSM125161 1 0.000 0.998 1.000 0.000 0.000
#> GSM125163 2 0.000 0.940 0.000 1.000 0.000
#> GSM125165 2 0.000 0.940 0.000 1.000 0.000
#> GSM125167 2 0.000 0.940 0.000 1.000 0.000
#> GSM125169 2 0.000 0.940 0.000 1.000 0.000
#> GSM125171 2 0.000 0.940 0.000 1.000 0.000
#> GSM125173 2 0.000 0.940 0.000 1.000 0.000
#> GSM125175 2 0.000 0.940 0.000 1.000 0.000
#> GSM125177 3 0.000 0.945 0.000 0.000 1.000
#> GSM125179 2 0.546 0.660 0.000 0.712 0.288
#> GSM125181 2 0.000 0.940 0.000 1.000 0.000
#> GSM125183 2 0.543 0.665 0.000 0.716 0.284
#> GSM125185 2 0.550 0.654 0.000 0.708 0.292
#> GSM125187 2 0.572 0.649 0.004 0.704 0.292
#> GSM125189 2 0.000 0.940 0.000 1.000 0.000
#> GSM125191 2 0.000 0.940 0.000 1.000 0.000
#> GSM125193 3 0.311 0.878 0.096 0.004 0.900
#> GSM125195 3 0.000 0.945 0.000 0.000 1.000
#> GSM125197 2 0.000 0.940 0.000 1.000 0.000
#> GSM125199 1 0.000 0.998 1.000 0.000 0.000
#> GSM125201 2 0.000 0.940 0.000 1.000 0.000
#> GSM125203 3 0.000 0.945 0.000 0.000 1.000
#> GSM125205 2 0.435 0.739 0.000 0.816 0.184
#> GSM125207 3 0.000 0.945 0.000 0.000 1.000
#> GSM125209 2 0.000 0.940 0.000 1.000 0.000
#> GSM125211 3 0.375 0.829 0.000 0.144 0.856
#> GSM125213 2 0.000 0.940 0.000 1.000 0.000
#> GSM125215 2 0.000 0.940 0.000 1.000 0.000
#> GSM125217 2 0.000 0.940 0.000 1.000 0.000
#> GSM125219 1 0.000 0.998 1.000 0.000 0.000
#> GSM125221 2 0.000 0.940 0.000 1.000 0.000
#> GSM125223 2 0.000 0.940 0.000 1.000 0.000
#> GSM125225 2 0.000 0.940 0.000 1.000 0.000
#> GSM125227 2 0.000 0.940 0.000 1.000 0.000
#> GSM125229 3 0.518 0.671 0.000 0.256 0.744
#> GSM125231 3 0.000 0.945 0.000 0.000 1.000
#> GSM125233 1 0.000 0.998 1.000 0.000 0.000
#> GSM125235 1 0.000 0.998 1.000 0.000 0.000
#> GSM125237 1 0.000 0.998 1.000 0.000 0.000
#> GSM125124 1 0.000 0.998 1.000 0.000 0.000
#> GSM125126 1 0.000 0.998 1.000 0.000 0.000
#> GSM125128 1 0.000 0.998 1.000 0.000 0.000
#> GSM125130 1 0.000 0.998 1.000 0.000 0.000
#> GSM125132 1 0.000 0.998 1.000 0.000 0.000
#> GSM125134 1 0.000 0.998 1.000 0.000 0.000
#> GSM125136 1 0.000 0.998 1.000 0.000 0.000
#> GSM125138 1 0.000 0.998 1.000 0.000 0.000
#> GSM125140 1 0.000 0.998 1.000 0.000 0.000
#> GSM125142 1 0.000 0.998 1.000 0.000 0.000
#> GSM125144 1 0.000 0.998 1.000 0.000 0.000
#> GSM125146 1 0.000 0.998 1.000 0.000 0.000
#> GSM125148 1 0.000 0.998 1.000 0.000 0.000
#> GSM125150 1 0.000 0.998 1.000 0.000 0.000
#> GSM125152 1 0.000 0.998 1.000 0.000 0.000
#> GSM125154 1 0.000 0.998 1.000 0.000 0.000
#> GSM125156 1 0.000 0.998 1.000 0.000 0.000
#> GSM125158 1 0.000 0.998 1.000 0.000 0.000
#> GSM125160 2 0.000 0.940 0.000 1.000 0.000
#> GSM125162 1 0.000 0.998 1.000 0.000 0.000
#> GSM125164 2 0.000 0.940 0.000 1.000 0.000
#> GSM125166 2 0.000 0.940 0.000 1.000 0.000
#> GSM125168 2 0.000 0.940 0.000 1.000 0.000
#> GSM125170 2 0.000 0.940 0.000 1.000 0.000
#> GSM125172 2 0.000 0.940 0.000 1.000 0.000
#> GSM125174 2 0.543 0.666 0.000 0.716 0.284
#> GSM125176 2 0.000 0.940 0.000 1.000 0.000
#> GSM125178 3 0.000 0.945 0.000 0.000 1.000
#> GSM125180 2 0.550 0.654 0.000 0.708 0.292
#> GSM125182 2 0.000 0.940 0.000 1.000 0.000
#> GSM125184 2 0.543 0.665 0.000 0.716 0.284
#> GSM125186 2 0.550 0.654 0.000 0.708 0.292
#> GSM125188 2 0.000 0.940 0.000 1.000 0.000
#> GSM125190 2 0.000 0.940 0.000 1.000 0.000
#> GSM125192 2 0.000 0.940 0.000 1.000 0.000
#> GSM125194 3 0.296 0.875 0.100 0.000 0.900
#> GSM125196 3 0.000 0.945 0.000 0.000 1.000
#> GSM125198 2 0.000 0.940 0.000 1.000 0.000
#> GSM125200 1 0.000 0.998 1.000 0.000 0.000
#> GSM125202 2 0.000 0.940 0.000 1.000 0.000
#> GSM125204 3 0.000 0.945 0.000 0.000 1.000
#> GSM125206 3 0.000 0.945 0.000 0.000 1.000
#> GSM125208 3 0.000 0.945 0.000 0.000 1.000
#> GSM125210 2 0.540 0.670 0.000 0.720 0.280
#> GSM125212 3 0.400 0.811 0.000 0.160 0.840
#> GSM125214 2 0.000 0.940 0.000 1.000 0.000
#> GSM125216 2 0.000 0.940 0.000 1.000 0.000
#> GSM125218 2 0.000 0.940 0.000 1.000 0.000
#> GSM125220 1 0.000 0.998 1.000 0.000 0.000
#> GSM125222 2 0.000 0.940 0.000 1.000 0.000
#> GSM125224 2 0.000 0.940 0.000 1.000 0.000
#> GSM125226 2 0.000 0.940 0.000 1.000 0.000
#> GSM125228 2 0.000 0.940 0.000 1.000 0.000
#> GSM125230 3 0.000 0.945 0.000 0.000 1.000
#> GSM125232 3 0.116 0.928 0.028 0.000 0.972
#> GSM125234 1 0.245 0.913 0.924 0.000 0.076
#> GSM125236 1 0.000 0.998 1.000 0.000 0.000
#> GSM125238 1 0.000 0.998 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.1022 0.979 0.968 0.000 0.000 0.032
#> GSM125125 1 0.0336 0.986 0.992 0.000 0.000 0.008
#> GSM125127 1 0.1118 0.978 0.964 0.000 0.000 0.036
#> GSM125129 1 0.1022 0.980 0.968 0.000 0.000 0.032
#> GSM125131 1 0.0592 0.985 0.984 0.000 0.000 0.016
#> GSM125133 1 0.0817 0.983 0.976 0.000 0.000 0.024
#> GSM125135 1 0.0707 0.983 0.980 0.000 0.000 0.020
#> GSM125137 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125139 1 0.0817 0.981 0.976 0.000 0.000 0.024
#> GSM125141 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125143 1 0.1211 0.981 0.960 0.000 0.000 0.040
#> GSM125145 1 0.1022 0.980 0.968 0.000 0.000 0.032
#> GSM125147 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125149 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125151 1 0.0921 0.979 0.972 0.000 0.000 0.028
#> GSM125153 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> GSM125155 1 0.0336 0.985 0.992 0.000 0.000 0.008
#> GSM125157 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125159 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125161 1 0.0592 0.985 0.984 0.000 0.000 0.016
#> GSM125163 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125165 4 0.3801 0.788 0.000 0.220 0.000 0.780
#> GSM125167 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125169 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125171 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125173 2 0.4382 0.538 0.000 0.704 0.000 0.296
#> GSM125175 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125177 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125179 4 0.1118 0.924 0.000 0.036 0.000 0.964
#> GSM125181 4 0.4008 0.756 0.000 0.244 0.000 0.756
#> GSM125183 4 0.1474 0.928 0.000 0.052 0.000 0.948
#> GSM125185 4 0.1118 0.924 0.000 0.036 0.000 0.964
#> GSM125187 4 0.1151 0.910 0.008 0.024 0.000 0.968
#> GSM125189 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125191 2 0.2216 0.874 0.000 0.908 0.000 0.092
#> GSM125193 3 0.0336 0.972 0.000 0.000 0.992 0.008
#> GSM125195 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125197 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125201 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125203 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125205 2 0.0592 0.948 0.000 0.984 0.016 0.000
#> GSM125207 3 0.0188 0.975 0.000 0.000 0.996 0.004
#> GSM125209 2 0.4830 0.279 0.000 0.608 0.000 0.392
#> GSM125211 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125213 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125215 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125217 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125219 1 0.1302 0.974 0.956 0.000 0.000 0.044
#> GSM125221 4 0.1637 0.925 0.000 0.060 0.000 0.940
#> GSM125223 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125229 3 0.1389 0.923 0.000 0.048 0.952 0.000
#> GSM125231 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125233 1 0.1302 0.974 0.956 0.000 0.000 0.044
#> GSM125235 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125237 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125124 1 0.0817 0.981 0.976 0.000 0.000 0.024
#> GSM125126 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125128 1 0.1022 0.983 0.968 0.000 0.000 0.032
#> GSM125130 1 0.1302 0.974 0.956 0.000 0.000 0.044
#> GSM125132 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125134 1 0.0336 0.985 0.992 0.000 0.000 0.008
#> GSM125136 1 0.0921 0.982 0.972 0.000 0.000 0.028
#> GSM125138 1 0.0707 0.982 0.980 0.000 0.000 0.020
#> GSM125140 1 0.0707 0.982 0.980 0.000 0.000 0.020
#> GSM125142 1 0.0188 0.985 0.996 0.000 0.000 0.004
#> GSM125144 1 0.0921 0.979 0.972 0.000 0.000 0.028
#> GSM125146 1 0.0336 0.985 0.992 0.000 0.000 0.008
#> GSM125148 1 0.0469 0.985 0.988 0.000 0.000 0.012
#> GSM125150 1 0.0336 0.985 0.992 0.000 0.000 0.008
#> GSM125152 1 0.0921 0.979 0.972 0.000 0.000 0.028
#> GSM125154 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> GSM125156 1 0.0336 0.985 0.992 0.000 0.000 0.008
#> GSM125158 1 0.0188 0.985 0.996 0.000 0.000 0.004
#> GSM125160 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0592 0.985 0.984 0.000 0.000 0.016
#> GSM125164 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125168 2 0.1792 0.903 0.000 0.932 0.000 0.068
#> GSM125170 2 0.0817 0.945 0.000 0.976 0.000 0.024
#> GSM125172 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125174 4 0.1398 0.925 0.000 0.040 0.004 0.956
#> GSM125176 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125178 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125180 4 0.1022 0.920 0.000 0.032 0.000 0.968
#> GSM125182 2 0.4008 0.649 0.000 0.756 0.000 0.244
#> GSM125184 4 0.1557 0.927 0.000 0.056 0.000 0.944
#> GSM125186 4 0.1118 0.924 0.000 0.036 0.000 0.964
#> GSM125188 4 0.4164 0.723 0.000 0.264 0.000 0.736
#> GSM125190 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125194 3 0.0336 0.972 0.000 0.000 0.992 0.008
#> GSM125196 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125198 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0188 0.986 0.996 0.000 0.000 0.004
#> GSM125202 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125204 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125206 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125208 3 0.0188 0.975 0.000 0.000 0.996 0.004
#> GSM125210 4 0.1474 0.928 0.000 0.052 0.000 0.948
#> GSM125212 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125214 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125220 1 0.0817 0.984 0.976 0.000 0.000 0.024
#> GSM125222 4 0.1557 0.927 0.000 0.056 0.000 0.944
#> GSM125224 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125226 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125228 2 0.0000 0.965 0.000 1.000 0.000 0.000
#> GSM125230 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM125232 3 0.5416 0.617 0.048 0.000 0.692 0.260
#> GSM125234 1 0.1389 0.971 0.952 0.000 0.000 0.048
#> GSM125236 1 0.1022 0.980 0.968 0.000 0.000 0.032
#> GSM125238 1 0.0469 0.985 0.988 0.000 0.000 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.3932 0.6861 0.328 0.000 0.000 0.000 0.672
#> GSM125125 1 0.3561 0.5190 0.740 0.000 0.000 0.000 0.260
#> GSM125127 5 0.3395 0.7391 0.236 0.000 0.000 0.000 0.764
#> GSM125129 5 0.3796 0.7215 0.300 0.000 0.000 0.000 0.700
#> GSM125131 1 0.0963 0.6789 0.964 0.000 0.000 0.000 0.036
#> GSM125133 1 0.3424 0.4237 0.760 0.000 0.000 0.000 0.240
#> GSM125135 1 0.4451 -0.2045 0.504 0.000 0.000 0.004 0.492
#> GSM125137 1 0.0290 0.6810 0.992 0.000 0.000 0.000 0.008
#> GSM125139 1 0.4268 0.0143 0.556 0.000 0.000 0.000 0.444
#> GSM125141 1 0.0510 0.6847 0.984 0.000 0.000 0.000 0.016
#> GSM125143 5 0.4235 0.4986 0.424 0.000 0.000 0.000 0.576
#> GSM125145 5 0.4383 0.3845 0.424 0.000 0.000 0.004 0.572
#> GSM125147 1 0.0290 0.6844 0.992 0.000 0.000 0.000 0.008
#> GSM125149 1 0.0290 0.6798 0.992 0.000 0.000 0.000 0.008
#> GSM125151 1 0.4306 -0.1487 0.508 0.000 0.000 0.000 0.492
#> GSM125153 1 0.3123 0.6138 0.812 0.000 0.000 0.004 0.184
#> GSM125155 1 0.2377 0.6597 0.872 0.000 0.000 0.000 0.128
#> GSM125157 1 0.0404 0.6807 0.988 0.000 0.000 0.000 0.012
#> GSM125159 2 0.0290 0.9491 0.000 0.992 0.000 0.000 0.008
#> GSM125161 1 0.1478 0.6567 0.936 0.000 0.000 0.000 0.064
#> GSM125163 2 0.0290 0.9491 0.000 0.992 0.000 0.000 0.008
#> GSM125165 4 0.4619 0.7103 0.000 0.216 0.000 0.720 0.064
#> GSM125167 2 0.0794 0.9419 0.000 0.972 0.000 0.000 0.028
#> GSM125169 2 0.0794 0.9413 0.000 0.972 0.000 0.000 0.028
#> GSM125171 2 0.0290 0.9486 0.000 0.992 0.000 0.000 0.008
#> GSM125173 2 0.5092 0.5823 0.000 0.688 0.008 0.236 0.068
#> GSM125175 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125177 3 0.0510 0.9465 0.000 0.000 0.984 0.000 0.016
#> GSM125179 4 0.0771 0.9029 0.000 0.004 0.000 0.976 0.020
#> GSM125181 4 0.4615 0.6599 0.000 0.252 0.000 0.700 0.048
#> GSM125183 4 0.0865 0.9040 0.000 0.004 0.000 0.972 0.024
#> GSM125185 4 0.0162 0.9048 0.000 0.004 0.000 0.996 0.000
#> GSM125187 4 0.0451 0.9040 0.000 0.004 0.000 0.988 0.008
#> GSM125189 2 0.0404 0.9479 0.000 0.988 0.000 0.000 0.012
#> GSM125191 2 0.3012 0.8235 0.000 0.852 0.000 0.124 0.024
#> GSM125193 3 0.3276 0.8788 0.032 0.000 0.836 0.000 0.132
#> GSM125195 3 0.1341 0.9432 0.000 0.000 0.944 0.000 0.056
#> GSM125197 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125199 1 0.0609 0.6850 0.980 0.000 0.000 0.000 0.020
#> GSM125201 2 0.0290 0.9486 0.000 0.992 0.000 0.000 0.008
#> GSM125203 3 0.1121 0.9449 0.000 0.000 0.956 0.000 0.044
#> GSM125205 2 0.0671 0.9425 0.000 0.980 0.004 0.000 0.016
#> GSM125207 3 0.0798 0.9449 0.000 0.000 0.976 0.016 0.008
#> GSM125209 2 0.4996 0.1785 0.000 0.548 0.000 0.420 0.032
#> GSM125211 3 0.1502 0.9370 0.000 0.004 0.940 0.000 0.056
#> GSM125213 2 0.0404 0.9485 0.000 0.988 0.000 0.000 0.012
#> GSM125215 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125217 2 0.0510 0.9461 0.000 0.984 0.000 0.000 0.016
#> GSM125219 5 0.3684 0.7022 0.280 0.000 0.000 0.000 0.720
#> GSM125221 4 0.1444 0.8968 0.000 0.012 0.000 0.948 0.040
#> GSM125223 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125227 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125229 3 0.2795 0.8876 0.000 0.064 0.880 0.000 0.056
#> GSM125231 3 0.2877 0.8758 0.004 0.000 0.848 0.004 0.144
#> GSM125233 5 0.3561 0.7446 0.260 0.000 0.000 0.000 0.740
#> GSM125235 1 0.2329 0.6424 0.876 0.000 0.000 0.000 0.124
#> GSM125237 1 0.0404 0.6833 0.988 0.000 0.000 0.000 0.012
#> GSM125124 1 0.4451 -0.1016 0.504 0.000 0.000 0.004 0.492
#> GSM125126 1 0.1851 0.6780 0.912 0.000 0.000 0.000 0.088
#> GSM125128 1 0.3796 0.3204 0.700 0.000 0.000 0.000 0.300
#> GSM125130 5 0.3366 0.7447 0.232 0.000 0.000 0.000 0.768
#> GSM125132 1 0.0703 0.6845 0.976 0.000 0.000 0.000 0.024
#> GSM125134 1 0.4009 0.4398 0.684 0.000 0.000 0.004 0.312
#> GSM125136 1 0.2773 0.5526 0.836 0.000 0.000 0.000 0.164
#> GSM125138 1 0.4430 0.0329 0.540 0.000 0.000 0.004 0.456
#> GSM125140 1 0.4249 0.0616 0.568 0.000 0.000 0.000 0.432
#> GSM125142 1 0.2719 0.6484 0.852 0.000 0.000 0.004 0.144
#> GSM125144 1 0.4448 -0.0580 0.516 0.000 0.000 0.004 0.480
#> GSM125146 1 0.4009 0.4577 0.684 0.000 0.000 0.004 0.312
#> GSM125148 1 0.0880 0.6836 0.968 0.000 0.000 0.000 0.032
#> GSM125150 1 0.1341 0.6834 0.944 0.000 0.000 0.000 0.056
#> GSM125152 1 0.4307 -0.1652 0.504 0.000 0.000 0.000 0.496
#> GSM125154 1 0.3550 0.5645 0.760 0.000 0.000 0.004 0.236
#> GSM125156 1 0.3508 0.5384 0.748 0.000 0.000 0.000 0.252
#> GSM125158 1 0.3210 0.5860 0.788 0.000 0.000 0.000 0.212
#> GSM125160 2 0.0290 0.9491 0.000 0.992 0.000 0.000 0.008
#> GSM125162 1 0.1671 0.6477 0.924 0.000 0.000 0.000 0.076
#> GSM125164 2 0.0162 0.9496 0.000 0.996 0.000 0.000 0.004
#> GSM125166 2 0.0162 0.9496 0.000 0.996 0.000 0.000 0.004
#> GSM125168 2 0.3608 0.7837 0.000 0.812 0.000 0.148 0.040
#> GSM125170 2 0.2830 0.8621 0.000 0.876 0.000 0.080 0.044
#> GSM125172 2 0.0290 0.9486 0.000 0.992 0.000 0.000 0.008
#> GSM125174 4 0.1357 0.8943 0.000 0.004 0.000 0.948 0.048
#> GSM125176 2 0.0162 0.9496 0.000 0.996 0.000 0.000 0.004
#> GSM125178 3 0.0609 0.9460 0.000 0.000 0.980 0.000 0.020
#> GSM125180 4 0.0510 0.9007 0.000 0.000 0.000 0.984 0.016
#> GSM125182 2 0.4583 0.5322 0.000 0.672 0.000 0.296 0.032
#> GSM125184 4 0.0865 0.9026 0.000 0.004 0.000 0.972 0.024
#> GSM125186 4 0.0162 0.9048 0.000 0.004 0.000 0.996 0.000
#> GSM125188 4 0.4840 0.6268 0.000 0.268 0.000 0.676 0.056
#> GSM125190 2 0.0609 0.9452 0.000 0.980 0.000 0.000 0.020
#> GSM125192 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125194 3 0.3485 0.8709 0.048 0.000 0.828 0.000 0.124
#> GSM125196 3 0.1341 0.9432 0.000 0.000 0.944 0.000 0.056
#> GSM125198 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125200 1 0.3109 0.5933 0.800 0.000 0.000 0.000 0.200
#> GSM125202 2 0.0290 0.9486 0.000 0.992 0.000 0.000 0.008
#> GSM125204 3 0.1121 0.9449 0.000 0.000 0.956 0.000 0.044
#> GSM125206 3 0.1270 0.9439 0.000 0.000 0.948 0.000 0.052
#> GSM125208 3 0.0693 0.9456 0.000 0.000 0.980 0.012 0.008
#> GSM125210 4 0.0324 0.9047 0.000 0.004 0.000 0.992 0.004
#> GSM125212 3 0.1502 0.9370 0.000 0.004 0.940 0.000 0.056
#> GSM125214 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125216 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125218 2 0.0290 0.9487 0.000 0.992 0.000 0.000 0.008
#> GSM125220 1 0.3876 0.3069 0.684 0.000 0.000 0.000 0.316
#> GSM125222 4 0.1408 0.8980 0.000 0.008 0.000 0.948 0.044
#> GSM125224 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125226 2 0.0703 0.9431 0.000 0.976 0.000 0.000 0.024
#> GSM125228 2 0.0000 0.9500 0.000 1.000 0.000 0.000 0.000
#> GSM125230 3 0.1341 0.9378 0.000 0.000 0.944 0.000 0.056
#> GSM125232 5 0.7504 -0.0945 0.048 0.000 0.360 0.208 0.384
#> GSM125234 5 0.3613 0.6876 0.160 0.000 0.016 0.012 0.812
#> GSM125236 5 0.3752 0.7319 0.292 0.000 0.000 0.000 0.708
#> GSM125238 1 0.0162 0.6836 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 6 0.3841 0.6472 0.256 0.000 0.000 0.000 0.028 0.716
#> GSM125125 1 0.4246 0.0186 0.580 0.000 0.000 0.000 0.020 0.400
#> GSM125127 6 0.3607 0.5591 0.112 0.000 0.000 0.000 0.092 0.796
#> GSM125129 6 0.3566 0.6496 0.224 0.000 0.000 0.000 0.024 0.752
#> GSM125131 1 0.1970 0.6560 0.912 0.000 0.000 0.000 0.028 0.060
#> GSM125133 1 0.4357 0.4207 0.700 0.000 0.000 0.000 0.076 0.224
#> GSM125135 6 0.4856 0.1837 0.464 0.000 0.000 0.000 0.056 0.480
#> GSM125137 1 0.1245 0.6704 0.952 0.000 0.000 0.000 0.016 0.032
#> GSM125139 6 0.4975 0.4007 0.428 0.000 0.000 0.000 0.068 0.504
#> GSM125141 1 0.0972 0.6697 0.964 0.000 0.000 0.000 0.008 0.028
#> GSM125143 6 0.4703 0.6019 0.312 0.000 0.000 0.000 0.068 0.620
#> GSM125145 6 0.5334 0.4553 0.344 0.000 0.000 0.000 0.120 0.536
#> GSM125147 1 0.0993 0.6712 0.964 0.000 0.000 0.000 0.012 0.024
#> GSM125149 1 0.0806 0.6651 0.972 0.000 0.000 0.000 0.020 0.008
#> GSM125151 6 0.4972 0.4811 0.392 0.000 0.000 0.000 0.072 0.536
#> GSM125153 1 0.4486 0.4785 0.704 0.000 0.000 0.000 0.112 0.184
#> GSM125155 1 0.3620 0.5471 0.772 0.000 0.000 0.000 0.044 0.184
#> GSM125157 1 0.1176 0.6631 0.956 0.000 0.000 0.000 0.020 0.024
#> GSM125159 2 0.1501 0.8956 0.000 0.924 0.000 0.000 0.076 0.000
#> GSM125161 1 0.2630 0.6128 0.872 0.000 0.000 0.000 0.064 0.064
#> GSM125163 2 0.0547 0.9086 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM125165 4 0.6083 0.4612 0.000 0.184 0.000 0.456 0.348 0.012
#> GSM125167 2 0.2362 0.8624 0.000 0.860 0.000 0.000 0.136 0.004
#> GSM125169 2 0.2402 0.8626 0.000 0.856 0.000 0.000 0.140 0.004
#> GSM125171 2 0.0458 0.9080 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM125173 2 0.6294 0.2795 0.000 0.516 0.012 0.188 0.268 0.016
#> GSM125175 2 0.0458 0.9075 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM125177 3 0.0692 0.7829 0.000 0.000 0.976 0.000 0.020 0.004
#> GSM125179 4 0.0777 0.7731 0.000 0.000 0.000 0.972 0.024 0.004
#> GSM125181 4 0.5704 0.5083 0.000 0.164 0.000 0.520 0.312 0.004
#> GSM125183 4 0.2146 0.7657 0.000 0.000 0.000 0.880 0.116 0.004
#> GSM125185 4 0.0865 0.7766 0.000 0.000 0.000 0.964 0.036 0.000
#> GSM125187 4 0.1610 0.7748 0.000 0.000 0.000 0.916 0.084 0.000
#> GSM125189 2 0.1531 0.8997 0.000 0.928 0.000 0.000 0.068 0.004
#> GSM125191 2 0.4427 0.6782 0.000 0.716 0.000 0.148 0.136 0.000
#> GSM125193 3 0.5513 0.5175 0.044 0.000 0.600 0.004 0.296 0.056
#> GSM125195 3 0.2311 0.7537 0.000 0.000 0.880 0.000 0.104 0.016
#> GSM125197 2 0.0146 0.9078 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125199 1 0.0891 0.6662 0.968 0.000 0.000 0.000 0.008 0.024
#> GSM125201 2 0.0260 0.9081 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM125203 3 0.1196 0.7800 0.000 0.000 0.952 0.000 0.040 0.008
#> GSM125205 2 0.2279 0.8514 0.000 0.900 0.048 0.000 0.048 0.004
#> GSM125207 3 0.2102 0.7740 0.000 0.000 0.908 0.012 0.068 0.012
#> GSM125209 2 0.5758 0.1096 0.000 0.476 0.000 0.340 0.184 0.000
#> GSM125211 3 0.3858 0.6892 0.000 0.004 0.724 0.000 0.248 0.024
#> GSM125213 2 0.1267 0.9012 0.000 0.940 0.000 0.000 0.060 0.000
#> GSM125215 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125217 2 0.1753 0.8922 0.000 0.912 0.000 0.000 0.084 0.004
#> GSM125219 6 0.3523 0.5136 0.180 0.000 0.000 0.000 0.040 0.780
#> GSM125221 4 0.3023 0.7326 0.000 0.004 0.000 0.784 0.212 0.000
#> GSM125223 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125225 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125227 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125229 3 0.4410 0.6648 0.000 0.052 0.724 0.000 0.204 0.020
#> GSM125231 3 0.4867 0.4440 0.008 0.000 0.684 0.004 0.208 0.096
#> GSM125233 6 0.2706 0.6223 0.160 0.000 0.000 0.000 0.008 0.832
#> GSM125235 1 0.3860 0.5188 0.728 0.000 0.000 0.000 0.036 0.236
#> GSM125237 1 0.0993 0.6693 0.964 0.000 0.000 0.000 0.012 0.024
#> GSM125124 6 0.5466 0.3644 0.404 0.000 0.000 0.000 0.124 0.472
#> GSM125126 1 0.3200 0.5747 0.788 0.000 0.000 0.000 0.016 0.196
#> GSM125128 1 0.4835 0.2466 0.592 0.000 0.000 0.000 0.072 0.336
#> GSM125130 6 0.2826 0.5786 0.128 0.000 0.000 0.000 0.028 0.844
#> GSM125132 1 0.1225 0.6707 0.952 0.000 0.000 0.000 0.012 0.036
#> GSM125134 1 0.5336 0.2037 0.572 0.000 0.000 0.000 0.144 0.284
#> GSM125136 1 0.3680 0.5362 0.784 0.000 0.000 0.000 0.072 0.144
#> GSM125138 1 0.5533 -0.2931 0.448 0.000 0.000 0.000 0.132 0.420
#> GSM125140 1 0.5034 -0.3455 0.472 0.000 0.000 0.000 0.072 0.456
#> GSM125142 1 0.4191 0.5167 0.732 0.000 0.000 0.000 0.088 0.180
#> GSM125144 6 0.5492 0.3705 0.400 0.000 0.000 0.000 0.128 0.472
#> GSM125146 1 0.5240 0.1229 0.544 0.000 0.000 0.000 0.108 0.348
#> GSM125148 1 0.2420 0.6450 0.884 0.000 0.000 0.000 0.040 0.076
#> GSM125150 1 0.2536 0.6319 0.864 0.000 0.000 0.000 0.020 0.116
#> GSM125152 6 0.4949 0.4983 0.380 0.000 0.000 0.000 0.072 0.548
#> GSM125154 1 0.5036 0.3479 0.632 0.000 0.000 0.000 0.140 0.228
#> GSM125156 1 0.4332 0.3615 0.672 0.000 0.000 0.000 0.052 0.276
#> GSM125158 1 0.3802 0.5061 0.748 0.000 0.000 0.000 0.044 0.208
#> GSM125160 2 0.1387 0.8989 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM125162 1 0.2744 0.6064 0.864 0.000 0.000 0.000 0.064 0.072
#> GSM125164 2 0.0865 0.9069 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM125166 2 0.0937 0.9053 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM125168 2 0.4980 0.6361 0.000 0.660 0.000 0.144 0.192 0.004
#> GSM125170 2 0.4233 0.7548 0.000 0.740 0.000 0.088 0.168 0.004
#> GSM125172 2 0.0858 0.9053 0.000 0.968 0.000 0.000 0.028 0.004
#> GSM125174 4 0.3733 0.6899 0.000 0.012 0.036 0.800 0.144 0.008
#> GSM125176 2 0.0891 0.9092 0.000 0.968 0.000 0.008 0.024 0.000
#> GSM125178 3 0.1531 0.7788 0.000 0.000 0.928 0.000 0.068 0.004
#> GSM125180 4 0.0777 0.7731 0.000 0.000 0.000 0.972 0.024 0.004
#> GSM125182 2 0.5884 0.2984 0.000 0.508 0.000 0.236 0.252 0.004
#> GSM125184 4 0.1411 0.7684 0.000 0.000 0.000 0.936 0.060 0.004
#> GSM125186 4 0.0865 0.7766 0.000 0.000 0.000 0.964 0.036 0.000
#> GSM125188 4 0.5758 0.4687 0.000 0.200 0.000 0.496 0.304 0.000
#> GSM125190 2 0.1644 0.8978 0.000 0.920 0.000 0.000 0.076 0.004
#> GSM125192 2 0.0790 0.9066 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM125194 3 0.5854 0.4437 0.080 0.000 0.572 0.000 0.288 0.060
#> GSM125196 3 0.2311 0.7537 0.000 0.000 0.880 0.000 0.104 0.016
#> GSM125198 2 0.0146 0.9078 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125200 1 0.3483 0.5205 0.764 0.000 0.000 0.000 0.024 0.212
#> GSM125202 2 0.0260 0.9081 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM125204 3 0.1124 0.7775 0.000 0.000 0.956 0.000 0.036 0.008
#> GSM125206 3 0.2170 0.7572 0.000 0.000 0.888 0.000 0.100 0.012
#> GSM125208 3 0.2159 0.7734 0.000 0.000 0.904 0.012 0.072 0.012
#> GSM125210 4 0.1007 0.7807 0.000 0.000 0.000 0.956 0.044 0.000
#> GSM125212 3 0.3858 0.6887 0.000 0.004 0.724 0.000 0.248 0.024
#> GSM125214 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125216 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125218 2 0.1349 0.9021 0.000 0.940 0.000 0.000 0.056 0.004
#> GSM125220 1 0.4798 0.3062 0.612 0.000 0.000 0.000 0.076 0.312
#> GSM125222 4 0.2902 0.7423 0.000 0.004 0.000 0.800 0.196 0.000
#> GSM125224 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125226 2 0.1531 0.8995 0.000 0.928 0.000 0.000 0.068 0.004
#> GSM125228 2 0.0146 0.9085 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125230 3 0.3333 0.7226 0.000 0.000 0.784 0.000 0.192 0.024
#> GSM125232 5 0.8419 0.0000 0.052 0.000 0.260 0.184 0.272 0.232
#> GSM125234 6 0.2790 0.5379 0.088 0.000 0.012 0.000 0.032 0.868
#> GSM125236 6 0.3852 0.6131 0.176 0.000 0.000 0.000 0.064 0.760
#> GSM125238 1 0.0405 0.6701 0.988 0.000 0.000 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 agent(p) individual(p) k
#> CV:skmeans 116 1.000 1.12e-05 2
#> CV:skmeans 116 0.962 1.49e-09 3
#> CV:skmeans 115 0.997 1.96e-11 4
#> CV:skmeans 99 0.978 4.56e-11 5
#> CV:skmeans 90 0.929 1.24e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.928 0.936 0.974 0.5034 0.496 0.496
#> 3 3 0.872 0.887 0.948 0.2995 0.804 0.621
#> 4 4 0.808 0.842 0.917 0.1381 0.895 0.701
#> 5 5 0.821 0.831 0.918 0.0549 0.946 0.795
#> 6 6 0.803 0.752 0.851 0.0453 0.959 0.810
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM125123 1 0.0000 0.974 1.000 0.000
#> GSM125125 1 0.0000 0.974 1.000 0.000
#> GSM125127 1 0.0000 0.974 1.000 0.000
#> GSM125129 1 0.0000 0.974 1.000 0.000
#> GSM125131 1 0.0000 0.974 1.000 0.000
#> GSM125133 1 0.0000 0.974 1.000 0.000
#> GSM125135 1 0.0000 0.974 1.000 0.000
#> GSM125137 1 0.0000 0.974 1.000 0.000
#> GSM125139 1 0.0000 0.974 1.000 0.000
#> GSM125141 1 0.0000 0.974 1.000 0.000
#> GSM125143 1 0.0000 0.974 1.000 0.000
#> GSM125145 1 0.0000 0.974 1.000 0.000
#> GSM125147 1 0.0000 0.974 1.000 0.000
#> GSM125149 1 0.0000 0.974 1.000 0.000
#> GSM125151 1 0.0000 0.974 1.000 0.000
#> GSM125153 1 0.0000 0.974 1.000 0.000
#> GSM125155 1 0.0000 0.974 1.000 0.000
#> GSM125157 1 0.0000 0.974 1.000 0.000
#> GSM125159 2 0.0000 0.970 0.000 1.000
#> GSM125161 1 0.0000 0.974 1.000 0.000
#> GSM125163 2 0.0000 0.970 0.000 1.000
#> GSM125165 2 0.0000 0.970 0.000 1.000
#> GSM125167 2 0.0000 0.970 0.000 1.000
#> GSM125169 2 0.0000 0.970 0.000 1.000
#> GSM125171 2 0.0000 0.970 0.000 1.000
#> GSM125173 2 0.0000 0.970 0.000 1.000
#> GSM125175 2 0.0000 0.970 0.000 1.000
#> GSM125177 2 0.0000 0.970 0.000 1.000
#> GSM125179 2 0.0376 0.967 0.004 0.996
#> GSM125181 2 0.0000 0.970 0.000 1.000
#> GSM125183 2 0.6148 0.817 0.152 0.848
#> GSM125185 2 0.0000 0.970 0.000 1.000
#> GSM125187 1 0.9833 0.255 0.576 0.424
#> GSM125189 2 0.0000 0.970 0.000 1.000
#> GSM125191 2 0.0000 0.970 0.000 1.000
#> GSM125193 1 0.8813 0.562 0.700 0.300
#> GSM125195 1 0.7219 0.739 0.800 0.200
#> GSM125197 2 0.0000 0.970 0.000 1.000
#> GSM125199 1 0.0000 0.974 1.000 0.000
#> GSM125201 2 0.0000 0.970 0.000 1.000
#> GSM125203 2 0.8763 0.588 0.296 0.704
#> GSM125205 2 0.0000 0.970 0.000 1.000
#> GSM125207 2 0.9580 0.397 0.380 0.620
#> GSM125209 2 0.0000 0.970 0.000 1.000
#> GSM125211 2 0.7139 0.758 0.196 0.804
#> GSM125213 2 0.0000 0.970 0.000 1.000
#> GSM125215 2 0.0000 0.970 0.000 1.000
#> GSM125217 2 0.0000 0.970 0.000 1.000
#> GSM125219 1 0.0000 0.974 1.000 0.000
#> GSM125221 2 0.0000 0.970 0.000 1.000
#> GSM125223 2 0.0000 0.970 0.000 1.000
#> GSM125225 2 0.0000 0.970 0.000 1.000
#> GSM125227 2 0.0000 0.970 0.000 1.000
#> GSM125229 2 0.0000 0.970 0.000 1.000
#> GSM125231 1 0.0376 0.971 0.996 0.004
#> GSM125233 1 0.0000 0.974 1.000 0.000
#> GSM125235 1 0.0000 0.974 1.000 0.000
#> GSM125237 1 0.0000 0.974 1.000 0.000
#> GSM125124 1 0.0000 0.974 1.000 0.000
#> GSM125126 1 0.0000 0.974 1.000 0.000
#> GSM125128 1 0.0000 0.974 1.000 0.000
#> GSM125130 1 0.0000 0.974 1.000 0.000
#> GSM125132 1 0.0000 0.974 1.000 0.000
#> GSM125134 1 0.0000 0.974 1.000 0.000
#> GSM125136 1 0.0000 0.974 1.000 0.000
#> GSM125138 1 0.0000 0.974 1.000 0.000
#> GSM125140 1 0.0000 0.974 1.000 0.000
#> GSM125142 1 0.0000 0.974 1.000 0.000
#> GSM125144 1 0.0000 0.974 1.000 0.000
#> GSM125146 1 0.0000 0.974 1.000 0.000
#> GSM125148 1 0.0000 0.974 1.000 0.000
#> GSM125150 1 0.0000 0.974 1.000 0.000
#> GSM125152 1 0.0000 0.974 1.000 0.000
#> GSM125154 1 0.0000 0.974 1.000 0.000
#> GSM125156 1 0.0000 0.974 1.000 0.000
#> GSM125158 1 0.0000 0.974 1.000 0.000
#> GSM125160 2 0.0000 0.970 0.000 1.000
#> GSM125162 1 0.0000 0.974 1.000 0.000
#> GSM125164 2 0.0000 0.970 0.000 1.000
#> GSM125166 2 0.0000 0.970 0.000 1.000
#> GSM125168 2 0.0000 0.970 0.000 1.000
#> GSM125170 2 0.0000 0.970 0.000 1.000
#> GSM125172 2 0.0000 0.970 0.000 1.000
#> GSM125174 2 0.0672 0.963 0.008 0.992
#> GSM125176 2 0.0000 0.970 0.000 1.000
#> GSM125178 2 0.8327 0.640 0.264 0.736
#> GSM125180 2 0.3733 0.906 0.072 0.928
#> GSM125182 2 0.0000 0.970 0.000 1.000
#> GSM125184 2 0.0000 0.970 0.000 1.000
#> GSM125186 2 0.3733 0.906 0.072 0.928
#> GSM125188 2 0.0000 0.970 0.000 1.000
#> GSM125190 2 0.0000 0.970 0.000 1.000
#> GSM125192 2 0.0000 0.970 0.000 1.000
#> GSM125194 1 0.0000 0.974 1.000 0.000
#> GSM125196 2 0.0000 0.970 0.000 1.000
#> GSM125198 2 0.0000 0.970 0.000 1.000
#> GSM125200 1 0.0000 0.974 1.000 0.000
#> GSM125202 2 0.0000 0.970 0.000 1.000
#> GSM125204 2 0.8327 0.649 0.264 0.736
#> GSM125206 2 0.0672 0.963 0.008 0.992
#> GSM125208 1 0.9732 0.310 0.596 0.404
#> GSM125210 2 0.0000 0.970 0.000 1.000
#> GSM125212 2 0.0000 0.970 0.000 1.000
#> GSM125214 2 0.0000 0.970 0.000 1.000
#> GSM125216 2 0.0000 0.970 0.000 1.000
#> GSM125218 2 0.0000 0.970 0.000 1.000
#> GSM125220 1 0.0000 0.974 1.000 0.000
#> GSM125222 2 0.0000 0.970 0.000 1.000
#> GSM125224 2 0.0000 0.970 0.000 1.000
#> GSM125226 2 0.0000 0.970 0.000 1.000
#> GSM125228 2 0.0000 0.970 0.000 1.000
#> GSM125230 1 0.0672 0.967 0.992 0.008
#> GSM125232 1 0.0376 0.971 0.996 0.004
#> GSM125234 1 0.0376 0.971 0.996 0.004
#> GSM125236 1 0.0000 0.974 1.000 0.000
#> GSM125238 1 0.0000 0.974 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125125 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125127 1 0.1289 0.9563 0.968 0.000 0.032
#> GSM125129 1 0.0892 0.9655 0.980 0.000 0.020
#> GSM125131 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125133 1 0.0747 0.9634 0.984 0.000 0.016
#> GSM125135 1 0.0424 0.9708 0.992 0.000 0.008
#> GSM125137 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125139 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125141 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125143 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125145 1 0.0592 0.9695 0.988 0.000 0.012
#> GSM125147 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125151 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125153 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125165 3 0.2165 0.8590 0.000 0.064 0.936
#> GSM125167 2 0.0424 0.9502 0.000 0.992 0.008
#> GSM125169 2 0.0424 0.9502 0.000 0.992 0.008
#> GSM125171 2 0.3619 0.8328 0.000 0.864 0.136
#> GSM125173 3 0.1753 0.8673 0.000 0.048 0.952
#> GSM125175 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125177 2 0.1860 0.9203 0.000 0.948 0.052
#> GSM125179 3 0.1031 0.8741 0.000 0.024 0.976
#> GSM125181 3 0.0747 0.8759 0.000 0.016 0.984
#> GSM125183 3 0.0848 0.8750 0.008 0.008 0.984
#> GSM125185 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125187 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125189 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125191 2 0.3816 0.8160 0.000 0.852 0.148
#> GSM125193 1 0.6235 0.1862 0.564 0.000 0.436
#> GSM125195 3 0.3359 0.8389 0.084 0.016 0.900
#> GSM125197 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125201 2 0.0747 0.9449 0.000 0.984 0.016
#> GSM125203 3 0.5791 0.7549 0.168 0.048 0.784
#> GSM125205 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125207 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125209 2 0.5216 0.6591 0.000 0.740 0.260
#> GSM125211 3 0.0424 0.8739 0.008 0.000 0.992
#> GSM125213 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125217 2 0.0592 0.9485 0.000 0.988 0.012
#> GSM125219 1 0.1529 0.9503 0.960 0.000 0.040
#> GSM125221 3 0.1860 0.8655 0.000 0.052 0.948
#> GSM125223 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125229 2 0.1411 0.9315 0.000 0.964 0.036
#> GSM125231 3 0.5706 0.5273 0.320 0.000 0.680
#> GSM125233 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125235 1 0.0237 0.9720 0.996 0.000 0.004
#> GSM125237 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125124 1 0.0892 0.9661 0.980 0.000 0.020
#> GSM125126 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125128 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125130 1 0.1529 0.9502 0.960 0.000 0.040
#> GSM125132 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125136 1 0.0424 0.9699 0.992 0.000 0.008
#> GSM125138 1 0.2537 0.8933 0.920 0.000 0.080
#> GSM125140 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125144 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125146 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125166 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125168 2 0.6154 0.2998 0.000 0.592 0.408
#> GSM125170 2 0.0747 0.9467 0.000 0.984 0.016
#> GSM125172 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125174 3 0.0747 0.8750 0.000 0.016 0.984
#> GSM125176 2 0.0424 0.9503 0.000 0.992 0.008
#> GSM125178 3 0.4796 0.7128 0.000 0.220 0.780
#> GSM125180 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125182 2 0.4178 0.7920 0.000 0.828 0.172
#> GSM125184 3 0.1643 0.8686 0.000 0.044 0.956
#> GSM125186 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125188 2 0.5948 0.4026 0.000 0.640 0.360
#> GSM125190 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125192 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125194 3 0.5835 0.4914 0.340 0.000 0.660
#> GSM125196 3 0.6505 0.0412 0.004 0.468 0.528
#> GSM125198 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.9730 1.000 0.000 0.000
#> GSM125202 2 0.0237 0.9515 0.000 0.996 0.004
#> GSM125204 3 0.5331 0.7960 0.076 0.100 0.824
#> GSM125206 3 0.5397 0.6110 0.000 0.280 0.720
#> GSM125208 3 0.0000 0.8745 0.000 0.000 1.000
#> GSM125210 3 0.1031 0.8737 0.000 0.024 0.976
#> GSM125212 3 0.4002 0.7782 0.000 0.160 0.840
#> GSM125214 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125220 1 0.0592 0.9666 0.988 0.000 0.012
#> GSM125222 3 0.1031 0.8742 0.000 0.024 0.976
#> GSM125224 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125226 2 0.0892 0.9437 0.000 0.980 0.020
#> GSM125228 2 0.0000 0.9522 0.000 1.000 0.000
#> GSM125230 3 0.3267 0.8176 0.116 0.000 0.884
#> GSM125232 3 0.5810 0.4942 0.336 0.000 0.664
#> GSM125234 1 0.6008 0.3920 0.628 0.000 0.372
#> GSM125236 1 0.0747 0.9678 0.984 0.000 0.016
#> GSM125238 1 0.0000 0.9730 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0921 0.883 0.972 0.000 0.000 0.028
#> GSM125125 4 0.1211 0.917 0.040 0.000 0.000 0.960
#> GSM125127 1 0.4365 0.786 0.784 0.000 0.028 0.188
#> GSM125129 4 0.3907 0.723 0.232 0.000 0.000 0.768
#> GSM125131 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125133 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125135 4 0.3610 0.765 0.200 0.000 0.000 0.800
#> GSM125137 4 0.2149 0.868 0.088 0.000 0.000 0.912
#> GSM125139 1 0.0707 0.882 0.980 0.000 0.000 0.020
#> GSM125141 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125143 1 0.0817 0.883 0.976 0.000 0.000 0.024
#> GSM125145 1 0.2647 0.858 0.880 0.000 0.000 0.120
#> GSM125147 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125149 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125151 1 0.0707 0.882 0.980 0.000 0.000 0.020
#> GSM125153 1 0.2814 0.851 0.868 0.000 0.000 0.132
#> GSM125155 4 0.4477 0.457 0.312 0.000 0.000 0.688
#> GSM125157 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125159 2 0.0469 0.940 0.000 0.988 0.012 0.000
#> GSM125161 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125163 2 0.0469 0.940 0.000 0.988 0.012 0.000
#> GSM125165 3 0.1557 0.873 0.000 0.056 0.944 0.000
#> GSM125167 2 0.0707 0.938 0.000 0.980 0.020 0.000
#> GSM125169 2 0.0336 0.941 0.000 0.992 0.008 0.000
#> GSM125171 2 0.3266 0.788 0.000 0.832 0.168 0.000
#> GSM125173 3 0.1474 0.875 0.000 0.052 0.948 0.000
#> GSM125175 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125177 2 0.2363 0.893 0.024 0.920 0.056 0.000
#> GSM125179 3 0.1004 0.877 0.004 0.024 0.972 0.000
#> GSM125181 3 0.0921 0.879 0.000 0.028 0.972 0.000
#> GSM125183 3 0.0376 0.875 0.004 0.000 0.992 0.004
#> GSM125185 3 0.1118 0.869 0.036 0.000 0.964 0.000
#> GSM125187 3 0.0336 0.875 0.008 0.000 0.992 0.000
#> GSM125189 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125191 2 0.3400 0.781 0.000 0.820 0.180 0.000
#> GSM125193 4 0.4284 0.709 0.020 0.000 0.200 0.780
#> GSM125195 3 0.3862 0.827 0.080 0.016 0.860 0.044
#> GSM125197 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125199 4 0.0188 0.938 0.004 0.000 0.000 0.996
#> GSM125201 2 0.1118 0.928 0.000 0.964 0.036 0.000
#> GSM125203 3 0.5885 0.708 0.028 0.064 0.728 0.180
#> GSM125205 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125207 3 0.1022 0.873 0.032 0.000 0.968 0.000
#> GSM125209 2 0.4454 0.578 0.000 0.692 0.308 0.000
#> GSM125211 3 0.0672 0.875 0.008 0.000 0.984 0.008
#> GSM125213 2 0.0707 0.936 0.000 0.980 0.020 0.000
#> GSM125215 2 0.0592 0.938 0.000 0.984 0.016 0.000
#> GSM125217 2 0.1022 0.932 0.000 0.968 0.032 0.000
#> GSM125219 1 0.0707 0.877 0.980 0.000 0.000 0.020
#> GSM125221 3 0.1474 0.874 0.000 0.052 0.948 0.000
#> GSM125223 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125229 2 0.1854 0.907 0.012 0.940 0.048 0.000
#> GSM125231 1 0.4761 0.413 0.628 0.000 0.372 0.000
#> GSM125233 1 0.0817 0.883 0.976 0.000 0.000 0.024
#> GSM125235 4 0.0921 0.925 0.028 0.000 0.000 0.972
#> GSM125237 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125124 1 0.0707 0.882 0.980 0.000 0.000 0.020
#> GSM125126 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125128 4 0.0188 0.937 0.004 0.000 0.000 0.996
#> GSM125130 1 0.0469 0.879 0.988 0.000 0.000 0.012
#> GSM125132 4 0.0592 0.932 0.016 0.000 0.000 0.984
#> GSM125134 1 0.2647 0.858 0.880 0.000 0.000 0.120
#> GSM125136 4 0.0336 0.935 0.000 0.000 0.008 0.992
#> GSM125138 1 0.2589 0.859 0.884 0.000 0.000 0.116
#> GSM125140 1 0.1118 0.882 0.964 0.000 0.000 0.036
#> GSM125142 1 0.3311 0.825 0.828 0.000 0.000 0.172
#> GSM125144 1 0.0707 0.882 0.980 0.000 0.000 0.020
#> GSM125146 1 0.3400 0.815 0.820 0.000 0.000 0.180
#> GSM125148 1 0.4866 0.472 0.596 0.000 0.000 0.404
#> GSM125150 4 0.2149 0.874 0.088 0.000 0.000 0.912
#> GSM125152 1 0.0817 0.882 0.976 0.000 0.000 0.024
#> GSM125154 1 0.2704 0.856 0.876 0.000 0.000 0.124
#> GSM125156 1 0.0817 0.882 0.976 0.000 0.000 0.024
#> GSM125158 1 0.4304 0.629 0.716 0.000 0.000 0.284
#> GSM125160 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125162 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM125164 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125168 2 0.4907 0.251 0.000 0.580 0.420 0.000
#> GSM125170 2 0.0592 0.939 0.000 0.984 0.016 0.000
#> GSM125172 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125174 3 0.1004 0.878 0.004 0.024 0.972 0.000
#> GSM125176 2 0.0336 0.941 0.000 0.992 0.008 0.000
#> GSM125178 3 0.4770 0.627 0.012 0.288 0.700 0.000
#> GSM125180 3 0.0469 0.875 0.012 0.000 0.988 0.000
#> GSM125182 2 0.3907 0.715 0.000 0.768 0.232 0.000
#> GSM125184 3 0.1557 0.874 0.000 0.056 0.944 0.000
#> GSM125186 3 0.1792 0.854 0.068 0.000 0.932 0.000
#> GSM125188 2 0.4713 0.404 0.000 0.640 0.360 0.000
#> GSM125190 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125192 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125194 3 0.5231 0.356 0.012 0.000 0.604 0.384
#> GSM125196 3 0.5716 0.194 0.028 0.420 0.552 0.000
#> GSM125198 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125200 1 0.1211 0.881 0.960 0.000 0.000 0.040
#> GSM125202 2 0.0188 0.942 0.000 0.996 0.004 0.000
#> GSM125204 3 0.5794 0.773 0.064 0.080 0.764 0.092
#> GSM125206 3 0.5137 0.593 0.024 0.296 0.680 0.000
#> GSM125208 3 0.1389 0.870 0.048 0.000 0.952 0.000
#> GSM125210 3 0.0592 0.878 0.000 0.016 0.984 0.000
#> GSM125212 3 0.3810 0.758 0.008 0.188 0.804 0.000
#> GSM125214 2 0.0469 0.940 0.000 0.988 0.012 0.000
#> GSM125216 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125220 4 0.0336 0.937 0.008 0.000 0.000 0.992
#> GSM125222 3 0.0469 0.879 0.000 0.012 0.988 0.000
#> GSM125224 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125226 2 0.0707 0.936 0.000 0.980 0.020 0.000
#> GSM125228 2 0.0000 0.942 0.000 1.000 0.000 0.000
#> GSM125230 3 0.3958 0.772 0.032 0.000 0.824 0.144
#> GSM125232 1 0.3688 0.726 0.792 0.000 0.208 0.000
#> GSM125234 1 0.1792 0.841 0.932 0.000 0.068 0.000
#> GSM125236 1 0.4972 0.251 0.544 0.000 0.000 0.456
#> GSM125238 4 0.0000 0.939 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.0404 0.8614 0.012 0.000 0.000 0.000 0.988
#> GSM125125 1 0.1197 0.9103 0.952 0.000 0.000 0.000 0.048
#> GSM125127 5 0.4406 0.7458 0.128 0.000 0.108 0.000 0.764
#> GSM125129 1 0.3707 0.6383 0.716 0.000 0.000 0.000 0.284
#> GSM125131 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125135 1 0.3661 0.6517 0.724 0.000 0.000 0.000 0.276
#> GSM125137 1 0.1671 0.8820 0.924 0.000 0.000 0.000 0.076
#> GSM125139 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125141 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125143 5 0.0162 0.8622 0.004 0.000 0.000 0.000 0.996
#> GSM125145 5 0.2179 0.8395 0.112 0.000 0.000 0.000 0.888
#> GSM125147 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125151 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125153 5 0.2471 0.8269 0.136 0.000 0.000 0.000 0.864
#> GSM125155 1 0.3796 0.4960 0.700 0.000 0.000 0.000 0.300
#> GSM125157 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.2127 0.8864 0.000 0.892 0.000 0.108 0.000
#> GSM125161 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125163 2 0.2230 0.8815 0.000 0.884 0.000 0.116 0.000
#> GSM125165 4 0.0404 0.8546 0.000 0.012 0.000 0.988 0.000
#> GSM125167 2 0.1732 0.9038 0.000 0.920 0.000 0.080 0.000
#> GSM125169 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125171 2 0.0609 0.9298 0.000 0.980 0.000 0.020 0.000
#> GSM125173 4 0.0609 0.8539 0.000 0.020 0.000 0.980 0.000
#> GSM125175 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125177 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125179 4 0.0000 0.8553 0.000 0.000 0.000 1.000 0.000
#> GSM125181 4 0.0290 0.8549 0.000 0.008 0.000 0.992 0.000
#> GSM125183 4 0.0794 0.8529 0.000 0.000 0.028 0.972 0.000
#> GSM125185 4 0.0162 0.8554 0.000 0.000 0.000 0.996 0.004
#> GSM125187 4 0.0290 0.8555 0.000 0.000 0.008 0.992 0.000
#> GSM125189 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125191 2 0.3274 0.7966 0.000 0.780 0.000 0.220 0.000
#> GSM125193 3 0.4354 0.6055 0.256 0.000 0.712 0.032 0.000
#> GSM125195 3 0.0510 0.8989 0.000 0.000 0.984 0.016 0.000
#> GSM125197 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125199 1 0.0162 0.9355 0.996 0.000 0.000 0.000 0.004
#> GSM125201 2 0.2605 0.8606 0.000 0.852 0.000 0.148 0.000
#> GSM125203 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125205 2 0.0566 0.9313 0.000 0.984 0.012 0.004 0.000
#> GSM125207 3 0.1608 0.8544 0.000 0.000 0.928 0.072 0.000
#> GSM125209 2 0.3274 0.7987 0.000 0.780 0.000 0.220 0.000
#> GSM125211 4 0.3612 0.6592 0.000 0.000 0.268 0.732 0.000
#> GSM125213 2 0.2773 0.8474 0.000 0.836 0.000 0.164 0.000
#> GSM125215 2 0.2690 0.8543 0.000 0.844 0.000 0.156 0.000
#> GSM125217 2 0.2852 0.8422 0.000 0.828 0.000 0.172 0.000
#> GSM125219 5 0.4287 0.0696 0.000 0.000 0.460 0.000 0.540
#> GSM125221 4 0.0290 0.8556 0.000 0.008 0.000 0.992 0.000
#> GSM125223 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125227 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125229 3 0.1270 0.8620 0.000 0.052 0.948 0.000 0.000
#> GSM125231 3 0.5154 0.2901 0.000 0.000 0.580 0.048 0.372
#> GSM125233 5 0.0290 0.8617 0.008 0.000 0.000 0.000 0.992
#> GSM125235 1 0.1197 0.9103 0.952 0.000 0.000 0.000 0.048
#> GSM125237 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125124 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125126 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125130 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125132 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125134 5 0.2230 0.8383 0.116 0.000 0.000 0.000 0.884
#> GSM125136 1 0.0609 0.9238 0.980 0.000 0.020 0.000 0.000
#> GSM125138 5 0.2179 0.8395 0.112 0.000 0.000 0.000 0.888
#> GSM125140 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125142 5 0.2891 0.7966 0.176 0.000 0.000 0.000 0.824
#> GSM125144 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125146 5 0.2966 0.7896 0.184 0.000 0.000 0.000 0.816
#> GSM125148 5 0.4242 0.3841 0.428 0.000 0.000 0.000 0.572
#> GSM125150 1 0.1544 0.8911 0.932 0.000 0.000 0.000 0.068
#> GSM125152 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000
#> GSM125154 5 0.2230 0.8380 0.116 0.000 0.000 0.000 0.884
#> GSM125156 5 0.0162 0.8618 0.004 0.000 0.000 0.000 0.996
#> GSM125158 5 0.3730 0.5974 0.288 0.000 0.000 0.000 0.712
#> GSM125160 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125162 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
#> GSM125164 2 0.0404 0.9333 0.000 0.988 0.000 0.012 0.000
#> GSM125166 2 0.0162 0.9353 0.000 0.996 0.000 0.004 0.000
#> GSM125168 2 0.3796 0.5469 0.000 0.700 0.000 0.300 0.000
#> GSM125170 2 0.0510 0.9302 0.000 0.984 0.000 0.016 0.000
#> GSM125172 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125174 4 0.3526 0.7732 0.000 0.072 0.096 0.832 0.000
#> GSM125176 2 0.0162 0.9353 0.000 0.996 0.000 0.004 0.000
#> GSM125178 3 0.0898 0.8938 0.000 0.008 0.972 0.020 0.000
#> GSM125180 4 0.1792 0.8245 0.000 0.000 0.084 0.916 0.000
#> GSM125182 2 0.3508 0.7586 0.000 0.748 0.000 0.252 0.000
#> GSM125184 4 0.2648 0.7525 0.000 0.152 0.000 0.848 0.000
#> GSM125186 4 0.1270 0.8320 0.000 0.000 0.000 0.948 0.052
#> GSM125188 4 0.4287 -0.0295 0.000 0.460 0.000 0.540 0.000
#> GSM125190 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125192 2 0.0162 0.9349 0.000 0.996 0.004 0.000 0.000
#> GSM125194 4 0.5017 0.6328 0.196 0.000 0.076 0.716 0.012
#> GSM125196 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125198 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125200 5 0.0290 0.8618 0.008 0.000 0.000 0.000 0.992
#> GSM125202 2 0.0162 0.9353 0.000 0.996 0.000 0.004 0.000
#> GSM125204 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125206 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125208 3 0.0000 0.9062 0.000 0.000 1.000 0.000 0.000
#> GSM125210 4 0.0000 0.8553 0.000 0.000 0.000 1.000 0.000
#> GSM125212 4 0.3715 0.6709 0.000 0.004 0.260 0.736 0.000
#> GSM125214 2 0.2605 0.8607 0.000 0.852 0.000 0.148 0.000
#> GSM125216 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125218 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125220 1 0.1671 0.8836 0.924 0.000 0.076 0.000 0.000
#> GSM125222 4 0.0955 0.8540 0.000 0.004 0.028 0.968 0.000
#> GSM125224 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125226 2 0.0290 0.9337 0.000 0.992 0.000 0.008 0.000
#> GSM125228 2 0.0000 0.9360 0.000 1.000 0.000 0.000 0.000
#> GSM125230 4 0.3983 0.5522 0.000 0.000 0.340 0.660 0.000
#> GSM125232 5 0.2873 0.7856 0.000 0.000 0.016 0.128 0.856
#> GSM125234 5 0.1211 0.8489 0.000 0.000 0.024 0.016 0.960
#> GSM125236 5 0.4283 0.2390 0.456 0.000 0.000 0.000 0.544
#> GSM125238 1 0.0000 0.9371 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.0363 0.8561 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM125125 5 0.1075 0.9091 0.048 0.000 0.000 0.000 0.952 0.000
#> GSM125127 1 0.3977 0.7457 0.760 0.000 0.096 0.000 0.144 0.000
#> GSM125129 5 0.3351 0.6313 0.288 0.000 0.000 0.000 0.712 0.000
#> GSM125131 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125133 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125135 5 0.3288 0.6511 0.276 0.000 0.000 0.000 0.724 0.000
#> GSM125137 5 0.1501 0.8806 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM125139 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125141 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125143 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125145 1 0.2048 0.8324 0.880 0.000 0.000 0.000 0.120 0.000
#> GSM125147 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125149 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125151 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125153 1 0.2260 0.8222 0.860 0.000 0.000 0.000 0.140 0.000
#> GSM125155 5 0.3409 0.4926 0.300 0.000 0.000 0.000 0.700 0.000
#> GSM125157 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125159 2 0.3624 0.6300 0.000 0.784 0.000 0.060 0.000 0.156
#> GSM125161 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125163 2 0.3681 0.6256 0.000 0.780 0.000 0.064 0.000 0.156
#> GSM125165 4 0.0717 0.8159 0.000 0.008 0.000 0.976 0.000 0.016
#> GSM125167 2 0.3318 0.6444 0.000 0.796 0.000 0.032 0.000 0.172
#> GSM125169 2 0.0260 0.7402 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125171 2 0.1391 0.7353 0.000 0.944 0.000 0.040 0.000 0.016
#> GSM125173 4 0.0520 0.8156 0.000 0.008 0.000 0.984 0.000 0.008
#> GSM125175 2 0.1387 0.6906 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM125177 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125179 4 0.0146 0.8147 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM125181 4 0.4798 0.6194 0.000 0.080 0.000 0.620 0.000 0.300
#> GSM125183 4 0.0000 0.8145 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125185 4 0.4408 0.6509 0.000 0.052 0.000 0.656 0.000 0.292
#> GSM125187 4 0.0508 0.8129 0.000 0.000 0.012 0.984 0.000 0.004
#> GSM125189 2 0.0260 0.7385 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125191 2 0.5042 0.4891 0.000 0.592 0.000 0.100 0.000 0.308
#> GSM125193 3 0.4027 0.6536 0.000 0.008 0.736 0.028 0.224 0.004
#> GSM125195 3 0.0291 0.9035 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM125197 6 0.3804 0.8051 0.000 0.424 0.000 0.000 0.000 0.576
#> GSM125199 5 0.0146 0.9344 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM125201 6 0.3384 0.5527 0.000 0.120 0.000 0.068 0.000 0.812
#> GSM125203 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125205 6 0.4018 0.8020 0.000 0.412 0.008 0.000 0.000 0.580
#> GSM125207 3 0.1152 0.8767 0.000 0.000 0.952 0.004 0.000 0.044
#> GSM125209 2 0.4829 0.5032 0.000 0.612 0.000 0.080 0.000 0.308
#> GSM125211 4 0.4277 0.6829 0.000 0.000 0.144 0.732 0.000 0.124
#> GSM125213 2 0.4859 0.4704 0.000 0.584 0.000 0.072 0.000 0.344
#> GSM125215 6 0.3352 0.5573 0.000 0.112 0.000 0.072 0.000 0.816
#> GSM125217 2 0.4720 0.5108 0.000 0.624 0.000 0.072 0.000 0.304
#> GSM125219 1 0.3868 -0.0659 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM125221 4 0.1265 0.8111 0.000 0.008 0.000 0.948 0.000 0.044
#> GSM125223 6 0.3810 0.8046 0.000 0.428 0.000 0.000 0.000 0.572
#> GSM125225 6 0.3862 0.7430 0.000 0.476 0.000 0.000 0.000 0.524
#> GSM125227 2 0.2092 0.6126 0.000 0.876 0.000 0.000 0.000 0.124
#> GSM125229 3 0.2999 0.8115 0.000 0.040 0.836 0.000 0.000 0.124
#> GSM125231 3 0.4493 0.3151 0.364 0.000 0.596 0.040 0.000 0.000
#> GSM125233 1 0.0260 0.8564 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125235 5 0.1141 0.9058 0.052 0.000 0.000 0.000 0.948 0.000
#> GSM125237 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125124 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125126 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125128 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125130 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125132 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125134 1 0.2092 0.8307 0.876 0.000 0.000 0.000 0.124 0.000
#> GSM125136 5 0.0458 0.9251 0.000 0.000 0.016 0.000 0.984 0.000
#> GSM125138 1 0.2048 0.8324 0.880 0.000 0.000 0.000 0.120 0.000
#> GSM125140 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125142 1 0.2631 0.7926 0.820 0.000 0.000 0.000 0.180 0.000
#> GSM125144 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125146 1 0.2697 0.7856 0.812 0.000 0.000 0.000 0.188 0.000
#> GSM125148 1 0.3817 0.3776 0.568 0.000 0.000 0.000 0.432 0.000
#> GSM125150 5 0.1327 0.8938 0.064 0.000 0.000 0.000 0.936 0.000
#> GSM125152 1 0.0000 0.8564 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125154 1 0.2092 0.8307 0.876 0.000 0.000 0.000 0.124 0.000
#> GSM125156 1 0.0146 0.8566 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125158 1 0.3330 0.5982 0.716 0.000 0.000 0.000 0.284 0.000
#> GSM125160 2 0.0632 0.7334 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125162 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125164 2 0.1124 0.7380 0.000 0.956 0.000 0.008 0.000 0.036
#> GSM125166 2 0.1267 0.6985 0.000 0.940 0.000 0.000 0.000 0.060
#> GSM125168 2 0.3819 0.3771 0.000 0.652 0.000 0.340 0.000 0.008
#> GSM125170 2 0.0777 0.7407 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM125172 2 0.1074 0.7312 0.000 0.960 0.000 0.028 0.000 0.012
#> GSM125174 4 0.1444 0.7711 0.000 0.072 0.000 0.928 0.000 0.000
#> GSM125176 2 0.0713 0.7410 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM125178 3 0.1074 0.8862 0.000 0.012 0.960 0.028 0.000 0.000
#> GSM125180 4 0.0603 0.8154 0.004 0.000 0.000 0.980 0.000 0.016
#> GSM125182 2 0.4814 0.5050 0.000 0.616 0.000 0.080 0.000 0.304
#> GSM125184 4 0.1327 0.7787 0.000 0.064 0.000 0.936 0.000 0.000
#> GSM125186 4 0.3732 0.7257 0.024 0.004 0.000 0.744 0.000 0.228
#> GSM125188 4 0.6112 0.0648 0.000 0.332 0.000 0.368 0.000 0.300
#> GSM125190 2 0.1267 0.7159 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM125192 2 0.0632 0.7309 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125194 4 0.4108 0.6644 0.008 0.000 0.072 0.756 0.164 0.000
#> GSM125196 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125198 6 0.3817 0.8025 0.000 0.432 0.000 0.000 0.000 0.568
#> GSM125200 1 0.0260 0.8565 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125202 6 0.3823 0.8026 0.000 0.436 0.000 0.000 0.000 0.564
#> GSM125204 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125206 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125208 3 0.0000 0.9064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125210 4 0.4443 0.6461 0.000 0.052 0.000 0.648 0.000 0.300
#> GSM125212 4 0.4593 0.6749 0.000 0.008 0.152 0.716 0.000 0.124
#> GSM125214 6 0.4176 0.5082 0.000 0.212 0.000 0.068 0.000 0.720
#> GSM125216 6 0.3838 0.7888 0.000 0.448 0.000 0.000 0.000 0.552
#> GSM125218 2 0.0260 0.7385 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125220 5 0.1765 0.8630 0.000 0.000 0.096 0.000 0.904 0.000
#> GSM125222 4 0.0146 0.8145 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM125224 6 0.3810 0.8046 0.000 0.428 0.000 0.000 0.000 0.572
#> GSM125226 2 0.1320 0.7284 0.000 0.948 0.000 0.036 0.000 0.016
#> GSM125228 2 0.1714 0.6642 0.000 0.908 0.000 0.000 0.000 0.092
#> GSM125230 4 0.4871 0.5934 0.000 0.000 0.224 0.652 0.000 0.124
#> GSM125232 1 0.2562 0.7610 0.828 0.000 0.000 0.172 0.000 0.000
#> GSM125234 1 0.1003 0.8442 0.964 0.000 0.028 0.004 0.000 0.004
#> GSM125236 1 0.3838 0.2595 0.552 0.000 0.000 0.000 0.448 0.000
#> GSM125238 5 0.0000 0.9360 0.000 0.000 0.000 0.000 1.000 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 agent(p) individual(p) k
#> CV:pam 113 1.000 2.89e-05 2
#> CV:pam 109 0.963 1.55e-06 3
#> CV:pam 108 0.292 5.35e-06 4
#> CV:pam 110 0.426 8.96e-09 5
#> CV:pam 107 0.532 9.03e-10 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.803 0.937 0.966 0.4923 0.511 0.511
#> 3 3 0.828 0.830 0.920 0.3111 0.839 0.684
#> 4 4 0.693 0.761 0.812 0.1030 0.939 0.829
#> 5 5 0.634 0.656 0.780 0.0761 0.901 0.682
#> 6 6 0.654 0.588 0.740 0.0417 0.964 0.851
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
#> GSM125123 1 0.000 0.996 1.000 0.000
#> GSM125125 1 0.000 0.996 1.000 0.000
#> GSM125127 1 0.000 0.996 1.000 0.000
#> GSM125129 1 0.000 0.996 1.000 0.000
#> GSM125131 1 0.000 0.996 1.000 0.000
#> GSM125133 1 0.000 0.996 1.000 0.000
#> GSM125135 1 0.000 0.996 1.000 0.000
#> GSM125137 1 0.000 0.996 1.000 0.000
#> GSM125139 1 0.000 0.996 1.000 0.000
#> GSM125141 1 0.000 0.996 1.000 0.000
#> GSM125143 1 0.000 0.996 1.000 0.000
#> GSM125145 1 0.000 0.996 1.000 0.000
#> GSM125147 1 0.000 0.996 1.000 0.000
#> GSM125149 1 0.000 0.996 1.000 0.000
#> GSM125151 1 0.000 0.996 1.000 0.000
#> GSM125153 1 0.000 0.996 1.000 0.000
#> GSM125155 1 0.000 0.996 1.000 0.000
#> GSM125157 1 0.000 0.996 1.000 0.000
#> GSM125159 2 0.000 0.943 0.000 1.000
#> GSM125161 1 0.000 0.996 1.000 0.000
#> GSM125163 2 0.000 0.943 0.000 1.000
#> GSM125165 2 0.000 0.943 0.000 1.000
#> GSM125167 2 0.000 0.943 0.000 1.000
#> GSM125169 2 0.000 0.943 0.000 1.000
#> GSM125171 2 0.000 0.943 0.000 1.000
#> GSM125173 2 0.000 0.943 0.000 1.000
#> GSM125175 2 0.000 0.943 0.000 1.000
#> GSM125177 2 0.689 0.814 0.184 0.816
#> GSM125179 2 0.000 0.943 0.000 1.000
#> GSM125181 2 0.000 0.943 0.000 1.000
#> GSM125183 2 0.000 0.943 0.000 1.000
#> GSM125185 2 0.000 0.943 0.000 1.000
#> GSM125187 2 0.000 0.943 0.000 1.000
#> GSM125189 2 0.000 0.943 0.000 1.000
#> GSM125191 2 0.000 0.943 0.000 1.000
#> GSM125193 2 0.781 0.760 0.232 0.768
#> GSM125195 2 0.909 0.617 0.324 0.676
#> GSM125197 2 0.000 0.943 0.000 1.000
#> GSM125199 1 0.000 0.996 1.000 0.000
#> GSM125201 2 0.000 0.943 0.000 1.000
#> GSM125203 2 0.886 0.653 0.304 0.696
#> GSM125205 2 0.634 0.836 0.160 0.840
#> GSM125207 2 0.697 0.810 0.188 0.812
#> GSM125209 2 0.000 0.943 0.000 1.000
#> GSM125211 2 0.469 0.881 0.100 0.900
#> GSM125213 2 0.000 0.943 0.000 1.000
#> GSM125215 2 0.000 0.943 0.000 1.000
#> GSM125217 2 0.000 0.943 0.000 1.000
#> GSM125219 1 0.000 0.996 1.000 0.000
#> GSM125221 2 0.000 0.943 0.000 1.000
#> GSM125223 2 0.000 0.943 0.000 1.000
#> GSM125225 2 0.000 0.943 0.000 1.000
#> GSM125227 2 0.000 0.943 0.000 1.000
#> GSM125229 2 0.714 0.802 0.196 0.804
#> GSM125231 2 0.821 0.728 0.256 0.744
#> GSM125233 1 0.000 0.996 1.000 0.000
#> GSM125235 1 0.000 0.996 1.000 0.000
#> GSM125237 1 0.000 0.996 1.000 0.000
#> GSM125124 1 0.000 0.996 1.000 0.000
#> GSM125126 1 0.000 0.996 1.000 0.000
#> GSM125128 1 0.000 0.996 1.000 0.000
#> GSM125130 1 0.000 0.996 1.000 0.000
#> GSM125132 1 0.000 0.996 1.000 0.000
#> GSM125134 1 0.000 0.996 1.000 0.000
#> GSM125136 1 0.000 0.996 1.000 0.000
#> GSM125138 1 0.000 0.996 1.000 0.000
#> GSM125140 1 0.000 0.996 1.000 0.000
#> GSM125142 1 0.000 0.996 1.000 0.000
#> GSM125144 1 0.000 0.996 1.000 0.000
#> GSM125146 1 0.000 0.996 1.000 0.000
#> GSM125148 1 0.000 0.996 1.000 0.000
#> GSM125150 1 0.000 0.996 1.000 0.000
#> GSM125152 1 0.000 0.996 1.000 0.000
#> GSM125154 1 0.000 0.996 1.000 0.000
#> GSM125156 1 0.000 0.996 1.000 0.000
#> GSM125158 1 0.000 0.996 1.000 0.000
#> GSM125160 2 0.000 0.943 0.000 1.000
#> GSM125162 1 0.000 0.996 1.000 0.000
#> GSM125164 2 0.000 0.943 0.000 1.000
#> GSM125166 2 0.000 0.943 0.000 1.000
#> GSM125168 2 0.000 0.943 0.000 1.000
#> GSM125170 2 0.000 0.943 0.000 1.000
#> GSM125172 2 0.000 0.943 0.000 1.000
#> GSM125174 2 0.000 0.943 0.000 1.000
#> GSM125176 2 0.000 0.943 0.000 1.000
#> GSM125178 2 0.680 0.818 0.180 0.820
#> GSM125180 2 0.000 0.943 0.000 1.000
#> GSM125182 2 0.000 0.943 0.000 1.000
#> GSM125184 2 0.000 0.943 0.000 1.000
#> GSM125186 2 0.000 0.943 0.000 1.000
#> GSM125188 2 0.000 0.943 0.000 1.000
#> GSM125190 2 0.000 0.943 0.000 1.000
#> GSM125192 2 0.000 0.943 0.000 1.000
#> GSM125194 2 0.697 0.811 0.188 0.812
#> GSM125196 2 0.821 0.728 0.256 0.744
#> GSM125198 2 0.000 0.943 0.000 1.000
#> GSM125200 1 0.000 0.996 1.000 0.000
#> GSM125202 2 0.000 0.943 0.000 1.000
#> GSM125204 2 0.891 0.646 0.308 0.692
#> GSM125206 2 0.814 0.734 0.252 0.748
#> GSM125208 2 0.706 0.806 0.192 0.808
#> GSM125210 2 0.000 0.943 0.000 1.000
#> GSM125212 2 0.416 0.892 0.084 0.916
#> GSM125214 2 0.000 0.943 0.000 1.000
#> GSM125216 2 0.000 0.943 0.000 1.000
#> GSM125218 2 0.000 0.943 0.000 1.000
#> GSM125220 1 0.000 0.996 1.000 0.000
#> GSM125222 2 0.000 0.943 0.000 1.000
#> GSM125224 2 0.000 0.943 0.000 1.000
#> GSM125226 2 0.000 0.943 0.000 1.000
#> GSM125228 2 0.000 0.943 0.000 1.000
#> GSM125230 2 0.697 0.810 0.188 0.812
#> GSM125232 2 0.605 0.845 0.148 0.852
#> GSM125234 1 0.671 0.761 0.824 0.176
#> GSM125236 1 0.000 0.996 1.000 0.000
#> GSM125238 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.1289 0.9699 0.968 0.000 0.032
#> GSM125125 1 0.1031 0.9727 0.976 0.000 0.024
#> GSM125127 1 0.1289 0.9699 0.968 0.000 0.032
#> GSM125129 1 0.1289 0.9699 0.968 0.000 0.032
#> GSM125131 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125133 1 0.0424 0.9744 0.992 0.000 0.008
#> GSM125135 1 0.0892 0.9734 0.980 0.000 0.020
#> GSM125137 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125139 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125141 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125143 1 0.1529 0.9652 0.960 0.000 0.040
#> GSM125145 1 0.1031 0.9727 0.976 0.000 0.024
#> GSM125147 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125149 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125151 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125153 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125155 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125157 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125159 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125161 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125163 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125165 2 0.4178 0.7465 0.000 0.828 0.172
#> GSM125167 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125169 2 0.4452 0.7186 0.000 0.808 0.192
#> GSM125171 3 0.5621 0.5848 0.000 0.308 0.692
#> GSM125173 2 0.5733 0.4762 0.000 0.676 0.324
#> GSM125175 2 0.3192 0.8071 0.000 0.888 0.112
#> GSM125177 3 0.0892 0.8096 0.000 0.020 0.980
#> GSM125179 3 0.6008 0.4772 0.000 0.372 0.628
#> GSM125181 2 0.4178 0.7485 0.000 0.828 0.172
#> GSM125183 2 0.6111 0.2829 0.000 0.604 0.396
#> GSM125185 3 0.6008 0.4773 0.000 0.372 0.628
#> GSM125187 3 0.5968 0.4978 0.000 0.364 0.636
#> GSM125189 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125191 2 0.0747 0.8819 0.000 0.984 0.016
#> GSM125193 3 0.2318 0.8036 0.028 0.028 0.944
#> GSM125195 3 0.1170 0.8085 0.008 0.016 0.976
#> GSM125197 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125199 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125201 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125203 3 0.2176 0.7980 0.032 0.020 0.948
#> GSM125205 3 0.5138 0.6280 0.000 0.252 0.748
#> GSM125207 3 0.1031 0.8093 0.000 0.024 0.976
#> GSM125209 2 0.0892 0.8805 0.000 0.980 0.020
#> GSM125211 3 0.6045 0.4259 0.000 0.380 0.620
#> GSM125213 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125217 2 0.0424 0.8856 0.000 0.992 0.008
#> GSM125219 1 0.1411 0.9679 0.964 0.000 0.036
#> GSM125221 2 0.5859 0.4505 0.000 0.656 0.344
#> GSM125223 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125229 3 0.2945 0.7806 0.004 0.088 0.908
#> GSM125231 3 0.0983 0.8091 0.004 0.016 0.980
#> GSM125233 1 0.1289 0.9699 0.968 0.000 0.032
#> GSM125235 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125237 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125124 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125126 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125128 1 0.0747 0.9745 0.984 0.000 0.016
#> GSM125130 1 0.1753 0.9593 0.952 0.000 0.048
#> GSM125132 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125134 1 0.0892 0.9734 0.980 0.000 0.020
#> GSM125136 1 0.0424 0.9744 0.992 0.000 0.008
#> GSM125138 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125140 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125142 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM125144 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125146 1 0.0892 0.9734 0.980 0.000 0.020
#> GSM125148 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125150 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125152 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125154 1 0.1031 0.9727 0.976 0.000 0.024
#> GSM125156 1 0.0000 0.9745 1.000 0.000 0.000
#> GSM125158 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125160 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125162 1 0.0237 0.9744 0.996 0.000 0.004
#> GSM125164 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125168 2 0.2356 0.8498 0.000 0.928 0.072
#> GSM125170 2 0.4235 0.7497 0.000 0.824 0.176
#> GSM125172 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125174 3 0.6204 0.3486 0.000 0.424 0.576
#> GSM125176 2 0.5098 0.6280 0.000 0.752 0.248
#> GSM125178 3 0.0892 0.8096 0.000 0.020 0.980
#> GSM125180 3 0.5835 0.5334 0.000 0.340 0.660
#> GSM125182 2 0.1964 0.8617 0.000 0.944 0.056
#> GSM125184 2 0.6192 0.1792 0.000 0.580 0.420
#> GSM125186 3 0.5968 0.4935 0.000 0.364 0.636
#> GSM125188 2 0.4346 0.7376 0.000 0.816 0.184
#> GSM125190 2 0.1289 0.8739 0.000 0.968 0.032
#> GSM125192 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125194 3 0.2313 0.8015 0.032 0.024 0.944
#> GSM125196 3 0.0747 0.8086 0.000 0.016 0.984
#> GSM125198 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125200 1 0.0747 0.9745 0.984 0.000 0.016
#> GSM125202 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125204 3 0.1482 0.8083 0.012 0.020 0.968
#> GSM125206 3 0.0747 0.8086 0.000 0.016 0.984
#> GSM125208 3 0.0892 0.8096 0.000 0.020 0.980
#> GSM125210 3 0.6244 0.3001 0.000 0.440 0.560
#> GSM125212 2 0.6291 0.0134 0.000 0.532 0.468
#> GSM125214 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125216 2 0.0237 0.8857 0.000 0.996 0.004
#> GSM125218 2 0.0592 0.8840 0.000 0.988 0.012
#> GSM125220 1 0.5016 0.7128 0.760 0.000 0.240
#> GSM125222 2 0.5733 0.4972 0.000 0.676 0.324
#> GSM125224 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125226 2 0.0237 0.8869 0.000 0.996 0.004
#> GSM125228 2 0.0000 0.8880 0.000 1.000 0.000
#> GSM125230 3 0.1453 0.8096 0.008 0.024 0.968
#> GSM125232 3 0.2031 0.8082 0.016 0.032 0.952
#> GSM125234 1 0.6434 0.4381 0.612 0.008 0.380
#> GSM125236 1 0.1289 0.9699 0.968 0.000 0.032
#> GSM125238 1 0.0237 0.9744 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.5630 0.799 0.608 0.000 0.032 0.360
#> GSM125125 1 0.4008 0.837 0.756 0.000 0.000 0.244
#> GSM125127 1 0.5548 0.807 0.628 0.000 0.032 0.340
#> GSM125129 1 0.5630 0.799 0.608 0.000 0.032 0.360
#> GSM125131 1 0.0592 0.824 0.984 0.000 0.000 0.016
#> GSM125133 1 0.1118 0.821 0.964 0.000 0.000 0.036
#> GSM125135 1 0.4535 0.831 0.704 0.000 0.004 0.292
#> GSM125137 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125139 1 0.4730 0.812 0.636 0.000 0.000 0.364
#> GSM125141 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125143 1 0.5432 0.815 0.652 0.000 0.032 0.316
#> GSM125145 1 0.4564 0.822 0.672 0.000 0.000 0.328
#> GSM125147 1 0.0817 0.820 0.976 0.000 0.000 0.024
#> GSM125149 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125151 1 0.4730 0.812 0.636 0.000 0.000 0.364
#> GSM125153 1 0.3400 0.841 0.820 0.000 0.000 0.180
#> GSM125155 1 0.1716 0.839 0.936 0.000 0.000 0.064
#> GSM125157 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125159 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125161 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125163 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125165 2 0.5674 0.526 0.000 0.720 0.132 0.148
#> GSM125167 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125169 2 0.7480 -0.247 0.000 0.500 0.276 0.224
#> GSM125171 3 0.5151 0.552 0.000 0.140 0.760 0.100
#> GSM125173 4 0.7448 0.570 0.000 0.400 0.172 0.428
#> GSM125175 2 0.4399 0.604 0.000 0.768 0.212 0.020
#> GSM125177 3 0.0376 0.868 0.000 0.004 0.992 0.004
#> GSM125179 4 0.6856 0.789 0.000 0.140 0.284 0.576
#> GSM125181 2 0.5719 0.514 0.000 0.716 0.132 0.152
#> GSM125183 4 0.7145 0.790 0.000 0.252 0.192 0.556
#> GSM125185 4 0.6876 0.787 0.000 0.140 0.288 0.572
#> GSM125187 4 0.7031 0.785 0.000 0.152 0.296 0.552
#> GSM125189 2 0.1867 0.819 0.000 0.928 0.072 0.000
#> GSM125191 2 0.0469 0.857 0.000 0.988 0.012 0.000
#> GSM125193 3 0.1909 0.859 0.008 0.004 0.940 0.048
#> GSM125195 3 0.0657 0.865 0.000 0.004 0.984 0.012
#> GSM125197 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125199 1 0.1022 0.821 0.968 0.000 0.000 0.032
#> GSM125201 2 0.0336 0.858 0.000 0.992 0.008 0.000
#> GSM125203 3 0.0967 0.862 0.004 0.004 0.976 0.016
#> GSM125205 3 0.2542 0.793 0.000 0.084 0.904 0.012
#> GSM125207 3 0.4539 0.583 0.000 0.008 0.720 0.272
#> GSM125209 2 0.0707 0.853 0.000 0.980 0.020 0.000
#> GSM125211 4 0.7684 0.421 0.000 0.216 0.388 0.396
#> GSM125213 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125215 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125217 2 0.2867 0.776 0.000 0.884 0.104 0.012
#> GSM125219 1 0.5728 0.798 0.600 0.000 0.036 0.364
#> GSM125221 4 0.7551 0.763 0.000 0.288 0.228 0.484
#> GSM125223 2 0.0188 0.859 0.000 0.996 0.000 0.004
#> GSM125225 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125229 3 0.1545 0.845 0.000 0.040 0.952 0.008
#> GSM125231 3 0.0844 0.868 0.004 0.004 0.980 0.012
#> GSM125233 1 0.5659 0.795 0.600 0.000 0.032 0.368
#> GSM125235 1 0.1022 0.823 0.968 0.000 0.000 0.032
#> GSM125237 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125124 1 0.4643 0.817 0.656 0.000 0.000 0.344
#> GSM125126 1 0.2149 0.839 0.912 0.000 0.000 0.088
#> GSM125128 1 0.2214 0.817 0.928 0.000 0.028 0.044
#> GSM125130 1 0.5929 0.790 0.596 0.000 0.048 0.356
#> GSM125132 1 0.1637 0.837 0.940 0.000 0.000 0.060
#> GSM125134 1 0.4500 0.825 0.684 0.000 0.000 0.316
#> GSM125136 1 0.1584 0.820 0.952 0.000 0.012 0.036
#> GSM125138 1 0.4605 0.820 0.664 0.000 0.000 0.336
#> GSM125140 1 0.4730 0.812 0.636 0.000 0.000 0.364
#> GSM125142 1 0.2081 0.843 0.916 0.000 0.000 0.084
#> GSM125144 1 0.4730 0.812 0.636 0.000 0.000 0.364
#> GSM125146 1 0.4509 0.829 0.708 0.000 0.004 0.288
#> GSM125148 1 0.0188 0.828 0.996 0.000 0.000 0.004
#> GSM125150 1 0.1716 0.838 0.936 0.000 0.000 0.064
#> GSM125152 1 0.4730 0.812 0.636 0.000 0.000 0.364
#> GSM125154 1 0.4382 0.828 0.704 0.000 0.000 0.296
#> GSM125156 1 0.2589 0.843 0.884 0.000 0.000 0.116
#> GSM125158 1 0.2704 0.841 0.876 0.000 0.000 0.124
#> GSM125160 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0921 0.818 0.972 0.000 0.000 0.028
#> GSM125164 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125168 2 0.2313 0.817 0.000 0.924 0.044 0.032
#> GSM125170 4 0.7607 0.611 0.000 0.388 0.200 0.412
#> GSM125172 2 0.2662 0.795 0.000 0.900 0.084 0.016
#> GSM125174 4 0.6897 0.792 0.000 0.144 0.284 0.572
#> GSM125176 2 0.7439 -0.258 0.000 0.500 0.296 0.204
#> GSM125178 3 0.1109 0.865 0.000 0.004 0.968 0.028
#> GSM125180 4 0.6928 0.767 0.000 0.136 0.308 0.556
#> GSM125182 2 0.1722 0.834 0.000 0.944 0.048 0.008
#> GSM125184 4 0.7190 0.773 0.000 0.272 0.184 0.544
#> GSM125186 4 0.6876 0.787 0.000 0.140 0.288 0.572
#> GSM125188 2 0.7300 -0.402 0.000 0.472 0.156 0.372
#> GSM125190 2 0.3160 0.767 0.000 0.872 0.108 0.020
#> GSM125192 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125194 3 0.3575 0.808 0.020 0.004 0.852 0.124
#> GSM125196 3 0.0376 0.867 0.000 0.004 0.992 0.004
#> GSM125198 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125200 1 0.2589 0.840 0.884 0.000 0.000 0.116
#> GSM125202 2 0.2101 0.822 0.000 0.928 0.060 0.012
#> GSM125204 3 0.0895 0.863 0.000 0.004 0.976 0.020
#> GSM125206 3 0.0188 0.867 0.000 0.004 0.996 0.000
#> GSM125208 3 0.4262 0.657 0.000 0.008 0.756 0.236
#> GSM125210 4 0.6856 0.789 0.000 0.140 0.284 0.576
#> GSM125212 2 0.7735 -0.367 0.000 0.444 0.280 0.276
#> GSM125214 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125218 2 0.2593 0.783 0.000 0.892 0.104 0.004
#> GSM125220 1 0.5257 0.784 0.752 0.000 0.104 0.144
#> GSM125222 4 0.7336 0.790 0.000 0.256 0.216 0.528
#> GSM125224 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125226 2 0.1716 0.829 0.000 0.936 0.064 0.000
#> GSM125228 2 0.0000 0.860 0.000 1.000 0.000 0.000
#> GSM125230 3 0.4612 0.746 0.020 0.012 0.780 0.188
#> GSM125232 3 0.4086 0.709 0.008 0.000 0.776 0.216
#> GSM125234 1 0.7795 0.384 0.404 0.000 0.344 0.252
#> GSM125236 1 0.5630 0.799 0.608 0.000 0.032 0.360
#> GSM125238 1 0.0921 0.818 0.972 0.000 0.000 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.1538 0.7610 0.036 0.000 0.008 0.008 0.948
#> GSM125125 5 0.3582 0.7063 0.224 0.000 0.000 0.008 0.768
#> GSM125127 5 0.2308 0.7484 0.048 0.000 0.036 0.004 0.912
#> GSM125129 5 0.1618 0.7626 0.040 0.000 0.008 0.008 0.944
#> GSM125131 1 0.3074 0.8376 0.804 0.000 0.000 0.000 0.196
#> GSM125133 1 0.2629 0.8682 0.860 0.000 0.004 0.000 0.136
#> GSM125135 5 0.3300 0.7416 0.204 0.000 0.004 0.000 0.792
#> GSM125137 1 0.2230 0.8717 0.884 0.000 0.000 0.000 0.116
#> GSM125139 5 0.2616 0.7750 0.076 0.000 0.000 0.036 0.888
#> GSM125141 1 0.2280 0.8745 0.880 0.000 0.000 0.000 0.120
#> GSM125143 5 0.4150 0.7016 0.180 0.000 0.044 0.004 0.772
#> GSM125145 5 0.3039 0.7501 0.192 0.000 0.000 0.000 0.808
#> GSM125147 1 0.2773 0.8638 0.836 0.000 0.000 0.000 0.164
#> GSM125149 1 0.2230 0.8731 0.884 0.000 0.000 0.000 0.116
#> GSM125151 5 0.2554 0.7738 0.072 0.000 0.000 0.036 0.892
#> GSM125153 5 0.4294 0.1768 0.468 0.000 0.000 0.000 0.532
#> GSM125155 5 0.4546 0.2252 0.460 0.000 0.000 0.008 0.532
#> GSM125157 1 0.2179 0.8716 0.888 0.000 0.000 0.000 0.112
#> GSM125159 2 0.1808 0.7898 0.012 0.936 0.008 0.044 0.000
#> GSM125161 1 0.2230 0.8712 0.884 0.000 0.000 0.000 0.116
#> GSM125163 2 0.0162 0.7911 0.000 0.996 0.000 0.004 0.000
#> GSM125165 2 0.6476 0.2644 0.024 0.544 0.124 0.308 0.000
#> GSM125167 2 0.1498 0.7924 0.016 0.952 0.008 0.024 0.000
#> GSM125169 2 0.6741 0.1786 0.024 0.540 0.236 0.200 0.000
#> GSM125171 3 0.5583 0.3263 0.024 0.200 0.680 0.096 0.000
#> GSM125173 4 0.7212 0.4014 0.040 0.336 0.176 0.448 0.000
#> GSM125175 2 0.6313 0.4833 0.056 0.636 0.192 0.116 0.000
#> GSM125177 3 0.0968 0.8148 0.000 0.004 0.972 0.012 0.012
#> GSM125179 4 0.5845 0.5308 0.020 0.064 0.344 0.572 0.000
#> GSM125181 2 0.6169 0.3815 0.020 0.608 0.136 0.236 0.000
#> GSM125183 4 0.6350 0.5874 0.016 0.220 0.180 0.584 0.000
#> GSM125185 4 0.5729 0.5345 0.020 0.064 0.312 0.604 0.000
#> GSM125187 4 0.6636 0.5116 0.024 0.120 0.396 0.460 0.000
#> GSM125189 2 0.2958 0.7624 0.020 0.880 0.024 0.076 0.000
#> GSM125191 2 0.1949 0.7779 0.012 0.932 0.040 0.016 0.000
#> GSM125193 3 0.3606 0.7848 0.036 0.004 0.852 0.080 0.028
#> GSM125195 3 0.1484 0.8133 0.000 0.000 0.944 0.008 0.048
#> GSM125197 2 0.3176 0.7564 0.048 0.868 0.012 0.072 0.000
#> GSM125199 1 0.2583 0.8730 0.864 0.000 0.000 0.004 0.132
#> GSM125201 2 0.3175 0.7745 0.044 0.872 0.020 0.064 0.000
#> GSM125203 3 0.0865 0.8166 0.000 0.000 0.972 0.004 0.024
#> GSM125205 3 0.4281 0.5897 0.016 0.152 0.784 0.048 0.000
#> GSM125207 3 0.4953 0.5800 0.024 0.000 0.688 0.260 0.028
#> GSM125209 2 0.2339 0.7651 0.008 0.912 0.052 0.028 0.000
#> GSM125211 4 0.7309 0.2585 0.048 0.168 0.356 0.428 0.000
#> GSM125213 2 0.0324 0.7910 0.004 0.992 0.000 0.004 0.000
#> GSM125215 2 0.2491 0.7630 0.036 0.896 0.000 0.068 0.000
#> GSM125217 2 0.3926 0.7205 0.020 0.820 0.048 0.112 0.000
#> GSM125219 5 0.1405 0.7412 0.016 0.000 0.020 0.008 0.956
#> GSM125221 4 0.7419 0.3822 0.036 0.356 0.232 0.376 0.000
#> GSM125223 2 0.3176 0.7589 0.048 0.868 0.012 0.072 0.000
#> GSM125225 2 0.1300 0.7835 0.016 0.956 0.000 0.028 0.000
#> GSM125227 2 0.2514 0.7640 0.044 0.896 0.000 0.060 0.000
#> GSM125229 3 0.4789 0.6746 0.028 0.044 0.756 0.168 0.004
#> GSM125231 3 0.1205 0.8169 0.000 0.000 0.956 0.004 0.040
#> GSM125233 5 0.1329 0.7605 0.032 0.000 0.008 0.004 0.956
#> GSM125235 1 0.2852 0.8622 0.828 0.000 0.000 0.000 0.172
#> GSM125237 1 0.2813 0.8607 0.832 0.000 0.000 0.000 0.168
#> GSM125124 5 0.2905 0.7748 0.096 0.000 0.000 0.036 0.868
#> GSM125126 1 0.4517 0.3055 0.556 0.000 0.000 0.008 0.436
#> GSM125128 1 0.3160 0.8387 0.808 0.000 0.004 0.000 0.188
#> GSM125130 5 0.1280 0.7395 0.008 0.000 0.024 0.008 0.960
#> GSM125132 1 0.4046 0.6836 0.696 0.000 0.000 0.008 0.296
#> GSM125134 5 0.3242 0.7347 0.216 0.000 0.000 0.000 0.784
#> GSM125136 1 0.2763 0.8538 0.848 0.000 0.004 0.000 0.148
#> GSM125138 5 0.3098 0.7648 0.148 0.000 0.000 0.016 0.836
#> GSM125140 5 0.2616 0.7745 0.076 0.000 0.000 0.036 0.888
#> GSM125142 5 0.4451 0.0994 0.492 0.000 0.000 0.004 0.504
#> GSM125144 5 0.2850 0.7751 0.092 0.000 0.000 0.036 0.872
#> GSM125146 5 0.3661 0.6680 0.276 0.000 0.000 0.000 0.724
#> GSM125148 1 0.3333 0.8173 0.788 0.000 0.000 0.004 0.208
#> GSM125150 1 0.4464 0.3357 0.584 0.000 0.000 0.008 0.408
#> GSM125152 5 0.2554 0.7738 0.072 0.000 0.000 0.036 0.892
#> GSM125154 5 0.3661 0.6672 0.276 0.000 0.000 0.000 0.724
#> GSM125156 5 0.4367 0.4901 0.372 0.000 0.000 0.008 0.620
#> GSM125158 5 0.4183 0.5644 0.324 0.000 0.000 0.008 0.668
#> GSM125160 2 0.0451 0.7911 0.008 0.988 0.000 0.004 0.000
#> GSM125162 1 0.2230 0.8712 0.884 0.000 0.000 0.000 0.116
#> GSM125164 2 0.0854 0.7909 0.008 0.976 0.004 0.012 0.000
#> GSM125166 2 0.0566 0.7915 0.012 0.984 0.000 0.004 0.000
#> GSM125168 2 0.4129 0.6828 0.016 0.808 0.076 0.100 0.000
#> GSM125170 2 0.7431 -0.2682 0.040 0.416 0.232 0.312 0.000
#> GSM125172 2 0.4173 0.7264 0.040 0.816 0.060 0.084 0.000
#> GSM125174 4 0.5430 0.5667 0.016 0.064 0.268 0.652 0.000
#> GSM125176 2 0.7202 -0.1615 0.040 0.444 0.344 0.172 0.000
#> GSM125178 3 0.1605 0.8118 0.004 0.000 0.944 0.040 0.012
#> GSM125180 4 0.5950 0.5218 0.024 0.064 0.352 0.560 0.000
#> GSM125182 2 0.3459 0.7094 0.004 0.844 0.072 0.080 0.000
#> GSM125184 4 0.5736 0.5874 0.024 0.156 0.144 0.676 0.000
#> GSM125186 4 0.5729 0.5345 0.020 0.064 0.312 0.604 0.000
#> GSM125188 2 0.7262 -0.0960 0.044 0.456 0.180 0.320 0.000
#> GSM125190 2 0.5018 0.6258 0.020 0.736 0.088 0.156 0.000
#> GSM125192 2 0.0324 0.7911 0.004 0.992 0.000 0.004 0.000
#> GSM125194 3 0.3232 0.7854 0.016 0.000 0.864 0.084 0.036
#> GSM125196 3 0.1251 0.8163 0.000 0.000 0.956 0.008 0.036
#> GSM125198 2 0.2426 0.7657 0.036 0.900 0.000 0.064 0.000
#> GSM125200 5 0.4327 0.4705 0.360 0.000 0.000 0.008 0.632
#> GSM125202 2 0.3923 0.7419 0.040 0.832 0.052 0.076 0.000
#> GSM125204 3 0.0703 0.8168 0.000 0.000 0.976 0.000 0.024
#> GSM125206 3 0.1168 0.8164 0.000 0.000 0.960 0.008 0.032
#> GSM125208 3 0.4080 0.6606 0.016 0.000 0.760 0.212 0.012
#> GSM125210 4 0.5484 0.5462 0.008 0.068 0.308 0.616 0.000
#> GSM125212 4 0.7521 0.4225 0.048 0.252 0.272 0.428 0.000
#> GSM125214 2 0.0566 0.7913 0.000 0.984 0.004 0.012 0.000
#> GSM125216 2 0.2125 0.7735 0.024 0.920 0.004 0.052 0.000
#> GSM125218 2 0.3666 0.7359 0.020 0.840 0.048 0.092 0.000
#> GSM125220 1 0.5698 0.5958 0.668 0.000 0.116 0.020 0.196
#> GSM125222 4 0.7468 0.4345 0.044 0.328 0.220 0.408 0.000
#> GSM125224 2 0.2645 0.7621 0.044 0.888 0.000 0.068 0.000
#> GSM125226 2 0.3149 0.7569 0.012 0.868 0.040 0.080 0.000
#> GSM125228 2 0.2747 0.7622 0.048 0.888 0.004 0.060 0.000
#> GSM125230 3 0.4625 0.5907 0.020 0.004 0.652 0.324 0.000
#> GSM125232 3 0.4356 0.6640 0.024 0.000 0.756 0.200 0.020
#> GSM125234 5 0.4675 0.2105 0.000 0.000 0.380 0.020 0.600
#> GSM125236 5 0.1200 0.7470 0.016 0.000 0.012 0.008 0.964
#> GSM125238 1 0.2648 0.8693 0.848 0.000 0.000 0.000 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.1320 0.72972 0.948 0.000 0.036 0.000 0.016 NA
#> GSM125125 1 0.4302 0.63889 0.668 0.000 0.004 0.000 0.292 NA
#> GSM125127 1 0.2320 0.71683 0.892 0.000 0.080 0.000 0.024 NA
#> GSM125129 1 0.1408 0.72835 0.944 0.000 0.036 0.000 0.020 NA
#> GSM125131 5 0.2678 0.79258 0.116 0.000 0.000 0.004 0.860 NA
#> GSM125133 5 0.1964 0.82603 0.056 0.000 0.008 0.004 0.920 NA
#> GSM125135 1 0.4133 0.67557 0.720 0.000 0.012 0.000 0.236 NA
#> GSM125137 5 0.0717 0.82800 0.008 0.000 0.000 0.000 0.976 NA
#> GSM125139 1 0.4511 0.72618 0.752 0.000 0.000 0.060 0.052 NA
#> GSM125141 5 0.0806 0.83007 0.008 0.000 0.000 0.000 0.972 NA
#> GSM125143 1 0.4244 0.67282 0.744 0.000 0.080 0.000 0.168 NA
#> GSM125145 1 0.3221 0.70028 0.772 0.000 0.004 0.000 0.220 NA
#> GSM125147 5 0.1858 0.80635 0.092 0.000 0.000 0.000 0.904 NA
#> GSM125149 5 0.0363 0.82707 0.000 0.000 0.000 0.000 0.988 NA
#> GSM125151 1 0.4632 0.71901 0.736 0.000 0.000 0.060 0.048 NA
#> GSM125153 5 0.4401 -0.16155 0.464 0.000 0.000 0.000 0.512 NA
#> GSM125155 1 0.4632 0.36032 0.520 0.000 0.000 0.000 0.440 NA
#> GSM125157 5 0.0363 0.82707 0.000 0.000 0.000 0.000 0.988 NA
#> GSM125159 2 0.0790 0.67689 0.000 0.968 0.000 0.000 0.000 NA
#> GSM125161 5 0.1053 0.82517 0.012 0.000 0.000 0.004 0.964 NA
#> GSM125163 2 0.1765 0.66953 0.000 0.904 0.000 0.000 0.000 NA
#> GSM125165 2 0.7305 0.18890 0.004 0.448 0.160 0.172 0.000 NA
#> GSM125167 2 0.0891 0.67580 0.000 0.968 0.000 0.008 0.000 NA
#> GSM125169 2 0.5805 0.43452 0.000 0.608 0.212 0.044 0.000 NA
#> GSM125171 3 0.5013 0.43810 0.000 0.192 0.688 0.032 0.000 NA
#> GSM125173 2 0.7617 0.00411 0.004 0.380 0.212 0.200 0.000 NA
#> GSM125175 2 0.5414 0.54894 0.004 0.612 0.128 0.008 0.000 NA
#> GSM125177 3 0.1194 0.70912 0.000 0.004 0.956 0.032 0.000 NA
#> GSM125179 4 0.3854 0.77257 0.000 0.048 0.188 0.760 0.000 NA
#> GSM125181 2 0.6947 0.26851 0.004 0.512 0.172 0.136 0.000 NA
#> GSM125183 4 0.7583 0.34377 0.004 0.244 0.200 0.380 0.000 NA
#> GSM125185 4 0.3651 0.76809 0.000 0.048 0.180 0.772 0.000 NA
#> GSM125187 4 0.5888 0.53506 0.000 0.176 0.316 0.500 0.000 NA
#> GSM125189 2 0.1285 0.67611 0.000 0.944 0.004 0.000 0.000 NA
#> GSM125191 2 0.4163 0.58837 0.004 0.784 0.084 0.024 0.000 NA
#> GSM125193 3 0.2743 0.69602 0.020 0.008 0.888 0.060 0.004 NA
#> GSM125195 3 0.1909 0.69672 0.024 0.000 0.920 0.052 0.000 NA
#> GSM125197 2 0.3857 0.47496 0.000 0.532 0.000 0.000 0.000 NA
#> GSM125199 5 0.1418 0.82537 0.032 0.000 0.000 0.000 0.944 NA
#> GSM125201 2 0.2664 0.64820 0.000 0.816 0.000 0.000 0.000 NA
#> GSM125203 3 0.0891 0.70945 0.024 0.000 0.968 0.008 0.000 NA
#> GSM125205 3 0.4964 0.49091 0.000 0.168 0.704 0.040 0.000 NA
#> GSM125207 3 0.3737 0.31106 0.000 0.000 0.608 0.392 0.000 NA
#> GSM125209 2 0.4452 0.57171 0.004 0.764 0.092 0.032 0.000 NA
#> GSM125211 3 0.7375 0.14495 0.004 0.136 0.424 0.216 0.000 NA
#> GSM125213 2 0.1285 0.67712 0.004 0.944 0.000 0.000 0.000 NA
#> GSM125215 2 0.3838 0.48052 0.000 0.552 0.000 0.000 0.000 NA
#> GSM125217 2 0.2652 0.65524 0.000 0.868 0.020 0.008 0.000 NA
#> GSM125219 1 0.1434 0.72309 0.940 0.000 0.048 0.000 0.012 NA
#> GSM125221 2 0.7534 0.00373 0.004 0.384 0.252 0.152 0.000 NA
#> GSM125223 2 0.3989 0.47385 0.000 0.528 0.004 0.000 0.000 NA
#> GSM125225 2 0.2597 0.64285 0.000 0.824 0.000 0.000 0.000 NA
#> GSM125227 2 0.3810 0.49153 0.000 0.572 0.000 0.000 0.000 NA
#> GSM125229 3 0.4731 0.61885 0.000 0.044 0.736 0.120 0.000 NA
#> GSM125231 3 0.1261 0.70780 0.024 0.000 0.952 0.024 0.000 NA
#> GSM125233 1 0.1577 0.73046 0.940 0.000 0.036 0.000 0.016 NA
#> GSM125235 5 0.2350 0.80004 0.100 0.000 0.000 0.000 0.880 NA
#> GSM125237 5 0.1700 0.81478 0.080 0.000 0.000 0.000 0.916 NA
#> GSM125124 1 0.4831 0.72072 0.728 0.000 0.004 0.060 0.052 NA
#> GSM125126 5 0.4470 0.26889 0.356 0.000 0.000 0.000 0.604 NA
#> GSM125128 5 0.2290 0.80257 0.084 0.000 0.020 0.004 0.892 NA
#> GSM125130 1 0.1429 0.72007 0.940 0.000 0.052 0.000 0.004 NA
#> GSM125132 5 0.3139 0.73098 0.160 0.000 0.000 0.000 0.812 NA
#> GSM125134 1 0.3695 0.67000 0.712 0.000 0.000 0.000 0.272 NA
#> GSM125136 5 0.1881 0.80794 0.052 0.000 0.004 0.004 0.924 NA
#> GSM125138 1 0.5014 0.71935 0.708 0.000 0.000 0.044 0.112 NA
#> GSM125140 1 0.4562 0.72239 0.744 0.000 0.000 0.060 0.048 NA
#> GSM125142 1 0.4592 0.30315 0.496 0.000 0.000 0.000 0.468 NA
#> GSM125144 1 0.4632 0.71901 0.736 0.000 0.000 0.060 0.048 NA
#> GSM125146 1 0.4074 0.59916 0.656 0.000 0.004 0.000 0.324 NA
#> GSM125148 5 0.2538 0.77139 0.124 0.000 0.000 0.000 0.860 NA
#> GSM125150 5 0.4584 0.02498 0.404 0.000 0.000 0.000 0.556 NA
#> GSM125152 1 0.4736 0.72231 0.736 0.000 0.004 0.060 0.048 NA
#> GSM125154 1 0.5033 0.61212 0.608 0.000 0.000 0.012 0.312 NA
#> GSM125156 1 0.4524 0.59516 0.628 0.000 0.000 0.000 0.320 NA
#> GSM125158 1 0.4480 0.56817 0.616 0.000 0.000 0.000 0.340 NA
#> GSM125160 2 0.0937 0.67721 0.000 0.960 0.000 0.000 0.000 NA
#> GSM125162 5 0.1148 0.82350 0.016 0.000 0.000 0.004 0.960 NA
#> GSM125164 2 0.1899 0.66903 0.004 0.928 0.028 0.008 0.000 NA
#> GSM125166 2 0.0405 0.67506 0.000 0.988 0.000 0.008 0.000 NA
#> GSM125168 2 0.5465 0.45280 0.000 0.664 0.152 0.052 0.000 NA
#> GSM125170 2 0.7220 0.11935 0.004 0.436 0.252 0.108 0.000 NA
#> GSM125172 2 0.3053 0.66213 0.000 0.812 0.012 0.004 0.000 NA
#> GSM125174 4 0.4597 0.73288 0.004 0.048 0.220 0.708 0.000 NA
#> GSM125176 2 0.6497 0.29491 0.004 0.520 0.272 0.060 0.000 NA
#> GSM125178 3 0.1462 0.69656 0.000 0.000 0.936 0.056 0.000 NA
#> GSM125180 4 0.3892 0.76117 0.000 0.048 0.212 0.740 0.000 NA
#> GSM125182 2 0.5451 0.48498 0.004 0.672 0.148 0.044 0.000 NA
#> GSM125184 4 0.6017 0.61696 0.004 0.108 0.160 0.628 0.000 NA
#> GSM125186 4 0.3651 0.76809 0.000 0.048 0.180 0.772 0.000 NA
#> GSM125188 2 0.6871 0.27107 0.000 0.500 0.176 0.120 0.000 NA
#> GSM125190 2 0.4236 0.60124 0.000 0.772 0.088 0.028 0.000 NA
#> GSM125192 2 0.1007 0.67537 0.000 0.956 0.000 0.000 0.000 NA
#> GSM125194 3 0.2939 0.68133 0.024 0.000 0.868 0.084 0.012 NA
#> GSM125196 3 0.1738 0.70091 0.016 0.000 0.928 0.052 0.000 NA
#> GSM125198 2 0.3823 0.49183 0.000 0.564 0.000 0.000 0.000 NA
#> GSM125200 1 0.4538 0.56498 0.612 0.000 0.000 0.000 0.340 NA
#> GSM125202 2 0.2706 0.66445 0.000 0.832 0.008 0.000 0.000 NA
#> GSM125204 3 0.0972 0.70878 0.028 0.000 0.964 0.008 0.000 NA
#> GSM125206 3 0.1296 0.70498 0.004 0.000 0.948 0.044 0.000 NA
#> GSM125208 3 0.3515 0.42490 0.000 0.000 0.676 0.324 0.000 NA
#> GSM125210 4 0.3746 0.77261 0.000 0.048 0.192 0.760 0.000 NA
#> GSM125212 3 0.7520 -0.01259 0.000 0.276 0.344 0.156 0.000 NA
#> GSM125214 2 0.2093 0.67485 0.004 0.900 0.004 0.004 0.000 NA
#> GSM125216 2 0.3954 0.59123 0.004 0.684 0.016 0.000 0.000 NA
#> GSM125218 2 0.2393 0.66271 0.000 0.884 0.020 0.004 0.000 NA
#> GSM125220 5 0.4050 0.67409 0.100 0.000 0.104 0.000 0.780 NA
#> GSM125222 2 0.7619 -0.03959 0.004 0.368 0.248 0.172 0.000 NA
#> GSM125224 2 0.3854 0.47577 0.000 0.536 0.000 0.000 0.000 NA
#> GSM125226 2 0.2240 0.65993 0.000 0.904 0.032 0.008 0.000 NA
#> GSM125228 2 0.3797 0.49985 0.000 0.580 0.000 0.000 0.000 NA
#> GSM125230 3 0.5190 0.52116 0.000 0.000 0.632 0.256 0.016 NA
#> GSM125232 3 0.4892 0.37129 0.032 0.000 0.620 0.324 0.008 NA
#> GSM125234 1 0.5234 0.09013 0.532 0.000 0.392 0.060 0.000 NA
#> GSM125236 1 0.1297 0.72562 0.948 0.000 0.040 0.000 0.012 NA
#> GSM125238 5 0.0717 0.83147 0.016 0.000 0.000 0.000 0.976 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> CV:mclust 116 1.000 6.52e-06 2
#> CV:mclust 102 0.976 5.06e-08 3
#> CV:mclust 110 0.985 1.80e-11 4
#> CV:mclust 95 0.891 2.21e-08 5
#> CV:mclust 84 0.817 4.91e-06 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.963 0.942 0.976 0.5035 0.496 0.496
#> 3 3 0.799 0.837 0.917 0.2866 0.820 0.651
#> 4 4 0.621 0.596 0.783 0.1141 0.891 0.713
#> 5 5 0.676 0.638 0.789 0.0483 0.921 0.752
#> 6 6 0.671 0.597 0.773 0.0453 0.939 0.778
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
#> GSM125123 1 0.000 0.9840 1.000 0.000
#> GSM125125 1 0.000 0.9840 1.000 0.000
#> GSM125127 1 0.000 0.9840 1.000 0.000
#> GSM125129 1 0.000 0.9840 1.000 0.000
#> GSM125131 1 0.000 0.9840 1.000 0.000
#> GSM125133 1 0.000 0.9840 1.000 0.000
#> GSM125135 1 0.000 0.9840 1.000 0.000
#> GSM125137 1 0.000 0.9840 1.000 0.000
#> GSM125139 1 0.000 0.9840 1.000 0.000
#> GSM125141 1 0.000 0.9840 1.000 0.000
#> GSM125143 1 0.000 0.9840 1.000 0.000
#> GSM125145 1 0.000 0.9840 1.000 0.000
#> GSM125147 1 0.000 0.9840 1.000 0.000
#> GSM125149 1 0.000 0.9840 1.000 0.000
#> GSM125151 1 0.000 0.9840 1.000 0.000
#> GSM125153 1 0.000 0.9840 1.000 0.000
#> GSM125155 1 0.000 0.9840 1.000 0.000
#> GSM125157 1 0.000 0.9840 1.000 0.000
#> GSM125159 2 0.000 0.9662 0.000 1.000
#> GSM125161 1 0.000 0.9840 1.000 0.000
#> GSM125163 2 0.000 0.9662 0.000 1.000
#> GSM125165 2 0.000 0.9662 0.000 1.000
#> GSM125167 2 0.000 0.9662 0.000 1.000
#> GSM125169 2 0.000 0.9662 0.000 1.000
#> GSM125171 2 0.000 0.9662 0.000 1.000
#> GSM125173 2 0.000 0.9662 0.000 1.000
#> GSM125175 2 0.000 0.9662 0.000 1.000
#> GSM125177 2 0.000 0.9662 0.000 1.000
#> GSM125179 2 0.224 0.9362 0.036 0.964
#> GSM125181 2 0.000 0.9662 0.000 1.000
#> GSM125183 2 0.388 0.8986 0.076 0.924
#> GSM125185 2 0.000 0.9662 0.000 1.000
#> GSM125187 1 0.204 0.9541 0.968 0.032
#> GSM125189 2 0.000 0.9662 0.000 1.000
#> GSM125191 2 0.000 0.9662 0.000 1.000
#> GSM125193 1 0.242 0.9460 0.960 0.040
#> GSM125195 1 0.913 0.4946 0.672 0.328
#> GSM125197 2 0.000 0.9662 0.000 1.000
#> GSM125199 1 0.000 0.9840 1.000 0.000
#> GSM125201 2 0.000 0.9662 0.000 1.000
#> GSM125203 2 0.767 0.7141 0.224 0.776
#> GSM125205 2 0.000 0.9662 0.000 1.000
#> GSM125207 2 0.000 0.9662 0.000 1.000
#> GSM125209 2 0.000 0.9662 0.000 1.000
#> GSM125211 2 0.000 0.9662 0.000 1.000
#> GSM125213 2 0.000 0.9662 0.000 1.000
#> GSM125215 2 0.000 0.9662 0.000 1.000
#> GSM125217 2 0.000 0.9662 0.000 1.000
#> GSM125219 1 0.000 0.9840 1.000 0.000
#> GSM125221 2 0.000 0.9662 0.000 1.000
#> GSM125223 2 0.000 0.9662 0.000 1.000
#> GSM125225 2 0.000 0.9662 0.000 1.000
#> GSM125227 2 0.000 0.9662 0.000 1.000
#> GSM125229 2 0.000 0.9662 0.000 1.000
#> GSM125231 1 0.000 0.9840 1.000 0.000
#> GSM125233 1 0.000 0.9840 1.000 0.000
#> GSM125235 1 0.000 0.9840 1.000 0.000
#> GSM125237 1 0.000 0.9840 1.000 0.000
#> GSM125124 1 0.000 0.9840 1.000 0.000
#> GSM125126 1 0.000 0.9840 1.000 0.000
#> GSM125128 1 0.000 0.9840 1.000 0.000
#> GSM125130 1 0.000 0.9840 1.000 0.000
#> GSM125132 1 0.000 0.9840 1.000 0.000
#> GSM125134 1 0.000 0.9840 1.000 0.000
#> GSM125136 1 0.000 0.9840 1.000 0.000
#> GSM125138 1 0.000 0.9840 1.000 0.000
#> GSM125140 1 0.000 0.9840 1.000 0.000
#> GSM125142 1 0.000 0.9840 1.000 0.000
#> GSM125144 1 0.000 0.9840 1.000 0.000
#> GSM125146 1 0.000 0.9840 1.000 0.000
#> GSM125148 1 0.000 0.9840 1.000 0.000
#> GSM125150 1 0.000 0.9840 1.000 0.000
#> GSM125152 1 0.000 0.9840 1.000 0.000
#> GSM125154 1 0.000 0.9840 1.000 0.000
#> GSM125156 1 0.000 0.9840 1.000 0.000
#> GSM125158 1 0.000 0.9840 1.000 0.000
#> GSM125160 2 0.000 0.9662 0.000 1.000
#> GSM125162 1 0.000 0.9840 1.000 0.000
#> GSM125164 2 0.000 0.9662 0.000 1.000
#> GSM125166 2 0.000 0.9662 0.000 1.000
#> GSM125168 2 0.000 0.9662 0.000 1.000
#> GSM125170 2 0.000 0.9662 0.000 1.000
#> GSM125172 2 0.000 0.9662 0.000 1.000
#> GSM125174 2 0.000 0.9662 0.000 1.000
#> GSM125176 2 0.000 0.9662 0.000 1.000
#> GSM125178 1 0.881 0.5564 0.700 0.300
#> GSM125180 1 0.615 0.8119 0.848 0.152
#> GSM125182 2 0.000 0.9662 0.000 1.000
#> GSM125184 2 0.000 0.9662 0.000 1.000
#> GSM125186 2 0.917 0.5184 0.332 0.668
#> GSM125188 2 0.000 0.9662 0.000 1.000
#> GSM125190 2 0.000 0.9662 0.000 1.000
#> GSM125192 2 0.000 0.9662 0.000 1.000
#> GSM125194 1 0.000 0.9840 1.000 0.000
#> GSM125196 2 0.855 0.6224 0.280 0.720
#> GSM125198 2 0.000 0.9662 0.000 1.000
#> GSM125200 1 0.000 0.9840 1.000 0.000
#> GSM125202 2 0.000 0.9662 0.000 1.000
#> GSM125204 2 0.973 0.3453 0.404 0.596
#> GSM125206 2 0.373 0.9022 0.072 0.928
#> GSM125208 2 0.999 0.0851 0.484 0.516
#> GSM125210 2 0.000 0.9662 0.000 1.000
#> GSM125212 2 0.000 0.9662 0.000 1.000
#> GSM125214 2 0.000 0.9662 0.000 1.000
#> GSM125216 2 0.000 0.9662 0.000 1.000
#> GSM125218 2 0.000 0.9662 0.000 1.000
#> GSM125220 1 0.000 0.9840 1.000 0.000
#> GSM125222 2 0.000 0.9662 0.000 1.000
#> GSM125224 2 0.000 0.9662 0.000 1.000
#> GSM125226 2 0.000 0.9662 0.000 1.000
#> GSM125228 2 0.000 0.9662 0.000 1.000
#> GSM125230 1 0.000 0.9840 1.000 0.000
#> GSM125232 1 0.000 0.9840 1.000 0.000
#> GSM125234 1 0.000 0.9840 1.000 0.000
#> GSM125236 1 0.000 0.9840 1.000 0.000
#> GSM125238 1 0.000 0.9840 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.1860 0.9101 0.948 0.000 0.052
#> GSM125125 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125127 1 0.1529 0.9292 0.960 0.000 0.040
#> GSM125129 1 0.0592 0.9377 0.988 0.000 0.012
#> GSM125131 1 0.0892 0.9355 0.980 0.000 0.020
#> GSM125133 1 0.1163 0.9311 0.972 0.000 0.028
#> GSM125135 1 0.0424 0.9405 0.992 0.000 0.008
#> GSM125137 1 0.0592 0.9377 0.988 0.000 0.012
#> GSM125139 1 0.4452 0.7395 0.808 0.000 0.192
#> GSM125141 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM125143 1 0.2261 0.8957 0.932 0.000 0.068
#> GSM125145 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125147 1 0.0747 0.9371 0.984 0.000 0.016
#> GSM125149 1 0.0747 0.9367 0.984 0.000 0.016
#> GSM125151 3 0.6299 0.1645 0.476 0.000 0.524
#> GSM125153 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125155 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125157 1 0.0592 0.9377 0.988 0.000 0.012
#> GSM125159 2 0.0424 0.9238 0.000 0.992 0.008
#> GSM125161 1 0.1031 0.9335 0.976 0.000 0.024
#> GSM125163 2 0.0000 0.9238 0.000 1.000 0.000
#> GSM125165 2 0.4346 0.8004 0.000 0.816 0.184
#> GSM125167 2 0.0424 0.9234 0.000 0.992 0.008
#> GSM125169 2 0.0829 0.9195 0.004 0.984 0.012
#> GSM125171 2 0.0592 0.9226 0.000 0.988 0.012
#> GSM125173 2 0.1031 0.9200 0.000 0.976 0.024
#> GSM125175 2 0.0424 0.9224 0.000 0.992 0.008
#> GSM125177 2 0.2356 0.8962 0.000 0.928 0.072
#> GSM125179 3 0.1267 0.8360 0.004 0.024 0.972
#> GSM125181 2 0.6280 0.3125 0.000 0.540 0.460
#> GSM125183 3 0.1860 0.8234 0.000 0.052 0.948
#> GSM125185 3 0.1289 0.8321 0.000 0.032 0.968
#> GSM125187 3 0.1482 0.8367 0.020 0.012 0.968
#> GSM125189 2 0.0237 0.9233 0.000 0.996 0.004
#> GSM125191 2 0.3116 0.8718 0.000 0.892 0.108
#> GSM125193 1 0.1491 0.9257 0.968 0.016 0.016
#> GSM125195 3 0.3325 0.8135 0.076 0.020 0.904
#> GSM125197 2 0.0424 0.9236 0.000 0.992 0.008
#> GSM125199 1 0.0424 0.9391 0.992 0.000 0.008
#> GSM125201 2 0.0424 0.9236 0.000 0.992 0.008
#> GSM125203 2 0.6488 0.6619 0.192 0.744 0.064
#> GSM125205 2 0.0892 0.9192 0.000 0.980 0.020
#> GSM125207 3 0.1163 0.8343 0.000 0.028 0.972
#> GSM125209 2 0.5058 0.7325 0.000 0.756 0.244
#> GSM125211 2 0.3028 0.8862 0.048 0.920 0.032
#> GSM125213 2 0.1031 0.9190 0.000 0.976 0.024
#> GSM125215 2 0.0424 0.9236 0.000 0.992 0.008
#> GSM125217 2 0.0424 0.9223 0.000 0.992 0.008
#> GSM125219 1 0.2448 0.8989 0.924 0.000 0.076
#> GSM125221 2 0.3272 0.8740 0.004 0.892 0.104
#> GSM125223 2 0.0747 0.9211 0.000 0.984 0.016
#> GSM125225 2 0.0000 0.9238 0.000 1.000 0.000
#> GSM125227 2 0.0237 0.9234 0.000 0.996 0.004
#> GSM125229 2 0.2903 0.8770 0.048 0.924 0.028
#> GSM125231 3 0.5254 0.6635 0.264 0.000 0.736
#> GSM125233 1 0.2625 0.8792 0.916 0.000 0.084
#> GSM125235 1 0.0424 0.9405 0.992 0.000 0.008
#> GSM125237 1 0.0592 0.9382 0.988 0.000 0.012
#> GSM125124 3 0.4291 0.7426 0.180 0.000 0.820
#> GSM125126 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125128 1 0.1031 0.9335 0.976 0.000 0.024
#> GSM125130 1 0.6008 0.3523 0.628 0.000 0.372
#> GSM125132 1 0.0000 0.9404 1.000 0.000 0.000
#> GSM125134 1 0.0424 0.9394 0.992 0.000 0.008
#> GSM125136 1 0.1031 0.9335 0.976 0.000 0.024
#> GSM125138 1 0.5591 0.5159 0.696 0.000 0.304
#> GSM125140 1 0.5291 0.6050 0.732 0.000 0.268
#> GSM125142 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125144 1 0.6267 0.0718 0.548 0.000 0.452
#> GSM125146 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125148 1 0.0424 0.9405 0.992 0.000 0.008
#> GSM125150 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125152 3 0.6244 0.2805 0.440 0.000 0.560
#> GSM125154 1 0.1529 0.9204 0.960 0.000 0.040
#> GSM125156 1 0.0424 0.9394 0.992 0.000 0.008
#> GSM125158 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125160 2 0.0237 0.9238 0.000 0.996 0.004
#> GSM125162 1 0.1031 0.9335 0.976 0.000 0.024
#> GSM125164 2 0.1289 0.9174 0.000 0.968 0.032
#> GSM125166 2 0.0592 0.9227 0.000 0.988 0.012
#> GSM125168 2 0.5706 0.6216 0.000 0.680 0.320
#> GSM125170 2 0.2261 0.8967 0.000 0.932 0.068
#> GSM125172 2 0.0000 0.9238 0.000 1.000 0.000
#> GSM125174 3 0.2711 0.7894 0.000 0.088 0.912
#> GSM125176 2 0.2448 0.8927 0.000 0.924 0.076
#> GSM125178 3 0.6858 0.7156 0.188 0.084 0.728
#> GSM125180 3 0.1315 0.8371 0.008 0.020 0.972
#> GSM125182 2 0.6180 0.4262 0.000 0.584 0.416
#> GSM125184 3 0.1753 0.8238 0.000 0.048 0.952
#> GSM125186 3 0.1315 0.8371 0.008 0.020 0.972
#> GSM125188 2 0.5529 0.6606 0.000 0.704 0.296
#> GSM125190 2 0.0424 0.9237 0.000 0.992 0.008
#> GSM125192 2 0.0424 0.9238 0.000 0.992 0.008
#> GSM125194 3 0.6274 0.2736 0.456 0.000 0.544
#> GSM125196 3 0.1129 0.8361 0.004 0.020 0.976
#> GSM125198 2 0.0424 0.9236 0.000 0.992 0.008
#> GSM125200 1 0.0237 0.9405 0.996 0.000 0.004
#> GSM125202 2 0.0237 0.9239 0.000 0.996 0.004
#> GSM125204 3 0.6446 0.6474 0.052 0.212 0.736
#> GSM125206 2 0.5180 0.7965 0.032 0.812 0.156
#> GSM125208 3 0.1315 0.8371 0.008 0.020 0.972
#> GSM125210 3 0.1753 0.8241 0.000 0.048 0.952
#> GSM125212 2 0.1031 0.9211 0.000 0.976 0.024
#> GSM125214 2 0.0424 0.9238 0.000 0.992 0.008
#> GSM125216 2 0.0237 0.9239 0.000 0.996 0.004
#> GSM125218 2 0.0747 0.9199 0.000 0.984 0.016
#> GSM125220 1 0.0892 0.9356 0.980 0.000 0.020
#> GSM125222 2 0.5859 0.5688 0.000 0.656 0.344
#> GSM125224 2 0.0424 0.9236 0.000 0.992 0.008
#> GSM125226 2 0.0237 0.9238 0.000 0.996 0.004
#> GSM125228 2 0.0237 0.9234 0.000 0.996 0.004
#> GSM125230 3 0.6095 0.4382 0.392 0.000 0.608
#> GSM125232 3 0.1289 0.8327 0.032 0.000 0.968
#> GSM125234 3 0.5397 0.6219 0.280 0.000 0.720
#> GSM125236 1 0.0892 0.9385 0.980 0.000 0.020
#> GSM125238 1 0.0424 0.9388 0.992 0.000 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.4839 0.7307 0.756 0.000 0.200 0.044
#> GSM125125 1 0.1022 0.8564 0.968 0.000 0.032 0.000
#> GSM125127 1 0.5344 0.6298 0.668 0.000 0.300 0.032
#> GSM125129 1 0.3836 0.7850 0.816 0.000 0.168 0.016
#> GSM125131 1 0.0469 0.8594 0.988 0.000 0.012 0.000
#> GSM125133 1 0.0707 0.8587 0.980 0.000 0.020 0.000
#> GSM125135 1 0.2281 0.8416 0.904 0.000 0.096 0.000
#> GSM125137 1 0.3688 0.7220 0.792 0.000 0.208 0.000
#> GSM125139 1 0.4127 0.7935 0.824 0.000 0.124 0.052
#> GSM125141 1 0.1716 0.8483 0.936 0.000 0.064 0.000
#> GSM125143 1 0.4114 0.7982 0.828 0.000 0.112 0.060
#> GSM125145 1 0.2216 0.8378 0.908 0.000 0.092 0.000
#> GSM125147 1 0.0817 0.8575 0.976 0.000 0.024 0.000
#> GSM125149 1 0.1940 0.8374 0.924 0.000 0.076 0.000
#> GSM125151 1 0.7776 0.0993 0.412 0.000 0.248 0.340
#> GSM125153 1 0.1022 0.8603 0.968 0.000 0.032 0.000
#> GSM125155 1 0.0817 0.8605 0.976 0.000 0.024 0.000
#> GSM125157 1 0.2149 0.8305 0.912 0.000 0.088 0.000
#> GSM125159 2 0.5229 0.7259 0.000 0.748 0.084 0.168
#> GSM125161 1 0.3219 0.7745 0.836 0.000 0.164 0.000
#> GSM125163 2 0.0592 0.8005 0.000 0.984 0.000 0.016
#> GSM125165 3 0.7839 -0.0656 0.000 0.264 0.384 0.352
#> GSM125167 2 0.4459 0.7407 0.000 0.780 0.032 0.188
#> GSM125169 2 0.3993 0.7822 0.004 0.844 0.060 0.092
#> GSM125171 2 0.1557 0.7779 0.000 0.944 0.056 0.000
#> GSM125173 2 0.6281 0.6170 0.000 0.656 0.128 0.216
#> GSM125175 2 0.0921 0.7907 0.000 0.972 0.028 0.000
#> GSM125177 3 0.6141 0.2887 0.004 0.428 0.528 0.040
#> GSM125179 4 0.2021 0.4880 0.000 0.040 0.024 0.936
#> GSM125181 4 0.7834 0.0192 0.000 0.320 0.276 0.404
#> GSM125183 4 0.6084 0.2713 0.004 0.080 0.252 0.664
#> GSM125185 4 0.1489 0.4882 0.000 0.004 0.044 0.952
#> GSM125187 4 0.3764 0.4261 0.000 0.072 0.076 0.852
#> GSM125189 2 0.2174 0.8028 0.000 0.928 0.020 0.052
#> GSM125191 2 0.5130 0.6219 0.000 0.668 0.020 0.312
#> GSM125193 3 0.6319 0.0286 0.436 0.000 0.504 0.060
#> GSM125195 3 0.6312 0.1216 0.048 0.016 0.616 0.320
#> GSM125197 2 0.2149 0.7459 0.000 0.912 0.088 0.000
#> GSM125199 1 0.2011 0.8363 0.920 0.000 0.080 0.000
#> GSM125201 2 0.2081 0.7491 0.000 0.916 0.084 0.000
#> GSM125203 3 0.7010 0.3861 0.036 0.328 0.576 0.060
#> GSM125205 2 0.3975 0.5147 0.000 0.760 0.240 0.000
#> GSM125207 4 0.4898 0.1396 0.000 0.000 0.416 0.584
#> GSM125209 2 0.5713 0.5311 0.000 0.604 0.036 0.360
#> GSM125211 3 0.7700 0.2823 0.056 0.140 0.600 0.204
#> GSM125213 2 0.4248 0.7272 0.000 0.768 0.012 0.220
#> GSM125215 2 0.1118 0.7873 0.000 0.964 0.036 0.000
#> GSM125217 2 0.4224 0.7756 0.000 0.824 0.076 0.100
#> GSM125219 1 0.5664 0.6928 0.716 0.004 0.200 0.080
#> GSM125221 2 0.7642 0.3000 0.004 0.472 0.192 0.332
#> GSM125223 2 0.1637 0.7719 0.000 0.940 0.060 0.000
#> GSM125225 2 0.0672 0.7995 0.000 0.984 0.008 0.008
#> GSM125227 2 0.1118 0.7880 0.000 0.964 0.036 0.000
#> GSM125229 3 0.5703 0.1413 0.012 0.480 0.500 0.008
#> GSM125231 3 0.5489 0.1202 0.040 0.000 0.664 0.296
#> GSM125233 1 0.6027 0.6246 0.664 0.000 0.244 0.092
#> GSM125235 1 0.0336 0.8599 0.992 0.000 0.008 0.000
#> GSM125237 1 0.1022 0.8557 0.968 0.000 0.032 0.000
#> GSM125124 4 0.6653 0.1858 0.104 0.000 0.328 0.568
#> GSM125126 1 0.0000 0.8598 1.000 0.000 0.000 0.000
#> GSM125128 1 0.1302 0.8579 0.956 0.000 0.044 0.000
#> GSM125130 1 0.7596 0.2450 0.456 0.000 0.332 0.212
#> GSM125132 1 0.0707 0.8581 0.980 0.000 0.020 0.000
#> GSM125134 1 0.2197 0.8417 0.916 0.000 0.080 0.004
#> GSM125136 1 0.1716 0.8475 0.936 0.000 0.064 0.000
#> GSM125138 1 0.6934 0.4739 0.572 0.000 0.276 0.152
#> GSM125140 1 0.4586 0.7750 0.796 0.000 0.136 0.068
#> GSM125142 1 0.0921 0.8602 0.972 0.000 0.028 0.000
#> GSM125144 1 0.7636 0.2649 0.468 0.000 0.284 0.248
#> GSM125146 1 0.1557 0.8495 0.944 0.000 0.056 0.000
#> GSM125148 1 0.0469 0.8600 0.988 0.000 0.012 0.000
#> GSM125150 1 0.0188 0.8600 0.996 0.000 0.004 0.000
#> GSM125152 4 0.7866 0.0269 0.328 0.000 0.284 0.388
#> GSM125154 1 0.3161 0.8246 0.864 0.000 0.124 0.012
#> GSM125156 1 0.0469 0.8613 0.988 0.000 0.012 0.000
#> GSM125158 1 0.0469 0.8594 0.988 0.000 0.012 0.000
#> GSM125160 2 0.3160 0.7919 0.000 0.872 0.020 0.108
#> GSM125162 1 0.2868 0.7979 0.864 0.000 0.136 0.000
#> GSM125164 2 0.3583 0.7628 0.000 0.816 0.004 0.180
#> GSM125166 2 0.3300 0.7800 0.000 0.848 0.008 0.144
#> GSM125168 2 0.6650 0.2800 0.000 0.484 0.084 0.432
#> GSM125170 2 0.5512 0.6160 0.000 0.660 0.040 0.300
#> GSM125172 2 0.0921 0.7906 0.000 0.972 0.028 0.000
#> GSM125174 4 0.3015 0.4696 0.000 0.024 0.092 0.884
#> GSM125176 2 0.2334 0.7994 0.000 0.908 0.004 0.088
#> GSM125178 3 0.5729 0.2305 0.016 0.024 0.656 0.304
#> GSM125180 4 0.2973 0.4317 0.000 0.000 0.144 0.856
#> GSM125182 4 0.7001 -0.1687 0.000 0.420 0.116 0.464
#> GSM125184 4 0.3247 0.4483 0.000 0.060 0.060 0.880
#> GSM125186 4 0.1389 0.4826 0.000 0.000 0.048 0.952
#> GSM125188 2 0.7564 0.1542 0.000 0.420 0.192 0.388
#> GSM125190 2 0.4578 0.7517 0.000 0.788 0.052 0.160
#> GSM125192 2 0.1474 0.8027 0.000 0.948 0.000 0.052
#> GSM125194 3 0.6717 0.1460 0.108 0.000 0.560 0.332
#> GSM125196 3 0.5735 -0.0355 0.020 0.004 0.540 0.436
#> GSM125198 2 0.1474 0.7779 0.000 0.948 0.052 0.000
#> GSM125200 1 0.0336 0.8611 0.992 0.000 0.008 0.000
#> GSM125202 2 0.1389 0.7810 0.000 0.952 0.048 0.000
#> GSM125204 3 0.6712 0.2282 0.028 0.076 0.640 0.256
#> GSM125206 3 0.6736 0.3749 0.032 0.288 0.620 0.060
#> GSM125208 4 0.4746 0.1827 0.000 0.000 0.368 0.632
#> GSM125210 4 0.1833 0.4899 0.000 0.024 0.032 0.944
#> GSM125212 3 0.7152 0.3131 0.004 0.264 0.568 0.164
#> GSM125214 2 0.1305 0.8031 0.000 0.960 0.004 0.036
#> GSM125216 2 0.0921 0.7909 0.000 0.972 0.028 0.000
#> GSM125218 2 0.2124 0.8039 0.000 0.932 0.028 0.040
#> GSM125220 1 0.1118 0.8539 0.964 0.000 0.036 0.000
#> GSM125222 4 0.7679 -0.0852 0.000 0.376 0.216 0.408
#> GSM125224 2 0.1474 0.7777 0.000 0.948 0.052 0.000
#> GSM125226 2 0.3659 0.7780 0.000 0.840 0.024 0.136
#> GSM125228 2 0.1118 0.7876 0.000 0.964 0.036 0.000
#> GSM125230 3 0.5908 0.2191 0.048 0.004 0.636 0.312
#> GSM125232 4 0.4560 0.3259 0.004 0.000 0.296 0.700
#> GSM125234 4 0.7555 0.0507 0.164 0.004 0.396 0.436
#> GSM125236 1 0.3545 0.7947 0.828 0.000 0.164 0.008
#> GSM125238 1 0.1637 0.8487 0.940 0.000 0.060 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.4217 0.7207 0.740 0.000 0.020 0.008 0.232
#> GSM125125 1 0.1478 0.8511 0.936 0.000 0.000 0.000 0.064
#> GSM125127 1 0.5362 0.6361 0.672 0.000 0.080 0.012 0.236
#> GSM125129 1 0.3795 0.7942 0.808 0.000 0.044 0.004 0.144
#> GSM125131 1 0.0404 0.8602 0.988 0.000 0.000 0.012 0.000
#> GSM125133 1 0.0703 0.8580 0.976 0.000 0.000 0.024 0.000
#> GSM125135 1 0.3120 0.8349 0.864 0.000 0.048 0.004 0.084
#> GSM125137 1 0.4575 0.5329 0.648 0.000 0.024 0.328 0.000
#> GSM125139 1 0.3010 0.8060 0.824 0.000 0.004 0.000 0.172
#> GSM125141 1 0.2011 0.8405 0.908 0.000 0.000 0.088 0.004
#> GSM125143 1 0.3489 0.7939 0.820 0.000 0.036 0.000 0.144
#> GSM125145 1 0.2783 0.8288 0.868 0.000 0.012 0.004 0.116
#> GSM125147 1 0.0671 0.8608 0.980 0.000 0.000 0.016 0.004
#> GSM125149 1 0.2127 0.8256 0.892 0.000 0.000 0.108 0.000
#> GSM125151 5 0.4726 0.2577 0.376 0.000 0.016 0.004 0.604
#> GSM125153 1 0.3405 0.8287 0.848 0.000 0.012 0.036 0.104
#> GSM125155 1 0.1197 0.8553 0.952 0.000 0.000 0.048 0.000
#> GSM125157 1 0.2471 0.8042 0.864 0.000 0.000 0.136 0.000
#> GSM125159 2 0.4204 0.6932 0.000 0.756 0.048 0.196 0.000
#> GSM125161 1 0.4054 0.6777 0.748 0.000 0.028 0.224 0.000
#> GSM125163 2 0.0771 0.8080 0.000 0.976 0.000 0.020 0.004
#> GSM125165 4 0.3843 0.4612 0.000 0.184 0.012 0.788 0.016
#> GSM125167 2 0.2674 0.7744 0.000 0.856 0.000 0.140 0.004
#> GSM125169 2 0.2233 0.7938 0.000 0.892 0.000 0.104 0.004
#> GSM125171 2 0.1956 0.7843 0.000 0.928 0.052 0.012 0.008
#> GSM125173 2 0.5374 0.4852 0.000 0.640 0.052 0.292 0.016
#> GSM125175 2 0.0693 0.8004 0.000 0.980 0.012 0.008 0.000
#> GSM125177 3 0.2067 0.6938 0.000 0.028 0.924 0.044 0.004
#> GSM125179 5 0.4455 0.4316 0.000 0.036 0.000 0.260 0.704
#> GSM125181 4 0.5442 0.3241 0.000 0.352 0.020 0.592 0.036
#> GSM125183 4 0.5218 0.1407 0.000 0.084 0.004 0.672 0.240
#> GSM125185 5 0.4618 0.4234 0.000 0.024 0.016 0.248 0.712
#> GSM125187 5 0.6335 0.0823 0.000 0.104 0.016 0.396 0.484
#> GSM125189 2 0.1697 0.8083 0.000 0.932 0.008 0.060 0.000
#> GSM125191 2 0.3750 0.6767 0.000 0.756 0.000 0.232 0.012
#> GSM125193 4 0.5616 -0.2828 0.080 0.000 0.384 0.536 0.000
#> GSM125195 3 0.3284 0.6678 0.000 0.000 0.828 0.024 0.148
#> GSM125197 2 0.3183 0.6851 0.000 0.828 0.156 0.016 0.000
#> GSM125199 1 0.2020 0.8305 0.900 0.000 0.000 0.100 0.000
#> GSM125201 2 0.3419 0.6524 0.000 0.804 0.180 0.016 0.000
#> GSM125203 3 0.1413 0.6967 0.000 0.020 0.956 0.012 0.012
#> GSM125205 3 0.4577 0.3671 0.000 0.296 0.676 0.024 0.004
#> GSM125207 3 0.5878 0.4931 0.000 0.000 0.548 0.116 0.336
#> GSM125209 2 0.4754 0.5330 0.000 0.664 0.012 0.304 0.020
#> GSM125211 3 0.4811 0.4146 0.008 0.004 0.548 0.436 0.004
#> GSM125213 2 0.2930 0.7568 0.000 0.832 0.000 0.164 0.004
#> GSM125215 2 0.1894 0.7747 0.000 0.920 0.072 0.008 0.000
#> GSM125217 2 0.3525 0.7573 0.004 0.816 0.024 0.156 0.000
#> GSM125219 1 0.4792 0.6834 0.712 0.000 0.052 0.008 0.228
#> GSM125221 2 0.4956 0.2717 0.008 0.548 0.000 0.428 0.016
#> GSM125223 2 0.2069 0.7690 0.000 0.912 0.076 0.012 0.000
#> GSM125225 2 0.0324 0.8046 0.000 0.992 0.004 0.004 0.000
#> GSM125227 2 0.1638 0.7834 0.000 0.932 0.064 0.004 0.000
#> GSM125229 3 0.4100 0.6493 0.016 0.028 0.784 0.172 0.000
#> GSM125231 3 0.4455 0.6189 0.000 0.000 0.744 0.068 0.188
#> GSM125233 1 0.4598 0.6592 0.700 0.000 0.028 0.008 0.264
#> GSM125235 1 0.0451 0.8611 0.988 0.000 0.000 0.008 0.004
#> GSM125237 1 0.1121 0.8531 0.956 0.000 0.000 0.044 0.000
#> GSM125124 5 0.3980 0.5004 0.080 0.000 0.012 0.092 0.816
#> GSM125126 1 0.0566 0.8612 0.984 0.000 0.000 0.004 0.012
#> GSM125128 1 0.1300 0.8584 0.956 0.000 0.016 0.028 0.000
#> GSM125130 5 0.5937 0.2158 0.368 0.004 0.068 0.012 0.548
#> GSM125132 1 0.0771 0.8599 0.976 0.000 0.000 0.020 0.004
#> GSM125134 1 0.3689 0.8024 0.816 0.000 0.008 0.032 0.144
#> GSM125136 1 0.2470 0.8173 0.884 0.000 0.012 0.104 0.000
#> GSM125138 5 0.6156 0.1866 0.388 0.000 0.012 0.096 0.504
#> GSM125140 1 0.3160 0.7943 0.808 0.000 0.000 0.004 0.188
#> GSM125142 1 0.3702 0.8186 0.832 0.000 0.008 0.080 0.080
#> GSM125144 5 0.5659 0.1578 0.404 0.000 0.008 0.060 0.528
#> GSM125146 1 0.3070 0.8296 0.860 0.000 0.012 0.016 0.112
#> GSM125148 1 0.0912 0.8619 0.972 0.000 0.000 0.016 0.012
#> GSM125150 1 0.0880 0.8587 0.968 0.000 0.000 0.000 0.032
#> GSM125152 5 0.3720 0.4958 0.228 0.000 0.012 0.000 0.760
#> GSM125154 1 0.5656 0.6337 0.672 0.000 0.016 0.136 0.176
#> GSM125156 1 0.1605 0.8606 0.944 0.000 0.004 0.012 0.040
#> GSM125158 1 0.1270 0.8544 0.948 0.000 0.000 0.000 0.052
#> GSM125160 2 0.2295 0.8006 0.000 0.900 0.008 0.088 0.004
#> GSM125162 1 0.3449 0.7528 0.812 0.000 0.024 0.164 0.000
#> GSM125164 2 0.2612 0.7811 0.000 0.868 0.000 0.124 0.008
#> GSM125166 2 0.2358 0.7921 0.000 0.888 0.000 0.104 0.008
#> GSM125168 2 0.5131 0.3743 0.000 0.588 0.000 0.364 0.048
#> GSM125170 2 0.3977 0.6916 0.000 0.764 0.000 0.204 0.032
#> GSM125172 2 0.1082 0.7957 0.000 0.964 0.028 0.008 0.000
#> GSM125174 5 0.5453 0.3740 0.000 0.048 0.016 0.324 0.612
#> GSM125176 2 0.1549 0.8083 0.000 0.944 0.000 0.040 0.016
#> GSM125178 3 0.3829 0.6478 0.000 0.000 0.776 0.196 0.028
#> GSM125180 5 0.3167 0.4870 0.000 0.008 0.008 0.148 0.836
#> GSM125182 2 0.7544 -0.2623 0.000 0.400 0.116 0.384 0.100
#> GSM125184 5 0.5902 0.2200 0.000 0.080 0.008 0.400 0.512
#> GSM125186 5 0.4125 0.4427 0.000 0.004 0.020 0.236 0.740
#> GSM125188 4 0.5780 0.3176 0.000 0.352 0.024 0.572 0.052
#> GSM125190 2 0.2798 0.7767 0.000 0.852 0.000 0.140 0.008
#> GSM125192 2 0.1282 0.8073 0.000 0.952 0.000 0.044 0.004
#> GSM125194 4 0.5094 -0.3381 0.024 0.000 0.412 0.556 0.008
#> GSM125196 3 0.3821 0.6356 0.000 0.000 0.764 0.020 0.216
#> GSM125198 2 0.2017 0.7687 0.000 0.912 0.080 0.008 0.000
#> GSM125200 1 0.1012 0.8620 0.968 0.000 0.000 0.012 0.020
#> GSM125202 2 0.1628 0.7854 0.000 0.936 0.056 0.008 0.000
#> GSM125204 3 0.3120 0.6796 0.000 0.012 0.856 0.016 0.116
#> GSM125206 3 0.2165 0.6862 0.000 0.036 0.924 0.024 0.016
#> GSM125208 3 0.6130 0.5023 0.000 0.000 0.556 0.264 0.180
#> GSM125210 5 0.4828 0.4187 0.000 0.036 0.016 0.244 0.704
#> GSM125212 3 0.4683 0.5237 0.008 0.012 0.624 0.356 0.000
#> GSM125214 2 0.0955 0.8085 0.000 0.968 0.000 0.028 0.004
#> GSM125216 2 0.1281 0.7968 0.000 0.956 0.032 0.012 0.000
#> GSM125218 2 0.1041 0.8084 0.000 0.964 0.004 0.032 0.000
#> GSM125220 1 0.1205 0.8545 0.956 0.000 0.004 0.040 0.000
#> GSM125222 2 0.5296 0.0666 0.000 0.484 0.000 0.468 0.048
#> GSM125224 2 0.1894 0.7741 0.000 0.920 0.072 0.008 0.000
#> GSM125226 2 0.2233 0.7936 0.000 0.892 0.000 0.104 0.004
#> GSM125228 2 0.1638 0.7826 0.000 0.932 0.064 0.004 0.000
#> GSM125230 3 0.4829 0.5022 0.008 0.000 0.604 0.372 0.016
#> GSM125232 5 0.4400 0.4474 0.000 0.000 0.060 0.196 0.744
#> GSM125234 5 0.5365 0.4276 0.176 0.004 0.104 0.012 0.704
#> GSM125236 1 0.4316 0.7741 0.780 0.000 0.056 0.012 0.152
#> GSM125238 1 0.1732 0.8416 0.920 0.000 0.000 0.080 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.3928 0.7169 0.764 0.000 0.012 0.192 0.024 0.008
#> GSM125125 1 0.1549 0.8467 0.944 0.000 0.004 0.024 0.024 0.004
#> GSM125127 1 0.6285 0.5267 0.620 0.000 0.108 0.148 0.108 0.016
#> GSM125129 1 0.3800 0.7605 0.800 0.000 0.020 0.140 0.032 0.008
#> GSM125131 1 0.0665 0.8507 0.980 0.000 0.004 0.000 0.008 0.008
#> GSM125133 1 0.0692 0.8491 0.976 0.000 0.000 0.000 0.004 0.020
#> GSM125135 1 0.2985 0.8262 0.872 0.000 0.032 0.048 0.044 0.004
#> GSM125137 1 0.5095 0.5694 0.632 0.000 0.000 0.008 0.104 0.256
#> GSM125139 1 0.3030 0.8159 0.848 0.000 0.000 0.056 0.092 0.004
#> GSM125141 1 0.3253 0.8091 0.832 0.000 0.000 0.004 0.096 0.068
#> GSM125143 1 0.3087 0.7682 0.820 0.000 0.004 0.160 0.012 0.004
#> GSM125145 1 0.3812 0.7846 0.812 0.000 0.016 0.036 0.116 0.020
#> GSM125147 1 0.2250 0.8370 0.888 0.000 0.000 0.000 0.092 0.020
#> GSM125149 1 0.2697 0.8272 0.864 0.000 0.000 0.000 0.044 0.092
#> GSM125151 4 0.4540 0.3833 0.324 0.000 0.000 0.632 0.036 0.008
#> GSM125153 5 0.4004 0.4214 0.328 0.000 0.004 0.000 0.656 0.012
#> GSM125155 1 0.1492 0.8513 0.940 0.000 0.000 0.000 0.036 0.024
#> GSM125157 1 0.2302 0.8156 0.872 0.000 0.000 0.000 0.008 0.120
#> GSM125159 2 0.4394 0.4490 0.000 0.608 0.020 0.008 0.000 0.364
#> GSM125161 1 0.3851 0.6913 0.740 0.000 0.012 0.004 0.012 0.232
#> GSM125163 2 0.0893 0.7883 0.000 0.972 0.004 0.004 0.004 0.016
#> GSM125165 6 0.5706 0.4025 0.000 0.296 0.004 0.032 0.088 0.580
#> GSM125167 2 0.3043 0.7091 0.000 0.796 0.000 0.004 0.004 0.196
#> GSM125169 2 0.2234 0.7655 0.000 0.872 0.000 0.004 0.000 0.124
#> GSM125171 2 0.3679 0.6887 0.000 0.816 0.120 0.008 0.028 0.028
#> GSM125173 5 0.7063 -0.1415 0.000 0.368 0.084 0.004 0.376 0.168
#> GSM125175 2 0.1065 0.7793 0.000 0.964 0.020 0.000 0.008 0.008
#> GSM125177 3 0.1753 0.6663 0.000 0.000 0.912 0.000 0.004 0.084
#> GSM125179 4 0.6509 0.2847 0.000 0.064 0.000 0.500 0.284 0.152
#> GSM125181 6 0.5136 0.4947 0.000 0.228 0.000 0.124 0.008 0.640
#> GSM125183 5 0.4899 0.2050 0.000 0.036 0.000 0.020 0.588 0.356
#> GSM125185 4 0.3386 0.4138 0.000 0.008 0.000 0.788 0.016 0.188
#> GSM125187 4 0.6223 -0.1722 0.000 0.128 0.000 0.468 0.040 0.364
#> GSM125189 2 0.2261 0.7782 0.000 0.884 0.008 0.004 0.000 0.104
#> GSM125191 2 0.4505 0.5462 0.000 0.668 0.000 0.056 0.004 0.272
#> GSM125193 6 0.5363 -0.2356 0.064 0.000 0.280 0.032 0.004 0.620
#> GSM125195 3 0.2909 0.6227 0.000 0.000 0.836 0.136 0.028 0.000
#> GSM125197 2 0.3566 0.6406 0.000 0.780 0.192 0.004 0.012 0.012
#> GSM125199 1 0.1588 0.8388 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM125201 2 0.3624 0.6467 0.000 0.780 0.188 0.004 0.012 0.016
#> GSM125203 3 0.1829 0.6677 0.000 0.000 0.920 0.024 0.000 0.056
#> GSM125205 3 0.4306 0.3438 0.000 0.248 0.708 0.012 0.008 0.024
#> GSM125207 3 0.5437 0.4133 0.000 0.000 0.484 0.416 0.008 0.092
#> GSM125209 2 0.5572 0.2764 0.000 0.548 0.000 0.152 0.004 0.296
#> GSM125211 3 0.5016 0.4895 0.000 0.000 0.488 0.012 0.044 0.456
#> GSM125213 2 0.3956 0.6147 0.000 0.712 0.000 0.036 0.000 0.252
#> GSM125215 2 0.1555 0.7711 0.000 0.932 0.060 0.004 0.000 0.004
#> GSM125217 2 0.3426 0.6518 0.000 0.720 0.000 0.000 0.004 0.276
#> GSM125219 1 0.4593 0.3306 0.584 0.000 0.016 0.384 0.012 0.004
#> GSM125221 2 0.4973 0.3016 0.012 0.548 0.000 0.036 0.004 0.400
#> GSM125223 2 0.2368 0.7477 0.000 0.888 0.092 0.004 0.008 0.008
#> GSM125225 2 0.0363 0.7843 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM125227 2 0.1908 0.7562 0.000 0.900 0.096 0.000 0.000 0.004
#> GSM125229 3 0.4466 0.6002 0.004 0.004 0.640 0.008 0.016 0.328
#> GSM125231 5 0.4795 0.1084 0.000 0.000 0.400 0.016 0.556 0.028
#> GSM125233 4 0.4579 -0.0713 0.476 0.000 0.012 0.496 0.016 0.000
#> GSM125235 1 0.0972 0.8506 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM125237 1 0.1010 0.8484 0.960 0.000 0.000 0.000 0.004 0.036
#> GSM125124 5 0.3739 0.5150 0.056 0.000 0.000 0.176 0.768 0.000
#> GSM125126 1 0.0551 0.8503 0.984 0.000 0.000 0.004 0.008 0.004
#> GSM125128 1 0.1801 0.8419 0.932 0.000 0.012 0.004 0.012 0.040
#> GSM125130 4 0.3901 0.4694 0.188 0.000 0.024 0.764 0.024 0.000
#> GSM125132 1 0.0458 0.8500 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM125134 5 0.4523 0.2229 0.416 0.000 0.000 0.016 0.556 0.012
#> GSM125136 1 0.2500 0.8097 0.868 0.000 0.004 0.000 0.012 0.116
#> GSM125138 5 0.2563 0.5756 0.076 0.000 0.004 0.040 0.880 0.000
#> GSM125140 1 0.3295 0.7994 0.816 0.000 0.000 0.056 0.128 0.000
#> GSM125142 5 0.3707 0.4402 0.312 0.000 0.000 0.000 0.680 0.008
#> GSM125144 5 0.4250 0.5116 0.144 0.000 0.000 0.108 0.744 0.004
#> GSM125146 1 0.4797 0.0934 0.512 0.000 0.008 0.012 0.452 0.016
#> GSM125148 1 0.3311 0.7539 0.780 0.000 0.000 0.004 0.204 0.012
#> GSM125150 1 0.1296 0.8493 0.948 0.000 0.000 0.004 0.044 0.004
#> GSM125152 4 0.5027 0.3805 0.272 0.000 0.000 0.624 0.100 0.004
#> GSM125154 5 0.2554 0.5785 0.092 0.000 0.004 0.000 0.876 0.028
#> GSM125156 1 0.1477 0.8527 0.940 0.000 0.000 0.004 0.048 0.008
#> GSM125158 1 0.0993 0.8481 0.964 0.000 0.000 0.012 0.024 0.000
#> GSM125160 2 0.2932 0.7563 0.000 0.836 0.020 0.004 0.000 0.140
#> GSM125162 1 0.3437 0.7404 0.788 0.000 0.004 0.008 0.012 0.188
#> GSM125164 2 0.2346 0.7631 0.000 0.868 0.000 0.008 0.000 0.124
#> GSM125166 2 0.1701 0.7831 0.000 0.920 0.000 0.008 0.000 0.072
#> GSM125168 2 0.5955 0.4008 0.000 0.576 0.008 0.020 0.156 0.240
#> GSM125170 2 0.4197 0.6762 0.000 0.752 0.000 0.016 0.060 0.172
#> GSM125172 2 0.2136 0.7646 0.000 0.908 0.064 0.000 0.012 0.016
#> GSM125174 5 0.4303 0.5045 0.000 0.052 0.008 0.084 0.788 0.068
#> GSM125176 2 0.0603 0.7883 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM125178 3 0.4665 0.6141 0.000 0.000 0.660 0.008 0.060 0.272
#> GSM125180 4 0.5012 0.3778 0.000 0.008 0.000 0.640 0.256 0.096
#> GSM125182 6 0.6839 0.3887 0.000 0.232 0.044 0.304 0.004 0.416
#> GSM125184 5 0.5155 0.4421 0.000 0.064 0.004 0.096 0.712 0.124
#> GSM125186 4 0.3202 0.4320 0.000 0.000 0.000 0.800 0.024 0.176
#> GSM125188 6 0.5629 0.4454 0.004 0.184 0.000 0.228 0.004 0.580
#> GSM125190 2 0.2544 0.7652 0.000 0.864 0.000 0.004 0.012 0.120
#> GSM125192 2 0.1285 0.7863 0.000 0.944 0.000 0.004 0.000 0.052
#> GSM125194 6 0.6391 -0.3782 0.004 0.000 0.344 0.016 0.208 0.428
#> GSM125196 3 0.3088 0.6126 0.000 0.000 0.808 0.172 0.020 0.000
#> GSM125198 2 0.2518 0.7455 0.000 0.884 0.088 0.004 0.012 0.012
#> GSM125200 1 0.0951 0.8504 0.968 0.000 0.000 0.008 0.020 0.004
#> GSM125202 2 0.2554 0.7465 0.000 0.880 0.088 0.000 0.012 0.020
#> GSM125204 3 0.2930 0.6486 0.000 0.000 0.840 0.124 0.000 0.036
#> GSM125206 3 0.1760 0.6480 0.000 0.012 0.936 0.020 0.028 0.004
#> GSM125208 3 0.6217 0.4137 0.000 0.000 0.432 0.272 0.008 0.288
#> GSM125210 4 0.4943 0.3458 0.000 0.048 0.000 0.688 0.052 0.212
#> GSM125212 3 0.4627 0.5255 0.000 0.000 0.532 0.012 0.020 0.436
#> GSM125214 2 0.1147 0.7889 0.000 0.960 0.004 0.004 0.004 0.028
#> GSM125216 2 0.1003 0.7801 0.000 0.964 0.028 0.004 0.000 0.004
#> GSM125218 2 0.1349 0.7870 0.000 0.940 0.000 0.004 0.000 0.056
#> GSM125220 1 0.1728 0.8394 0.924 0.000 0.000 0.004 0.008 0.064
#> GSM125222 2 0.5931 0.2184 0.000 0.516 0.000 0.048 0.084 0.352
#> GSM125224 2 0.2068 0.7571 0.000 0.904 0.080 0.000 0.008 0.008
#> GSM125226 2 0.2856 0.7589 0.000 0.844 0.004 0.004 0.012 0.136
#> GSM125228 2 0.1957 0.7619 0.000 0.912 0.072 0.000 0.008 0.008
#> GSM125230 3 0.5344 0.5278 0.000 0.000 0.528 0.008 0.088 0.376
#> GSM125232 5 0.3360 0.5280 0.000 0.000 0.032 0.084 0.840 0.044
#> GSM125234 4 0.3971 0.4707 0.088 0.000 0.024 0.808 0.068 0.012
#> GSM125236 1 0.4088 0.7813 0.804 0.000 0.028 0.096 0.052 0.020
#> GSM125238 1 0.2401 0.8380 0.892 0.000 0.000 0.004 0.060 0.044
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n agent(p) individual(p) k
#> CV:NMF 113 0.7784 1.13e-04 2
#> CV:NMF 108 0.3500 5.57e-06 3
#> CV:NMF 76 1.0000 1.02e-03 4
#> CV:NMF 87 0.5994 8.94e-05 5
#> CV:NMF 81 0.0605 1.32e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 0.973 0.986 0.4941 0.505 0.505
#> 3 3 0.829 0.929 0.933 0.3069 0.837 0.677
#> 4 4 0.829 0.865 0.915 0.0713 0.991 0.974
#> 5 5 0.766 0.726 0.856 0.0659 0.945 0.834
#> 6 6 0.761 0.710 0.833 0.0437 0.939 0.789
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
#> GSM125123 1 0.0000 0.986 1.000 0.000
#> GSM125125 1 0.0000 0.986 1.000 0.000
#> GSM125127 1 0.0000 0.986 1.000 0.000
#> GSM125129 1 0.0000 0.986 1.000 0.000
#> GSM125131 1 0.0000 0.986 1.000 0.000
#> GSM125133 1 0.0000 0.986 1.000 0.000
#> GSM125135 1 0.0000 0.986 1.000 0.000
#> GSM125137 1 0.0000 0.986 1.000 0.000
#> GSM125139 1 0.0000 0.986 1.000 0.000
#> GSM125141 1 0.0000 0.986 1.000 0.000
#> GSM125143 1 0.0000 0.986 1.000 0.000
#> GSM125145 1 0.0000 0.986 1.000 0.000
#> GSM125147 1 0.0000 0.986 1.000 0.000
#> GSM125149 1 0.0000 0.986 1.000 0.000
#> GSM125151 1 0.0000 0.986 1.000 0.000
#> GSM125153 1 0.0000 0.986 1.000 0.000
#> GSM125155 1 0.0000 0.986 1.000 0.000
#> GSM125157 1 0.0000 0.986 1.000 0.000
#> GSM125159 2 0.0000 0.986 0.000 1.000
#> GSM125161 1 0.0000 0.986 1.000 0.000
#> GSM125163 2 0.0000 0.986 0.000 1.000
#> GSM125165 2 0.0000 0.986 0.000 1.000
#> GSM125167 2 0.0000 0.986 0.000 1.000
#> GSM125169 2 0.0000 0.986 0.000 1.000
#> GSM125171 2 0.0000 0.986 0.000 1.000
#> GSM125173 2 0.2603 0.963 0.044 0.956
#> GSM125175 2 0.0000 0.986 0.000 1.000
#> GSM125177 2 0.2423 0.967 0.040 0.960
#> GSM125179 2 0.1633 0.977 0.024 0.976
#> GSM125181 2 0.0000 0.986 0.000 1.000
#> GSM125183 2 0.0938 0.983 0.012 0.988
#> GSM125185 2 0.1633 0.977 0.024 0.976
#> GSM125187 2 0.1633 0.977 0.024 0.976
#> GSM125189 2 0.0000 0.986 0.000 1.000
#> GSM125191 2 0.0000 0.986 0.000 1.000
#> GSM125193 2 0.2423 0.967 0.040 0.960
#> GSM125195 2 0.4022 0.929 0.080 0.920
#> GSM125197 2 0.0000 0.986 0.000 1.000
#> GSM125199 1 0.0000 0.986 1.000 0.000
#> GSM125201 2 0.0000 0.986 0.000 1.000
#> GSM125203 2 0.2423 0.967 0.040 0.960
#> GSM125205 2 0.0000 0.986 0.000 1.000
#> GSM125207 2 0.2423 0.967 0.040 0.960
#> GSM125209 2 0.0000 0.986 0.000 1.000
#> GSM125211 2 0.1843 0.973 0.028 0.972
#> GSM125213 2 0.0000 0.986 0.000 1.000
#> GSM125215 2 0.0000 0.986 0.000 1.000
#> GSM125217 2 0.0000 0.986 0.000 1.000
#> GSM125219 1 0.0000 0.986 1.000 0.000
#> GSM125221 2 0.0672 0.984 0.008 0.992
#> GSM125223 2 0.0000 0.986 0.000 1.000
#> GSM125225 2 0.0000 0.986 0.000 1.000
#> GSM125227 2 0.0000 0.986 0.000 1.000
#> GSM125229 2 0.1843 0.973 0.028 0.972
#> GSM125231 1 0.9580 0.374 0.620 0.380
#> GSM125233 1 0.0000 0.986 1.000 0.000
#> GSM125235 1 0.0000 0.986 1.000 0.000
#> GSM125237 1 0.0000 0.986 1.000 0.000
#> GSM125124 1 0.0000 0.986 1.000 0.000
#> GSM125126 1 0.0000 0.986 1.000 0.000
#> GSM125128 1 0.0000 0.986 1.000 0.000
#> GSM125130 1 0.0000 0.986 1.000 0.000
#> GSM125132 1 0.0000 0.986 1.000 0.000
#> GSM125134 1 0.0000 0.986 1.000 0.000
#> GSM125136 1 0.0000 0.986 1.000 0.000
#> GSM125138 1 0.0000 0.986 1.000 0.000
#> GSM125140 1 0.0000 0.986 1.000 0.000
#> GSM125142 1 0.0000 0.986 1.000 0.000
#> GSM125144 1 0.0000 0.986 1.000 0.000
#> GSM125146 1 0.0000 0.986 1.000 0.000
#> GSM125148 1 0.0000 0.986 1.000 0.000
#> GSM125150 1 0.0000 0.986 1.000 0.000
#> GSM125152 1 0.0000 0.986 1.000 0.000
#> GSM125154 1 0.0000 0.986 1.000 0.000
#> GSM125156 1 0.0000 0.986 1.000 0.000
#> GSM125158 1 0.0000 0.986 1.000 0.000
#> GSM125160 2 0.0000 0.986 0.000 1.000
#> GSM125162 1 0.0000 0.986 1.000 0.000
#> GSM125164 2 0.0000 0.986 0.000 1.000
#> GSM125166 2 0.0000 0.986 0.000 1.000
#> GSM125168 2 0.0000 0.986 0.000 1.000
#> GSM125170 2 0.0000 0.986 0.000 1.000
#> GSM125172 2 0.0000 0.986 0.000 1.000
#> GSM125174 2 0.2603 0.963 0.044 0.956
#> GSM125176 2 0.0000 0.986 0.000 1.000
#> GSM125178 2 0.2423 0.967 0.040 0.960
#> GSM125180 2 0.1633 0.977 0.024 0.976
#> GSM125182 2 0.0000 0.986 0.000 1.000
#> GSM125184 2 0.0938 0.983 0.012 0.988
#> GSM125186 2 0.1633 0.977 0.024 0.976
#> GSM125188 2 0.0000 0.986 0.000 1.000
#> GSM125190 2 0.0000 0.986 0.000 1.000
#> GSM125192 2 0.0000 0.986 0.000 1.000
#> GSM125194 2 0.2423 0.967 0.040 0.960
#> GSM125196 2 0.4022 0.929 0.080 0.920
#> GSM125198 2 0.0000 0.986 0.000 1.000
#> GSM125200 1 0.0000 0.986 1.000 0.000
#> GSM125202 2 0.0000 0.986 0.000 1.000
#> GSM125204 2 0.2423 0.967 0.040 0.960
#> GSM125206 2 0.4022 0.929 0.080 0.920
#> GSM125208 2 0.2423 0.967 0.040 0.960
#> GSM125210 2 0.0000 0.986 0.000 1.000
#> GSM125212 2 0.1843 0.973 0.028 0.972
#> GSM125214 2 0.0000 0.986 0.000 1.000
#> GSM125216 2 0.0000 0.986 0.000 1.000
#> GSM125218 2 0.0000 0.986 0.000 1.000
#> GSM125220 1 0.0000 0.986 1.000 0.000
#> GSM125222 2 0.0672 0.984 0.008 0.992
#> GSM125224 2 0.0000 0.986 0.000 1.000
#> GSM125226 2 0.0000 0.986 0.000 1.000
#> GSM125228 2 0.0000 0.986 0.000 1.000
#> GSM125230 2 0.2236 0.967 0.036 0.964
#> GSM125232 1 0.8813 0.561 0.700 0.300
#> GSM125234 1 0.0000 0.986 1.000 0.000
#> GSM125236 1 0.0000 0.986 1.000 0.000
#> GSM125238 1 0.0000 0.986 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0592 0.969 0.988 0.000 0.012
#> GSM125125 1 0.0592 0.969 0.988 0.000 0.012
#> GSM125127 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125129 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125131 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125133 1 0.0592 0.969 0.988 0.000 0.012
#> GSM125135 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125137 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125139 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125143 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125145 1 0.1289 0.964 0.968 0.000 0.032
#> GSM125147 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125153 1 0.1031 0.966 0.976 0.000 0.024
#> GSM125155 1 0.0237 0.970 0.996 0.000 0.004
#> GSM125157 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125159 2 0.1163 0.961 0.000 0.972 0.028
#> GSM125161 1 0.0237 0.969 0.996 0.000 0.004
#> GSM125163 2 0.0747 0.964 0.000 0.984 0.016
#> GSM125165 3 0.6140 0.558 0.000 0.404 0.596
#> GSM125167 2 0.3038 0.895 0.000 0.896 0.104
#> GSM125169 2 0.2711 0.912 0.000 0.912 0.088
#> GSM125171 2 0.0424 0.964 0.000 0.992 0.008
#> GSM125173 3 0.1643 0.870 0.000 0.044 0.956
#> GSM125175 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125177 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125179 3 0.4862 0.917 0.020 0.160 0.820
#> GSM125181 3 0.4842 0.872 0.000 0.224 0.776
#> GSM125183 3 0.4291 0.919 0.008 0.152 0.840
#> GSM125185 3 0.4862 0.917 0.020 0.160 0.820
#> GSM125187 3 0.4862 0.917 0.020 0.160 0.820
#> GSM125189 2 0.1031 0.962 0.000 0.976 0.024
#> GSM125191 2 0.4291 0.755 0.000 0.820 0.180
#> GSM125193 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125195 3 0.2116 0.874 0.012 0.040 0.948
#> GSM125197 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125201 2 0.0747 0.963 0.000 0.984 0.016
#> GSM125203 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125205 2 0.0747 0.963 0.000 0.984 0.016
#> GSM125207 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125209 3 0.4750 0.880 0.000 0.216 0.784
#> GSM125211 3 0.4615 0.914 0.020 0.144 0.836
#> GSM125213 2 0.0892 0.963 0.000 0.980 0.020
#> GSM125215 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125217 2 0.2356 0.927 0.000 0.928 0.072
#> GSM125219 1 0.1529 0.961 0.960 0.000 0.040
#> GSM125221 3 0.5480 0.826 0.004 0.264 0.732
#> GSM125223 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125225 2 0.1289 0.958 0.000 0.968 0.032
#> GSM125227 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125229 3 0.4615 0.914 0.020 0.144 0.836
#> GSM125231 1 0.6468 0.333 0.552 0.004 0.444
#> GSM125233 1 0.1860 0.956 0.948 0.000 0.052
#> GSM125235 1 0.1753 0.959 0.952 0.000 0.048
#> GSM125237 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125124 1 0.1163 0.965 0.972 0.000 0.028
#> GSM125126 1 0.0592 0.969 0.988 0.000 0.012
#> GSM125128 1 0.0237 0.969 0.996 0.000 0.004
#> GSM125130 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125132 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125134 1 0.1529 0.962 0.960 0.000 0.040
#> GSM125136 1 0.0237 0.969 0.996 0.000 0.004
#> GSM125138 1 0.1163 0.965 0.972 0.000 0.028
#> GSM125140 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125144 1 0.0892 0.967 0.980 0.000 0.020
#> GSM125146 1 0.1289 0.964 0.968 0.000 0.032
#> GSM125148 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125154 1 0.1031 0.966 0.976 0.000 0.024
#> GSM125156 1 0.0237 0.970 0.996 0.000 0.004
#> GSM125158 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125160 2 0.1163 0.961 0.000 0.972 0.028
#> GSM125162 1 0.0237 0.969 0.996 0.000 0.004
#> GSM125164 2 0.1031 0.962 0.000 0.976 0.024
#> GSM125166 2 0.0892 0.964 0.000 0.980 0.020
#> GSM125168 2 0.3038 0.895 0.000 0.896 0.104
#> GSM125170 2 0.2711 0.912 0.000 0.912 0.088
#> GSM125172 2 0.0424 0.964 0.000 0.992 0.008
#> GSM125174 3 0.1643 0.870 0.000 0.044 0.956
#> GSM125176 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125178 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125180 3 0.4862 0.917 0.020 0.160 0.820
#> GSM125182 3 0.4842 0.872 0.000 0.224 0.776
#> GSM125184 3 0.4291 0.919 0.008 0.152 0.840
#> GSM125186 3 0.4862 0.917 0.020 0.160 0.820
#> GSM125188 3 0.4399 0.900 0.000 0.188 0.812
#> GSM125190 2 0.1031 0.962 0.000 0.976 0.024
#> GSM125192 2 0.0892 0.964 0.000 0.980 0.020
#> GSM125194 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125196 3 0.2116 0.874 0.012 0.040 0.948
#> GSM125198 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.970 1.000 0.000 0.000
#> GSM125202 2 0.0747 0.963 0.000 0.984 0.016
#> GSM125204 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125206 3 0.2116 0.874 0.012 0.040 0.948
#> GSM125208 3 0.3375 0.917 0.008 0.100 0.892
#> GSM125210 3 0.4750 0.880 0.000 0.216 0.784
#> GSM125212 3 0.4615 0.914 0.020 0.144 0.836
#> GSM125214 2 0.0892 0.963 0.000 0.980 0.020
#> GSM125216 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125218 2 0.2356 0.927 0.000 0.928 0.072
#> GSM125220 1 0.1529 0.961 0.960 0.000 0.040
#> GSM125222 3 0.5480 0.826 0.004 0.264 0.732
#> GSM125224 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125226 2 0.1289 0.958 0.000 0.968 0.032
#> GSM125228 2 0.0000 0.962 0.000 1.000 0.000
#> GSM125230 3 0.4485 0.913 0.020 0.136 0.844
#> GSM125232 1 0.5988 0.524 0.632 0.000 0.368
#> GSM125234 1 0.2165 0.950 0.936 0.000 0.064
#> GSM125236 1 0.1753 0.959 0.952 0.000 0.048
#> GSM125238 1 0.0000 0.970 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0779 0.930 0.980 0.000 0.004 0.016
#> GSM125125 1 0.0779 0.930 0.980 0.000 0.004 0.016
#> GSM125127 1 0.3443 0.883 0.848 0.000 0.016 0.136
#> GSM125129 1 0.3547 0.876 0.840 0.000 0.016 0.144
#> GSM125131 1 0.0707 0.928 0.980 0.000 0.000 0.020
#> GSM125133 1 0.1576 0.925 0.948 0.000 0.004 0.048
#> GSM125135 1 0.3547 0.875 0.840 0.000 0.016 0.144
#> GSM125137 1 0.0592 0.929 0.984 0.000 0.000 0.016
#> GSM125139 1 0.0188 0.930 0.996 0.000 0.000 0.004
#> GSM125141 1 0.0336 0.930 0.992 0.000 0.000 0.008
#> GSM125143 1 0.3495 0.879 0.844 0.000 0.016 0.140
#> GSM125145 1 0.2859 0.898 0.880 0.000 0.008 0.112
#> GSM125147 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> GSM125149 1 0.0707 0.928 0.980 0.000 0.000 0.020
#> GSM125151 1 0.0921 0.930 0.972 0.000 0.000 0.028
#> GSM125153 1 0.1807 0.923 0.940 0.000 0.008 0.052
#> GSM125155 1 0.0592 0.931 0.984 0.000 0.000 0.016
#> GSM125157 1 0.0592 0.929 0.984 0.000 0.000 0.016
#> GSM125159 2 0.1174 0.957 0.000 0.968 0.012 0.020
#> GSM125161 1 0.1302 0.919 0.956 0.000 0.000 0.044
#> GSM125163 2 0.0804 0.959 0.000 0.980 0.012 0.008
#> GSM125165 3 0.5339 0.389 0.000 0.272 0.688 0.040
#> GSM125167 2 0.3013 0.899 0.000 0.888 0.080 0.032
#> GSM125169 2 0.2699 0.912 0.000 0.904 0.068 0.028
#> GSM125171 2 0.0469 0.958 0.000 0.988 0.000 0.012
#> GSM125173 4 0.4193 1.000 0.000 0.000 0.268 0.732
#> GSM125175 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125177 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125179 3 0.2101 0.803 0.000 0.012 0.928 0.060
#> GSM125181 3 0.3548 0.768 0.000 0.068 0.864 0.068
#> GSM125183 3 0.2748 0.806 0.004 0.020 0.904 0.072
#> GSM125185 3 0.2101 0.803 0.000 0.012 0.928 0.060
#> GSM125187 3 0.2101 0.803 0.000 0.012 0.928 0.060
#> GSM125189 2 0.1182 0.957 0.000 0.968 0.016 0.016
#> GSM125191 2 0.4290 0.691 0.000 0.772 0.212 0.016
#> GSM125193 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125195 3 0.4543 0.358 0.000 0.000 0.676 0.324
#> GSM125197 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0336 0.930 0.992 0.000 0.000 0.008
#> GSM125201 2 0.1411 0.946 0.000 0.960 0.020 0.020
#> GSM125203 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125205 2 0.1411 0.946 0.000 0.960 0.020 0.020
#> GSM125207 3 0.2342 0.799 0.000 0.008 0.912 0.080
#> GSM125209 3 0.3081 0.781 0.000 0.064 0.888 0.048
#> GSM125211 3 0.3142 0.772 0.000 0.008 0.860 0.132
#> GSM125213 2 0.0927 0.957 0.000 0.976 0.016 0.008
#> GSM125215 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125217 2 0.2256 0.932 0.000 0.924 0.056 0.020
#> GSM125219 1 0.2329 0.918 0.916 0.000 0.012 0.072
#> GSM125221 3 0.3731 0.711 0.000 0.120 0.844 0.036
#> GSM125223 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125225 2 0.1452 0.950 0.000 0.956 0.036 0.008
#> GSM125227 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125229 3 0.3142 0.772 0.000 0.008 0.860 0.132
#> GSM125231 1 0.7007 0.123 0.452 0.000 0.116 0.432
#> GSM125233 1 0.3108 0.896 0.872 0.000 0.016 0.112
#> GSM125235 1 0.2593 0.912 0.904 0.000 0.016 0.080
#> GSM125237 1 0.0592 0.929 0.984 0.000 0.000 0.016
#> GSM125124 1 0.2831 0.896 0.876 0.000 0.004 0.120
#> GSM125126 1 0.0779 0.930 0.980 0.000 0.004 0.016
#> GSM125128 1 0.1474 0.921 0.948 0.000 0.000 0.052
#> GSM125130 1 0.3547 0.876 0.840 0.000 0.016 0.144
#> GSM125132 1 0.0707 0.928 0.980 0.000 0.000 0.020
#> GSM125134 1 0.2480 0.913 0.904 0.000 0.008 0.088
#> GSM125136 1 0.1302 0.919 0.956 0.000 0.000 0.044
#> GSM125138 1 0.2831 0.896 0.876 0.000 0.004 0.120
#> GSM125140 1 0.0188 0.930 0.996 0.000 0.000 0.004
#> GSM125142 1 0.0336 0.930 0.992 0.000 0.000 0.008
#> GSM125144 1 0.2216 0.912 0.908 0.000 0.000 0.092
#> GSM125146 1 0.2859 0.898 0.880 0.000 0.008 0.112
#> GSM125148 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> GSM125150 1 0.0707 0.928 0.980 0.000 0.000 0.020
#> GSM125152 1 0.0921 0.930 0.972 0.000 0.000 0.028
#> GSM125154 1 0.1807 0.923 0.940 0.000 0.008 0.052
#> GSM125156 1 0.0592 0.931 0.984 0.000 0.000 0.016
#> GSM125158 1 0.0592 0.929 0.984 0.000 0.000 0.016
#> GSM125160 2 0.1174 0.957 0.000 0.968 0.012 0.020
#> GSM125162 1 0.1302 0.919 0.956 0.000 0.000 0.044
#> GSM125164 2 0.1042 0.958 0.000 0.972 0.020 0.008
#> GSM125166 2 0.0927 0.959 0.000 0.976 0.016 0.008
#> GSM125168 2 0.3013 0.899 0.000 0.888 0.080 0.032
#> GSM125170 2 0.2699 0.912 0.000 0.904 0.068 0.028
#> GSM125172 2 0.0469 0.958 0.000 0.988 0.000 0.012
#> GSM125174 4 0.4193 1.000 0.000 0.000 0.268 0.732
#> GSM125176 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125178 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125180 3 0.2101 0.803 0.000 0.012 0.928 0.060
#> GSM125182 3 0.3548 0.768 0.000 0.068 0.864 0.068
#> GSM125184 3 0.2748 0.806 0.004 0.020 0.904 0.072
#> GSM125186 3 0.2101 0.803 0.000 0.012 0.928 0.060
#> GSM125188 3 0.3013 0.789 0.000 0.032 0.888 0.080
#> GSM125190 2 0.1182 0.957 0.000 0.968 0.016 0.016
#> GSM125192 2 0.0927 0.959 0.000 0.976 0.016 0.008
#> GSM125194 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125196 3 0.4543 0.358 0.000 0.000 0.676 0.324
#> GSM125198 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0336 0.930 0.992 0.000 0.000 0.008
#> GSM125202 2 0.1411 0.946 0.000 0.960 0.020 0.020
#> GSM125204 3 0.2546 0.796 0.000 0.008 0.900 0.092
#> GSM125206 3 0.4543 0.358 0.000 0.000 0.676 0.324
#> GSM125208 3 0.2342 0.799 0.000 0.008 0.912 0.080
#> GSM125210 3 0.3081 0.781 0.000 0.064 0.888 0.048
#> GSM125212 3 0.3142 0.772 0.000 0.008 0.860 0.132
#> GSM125214 2 0.0927 0.957 0.000 0.976 0.016 0.008
#> GSM125216 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125218 2 0.2256 0.932 0.000 0.924 0.056 0.020
#> GSM125220 1 0.2329 0.918 0.916 0.000 0.012 0.072
#> GSM125222 3 0.3731 0.711 0.000 0.120 0.844 0.036
#> GSM125224 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125226 2 0.1452 0.950 0.000 0.956 0.036 0.008
#> GSM125228 2 0.0000 0.958 0.000 1.000 0.000 0.000
#> GSM125230 3 0.2814 0.765 0.000 0.000 0.868 0.132
#> GSM125232 1 0.6407 0.347 0.520 0.000 0.068 0.412
#> GSM125234 1 0.3695 0.867 0.828 0.000 0.016 0.156
#> GSM125236 1 0.2593 0.912 0.904 0.000 0.016 0.080
#> GSM125238 1 0.0592 0.929 0.984 0.000 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2488 0.7165 0.872 0.000 0.004 0.000 0.124
#> GSM125125 1 0.2488 0.7165 0.872 0.000 0.004 0.000 0.124
#> GSM125127 5 0.4504 0.6540 0.428 0.000 0.008 0.000 0.564
#> GSM125129 5 0.4657 0.7368 0.380 0.000 0.008 0.008 0.604
#> GSM125131 1 0.0703 0.7358 0.976 0.000 0.000 0.000 0.024
#> GSM125133 1 0.1864 0.7117 0.924 0.000 0.004 0.004 0.068
#> GSM125135 5 0.4403 0.7298 0.384 0.000 0.008 0.000 0.608
#> GSM125137 1 0.1121 0.7394 0.956 0.000 0.000 0.000 0.044
#> GSM125139 1 0.2377 0.7088 0.872 0.000 0.000 0.000 0.128
#> GSM125141 1 0.1270 0.7451 0.948 0.000 0.000 0.000 0.052
#> GSM125143 5 0.4668 0.7326 0.384 0.000 0.008 0.008 0.600
#> GSM125145 1 0.4449 -0.3998 0.512 0.000 0.004 0.000 0.484
#> GSM125147 1 0.1043 0.7474 0.960 0.000 0.000 0.000 0.040
#> GSM125149 1 0.0510 0.7403 0.984 0.000 0.000 0.000 0.016
#> GSM125151 1 0.3395 0.5835 0.764 0.000 0.000 0.000 0.236
#> GSM125153 1 0.3906 0.4001 0.704 0.000 0.004 0.000 0.292
#> GSM125155 1 0.1792 0.7356 0.916 0.000 0.000 0.000 0.084
#> GSM125157 1 0.0609 0.7474 0.980 0.000 0.000 0.000 0.020
#> GSM125159 2 0.1299 0.9458 0.000 0.960 0.008 0.020 0.012
#> GSM125161 1 0.1410 0.7089 0.940 0.000 0.000 0.000 0.060
#> GSM125163 2 0.1200 0.9465 0.000 0.964 0.008 0.016 0.012
#> GSM125165 3 0.5887 0.4880 0.000 0.232 0.644 0.096 0.028
#> GSM125167 2 0.3427 0.8809 0.000 0.860 0.048 0.064 0.028
#> GSM125169 2 0.2980 0.8990 0.000 0.884 0.036 0.056 0.024
#> GSM125171 2 0.0807 0.9464 0.000 0.976 0.000 0.012 0.012
#> GSM125173 4 0.1544 1.0000 0.000 0.000 0.068 0.932 0.000
#> GSM125175 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125177 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125179 3 0.2193 0.8317 0.000 0.000 0.912 0.060 0.028
#> GSM125181 3 0.3949 0.8028 0.000 0.036 0.828 0.088 0.048
#> GSM125183 3 0.3044 0.8127 0.000 0.004 0.840 0.148 0.008
#> GSM125185 3 0.2193 0.8317 0.000 0.000 0.912 0.060 0.028
#> GSM125187 3 0.2193 0.8317 0.000 0.000 0.912 0.060 0.028
#> GSM125189 2 0.1483 0.9434 0.000 0.952 0.012 0.028 0.008
#> GSM125191 2 0.4444 0.6726 0.000 0.752 0.200 0.024 0.024
#> GSM125193 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125195 3 0.4550 0.5671 0.000 0.000 0.688 0.036 0.276
#> GSM125197 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125199 1 0.0880 0.7496 0.968 0.000 0.000 0.000 0.032
#> GSM125201 2 0.1399 0.9356 0.000 0.952 0.020 0.000 0.028
#> GSM125203 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125205 2 0.1399 0.9356 0.000 0.952 0.020 0.000 0.028
#> GSM125207 3 0.1668 0.8298 0.000 0.000 0.940 0.028 0.032
#> GSM125209 3 0.3405 0.8146 0.000 0.040 0.860 0.072 0.028
#> GSM125211 3 0.3471 0.7857 0.000 0.000 0.836 0.072 0.092
#> GSM125213 2 0.0798 0.9471 0.000 0.976 0.016 0.000 0.008
#> GSM125215 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125217 2 0.2825 0.9085 0.000 0.892 0.048 0.040 0.020
#> GSM125219 1 0.4338 0.3739 0.684 0.000 0.008 0.008 0.300
#> GSM125221 3 0.4281 0.7616 0.000 0.080 0.800 0.100 0.020
#> GSM125223 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125225 2 0.1787 0.9355 0.000 0.940 0.032 0.016 0.012
#> GSM125227 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125229 3 0.3471 0.7857 0.000 0.000 0.836 0.072 0.092
#> GSM125231 5 0.5445 0.2557 0.088 0.000 0.132 0.056 0.724
#> GSM125233 5 0.4787 0.5906 0.444 0.000 0.008 0.008 0.540
#> GSM125235 1 0.4751 -0.1159 0.564 0.000 0.008 0.008 0.420
#> GSM125237 1 0.0290 0.7479 0.992 0.000 0.000 0.000 0.008
#> GSM125124 1 0.4201 0.0246 0.592 0.000 0.000 0.000 0.408
#> GSM125126 1 0.2488 0.7165 0.872 0.000 0.004 0.000 0.124
#> GSM125128 1 0.1956 0.7071 0.916 0.000 0.000 0.008 0.076
#> GSM125130 5 0.4657 0.7368 0.380 0.000 0.008 0.008 0.604
#> GSM125132 1 0.0703 0.7358 0.976 0.000 0.000 0.000 0.024
#> GSM125134 1 0.4331 -0.0224 0.596 0.000 0.004 0.000 0.400
#> GSM125136 1 0.1410 0.7089 0.940 0.000 0.000 0.000 0.060
#> GSM125138 1 0.4201 0.0246 0.592 0.000 0.000 0.000 0.408
#> GSM125140 1 0.2377 0.7088 0.872 0.000 0.000 0.000 0.128
#> GSM125142 1 0.1270 0.7451 0.948 0.000 0.000 0.000 0.052
#> GSM125144 1 0.3966 0.3370 0.664 0.000 0.000 0.000 0.336
#> GSM125146 1 0.4449 -0.3998 0.512 0.000 0.004 0.000 0.484
#> GSM125148 1 0.1043 0.7474 0.960 0.000 0.000 0.000 0.040
#> GSM125150 1 0.0510 0.7403 0.984 0.000 0.000 0.000 0.016
#> GSM125152 1 0.3395 0.5835 0.764 0.000 0.000 0.000 0.236
#> GSM125154 1 0.3906 0.4001 0.704 0.000 0.004 0.000 0.292
#> GSM125156 1 0.1792 0.7356 0.916 0.000 0.000 0.000 0.084
#> GSM125158 1 0.0609 0.7474 0.980 0.000 0.000 0.000 0.020
#> GSM125160 2 0.1299 0.9458 0.000 0.960 0.008 0.020 0.012
#> GSM125162 1 0.1410 0.7089 0.940 0.000 0.000 0.000 0.060
#> GSM125164 2 0.1419 0.9448 0.000 0.956 0.016 0.016 0.012
#> GSM125166 2 0.0807 0.9485 0.000 0.976 0.012 0.000 0.012
#> GSM125168 2 0.3427 0.8809 0.000 0.860 0.048 0.064 0.028
#> GSM125170 2 0.2980 0.8990 0.000 0.884 0.036 0.056 0.024
#> GSM125172 2 0.0807 0.9464 0.000 0.976 0.000 0.012 0.012
#> GSM125174 4 0.1544 1.0000 0.000 0.000 0.068 0.932 0.000
#> GSM125176 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125178 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125180 3 0.2193 0.8317 0.000 0.000 0.912 0.060 0.028
#> GSM125182 3 0.3949 0.8028 0.000 0.036 0.828 0.088 0.048
#> GSM125184 3 0.3044 0.8127 0.000 0.004 0.840 0.148 0.008
#> GSM125186 3 0.2193 0.8317 0.000 0.000 0.912 0.060 0.028
#> GSM125188 3 0.3412 0.8153 0.000 0.008 0.848 0.096 0.048
#> GSM125190 2 0.1483 0.9434 0.000 0.952 0.012 0.028 0.008
#> GSM125192 2 0.0807 0.9485 0.000 0.976 0.012 0.000 0.012
#> GSM125194 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125196 3 0.4550 0.5671 0.000 0.000 0.688 0.036 0.276
#> GSM125198 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125200 1 0.0880 0.7496 0.968 0.000 0.000 0.000 0.032
#> GSM125202 2 0.1399 0.9356 0.000 0.952 0.020 0.000 0.028
#> GSM125204 3 0.1753 0.8274 0.000 0.000 0.936 0.032 0.032
#> GSM125206 3 0.4550 0.5671 0.000 0.000 0.688 0.036 0.276
#> GSM125208 3 0.1668 0.8298 0.000 0.000 0.940 0.028 0.032
#> GSM125210 3 0.3405 0.8146 0.000 0.040 0.860 0.072 0.028
#> GSM125212 3 0.3471 0.7857 0.000 0.000 0.836 0.072 0.092
#> GSM125214 2 0.0798 0.9471 0.000 0.976 0.016 0.000 0.008
#> GSM125216 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125218 2 0.2825 0.9085 0.000 0.892 0.048 0.040 0.020
#> GSM125220 1 0.4338 0.3739 0.684 0.000 0.008 0.008 0.300
#> GSM125222 3 0.4281 0.7616 0.000 0.080 0.800 0.100 0.020
#> GSM125224 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125226 2 0.1787 0.9355 0.000 0.940 0.032 0.016 0.012
#> GSM125228 2 0.0290 0.9474 0.000 0.992 0.000 0.000 0.008
#> GSM125230 3 0.3579 0.7813 0.000 0.000 0.828 0.072 0.100
#> GSM125232 5 0.4233 0.3718 0.092 0.000 0.072 0.028 0.808
#> GSM125234 5 0.4594 0.7314 0.360 0.000 0.008 0.008 0.624
#> GSM125236 1 0.4758 -0.1370 0.560 0.000 0.008 0.008 0.424
#> GSM125238 1 0.0290 0.7479 0.992 0.000 0.000 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2805 0.7237 0.812 0.000 0.000 0.000 0.184 0.004
#> GSM125125 1 0.2805 0.7237 0.812 0.000 0.000 0.000 0.184 0.004
#> GSM125127 5 0.2879 0.7298 0.176 0.000 0.004 0.000 0.816 0.004
#> GSM125129 5 0.2178 0.7290 0.132 0.000 0.000 0.000 0.868 0.000
#> GSM125131 1 0.0363 0.7954 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM125133 1 0.1700 0.7529 0.916 0.000 0.004 0.000 0.080 0.000
#> GSM125135 5 0.2462 0.7256 0.132 0.000 0.004 0.000 0.860 0.004
#> GSM125137 1 0.0865 0.7959 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM125139 1 0.2558 0.7501 0.840 0.000 0.000 0.000 0.156 0.004
#> GSM125141 1 0.1501 0.7975 0.924 0.000 0.000 0.000 0.076 0.000
#> GSM125143 5 0.2219 0.7307 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM125145 5 0.3996 0.5518 0.352 0.000 0.004 0.000 0.636 0.008
#> GSM125147 1 0.1327 0.8013 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM125149 1 0.0146 0.7989 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125151 1 0.3728 0.4536 0.652 0.000 0.000 0.000 0.344 0.004
#> GSM125153 1 0.3841 0.2720 0.616 0.000 0.000 0.000 0.380 0.004
#> GSM125155 1 0.2558 0.7425 0.840 0.000 0.000 0.000 0.156 0.004
#> GSM125157 1 0.0713 0.8051 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125159 2 0.1768 0.9297 0.000 0.932 0.044 0.012 0.008 0.004
#> GSM125161 1 0.1285 0.7570 0.944 0.000 0.004 0.000 0.052 0.000
#> GSM125163 2 0.1768 0.9286 0.000 0.932 0.044 0.012 0.008 0.004
#> GSM125165 4 0.5672 0.1313 0.000 0.212 0.072 0.644 0.008 0.064
#> GSM125167 2 0.3649 0.8688 0.000 0.828 0.092 0.036 0.008 0.036
#> GSM125169 2 0.3003 0.8930 0.000 0.868 0.068 0.028 0.004 0.032
#> GSM125171 2 0.1464 0.9232 0.000 0.944 0.036 0.000 0.004 0.016
#> GSM125173 6 0.1075 1.0000 0.000 0.000 0.000 0.048 0.000 0.952
#> GSM125175 2 0.0508 0.9343 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM125177 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125179 4 0.0508 0.6372 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM125181 4 0.2874 0.5494 0.000 0.020 0.072 0.876 0.012 0.020
#> GSM125183 4 0.3509 0.5266 0.000 0.004 0.060 0.816 0.004 0.116
#> GSM125185 4 0.0508 0.6372 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM125187 4 0.0508 0.6372 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM125189 2 0.1873 0.9263 0.000 0.924 0.048 0.020 0.000 0.008
#> GSM125191 2 0.3933 0.6818 0.000 0.740 0.032 0.220 0.008 0.000
#> GSM125193 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125195 4 0.4531 0.2868 0.000 0.000 0.464 0.504 0.032 0.000
#> GSM125197 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125199 1 0.1010 0.8062 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM125201 2 0.1757 0.9046 0.000 0.916 0.076 0.000 0.008 0.000
#> GSM125203 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125205 2 0.1757 0.9046 0.000 0.916 0.076 0.000 0.008 0.000
#> GSM125207 4 0.2883 0.6262 0.000 0.000 0.212 0.788 0.000 0.000
#> GSM125209 4 0.2037 0.5987 0.000 0.028 0.028 0.924 0.008 0.012
#> GSM125211 3 0.4912 0.9937 0.000 0.000 0.516 0.432 0.008 0.044
#> GSM125213 2 0.1078 0.9332 0.000 0.964 0.012 0.016 0.008 0.000
#> GSM125215 2 0.0508 0.9339 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM125217 2 0.3090 0.8923 0.000 0.856 0.092 0.024 0.004 0.024
#> GSM125219 5 0.3868 0.1999 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM125221 4 0.4001 0.4455 0.000 0.056 0.084 0.800 0.000 0.060
#> GSM125223 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125225 2 0.2202 0.9200 0.000 0.908 0.052 0.028 0.000 0.012
#> GSM125227 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125229 3 0.4912 0.9937 0.000 0.000 0.516 0.432 0.008 0.044
#> GSM125231 5 0.6013 0.2507 0.040 0.000 0.336 0.056 0.544 0.024
#> GSM125233 5 0.2762 0.7190 0.196 0.000 0.000 0.000 0.804 0.000
#> GSM125235 5 0.3578 0.5797 0.340 0.000 0.000 0.000 0.660 0.000
#> GSM125237 1 0.0790 0.8052 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125124 1 0.4310 -0.0529 0.512 0.000 0.004 0.000 0.472 0.012
#> GSM125126 1 0.2805 0.7237 0.812 0.000 0.000 0.000 0.184 0.004
#> GSM125128 1 0.1644 0.7509 0.920 0.000 0.004 0.000 0.076 0.000
#> GSM125130 5 0.2178 0.7290 0.132 0.000 0.000 0.000 0.868 0.000
#> GSM125132 1 0.0363 0.7954 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM125134 5 0.4033 0.4766 0.404 0.000 0.004 0.000 0.588 0.004
#> GSM125136 1 0.1285 0.7570 0.944 0.000 0.004 0.000 0.052 0.000
#> GSM125138 1 0.4310 -0.0529 0.512 0.000 0.004 0.000 0.472 0.012
#> GSM125140 1 0.2558 0.7501 0.840 0.000 0.000 0.000 0.156 0.004
#> GSM125142 1 0.1501 0.7975 0.924 0.000 0.000 0.000 0.076 0.000
#> GSM125144 1 0.3907 0.2575 0.588 0.000 0.000 0.000 0.408 0.004
#> GSM125146 5 0.3996 0.5518 0.352 0.000 0.004 0.000 0.636 0.008
#> GSM125148 1 0.1327 0.8013 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM125150 1 0.0146 0.7989 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125152 1 0.3728 0.4536 0.652 0.000 0.000 0.000 0.344 0.004
#> GSM125154 1 0.3841 0.2720 0.616 0.000 0.000 0.000 0.380 0.004
#> GSM125156 1 0.2558 0.7425 0.840 0.000 0.000 0.000 0.156 0.004
#> GSM125158 1 0.0713 0.8051 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM125160 2 0.1768 0.9297 0.000 0.932 0.044 0.012 0.008 0.004
#> GSM125162 1 0.1285 0.7570 0.944 0.000 0.004 0.000 0.052 0.000
#> GSM125164 2 0.1950 0.9269 0.000 0.924 0.044 0.020 0.008 0.004
#> GSM125166 2 0.0881 0.9353 0.000 0.972 0.008 0.012 0.008 0.000
#> GSM125168 2 0.3649 0.8688 0.000 0.828 0.092 0.036 0.008 0.036
#> GSM125170 2 0.3003 0.8930 0.000 0.868 0.068 0.028 0.004 0.032
#> GSM125172 2 0.1464 0.9232 0.000 0.944 0.036 0.000 0.004 0.016
#> GSM125174 6 0.1075 1.0000 0.000 0.000 0.000 0.048 0.000 0.952
#> GSM125176 2 0.0508 0.9343 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM125178 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125180 4 0.0508 0.6372 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM125182 4 0.2874 0.5494 0.000 0.020 0.072 0.876 0.012 0.020
#> GSM125184 4 0.3509 0.5266 0.000 0.004 0.060 0.816 0.004 0.116
#> GSM125186 4 0.0508 0.6372 0.000 0.000 0.000 0.984 0.012 0.004
#> GSM125188 4 0.2007 0.5906 0.000 0.008 0.040 0.924 0.012 0.016
#> GSM125190 2 0.1873 0.9263 0.000 0.924 0.048 0.020 0.000 0.008
#> GSM125192 2 0.0881 0.9353 0.000 0.972 0.008 0.012 0.008 0.000
#> GSM125194 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125196 4 0.4531 0.2868 0.000 0.000 0.464 0.504 0.032 0.000
#> GSM125198 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125200 1 0.1010 0.8062 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM125202 2 0.1757 0.9046 0.000 0.916 0.076 0.000 0.008 0.000
#> GSM125204 4 0.3023 0.6181 0.000 0.000 0.232 0.768 0.000 0.000
#> GSM125206 4 0.4531 0.2868 0.000 0.000 0.464 0.504 0.032 0.000
#> GSM125208 4 0.2883 0.6262 0.000 0.000 0.212 0.788 0.000 0.000
#> GSM125210 4 0.2037 0.5987 0.000 0.028 0.028 0.924 0.008 0.012
#> GSM125212 3 0.4912 0.9937 0.000 0.000 0.516 0.432 0.008 0.044
#> GSM125214 2 0.1078 0.9332 0.000 0.964 0.012 0.016 0.008 0.000
#> GSM125216 2 0.0508 0.9339 0.000 0.984 0.012 0.000 0.004 0.000
#> GSM125218 2 0.3090 0.8923 0.000 0.856 0.092 0.024 0.004 0.024
#> GSM125220 5 0.3868 0.1999 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM125222 4 0.4001 0.4455 0.000 0.056 0.084 0.800 0.000 0.060
#> GSM125224 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125226 2 0.2202 0.9200 0.000 0.908 0.052 0.028 0.000 0.012
#> GSM125228 2 0.0405 0.9336 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125230 3 0.4903 0.9813 0.000 0.000 0.524 0.424 0.008 0.044
#> GSM125232 5 0.5499 0.3490 0.040 0.000 0.276 0.040 0.624 0.020
#> GSM125234 5 0.1910 0.7069 0.108 0.000 0.000 0.000 0.892 0.000
#> GSM125236 5 0.3563 0.5863 0.336 0.000 0.000 0.000 0.664 0.000
#> GSM125238 1 0.0790 0.8052 0.968 0.000 0.000 0.000 0.032 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 agent(p) individual(p) k
#> MAD:hclust 115 1.000 8.53e-06 2
#> MAD:hclust 115 0.994 4.62e-09 3
#> MAD:hclust 110 1.000 4.34e-13 4
#> MAD:hclust 101 0.867 1.75e-12 5
#> MAD:hclust 98 0.983 1.05e-15 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 1.000 0.4975 0.503 0.503
#> 3 3 0.798 0.949 0.938 0.3169 0.806 0.626
#> 4 4 0.735 0.724 0.800 0.1046 0.981 0.943
#> 5 5 0.706 0.607 0.735 0.0695 0.884 0.641
#> 6 6 0.691 0.670 0.727 0.0406 0.952 0.792
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
#> GSM125123 1 0.0000 1.000 1.000 0.000
#> GSM125125 1 0.0000 1.000 1.000 0.000
#> GSM125127 1 0.0000 1.000 1.000 0.000
#> GSM125129 1 0.0000 1.000 1.000 0.000
#> GSM125131 1 0.0000 1.000 1.000 0.000
#> GSM125133 1 0.0000 1.000 1.000 0.000
#> GSM125135 1 0.0000 1.000 1.000 0.000
#> GSM125137 1 0.0000 1.000 1.000 0.000
#> GSM125139 1 0.0000 1.000 1.000 0.000
#> GSM125141 1 0.0000 1.000 1.000 0.000
#> GSM125143 1 0.0000 1.000 1.000 0.000
#> GSM125145 1 0.0000 1.000 1.000 0.000
#> GSM125147 1 0.0000 1.000 1.000 0.000
#> GSM125149 1 0.0000 1.000 1.000 0.000
#> GSM125151 1 0.0000 1.000 1.000 0.000
#> GSM125153 1 0.0000 1.000 1.000 0.000
#> GSM125155 1 0.0000 1.000 1.000 0.000
#> GSM125157 1 0.0000 1.000 1.000 0.000
#> GSM125159 2 0.0000 1.000 0.000 1.000
#> GSM125161 1 0.0000 1.000 1.000 0.000
#> GSM125163 2 0.0000 1.000 0.000 1.000
#> GSM125165 2 0.0000 1.000 0.000 1.000
#> GSM125167 2 0.0000 1.000 0.000 1.000
#> GSM125169 2 0.0000 1.000 0.000 1.000
#> GSM125171 2 0.0000 1.000 0.000 1.000
#> GSM125173 2 0.0000 1.000 0.000 1.000
#> GSM125175 2 0.0000 1.000 0.000 1.000
#> GSM125177 2 0.0000 1.000 0.000 1.000
#> GSM125179 2 0.0000 1.000 0.000 1.000
#> GSM125181 2 0.0000 1.000 0.000 1.000
#> GSM125183 2 0.0000 1.000 0.000 1.000
#> GSM125185 2 0.0000 1.000 0.000 1.000
#> GSM125187 2 0.0376 0.996 0.004 0.996
#> GSM125189 2 0.0000 1.000 0.000 1.000
#> GSM125191 2 0.0000 1.000 0.000 1.000
#> GSM125193 2 0.0000 1.000 0.000 1.000
#> GSM125195 2 0.0000 1.000 0.000 1.000
#> GSM125197 2 0.0000 1.000 0.000 1.000
#> GSM125199 1 0.0000 1.000 1.000 0.000
#> GSM125201 2 0.0000 1.000 0.000 1.000
#> GSM125203 2 0.0000 1.000 0.000 1.000
#> GSM125205 2 0.0000 1.000 0.000 1.000
#> GSM125207 2 0.0000 1.000 0.000 1.000
#> GSM125209 2 0.0000 1.000 0.000 1.000
#> GSM125211 2 0.0000 1.000 0.000 1.000
#> GSM125213 2 0.0000 1.000 0.000 1.000
#> GSM125215 2 0.0000 1.000 0.000 1.000
#> GSM125217 2 0.0000 1.000 0.000 1.000
#> GSM125219 1 0.0000 1.000 1.000 0.000
#> GSM125221 2 0.0000 1.000 0.000 1.000
#> GSM125223 2 0.0000 1.000 0.000 1.000
#> GSM125225 2 0.0000 1.000 0.000 1.000
#> GSM125227 2 0.0000 1.000 0.000 1.000
#> GSM125229 2 0.0000 1.000 0.000 1.000
#> GSM125231 1 0.0000 1.000 1.000 0.000
#> GSM125233 1 0.0000 1.000 1.000 0.000
#> GSM125235 1 0.0000 1.000 1.000 0.000
#> GSM125237 1 0.0000 1.000 1.000 0.000
#> GSM125124 1 0.0000 1.000 1.000 0.000
#> GSM125126 1 0.0000 1.000 1.000 0.000
#> GSM125128 1 0.0000 1.000 1.000 0.000
#> GSM125130 1 0.0000 1.000 1.000 0.000
#> GSM125132 1 0.0000 1.000 1.000 0.000
#> GSM125134 1 0.0000 1.000 1.000 0.000
#> GSM125136 1 0.0000 1.000 1.000 0.000
#> GSM125138 1 0.0000 1.000 1.000 0.000
#> GSM125140 1 0.0000 1.000 1.000 0.000
#> GSM125142 1 0.0000 1.000 1.000 0.000
#> GSM125144 1 0.0000 1.000 1.000 0.000
#> GSM125146 1 0.0000 1.000 1.000 0.000
#> GSM125148 1 0.0000 1.000 1.000 0.000
#> GSM125150 1 0.0000 1.000 1.000 0.000
#> GSM125152 1 0.0000 1.000 1.000 0.000
#> GSM125154 1 0.0000 1.000 1.000 0.000
#> GSM125156 1 0.0000 1.000 1.000 0.000
#> GSM125158 1 0.0000 1.000 1.000 0.000
#> GSM125160 2 0.0000 1.000 0.000 1.000
#> GSM125162 1 0.0000 1.000 1.000 0.000
#> GSM125164 2 0.0000 1.000 0.000 1.000
#> GSM125166 2 0.0000 1.000 0.000 1.000
#> GSM125168 2 0.0000 1.000 0.000 1.000
#> GSM125170 2 0.0000 1.000 0.000 1.000
#> GSM125172 2 0.0000 1.000 0.000 1.000
#> GSM125174 2 0.0000 1.000 0.000 1.000
#> GSM125176 2 0.0000 1.000 0.000 1.000
#> GSM125178 2 0.0000 1.000 0.000 1.000
#> GSM125180 2 0.0000 1.000 0.000 1.000
#> GSM125182 2 0.0000 1.000 0.000 1.000
#> GSM125184 2 0.0000 1.000 0.000 1.000
#> GSM125186 2 0.0000 1.000 0.000 1.000
#> GSM125188 2 0.0000 1.000 0.000 1.000
#> GSM125190 2 0.0000 1.000 0.000 1.000
#> GSM125192 2 0.0000 1.000 0.000 1.000
#> GSM125194 1 0.1633 0.975 0.976 0.024
#> GSM125196 2 0.0000 1.000 0.000 1.000
#> GSM125198 2 0.0000 1.000 0.000 1.000
#> GSM125200 1 0.0000 1.000 1.000 0.000
#> GSM125202 2 0.0000 1.000 0.000 1.000
#> GSM125204 2 0.0000 1.000 0.000 1.000
#> GSM125206 2 0.0000 1.000 0.000 1.000
#> GSM125208 2 0.0000 1.000 0.000 1.000
#> GSM125210 2 0.0000 1.000 0.000 1.000
#> GSM125212 2 0.0000 1.000 0.000 1.000
#> GSM125214 2 0.0000 1.000 0.000 1.000
#> GSM125216 2 0.0000 1.000 0.000 1.000
#> GSM125218 2 0.0000 1.000 0.000 1.000
#> GSM125220 1 0.0000 1.000 1.000 0.000
#> GSM125222 2 0.0000 1.000 0.000 1.000
#> GSM125224 2 0.0000 1.000 0.000 1.000
#> GSM125226 2 0.0000 1.000 0.000 1.000
#> GSM125228 2 0.0000 1.000 0.000 1.000
#> GSM125230 2 0.0672 0.992 0.008 0.992
#> GSM125232 1 0.0000 1.000 1.000 0.000
#> GSM125234 1 0.0000 1.000 1.000 0.000
#> GSM125236 1 0.0000 1.000 1.000 0.000
#> GSM125238 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125125 1 0.0747 0.959 0.984 0.000 0.016
#> GSM125127 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125129 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125131 1 0.1163 0.959 0.972 0.000 0.028
#> GSM125133 1 0.1411 0.958 0.964 0.000 0.036
#> GSM125135 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125137 1 0.1031 0.959 0.976 0.000 0.024
#> GSM125139 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125141 1 0.1031 0.959 0.976 0.000 0.024
#> GSM125143 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125145 1 0.2959 0.956 0.900 0.000 0.100
#> GSM125147 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125149 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125151 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125153 1 0.2356 0.960 0.928 0.000 0.072
#> GSM125155 1 0.1031 0.959 0.976 0.000 0.024
#> GSM125157 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125159 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125161 1 0.1289 0.958 0.968 0.000 0.032
#> GSM125163 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125165 3 0.3551 0.950 0.000 0.132 0.868
#> GSM125167 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125173 3 0.3340 0.960 0.000 0.120 0.880
#> GSM125175 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125177 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125179 3 0.3192 0.960 0.000 0.112 0.888
#> GSM125181 3 0.3412 0.957 0.000 0.124 0.876
#> GSM125183 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125185 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125187 3 0.3192 0.960 0.000 0.112 0.888
#> GSM125189 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125191 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125193 3 0.2537 0.933 0.000 0.080 0.920
#> GSM125195 3 0.3192 0.960 0.000 0.112 0.888
#> GSM125197 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125199 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125201 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125203 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125205 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125207 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125209 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125211 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125213 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125219 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125221 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125223 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125229 3 0.3340 0.960 0.000 0.120 0.880
#> GSM125231 3 0.1267 0.854 0.024 0.004 0.972
#> GSM125233 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125235 1 0.1411 0.958 0.964 0.000 0.036
#> GSM125237 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125124 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125126 1 0.0747 0.959 0.984 0.000 0.016
#> GSM125128 1 0.1289 0.958 0.968 0.000 0.032
#> GSM125130 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125132 1 0.0747 0.959 0.984 0.000 0.016
#> GSM125134 1 0.2625 0.958 0.916 0.000 0.084
#> GSM125136 1 0.1411 0.958 0.964 0.000 0.036
#> GSM125138 1 0.2625 0.958 0.916 0.000 0.084
#> GSM125140 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125142 1 0.1529 0.962 0.960 0.000 0.040
#> GSM125144 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125146 1 0.2625 0.958 0.916 0.000 0.084
#> GSM125148 1 0.1031 0.959 0.976 0.000 0.024
#> GSM125150 1 0.0892 0.959 0.980 0.000 0.020
#> GSM125152 1 0.2537 0.957 0.920 0.000 0.080
#> GSM125154 1 0.2537 0.959 0.920 0.000 0.080
#> GSM125156 1 0.1860 0.962 0.948 0.000 0.052
#> GSM125158 1 0.1289 0.962 0.968 0.000 0.032
#> GSM125160 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125162 1 0.1289 0.958 0.968 0.000 0.032
#> GSM125164 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125168 3 0.6111 0.535 0.000 0.396 0.604
#> GSM125170 2 0.6126 0.137 0.000 0.600 0.400
#> GSM125172 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125174 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125176 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125178 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125180 3 0.3192 0.960 0.000 0.112 0.888
#> GSM125182 3 0.6126 0.527 0.000 0.400 0.600
#> GSM125184 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125186 3 0.3192 0.960 0.000 0.112 0.888
#> GSM125188 3 0.3412 0.957 0.000 0.124 0.876
#> GSM125190 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125194 3 0.0237 0.858 0.000 0.004 0.996
#> GSM125196 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125198 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.961 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125204 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125206 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125208 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125210 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125212 3 0.3340 0.960 0.000 0.120 0.880
#> GSM125214 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125220 1 0.1411 0.958 0.964 0.000 0.036
#> GSM125222 3 0.3267 0.962 0.000 0.116 0.884
#> GSM125224 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.986 0.000 1.000 0.000
#> GSM125230 3 0.2796 0.944 0.000 0.092 0.908
#> GSM125232 3 0.1289 0.849 0.032 0.000 0.968
#> GSM125234 1 0.2878 0.955 0.904 0.000 0.096
#> GSM125236 1 0.2796 0.956 0.908 0.000 0.092
#> GSM125238 1 0.0892 0.959 0.980 0.000 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.4907 0.808 0.580 0.000 0.000 0.420
#> GSM125125 1 0.2408 0.840 0.896 0.000 0.000 0.104
#> GSM125127 1 0.4916 0.807 0.576 0.000 0.000 0.424
#> GSM125129 1 0.4907 0.808 0.580 0.000 0.000 0.420
#> GSM125131 1 0.1022 0.823 0.968 0.000 0.000 0.032
#> GSM125133 1 0.3032 0.803 0.868 0.000 0.008 0.124
#> GSM125135 1 0.4916 0.807 0.576 0.000 0.000 0.424
#> GSM125137 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> GSM125139 1 0.4746 0.827 0.688 0.000 0.008 0.304
#> GSM125141 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> GSM125143 1 0.4907 0.808 0.580 0.000 0.000 0.420
#> GSM125145 1 0.5498 0.809 0.576 0.000 0.020 0.404
#> GSM125147 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> GSM125149 1 0.0000 0.823 1.000 0.000 0.000 0.000
#> GSM125151 1 0.4792 0.826 0.680 0.000 0.008 0.312
#> GSM125153 1 0.4139 0.842 0.800 0.000 0.024 0.176
#> GSM125155 1 0.2053 0.838 0.924 0.000 0.004 0.072
#> GSM125157 1 0.0000 0.823 1.000 0.000 0.000 0.000
#> GSM125159 2 0.2973 0.855 0.000 0.856 0.000 0.144
#> GSM125161 1 0.2271 0.812 0.916 0.000 0.008 0.076
#> GSM125163 2 0.1302 0.900 0.000 0.956 0.000 0.044
#> GSM125165 4 0.6082 0.272 0.000 0.044 0.476 0.480
#> GSM125167 2 0.3726 0.818 0.000 0.788 0.000 0.212
#> GSM125169 2 0.4134 0.762 0.000 0.740 0.000 0.260
#> GSM125171 2 0.1118 0.900 0.000 0.964 0.000 0.036
#> GSM125173 3 0.5712 0.167 0.000 0.032 0.584 0.384
#> GSM125175 2 0.1211 0.899 0.000 0.960 0.000 0.040
#> GSM125177 3 0.1488 0.661 0.000 0.032 0.956 0.012
#> GSM125179 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125181 3 0.6077 -0.393 0.000 0.044 0.496 0.460
#> GSM125183 3 0.5432 0.391 0.000 0.032 0.652 0.316
#> GSM125185 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125187 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125189 2 0.3444 0.840 0.000 0.816 0.000 0.184
#> GSM125191 2 0.4222 0.673 0.000 0.728 0.000 0.272
#> GSM125193 3 0.1771 0.650 0.004 0.012 0.948 0.036
#> GSM125195 3 0.1837 0.658 0.000 0.028 0.944 0.028
#> GSM125197 2 0.0336 0.900 0.000 0.992 0.000 0.008
#> GSM125199 1 0.0000 0.823 1.000 0.000 0.000 0.000
#> GSM125201 2 0.0469 0.899 0.000 0.988 0.000 0.012
#> GSM125203 3 0.1610 0.661 0.000 0.032 0.952 0.016
#> GSM125205 2 0.0817 0.894 0.000 0.976 0.000 0.024
#> GSM125207 3 0.1356 0.662 0.000 0.032 0.960 0.008
#> GSM125209 2 0.5203 0.304 0.000 0.576 0.008 0.416
#> GSM125211 3 0.3581 0.579 0.000 0.032 0.852 0.116
#> GSM125213 2 0.1389 0.899 0.000 0.952 0.000 0.048
#> GSM125215 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> GSM125217 2 0.3726 0.820 0.000 0.788 0.000 0.212
#> GSM125219 1 0.5236 0.802 0.560 0.000 0.008 0.432
#> GSM125221 3 0.5827 -0.138 0.000 0.032 0.532 0.436
#> GSM125223 2 0.0707 0.901 0.000 0.980 0.000 0.020
#> GSM125225 2 0.0592 0.904 0.000 0.984 0.000 0.016
#> GSM125227 2 0.0592 0.902 0.000 0.984 0.000 0.016
#> GSM125229 3 0.3598 0.572 0.000 0.028 0.848 0.124
#> GSM125231 3 0.1637 0.615 0.000 0.000 0.940 0.060
#> GSM125233 1 0.4907 0.808 0.580 0.000 0.000 0.420
#> GSM125235 1 0.2714 0.815 0.884 0.000 0.004 0.112
#> GSM125237 1 0.0000 0.823 1.000 0.000 0.000 0.000
#> GSM125124 1 0.5206 0.823 0.668 0.000 0.024 0.308
#> GSM125126 1 0.1557 0.834 0.944 0.000 0.000 0.056
#> GSM125128 1 0.3088 0.803 0.864 0.000 0.008 0.128
#> GSM125130 1 0.5337 0.801 0.564 0.000 0.012 0.424
#> GSM125132 1 0.0000 0.823 1.000 0.000 0.000 0.000
#> GSM125134 1 0.5161 0.826 0.676 0.000 0.024 0.300
#> GSM125136 1 0.3032 0.803 0.868 0.000 0.008 0.124
#> GSM125138 1 0.5206 0.823 0.668 0.000 0.024 0.308
#> GSM125140 1 0.4722 0.829 0.692 0.000 0.008 0.300
#> GSM125142 1 0.3659 0.842 0.840 0.000 0.024 0.136
#> GSM125144 1 0.5206 0.823 0.668 0.000 0.024 0.308
#> GSM125146 1 0.5252 0.828 0.644 0.000 0.020 0.336
#> GSM125148 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> GSM125150 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> GSM125152 1 0.4792 0.826 0.680 0.000 0.008 0.312
#> GSM125154 1 0.4267 0.842 0.788 0.000 0.024 0.188
#> GSM125156 1 0.4401 0.835 0.724 0.000 0.004 0.272
#> GSM125158 1 0.4372 0.836 0.728 0.000 0.004 0.268
#> GSM125160 2 0.2704 0.868 0.000 0.876 0.000 0.124
#> GSM125162 1 0.2271 0.812 0.916 0.000 0.008 0.076
#> GSM125164 2 0.1302 0.900 0.000 0.956 0.000 0.044
#> GSM125166 2 0.1716 0.899 0.000 0.936 0.000 0.064
#> GSM125168 4 0.7323 0.734 0.000 0.164 0.352 0.484
#> GSM125170 4 0.7516 0.643 0.000 0.240 0.264 0.496
#> GSM125172 2 0.1118 0.900 0.000 0.964 0.000 0.036
#> GSM125174 3 0.5247 0.446 0.000 0.032 0.684 0.284
#> GSM125176 2 0.2888 0.868 0.000 0.872 0.004 0.124
#> GSM125178 3 0.1488 0.661 0.000 0.032 0.956 0.012
#> GSM125180 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125182 4 0.7480 0.721 0.000 0.180 0.376 0.444
#> GSM125184 3 0.5321 0.428 0.000 0.032 0.672 0.296
#> GSM125186 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125188 3 0.5850 -0.308 0.000 0.032 0.512 0.456
#> GSM125190 2 0.3726 0.818 0.000 0.788 0.000 0.212
#> GSM125192 2 0.0921 0.902 0.000 0.972 0.000 0.028
#> GSM125194 3 0.1209 0.643 0.004 0.000 0.964 0.032
#> GSM125196 3 0.1833 0.658 0.000 0.032 0.944 0.024
#> GSM125198 2 0.0336 0.900 0.000 0.992 0.000 0.008
#> GSM125200 1 0.3494 0.846 0.824 0.000 0.004 0.172
#> GSM125202 2 0.0469 0.899 0.000 0.988 0.000 0.012
#> GSM125204 3 0.1610 0.661 0.000 0.032 0.952 0.016
#> GSM125206 3 0.1488 0.659 0.000 0.032 0.956 0.012
#> GSM125208 3 0.1356 0.662 0.000 0.032 0.960 0.008
#> GSM125210 3 0.5222 0.458 0.000 0.032 0.688 0.280
#> GSM125212 3 0.3581 0.579 0.000 0.032 0.852 0.116
#> GSM125214 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.902 0.000 1.000 0.000 0.000
#> GSM125218 2 0.3726 0.820 0.000 0.788 0.000 0.212
#> GSM125220 1 0.3088 0.801 0.864 0.000 0.008 0.128
#> GSM125222 3 0.5800 -0.044 0.000 0.032 0.548 0.420
#> GSM125224 2 0.0592 0.902 0.000 0.984 0.000 0.016
#> GSM125226 2 0.3726 0.818 0.000 0.788 0.000 0.212
#> GSM125228 2 0.0592 0.902 0.000 0.984 0.000 0.016
#> GSM125230 3 0.1545 0.643 0.000 0.008 0.952 0.040
#> GSM125232 3 0.2081 0.579 0.000 0.000 0.916 0.084
#> GSM125234 1 0.6050 0.772 0.524 0.000 0.044 0.432
#> GSM125236 1 0.4916 0.807 0.576 0.000 0.000 0.424
#> GSM125238 1 0.0188 0.822 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.4679 0.6439 0.136 0.000 0.000 0.124 0.740
#> GSM125125 5 0.4917 -0.5719 0.416 0.000 0.000 0.028 0.556
#> GSM125127 5 0.4717 0.6450 0.144 0.000 0.000 0.120 0.736
#> GSM125129 5 0.4624 0.6436 0.144 0.000 0.000 0.112 0.744
#> GSM125131 1 0.4415 0.8154 0.604 0.000 0.000 0.008 0.388
#> GSM125133 1 0.4983 0.6511 0.664 0.000 0.000 0.064 0.272
#> GSM125135 5 0.4671 0.6460 0.144 0.000 0.000 0.116 0.740
#> GSM125137 1 0.4397 0.8344 0.564 0.000 0.000 0.004 0.432
#> GSM125139 5 0.0865 0.6283 0.024 0.000 0.000 0.004 0.972
#> GSM125141 1 0.4262 0.8304 0.560 0.000 0.000 0.000 0.440
#> GSM125143 5 0.4577 0.6447 0.144 0.000 0.000 0.108 0.748
#> GSM125145 5 0.5388 0.6333 0.152 0.000 0.004 0.164 0.680
#> GSM125147 1 0.4256 0.8335 0.564 0.000 0.000 0.000 0.436
#> GSM125149 1 0.4249 0.8353 0.568 0.000 0.000 0.000 0.432
#> GSM125151 5 0.0566 0.6403 0.004 0.000 0.000 0.012 0.984
#> GSM125153 5 0.4682 0.2419 0.212 0.000 0.004 0.060 0.724
#> GSM125155 5 0.4288 -0.4897 0.384 0.000 0.000 0.004 0.612
#> GSM125157 1 0.4256 0.8356 0.564 0.000 0.000 0.000 0.436
#> GSM125159 2 0.3910 0.7899 0.032 0.772 0.000 0.196 0.000
#> GSM125161 1 0.5080 0.7669 0.604 0.000 0.000 0.048 0.348
#> GSM125163 2 0.1740 0.8692 0.012 0.932 0.000 0.056 0.000
#> GSM125165 4 0.4993 0.6798 0.032 0.020 0.268 0.680 0.000
#> GSM125167 2 0.4924 0.7113 0.060 0.668 0.000 0.272 0.000
#> GSM125169 2 0.5302 0.6034 0.064 0.592 0.000 0.344 0.000
#> GSM125171 2 0.2775 0.8562 0.076 0.884 0.004 0.036 0.000
#> GSM125173 4 0.6016 0.4057 0.092 0.008 0.388 0.512 0.000
#> GSM125175 2 0.1893 0.8651 0.048 0.928 0.000 0.024 0.000
#> GSM125177 3 0.0613 0.6761 0.008 0.004 0.984 0.004 0.000
#> GSM125179 3 0.5934 -0.0941 0.068 0.008 0.496 0.424 0.004
#> GSM125181 4 0.5110 0.6643 0.032 0.016 0.308 0.644 0.000
#> GSM125183 4 0.5706 0.2000 0.060 0.008 0.448 0.484 0.000
#> GSM125185 3 0.5965 -0.0752 0.072 0.008 0.508 0.408 0.004
#> GSM125187 3 0.5965 -0.0752 0.072 0.008 0.508 0.408 0.004
#> GSM125189 2 0.4272 0.7824 0.052 0.752 0.000 0.196 0.000
#> GSM125191 2 0.5033 0.3487 0.024 0.524 0.004 0.448 0.000
#> GSM125193 3 0.1399 0.6708 0.020 0.000 0.952 0.028 0.000
#> GSM125195 3 0.1059 0.6748 0.020 0.004 0.968 0.008 0.000
#> GSM125197 2 0.0880 0.8641 0.032 0.968 0.000 0.000 0.000
#> GSM125199 1 0.4262 0.8333 0.560 0.000 0.000 0.000 0.440
#> GSM125201 2 0.1041 0.8647 0.032 0.964 0.000 0.004 0.000
#> GSM125203 3 0.0854 0.6762 0.012 0.004 0.976 0.008 0.000
#> GSM125205 2 0.1830 0.8469 0.052 0.932 0.012 0.004 0.000
#> GSM125207 3 0.0833 0.6756 0.004 0.004 0.976 0.016 0.000
#> GSM125209 4 0.5527 0.3197 0.032 0.312 0.036 0.620 0.000
#> GSM125211 3 0.3893 0.5652 0.052 0.004 0.804 0.140 0.000
#> GSM125213 2 0.2236 0.8615 0.024 0.908 0.000 0.068 0.000
#> GSM125215 2 0.0162 0.8702 0.004 0.996 0.000 0.000 0.000
#> GSM125217 2 0.4972 0.7214 0.068 0.672 0.000 0.260 0.000
#> GSM125219 5 0.5083 0.6161 0.160 0.000 0.000 0.140 0.700
#> GSM125221 4 0.5497 0.5942 0.056 0.012 0.328 0.604 0.000
#> GSM125223 2 0.0404 0.8687 0.012 0.988 0.000 0.000 0.000
#> GSM125225 2 0.0798 0.8732 0.016 0.976 0.000 0.008 0.000
#> GSM125227 2 0.0162 0.8704 0.004 0.996 0.000 0.000 0.000
#> GSM125229 3 0.3758 0.5656 0.052 0.004 0.816 0.128 0.000
#> GSM125231 3 0.3043 0.6150 0.020 0.000 0.872 0.088 0.020
#> GSM125233 5 0.4535 0.6462 0.140 0.000 0.000 0.108 0.752
#> GSM125235 1 0.4823 0.6694 0.644 0.000 0.000 0.040 0.316
#> GSM125237 1 0.4256 0.8356 0.564 0.000 0.000 0.000 0.436
#> GSM125124 5 0.1731 0.6250 0.004 0.000 0.004 0.060 0.932
#> GSM125126 1 0.4905 0.7522 0.500 0.000 0.000 0.024 0.476
#> GSM125128 1 0.5598 0.5600 0.612 0.000 0.000 0.112 0.276
#> GSM125130 5 0.4732 0.6435 0.144 0.000 0.004 0.108 0.744
#> GSM125132 1 0.4403 0.8353 0.560 0.000 0.000 0.004 0.436
#> GSM125134 5 0.2172 0.6134 0.020 0.000 0.004 0.060 0.916
#> GSM125136 1 0.4983 0.6518 0.664 0.000 0.000 0.064 0.272
#> GSM125138 5 0.1731 0.6250 0.004 0.000 0.004 0.060 0.932
#> GSM125140 5 0.1121 0.6113 0.044 0.000 0.000 0.000 0.956
#> GSM125142 5 0.4769 0.0520 0.256 0.000 0.000 0.056 0.688
#> GSM125144 5 0.1731 0.6250 0.004 0.000 0.004 0.060 0.932
#> GSM125146 5 0.5581 0.5769 0.192 0.000 0.004 0.148 0.656
#> GSM125148 1 0.4268 0.8264 0.556 0.000 0.000 0.000 0.444
#> GSM125150 1 0.4291 0.8014 0.536 0.000 0.000 0.000 0.464
#> GSM125152 5 0.0566 0.6403 0.004 0.000 0.000 0.012 0.984
#> GSM125154 5 0.4555 0.2809 0.196 0.000 0.004 0.060 0.740
#> GSM125156 5 0.2286 0.5386 0.108 0.000 0.000 0.004 0.888
#> GSM125158 5 0.2338 0.5316 0.112 0.000 0.000 0.004 0.884
#> GSM125160 2 0.3876 0.7929 0.032 0.776 0.000 0.192 0.000
#> GSM125162 1 0.5080 0.7669 0.604 0.000 0.000 0.048 0.348
#> GSM125164 2 0.1670 0.8693 0.012 0.936 0.000 0.052 0.000
#> GSM125166 2 0.2074 0.8659 0.036 0.920 0.000 0.044 0.000
#> GSM125168 4 0.5894 0.6527 0.044 0.084 0.212 0.660 0.000
#> GSM125170 4 0.6092 0.5795 0.048 0.160 0.132 0.660 0.000
#> GSM125172 2 0.2473 0.8610 0.072 0.896 0.000 0.032 0.000
#> GSM125174 3 0.6100 -0.1534 0.096 0.008 0.472 0.424 0.000
#> GSM125176 2 0.3779 0.8123 0.056 0.816 0.004 0.124 0.000
#> GSM125178 3 0.0854 0.6742 0.008 0.004 0.976 0.012 0.000
#> GSM125180 3 0.5934 -0.0941 0.068 0.008 0.496 0.424 0.004
#> GSM125182 4 0.5834 0.6644 0.032 0.076 0.252 0.640 0.000
#> GSM125184 3 0.5710 -0.2031 0.060 0.008 0.472 0.460 0.000
#> GSM125186 3 0.5965 -0.0752 0.072 0.008 0.508 0.408 0.004
#> GSM125188 4 0.4906 0.6499 0.028 0.008 0.324 0.640 0.000
#> GSM125190 2 0.4840 0.7303 0.064 0.688 0.000 0.248 0.000
#> GSM125192 2 0.0566 0.8720 0.004 0.984 0.000 0.012 0.000
#> GSM125194 3 0.1117 0.6734 0.016 0.000 0.964 0.020 0.000
#> GSM125196 3 0.1059 0.6748 0.020 0.004 0.968 0.008 0.000
#> GSM125198 2 0.0880 0.8641 0.032 0.968 0.000 0.000 0.000
#> GSM125200 5 0.3707 -0.0333 0.284 0.000 0.000 0.000 0.716
#> GSM125202 2 0.1041 0.8647 0.032 0.964 0.000 0.004 0.000
#> GSM125204 3 0.0854 0.6762 0.012 0.004 0.976 0.008 0.000
#> GSM125206 3 0.0932 0.6753 0.020 0.004 0.972 0.004 0.000
#> GSM125208 3 0.0833 0.6756 0.004 0.004 0.976 0.016 0.000
#> GSM125210 3 0.5979 -0.1159 0.072 0.008 0.496 0.420 0.004
#> GSM125212 3 0.3936 0.5598 0.052 0.004 0.800 0.144 0.000
#> GSM125214 2 0.0324 0.8710 0.004 0.992 0.000 0.004 0.000
#> GSM125216 2 0.0162 0.8702 0.004 0.996 0.000 0.000 0.000
#> GSM125218 2 0.4923 0.7253 0.068 0.680 0.000 0.252 0.000
#> GSM125220 1 0.5265 0.6089 0.636 0.000 0.000 0.080 0.284
#> GSM125222 4 0.5540 0.5746 0.056 0.012 0.340 0.592 0.000
#> GSM125224 2 0.0404 0.8687 0.012 0.988 0.000 0.000 0.000
#> GSM125226 2 0.4914 0.7186 0.064 0.676 0.000 0.260 0.000
#> GSM125228 2 0.0000 0.8706 0.000 1.000 0.000 0.000 0.000
#> GSM125230 3 0.2228 0.6496 0.040 0.000 0.912 0.048 0.000
#> GSM125232 3 0.5622 0.4993 0.076 0.000 0.712 0.136 0.076
#> GSM125234 5 0.5315 0.6189 0.152 0.000 0.024 0.108 0.716
#> GSM125236 5 0.4720 0.6411 0.140 0.000 0.000 0.124 0.736
#> GSM125238 1 0.4249 0.8353 0.568 0.000 0.000 0.000 0.432
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.5316 0.6159 0.600 0.000 0.000 0.012 0.104 NA
#> GSM125125 5 0.5102 0.5940 0.428 0.000 0.000 0.012 0.508 NA
#> GSM125127 1 0.5008 0.6193 0.612 0.000 0.000 0.000 0.108 NA
#> GSM125129 1 0.5042 0.6181 0.604 0.000 0.000 0.000 0.108 NA
#> GSM125131 5 0.3468 0.8183 0.284 0.000 0.000 0.000 0.712 NA
#> GSM125133 5 0.5223 0.6643 0.188 0.000 0.000 0.044 0.676 NA
#> GSM125135 1 0.5011 0.6219 0.616 0.000 0.000 0.000 0.112 NA
#> GSM125137 5 0.3835 0.8261 0.320 0.000 0.000 0.012 0.668 NA
#> GSM125139 1 0.1718 0.6010 0.932 0.000 0.000 0.008 0.044 NA
#> GSM125141 5 0.3668 0.8218 0.328 0.000 0.000 0.004 0.668 NA
#> GSM125143 1 0.5042 0.6176 0.604 0.000 0.000 0.000 0.108 NA
#> GSM125145 1 0.5231 0.6108 0.612 0.000 0.000 0.008 0.112 NA
#> GSM125147 5 0.3684 0.8215 0.332 0.000 0.000 0.004 0.664 NA
#> GSM125149 5 0.3482 0.8271 0.316 0.000 0.000 0.000 0.684 NA
#> GSM125151 1 0.1821 0.6194 0.928 0.000 0.000 0.008 0.024 NA
#> GSM125153 1 0.4205 0.3504 0.728 0.000 0.000 0.004 0.204 NA
#> GSM125155 1 0.3672 -0.2273 0.632 0.000 0.000 0.000 0.368 NA
#> GSM125157 5 0.3619 0.8273 0.316 0.000 0.000 0.000 0.680 NA
#> GSM125159 2 0.5015 0.7259 0.000 0.692 0.000 0.140 0.024 NA
#> GSM125161 5 0.5064 0.7425 0.232 0.000 0.000 0.044 0.668 NA
#> GSM125163 2 0.2895 0.8161 0.000 0.868 0.000 0.052 0.016 NA
#> GSM125165 4 0.5563 0.6240 0.000 0.008 0.124 0.632 0.020 NA
#> GSM125167 2 0.5917 0.5809 0.000 0.508 0.000 0.168 0.012 NA
#> GSM125169 2 0.6110 0.4399 0.000 0.416 0.000 0.236 0.004 NA
#> GSM125171 2 0.3578 0.7946 0.000 0.796 0.000 0.016 0.028 NA
#> GSM125173 4 0.6274 0.5950 0.000 0.004 0.200 0.564 0.048 NA
#> GSM125175 2 0.2810 0.8048 0.000 0.832 0.000 0.004 0.008 NA
#> GSM125177 3 0.0622 0.8531 0.000 0.000 0.980 0.012 0.000 NA
#> GSM125179 4 0.3756 0.6112 0.000 0.004 0.316 0.676 0.000 NA
#> GSM125181 4 0.5884 0.6115 0.000 0.012 0.140 0.620 0.032 NA
#> GSM125183 4 0.4244 0.6248 0.000 0.004 0.280 0.684 0.004 NA
#> GSM125185 4 0.3861 0.6112 0.000 0.004 0.316 0.672 0.000 NA
#> GSM125187 4 0.3741 0.6046 0.000 0.000 0.320 0.672 0.000 NA
#> GSM125189 2 0.4954 0.7078 0.000 0.628 0.000 0.112 0.000 NA
#> GSM125191 4 0.6153 -0.1448 0.000 0.408 0.000 0.420 0.024 NA
#> GSM125193 3 0.2521 0.8355 0.000 0.000 0.892 0.056 0.020 NA
#> GSM125195 3 0.2045 0.8415 0.000 0.000 0.920 0.024 0.028 NA
#> GSM125197 2 0.1858 0.8018 0.000 0.924 0.000 0.012 0.012 NA
#> GSM125199 5 0.3684 0.8228 0.332 0.000 0.000 0.000 0.664 NA
#> GSM125201 2 0.2247 0.7998 0.000 0.904 0.000 0.012 0.024 NA
#> GSM125203 3 0.1353 0.8486 0.000 0.000 0.952 0.024 0.012 NA
#> GSM125205 2 0.3115 0.7781 0.000 0.864 0.016 0.016 0.032 NA
#> GSM125207 3 0.1296 0.8495 0.000 0.000 0.952 0.032 0.004 NA
#> GSM125209 4 0.5907 0.4497 0.000 0.192 0.004 0.600 0.032 NA
#> GSM125211 3 0.4930 0.7133 0.000 0.000 0.728 0.088 0.084 NA
#> GSM125213 2 0.3515 0.8008 0.000 0.828 0.000 0.064 0.024 NA
#> GSM125215 2 0.1297 0.8218 0.000 0.948 0.000 0.000 0.012 NA
#> GSM125217 2 0.5720 0.6359 0.000 0.548 0.000 0.148 0.012 NA
#> GSM125219 1 0.5479 0.5900 0.552 0.000 0.000 0.008 0.116 NA
#> GSM125221 4 0.4464 0.6589 0.000 0.004 0.172 0.732 0.008 NA
#> GSM125223 2 0.1082 0.8206 0.000 0.956 0.000 0.000 0.004 NA
#> GSM125225 2 0.1625 0.8235 0.000 0.928 0.000 0.000 0.012 NA
#> GSM125227 2 0.1010 0.8211 0.000 0.960 0.000 0.000 0.004 NA
#> GSM125229 3 0.4786 0.7247 0.000 0.000 0.740 0.096 0.088 NA
#> GSM125231 3 0.3878 0.7631 0.056 0.000 0.824 0.036 0.020 NA
#> GSM125233 1 0.5241 0.6169 0.600 0.000 0.000 0.008 0.104 NA
#> GSM125235 5 0.4760 0.7081 0.232 0.000 0.000 0.020 0.684 NA
#> GSM125237 5 0.3515 0.8268 0.324 0.000 0.000 0.000 0.676 NA
#> GSM125124 1 0.1615 0.6023 0.928 0.000 0.000 0.004 0.004 NA
#> GSM125126 5 0.4468 0.7723 0.364 0.000 0.000 0.008 0.604 NA
#> GSM125128 5 0.6239 0.4647 0.188 0.000 0.000 0.044 0.544 NA
#> GSM125130 1 0.5160 0.6179 0.604 0.000 0.004 0.000 0.108 NA
#> GSM125132 5 0.3668 0.8246 0.328 0.000 0.000 0.000 0.668 NA
#> GSM125134 1 0.1745 0.5980 0.920 0.000 0.000 0.000 0.012 NA
#> GSM125136 5 0.5223 0.6643 0.188 0.000 0.000 0.044 0.676 NA
#> GSM125138 1 0.1728 0.6005 0.924 0.000 0.000 0.004 0.008 NA
#> GSM125140 1 0.2013 0.5748 0.908 0.000 0.000 0.008 0.076 NA
#> GSM125142 1 0.4117 0.3504 0.740 0.000 0.000 0.004 0.192 NA
#> GSM125144 1 0.1615 0.6023 0.928 0.000 0.000 0.004 0.004 NA
#> GSM125146 1 0.5194 0.5896 0.632 0.000 0.000 0.008 0.128 NA
#> GSM125148 5 0.3864 0.8088 0.344 0.000 0.000 0.004 0.648 NA
#> GSM125150 5 0.3672 0.7876 0.368 0.000 0.000 0.000 0.632 NA
#> GSM125152 1 0.1821 0.6194 0.928 0.000 0.000 0.008 0.024 NA
#> GSM125154 1 0.3959 0.3964 0.760 0.000 0.000 0.004 0.172 NA
#> GSM125156 1 0.2597 0.4555 0.824 0.000 0.000 0.000 0.176 NA
#> GSM125158 1 0.2772 0.4517 0.816 0.000 0.000 0.000 0.180 NA
#> GSM125160 2 0.4713 0.7460 0.000 0.720 0.000 0.120 0.020 NA
#> GSM125162 5 0.5064 0.7425 0.232 0.000 0.000 0.044 0.668 NA
#> GSM125164 2 0.2895 0.8161 0.000 0.868 0.000 0.052 0.016 NA
#> GSM125166 2 0.3275 0.8103 0.000 0.828 0.000 0.044 0.008 NA
#> GSM125168 4 0.6057 0.5886 0.000 0.048 0.080 0.580 0.016 NA
#> GSM125170 4 0.5838 0.5625 0.000 0.060 0.060 0.564 0.004 NA
#> GSM125172 2 0.3542 0.7974 0.000 0.800 0.000 0.016 0.028 NA
#> GSM125174 4 0.5219 0.5867 0.000 0.004 0.268 0.636 0.024 NA
#> GSM125176 2 0.5102 0.7194 0.000 0.668 0.004 0.140 0.008 NA
#> GSM125178 3 0.0717 0.8523 0.000 0.000 0.976 0.016 0.000 NA
#> GSM125180 4 0.3756 0.6112 0.000 0.004 0.316 0.676 0.000 NA
#> GSM125182 4 0.6393 0.5996 0.000 0.060 0.112 0.604 0.032 NA
#> GSM125184 4 0.4040 0.6167 0.000 0.004 0.304 0.676 0.004 NA
#> GSM125186 4 0.3861 0.6112 0.000 0.004 0.316 0.672 0.000 NA
#> GSM125188 4 0.5688 0.6184 0.000 0.008 0.144 0.640 0.032 NA
#> GSM125190 2 0.5563 0.6250 0.000 0.528 0.000 0.136 0.004 NA
#> GSM125192 2 0.1562 0.8236 0.000 0.940 0.000 0.024 0.004 NA
#> GSM125194 3 0.2328 0.8397 0.000 0.000 0.904 0.044 0.020 NA
#> GSM125196 3 0.2045 0.8415 0.000 0.000 0.920 0.024 0.028 NA
#> GSM125198 2 0.1858 0.8018 0.000 0.924 0.000 0.012 0.012 NA
#> GSM125200 1 0.3864 -0.0176 0.648 0.000 0.000 0.004 0.344 NA
#> GSM125202 2 0.2247 0.7998 0.000 0.904 0.000 0.012 0.024 NA
#> GSM125204 3 0.1353 0.8486 0.000 0.000 0.952 0.024 0.012 NA
#> GSM125206 3 0.1794 0.8452 0.000 0.000 0.932 0.016 0.028 NA
#> GSM125208 3 0.1296 0.8495 0.000 0.000 0.952 0.032 0.004 NA
#> GSM125210 4 0.3844 0.6149 0.000 0.004 0.312 0.676 0.000 NA
#> GSM125212 3 0.4930 0.7133 0.000 0.000 0.728 0.088 0.084 NA
#> GSM125214 2 0.0984 0.8194 0.000 0.968 0.000 0.008 0.012 NA
#> GSM125216 2 0.1225 0.8214 0.000 0.952 0.000 0.000 0.012 NA
#> GSM125218 2 0.5688 0.6410 0.000 0.548 0.000 0.140 0.012 NA
#> GSM125220 5 0.5873 0.5796 0.188 0.000 0.000 0.048 0.608 NA
#> GSM125222 4 0.4479 0.6587 0.000 0.004 0.180 0.728 0.008 NA
#> GSM125224 2 0.1082 0.8206 0.000 0.956 0.000 0.000 0.004 NA
#> GSM125226 2 0.5771 0.6126 0.000 0.520 0.000 0.140 0.012 NA
#> GSM125228 2 0.0865 0.8208 0.000 0.964 0.000 0.000 0.000 NA
#> GSM125230 3 0.3508 0.7985 0.000 0.000 0.832 0.036 0.080 NA
#> GSM125232 3 0.7200 0.2405 0.200 0.000 0.464 0.244 0.020 NA
#> GSM125234 1 0.5798 0.6001 0.576 0.000 0.012 0.020 0.100 NA
#> GSM125236 1 0.5241 0.6155 0.600 0.000 0.000 0.008 0.104 NA
#> GSM125238 5 0.3531 0.8225 0.328 0.000 0.000 0.000 0.672 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> MAD:kmeans 116 1.000 1.12e-05 2
#> MAD:kmeans 115 0.826 2.49e-08 3
#> MAD:kmeans 100 0.370 7.98e-08 4
#> MAD:kmeans 97 0.924 7.87e-08 5
#> MAD:kmeans 104 0.912 2.93e-10 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.992 0.997 0.5011 0.499 0.499
#> 3 3 0.922 0.967 0.982 0.2836 0.829 0.667
#> 4 4 0.954 0.931 0.950 0.0796 0.941 0.837
#> 5 5 0.799 0.769 0.824 0.0881 0.917 0.737
#> 6 6 0.732 0.659 0.808 0.0508 0.941 0.766
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
#> GSM125123 1 0.0000 0.997 1.000 0.000
#> GSM125125 1 0.0000 0.997 1.000 0.000
#> GSM125127 1 0.0000 0.997 1.000 0.000
#> GSM125129 1 0.0000 0.997 1.000 0.000
#> GSM125131 1 0.0000 0.997 1.000 0.000
#> GSM125133 1 0.0000 0.997 1.000 0.000
#> GSM125135 1 0.0000 0.997 1.000 0.000
#> GSM125137 1 0.0000 0.997 1.000 0.000
#> GSM125139 1 0.0000 0.997 1.000 0.000
#> GSM125141 1 0.0000 0.997 1.000 0.000
#> GSM125143 1 0.0000 0.997 1.000 0.000
#> GSM125145 1 0.0000 0.997 1.000 0.000
#> GSM125147 1 0.0000 0.997 1.000 0.000
#> GSM125149 1 0.0000 0.997 1.000 0.000
#> GSM125151 1 0.0000 0.997 1.000 0.000
#> GSM125153 1 0.0000 0.997 1.000 0.000
#> GSM125155 1 0.0000 0.997 1.000 0.000
#> GSM125157 1 0.0000 0.997 1.000 0.000
#> GSM125159 2 0.0000 0.996 0.000 1.000
#> GSM125161 1 0.0000 0.997 1.000 0.000
#> GSM125163 2 0.0000 0.996 0.000 1.000
#> GSM125165 2 0.0000 0.996 0.000 1.000
#> GSM125167 2 0.0000 0.996 0.000 1.000
#> GSM125169 2 0.0000 0.996 0.000 1.000
#> GSM125171 2 0.0000 0.996 0.000 1.000
#> GSM125173 2 0.0000 0.996 0.000 1.000
#> GSM125175 2 0.0000 0.996 0.000 1.000
#> GSM125177 2 0.0000 0.996 0.000 1.000
#> GSM125179 2 0.0672 0.989 0.008 0.992
#> GSM125181 2 0.0000 0.996 0.000 1.000
#> GSM125183 2 0.0000 0.996 0.000 1.000
#> GSM125185 2 0.0000 0.996 0.000 1.000
#> GSM125187 2 0.7299 0.744 0.204 0.796
#> GSM125189 2 0.0000 0.996 0.000 1.000
#> GSM125191 2 0.0000 0.996 0.000 1.000
#> GSM125193 1 0.5178 0.870 0.884 0.116
#> GSM125195 2 0.0000 0.996 0.000 1.000
#> GSM125197 2 0.0000 0.996 0.000 1.000
#> GSM125199 1 0.0000 0.997 1.000 0.000
#> GSM125201 2 0.0000 0.996 0.000 1.000
#> GSM125203 2 0.0000 0.996 0.000 1.000
#> GSM125205 2 0.0000 0.996 0.000 1.000
#> GSM125207 2 0.0000 0.996 0.000 1.000
#> GSM125209 2 0.0000 0.996 0.000 1.000
#> GSM125211 2 0.0000 0.996 0.000 1.000
#> GSM125213 2 0.0000 0.996 0.000 1.000
#> GSM125215 2 0.0000 0.996 0.000 1.000
#> GSM125217 2 0.0000 0.996 0.000 1.000
#> GSM125219 1 0.0000 0.997 1.000 0.000
#> GSM125221 2 0.0000 0.996 0.000 1.000
#> GSM125223 2 0.0000 0.996 0.000 1.000
#> GSM125225 2 0.0000 0.996 0.000 1.000
#> GSM125227 2 0.0000 0.996 0.000 1.000
#> GSM125229 2 0.0000 0.996 0.000 1.000
#> GSM125231 1 0.0000 0.997 1.000 0.000
#> GSM125233 1 0.0000 0.997 1.000 0.000
#> GSM125235 1 0.0000 0.997 1.000 0.000
#> GSM125237 1 0.0000 0.997 1.000 0.000
#> GSM125124 1 0.0000 0.997 1.000 0.000
#> GSM125126 1 0.0000 0.997 1.000 0.000
#> GSM125128 1 0.0000 0.997 1.000 0.000
#> GSM125130 1 0.0000 0.997 1.000 0.000
#> GSM125132 1 0.0000 0.997 1.000 0.000
#> GSM125134 1 0.0000 0.997 1.000 0.000
#> GSM125136 1 0.0000 0.997 1.000 0.000
#> GSM125138 1 0.0000 0.997 1.000 0.000
#> GSM125140 1 0.0000 0.997 1.000 0.000
#> GSM125142 1 0.0000 0.997 1.000 0.000
#> GSM125144 1 0.0000 0.997 1.000 0.000
#> GSM125146 1 0.0000 0.997 1.000 0.000
#> GSM125148 1 0.0000 0.997 1.000 0.000
#> GSM125150 1 0.0000 0.997 1.000 0.000
#> GSM125152 1 0.0000 0.997 1.000 0.000
#> GSM125154 1 0.0000 0.997 1.000 0.000
#> GSM125156 1 0.0000 0.997 1.000 0.000
#> GSM125158 1 0.0000 0.997 1.000 0.000
#> GSM125160 2 0.0000 0.996 0.000 1.000
#> GSM125162 1 0.0000 0.997 1.000 0.000
#> GSM125164 2 0.0000 0.996 0.000 1.000
#> GSM125166 2 0.0000 0.996 0.000 1.000
#> GSM125168 2 0.0000 0.996 0.000 1.000
#> GSM125170 2 0.0000 0.996 0.000 1.000
#> GSM125172 2 0.0000 0.996 0.000 1.000
#> GSM125174 2 0.0000 0.996 0.000 1.000
#> GSM125176 2 0.0000 0.996 0.000 1.000
#> GSM125178 2 0.0000 0.996 0.000 1.000
#> GSM125180 2 0.0672 0.989 0.008 0.992
#> GSM125182 2 0.0000 0.996 0.000 1.000
#> GSM125184 2 0.0000 0.996 0.000 1.000
#> GSM125186 2 0.0376 0.993 0.004 0.996
#> GSM125188 2 0.0000 0.996 0.000 1.000
#> GSM125190 2 0.0000 0.996 0.000 1.000
#> GSM125192 2 0.0000 0.996 0.000 1.000
#> GSM125194 1 0.0000 0.997 1.000 0.000
#> GSM125196 2 0.0000 0.996 0.000 1.000
#> GSM125198 2 0.0000 0.996 0.000 1.000
#> GSM125200 1 0.0000 0.997 1.000 0.000
#> GSM125202 2 0.0000 0.996 0.000 1.000
#> GSM125204 2 0.0000 0.996 0.000 1.000
#> GSM125206 2 0.0000 0.996 0.000 1.000
#> GSM125208 2 0.0000 0.996 0.000 1.000
#> GSM125210 2 0.0000 0.996 0.000 1.000
#> GSM125212 2 0.0000 0.996 0.000 1.000
#> GSM125214 2 0.0000 0.996 0.000 1.000
#> GSM125216 2 0.0000 0.996 0.000 1.000
#> GSM125218 2 0.0000 0.996 0.000 1.000
#> GSM125220 1 0.0000 0.997 1.000 0.000
#> GSM125222 2 0.0000 0.996 0.000 1.000
#> GSM125224 2 0.0000 0.996 0.000 1.000
#> GSM125226 2 0.0000 0.996 0.000 1.000
#> GSM125228 2 0.0000 0.996 0.000 1.000
#> GSM125230 1 0.3431 0.932 0.936 0.064
#> GSM125232 1 0.0000 0.997 1.000 0.000
#> GSM125234 1 0.0000 0.997 1.000 0.000
#> GSM125236 1 0.0000 0.997 1.000 0.000
#> GSM125238 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125125 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125127 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125129 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125131 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125133 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125135 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125137 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125139 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125141 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125143 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125145 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125147 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125149 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125151 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125153 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125155 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125157 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125161 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125165 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125167 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125173 2 0.0237 0.975 0.000 0.996 0.004
#> GSM125175 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125177 3 0.0237 0.942 0.000 0.004 0.996
#> GSM125179 3 0.3038 0.918 0.000 0.104 0.896
#> GSM125181 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125183 3 0.3267 0.909 0.000 0.116 0.884
#> GSM125185 3 0.3038 0.918 0.000 0.104 0.896
#> GSM125187 3 0.3193 0.920 0.004 0.100 0.896
#> GSM125189 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125191 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125193 3 0.3528 0.878 0.092 0.016 0.892
#> GSM125195 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125197 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125199 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125203 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125205 2 0.1529 0.942 0.000 0.960 0.040
#> GSM125207 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125209 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125211 2 0.5968 0.465 0.000 0.636 0.364
#> GSM125213 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125219 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125221 2 0.0747 0.965 0.000 0.984 0.016
#> GSM125223 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125229 2 0.3116 0.871 0.000 0.892 0.108
#> GSM125231 3 0.0892 0.935 0.020 0.000 0.980
#> GSM125233 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125235 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125237 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125124 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125126 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125128 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125130 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125132 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125134 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125136 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125138 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125140 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125142 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125144 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125146 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125148 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125150 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125152 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125154 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125156 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125158 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125162 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125168 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125170 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125172 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125174 3 0.3038 0.918 0.000 0.104 0.896
#> GSM125176 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125178 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125180 3 0.3038 0.918 0.000 0.104 0.896
#> GSM125182 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125184 3 0.3267 0.909 0.000 0.116 0.884
#> GSM125186 3 0.3038 0.918 0.000 0.104 0.896
#> GSM125188 2 0.0592 0.968 0.000 0.988 0.012
#> GSM125190 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125194 3 0.2796 0.882 0.092 0.000 0.908
#> GSM125196 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125198 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125200 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125204 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125206 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125208 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125210 3 0.3116 0.915 0.000 0.108 0.892
#> GSM125212 2 0.5254 0.669 0.000 0.736 0.264
#> GSM125214 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125220 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125222 2 0.3686 0.826 0.000 0.860 0.140
#> GSM125224 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.978 0.000 1.000 0.000
#> GSM125230 3 0.0000 0.943 0.000 0.000 1.000
#> GSM125232 3 0.0237 0.942 0.004 0.000 0.996
#> GSM125234 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125236 1 0.0000 1.000 1.000 0.000 0.000
#> GSM125238 1 0.0000 1.000 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125125 1 0.1209 0.969 0.964 0.000 0.032 0.004
#> GSM125127 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125129 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125131 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125133 1 0.2198 0.960 0.920 0.000 0.072 0.008
#> GSM125135 1 0.0188 0.968 0.996 0.000 0.004 0.000
#> GSM125137 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125139 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125141 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125143 1 0.0469 0.967 0.988 0.000 0.012 0.000
#> GSM125145 1 0.0336 0.967 0.992 0.000 0.008 0.000
#> GSM125147 1 0.1970 0.962 0.932 0.000 0.060 0.008
#> GSM125149 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125151 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125153 1 0.0779 0.970 0.980 0.000 0.016 0.004
#> GSM125155 1 0.1151 0.969 0.968 0.000 0.024 0.008
#> GSM125157 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125159 2 0.0188 0.957 0.000 0.996 0.000 0.004
#> GSM125161 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125163 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125165 2 0.4331 0.644 0.000 0.712 0.000 0.288
#> GSM125167 2 0.0188 0.957 0.000 0.996 0.000 0.004
#> GSM125169 2 0.0188 0.957 0.000 0.996 0.000 0.004
#> GSM125171 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125173 2 0.3718 0.802 0.000 0.820 0.012 0.168
#> GSM125175 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125177 3 0.2334 0.933 0.000 0.004 0.908 0.088
#> GSM125179 4 0.1305 0.922 0.000 0.004 0.036 0.960
#> GSM125181 2 0.4482 0.674 0.000 0.728 0.008 0.264
#> GSM125183 4 0.0937 0.911 0.000 0.012 0.012 0.976
#> GSM125185 4 0.1305 0.922 0.000 0.004 0.036 0.960
#> GSM125187 4 0.1118 0.920 0.000 0.000 0.036 0.964
#> GSM125189 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125191 2 0.0921 0.942 0.000 0.972 0.000 0.028
#> GSM125193 3 0.1584 0.864 0.012 0.000 0.952 0.036
#> GSM125195 3 0.2281 0.932 0.000 0.000 0.904 0.096
#> GSM125197 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125199 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125201 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125203 3 0.2216 0.933 0.000 0.000 0.908 0.092
#> GSM125205 2 0.0592 0.948 0.000 0.984 0.016 0.000
#> GSM125207 3 0.2281 0.931 0.000 0.000 0.904 0.096
#> GSM125209 2 0.3528 0.782 0.000 0.808 0.000 0.192
#> GSM125211 3 0.3239 0.862 0.000 0.068 0.880 0.052
#> GSM125213 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125215 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125217 2 0.0376 0.956 0.000 0.992 0.004 0.004
#> GSM125219 1 0.1004 0.968 0.972 0.000 0.024 0.004
#> GSM125221 4 0.3495 0.772 0.000 0.140 0.016 0.844
#> GSM125223 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125229 3 0.3450 0.756 0.000 0.156 0.836 0.008
#> GSM125231 3 0.2871 0.909 0.032 0.000 0.896 0.072
#> GSM125233 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125235 1 0.2124 0.962 0.924 0.000 0.068 0.008
#> GSM125237 1 0.1970 0.962 0.932 0.000 0.060 0.008
#> GSM125124 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125126 1 0.1722 0.967 0.944 0.000 0.048 0.008
#> GSM125128 1 0.2198 0.960 0.920 0.000 0.072 0.008
#> GSM125130 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125132 1 0.1807 0.965 0.940 0.000 0.052 0.008
#> GSM125134 1 0.0188 0.968 0.996 0.000 0.004 0.000
#> GSM125136 1 0.2124 0.961 0.924 0.000 0.068 0.008
#> GSM125138 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125140 1 0.0188 0.968 0.996 0.000 0.004 0.000
#> GSM125142 1 0.1109 0.969 0.968 0.000 0.028 0.004
#> GSM125144 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125146 1 0.0336 0.967 0.992 0.000 0.008 0.000
#> GSM125148 1 0.1890 0.964 0.936 0.000 0.056 0.008
#> GSM125150 1 0.1545 0.967 0.952 0.000 0.040 0.008
#> GSM125152 1 0.0376 0.967 0.992 0.000 0.004 0.004
#> GSM125154 1 0.0376 0.969 0.992 0.000 0.004 0.004
#> GSM125156 1 0.0000 0.968 1.000 0.000 0.000 0.000
#> GSM125158 1 0.0188 0.969 0.996 0.000 0.004 0.000
#> GSM125160 2 0.0188 0.957 0.000 0.996 0.000 0.004
#> GSM125162 1 0.2048 0.961 0.928 0.000 0.064 0.008
#> GSM125164 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125168 2 0.1637 0.920 0.000 0.940 0.000 0.060
#> GSM125170 2 0.2216 0.894 0.000 0.908 0.000 0.092
#> GSM125172 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125174 4 0.1356 0.917 0.000 0.008 0.032 0.960
#> GSM125176 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125178 3 0.2081 0.932 0.000 0.000 0.916 0.084
#> GSM125180 4 0.1305 0.922 0.000 0.004 0.036 0.960
#> GSM125182 2 0.1940 0.907 0.000 0.924 0.000 0.076
#> GSM125184 4 0.1174 0.915 0.000 0.012 0.020 0.968
#> GSM125186 4 0.1305 0.922 0.000 0.004 0.036 0.960
#> GSM125188 2 0.5233 0.528 0.000 0.648 0.020 0.332
#> GSM125190 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125194 3 0.1854 0.878 0.012 0.000 0.940 0.048
#> GSM125196 3 0.2281 0.932 0.000 0.000 0.904 0.096
#> GSM125198 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0524 0.970 0.988 0.000 0.008 0.004
#> GSM125202 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125204 3 0.2216 0.933 0.000 0.000 0.908 0.092
#> GSM125206 3 0.2281 0.932 0.000 0.000 0.904 0.096
#> GSM125208 3 0.2216 0.932 0.000 0.000 0.908 0.092
#> GSM125210 4 0.1545 0.922 0.000 0.008 0.040 0.952
#> GSM125212 3 0.3399 0.833 0.000 0.092 0.868 0.040
#> GSM125214 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0376 0.956 0.000 0.992 0.004 0.004
#> GSM125220 1 0.2124 0.961 0.924 0.000 0.068 0.008
#> GSM125222 4 0.3166 0.805 0.000 0.116 0.016 0.868
#> GSM125224 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125226 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125228 2 0.0000 0.959 0.000 1.000 0.000 0.000
#> GSM125230 3 0.2281 0.923 0.000 0.000 0.904 0.096
#> GSM125232 4 0.4678 0.661 0.024 0.000 0.232 0.744
#> GSM125234 1 0.1297 0.954 0.964 0.000 0.016 0.020
#> GSM125236 1 0.0657 0.965 0.984 0.000 0.012 0.004
#> GSM125238 1 0.1970 0.962 0.932 0.000 0.060 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.2127 0.7085 0.108 0.000 0.000 0.000 0.892
#> GSM125125 5 0.4182 -0.2932 0.400 0.000 0.000 0.000 0.600
#> GSM125127 5 0.1732 0.6932 0.080 0.000 0.000 0.000 0.920
#> GSM125129 5 0.2179 0.7070 0.112 0.000 0.000 0.000 0.888
#> GSM125131 1 0.4201 0.9003 0.592 0.000 0.000 0.000 0.408
#> GSM125133 1 0.3983 0.8664 0.660 0.000 0.000 0.000 0.340
#> GSM125135 5 0.1908 0.7184 0.092 0.000 0.000 0.000 0.908
#> GSM125137 1 0.4171 0.9038 0.604 0.000 0.000 0.000 0.396
#> GSM125139 5 0.1965 0.7028 0.096 0.000 0.000 0.000 0.904
#> GSM125141 1 0.4192 0.9022 0.596 0.000 0.000 0.000 0.404
#> GSM125143 5 0.2732 0.6935 0.160 0.000 0.000 0.000 0.840
#> GSM125145 5 0.1908 0.7060 0.092 0.000 0.000 0.000 0.908
#> GSM125147 1 0.4201 0.8988 0.592 0.000 0.000 0.000 0.408
#> GSM125149 1 0.4138 0.9108 0.616 0.000 0.000 0.000 0.384
#> GSM125151 5 0.1341 0.7197 0.056 0.000 0.000 0.000 0.944
#> GSM125153 5 0.4047 0.2013 0.320 0.000 0.004 0.000 0.676
#> GSM125155 5 0.4227 -0.3800 0.420 0.000 0.000 0.000 0.580
#> GSM125157 1 0.4171 0.9121 0.604 0.000 0.000 0.000 0.396
#> GSM125159 2 0.0794 0.9155 0.028 0.972 0.000 0.000 0.000
#> GSM125161 1 0.4060 0.8950 0.640 0.000 0.000 0.000 0.360
#> GSM125163 2 0.0404 0.9178 0.012 0.988 0.000 0.000 0.000
#> GSM125165 2 0.6071 0.4669 0.140 0.568 0.004 0.288 0.000
#> GSM125167 2 0.1608 0.8988 0.072 0.928 0.000 0.000 0.000
#> GSM125169 2 0.1608 0.8985 0.072 0.928 0.000 0.000 0.000
#> GSM125171 2 0.0290 0.9175 0.008 0.992 0.000 0.000 0.000
#> GSM125173 2 0.5604 0.6537 0.116 0.680 0.020 0.184 0.000
#> GSM125175 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125177 3 0.0162 0.9114 0.000 0.000 0.996 0.004 0.000
#> GSM125179 4 0.0162 0.9234 0.000 0.000 0.000 0.996 0.004
#> GSM125181 2 0.6191 0.3770 0.164 0.528 0.000 0.308 0.000
#> GSM125183 4 0.1557 0.9140 0.052 0.000 0.008 0.940 0.000
#> GSM125185 4 0.0671 0.9233 0.016 0.000 0.000 0.980 0.004
#> GSM125187 4 0.1205 0.9186 0.040 0.000 0.000 0.956 0.004
#> GSM125189 2 0.0794 0.9144 0.028 0.972 0.000 0.000 0.000
#> GSM125191 2 0.2067 0.8868 0.032 0.920 0.000 0.048 0.000
#> GSM125193 3 0.3210 0.8461 0.212 0.000 0.788 0.000 0.000
#> GSM125195 3 0.2110 0.9058 0.072 0.000 0.912 0.016 0.000
#> GSM125197 2 0.0162 0.9177 0.004 0.996 0.000 0.000 0.000
#> GSM125199 1 0.4182 0.9113 0.600 0.000 0.000 0.000 0.400
#> GSM125201 2 0.0290 0.9175 0.008 0.992 0.000 0.000 0.000
#> GSM125203 3 0.1845 0.9086 0.056 0.000 0.928 0.016 0.000
#> GSM125205 2 0.1493 0.8952 0.028 0.948 0.024 0.000 0.000
#> GSM125207 3 0.1117 0.9112 0.016 0.000 0.964 0.020 0.000
#> GSM125209 2 0.4769 0.6340 0.056 0.688 0.000 0.256 0.000
#> GSM125211 3 0.2932 0.8775 0.112 0.020 0.864 0.004 0.000
#> GSM125213 2 0.0609 0.9165 0.020 0.980 0.000 0.000 0.000
#> GSM125215 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125217 2 0.1478 0.9056 0.064 0.936 0.000 0.000 0.000
#> GSM125219 5 0.3274 0.6138 0.220 0.000 0.000 0.000 0.780
#> GSM125221 4 0.3161 0.8576 0.092 0.044 0.004 0.860 0.000
#> GSM125223 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125225 2 0.0290 0.9180 0.008 0.992 0.000 0.000 0.000
#> GSM125227 2 0.0290 0.9179 0.008 0.992 0.000 0.000 0.000
#> GSM125229 3 0.3176 0.8538 0.080 0.064 0.856 0.000 0.000
#> GSM125231 3 0.5330 0.6379 0.068 0.000 0.684 0.020 0.228
#> GSM125233 5 0.2074 0.7072 0.104 0.000 0.000 0.000 0.896
#> GSM125235 1 0.4201 0.8968 0.592 0.000 0.000 0.000 0.408
#> GSM125237 1 0.4192 0.9109 0.596 0.000 0.000 0.000 0.404
#> GSM125124 5 0.1357 0.7075 0.048 0.000 0.004 0.000 0.948
#> GSM125126 5 0.4307 -0.6857 0.496 0.000 0.000 0.000 0.504
#> GSM125128 1 0.4088 0.7811 0.632 0.000 0.000 0.000 0.368
#> GSM125130 5 0.1671 0.6946 0.076 0.000 0.000 0.000 0.924
#> GSM125132 1 0.4268 0.8447 0.556 0.000 0.000 0.000 0.444
#> GSM125134 5 0.2719 0.6580 0.144 0.000 0.004 0.000 0.852
#> GSM125136 1 0.3966 0.8604 0.664 0.000 0.000 0.000 0.336
#> GSM125138 5 0.1768 0.7056 0.072 0.000 0.004 0.000 0.924
#> GSM125140 5 0.2471 0.6767 0.136 0.000 0.000 0.000 0.864
#> GSM125142 5 0.4126 -0.0785 0.380 0.000 0.000 0.000 0.620
#> GSM125144 5 0.1430 0.7078 0.052 0.000 0.004 0.000 0.944
#> GSM125146 5 0.2970 0.6311 0.168 0.000 0.004 0.000 0.828
#> GSM125148 1 0.4242 0.8661 0.572 0.000 0.000 0.000 0.428
#> GSM125150 1 0.4300 0.7489 0.524 0.000 0.000 0.000 0.476
#> GSM125152 5 0.1121 0.7221 0.044 0.000 0.000 0.000 0.956
#> GSM125154 5 0.3814 0.3790 0.276 0.000 0.004 0.000 0.720
#> GSM125156 5 0.3242 0.5569 0.216 0.000 0.000 0.000 0.784
#> GSM125158 5 0.3395 0.5019 0.236 0.000 0.000 0.000 0.764
#> GSM125160 2 0.0703 0.9162 0.024 0.976 0.000 0.000 0.000
#> GSM125162 1 0.4060 0.8950 0.640 0.000 0.000 0.000 0.360
#> GSM125164 2 0.0290 0.9181 0.008 0.992 0.000 0.000 0.000
#> GSM125166 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125168 2 0.4179 0.7739 0.072 0.776 0.000 0.152 0.000
#> GSM125170 2 0.4847 0.6850 0.080 0.704 0.000 0.216 0.000
#> GSM125172 2 0.0290 0.9175 0.008 0.992 0.000 0.000 0.000
#> GSM125174 4 0.1444 0.9134 0.040 0.000 0.012 0.948 0.000
#> GSM125176 2 0.0451 0.9175 0.004 0.988 0.000 0.008 0.000
#> GSM125178 3 0.0451 0.9110 0.008 0.000 0.988 0.004 0.000
#> GSM125180 4 0.0162 0.9234 0.000 0.000 0.000 0.996 0.004
#> GSM125182 2 0.4334 0.7654 0.092 0.768 0.000 0.140 0.000
#> GSM125184 4 0.0833 0.9215 0.016 0.004 0.004 0.976 0.000
#> GSM125186 4 0.0671 0.9233 0.016 0.000 0.000 0.980 0.004
#> GSM125188 2 0.7054 0.2063 0.140 0.468 0.044 0.348 0.000
#> GSM125190 2 0.1197 0.9079 0.048 0.952 0.000 0.000 0.000
#> GSM125192 2 0.0000 0.9180 0.000 1.000 0.000 0.000 0.000
#> GSM125194 3 0.3874 0.8379 0.200 0.000 0.776 0.008 0.016
#> GSM125196 3 0.2110 0.9058 0.072 0.000 0.912 0.016 0.000
#> GSM125198 2 0.0162 0.9177 0.004 0.996 0.000 0.000 0.000
#> GSM125200 5 0.3876 0.2443 0.316 0.000 0.000 0.000 0.684
#> GSM125202 2 0.0290 0.9175 0.008 0.992 0.000 0.000 0.000
#> GSM125204 3 0.1845 0.9086 0.056 0.000 0.928 0.016 0.000
#> GSM125206 3 0.1942 0.9076 0.068 0.000 0.920 0.012 0.000
#> GSM125208 3 0.1117 0.9112 0.016 0.000 0.964 0.020 0.000
#> GSM125210 4 0.0510 0.9237 0.016 0.000 0.000 0.984 0.000
#> GSM125212 3 0.2932 0.8775 0.112 0.020 0.864 0.004 0.000
#> GSM125214 2 0.0162 0.9177 0.004 0.996 0.000 0.000 0.000
#> GSM125216 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125218 2 0.1341 0.9058 0.056 0.944 0.000 0.000 0.000
#> GSM125220 1 0.3983 0.8561 0.660 0.000 0.000 0.000 0.340
#> GSM125222 4 0.2835 0.8739 0.080 0.036 0.004 0.880 0.000
#> GSM125224 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125226 2 0.1270 0.9068 0.052 0.948 0.000 0.000 0.000
#> GSM125228 2 0.0162 0.9179 0.004 0.996 0.000 0.000 0.000
#> GSM125230 3 0.1638 0.9017 0.064 0.000 0.932 0.004 0.000
#> GSM125232 4 0.6853 0.4321 0.044 0.000 0.148 0.544 0.264
#> GSM125234 5 0.2046 0.6732 0.068 0.000 0.000 0.016 0.916
#> GSM125236 5 0.1908 0.7031 0.092 0.000 0.000 0.000 0.908
#> GSM125238 1 0.4182 0.9059 0.600 0.000 0.000 0.000 0.400
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.4044 0.7061 0.704 0.000 0.000 0.000 0.256 0.040
#> GSM125125 5 0.3934 0.3686 0.304 0.000 0.000 0.000 0.676 0.020
#> GSM125127 1 0.2948 0.7130 0.848 0.000 0.000 0.000 0.092 0.060
#> GSM125129 1 0.3612 0.7355 0.764 0.000 0.000 0.000 0.200 0.036
#> GSM125131 5 0.1700 0.7691 0.048 0.000 0.000 0.000 0.928 0.024
#> GSM125133 5 0.2328 0.7299 0.056 0.000 0.000 0.000 0.892 0.052
#> GSM125135 1 0.3802 0.7536 0.748 0.000 0.000 0.000 0.208 0.044
#> GSM125137 5 0.0520 0.7723 0.008 0.000 0.000 0.000 0.984 0.008
#> GSM125139 1 0.4155 0.6586 0.616 0.000 0.000 0.000 0.364 0.020
#> GSM125141 5 0.0692 0.7716 0.020 0.000 0.000 0.000 0.976 0.004
#> GSM125143 1 0.3727 0.7473 0.748 0.000 0.000 0.000 0.216 0.036
#> GSM125145 1 0.4428 0.7018 0.684 0.000 0.000 0.000 0.244 0.072
#> GSM125147 5 0.0891 0.7711 0.024 0.000 0.000 0.000 0.968 0.008
#> GSM125149 5 0.0717 0.7719 0.008 0.000 0.000 0.000 0.976 0.016
#> GSM125151 1 0.3802 0.7173 0.676 0.000 0.000 0.000 0.312 0.012
#> GSM125153 5 0.4974 0.1573 0.324 0.000 0.000 0.000 0.588 0.088
#> GSM125155 5 0.3398 0.5122 0.252 0.000 0.000 0.000 0.740 0.008
#> GSM125157 5 0.0603 0.7704 0.004 0.000 0.000 0.000 0.980 0.016
#> GSM125159 2 0.2300 0.7405 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM125161 5 0.1780 0.7464 0.028 0.000 0.000 0.000 0.924 0.048
#> GSM125163 2 0.0790 0.8034 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM125165 2 0.5996 -0.6899 0.004 0.408 0.000 0.196 0.000 0.392
#> GSM125167 2 0.3337 0.5854 0.004 0.736 0.000 0.000 0.000 0.260
#> GSM125169 2 0.3547 0.5249 0.004 0.696 0.000 0.000 0.000 0.300
#> GSM125171 2 0.0865 0.7981 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM125173 2 0.6322 -0.4545 0.016 0.460 0.016 0.140 0.000 0.368
#> GSM125175 2 0.1007 0.7986 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM125177 3 0.0820 0.8216 0.012 0.000 0.972 0.000 0.000 0.016
#> GSM125179 4 0.0000 0.8457 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125181 6 0.6182 0.8258 0.004 0.316 0.008 0.208 0.000 0.464
#> GSM125183 4 0.2100 0.8051 0.004 0.000 0.000 0.884 0.000 0.112
#> GSM125185 4 0.0603 0.8457 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM125187 4 0.1010 0.8421 0.000 0.000 0.004 0.960 0.000 0.036
#> GSM125189 2 0.2135 0.7574 0.000 0.872 0.000 0.000 0.000 0.128
#> GSM125191 2 0.2912 0.7064 0.000 0.844 0.000 0.040 0.000 0.116
#> GSM125193 3 0.5459 0.6315 0.036 0.000 0.528 0.000 0.052 0.384
#> GSM125195 3 0.2214 0.8077 0.016 0.000 0.888 0.000 0.000 0.096
#> GSM125197 2 0.0146 0.8032 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125199 5 0.0603 0.7735 0.016 0.000 0.000 0.000 0.980 0.004
#> GSM125201 2 0.0865 0.7945 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM125203 3 0.1462 0.8182 0.008 0.000 0.936 0.000 0.000 0.056
#> GSM125205 2 0.2144 0.7404 0.004 0.908 0.040 0.000 0.000 0.048
#> GSM125207 3 0.2151 0.8190 0.008 0.000 0.904 0.016 0.000 0.072
#> GSM125209 2 0.5095 0.1193 0.000 0.632 0.000 0.180 0.000 0.188
#> GSM125211 3 0.4827 0.6796 0.048 0.012 0.612 0.000 0.000 0.328
#> GSM125213 2 0.1444 0.7858 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM125215 2 0.0363 0.8056 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM125217 2 0.3175 0.6109 0.000 0.744 0.000 0.000 0.000 0.256
#> GSM125219 1 0.4617 0.5734 0.664 0.000 0.000 0.000 0.252 0.084
#> GSM125221 4 0.3927 0.5949 0.004 0.024 0.000 0.712 0.000 0.260
#> GSM125223 2 0.0363 0.8039 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM125225 2 0.1007 0.8021 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM125227 2 0.0260 0.8041 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125229 3 0.4897 0.7150 0.040 0.052 0.684 0.000 0.000 0.224
#> GSM125231 3 0.6137 0.5216 0.236 0.000 0.568 0.020 0.016 0.160
#> GSM125233 1 0.3344 0.7156 0.804 0.000 0.000 0.000 0.152 0.044
#> GSM125235 5 0.2868 0.7119 0.132 0.000 0.000 0.000 0.840 0.028
#> GSM125237 5 0.0622 0.7733 0.012 0.000 0.000 0.000 0.980 0.008
#> GSM125124 1 0.4544 0.7113 0.668 0.000 0.000 0.000 0.256 0.076
#> GSM125126 5 0.2841 0.6551 0.164 0.000 0.000 0.000 0.824 0.012
#> GSM125128 5 0.3790 0.6189 0.156 0.000 0.000 0.000 0.772 0.072
#> GSM125130 1 0.2934 0.7096 0.844 0.000 0.000 0.000 0.112 0.044
#> GSM125132 5 0.1757 0.7513 0.076 0.000 0.000 0.000 0.916 0.008
#> GSM125134 1 0.5063 0.5430 0.544 0.000 0.000 0.000 0.372 0.084
#> GSM125136 5 0.2680 0.7099 0.076 0.000 0.000 0.000 0.868 0.056
#> GSM125138 1 0.4705 0.7023 0.652 0.000 0.000 0.000 0.260 0.088
#> GSM125140 1 0.4310 0.6064 0.580 0.000 0.000 0.000 0.396 0.024
#> GSM125142 5 0.4527 0.3625 0.272 0.000 0.000 0.000 0.660 0.068
#> GSM125144 1 0.4592 0.7092 0.664 0.000 0.000 0.000 0.256 0.080
#> GSM125146 1 0.5086 0.4970 0.532 0.000 0.000 0.000 0.384 0.084
#> GSM125148 5 0.1926 0.7492 0.068 0.000 0.000 0.000 0.912 0.020
#> GSM125150 5 0.2558 0.6792 0.156 0.000 0.000 0.000 0.840 0.004
#> GSM125152 1 0.3766 0.7229 0.684 0.000 0.000 0.000 0.304 0.012
#> GSM125154 5 0.5157 -0.1881 0.404 0.000 0.000 0.000 0.508 0.088
#> GSM125156 1 0.4246 0.4785 0.532 0.000 0.000 0.000 0.452 0.016
#> GSM125158 5 0.4152 -0.2267 0.440 0.000 0.000 0.000 0.548 0.012
#> GSM125160 2 0.1863 0.7708 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM125162 5 0.1780 0.7464 0.028 0.000 0.000 0.000 0.924 0.048
#> GSM125164 2 0.0632 0.8050 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125166 2 0.0632 0.8048 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125168 2 0.5049 0.2510 0.004 0.624 0.000 0.104 0.000 0.268
#> GSM125170 2 0.5468 0.0803 0.004 0.572 0.000 0.148 0.000 0.276
#> GSM125172 2 0.1075 0.7971 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM125174 4 0.2264 0.8106 0.012 0.000 0.004 0.888 0.000 0.096
#> GSM125176 2 0.1461 0.7935 0.000 0.940 0.000 0.016 0.000 0.044
#> GSM125178 3 0.1442 0.8208 0.012 0.000 0.944 0.004 0.000 0.040
#> GSM125180 4 0.0000 0.8457 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125182 2 0.5230 0.0962 0.004 0.592 0.008 0.080 0.000 0.316
#> GSM125184 4 0.0865 0.8402 0.000 0.000 0.000 0.964 0.000 0.036
#> GSM125186 4 0.0603 0.8457 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM125188 6 0.7019 0.8293 0.008 0.292 0.048 0.252 0.000 0.400
#> GSM125190 2 0.2805 0.7014 0.004 0.812 0.000 0.000 0.000 0.184
#> GSM125192 2 0.0260 0.8042 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125194 3 0.5991 0.6227 0.060 0.000 0.516 0.000 0.076 0.348
#> GSM125196 3 0.2163 0.8085 0.016 0.000 0.892 0.000 0.000 0.092
#> GSM125198 2 0.0000 0.8034 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125200 5 0.3547 0.3726 0.300 0.000 0.000 0.000 0.696 0.004
#> GSM125202 2 0.0790 0.7952 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM125204 3 0.1398 0.8175 0.008 0.000 0.940 0.000 0.000 0.052
#> GSM125206 3 0.2112 0.8094 0.016 0.000 0.896 0.000 0.000 0.088
#> GSM125208 3 0.1957 0.8197 0.008 0.000 0.912 0.008 0.000 0.072
#> GSM125210 4 0.0692 0.8449 0.000 0.000 0.004 0.976 0.000 0.020
#> GSM125212 3 0.4841 0.6764 0.048 0.012 0.608 0.000 0.000 0.332
#> GSM125214 2 0.0260 0.8037 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125216 2 0.0260 0.8047 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125218 2 0.2823 0.6820 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM125220 5 0.3686 0.6393 0.124 0.000 0.000 0.000 0.788 0.088
#> GSM125222 4 0.3656 0.6308 0.004 0.012 0.000 0.728 0.000 0.256
#> GSM125224 2 0.0260 0.8041 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125226 2 0.2838 0.6943 0.004 0.808 0.000 0.000 0.000 0.188
#> GSM125228 2 0.0260 0.8041 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125230 3 0.3422 0.7898 0.040 0.000 0.792 0.000 0.000 0.168
#> GSM125232 4 0.7372 0.2135 0.260 0.000 0.176 0.428 0.008 0.128
#> GSM125234 1 0.3004 0.6999 0.860 0.000 0.004 0.008 0.080 0.048
#> GSM125236 1 0.3624 0.7106 0.784 0.000 0.000 0.000 0.156 0.060
#> GSM125238 5 0.0603 0.7721 0.016 0.000 0.000 0.000 0.980 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 agent(p) individual(p) k
#> MAD:skmeans 116 1.000 1.12e-05 2
#> MAD:skmeans 115 0.789 9.02e-08 3
#> MAD:skmeans 116 0.933 1.12e-10 4
#> MAD:skmeans 105 0.943 1.90e-09 5
#> MAD:skmeans 101 0.994 3.09e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.893 0.926 0.969 0.5025 0.496 0.496
#> 3 3 0.833 0.845 0.924 0.2994 0.797 0.610
#> 4 4 0.667 0.731 0.856 0.1369 0.877 0.656
#> 5 5 0.807 0.761 0.875 0.0598 0.927 0.731
#> 6 6 0.778 0.570 0.790 0.0474 0.927 0.688
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
#> GSM125123 1 0.0000 0.9632 1.000 0.000
#> GSM125125 1 0.0000 0.9632 1.000 0.000
#> GSM125127 1 0.0000 0.9632 1.000 0.000
#> GSM125129 1 0.0000 0.9632 1.000 0.000
#> GSM125131 1 0.0000 0.9632 1.000 0.000
#> GSM125133 1 0.0000 0.9632 1.000 0.000
#> GSM125135 1 0.0000 0.9632 1.000 0.000
#> GSM125137 1 0.0000 0.9632 1.000 0.000
#> GSM125139 1 0.0000 0.9632 1.000 0.000
#> GSM125141 1 0.0000 0.9632 1.000 0.000
#> GSM125143 1 0.0000 0.9632 1.000 0.000
#> GSM125145 1 0.0000 0.9632 1.000 0.000
#> GSM125147 1 0.0000 0.9632 1.000 0.000
#> GSM125149 1 0.0000 0.9632 1.000 0.000
#> GSM125151 1 0.0000 0.9632 1.000 0.000
#> GSM125153 1 0.0000 0.9632 1.000 0.000
#> GSM125155 1 0.0000 0.9632 1.000 0.000
#> GSM125157 1 0.0000 0.9632 1.000 0.000
#> GSM125159 2 0.0000 0.9707 0.000 1.000
#> GSM125161 1 0.0000 0.9632 1.000 0.000
#> GSM125163 2 0.0000 0.9707 0.000 1.000
#> GSM125165 2 0.0000 0.9707 0.000 1.000
#> GSM125167 2 0.0000 0.9707 0.000 1.000
#> GSM125169 2 0.0000 0.9707 0.000 1.000
#> GSM125171 2 0.0000 0.9707 0.000 1.000
#> GSM125173 2 0.1184 0.9595 0.016 0.984
#> GSM125175 2 0.0000 0.9707 0.000 1.000
#> GSM125177 2 0.0000 0.9707 0.000 1.000
#> GSM125179 2 0.3733 0.9102 0.072 0.928
#> GSM125181 2 0.0000 0.9707 0.000 1.000
#> GSM125183 2 0.4298 0.8931 0.088 0.912
#> GSM125185 2 0.2423 0.9399 0.040 0.960
#> GSM125187 1 0.6712 0.7800 0.824 0.176
#> GSM125189 2 0.0000 0.9707 0.000 1.000
#> GSM125191 2 0.0000 0.9707 0.000 1.000
#> GSM125193 1 0.6247 0.8064 0.844 0.156
#> GSM125195 1 1.0000 0.0173 0.504 0.496
#> GSM125197 2 0.0000 0.9707 0.000 1.000
#> GSM125199 1 0.0000 0.9632 1.000 0.000
#> GSM125201 2 0.0000 0.9707 0.000 1.000
#> GSM125203 2 0.9491 0.4173 0.368 0.632
#> GSM125205 2 0.0000 0.9707 0.000 1.000
#> GSM125207 2 0.7453 0.7302 0.212 0.788
#> GSM125209 2 0.0000 0.9707 0.000 1.000
#> GSM125211 2 0.3431 0.9174 0.064 0.936
#> GSM125213 2 0.0000 0.9707 0.000 1.000
#> GSM125215 2 0.0000 0.9707 0.000 1.000
#> GSM125217 2 0.0000 0.9707 0.000 1.000
#> GSM125219 1 0.0000 0.9632 1.000 0.000
#> GSM125221 2 0.0000 0.9707 0.000 1.000
#> GSM125223 2 0.0000 0.9707 0.000 1.000
#> GSM125225 2 0.0000 0.9707 0.000 1.000
#> GSM125227 2 0.0000 0.9707 0.000 1.000
#> GSM125229 2 0.0000 0.9707 0.000 1.000
#> GSM125231 1 0.8327 0.6463 0.736 0.264
#> GSM125233 1 0.0000 0.9632 1.000 0.000
#> GSM125235 1 0.0000 0.9632 1.000 0.000
#> GSM125237 1 0.0000 0.9632 1.000 0.000
#> GSM125124 1 0.0000 0.9632 1.000 0.000
#> GSM125126 1 0.0000 0.9632 1.000 0.000
#> GSM125128 1 0.0000 0.9632 1.000 0.000
#> GSM125130 1 0.0000 0.9632 1.000 0.000
#> GSM125132 1 0.0000 0.9632 1.000 0.000
#> GSM125134 1 0.0000 0.9632 1.000 0.000
#> GSM125136 1 0.0000 0.9632 1.000 0.000
#> GSM125138 1 0.0000 0.9632 1.000 0.000
#> GSM125140 1 0.0000 0.9632 1.000 0.000
#> GSM125142 1 0.0000 0.9632 1.000 0.000
#> GSM125144 1 0.0000 0.9632 1.000 0.000
#> GSM125146 1 0.0000 0.9632 1.000 0.000
#> GSM125148 1 0.0000 0.9632 1.000 0.000
#> GSM125150 1 0.0000 0.9632 1.000 0.000
#> GSM125152 1 0.0000 0.9632 1.000 0.000
#> GSM125154 1 0.0000 0.9632 1.000 0.000
#> GSM125156 1 0.0000 0.9632 1.000 0.000
#> GSM125158 1 0.0000 0.9632 1.000 0.000
#> GSM125160 2 0.0000 0.9707 0.000 1.000
#> GSM125162 1 0.0000 0.9632 1.000 0.000
#> GSM125164 2 0.0000 0.9707 0.000 1.000
#> GSM125166 2 0.0000 0.9707 0.000 1.000
#> GSM125168 2 0.0000 0.9707 0.000 1.000
#> GSM125170 2 0.0000 0.9707 0.000 1.000
#> GSM125172 2 0.0000 0.9707 0.000 1.000
#> GSM125174 2 0.0376 0.9680 0.004 0.996
#> GSM125176 2 0.0000 0.9707 0.000 1.000
#> GSM125178 2 0.1633 0.9528 0.024 0.976
#> GSM125180 2 0.5294 0.8581 0.120 0.880
#> GSM125182 2 0.0000 0.9707 0.000 1.000
#> GSM125184 2 0.0000 0.9707 0.000 1.000
#> GSM125186 2 0.7139 0.7580 0.196 0.804
#> GSM125188 2 0.0000 0.9707 0.000 1.000
#> GSM125190 2 0.0000 0.9707 0.000 1.000
#> GSM125192 2 0.0000 0.9707 0.000 1.000
#> GSM125194 1 0.0672 0.9565 0.992 0.008
#> GSM125196 2 0.0672 0.9654 0.008 0.992
#> GSM125198 2 0.0000 0.9707 0.000 1.000
#> GSM125200 1 0.0000 0.9632 1.000 0.000
#> GSM125202 2 0.0000 0.9707 0.000 1.000
#> GSM125204 2 0.9815 0.2757 0.420 0.580
#> GSM125206 2 0.0000 0.9707 0.000 1.000
#> GSM125208 1 0.9000 0.5494 0.684 0.316
#> GSM125210 2 0.1414 0.9564 0.020 0.980
#> GSM125212 2 0.0000 0.9707 0.000 1.000
#> GSM125214 2 0.0000 0.9707 0.000 1.000
#> GSM125216 2 0.0000 0.9707 0.000 1.000
#> GSM125218 2 0.0000 0.9707 0.000 1.000
#> GSM125220 1 0.0000 0.9632 1.000 0.000
#> GSM125222 2 0.0000 0.9707 0.000 1.000
#> GSM125224 2 0.0000 0.9707 0.000 1.000
#> GSM125226 2 0.0000 0.9707 0.000 1.000
#> GSM125228 2 0.0000 0.9707 0.000 1.000
#> GSM125230 1 0.8267 0.6532 0.740 0.260
#> GSM125232 1 0.8144 0.6667 0.748 0.252
#> GSM125234 1 0.0000 0.9632 1.000 0.000
#> GSM125236 1 0.0000 0.9632 1.000 0.000
#> GSM125238 1 0.0000 0.9632 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125125 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125127 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125129 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125131 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125135 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125137 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125139 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125141 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125143 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125145 1 0.1163 0.9731 0.972 0.000 0.028
#> GSM125147 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125151 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125153 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125155 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125157 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125163 2 0.0747 0.8963 0.000 0.984 0.016
#> GSM125165 3 0.1964 0.8246 0.000 0.056 0.944
#> GSM125167 2 0.4750 0.7570 0.000 0.784 0.216
#> GSM125169 2 0.4931 0.7392 0.000 0.768 0.232
#> GSM125171 2 0.5138 0.7154 0.000 0.748 0.252
#> GSM125173 3 0.1860 0.8262 0.000 0.052 0.948
#> GSM125175 2 0.1529 0.8909 0.000 0.960 0.040
#> GSM125177 2 0.2261 0.8796 0.000 0.932 0.068
#> GSM125179 3 0.0592 0.8324 0.000 0.012 0.988
#> GSM125181 3 0.1411 0.8300 0.000 0.036 0.964
#> GSM125183 3 0.1491 0.8307 0.016 0.016 0.968
#> GSM125185 3 0.0000 0.8288 0.000 0.000 1.000
#> GSM125187 3 0.4235 0.6961 0.176 0.000 0.824
#> GSM125189 2 0.1289 0.8927 0.000 0.968 0.032
#> GSM125191 2 0.2537 0.8760 0.000 0.920 0.080
#> GSM125193 1 0.7107 0.3535 0.624 0.036 0.340
#> GSM125195 3 0.0829 0.8328 0.004 0.012 0.984
#> GSM125197 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125201 2 0.3482 0.7897 0.000 0.872 0.128
#> GSM125203 3 0.7129 0.3357 0.392 0.028 0.580
#> GSM125205 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125207 3 0.0592 0.8321 0.000 0.012 0.988
#> GSM125209 2 0.4605 0.7839 0.000 0.796 0.204
#> GSM125211 3 0.1878 0.8290 0.004 0.044 0.952
#> GSM125213 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125217 2 0.5948 0.4871 0.000 0.640 0.360
#> GSM125219 1 0.1643 0.9653 0.956 0.000 0.044
#> GSM125221 3 0.1860 0.8262 0.000 0.052 0.948
#> GSM125223 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125229 2 0.4121 0.8066 0.000 0.832 0.168
#> GSM125231 3 0.3031 0.8019 0.076 0.012 0.912
#> GSM125233 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125235 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125237 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125124 1 0.1411 0.9706 0.964 0.000 0.036
#> GSM125126 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125130 1 0.1411 0.9705 0.964 0.000 0.036
#> GSM125132 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125138 1 0.1964 0.9434 0.944 0.000 0.056
#> GSM125140 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125142 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125144 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125146 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125152 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125154 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125156 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125158 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125160 2 0.0892 0.8957 0.000 0.980 0.020
#> GSM125162 1 0.0000 0.9744 1.000 0.000 0.000
#> GSM125164 2 0.0424 0.8956 0.000 0.992 0.008
#> GSM125166 2 0.0592 0.8966 0.000 0.988 0.012
#> GSM125168 3 0.6168 0.2166 0.000 0.412 0.588
#> GSM125170 3 0.6267 0.0761 0.000 0.452 0.548
#> GSM125172 2 0.4235 0.8005 0.000 0.824 0.176
#> GSM125174 3 0.1529 0.8295 0.000 0.040 0.960
#> GSM125176 2 0.2448 0.8766 0.000 0.924 0.076
#> GSM125178 2 0.6235 0.1943 0.000 0.564 0.436
#> GSM125180 3 0.0475 0.8303 0.004 0.004 0.992
#> GSM125182 2 0.5216 0.7077 0.000 0.740 0.260
#> GSM125184 3 0.1753 0.8275 0.000 0.048 0.952
#> GSM125186 3 0.0000 0.8288 0.000 0.000 1.000
#> GSM125188 3 0.5905 0.4423 0.000 0.352 0.648
#> GSM125190 2 0.2537 0.8749 0.000 0.920 0.080
#> GSM125192 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125194 3 0.6008 0.4287 0.372 0.000 0.628
#> GSM125196 2 0.6045 0.4957 0.000 0.620 0.380
#> GSM125198 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125200 1 0.0892 0.9738 0.980 0.000 0.020
#> GSM125202 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125204 3 0.9305 0.2487 0.380 0.164 0.456
#> GSM125206 3 0.3879 0.7452 0.000 0.152 0.848
#> GSM125208 3 0.0983 0.8277 0.016 0.004 0.980
#> GSM125210 3 0.0747 0.8324 0.000 0.016 0.984
#> GSM125212 3 0.2878 0.8012 0.000 0.096 0.904
#> GSM125214 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125218 2 0.1964 0.8841 0.000 0.944 0.056
#> GSM125220 1 0.0237 0.9743 0.996 0.000 0.004
#> GSM125222 3 0.3752 0.7626 0.000 0.144 0.856
#> GSM125224 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125226 3 0.6260 0.1147 0.000 0.448 0.552
#> GSM125228 2 0.0000 0.8968 0.000 1.000 0.000
#> GSM125230 3 0.1999 0.8281 0.036 0.012 0.952
#> GSM125232 3 0.2625 0.7852 0.084 0.000 0.916
#> GSM125234 1 0.3267 0.8908 0.884 0.000 0.116
#> GSM125236 1 0.1289 0.9726 0.968 0.000 0.032
#> GSM125238 1 0.0000 0.9744 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.2345 0.81474 0.900 0.000 0.000 0.100
#> GSM125125 4 0.4040 0.81559 0.248 0.000 0.000 0.752
#> GSM125127 1 0.0188 0.87828 0.996 0.000 0.000 0.004
#> GSM125129 4 0.4877 0.54605 0.408 0.000 0.000 0.592
#> GSM125131 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125133 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125135 1 0.4907 -0.00965 0.580 0.000 0.000 0.420
#> GSM125137 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125139 1 0.0188 0.87801 0.996 0.000 0.000 0.004
#> GSM125141 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125143 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125145 1 0.1118 0.87169 0.964 0.000 0.000 0.036
#> GSM125147 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125149 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125151 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125153 1 0.2345 0.83423 0.900 0.000 0.000 0.100
#> GSM125155 4 0.4103 0.80947 0.256 0.000 0.000 0.744
#> GSM125157 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125159 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125161 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125163 2 0.0817 0.84937 0.000 0.976 0.024 0.000
#> GSM125165 3 0.0592 0.79559 0.000 0.016 0.984 0.000
#> GSM125167 2 0.4804 0.46513 0.000 0.616 0.384 0.000
#> GSM125169 2 0.4866 0.42593 0.000 0.596 0.404 0.000
#> GSM125171 2 0.4697 0.51994 0.000 0.644 0.356 0.000
#> GSM125173 3 0.0469 0.79661 0.000 0.012 0.988 0.000
#> GSM125175 2 0.1557 0.83977 0.000 0.944 0.056 0.000
#> GSM125177 2 0.5167 0.72565 0.000 0.760 0.108 0.132
#> GSM125179 3 0.2530 0.76480 0.100 0.000 0.896 0.004
#> GSM125181 3 0.0188 0.79671 0.000 0.004 0.996 0.000
#> GSM125183 3 0.0000 0.79606 0.000 0.000 1.000 0.000
#> GSM125185 3 0.2401 0.76750 0.092 0.000 0.904 0.004
#> GSM125187 3 0.5773 0.37828 0.336 0.000 0.620 0.044
#> GSM125189 2 0.1302 0.84377 0.000 0.956 0.044 0.000
#> GSM125191 2 0.2589 0.81161 0.000 0.884 0.116 0.000
#> GSM125193 4 0.3172 0.58137 0.000 0.000 0.160 0.840
#> GSM125195 3 0.3763 0.74865 0.024 0.000 0.832 0.144
#> GSM125197 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125199 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125201 2 0.2704 0.75694 0.000 0.876 0.124 0.000
#> GSM125203 3 0.5539 0.41255 0.008 0.008 0.552 0.432
#> GSM125205 2 0.1474 0.82720 0.000 0.948 0.000 0.052
#> GSM125207 3 0.3024 0.75281 0.000 0.000 0.852 0.148
#> GSM125209 2 0.4277 0.65412 0.000 0.720 0.280 0.000
#> GSM125211 3 0.0469 0.79661 0.000 0.012 0.988 0.000
#> GSM125213 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125215 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125217 2 0.4992 0.18345 0.000 0.524 0.476 0.000
#> GSM125219 1 0.2149 0.81033 0.912 0.000 0.000 0.088
#> GSM125221 3 0.0469 0.79661 0.000 0.012 0.988 0.000
#> GSM125223 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125229 2 0.6955 0.43372 0.000 0.560 0.296 0.144
#> GSM125231 1 0.5705 0.58937 0.704 0.000 0.204 0.092
#> GSM125233 1 0.1302 0.86359 0.956 0.000 0.000 0.044
#> GSM125235 4 0.4072 0.81213 0.252 0.000 0.000 0.748
#> GSM125237 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125124 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125126 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125128 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125130 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125132 4 0.3123 0.89938 0.156 0.000 0.000 0.844
#> GSM125134 1 0.2345 0.83423 0.900 0.000 0.000 0.100
#> GSM125136 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125138 1 0.1716 0.85856 0.936 0.000 0.000 0.064
#> GSM125140 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125142 1 0.2345 0.83423 0.900 0.000 0.000 0.100
#> GSM125144 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125146 1 0.2345 0.83423 0.900 0.000 0.000 0.100
#> GSM125148 4 0.3942 0.80832 0.236 0.000 0.000 0.764
#> GSM125150 1 0.4981 -0.01337 0.536 0.000 0.000 0.464
#> GSM125152 1 0.0000 0.87798 1.000 0.000 0.000 0.000
#> GSM125154 1 0.2345 0.83423 0.900 0.000 0.000 0.100
#> GSM125156 1 0.0188 0.87790 0.996 0.000 0.000 0.004
#> GSM125158 1 0.2081 0.83268 0.916 0.000 0.000 0.084
#> GSM125160 2 0.1211 0.84541 0.000 0.960 0.040 0.000
#> GSM125162 4 0.3024 0.90582 0.148 0.000 0.000 0.852
#> GSM125164 2 0.0188 0.85082 0.000 0.996 0.004 0.000
#> GSM125166 2 0.0707 0.85008 0.000 0.980 0.020 0.000
#> GSM125168 3 0.4790 0.24855 0.000 0.380 0.620 0.000
#> GSM125170 3 0.4916 0.10321 0.000 0.424 0.576 0.000
#> GSM125172 2 0.4406 0.60687 0.000 0.700 0.300 0.000
#> GSM125174 3 0.0188 0.79670 0.000 0.004 0.996 0.000
#> GSM125176 2 0.2469 0.81298 0.000 0.892 0.108 0.000
#> GSM125178 2 0.7073 0.18258 0.000 0.504 0.364 0.132
#> GSM125180 3 0.3448 0.70544 0.168 0.000 0.828 0.004
#> GSM125182 2 0.4843 0.44528 0.000 0.604 0.396 0.000
#> GSM125184 3 0.0469 0.79661 0.000 0.012 0.988 0.000
#> GSM125186 3 0.2530 0.76391 0.100 0.000 0.896 0.004
#> GSM125188 3 0.4936 0.32198 0.000 0.372 0.624 0.004
#> GSM125190 2 0.2647 0.80775 0.000 0.880 0.120 0.000
#> GSM125192 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125194 3 0.5279 0.24355 0.012 0.000 0.588 0.400
#> GSM125196 3 0.7283 -0.10947 0.000 0.420 0.432 0.148
#> GSM125198 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125200 1 0.1474 0.86848 0.948 0.000 0.000 0.052
#> GSM125202 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125204 4 0.8561 -0.31241 0.104 0.092 0.384 0.420
#> GSM125206 3 0.5080 0.69513 0.000 0.092 0.764 0.144
#> GSM125208 3 0.4590 0.74498 0.060 0.000 0.792 0.148
#> GSM125210 3 0.0188 0.79578 0.000 0.000 0.996 0.004
#> GSM125212 3 0.0707 0.79490 0.000 0.020 0.980 0.000
#> GSM125214 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125218 2 0.2281 0.81907 0.000 0.904 0.096 0.000
#> GSM125220 4 0.2589 0.86003 0.116 0.000 0.000 0.884
#> GSM125222 3 0.3444 0.65498 0.000 0.184 0.816 0.000
#> GSM125224 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125226 3 0.4713 0.31661 0.000 0.360 0.640 0.000
#> GSM125228 2 0.0000 0.85150 0.000 1.000 0.000 0.000
#> GSM125230 3 0.0469 0.79605 0.000 0.000 0.988 0.012
#> GSM125232 1 0.3172 0.72249 0.840 0.000 0.160 0.000
#> GSM125234 1 0.0657 0.87347 0.984 0.000 0.012 0.004
#> GSM125236 1 0.3726 0.64347 0.788 0.000 0.000 0.212
#> GSM125238 4 0.3024 0.90582 0.148 0.000 0.000 0.852
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.2561 0.8166 0.144 0.000 0.000 0.000 0.856
#> GSM125125 1 0.1478 0.8780 0.936 0.000 0.000 0.000 0.064
#> GSM125127 5 0.0162 0.9210 0.004 0.000 0.000 0.000 0.996
#> GSM125129 1 0.4045 0.4432 0.644 0.000 0.000 0.000 0.356
#> GSM125131 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125135 5 0.4291 0.1011 0.464 0.000 0.000 0.000 0.536
#> GSM125137 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125139 5 0.0162 0.9213 0.004 0.000 0.000 0.000 0.996
#> GSM125141 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125143 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125145 5 0.0510 0.9202 0.016 0.000 0.000 0.000 0.984
#> GSM125147 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125151 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125153 5 0.1478 0.9033 0.064 0.000 0.000 0.000 0.936
#> GSM125155 1 0.1608 0.8740 0.928 0.000 0.000 0.000 0.072
#> GSM125157 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.0510 0.8184 0.000 0.984 0.016 0.000 0.000
#> GSM125161 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125163 2 0.1168 0.8172 0.000 0.960 0.032 0.008 0.000
#> GSM125165 4 0.3949 0.7457 0.000 0.000 0.332 0.668 0.000
#> GSM125167 2 0.4747 0.5923 0.000 0.636 0.332 0.032 0.000
#> GSM125169 2 0.5613 0.5187 0.000 0.576 0.332 0.092 0.000
#> GSM125171 2 0.4878 0.6313 0.000 0.676 0.264 0.060 0.000
#> GSM125173 4 0.4066 0.7484 0.000 0.004 0.324 0.672 0.000
#> GSM125175 2 0.1386 0.8152 0.000 0.952 0.032 0.016 0.000
#> GSM125177 3 0.4339 0.4826 0.000 0.336 0.652 0.012 0.000
#> GSM125179 4 0.0794 0.6575 0.000 0.000 0.000 0.972 0.028
#> GSM125181 4 0.2773 0.7279 0.000 0.000 0.164 0.836 0.000
#> GSM125183 4 0.3857 0.7535 0.000 0.000 0.312 0.688 0.000
#> GSM125185 4 0.0880 0.6544 0.000 0.000 0.000 0.968 0.032
#> GSM125187 4 0.1410 0.6165 0.000 0.000 0.060 0.940 0.000
#> GSM125189 2 0.1557 0.8115 0.000 0.940 0.052 0.008 0.000
#> GSM125191 2 0.3116 0.7813 0.000 0.860 0.076 0.064 0.000
#> GSM125193 1 0.4341 0.4093 0.628 0.000 0.364 0.008 0.000
#> GSM125195 3 0.2020 0.6909 0.000 0.000 0.900 0.100 0.000
#> GSM125197 2 0.0162 0.8185 0.000 0.996 0.004 0.000 0.000
#> GSM125199 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125201 2 0.2763 0.6925 0.000 0.848 0.004 0.148 0.000
#> GSM125203 3 0.2727 0.6587 0.116 0.000 0.868 0.016 0.000
#> GSM125205 2 0.3143 0.5911 0.000 0.796 0.204 0.000 0.000
#> GSM125207 3 0.3949 0.6514 0.000 0.000 0.668 0.332 0.000
#> GSM125209 2 0.5500 0.6055 0.000 0.648 0.140 0.212 0.000
#> GSM125211 4 0.3895 0.7515 0.000 0.000 0.320 0.680 0.000
#> GSM125213 2 0.0162 0.8185 0.000 0.996 0.004 0.000 0.000
#> GSM125215 2 0.0162 0.8185 0.000 0.996 0.004 0.000 0.000
#> GSM125217 2 0.5732 0.5338 0.000 0.588 0.296 0.116 0.000
#> GSM125219 5 0.0794 0.9127 0.000 0.000 0.028 0.000 0.972
#> GSM125221 4 0.4029 0.7514 0.000 0.004 0.316 0.680 0.000
#> GSM125223 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.0162 0.8185 0.000 0.996 0.004 0.000 0.000
#> GSM125227 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125229 3 0.1732 0.6793 0.000 0.080 0.920 0.000 0.000
#> GSM125231 5 0.3562 0.7452 0.000 0.000 0.196 0.016 0.788
#> GSM125233 5 0.1121 0.9092 0.044 0.000 0.000 0.000 0.956
#> GSM125235 1 0.1544 0.8758 0.932 0.000 0.000 0.000 0.068
#> GSM125237 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125124 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125126 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125130 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125132 1 0.0404 0.9156 0.988 0.000 0.000 0.000 0.012
#> GSM125134 5 0.1478 0.9033 0.064 0.000 0.000 0.000 0.936
#> GSM125136 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125138 5 0.1197 0.9107 0.048 0.000 0.000 0.000 0.952
#> GSM125140 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125142 5 0.1478 0.9033 0.064 0.000 0.000 0.000 0.936
#> GSM125144 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125146 5 0.1478 0.9033 0.064 0.000 0.000 0.000 0.936
#> GSM125148 1 0.1732 0.8541 0.920 0.000 0.000 0.000 0.080
#> GSM125150 1 0.4201 0.2497 0.592 0.000 0.000 0.000 0.408
#> GSM125152 5 0.0000 0.9206 0.000 0.000 0.000 0.000 1.000
#> GSM125154 5 0.1478 0.9033 0.064 0.000 0.000 0.000 0.936
#> GSM125156 5 0.0162 0.9211 0.004 0.000 0.000 0.000 0.996
#> GSM125158 5 0.1908 0.8716 0.092 0.000 0.000 0.000 0.908
#> GSM125160 2 0.1549 0.8125 0.000 0.944 0.040 0.016 0.000
#> GSM125162 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
#> GSM125164 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125166 2 0.0404 0.8194 0.000 0.988 0.012 0.000 0.000
#> GSM125168 2 0.6653 0.2005 0.000 0.432 0.328 0.240 0.000
#> GSM125170 2 0.6572 0.2593 0.000 0.452 0.328 0.220 0.000
#> GSM125172 2 0.4404 0.6636 0.000 0.712 0.252 0.036 0.000
#> GSM125174 4 0.3876 0.7523 0.000 0.000 0.316 0.684 0.000
#> GSM125176 2 0.2983 0.7817 0.000 0.868 0.076 0.056 0.000
#> GSM125178 3 0.4339 0.4893 0.000 0.336 0.652 0.012 0.000
#> GSM125180 4 0.1121 0.6455 0.000 0.000 0.000 0.956 0.044
#> GSM125182 2 0.4890 0.5845 0.000 0.628 0.332 0.040 0.000
#> GSM125184 4 0.4029 0.7514 0.000 0.004 0.316 0.680 0.000
#> GSM125186 4 0.0963 0.6521 0.000 0.000 0.000 0.964 0.036
#> GSM125188 4 0.3366 0.5633 0.000 0.140 0.032 0.828 0.000
#> GSM125190 2 0.3536 0.7605 0.000 0.832 0.084 0.084 0.000
#> GSM125192 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125194 4 0.4088 0.4121 0.368 0.000 0.000 0.632 0.000
#> GSM125196 3 0.2813 0.7068 0.000 0.000 0.832 0.168 0.000
#> GSM125198 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125200 5 0.1410 0.9071 0.060 0.000 0.000 0.000 0.940
#> GSM125202 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125204 3 0.4039 0.6833 0.004 0.000 0.720 0.268 0.008
#> GSM125206 3 0.1124 0.6017 0.000 0.004 0.960 0.036 0.000
#> GSM125208 3 0.4183 0.6514 0.000 0.000 0.668 0.324 0.008
#> GSM125210 4 0.0000 0.6704 0.000 0.000 0.000 1.000 0.000
#> GSM125212 4 0.4101 0.7438 0.000 0.004 0.332 0.664 0.000
#> GSM125214 2 0.0162 0.8185 0.000 0.996 0.004 0.000 0.000
#> GSM125216 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125218 2 0.2769 0.7847 0.000 0.876 0.092 0.032 0.000
#> GSM125220 1 0.1300 0.8998 0.956 0.000 0.028 0.000 0.016
#> GSM125222 4 0.5263 0.6526 0.000 0.144 0.176 0.680 0.000
#> GSM125224 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125226 2 0.6740 0.0968 0.000 0.404 0.328 0.268 0.000
#> GSM125228 2 0.0000 0.8191 0.000 1.000 0.000 0.000 0.000
#> GSM125230 4 0.4166 0.7345 0.004 0.000 0.348 0.648 0.000
#> GSM125232 5 0.0880 0.9094 0.000 0.000 0.000 0.032 0.968
#> GSM125234 5 0.1341 0.8956 0.000 0.000 0.000 0.056 0.944
#> GSM125236 5 0.3395 0.6889 0.236 0.000 0.000 0.000 0.764
#> GSM125238 1 0.0000 0.9230 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2300 0.8184 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM125125 5 0.1267 0.8816 0.060 0.000 0.000 0.000 0.940 0.000
#> GSM125127 1 0.0146 0.9155 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125129 5 0.3684 0.3926 0.372 0.000 0.000 0.000 0.628 0.000
#> GSM125131 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125133 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125135 1 0.3843 0.1584 0.548 0.000 0.000 0.000 0.452 0.000
#> GSM125137 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125139 1 0.0146 0.9157 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125141 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125143 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125145 1 0.0458 0.9149 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM125147 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125149 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125151 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125153 1 0.1267 0.9019 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125155 5 0.1387 0.8777 0.068 0.000 0.000 0.000 0.932 0.000
#> GSM125157 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125159 2 0.3330 0.2129 0.000 0.716 0.000 0.000 0.000 0.284
#> GSM125161 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125163 2 0.4384 0.1653 0.000 0.616 0.000 0.036 0.000 0.348
#> GSM125165 4 0.2631 0.6703 0.000 0.008 0.000 0.840 0.000 0.152
#> GSM125167 2 0.6024 0.0630 0.000 0.388 0.000 0.244 0.000 0.368
#> GSM125169 6 0.5701 0.1102 0.000 0.228 0.000 0.248 0.000 0.524
#> GSM125171 6 0.5688 0.3222 0.000 0.144 0.004 0.364 0.000 0.488
#> GSM125173 4 0.0790 0.7833 0.000 0.000 0.000 0.968 0.000 0.032
#> GSM125175 6 0.4420 0.3152 0.000 0.360 0.000 0.036 0.000 0.604
#> GSM125177 3 0.2918 0.8361 0.000 0.088 0.856 0.004 0.000 0.052
#> GSM125179 4 0.3867 0.6926 0.000 0.000 0.052 0.748 0.000 0.200
#> GSM125181 2 0.5962 0.1618 0.000 0.488 0.004 0.260 0.000 0.248
#> GSM125183 4 0.0260 0.7902 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM125185 2 0.6695 -0.0252 0.000 0.440 0.052 0.308 0.000 0.200
#> GSM125187 4 0.4809 0.6378 0.000 0.000 0.140 0.668 0.000 0.192
#> GSM125189 6 0.4151 0.3385 0.000 0.264 0.000 0.044 0.000 0.692
#> GSM125191 2 0.4173 0.2134 0.000 0.712 0.000 0.228 0.000 0.060
#> GSM125193 5 0.4116 0.2632 0.000 0.000 0.416 0.012 0.572 0.000
#> GSM125195 3 0.0713 0.9050 0.000 0.000 0.972 0.028 0.000 0.000
#> GSM125197 2 0.3867 -0.2612 0.000 0.512 0.000 0.000 0.000 0.488
#> GSM125199 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125201 2 0.1610 0.1790 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM125203 3 0.1340 0.9000 0.000 0.040 0.948 0.000 0.008 0.004
#> GSM125205 2 0.5259 -0.1729 0.000 0.536 0.108 0.000 0.000 0.356
#> GSM125207 3 0.2398 0.8666 0.000 0.028 0.888 0.004 0.000 0.080
#> GSM125209 2 0.5190 0.2264 0.000 0.584 0.004 0.100 0.000 0.312
#> GSM125211 4 0.0632 0.7871 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM125213 2 0.0632 0.2222 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM125215 2 0.3101 0.0271 0.000 0.756 0.000 0.000 0.000 0.244
#> GSM125217 2 0.5736 0.1581 0.000 0.492 0.000 0.188 0.000 0.320
#> GSM125219 1 0.1075 0.8971 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM125221 4 0.0692 0.7869 0.000 0.004 0.000 0.976 0.000 0.020
#> GSM125223 6 0.3860 0.2213 0.000 0.472 0.000 0.000 0.000 0.528
#> GSM125225 6 0.3817 0.2327 0.000 0.432 0.000 0.000 0.000 0.568
#> GSM125227 2 0.3868 -0.2685 0.000 0.504 0.000 0.000 0.000 0.496
#> GSM125229 3 0.3049 0.8810 0.000 0.044 0.864 0.052 0.000 0.040
#> GSM125231 1 0.3999 0.6073 0.696 0.000 0.272 0.032 0.000 0.000
#> GSM125233 1 0.1007 0.9051 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM125235 5 0.1327 0.8795 0.064 0.000 0.000 0.000 0.936 0.000
#> GSM125237 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125124 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125126 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125128 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125130 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125132 5 0.0363 0.9147 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM125134 1 0.1267 0.9019 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125136 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125138 1 0.1075 0.9069 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM125140 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125142 1 0.1267 0.9019 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125144 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125146 1 0.1267 0.9019 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125148 5 0.1556 0.8552 0.080 0.000 0.000 0.000 0.920 0.000
#> GSM125150 5 0.3756 0.2801 0.400 0.000 0.000 0.000 0.600 0.000
#> GSM125152 1 0.0000 0.9150 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125154 1 0.1267 0.9019 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125156 1 0.0146 0.9156 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125158 1 0.1714 0.8694 0.908 0.000 0.000 0.000 0.092 0.000
#> GSM125160 2 0.4879 -0.1798 0.000 0.544 0.000 0.064 0.000 0.392
#> GSM125162 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125164 2 0.3672 -0.1334 0.000 0.632 0.000 0.000 0.000 0.368
#> GSM125166 6 0.4252 0.3049 0.000 0.372 0.000 0.024 0.000 0.604
#> GSM125168 4 0.3869 -0.1262 0.000 0.000 0.000 0.500 0.000 0.500
#> GSM125170 6 0.3867 0.0471 0.000 0.000 0.000 0.488 0.000 0.512
#> GSM125172 6 0.4934 0.3734 0.000 0.112 0.000 0.256 0.000 0.632
#> GSM125174 4 0.0260 0.7902 0.000 0.000 0.008 0.992 0.000 0.000
#> GSM125176 6 0.6061 0.1112 0.000 0.368 0.000 0.260 0.000 0.372
#> GSM125178 3 0.3140 0.8229 0.000 0.092 0.844 0.008 0.000 0.056
#> GSM125180 4 0.4006 0.6919 0.004 0.000 0.052 0.744 0.000 0.200
#> GSM125182 2 0.5935 0.1390 0.000 0.456 0.000 0.244 0.000 0.300
#> GSM125184 4 0.0405 0.7874 0.000 0.008 0.000 0.988 0.000 0.004
#> GSM125186 4 0.4210 0.6901 0.012 0.000 0.052 0.736 0.000 0.200
#> GSM125188 2 0.5983 0.1497 0.000 0.504 0.008 0.236 0.000 0.252
#> GSM125190 6 0.5449 0.2966 0.000 0.124 0.000 0.388 0.000 0.488
#> GSM125192 2 0.3864 -0.2408 0.000 0.520 0.000 0.000 0.000 0.480
#> GSM125194 4 0.4088 0.3884 0.016 0.000 0.000 0.616 0.368 0.000
#> GSM125196 3 0.0458 0.9072 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM125198 2 0.3868 -0.2645 0.000 0.508 0.000 0.000 0.000 0.492
#> GSM125200 1 0.1267 0.9035 0.940 0.000 0.000 0.000 0.060 0.000
#> GSM125202 2 0.4175 -0.2641 0.000 0.524 0.000 0.012 0.000 0.464
#> GSM125204 3 0.0000 0.9046 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM125206 3 0.2333 0.8377 0.000 0.004 0.872 0.120 0.000 0.004
#> GSM125208 3 0.1552 0.8933 0.000 0.020 0.940 0.004 0.000 0.036
#> GSM125210 2 0.6695 -0.0252 0.000 0.440 0.052 0.308 0.000 0.200
#> GSM125212 4 0.1679 0.7744 0.000 0.016 0.012 0.936 0.000 0.036
#> GSM125214 2 0.2697 0.0833 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM125216 2 0.3868 -0.2663 0.000 0.504 0.000 0.000 0.000 0.496
#> GSM125218 6 0.4196 0.3526 0.000 0.144 0.000 0.116 0.000 0.740
#> GSM125220 5 0.1367 0.8894 0.012 0.000 0.044 0.000 0.944 0.000
#> GSM125222 4 0.1838 0.7401 0.000 0.068 0.000 0.916 0.000 0.016
#> GSM125224 6 0.3869 0.1884 0.000 0.500 0.000 0.000 0.000 0.500
#> GSM125226 6 0.5112 0.2867 0.000 0.088 0.000 0.376 0.000 0.536
#> GSM125228 6 0.3747 0.2809 0.000 0.396 0.000 0.000 0.000 0.604
#> GSM125230 4 0.2558 0.7052 0.000 0.000 0.156 0.840 0.000 0.004
#> GSM125232 1 0.2178 0.8204 0.868 0.000 0.000 0.132 0.000 0.000
#> GSM125234 1 0.1391 0.8956 0.944 0.000 0.016 0.000 0.000 0.040
#> GSM125236 1 0.2996 0.7034 0.772 0.000 0.000 0.000 0.228 0.000
#> GSM125238 5 0.0000 0.9219 0.000 0.000 0.000 0.000 1.000 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 agent(p) individual(p) k
#> MAD:pam 113 1.000 4.84e-05 2
#> MAD:pam 105 0.871 2.78e-06 3
#> MAD:pam 99 0.313 1.66e-05 4
#> MAD:pam 106 0.590 4.15e-08 5
#> MAD:pam 71 0.464 9.00e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.997 0.4901 0.511 0.511
#> 3 3 0.682 0.808 0.790 0.2925 0.830 0.668
#> 4 4 0.691 0.837 0.868 0.1572 0.845 0.589
#> 5 5 0.860 0.889 0.916 0.0892 0.889 0.605
#> 6 6 0.829 0.792 0.850 0.0292 0.968 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
#> GSM125123 1 0.0000 0.997 1.000 0.000
#> GSM125125 1 0.0000 0.997 1.000 0.000
#> GSM125127 1 0.0672 0.992 0.992 0.008
#> GSM125129 1 0.0000 0.997 1.000 0.000
#> GSM125131 1 0.0000 0.997 1.000 0.000
#> GSM125133 1 0.0376 0.995 0.996 0.004
#> GSM125135 1 0.0000 0.997 1.000 0.000
#> GSM125137 1 0.0000 0.997 1.000 0.000
#> GSM125139 1 0.0000 0.997 1.000 0.000
#> GSM125141 1 0.0000 0.997 1.000 0.000
#> GSM125143 1 0.1414 0.981 0.980 0.020
#> GSM125145 1 0.0000 0.997 1.000 0.000
#> GSM125147 1 0.0000 0.997 1.000 0.000
#> GSM125149 1 0.0000 0.997 1.000 0.000
#> GSM125151 1 0.0000 0.997 1.000 0.000
#> GSM125153 1 0.0000 0.997 1.000 0.000
#> GSM125155 1 0.0000 0.997 1.000 0.000
#> GSM125157 1 0.0000 0.997 1.000 0.000
#> GSM125159 2 0.0000 0.996 0.000 1.000
#> GSM125161 1 0.0000 0.997 1.000 0.000
#> GSM125163 2 0.0000 0.996 0.000 1.000
#> GSM125165 2 0.0000 0.996 0.000 1.000
#> GSM125167 2 0.0000 0.996 0.000 1.000
#> GSM125169 2 0.0000 0.996 0.000 1.000
#> GSM125171 2 0.0000 0.996 0.000 1.000
#> GSM125173 2 0.0000 0.996 0.000 1.000
#> GSM125175 2 0.0000 0.996 0.000 1.000
#> GSM125177 2 0.0000 0.996 0.000 1.000
#> GSM125179 2 0.0000 0.996 0.000 1.000
#> GSM125181 2 0.0000 0.996 0.000 1.000
#> GSM125183 2 0.0000 0.996 0.000 1.000
#> GSM125185 2 0.0000 0.996 0.000 1.000
#> GSM125187 2 0.0376 0.993 0.004 0.996
#> GSM125189 2 0.0000 0.996 0.000 1.000
#> GSM125191 2 0.0000 0.996 0.000 1.000
#> GSM125193 2 0.0938 0.986 0.012 0.988
#> GSM125195 2 0.0000 0.996 0.000 1.000
#> GSM125197 2 0.0000 0.996 0.000 1.000
#> GSM125199 1 0.0000 0.997 1.000 0.000
#> GSM125201 2 0.0000 0.996 0.000 1.000
#> GSM125203 2 0.0376 0.993 0.004 0.996
#> GSM125205 2 0.0000 0.996 0.000 1.000
#> GSM125207 2 0.0000 0.996 0.000 1.000
#> GSM125209 2 0.0000 0.996 0.000 1.000
#> GSM125211 2 0.0000 0.996 0.000 1.000
#> GSM125213 2 0.0000 0.996 0.000 1.000
#> GSM125215 2 0.0000 0.996 0.000 1.000
#> GSM125217 2 0.0000 0.996 0.000 1.000
#> GSM125219 1 0.0376 0.995 0.996 0.004
#> GSM125221 2 0.0000 0.996 0.000 1.000
#> GSM125223 2 0.0000 0.996 0.000 1.000
#> GSM125225 2 0.0000 0.996 0.000 1.000
#> GSM125227 2 0.0000 0.996 0.000 1.000
#> GSM125229 2 0.0000 0.996 0.000 1.000
#> GSM125231 2 0.6148 0.823 0.152 0.848
#> GSM125233 1 0.0000 0.997 1.000 0.000
#> GSM125235 1 0.0000 0.997 1.000 0.000
#> GSM125237 1 0.0000 0.997 1.000 0.000
#> GSM125124 1 0.0000 0.997 1.000 0.000
#> GSM125126 1 0.0000 0.997 1.000 0.000
#> GSM125128 1 0.0376 0.995 0.996 0.004
#> GSM125130 1 0.1414 0.981 0.980 0.020
#> GSM125132 1 0.0000 0.997 1.000 0.000
#> GSM125134 1 0.0000 0.997 1.000 0.000
#> GSM125136 1 0.0376 0.995 0.996 0.004
#> GSM125138 1 0.0000 0.997 1.000 0.000
#> GSM125140 1 0.0000 0.997 1.000 0.000
#> GSM125142 1 0.0000 0.997 1.000 0.000
#> GSM125144 1 0.0000 0.997 1.000 0.000
#> GSM125146 1 0.0376 0.995 0.996 0.004
#> GSM125148 1 0.0000 0.997 1.000 0.000
#> GSM125150 1 0.0000 0.997 1.000 0.000
#> GSM125152 1 0.0000 0.997 1.000 0.000
#> GSM125154 1 0.0000 0.997 1.000 0.000
#> GSM125156 1 0.0000 0.997 1.000 0.000
#> GSM125158 1 0.0000 0.997 1.000 0.000
#> GSM125160 2 0.0000 0.996 0.000 1.000
#> GSM125162 1 0.0000 0.997 1.000 0.000
#> GSM125164 2 0.0000 0.996 0.000 1.000
#> GSM125166 2 0.0000 0.996 0.000 1.000
#> GSM125168 2 0.0000 0.996 0.000 1.000
#> GSM125170 2 0.0000 0.996 0.000 1.000
#> GSM125172 2 0.0000 0.996 0.000 1.000
#> GSM125174 2 0.0000 0.996 0.000 1.000
#> GSM125176 2 0.0000 0.996 0.000 1.000
#> GSM125178 2 0.0000 0.996 0.000 1.000
#> GSM125180 2 0.0376 0.993 0.004 0.996
#> GSM125182 2 0.0000 0.996 0.000 1.000
#> GSM125184 2 0.0000 0.996 0.000 1.000
#> GSM125186 2 0.0000 0.996 0.000 1.000
#> GSM125188 2 0.0000 0.996 0.000 1.000
#> GSM125190 2 0.0000 0.996 0.000 1.000
#> GSM125192 2 0.0000 0.996 0.000 1.000
#> GSM125194 2 0.1414 0.978 0.020 0.980
#> GSM125196 2 0.0000 0.996 0.000 1.000
#> GSM125198 2 0.0000 0.996 0.000 1.000
#> GSM125200 1 0.0000 0.997 1.000 0.000
#> GSM125202 2 0.0000 0.996 0.000 1.000
#> GSM125204 2 0.0376 0.993 0.004 0.996
#> GSM125206 2 0.0000 0.996 0.000 1.000
#> GSM125208 2 0.0000 0.996 0.000 1.000
#> GSM125210 2 0.0000 0.996 0.000 1.000
#> GSM125212 2 0.0000 0.996 0.000 1.000
#> GSM125214 2 0.0000 0.996 0.000 1.000
#> GSM125216 2 0.0000 0.996 0.000 1.000
#> GSM125218 2 0.0000 0.996 0.000 1.000
#> GSM125220 1 0.1414 0.981 0.980 0.020
#> GSM125222 2 0.0000 0.996 0.000 1.000
#> GSM125224 2 0.0000 0.996 0.000 1.000
#> GSM125226 2 0.0000 0.996 0.000 1.000
#> GSM125228 2 0.0000 0.996 0.000 1.000
#> GSM125230 2 0.0938 0.986 0.012 0.988
#> GSM125232 2 0.2778 0.950 0.048 0.952
#> GSM125234 1 0.2423 0.961 0.960 0.040
#> GSM125236 1 0.0376 0.995 0.996 0.004
#> GSM125238 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125125 1 0.1643 0.8603 0.956 0.000 0.044
#> GSM125127 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125129 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125131 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125135 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125137 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125139 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125141 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125143 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125145 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125147 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125151 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125153 1 0.5591 0.8744 0.696 0.000 0.304
#> GSM125155 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125159 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125161 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125163 2 0.0424 0.8474 0.000 0.992 0.008
#> GSM125165 2 0.5785 0.2295 0.000 0.668 0.332
#> GSM125167 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125169 2 0.6215 -0.2708 0.000 0.572 0.428
#> GSM125171 2 0.4702 0.5046 0.000 0.788 0.212
#> GSM125173 3 0.6252 0.7079 0.000 0.444 0.556
#> GSM125175 2 0.2959 0.7419 0.000 0.900 0.100
#> GSM125177 3 0.6079 0.8924 0.000 0.388 0.612
#> GSM125179 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125181 2 0.5882 0.1579 0.000 0.652 0.348
#> GSM125183 3 0.5905 0.8636 0.000 0.352 0.648
#> GSM125185 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125187 3 0.5678 0.8862 0.000 0.316 0.684
#> GSM125189 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125191 2 0.1163 0.8344 0.000 0.972 0.028
#> GSM125193 3 0.5760 0.8838 0.000 0.328 0.672
#> GSM125195 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125197 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125203 3 0.5785 0.8966 0.000 0.332 0.668
#> GSM125205 2 0.4002 0.6307 0.000 0.840 0.160
#> GSM125207 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125209 2 0.2796 0.7814 0.000 0.908 0.092
#> GSM125211 3 0.6299 0.6257 0.000 0.476 0.524
#> GSM125213 2 0.0747 0.8456 0.000 0.984 0.016
#> GSM125215 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125217 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125219 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125221 3 0.5835 0.8768 0.000 0.340 0.660
#> GSM125223 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125225 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125227 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125229 3 0.6260 0.6869 0.000 0.448 0.552
#> GSM125231 3 0.8222 0.6875 0.100 0.308 0.592
#> GSM125233 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125235 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125124 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125126 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125130 1 0.5760 0.8666 0.672 0.000 0.328
#> GSM125132 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125134 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125136 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125138 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125140 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125142 1 0.5560 0.8745 0.700 0.000 0.300
#> GSM125144 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125146 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125148 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125150 1 0.0424 0.8573 0.992 0.000 0.008
#> GSM125152 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125154 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125156 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125158 1 0.5650 0.8741 0.688 0.000 0.312
#> GSM125160 2 0.1643 0.8357 0.000 0.956 0.044
#> GSM125162 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125168 2 0.3192 0.7302 0.000 0.888 0.112
#> GSM125170 3 0.6235 0.7548 0.000 0.436 0.564
#> GSM125172 2 0.0892 0.8381 0.000 0.980 0.020
#> GSM125174 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125176 2 0.5098 0.3965 0.000 0.752 0.248
#> GSM125178 3 0.5905 0.9033 0.000 0.352 0.648
#> GSM125180 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125182 2 0.4702 0.5593 0.000 0.788 0.212
#> GSM125184 3 0.6235 0.8329 0.000 0.436 0.564
#> GSM125186 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125188 2 0.6008 0.0307 0.000 0.628 0.372
#> GSM125190 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125192 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125194 3 0.5678 0.8862 0.000 0.316 0.684
#> GSM125196 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125198 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125200 1 0.3941 0.8677 0.844 0.000 0.156
#> GSM125202 2 0.1411 0.8252 0.000 0.964 0.036
#> GSM125204 3 0.5882 0.9026 0.000 0.348 0.652
#> GSM125206 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125208 3 0.5859 0.9016 0.000 0.344 0.656
#> GSM125210 3 0.6008 0.9025 0.000 0.372 0.628
#> GSM125212 2 0.5706 0.2800 0.000 0.680 0.320
#> GSM125214 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125218 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125220 1 0.0000 0.8565 1.000 0.000 0.000
#> GSM125222 3 0.5678 0.8862 0.000 0.316 0.684
#> GSM125224 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125226 2 0.1964 0.8297 0.000 0.944 0.056
#> GSM125228 2 0.0000 0.8482 0.000 1.000 0.000
#> GSM125230 3 0.5678 0.8862 0.000 0.316 0.684
#> GSM125232 3 0.8452 0.7577 0.096 0.372 0.532
#> GSM125234 1 0.5968 0.8405 0.636 0.000 0.364
#> GSM125236 1 0.5678 0.8735 0.684 0.000 0.316
#> GSM125238 1 0.0000 0.8565 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125125 1 0.4477 0.3517 0.688 0.000 0.000 0.312
#> GSM125127 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125129 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125131 4 0.4304 0.8997 0.284 0.000 0.000 0.716
#> GSM125133 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125135 1 0.0188 0.9210 0.996 0.000 0.000 0.004
#> GSM125137 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125139 1 0.0188 0.9210 0.996 0.000 0.000 0.004
#> GSM125141 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125143 1 0.0657 0.9165 0.984 0.000 0.004 0.012
#> GSM125145 1 0.0469 0.9178 0.988 0.000 0.000 0.012
#> GSM125147 4 0.3837 0.9559 0.224 0.000 0.000 0.776
#> GSM125149 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125151 1 0.0188 0.9210 0.996 0.000 0.000 0.004
#> GSM125153 1 0.1389 0.8939 0.952 0.000 0.000 0.048
#> GSM125155 1 0.4992 -0.3674 0.524 0.000 0.000 0.476
#> GSM125157 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125159 2 0.4104 0.8429 0.000 0.832 0.080 0.088
#> GSM125161 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125163 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125165 3 0.5948 0.7344 0.000 0.144 0.696 0.160
#> GSM125167 2 0.3820 0.8487 0.000 0.848 0.064 0.088
#> GSM125169 2 0.6693 0.2614 0.000 0.488 0.424 0.088
#> GSM125171 2 0.5543 0.4993 0.000 0.612 0.360 0.028
#> GSM125173 3 0.3877 0.8472 0.000 0.048 0.840 0.112
#> GSM125175 2 0.3831 0.7779 0.000 0.792 0.204 0.004
#> GSM125177 3 0.1059 0.8801 0.000 0.012 0.972 0.016
#> GSM125179 3 0.1118 0.8765 0.000 0.000 0.964 0.036
#> GSM125181 3 0.5855 0.7430 0.000 0.136 0.704 0.160
#> GSM125183 3 0.2799 0.8659 0.000 0.008 0.884 0.108
#> GSM125185 3 0.1118 0.8768 0.000 0.000 0.964 0.036
#> GSM125187 3 0.1474 0.8789 0.000 0.000 0.948 0.052
#> GSM125189 2 0.4525 0.8258 0.000 0.804 0.116 0.080
#> GSM125191 2 0.4139 0.8149 0.000 0.816 0.144 0.040
#> GSM125193 3 0.1637 0.8744 0.000 0.000 0.940 0.060
#> GSM125195 3 0.0921 0.8764 0.000 0.000 0.972 0.028
#> GSM125197 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125199 4 0.3907 0.9525 0.232 0.000 0.000 0.768
#> GSM125201 2 0.0376 0.8852 0.000 0.992 0.004 0.004
#> GSM125203 3 0.0469 0.8795 0.000 0.000 0.988 0.012
#> GSM125205 2 0.4283 0.7093 0.000 0.740 0.256 0.004
#> GSM125207 3 0.0921 0.8789 0.000 0.000 0.972 0.028
#> GSM125209 3 0.5599 0.6294 0.000 0.276 0.672 0.052
#> GSM125211 3 0.5042 0.7970 0.000 0.096 0.768 0.136
#> GSM125213 2 0.2399 0.8729 0.000 0.920 0.048 0.032
#> GSM125215 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125217 2 0.5293 0.7800 0.000 0.748 0.152 0.100
#> GSM125219 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125221 3 0.2775 0.8662 0.000 0.020 0.896 0.084
#> GSM125223 2 0.0188 0.8860 0.000 0.996 0.004 0.000
#> GSM125225 2 0.0592 0.8834 0.000 0.984 0.000 0.016
#> GSM125227 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125229 3 0.3732 0.8396 0.000 0.056 0.852 0.092
#> GSM125231 3 0.4775 0.6647 0.232 0.000 0.740 0.028
#> GSM125233 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125235 4 0.3837 0.9554 0.224 0.000 0.000 0.776
#> GSM125237 4 0.3873 0.9541 0.228 0.000 0.000 0.772
#> GSM125124 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125126 4 0.4746 0.7815 0.368 0.000 0.000 0.632
#> GSM125128 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125130 1 0.0469 0.9107 0.988 0.000 0.000 0.012
#> GSM125132 4 0.4679 0.8097 0.352 0.000 0.000 0.648
#> GSM125134 1 0.0469 0.9190 0.988 0.000 0.000 0.012
#> GSM125136 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125138 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125140 1 0.0188 0.9210 0.996 0.000 0.000 0.004
#> GSM125142 1 0.1867 0.8743 0.928 0.000 0.000 0.072
#> GSM125144 1 0.0188 0.9210 0.996 0.000 0.000 0.004
#> GSM125146 1 0.0707 0.9131 0.980 0.000 0.000 0.020
#> GSM125148 4 0.4543 0.8503 0.324 0.000 0.000 0.676
#> GSM125150 1 0.3024 0.7702 0.852 0.000 0.000 0.148
#> GSM125152 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125154 1 0.0469 0.9186 0.988 0.000 0.000 0.012
#> GSM125156 1 0.2530 0.8226 0.888 0.000 0.000 0.112
#> GSM125158 1 0.2408 0.8327 0.896 0.000 0.000 0.104
#> GSM125160 2 0.2675 0.8702 0.000 0.908 0.044 0.048
#> GSM125162 4 0.3764 0.9571 0.216 0.000 0.000 0.784
#> GSM125164 2 0.0376 0.8856 0.000 0.992 0.004 0.004
#> GSM125166 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125168 3 0.5668 0.5910 0.000 0.300 0.652 0.048
#> GSM125170 3 0.2282 0.8779 0.000 0.024 0.924 0.052
#> GSM125172 2 0.2714 0.8537 0.000 0.884 0.112 0.004
#> GSM125174 3 0.1637 0.8794 0.000 0.000 0.940 0.060
#> GSM125176 3 0.5888 0.0213 0.000 0.424 0.540 0.036
#> GSM125178 3 0.0592 0.8794 0.000 0.000 0.984 0.016
#> GSM125180 3 0.1118 0.8765 0.000 0.000 0.964 0.036
#> GSM125182 3 0.5732 0.6370 0.000 0.264 0.672 0.064
#> GSM125184 3 0.2867 0.8695 0.000 0.012 0.884 0.104
#> GSM125186 3 0.0921 0.8764 0.000 0.000 0.972 0.028
#> GSM125188 3 0.5670 0.7531 0.000 0.128 0.720 0.152
#> GSM125190 2 0.5432 0.7851 0.000 0.740 0.136 0.124
#> GSM125192 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125194 3 0.1022 0.8777 0.000 0.000 0.968 0.032
#> GSM125196 3 0.0921 0.8764 0.000 0.000 0.972 0.028
#> GSM125198 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125200 1 0.2589 0.8173 0.884 0.000 0.000 0.116
#> GSM125202 2 0.2999 0.8394 0.000 0.864 0.132 0.004
#> GSM125204 3 0.0707 0.8792 0.000 0.000 0.980 0.020
#> GSM125206 3 0.0592 0.8787 0.000 0.000 0.984 0.016
#> GSM125208 3 0.0592 0.8803 0.000 0.000 0.984 0.016
#> GSM125210 3 0.1389 0.8785 0.000 0.000 0.952 0.048
#> GSM125212 3 0.5948 0.7336 0.000 0.144 0.696 0.160
#> GSM125214 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125218 2 0.4773 0.8173 0.000 0.788 0.120 0.092
#> GSM125220 4 0.4675 0.9260 0.244 0.000 0.020 0.736
#> GSM125222 3 0.2149 0.8719 0.000 0.000 0.912 0.088
#> GSM125224 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125226 2 0.5031 0.7996 0.000 0.768 0.140 0.092
#> GSM125228 2 0.0000 0.8859 0.000 1.000 0.000 0.000
#> GSM125230 3 0.2675 0.8668 0.000 0.008 0.892 0.100
#> GSM125232 3 0.3907 0.7708 0.140 0.000 0.828 0.032
#> GSM125234 1 0.3367 0.7539 0.864 0.000 0.108 0.028
#> GSM125236 1 0.0000 0.9208 1.000 0.000 0.000 0.000
#> GSM125238 4 0.3837 0.9559 0.224 0.000 0.000 0.776
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.0510 0.956 0.016 0.000 0.000 0.000 0.984
#> GSM125125 5 0.3508 0.695 0.252 0.000 0.000 0.000 0.748
#> GSM125127 5 0.0609 0.958 0.020 0.000 0.000 0.000 0.980
#> GSM125129 5 0.0794 0.960 0.028 0.000 0.000 0.000 0.972
#> GSM125131 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125133 1 0.0510 0.966 0.984 0.000 0.000 0.000 0.016
#> GSM125135 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125137 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125139 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125141 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125143 5 0.1341 0.954 0.056 0.000 0.000 0.000 0.944
#> GSM125145 5 0.1270 0.957 0.052 0.000 0.000 0.000 0.948
#> GSM125147 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125149 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125151 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125153 5 0.2561 0.878 0.144 0.000 0.000 0.000 0.856
#> GSM125155 1 0.3837 0.559 0.692 0.000 0.000 0.000 0.308
#> GSM125157 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125159 4 0.3366 0.801 0.000 0.232 0.000 0.768 0.000
#> GSM125161 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125163 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125165 4 0.1357 0.826 0.004 0.048 0.000 0.948 0.000
#> GSM125167 4 0.3424 0.795 0.000 0.240 0.000 0.760 0.000
#> GSM125169 4 0.2390 0.811 0.008 0.024 0.060 0.908 0.000
#> GSM125171 2 0.2672 0.834 0.008 0.872 0.116 0.004 0.000
#> GSM125173 4 0.4148 0.604 0.004 0.028 0.216 0.752 0.000
#> GSM125175 2 0.1836 0.907 0.008 0.936 0.040 0.016 0.000
#> GSM125177 3 0.2439 0.898 0.004 0.000 0.876 0.120 0.000
#> GSM125179 3 0.0162 0.921 0.000 0.000 0.996 0.004 0.000
#> GSM125181 4 0.1547 0.818 0.004 0.032 0.016 0.948 0.000
#> GSM125183 3 0.3048 0.865 0.004 0.000 0.820 0.176 0.000
#> GSM125185 3 0.0794 0.919 0.000 0.000 0.972 0.028 0.000
#> GSM125187 3 0.0609 0.922 0.000 0.000 0.980 0.020 0.000
#> GSM125189 4 0.4015 0.686 0.000 0.348 0.000 0.652 0.000
#> GSM125191 4 0.3932 0.713 0.000 0.328 0.000 0.672 0.000
#> GSM125193 4 0.1851 0.781 0.000 0.000 0.088 0.912 0.000
#> GSM125195 3 0.0794 0.923 0.000 0.000 0.972 0.028 0.000
#> GSM125197 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125199 1 0.0703 0.962 0.976 0.000 0.000 0.000 0.024
#> GSM125201 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125203 3 0.2674 0.884 0.004 0.000 0.856 0.140 0.000
#> GSM125205 2 0.1913 0.904 0.008 0.932 0.044 0.016 0.000
#> GSM125207 3 0.0963 0.920 0.000 0.000 0.964 0.036 0.000
#> GSM125209 4 0.3724 0.825 0.000 0.184 0.028 0.788 0.000
#> GSM125211 4 0.1412 0.821 0.004 0.036 0.008 0.952 0.000
#> GSM125213 4 0.4294 0.458 0.000 0.468 0.000 0.532 0.000
#> GSM125215 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125217 4 0.2966 0.823 0.000 0.184 0.000 0.816 0.000
#> GSM125219 5 0.0290 0.951 0.008 0.000 0.000 0.000 0.992
#> GSM125221 4 0.1638 0.795 0.000 0.004 0.064 0.932 0.000
#> GSM125223 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125225 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125227 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125229 4 0.2037 0.802 0.004 0.012 0.064 0.920 0.000
#> GSM125231 3 0.1251 0.911 0.000 0.000 0.956 0.008 0.036
#> GSM125233 5 0.0609 0.958 0.020 0.000 0.000 0.000 0.980
#> GSM125235 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125237 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125124 5 0.0794 0.961 0.028 0.000 0.000 0.000 0.972
#> GSM125126 1 0.2280 0.876 0.880 0.000 0.000 0.000 0.120
#> GSM125128 1 0.0703 0.961 0.976 0.000 0.000 0.000 0.024
#> GSM125130 5 0.0290 0.951 0.008 0.000 0.000 0.000 0.992
#> GSM125132 1 0.1341 0.940 0.944 0.000 0.000 0.000 0.056
#> GSM125134 5 0.1043 0.959 0.040 0.000 0.000 0.000 0.960
#> GSM125136 1 0.0510 0.966 0.984 0.000 0.000 0.000 0.016
#> GSM125138 5 0.0880 0.961 0.032 0.000 0.000 0.000 0.968
#> GSM125140 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125142 5 0.1908 0.928 0.092 0.000 0.000 0.000 0.908
#> GSM125144 5 0.0880 0.961 0.032 0.000 0.000 0.000 0.968
#> GSM125146 5 0.1908 0.928 0.092 0.000 0.000 0.000 0.908
#> GSM125148 1 0.1671 0.923 0.924 0.000 0.000 0.000 0.076
#> GSM125150 5 0.2813 0.847 0.168 0.000 0.000 0.000 0.832
#> GSM125152 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125154 5 0.1671 0.940 0.076 0.000 0.000 0.000 0.924
#> GSM125156 5 0.1043 0.961 0.040 0.000 0.000 0.000 0.960
#> GSM125158 5 0.1121 0.960 0.044 0.000 0.000 0.000 0.956
#> GSM125160 4 0.4227 0.566 0.000 0.420 0.000 0.580 0.000
#> GSM125162 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
#> GSM125164 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125166 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125168 4 0.6245 0.618 0.000 0.220 0.236 0.544 0.000
#> GSM125170 3 0.4525 0.483 0.000 0.016 0.624 0.360 0.000
#> GSM125172 2 0.0324 0.958 0.000 0.992 0.004 0.004 0.000
#> GSM125174 3 0.1205 0.918 0.004 0.000 0.956 0.040 0.000
#> GSM125176 2 0.4260 0.565 0.008 0.680 0.308 0.004 0.000
#> GSM125178 3 0.2020 0.907 0.000 0.000 0.900 0.100 0.000
#> GSM125180 3 0.0162 0.921 0.000 0.000 0.996 0.004 0.000
#> GSM125182 4 0.3134 0.833 0.000 0.120 0.032 0.848 0.000
#> GSM125184 3 0.2068 0.900 0.004 0.000 0.904 0.092 0.000
#> GSM125186 3 0.0703 0.920 0.000 0.000 0.976 0.024 0.000
#> GSM125188 4 0.1471 0.812 0.004 0.020 0.024 0.952 0.000
#> GSM125190 4 0.3300 0.813 0.000 0.204 0.004 0.792 0.000
#> GSM125192 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125194 3 0.2424 0.895 0.000 0.000 0.868 0.132 0.000
#> GSM125196 3 0.0404 0.921 0.000 0.000 0.988 0.012 0.000
#> GSM125198 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125200 5 0.0963 0.962 0.036 0.000 0.000 0.000 0.964
#> GSM125202 2 0.0693 0.949 0.000 0.980 0.008 0.012 0.000
#> GSM125204 3 0.1410 0.920 0.000 0.000 0.940 0.060 0.000
#> GSM125206 3 0.1478 0.919 0.000 0.000 0.936 0.064 0.000
#> GSM125208 3 0.1197 0.923 0.000 0.000 0.952 0.048 0.000
#> GSM125210 3 0.0880 0.920 0.000 0.000 0.968 0.032 0.000
#> GSM125212 4 0.1430 0.827 0.004 0.052 0.000 0.944 0.000
#> GSM125214 2 0.0162 0.960 0.000 0.996 0.000 0.004 0.000
#> GSM125216 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125218 4 0.3274 0.808 0.000 0.220 0.000 0.780 0.000
#> GSM125220 1 0.0963 0.958 0.964 0.000 0.000 0.000 0.036
#> GSM125222 3 0.3928 0.724 0.004 0.000 0.700 0.296 0.000
#> GSM125224 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125226 4 0.3074 0.820 0.000 0.196 0.000 0.804 0.000
#> GSM125228 2 0.0000 0.960 0.000 1.000 0.000 0.000 0.000
#> GSM125230 3 0.3300 0.850 0.004 0.000 0.792 0.204 0.000
#> GSM125232 3 0.1571 0.901 0.000 0.000 0.936 0.004 0.060
#> GSM125234 5 0.1764 0.901 0.000 0.000 0.064 0.008 0.928
#> GSM125236 5 0.0290 0.951 0.008 0.000 0.000 0.000 0.992
#> GSM125238 1 0.0404 0.968 0.988 0.000 0.000 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.0508 0.9400 0.984 0.000 0.004 0.000 0.012 0.000
#> GSM125125 1 0.3636 0.5615 0.676 0.000 0.000 0.000 0.320 0.004
#> GSM125127 1 0.1226 0.9228 0.952 0.000 0.004 0.000 0.004 0.040
#> GSM125129 1 0.0692 0.9432 0.976 0.000 0.004 0.000 0.020 0.000
#> GSM125131 5 0.0713 0.9448 0.028 0.000 0.000 0.000 0.972 0.000
#> GSM125133 5 0.0458 0.9518 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM125135 1 0.1155 0.9465 0.956 0.000 0.004 0.000 0.036 0.004
#> GSM125137 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125139 1 0.1082 0.9468 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125141 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125143 1 0.1644 0.9320 0.920 0.000 0.004 0.000 0.076 0.000
#> GSM125145 1 0.1889 0.9430 0.920 0.000 0.004 0.000 0.056 0.020
#> GSM125147 5 0.0260 0.9540 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125149 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125151 1 0.1082 0.9468 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125153 1 0.2704 0.8835 0.844 0.000 0.000 0.000 0.140 0.016
#> GSM125155 5 0.3584 0.5522 0.308 0.000 0.000 0.000 0.688 0.004
#> GSM125157 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125159 6 0.4890 0.7162 0.000 0.140 0.204 0.000 0.000 0.656
#> GSM125161 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125163 2 0.1075 0.9125 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM125165 3 0.3087 0.5239 0.000 0.012 0.808 0.004 0.000 0.176
#> GSM125167 6 0.4821 0.7188 0.000 0.148 0.184 0.000 0.000 0.668
#> GSM125169 6 0.4963 0.3657 0.000 0.008 0.368 0.056 0.000 0.568
#> GSM125171 2 0.3657 0.7755 0.000 0.816 0.028 0.104 0.000 0.052
#> GSM125173 3 0.3423 0.6029 0.000 0.008 0.808 0.148 0.000 0.036
#> GSM125175 2 0.1950 0.8941 0.000 0.924 0.016 0.028 0.000 0.032
#> GSM125177 4 0.3950 0.7474 0.000 0.000 0.240 0.720 0.000 0.040
#> GSM125179 4 0.0405 0.8204 0.000 0.000 0.004 0.988 0.000 0.008
#> GSM125181 3 0.3343 0.5504 0.000 0.004 0.796 0.024 0.000 0.176
#> GSM125183 4 0.4093 0.5276 0.000 0.000 0.404 0.584 0.000 0.012
#> GSM125185 4 0.0858 0.8214 0.000 0.000 0.004 0.968 0.000 0.028
#> GSM125187 4 0.2882 0.7929 0.000 0.000 0.180 0.812 0.000 0.008
#> GSM125189 6 0.5138 0.7019 0.000 0.208 0.168 0.000 0.000 0.624
#> GSM125191 6 0.5955 0.5324 0.000 0.280 0.208 0.008 0.000 0.504
#> GSM125193 3 0.1908 0.6180 0.000 0.000 0.900 0.096 0.000 0.004
#> GSM125195 4 0.2956 0.8090 0.000 0.000 0.120 0.840 0.000 0.040
#> GSM125197 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125199 5 0.0547 0.9478 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125201 2 0.0260 0.9361 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125203 4 0.4561 0.3935 0.000 0.000 0.428 0.536 0.000 0.036
#> GSM125205 2 0.2172 0.8855 0.000 0.912 0.020 0.024 0.000 0.044
#> GSM125207 4 0.1575 0.8225 0.000 0.000 0.032 0.936 0.000 0.032
#> GSM125209 6 0.5717 0.3905 0.000 0.100 0.348 0.024 0.000 0.528
#> GSM125211 3 0.2400 0.5839 0.000 0.008 0.872 0.004 0.000 0.116
#> GSM125213 6 0.4282 0.6054 0.000 0.304 0.040 0.000 0.000 0.656
#> GSM125215 2 0.0547 0.9313 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125217 6 0.4662 0.6907 0.000 0.096 0.236 0.000 0.000 0.668
#> GSM125219 1 0.0935 0.9233 0.964 0.000 0.004 0.000 0.000 0.032
#> GSM125221 3 0.1500 0.6234 0.000 0.000 0.936 0.052 0.000 0.012
#> GSM125223 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125225 2 0.1863 0.8548 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM125227 2 0.0260 0.9370 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM125229 3 0.4289 0.4097 0.000 0.008 0.696 0.040 0.000 0.256
#> GSM125231 4 0.4017 0.7676 0.064 0.000 0.064 0.800 0.000 0.072
#> GSM125233 1 0.0458 0.9408 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM125235 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125237 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125124 1 0.1225 0.9463 0.952 0.000 0.000 0.000 0.036 0.012
#> GSM125126 5 0.2300 0.8356 0.144 0.000 0.000 0.000 0.856 0.000
#> GSM125128 5 0.0547 0.9493 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125130 1 0.1429 0.9117 0.940 0.000 0.004 0.004 0.000 0.052
#> GSM125132 5 0.1444 0.9128 0.072 0.000 0.000 0.000 0.928 0.000
#> GSM125134 1 0.1672 0.9447 0.932 0.000 0.004 0.000 0.048 0.016
#> GSM125136 5 0.0547 0.9493 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM125138 1 0.1297 0.9464 0.948 0.000 0.000 0.000 0.040 0.012
#> GSM125140 1 0.0937 0.9462 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125142 1 0.2112 0.9259 0.896 0.000 0.000 0.000 0.088 0.016
#> GSM125144 1 0.1225 0.9463 0.952 0.000 0.000 0.000 0.036 0.012
#> GSM125146 1 0.2669 0.9101 0.864 0.000 0.004 0.000 0.108 0.024
#> GSM125148 5 0.1501 0.9035 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM125150 1 0.2402 0.8764 0.856 0.000 0.000 0.000 0.140 0.004
#> GSM125152 1 0.1082 0.9468 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125154 1 0.2006 0.9304 0.904 0.000 0.000 0.000 0.080 0.016
#> GSM125156 1 0.1152 0.9462 0.952 0.000 0.000 0.000 0.044 0.004
#> GSM125158 1 0.1082 0.9463 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125160 6 0.3979 0.6447 0.000 0.256 0.036 0.000 0.000 0.708
#> GSM125162 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM125164 2 0.1556 0.8903 0.000 0.920 0.000 0.000 0.000 0.080
#> GSM125166 2 0.1007 0.9194 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM125168 6 0.7018 0.3407 0.000 0.132 0.260 0.148 0.000 0.460
#> GSM125170 3 0.4381 -0.0403 0.000 0.000 0.536 0.440 0.000 0.024
#> GSM125172 2 0.0820 0.9301 0.000 0.972 0.012 0.000 0.000 0.016
#> GSM125174 4 0.1616 0.8155 0.000 0.000 0.048 0.932 0.000 0.020
#> GSM125176 2 0.5592 0.5187 0.000 0.632 0.060 0.224 0.000 0.084
#> GSM125178 4 0.3791 0.7528 0.000 0.000 0.236 0.732 0.000 0.032
#> GSM125180 4 0.0405 0.8204 0.000 0.000 0.004 0.988 0.000 0.008
#> GSM125182 6 0.5708 0.3161 0.000 0.080 0.376 0.032 0.000 0.512
#> GSM125184 4 0.2250 0.7908 0.000 0.000 0.092 0.888 0.000 0.020
#> GSM125186 4 0.0632 0.8211 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM125188 3 0.3424 0.5594 0.000 0.004 0.796 0.032 0.000 0.168
#> GSM125190 6 0.5076 0.6910 0.000 0.132 0.248 0.000 0.000 0.620
#> GSM125192 2 0.0146 0.9373 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125194 4 0.4184 0.2587 0.000 0.000 0.488 0.500 0.000 0.012
#> GSM125196 4 0.1780 0.8234 0.000 0.000 0.048 0.924 0.000 0.028
#> GSM125198 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125200 1 0.1082 0.9463 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125202 2 0.0806 0.9316 0.000 0.972 0.008 0.000 0.000 0.020
#> GSM125204 4 0.3555 0.7811 0.000 0.000 0.184 0.776 0.000 0.040
#> GSM125206 4 0.3450 0.7870 0.000 0.000 0.188 0.780 0.000 0.032
#> GSM125208 4 0.2201 0.8202 0.000 0.000 0.076 0.896 0.000 0.028
#> GSM125210 4 0.0993 0.8226 0.000 0.000 0.012 0.964 0.000 0.024
#> GSM125212 3 0.3253 0.4843 0.000 0.020 0.788 0.000 0.000 0.192
#> GSM125214 2 0.0146 0.9373 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125216 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125218 6 0.4804 0.6985 0.000 0.112 0.232 0.000 0.000 0.656
#> GSM125220 5 0.0632 0.9498 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM125222 3 0.3221 0.4101 0.000 0.000 0.736 0.264 0.000 0.000
#> GSM125224 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125226 6 0.4895 0.7102 0.000 0.124 0.228 0.000 0.000 0.648
#> GSM125228 2 0.0000 0.9379 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125230 3 0.4192 -0.1250 0.000 0.000 0.572 0.412 0.000 0.016
#> GSM125232 4 0.1720 0.8025 0.032 0.000 0.000 0.928 0.000 0.040
#> GSM125234 1 0.2998 0.8392 0.852 0.000 0.004 0.076 0.000 0.068
#> GSM125236 1 0.1082 0.9201 0.956 0.000 0.004 0.000 0.000 0.040
#> GSM125238 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 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 agent(p) individual(p) k
#> MAD:mclust 116 1.000 6.52e-06 2
#> MAD:mclust 110 0.968 5.70e-08 3
#> MAD:mclust 111 0.690 1.18e-06 4
#> MAD:mclust 114 0.387 7.53e-07 5
#> MAD:mclust 105 0.328 1.62e-07 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.946 0.943 0.977 0.5032 0.496 0.496
#> 3 3 0.797 0.869 0.931 0.2870 0.806 0.627
#> 4 4 0.637 0.655 0.820 0.1187 0.895 0.712
#> 5 5 0.647 0.609 0.764 0.0572 0.933 0.770
#> 6 6 0.695 0.578 0.771 0.0292 0.909 0.687
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
#> GSM125123 1 0.0000 0.975 1.000 0.000
#> GSM125125 1 0.0000 0.975 1.000 0.000
#> GSM125127 1 0.0000 0.975 1.000 0.000
#> GSM125129 1 0.0000 0.975 1.000 0.000
#> GSM125131 1 0.0000 0.975 1.000 0.000
#> GSM125133 1 0.0000 0.975 1.000 0.000
#> GSM125135 1 0.0000 0.975 1.000 0.000
#> GSM125137 1 0.0000 0.975 1.000 0.000
#> GSM125139 1 0.0000 0.975 1.000 0.000
#> GSM125141 1 0.0000 0.975 1.000 0.000
#> GSM125143 1 0.0000 0.975 1.000 0.000
#> GSM125145 1 0.0000 0.975 1.000 0.000
#> GSM125147 1 0.0000 0.975 1.000 0.000
#> GSM125149 1 0.0000 0.975 1.000 0.000
#> GSM125151 1 0.0000 0.975 1.000 0.000
#> GSM125153 1 0.0000 0.975 1.000 0.000
#> GSM125155 1 0.0000 0.975 1.000 0.000
#> GSM125157 1 0.0000 0.975 1.000 0.000
#> GSM125159 2 0.0000 0.976 0.000 1.000
#> GSM125161 1 0.0000 0.975 1.000 0.000
#> GSM125163 2 0.0000 0.976 0.000 1.000
#> GSM125165 2 0.0000 0.976 0.000 1.000
#> GSM125167 2 0.0000 0.976 0.000 1.000
#> GSM125169 2 0.0000 0.976 0.000 1.000
#> GSM125171 2 0.0000 0.976 0.000 1.000
#> GSM125173 2 0.0000 0.976 0.000 1.000
#> GSM125175 2 0.0000 0.976 0.000 1.000
#> GSM125177 2 0.0000 0.976 0.000 1.000
#> GSM125179 2 0.6801 0.789 0.180 0.820
#> GSM125181 2 0.0000 0.976 0.000 1.000
#> GSM125183 2 0.1184 0.963 0.016 0.984
#> GSM125185 2 0.0000 0.976 0.000 1.000
#> GSM125187 1 0.4298 0.883 0.912 0.088
#> GSM125189 2 0.0000 0.976 0.000 1.000
#> GSM125191 2 0.0000 0.976 0.000 1.000
#> GSM125193 1 0.0376 0.971 0.996 0.004
#> GSM125195 1 0.9922 0.167 0.552 0.448
#> GSM125197 2 0.0000 0.976 0.000 1.000
#> GSM125199 1 0.0000 0.975 1.000 0.000
#> GSM125201 2 0.0000 0.976 0.000 1.000
#> GSM125203 2 0.7815 0.713 0.232 0.768
#> GSM125205 2 0.0000 0.976 0.000 1.000
#> GSM125207 2 0.0000 0.976 0.000 1.000
#> GSM125209 2 0.0000 0.976 0.000 1.000
#> GSM125211 2 0.0000 0.976 0.000 1.000
#> GSM125213 2 0.0000 0.976 0.000 1.000
#> GSM125215 2 0.0000 0.976 0.000 1.000
#> GSM125217 2 0.0000 0.976 0.000 1.000
#> GSM125219 1 0.0000 0.975 1.000 0.000
#> GSM125221 2 0.0000 0.976 0.000 1.000
#> GSM125223 2 0.0000 0.976 0.000 1.000
#> GSM125225 2 0.0000 0.976 0.000 1.000
#> GSM125227 2 0.0000 0.976 0.000 1.000
#> GSM125229 2 0.0000 0.976 0.000 1.000
#> GSM125231 1 0.0000 0.975 1.000 0.000
#> GSM125233 1 0.0000 0.975 1.000 0.000
#> GSM125235 1 0.0000 0.975 1.000 0.000
#> GSM125237 1 0.0000 0.975 1.000 0.000
#> GSM125124 1 0.0000 0.975 1.000 0.000
#> GSM125126 1 0.0000 0.975 1.000 0.000
#> GSM125128 1 0.0000 0.975 1.000 0.000
#> GSM125130 1 0.0000 0.975 1.000 0.000
#> GSM125132 1 0.0000 0.975 1.000 0.000
#> GSM125134 1 0.0000 0.975 1.000 0.000
#> GSM125136 1 0.0000 0.975 1.000 0.000
#> GSM125138 1 0.0000 0.975 1.000 0.000
#> GSM125140 1 0.0000 0.975 1.000 0.000
#> GSM125142 1 0.0000 0.975 1.000 0.000
#> GSM125144 1 0.0000 0.975 1.000 0.000
#> GSM125146 1 0.0000 0.975 1.000 0.000
#> GSM125148 1 0.0000 0.975 1.000 0.000
#> GSM125150 1 0.0000 0.975 1.000 0.000
#> GSM125152 1 0.0000 0.975 1.000 0.000
#> GSM125154 1 0.0000 0.975 1.000 0.000
#> GSM125156 1 0.0000 0.975 1.000 0.000
#> GSM125158 1 0.0000 0.975 1.000 0.000
#> GSM125160 2 0.0000 0.976 0.000 1.000
#> GSM125162 1 0.0000 0.975 1.000 0.000
#> GSM125164 2 0.0000 0.976 0.000 1.000
#> GSM125166 2 0.0000 0.976 0.000 1.000
#> GSM125168 2 0.0000 0.976 0.000 1.000
#> GSM125170 2 0.0000 0.976 0.000 1.000
#> GSM125172 2 0.0000 0.976 0.000 1.000
#> GSM125174 2 0.0000 0.976 0.000 1.000
#> GSM125176 2 0.0000 0.976 0.000 1.000
#> GSM125178 2 0.8207 0.673 0.256 0.744
#> GSM125180 1 0.9170 0.488 0.668 0.332
#> GSM125182 2 0.0000 0.976 0.000 1.000
#> GSM125184 2 0.0000 0.976 0.000 1.000
#> GSM125186 2 0.7745 0.719 0.228 0.772
#> GSM125188 2 0.0000 0.976 0.000 1.000
#> GSM125190 2 0.0000 0.976 0.000 1.000
#> GSM125192 2 0.0000 0.976 0.000 1.000
#> GSM125194 1 0.0000 0.975 1.000 0.000
#> GSM125196 2 0.6801 0.789 0.180 0.820
#> GSM125198 2 0.0000 0.976 0.000 1.000
#> GSM125200 1 0.0000 0.975 1.000 0.000
#> GSM125202 2 0.0000 0.976 0.000 1.000
#> GSM125204 1 0.9963 0.108 0.536 0.464
#> GSM125206 2 0.2778 0.934 0.048 0.952
#> GSM125208 2 0.7745 0.718 0.228 0.772
#> GSM125210 2 0.0000 0.976 0.000 1.000
#> GSM125212 2 0.0000 0.976 0.000 1.000
#> GSM125214 2 0.0000 0.976 0.000 1.000
#> GSM125216 2 0.0000 0.976 0.000 1.000
#> GSM125218 2 0.0000 0.976 0.000 1.000
#> GSM125220 1 0.0000 0.975 1.000 0.000
#> GSM125222 2 0.0000 0.976 0.000 1.000
#> GSM125224 2 0.0000 0.976 0.000 1.000
#> GSM125226 2 0.0000 0.976 0.000 1.000
#> GSM125228 2 0.0000 0.976 0.000 1.000
#> GSM125230 1 0.0000 0.975 1.000 0.000
#> GSM125232 1 0.0000 0.975 1.000 0.000
#> GSM125234 1 0.0000 0.975 1.000 0.000
#> GSM125236 1 0.0000 0.975 1.000 0.000
#> GSM125238 1 0.0000 0.975 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.2959 0.905 0.900 0.000 0.100
#> GSM125125 1 0.1031 0.939 0.976 0.000 0.024
#> GSM125127 1 0.3412 0.883 0.876 0.000 0.124
#> GSM125129 1 0.2261 0.926 0.932 0.000 0.068
#> GSM125131 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125135 1 0.1860 0.933 0.948 0.000 0.052
#> GSM125137 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125139 1 0.4178 0.828 0.828 0.000 0.172
#> GSM125141 1 0.0237 0.940 0.996 0.000 0.004
#> GSM125143 1 0.2448 0.922 0.924 0.000 0.076
#> GSM125145 1 0.2066 0.930 0.940 0.000 0.060
#> GSM125147 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125151 1 0.6274 0.163 0.544 0.000 0.456
#> GSM125153 1 0.1411 0.938 0.964 0.000 0.036
#> GSM125155 1 0.0892 0.940 0.980 0.000 0.020
#> GSM125157 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125159 2 0.0237 0.955 0.004 0.996 0.000
#> GSM125161 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125165 2 0.0829 0.952 0.012 0.984 0.004
#> GSM125167 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125169 2 0.3752 0.829 0.144 0.856 0.000
#> GSM125171 2 0.0237 0.955 0.000 0.996 0.004
#> GSM125173 2 0.0237 0.955 0.000 0.996 0.004
#> GSM125175 2 0.0237 0.955 0.000 0.996 0.004
#> GSM125177 2 0.5431 0.620 0.000 0.716 0.284
#> GSM125179 3 0.0424 0.840 0.000 0.008 0.992
#> GSM125181 2 0.1647 0.941 0.004 0.960 0.036
#> GSM125183 3 0.5216 0.631 0.000 0.260 0.740
#> GSM125185 3 0.1289 0.836 0.000 0.032 0.968
#> GSM125187 3 0.0592 0.836 0.012 0.000 0.988
#> GSM125189 2 0.0237 0.955 0.004 0.996 0.000
#> GSM125191 2 0.1753 0.934 0.000 0.952 0.048
#> GSM125193 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125195 3 0.0592 0.841 0.000 0.012 0.988
#> GSM125197 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125199 1 0.0237 0.940 0.996 0.000 0.004
#> GSM125201 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125203 2 0.6054 0.704 0.180 0.768 0.052
#> GSM125205 2 0.0592 0.953 0.000 0.988 0.012
#> GSM125207 3 0.1031 0.838 0.000 0.024 0.976
#> GSM125209 2 0.4062 0.828 0.000 0.836 0.164
#> GSM125211 2 0.2066 0.919 0.060 0.940 0.000
#> GSM125213 2 0.0592 0.953 0.000 0.988 0.012
#> GSM125215 2 0.0237 0.955 0.000 0.996 0.004
#> GSM125217 2 0.1529 0.934 0.040 0.960 0.000
#> GSM125219 1 0.2625 0.916 0.916 0.000 0.084
#> GSM125221 2 0.2066 0.919 0.060 0.940 0.000
#> GSM125223 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125229 2 0.3686 0.834 0.140 0.860 0.000
#> GSM125231 3 0.5138 0.648 0.252 0.000 0.748
#> GSM125233 1 0.3340 0.887 0.880 0.000 0.120
#> GSM125235 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125124 3 0.3267 0.779 0.116 0.000 0.884
#> GSM125126 1 0.0424 0.940 0.992 0.000 0.008
#> GSM125128 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125130 3 0.5621 0.534 0.308 0.000 0.692
#> GSM125132 1 0.0424 0.940 0.992 0.000 0.008
#> GSM125134 1 0.2537 0.919 0.920 0.000 0.080
#> GSM125136 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125138 3 0.6111 0.364 0.396 0.000 0.604
#> GSM125140 1 0.4121 0.832 0.832 0.000 0.168
#> GSM125142 1 0.1643 0.936 0.956 0.000 0.044
#> GSM125144 3 0.6168 0.312 0.412 0.000 0.588
#> GSM125146 1 0.1964 0.932 0.944 0.000 0.056
#> GSM125148 1 0.0237 0.940 0.996 0.000 0.004
#> GSM125150 1 0.0892 0.940 0.980 0.000 0.020
#> GSM125152 3 0.6095 0.358 0.392 0.000 0.608
#> GSM125154 1 0.3412 0.879 0.876 0.000 0.124
#> GSM125156 1 0.1964 0.932 0.944 0.000 0.056
#> GSM125158 1 0.1753 0.934 0.952 0.000 0.048
#> GSM125160 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125164 2 0.0592 0.953 0.000 0.988 0.012
#> GSM125166 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125168 2 0.3116 0.885 0.000 0.892 0.108
#> GSM125170 2 0.0424 0.954 0.000 0.992 0.008
#> GSM125172 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125174 3 0.2261 0.826 0.000 0.068 0.932
#> GSM125176 2 0.3941 0.833 0.000 0.844 0.156
#> GSM125178 3 0.5696 0.769 0.056 0.148 0.796
#> GSM125180 3 0.0000 0.838 0.000 0.000 1.000
#> GSM125182 2 0.3267 0.880 0.000 0.884 0.116
#> GSM125184 3 0.2356 0.821 0.000 0.072 0.928
#> GSM125186 3 0.0237 0.839 0.000 0.004 0.996
#> GSM125188 2 0.2301 0.927 0.004 0.936 0.060
#> GSM125190 2 0.0237 0.955 0.004 0.996 0.000
#> GSM125192 2 0.0424 0.954 0.000 0.992 0.008
#> GSM125194 1 0.5760 0.505 0.672 0.000 0.328
#> GSM125196 3 0.0592 0.840 0.000 0.012 0.988
#> GSM125198 2 0.0237 0.955 0.000 0.996 0.004
#> GSM125200 1 0.1529 0.937 0.960 0.000 0.040
#> GSM125202 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125204 3 0.4209 0.788 0.020 0.120 0.860
#> GSM125206 3 0.5873 0.511 0.004 0.312 0.684
#> GSM125208 3 0.0424 0.840 0.000 0.008 0.992
#> GSM125210 3 0.2356 0.820 0.000 0.072 0.928
#> GSM125212 2 0.0892 0.947 0.020 0.980 0.000
#> GSM125214 2 0.0592 0.953 0.000 0.988 0.012
#> GSM125216 2 0.0892 0.951 0.000 0.980 0.020
#> GSM125218 2 0.1411 0.937 0.036 0.964 0.000
#> GSM125220 1 0.0000 0.939 1.000 0.000 0.000
#> GSM125222 2 0.3038 0.885 0.000 0.896 0.104
#> GSM125224 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125226 2 0.0237 0.955 0.004 0.996 0.000
#> GSM125228 2 0.0000 0.956 0.000 1.000 0.000
#> GSM125230 3 0.6026 0.466 0.376 0.000 0.624
#> GSM125232 3 0.0424 0.837 0.008 0.000 0.992
#> GSM125234 3 0.1643 0.824 0.044 0.000 0.956
#> GSM125236 1 0.2261 0.927 0.932 0.000 0.068
#> GSM125238 1 0.0000 0.939 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.3105 0.8511 0.868 0.000 0.012 0.120
#> GSM125125 1 0.0469 0.8965 0.988 0.000 0.000 0.012
#> GSM125127 1 0.6223 0.5574 0.656 0.020 0.052 0.272
#> GSM125129 1 0.3215 0.8588 0.876 0.000 0.032 0.092
#> GSM125131 1 0.0188 0.8973 0.996 0.000 0.004 0.000
#> GSM125133 1 0.0592 0.8958 0.984 0.000 0.016 0.000
#> GSM125135 1 0.2775 0.8701 0.896 0.000 0.020 0.084
#> GSM125137 1 0.4008 0.7014 0.756 0.000 0.244 0.000
#> GSM125139 1 0.2149 0.8777 0.912 0.000 0.000 0.088
#> GSM125141 1 0.2081 0.8728 0.916 0.000 0.084 0.000
#> GSM125143 1 0.1767 0.8924 0.944 0.000 0.012 0.044
#> GSM125145 1 0.3219 0.8523 0.868 0.000 0.020 0.112
#> GSM125147 1 0.0921 0.8936 0.972 0.000 0.028 0.000
#> GSM125149 1 0.2469 0.8528 0.892 0.000 0.108 0.000
#> GSM125151 1 0.5284 0.4170 0.616 0.000 0.016 0.368
#> GSM125153 1 0.2256 0.8846 0.924 0.000 0.020 0.056
#> GSM125155 1 0.1635 0.8931 0.948 0.000 0.044 0.008
#> GSM125157 1 0.2345 0.8589 0.900 0.000 0.100 0.000
#> GSM125159 2 0.5285 0.0791 0.000 0.524 0.468 0.008
#> GSM125161 1 0.3688 0.7463 0.792 0.000 0.208 0.000
#> GSM125163 2 0.1211 0.7990 0.000 0.960 0.040 0.000
#> GSM125165 3 0.3300 0.6976 0.000 0.144 0.848 0.008
#> GSM125167 2 0.4872 0.4359 0.000 0.640 0.356 0.004
#> GSM125169 2 0.4920 0.6590 0.052 0.756 0.192 0.000
#> GSM125171 2 0.3174 0.7202 0.008 0.892 0.048 0.052
#> GSM125173 3 0.5372 0.1833 0.000 0.444 0.544 0.012
#> GSM125175 2 0.0336 0.7986 0.000 0.992 0.008 0.000
#> GSM125177 2 0.7731 -0.1486 0.000 0.428 0.332 0.240
#> GSM125179 4 0.3257 0.5811 0.000 0.004 0.152 0.844
#> GSM125181 3 0.3547 0.6982 0.000 0.144 0.840 0.016
#> GSM125183 3 0.3969 0.5326 0.000 0.016 0.804 0.180
#> GSM125185 4 0.4730 0.3183 0.000 0.000 0.364 0.636
#> GSM125187 3 0.5070 0.2156 0.000 0.004 0.580 0.416
#> GSM125189 2 0.2760 0.7554 0.000 0.872 0.128 0.000
#> GSM125191 2 0.5376 0.2610 0.000 0.588 0.396 0.016
#> GSM125193 3 0.4019 0.4843 0.196 0.012 0.792 0.000
#> GSM125195 4 0.5037 0.5728 0.040 0.048 0.112 0.800
#> GSM125197 2 0.0592 0.7943 0.000 0.984 0.016 0.000
#> GSM125199 1 0.1940 0.8756 0.924 0.000 0.076 0.000
#> GSM125201 2 0.0707 0.7921 0.000 0.980 0.020 0.000
#> GSM125203 2 0.8605 -0.1123 0.144 0.428 0.360 0.068
#> GSM125205 2 0.2385 0.7450 0.000 0.920 0.052 0.028
#> GSM125207 4 0.4661 0.3652 0.000 0.000 0.348 0.652
#> GSM125209 3 0.6192 0.2215 0.000 0.436 0.512 0.052
#> GSM125211 3 0.3048 0.6794 0.016 0.108 0.876 0.000
#> GSM125213 2 0.5093 0.4080 0.000 0.640 0.348 0.012
#> GSM125215 2 0.0817 0.8016 0.000 0.976 0.024 0.000
#> GSM125217 2 0.5193 0.3058 0.008 0.580 0.412 0.000
#> GSM125219 1 0.2727 0.8729 0.900 0.004 0.012 0.084
#> GSM125221 3 0.4462 0.6662 0.044 0.164 0.792 0.000
#> GSM125223 2 0.0469 0.7961 0.000 0.988 0.012 0.000
#> GSM125225 2 0.1389 0.7958 0.000 0.952 0.048 0.000
#> GSM125227 2 0.0592 0.8008 0.000 0.984 0.016 0.000
#> GSM125229 3 0.6508 0.4395 0.104 0.296 0.600 0.000
#> GSM125231 4 0.6511 0.5443 0.196 0.016 0.116 0.672
#> GSM125233 1 0.2737 0.8648 0.888 0.000 0.008 0.104
#> GSM125235 1 0.1302 0.8890 0.956 0.000 0.044 0.000
#> GSM125237 1 0.1716 0.8813 0.936 0.000 0.064 0.000
#> GSM125124 4 0.3907 0.5968 0.120 0.000 0.044 0.836
#> GSM125126 1 0.0657 0.8974 0.984 0.000 0.012 0.004
#> GSM125128 1 0.1118 0.8945 0.964 0.000 0.036 0.000
#> GSM125130 4 0.6398 0.2287 0.396 0.012 0.044 0.548
#> GSM125132 1 0.0188 0.8971 0.996 0.000 0.004 0.000
#> GSM125134 1 0.3598 0.8363 0.848 0.000 0.028 0.124
#> GSM125136 1 0.1867 0.8774 0.928 0.000 0.072 0.000
#> GSM125138 4 0.5769 0.3073 0.376 0.000 0.036 0.588
#> GSM125140 1 0.2530 0.8701 0.896 0.000 0.004 0.100
#> GSM125142 1 0.1174 0.8958 0.968 0.000 0.012 0.020
#> GSM125144 4 0.5611 0.2069 0.412 0.000 0.024 0.564
#> GSM125146 1 0.3051 0.8634 0.884 0.000 0.028 0.088
#> GSM125148 1 0.0188 0.8979 0.996 0.000 0.004 0.000
#> GSM125150 1 0.0469 0.8965 0.988 0.000 0.000 0.012
#> GSM125152 4 0.5237 0.3779 0.356 0.000 0.016 0.628
#> GSM125154 1 0.4436 0.7837 0.800 0.000 0.052 0.148
#> GSM125156 1 0.0657 0.8972 0.984 0.000 0.004 0.012
#> GSM125158 1 0.0707 0.8956 0.980 0.000 0.000 0.020
#> GSM125160 2 0.4746 0.5249 0.000 0.688 0.304 0.008
#> GSM125162 1 0.3123 0.8076 0.844 0.000 0.156 0.000
#> GSM125164 2 0.1824 0.7929 0.000 0.936 0.060 0.004
#> GSM125166 2 0.1637 0.7930 0.000 0.940 0.060 0.000
#> GSM125168 3 0.6061 0.3256 0.000 0.400 0.552 0.048
#> GSM125170 2 0.4453 0.6436 0.000 0.744 0.244 0.012
#> GSM125172 2 0.0336 0.7985 0.000 0.992 0.008 0.000
#> GSM125174 4 0.4483 0.5150 0.000 0.004 0.284 0.712
#> GSM125176 2 0.1913 0.7843 0.000 0.940 0.020 0.040
#> GSM125178 3 0.5161 0.0817 0.000 0.008 0.592 0.400
#> GSM125180 4 0.2149 0.5989 0.000 0.000 0.088 0.912
#> GSM125182 3 0.6240 0.4944 0.000 0.320 0.604 0.076
#> GSM125184 4 0.4936 0.4381 0.000 0.008 0.340 0.652
#> GSM125186 4 0.4522 0.4015 0.000 0.000 0.320 0.680
#> GSM125188 3 0.4194 0.6891 0.000 0.172 0.800 0.028
#> GSM125190 2 0.3486 0.7200 0.000 0.812 0.188 0.000
#> GSM125192 2 0.1211 0.7988 0.000 0.960 0.040 0.000
#> GSM125194 3 0.2751 0.5658 0.056 0.000 0.904 0.040
#> GSM125196 4 0.3142 0.5892 0.000 0.008 0.132 0.860
#> GSM125198 2 0.0469 0.7961 0.000 0.988 0.012 0.000
#> GSM125200 1 0.0592 0.8965 0.984 0.000 0.000 0.016
#> GSM125202 2 0.1004 0.7875 0.000 0.972 0.024 0.004
#> GSM125204 4 0.7177 0.3407 0.028 0.080 0.336 0.556
#> GSM125206 4 0.7944 0.2167 0.024 0.324 0.164 0.488
#> GSM125208 3 0.4955 0.1445 0.000 0.000 0.556 0.444
#> GSM125210 4 0.4103 0.4884 0.000 0.000 0.256 0.744
#> GSM125212 3 0.3681 0.6815 0.008 0.176 0.816 0.000
#> GSM125214 2 0.1118 0.7998 0.000 0.964 0.036 0.000
#> GSM125216 2 0.0336 0.8006 0.000 0.992 0.008 0.000
#> GSM125218 2 0.3649 0.6968 0.000 0.796 0.204 0.000
#> GSM125220 1 0.2081 0.8708 0.916 0.000 0.084 0.000
#> GSM125222 3 0.3447 0.6911 0.000 0.128 0.852 0.020
#> GSM125224 2 0.0469 0.7988 0.000 0.988 0.012 0.000
#> GSM125226 2 0.4431 0.5539 0.000 0.696 0.304 0.000
#> GSM125228 2 0.0336 0.8006 0.000 0.992 0.008 0.000
#> GSM125230 3 0.2635 0.5895 0.020 0.000 0.904 0.076
#> GSM125232 4 0.3257 0.5915 0.004 0.000 0.152 0.844
#> GSM125234 4 0.5925 0.5498 0.196 0.036 0.048 0.720
#> GSM125236 1 0.3970 0.8254 0.836 0.004 0.036 0.124
#> GSM125238 1 0.1716 0.8813 0.936 0.000 0.064 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2464 0.8493 0.904 0.000 0.048 0.004 0.044
#> GSM125125 1 0.0798 0.8645 0.976 0.000 0.016 0.000 0.008
#> GSM125127 1 0.5678 0.6485 0.692 0.012 0.156 0.012 0.128
#> GSM125129 1 0.3187 0.8358 0.864 0.000 0.088 0.012 0.036
#> GSM125131 1 0.0865 0.8660 0.972 0.000 0.024 0.004 0.000
#> GSM125133 1 0.0693 0.8670 0.980 0.000 0.008 0.012 0.000
#> GSM125135 1 0.2506 0.8514 0.904 0.000 0.052 0.008 0.036
#> GSM125137 1 0.4359 0.6617 0.704 0.000 0.004 0.272 0.020
#> GSM125139 1 0.2395 0.8528 0.904 0.000 0.016 0.008 0.072
#> GSM125141 1 0.2623 0.8400 0.884 0.000 0.004 0.096 0.016
#> GSM125143 1 0.2812 0.8414 0.876 0.000 0.096 0.024 0.004
#> GSM125145 1 0.3183 0.8226 0.856 0.000 0.028 0.008 0.108
#> GSM125147 1 0.0693 0.8661 0.980 0.000 0.000 0.012 0.008
#> GSM125149 1 0.2439 0.8300 0.876 0.000 0.000 0.120 0.004
#> GSM125151 1 0.5346 0.5564 0.660 0.000 0.068 0.012 0.260
#> GSM125153 1 0.4134 0.6466 0.720 0.000 0.008 0.008 0.264
#> GSM125155 1 0.1522 0.8634 0.944 0.000 0.000 0.044 0.012
#> GSM125157 1 0.2497 0.8341 0.880 0.000 0.004 0.112 0.004
#> GSM125159 4 0.5922 0.2983 0.000 0.388 0.108 0.504 0.000
#> GSM125161 1 0.3906 0.7011 0.744 0.000 0.016 0.240 0.000
#> GSM125163 2 0.0955 0.7681 0.000 0.968 0.004 0.028 0.000
#> GSM125165 4 0.3962 0.5180 0.012 0.180 0.004 0.788 0.016
#> GSM125167 2 0.4088 0.4097 0.000 0.632 0.000 0.368 0.000
#> GSM125169 2 0.4472 0.6189 0.032 0.732 0.004 0.228 0.004
#> GSM125171 2 0.3154 0.7243 0.004 0.868 0.088 0.008 0.032
#> GSM125173 4 0.5683 0.1904 0.000 0.420 0.032 0.520 0.028
#> GSM125175 2 0.0324 0.7696 0.000 0.992 0.004 0.004 0.000
#> GSM125177 3 0.5317 0.6396 0.000 0.100 0.708 0.172 0.020
#> GSM125179 5 0.3288 0.5947 0.000 0.020 0.040 0.076 0.864
#> GSM125181 4 0.3646 0.4877 0.000 0.072 0.064 0.844 0.020
#> GSM125183 4 0.5723 0.1729 0.000 0.076 0.004 0.532 0.388
#> GSM125185 5 0.6740 0.1639 0.000 0.004 0.316 0.232 0.448
#> GSM125187 4 0.6881 0.1707 0.000 0.020 0.216 0.500 0.264
#> GSM125189 2 0.2536 0.7259 0.000 0.868 0.004 0.128 0.000
#> GSM125191 2 0.5592 0.2907 0.000 0.576 0.056 0.356 0.012
#> GSM125193 4 0.4873 0.2398 0.068 0.000 0.244 0.688 0.000
#> GSM125195 3 0.2368 0.7341 0.008 0.024 0.920 0.032 0.016
#> GSM125197 2 0.2890 0.6911 0.000 0.836 0.160 0.004 0.000
#> GSM125199 1 0.2179 0.8410 0.896 0.000 0.004 0.100 0.000
#> GSM125201 2 0.3582 0.6195 0.000 0.768 0.224 0.008 0.000
#> GSM125203 3 0.4224 0.7229 0.044 0.024 0.796 0.136 0.000
#> GSM125205 2 0.4666 0.3441 0.004 0.596 0.388 0.012 0.000
#> GSM125207 3 0.5025 0.6525 0.000 0.000 0.704 0.172 0.124
#> GSM125209 4 0.7088 0.4085 0.000 0.300 0.236 0.444 0.020
#> GSM125211 4 0.4400 0.3227 0.008 0.020 0.236 0.732 0.004
#> GSM125213 2 0.5798 0.2016 0.000 0.556 0.108 0.336 0.000
#> GSM125215 2 0.2248 0.7567 0.000 0.900 0.088 0.012 0.000
#> GSM125217 4 0.4591 -0.0252 0.004 0.476 0.004 0.516 0.000
#> GSM125219 1 0.3077 0.8330 0.864 0.000 0.100 0.008 0.028
#> GSM125221 4 0.4996 0.4068 0.016 0.300 0.000 0.656 0.028
#> GSM125223 2 0.1892 0.7496 0.000 0.916 0.080 0.004 0.000
#> GSM125225 2 0.1041 0.7674 0.000 0.964 0.004 0.032 0.000
#> GSM125227 2 0.1357 0.7654 0.000 0.948 0.048 0.004 0.000
#> GSM125229 4 0.6179 0.0136 0.064 0.036 0.356 0.544 0.000
#> GSM125231 5 0.5228 0.4037 0.048 0.000 0.276 0.016 0.660
#> GSM125233 1 0.3420 0.8126 0.836 0.000 0.124 0.004 0.036
#> GSM125235 1 0.0880 0.8634 0.968 0.000 0.000 0.032 0.000
#> GSM125237 1 0.1557 0.8593 0.940 0.000 0.008 0.052 0.000
#> GSM125124 5 0.2473 0.6073 0.072 0.000 0.032 0.000 0.896
#> GSM125126 1 0.0000 0.8653 1.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.1907 0.8622 0.928 0.000 0.044 0.028 0.000
#> GSM125130 1 0.6992 -0.1517 0.388 0.000 0.372 0.012 0.228
#> GSM125132 1 0.0000 0.8653 1.000 0.000 0.000 0.000 0.000
#> GSM125134 1 0.4598 0.5649 0.664 0.000 0.016 0.008 0.312
#> GSM125136 1 0.1740 0.8571 0.932 0.000 0.012 0.056 0.000
#> GSM125138 5 0.3482 0.5744 0.168 0.000 0.012 0.008 0.812
#> GSM125140 1 0.2694 0.8292 0.864 0.000 0.004 0.004 0.128
#> GSM125142 1 0.3905 0.6901 0.752 0.000 0.004 0.012 0.232
#> GSM125144 5 0.3974 0.5413 0.228 0.000 0.016 0.004 0.752
#> GSM125146 1 0.4010 0.7583 0.784 0.000 0.032 0.008 0.176
#> GSM125148 1 0.0865 0.8655 0.972 0.000 0.004 0.000 0.024
#> GSM125150 1 0.0609 0.8654 0.980 0.000 0.000 0.000 0.020
#> GSM125152 5 0.6069 0.0784 0.444 0.000 0.092 0.008 0.456
#> GSM125154 5 0.4954 0.2781 0.380 0.000 0.012 0.016 0.592
#> GSM125156 1 0.0771 0.8665 0.976 0.000 0.000 0.004 0.020
#> GSM125158 1 0.1018 0.8641 0.968 0.000 0.016 0.000 0.016
#> GSM125160 2 0.5047 0.4419 0.000 0.652 0.064 0.284 0.000
#> GSM125162 1 0.3391 0.7650 0.800 0.000 0.012 0.188 0.000
#> GSM125164 2 0.2249 0.7442 0.000 0.896 0.000 0.096 0.008
#> GSM125166 2 0.2722 0.7278 0.000 0.868 0.004 0.120 0.008
#> GSM125168 2 0.6501 0.0687 0.000 0.488 0.012 0.360 0.140
#> GSM125170 2 0.6369 0.3016 0.000 0.544 0.004 0.216 0.236
#> GSM125172 2 0.0880 0.7675 0.000 0.968 0.032 0.000 0.000
#> GSM125174 5 0.3129 0.5918 0.000 0.020 0.032 0.076 0.872
#> GSM125176 2 0.2308 0.7598 0.000 0.912 0.004 0.048 0.036
#> GSM125178 3 0.5983 0.5631 0.000 0.000 0.580 0.252 0.168
#> GSM125180 5 0.2899 0.5907 0.000 0.004 0.096 0.028 0.872
#> GSM125182 4 0.6542 0.3619 0.000 0.140 0.288 0.548 0.024
#> GSM125184 5 0.3817 0.5589 0.000 0.032 0.020 0.128 0.820
#> GSM125186 5 0.6172 0.3181 0.000 0.000 0.280 0.176 0.544
#> GSM125188 4 0.4568 0.3869 0.000 0.036 0.208 0.740 0.016
#> GSM125190 2 0.4316 0.6292 0.000 0.748 0.004 0.208 0.040
#> GSM125192 2 0.1331 0.7647 0.000 0.952 0.000 0.040 0.008
#> GSM125194 4 0.4408 0.2837 0.032 0.000 0.224 0.736 0.008
#> GSM125196 3 0.3119 0.7322 0.000 0.000 0.860 0.068 0.072
#> GSM125198 2 0.2233 0.7371 0.000 0.892 0.104 0.004 0.000
#> GSM125200 1 0.0566 0.8649 0.984 0.000 0.004 0.000 0.012
#> GSM125202 2 0.2497 0.7293 0.000 0.880 0.112 0.004 0.004
#> GSM125204 3 0.3147 0.7515 0.012 0.012 0.864 0.104 0.008
#> GSM125206 3 0.5084 0.6652 0.008 0.064 0.768 0.064 0.096
#> GSM125208 3 0.5188 0.5089 0.000 0.000 0.600 0.344 0.056
#> GSM125210 5 0.6089 0.3672 0.000 0.004 0.256 0.160 0.580
#> GSM125212 4 0.4498 0.2779 0.000 0.032 0.280 0.688 0.000
#> GSM125214 2 0.0865 0.7689 0.000 0.972 0.004 0.024 0.000
#> GSM125216 2 0.1124 0.7660 0.000 0.960 0.036 0.004 0.000
#> GSM125218 2 0.3366 0.6656 0.000 0.784 0.004 0.212 0.000
#> GSM125220 1 0.2193 0.8457 0.900 0.000 0.008 0.092 0.000
#> GSM125222 4 0.6524 0.3418 0.004 0.312 0.004 0.512 0.168
#> GSM125224 2 0.1952 0.7475 0.000 0.912 0.084 0.004 0.000
#> GSM125226 2 0.4029 0.5092 0.000 0.680 0.004 0.316 0.000
#> GSM125228 2 0.0693 0.7694 0.000 0.980 0.012 0.008 0.000
#> GSM125230 4 0.4914 0.0823 0.004 0.000 0.336 0.628 0.032
#> GSM125232 5 0.2074 0.5955 0.000 0.000 0.044 0.036 0.920
#> GSM125234 5 0.7161 0.2745 0.188 0.012 0.368 0.012 0.420
#> GSM125236 1 0.2730 0.8451 0.892 0.000 0.056 0.008 0.044
#> GSM125238 1 0.2349 0.8466 0.900 0.000 0.004 0.084 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.2383 0.85071 0.900 0.000 0.000 0.020 0.052 0.028
#> GSM125125 1 0.0820 0.86304 0.972 0.000 0.000 0.000 0.016 0.012
#> GSM125127 1 0.4829 0.68882 0.700 0.000 0.008 0.048 0.028 0.216
#> GSM125129 1 0.2891 0.84109 0.868 0.000 0.008 0.012 0.024 0.088
#> GSM125131 1 0.0363 0.86420 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM125133 1 0.0632 0.86354 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM125135 1 0.2859 0.83995 0.868 0.000 0.008 0.012 0.020 0.092
#> GSM125137 1 0.5250 0.57777 0.640 0.000 0.044 0.060 0.000 0.256
#> GSM125139 1 0.2582 0.85587 0.888 0.000 0.000 0.020 0.060 0.032
#> GSM125141 1 0.3332 0.79347 0.808 0.000 0.000 0.048 0.000 0.144
#> GSM125143 1 0.3702 0.80858 0.808 0.000 0.016 0.000 0.104 0.072
#> GSM125145 1 0.2542 0.84321 0.876 0.000 0.000 0.044 0.000 0.080
#> GSM125147 1 0.1434 0.86195 0.940 0.000 0.000 0.012 0.000 0.048
#> GSM125149 1 0.2196 0.83732 0.884 0.000 0.004 0.004 0.000 0.108
#> GSM125151 1 0.4546 0.63321 0.660 0.000 0.000 0.040 0.288 0.012
#> GSM125153 4 0.5291 0.07881 0.448 0.000 0.016 0.476 0.000 0.060
#> GSM125155 1 0.0713 0.86328 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM125157 1 0.2377 0.83023 0.868 0.000 0.004 0.004 0.000 0.124
#> GSM125159 2 0.6610 -0.20666 0.000 0.408 0.388 0.000 0.064 0.140
#> GSM125161 1 0.4341 0.71105 0.732 0.000 0.080 0.000 0.008 0.180
#> GSM125163 2 0.0692 0.76366 0.000 0.976 0.004 0.000 0.000 0.020
#> GSM125165 6 0.7925 -0.16504 0.000 0.192 0.288 0.084 0.060 0.376
#> GSM125167 2 0.3415 0.69180 0.000 0.824 0.020 0.000 0.036 0.120
#> GSM125169 2 0.3281 0.69975 0.036 0.828 0.000 0.000 0.012 0.124
#> GSM125171 2 0.3154 0.66676 0.000 0.800 0.000 0.012 0.004 0.184
#> GSM125173 3 0.7644 -0.13023 0.000 0.168 0.396 0.284 0.020 0.132
#> GSM125175 2 0.1075 0.75685 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM125177 3 0.4255 0.50418 0.000 0.004 0.692 0.012 0.020 0.272
#> GSM125179 4 0.4783 0.38143 0.000 0.044 0.000 0.612 0.332 0.012
#> GSM125181 3 0.7518 -0.25811 0.000 0.164 0.340 0.000 0.204 0.292
#> GSM125183 4 0.6015 0.40085 0.004 0.024 0.084 0.624 0.040 0.224
#> GSM125185 5 0.1847 0.45964 0.000 0.008 0.008 0.048 0.928 0.008
#> GSM125187 5 0.5984 0.35572 0.008 0.096 0.040 0.044 0.672 0.140
#> GSM125189 2 0.1616 0.75857 0.000 0.932 0.020 0.000 0.000 0.048
#> GSM125191 2 0.4691 0.59837 0.000 0.728 0.028 0.000 0.144 0.100
#> GSM125193 3 0.5640 0.30682 0.036 0.004 0.632 0.000 0.116 0.212
#> GSM125195 3 0.5513 0.41324 0.000 0.004 0.532 0.004 0.108 0.352
#> GSM125197 2 0.3424 0.62795 0.000 0.772 0.024 0.000 0.000 0.204
#> GSM125199 1 0.1663 0.84874 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM125201 2 0.5493 0.21423 0.000 0.576 0.136 0.008 0.000 0.280
#> GSM125203 3 0.4764 0.50442 0.012 0.004 0.664 0.000 0.052 0.268
#> GSM125205 6 0.6240 -0.22400 0.000 0.236 0.292 0.008 0.004 0.460
#> GSM125207 3 0.4181 0.47959 0.000 0.000 0.644 0.000 0.328 0.028
#> GSM125209 5 0.6156 -0.02967 0.000 0.392 0.052 0.000 0.460 0.096
#> GSM125211 3 0.2201 0.52443 0.000 0.000 0.904 0.036 0.004 0.056
#> GSM125213 2 0.4635 0.62295 0.000 0.744 0.052 0.000 0.132 0.072
#> GSM125215 2 0.1745 0.75068 0.000 0.920 0.012 0.000 0.000 0.068
#> GSM125217 2 0.5419 0.42950 0.000 0.628 0.160 0.000 0.016 0.196
#> GSM125219 1 0.3240 0.81344 0.820 0.000 0.000 0.008 0.144 0.028
#> GSM125221 2 0.6523 0.27603 0.008 0.540 0.068 0.012 0.080 0.292
#> GSM125223 2 0.2135 0.72303 0.000 0.872 0.000 0.000 0.000 0.128
#> GSM125225 2 0.0458 0.76343 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM125227 2 0.2118 0.73480 0.000 0.888 0.008 0.000 0.000 0.104
#> GSM125229 3 0.2375 0.54964 0.012 0.000 0.888 0.000 0.012 0.088
#> GSM125231 4 0.5767 0.18280 0.004 0.000 0.256 0.532 0.000 0.208
#> GSM125233 1 0.3602 0.78138 0.784 0.000 0.000 0.008 0.176 0.032
#> GSM125235 1 0.0632 0.86288 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM125237 1 0.1267 0.85940 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM125124 4 0.3194 0.59740 0.032 0.000 0.000 0.828 0.132 0.008
#> GSM125126 1 0.0891 0.86497 0.968 0.000 0.000 0.000 0.008 0.024
#> GSM125128 1 0.2295 0.86008 0.904 0.000 0.028 0.000 0.016 0.052
#> GSM125130 5 0.4979 0.14482 0.356 0.000 0.000 0.028 0.584 0.032
#> GSM125132 1 0.0520 0.86391 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM125134 1 0.4808 -0.06135 0.476 0.000 0.000 0.472 0.000 0.052
#> GSM125136 1 0.1882 0.85938 0.920 0.000 0.012 0.000 0.008 0.060
#> GSM125138 4 0.2084 0.62998 0.024 0.000 0.000 0.916 0.044 0.016
#> GSM125140 1 0.2340 0.85883 0.900 0.000 0.000 0.024 0.016 0.060
#> GSM125142 4 0.5190 0.05214 0.448 0.000 0.000 0.464 0.000 0.088
#> GSM125144 4 0.4426 0.54165 0.152 0.000 0.000 0.748 0.072 0.028
#> GSM125146 1 0.4934 0.49839 0.632 0.000 0.000 0.256 0.000 0.112
#> GSM125148 1 0.2340 0.84725 0.896 0.000 0.004 0.056 0.000 0.044
#> GSM125150 1 0.0717 0.86354 0.976 0.000 0.000 0.008 0.000 0.016
#> GSM125152 1 0.5733 0.43306 0.560 0.000 0.000 0.108 0.304 0.028
#> GSM125154 4 0.3246 0.59447 0.072 0.000 0.016 0.844 0.000 0.068
#> GSM125156 1 0.1092 0.86499 0.960 0.000 0.000 0.020 0.000 0.020
#> GSM125158 1 0.1167 0.86244 0.960 0.000 0.000 0.012 0.008 0.020
#> GSM125160 2 0.4453 0.62505 0.000 0.756 0.136 0.000 0.052 0.056
#> GSM125162 1 0.3190 0.80085 0.820 0.000 0.044 0.000 0.000 0.136
#> GSM125164 2 0.1176 0.75797 0.000 0.956 0.000 0.000 0.020 0.024
#> GSM125166 2 0.0777 0.75942 0.000 0.972 0.000 0.000 0.004 0.024
#> GSM125168 2 0.6639 0.34689 0.000 0.580 0.048 0.204 0.052 0.116
#> GSM125170 2 0.4029 0.67442 0.000 0.792 0.000 0.080 0.032 0.096
#> GSM125172 2 0.2615 0.71211 0.000 0.852 0.004 0.008 0.000 0.136
#> GSM125174 4 0.2276 0.61816 0.000 0.008 0.020 0.912 0.040 0.020
#> GSM125176 2 0.0881 0.76376 0.000 0.972 0.000 0.008 0.008 0.012
#> GSM125178 3 0.3618 0.54903 0.000 0.000 0.812 0.076 0.012 0.100
#> GSM125180 4 0.4421 0.27775 0.000 0.020 0.000 0.552 0.424 0.004
#> GSM125182 5 0.7095 0.01739 0.000 0.152 0.320 0.000 0.408 0.120
#> GSM125184 4 0.3087 0.60914 0.000 0.008 0.020 0.856 0.096 0.020
#> GSM125186 5 0.2044 0.44459 0.000 0.004 0.008 0.076 0.908 0.004
#> GSM125188 5 0.6670 0.00481 0.000 0.048 0.380 0.000 0.384 0.188
#> GSM125190 2 0.2742 0.72419 0.000 0.856 0.008 0.008 0.004 0.124
#> GSM125192 2 0.0603 0.76350 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM125194 3 0.4432 0.36532 0.008 0.000 0.720 0.036 0.016 0.220
#> GSM125196 3 0.6246 0.41978 0.000 0.004 0.508 0.020 0.200 0.268
#> GSM125198 2 0.2704 0.70371 0.000 0.844 0.016 0.000 0.000 0.140
#> GSM125200 1 0.1232 0.86159 0.956 0.000 0.000 0.004 0.016 0.024
#> GSM125202 2 0.4137 0.57876 0.000 0.732 0.024 0.016 0.004 0.224
#> GSM125204 3 0.5792 0.46009 0.004 0.004 0.552 0.000 0.208 0.232
#> GSM125206 3 0.4859 0.37768 0.000 0.004 0.548 0.028 0.012 0.408
#> GSM125208 3 0.3686 0.52253 0.000 0.000 0.748 0.000 0.220 0.032
#> GSM125210 5 0.2692 0.38691 0.000 0.012 0.000 0.148 0.840 0.000
#> GSM125212 3 0.1251 0.53974 0.000 0.000 0.956 0.008 0.012 0.024
#> GSM125214 2 0.0777 0.76292 0.000 0.972 0.000 0.000 0.004 0.024
#> GSM125216 2 0.1267 0.75470 0.000 0.940 0.000 0.000 0.000 0.060
#> GSM125218 2 0.2126 0.74562 0.000 0.904 0.020 0.000 0.004 0.072
#> GSM125220 1 0.2214 0.85127 0.892 0.000 0.004 0.000 0.012 0.092
#> GSM125222 2 0.7528 0.08542 0.000 0.460 0.048 0.168 0.080 0.244
#> GSM125224 2 0.2346 0.72091 0.000 0.868 0.008 0.000 0.000 0.124
#> GSM125226 2 0.3160 0.70785 0.000 0.836 0.012 0.004 0.020 0.128
#> GSM125228 2 0.1610 0.74763 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM125230 3 0.2307 0.53816 0.000 0.000 0.896 0.068 0.004 0.032
#> GSM125232 4 0.2325 0.62037 0.000 0.000 0.048 0.900 0.044 0.008
#> GSM125234 5 0.6170 0.17774 0.192 0.000 0.000 0.124 0.592 0.092
#> GSM125236 1 0.2898 0.83859 0.864 0.000 0.000 0.024 0.024 0.088
#> GSM125238 1 0.2790 0.82363 0.844 0.000 0.000 0.024 0.000 0.132
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 agent(p) individual(p) k
#> MAD:NMF 113 1.0000 4.12e-05 2
#> MAD:NMF 111 0.1856 1.08e-05 3
#> MAD:NMF 88 0.5445 1.24e-06 4
#> MAD:NMF 84 0.1295 4.62e-05 5
#> MAD:NMF 81 0.0235 3.01e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.805 0.923 0.958 0.4337 0.568 0.568
#> 3 3 0.920 0.917 0.956 0.5114 0.770 0.595
#> 4 4 0.877 0.837 0.859 0.0672 1.000 1.000
#> 5 5 0.876 0.837 0.876 0.0242 0.933 0.804
#> 6 6 0.830 0.778 0.870 0.0288 0.968 0.892
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM125123 1 0.0000 0.951 1.000 0.000
#> GSM125125 1 0.0000 0.951 1.000 0.000
#> GSM125127 1 0.0000 0.951 1.000 0.000
#> GSM125129 1 0.0000 0.951 1.000 0.000
#> GSM125131 1 0.0000 0.951 1.000 0.000
#> GSM125133 1 0.0000 0.951 1.000 0.000
#> GSM125135 1 0.0000 0.951 1.000 0.000
#> GSM125137 1 0.0000 0.951 1.000 0.000
#> GSM125139 1 0.0000 0.951 1.000 0.000
#> GSM125141 1 0.0000 0.951 1.000 0.000
#> GSM125143 1 0.0000 0.951 1.000 0.000
#> GSM125145 1 0.0000 0.951 1.000 0.000
#> GSM125147 1 0.0000 0.951 1.000 0.000
#> GSM125149 1 0.0000 0.951 1.000 0.000
#> GSM125151 1 0.0000 0.951 1.000 0.000
#> GSM125153 1 0.0000 0.951 1.000 0.000
#> GSM125155 1 0.0000 0.951 1.000 0.000
#> GSM125157 1 0.0000 0.951 1.000 0.000
#> GSM125159 2 0.0000 0.960 0.000 1.000
#> GSM125161 1 0.0000 0.951 1.000 0.000
#> GSM125163 2 0.0000 0.960 0.000 1.000
#> GSM125165 1 0.4939 0.908 0.892 0.108
#> GSM125167 2 0.1184 0.954 0.016 0.984
#> GSM125169 2 0.8861 0.564 0.304 0.696
#> GSM125171 2 0.0672 0.957 0.008 0.992
#> GSM125173 1 0.4562 0.917 0.904 0.096
#> GSM125175 2 0.0000 0.960 0.000 1.000
#> GSM125177 1 0.4690 0.914 0.900 0.100
#> GSM125179 1 0.4431 0.919 0.908 0.092
#> GSM125181 1 0.7815 0.752 0.768 0.232
#> GSM125183 1 0.4562 0.917 0.904 0.096
#> GSM125185 1 0.4431 0.919 0.908 0.092
#> GSM125187 1 0.4431 0.919 0.908 0.092
#> GSM125189 2 0.4298 0.897 0.088 0.912
#> GSM125191 2 0.3733 0.911 0.072 0.928
#> GSM125193 1 0.4298 0.921 0.912 0.088
#> GSM125195 1 0.4690 0.914 0.900 0.100
#> GSM125197 2 0.0000 0.960 0.000 1.000
#> GSM125199 1 0.0000 0.951 1.000 0.000
#> GSM125201 2 0.0000 0.960 0.000 1.000
#> GSM125203 1 0.4690 0.914 0.900 0.100
#> GSM125205 2 0.0000 0.960 0.000 1.000
#> GSM125207 1 0.4690 0.914 0.900 0.100
#> GSM125209 2 0.3733 0.911 0.072 0.928
#> GSM125211 1 0.4690 0.914 0.900 0.100
#> GSM125213 2 0.0000 0.960 0.000 1.000
#> GSM125215 2 0.0000 0.960 0.000 1.000
#> GSM125217 2 0.2423 0.939 0.040 0.960
#> GSM125219 1 0.0376 0.951 0.996 0.004
#> GSM125221 1 0.4431 0.919 0.908 0.092
#> GSM125223 2 0.0000 0.960 0.000 1.000
#> GSM125225 2 0.0000 0.960 0.000 1.000
#> GSM125227 2 0.0000 0.960 0.000 1.000
#> GSM125229 1 0.9944 0.206 0.544 0.456
#> GSM125231 1 0.1633 0.945 0.976 0.024
#> GSM125233 1 0.0000 0.951 1.000 0.000
#> GSM125235 1 0.0000 0.951 1.000 0.000
#> GSM125237 1 0.0000 0.951 1.000 0.000
#> GSM125124 1 0.0000 0.951 1.000 0.000
#> GSM125126 1 0.0000 0.951 1.000 0.000
#> GSM125128 1 0.0000 0.951 1.000 0.000
#> GSM125130 1 0.0000 0.951 1.000 0.000
#> GSM125132 1 0.0000 0.951 1.000 0.000
#> GSM125134 1 0.0000 0.951 1.000 0.000
#> GSM125136 1 0.0000 0.951 1.000 0.000
#> GSM125138 1 0.0000 0.951 1.000 0.000
#> GSM125140 1 0.0000 0.951 1.000 0.000
#> GSM125142 1 0.0000 0.951 1.000 0.000
#> GSM125144 1 0.0000 0.951 1.000 0.000
#> GSM125146 1 0.0000 0.951 1.000 0.000
#> GSM125148 1 0.0000 0.951 1.000 0.000
#> GSM125150 1 0.0000 0.951 1.000 0.000
#> GSM125152 1 0.0000 0.951 1.000 0.000
#> GSM125154 1 0.0000 0.951 1.000 0.000
#> GSM125156 1 0.0000 0.951 1.000 0.000
#> GSM125158 1 0.0000 0.951 1.000 0.000
#> GSM125160 2 0.0000 0.960 0.000 1.000
#> GSM125162 1 0.0000 0.951 1.000 0.000
#> GSM125164 2 0.0000 0.960 0.000 1.000
#> GSM125166 2 0.0000 0.960 0.000 1.000
#> GSM125168 2 0.1184 0.954 0.016 0.984
#> GSM125170 2 0.8861 0.564 0.304 0.696
#> GSM125172 2 0.0000 0.960 0.000 1.000
#> GSM125174 1 0.4562 0.917 0.904 0.096
#> GSM125176 2 0.7674 0.711 0.224 0.776
#> GSM125178 1 0.4690 0.914 0.900 0.100
#> GSM125180 1 0.4431 0.919 0.908 0.092
#> GSM125182 2 0.1184 0.954 0.016 0.984
#> GSM125184 1 0.4562 0.917 0.904 0.096
#> GSM125186 1 0.4431 0.919 0.908 0.092
#> GSM125188 1 0.4690 0.914 0.900 0.100
#> GSM125190 2 0.4298 0.897 0.088 0.912
#> GSM125192 2 0.0000 0.960 0.000 1.000
#> GSM125194 1 0.4298 0.921 0.912 0.088
#> GSM125196 1 0.4690 0.914 0.900 0.100
#> GSM125198 2 0.0000 0.960 0.000 1.000
#> GSM125200 1 0.0000 0.951 1.000 0.000
#> GSM125202 2 0.0000 0.960 0.000 1.000
#> GSM125204 1 0.4690 0.914 0.900 0.100
#> GSM125206 1 0.4690 0.914 0.900 0.100
#> GSM125208 1 0.4690 0.914 0.900 0.100
#> GSM125210 1 0.9044 0.594 0.680 0.320
#> GSM125212 1 0.4690 0.914 0.900 0.100
#> GSM125214 2 0.0000 0.960 0.000 1.000
#> GSM125216 2 0.0000 0.960 0.000 1.000
#> GSM125218 2 0.2236 0.942 0.036 0.964
#> GSM125220 1 0.0376 0.951 0.996 0.004
#> GSM125222 1 0.4431 0.919 0.908 0.092
#> GSM125224 2 0.0000 0.960 0.000 1.000
#> GSM125226 2 0.1184 0.954 0.016 0.984
#> GSM125228 2 0.0000 0.960 0.000 1.000
#> GSM125230 1 0.1633 0.945 0.976 0.024
#> GSM125232 1 0.1633 0.945 0.976 0.024
#> GSM125234 1 0.0376 0.951 0.996 0.004
#> GSM125236 1 0.0000 0.951 1.000 0.000
#> GSM125238 1 0.0000 0.951 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125127 1 0.1411 0.962 0.964 0.000 0.036
#> GSM125129 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125131 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125133 1 0.1411 0.962 0.964 0.000 0.036
#> GSM125135 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125137 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125139 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125143 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125145 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125147 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125153 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125159 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125161 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125163 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125165 3 0.0424 0.920 0.000 0.008 0.992
#> GSM125167 2 0.2537 0.916 0.000 0.920 0.080
#> GSM125169 2 0.6140 0.457 0.000 0.596 0.404
#> GSM125171 2 0.1411 0.923 0.000 0.964 0.036
#> GSM125173 3 0.0237 0.925 0.004 0.000 0.996
#> GSM125175 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125177 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125179 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125181 3 0.3551 0.787 0.000 0.132 0.868
#> GSM125183 3 0.0237 0.925 0.004 0.000 0.996
#> GSM125185 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125187 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125189 2 0.4178 0.847 0.000 0.828 0.172
#> GSM125191 2 0.3686 0.877 0.000 0.860 0.140
#> GSM125193 3 0.1031 0.917 0.024 0.000 0.976
#> GSM125195 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125197 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125201 2 0.0424 0.919 0.000 0.992 0.008
#> GSM125203 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125205 2 0.0424 0.919 0.000 0.992 0.008
#> GSM125207 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125209 2 0.3686 0.877 0.000 0.860 0.140
#> GSM125211 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125213 2 0.1411 0.923 0.000 0.964 0.036
#> GSM125215 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125217 2 0.3619 0.882 0.000 0.864 0.136
#> GSM125219 1 0.2066 0.937 0.940 0.000 0.060
#> GSM125221 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125223 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125229 3 0.5926 0.325 0.000 0.356 0.644
#> GSM125231 3 0.5810 0.526 0.336 0.000 0.664
#> GSM125233 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125124 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125126 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125130 1 0.1031 0.973 0.976 0.000 0.024
#> GSM125132 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125138 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125144 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125146 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125160 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125162 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125164 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125166 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125168 2 0.2537 0.916 0.000 0.920 0.080
#> GSM125170 2 0.6140 0.457 0.000 0.596 0.404
#> GSM125172 2 0.1031 0.922 0.000 0.976 0.024
#> GSM125174 3 0.0237 0.925 0.004 0.000 0.996
#> GSM125176 2 0.5706 0.626 0.000 0.680 0.320
#> GSM125178 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125180 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125182 2 0.2625 0.915 0.000 0.916 0.084
#> GSM125184 3 0.0237 0.925 0.004 0.000 0.996
#> GSM125186 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125188 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125190 2 0.4178 0.847 0.000 0.828 0.172
#> GSM125192 2 0.2165 0.922 0.000 0.936 0.064
#> GSM125194 3 0.1031 0.917 0.024 0.000 0.976
#> GSM125196 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125198 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125202 2 0.0424 0.919 0.000 0.992 0.008
#> GSM125204 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125206 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125208 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125210 3 0.4796 0.653 0.000 0.220 0.780
#> GSM125212 3 0.0000 0.925 0.000 0.000 1.000
#> GSM125214 2 0.1411 0.923 0.000 0.964 0.036
#> GSM125216 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125218 2 0.3551 0.885 0.000 0.868 0.132
#> GSM125220 1 0.2066 0.937 0.940 0.000 0.060
#> GSM125222 3 0.0747 0.923 0.016 0.000 0.984
#> GSM125224 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125226 2 0.2959 0.906 0.000 0.900 0.100
#> GSM125228 2 0.0000 0.917 0.000 1.000 0.000
#> GSM125230 3 0.5785 0.534 0.332 0.000 0.668
#> GSM125232 3 0.5810 0.526 0.336 0.000 0.664
#> GSM125234 1 0.2165 0.932 0.936 0.000 0.064
#> GSM125236 1 0.0000 0.994 1.000 0.000 0.000
#> GSM125238 1 0.0000 0.994 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0469 0.984 0.988 0.000 0.000 0.012
#> GSM125125 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125127 1 0.1733 0.952 0.948 0.000 0.028 0.024
#> GSM125129 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125131 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125133 1 0.1733 0.952 0.948 0.000 0.028 0.024
#> GSM125135 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125137 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125143 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125145 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125147 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125161 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125163 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125165 3 0.2610 0.846 0.000 0.012 0.900 0.088
#> GSM125167 2 0.0657 0.765 0.000 0.984 0.004 0.012
#> GSM125169 2 0.7016 0.385 0.000 0.572 0.176 0.252
#> GSM125171 2 0.4817 0.716 0.000 0.612 0.000 0.388
#> GSM125173 3 0.2281 0.835 0.000 0.000 0.904 0.096
#> GSM125175 2 0.3219 0.754 0.000 0.836 0.000 0.164
#> GSM125177 3 0.1824 0.853 0.000 0.004 0.936 0.060
#> GSM125179 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125181 3 0.5452 0.724 0.000 0.156 0.736 0.108
#> GSM125183 3 0.1118 0.855 0.000 0.000 0.964 0.036
#> GSM125185 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125187 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125189 2 0.4244 0.682 0.000 0.804 0.036 0.160
#> GSM125191 2 0.2300 0.739 0.000 0.924 0.048 0.028
#> GSM125193 3 0.3238 0.844 0.008 0.020 0.880 0.092
#> GSM125195 3 0.5905 0.703 0.000 0.060 0.636 0.304
#> GSM125197 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125199 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125201 2 0.4961 0.690 0.000 0.552 0.000 0.448
#> GSM125203 3 0.1824 0.853 0.000 0.004 0.936 0.060
#> GSM125205 2 0.4961 0.690 0.000 0.552 0.000 0.448
#> GSM125207 3 0.1743 0.853 0.000 0.004 0.940 0.056
#> GSM125209 2 0.2300 0.739 0.000 0.924 0.048 0.028
#> GSM125211 3 0.1824 0.854 0.000 0.004 0.936 0.060
#> GSM125213 2 0.1211 0.769 0.000 0.960 0.000 0.040
#> GSM125215 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125217 2 0.3479 0.710 0.000 0.840 0.012 0.148
#> GSM125219 1 0.2319 0.930 0.924 0.000 0.040 0.036
#> GSM125221 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125223 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125225 2 0.4994 0.680 0.000 0.520 0.000 0.480
#> GSM125227 2 0.4994 0.680 0.000 0.520 0.000 0.480
#> GSM125229 3 0.7734 0.205 0.000 0.344 0.420 0.236
#> GSM125231 3 0.6412 0.486 0.320 0.000 0.592 0.088
#> GSM125233 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125235 1 0.0336 0.987 0.992 0.000 0.000 0.008
#> GSM125237 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125128 1 0.0188 0.989 0.996 0.000 0.000 0.004
#> GSM125130 1 0.1297 0.965 0.964 0.000 0.020 0.016
#> GSM125132 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125136 1 0.0188 0.989 0.996 0.000 0.000 0.004
#> GSM125138 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125146 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125148 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125160 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125164 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125168 2 0.0657 0.765 0.000 0.984 0.004 0.012
#> GSM125170 2 0.7016 0.385 0.000 0.572 0.176 0.252
#> GSM125172 2 0.4776 0.719 0.000 0.624 0.000 0.376
#> GSM125174 3 0.2281 0.835 0.000 0.000 0.904 0.096
#> GSM125176 2 0.6308 0.512 0.000 0.648 0.120 0.232
#> GSM125178 3 0.1824 0.853 0.000 0.004 0.936 0.060
#> GSM125180 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125182 2 0.0779 0.764 0.000 0.980 0.004 0.016
#> GSM125184 3 0.1211 0.855 0.000 0.000 0.960 0.040
#> GSM125186 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125188 3 0.3205 0.836 0.000 0.024 0.872 0.104
#> GSM125190 2 0.4244 0.682 0.000 0.804 0.036 0.160
#> GSM125192 2 0.0000 0.768 0.000 1.000 0.000 0.000
#> GSM125194 3 0.3238 0.844 0.008 0.020 0.880 0.092
#> GSM125196 3 0.5839 0.709 0.000 0.060 0.648 0.292
#> GSM125198 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125200 1 0.0000 0.992 1.000 0.000 0.000 0.000
#> GSM125202 2 0.4961 0.690 0.000 0.552 0.000 0.448
#> GSM125204 3 0.1824 0.853 0.000 0.004 0.936 0.060
#> GSM125206 3 0.5905 0.703 0.000 0.060 0.636 0.304
#> GSM125208 3 0.1743 0.853 0.000 0.004 0.940 0.056
#> GSM125210 3 0.5247 0.649 0.000 0.228 0.720 0.052
#> GSM125212 3 0.1824 0.854 0.000 0.004 0.936 0.060
#> GSM125214 2 0.1211 0.769 0.000 0.960 0.000 0.040
#> GSM125216 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125218 2 0.3428 0.712 0.000 0.844 0.012 0.144
#> GSM125220 1 0.2319 0.930 0.924 0.000 0.040 0.036
#> GSM125222 3 0.1722 0.850 0.000 0.008 0.944 0.048
#> GSM125224 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125226 2 0.1452 0.758 0.000 0.956 0.008 0.036
#> GSM125228 2 0.4999 0.674 0.000 0.508 0.000 0.492
#> GSM125230 3 0.6394 0.494 0.316 0.000 0.596 0.088
#> GSM125232 3 0.6412 0.486 0.320 0.000 0.592 0.088
#> GSM125234 1 0.2411 0.926 0.920 0.000 0.040 0.040
#> GSM125236 1 0.0469 0.984 0.988 0.000 0.000 0.012
#> GSM125238 1 0.0000 0.992 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.0404 0.9826 0.988 0.000 0.012 0.000 0.000
#> GSM125125 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125127 1 0.1270 0.9477 0.948 0.000 0.052 0.000 0.000
#> GSM125129 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125131 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.1270 0.9477 0.948 0.000 0.052 0.000 0.000
#> GSM125135 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125137 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125143 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125145 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125147 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125161 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125163 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125165 3 0.3485 0.7740 0.000 0.124 0.828 0.048 0.000
#> GSM125167 2 0.4084 0.7725 0.000 0.668 0.004 0.000 0.328
#> GSM125169 2 0.3267 0.4521 0.000 0.844 0.112 0.044 0.000
#> GSM125171 5 0.3895 0.5247 0.000 0.320 0.000 0.000 0.680
#> GSM125173 3 0.4394 0.6685 0.000 0.100 0.764 0.136 0.000
#> GSM125175 2 0.4171 0.6107 0.000 0.604 0.000 0.000 0.396
#> GSM125177 3 0.2962 0.7944 0.000 0.084 0.868 0.048 0.000
#> GSM125179 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125181 3 0.4907 0.5688 0.000 0.280 0.664 0.056 0.000
#> GSM125183 3 0.2423 0.7953 0.000 0.080 0.896 0.024 0.000
#> GSM125185 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125187 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125189 2 0.3452 0.7116 0.000 0.820 0.032 0.000 0.148
#> GSM125191 2 0.5359 0.7410 0.000 0.628 0.040 0.020 0.312
#> GSM125193 3 0.2339 0.7709 0.008 0.052 0.912 0.028 0.000
#> GSM125195 4 0.2927 0.9864 0.000 0.040 0.092 0.868 0.000
#> GSM125197 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125199 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125201 5 0.2329 0.8160 0.000 0.124 0.000 0.000 0.876
#> GSM125203 3 0.2962 0.7944 0.000 0.084 0.868 0.048 0.000
#> GSM125205 5 0.2329 0.8160 0.000 0.124 0.000 0.000 0.876
#> GSM125207 3 0.2903 0.7953 0.000 0.080 0.872 0.048 0.000
#> GSM125209 2 0.5359 0.7410 0.000 0.628 0.040 0.020 0.312
#> GSM125211 3 0.3033 0.7947 0.000 0.084 0.864 0.052 0.000
#> GSM125213 2 0.4150 0.7158 0.000 0.612 0.000 0.000 0.388
#> GSM125215 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125217 2 0.3402 0.7322 0.000 0.804 0.008 0.004 0.184
#> GSM125219 1 0.1671 0.9235 0.924 0.000 0.076 0.000 0.000
#> GSM125221 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125223 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125225 5 0.0703 0.8868 0.000 0.024 0.000 0.000 0.976
#> GSM125227 5 0.0703 0.8868 0.000 0.024 0.000 0.000 0.976
#> GSM125229 2 0.5155 -0.0291 0.000 0.596 0.352 0.052 0.000
#> GSM125231 3 0.5647 0.2669 0.320 0.056 0.604 0.020 0.000
#> GSM125233 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125235 1 0.0290 0.9855 0.992 0.000 0.008 0.000 0.000
#> GSM125237 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.0162 0.9883 0.996 0.000 0.004 0.000 0.000
#> GSM125130 1 0.0963 0.9621 0.964 0.000 0.036 0.000 0.000
#> GSM125132 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125136 1 0.0162 0.9883 0.996 0.000 0.004 0.000 0.000
#> GSM125138 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125146 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125148 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125160 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125162 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125164 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125166 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125168 2 0.4084 0.7725 0.000 0.668 0.004 0.000 0.328
#> GSM125170 2 0.3267 0.4521 0.000 0.844 0.112 0.044 0.000
#> GSM125172 5 0.3837 0.5304 0.000 0.308 0.000 0.000 0.692
#> GSM125174 3 0.4394 0.6685 0.000 0.100 0.764 0.136 0.000
#> GSM125176 2 0.4318 0.5774 0.000 0.808 0.072 0.044 0.076
#> GSM125178 3 0.2962 0.7944 0.000 0.084 0.868 0.048 0.000
#> GSM125180 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125182 2 0.4066 0.7729 0.000 0.672 0.004 0.000 0.324
#> GSM125184 3 0.2482 0.7950 0.000 0.084 0.892 0.024 0.000
#> GSM125186 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125188 3 0.3950 0.7575 0.000 0.136 0.796 0.068 0.000
#> GSM125190 2 0.3452 0.7116 0.000 0.820 0.032 0.000 0.148
#> GSM125192 2 0.3999 0.7661 0.000 0.656 0.000 0.000 0.344
#> GSM125194 3 0.2339 0.7709 0.008 0.052 0.912 0.028 0.000
#> GSM125196 4 0.3307 0.9723 0.000 0.052 0.104 0.844 0.000
#> GSM125198 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125200 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
#> GSM125202 5 0.2329 0.8160 0.000 0.124 0.000 0.000 0.876
#> GSM125204 3 0.2962 0.7944 0.000 0.084 0.868 0.048 0.000
#> GSM125206 4 0.2927 0.9864 0.000 0.040 0.092 0.868 0.000
#> GSM125208 3 0.2903 0.7953 0.000 0.080 0.872 0.048 0.000
#> GSM125210 3 0.5384 0.5071 0.000 0.268 0.660 0.036 0.036
#> GSM125212 3 0.3033 0.7947 0.000 0.084 0.864 0.052 0.000
#> GSM125214 2 0.4150 0.7158 0.000 0.612 0.000 0.000 0.388
#> GSM125216 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125218 2 0.3282 0.7335 0.000 0.804 0.008 0.000 0.188
#> GSM125220 1 0.1671 0.9235 0.924 0.000 0.076 0.000 0.000
#> GSM125222 3 0.0324 0.7961 0.000 0.004 0.992 0.004 0.000
#> GSM125224 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125226 2 0.3949 0.7720 0.000 0.696 0.004 0.000 0.300
#> GSM125228 5 0.0000 0.8919 0.000 0.000 0.000 0.000 1.000
#> GSM125230 3 0.5631 0.2728 0.316 0.056 0.608 0.020 0.000
#> GSM125232 3 0.5647 0.2669 0.320 0.056 0.604 0.020 0.000
#> GSM125234 1 0.1732 0.9192 0.920 0.000 0.080 0.000 0.000
#> GSM125236 1 0.0404 0.9826 0.988 0.000 0.012 0.000 0.000
#> GSM125238 1 0.0000 0.9908 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.0547 0.9611 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM125125 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125127 1 0.2053 0.8563 0.888 0.000 0.000 0.004 0.108 0.000
#> GSM125129 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125131 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125133 1 0.2053 0.8563 0.888 0.000 0.000 0.004 0.108 0.000
#> GSM125135 1 0.0146 0.9744 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125137 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125143 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125145 1 0.0146 0.9744 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125147 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125161 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125163 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125165 4 0.2174 0.7162 0.000 0.008 0.008 0.896 0.088 0.000
#> GSM125167 2 0.0820 0.7582 0.000 0.972 0.000 0.000 0.012 0.016
#> GSM125169 2 0.6862 0.4238 0.000 0.552 0.032 0.176 0.172 0.068
#> GSM125171 2 0.5687 0.2679 0.000 0.508 0.004 0.000 0.152 0.336
#> GSM125173 4 0.5190 0.0651 0.000 0.000 0.080 0.524 0.392 0.004
#> GSM125175 2 0.4060 0.6058 0.000 0.684 0.000 0.000 0.032 0.284
#> GSM125177 4 0.0520 0.7495 0.000 0.000 0.008 0.984 0.008 0.000
#> GSM125179 4 0.2234 0.7205 0.000 0.000 0.004 0.872 0.124 0.000
#> GSM125181 4 0.4428 0.5372 0.000 0.156 0.012 0.736 0.096 0.000
#> GSM125183 4 0.2389 0.7343 0.000 0.000 0.008 0.864 0.128 0.000
#> GSM125185 4 0.2234 0.7205 0.000 0.000 0.004 0.872 0.124 0.000
#> GSM125187 4 0.2234 0.7205 0.000 0.000 0.004 0.872 0.124 0.000
#> GSM125189 2 0.3961 0.6845 0.000 0.804 0.008 0.024 0.100 0.064
#> GSM125191 2 0.1946 0.7372 0.000 0.912 0.000 0.072 0.004 0.012
#> GSM125193 4 0.4011 0.6058 0.000 0.000 0.024 0.672 0.304 0.000
#> GSM125195 3 0.2003 0.9738 0.000 0.000 0.884 0.116 0.000 0.000
#> GSM125197 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125199 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125201 2 0.5738 -0.1348 0.000 0.432 0.004 0.000 0.144 0.420
#> GSM125203 4 0.0520 0.7495 0.000 0.000 0.008 0.984 0.008 0.000
#> GSM125205 2 0.5738 -0.1461 0.000 0.428 0.004 0.000 0.144 0.424
#> GSM125207 4 0.0405 0.7497 0.000 0.000 0.008 0.988 0.004 0.000
#> GSM125209 2 0.1946 0.7372 0.000 0.912 0.000 0.072 0.004 0.012
#> GSM125211 4 0.0806 0.7445 0.000 0.000 0.008 0.972 0.020 0.000
#> GSM125213 2 0.1588 0.7340 0.000 0.924 0.004 0.000 0.000 0.072
#> GSM125215 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125217 2 0.3238 0.7069 0.000 0.844 0.000 0.016 0.080 0.060
#> GSM125219 1 0.2595 0.7796 0.836 0.000 0.000 0.004 0.160 0.000
#> GSM125221 4 0.2278 0.7204 0.000 0.000 0.004 0.868 0.128 0.000
#> GSM125223 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125225 6 0.2278 0.9453 0.000 0.128 0.004 0.000 0.000 0.868
#> GSM125227 6 0.2278 0.9453 0.000 0.128 0.004 0.000 0.000 0.868
#> GSM125229 2 0.7662 -0.0011 0.000 0.316 0.040 0.312 0.276 0.056
#> GSM125231 5 0.5982 1.0000 0.228 0.000 0.000 0.380 0.392 0.000
#> GSM125233 1 0.0146 0.9744 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125235 1 0.0865 0.9473 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM125237 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125128 1 0.0146 0.9740 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125130 1 0.0937 0.9388 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125132 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125136 1 0.0146 0.9740 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125138 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125146 1 0.0146 0.9744 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125148 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125160 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125162 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125164 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125166 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125168 2 0.0820 0.7582 0.000 0.972 0.000 0.000 0.012 0.016
#> GSM125170 2 0.6862 0.4238 0.000 0.552 0.032 0.176 0.172 0.068
#> GSM125172 2 0.5529 0.2780 0.000 0.516 0.000 0.000 0.148 0.336
#> GSM125174 4 0.5196 0.0545 0.000 0.000 0.080 0.520 0.396 0.004
#> GSM125176 2 0.6239 0.5259 0.000 0.632 0.028 0.128 0.140 0.072
#> GSM125178 4 0.0520 0.7495 0.000 0.000 0.008 0.984 0.008 0.000
#> GSM125180 4 0.2234 0.7205 0.000 0.000 0.004 0.872 0.124 0.000
#> GSM125182 2 0.0725 0.7583 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM125184 4 0.2346 0.7349 0.000 0.000 0.008 0.868 0.124 0.000
#> GSM125186 4 0.2234 0.7205 0.000 0.000 0.004 0.872 0.124 0.000
#> GSM125188 4 0.3345 0.6316 0.000 0.000 0.028 0.788 0.184 0.000
#> GSM125190 2 0.3961 0.6845 0.000 0.804 0.008 0.024 0.100 0.064
#> GSM125192 2 0.0547 0.7573 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM125194 4 0.4011 0.6058 0.000 0.000 0.024 0.672 0.304 0.000
#> GSM125196 3 0.2416 0.9466 0.000 0.000 0.844 0.156 0.000 0.000
#> GSM125198 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125200 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125202 2 0.5738 -0.1348 0.000 0.432 0.004 0.000 0.144 0.420
#> GSM125204 4 0.0520 0.7495 0.000 0.000 0.008 0.984 0.008 0.000
#> GSM125206 3 0.2003 0.9738 0.000 0.000 0.884 0.116 0.000 0.000
#> GSM125208 4 0.0405 0.7497 0.000 0.000 0.008 0.988 0.004 0.000
#> GSM125210 4 0.3217 0.5068 0.000 0.224 0.000 0.768 0.008 0.000
#> GSM125212 4 0.0806 0.7445 0.000 0.000 0.008 0.972 0.020 0.000
#> GSM125214 2 0.1588 0.7340 0.000 0.924 0.004 0.000 0.000 0.072
#> GSM125216 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125218 2 0.3047 0.7096 0.000 0.852 0.000 0.008 0.080 0.060
#> GSM125220 1 0.2595 0.7796 0.836 0.000 0.000 0.004 0.160 0.000
#> GSM125222 4 0.2278 0.7204 0.000 0.000 0.004 0.868 0.128 0.000
#> GSM125224 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125226 2 0.0632 0.7563 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM125228 6 0.1663 0.9845 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM125230 4 0.5970 -0.9768 0.224 0.000 0.000 0.392 0.384 0.000
#> GSM125232 5 0.5982 1.0000 0.228 0.000 0.000 0.380 0.392 0.000
#> GSM125234 1 0.2668 0.7661 0.828 0.000 0.000 0.004 0.168 0.000
#> GSM125236 1 0.0937 0.9433 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125238 1 0.0000 0.9768 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> ATC:hclust 115 1.000 9.46e-05 2
#> ATC:hclust 113 0.988 3.59e-08 3
#> ATC:hclust 110 1.000 4.55e-08 4
#> ATC:hclust 110 0.930 9.45e-13 5
#> ATC:hclust 105 0.984 1.52e-17 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.987 0.994 0.5018 0.499 0.499
#> 3 3 1.000 0.980 0.991 0.3238 0.794 0.606
#> 4 4 0.851 0.796 0.883 0.0884 0.939 0.819
#> 5 5 0.787 0.797 0.856 0.0502 0.940 0.788
#> 6 6 0.728 0.821 0.820 0.0427 0.963 0.846
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
#> GSM125123 1 0.0000 1.000 1.000 0.000
#> GSM125125 1 0.0000 1.000 1.000 0.000
#> GSM125127 1 0.0000 1.000 1.000 0.000
#> GSM125129 1 0.0000 1.000 1.000 0.000
#> GSM125131 1 0.0000 1.000 1.000 0.000
#> GSM125133 1 0.0000 1.000 1.000 0.000
#> GSM125135 1 0.0000 1.000 1.000 0.000
#> GSM125137 1 0.0000 1.000 1.000 0.000
#> GSM125139 1 0.0000 1.000 1.000 0.000
#> GSM125141 1 0.0000 1.000 1.000 0.000
#> GSM125143 1 0.0000 1.000 1.000 0.000
#> GSM125145 1 0.0000 1.000 1.000 0.000
#> GSM125147 1 0.0000 1.000 1.000 0.000
#> GSM125149 1 0.0000 1.000 1.000 0.000
#> GSM125151 1 0.0000 1.000 1.000 0.000
#> GSM125153 1 0.0000 1.000 1.000 0.000
#> GSM125155 1 0.0000 1.000 1.000 0.000
#> GSM125157 1 0.0000 1.000 1.000 0.000
#> GSM125159 2 0.0000 0.989 0.000 1.000
#> GSM125161 1 0.0000 1.000 1.000 0.000
#> GSM125163 2 0.0000 0.989 0.000 1.000
#> GSM125165 2 0.0000 0.989 0.000 1.000
#> GSM125167 2 0.0000 0.989 0.000 1.000
#> GSM125169 2 0.0000 0.989 0.000 1.000
#> GSM125171 2 0.0000 0.989 0.000 1.000
#> GSM125173 2 0.0000 0.989 0.000 1.000
#> GSM125175 2 0.0000 0.989 0.000 1.000
#> GSM125177 2 0.0000 0.989 0.000 1.000
#> GSM125179 2 0.6887 0.781 0.184 0.816
#> GSM125181 2 0.0000 0.989 0.000 1.000
#> GSM125183 2 0.6973 0.776 0.188 0.812
#> GSM125185 2 0.0000 0.989 0.000 1.000
#> GSM125187 1 0.0000 1.000 1.000 0.000
#> GSM125189 2 0.0000 0.989 0.000 1.000
#> GSM125191 2 0.0000 0.989 0.000 1.000
#> GSM125193 2 0.8327 0.653 0.264 0.736
#> GSM125195 2 0.0000 0.989 0.000 1.000
#> GSM125197 2 0.0000 0.989 0.000 1.000
#> GSM125199 1 0.0000 1.000 1.000 0.000
#> GSM125201 2 0.0000 0.989 0.000 1.000
#> GSM125203 2 0.0000 0.989 0.000 1.000
#> GSM125205 2 0.0000 0.989 0.000 1.000
#> GSM125207 2 0.0000 0.989 0.000 1.000
#> GSM125209 2 0.0000 0.989 0.000 1.000
#> GSM125211 2 0.0000 0.989 0.000 1.000
#> GSM125213 2 0.0000 0.989 0.000 1.000
#> GSM125215 2 0.0000 0.989 0.000 1.000
#> GSM125217 2 0.0000 0.989 0.000 1.000
#> GSM125219 1 0.0000 1.000 1.000 0.000
#> GSM125221 2 0.0000 0.989 0.000 1.000
#> GSM125223 2 0.0000 0.989 0.000 1.000
#> GSM125225 2 0.0000 0.989 0.000 1.000
#> GSM125227 2 0.0000 0.989 0.000 1.000
#> GSM125229 2 0.0000 0.989 0.000 1.000
#> GSM125231 1 0.0000 1.000 1.000 0.000
#> GSM125233 1 0.0000 1.000 1.000 0.000
#> GSM125235 1 0.0000 1.000 1.000 0.000
#> GSM125237 1 0.0000 1.000 1.000 0.000
#> GSM125124 1 0.0000 1.000 1.000 0.000
#> GSM125126 1 0.0000 1.000 1.000 0.000
#> GSM125128 1 0.0000 1.000 1.000 0.000
#> GSM125130 1 0.0000 1.000 1.000 0.000
#> GSM125132 1 0.0000 1.000 1.000 0.000
#> GSM125134 1 0.0000 1.000 1.000 0.000
#> GSM125136 1 0.0000 1.000 1.000 0.000
#> GSM125138 1 0.0000 1.000 1.000 0.000
#> GSM125140 1 0.0000 1.000 1.000 0.000
#> GSM125142 1 0.0000 1.000 1.000 0.000
#> GSM125144 1 0.0000 1.000 1.000 0.000
#> GSM125146 1 0.0000 1.000 1.000 0.000
#> GSM125148 1 0.0000 1.000 1.000 0.000
#> GSM125150 1 0.0000 1.000 1.000 0.000
#> GSM125152 1 0.0000 1.000 1.000 0.000
#> GSM125154 1 0.0000 1.000 1.000 0.000
#> GSM125156 1 0.0000 1.000 1.000 0.000
#> GSM125158 1 0.0000 1.000 1.000 0.000
#> GSM125160 2 0.0000 0.989 0.000 1.000
#> GSM125162 1 0.0000 1.000 1.000 0.000
#> GSM125164 2 0.0000 0.989 0.000 1.000
#> GSM125166 2 0.0000 0.989 0.000 1.000
#> GSM125168 2 0.0000 0.989 0.000 1.000
#> GSM125170 2 0.0000 0.989 0.000 1.000
#> GSM125172 2 0.0000 0.989 0.000 1.000
#> GSM125174 2 0.0000 0.989 0.000 1.000
#> GSM125176 2 0.0000 0.989 0.000 1.000
#> GSM125178 2 0.0000 0.989 0.000 1.000
#> GSM125180 2 0.0938 0.979 0.012 0.988
#> GSM125182 2 0.0000 0.989 0.000 1.000
#> GSM125184 2 0.0000 0.989 0.000 1.000
#> GSM125186 2 0.1414 0.971 0.020 0.980
#> GSM125188 2 0.0000 0.989 0.000 1.000
#> GSM125190 2 0.0000 0.989 0.000 1.000
#> GSM125192 2 0.0000 0.989 0.000 1.000
#> GSM125194 1 0.0000 1.000 1.000 0.000
#> GSM125196 2 0.0000 0.989 0.000 1.000
#> GSM125198 2 0.0000 0.989 0.000 1.000
#> GSM125200 1 0.0000 1.000 1.000 0.000
#> GSM125202 2 0.0000 0.989 0.000 1.000
#> GSM125204 2 0.0000 0.989 0.000 1.000
#> GSM125206 2 0.0000 0.989 0.000 1.000
#> GSM125208 2 0.0000 0.989 0.000 1.000
#> GSM125210 2 0.0000 0.989 0.000 1.000
#> GSM125212 2 0.0000 0.989 0.000 1.000
#> GSM125214 2 0.0000 0.989 0.000 1.000
#> GSM125216 2 0.0000 0.989 0.000 1.000
#> GSM125218 2 0.0000 0.989 0.000 1.000
#> GSM125220 1 0.0000 1.000 1.000 0.000
#> GSM125222 2 0.0000 0.989 0.000 1.000
#> GSM125224 2 0.0000 0.989 0.000 1.000
#> GSM125226 2 0.0000 0.989 0.000 1.000
#> GSM125228 2 0.0000 0.989 0.000 1.000
#> GSM125230 1 0.0000 1.000 1.000 0.000
#> GSM125232 1 0.0000 1.000 1.000 0.000
#> GSM125234 1 0.0000 1.000 1.000 0.000
#> GSM125236 1 0.0000 1.000 1.000 0.000
#> GSM125238 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125127 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125129 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125131 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125135 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125137 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125139 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125141 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125143 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125145 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125147 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125149 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125151 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125153 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125155 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125157 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125159 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125163 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125165 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125167 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125169 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125171 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125173 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125175 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125177 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125179 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125181 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125183 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125185 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125187 3 0.0000 0.996 0.000 0.000 1.000
#> GSM125189 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125191 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125193 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125195 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125197 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125201 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125203 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125205 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125207 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125209 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125211 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125213 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125215 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125217 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125219 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125221 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125223 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125225 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125227 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125229 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125231 3 0.0000 0.996 0.000 0.000 1.000
#> GSM125233 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125124 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125126 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125130 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125132 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125134 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125136 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125138 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125140 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125142 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125144 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125146 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125148 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125150 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125152 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125154 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125156 1 0.0237 0.981 0.996 0.000 0.004
#> GSM125158 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125160 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125164 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125166 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125168 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125170 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125172 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125174 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125176 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125178 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125180 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125182 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125184 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125186 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125188 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125190 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125192 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125194 3 0.0000 0.996 0.000 0.000 1.000
#> GSM125196 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125198 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125202 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125204 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125206 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125208 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125210 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125212 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125214 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125216 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125218 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125220 1 0.6026 0.406 0.624 0.000 0.376
#> GSM125222 3 0.0237 0.999 0.000 0.004 0.996
#> GSM125224 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125226 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125228 2 0.0000 1.000 0.000 1.000 0.000
#> GSM125230 3 0.0000 0.996 0.000 0.000 1.000
#> GSM125232 3 0.0424 0.988 0.008 0.000 0.992
#> GSM125234 1 0.6235 0.242 0.564 0.000 0.436
#> GSM125236 1 0.0000 0.981 1.000 0.000 0.000
#> GSM125238 1 0.0237 0.981 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 4 0.4985 0.858 0.468 0.000 0.000 0.532
#> GSM125125 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125127 4 0.4955 0.875 0.444 0.000 0.000 0.556
#> GSM125129 1 0.4746 -0.349 0.632 0.000 0.000 0.368
#> GSM125131 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125133 4 0.4955 0.875 0.444 0.000 0.000 0.556
#> GSM125135 1 0.4843 -0.463 0.604 0.000 0.000 0.396
#> GSM125137 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125143 4 0.4977 0.875 0.460 0.000 0.000 0.540
#> GSM125145 1 0.4761 -0.365 0.628 0.000 0.000 0.372
#> GSM125147 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0336 0.898 0.000 0.992 0.008 0.000
#> GSM125161 1 0.0336 0.843 0.992 0.000 0.000 0.008
#> GSM125163 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125165 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM125167 2 0.0336 0.898 0.000 0.992 0.008 0.000
#> GSM125169 3 0.5203 0.501 0.000 0.348 0.636 0.016
#> GSM125171 2 0.0592 0.896 0.000 0.984 0.016 0.000
#> GSM125173 3 0.0817 0.958 0.000 0.000 0.976 0.024
#> GSM125175 2 0.0592 0.898 0.000 0.984 0.000 0.016
#> GSM125177 3 0.1118 0.955 0.000 0.000 0.964 0.036
#> GSM125179 3 0.0817 0.956 0.000 0.000 0.976 0.024
#> GSM125181 3 0.1510 0.947 0.000 0.028 0.956 0.016
#> GSM125183 3 0.0817 0.957 0.000 0.000 0.976 0.024
#> GSM125185 3 0.0469 0.958 0.000 0.000 0.988 0.012
#> GSM125187 3 0.1118 0.952 0.000 0.000 0.964 0.036
#> GSM125189 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125191 2 0.1004 0.892 0.000 0.972 0.024 0.004
#> GSM125193 3 0.1118 0.957 0.000 0.000 0.964 0.036
#> GSM125195 3 0.1867 0.944 0.000 0.000 0.928 0.072
#> GSM125197 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125199 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125201 2 0.1302 0.893 0.000 0.956 0.000 0.044
#> GSM125203 3 0.1118 0.955 0.000 0.000 0.964 0.036
#> GSM125205 2 0.4431 0.797 0.000 0.696 0.000 0.304
#> GSM125207 3 0.0921 0.957 0.000 0.000 0.972 0.028
#> GSM125209 2 0.0895 0.894 0.000 0.976 0.020 0.004
#> GSM125211 3 0.1118 0.957 0.000 0.000 0.964 0.036
#> GSM125213 2 0.2149 0.881 0.000 0.912 0.000 0.088
#> GSM125215 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125217 2 0.1356 0.886 0.000 0.960 0.032 0.008
#> GSM125219 4 0.4955 0.875 0.444 0.000 0.000 0.556
#> GSM125221 3 0.0469 0.958 0.000 0.000 0.988 0.012
#> GSM125223 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125225 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125227 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125229 3 0.3821 0.849 0.000 0.120 0.840 0.040
#> GSM125231 3 0.1867 0.945 0.000 0.000 0.928 0.072
#> GSM125233 1 0.4746 -0.349 0.632 0.000 0.000 0.368
#> GSM125235 1 0.4989 -0.720 0.528 0.000 0.000 0.472
#> GSM125237 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125128 4 0.4977 0.875 0.460 0.000 0.000 0.540
#> GSM125130 4 0.4972 0.876 0.456 0.000 0.000 0.544
#> GSM125132 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125136 4 0.4977 0.875 0.460 0.000 0.000 0.540
#> GSM125138 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125146 1 0.4761 -0.365 0.628 0.000 0.000 0.372
#> GSM125148 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125160 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125162 1 0.5000 -0.796 0.500 0.000 0.000 0.500
#> GSM125164 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125168 2 0.0707 0.895 0.000 0.980 0.020 0.000
#> GSM125170 3 0.1584 0.941 0.000 0.036 0.952 0.012
#> GSM125172 2 0.0592 0.896 0.000 0.984 0.016 0.000
#> GSM125174 3 0.1118 0.956 0.000 0.000 0.964 0.036
#> GSM125176 2 0.1388 0.886 0.000 0.960 0.028 0.012
#> GSM125178 3 0.1022 0.956 0.000 0.000 0.968 0.032
#> GSM125180 3 0.0592 0.958 0.000 0.000 0.984 0.016
#> GSM125182 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125184 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM125186 3 0.0592 0.958 0.000 0.000 0.984 0.016
#> GSM125188 3 0.0707 0.957 0.000 0.000 0.980 0.020
#> GSM125190 2 0.1284 0.889 0.000 0.964 0.024 0.012
#> GSM125192 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM125194 3 0.1474 0.945 0.000 0.000 0.948 0.052
#> GSM125196 3 0.1867 0.944 0.000 0.000 0.928 0.072
#> GSM125198 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125200 1 0.0000 0.854 1.000 0.000 0.000 0.000
#> GSM125202 2 0.1489 0.893 0.000 0.952 0.004 0.044
#> GSM125204 3 0.1302 0.957 0.000 0.000 0.956 0.044
#> GSM125206 3 0.1867 0.944 0.000 0.000 0.928 0.072
#> GSM125208 3 0.1211 0.958 0.000 0.000 0.960 0.040
#> GSM125210 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM125212 3 0.1211 0.957 0.000 0.000 0.960 0.040
#> GSM125214 2 0.2149 0.881 0.000 0.912 0.000 0.088
#> GSM125216 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125218 2 0.0188 0.899 0.000 0.996 0.004 0.000
#> GSM125220 4 0.7093 0.640 0.272 0.000 0.172 0.556
#> GSM125222 3 0.0469 0.958 0.000 0.000 0.988 0.012
#> GSM125224 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125226 2 0.0469 0.897 0.000 0.988 0.012 0.000
#> GSM125228 2 0.4679 0.775 0.000 0.648 0.000 0.352
#> GSM125230 3 0.1867 0.945 0.000 0.000 0.928 0.072
#> GSM125232 3 0.2773 0.898 0.004 0.000 0.880 0.116
#> GSM125234 4 0.7128 0.623 0.260 0.000 0.184 0.556
#> GSM125236 4 0.4977 0.875 0.460 0.000 0.000 0.540
#> GSM125238 1 0.0000 0.854 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.4671 0.862 0.332 0.028 0.000 0.000 0.640
#> GSM125125 1 0.1557 0.836 0.940 0.052 0.000 0.000 0.008
#> GSM125127 5 0.4728 0.868 0.296 0.040 0.000 0.000 0.664
#> GSM125129 1 0.5461 -0.347 0.528 0.064 0.000 0.000 0.408
#> GSM125131 1 0.1484 0.836 0.944 0.048 0.000 0.000 0.008
#> GSM125133 5 0.5325 0.870 0.308 0.076 0.000 0.000 0.616
#> GSM125135 1 0.5498 -0.455 0.496 0.064 0.000 0.000 0.440
#> GSM125137 1 0.0290 0.857 0.992 0.008 0.000 0.000 0.000
#> GSM125139 1 0.0290 0.857 0.992 0.008 0.000 0.000 0.000
#> GSM125141 1 0.0290 0.857 0.992 0.008 0.000 0.000 0.000
#> GSM125143 5 0.4679 0.866 0.316 0.032 0.000 0.000 0.652
#> GSM125145 1 0.5579 -0.397 0.508 0.072 0.000 0.000 0.420
#> GSM125147 1 0.0609 0.858 0.980 0.020 0.000 0.000 0.000
#> GSM125149 1 0.0510 0.855 0.984 0.016 0.000 0.000 0.000
#> GSM125151 1 0.1341 0.844 0.944 0.056 0.000 0.000 0.000
#> GSM125153 1 0.1544 0.841 0.932 0.068 0.000 0.000 0.000
#> GSM125155 1 0.0510 0.856 0.984 0.016 0.000 0.000 0.000
#> GSM125157 1 0.1121 0.844 0.956 0.044 0.000 0.000 0.000
#> GSM125159 2 0.4206 0.892 0.000 0.696 0.000 0.288 0.016
#> GSM125161 1 0.2793 0.764 0.876 0.088 0.000 0.000 0.036
#> GSM125163 2 0.4003 0.894 0.000 0.704 0.000 0.288 0.008
#> GSM125165 3 0.0771 0.935 0.000 0.004 0.976 0.000 0.020
#> GSM125167 2 0.3730 0.894 0.000 0.712 0.000 0.288 0.000
#> GSM125169 2 0.5359 0.312 0.000 0.608 0.316 0.000 0.076
#> GSM125171 2 0.4735 0.885 0.000 0.672 0.000 0.284 0.044
#> GSM125173 3 0.2270 0.914 0.000 0.020 0.904 0.000 0.076
#> GSM125175 2 0.5213 0.855 0.000 0.616 0.000 0.320 0.064
#> GSM125177 3 0.2362 0.922 0.000 0.024 0.900 0.000 0.076
#> GSM125179 3 0.0703 0.933 0.000 0.000 0.976 0.000 0.024
#> GSM125181 3 0.2927 0.895 0.000 0.060 0.872 0.000 0.068
#> GSM125183 3 0.1041 0.932 0.000 0.004 0.964 0.000 0.032
#> GSM125185 3 0.0162 0.934 0.000 0.000 0.996 0.000 0.004
#> GSM125187 3 0.0880 0.932 0.000 0.000 0.968 0.000 0.032
#> GSM125189 2 0.5124 0.868 0.000 0.644 0.000 0.288 0.068
#> GSM125191 2 0.4109 0.894 0.000 0.700 0.000 0.288 0.012
#> GSM125193 3 0.1764 0.933 0.000 0.008 0.928 0.000 0.064
#> GSM125195 3 0.3631 0.886 0.000 0.072 0.824 0.000 0.104
#> GSM125197 4 0.0290 0.900 0.000 0.000 0.000 0.992 0.008
#> GSM125199 1 0.0703 0.852 0.976 0.024 0.000 0.000 0.000
#> GSM125201 2 0.5077 0.722 0.000 0.568 0.000 0.392 0.040
#> GSM125203 3 0.2236 0.923 0.000 0.024 0.908 0.000 0.068
#> GSM125205 4 0.3214 0.713 0.000 0.120 0.000 0.844 0.036
#> GSM125207 3 0.1697 0.929 0.000 0.008 0.932 0.000 0.060
#> GSM125209 2 0.4003 0.894 0.000 0.704 0.000 0.288 0.008
#> GSM125211 3 0.1981 0.928 0.000 0.016 0.920 0.000 0.064
#> GSM125213 2 0.4830 0.527 0.000 0.492 0.000 0.488 0.020
#> GSM125215 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125217 2 0.4713 0.877 0.000 0.676 0.000 0.280 0.044
#> GSM125219 5 0.5218 0.864 0.296 0.072 0.000 0.000 0.632
#> GSM125221 3 0.0510 0.934 0.000 0.000 0.984 0.000 0.016
#> GSM125223 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125225 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125227 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125229 3 0.5224 0.719 0.000 0.176 0.684 0.000 0.140
#> GSM125231 3 0.2625 0.915 0.000 0.016 0.876 0.000 0.108
#> GSM125233 1 0.5359 -0.352 0.532 0.056 0.000 0.000 0.412
#> GSM125235 5 0.5295 0.705 0.408 0.052 0.000 0.000 0.540
#> GSM125237 1 0.0510 0.855 0.984 0.016 0.000 0.000 0.000
#> GSM125124 1 0.1197 0.848 0.952 0.048 0.000 0.000 0.000
#> GSM125126 1 0.1484 0.836 0.944 0.048 0.000 0.000 0.008
#> GSM125128 5 0.4770 0.875 0.320 0.036 0.000 0.000 0.644
#> GSM125130 5 0.4329 0.879 0.312 0.016 0.000 0.000 0.672
#> GSM125132 1 0.0609 0.853 0.980 0.020 0.000 0.000 0.000
#> GSM125134 1 0.1478 0.841 0.936 0.064 0.000 0.000 0.000
#> GSM125136 5 0.5213 0.874 0.320 0.064 0.000 0.000 0.616
#> GSM125138 1 0.1341 0.844 0.944 0.056 0.000 0.000 0.000
#> GSM125140 1 0.0290 0.857 0.992 0.008 0.000 0.000 0.000
#> GSM125142 1 0.1197 0.848 0.952 0.048 0.000 0.000 0.000
#> GSM125144 1 0.1341 0.844 0.944 0.056 0.000 0.000 0.000
#> GSM125146 1 0.5579 -0.397 0.508 0.072 0.000 0.000 0.420
#> GSM125148 1 0.0609 0.858 0.980 0.020 0.000 0.000 0.000
#> GSM125150 1 0.0609 0.854 0.980 0.020 0.000 0.000 0.000
#> GSM125152 1 0.1341 0.844 0.944 0.056 0.000 0.000 0.000
#> GSM125154 1 0.1410 0.842 0.940 0.060 0.000 0.000 0.000
#> GSM125156 1 0.0703 0.855 0.976 0.024 0.000 0.000 0.000
#> GSM125158 1 0.0609 0.853 0.980 0.020 0.000 0.000 0.000
#> GSM125160 2 0.4206 0.892 0.000 0.696 0.000 0.288 0.016
#> GSM125162 5 0.5752 0.728 0.412 0.088 0.000 0.000 0.500
#> GSM125164 2 0.4003 0.894 0.000 0.704 0.000 0.288 0.008
#> GSM125166 2 0.4003 0.894 0.000 0.704 0.000 0.288 0.008
#> GSM125168 2 0.4003 0.893 0.000 0.704 0.000 0.288 0.008
#> GSM125170 3 0.3110 0.862 0.000 0.080 0.860 0.000 0.060
#> GSM125172 2 0.4442 0.887 0.000 0.688 0.000 0.284 0.028
#> GSM125174 3 0.2448 0.912 0.000 0.020 0.892 0.000 0.088
#> GSM125176 2 0.5301 0.850 0.000 0.648 0.004 0.272 0.076
#> GSM125178 3 0.2236 0.923 0.000 0.024 0.908 0.000 0.068
#> GSM125180 3 0.0703 0.933 0.000 0.000 0.976 0.000 0.024
#> GSM125182 2 0.4109 0.893 0.000 0.700 0.000 0.288 0.012
#> GSM125184 3 0.0000 0.934 0.000 0.000 1.000 0.000 0.000
#> GSM125186 3 0.0703 0.933 0.000 0.000 0.976 0.000 0.024
#> GSM125188 3 0.1469 0.931 0.000 0.016 0.948 0.000 0.036
#> GSM125190 2 0.5301 0.850 0.000 0.648 0.004 0.272 0.076
#> GSM125192 2 0.4109 0.893 0.000 0.700 0.000 0.288 0.012
#> GSM125194 3 0.1638 0.923 0.000 0.004 0.932 0.000 0.064
#> GSM125196 3 0.3631 0.886 0.000 0.072 0.824 0.000 0.104
#> GSM125198 4 0.0290 0.900 0.000 0.000 0.000 0.992 0.008
#> GSM125200 1 0.0703 0.852 0.976 0.024 0.000 0.000 0.000
#> GSM125202 2 0.5077 0.722 0.000 0.568 0.000 0.392 0.040
#> GSM125204 3 0.2236 0.923 0.000 0.024 0.908 0.000 0.068
#> GSM125206 3 0.3639 0.886 0.000 0.076 0.824 0.000 0.100
#> GSM125208 3 0.1697 0.929 0.000 0.008 0.932 0.000 0.060
#> GSM125210 3 0.0404 0.934 0.000 0.000 0.988 0.000 0.012
#> GSM125212 3 0.2408 0.926 0.000 0.016 0.892 0.000 0.092
#> GSM125214 4 0.4829 -0.557 0.000 0.480 0.000 0.500 0.020
#> GSM125216 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125218 2 0.5124 0.868 0.000 0.644 0.000 0.288 0.068
#> GSM125220 5 0.6365 0.746 0.192 0.072 0.100 0.000 0.636
#> GSM125222 3 0.0510 0.934 0.000 0.000 0.984 0.000 0.016
#> GSM125224 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125226 2 0.5233 0.864 0.000 0.636 0.000 0.288 0.076
#> GSM125228 4 0.0000 0.904 0.000 0.000 0.000 1.000 0.000
#> GSM125230 3 0.2519 0.919 0.000 0.016 0.884 0.000 0.100
#> GSM125232 3 0.3183 0.869 0.000 0.016 0.828 0.000 0.156
#> GSM125234 5 0.5927 0.728 0.176 0.040 0.116 0.000 0.668
#> GSM125236 5 0.4165 0.875 0.320 0.008 0.000 0.000 0.672
#> GSM125238 1 0.0404 0.854 0.988 0.012 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 5 0.4259 0.807 0.188 0.000 0.028 0.000 0.744 NA
#> GSM125125 1 0.2907 0.840 0.860 0.000 0.028 0.000 0.016 NA
#> GSM125127 5 0.4422 0.800 0.172 0.000 0.008 0.008 0.740 NA
#> GSM125129 5 0.5870 0.624 0.356 0.000 0.040 0.000 0.516 NA
#> GSM125131 1 0.2230 0.854 0.904 0.000 0.016 0.000 0.016 NA
#> GSM125133 5 0.5102 0.795 0.184 0.000 0.028 0.000 0.680 NA
#> GSM125135 5 0.5898 0.641 0.348 0.000 0.056 0.000 0.524 NA
#> GSM125137 1 0.1334 0.884 0.948 0.000 0.032 0.000 0.000 NA
#> GSM125139 1 0.1408 0.884 0.944 0.000 0.036 0.000 0.000 NA
#> GSM125141 1 0.0777 0.888 0.972 0.000 0.004 0.000 0.000 NA
#> GSM125143 5 0.4022 0.807 0.188 0.000 0.016 0.000 0.756 NA
#> GSM125145 5 0.6324 0.557 0.372 0.000 0.072 0.000 0.464 NA
#> GSM125147 1 0.1765 0.881 0.924 0.000 0.024 0.000 0.000 NA
#> GSM125149 1 0.1124 0.880 0.956 0.000 0.008 0.000 0.000 NA
#> GSM125151 1 0.3167 0.842 0.832 0.000 0.096 0.000 0.000 NA
#> GSM125153 1 0.3560 0.826 0.808 0.000 0.084 0.000 0.004 NA
#> GSM125155 1 0.1492 0.883 0.940 0.000 0.036 0.000 0.000 NA
#> GSM125157 1 0.1672 0.870 0.932 0.000 0.016 0.000 0.004 NA
#> GSM125159 2 0.0458 0.854 0.000 0.984 0.000 0.000 0.000 NA
#> GSM125161 1 0.3994 0.726 0.792 0.000 0.036 0.000 0.056 NA
#> GSM125163 2 0.0260 0.858 0.000 0.992 0.000 0.000 0.000 NA
#> GSM125165 4 0.1913 0.862 0.000 0.000 0.000 0.908 0.012 NA
#> GSM125167 2 0.1010 0.858 0.000 0.960 0.000 0.000 0.004 NA
#> GSM125169 2 0.6557 0.424 0.000 0.516 0.004 0.192 0.052 NA
#> GSM125171 2 0.3253 0.832 0.000 0.832 0.000 0.004 0.068 NA
#> GSM125173 4 0.3581 0.819 0.000 0.000 0.036 0.824 0.044 NA
#> GSM125175 2 0.4402 0.765 0.000 0.764 0.048 0.000 0.068 NA
#> GSM125177 4 0.3428 0.811 0.000 0.000 0.000 0.696 0.000 NA
#> GSM125179 4 0.0622 0.858 0.000 0.000 0.000 0.980 0.008 NA
#> GSM125181 4 0.4447 0.788 0.000 0.064 0.000 0.704 0.008 NA
#> GSM125183 4 0.0820 0.855 0.000 0.000 0.000 0.972 0.016 NA
#> GSM125185 4 0.0260 0.860 0.000 0.000 0.000 0.992 0.000 NA
#> GSM125187 4 0.0717 0.856 0.000 0.000 0.000 0.976 0.016 NA
#> GSM125189 2 0.3432 0.797 0.000 0.800 0.000 0.000 0.052 NA
#> GSM125191 2 0.1296 0.857 0.000 0.948 0.000 0.004 0.004 NA
#> GSM125193 4 0.2066 0.863 0.000 0.000 0.000 0.904 0.024 NA
#> GSM125195 4 0.4669 0.719 0.000 0.000 0.016 0.556 0.020 NA
#> GSM125197 3 0.2994 0.967 0.000 0.208 0.788 0.000 0.004 NA
#> GSM125199 1 0.1340 0.877 0.948 0.000 0.008 0.000 0.004 NA
#> GSM125201 2 0.3601 0.755 0.000 0.828 0.072 0.000 0.048 NA
#> GSM125203 4 0.3409 0.813 0.000 0.000 0.000 0.700 0.000 NA
#> GSM125205 3 0.5517 0.643 0.000 0.356 0.548 0.000 0.052 NA
#> GSM125207 4 0.3126 0.832 0.000 0.000 0.000 0.752 0.000 NA
#> GSM125209 2 0.1296 0.857 0.000 0.948 0.000 0.004 0.004 NA
#> GSM125211 4 0.2905 0.856 0.000 0.000 0.008 0.836 0.012 NA
#> GSM125213 2 0.3488 0.668 0.000 0.804 0.152 0.000 0.012 NA
#> GSM125215 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125217 2 0.2356 0.842 0.000 0.884 0.000 0.004 0.016 NA
#> GSM125219 5 0.5255 0.790 0.172 0.000 0.028 0.008 0.684 NA
#> GSM125221 4 0.0363 0.857 0.000 0.000 0.000 0.988 0.012 NA
#> GSM125223 3 0.2994 0.967 0.000 0.208 0.788 0.000 0.004 NA
#> GSM125225 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125227 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125229 4 0.5316 0.627 0.000 0.104 0.000 0.480 0.000 NA
#> GSM125231 4 0.2791 0.845 0.000 0.000 0.008 0.864 0.032 NA
#> GSM125233 5 0.5877 0.629 0.352 0.000 0.044 0.000 0.520 NA
#> GSM125235 5 0.5604 0.746 0.260 0.000 0.044 0.000 0.608 NA
#> GSM125237 1 0.1124 0.880 0.956 0.000 0.008 0.000 0.000 NA
#> GSM125124 1 0.2672 0.859 0.868 0.000 0.080 0.000 0.000 NA
#> GSM125126 1 0.2449 0.849 0.888 0.000 0.020 0.000 0.012 NA
#> GSM125128 5 0.4022 0.809 0.188 0.000 0.016 0.000 0.756 NA
#> GSM125130 5 0.3587 0.809 0.188 0.000 0.000 0.000 0.772 NA
#> GSM125132 1 0.1340 0.877 0.948 0.000 0.008 0.000 0.004 NA
#> GSM125134 1 0.3572 0.827 0.812 0.000 0.080 0.000 0.008 NA
#> GSM125136 5 0.4886 0.799 0.188 0.000 0.024 0.000 0.696 NA
#> GSM125138 1 0.3167 0.842 0.832 0.000 0.096 0.000 0.000 NA
#> GSM125140 1 0.1408 0.884 0.944 0.000 0.036 0.000 0.000 NA
#> GSM125142 1 0.3063 0.847 0.840 0.000 0.092 0.000 0.000 NA
#> GSM125144 1 0.3167 0.842 0.832 0.000 0.096 0.000 0.000 NA
#> GSM125146 5 0.6324 0.557 0.372 0.000 0.072 0.000 0.464 NA
#> GSM125148 1 0.1633 0.881 0.932 0.000 0.024 0.000 0.000 NA
#> GSM125150 1 0.1124 0.880 0.956 0.000 0.008 0.000 0.000 NA
#> GSM125152 1 0.3167 0.842 0.832 0.000 0.096 0.000 0.000 NA
#> GSM125154 1 0.3172 0.842 0.832 0.000 0.092 0.000 0.000 NA
#> GSM125156 1 0.2119 0.873 0.904 0.000 0.060 0.000 0.000 NA
#> GSM125158 1 0.1196 0.878 0.952 0.000 0.008 0.000 0.000 NA
#> GSM125160 2 0.0547 0.853 0.000 0.980 0.000 0.000 0.000 NA
#> GSM125162 5 0.6062 0.633 0.356 0.000 0.036 0.000 0.492 NA
#> GSM125164 2 0.0405 0.858 0.000 0.988 0.000 0.000 0.004 NA
#> GSM125166 2 0.0405 0.858 0.000 0.988 0.000 0.000 0.004 NA
#> GSM125168 2 0.1155 0.857 0.000 0.956 0.000 0.004 0.004 NA
#> GSM125170 4 0.4647 0.761 0.000 0.060 0.004 0.736 0.036 NA
#> GSM125172 2 0.2533 0.843 0.000 0.884 0.000 0.004 0.056 NA
#> GSM125174 4 0.3445 0.809 0.000 0.000 0.036 0.836 0.048 NA
#> GSM125176 2 0.3752 0.782 0.000 0.776 0.000 0.004 0.052 NA
#> GSM125178 4 0.3409 0.813 0.000 0.000 0.000 0.700 0.000 NA
#> GSM125180 4 0.0622 0.858 0.000 0.000 0.000 0.980 0.008 NA
#> GSM125182 2 0.0508 0.858 0.000 0.984 0.000 0.000 0.004 NA
#> GSM125184 4 0.0713 0.862 0.000 0.000 0.000 0.972 0.000 NA
#> GSM125186 4 0.0622 0.858 0.000 0.000 0.000 0.980 0.008 NA
#> GSM125188 4 0.2980 0.843 0.000 0.000 0.000 0.800 0.008 NA
#> GSM125190 2 0.3752 0.782 0.000 0.776 0.000 0.004 0.052 NA
#> GSM125192 2 0.0405 0.858 0.000 0.988 0.000 0.000 0.004 NA
#> GSM125194 4 0.1528 0.848 0.000 0.000 0.000 0.936 0.048 NA
#> GSM125196 4 0.4669 0.719 0.000 0.000 0.016 0.556 0.020 NA
#> GSM125198 3 0.2994 0.967 0.000 0.208 0.788 0.000 0.004 NA
#> GSM125200 1 0.1340 0.877 0.948 0.000 0.008 0.000 0.004 NA
#> GSM125202 2 0.3601 0.755 0.000 0.828 0.072 0.000 0.048 NA
#> GSM125204 4 0.3409 0.813 0.000 0.000 0.000 0.700 0.000 NA
#> GSM125206 4 0.4738 0.719 0.000 0.000 0.020 0.556 0.020 NA
#> GSM125208 4 0.3076 0.835 0.000 0.000 0.000 0.760 0.000 NA
#> GSM125210 4 0.1501 0.862 0.000 0.000 0.000 0.924 0.000 NA
#> GSM125212 4 0.3533 0.842 0.000 0.000 0.004 0.748 0.012 NA
#> GSM125214 2 0.3734 0.638 0.000 0.784 0.164 0.000 0.012 NA
#> GSM125216 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125218 2 0.3432 0.797 0.000 0.800 0.000 0.000 0.052 NA
#> GSM125220 5 0.5757 0.722 0.104 0.000 0.028 0.076 0.684 NA
#> GSM125222 4 0.0363 0.857 0.000 0.000 0.000 0.988 0.012 NA
#> GSM125224 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125226 2 0.3394 0.801 0.000 0.804 0.000 0.000 0.052 NA
#> GSM125228 3 0.2854 0.968 0.000 0.208 0.792 0.000 0.000 NA
#> GSM125230 4 0.2763 0.846 0.000 0.000 0.008 0.868 0.036 NA
#> GSM125232 4 0.3371 0.811 0.000 0.000 0.012 0.832 0.080 NA
#> GSM125234 5 0.5029 0.722 0.096 0.000 0.012 0.084 0.736 NA
#> GSM125236 5 0.3724 0.809 0.188 0.000 0.012 0.000 0.772 NA
#> GSM125238 1 0.1049 0.880 0.960 0.000 0.008 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> ATC:kmeans 116 1.000 3.22e-05 2
#> ATC:kmeans 114 0.974 3.76e-08 3
#> ATC:kmeans 109 0.986 6.55e-09 4
#> ATC:kmeans 109 0.963 1.24e-10 5
#> ATC:kmeans 115 0.918 1.10e-12 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 0.974 0.991 0.5034 0.496 0.496
#> 3 3 0.969 0.948 0.977 0.2574 0.835 0.677
#> 4 4 0.938 0.876 0.947 0.0662 0.927 0.807
#> 5 5 0.886 0.852 0.914 0.0349 0.981 0.940
#> 6 6 0.883 0.814 0.899 0.0302 0.996 0.987
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
#> GSM125123 1 0.000 0.9823 1.000 0.000
#> GSM125125 1 0.000 0.9823 1.000 0.000
#> GSM125127 1 0.000 0.9823 1.000 0.000
#> GSM125129 1 0.000 0.9823 1.000 0.000
#> GSM125131 1 0.000 0.9823 1.000 0.000
#> GSM125133 1 0.000 0.9823 1.000 0.000
#> GSM125135 1 0.000 0.9823 1.000 0.000
#> GSM125137 1 0.000 0.9823 1.000 0.000
#> GSM125139 1 0.000 0.9823 1.000 0.000
#> GSM125141 1 0.000 0.9823 1.000 0.000
#> GSM125143 1 0.000 0.9823 1.000 0.000
#> GSM125145 1 0.000 0.9823 1.000 0.000
#> GSM125147 1 0.000 0.9823 1.000 0.000
#> GSM125149 1 0.000 0.9823 1.000 0.000
#> GSM125151 1 0.000 0.9823 1.000 0.000
#> GSM125153 1 0.000 0.9823 1.000 0.000
#> GSM125155 1 0.000 0.9823 1.000 0.000
#> GSM125157 1 0.000 0.9823 1.000 0.000
#> GSM125159 2 0.000 0.9984 0.000 1.000
#> GSM125161 1 0.000 0.9823 1.000 0.000
#> GSM125163 2 0.000 0.9984 0.000 1.000
#> GSM125165 2 0.000 0.9984 0.000 1.000
#> GSM125167 2 0.000 0.9984 0.000 1.000
#> GSM125169 2 0.000 0.9984 0.000 1.000
#> GSM125171 2 0.000 0.9984 0.000 1.000
#> GSM125173 2 0.000 0.9984 0.000 1.000
#> GSM125175 2 0.000 0.9984 0.000 1.000
#> GSM125177 2 0.000 0.9984 0.000 1.000
#> GSM125179 1 0.998 0.1162 0.528 0.472
#> GSM125181 2 0.000 0.9984 0.000 1.000
#> GSM125183 1 1.000 0.0435 0.508 0.492
#> GSM125185 2 0.000 0.9984 0.000 1.000
#> GSM125187 1 0.000 0.9823 1.000 0.000
#> GSM125189 2 0.000 0.9984 0.000 1.000
#> GSM125191 2 0.000 0.9984 0.000 1.000
#> GSM125193 1 0.000 0.9823 1.000 0.000
#> GSM125195 2 0.000 0.9984 0.000 1.000
#> GSM125197 2 0.000 0.9984 0.000 1.000
#> GSM125199 1 0.000 0.9823 1.000 0.000
#> GSM125201 2 0.000 0.9984 0.000 1.000
#> GSM125203 2 0.000 0.9984 0.000 1.000
#> GSM125205 2 0.000 0.9984 0.000 1.000
#> GSM125207 2 0.000 0.9984 0.000 1.000
#> GSM125209 2 0.000 0.9984 0.000 1.000
#> GSM125211 2 0.000 0.9984 0.000 1.000
#> GSM125213 2 0.000 0.9984 0.000 1.000
#> GSM125215 2 0.000 0.9984 0.000 1.000
#> GSM125217 2 0.000 0.9984 0.000 1.000
#> GSM125219 1 0.000 0.9823 1.000 0.000
#> GSM125221 2 0.000 0.9984 0.000 1.000
#> GSM125223 2 0.000 0.9984 0.000 1.000
#> GSM125225 2 0.000 0.9984 0.000 1.000
#> GSM125227 2 0.000 0.9984 0.000 1.000
#> GSM125229 2 0.000 0.9984 0.000 1.000
#> GSM125231 1 0.000 0.9823 1.000 0.000
#> GSM125233 1 0.000 0.9823 1.000 0.000
#> GSM125235 1 0.000 0.9823 1.000 0.000
#> GSM125237 1 0.000 0.9823 1.000 0.000
#> GSM125124 1 0.000 0.9823 1.000 0.000
#> GSM125126 1 0.000 0.9823 1.000 0.000
#> GSM125128 1 0.000 0.9823 1.000 0.000
#> GSM125130 1 0.000 0.9823 1.000 0.000
#> GSM125132 1 0.000 0.9823 1.000 0.000
#> GSM125134 1 0.000 0.9823 1.000 0.000
#> GSM125136 1 0.000 0.9823 1.000 0.000
#> GSM125138 1 0.000 0.9823 1.000 0.000
#> GSM125140 1 0.000 0.9823 1.000 0.000
#> GSM125142 1 0.000 0.9823 1.000 0.000
#> GSM125144 1 0.000 0.9823 1.000 0.000
#> GSM125146 1 0.000 0.9823 1.000 0.000
#> GSM125148 1 0.000 0.9823 1.000 0.000
#> GSM125150 1 0.000 0.9823 1.000 0.000
#> GSM125152 1 0.000 0.9823 1.000 0.000
#> GSM125154 1 0.000 0.9823 1.000 0.000
#> GSM125156 1 0.000 0.9823 1.000 0.000
#> GSM125158 1 0.000 0.9823 1.000 0.000
#> GSM125160 2 0.000 0.9984 0.000 1.000
#> GSM125162 1 0.000 0.9823 1.000 0.000
#> GSM125164 2 0.000 0.9984 0.000 1.000
#> GSM125166 2 0.000 0.9984 0.000 1.000
#> GSM125168 2 0.000 0.9984 0.000 1.000
#> GSM125170 2 0.000 0.9984 0.000 1.000
#> GSM125172 2 0.000 0.9984 0.000 1.000
#> GSM125174 2 0.000 0.9984 0.000 1.000
#> GSM125176 2 0.000 0.9984 0.000 1.000
#> GSM125178 2 0.000 0.9984 0.000 1.000
#> GSM125180 2 0.141 0.9785 0.020 0.980
#> GSM125182 2 0.000 0.9984 0.000 1.000
#> GSM125184 2 0.000 0.9984 0.000 1.000
#> GSM125186 2 0.373 0.9209 0.072 0.928
#> GSM125188 2 0.000 0.9984 0.000 1.000
#> GSM125190 2 0.000 0.9984 0.000 1.000
#> GSM125192 2 0.000 0.9984 0.000 1.000
#> GSM125194 1 0.000 0.9823 1.000 0.000
#> GSM125196 2 0.000 0.9984 0.000 1.000
#> GSM125198 2 0.000 0.9984 0.000 1.000
#> GSM125200 1 0.000 0.9823 1.000 0.000
#> GSM125202 2 0.000 0.9984 0.000 1.000
#> GSM125204 2 0.000 0.9984 0.000 1.000
#> GSM125206 2 0.000 0.9984 0.000 1.000
#> GSM125208 2 0.000 0.9984 0.000 1.000
#> GSM125210 2 0.000 0.9984 0.000 1.000
#> GSM125212 2 0.000 0.9984 0.000 1.000
#> GSM125214 2 0.000 0.9984 0.000 1.000
#> GSM125216 2 0.000 0.9984 0.000 1.000
#> GSM125218 2 0.000 0.9984 0.000 1.000
#> GSM125220 1 0.000 0.9823 1.000 0.000
#> GSM125222 2 0.000 0.9984 0.000 1.000
#> GSM125224 2 0.000 0.9984 0.000 1.000
#> GSM125226 2 0.000 0.9984 0.000 1.000
#> GSM125228 2 0.000 0.9984 0.000 1.000
#> GSM125230 1 0.000 0.9823 1.000 0.000
#> GSM125232 1 0.000 0.9823 1.000 0.000
#> GSM125234 1 0.000 0.9823 1.000 0.000
#> GSM125236 1 0.000 0.9823 1.000 0.000
#> GSM125238 1 0.000 0.9823 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125127 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125129 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125131 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125135 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125137 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125139 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125143 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125145 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125147 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125153 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125159 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125161 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125163 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125165 3 0.4504 0.746 0.000 0.196 0.804
#> GSM125167 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125169 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125173 3 0.2066 0.866 0.000 0.060 0.940
#> GSM125175 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125177 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125179 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125181 2 0.0237 0.992 0.000 0.996 0.004
#> GSM125183 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125185 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125187 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125189 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125191 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125193 1 0.5760 0.497 0.672 0.000 0.328
#> GSM125195 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125197 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125203 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125205 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125207 3 0.1163 0.883 0.000 0.028 0.972
#> GSM125209 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125211 3 0.0592 0.889 0.000 0.012 0.988
#> GSM125213 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125217 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125219 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125221 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125223 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125229 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125231 3 0.6126 0.376 0.400 0.000 0.600
#> GSM125233 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125124 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125126 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125130 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125132 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125138 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125144 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125146 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125168 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125170 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125172 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125174 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125176 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125178 3 0.3879 0.793 0.000 0.152 0.848
#> GSM125180 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125182 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125184 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125186 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125188 2 0.3816 0.812 0.000 0.852 0.148
#> GSM125190 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125192 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125194 1 0.1031 0.968 0.976 0.000 0.024
#> GSM125196 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125198 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125202 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125204 3 0.6045 0.448 0.000 0.380 0.620
#> GSM125206 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125208 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125210 3 0.6225 0.323 0.000 0.432 0.568
#> GSM125212 3 0.1529 0.878 0.000 0.040 0.960
#> GSM125214 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125218 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125220 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125222 3 0.0000 0.892 0.000 0.000 1.000
#> GSM125224 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125226 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125228 2 0.0000 0.996 0.000 1.000 0.000
#> GSM125230 3 0.3267 0.816 0.116 0.000 0.884
#> GSM125232 3 0.6140 0.366 0.404 0.000 0.596
#> GSM125234 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125236 1 0.0000 0.992 1.000 0.000 0.000
#> GSM125238 1 0.0000 0.992 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125125 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125127 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125129 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125131 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125133 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125135 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125137 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125143 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125145 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125147 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125161 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125163 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125165 2 0.6785 0.1769 0.000 0.540 0.352 0.108
#> GSM125167 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125169 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125171 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125173 3 0.7628 0.1144 0.000 0.348 0.440 0.212
#> GSM125175 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125177 4 0.3351 0.7642 0.000 0.148 0.008 0.844
#> GSM125179 3 0.0188 0.7537 0.000 0.000 0.996 0.004
#> GSM125181 2 0.1610 0.9205 0.000 0.952 0.016 0.032
#> GSM125183 3 0.0469 0.7517 0.000 0.000 0.988 0.012
#> GSM125185 3 0.0336 0.7531 0.000 0.000 0.992 0.008
#> GSM125187 3 0.0188 0.7526 0.000 0.000 0.996 0.004
#> GSM125189 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125191 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125193 4 0.5936 0.3814 0.056 0.000 0.324 0.620
#> GSM125195 4 0.2973 0.7654 0.000 0.144 0.000 0.856
#> GSM125197 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125199 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125201 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125203 4 0.2412 0.7907 0.000 0.084 0.008 0.908
#> GSM125205 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125207 4 0.1970 0.7762 0.000 0.008 0.060 0.932
#> GSM125209 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125211 4 0.4679 0.3563 0.000 0.000 0.352 0.648
#> GSM125213 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125215 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125217 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125219 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125221 3 0.1118 0.7406 0.000 0.000 0.964 0.036
#> GSM125223 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125225 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125227 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125229 2 0.1211 0.9253 0.000 0.960 0.000 0.040
#> GSM125231 3 0.7748 0.2221 0.280 0.000 0.440 0.280
#> GSM125233 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125235 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125237 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125128 1 0.0336 0.9846 0.992 0.000 0.000 0.008
#> GSM125130 1 0.0469 0.9818 0.988 0.000 0.000 0.012
#> GSM125132 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125136 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125138 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125146 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125148 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125160 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125164 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125168 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125170 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125172 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125174 3 0.3583 0.6302 0.000 0.004 0.816 0.180
#> GSM125176 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125178 4 0.2376 0.7823 0.000 0.016 0.068 0.916
#> GSM125180 3 0.0188 0.7537 0.000 0.000 0.996 0.004
#> GSM125182 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125184 3 0.1890 0.7266 0.000 0.008 0.936 0.056
#> GSM125186 3 0.0188 0.7537 0.000 0.000 0.996 0.004
#> GSM125188 2 0.6134 -0.0175 0.000 0.508 0.048 0.444
#> GSM125190 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125194 1 0.6613 0.4156 0.628 0.000 0.200 0.172
#> GSM125196 4 0.2973 0.7654 0.000 0.144 0.000 0.856
#> GSM125198 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125200 1 0.0000 0.9896 1.000 0.000 0.000 0.000
#> GSM125202 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125204 4 0.2124 0.7890 0.000 0.028 0.040 0.932
#> GSM125206 4 0.2973 0.7654 0.000 0.144 0.000 0.856
#> GSM125208 4 0.1716 0.7695 0.000 0.000 0.064 0.936
#> GSM125210 2 0.4690 0.5986 0.000 0.724 0.260 0.016
#> GSM125212 4 0.4434 0.5980 0.000 0.016 0.228 0.756
#> GSM125214 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125216 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125218 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125220 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125222 3 0.1022 0.7434 0.000 0.000 0.968 0.032
#> GSM125224 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125226 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125228 2 0.0000 0.9642 0.000 1.000 0.000 0.000
#> GSM125230 3 0.7443 0.1225 0.172 0.000 0.436 0.392
#> GSM125232 3 0.6091 0.3496 0.344 0.000 0.596 0.060
#> GSM125234 1 0.0592 0.9791 0.984 0.000 0.000 0.016
#> GSM125236 1 0.0188 0.9872 0.996 0.000 0.000 0.004
#> GSM125238 1 0.0000 0.9896 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.0510 0.9699 0.984 0.000 0.000 0.000 0.016
#> GSM125125 1 0.0162 0.9740 0.996 0.000 0.000 0.000 0.004
#> GSM125127 1 0.2230 0.8936 0.884 0.000 0.000 0.000 0.116
#> GSM125129 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125131 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125133 1 0.2329 0.8825 0.876 0.000 0.000 0.000 0.124
#> GSM125135 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125137 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125139 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125141 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125143 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125145 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125147 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125149 1 0.0000 0.9743 1.000 0.000 0.000 0.000 0.000
#> GSM125151 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125153 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125155 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125157 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125159 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125161 1 0.0404 0.9716 0.988 0.000 0.000 0.000 0.012
#> GSM125163 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125165 2 0.7203 0.0467 0.000 0.488 0.040 0.220 0.252
#> GSM125167 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125169 2 0.0510 0.9433 0.000 0.984 0.000 0.000 0.016
#> GSM125171 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125173 5 0.7526 0.2348 0.000 0.224 0.096 0.176 0.504
#> GSM125175 2 0.0290 0.9500 0.000 0.992 0.000 0.000 0.008
#> GSM125177 3 0.0794 0.8436 0.000 0.028 0.972 0.000 0.000
#> GSM125179 4 0.0451 0.8696 0.000 0.000 0.004 0.988 0.008
#> GSM125181 2 0.4292 0.6792 0.000 0.748 0.012 0.024 0.216
#> GSM125183 4 0.3003 0.7227 0.000 0.000 0.000 0.812 0.188
#> GSM125185 4 0.0579 0.8677 0.000 0.000 0.008 0.984 0.008
#> GSM125187 4 0.1608 0.8463 0.000 0.000 0.000 0.928 0.072
#> GSM125189 2 0.0510 0.9433 0.000 0.984 0.000 0.000 0.016
#> GSM125191 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125193 5 0.6615 0.0777 0.024 0.000 0.232 0.184 0.560
#> GSM125195 3 0.2522 0.8288 0.000 0.024 0.896 0.004 0.076
#> GSM125197 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125199 1 0.0162 0.9740 0.996 0.000 0.000 0.000 0.004
#> GSM125201 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125203 3 0.0290 0.8519 0.000 0.008 0.992 0.000 0.000
#> GSM125205 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125207 3 0.2625 0.7873 0.000 0.000 0.876 0.016 0.108
#> GSM125209 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125211 5 0.6199 0.0873 0.000 0.000 0.392 0.140 0.468
#> GSM125213 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125215 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125217 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125219 1 0.2424 0.8743 0.868 0.000 0.000 0.000 0.132
#> GSM125221 4 0.3282 0.7543 0.000 0.000 0.008 0.804 0.188
#> GSM125223 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125225 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125227 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125229 2 0.2389 0.8397 0.000 0.880 0.116 0.000 0.004
#> GSM125231 5 0.7739 0.4181 0.144 0.000 0.132 0.248 0.476
#> GSM125233 1 0.0162 0.9740 0.996 0.000 0.000 0.000 0.004
#> GSM125235 1 0.0162 0.9740 0.996 0.000 0.000 0.000 0.004
#> GSM125237 1 0.0000 0.9743 1.000 0.000 0.000 0.000 0.000
#> GSM125124 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125126 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125128 1 0.1410 0.9390 0.940 0.000 0.000 0.000 0.060
#> GSM125130 1 0.2020 0.9050 0.900 0.000 0.000 0.000 0.100
#> GSM125132 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125134 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125136 1 0.2230 0.8904 0.884 0.000 0.000 0.000 0.116
#> GSM125138 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125140 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125142 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125144 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125146 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125148 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125150 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125152 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125154 1 0.0290 0.9737 0.992 0.000 0.000 0.000 0.008
#> GSM125156 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
#> GSM125158 1 0.0162 0.9740 0.996 0.000 0.000 0.000 0.004
#> GSM125160 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125162 1 0.0880 0.9596 0.968 0.000 0.000 0.000 0.032
#> GSM125164 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125166 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125168 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125170 2 0.0510 0.9433 0.000 0.984 0.000 0.000 0.016
#> GSM125172 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125174 5 0.5206 0.0431 0.000 0.000 0.044 0.428 0.528
#> GSM125176 2 0.0510 0.9433 0.000 0.984 0.000 0.000 0.016
#> GSM125178 3 0.0486 0.8509 0.000 0.004 0.988 0.004 0.004
#> GSM125180 4 0.0451 0.8696 0.000 0.000 0.004 0.988 0.008
#> GSM125182 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125184 4 0.3053 0.7701 0.000 0.044 0.008 0.872 0.076
#> GSM125186 4 0.0693 0.8688 0.000 0.000 0.008 0.980 0.012
#> GSM125188 2 0.7822 -0.1156 0.000 0.412 0.256 0.076 0.256
#> GSM125190 2 0.0510 0.9433 0.000 0.984 0.000 0.000 0.016
#> GSM125192 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125194 5 0.6556 0.2140 0.356 0.000 0.036 0.096 0.512
#> GSM125196 3 0.2396 0.8312 0.000 0.024 0.904 0.004 0.068
#> GSM125198 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125200 1 0.0290 0.9730 0.992 0.000 0.000 0.000 0.008
#> GSM125202 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125204 3 0.0324 0.8513 0.000 0.004 0.992 0.004 0.000
#> GSM125206 3 0.2522 0.8288 0.000 0.024 0.896 0.004 0.076
#> GSM125208 3 0.2824 0.7765 0.000 0.000 0.864 0.020 0.116
#> GSM125210 2 0.4313 0.5993 0.000 0.716 0.008 0.260 0.016
#> GSM125212 3 0.6049 0.0102 0.000 0.008 0.488 0.092 0.412
#> GSM125214 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125216 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125218 2 0.0404 0.9455 0.000 0.988 0.000 0.000 0.012
#> GSM125220 1 0.2424 0.8745 0.868 0.000 0.000 0.000 0.132
#> GSM125222 4 0.3171 0.7643 0.000 0.000 0.008 0.816 0.176
#> GSM125224 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125226 2 0.0000 0.9510 0.000 1.000 0.000 0.000 0.000
#> GSM125228 2 0.0162 0.9511 0.000 0.996 0.000 0.000 0.004
#> GSM125230 5 0.7040 0.4267 0.076 0.000 0.156 0.204 0.564
#> GSM125232 5 0.6800 0.2774 0.184 0.000 0.012 0.368 0.436
#> GSM125234 1 0.2280 0.8897 0.880 0.000 0.000 0.000 0.120
#> GSM125236 1 0.0963 0.9601 0.964 0.000 0.000 0.000 0.036
#> GSM125238 1 0.0162 0.9743 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.0865 0.921 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM125125 1 0.0146 0.934 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125127 1 0.3653 0.650 0.692 0.000 0.000 0.000 0.300 0.008
#> GSM125129 1 0.0692 0.933 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125131 1 0.0146 0.934 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125133 1 0.3464 0.616 0.688 0.000 0.000 0.000 0.312 0.000
#> GSM125135 1 0.1152 0.925 0.952 0.000 0.000 0.000 0.044 0.004
#> GSM125137 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125139 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125141 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125143 1 0.1010 0.928 0.960 0.000 0.000 0.000 0.036 0.004
#> GSM125145 1 0.0777 0.931 0.972 0.000 0.000 0.000 0.024 0.004
#> GSM125147 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125149 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125151 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125153 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125155 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125157 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125159 2 0.0405 0.927 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM125161 1 0.0790 0.922 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125163 2 0.0146 0.930 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125165 2 0.7903 -0.346 0.000 0.352 0.068 0.144 0.092 0.344
#> GSM125167 2 0.0146 0.930 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125169 2 0.2255 0.866 0.000 0.892 0.000 0.000 0.080 0.028
#> GSM125171 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125173 6 0.5770 0.475 0.000 0.112 0.036 0.108 0.060 0.684
#> GSM125175 2 0.0972 0.913 0.000 0.964 0.000 0.000 0.028 0.008
#> GSM125177 3 0.1768 0.798 0.000 0.044 0.932 0.008 0.004 0.012
#> GSM125179 4 0.0508 0.829 0.000 0.000 0.000 0.984 0.004 0.012
#> GSM125181 2 0.6380 0.357 0.000 0.564 0.028 0.028 0.236 0.144
#> GSM125183 4 0.4044 0.593 0.000 0.000 0.000 0.704 0.040 0.256
#> GSM125185 4 0.0692 0.828 0.000 0.000 0.000 0.976 0.004 0.020
#> GSM125187 4 0.2712 0.790 0.000 0.000 0.000 0.864 0.088 0.048
#> GSM125189 2 0.1682 0.892 0.000 0.928 0.000 0.000 0.052 0.020
#> GSM125191 2 0.0405 0.927 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM125193 5 0.6365 0.331 0.004 0.000 0.140 0.064 0.556 0.236
#> GSM125195 3 0.3350 0.746 0.000 0.012 0.824 0.000 0.124 0.040
#> GSM125197 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125199 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125201 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125203 3 0.1465 0.816 0.000 0.020 0.948 0.004 0.004 0.024
#> GSM125205 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125207 3 0.4494 0.636 0.000 0.000 0.732 0.036 0.048 0.184
#> GSM125209 2 0.0405 0.927 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM125211 6 0.5353 0.534 0.000 0.000 0.256 0.084 0.032 0.628
#> GSM125213 2 0.0291 0.929 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM125215 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125217 2 0.0291 0.929 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM125219 1 0.3804 0.563 0.656 0.000 0.000 0.000 0.336 0.008
#> GSM125221 4 0.4527 0.681 0.000 0.000 0.008 0.724 0.132 0.136
#> GSM125223 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125225 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125227 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125229 2 0.2757 0.824 0.000 0.864 0.104 0.000 0.016 0.016
#> GSM125231 6 0.5816 0.562 0.064 0.000 0.056 0.152 0.052 0.676
#> GSM125233 1 0.0858 0.931 0.968 0.000 0.000 0.000 0.028 0.004
#> GSM125235 1 0.0260 0.934 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125237 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125124 1 0.0603 0.933 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM125126 1 0.0146 0.934 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125128 1 0.2260 0.834 0.860 0.000 0.000 0.000 0.140 0.000
#> GSM125130 1 0.3217 0.747 0.768 0.000 0.000 0.000 0.224 0.008
#> GSM125132 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125134 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125136 1 0.3126 0.710 0.752 0.000 0.000 0.000 0.248 0.000
#> GSM125138 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125140 1 0.0260 0.935 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM125142 1 0.0508 0.934 0.984 0.000 0.000 0.000 0.012 0.004
#> GSM125144 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125146 1 0.0858 0.930 0.968 0.000 0.000 0.000 0.028 0.004
#> GSM125148 1 0.0146 0.935 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125150 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125152 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125154 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125156 1 0.0692 0.932 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM125158 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125160 2 0.0291 0.929 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM125162 1 0.1204 0.906 0.944 0.000 0.000 0.000 0.056 0.000
#> GSM125164 2 0.0146 0.930 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125166 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125168 2 0.0146 0.930 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125170 2 0.2384 0.859 0.000 0.884 0.000 0.000 0.084 0.032
#> GSM125172 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125174 6 0.4637 0.532 0.000 0.008 0.016 0.232 0.044 0.700
#> GSM125176 2 0.1765 0.889 0.000 0.924 0.000 0.000 0.052 0.024
#> GSM125178 3 0.1890 0.806 0.000 0.000 0.924 0.024 0.008 0.044
#> GSM125180 4 0.0508 0.829 0.000 0.000 0.000 0.984 0.004 0.012
#> GSM125182 2 0.0291 0.929 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM125184 4 0.3805 0.699 0.000 0.016 0.020 0.788 0.012 0.164
#> GSM125186 4 0.0692 0.828 0.000 0.000 0.000 0.976 0.004 0.020
#> GSM125188 2 0.8312 -0.374 0.000 0.324 0.184 0.052 0.260 0.180
#> GSM125190 2 0.2009 0.878 0.000 0.908 0.000 0.000 0.068 0.024
#> GSM125192 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125194 5 0.7104 0.387 0.208 0.000 0.040 0.032 0.460 0.260
#> GSM125196 3 0.3208 0.752 0.000 0.008 0.832 0.000 0.120 0.040
#> GSM125198 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125200 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125202 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125204 3 0.1138 0.817 0.000 0.004 0.960 0.012 0.000 0.024
#> GSM125206 3 0.3208 0.752 0.000 0.008 0.832 0.000 0.120 0.040
#> GSM125208 3 0.4693 0.639 0.000 0.000 0.720 0.036 0.064 0.180
#> GSM125210 2 0.4804 0.566 0.000 0.688 0.008 0.236 0.020 0.048
#> GSM125212 6 0.6050 0.311 0.000 0.016 0.368 0.056 0.048 0.512
#> GSM125214 2 0.0146 0.930 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM125216 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125218 2 0.1049 0.911 0.000 0.960 0.000 0.000 0.032 0.008
#> GSM125220 1 0.3652 0.585 0.672 0.000 0.000 0.000 0.324 0.004
#> GSM125222 4 0.4260 0.707 0.000 0.000 0.004 0.744 0.116 0.136
#> GSM125224 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125226 2 0.0146 0.929 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM125228 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125230 6 0.5060 0.595 0.032 0.000 0.068 0.112 0.048 0.740
#> GSM125232 6 0.5856 0.417 0.128 0.000 0.000 0.204 0.056 0.612
#> GSM125234 1 0.3927 0.569 0.644 0.000 0.000 0.000 0.344 0.012
#> GSM125236 1 0.1806 0.890 0.908 0.000 0.000 0.000 0.088 0.004
#> GSM125238 1 0.0000 0.935 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 agent(p) individual(p) k
#> ATC:skmeans 114 1.000 3.19e-05 2
#> ATC:skmeans 111 0.950 2.16e-07 3
#> ATC:skmeans 107 0.937 2.21e-11 4
#> ATC:skmeans 105 0.971 1.45e-11 5
#> ATC:skmeans 108 0.994 4.97e-13 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.992 0.997 0.5006 0.499 0.499
#> 3 3 0.873 0.872 0.951 0.3139 0.781 0.585
#> 4 4 0.927 0.907 0.962 0.0694 0.934 0.811
#> 5 5 0.777 0.710 0.805 0.0899 0.976 0.922
#> 6 6 0.785 0.765 0.860 0.0607 0.876 0.577
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
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
#> GSM125123 1 0.0000 0.993 1.000 0.000
#> GSM125125 1 0.0000 0.993 1.000 0.000
#> GSM125127 1 0.0000 0.993 1.000 0.000
#> GSM125129 1 0.0000 0.993 1.000 0.000
#> GSM125131 1 0.0000 0.993 1.000 0.000
#> GSM125133 1 0.0000 0.993 1.000 0.000
#> GSM125135 1 0.0000 0.993 1.000 0.000
#> GSM125137 1 0.0000 0.993 1.000 0.000
#> GSM125139 1 0.0000 0.993 1.000 0.000
#> GSM125141 1 0.0000 0.993 1.000 0.000
#> GSM125143 1 0.0000 0.993 1.000 0.000
#> GSM125145 1 0.0000 0.993 1.000 0.000
#> GSM125147 1 0.0000 0.993 1.000 0.000
#> GSM125149 1 0.0000 0.993 1.000 0.000
#> GSM125151 1 0.0000 0.993 1.000 0.000
#> GSM125153 1 0.0000 0.993 1.000 0.000
#> GSM125155 1 0.0000 0.993 1.000 0.000
#> GSM125157 1 0.0000 0.993 1.000 0.000
#> GSM125159 2 0.0000 0.999 0.000 1.000
#> GSM125161 1 0.0000 0.993 1.000 0.000
#> GSM125163 2 0.0000 0.999 0.000 1.000
#> GSM125165 2 0.0000 0.999 0.000 1.000
#> GSM125167 2 0.0000 0.999 0.000 1.000
#> GSM125169 2 0.0000 0.999 0.000 1.000
#> GSM125171 2 0.0000 0.999 0.000 1.000
#> GSM125173 2 0.0000 0.999 0.000 1.000
#> GSM125175 2 0.0000 0.999 0.000 1.000
#> GSM125177 2 0.0000 0.999 0.000 1.000
#> GSM125179 2 0.1184 0.984 0.016 0.984
#> GSM125181 2 0.0000 0.999 0.000 1.000
#> GSM125183 2 0.0938 0.988 0.012 0.988
#> GSM125185 2 0.0000 0.999 0.000 1.000
#> GSM125187 1 0.9358 0.456 0.648 0.352
#> GSM125189 2 0.0000 0.999 0.000 1.000
#> GSM125191 2 0.0000 0.999 0.000 1.000
#> GSM125193 2 0.0672 0.992 0.008 0.992
#> GSM125195 2 0.0000 0.999 0.000 1.000
#> GSM125197 2 0.0000 0.999 0.000 1.000
#> GSM125199 1 0.0000 0.993 1.000 0.000
#> GSM125201 2 0.0000 0.999 0.000 1.000
#> GSM125203 2 0.0000 0.999 0.000 1.000
#> GSM125205 2 0.0000 0.999 0.000 1.000
#> GSM125207 2 0.0000 0.999 0.000 1.000
#> GSM125209 2 0.0000 0.999 0.000 1.000
#> GSM125211 2 0.0000 0.999 0.000 1.000
#> GSM125213 2 0.0000 0.999 0.000 1.000
#> GSM125215 2 0.0000 0.999 0.000 1.000
#> GSM125217 2 0.0000 0.999 0.000 1.000
#> GSM125219 1 0.0000 0.993 1.000 0.000
#> GSM125221 2 0.0000 0.999 0.000 1.000
#> GSM125223 2 0.0000 0.999 0.000 1.000
#> GSM125225 2 0.0000 0.999 0.000 1.000
#> GSM125227 2 0.0000 0.999 0.000 1.000
#> GSM125229 2 0.0000 0.999 0.000 1.000
#> GSM125231 1 0.0376 0.989 0.996 0.004
#> GSM125233 1 0.0000 0.993 1.000 0.000
#> GSM125235 1 0.0000 0.993 1.000 0.000
#> GSM125237 1 0.0000 0.993 1.000 0.000
#> GSM125124 1 0.0000 0.993 1.000 0.000
#> GSM125126 1 0.0000 0.993 1.000 0.000
#> GSM125128 1 0.0000 0.993 1.000 0.000
#> GSM125130 1 0.0000 0.993 1.000 0.000
#> GSM125132 1 0.0000 0.993 1.000 0.000
#> GSM125134 1 0.0000 0.993 1.000 0.000
#> GSM125136 1 0.0000 0.993 1.000 0.000
#> GSM125138 1 0.0000 0.993 1.000 0.000
#> GSM125140 1 0.0000 0.993 1.000 0.000
#> GSM125142 1 0.0000 0.993 1.000 0.000
#> GSM125144 1 0.0000 0.993 1.000 0.000
#> GSM125146 1 0.0000 0.993 1.000 0.000
#> GSM125148 1 0.0000 0.993 1.000 0.000
#> GSM125150 1 0.0000 0.993 1.000 0.000
#> GSM125152 1 0.0000 0.993 1.000 0.000
#> GSM125154 1 0.0000 0.993 1.000 0.000
#> GSM125156 1 0.0000 0.993 1.000 0.000
#> GSM125158 1 0.0000 0.993 1.000 0.000
#> GSM125160 2 0.0000 0.999 0.000 1.000
#> GSM125162 1 0.0000 0.993 1.000 0.000
#> GSM125164 2 0.0000 0.999 0.000 1.000
#> GSM125166 2 0.0000 0.999 0.000 1.000
#> GSM125168 2 0.0000 0.999 0.000 1.000
#> GSM125170 2 0.0000 0.999 0.000 1.000
#> GSM125172 2 0.0000 0.999 0.000 1.000
#> GSM125174 2 0.0000 0.999 0.000 1.000
#> GSM125176 2 0.0000 0.999 0.000 1.000
#> GSM125178 2 0.0000 0.999 0.000 1.000
#> GSM125180 2 0.0000 0.999 0.000 1.000
#> GSM125182 2 0.0000 0.999 0.000 1.000
#> GSM125184 2 0.0000 0.999 0.000 1.000
#> GSM125186 2 0.0000 0.999 0.000 1.000
#> GSM125188 2 0.0000 0.999 0.000 1.000
#> GSM125190 2 0.0000 0.999 0.000 1.000
#> GSM125192 2 0.0000 0.999 0.000 1.000
#> GSM125194 1 0.0000 0.993 1.000 0.000
#> GSM125196 2 0.0000 0.999 0.000 1.000
#> GSM125198 2 0.0000 0.999 0.000 1.000
#> GSM125200 1 0.0000 0.993 1.000 0.000
#> GSM125202 2 0.0000 0.999 0.000 1.000
#> GSM125204 2 0.0000 0.999 0.000 1.000
#> GSM125206 2 0.0000 0.999 0.000 1.000
#> GSM125208 2 0.0000 0.999 0.000 1.000
#> GSM125210 2 0.0000 0.999 0.000 1.000
#> GSM125212 2 0.0000 0.999 0.000 1.000
#> GSM125214 2 0.0000 0.999 0.000 1.000
#> GSM125216 2 0.0000 0.999 0.000 1.000
#> GSM125218 2 0.0000 0.999 0.000 1.000
#> GSM125220 1 0.0000 0.993 1.000 0.000
#> GSM125222 2 0.0000 0.999 0.000 1.000
#> GSM125224 2 0.0000 0.999 0.000 1.000
#> GSM125226 2 0.0000 0.999 0.000 1.000
#> GSM125228 2 0.0000 0.999 0.000 1.000
#> GSM125230 1 0.0000 0.993 1.000 0.000
#> GSM125232 1 0.0000 0.993 1.000 0.000
#> GSM125234 1 0.0000 0.993 1.000 0.000
#> GSM125236 1 0.0000 0.993 1.000 0.000
#> GSM125238 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125125 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125127 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125129 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125131 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125133 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125135 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125137 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125139 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125143 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125145 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125147 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125151 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125153 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125155 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125157 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125159 2 0.5291 0.6497 0.000 0.732 0.268
#> GSM125161 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125163 2 0.0237 0.8785 0.000 0.996 0.004
#> GSM125165 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125167 2 0.3412 0.8093 0.000 0.876 0.124
#> GSM125169 2 0.6302 0.1872 0.000 0.520 0.480
#> GSM125171 2 0.5859 0.5290 0.000 0.656 0.344
#> GSM125173 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125175 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125177 3 0.3116 0.8091 0.000 0.108 0.892
#> GSM125179 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125181 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125183 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125185 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125187 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125189 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125191 3 0.6244 0.0735 0.000 0.440 0.560
#> GSM125193 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125195 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125197 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125201 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125203 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125205 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125207 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125209 3 0.6309 -0.1467 0.000 0.496 0.504
#> GSM125211 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125213 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125215 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125217 3 0.6235 0.0872 0.000 0.436 0.564
#> GSM125219 1 0.0237 0.9922 0.996 0.000 0.004
#> GSM125221 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125223 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125225 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125227 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125229 3 0.6302 -0.0888 0.000 0.480 0.520
#> GSM125231 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125233 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125124 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125126 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125128 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125130 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125132 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125136 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125138 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125144 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125146 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125148 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125150 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125152 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125154 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125156 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125158 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125160 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125162 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125164 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125166 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125168 2 0.6244 0.3134 0.000 0.560 0.440
#> GSM125170 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125172 2 0.5905 0.5141 0.000 0.648 0.352
#> GSM125174 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125176 2 0.6252 0.3027 0.000 0.556 0.444
#> GSM125178 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125180 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125182 2 0.0237 0.8785 0.000 0.996 0.004
#> GSM125184 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125186 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125188 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125190 2 0.6252 0.3027 0.000 0.556 0.444
#> GSM125192 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125194 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125196 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125198 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125200 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125202 2 0.3619 0.7992 0.000 0.864 0.136
#> GSM125204 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125206 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125208 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125210 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125212 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125214 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125216 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125218 2 0.3412 0.8093 0.000 0.876 0.124
#> GSM125220 1 0.4062 0.8044 0.836 0.000 0.164
#> GSM125222 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125224 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125226 2 0.3412 0.8093 0.000 0.876 0.124
#> GSM125228 2 0.0000 0.8800 0.000 1.000 0.000
#> GSM125230 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125232 3 0.0000 0.9262 0.000 0.000 1.000
#> GSM125234 3 0.4974 0.6351 0.236 0.000 0.764
#> GSM125236 1 0.0000 0.9962 1.000 0.000 0.000
#> GSM125238 1 0.0000 0.9962 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125125 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125127 1 0.1940 0.906 0.924 0.000 0.076 0.000
#> GSM125129 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125131 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125133 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125135 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125137 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125139 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125141 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125143 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125145 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125147 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125149 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125151 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125155 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125157 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125159 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125161 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125163 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125165 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125167 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125169 2 0.4730 0.465 0.000 0.636 0.364 0.000
#> GSM125171 2 0.0188 0.890 0.000 0.996 0.004 0.000
#> GSM125173 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125175 2 0.4605 0.422 0.000 0.664 0.000 0.336
#> GSM125177 3 0.4746 0.428 0.000 0.368 0.632 0.000
#> GSM125179 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125181 3 0.4193 0.641 0.000 0.268 0.732 0.000
#> GSM125183 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125185 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125187 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125189 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125191 2 0.4697 0.437 0.000 0.644 0.356 0.000
#> GSM125193 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125195 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125197 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125199 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125201 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125203 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125205 4 0.2281 0.892 0.000 0.096 0.000 0.904
#> GSM125207 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125209 2 0.3873 0.682 0.000 0.772 0.228 0.000
#> GSM125211 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125213 2 0.3024 0.763 0.000 0.852 0.000 0.148
#> GSM125215 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125217 2 0.4898 0.272 0.000 0.584 0.416 0.000
#> GSM125219 1 0.1302 0.944 0.956 0.000 0.044 0.000
#> GSM125221 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125223 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125225 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125227 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125229 2 0.4008 0.660 0.000 0.756 0.244 0.000
#> GSM125231 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125233 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125235 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125237 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125124 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125126 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125128 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125130 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125132 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125134 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125136 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125138 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125140 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125142 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125144 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125146 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125148 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125150 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125152 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125154 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125156 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125158 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125160 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125162 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125164 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125166 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125168 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125170 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125172 2 0.0336 0.888 0.000 0.992 0.008 0.000
#> GSM125174 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125176 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125178 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125180 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125182 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125184 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125186 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125188 3 0.4008 0.680 0.000 0.244 0.756 0.000
#> GSM125190 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125192 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125194 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125196 3 0.3688 0.731 0.000 0.208 0.792 0.000
#> GSM125198 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125200 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125202 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125204 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125206 3 0.0817 0.915 0.000 0.024 0.976 0.000
#> GSM125208 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125210 3 0.4134 0.655 0.000 0.260 0.740 0.000
#> GSM125212 3 0.3688 0.731 0.000 0.208 0.792 0.000
#> GSM125214 2 0.1211 0.867 0.000 0.960 0.000 0.040
#> GSM125216 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125218 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125220 1 0.4103 0.650 0.744 0.000 0.256 0.000
#> GSM125222 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125224 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125226 2 0.0000 0.893 0.000 1.000 0.000 0.000
#> GSM125228 4 0.0000 0.989 0.000 0.000 0.000 1.000
#> GSM125230 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125232 3 0.0000 0.932 0.000 0.000 1.000 0.000
#> GSM125234 3 0.3942 0.619 0.236 0.000 0.764 0.000
#> GSM125236 1 0.0000 0.991 1.000 0.000 0.000 0.000
#> GSM125238 1 0.0000 0.991 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.2179 0.7984 0.888 0.000 0.000 NA 0.000
#> GSM125125 1 0.0162 0.7961 0.996 0.000 0.000 NA 0.000
#> GSM125127 1 0.6377 0.5579 0.484 0.000 0.180 NA 0.000
#> GSM125129 1 0.3774 0.7742 0.704 0.000 0.000 NA 0.000
#> GSM125131 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125133 1 0.1908 0.7687 0.908 0.000 0.000 NA 0.000
#> GSM125135 1 0.4297 0.7256 0.528 0.000 0.000 NA 0.000
#> GSM125137 1 0.2813 0.7864 0.832 0.000 0.000 NA 0.000
#> GSM125139 1 0.1608 0.7984 0.928 0.000 0.000 NA 0.000
#> GSM125141 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125143 1 0.4249 0.7337 0.568 0.000 0.000 NA 0.000
#> GSM125145 1 0.4262 0.7337 0.560 0.000 0.000 NA 0.000
#> GSM125147 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125149 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125151 1 0.4249 0.7366 0.568 0.000 0.000 NA 0.000
#> GSM125153 1 0.4182 0.7458 0.600 0.000 0.000 NA 0.000
#> GSM125155 1 0.1544 0.7975 0.932 0.000 0.000 NA 0.000
#> GSM125157 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125159 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125161 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125163 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125165 3 0.1965 0.7525 0.000 0.000 0.904 NA 0.096
#> GSM125167 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125169 2 0.5756 0.4041 0.000 0.576 0.312 NA 0.112
#> GSM125171 2 0.1965 0.8697 0.000 0.904 0.000 NA 0.096
#> GSM125173 3 0.0794 0.7744 0.000 0.000 0.972 NA 0.028
#> GSM125175 2 0.4832 0.5496 0.000 0.712 0.000 NA 0.200
#> GSM125177 5 0.6491 -0.1933 0.000 0.264 0.244 NA 0.492
#> GSM125179 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125181 3 0.5215 0.5320 0.000 0.240 0.664 NA 0.096
#> GSM125183 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125185 3 0.1908 0.7541 0.000 0.000 0.908 NA 0.092
#> GSM125187 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125189 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125191 2 0.3159 0.8269 0.000 0.856 0.056 NA 0.088
#> GSM125193 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125195 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125197 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125199 1 0.2813 0.7864 0.832 0.000 0.000 NA 0.000
#> GSM125201 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125203 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125205 5 0.6294 0.5984 0.000 0.156 0.000 NA 0.468
#> GSM125207 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125209 2 0.2740 0.8483 0.000 0.876 0.028 NA 0.096
#> GSM125211 3 0.0510 0.7758 0.000 0.000 0.984 NA 0.016
#> GSM125213 2 0.2964 0.7893 0.000 0.856 0.000 NA 0.120
#> GSM125215 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125217 2 0.3866 0.7681 0.000 0.808 0.096 NA 0.096
#> GSM125219 1 0.3741 0.7118 0.816 0.000 0.076 NA 0.000
#> GSM125221 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125223 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125225 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125227 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125229 5 0.5351 -0.2860 0.000 0.464 0.052 NA 0.484
#> GSM125231 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125233 1 0.3999 0.7673 0.656 0.000 0.000 NA 0.000
#> GSM125235 1 0.1410 0.7919 0.940 0.000 0.000 NA 0.000
#> GSM125237 1 0.0290 0.7957 0.992 0.000 0.000 NA 0.000
#> GSM125124 1 0.4249 0.7366 0.568 0.000 0.000 NA 0.000
#> GSM125126 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125128 1 0.1544 0.7733 0.932 0.000 0.000 NA 0.000
#> GSM125130 1 0.3857 0.7584 0.688 0.000 0.000 NA 0.000
#> GSM125132 1 0.1197 0.7939 0.952 0.000 0.000 NA 0.000
#> GSM125134 1 0.4249 0.7366 0.568 0.000 0.000 NA 0.000
#> GSM125136 1 0.1544 0.7733 0.932 0.000 0.000 NA 0.000
#> GSM125138 1 0.4249 0.7366 0.568 0.000 0.000 NA 0.000
#> GSM125140 1 0.4138 0.7526 0.616 0.000 0.000 NA 0.000
#> GSM125142 1 0.4150 0.7490 0.612 0.000 0.000 NA 0.000
#> GSM125144 1 0.4242 0.7377 0.572 0.000 0.000 NA 0.000
#> GSM125146 1 0.4242 0.7356 0.572 0.000 0.000 NA 0.000
#> GSM125148 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125150 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125152 1 0.4219 0.7401 0.584 0.000 0.000 NA 0.000
#> GSM125154 1 0.4192 0.7448 0.596 0.000 0.000 NA 0.000
#> GSM125156 1 0.4219 0.7401 0.584 0.000 0.000 NA 0.000
#> GSM125158 1 0.3336 0.7877 0.772 0.000 0.000 NA 0.000
#> GSM125160 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125162 1 0.0000 0.7958 1.000 0.000 0.000 NA 0.000
#> GSM125164 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125166 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125168 2 0.2124 0.8674 0.000 0.900 0.004 NA 0.096
#> GSM125170 3 0.1965 0.7525 0.000 0.000 0.904 NA 0.096
#> GSM125172 2 0.2124 0.8675 0.000 0.900 0.004 NA 0.096
#> GSM125174 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125176 2 0.0510 0.9133 0.000 0.984 0.000 NA 0.016
#> GSM125178 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125180 3 0.3949 0.6167 0.000 0.000 0.668 NA 0.332
#> GSM125182 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125184 3 0.0703 0.7750 0.000 0.000 0.976 NA 0.024
#> GSM125186 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125188 3 0.6222 0.4602 0.000 0.236 0.548 NA 0.216
#> GSM125190 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125192 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125194 3 0.1121 0.7518 0.000 0.000 0.956 NA 0.000
#> GSM125196 5 0.6272 -0.3512 0.000 0.160 0.348 NA 0.492
#> GSM125198 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125200 1 0.4182 0.7439 0.600 0.000 0.000 NA 0.000
#> GSM125202 2 0.0290 0.9157 0.000 0.992 0.000 NA 0.008
#> GSM125204 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125206 3 0.4306 0.5279 0.000 0.000 0.508 NA 0.492
#> GSM125208 3 0.4150 0.5750 0.000 0.000 0.612 NA 0.388
#> GSM125210 3 0.5354 0.5268 0.000 0.240 0.652 NA 0.108
#> GSM125212 5 0.6381 -0.3622 0.000 0.172 0.364 NA 0.464
#> GSM125214 2 0.1106 0.8972 0.000 0.964 0.000 NA 0.024
#> GSM125216 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125218 2 0.0000 0.9174 0.000 1.000 0.000 NA 0.000
#> GSM125220 1 0.5818 -0.0192 0.464 0.000 0.444 NA 0.000
#> GSM125222 3 0.0000 0.7766 0.000 0.000 1.000 NA 0.000
#> GSM125224 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125226 2 0.0794 0.9083 0.000 0.972 0.000 NA 0.028
#> GSM125228 5 0.4306 0.7173 0.000 0.000 0.000 NA 0.508
#> GSM125230 3 0.1121 0.7518 0.000 0.000 0.956 NA 0.000
#> GSM125232 3 0.1478 0.7373 0.000 0.000 0.936 NA 0.000
#> GSM125234 3 0.6819 0.1951 0.064 0.000 0.436 NA 0.076
#> GSM125236 1 0.2179 0.7753 0.888 0.000 0.000 NA 0.000
#> GSM125238 1 0.0609 0.7949 0.980 0.000 0.000 NA 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 5 0.3852 0.6127 0.384 0.000 0.004 0.000 0.612 0.000
#> GSM125125 5 0.3464 0.7367 0.312 0.000 0.000 0.000 0.688 0.000
#> GSM125127 5 0.4637 -0.1574 0.408 0.000 0.028 0.008 0.556 0.000
#> GSM125129 1 0.2969 0.5734 0.776 0.000 0.000 0.000 0.224 0.000
#> GSM125131 5 0.3428 0.7369 0.304 0.000 0.000 0.000 0.696 0.000
#> GSM125133 5 0.1713 0.5230 0.044 0.000 0.028 0.000 0.928 0.000
#> GSM125135 1 0.1327 0.7705 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM125137 5 0.3868 0.4482 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM125139 5 0.3774 0.6475 0.408 0.000 0.000 0.000 0.592 0.000
#> GSM125141 5 0.3446 0.7369 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM125143 1 0.2730 0.7223 0.836 0.000 0.012 0.000 0.152 0.000
#> GSM125145 1 0.1806 0.7712 0.908 0.000 0.004 0.000 0.088 0.000
#> GSM125147 5 0.3464 0.7362 0.312 0.000 0.000 0.000 0.688 0.000
#> GSM125149 5 0.3446 0.7369 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM125151 1 0.0146 0.7997 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125153 1 0.1141 0.7914 0.948 0.000 0.000 0.000 0.052 0.000
#> GSM125155 1 0.3867 -0.4575 0.512 0.000 0.000 0.000 0.488 0.000
#> GSM125157 5 0.3446 0.7369 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM125159 2 0.0260 0.9299 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM125161 5 0.3446 0.7369 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM125163 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125165 4 0.2092 0.8325 0.000 0.000 0.124 0.876 0.000 0.000
#> GSM125167 2 0.0363 0.9294 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM125169 2 0.5200 0.5886 0.000 0.632 0.192 0.172 0.004 0.000
#> GSM125171 2 0.2595 0.8541 0.000 0.836 0.160 0.000 0.004 0.000
#> GSM125173 4 0.1141 0.8897 0.000 0.000 0.052 0.948 0.000 0.000
#> GSM125175 2 0.3152 0.7292 0.000 0.792 0.008 0.000 0.004 0.196
#> GSM125177 3 0.1265 0.9040 0.000 0.008 0.948 0.044 0.000 0.000
#> GSM125179 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125181 4 0.4482 0.6457 0.000 0.168 0.124 0.708 0.000 0.000
#> GSM125183 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125185 4 0.1814 0.8510 0.000 0.000 0.100 0.900 0.000 0.000
#> GSM125187 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125189 2 0.0405 0.9273 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125191 2 0.2003 0.8815 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM125193 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125195 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125197 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125199 5 0.3868 0.4482 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM125201 2 0.0146 0.9297 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125203 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125205 6 0.2454 0.8052 0.000 0.160 0.000 0.000 0.000 0.840
#> GSM125207 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125209 2 0.1863 0.8840 0.000 0.896 0.104 0.000 0.000 0.000
#> GSM125211 4 0.1007 0.8934 0.000 0.000 0.044 0.956 0.000 0.000
#> GSM125213 2 0.2340 0.8062 0.000 0.852 0.000 0.000 0.000 0.148
#> GSM125215 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125217 2 0.2949 0.8298 0.000 0.832 0.140 0.028 0.000 0.000
#> GSM125219 5 0.1970 0.5141 0.060 0.000 0.028 0.000 0.912 0.000
#> GSM125221 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125223 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125225 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125227 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125229 3 0.1391 0.8599 0.000 0.040 0.944 0.016 0.000 0.000
#> GSM125231 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125233 1 0.2092 0.7144 0.876 0.000 0.000 0.000 0.124 0.000
#> GSM125235 5 0.3883 0.6748 0.332 0.000 0.012 0.000 0.656 0.000
#> GSM125237 5 0.3515 0.7296 0.324 0.000 0.000 0.000 0.676 0.000
#> GSM125124 1 0.0000 0.7999 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125126 5 0.3446 0.7369 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM125128 5 0.2383 0.6118 0.096 0.000 0.024 0.000 0.880 0.000
#> GSM125130 5 0.4118 0.0992 0.312 0.000 0.028 0.000 0.660 0.000
#> GSM125132 5 0.3684 0.6746 0.372 0.000 0.000 0.000 0.628 0.000
#> GSM125134 1 0.0458 0.8005 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM125136 5 0.2255 0.6019 0.080 0.000 0.028 0.000 0.892 0.000
#> GSM125138 1 0.0146 0.7999 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125140 1 0.1957 0.7312 0.888 0.000 0.000 0.000 0.112 0.000
#> GSM125142 1 0.1610 0.7807 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM125144 1 0.0146 0.7998 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125146 1 0.2402 0.7496 0.868 0.000 0.012 0.000 0.120 0.000
#> GSM125148 5 0.3482 0.7349 0.316 0.000 0.000 0.000 0.684 0.000
#> GSM125150 5 0.3428 0.7369 0.304 0.000 0.000 0.000 0.696 0.000
#> GSM125152 1 0.0937 0.7851 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125154 1 0.1075 0.7932 0.952 0.000 0.000 0.000 0.048 0.000
#> GSM125156 1 0.0937 0.7851 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM125158 1 0.3797 -0.2319 0.580 0.000 0.000 0.000 0.420 0.000
#> GSM125160 2 0.0146 0.9297 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM125162 5 0.3309 0.7306 0.280 0.000 0.000 0.000 0.720 0.000
#> GSM125164 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125166 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125168 2 0.2003 0.8803 0.000 0.884 0.116 0.000 0.000 0.000
#> GSM125170 4 0.2320 0.8281 0.000 0.000 0.132 0.864 0.004 0.000
#> GSM125172 2 0.2100 0.8803 0.000 0.884 0.112 0.004 0.000 0.000
#> GSM125174 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125176 2 0.0777 0.9275 0.000 0.972 0.024 0.000 0.004 0.000
#> GSM125178 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125180 3 0.3221 0.7182 0.000 0.000 0.736 0.264 0.000 0.000
#> GSM125182 2 0.0363 0.9294 0.000 0.988 0.012 0.000 0.000 0.000
#> GSM125184 4 0.1141 0.8897 0.000 0.000 0.052 0.948 0.000 0.000
#> GSM125186 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125188 4 0.5346 0.4121 0.000 0.164 0.252 0.584 0.000 0.000
#> GSM125190 2 0.0405 0.9273 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125192 2 0.0000 0.9292 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM125194 4 0.0622 0.8951 0.000 0.000 0.012 0.980 0.008 0.000
#> GSM125196 3 0.1265 0.9040 0.000 0.008 0.948 0.044 0.000 0.000
#> GSM125198 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125200 1 0.1387 0.7679 0.932 0.000 0.000 0.000 0.068 0.000
#> GSM125202 2 0.0547 0.9287 0.000 0.980 0.020 0.000 0.000 0.000
#> GSM125204 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125206 3 0.1141 0.9090 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM125208 3 0.3023 0.7585 0.000 0.000 0.768 0.232 0.000 0.000
#> GSM125210 4 0.4595 0.6330 0.000 0.168 0.136 0.696 0.000 0.000
#> GSM125212 3 0.5147 0.3117 0.000 0.096 0.548 0.356 0.000 0.000
#> GSM125214 2 0.0790 0.9162 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM125216 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125218 2 0.0405 0.9273 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM125220 5 0.4214 0.3414 0.032 0.000 0.028 0.200 0.740 0.000
#> GSM125222 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM125224 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125226 2 0.1152 0.9212 0.000 0.952 0.044 0.000 0.004 0.000
#> GSM125228 6 0.0000 0.9794 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125230 4 0.0622 0.8951 0.000 0.000 0.012 0.980 0.008 0.000
#> GSM125232 4 0.1003 0.8816 0.000 0.000 0.020 0.964 0.016 0.000
#> GSM125234 1 0.6923 0.2098 0.396 0.000 0.092 0.152 0.360 0.000
#> GSM125236 5 0.2282 0.5281 0.088 0.000 0.024 0.000 0.888 0.000
#> GSM125238 5 0.3578 0.7107 0.340 0.000 0.000 0.000 0.660 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 agent(p) individual(p) k
#> ATC:pam 115 0.918 2.48e-05 2
#> ATC:pam 108 0.879 6.53e-07 3
#> ATC:pam 111 0.699 6.67e-09 4
#> ATC:pam 108 0.893 8.71e-09 5
#> ATC:pam 106 0.805 1.12e-09 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 0.991 0.997 0.4853 0.514 0.514
#> 3 3 0.723 0.753 0.840 0.2958 0.841 0.691
#> 4 4 0.681 0.770 0.801 0.1104 0.850 0.626
#> 5 5 0.806 0.859 0.885 0.0879 0.889 0.655
#> 6 6 0.884 0.864 0.890 0.0551 0.942 0.759
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
#> GSM125123 1 0.000 0.991 1.000 0.000
#> GSM125125 1 0.000 0.991 1.000 0.000
#> GSM125127 1 0.000 0.991 1.000 0.000
#> GSM125129 1 0.000 0.991 1.000 0.000
#> GSM125131 1 0.000 0.991 1.000 0.000
#> GSM125133 1 0.000 0.991 1.000 0.000
#> GSM125135 1 0.000 0.991 1.000 0.000
#> GSM125137 1 0.000 0.991 1.000 0.000
#> GSM125139 1 0.000 0.991 1.000 0.000
#> GSM125141 1 0.000 0.991 1.000 0.000
#> GSM125143 2 0.000 1.000 0.000 1.000
#> GSM125145 1 0.000 0.991 1.000 0.000
#> GSM125147 1 0.000 0.991 1.000 0.000
#> GSM125149 1 0.000 0.991 1.000 0.000
#> GSM125151 1 0.000 0.991 1.000 0.000
#> GSM125153 1 0.000 0.991 1.000 0.000
#> GSM125155 1 0.000 0.991 1.000 0.000
#> GSM125157 1 0.000 0.991 1.000 0.000
#> GSM125159 2 0.000 1.000 0.000 1.000
#> GSM125161 1 0.000 0.991 1.000 0.000
#> GSM125163 2 0.000 1.000 0.000 1.000
#> GSM125165 2 0.000 1.000 0.000 1.000
#> GSM125167 2 0.000 1.000 0.000 1.000
#> GSM125169 2 0.000 1.000 0.000 1.000
#> GSM125171 2 0.000 1.000 0.000 1.000
#> GSM125173 2 0.000 1.000 0.000 1.000
#> GSM125175 2 0.000 1.000 0.000 1.000
#> GSM125177 2 0.000 1.000 0.000 1.000
#> GSM125179 2 0.000 1.000 0.000 1.000
#> GSM125181 2 0.000 1.000 0.000 1.000
#> GSM125183 2 0.000 1.000 0.000 1.000
#> GSM125185 2 0.000 1.000 0.000 1.000
#> GSM125187 2 0.000 1.000 0.000 1.000
#> GSM125189 2 0.000 1.000 0.000 1.000
#> GSM125191 2 0.000 1.000 0.000 1.000
#> GSM125193 2 0.000 1.000 0.000 1.000
#> GSM125195 2 0.000 1.000 0.000 1.000
#> GSM125197 2 0.000 1.000 0.000 1.000
#> GSM125199 1 0.000 0.991 1.000 0.000
#> GSM125201 2 0.000 1.000 0.000 1.000
#> GSM125203 2 0.000 1.000 0.000 1.000
#> GSM125205 2 0.000 1.000 0.000 1.000
#> GSM125207 2 0.000 1.000 0.000 1.000
#> GSM125209 2 0.000 1.000 0.000 1.000
#> GSM125211 2 0.000 1.000 0.000 1.000
#> GSM125213 2 0.000 1.000 0.000 1.000
#> GSM125215 2 0.000 1.000 0.000 1.000
#> GSM125217 2 0.000 1.000 0.000 1.000
#> GSM125219 1 0.000 0.991 1.000 0.000
#> GSM125221 2 0.000 1.000 0.000 1.000
#> GSM125223 2 0.000 1.000 0.000 1.000
#> GSM125225 2 0.000 1.000 0.000 1.000
#> GSM125227 2 0.000 1.000 0.000 1.000
#> GSM125229 2 0.000 1.000 0.000 1.000
#> GSM125231 2 0.000 1.000 0.000 1.000
#> GSM125233 1 0.000 0.991 1.000 0.000
#> GSM125235 1 0.000 0.991 1.000 0.000
#> GSM125237 1 0.000 0.991 1.000 0.000
#> GSM125124 1 0.000 0.991 1.000 0.000
#> GSM125126 1 0.000 0.991 1.000 0.000
#> GSM125128 1 0.000 0.991 1.000 0.000
#> GSM125130 1 0.000 0.991 1.000 0.000
#> GSM125132 1 0.000 0.991 1.000 0.000
#> GSM125134 1 0.000 0.991 1.000 0.000
#> GSM125136 1 0.000 0.991 1.000 0.000
#> GSM125138 1 0.000 0.991 1.000 0.000
#> GSM125140 1 0.000 0.991 1.000 0.000
#> GSM125142 1 0.000 0.991 1.000 0.000
#> GSM125144 1 0.000 0.991 1.000 0.000
#> GSM125146 1 0.000 0.991 1.000 0.000
#> GSM125148 1 0.000 0.991 1.000 0.000
#> GSM125150 1 0.000 0.991 1.000 0.000
#> GSM125152 1 0.000 0.991 1.000 0.000
#> GSM125154 1 0.000 0.991 1.000 0.000
#> GSM125156 1 0.000 0.991 1.000 0.000
#> GSM125158 1 0.000 0.991 1.000 0.000
#> GSM125160 2 0.000 1.000 0.000 1.000
#> GSM125162 1 0.000 0.991 1.000 0.000
#> GSM125164 2 0.000 1.000 0.000 1.000
#> GSM125166 2 0.000 1.000 0.000 1.000
#> GSM125168 2 0.000 1.000 0.000 1.000
#> GSM125170 2 0.000 1.000 0.000 1.000
#> GSM125172 2 0.000 1.000 0.000 1.000
#> GSM125174 2 0.000 1.000 0.000 1.000
#> GSM125176 2 0.000 1.000 0.000 1.000
#> GSM125178 2 0.000 1.000 0.000 1.000
#> GSM125180 2 0.000 1.000 0.000 1.000
#> GSM125182 2 0.000 1.000 0.000 1.000
#> GSM125184 2 0.000 1.000 0.000 1.000
#> GSM125186 2 0.000 1.000 0.000 1.000
#> GSM125188 2 0.000 1.000 0.000 1.000
#> GSM125190 2 0.000 1.000 0.000 1.000
#> GSM125192 2 0.000 1.000 0.000 1.000
#> GSM125194 2 0.000 1.000 0.000 1.000
#> GSM125196 2 0.000 1.000 0.000 1.000
#> GSM125198 2 0.000 1.000 0.000 1.000
#> GSM125200 1 0.000 0.991 1.000 0.000
#> GSM125202 2 0.000 1.000 0.000 1.000
#> GSM125204 2 0.000 1.000 0.000 1.000
#> GSM125206 2 0.000 1.000 0.000 1.000
#> GSM125208 2 0.000 1.000 0.000 1.000
#> GSM125210 2 0.000 1.000 0.000 1.000
#> GSM125212 2 0.000 1.000 0.000 1.000
#> GSM125214 2 0.000 1.000 0.000 1.000
#> GSM125216 2 0.000 1.000 0.000 1.000
#> GSM125218 2 0.000 1.000 0.000 1.000
#> GSM125220 1 0.973 0.322 0.596 0.404
#> GSM125222 2 0.000 1.000 0.000 1.000
#> GSM125224 2 0.000 1.000 0.000 1.000
#> GSM125226 2 0.000 1.000 0.000 1.000
#> GSM125228 2 0.000 1.000 0.000 1.000
#> GSM125230 2 0.000 1.000 0.000 1.000
#> GSM125232 2 0.000 1.000 0.000 1.000
#> GSM125234 1 0.000 0.991 1.000 0.000
#> GSM125236 1 0.000 0.991 1.000 0.000
#> GSM125238 1 0.000 0.991 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.1163 0.9723 0.972 0.000 0.028
#> GSM125125 1 0.0747 0.9745 0.984 0.000 0.016
#> GSM125127 1 0.1753 0.9705 0.952 0.000 0.048
#> GSM125129 1 0.1529 0.9705 0.960 0.000 0.040
#> GSM125131 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125133 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125135 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125137 1 0.0592 0.9746 0.988 0.000 0.012
#> GSM125139 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125141 1 0.1163 0.9729 0.972 0.000 0.028
#> GSM125143 2 0.6968 0.3723 0.080 0.716 0.204
#> GSM125145 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125147 1 0.0592 0.9748 0.988 0.000 0.012
#> GSM125149 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125151 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125153 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125155 1 0.1163 0.9728 0.972 0.000 0.028
#> GSM125157 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125159 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125161 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125163 2 0.3412 0.5732 0.000 0.876 0.124
#> GSM125165 2 0.6026 -0.1341 0.000 0.624 0.376
#> GSM125167 2 0.5058 0.3989 0.000 0.756 0.244
#> GSM125169 2 0.0237 0.6573 0.000 0.996 0.004
#> GSM125171 2 0.4555 0.6311 0.000 0.800 0.200
#> GSM125173 3 0.5621 0.8862 0.000 0.308 0.692
#> GSM125175 2 0.4504 0.6324 0.000 0.804 0.196
#> GSM125177 3 0.6079 0.8528 0.000 0.388 0.612
#> GSM125179 3 0.5621 0.8862 0.000 0.308 0.692
#> GSM125181 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125183 3 0.6062 0.8558 0.000 0.384 0.616
#> GSM125185 3 0.5591 0.8854 0.000 0.304 0.696
#> GSM125187 3 0.6140 0.8342 0.000 0.404 0.596
#> GSM125189 2 0.1289 0.6473 0.000 0.968 0.032
#> GSM125191 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125193 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125195 2 0.1031 0.6537 0.000 0.976 0.024
#> GSM125197 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125199 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125201 2 0.4452 0.6334 0.000 0.808 0.192
#> GSM125203 2 0.6274 -0.4611 0.000 0.544 0.456
#> GSM125205 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125207 3 0.5591 0.8854 0.000 0.304 0.696
#> GSM125209 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125211 3 0.5733 0.8855 0.000 0.324 0.676
#> GSM125213 2 0.5178 0.3747 0.000 0.744 0.256
#> GSM125215 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125217 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125219 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125221 3 0.6274 0.7399 0.000 0.456 0.544
#> GSM125223 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125225 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125227 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125229 2 0.5327 0.3393 0.000 0.728 0.272
#> GSM125231 2 0.3192 0.5828 0.000 0.888 0.112
#> GSM125233 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125235 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125237 1 0.0237 0.9746 0.996 0.000 0.004
#> GSM125124 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125126 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125128 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125130 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125132 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125134 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125136 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125138 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125140 1 0.0592 0.9746 0.988 0.000 0.012
#> GSM125142 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125144 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125146 1 0.1964 0.9674 0.944 0.000 0.056
#> GSM125148 1 0.0592 0.9747 0.988 0.000 0.012
#> GSM125150 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125152 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125154 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125156 1 0.1860 0.9677 0.948 0.000 0.052
#> GSM125158 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125160 2 0.4346 0.5062 0.000 0.816 0.184
#> GSM125162 1 0.1529 0.9662 0.960 0.000 0.040
#> GSM125164 2 0.1289 0.6475 0.000 0.968 0.032
#> GSM125166 2 0.0000 0.6569 0.000 1.000 0.000
#> GSM125168 2 0.5291 0.3467 0.000 0.732 0.268
#> GSM125170 2 0.2356 0.6223 0.000 0.928 0.072
#> GSM125172 2 0.2356 0.6534 0.000 0.928 0.072
#> GSM125174 3 0.5621 0.8862 0.000 0.308 0.692
#> GSM125176 2 0.0237 0.6573 0.000 0.996 0.004
#> GSM125178 3 0.5591 0.8854 0.000 0.304 0.696
#> GSM125180 3 0.5621 0.8862 0.000 0.308 0.692
#> GSM125182 2 0.5327 0.3400 0.000 0.728 0.272
#> GSM125184 3 0.5591 0.8854 0.000 0.304 0.696
#> GSM125186 3 0.5621 0.8862 0.000 0.308 0.692
#> GSM125188 2 0.5431 0.3089 0.000 0.716 0.284
#> GSM125190 2 0.0000 0.6569 0.000 1.000 0.000
#> GSM125192 2 0.1163 0.6493 0.000 0.972 0.028
#> GSM125194 3 0.6309 0.6359 0.000 0.496 0.504
#> GSM125196 3 0.5706 0.8829 0.000 0.320 0.680
#> GSM125198 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125200 1 0.0592 0.9740 0.988 0.000 0.012
#> GSM125202 2 0.4605 0.6296 0.000 0.796 0.204
#> GSM125204 3 0.5905 0.8672 0.000 0.352 0.648
#> GSM125206 3 0.6307 0.6207 0.000 0.488 0.512
#> GSM125208 3 0.6079 0.8526 0.000 0.388 0.612
#> GSM125210 3 0.5591 0.8854 0.000 0.304 0.696
#> GSM125212 2 0.5905 0.0484 0.000 0.648 0.352
#> GSM125214 2 0.3879 0.6437 0.000 0.848 0.152
#> GSM125216 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125218 2 0.0424 0.6554 0.000 0.992 0.008
#> GSM125220 1 0.4749 0.8382 0.844 0.116 0.040
#> GSM125222 3 0.6140 0.8342 0.000 0.404 0.596
#> GSM125224 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125226 2 0.2711 0.6279 0.000 0.912 0.088
#> GSM125228 2 0.4702 0.6263 0.000 0.788 0.212
#> GSM125230 3 0.6154 0.8281 0.000 0.408 0.592
#> GSM125232 3 0.6307 0.6646 0.000 0.488 0.512
#> GSM125234 1 0.1753 0.9705 0.952 0.000 0.048
#> GSM125236 1 0.1643 0.9709 0.956 0.000 0.044
#> GSM125238 1 0.0424 0.9747 0.992 0.000 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.3837 0.80160 0.776 0.224 0.000 0.000
#> GSM125125 1 0.1118 0.86642 0.964 0.036 0.000 0.000
#> GSM125127 1 0.4356 0.76579 0.708 0.292 0.000 0.000
#> GSM125129 1 0.1637 0.86044 0.940 0.060 0.000 0.000
#> GSM125131 1 0.1118 0.86424 0.964 0.036 0.000 0.000
#> GSM125133 1 0.4477 0.73381 0.688 0.312 0.000 0.000
#> GSM125135 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125137 1 0.0592 0.86646 0.984 0.016 0.000 0.000
#> GSM125139 1 0.0817 0.86629 0.976 0.024 0.000 0.000
#> GSM125141 1 0.0817 0.86605 0.976 0.024 0.000 0.000
#> GSM125143 1 0.8958 0.19698 0.432 0.192 0.296 0.080
#> GSM125145 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125147 1 0.1211 0.86607 0.960 0.040 0.000 0.000
#> GSM125149 1 0.0707 0.86597 0.980 0.020 0.000 0.000
#> GSM125151 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125153 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125155 1 0.0707 0.86632 0.980 0.020 0.000 0.000
#> GSM125157 1 0.3356 0.81636 0.824 0.176 0.000 0.000
#> GSM125159 2 0.7537 0.85908 0.000 0.456 0.348 0.196
#> GSM125161 1 0.4431 0.73992 0.696 0.304 0.000 0.000
#> GSM125163 2 0.7685 0.88649 0.000 0.456 0.288 0.256
#> GSM125165 3 0.2032 0.83753 0.000 0.028 0.936 0.036
#> GSM125167 2 0.7568 0.86728 0.000 0.456 0.340 0.204
#> GSM125169 2 0.7399 0.80649 0.000 0.512 0.208 0.280
#> GSM125171 4 0.5367 0.50905 0.000 0.304 0.032 0.664
#> GSM125173 3 0.0524 0.86056 0.000 0.008 0.988 0.004
#> GSM125175 4 0.4617 0.67745 0.000 0.204 0.032 0.764
#> GSM125177 3 0.0524 0.86061 0.000 0.008 0.988 0.004
#> GSM125179 3 0.0188 0.85821 0.000 0.004 0.996 0.000
#> GSM125181 3 0.3308 0.76768 0.000 0.036 0.872 0.092
#> GSM125183 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125185 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125187 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125189 2 0.7706 0.88222 0.000 0.452 0.268 0.280
#> GSM125191 2 0.7323 0.78142 0.000 0.456 0.388 0.156
#> GSM125193 3 0.2965 0.79375 0.000 0.036 0.892 0.072
#> GSM125195 3 0.7079 -0.00893 0.000 0.276 0.556 0.168
#> GSM125197 4 0.1211 0.83468 0.000 0.040 0.000 0.960
#> GSM125199 1 0.0707 0.86597 0.980 0.020 0.000 0.000
#> GSM125201 4 0.6799 -0.41651 0.000 0.440 0.096 0.464
#> GSM125203 3 0.3919 0.73279 0.000 0.104 0.840 0.056
#> GSM125205 4 0.2742 0.80278 0.000 0.076 0.024 0.900
#> GSM125207 3 0.0188 0.86116 0.000 0.000 0.996 0.004
#> GSM125209 3 0.7286 -0.58838 0.000 0.364 0.480 0.156
#> GSM125211 3 0.0779 0.85962 0.000 0.016 0.980 0.004
#> GSM125213 2 0.7568 0.86594 0.000 0.456 0.340 0.204
#> GSM125215 4 0.0000 0.83518 0.000 0.000 0.000 1.000
#> GSM125217 2 0.7442 0.82545 0.000 0.456 0.368 0.176
#> GSM125219 1 0.4406 0.74118 0.700 0.300 0.000 0.000
#> GSM125221 3 0.1576 0.84407 0.000 0.004 0.948 0.048
#> GSM125223 4 0.1978 0.82601 0.000 0.068 0.004 0.928
#> GSM125225 4 0.0000 0.83518 0.000 0.000 0.000 1.000
#> GSM125227 4 0.0336 0.83689 0.000 0.008 0.000 0.992
#> GSM125229 3 0.6602 -0.53270 0.000 0.436 0.484 0.080
#> GSM125231 3 0.4724 0.70600 0.000 0.096 0.792 0.112
#> GSM125233 1 0.2704 0.84467 0.876 0.124 0.000 0.000
#> GSM125235 1 0.0921 0.86776 0.972 0.028 0.000 0.000
#> GSM125237 1 0.0707 0.86646 0.980 0.020 0.000 0.000
#> GSM125124 1 0.3266 0.82711 0.832 0.168 0.000 0.000
#> GSM125126 1 0.1118 0.86424 0.964 0.036 0.000 0.000
#> GSM125128 1 0.4040 0.77684 0.752 0.248 0.000 0.000
#> GSM125130 1 0.4431 0.73992 0.696 0.304 0.000 0.000
#> GSM125132 1 0.1022 0.86479 0.968 0.032 0.000 0.000
#> GSM125134 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125136 1 0.4522 0.72732 0.680 0.320 0.000 0.000
#> GSM125138 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125140 1 0.0469 0.86673 0.988 0.012 0.000 0.000
#> GSM125142 1 0.2921 0.83955 0.860 0.140 0.000 0.000
#> GSM125144 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125146 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125148 1 0.0817 0.86614 0.976 0.024 0.000 0.000
#> GSM125150 1 0.1022 0.86479 0.968 0.032 0.000 0.000
#> GSM125152 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125154 1 0.3400 0.82390 0.820 0.180 0.000 0.000
#> GSM125156 1 0.3266 0.82924 0.832 0.168 0.000 0.000
#> GSM125158 1 0.1022 0.86479 0.968 0.032 0.000 0.000
#> GSM125160 2 0.7658 0.88282 0.000 0.456 0.308 0.236
#> GSM125162 1 0.4431 0.73992 0.696 0.304 0.000 0.000
#> GSM125164 2 0.7685 0.87749 0.000 0.456 0.256 0.288
#> GSM125166 2 0.7681 0.87380 0.000 0.456 0.252 0.292
#> GSM125168 2 0.7537 0.85844 0.000 0.456 0.348 0.196
#> GSM125170 3 0.6740 0.12902 0.000 0.256 0.600 0.144
#> GSM125172 2 0.7620 0.82761 0.000 0.460 0.224 0.316
#> GSM125174 3 0.0524 0.86056 0.000 0.008 0.988 0.004
#> GSM125176 2 0.7450 0.82066 0.000 0.504 0.216 0.280
#> GSM125178 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125180 3 0.0188 0.85821 0.000 0.004 0.996 0.000
#> GSM125182 2 0.7553 0.86265 0.000 0.456 0.344 0.200
#> GSM125184 3 0.0188 0.86116 0.000 0.000 0.996 0.004
#> GSM125186 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125188 3 0.3107 0.78406 0.000 0.036 0.884 0.080
#> GSM125190 2 0.7603 0.85996 0.000 0.476 0.244 0.280
#> GSM125192 2 0.7685 0.87790 0.000 0.456 0.256 0.288
#> GSM125194 3 0.1767 0.84226 0.000 0.012 0.944 0.044
#> GSM125196 3 0.1978 0.82160 0.000 0.068 0.928 0.004
#> GSM125198 4 0.0188 0.83642 0.000 0.004 0.000 0.996
#> GSM125200 1 0.1022 0.86479 0.968 0.032 0.000 0.000
#> GSM125202 4 0.5712 0.41940 0.000 0.308 0.048 0.644
#> GSM125204 3 0.1109 0.85488 0.000 0.004 0.968 0.028
#> GSM125206 3 0.3958 0.69425 0.000 0.160 0.816 0.024
#> GSM125208 3 0.0188 0.86116 0.000 0.000 0.996 0.004
#> GSM125210 3 0.0376 0.86123 0.000 0.004 0.992 0.004
#> GSM125212 3 0.2797 0.80251 0.000 0.032 0.900 0.068
#> GSM125214 2 0.7379 0.64310 0.000 0.452 0.164 0.384
#> GSM125216 4 0.0000 0.83518 0.000 0.000 0.000 1.000
#> GSM125218 2 0.7690 0.88077 0.000 0.456 0.264 0.280
#> GSM125220 1 0.4522 0.72732 0.680 0.320 0.000 0.000
#> GSM125222 3 0.0000 0.85961 0.000 0.000 1.000 0.000
#> GSM125224 4 0.0000 0.83518 0.000 0.000 0.000 1.000
#> GSM125226 2 0.7685 0.88788 0.000 0.456 0.288 0.256
#> GSM125228 4 0.1743 0.83059 0.000 0.056 0.004 0.940
#> GSM125230 3 0.0188 0.86116 0.000 0.000 0.996 0.004
#> GSM125232 3 0.1520 0.85374 0.000 0.020 0.956 0.024
#> GSM125234 1 0.4697 0.75530 0.696 0.296 0.000 0.008
#> GSM125236 1 0.3610 0.81892 0.800 0.200 0.000 0.000
#> GSM125238 1 0.0592 0.86646 0.984 0.016 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 5 0.3242 0.781 0.216 0.000 0.000 0.000 0.784
#> GSM125125 1 0.1764 0.844 0.928 0.000 0.000 0.008 0.064
#> GSM125127 5 0.1197 0.884 0.048 0.000 0.000 0.000 0.952
#> GSM125129 1 0.3898 0.828 0.804 0.000 0.000 0.116 0.080
#> GSM125131 1 0.3047 0.833 0.868 0.004 0.000 0.044 0.084
#> GSM125133 5 0.0510 0.876 0.016 0.000 0.000 0.000 0.984
#> GSM125135 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125137 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125139 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125141 1 0.1478 0.845 0.936 0.000 0.000 0.000 0.064
#> GSM125143 5 0.5775 0.752 0.180 0.076 0.036 0.012 0.696
#> GSM125145 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125147 1 0.1270 0.845 0.948 0.000 0.000 0.000 0.052
#> GSM125149 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125151 1 0.3309 0.817 0.836 0.000 0.000 0.128 0.036
#> GSM125153 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125155 1 0.2888 0.841 0.880 0.004 0.000 0.060 0.056
#> GSM125157 1 0.4736 0.550 0.656 0.004 0.000 0.028 0.312
#> GSM125159 2 0.2020 0.898 0.000 0.900 0.100 0.000 0.000
#> GSM125161 5 0.3039 0.805 0.192 0.000 0.000 0.000 0.808
#> GSM125163 2 0.1851 0.903 0.000 0.912 0.088 0.000 0.000
#> GSM125165 3 0.1121 0.948 0.000 0.044 0.956 0.000 0.000
#> GSM125167 2 0.1851 0.903 0.000 0.912 0.088 0.000 0.000
#> GSM125169 2 0.1990 0.878 0.000 0.920 0.068 0.008 0.004
#> GSM125171 2 0.1408 0.814 0.000 0.948 0.008 0.044 0.000
#> GSM125173 3 0.1461 0.942 0.000 0.028 0.952 0.016 0.004
#> GSM125175 2 0.1894 0.783 0.000 0.920 0.008 0.072 0.000
#> GSM125177 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125179 3 0.1299 0.948 0.000 0.020 0.960 0.012 0.008
#> GSM125181 3 0.2690 0.820 0.000 0.156 0.844 0.000 0.000
#> GSM125183 3 0.0324 0.958 0.000 0.004 0.992 0.004 0.000
#> GSM125185 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125187 3 0.0290 0.957 0.000 0.000 0.992 0.008 0.000
#> GSM125189 2 0.1671 0.901 0.000 0.924 0.076 0.000 0.000
#> GSM125191 2 0.2280 0.878 0.000 0.880 0.120 0.000 0.000
#> GSM125193 3 0.1043 0.948 0.000 0.040 0.960 0.000 0.000
#> GSM125195 3 0.3086 0.808 0.000 0.180 0.816 0.000 0.004
#> GSM125197 4 0.3586 0.953 0.000 0.264 0.000 0.736 0.000
#> GSM125199 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125201 2 0.1012 0.850 0.000 0.968 0.020 0.012 0.000
#> GSM125203 3 0.1990 0.941 0.000 0.068 0.920 0.008 0.004
#> GSM125205 2 0.3353 0.557 0.000 0.796 0.008 0.196 0.000
#> GSM125207 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125209 2 0.2424 0.865 0.000 0.868 0.132 0.000 0.000
#> GSM125211 3 0.0324 0.958 0.000 0.004 0.992 0.004 0.000
#> GSM125213 2 0.1908 0.902 0.000 0.908 0.092 0.000 0.000
#> GSM125215 4 0.3242 0.948 0.000 0.216 0.000 0.784 0.000
#> GSM125217 2 0.2127 0.891 0.000 0.892 0.108 0.000 0.000
#> GSM125219 5 0.0510 0.876 0.016 0.000 0.000 0.000 0.984
#> GSM125221 3 0.1121 0.948 0.000 0.044 0.956 0.000 0.000
#> GSM125223 4 0.3707 0.938 0.000 0.284 0.000 0.716 0.000
#> GSM125225 4 0.3684 0.921 0.000 0.280 0.000 0.720 0.000
#> GSM125227 2 0.4256 -0.264 0.000 0.564 0.000 0.436 0.000
#> GSM125229 2 0.4060 0.499 0.000 0.640 0.360 0.000 0.000
#> GSM125231 3 0.2452 0.929 0.000 0.084 0.896 0.016 0.004
#> GSM125233 1 0.3532 0.823 0.824 0.000 0.000 0.128 0.048
#> GSM125235 1 0.4297 0.139 0.528 0.000 0.000 0.000 0.472
#> GSM125237 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125124 1 0.3197 0.827 0.836 0.000 0.000 0.140 0.024
#> GSM125126 1 0.3021 0.835 0.872 0.004 0.000 0.060 0.064
#> GSM125128 5 0.2852 0.836 0.172 0.000 0.000 0.000 0.828
#> GSM125130 5 0.0880 0.885 0.032 0.000 0.000 0.000 0.968
#> GSM125132 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125134 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125136 5 0.0880 0.885 0.032 0.000 0.000 0.000 0.968
#> GSM125138 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125140 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125142 1 0.2969 0.825 0.852 0.000 0.000 0.128 0.020
#> GSM125144 1 0.3229 0.819 0.840 0.000 0.000 0.128 0.032
#> GSM125146 1 0.3669 0.803 0.816 0.000 0.000 0.128 0.056
#> GSM125148 1 0.1197 0.845 0.952 0.000 0.000 0.000 0.048
#> GSM125150 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125152 1 0.3309 0.817 0.836 0.000 0.000 0.128 0.036
#> GSM125154 1 0.3309 0.817 0.836 0.000 0.000 0.128 0.036
#> GSM125156 1 0.2727 0.827 0.868 0.000 0.000 0.116 0.016
#> GSM125158 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125160 2 0.1792 0.903 0.000 0.916 0.084 0.000 0.000
#> GSM125162 5 0.2773 0.838 0.164 0.000 0.000 0.000 0.836
#> GSM125164 2 0.1851 0.903 0.000 0.912 0.088 0.000 0.000
#> GSM125166 2 0.1851 0.903 0.000 0.912 0.088 0.000 0.000
#> GSM125168 2 0.1965 0.900 0.000 0.904 0.096 0.000 0.000
#> GSM125170 3 0.2805 0.911 0.000 0.108 0.872 0.012 0.008
#> GSM125172 2 0.1331 0.877 0.000 0.952 0.040 0.008 0.000
#> GSM125174 3 0.1461 0.942 0.000 0.028 0.952 0.016 0.004
#> GSM125176 2 0.1717 0.883 0.000 0.936 0.052 0.008 0.004
#> GSM125178 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125180 3 0.1200 0.950 0.000 0.016 0.964 0.012 0.008
#> GSM125182 2 0.1908 0.902 0.000 0.908 0.092 0.000 0.000
#> GSM125184 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125186 3 0.1074 0.952 0.000 0.016 0.968 0.012 0.004
#> GSM125188 3 0.1478 0.931 0.000 0.064 0.936 0.000 0.000
#> GSM125190 2 0.1991 0.899 0.000 0.916 0.076 0.004 0.004
#> GSM125192 2 0.1851 0.903 0.000 0.912 0.088 0.000 0.000
#> GSM125194 3 0.1043 0.948 0.000 0.040 0.960 0.000 0.000
#> GSM125196 3 0.0609 0.955 0.000 0.020 0.980 0.000 0.000
#> GSM125198 4 0.3508 0.955 0.000 0.252 0.000 0.748 0.000
#> GSM125200 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
#> GSM125202 2 0.1012 0.840 0.000 0.968 0.012 0.020 0.000
#> GSM125204 3 0.1492 0.952 0.000 0.040 0.948 0.004 0.008
#> GSM125206 3 0.0609 0.955 0.000 0.020 0.980 0.000 0.000
#> GSM125208 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM125210 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125212 3 0.1121 0.948 0.000 0.044 0.956 0.000 0.000
#> GSM125214 2 0.1408 0.869 0.000 0.948 0.044 0.008 0.000
#> GSM125216 4 0.3242 0.948 0.000 0.216 0.000 0.784 0.000
#> GSM125218 2 0.1671 0.901 0.000 0.924 0.076 0.000 0.000
#> GSM125220 5 0.0671 0.875 0.016 0.004 0.000 0.000 0.980
#> GSM125222 3 0.0162 0.958 0.000 0.004 0.996 0.000 0.000
#> GSM125224 4 0.3242 0.948 0.000 0.216 0.000 0.784 0.000
#> GSM125226 2 0.1671 0.901 0.000 0.924 0.076 0.000 0.000
#> GSM125228 4 0.3661 0.947 0.000 0.276 0.000 0.724 0.000
#> GSM125230 3 0.0579 0.956 0.000 0.008 0.984 0.008 0.000
#> GSM125232 3 0.2206 0.940 0.000 0.068 0.912 0.016 0.004
#> GSM125234 5 0.1408 0.882 0.044 0.008 0.000 0.000 0.948
#> GSM125236 5 0.1732 0.877 0.080 0.000 0.000 0.000 0.920
#> GSM125238 1 0.3018 0.836 0.872 0.004 0.000 0.068 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 5 0.1866 0.8773 0.084 0.000 0.008 0.000 0.908 0.000
#> GSM125125 1 0.2912 0.6604 0.784 0.000 0.216 0.000 0.000 0.000
#> GSM125127 5 0.0777 0.9064 0.024 0.000 0.004 0.000 0.972 0.000
#> GSM125129 1 0.0458 0.8401 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM125131 1 0.5724 -0.4121 0.424 0.000 0.412 0.000 0.164 0.000
#> GSM125133 5 0.0146 0.9011 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM125135 1 0.0146 0.8373 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM125137 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125139 3 0.3371 0.9111 0.292 0.000 0.708 0.000 0.000 0.000
#> GSM125141 1 0.3101 0.5957 0.756 0.000 0.244 0.000 0.000 0.000
#> GSM125143 5 0.2208 0.8893 0.052 0.008 0.012 0.016 0.912 0.000
#> GSM125145 1 0.0000 0.8348 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125147 1 0.3198 0.5775 0.740 0.000 0.260 0.000 0.000 0.000
#> GSM125149 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125151 1 0.1267 0.8514 0.940 0.000 0.060 0.000 0.000 0.000
#> GSM125153 1 0.0000 0.8348 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM125155 3 0.3854 0.4986 0.464 0.000 0.536 0.000 0.000 0.000
#> GSM125157 5 0.5848 -0.0692 0.256 0.000 0.256 0.000 0.488 0.000
#> GSM125159 2 0.0865 0.9498 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM125161 5 0.1267 0.8957 0.060 0.000 0.000 0.000 0.940 0.000
#> GSM125163 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125165 4 0.0909 0.9403 0.000 0.020 0.012 0.968 0.000 0.000
#> GSM125167 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125169 2 0.3315 0.8741 0.000 0.820 0.104 0.076 0.000 0.000
#> GSM125171 2 0.2422 0.9064 0.000 0.892 0.072 0.012 0.000 0.024
#> GSM125173 4 0.1924 0.9157 0.000 0.028 0.048 0.920 0.004 0.000
#> GSM125175 2 0.3077 0.8787 0.000 0.852 0.084 0.012 0.000 0.052
#> GSM125177 4 0.0870 0.9406 0.000 0.012 0.012 0.972 0.004 0.000
#> GSM125179 4 0.0806 0.9410 0.000 0.008 0.020 0.972 0.000 0.000
#> GSM125181 4 0.4823 0.3785 0.000 0.348 0.068 0.584 0.000 0.000
#> GSM125183 4 0.1584 0.9319 0.000 0.008 0.064 0.928 0.000 0.000
#> GSM125185 4 0.0767 0.9407 0.000 0.008 0.012 0.976 0.004 0.000
#> GSM125187 4 0.1956 0.9243 0.000 0.008 0.080 0.908 0.004 0.000
#> GSM125189 2 0.2058 0.9403 0.000 0.908 0.056 0.036 0.000 0.000
#> GSM125191 2 0.0937 0.9496 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125193 4 0.1584 0.9313 0.000 0.008 0.064 0.928 0.000 0.000
#> GSM125195 4 0.3150 0.8220 0.000 0.120 0.052 0.828 0.000 0.000
#> GSM125197 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125199 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125201 2 0.1546 0.9392 0.000 0.944 0.016 0.020 0.000 0.020
#> GSM125203 4 0.0748 0.9405 0.000 0.004 0.016 0.976 0.004 0.000
#> GSM125205 2 0.2925 0.8868 0.000 0.864 0.060 0.012 0.000 0.064
#> GSM125207 4 0.0862 0.9413 0.000 0.008 0.016 0.972 0.004 0.000
#> GSM125209 2 0.0937 0.9496 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM125211 4 0.1082 0.9349 0.000 0.000 0.040 0.956 0.004 0.000
#> GSM125213 2 0.0865 0.9498 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM125215 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125217 2 0.0865 0.9498 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM125219 5 0.0260 0.9048 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125221 4 0.1196 0.9367 0.000 0.008 0.040 0.952 0.000 0.000
#> GSM125223 6 0.0260 0.9166 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM125225 6 0.1387 0.8651 0.000 0.068 0.000 0.000 0.000 0.932
#> GSM125227 6 0.3789 0.2436 0.000 0.416 0.000 0.000 0.000 0.584
#> GSM125229 2 0.3394 0.7631 0.000 0.776 0.024 0.200 0.000 0.000
#> GSM125231 4 0.3314 0.8561 0.000 0.032 0.144 0.816 0.008 0.000
#> GSM125233 1 0.0547 0.8420 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM125235 5 0.3236 0.7391 0.180 0.000 0.024 0.000 0.796 0.000
#> GSM125237 3 0.3266 0.9307 0.272 0.000 0.728 0.000 0.000 0.000
#> GSM125124 1 0.1814 0.8288 0.900 0.000 0.100 0.000 0.000 0.000
#> GSM125126 3 0.4652 0.8109 0.312 0.000 0.624 0.000 0.064 0.000
#> GSM125128 5 0.0935 0.9070 0.032 0.000 0.004 0.000 0.964 0.000
#> GSM125130 5 0.0363 0.9068 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM125132 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125134 1 0.0146 0.8373 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM125136 5 0.0260 0.9048 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125138 1 0.1501 0.8450 0.924 0.000 0.076 0.000 0.000 0.000
#> GSM125140 3 0.3198 0.9374 0.260 0.000 0.740 0.000 0.000 0.000
#> GSM125142 1 0.1327 0.8505 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM125144 1 0.1387 0.8502 0.932 0.000 0.068 0.000 0.000 0.000
#> GSM125146 1 0.0790 0.8148 0.968 0.000 0.000 0.000 0.032 0.000
#> GSM125148 1 0.3330 0.5134 0.716 0.000 0.284 0.000 0.000 0.000
#> GSM125150 3 0.3468 0.9185 0.284 0.000 0.712 0.000 0.004 0.000
#> GSM125152 1 0.1327 0.8510 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM125154 1 0.1267 0.8514 0.940 0.000 0.060 0.000 0.000 0.000
#> GSM125156 1 0.1444 0.8501 0.928 0.000 0.072 0.000 0.000 0.000
#> GSM125158 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125160 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125162 5 0.0458 0.9079 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM125164 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125166 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125168 2 0.1196 0.9488 0.000 0.952 0.008 0.040 0.000 0.000
#> GSM125170 4 0.2136 0.9161 0.000 0.048 0.048 0.904 0.000 0.000
#> GSM125172 2 0.1952 0.9261 0.000 0.920 0.052 0.016 0.000 0.012
#> GSM125174 4 0.1924 0.9157 0.000 0.028 0.048 0.920 0.004 0.000
#> GSM125176 2 0.2537 0.9199 0.000 0.872 0.096 0.032 0.000 0.000
#> GSM125178 4 0.0767 0.9407 0.000 0.008 0.012 0.976 0.004 0.000
#> GSM125180 4 0.0622 0.9418 0.000 0.008 0.012 0.980 0.000 0.000
#> GSM125182 2 0.0790 0.9496 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM125184 4 0.0767 0.9407 0.000 0.008 0.012 0.976 0.004 0.000
#> GSM125186 4 0.0622 0.9418 0.000 0.008 0.012 0.980 0.000 0.000
#> GSM125188 4 0.2179 0.9181 0.000 0.036 0.064 0.900 0.000 0.000
#> GSM125190 2 0.2474 0.9304 0.000 0.880 0.080 0.040 0.000 0.000
#> GSM125192 2 0.0935 0.9493 0.000 0.964 0.004 0.032 0.000 0.000
#> GSM125194 4 0.2174 0.9193 0.000 0.008 0.088 0.896 0.008 0.000
#> GSM125196 4 0.0767 0.9407 0.000 0.008 0.012 0.976 0.004 0.000
#> GSM125198 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125200 3 0.3151 0.9414 0.252 0.000 0.748 0.000 0.000 0.000
#> GSM125202 2 0.2317 0.9161 0.000 0.900 0.064 0.016 0.000 0.020
#> GSM125204 4 0.0508 0.9412 0.000 0.004 0.012 0.984 0.000 0.000
#> GSM125206 4 0.0837 0.9406 0.000 0.004 0.020 0.972 0.004 0.000
#> GSM125208 4 0.0972 0.9395 0.000 0.008 0.028 0.964 0.000 0.000
#> GSM125210 4 0.0767 0.9407 0.000 0.008 0.012 0.976 0.004 0.000
#> GSM125212 4 0.0891 0.9395 0.000 0.024 0.008 0.968 0.000 0.000
#> GSM125214 2 0.1346 0.9447 0.000 0.952 0.008 0.024 0.000 0.016
#> GSM125216 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125218 2 0.2058 0.9403 0.000 0.908 0.056 0.036 0.000 0.000
#> GSM125220 5 0.0260 0.9048 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM125222 4 0.0806 0.9401 0.000 0.008 0.020 0.972 0.000 0.000
#> GSM125224 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125226 2 0.1082 0.9490 0.000 0.956 0.004 0.040 0.000 0.000
#> GSM125228 6 0.0000 0.9209 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM125230 4 0.2488 0.8938 0.000 0.004 0.124 0.864 0.008 0.000
#> GSM125232 4 0.3043 0.8708 0.000 0.020 0.140 0.832 0.008 0.000
#> GSM125234 5 0.1088 0.9038 0.024 0.000 0.016 0.000 0.960 0.000
#> GSM125236 5 0.1643 0.8910 0.068 0.000 0.008 0.000 0.924 0.000
#> GSM125238 3 0.3151 0.9414 0.252 0.000 0.748 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 agent(p) individual(p) k
#> ATC:mclust 115 1.000 1.49e-05 2
#> ATC:mclust 100 0.739 4.72e-06 3
#> ATC:mclust 109 0.531 1.61e-08 4
#> ATC:mclust 113 0.998 2.41e-10 5
#> ATC:mclust 111 0.993 2.84e-11 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21168 rows and 116 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 0.981 0.992 0.5027 0.498 0.498
#> 3 3 0.933 0.923 0.964 0.3076 0.799 0.612
#> 4 4 0.789 0.748 0.876 0.0672 0.982 0.947
#> 5 5 0.704 0.611 0.795 0.0520 0.965 0.895
#> 6 6 0.703 0.622 0.783 0.0283 0.964 0.883
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
#> GSM125123 1 0.0000 0.995 1.000 0.000
#> GSM125125 1 0.0000 0.995 1.000 0.000
#> GSM125127 1 0.0000 0.995 1.000 0.000
#> GSM125129 1 0.0000 0.995 1.000 0.000
#> GSM125131 1 0.0000 0.995 1.000 0.000
#> GSM125133 1 0.0000 0.995 1.000 0.000
#> GSM125135 1 0.0000 0.995 1.000 0.000
#> GSM125137 1 0.0000 0.995 1.000 0.000
#> GSM125139 1 0.0000 0.995 1.000 0.000
#> GSM125141 1 0.0000 0.995 1.000 0.000
#> GSM125143 1 0.0000 0.995 1.000 0.000
#> GSM125145 1 0.0000 0.995 1.000 0.000
#> GSM125147 1 0.0000 0.995 1.000 0.000
#> GSM125149 1 0.0000 0.995 1.000 0.000
#> GSM125151 1 0.0000 0.995 1.000 0.000
#> GSM125153 1 0.0000 0.995 1.000 0.000
#> GSM125155 1 0.0000 0.995 1.000 0.000
#> GSM125157 1 0.0000 0.995 1.000 0.000
#> GSM125159 2 0.0000 0.989 0.000 1.000
#> GSM125161 1 0.0000 0.995 1.000 0.000
#> GSM125163 2 0.0000 0.989 0.000 1.000
#> GSM125165 2 0.0000 0.989 0.000 1.000
#> GSM125167 2 0.0000 0.989 0.000 1.000
#> GSM125169 2 0.0000 0.989 0.000 1.000
#> GSM125171 2 0.0000 0.989 0.000 1.000
#> GSM125173 2 0.0000 0.989 0.000 1.000
#> GSM125175 2 0.0000 0.989 0.000 1.000
#> GSM125177 2 0.0000 0.989 0.000 1.000
#> GSM125179 2 0.9129 0.517 0.328 0.672
#> GSM125181 2 0.0000 0.989 0.000 1.000
#> GSM125183 2 0.8763 0.584 0.296 0.704
#> GSM125185 2 0.0000 0.989 0.000 1.000
#> GSM125187 1 0.0376 0.991 0.996 0.004
#> GSM125189 2 0.0000 0.989 0.000 1.000
#> GSM125191 2 0.0000 0.989 0.000 1.000
#> GSM125193 1 0.7815 0.692 0.768 0.232
#> GSM125195 2 0.0000 0.989 0.000 1.000
#> GSM125197 2 0.0000 0.989 0.000 1.000
#> GSM125199 1 0.0000 0.995 1.000 0.000
#> GSM125201 2 0.0000 0.989 0.000 1.000
#> GSM125203 2 0.0000 0.989 0.000 1.000
#> GSM125205 2 0.0000 0.989 0.000 1.000
#> GSM125207 2 0.0000 0.989 0.000 1.000
#> GSM125209 2 0.0000 0.989 0.000 1.000
#> GSM125211 2 0.0000 0.989 0.000 1.000
#> GSM125213 2 0.0000 0.989 0.000 1.000
#> GSM125215 2 0.0000 0.989 0.000 1.000
#> GSM125217 2 0.0000 0.989 0.000 1.000
#> GSM125219 1 0.0000 0.995 1.000 0.000
#> GSM125221 2 0.0672 0.982 0.008 0.992
#> GSM125223 2 0.0000 0.989 0.000 1.000
#> GSM125225 2 0.0000 0.989 0.000 1.000
#> GSM125227 2 0.0000 0.989 0.000 1.000
#> GSM125229 2 0.0000 0.989 0.000 1.000
#> GSM125231 1 0.0672 0.988 0.992 0.008
#> GSM125233 1 0.0000 0.995 1.000 0.000
#> GSM125235 1 0.0000 0.995 1.000 0.000
#> GSM125237 1 0.0000 0.995 1.000 0.000
#> GSM125124 1 0.0000 0.995 1.000 0.000
#> GSM125126 1 0.0000 0.995 1.000 0.000
#> GSM125128 1 0.0000 0.995 1.000 0.000
#> GSM125130 1 0.0000 0.995 1.000 0.000
#> GSM125132 1 0.0000 0.995 1.000 0.000
#> GSM125134 1 0.0000 0.995 1.000 0.000
#> GSM125136 1 0.0000 0.995 1.000 0.000
#> GSM125138 1 0.0000 0.995 1.000 0.000
#> GSM125140 1 0.0000 0.995 1.000 0.000
#> GSM125142 1 0.0000 0.995 1.000 0.000
#> GSM125144 1 0.0000 0.995 1.000 0.000
#> GSM125146 1 0.0000 0.995 1.000 0.000
#> GSM125148 1 0.0000 0.995 1.000 0.000
#> GSM125150 1 0.0000 0.995 1.000 0.000
#> GSM125152 1 0.0000 0.995 1.000 0.000
#> GSM125154 1 0.0000 0.995 1.000 0.000
#> GSM125156 1 0.0000 0.995 1.000 0.000
#> GSM125158 1 0.0000 0.995 1.000 0.000
#> GSM125160 2 0.0000 0.989 0.000 1.000
#> GSM125162 1 0.0000 0.995 1.000 0.000
#> GSM125164 2 0.0000 0.989 0.000 1.000
#> GSM125166 2 0.0000 0.989 0.000 1.000
#> GSM125168 2 0.0000 0.989 0.000 1.000
#> GSM125170 2 0.0000 0.989 0.000 1.000
#> GSM125172 2 0.0000 0.989 0.000 1.000
#> GSM125174 2 0.0000 0.989 0.000 1.000
#> GSM125176 2 0.0000 0.989 0.000 1.000
#> GSM125178 2 0.0000 0.989 0.000 1.000
#> GSM125180 2 0.1184 0.975 0.016 0.984
#> GSM125182 2 0.0000 0.989 0.000 1.000
#> GSM125184 2 0.0000 0.989 0.000 1.000
#> GSM125186 2 0.1843 0.963 0.028 0.972
#> GSM125188 2 0.0000 0.989 0.000 1.000
#> GSM125190 2 0.0000 0.989 0.000 1.000
#> GSM125192 2 0.0000 0.989 0.000 1.000
#> GSM125194 1 0.0000 0.995 1.000 0.000
#> GSM125196 2 0.0000 0.989 0.000 1.000
#> GSM125198 2 0.0000 0.989 0.000 1.000
#> GSM125200 1 0.0000 0.995 1.000 0.000
#> GSM125202 2 0.0000 0.989 0.000 1.000
#> GSM125204 2 0.0000 0.989 0.000 1.000
#> GSM125206 2 0.0000 0.989 0.000 1.000
#> GSM125208 2 0.0000 0.989 0.000 1.000
#> GSM125210 2 0.0000 0.989 0.000 1.000
#> GSM125212 2 0.0000 0.989 0.000 1.000
#> GSM125214 2 0.0000 0.989 0.000 1.000
#> GSM125216 2 0.0000 0.989 0.000 1.000
#> GSM125218 2 0.0000 0.989 0.000 1.000
#> GSM125220 1 0.0000 0.995 1.000 0.000
#> GSM125222 2 0.0672 0.982 0.008 0.992
#> GSM125224 2 0.0000 0.989 0.000 1.000
#> GSM125226 2 0.0000 0.989 0.000 1.000
#> GSM125228 2 0.0000 0.989 0.000 1.000
#> GSM125230 1 0.0938 0.984 0.988 0.012
#> GSM125232 1 0.0000 0.995 1.000 0.000
#> GSM125234 1 0.0000 0.995 1.000 0.000
#> GSM125236 1 0.0000 0.995 1.000 0.000
#> GSM125238 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM125123 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125125 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125127 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125129 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125131 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125133 1 0.0475 0.99417 0.992 0.004 0.004
#> GSM125135 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125137 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125139 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125141 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125143 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125145 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125147 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125149 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125151 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125153 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125155 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125157 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125159 2 0.5216 0.67989 0.000 0.740 0.260
#> GSM125161 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125163 2 0.0892 0.93299 0.000 0.980 0.020
#> GSM125165 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125167 2 0.2448 0.89695 0.000 0.924 0.076
#> GSM125169 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125171 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125173 3 0.3116 0.85669 0.000 0.108 0.892
#> GSM125175 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125177 3 0.5948 0.43794 0.000 0.360 0.640
#> GSM125179 3 0.0475 0.93207 0.004 0.004 0.992
#> GSM125181 3 0.0424 0.93279 0.000 0.008 0.992
#> GSM125183 3 0.0475 0.93207 0.004 0.004 0.992
#> GSM125185 3 0.0424 0.93293 0.000 0.008 0.992
#> GSM125187 3 0.0237 0.93017 0.004 0.000 0.996
#> GSM125189 2 0.0237 0.93689 0.000 0.996 0.004
#> GSM125191 3 0.5733 0.52150 0.000 0.324 0.676
#> GSM125193 3 0.0424 0.92775 0.008 0.000 0.992
#> GSM125195 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125197 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125199 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125201 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125203 2 0.5431 0.63776 0.000 0.716 0.284
#> GSM125205 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125207 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125209 3 0.4504 0.75040 0.000 0.196 0.804
#> GSM125211 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125213 2 0.4002 0.81392 0.000 0.840 0.160
#> GSM125215 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125217 2 0.4887 0.73084 0.000 0.772 0.228
#> GSM125219 1 0.0475 0.99417 0.992 0.004 0.004
#> GSM125221 3 0.0237 0.93208 0.000 0.004 0.996
#> GSM125223 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125225 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125227 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125229 2 0.1529 0.92325 0.000 0.960 0.040
#> GSM125231 3 0.0983 0.92494 0.016 0.004 0.980
#> GSM125233 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125235 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125237 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125124 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125126 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125128 1 0.0424 0.99440 0.992 0.000 0.008
#> GSM125130 1 0.0424 0.99440 0.992 0.000 0.008
#> GSM125132 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125134 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125136 1 0.0424 0.99440 0.992 0.000 0.008
#> GSM125138 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125140 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125142 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125144 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125146 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125148 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125150 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125152 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125154 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125156 1 0.0237 0.99534 0.996 0.000 0.004
#> GSM125158 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125160 2 0.1643 0.91912 0.000 0.956 0.044
#> GSM125162 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125164 2 0.0747 0.93453 0.000 0.984 0.016
#> GSM125166 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125168 2 0.6111 0.37273 0.000 0.604 0.396
#> GSM125170 2 0.2537 0.89340 0.000 0.920 0.080
#> GSM125172 2 0.0424 0.93698 0.000 0.992 0.008
#> GSM125174 3 0.1031 0.92700 0.000 0.024 0.976
#> GSM125176 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125178 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125180 3 0.1289 0.92168 0.000 0.032 0.968
#> GSM125182 2 0.5254 0.67252 0.000 0.736 0.264
#> GSM125184 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125186 3 0.0475 0.93207 0.004 0.004 0.992
#> GSM125188 3 0.0424 0.93279 0.000 0.008 0.992
#> GSM125190 2 0.0592 0.93460 0.000 0.988 0.012
#> GSM125192 2 0.0424 0.93689 0.000 0.992 0.008
#> GSM125194 3 0.0424 0.92775 0.008 0.000 0.992
#> GSM125196 3 0.6305 0.00457 0.000 0.484 0.516
#> GSM125198 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125200 1 0.0000 0.99679 1.000 0.000 0.000
#> GSM125202 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125204 3 0.3482 0.83553 0.000 0.128 0.872
#> GSM125206 2 0.4504 0.77129 0.000 0.804 0.196
#> GSM125208 3 0.0237 0.93208 0.000 0.004 0.996
#> GSM125210 3 0.1031 0.92738 0.000 0.024 0.976
#> GSM125212 3 0.0592 0.93292 0.000 0.012 0.988
#> GSM125214 2 0.0892 0.93292 0.000 0.980 0.020
#> GSM125216 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125218 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125220 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125222 3 0.0424 0.93293 0.000 0.008 0.992
#> GSM125224 2 0.0237 0.93747 0.000 0.996 0.004
#> GSM125226 2 0.1031 0.93131 0.000 0.976 0.024
#> GSM125228 2 0.0000 0.93626 0.000 1.000 0.000
#> GSM125230 3 0.0424 0.92955 0.008 0.000 0.992
#> GSM125232 3 0.0592 0.92644 0.012 0.000 0.988
#> GSM125234 1 0.1765 0.96004 0.956 0.040 0.004
#> GSM125236 1 0.0237 0.99612 0.996 0.000 0.004
#> GSM125238 1 0.0000 0.99679 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM125123 1 0.0336 0.9584 0.992 0.000 0.000 0.008
#> GSM125125 1 0.0336 0.9584 0.992 0.000 0.000 0.008
#> GSM125127 1 0.0469 0.9591 0.988 0.000 0.000 0.012
#> GSM125129 1 0.0188 0.9583 0.996 0.000 0.000 0.004
#> GSM125131 1 0.0336 0.9575 0.992 0.000 0.000 0.008
#> GSM125133 1 0.2011 0.9241 0.920 0.000 0.000 0.080
#> GSM125135 1 0.0469 0.9582 0.988 0.000 0.000 0.012
#> GSM125137 1 0.0469 0.9582 0.988 0.000 0.000 0.012
#> GSM125139 1 0.0336 0.9585 0.992 0.000 0.000 0.008
#> GSM125141 1 0.0592 0.9591 0.984 0.000 0.000 0.016
#> GSM125143 1 0.2469 0.9004 0.892 0.000 0.000 0.108
#> GSM125145 1 0.1118 0.9519 0.964 0.000 0.000 0.036
#> GSM125147 1 0.0469 0.9580 0.988 0.000 0.000 0.012
#> GSM125149 1 0.0921 0.9521 0.972 0.000 0.000 0.028
#> GSM125151 1 0.2466 0.9107 0.900 0.000 0.004 0.096
#> GSM125153 1 0.1716 0.9376 0.936 0.000 0.000 0.064
#> GSM125155 1 0.0336 0.9586 0.992 0.000 0.000 0.008
#> GSM125157 1 0.1302 0.9450 0.956 0.000 0.000 0.044
#> GSM125159 2 0.7803 -0.2825 0.000 0.404 0.256 0.340
#> GSM125161 1 0.2760 0.8846 0.872 0.000 0.000 0.128
#> GSM125163 2 0.1174 0.8660 0.000 0.968 0.012 0.020
#> GSM125165 3 0.3942 0.5479 0.000 0.000 0.764 0.236
#> GSM125167 2 0.2494 0.8520 0.000 0.916 0.036 0.048
#> GSM125169 2 0.2530 0.8231 0.000 0.888 0.000 0.112
#> GSM125171 2 0.2174 0.8512 0.000 0.928 0.020 0.052
#> GSM125173 3 0.4957 0.5824 0.000 0.112 0.776 0.112
#> GSM125175 2 0.0817 0.8627 0.000 0.976 0.000 0.024
#> GSM125177 3 0.6987 0.2650 0.000 0.160 0.568 0.272
#> GSM125179 3 0.3300 0.6154 0.000 0.008 0.848 0.144
#> GSM125181 4 0.4804 0.4472 0.000 0.000 0.384 0.616
#> GSM125183 3 0.2530 0.6447 0.000 0.000 0.888 0.112
#> GSM125185 3 0.1661 0.6575 0.000 0.004 0.944 0.052
#> GSM125187 3 0.2814 0.6279 0.000 0.000 0.868 0.132
#> GSM125189 4 0.6207 -0.1100 0.000 0.452 0.052 0.496
#> GSM125191 3 0.6238 0.2567 0.000 0.296 0.620 0.084
#> GSM125193 4 0.4820 0.5202 0.012 0.000 0.296 0.692
#> GSM125195 2 0.1867 0.8454 0.000 0.928 0.000 0.072
#> GSM125197 2 0.0779 0.8647 0.000 0.980 0.004 0.016
#> GSM125199 1 0.0592 0.9562 0.984 0.000 0.000 0.016
#> GSM125201 2 0.1610 0.8595 0.000 0.952 0.032 0.016
#> GSM125203 2 0.5767 0.5068 0.000 0.660 0.280 0.060
#> GSM125205 2 0.1151 0.8644 0.000 0.968 0.008 0.024
#> GSM125207 3 0.2921 0.6263 0.000 0.000 0.860 0.140
#> GSM125209 3 0.7231 0.1320 0.000 0.192 0.540 0.268
#> GSM125211 3 0.1109 0.6622 0.000 0.004 0.968 0.028
#> GSM125213 2 0.3787 0.7659 0.000 0.840 0.124 0.036
#> GSM125215 2 0.0336 0.8649 0.000 0.992 0.000 0.008
#> GSM125217 2 0.6064 0.5073 0.000 0.672 0.220 0.108
#> GSM125219 1 0.0469 0.9573 0.988 0.000 0.000 0.012
#> GSM125221 3 0.4967 -0.0845 0.000 0.000 0.548 0.452
#> GSM125223 2 0.0188 0.8649 0.000 0.996 0.000 0.004
#> GSM125225 2 0.0336 0.8648 0.000 0.992 0.000 0.008
#> GSM125227 2 0.0336 0.8648 0.000 0.992 0.000 0.008
#> GSM125229 2 0.4944 0.6896 0.000 0.768 0.072 0.160
#> GSM125231 3 0.6019 0.4541 0.044 0.020 0.672 0.264
#> GSM125233 1 0.0336 0.9586 0.992 0.000 0.000 0.008
#> GSM125235 1 0.0336 0.9585 0.992 0.000 0.000 0.008
#> GSM125237 1 0.0921 0.9521 0.972 0.000 0.000 0.028
#> GSM125124 1 0.0707 0.9563 0.980 0.000 0.000 0.020
#> GSM125126 1 0.0336 0.9575 0.992 0.000 0.000 0.008
#> GSM125128 1 0.3444 0.8270 0.816 0.000 0.000 0.184
#> GSM125130 1 0.2589 0.8953 0.884 0.000 0.000 0.116
#> GSM125132 1 0.0592 0.9560 0.984 0.000 0.000 0.016
#> GSM125134 1 0.0592 0.9571 0.984 0.000 0.000 0.016
#> GSM125136 1 0.4925 0.4149 0.572 0.000 0.000 0.428
#> GSM125138 1 0.1474 0.9444 0.948 0.000 0.000 0.052
#> GSM125140 1 0.0469 0.9580 0.988 0.000 0.000 0.012
#> GSM125142 1 0.1211 0.9499 0.960 0.000 0.000 0.040
#> GSM125144 1 0.1389 0.9461 0.952 0.000 0.000 0.048
#> GSM125146 1 0.1022 0.9528 0.968 0.000 0.000 0.032
#> GSM125148 1 0.0469 0.9580 0.988 0.000 0.000 0.012
#> GSM125150 1 0.0336 0.9575 0.992 0.000 0.000 0.008
#> GSM125152 1 0.1792 0.9346 0.932 0.000 0.000 0.068
#> GSM125154 1 0.3032 0.8819 0.868 0.000 0.008 0.124
#> GSM125156 1 0.0592 0.9571 0.984 0.000 0.000 0.016
#> GSM125158 1 0.0336 0.9575 0.992 0.000 0.000 0.008
#> GSM125160 2 0.2399 0.8446 0.000 0.920 0.048 0.032
#> GSM125162 1 0.2704 0.8878 0.876 0.000 0.000 0.124
#> GSM125164 2 0.1151 0.8644 0.000 0.968 0.008 0.024
#> GSM125166 2 0.0895 0.8650 0.000 0.976 0.004 0.020
#> GSM125168 2 0.5955 0.4184 0.000 0.616 0.328 0.056
#> GSM125170 2 0.3581 0.8078 0.000 0.852 0.032 0.116
#> GSM125172 2 0.2032 0.8598 0.000 0.936 0.028 0.036
#> GSM125174 3 0.4274 0.6156 0.000 0.044 0.808 0.148
#> GSM125176 2 0.1118 0.8600 0.000 0.964 0.000 0.036
#> GSM125178 3 0.3208 0.6250 0.000 0.004 0.848 0.148
#> GSM125180 3 0.4149 0.5978 0.000 0.036 0.812 0.152
#> GSM125182 2 0.7414 -0.1410 0.000 0.460 0.172 0.368
#> GSM125184 3 0.1978 0.6522 0.000 0.004 0.928 0.068
#> GSM125186 3 0.2266 0.6504 0.000 0.004 0.912 0.084
#> GSM125188 4 0.4679 0.5053 0.000 0.000 0.352 0.648
#> GSM125190 2 0.3626 0.7591 0.000 0.812 0.004 0.184
#> GSM125192 2 0.0817 0.8652 0.000 0.976 0.000 0.024
#> GSM125194 3 0.4996 -0.1879 0.000 0.000 0.516 0.484
#> GSM125196 3 0.6919 0.0868 0.000 0.368 0.516 0.116
#> GSM125198 2 0.0779 0.8647 0.000 0.980 0.004 0.016
#> GSM125200 1 0.0336 0.9575 0.992 0.000 0.000 0.008
#> GSM125202 2 0.1733 0.8594 0.000 0.948 0.028 0.024
#> GSM125204 3 0.4549 0.6007 0.000 0.100 0.804 0.096
#> GSM125206 2 0.5998 0.5630 0.000 0.684 0.200 0.116
#> GSM125208 3 0.4624 0.3482 0.000 0.000 0.660 0.340
#> GSM125210 3 0.3647 0.6361 0.000 0.040 0.852 0.108
#> GSM125212 3 0.3486 0.5903 0.000 0.000 0.812 0.188
#> GSM125214 2 0.1406 0.8615 0.000 0.960 0.024 0.016
#> GSM125216 2 0.0469 0.8651 0.000 0.988 0.000 0.012
#> GSM125218 2 0.3257 0.7880 0.000 0.844 0.004 0.152
#> GSM125220 1 0.1637 0.9368 0.940 0.000 0.000 0.060
#> GSM125222 3 0.3942 0.5251 0.000 0.000 0.764 0.236
#> GSM125224 2 0.0336 0.8649 0.000 0.992 0.000 0.008
#> GSM125226 2 0.1975 0.8587 0.000 0.936 0.016 0.048
#> GSM125228 2 0.0817 0.8639 0.000 0.976 0.000 0.024
#> GSM125230 3 0.1211 0.6591 0.000 0.000 0.960 0.040
#> GSM125232 3 0.3837 0.5499 0.000 0.000 0.776 0.224
#> GSM125234 1 0.1798 0.9425 0.944 0.016 0.000 0.040
#> GSM125236 1 0.0336 0.9591 0.992 0.000 0.000 0.008
#> GSM125238 1 0.0592 0.9564 0.984 0.000 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM125123 1 0.0671 0.9226 0.980 0.000 0.000 0.004 0.016
#> GSM125125 1 0.0290 0.9217 0.992 0.000 0.000 0.000 0.008
#> GSM125127 1 0.1668 0.9158 0.940 0.000 0.000 0.028 0.032
#> GSM125129 1 0.1579 0.9139 0.944 0.000 0.000 0.024 0.032
#> GSM125131 1 0.0693 0.9210 0.980 0.000 0.000 0.012 0.008
#> GSM125133 1 0.2291 0.8950 0.908 0.000 0.000 0.036 0.056
#> GSM125135 1 0.1281 0.9205 0.956 0.000 0.000 0.032 0.012
#> GSM125137 1 0.1195 0.9216 0.960 0.000 0.000 0.028 0.012
#> GSM125139 1 0.0798 0.9231 0.976 0.000 0.000 0.008 0.016
#> GSM125141 1 0.1493 0.9197 0.948 0.000 0.000 0.028 0.024
#> GSM125143 1 0.5101 0.6260 0.676 0.000 0.008 0.060 0.256
#> GSM125145 1 0.1750 0.9122 0.936 0.000 0.000 0.036 0.028
#> GSM125147 1 0.0510 0.9213 0.984 0.000 0.000 0.000 0.016
#> GSM125149 1 0.0798 0.9197 0.976 0.000 0.000 0.016 0.008
#> GSM125151 1 0.3661 0.8497 0.836 0.000 0.012 0.096 0.056
#> GSM125153 1 0.2974 0.8757 0.868 0.000 0.000 0.080 0.052
#> GSM125155 1 0.0693 0.9217 0.980 0.000 0.000 0.008 0.012
#> GSM125157 1 0.1907 0.9090 0.928 0.000 0.000 0.044 0.028
#> GSM125159 2 0.7608 -0.3652 0.000 0.364 0.224 0.052 0.360
#> GSM125161 1 0.3420 0.8420 0.840 0.000 0.000 0.076 0.084
#> GSM125163 2 0.2407 0.7682 0.000 0.896 0.004 0.088 0.012
#> GSM125165 3 0.4713 -0.1414 0.000 0.000 0.544 0.440 0.016
#> GSM125167 2 0.3530 0.7077 0.000 0.784 0.012 0.204 0.000
#> GSM125169 2 0.4335 0.6765 0.004 0.728 0.004 0.244 0.020
#> GSM125171 2 0.3160 0.7561 0.000 0.876 0.032 0.052 0.040
#> GSM125173 3 0.6708 -0.3157 0.000 0.168 0.452 0.368 0.012
#> GSM125175 2 0.0955 0.7795 0.000 0.968 0.000 0.028 0.004
#> GSM125177 5 0.5903 0.2855 0.000 0.092 0.312 0.012 0.584
#> GSM125179 3 0.4238 0.4145 0.000 0.028 0.804 0.112 0.056
#> GSM125181 4 0.7001 0.0552 0.000 0.012 0.244 0.420 0.324
#> GSM125183 3 0.4130 0.2031 0.000 0.000 0.696 0.292 0.012
#> GSM125185 3 0.2885 0.4894 0.000 0.004 0.880 0.052 0.064
#> GSM125187 3 0.3946 0.4490 0.000 0.000 0.800 0.120 0.080
#> GSM125189 2 0.6273 0.3889 0.000 0.524 0.000 0.292 0.184
#> GSM125191 3 0.6355 0.0874 0.000 0.296 0.564 0.024 0.116
#> GSM125193 5 0.6476 -0.2099 0.012 0.000 0.164 0.288 0.536
#> GSM125195 2 0.4891 0.0617 0.000 0.532 0.008 0.012 0.448
#> GSM125197 2 0.1341 0.7693 0.000 0.944 0.000 0.000 0.056
#> GSM125199 1 0.1012 0.9211 0.968 0.000 0.000 0.020 0.012
#> GSM125201 2 0.3339 0.6983 0.000 0.836 0.040 0.000 0.124
#> GSM125203 5 0.7247 0.3682 0.000 0.264 0.296 0.024 0.416
#> GSM125205 2 0.3652 0.6266 0.000 0.784 0.012 0.004 0.200
#> GSM125207 3 0.4890 0.3281 0.000 0.000 0.628 0.040 0.332
#> GSM125209 3 0.7462 0.1661 0.000 0.212 0.516 0.096 0.176
#> GSM125211 3 0.2416 0.5041 0.000 0.000 0.888 0.012 0.100
#> GSM125213 2 0.4997 0.5472 0.000 0.728 0.128 0.008 0.136
#> GSM125215 2 0.1205 0.7752 0.000 0.956 0.000 0.004 0.040
#> GSM125217 2 0.5234 0.6402 0.000 0.736 0.136 0.084 0.044
#> GSM125219 1 0.0798 0.9224 0.976 0.000 0.000 0.016 0.008
#> GSM125221 3 0.6336 -0.1948 0.000 0.000 0.468 0.368 0.164
#> GSM125223 2 0.1041 0.7768 0.000 0.964 0.000 0.004 0.032
#> GSM125225 2 0.0609 0.7790 0.000 0.980 0.000 0.020 0.000
#> GSM125227 2 0.0771 0.7780 0.000 0.976 0.000 0.004 0.020
#> GSM125229 5 0.5974 0.2409 0.000 0.404 0.052 0.028 0.516
#> GSM125231 3 0.6750 0.2265 0.028 0.016 0.596 0.212 0.148
#> GSM125233 1 0.0912 0.9226 0.972 0.000 0.000 0.016 0.012
#> GSM125235 1 0.0290 0.9218 0.992 0.000 0.000 0.000 0.008
#> GSM125237 1 0.1018 0.9179 0.968 0.000 0.000 0.016 0.016
#> GSM125124 1 0.2645 0.8866 0.888 0.000 0.000 0.068 0.044
#> GSM125126 1 0.0451 0.9206 0.988 0.000 0.000 0.008 0.004
#> GSM125128 1 0.5284 0.6119 0.660 0.000 0.000 0.104 0.236
#> GSM125130 1 0.5223 0.6358 0.672 0.000 0.000 0.108 0.220
#> GSM125132 1 0.0912 0.9187 0.972 0.000 0.000 0.012 0.016
#> GSM125134 1 0.1211 0.9189 0.960 0.000 0.000 0.016 0.024
#> GSM125136 1 0.6318 0.2531 0.488 0.000 0.000 0.168 0.344
#> GSM125138 1 0.3427 0.8581 0.844 0.000 0.004 0.096 0.056
#> GSM125140 1 0.1668 0.9167 0.940 0.000 0.000 0.032 0.028
#> GSM125142 1 0.2520 0.8925 0.896 0.000 0.000 0.056 0.048
#> GSM125144 1 0.3120 0.8700 0.864 0.000 0.004 0.084 0.048
#> GSM125146 1 0.1106 0.9196 0.964 0.000 0.000 0.024 0.012
#> GSM125148 1 0.0865 0.9208 0.972 0.000 0.000 0.004 0.024
#> GSM125150 1 0.0566 0.9216 0.984 0.000 0.000 0.012 0.004
#> GSM125152 1 0.2694 0.8864 0.888 0.000 0.004 0.076 0.032
#> GSM125154 1 0.4296 0.8097 0.796 0.000 0.024 0.124 0.056
#> GSM125156 1 0.0912 0.9198 0.972 0.000 0.000 0.012 0.016
#> GSM125158 1 0.0000 0.9217 1.000 0.000 0.000 0.000 0.000
#> GSM125160 2 0.2828 0.7333 0.000 0.872 0.020 0.004 0.104
#> GSM125162 1 0.3506 0.8343 0.832 0.000 0.000 0.064 0.104
#> GSM125164 2 0.2362 0.7678 0.000 0.900 0.008 0.084 0.008
#> GSM125166 2 0.1990 0.7733 0.000 0.920 0.008 0.068 0.004
#> GSM125168 2 0.5670 0.4841 0.000 0.636 0.248 0.108 0.008
#> GSM125170 2 0.4876 0.4056 0.000 0.544 0.012 0.436 0.008
#> GSM125172 2 0.2665 0.7730 0.000 0.900 0.032 0.048 0.020
#> GSM125174 4 0.6263 0.0349 0.000 0.084 0.380 0.512 0.024
#> GSM125176 2 0.2653 0.7671 0.000 0.880 0.000 0.096 0.024
#> GSM125178 3 0.4943 0.1667 0.000 0.008 0.556 0.016 0.420
#> GSM125180 3 0.4786 0.4315 0.000 0.048 0.776 0.088 0.088
#> GSM125182 2 0.7605 -0.0333 0.000 0.452 0.100 0.136 0.312
#> GSM125184 3 0.1369 0.4784 0.000 0.008 0.956 0.028 0.008
#> GSM125186 3 0.1894 0.4696 0.000 0.000 0.920 0.072 0.008
#> GSM125188 5 0.6347 -0.2114 0.000 0.000 0.228 0.248 0.524
#> GSM125190 2 0.5059 0.4349 0.000 0.548 0.000 0.416 0.036
#> GSM125192 2 0.1549 0.7802 0.000 0.944 0.000 0.016 0.040
#> GSM125194 3 0.6674 -0.1495 0.000 0.000 0.408 0.236 0.356
#> GSM125196 5 0.6449 0.3013 0.000 0.148 0.352 0.008 0.492
#> GSM125198 2 0.1410 0.7679 0.000 0.940 0.000 0.000 0.060
#> GSM125200 1 0.0579 0.9206 0.984 0.000 0.000 0.008 0.008
#> GSM125202 2 0.2940 0.7457 0.000 0.876 0.048 0.004 0.072
#> GSM125204 3 0.5873 -0.0243 0.000 0.068 0.508 0.012 0.412
#> GSM125206 5 0.6630 0.4379 0.000 0.300 0.180 0.012 0.508
#> GSM125208 3 0.5968 0.0741 0.000 0.000 0.448 0.108 0.444
#> GSM125210 3 0.4091 0.3687 0.000 0.092 0.804 0.096 0.008
#> GSM125212 3 0.4584 0.4483 0.000 0.000 0.716 0.056 0.228
#> GSM125214 2 0.1800 0.7667 0.000 0.932 0.020 0.000 0.048
#> GSM125216 2 0.1205 0.7744 0.000 0.956 0.000 0.004 0.040
#> GSM125218 2 0.3897 0.7051 0.000 0.768 0.000 0.204 0.028
#> GSM125220 1 0.1579 0.9122 0.944 0.000 0.000 0.024 0.032
#> GSM125222 3 0.5053 0.3354 0.000 0.000 0.688 0.216 0.096
#> GSM125224 2 0.1124 0.7751 0.000 0.960 0.000 0.004 0.036
#> GSM125226 2 0.3883 0.6797 0.000 0.744 0.008 0.244 0.004
#> GSM125228 2 0.1830 0.7796 0.000 0.932 0.000 0.028 0.040
#> GSM125230 3 0.3289 0.4984 0.000 0.000 0.844 0.048 0.108
#> GSM125232 3 0.4400 0.3460 0.000 0.000 0.744 0.196 0.060
#> GSM125234 1 0.4257 0.8319 0.816 0.048 0.004 0.088 0.044
#> GSM125236 1 0.1012 0.9227 0.968 0.000 0.000 0.012 0.020
#> GSM125238 1 0.0807 0.9216 0.976 0.000 0.000 0.012 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM125123 1 0.1296 0.9055 0.948 0.000 0.004 0.000 0.004 0.044
#> GSM125125 1 0.0748 0.9086 0.976 0.000 0.004 0.000 0.004 0.016
#> GSM125127 1 0.3588 0.8327 0.824 0.000 0.032 0.000 0.052 0.092
#> GSM125129 1 0.2501 0.8819 0.888 0.000 0.012 0.000 0.072 0.028
#> GSM125131 1 0.1232 0.9078 0.956 0.000 0.004 0.000 0.024 0.016
#> GSM125133 1 0.3018 0.8540 0.848 0.000 0.016 0.000 0.112 0.024
#> GSM125135 1 0.2377 0.8827 0.892 0.000 0.024 0.000 0.008 0.076
#> GSM125137 1 0.1074 0.9091 0.960 0.000 0.000 0.000 0.012 0.028
#> GSM125139 1 0.0603 0.9099 0.980 0.000 0.000 0.000 0.004 0.016
#> GSM125141 1 0.0806 0.9097 0.972 0.000 0.000 0.000 0.008 0.020
#> GSM125143 1 0.5126 0.3635 0.568 0.000 0.032 0.016 0.372 0.012
#> GSM125145 1 0.2339 0.8859 0.896 0.000 0.012 0.000 0.020 0.072
#> GSM125147 1 0.0146 0.9081 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM125149 1 0.1074 0.9071 0.960 0.000 0.000 0.000 0.028 0.012
#> GSM125151 1 0.2806 0.8594 0.872 0.000 0.000 0.012 0.056 0.060
#> GSM125153 1 0.1708 0.8938 0.932 0.000 0.000 0.004 0.040 0.024
#> GSM125155 1 0.0291 0.9080 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM125157 1 0.1605 0.9019 0.936 0.000 0.004 0.000 0.044 0.016
#> GSM125159 2 0.7069 -0.0617 0.000 0.368 0.048 0.260 0.316 0.008
#> GSM125161 1 0.3377 0.8081 0.812 0.000 0.012 0.000 0.148 0.028
#> GSM125163 2 0.2650 0.7290 0.000 0.880 0.004 0.040 0.004 0.072
#> GSM125165 4 0.5847 0.3152 0.000 0.020 0.020 0.536 0.072 0.352
#> GSM125167 2 0.3992 0.6460 0.000 0.756 0.004 0.064 0.000 0.176
#> GSM125169 2 0.4879 0.5042 0.000 0.688 0.020 0.020 0.036 0.236
#> GSM125171 2 0.4609 0.6619 0.000 0.772 0.064 0.064 0.016 0.084
#> GSM125173 4 0.6288 -0.0771 0.000 0.156 0.012 0.424 0.012 0.396
#> GSM125175 2 0.1643 0.7288 0.000 0.924 0.008 0.000 0.000 0.068
#> GSM125177 3 0.4648 0.5887 0.000 0.036 0.740 0.108 0.116 0.000
#> GSM125179 4 0.4520 0.5497 0.000 0.040 0.044 0.780 0.036 0.100
#> GSM125181 5 0.6685 0.3529 0.000 0.012 0.056 0.176 0.524 0.232
#> GSM125183 4 0.4391 0.5149 0.000 0.004 0.020 0.728 0.040 0.208
#> GSM125185 4 0.3520 0.5613 0.000 0.036 0.020 0.840 0.084 0.020
#> GSM125187 4 0.4178 0.5200 0.000 0.004 0.048 0.780 0.132 0.036
#> GSM125189 2 0.6294 0.1232 0.000 0.556 0.036 0.008 0.188 0.212
#> GSM125191 4 0.6336 0.2159 0.000 0.324 0.064 0.524 0.072 0.016
#> GSM125193 5 0.4609 0.4447 0.000 0.000 0.140 0.084 0.740 0.036
#> GSM125195 3 0.3444 0.5913 0.000 0.140 0.812 0.000 0.012 0.036
#> GSM125197 2 0.1810 0.7325 0.000 0.932 0.036 0.020 0.008 0.004
#> GSM125199 1 0.0717 0.9085 0.976 0.000 0.000 0.000 0.016 0.008
#> GSM125201 2 0.4722 0.6365 0.000 0.744 0.056 0.152 0.028 0.020
#> GSM125203 3 0.6180 0.5746 0.000 0.128 0.608 0.200 0.036 0.028
#> GSM125205 2 0.4690 0.5022 0.000 0.692 0.244 0.032 0.012 0.020
#> GSM125207 4 0.5952 0.0840 0.000 0.000 0.364 0.476 0.144 0.016
#> GSM125209 4 0.6846 0.2352 0.000 0.280 0.036 0.480 0.180 0.024
#> GSM125211 4 0.4364 0.5098 0.000 0.004 0.168 0.752 0.028 0.048
#> GSM125213 2 0.4628 0.5996 0.000 0.732 0.064 0.172 0.028 0.004
#> GSM125215 2 0.1124 0.7391 0.000 0.956 0.036 0.008 0.000 0.000
#> GSM125217 2 0.5405 0.5120 0.000 0.656 0.008 0.224 0.072 0.040
#> GSM125219 1 0.2172 0.8960 0.912 0.000 0.020 0.000 0.024 0.044
#> GSM125221 4 0.6111 0.0947 0.000 0.004 0.012 0.476 0.340 0.168
#> GSM125223 2 0.0820 0.7361 0.000 0.972 0.012 0.000 0.000 0.016
#> GSM125225 2 0.0951 0.7380 0.000 0.968 0.004 0.008 0.000 0.020
#> GSM125227 2 0.0748 0.7379 0.000 0.976 0.004 0.004 0.000 0.016
#> GSM125229 3 0.7735 0.2689 0.000 0.276 0.352 0.068 0.264 0.040
#> GSM125231 4 0.7505 0.0639 0.008 0.004 0.296 0.400 0.120 0.172
#> GSM125233 1 0.1850 0.8988 0.924 0.000 0.008 0.000 0.016 0.052
#> GSM125235 1 0.0405 0.9086 0.988 0.000 0.000 0.000 0.004 0.008
#> GSM125237 1 0.1082 0.9066 0.956 0.000 0.000 0.000 0.040 0.004
#> GSM125124 1 0.3030 0.8561 0.872 0.000 0.016 0.016 0.044 0.052
#> GSM125126 1 0.0458 0.9083 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM125128 1 0.4747 0.3630 0.548 0.000 0.016 0.000 0.412 0.024
#> GSM125130 1 0.5338 0.2563 0.508 0.000 0.020 0.000 0.412 0.060
#> GSM125132 1 0.0935 0.9080 0.964 0.000 0.000 0.000 0.032 0.004
#> GSM125134 1 0.0405 0.9076 0.988 0.000 0.000 0.000 0.004 0.008
#> GSM125136 5 0.4611 0.0712 0.380 0.000 0.016 0.000 0.584 0.020
#> GSM125138 1 0.3686 0.8207 0.828 0.000 0.016 0.016 0.076 0.064
#> GSM125140 1 0.1382 0.9051 0.948 0.000 0.008 0.000 0.008 0.036
#> GSM125142 1 0.1624 0.8959 0.936 0.000 0.000 0.004 0.020 0.040
#> GSM125144 1 0.1719 0.8953 0.932 0.000 0.004 0.000 0.032 0.032
#> GSM125146 1 0.1003 0.9071 0.964 0.000 0.004 0.000 0.004 0.028
#> GSM125148 1 0.0291 0.9080 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM125150 1 0.0520 0.9099 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM125152 1 0.1480 0.9016 0.940 0.000 0.000 0.000 0.020 0.040
#> GSM125154 1 0.3495 0.8119 0.828 0.000 0.000 0.020 0.076 0.076
#> GSM125156 1 0.0260 0.9082 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM125158 1 0.0405 0.9089 0.988 0.000 0.000 0.000 0.008 0.004
#> GSM125160 2 0.4094 0.6806 0.000 0.800 0.036 0.108 0.040 0.016
#> GSM125162 1 0.3457 0.7970 0.800 0.000 0.016 0.000 0.164 0.020
#> GSM125164 2 0.2451 0.7269 0.000 0.888 0.000 0.040 0.004 0.068
#> GSM125166 2 0.2527 0.7219 0.000 0.880 0.000 0.032 0.004 0.084
#> GSM125168 2 0.5450 0.4888 0.000 0.652 0.024 0.204 0.008 0.112
#> GSM125170 2 0.5282 -0.0475 0.000 0.504 0.004 0.064 0.008 0.420
#> GSM125172 2 0.4128 0.6916 0.000 0.788 0.016 0.116 0.012 0.068
#> GSM125174 6 0.5006 -0.1974 0.004 0.020 0.036 0.308 0.004 0.628
#> GSM125176 2 0.2830 0.7110 0.000 0.868 0.012 0.012 0.012 0.096
#> GSM125178 3 0.5567 0.2611 0.000 0.008 0.532 0.368 0.080 0.012
#> GSM125180 4 0.5747 0.5224 0.004 0.068 0.060 0.704 0.072 0.092
#> GSM125182 2 0.7169 -0.0476 0.000 0.444 0.328 0.032 0.124 0.072
#> GSM125184 4 0.2495 0.5780 0.000 0.004 0.036 0.896 0.012 0.052
#> GSM125186 4 0.2808 0.5804 0.000 0.008 0.044 0.880 0.012 0.056
#> GSM125188 5 0.5008 0.4582 0.000 0.004 0.136 0.132 0.704 0.024
#> GSM125190 6 0.5457 -0.1800 0.000 0.444 0.012 0.016 0.048 0.480
#> GSM125192 2 0.2483 0.7390 0.000 0.904 0.016 0.024 0.020 0.036
#> GSM125194 5 0.7163 0.2642 0.000 0.000 0.172 0.284 0.420 0.124
#> GSM125196 3 0.3177 0.6479 0.000 0.068 0.860 0.044 0.012 0.016
#> GSM125198 2 0.2302 0.7275 0.000 0.908 0.044 0.032 0.008 0.008
#> GSM125200 1 0.0603 0.9092 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM125202 2 0.4632 0.6537 0.000 0.756 0.060 0.136 0.020 0.028
#> GSM125204 3 0.5002 0.5419 0.000 0.024 0.676 0.244 0.036 0.020
#> GSM125206 3 0.2892 0.6354 0.000 0.068 0.876 0.016 0.012 0.028
#> GSM125208 5 0.6144 0.1547 0.000 0.000 0.228 0.320 0.444 0.008
#> GSM125210 4 0.3730 0.5357 0.000 0.096 0.016 0.820 0.012 0.056
#> GSM125212 4 0.5995 0.3686 0.000 0.016 0.236 0.592 0.132 0.024
#> GSM125214 2 0.2898 0.7179 0.000 0.868 0.028 0.084 0.016 0.004
#> GSM125216 2 0.0806 0.7376 0.000 0.972 0.020 0.000 0.000 0.008
#> GSM125218 2 0.3516 0.6470 0.000 0.792 0.000 0.012 0.024 0.172
#> GSM125220 1 0.1225 0.9066 0.952 0.000 0.000 0.000 0.036 0.012
#> GSM125222 4 0.4985 0.4747 0.000 0.004 0.052 0.712 0.168 0.064
#> GSM125224 2 0.0717 0.7386 0.000 0.976 0.016 0.000 0.000 0.008
#> GSM125226 2 0.3874 0.5944 0.000 0.744 0.004 0.020 0.008 0.224
#> GSM125228 2 0.1908 0.7236 0.000 0.916 0.028 0.000 0.000 0.056
#> GSM125230 4 0.5604 0.3422 0.000 0.000 0.280 0.588 0.028 0.104
#> GSM125232 4 0.6205 0.3978 0.000 0.000 0.116 0.588 0.100 0.196
#> GSM125234 1 0.6162 0.5939 0.644 0.040 0.108 0.000 0.060 0.148
#> GSM125236 1 0.2317 0.8857 0.900 0.000 0.016 0.000 0.020 0.064
#> GSM125238 1 0.0972 0.9075 0.964 0.000 0.000 0.000 0.028 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 agent(p) individual(p) k
#> ATC:NMF 116 1.000 1.90e-05 2
#> ATC:NMF 113 0.991 1.71e-07 3
#> ATC:NMF 102 0.928 2.49e-06 4
#> ATC:NMF 76 0.616 4.24e-06 5
#> ATC:NMF 87 0.992 1.52e-07 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